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Shades of red and green : the colorful diversity and ecology of picocyanobacteria in the BalticSea

Haverkamp, T.H.A.

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Citation for published version (APA):Haverkamp, T. H. A. (2008). Shades of red and green : the colorful diversity and ecology of picocyanobacteria inthe Baltic Sea. Yerseke: Netherlands Institute of Ecology (NIOO) - Royal Netherlands Academy of Arts andSciences.

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Shades of red and greenTh

omas H

averkamp

Uitnodiging

voor het bijwonen van de openbare verdediging van mijn proefschrift:

Shades of red and greenThe colorful diversity and

ecology of picocyanobacteria in the Baltic Sea

op dinsdag 14 oktober 2008om 14:00 uur in de

Agnietenkapel van de UvA Oudezijds Voorburgwal 231

1012 EZ Amsterdam

na afloop bent u van harte welkom

op de receptie

Thomas HaverkampBoven Zevenwouden 19

3524 CK [email protected]

paranimfen:Edward Kuijer

Mabula [email protected]

Shades of red and green

The colorful diversity and ecology of picocyanobacteria in the Baltic Sea

Thomas Haverkamp

NIO

O Th

esis 67

Shades of red and green

The colorful diversity and ecology of picocyanobacteria in the Baltic Sea

ACADEMISCH PROEFSCHRIFT

ter verkrijging van de graad van doctoraan de Universiteit van Amsterdamop gezag van de Rector Magnificus

prof. dr. D.C. van den Boomten overstaan van een door het college voor promoties ingestelde

commissie, in het openbaar te verdedigen in de Agnietenkapel der Universiteitop dinsdag 14 oktober 2008, te 14:00 uur

door

Thomas Hendricus Augustus Haverkampgeboren te Veghel

PROMOTIECOMMISSIE

Promotores: Prof. dr. L.J. Stal Prof. dr. J. Huisman

Overige leden: Prof. dr. K.J. Hellingwerf Dr. H.C.P. Matthijs Dr. G. Muyzer Prof. dr. P.H. van Tienderen Dr. M. Veldhuis Dr. A. Wilmotte Prof. dr. A.M. Wood

Faculteit der Natuurwetenschappen, Wiskunde & Informatica.

2008 © Thomas Haverkamp

ISBN Correspondence [email protected] Gildeprint (www.gildeprint.nl)Cover and Layout Arne Heijenga, Riverside California (www.stukjewebgebeuren.nl)Font Garamond Premier Pro (13pt) Helvetica Neue LT Std (10pt)

The research reported in this thesis was carried out at the Department of Marine Microbiology of the Netherlands Institute of Ecology of the Royal Netherlands Academy of Arts and Sciences (NIOO-KNAW) in Yerseke. The investigations were supported in part by the European Commission through the project ‘MIRACLE’ (EVK3-CT-2002-00087).

Table of Contents

Chapter 1 7General introduction

Chapter 2 19Colourful coexistence of red and green picocyanobacteria in lakes and seas

Chapter 3 43Diversity and phylogeny of Baltic Sea picocyanobacteria inferred from their ITS and phycobiliprotein operons

Chapter 4 75Rapid diversification of red and green Synechococcus strains in the Baltic Sea

Chapter 5 105Phenotypic and genetic diversification of Pseudanabaena spp. (Cyanobacteria)

Chapter 6 131General discussion

Chapter 7 143English summary

Chapter 8 149Samenvatting

Chapter 9 155References

Chapter 10 171Dankwoord / Acknowledgements

Chapter 1General introduction

General introduction

9

Microbial diversityLife on Earth is subdivided in three major domains, the Archaea, the Bacteria and the Eukarya

(Woese and Fox, 1977). The Eukarya comprises both macro- and microorganisms while the other two domains are exclusively microbial. With the invention of the microscope in the 17th century by Antonie van Leeuwenhoek, microbial life became visible for the human eye and allowed the exploration of this fascinating world. For a very long time, the microscope was the only instrument that allowed researchers to study microorganisms in the natural environment without the need for isolation and cultivation. However, the morphological characteristics of microorganisms are few and their isolation into laboratory cultures yielded only low numbers of species. For many years the known species diversity in the microbial world was therefore low, although microbiologists realized that the vast majority remained undiscovered. Only with the application of culture independent molecular biological techniques it became clear that there is a staggering diversity in the microbial world, which will probably prove to be beyond our comprehension.

Microorganisms are the dominant component of any ecosystem on Earth and many ecosystems are exclusively microbial. Microbes are found in a wide range of environmental conditions. For instance, microorganisms grow in environments with temperatures ranging from below 0 to above 100ºC (Blank et al., 2002; Sattley and Madigan, 2007; van der Meer et al., 2007). Other environmental extremes under which microorganisms may proliferate include hypersaline, extremely acidic or alkaline and arid environments as well as systems where hyperbaric pressure or electromagnetic radiation prevent any other lifeform (van der Wielen et al., 2005; Appukuttan et al., 2006; Dong et al., 2007; Nogi et al., 2007; Islam et al., 2008). Microorganisms evolved specific adaptations that enable them to live in these harsh environments (Makarova et al., 2001; Swire, 2007).

The high species diversity among microbes is not only related to their survival in a wide range of different ecosystems, but also within a particular ecosystem the microbial diversity is usually very large. This is especially intriguing for the largely unstructured aquatic ecosystems such as oceans, seas and lakes. In aquatic ecosystems the plankton community consists of many coexisting species competing for the same resources. Hutchinson (1961) called this “The paradox of the plankton”. This is an interesting paradox because of the nutrient deficiencies that occur in summer in most aquatic ecosystems. One would expect that nutrient deficiency leads to fierce competition among species depending on these nutrients. Nevertheless, even under these resource limiting conditions the species diversity in aquatic environments usually remains high. In order to solve the paradox formulated by Hutchinson it is necessary to understand the mechanisms responsible for the existence of the high species diversity within the plankton specifically and for microbial communities in general. These mechanisms can be studied using clearly distinguishable microorganisms (morphological, phenotypical or genotypic differences) that can be compared for their adaptation to specific environmental conditions. Since many microorganisms have only limited morphological differences it was difficult to study the mechanisms underlying their diversity until the introduction of molecular techniques.

10

Chapter 1

The introduction of culture independent molecular biological techniques in microbial ecology in the 1980’s revealed far greater microbial species diversity in many ecosystems than had been expected. For instance, the polymerase chain reaction (PCR) enabled the amplification of ribosomal genes in DNA samples extracted directly from the environment without the need to cultivate microorganisms (Olsen et al., 1986; Pace et al., 1986). After the initial work of Norman Pace and collaborators many researchers have used PCR and PCR-dependent techniques in order to describe the microbial diversity for the three domains of life in a wide range of ecosystems. This revealed that hundreds of species might coexist in one milliliter of ocean water, while in one gram of soil more than 10.000 different species have been detected (Torsvik et al., 1996; Curtis and Sloan, 2004; Schloss and Handelsman, 2005). More recently, it was shown that the number of species could be in the range of millions for marine and soil ecosystems (Gans et al., 2005; Sogin et al., 2006). These very large numbers of species present in the environment contrast strongly with the low number of microorganisms that have been cultivated. Culture-independent approaches indicated that many of the newly identified taxa and species have not been cultivated and are only known by their 16S rRNA sequences (Ward et al., 1990; Rappe and Giovannoni, 2003; Sogin et al., 2006). We do not know who they are, what they look like and what they are doing.

The discrepancy between the number of microbial species in the environment and the number of cultivated microorganisms was known long before molecular biology techniques were applied in microbiology. It has been noted for a long time that there is a large discrepancy between the number of colony-forming bacteria and the number of bacteria that were counted microscopically. The cultivation efficiency of bacteria in marine water samples using standard marine media ranges between 0.1 and 0.01% (Kogure et al., 1979). This difference between cell counts using microscopy or flow-cytometry and the number of cultivated bacteria in the same sample is known as “The great plate count anomaly” (Staley and Konopka, 1985). Moreover, we now know that many of the uncultivated bacteria appear to be dominant in the natural environment while most cultivated bacteria are rare (for reviews, see e.g. Rappe and Giovannoni, 2003; Pedros-Alio, 2006). This led to the development of new and improved methods to enhance the number of bacteria that can be isolated and cultivated in the laboratory, specifically those species that are numerically important in natural communities (Button et al., 1993; Bruns et al., 2002; Connon and Giovannoni, 2002; Zengler et al., 2002; Ferrari et al., 2005). With the introduction of these novel cultivation techniques several new microorganisms have been isolated and can now be grown in the laboratory, but they still represent only a tiny fraction of the enormous diversity found in the microbial world.


11

The microbial species definitionHistorically, bacterial species are defined based on morphology, physiology and metabolic

capacities of pure cultures (Gevers et al., 2005). However, taxonomy of microorganisms based on these characteristics do not reflect their phylogenetic relationships. For instance, Gram-negative bacteria belonging to the group of pseudomonads were characterized by the usage of only one carbon source, acetate. Later on it was discovered through 16S rRNA gene sequencing that these organisms belonged to three different evolutionary groups, the Alpha-, Beta-, and Gammaproteobacteria (Staley, 2006). Nowadays, phylogenetic inference of prokaryotes utilizes often the ribosomal genes (particularly the 16S and 18S rRNA genes in Bacteria/Archaea and Eukarya, respectively) since they are present in all forms of life and are highly conserved. The usage of the 16S rRNA gene sequences led to the discovery of many unknown taxa at various taxonomic levels from species to genera and even to the discovery of the third kingdom of life: the Archaea (Woese and Fox, 1977; Pace, 1997; Rappe and Giovannoni, 2003). With the sequencing of the gene coding for the 16S small ribosomal subunit and the rapidly expanding number of such sequences deposited in databases, it became possible to produce a reasonable sound phylogenetic taxonomy of Bacteria and Archaea (Woese et al., 1990). The problem with this 16S rRNA taxonomy was that it did neither reflect morphology, nor the physiology and metabolic capacities of the organisms in a consistent way. In general, a prokaryotic species would include a collection of strains with approximately 70% or greater DNA-DNA hybridization (DDH) values (Wayne et al., 1987; Stackebrandt et al., 2002). This agrees in many cases with the proposed limit of 97% similarity between the 16S rRNA gene sequences to separate species. Strains showing less than 97% 16S rRNA similarity are considered different species, while strains with more than 97% 16S rRNA similarity could be the same species (Stackebrandt and Goebel, 1994). Likewise 95% similarity of the 16S rRNA has been considered to mark the level of the genus (Konstantinidis and Tiedje, 2007). Recently, genomic analysis of closely related species indicated that the average nucleotide identity (ANI) of the shared genes between genomes could be around 94%. This measure corresponded with the 70% DDH standard of the above species definition (Konstantinidis and Tiedje, 2005).

The discussion above shows that the bacterial species definition changed from a phenotypically based approach to one that is pure phylogenetic. The most recent species definitions for prokaryotes use distinct DNA sequences of one or more genes, or genomes, to describe monophyletic clusters (Gevers et al., 2005; Staley, 2006; Cohan and Perry, 2007). The reasoning behind this is that through time each cluster independently evolved of other clusters while acquiring distinct adaptations that are only shared by the entire cluster (Cohan and Perry, 2007).

12

Chapter 1

EcotypesThe ease of use of the 16S rRNA gene sequences to describe novel microbial species has

led to many discoveries and has revolutionized our knowledge on microbial diversity. But the conserved nature of the 16S rRNA gene has also a downside, since it is possible that microorganisms with identical 16S rRNA genes should be assigned to different species when DNA-DNA hybridization reveals less than 70% similarity (Fox et al., 1992). Furthermore, with the increase of the number of ribosomal sequences in the databases it became apparent that closely related 16S or 18S rRNA sequences (similarity > 96%) often belong to clusters of species with different physiologies that inhabit distinct niches in the environment (Rappe and Giovannoni, 2003). The 16S rRNA microdiversity clusters are often accompanied by large differences between the genomes of closely related species. Processes like Horizontal Gene Transfer (HGT), which causes genes to be exchanged between bacteria, can be responsible for the differences found between closely related genomes. In this way, HGT could complicate the analysis of phylogenetic relationships between microorganisms because species boundaries become blurred (Zhaxybayeva et al., 2006; Choi and Kim, 2007). This suggests that the microdiversity clusters of 16S rRNA, and other genes, are of evolutionary and of ecological importance (Cohan, 2001; Rappe and Giovannoni, 2003; Acinas et al., 2004).

An example of the ecological distinctiveness of strains with closely related 16S rRNA gene sequences is the comparison of two groups of picocyanobacteria both belonging to the genus Prochlorococcus. Prochlorococcus differs from all other cyanobacteria because they use divinyl chlorophyll a2 and b for light harvesting instead of the phycobiliproteins. The strains MED4 and MIT9313 differ with regard to the optimum light intensity for growth (Moore et al., 1998). Strain MED4 tolerates high light intensities, has a low Chl b/a2 ratio and belongs phylogenetically to the low B/A clade. Strain MIT9313 is sensitive to high light, has a high Chl b/a2 ratio and belongs to the high B/A clade (Moore and Chisholm, 1999). Despite these differences the sequences of the 16S rRNA genes of both strains diverge less than 3% (Rocap et al., 2003). However, the genomes are much more divergent. The genome of strain MED4 has 1716 genes, while that of strain MIT9313 contains 2275 genes. Both genomes share 1352 genes. The remaining genes are strain specific. However, the 923 genes specific of strain MIT9313 are shared with Synechococcus WH8102 (Rocap et al., 2003; Hess, 2004), another marine picocyanobacterium. The differences between the genomes of both Prochlorococcus strains were attributed to the specific ecological niches where they thrive. Prochlorococcus MED4 occurs in the surface layers where it is exposed to high light while MIT9313 thrives at greater depth with lower light intensities (Moore et al., 1998; West and Scanlan, 1999). Based on the different ecological niches of these Prochlorococcus strains, they were assigned as “ecotypes” of the same species (Rocap et al., 2003).

Cohan and Perry (2007) defined “ecotype” in the following way: “A group of bacteria that are ecologically similar to one another, so similar that genetic diversity within the ecotype is limited by a cohesive force, either periodic selection or genetic drift, or both”. The above example on Prochlorococcus ecotypes, and other examples not discussed here, indicate that a microbial species in the microbial world consists of assemblages of closely related 16S rRNA


13

genomes, but that these assemblages may proliferate as ecologically distinct populations or ecotypes (Rocap et al., 2003; Lopez-Lopez et al., 2005; Staley, 2006; Cohan and Perry, 2007; Ward et al., 2008).

The color of lightThe ecotype differentiation in Prochlorococcus is mainly based on its ability to grow optimal

at high or low light levels. However, the light gradient in the water column alone does not explain the huge diversity of the phytoplankton. The phytoplankton community consists of many different species, each with their own nutrient requirements and nutrient uptake characteristics, their strategies to escape predation, differences in light harvesting properties and pigmentation, and many other factors (Irigoien et al., 2004; Wilson et al., 2007). Stomp et al. (2004) used two strains of Synechococcus for competition experiments in chemostats. One strain (BS4) contained only the blue-green pigment phycocyanin (PC), while the other (BS5) contained mainly the red phycoerythrin (PE). These authors demonstrated that red and green cyanobacteria can coexist under white light.

The light harvesting complexes of cyanobacteria, the phycobilisomes, consist of phycobiliproteins with different chromophores and, hence, different light harvesting capacities (Fig. 1.1). These protein complexes differ in their light absorbance maxima by the chromophores they bind. PE binds phycoerythrobilin (PEB) (λmax: 540 to 560 nm) which results in a maximal absorption peak at ~565nm. PC binds phycocyanobilin (PCB) which results in an absorption maximum at ~620nm (Ong and Glazer, 1991). The difference in light absorption capacities of the pigments enables the Synechococcus strains BS4 and BS5 to coexist under white light in the laboratory (Stomp et al., 2004) because they each use a different part of the light spectrum. These differently pigmented Synechococcus species can be regarded as ecotypes.

Another pigment ecotype exists among Synechococcus. This ecotype binds an additional pigment, phycourobilin (PUB) (λmax ~ 490 nm) to its phycoerythrin protein complex (Wood et al., 1985; Ong and Glazer, 1991). Synechococcus species that possess PUB as well as PEB chromophores exhibit a large range of variation in the PUB/PEB ratio (Fuller et al., 2003), resulting in optically different phenotypes (red to orange). Some marine Synechococcus strains are capable of a special form of chromatic adaptation by which they change the PUB/PEB ratio in response to changes in the light spectrum (Palenik, 2001). Cyanobacteria possessing PUB are exclusively found in the oceans, suggesting that the evolution of PUB is an adaptation to blue light that prevails in the marine environment.

In contrast to the Prochlorococcus ecotypes, Synechococcus pigment ecotypes are not clearly distinguishable from their 16S rRNA and ITS-1 phylogenies. Many clusters contain isolates that possess different pigmentation phenotypes (Crosbie et al., 2003; Ernst et al., 2003; Fuller et al., 2003). For example, the two Synechococcus strains used in the experiments of Stomp et al. (2004) were identical for the 16S rRNA gene and nearly identical (similarity > 99%) for the ribosomal internal transcribed spacer region (Crosbie et al., 2003; Ernst et al., 2003).

14

Chapter 1

Figure 1.1 A schematic representation of the phycobilisomes in picocyanobacteria. The phycobilisome has a core consisting of allophycocyanin (APC) protein complexes. The allophycocyanin core is attached to photosystem I or II present in the thylakoids membrane and it transfers the light energy absorbed by the rods to the photosynthetic reaction centers. Attached to the APC core are the rods that harvest the light energy. Close to the core, discs consisting of hexameric complexes of phycocyanin proteins with PCB as the pigment (A). The next discs are composed of hexameric complexes with phycoerythrin (PE) I (B) and the most distal discs contain PE II proteins (C and D). In general, PE I binds only PEB (B), while PUB binds to PE II (C and D). Picture reproduced from Six et al. (2007) under the Creative Commons Attribution License version 2.0.


15

Furthermore, Synechococcus strains may not only vary with respect to their pigment composition, but they may also use different nitrogen sources and may differ with respect to motility. None of these properties are reflected in the 16S rRNA phylogeny (Fuller et al., 2003; Scanlan, 2003; Ernst et al., 2005).

Hence, it is difficult to explain the evolution of Synechococcus ecotypes from the level of the ribosomal operon as was done in the case of Prochlorococcus. The Synechococcus strains BS4 and BS5 were isolated from the Bornholm Sea together with a thin brown filamentous cyanobacterium that is capable of complementary chromatic adaptation (CCA) (Stal et al., 2003). This cyanobacterium was identified as Pseudanabaena sp. Cyanobacteria capable of CCA are able to change their relative amounts of phycoerythrin and phycocyanin, in response to the color of the light they are exposed to. Under white light they absorb light using both PE and PC, rendering the organism a brown to black appearance. These cyanobacteria are able to compensate for changes in the light spectrum by adjustment of the PE and PC composition in their phycobilisomes (Kehoe and Gutu, 2006). Hence, in red light the organism turns (blue) green and in green light it turns red.

Stomp et al. (2004) used as a model organism the filamentous cyanobacterium Tolypothrix that is capable of CCA. These authors showed that Tolypothrix coexisted with either BS4 or BS5. In competition against the green Synechococcus strains BS4, Tolypothrix turned green. Conversely, in competition against the red Synechococcus strain BS5, Tolypothrix turned green. Thus, the species capable of CCA took advantage of the photons that were not used by its competitors. The rate at which species can change their pigmentation also plays a key role in phytoplankton competition, as has been shown in competition experiments with Pseudanabaena strains capable of CCA (Stomp et al., in press). To assess the pigmentation of Synechococcus and Pseudanabaena in their natural habitat, many strains of Synechococcus and Pseudanabaena were isolated from the Baltic Sea. The investigation of these isolates raised questions with regard to their diversity, abundance and distribution in the natural environment. This thesis aimed at answering some of these questions.

16

Chapter 1

Aim of this thesisThe aim of this thesis was to investigate the distribution of differently pigmented co-

existing picocyanobacteria in the natural environment and to study the mechanisms of ecotype differentiation in Synechococcus and Pseudanabaena populations at the genetic level using culture independent methods as well as cultivated isolates.

OutlineThis thesis consists of four scientific papers and a synthesis.

Chapter 2: Colourful coexistence of red and green picocyanobacteria in lakes and seas.In this chapter 70 different aquatic ecosystems (ranging from the oligotrophic ocean to

turbid brown peat lakes) were analyzed with respect to the underwater light spectrum and the abundance of red and green picocyanobacteria (Stomp et al. 2007). The results were compared with a parameterized competition model that predicted opportunities for coexistence of red and green phytoplankton. The field data were consistent with laboratory experiments showing the coexistence of red and green picocyanobacteria (Stomp et al., 2004), and proved coexistence of red and green phytoplankton species in lakes and seas by niche differentiation along the light spectrum.

Figure 1.2 The operon structure encoding the alpha and beta subunit genes of the phycoerythrin (cpe) and phycocyanin (cpc) proteins. The transcription of the genes is from left to right. Indicated by the arrows are the forward (black) and reverse (white) primers used in this thesis. Primer names accompany the arrows. The information of the mRNA is translated in amino-acid chains encoding the alpha and beta subunits. Both amino-acid chains are subsequently assembled into one protein.


17

Chapter 3: Diversity and phylogeny of Baltic Sea picocyanobacteria inferred from their ITS and phycobiliprotein operons.

In this chapter the distribution of red and green picocyanobacteria in the Baltic Sea is reported (Haverkamp et al. 2008). Using flow-cytometry and clone libraries derived from DNA extracted from environmental samples it is demonstrated that phycoerythrin (PE) (red) and phycocyanin (PC) (green) rich Synechococcus coexist. The Baltic Sea picocyanobacteria are related to freshwater Synechococcus. Furthermore, analysis of the genes encoding for the PE and PC proteins (Fig 1.2) showed that the Synechococcus group separated phylogenetically into three different ecotypes, each characterized by a different pigmentation.

Chapter 4: Rapid diversification of red and green Synechococcus strains in the Baltic Sea.Almost 50 closely related Synechococcus isolates were analyzed in order to assess the

importance of microdiversity within this genus. By using a multi-locus sequence approach it was demonstrated that closely related genotypes at the 16S rRNA level can have different pigmentation phenotypes. Due to this microdiversity it is difficult to interpret environmental studies using clone libraries of Synechococcus 16S rRNA genes. Closely related 16S rRNA genotypes may exhibit totally different phenotypes. This can only be resolved through cultures that provide the whole genome. Using an extensive culture collection of Baltic Sea Synechococcus strains it was discovered that horizontal gene transfer was probably involved in the generation of microdiversity among this group of picocyanobacteria.

Chapter 5: Phenotypic and genetic diversification of Pseudanabaena.This chapter focuses on the extension of the present genetic knowledge of the small

filamentous cyanobacteria Pseudanabaena. Isolates of Pseudanabaena from the Baltic Sea and from the Albufera de Valencia (Spain) were used to identify various lineages that showed high levels of microdiversity. The results hinted to the presence of endemic and cosmopolitan species present in both ecosystems. Furthermore, it was found that purifying selection at the locus of the phycocyanin operon promoted evolutionary diversification in populations of Pseudanabaena.

Chapter 6: General Discussion.This final chapter integrates the research presented in this thesis and discusses the results in

their connection to one another, reaching some overall conclusions.

Chapter 2Colourful coexistence of red and green picocyanobacteria in lakes and seas

Maayke Stomp1

Jef Huisman1*

Lajos Vörös2

Frances R. Pick3

Maria Laamanen4

Thomas Haverkamp5

Lucas J. Stal5

1Aquatic Microbiology, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Nieuwe Achtergracht 127, 1018 WS Amsterdam, The Netherlands

2Balaton Limnological Research Institute of the Hungarian Academy of Sciences, PO Box 35, H-8237 Tihany, Hungary

3Biology Department, University of Ottawa, PO Box 450, Ottawa, ON K1N 6N5, Canada

4|Finnish Institute of Marine Research, PO Box 2, FIN-00561 Helsinki, Finland (Present address: Ministry of the Environment, PO Box 35, FIN-00023 Government, Finland)

5Netherlands Institute of Ecology (NIOO-KNAW), Centre for Estuarine and Marine Ecology, PO Box 140, 4400 AC Yerseke, The Netherlands

Published in: Ecology Letters. Volume: 10 Pages: 290-298 Year: 2007.Running title: Coexistence in the light spectrum.Key words: adaptive radiation, biodiversity, competition for light, coexistence, cyanobacteria, light spectrum, phycocyanin, phycoerythrin, resource competition, Synechococcus

20

Chapter 2

Abstract The paradox of the plankton inspired many studies on the mechanisms of species coexistence.

Recent laboratory experiments showed that partitioning of white light allows stable coexistence of red and green picocyanobacteria. Here, we investigate to what extent these laboratory find-ings can be extrapolated to natural waters. We predict from a parameterised competition model that the underwater light colour of lakes and seas provides ample opportunities for coexistence of red and green phytoplankton species. To test this prediction, we sampled picocyanobacteria of 70 aquatic ecosystems, ranging from clear blue oceans to turbid brown peat lakes. As predicted, red pi-cocyanobacteria dominated in clear waters whereas green picocyanobacteria dominated in turbid waters. We found widespread coexistence of red and green picocyanobacteria in waters of interme-diate turbidity. These field data support the hypothesis that niche differentiation along the light spectrum promotes phytoplankton biodiversity, thus providing a colourful solution to Hutchinson’s plankton paradox.

Coexistence of red and green picocyanobacteria

21

IntroductionPhytoplankton species compete for only a handful of resources (e.g., nitrogen, phosphorus,

iron, silica, light). This suggests limited opportunity for niche differentiation. Yet, a single millilitre of water may contain dozens of different phytoplankton species. What explains the surprising biodiversity of the plankton? This paradox of the plankton, formulated by Hutchinson (1961), has motivated a plethora of studies on competition and community structure (Tilman 1982; Sommer 1985; Grover 1997; Huisman & Weissing 1999; Litchman & Klausmeier 2001). Classic ecological theory predicts that niche differentiation reduces competition among species, and thereby facilitates coexistence (Gause 1934; MacArthur & Levins 1967; Hutchinson 1978). Darwin’s finches are a famous example (Darwin 1859; Lack 1974). A rich variety of finch species coexist on the Galápagos islands, as adaptive radiation in beak morphology has enabled niche differentiation of the finch species along a spectrum of different seed sizes (Grant & Grant 2002).

Similarly, light offers a spectrum of resources, ranging from blue light at short wavelengths, via green and yellow, to red light at long wavelengths. Although competition theory has largely ignored the light spectrum as a major axis of niche differentiation, plankton ecologists have long recognized that a rich diversity of photosynthetic pigments allows phytoplankton species to utilize different wavelengths (Engelmann 1883; Bricaud et al. 1983; Wood 1985; Sathyendranath & Platt 1989; Kirk 1994; Falkowski et al. 2004). For instance, red picocyanobacteria use the pigment phycoerythrin to absorb green light, whereas green picocyanobacteria use the pigment phycocyanin to absorb red light (Fig. 2.1a). Hence, one might hypothesize that they can share the light spectrum by specialization on different wavelengths. Indeed, recent competition models and laboratory experiments showed that red picocyanobacteria win the competition in green light, green picocyanobacteria win in red light, while red and green picocyanobacteria coexist in the full spectrum provided by white light (Stomp et al. 2004). One might argue, however, that underwater light fields do not resemble a white spectrum, because water, dissolved organic matter, and other constituents bring colour into the water column. Can these models and laboratory experiments be extrapolated to natural waters? Does partitioning of the underwater light spectrum mediate the coexistence of a colourful mixture of phytoplankton species in aquatic ecosystems?

To address these questions, we apply a fully parameterised competition model to predict the outcome of competition between red and green phytoplankton species in different natural waters. We test the model predictions by sampling red and green picocyanobacteria from many different aquatic ecosystems, ranging from clear blue oceans to dark brown peat lakes.

Competition modelThe underwater light spectrum of natural waters largely depends on light attenuation by water itself, by the “background turbidity” caused by dissolved organic matter (known as gilvin in the optics literature) and inanimate suspended particles (tripton, like sediment and detritus), and by the phytoplankton species present in the water column (Kirk 1994). Water absorbs

22

Chapter 2

strongly in the red part of the spectrum, whereas the background turbidity is responsible for rapid attenuation of blue wavelengths (Fig. 2.1b). Hence, with increasing background turbidity, the underwater light spectrum is shifted towards the red. The total light absorption by all these constituents determines the underwater light spectrum. For example, in the Baltic Sea light absorption in the blue and the red end of the spectrum is of a similar magnitude (Fig. 2.1b), resulting in an underwater light spectrum that narrows to the green wavelengths (Fig. 2.1c).

We consider a vertical water column, in which the phytoplankton species, gilvin and tripton are all homogeneously mixed throughout the surface mixed layer. Let I(λ,z) denote the light intensity of wavelength λ at depth z. Sunlight enters the water column with an incident light spectrum Iin(λ). According to a spectrally explicit version of Lambert-Beer’s law, the underwater light spectrum changes with depth (Sathyendranath & Platt 1989; Kirk 1994; Stomp et al. 2004):

Formules Thomas Haverkamp

Arne Heijenga

July 26, 2008

1

(λ, z) = Iin(λ)EXP

−KW (λ)z −KBG(λ)z −

ni=1

ki(λ)Niz

2

γi(z) = 700

400

ai(λ)ki(λ)I(λ, z)dλ

3

dNi

dt=

Ni

zm

zm

0

pmax,iγi(z)(pmax,i/φi) + γi(z)

dz − LiNi i = 1, ..., n

4

KBG(λ) = KBG(484)EXP (−S(λ− 484))

1

(1)

where KW(λ) is the absorption spectrum of water, KBG(λ) is the absorption spectrum of the background turbidity (tripton plus gilvin), ki(λ) is the specific absorption spectrum of phytoplankton species i, Ni is the population density of phytoplankton species i, and n is the number of phytoplankton species. We note, from Eq.1, that the underwater light spectrum is dynamic. For instance, changes in the population densities of phytoplankton species can shift the underwater light spectrum.

The number of absorbed photons available for photosynthesis by a phytoplankton species i at a given depth z depends on its photosynthetic action spectrum and on the light spectrum at this depth (Sathyendranath & Platt 1989; Stomp et al. 2004):


Arne Heijenga

July 26, 2008

1



ni=1

ki(λ)Niz

2

γi(z) = 700

400


3

dNi

dt=

Ni

zm

zm

0


dz − LiNi i = 1, ..., n

4

KBG(λ) = KBG(484)EXP (−S(λ− 484))

1

(2)

where ai(λ) converts the absorption spectrum into the action spectrum of phytoplankton species i. In many species, photons that have been absorbed are utilized with equal efficiency, irrespective of their wavelengths. That is, the absorption spectrum and action spectrum are often quite similar (Kirk 1994; Lewis et al. 1985). For simplicity, therefore, we here assume that the absorption spectrum and action spectrum have the same shape (i.e., ai(λ) = 1 for all λ). We further assume that the specific growth rate of each phytoplankton species i is an increasing, saturating function of the number of photons it has absorbed (Sathyendranath & Platt 1989):


Arne Heijenga

July 26, 2008

1



ni=1

ki(λ)Niz

2

γi(z) = 700

400


3

dNi

dt=

Ni

zm

zm

0


dz − LiNi i = 1, ..., n

4

KBG(λ) = KBG(484)EXP (−S(λ− 484))

1

(3)

where pmax,i is the maximum specific growth rate of species i, φi is the growth efficiency (‘quantum yield’) at low light intensities, Li is the specific loss rate due to factors such as grazing


23

and sinking, and zm is the depth of the surface mixed layer. Essentially, Eq.3 states that the growth rates of the species are governed by the photons they have absorbed. That is, there is no direct interference between the species. Instead, the species compete for light by absorption of photons in specific regions of the light spectrum. Species with similar light absorption spectra will therefore face stronger competition for light.

Numerical simulations of the model were based on a fourth order Runga-Kutta procedure for time integration, and Simpson’s rule for depth integration. Model parameters for our simulations were obtained as follows. For the incident light spectrum, Iin(λ), we used the

Figure 2.1 Optical characteristics of red and green picocyanobacteria and their environment. (a) Absorption spectra of red and green picocyanobacteria isolated from the Baltic Sea. (b) Light absorption spectra of pure water (blue line) and gilvin plus tripton in the Pacific Ocean (light brown line), the Baltic Sea (medium brown), and a peat lake (dark brown). (c) Underwater light spectra measured in the Baltic Sea. The spectrum narrows to the green waveband with increasing depth.

24

Chapter 2

surface spectrum measured at the Baltic Sea on July 2004 (Fig. 2.1c). The absorption spectrum of pure water was taken from the literature (Pope & Fry 1997). The absorption spectrum of the background turbidity can be described as an exponentially decreasing function of wavelength (Bricaud et al. 1981; Kirk 1994):


Arne Heijenga

July 26, 2008

1



ni=1

ki(λ)Niz

2

γi(z) = 700

400


3

dNi

dt=

Ni

zm

zm

0


dz − LiNi i = 1, ..., n

4

KBG(λ) = KBG(484)EXP (−S(λ− 484))

1

(4)

where KBG(484) is the background turbidity at a reference wavelength of 484 nm, and S is the slope of the exponential decline. The value of KBG(484) depends on the concentration of gilvin and tripton (see Supplementary Material). The slope S varies between 0.010 and 0.020 nm-1, and we will here assume a typical value of S = 0.017 nm-1 (Kirk 1994). The growth and loss parameters of the picocyanobacteria (pmax, φ, L) were estimated from our earlier studies (Lavallée & Pick 2002; Stomp et al. 2004). We assumed that the parameter values of red and green picocyanobacteria are identical, except for their absorption spectra. The specific absorption spectra of red and green picocyanobacteria were measured with an AMINCO DW-2000 double-beam spectrophotometer (Stomp et al. 2004), and are shown in Fig. 2.1a. Parameter values and their sources are listed in Table 2.1.


25

Table 2.1 Parameter values and their interpretation

Symbol Interpretation Units Value

Independent variables

t time d -

z depth m -

λ wavelength nm -

Dependent variables

Ni Population density of spe-cies i

cells m-3 -

γi (z) Absorbed photons by spe-cies i

μmol photons cell-1 s-1 -

I ( λ, z) Underwater light spectrum μmol photons m-2 s-1 nm-1 -

Parameters

Iin( λ) Spectrum of incident light μmol photons m-2 s-1 nm-1 Measured (Fig.1c)

KW( λ) Absorption spectrum of pure water

m-1 Literature*

KBG( λ) Absorption spectrum of background turbidity (tripton plus gilvin)

m-1 Calculated (Eq.2)

KBG(484) Absorption of background turbidity at 484 nm

m-1 Measured range (0.03 – 7.0)

S Exponential decline of ab-sorption spectrum of back-ground turbidity

nm-1 0.017†

ki( λ) Absorption spectrum of spe-cies i

m2 cell-1 Measured (Fig.1a)

ai( λ) Conversion of absorption spectrum into action spec-trum of species i

- 1

zm Depth of surface mixed layer m Wide range (1 – 100)

Li Specific loss rate of species i d-1 0.67‡

pmax,i Maximum growth rate of species i

d-1 1.0‡

φi Photosynthetic efficiency of species i

cells d-1 (μmol photons s-1)-1 2.0 x 1012 §

Notes: *Pope & Fry (1997); †Kirk (1994); ‡Lavallée & Pick (2002); §Stomp et al. (2004).

26

Chapter 2

Materials and Methods

Sampling picocyanobacteria.We sampled picocyanobacteria from a wide variety of waters covering a large range of

background turbidities. Our sampling sites included station ALOHA in the subtropical Pacific Ocean, 9 sampling stations in the Baltic Sea, and 60 lakes in Canada, Hungary, Italy, Nepal and New Zealand. An overview of all 70 sampling stations is given in the Supplementary Material.

Counting picocyanobacteria.The concentrations of red and green picocyanobacteria in samples from the Baltic Sea and

Pacific Ocean were counted by flow cytometry (Jonker et al. 1995; Vives-Rego et al. 2000), using a Coulter Epics Elite ESP flow cytometer (Beckman Coulter Nederland BV, Mijdrecht, Netherlands) equipped with a green laser (525 nm) and a red laser (670 nm). The flow cytometer distinguished between picocyanobacteria and larger phytoplankton by their size (using side scattering). Red and green picocyanobacteria were distinguished based upon their different fluorescence signals. Cells rich in phycoerythrin emitted orange light (550-620 nm) when excited by the green laser, whereas cells rich in phycocyanin emitted far red light (> 670 nm) when excited by the red laser.

The concentrations of red and green picocyanobacteria in the lake samples were counted by epifluorescence microscopy using blue and green filters (Pick 1991; Vörös et al. 1998). When excited by blue light, cells rich in phycoerythrin emit yellow to orange light, while cells without phycoerythrin appear dull red. When excited by green light, both red and green picocyanobacteria emit an intense red light. Both groups of picocyanobacteria can be easily distinguished from eukaryotic picoplankton or prochlorophytes, which fluoresce a very faint red or not at all.

Light spectra and absorption spectra.Spectra of the incident light and underwater light spectra were measured with a RAMSES-

ACC-VIS spectroradiometer (TriOS, Oldenburg, Germany). Absorption spectra of background turbidity were calculated by Eq.2, from the light attenuation of background turbidity at the reference wavelength of 484 nm, KBG(484). Further methodological details can be found in the Supplementary Material.


27

Results

Model PredictionsWe used the model to simulate competition for light between red and green picocyanobacteria

in different underwater light fields. As a first check, we ran a large number of simulations to investigate the model’s behaviour. The model did not display non-equilibrium dynamics or

multiple stable states. Each simulation was run until changes in population densities approached zero, and hence an equilibrium had been reached. In all simulations, the final outcome of competition was always independent of the initial abundances of the species.

Fig. 2.2a shows the underwater light spectra at the photic depth (defined as the depth at which the PAR-integrated irradiance equals 1% of the surface irradiance), calculated from Eqs.1 and 2, for three waters with different background turbidities. When background turbidity is low, typical of oligotrophic lakes, the underwater light spectrum is green (Fig. 2.2a), which matches the absorption spectrum of red picocyanobacteria (Fig. 2.1a). In this environment, the model predicts that red picocyanobacteria win (Fig. 2.2b). At intermediate background

Figure 2.2 Model simulations. (a) Light spectra at the photic depth in waters with, I, a low background turbidity (KBG(484) = 0.3 m-1), II, intermediate background turbidity (KBG(484) = 1.1 m-1), and III, high background turbidity (KBG(484) = 7 m-1). (b) Red picocyanobacteria win in clear waters with a deep surface-mixed layer (KBG(484)=0.3 m-1; zm=36 m). (c) Stable coexistence of red and green picocyanobacteria in waters of intermediate turbidity and mixing depth (KBG(484)=1.1 m-1; zm=17 m). (d) Green picocyanobacteria win in turbid waters with a shallow surface-mixed layer (KBG(484)=7 m-1; zm=8 m).

28

Chapter 2

turbidity typical for mesotrophic lakes and the coastal zone, the underwater light spectrum (Fig. 2.2a) overlaps with the absorption spectra of both picocyanobacteria. Here, the model predicts stable coexistence of red and green picocyanobacteria (Fig. 2.2c). At high background turbidity, typical of eutrophic lakes, the underwater light spectrum is shifted towards the red (Fig. 2.2a), and here green picocyanobacteria are the superior competitors (Fig. 2.2d). Thus, along a gradient of background turbidity, theory predicts that red picocyanobacteria are gradually replaced by green picocyanobacteria.

Fig. 2.3 plots the outcome of competition as a function of background turbidity and mixing depth of the surface mixed layer. If the surface mixed layer is deep and the background turbidity is high (upper right area in Fig. 2.3), conditions are too dark for the growth of picocyanobacteria. If the surface mixed layer is shallow (lower part of Fig. 2.3), the picocyanobacteria are exposed to the white light spectrum near the water surface, in which both the red and green species can coexist. If the surface mixed layer has an intermediate depth, the model predicts a gradual transition from red to green picocyanobacteria with increasing background turbidity (Fig. 2.3).

Testing the Model Predic-tions in Lakes and Seas

We first tested the model predictions in the Baltic Sea. Here, we found widespread coexistence of red and green picocyanobacteria. At sampling stations with a deep surface mixed layer, the reds and greens typically coexisted throughout

Figure 2.3 The predicted outcome of competition plotted as function of background turbidity and surface-mixed-layer depth. The graph is based on a grid of 100 x 100 simulations. Dashed line indicates the photic depth, which depends on the background turbidity of the water column. Points I, II, and III correspond to the simulations shown in Figure 2.2. Model parameters: see Table 2.1.


