SOIL FUNGAL COMMUNITIES ASSOCIATED WITH PLANT HEALTH AS REVEALED BY NEXT-GENERATION SEQUENCING

LIHUI XUPhD THESIS • SCIENCE AND TECHNOLOGY • 2011

Department of AgroecologyScience and TechnologyAarhus UniversityForsøgsvej 14200 Slagelse

SOIL FUNGAL COMMUNITIES ASSOCIATED WITH PLANT HEALTH AS REVEALED BY NEXT-GENERATION SEQUENCING

LIHUI XUPhD thesis • science anD technology • 2011

Tryk: www.digisource.dkISBN: 978-87-91949-99-9

Ph.d_58448_Lihui_Xu.indd 3 02/12/11 10.25

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Preface

This thesis is submitted to fulfill the requirements for obtaining the Ph.D. degree

at the Faculty of Science and Technology, Aarhus University. The Ph.D. project was

carried out at Research Centre Flakkebjerg, Department of Agroecology, Faculty of

Science and Technology, Aarhus University.

The present project is based on three main experimental studies resulted in three

manuscripts: (i) Influence of DNA extraction and PCR amplification on amplicon

sequencing-based studies of soil fungal communities; (ii) Soil fungal community

structure along a soil health gradient in pea fields examined using deep amplicon

sequencing; (iii) Fungal community structure in roots, rhizosphere, and bulk soil

associated with plant health as examined by deep amplicon sequencing.

I would like to acknowledge all the people who have been helping me in various

ways.

Foremost, I would like to express my sincere gratitude to my principle

supervisor Dr. Mogens Nicolaisen. His enthusiasm, inspiration, and expert guidance

helped me throughout all the time of my research and thesis writing. I am also greatly

indebted to my co-supervisors Dr. Sabine Ravnskov and Dr. John Larsen for their

thoughtful guidance, wise advice, and enormous encouragement during my Ph.D.

study. It has been a great pleasure working with such a great supervising group.

I am very thankful to all of the technical staff, Anne-Pia Larsen, Ellen

Frederiksen, Henriette Nyskjold, Jette Them Lilholt, Steen Meier, and Tina Tønnersen

for excellent technical assistance in the laboratory and in the greenhouse.

My special thanks go to Kristian Kristensen, Niels Holst, and Bernd

Wollenweber for their valuable advice on statistical analysis.

I sincerely acknowledge Karen O´Hanlon, Stephanie Walter, and Kirsten Jensen

for indispensable proofreading of the thesis and manuscripts.

I would like to thank all the colleagues and friends at Research Centre

Flakkebjerg for their kind assistance and for providing a pleasant working

environment.

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I send special thanks to Valeria Bianciotto, Erica Lumini, and Alberto Orgiazzi

at University of Turin, Italy for giving great suggestions on sequence analysis.

I am grateful to Professor Jo Handelsman and the entire Handelsman Lab at

Yale University for their hospitality and for inspiring me in my work. It was my

immense pleasure to stay in your lab. During the three months stay, I managed to

generate new amplicon libraries for pyrosequencing and to learn techniques for

sequence analysis.

Last but not least, I would like to thank my beloved family and friends for their

continuous love and tremendous support at all time.

Lihui Xu

October 2011

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Contents

Summary ....................................................................................................................... 1

Sammendrag ................................................................................................................. 3

1 Introduction ............................................................................................................... 5

1.1 The soil environment .......................................................................................... 5

1.1.1 Physical, chemical, and biological components ......................................... 5

1.1.2 Soil functions ................................................................................................ 6

1.1.3 Soil health ..................................................................................................... 7

1.1.4 Root and rhizosphere .................................................................................. 9

1.2 Soil fungi............................................................................................................ 10

1.2.1 Taxonomic groups of soil fungi ................................................................ 11

1.2.2 Soil fungal life cycles .................................................................................. 12

1.2.3 Role of fungi in the soil ecosystem ............................................................ 12

1.2.4 Soil fungal diversity ................................................................................... 14

1.3 Soil-borne pathogens ........................................................................................ 16

1.3.1 Pea root diseases caused by soil-borne fungal pathogens ...................... 16

1.3.2 Interactions among fungal pathogens ...................................................... 18

1.3.3 Management of soil-borne pathogens ...................................................... 19

1.4 Methods to study soil fungal diversity ............................................................ 21

1.4.1 Classical and biochemical-based techniques ........................................... 21

1.4.2 Molecular-based techniques: DNA fingerprinting and microarray ..... 23

1.4.3 Sequencing techniques .............................................................................. 28

1.5 Motivation and objectives ................................................................................ 37

2 Paper I. Influence of DNA extraction and PCR amplification on studies of soil

fungal communities based on amplicon sequencing ............................................... 39

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3 Paper II. Soil fungal community structure along a soil health gradient in pea

fields examined using deep amplicon sequencing ................................................... 51

4 Paper III. Fungal community structure in roots, rhizosphere, and bulk soil

associated with plant root health as examined by deep amplicon sequencing ..... 73

5 General discussion ................................................................................................ 127

6 Conclusions and further perspectives ................................................................. 131

References ................................................................................................................. 135

1

Summary

This project investigated fungal communities associated with plant root health

in agricultural soils using next-generation amplicon sequencing.

Initially, DNA extraction and PCR effects on the variation of read abundances

of pyrosequencing generated operational taxonomic units (OTUs) were investigated

using soil samples from a pea field. Results showed that species richness was

consistent among replicates. Variation among dominant OTUs was low across

replicates, whereas rare OTUs showed higher variation among replicates. Results

further indicated that pooling of several DNA extractions and PCR amplicons will

decrease variation among samples.

Soil fungal communities along a soil health gradient in nine pea field soils were

explored. Soil fungal communities from each soil were different and were strongly

dominated by Ascomycota and Basidiomycota. Several soil-borne fungal pathogens

were detected in the bulk soil. Phoma, Podospora, Pseudaleuria and Veronaea, at the

genus level, correlated to the disease severity index (DSI) of pea roots; Phoma was

most abundant in soils with high DSI, whereas Podospora, Pseudaleuria, and

Veronaea were most abundant in soils with low DSI.

Fungal communities in pea plant roots, the surrounding rhizosphere, and bulk

soil from three pea fields were examined in relation to root health. Fungal diversity in

terms of richness was highest in bulk soil and lowest in roots. Fungal communities in

all samples were strongly dominated by Dikarya and differed significantly among the

three environments. Fusarium oxysporum and Aphanomyces euteiches were the likely

causes of pea root rot in the respective fields as assessed by pyrosequencing data and

quantitative PCR. Glomus and Fusarium were significantly more abundant in roots,

whereas Cryptococcus and Mortierella were almost exclusively found in rhizosphere

and bulk soil. A clear correlation was demonstrated between health status of roots and

their fungal communities. The results showed that fungal community structures are

highly variable in response to the three different ecological niches, between healthy

and diseased roots, and across different fields.

The results presented in this project revealed a high diversity of fungal

communities in agricultural soils and provided information on the different functional

fungal groups, including pathogens, and their dynamics in relation to root health. This

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knowledge will further improve the understanding of soil fungal communities with

regard to plant diseases.

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Sammendrag

I dette projekt blev jordens svampesamfund undersøgt i relation til planters

sundhed ved hjælp af amplicon pyrosekventering.

Første blev effekten af DNA-ekstraktion og PCR på variation af mængderne af

pyrosekventering genererede operationelle taksonomiske enheder (OTU) undersøgt i

jordprøver fra en ærtemark. Resultaterne viste, at artsrigdommen var stabil mellem

replikater. Variationen blandt dominerende OTU var lav på tværs af replikater, mens

sjældne OTU viste højere variation. Resultaterne viser, at sammenlægning af flere

DNA ekstraktioner og PCR produkter vil mindske variation blandt prøver.

Svampesamfund i ærtejorde med forskellig sygdomspåvirkning blev undersøgt.

Svampesamfundenes sammensætning var afhængig af sygdomstrykket i den enkelte

mark. Samfundene i ni jorde var stærkt domineret af Ascomycota og Basidiomycota,

og flere jordbårne plantepatogener blev påvist i jorden. Især Phoma, Podospora,

Pseudaleuria og Veronaea korrelerede med sygdomstrykket i markerne; Phoma var

mest forekommende i jorde med syge planter, mens Podospora, Pseudaleuria, og

Veronaea var mest udbredt i sunde jorde.

Svampesamfund i ærterødder, deres omgivende rhizosfære, og den tilstødende

bulkjord fra tre ærtemarker blev undersøgt og relateret til rodsundhed. Der blev fundet

størst artsrigdom i jord og mindst i rødder. Svampesamfundene i alle tre miljøer var

stærkt domineret af Dikarya, men varierede signifikant blandt de tre miljøer.

Fusarium oxysporum og Aphanomyces euteiches blev, på baggrund af

pyrosekventering og kvantitativ PCR, vurderet til at være den sandsynlige årsag til

den forekommende rodråd. Glomus og Fusarium var signifikant oftere forekommende

i rødder, mens Cryptococcus og Mortierella næsten udelukkende blev fundet i

rhizosfære- og bulkjord. En klar sammenhæng blev påvist mellem sundhedstilstanden

af rødder og deres svampesamfund. Resultaterne viste, at strukturen af

svampesamfund varierer mellem de forskellige økologiske nicher (rødder, rhizosfære

og den omgivende bulkjord), mellem sunde og syge rødder, og mellem forskellige

marker.

Resultaterne fra dette projekt viser en stor mangfoldighed i svampesamfundene i

landbrugsjorde og klare sammenhænge mellem sygdomstryk i markerne og

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forekomsten af enkelte svampegrupper. Projektet har medvirket til en øget forståelse

af dynamikken i jordens svampesamfund i relation til plantesygdomme.

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1 Introduction

1.1 The soil environment

Soil is a highly complex and dynamic environment, in which the biological

activity is mostly dominated by microorganisms. Soil microorganisms have many

beneficial effects, including nitrogen fixation, phosphorous solubilization, and organic

matter decomposition, which together enhance the bioavailability of plant nutrients

essential for primary production in all terrestrial ecosystems (Gomes et al., 2003).

1.1.1 Physical, chemical, and biological components

The properties of a soil ecosystem are the product of intricate interactions

between a physical and chemical matrix of highly variable composition and living

communities composed of essentially all life forms.

Sand, silt and clay are basic soil components determining soil texture, which in

combination with humic substances and biological components provide the physical

structure of the soil. Micro- and macro-aggregates secure an important balance

between water availability and aeration, which is essential for plant growth. Although

soil aggregates provide surfaces for microbial colonial development, clay and

colloidal organic matter have the smallest diameters, and hence present the largest

surface area for interaction with soil microorganisms and their products (Tate, 2000).

The chemical components of soil including organic compounds and inorganic

minerals derived mainly from organic matter decomposition are essential for all soil

organisms. In relation to plant growth, mineral nutrients are divided into

macronutrients (N, P, K, Ca, Mg, and S) and micronutrients (Fe, Mn, B, Zn, Cu, Cl,

and Mo) (Whitehead, 2000). The availability of plant nutrients is strongly dependent

on soil pH and cation exchange capacity of the soil (Lauber et al., 2009; Rousk et al.,

2010).

The soil is a complex ecosystem with a diverse community of organisms

performing vital functions within. The most widely used system for classifying soil

organisms is according to size: macrobiota, mesobiota and microbiota (Wallwork,

1970; Swift et al., 1979). One gram of soil may contain up to 10 billion

microorganisms of possibly thousands of different species (Rossello-Mora & Amann,

2001). Soil microorganisms exist in large numbers and display an enormous diversity

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of forms and functions. The major microbial groups in soil are fungi, bacteria

(including actinomycetes), algae (including cyanobacteria) and protozoa. All the soil

characteristics interact with each other, and in particular, the biological components

and functions of soils depend on, and emerge from, the physical and chemical

components (Girvan et al., 2003). Microbial biomass plays a dual role in the soil: first,

it is essential for the organic matter decomposition with concurrent release of

nutrients, and second, it is a labile pool of nutrients for plants (Stevenson, 1994).

A complex array of physical, chemical, and biological interactions is involved

in soil organic matter decomposition, ensuring the completion of the biogeochemical

nutrient cycles (Robertson & Paul, 2000).

1.1.2 Soil functions

Soils provide the following basic functions, with the actual combinations and

relative individual importance depending on the specific function in question

(Nortcliff, 2002):

(i) Provide a physical, chemical and biological setting for living organisms

(ii) Regulate and partition water flow, storage and recycling of nutrients and

other elements

(iii) Support biological activity and diversity for plant growth and animal

productivity

(iv) Filter, buffer, degrade, immobilize and detoxify organic and inorganic

substances

(v) Provide mechanical support for living organisms and their structures

These basic soil functions are often combined to provide more general functions,

and soils usually perform several functions simultaneously. These functions refer to

the capacity of a soil to maintain soil ecosystem health (Nortcliff, 2002). The

multifunctional role of soil must be considered for any soil health evaluation. The

ability of soil to perform specific functions depends strongly on climatic conditions,

which vary among climatic zones, but climate also varies at any given location during

the year. Therefore, it is important to consider climate when defining soil health

(Bouma, 2002).

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To characterize soil function, suitable indicators are necessary to understand the

causal relationship between the soil health indicators and the specific soil functions

under consideration, and to define soil properties. Several physical, chemical, and

biological indicators have been proposed to determine soil health and quality (Arias et

al., 2005).

1.1.3 Soil health

Soil is a reservoir of essential nutrients for plant growth and therefore soil health

is of great importance, particularly in agricultural soils. Soil health is defined as “the

continued capacity of soil to function as a vital living system, within ecosystem and

land-use boundaries, to sustain biological productivity, maintain the quality of air and

water environments, and promote plant, animal, and human health” (Doran et al.,

1996). The concept of soil health refers to the biological, physical and chemical

features which are imperative for long-term, sustainable agricultural productivity with

minimal environmental impact. The term soil health is not synonymous with soil

quality, and they should not substitute each other. Soil quality was defined as “the

capacity of a specific kind of soil to function, within natural or managed ecosystem

boundaries, to sustain plant and animal productivity, maintain or enhance water and

air quality, and support human health and habitation” (Karlen et al., 1997). The two

definitions may appear similar, but soil quality is related to soil functions, while soil

health presents the soil as a finite and dynamic living resource (Doran & Zeiss, 2000).

Due to the multifunctional nature of soil ecosystems, it is difficult to define a healthy

soil without first defining the targeted goals such as plant health, atmospheric balance,

or erosion avoidance. In the present work, plant health is defined as a specific target

goal or aim in order to define a healthy soil. Healthy soils maintain a diverse

community of soil organisms that can help to: (i) control plant diseases as well as

insect and weed pests; (ii) form beneficial symbiotic associations with plant roots (e.g.

nitrogen-fixing bacteria and mycorrhizal fungi); (iii) recycle plant nutrients; (iv)

improve soil structure with positive repercussions for its water- and nutrient-holding

capacity; (v) improve crop production (Arias et al., 2005).

One of the most important objectives in determining soil health is to acquire

indicators for evaluation of the current status of soil. Since soil function is very

complex, one unique indicator is not enough to assess soil health. Doran et al. (1996)

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proposed a limited number of indicators to describe soil health. Indicators should (i)

encompass ecosystem processes and relate to process-oriented modeling; (ii) integrate

soil physical, chemical, and biological properties and processes; (iii) be accessible to

many users and applicable to field conditions; (iv) be sensitive to variations in

management and climate at an appropriate time-scale; and (v) when possible, be

components of existing soil databases.

The ability of soil to suppress plant diseases can result from several different

mechanisms: the pathogen (i) does not establish or persist, (ii) establishes but causes

little or no damage, or (iii) establishes and causes disease for a while but thereafter the

disease is less important, although the pathogen may persist in the soil (Baker & Cook,

1974). Given a susceptible host, disease suppression is the result of pathogen

suppression (Termorshuizen & Jeger, 2008). Two classical types of suppressiveness

are classified: specific and general suppression (Baker & Cook, 1974). Specific

suppression is caused by individual or selected groups of microorganisms and is

transferable, whereas general suppression is caused by multiple microorganisms and

is not transferable between soils (Weller et al., 2002). Suppressive soils have been

described for many soil-borne pathogens. Several soil-borne pathogens, such as

Fusarium oxysporum (the cause of vascular wilts), Gaeumannomyces graminis (the

cause of take-all disease in wheat), Phytophthora infestans (a cause of foliar disease),

Pythium spp. (a cause of damping-off), have been shown to be suppressible in certain

soils (Martin & Hancock, 1986; Alabouvette et al., 1993; Andrivon, 1994; Hornby et

al., 1998; Weller et al., 2002). The mechanisms by which soils are suppressive to

different pathogens can involve biotic (soil microflora) and/or abiotic factors (soil

physicochemical properties) (Garbeva et al., 2004). Generally, suppressive soils can

be considered as healthy soils (Janvier et al., 2007).

Some biological, physical, and chemical indicators have been used for

determining soil health, such as microbial biomass, microbial activity, carbon cycling,

nitrogen cycling, biodiversity and microbial resilience, bioavailability of contaminants,

and physical and chemical properties (Arias et al., 2005). The validation of the

relevance of the chosen abiotic or biotic indicators in several agronomic situations is

important when describing the soil health and soil suppressiveness (Janvier et al.,

2007).

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1.1.4 Root and rhizosphere

Plant roots grow mostly within the soil and have wide-ranging, long-lasting

effects on plant populations both above and below ground, and hence are included in

soil biota. Although plant roots are generally considered as a relatively mundane

habitat, their examination revealed an extensive fungal diversity (Vandenkoornhuyse

et al., 2002).

Microbial growth is generally enhanced around plant roots, which is usually

assigned to rhizosphere effect. The rhizosphere, as originally conceived by Hiltner

(1904), was the narrow region of soil surrounding plant roots affected by the living

roots. It is a very dynamic environment where plants, soil, and microorganisms

interact. Plant root exudates are the main food source for microorganisms and the

driving force of their population density and activities (Raaijmakers et al., 2009).

Root exudates have been shown to increase the mass and activity of soil

microorganisms and fauna in the rhizosphere (Butler et al., 2003). Important

parameters, such as the quantity and the quality of available carbon compounds

originating from plants, as well as novel sites for microbial attachment discriminate

rhizosphere from bulk soil (Curl & Truelove, 1986).

Microorganisms in the rhizosphere play crucial roles in plant growth and health.

Microbial communities in the rhizosphere can have deleterious, beneficial, or neutral

effects on the plant. Microorganisms that adversely affect plant growth and health are

pathogenic fungi, oomycetes, bacteria and nematodes, whereas beneficial

microorganisms include mycorrhizal fungi, nitrogen-fixing bacteria, and plant growth

promoting rhizobacteria. Many microorganisms have a neutral effect on the plant, but

are part of the complex food web that utilizes the large amounts of carbon that is fixed

by the plant and released into the rhizosphere (i.e. rhizodeposits) (Raaijmakers et al.,

2009).

Rhizodeposition describes the total carbon transfer from plant roots to soil and

comprises water-soluble exudates, secretions, lysates from dead cells and mucilage

(Grayston et al., 1997). Plant roots may release massive amounts of organic

compounds via rhizodeposition, which ultimately may lead to benefits provided by

some microorganisms. Therefore, rhizodeposits play an important role in the

regulation of symbiotic and protective associations between plants and soil

microorganisms (Lambers et al., 2009).

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The rhizosphere fungal community has been examined and saprotrophic fungi

with representatives from all major terrestrial phyla - Ascomycota, Basidiomycota and

Zygomycota have been identified (Gomes et al., 2003; Renker et al., 2004; Vujanovic

et al., 2007). The saprotrophic fungi of the rhizosphere may be involved in the

degradation of both simple root exudates and the more complex compounds in

sloughed-off root cells (Buée et al., 2009a).

Mycorrhizal interactions influence the species composition, diversity, and

stability of microbial communities. The area of soil under the influence of

mycorrhizal roots as opposed to non-mycorrhizal roots and extraradical mycelium

was defined as the mycorrhizosphere (Rambelli, 1973). The term “mycorrhizosphere”

was coined to describe the unique properties of the rhizosphere surrounding and

influenced by mycorrhizas (Linderman, 1988). Mycorrhizal fungi frequently stimulate

plants to reduce root biomass while simultaneously expanding nutrient uptake

capacity, by extending mycelium far beyond root surfaces and proliferating in soil

pores that are too small for root hairs to enter (Johnson & Gehring, 2007). Mycelial

networks of mycorrhizal fungi can connect plant root systems and soil particles over

broad areas. These fungi often comprise the largest portion of soil microbial biomass

(Olsson et al., 1999; Hogberg & Hogberg, 2002). Therefore, mycorrhizal symbioses

structure the physical and chemical composition in the rhizosphere, and impact the

biological communities and ecosystems.

1.2 Soil fungi

Soil fungi are an immensely diverse group of organisms, which exist in a wide

range of forms from the microscopic single-celled yeasts to large macrofungi. Fungi

are usually the most abundant component of the soil microorganisms in terms of

biomass (Lin & Brookes, 1999). In an ecological classification of the soil fungi, a

number of groups can be differentiated, such as obligate saprophytes, root inhabiting

fungi, mycoparasitic fungi, nematophagous fungi, and insect pathogenic fungi. The

specialized plant parasites, together with mycorrhizal fungi, have been grouped

together as root inhabiting fungi. The remainder of the root infecting fungi, together

with the obligate saprophytes, have been designated as soil inhabiting fungi (Garrett,

1950). The following introduction of soil fungi will be presented as a combination of

root inhabiting fungi and soil inhabiting fungi.

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1.2.1 Taxonomic groups of soil fungi

Soil fungi comprise all major fungal phyla, Ascomycota, Basidiomycota,

Chytridiomycota, Glomeromycota, Zygomycota, and Oomycota (not true fungi)

(Webster, 1980).

The fungal group Ascomycota is characterized by the presence of an ascus, a

microscopic sexual structure in which nonmotile spores (ascospores) are formed.

However, some species of Ascomycota are asexual, and they do not form asci or

ascospores. Examples of Ascomycota include Fusarium sp., Aspergillus sp.,

Penicillium sp., and Trichoderma sp. Basidiomycota are filamentous fungi which

form hyphae (except for those forming yeasts) and reproduce sexually through the

formation of specialized basidia and basidiospores. However, some Basidiomycota

reproduce asexually, and may or may not also reproduce sexually. Examples are

Cryptococcus sp., Rhizoctonia sp., Rhodotorula sp., and Sistotrema sp.

Chytridiomycota (chytrids) is the only true fungi that reproduces with motile spores

(zoospores), which are typically propelled by a single, posteriorly directed flagellum

(James et al., 2006). These organisms are often referred to as chytrid fungi or chytrids.

The majority of chytrid species occur in terrestrial habitats (Barr, 2001) such as forest,

agricultural and desert soils, as saprotrophs of refractory substrata including pollen,

chitin, keratin and cellulose. Chytrids are also obligate parasites of a wide variety of

vascular plants in soil, such as potatoes (Synchytrium) and cucurbits (Olpidium).

Glomeromycota have generally coenocytic mycelia and reproduce asexually through

blastic development of the hyphal tip to produce glomerospores (Schussler et al.,

2001). The Glomeromycota, such as the members of the Glomus genus comprise

ubiquitous symbionts of a multitude of plants which form arbuscular mycorrhiza.

Zygomycota are able to reproduce both sexually and asexually. During sexual

reproduction, zygospores develop in zygosporangia following gametangial fusion.

Sexual reproduction is haploid-dominant, while asexual reproduction makes use of

aplanospores. With asexual reproduction, asexual spores called sporangiospores are

produced either endogenously in sporangia or exogenously. For example,

Conidiobolus sp. and Mortierella sp. Oomycota from the kingdom Chromista (or

Straminipila) are filamentous, fungus-like eukaryotic microorganisms, which

reproduce both sexually and asexually. Most of the oomycetes (syn.

peronosporomycetes) produce two morphologically distinct types of spores, which are

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asexual, self-motile spores called zoospores, and the sexual spores called oospores.

The class oomycetes comprises organisms which resemble fungi with regard to both

morphological and physiological traits, but they are phylogenetically related to

diatoms, chromophyte algae and other heterokont protists (Dick et al., 1999). Notable

examples are Aphanomyces euteiches, Phytophthora infestans and Pythium ultimum.

1.2.2 Soil fungal life cycles

Composition and abundance of soil fungal communities can be influenced by

fungal life cycles and different forms of fungal structures in variable fungal phyla.

Fungi are present in soil as both actively growing organisms and as dormant

propagules (Warcup, 1951). The majority soil fungi are present as mycelium, sexual

or asexual spores, chlamydospores or sclerotial bodies (Bridge & Spooner, 2001).

Only mycelial states tend to have considerable metabolic activity, while the latter

stages are dormant survival structures with little activity and limited importance in

soil metabolism. For example, the life cycle of Aphanomyces euteiches includes

asexual and sexual stages that occur only in soil and allow an efficient dissemination

and conservation of the parasite. The infection of plant roots is initiated by oospore

germination in close vicinity of a plant host. Aphanomyces spp. can survive in soil as

oospores, which are generally associated with organic debris and are found primarily

in the plowed layer of soil (Pfender, 2001). Chlamydospores are the survival

structures of e.g. Fusarium solani and Fusarium oxysporum in naturally infested soil.

Pythium spp. are common soil inhabitants that persist in root debris as oospores or

thick-walled sporangia (Kraft & Pfleger, 2001). Phoma medicaginis var. pinodella

only produces pycnidiospores during the epidemic phase, but can survive in the

ground as the form of chlamydospores over a long period of time (Allard et al., 1993).

When analyzing soil fungal communities, it is important to consider that the

relative abundance of fungi may depend on the specific environment and stage in the

fungal life cycles at the time of sampling.

1.2.3 Role of fungi in the soil ecosystem

Soil fungi play fundamental roles in nutrient cycling processes in most

terrestrial ecosystems, notably through forming symbiotic associations such as

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mycorrhiza with plants and through organic matter decomposition (Stajich et al.,

2009).

Soil fungi can be classified into two main general functional groups based on

the mode of nutrition: saprophytic fungi (living on dead organic matter) or symbiotic

fungi (living in association with a host in a mutual, pathogenic or parasitic relation).

Some fungi are obligate saprotrophs or symbionts, whereas others are facultative in

relation to energy supply.

Saprophytic fungi are decomposers that convert dead organic material into

fungal biomass, carbon dioxide (CO2), and small molecules such as organic acids.

There are many forms of dead organic matter, such as leaf litter, dung, dead animals,

and wood. Saprotrophic fungi generally obtain their nutrients by decomposing

recalcitrant organic residues with a high cellulose and lignin content (De Boer et al.,

2005). Moreover, saprotrophic fungi release nutrients that can also be used by other

soil living organisms, making the fungi vital to the health of soil ecosystems.

Arbuscular mycorrhiza (AM) and ectomycorrhiza (ECM) are two major types of

symbiotic plant-fungus associations. AM is the most common mycorrhizal type being

found associated with about 80% of all terrestrial plants, while ECM is formed by

only approximately 8,000 plants species (Smith & Read, 2008). AM fungi are obligate

biotrophs which colonize a wide range of land plant species and can be found in all

ecosystems. The presence of AM fungi at the interface between plant roots and soil

makes them an important functional group of soil fungi which strongly influences

ecosystem processes (Gianinazzi et al., 2010). AM fungi play a vital role in plant

phosphorus supply, whilst the host plant provides carbon assimilates reciprocally

(Smith & Read, 2008). AM fungi can protect the plants from pathogens (Whipps,

2004), and can influence plant growth traits (StreitwolfEngel et al., 1997).

Furthermore, AM fungal diversity can determine plant community structure,

ecosystem variability and productivity (van der Heijden et al., 1998). The beneficial

effects of AM fungi on plant performance and soil health are essential for the

sustainable management of agricultural ecosystems (Jeffries et al., 2003; Barrios,

2007).

The occurrence of pathogenic or parasitic fungi can cause reduced plant

production or even plant death when they colonize roots. Soil-borne pathogens can

result in economically important losses in a wide variety of plants. For example, the

genera Fusarium and Verticillium cause vascular wilt diseases and lead to a

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particularly fast and effective killing of their hosts (Tarkka et al., 2008). AM fungi

and some root pathogens such as Aphanomyces euteiches, are biotrophs with similar

trophic requirements, but they show different functions (Graham, 2001).

The ecological roles of distinct fungi can be difficult to classify, as shifts in the

functionality of different species can occur in response to resource availability and

other variable factors (Termorshuizen & Jeger, 2008). For example, Fusarium sp.

displayed from parasitic to saprotrophic behavior in grassland systems differing

management regimes (Wilberforce et al., 2003). While functional groups remain a

powerful concept in describing the role of fungi in soil ecosystems, pathogens

however have the ability to switch growth strategies under different circumstances

(Kjøller & Struwe, 1992).

1.2.4 Soil fungal diversity

Soil microbial diversity comprises species diversity, genetic diversity, and

ecosystem biodiversity (Solbrig, 1991). Species diversity consists of two components:

the total number of species present (species richness) and the distribution of

individuals among species (species evenness or equitability) (Øvreås, 2000). The

concepts of species diversity were defined as: species diversity within and among

communities (α- and β-diversity), and total species diversity in a set of communities

(γ-diversity) (Whittaker, 1960; Whittaker, 1972). Diversity has been partitioned into

local diversity (α) and regional diversity (γ), with the two linked by the extent of

species composition variations over space (β) considering the relationship between

species diversity and scale (Godfray & Lawton, 2001). The relationship between the

three quantities has been described as additive (γ = + β) (Lande, 1996; Loreau,

2000).

A measure of species diversity should be nonparametric and statistically

accurate. Species richness, Shannon information, and Simpson diversity are the three

most commonly used nonparametric measures of species diversity (Lande, 1996).

Simpson index is a diversity index biased towards evenness (Magurran, 1988), while

Shannon index is more biased towards richness. Therefore, microbial diversity has

generally been compared using different indices to ensure that the diversity ordering

is robust. Futhermore, some classic indices of compositional similarity are sensitive to

sample size, especially for assemblages with numerous rare species, and are based

15

only on presence-absence data, thus accurate estimators for them are unattainable

(Chao et al., 2005). Estimators were proposed by Chao et al. (2005) for these indices,

which include the effect of unseen shared species, based on either (replicated)

incidence- or abundance-based sample data.

