THE IMPACT OF TEMPERATURE ON MARINE PHYTOPLANKTON … · Temperature at the sea-ice-water...

Supporting information

THE IMPACT OF TEMPERATURE ON MARINE PHYTOPLANKTON

METABOLISM AND RESOURCE ALLOCATION

Andrew Toseland1, Stuart Daines2, James Clark2, Amy Kirkham3, Jan Strauss3,

Christiane Uhlig4, Timothy M. Lenton2, Klaus Valentin4, Gareth Pearson5, Vincent

Moulton1, Thomas Mock3

Author affiliations:

1 School of Computing Sciences, University of East Anglia, Norwich Research Park,

Norwich, UK

2 College of Life and Environmental Sciences, University of Exeter, UK

3 School of Environmental Sciences, University of East Anglia, Norwich Research Park,

Norwich, UK

4 Alfred-Wegener Institute for Polar and Marine Research, Bremerhaven, Germany

5 Centre of Marine Sciences, University of the Algarve, Portugal

The impact of temperature on marine phytoplankton resource allocation and metabolism

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NCLIMATE1989

NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange 1

© 2013 Macmillan Publishers Limited. All rights reserved.

http://www.nature.com/doifinder/10.1038/nclimate1989

Contents

Supplementary materials & methods for: Sampling, Molecular Biology & Bioinformatics

Supplementary tables: S1-S9

Supplementary figures: S1-S12

Supplementary materials & methods for: Modelling

Supplementary tables: S10-S12*

Supplementary figures: S13-S14*

*Tables and figures for the modelling section of supporting information are embedded within the

main text.

2 NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange

SUPPLEMENTARY INFORMATION DOI: 10.1038/NCLIMATE1989



Supplementary Materials and Methods (Sampling, Molecular Biology & Bioinformatics)

1 Sampling

1.1 EPAC (Equatorial Pacific)

Samples were taken on two stations (EPAC1: 0°, 155°W; EPAC2: 0°, 140°W) (Fig. S1) during a

cruise to the equatorial Pacific Ocean from 15th to 2nd of October 2006 onboard the RV ‘Kilo

Moana’.

1.2 NPAC (North-East Pacific, Puget Sound)

Samples were taken on one station (NPAC: 47°55.19 N; 122°20'38 W) (Fig. S1) during a Puget

Sound cruise on the 15th of August 2007 onboard the ‘Sorcerer’ (Craig Venter Institute, US). Water

for RNA samples was pumped from about 8m depth onboard with a hose and peristaltic pump

(Table S1). Cells were immediately filtered onto autoclaved Nucleopore filters (25mm) with a pore

size of 2μm. Not more than 500ml were filtered at a time in order to keep the filtration time <5

minutes per filter. Filters were subsequently flash frozen in liquid nitrogen and stored in the

laboratory at -80°C. Phytoplankton were collected and concentrated by net tows from about 10m

depth to the surface, using 0.25 m diameter nets with a mesh size of 10 μm (Research Nets Inc.

Redmond, WA, USA).

1.3 ANT (Southern Ocean, Weddell Sea)

Samples were taken on two stations (ANT1: 65°06.11 S, 57°23.55 W; ANT2: 60°07.11 S, 47°54.55

W) (Fig. S1) during the WWOS (Winter Weddell Outflow Study) cruise in Austral summer 2006

with the German Icebreaker ‘Polarstern’. Samples on ANT1 were obtained by icecore drilling and

collecting microorganism communities from the lowermost cm (ice-waterinterface) of the ice core

(Table S1). For RNA extraction ice samples were melted in or washed with prefiltered (0.2μm) sea





water or brine and cells were subsequently filtered onto Isopore filters (Millipore) (25mm) with a

pore size of 1.2μm. Filters were subsequently flash frozen and stored in liquid nitrogen. Samples on

ANT2 were obtained by fishing ice floes and collecting microorganisms from the ice water‐

interface as done on ANT1.

1.4 ARC (Arctic) & NATL (North Atlantic)

Phytoplankton community samples were taken in June 2009 on board the RV “Jan Mayan”. Water

samples at the DCM were taken directly from the CTD rosette (12.5 L Niskin bottles) in waters

characterised as Arctic (June 20 SW Spitsbergen at 76' 36”N; 18' 11”E, temperature -1ºC at 35 m)

and Atlantic influenced (June 16 at the Polar front south of Bear Island at 73' 55”N; 18' 46”E,

temperature +2.1ºC at 50 m). Cells were collected by filtration on 5 μm pore-etched polycarbonate

filters, flash–frozen in liquid nitrogen, and stored in a cryoshipper for transport to the laboratory.

2 Temperature, nutrients, chlorophyll a

2.1 EPAC (Equatorial Pacific)

Physical properties (e.g. temperature) were measured with a Seabird 911+ conductivity,

temperature, and depth (CTD) profiler (1). Nutrients samples were frozen at -20°C until onshore

analysis. Within 2 months after the cruise, the dissolved inorganic N was determined using an

Astoria Autoanalyzer. [Si(OH)4] in ambient sea water was measured on board

spectrophotometrically (2). Chl a was determined onboard ship by extraction with 90% acetone at

-20°C for 24h and measured by in vitro fluorometry on a Turner Designs Trilogy fluorometer using

the acidification method (3).


Temperature of freshly collected water was measured onboard using a mercury thermometer. Water





for nutrient measurements was collected in sterile 15mL Falcon tubes and subsequently frozen at

-20°C. Nutrient analysis ([Si(OH)4], NO3, PO4) was conducted using a Technicon Autoanalyzer

Model AAII within 3 months after sampling (Table S4). The Hansville buoy (ORCA) at

47°54.44”N and 122°37.62W was used to obtain oceanographic profiles of T, S, density, O2, and in

situ fluorescence close to the sampling site (Fig. S1) We retrieved data for 7 profiles from the

surface to 20m depth on the 15th of August 2007 (Fig. S8). Four profiles were measured before

sampling, one at the time of sampling and 2 afterwards. These data allowed to reconstructing the

development of a phytoplankton bloom close to the sampling site. We acknowledge Al Devol and

Wendi Ruef for making these data available to us.

2.3 ANT (Southern Ocean, Weddell Sea)

Temperature at the sea-ice-water interphase was measured with a Testo 720 thermometer with PT-

100 sensor. Nutrients ([Si(OH)4], NO3, PO4) were measure on melted sea ice onboard ‘Polarstern’

using an Autoanalyser. Chl a was determined onboard ship by extraction with 90% acetone at -20°C

for 24h and measured by in vitro fluorometry on a Turner Designs Trilogy fluorometer using the

acidification method (3) (Table S4).

3 Independent taxonomic identification of dominant eukaryotic phytoplankton


Cells from net tow samples were fixed with 1% Lugol (final concentration) and counted with an

Olympus BX43 microscope with DIC optics at ×100 and x400 magnification. A magnification of

x1000 (oil immersion) was used for species identification (Table S5).

4 Molecular biology





4.1 Metatranscriptome (RNA, cDNA, sequencing)

4.1.1 ANT (Antarctic), EPAC (Equatorial Pacific) & NPAC (North Pacific)

Several samples per station and ecosystem (EPAC, NPAC, ANT) were filtered onto 2μ pore size

filters and subsequently flush frozen in liquid N and stored at -80˚C. RNA extraction was performed

with the ToTally RNA extraction kit (Ambion) according to the manufacturers recommendation.

Eukaryotic mRNA was extracted with the Oligotex mRNA purification kit (Qiagen). The same kit

was used to do an additional purification with the purified mRNA from the first time to reduce the

contamination by rRNA and bacterial mRNA. Due to a very limited amount of double purified

eukaryotic mRNA, we pooled all samples from each ecosystem (EPAC, NPAC, ANT). CDNA

synthesis on the pooled samples was conducted with the SuperSmart PCR cDNA kit (Clontech)

according to manufacturers recommendations. Libraries for next generation sequencing were

constructed according to protocols for Roche 454 GS-FLX and GS-Titanium sequencing. GS-FLX

sequencing was done at the NERC sequencing facility in Liverpool (UK) and GS-Titanium

sequencing was done at Roche 454 (US).

4.1.2 ARC (Arctic) & NATL (North Atlantic)

Extraction of total RNA from replicate filters was performed following standard protocols and a

commercial kit (RNAeasy, Qiagen). Synthesis of full-length double-stranded cDNA (ds-cDNA) was

performed from 250 ng of total RNA of each sample (SMARTer PCR cDNA Synthesis Kit;

Clontech) according to the manufacturer's instructions, allowing synthesis of full-length transcripts

while maintaining the gene representation of unamplified samples. Full-length single-stranded DNA

templates were then amplified by long-distance PCR using the Advantage 2 PCR Kit (Clontech).

Replicate PCR reactions were performed for each library in order to obtain the amount required for

sequencing (3 – 5 μg), and subsequently pooled and purified using the MiniElute PCR Purification

kit (Qiagen). The cDNA libraries were quantified using NanoDrop (ThermoScientific), and the





quality of final samples was verified using agarose gel electrophoresis. Libraries were sequenced by

a commercial service provider (BioCant, Portugal) using 454 FLX Titanium chemistry.

4.2 Fragilariopsis cylindrus culture experiments and ribosomal gene expression

4.2.1 Culture conditions

Fragilariopsis cylindrus (Grunow) Krieger CCMP1102 was obtained from the Provasoli-Guillard

National Centre for Marine Algae and Microbiota (NCMA, https://ncma.bigelow.org/, West

Boothbay Harbor, USA, formerly CCMP). All cultures were grown and maintained in filtersterilised

(0.2 μm pore size) Aquil medium (4) at 4°C under continuous illumination at a photon flux density

of 35 μmol photons m-2 s-1. Cultures of F. cylindrus were handled under strict sterile conditions

and potential bacterial contamination was eliminated as stock cultures were subjected to a multi-

antibiotic treatment with Ampicillin (50 μg mL-1), Gentamycin (1 μg mL-1), Streptomycin (25 μg

mL-1), Chloramphenicol (1 μg mL-1) and Ciprofloxacin (10 μg mL-1) (5). Epifluorescence

microscopy was used to confirm axenic cultures using 4',6-diamidino-2-phenylindole (DAPI)

fluorescent nucleic acid staining before the beginning of the experiment. During exponential

growth, stock cultures were used to inoculate triplicates of 2L experimental batch cultures for

optimal (+4°C), high (+10°C) and low (-2°C) temperature treatments. Bubbling with sterile filtered

air and shaking of the culture bottles ensured sufficient CO2 supply and mixing during experimental

treatments. Experimental cultures were grown to mid-exponential phase (approximately 500,000

cells mL-1) at +4°C before temperatures were amended to the final experimental temperature

(+10°C to -2°C). Subsamples were taken on a daily basis throughout the experiment to determine

the maximum quantum yield of photosystem II (Fv/Fm) by pulse-amplitude-modulated fluorometry

(Phyto-PAM fluorometer, Walz GmbH, Effeltrich, Germany) and cell counts (Multisizer 3 particle

counter, Beckman Coulter, Brea, USA).





4.2.2 RNA extraction and purification.

Cells were harvested on the third day after the cultures reached the experimental temperatures using

1.2 μm membrane filters (Isopore Membrane, Millipore, MA, USA). The volume of F. cylindrus

culture per filter sample was recorded to calculate the number of cells per filter sample. Total RNA

was extracted using TRI Reagent (Sigma-Aldrich, St. Louis, USA) followed by DNase I (Qiagen,

Hilden, Germany) treatment (1h, 37°C) and purification using RNeasy MinElute Cleanup Kit

(Qiagen, Hilden, Germany). Purity of RNA was checked on a NanoDrop (Thermo Fisher Scientific,

Waltham, USA) and integrity using denaturing 2% formaldehyde gels. Concentrations after RNA

cleanup were determined in duplicate readings using a NanoDrop.

4.2.3 Total RNA concentration per Fragilariopsis cylindrus cell as a function of growth

temperature.

The total RNA yield obtained for each filter sample was used to calculate the RNA concentration

per cell. Therefore the total RNA yield per filter was divided by the number of cells in each filter

sample. The obtained total RNA concentration per cell was plotted as a function of growth

temperature and used for a linear regression analysis.

4.2.4 Reverse transcription, primer design, and Q-PCR conditions.

First strand synthesis was performed using Superscript II reverse transcriptase (Invitrogen,

Carlsbad, USA) utilising Anchored Oligo(dT)20 Primer (Invitrogen, Carlsbad, USA). Reverse

transcription (RT) of 500 ng of total RNA was carried out according to manufacturer’s

recommendations in 20μL reactions at 42°C for 50 minutes, followed by inactivation at 70°C for 15

minutes. Immediately prior to transcription all reaction mix was spiked with artificial RNA of the

major allergen (MA) gene of the butterfly Pieris rapae (cabbage white butterfly, Lepidoptera:

Pieridae) to verify efficiency of RT reactions and to provide an exogenous control. The MA gene





provides an ideal control, because few insects are present in the marine environment and P. rapae is

considered alien to polar or marine diatoms. The control gene MA was constantly detected in all

samples at a cycle threshold of 36.14 (±0.17, n=12) indicating consistent efficiency of the RT

reaction. As a control for DNA contamination, RNA was pooled from each biological replicate and

first strand synthesis reaction mix was added omitting reverse transcriptase. Oligonucleotides (Table

S8) were designed towards the 3’ end of the gene of interest using the webbased RealTimeDesign

Software (available at http://www.biosearchtech.com/realtimedesign, Biosearch Technologies,

Novato, USA). BLAST searches of the primer sequences against the F. cylindrus genome sequence

(http://genome.jgi-psf.org/Fracy1/Fracy1.home.html) were performed to check for target specificity

and if necessary primer sequences were modified manually. Oligonucleotides were assessed for

melting temperature, hairpins, and primer dimers using the webbased tool OligoAnalyzer 3.1

(available at http://eu.idtdna.com/analyzer/Applications/OligoAnalyzer; Integrated DNA

Technologies, Coralville, USA) and synthesised by Eurofins MWG Operon (Ebersberg, Germany).

For second strand amplification, 5 μL of a 10-fold diluted RT reaction mix was supplemented with

20 μL 2× SensiMix SYBR Green NoROX Master Mix (Bioline, London, UK). Each primer was

added at a concentration of 200 nM. Amplifications were performed in white 96-well plates on a

CFX96 Real Time System (Bio-Rad, Hercules, USA) using the following conditions: initial

denaturation 95°C, 10 minutes, followed by 40 amplification and quantification cycles of 15

seconds at 95°C, 15 seconds at 59°C, 10 seconds at 72°C. Finally, a melting curve analysis (65°C to

95°C, increments of 0.5 °C, dwelling time 5 seconds) was carried out to check for primer dimers

and non-specific amplification.

4.2.5 qPCR data analysis.

The cycle thresholds were automatically determined using the CFX Manager Software Version 1.1

(Bio-Rad, Hercules, USA). The REST-MCS© software (available at

http://rest.genequantificationinfo/) was used to test the expression of target genes under both





experimental conditions normalised by a reference gene index containing the endogenous and

exogenous controls TBP, RNAP II and MA and significances were tested by a Pair Wise Fixed

Reallocation Randomisation Test using 2000 iterations.

4.3 Biochemical studies

4.3.1 Diatom Cultures

Diatom cultures (Thalassiosira pseudonana CCMP1335 and Fragilariopsis cylindrus CCMP1102)

were grown in artificial seawater (NEPC) under 24 hours light at 100 μE. Photosynthetic health was

estimated using phytoPAM ED (WALZ) spectrometer. Healthy cultures (fv/fm >0.6) were

transferred to incubators (Sanyo) at the experimental temperatures and allowed to acclimatise for 24

hours before 25000 cells/ml was diluted into new media that was pre-warmed or cooled to the

experimental temperature. The cultures’ growth was monitored daily using a coulter counter

(Beckman).

4.3.2 Western blots

100 ml of mid-exponential phase culture was pelleted by centrifugation and total proteins were

extracted by adding 50 μl of lysis buffer (50 mM Tris pH 6.8, 2% SDS) to the cell pellet. Cell

lysates were incubated at room temperature for 30 min before separation from cellular debris by

centrifuging at 10,000 g at 4 °C for 30 minutes. 35 μg protein extracts were resolved on 12.5 %

SDS-PAGE gels and transferred to nitrocellulose transfer membranes using criterion blotter

(BioRad). Loading was checked by incubating the membrane with the protein stain Ponceau S for

20 minutes at room temperature. The S14 ribosomal protein was hybridised with 1:1000 dilution of

S14 antibody (AS09 477, Agrisera), for 1 hour at room temperature, followed by a 1:10,000 dilution

of horseradish peroxidase (HRP)-conjugated goat anti-rabbit secondary antibody (Promega) in

1×PBS, 1% milk, 0.1% Tween 20. Signals were visualised using the enchanced chemiluminescence





(ECL) kit (Amersham Biosciences) and Lasimager 2000 software (Fuji).

4.3.3 Translation efficiency experiment

A transgenic T. pseudonana strain expressing a novel gene fused to eGFP on the inducible nitrate

reductase (NR) promotor was grown in NEPC containing 550 μM NH4Cl as the sole nitrogen

source, repressing the expression of the transgene. Mid-exponential phase cells were transferred to

nitrogen-free NEPC for one generation time (approx. 12h at 20°C, 24h at 11°C and 48h at 4°C)

before 550 μM NaNO3 was added to activate the NR cassette. Triplicate cultures were monitored

using a FACSCalibur (Beckton Dickinson) Flow Cytometer. Cells were discriminated by plastid red

autofluorescence versus the eGFP green fluorescence arising when the eGFP was translated.

Populations were gated and percentage of total population calculated. The lag phase was calculated

as the first time point at which the percentage of cells in the eGFP gate was found to be significantly

higher than the T0 measurement (t-test, p<0.05). Translation efficiency (m) was calculated as the

slope of the curve after the lag phase.

5 Bioinformatics

5.1 Quality filtering

Quality clipping was performed as in Marchetti et al. (6) using a single base sliding window each

sequence was trimmed from 3' to 5' until a base is reached with a Phred quality score of >=14 is

met. To identify potential sequencing artifacts, all sequences were clustered with CD-HIT-est (7) at

100% requiring 100% coverage of both sequences. Only the cluster representatives were retained,

cluster members (exact duplicates) were deemed potential artifacts omitted and from further

processing. The 5' primer (AAGCAGTGGTATCAACGCAGAGT) was detected using PatMan (8)

allowing up to 4 mismatches and 2 gaps, match coordinates from PatMan were used to trim primer

regions using a custom BioPerl script. The 3', 17 base oligo-dt primer was identified using Dust





(word size 2, complexity value of 50) to get the coordinates of low complexity regions. Each

identified region was examined and, if it was of an appropriate length (>= 15 bases) and composed

of >=75% adenine or >=75% thymine the region was trimmed out. Low complexity sequences were

identified using Dust. Using default parameters, sequences were run through Dust and low

complexity regions masked with Xs. The proportion of masked bases for each sequence was

calculated and sequences comprising of >= 70% low complexity region were filtered out. Finally

any sequences less than 50bp in length were removed.

Despite specifically targeting eukaryotic mRNA by attaching oligo-dt primers to the poly-A region

of transcripts, some non mRNA may be present in the samples. John et al. (9) reported that ~2% of

sequences of a small scale eukaryotic metatranscriptome held significant similarity to ribosomal rna

(rRNA). In order to detect putative rRNA sequences we performed blastn (10) searches (Default

settings, no complexity filtering) against both the large and small subunit databases of the Silva

ribosomal database (11). Sequences returning hits with bit scores 50 were deemed putative rRNA⩾

and excluded from further analysis. The final processing stage was to cluster sequence sets to

remove redundancy and speed up homology searches. Each sequence set was clustered using CD-

HI-est at >=95% overall identity and requiring >=50% coverage of the representative sequence. A

lookup table of cluster details (Cluster representative ID, Cluster size, Cluster member Ids) was

created in order to scale the annotation results of cluster representatives accordingly.

5.2 Exploring data set composition through sequence clustering

Sequences from all 5 data sets had an environment specific prefix added to their accession and were

pooled together. All sequences were then translated into their longest open reading frames

(minimum length >= 10 amino acids) and clustered with CD-HIT (>=60% overall identity, >=50%

coverage of the representative sequence). Using a custom Perl script, the resulting clusters were

examined individually and the sequence ids of all cluster members were appended to a list for each

environment involved in that cluster. The resulting lists were used to create the 5 group venn





diagram in R using the 'venn' function of the gplots package. Lists of sequence ids for each of the

31 sections of the venn diagram were produced using R set operators for downstream analysis.

5.3 PhymmBL taxonomic affiliations

The taxonomic composition of the samples were determined using PhymmBL (12), a hybrid

classifier which combines BLAST alignments with nucleotide composition based interpolated

markov models. By default, PhymmBL uses bacterial and archaeal genomes from NCBI GenBank

(13) as a reference. It is however, extensible and has been successfully applied to eukaryotic data

(14). We created a representative set of 44 eukaryote organisms using genomes and ESTs covering

the major eukaryote groups but with a focus on algal species for this analysis (See Table S3 for list

of organisms used and taxonomic labels). Genome sequences were downloaded from NCBI

GenBank and JGI (with 4 exceptions: Cyanidioschyzon merolae from Cyanidioschyzon merolae

Genome Project http://merolae.biol.s.u-tokyo.ac.jp/download; Strongylocentrotus purpuratus from

Sea Urchin Genome Project http://www.hgsc.bcm.tmc.edu/projectspecies-o- Strongylocentrotus;

Danio rerio from UCSC http://genome.ucsc.edu/cgi-bin/hgGateway?db=danRer5; and Homo

sapiens from Genome Reference Consortium

http://hgdownload.cse.ucsc.edu/goldenPath/hg19/chromosomes/). EST sequences were downloaded

from NCBI-dbEST and clustered with CD-HIT-est at 95% similarity to ensure non-redundancy of

sequences. Taxonomic classifications for the PhymmBL configuration file were taken from the

NCBI taxonomy (15) and AlgaeBase (16). The sequence files and taxonomic details were added to

PhymmBL in batch mode and IMMs created for each new organism. PhymmBL results were

filtered with a confidence score cutoff of 0.9 at the phylum level⩾ . Subsets of sequences matching

to the phyla Bacillariophyta and Dinoflagellata were extracted for further analysis.

5.4 Pfam

Cluster representative sequences were translated into all 6 reading frames (Min length 10aa) and





homology searches against the Pfam protein database (17) performed using pfam_scan.pl (Pfam-A

only, default gathering thresholds used). Results filtered using custom Perl script to detect best

match(es), remove conflicting matches across different reading frames and scale results to cluster

size.

5.5 GO Term Enrichment & Term Clouds

All detected Pfam domains were mapped to their corresponding GO term(s) (18) using a custom

Perl script and the mapping file Pfam2Go (http://www.geneontology.org/external2go/pfam2go).

Then, for each possible pair of environments we performed a Fisher's exact test on each GO term.

Enriched GO terms were identified using a Bonferroni corrected p-value <0.001 and used to create

term clouds (One for each environment in the pairwise comparison). Lists of enriched GO terms

were created - one for each environment, with the frequency of a GO term in the list determined by

the absolute difference in the normalised abundance of the term between the two environments. The

term clouds were created with Worditout.com using direct colour blending from blue (low

frequency) to red (high frequency). See Fig. S12 for example term cloud.