29

the surface layer (Fig. 2.4a). At sampling stations with a shallower surface mixed layer, the red and green picocyanobacteria coexisted near the surface while red picocyanobacteria formed a deep chlorophyll maximum underneath (Fig. 2.4b). Isolation of picoplankton strains from the Baltic Sea revealed a colourful community of picocyanobacteria and pico-eukaryotes (Fig. 2.4c), spanning a full rainbow from red to green pigmentation. Analysis of the sequences of the 16S rRNA gene and the ribosomal internally transcribed spacer (ITS-1) region show that the varicoloured picocyanobacteria of the Baltic Sea are all closely related and fall within the subalpine cluster II and the Bornholm Sea cluster of the Synechococcus complex (Crosbie et al. 2003; Ernst et al. 2003). Fig. 2.4c thus illustrates that closely related picocyanobacteria may radiate into a rich variety of differently pigmented strains.

As a next step, we extended the analysis to the complete data set of 70 sampling stations, covering a wide range of background turbidities (see Table S1 of the Supplementary Materials for details). At low background turbidity (KBG(484) < 0.6 m-1), red picocyanobacteria were dominant (Fig. 2.5). At high background turbidity (KBG(484) > 3 m-1), green picocyanobacteria were dominant. The data set shows coexistence of reds and greens in a large window of intermediate background turbidities. For comparison, model predictions are plotted by the

Figure 2.4 Coexistence of red and green picocyanobacteria in the Baltic Sea. (a) Depth profiles from a sampling station with a homogeneous distribution of coexisting red and green picocyanobacteria up to a depth of 18 m. (b) Depth profiles from a sampling station with a homogeneous distribution of coexisting reds and greens near the surface, and a deep chlorophyll maximum of red picocyanobacteria underneath. Red circles indicate red picocyanobacteria, green circles indicate green p i c o c y a n o b a c t e r i a , yellow triangles indicate temperature. (c) Picoplankton strains isolated from the Baltic Sea, illustrating a colourful biodiversity of green pico-eukaryotes (the wells indicated by a *) and varicoloured picocyanobacteria of the subalpine cluster II of Synechococcus (all other wells).

30

Chapter 2

solid lines in Fig. 2.5, assuming that the surface-mixed-layer depth equals the photic depth, which corresponds to a slice along the dashed line in Fig. 2.3. The competition model predicts a similar transition from red to green picocyanobacteria as observed in the sampled lakes and seas. Linear regression of predicted versus observed relative abundances revealed that the model explained 54% of the variation in the data set (R2 = 0.54, n = 70, P < 0.0001). Linear regression of the residuals versus background turbidity was not significant (R2 = 0.01, n = 70, P = 0.20). This indicates that the model effectively captured the relationship between the relative abundances of red and green picocyanobacteria and background turbidity.

As a final check, we tested the sensitivity of the model predictions to our simplifying assumption that the surface-mixed-layer depth equaled the photic depth (where irradiance is 1% of surface irradiance). For this purpose, we ran the model using a shallower and a deeper surface mixed layer, corresponding to 0.5% and 5% of the surface irradiance, respectively. This showed that the model predictions were not very sensitive to our assumption. The coexistence window in Fig. 2.5 slightly widened or narrowed, respectively, and the model still explained 43% to 33% of the variation in the data set.

Figure 2.5 Relative abundances of red picocyanobacteria (red symbols) and green picocyanobacteria (green symbols) observed in lakes and seas plotted against background turbidity. Data are from 25 European lakes (triangles), 30 Canadian lakes (squares), 5 lakes in Nepal and New Zealand (diamonds), and 9 sampling stations in the Baltic Sea (circles). At sampling station ALOHA, in the subtropical Pacific, background turbidity was below the range shown in the graph, but the picocyanobacteria of the Synechococcus group were dominated by nearly 100% red cells. The red and green curves indicate the model predictions for red and green picocyanobacteria, respectively, assuming a surface-mixed-layer depth equal to the photic depth. Model parameters: see Table 2.1.


31

DiscussionMany previous studies have focused on light intensity as a major axis of niche differentiation in

aquatic and terrestrial plant communities. Theory and experiments have shown that competition for light can be successfully predicted from knowledge of species traits and environmental conditions (Huisman et al. 1999; Litchman 2003; Passarge et al. 2006). Field studies have shown that light intensity is an important selective factor in phytoplankton communities (Sommer 1993; Rocap et al. 2003; Huisman et al. 2004). For instance, the Prochlorococcus complex in the oligotrophic ocean is differentiated into several different ecotypes (Moore et al. 1998; Rocap et al. 2003; Johnson et al. 2006). Some of these ecotypes are adapted to high light intensities near the water surface, whereas other ecotypes are adapted to low light intensities encountered at greater depths.

This study builds on previous work of plankton ecologists, who have pointed out that the light spectrum is an important additional axis of niche differentiation (Engelmann 1883; Wood 1985; Kirk 1994), and may play a major selective role in phytoplankton communities (Béjà et al. 2001; Rocap et al. 2003). Recent laboratory competition experiments demonstrated that partitioning of the light spectrum enables stable coexistence of red and green picocyanobacteria in white light (Stomp et al. 2004). Our results show that, essentially, these lab findings can be extrapolated to natural waters. Distribution patterns of picocyanobacteria of the Synechococcus complex are strongly related to the underwater light colour, with a gradual transition from predominance of red strains in clear waters to green strains in turbid waters (Fig. 2.5). Moreover, consistent with the model predictions, we found widespread coexistence of red and green picocyanobacteria in many aquatic ecosystems all over the world. This global pattern is consistent with various local studies, which have shown dominance of red picocyanobacteria in the open ocean (Li et al. 1983; Platt et al. 1983; Campbell & Carpenter 1987; Campbell & Vaulot 1993), and coexistence of red and green picocyanobacteria in waters of intermediate turbidity, such as coastal ecosystems, estuaries and lakes (Pick 1991; Vörös et al. 1998; Murrell & Lores 2004; Katano et al. 2005; Mózes et al. 2006).

Although we focused here on red and green picocyanobacteria, other phytoplankton groups will be involved in competition for light as well. For instance, the absorption spectra of green algae, diatoms, and prochlorophytes all partially overlap with the absorption spectra of red and green picocyanobacteria, and may thereby suppress their numbers. Adding Prochlorococcus to our model (results not shown) revealed that, due to their pigmentation in the blue part of the spectrum, Prochlorococcus is predicted to dominate competition for light in the clearest oceans. In slightly more turbid waters, Prochlorococcus was gradually replaced by red picocyanobacteria, which in turn were gradually replaced by green picocyanobacteria in turbid waters (as in Fig. 2.5). Thus, in principle at least, the theoretical framework presented here can be further extended to define the spectral niches of other phytoplankton groups as well.

A restriction of our competition model is that it assumes complete mixing of the phytoplankton species throughout the surface mixed layer. This may be a reasonable approximation for turbulent surface waters, and demonstrates that vertical stratification is not

32

Chapter 2

required for the coexistence of red and green phytoplankton species. Many waters, however, are not well mixed. Moreover, some cyanobacterial species can regulate their buoyancy, and thereby adjust their vertical position within the water column. An example is Planktothrix rubescens, a red filamentous cyanobacterium that can develop dense monolayers in the metalimnion of stratified lakes (Dokulil & Teubner 2000; Walsby 2005). In principle, our phytoplankton competition models can be extended to include weak vertical mixing, using systems of partial differential equations (Klausmeier & Litchman 2001; Huisman et al. 2006). It would be an interesting next step to investigate how weak mixing favours species with different pigment composition at different depths.

The analogy between niche differentiation of picocyanobacteria and niche differentiation of Darwin’s finches (Darwin 1859; Lack 1974; Grant & Grant 2002) is interesting. Niche differentiation among Darwin’s finches has been ascribed to the evolutionary process of adaptive radiation, during which a single ancestor radiated into different species occupying different niches along the spectrum of different seed sizes. Is niche differentiation of picocyanobacteria along the light spectrum the result of a similar process of adaptive radiation? All cyanobacteria contain the bluegreen pigment phycocyanin, whereas only some strains contain the red pigment phycoerythrin. Molecular phylogenies have shown that clusters of closely related picocyanobacteria often contain both red and green strains (Crosbie et al. 2003; Ernst et al. 2003), as exemplified by the closely related red and green picocyanobacteria from the Baltic Sea (Fig. 2.4c). This may indicate that the ancestral strains of these clusters all contained both phycocyanin and phycoerythrin, or that different clusters acquired red pigments during independent adaptive radiations, by mutation or horizontal gene transfer (Ernst et al. 2003). Perhaps evolutionary experiments, similar to ongoing experiments with E. coli (Lenski & Travisano 1994), might shed further light on the potential for adaptive radiation in these varicoloured picocyanobacteria.

In conclusion, the theory and field data presented here show that niche differentiation along the underwater light spectrum offers ample opportunities for coexistence of phytoplankton species. These findings add a colourful new solution to Hutchinson’s (1961) classic paradox of the plankton, and suggest that the underwater light spectrum deserves full attention in future studies of phytoplankton competition.


33

AcknowledgementsWe thank the crew of the research vessels Aranda and Kilo Moana for help during sampling,

D.M. Karl for the opportunity to join HOT cruise 174, and B. Pex, H. van Overzee and R. Poutsma for their help in the Dutch lakes. We also thank A. Wijnholds-Vreman for support with the flow cytometer, H.J. Gons, S.G.H. Simis and P. Stol for help with the filterpad method, and G.G. Mittelbach and the anonymous referees for their helpful comments on the manuscript. M.S. and J.H. were supported by the Earth and Life Sciences Foundation (ALW), which is subsidised by the Netherlands Organization for Scientific Research (NWO). L.V. was supported by the Hungarian Research Fund (OTKA TO-42977). T.H. and L.J.S. acknowledge support from the European Commission through the project MIRACLE (EVK3-CT-2002–00087).

Supplementary MaterialThe following supplementary material is available for this article:

Table S1 Overview of the 70 sampling stations used in this study.

Appendix S1 Sampling stations.

Appendix S2 Measurement of background turbidity.

Appendix S3 Algorithm to calculate background turbidity.

Figure S1 Light attenuation by phytoplankton versus chlorophyll concentration.

Figure S2 Predicted versus observed background turbidity.

This material is available as part of the online article from: http://www.blackwell-synergy.com/doi/full/10.1111/j.1461-0248.2007.01026.x

Please note: Blackwell Publishing is not responsible for the content or functionality of any supplementary materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.

34

Chapter 2

Table S1 Sampling stations and some of their characteristics.

Sampling stations Area (km2)

Average depth (m)

Sampling depth (m)

KBG(484)(m-1)

Red picos(%)

Hungary, lakes

L. Balaton (Fűzfő Basin) 596 3.2 0 – 2 1.65 73

L. Balaton (Tihany basin) 596 3.2 0 - 3.7* 2.73 57

L. Balaton (Zánka basin) 596 3.2 0 – 2 1.94 63

L. Balaton (Szigliget basin) 596 3.2 0 – 2 1.49 27

L. Balaton (Keszthely basin) 596 3.2 0 - 2.3* 2.17 73

L. Balaton (Zala river) - - - 3.03 6

Kis-Balaton (upper res.) 18 1 0 – 1* 3.82 0

Kis-Balaton (lower res.) 16 0.8 0 - 0.8* 4.66 0

Marcali reservoir 4 1.8 0 - 1.8* 3.48 0

Monostorapáti reservoir 0.3 2 0 – 2* 6.00 4

L. Pécsi 0.75 3.3 0 - 3.3* 1.41 78

L. Herman Otto 0.29 1 0 – 1* 3.95 32

Deseda reservoir 2.2 2.9 0 - 2.9* 5.90 2

Italy, lakes

L. Como 146 154 0 – 20* 0.28 98

L. Maggiore 212 177 0 – 20* 0.43 96

L. Garda 368 133 0 – 20* 0.22 98

L. Iseo 62 123 0 – 20* 0.29 99

L. Orta 18 72 0 – 20* 0.46 100

L. Mergozzo 1.8 45 0 – 20* 0.25 99

L. Varese 15 11 0 – 11* 0.69 54

L. Candia 1.3 5.9 0 - 5.9* 0.49 50

L. Paione Superiore 0.014 5.1 0 - 5.1* 0.35 100

L. Paione Inferiore 0.014 7.3 0 - 7.3* 0.12 100

L. Azzuro 0.003 2 0 – 2* 0.30 100

L. Devero 1 20 0 – 20* 0.35 100

Nepal, lakes

L. Piramide Superiore 0.6 8.2 0 - 8.2* 0.21 100

L. Piramide Inferiore 1.7 14.8 0 - 14.8* 0.12 100

New Zealand, lakes

Okareka 3.5 12 2 0.30 55

Tarawera 41 50 2 0.36 100

Rotorua 80 6.8 2 0.80 60

Ontario, lakes

Superior 81900 145 2 0.19 100

Erie (east) 6150 27 2 0.42 100

Erie (central) 15390 18 2 0.32 100

Erie (west) 3680 7.6 2 0.94 67


35

Sampling stations Area (km2)

Average depth (m)

Sampling depth (m)

KBG(484)(m-1)

Red picos(%)

Ontario 19680 90 2 0.41 100

Bay of Quinte 257 8.3 2 2.15 11

Cherry 0.22 5.5 2 0.83 16

Triangle 0.27 4.7 2 0.59 49

Bay 1.6 11 2 0.35 100

Buller 0.31 20 2 0.46 100

Halls 5.7 ? 2 0.24 88

Koshlong 4.1 10 2 0.48 68

Anstruther 6.3 13 2 0.69 19

L’Amable 1.8 23 2 0.48 100

Opeongo 22 ? 2 1.21 21

St. Nora ? ? 2 0.91 52

Crawford 0.02 ? 2 0.45 100

Drag 10 18 2 0.48 92

Wolf 1.2 4.8 2 0.86 35

Picard 0.76 10 2 0.48 99

Salmon 1.7 11 2 0.35 100

Bobs GB 4.8 14 2 0.41 93

Chub 0.34 8.8 2 0.85 0

Jacks 5.1 17 2 0.57 95

Bobs WB 9.4 9.5 2 0.74 66

St. George 0.10 / 2 0.66 53

Rice 100 2.4 2 1.52 18

Heart 0.18 3.7 2 2.34 0

Alberta, lakes

Island 7.8 3.7 2 0.76 90

Amisk 5.2 16 2 1.11 91

Baltic Sea

LL3A 3.7 x 105 69 0 – 20* 0.86 55

CYA04_2 3.7 x 105 75 0 – 14* 0.31 77

CYA04_3 3.7 x 105 63 0 – 17* 0.70 69

CYA04_7 3.7 x 105 85 0 – 14* 0.63 72

CYA04_11 3.7 x 105 77 0 – 15* 1.17 39

CYA04_15 3.7 x 105 69 0 – 15* 0.67 66

CYA04_20 3.7 x 105 62 0 – 30* 0.56 51

CYA04_22 3.7 x 105 90 0 – 20* 0.71 52

CYA04_28 3.7 x 105 111 0 – 40* 0.52 65

Pacific Ocean, Hawaii

ALOHA N.A. ~4000 0 – 120* 0.016 100

*Samples were integrated over the depth of the surface mixed layer

36

Chapter 2

Appendix S1

Sampling stationsDuring the summers of 1986-1988, 30 lakes in Canada and 3 lakes in New Zealand were

sampled, covering a wide range in background turbidities and water-column depths (Pick 1991). For each lake, 8-12 samples were taken from 2 m depth using a Van Dorn sampler. These samples were mixed.

During the summers of 1994 and 1995, 13 lakes in Hungary, 12 lakes in Italy and 2 lakes in Nepal were sampled (Vörös et al. 1998). In the deep lakes, the first 20 m of the water column was sampled with an integrating sampler. In the shallow lakes, ponds and reservoirs the whole water column was sampled by a Van Dorn sampler using an interval of 1 m, and these samples were mixed.

From 12 to 19 July 2004, 9 stations in the Baltic Sea (from 59.1oN to 60.0oN and from 22.2oE to 26.2oE) were sampled from the research vessel Aranda on Cruise Cyano-04 08/2004. Water samples were taken with a Rosette sampler from 0 to 30 m depth using a sample interval of 3 m. Temperature was measured using the Seabird 911 plus CTD sonde.

From 5 to 11 October 2005, Station ALOHA (23.4oN, 158oW) of the Hawaiian Ocean Time series (HOT) in the North Subtropical Pacific Ocean was sampled from the research vessel Kilo Moana on cruise number 174. Water samples were taken from 12 depths within the upper 200 m with a SeaBird (Model SBE-09) CTD Rosette system. An overview of all 70 sampling stations is given in Table S1.


37

Appendix S2

Measurement of background turbidity To calculate the underwater light field, the model uses the background turbidity at the

reference wavelength of 484 nm, KBG(484), as input parameter (Eq.4 of the main text). We determined KBG(484) spectrophotometrically, as the sum of the light absorption by gilvin, KGIL (484), and the light absorption by tripton, KTRIP (484).

Absorption by gilvinDissolved organic matter is known as ‘gilvin’ in the optics literature. To determine light

absorption by gilvin, water samples were filtered through 0.2 µm cellulose acetate filters (Schleicher and Schuell). Absorption spectra of the filtrate were measured by a Lambda 800 UV/VIS spectrophotometer (Perkin-Elmer, Wellesley, MA, USA) using a 5 cm quartz cuvet, with milli-Q water as reference (Simis et al. 2005). The parameter KGIL(484) is the light absorption by gilvin measured at 484 nm.

Absorption by triptonTripton refers to inanimate suspended particles in the water column. Absorption spectra

of suspended matter were determined on GF/F filters using the filterpad method (Yentsch 1962; Cleveland & Weidemann 1993; Simis et al. 2005). The spectra were measured with a Lambda 800 UV/VIS spectrophotometer (Perkin-Elmer, Wellesley, MA, USA) equipped with a 150-mm integrating sphere (Labsphere, North Sotton, NH, USA). For the correction of path length amplification the method of Cleveland and Weidemann (1993) was used. First, the absorption spectrum of the loaded filter, obtained after filtration of the water sample, was measured. This includes all seston (phytoplankton plus tripton). As a next step, the absorption spectrum of tripton on the filter was measured, after bleaching of phytoplankton pigments by boiling ethanol. The parameter KTRIP(484) is the light absorption by tripton measured at 484 nm.

38

Chapter 2

Appendix S3

An algorithm to calculate background turbidityIdeally, one would like to determine the background turbidity from direct measurements of

the light absorption by gilvin and tripton, as described in Appendix S2. However, for several sampling stations we did not have data on the absorption by gilvin and tripton. Therefore, we developed a simple algorithm to calculate the background turbidity from the total light attenuation coefficient and the chlorophyll concentration in the water column. This Appendix presents a concise description of the algorithm.

Partitioning of the total light attenuation The total light attenuation, KD, in natural waters is governed by light attenuation by gilvin

and tripton, KBG, attenuation by water itself, KW, and attenuation by phytoplankton, KPHYT (Kirk 1994). Hence, the total light attenuation at the reference wavelength of 484 nm can be partitioned as follows:

Formules Thomas Haverkamp Chapter 2

Supplemental

Arne Heijenga

August 2, 2008

1

KD(484) = KBG(484) + KW (484) + KPHY T (484)

2

10log [KD(484)] = 1.135310log [KD(P AR)] + 0.2023

3

KPHY T (484) = 0.0368 [Chl]

4

10log [KBG,pred(484)] = 0.900610log [KBG,meas(484)]

5

KBG(484) =1.593 [KD(P AR)]1.135 − 0.0136− 0.0368 [Chl]

1.11

1

(S1)

Accordingly, KBG(484) can be calculated if the values of the other attenuation coefficients in Eq S1 are known. The total light attenuation coefficient at 484 nm, KD(484), was estimated

Figure S1 Light attenuation coefficient of phytoplankton at 484 nm, KPHYT(484), as function of the chlorophyll a concentration.


39

from the attenuation coefficient of photosynthetic active radiation, KD(PAR), using the empirical relation (Balogh et al. 2000):


Supplemental

Arne Heijenga

August 2, 2008

1

KD(484) = KBG(484) + KW (484) + KPHY T (484)

2

10log [KD(484)] = 1.135310log [KD(P AR)] + 0.2023

3

KPHY T (484) = 0.0368 [Chl]

4


5

KBG(484) =1.593 [KD(P AR)]1.135 − 0.0136− 0.0368 [Chl]

1.11

1

(S2)

where KD(PAR) was estimated from vertical light profiles (PAR range, 400-700 nm), measured with a Licor Li-185 quantum sensor for the Baltic Sea and the lakes in Hungary, Italy and Nepal and with a Licor Li-190 quantum sensor for the lakes in Canada and New Zealand. Light attenuation by pure water at 484 nm is known, i.e., KW(484) = 0.0136 m-1 (Pope & Fry 1997). Light attenuation by phytoplankton, KPHYT(484), was calculated from chlorophyll a concentrations, as described below.

Absorption by phytoplankton at 484 nmWe established a relationship between KPHYT(484) and the chlorophyll concentration. For

this purpose, samples from 10 sampling stations in the Baltic Sea, at 11 different depths per sampling station, were each split into two subsamples. One set of subsamples was used for chlorophyll analysis while the other set of subsamples was used to determine the phytoplankton absorption spectra. Chlorophyll a concentrations were measured spectrophotometrically after hot ethanol extraction of phytoplankton collected on Whatman GF/F filters (Nusch 1980).

Figure S2 Background turbidity predicted from Eqs S1-S3 against measured background turbidity. Data points represent samples taken from five Dutch lakes (Lake Loosdrecht, Lake Proost, Lake Groote Moost, Lake t’Elfde, Lake IJsselmeer), nine sampling stations in the Baltic Sea, and two sampling stations near station ALOHA (Pacific Ocean, Hawaii).

40

Chapter 2

Light absorption spectra of the phytoplankton communities were obtained from the filterpad method described in Appendix S2, as the difference between the absorption spectrum of seston (phytoplankton plus tripton) and the absorption spectrum of tripton. The results show a strong relationship between the phytoplankton light absorption at 484 nm and the chlorophyll a concentration (Fig. S1):


Supplemental

Arne Heijenga

August 2, 2008

1

KD(484) = KBG(484) + KW (484) + KPHY T (484)

2

10log [KD(484)] = 1.135310log [KD(P AR)] + 0.2023

3

KPHY T (484) = 0.0368 [Chl]

4


5

KBG(484) =1.593 [KD(P AR)]1.135 − 0.0136− 0.0368 [Chl]

1.11

1

(S3)

where [Chl] is the chlorophyll a concentration in μg Chl L-1 (linear regression forced through the origin: R2 = 0.93, n=110, p<0.0001). Eq. S3 was used to calculate KPHYT(484) from the chlorophyll a concentrations for all sampling stations.

Calibration of the algorithmThe background turbidity, KBG(484), can now be calculated from Eqs S1-S3. To test this,

we compared the predicted background turbidity (Eqs.S1-S3) with the measured background turbidity (Appendix S2). For this purpose, we applied Eqs S1-S3 to an independent data set consisting of five Dutch lakes (Lake Loosdrecht, Lake Proost, Lake Groote Moost, Lake t’Elfde, Lake IJsselmeer), nine sampling stations in the Baltic Sea, and two stations near station ALOHA (Pacific Ocean, Hawaii), and at these sites we also measured the background turbidity following the procedures described in Appendix S2.

This showed a close correspondence between the predicted and measured background turbidity (Fig. S2):


Supplemental

Arne Heijenga

August 2, 2008

1

KD(484) = KBG(484) + KW (484) + KPHY T (484)

2

10log [KD(484)] = 1.135310log [KD(P AR)] + 0.2023

3

KPHY T (484) = 0.0368 [Chl]

4


5

KBG(484) =1.593 [KD(P AR)]1.135 − 0.0136− 0.0368 [Chl]

1.11

1

(S4)

based on linear regression forced through the origin, after log-transformation of the data (R2= 0.98, n=16, p<0.0001). The factor 0.9006 in Eq.S4 was incorporated as correction factor in the algorithm to improve our predictions.

Hence, combining the information in Eqs S1-S4, the following algorithm was obtained to predict the background turbidity at 484 nm from the light attenuation coefficient and chlorophyll a concentration:


Supplemental

Arne Heijenga

August 2, 2008

1

KD(484) = KBG(484) + KW (484) + KPHY T (484)

2

10log [KD(484)] = 1.135310log [KD(P AR)] + 0.2023

3

KPHY T (484) = 0.0368 [Chl]

4


5

KBG(484) =1.593 [KD(P AR)]1.135 − 0.0136− 0.0368 [Chl]

1.11

1

(S5)

We applied this semi-empirical algorithm to calculate the background turbidity at all 71 sampling stations. Furthermore, we suggest that the algorithm may also find application in other studies.


41

Supplementary ReferencesBalogh, K.V., Koncz, E. & Vörös, L. (2000). An empirical model describing the contribution of colour,

algae and particles to the light climate of shallow lakes. Verh. Internat. Verein. Limnol., 27, 2678-2681.

Cleveland, J.S. & Weidemann, A.D. (1993). Quantifying absorption by aquatic particles: a multiple scattering correction for glass-fiber filters. Limnol. Oceanogr., 38, 1321-1327.

Kirk, J.T.O. (1994). Light and Photosynthesis in Aquatic Ecosystems (2nd edn). Cambridge Univ. Press, Cambridge.

Nusch, E.A. (1980). Comparison of different methods for chlorophyll and phaeopigment determination. Arch. Hydrobiol. Beih. Ergebn. Limnol., 14, 14-36.

Pope, R.M. & Fry, E.S. (1997). Absorption spectrum (380-700 nm) of pure water. II. Integrating cavity measurements. Appl. Opt., 36, 8710-8723.

Pick, F.R. (1991). The abundance and composition of freshwater picocyanobacteria in relation to light penetration. Limnol. Oceanogr., 36, 1457-1462.

Simis, S.G.H., Peters, S.W.M. & Gons, H.J. (2005). Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water. Limnol. Oceanogr., 50, 237-245.

Vörös, L., Callieri, C., Balogh, K.V. & Bertoni, R. (1998). Freshwater picocyanobacteria along a trophic gradient and light quality range. Hydrobiologia, 370, 117-125.

Yentsch, C.S. (1962). Measurement of visible light absorption by particulate matter in the ocean. Limnol. Oceanogr., 7, 207-217.

Chapter 3Diversity and phylogeny of Baltic Sea picocyanobacteria inferred from

their ITS and phycobiliprotein operons

Thomas Haverkamp1

Silvia G. Acinas1†

Marije Doeleman1

Maayke Stomp2

Jef Huisman1, 2 Lucas J. Stal1, 2*

1Department of Marine Microbiology, Netherlands Institute of Ecology, NIOO-KNAW, P.O.Box 140, 4400 AC Yerseke, The Netherlands

2Aquatic Microbiology, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Nieuwe Achtergracht 127, 1018 WS Amsterdam, The Netherlands

† Present address: Institut de Ciencies del Mar, CMIMA, 08003, Barcelona, Spain

*For correspondence. E-mail [email protected]; Tel. (+31) 113 577300; Fax (+31) 113 573616.

Published in: Environmental Microbiology. Volume 10 pages: 174-188. Year: 2008Running title: Phylogeny of red and green picocyanobacteria.

44!

Chapter 3

SummaryPicocyanobacteria of the genus Synechococcus span a range of different colours, from red strains

rich in phycoerythrin (PE) to green strains rich in phycocyanin (PC). Here, we show that coexis-tence of red and green picocyanobacteria in the Baltic Sea is widespread. The diversity and phylog-eny of red and green picocyanobacteria was analysed using three different genes: 16S rRNA-ITS, the cpeBA operon of the red PE pigment, and the cpcBA operon of the green PC pigment. Sequenc-ing of 209 clones showed that Baltic Sea picocyanobacteria exhibit high levels of microdiversity. The partial nucleotide sequences of the cpcBA and cpeBA operons from the clone libraries of the Baltic Sea revealed two distinct phylogenetic clades: one clade containing mainly sequences from cultured PC-rich picocyanobacteria, while the other contains only sequences from cultivated PE-rich strains. A third clade of phycourobilin (PUB) containing strains of PE-rich Synechococcus spp. did not contain sequences from the Baltic Sea clone libraries. These findings differ from previously published phylogenies based on 16S rRNA gene analysis. Our data suggest that, in terms of their pigmentation, Synechococcus spp. represent three different lineages occupying different ecological niches in the underwater light spectrum. Strains from different lineages can coexist in light envi-ronments that overlap with their light absorption spectra.

Diversity and phylogeny of Baltic Sea picocyanobacteria

45

IntroductionPicocyanobacteria of the Synechococcus group span a range of different colours, depending

on their pigment composition (Wood, 1985; Olson et al., 1990; Pick, 1991; Vörös et al., 1998; Stomp et al., 2007). Picocyanobacteria with high concentrations of the pigment phycoerythrin (PE) absorb green light effectively, and have a red appearance. Picocyanobacteria with high concentrations of phycocyanin (PC) absorb red light effectively, and have a blue-green colour. Recent competition models and laboratory experiments showed that red picocyanobacteria win the competition in green light, green picocyanobacteria win in red light, while red and green picocyanobacteria can coexist in white light by partitioning of the light spectrum (Stomp et al., 2004). This matches their distribution patterns. Red picocyanobacteria are dominant components of the Synechococcus group in open ocean waters (Li et al., 1983; Platt et al., 1983; Campbell and Carpenter, 1987; Campbell and Vaulot, 1993), where green and particularly blue light penetrate deeply into the water column. Moreover, red picocyanobacteria can have two different bilin pigments known as phycoerythrobilin (PEB) and phycourobilin (PUB), which both bind to the apoprotein phycoerythrin. The absorption peak of PUB is shifted slightly further to the blue part of the spectrum, and picocyanobacteria with a high PUB/PEB ratio are typically dominant in oligotrophic regions of the oceans where blue light prevails (Olson et al., 1990; Wood et al., 1998; Toledo et al., 1999). In addition, some strains are able to modify their pigmentation through the synthesis of PE with two alternative chromophores, PEB and PUB (Type IV CA; Everroad et al., 2006). Green picocyanobacteria dominate in turbid waters, where red light prevails (Stomp et al., 2007). Coexistence of red and green picocyanobacteria can be found in waters of intermediate colouration, including coastal seas and many freshwater lakes (Pick, 1991; Vörös et al., 1998; Murrell and Lores, 2004; Katano et al., 2005; Mózes et al., 2006; Stomp et al., 2007).

The genus Synechococcus is polyphyletic. Several clusters have been identified, based on photosynthetic pigmentation, nitrogen requirements, motility and salinity (Herdman et al., 2001). In marine environments, Synechococcus spp. are dominated by members of cluster 5. Synechococcus cluster 5 is divided in two sub-clusters, 5.1 and 5.2. Both sub-clusters consist of isolates from the ocean as well as from coastal origin. Members of cluster 5.1 typically have a red colour. They produce PE as their main photosynthetic pigment, have a GC content between 55-62%, and require elevated salt levels for growth. In contrast, members of cluster 5.2 have a green appearance. They produce the pigment PC but lack PE, have a GC content between 63- 66%, and are often able to grow without elevated salt requirements (Herdman et al., 2001).

Freshwater picocyanobacteria are often assigned to Cyanobium, a genus closely related to Synechococcus. Cyanobium is only known from freshwater and brackish environments (Crosbie et al., 2003; Ernst et al., 2003). It contains PC as its main photosynthetic pigment and possesses a high GC content (66-71%). Cyanobium is composed of clusters that are distinguished by salt-tolerance and GC content (Herdman et al., 2001).

The phylogenetic tree of picocyanobacteria is not always consistent with their pigmentation type. Some strains isolated from marine and freshwater environments produce PE, but are

46

Chapter 3

related to the Cyanobium cluster according to sequence information of their 16S rRNA gene, the ribosomal internally transcribed spacer (ITS) region, and the rpoC1 gene (Crosbie et al., 2003; Ernst et al., 2003; Everroad and Wood, 2006). Conversely, most members of Synechococcus cluster 5.1 are rich in PE, but PC-rich isolates were obtained from the Red Sea. Although the genomic GC content of one of these isolates, strain RS9917, (64%) is within the range of Cyanobium, it is unknown whether this is also the case for the other strains of that clade (VIII) of cluster 5.1 (Fuller et al., 2003).

Here, we studied natural communities of picocyanobacteria from the Baltic Sea by constructing clone libraries of partial sequences of the 16S rRNA-ITS, cpeBA and cpcBA operons. The latter two encode for the pigments PE and PC, respectively. Earlier studies suggested that the phylogeny of cpcBA of freshwater picocyanobacteria correlated with pigmentation (Neilan et al., 1995; Robertson et al., 2001; Crosbie et al., 2003). Our results demonstrate that a phylogeny based on the operons encoding for phycocyanin and phycoerythrin in picocyanobacteria differs from earlier phylogenies based on the 16S rRNA-ITS operon.

Figure 3.1. The sampling stations S298, S300, S314 and S320 along the East-West transect from the Gulf of Finland to the Baltic Sea during the CYANO-cruise in 2004.


47

Results

Environmental conditionsStratification

In July 2004, water was sampled at 4 stations in the Gulf of Finland and Baltic Sea proper (Fig. 3.1). Station S298 had a triple thermal stratification at 5, 10 and 20 m depth (Fig. 3.2). The density profile of this station revealed that the upper 5 m was well mixed, while density gradually increased with depth below this shallow surface-mixed layer. Station S300 showed a clear surface-mixed layer of ~15 m depth. Station S314 had a slightly shallower surface-mixed layer, with a thermocline and pycnocline at 10-12 m depth. Both stations S300 and S314 had a subtle secondary stratification at ~21 m depth. Station S320 was not stratified, but showed nearly homogeneous vertical profiles of salinity, temperature, and density up to 30 m depth (Fig. 3.2).

Underwater light spectraThe underwater light spectrum of natural waters largely depends on light attenuation

by water itself, by the ‘background turbidity’ caused by dissolved organic matter (known as gilvin in the optics literature) and inanimate suspended particles (tripton, like sediment and

Figure 3.2. Vertical profiles of salinity, temperature, and density at the stations S298, S300, S314 and S320.

48

Chapter 3

detritus), and by phytoplankton species present in the water column (Kirk, 1994). Water absorbs strongly in the red part of the spectrum, whereas gilvin and tripton are responsible for rapid attenuation of blue wavelengths. In the Baltic Sea, light absorption in the blue and the red end of the spectrum is of a similar magnitude. At all 4 stations, this yielded an underwater light spectrum that narrowed to green wavelengths with increasing depth (Fig. 3.3A). The light absorption spectra of a red and a green strain of Baltic Sea picocyanobacteria are depicted in Fig. 3.3B as an example to illustrate how they are tuned to the underwater light spectrum. PE-rich strains have an absorption peak at ~560 nm, and hence absorb green light effectively. PC-rich strains have an absorption peak at ~625 nm, and absorb orange-red light effectively. Chlorophyll peaks were also clearly visible in the absorption spectra at 440 nm (Soret band) and 680 nm.

We found euphotic depths of 10.5 m at station S298, 15.3 m at station S300, and 20.3 m at stations S314 and S320, where the euphotic depth is defined as the depth at which the irradiance [PAR( Photosynthetically active radiation), 400-700 nm] equals 1% of the surface irradiance. Hence, the background turbidity of the surface water decreased from the Eastern towards the Western part of the Gulf of Finland.

Figure 3.3. Comparison of the underwater light spectrum and the light absorption spectra of PE-rich and PC-rich picocyanobacteria.A. Underwater light spectra measured at station S320 in the (Gulf of Finland (Baltic Sea). The spectrum narrows to the green waveband with increasing depth. Underwater light spectra at the three other stations were similar. B. Absorption spectra of the PC-rich strain CCY0417 and the PE-rich strain CCY0448 isolated from the Gulf of Finland (Baltic Sea). Absorption spectra are scaled to their maximum value.


49

NutrientsDissolved inorganic nitrogen and phosphorus were measured in water samples from the

surface (0 m) and from 30 m depth (Table 3.1). At all stations, nitrogen and phosphorus concentrations were lower at the surface than at depth. Nitrate and nitrite concentrations at the surface were at or below the detection limit of 0.01 µM. At station S320, the phosphorus concentration at the surface was also below the detection limit. At all stations, the N:P ratios were well below the Redfield ratio of 16 (Table 3.1), indicating that nitrogen was relatively more limiting for phytoplankton growth than phosphorus.

Figure 3.4. Vertical profiles of chlorophyll a and picocyanobacteria at stations S298, S300, S314, and S320. A. Concentration of chlorophyll a in the large size fraction (blue dots; > 20 µm) and in the small size fraction (green triangles; < 20 µm). Total concentration of chlorophyll a is shown in black.B. Concentration of PC-rich picocyanobacteria (green dots) and PE-rich picocyanobacteria (red triangles). In black is shown the total number of picocyanobacterial cells.

Table 3.1. Concentrations of PO43-, NO3

-, NO2-, and NH4

+ (in μmol L-1) measured at the four sampling stations, both from the surface layer and at 30 m depth.

StationsPO4

3- (μM) NO3- (μM) NO2

- (μM) NH4+ (μM) N:P

0m 30m 0m 30m 0m 30m 0m 30m 0m 30m

S298 0.02 0.28* 0 0.01* 0 0.01* 0.18 0.17* 9 0.68*S300 0.18 0.85 0 1.11 0 0.29 0.21 0.86 1.17 2.66S314 0.09 0.32 0 0.32 0 0.08 0.11 1.56 1.22 6.13S320 0 0.19 0.03 0.09 0 0 0.07 1.3 n.d. 7.32

*At station S298, deep samples were from 20 m instead of 30m.

50

Chapter 3

Distribution of picocyanobacteriaChlorophyll a was measured in two size fractions, a small size fraction (< 20 µm) and a

large size fraction (> 20 µm). Microscopic examination indicated that the small size fraction in the Baltic Sea contained mainly picocyanobacteria (< 2 μm) and also small filaments of Pseudanabaena spp., consistent with earlier studies (Albertano et al., 1997; Stal and Walsby, 2000; Stal et al., 2003). The large size fraction was dominated by the filamentous, N2-fixing cyanobacteria Nodularia spumigena, Anabaena spp. and Aphanizomenon flos-aquae, which were mainly concentrated in the upper 10 m of the water column (Fig. 3.4). Picocyanobacteria were mainly distributed over the upper 15-20 m at stations S300, S314 and S320, and even down to 30 m at station S298. The small size fraction represented 70-80% of the total chlorophyll a in the upper 10 m, and even more than 90% of the total chlorophyll a below 10 m (Fig. 3.4).

Red and green picocyanobacteria were counted by flow cytometry, on the basis of their size and pigment composition. The depth distributions revealed that red and green picocyanobacteria coexisted throughout the upper 30 m (Fig. 3.4). The cell numbers of the

Figure 3.5. Diversity patterns of the Baltic Sea picocyanobacteria using 16S rRNA-ITS sequences.A. Rarefaction curves of the number of observed OTUs at 100, 99, 98, 97 and 96 % similarity cut-offs.B. Number of OTUs plotted against different cluster cut-off values in 1.0% increments for sequences grouped into similarity clusters.


51

Figure 3.6. Neighbor-joining tree of the picocyanobacterial cpcBA genes. Clades were condensed for clarity, showing the group designations following Crosbie et al. (2003) (Fig. S2). BS-group designations are assigned to clades formed solely by clone sequences from the Baltic Sea. For condensed groups, the number of cpcBA sequences is indicated within brackets. For single sequences, the GenBank accession number and the strain designation are given. For each clade with known isolates, the pigment phenotype is indicated with the colours red (PE-rich) and green (PC-rich). Numbers indicate the mean ENC number and the mean GC content, respectively. The tree was calculated with the software MEGA with the neighbor-joining method using the Kimura- two parameter model of nucleotide substitution with 1000 replicates (Kumar et al., 2004). Bootstrap values (>50%) are shown at the nodes. As out groups were used the cpcBA sequences of Synechococcus cluster 1 (strains PCC6301, PCC7942 and PCC7943), Synechococcus cluster 2 (strains PCC6716, PCC6717, Synechococcus elongates, JA-2-3b and JA-3-3b), and Synechococcus cluster 3 (PCC7002).

52

Chapter 3

green picocyanobacteria showed a gradual decline with depth, while the red picocyanobacteria formed a subsurface maximum. At stations S298, S300 and S314, the subsurface maximum of the red picocyanobacteria was at the euphotic depth. At station S320, which lacked a clear stratification pattern (Fig. 3.2), the subsurface maximum at ~8 m was less pronounced (Fig. 3.4).

The 16S rRNA and ITS regionThe diversity of picocyanobacteria was assessed by sequencing environmental clone libraries

containing PCR fragments with a part of the 16S rRNA gene and the internally transcribed

Figure 3.7. Unrooted neighbor-joining tree of the picocyanobacterial cpeBA genes. Sequences were obtained from the Baltic Sea and from Synechococcus strains with sequenced genomes spanning the cpeBA-IGS region. Baltic Sea clusters indicate clades formed solely by clone sequences from the Baltic Sea. The number of cpeBA clone sequences is indicated within brackets. Synechococcus sequences extracted from existing genome sequences or GenBank are shown in bold. Additional Synechococcus sequences from strains used in this study are shown in italics. The tree revealed that the cpeBA sequences separated into clades containing PEB only and PUB/PEB-producing clades. The Baltic Sea sequences separated into 4 clusters and one single clone (S298-3m-9). Bootstrap values (>50%) based on 1000 replicates are shown at the nodes, using distance analysis (first number) and maximum parsimony analyses (second number). A ‘-‘ indicates not significant.