The resilience capacity of the soil is positively associated with the soil microbial

diversity (Arias et al., 2005). Microbial diversity is also considered as one of the main

components of soil suppressiveness to soil-borne diseases (Garbeva et al., 2004).

However, the relationship between soil biodiversity and disease suppression is unclear

and the assumption that the soil becomes more suppressive when diversity increases is

untested (Reeleder, 2003).

Soil is a habitat of high fungal diversity (Blackwell, 2011). Extensive studies

examined the fungal diversity in different soil types with various methods, such as

soil planted maize or potato with denaturing gradient gel electrophoresis (DGGE)

(Gomes et al., 2003; Manici & Caputo, 2009), or potato farm or forest soil or tallgrass

prairie soil with 454 pyrosequencing (Buée et al., 2009b; Jumpponen et al., 2010;

Lumini et al., 2010; Sugiyama et al., 2010). Generally, 454 pyrosequencing has a

much higher resolution than fingerprinting-based methods. Non-parametric index

Chao1 estimated that the OTU richness at 97% sequence similarity close to 2240 (±

360) in forest soil (Buée et al., 2009b), 1652 OTUs in other forest soils (Lim et al.,

2010), an average of 1,674 OTUs in organic and conventional potato farms

(Sugiyama et al., 2010). In these studies, forest soils and agricultural soils had

relatively similar fungal diversity based on the estimated number of OTUs. However,

the use of different primers, differences in the processing of sequences and the level

of detail reported make precise comparisons difficult.

Generally, previous studies showed that majority of fungi in soil belonged to

Dikarya (Ascomycota and Basidiomycota) (Buée et al., 2009b; Jumpponen et al.,

2010; Sugiyama et al., 2010). Buée et al. (2009b) found that 81% of the fungi in

forest soils belonged to the Dikarya, and identified the Agaricomycetes as the

dominant fungal class, and Ceratobasidium sp., Cryptococcus podzolicus, Lactarius

sp., and Scleroderma sp. as the most abundant species using primers from nuclear

ribosomal internal transcribed spacer-1 (ITS1). As the most abundant OTUs,

Jumpponen et al. (2010) identified Basidiomycota, Ascomycota, basal fungal lineages

and Glomeromycota in order of decreasing frequency in tallgrass prairie soil by

pyrosequencing of ITS2 region. Sugiyama et al. (2010) found most of the major

16

fungal phyla in potato fields including a variety of known potato fungal pathogens

(e.g., Alternaria spp., Ulocladium spp., Pythium ultimum and Alternaria solani) using

primers from ITS1 region. Obviously, the choice of primer and different environments

might have crucial influence on the study of soil fungal diversity.

Soil fungal diversity can be influenced by several factors, such as soil pH, soil

type, plant species, soil depth, and management strategies. Soil pH has a relatively

weak effect on fungal diversity compared to bacterial diverstity (Rousk et al., 2010).

Berg & Smalla (2009) reviewed that plant species and soil type cooperatively shape

the structure and function of microbial communities in the rhizosphere. Jumpponen et

al. (2010) found that the fungal community differed across vertical profiles, and

diversity estimator decreased with increasing depth. Organic potato farms showed a

slightly higher diversity and evenness within the fungal community compared with

conventional farming (Sugiyama et al., 2010). In conclusion, soil fungal diversity

varies under different circumstances. More studies of soil fungal diversity into

different angles will improve the understanding of the structure of soil fungal

communities.

1.3 Soil-borne pathogens

Soil-borne pathogens often become injurious, hampering plant root growth, and

reducing crop yield and quality substantially (Weller et al., 2002). Some of these

pathogens are especially challenging since they often survive in soil for several years

and each plant species is often susceptible to more than one pathogen (Fitt et al.,

2006). Many soil-borne fungi and fungus-like organisms persist in the soil under

unfavorable conditions for extended periods, because they produce resilient survival

structures such as melanized mycelium, chlamydospores, oospores, or sclerotia (Kraft

& Pfleger, 2001). It is difficult to predict, detect, and diagnose many plant diseases

caused by soil-borne pathogens before serious damage occurs. Generally, soil is a

complex environment, which makes it challenging to predict all the ongoing disease

dynamics.

1.3.1 Pea root diseases caused by soil-borne fungal pathogens

Soil-borne fungal pathogens are causal agents of legume diseases of increasing

economic importance such as root rots, seedling damping-off, and vascular wilts

17

(Lichtenzveig et al., 2006). A root disease is the result of an interaction among the

pathogen, the host, and environmental conditions which are conducive to disease

development. Fungi are the most common causal agents of pea diseases (Kraft &

Pfleger, 2001). Field pea (Pisum sativum L.), grown for fodder and for human

consumption is subject to a number of soil-borne diseases the severity of which

increases in severity as pea cropping intensifies (Bødker et al., 1993a). These diseases,

commonly referred to as the pea root rot complex, are caused by single or multiple

pathogens, including Alternaria alternata, Aphanomyces euteiches, Fusarium

oxysporum f. sp. pisi, F. solani f. sp. pisi, Mycosphaerella pinodes, Phoma

medicaginis var. pinodella (formerly Ascochyta pinodella), Pythium spp., Rhizoctonia

solani, Sclerotinia sclerotiorum, and Thielaviopsis basicola (Bødker et al., 1993b;

Persson et al., 1997; Bretag et al., 2006; Gaulin et al., 2007). These pathogens, either

individually or in combination, cause symptoms such as seed decay, root rot, foot rot,

seedling blight, or wilt (Figure 1).

(a) (b)

Figure 1. Pea fields with diseased plants (a), and healthy plants (b) in Denmark 2008.

One of the most widespread and destructive diseases of pea is Aphanomyces

root rot caused by A. euteiches, also known as common root rot, which occurs most

frequently and severely in wet soils. Aphanomyces root rot has been recognized as a

serious soil-borne disease in several American states and in Europe (Allmaras et al.,

2003; Levenfors et al., 2003). The disease starts with the yellowing of root tissue. At a

later stage, infected roots become brown and the hypocotyl darkens at the soil line.

18

The pathogen infects the cortex of primary and lateral roots and oospores are formed

within the root tissues.

Fusarium root rot caused by F. solani can occur in conjunction with other root

diseases of pea such as Aphanomyces, Rhizoctonia, or Pythium root rot (Kraft &

Pfleger, 2001). Fusarium wilt of pea caused by F. oxysporum can often be severe

when short rotations with other crops are practiced (Kraft, 1994), eventually resulting

in wilted plants. Pea diseases caused by Pythium spp. are most often categorized as

damping-off, seed rot, or root rot (Martin & Loper, 1999). T. basicola causes

Thielaviopsis root rot, and the pathogen is widely distributed with an extensive host

range (Lucas, 1958). It causes a very characteristic black rot of the entire root system

and stem base. Severe infection can result in wilting of lower leaves and stunting of

plants (Bødker et al., 1993b). Diseases caused by Ascochyta spp. are characterized by

leaf, stem, and pod lesions as well as discoloration of the cotyledon, hypocotyl, and

root areas. In 1927, L. K. Jones clarified and described the disease symptoms and

mycological characteristics of the three Ascochyta species that cause diseases of pea:

Ascochyta pisi Lib., which causes leaf and pod spot; Mycosphaerella pinodes (Berk.

& Bloxam) Vestergr., the perfect stage of A. pinodes, which causes blight; and A.

pinodella, which is now designated as P. medicaginis var. pinodella (L. K. Jones)

Boerema, which causes foot rot (Bretag & Ramsey, 2001).

1.3.2 Interactions among fungal pathogens

Disease complex involving several different pathogenic species cause similar

symptoms on the same host plant. Co-occurring plant pathogens may interact with

each other through antagonism and/or synergism. Species utilizing the same resource

have the potential to affect each other in two main ways: antagonism, where one

pathogen has a negative effect on the development of the other, and synergism, where

one pathogen promotes the development of the other (Begon et al., 2006). Different

interaction mechanisms, such as competition for space or nutrients, altered host

susceptibility through induced resistance or toxin production by one pathogen

suppressing the development of the other, may result in different effects (Le May et

al., 2009).

Interactions among pathogens might be one of the major forces shaping

pathogen community structures, and hence the dynamics and severity of diseases in

19

the field. Le May et al. (2009) studied the effects of co-occurrence on the

development of pathogens and disease severity of pea using two pathogens (M.

pinodes and P. medicaginis var. pinodella), and showed that the presence of the two

pathogens on the same host plant organ limited the disease development and their

reproduction, however, damages increased by a subsequent inoculation of the other

pathogen. Also when pea roots are infected by Aphanomyces spp., other soil-borne

fungi are generally involved in the disease complex. When Aphanomyces spp. are

present at low or moderate inoculum levels, infection of roots by fungi such as

Fusarium or Pythium spp. can increase disease severity (Pfender, 2001). For example,

co-inoculation of pea seedlings with A. euteiches and a nonpathogenic isolate of F.

solani resulted in significantly greater disease severity of pea root rot than inoculation

with A. euteiches alone (Peters & Grau, 2002). Antagonism between pathogens and

other microorganisms can be exploited by the use of biocontrol agents to limit

diseases, see the following section.

1.3.3 Management of soil-borne pathogens

Management of soil-borne diseases requires comprehensive knowledge of the

pathogen, the host plant, and the environmental conditions that favor infection. A

better understanding of the pathogen-host-environment dynamics will assist in the

design of improved disease management strategies.

Generally, soil-borne disease control strategies include host resistance, cultural

control, chemical control and biological control. Disease-resistant cultivars are an

obvious and effective control method because resistance to pathogens can be long

lasting. A plant can express resistance through the action of a single gene that confers

immunity or through multiple genes that result in a broad resistance to many

pathogens. For example, differential cultivars resistant to different races of F.

oxysporum have been widely used (Kraft, 1994). Cultural control methods involve

two main aspects: reducing inoculum in the environment of the host plant, and

creating environmental conditions unfavorable for disease development. The use of

organic matter has been proposed, for both conventional and organic agriculture

systems, to decrease the incidence of plant diseases caused by soil-borne pathogens

(Bonanomi et al., 2007). Increased crop diversity in rotations can also reduce root

disease severity of field pea (Bailey et al., 2001; Lupwayi & Kennedy, 2007). Crop

20

management such as crop rotation, residue retention and sowing time, is the main

method used to reduce the severity of Ascochyta blight of field pea and to minimize

yield losses, although with varying degrees of success (McDonald & Peck, 2009).

Agricultural chemicals can sometimes be used to manage soil-borne pathogens, such

as pre-plant fumigants or fungicide-treated seeds. For example, Pythium control is

improved by planting seed that has been treated with a fungicidal seed protectant

(Kraft & Papavizas, 1983).

Biological control uses a natural antagonist of a pathogen in order to reduce the

level or prevalence of a disease (Baker, 1987). Biological control agents containing

viable antagonistic organisms can be used to combat pathogens. At present, only

cultural and prophylactic methods of disease management, such as crop rotation and

bioassay methods to detect any potential inoculum in soil before sowing, are

recommended for the control of Aphanomyces root rot (Vandemark et al., 2000).

Additionally, organic amendments applied to field soils were shown to confer control

of soil-borne diseases caused by A. euteiches (Lumsden et al., 1983; Fritz et al., 1995;

Stone et al., 2003). Also, microbial antagonists or plant beneficial microorganisms

can limit Aphanomyces root rot. For example, inoculation of soil with bacteria such as

Pseudomonas aureofaciens (Carruthers et al., 1994) or Burkholderia cepacia

(Heungens & Parke, 2000) was demonstrated to control A. euteiches infection.

Likewise, AM fungi are able to reduce development of pea root rot caused by A.

euteiches (Larsen & Bødker, 2001; Bødker et al., 2002; Thygesen et al., 2004).

Alabouvette et al. (2009) found that Pseudomonas spp. and Trichoderma spp.

are the two most widely studied groups of biological control agents against F.

oxysporum. In addition, non-pathogenic F. oxysporum strains can be used to control

wilt induced by pathogenic strains. However, the success of biological control

depends not only on plant-microbial interactions but also on the ecological fitness of

the biological control agents (Alabouvette et al., 2009). Some Rhizobium

leguminosarum bv. viceae strains have the potential for biological control of Pythium

damping-off of field pea (Bardin et al., 2004). A strain of Clonostachys rosea was

identified as a mycoparasite against most of the pathogens causing pea root rot

complex, and can be used as a biological control agent of pea diseases (Xue, 2003).

However, effective biological control requires careful matching of antagonists to

pathosystems (Cunniffe & Gilligan, 2011). In addition, control of soil-borne

21

pathogens can be achieved by disease-suppressive soils (Schroth & Hancock, 1982;

Weller et al., 2002).

1.4 Methods to study soil fungal diversity

Fungal species diversity comprises species richness, abundance, evenness, and

distribution (Trevors, 1998; Øvreås, 2000). Methods to measure microbial diversity in

soil can be categorized into classical techniques, biochemical-based techniques, and

molecular-based techniques (Kirk et al., 2004). In general, molecular-based methods

consist of DNA fingerprinting, microarray and sequencing techniques.

1.4.1 Classical and biochemical-based techniques

Classical and biochemical techniques include e.g. plate counts, sole carbon

source utilization patterns/community level physiological profiling (CLPP), and fatty

acid methyl ester (FAME) analysis.

1.4.1.1 Plate counts

Traditionally, the diversity of soil microbial communities has been assessed by

culturing techniques that use various culture media specific for different microbial

species. This method is relatively fast, inexpensive, and ensures that only the active,

heterotrophic component of the microbial population is examined. However, it may be

difficult to isolate microorganisms from soil particles, to select specific growth media

(Tabacchioni et al., 2000), and finally, many species are non-culturable using current

culture media formulations (Atlas & Bartha, 1998). All of these limitations can

influence estimations of microbial diversity.

1.4.1.2 Sole carbon source utilization patterns/community level physiological

profiling (CLPP)

The commercially available BIOLOG MicroPlate™ bacterial identification

system was introduced to assess the potential functional diversity of microorganisms

from environmental samples through sole source carbon utilization (SSCU) patterns

(Garland & Mills, 1991). The gram-negative (GN) or gram-positive (GP) plate for

bacteria contains 95 different carbon sources and one control well without a substrate.

22

Metabolism of specific substrates in particular wells results in a color change of

tetrazolium dye. Individual species may be identified based on the specific pattern of

color change on the plate, thus providing an identifiable metabolic fingerprint.

Though there are currently few reports of fingerprinting fungal communities,

fungal specific plates BIOLOG SF-N and SF-P, which contain the same carbon

sources as the corresponding GN or GP plates, can be used for assessment of fungal

activity (Dobranic & Zak, 1999; Buyer et al., 2001; Classen et al., 2003; Grizzle &

Zak, 2006). BIOLOG FF plates have been made available specifically for fungi, and

contain a different set of carbon substrates compared to GN and GP plates, and a

different tetrazolium dye that can be metabolized by fungi (Preston-Mafham et al.,

2002). A method based on the soil FungiLog method (Sobek & Zak, 2003) was

developed in order to evaluate soil fungal functional diversity by examining the

utilization of different N substances (Nitrolog) on the PM3 plate (Biolog Inc.)

(Grizzle & Zak, 2006).

The advantage of CLPPs are, that they can distinguish fungal communities, that

they are relatively simple and reproducible, and that they produce a large amount of

information on metabolic characteristics of the communities (Zak et al., 1994).

However, they can only be applied to culturable microorganisms, particularly fast-

growing microorganisms (Yao et al., 2000), and they reflect the potential, and not the

in situ, metabolic diversity (Garland & Mills, 1991).

1.4.1.3 Fatty acid methyl ester (FAME) analysis

Several studies showed that fungi differ in fatty acid composition with some

fatty acids being specific to certain groups of fungi (Muller et al., 1994; Stahl & Klug,

1996; Zelles, 1997; Kock & Botha, 1998; Larsen et al., 1998). Fatty acid methyl ester

(FAME) analysis provides information on the microbial community composition

based on the fact that different groups of fungi contain different fatty acids (Ibekwe &

Kennedy, 1998). Fatty acids constitute a relatively constant proportion of the cell

biomass and signature fatty acids exist that can differentiate major taxonomic groups

within a microbial community. However, FAME from whole soil may be derived not

only from living fungi, but also from dead cells, humic materials, as well as plant and

root exudates.

The separate measurement of neutral lipid fatty acids (NLFAs) and

phospholipid fatty acids (PLFAs) is very useful for interpretation of perturbation

23

effects on soil and compost microorganisms (Bååth, 2003). PLFAs are constituents of

biological membranes and can be used to estimate biomass of fungi since biovolume

and cell surface are well correlated (Tunlid & White, 1992). NLFAs serve as energy

reserves in many fungi including AM fungi and oomycete fungi, such as A. euteiches.

The NLFA/PLFA ratio has been suggested as an indicator of the fungal nutrient status

or physiological state (Tunlid & White, 1990).

Although FAME analysis does not rely on cultivation of microorganisms, this

method is fraught with limitations. Cellular fatty acid composition can be influenced

by factors such as growth conditions and environmental stresses, moreover, other

organisms can confound the FAME profiles (Graham et al., 1995). Frostegård et al.

(2011) reviewed the use and misuse of PLFA measurements in soils, such as PLFA

interpretation, the extent of turn-over of PLFAs in soil, and the flawed use of diversity

indices to evaluate PLFA patterns.

1.4.2 Molecular-based techniques: DNA fingerprinting and

microarray

Various molecular-based techniques to assess fungal communities in

environmental samples have been developed, and have contributed to a better

understanding of the role of fungi in ecological habitats. Initially, properties such as

guanine plus cytosine (G+C) content (Nusslein & Tiedje, 1999), DNA reassociation

(Torsvik et al., 1996), DNA-DNA and mRNA-DNA hybridization (Schramm et al.,

1996) were used to measure the microbial diversity. However, they are now largely

obsolete due to the emergence of higher-resolution DNA fingerprinting, microarray

and sequencing technologies. The majortity of the molecular techniques currently

used rely on polymerase chain reaction (PCR) (Figure 2).

Selection of PCR target for the required taxonomic resolution is important.

PCR-based methods targeting the ribosomal DNA gene have been extensively used to

investigate fungal communities (Kirk et al., 2004). Comprehensive diversity studies

can be performed using the nuclear small (the 18S rDNA subunit-SSU) or the large

(the 25S or 28S rDNA subunit-LSU) ribosomal DNA gene (Figure 3). They have

been used predominantly in phylogenetic studies to determine evolutionary

relationships between taxa, and these sequences provide critical information for

identifying environmentally amplified rDNA signals (Mitchell & Zuccaro, 2006).

24

These two regions have different levels of sequence variation. The nuclear small

subunit rRNA gene is the most conserved among rRNA genes, and therefore, has only

limited phylogenetic resolution beyond the family level (Horton & Bruns, 2001). The

nuclear large subunit rRNA gene is more variable, especially in domains D2 and D8

in the 28S (Hopple & Vilgalys, 1999), and provides adequate variation to discriminate

sequences at the genus level.

Figure 2. PCR-based approaches for analysis of environmental nucleic acids. DNA is

extracted from the environmental source and is subjected to PCR amplification to produce a

heterogeneous mixture of sequences. These are separated into individual molecules by

cloning or electrophoresis techniques (DGGE/TGGE-denaturing gradient gel

electrophoresis/temperature gradient gel electrophoresis; SSCP-single stranded

conformational polymorphism; (T-) RFLP-(terminal-) restriction fragment length

polymorphism; ARDRA-amplified rDNA restriction analysis; ARISA-amplified ribosomal

intergenic spacer analysis). The electrophoresis techniques give banding patterns that

represent the individually separated sequences, and these profiles can be used to characterize

the PCR-amplified DNA from the environment. They can be used to make diversity

assessments after the molecules have been identified by sequencing or by comparing

electrophoretic mobility of the fragments. The signals on the array and the number of

sequences can be used for estimation of diversity indices (modified from Mitchell & Zuccaro,

2006).

25

Figure 3. The ribosomal DNA gene cluster contains three main genes (5.8S, 18S, 28S),

interspersed between intergenic spacer (IGS), non-transcribed spacer (NTS), external

transcribed spacer (ETS) and internal transcribed spacer (ITS). The degree of sequence

conservation varies between these genetic regions and within the genes. Relative position of

some primers are as shown above (forward primers) or below (reverse). The 18S rDNA gene

is generally used to discriminate from kingdom to family level, whereas the variation in the

28S rDNA can be used to separate sequences from family to genus level. The ITS region is

highly variable and can most often be used as a DNA barcode for fungal identification at

species level.

Larger sequence variation is required to identify environmental samples at the

species, strain or biovar level. To date, the internal transcribed spacer regions (ITS

regions 1 and 2), which display a large sequence and size variation, have been

validated as a suitable DNA barcode marker for the identification of fungal species

(Seifert, 2008; Seifert, 2009). This nuclear region, which is well-known in molecular

ecology and fungal systematics, is located between the SSU and LSU rRNA genes

and contains two noncoding spacer regions separated by the 5.8S rRNA gene. In fungi

it is typically about 650-900 bp in size, including the 5.8S gene. ITS can be used to

identify sequences at the species level, and even strain level, depending on the

taxonomic group. The variability is due to indels, repetitions, and nucleotide

substitutions, which, however, increase the difficulty of alignments and subsequent

phylogenetic analysis (Bruns, 2001). Moreover, recently evolved species might not

have enough variability within the ITS regions to be identified at the strain or biovar

level.

The most commonly used primers for fungal ITS amplification are the universal

primer pair-ITS1 and ITS4 (White et al., 1990; Gardes et al., 1991), or the fungal

specific-ITS1F (Gardes & Bruns, 1993) and ITS4. In contrast to ITS1F, ITS1

amplifies oomycetes, but can also co-amplifies plant ribosomal sequences. In order to

26

reduce co-amplification bias, specific primers have been designed for specific groups

of fungi, such as ITS4A for ascomycetes (Larena et al., 1999), ITS4B for

basidiomycetes (Gardes & Bruns, 1993), and primer set NSI1 and NLB4 for

Dikaryomycota (Martin & Rygiewicz, 2005).

1.4.2.1 Denaturing gradient gel electrophoresis (DGGE)/temperature gradient

gel electrophoresis (TGGE)

Denaturing gradient gel electrophoresis (DGGE) and temperature gradient gel

electrophoresis (TGGE) are two similar methods for studying microbial communities

(Muyzer et al., 1993). They utilize either a chemical or temperature gradient to

denature the sample as it moves across a polyacrylamide gel. DGGE and TGGE can

be applied to nucleic acids such as DNA and RNA, and less commonly to proteins.

Mixtures of PCR products with the same length differing only in sequence can be

separated by this technique. This method has the strength of being applicable to

multiple parallel samples concurrently, which enables the study of changes in

microbial populations from natural ecosystems (Muyzer, 1999). Another main

advantage is that they provide the possibility to further analyze sequences from

fingerprints using molecular methods, and thus to identify individual bands

(Valaskova & Baldrian, 2009). However, some limitations can influence the final

results, such as variable extraction efficiency of DNA (Theron & Cloete, 2000) and

amplification bias (von Wintzingerode et al., 1997). Additionally, one band may not

necessarily represent one species (Gelsomino et al., 1999), and one species may result

in multiple bands (Niemi et al., 2001).

1.4.2.2 Single strand confirmation polymorphism (SSCP)

Single strand confirmation polymorphism (SSCP) also relies on electrophoretic

separation based on differences in DNA sequences under certain experimental

conditions. Single-stranded DNA molecules are separated based on differences in

their secondary structures (Lee et al., 1996). When DNA fragments are of same size

and denaturant is absent, folding, and thus mobility, will be dependent on the DNA

sequences. As an example, SSCP has been used to study mycorrhizal fungi in roots

(Simon et al., 1993; Kjøller & Rosendahl, 2000). This technique has the same

limitations as DGGE, as one sequence may be represented by more than one band on

27

the gel due to the variant folding of single-stranded DNA molecules (Tiedje et al.,

1999).

1.4.2.3 Restriction fragment length polymorphism (RFLP)/amplified ribosomal

DNA restriction analysis (ARDRA)/terminal restriction fragment length

polymorphism (T-RFLP)/ribosomal intergenic spacer analysis (RISA)/

automated ribosomal intergenic spacer analysis (ARISA)

Restriction fragment length polymorphism (RFLP) also known as amplified

ribosomal DNA restriction analysis (ARDRA) detects differences in the localization

of restriction sites in DNA sequences. In the case of community analysis, the DNA

sample is digested by restriction enzymes and the resulting restriction fragments are

separated according to their lengths by gel electrophoresis (Liu et al., 1997). Terminal

restriction fragment length polymorphism (T-RFLP) uses a similar principle as RFLP

with the exception that one PCR primer is labeled with a fluorescent dye. This

technique for profiling of microbial communities is based on the position of the

restriction site closest to a labeled end of an amplified gene. A mixture of PCR

amplified variants of a single gene is digested using one or more restriction enzymes,

and the individual resulting terminal fragments are separated and detected using a

DNA sequencer. This technique has been widely used for describing fungal species

richness and structure (Brodie et al., 2003; Avis et al., 2010) and for identifying

species in a community (Buchan et al., 2003). T-RFLP has a relatively high resolution,

however, it may overestimate the diversity due to incomplete digestion by restriction

enzymes (Osborn et al., 2000). Moreover, different species have different gene copy

numbers, which could bias the results (Liu et al., 1997).

Ribosomal intergenic spacer analysis (RISA) and automated ribosomal

intergenic spacer analysis (ARISA) are similar in principle to RFLP and T-RFLP.

These methods separate sequences differing in length, thus providing ribosomal-based

fingerprinting of microbial communities. ARISA has become a commonly used

molecular technique for the study of microbial populations in environmental samples.

It has been used to examine and compare the composition of fungal communities

associated with different ecological samples (Ranjard et al., 2001; Torzilli et al., 2006;

Gillevet et al., 2009; Slabbert et al., 2010). ARISA is a relatively high-resolution,

highly reproducible and robust method for assessing and discriminating between

microbial communities.

28

1.4.2.4 DNA microarray

A DNA microarray (also commonly known as gene chip, DNA chip, or biochip)

consists of an array of DNA spots attached to a solid surface. Each DNA spot contains

a specific DNA sequence, known as probes, which are used to hybridize to a target of

DNA, cDNA or cRNA from e.g. an environmental sample. Since an array can contain

tens of thousands of probes, a microarray experiment can test for multiple species in

parallel.

Microarrays have been developed to accommodate many types of studies.

Microbial diagnostic microarrays for microbial community analysis have been

classified into three main categories based on the nature of probe and target molecules

(Zhou, 2003). They are (i) phylogenetic oligonucleotide microarrays (phylochips)

with short oligonucleotides designed against a phylogenetic marker gene, (ii)

functional gene arrays (FGAs) using gene fragments or oligonucleotides targeting

genes with the function of interest as probes, and (iii) community genome arrays

(CGAs) employing whole bacterial genomes as probes. Microbial diagnostic

microarrays represent a powerful tool for the parallel, high-throughput identification

of many microorganisms (Bodrossy & Sessitsch, 2004; Sessitsch et al., 2006). One

major problem of the microarray technique is cross-hybridization between closely

related species and features on the array, however, cross-hybridization quickly

decrease as sequence identities decrease (Shiu & Borevitz, 2008).

Phylochips and FGAs have been widely used to study dynamics and functions

of bacterial communities, and fungal phylochips have mostly been employed for

examining pathogenic fungi (Lievens et al., 2003; Tambong et al., 2006) and fungi in

compost communities (Hultman et al., 2008). Finally, phylochips have the limitation

that they only detect those taxa for which probes are available.

1.4.3 Sequencing techniques

Sequencing techniques rely on the identification of taxa on the basis of sequence

information from e.g. the ITS region. Sequencing can be divided in two main

approaches: cloning followed by Sanger sequencing and the more recent approach of

high throughput sequencing technologies.

29

1.4.3.1 Sanger sequencing

To be able to sequence individual amplicons, Sanger sequencing technology

(Sanger & Coulson, 1975) for studying microbial communities involves cloning of the

amplicons in suitable vectors, typically in bacterial cells, thereby putting a limit on the

number of individuals that can be identified. Large-scale Sanger sequencing has been

used to analyze soil fungal community and in one such study ~1,000 fungal sequences

were obtained (O'Brien et al., 2005). The cloning step and subsequent sequencing are

laborious and expensive. Furthermore, lower intensities and missing termination

variants may lead to sequencing errors accumulating toward the end of long

sequences (Kircher & Kelso, 2010).

1.4.3.2 Next generation sequencing-454 pyrosequencing

Next generation of non-Sanger-based high-throughput sequencing technologies

has advanced DNA sequencing at an unprecedented speed, thereby revolutionizing

today‟s biology (Schuster, 2008). Next-generation sequencing (NGS) technologies

include several sequencing platforms, such as 454 sequencing (used in the 454

Genome Sequencers, Roche Applied Science; Basel), Solexa technology (used in the

Illumina (San Diego) Genome Analyzer), the SOLiD platform (Applied Biosystems;

Foster City, CA, USA), the Polonator (Dover/Harvard), the HeliScope Single

Molecule Sequencer technology (Helicos; Cambridge, MA, USA), the Pacific

Biosciences real-time sequencing (Pacific Biosciences; Menlo Park, CA, USA) and

the Ion semiconductor sequencing (Ion Torrent Systems Inc.; Guilford, CT, San

Francisco, CA & Beverly, MA) (Shendure & Ji, 2008; Metzker, 2010; Rusk, 2011).

454 pyrosequencing, is one of the leading techniques supplanting Sanger sequencing

for comparative genomics and metagenomics, and was the first next-generation

sequencing platform available as a commercial product (Margulies et al., 2005). 454

pyrosequencing provides new solutions to the three bottlenecks - sample preparation,

library construction, sequencing, therefore ensuring overall simplification of the

tedious procedure of Sanger sequencing. It uses a large-scale parallel pyrosequencing

system with the ability to sequence approximately roughly 400-600 megabases of

DNA per 10-hour run on the Genome Sequencer FLX instrument (Figure 4). The

longer read length of 454 pyrosequencing is preferable to the other NGS methods for

fungal identification.