5.6 KEGG

KEGG pathways (19) for cluster representative sequences were identified using the KEGG/KAAS

web-server (20) (Using single-directional best hit EST mode against a eukaryote representative

gene set, bit-score cut-off >=40). The resulting KO (Kegg Orthology) list were scaled by cluster

size and filtered using MinPath (21) to get a minimal set of pathways. Hits for KEGG pathways

K000230: Purine metabolism and K000240: Pyrimidine metabolism were summed and plotted

against temperature for each environment

5.7 CCA

Canonical Correspondance Analysis was performed using the VEGAN package in R. We treated the





transpose of the normalised Pfam count tables as our species data and created a second table of

environmental factors: temperature, salinity, latitude, longitude, nutrient levels etc. Where

environmental data was unavailable we used the World Ocean Atlas

(http://www.nodc.noaa.gov/OC5/SELECT/woaselect/woaselect.html for nutrient levels, taking the

annual surface mean measurements). For light levels we used the Pangaea information system

website (htpp://www.pangaea.de) to find in-situ PAR readings over a depth gradient for

environments analogous to our samples. By plotting PAR against depth and fitting an exponential

regression line we could extract the equation for the PAR – depth relationship and plug in our depth

measurement to get an estimated PAR. The data sets used were:

ANT: Nicolaus, M et al. (2012): Downward spectral solar irradiance as measured in different depths

under sea ice (transmitted irradiance) at sea ice station PS78/267-1.

doi:10.1594/PANGAEA.786857,

EPAC: Eldin, Gerard; Rodier, Martine; Dupouy, D (2004): Physical oceanography at CTD station

FLUPAC_119. doi:10.1594/PANGAEA.186766

NATL & ARC: Fosså, Jan Helge; Kutti, Tina; Bergstad, Odd Aksel; Knutsen, Tor; Svellingen,

Ingvald; Wangensten, Jarle; Johannessen, Reidar; Steinsland, Asgeir (2011): Physical oceanography

during R/V H. Mosby cruise IMR-2009615. Institute of Marine Research, Bergen,

doi:10.1594/PANGAEA.756308

NPAC: Whitney, Frank (2002): Physical oceanography at station IOS_97-11_CTD045.

doi:10.1594/PANGAEA.79563

In the case of ANT and EPAC where there were multiple samples we took the mean values. All

environmental data was log2 transformed and an offset added to temperature values to make them

positive. To highlight specific proteins for nitrate reductases, fucoxanthin chlorophyll binding

proteins (FCPs), ribosomal proteins and silicon transporters we took one gene of each type from 3





diatoms: Thalassiosira pseudonana, Phaeodactylum tricornutum and Cylindrotheca fusiformis

(From NCBI RefSeq/GenBank see Table S9). Each gene was compared to Pfam-A (gathering

threshold cutoff) and the detected domains used to represent that gene. One hundred percent of the

total inertia (1.563) was explained by the set of environmental constraints (temperature, light,

nitrate and phosphate). The four CCA dimensions accounted for 0.58310 (37.31%), 0.49761

(31.84%), 0.41433 (26.50864) and 0.06816 (4.36%) respectively.

Correlations between environmental factors and the normalised abundance of hits to the GO term

for translation was performed using the Pairs function in R.

5.8 Heatmaps

All heatmaps were created using the Heatmap.2 function in R. For the taxonomy heatmap, only

phymmBL classified algal groups were used. The percentage of hits to each phyla were read in as a

table and used to create two correlation matrices (One for the table and one for it's transpose).

Distances were measured as 1 – Pearson correlation coefficient between rows/columns and the

matrices were used to create row and column dendrograms (complete linkage clustering). The

abundance data was finally scaled and centred by column. For GO terms the heatmap was created

as above, but only biological process GO terms over an abundance cutoff (>=0.5% of hits in at least

one data set).

5.9 Rarefaction

Rarefraction curves were produced with the online Rarefaction tool

(http://www.biology.ualberta.ca/jbrzusto/rarefact.php#Calculator) using Chao’s estimator for

species richness. A list of raw totals for each detected Pfam domain were entered (plus the number

of sequences providing no hits) and sampled at 50,000 sequence intervals..

5.10 Thalassiosira pseudonana transcriptome





As ~60% of sequences in the NPAC sample had taxonomic affiliations with T. pseudonana we

chose this data set to perform a comparison with expression data from a T. pseudonana genomewide

microarray experiment (22). First a spreadsheeet was compiled of differentially expressed (log2

fold change >= 1, p-value <0.05) T. pseudonana genes (Table S6) and expression values under low

temperature (4°C), and silicate, nitrate, iron and CO2 limitation. Columns were added to each gene

for GO, KEGG, KOG (annotations taken from JGI

http://genome.jgipsf.org/Thaps3/Thaps3.download.ftp.html)) and Pfam annotations (performed

ourselves, search against Pfam-A, gathering threshold cutoff). Sequences from the NPAC sample

classified as Bacillariophyta (PhymmBL phylum confidence score >= 0.9) were extracted and

BLASTed against the JGI T. pseudonana gene models (BLASTx, e-value <= 1e-5, using soft

masking, requiring >=50% coverage of the query and >= 75% overall identity and taking the single

best hit). Total matches to each differentially expressed gene were added to the table.

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Supplementary Tables

Table S1: Geographical locations, method of sampling and water depth. ANT 1, 2: Southern

Ocean; NPAC: North-East Pacific; EPAC 1, 2: Equatorial Pacific. NATL North Atlantic and

ARC Arctic.

Location Date Latitude Longitude Sampling method Water depth

ANT1 08/10/06 65°06.11 S 57°23.55 W Ice core drillingIce-water

interface

ANT2 23/09/06 60°07.11 S 47°54.55 W Fishing ice floesIce- water

interface

ARC 20/06/09 76°36N 18° 11E Rosette 35m

EPAC1 27/09/06 0° 155° W Rosette 10m

EPAC2 25/08/06 0° 140° W Rosette 40m

NATL 16/06/09 73°55N 18°46E Rosette 50m

NPAC 15/08/07 47°55.19 N 122°20.38 W Membrane pump 8m





Table S2: Summary of 454 sequence data for Antarctic (ANT), Arctic (ARC), Equatorial Pacific

(EPAC), North Atlantic (NATL), and North Pacific (NPAC) metatranscriptomes. 1Only exact

duplicates were removed: CD-HIT-est clustering at 100% identity requiring 100% coverage of

both sequences. 2

Blastn against Silva SSU & LSU database – Best hit, no complexity filtering,

bit-score cutoff ≥50. 3CD-HIT-est clustering ≥95% overall identity, requiring ≥50% coverage

cluster representative.

ANT ARC EPAC NATL NPAC

# Raw Reads 391,614 514,223 342,252 513,985 313,910

Avg Length

(bp)

168.1 278.1 158.9 310.7 258.0

Total Size (Mb) 65.83 143.03 54.4 159.67 81

Potential

Artifacts1

49,093 3,175 21,942 5,172 14,172

Putative rRNA

(%)2

3,595 (0.92%) 38,651

(7.52%)

1,324 (0.39%) 68,009

(13.23%)

1,254 (0.4%)

# Filtered

Reads

220,844 421,107 246,534 394,187 250,841

Avg Length

(bp)

209.3 252.1 161.0 285.6 268.2

Total Size (Mb) 46.22 106.18 39.69 112.58 67.26

GC% 43.43 43.82 47.30 43.99 44.44

# Clusters3 29,840 254,423 119,783 252,031 76,564





Table S3a: List of eukaryotic genomes and their taxonomic classifications added to PhymmBL reference database.

phylum class order family genus species strain

Bacillariophyta Coscinodiscophyceae Thalassiosirales Thalassiosiraceae Thalassiosira pseudonana CCMP1335

Bacillariophyta Bacillariophyceae Naviculales Phaeodactylaceae Phaeodactylum tricornutum CCAP1055/1

Bacillariophyta Bacillariophyceae Bacillariales Bacillariaceae Fragilariopsis cylindrus NO_VALUE

Ciliophora Oligohymenophorea Peniculida Parameciidae Paramecium tetraurelia sd4-2

Ciliophora Oligohymenophorea Hymenostomatida Tetrahymenidae Tetrahymena thermophila SB210

Apicomplexa Coccidia Eucoccidiorida Cryptosporidiidae Cryptosporidium parvum IowaII

Apicomplexa Aconoidasida NO_VALUE Theileriidae Theileria annulata Ankara

Apicomplexa Aconoidasida Haemosporida NO_VALUE Plasmodium yoelii 17XNL

NO_VALUE Lobosa Amoebida Entamoebidae Entamoeba histolytica HM-1:IMSS

Mycetozoa Dictyostelia Dictyosteliida NO_VALUE Dictyostelium discoideum AX4

NO_VALUE NO_VALUE Choanoflagellida Codonosigidae Monosiga brevicollis MX1

Microsporidia NO_VALUE NO_VALUE Unikaryonidae Encephalitozoon cuniculi GB-M1

Basidiomycota Tremellomycetes Tremellales Tremellaceae Cryptococcus neoformans JEC21

Ascomycota Schizosaccharomycetes Schizosaccharomyceta

les

Schizosaccharomyce

taceae

Schizosaccharom

yces

pombe 972h-

Ascomycota Pezizomycetes Pezizales Tuberaceae Tuber melanosporum Mel28

Ascomycota Dothideomycetes Pleosporales Phaeosphaeriaceae Phaeosphaeria nodorum SN15

Ascomycota Eurotiomycetes Eurotiales Trichocomaceae Aspergillus nidulans FGSC_A4

Ascomycota LeotiomyceteS Helotiales Sclerotiniaceae Sclerotinia sclerotiorum 1980_UF-70

Ascomycota Sordariomycetes Sordariales Sordariaceae Neurospora crassa OR74A

Ascomycota Saccharomycetes Saccharomycetales Saccharomycetaceae Saccharomyces cerevisiae S288c

Rhodophyta Cyanidiophyceae Cyanidiales Cyanidiaceae Cyanidioschyzon merolae 10D





Chlorophyta Mamiellophyceae Mamiellales Bathycoccaceae Ostreococcus lucimarinus CCE9901

Chlorophyta Chlorophyceae Chlamydomonadales Volvocaceae Volvox carteri f_nagariensis

Chlorophyta Chlorophyceae Chlamydomonadales Chlamydomonadace

ae

Chlamydomonas reinhardtii NO_VALUE

Streptophyta Liliopsida Poales Poaceae Oryza sativa japonica

Streptophyta NO_VALUE Brassicales Brassicaceae Arabidopsis thaliana NO_VALUE

Nematoda Chromadorea Rhabditida Rhabditidae Caenorhabditis elegans NO_VALUE

Arthropoda Branchiopoda Diplostraca Daphniidae Daphnia pulex NO_VALUE

Arthropoda Insecta Diptera Drosophilidae Drosophila melanogaster NO_VALUE

Echinodermata Echinoidea Echinoida Strongylocentrotidae Strongylocentrot

us

purpuratus NO_VALUE

Chordata Actinopterygii Cypriniformes Cyprinidae Danio rerio NO_VALUE

Chordata Mammalia Primates Hominidae Homo sapiens GRCh37

Bacillariophyta Pelagophyceae Pelagomonadales Pelagomonadaceae Aureococcus anophagefferens NO_VALUE

Chlorophyta Mamiellophyceae Mamiellales Mamiellaceae Micromonas pusilla CCMP1545





Table S3b: List of eukaryotic EST libraries added to PhymmBL reference database. Sequences downloaded from NCBI DB-EST and clustered with

CD-HIT to ensure non-redundancy.

phylum class order family genus species strain

Dinoflagellata Dinophyceae Gonyaulacales Gonyaulacaceae Alexandrium catenella NO_VALUE

Bacillariophyta Coscinodiscophyceae Chaetocerotales Chaetocerotaceae Chaetoceros neogracile NO_VALUE

Glaucophyta Glaucophyceae Glaucocystales Glaucocystaceae Cyanophora paradoxa NO_VALUE

Ochrophyta Phaeophyceae Ectocarpales Ectocarpaceae Ectocarpus siliculosus NO_VALUE

Haptophyta Prymnesiophyceae Isochrysidales Noelaerhabdaceae Emiliania huxleyi NO_VALUE

Cryptophyta Cryptophyceae Pyrenomonadales Geminigeraceae Guillardia theta NO_VALUE

Dinoflagellata Dinophyceae Peridiniales Heterocapsaceae Heterocapsa triquetra NO_VALUE

Dinoflagellata Dinophyceae Gymnodiniales Gymnodiniaceae Karenia brevis NO_VALUE

Dinoflagellata Dinophyceae Gymnodiniales Gymnodiniaceae Karlodinium micrum NO_VALUE

Haptophyta Pavlovophyceae Pavlovales Pavlovaceae Pavlova lutheri NO_VALUE





Table S4: Temperature (T), salinity (in PSU), photosynthetically active radiation (PAR) andmajor nutrients given in mol/L. Chlorophyll a (Chl a) given in g/L or expressed as in-situμ μ

fluorescence and light, day length. ANT 1, 2: Antarctic, ARC: Arctic, EPAC 1, 2: EquatorialPacific, NATL: North Atlantic, NPAC: North-East Pacific. N.d. = no data available. S = Takenfrom “SPINDLER, Michael. NEOGLOBOQUADRINA PACHYDERMA FROM ANTARCTICSEA ICE. Proc. NIPR Symp. Polar Biol. Vol. 9. (1996).“ P = Data derived from depth-corrected,in-situ measurements from analogous environments from Pangea information system (Seesupplementary materials and methods for full details). W = Data derived from World Ocean Atlas(Annual mean surface measurements). L= Day lengths calculated using formula from(http://ocean.stanford.edu/courses/EESS151/).

Location T

[C°]

NO3 Si(OH)4 PO4 Salinity

(PSU)

PAR

(W/m2)P

Chl a (ug/L) or

in situfluorescence

Day Length

(Hours)L

ANT1 -2 7.8 6.1 2.2 39.3S 0.02 93ug/L 13.73

ANT2 -2 n.d. n.d. n.d. n.d. 0.02 n.d. 12.01

ARC -1.1 5W 2.5W 0.5W 34.2 2.19 n.d. 24

EPAC1 27 4.72 2.42 0.5 35.3W 279.62 0.26 ug /L 11.97

EPAC2 27 4.4 1.88 0.5 35.3W 60.02 0.29 ug/L 11.97

NATL 2.1 5W 2.5W 0.5W 34.9 0.32 n.d. 24

NPAC 12 12.47 30.02 1.71 30 324.37 6.98 in situfluorescence

14.1





Table S5: Taxonomic composition of major eukaryotic phytoplankton species in NPAC (North-

East Pacific (Puget Sound)) on 15th

of August 2006. N=3.

Dominant phytoplankton Cells / L

Coscinodiscus walesii 5472 ±894

Chaetoceros spp. single cells 13105 ± 1983

Chaetoceros spp. chains 6280 ± 453

Thalassiosira spp. 91129 ± 7998

Thalassiosira nitzschioides <1000

Pennate diatoms <100

Dinoflagellates <100

Unidentified flagellates <1000





Table S6: Significantly differentially expressed (p < 0.05; log2 fold change >1) Thalassiosira

pseudonana genes and expression values under different growth limitations in relation to control

growth.