53

spacer between the 16S and 23S rRNA genes (ITS). At all 4 stations, samples were taken at 3 and 12 m depth, where both PC-rich and PE-rich picocyanobacteria were abundant (Fig. 3.4). The samples were size fractionated, to separate the small cyanobacteria (< 20 µm) from the larger phytoplankton. This yielded a total of 8 samples, from which DNA was extracted and PCR amplified using oligonucleotide primers specific for cyanobacteria. We sequenced the last 400 bases of the 16S rRNA gene and the complete ITS of 74 clones, and compared these sequences against existing databases (NCBI, RDP-II) (Table S1, Fig. S1). One clone appeared to be from the filamentous heterocystous cyanobacterium Anabaena flos-aquae (99% similarity to the 16S rRNA sequence; AJ630422), and was therefore not further considered.

The vast majority of clones (65 of the 74) exhibited high sequence similarity (96 to 99%) to several closely related Synechococcus strains (LM94, BO8807 and S. rubescens), which all belong to freshwater group B (Crosbie et al., 2003; Ernst et al., 2003) (Table S1, Fig. S1). This is consistent with earlier studies, which have shown that strains of group B are more than 99% similar at the 16S-rRNA level (Crosbie et al., 2003), and more than 95% similar at the ITS sequence (Ernst et al., 2003). The remaining clones displayed high sequence similarity (96 to 98%) to other freshwater Synechococcus strains (Table S1, Fig. S1). One of our clone sequences (TH320-12-6) had a 99% similarity to the 16S rRNA gene of Synechococcus strain MH305 (Crosbie et al., 2003). The ITS sequence of this clone was completely disparate from the other clones, except for the tRNA genes. The position of the clone TH320-12-6 in our phylogenetic analysis confirms this by placing the sequence close to the root of the tree with low bootstrap support (Fig. S1). We observed large variations in ITS length and GC content in our clone libraries, consistent with earlier studies (Laloui et al., 2002; Rocap et al., 2002; Ernst et al., 2003; Chen et al., 2006).

Comparison of the clone libraries from 3 m and 12 m depth, using the program Web-Libshuff (Singleton et al., 2001), revealed that there was no significant difference between the

Table 3.2. Diversity estimators for the clone libraries of the 16S rRNA-ITS, 16S rRNA, cpcBA operon and cpeBA operon, with and without intergenic spacers. The number of Operational Taxonomic Units (OTUs) is shown at 100%, 99% and 97% similarity cut-off values. The coverage is expressed as defined by Good (1953). The Chao-1 richness, ACE richness, Shannon diversity index and Simpson diversity index use 99% similarity cut-off values. Numbers within parentheses for the Chao-1 and ACE richness estimators are 95% confidence intervals.

Gene Number of clones

OTUs (100% / 99% / 97%)

Good’s Coverage (%)

Chao-1 S-ACE Shannon index

Simpson index (1/D)

16S-ITS complete 73 40 / 22 / 11 86.3 37 (26-86) 36 ( 26-66 ) 2.64 10.90

16S without ITS 73 19 / 6 / 1 95.9 9 (6-31) 14 ( 7-79) 0.89 1.85

cpcBA operon 68 24 / 11 / 8 92.65 21 (13-63) 16 ( 12-37) 1.52 2.76

cpcBA without IGS 68 20 / 10 / 8 94.12 16 (11-48) 13 ( 11-30) 1.49 2.75

cpeBA operon 68 24 / 11 / 5 91.8 26 (14-79) 28 (14-107) 1.85 5.52

cpeBA without IGS 68 24 / 12 / 6 91.8 27 (15-80) 23 (14-70) 2.01 6.66

54

Chapter 3

libraries obtained from the two sampling depths (P > 0.05). We therefore assumed that the libraries from 3 m and 12 m depth have the same composition, and they were lumped in our diversity analysis. The diversity in the clone libraries was analysed using the program DOTUR that calculates several diversity estimators and can be used to create rarefaction curves and similarity plots (Schloss and Handelsman, 2005). Rarefaction was used to determine the diversity structure within the 16S rRNA gene - ITS clone library (Fig. 3.5A, Table 3.2). These results indicate a high degree of microdiversity in our clone library, suggesting that many of the sequences belong to the same or closely related “species”. When the similarity was further reduced, the number of OTUs continued to decrease until all clones merged into a single OTU at 73% similarity (Fig. 3.5B).

Because the ITS region is highly variable, we also tested the diversity within our library by using only the sequences encoding part of the 16S rRNA gene (487 bp). This revealed that 68% of the partial 16S rRNA sequences fall into the 99% clusters (Table 3.2).

Several diversity estimators were calculated, such as the Shannon-Weaver and Simpson diversity indices, Good’s Coverage, and the Chao and ACE richness estimates (Good, 1953; Chao and Lee, 1992; Magurran, 1988). Assuming a 99% similarity criterion, the Chao and ACE richness estimates indicated a species richness of 37 and 36, respectively (Table 3.2).

The phycocyanin operonWe included known cpcBA sequences in our alignment for comparison with the 68 clones

that we obtained from the Baltic Sea. The lengths of the sequences available in GenBank ranged from 320 bp to almost 500 bp (excluding the intergenic spacer, IGS), complicating phylogenetic analysis of the cpcBA genes. We decided to remove sequences shorter than 380 bp (IGS excluded) from our alignment to avoid incorrect topologies (Nei et al., 1998; Tamura et al., 2004). This approach gave a more robust phylogenetic tree of the cpcBA gene.

Figure 6 shows the phylogenetic tree that we obtained for the partial cpcBA gene sequences. Many of the picocyanobacteria of the Baltic Sea are closely related to the known groups A, B, H, and I (Robertson et al., 2001; Crosbie et al., 2003; Table S2), confirming the results based on the 16S rRNA-ITS operon. The Baltic Sea Group 3 is probably a novel taxon within the picocyanobacteria, since these sequences form a monophyletic group that separates with a long branch and with good bootstrap support from the other sequences. We can not exclude that the other Baltic Sea groups might also represent unique groups although the branch lengths separating these sequences from known sequences are small. Hence, this might as well represent microdiversity between the clusters.

There are also some striking differences between the cpcBA phylogeny and the existing 16S rRNA phylogenies (Crosbie et al., 2003; Fuller et al., 2003). First, the cpcBA phylogeny separated most picocyanobacteria with a green phenotype from picocyanobacteria with a red phenotype, although there were a few red strains within the green clusters (Fig. 3.6, Fig. S2). Second, in contrast to the 16S rRNA phylogeny, in the cpcBA phylogeny green picocyanobacteria isolated from marine environments (e.g., strains RS9917 and WH5701) clustered with green


55

freshwater picocyanobacteria. Third, the green Cyanobium strain CCY9201 (previously known as BS4) and the red Cyanobium strain CCY9202 (previously known as BS5), which were nearly identical according to the 16S rRNA-ITS phylogeny (Crosbie et al., 2003; Ernst et al., 2003), were completely separated in the cpcBA phylogeny. Fourth, the cpcBA phylogeny revealed that phycourobilin (PUB)-producing picocyanobacteria form a distinct cluster within the red picocyanobacteria.

The cpcBA phylogeny pointed at a close correlation between pigment phenotype and GC content (Fig. 3.6). PC-rich isolates had GC-contents higher than 60%, while most PE-rich isolates had GC contents less than 60% although there were a few exceptions. The difference in GC content between the cpcBA sequences was mainly caused by higher GC content at the third codon position, resulting in synonymous mutations in most of the codons investigated. Likewise, the cpcBA phylogeny pointed at a close correlation between pigment phenotype and the effective number of codons (ENC). The ENC number represents a measure for the codon usage bias (Comeron and Aguade, 1998). An ENC number of 20 means that only one codon is used for each amino acid, while an ENC number of 61 indicates that all codons are used equally often and in that case there is no bias in codon usage (Wright, 1990). PC-rich isolates had a low ENC number in the range of 23-32, while almost all PE-rich isolates had a high ENC number ranging from 33 to 45 (Fig. 3.6). Interestingly, PE-rich strains with a GC content exceeding 60% and an ENC-number below 33 clustered with the PC-rich strains.

The phycoerythrin operonPCR amplification of the cpeBA operon encoding the pigment phycoerythrin resulted in 68

clones (for primers see Everroad and Wood, 2006). The number of cpeBA sequences available in existing databases such as GenBank was limited to 37 full-length sequences of different cyanobacteria and red algae. BLASTn searches using the nucleotide sequences of all our cpeBA clones returned only one of two different top hits, marine Synechococcus strains WH7803 (X72961) and WH8102 (BX569694) (Table S3). Our sequences showed only 81% to 90% similarity with these two sequences. BLASTp searches using our cpeBA sequences as query were done using the CPE-A and the CPE-B protein coding sequences. Both fragments showed the highest similarity with the CPE-A (range 86 to 93%) and CPE-B (91 to 97%) proteins from the marine Synechococcus strain WH7805 (Table S3).

We performed a phylogenetic analysis using our Baltic Sea partial cpeBA nucleotide sequences and those recovered from existing databases. Analysis of the phenotypes revealed that all cultured strains within the cpeBA phylogeny were PE-rich strains with a GC content between 53 and 63 % and a ENC number ranging from 30 to 45 (Fig. S3). The cpeBA phylogeny yielded two major groups (Fig. 3.7). Again these two groups matched the pigmentation of picocyanobacteria. The first group was formed by cpeBA genes from freshwater and marine Synechococcus strains producing PEB only, while the second group consisted of marine strains producing both PUB and PEB. This topology was consistent with the cpcBA phylogeny, where the PUB-producing picocyanobacteria formed a distinct cluster (Fig. 3.6). All cpeBA sequences that we obtained from the Baltic Sea were constrained within the PEB group (Fig. 3.7). These

56

Chapter 3

Baltic Sea sequences were separated into two major clades, one clade comprising the clusters 1 and 2, and the other clade formed by clusters 3 and 4. Comparison of the overall similarity at the amino acid level showed that the similarity within each of these two clades is more than 98%, while the similarity between the two clades is only 86.6%.

Calculation of diversity estimators showed that the diversity in the cpcBA library and cpeBA library is low compared to the 16S rRNA-ITS library (Table 3.2). This might be attributed to inherent differences in variability between these libraries, but also to differences in length between the 16S rRNA-ITS sequences and the cpcBA and cpeBA sequences. The number of OTUs was rather similar for the cpcBA and cpeBA operons. According to the Chao-1 and ACE richness estimates and the Shannon and Simpson diversity indices, however, the diversity at the cpeBA operon encoding for phycoerythrin was slightly higher than the diversity at the cpcBA operon encoding for phycocyanin (Table 3.2).

Discussion

Colourful coexistence of red and green picocyanobacteriaOur results show that PC-rich and PE-rich picocyanobacteria coexist in the Baltic Sea,

where they are approximately equally abundant players in the cyanobacterial community (Fig. 3.4). This confirms earlier results of Stomp et al. (2004, 2007). PC-rich picocyanobacteria were slightly more abundant in the upper 5 m of the water column, while PE-rich picocyanobacteria were numerically more dominant at 5-15 m depth. This vertical distribution matches the underwater light spectrum, since green light penetrates more deeply into the Baltic Sea than red light (Fig. 3.3A). Remarkably, the PC-rich and PE-rich picocyanobacteria maintained their vertical distribution even in waters with a nearly homogeneous temperature and density profile (Station S320, Fig. 3.2 and Fig. 3.4). Since picocyanobacteria lack buoyancy regulation, this indicates that the local growth rates of the PE-rich and PC-rich populations at these depths exceeded the rate of vertical mixing by hydrodynamic processes (Huisman et al., 1999).

Sequencing of 209 clones revealed that picocyanobacteria of the Baltic Sea exhibit high levels of microdiversity. Approximately 46% to 54% of the OTUs present in each clone library were constrained at 99% similarity clusters (micro-clusters; Fig. 3.5, Table 3.2). Such high levels of microdiversity have also been detected by many previous studies of marine microbial communities and other natural bacterial populations (Acinas et al., 2004; Lopez-Lopez et al., 2005; Pommier et al., 2007; Rusch et al., 2007). The high microdiversity of Synechococcus spp. genes found in our clone libraries may reflect local adaptive radiation of picocyanobacteria which allows them to proliferate under a wide range of different conditions in the Baltic Sea.

Phylogeny of red and green picocyanobacteriaOur results show that a phylogeny based on the cpcBA gene (phycocyanin) and cpeBA gene

(phycoerythrin) differs from a phylogeny based on 16S rRNA gene sequences. This is especially


57

clear for the cpcBA dataset, where clustering of the different phylotypes largely matched the pigment composition of the picocyanobacteria (see also Robertson et al., 2001; Crosbie et al., 2003). This is exemplified by the green CCY9201 (previously known as BS4) and red CCY9202 (previously known as BS5) strains used in the competition experiments of Stomp et al. (2004). On the basis of their ITS sequences, these two strains are more than 99% similar (Ernst et al., 2003), whereas their cpcBA gene sequences are well separated (Fig. 3.6), where the green strain CCY9201 clusters in the group of PC-rich picocyanobacteria while the red strain CCY9202 clusters in the group of PE-rich picocyanobacteria (Fig. 3.6). The few sequences of red strains that cluster with the cpcBA operons of green isolates can be explained by horizontal gene transfer (HGT).

Another example is the placement of the PC-rich marine isolate RS9917. This strain forms a distinct cluster with other PC-rich isolates within the marine picocyanobacteria based on the 16S rRNA gene sequences (Fuller et al., 2003). According to our phylogenetic analysis, the partial cpcBA sequences of strain RS9917 clusters with the cpcBA sequences of PC-rich freshwater picocyanobacteria. This could have been caused by HGT of the cpcBA operon of a freshwater picocyanobacterium. Likewise, clustering of similar pigmentation types is also evident from the placement of PUB/PEB-producing marine Synechococcus in both the cpcBA and cpeBA phylogeny. The marine strain WH7805 produces PEB, but in contrast to other PE-rich marine Synechococcus strains it is not capable of producing PUB (Fuller et al., 2003). In the cpeBA and cpcBA phylogenetic trees, strain WH7805 is clustered separately from the PUB–producing marine Synechococcus strains. Only strains that produce PUB might possess the capacity of chromatic adaptation of type IV. We have not retrieved any sequences in our Baltic Sea clone libraries that are related to PUB-producing picocyanobacteria.

Overall, our phylogenetic analyses extend earlier findings of Robertson et al. (2001) and Crosbie et al. (2003), who showed that the cpcBA operon separates PE-rich and PC-rich picocyanobacterial isolates from freshwater lakes. In our analysis, we included picocyanobacteria from brackish waters and marine ecosystems, and studied not only the cpcBA operon but also the cpeBA operon. This revealed three distinct groups of picocyanobacteria separated in line with their pigmentation, namely PUB/PEB producing strains, PEB producing strains, and PC producing strains. All are members of the monophyletic clade formed by Synechococcus and Cyanobium.

Correlations with GC content and ENC numberDifferences in pigmentation in the cpcBA phylogeny correlated with the ENC number

and the GC content of the sequences. PC-rich picocyanobacteria had higher GC contents and lower ENC numbers than PE-rich picocyanobacteria (Fig. 3.6). One possible explanation for differences in GC content in PE-rich and PC-rich picocyanobacteria is that it may reflect differences in expression levels of the cpcBA gene. In fact, highly expressed genes in Prochlorococcus strain MED4 had a higher GC content compared to low expressed genes (Banerjee and Ghosh, 2006). A PE-rich cyanobacterial phycobilisome has one disk of PC proteins while containing multiple disks of PE proteins. A PC-rich phycobilisome usually has

58

Chapter 3

several disks of PC. The higher demand for phycocyanin might require a higher expression level and, hence a higher GC content of the cpcBA operon in PC-rich cyanobacteria. Alternatively, it could also be that the genomes of PC-rich picocyanobacteria have a higher GC-content. We tested this hypothesis by analyzing the GC-content of the protein-coding genes of the genome sequences of Synechococcus spp. present in GenBank (Table S4). This showed that the overall GC-content of the protein-coding genes of the PC-rich Synechococcus strains WH5701 and RS9917 is higher compared to those of the PE-rich picocyanobacteria (Table S4). This would contradict the theory that higher expression levels cause the higher GC-content in the cpcBA operons of PC-rich Synechococcus spp. It also confirms the placement of RS9917 among the freshwater picocyanobacteria in our phylogenetic analysis and that it is unlikely that this is caused by HGT of phycobiliprotein genes.

Another explanation for the relationship between GC content and pigmentation might come from the environment. Comparative studies suggest that the GC contents of microbial genomes or environmental shotgun libraries vary among habitats of different productivity (Goo et al., 2004; Carbone et al., 2005; Foerstner et al., 2005). For instance, Foerstner et al. (2005) observed that the average GC-content of open reading frames (ORFs) from the oligotrophic Sargasso Sea is only 34%, whereas the GC content of ORFs from productive Minnesota soil samples is 61%. These large differences in GC content were not merely an effect of differences in species composition between these two contrasting environments, but remained when the same analysis was focused on phyla present in both environments or on genes present in both environments. Extrapolated to the cpcBA phylogeny, this would mean that the high GC sequences of PC-rich picocyanobacteria come from environments with higher levels of nutrients than the sequences of PE-rich picocyanobacteria that have a lower GC content. This explanation is consistent with the global distribution pattern of picocyanobacteria (e.g., Stomp et al., 2007), where PC-rich picocyanobacteria dominate in productive lakes and coastal waters while PE-rich picocyanobacteria dominate in the oligotrophic open ocean.

ConclusionsWe have found high microdiversity among picocyanobacteria of the Baltic Sea, where

coexistence of red and green Synechococcus strains is widespread. Analysis of the cpcBA and cpeBA operons revealed a phylogenetic tree in which picocyanobacteria are divided into three different pigment groups: PC-rich, only PEB-producing, and PUB/PEB-producing strains. The PC-rich strains had consistently higher GC contents and lower ENC numbers than the two other pigment groups. These findings differ from the picocyanobacterial phylogeny based on 16S rRNA, which separates marine and freshwater species but not the pigmentation groups. This indicates that picocyanobacterial phylogenies based on the phycocyanin and phycoerythrin genes are not easily compared with the 16S rRNA phylogeny. The topologies can be dissimilar because of different evolutionary histories of the different genes within the same group of organisms.


59

Experimental procedures

Sample collectionWater samples from the Baltic Sea were collected from 12 - 19 July 2004 during a research

cruise with the Finnish RV Aranda. For the work reported here, we sampled 4 stations (stations S298, S300, S314, S320; Fig. 3.1), positioned along an East-West transect from the Gulf of Finland into the Baltic Sea proper (from N 59.1 - 60.0 oN and E 22.2 - 26.2 oE to 59.1 oN 22.2 oE). Samples were taken at 3 m depth intervals from the surface to 30 m depth using a rosette sampler. A Seabird 911 CTD was connected to the rosette sampler, to measure temperature and salinity along these depth profiles. Nutrient concentrations in the water samples were analysed according to standard methods (Grasshoff et al., 1983).

Underwater light spectraSpectra of the incident light and underwater light spectra were measured with a RAMSES-

ACC-VIS spectroradiometer (TriOS, Oldenburg, Germany). Light absorption spectra of isolated strains were measured using a Cary 100 Bio equipped with an integrating sphere DRA-CA-3300, with distilled water as a reference.

Chlorophyll analysisFor chlorophyll a analysis, the phytoplankton was divided into two size classes. Total

chlorophyll a was obtained by filtering 0.5 L on GF/F filters (Whatman, nominal pore size 0.7 μm). Chlorophyll a of the large size fraction of phytoplankton was obtained by filtering 1 L on 20 μm nylon mesh (plankton net). Chlorophyll a of the small size fraction was calculated as the difference between total chlorophyll a and chlorophyll a of the large size fraction. This procedure largely discriminates between picoplankton and the larger filamentous cyanobacteria in the Baltic Sea (Stal and Walsby, 2000). Chlorophyll a was extracted overnight in the dark at room temperature by 96% ethanol and absorption was measured spectrophotometrically at 665 nm. Chlorophyll concentration was calculated using an absorption coefficient of 72.3 ml mg-1 cm-1 (Stal et al., 1999).

Counting red and green picocyanobacteriaThe concentrations of red and green picocyanobacteria in the samples were counted by

flow cytometry (Jonker et al., 1995; Stomp et al., 2007), using a Coulter Epics Elite ESP flow cytometer (Beckman Coulter Nederland BV, Mijdrecht, Netherlands) equipped with a green laser (525 nm) and a red laser (670 nm). The flow cytometer distinguished between picocyanobacteria and larger phytoplankton by their size (using side scattering). Red and green picocyanobacteria were distinguished based upon their different fluorescence signals. Cells rich in PE emitted orange light (550-620 nm) when excited by the green laser, whereas cells rich in PC emitted far red light (> 670 nm) when excited by the red laser.

60

Chapter 3

Extraction of nucleic acidsFrom each station 1 L of seawater from each sampling depth was pre-filtered through 20 μm

nylon mesh and collected in polycarbonate bottles that were rinsed by 0.5 M NaOH. The pre-filtered seawater was immediately filtered through 0.2 μm Sterivex filtration units (Millipore) using a peristaltic pump. Subsequently, the Sterivex filters were filled with 2 ml lysis buffer (400 mM NaCl, 20 mM EDTA, 50 mM Tris-HCl [pH 9.0], 0.75 M sucrose) (Massana et al., 1997; Moon-van der Staay et al., 2001) and stored at -20 ºC.

Nucleic acids were extracted as described by Massana et al. (1997) with modifications. In brief, lysozyme (final concentration 1 mg ml-1) was added to the Sterivex unit and incubated for 45 min at 37 ºC. Subsequently, proteinase-K (final concentration 50 μg ml-1) and sodium dodecyl sulfate (SDS) (1% w/v) were added and incubation was continued overnight at 55 ºC. The lysate was recovered from the Sterivex unit by extracting it twice with an equal amount of phenol-chloroform-isoamyl alcohol (25:24:1; pH 8) and once with the same volume of chloroform-isoamyl alcohol (24:1). The extracts were centrifuged (Sigma 4k15 with a swing-out rotor, nr.11156) for 15 min at 1300 rpm and 25˚C. The aqueous phase was transferred to a 15 ml Greiner tube and two volumes of 96% ethanol and 1/10 volume 3 M Na-acetate were added and subsequently incubated for 2 h at -70 ºC to precipitate the DNA. Subsequently, the DNA was centrifuged for 20 min at 14000 rpm and 4 ºC. The pellet was washed with cold 70% ethanol (-20 ºC) and centrifuged for 5 min at 14000 rpm and 4 oC. The supernatant was removed by pipetting and the pellet was air dried. The dry pellet was suspended in 100 μl 10 mM Tris-HCl (pH 8.5). Because the DNA was not PCR grade after this procedure, it was further purified using the Powersoil DNA extraction kit (MoBio Laboratories) following the manufacturer’s recommendations.

Primer designFor amplification of part of the 16S rRNA gene and the internal transcribed spacer between

the 16S and 23S rRNA genes, we designed oligonucleotide primers that bind to the 5‘ region of the 23S rRNA sequences of cyanobacteria (Table 3.3). Cyanobacterial 23S rRNA gene sequences were obtained from GenBank and aligned using the Clustal-W program in Bioedit (Thompson et al., 1994; Hall, 1999). The alignment was imported to Primer Premier software (Premier Biosoft International, version 5.0) and 23S rRNA gene oligonucleotide primers were designed using B1055 as the forward 16S rRNA primer (Singh et al., 1998; Zaballos et al., 2006; Table 3). Primer sequences were checked for their specificity by performing BLASTn searches against the GenBank database.

PCR primers targeting the phycocyanin cpcBA operons in a wide range of cyanobacteria were available from the literature (Neilan et al., 1995; Robertson et al., 2001; Crosbie et al., 2003). Recently, genome sequences from a variety of picocyanobacteria became available providing the opportunity to design primers that target specifically the cpcBA genes from Synechococcus-like cyanobacteria. Using the Integrated Microbial Genomes database (http://img.jgi.doe.gov/cgi-bin/pub/main.cgi), cpcBA operons were obtained from the following (un-)finished picocyanobacterial genomes: Synechococcus PCC6301 (AP008231), PCC7942


61

(CP000100), CC9311 (CP000435), CC9605 (CP000110), CC9902 (CP000097), RS9917 (AANP01000000), WH5701 (AANO01000000), WH7805 (AAOK01000000), and WH8102 (BX548020) (Markowitz et al., 2006). The cpcBA operons M95288 and M95289 from Synechococcus strain WH8020 were downloaded from GenBank (Delorimier et al., 1993). The full length cpcBA operons were aligned in Bioedit using the ClustalW algorithm. The alignment was imported in Primer Premier 5.0 and used to design primers specifically targeting the cpcBA genes from the marine cluster B (Synechococcus WH5701) (Table 3.3).

PCR and clone library construction DNA obtained from 3 and 12 m depth of stations S298, S300, S314 and S320 were used to

amplify the cyanobacterial 16S rRNA-ITS region, the cpeBA operon and the cpcBA operons using the primers listed in Table 3.3. The PCR reaction mixture was composed of 1 μl of template DNA (1 - 20 ng μl-1), 2.5 μl of 10X PCR buffer (Qiagen), 0.5 μl of 10 mM dNTP’s mixture (Roche) and 0.62 units of HotStarTaq DNA polymerase (Qiagen). We added 10 pmol of each forward and reverse primer, except for the 16S rRNA-ITS PCR where was 5 pmol was used. Sterile MilliQ grade water was added to a final reaction volume of 25 μl.

The PCR reactions were run on a GeneAmp System 2700 thermocycler. The program for the 16S-ITS amplification consisted of 15 min hot start at 94 ºC; 35 cycles of 1 min at 94 ºC; 1 min at 62 ºC; and 1 min at 72ºC; which was followed by a final elongation step at 72ºC for 10 min. For amplification of the cpeBA genes the following program was applied: 15 min at 94 ºC, 40 cycles of 30 seconds at 94 ºC, 30 seconds at 55 ºC, and 1.5 min at 72 ºC. The final elongation step was 10 min at 72 ºC. The same program was used to amplify cpcBA except that the elongation step was only 1 min.

PCR-reactions were done in triplicate to decrease variations in amplification (Polz and Cavanaugh, 1998). The PCR products of the triplicate reactions were pooled and cloned. Cloning was done using the TOPO TA cloning kit for sequencing (Invitrogen) following the instructions of the manufacturer. For each sample and PCR product 20 clones were picked

Table 3.3. Oligonucleotide primers used in this study.

Primer Name

Target gene

Sequence 5’ to 3’ Tm ( ºC)

Reference

B1055 16S rRNA ATG GCT GTC GTC AGC TCGT 66 Zaballos et al., 2006

Cya23S-58r2 23S rRNA CGT CCT TCA TCG CCT CTG 58 This study

PITS 1 ITS TCA GTT GGT AGA GCG CCT GC 56 Ernst et al., 2003

PITS 3 ITS GTTAGCGGACTCGAACCGC 65 Ernst et al., 2003

SyncpcB-Fw cpcB ATGGCTGCTTGCCTGCG 61 This study

SyncpcA-Rev cpcA ATCTGGGTGGTGTAGGG 50 This study

B3FW cpeB TCA AGG AGA CCT ACA TCG 58 Everroad and Wood, 2006

SynA1R cpeA CAG TAG TTG ATC AGR CGC AGG T 64 Everroad and Wood, 2006

62

Chapter 3

using sterile toothpicks. The cells were transferred to 200 μl of sterile LB-Broth and grown overnight. Twenty-five μl of culture was mixed with 25 μl of Milli-Q water and heated at 94 ºC for 10 minutes. Five μl of the mixture was used for PCR amplification of the insert using the T3 and T7 primers of the vector. Subsequently, 10 positive PCR reactions were chosen per sample and purified using the DNA Clean & Concentrator (Zymo Research). The DNA concentration was measured using a Nanodrop ND1000 (NanoDrop Technologies) spectrophotometer. The PCR product was sequenced using the Big Dye Terminator v1.1 Cycle sequencing kit (Applied Biosystems) according to the manufacturer’s instructions. The clones containing cpeBA and cpcBA fragments were sequenced using the T3 and T7 primers, while the 16S rRNA-ITS clones were sequenced with the primers B1055, Cya23S-58R2, PITS1 and PITS3 (Table 3.3). Sequencing was done with a 3130 Genetic Analyzer (Applied Biosystems). For each clone, the forward and reverse sequences were manually aligned in Bioedit and the sequences were checked against GenBank using BLASTn and BLASTp (Altschul et al., 1990; McGinnis and Madden, 2004). Furthermore, the 16S rRNA clone sequences were compared to the RDP-II database (Cole et al., 2005).

Diversity calculations and phylogenetic analysisFor the diversity calculations, the clone sequences of the different sampling stations were

grouped together. The program DOTUR was used for calculating rarefaction, library coverage, Shannon-Weaver diversity index (H’), Simpson index (D), Chao-1 non-parametric richness estimator and the ACE coverage-based richness estimator (Schloss and Handelsman, 2005). Calculations were performed on a Jukes-Cantor corrected distance matrix created with the DNADIST program from the PHYLIP Package (Felsenstein, 1989).

Sequences previously identified to be closely related by BLASTn comparison were imported from GenBank into Bioedit and aligned against the clone sequences using ClustalW. Alignments of the 16S rRNA-ITS sequences were done manually in Bioedit by reference of the ITS alignment of the predicted secondary structure models proposed in several papers describing the cyanobacterial ITS sequences (Iteman et al., 2000; Laloui et al., 2002; Rocap et al., 2002; Taton et al., 2003). Sequence comparison and phylogenetic analyses were performed using the software MEGA3.1 (Kumar et al., 2004). For the 16S rRNA-ITS region the sequences were compared using the neighbor-joining algorithm with Jukes-Cantor correction and 1000 bootstraps. The coding regions of the cpeBA and cpcBA operon were both used in phylogenetic analyses. Both data sets were separately analysed using the following approach. Phylogenetic analyses were done with the neighbor-joining method as well as with maximum parsimony. Neighbor-joining was performed with the Kimura-2-parameter model for nucleotide evolution with 1000 bootstraps. Maximum parsimony was used with the close-neighbor-interchange search algorithm with random tree addition using 100 bootstraps. Codon usage in the cpcBA and cpeBA coding regions was analysed using DnaSP version 4.0 (Rozas et al., 2003).


63

Nucleotide sequence accession numbersThe sequence data reported in this paper have been submitted to the GenBank database

under accession numbers: 16S rRNA- ITS clones (EF513279 – EF513350); cpcBA clones (EF513351 – EF513418); cpeBA clones (EF513418 – EF513486); BO8805 cpcBA (EF513487); CCY9201 cpcBA (EF513488); CCY9202 cpcBA (EF513489); CCY9202 cpeBA (EF513490); Anabaena-like 16S-ITS clone TH298-12-6 (EF530539).

AcknowledgementsWe thank M. Laamanen for the opportunity to join cruise CYANO-04, and the crew of

the research vessel Aranda for help during sampling. We also thank A. Wijnholds-Vreman for carrying out the flow cytometry analyses. We thank C. Everroad and A.M. Wood for sharing with us their cpeBA primer sequences before publication. We gratefully acknowledge the comments of two anonymous referees. M.S. and J.H. were supported by the Earth and Life Sciences Foundation (ALW), which is subsidised by the Netherlands Organization for Scientific Research (NWO). T.H. and L.J.S. acknowledge support from the European Commission through the project MIRACLE (EVK3-CT-2002–00087). This is NIOO-KNAW publication nr: 4153.

64

Chapter 3

SUPPLEMENTARY MATERIAL CHAPTER 3The following supplementary material is available for this article:

Figure S1. Full length neighbor-joining tree of the picocyanobacterial ITS sequences.

Figure S2. Neighbor-joining tree of the partial sequences of picocyanobacterial cpcBA genes, showing the positions of all cpcBA clones obtained from the Baltic Sea.

Figure S3. Neighbor-joining tree of the partial sequences of picocyanobacterial cpeBA genes, showing the positions of all cpeBA clones obtained from the Baltic Sea.

Table S1. Comparison of 16S rRNA- ITS clones with GenBank and RDPII databases.

Table S2. Comparison of cpcBA clones with GenBank databases.

Table S3. Comparison of cpeBA clones with GenBank databases.

Table S4a, Table S4b. Comparison of GC-content from cyanobacterial genomes and the protein coding genes found in these genomes.

ReferencesChen, F., Wang, K., Kan, J.J., Suzuki, M.T., and Wommack, K.E. (2006) Diverse and unique

picocyanobacteria in Chesapeake Bay, revealed by 16S-23S rRNA internal transcribed spacer sequences. Appl Environ Microbiol 72: 2239-2243.

Crosbie, N.D., Pöckl, M., and Weisse, T. (2003) Dispersal and phylogenetic diversity of nonmarine picocyanobacteria, inferred from 16S rRNA gene and cpcBA-intergenic spacer sequence analyses. Appl Environ Microbiol 69: 5716-5721.

Kumar, S., Tamura, K., and Nei, M. (2004) MEGA3: Integrated software for molecular evolutionary genetics analysis and sequence alignment. Brief Bioinform 5: 150-163.

Rocap, G., Distel, D.L., Waterbury, J.B., and Chisholm, S.W. (2002) Resolution of Prochlorococcus and Synechococcus ecotypes by using 16S-23S ribosomal DNA internal transcribed spacer sequences. Appl Environ Microbiol 68: 1180-1191.


65

Figure S1: Neighbor-joining tree of ITS sequences (439 positions) of Synechococcus isolates and Baltic Sea clone sequences. The group designations follow Rocap et al. (2002) for the marine Synechococcus spp., Crosbie et al. (2003) for freshwater picocyanobacteria and Chen et al. (2006) for isolates from Chesapeake Bay. For isolates the pigmentation phenotype is indicated as far as it was available to us. The phenotype is represented by the colours green (PC rich), red (only PEB producing) and orange (PUB and PEB producing). The tree was calculated using

the software MEGA with the neighbor-joining algorithm with 1000 bootstrap replicates. The Jukes-Cantor model of nucleotide substitution was used. Only bootstrap values above 50 are shown for the nodes in the tree. The tree was rooted using the ITS sequence of Synechococcus PCC6301.

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Chapter 3

Figure S2: Neighbor-joining tree of the picocyanobacterial cpcBA genes. The group designations follow Crosbie et al. (2003). The BS-group designations are used for clades formed solely by sequences from the Baltic Sea. For isolates the pigment phenotype is indicated in colours red (PE-rich) and green (PC-rich). Numbers indicated the mean ENC number and the mean GC content, respectively. The tree was calculated with the software MEGA with the neighbor-joining method using the Kimura- two parameter model of nucleotide substitution with 1000 replicates (Kumar et al., 2004). Bootstrap values >50% are shown at the nodes. As out groups were used the cpcBA sequences of Synechococcus cluster 1 (strains PCC6301, PCC7942 and PCC7943) Synechococcus cluster 2 (strains PCC6716, PCC6717, Synechococcus elongatus, JA-2-3b and JA-3-3b) and Synechococcus cluster 3 (PCC7002).


67

Tab

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160

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6.21

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echo

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3024

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MW

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298-

3-2

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4º N

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3EF

5132

8014

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BO

8807

AF3

1707

496

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985

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26.

21º E

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8114

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460

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3024

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MW

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6

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1.00

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560

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º E3

EF51

3283

1437

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p. B

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3115

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º E3

EF51

3284

1482

Syn

echo

cocc

us s

p. B

O88

07A

F317

074

96%

Syn

echo

cocc

us s

p. M

W77

D1

AY

1512

461.

000

BTH

298-

3-8

60.0

4º N

26.

21º E

3EF

5132

8514

12U

ncul

ture

d S

ynec

hoco

ccus

sp.

clo

ne C

B11

G10

AY85

5305

98%

Syn

echo

cocc

us s

p. M

W76

B2

AY

1512

450.

985

BTH

298-

3-9

60.0

4º N

26.

21º E

3EF

5132

8613

92S

ynec

hoco

ccus

sp.

BO

8807

AF3

1707

496

%S

ynec

hoco

ccus

sp.

MW

72C

6

AY15

1240

0.99

8 B

TH29

8-3-

1060

.04º

N 2

6.21

º E3

EF51

3287

1412

Syn

echo

cocc

us s

p. B

O88

07A

F317

074

96%

Syn

echo

cocc

us s

p. M

W72

C6

AY

1512

400.

998

BTH

298-

12-1

60.0

4º N

26.

21º E

12EF

5132

8813

57S

ynec

hoco

ccus

sp.

BO

0014

A

F330

251

97%

Syn

echo

cocc

us s

p. B

O 8

805

AF3

1707

30.

996

Sub

alpi

ne c

lust

er II

TH29

8-12

-360

.04º

N 2

6.21

º E12

EF51

3289

1412

Syn

echo

cocc

us s

p. B

O88

07A

F317

074

96%

Syn

echo

cocc

us s

p. M

W72

C6

AY

1512

401.

000

BTH

298-

12-4

60.0

4º N

26.

21º E

12EF

5132

9014

13S

ynec

hoco

ccus

sp.

LM

94A

F330

248

96%

Syn

echo

cocc

us s

p. M

W15

-2

AY15

1232

0.97

2 B

TH29

8-12

-560

.04º

N 2

6.21

º E12

EF51

3291

1412

Syn

echo

cocc

us s

p. B

O88

07A

F317

074

96%

Syn

echo

cocc

us s

p. M

W77

D1

AY

1512

460.

987

BTH

298-

12-6

60.0

4º N

26.

21º E

12EF

5305

3910

36 A

naba

ena

flos-

aqua

e 1t

u30s

4 A

J630

422

99%

Aph

aniz

omen

on fl

os-a

quae

var

. kle

bahn

ii; 2

18A

J293

123

0.98

9A

naba

ena

sp.

TH29

8-12

-760

.04º

N 2

6.21

º E12

EF51

3292

1439

Syn

echo

cocc

us s

p. B

O88

07A

F317

074

97%

Syn

echo

cocc

us s

p. M

W76

B2

AY

1512

450.

955

BTH

298-

12-8

60.0

4º N

26.

21º E

12EF

5132

9314

12S

ynec

hoco

ccus

rube

scen

sA

F317

076

96%

Syn

echo

cocc

us s

p. M

W77

D1

AY

1512

461.

000

BTH

298-

12-9

60.0

4º N

26.

21º E

12EF

5132

9414

38S

ynec

hoco

ccus

sp.

LM

94A

F330

248

97%

Syn

echo

cocc

us s

p. M

W77

D1

AY

1512

460.

991

BTH

298-

12-1

160

.04º

N 2

6.21

º E12

EF51

3295

1412

Syn

echo

cocc

us s

p. B

O88

07A

F317

074

96%

Syn

echo

cocc

us s

p. M

W77

D1

AY

1512

460.

985

BTH

300-

3-1

59.3

3º N

23.

10º E

3EF

5132

9614

12S

ynec

hoco

ccus

sp.

BO

8807

AF3

1707

496

%S

ynec

hoco

ccus

sp.

MW

77D

1

AY15

1246

0.99

4 B

TH30

0-3-

259

.33º

N 2

3.10

º E3

EF51

3299

1412

Syn

echo

cocc

us s

p. B

O88

07A

F317

074

96%

Syn

echo

cocc

us s

p. M

W72

C6

AY

1512

401.

000

BTH

300-

3-3

59.3

3º N

23.

10º E

3EF

5133

0014

37S

ynec

hoco

ccus

sp.

LM

94A

F330

248

96%

Syn

echo

cocc

us s

p. M

W72

C6

AY

1512

400.

987

BTH

300-

3-4

59.3

3º N

23.

10º E

3EF

5133

0114

12S

ynec

hoco

ccus

rube

scen

s A

F317

076

96%

Syn

echo

cocc

us s

p. M

W77

D1

AY

1512

460.

958

BTH

300-

3-6

59.3

3º N

23.

10º E

3EF

5133

0214

12S

ynec

hoco

ccus

sp.

LM

94A

F330

248

96%

Syn

echo

cocc

us s

p. M

W72

C6

AY

1512

401.

000

BTH

300-

3-7

59.3

3º N

23.

10º E

3EF

5133

0314

12S

ynec

hoco

ccus

sp.

LM

94A

F330

248

96%

Syn

echo

cocc

us s

p. M

W77

D1

AY

1512

460.

991

BTH

300-

3-8

59.3

3º N

23.