30

The rapid development of 454 pyrosequencing technology and its capability to

sequence any double-stranded DNA has led to its application in a broad range of

different research fields, including de novo whole genome sequencing, re-sequencing

of whole genomes and target DNA regions, metagenomics and transcriptomic analysis

(Table 1). Whole genome sequencing is to sequence the entire genome of an organism,

for example, humans, other animals, or microorganisms such as fungi, bacteria, or

viruses (Green et al., 2008). Amplicon (ultra deep) sequencing aims to detect

mutations at extremely low levels, and target amplified specific DNA regions for

assessments of microbial community diversity (Jumpponen et al., 2010).

Transcriptome sequencing enables small RNA profiling and discovery, analysis of

full-length mRNA transcripts, and mRNA transcript expression analysis (full-length

mRNA, expressed sequence tags (ESTs) and ditags, and allele-specific expression).

Transcriptome sequencing has advanced the study of various areas, including the

discovery of novel genes, single nucleotide polymorphisms (SNPs),

insertions/deletions and splice-variants, the identification of gene space in novel

genomes, the assembly of full-length genes (Barbazuk et al., 2007; Franssen et al.,

2011). Metagenomics is the study of the genomic content in a complex sample. This

approach aims to characterize all the organisms present in a sample and to identify the

function of each organism within a specific environment. Metagenomic samples can

be taken from any ecological niche depending on the research question and have been

taken from the human body, soil samples, extreme environments like deep mines and

the various layers within the ocean (Handelsman, 2004).

31

Figure 4. Overview of the 454 sequencing technology. (a) Genomic DNA is isolated,

fragmented, ligated to adapters and separated into single strands. (b) Fragments are bound to

beads under conditions that favor one fragment per bead, the beads are isolated and

compartmentalized in the droplets of a PCR-reaction-mixture-in-oil emulsion and PCR

amplification occurs within each droplet, resulting in beads each carrying ten million copies

of a unique DNA template. (c) The emulsion is broken, the DNA strands are denatured, and

beads carrying single-stranded DNA templates are enriched (not shown) and deposited into

wells of a fiber-optic slide. (d) Smaller beads carrying immobilized enzymes required for a

solid phase pyrophosphate sequencing reaction are deposited into each well. (e) Scanning

electron micrograph of a portion of a fiber-optic slide, showing fiber-optic cladding and wells

before bead deposition. (f) The 454 sequencing instrument consists of the following major

subsystems: a fluidic assembly (object i), a flow cell that includes the well-containing fiber-

optic slide (object ii), a CCD camera-based imaging assembly with its own fiber-optic bundle

used to image the fiber-optic slide (part of object iii), and a computer that provides the

necessary user interface and instrument control (part of object iii) (from Rothberg & Leamon,

2008).

32

Pyrosequencing of ribosomal RNA amplicons (pyrotags), in particular ITS

rDNA amplicons for fungi, has been applied for profiling the phylogenetic diversity

within microbial communities. The ITS region is widely used for identification of

fungi due to the relatively high variability combined with the flanking conserved

regions (18S and 28S) for primer annealing (Begerow et al., 2010). The ITS region

was tested for its feasibility for characterization of fungal communities using

pyrosequencing, and further, it was validated that the ITS1 region with an average

length of approximately 250 bp proofed adequate for identification of fungi at genus

or even at species level (Nilsson et al., 2009a).

Table 1. Applications using the novel 454 pyrosequencing technique.

Application Research project Reference

Bacterial genome sequencing Mycobacterium tuberculosis (Andries et al., 2005)

Human whole genome

sequencing Homo sapiens (Wheeler et al., 2008)

Metagenomics Microbial community in deep mine (Edwards et al., 2006)

Transcriptome mRNA trancript from Arabidopsis (Weber et al., 2007)

Genome structure Variation in human genome (Korbel et al., 2007)

Amplicon analysis Forest soil fungal community (Buée et al., 2009b)

454 pyrosequencing is still relatively expensive, therefore, methods have been

developed to enable the pooling of several samples in one sequencing reaction.

Tagged PCR primers enable the assignment of DNA sequences in a sequenced pool to

the correct sample once sequencing anomalies are accounted for (miss-assignment

rate < 0.4%). Therefore, the method enables accurate sequencing and assignment of

DNA sequences from multiple sources in a single run, thus reducing expenses

(Binladen et al., 2007) (Figure 5).

33

Figure 5. Structure of an MID containing PCR fragment (Eurofins MWG Operon, Ebersberg,

Germany). Primer A and Primer B are forward and reverse fusion-primers for pyrosequencing.

Depending on the sequencing needs, MID on forward and reverse fusion-primer may be

identical or different. When sequencing from both sides, identical MIDs are recommended for

each PCR fragment. The sequencing key (TCAG) is recognizable by the system software and

the priming sequences. Multiplex Identifiers (MIDs) are used to label the targeted primers.

Treatment of pyrosequencing data generally involves several steps: quality

control, sequence clustering, BLAST (Basic Local Alignment Search Tool) for

identification of individual clusters and subsequent statistical analysis. To date,

methods for prokaryotes have been leading the way in this regard and various

streamlined software pipelines are available. Open-source as well as web-accessible

software packages such as the Ribosomal Database Project (RDP) (Maidak et al.,

2001), Greengenes (DeSantis et al., 2006b), Mothur (Schloss et al., 2009), and

Quantitative Insights Into Microbial Ecology (QIIME) (Caporaso et al., 2010b), have

been developed and are now widely used for microbial community analysis.

Unfortunately, many methods developed for prokaryotes are not appropriate for fungi.

The ITS region is highly variable among fungal species, making it difficult for a

proper alignment of sequenced ITS regions, and thus impacts the discrimination of

fungal species.

Besides self-developed sets of tools (Taylor & Houston, 2011), a variety of

software packages can be used to accomplish reliable quality control of

pyrosequencing data properly. The Lucy DNA sequence quality and vector trimming

tool (Chou & Holmes, 2001) can be used to clean raw sequences. SeqTrim is a high-

throughput pipeline for pre-processing any type of sequence reads, including next-

generation sequencing (Falgueras et al., 2010). To remove bad quality reads,

PyroNoise and AmpliconNoise can be applied to model sequencing noise from the

34

flowgrams using a distance measure (Quince et al., 2009; Quince et al., 2011).

Software specifically developed to detect 16S rRNA gene chimeras is not appropriate

for fungal ITS sequences (Huber et al., 2004; Ashelford et al., 2006).

There are many software packages available for multiple sequence alignment,

such as ClustalW (Larkin et al., 2007), MAFFT (Katoh et al., 2002), MUSCLE

(Edgar, 2004), T-Coffee (Notredame et al., 2000), NAST (DeSantis et al., 2006a),

PyNAST (Caporaso et al., 2010a), or the pairwise alignment program ESPRIT (Sun et

al., 2009). The choice depends on the scalability, speed, and accuracy desired with the

increasing amounts of sequence data, if many sequences are analyzed, alignment may

not be possible or in some cases alignment is difficult (e.g. ITS region).

Various approaches for generating operational taxonomic units (OTUs) have

been selected for different studies, such as BLASTclust (Dondoshansky, 2002) for

forest soil (Buée et al., 2009b), CAP3 (Huang & Madan, 1999) for tallgrass prairie

soil (Jumpponen et al., 2010), CD-HIT for an arable soil (Rousk et al., 2010), and

TGICL (Pertea et al., 2003) for tropical mycorrhizal fungi (Tedersoo et al., 2010),

respectively. Moreover, more recent clustering methods are available, such as

UCLUST (Edgar, 2010) and SEED (Bao et al., 2011).

The most widely used approach to identify fungal taxa represented by OTUs is

BLAST-based similarity searches (Altschul et al., 1997) in Genbank (Benson et al.,

2011). Moreover, open-source software exist (Nilsson et al., 2009b) and custom

curated databases are available now, such as UNITE (Kõljalg et al., 2005) and FESIN

(http://www.bio.utk.edu/fesin/title.htm).

For the subsequent statistical analyses, several programs exist for different

purposes, such as Analytic Rarefaction v1.3 for rarefaction analyses (Hunt Mountain

software, Department of Geology, University of Georgia, Athens, GA, USA),

EstimateS for diversity analyses (Colwell & Coddington, 1994), PC-ORD, PRIMER,

or Vegan for community ordination analyses (McCune & Mefford, 1999; Clarke &

Warwick, 2001; Oksanen et al., 2007), and UniFrac or Phylocom for phylogenetic

community analyses (Lozupone & Knight, 2005; Webb et al., 2008).

Newly developed web-based pipelines for processing fungal pyrosequencing

data such as CLOTU (http://www.bioportal.uio.no) (Kumar et al., 2011), SCATA

(scata.mykopat.slu.se), PlutoF (http://unite.ut.ee), and Metagenomics of Alaskan

Fungi (http://www.borealfungi.uaf.edu), have provided different ways of sequence

clustering and identification.

35

Although pyrosequencing technology has enabled significant progress in the

describing and comparing of complex microbial communities, some aspects are still

challenging and conclusions should be drawn with great caution. In the process of

generating sequence data, besides the commonly known biases in DNA extraction

(Feinstein et al., 2009) and PCR amplification (Meyerhans et al., 1990), several other

different steps can introduce various types of incompletely understood biases.

Above all, the choice of primer, particularly for studying fungal communities, is

critical. Some of the ITS primers appear to introduce taxonomic biases during PCR,

such as ITS1-F, ITS1 and ITS5, which were biased towards amplification of

basidiomycetes, whereas others, e.g. ITS2, ITS3 and ITS4, were biased towards

ascomycetes (Bellemain et al., 2010). Therefore, different primer combinations or

different parts of the ITS region should be analyzed in parallel, and identification of

alternative ITS primers would be advisable (Bellemain et al., 2010).

Pyrosequencing error is another challenge for the assessment of microbial

communities (reviewed by Kircher & Kelso, 2010). Many rare OTUs correspond to

low-abundance taxa that comprise the rare biosphere, i.e. the long tail of the species

abundance distribution (Pedros-Alio, 2007). The majority of low-quality reads

represent the accumulation of small sequencing errors (Reeder & Knight, 2009),

which can lead to artificial inflation of diversity estimates unless relatively stringent

read quality filtering and low clustering thresholds are applied, such as the use of

quality trimming to 0.2% error probability and a clustering threshold of 97% identity

which was used for bacterial studies (Kunin et al., 2010). Tedersoo et al. (2010) also

established 97% sequence similarity of the ITS region as a barcoding threshold for

fungal species. Depth of sequencing is also one of the most pressing aspects, as the

number of required sequences varies from different target regions (Anderson et al.,

2003) and the complexity of the sample. Alignment quality, distance calculation

method, and clustering accuracy (Huse et al., 2010) have a profound effect on

downstream analysis, and hence impact the interpretation of microbial analyses

(Schloss, 2010). In addition, there is concern regarding the relative read abundance

counts when quantifying microbial communities with 454 pyrosequencing. It has been

shown that read abundance is approximately quantitative within species, but between-

species comparisons can be biased by innate sequence structure (Amend et al., 2010a).

Another major limitation in investigating fungal diversity in environmental

samples is a feature of databases, in which large quantities of unidentified and

36

misidentified sequences hinder the utility of BLAST searches. In Genbank, about 20%

of the fungal DNA sequences may be incorrectly identified to species level, and the

majority of sequences lack descriptive and up-to-date annotations (Nilsson et al.,

2006). Despite the availability of pipelines and curated databases, automated BLAST

can also lead to misinterpretation because the representative sequence of each cluster

for BLAST search and the highest-ranked BLAST hits may not be optimal. 454

pyrosequencing requires faster and more powerful computational resources than

Sanger sequencing. Furthermore, metagenomic analysis of microbial communities is

based on the inference from our existing knowledge, thus the uncharacterized species

will hamper in-depth understanding (Hugenholtz & Tyson, 2008).

Despite these limitations, pyrosequencing technologies hold great promise for

comprehensive community analyses and contribute a significant and growing impact

on the understanding of the dynamics and mechanisms of microbial communities in

ecosystems, which will further our understanding of microorganisms and their

important roles.

Concluding remarks:

To date, there is no settled criterion for analyzing environmental next-

generation sequencing data, because many steps of the processing and analysis are

still tentative and differ depending on the scope of the study. However, a proposal on

how NGS studies of fungal communities should be reported and disseminated to the

scientific community has been described (Nilsson et al., 2011). The following

questions have to be considered when analyzing NGS data of fungal communities.

1. Is the initial sequence trimming, filtering, and de-noising good enough to

proceed? How to set the threshold for the processing?

2. How to handle singletons?

3. Is multiple alignment needed/feasible? Which program is more appropriate for

the sequence data at hand?

4. Does the chosen clustering program generate high quality output?

5. Which sequence should be selected as a representative for a given OTU for

BLAST searches? -the longest or randomly selected sequences within each

cluster?

6. What database should be used for taxonomic assignment - a reference database

or a public database or a combination of both? Are the results credible?

37

7. How to interpret the data so as to answer the research questions?

8. Are the results quantitatively or qualitatively comparable to other published

findings?

1.5 Motivation and objectives

Soil health is one of the basic requirements for plant production in agricultural

systems. The balance between pathogenic and beneficial populations forms an

important element in the determination of soil health. Both abiotic and biotic

indicators have been proposed to estimate soil health (Janvier et al., 2007). Most soil-

borne diseases, such as plant root rot, are caused by fungi or fungus-like organisms,

emphasizing the importance of studying soil fungal communities in relation to

improving soil health. Fungal soil-borne pathogens are deleterious and can survive in

soil for a long time. These pathogens often occur together, which can significantly

intensify the disease severity. Furthermore, the complexity of the soil environment

makes it challenging to comprehend all diseases dynamics.

Several technologies have been applied for the study of fungal communities.

Cultivation has several drawbacks as different fungi have different growth rates and

growth requirements, some fungi cannot easily be identified by their morphology, and

many fungi cannot be cultured in vitro in the first place (O'Brien et al., 2005). To

overcome these difficulties, molecular techniques have become widely used.

Approaches such as DGGE and Sanger sequencing of cloned PCR products have been

among the preferred methods. However, these methods lack resolution (DGGE) or are

very costly and time-consuming (Sanger sequencing). The development of next-

generation sequencing, particularly 454 deep amplicon sequencing (Margulies et al.,

2005), offers new opportunities for the study of soil fungal communities as high

numbers of individuals can be analyzed without the need for cloning. Moreover, the

use of tagged primers allows the pooling of several samples, thus reducing costs and

increasing sample throughput (Binladen et al., 2007).

In this project, the following hypotheses have been tested:

1. Fungal community profiling by pyrosequencing can be used to characterize

soil health, and soil-borne fungal pathogens can be detected in the bulk soil by

pyrosequencing.

38

2. The fungal communities differ among soils where plants show symptoms of

disease and soils where plants are healthy.

3. Fungal communities differ among plant roots, rhizosphere and bulk soil, and

differences in fungal communities can be found between diseased and healthy

plants in the three environments, respectively.

4. Certain soil fungi can be used as soil health indicators.

The overall objectives of the project were:

1. To test the feasibility of pyrosequencing for studying soil fungi in relation to

root health.

2. To characterize fungal communities along a soil health gradient in pea field

soils and to assess the possibility of certain soil fungi to be used as soil health

indicators.

3. To profile the fungal communities in plant roots, the rhizosphere, and the

surrounding bulk soil in relation to root health.

39

2 Paper I

Influence of DNA extraction and PCR amplification on studies of soil

fungal communities based on amplicon sequencing

Lihui Xu, Sabine Ravnskov, John Larsen, and Mogens Nicolaisen

L.H. Xu, S. Ravnskov and M. Nicolaisen. Department of Agroecology, Faculty of

Science and Technology, Aarhus University, Forsøgsvej 1, 4200 Slagelse, Denmark

E-mail: Lihui.Xu@agrsci.dk; Sabine.Ravnskov@agrsci.dk

J. Larsen. Centro de Investigaciones en Ecosistemas, Universidad Nacional

Autónoma de México, Antigua Carretera a Pátzcuaro 8701 Col. Ex Hacienda de San

José de la Huerta, C.P. 58190 Morelia, Michoacán, México

E-mail: jlarsen@oikos.unam.mx

Corresponding author: Mogens Nicolaisen

Tel.: +45-8715 8137; Fax: +45-8715 6082

E-mail: Mogens.Nicolaisen@agrsci.dk

(Accepted by Canadian Journal of Microbiology)

40

Abstract

Most studies involving next-generation amplicon sequencing of microbial

communities from environmental studies lack replicates. DNA extraction and PCR

effects on the variation of read abundances of operational taxonomic units generated

from deep amplicon 454 pyrosequencing was investigated using soil samples from an

agricultural field with diseased pea. One sample was extracted four times, and one of

these samples was PCR amplified four times to obtain eight replicates in total. Results

showed that species richness was consistent among replicates. Variation among

dominant taxa was low across replicates, whereas rare operational taxonomic units

showed higher variation among replicates. The results indicate that pooling of several

extractions and PCR amplicons will decrease variation among samples.

Key words: soil fungi, DNA extraction, PCR, pyrosequencing, replication

41

Soil fungal communities are extremely complex and diverse (Hibbett et al.

2011). 454 amplicon pyrosequencing (Margulies et al. 2005), a next-generation

sequencing technology, has provided new powerful tools for analyzing this diversity,

both qualitatively and semi-quantitatively, and has been used to study fungi in

different types of soil (Buée et al. 2009; Lumini et al. 2010; Rousk et al. 2010;

Sugiyama et al. 2010). However, most of these first studies lacked technical replicates.

Several steps in the process of generating pyrosequencing data may generate variation,

such as sampling, DNA extraction (Feinstein et al. 2009), initial PCR amplification of

samples including primer choice (Engelbrektson et al. 2010; Bellemain et al. 2010 ),

emulsion PCR (emPCR), and the sequencing process itself (Huse et al. 2007; Kunin et

al. 2010; Porazinska et al. 2010). Incorrect clustering during the sequence analysis

(Huse et al., 2010) or different ordination analyses techniques and data transformation

(Kuczynski et al. 2010; Zhou et al. 2011) may influence the description of microbial

communities. In addition, several factors may complicate comparisons within samples,

such as different DNA extraction and PCR efficiencies for different fungal species

(Bellemain et al. 2010) and differences in copy number of the PCR target region

between species (e.g., Lindner and Banik 2011); however, this should not compromise

sample-to-sample comparisons. In support of this, Amend et al. (2010) found that read

abundance varied with an order of magnitude among species that were added in equal

quantities in an analysis of pyrosequencing reads from samples that were spiked with

different numbers of spores of different species, whereas the number of spores of

single species and the number of reads correlated well.

In this study, the variation among technical replicates during DNA extraction

and PCR was studied to test the reliability of pyrosequencing data. Several factors

may lead to variation in these two steps: technical error is important during both

processes, and sample heterogeneity is probably the main cause of variation during

DNA extraction, whereas stochastic variation in early amplification is probably the

major factor responsible for variation during PCR. DNA was extracted four times

from one soil sample, and from one of these extractions, four independent PCR

amplifications were conducted before pyrosequencing. To examine differences among

replicates, operational taxonomic units (OTUs) (Blaxter et al. 2005) were identified.

OTU richness (number of OTUs) and composition (relative abundance of OTUs) were

assessed and compared among replicates.

42

One soil sample was collected from a pea field, in which plants showed severe

symptoms of root rot (Persson et al. 1997), by taking five random subsamples each of

1 kg to a depth of 20 cm within a diameter of 2 m. These samples were pooled and

thoroughly mixed before a subsample of 50 g was taken for DNA extraction. After

freeze drying, the soil was homogenized in a Retsch MM301 bead mill for 8 min with

three steel balls (diameter 11.3 mm) that had been prechilled in liquid nitrogen. Total

soil DNA was extracted from four subsamples (3 g each) using the PowerMaxTM

Soil

DNA Isolation Kit (Mo Bio Laboratories, Inc., Carlsbad, California, USA). The

internal transcribed spacer-1 (ITS1), which is widely used as a barcode for fungal

identification (Seifert 2009), was used in this experiment. To amplify ITS1, primer

pair ITS1 (White et al. 1990) and 58A2R (Martin and Rygiewicz 2005) was used in

two rounds of PCR. In the second round, the primers ITS1 and 58A2R were used with

attached tags and adaptors according to the sequencing company (Eurofins MWG

Operon, Germany) for use during the pyrosequencing process. To examine variation

among DNA extractions, the ITS1 region was amplified from the DNA of the four

subsamples. Moreover, to examine variation in the amplification step, the ITS1 PCR

was conducted four times from one of the extractions. PCR for both PCR

amplifications contained 1× PCR buffer (Invitrogen Corporation, Carlsbad, California,

USA), 1.5 mmol/L MgCl2, 0.4 mmol/L dNTPs, 1 μmol/L each primer, 1U of Taq

DNA recombinant polymerase (Invitrogen), and 1 μL of DNA template, in a final

volume of 25 μL. All amplifications were conducted in a GeneAmp® PCR System

9700 thermal cycler (Applied Biosystems Inc., Foster City, California, USA) using an

initial step of 94 ◦C for 5 min, followed by 20 cycles at 94

◦C for 15 s, 48

◦C for 30 s,

72 ◦C for 30 s, and a final elongation at 72

◦C for 7 min. The eight tagged reactions

were pooled in equal amounts based on the concentration measured by

spectrophotometry (NanoDrop ND-1000 Spectrophotometer). The combined PCR

products were gel purified using the MinElute Gel Extraction Kit (Qiagen GmbH,

Hilden, Germany). Finally, the pooled sample was sequenced by Eurofins MWG on a

454 Life Sciences Genome Sequencer FLX (Roche Diagnostics) using a 1/16 plate,

and results were delivered as tag-sorted sequences.

All sequences generated from the four replicate DNA extractions and the four

replicate PCR amplifications were analyzed together to identify OTUs. Clustering of

sequences was performed using BLASTCLUST (Altschul et al. 1997) at 97%

sequence similarity, which is a frequently used cut-off value for OTU delimitation

43

(Tedersoo et al. 2010), on the freely available computational resource Bioportal at the

University of Oslo (http://www.bioportal.uio.no) using CLOTU software (Kumar et al.

2011). Analytic Rarefaction version 1.3 (Hunt Mountain Software, Department of

Geology, University of Georgia, Athens, Georgia, USA) was used for rarefaction

analysis. Calculation of the diversity (Chao1) index was performed using the

EstimateS version 8.2 software package (Colwell 2009).

After filtering, a total of 6490 sequences from the eight samples passed the

quality control (tags and primers found in reads; no sequence ambiguities; length >

200 nt). These sequences were clustered into 363 OTUs, including 183 singletons, at

97% sequence similarity. Excluding singletons, which may arise from sequencing

artefacts (Tedersoo et al. 2010), the remaining 180 nonsingletons were used in the

downstream analysis. The most abundant OTUs overall were Phoma medicaginis var.

pinodella (30.7%), which is known to be involved in pea foot rot (Bretag and Ramsey

2001), followed up by Verticillium dahliae (25.0%), Dokmaia sp. (14.4%),

Plectosphaerella cucumerina (2.3%), and Cryptococcus aerius (1.3%). The first five

most abundant OTUs accounted for 73.7% of all reads.

To estimate species richness, rarefaction curves were generated by randomly

sampling sequences and plotting the number of OTUs observed against the number of

sequences sampled (Fig. 1). The number of OTUs observed increased with the

number of sequences sampled, and none of the curves reached a plateau at 97%

similarity level. The amplicons of the four PCR replicates produced OTU richness

estimates of 83 (± 18) OTUs using the nonparametric Chao1 estimator (Chao et al.,

2005), whereas the amplicons of the four DNA extraction replicates produced

estimates of 69 (± 6) OTUs, indicating that not all OTUs were sampled and that there

was significant variation within individual samples. This also indicated that Chao1

estimates in individual replicates significantly underestimated OTU richness, as 180

nonsingletons were identified when analyzing pooled reads.

44

Fig. 1. Rarefaction curves depicting the effect of the total number of sequences sampled on

the number of operational taxonomic units (OTUs) identified from the four replicate DNA

extractions (E) and four replicate PCR amplifications (P).

The variance of the relative number of reads within each OTU from the four

DNA extractions and four PCR amplifications was plotted against the number of

sequences in each OTU (Fig. 2). Not surprisingly, the variance of the relative

abundance of reads in dominant OTUs was lower than the variance in rare OTUs,

indicating that only the most abundant OTUs are reliably quantified. A similar

observation was done by Unterseher et al. (2011), who also found different species

abundance distributions among rare (satellite) and abundant (core) OTUs. In this

study, only relatively few sequences were analyzed. To obtain more reliable

quantitative data on the rare OTUs, significantly more sequences have to be analyzed.

However, if only considering the dominant OTUs, these results show that the variance

is minor.

45

Fig. 2. Variance of the relative abundance of reads in each operational taxonomic unit (OTU)

from the four DNA extractions and four PCR amplifications. The y-axis shows variance

between replicates of the relative abundance of sequences within each OTU, while the x-axis

is the number of sequences in each OTU after log10 transformation.

Rank abundance curves of the dominant OTUs (relative abundance > 0.1%)

were plotted to compare read abundances in the two different experimental steps. First,

read abundances were plotted from individual samples (Fig. 3A), and read abundances

from the DNA extraction and the PCR steps were then averaged, respectively (Fig.

3B). When plotted individually, the abundance varied significantly across samples;

however, the samples produced very similar plots of read abundance for the dominant

OTUs, when averaged. This indicates that variation among replicates can be

decreased by performing multiple individual DNA extracts and PCR amplifications

and then pooling in the experimental stage or during data treatment, even for the more

rare OTUs.

46

Fig. 3. Individual abundance plot (A) or averaged rank abundance plot (B) of the most

abundant operational taxonomic units (OTUs) (representing 99% of the total number of

sequences) of DNA extraction replicates and PCR amplification replicates. The y-axis shows

the relative abundance of OTUs after log10 transformation, while the x-axis ranks each OTU

from most to least abundant.

To compare the diversity uncovered in the eight different samples at the phylum

level, all nonsingletons were classified using NCBI BLASTn (Altschul et al. 1997)

against the nonredundant GenBank database (Benson et al. 2011) and amalgamated at

the phylum level. Ascomycota constituted 89.9% (± 2.95%) of the reads in each

amplicon data set, followed by Basidiomycota (6.8% ± 2.1%), whereas other phyla,

including Chytridiomycota, Glomeromycota, Zygomycota, and the nonfungal

Oomycota, each represented ~1% of reads (data not shown). This showed that by

clustering at higher levels, variation between individual replicates was minor;

however, this will lead to a dramatic loss of detail.

In conclusion, the variation of technical replicates performed during DNA

extraction and PCR amplification for amplicon-based pyrosequencing of soil fungi

was high, especially for rare OTUs. This study only included relatively few sequences;

to obtain quantitative data for rare OTUs, many more sequence reads are needed. To

detect a larger part of the fungal diversity, technical replicates of DNA extraction and

replicates of PCR amplifications from each extraction should be included in fungal

community studies. To save resources, our results indicate that replicates may be

pooled before the pyrosequencing step. Furthermore, to improve statistical analyses,

these replicates could be tagged individually.

47

Acknowledgements

This study was financed by the Faculty of Science and Technology, Aarhus

University, Denmark. We thank Karsten Malmskov (Ardo A/S) for assistance and for

providing access to the study site. We are grateful to the editor and to two anonymous

reviewers for their critical comments and suggestions.

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51

3 Paper II

Soil fungal community structure along a soil health gradient in pea

fields examined using deep amplicon sequencing

Lihui Xua, Sabine Ravnskov

a, John Larsen

b, R. Henrik Nilsson

c, Mogens

Nicolaisena,*

aDepartment of Agroecology, Faculty of Science and Technology, Aarhus University,

4200 Slagelse, Denmark

E-mail: Lihui.Xu@agrsci.dk; Sabine.Ravnskov@agrsci.dk

bCentro de Investigaciones en Ecosistemas, Universidad Nacional Autónoma de

México, C.P. 58190 Morelia, Michoacán, México

E-mail: jlarsen@oikos.unam.mx

cDepartment of Plant and Environmental Sciences, University of Gothenburg, Box

461, 405 30 Gothenburg, Sweden

E-mail: henrik.nilsson@dpes.gu.se

*Corresponding author: Mogens Nicolaisen, Department of Agroecology, Faculty of

Science and Technology, Aarhus University, Forsøgsvej 1, 4200 Slagelse, Denmark.

Tel.: +45-8715 8137; Fax: +45-8715 6082

E-mail address: Mogens.Nicolaisen@agrsci.dk

(Accepted by Soil Biology & Biochemistry)

52

Abstract

Soil fungi and oomycetes (syn. peronosporomycetes) are the most common

causes of pea diseases, and these pathogens often occur in complexes involving

several species. Information on the dynamics within this complex of pathogens, and

also between the complex of pathogens and other fungi in the development of root

disease is limited. In this study, next-generation sequencing of nuclear ribosomal

internal transcribed spacer-1 was used to characterize fungal communities in

agricultural soils from nine pea fields, in which pea roots showed different degrees of

disease. Fungal species richness, diversity, and community composition were

analyzed and compared among the different pea soils. After filtering for quality and

excluding non-fungal sequences, 55,460 sequences clustering into 434 operational

taxonomic units (OTUs), were obtained from the nine soil samples. These sequences

were found to correspond to 145-200 OTUs in each soil. The fungal communities in

the nine soils were strongly dominated by Ascomycota and Basidiomycota. Phoma,

Podospora, Pseudaleuria, and Veronaea, at genus level, correlated to the disease

severity index of pea roots; Phoma was most abundant in soils with diseased plants,

whereas Podospora, Pseudaleuria, and Veronaea were most abundant in healthy soils.

No correlation was found between the disease severity index and the abundance of

some of the other fungi and oomycetes normally considered as root pathogens in pea.