ID Si Fe N T Co2 PFAM

6363 0 0 0 1.27 0 Ribosomal_L23eN

5259 0 0 0 1.14 0 Ribosomal_L44

2848 -2.6 -1.56 -4.74 -1.55 0 PsbU

25772 0 0 -1.33 0 0 Actin

28496 -2.89 1.44 -2.04 2.42 0 AdoHcyase

38715 -5.85 -1.17 -3.96 -3.62 0 Chloroa_b-bind

41829 1.38 0 0 0 0 GTP_EFTU;GTP_EFTU_D2;GTP_EFTU_D3

6285 0 0 0 1.34 0 HATPase_c;HSP90

42962 0 0 1.38 0 0 Chloroa_b-bind

31383 -3.61 -1.01 -3.43 1.04 0 Gp_dh_N;Gp_dh_C

575 -3.58 0 -4.11 0 0 Aminotran_3

866 0 0 -1.32 0 0 CLP_protease

12152 -2.94 0 -2.9 2.28 0 ketoacyl-synt;Ketoacyl-synt_C

26893 0 0 0 1.47 0 Ribosomal_S13

25933 0 0 -1.62 0 0 TPT

41256 -1.31 0 0 0 0 ATP-synt_ab_N;ATP-synt_ab_C

41548 0 0 0 0 1.7 Epimerase

26051 0 0 0 1.68 0 Gln-synt_N;Gln-synt_C

31705 -1.84 -1.13 0 -2.18 0 Mpv17_PMP22

264201 0 0 0 1.28 0 Ribosomal_L2;Ribosomal_L2_C

38583 -4.98 -1.28 -4.31 -2.28 0 Chloroa_b-bind

9716 0 0 0 1.69 0 DEAD;Helicase_C

268127 -5.69 -1.11 -7.68 -5.58 0 Chloroa_b-bind

268304 -3.86 -1.19 -4.27 0 0 0

33018 -6.19 -1.67 -6.41 -3.45 0 Chloroa_b-bind

802 0 0 0 1.02 0 Ribosomal_L18p

32924 -3.56 -2.49 -3.43 0 0 Ribul_P_3_epim

39143 1.16 0 0 0 0 Mito_carr

264921 -4.12 -2.46 -1.86 2.68 0 Chloroa_b-bind

428 -4.88 -2.57 -2.2 0 0 F_bP_aldolase

1326 -2.16 0 0 0 0 ATP-sulfurylase

29825 0 0 0 1.23 0 Ribosomal_S8e

32201 -4.33 0 -3.08 0 0 Mg_chelatase;VWA

39936 0 0 0 1.57 0 Metallophos

1738 0 0 -1.59 0 0 CLP_protease

21175 -4.53 -1.14 -5.62 0 0 Transketolase_N;Transket_pyr;Transketolase_

C

32546 0 0 -1.6 2.13 0 Cyt-b5;FA_desaturase





262125 -3.45 -1.49 0 0 0 NIR_SIR_ferr

3622 -1.09 0 -1.19 0 0 IspD

21815 0 1.47 -1.84 1.53 0 S-AdoMet_synt_N;S-AdoMet_synt_M;S-

AdoMet_synt_C

22565 -2.12 0 1.44 0 0 Sugar_tr

26046 0 0 0 1.11 0 Ribosomal_S3Ae

28189 0 0 0 3.65 0 HSP70;NAD-GH

31012 -3.51 -1.87 -2.97 0 0 Coprogen_oxidas

31516 0 0 0 1.46 0 NOP5NT;NOSIC;Nop

32555 -1.58 0 0 0 0 Ribonuc_red_sm

32752 0 0 0 1.16 0 Ribosomal_L24e

39278 -1.17 0 0 0 0 ATP-synt_C

42326 -2.22 0 -2.37 -1.87 0 UDPGP

269274 -3.27 0 0 -2 0 MFS_1

2343 -2.21 0 -3.64 1.87 0 0

3741 -1.71 -1.41 0 2.13 0 ELO

4830 0 1.64 0 0 0 Cofilin_ADF

5021 0 0 0 2.02 0 ketoacyl-synt;Ketoacyl-synt_C

5174 -3.9 0 -5.07 -4.63 0 Chloroa_b-bind

10234 -4.48 -2.55 -3.98 -1.15 0 FAD_binding_3

11411 0 0 2.09 0 0 Citrate_synt

20603 -6.96 -2.64 -7.64 -2.61 0 0

25892 -4.33 -1.44 -3.64 0 0 NAD_binding_1

26221 0 0 0 1.02 0 Ribosomal_S13_N;Ribosomal_S15

26367 0 0 0 1.21 0 Ribosomal_S8

28125 -1.81 0 0 0 0 GlutR_N;Shikimate_DH;GlutR_dimer

31446 0 0 0 1.11 0 Ribosomal_S21

33008 0 0 0 1.86 0 EPSP_synthase

36235 -2.24 0 -1.52 0 0 p450

36979 0 0 0 2.01 0 Fcf1

38667 -5.92 -1.49 -4.18 -3.48 0 Chloroa_b-bind

39901 0 0 0 0 1.39 PGK

40391 -1.82 0 1.83 0 0 Enolase_N;Enolase_C

269240 0 0 1.83 1.63 0 HSP70;NAD-GH

3815 -5.93 -3.43 -3.8 -5.97 0 Chloroa_b-bind

4914 -3.23 0 -3.32 0 0 NAD_binding_1

5219 -2.66 -2.41 -2.69 0 0 Acyl_transf_1

5240 -2.5 0 -2.92 -3.08 0 ALAD

8522 -2.23 0 0 0 -2.35 Ribonuc_red_sm

9021 -2.95 0 0 0 0 Porphobil_deam;Porphobil_deamC

10233 -1.74 0 -1.45 0 0 0

11501 -5.78 -1.87 -5.7 -2.24 0 Chloroa_b-bind





13806 -1.17 0 0 0 0 tRNA-synt_1c;tRNA-synt_1c_C

20008 0 0 0 1.36 0 Ribosomal_S26e

20965 -7.71 -2.61 -7.03 -3.37 -1.4 0

21261 -2.28 0 0 0 0 Rubredoxin

21292 1.31 0 0 0 0 CitMHS

21327 -1.62 0 -1.93 0 0 DUF1625

21972 -1.75 0 0 3.01 0 0

22350 0 0 0 1.38 0 Ribosomal_S21e

22476 0 0 0 1.43 0 Ribosomal_L13e

23283 -1.74 0 -2.07 0 0 0

24250 -1.5 0 1.76 0 0 SSF

26436 0 0 0 1.7 0 Peptidase_M3

26573 -3.65 -1.62 -3.28 0 0 DUF3479;CobN-Mg_chel

27873 0 0 1.55 0 0 IMPDH

29217 0 0 0 1.17 0 Ribosomal_L7Ae

29375 -5.89 -1.9 -6.91 -4 0 Chloroa_b-bind

29728 0 0 0 0 1.67 Epimerase

29842 -4.9 -1.85 -4.56 -1.78 0 0

29861 0 0 1.65 0 0 GATase_2;Glu_syn_central;Glu_synthase;GX

GXG

31014 0 0 0 1.59 0 Adap_comp_sub

31091 -2.13 0 -1.72 0 0 NDK

31851 -1.58 0 -1.94 2.95 0 adh_short

31912 -1.36 0 0 0 0 Pro_isomerase

32955 -2.59 -1.71 -3.24 0 0 ADH_N;ADH_zinc_N

33871 0 0 0 1.69 0 Metallophos

34585 -2.72 -2.43 -3.81 0 0 Radical_SAM;BATS

34830 -4.34 -1.9 -5.16 -1.91 0 MSP

34864 0 0 -1.2 2.39 0 FKBP_N;FKBP_C

35180 -7.04 0 -1.56 -1.08 -1.87 Mpv17_PMP22

35712 -4.7 0 -5.16 1.56 0 PGK

35816 0 0 -1.51 0 0 Gp_dh_N;Gp_dh_C

35934 -4.71 -1.26 -4.14 0 0 Cytochrom_C

37083 0 0 0 1.17 0 DnaJ;DnaJ_C

39003 0 0 2.91 3.93 0 UPF0113

39666 -1.14 0 0 0 0 cobW;CobW_C

40156 -2.65 0 -2.08 0 0 ATP-synt

40312 0 0 0 1.15 0 Ribosomal_S9

41005 -2.34 0 -2.43 0 0 Transket_pyr;Transketolase_C

41113 0 0 0 1.79 0 Cyt-b5;FA_desaturase

262796 -3.1 -1.28 -4.57 0 0 PP-binding

264039 -2.02 0 -2.37 0 0 p450





268374 0 0 2.49 0 0 E1_dh

268895 7.15 0 -1.35 0 0 Silic_transp

269348 -1.97 0 -2.4 0 0 Pyrophosphatase

269942 -2.92 0 -2.57 0 0 SHMT

105 0 0 0 1.55 0 S1;EIF_2_alpha

174 0 0 1.6 0 0 CoA_trans

225 0 0 -1.44 0 0 RRF

233 -7.44 -6.88 -5.57 -7.99 0 0

283 -1.8 0 -1.19 0 0 HIT

370 -3.98 0 0 2.63 -1.51 Ribonuc_red_lgC;ATP-cone;Ribonuc_red_lgN

411 0 0 0 1.64 0 CoA_binding;Ligase_CoA

412 -4.68 -2.58 -3.32 -2.01 0 Aminotran_1_2

511 0 0 0 1.2 0 FeoB_N;MMR_HSR1;YchF-GTPase_C

547 -2.07 0 -2.82 2.11 0 Biotin_lipoyl;E3_binding;2-oxoacid_dh

574 -1.83 0 -1.9 0 0 TPP_enzyme_C;Transket_pyr;Transketolase_C

595 0 0 0 1.39 0 eIF-1a

644 -3.46 0 -2.63 1.37 0 PfkB

656 0 -1.66 2.43 0 0 GST_C

681 3.06 0 0 0 0 0

711 0 0 -1.29 0 0 SBF

795 2.64 0 4.83 -2.61 0 E1_dh

799 0 0 0 1.25 0 ABC_membrane;ABC_tran

843 -1.08 0 0 0 0 Ras

874 -6.26 -1.87 -5.24 0 0 0

896 1.62 1.94 0 0 0 0

899 0 0 2.14 -1.96 1.95 0

928 0 0 0 1.58 1.87 DAGAT

932 0 1.2 1.2 0 0 Choline_transpo

985 0 0 2.3 0 1.89 0

997 -1.34 0 0 0 0 OTCace_N

1010 0 0 1.56 0 0 0

1022 0 0 0 2.11 0 0

1049 -3.57 -2.35 -3.67 -2.79 0 GFO_IDH_MocA;GFO_IDH_MocA_C

1060 0 0 1.97 1.87 0 GFA

1093 -3.69 -2.84 -3.82 -2.77 0 DUF803

1122 0 0 0 1.17 0 Nuc_sug_transp

1160 1.99 1.65 0 0 0 FKBP_C

1203 0 0 0 2.07 0 PPI_Ypi1

1210 0 1.3 0 0 -1.94 0

1238 0 0 0 -1.65 0 Glyoxalase

1243 -1.78 0 0 0 -1.35 0

1247 1.63 1.53 -1.49 0 0 PALP





1254 -2.59 0 -2.32 -1.92 0 Chalcone

1309 1.77 0 0 0 0 0

1344 2.32 2.3 0 0 0 0

1356 0 0 0 -1.06 0 0

1374 0 0 1.24 2.32 0 RRM_1

1388 0 0 1.9 0 0 0

1393 -1.39 1.24 0 0 0 SMC_N

1397 0 0 0 1.1 -1.03 0

1456 0 0 -1.38 0 0 IDH

1463 2.63 0 0 0 0 zf-DHHC

1481 0 0 0 -1.26 0 0

1515 -1.39 0 0 0 0 NifU

1526 0 0 0 2.3 0 CRAL_TRIO

1557 0 0 0 2.26 0 0

1584 -1.89 0 -1.56 0 0 Pro_isomerase

1591 4.04 2.39 0 0 0 0

1594 -2.3 0 0 0 0 0

1637 -2.6 -2.29 -2.58 0 0 0

1665 -1.56 0 0 0 0 Fer2

1666 0 0 0 2.63 0 Tic22

1711 0 0 1.69 -1.58 0 CRAL_TRIO

1724 -1.26 0 -1.36 0 0 0

1734 0 0 0 2 0 PPR

1754 0 0 -1.7 0 0 GST_C

1761 0 0 0 2.18 0 DUF1499

1779 0 0 1.37 -1.61 0 SPX

1798 0 0 1.49 0 0 0

1843 4.31 2.56 0 0 0 Bestrophin

1869 0 0 0 1.7 0 0

1870 -1.29 0 0 3.24 0 0

1889 -1.19 0 0 0 0 EFP_N;Elong-fact-P_C

1899 0 0 0 -2.31 1.63 0

1903 1.55 0 0 0 0 0

1908 -1.63 0 0 0 0 Spindle_Spc25

1909 0 0 0 2.09 0 0

1932 5.42 2.98 3.15 -1.23 0 HA

1945 2.68 0 0 0 0 zf-C3HC4

1951 0 0 0 2.23 0 SET

1953 -4.58 -1.23 -3.92 0 0 0

1962 0 0 0 1.56 0 Peptidase_M48

1965 -1.26 0 -2.31 -1.44 0 0

1968 -1.57 0 0 0 0 0





1989 -2.27 0 -1.95 -1.81 0 FKBP_C

2028 2.43 0 1.71 0 0 RCC1

2078 -7.16 -6.32 -5.72 -7.58 0 0

2157 0 0 1.03 0 0 D123

2220 1.37 0 0 0 0 HSF_DNA-bind

2237 1.67 0 0 0 0 0

2243 1.62 1.54 0 0 0 0

2287 0 0 -1.12 -1.19 0 0

2320 0 0 1.39 -1.42 0 0

2335 -1.8 0 0 0 -1.68 CXC

2352 -1.8 0 0 0 0 ClpS

2375 0 0 0 2.04 0 Polyketide_cyc

2420 0 0 0 2.87 0 S1

2462 -2.54 -1.44 0 0 0 0

2465 -1.94 0 0 -1.91 0 0

2483 0 0 1.65 0 0 0

2524 0 -2.37 0 0 0 Cyclin_N;Cyclin_C

2535 0 0 0 1.76 0 0

2540 0 0 0 1.19 0 Acetyltransf_1

2553 -1.29 0 0 -1.34 0 ADH_N

2577 -1.17 0 -1.19 0 0 0

2601 -4.6 -1.7 -4.57 -2.1 0 Chloroa_b-bind

2642 2.17 0 0 0 0 0

2671 0 0 0 1.13 0 NUDIX

2673 -3.4 0 -2.8 -3.52 0 Rieske

2698 0 0 0 -1.52 0 0

2769 -1.28 -1.36 -1.2 0 0 Mito_carr

2772 0 0 1.56 1.54 0 Utp14

2818 0 -1.27 0 0 0 0

2845 -4.81 -1.42 -5.5 -1.95 0 Chloroa_b-bind

2846 -1.68 0 -1.76 -1.6 0 2-Hacid_dh;2-Hacid_dh_C

2877 2.05 0 0 0 3.27 0

2880 0 0 0 0 1.26 TspO_MBR

2892 0 0 0 2.77 0 UBA;EF_TS

2916 1.22 1.26 -1.72 0 0 TENA_THI-4

2921 0 0 0 1.09 0 SAP;RRM_1

2928 0 0 0 -1.92 0 0

2942 2.46 1.32 0 0 0 0

2957 0 0 0 -1.71 0 0

3073 0 0 0 1.34 0 Polyketide_cyc

3085 0 1.53 0 0 0 0

3105 0 0 0 1.43 0 SKN1





3111 0 0 0 -1.65 0 0

3114 0 0 0 6.9 0 AhpC-TSA

3132 0 0 0 1.64 0 Ank

3141 -2.57 0 -2.69 0 0 0

3143 -1.98 -1.86 -2.16 2.38 0 FA_desaturase

3148 0 0 0 -1.51 0 Inhibitor_I29;Peptidase_C1

3189 0 0 0 1.45 0 WHEP-TRS

3201 4.58 2.01 0 1.38 0 0

3215 0 0 0 1.71 0 Cyclin_N;Cyclin_C

3222 1.78 0 0 0 0 TauD

3244 0 0 0 -1.39 1.94 PLA2G12

3250 5.85 0 0 1.56 1.58 0

3275 0 -1.71 1.49 0 0 TLD

3298 0 0 0 2.35 0 MtN3_slv

3300 0 0 0 1.81 0 0

3313 3.62 2.8 0 3.09 0 peroxidase

3322 0 2 -2.47 0 0 0

3326 -2.5 -1.96 -1.69 0 0 TauD

3330 0 0 0 0 2.15 cNMP_binding;RGS

3344 0 0 0 2.82 0 Mpv17_PMP22

3353 -4.96 -3.07 -4.51 0 0 FMN_dh

3397 1.57 0 0 0 0 ETF_QO

3426 0 0 0 0 1.53 zf-DNL

3428 -2.4 0 -2.08 0 0 0

3453 -2.31 -2.19 -2.73 -2.7 -1.92 0

3463 -3.73 -2.36 -2.84 0 0 0

3481 0 0 -2.17 0 0 0

3482 -2.48 0 0 0 -1.51 0

3524 1.22 0 0 0 0 Mito_carr

3554 0 0 2.52 0 0 0

3583 0 0 1.34 0 0 0

3601 1.09 0 1.53 2.13 0 Ion_trans_2

3604 1.69 1.98 0 0 3.2 0

3614 0 0 2.65 2.11 2.1 0

3627 1.65 0 0 0 0 Thiolase_N;Thiolase_C

3706 3.25 0 0 0 0 0

3748 -1.98 0 0 6.58 0 Mito_carr

3755 0 0 0 1.48 0 0

3781 1.77 0 1.97 3.55 0 0

3839 1.28 1.27 0 0 0 0

3840 0 1.54 0 0 0 PPR

3845 0 0 0 1.54 0 0





3863 3.52 0 0 0 0 DAO

3878 -1.67 0 -1.66 0 0 Pro_isomerase

3883 -1.7 0 -2.5 1.29 0 0

3903 0 0 0 1.57 0 adh_short

3906 0 0 1.76 0 0 Mito_carr

3944 0 0 0 1.42 0 MMR_HSR1

3956 0 0 0 2.11 0 FUN14

3974 -2.85 -1.4 0 -1.7 0 URO-D

3975 3.32 2.97 0 0 0 0

3976 3.22 2.71 0 0 0 0

4000 0 0 1.28 0 0 0

4002 -1.83 0 -1.87 -1.53 0 Peptidase_M14

4007 0 0 1.97 0 0 0

4026 0 0 0 0 3.06 0

4050 3 1.76 0 0 1.54 0

4058 0 0 0 -2.7 2.38 Cyclin_N

4067 0 0 2.33 0 0 0

4086 0 0 1.95 0 0 0

4117 0 0 0 2.72 0 0

4146 0 0 0 0 1.95 0

4170 -1.62 0 0 0 -1.21 0

4214 -1.18 0 0 0 0 0

4225 0 1.52 -2.05 0 1.91 0

4261 0 0 0 -1.73 0 0

4270 -1.43 0 0 0 0 Scramblase

4315 -1.76 0 0 -2.65 0 0

4349 0 0 0 2.99 0 0

4355 0 0 3.27 0 0 0

4376 -1.73 0 0 0 0 PRK

4382 0 0 -1.69 3.33 0 DUF2854

4439 -5.15 -2.67 -5.37 0 0 0

4456 0 0 0 -1.19 0 0

4495 -1.41 0 0 0 0 0

4536 0 0 0 0 -1.9 0

4576 0 0 0 1.59 0 MatE

4608 1.51 1.49 0 0 0 0

4616 2.34 3.01 0 0 0 0

4624 0 0 0 1.24 0 DEAD;Helicase_C

4633 0 0 0 1.31 0 0

4654 0 0 3.05 0 0 0

4659 0 0 0 0 -1.75 0

4668 -1.96 0 0 0 0 0





4700 -2.93 0 -1.38 0 0 0

4718 -1.55 0 -1.71 -1.6 0 Epimerase

4726 0 0 0 -2.07 0 0

4755 0 0 2 0 0 0

4765 1.82 0 0 0 0 RRM_1

4767 0 0 -1.41 0 0 0

4781 -1.52 0 0 0 0 0

4782 0 0 0 0 -1.19 0

4804 0 0 -1.58 0 0 Aldo_ket_red

4819 -3.98 -3.64 -4.19 -3.27 0 Bestrophin

4820 -7.85 -4.81 -6.44 -5.62 0 Bestrophin

4834 0 0 0 -2.01 0 PTPS

4875 0 0 -1.83 0 0 PK;PK_C

4878 3.1 2.49 0 0 0 0

4888 -1.89 -1.57 -2.55 0 0 0

4891 0 0 0 1.59 0 DSPc

4900 0 0 0 1.38 0 DUF1997

4919 0 0 2.51 0 0 0

4985 0 1.39 0 1.57 1.33 0

5009 0 -1.17 0 0 0 0

5026 -1.16 0 0 0 0 ATP-synt

5028 1.25 0 0 0 0 RdRP

5077 -3.74 -1.45 -2.74 -1.53 0 adh_short

5078 0 0 0 3.14 0 tRNA-synt_2b;HGTP_anticodon

5108 0 0 0 1.76 0 DUF3727

5110 7.62 7.82 0 0 0 0

5130 0 0 2.11 2.62 0 Sel1

5147 -1.71 0 0 0 0 0

5186 0 0 1.56 -2.21 0 PEPCK_ATP

5193 0 0 1.12 0 0 DUF2263

5201 0 0 0 -1.29 1.45 MatE

5293 0 0 0 1.7 0 Kri1;Iso_dh;MobB;Clp1

5362 0 0 0 1.35 0 0

5371 0 0 1.4 0 0 0

5377 0 0 0 2.76 0 0

5380 0 0 0 2.36 0 DUF1092

5393 0 0 0 -1.33 0 0

5394 -1.96 0 2.8 1.47 0 0

5424 -2.12 -1.24 2.74 -3.55 0 zf-C3HC4

5470 -1.6 0 0 0 0 CBS

5484 0 0 0 1.39 0 DUF914

5500 0 -3.03 0 -2.77 1.5 PPDK_N;PEP-utilizers_C





5513 0 0 0 2.06 0 0

5515 0 0 0 1.32 0 SAM_2

5532 0 0 0 3.57 0 0

5533 0 0 0 5 0 Chloroa_b-bind

5545 -1.27 0 -1.75 2.95 1.37 0

5584 0 0 0 0 1.15 2OG-FeII_Oxy

5600 0 -1.56 0 0 0 0

5607 -1.53 0 0 -1.68 0 Choline_transpo

5647 0 0 0 1.05 0 0

5675 -1.24 0 0 1.23 0 0

5763 1.25 0 0 0 0 SAP

5778 0 0 0 -1.71 1.68 0

5832 0 0 0 0 1.68 Cytochrom_B561

5835 4.22 2.47 0 0 1.63 0

5954 -3.12 1.59 -2.12 2.55 0 AdoHcyase

5989 1.91 0 0 0 0 Inositol_P

6048 0 0 2.99 -1.93 0 0

6114 0 0 0 0 1.31 Guanylate_cyc_2

6123 0 0 0 1.84 0 Ank

6139 -1.85 -1.56 -1.76 0 0 Chloroa_b-bind

6155 -2.35 -1.69 -2.66 0 0 0

6203 -2.3 0 0 -2.63 0 Bestrophin

6204 0 0 0 -2.13 0 Bestrophin

6211 5.74 4.5 0 0 0 Cyclin_N

6248 0 0 0 -1.75 0 0

6250 0 0 1.53 1.34 0 0

6253 0 0 0 1.47 0 0

6258 0 0 0 -1.43 0 NUDIX

6290 0 0 -1.9 2.89 0 NDK

6330 0 0 0 0 -1.87 0

6332 0 0 0 -3.06 0 0

6383 0 0 0 2.08 0 RNA_pol_Rpb7_N;S1

6489 0 0 0 1.69 0 0

6551 0 1.72 -3.16 0 0 0

6562 -1.25 0 0 0 0 Ribosomal_L23

6564 0 0 -1.38 0 0 TspO_MBR

6581 1.5 0 0 0 0 0

6607 0 0 0 1.73 0 SpoU_methylase

6670 0 0 0 -1.58 0 ATG_C;DUF1162

6731 0 0 -3.23 -2.64 0 0

6743 1.57 2.33 0 0 0 0

6750 0 0 0 -2.56 0 0





6770 -4.39 -1.46 -4.97 -1.74 0 CPSase_L_chain;CPSase_L_D2;Biotin_carb_

C;Biotin_lipoyl;ACC_central;Carboxyl_trans

6807 0 0 0 0 -1.25 0

6817 -1.69 0 -1.42 0 0 PAP_fibrillin;PEMT

6876 0 0 0 -1.73 0 0

6894 0 0 3.71 1.45 0 Pirin;Pirin_C

6931 0 0 0 1.45 0 Mpv17_PMP22

6948 0 0 0 1.14 0 Histone

6949 0 0 0 0 1.59 Peptidase_S41

6950 1.13 0 0 0 0 Peptidase_S41

6958 0 0 1.31 0 0 HSF_DNA-bind

6971 0 0 2.28 0 0 0

6979 0 0 0 0 1.58 DJ-1_PfpI

6988 0 0 -1.47 0 0 tRNA_anti;tRNA-synt_2

7019 0 0 1.25 0 0 mTERF

7023 -1.74 0 0 0 -1.35 0

7031 0 0 0 1.2 0 CIA30

7060 -1.77 0 -1.54 0 0 0

7070 0 0 1.93 0 0 0

7093 0 0 0 1.57 0 Lipase_3

7094 0 0 0 1.47 0 Amino_oxidase

7110 0 0 0 -1.77 -1.45 0

7123 -1.28 0 0 0 0 Exonuc_X-T

7134 0 1.66 1.62 0 0 Mem_trans

7155 -1.05 0 -1.13 0 0 0

7166 0 0 0 1.46 0 PGAM

7186 3.31 3.13 0 -2.18 1.99 0

7265 0 0 0 1.27 0 NOB1_Zn_bind

7268 0 0 0 -2.46 1.09 Cation_ATPase_N;E1-E2_ATPase;Hydrolase

7270 -2.31 -2.04 -1.7 -1.93 0 0

7341 0 0 -1.67 0 0 TPT