10º E

3EF

5133

0414

12S

ynec

hoco

ccus

rube

scen

sA

F317

076

96%

Syn

echo

cocc

us s

p. M

W15

-2

AY15

1232

0.98

5 B

TH30

0-3-

959

.33º

N 2

3.10

º E3

EF51

3305

1412

Syn

echo

cocc

us ru

besc

ens

AF3

1707

696

%S

ynec

hoco

ccus

sp.

MW

15-2

AY

1512

321.

000

BTH

300-

3-10

59.3

3º N

23.

10º E

3EF

5132

9713

66S

ynec

hoco

ccus

sp.

BO

8807

AF3

1707

499

%S

ynec

hoco

ccus

sp.

MW

76B

2

AY15

1245

0.98

5 B

TH30

0-3-

1159

.33º

N 2

3.10

º E3

EF51

3298

1365

Syn

echo

cocc

us s

p. B

O88

07A

F317

074

99%

Syn

echo

cocc

us s

p. M

W76

B2

AY

1512

450.

985

BTH

300-

12-2

59.3

3º N

23.

10º E

12EF

5133

0814

38S

ynec

hoco

ccus

sp.

LM

94A

F330

248

96%

Syn

echo

cocc

us s

p. M

W72

C6

AY

1512

400.

960

BTH

300-

12-4

59.3

3º N

23.

10º E

12EF

5133

0914

38S

ynec

hoco

ccus

sp.

LM

94A

F330

248

97%

Syn

echo

cocc

us s

p. M

W72

C6

AY

1512

400.

977

BTH

300-

12-5

59.3

3º N

23.

10º E

12EF

5133

1014

12S

ynec

hoco

ccus

rube

scen

sA

F317

076

97%

Syn

echo

cocc

us s

p. M

W77

D1

AY

1512

461.

000

BTH

300-

12-6

59.3

3º N

23.

10º E

12EF

5133

1114

12S

ynec

hoco

ccus

rube

scen

sA

F317

076

96%

Syn

echo

cocc

us s

p. M

W77

D1

AY

1512

461.

000

BTH

300-

12-7

59.3

3º N

23.

10º E

12EF

5133

1214

12S

ynec

hoco

ccus

rube

scen

sA

F317

076

97%

Syn

echo

cocc

us s

p. M

W77

D1

AY

1512

460.

987

BTH

300-

12-8

59.3

3º N

23.

10º E

12EF

5133

1314

12S

ynec

hoco

ccus

rube

scen

sA

F317

076

96%

Syn

echo

cocc

us s

p. M

W15

-2

AY15

1232

0.99

1 B

TH30

0-12

-959

.33º

N 2

3.10

º E12

EF51

3314

1440

Syn

echo

cocc

us s

p. B

O88

07A

F317

074

97%

Syn

echo

cocc

us s

p. M

W76

B2

AY

1512

450.

970

BTH

300-

12-1

059

.33º

N 2

3.10

º E12

EF51

3306

1366

Syn

echo

cocc

us s

p. B

O88

07A

F317

074

99%

Syn

echo

cocc

us s

p. M

W76

B2

AY

1512

450.

985

BTH

300-

12-1

159

.33º

N 2

3.10

º E12

EF51

3307

1412

Syn

echo

cocc

us s

p. B

O88

07A

F317

074

96%

Syn

echo

cocc

us s

p. M

W72

C6

AY

1512

401.

000

BTH

314-

3-1

59.3

0º N

22.

40º E

3EF

5133

1514

12S

ynec

hoco

ccus

rube

scen

sA

F317

076

96%

Syn

echo

cocc

us s

p. M

W15

-2

AY15

1232

1.00

0 B

TH31

4-3-

259

.30º

N 2

2.40

º E3

EF51

3317

1412

Syn

echo

cocc

us ru

besc

ens

AF3

1707

696

%S

ynec

hoco

ccus

sp.

MW

15-2

AY

1512

320.

985

BTH

314-

3-3

59.3

0º N

22.

40º E

3EF

5133

1813

66S

ynec

hoco

ccus

sp.

BO

8807

AF3

1707

499

%S

ynec

hoco

ccus

sp.

MW

76B

2

AY15

1245

0.97

7 B

TH31

4-3-

459

.30º

N 2

2.40

º E3

EF51

3319

1366

Syn

echo

cocc

us s

p. B

O88

07A

F317

074

99%

Syn

echo

cocc

us s

p. B

O 8

807

AF3

1707

40.

981

BTH

314-

3-5

59.3

0º N

22.

40º E

3EF

5133

2014

38S

ynec

hoco

ccus

sp.

LM

94A

F330

248

96%

Syn

echo

cocc

us s

p. M

W73

D5

AY15

1241

0.97

2 B

TH31

4-3-

659

.30º

N 2

2.40

º E3

EF51

3321

1438

Syn

echo

cocc

us s

p. L

M94

AF3

3024

896

% S

ynec

hoco

ccus

sp.

MW

72C

6

AY15

1240

0.98

7 B

TH31

4-3-

759

.30º

N 2

2.40

º E3

EF51

3322

1438

Syn

echo

cocc

us s

p. L

M94

AF3

3024

896

%S

ynec

hoco

ccus

sp.

MW

72C

6

AY15

1240

0.95

1 B

TH31

4-3-

859

.30º

N 2

2.40

º E3

EF51

3323

1412

Syn

echo

cocc

us ru

besc

ens

AF3

1707

697

%S

ynec

hoco

ccus

sp.

MW

77D

1

AY15

1246

0.97

0 B

TH31

4-3-

1059

.30º

N 2

2.40

º E3

EF51

3316

1412

Syn

echo

cocc

us ru

besc

ens

AF3

1707

696

%S

ynec

hoco

ccus

sp.

MW

15-2

AY

1512

320.

991

BTH

314-

12-1

59.3

0º N

22.

40º E

12EF

5133

2414

40S

ynec

hoco

ccus

sp.

BO

8807

AF3

1707

497

%S

ynec

hoco

ccus

sp.

MW

76B

2

AY15

1245

0.96

0 B

TH31

4-12

-259

.30º

N 2

2.40

º E12

EF51

3325

1366

Syn

echo

cocc

us s

p. B

O88

07A

F317

074

99%

Syn

echo

cocc

us s

p. M

W76

B2

AY

1512

450.

972

BTH

314-

12-3

59.3

0º N

22.

40º E

12EF

5133

2614

12S

ynec

hoco

ccus

sp.

LM

94A

F330

248

96%

Syn

echo

cocc

us s

p. M

W77

D1

AY

1512

461.

000

BTH

314-

12-4

59.3

0º N

22.

40º E

12EF

5133

2714

12S

ynec

hoco

ccus

rube

scen

sA

F317

076

96%

Syn

echo

cocc

us s

p. M

W15

-2

AY15

1232

0.98

5 B

TH31

4-12

-559

.30º

N 2

2.40

º E12

EF51

3328

1391

Syn

echo

cocc

us s

p. B

O88

07A

F317

074

99%

Syn

echo

cocc

us s

p. M

W76

B2

AY

1512

450.

970

BTH

314-

12-7

59.3

0º N

22.

40º E

12EF

5133

2914

38S

ynec

hoco

ccus

sp.

LM

94A

F330

248

96%

Syn

echo

cocc

us s

p. M

W72

C6

AY

1512

400.

977

BTH

314-

12-8

59.3

0º N

22.

40º E

12EF

5133

3013

64S

ynec

hoco

ccus

sp.

BO

8807

AF3

1707

499

%S

ynec

hoco

ccus

sp.

MW

76B

2

AY15

1245

0.98

5 B

TH31

4-12

-959

.30º

N 2

2.40

º E12

EF51

3331

1412

Syn

echo

cocc

us s

p. B

O88

07A

F317

074

96%

Syn

echo

cocc

us s

p. M

W72

C6

AY

1512

401.

000

BTH

320-

3-1

59.1

3º N

22.

29º E

3EF

5133

3214

38S

ynec

hoco

ccus

sp.

LM

94A

F330

248

97%

Syn

echo

cocc

us s

p. M

W77

D1

AY

1512

461.

000

BTH

320-

3-2

59.1

3º N

22.

29º E

3EF

5133

3414

12S

ynec

hoco

ccus

rube

scen

sA

F317

076

96%

Syn

echo

cocc

us s

p. M

W15

-2

AY15

1232

0.98

5 B

TH32

0-3-

359

.13º

N 2

2.29

º E3

EF51

3335

1412

Syn

echo

cocc

us s

p. L

M94

AF3

3024

896

%S

ynec

hoco

ccus

sp.

MW

77D

1

AY15

1246

1.00

0 B

TH32

0-3-

459

.13º

N 2

2.29

º E3

EF51

3336

1412

Syn

echo

cocc

us s

p. B

O88

07A

F317

074

96%

Syn

echo

cocc

us s

p. M

W72

C6

AY

1512

401.

000

BTH

320-

3-5

59.1

3º N

22.

29º E

3EF

5133

3713

65S

ynec

hoco

ccus

sp.

BO

8807

AF3

1707

499

%S

ynec

hoco

ccus

sp.

MW

76B

2

AY15

1245

0.97

2 B

TH32

0-3-

659

.13º

N 2

2.29

º E3

EF51

3338

1412

Syn

echo

cocc

us ru

besc

ens

AF3

1707

697

%S

ynec

hoco

ccus

sp.

MW

15-2

AY

1512

321.

000

BTH

320-

3-7

59.1

3º N

22.

29º E

3EF

5133

3914

38S

ynec

hoco

ccus

sp.

LM

94A

F330

248

96%

Syn

echo

cocc

us s

p. M

W72

C6

AY

1512

400.

987

BTH

320-

3-8

59.1

3º N

22.

29º E

3EF

5133

4014

37S

ynec

hoco

ccus

sp.

LM

94A

F330

248

96%

Syn

echo

cocc

us s

p. M

W72

C6

AY

1512

400.

972

BTH

320-

3-9

59.1

3º N

22.

29º E

3EF

5133

4114

38S

ynec

hoco

ccus

sp.

LM

94A

F330

248

97%

Syn

echo

cocc

us s

p. M

W72

C6

AY

1512

400.

987

BTH

320-

3-10

59.1

3º N

22.

29º E

3EF

5133

3312

05S

ynec

hoco

ccus

sp.

LB

P1

AF3

3024

798

%S

ynec

hoco

ccus

sp.

MW

25B

5AY

1512

331.

000

HTH

320-

12-1

59.1

3º N

22.

29º E

12EF

5133

4214

38S

ynec

hoco

ccus

rube

scen

sA

F317

076

97%

Syn

echo

cocc

us s

p. M

W72

C6

AY

1512

400.

974

BTH

320-

12-2

59.1

3º N

22.

29º E

12EF

5133

4414

38S

ynec

hoco

ccus

sp.

LM

94A

F330

248

96%

Syn

echo

cocc

us s

p. M

W72

C6

AY

1512

400.

987

BTH

320-

12-3

59.1

3º N

22.

29º E

12EF

5133

4514

38S

ynec

hoco

ccus

sp.

LM

94A

F330

248

96%

Syn

echo

cocc

us s

p. M

W72

C6

AY

1512

400.

964

BTH

320-

12-4

59.1

3º N

22.

29º E

12EF

5133

4614

37S

ynec

hoco

ccus

sp.

LM

94A

F330

248

96%

Syn

echo

cocc

us s

p. M

W72

C6

AY

1512

400.

966

BTH

320-

12-5

59.1

3º N

22.

29º E

12EF

5133

4713

57S

ynec

hoco

ccus

sp.

BO

0014

AF3

3025

197

%S

ynec

hoco

ccus

sp.

BO

880

5A

F317

073

0.99

6S

ubal

pine

clu

ster

IITH

320-

12-6

59.1

3º N

22.

29º E

12EF

5133

4813

62S

ynec

hoco

ccus

sp.

MH

305

AY22

4198

99%

Syn

echo

cocc

us s

p. M

H30

5AY

2241

980.

991

MH

305

clad

eTH

320-

12-8

59.1

3º N

22.

29º E

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scen

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., 20

03 a

nd E

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al.,2

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68

Chapter 3


69

Figure S3 (previous page): Unrooted neighbor-joining tree of cpeBA sequences obtained from the Baltic Sea and sequences from Synechococcus strains spanning the cpeBA-IGS region. Isolates producing only PEB are shown in red. The PUB/PEB producing isolates are shown in orange. Numbers indicated the mean ENC number and the mean GC content, respectively. The tree revealed that the cpeBA sequences separated into clades containing PEB only and PUB/PEB producing clades. The Baltic Sea sequences separated into 4 clusters and one single clone (S298-3m-9). Significant (<50%) percentage values of 1000 bootstraps are given as numbers at branching points for distance / maximum parsimony analyses (in order NJ-distance / MP) (‘-‘ indicates not significant).

70

Chapter 3

Tab

le S

2: c

omp

aris

on o

f cp

cBA

clo

nes

with

Gen

ban

k d

atab

ases

.S

eque

nce

IDS

amp

ling

Lo

ca-

tio

nD

epth

(m

)G

enB

ank

Acc

esio

n N

umb

er

clo

ne

Leng

th

(bp

)C

lose

st r

elat

ive

to G

en-

Ban

k (B

last

N)

Gen

Ban

k A

cc.

Num

ber

.

Sim

i-la

rity

S

core

(%

)

Bes

t B

last

P h

it (c

pcB

)G

enB

ank

Acc

. Num

-b

er.

Sim

i-la

rity

S

core

(%

)

Bes

t B

last

P h

it (c

pcA

)G

enB

ank

Acc

. Num

-b

er.

Sim

i-la

rity

S

core

(%

)

Phy

log

enet

ic

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ter

or

BS

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roup

(Fig

. 6)

S29

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cocc

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805

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350

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4º N

26.

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5133

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301

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4205

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O 8

801

|AA

P40

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933

S29

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960

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6.21

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EF51

3359

524

Syn

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F600

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298-

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159

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799

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S30

0-3-

259

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3.10

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EF51

3362

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Syn

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701

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799

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S30

0-3-

359

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EF51

3363

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Syn

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701

ZP_0

1083

799

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S30

0-3-

459

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3.10

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EF51

3364

539

Syn

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570

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echo

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701

ZP_0

1083

799

951

S30

0-3-

559

.33º

N 2

3.10

º E3

EF51

3365

539

Syn

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cocc

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570

1ZP

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Syn

echo

cocc

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H 5

701

ZP_0

1083

799

951

S30

0-3-

659

.33º

N 2

3.10

º E3

EF51

3366

539

Syn

echo

cocc

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W10

0C3

AY15

1224

95S

ynec

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WH

570

1ZP

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8380

095

Syn

echo

cocc

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p. W

H 5

701

ZP_0

1083

799

951

S30

0-3-

759

.33º

N 2

3.10

º E3

EF51

3367

539

Syn

echo

cocc

us s

p. M

W10

0C3

AY15

1224

95S

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570

1ZP

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H 5

701

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1083

799

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S30

0-3-

859

.33º

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3.10

º E3

EF51

3368

539

Syn

echo

cocc

us s

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W10

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1224

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570

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cocc

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701

ZP_0

1083

799

951

S30

0-3-

959

.33º

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3.10

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EF51

3369

539

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echo

cocc

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1224

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570

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cocc

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701

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799

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S30

0-3-

1059

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3.10

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EF51

3370

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Syn

echo

cocc

us s

p. M

W10

0C3

AY15

1224

95S

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WH

570

1ZP

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cocc

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701

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799

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S30

0-12

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3.10

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3371

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cocc

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W10

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1224

97S

ynec

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WH

570

1ZP

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Syn

echo

cocc

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701

ZP_0

1083

799

962

S30

0-12

-259

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3.10

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EF51

3372

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Syn

echo

cocc

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1512

2899

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cocc

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W6C

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3712

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780

5ZP

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2335

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grou

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S30

0-12

-359

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3.10

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EF51

3373

539

Syn

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cocc

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p. M

W10

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AY15

1224

95S

ynec

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sp.

WH

570

1ZP

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8380

095

Syn

echo

cocc

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701

ZP_0

1083

799

951

S30

0-12

-459

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3.10

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3374

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Syn

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570

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Syn

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701

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799

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S30

0-12

-559

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3.10

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EF51

3375

539

Syn

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799

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S30

0-12

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EF51

3376

524

Syn

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301

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Syn

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cocc

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S72

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0099

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S30

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Syn

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570

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Syn

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859

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3386

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echo

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3387

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Syn

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59.3

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MW

100C

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2496

Syn

echo

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570

1ZP

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59.3

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5133

9453

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100C

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1512

2497

Syn

echo

cocc

us s

p. W

H 5

701

ZP_0

1083

800

95S

ynec

hoco

ccus

sp.

WH

570

1ZP

_010

8379

996

2S

314-

12-7

59.3

0º N

22.

40º E

12EF

5133

9553

9S

ynec

hoco

ccus

sp.

MW

100C

3 AY

1512

2495

Syn

echo

cocc

us s

p. W

H 5

701

ZP_0

1083

800

95S

ynec

hoco

ccus

sp.

WH

570

1ZP

_010

8379

995

1S

314-

12-8

59.3

0º N

22.

40º E

12EF

5133

9653

9S

ynec

hoco

ccus

sp.

MW

100C

3 AY

1512

2495

Syn

echo

cocc

us s

p. W

H 5

701

ZP_0

1083

800

95S

ynec

hoco

ccus

sp.

WH

570

1ZP

_010

8379

995

1S

314-

12-9

59.3

0º N

22.

40º E

12EF

5133

9753

9S

ynec

hoco

ccus

sp.

MW

100C

3 AY

1512

2496

Syn

echo

cocc

us s

p. W

H 5

701

ZP_0

1083

800

95S

ynec

hoco

ccus

sp.

WH

570

1ZP

_010

8379

996

2S

314-

12-1

059

.30º

N 2

2.40

º E12

EF51

3398

499

Syn

echo

cocc

us s

p. P

S72

5 A

F223

446

98S

ynec

hoco

ccus

sp.

PS

720

AA

F601

2897

Syn

echo

cocc

us s

p. B

O 8

807

AA

P40

810

74gr

oup

BS

314-

12-1

159

.30º

N 2

2.40

º E12

EF51

3399

539

Syn

echo

cocc

us s

p. M

W10

0C3

AY15

1224

95S

ynec

hoco

ccus

sp.

MW

100C

3A

AO

3711

798

Syn

echo

cocc

us s

p. W

H 5

701

ZP_0

1083

799

951

S32

0-3-

159

.13º

N 2

2.29

º E3

EF51

3401

539

Syn

echo

cocc

us s

p. M

W10

0C3

AY15

1224

95S

ynec

hoco

ccus

sp.

WH

570

1ZP

_010

8380

095

Syn

echo

cocc

us s

p. W

H 5

701

ZP_0

1083

799

951

S32

0-3-

259

.13º

N 2

2.29

º E3

EF51

3402

539

Syn

echo

cocc

us s

p. M

W10

0C3

AY15

1224

95S

ynec

hoco

ccus

sp.

WH

570

1ZP

_010

8380

095

Syn

echo

cocc

us s

p. W

H 5

701

ZP_0

1083

799

951

S32

0-3-

359

.13º

N 2

2.29

º E3

EF51

3403

539

Syn

echo

cocc

us s

p. M

W10

0C3

AY15

1224

95S

ynec

hoco

ccus

sp.

WH

570

1ZP

_010

8380

095

Syn

echo

cocc

us s

p. W

H 5

701

ZP_0

1083

799

951

S32

0-3-

459

.13º

N 2

2.29

º E3

EF51

3404

499

Syn

echo

cocc

us s

p. P

S72

5 A

F223

446

98S

ynec

hoco

ccus

sp.

PS

720

AA

F601

2897

Syn

echo

cocc

us s

p. B

O 8

807

AA

P40

810

74gr

oup

BS

320-

3-5

59.1

3º N

22.

29º E

3EF

5134

0553

9S

ynec

hoco

ccus

sp.

MW

100C

3 AY

1512

2495

Syn

echo

cocc

us s

p. W

H 5

701

ZP_0

1083

800

95S

ynec

hoco

ccus

sp.

WH

570

1ZP

_010

8379

995

1S

320-

3-6

59.1

3º N

22.

29º E

3EF

5134

0649

9S

ynec

hoco

ccus

sp.

PS

725

AF2

2344

698

Syn

echo

cocc

us s

p. P

S72

0A

AF6

0128

97S

ynec

hoco

ccus

sp.

PS

725

AA

F601

2175

grou

p B

S32

0-3-

759

.13º

N 2

2.29

º E3

EF51

3407

539

Syn

echo

cocc

us s

p. M

W10

0C3

AY15

1224

95S

ynec

hoco

ccus

sp.

WH

570

1ZP

_010

8380

094

Syn

echo

cocc

us s

p. W

H 5

701

ZP_0

1083

799

921

S32

0-3-

959

.13º

N 2

2.29

º E3

EF51

3408

539

Syn

echo

cocc

us s

p. M

W10

0C3

AY15

1224

95S

ynec

hoco

ccus

sp.

WH

570

1ZP

_010

8380

095

Syn

echo

cocc

us s

p. W

H 5

701

ZP_0

1083

799

951

S32

0-3-

1059

.13º

N 2

2.29

º E3

EF51

3409

539

Syn

echo

cocc

us s

p. M

W10

0C3

AY15

1224

95S

ynec

hoco

ccus

sp.

WH

570

1ZP

_010

8380

095

Syn

echo

cocc

us s

p. W

H 5

701

ZP_0

1083

799

931

S32

0-3-

1159

.13º

N 2

2.29

º E3

EF51

3400

539

Syn

echo

cocc

us s

p. M

W10

0C3

AY15

1224

95S

ynec

hoco

ccus

sp.

WH

570

1ZP

_010

8380

094

Syn

echo

cocc

us s

p. W

H 5

701

ZP_0

1083

799

951

S32

0-12

-159

.13º

N 2

2.29

º E12

EF51

3410

539

Syn

echo

cocc

us s

p. M

W10

0C3

AY15

1224

95S

ynec

hoco

ccus

sp.

WH

570

1ZP

_010

8380

095

Syn

echo

cocc

us s

p. W

H 5

701

ZP_0

1083

799

951

S32

0-12

-259

.13º

N 2

2.29

º E12

EF51

3411

539

Syn

echo

cocc

us s

p. M

W10

0C3

AY15

1224

95S

ynec

hoco

ccus

sp.

WH

570

1ZP

_010

8380

095

Syn

echo

cocc

us s

p. W

H 5

701

ZP_0

1083

799

951

S32

0-12

-359

.13º

N 2

2.29

º E12

EF51

3412

539

Syn

echo

cocc

us s

p. M

W10

0C3

AY15

1224

95S

ynec

hoco

ccus

sp.

WH

570

1ZP

_010

8380

095

Syn

echo

cocc

us s

p. W

H 5

701

ZP_0

1083

799

951

S32

0-12

-559

.13º

N 2

2.29

º E12

EF51

3413

539

Syn

echo

cocc

us s

p. M

W10

0C3

AY15

1224

95S

ynec

hoco

ccus

sp.

WH

570

1ZP

_010

8380

095

Syn

echo

cocc

us s

p. W

H 5

701

ZP_0

1083

799

951

S32

0-12

-659

.13º

N 2

2.29

º E12

EF51

3414

499

Syn

echo

cocc

us s

p. P

S72

5 A

F223

446

98S

ynec

hoco

ccus

sp.

PS

720

AA

F601

2896

Syn

echo

cocc

us s

p. B

O 8

807

AA

P40

810

74gr

oup

BS

320-

12-7

59.1

3º N

22.

29º E

12EF

5134

1551

1S

ynec

hoco

ccus

sp.

MW

100C

3 AY

1512

2493

/89

Syn

echo

cocc

us s

p. W

H 5

701

ZP_0

1083

800

100

Syn

echo

cocc

us s

p. W

H 5

701

ZP_0

1083

799

100

n.a

S32

0-12

-859

.13º

N 2

2.29

º E12

EF51

3416

499

Syn

echo

cocc

us s

p. P

S72

5 A

F223

446

98S

ynec

hoco

ccus

sp.

PS

720

AA

F601

2897

Syn

echo

cocc

us s

p. B

O 8

807

AA

P40

810

74gr

oup

BS

320-

12-9

59.1

3º N

22.

29º E

12EF

5134

1753

9S

ynec

hoco

ccus

sp.

MW

100C

3 AY

1512

2495

Syn

echo

cocc

us s

p. W

H 5

701

ZP_0

1083

800

95S

ynec

hoco

ccus

sp.

WH

570

1ZP

_010

8379

995

1S

320-

12-1

059

.13º

N 2

2.29

º E12

EF51

3418

539

Syn

echo

cocc

us s

p. M

W10

0C3

AY15

1224

95S

ynec

hoco

ccus

sp.

WH

570

1ZP

_010

8380

094

Syn

echo

cocc

us s

p. W

H 5

701

ZP_0

1083

799

951


71

Su

pp

lem

ent

tab

le 3

: com

par

ison

of c

peB

A c

lone

s w

ith G

enb

ank

dat

abas

es.

Seq

uenc

e ID

Sam

plin

g

Loca

tio

nD

epth

(m

)G

enB

ank

Acc

esio

n N

umb

er

clo

ne

Leng

th

(bp

)C

lose

st r

elat

ive

to G

en-

Ban

k (B

last

N)

Gen

Ban

k A

cc.

Num

ber

.

Sim

i-la

rity

sc

ore

(%

)

Bes

t B

last

P h

it C

PE

-BG

enB

ank

Acc

. Num

-b

er.

Sim

i-la

rity

sc

ore

(%

)

Bes

t B

last

P h

it C

PE

-AG

enB

ank

Acc

. Num

-b

er.

Sim

i-la

rity

sc

ore

(%

)

BS

cl

uste

r

(Fig

. 7)

S29

8-3m

-360

.04º

N 2

6.21

º E3

EF51

3419

531

Syn

echo

cocc

us s

p. W

H78

03X7

2961

84S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

397

%S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

290

%1

S29

8-3M

-460

.04º

N 2

6.21

º E3

EF51

3422

523

Syn

echo

cocc

us s

p. W

H78

03X7

2961

84S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

394

%S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

290

%1

S29

8-3M

-760

.04º

N 2

6.21

º E3

EF51

3420

526

Syn

echo

cocc

us s

p. W

H78

03X7

2961

85S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

392

%S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

288

%4

S29

8-3M

-860

.04º

N 2

6.21

º E3

EF51

3423

532

Syn

echo

cocc

us s

p. W

H81

02B

X569

694

81S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

397

%S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

290

%2

S29

8-3m

-960

.04º

N 2

6.21

º E3

EF51

3421

531

Syn

echo

cocc

us s

p. W

H81

02B

X569

694

81S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

392

%S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

288

%S

ingl

e S

298-

3m10

60.0

4º N

26.

21º E

3EF

5134

2453

2S

ynec

hoco

ccus

sp.

WH

7803

X729

6185

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

94%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

89%

1S

298-

3m11

60.0

4º N

26.

21º E

3EF

5134

2553

2S

ynec

hoco

ccus

sp.

WH

7803

X729

6185

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

92%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

90%

1S

298-

3m12

60.0

4º N

26.

21º E

3EF

5134

2653

1S

ynec

hoco

ccus

sp.

WH

7803

X729

6186

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

95%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

90%

1S

298-

3m13

60.0

4º N

26.

21º E

3EF

5134

2753

1S

ynec

hoco

ccus

sp.

WH

7803

X729

6184

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

97%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

90%

2S

298-

3m15

60.0

4º N

26.

21º E

3EF

5134

2853

1S

ynec

hoco

ccus

sp.

WH

7803

X729

6184

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

95%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

89%

2S

298-

12m

260

.04º

N 2

6.21

º E12

EF51

3459

523

Syn

echo

cocc

us s

p. W

H81

02B

X569

694

81S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

392

%S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

287

%4

S29

8-12

m5

60.0

4º N

26.

21º E

12EF

5134

6052

3S

ynec

hoco

ccus

sp.

WH

8102

BX5

6969

481

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

92%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

88%

4S

298-

12m

1160

.04º

N 2

6.21

º E12

EF51

3461

523

Syn

echo

cocc

us s

p. W

H81

02B

X569

694

81S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

392

%S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

288

%4

S30

0-3m

159

.33º

N 2

3.10

º E3

EF51

3429

531

Syn

echo

cocc

us s

p. W

H78

03X7

2961

83S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

394

%S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

290

%1

S30

0-3m

259

.33º

N 2

3.10

º E3

EF51

3430

531

Syn

echo

cocc

us s

p. W

H78

03X7

2961

85S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

394

%S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

290

%1

S30

0-3m

1459

.33º

N 2

3.10

º E3

EF51

3431

531

Syn

echo

cocc

us s

p. W

H78

03X7

2961

85S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

395

%S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

289

%1

S30

0-3m

1759

.33º

N 2

3.10

º E3

EF51

3432

531

Syn

echo

cocc

us s

p. W

H78

03X7

2961

85S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

395

%S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

290

%1

S30

0-3m

1859

.33º

N 2

3.10

º E3

EF51

3453

531

Syn

echo

cocc

us s

p. W

H81

02B

X569

694

89S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

392

%S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

287

%3

S30

0-3m

2059

.33º

N 2

3.10

º E3

EF51

3433

531

Syn

echo

cocc

us s

p. W

H78

03X7

2961

85S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

395

%S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

290

%1

S30

0-3m

2159

.33º

N 2

3.10

º E3

EF51

3454

531

Syn

echo

cocc

us s

p. W

H81

02B

X569

694

89S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

394

%S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

286

%3

S30

0-3m

2259

.33º

N 2

3.10

º E3

EF51

3434

531

Syn

echo

cocc

us s

p. W

H78

03X7

2961

86S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

394

%S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

290

%1

S30

0-3m

2359

.33º

N 2

3.10

º E3

EF51

3435

531

Syn

echo

cocc

us s

p. W

H78

03X7

2961

85S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

395

%S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

290

%1

S30

0-3m

2459

.33º

N 2

3.10

º E3

EF51

3458

523

Syn

echo

cocc

us s

p. W

H78

03X7

2961

87S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

392

%S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

287

%4

S30

0-12

m1

59.3

3º N

23.

10º E

12EF

5134

6253

0S

ynec

hoco

ccus

sp.

WH

8102

BX5

6969

490

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

95%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

90%

2S

300-

12m

259

.33º

N 2

3.10

º E12

EF51

3463

530

Syn

echo

cocc

us s

p. W

H81

02B

X569

694

90S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

396

%S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

290

%2

S30

0-12

m3

59.3

3º N

23.

10º E

12EF

5134

6453

0S

ynec

hoco

ccus

sp.

WH

7803

X729

6190

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

95%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

90%

1S

300-

12m

459

.33º

N 2

3.10

º E12

EF51

3465

530

Syn

echo

cocc

us s

p. W

H81

02B

X569

694

90S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

397

%S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

290

%2

S30

0-12

m5

59.3

3º N

23.

10º E

12EF

5134

6652

9S

ynec

hoco

ccus

sp.

WH

7803

X729

6189

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

95%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

90%

1S

300-

12m

659

.33º

N 2

3.10

º E12

EF51

3467

530

Syn

echo

cocc

us s

p. W

H78

03X7

2961

90S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

395

%S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

290

%1

S30

0-12

m7

59.3

3º N

23.

10º E

12EF

5134

6853

0S

ynec

hoco

ccus

sp.

WH

8102

BX5

6969

490

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

97%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

90%

2S

300-

12m

959

.33º

N 2

3.10

º E12

EF51

3469

531

Syn

echo

cocc

us s

p. W

H78

03X7

2961

88S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

394

%S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

290

%1

S30

0-12

m11

59.3

3º N

23.

10º E

12EF

5134

7053

0S

ynec

hoco

ccus

sp.

WH

8102

BX5

6969

490

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

97%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

90%

2S

314-

3m1

59.3

0º N

22.

40º E

3EF

5134

3753

2S

ynec

hoco

ccus

sp.

WH

7803

X729

6184

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

92%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

88%

1S

314-

3m2

59.3

0º N

22.

40º E

3EF

5134

3853

1S

ynec

hoco

ccus

sp.

WH

7803

X729

6184

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

97%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

90%

2S

314-

3m3

59.3

0º N

22.

40º E

3EF

5134

3952

3S

ynec

hoco

ccus

sp.

WH

8102

BX5

6969

481

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

92%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

88%

4S

314-

3m5

59.3

0º N

22.

40º E

3EF

5134

4053

1S

ynec

hoco

ccus

sp.

WH

7803

X729

6184

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

97%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

89%

2S

314-

3m8

59.3

0º N

22.

40º E

3EF

5134

4153

2S

ynec

hoco

ccus

sp.

WH

7803

X729

6185

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

95%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

89%

1S

314-

3m9

59.3

0º N

22.

40º E

3EF

5134

4253

1S

ynec

hoco

ccus

sp.

WH

7803

X729

6185

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

94%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

90%

1S

314-

3m10

59.3

0º N

22.

40º E

3EF

5134

4353

1S

ynec

hoco

ccus

sp.

WH

7803

X729

6184

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

95%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

90%

2S

314-

3m11

59.3

0º N

22.

40º E

3EF

5134

3653

1S

ynec

hoco

ccus

sp.

WH

7803

X729

6184

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

95%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

90%

2S

314-

3m14

59.3

0º N

22.

40º E

3EF

5134

4453

1S

ynec

hoco

ccus

sp.

WH

7803

X729

6184

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

97%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

90%

2S

314-

3m15

59.3

0º N

22.

40º E

3EF

5134

4553

2S

ynec

hoco

ccus

sp.

WH

7803

X729

6185

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

95%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

90%

1S

314-

12m

659

.30º

N 2

2.40

º E12

EF51

3471

530

Syn

echo

cocc

us s

p. W

H81

02B

X569

694

90S

ynec

hoco

ccus

sp.

WH

780

5ZP

_011

2334

397

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ynec

hoco

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sp.

WH

780

5ZP

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2334

290

%2

S31

4-12

m7

59.3

0º N

22.

40º E

12EF

5134

7253

0S

ynec

hoco

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7803

X729

6189

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

94%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

93%

1S

314-

12m

859

.30º

N 2

2.40

º E12

EF51

3473

530

Syn

echo

cocc

us s

p. W

H81

02B

X569

694

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sp.

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780

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ynec

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780

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2334

290

%2

S31

4-12

m11

59.3

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40º E

12EF

5134

7452

3S

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hoco

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sp.

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8102

BX5

6969

488

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

94%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

88%

4S

314-

12m

1359

.30º

N 2

2.40

º E12

EF51

3475

531

Syn

echo

cocc

us s

p. W

H78

03X7

2961

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ynec

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780

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2334

290

%1

S31

4-12

m14

59.3

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12EF

5134

7653

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8102

BX5

6969

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Syn

echo

cocc

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p. W

H 7

805

ZP_0

1123

343

92%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

87%

3S

314-

12m

1559

.30º

N 2

2.40

º E12

EF51

3477

530

Syn

echo

cocc

us s

p. W

H81

02B

X569

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7853

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8102

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Syn

echo

cocc

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H 7

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343

94%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

87%

3S

320-

3m1

59.1

3º N

22.

29º E

3EF

5134

5553

1S

ynec

hoco

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7803

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Syn

echo

cocc

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H 7

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92%

Syn

echo

cocc

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H 7

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87%

3S

320-

3m2

59.1

3º N

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5134

4653

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7803

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Syn

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H 7

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95%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

90%

1S

320-

3m5

59.1

3º N

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29º E

3EF

5134

4753

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ynec

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7803

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Syn

echo

cocc

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H 7

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ZP_0

1123

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94%

Syn

echo

cocc

us s

p. W

H 7

805

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1123

342

90%

1S

320-

3m7

59.1

3º N

22.

29º E

3EF

5134

4853

1S

ynec

hoco

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sp.

WH

7803

X729

6190

Syn

echo

cocc

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p. W

H 7

805

ZP_0

1123

343

95%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

90%

1S

320-

3m8

59.1

3º N

22.

29º E

3EF

5134

5653

0S

ynec

hoco

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sp.

WH

8102

BX5

6969

487

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

91%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

84%

3S

320-

3m9

59.1

3º N

22.

29º E

3EF

5134

4953

1S

ynec

hoco

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sp.

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7803

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6188

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

97%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

89%

2S

320-

3m10

59.1

3º N

22.

29º E

3EF

5134

5753

1S

ynec

hoco

ccus

sp.

WH

8102

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6969

489

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

94%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

87%

3S

320-

3m12

59.1

3º N

22.

29º E

3EF

5134

5053

1S

ynec

hoco

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Syn

echo

cocc

us s

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H 7

805

ZP_0

1123

343

95%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

90%

1S

320-

3m13

59.1

3º N

22.

29º E

3EF

5134

5153

1S

ynec

hoco

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sp.

WH

7803

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6189

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

95%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

89%

1S

320-

3m14

59.1

3º N

22.

29º E

3EF

5134

5253

1S

ynec

hoco

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sp.

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7803

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6189

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

95%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

89%

1S

320-

12m

159

.13º

N 2

2.29

º E12

EF51

3479

523

Syn

echo

cocc

us s

p. W

H81

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X569

694

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ynec

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sp.

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780

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ynec

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780

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288

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S32

0-12

m3

59.1

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12EF

5134

8052

3S

ynec

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sp.

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8102

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6969

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Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

92%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

88%

4S

320-

12m

459

.13º

N 2

2.29

º E12

EF51

3481

523

Syn

echo

cocc

us s

p. W

H81

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780

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392

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ynec

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780

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2334

287

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S32

0-12

m6

59.1

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29º E

12EF

5134

8252

3S

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sp.

WH

8102

BX5

6969

488

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

92%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

88%

4S

320-

12m

959

.13º

N 2

2.29

º E12

EF51

3483

523

Syn

echo

cocc

us s

p. W

H81

02B

X569

694

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ynec

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sp.

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780

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2334

392

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ynec

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780

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_011

2334

288

%4

S32

0-12

m10

59.1

3º N

22.

29º E

12EF

5134

8452

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hoco

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sp.

WH

8102

BX5

6969

488

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

92%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

88%

4S

320-

12m

1459

.13º

N 2

2.29

º E12

EF51

3485

523

Syn

echo

cocc

us s

p. W

H81

02B

X569

694

88S

ynec

hoco

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sp.

WH

780

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2334

392

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ynec

hoco

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sp.

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780

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_011

2334

288

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S32

0-12

m16

59.1

3º N

22.

29º E

12EF

5134

8652

3S

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hoco

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sp.

WH

8102

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6969

488

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

343

92%

Syn

echo

cocc

us s

p. W

H 7

805

ZP_0

1123

342

88%

4

72

Chapter 3

Table S4a: GC content of picocyanobacterial genomes

Data obtained from the IMG database: http://img.jgi.doe.gov/cgi-bin/pub/main.cgi. Genomes organized according to GC-content.

Genome Name Mbp Total Genes

Protein coding genes

genomic GC con-tent %

pheno-type

PUB Site of isolation

Synechococcus elongatus PCC 6301 (outgroup) 2.7 2578 2527 55 PC-rich n.a. Waller Creek, Austin, Texas, U.S.A.

Synechococcus sp. CC9311 2.6 2942 2892 52 PE-rich Yes California current

Synechococcus sp. BL107 2.3 2553 2507 54 PE-rich Yes* Blanes Bay, Mediterranean Sea


Synechococcus sp. WH 7805 2.6 2934 2883 58 PE-rich No Sargasso Sea, 33º45Ń, 67º30´W


Synechococcus sp. WH 8102 2.4 2580 2528 59 PE-rich Yes Tropical Atlantic, 22º30Ń, 65º36´W

Synechococcus sp. RS9916 2.7 3009 2961 60 PE-rich Yes Gulf of Aqaba, 29º28Ń, 34º55É

Synechococcus sp. RS9917 2.6 2820 2770 64 PC-rich n.a. Gulf of Aqaba, 29º28Ń, 34º55É

Synechococcus sp. WH 5701 3.0 3401 3346 65 PC-rich n.a. Long Island Sound

* D.Scanlan Personal Communication


73

Table S4b: GC content of protein coding genes of PE-rich and PC-rich Synechococcus genomesData used to produce the above plots.