Key words: Soil fungal community; Fungal diversity; Pea diseases; Nuclear

ribosomal internal transcribed spacer-1 (ITS1); Pyrosequencing

53

1. Introduction

The health status of plant roots is a result of complex interactions between the

plant, the physical and chemical soil environment, and microorganisms in the soil,

both the pathogens themselves, but also other microorganisms. Microbial diversity in

soil is one of the main components determining soil health (Garbeva et al., 2004), and

is believed to be one of the main drivers in soil suppressiveness. Representatives of a

range of fungal groups such as non-pathogenic Fusarium spp., Penicillium,

Trichoderma, have been identified as antagonists of soil-borne plant pathogens

(Garbeva et al., 2004). Moreover, arbuscular mycorrhizal (AM) fungi have been

shown to reduce plant root diseases (Gianinazzi et al., 2010; Whipps, 2004). Under

unfavorable conditions, these interactions may lead to development of disease, and

under other conditions to soil suppressiveness. Suppressive soil has been defined as

soil in which the disease severity or incidence remains low in spite of the presence of

pathogens, susceptible host plants, and climatic conditions favorable for disease

development (Baker and Cook, 1974). Although soil health is one of the most

important requirements for plant production in agricultural systems, it is not easily

described. However, several indicators, both abiotic and biotic, have been proposed to

estimate soil health (Janvier et al., 2007).

Fungi and fungus-like organisms are an important and diverse group of

microorganisms in the soil ecosystem (Fierer et al., 2007), including multiple

functional groups such as decomposers, mycorrhizal fungi, and many plant pathogens

(Stajich et al., 2009). All major fungal phyla, viz. Ascomycota, Basidiomycota,

Chytridiomycota, Glomeromycota, and Zygomycota, are present in the soil ecosystem

together with the Oomycota. However, only a small fraction of this diversity has been

analyzed to date (Hibbett et al., 2011), emphasizing the importance of studying the

soil fungal communities in relation to improving soil health.

Field pea (Pisum sativum L.) grown for fodder and for human consumption is

subject to a number of soil-borne diseases that can increase in severity as pea

cropping intensifies (Bødker et al., 1993a). These diseases, commonly referred to as

the pea root rot complex, are caused by single or combinations of pathogens,

including Alternaria alternata, Aphanomyces euteiches, Fusarium oxysporum f. sp.

pisi, Fusarium solani f. sp. pisi, Mycosphaerella pinodes, Phoma medicaginis var.

pinodella (formerly Ascochyta pinodella), Pythium spp., Rhizoctonia solani,

54

Sclerotinia sclerotiorum, and Thielaviopsis basicola (Bretag et al., 2006; Bødker et al.,

1993b; Gaulin et al., 2007; Persson et al., 1997). The pathogens, individually or in

combination, cause symptoms such as seed decay, root rot, foot rot, seedling blight,

and wilt. The incidence of the pea root rot complex varies between years, depending

on climate, crop rotation (Bødker et al., 1993a), and agricultural practices (Davidson

and Ramsey, 2000). Although it is clear that pea root rot is caused by the above

mentioned pathogens, limited information is available on the fungal communities in

the soil and their influence on pea plant health. Moreover, the interaction between

these pathogens and other soil living fungi is not well investigated.

Next-generation sequencing (NGS) technologies, particularly 454 deep

amplicon sequencing (Margulies et al., 2005), offer new opportunities for studies of

such interactions by profiling fungal communities, as high numbers of individuals can

be analyzed without cloning. Furthermore, the use of tagged primers allows pooling

of several samples, thus reducing costs and increasing sample throughput (Binladen et

al., 2007). Only a few studies on fungi have been performed using NGS (e.g. Buée et

al., 2009; Jumpponen and Jones, 2009; Lumini et al., 2010; Rousk et al., 2010;

Sugiyama et al., 2010), and even fewer studies have focused on fungal communities

in soils from intensive cropping systems (Sugiyama et al., 2010).

The internal transcribed spacer (ITS) region is widely used for identification of

fungi due to the relatively high variability combined with the flanking conserved

regions (18S and 28S) for primer annealing (Begerow et al., 2010). Due to length

constraints of early 454 pyrosequencing technology, only parts of the ITS region

could be used in initial studies. Nilsson et al. (2009) tested the ITS region as a target

for characterization of fungal communities using pyrosequencing, and found that the

ITS1 region with an average length of approximately 250 bp was adequate to identify

fungi at genus or even at species level. Also Buée et al. (2009) and Jumpponen and

Jones (2009) used the ITS1 region as a genetic marker to amplify environmental

samples for pyrosequencing fungi.

The aim of this study was to characterize fungal communities along a soil

health gradient in pea field soils to answer the following questions: (i) Does the

overall soil fungal community differ between soils in which pea plants show

symptoms of disease and in soils with healthy plants? (ii) Can fungal soil-borne

pathogens be detected in the bulk soil? (iii) Can certain soil fungi be used as soil

health indicators?

55

2. Materials and Methods

2.1. Soil sampling

Soil samples were collected from nine pea fields in Denmark on September 1,

2008 just before harvest. Soil characteristics of these fields were shown in Table 1.

All fields had been sown with the same pea cultivar, Rainier. Based on visual

observation of pea root rot symptoms, six fields with apparently diseased plants and

three fields with apparently healthy plants were selected. Each soil sample of

approximately 5 kg was collected by taking five random sub-samples between plants

within a diameter of 2 m to a depth of 20 cm. These five samples from each field were

pooled and homogenized. One sub-sample of 50 g from this was taken and frozen at -

80 ◦C pending further processing.

Table 1 Soil characteristics of nine pea fields in Denmark.

Pea

field

Latitude,

longitude

Soil

type

Yield

(kg/ha) Pre-crop pH

Nutrient (mg/100g soil)

P K Mg

1 55°01'N,

12°17'E

Heavy

clay 2,461 Beet 6.5 2.5 13.5 10

2 55°01'N,

12°17'E

Heavy

clay 2,461 Beet 6.5 2.5 13.5 10

3 54°59'N,

12°18'E

Heavy

Clay 2,482

Spring

barley 6.4 3.1 10.5 7.0

4 54°58'N,

12°25'E Clay 976

Spring

barley 7.1 2.6 10.1 4.8

5 54°57'N,

12°30'E Clay 2,330

Spring

barley 7.4 5.4 11.2 4.6

6 54°56'N,

12°30'E

Heavy

clay

Not

harvested

Spring

barley 6.9 3.4 11.7 7.4

7 54°58'N,

12°24'E

Heavy

clay 3,558

Winter

wheat 7.3 4.2 12.5 6.1

8 54°58'N,

12°20'E

Heavy

clay

Not

harvested Pea 7.0 3.4 11.9 9.8

9 54°59'N,

11°59'E Clay 2,070

Spring

barley 6.6 4.2 9.9 5.1

2.2. Evaluation of field soils in a pot experiment

The nine soils were compared in a pot experiment to evaluate disease

development in pea plants grown in the respective soils and to confirm observations

from the fields. The pea cultivar Rainier was sown in pots with five replicates of each

soil. Pots with 1100 g soil and seven seeds were placed randomly in a greenhouse

with controlled growth conditions (16 h, 20 ◦C light; 8 h, 18

◦C dark). After six weeks,

56

pea plants were harvested, soil was removed from roots and lower stems by washing,

and the roots were scored for root and tap-root rotting on a 0-6 scale disease severity

index (DSI) as follows: 0 = healthy plant without any visible symptoms; 1 =

discoloration of less than 10% on a single root; 2 = discoloration of about 25% of the

root system; 3 = about 50% of the root system was dark and affected; 4 = about 75%

of the root system was dark and affected but no symptoms on epicotyl or leaves; 5 =

the whole root system, together with the epicotyl, was dark and affected and the

lowest leaves were wilted; and 6 = dead plant. Sub-samples of roots were stained with

0.05% trypan blue in lactophenol (Phillips and Hayman, 1970) for characterization of

the fungal composition by microscopy. The total root length was calculated by the

grid-line intersect method (Giovannetti and Mosse, 1980).

2.3. DNA extraction and PCR amplification

Freeze-dried soil samples were homogenized in a Retsch MM301 bead mill for

8 min in vibrating bowls with three steel balls (diameter = 1.13 cm) that had been pre-

chilled in liquid nitrogen. Total soil DNA was extracted from 3 g of milled soil using

the PowerMaxTM

Soil DNA Isolation Kit (Mo Bio Laboratories, Inc., Carlsbad, CA,

USA) according to the manufacturer‟s instructions.

To amplify ITS1, the primers ITS1 (TCC GTA GGT GAA CCT GCGG) (White

et al., 1990) and 58A2R (CTG CGT TCT TCA TCG AT) (Martin and Rygiewicz,

2005) were used. To generate amplicons for 454 pyrosequencing, two rounds of PCR

were performed. The first PCR step was performed with primer pair ITS1 and 58A2R

to amplify the region of interest from the soil DNA. Then 1 μl of the ITS1 PCR

product was used as template for a second amplification. This step was performed

with the primers A-key-MID tag-ITS1 and B-key-58A2R, of which the A adapter

(GCC TCC CTC GCG CCA TCAG) and the B adapter (GCC TTG CCA GCC CGC

TCAG) were pyrosequencing primers and the hexamer MID tags were required for

sample identification after pooling. The nine tags were selected from the list of

recommended tags from Eurofins MWG Operon (Ebersberg, Germany). Primers were

synthesized by Eurofins MWG Operon.

PCR reactions for both PCR amplifications contained 1× PCR reaction buffer,

1.5 mM MgCl2, 0.4 mM dNTPs, 1 μM each primer, 1U of Taq DNA recombinant

polymerase (Invitrogen Corporation, Carlsbad, CA, USA), and 1 μl of DNA template

with a final volume of 25 μl. All amplifications were conducted in a GeneAmp® PCR

57

System 9700 thermal cycler (Applied Biosystems Inc., Foster City, CA, USA) using

an initial DNA denaturation step of 94 ◦C for 5 min, followed by 20 cycles of

denaturation at 94 ◦C for 15 s, annealing at 48

◦C for 30 s, extension at 72

◦C for 30 s,

and a final elongation at 72 ◦C for 7 min. PCR products were analyzed by gel

electrophoresis in 2% agarose.

2.4. PCR purification and pyrosequencing

The concentration of the amplicons was estimated through both

spectrophotometry (NanoDrop ND-1000 Spectrophotometer) and by analyzing

agarose gel pictures using Kodak Molecular Imaging Software (Eastman Kodak

Company, Rochester, NY, USA). After pooling approximately equimolar of PCR

amplicons from each of the nine samples, the combined PCR products at 280-320 bp

were excised from an agarose gel and purified using the MinElute Gel Extraction Kit

(Qiagen GmbH, Hilden, Germany). After precipitating the DNA and resuspending in

10 µl TE buffer, the pooled sample was sequenced by Eurofins MWG on a 454 Life

Sciences Genome Sequencer FLX (Roche Diagnostics) using a ¼ plate, and the

results were delivered as tag-sorted sequences.

2.5. OTU (Operational Taxonomic Unit)-based sequence analysis

All sequences from the nine soils were analyzed together to identify OTUs. The

entire ITS1, excluding tag-, primer-, 18S-, and 5.8S-sequences, was extracted from all

sequences by the ITS extractor (Nilsson et al., 2010) and clustered to hypothetical

species using CAP3 (Huang and Madan, 1999) (97% similarity over ≥ 90% of the

alignment length) as implemented in the pyrosequencing pipeline of Tedersoo et al.

(2010). A majority-rule consensus sequence was computed for each cluster to

minimize the impact of poor reads (the lower limit for the length of ITS1 was 50 bp,

and sequences with more than one DNA ambiguity symbol were discarded). The

consensus sequence was then used as query for subsequent BLAST searches using

NCBI-BLASTn (Altschul et al., 1997) against the non-redundant GenBank database

(Benson et al., 2011) and a custom-curated database (C-DB), which contained all fully

identified fungal ITS sequences screened from the GenBank and UNITE databases

(Abarenkov et al., 2010). OTUs at 97% sequence similarity that could not be

identified using custom-curated databases were recovered by BLAST search against

58

GenBank to obtain information about their taxonomic distribution and their similarity

to the nearest relative in the NCBI database.

OTUs defined at 97% sequence similarity were used to generate rarefaction

curves and to estimate the richness by non-parametric indices ACE (abundance-based

coverage estimates) and Chao1 (Chao et al., 2005). Rarefaction analysis was

performed using Analytic Rarefaction v1.3 (Hunt Mountain software, Department of

Geology, University of Georgia, Athens, GA, USA). Calculation of richness indices

(ACE and Chao1) was performed using the EstimateS software package with default

settings (Colwell, 2009).

2.6. Statistical analyses

All variables employed to characterize the nine different pea field soils (Table 2)

were subjected to one-way ANOVA using STATGRAPHICS Plus, version 5.1

(Copyright Manugistics Inc.). Prior to ANOVA, the data were analyzed for normal

distribution and variance homogeneity by Bartlett‟s test. Post-ANOVA mean

comparisons were performed with least significant difference (LSD) values. To

compare the fungal communities in soils collected from fields with high (DSI > 1) and

low (DSI ≤ 1) DSI, respectively, linear discriminant analysis (LDA) was performed

by SAS

9.2 (SAS Institute Inc., Cary, NC, USA). A forward elimination method was

used to select the best variables from 178 identified genera for discrimination. Simple

linear regressions analyses were used to correlate DSI with different soil

characteristics listed in Table 1, with the fungi identified at the phylum level, and with

eight fungal genera that were responding significantly to health status according to the

LDA analysis.

The software package PRIMER-E v6 (Clarke and Warwick, 2001) was used for

testing whether there were differences in fungal community composition or variability

across treatments by analysis of similarity (ANOSIM) (Clarke, 1993). Rank-order

Bray-Curtis distance was used to determine distinction in either mean on variability

refers to differences in the variability around a centroid in ordination space. All tests

were permutated, assuming no underlying distribution of the community.

59

3. Results

In six soils, plants showed root rot symptoms in the field (field numbers 1, 2, 4,

6, 8, 9) and in three soils, plants did not show any significant disease symptoms (field

numbers 3, 5, 7). In field numbers 1, 2, 4, 6, 8, and 9, pea plants were severely stunted;

the leaves were yellow and the number of pods was reduced, each pod in addition

carrying a reduced number of seeds. The roots were small and had a yellow to dark

brown color; some plants had wilted. The DSI of roots in the pot experiment

confirmed observations in the field (Table 2). Disease symptoms were obvious on pea

plants, which were planted in soils from field numbers 1, 2, 4, 6, 8, and 9 (DSI > 1),

whereas fewer disease symptoms were visible on plants planted in soil from field

numbers 3, 5, and 7 (DSI ≤ 1) (Table 2). In general, all recorded plant growth

characteristics followed the same pattern as the DSI, so that plants grown in soils from

fields with diseased plants showed less vigorous growth than that of plants grown in

soil from fields without any apparently diseased plants (Table 2). Microscopy showed

that arbuscular mycorrhizal (AM) root colonization was in the range of 10-31% areal

root coverage between soils from the different fields (Table 2). Regression analysis

showed a significant negative correlation between AM root colonization and DSI (R2

= 0.25, P = 0.001).

After quality filtering, a total of 68,811 ITS1 sequences were obtained from all

soil samples. These were clustered into 1145 OTUs containing 748 non-singletons

and 397 singletons at 97% sequence similarity. Singletons constituted 0.58% of the

total number of reads and were excluded from further analysis. All the non-singletons

were used to BLAST against the non-redundant GenBank database and C-DB. In total,

434 OTUs, which contained 55,460 sequences were identified as fungi and oomycetes,

excluding 8440 sequences with BLAST hits to plant sequences, 1345 to animal

sequences, 49 to bacterial sequences, and 3120 sequences with no significant matches.

The number of sequences from each soil ranged from 3309 to 7757, which resulted in

the number of OTUs per soil sample ranging from 145 to 200.

60

Table 2 Plant growth performance, arbuscular mycorrhizal (AM) root colonization,

and disease severity index of pea plants grown in a greenhouse pot experiment with

soil collected from the different pea fields included in the present study. Different

letters indicate significant differences among soils for the individual variables (n = 5).

Pea

field

Shoot dry

weight

(g)

Root dry

weight

(g)

Root length

(m)

AM root

colonization

(%)

Disease

severity

index

1 2.05 b 0.61 a 5.49 a 20.1 cd 3.0 bc

2 2.58 d 0.62 a 8.52 bcd 15.9 bcd 2.4 b

3 2.46 cd 0.69 a 13.30 e 14.6 bc 0.6 a

4 1.53 a 0.53 a 9.29 cd 9.7 ab 5.0 d

5 2.32 bcd 0.71 a 10.02 d 30.4 e 1.0 a

6 2.31 bcd 0.75 a 7.52 abc 6.9 a 3.2 bc

7 2.39 cd 0.64 a 10.30 d 30.7 e 0.8 a

8 1.42 a 0.60 a 6.81 ab 22.9 d 4.0 cd

9 2.15 bc 0.68 a 8.54 bcd 18.3 cd 3.0 bc

P-value < 0.0000 0.4243 < 0.0000 < 0.0000 < 0.0000

LSD 0.34 NS 2.14 7.1 1.1

NS = Not significant

LSD = Least significant difference

Rarefaction curves showed that the number of OTUs observed increased with

the number of sequences sampled in each of the soils and that none of the curves

reached a plateau at 97% similarity level (Fig. 1), while a plateau was reached at

lower levels of similarity, 85% or 80% (data not shown). There was no clear trend

between the number of OTUs observed in the samples and the health status as

measured by DSI. In all soils, the number of OTUs observed was lower than the

number of OTUs estimated with the non-parametric ACE and Chao1 (both from 172

to 212) indices at 97% similarity. In addition, the estimated richness in the individual

soils did not correlate to health status (data not shown).

61

Fig. 1. Rarefaction curves of soil fungal communities at 97% sequence similarity level in the

nine soils from pea fields. Between 3309 and 7757 sequences were obtained from each soil,

corresponding to 145-200 OTUs. Number at the end of each curve indicates the pea field

number. Red color: six soils with disease severity index (DSI) > 1; green color: three soils

with DSI ≤ 1.

The 30 most abundant OTUs accounted for 68.6% of the total number of

sequences (Table 3) and the four most abundant and fully identified species were P.

medicaginis var. pinodella, Verticillium nigrescens, Guehomyces pullulans, and

Cryptococcus aerius, which represented 54.8% of all reads. After amalgamation of

OTUs at phylum level, the total abundance of each phylum in all soils was:

Ascomycota (62.5%), Basidiomycota (27.1%), Chytridiomycota (0.1%),

Glomeromycota (0.03%), Oomycota (1.3%), and Zygomycota (7.4%). No correlation

could be found for Ascomycota, Basidiomycota, Chytridiomycota, Glomeromycota,

Oomycota, and Zygomycota, between their relative abundance in the individual soils

and DSI of roots (data not shown).

62

Table 3 Identification and abundance of the 30 most common fungal operational taxonomic units

(OTUs) recovered from the nine pea field soils.

Closest NCBI database match Closest

accession

number

Query

Coverage

Similarity Number

of

sequences

Relative

abundance

(%) (%) (%)

Phoma medicaginis var. pinodella FJ032641 100 97 13,836 24.95

Verticillium nigrescens FN386267 100 98 6,521 11.76

Guehomyces pullulans AF444417 100 93 6,164 11.11

Cryptococcus aerius AB032666 97 96 3,855 6.95

Leptosphaeria sp. AM924151 100 98 3,528 6.36

Mortierella elongata FJ161928 100 97 1,976 3.56

Uncultured Cantharellaceae DQ273371 96 81 1,833 3.31

Tetracladium sp. FJ000376 91 92 1,382 2.49

Mortierella hyalina FJ590596 100 98 963 1.74

Uncultured Tetracladium EU754979 100 83 728 1.31

Exophiala salmonis AF050274 100 90 674 1.22

Pseudeurotium bakeri FJ903285 100 96 615 1.11

Waitea circinata EU693448 100 81 615 1.11

Thelebolus microsporus DQ028268 100 98 423 0.76

Pythium intermedium DQ083532 100 99 362 0.65

Cladosporium tenuissimum GU248330 100 100 356 0.64

Mortierella sp. EF601628 98 83 339 0.61

Mrakia frigida DQ831018 100 95 323 0.58

Microdochium bolleyi AJ279475 100 99 321 0.58

Uncultured fungus FN397434 97 92 288 0.52

Cryptococcus terricola FN298664 100 97 281 0.51

Fusarium merismoides EU860057 100 99 264 0.48

Cryptococcus elinovii AF145318 100 98 259 0.47

Peziza phyllogena AY789329 97 82 247 0.45

Tetracladium maxilliforme FJ000371 100 90 202 0.36

Batcheloromyces leucadendri EU707889 88 81 187 0.34

Geomyces vinaceus AJ608972 97 98 179 0.32

Sistotrema coronilla DQ397337 96 83 172 0.31

Plectosphaerella cucumerina GU062300 100 100 170 0.31

Gymnostellatospora alpina DQ117459 83 90 168 0.30

63

The fungal community structures in all soils were compared to identify OTUs

overlapping among the nine soils. Forty OTUs, which represented 80.6% of the total

sequences, were shared by all soils, whereas between 4 and 21 OTUs were found in

only one soil. However, these OTUs only accounted for 0.01-0.27% of the total

number of sequences. There were 270 OTUs shared between 2 and 8 soils, accounting

for 16.8% of the sequences. The fungal communities in soils with diseased plants

(field numbers 1, 2, 4, 6, 8, and 9) and in soils with healthy plants (field numbers 3, 5,

and 7) were different from each other in composition (relative abundance of OTUs)

by ANOSIM (R = 0.451, P = 0.036).

LDA was used to test whether there were any significant differences in fungal

communities by individual taxonomic units along the soil health gradient as defined

by DSI. Eight genera (P < 0.05) could be used to separate soils along the soil health

gradient. They were Phoma, Podospora, Pseudaleuria, Gaeumannomyces,

Paraglomus, Phialocephala, Veronaea, and Trichocladium. Of these, Phoma was the

most abundant with 23.74% (± 10.6%) on average in nine soils, whereas Podospora

(0.13 ± 0.1%), Pseudaleuria (0.13 ± 0.1%), Veronaea (0.06 ± 0.1%),

Gaeumannomyces (0.01 ± 0.01%), Paraglomus (0.01 ± 0.01%), Phialocephala (0.01

± 0.01%), and Trichocladium (0.01 ± 0.01%) were present in much lower abundance.

Considering their extremely low abundance, Gaeumannomyces, Paraglomus,

Phialocephala, and Trichocladium were not considered further as their distribution

could have been random. When the relative abundance of P. medicaginis var.

pinodella was plotted against DSI, a strong positive correlation (R2

= 0.84, P = 0.001)

was observed (Fig. 2). Also the abundance of Podospora (R2

= 0.69, P = 0.006),

Pseudaleuria (R2

= 0.81, P = 0.001), and Veronaea (R2

= 0.44, P = 0.051) was plotted

against DSI (Fig. 2). To rule out any underlying correlation between these fungi and

other soil characteristics, a linear regression analysis was performed. No significant

correlations could be found, except that there was a weak correlation between

phosphorous content and Podospora (R2

= 0.50, P = 0.034). Other fungi generally

considered as major causal agents of pea root rot were only present in limited amounts,

and their presence was not correlated to DSI (data not shown). The relative abundance

of other pea root rot pathogens recovered from the nine field soils was generally low:

Alternaria sp. (0.14%), Aphanomyces sp. (0.02%), Fusarium sp. (1.16%), Pythium

spp. (1.26%), and Thielaviopsis sp. (0.01%).

64

Fig. 2. Linear regressions between relative abundance (%) of four fungal genera responding

to soil health status and disease severity index (DSI) of plants grown in the nine respective

soils. (a) Phoma sp., (b) Podospora sp., (c) Pseudaleuria sp., (d) Veronaea sp.

4. Discussion

Analyses of fungal community structure in nine pea field soils revealed

significant differences between fungal communities in soils with diseased plants as

compared to soils with healthy plants, showing a remarkable change in soil fungal

community structure along the soil health gradient. A number of OTUs which showed

significant differences in abundance between soils with high and low DSI were

identified. A clear positive correlation was observed between the abundance of

sequences from an OTU which was closely related to P. medicaginis var. pinodella,

and the DSI of pea plants grown in soils from the different fields. P. medicaginis var.

pinodella has previously been isolated from pea roots with rot symptoms and was the

most frequently isolated pathogen in Southern Scandinavia in a pea field survey

(Persson et al., 1997). P. medicaginis var. pinodella can survive in soil for several

years as chlamydospores (Wallen and Jeun, 1968), and it is highly influenced by crop

rotation practices (Davidson and Ramsey, 2000). A negative correlation was found

65

between Podospora, Pseudaleuria, and Veronaea, and the DSI. To the best of our

knowledge, none of these fungi have previously been reported to be involved in

interactions with plant roots, but results from this study may indicate a role of these

fungi in pea root health and in soil disease suppressiveness. Other major pea root

pathogens, such as A. alternata, A. euteiches, F. oxysporum f. sp. pisi, F. solani f. sp.

pisi, M. pinodes, Pythium spp., R. solani, S. sclerotiorum, and T. basicola, were low in

abundance and did not correlate with the DSI of plants grown in the different soils,

which could be caused by several factors. Firstly, P. medicaginis var. pinodella may

be the only causal agent of pea diseases in these fields. Secondly, it has been shown to

inhibit the growth of M. pinodes in a pea leaf assay (Le May et al., 2009). Thirdly,

some fungi, such as obligate parasites, are dominant in plant roots while others, such

as saprophytic fungi, are dominant in soil (Garrett, 1950). Finally, different fungi

infect pea at different growth stages. Pythium spp., for example, primarily infects

during or immediately after seed germination (Kraft and Pfleger, 2001).

AM fungi are known to play important roles in pea root health (Larsen and

Bødker, 2001; Thygesen et al., 2004). In this study, AM fungi were generally found in

extremely low amounts, and no correlation was found between the abundance of AM

fungi in the nine soils and the DSI. This finding is in contrast to the fact that DSI

correlated negatively with root colonization by AM fungi of the corresponding pea

plants as assessed by microscopy. However, the abundance of AM fungi in soil is not

always correlated to the colonization of roots (Wang et al., 2008). The negative

correlation between AM fungal colonization of roots and DSI confirms several studies

of the antagonistic potential of AM fungi against pathogens in plant roots as reviewed

by e.g. Whipps (2004).

After amalgamation of OTUs at phylum level, no correlations could be observed

between the health status of the soils and the relative abundance of each phylum. In

all soils, Ascomycota was dominant followed by Basidiomycota which is in

accordance with Klaubauf et al. (2010), who found 77.7-88.2% of the clones in the

respective libraries to be Ascomycota, and 7.5-21.3% of Basidiomycota in four arable

soils and one grassland. The low abundance of Oomycota and Glomeromycota is

supported by Sugiyama et al. (2010), who found 0.4% of Oomycota sequences and

0.1% of Glomeromycota sequences in soil from potato fields. Also Jumpponen et al.

(2010) found low levels of Glomeromycota (1%) in a tallgrass prairie soil. However,

66

different primer sets were used in these studies, making a direct comparison difficult

(Bellemain et al., 2010).

The richness of the nine soils was analyzed by constructing rarefaction curves

and by estimating richness using ACE and Chao1 indices. The full richness of the

soils was not recovered in the sampled sequences. The number of OTUs estimated in

the soil samples with Chao1 index at 97% similarity (1145 including singletons),

which was comparable to results from Sugyama et al. (2010), who found estimated

average Chao1 values of 1674 for fungi in potato soils. The numbers of observed and

estimated OTUs were similar in all soils and no correlation could be found to the DSI

of the soils, indicating that the overall fungal diversity of the soils was not affected by

health status. In contrast, Manici and Caputo (2009) found that soil fungal diversity

and the abundance of pathogens in potato roots were negatively correlated, however,

in this study, samples were from soils with different rotation practices, which may

have been the major cause of the lower diversity in soils with high potato cropping

intensity.

Strikingly, 40 OTUs representing 80.6% of the total number of sequences, were

shared by all nine soils, whereas 124 OTUs representing 2.6% of the total number of

sequences, were present in only one of the soils. Klaubauf et al. (2010) only identified

four out of 116 OTUs that were common in at least three soils when comparing five

agricultural soils. However, these soils were selected to “represent different bedrocks,

soil textures, pH values, water, and humus contents”, whereas the soils in the present

experiment were similar in soil characteristics, were sampled within a distance of

approximately 25 km, and were all sown with pea, which may explain the higher

proportion of shared OTUs. Furthermore, the four most abundant OTUs (for all nine

soils) constituted more than half of all reads. In a study of forest soils (Buée et al.,

2009), the four most abundant OTUs constituted 36% of the reads, indicating that, in

general, only a few species dominate fungal communities in soils. This shows that a

few OTUs constitute the majority of fungal biomass in the soils and that these are

shared by most of the analyzed soils. A highly diverse reservoir of less abundant

OTUs was present in the soils. These low abundance fungi may represent a microbial

reservoir that play important functions under environmental stress, when new carbon

sources become available or they may have functions in plant disease that were not

detected in this study.

67

In conclusion, the results obtained from the present NGS study on soils with

diseased and healthy plants, respectively, show that (i) the nine soils could be

discriminated on the basis of their fungal communities, (ii) fungal soil-borne

pathogens could be detected in the bulk soils, and (iii) some of the fungi that

responded to plant health may be used as soil health indicators. A positive correlation

was found between P. medicaginis var. pinodella and DSI, and negative correlations

were found between the DSI and Podospora, Pseudaleuria, and Veronaea, indicating

that these fungi interact with disease development in pea roots. Finally, it was

demonstrated that NGS provides a powerful tool to examine fungal communities

related to plant disease in agricultural soils.

Acknowledgements

This study was financed by the Faculty of Science and Technology, Aarhus

University, Denmark. We thank Karsten Malmskov (Ardo A/S) for assistance and

providing access to the study sites. We are grateful to the editor and to two

anonymous reviewers for their critical comments and suggestions.