7349 0 0 0 0 3.25 0

7372 0 0 0 0 2.3 Trypsin

7491 0 0 3.2 0 0 HA

7509 1.03 0 0 0 0 0

7558 0 0 0 2.14 0 DUF525

7582 0 0 0 0 2.65 Guanylate_cyc

7583 0 0 0 1.51 0 DUF1995

7642 0 0 0 2.07 0 Hydrolase_4

7669 0 0 0 -1.98 0 0

7678 0 0 0 1.33 0 0

7704 0 0 0 -2.1 0 Pyr_redox_2;Pyr_redox_dim;Rhodanese





7709 -3.68 -2.02 3.15 -3.82 0 0

7719 2.25 0 0 0 0 YbaK

7742 0 0 0 2.11 0 KTI12

7752 0 0 1.37 -1.56 0 0

7776 6.44 3.91 0 0 1.6 0

7805 0 0 0 4.78 0 Aldo_ket_red

7815 0 0 0 -2.34 0 0

7838 0 0 0 1.62 0 0

7859 0 0 0 0 1.31 0

7863 1.31 0 -1.39 0 0 0

7865 2.76 1.74 0 0 0 DUF839

7873 -1.58 0 0 0 0 0

7881 -2.62 0 -3.3 0 0 0

7883 -4.65 -2.83 -2.25 1.71 0 0

7887 0 0 0 0 1.51 Snf7

7916 -6.56 -2.87 -6.92 -5.96 0 Chloroa_b-bind

7940 -2.64 0 0 2.93 0 0

7950 -2.56 -1.71 0 -2.5 0 0

7974 0 0 0 -2.87 0 0

7997 -1.4 0 0 0 0 TLD

7999 2.16 0 0 1.96 0 0

8014 0 0 -1.56 0 0 0

8028 -1.66 -1.04 0 -1 0 0

8031 -2.29 0 0 -1.36 0 0

8044 -1.39 -1.41 0 0 1.27 Pentapeptide

8062 0 0 0 1.17 0 0

8073 -1.86 0 -1.61 -2.19 0 TauD

8086 2.67 1.69 0 0 0 0

8164 0 0 0 1.78 0 0

8180 2.29 0 0 0 0 0

8181 0 0 0 0 -1.46 0

8216 0 -1.37 1.58 0 0 0

8219 -6.31 0 0 0 0 0

8248 -3.24 -1.73 -2.99 0 0 HopJ

8270 0 0 0 1.47 0 0

8281 0 0 1.6 2.66 0 0

8329 -1.94 -1.41 0 0 0 0

8355 0 0 0 -1.44 0 0

8365 1.93 2.62 0 0 0 0

8407 2.84 0 2.91 -4.6 1.94 0

8409 -1.72 0 0 0 0 0

8426 -1.26 0 0 0 0 Mito_carr





8437 0 0 0 1.45 0 E1-E2_ATPase

8439 1.17 1.07 1.57 0 0 Epimerase

8469 1.16 0 1.49 1.65 0 0

8521 0 0 4.56 1.55 0 PseudoU_synth_1

8537 0 2.28 0 0 0 Peptidase_M50

8571 0 0 0 1.6 0 0

8611 0 0 0 2 0 FG-GAP

8670 -1.33 -1.53 -1.61 2.06 0 0

8672 -1.58 -1.85 0 -3.3 0 NAD_binding_4

8673 1.33 0 0 0 0 0

8698 1.56 0 1.62 0 0 0

8704 -1.38 0 0 0 -1.65 0

8713 -5.78 -2.91 -5.09 0 0 0

8733 0 0 0 -1.35 0 BHD_1;BHD_2;BHD_3

8740 2.62 1.82 0 0 0 Bestrophin

8775 0 0 0 2.84 0 Peptidase_S9

8778 0 0 2.99 0 0 Transket_pyr;Transketolase_C

8808 0 0 0 2.43 0 SNARE_assoc

8811 0 0 0 1.26 0 RPE65

8845 1.74 0 0 0 0 0

8861 0 0 1.75 2.06 0 GST_C

8952 -1.9 0 0 -2.5 2.56 GATase_2;SIS

8974 -2.14 -2.25 0 0 0 0

8979 1.24 0 0 0 0 MS_channel

8982 0 1.67 0 0 0 0

8997 0 0 0 1.68 0 DEAD;Helicase_C

9003 0 0 0 0 1.52 0

9007 1.69 0 0 0 0 0

9057 0 0 0 1.28 0 0

9060 0 0 1.72 0 0 0

9086 0 0 0 1.62 0 0

9087 0 0 0 1.51 0 Mito_carr

9118 0 0 0 2.17 0 MoaC

9122 0 0 0 1.84 -1.95 0

9152 0 0 0 -2.38 2.8 0

9173 0 0 0 -2 0 0

9240 -2.84 -2.88 0 -2.37 0 PT

9242 -2.55 -2.7 0 -2.33 0 0

9252 0 0 -1.63 0 0 SET

9255 0 1.29 0 0 0 0

9268 -1.63 -1.82 -2.06 -2.4 0 0

9284 0 0 0 -1.25 0 Guanylate_cyc





9352 -2.07 0 -3.15 0 0 LrgB

9394 0 0 -1.5 0 0 0

9406 0 -1.09 0 -1.86 1.45 0

9432 0 1.33 0 0 0 CAP

9442 0 0 0 2.96 0 Fibrillarin

9475 0 0 1.26 0 0 0

9479 2.14 1.62 0 0 0 ATG22

9485 -1.94 -1.47 0 0 0 mTERF

9499 0 0 1.71 0 0 Hydrolase_3

9502 -1.9 0 0 0 0 0

9511 0 0 0 1.96 0 ADK

9524 2.55 1.78 0 0 0 PAP_fibrillin

9557 -6.42 0 0 0 -2.1 0

9558 -2.53 0 0 0 0 0

9619 6.95 0 0 0 0 0

9689 0 0 0 2.19 0 PAS

9705 0 0 0 1.66 0 DUF2431

9714 1.14 1.93 1.34 -1.35 0 0

9722 0 0 0 -1.08 0 0

9737 0 0 1.02 0 0 0

9742 0 0 0 3.43 0 HA

9746 0 0 1.2 0 0 RNA_pol_Rpb4

9754 0 0 1.46 0 0 0

9795 0 0 -1.84 0 0 0

9830 0 0 -1.71 0 0 Cys_Met_Meta_PP

9840 0 1.9 0 0 0 Nramp

9874 0 0 0 0 -1.57 0

9878 -1.82 0 0 0 0 0

9903 -2.24 -2.11 -1.8 -2.44 0 0

9918 -2.08 0 -2.5 0 0 Cupin_3

9958 0 0 5.88 0 0 0

9976 0 0 0 1.3 0 0

10081 0 0 0 -1.1 0 Metallophos

10093 0 0 0 1.72 0 FAD_binding_3

10106 -1.12 0 0 0 -1.46 0

10156 -1.31 0 0 0 -1.35 Tcp10_C

10181 -1.51 0 0 0 0 0

10228 0 0 0 -1.48 0 0

10254 0 0 0 3.98 0 FAD_binding_2

10256 0 0 0 -2.55 0 0

10311 0 0 0 -3.97 1.75 0

10313 -1.61 0 0 -3.41 1.6 0





10360 -1.74 0 -1.54 -1.73 0 0

10363 5.41 0 1.36 -2.07 1.89 0

10378 -1.12 -1.11 0 0 0 0

10385 3.85 3.51 0 0 1.56 CMD

10416 0 0 0 1.47 0 0

10417 -3.3 0 -2.54 0 0 0

10425 0 1.48 0 -1.47 0 DUF1409

10457 0 0 0 -1.14 0 Alpha_kinase

10472 0 0 0 1.59 0 0

10497 0 -1.22 0 0 0 Gly_transf_sug

10518 0 0 1.33 0 0 0

10552 4.71 3 0 0 0 Beta_propel

10556 1.49 0 0 0 0 0

10604 0 0 0 3.31 0 Epimerase

10626 0 0 1.15 0 0 0

10661 0 1.58 0 0 0 0

10677 0 0 0 1.64 0 FA_desaturase

10723 1.83 0 -1.92 0 0 0

10741 0 0 0 -1.52 0 0

10767 -2.28 -1.78 -1.86 0 0 0

10791 -2.14 -2.57 0 -2.84 -2.78 RCC1

10850 0 1.26 0 0 0 FA_hydroxylase

10885 0 0 0 2.35 2.16 0

10899 3.17 0 0 -3.23 0 0

10945 0 0 3.03 0 0 0

10983 1.54 0 0 0 0 0

10997 4.09 4.05 0 0 0 0

11029 0 0 0 -1.77 0 0

11101 1.62 1.69 0 0 0 TauE

11118 0 0 -2.12 1.77 0 0

11133 -1.15 0 0 0 0 0

11145 0 0 1.67 0 0 0

11320 0 0 0 2.53 2.42 DAHP_synth_2

11352 0 0 1.35 0 0 0

11357 0 0 0 0 -1.62 0

11360 1.51 1.41 0 -1 0 0

11366 2.09 2.29 0 -1.85 0 0

11375 0 0 0 -1.24 0 Ferric_reduct;FAD_binding_8;NAD_binding_

6

11383 0 0 0 0 3.03 0

11392 0 1.98 -1.49 0 0 0

11430 -1.26 -1.37 0 0 0 0





11487 0 1.95 1.2 3.83 0 PhyH

11499 -1.04 0 0 0 0 0

11500 0 0 0 1.98 0 0

11508 0 0 0 1.82 0 Prefoldin_2

11523 0 -1.52 0 -1.54 0 Sulfotransfer_1

11548 -1.3 0 0 0 0 0

11557 0 0 0 0 -1.09 PhyH

11576 0 0 0 0 1.58 0

11676 0 0 0 1.06 0 LON

11757 -2.44 0 -2.53 0 0 0

11777 0 0 0 1.58 0 Methyltransf_16

11794 0 0 2.13 -2.2 0 0

11796 0 0 1.6 -2.45 0 0

11824 0 0 0 1.59 0 GidB

11836 -1.75 0 0 1.42 0 0

11858 0 -1.41 0 0 0 0

11902 -4.98 -1.28 -4.31 -2.28 0 Telo_bind

11905 0 2.07 0 0 0 0

11924 -2.23 0 -1.69 -2.25 0 PPR

11977 1.49 0 0 0 0 0

11978 0 0 0 2.21 0 Cyt-b5;DAO

12012 0 0 0 0 1.81 DSBA

12040 -1.47 0 -1.27 -1.58 2.34 0

12047 0 0 0 0 2.21 0

12061 0 0 0 1.78 0 0

12070 0 0 0 2.67 0 NDK

12072 0 0 0 0 1.9 0

12141 0 0 0 1.5 0 Abhydrolase_1

12171 0 1.65 0 0 1.52 0

12179 6.15 3.56 0 0 0 0

12594 2.11 4 0 0 0 Chitin_bind_1

12637 1.21 0 0 0 0 Myosin_head

12695 -2.2 0 -4.1 0 -1.46 Glucan_synthase

13064 0 0 2.15 0 0 GCS

13089 1.95 1.51 0 0 2.6 ABC_tran

13224 0 1.25 0 0 0 RRM_1;PABP

13254 0 0 0 1.89 0 DEAD;Helicase_C

13459 0 0 0 -1.34 0 AAA_2;ClpB_D2-small

13485 -2.79 -1.78 3.07 -4.51 0 Xan_ur_permease

13982 0 0 0 3.2 0 DEAD;Helicase_C

14147 -2.05 -2.19 -1.94 -2.23 0 PALP

14322 4.06 0 0 0 2.4 Pkinase





14370 1.52 0 0 0 0 WD40

14389 0 0 0 3.06 0 Pyridoxal_deC

14563 -1.56 0 0 0 0 Kinesin;6PF2K;PGAM

14597 0 0 1.72 0 0 DEAD;Helicase_C

14700 1.52 0 0 0 0 WD40

14942 0 0 -1.12 0 0 Kinesin

15027 0 0 0 1.47 0 CitMHS

15093 0 0 1.1 -1.4 0 Peptidase_S10

15226 0 0 0 1.07 0 GHMP_kinases_N

15259 0 0 0 1.43 0 S1

15638 1.52 0 0 -1.31 0 PHD;SET

15916 0 1.33 0 0 0 cNMP_binding;Pkinase

15961 2.17 1.26 0 0 0 5-FTHF_cyc-lig

16210 1.5 0 2.26 -2.33 0 0

16344 -2.52 -1.59 -2.77 -3.08 0 Nuc_sug_transp

16372 0 0 0 0 1.43 Aldo_ket_red

16390 0 0 1.36 0 0 Peptidase_C1;Inhibitor_I29

16746 0 2.11 0 2.41 0 Mito_carr

16772 0 0 0 1.47 0 Mito_carr

16777 0 0 0 0 1.54 0

16947 0 0 0 1.43 0 DEAD;Helicase_C

17031 2.08 2.14 0 -1.41 0 PI3_PI4_kinase

17073 -1.15 0 0 0 0 DUF1295

17140 3.04 2.52 0 0 0 Spermine_synth

17242 0 0 0 0 1.07 Band_7

17302 0 0 1.87 1.58 0 Inositol_P

17362 0 0 0 4.43 0 Kua-UEV1_localn

17439 -1.08 0 0 0 0 DUF818

17443 0 0 -1.6 1.86 0 Inositol_P

17480 0 0 0 1.58 0 Pkinase

17492 1.55 0 0 0 0 ThiF

17623 0 0 2.59 0 0 TP_methylase

17687 0 0 1.82 0 0 AAA

17704 -1.09 0 -1.5 0 0 Peptidase_C12

17854 0 0 -1.91 0 0 Heme_oxygenase

17859 0 0 0 1.47 0 DER1

17961 0 0 0 2.1 0 UPF0005

18076 2.43 0 0 2.24 0 ADK

18099 1.42 1.63 0 0 1.7 Nfu_N;NifU

18109 0 0 0 2.27 0 UPF0016

18198 0 0 1.5 1.61 0 RRM_1

18351 0 0 0 2.39 0 Maf





18536 1.42 1.48 -3.01 1.46 1.29 Chloroa_b-bind

18624 0 0 3.07 -2.01 0 Mpv17_PMP22

18662 0 0 2.17 0 0 Ank;ADK

18741 0 0 0 0 1.26 0

18820 1.04 0 0 0 0 Myb_DNA-binding

18841 0 0 0 0 1.59 RRM_1

19048 3.16 2.35 0 0 0 Pkinase

19094 0 0 0 0 1.54 Pkinase

19141 0 0 2.57 3.36 0 Flavodoxin_1

19351 0 0 0 1.22 0 LRR_1

19421 0 0 0 -1.4 0 HECT

19501 0 0 0 1.1 0 S10_plectin

19541 0 0 0 1.18 0 Ribosomal_L28e

19793 -3.03 0 0 1.23 0 Histone

19813 2.39 2.23 0 0 1.9 AdoMet_dc;Spermine_synth

19818 0 0 0 1.21 0 Ribosomal_S15

19895 1.58 0 0 0 0 BTB

19928 0 0 0 2.16 0 Ribosomal_L20

20065 -1.35 0 0 0 0 bZIP_1

20186 -1.61 -1.77 4.68 -1.74 2.02 DnaJ

20194 -3.7 0 -3.37 1.77 0 zf-CDGSH

20223 0 0 0 2.18 0 RRM_1

20266 0 0 0 2.25 0 RRM_1

20335 -3.97 -1.82 -5.32 -3.49 0 Rotamase

20567 1.47 0 0 0 0 MS_channel

20590 2.52 2.03 0 0 0 IQ

20593 0 0 -1.22 3.42 0 FA_hydroxylase

20605 2.23 1.01 0 0 0 DEAD

20609 0 0 1.9 0 0 0

20625 0 0 0 1.48 0 0

20629 0 0 0 1.73 0 YCII

20641 0 0 0 1.82 0 0

20648 0 0 0 0 1.7 0

20649 0 0 0 -2.18 0 0

20658 0 0 0 1.85 0 Nop53

20670 0 0 2.31 0 -1.84 0

20671 2.95 2.41 0 0 0 0

20678 0 0 0 1.53 0 Cupin_4

20707 0 0 0 0 1.04 0

20731 0 0 0 2.63 0 Mito_carr

20740 0 0 0 0 1.17 0

20743 1.31 0 0 0 0 0





20751 0 0 0 -1.53 1.47 MORN

20786 0 1.41 0 0 0 0

20795 0 1.69 0 0 0 CAP

20797 -1.2 0 -1.51 0 0 Methyltransf_11

20810 0 0 0 -1.11 0 FTR1

20812 -1.22 0 -2.05 0 0 0

20814 0 0 0 2.21 0 0

20816 0 0 2.11 0 0 Aminotran_4

20827 -1.94 0 2.27 0 0 TauE;GYF

20829 0 0 1.26 0 0 HPP

20832 0 0 0 -1.27 0 HPP

20833 0 0 0 3.01 0 Methyltransf_16

20837 0 0 2.17 -1.47 0 0

20845 1.27 0 0 0 0 Tic22

20847 -4.37 1.88 0 2.11 -1.94 0

20877 0 0 0 1.66 0 Ubiq_cyt_C_chap;WW

20880 1.49 0 -2.58 0 0 PhoD

20888 -1.36 0 0 0 0 0

20909 0 0 0 0 1.53 0

20930 0 0 0 1.26 0 0

20931 -2.36 0 0 0 -1.62 0

20946 0 0 0 0 2.55 0

20950 0 0 0 1.2 1.68 DAGAT

20953 0 0 0 -1.44 0 Trypsin

20959 1.2 1.95 0 -1.96 0 Myb_DNA-binding

20962 2.17 0 0 0 0 0

20966 0 0 0 0 1.71 CIA30

20972 2.56 0 0 0 0 0

20974 0 0 0 -2.22 0 0

20993 0 0 0 0 1.39 AAA_5;AAA_2;ClpB_D2-small

20998 0 0 0 0 1.59 HOOK

20999 0 -1.8 0 0 0 Cyclin_N

21000 0 -1.16 0 -2.07 1.63 Cyclin_N

21001 0 0 0 -2.21 1.44 Cyclin_N;Cyclin_C

21020 0 2.82 0 0 0 0

21043 -1.1 0 0 0 0 0

21050 0 0 0 1.85 0 NOG1

21059 -1.4 -1.62 -1.47 -1.29 0 0

21061 0 0 0 0 1.83 0

21067 0 0 2.5 0 0 FMN_red

21068 1.92 2.32 0 0 0 0

21076 -1.74 -2.21 5.2 0 1.59 0





21081 -5 -2.86 -6.27 -3.28 0 0

21085 4.31 4.57 0 0 0 0

21087 1.35 0 0 0 0 0

21094 0 0 0 0 -2.22 0

21124 0 0 0 -1.69 0 0

21139 0 0 1.29 0 0 0

21149 0 1.51 1.62 2.64 0 0

21152 0 0 0 0 1.11 zf-C3HC4

21159 0 1.16 0 0 0 Cyclin_N;Cyclin_C

21161 0 0 0 0 1.53 Patched

21170 0 0 0 -1.53 0 0

21177 0 0 1.82 0 0 Biotin_lipoyl;UBX

21193 0 0 0 1.48 0 ubiquitin

21203 0 0 0 -1.55 0 SET

21208 0 0 1.76 -1.72 2.05 0

21213 0 0 0 0 1.63 0

21216 3.01 2.96 0 0 0 0

21224 0 0 3.28 2.45 0 0

21234 1.31 0 0 0 0 0

21237 -1.65 0 0 0 0 TPR_1

21240 0 0 0 0 1.66 0

21250 0 0 -1.89 0 0 0

21255 -1.77 0 0 -1.48 1.22 DUF500

21258 0 0 0 2.43 0 DUF3523;AAA

21279 0 0 0 0 1.52 0

21290 -2.27 0 0 0 0 0

21299 0 0 2.77 0 0 AMP-binding

21300 0 0 -1.81 0 0 Epimerase

21306 -2.17 0 0 0 0 0

21312 0 0 2.93 0 0 0

21342 0 0 0 0 1.53 0

21348 -3.24 0 -3.41 0 0 0

21351 -1.07 0 0 0 0 0

21362 1.08 1.06 1.7 0 0 0

21374 0 0 1.24 1.8 0 Muc_lac_enz

21381 1.88 2.19 0 0 0 0

21386 0 0 0 0 2.47 Aldo_ket_red

21389 -2.5 0 0 -1.42 0 0

21390 -2.48 0 0 0 0 0

21392 1.11 0 0 0 1.11 0

21403 -1.61 0 0 0 0 0

21416 1.7 0 0 0 0 0





21438 0 0 0 -1.96 0 0

21446 1.42 0 1.3 0 0 0

21449 0 0 0 -1.49 0 0

21472 -5.28 -2.04 -4.24 -2.43 0 Chloroa_b-bind

21477 -2.99 -1.09 -1.56 0 0 0

21480 1.61 0 0 0 0 Choline_transpo

21489 0 0 0 1.3 0 0

21515 0 0 0 0 1.24 0

21517 -1.89 -1.74 -1.65 0 0 Methyltransf_6

21519 0 0 0 2.42 0 0

21587 0 2.88 0 0 0 0

21594 0 0 0 0 1.46 0

21598 0 0 0 0 1.19 Acetyltransf_1

21605 0 1.97 0 0 -1.92 0

21611 0 0 0 -1.37 0 Inhibitor_I29;Peptidase_C1

21612 0 0 0 1.86 0 GARS_A

21613 4.91 2.79 0 0 0 0

21640 0 0 1.73 0 0 Iso_dh

21651 0 0 0 -1.53 0 Abhydrolase_1

21656 2.16 0 0 0 0 0

21661 0 0 0 -3.04 1.57 Pro_isomerase

21663 0 0 0 -1.7 1.29 0

21664 2.43 1.15 0 0 0 0

21665 5.85 0 0 1.56 1.58 0

21666 0 -1.2 0 -1.84 0 0

21680 0 0 0 -1.26 0 0

21683 4.82 4.32 0 0 0 Kringle;peroxidase

21690 0 0 0 -1.86 0 0

21692 -1.94 0 -1.43 0 0 DUF2470

21707 3.2 2.19 0 0 0 0

21720 0 0 1.96 0 1.58 ZZ

21725 0 0 1.62 2.63 0 Brix

21727 1.16 0 0 0 0 DENN;PDZ

21748 -2.45 0 -2.16 0 0 F_bP_aldolase

21753 0 0 1.65 0 0 PGAM

21774 0 0 0 -1.59 0 0

21776 2.4 0 0 0 -1.47 0

21779 1.64 2.06 0 0 0 0

21781 0 0 1.17 2.43 0 0

21784 1.45 1.5 0 0 0 0

21785 -1.37 0 2.65 2.32 0 Pyr_redox_2

21795 3.54 3.54 0 0 0 0





21807 0 0 0 -2.27 0 PDZ

21808 0 0 0 -1.36 0 0

21821 -2.49 0 -3.19 0 0 0

21830 0 0 0 0 2.41 WLM

21844 2.32 2.64 0 0 1.84 0

21847 0 0 0 1.62 0 FAD_binding_2

21850 1.96 1.81 0 0 -1.52 Cyclin_N

21855 1.91 0 1.77 1.48 0 PH;DUF1336

21886 0 0 0 1.41 0 0

21896 0 0 0 0 1.48 0

21897 -2.76 0 0 -1.92 2.55 0

21898 -1.62 0 0 0 0 0

21900 0 0 0 1.55 0 FAD_binding_2

21923 0 0 0 0 -1.39 0

21948 -1.29 0 0 0 0 DUF179

21965 0 0 0 0 2.08 Thioredoxin

21966 0 0 1.49 2.5 0 0

21968 3.62 0 0 0 0 0

21979 0 0 1.69 1.7 0 DUF2373

22002 0 0 0 -1.38 0 0

22016 -1.37 0 0 0 0 ABC_membrane;ABC_tran

22025 0 0 0 0 1.31 0

22030 4.4 3.02 0 0 0 Hydrolase

22052 0 0 3.27 0 0 0

22057 1.7 0 0 0 0 0

22064 0 0 0 -2.8 2.39 0

22074 -1.87 -2.93 0 -2.44 0 0

22114 0 0 0 3.86 0 0

22117 -5.04 -1.76 -1.84 -1.24 0 0

22127 0 0 0 -2.18 0 0

22155 0 0 0 0 1.56 K_tetra

22163 0 0 0 1.24 0 0

22166 0 0 0 -1.3 0 0

22171 -1.04 0 0 0 0 Cation_efflux

22187 0 0 0 0 3.04 HSP20

22194 -1.36 -1.59 -1.73 -2.44 0 DUF1995

22197 0 0 0 2.18 0 Aldo_ket_red

22198 -1.33 0 -1.73 2.59 0 0

22199 0 1.43 0 0 0 0

22200 0 0 1.83 2.45 0 DnaJ

22203 0 0 1.93 1.81 0 0

22208 -3.1 -2.45 -2.56 -2.31 0 Aminotran_5





22213 0 0 1.61 1.72 0 PFK

22214 -2.04 0 -2.64 0 0 0

22215 0 0 0 -1.37 0 CBM_14

22218 0 1.99 0 0 0 0

22220 -3.75 0 0 0 0 0

22223 0 0 0 0 1.85 Serinc

22227 0 0 0 0 1.83 0

22231 0 0 0 -1.67 0 0

22234 0 0 2.1 0 0 PAS

22240 0 0 0 1.71 0 eRF1_1;eRF1_2;eRF1_3

22251 0 0 2.36 0 0 0

22277 1.27 1.02 0 0 0 0

22323 0 0 0 2.3 0 SRP19

22332 3.42 2.22 0 0 1.61 Peptidase_S51

22337 1.56 0 0 1.21 0 0

22339 0 0 0 -1.43 1.28 DnaJ;U-box

22345 0 0 6.21 4.07 0 PK;PK_C

22349 0 1.31 0 0 0 0

22356 0 0 0 1.3 0 TruD