GC content per gene (%)

PCC6301 (outgroup_PC)

CC9311 (PE)

BL107 (PE)

CC9902 (PE)

WH7805 (PE)

CC9605 (PE)

WH8102 (PE)

RS9916 (PE)

RS9917 (PC)

WH5701 (PC)

23 0 0 0 0 0 0 0 0 0 024 0 0 1 0 0 0 0 0 0 025 0 0 0 0 0 0 0 0 0 026 0 0 0 0 0 0 0 0 0 027 0 0 0 0 0 0 0 0 0 028 0 0 0 0 0 0 1 0 0 029 0 0 0 0 0 0 0 0 0 030 0 0 0 1 0 0 0 0 0 031 0 2 0 3 0 0 0 0 0 032 0 3 1 4 0 0 0 0 0 033 0 1 3 3 2 4 2 1 0 034 1 1 4 5 1 2 4 2 0 035 1 7 10 5 4 3 7 1 1 036 4 10 4 9 2 7 7 2 0 137 2 17 8 5 3 4 5 2 0 038 3 13 15 7 7 7 6 6 0 039 4 24 14 9 6 12 8 7 1 040 3 46 17 15 9 9 11 6 2 141 8 49 22 19 5 17 11 7 1 142 9 59 27 19 15 19 10 2 0 043 10 60 19 25 16 27 12 6 1 144 11 66 22 27 18 14 12 11 0 145 15 73 39 24 16 18 16 18 1 446 19 79 37 29 22 16 10 19 0 747 28 88 33 38 22 20 17 21 5 348 23 99 49 37 25 28 16 22 5 949 32 103 40 42 28 18 14 23 6 1050 39 134 63 55 36 19 9 35 4 1251 44 171 74 69 43 32 22 33 5 1252 52 162 91 73 46 26 22 38 5 2053 97 208 111 98 85 40 40 45 6 2254 109 245 144 148 106 52 46 50 13 3555 183 284 191 176 124 60 42 60 14 4956 231 278 245 226 133 78 62 83 23 3257 352 218 289 267 181 100 74 117 33 7458 416 173 268 243 262 96 89 172 38 6659 387 112 227 223 273 143 143 183 72 7760 266 61 189 178 279 209 179 268 81 9561 111 27 110 121 298 255 235 274 130 7762 41 14 79 57 237 283 260 330 195 13863 17 5 40 28 212 306 302 307 220 14164 5 0 15 11 154 256 252 279 249 16165 0 0 2 5 114 231 244 206 302 23266 2 0 2 3 57 148 152 170 313 23767 1 0 1 0 26 78 100 101 286 28468 1 0 1 0 9 39 53 39 279 28269 0 0 0 0 7 14 22 9 219 31170 0 0 0 0 0 9 8 2 135 29171 0 0 0 0 0 3 2 2 85 24672 0 0 0 0 0 0 1 1 24 21273 0 0 0 0 0 0 0 0 12 10774 0 0 0 0 0 0 0 0 3 5375 0 0 0 0 0 0 0 0 1 2876 0 0 0 0 0 0 0 1 0 1377 0 0 0 0 0 0 0 0 0 0

Chapter 4Rapid diversification of red and green Synechococcus strains

in the Baltic Sea

Thomas H.A. Haverkamp1

Daphne Schouten1

Marije Doeleman1

Ute Wollenzien1

Jef Huisman2

Lucas J. Stal1,2*

1Department of Marine Microbiology, Netherlands Institute of Ecology (NIOO-KNAW), PO Box 140, 4400 AC Yerseke, The Netherlands.

2Aquatic Microbiology, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Nieuwe Achtergracht 127, 1018 WS Amsterdam, The Netherlands.

Submitted to: The ISME journal.Running title: Microdiversity among Baltic Sea picocyanobacteriaKeywords: Baltic Sea; biogeography; horizontal gene transfer; microdiversity; phycocyanin; SynechococcusSubject Category: Microbial population and community ecology

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AbstractSynechococcus is a cosmopolitan genus of picocyanobacteria living in the photic zone of fresh-

water and marine aquatic environments. Here, we describe the isolation of 46 closely related picocyanobacterial strains from the Baltic Sea. The isolates show considerable variation in their pigmentation phenotypes. Furthermore, small differences between the strains were observed with respect to cell volume and preference for either ammonium or nitrate as the main source of nitro-gen. At the molecular level we found that 39 strains, designated BSea1, had almost identical 16S rRNA – ITS sequences with Synechococcus strain WH5701. Despite having similar 16S rRNA – ITS sequences, the BSea1 strains could be separated into several different clusters when compar-ing the phycocyanin (cpcBA) operon. This separation corresponds to the pigmentation of the differ-ent BSea1 strains. The majority of phycocyanin (PC) rich Bsea1 strains clustered with WH5701. Remarkably, the phycoerythrin (PE) rich strains of BSea1 formed an as yet unidentified cluster within the cpcBA phylogeny, distantly related to other PE-rich groups. Detailed analysis of the cpcBA operon using neighbour net analysis indicates that the PE-rich BSea1 strains are probably endemic for the Baltic Sea. Comparison of the phylogenies obtained by using the 16S rRNA–ITS, the cpcBA as well as the concatenated 16S rRNA-ITS and cpcBA operon sequences, revealed pos-sible events of horizontal gene transfer among different Synechococcus lineages. Our results show that microdiversity is important in Synechococcus populations, and that it can reflect different phenotypes with different ecological roles.

Rapid diversification of red and green Synechococcus strains

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IntroductionThe picocyanobacterium Synechococcus is a typical cosmopolitan genus (Schmidt et al., 1991).

Synechococcus spp. can be found in a wide range of marine and freshwater ecosystems around the world, as well as in benthic systems and even in extreme environments such as hot springs (Schmidt et al., 1991; Ramsing et al., 2000; Crosbie et al., 2003; Ernst et al., 2003; Fuller et al., 2003; Ivanikova et al., 2007). Synechococcus comprises both picocyanobacteria (by definition <2 µm) as well as species that are larger. The genus is polyphyletic and genetically highly diverse. Synechococcus species are phylogenetically divided in several major clusters (Herdman et al., 2001). The picocyanobacteria often found and isolated from marine, brackish and freshwaters are related to Synechococcus cluster 5 (Crosbie et al., 2003; Ernst et al., 2003). At the 16S rRNA level this cluster is closely related to the sister groups Prochlorococcus and Cyanobium. The ribosomal genes of these species form a monophyletic clade in the cyanobacterial phylogenetic tree (Herdman et al., 2001; Ernst et al., 2003; Fuller et al., 2003).

The study of the diversity of Synechococcus and its sister groups has been mainly performed by analysis of the 16S rRNA gene obtained from environmental clone libraries or from isolated strains from various environments throughout the world. However, the 16S rRNA gene is conserved and has limited resolution. Several authors have therefore used additional genetic markers to study the diversity of picocyanobacteria. Examples of less conserved genetic markers used in cyanobacterial studies include the internal transcribed spacer (ITS) region of the ribosomal operon (Rocap et al., 2002; Ernst et al., 2003; Chen et al., 2006; Haverkamp et al., 2008); the phycocyanin operon (cpcBA) (Robertson et al., 2001; Crosbie et al., 2003; Ivanikova et al., 2007; Haverkamp et al., 2008); the phycoerythrin operon (cpeBA) (Everroad and Wood, 2006; Haverkamp et al., 2008); the RNA polymerase core subunit gene (rpoC1) (Mühling et al., 2006); the gene encoding for protein D1 of photosystem II (psbA) (Zeidner et al., 2003); the RubisCO gene (rbcL) (Chen et al., 2004; Everroad and Wood, 2006; Wilhelm et al., 2006); the gene encoding the GG-phosphate synthetase (ggpS) and the isiA gene (encoding a chlorophyll a binding protein) (Geiss et al., 2004). These studies used either isolated strains or clone libraries derived from environmental DNA. An important advantage of using cultivated strains is that it is possible to carry out phylogenetic comparisons using multiple different genetic markers. This is nowadays current practice for the analysis of pathogenic organisms in order to study the genetics behind the virulence of different strains (Hold et al., 2001; Li et al., 2006; Vassileva et al., 2006). This approach has been rarely applied in the study of picocyanobacteria (Crosbie et al., 2003; Ernst et al., 2003; Chen et al., 2006; Everroad and Wood, 2006). Everroad and Wood (2006) concatenated the 16S rRNA with the rpoC1 gene in order to identify the phylogenetic position of their isolates. Crosbie et al. (2003) compared the phylogenies of the 16S rRNA and the cpcBA, Ernst et al. (2003) studied the 16S rRNA and the ITS, and Chen et al. (2006) investigated the relationship between the ITS and the rbcL genes.

The Synechococcus genus consists of both red strains rich in the pigment phycoerythrin, and green strains rich in phycocyanin. Competition experiments demonstrated that these red and green strains can coexist in white light, through niche differentiation in the light spectrum (Stomp et al. 2004). This spectral niche differentiation also seems to explain the distribution of

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red and green strains in natural waters. A recent field survey of 70 aquatic ecosystems showed that red strains dominate in clear ocean waters, whereas green strains dominate in turbid inlands waters (Stomp et al. 2007a). Widespread coexistence of red and green picocyanobacteria was found in waters of intermediate turbidity, such as mesotrophic lakes and coastal seas. Based on these previous studies (Stomp et al. 2004, 2007a, 2007b; Haverkamp et al. 2008), we hypothesize that waters of intermediate turbidity, such as the Baltic Sea, are hotspots for the diversification of red and green strains.

In this study the partial 16S rRNA, the ITS region and the cpcBA operon sequences were concatenated in order to study the phylogenetic relationships of strains isolated from the Baltic Sea with other known picocyanobacteria. The datasets were analysed phylogenetically and by using rarefaction. We show that the concatenated dataset yielded higher estimates of cyanobacterial diversity than analysis of the datasets separately. Furthermore, we show that the multilocus approach hinted to geographical endemism and horizontal gene transfer, an observation that hints at rapid diversification of the Synechococcus strains in the Baltic Sea.

Materials and Methods

Sample CollectionWater samples were collected during a research cruise with the Finnish RV Aranda in the

Gulf of Finland (Baltic Sea) from 12 - 19 July 2004. Samples were taken using a rosette sampler with Niskin Bottles. Eight stations (between N 59.1 E 22.2 and N60.0 E 26.2) (Table SM1) were sampled. A Seabird 911 CTD was connected to the rosette sampler that recorded temperature, salinity, density and oxygen concentration in the water column (Figure SM1). Water samples were taken for analysis of nutrients and processed using standard methods (Grasshoff et al., 1983).

Isolation of Baltic Sea picocyanobacteriaFor isolation of picocyanobacteria water samples were fractionated using a 20 μm mesh

plankton net. The filtrate was used for inoculation. The filtrate was serially diluted in 96 deep well microtiterplates (Nunc, Roskilde, Denmark) containing BSea6 medium (mixture of 1 part ASNIII and 4 parts BG11 with a final salinity of 6.0 psu) (Rippka et al., 1979). Instead of NaNO3, NH4Cl was used as nitrogen source (1 mM). Incubation with 10 μmol photons m-2 s-1 of light was started while still on board the ship. During transport of the enrichments to the laboratory the cultures were in the dark (for about 36 hours). Upon returning to the laboratory incubation was continued for 2 weeks at 20 ºC and 10 μmol photons m-2 s-1. Subsequently, the light level was increased to 20 μmol photons m-2 s-1 and the incubation was continued for 6 weeks until colonies became visible at the bottom of the wells. Colonies were picked with sterile glass capillaries and transferred to sterile BSea6 medium and incubated at 20 μmol photons m-2 s-1. Clonal isolates were obtained by using the soft-agarose plating technique (Brahamsha, 1996). Briefly, dilution series were made in 96 well plates (type 655180, Greiner


79

Bio-one, Frickenhausen, Germany) filled with 200 μl BSea6 medium. From each dilution step 50 μl was used to inoculate 25 ml of BSea6 medium containing 0.15% (wt/vol) agarose (Sigma-Aldrich, St. Louis, MO, USA) at 40ºC. After inoculation the agarose plates were allowed to solidify and left to dry. Plates were incubated under low-light (10 μmol photons m-2 s-1) for two weeks after which the light intensity was increased to 20 μmol photons m-2 s-1 and incubation was continued until colonies appeared that were sufficiently large to be picked. Colonies were transferred to sterile BSea6 medium. After three cycles of soft-agarose plating and subsequent picking of colonies, strains were considered to be axenic and clonal and cultured in liquid BSea6 medium for experimental purposes. Strains were maintained in BSea6 amended with NaNO3 (1.8 mM) and NaHCO3 (0.012 mM).

Morphology and microscopyExponentially growing or early stationary phase cultures were collected and fixed in a

mixture of 1% formaldehyde and 0.05% glutaraldehyde. The samples were stored at -80 ºC until analysis. Microscope slides were prepared by covering them with a thin layer of 1% hot agarose (50 ºC) that was subsequently allowed to solidify. Cells were applied to the agarose and covered with a cover slip. Observations were performed using a Zeiss Axiophot microscope (Oberkochen, Germany). Microphotographs were taken with a ProgRes C10 plus digital imaging system (JENOPTIK Laser, Optik, Systeme GmbH, Jena, Germany). The pictures were processed using ProgRes CapturePro2.0 software (JENOPTIK Laser, Optik, Systeme GmbH). At least 40 measurements of cell width and length were taken and averaged for each culture. The cell volume was calculated using the formula for a cylinder with hemispherical ends for all cells (V = π (L-W) W2/4 + πW3/6) (Sieracki et al., 1989).

In vivo absorption spectraCultures in the exponential to early stationary phase were used for recording in vivo

absorption spectra. An aliquot of the culture was transferred to a cuvet (4ml) and the in vivo absorption spectrum was measured from 400 – 750 nm using a Varian Cary 100 Bio equipped with an integrating sphere DRA-CA-3300 (Palo Alto, CA, USA).

Growth characteristics of picocyanobacterial isolatesThe strains CCY0441 - CCY0492 were tested for growth with different nitrogen sources

using BSea6 medium amended with NaHCO3 (0.012 mM). One treatment only contained NaNO3 (1.7 mM) as nitrogen source while the second treatment contained both NaNO3 (1.7 mM) and NH4Cl (1 mM) as nitrogen sources. Strains were incubated in duplicate for 3 weeks in 24-well microtiterplates (type 662160, Greiner Bio-one). After incubation, absorption spectra were measured for each strain and treatment.

DNA extractionStationary phase cultures were used for DNA extraction. One ml of culture was transferred

to an Eppendorf tube provided by the UltraClean Soil DNA extraction kit (MoBio Laboratories

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Inc., Carlsbad, CA, USA). DNA extraction was performed following the instructions of the manufacturer, except that the initial vortexing step was decreased to 5 min at maximum speed. DNA was eluted with 50 μl of TE-buffer. The DNA concentration was measured using a ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA).

PCR reactionsPCR reactions were performed with 20-100 ng of DNA in a 25 μl reaction volume. The

16S-ITS-operon was amplified using the primers B1055F and PitsE-cyanR (Ernst et al., 2003; Zaballos et al., 2006), while in a separate reaction the cpcBA-operon was amplified using the primers cpcBF (UFP) / cpcAR (URP) (Robertson et al., 2001). For each PCR reaction the mixture contained 0.625 units of HotStarTaq (Qiagen, Venlo, The Netherlands), 0.2mM of each dNTP (Roche, Woerden, The Netherlands), 1 x PCR buffer (Qiagen), forward and reverse primers were used at a final concentration of 5 pmol and MQ-grade H2O was added to a final volume of 25 μl.

A GeneAmp PCR System 2700 (Applied Biosystems, Foster City, CA, USA) was used to perform the PCR reactions. The PCR program to amplify the 16S rRNA-ITS-1 region consisted of a hot start at 94ºC of 15 min, followed by 35 cycles of 1 min at 94ºC, 1 min at 62ºC and 1 min at 72ºC and a final elongation step of 10 min at 72ºC. The PCR of the cpcBA-operon was identical except that the annealing step was at 55ºC and the number of cycles was 40. PCR products were checked on a 1% agarose (Sigma Aldrich) gel.

Cloning and sequencingPCR products were cloned using the TOPO TA Cloning Kit (Invitrogen, Breda, The

Netherlands) following the instructions of the manufacturer. After overnight incubation of the transformed cells 5 colonies were picked for each PCR product and inoculated in microtiterplates (type 655180, Greiner Bio-one) containing 200 μl of liquid LB-medium. After overnight incubation at 37ºC, 25 μl of culture was mixed with 25 μl of sterile MQ grade H2O. The mixture was heated for 10 min at 95ºC and subsequently used as template in a PCR reaction containing the T7 and T3 primers of the vector in order to amplify the inserted gene fragments. The PCR reaction mixture contained 0.625 units HotStarTaq (Qiagen), 0.2 mM of each DNTP (Roche), 1 x PCR Buffer (Qiagen), 5 pmol of each of the T7 and T3 primers, 2 μl of the bacterial suspension. Sterile MQ-grade H2O was added to a final volume of 50 μl. The PCR program was run on a GeneAmp PCR System 2700 with the following settings: 15 min hot start at 94ºC, 35 cycles of 30 sec at 94ºC, 30 sec at 55ºC, 1 min at 72 º C and a final step of 10 min elongation at 72ºC. PCR products were checked on a 1% agarose (Sigma Aldrich) gel. Positive PCR reactions were purified using the DNA clean & concentrator-5 kit (Zymo Research, Orange, CA, USA) and eluted in 20 μl of sterile MQ-grade H2O. DNA concentrations of purified PCR products were checked with the ND1000-spectrophotometer.

For each of the loci four clones were sequenced in order to minimize errors caused by the polymerase. The sequencing reactions were performed by using the Big Dye Terminator v1.1 Cycle sequencing kit (Applied Biosystems) following the instructions of the manufacturer.


81

Sequence products were analyzed with a 3130 Genetic Analyzer (Applied Biosystems). For each clone, the forward and reverse sequences were manually aligned in BioEdit (Hall, 1999) and the consensus sequences were checked against GenBank using BlastN and BlastP (Altschul et al., 1997; McGinnis and Madden, 2004). For all strains the 16S rRNA-ITS-1 sequences and cpcBA operon sequences have been submitted to the GenBank databases under accession No. EU386608 – EU386699.

Phylogenetic and diversity analysisPhylogenetic analysis was performed using alignments of the 16S rRNA-ITS-1 and the

cpcBA operon sequences by using closely related sequences obtained from GenBank. For the 16S rRNA-ITS-1 the highly variable regions that could not be reliably aligned were removed from the analysis, except when stated otherwise. Also, the intergenic spacer region (IGS) was removed from the cpcBA operon sequences before analysis for reasons of reliable alignment problems. Additionally, for those strains of which both the 16S rRNA-ITS-1 and the cpcBA operon are known we constructed concatenated sequences using the program DAMBE (Xia and Xie, 2001). Phylogenetic tree construction was performed using the MEGA4.0 software (Tamura et al., 2007). Neighbour-net network analysis was performed using the SplitsTree4 program (Huson and Bryant, 2006). Maximum likelihood analysis using the 16S rRNA-ITS-1 region and the cpcBA operon was performed using PAUP* (Swofford, 2003). Analysis of the diversity at the different loci between the picocyanobacterial isolates was performed using rarefaction analysis with the software DOTUR (Schloss and Handelsman, 2005). As input for rarefaction analysis we created a distance matrix of the sequence alignments of the different loci using the program DNAdist in the Phylip package using Jukes-Cantor distances and standard settings (Felsenstein, 1989). The distance matrix was subsequently analyzed with DOTUR using standard conditions with the rarefaction option enabled. The output was used to create the rarefaction curves and similarity plots.

Results

Sampling sitesIn the summer of 2004 eight stations were sampled in the Gulf of Finland (Baltic Sea).

The geographic coordinates, water depth and date of sampling are summarized in Table SM1 of the supplementary materials. Vertical profiles of salinity, temperature, density, and oxygen saturation at the sampling stations are provided in Figure SM1 of the supplementary materials.

The concentrations of NH4+, NO3

-, NO2- and PO4

3- at the surface and at 30 m depth are summarized in Table 4.1. At the water surface the N:P ratios were between 1 and 2, except at station 298 where the N:P ratio was 9 due to a very low phosphate concentration. At 30 m depth the N:P ratios were higher and ranged from 3.5 – 10.6. The higher N:P ratios were mainly caused by a higher concentration of NH4

+. Station 298 was again exceptional. The N:P ratio at

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20 m was only 0.68 and was caused by low NH4+ and relatively high phosphate concentrations.

The nutrient concentrations at this station were lower than at the other stations and the N:P ratio seemed to be largely influenced by the dynamics of phosphate (Table 4.1).

Morphology and pigmentationForty-six picocyanobacteria were isolated from water samples of the Gulf of Finland. All of the isolates could be assigned to the picoplankton frac-tion based on their size and their coccoid or rod-like shape. All strains were maintained in the same cultivation medium BSea6 containing 1 mM NH4Cl and 1.8 mM NaNO3 as nitro-gen sources and 0.012 mM NaHCO3. Comparison of the strains showed that cell sizes and volumes varied considerably between the isolates. The list of strains, their CCY number, clade assignment, station and depth of isolation, pigmentation and size and volumes are listed in Table 4.2. The coccoid isolate CCY0452 was the smallest among the isolates (bio-volume of 0.66 μm3) and the rod CCY0454 was the largest (bio-volume of 2.43 μm3). Cell width varied only little between strains (0.8 - 1.4 μm), while cell length varied from 1.2 - 2.9 μm causing the bio-volume differences. Thirty-three strains contained high contents of phycoerythrin but only a little phycocyanin (PE-rich strains). The other 13 strains lacked phycoerythrin and contained larger amounts of phycocyanin (PC-rich strains) (Table 4.2). We observed that the relative absorption spectra of the strains CCY0441 - CCY0492, grown in Bsea6 medium with NaNO3 and NH4Cl, showed considerable phenotypic variation with respect to the phycobiliprotein (either PE or PC) to Chla ratio, despite the fact that all strains were maintained under identical conditions (Figure 4.1; Table 4.3; Figure SM2). Furthermore, when strains CCY0441 - CCY0492 were grown in Bsea6 medium with only 1 mM NaNO3

as source of nitrogen, the PE:Chla or PC:Chla ratios decreased in most strains (Table 4.3). All PC-rich strains showed a lower PC:Chla ratio when grown with NO3

- (Table 4.3). Many of the PE-rich strains showed a decrease in the PE:Chla ratio as well, although not as strong as the PC-rich strains. The PE-rich strain CCY0456 did not show a ratio change, while the

Table 4.1. Concentrations of PO43-, NO3-, NO2

-, and NH4+ (in μM) measured at the sampling stations

at the surface and at 30 m depth. For station 305 we lacked data and was therefore omitted from this list.

Stations PO43- (μM) NO3- (μM) NO2- (μM) NH4+ (μM) N:P

0m 30m 0m 30m 0m 30m 0m 30m 0m 30m

S298 0.02 0.28* 0 0.01* 0 0.01* 0.18 0.17* 9 0.68*

S301 0.15 0.61 0 0.20 0 0.20 0.23 1.78 1.53 3.57

S307 0.09 0.48 0 0.46 0.02 0.18 0.13 1.88 1.67 5.25

S310 0.09 0.60 0.01 0.46 0.04 0.29 0.14 1.58 2.11 3.88

S314 0.09 0.32 0 0.32 0 0.08 0.11 1.56 1.22 6.13

S322 0.10 0.36 0.03 0.36 0.01 0.12 0.11 1.48 1.50 5.44

S327 0 0.18 0 0.25 0 0.01 0.06 1.65 n.d. 10.61

*At station S298, deep samples were from 20 m instead of 30m.


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Table 4.2. Picocyanobacteria isolated in this study. Clade designation is based the highest similarity between the 16S rRNA-ITS sequence with known sequences found in GenBank. Cell length and width are averages of 40 cell measurements. Bio-volume of the cells was calculated from the average values of length and width using the formula for a cylinder with hemispherical ends (Sieracki et al., 1989). Strains indicated by the pigmentation PE contained high contents of phycoerythrin and only a little phycocyanin, while strains indicated by PC contained phycocyanin only.

Strain Clade Site of Isolation Station Isolation depth

cell length

(μm)

cell width

(μm)

cell bio-volume

(μm3)

Pigmentation

CCY0415 Subalpine C II 59º 30’ N, 22º 40’ E S314 0 1.8 1.2 1.47 PCCCY0416 Subalpine C II 59º 30’ N, 22º 40’ E S314 0 nd nd nd PCCCY0417 Subalpine C II 59º 30’ N, 22º 40’ E S314 0 1.5 1.1 1.07 PCCCY0418 Subalpine C II 59º 30’ N, 22º 40’ E S314 0 2.1 1.1 1.74 PECCY0419 Subalpine C II 59º 30’ N, 22º 40’ E S314 0 1.6 1.1 1.21 PECCY0420 Bornholm Sea 59º 30’ N, 22º 40’ E S314 0 nd nd nd PECCY0421 Subalpine C II 59º 30’ N, 22º 40’ E S314 15 1.3 1.1 0.89 PECCY0422 Subalpine C II 59º 30’ N, 22º 40’ E S314 15 1.6 1.1 1.11 PECCY0423 Subalpine C II 59º 13’ N, 22º 19’ E S327 15 1.9 1.1 1.48 PECCY0424 Subalpine C II 59º 13’ N, 22º 19’ E S327 15 1.5 1.1 1.05 PECCY0426 Group A Cyanobium 59º 30’ N, 22º 43’ E S310 0 2.0 1.3 1.90 PCCCY0431 Subalpine C II 59º 30’ N, 22º 40’ E S314 15 1.7 1.1 1.26 PECCY0432 Group I 60º 04’ N, 26º 21’ E S298 0 nd nd nd PECCY0434 Subalpine C II 59º 30’ N, 22º 40’ E S314 0 1.5 1.2 1.20 PECCY0435 Subalpine C II 59º 30’ N, 22º 40’ E S314 0 1.8 1.1 1.34 PECCY0436 Subalpine C II 59º 24’ N, 22º 26’ E S322 0 1.6 1.1 1.23 PECCY0437 Subalpine C II 59º 13’ N, 22º 19’ E S327 0 1.7 1.2 1.50 PCCCY0439 Subalpine C II 59º 30’ N, 22º 40’ E S314 0 1.6 1.1 1.17 PECCY0441 Subalpine C II 59º 28’ N, 22º 39’ E S305 0 1.8 1.1 1.25 PCCCY0443 Subalpine C II 59º 28’ N, 22º 39’ E S305 0 2.0 1.0 1.21 PCCCY0444 Subalpine C II 59º 30’ N, 22º 50’ E S307 0 2.1 1.1 1.57 PECCY0446 Subalpine C II 59º 30’ N, 22º 43’ E S310 0 1.3 1.1 0.83 PECCY0448 Subalpine C II 59º 30’ N, 22º 43’ E S310 12 1.5 1.4 1.59 PECCY0449 Subalpine C II 59º 30’ N, 22º 43’ E S310 12 2.0 1.1 1.58 PECCY0450 Subalpine C II 59º 30’ N, 22º 43’ E S310 12 1.6 1.0 0.97 PECCY0451 Subalpine C II 59º 30’ N, 22º 43’ E S310 12 1.7 1.0 1.07 PECCY0452 Subalpine C II 59º 30’ N, 22º 43’ E S310 12 1.2 1.0 0.66 PCCCY0454 Bornholm Sea 59º 30’ N, 22º 43’ E S310 0 2.9 1.1 2.43 PECCY0455 Subalpine C II 59º 30’ N, 22º 40’ E S314 0 2.1 1.1 1.62 PCCCY0456 Subalpine C II 59º 32’ N, 22º 50’ E S301 0 1.5 1.1 1.08 PECCY0457 Subalpine C II 59º 30’ N, 22º 40’ E S314 0 1.7 1.0 1.10 PECCY0458 Subalpine C II 59º 30’ N, 22º 40’ E S314 0 2.0 0.9 1.17 PECCY0461 Subalpine C II 59º 32’ N, 22º 50’ E S301 0 1.5 0.9 0.80 PECCY0462 Subalpine C II 59º 30’ N, 22º 40’ E S314 15 1.4 1.0 0.82 PCCCY0463 Subalpine C II 59º 30’ N, 22º 40’ E S314 15 1.7 1.1 1.24 PECCY0464 Subalpine C II 59º 30’ N, 22º 40’ E S314 15 1.6 1.2 1.25 PECCY0465 Subalpine C II 59º 30’ N, 22º 40’ E S314 15 1.8 1.1 1.31 PECCY0466 Subalpine C II 59º 24’ N, 22º 26’ E S322 0 1.3 1.1 0.88 PECCY0467 Subalpine C II 59º 24’ N, 22º 26’ E S322 0 1.4 1.2 1.05 PECCY0468 Group I 59º 32’ N, 22º 50’ E S301 0 1.8 0.8 0.83 PECCY0469 Subalpine C II 59º 24’ N, 22º 26’ E S322 15 1.3 1.0 0.82 PCCCY0470 Bornholm Sea 59º 28’ N, 22º 39’ E S305 0 1.7 1.1 1.14 PECCY0489 Subalpine C II 59º 28’ N, 22º 39’ E S305 0 1.7 1.0 1.18 PECCY0490 Group I 59º 28’ N, 22º 39’ E S305 0 1.8 0.9 0.86 PCCCY0491 Subalpine C II 59º 28’ N, 22º 39’ E S305 0 1.7 1.0 1.13 PECCY0492 Subalpine C II 59º 28’ N, 22º 39’ E S305 0 1.3 1.0 0.76 PC

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strains CCY0444, CCY0454, CCY0461, CCY0468 and CCY0470 showed an increase in the PE:Chla ratio (Table 4.3). Phycobilisomes can be used for nitrogen storage. Hence, these results might indicate different preferences for N-sources between the strains as has been shown for other species of freshwater Synechococcus (Scanlan, 2003; Ernst et al., 2005).

Molecular identification of the Baltic Sea picocyanobacterial isolates.Molecular identification of the 46 strains isolated in this study revealed representatives

of several clusters of picocyanobacteria. Based on sequence comparison of the ribosomal 16S rRNA - ITS-1 region, all strains were highly similar to freshwater picocyanobacteria (Table 4.2 and Figure 4.2). The isolates belong to 4 major groups within the 16S rRNA-ITS-1 phylogeny. The 16S rRNA-ITS-1 sequence of strain CCY0426 clustered with strains belonging to group A of the freshwater picocyanobacteria (Ernst et al., 2003; Chen et al., 2006). The second group of isolates contains the strains CCY0420, CCY0454 and CCY0470. Their 16S rRNA-ITS-1 sequences form a monophyletic clade with CCY9201 (previously BS4) and CCY9202 (previously BS5) that were previously isolated from the Baltic Sea (Ernst et al., 2003; Stomp et al. 2004) and were assigned as ‘Bornholm Sea Group’. A third clade comprises the ITS-1 sequences of the strains CCY0432, CCY0468 and CCY0490. These three strains form

Figure 4.1. Comparison of in vivo absorption spectra of two PE-rich and two PC-rich Baltic Sea isolates grown under the same light conditions. Note that, despite their striking difference in pigmentation, the strains CCY0417 and CCY0443 belong to the same phylogenetic clade as determined with the 16S rRNA-ITS-1 region.


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within the picocyanobacterial ITS-1 phylogeny, another deep branching cluster only related with the ribosomal 16S rRNA sequences of strains from group I (Crosbie et al., 2003). The fourth and largest group of isolates contains 39 strains and is designated Baltic Sea group 1 (BSea1). The 16S rRNA - ITS-1 sequences of these strains show the highest similarity to those of Synechococcus WH5701 and BO8805 that belong to the Marine cluster B or the Sub-alpine Cluster II (Ernst et al., 2003; Chen et al., 2006). The 16S rRNA-ITS sequences of BSea1

Table 4.3. The ratio of phycobilisome absorption over Chla absorption of strains CCY0441 – CCY0492, using either both NaNO3 and NH4Cl as a nitrogen source or only NaNO3. For both treatments we determined the absorption peaks of the dominant phycobiliprotein (PE at 570 nm, or PC at 625 nm) and Chla (680 nm), and calculated the ratio between these pigments. A ratio > 1 indicates a higher absorption by the phycobiliprotein than by Chla. Absorption spectra for every strain can be found in Supplementary Figure 1.

Strains Pigmentation NH4Cl + NaNO3 NaNO3 Only Observed difference

CCY0492 PC 1.2 0.5 -0.6

CCY0469 PC 1.2 0.6 -0.6

CCY0452 PC 1.1 0.6 -0.5

CCY0462 PC 1.2 0.7 -0.5

CCY0443 PC 1.1 0.7 -0.5

CCY0455 PC 1.2 0.7 -0.5

CCY0441 PC 1.2 0.9 -0.3

CCY0490 PC 1.2 1.0 -0.2

CCY0466 PE 1.2 0.7 -0.5

CCY0463 PE 1.0 0.6 -0.4

CCY0448 PE 1.0 0.6 -0.4

CCY0449 PE 1.0 0.7 -0.4

CCY0464 PE 1.0 0.7 -0.3

CCY0467 PE 1.0 0.7 -0.3

CCY0491 PE 1.0 0.7 -0.3

CCY0489 PE 1.0 0.7 -0.2

CCY0451 PE 1.2 1.0 -0.2

CCY0465 PE 0.9 0.7 -0.2

CCY0457 PE 1.0 0.8 -0.2

CCY0446 PE 1.2 1.0 -0.2

CCY0450 PE 1.2 1.0 -0.2

CCY0458 PE 1.0 0.8 -0.2

CCY0456 PE 0.9 0.9 0.0

CCY0444 PE 1.4 1.5 0.1

CCY0470 PE 1.3 1.5 0.2

CCY0468 PE 1.5 1.7 0.2

CCY0461 PE 1.5 1.9 0.3

CCY0454 PE 1.0 1.4 0.4

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Figure 4.2. Neighbour-joining tree based on the 16S rRNA-ITS-1 sequences from 112 picocyanobacterial isolates. The partial sequences used contained the highly conserved regions of the 16S rRNA and the ITS-1 region (862 nucleotides). The tree was rooted with the sequence of Synechococcus PCC6301. Construction of the Neighbour-joining tree was done using a Jukes-Cantor model of nucleotide evolution. Bootstrap values (1000 replicates) are shown for those nodes that have a confidence level higher than 50%. Taxa containing picocyanobacterial isolates from the Baltic Sea are marked by boxes. For clustered taxa it is indicated how many isolates are contained within the taxon by the numbers between brackets.


87

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isolates can be completely aligned with WH5701 and BO8805. Nonetheless, the sequences of the BSea1 strains show on average 7.3% and 8.6% nucleotide difference at the 16S rRNA-ITS-1 region in comparison with WH5701 and BO8805, respectively. Detailed comparison of the ITS-1 of WH5701 and the BSea1 isolates revealed that most of the nucleotide differences are found in the hyper variable regions that form the hairpins (Rocap et al., 2002). Within the BSea1 group, microdiversity is observed when the full length ITS-1 sequences are used for the phylogenetic analysis (Figure SM3). Four strains form a distinct cluster (BSea1B) that has on average 1.3% sequence difference with the other major sub-cluster, BSea1A (Figure SM3). Synechococcus WH5701 was previously assigned to Synechococcus cluster 5.2 and all members

Table 4.4. Comparison of diversity of picocyanobacterial cpcBA genes obtained in different datasets. Number of OTU is given at the similarity cut-off levels 100, 99, 97, 94 and 90%.

Dataset Sequences OTU at

100% 99% 97% 94% 90%

Baltic Sea isolates 46 18 9 6 4 4

Baltic Sea clone libraries * 68 20 10 7 6 6

Isolates and clones 114 36 16 10 8 8

Picocyanobacterial GenBank 331 143 83 58 42 31

* Data from Haverkamp et al., 2008

Figure 4.4. Comparison of diversity at different loci for the Baltic Sea isolates. A) 100% similarity rarefaction curves and B) similarity plots of the 16S rRNA-ITS sequences (closed circles), cpcBA-operon (open triangles) and the concatenated 16S rRNA-ITS and cpcBA operon sequences (closed squares).


89

of this clade were assumed to have phycocyanin as their main photosynthetic pigment (i.e. are green) (Herdman et al., 2001). However, many of our Baltic Sea isolates are red and contain phycoerythrin as their main photosynthetic pigment.

Phycocyanin diversity of picocyanobacteriaThe cpcBA gene found in picocyanobacteria can be used to separate strains into three

different phylogenetic groups that have clearly different pigment phenotypes (Six et al., 2007; Haverkamp et al., 2008). In order to test whether the PE- and PC-rich Baltic Sea isolates can be separated according to pigment type, the 46 isolates were compared to known cpcBA operon sequences available in GenBank. The Baltic Sea PE and PC-rich isolates do cluster according to pigment phenotype as was found before (Figure SM4) (Haverkamp et al., 2008). This is especially clear for the BSea1 group isolates which is split in the cpcBA phylogeny in a PE-rich group, clustering with other PE-rich isolates, and a PC-rich group which clusters closely with strain WH5701 and the PC-rich Group I (Figure SM4) (Crosbie et al., 2003; Haverkamp

Figure 4.5. A Neighbour-network constructed using 42 representative cpcBA sequences (determined by rarefaction analysis with a cut-off of 94%). Bootstrap support values for the nodes are not shown for clarity. The out-group is represented by the cpcBA sequence of Synechococcus PCC6301 (■). The PE-rich BSea1 cluster is represented by strain CCY0418. Pigmentation phenotypes are in indicated for the strains used here; PE-rich strains (•), PC-rich strains (o). For a complete analysis using all known cpcBA sequences see Figure SM7.

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et al., 2008). Nonetheless, a more detailed representation of the same phylogenetic tree shows that several PC-rich strains do cluster with the PE-rich isolates of the BSea1 group (Figure SM5). Remarkably, the PE-rich isolates CCY0432 and CCY0468 fall into the Baltic Sea cluster of clones in Group I, which until now only contained PC-rich isolates (Figure SM5). The cpcBA sequences of PE-rich strains CCY0420, CCY0450, CC0454 and CCY0470 form a monophyletic clade with CCY9202 and strains belonging to PE-rich group E. Finally, the cpcBA operon sequence of PC-rich strain CCY0426 clusters together with the PC-rich group A (Crosbie et al., 2003) (Figure SM4; Figure SM5).

In addition to the study of isolates from the Baltic Sea, clone libraries were constructed from environmental DNA extracted from the same water samples (Haverkamp et al., 2008). The combination of the cpcBA dataset from clone libraries and from the isolates revealed the highest diversity in the combined dataset (Table 4.4; Figure SM5). Phylogenetic analysis revealed that this is caused by the presence of groups that are only found in one of the data sets, thus increasing the overall diversity (Figure SM5). Rarefaction analysis of the cpcBA sequences of the isolates, the clone libraries and additional sequences obtained from GenBank (331 sequences in total), showed that even at the 90% similarity level, the global diversity of the Synechococcus cpcBA operon was not saturated (data not shown). In total 31 OTUs representing major clades within the picocyanobacteria were found using a 90% similarity cut-off (Table 4.4).

Comparison of the 16S rRNA-ITS-1 and cpcBA phylogeniesUsing 16S rRNA we found a close relationship between the PC-rich strains WH5701,

BO8805 and the BSea1 group (both PE- and PC-rich) of our isolates. In addition, the cpcBA operon suggests a split between the PE and PC-rich picocyanobacteria from the BSea1 group. However, a clear comparison between the two phylogenetic trees is not possible since both are us-ing different sequences which can create differences in the phylogenetic analysis. Therefore we used only cpcBA sequences from isolates for which also the 16S rRNA-ITS-1 sequences are available from GenBank for the phylogenetic analyses. This allowed for comparison of the phylogenies of the 16S rRNA-ITS-1 and cpcBA sequences of these strains and provided the possibility of concatenation of these sequences.

Most of the strains that are closely related at the 16S rRNA-ITS level were also closely related for the cpcBA region (Figure 4.3; Figure SM6). However, several strains have different positions within the cpcBA phylogeny when compared to the 16S rRNA-ITS-1 phylogeny (Figure 4.3). This is especially obvious for strain CCY0450, which belongs to the BSea1 group at the 16S rRNA-ITS-1 level, but clusters with the Bornholm Sea group based on cpcBA phylogeny. In the 16S rRNA-ITS phylogeny the Bornholm Sea group is clearly separated from BSea1. Other strains that behave in a similar way are CCY0416 (BSea1), CCY0490 (Bsea2), CCY9201 (Bornholm Sea) and the marine Synechococcus strains RS9917, and RC307 (Figure SM6) (Fuller et al., 2003; Six et al., 2007; Haverkamp et al., 2008).

Interestingly, based on the cpcBA phylogeny, the BSea1 group is split into a group composed mainly of PE-rich strains and a group of PC-rich strains that clusters with the PC-rich strains WH5701 and BO8805. This contradicts the 16S rRNA-ITS-1 phylogeny in which PE-rich


91

strains of the BSea1 group form a monophyletic clade with PC-rich strains including WH5701 and BO8805. Hence, the phylogeny of the 16S rRNA-ITS is incongruent with that of the cpcBA (Figure 4.3). It is difficult to compare the phylogenetic trees of 16S rRNA-ITS and cpcBA because of assumptions used to create the former. In order to construct the 16S rRNA-ITS-1 phylogenetic tree large variable regions had to be removed. However, the closely related strains of the BSea1 group, WH5701, BO8805 and the less similar group I strains can be used to test if the topologies are different using Maximum Likelihood. The likelihoods of trees of 16S rRNA-ITS and the cpcBA operon were evaluated using the Jukes Cantor model of nucleotide substitution by fitting the tree of one gene to the ML tree of the other followed by re-sampling (Swofford, 2003). The resulting distributions of log-likelihoods and associated p-values were compared to that of the best ML topology (Table 4.5). The results of this exercise indicated that for the tested clade the 16S rRNA-ITS-1 phylogeny is significantly different from that of the cpcBA operon. Removal of the group I from the analysis showed no significant differences between the two phylogenies.