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73

4 Paper III

Fungal community structure in roots, rhizosphere, and bulk soil

associated with plant root health as examined by deep amplicon

sequencing

Lihui Xu1, Sabine Ravnskov

1, John Larsen

2, Mogens Nicolaisen

1

1Department of Agroecology, Faculty of Science and Technology, Aarhus University,

Slagelse, Denmark

2Centro de Investigaciones en Ecosistemas, Universidad Nacional Autónoma de

México, C.P. 58190 Morelia, Michoacán, México

Correspondence: Mogens Nicolaisen, Department of Agroecology, Faculty of Science

and Technology, Aarhus University, Forsøgsvej 1, 4200 Slagelse, Denmark.

E-mail: Mogens.Nicolaisen@agrsci.dk

(Manuscript in preparation)

74

Abstract

Fungi play a pivotal role in the function of plant roots and their interaction with

the surrounding soil, having strong effects on plant health, both as pathogens but also

as suppressors of plant disease. In this study, next-generation amplicon sequencing

was explored in order to characterize fungal communities in diseased and healthy

plant roots, their surrounding rhizosphere and the adjacent bulk soil. Samples were

taken from three agricultural fields. Fungal species richness, diversity, and community

composition was analyzed and compared among the three environments among the

three fields and between diseased and healthy samples. Fungal species richness was

highest in bulk soil and lowest in roots. Fungal communities in all samples were

strongly dominated by Dikarya, and differed significantly among the three

environments. Fusarium oxysporum and Aphanomyces euteiches were the likely

causes of root rot in the respective fields as assessed by pyrosequencing data and

quantitative PCR. Glomus and Fusarium species were significantly more abundant in

roots, whereas Cryptococcus and Mortierella species were almost exclusively found

in the rhizosphere and bulk soil. A clear correlation was demonstrated between health

status of roots and their fungal communities. The results showed that fungal

community structures are highly variable in the three different ecological niches,

between healthy and diseased roots, and across different fields.

Key words: fungal community, root fungi, soil fungi, nuclear ribosomal internal

transcribed spacer-1 (ITS1), pea root rot, rhizosphere, bulk soil, pyrosequencing.

75

Introduction

Microorganisms in rhizosphere and bulk soil include fungi, bacteria, algae,

protozoa, and nematodes (Raaijmakers et al., 2009). Fungi are an immensely diverse

group of organisms that play crucial roles in agricultural soils as degraders of organic

material, as plant pathogens, or as beneficial organisms that suppress plant pathogens

or promote plant growth. The interaction between fungi and plant roots can, under

unfavorable conditions, lead to disease, which in many cases is caused by complexes

of different fungi. Pea roots, for instance, can be infected by a number of fungi or

fungus-like organisms that together, or alone, are able to cause disease (Kraft &

Pfleger, 2001). Arbuscular mycorrhizal (AM) fungi can improve the nutrient status of

their host plants (Smith & Read, 2008), but can also have a strong influence on plant

health by suppressing various pathogens (Gianinazzi et al., 2010; Larsen & Bødker,

2001; Whipps, 2004). The root, rhizosphere, and bulk soil are all important reservoirs

for fungi and other microorganisms that may cause disease. However, they represent

different habitats, which are reflected in the fungal communities that make up the

three environments. Roots can host an extensive fungal diversity (Vandenkoornhuyse

et al., 2002) including symbiotic and parasitic fungi that live on plant sugars whereas

soils, which are rich in organic matter, are abundant in saprophytic fungi. The bulk

soil is the main reservoir of fungi in the rhizosphere (Berg & Smalla, 2009), an

environment that is rich in both root exudates and soil organic matter, both of which

are driving forces in the population density and activities of the communities

(Raaijmakers et al., 2009).

The interaction between soil health and plant roots has mainly been investigated

by studying individual species or groups of microorganisms. Molecular methods have

dramatically advanced fungal discovery (Blackwell, 2011), and various biochemical-

based and molecular-based techniques have been developed to assess fungal

communities in soil. However, many of these technologies lack resolution. With the

recent development of next-generation sequencing (NGS), in particular 454 deep

amplicon sequencing (Margulies et al., 2005), studies of microbial communities from

different ecological niches have reached a hitherto unseen amount of data and

resolution (Buée et al., 2009; Jumpponen & Jones, 2009; Jumpponen et al., 2010;

Lumini et al., 2010; Rousk et al., 2010; Sugiyama et al., 2010).

76

Field pea is susceptible to a number of soil-borne diseases that can increase in

severity as pea cropping intensifies (Bødker et al., 1993a). These diseases, commonly

referred to as the pea root rot complex, are caused by single or combinations of

pathogens, including Alternaria alternata, Aphanomyces euteiches, Fusarium

oxysporum f. sp. pisi, F. solani f. sp. pisi, Mycosphaerella pinodes, Phoma

medicaginis var. pinodella (formerly Ascochyta pinodella), Pythium spp., Rhizoctonia

solani, Sclerotinia sclerotiorum, and Thielaviopsis basicola (Bødker et al., 1993b;

Persson et al., 1997; Bretag et al., 2006; Gaulin et al., 2007). The pathogens, either

individually or in combination, cause symptoms such as seed decay, root rot, foot rot,

seedling blight, and wilt.

Understanding the complex interactions of plant roots and microorganisms in

their surrounding soil is important and relevant for the development of sustainable

disease management. Whereas the involvement of single species of pathogens in

disease is well investigated, not much is known about fungal communities and their

role in disease development. To investigate the interaction of plant roots and

associated fungal communities, and their impact on root health, pea (Pisum sativum L.)

was chosen as a model organism. Fungal communities from roots and their

surrounding rhizosphere and bulk soil were examined using NGS deep amplicon

sequencing. Communities were studied in both diseased and healthy plants from three

fields. The specific aims of this study were: (i) to profile and compare fungal

communities in plant roots, their rhizosphere, and bulk soil, (ii) to compare fungal

communities with respect to root health in the three environments, and (iii) to identify

possible causal agents of disease and other fungal taxa that were responsive of health

status.

Materials and Methods

Plants and soil sampling

Plant and soil samples were collected from three pea fields located in Southern

Zealand (F1: 55˚15‟25”N, 11˚26‟46”E; F2: 55˚14‟60”N, 11˚41‟20”E) and Lolland (F3)

in Denmark (54˚49‟12”N, 11˚38‟42”E) in 2010. These fields contained sandy loam

soils, and had been sown with the same pea cultivar, Bingo. Based on visual

inspection of pea roots, five diseased and five healthy plants were sampled from each

field. Each individual pea plant was collected together with its corresponding

77

rhizosphere soil and 50 g of adjacent bulk soil. Rhizosphere soil was collected by

carefully brushing roots, and root hairs were subsequently removed from the collected

soil. Soil was removed from roots by washing, and roots were then scored for root and

tap-root rotting on a 0-6 scale of disease severity index (DSI) as follows: 0 = healthy

plant without any visible symptoms; 1 = discoloration of less than 10% on a single

root; 2 = discoloration of approximately 25% of the root system; 3 = about 50% of the

root system was dark and affected; 4 = about 75% of the root system was dark and

affected but no symptoms on epicotyl or leaves; 5 = the whole root system, together

with the epicotyl, was dark and affected and the lowest leaves were wilted; and 6 =

dead plant. After scoring, roots were frozen at -80 ◦C.

DNA extraction

Freeze dried soil samples and pea plant roots were homogenized in a

Geno/Grinder 2000 (SPEX CertiPrep, Metuchen, NJ) 6 times for 40s in plastic tubes

with eight steel beads, which had been pre-chilled in liquid nitrogen. DNA was

extracted from rhizosphere soil and bulk soil using the PowerSoil DNA Isolation Kit

(Mo Bio Laboratories, Inc., Carlsbad, CA, USA), and from roots using the DNeasy

Plant Mini Kit (Qiagen GmbH, Hilden, Germany) according to the manufacturer‟s

instructions.

Quantitative PCR analysis

Due to primer choice, oomycetes could not be detected during pyrosequencing.

In order to test for the presence of A. euteiches in the 30 pea root samples, a Q-PCR

assay was performed in a 384-well optical reaction plate, using the primers

136F/211R and the probe 161T (Vandemark et al., 2002). In a final volume of 15 μl,

assays contained 2 μl of DNA template, 7.5 μl of TaqMan Universal PCR Mastermix

(Applied Biosystems Inc., Foster City, CA, USA), 900 nM of each primer (136F,

211R) and 500 nM of probe (161T). The thermal cycle protocol was used as described

in (Sauvage et al., 2007) in the ABI 7900HT Sequence Detection System (Applied

Biosystems Inc., Foster City, CA, USA). Samples were tested in triplicate.

PCR amplification, purification and pyrosequencing

To amplify the ITS1 region, primers ITS1-F (CTT GGT CAT TTA GAG GAA

GTAA) (Gardes & Bruns, 1993) and 58A2R (CTG CGT TCT TCA TCG AT) (Martin

78

& Rygiewicz, 2005) were used. To generate amplicons for 454 pyrosequencing, PCR

amplification was performed as a two-step program. The first PCR step was

performed with primer pair ITS1F and 58A2R, and then 1 μl of PCR product was

used as template for the next step. The second PCR step was performed with a tag

encoded primer set using the forward primer: (5‟-

CGTATCGCCTCCCTCGCGCCATCAG-MID-ITS1F-3‟) and reverse primer (5‟-

CTATGCGCCTTGCCAGCCCGCTCAG-MID-58A2R-3‟). Thirty 10-nucleotide

MID primer tags for sample identification after pooling were selected randomly from

the list of recommended MID primer tags from Eurofins MWG GmbH (Germany).

Primers were synthesized by Eurofins MWG GmbH (Germany). PCR reactions for

both PCR steps contained 1 × PCR reaction buffer, 1.5 mM MgC12, 0.4 mM dNTPs,

1 μM each primer, 1 U of Taq DNA recombinant polymerase (Invitrogen Corporation,

Carlsbad, USA) and 1 μl of DNA template in a final volume of 25 μl. All

amplifications were conducted in a GeneAmp PCR System 9700 thermal cycler (PE

Applied Biosystems) using an initial DNA denaturation step of 94 ◦C for 5 min,

followed by 20 cycles at 94 ◦C for 15 s, 50

◦C for 30 s, 72

◦C for 30 s, and a final

elongation at 72 ◦C for 7 min. PCR products were analyzed by electrophoresis in a

1.5% agarose gel.

The concentration of amplicons was measured by a 2100 Bioanalyzer (Agilent

Technologies, Inc., Santa Clara, CA, USA) according to the manufacturer‟s

instructions. PCR amplicons of roots, rhizosphere soil, and bulk soil were pooled in

equimolar amounts from the 30 samples (health status (2) × environment (3) ×

replicates (5)) of all three fields, precipitated, and then redissolved in 10 µl TE buffer.

The pooled amplicons were electrophoresed in 1.5% agarose gels, and a smear of

PCR products at 320-360 bp were cut from a gel and purified using QIAquick Gel

Extraction Kit (Qiagen GmbH, Hilden, Germany). The final three samples were

sequenced by Eurofins MWG on a 454 Life Sciences Genome Sequencer FLX

machine using Titanium series chemistry (Roche Diagnostics) on a 1/4 plate each and

sequence data were delivered as MID tag-sorted sequences.

OTU (Operational Taxonomic Unit)-based sequence analysis

Sequence filtering, clustering, and BLAST searches were performed on the

freely available computational resource Bioportal at the University of Oslo

(http://www.bioportal.uio.no) using the CLOTU application (Kumar et al., 2011). All

79

sequences generated from each of the three environments (30 samples) were analyzed

together to identify operational taxonomic units (OTUs). Initially, reads were filtered

by discarding sequences in which primers and tag could not be identified and which

were shorter than 150 bp. Remaining sequences were clustered using BLASTclust at

97% similarity and 90% coverage, and singletons were subsequently omitted from the

dataset. BLAST searches were performed using a combination of (i) the BLAST

feature in the CLOTU program, which selects one sequence from each cluster (the

longest) and uses NCBI-BLASTn against the non-redundant GenBank database, and

(ii) manual BLAST searches in GenBank using a set of randomly selected sequences.

In both approaches, uncultured fungi were filtered from the results. Rarefaction

analysis was performed using Analytic Rarefaction v.1.3 (Hunt Mountain Software,

Department of Geology, University of Georgia, Athens, GA, USA). The non-

parametric estimators Abundance Coverage Estimator (ACE) and Chao1 were

calculated using the EstimateS v.8.2 software package (Colwell, 2009) with default

settings.

Statistical analyses

Fungal diversity observed and estimated by ACE and Chao1, the abundance of

fungi at phylum level, and the 10 most abundant OTUs from roots, rhizosphere and

bulk soil were subjected to one- and two-way analysis of variance (ANOVA) to

examine levels of significance (P < 0.05) of main factors and their interactions. To

identify fungi among the rare OTUs that were significantly different between diseased

and healthy plants, one-way ANOVA for OTUs from the three environments were

performed with an abundance limit value of 0.1% of all reads. Prior to ANOVA, the

data were analyzed for normal distribution and variance homogeneity by Bartlett‟s

test (P > 0.05). Post-ANOVA mean comparisons were performed with least

significant difference (LSD) values. Data were log- or square root-transformed under

the statistical analysis, when required according to Bartlett‟s test.

Principal component analysis (PCA) was performed on the 10 most abundant

OTUs in roots, rhizosphere and bulk soil, respectively. The scores of the first two

components from the PCA were used to compare differences in fungal communities

between plants of differing health status and across the fields. PCA was performed at

phylum level to compare the fungal community in different environments. Standard

80

errors (SE) of the mean of component 1 and component 2 for the three fields were

calculated to evaluate data variation.

The software STATGRAPHICS Plus, version 5.1 (Copyright Manugistics Inc.)

was used to perform the statistical analyses.

Results

Sample characterization

In pea fields F1 and F2, diseased plants were scattered across the field, whereas

in F3 diseased plants occurred in a large area. Roots from healthy plants showed less

darkening and had significantly more root hairs, and also they exhibited higher shoot

dry weight, higher root fresh weight, and lower DSI (for details and photographs, see

Table 1 and supplementary Fig. S1).

Using a qPCR Taqman assay, A. euteiches was detected only in root samples

from F3. Moreover, the presence of A. euteiches in diseased roots (Ct value = 29 ± 1)

differed significantly from that in healthy roots (Ct value = 39 ± 1) (P < 0.001).

Assuming 100% amplification efficiency, this means that the amount of A. euteiches

was approximately 1000 times higher in symptomatic roots.

DSI: Disease severity index; ***, P ≤ 0.001

Table 1. Characteristics of plants with and without root disease symptoms

from three different fields. Different letters indicate significant

differences among plants for the individual variables (n = 5).

Field Health

status

Shoot dry

weight (g)

Root fresh

weight (g)

DSI

Root

DSI

Stem

1 Diseased 3.30 b 0.14 a 4.8 b 4.8 c

1 Healthy 7.15 cd 0.51 b 1.0 a 1.0 b

2 Diseased 4.72 bc 0.14 a 5.0 b 5.0 c

2 Healthy 7.48 cd 0.62 b 0.9 a 1.0 b

3 Diseased 0.65 a 0.13 a 4.8 b 5.0 c

3 Healthy 2.85 b 0.67 b 0.8 a 0 a

Analysis of variance P values

Field (F) *** 0.92 0.22 ***

Health status (H) *** *** *** ***

F x H 0.01 0.77 0.38 ***

81

OTUs abundance and richness

After quality filtering, the root dataset contained 151,563 sequences, which

were clustered into 123 non-singleton OTUs (139,309 reads), the rhizosphere soil

dataset contained 182,577 reads, which were clustered into 271 non-singleton OTUs

(165,364 reads), and the bulk soil data set contained 166,321 reads, which were

clustered into 440 non-singleton OTUs (155,043 reads). The average number of

filtered reads in the 90 samples was 5108 (± 1164).

BLAST searches in the GenBank database identified all non-singletons as fungi.

The average relative abundance of five biological replicates of each OTU is listed in

the supplementary material (Table S1) together with their best BLAST hits in

GenBank.

Plotting the number of OTUs observed versus the number of sequences sampled

resulted in rarefaction curves that did not reach a plateau in the bulk soil samples,

whereas the curves from root and rhizosphere samples were close to reaching a

plateau (data not shown). At 97% sequence similarity, ACE and Chao1 richness

estimators were calculated (Table 2). One-way ANOVA indicated that the estimated

fungal community richness was significantly different across the three environments,

with bulk soil showing the highest diversity, while roots showed the lowest diversity.

Two-way ANOVA indicated that the factor environment (E) had a statistically

significant effect on the OTUs observed at the 95% confidence level. However,

generally no clear trend was observed in health status and diversity.

82

ANOVA, Analysis of variance; E, Environment; H, Health status

*, P < 0.05; **, P < 0.01; ***, P < 0.001

Table 2. Fungal diversity observed, and estimated by ACE and Chao1 in roots,

rhizosphere and bulk soils from diseased and healthy areas of three different pea

fields. Different letters indicate significant difference between treatment means

as examined in terms of multiple range test (n = 5) (Variance homogeneity was

unavailable with Bartlett's test in Field 1).

Field Environment Health status Observed ACE Chao1

Field 1 Root Diseased 39 a 69 a 74 a

Root Healthy 55 a 70 a 73 a

Rhizosphere Diseased 151 b 203 bc 206 bc

Rhizosphere Healthy 134 b 172 b 175 b

Soil Diseased 217 d 348 d 355 d

Soil Healthy 183 c 241 c 244 c

P values

ANOVA Environment *** *** ***

Health status 0.16 * *

E × H 0.06 * 0.05

Field 2 Root Diseased 11 a 24 a 25 a

Root Healthy 32 b 41 a 44 a

Rhizosphere Diseased 105 c 134 b 138 b

Rhizosphere Healthy 98 c 119 b 122 b

Soil Diseased 147 d 187 c 190 c

Soil Healthy 146 d 191 c 194 c

P values

ANOVA Environment *** *** ***

Health status 0.16 0.81 0.77

E × H ** 0.27 0.24

Field 3 Root Diseased 25 a 35 a 43 a

Root Healthy 30 a 40 a 44 a

Rhizosphere Diseased 98 c 119 b 121 b

Rhizosphere Healthy 85 b 100 b 103 b

Soil Diseased 136 d 171 c 174 c

Soil Healthy 131 d 180 c 185 c

P values

ANOVA Environment *** *** ***

Health status 0.14 0.85 0.84

E × H * 0.31 0.37

83

Comparison of fungal communities in roots, rhizosphere and bulk soil

Initially, all OTUs were classified and then amalgamated at phylum level. The

majority of fungal reads recovered belonged to the Dikarya (Ascomycota and

Basidiomycota), accounting for 96.5%, 87.8%, and 82.9% of the reads in the roots,

rhizosphere, and bulk soil, respectively. A PCA scatter plot showed that root fungal

communities at phylum level differed from rhizosphere and bulk soil (Fig. 1A).

Ascomycota and Glomeromycota were highly abundant in roots, whereas

Basidiomycota and Zygomycota were more frequent in the rhizosphere and bulk soil.

Chytridiomycota were almost absent from the whole dataset, however, members from

this phylum were found in low amounts in rhizophere and bulk soil. Uncultured and

unidentified fungi were mostly associated with the bulk soil (Fig. 1B).

Next, individual OTUs were amalgamated into genera. The abundance of reads

in each genus in the three environments is shown in supplementary material (Table

S2). A higher diversity was observed in bulk soil (154 genera present) compared to

rhizosphere soil (114 genera) and roots (49 genera). A selection of genera that

differed significantly among the three environments in this dataset are shown in Table

3. The AM fungal genus Glomus was dominant in roots, Cryptococcus and

Mortierella species were abundant in the rhizosphere and bulk soil and rare in roots,

whereas the presence of Fusarium gradually decreased from roots over the

rhizosphere to the bulk soil.

The ten most abundant individual OTUs accounting for 96.53% (roots), 74.49%

(rhizosphere), and 65.39% (bulk soil) of the total number of sequences differed

significantly among the three environments (Table 4). F. oxysporum was most

abundant in roots, while Verticillium dahliae was the most abundant OTU in

rhizosphere and bulk soil. Exophiala salmonis was present in all three different

environments among the 10 most abundant OTUs, but most abundant in roots.

84

Fig. 1. Two-dimensional principle component analysis (PCA) of fungal communities of fungi

recovered at the phylum level in roots (triangle), rhizosphere (square), and bulk soil (circle)

with different root health status from three fields. The different fields are indicated by color –

Field 1 (white), Field 2 (grey), and Field 3 (black). Root health status is indicated by D

(diseased) and H (Healthy). Scores and loadings from PCA of the fungal communities of

fungi are presented in (A) and (B), repectively. Error bars represent standard errors of the

mean (n = 5).

85

Table 3. Mean relative abundance (%) and total number of species of selected fungal

genera recovered from roots, rhizosphere and bulk soil, respectively, from three pea fields

(n = 30).

Relative abundance Species

Fungi Root Rhizosphere Soil Root Rhizosphere Soil

Glomus 2.5 (± 0.9) 0.2 (± 0.0) 0.1 (± 0.0) 9 2 3

Fusarium 53.4 (± 5.9) 21.1 (± 3.8) 8.7 (± 0.9) 5 7 9

Cryptococcus 0.1 (± 0.0) 6.8 (± 0.6) 8.7 (± 0.3) 8 9 12

Mortierella 0.3 (± 0.1) 11.4 (± 1.2) 14.3 (± 1.1) 2 6 7

*Values in brackets after mean values represent standard errors of mean (n = 30).

Comparison of fungal communities in diseased vs. healthy plants

At the phylum level, Glomeromycota was almost exclusively present in healthy

roots in the three fields, and the abundance was significantly different between

diseased and healthy roots. Furthermore, the presence of Zygomycota corresponded

significantly to the health status of roots, being more abundant in healthy roots.

Significant differences of all phyla from three fields were observed in the bulk soil

(Table 5).

To analyze the relationship between diseased and healthy samples from three

fields, we produced PCA scatter plots including the most abundant OTUs from each

environment. In the plot of root data, first and second principal components explained

30.6% and 24.1% of the total variance, respectively (Fig. 2A). The main loadings of

the two principal components were the eight most abundant species from roots with

significant response to either field or health status. Root fungal communities differed

significantly between health status and across fields. The relationship between fungal

species and root samples are shown in Fig. 2B. In the plot of rhizosphere soil data,

first and the second principal components explained 32.8% and 24.2% of the total

variance, respectively (Fig. 3A). The main loadings of the two principal components

were the 10 most abundant species recovered from rhizosphere with significant

response to either field or health status. No differences in the fungal communities

were found between diseased and healthy status. Fungal communities in the

rhizosphere soil from F1 were distinct from F2 and F3 along component 1. The

relationship between fungal species and rhizosphere samples are shown in Fig. 3B. In

86

Table 4. Relative abundance (%) of ten most dominant fungal OTUs recovered from pea roots,

rhizosphere soil, and bulk soil sampled from three different pea fields from diseased (D) and healthy (H)

areas of the respective fields. Different letters indicate significant differences between treatment means

according to multiple range test (n = 5).

Field 1 Field 2 Field 3 P values from

ANOVA

D H D H D H Field

(F)

Health

(H)

F x H

Roots

Fusarium oxysporum 73.9cd 19.0a 94.3e 42.4ab 21.8a 65.2bc * ** ***

Exophiala salmonis 19.4c 63.0d 0.6a 16.2bc 2.5ab 2.8ab *** *** ***

Leohumicola minima 0a 0a 0a 0a 55.4b 4.3a *** *** ***

Epicoccum nigrum 0.1a 4.7a 0a 30.5b 0a 0a * * *

Neonectria radicicola 0.7a 0.02a 0a 0.2a 14.9b 15.9b *** 0.91 0.96

Glomus mosseae 0.1a 1.8ab 0a 6.7b 0a 0.2ab 0.29 * 0.27

Bionectria ochroleuca 2.4ab 1.2ab 4.4b 0.1ab 0a 1.3ab 0.35 0.33 0.23

Dendryphion nanum 0a 0a 0a 0a 2.3b 0.1ab * 0.29 0.32

Uncultured fungus 0.2a 2.4b 0a 0.8a 0a 0a 0.06 * 0.11

Fusarium culmorum 0a 0a 0a 0a 0a 2.6a 0.37 0.32 0.37

Rhizosphere soil

Verticillium dahliae 5.9a 5.8a 30.1c 34.6c 14.7b 21.6b *** 0.11 0.45

Fusarium oxysporum 42.7b 33.2b 8.3a 7.7a 3.1a 5.3a *** 0.50 0.65

Trichocladium asperum 2.5bc 3.1bc 2.9ab 0.3a 24.6cd 26.3d *** 0.97 0.64

Bionectria ochroleuca 2.8b 3.1b 13.5c 12.3c 0.2a 0.4a *** 0.31 0.27

Cryptococcus aerius 3.1a 3.8ab 5.5ab 6.1b 5.8ab 5.8ab * 0.60 0.92

Mortierella sp. 1.7a 4.4ab 2.8ab 2.3ab 9.1c 6.1bc ** 0.82 0.18

Phoma eupyrena 2.6a 2.7a 4.1ab 5.2b 5.7b 4.2ab * 0.83 0.26

Mortierella elongata 4.3abc 6.2c 2.4a 2.1a 3.2ab 5.1bc ** 0.09 0.28

Fusarium merismoides 2.8ab 3.2ab 1.7a 1.6a 5.7b 4.5ab * 0.74 0.78

Exophiala salmonis 3.0bc 3.3c 2.0ab 1.9bc 1.2ab 0.4a *** 0.69 0.41

Bulk soil

Verticillium dahliae 11.7a 11.5a 38.0c 38.1c 20.7b 34.2c *** ** ***

Phoma eupyrena 3.9a 4.4ab 5.6bc 6.4c 10.1c 8.6c *** 0.87 0.12

Cryptococcus aerius 5.2a 5.6ab 6.1ab 6.5abc 7.8c 7.1bc ** 0.99 0.51

Mortierella sp. 3.7a 5.5ab 3.0a 3.2a 10.2b 7.2ab * 0.92 0.54

Mortierella elongata 7.3c 8.5c 2.7a 2.0a 2.6a 4.6b *** 0.07 0.06

Fusarium oxysporum 10.1c 5.5b 3.4ab 3.0ab 1.8a 2.1ab *** 0.12 0.11

Fusarium merismoides 4.9bc 6.0c 2.1a 2.0a 4.1b 4.8b *** 0.10 0.32

Exophiala salmonis 4.7d 6.0e 2.5c 2.4c 1.8b 0.9a *** 0.46 ***

Dokmaia monthadangii 2.0a 1.6a 3.6c 3.0bc 1.7a 2.3ab *** 0.62 0.17

Aleuria aurantia 3.6b 7.6c 0.1a 0.2a 2.5b 0.6a *** 0.08 ***

*, P < 0.05; **, P < 0.01; ***, P < 0.001

87

the plot of bulk soil data, the first and the second principal components explained

49.7% and 20.2% of the total variance, respectively (Fig. 4A). The main loadings of

the two principal components were the 10 most abundant species identified from soil

with significantly response to either field or health status. The difference between

fungal communities across fields was distinct, but differed only moderately between

plants of differing health status. The relationship between fungal species and bulk soil

samples are shown in Fig. 4B.

A number of individual OTUs differed significantly based on health status in

individual fields (Table 4, and Table S1). Ten OTUs in roots, 3 OTUs in the

rhizosphere soil, and 26 OTUs in the bulk soil responded significantly to health status

(Table S1). In Table 4, the 10 most abundant OTUs are listed. The four most abundant

species in roots, F. oxysporum, E. salmonis, Leohumicola minima, and Epicoccum

nigrum were all significantly different between diseased and healthy samples within

each field, and also across fields. The most dominant species in bulk soil, V. dahliae,

was significantly different between diseased and healthy plants in F3. None of the 10

most abundant OTUs from the rhizosphere soil were significantly different between

diseased and healthy samples. In all three environments in F1 and F2, F. oxysporum

was more abundant in diseased samples compared to healthy samples, while the

reverse was observed in F3. E. salmonis was more abundant in healthy roots

compared to diseased roots in F1 and F2, but with no significant difference in F3. In

the bulk soil of F3, V. dahlia was more dominant in healthy than in diseased samples,

but with a similar abundance in F1 and F2. The abundance of Glomus mosseae

correlated with the health status being less abundant or even absent in diseased roots.

88

Table 5. Fungi at the phylum level in roots, rhizosphere, and bulk soils from diseased and

healthy areas of three pea fields. Different letters indicate significant differences between

treatment means as examined with multiple range test (n = 5) (Uncultured and unidentified

fungi were excluded).

Environment Field Health

Status

Asco-

mycota

Basidio-

mycota

Chytridio-

mycota

Glomero-

mycota

Zygo-

mycota

Root Field 1 Diseased 98.5 b 0.32 ab 0 0.8 ab 0.1 a

Field 1 Healthy 91.4 a 0.18 b 0 5.12 c 0.66 b

Field 2 Diseased 100.0 b 0.02 ab 0 0.01 ab 0.003 a

Field 2 Healthy 91.1 a 0.13 ab 0 7.21 c 0.65 b

Field 3 Diseased 99.8 b 0.04 a 0 0.02 a 0.07 a

Field 3 Healthy 97.5 b 0.05 ab 0 2.2 bc 0.2 ab

P values

ANOVA Field (F) 0.13 * - 0.07 0.49

Health

(H)

*** 0.37 - *** **

F x H 0.22 0.76 - 0.74 0.23

Rhizosphere Field 1 Diseased 82.8 a 6.6 a 0.26 bc 0.14 a 8.4 a

Field 1 Healthy 75.7 a 8.3 ab 0.34 c 0.22 a 13.8 ab

Field 2 Diseased 79.0 a 11.8 bc 0.06 a 0.13 a 8.0 a

Field 2 Healthy 78.2 a 14.0 c 0.06 ab 0.19 a 7.0 a

Field 3 Diseased 71.9 a 8.9 ab 0.06 a 0.32 a 18.0 b

Field 3 Healthy 77.6 a 8.6 ab 0.03 a 0.23 a 13.2 ab

P values

ANOVA Field (F) 0.50 ** *** 0.71 0.02

Health

(H)

0.82 0.36 0.63 0.68 0.94

F x H 0.30 0.70 0.87 0.68 0.18

Bulk soil Field 1 Diseased 68.5 bc 11.8 a 0.37 d 0.20 b 15.2 b

Field 1 Healthy 65.4 b 12.4 a 0.50 d 0.19 b 18.4 b

Field 2 Diseased 73.8 d 15.7 b 0.04 ab 0.13 ab 8.8 a

Field 2 Healthy 71.9 cd 17.4 b 0.04 a 0.14 ab 8.9 a

Field 3 Diseased 60.0 a 16.5 b 0.10 bc 0.08 a 17.8 b

Field 3 Healthy 70.6 cd 11.2 a 0.23 cd 0.10 ab 17.0 b

P values

ANOVA Field (F) *** *** ** * ***

Health

(H)

0.20 0.27 0.75 0.83 0.34

F x H *** ** 0.21 0.92 0.87

ANOVA, Analysis of variance; *, P < 0.05; **, P < 0.01; ***, P < 0.001

89

Fig. 2. Two-dimensional principle component analysis (PCA) of fungal communities of

selected fungi in roots with different health status from three fields. Scores and loadings from

PCA of the fungal communities of pea root-inhabiting fungi are presented in (A) and (B),

repectively. Eight fungi responding significantly to either factor F or H were included in the

PCA. Error bars represent standard errors of the mean (n = 5).