22368 0 0 0 1.36 1.96 ArsA_ATPase;ParA

22378 0 0 0 1.33 0 0

22379 0 0 0 2.64 0 0

22396 -2.21 -1.04 -1.66 0 0 0

22404 0 0 0 1.56 0 DEAD;Helicase_C

22413 -1.54 -1.16 0 -1.25 0 0

22428 0 1.44 0 0 0 0

22429 0 0 0 3.09 0 0

22430 0 0 1.23 0 0 SOUL

22454 -1.77 -2.05 1.9 0 0 HSF_DNA-bind

22455 0 0 0 -1.97 0 0

22462 0 0 1.45 1.27 0 FtsJ

22481 0 0 0 0 1.89 0

22483 0 0 1.42 0 0 0

22486 0 1.87 0 0 0 0

22490 0 0 0 0 1.5 DOMON;Cytochrom_B561

22495 1.32 2.23 0 2.05 1.47 Cyclin_N

22522 0 1.9 0 0 0 0

22526 -1.3 0 0 0 0 ABC_membrane;ABC_tran;DUF1602

22527 -1.48 0 -1.55 0 0 NAD_binding_1

22566 0 0 1.75 0 0 0

22582 0 0 0 1.28 0 S1

22610 0 0 0 1.05 0 Ribosomal_L4;NAC;CIA30





22635 0 0 0 1.94 0 Nop;CSD;NOP5NT;NOSIC;IF3_N;IF3_C;Pet1

91_N

22640 0 0 3.17 0 0 0

22643 0 1.07 0 0 0 0

22645 1.3 0 0 0 0 0

22656 0 0 0 0 1.27 TatD_DNase

22658 1.52 0 0 0 0 DUF1800;DUF1501

22670 0 0 2.61 0 0 0

22671 0 0 2.83 0 0 FAD_binding_3

22683 0 0 0 -1.38 0 0

22701 -4.01 0 0 0 0 0

22702 -1.85 0 1.37 0 0 0

22712 0 0 0 2.08 0 Fer2

22714 0 0 -1.35 0 0 0

22721 0 0 2.07 0 0 0

22725 0 0 0 1.25 0 SAP

22731 0 0 1.69 0 0 zf-C3HC4

22734 -3.31 0 0 0 -1.8 0

22748 -2.19 -1.44 0 0 0 0

22752 0 0 1.42 0 0 0

22772 0 1.68 0 0 0 0

22781 2.29 1.99 0 0 0 0

22783 0 0 0 0 1.62 CRCB

22794 0 0 -1.73 0 0 TRAM_LAG1_CLN8

22795 0 0 2.43 -1.55 0 0

22796 0 0 0 0 1.33 UBA;PUB

22855 0 0 0 -2.15 0 0

22860 -2.03 0 0 0 0 0

22861 0 0 1.54 1.76 0 0

22863 0 0 -1.89 1.34 0 Peptidase_M16;Peptidase_M16_C;M16C_asso

c

22864 0 0 0 -1.54 0 DAO;GCV_T;GCV_T_C

22867 -1.44 0 0 0 0 0

22876 0 0 0 1 0 0

22877 0 0 0 -1.4 0 MgtC

22879 0 0 0 1.55 0 0

22880 0 0 0 -1.88 0 Fasciclin

22891 -1.74 0 0 0 0 0

22941 -3.88 -3.92 0 -4.33 0 peroxidase

22984 2.01 1.55 0 0 0 Gelsolin

22985 0 0 0 1.84 0 NLE;WD40

22993 2.87 3 0 0 0 GARS_A

23036 0 1.82 0 0 0 0





23040 0 1.68 1.73 0 0 DUF1800;DUF1501

23071 1.65 0 0 0 0 0

23143 4.36 2.21 0 2.8 0 Cyclin_N

23159 1.3 0 0 0 0 0

23160 2.86 2.37 4.13 0 0 0

23209 0 0 0 1.27 0 0

23224 0 1.52 1.57 1.98 0 0

23225 0 0 0 0 -1.67 0

23232 0 0 0 2.41 0 0

23245 0 0 1.45 1.6 0 0

23246 0 0 0 3.02 0 0

23253 -2.99 0 0 0 0 0

23264 0 1.56 1.39 0 0 0

23272 0 0 0 0 1.39 Adap_comp_sub

23292 0 0 0 2.39 0 FA_desaturase

23295 -2.07 0 0 -4.34 0 Trp_Tyr_perm

23296 0 0 1.53 0 0 0

23303 0 -1.3 0 0 0 0

23308 -1.02 0 0 -1.12 1.36 0

23329 0 0 0 1.4 0 Cpn60_TCP1

23354 0 0 0 2.26 0 CPL

23357 0 0 -1.56 1.61 0 Hydrolase

23359 0 0 0 -1.32 0 0

23363 0 -3.54 -4.44 -5.52 0 0

23369 0 1.6 0 0 2.48 0

23374 0 0 0 -2.64 2.26 0

23377 0 0 1.63 0 0 VWA

23390 -1.5 0 0 0 0 0

23391 0 0 1.45 -1.66 0 Ammonium_transp;Cyt-b5;FA_desaturase

23393 0 0 0 1.66 0 0

23396 2.23 1.39 0 -1.18 0 0

23399 -1.86 0 0 0 0 efhand

23409 0 0 0 0 1.01 0

23416 1.01 1.45 0 0 0 0

23431 0 0 0 -1.88 1.7 0

23446 0 0 0 -1.87 1.79 0

23466 0 0 1.15 0 0 0

23481 -1.18 0 0 0 0 Sulfotransfer_1

23486 0 0 0 0 3.16 0

23503 0 0 1.23 -1.1 0 Ank

23504 3.79 1.37 0 0 0 0

23505 2.86 1.79 0 0 0 0





23509 0 1.64 0 0 0 0

23510 2.87 3.49 0 0 0 0

23511 0 1.51 0 0 0 0

23519 0 0 0 -1.6 0 PAH;HDAC_interact;Pkinase

23526 1.44 0 0 0 0 Glyoxalase

23527 0 0 0 1.66 0 Prefoldin_2

23528 0 0 -1.36 0 0 Ras

23543 0 0 0 -1.43 2.04 0

23565 0 0 0 -1.04 0 0

23603 -1.03 0 0 0 0 0

23607 0 0 -1.72 0 0 TRAM_LAG1_CLN8

23620 -1.25 0 -1.73 -1.88 5.16 0

23623 -2.08 -1.79 4.82 0 0 0

23624 2.19 2.11 0 0 0 CAP

23653 0 0 0 0 1.71 Cyclin_N

23654 0 -1.27 0 0 0 0

23655 0 0 0 1.66 0 MAM33

23657 -1.98 0 0 0 0 Globin

23662 0 0 0 -1.87 3.05 Trypsin

23665 -1.99 0 0 0 0 HSF_DNA-bind

23669 0 0 0 0 1.4 0

23671 1.6 0 0 0 0 0

23684 -1.94 0 0 0 1.21 0

23685 -1.94 0 0 0 1.21 0

23700 0 0 0 -1.49 0 0

23701 0 -1.35 0 -2.28 0 0

23716 0 0 1.62 0 0 0

23719 1.79 1.99 0 0 0 0

23720 2.17 2.81 0 0 0 0

23757 -1.16 0 0 0 0 0

23771 -2.9 0 0 0 1.95 0

23794 -1.13 -1.23 0 -1.99 1.21 0

23796 1.69 0 0 0 1.66 0

23798 0 0 0 2.89 0 FA_desaturase

23801 -2.01 0 1.36 0 0 HSF_DNA-bind

23811 0 0 0 3.89 0 ABC_tran

23813 0 1.57 -1.56 0 0 Kazal_1

23814 0 1.73 -1.56 0 0 Kazal_1

23821 0 -1.54 0 -2.45 0 0

23827 0 0 0 -2.48 0 0

23850 -1.48 0 0 0 0 WD40

23857 0 1.78 0 0 1.67 Thioredoxin;PUB





23861 2.46 2.72 0 0 0 0

23862 1.14 0 0 0 0 0

23867 0 0 2.18 -3.78 0 PCMT

23872 0 0 1.79 3.6 0 GLTP

23881 -1.22 0 0 0 0 0

23888 0 0 0 -2.29 0 0

23899 0 0 0 0 1.23 0

23918 -3.89 0 -4.57 0 0 0

23927 1.75 1.9 0 -2.7 2.77 Smr

23929 0 0 0 1.94 0 0

23932 1.92 2.1 0 0 0 0

23933 0 0 0 -1.31 0 0

23934 -2.28 -2.05 0 0 2.43 0

23937 0 0 0 -1.36 0 0

23947 0 0 0 -1.23 0 0

23976 2.23 2.75 0 0 0 SBF

23986 2.09 1.84 0 -1.95 4.92 0

23993 -1.63 -1.77 0 0 1.75 0

24000 0 0 0 0 1.43 uDENN

24008 0 1.55 0 0 0 0

24011 0 0 0 -1.33 0 0

24016 0 0 0 1.31 0 Ebp2

24017 2.37 0 0 0 0 0

24023 1.73 0 0 0 0 HA

24028 2.63 1.68 0 0 0 0

24039 2.6 1.82 0 0 0 0

24046 0 0 0 0 2.45 0

24056 -1.16 0 0 0 0 Pkinase

24060 -2.29 0 0 0 0 GFO_IDH_MocA

24072 0 0 0 1.33 0 0

24074 0 0 0 0 1.35 0

24077 0 0 0 -1.47 0 0

24099 -1.13 0 -1.55 0 0 0

24112 0 0 0 0 1.32 HSF_DNA-bind

24126 0 0 0 1.59 0 Sld5

24134 0 0 0 -1.73 1.73 0

24149 0 0 0 1.96 2.33 0

24153 2.05 0 0 0 0 0

24160 -3.58 0 0 0 -1.97 0

24163 0 0 -2.17 0 0 tRNA-synt_2d;FDX-ACB

24164 4.12 2.01 0 2.18 1.98 0

24165 1.25 0 0 0 0 0





24172 0 0 0 1.75 0 0

24198 0 0 0 -3.44 3.96 0

24199 0 0 0 0 1.8 0

24205 0 0 0 0 1.21 0

24217 0 0 1.87 1.83 0 CS;SAP

24224 -1.37 0 0 0 0 0

24225 -1.72 0 0 0 0 ClpS

24248 -3.79 -2.97 -2.46 -2.59 0 GATase;CPSase_L_chain;CPSase_L_D2;CPSa

se_L_D3;MGS;CPSase_sm_chain

24257 0 0 0 -1.93 0 0

24261 0 0 1.68 1.87 0 0

24292 1.35 0 0 0 0 zf-CCCH

24303 1.47 1.35 0 1.16 0 DUF647

24307 2.59 2.5 0 -2.08 0 0

24309 0 0 -2.43 0 0 PsbP

24319 1.26 0 0 1.36 0 0

24325 0 0 0 -3.38 0 Cna_B

24341 0 0 0 1.4 0 DnaJ

24346 0 0 0 1.24 0 0

24361 0 0 1.01 0 0 0

24362 0 0 1.58 0 0 0

24369 0 0 0 1.88 0 RRM_1

24376 -1.38 0 0 0 2.44 C2;TerD

24379 0 0 2.84 1.56 0 0

24385 4.18 3.61 -2.22 2.09 2.22 0

24396 0 0 1.33 2.04 0 HSF_DNA-bind

24428 0 1.17 0 0 0 0

24441 2.1 2.17 0 0 1.65 0

24443 0 2.21 0 0 0 0

24445 1.24 0 0 0 0 0

24448 0 0 1.78 0 0 0

24456 0 0 0 0 1.69 0

24486 0 0 0 0 1.29 0

24500 0 0 0 -3.45 2.54 0

24507 0 0 0 1.49 0 0

24511 0 0 0 0 1.45 0

24512 -2.79 0 -3.01 0 0 0

24521 0 0 0 -2.31 2.16 GATase_2;SIS

24534 0 -2.24 1.86 -3.17 0 0

24535 -2.23 0 0 0 0 ADH_N

24557 -1.17 -1.47 0 -1.88 0 0

24559 -1.58 -1.87 0 0 0 0





24564 -2.68 -2.99 0 -2.28 0 0

24566 -2.22 -1.94 0 -1.97 -2.37 0

24569 -1.86 0 0 0 0 0

24571 -2.02 -1.97 0 -1.55 0 0

24575 0 0 0 -1.23 0 Guanylate_cyc

24597 0 0 0 1.45 0 0

24599 0 0 0 0 2.12 0

24602 1.76 0 0 0 0 0

24606 0 -1.5 0 0 1.91 0

24649 -2.78 0 0 0 -2.33 0

24655 0 0 1.41 0 0 0

24657 1.4 0 0 0 0 0

24659 0 0 0 -1.47 2 3_5_exonuc;KH_1

24669 -4.43 -2.26 -4.11 0 0 0

24677 0 -1.84 0 -2.13 1.51 Smr

24680 1.48 1.28 0 0 0 Sulfotransfer_2

24694 0 0 -1.12 -1.94 0 DUF1800;DUF1501

24699 -2.01 0 0 0 0 0

24700 -1.73 0 -2.1 0 0 PMSR

24704 0 0 1.52 -1.63 0 0

24718 0 0 0 1.63 0 ABC1

24726 3.38 2.48 0 0 0 0

24738 0 0 0 0 1.53 0

24760 4.72 3.56 0 -4.01 2.54 0

24761 5.16 3.58 0 -4.04 2.57 0

24769 -4.51 -1.02 -4.87 -1.7 0 0

24770 0 0 -1.14 0 0 DUF498

24812 0 0 0 2.26 0 GFO_IDH_MocA

24816 0 1.63 0 2.32 -2.26 AsnA

24818 4.19 4.03 0 0 0 0

24849 0 0 1.41 0 0 Exonuc_X-T

24850 0 0 0 1.21 0 0

24858 6.34 5.13 0 2.23 0 0

24862 0 0 0 -1.74 0 0

24882 -1.89 0 -1.43 -1.83 0 0

24900 1.84 1.6 0 0 0 0

24904 0 0 1.18 2.33 0 Fcf1

24910 0 0 0 0 1.99 0

24918 2.73 1.04 0 0 0 0

24923 0 0 5.21 0 0 0

24932 0 0 -1.06 0 0 Thioredoxin

24935 -2.11 0 0 -1.77 0 0





24961 0 0 0 0 1.84 Kelch_1

24962 0 0 0 0 1.4 0

24963 2.12 2.51 0 0 0 0

24977 0 0 2.05 0 0 TRAUB

24980 1.21 1.23 0 1.51 0 0

24999 -1.76 -1.33 3.53 -2.63 0 0

25012 0 -1.33 0 1.15 0 MAPEG

25041 -1.31 0 0 0 0 TPR_1

25058 -4.81 -4.09 0 -3.73 -3.21 0

25059 0 0 0 2.6 0 0

25060 -4.54 -4.91 0 -4.19 -3.17 0

25061 -4.41 -4.24 0 -4.32 -3.84 0

25064 0 0 0 1.91 0 0

25069 0 0 0 0 1.97 0

25075 0 0 0 1.74 0 0

25092 0 0 0 0 1.8 0

25100 0 0 0 2.22 0 PAP_fibrillin

25104 -1.89 0 0 1.65 -2.35 0

25107 0 0 0 -1.36 2.17 U-box

25115 1.17 0 0 0 0 0

25116 -2.01 0 -1.38 0 0 PGK

25123 0 0 1.34 0 0 0

25127 0 0 0 0 2.05 DUF1625

25130 0 0 0 2.45 0 2-Hacid_dh_C

25156 0 1.23 -1.66 0 2.08 0

25157 0 0 0 -1.38 1.79 0

25159 0 0 2.35 2.32 0 0

25161 0 0 1.48 0 0 0

25164 0 0 0 -2.56 0 0

25171 -1.49 0 0 0 1.85 DUF1517

25183 0 0 0 1.44 0 0

25190 0 0 0 -1.29 0 0

25193 -1.92 0 0 0 0 0

25202 1.75 0 0 0 0 0

25205 4.16 2.58 0 0 0 Beta_propel

25206 0 0 0 0 2.78 0

25207 0 0 0 0 2.07 0

25217 0 0 0 -1.69 0 0

25231 0 0 0 0 2.01 Acetyltransf_1

25235 0 0 0 0 -1.19 0

25243 0 1.9 1.54 1.77 0 0

25246 1.45 0 0 0 0 RRM_1





25264 0 0 0 -2 0 0

25277 0 0 0 1.92 0 0

25280 6.74 5.45 0 0 2.02 0

25290 2.11 1.72 0 0 0 0

25295 0 0 0 1.51 0 0

25299 -2.05 0 0 0 0 Oxidored_molyb;Mo-co_dimer;Cyt-

b5;FAD_binding_6;NAD_binding_1;Glyco_hy

dro_38C

25306 0 0 0 -1.45 0 0

25335 0 0 0 0 1.62 0

25337 -2.34 -2.54 0 -3.62 -3.18 RCC1

25347 -1.15 -1.51 0 0 0 0

25353 0 0 0 0 1.83 0

25355 0 0 0 1.33 0 Plug_translocon;SecY

25357 6.02 5.39 0 0 0 0

25372 1.51 1.92 0 0 0 0

25381 1.79 1.34 0 0 0 0

25382 0 1.35 0 0 0 Glyco_transf_10

25386 0 0 0 0 2.33 0

25387 -1.5 0 0 0 0 0

25389 0 0 4.43 0 0 0

25393 -1.67 0 0 0 0 0

25396 0 0 0 0 1.2 0

25398 1.44 0 0 0 0 0

25414 4.03 2.91 0 0 2.65 0

25415 0 0 0 -1.62 1.67 0

25428 0 0 0 -1.89 0 0

25430 0 0 0 -1.84 0 0

25432 1.72 2.38 0 0 0 0

25439 -2.96 -2.43 0 -1.66 0 0

25463 0 0 0 0 1.76 0

25464 -2.45 0 0 0 -1.7 0

25528 0 0 0 1.62 -1.77 0

25544 0 0 -1.21 0 0 0

25548 1.2 0 0 0 0 0

25551 0 -1.37 0 0 0 0

25560 -2.62 -1.78 -2 0 0 PseudoU_synth_2

25572 0 0 2.45 0 0 Acetyltransf_1;CoA_binding

25582 3.17 0 0 0 1.96 RRM_1

25590 0 0 0 -1.97 2.02 0

25594 0 -1.4 0 0 0 0

25604 0 0 0 1.52 0 0

25610 -1.22 0 -1.87 0 0 Fasciclin;Methyltransf_11





25613 0 0 0 -2.72 0 FAT

25623 1.62 0 0 0 0 0

25626 0 1.44 0 0 0 0

25629 0 0 0 1.77 0 GTP_EFTU;GTP_EFTU_D2;EFG_IV;EFG_C

25635 0 0 0 0 2.33 0

25637 1.42 2 0 0 1.45 0

25639 0 0 0 0 1.75 Choline_kinase

25649 1.35 2.26 0 0 0 0

25650 0 0 1.01 0 0 0

25659 -1.57 0 1.9 -1.71 1.46 0

25692 -1.45 0 0 0 0 0

25698 0 0 0 1.74 0 IFRD

25713 3.54 3.69 0 0 0 0

25739 1.3 0 0 0 0 VWA

25742 1.68 1.02 0 0 0 0

25746 0 0 0 1.6 0 DUF2039

25750 0 0 0 0 1.59 0

25766 0 0 0 1.65 0 0

25775 1.42 0 0 0 0 zf-C3HC4

25783 1.2 2.09 0 0 0 0

25797 0 0 0 2.58 0 0

25805 2.6 3.05 0 1.47 0 0

25807 -3.59 0 0 0 0 0

25814 1.4 0 0 0 0 0

25839 0 0 -1.33 0 0 0

25840 -5.65 -3.03 -1.33 -4.98 0 CdCA1

25843 0 0 0 0 1.75 0

25848 -1.42 0 -1.28 -1.59 2.27 0

25898 -4.03 0 0 0 0 0

25904 4.55 3.82 0 0 2.76 0

25905 1.26 0 0 0 0 DUF23

25908 1.47 0 0 0 0 0

25909 0 0 1.57 0 2.06 0

25912 -1.9 0 0 0 0 0

25917 0 0 0 0 -1.98 0

25921 -5.64 0 0 0 0 0

25949 0 0 0 1.14 0 Ribosomal_L5;Ribosomal_L5_C

26022 0 0 2.48 2.05 0 Pam16

26031 0 0 0 2.12 0 SHMT

26041 6.18 6.58 0 0 0 Chitin_bind_1

26131 -4.22 -1.84 -2.16 0 0 CytB6-F_Fe-S;Rieske

26137 0 0 0 1.21 0 Ribosomal_L6





26190 0 0 0 1.63 0 UCH;SHMT

26365 2.18 0 0 -3.32 0 ECH;3HCDH_N

26366 0 0 3.3 2.84 0 Mito_carr

26418 0 0 0 1.34 0 Peptidase_S24

26470 0 0 0 0 1.47 Epimerase

26473 1.91 1.68 0 0 0 Metallophos

26492 0 0 0 1.37 0 GARS_N;GARS_A;GARS_C;Formyl_trans_N

;AIRS;AIRS_C

26530 0 0 -1.95 0 0 Cation_efflux

26548 -3.82 -2.26 -3.88 -3.02 0 GFO_IDH_MocA;GFO_IDH_MocA_C

26678 0 0 0 1.79 0 Transketolase_N;Transket_pyr;Transketolase_

C

26686 0 0 0 1.61 0 WD40

26759 -1.58 0 0 0 -1.22 adh_short

26991 -1.83 0 0 0 0 Glutaredoxin

27083 0 0 0 1.62 0 PCI;eIF3_N

27273 -2.43 0 -2.3 1.69 0 MTHFR

27292 0 0 0 1.27 0 RS4NT;Ribosomal_S4e

27414 -3.05 0 0 -2.03 0 MFS_1

27550 0 1.18 2.02 2.29 0 THF_DHG_CYH;THF_DHG_CYH_C;cNMP

_binding

27776 0 0 0 0 1.1 Amidohydro_1

27836 0 0 0 -2.17 0 Chitin_synth_2

27850 0 0 2.99 0 0 PGAM

28028 0 0 -1.54 0 0 PCI

28300 0 0 1.86 0 0 RrnaAD

28326 0 -1.6 -1.64 0 0 LYTB

28350 0 0 0 2.4 -1.26 PGAM

28521 -1.72 0 -2.66 0 0 GCV_H

28544 0 1.65 1.48 4.03 0 DapB_N;DapB_C

28570 0 0 0 2.77 0 Ribosomal_L7Ae

28682 0 0 0 1.57 0 Cpn60_TCP1

28755 0 0 2 2.57 0 Ribosomal_S8e

28825 3.93 4.53 0 0 0 Cyt-b5;Chitin_synth_2

28842 1.22 0 0 0 0 Thioredoxin

28865 0 0 0 1.36 0 ABC1;APH

28920 0 0 0 -3.2 2.42 Pkinase

28998 0 0 0 1.71 1.68 Aldo_ket_red

29008 0 0 0 1.26 0 AA_kinase;NAD_binding_3;Homoserine_dh

29049 0 0 0 2.65 0 IF-2;IF2_N;GTP_EFTU

29183 0 0 0 1.45 0 HMGL-like;LeuA_dimer

29244 -1.23 0 0 0 0 Pro_isomerase

29314 -1.99 0 -2.04 0 0 Peptidase_M17





29506 0 0 0 0 1.14 Cpn10

29771 3.16 2.68 1.86 0 0 PI3_PI4_kinase;DUF3385;FAT;Rapamycin_bi

nd;FATC

29782 0 0 0 0 1.46 eIF-6

30385 -4.26 -1.45 -5.35 -3.14 0 Chloroa_b-bind

30851 0 0 -2.68 0 0 AAA;AAA_2;ClpB_D2-small;DUF3170

30862 0 0 0 2.92 0 zf-CCHC

30871 0 0 2.36 2.91 0 Ham1p_like

30887 0 0 1.6 0 0 Cyt-b5

30939 -1.66 0 0 2.94 0 eIF-1a

30976 -2.39 0 -2.06 0 0 FKBP_C

30977 0 0 0 2.15 0 Pep_deformylase

30979 0 0 0 1.41 0 HIT

31001 1.46 0 0 0 0 Glycos_trans_3N;Glycos_transf_3;PYNP_C

31006 -1.45 0 0 0 0 Ribosomal_S30AE

31011 0 0 0 -1.61 0 Mito_carr

31035 0 0 0 2.05 0 Trypsin

31037 0 0 0 3.15 0 DEAD;Helicase_C

31047 0 0 0 2.13 0 Brix

31085 2.74 3.15 0 0 0 Aa_trans

31108 0 0 0 -1.69 0 Glyco_transf_20;Trehalose_PPase

31125 0 0 0 1.88 0 adh_short

31128 -4.22 -4.03 -4.35 0 0 Chloroa_b-bind

31166 0 2.11 0 0 0 AdoMet_dc

31169 -2.09 0 0 -1.6 0 AhpC-TSA

31216 0 0 -1.27 0 0 Aminotran_1_2

31226 1.24 0 0 0 0 Sugar_tr

31232 0 0 1.96 0 0 PFK

31259 0 0 0 1.75 0 HA

31266 0 0 1.73 0 0 NUDIX

31362 -1.26 0 0 0 0 ATP-grasp_2;Ligase_CoA

31394 0 0 -2.24 1.6 0 Aminotran_1_2

31402 0 0 -1.74 1.52 0 DUF3007

31406 1.21 0 1.34 0 0 Arf

31412 0 0 0 1.47 0 THF_DHG_CYH_C

31415 0 0 0 1.35 0 Cons_hypoth95

31447 0 0 0 1.63 0 Mito_carr

31451 0 0 0 1.55 0 Ribosomal_L18e

31465 0 0 0 2.52 0 Brix

31535 -1.74 0 0 0 0 FKBP_C;Pro_isomerase

31564 0 0 -1.24 0 0 LSM