Finally, both datasets were used to create concatenated sequences of the 16S rRNA-ITS-1 and the cpcBA operon. The concatenated sequences were used to construct a phylogeny revealing a tree resembling the cpcBA (Figure 4.3). Most clades found in the cpcBA phylogeny appeared also in the concatenated dataset, showing the high diversity of cpcBA. Yet, in the concatenated dataset CCY0490 and RS9917 cluster with strains for which they have a closely related 16S-rRNA-ITS-1 sequence, but a different cpcBA sequence. Hence, concatenated 16S-rRNA-ITS and cpcBA sequences changes the picture only slightly when compared to the cpcBA alone (Figure 4.3 and Figure SM6). This conclusion is supported by rarefaction analysis of the datasets, where concatenation increased the diversity at the 100% similarity level to 22 OTU compared to 20 and 13 OTU for the cpcBA operon and 16S rRNA-ITS1, respectively (Figure 4.4).

Split network analysisThe cpcBA phylogeny is represented by well supported clades deep in the tree (Figure SM4

and SM5). However, the basal taxa of the cpcBA phylogeny are shown with long branches and the nodes have low bootstrap support (<50%) (Figure SM4). This could be an indication for homoplasy (backward and/or parallel mutations). Calculation of the homoplasy index HI in MEGA4.0 revealed high levels of homoplasy in the data set (HI=0.74 for parsimony-informative sites). This high value of the HI suggests that there is a large amount of ambiguity within the data that could have an effect on the phylogenetic three. A possible way to study this is by analyzing the cpcBA dataset using Neighbour-net networks (Bryant and Moulton, 2004). Neighbour-net networks can be used to study aligned sequence data by creating a network rather than a tree in order to show conflicting data within the phylogeny by representing the data and the evolutionary distances (Huson and Bryant, 2006). A Neighbour-net network was created using the cpcBA sequences (Figure 4.5; the same analysis using the complete set of sequences can be found Figure SM7). The occurrence of block shaped patterns instead of a tree like branching pattern indicates a high uncertainty in the phylogenetic signal that is probably caused by homoplasy or recombination. This view is consistent with the finding that

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the phylogenies of the 16S rRNA-ITS-1 sequences and the cpcBA operon sequences are not entirely congruent. Nonetheless, the Neighbour-net network shows by the long branches that the cpcBA sequences of the PE-rich BSea1 cluster (represented by strain CCY0418) and the other groups form distinct taxa within the cpcBA phylogeny of the picocyanobacteria (Figure 4.5 and Figure SM7). In addition it shows a clear separation between the cpcBA genes of PE-rich and PC rich picocyanobacteria (Figure 4.5).

Discussion

Nitrogen preference of Baltic Sea isolates.The isolation of 46 Synechococcus strains from the Baltic Sea enabled us to study the

microdiversity found in this group of cyanobacteria. The strains were compared using morphological, physiological and molecular approaches. At the morphological level the main difference between isolates is that they are either PE-rich or PC-rich. This division is genetically supported using the cpcBA genes (Six et al., 2007; Haverkamp et al., 2008). Other features like cell volume and nitrogen preferences could not easily be separated into different clusters at the genetic level. Nonetheless, several strains showed a preference for nitrate over ammonium and two of these strains, CCY0454 and CCY0470, fall in a distinct cluster and are closely related to the Bornholm Sea strains CCY9201 (BS4) and CCY9202 (BS5) (Figure 4.2). The latter strains were isolated previously using a mixture of ASNIII and BG11 containing NO3

- as the main nitrogen source (Ernst et al., 2003). This suggests that isolates of the Bornholm Sea group are better adapted to nitrate than the majority of our Baltic Sea isolates who seem to prefer ammonium. The ammonium preference of the majority of our strains is logical in several ways. Ammonium was the dominant nitrogen source at our sampling stations (Table 4.1). In addition, ammonium was the nitrogen source used for isolation, thus we selected for isolation of species growing efficiently on ammonium. Furthermore, previous studies showed that nitrogen addition using NH4

+ increased the abundance of picocyanobacteria in Baltic Sea waters to a much greater extent than nitrate (Stal et al., 1999; Kuuppo et al., 2003; Lagus et al., 2007), suggesting that the majority of Baltic Sea picocyanobacteria have a preference of ammonium over nitrate.

The ammonium preference of our Baltic Sea isolates can be explained in the light of the recent publication of Agawin et al. (2007). They found coexistence between a N2-fixing Cyanothece strain and a Synechococcus strain in continuous cultures. The low nitrate input supplied to the continuous cultures could not explain the high abundance of Synechococcus cells observed in the species mixture. Instead, excretion of fixed nitrogen by the Cyanothece strain provided enough additional nitrogen in the growth medium to allow a high abundance of Synechococcus cells (Agawin et al., 2007). At our sampling stations, nitrate concentrations were very low and the larger phytoplankton (<20 um) consisted of large filamentous N2-fixing cyanobacteria such as Nodularia spumigena (Haverkamp et al., 2008). Fixed nitrogen is released by N2-fixing cyanobacteria in the form of ammonium or dissolved organic nitrogen (DON), which could


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select for a picocyanobacterial population with a higher affinity for ammonium than for nitrate (Ohlendieck et al., 2007). This would explain why we found only a few isolates from the Bornholm Sea group since they seem to prefer nitrate over ammonium and thus would be out competed by species with an ammonium preference in the setup of our isolation strategy. Moreover, this suggests that strains belonging to the Bornholm Sea group are not dominant members of the picoplankton in the Baltic Sea under nitrate-limited conditions (Stal et al., 2003).

Diversity of Baltic Sea Synechococcus.The use of molecular markers to analyze phylogenetic relationships of our Synechococcus strains

from the Baltic Sea with known isolates and clone sequences allowed us to study the diversity found in the Baltic Sea. The markers were the 16S rRNA-ITS region and the cpcBA operon. In a previous study these loci were used to study the diversity within clone libraries constructed from environmental DNA (Haverkamp et al., 2008). Several groups of picocyanobacteria found in the clone libraries were not found among the cultured isolates, and vice versa (Table 4; Figure SM5). This demonstrated that neither approach sampled the full existing diversity of picocyanobacteria. Hence, clone libraries and culture collection complement each other (Kisand and Wikner, 2003; Alonso et al., 2007). The combination of cpcBA sequences from cultures and environmental clone libraries increased the picocyanobacterial cpcBA sequence diversity found in the Baltic Sea considerably.

Isolated strains, however, have an advantage for the study of diversity in comparison to clone libraries, since they give the possibility to relate genotype to phenotype. In our study, strains of the BSea 1 group are closely related at the ITS level to the Marine Cluster 5.2 and Subalpine Cluster II strains WH5701 and BO8805 (Herdman et al., 2001; Crosbie et al., 2003; Ernst et al., 2003; Becker et al., 2004; Chen et al., 2006). We found both PC-rich and PE-rich isolates in the BSea 1 group, all closely related at the ITS level. The different pigmentation types could not be separated using the ribosomal 16S rRNA-ITS sequences.

Conversely, analysis of the cpcBA operon shows that PE-rich and PC-rich strains from the BSea 1 group can be separated. This implies that although PE-rich and PC-rich Synechococcus strains have similar 16S rRNA-ITS-1 sequences, they have quite different pigment genotypes, enabling their adaptation to different light climates (Six et al., 2007; Stomp et al., 2007a; Haverkamp et al., 2008). Furthermore, our closely related isolates behave quite differently towards different nitrogen sources. Therefore, quantifying 16S rRNA or ITS-1 genotypes using real-time PCR or FISH based probing in the environment while assuming certain phenotypic traits connected to these ribosomal genotypes (usually based on a few isolated strains) seems prone to and misinterpretation. This is especially difficult in the light of the extensive microdiversity found in bacteria in general (Acinas et al., 2004). In our opinion, it would be better to use genes closely connected to certain phenotypes, like the cpcBA operon, in order to assess the ecological distribution of these different phenotypes in the environment. That would probably give more meaningful observations related to the ecological niche of bacteria.

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Micro-diversity, recombination and endemismOur results suggest that the diversity of picocyanobacteria in the Baltic Sea is substantially

higher than we could measure. Rarefaction analysis using all known picocyanobacterial cpcBA sequences shows that the diversity found within the picocyanobacterial cpcBA gene is not levelling off, even not at the lower similarity levels (Table 4.4). Hence, it is not surprising that when an unexplored ecosystem, like the Baltic Sea, is sampled the first time it will yield new additional taxa, thereby increasing the global diversity of picocyanobacterial cpcBA sequences (Kemp and Aller, 2004). One possible explanation for the high microdiversity of Synechococcus strains in the Baltic Sea is that the cpcBA genes are quickly evolving, with the third codon position showing many synonymous mutations that increases microdiversity (Haverkamp et al., 2008). Additionally, the high picocyanobacterial diversity could be caused by high recombination in the form of horizontal gene transfer. We considered the possibility of recombination within the genomes of isolated strains by comparing the phylogenies of the 16S rRNA-ITS-1, the cpcBA operon, and a concatenate of both sequences. A Maximum likelihood analysis of the phylogenies suggests that recombination has taken place between the different strains thereby creating higher diversity. In addition, we analyzed the cpcBA operon using the Neighbour-net analysis, which showed high ambiguity between the sequences. This indicated that evolutionary processes within the cpcBA operon do not behave in a tree-like manner. This behaviour can be explained in three ways, by extensive homoplasy (backward and/or parallel mutations), horizontal gene transfer of the cpcBA operon, or by a lack of data. These results are in line with genome analysis of the phycobiliprotein gene complexes of several marine Synechococcus strains. The genes encoding phycocyanin, phycoerythrin I and II showed different evolutionary relationships in comparison to the genes belonging to the genome core such as the allophycocyanin gene or the ribosomal 16S rRNA-ITS sequences (Six et al., 2007).

Finally, the neighbour-net analysis indicated that on the basis of the cpcBA phylogeny the PE-rich isolates of the BSea1 group are distantly related to other Synechococcus isolates and in that way might be specific for the Baltic Sea with its unique environmental conditions. Studies on large ecosystems such as Lake Superior and the Baltic Sea indicate that these environments inhabit locally adapted Synechococcus spp. (Ivanikova et al., 2007; Haverkamp et al., 2008). These results indicate that the cpcBA operon can be used to study diversity of Synechococcus species from distinct environments. This is consistent with results of Pommier et al. (2007), who found signals of endemism within the global bacterioplankton community. In line with our previous and current findings, one likely explanation for the tremendous diversity of picocyanobacteria in the Baltic Sea is that its underwater light spectrum offers suitable niches for the coexistence of red and green picocyanobacteria, which may favour the rapid diversification of PE-rich and PC-rich strains.


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AcknowledgmentsWe thank M. Laamanen for the opportunity to join cruise CYANO-04, M. Stomp and

the crew of the research vessel Aranda for help during sampling. We also thank T. Mes for constructive discussion and help with the Maximum likelihood comparisons. T.H. and L.J.S. acknowledge support from the European Commission through the project MIRACLE (EVK3-CT-2002–00087). The research of J.H. was supported by the Earth and Life Sciences Foundation (ALW), which is subsidized by the Netherlands Organization for Scientific Research (NWO). This is publication xxxx of NIOO-KNAW.

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Table SM1: Baltic Sea sampling station, geographic location, depth of the water column and the sampling date.

Station Latitude Longitude Bottom depth (m) Date

St298 60,04 26,21 69 120704

St301 59,32 22,50 75 140704

St305 59,28 22,39 79 150704

St307 59,30 22,50 85 150704

St310 59,30 22,44 77 160704

St314 59,30 22,40 69 170704

St322 59,24 22,26 90 180704

St327 59,13 22,19 112 180704

Supplementary Information.The following supplementary material is available for this article:

Table SM1 Overview of the location, depth and the time of sampling for all Baltic Sea Sam-pling stations.

Figure SM1 Depth profiles for A) salinity, B) temperature, C) density and D) oxygen satura-tion at the stations 298, 301, 305, 307, 310, 314, 322 and 327.

Figure SM2 Absorption spectra for all tested strains.

Figure SM3 Neighbour-joining tree using 16S rRNA-ITS-1 sequences (1045 nucleotides) from the isolates of the Baltic Sea 1 group and the closely related Synechococcus isolates WH5701 and BO8805.

Figure SM4 Phylogenetic tree based on the cpcBA operon sequences of the Baltic Sea Syn-echococcus isolates and available sequences obtained from GenBank (498 positions).

Figure SM5 Radial phylogenetic tree using cpcBA operon sequences of the Baltic Sea Syn-echococcus isolates and available sequences obtained from GenBank (498 positions).

Figure SM6 Comparison of phylogenetic trees of the 16S rRNA-ITS region, the cpcBA operon and a tree based on the concatenation of the mentioned sequences.

Figure SM7 Neighbour-net network based on the 342 cpcBA operon sequences.


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Chapter 5Phenotypic and genetic diversification of Pseudanabaena spp. (Cyanobacteria)

Thomas H. A. Haverkamp1

Silvia G. Acinas 1,2

Jef Huisman3

Lucas J. Stal1,3

1 Department of Marine Microbiology, Netherlands Institute of Ecology, NIOO-KNAW, P.O.Box 140, 4400 AC Yerseke, The Netherlands

2 Present addresses: Institut de Ciències del Mar, CMIMA-CSIC, 08003, Barcelona, Spain.3 Aquatic Microbiology, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam,

Nieuwe Achtergracht 127, 1018 WS Amsterdam, The Netherlands.

Accepted for publication in: The ISME journal.Running Title: Diversity of Pseudanabaena populations.Subject Category: Microbial population and community ecologyKeywords: Pseudanabaena / Cyanobacteria/ Complementary chromatic adaptation/ Microbial diversity/ Biogeography/ Evolutionary diversification

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AbstractPseudanabaena species are poorly known filamentous bloom-forming cyanobacteria closely

related to Limnothrix. Because of their small size, the importance of Pseudanabaena has been overlooked and they have not been recognized as dominant organisms in blooms. We isolated 28 Pseudanabaena strains from the Baltic Sea and the Albufera de Valencia (Spain). By combin-ing phenotypic and genotypic approaches the phylogeny, diversity, biogeography and evolution-ary diversification of these isolates were explored. Analysis of the in vivo absorption spectra of the Pseudanabaena strains revealed two coexisting pigmentation phenotypes: (i) phycocyanin-rich strains, and (ii) strains containing both phycocyanin and phycoerythrin. Strains of the latter phe-notype were all capable of complementary chromatic adaptation (CCA). About 65kb of each of the Pseudanabaena genomes, containing the16 and 23S rRNA genes, the ribosomal intergenic spacer ITS-1, the cpcBA operon encoding phycocyanin, and the intergenic spacer (IGS) between cpcA and cpcB, were sequenced. In addition, the presence of nifH, one of the structural genes of nitrogenase, was investigated. Sequence analysis of ITS and cpcBA-IGS allowed for the differen-tiation between Pseudanabaena isolates exhibiting high levels of microdiversity. This multi-locus sequencing approach revealed specific clusters for the Baltic Sea, the Albufera de Valencia, and a mixed cluster with strains from both ecosystems. The latter comprised exclusively CCA phenotypes. The phylogenies of the 16 and 23S rRNA genes are consistent, but analysis of other loci indicated loss of substructure, suggesting that recombination between these loci has occurred. Population genetic analyses of the phycocyanin genes suggest an evolutionary diversification of Pseudanabaena through purifying selection.

Phenotypic and genetic diversification of Pseudanabaena spp.

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IntroductionCyanobacteria are the dominant component of the phytoplankton in many freshwater and

marine environments where they may form nuisance blooms (Chorus and Bartram, 1999; Huisman et al., 2005; Granéli and Turner, 2006). The attention is usually towards the larger species that form aggregates and possess gas vesicles that make them buoyant and therefore accumulate at the surface. These species are either toxic or fix N2 or both and may cause serious environmental and socio-economical problems. However, often it is not recognized that smaller cyanobacteria exceed the larger species in terms of biomass and activity and therefore play a major role in the ecosystem dynamics. Several reports mentioned the occurrence of the tiny filamentous Pseudanabaena in cyanobacterial blooms in brackish and freshwater ecosystems (Vasconcelos and Pereira, 2001; Stal et al., 2003; Gkelis et al., 2005; Zwart et al., 2005; Kim et al., 2006; Willame et al., 2006). Nevertheless, Pseudanabaena spp. have been overlooked, perhaps because they have not been recognized as a dominant component of the phytoplankton. As a consequence, Pseudanabaena is a poorly known cyanobacterium.

Pseudanabaena species are non-heterocystous cyanobacteria belonging to the order of Oscillatoriales. The family of Pseudanabaenaceae is characterized by simple trichomes with a width less than 4 µm. The cells are longer than wide, possess parietal thylakoids, contain polar gas vesicles, and the cross walls are conspicuously constricted (Castenholz et al., 2001; Komárek, 2003). Some strains display complementary chromatic adaptation (CCA). This process allows these organisms to regulate the ratio of the accessory photosynthetic pigments phycocyanin (PC) and phycoerythrin (PE), which helps them to adapt to the prevailing light spectrum (reviewed in Kehoe and Gutu (2006)) thereby favoring their persistence in competition against other species (Stomp et al., 2004; Stomp et al., in press). Most cultured strains reveal gliding motility and some are capable of anaerobic N2-fixation (Rippka and Herdman, 1992).

Morphologically, Pseudanabaena resembles Limnothrix making their identification difficult. The main differences are the somewhat wider cells (1- 6 µm) and the less distinct constriction of the cross walls in Limnothrix (Castenholz et al., 2001). Although they are rarely recognized as dominant organisms, Pseudanabaena as well as Limnothrix species occur and form blooms in eutrophic water bodies and occasionally dominate the phytoplankton (Mayer et al., 1997; Rücker et al., 1997; Zwart et al., 2005). Limnothrix is typically found in meso- to eutrophic freshwater ecosystems, whereas Pseudanabaena is more widely distributed and occurs in diverse aquatic environments (Castenholz et al., 2001; Zwart et al., 2005; Diez et al., 2007). Based on the 16S rRNA gene, Pseudanabaena and Limnothrix form a monophyletic cluster within the cyanobacteria (Zwart et al., 2005; Willame et al., 2006). However, the available molecular data of the Pseudanabaena / Limnothrix group is scarce and consists of a limited number of environmental sequences and a few isolates. This limited data does not resolve the phylogeny of the Pseudanabaena / Limnothrix group.

Here we present a multi-phasic phenotypic and genotypic approach to explore the evolutionary diversification of Pseudanabaena strains isolated from two distant geographical regions in Europe, the Baltic Sea, a large brackish basin in the North, and Albufera de Valencia,

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a coastal lagoon in the South of Europe. The Baltic Sea is one of the largest bodies of brackish water in the world. It is a eutrophic system that develops blooms of the conspicuous N2-fixing heterocystous cyanobacteria Aphanizomenon and Nodularia, of which the latter is toxic (Stal et al., 2003). However, the dominant component of the cyanobacterial community in the Baltic Sea consists of a colorful mixture of unicellular picocyanobacteria of the Synechococcus group and the tiny filamentous Pseudanabaena (Stal et al., 2003; Stomp et al., 2007; Haverkamp et al., 2008). The Albufera de Valencia is a highly eutrophic coastal freshwater lagoon in Spain that is fed by streams, rivers and irrigation channels carrying fertilizer of the surrounding rice fields. The Albufera is characterized by dense water blooms of cyanobacteria among which Pseudanabaena spp. is a dominant group of organisms (Romo and Miracle, 1994; Villena and Romo, 2003). This is the first report on the diversity, biogeography and evolution of Pseudanabaena and a crucial step towards an understanding of the ecology of this remarkable but hitherto neglected organism.

Materials and methods

Isolation, cultivation and strain collectionThe Pseudanabaena strains used in this study were isolated between 1995 and 2004 (Table

5.1). Strain CCY9508 was isolated in 1995 from the Baltic Sea (Bornholm Sea). The Spanish strains were isolated in 1997 from Albufera de Valencia. For the isolation of these strains, water was pre-filtered through 2 µm mesh plankton net. Subsequently, the filtrate was spread onto 0.7% agarose medium in Petri dishes. Strain CCY9508 was isolated on a mixture consisting of 1/3 volume ASNIII + 2/3 volume BG11 medium with a salinity of 12.2‰ (Rippka et al., 1979). The Spanish isolates were isolated on the freshwater BG11 medium. Single colonies were picked from the agarose plates and repeatedly transferred until axenic monoclonal strains were obtained. The other Baltic Sea strains were isolated from samples collected at various stations in the Gulf of Finland (from 59.1 oN 22.2 oE to 60.0 oN 26.2 oE) during a research cruise in July 2004. Water samples were collected from defined depths using a rosette sampler. Pseudanabaena strains were isolated using two different approaches. In one approach, water samples were fractionated, first using one layer and subsequently two layers of plankton net (20 µm mesh) under gentle vacuum. This filtrate was successively filtered through 5 µm, 1 µm and 0.45 µm membrane filters. Finally, the 0.45 µm filter was transferred to a sterile 10 cm Petri dish filled with a mixture of 4/5 parts BG11 and 1/5 parts ASNIII medium, containing NH4Cl (0.05 g·l-1) as the nitrogen source. In the other approach, water was filtered through 20 µm plankton net and subsequently diluted to extinction in 96 deep well microtiterplates (Nunc, Inc.) containing a mixture of 4/5 parts BG11 and 1/5 parts ASNIII medium containing NH4Cl (0.05 g·l-1). In both approaches the cells were first grown under a light regime of 10 μmol photons·m-2·s-1 for two weeks. Subsequently, the light intensity was increased to 20 μmol photons·m-2·s-1 and the cultures were incubated for another 6 weeks. Trichomes growing on the filters or at the surface of the wells were picked and transferred to solid media. In order


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Table 5.1. Characteristics of Pseudanabaena strains used in this study, year and location of isolation and PCR amplification for nifH genes.

Isolation Details Growth Morphological Characteristics

CCAe Absorption Ratio nifH PCR

Strain Origin Year Mediuma Cell Size (µm)b

PGVc Mo-tilityd

570 / 625f

680 / 570g

680 / 625h

Amplifi-cation

CCY9701 Albufera de Valencia 1997 BG 11 6.1x1.5 + - - 0.75 1.22 0.91 +

CCY9702 Albufera de Valencia 1997 BG 11 6.7x1.4 + - ND ND ND ND +

CCY9703 Albufera de Valencia 1997 BG 11 5.5x1.5 + + + 1.07 0.9 0.96 ND

CCY9704 Albufera de Valencia 1997 BG 11 6.0x1.6 + - + 1.02 1.07 1.1 ND



CCY9710 Albufera de Valencia 1997 BG 11 3.5x1.4 - + - 0.82 1.21 0.99 ND

CCY9712 Albufera de Valencia 1997 BG 11 4.9x1.5 + + - 0.67 1.22 0.81 ND

CCY9714 Albufera de Valencia 1997 BG 11 4.9x2.0 + + - 0.69 1.41 0.97 ND

CCY9715 Albufera de Valencia 1997 BG 11 4.9x1.4 + - + 1.18 1.08 1.27 ND

CCY9508 Baltic Sea 1995 1/3A+2/3B 6.1x1.5 + + + 0.67 1.51 1.01 ND

CCY0471 Baltic Sea 2004 1/5A+4/5B 3.2x1.4 + + + 1.04 1.48 1.53 +



CCY0474 Baltic Sea 2004 1/5A+4/5B 2.8x1.5 + + + 1.49 1.18 1.75 -


CCY0476 Baltic Sea 2004 1/5A+4/5B 2.2x1.6 + + - 0.62 1.45 0.89 -












a. Growth media description can be found in Rippka et al., 1979.[49] (1/3A +2/3B: 1 part ASNIII medium + 2 parts BG11 medium)

b. Cell size based on the average length x width of at least 40 cells.c. PGV: Polar Gas Vesicles.d. Motility on agarose plates.e. CCA: Complementary Chromatic Adaptation.f. Ratio between absorption at 570 nm (phycoerythrin) and 625 nm (phycocyanin). g. Ratio between absorption at 680 nm (Chla) and 570 nm (phycoerythrin). h. Ratio between absorption at 680 nm (Chla) and 625 nm (phycocyanin). ND. Not determined.

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to obtain monoclonal axenic strains, trichomes were repeatedly transferred. Once obtained, monoclonal axenic strains were maintained in their specific growth medium in the Culture Collection Yerseke (CCY) (Table 5.1).

Morphology and microscopy.Cells were collected from exponential or stationary liquid cultures and were fixed in a

mixture of 1% (w/v) formaldehyde and 0.05% (w/v) glutaraldehyde and subsequently stored at -80oC until they were analyzed (Biegala et al., 2003). Microscope slides were prepared by covering them with a thin layer of 1% (w/v) molten agarose (50 ºC) (Sigma-Aldrich) that was allowed to solidify shortly before cells were applied. The slides were examined using a Zeiss Axiophot microscope equipped with a ProgRes C10 plus digital imaging system (JENOPTIK Laser, Optik, Systeme GmbH). The images were subsequently processed using ProgRes CapturePro2.0 software (JENOPTIK Laser, Optik, Systeme GmbH). From at least 30 cells in each culture the width and length were measured.

In vivo absorption spectraExponential or stationary liquid cultures grown under white light (20 μmol photons·m-2·s-1)

were used for determination of the in vivo absorption spectra. Spectra were measured from 400 to 750 nm using a Varian Cary 100 Bio equipped with an integrating sphere DRA-CA-3300. Distilled water was used as reference.

Determination of CCA In order to test for the capacity of CCA the strains were cultured on solid (agarose) media.

Each strain was inoculated in two Petri dishes (Greiner Bio-One) that were incubated under a different color of light. Green light was obtained using Lee filter no. 124 (dark green) and red light through Lee filter no. 26 (red). The incident white light intensity was 100 μmol photons·m-

2·s-1. In order to document CCA, the cultures were photographed after two weeks of growth under monochromatic light. Subsequently, the cultures were changed to the other color of monochromatic light and incubated another two weeks after which they were documented again. A change from green to reddish / black phenotype and vice versa, was taken as evidence for CCA.

The strains CCY9703 and CCY9710 were also cultured in liquid medium. Three liquid cultures of each strain were grown under white light conditions (14 μmol photons·m-2·s-1) until stationary phase was reached. Subsequently, the three cultures were grown under white, green or red light. After two weeks the cultures were sampled and the in vivo absorption spectra were measured as described above.

DNA isolationFor DNA extraction cells were collected from exponentially growing or stationary phase

cultures. Briefly, two ml of culture was centrifuged in a table top centrifuge (Eppendorf type 5424)at 10000 r.p.m. for 1 minute at room temperature. The supernatant was removed and


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the cell pellet was re-suspended in lysis buffer provided by the Powersoil DNA extraction kit (MoBio). DNA extraction was performed following the instructions of the manufacturer. DNA quantity and quality were checked by running agarose gels as well as spectrophotometrically (Nanodrop ND1000).

PCR and sequencingPCR reactions were performed using the PCR primers listed in Table 5.2. The B1055F and

PitsE-cyanR were used to amplify the end of the 16S rRNA gene plus the ITS-1. The 23S rRNA gene was amplified with 129F and 2241R primers and the phycocyanin operon (cpcBA) with cpcAR and cpcBF primers. Finally nifH PCR amplification were performed by the set of primers described in Table 5.2. Each reaction contained 0.2 mM of each dNTP, 2 mM MgCl2, 5 or 10 pmol of each primer, 1 µl template DNA (5-10 ng), 1x PCR buffer and 1 units of HotStarTaq (Qiagen). MQ-grade H2O was added to a final volume of 30 μl. The PCR reactions were carried out in a GeneAmp System 2700 thermocycler (Applied Biosystems). The PCR program used to amplify the 16S rRNA-ITS-1 region, the 23S rRNA gene and the cpcBA operon were as follows: a hot start at 94ºC of 15 minutes, followed by 30 cycles of 1 minute 94ºC, 1 minute of 55ºC and 1 minute of 72ºC. Following the last cycle an elongation step of 10 minutes at 72ºC was applied. The PCR program for amplification of the nifH gene was according the nested PCR protocol as described by Zani et al. (2000) with modifications. In brief, the first PCR using primers nifH 1 and 2 was started with a hot start of 15 minutes at 96ºC and followed by 35 cycles of 1 minute 94ºC, 1 minute at 57ºC, 1 minute at 72ºC, followed by 10 minutes of elongation at 72ºC. One µl of the PCR product was then used in the second PCR using the primers nifH 3 and 4. The PCR program started with a 15 minute hot

Table 5.2. List of the primers used for PCR and sequencing in this study.

Primer Name Target Sequence (5'-3') Reference

Bact1055Fa 16S rRNA gene AATGGCTGTCGTCAGCTCGT Garcia-Martinez et al., 1999

PitsE-cyanRa 23S rRNA gene CTCTGTGTGCCAAGGTATC Ernst et al., 2003

129Fb 23S rRNA gene CYGAATGGGRVAACC Hunt et al, 2006

2241Rb 23S rRNA gene ACCGCCCCAGTHAAACT Lane et al, 1992*

cpcARc Phycocyanic operon TAGTGTAAAACGACGGCCAGTTGYYTKCGCGACATGGA Robertson et al., 2001

cpcBFc Phycocyanic operon TAGCAGGAAACAGCTATGACGTGGTGTARGGGAAYTT Robertson et al., 2001

nifH1 nifH TGYGAYCCNAARGCNGA Zani et al., 2000

nifH2 nifH ADNGCCATCATYTCNCC Zani et al., 2000

nifH3 NifH (internal primer) ATRTTRTTNGCNGCRTA Zani et al., 2000

nifH4 NifH (internal primer) TTYTAYGGNAARGGNGG Zani et al., 2000

a. These primers were also used to obtain the complete ITS-1 sequence.b. These primers were used to amplify the complete 23S rRNA gene from the Pseudanabaena

strains.c. These primers were used to amplify the subunits beta and alpha of the phycocyanin operon plus

the intergenic spacer (IGS) between both genes.* This primer was revised recently by Hunt et al, 2006.[50]

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start at 94ºC, followed by 35 cycles of 1 minute 94ºC, 1 minute at 54ºC, 1 minute at 72ºC, followed by 10 minutes of elongation at 72ºC.

The DNA clean & concentrator-5 kit (Zymogram) was used in order to remove primer dimers from the PCR reactions following the instructions of the manufacturer. DNA concentration of the purified PCR products was checked spectrophotometrically (Nanodrop, ND1000). For sequencing reactions 3.5 µl of the purified and concentrated PCR product served as template using 10 µM of the sequencing primer and the Big Dye Terminator v1.1 cycle sequencing kit (Applied Biosystems) following the manufacturer’s instructions. Sequencing primers were the same forward and the reverse primers as were used for PCR amplification (Table 5.2). Sequence products were analyzed using a 3130 Genetic Analyzer (Applied Biosystems). Sequences were edited manually using ChromasPro V 1.41 (Technelysium Pty Ltd) and manually checked for errors in base calling. Only high quality sequences were included in the final dataset. The sequences were deposited in GenBank under the following accession numbers: 16S rRNA (EU025781-EU025806), ITS-1 region (EU119301-EU119325), 23S-129F (EU025807-EU025831), 23S-2241R (EU025756-EU25780) and the cpcBA operon (EU119326-EU119352).

Figure 5.1. The effect of light colour on the pigment composition of Pseudanabaena strains CCY9703 and CCY9710. Three cultures of each strain were grown under white light until the cultures reached the stationary phase. The cultures were then transferred and grown under white (A), green (B) or red (C) light. Strain CCY9703 shows a decreased PE absorption under the influence of red light and an increased PC absorption indicating its capacity for complementary chromatic adaptation (CCA). Strain CC9710 showed a decrease in PC absorption under influence of red light (PC-rich strain).


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Phylogenetic analysisAll Pseudanabaena sequences obtained from this study were aligned using CLUSTALW

integrated into the package BioEdit (Hall, 1999). These sequences were aligned against sequences closely related to Pseudanabaena/Limnothrix group previously identified by BLASTN comparison from GenBank and other references identified in previous published studies. Sequence comparison and phylogenetic analyses of the partial sequencing of 16S rRNA, 23S rRNA, ITS-1, cpcBA and IGS were performed using the software MEGA3.1 (Kumar et al., 2004). Neighbor-joining with Jukes–Cantor correction and 1000 bootstraps was used to build the corresponding phylogenetic trees. Partial amino acid sequences of the coding region of the cpcBA operon were also used in the phylogenetic analyses performed with the neighbor-joining method as well as with maximum parsimony. Maximum parsimony was used with the close-neighbor-interchange search algorithm with random tree addition using 100 bootstraps.

Figure 5.2. Neighbor Joining Tree based on comparison of the partial sequences of the 23S rRNA (A, 129F primer) and 16S rRNA (B, Bact1055F primer) genes of the Pseudanabaena strains collected in this study. All Pseudanabaena strains grouped in a single 99% cluster, for both the 23- and the 16S rRNA genes (shadowed area).

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Population genetic analysis.The protein coding nucleotide sequences from the cpcBA locus aligned by ClustalW were

analyzed using DnaSP version 4.0 (Rozas et al., 2003) to calculate the following parameters: (i) synonymous and non-synonymous polymorphic sites, (ii) estimation of Ka/Ks divergence ratio, (iii) The McDonald-Kreitman test (McDonald and Kreitman, 1991) to detect positive or purifying selection and the HKA test based on neutral theory of molecular evolution (Hudson et al., 1987), (iv) degree of genetic differentiation between populations, estimated by Fst (Hudson et al., 1992), (v) estimation of the recombination parameter (R) and minimum number of recombination events (Hudson et al., 1987).

Figure 5.3. Microdiversity of Pseudanabaena spp. as revealed by ITS-1 analysis. The ITS allowed further differentiation showing geographical patterns, with clusters specific for the Baltic Sea (Cluster I) and Albufera de Valencia, Spain (Cluster III), but also a cluster with representatives of both locations (Cluster II). Strain CCY9710 from the Albufera de Valencia represents the only exception and clustered with the Baltic Sea strains.


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Finally, Selecton version 2.2 (http://selecton.bioinfo.tau.ac.il) was used to identify positive and purifying selection at each of the amino acids using a Bayesian inference approach (Stern et al., 2007). The Selecton server automatically calculates the ratio between Ka and Ks (ω) at each codon site using a maximum likelihood (ML) approach. The value of ω at each site is translated to a discrete color scale projected onto one of the homologous sequences for each sequence clusters. Colors 1 to 2 (dark and light yellow) indicate ω >1 and stand for sites with positive selection while shades of white through magenta (colors 3 through 7) indicated various levels of ω≤1 where Selecton results can be accurately used to infer sites undergoing purifying selection.

Figure 5.4. Correlation between cell length and the ranking of Pseudanabaena strains in the phylogenetic tree derived from the ITS-1 sequences. BSC correspond to the Baltic Sea Cluster, MABSC to the Mixed Albufera and Baltic Sea Cluster and AVC to the Albufera de Valencia Cluster. The blue dot corresponds to strain CCY9709.

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ResultsPhenotypic traits: morphological characteristics and photosynthetic pigment composition.

A total of 28 strains were isolated from two geographically distant locations: Albufera de Valencia, Spain (10 strains isolated in 1997) and the Baltic Sea (1 strains isolated in 1995, 17 strains in 2004). The isolates were assigned to Pseudanabaena based on their morphological characteristics, such as cell size, motility on agarose plates and the presence of polar gas vesicles (Table 5.1). The isolates displayed different cell dimensions ranging from 2.1×1.9 to 6.7×1.4 µm (length × width) (Figure 1SM). While cell width remained within a narrow range, cell length varied considerably. All strains, except CCY9710 from Albufera de Valencia, possessed polar gas vesicles (Table 5.1). Gliding motility was observed in most of the strains except for 4 isolates from Albufera de Valencia (Table 5.1).

Figure 5.5. (a) Phylogenetic relationships of Pseudanabaena strains using 150 amino acids of the phycocyanin operon (cpcBA) genes. The green labeled strains represent PC-rich Pseudanabaena spp. All other strains contained both phycocyanin and phycoerythrin and are capable of complementary chromatic adaptation (CCA). Asterisks indicate nifH positive strains. Cluster II consists entirely of CCA strains, and is identical to Cluster MABSC in the ITS-based phylogenetic tree (see Figure 3). (b) Phylogenetic relationships derived from the cpcBA operon concatenated with the Intergenic Spacer (IGS) (about 600 bp). This revealed different sub-clusters (‘geotypes’) from the Baltic Sea (BS) and Albufera de Valencia (AV) within Cluster I.


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The presence of the major light-harvesting pigments chlorophyll a, phycocyanin (PC) and phycoerythrin (PE) was determined by in vivo absorption spectra, from which the ratios PE:PC, Chla:PE and Chla:PC were calculated (Table 5.1). Pseudanabaena isolates were divided into strains that have both PE and PC pigments (63% of all strains) and those with only PC (37%) as their major pigment (Figure 5.1). All strains possessing both PE and PC pigments were capable of complementary chromatic adaptation (CCA). PC-rich strains reveal an absorption peak at ~625 nm and, hence, harvest orange-red light effectively. The proportion of strains capable of CCA that were isolated from the Baltic Sea was 66%, slightly higher than the 55% for the Albufera de Valencia. The majority of strains that were positive for CCA possessed a ratio of the absorption at 570 and 625 nm of approximately 1 or more when incubated in white light. When incubated under red or green light cultures changed pigmentation towards green and red, respectively (Figure 5.1b and 5.1c and Figure 2SM). Chlorophyll a showed absorption peaks at 440 nm (Soret band) and 680 nm. The absorption peaks of the three major light harvesting pigments were at the same wavelengths in all isolates but their relative heights varied substantially (Table 5.1).

The phylogeny of Pseudanabaena revealed from their 16- and 23S rRNA genesIn order to determine the phylogenetic relationships of the Pseudanabaena isolates, the 16-

and 23S rRNA genes were partly sequenced and analyzed. Because the 23S rRNA gene offers a higher phylogenetic resolution, the start and end of the 23S rRNA gene were both sequenced

Figure 5.6. Selecton results for the cpcBA locus on 20 sequences from Cluster I and 6 sequences from Cluster II using the E8 model (Stern et al., 2007). Different levels of purifying selection are colored in shades of magenta through 150 amino acids of the cpcBA locus. The scale bar indicates selection ranging from positive (yellow colors 1 and 2) to purifying selection (colors 3 through 7). No positive selection is observed. The star symbols represent the non-synonymous sites (amino acids changes) between Cluster I and Cluster II.

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using the forward 129F and reverse 2241R primers, respectively (Table 5.2). This resulted in two products with a sequence length of 440bp (129F) and 527 bp (2241R) respectively. The sequences obtained by using these primers gave similar phylogenetic relationships, although a larger number of polymorphisms were observed when using primer 129F (data not shown). Figure 5.2 depicts the phylogeny based on neighbor-joining analysis and compares the 23S rRNA (Figure 5.2a) with the 16S rRNA gene tree topologies (Figure 5.2b) obtained using the primers 129F (440bp) and Bact1055F (400bp) respectively. In both trees all isolates grouped into a single 99% similarity cluster indicating congruency in the phylogeny of the 16- and 23S rRNA genes. Moreover, the 23S rRNA gene displayed more polymorphisms than the 16S rRNA gene, which was virtually identical in virtually all isolates. The phylogenetic analysis of the 16S rRNA gene confirmed that all isolates belong to the Pseudanabaena/Limnothrix group with 99% cluster similarity (Zwart et al., 2005; Willame et al., 2006). From BLAST searches against the GenBank database we observed that 42% of the isolates were 100% identical to Pseudanabaena sp. PCC6903 (AM709632). The other strains possess high similarity (99%) to Pseudanabaena sp. 1tu24s9 (AM259269), which originated from the Finnish freshwater Lake Tuusulanjarvi (Table 1SM). Only strain CCY9709 from Albufera de Valencia exhibited a higher divergence based on the 23S rRNA sequence (Figure 5.2a) showing a 94% similarity with Pseudanabaena sp. PCC6903 (Table 1SM).

Microdiversity within Pseudanabaena strains: ITS sequencing analysisThe partial sequencing of the 16- and 23S rRNA genes was not sufficient to resolve the

phylogeny of the Pseudanabaena/Limnothrix group. Therefore the internal transcribed spacer (ITS) region located between the 16- and 23S rRNA genes was sequenced. All Pseudanabaena ITS sequences revealed the same structure and contained two tRNA genes, tRNAIle and tRNAAla (data not shown). However, there was a high nucleotide divergence and length variability among the ITS sequences. The phylogenetic analysis of the ITS revealed a higher level of differentiation within the Pseudanabaena/Limnothrix group (Figure 5.3), which constrained most of the strains in three major 99% clusters (“microdiversity clusters”). This indicates a high level of microdiversity among the Pseudanabaena isolates. Unfortunately, no ITS sequences related to Pseudanabaena species have been published to date, and therefore our sequences clustered with the two known Limnothrix ITS sequences.