90

Fig. 3. Two-dimensional principle component analysis (PCA) of fungal communities of

selected fungi in rhizosphere soil with different root health status from three fields. Scores

and loadings from PCA of the fungal communities in rhizosphere soil are presented in (A)

and (B), repectively. Ten fungi responding significantly to either factor F or H were included

in the PCA. Error bars represent standard errors of the mean (n = 5).

91

Fig. 4. Two-dimensional principle component analysis (PCA) of fungal communities of

selected fungi in bulk soil with different root health status from three fields. Scores and

loadings from PCA of the fungal communities in bulk soil are presented in (A) and (B),

repectively. Ten fungi responding significantly to either factor F or H were included in the

PCA. Error bars represent standard errors of the mean (n = 5).

92

Discussion

This study employed NGS to characterize fungal communities from soil,

rhizosphere and root samples taken from pea plants with and without signs of root rot.

The method detected clear differences in fungal community structures depending on

the different factors investigated; environment, health status and field. Environment

had the strongest impact on fungal community structures, followed by field and

finally health, with the exception that in roots, fungal communities were strongly

affected by health status.

In this study, the two non-parametric ACE and Chao1 indices at 97% similarity

showed highly significant differences between the three fields, which might have been

caused by differences in factors such as edaphic conditions, content of nutrients and

microelements and/or microclimatic differences. However, this was not investigated

in the present study. Geographic distance is one of the major determinants of

microbial diversity as found for bacteria (Fierer & Jackson, 2006; Fulthorpe et al.,

2008). Significant differences were also observed between the three environments,

probably reflecting differences in the complexity of the three environments and the

role of root exudates in regulating soil fungal community composition and diversity

(Broeckling et al., 2008). Conversely, no clear trend in estimated richness was seen

between diseased and healthy samples. In another study, soil fungal diversity and

abundance of pathogens in potato roots were reported to be negatively correlated

(Manici & Caputo, 2009). Rarefaction curves at 97% sequence similarity level also

showed a decreasing diversity from bulk soil over the rhizosphere soil to the roots,

and they indicated that not all fungal diversity had been fully represented. Finally,

OTUs that only could be assigned to „uncultured‟ and „unidentified fungi‟ were

mostly associated with bulk soil, also indicating that diversity was highest in the soil

environment.

Fungal communities in roots, rhizosphere and bulk soil

The composition of fungal communities differed significantly among the three

different environments. The fungal communities in roots were highly dominated by

Ascomycota, as observed in culture-based analyses of endophytic fungi (Arnold et al.,

2000), whereas Basidiomycota, mainly consisting of saprotrophic yeasts, were highly

abundant in rhizosphere and bulk soil. Zygomycota (mainly Mortierella) was found in

93

high abundance in the rhizosphere and bulk soil. This group mainly consists of

saprophytes (e.g. Thormann et al., 2001). Mortierellaceae is commonly encountered

in soil (O'Donnell et al., 2001; Benny & Blackwell, 2004) and soil-borne organic

substrates (O'Donnell et al., 2001; Thormann et al., 2001). Chytridiomycota was

found in low amounts in the rhizophere and bulk soil, which is consistent with

previous studies (Letcher & Powell, 2001; Letcher et al., 2004).

At the genus level, Glomus was most frequently found in roots. However, as the

fungus takes up nutrients from the surrounding soil, the low abundance of AM fungal

sequences from rhizosphere and bulk soil was unexpected. This is in strong contrast to

the finding that biomass of the extraradical mycelium of AM fungi was aprroximately

10 times as high as the biomass of intraradical mycelium and that extraradical

mycelium constituted the largest fraction of soil microbial biomass as examined using

biomarker fatty acids (Olsson et al., 1999). Fusarium was significantly more abundant

in roots and gradually decreased over the rhizosphere soil to the bulk soil. Different

Fusarium species colonize different environments, e.g. F. oxysporum f. sp. pisi is an

efficient root colonizer and soil saprophyte (Kraft & Pfleger, 2001), whereas other

Fusarium species such as F. culmorum and F. vasinfectum are mainly soil-inhabiting

fungi (Garrett, 1950). Cryptococcus and Mortierella were almost exclusively found in

the rhizosphere and bulk soil, reflecting the fact that these yeasts and filamentous

fungi are saprophytes. Most soil yeasts, including Cryptococcus, are considered to be

saprotrophs associated with plants, and some can enhance plant growth, maintain soil

structure, and transform nutrients (Botha, 2011).

Different functional groups of fungi, such as AM fungi, pathogenic fungi and

saprotrophic fungi, occupy their own niche and utilize different resources. A highly

diverse population of different functional fungal groups is more resistant or resilient

to stress, and more capable of adapting with environmental changes (Allison &

Martiny, 2008).

Comparison of fungal communities in diseased vs. healthy plants

Different disease patterns observed in the three fields indicated different causal

pathogens and disease aetiology. This was confirmed by a specific qPCR showing the

presence of A. euteiches in diseased roots from F3 but absence in the other fields.

Assessment of DSI, and determination of shoot dry weight and root fresh weight

further confirmed visual disease assessments in the fields.

94

F. oxysporum was significantly more abundant in the roots of diseased plants

compared to healthy plants in F1 and F2, whereas the opposite was observed in F3. F.

oxysporum is a common pathogen of pea diseases (Kraft & Pfleger, 2001), thus the

high abundance in diseased roots is not surprising (Persson et al., 1997). The lower

abundance of F. oxysporum in diseased roots in F3 may be explained by the fact that

A. euteiches, another pea pathogen, was only found in F3. These results suggest a

possible competition between the two pathogens but this remains to be examined

under controlled experimental conditions. Co-occurring pathogens may interact with

each other, through antagonism and/or synergism (Le May et al., 2009). A. euteiches

is associated with reduced amounts of Phytophthora medicaginis in Alfalfa that is co-

inoculated with both pathogens as studied by Real-time PCR (Vandemark et al.,

2010). When Aphanomyces spp. are present at low or moderate inoculum levels,

infection of roots by fungi such as Fusarium or Pythium spp. can increase disease

severity (Pfender, 2001). This trend in the presence of F. oxysporum in the three fields

was also observed in the rhizosphere and bulk soil, although the differences were not

significant in between these two environments. Also in contrast, an OTU with 92%

similarity to Leohumicola sp. was highly abundant in diseased roots compared to

healthy roots in F3, but this OTU was not found in significant amounts in the

rhizosphere and bulk soil. These results may indicate an involvement of this fungus in

the pea root disease complex associated with A. euteiches. However, to the best of our

knowledge, this fungus has not previously been reported to be involved in plant root

diseases.

E. salmonis and E. nigrum were found in high abundance in roots of healthy

plants in F1 and F2. Exophiala belong to the group of dark-septate endophytes

(Jumpponen & Trappe, 1998), some of which have previously been shown to suppress

plant pathogens (Narisawa et al., 2004). Epicoccum is a well-known biocontrol agent

of plant pathogens (Madrigal et al., 1994; Reeleder, 2004). These two fungi may be

interacting with F. oxysporum in healthy roots. The dominance of the AM fungi

(phylum Glomeromycota, species Glomus mosseae) in healthy roots compared to

diseased roots in the three fields support previous findings that AM fungi suppress a

broad range of root pathogens (Whipps, 2004). Furthermore, in pea roots, AM fungi

have been shown to reduce development of root rot caused by A. euteiches (Thygesen

et al., 2004). Finally, some of the rare OTUs responded significantly to health status

in the three environments (Table S1). These findings should be further investigated.

95

The abundance of only a few OTUs was significantly different in individual

fields among the ten most abundant OTUs in each environment. For example, the soil-

borne fungal pathogen V. dahliae was found at high incidence in rhizosphere and bulk

soil. However a significant difference between diseased and healthy samples was

found only in the bulk soil in F3, F. oxysporum was more abundant in diseased roots,

and corresponding rhizosphere and bulk soils than healthy samples in F1. The

abundance of almost all other OTUs in the bulk soil was strikingly similar among

diseased and healthy samples indicating that abundance of individual species is not

the only determinant of disease. Although the main differences between diseased and

healthy samples were found in roots, it is surprising that these differences were not

found in the rhizosphere. This indicates that the disease and its effects are very

localized and that the rhizosphere may not be significantly affected by a higher

abundance of specific fungi.

Generally, sequences provided sufficient taxonomic information for reliable

identification. For most OTUs, a high degree of identity (97-100%) and coverage (>

90%) to sequences in GenBank was found, indicating a high accuracy of sequencing.

At phylum level, most sequences belonged to Dikarya, which was not surprising, as

the forward primer ITS1F is biased towards amplification of basidiomycetes

(Bellemain et al., 2010), and the reverse primer 58A2R is specific to Dikaryomycota

(Martin & Rygiewicz, 2005). This means that oomycetes, including pea pathogens

such as A. euteiches and Pythium spp., are not detected using these primers. This was

indeed confirmed by the fact that a qPCR analysis showed high abundance of A.

euteiches in roots, whereas the sequencing did not reveal any sequences from this

species. The Glomeromycota and Chytridiomycota were also probably underestimated

due to primer choice and taxonomic misidentification in GenBank (Vilgalys, 2003;

Nilsson et al., 2008). Furthermore, it has been reported that Glomus belonging to

Glomeromycota, is one of the genera represented by the highest number of

insufficiently identified ITS sequences in GenBank (Ryberg et al., 2009).

In conclusion, fungal communities in the three environments; root, rhizosphere,

and bulk soil were distinct and varied with respect to community composition and

diversity, probably as a cause of the widely different availability of nutrients in the

three environments. The study also demonstrated a clear relationship between health

status of roots and their fungal communities, and the results indicated that interactions

96

between different pathogens or interactions between pathogens and non-pathogens

may affect the development of pea disease together with the plant host.

Acknowledgements

This study was supported by the Faculty of Science and Technology, Aarhus

University, Denmark. Karsten Malmskov (Ardo A/S) is acknowledged for assisting in

collecting samples.

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101

Figure S1. Examples of pea roots from three fields.

D: diseased roots; H: healthy roots

102

Table S1. Relative abundance (%) of fungal operational taxonomic units (OTUs) recovered

from three different environments in three pea fields. Gray highlighted OTUs responded

significantly to root health status. Red and green colors respectively indicate an increase and

decrease in relative abundance in each environment from diseased plants as compared to that

of healthy plants. Roots (a), rhizosphere soil (b), and bulk soil (c).

Root (a)

OTU Closest hit F1D F1H F2D F2H F3D F3H

1 Fusarium oxysporum 73.90 18.98 94.31 42.37 21.84 65.24

2 Exophiala salmonis 19.44 62.97 0.55 16.16 2.46 2.83

3 Leohumicola minima 0 0.01 0.01 0.05 55.50 4.27

4 Epicoccum nigrum 0.54 4.72 0.13 30.44 0.35 0.17

5 Neonectria radicicola 0.71 0.21 0.04 0.22 14.89 15.94

6 Glomus mosseae 0.12 1.79 0.01 6.76 0 1.79

7 Bionectria ochroleuca 2.38 1.25 4.39 0.92 0.01 1.32

8 Dendryphion nanum 0.01 0.02 0 0.01 2.35 0.72

9 Uncultured fungus 0.17 2.42 0 0.82 0.01 0.02

10 Fusarium culmorum 0.01 0.02 0.02 0.01 0.01 2.55

11 Monacrosporium elegan 0.35 0.31 0.14 0.41 0.57 0.02

12 Trichocladium asperum 0.04 0.02 0.01 0.01 0.46 1.41

13 Uncultured Pyronemataceae 0 0 0 0 0 1.48

14 Mortierella elongata 0.05 0.57 0 0.25 0.06 0.09

15 Glomus caledonium 0.08 0.73 0 0.07 0 0.13

16 Fusarium avenaceum 0 0.06 0.01 0.02 0.20 0.52

17 Eucasphaeria capensis 0.11 0.58 0.01 0 0 0.13

18 Fusidium griseum 0 0.06 0.11 0.03 0.54 0.01

19 Glomus intraradices 0.01 0.75 0 0 0 0

20 Cudoniella clavus 0.22 0.56 0.01 0 0.03 0

21 Glomus mosseae 0.02 0.28 0 0.24 0 0.02

22 Verticillium dahliae 0.06 0.13 0.09 0.11 0.09 0.09

23 Glomus intraradices 0.07 0.46 0 0 0 0

24 Cladosporium cucumerinum 0.17 0.26 0.01 0.06 0 0.03

25 Mortierella sp. 0.03 0.01 0 0.22 0.01 0.11

26 Glomus sp. 0.16 0.24 0 0 0 0

27 No 0.03 0.17 0 0.07 0 0.03

28 Microdochium bolleyi 0 0.03 0.03 0.03 0.20 0.02

29 Uncultured Davidiella 0.02 0.28 0 0 0 0

30 Glomus mosseae 0.02 0.11 0 0.01 0 0.12

31 Periconia macrospinos 0.02 0.12 0.01 0.01 0.02 0.08

32 Pyrenochaeta sp. 0 0 0 0 0.17 0.01

33 Glomus versiforme 0.06 0.16 0.01 0.02 0 0.03

34 Coprinellus mitrinodulisporum 0.21 0 0 0.05 0 0

35 Leohumicola minima 0.02 0 0 0 0 0.20

36 Lewia infectori 0.05 0.12 0 0.04 0 0.01

37 Leohumicola minima 0.01 0.15 0 0.03 0.01 0

38 Leptodontidium orchidicola 0.02 0.07 0 0.01 0.02 0.05

103

39 Neonectria veuillotiana 0 0 0 0 0.02 0.15

40 Glomus eburneum 0.04 0.12 0 0.01 0 0

41 Mortierella elongata 0.01 0.04 0 0.09 0 0

42 Didymella exitialis 0.02 0.10 0.01 0.03 0 0

43 Glomus mosseae 0.02 0.09 0 0.01 0.01 0.01

44 Mortierella alpina 0.01 0.04 0 0.09 0 0

45 Fusarium merismoides 0.01 0.02 0 0.03 0.03 0.04

46 Cryptococcus aerius 0.01 0.03 0.02 0 0 0.05

47 Paraglomus laccatum 0.02 0.09 0 0.01 0 0

48 Glomus claroideum 0.01 0.03 0 0 0 0.06

49 Uncultured fungus 0.01 0 0 0 0.06 0

50 Fusarium solani 0 0 0.01 0 0.01 0.06

51 Alternaria alternata 0.04 0.04 0 0.02 0 0

52 Cryptococcus victoriae 0.02 0.06 0 0.01 0 0

53 Leptodontidium elatius 0.01 0.01 0 0 0 0.08

54 Cladosporium tenuissimum 0.02 0.05 0 0.01 0 0

55 Glomus geosporum 0 0.05 0 0.02 0 0.01

56 Cryptococcus tephrensis 0.01 0.02 0 0.05 0 0

57 Aquaticola hongkongensis 0.02 0.07 0 0 0 0

58 Acaulospora trappei 0.02 0.05 0 0 0 0

59 Dokmaia monthadangii 0.03 0.02 0.01 0.01 0 0

60 Parapleurotheciopsis inaequiseptata 0.01 0 0.01 0.04 0 0

61 Acaulospora trappei 0.02 0.03 0 0.01 0 0

62 Ambispora leptoticha 0.02 0.01 0 0.03 0 0

63 Cryptococcus chernovii 0 0.03 0 0.01 0 0

64 Eucasphaeria capensis 0 0.04 0 0 0 0

65 Exophiala salmonis 0.02 0.02 0 0 0 0

66 Cryptococcus laurentii 0.02 0 0 0 0.01 0

67 Dokmaia monthadangii 0 0.02 0 0.01 0 0

68 Glomus intraradices 0.01 0.03 0 0 0 0

69 Glomus caledonium 0 0.02 0 0 0.01 0

70 Glomus eburneum 0 0 0 0.02 0 0.01

71 Cryptococcus aerius 0 0 0 0.01 0.01 0

72 Hypocrea pachybasioides 0.02 0.01 0 0 0 0

73 Arthrobotrys oligospora 0.01 0.02 0 0 0 0

74 Uncultured fungus 0.01 0 0.01 0 0 0

75 Pochonia chlamydosporia 0.02 0 0 0 0 0.01

76 Fusarium solani 0 0 0.01 0 0.01 0

77 Trichocladium asperum 0 0 0 0 0 0.01

78 Uncultured fungus 0.01 0.01 0 0 0 0

79 Leptosphaerulina chartarum 0 0.02 0 0 0 0

80 Cryptococcus aureus 0.01 0.01 0 0 0 0

81 Dioszegia crocea 0 0.01 0 0.01 0 0

82 Glomus claroideum 0 0 0 0 0 0.02

83 Scutellospora reticulata 0.01 0.02 0 0 0 0

104

84 Fusarium oxysporum 0.01 0 0 0 0 0

85 Pleospora herbarum 0 0.01 0 0 0 0

86 Ijuhya paraparilis 0.01 0.01 0 0 0 0

87 Exophiala salmonis 0 0.01 0 0 0 0

88 Uncultured Glomus 0 0.01 0 0 0 0

89 Paecilomyces carneus 0.01 0 0 0 0 0

90 Cryptococcus rajasthanensis 0.01 0 0 0 0.01 0

91 Cudoniella clavus 0.01 0 0 0 0 0

92 Didymella exitialis 0.02 0 0 0 0 0

93 Dwayaangam colodena 0 0.01 0 0 0 0

94 Glomus rubiforme 0.01 0.01 0 0 0 0

95 Acremonium rutilum 0.01 0 0 0 0 0

96 Cosmospora vilior 0 0.01 0 0 0 0

97 Glomus intraradices 0.01 0 0 0 0 0

98 Neonectria radicicola 0.01 0 0 0 0 0.01

99 Ambispora fennica 0.01 0.01 0 0 0 0

100 Ambispora leptoticha 0.01 0 0 0 0 0

101 Glomus mosseae 0.02 0 0 0 0 0

102 Neonectria radicicola 0 0 0 0 0 0.01

103 No 0 0 0 0 0 0

104 Uncultured Glomus 0.01 0 0 0 0 0

105 Trichocladium opacum 0 0 0 0 0 0

106 Fusarium oxysporum 0.01 0 0 0 0 0

107 Rhodotorula cresolica 0.01 0 0 0 0 0

108 Glomus walkeri 0 0 0 0 0 0

109 Cryptococcus dimennae 0.01 0 0 0 0 0

110 Bionectria ochroleuca 0.01 0 0.01 0 0 0

111 Fusarium solani 0.01 0 0 0 0 0

112 Trichoderma koningiopsis 0.01 0 0 0 0 0

113 Exophiala salmonis 0.01 0 0 0 0 0

114 Uncultured fungus 0.01 0 0 0 0 0

115 Monacrosporium psychrophilum 0 0 0 0 0 0

116 Fusarium oxysporum 0.01 0 0 0 0 0.01

117 Fusarium oxysporum 0 0 0 0 0.01 0

118 No 0.01 0 0 0 0 0

119 Exophiala salmonis 0 0 0 0 0 0

120 Fusarium solani 0 0 0 0 0 0

121 Fusarium solani 0 0 0.01 0 0 0

122 Fusarium solani 0 0 0.01 0 0 0

123 Pochonia suchlasporia 0 0 0 0 0 0

105

Rhizosphere soil (b)