31569 -2.09 0 0 0 0 Tubulin;Tubulin_C





31635 0 0 0 3.59 0 TPT

31636 -1.91 -2.2 -1.57 0 0 Aldose_epim

31673 0 0 -1.46 0 0 Radical_SAM

31732 0 0 0 -1.78 0 Ank

31749 -6.62 -3.19 -7.08 -5.07 0 Chloroa_b-bind

31771 0 0 0 1.08 0 ABC1

31783 2.48 2.75 0 0 0 PrmA

31818 0 0 3.08 0 0 Mito_carr

31819 0 0 0 1.71 0 DUF2419

31923 0 0 0 1.37 0 0

31930 -1.39 0 -1.63 1.51 0 AAA;Peptidase_M41

31938 0 0 0 2.01 0 PUF

31951 -1.69 0 0 0 0 Cation_efflux

31979 0 0 0 1.05 0 HhH-GPD

31983 -2.41 -1.92 -2.72 -2.38 0 Chloroa_b-bind

32003 1.49 0 0 0 1.54 Lipase_GDSL

32029 0 0 0 -1.63 0 Methyltransf_11;Sterol_MT_C

32053 0 0 -1.51 0 0 WHEP-TRS;tRNA-

synt_2b;HGTP_anticodon;ProRS-C_1

32066 -1.11 0 0 1.87 0 Mito_carr

32067 0 0 1.67 -1.88 0 Transket_pyr;Transketolase_C

32137 0 0 0 2.22 0 Hydrolase

32140 0 0 0 2.73 0 DHDPS

32145 -2.75 0 -1.6 1.85 0 Lipid_DES;FA_desaturase

32153 -1.1 0 -1.57 2.31 0 0

32158 0 0 1.83 1.27 0 NOC3p;CBF

32176 0 0 0 0 1.06 MAP1_LC3

32216 0 0 0 1.35 0 Ribosomal_S5;Ribosomal_S5_C

32223 -1.27 0 -1.5 0 0 Ras

32252 0 0 0 1.46 0 Rib_5-P_isom_A

32261 -2.15 0 -1.9 0 0 DUF2237

32325 0 0 0 1.38 0 ABC_membrane;ABC_tran;DUF1602

32332 -3.45 -2.57 -2.85 1.85 0 Rib_5-P_isom_A

32374 0 0 0 1.44 0 0

32430 1.59 0 0 0 0 DUF1336

32431 -1.53 -1.4 -1.06 0 0 Fe_bilin_red

32459 -1.72 0 1.61 0 0 PspA_IM30

32485 0 0 0 -1.62 0 Pro_dh

32493 0 0 2.42 -1.52 0 Aldo_ket_red

32557 -2.73 0 0 0 0 zf-C3HC4

32577 0 0 0 0 1.43 Semialdhyde_dh;Semialdhyde_dhC

32596 0 1.56 0 0 0 Trypsin





32610 0 0 0 0 1.42 EamA

32678 1.76 1.83 0 0 0 Archease

32723 -4.08 -1.84 -4.11 -4.05 0 Chloroa_b-bind

32736 0 0 0 0 2.02 DEAD;Helicase_C

32738 4.38 4.08 0 -1.83 0 Pkinase

32795 0 0 0 1.23 0 Arf

32807 0 0 0 2.07 0 DEAD;Helicase_C

32827 0 0 0 1.25 0 Metallophos

32860 -1.18 0 -1.69 3.26 0 0

32874 -2.36 0 -2.02 -1.7 0 Sod_Fe_N;Sod_Fe_C

32964 -6.19 -2.6 -5.39 -1.95 -1.2 PsbM

32971 0 0 0 -1.86 0 Trypsin

33000 -3.48 -1.99 -3.07 0 0 FTHFS

33021 0 0 0 4.2 0 FBPase

33024 0 0 0 -2.37 0 Isochorismatase

33035 0 0 -1.43 0 0 YGGT

33044 0 0 0 1.5 0 WHEP-TRS;tRNA_anti;tRNA-synt_2

33067 -1.51 0 0 0 0 CBFD_NFYB_HMF

33131 -6.1 -2.94 -6.52 -5.2 0 Chloroa_b-bind

33169 0 0 0 2.52 0 Mpv17_PMP22

33219 0 0 0 3.11 0 Sdh5

33220 0 0 0 2.86 2.56 PMSR

33241 0 0 0 1.12 0 Ribosomal_L22

33270 -2.45 0 0 1.25 0 Histone

33316 0 0 -1.37 0 1.44 Epimerase

33330 0 0 0 1.65 0 Glycos_transf_2

33340 -1.63 0 0 0 0 bZIP_1;PAS

33343 -1.53 0 0 0 0 NAD_binding_2;6PGD

33407 -1.34 0 0 0 0 bZIP_2;PAS

33476 0 0 0 2.45 0 Diphthamide_syn

33558 0 0 0 1.42 0 SecA_DEAD;SecA_SW

33578 -1.13 0 0 0 0 Ras

33580 0 0 1.49 0 0 ABC_membrane;ABC_tran

33589 0 0 0 2.4 0 Brix

33606 -5.58 -2.25 -5.52 -5.05 0 Chloroa_b-bind

33653 0 2.35 0 2.51 0 GrpE

33663 -1.47 0 -1.06 0 0 PGI

33680 0 2.29 0 0 0 HMG-CoA_red

33701 -3.21 -1.73 -3.18 0 0 0

33718 0 0 1.73 3.25 0 Methyltransf_4

33772 2.26 2.43 0 0 0 Pkinase

33855 0 0 -2.38 0 0 FKBP_C





33883 -1.69 0 0 0 -2.03 Cyclin_N;Cyclin_C

33891 -2.42 -1.43 -1.72 0 0 Sigma70_r2;Sigma70_r3;Sigma70_r4

33911 0 0 0 3.2 0 Steroid_dh

33937 0 0 0 1.98 0 FAD_binding_6;NAD_binding_1

33941 0 0 0 2.56 0 Mito_carr

33973 0 0 0 -1.17 0 DUF3336;Patatin

33979 -1.72 0 0 0 -1.53 Pkinase;POLO_box

33985 0 0 0 1.28 0 PseudoU_synth_1

33995 0 0 0 1.71 0 DUF1077

34006 0 0 0 1.84 0 Nol1_Nop2_Fmu

34030 -2.64 0 -2.46 0 0 malic;Malic_M

34044 0 0 0 1.96 0 Peptidase_M24

34094 0 0 -2.44 -2.92 2.32 0

34104 0 0 0 2.37 0 FKBP_C

34125 -7.14 -6.86 -8.58 -6.92 0 0

34170 0 0 0 1.52 0 Peptidase_M18

34191 1.12 1.04 0 0 0 CobN-Mg_chel

34210 -3.81 0 0 0 0 Histone

34211 -2 0 0 -1.9 1.85 Cytochrom_C

34276 -6.26 -2.41 -6.38 -4.6 0 Chloroa_b-bind

34280 0 0 0 -1.31 2.24 0

34283 0 0 0 1.95 0 adh_short

34340 0 0 0 1.42 0 0

34357 0 0 0 1.85 0 Fcf2

34379 0 0 -1.23 0 -1.13 NIF

34447 0 0 0 1.1 0 Cyt-b5

34543 -2.6 0 -2.79 0 0 PEPcase

34551 0 0 -2.46 0 0 60KD_IMP

34554 0 0 0 3.49 0 Psb28

34559 0 0 0 1.16 0 Pkinase

34592 1.48 0 2.18 2.28 0 FAD_binding_6;NAD_binding_1

34681 0 0 -1.59 2.16 0 FabA

34738 0 0 0 0 1.81 0

34746 0 0 1.5 0 0 Abhydrolase_2

34771 0 0 0 1.74 0 Formyl_trans_N;Formyl_trans_C

34809 2.7 0 0 0 0 Thiolase_N;Thiolase_C

34878 1.93 0 0 0 0 Abhydrolase_1

34881 0 0 0 1.66 0 Pescadillo_N;BRCT

35041 0 0 2.4 1.86 0 0

35094 0 0 2.94 0 0 2-Hacid_dh_C;Aldolase_II

35189 1.8 0 0 0 0 DNA_pol_B_exo

35206 0 0 1.82 0 0 LRR_1





35310 -1.74 1.43 0 0 -1.6 Kinesin

35407 -1.74 0 0 1.39 0 Histone

35409 0 0 -2.51 0 0 peroxidase

35464 -1.31 0 0 0 0 SAP;HhH-GPD

35499 0 0 0 -1.45 0 SMC_N;SMC_hinge

35523 0 0 -1.18 -1.6 0 ICL

35532 0 0 -1.11 -1.46 0 Epimerase

35639 0 0 0 3.22 0 Abhydrolase_1

35685 0 0 1.82 0 1.68 CS

35710 1.56 0 0 0 0 Acyl-CoA_dh_N;Acyl-CoA_dh_M;Acyl-

CoA_dh_1

35740 0 0 0 1.93 0 adh_short

35871 -2.96 -1.93 -2.48 0 0 2-Hacid_dh_C

35878 -1.96 0 -1.47 0 0 PGM_PMM_I;PGM_PMM_II;PGM_PMM_III

35911 0 0 0 2.34 0 Methyltrans_SAM

35963 1.63 1.36 1.73 0 0 Ureidogly_hydro;Polysacc_deac_1;Isochorism

atase

35968 -3.03 0 -1.3 1.64 -1.58 0

36015 0 0 3.33 0 0 Mpv17_PMP22

36053 0 0 0 1.45 0 Ank;Pkinase;Pkinase_Tyr

36078 0 0 -1.75 0 0 Redoxin

36081 -5.96 -2.32 -4.98 -5.35 0 Chloroa_b-bind

36186 0 0 0 1.99 0 DUF143

36208 -2.71 -1.62 -1.53 -1.44 0 GCV_T;GCV_T_C

36263 0 1.32 0 0 0 Ammonium_transp

36291 0 0 2.12 0 0 Biotin_lipoyl;E3_binding;2-oxoacid_dh

36297 0 0 0 -2 0 Asp

36322 0 0 0 2.41 0 tRNA_U5-meth_tr

36339 0 0 1.22 0 0 tRNA-synt_1;Anticodon_1

36406 0 0 2.28 1.73 0 Ribosomal_L30

36420 0 2.23 0 0 0 HATPase_c;DNA_gyraseB;DNA_topoisoIV

36431 0 0 -1.25 0 0 Pro_isomerase

36434 0 0 0 1.87 0 DUF1253

36456 0 0 0 1.94 0 eIF-5_eIF-2B

36462 -4.11 0 -4.06 0 0 TIM

36477 1.06 0 0 0 0 ABC_tran;ABC2_membrane;DUF1602

36522 0 1.62 0 0 0 HATPase_c;DNA_gyraseB;DNA_topoisoIV

36527 0 0 0 1.91 0 Methyltransf_11

36539 0 0 1.65 0 0 Trm112p

36557 0 0 0 3.61 0 PAP_fibrillin

36572 0 0 0 -1 0 GFO_IDH_MocA

36576 0 0 0 2.65 0 GTP_EFTU;GTP_EFTU_D2;EFG_C

36679 0 1.1 0 2.94 0 ICMT





36689 0 0 1.87 0 1.13 PFL;Gly_radical

36702 0 0 1.42 0 1.06 PFL;Gly_radical

36709 0 0 -1.25 2.99 0 Epimerase

36716 0 0 2.37 0 0 Pyr_redox_2;Pyr_redox_dim

36788 -1.8 0 0 0 0 NAD_binding_5

36837 0 0 0 -1.71 0 ABC_membrane;ABC_tran;DUF1602

36917 0 0 0 1.36 0 TGT

36929 0 0 0 1.86 0 KH_1

36958 1.83 0 0 0 0 Pkinase_Tyr

36995 0 0 0 0 1.26 0

37071 0 1.64 1.81 3.1 0 OMPdecase;Pribosyltran

37098 0 0 1.7 1.77 0 Pterin_4a

37127 -2.07 1.16 0 0 -1.42 Glyco_hydro_31

37280 -1.26 0 0 2.35 0 ABC1

37288 0 0 -2.25 0 0 Amino_oxidase

37294 -2.54 0 -2.41 -2.02 0 ABC1

37306 1.23 0 0 0 0 Sigma70_r2;Sigma70_r4

37322 2.08 2.81 0 0 0 Pkinase

37338 0 0 -1.82 0 0 Thioredoxin

37357 -1.67 0 0 0 0 Histone

37359 0 0 0 1.59 0 CLP_protease

37376 0 1.34 0 0 0 Myosin_head;IQ

37431 -1.58 0 0 1.21 0 Histone

37444 0 0 0 3.42 0 Methyltransf_11

37450 0 0 -1.71 0 0 Peptidase_M16;Peptidase_M16_C

37493 0 0 0 -1.51 0 Formyl_trans_N

37509 -1.72 0 0 0 0 vATP-synt_AC39

37534 -1.67 0 -1.68 -1.89 0 DUF59;ParA;DUF971

37562 0 0 0 1.26 0 Pkinase;Pkinase_Tyr

37584 0 0 0 -3.05 0 Cupin_3

37592 0 1.42 0 0 0 Pkinase

37599 0 0 0 2.99 0 Peptidase_M41

37615 -1.08 0 0 -1.24 0 Epimerase

37707 2.44 1.63 0 0 0 MORN

37739 0 0 0 0 1.47 adh_short

37803 3.25 2.25 0 0 0 DUF208

37928 0 0 1.67 0 0 MOZ_SAS

37961 0 0 0 2.3 0 zf-Tim10_DDP

37965 0 0 1.35 1.43 0 S1

37976 -1.34 0 -2.19 0 0 Pro_isomerase

37988 0 1.56 0 0 0 Arf

38006 0 0 1.61 1.63 0 FKBP_C





38030 0 0 -1.5 0 0 ABM

38051 0 0 1.64 0 0 6PF2K;PGAM

38054 -1.43 0 0 0 0 MtN3_slv

38085 -3.02 0 -3.5 0 0 EamA;TPT

38121 0 0 0 -1.96 0 AAA_2;ClpB_D2-small;DUF3170

38122 -5.89 -3 -5.71 -5.99 0 Chloroa_b-bind

38139 0 0 0 3.02 0 Chloroa_b-bind

38191 0 1.57 0 1.64 0 Cpn60_TCP1

38221 0 0 0 2.01 0 0

38360 0 0 1.31 0 0 Peptidase_C1

38494 -3.95 0 -3.64 -2.65 0 Chloroa_b-bind

38512 -1.97 0 -2.2 0 0 Hydrolase_4

38513 0 0 0 2.47 0 ABC1

38574 0 0 0 0 1.9 DnaJ

38575 -1.37 0 0 0 0 adh_short

38597 0 0 1.42 0 1.06 PFL;Gly_radical

38608 -1.95 0 -1.72 1.97 0 Semialdhyde_dh;NmrA

38646 1.39 0 1.93 1.82 0 Thioredoxin

38724 -3.08 0 -1.76 0 0 peroxidase

38760 0 0 1.87 0 1.13 PFL;Gly_radical

38767 0 0 0 0 1.78 Peptidase_C1

38776 0 0 0 1.2 0 BOP1NT;WD40

38780 0 0 0 -1.35 0 DSPc

38800 0 0 -1.05 0 0 peroxidase

38807 -4.06 -2.32 -3.63 0 0 Aldolase

38845 1.15 0 0 0 0 ABC_membrane;ABC_tran

38879 -4.6 -2.6 -1.84 2.65 0 Chloroa_b-bind

38924 0 0 -1.88 0 0 tRNA-synt_1c;tRNA_bind

38964 0 0 0 1.98 0 Chorismate_synt

38973 0 0 0 0 2.24 DEP;TBC

38987 0 -1.6 0 2.58 0 ABC_tran;DUF1602

39032 0 0 0 0 1.29 Fe-S_biosyn

39098 0 1.25 0 0 0 HSP70

39149 0 0 0 0 1.17 HATPase_c;HSP90

39173 -1.15 0 0 0 0 ATP-synt_D

39286 0 0 1.57 1.69 0 ABC_tran;DUF1602

39550 0 0 0 1.23 0 Ribosomal_S7e

39622 0 0 0 1.72 0 NAC;UBA

39677 -1.82 1.66 0 0 0 Myb_DNA-binding

39710 0 0 0 2.12 0 MMR_HSR1;YchF-GTPase_C

39799 -4.64 -2.1 -4.38 -2.54 0 GDC-P

39813 -5.1 -1.47 -3.87 -3.26 0 Chloroa_b-bind





39824 1.5 0 0 0 2.36 Clp_N;AAA;AAA_2;ClpB_D2-

small;DUF3170

39845 0 0 0 1.53 0 DNA_topoisoIV;DNA_gyraseA_C

39864 0 0 0 0 1.51 DEAD;Helicase_C

39924 0 0 0 1.98 0 HATPase_c;DNA_gyraseB;Toprim;DNA_gyra

seB_C

39941 0 0 0 2.34 0 Aminotran_4

39953 0 0 1.88 0 0 DEAD;Helicase_C

40044 0 0 0 1.77 0 Peptidase_M24

40193 0 0 -3.93 0 0 AAA

40233 0 0 0 1.59 0 CSD

40323 -4.55 -2.94 -1.24 0 0 CPSase_sm_chain;GATase;CPSase_L_chain;C

PSase_L_D2;CPSase_L_D3;MGS

40329 0 0 0 1.29 0 Ribosomal_L18ae

40341 0 0 0 2.97 0 WD40

40385 0 0 -1.07 0 0 ABC_membrane;ABC_tran;DUF1602

40393 0 0 6.17 4.24 0 PK;PK_C

40509 0 0 2.01 2.53 0 Ribosomal_L24e

40586 -2.06 0 0 0 1.61 Epimerase

40597 0 0 0 1.39 0 RPE65

40630 -2.06 0 0 0 0 Band_7

40747 -5.83 -1.96 -5.21 -4.24 0 Chloroa_b-bind

40771 0 0 0 1.58 0 Enolase_N;Enolase_C

40788 -2.09 0 -2.04 0 0 RmlD_sub_bind

40801 0 0 0 2.55 0 polyprenyl_synt

40958 3.22 3.49 3.3 4.92 0 TIM

40966 0 0 0 2.38 0 Sigma70_r2;Sigma70_r3

40985 0 0 0 2.71 0 GN3L_Grn1;MMR_HSR1

41014 0 0 0 5.33 0 FA_desaturase

41169 0 0 0 0 1.68 Peptidase_M16;Peptidase_M16_C

41178 0 0 0 1.69 0 HisG;HisG_C

41392 3.15 0 -1.45 0 0 Silic_transp

41425 0 0 2.12 -1.53 1.82 Ldh_1_N;Ldh_1_C

41433 -1.81 0 3.04 0 0 Pyr_redox_dim;Pyr_redox_2

41632 -1.54 0 0 0 0 Asp

41655 0 0 0 2.79 0 Chloroa_b-bind

41697 -1.29 0 0 0 0 Thioredoxin

41733 0 -1.37 0 0 -1.28 ThiC

41979 0 0 1.83 0 0 Pyr_redox_2;Pyr_redox_dim

42123 0 0 0 -2.49 2.32 GATase_2;SIS

42133 0 0 0 -1.55 0 Guanylate_cyc

42194 0 0 0 2.42 0 cNMP_binding

42258 4.7 3.41 0 0 0 WD40





42320 -1.66 0 0 0 1.72 Snf7

42515 0 0 0 1.85 0 Helicase_C;DEAD

42545 0 0 0 0 1.62 PA

42577 -2.28 -1.06 0 -2.04 0 PGK

42594 0 0 2.28 2.74 0 Ribosomal_L10

42612 0 0 0 1.41 0 SOR_SNZ

42660 0 0 0 2.21 0 0

42704 0 0 0 1.15 0 Aconitase;Aconitase_C

42804 0 0 0 0 1.5 Trypsin

43120 0 0 0 1.67 0 Thioredoxin;COPIIcoated_ERV

43128 0 0 -1.27 0 0 Saccharop_dh

260748 0 0 0 2.59 0 TRM

260761 -1.8 0 0 0 0 HA

260906 1.55 0 0 0 0 Phos_pyr_kin

260925 1.71 0 0 0 0 dCMP_cyt_deam_1

260926 -1.92 0 0 0 0 LRR_1

260934 0 0 3.08 -1.35 0 Aminotran_4

260941 0 0 0 1.26 0 CDP-OH_P_transf

260942 -2.67 -1.69 -2.47 -2.13 0 Ribonuc_L-PSP

260953 -6.13 -4.4 -5.91 -3.01 0 OCD_Mu_crystall

260962 -1.13 0 0 0 0 Rhodanese

260974 0 0 -2.02 0 0 0

260975 0 0 1.38 0 0 Na_H_Exchanger

260991 0 0 0 1.7 0 DnaJ;DnaJ_C

261036 -3.83 0 0 0 -2.26 LRR_1

261087 0 0 0 -3.48 0 Aminotran_3

261109 0 0 0 3.44 0 HSP70

261112 0 0 0 1.45 0 NUDIX

261124 1.47 0 0 0 0 AlaDh_PNT_C;Saccharop_dh_N

261161 0 2.5 0 1.4 0 Spermine_synth

261226 0 0 0 2.19 0 Pentapeptide

261232 -1.94 0 -1.57 0 0 URO-D

261242 0 0 0 2.62 0 PsbP

261275 -1.52 0 0 2.42 0 ThylakoidFormat

261284 0 0 0 1.54 0 Aldo_ket_red

261287 -1.19 0 0 0 0 SUI1

261476 0 0 0 0 1.48 Biotin_lipoyl

261525 0 0 0 2.54 0 p450

261541 -1.35 0 0 0 0 RRM_1

261636 0 0 0 1.81 0 Glutaredoxin

261641 -3.32 -2.13 0 -3.44 2.35 Ferric_reduct;FAD_binding_8;NAD_binding_

6





261684 0 0 -1.81 0 0 tRNA-synt_1;Anticodon_1

261690 0 0 -1.37 0 0 ABC_tran;ABC2_membrane

261702 2.2 2.43 0 0 2.77 HSP20

261705 0 -1.38 0 0 0 Trypsin

261711 0 0 0 -1.38 1.5 Sulfotransfer_1

261726 0 0 0 -2.24 0 Metallophos;Acetyltransf_1

261727 1.2 0 0 0 0 Kringle

261748 -2.48 -1.84 -3.15 -2.06 0 cobW;CobW_C

261777 0 0 0 1.71 0 MIF4G

261820 3.27 2.36 0 0 0 Pkinase

261823 0 0 0 1.65 0 FBPase

261827 -2.1 0 0 0 0 Thioredoxin

261878 0 0 0 1.71 0 Peptidase_M50

261882 -1.77 0 0 0 1.62 Glutaredoxin

261885 0 0 1.64 0 0 RRM_1

261895 0 0 0 0 1.33 Glyco_hydro_18

261925 -1.45 0 0 0 0 PP2C

261935 0 0 0 1.39 0 Thioredoxin

261965 3.3 2.97 0 0 0 HSF_DNA-bind

261966 1.5 0 0 0 0 Pyr_redox_2

262006 0 0 0 0 -1.49 Carb_anhydrase

262009 -3.07 0 0 0 -1.98 Carb_anhydrase

262032 -2.07 0 0 -1.84 0 0

262041 0 0 1.74 1.92 0 MIF4G;MA3

262099 2.4 2.05 0 0 0 Ala_racemase_N;Ala_racemase_C

262129 2.06 2.15 0 0 0 zf-C3HC4

262146 0 0 0 -1.71 0 Cyt-b5;Chitin_synth_2

262148 0 0 0 -1.72 0 Cyt-b5

262149 0 0 0 -1.75 0 Cyt-b5


262153 0 0 0 -3.16 0 PT;Glyco_hydro_18

262217 -1.44 0 0 0 0 LRR_1

262229 0 0 0 1.65 0 adh_short

262236 0 0 1.42 0 0 AA_permease

262242 1.98 0 0 -1.83 0 AMP-binding

262249 -2.16 0 0 0 0 LRR_1

262250 0 0 0 0 1.22 TPR_4;IU_nuc_hydro

262254 0 0 -1.17 1.66 0 ABC_tran

262258 0 0 0 1.11 0 CitMHS

262279 -3.07 0 -2.77 1.67 0 UbiA

262307 0 0 0 -1.43 0 BCCT

262313 -5.67 -1.8 -4.27 -6.09 0 Chloroa_b-bind





262322 0 0 0 0 -1.23 Homeobox

262332 -6.81 -2.83 -6.38 -5.06 0 Chloroa_b-bind

262335 0 0 0 1.48 0 Methyltransf_11

262414 0 0 0 2.02 0 CSD

262433 0 0 1.98 -3.26 0 PUF

262455 0 0 0 0 1.86 HlyIII

262457 0 0 0 2.67 0 60KD_IMP

262506 0 0 -1.17 0 0 0

262517 1.81 0 0 -1.11 0 Glyco_hydro_18

262535 0 0 2.48 0 0 TPR_4;TPR_1

262564 -1.73 0 0 0 0 V-ATPase_H_N;V-ATPase_H_C

262567 0 0 0 0 1.75 Aldo_ket_red

262572 0 0 -1.86 0 0 Octopine_DH

262610 0 0 0 1.19 0 NUDIX

262619 0 0 0 -1.35 0 Spc97_Spc98

262620 0 0 0 4.17 0 Ank

262630 0 0 0 1.92 0 DnaJ

262659 1.94 1.99 0 0 1.31 Trypsin

262677 1.8 2.27 0 0 0 PrmA