The ITS analysis uncovered biogeographical patterns with clusters containing isolates specific for the Baltic Sea, isolates specific for the Albufera de Valencia, and a mixed cluster containing isolates from both locations. Most of the Baltic Sea strains grouped in the Baltic Sea Cluster (BSC) with 99.6% similarity. BSC contained 12 isolates from the Baltic Sea but also one strain from the Albufera de Valencia (CCY9710) and Limnothrix sp. MR1 (isolated from Lake Loosdrecht, The Netherlands (Zwart et al., 2005)). The Mixed Albufera and Baltic Sea Cluster (MABSC) contained 5 identical sequences retrieved from isolates from both locations. The fact that strains originating from such distant locations and with 7 years between their isolation possess identical ITS sequences is remarkable. The Albufera de Valencia Cluster (AVC) comprised 6 isolates from Albufera de Valencia possessing 99.2% similarity. Strain CCY9709 grouped in a fourth cluster together with Limnothrix redekei CCAP 1443/1. Furthermore,


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unique insertion sequences (IS) were found in some clusters. A unique 36 bp IS was found in the MABSC. In CCY9709 three specific IS of 23, 6 and 5 base pairs were found. The position of the isolates in the ITS phylogenetic tree showed a close relationship with cell length (Figure 5.4). The cell length of all BSC strains, including CCY9710 from Albufera de Valencia, ranged from 2.1 - 3.5 µm. The MABSC cluster was characterized by cells that are slightly longer, ranging from 3.2 - 6.1 µm. Finally, the AVC cluster comprised the strains with the longest cells, ranging from 4.3 to 6.7 µm. Strain CCY9709 possessed cells of 4.9 µm long and grouped in an independent monophyletic cluster with Limnothrix redekei CCAP 1443/1 which, however, has considerably longer cells (6-10 µm, personal communication of the Culture Collection of Algae and Protozoa (CCAP)).

Phylogeny of the phycocyanin operon: correlation of the cpcBA gene clusters with light absorption spectra

A major limitation of the use of ribosomal genes as molecular markers is the impossibility to attribute eco-physiological traits to these genes. Therefore, we also sequenced the phycocyanin operon (cpcBA). CpcBA encodes the two subunits of phycocyanin, which is part of the phycobilisome, the major light harvesting complex of cyanobacteria. The main goal was to assign sequence clusters of cpcBA to the in vivo light absorption spectra of the isolated strains in order to reveal ecologically different populations (ecotypes) within Pseudanabaena (Figure 5.5). The cpcBA locus has been widely used for the study of cyanobacterial diversity and phylogeny and it is therefore suitable for our purpose (Ivanikova et al., 2007; Six et al., 2007; Haverkamp et al., 2008). In addition, the intergenic spacer (IGS) between cpcB and cpcA (cpcBA-IGS) was sequenced (Figure 3SM and Figure 5.5b) in order to explore whether coding and non-coding regions of the cpcBA operon display different evolutionary rates resulting in different phylogenies.

Phylogenetic analysis of the partial sequences of cpcBA encoding for 150 amino acids, displayed two well-supported clusters (Figure 5.5a) with similarities higher than 99% (“microdiversity clusters”). Cluster I comprises sequences with 99.9% similarity and grouped 20 isolates from both locations. Half of these isolates were rich in phycocyanin (PC-rich) and unable to perform CCA, while the other 10 strains were capable of CCA. This cluster is closely related to Pseudanabaena sp. PCC7409 that is capable of CCA. Cluster II contained only strains capable of CCA, both from the Baltic Sea and the Alfubera de Valencia and possess 100% sequence similarity. Half of the strains of Cluster II investigated for nifH PCR amplification possessed nifH encoding for dinitrogenase reductase, which is a component of nitrogenase. Among Cluster I there was only one strain (CCY0477) that possessed nifH in a total of 20 isolates. NifH was found in strains originating from both environments. CpcBA cluster II in Figure 5.5 corresponds to the MABSC cluster of ITS sequences in Figure 5.4. This hints to the presence of a conserved and coherent lineage within Pseudanabaena. Again the CCA positive strain CCY9709 displayed the most divergent position in the cpcBA phylogeny.

The phylogeny of the concatenated cpcBA-IGS sequences revealed biogeographical patterns that were not shown in the cpcBA phylogeny alone (Figure 5.5b). The tree topology of the

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concatenated cpcBA-IGS phylogeny was consistent with the topology of the cpcBA phylogeny without the IGS region. Moreover, the heterogeneity and length variability of the IGS sequences allowed a higher level of differentiation and revealed different sub-clusters from specific geographic locations (“geotypes”). Four geotypes were discerned within cluster I (PC-rich/CCA), two of them originated from the Baltic Sea and the other two from Alfubera de Valencia (Figure 5.5b). In all geotypes, specific base pair signatures were assigned (Figure 3SM). Complete IGS sequence length ranged from 104 to 212 bp. The strains of Cluster I (PC-rich/CCA) possessed an IGS of 153 bp, while the IGS sequences of cluster II (only CCA) were shorter and possessed only 104 base pairs. Strain CCY9709 had the longest (212 bp) and most divergent IGS (Figure 3SM). Moreover, Cluster II (only CCA) comprised different IGS sequences compared to Cluster I-IGS and contained several deletions (in total 49 base pairs). Nonetheless, Cluster II-IGS sequences were conserved and only a few specific signatures were found. For example, the T at position 120 or the C at position 132 are representative for 2 of the 3 Baltic Sea strains that possess nifH (Figure 3SM).

Evolutionary forces operating on the phycocyanin operon (cpcBA) genesBased on the analysis of the cpcBA operon, the evolutionary processes shaping the

phycocyanin genes among Pseudanabaena spp. were investigated. Partial sequencing of 450 nucleotides constrained 26 strains in two major lineages, Cluster I (20 isolates) and Cluster II (6 isolates) (Figure 5.5a). The number of polymorphisms within and between both lineages was examined. A total of 16 single nucleotide polymorphisms (SNPs) were found between both lineages and therefore the nucleotide divergence observed between both clusters was 3.82%. The number of synonymous (silent) nucleotide substitutions per synonymous site (Ks = 0.0809) was 12.3 fold higher than the non-synonymous substitutions (amino acid-changing) per non-synonymous site (Ka=0.00655). The ratio of non-synonymous and synonymous nucleotide substitutions in protein-coding genes (Ka/Ks) may give important clues about the selection pressure on and evolution of protein coding genes (McDonald and Kreitman, 1991). The ratio Ka/Ks observed for cpcBA was 0.074. Ratios of Ka/Ks <1 indicate that purifying selection takes place. Hence, this is the case for the cpcBA clusters I and II. Purifying selection means that non-synonymous vs. synonymous mutations is favored in the direction of the latter. As a consequence, over time, slightly deleterious non-synonymous mutations are continuously removed from the population leaving only synonymous mutations. In addition, the possibility to detect different types of selection (positive or purifying) on specific amino acids within the cpcBA operon was explored (Stern et al., 2007). Positive selection was not detected in any of the amino acids, which indicates that all amino acids must have been under different levels of purifying selection (Figure 5.6). Furthermore, the degree of genetic differentiation between both clusters (Pseudanabaena subpopulations) was also explored by estimating the fixation index Fst, which indicates the amount of gene flow between populations (Hudson et al., 1992). Values of Fst range between 0-1. A value of 0 indicates that the populations share the same alleles, while a value of 1 shows that the populations are fixed for different alleles. The Fst value for the cpcBA loci was 0.886, which indicated high gene flow compared with other described bacterial populations (Whitaker et al., 2003; Miller et al., 2006).


121

Finally, the number of amino acid changes (non-synonymous sites) was assessed for the 150 codons analyzed. Only two amino acid changes were detected, yielding a 1.4% amino acid divergence. In both cases, the identity of the amino acids differed systematically between Cluster I and Cluster II of the Pseudanabaena cpcBA lineages. At position 42 at the end of the beta subunit (cpcB), a serine (S, alcohol polar R-group) found in cluster I was exchanged in Cluster II by an alanine (A, aliphatic R-group, a hydrophobic molecule). Moreover, at position 100, at the beginning of the alpha subunit (cpcA) a threonine (T, a hydrophilic and hydroxyl-containing amino acid) in cluster I was replaced by alanine (A) in Cluster II. However, neither of these two amino acids were found to be under positive selection (white color in Figure 5.6).

122

Chapter 5

Discussion

Pseudanabaena/Limnothrix group: agreement of phenotype with genetic dataPseudanabaena is morphologically similar to Limnothrix. The large variation of cell lengths

observed among the strains isolated in this study suggests that Pseudanabaena shows a high level of plasticity. Accordingly, cell length is not very useful for distinguishing Pseudanabaena from Limnothrix.

Based on 16S rRNA gene sequences, earlier studies showed that Pseudanabaena and some strains of Limnothrix cluster with Pseudanabaena spp., including one isolate assigned to Limnothrix redekei. This cluster is commonly referred to as the Pseudanabaena/Limnothrix group (Gkelis et al., 2005; Willame et al., 2006). This group also comprised several environmental sequences as well as other isolates including the type strain Pseudanabaena PCC7408, strains belonging to Limnothrix redekei (Van Goor, Meffert) (Limnothrix sp. MR1 from Lake Loosdrecht, L. redekei CCAP 1443/1 and L. redekei CCAP 227/1) and several other isolates from Lake Loosdrecht, The Netherlands (Zwart et al., 2005; Willame et al., 2006). A second cluster, comprising only Limnothrix redekei strains isolated from Lake Kastoria (Greece), has also been observed (Gkelis et al., 2005; Willame et al., 2006). Based on partial sequencing of the 16- and 23S rRNA genes all Pseudanabaena strains isolated in this study cluster with the Pseudanabaena/Limnothrix group. Neither 16- nor 23S rRNA gene analysis could distinguish between Pseudanabaena and Limnothrix, confirming previous reports (Zwart et al., 2005; Willame et al., 2006). However, ITS analysis of the Pseudanabaena isolates shows a high nucleotide divergence and size variability revealing a higher level of differentiation in this group. For instance, strains belonging to the AV Cluster and strain CCY9709 all possessed cells that were almost 3 times longer than wide (Figure 5.4), a characteristic that is considered typical for Limnothrix. This could indicate that these strains are indeed closely related to Limnothrix redekei. Nonetheless, in order to confirm these observations, it is necessary to increase the number of ITS sequences of Limnothrix redekei and Pseudanabaena. Moreover, it will be necessary to re-evaluate the morphological basis on which the genera Limnothrix and Pseudanabaena are separated.

Biogeography of PseudanabaenaThe distribution of Pseudanabaena is clearly more widespread than previously anticipated.

Members of this genus have been reported from a variety of different environments including freshwater lakes (Gkelis et al., 2005; Zwart et al., 2005; Kim et al., 2006; Willame et al., 2006), brackish environments (Stal et al., 2003), hot springs (Castenholz et al., 2001) as well as from epilithic cyanobacterial communities of beach rock (Heron Island, Great Barrier Reef) (Diez et al., 2007). Our study revealed several lineages of Pseudanabaena based on a multilocus sequence typing approach using 5 loci of 28 isolates. The average genetic divergence of each of the markers varied from 1% in the small (SSU) and large subunit (LSU) ribosomal RNA genes to 3.5% in ITS-1 and 3.82% in cpcBA. The Baltic Sea is geographically distant from the Albufera de Valencia. Moreover, they represent quite different habitats (i.e., brackish


123

vs. freshwater) and it is therefore not surprising to find different Pseudanabaena genotypes at these two locations. Endemic clusters (“geotypes”) were detected in the Baltic Sea (BS Cluster) and Albufera de Valencia (AV Cluster) based on the analysis of ITS sequences (Figure 5.3). The analysis of the cpcBA-IGS operon revealed even a better differentiation of these geotypes (Figures 5.5b, 3SM).

Yet, the Baltic Sea is connected to the North Sea, and via the Atlantic Ocean and the Mediterranean Sea it ultimately links to Albufera de Valencia. This connection could allow the long-range dispersal of Pseudanabaena genotypes, although also other mechanisms for long-range dispersal could be envisioned (such as transport by birds). Salinity differences between the Baltic Sea and the Albufera de Valencia are minor, and organisms might adapt quickly to somewhat higher or lower salt levels. Indeed, a coherent and “cosmopolitan” monophyletic cluster, comprising members from both locations was found for several loci (Cluster MABSC by ITS and Cluster II by cpcBA genes or cpcBA-IGS analysis) suggesting a conserved and homogeneous lineage within Pseudanabaena (Figures 5.3, 5.5). This lineage clustered the strains isolated from the two distant locations, even though the isolation at the two locations was separated by 7 years. This could point to global dispersal of these Pseudanabaena strains. However, more Pseudanabaena strains from other environments and locations should be included in order to confirm the possibility of global dispersal. Based on analysis of the genes of the phycocyanin operon and the light absorption spectra associated with them, it is proposed that this lineage or subpopulation of Pseudanabaena (Cluster MABSC by ITS and Cluster II by cpcBA genes or cpcBA-IGS analysis) represents an “ecotype” that possesses the ability of CCA. Interestingly, this ecotype also possesses the nifH gene in at least half of their members and therefore might be capable of N2 fixation (Figure 5.5). This ecotype proposed with the name of PCCA ecotype seems to be widely distributed, and the ability of CCA provides it with a selective advantage.

Genetic diversification of Pseudanabaena populationsCorrelation of the cpcBA gene clusters with the light absorption spectra hinted at the

coexistence of two Pseudanabaena populations with a niche differentiation along the light spectrum (Figure 5.5, Cluster I and II). Cluster II or the “PCCA ecotype” occurs as a group with 99% similarity (“microdiversity cluster”), at a variety of different loci (ITS, cpcBA gene and cpcBA-IGS analysis). Previous studies indicate that such microdiversity clusters could represent important units of differentiation as ecotypes in natural populations of bacteria (Palys et al., 1997; Moore et al., 1998; Rocap et al., 2003; Konstantinidis and Tiedje, 2005; Lopez-Lopez et al., 2005; Thompson et al., 2005; Cohan, 2006; Polz et al., 2006; Cohan and Perry, 2007), and are often observed in environmental clone libraries (Field et al., 1997; Acinas et al., 2004; Morris et al., 2005; Johnson et al., 2006; Pommier et al., 2007). The microdiversity clusters identified here are correlated with morphological and ecophysiological traits such as cell length and the capacity to perform CCA (Figures 5.4, 5.5), providing further support for the designation as an ‘ecotype’ (Ahlgren and Rocap, 2006; Johnson et al., 2006; Polz et al., 2006; Ward et al., 2006). Although this genetic pattern agrees with the ecotype model for bacterial species (Palys et al., 1997; Cohan, 2002; Gevers et al., 2005; Cohan and Perry, 2007),

124

Chapter 5

other mechanisms causing the genetic diversification of Pseudanabaena populations cannot be excluded. Indeed, the loss of substructure in the tree topology occurred when different loci were compared (e.g. ITS vs. cpcBA-IGS). For instance, strain CCY9710 from Albufera de Valencia fell into the Baltic Sea Cluster based on its ITS sequence and it shared with all strains a similar cell length. However, this strain grouped with other isolates from Albufera de Valencia when considering the phylogeny based on cpcBA-IGS sequences. Similarly, the MABSC cluster was an independent branch in the phylogeny of the cpcBA locus while it was sister to the BSC using ITS. Moreover, at least 12 recombination events (Rm) were detected at the cpcBA locus, emphasizing the importance of homologous recombination (HR) and that this process should be taken into account.

By using the ratio of non-synonymous vs. synonymous fixation as a measure of the level of selective pressure on the phycocyanin operon (cpcBA), it was concluded that purifying selection is involved in the evolutionary diversification of Pseudanabaena populations. Other population genetic analyses such Tajima s D and the Mc Donald-Kreitman (MK) tests for selection were not significant (data not shown), further supporting our finding that the mutations at cpcBA do not deviate from those expected from neutrality and, hence, are not under positive selection. The results show that divergence of the cpcBA in Cluster I and II is promoted by purifying selection in both populations.

Recent laboratory experiments investigated the role of complementary chromatic adaptation in the competition of Pseudanabaena against red and green Synechococcus strains (Stomp et al., in press). The competition experiments showed that Pseudanabaena was a strong competitor in fluctuating light environments, provided that it had sufficient time to adjust its pigment composition to the prevailing light spectrum. Pseudanabaena can change its pigmentation from red to green, and vice versa, within ~7 days. Thus, Pseudanabaena benefited from CCA only if fluctuations in underwater light color were slow compared to the time required for CCA, corresponding to slow mixing processes or infrequent storms in their natural habitat (Stomp et al., in press). We hypothesize that phycocyanin-rich strains (PC-rich) in Cluster I have lost the capacity of CCA recently by the loss or presence of dysfunctional genes required to synthesize the phycoerythrin disks in the phycobilisome, or the genes like rcaE which is needed for the control of CCA (Terauchi et al., 2004; Kehoe and Gutu, 2006). Knock-out experiments targeting the rcaE gene showed that this gene is needed for responsiveness to both red and green light under complementary chromatic adaptation (Terauchi et al., 2004). The loss or the presence of a dysfunctional copy of rcaE or other genes involved in the phycobilisomes or CCA may be caused by deleterious mutations, resulting in strains that have lost phycoerythrin and are only able to use phycocyanin and chlorophyll a as their main light harvesting pigments. Phycocyanin absorbs photons in the orange-red part of the light spectrum. Accordingly, loss of CCA is likely to be advantageous in moderately turbid waters where orange-red light predominates (Stomp et al., 2007), and during storm periods with rapid mixing when CCA is too slow to track changes in the underwater light spectrum experienced by the entrained Pseudanabaena filaments (Stomp et al., in press). Variation in the underwater light spectrum at a range of different time scales could thus have induced genetic divergence between PC-rich


125

and CCA strains because of differences in fitness. The proposed mechanism of selective sweeps could enable such genetic diversification (Cohan, 2002; Gevers et al., 2005; Cohan, 2006; Cohan and Perry, 2007).

In summary, multi-locus sequencing of 5 independent loci revealed the existence of several lineages or subpopulations within Pseudanabaena. The phylogenies of the 16- 23S rRNA genes are consistent, but analysis of the other loci indicated loss of substructure, suggesting recombination between these loci. Pseudanabaena isolates exhibited high levels of microdiversity unveiling biogeographical patterns with both local as well as more globally dispersed populations. A conserved Pseudanabaena lineage proposed as “PCCA ecotype” was characterized by the capacity of chromatic adaptation and possibility for N2 fixation. Population genetic analyses of the phycocyanin genes suggest an evolutionary diversification of Pseudanabaena through purifying selection.

The isolation of additional Pseudanabaena/Limnothrix strains from a variety of different environments is required to further elucidate of the ecology, biogeography and evolution of this enigmatic but understudied group of cyanobacteria.

AcknowledgementsThe authors are grateful to Marije Doeleman for her assistance in sequencing and to Ute

Wollenzien for isolation, culturing and maintenance of the Pseudanabaena strains. We thank Maayke Stomp and the crew of the RV Aranda for their great help during sampling of the Baltic Sea. Special thanks to Caroline Jenkins for providing us with 0.45 μm filters during the Cyano-cruise in 2004. The research of JH was supported by the Earth and Life Sciences Foundation (ALW), which is subsidized by the Netherlands Organization for Scientific Research (NWO). SGA was supported in the framework of the Flemish-Dutch collaboration for Sea Research (VLANEZO), TH and LJS acknowledge the support from the European Commission through the project MIRACLE (EVK3-CT-2002–00087). This is publication xxxx of NIOO-KNAW.

126

Chapter 5

Supplementary InformationThe following supplementary material is available for this article:

Figure 1SM. Light microscopy images of some representative Pseudanabaena isolates from the Baltic Sea and Albufera de Valencia, Spain. Scale bar, 5µm.

Figure 2SM. The effect of light color on the pigment composition of Pseudanabaena strain CCY9703. Three cultures of strain CCY9703 were grown under different light: white light (black line), green filtered (green line) or red filtered (red line) light, until the cultures reached the stationary phase. Strain CCY9703 shows a decreased PE and an increased PC absorption under red light. Under green light CCY9703 shows some increase in PE absorption and a decrease of PC absorption.

Figure 3SM. IGS between the cpcB and cpcA genes. In the alignment the different signatures for the distinct endemic and cosmopolitan clusters of Pseudanabaena spp. strains are indicated with different colors.

Table 1SM. BLAST results of the sequences of 5 loci from Pseudanabaena strains used in this study


127

Figure 1SM

Figure 2SM

128

Chapter 5

Figure 3SM


129

Tab

le 1

SM

. B

last

res

ults

of t

he s

eque

ncin

g of

5 lo

ci fr

om P

seud

anab

aena

str

ains

use

d in

thi

s st

udy.

Str

ain

Nam

e16

S r

RN

A g

ene

(Bac

t105

5F)

23S

rR

NA

gen

e (1

29F

pri

mer

)23

S r

RN

A g

ene

(224

1R p

rim

er)

ITS

1 (In

terg

enic

Sp

acer

1)

cpcB

A g

enes

plu

s IG

S

bpC

lose

st re

lativ

e(%

) to

Gen

bank

/Acc

. Nr.

bpC

lose

st re

lativ

e(%

) to

Gen

bank

/Acc

. Nr.

bpC

lose

st re

lativ

e(%

) to

Gen

bank

/Acc

. Nr.

bpC

lose

st re

lativ

e(%

) to

Gen

bank

/Acc

. Nr.

bpC

lose

st re

lativ

e(%

sim

ilarit

y) to

Gen

bank

/Acc

. Nr.

CC

Y97

0263

3P

seud

anab

aena

sp.

1t

u24s

9 (9

9%)(p

artia

l seq

455

bp)

323

Lim

noth

rix s

p.

MR

1 (9

9%) /

AJ5

8000

8 51

8Tr

icho

desm

ium

ery

thra

eum

IM

S10

1 (9

3%)/C

P00

0393

497

Lim

noth

rix s

p.

MR

1 (9

6%)/A

J580

008

548

Pse

udan

abae

na s

p.

PC

C 7

409

cpcB

A (9

6%)/M

9942

6C

CY

9703

553

Pse

udan

abae

na

PC

C 6

903

(100

%) (

parti

al s

eq 3

22bp

)45

8S

ynec

hoco

ccus

elo

ngat

us

PC

C 6

301(

92%

)/AP

0082

31*

510

Tric

hode

smiu

m e

ryth

raeu

m

IMS

101

(93%

)/CP

0003

9353

6Li

mno

thrix

sp.

M

R1

(99%

)/AJ5

8000

8 55

9C

yano

bact

eriu

m

LS

0580

cpc

BA

(91%

)/DQ

5264

05

CC

Y97

0445

9P

seud

anab

aena

sp.

1t

u24s

9 (9

9%) (

parti

al s

eq 4

55bp

)47

1S

ynec

hoco

ccus

elo

ngat

us

PC

C 6

301(

92%

)/AP

0082

31*

461

Tric

hode

smiu

m e

ryth

raeu

m

IMS

101

(93%

)/CP

0003

9353

6Li

mno

thrix

sp.

M

R1

(99%

)/AJ5

8000

8 55

9C

yano

bact

eriu

m

LS

0580

cpc

BA

(91%

)/DQ

5264

05

CC

Y97

0564

6P

seud

anab

aena

sp.

1t

u24s

9 (9

9%) (

parti

al s

eq 4

55bp

)35

2Li

mno

thrix

sp.

M

R1

(98%

) / A

J580

008

522

Tric

hode

smiu

m e

ryth

raeu

m

IMS

101

(93%

)/CP

0003

9349

9Li

mno

thrix

sp.

M

R1

(96%

)/AJ5

8000

8 60

9P

seud

anab

aena

sp.

P

CC

740

9 cp

cBA

(96%

)/M99

426

CC

Y97

0963

4P

seud

anab

aena

sp.

1t

u24s

9 (9

9%) (

parti

al s

eq 4

55bp

)45

5S

ynec

hoco

ccus

elo

ngat

us

PC

C 6

301(

88%

)/AP

0082

3154

9Tr

icho

desm

ium

ery

thra

eum

IM

S10

1 (9

2%)/C

P00

0393

531

Lim

noth

rix s

p.

MR

1 (1

00%

)/AJ5

8000

8 (7

7% c

over

age)

548

Cya

noba

cter

ium

L

S05

80 c

pcB

A (9

1%)/D

Q52

6405

C

CY

9710

603

Pse

udan

abae

na s

p.

1tu2

4s9

(99%

) (pa

rtial

seq

455

bp)

468

Syn

echo

cocc

us e

long

atus

P

CC

630

1(91

%)/A

P00

8231

*57

6Tr

icho

desm

ium

ery

thra

eum

IM

S10

1 (9

1%)/C

P00

0393

499

Lim

noth

rix s

p.

MR

1 (9

9%)/A

J580

008

608

Pse

udan

abae

na s

p.

PC

C 7

409

cpcB

A (9

5%)/M

9942

6C

CY

9712

668

Pse

udan

abae

na s

p.

1tu2

4s9

(99%

) (pa

rtial

seq

455

bp)

471

Syn

echo

cocc

us e

long

atus

P

CC

630

1(92

%)/A

P00

8231

*58

2Tr

icho

desm

ium

ery

thra

eum

IM

S10

1 (9

1%)/C

P00

0393

495

Lim

noth

rix s

p.

MR

1 (9

6%)/A

J580

008

609

Pse

udan

abae

na s

p.

PC

C 7

409

cpcB

A (9

6%)/M

9942

6C

CY

9714

642

Pse

udan

abae

na s

p.

1tu2

4s9

(99%

) (pa

rtial

seq

455

bp)

472

Syn

echo

cocc

us e

long

atus

P

CC

630

1(92

%)/A

P00

8231

*59

7Tr

icho

desm

ium

ery

thra

eum

IM

S10

1 (9

1%)/C

P00

0393

495

Lim

noth

rix s

p.

MR

1 (9

6%)/A

J580

008

608

Pse

udan

abae

na s

p.

PC

C 7

409

cpcB

A (9

6%)/M

9942

6C

CY

9715

672

Pse

udan

abae

na s

p.

1tu2

4s9

(99%

) (pa

rtial

seq

455

bp)

472

Syn

echo

cocc

us e

long

atus

P

CC

630

1(92

%)/A

P00

8231

*61

2Tr

icho

desm

ium

ery

thra

eum

IM

S10

1 (9

1%)/C

P00

0393

499

Lim

noth

rix s

p.

MR

1 (9

6%)/A

J580

008

609

Pse

udan

abae

na s

p.

PC

C 7

409

cpcB

A (9

6%)/M

9942

6C

CY

9508

640

Pse

udan

abae

na s

p.

1tu2

4s9

(99%

) (pa

rtial

seq

455

bp)

450

Syn

echo

cocc

us e

long

atus

P

CC

630

1(91

%)/A

P00

8231

*52

0Tr

icho

desm

ium

ery

thra

eum

IM

S10

1 (9

3%)/C

P00

0393

536

Lim

noth

rix s

p.

MR

1 (9

9%)/A

J580

008

(85%

cov

erag

e)55

9C

yano

bact

eriu

m

LS

0580

cpc

BA

(91%

)/DQ

5264

05 (9

2% c

over

age)

CC

Y04

7165

0P

seud

anab

aena

sp.

1t

u24s

9 (9

9%) (

parti

al s

eq 4

55bp

)47

3S

ynec

hoco

ccus

elo

ngat

us

PC

C 6

301(

92%

)/AP

0082

31*

556

Tric

hode

smiu

m e

ryth

raeu

m

IMS

101

(91%

)/CP

0003

9353

6Li

mno

thrix

sp.

M

R1

(99%

)/AJ5

8000

8 (8

5% c

over

age)

580

Cya

noba

cter

ium

L

S05

80 c

pcB

A (9

1%)/D

Q52

6405

(92%

cov

erag

e)C

CY

0472

538

Pse

udan

abae

na

PC

C69

03 (1

00%

) (pa

rtial

seq

322

bp)

473

Syn

echo

cocc

us e

long

atus

P

CC

630

1(92

%)/A

P00

8231

*54

2Tr

icho

desm

ium

ery

thra

eum

IM

S10

1 (9

1%)/C

P00

0393

447

Lim

noth

rix s

p.

MR

1 (9

9%)/A

J580

008

(82%

cov

erag

e)58

0C

yano

bact

eriu

m

LS

0580

cpc

BA

(91%

)/DQ

5264

05 (9

2% c

over

age)

CC

Y04

7347

7P

seud

anab

aena

P

CC

6903

(100

%) (

parti

al s

eq 3

22bp

)47

1S

ynec

hoco

ccus

elo

ngat

us

PC

C 6

301(

92%

)/AP

0082

31*

366

Tric

hode

smiu

m e

ryth

raeu

m

IMS

101

(94%

)/CP

0003

93N

DN

D58

0C

yano

bact

eriu

m

LS

0580

cpc

BA

(91%

)/DQ

5264

05 (9

2% c

over

age)

CC

Y04

7450

8P

seud

anab

aena

P

CC

6903

(100

%) (

parti

al s

eq 3

22bp

)N

DN

DN

DN

D49

5Li

mno

thrix

sp.

M

R1

(97%

)/AJ5

8000

8 60

8P

seud

anab

aena

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rtial

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ND

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udan

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na s

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630

1(92

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9942

6C

CY

0481

625

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udan

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na

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) (pa

rtial

seq

322

bp)

473

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cocc

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long

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P

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630

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%)/A

P00

8231

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5Tr

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desm

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P00

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406

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Pse

udan

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CC

630

1(92

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1 (9

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593

Pse

udan

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p.

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C 7

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A (9

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CY

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376

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noth

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504

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hode

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m e

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m

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101

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DN

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8462

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hoco

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PC

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92%

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0082

31*

449

Tric

hode

smiu

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9340

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sp.

M

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8000

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8P

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sp.

P

CC

740

9 cp

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(96%

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8555

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sp.

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9 (9

9%) (

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1Li

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sp.

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R1

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008

554

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m e

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740

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sp.

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9 (9

9%) (

parti

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eq 4

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)35

4Li

mno

thrix

sp.

M

R1

(98%

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J580

008

478

Tric

hode

smiu

m e

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Chapter 6General discussion

General discussion

133

Cyanobacteria are photoautotrophic organisms. They use solar energy to sustain their metabolic processes, which enable cyanobacteria to proliferate in a wide range of aquatic and terrestrial environments. In aquatic environments cyanobacteria are an important component of the phytoplankton, together with a plethora of photoautotrophic eukaryotic micro-organisms. Phytoplankton species compete for resources such as light and nutrients. Although pelagic ecosystems offer rather unstructured habitats lacking the intricate physical structure of, say, tropical rain forests or coral reefs, phytoplankton is known for its unexplained high species diversity. This conundrum stimulated Hutchinson (1961) to formulate the paradox of the plankton, which has been a source of inspiration for many studies investigating the mechanisms maintaining biodiversity.

This thesis aimed at the identification of the genetic background of one possible solution for the paradox of the plankton, that is, niche differentiation with respect to the color of light. For this purpose, we investigated the distribution of differently pigmented picocyanobacteria as a function of the underwater light spectrum (Chapter 2). Furthermore, we investigated the phylogeny and diversity of two genera belonging to the picocyanobacteria, namely unicellular picocyanobacteria of the Synechococcus group (Chapters 3 and 4) and the tiny filamentous Pseudanabaena spp. (Chapter 5). Our phylogenetic studies focused on the genes coding for the phycobilin pigments phycoerythrin (PE) and phycocyanin (PC), which are responsible for the characteristic red and green pigmentation of cyanobacteria. This chapter discusses the results obtained in the preceding chapters, and evaluates our hypothesis on the spectral niche differentiation of cyanobacteria.

Colorful phytoplanktonThe color of light could be one important factor allowing the maintenance of the high

species diversity observed in the phytoplankton (Stomp et al., 2004). Chemostat experiments showed that differently pigmented Synechococcus strains can coexist in white light, because their pigments enable utilization of different parts of the light spectrum. Moreover, further chemostat experiments showed that the filamentous cyanobacterium Tolypothrix can coexist with either a red or a green strain of Synechococcus by using the part of the light spectrum left unused by its competitors (Stomp et al., 2004). This is possible since Tolypothrix is capable of tuning its pigmentation to the prevailing light spectrum through the mechanism of complementary chromatic adaptation (CCA).

In this thesis, the coexistence of differently pigmented picocyanobacteria was verified for natural environments. Seventy aquatic ecosystems characterized by a range of different turbidities were compared with respect to the underwater light spectrum and attenuation in the water column (Chapter 2). This led to the conclusion that red light dominates in turbid waters. This favors the growth of PC-rich picocyanobacteria. Aquatic ecosystems with lower turbidity enable light to penetrate deeper into the water column while the underwater light spectrum shifts towards green and blue light. In transparent oligotrophic waters, such as the open oceans,

134

Chapter 6

PE-rich picocyanobacteria predominate. In water bodies with intermediate turbidity, such as the Baltic Sea, PE-rich and PC-rich picocyanobacteria co-exist with more or less equal cell abundances (Stomp et al. 2007a; Chapter 2).

Pigmentation of Synechococcus in the Baltic SeaIn the Baltic Sea, PE-rich and PC-rich picocyanobacteria reveal distinct depth distributions.

PE-rich picocyanobacteria dominate the phytoplankton in the deeper part of the euphotic zone, while PE-rich and PC-rich picocyanobacteria coexist in the upper mixed layer (Stomp et al., 2007a; Haverkamp et al., 2008; Chapter 2 and 3). This distribution was the result of the underwater light spectrum, changing from white light at the surface towards green light at depth. Water mainly absorbs red light. Red light is therefore not available in the deeper part of the euphotic zone, which restricts the growth of PC-rich cyanobacteria to the upper mixed layer.

The depth distribution of PE-rich and PC-rich picocyanobacteria in Baltic Sea waters resembles that of spectrally different pigment types (PC versus bacteriochlorophyll) in microbial mats (Ward, 2006). In hot spring microbial mats various ecotypes of a thermophilic PC-rich Synechococcus differed in their photosynthetic characteristics because of light quality gradients and were therefore found at different depths (Ramsing et al., 2000; Allewalt et al., 2006; Ward et al., 2006; Kilian et al., 2007). This example is also analogous to the distributions of high- and low-light ecotypes of Prochlorococcus species (Moore et al., 1998; West and Scanlan, 1999). In Prochlorococcus the chl b/a2 ratios are different in the high- and low-light ecotypes (Moore and Chisholm, 1999). The low-light ecotypes have the highest chl b/a2 ratios and are able to grow under low light conditions found at the bottom of the euphotic zone. In the Red Sea and other (sub)-tropical oligotropic marine environments Prochlorococcus is the dominant phototroph when the water column is stratified. In oceanic environments deep mixing causes Prochlorococcus to disappear in favor of other phytoplankton species, particularly Synechococcus (Olson et al., 1990; Lindell and Post, 1995; Campbell et al., 1998; Fuller et al., 2005; Ahlgren and Rocap, 2006; Al-Najjar et al., 2007).

Studies on marine Synechococcus species have identified different ecotypes that show distinct geographic distributions (Fuller et al., 2006; Zwirglmaier et al., 2007; Zwirglmaier et al., 2008). However, the typical vertical distribution of Synechococcus ecotypes in the Baltic Sea, characterized by coexistence of PC-rich and PE-rich ecotypes near the water surface and the predominance of PE-rich picocyanobacteria in the deeper water layers underneath (Stomp et al., 2007a; Haverkamp et al., 2008; Chapters 2 and 3), has not been found in open-ocean ecosystems. In the Baltic Sea, the underwater light climate has predominantly a green color, which is effectively absorbed by PE-rich picocyanobacteria. In open ocean waters, water clarity is typically much higher than in the Baltic Sea, which shifts the underwater light color towards the blue part of the spectrum. As a result, the deeper water layers of the open ocean are dominated by low-light Prochlorococcus ecotypes, whose spectra are much better tuned to the prevailing blue light than the PE-rich picocyanobacteria of the Baltic Sea (Stomp et al., 2007b).

General discussion

135

Synechococcus is more abundant than Prochlorococcus in coastal oceanic regions, where water transparency is much lower than in the open ocean (Partensky et al., 1999) and the underwater light color is shifted towards the green part of the light spectrum (Stomp et al., 2007b). The Synechococcus species found in the different oceanic regions belong to clades I or III of Synechococcus cluster 5.1. The cluster 5.1 strains require elevated levels of Na+, Mg2+ and Ca2+ for growth reflecting the marine environment in which they thrive. Synechococcus species belonging to other clusters are often just halotolerant (Herdman et al., 2001; Fuller et al., 2003). A unique feature found only in Synechococcus strains belonging to clades I and III is the capacity for type IV complementary chromatic adaptation (CCA) (Fuller et al., 2003; Palenik et al., 2006; Zwirglmaier et al., 2007; Zwirglmaier et al., 2008). Many marine Synechococcus strains contain phycobilisome complexes with two pigments, phycourobilin (PUB) and phycoerythrobilin (PEB) that are present in the cell in a constant, strain specific ratio (Alberte et al., 1984; Wood et al., 1985; Fuller et al., 2003; Six et al., 2004). Synechococcus species capable of CCA are able to change the PUB/PEB ratio in response to changes in the spectral quality of light. Blue light stimulates a high PUB/PEB ratio, while green light induces a low PUB/PEB ratio. CCA leads to subtle but critical changes in the absorbance characteristics of their phycobilisomes (Palenik, 2001; Everroad et al., 2006). During deep mixing marine Synechococcus species encounter changes in spectral light quality from white light near the surface to blue or green light at greater depth. It is therefore likely that the CCA capacity found in Synechococcus clades I and III is an adaptation to the mixing conditions they experience in marine environments (Fuller et al., 2006; Stomp et al., in press).

During this study, picocyanobacteria related to the Synechococcus cluster 5.1 were not encountered in the Baltic Sea. Instead, analysis of the 16S rRNA and ITS-1 region of clone libraries and isolated strains confirmed the presence of Synechococcus cluster 5.2 or Cyanobium-like species (Crosbie et al., 2003; Ernst et al., 2003; Chen et al., 2006). Cluster 5.2 and Cyanobium are closely related to the Synechococcus cluster 5.1 (Herdman et al., 2001). Isolates of Synechococcus cluster 5.2 and Cyanobium have been obtained from brackish as well as freshwater environments and it is therefore not surprising that species related to these groups were identified in the brackish Baltic Sea (Crosbie et al., 2003; Ernst et al., 2003).

So far, CCA type IV has not been detected among picocyanobacteria other than Synechococcus cluster 5.1. Hence, it did not come as a surprise that CCA was absent among the Baltic Sea Synechococcus strains that were isolated in the course of this study. Variation in pigmentation was observed within several PE-rich or PC-rich isolates from the Baltic Sea, but this variation was attributed to differences in nitrogen source (i.e., ammonium versus nitrate) and not to chromatic adaptation (Chapter 4). The absence of CCA type IV is further supported by the light regime in the Baltic Sea. Green light dominates throughout the photic zone and this is not absorbed by the PUB pigments used in CCA type IV.

Chromatic adaptation in Pseudanabaena.While the Baltic Sea Synechococcus isolates were negative with respect to complementary

chromatic adaptation, many Baltic Sea isolates of the filamentous Pseudanabaena were able to

136

Chapter 6

perform CCA type III. The Pseudanabaena isolates were divided into two groups with respect to pigment and CCA: a PC-rich group and the CCA+ group (Chapter 5). The CCA+ group comprises strains that are able to regulate the amounts of both the PE and PC pigments. In this respect the CCA+ Pseudanabaena species are similar to the Tolypothrix sp. used by Stomp et al. (2004) in their study of the competition and coexistence of differently pigmented cyanobacteria. Remarkably, every PE-rich Pseudanabaena isolate known and tested so far is capable to perform CCA type III, except for strain PCC 7367 (Castenholz et al., 2001). The latter strain is also unique among Pseudanabaena because it was isolated from a marine intertidal sediment and it grows in full strength seawater medium ASNIII (Castenholz et al., 2001). As far as known, all other Pseudanabaena isolates have been obtained from brackish and freshwater environments (Postius et al., 2001; Stal et al., 2003; Zwart et al., 2005). It is not clear why no other PE-rich Pseudanabaena strains incapable of CCA type III have been isolated from the environment. If such strains do not exist it would suggest that CCA provides Pseudanabaena with a selective advantage over PE-rich species that are unable of chromatic adaptation.