OTU Closest hit F1D F1H F2D F2H F3D F3H

1 Verticillium dahliae 5.94 5.84 30.06 34.62 14.70 21.59

2 Fusarium oxysporum 42.74 33.23 8.28 7.70 3.09 5.28

3 Trichocladium asperum 2.50 3.15 2.94 0.33 24.61 26.29

4 Bionectria ochroleuca 2.78 3.13 13.48 12.32 0.21 0.43

5 Cryptococcus aerius 3.07 3.84 5.53 6.09 5.81 5.80

6 Mortierella sp. 1.71 4.42 2.77 2.27 9.13 6.09

7 Phoma eupyrena 2.61 2.66 4.11 5.16 5.71 4.21

8 Mortierella elongata 4.29 6.19 2.41 2.08 3.25 5.09

9 Fusarium merismoides 2.85 3.22 1.70 1.59 5.73 4.55

10 Exophiala salmonis 2.95 3.34 1.96 1.95 1.24 0.41

11 Leptodontidium elatius 0.49 0.52 2.27 2.57 2.49 2.89

12 Dokmaia monthadangii 1.08 0.79 2.06 2.32 1.29 1.34

13 Cryptococcus terricola 0.49 0.89 2.91 3.79 0.21 0.01

14 Uncultured fungus 3.35 4.43 0.05 0.15 1.04 0.08

15 Eucasphaeria capensis 2.89 1.40 2.13 0.08 0.10 0.14

16 Cercophora sparsa 2.83 3.39 0.44 0.60 0.15 0.02

17 Trichosporon pullulans 0.04 0.03 1.36 1.73 1.83 1.39

18 Fusarium solani 0.98 0.40 1.99 1.46 0.60 0.48

19 Tetracladium maxilliforme 0.20 0.22 0.56 0.65 2.03 2.42

20 Mortierella horticola 0.26 0.18 0.03 0.04 4.76 0.78

21 Mortierella alpina 0.74 1.25 1.58 1.23 0.11 0.25

22 Monacrosporium psychrophilum 1.21 1.29 0.72 0.61 0.39 0.01

23 Apodus deciduus 0.74 1.26 0.54 0.31 0.22 0.13

24 Mortierella elongata 0.79 0.85 0.42 0.59 0.19 0.12

25 Neonectria radicicola 0.14 0.22 0.18 0.20 1.08 0.86

26 Candida sake 0.07 0.02 0.66 0.77 0.62 0.45

27 Microdochium bolleyi 0.36 0.60 0.57 0.46 0.22 0.27

28 Mortierella elongata 0.51 0.73 0.48 0.59 0.03 0.01

29 Cryptotrichosporon anacardii 0.23 0.57 0.62 0.81 0.09 0.03

30 Trichosporon vadense 0.23 0.28 0.24 0.33 0.33 0.75

31 Pseudeurotium bakeri 0.03 0.03 0.33 0.28 0.83 0.82

32 Acremonium rutilum 0.15 0.21 0.49 0.61 0.34 0.29

33 Leohumicola minima 0.06 0.03 0.10 0.18 1.11 0.03

34 Cryptococcus laurentii 0.50 0.82 0.02 0.02 0.11 0.33

35 Trichocladium opacum 0.16 0.13 0.25 0.33 0.33 0.23

36 Uncultured Minimedusa 0.77 0.40 0.04 0.03 0.07 0.05

37 Cryptococcus podzolicus 0.05 0.25 0.31 0.64 0.01 0

38 Pseudallescheria fimeti 0.59 0.45 0.01 0 0.06 0.19

39 Leptodontidium orchidicola 0.44 0.26 0.06 0.08 0.24 0.01

40 Uncultured fungus 0.33 0.37 0.14 0.15 0.08 0.04

41 Cylindrocarpon didymum 0.09 0.16 0.15 0.12 0.15 0.33

42 Lachnella alboviolascen 0.33 0.35 0.09 0.01 0.15 0.02

43 Cladosporium cucumerinum 0.25 0.10 0.36 0.07 0.01 0.02

106

44 Mortierella alpina 0.02 0.03 0.03 0.02 0.27 0.52

45 Dendryphion nanum 0.14 0.17 0.02 0.01 0.15 0.29

46 Pseudallescheria africana 0.14 0.13 0.08 0.12 0.11 0.16

47 Aquaticola hongkongensis 0.17 0.58 0 0 0 0

48 Paecilomyces carneu 0.16 0.19 0.18 0.13 0.03 0

49 Devriesia pseudoamericana 0.08 0.06 0.23 0.26 0 0.01

50 Preussia africana 0.06 0.04 0 0 0.24 0.36

51 Sclerotinia homoeocarpa 0.14 0.51 0 0 0 0

52 Uncultured fungus 0.23 0.14 0 0 0.11 0.15

53 Fusarium solani 0.08 0.03 0.04 0.05 0.18 0.25

54 Geomyces pannorum 0.12 0.10 0 0 0.20 0.22

55 Uncultured fungus 0.15 0.23 0.10 0.09 0.03 0.02

56 Ophiosphaerella agrostis 0.11 0.14 0.05 0.07 0.10 0.15

57 Preussia flanaganii 0.01 0.01 0.01 0 0.24 0.38

58 Rhinocladiella sp. 0.12 0.18 0.09 0.18 0 0

59 Mortierella gamsii 0 0.02 0.01 0 0.25 0.32

60 Neonectria radicicola 0.02 0.02 0.09 0.11 0.16 0.16

61 Glomus mosseae 0.04 0.15 0.09 0.16 0 0.17

62 Cudoniella clavus 0.05 0.08 0.04 0 0.41 0

63 Uncultured fungus 0.01 0.02 0.45 0 0 0.01

64 Mortierella hyalina 0.05 0.07 0.21 0.13 0.03 0

65 No 0.10 0.12 0.04 0.04 0.12 0.07

66 Podospora didyma 0.09 0.13 0.08 0.17 0.04 0

67 Olpidium brassicae 0.21 0.29 0.01 0.02 0 0

68 Rhodotorula pustul 0.05 0.05 0.17 0.13 0.02 0.04

69 Neonectria veuillotiana 0 0 0 0.02 0.10 0.35

70 Cudoniella clavus 0.11 0.01 0.28 0.03 0 0

71 Hypocrea pachybasioides 0.08 0.16 0.09 0.10 0.01 0.01

72 Pyrenochaeta sp. 0.01 0 0 0 0.40 0.01

73 Rhodotorula auriculariae 0.13 0.33 0 0.01 0 0.01

74 Coprinopsis semitalis 0.01 0.01 0.11 0.17 0.09 0.03

75 Calcarisporium arbuscula 0.08 0.18 0.06 0.07 0 0

76 Stachybotrys chartarum 0.09 0.09 0.01 0 0.08 0.16

77 Cladosporium tenuissimum 0.20 0.08 0.03 0.04 0.02 0.01

78 Rhodotorula sp. 0.01 0 0.06 0.07 0.12 0.08

79 Myrmecridium schulzeri 0.02 0.05 0.10 0.08 0.04 0.02

80 Exophiala salmonis 0.13 0.14 0.03 0.03 0 0

81 Leohumicola minima 0.07 0.19 0.03 0.02 0.01 0

82 Uncultured fungus 0.04 0.02 0.01 0.03 0.21 0

83 Gaeumannomyces graminis 0.06 0.10 0.06 0.08 0 0

84 Cryptococcus macerans 0.17 0.12 0 0.01 0.01 0.03

85 Lewia infectoria 0.09 0.08 0.10 0.02 0 0

86 Uncultured fungus 0.07 0.12 0.05 0.02 0.02 0

87 Scytalidium cuboideum 0.04 0.05 0.06 0.15 0 0

88 Fusidium griseum 0.03 0.05 0.02 0.06 0.08 0.05

107

89 Uncultured fungus 0.04 0.10 0.04 0.07 0.01 0

90 Uncultured fungus 0.06 0.07 0.06 0.06 0 0

91 Bionectria ochroleuca 0.09 0.06 0.01 0.01 0.02 0.10

92 Phialophora sp. 0.04 0.21 0 0 0 0

93 Neonectria radicicola 0.01 0 0 0 0.13 0.10

94 Trichoderma koningiopsis 0.09 0.03 0.01 0.07 0.03 0.01

95 Olpidium brassicae 0.04 0.03 0.04 0.03 0.05 0.03

96 Acremonium murorum 0.01 0.01 0.06 0.06 0.03 0.06

97 Neofabraea eucalypti 0.05 0.05 0.05 0.01 0 0.06

98 Petrakia sp. 0 0 0.03 0.11 0.01 0.05

99 No 0.08 0.12 0 0.01 0 0

100 Uncultured fungus 0.07 0.06 0 0 0.07 0

101 Chaetosphaeria sp. 0 0.05 0.03 0.10 0 0

102 Paecilomyces marquandii 0.03 0.08 0.01 0 0.02 0.06

103 Cercophora areolata 0.02 0.04 0.03 0.01 0.08 0.01

104 Glomus caledonium 0.01 0 0.01 0 0.17 0

105 Arthrobotrys oligospora 0.11 0.02 0 0.04 0 0

106 Geomyces pannorum 0.06 0.09 0.02 0.01 0.01 0

107 Galactomyces geotrichum 0 0 0 0 0 0.18

108 Uncultured fungus 0.01 0.02 0.05 0.05 0.01 0.01

109 Cryptococcus victoriae 0.10 0.04 0.01 0 0 0

110 Degelia plumbea 0.02 0.05 0.04 0.04 0.01 0

111 Arthrographis alba 0.03 0.03 0.03 0.03 0.02 0.01

112 Cosmospora vilior 0.05 0.08 0.01 0 0 0.01

113 Cryptococcus festucosus 0.07 0.05 0.01 0.01 0 0

114 Trichoderma rossicum 0.03 0.02 0.03 0.01 0.01 0.04

115 Polytolypa hystricis 0.03 0.06 0 0.01 0.02 0.01

116 Heydenia alpina 0.07 0.03 0 0 0.03 0.01

117 Cudoniella clavus 0.02 0.03 0.03 0.01 0.03 0.01

118 Sporobolomyces roseus 0.01 0.01 0.05 0.04 0 0

119 No 0.04 0.05 0.02 0.01 0 0

120 Paraglomus laccatum 0 0 0 0 0.11 0

121 Amaurodon viridis 0.12 0 0 0 0 0

122 Periconia macrospinosa 0.01 0.04 0.01 0.02 0.02 0.01

123 Rhodotorula minuta 0.01 0.01 0.06 0.02 0 0

124 Entrophospora sp. 0.03 0.02 0.01 0.03 0.02 0

125 Podospora didyma 0.02 0.05 0.01 0.02 0 0.02

126 Nectria inventa 0.05 0.04 0 0.01 0.01 0.01

127 Fusarium domesticum 0.04 0.05 0.01 0 0 0.01

128 Uncultured fungus 0.05 0.03 0.01 0 0.01 0.01

129 Hydropisphaera erubescens 0.09 0 0 0 0 0

130 Phaeophleospora stonei 0.02 0.02 0.03 0.02 0.02 0

131 Leohumicola minima 0 0 0.01 0 0.02 0.04

132 Cercophora coprophila 0 0 0 0 0.05 0.05

133 Coprinellus flocculosus 0 0 0.09 0.01 0 0

108

134 Sporidiobolus ruineniae 0.02 0.01 0.05 0.03 0 0

135 Microbotryum pinguiculae 0.03 0.08 0 0 0 0

136 Didymella exitialis 0.02 0 0.04 0.02 0 0

137 Cosmospora vilior 0.04 0.04 0.01 0.01 0 0

138 Cryptococcus tephrensis 0.02 0.01 0.01 0.03 0 0

139 Botryotinia fuckeliana 0.04 0.05 0 0 0 0

140 Uncultured fungus 0.04 0.01 0.01 0.01 0 0

141 Ijuhya paraparilis 0.04 0.02 0 0.02 0 0

142 Paecilomyces inflatus 0.04 0.04 0 0 0 0

143 Coniothyrium cereale 0.01 0 0.02 0.01 0 0.05

144 Ascobolus crenulatus 0.02 0 0 0 0.03 0.02

145 Schizothecium curvisporum 0.01 0.01 0 0 0 0.07

146 Parapleurotheciopsis inaequiseptata 0.02 0 0 0.06 0 0

147 Uncultured fungus 0.02 0.07 0 0 0 0

148 Uncultured fungus 0.02 0.02 0.02 0 0.01 0

149 Peziza domicilian 0.06 0 0 0 0 0

150 Epicoccum nigrum 0.03 0.02 0.01 0.01 0 0

151 Leucopaxillus tricolor 0.01 0.06 0 0 0 0

152 No 0.03 0.03 0 0.01 0 0.01

153 Cladorrhinum brunnescens 0.04 0.02 0 0.01 0.01 0.01

154 Uncultured fungus 0.02 0.01 0.01 0.01 0.01 0.01

155 Nectria bactridioides 0.02 0.02 0.01 0.01 0 0

156 Schizothecium glutinans 0.03 0.02 0 0 0.01 0

157 Capronia peltigerae 0.02 0.01 0.02 0 0 0

158 Glomus caledonium 0.01 0 0 0 0.01 0.05

159 Alternaria alternata 0.01 0.01 0.03 0.01 0 0

160 Clavulinaceae sp. 0.01 0 0.03 0.02 0 0

161 Exophiala salmonis 0.02 0.04 0 0 0 0

162 Uncultured fungus 0.05 0 0 0 0 0

163 Schizothecium carpinicola 0.01 0.01 0.01 0 0 0.03

164 Cryptococcus podzolicus 0.01 0 0.03 0 0 0

165 Pyrenochaeta lycopersici 0.01 0 0 0 0.03 0.02

166 Uncultured fungus 0.01 0 0.01 0.01 0 0.03

167 Volutella ciliata 0.03 0 0 0 0.02 0

168 Uncultured fungus 0.01 0 0.03 0 0 0

169 Mortierella indohii 0.01 0.03 0 0 0 0.01

170 Phialocephala xalapensis 0.02 0.01 0.01 0 0 0

171 Uncultured fungus 0.01 0.03 0 0 0 0

172 Ascobolus crenulatus 0.01 0 0 0 0 0.04

173 Schizothecium glutinans 0 0 0.01 0.01 0 0.02

174 Metarhizium flavoviride 0.02 0.01 0 0.01 0 0

175 Glomus mosseae 0.01 0.01 0 0 0 0.01

176 No 0.01 0.01 0 0.01 0.01 0

177 Entrophospora infrequens 0.02 0.01 0 0 0 0

178 Uncultured fungus 0.02 0.02 0 0 0 0

109

179 Uncultured fungus 0.03 0 0 0 0 0

180 Alternaria brassicae 0.01 0.02 0 0 0 0

181 Schizothecium glutinans 0 0.01 0.01 0 0.01 0

182 Uncultured fungus 0.02 0 0.01 0 0 0.01

183 Clitopilus passeckerianus 0.01 0 0 0 0.02 0

184 Gymnostellatospora subnuda 0 0.01 0.01 0 0.01 0

185 Cryptococcus chernovii 0 0.02 0.01 0 0 0

186 Rhizophlyctis rosea 0.01 0.01 0 0 0 0

187 Pseudofavolus cucullatus 0.01 0.02 0 0 0 0.01

188 Glomus mosseae 0.01 0.01 0.01 0 0 0

189 Uncultured fungus 0.01 0.01 0 0 0 0

190 Crocicreas coronatum 0 0.03 0 0 0 0

191 Uncultured fungus 0.02 0 0 0 0 0

192 Uncultured fungus 0.02 0 0 0 0 0

193 Stromatonectria caraganae 0.03 0 0 0 0 0

194 Uncultured fungus 0 0 0 0 0.02 0

195 Eremomyces langeronii 0.01 0 0 0 0.01 0

196 Cochliobolus sativus 0 0 0 0.02 0 0

197 Acremonium persicinum 0 0 0.01 0.01 0 0

198 Glomus sp. 0.01 0.01 0 0 0 0

199 Myrothecium roridum 0.01 0.01 0 0 0 0

200 Rhodotorula cresolica 0.01 0.01 0 0 0 0

201 No 0.01 0.01 0 0 0 0

202 Podospora curvicolla 0 0 0 0 0 0.01

203 Uncultured fungus 0.02 0 0 0 0 0.01

204 Verticillium tricorpus 0.01 0 0 0 0 0.01

205 Uncultured fungus 0.01 0 0 0.01 0 0

206 Mortierella elongata 0.01 0 0 0 0 0

207 No 0.01 0 0 0 0 0

208 Fusarium larvarum 0.01 0 0 0 0 0

209 Trichocladium asperum 0.01 0 0 0 0 0

210 Trichocladium asperum 0.01 0 0 0 0 0

211 No 0.01 0.01 0 0 0 0

212 Bionectria ochroleuca 0.01 0 0 0 0 0

213 Pyrenochaeta inflorescentiae 0.01 0 0 0 0 0

214 Penicillium brevicompactum 0 0 0.01 0 0 0.01

215 Leohumicola minima 0.02 0 0 0 0 0

216 Tilletiopsis pallescens 0.01 0.01 0 0 0 0

217 Psilocybe crobula 0.01 0 0 0 0 0

218 Uncultured fungus 0.01 0 0 0 0 0

219 Fusarium acuminatum 0.01 0.01 0 0 0 0

220 Beauveria brongniartii 0 0 0 0 0 0.01

221 Leohumicola minima 0 0 0 0 0 0

222 Cephalosporium maydis 0.01 0 0 0 0 0

223 Acremonium cyanophagus 0 0 0 0 0 0

110

224 Pseudaleuria quinaultiana 0.01 0 0 0 0 0

225 Coprinopsis latispora 0.01 0 0 0 0 0

226 No 0.01 0 0 0 0 0

227 Fusarium oxysporum 0.01 0 0 0 0 0

228 Fusarium oxysporum 0 0.01 0 0 0 0

229 No 0 0 0 0 0 0

230 Uncultured fungus 0.01 0 0 0 0 0

231 Conidiobolus coronatus 0.01 0 0 0 0 0

232 Fusarium larvarum 0.01 0 0 0 0 0

233 Peziza echinispora 0.01 0 0 0 0 0

234 Uncultured fungus 0.01 0 0 0 0 0

235 Fusarium oxysporum 0 0 0 0 0 0

236 No 0 0 0 0 0.01 0

237 Sphaerodes sp. 0.01 0 0 0 0 0

238 Uncultured fungus 0 0.01 0 0 0 0

239 Mortierella elongata 0.01 0 0 0 0 0

240 Uncultured fungus 0.01 0 0 0 0 0

241 Uncultured fungus 0 0 0 0 0 0

242 Fusarium oxysporum 0 0 0 0 0 0

243 Monacrosporium elegans 0 0 0 0 0 0

244 Fusarium oxysporum 0 0 0 0 0 0

245 Gaeumannomyces graminis 0 0 0 0 0 0

246 Cosmospora vilior 0 0 0 0 0 0

247 No 0.01 0 0 0 0 0

248 Pseudeurotium bakeri 0.01 0 0 0 0 0

249 Fusarium oxysporum 0 0 0 0 0 0

250 Fusarium oxysporum 0 0 0 0 0 0

251 Mortierella elongata 0 0 0 0 0 0

252 Mortierella sp. 0 0 0 0 0 0

253 Psathyrella pyrotricha 0 0 0 0 0 0

254 Fusarium oxysporum 0.01 0 0 0 0 0

255 Myrothecium verrucaria 0.01 0 0 0 0 0

256 Fusarium oxysporum 0 0 0 0 0 0

257 Fusarium oxysporum 0 0 0 0 0 0

258 Uncultured fungus 0 0 0 0 0 0

259 Tetracladium furcatum 0 0 0 0 0 0

260 Coprinopsis cinerea 0 0 0 0 0 0

261 Apodus deciduus 0 0 0 0 0 0

262 Mortierella sp. 0 0 0 0 0 0

263 Uncultured Fusarium 0 0 0 0 0 0

264 Verticillium dahliae 0 0 0 0 0 0

265 Hyphodiscus hymeniophilus 0 0 0 0 0 0

266 Mortierella elongata 0.01 0 0 0 0 0

267 Uncultured fungus 0 0 0 0 0 0

268 Fusarium culmorum 0 0 0 0 0 0

111

269 Conocybe rickenii 0.01 0 0 0 0 0

270 Uncultured fungus 0 0 0 0 0 0

271 Fusarium oxysporum 0 0 0 0 0 0

112

Bulk soil (c)