262679 -1.88 0 0 -1.55 0 Cation_ATPase_N;E1-

E2_ATPase;Hydrolase;Cation_ATPase_C

262743 -1.94 0 0 0 1.61 Choline_transpo

262753 -3.49 0 -3.7 3.35 0 peroxidase

262775 0 0 1.36 2.04 0 RBFA

262849 -3.61 -2 -4.3 -2.39 0 Chromate_transp

262946 0 0 0 2.3 0 DNA_photolyase;FAD_binding_7

262963 1.61 0 0 0 0 Phos_pyr_kin;TMP-TENI

262977 0 0 0 2.02 0 Fer2

263081 2.55 1.35 0 0 0 LRR_1

263116 0 0 0 1.48 0 Rubredoxin

263121 2.92 3.65 0 0 0 Peptidase_M8

263132 -1.74 0 -1.86 0 0 Phytochelatin

263142 1.59 1.4 0 0 0 FtsJ

263182 0 0 0 1.43 0 DUF1350

263212 1.08 1.42 -1.22 0 0 CAP

263213 1.37 1.47 0 0 0 CAP

263240 1.51 0 0 0 0 SNF2_N

263246 2.08 0 0 0 0 PI-PLC-X;PI-PLC-Y;C2

263268 0 0 0 -1.31 0 Peptidase_M8

263271 -1.8 0 0 -1.36 0 LRR_1

263287 0 0 0 0 4.27 DUF347







263313 2.05 1.73 0 0 0 Peptidase_M8

263343 1.3 0 0 0 0 HpcH_HpaI;MaoC_dehydratas

263346 0 0 0 0 1.74 LRR_1

263355 -1.46 0 0 0 0 0

263365 0 0 0 2.42 0 WW;SpoIIE

263371 0 0 0 1.54 0 DcpS_C

263431 0 0 -1.39 0 0 Peptidase_M16;Peptidase_M16_C

263451 1.51 0 0 0 0 Trypsin

263452 -1.48 0 0 0 1.78 Trypsin

263456 2.49 2.09 0 1.43 0 DUF938

263461 0 0 0 0 1.41 Methyltransf_11

263528 -2.02 0 0 -1.9 0 zf-TRAF

263641 0 0 0 1.45 0 GTP_EFTU;IF-2

263658 -1.87 0 -2.34 0 0 GUN4

263660 0 0 0 1.57 0 Acyltransferase

263661 0 0 0 3.06 0 Mito_carr

263707 0 0 0 1.61 0 Mito_carr

263781 0 0 -1.99 0 0 Sulfate_transp;STAS

263801 0 1.89 0 2.74 1.47 PseudoU_synth_2

263816 0 1.28 0 0 0 Chromo;SNF2_N;Helicase_C

263830 0 1.58 4.01 0 3.21 PFL;Gly_radical

263883 0 0 0 0 1.48 0

263887 0 0 0 0 2.83 Pkinase

263902 0 0 0 1.71 0 UbiA

263906 0 0 0 -2.75 1.81 GSHPx

263924 -1.86 -1.45 -2.05 -2.23 0 MFS_1

263935 -1.81 -1.89 -2.26 0 1.09 PAS

263938 0 0 0 -1.42 2.12 0

263953 0 0 0 -2.08 0 Guanylate_cyc

263992 -1.84 0 -1.44 -2.67 0 Thioredoxin

264004 0 1.38 0 0 0 E1-E2_ATPase

264005 0 0 2.63 0 0 Glyco_hydro_16

264008 2.25 3.53 0 0 2.12 TylF

264043 0 0 0 0 -1.1 Myb_DNA-binding

264095 0 0 0 0 1.49 TBP

264109 0 0 0 -1.51 0 adh_short

264149 -1.37 0 0 0 0 Ribosomal_L28

264157 0 0 0 -2.49 1.19 CBS

264181 -7.87 -7.66 -7.09 -9.28 0 CaMKII_AD

264269 0 0 0 0 1.49 0

264275 0 0 -1.44 0 0 CRAL_TRIO





264289 0 0 2.36 0 0 HSP20

264293 1.5 1.74 0 1.59 0 0

264295 0 1.08 0 0 0 PP2C

264335 0 0 0 2.55 0 PIP5K

264337 0 0 0 -1.8 0 0

264353 2.34 0 0 0 0 Mito_carr

264361 -1.99 0 0 0 0 Peptidase_M1;DUF3458

264384 -2.04 -2.66 -2.42 -2.48 3 0

264395 1.79 0 0 0 0 PI3_PI4_kinase

264421 -1.45 0 0 -1.35 0 ABC2_membrane

264439 0 0 0 -1.15 0 DAO

264494 0 0 0 -2.13 1.76 SIR2

264496 0 0 -1.79 0 0 Aldo_ket_red

264651 0 0 0 -2.58 1.64 Thioredoxin

264663 0 0 0 1.87 0 Thioredoxin

264664 0 0 1.66 0 0 Thioredoxin

264670 0 0 1.57 0 0 Redoxin

264671 1.01 0 0 -1.63 2.52 Pkinase

264688 0 0 0 1.97 1.29 GSHPx

264730 2.72 3.05 0 0 2.16 AdoMet_dc;Spermine_synth

264732 1.8 2.27 0 0 0 PrmA

264753 0 1.58 4.01 0 3.21 PFL;Gly_radical

264781 0 0 0 1.82 0 0

264786 0 0 0 1.27 0 RNA_pol_Rpc34

264804 0 0 0 -1.44 2.09 0

264807 -2.4 0 0 0 0 ATP-synt_C

264815 0 0 0 0 1.7 Peptidase_M3

264846 0 0 0 -1.44 1.84 Inhibitor_I29;Peptidase_C1

264854 -1.72 0 0 0 0 Pkinase

264865 -1.42 0 -2.03 0 0 CSD

264891 0 0 2.34 0 0 Glyco_hydro_30;Ricin_B_lectin

264901 -1.29 0 0 0 0 Amino_oxidase

264903 0 0 0 0 1.35 Glyco_hydro_18

267958 1.61 1.75 0 0 0 An_peroxidase

267971 0 0 1.75 0 0 MORN

268003 0 0 2.2 0 0 0

268009 0 0 0 -1.11 0 FTR1

268024 0 0 0 -2.32 0 0

268027 0 0 0 1.55 0 Met_10

268043 0 0 0 0 1.74 DUF231

268054 0 0 0 0 1.82 DUF1632;Sugar_transport

268059 0 0 0 1 0 0





268062 0 0 0 1.62 0 DnaJ;DnaJ_CXXCXGXG

268064 0 0 0 0 1.93 HSF_DNA-bind

268070 0 0 0 1.1 0 0

268117 0 1.63 0 0 -1.66 Histone

268160 -2.01 0 0 0 0 Band_7

268172 0 0 0 1.65 1.29 0

268185 0 -1.64 0 0 1.06 0

268187 0 0 -1.52 0 0 YGGT

268204 3.87 0 0 0 4.06 0

268220 0 0 0 -1.36 1.49 Peptidase_M6

268234 0 0 0 -1.78 0 0

268238 0 0 0 1.88 0 GIDA

268242 -2.18 -1.34 0 0 0 0

268270 0 0 0 1.96 0 Patched

268271 0 0 0 2.53 0 SRP54;SRP_SPB;SRP54_N

268296 0 0 0 1.59 0 Ribosomal_L12

268300 0 0 0 0 1.84 DUF493

268316 2.34 3 0 0 1.45 Annexin

268329 3.33 3.18 0 -1.42 0 Synaptobrevin

268335 0 0 0 -2.46 0 Bac_GDH;ELFV_dehydrog

268343 -2.17 -1.59 -2.21 1.48 0 0

268350 0 0 0 1.79 0 SpoU_methylase

268354 0 0 0 -1.39 1.35 Cyclin_N

268410 1.88 0 0 0 0 Pkinase

268447 -2.09 -1.63 0 0 0 Thioredoxin

268449 0 0 0 2.69 0 GST_C

268474 -2.52 0 0 0 0 NmrA

268480 -3.19 -1.66 -1.84 -1.7 0 polyprenyl_synt

268481 0 0 0 0 1.5 HSF_DNA-bind

268500 0 0 1.29 0 0 HATPase_c;HSP90;NAD_binding_1;FAD_bin

ding_6

268546 -3.15 -2.59 0 -2.79 0 PEPcase

268548 1.48 1.49 0 0 0 Esterase;ADH_N

268594 0 0 3.63 0 0 Aminotran_1_2

268596 0 0 0 1.26 0 CBF

268619 0 0 0 1.21 0 Arf

268621 -2.57 0 -2.12 0 0 PGM_PMM_I;PGM_PMM_II;PGM_PMM_III

;PGM_PMM_IV

268644 0 0 0 1.07 0 RNase_PH;RNase_PH_C;PNPase;KH_1;S1

268653 0 0 0 0 1.59 0

268657 0 0 -1.19 2.72 0 Pyr_redox_2;Pyr_redox_dim

268669 0 0 0 1.49 0 Rieske;PaO

268678 0 0 0 1.22 0 DnaJ;Myb_DNA-binding





268695 0 0 1.47 0 1.48 GTP_cyclohydro2;GTP_CH_N

268713 0 0 -2.64 0 0 0

268714 0 0 0 1.38 0 GTP_EFTU;GTP_EFTU_D2;EFG_C

268773 0 0 -1.18 0 0 0

268788 0 0 1.78 0 0 0

268839 2.58 2.04 0 0 0 RRM_1

268858 2.23 2.61 0 0 0 0

268881 0 0 0 0 1.46 Mod_r

268889 0 0 0 0 1.48 SNF2_N

268958 0 0 0 0 -2.3 0

268963 -2.12 0 1.63 -1.55 0 PUF

268965 0 0 1.96 0 0 Aconitase_B_N;Aconitase_2_N

268966 0 0 0 -1.83 0 Glyco_hydro_18

268970 1.42 0 1.62 2.39 0 2-Hacid_dh_C

269049 0 0 0 1.88 0 eIF-1a

269057 2.98 1.86 0 0 0 PGK

269086 0 0 0 1.09 0 Ribosomal_S24e

269095 -1.77 -1.8 4.45 0 0 Methyltransf_11

269115 0 1.34 0 0 0 0

269127 0 0 2 0 0 Acyl-CoA_dh_N;Acyl-CoA_dh_M;Acyl-

CoA_dh_1

269135 0 0 0 0 1.56 Tic22

269141 0 0 2.4 0 0 0

269160 0 0 1.71 0 0 Pyr_redox_2

269238 0 0 2.57 0 0 HSF_DNA-bind

269258 1.86 1.69 0 0 1.72 0

269273 0 0 0 -1.56 1.9 0

269325 -2.1 0 0 0 0 HSF_DNA-bind

269328 0 0 0 -2 0 Carboxyl_trans

269355 0 0 0 0 2.65 Kazal_1

269386 0 1.49 0 0 0 Trypsin

269393 -1.82 0 0 0 0 Epimerase

269434 0 0 1.44 1.29 0 Methyltrans_SAM

269459 -1.52 0 0 0 0 0

269475 0 0 0 0 1.6 SIR2

269513 0 0 0 -1.92 1.41 Acyl-CoA_dh_N;Acyl-CoA_dh_1;Acyl-

CoA_dh_M

269541 0 0 0 0 2.79 Peptidase_M20

269557 -3.08 0 1.56 -1.57 0 NAD_binding_4

269559 0 0 0 1.56 0 rRNA_processing

269575 0 0 0 0 1.27 0

269633 0 4.34 0 0 0 0

269653 -2.45 -1.49 0 -1.93 2.27 0





269655 0 2.53 0 0 0 Tubulin;FtsZ_C

269696 -4.53 -3.62 -5.46 -2.25 3.03 0

269699 -4.53 -3.62 -5.49 -2.22 3.03 0

269714 0 1.2 0 0 0 APS_kinase;ATP-sulfurylase;Pyrophosphatase

269764 0 0 0 2.19 0 PseudoU_synth_2

269776 0 0 1.84 2.23 0 0

269779 0 0 0 1.42 0 Ribosomal_S6e

269792 0 0 0 0 2.04 Annexin;WD40

269826 0 0 0 -1.95 0 Cyclin_N

269844 -1.41 0 1.61 -1.66 0 Glyco_transf_28;UDPGT

269866 0 -1.13 1.67 0 0 GST_N;GST_C

269876 0 -1.54 0 -2.08 2 0

269889 0 0 -1.26 -1.18 0 Pkinase

269900 0 0 1.89 0 0 GATase_2;Glu_syn_central;Glu_synthase;GX

GXG

269908 -3.97 -2.29 -3.72 -2.69 0 CPSase_L_chain;CPSase_L_D2;Biotin_carb_

C;HMGL-like;PYC_OADA;Biotin_lipoyl

269952 2.14 1.77 0 0 0 MtN3_slv

269968 0 0 -2.07 -1.47 0 Rhodanese

269975 0 0 1.51 1.65 0 0

269997 1.68 0 0 0 0 0

270013 0 0 0 1.52 0 3_5_exonuc;SAP;DNA_pol_A

270038 -1.25 0 0 0 0 Peptidase_C14

270092 -3.73 -2.63 -3.66 -2.45 -1.58 Chloroa_b-bind

270113 0 0 0 0 2.15 cNMP_binding

270127 0 0 0 0 1.64 Glyco_hydro_18;CBM_14

270136 0 0 1.77 0 0 Aminotran_3

270137 -1.85 0 0 -5.1 0 efhand





Table S7: List of Thalassiosira pseudonana proteins returning hits from NPAC, bacillariophyta-

like sequences.

ID # Hits Si Fe N T Co2 PFAM

6363 7821 0 0 0 1.27 0 Ribosomal_L23eN

5259 1228 0 0 0 1.14 0 Ribosomal_L44

2848 111 -2.6 -1.56 -4.74 -1.55 0 PsbU

25772 73 0 0 -1.33 0 0 Actin

28496 40 -2.89 1.44 -2.04 2.42 0 AdoHcyase

38715 29 -5.85 -1.17 -3.96 -3.62 0 Chloroa_b-bind

41829 28 1.38 0 0 0 0 GTP_EFTU;GTP_EFTU_D2;GTP_

EFTU_D3

6285 20 0 0 0 1.34 0 HATPase_c;HSP90

42962 20 0 0 1.38 0 0 Chloroa_b-bind

31383 19 -3.61 -1.01 -3.43 1.04 0 Gp_dh_N;Gp_dh_C

575 17 -3.58 0 -4.11 0 0 Aminotran_3

866 16 0 0 -1.32 0 0 CLP_protease

12152 15 -2.94 0 -2.9 2.28 0 ketoacyl-synt;Ketoacyl-synt_C

26893 14 0 0 0 1.47 0 Ribosomal_S13

25933 13 0 0 -1.62 0 0 TPT

41256 12 -1.31 0 0 0 0 ATP-synt_ab_N;ATP-synt_ab_C

41548 12 0 0 0 0 1.7 Epimerase

26051 11 0 0 0 1.68 0 Gln-synt_N;Gln-synt_C

31705 10 -1.84 -1.13 0 -2.18 0 Mpv17_PMP22

264201 10 0 0 0 1.28 0 Ribosomal_L2;Ribosomal_L2_C

38583 9 -4.98 -1.28 -4.31 -2.28 0 Chloroa_b-bind

9716 8 0 0 0 1.69 0 DEAD;Helicase_C

268127 8 -5.69 -1.11 -7.68 -5.58 0 Chloroa_b-bind

268304 8 -3.86 -1.19 -4.27 0 0

33018 7 -6.19 -1.67 -6.41 -3.45 0 Chloroa_b-bind

802 6 0 0 0 1.02 0 Ribosomal_L18p

32924 6 -3.56 -2.49 -3.43 0 0 Ribul_P_3_epim

39143 6 1.16 0 0 0 0 Mito_carr

264921 6 -4.12 -2.46 -1.86 2.68 0 Chloroa_b-bind

428 5 -4.88 -2.57 -2.2 0 0 F_bP_aldolase

1326 5 -2.16 0 0 0 0 ATP-sulfurylase

29825 5 0 0 0 1.23 0 Ribosomal_S8e

32201 5 -4.33 0 -3.08 0 0 Mg_chelatase;VWA

39936 5 0 0 0 1.57 0 Metallophos

1738 4 0 0 -1.59 0 0 CLP_protease

21175 4 -4.53 -1.14 -5.62 0 0 Transketolase_N;Transket_pyr;Trans

ketolase_C

32546 4 0 0 -1.6 2.13 0 Cyt-b5;FA_desaturase





262125 4 -3.45 -1.49 0 0 0 NIR_SIR_ferr

3622 3 -1.09 0 -1.19 0 0 IspD

21815 3 0 1.47 -1.84 1.53 0 S-AdoMet_synt_N;S-

AdoMet_synt_M;S-AdoMet_synt_C

22565 3 -2.12 0 1.44 0 0 Sugar_tr

26046 3 0 0 0 1.11 0 Ribosomal_S3Ae

28189 3 0 0 0 3.65 0 HSP70;NAD-GH

31012 3 -3.51 -1.87 -2.97 0 0 Coprogen_oxidas

31516 3 0 0 0 1.46 0 NOP5NT;NOSIC;Nop

32555 3 -1.58 0 0 0 0 Ribonuc_red_sm

32752 3 0 0 0 1.16 0 Ribosomal_L24e

39278 3 -1.17 0 0 0 0 ATP-synt_C

42326 3 -2.22 0 -2.37 -1.87 0 UDPGP

269274 3 -3.27 0 0 -2 0 MFS_1

2343 2 -2.21 0 -3.64 1.87 0

3741 2 -1.71 -1.41 0 2.13 0 ELO

4830 2 0 1.64 0 0 0 Cofilin_ADF

5021 2 0 0 0 2.02 0 ketoacyl-synt;Ketoacyl-synt_C

5174 2 -3.9 0 -5.07 -4.63 0 Chloroa_b-bind

10234 2 -4.48 -2.55 -3.98 -1.15 0 FAD_binding_3

11411 2 0 0 2.09 0 0 Citrate_synt

20603 2 -6.96 -2.64 -7.64 -2.61 0

25892 2 -4.33 -1.44 -3.64 0 0 NAD_binding_1

26221 2 0 0 0 1.02 0 Ribosomal_S13_N;Ribosomal_S15

26367 2 0 0 0 1.21 0 Ribosomal_S8

28125 2 -1.81 0 0 0 0 GlutR_N;Shikimate_DH;GlutR_dim

er

31446 2 0 0 0 1.11 0 Ribosomal_S21

33008 2 0 0 0 1.86 0 EPSP_synthase

36235 2 -2.24 0 -1.52 0 0 p450

36979 2 0 0 0 2.01 0 Fcf1

38667 2 -5.92 -1.49 -4.18 -3.48 0 Chloroa_b-bind

39901 2 0 0 0 0 1.39 PGK

40391 2 -1.82 0 1.83 0 0 Enolase_N;Enolase_C

269240 2 0 0 1.83 1.63 0 HSP70;NAD-GH

3815 1 -5.93 -3.43 -3.8 -5.97 0 Chloroa_b-bind

4914 1 -3.23 0 -3.32 0 0 NAD_binding_1

5219 1 -2.66 -2.41 -2.69 0 0 Acyl_transf_1

5240 1 -2.5 0 -2.92 -3.08 0 ALAD

8522 1 -2.23 0 0 0 -2.35 Ribonuc_red_sm

9021 1 -2.95 0 0 0 0 Porphobil_deam;Porphobil_deamC

10233 1 -1.74 0 -1.45 0 0

11501 1 -5.78 -1.87 -5.7 -2.24 0 Chloroa_b-bind





13806 1 -1.17 0 0 0 0 tRNA-synt_1c;tRNA-synt_1c_C

20008 1 0 0 0 1.36 0 Ribosomal_S26e

20965 1 -7.71 -2.61 -7.03 -3.37 -1.4

21261 1 -2.28 0 0 0 0 Rubredoxin

21292 1 1.31 0 0 0 0 CitMHS

21327 1 -1.62 0 -1.93 0 0 DUF1625

21972 1 -1.75 0 0 3.01 0

22350 1 0 0 0 1.38 0 Ribosomal_S21e

22476 1 0 0 0 1.43 0 Ribosomal_L13e

23283 1 -1.74 0 -2.07 0 0

24250 1 -1.5 0 1.76 0 0 SSF

26436 1 0 0 0 1.7 0 Peptidase_M3

26573 1 -3.65 -1.62 -3.28 0 0 DUF3479;CobN-Mg_chel

27873 1 0 0 1.55 0 0 IMPDH

29217 1 0 0 0 1.17 0 Ribosomal_L7Ae

29375 1 -5.89 -1.9 -6.91 -4 0 Chloroa_b-bind

29728 1 0 0 0 0 1.67 Epimerase

29842 1 -4.9 -1.85 -4.56 -1.78 0

29861 1 0 0 1.65 0 0 GATase_2;Glu_syn_central;Glu_syn

thase;GXGXG

31014 1 0 0 0 1.59 0 Adap_comp_sub

31091 1 -2.13 0 -1.72 0 0 NDK

31851 1 -1.58 0 -1.94 2.95 0 adh_short

31912 1 -1.36 0 0 0 0 Pro_isomerase

32955 1 -2.59 -1.71 -3.24 0 0 ADH_N;ADH_zinc_N

33871 1 0 0 0 1.69 0 Metallophos

34585 1 -2.72 -2.43 -3.81 0 0 Radical_SAM;BATS

34830 1 -4.34 -1.9 -5.16 -1.91 0 MSP

34864 1 0 0 -1.2 2.39 0 FKBP_N;FKBP_C

35180 1 -7.04 0 -1.56 -1.08 -1.87 Mpv17_PMP22

35712 1 -4.7 0 -5.16 1.56 0 PGK

35816 1 0 0 -1.51 0 0 Gp_dh_N;Gp_dh_C

35934 1 -4.71 -1.26 -4.14 0 0 Cytochrom_C

37083 1 0 0 0 1.17 0 DnaJ;DnaJ_C

39003 1 0 0 2.91 3.93 0 UPF0113

39666 1 -1.14 0 0 0 0 cobW;CobW_C

40156 1 -2.65 0 -2.08 0 0 ATP-synt

40312 1 0 0 0 1.15 0 Ribosomal_S9

41005 1 -2.34 0 -2.43 0 0 Transket_pyr;Transketolase_C

41113 1 0 0 0 1.79 0 Cyt-b5;FA_desaturase

262796 1 -3.1 -1.28 -4.57 0 0 PP-binding

264039 1 -2.02 0 -2.37 0 0 p450





268374 1 0 0 2.49 0 0 E1_dh

268895 1 7.15 0 -1.35 0 0 Silic_transp

269348 1 -1.97 0 -2.4 0 0 Pyrophosphatase

269942 1 -2.92 0 -2.57 0 0 SHMT





Table S8. Genes investigated during this study and sequences of the primers used to amplify

target genes by qPCR.

Gene Target/protein ID Primer sequence (5' - 3')Amplicon

size (bp)

Large ribosomal protein

L27e/269038

Fwd: GTCCGTCATATCTTCCCAACAC

Rev: TACTCGACGTTCCGCATCAAC

93


L22/270383

Fwd: TGCACATGGTCGAATTGGTA

Rev: GTTTGGCGGCCATCTTTCTG

131


L14/271911

Fwd: TTGCCCTAACGGATTTAACTGTG

Rev: AGACGTGTCTTCTTGGATTGC

142

Small ribosomal protein

RPS11/268264

Fwd: TACTGCCTTACACATCAAAGTTC

Rev: AGAGGGGATTGGTGTGACATC

142

Small ribosomal protein

S1/274976

Fwd: GATTCCCTCGATGGATTAGGTGA

Rev: GAATCAAGAGAATCAGAAACATCCG

89

TATA-box binding protein

TBP/143154

Fwd: GCATTTGCCTCCTATGAACCAGA

Rev: CTTTGCACCTGTTATCACAACCTTC

114

RNA polymerase RNAP

II/183218

Fwd: TCGGAGCTGCTTCCTTTTCTC

Rev: TTGTGGACTGGATGGGTTGTAAC

128

Major allergen MA control Fwd: TCGGTTGACAGATACCTTAAAGGAA

Rev: TCAAAGGTGACGTTCGAGTTCAT

100





Table S9: NCBI accessions and Pfam domains detected for Fucoxanthin chlorophyll binding

proteins (FCPs), Ntirate reductases and Silicon transporters for Cylindrotheca fusiformis (C.F.),

Phaedactylum tricornutum (P.T.) and Thalassiosira pseudonana (T.P.). *Downloaded from NCBI

RefSeq, all other gene sequences downloaded from NCBI Genbank. Based on HMM searches of

Pfam-A using default gathering thresholds.