Despite extensive research, the ecological role of CCA has not yet fully been elucidated (Kehoe and Gutu, 2006; but see Stomp et al., in press). Detecting CCA in the environment is difficult, because cyanobacteria capable of CCA may be mixed with other phytoplankton with similar pigmentation. However, one study assigned the capability of type III CCA to isolates from the periphyton of macrophytes in the deep literal zone but only to a few isolates from the pelagic zone (Postius et al., 2001). In their study, the capability of type III CCA seemed to be connected to the presence of the nifH gene and anaerobic N2-fixation. In the deep littoral zone, biofilms are formed on macrophytes creating microhabitats characterized by low oxygen concentrations, poor nitrogen availability and strong light attenuation (Thomas et al., 2006; Vis et al., 2006; Uku et al., 2007). Postius et al. (2001) concluded that the ability to perform anaerobic N2 fixation in combination with CCA type III might give an advantage to organisms colonizing macrophytes because it would enable them to adapt to changing light and nutrient conditions that affect the growth of the organisms.

Changing light and nutrient conditions also occur in the Baltic Sea where stratification and mixing of the water column are important factors for the development of large cyanobacterial blooms (Janssen et al., 2004). Pseudanabaena can become dominant in environments with high nutrient concentrations, irradiance levels and strong mixing (Chomerat et al., 2007). In the Baltic Sea Pseudanabaena is part of the phytoplankton (Suikkanen et al., 2005; Kangro et al., 2007; Riemann et al., 2008; this thesis) Under certain conditions Pseudanabaena can become dominant at the surface following a bloom of Aphanizomenon (Riemann et al., 2008). Similar observations have been made in the German Melangsee (Mischke and Nixdorf, 2003). The exact reasons causing the succession from Aphanizomenon to Pseudanabaena are not clear. For the German Melangsee it was concluded that the collapse of the Aphanizomenon bloom released nitrogen into the lake waters which could be used by the smaller filamentous species Limnothrix and Pseudanabaena (Mischke and Nixdorf, 2003). A similar event might be possible in the Baltic Sea. Another reason for the succession from Aphanizomenon to Pseudanabaena could be the difference in buoyancy of the cells of the two species. Buoyancy

General discussion

137

is regulated by the volume of the gas vesicles and the density of the cell, the latter depends mainly on the carbohydrate content (Visser et al., 1995; Walsby, 2005). For Limnothrix spp. it was estimated that their sinking speed was less than 1 m per month (Walsby, 2005). This suggests that Pseudanabaena, which has a size comparable to Limnothix, has a very slow sinking velocity. Moreover, Pseudanabaena possesses only 2 small gas vesicles at the polar ends of the cell and these are built of only one protein (gvpA), whereas other cyanobacteria require three proteins to make gas vesicles (Damerval et al., 1991). The gas vesicles of Pseudanabaena may therefore serve another function than providing buoyancy. Larger PC-rich filamentous, heterocystous cyanobacteria like Aphanizomenon will float faster upwards and form surface blooms that change the spectral quality of light at greater depth. Under such conditions it might be beneficial for Pseudanabaena to use PE for light absorption. In contrast, when thriving at the surface, Pseudanabaena encounters a rather white light spectrum, and will thus benefit from a combination of PE and PC pigments. Accordingly, the capability of CCA might be beneficial because of the fluctuations in spectral quality encountered during vertical mixing in the water column.

In addition, Pseudanabaena might survive during periods of low nitrogen availability by means of N2-fixation. In Pseudanabaena N2-fixation has only been found under anaerobic conditions (Kallas et al., 1985; Bergman et al., 1997; Postius et al., 2001). However, in the dark, microaerobic patches may be formed within aggregates of cyanobacteria creating a niche for anaerobic N2-fixation (Ploug et al., 1997; Postius et al., 2001; Tuomainen et al., 2003). This might alleviate N-limitation in Pseudanabaena in surface waters. Together with their ability for CCA, this would enhance the competitive potential of Pseudanabaena. This suggests that Pseudanabaena spp. are opportunistic species that are capable to cope with a wide variety of environmental conditions.

In summary, the capacity of CCA type III or IV might be a prerequisite for cyanobacteria that migrate or are exposed to vertical mixing through the water column because they will experience considerable spectral changes in the underwater light. Obviously, vertical migration or mixing is not sufficient to explain the presence of CCA in cyanobacteria, since not all cyanobacteria show this ability. The Baltic Sea Synechococcus isolates were not able to perform CCA of any kind, while many of the Pseudanabaena isolates were capable of CCA. Factors such as the generation time, the speed of vertical mixing and/or vertical migration and the depth of the photic zone all influence the success of organisms and the benefits they may obtain from the ability of CCA.

Bacterial speciation and ecotypesIn taxonomic studies, different species, ecotypes or strains are compared in order to assess

their relatedness and evolutionary divergence. In microbial systematics and taxonomy this is achieved by analyzing gene sequences because of the lack of sufficient distinguishable phenotypic characteristics. The analysis of the 16S rRNA gene sequence allows quick identification of

138

Chapter 6

newly isolated strains. However, molecular methods also revealed that closely related strains may display considerable phenotypical variation within what would be regarded as a single species based on their 16S rRNA sequence (Feldgarden et al., 2003; Polz et al., 2006).

In this thesis, we studied the genus Synechococcus and its closely related sister-genus Cyanobium. We observed extensive phenotypic differences between different isolates. In fact, the within-genus variation was at least as large as the variation between the genera Synechococcus and Cyanobium. This raises the question whether Synechococcus and Cyanobium should be pooled into the same genus. The same question can be posed for Pseudanabaena and Limnothrix. The close relation of Synechococcus with Cyanobium, and also of Pseudanabaena with Limnothrix emphasizes that the taxonomical descriptions for these genera are difficult to apply, and that assignment of strains to either genus is not straightforward.

Morphology, phenotypes and molecular dataAt the 16S rRNA level the genus Synechococcus is closely related to Cyanobium, while

Pseudanabaena is closely related to Limnothrix. The use of the 16S rRNA gene to distinguish between the different genera is cumbersome because the phylogenies do not clearly separate the species (Crosbie et al., 2003; Ernst et al., 2003; Zwart et al., 2005; this thesis).

Historically, taxonomists have described genera by using morphological, physiological and other phenotypic characteristics to discriminate species (Herdman et al., 2001). For instance, pigmentation and cell elongation have been used as important descriptive characters to separate Synechococcus and Cyanobium (Komárek et al., 1999; Rippka et al., 2001). According to the description in Bergey’s Manual of Systematic Bacteriology Cyanobium spp. do not produce phycoerythrin (PE), while Synechococcus species may (Boone and Castenholz, 2001). Within Synechococcus, strains are found that produce PE while other strains do not (Herdman et al., 2001). However, in the newest taxonomic descriptions it is mentioned that PE can also be found among Cyanobium (Komárek et al., 1999). These authors concluded that the PE/PC ratio among Cyanobium spp. is strain or species specific. Moreover, the phylogeny based on 16S rRNA gene and ITS sequences is not congruent with the possession of the genes for PE synthesis. Hence, pigmentation is a characteristic with little taxonomic value (Komárek et al., 1999).

Using the cpcBA operon it was possible to phylogenetically separate PC-rich from PE-rich strains. In addition, a further separation of the latter into those producing PUB/PEB and those producing only PEB was also possible (Six et al., 2007; Haverkamp et al., 2008; this thesis). The phylogenies based on the cpcBA / cpeBA genes form clades of closely related sequences obtained from isolates from marine, brackish and freshwater environments with similar pigmentations (Chapters 3 and 4). This phylogenetic pattern is strikingly different from the pattern observed using the 16S rRNA gene which does not separate different pigmentation types (Chapter 3).

Recently, analysis of a number of cyanobacteria for which the whole genome has been sequenced indicated the existence of a core genome consisting of a set of highly conserved genes giving highly identical phylogenetic topologies (Shi and Falkowski, 2008). The PE and PC

General discussion

139

genes are not part of the core genome, but belong to the variable part that exchanges among different closely related lineages, which is in line with our results (Six et al., 2007; this thesis Chapter 4).

Cell elongation has also been used to differentiate between Cyanobium and Synechococcus (Komárek et al., 1999; Jezberova and Komarkova, 2007). Cell elongation occurs in Synechococcus when grown under suboptimal conditions but not in Cyanobium (Jezberova and Komarkova, 2007). Furthermore, cell elongation is accompanied by asymmetrical cell division, while under optimal conditions division occurs symmetrically in Synechococcus (Komárek et al., 1999). This indicates a close relationship between cell elongation and cell division.

Cell division is a highly coordinated process. The characteristics of cell elongation and asymmetrical cell division typical for Synechococcus but absent in Cyanobium could be the result of differences in the regulation mechanisms in these species when dealing with suboptimal conditions. Furthermore, it is known that different culture conditions can have profound effects on cell size and growth efficiency in Synechococcus (Burns et al., 2005; Ernst et al., 2005; Schwarz and Forchhammer, 2005; Burns et al., 2006; Jezberova and Komarkova, 2007). In Synechococcus PCC7942 a large number of genes have been identified that are involved in the regulation of cell division (Koksharova and Wolk, 2002; Miyagishima et al., 2005; Koksharova et al., 2007). Knock-out mutants of these genes show different length phenotypes in PCC7492 under logarithmic growth (Koksharova and Wolk, 2002; Miyagishima et al., 2005). The expression of many genes involved in processes such as protein synthesis, cell division, cell morphology, chromosome segregation, photosynthesis and carbon fixation, are either up- or down-regulated when genes involved in cell division are knocked-out in PCC7492 (Koksharova et al., 2007). It should be noted that when regulatory genes become mutated this may have important consequences for the phenotype. Hence, mutations in regulatory genes and culture conditions affecting the phenotype and morphology make the use of such characteristics for taxonomic purposes cumbersome. Nonetheless, observations on phenotypes under laboratory conditions can be meaningful when the different phenotypes can be related to specific changes in culture conditions. For instance, Synechococcus-like cells grown on BG11 exhibited larger cell size compared to those grown on WC medium which contains much lower nutrient concentrations (Guillard and Lorenzen, 1972; Jezberova and Komarkova, 2007). In contrast with Synechococcus, one strain of Cyanobium did not show differences in cell size when grown on BG11 or the WC-medium (Jezberova and Komarkova, 2007).

These observations imply that, possibly, the environmental conditions separating Cyanobium and Synechococcus could involve the nutrient status of the ecological niches that they inhabit. Cyanobium might be better capable to cope with suboptimal conditions than Synechococcus. As to date, however, the ecological preferences for either species remain unclear. If Cyanobium prefers eutrophic or mesotrophic conditions and Synechococcus rather occurs in mesotrophic to oligotrophic environments, one might consider Cyanobium and Synechococcus as different nutrient ecotypes of the same genus. If this were the case, using the appropriate genes, one might be able to identify these ecotypes with higher confidence than when using the 16S rRNA gene.

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Obviously, speciation among picocyanobacteria is affected by a range of environmental factors including spectral light quality, salinity and nutrient levels.

Another example is the Pseudanabaena / Limnothrix group that was investigated during this study. A polyphasic approach was applied in this study, combining morphology and molecular data, in order to determine the phylogenetic relationships of our isolates with known strains. Pseudanabaena / Limnothrix show overlap in their phenotypic characters (e.g. cell size, pigmentation, etc.) (Komárek, 2003). At the molecular level, the 16S rRNA gene sequences indicate that Pseudanabaena and Limnothrix are polyphyletic and strains with different taxonomical assignments appear in monophyletic clusters (Herdman et al., 2001; Zwart et al., 2005; Chapter 5). Using highly divergent sequences such as the ITS and the cpcBA-IGS region, several clades of the Pseudanabaena / Limnothrix cluster were found with different cell morphologies and geographical distributions (Chapter 5). Some of these clusters contain only strains with Limnothrix-like morphologies, while others contained isolates with morphologies characteristic of both Pseudanabaena and Limnothrix. These results indicate that, despite our detailed molecular analysis, it was still impossible to give a proper taxonomical assignment of our isolates. The small morphological differences between Pseudanabaena and Limnothrix could be the result of genetic or environmental differences that affect morphology. Unfortunately, experimental data describing the effect of environmental conditions on the morphology of Pseudanabaena and Limnothrix is lacking.

Species and ecotypesThe use of combined molecular or morphological and physiological characteristics may be

confusing when attempting to understand the taxonomy and speciation of closely related micro-organisms. This is especially true when only a few genes are used to delineate species. Genomics and multi-locus sequence approaches could improve the resolution for discriminating species.

Ideally, a polyphasic taxonomy of microorganisms using molecular as well as morphological and physiological phenotypic characteristics as complementary tools should produce similar phylogenies displaying the same bifurcations in the phylogenetic tree. For the cyanobacteria investigated in this thesis it was found that the species relationship at one locus differed from that at another shared locus. One explanation for this observation is that different genes may be under different selective forces causing different evolutionary patterns between closely related species. Other processes such as lateral gene transfer, recombination and gene exchange through conjugation or viral intermediates could also disturb clonal propagation of species.

Analysis of the available microbial genomes indicates that, within the cyanobacteria and especially between the closely related genera Synechococcus and Prochlorococcus, extensive lateral gene transfer must have taken place (Beiko et al., 2005; Zhaxybayeva et al., 2006; Shi and Falkowski, 2008). Analysis of the Prochlorococcus and Synechococcus genomes indicates that extensive reshuffling of genes has taken place (Hess, 2004). Since Cyanobium is sister to Synechococcus and Prochlorococcus, it can be expected that also between these genera extensive lateral gene transfer might have occurred. This could have obscured the possible boundaries between these species. The identification of core genes in cyanobacterial isolates and using them

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for MLSA typing may help to solve taxonomical questions as has been done for pathogenic prokaryotes (Zhaxybayeva et al., 2006; Shi and Falkowski, 2008).

Cluster 5 Synechococcus and Cyanobium spp. are closely related, they have only small and often overlapping morphological and other phenotypic differences, and equally important, they have ecologically similar or overlapping roles in the environment (Komárek et al., 1999; Ernst et al., 2003; this thesis). Therefore, we suggest that they should be classified as different ecotypes within the same genus (Konstantinidis and Tiedje, 2005; Cohan and Perry, 2007). The differences between Pseudanabaena and Limnothrix are equally small as within the picocyanobacteria. Therefore Pseudanabaena and Limnothrix might also be classified as one genus comprising closely related ecotypes. These ecotypes could be assigned based on their response to light, salinity and nutrients (Ahlgren and Rocap, 2006; Zwirglmaier et al., 2008; this thesis). Obviously, much more ecological, physiological, and genetic data is required from many more isolates of the Synechococcus/Cyanobium and Pseudanabaena/Limnothrix groups in order to solve this taxonomical conundrum.

ConclusionsCyanobacteria are a monophyletic group of phototrophic organisms with considerable

variation in genetic, physiological and morphological characteristics. Their extensive diversity allows cyanobacteria to thrive in many different habitats occupying a large variety of ecological niches. One important selective factor creating different niches in pelagic habitats is the color of light in the euphotic zone. Cyanobacteria with different photosynthetic pigments are capable of occupying niches characterized by different light colors. Here it was shown how the relative abundance of red (PE-rich) and green (PC-rich) picocyanobacteria varies with the underwater light spectrum. Although these differently pigmented picocyanobacteria cannot be distinguished on the basis of their 16S rRNA gene sequences, they can be distinguished using the genes encoding PC and PE proteins. For Pseudanabaena species it was found that the non-coding regions of these genes separated groups according to their geographic distributions. However, much more knowledge on the ecology of the different Pseudanabaena geotypes is required in order to determine their ecological distinctiveness and dispersal capabilities and to understand their success in many freshwater environments.

Taken together, the results presented in this thesis show that highly divergent gene sequences can be used to identify distinct ecotypes. This is only possible when DNA sequences can be linked to well-defined phenotypes, which requires the availability of cultured strains.

Chapter 7English summary

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The open water of aquatic ecosystems offers a rather homogenous environment with little spatial structure compared to, for instance, tropical rain forests or coral reefs. Yet, surprisingly, aquatic environments are inhabited by a highly diverse phytoplankton community with numerous coexisting species competing for only a limited number of nutrients. One potential niche axis contributing to the species diversity of the plankton might be the underwater light spectrum. This thesis describes the ecology and phylogeny of coexisting picocyanobacteria in relationship to the color of light. In particular, it was investigated how the color of light affects the distribution, diversity and pigment composition of the cyanobacteria Synechococcus and Pseudanabaena in the Baltic Sea.

In Chapter 2 the distribution of red and green pigmented picocyanobacteria was investigated in relation to the light regime in natural waters. Previously, it was found in laboratory experiments that red and green picocyanobacteria can coexist under white light. Here, a parameterized competition model was used to describe the coexistence of red and green phytoplankton species in relation with the underwater light color of lakes and seas. To test the predictions of the model, picocyanobacteria were sampled from 70 aquatic ecosystems, ranging from clear blue oceans to turbid brown peat lakes. As predicted, red picocyanobacteria dominated in clear waters whereas green picocyanobacteria dominated in turbid waters. Widespread coexistence of red and green picocyanobacteria occurred in waters of intermediate turbidity. These data support the hypothesis that niche differentiation along the light spectrum promotes phytoplankton biodiversity, thus providing a colorful solution to Hutchinson’s plankton paradox.

In Chapter 3 the distribution and diversity of differently colored picocyanobacteria in the Baltic Sea is reported. It was found that coexistence of red and green picocyanobacteria in the Baltic Sea is widespread. The diversity and phylogeny of red and green picocyanobacteria was analyzed using three different genes: 16S rRNA-ITS, the cpeBA operon of the red pigment phycoerythrin (PE), and the cpcBA operon of the blue pigment phycocyanin (PC). Sequencing of 209 environmental clones from the Baltic Sea showed that picocyanobacteria exhibit high levels of microdiversity. The partial nucleotide sequences of the cpcBA and cpeBA operons from the clone libraries of the Baltic Sea revealed two distinct phylogenetic clades: one clade contained mainly sequences from cultured PC-rich picocyanobacteria, while the other clade contained only sequences from cultivated PE-rich strains. A third clade of phycourobilin (PUB) containing strains of PE-rich Synechococcus spp. did not comprise sequences from the Baltic Sea clone libraries. These findings differ from previously published phylogenies based on 16S rRNA gene analysis. Our data suggest that, in terms of their pigmentation, Synechococcus spp. represent three different lineages occupying different ecological niches in the underwater light spectrum. Strains from different lineages can coexist in light environments that overlap with their light absorption spectra.

Chapter 4 describes the isolation of 46 closely related picocyanobacterial strains from the Baltic Sea. The isolates show considerable variation in their pigmentation phenotypes. Furthermore, small differences between the strains were observed with respect to cell volume and preference for either ammonium or nitrate as the main source of nitrogen. At the molecular level we found that 39 strains, designated BSea1, had almost identical 16S rRNA–ITS sequences

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with Synechococcus strain WH5701. Despite having similar 16S rRNA–ITS sequences, the BSea1 strains could be separated into several different clusters when comparing the phycocyanin (cpcBA) operon. This separation corresponds to the pigmentation of the different BSea1 strains. The majority of PC-rich Bsea1 strains clustered with WH5701. Remarkably, the PE-rich strains of BSea1 formed an as yet unidentified cluster within the cpcBA phylogeny, distantly related to other PE-rich groups. Detailed analysis of the cpcBA operon using neighbour net analysis indicated that the PE-rich BSea1 strains are probably endemic for the Baltic Sea. Comparison of the phylogenies obtained by using the 16S rRNA–ITS, the cpcBA as well as the concatenated 16S rRNA-ITS and cpcBA operon sequences, revealed possible events of horizontal gene transfer among different Synechococcus lineages. Our results show that microdiversity is widespread in Synechococcus populations, and that it can reflect different phenotypes with different ecological roles.

The topic discussed in chapter 5 is the isolation and diversity of Pseudanabaena strains from two different ecosystems. Pseudanabaena species are poorly known filamentous bloom-forming cyanobacteria closely related to Limnothrix. Because of their small size, the importance of Pseudanabaena has been overlooked and they have not been recognized as dominant organisms in blooms. We isolated 28 Pseudanabaena strains from the Baltic Sea and the Albufera de Valencia (Spain). By combining phenotypic and genotypic approaches the phylogeny, diversity, biogeography and evolutionary diversification of these isolates were explored. Analysis of the in vivo absorption spectra of the Pseudanabaena strains revealed two coexisting pigmentation phenotypes: (i) phycocyanin-rich strains, and (ii) strains containing both phycocyanin and phycoerythrin. Strains of the latter phenotype were all capable of complementary chromatic adaptation (CCA). We compared the different Pseudanabaena strains using a multi-locus sequence approach which spanned the 16S and 23S rRNA genes, the ribosomal intergenic spacer ITS-1, the cpcBA operon encoding phycocyanin, and the intergenic spacer (IGS) between cpcA and cpcB. In addition, the presence of nifH, one of the structural genes of nitrogenase, was investigated. Sequence analysis of ITS and cpcBA-IGS allowed for the differentiation between Pseudanabaena isolates exhibiting high levels of microdiversity. This multi-locus sequencing approach revealed specific clusters for the Baltic Sea, the Albufera de Valencia, and a mixed cluster with strains from both ecosystems. The latter comprised exclusively CCA phenotypes. The phylogenies of the 16S and 23S rRNA genes were consistent, but analysis of other loci indicated loss of substructure, suggesting that recombination between these loci has occurred. Population genetic analyses of the phycocyanin genes suggest an evolutionary diversification of Pseudanabaena through purifying selection.

Summarizing this research, we have found that picocyanobacteria of the Baltic Sea show an extensive genetic differentiation that enables them to display a wide variety of physiological and morphological characteristics. This extensive microdiversity allows cyanobacteria to occupy a variety of different ecological niches. In particular, the light spectrum offers an important axis for niche differentiation in pelagic ecosystems that can be occupied by cyanobacteria with different photosynthetic pigments. It was demonstrated that picocyanobacteria with the pigments phycocyanin and phycoerythrin have different abundances related to the light

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quality. Interestingly, while the differently pigmented picocyanobacteria could not be separated phylogentically on the basis of the 16S rRNA gene, they were clearly separated into different endemic and ecologically important groups when using the genes encoding for their pigment proteins phycocyanin and phycoerythrin. For Pseudanabaena it was found that the non-coding regions separated endemic and cosmopolitan groups. More ecological data will be required to fully understand the ecological niches of Pseudanabaena in aquatic environments.

In conclusion, our results show that functional genes can be used to identify a rich microdiversity of genotypes occupying different niches in the environment. A major ingredient in our approach is that the genotypes can be linked to distinct phenotypes, for which a dedicated collection of cultured isolates is indispensible.

Chapter 8Samenvatting

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Aquatische ecosystemen kenmerken zich door een gelijkmatig milieu, zonder veel ruimtelijke variatie of heterogeniteit. Desondanks is de soortenrijkdom van het fytoplankton verbazingwekkend groot. Omdat al deze soorten wedijveren om de spaarzaam aanwezige voedingsbronnen zou je verwachten dat er maar enkele soorten over zullen blijven. Dit blijkt echter niet het geval. Het onderzoek in dit proefschrift beschrijft hoe de diversiteit van fytoplanktonsoorten wordt beïnvloed door de kleur van het licht onder water. Dit onderzoek richtte zich met name op cyanobacteriën, een soortenrijke groep van oxygene fototrofe micro-organismen uit het fytoplankton van de Oostzee.

In hoofdstuk 2 wordt het onderzoek beschreven van de verdeling van rood en groen gekleurde picocyanobacteriën in relatie tot de lichtcondities in aquatische ecosystemen. Het was al bekend dat rode en groene picocyanobacteriën in wit licht kunnen co-existeren, doordat ze elk een ander deel van het lichtspectrum gebruiken. Verder is onder groen licht de rode soort in het voordeel, terwijl onder rood licht de groene soort in het voordeel is. Op basis van dit gegeven werd een wiskundig competitiemodel ontwikkeld dat de co-existentie van rode en groene picocyanobacteriën verklaart als functie van het onderwater lichtspectrum in meren en zeeën. Om dit model te testen werden van 70 verschillende ecosystemen de samenstelling van groene en rode picocyanobacteriën gemeten. Deze ecosystemen varieerden van de open oceaan tot zeer troebele ondiepe meren. Zoals verwacht kwamen de rode picocyanobacteriën vooral in helder water voor terwijl de groene picocyanobacteriën het troebele water domineren. In aquatische systemen met een middelmatig doorzicht co-existeren de rode en groene picocyanobacteriën. Deze resultaten ondersteunen de hypothese dat niche differentiatie met betrekking tot het licht spectrum de diversiteit van het fytoplankton kan bevorderen. Dit is een nieuwe kleurrijke oplossing voor de planktonparadox van Hutchinson.

Hoofdstuk 3 beschrijft de verdeling en de diversiteit van de verschillende gepigmenteerde pico-cyanobacteriën in de Oostzee. In alle monsters van de Oostzee werd co-existentie van rode en groene picocyanobacteriën gevonden. De diversiteit en de fylogenetische relaties tussen de rode en groene picocyanobacteriën werden geanalyseerd aan de hand van drie verschillende genen: het 16S rRNA-ITS, het cpeBA operon (coderend voor het rode pigment phycoerythrine (PE)), en het cpcBA operon (coderend voor het blauwe pigment phycocyanine (PC)). De analyse van de sequenties van 209 klonen liet zien dat de picocyanobacteriën van de Oostzee een grote microdiversiteit vertoont. Sequenties van de cpcBA en cpeBA operons onthulden twee duidelijk verschillende fylogenetische taxa van picocyanobacteriën. Het ene taxon omvat voornamelijk sequenties afkomstig van PC-rijke (groene) picocyanobacteriën, terwijl het andere taxon voornamelijk sequenties omvat van PE-rijke (rode) pico-cyanobacteriën. Er werd nog een derde taxon gevonden van PE-rijke, phycourobiline (PUB) producerende picocyanobacteriën, maar in de kloonbibliotheek van de Oostzee werden geen sequenties aangetroffen die tot dit derde taxon behoren. Deze resultaten verschillen met de voorheen gepubliceerde fylogenetische stambomen die gebaseerd zijn op het 16S rRNA gen. Dus de resultaten laten zien dat Synechococcus op basis van de pigmentsamenstelling onder te verdelen is in drie verschillende afstammingslijnen die ieder een verschillende ecologische niche in het onderwater licht spectrum innemen. Deze

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verschillende taxa kunnen co-existeren als het onderwater licht spectrum de absorptie spectra van de verschillende pigmenten bestrijkt.

Het onderzoek in hoofdstuk 4 beschrijft de isolatie van 46 stammen van picocyanobacteriën uit de Oostzee. Deze stammen variëren aanzienlijk met betrekking tot hun pigmentatie. Verder vertonen ze verschillen met betrekking tot hun celvolume en hun voorkeur voor ammonium of nitraat als stikstofbron. Het grootste deel had een vrijwel identiek 16S rRNA-ITS dat overeenkomt met Synechococcus WH5701. Ondanks de vrijwel identieke 16S rRNA-ITS, konden deze stammen onderscheiden worden in verschillende clusters op basis van de sequenties van het phycocyanine (cpcBA) operon. Deze onderverdeling komt overeen met de pigmentatie van die clusters. Het merendeel van de PC-rijke stammen clusteren fylogenetisch met Synechococcus WH5701. Onverwacht was daarentegen, dat de PE-rijke stammen een niet eerder gevonden cluster vormen binnen de fylogenie van het cpcBA (phycocyanine) operon die niet nauw gerelateerd is aan andere PE-rijke clusters. Een gedetailleerde analyse van het cpcBA operon met een neighbour-net analyse liet zien dat deze PE-rijke stammen waarschijnlijk endemisch zijn voor de Oostzee. Vergelijking van de fylogenetische stambomen van het 16S rRNA-ITS, het cpcBA operon en de gecombineerde 16S rRNA-ITS en cpcBA operon sequenties suggereert dat er horizontale gentransfer heeft plaatsgevonden tussen de verschillende Synechococcus clusters. De resultaten laten zien dat microdiversiteit belangrijk is voor de Synechococcus populaties omdat de verschillende fenotypes mogelijk een verschillende ecologische rol hebben. Deze verschillende fenotypes kunnen dan ook als ecotypes worden aangemerkt.

Het onderwerp van hoofdstuk 5 is de isolatie en de diversiteit van verschillende Pseudanabaena stammen uit twee niet geografisch verbonden ecosystemen. Pseudanabaena is een weinig bestudeerde, draadvormige cyanobacterie die nauw verwant is met Limnothrix. Omdat de trichomen van Pseudanabaena erg dun zijn vallen deze cyanobacteriën meestal weinig op tijdens het bestuderen van de grotere soorten van een bloei, terwijl ze toch in grote aantallen kunnen voorkomen. Tijdens deze studie zijn 28 Pseudanabaena stammen geïsoleerd uit de Oostzee en uit de Albufera de Valencia (Spanje). De fylogenie, diversiteit, biogeografie en de evolutionaire diversificatie van de isolaten is onderzocht met behulp van fenotypische en genotypische methodes. Analyse van de in vivo absorptie spectra van de Pseudanabaena stammen liet zien dat er twee co-existerende pigment fenotypes zijn: (i) stammen die alleen PC bevatten, en (ii) stammen die zowel PC en PE bevatten. De stammen met het tweede fenotype waren allemaal in staat tot complementaire chromatische adaptatie (CCA). Door CCA kan een organisme zijn pigmentensamenstelling aanpassen aan de kleur van het licht waardoor optimaal gebruik kan maken van de beschikbare lichtenergie.

De verschillende Pseudanabaena stammen werden op meerdere loci genetisch vergeleken. De loci die werden geanalyseerd waren de 16 en 23S rRNA genen, de ribosomale transcribed spacer ITS-1, de cpcBA operon en de intergenic spacer (IGS) die tussen de cpcA en cpcB ligt. Verder werd op de aanwezigheid van het nifH gen gecontroleerd. NifH codeert voor nitrogenase reductase, één van de enzymen die het N2-fixerende enzymcomplex nitrogenase vormt. Analyse van de ITS en de cpcBA-IGS sequenties liet een grote microdiversiteit van de Pseudanabaena isolaten zien. Deze multi-locus analyse onthulde specifieke clusters voor de Oostzee en voor de

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Albufera de Valencia, terwijl er ook een cluster is met stammen uit beide ecosystemen. Deze laatste groep bestond alleen uit CCA fenotypes. De fylogenetische stambomen van de 16S en de 23S rRNA genen zijn consistent, maar de analyse van de andere loci liet zien dat er een verlies van de substructuur van de fylogenie optreedt, hetgeen een aanwijzing is voor recombinatie van de verschillende loci. De populatie genetische analyses van de cpcBA genen bij Pseudanabaena geven aan dat de diversificatie plaats heeft gevonden via ‘zuiverende selectie’.

De resultaten in dit proefschrift leiden tot de conclusie dat er onder de picocyanobacteriën van de Oostzee een grote genetische diversiteit aanwezig is die de basis vormt voor een indrukwekkende variatie in fysiologische en morfologische kenmerken. Deze diversiteit stelt cyanobacteriën in staat om de zeer verschillende ecologische niches succesvol te bezetten. In dit proefschrift is gekeken naar de kleur van het licht als een factor die niches in pelagische ecosystemen definieert. De verschillende fotosynthese pigmenten en de adaptatie ten aanzien van de kleur van het licht stellen cyanobacteriën in staat deze niches te bezetten. Als gevolg daarvan hebben picocyanobacteriën met de pigmenten PC en PE verschillende verdelingen in ecosystemen, gerelateerd aan de kleur van het licht.

De fylogenie van het 16S rRNA gen brengt deze verschillend gekleurde picocyanobacteriën samen in één groep of cluster. Dit doet echter geen recht aan hun verschillende functie in het ecosysteem. De fylogenie van de functionele genen voor de PE en PC eiwitten scheiden de cyanobacteriën wel in verschillende functionele clusters. De niet-coderende sequenties van Pseudanabaena konden gebruikt worden voor het onderscheid tussen endemische en kosmopolitische clusters. Tot nu toe is de hoeveelheid ecologisch relevante data te beperkt om de ecologische rol en de dispersie capaciteiten van de verschillende Pseudanabaena geotypes volledig te begrijpen, en we weten nog niet precies wat de basis is van hun succes in het bijzonder in zoetwater ecosystemen.

Samenvattend laten de resultaten van dit proefschrift zien dat snel evoluerende genen in staat zijn om verschillende ecotypes te vormen die gekoppeld zijn aan een niche differentiërende factor, zoals de kleur van licht. Een cruciale stap in dit onderzoek was dat de DNA sequenties van functionele genen werden onderzocht (dus niet alleen het 16S rRNA), en dat deze sequenties gerelateerd konden worden aan de relevante fenotypische eigenschappen van opgekweekte stammen. Het analyseren van DNA dat rechtstreeks uit het milieu is geëxtraheerd, kan alleen vragen beantwoorden over de ecologie en de diversiteit van micro-organismen, wanneer deze DNA sequenties gekoppeld kunnen worden aan organismen waarvan de genetische, fysiologische en morfologische eigenschappen bekend zijn. In het algemeen betekent dit dat micro-organismen geïsoleerd moeten worden en in het laboratorium gekweekt en onderzocht op hun eigenschappen.

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Chapter 10Dankwoord / Acknowledgements

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Na ruim vijf jaar in het Zeeuwse land is het mij gelukt om het eindpunt te halen van mijn AIO-schap met dit proefschrift. Bij het schrijven van dit dankwoord weet ik dat ik Zeeland ga verlaten. Dit doe ik met gemengde gevoelens. Ik ben treurig dat ik naast mijn collega’s ook het wonderschone uitzicht van het CEME op de Oosterschelde zal moeten missen. Elke dag was het een genot om te zien hoe de Oosterschelde er bij lag. Soms grijs en grauw van de dichte nevel en andermaal glashelder met prachtige wolkenluchten zover het oog rijkte. Het getij gaf dit alles een dynamiek die dit uitzicht compleet maakte. Op een zomerse vrijdag lunchen aan de waterkant, mooier kan bijna niet.

Onder het genot van dit prachtige uitzicht heb ik in de afgelopen jaren gewerkt aan mijn proefschrift. Dit heb ik niet alleen kunnen doen. Zonder de hulp en inspiratie van mijn collega’s, studenten, vrienden en familie, was ik nooit op dit punt aanbeland.

Bij de start van mijn promotie onderzoek wist ik niets van cyanobacteriën en al helemaal niet hoe ik ze moest kweken. Dat ik de mogelijkheid kreeg om hier toch mee aan de slag te gaan was onverwacht. Achteraf kan ik alleen maar zeggen dat ik er blij mee ben. Lucas, dankzij de ruimte die je me gaf, heb ik de cyanobacteriën langzaam leren kennen en ben ik ze zelfs gaan bewonderen vanwege hun prachtige kleuren en vormen. Al werkend in het CEME heb ik de mogelijkheid gekregen om te ontdekken hoe divers micro-organismen zijn en hoe belangrijk ze zijn voor het functioneren van deze planeet. Dit is een les die ik niet snel zal vergeten. Daarnaast wil ik je danken voor het geduld en de mogelijkheid om op het NIOO te kunnen werken lang nadat mijn contract was afgerond. Dat had ik nodig.

Wat ik ook niet zal vergeten is dat het goed is om input te krijgen van collega’s die op een andere manier tegen de zaken aankijken. Jef en Maayke jullie input voor dit proefschrift was heel belangrijk. Zonder de ideeën over het verband tussen lichtkleur en de pigmenten van de cyanobacteriën hadden de hoofdstukken over diversiteit binnen de verschillende soorten organismen niet zulke leuke resultaten op geleverd. Jef, je kritische vragen over mijn resultaten en mijn manuscripten in wording, motiveerde mij vaak ook nog eens extra naar mijn resultaten te kijken. Maayke, de discussies over ons onderzoek waren heel inspirerend voor mij en gaven mij veel stof tot nadenken. Ik ben dan ook blij dat we elkaar hebben leren kennen tijdens de expeditie naar de Baltische zee en dat we daarna geregeld contact bleven houden. Ik hoop dan ook dat we dat nog lang gedachten uit kunnen wisselen, niet alleen over werk, maar ook over fotografie, reizen en relaties en alles wat er op ons pad komt.

Silvia, it was a wonderful experience to have you in our group at the NIOO and I consider myself very lucky to have you as a colleague working closely with me on a big part of my thesis. Your enthusiasm and ideas about diversity of micro-organisms and on photography inspired me and gave me the insights and energy that I needed to finish this thesis.

Veronique you were my first roommate at the NIOO and I have been very happy with that. The discussions with you about science, politics, our research projects, molecular biology and of course everyday life were great. I learned a lot on how things were organized at our institute from you.

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Hier wil ik ook mijn andere collega’s van MM bedanken die invloed hebben gehad op mijn werk. Jacco, het was prettig dat ik altijd je kamer kon binnenlopen om vragen af te vuren, over fytoplankton, fotosynthese of andere zaken. Ted, je kritische blik en discussies over fylogenie en populatie biologie waren voor mij broodnodig en zijn belangrijk geweest om te begrijpen hoe genen zich gedragen in de enorme populaties van bacterien. Henk, ik leerde je kennen in Helsinki en was dan ook erg blij met je komst naar Yerseke. Niet alleen voor mijzelf, maar ook voor MM, want een echte moleculair bioloog was nodig in de groep. Ik hoop dan ook dat je tijd op het NIOO succesvol gaat zijn.

Marije, Ute en Anita, jullie hulp bij mijn werk was onmisbaar of het nu ging over het kweken van bacterien, sequencen of flow-cytometry. Zonder jullie ervaring en kunde had ik een aantal zaken niet uitgeprobeerd die soms succesvol waren en soms ook niet, daarvoor mijn dank.

Nicole, ik wil je hier bedanken voor alle keren dat ik mee mocht rijden met de auto wanneer het regende, ondanks mijn foute opmerkingen eerder die dag. Daarnaast stond je altijd klaar met een luisterend oor om mijn frustraties aan te horen, of mij te wijzen op de fouten in mijn denken. Die duidelijkheid wordt zeer gewaardeerd.

Verder wil ik alle andere collega’s van MM en het CEME bedanken voor hun gezelligheid aan de koffietafel, hun collegialiteit en hulpvaardigheid in het lab. Een prettige en open werksfeer is een eerste vereiste om onderzoek te doen en op het NIOO is dit mogelijk.

Ik wil hier ook mijn beide studenten bedanken voor hun werk en inzet. Lianne, je tijd op het NIOO was maar kort, maar voor mij was het een goede ervaring om je te begeleiden. Daphne, je hebt heel veel voor mijn gecloned, gesequenced en PCR’s gedraaid, zodat we al mijn isolaten konden analyseren. Zonder die precieze hulp van jou had dit proefschrift nog een stuk langer geduurd.

Naast het werk op het NIOO moesten er natuurlijk ook spelletjes gespeeld worden. Iris, jij veroorzaakte een golf van jarenlange kolonisten op het NIOO, die werd ingeluid samen met Stef en Mark. Samen met jullie, en de laatste jaren met Henk, Nicole, Ellen, Nienke en Paul werd er weer heel wat afgetroggeld, gemanipuleerd en af en toe op onschuldige wijze valsgespeeld. Dit bezorgde mij veel plezierige uurtjes en af en toe ook heel intelligente momenten en inzichten. Tellen tot 8 zal altijd wel een probleem blijven voor mij.

Edward, Chris, Bas, Hans-Peter, Cas, Gerlach, mijn vrienden uit Utrecht en andere verre oorden. Jullie positieve en negatieve ervaringen met proefschriften, relaties of de rest van het leven was voor mij een inspiratie om vooral niet op te geven, maar om door te gaan. De avondjes stappen, de spelletjes catan (met vooral veel messen), de feestjes, en de stedentripjes door de loop van de jaren heen, brachten mij de nodige afleiding en relativering. Ik verheug me dan ook weer op een volgend tripje naar een leuke stad.

Mabula en Stefanie Ik weet niet of het altijd duidelijk was voor jullie wat ik deed de afgelopen jaren in Zeeland. Maar op de momenten dat ik jullie zag werd ik er vaak aan herrinerd dat er meer is dan mijn kleine wereldje in de wetenschap. Stefanie, je levensvisie staat vaak haaks op mijn ideeen, maar dat maakt het voor mij extra boeiend om te horen hoe jij denkt over de

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wereld. Het geeft mij veel vragen. Mabula, je kalme en scherpe blik kunnen mij altijd verbazen en dwingen mij omdat telkens weer te testen wanneer ik je zie.

Hilde en Gerard, met behulp van jullie steun en geloof in mij kan ik mijn eigen weg door dit leven uitstippelen zonder gehinderd te worden door beperkingen. Niets is onmogelijk en een helpende hand is altijd daar wanneer ik het nodig heb. Deze vrijheid geeft mij de kans om te ontdekken en te zijn wie ik ben.

Anke, meeting you on the way to Pau was the best thing that happened during the years of my thesis. You made me laugh with all your sharp remarks, your wit and your intelligence. During the last two years that kept me going to the end of this thesis with a smile on my face. I do hope that I can continue to make you smile as well and for as long as possible.

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