OTU Closest hit F1D F1H F2D F2H F3D F3H

1 Verticillium dahliae 11.68 11.45 38.05 38.12 20.67 34.21

2 Phoma eupyrena 3.93 4.36 5.62 6.44 10.11 8.62

3 Cryptococcus aerius 5.22 5.57 6.08 6.47 7.80 7.08

4 Mortierella sp. 3.73 5.48 2.97 3.22 10.17 7.72

5 Mortierella elongata 7.28 8.48 2.66 2.00 2.56 4.60

6 Fusarium oxysporum 10.09 5.45 3.43 2.98 1.76 2.07

7 Fusarium merismoides 4.95 5.97 2.09 1.97 4.14 4.83

8 Exophiala salmonis 4.66 5.97 2.52 2.39 1.79 0.95

9 Dokmaia monthadangii 2.00 1.57 3.59 3.00 1.69 2.31

10 Aleuria aurantia 3.62 7.64 0.05 0.20 2.54 0.55

11 Leptodontidium elatius 0.79 0.70 2.46 2.65 3.02 2.71

12 Bionectria ochroleuca 2.41 1.56 3.29 3.70 0.26 0.59

13 Cercophora sparsa 4.13 5.10 0.92 0.72 0.16 0.01

14 Cryptococcus terricola 0.77 1.39 2.95 3.78 0.20 0.05

15 Trichosporon pullulans 0.08 0.07 2.01 1.59 3.38 2.03

16 Sistotrema sernanderi 0.01 0 1.31 3.32 3.31 0.19

17 Trichocladium asperum 2.62 1.35 0.22 0.26 2.48 1.89

18 Mortierella alpina 1.17 1.54 1.49 1.42 0.25 0.68

19 Tetracladium furcatum 0.43 0.32 0.80 0.90 1.67 1.98

20 Mortierella horticola 0.31 0.19 0.03 0.01 3.44 1.12

21 Tetracladium maxilliforme 0.01 0 1.51 0.98 0.89 1.18

22 Mortierella elongata 1.18 1.21 0.45 0.94 0.16 0.07

23 Mortierella elongata 0.93 0.99 0.66 0.77 0.06 0.04

24 Apodus deciduus 1.09 1.41 0.38 0.23 0.27 0.09

25 Microdochium bolleyi 0.80 0.85 0.48 0.45 0.28 0.49

26 Eucasphaeria capensis 1.75 1.22 0.19 0.10 0.06 0.18

27 Trichosporon vadense 0.95 0.59 0.46 0.32 0.61 0.30

28 Acremonium rutilum 0.33 0.38 0.62 0.57 0.35 0.60

29 Uncultured fungus 0.04 0.06 0.05 0.06 2.38 0.04

30 Candida sake 0.17 0.07 0.66 0.71 0.62 0.34

31 Neonectria radicicol 0.41 0.35 0.17 0.13 0.75 0.84

32 Mortierella gamsii 0.29 0.34 0.19 0.18 0.60 0.93

33 Cryptotrichosporon anacardii 0.57 0.69 0.51 0.40 0.20 0.04

34 Cryptococcus laurentii 0.87 0.93 0.01 0.03 0.08 0.48

35 Mortierella alpina 0.09 0.02 0.04 0.03 0.31 1.48

36 Trichocladium opacum 0.32 0.27 0.29 0.23 0.48 0.26

37 Lachnella alboviolascens 0.49 0.79 0.04 0.08 0.09 0.25

38 Cladosporium cucumerinum 0.47 0.41 0.39 0.27 0.05 0.06

39 Pseudallescheria fimeti 0.91 0.50 0 0 0.12 0.23

40 Uncultured fungus 0.66 0.64 0.12 0.06 0.05 0.14

41 Pseudeurotium bakeri 0.04 0.02 0.24 0.23 0.64 0.28

42 Aquaticola hongkongensis 0.58 0.99 0 0 0 0

43 Uncultured fungus 0.45 0.37 0.19 0.13 0.15 0.08

113

44 Peziza domiciliana 0.57 0 1.04 0.05 0 0

45 Cryptococcus podzolicus 0.20 0.31 0.28 0.50 0 0.01

46 Leptodontidium orchidicola 0.62 0.37 0.08 0.04 0.33 0.01

47 Fusarium solani 0.41 0.01 0.11 0.25 0.17 0.19

48 Uncultured fungus 0 0.02 0.01 0.01 0.96 0

49 Ophiosphaerella herpotricha 0.19 0.13 0.11 0.11 0.18 0.32

50 Uncultured fungus 0.01 0.01 0.33 0.49 0 0.01

51 Neonectria ramulariae 0.10 0.17 0.08 0.14 0.18 0.26

52 Dendryphion nanum 0.16 0.21 0.02 0.01 0.26 0.29

53 Paecilomyces carneus 0.24 0.33 0.19 0.16 0.03 0

54 Acremonium persicinum 0.03 0.05 0.13 0.08 0.07 0.50

55 No 0.22 0.23 0.07 0.06 0.15 0.17

56 Uncultured fungus 0.04 0.06 0.01 0 0.69 0

57 Preussia funiculata 0.03 0.03 0.06 0 0.26 0.43

58 Cyphellophora laciniata 0.24 0.28 0.14 0.17 0 0

59 Geomyces pannorum 0.16 0.12 0.01 0 0.17 0.33

60 Uncultured fungus 0.46 0.19 0 0 0.07 0.16

61 Preussia africana 0.06 0.03 0.01 0.01 0.28 0.39

62 Pseudallescheria africana 0.24 0.21 0.12 0.08 0.08 0.08

63 Cudoniella clavus 0.30 0.11 0.40 0.07 0 0

64 Leohumicola minima 0.06 0.06 0.22 0.11 0.23 0.05

65 Mrakia nivalis 0.03 0 0.18 0.17 0.18 0.11

66 Sistotrema brinkmannii 0.29 0.11 0.31 0 0 0.06

67 Pyrenochaeta sp. 0.03 0 0.01 0 0.60 0.04

68 Plectosphaerella cucumerina 0.02 0.02 0.15 0.24 0.06 0.14

69 Mortierella hyalina 0.07 0.11 0.21 0.24 0.02 0.01

70 Uncultured fungus 0.20 0.16 0.12 0.06 0.10 0.05

71 Exophiala salmonis 0.32 0.29 0.09 0.02 0 0.01

72 Olpidium brassicae 0.30 0.42 0 0.01 0 0

73 Monacrosporium elegans 0.22 0.15 0.06 0.10 0.15 0

74 Sclerotinia homoeocarpa 0.44 0.21 0.01 0.03 0.01 0.01

75 Devriesia sp. 0.08 0.10 0.16 0.24 0 0

76 Lewia infectoria 0.11 0.07 0.20 0.16 0 0.02

77 Rhodotorula pustula 0.06 0.05 0.20 0.09 0.05 0.11

78 Neonectria radicicola 0.04 0.07 0.07 0.15 0.16 0.04

79 Athelia bombacina 0.29 0.23 0.06 0 0 0

80 Didymella exitialis 0.02 0.08 0.17 0.19 0.01 0.02

81 Fusarium solani 0.21 0.03 0.01 0.03 0.08 0.17

82 Hypocrea pachybasioides 0.20 0.19 0.05 0.08 0.02 0.01

83 No 0.02 0.02 0.15 0.21 0.06 0

84 No 0.01 0.04 0.01 0.05 0.38 0

85 Leohumicola minima 0.14 0.29 0.04 0.04 0.01 0

86 Mortierella gamsii 0.01 0 0 0.01 0.17 0.27

87 Stachybotrys chartarum 0.08 0.17 0.03 0.01 0.08 0.13

88 Scedosporium apiospermum 0.03 0.21 0.08 0.10 0.02 0.02

114

89 Chalara microchona 0.12 0.34 0.02 0 0 0

90 Waitea circinata 0.32 0.16 0.01 0 0 0

91 Thielaviopsis basicola 0.01 0 0.17 0.21 0 0

92 Podospora didyma 0.11 0.10 0.07 0.12 0.03 0.01

93 Clonostachys divergens 0.03 0 0.17 0.15 0 0.04

94 Cudoniella clavus 0.23 0.13 0.01 0.01 0.06 0

95 Periconia macrospinosa 0.05 0.05 0 0.03 0.11 0.16

96 Uncultured fungus 0.14 0.12 0.07 0.04 0.03 0.02

97 Coprinopsis semitalis 0.01 0 0.10 0.14 0.08 0.04

98 Olpidium brassicae 0.04 0.07 0.02 0.03 0.08 0.17

99 Calcarisporium arbuscula 0.22 0.12 0.04 0.04 0 0.02

100 Peziza echinispora 0.50 0 0 0 0 0

101 Waitea circinata 0.03 0.07 0.04 0 0.11 0.14

102 Neonectria veuillotiana 0.02 0.01 0.02 0.01 0.12 0.18

103 Waitea circinata 0 0 0.42 0 0 0

104 Scytalidium cuboideum 0.03 0.12 0.08 0.08 0.02 0

105 Glomus mosseae 0.05 0.06 0.06 0.10 0 0.04

106 No 0.09 0.27 0 0 0 0

107 Myrmecridium schulzeri 0.07 0.06 0.08 0.04 0.05 0.01

108 Pseudaleuria quinaultiana 0.01 0 0.06 0.08 0.13 0.02

109 Fusarium domesticum 0.14 0.12 0.04 0.01 0.03 0.02

110 Uncultured fungus 0.01 0 0.03 0.02 0.21 0

111 Cercophora areolata 0.05 0.06 0.02 0.04 0.12 0

112 Cryptococcus macerans 0.17 0.10 0 0.01 0.02 0.02

113 Sporobolomyces roseus 0.06 0.04 0.12 0.05 0 0.02

114 Neofabraea eucalypti 0.07 0.06 0.03 0.05 0.02 0.08

115 Trichoderma koningiopsis 0.10 0.07 0.03 0.04 0.04 0.01

116 Schizothecium carpinicola 0.25 0 0.04 0.01 0 0.03

117 Fusidium griseum 0.03 0.06 0.04 0.06 0.07 0

118 Rhodotorula sp. 0.04 0.06 0.08 0.07 0.01 0

119 Uncultured fungus 0.09 0.07 0.03 0.07 0.01 0

120 Botryotinia fuckeliana 0.10 0.08 0.03 0.04 0.01 0

121 Cryptococcus victoriae 0.15 0.11 0 0.01 0 0.01

122 Degelia plumbea 0.06 0.05 0.05 0.08 0.01 0

123 Alternaria alternata 0.07 0.09 0.05 0.05 0 0.01

124 Arthrographis alba 0.09 0.02 0.05 0.05 0.01 0.03

125 Geomyces pannorum 0.09 0.14 0.03 0 0.01 0.01

126 Dictyosporium toruloides 0.01 0 0.05 0.03 0.03 0.11

127 Paecilomyces marquandii 0.09 0.06 0.02 0.02 0.03 0.03

128 Gaeumannomyces graminis 0.07 0.08 0.04 0.04 0.01 0

129 Pleospora herbarum 0.02 0.02 0.14 0.05 0 0

130 Rhodotorula auriculariae 0.14 0.10 0 0.01 0 0

131 Cosmospora vilior 0.06 0.16 0 0 0 0

132 Uncultured fungus 0.10 0.08 0 0 0.04 0

133 Petrakia sp. 0.04 0 0.02 0.05 0.03 0.06

115

134 Leucopaxillus tricolor 0.05 0.18 0 0 0 0

135 Occultifur externus 0.01 0 0.13 0.03 0 0.02

136 Nectria inventa 0.13 0.04 0.01 0.02 0 0.02

137 Gymnostellatospora subnuda 0.08 0.07 0.02 0 0.03 0.02

138 Acremonium cereale 0.01 0 0.03 0.01 0.03 0.09

139 Rhodotorula minuta 0.13 0.01 0.05 0.01 0 0.01

140 Rhizoctonia sp. 0 0.01 0 0 0.15 0

141 Pyrenochaeta lycopersici 0.04 0.03 0 0 0.06 0.04

142 Sistotrema coronilla 0 0.13 0 0.04 0 0

143 Gelasinospora cratophora 0.02 0.01 0 0.01 0.04 0.09

144 Schizothecium glutinans 0.05 0.07 0.02 0.01 0.02 0

145 Microbotryum pinguiculae 0.11 0.06 0 0 0 0

146 Phaeophleospora stonei 0.06 0.04 0.02 0.01 0.02 0

147 Peziza arvernensis 0.17 0.01 0 0 0 0

148 Uncultured fungus 0.01 0.01 0.06 0.04 0 0.01

149 Ijuhya paraparilis 0.09 0.04 0 0.03 0 0

150 Heydenia alpina 0.05 0.04 0 0 0.06 0.01

151 Rhodotorula cresolica 0.02 0.09 0 0 0.03 0

152 Acremonium rutilum 0.04 0 0.01 0.02 0.02 0.04

153 Polytolypa hystricis 0.05 0.06 0.01 0 0.01 0.01

154 Uncultured fungus 0 0.01 0.03 0.06 0 0.01

155 Rhodotorula ferulica 0.01 0.01 0 0.01 0.08 0.01

156 No 0.01 0.04 0.05 0.02 0 0

157 Cercophora coprophila 0.02 0 0 0 0.06 0.04

158 Amaurodon viridis 0.03 0.06 0.02 0.02 0 0

159 Acremonium murorum 0 0 0.04 0.03 0.01 0.04

160 Uncultured fungus 0.02 0.03 0.03 0.01 0.01 0.02

161 Cryptococcus festucosus 0.02 0.06 0.01 0.02 0 0

162 Entrophospora sp. 0.02 0.04 0.04 0.02 0 0

163 Coniothyrium cereale 0.01 0.01 0.02 0.01 0.01 0.05

164 Neonectria lucida 0.01 0.09 0 0 0 0

165 Uncultured fungus 0.02 0.01 0.01 0.01 0.02 0.04

166 Cudoniella clavus 0.04 0.02 0.01 0.02 0 0.01

167 Olpidium brassicae 0.01 0.01 0.01 0.01 0.01 0.06

168 Uncultured fungus 0.02 0 0.01 0.02 0.06 0

169 No 0.04 0.08 0 0 0 0

170 Ascobolus crenulatus 0.05 0.01 0.01 0 0.01 0.03

171 Podospora pyriformis 0.02 0.02 0.03 0 0.01 0.02

172 No 0.01 0 0 0.02 0.01 0.06

173 Tubeufia helicomyces 0.02 0 0.01 0.06 0 0

174 Acremonium rutilum 0.01 0.01 0 0.01 0.03 0.04

175 Uncultured fungus 0 0 0.03 0.05 0 0

176 Nectria bactridioides 0.02 0.05 0.01 0.02 0 0

177 Rhodotorula lamellibrachiae 0.01 0.03 0 0.01 0 0.04

178 Trichoderma rossicum 0.01 0 0.01 0.01 0.02 0.05

116

179 Coprinellus bisporus 0.08 0.03 0 0 0.01 0

180 Rhodotorula glutinis 0.01 0 0.05 0.02 0 0

181 Leohumicola minima 0.01 0.02 0.02 0.02 0.03 0

182 Basidiobolus haptosporus 0.06 0.01 0.01 0 0 0.02

183 Uncultured fungus 0.10 0 0 0 0 0

184 Chaetosphaeria myriocarpa 0.03 0.05 0.01 0.01 0 0

185 Cryptococcus tephrensis 0.03 0.01 0.03 0.01 0 0

186 Mortierella sp. 0 0.03 0.04 0.01 0 0

187 Glomus caledonium 0.02 0.03 0 0 0 0.03

188 Udeniomyces pannonicus 0.03 0.01 0.01 0.03 0 0.01

189 Preussia africana 0.04 0.02 0 0.01 0.01 0.02

190 Pyrenochaeta inflorescentiae 0.01 0.03 0 0.02 0.01 0.01

191 Cryptococcus taibaiensis 0.07 0.02 0 0 0 0.01

192 Mortierella macrocystis 0.01 0 0 0.01 0 0.05

193 Glomus caledonium 0.02 0.01 0.01 0.01 0.03 0

194 Uncultured fungus 0.01 0 0.03 0.03 0 0

195 Paecilomyces inflatus 0.02 0.06 0 0 0 0

196 Uncultured fungus 0.01 0.07 0 0 0 0

197 Uncultured fungus 0.02 0.03 0.01 0.01 0 0

198 Crocicreas coronatum 0.01 0.04 0 0 0.02 0.01

199 No 0.09 0 0 0 0 0

200 Schizothecium glutinans 0.01 0.02 0.01 0.01 0.01 0.01

201 Cladorrhinum brunnescens 0.03 0.04 0 0 0 0

202 Leptosphaeria biglobosa 0.02 0.05 0 0 0 0

203 Uncultured fungus 0.02 0 0.01 0.01 0.03 0

204 No 0.09 0 0 0 0 0

205 Cryptococcus podzolicus 0.02 0.01 0 0.03 0 0

206 Podospora didyma 0 0 0 0.03 0 0.02

207 No 0.01 0.06 0 0 0 0

208 Capronia peltigerae 0.01 0.05 0.01 0 0 0

209 Glomus mosseae 0.02 0.01 0.01 0.01 0.01 0.01

210 Volutella ciliata 0.02 0 0 0 0.01 0.03

211 Tilletiopsis pallescens 0.02 0.02 0.01 0.01 0 0

212 Cladorrhinum samala 0.02 0.02 0 0.01 0.01 0.02

213 Cercophora coprophila 0.01 0.01 0 0 0.03 0.01

214 Pseudallescheria fimeti 0.01 0.02 0 0.01 0.01 0.01

215 Mycoarthris corallinus 0.02 0 0.03 0.01 0 0

216 Laetisaria arvalis 0.01 0.06 0 0 0 0

217 Hyphodontia alutaria 0.02 0.02 0.01 0.01 0 0

218 Rhodotorula lamellibrachiae 0.01 0 0 0.01 0.02 0.01

219 Pseudaleuria quinaultiana 0.02 0.04 0 0 0 0

220 Podospora curvicolla 0.02 0 0.01 0 0.01 0.02

221 Peziza domiciliana 0.01 0 0 0 0.05 0

222 Dioszegia fristingensis 0.02 0 0.03 0.01 0 0

223 Rhodotorula ferulica 0.01 0.01 0.01 0.01 0 0.01

117

224 Mortierella polycephala 0.01 0 0 0.01 0.02 0.01

225 Stilbum vulgare 0 0.01 0.04 0 0 0.01

226 No 0.02 0.01 0.01 0.02 0 0

227 Verticillium dahliae 0.02 0.03 0 0 0 0

228 Uncultured fungus 0.03 0.01 0 0 0.02 0

229 Uncultured fungus 0.04 0.01 0 0 0 0.01

230 Uncultured fungus 0.02 0.02 0.01 0 0 0

231 Entyloma majewskii 0.02 0.03 0 0 0 0.01

232 Mortierella alpina 0.01 0 0 0.01 0.01 0.01

233 Metarhizium flavoviride 0.04 0 0 0 0 0.01

234 Leptodontidium elatius 0.01 0 0.01 0 0.01 0.02

235 Uncultured fungus 0.02 0 0.02 0.01 0.01 0

236 Uncultured fungus 0.02 0.01 0.02 0 0 0

237 Sarea resinae 0.01 0.01 0.01 0.01 0 0

238 Cosmospora vilior 0.01 0.04 0 0 0 0

239 Rhodotorula lignophila 0 0.03 0 0 0 0.01

240 Uncultured fungus 0.03 0.02 0 0 0 0

241 Clitopilus passeckerianus 0.02 0 0.02 0 0 0.01

242 Schizothecium glutinans 0.01 0.01 0.01 0.01 0 0

243 Phialocephala xalapensis 0.01 0.03 0 0.01 0 0

244 Hydropisphaera erubescens 0.01 0 0 0 0 0.03

245 No 0.01 0 0 0 0 0.02

246 Lepista sordida 0.05 0 0 0 0 0

247 Cochliobolus sativus 0.01 0 0.01 0.02 0 0

248 Galerina arctica 0.01 0.02 0.01 0 0 0

249 Pseudofavolus cucullatus 0.02 0 0 0 0 0.02

250 Myrothecium roridum 0.01 0.02 0 0 0 0

251 Omphalina rustica 0.02 0.02 0 0.01 0 0

252 Leptosphaeria biglobosa 0.05 0 0 0 0 0

253 Uncultured fungus 0.01 0.02 0 0.01 0 0.01

254 Pyrenochaeta sp. 0.02 0 0 0 0.02 0

255 Colletotrichum trichellum 0.01 0 0 0 0.02 0

256 Uncultured fungus 0.04 0 0 0 0 0

257 Cryptococcus chernovii 0.01 0.01 0 0.01 0 0

258 Uncultured fungus 0.01 0.01 0.01 0.01 0 0

259 Schizothecium glutinans 0.01 0 0.01 0 0.01 0

260 Uncultured fungus 0.02 0.01 0 0 0 0

261 Fusarium merismoides 0.01 0 0 0 0 0.03

262 Pseudeurotium bakeri 0.02 0.01 0 0 0 0.01

263 Archaeospora sp. 0.01 0 0 0 0.03 0

264 Acremonium psammosporum 0.01 0.02 0 0 0 0

265 Preussia africana 0.01 0 0 0 0.01 0.01

266 Chaetomium aureum 0.01 0.01 0 0 0 0.01

267 Schizothecium glutinans 0.03 0.01 0 0 0 0

268 Cephaliophora tropica 0.01 0.01 0 0 0 0.01

118

269 No 0.01 0 0 0.01 0 0

270 Bionectria ochroleuca 0.01 0.01 0.01 0 0 0

271 Cyphellophora eucalypti 0 0 0.01 0.02 0 0

272 Schizothecium curvisporum 0.02 0.01 0 0 0 0

273 Uncultured fungus 0.04 0 0 0 0 0

274 Glomus mosseae 0.01 0 0 0 0 0.02

275 No 0.01 0 0 0 0 0

276 Podospora curvicolla 0.01 0 0.01 0 0 0.01

277 Uncultured fungus 0.01 0.02 0 0 0 0

278 Uncultured fungus 0.01 0.01 0 0 0.01 0

279 Uncultured fungus 0.04 0 0 0 0 0

280 Conocybe subcrispa 0.04 0 0 0 0 0

281 Uncultured fungus 0 0.01 0 0.02 0 0

282 Uncultured fungus 0.01 0.02 0 0 0 0

283 Cryptococcus wieringae 0 0 0.02 0 0 0

284 Cercophora sparsa 0.01 0.03 0 0 0 0

285 No 0.01 0.02 0 0 0 0

286 Ramariopsis kunzei 0.02 0.01 0 0 0 0

287 Glomus mosseae 0.01 0.02 0 0 0 0

288 Fusarium merismoides 0.01 0 0 0 0.01 0

289 Hypholoma fasciculare 0.01 0 0 0 0.01 0.01

290 Gaeumannomyces 0.01 0 0.01 0.01 0 0

291 Arthrobotrys oligospora 0.02 0.01 0 0.01 0 0

292 Teratosphaeria ohnowa 0.01 0 0 0.01 0 0

293 Pseudeurotium bakeri 0.01 0.01 0 0.01 0 0

294 Uncultured fungus 0 0 0 0 0 0.01

295 Schizothecium curvisporum 0.01 0.01 0 0 0 0

296 Uncultured fungus 0.01 0 0 0 0.01 0

297 Uncultured fungus 0.01 0.01 0 0 0 0

298 No 0.01 0 0.01 0 0 0

299 Rhizophlyctis rosea 0.01 0 0.01 0 0 0

300 No 0.01 0.01 0 0 0 0

301 Uncultured fungus 0.02 0.01 0 0 0 0.01

302 Uncultured fungus 0.02 0 0 0 0 0

303 Tricholoma ustale 0.02 0.01 0 0 0 0

304 Scedosporium apiospermum 0.02 0 0 0 0 0

305 Uncultured fungus 0.02 0 0 0 0 0

306 Acremonium persicinum 0 0 0 0 0 0.01

307 Phoma herbarum 0 0 0 0 0.02 0

308 Stromatonectria caraganae 0.01 0.01 0 0 0 0

309 Uncultured fungus 0.01 0 0 0.01 0 0

310 Rhodotorula ferulica 0 0 0 0.01 0 0

311 Sphaerodes sp. 0.01 0.01 0 0 0 0

312 No 0.01 0.01 0 0 0 0

313 Uncultured fungus 0.02 0.01 0 0 0 0

119

314 Trichocladium opacum 0.01 0 0 0 0 0

315 Acremonium strictum 0.02 0 0 0 0 0

316 Uncultured fungus 0 0.01 0 0 0 0

317 Uncultured fungus 0.01 0 0 0 0 0

318 Uncultured fungus 0 0 0 0 0 0.01

319 Uncultured fungus 0 0 0 0 0 0.01

320 No 0.02 0 0 0 0 0

321 Kurtzmanomyces tardus 0 0 0 0 0 0.01

322 Fusarium culmorum 0.01 0 0.01 0 0 0

323 Paraphoma chrysanthemicola 0.01 0 0 0 0 0.02

324 Stachybotrys chartarum 0.01 0 0 0 0 0

325 Panaeolus uliginosus 0.01 0.01 0 0 0 0

326 Rickenella pseudogrisella 0.01 0 0 0 0 0.01

327 Trichocladium asperum 0.01 0 0 0 0 0

328 No 0.01 0 0 0 0.02 0

329 Uncultured fungus 0.01 0 0 0.01 0 0

330 Uncultured fungus 0.01 0 0 0.01 0 0

331 Pseudaleuria quinaultiana 0.01 0 0.01 0 0 0

332 Pachnocybe ferruginea 0.01 0 0 0 0 0

333 Uncultured fungus 0.01 0.01 0 0 0 0

334 Entrophospora infrequens 0.01 0 0 0 0 0

335 Ceratobasidium sp. 0.01 0.01 0 0 0 0

336 Uncultured fungus 0.02 0 0 0 0 0

337 Mortierella sp. 0.01 0 0 0 0 0

338 Uncultured fungus 0.01 0 0 0 0 0

339 Uncultured fungus 0.02 0 0 0 0 0

340 Uncultured fungus 0.01 0 0 0 0 0

341 Uncultured fungus 0 0.01 0 0 0 0

342 Leohumicola minima 0.01 0 0.01 0 0 0

343 Mortierella elongata 0.01 0 0 0 0 0

344 No 0.01 0 0.01 0 0 0

345 Kernia pachypleura 0.01 0 0 0 0 0.01

346 Ajellomyces dermatitidis 0.01 0 0 0 0 0

347 Fusarium avenaceum 0.01 0 0 0 0 0

348 Cylindrocarpon didymum 0.01 0 0 0 0 0.01

349 Pyrenochaeta sp. 0.01 0 0 0 0.01 0

350 Uncultured fungus 0.02 0 0 0 0 0

351 Dictyosporium strelitziae 0.01 0 0 0.01 0 0

352 Hypocrea aeruginea 0.01 0 0.01 0 0 0

353 Sarea difformis 0 0.01 0 0 0 0

354 Cephalotheca sulfurea 0.01 0 0.01 0 0 0

355 Uncultured fungus 0.01 0 0 0 0 0

356 Hypocrea pachybasioides 0.01 0 0 0 0 0

357 Fusarium solani 0.01 0 0 0 0 0

358 Uncultured fungus 0.01 0 0 0 0 0

120

359 No 0.02 0 0 0 0 0

360 No 0.01 0.01 0 0 0 0

361 Cosmospora vilior 0.01 0 0 0 0 0

362 No 0.01 0 0 0 0 0

363 Cercophora sp. 0.01 0 0 0 0 0

364 Dokmaia monthadangii 0.01 0 0 0 0 0

365 Cosmospora vilior 0 0.01 0 0 0 0

366 Dokmaia monthadangii 0 0 0 0 0 0.01

367 Ascobolus crenulatus 0.01 0 0 0 0 0

368 No 0.01 0 0 0 0 0

369 Sphaerodes sp. 0.01 0 0 0 0 0

370 Fusarium oxysporum 0 0 0 0 0 0

371 Pseudeurotium bakeri 0.01 0 0 0 0.01 0

372 No 0.01 0 0 0 0 0

373 Cadophora fastigiata 0.01 0 0 0 0 0

374 Uncultured fungus 0.01 0 0 0 0 0

375 No 0.01 0 0 0 0.01 0

376 No 0.01 0 0 0.01 0 0

377 Uncultured fungus 0.01 0 0 0 0 0

378 Lasiosphaeria sorbina 0.01 0 0 0 0 0

379 Coprinopsis latispora 0.02 0 0 0 0 0

380 Cosmospora vilior 0.01 0.01 0 0 0 0

381 Leohumicola minima 0 0 0 0.01 0 0

382 No 0.01 0 0 0 0 0

383 Mortierella sp. 0.01 0 0 0 0 0

384 Fusarium incarnatum 0.01 0 0 0 0 0

385 No 0.01 0 0 0 0 0

386 No 0.01 0 0 0 0 0

387 Cosmospora vilior 0.01 0 0 0 0 0

388 Cercophora sparsa 0.01 0 0 0 0 0

389 Melanoxa oxalidis 0.01 0 0 0 0 0

390 No 0.01 0 0 0 0 0

391 Fusarium culmorum 0.01 0 0 0 0 0

392 No 0.01 0 0 0 0 0

393 Bionectria ochroleuca 0.01 0 0 0 0 0

394 Glomus mosseae 0.01 0 0 0 0 0

395 Ascobolus crenulatus 0.01 0 0 0 0 0

396 No 0.01 0 0 0 0 0

397 Trichosporon mycotoxinivorans 0.01 0 0 0 0 0

398 Cudoniella clavus 0.01 0 0 0 0 0

399 Paecilomyces carneus 0.01 0 0 0 0 0

400 Glomus mosseae 0.01 0 0 0 0 0

401 Podospora curvicolla 0.01 0 0 0 0 0

402 Fusarium incarnatum 0.01 0 0 0 0 0

403 Exophiala salmonis 0 0 0 0 0 0

121

404 Chaetomium sp. 0 0 0 0 0 0

405 Fusarium lactis 0 0 0 0 0 0

406 Helicoma vaccinii 0.01 0 0 0 0 0

407 Phialemonium dimorphosporum 0 0 0 0 0 0

408 Fusarium larvarum 0 0 0 0 0 0

409 Fusarium merismoides 0 0 0 0 0 0

410 No 0.01 0 0 0 0 0

411 Eucasphaeria capensis 0 0 0 0 0 0

412 Fusarium solani 0 0 0 0 0 0

413 Fusarium merismoides 0.01 0 0 0 0 0

414 Glomus sp. 0.01 0 0 0 0 0

415 Scutellospora reticulata 0.01 0 0 0 0 0

416 Olpidium brassicae 0.01 0 0 0 0 0

417 Pseudaleuria quinaultiana 0.01 0 0 0 0 0

418 Eucasphaeria capensis 0 0 0 0 0 0

419 Fusarium avenaceum 0.01 0 0 0 0 0

420 Apodus deciduus 0.01 0 0 0 0 0

421 Fusarium larvarum 0.01 0 0 0 0 0

422 No 0 0 0 0 0 0

423 Mortierella elongata 0.01 0 0 0 0 0

424 Glomus mosseae 0.01 0 0 0 0 0

425 Fusarium oxysporum 0.01 0 0 0 0 0

426 Uncultured fungus 0.01 0 0 0 0 0

427 Glomus claroideum 0.01 0 0 0 0 0

428 Acremonium cyanophagus 0.01 0 0 0 0 0

429 Pulvinula constellatio 0.01 0 0 0 0 0

430 Leohumicola minima 0.01 0 0 0 0 0

431 Apinisia racovitzae 0.01 0 0 0 0 0

432 Cryptococcus gastricus 0.01 0 0 0 0 0

433 Uncultured fungus 0 0 0 0 0 0

434 Uncultured fungus 0.01 0 0 0 0 0

435 Acremonium persicinum 0.01 0 0 0 0 0

436 Acaulospora trappei 0 0 0 0 0 0

437 No 0 0 0 0 0 0

438 Mastigobasidium intermedium 0.01 0 0 0 0 0

439 Oculimacula yallundae 0.01 0 0 0 0 0

440 No 0 0 0 0 0 0

122

Table S2. The standardized number of total sequences from root, rhizosphere, and bulk soil,

respectively, which were identified at the genus level (n = 30).

Genera Root Rhizosphere soil Bulk soil

Acaulospora 30 0 2

Acremonium 3 631 1136

Ajellomyces 0 0 4

Aleuria 0 0 3510

Alternaria 22 27 66

Amaurodon 0 32 32

Ambispora 18 0 0

Apinisia 0 0 2

Apodus 0 812 831

Aquaticola 18 184 346

Archaeospora 0 0 9

Arthrobotrys 6 48 7

Arthrographis 0 40 65

Ascobolus 0 34 31

Athelia 0 0 136

Basidiobolus 0 0 22

Beauveria 0 4 0

Bionectria 2034 9270 3146

Botryotinia 0 23 68

Cadophora 0 0 3

Calcarisporium 0 106 101

Candida 0 703 695

Capronia 0 17 17

Cephaliophora 0 0 8

Cephalosporium 0 4 0

Cephalotheca 0 0 4

Ceratobasidium 0 0 4

Cercophora 0 1964 2697

Chaetomium 0 0 10

Chaetosphaeria 0 51 22

Chalara 0 0 111

Cladorrhinum 0 18 34

Cladosporium 131 319 419

Clavulinaceae 0 17 0

Clitopilus 0 9 12

Clonostachys 0 0 109

Cochliobolus 0 7 11

Colletotrichum 0 0 10

Conidiobolus 0 3 0

Coniothyrium 0 22 29

123

Conocybe 0 2 8

Coprinellus 57 27 25

Coprinopsis 0 119 106

Cosmospora 3 66 77

Crocicreas 0 7 19

Cryptococcus 104 11271 13669

Cryptotrichosporon 0 625 592

Cudoniella 174 304 326

Cylindrocarpon 0 257 4

Cyphellophora 0 0 218

Degelia 0 40 67

Dendryphion 980 195 240

Devriesia 0 174 151

Dictyosporium 0 0 67

Didymella 40 25 133

Dioszegia 5 0 15

Dokmaia 24 2397 3805

Dwayaangam 4 0 0

Entrophospora 0 41 33

Entyloma 0 0 13

Epicoccum 8265 20 0

Eremomyces 0 7 0

Eucasphaeria 196 1893 806

Exophiala 24466 3249 4694

Fusarium 71076 36716 12744

Fusidium 181 74 69

Gaeumannomyces 0 83 67

Galactomyces 0 44 0

Galerina 0 0 11

Gelasinospora 0 0 44

Geomyces 0 210 266

Glomus 3439 238 168

Gymnostellatospora 0 9 50

Helicoma 0 0 2

Heydenia 0 35 38

Hydropisphaera 0 28 11

Hyphodiscus 0 2 0

Hyphodontia 0 0 15

Hypholoma 0 0 7

Hypocrea 7 119 136

Ijuhya 4 23 38

Kernia 0 0 4

Kurtzmanomyces 0 0 5

Lachnella 0 247 429

124

Laetisaria 0 0 15

Lasiosphaeria 0 0 3

Leohumicola 17106 585 352

Lepista 0 0 11

Leptodontidium 62 3331 3703

Leptosphaeria 0 0 28

Leptosphaerulina 5 0 0

Leucopaxillus 0 19 51

Lewia 48 78 147

Mastigobasidium 0 0 2

Melanoxa 0 0 2

Metarhizium 0 12 13

Microbotryum 0 26 40

Microdochium 70 653 821

Monacrosporium 472 1193 169

Mortierella 401 17974 21728

Mrakia 0 0 188

Mycoarthris 0 0 16

Myrmecridium 0 85 81

Myrothecium 0 8 10

Nectria 0 47 76

Neofabraea 0 57 72

Neonectria 7699 1041 1167

Occultifur 0 0 51

Oculimacula 0 0 2

Olpidium 0 191 301

Omphalina 0 0 10

Ophiosphaerella 0 160 269

Pachnocybe 0 0 5

Paecilomyces 4 256 323

Panaeolus 0 0 5

Paraglomus 27 32 5

Parapleurotheciopsis 15 22 0

Penicillium 0 5 0

Periconia 61 31 104

Petrakia 0 56 53

Peziza 0 23 492

Phaeophleospora 0 0 40

Phaeophleospora 0 28 0

Phialemonium 0 0 2

Phialocephala 0 13 11

Phialophora 0 65 0

Phoma 0 6526 10296

Plectosphaerella 0 0 173

125

Pleospora 4 0 59

Pochonia 8 0 0

Podospora 0 167 179

Polytolypa 0 35 34

Preussia 0 329 440

Psathyrella 0 2 0

Pseudaleuria 0 3 103

Pseudallescheria 0 516 617

Pseudeurotium 0 598 410

Pseudofavolus 0 8 10

Psilocybe 0 4 0

Pulvinula 0 0 2

Pyrenochaeta 61 138 258

Ramariopsis 0 0 7

Rhinocladiella 0 152 0

Rhizoctonia 0 0 45

Rhizophlyctis 0 8 6

Rhodotorula 2 373 486

Rickenella 0 0 5

Sarea 0 0 16

Scedosporium 0 0 124

Schizothecium 0 77 172

Sclerotinia 0 169 159

Scutellospora 5 0 2

Scytalidium 0 75 86

Sistotrema 0 0 2558

Sphaerodes 0 3 9

Sporidiobolus 0 27 0

Sporobolomyces 0 33 73

Stachybotrys 0 104 130

Stilbum 0 0 14

Stromatonectria 0 7 6

Teratosphaeria 0 0 7

Tetracladium 0 1622 2856

Thielaviopsis 0 0 111

Tilletiopsis 0 4 17

Trichocladium 430 18253 2652

Trichoderma 2 101 95

Tricholoma 0 0 6

Trichosporon 0 2326 3244

Tubeufia 0 0 26

Udeniomyces 0 0 21

Uncultured Davidiella 68 0 0

Uncultured fungus 911 3590 3421

126

Uncultured Fusarium 0 2 0

Uncultured Glomus 7 0 0

Uncultured Minimedusa 0 376 0

Uncultured Pyronemataceae 352 0 0

Verticillium 125 30561 41729

Volutella 0 14 17

Waitea 0 0 295

127

5 General discussion

This work presents a comprehensive view of fungal communities in pea fields,

provides new information on the pea root rot complex, and improves the

understanding of the role of fungi in soil and root health by means of high throughput

next-generation pyrosequencing.

Pyrosequencing disclosed fungal communities in three different ecological

niches - roots, rhizosphere, and bulk soil. The high diversity of fungi in the examined

agricultural soils was comparable to results obtained by Sugiyama et al. (2010), who

studied soil fungal communities in organic and conventional potato farms. However,

many studies suggest that the diversity of soil fungi is higher in natural ecosystems

than in agroecosystems (Buée et al., 2009; Jumpponen et al., 2010).

Pea diseases are the result of the interaction among pathogen, host, and

environmental conditions conducive to disease development. In the examined pea

fields, different pathogens were found to be the possible causal agents of pea diseases.

Interestingly, the abundance of Phoma medicaginis var. pinodella strongly correlated

to the disease severity index of pea roots in 2008, while Fusarium oxysporum and

Aphanomyces euteiches were the possible pathogens of pea root rot in the fields in

2010. In general, previous studies of pea diseases usually focused on one or two

pathogens (Bødker et al., 1993; Gaulin et al., 2007), whereas in this project, the total

fungal communities, including a complex of fungi associated with root health were

revealed. Furthermore, some fungi were mainly associated with healthy roots, but

whether these non-pathogenic fungi play a role in root health as biocontrol agents,

remains to be investigated.

The study of fungal communities in roots, rhizosphere, and bulk soil revealed

that the fungal communities that could be identified in diseased roots as the probable

causes of diseases, could not be found in the rhizosphere and bulk soil. This indicates

that causal agents of pea diseases in the bulk soil may exist as resting structures

mainly. Generally, the distinct fungal communities in three different environments

might be due to the different complexity of nutrients from these ecological niches and

due to the role of root exudates in regulating fungal community composition and

diversity in the surrounding soil (Broeckling et al., 2008). The higher abundance of

AM fungi in healthy roots compared to diseased roots confirms the important roles of

128

AM fungi in relation to pea root health (Larsen & Bødker, 2001; Thygesen et al.,

2004) and the biocontrol potential of AM fungi against root pathogens (Whipps,

2004). In addition, a large number of AM fungi are shown to be suitable as bio-

indicators in agricultural soils (Oehl et al., 2011). Nevertheless, the low rate of AM

fungi in rhizosphere and bulk soil is in strong contrast to the findings that these fungi

often constitute a dominant portion of soil microbial biomass (Olsson et al., 1999;

Hogberg & Hogberg, 2002).

Fungal cultivation methods have often shown a high abundance of e.g.

Penicillium, Aspergillus, and Trichoderma in agricultural soils (Elmholt & Labouriau,

2005; Harman, 2006), but interestingly, these genera were very rare in the examined

soils. The reason for this is unknown, but these genera may be overestimated in

culturing methods, as they grow well on most substrata. Elmholt & Labouriau (2005)

reported that the Zygomycota genus Mortierella was dominant in Danish agricultural

soils, which is consistent with results obtained in the present study. As decomposers,

Mortierella are commonly encountered in soil or soil-borne organic substrates

(O'Donnell et al., 2001). Yeasts such as Cryptococcus aerius and Guehomyces

pollulans, were highly abundant in the soils. Yeasts are considered to be mainly

saprotrophs able to utilize plant debris or plant exudates, and some are known to be

plant growth promoters due to their phosphorus solubilizing abilities (Botha, 2011).

Considering the high abundance of yeasts in these soils, their functional traits should

be further explored in relation to plant nutrition and health.

The key factors determining soil microbial diversity are linked to the

complexity of the microbial interactions in soil (Garbeva et al., 2004). The potential

determinants of fungal community structure in the studied soils could be health status,

environment, location, and edaphic differences. Many factors including soil type,

plant type, and soil management regime (such as crop rotation, tillage, fertilizer,

compost, manure, or pesticide applications and irrigation) strongly affect the

microbial diversity of soil (reviewed by Garbeva et al., 2004). Among the factors

examined in the present study, location (fields) and environment (root, rhizosphere, or

bulk soil) were stronger determinants of fungal community structure than health status.

454 amplicon sequencing has been widely used for investigation of microbial

communities in different environmental samples, such as agricultural soil (Sugiyama

et al., 2010), forest soil (Buée et al., 2009b), Quercus phyllosphere (Jumpponen &

Jones, 2009), and indoor dust (Amend et al., 2010). Nevertheless, the accuracy and

129

quality of massively parallel DNA pyrosequencing can be compromised due to factors

such as DNA extraction and PCR bias, pyrosequencing errors (Huse et al., 2007) or

incorrect data analysis, which are challenging for the assessment of microbial

community. Pyrosequencing can be subject to errors during several steps. In the

present study, pyrosequencing errors were minimized by quality control and a series

of other subsequent analyzing procedures, such as generating sequence clusters at

97% sequence similarity (Tedersoo et al., 2010), excluding singletons due to the

possibility of sequencing artifacts (Tedersoo et al., 2010), and BLAST searches

against both GenBank database (Benson et al., 2011) and a custom-curated database

derived from the GenBank and UNITE (Kõljalg et al., 2005) databases. Different

studies have employed different approaches for sequence filtering and analysis.

Hibbett et al. (2011) surveyed 10 recent pyrosequencing studies of fungal

communities in various environments.

The fungal communities that were recovered from the examined fields were

dominated by Ascomycota and Basidiomycota, which was probably also influenced

by the choice of primers. This is in line with Buée et al. (2009b), who studied the

fungal communities using similar primers in forest soils. The ITS region has been

commonly used as a target for characterization of fungal communities due to the

variability even within species (Nilsson et al., 2008). Notwithstanding, Nilsson et al.

(2009a) suggested that the OTUs assignments using the two different sub-regions

(ITS1 or ITS2) may not always be consistent. Furthermore, some of the ITS primers

have been suggested to have amplification biases (Bellemain et al., 2010), thus

different primer combinations or different parts of the ITS region could be analyzed in

parallel.

130

131

6 Conclusions and further perspectives

In the present project, the repeatability of pyrosequencing was tested using

parallel DNA extractions and PCR experiments, and by studying the variation of read

abundances of the generated clusters. It was concluded that pooling of several

extractions and PCR amplicons will decrease variation across replicates, and thus

result in more reliable estimates of fungal abundances.

Soil fungal communities along a soil health gradient in nine pea field soils were

examined. The soil fungal community composition and diversity varied, and

particularly four fungal genera correlated with the disease severity index (DSI) of pea

roots. The study of fungal communities in root, rhizosphere, and bulk soils in relation

to diseased and healthy pea roots, revealed clear differences of fungal diversity and

community structures, and a strong correlation between the fungal communities in

roots and root health status. In general, pyrosequencing of fungal communities in pea

soils provided comprehensive knowledge of the dynamics of fungal communities and

their interaction with plant roots in relation to plant disease.

The present work focused on the dominant fungal species, but it would be

interesting also to further explore the less abundant fungi of the “rare biosphere”,

because the number of rare species of microorganisms is potentially enormous

(Pedros-Alio, 2007), however, this would require even deeper sequencing efforts. In

this study, several fungal species were identified that correlated negatively with the

DSI. Their potential role in biocontrol of plant pathogens could be further investigated

in bio-assays.

Although fungi and oomycetes are the most common causes, pea diseases can

also be caused by other microorganisms, such as bacteria, and nematodes (Kraft &

Pfleger, 2001). Thus, a parallel analysis of bacterial and nematode communities will

give important complementary information, since among other interactions, fungal

biocontrol processes in the soil by bacteria are well known (Pliego et al., 2011).

Furthermore, it will also be very informative to compare the diversity of the major

microbial taxa, i.e., fungi, bacteria, archaea, protozoa, and nematodes from soil

samples with different plant health status using a metagenomic approach. A

comprehensive survey will reveal different microbial groups resulting in a better

understanding of the soil microbial communities.

132

Combining measures of microbial structural diversity with functional traits

should be explored in relation to soil and root health in agricultural systems.

Pyrosequencing not only reveals “who‟s there” (richness), but also answers questions

such as “how many are there” (abundance) as well as “what are they doing there”

(function). Investigating functional diversity in agricultural soils will give a much

more detailed picture of the interactions among microorganisms, soil, and plants.

Functional metagenomics has the capability to probe genetic and biochemical

diversity in microbial communities. Moreover, gene-targeted metagenomics (Iwai et

al., 2011) would be employed to investigate specific ecological functional diversity,

such as biodegradation, production of bioactive secondary metabolites, and

pathogenesis. For instance, nitrogen fixing bacteria in soil could be examined,

because nitrogen is an important soil component, and nitrogen fixation is mediated by

diverse phylogenetic groups of prokaryotes.

Soil from pea fields was selected as an example of agricultural soils to examine

fungal communities in this project. Plant type is one of the main drivers of soil

microbial community structure, making it interesting to investigate the soil microbial

communities from soils grown with other plants, in particular the previously rotated

crops. Further, plants also have strong impacts on soil microbial communities in a

functional way, thus the effects of plants on microbial communities in soil,

particularly beneficial effects, are necessary to explore in the future.

Plants and microorganisms are interdependent for nutrient supply. Plants

provide rhizosphere microorganisms with a carbon source, while microorganisms

provide nitrogen and phosphorus, and also protect plants from pathogens (Singh et al.,

2004). The development of stable isotope probing (SIP) advances the studies of

linking community structure to functional activity (Radajewski et al., 2000; Dumont

& Murrell, 2005). The combinations of SIP-microarray and SIP-metagenomics offer

more insights into the plant-microbe interactions. The SIP-microarray would be

applied to identify microbes utilizing plant carbon exudates and to estimate the

microbial gene expression in the rhizosphere. The SIP-metagenomics approach

involves pyrosequencing 13

C-labelled DNA from microorganisms in the soil,

therefore, the structure-functional relationship of rhizosphere microbes will be

determined.

In conclusion, most previous studies on plant diseases have focused on single or

few pathogens, however, the advent of high throughput pyrosequencing is very

133

promising for microbial community analysis and has enabled detailed studies of

overall fungal communities, which will lead to a new understanding of plant

pathology and give a much more detailed picture of the interactions between

microorganisms and plants.

134

135

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Soil fungi play extremely important roles in plant root health. Soil fungal communities associ-ated with plant root health were investigated using amplicon pyrosequencing. Initially, it was found that DNA extraction and PCR amplification affect the variation of read abundances of pyrosequencing generated operational taxonomic units, and that pooling of several DNA extractions and PCR amplicons will decrease variation among technical replicates. Soil fungal communities differed along a soil health gradient in nine pea fields. Phoma, Podospora, Pseu-daleuria, and Veronaea, at the genus level, correlated to the disease severity index of plant roots. Fungal communities also clearly varied in diseased and healthy pea roots, rhizosphere, and bulk soil from three pea fields in terms of community composition and diversity. Fusarium oxysporum and Aphanomyces euteiches were the likely causes of pea root rot in the respective fields. Glomus and Fusarium were significantly more abundant in roots, whereas Cryptococ-cus and Mortierella were almost exclusively found in rhizosphere and bulk soil. Generally, this project demonstrated clear relationships between fungal communities and plant root health.