C.F. P.T. T.P. Pfam domain(s)

FCPs AAN08838 EEC51450 ACI64366 chloroa_b-bind

Nitrate Reductase AAY59538 AAV66996XP_002294410

* Oxidored_molyb, Mo-

co_dimer, Cyt-b5,

FAD_binding_6,

FAD_binding_1

Silicon Transporter AAD13807 ABB81809 ABB81827 Silic_transp





Supplementary Figures

Figure S1: Sampling sites for: A) EPAC 1, 2 (Equatorial Pacific), B) NPAC (North Pacific),

Hansville buoy (ORCA) (yellow X) at 47°54.44”N and 122°37.62W was used to obtain

oceanographic profiles of T, S, density, O2, and in situ fluorescence close to the sampling site. C)

ANT 1, 2 (Antarctic), D) ARC (Arctic) and NATL (North Atlantic). Maps obtained from Google

Maps.





Figure S2: Top: Pfam protein domain rarefaction curves for Equatorial Pacific (EPAC), NorthPacific (NPAC), Antarctic (ANT), North Altantic (NATL) and Arctic (ARC)metatranscriptomes. (Chao-1 estimator of species richness using 50,000 sequenceincrements. http://www.biology.ualberta.ca/jbrzusto/rarefact.php). Bottom: table showing totalnumber of unique Pfam domains detected in each sample, the Chao-1 estimate of uniquedomains and the percentage of Chao-1 estimate detected in each sample

0

5000

0

1000

00

1500

00

2000

00

2500

00

3000

00

3500

00

4000

00

0

500

1000

1500

2000

2500

3000

3500

EPAC

NPAC

ANT

NATL

ARC

Sample Size

# U

niq

ue

Pfa

m D

om

ain

s D

ete

cte

d

ANT ARC EPAC NATL NPAC

# Unique Pfam domains detected 583.00 3316.00 1133.00 3369.00 1746.00

Chao-1 Estimate 720.20 3873.14 1652.39 4064.53 2239.90

% of estimate detected 80.95 85.62 68.57 82.89 77.95





Figure S3: Heatmap for all PhymmBL-classified sequences at the Phylum level (confidence

score of ≥ 0.9) Complete linkage clustering was performed based on a correlation matrix (1-

Pearson correlation coefficient) of relative abundances. Heatmaps scaled and centred by column.





Figure S4: Canonical correspondence analysis (CCA) between protein family (Pfam) abundance and environmental conditions (Temperature,

Light, Nitrate and Phosphate) deduced from ocean samples in this study, red dots represent ribosomal transcripts. Figures represent dimensions 1

and 2 (Left) and 1 and 3 (Right).





Figure S5: Correlation analysis between the normalised abundance of sequences associated with the GO term for translation and in-situ temperature of metatranscriptomes.





Figure S6: Multiple correlation plot between normalised abundance of metatranscriptomesequences associated with GO:0006412 – translation and environmental factors. Lower triangledisplays scatter plot of factors from the central diagonal. Upper triangle displays scaledcorrelation coefficient between factors from central diagonal.





Figure S7: qPCR analysis of 5 ribosomal genes determined in the polar diatom F. cylindrus

at the freezing point of seawater (-2°C). Changes in expression are shown as log2 of fold

changes relative to F. cylindrus grown at 10-11°C. Data was normalised to the geometric

mean of 3 reference genes (TBP, RNAP II, MA) and represents mean values and standard

error from biological replicates (n=3) and technical replicates (n=2). Significances (p <

0.05) were tested using pair wise fixed reallocation randomisation test (Relative Expression

Software Tool).





Figure S8: Hansville buoy (ORCA) (47°54.44”N, 122°37.62W) oceanographic profiles of T,

S, O2, and in situ fluorescence. Red line indicates time point (12:37pm) of sampling. C) and

D) indicates subsurface bloom development with maximum chl a concentration between 5

and 15 m depth.





Figure S9: Top: KRONA chart of NPAC (North Pacific) taxonomic affiliations at the

genus level, coloured by average confidence score. Bottom: Frequency of NPAC

Thalassiosira-like sequences matching to Thalassiosira pseudonana genes (blastx with

e-value cutoff < 1e-5, requiring >= 75% identity and >=50% coverage of the query

sequence) upregulated under different growth limitations (Note log scale on y-axis).

Co2 N Si Fe T1

10

100

1000

10000

Growth Limitation

# S

eqs





Figure S10: Plot of normalised abundance of sequences matching to nucleotide

metabolism (KEGG pathways K000230: Purine metabolism and K000240:

Pyrimidine metabolism) against temperature with exponential regression line.

-5 0 5 10 15 20 25 300

5

10

15

20

25

30

35

40

45

EPAC

NATL

NPAC

ANT

ARC

f(x) = 26.49·0.94^xR² = 0.93

Temperature ºC

Hits p

er

Mb

to

nucle

otid

e m

eta

bo

lism





Figure S11: Correlation analysis between total RNA per cell and growth temperature. Shown are

total RNA concentrations per Fragilariopsis cylindrus cell as calculated from biological

replicates grown under -2°C, +4°C and +10°C.

-4 -2 0 2 4 6 8 10 120.00000

0.00005

0.00010

0.00015

0.00020

0.00025

0.00030

0.00035

f(x) = -0.00001x + 0.00025

R² = 0.89773

Growth temperature [°C]

tota

l R

NA

[n

g/c

ell

]





Figure S12: Enriched GO terms from a pairwise comparison between EPAC (Equatorial Pacific)

and ANT (Antarctic). Top: Terms enriched in EPAC compared to ANT, Bottom: Terms enriched in

ANT compared to EPAC. Enriched GO terms were identified through pairwise Fisher's exact tests

on relative GO term abundances (Bonferroni corected P-value < 0.001). Term clouds created with

http://www.worditout.com scaling terms by the absolute difference in the relative abundance of the

enriched term and using direct colour blending from blue (low frequency) to red (high frequency).





6 Phytoplankton growth strategies and resource allocation in a global

marine ecosystem model

6.1 Model description

The agent and trait-based global marine ecosystem model is described fully in previous publications: the

agent-based modelling approach is described in Clark etal (2011)23; the sub-cellular resource allocation

model, integration with the agent-based model, and the coupling to the MIT-gcm is described in Clark

etal (2013)24; and the represention of rRNA, cellular stoichiometry, and extensions to a global model

including predation and cell-size parameterisation for high latitudes are described in Daines etal (2013)25.

Here we summarize the key features of the model. Phytoplankton diversity is represented by a trait-

based generic cell model which includes a coarse-grained representation of physiology and allocation to

subcellular compartments. Phytoplankton growth strategies (PGS) and hence cellular resource allocation

and composition are then an emergent result of environmental selection including multiple drivers (light,

nutrients and temperature).

The cell model is described fully in references Clark etal (2013)24 and Daines etal (2013)25. The

phytoplankton cell model represents allocation to three components: L represents cellular resource allo-

cation (fraction of cell nitrogen) to the photosynthetic light harvesting apparatus (including chlorophyll

and accessory pigments), E represents the biosynthetic apparatus (including aggregated enzyme systems

and ribosomes involved in small and large molecule biosynthesis), and a size-dependent allocation to cell

structure S(r) including cell surface associated components involved in nutrient acquisition and assimila-

tion, and all ’other’ components not directly involved in either photosynthesis or biosynthesis. Two traits,

for cell radius r which determines S(r), and (2) relative allocation to L vs E, combined with the constraint

that L+ E + S(r) = 1, then determine a two-dimensional trait space. Growth rate is then given by the

most limiting of light-harvesting mass-specific rate, fp = kpIzL (where Iz is incident light intensity and kp

is an empirically determined rate constant), temperature-dependent biosynthesis rate, ksQ10(T−To)/10E

(where temperature dependence is represented by the factor Q10, and kp is an empirically determined rate

constant), and mass-specific nutrient uptake (assumed diffusion limited and hence related to cell radius

r by fN ∝ 1/r2). C:N stoichiometry and fractional contribution of carbon and nitrogen to dry mass

are assumed constant and fixed for each compartment, hence relative cellular pool allocation as biomass,

carbon and nitrogen are equal. Internal fluxes are represented in carbon units. Phosphorus content is

pool-specific.





The ecosystem model uses an agent-based approach to represent a diverse phytoplankton population,

sampling a trait-space (where the two traits represent cell size, and subcellular resource allocation). A

population of ∼ 800 individual Lagrangian agents (each representative of many real-life individuals,26,27)

is maintained in each grid cell of a physical Eulerian ocean model, along with the concentration of a

single limiting nutrient, light, and temperature. Agents consume resources, grow, reproduce, and may be

consumed by predation and cycled through particulate and dissolved detrital pools to inorganic nutreint.

Biotic interactions are represented by a parameterisation of size-dependent predation.

6.2 Emergent phytoplankton growth strategies and resource allocation

The model captures major patterns between permanently-stratified, resource-limited oligotrophic gyres,

high-latitude bloom-forming regions, and equatorial upwelling regions. Nutrient supply is the major driver

for ecosystem structure in the oligotrophic gyres, resulting in selection for small cell size limited by an

imposed trade-off such that small cells pay an increased cost in allocation to S (representing cell-surface

associated structure and nutrient-uptake machinery), and slow, nutrient-limited growth rate (Figure S-

13). High-latitude, bloom forming regions favour larger, fast-growing cells. In these regions, temperature

and light then determine the relative allocation to biosynthesis and light-harvesting machinery.

6.3 Relating resource allocation to metagenomics

The resource-allocation patterns for S, L and E in the global model (Figure S-13) may be identified with

broad classes of protein families in the global metatranscriptomic dataset (Figure S12).

7 rRNA allocation

We summarise here results from Daines etal (2013)25. We assume that per-ribosome peptide synthesis

rate (φaσa)T = (φaσa)T0Q(T−T0)/1010 is conserved across eukaryotic taxa, with a temperature dependence

defined by a Q10 scaling. The minimum rRNA requirements (relative to cellular protein) for protein

synthesis and growth are then a function of growth rate and temperature. In the coarse-grained cell

model, the biosynthesis pool E represents both small and large molecule biosynthesis. This is partitioned

optimally into rRNA and small molecule biosynthesis, based on a parameterization of ribosomal transcrip-

tion rate and the parameterisation of overall mass-specific biomass generation rate by the E pool, with

κS derived from laboratory measurements28,29. Combined with phytoplankton growth strategies derived

2NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange 95




0 100 200 300

−80

−60

−40

−20

0

20

40

60

80

Surface (0−50m) mean cell radius

0

1

2

3

4

5

6A

0 100 200 300

−80

−60

−40

−20

0

20

40

60

80

Surface (0−50m) mean S

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8BB

0 100 200 300

−80

−60

−40

−20

0

20

40

60

80

Surface (0−50m) mean E

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8C

0 100 200 300

−80

−60

−40

−20

0

20

40

60

80

Surface (0−50m) mean L

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8D

Figure S-13: Model emergent biogeography, phytoplankton growth strategies, and cellular nitrogen allo-cation. A (top left): cell size. B (top right): allocation to “structure” including cell-surface associatedmachinery. C (bottom left): Allocation to biosynthesis machinery. D (bottom right): allocation to lightharvesting machinery. All properties are biomass-weighted annual means for the model surface layer(0-50m).

3





Table S-10: Stoichiometry of macromolecules and cellular components30. Eukaryotic ribosome is assumedto have composition rRNA:protein ratio 1.2:1

Macromolecule / component %C %N %P C:N:P

protein 53 17 0 3.6:1:0RNA 32.7 14.5 8.7 9.7:3.7:1

phospholipid 65 1.6 4.2 40.0:0.84:1eukaryotic ribosome 41.9 15.6 4.7 23.0:7.3:1

membrane (70% protein, 30% phospholipid) 56.6 12.38 1.26 116.0:21.8:1bacterial membrane30 55 13.7 0.86 162:35:1

’Redfield’ autotroph with gC/gdw = 0.5 50 8.81 1.22 106:16:1

Table S-11: Biological constants determining minimum rRNA requirements for growth

Parameter Description Units

mtota Average mass of an amino acid in peptide changes 110 Da

mtotr Average mass of a ribonucleotide 340 Daσa Peptide elongation rate aa rib−1 s−1

φa Fraction of ribosomes actively translating 0.8 -lr Length in ribonucleotides of rRNA in one eukaryotic ribosome 6860

from the global model, this then determines patterns in allocation to rRNA. We may further use absolute

laboratory calibrations for per-ribosome protein synthesis rate to determine the rRNA contribution to

N:P stoichiometry.

7.1 rRNA requirements for protein synthesis and growth

The minimum rRNA required to provide protein synthesis for growth (mass ratio of rRNA to protein)

as a function of growth rate µ depends (only) on the empirically determined per-ribosome protein syn-

thesis rate, and other biological parameters which are more directly related to “structural” properties of

biomolecules (Table S-11, data from Loladze and Elser (2011)31).

The minimum cellular-level ratio of rRNA dry mass to protein dry mass required for protein synthesis

for growth rmasscellrRNA:prot = mtot

cellrRNA/mtotcellprot is given by:

rmasscellrRNA:prot = 5.9

( µ

1 h−1

)(φaσa

1 aa rib−1 s−1

)−1

αcomp (S-1)

where dependency on structural parameters (Table S-11) is captured by:

αcomp =

(mtot

r

340 Da

)(lr

6860

)(mtot

a

110 Da

)−1

(S-2)





Using the stoichiometries from Table S-10, the (mass) ratio of phosphorus to nitrogen in the combi-

nation of cellular rRNA and protein is then

%Pcell

%Ncell=

8.7rmasscellrRNA:prot

17 + 14.5rmasscellrRNA:prot

(S-3)

where the major trend is from the ratio of phosphate-rich rRNA to nitrogen-rich protein, and the

dependency on rmasscellrRNA:prot in the denominator is a small correction due to the nitrogen content of

rRNA. The fraction of cell nitrogen in ribosomes (including both the rRNA and ribosome-associated

protein in ratio 1.2:1 for a eukaryotic ribosome30) is

%Nrib

%Ncell=



(S-4)

and the ratio of rRNA mass to cell carbon mass (given an overall bulk cell C:N stoichiometry) is

mtotrRNA

mCcell

=100rmass

cellrRNA:prot


(%Ncell

%Ccell

)(S-5)

7.2 Relating rRNA allocation to coarse-grained phytoplankton model

The optimal allocation of cellular nitrogen to the E pool is given by Eopt = %NE/%Ncell = µ/κs. Both

the allocation to E and the allocation to rRNA therefore scale linearly with growth rate, hence.assuming

the same temperature dependence for these rates, the optimal partitioning into rRNA and small-molecule

biosynthesis within the E pool follows from the two empirical rate constants:

%Nrib

%NE=

κsµ



= 1.67

(κs

0.168 h−1

)25C

(φaσa

1 aa rib−1 s−1

)−1

25C(1 + 0.85Eopt

(κs

0.168 h−1

)25C

(φaσa

1 aa rib−1 s−1

)−1

25C

)αcomp

The ratio %Nrib/%NEis therefore close to a constant value determined by the two rate constants, with

a small correction factor (arising from the non-protein contribution to cellular nitrogen in the rRNA).

The growth-strategy-dependent allocation to E then determines the allocation to rRNA.





Table S-12: Representative calculations for minimum rRNA requirements for growth. Per-ribosomeprotein synthesis rate (σaφa)25C scaled for temperature of 25C derived assuming temperature-dependencegiven by a factor Q10 = 2.

Description µ σaφa rmasscellrRNA:prot

%Nrib%Ncell

%Pcell%Ncell

N:P (σaφa)25Caa rib−1 s−1 aa rib−1 s−1

yeast, 30C (lr = 5470) 0.43 h−1 8 0.25 0.35 16.2 5.7T. weissflogii 20C29 1.29 d−1 1.9 0.167 0.246 0.075 29.6 2.7

7.3 Absolute laboratory calibration of rRNA synthesis rate and rRNA contribution

to N:P

The absolute value of the rRNA content and hence the N:P stoichiometry of the E compartment de-

termines the minimum rRNA contribution to overall stoichiometry, %PE/%NE = 0.304(%Nrib/%NE),

(N : P)E = 2.21/(%PE/%NE). This depends on a calibration of the per-ribosome protein synthesis

rate. Two representative values are shown in Table S-12, and a full sensitivity study is provided in

Daines etal (2013)25. The calculation for yeast at 30C demonstrates consistency with the data com-

pilation and calculation from Loladze and Elser (2011)31. The autotroph value for the diatom T.

weissflogii is derived by comparison with the measured RNA content of the diatom at observed max-

imum rate of light-limited growth at 20C29, given the measured ratio of RNA mass to cell carbon mass

mtotrRNA/m

Ccell = 0.151 gRNA(gC)−1 and measured stoichiometry with %Ncell/%Ccell = 0.176 gN(gC)−1

(Daines etal, 2013)25.

The lowest value (φaσa)25C = 2.7aa rib−1 s−1 consistent with the T. weissflogii dataset29 (Table S-12,

scaled to a temperature of 25C by assuming Q10 = 2) then gives %Nrib/%NE = 0.62 in the limit of small

allocation to E, and for the highest value Eopt = 0.8 (in the high-light limit where allocation to L is small

and S is 0.2), gives %Nrib/%NE = 0.49, (N : P)E = 14.8 and %Nrib/%Ncell = 0.40, (N : P)cell < 18.5

(this demonstrates that the correction term 0.85Eopt... is relatively small and the optimal small and

large molecule biosynthesis contributions to E have only a small sensitivity to other parameters). Taking

the per-ribosome transcription rate from yeast (φaσa)25C = 5.7 aa rib−1 s−1 (Table S-12, scaled to a

temperature of 25C by assuming Q10 = 2) approximately halves this value for rRNA and P content at

optimal growth. The highest values for maximum optimal cellular allocation to rRNA and hence maximum

rRNA contribution to cellular P content are approximately half those of Klausmeier etal (2004)32 as we

have enforced an additional constraint on per-ribosome transcription rate from consistency with laboratory

data. Another overall consistency argument is that we might expect the maximum rRNA to protein ratio





in an autotroph (with additional protein requirements for growth including for light harvesting and carbon

fixation, relative to a heterotroph growing on simple substrates) will always be less than the maximum in

yeast.

The calculations shown here for N:P (main paper Figure 4) use(φaσa)25C = 2.7 aa rib−1 s−1, and

include additional P contribution from phospholipid assumed a constant 10% of total cell mass33. A full

parameter sensitivity study, discussion of optimal allocation to rRNA (the Growth Rate Hypothesis) and

of potential multiple contributions to phytoplankton N:P is contained in Daines etal (2013)25. Evidence

for optimal allocation to rRNA in laboratory experiments is discussed in Loladze and Elser (2011)31.

However, some data on microalgae do not completely agree34 - the discrepancy is likely a combination of

experimental uncertainties, and additional rRNA requirements in dynamic environments. The essential

point for the interpretation here is that rRNA allocation for protein synthesis must represent a minimum

requirement for growth (based in the model approach here on laboratory calibration) and this part of P

allocation must be affected by temperature.

8 Temperature and light-dependence of optimal allocation in nutrient-

unlimited environments

A simplified steady-state optimality analysis provides additional insight into the temperature dependence

of cellular allocation strategies for exponential growth (unlimited nutrient) appropriate to large phyto-

plankton such as diatoms in high-latitude bloom forming environments or in upwelling regions. Here we

solve numerically for the optimal resource allocation to L and E that gives maximum growth rate as a

function of light and temperature, with cell radius fixed at a large value (the method is as described in24),

Figure S-14.





A) Photosynthesis investmentLi

ght i

nten

sity

(µE

m−2

s−1)

Temperature (deg C)0 10 20 30

0

50

100

150

200B) Biosynthesis investment

Temperature (deg C)0 10 20 30

Investment (0 − 1)

0

0.2

0.4

0.6

0.8

1

0 10 20 300

0.2

0.4

0.6

0.8

1A) Low light (16 µE m−2 s−1)

Inve

stm

ent (

0 −

1)

Temperature (deg C)

SLE (sm)E (rib)

0 10 20 30

B) High light (100 µE m−2 s−1)

Temperature (deg C)

SLE (sm)E (rib)

0 10 20 300

0.2

0.4

0.6

0.8

1A) Low light (16 µE m−2 s−1)

Inve

stm

ent (

0 −

1)

Temperature (deg C)

SLE (sm)E (rib)

0 10 20 30

B) High light (100 µE m−2 s−1)

Temperature (deg C)

SLE (sm)E (rib)

Figure S-14: Optimal allocation (fraction of cellular nitrogen) for nutrient-unlimited growth as a functionof light and temperature, and demonstration of sensitivity to ribosome synthesis rate. Top row: Optimalallocation to light harvesting and biosynthesis as a function of light and temperature. Middle row:optimal allocation as a function of temperature for two light levels, showing allocation within biosynthesiscomponent E to small and large molecule biosynthesis (ribosomes) for the lowest value (φaσa)25C =2.7 aa rib−1 s−1 consistent with the T. weissflogii dataset described in (Table S-12). Bottom row: asmiddle row, for (φaσa)25C = 5.7 aa rib−1 s−1 determined for yeast31 (Table S-12).





Supplementary References

23. Clark, J. R., Daines, S. J., Lenton, T. M., Watson, A. J. & Williams, H. T. P. Individual-based

modelling of adaptation in marine microbial populations using genetically defined physiological pa-

rameters. Ecological Modelling 222, 3823–3837 (2011).

24. Clark, J. R., Lenton, T. M., Williams, H. T. P. & Daines, S. J. Environmental selection and resource

allocation determine spatial patterns in picophytoplankton cell size. Limnology and Oceanography

58, 1008–1022 (2013).

25. Daines, S. J., Clark, J. R. & Lenton, T. M. Multiple environmental controls on phytoplankton growth

strategies suggest a key contribution of P storage to the N:P ratio. Ecology letters in revision (2013).

26. Rose, K. Individual-based modeling of populations with high mortality: A new method based on

following a fixed number of model individuals. Ecological Modelling 68, 273–292 (1993).

27. Scheffer, M., Baveco, J., DeAngelis, D. L., Rose, K. & van Nes, E. Super-individuals a simple solution

for modelling large populations on an individual basis. Ecological Modelling 80, 161–170 (1995).

28. Shuter, B. A model of physiological adaptation in unicellular algae. Journal of theoretical biology 78,

519–52 (1979).

29. Laws, E. A., Redalje, D., Karl, D. M. & Chalup, M. S. A theoretical and experimental examination

of the predictions of two recent models of phytoplankton growth. Journal of Theoretical Biology 105,

469–491 (1983).

30. Sterner, R. & Elser, J. J. Ecological stoichiometry: The biology of elements from molecules to the

biosphere (Princeton University Press, Princeton, 2002).

31. Loladze, I. & Elser, J. J. The origins of the Redfield nitrogen-to-phosphorus ratio are in a homoeostatic

protein-to-rRNA ratio. Ecology letters 14, 244–50 (2011).

32. Klausmeier, C. A., Litchman, E., Daufresne, T. & Levin, S. A. Optimal nitrogen-to-phosphorus

stoichiometry of phytoplankton. Nature 429, 171–4 (2004).

33. Geider, R. J. & LaRoche, J. Redfield revisited: variability of C:N:P in marine microalgae and its

biochemical basis. European Journal of Phycology 37, 1–17 (2002).





34. Flynn, K. J. et al. Is the Growth Rate Hypothesis Applicable To Microalgae? Journal of Phycology

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THE IMPACT OF TEMPERATURE ON MARINE PHYTOPLANKTON … · Temperature at the sea-ice-water...

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Transcript of THE IMPACT OF TEMPERATURE ON MARINE PHYTOPLANKTON … · Temperature at the sea-ice-water...