Soil Fungal Communities Associated with Plant Health As Revealed By

SOIL FUNGAL COMMUNITIES ASSOCIATED WITH PLANT HEALTH AS REVEALED BY NEXT-GENERATION SEQUENCING LIHUI XU PhD THESIS • SCIENCE AND TECHNOLOGY • 2011 SOIL FUNGAL COMMUNITIES ASSOCIATED WITH PLANT HEALTH AS REVEALED BY NEXT-GENERATION SEQUENCING Lihui Xu PhD thesis • Science and Technology • 2011

Department of Agroecology Science and Technology Aarhus University Forsøgsvej 1 4200 Slagelse

Tryk: www.digisource.dk ISBN: 978-87-91949-99-9

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

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.

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

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

iii

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

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

knowledge will further improve the understanding of soil fungal communities with regard to plant diseases.

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

forekomsten af enkelte svampegrupper. Projektet har medvirket til en øget forståelse af dynamikken i jordens svampesamfund i relation til plantesygdomme.

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

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).

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)

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).

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).

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.

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

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

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

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

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

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

(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.

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

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

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

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.

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

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).

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).

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

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

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.

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.

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.

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).

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).

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 Homo sapiens (Wheeler et al., 2008) sequencing 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).

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

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.

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

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?

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.

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.

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: [email protected]; [email protected]

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: [email protected]

Corresponding author: Mogens Nicolaisen Tel.: +45-8715 8137; Fax: +45-8715 6082 E-mail: [email protected]

(Accepted by Canadian Journal of Microbiology)

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

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.

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

(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.

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.

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.

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.

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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3 Paper II

Soil fungal community structure along a soil health gradient in pea fields examined using deep amplicon sequencing

Lihui Xua, Sabine Ravnskova, John Larsenb, R. Henrik Nilssonc, Mogens Nicolaisena,* aDepartment of Agroecology, Faculty of Science and Technology, Aarhus University, 4200 Slagelse, Denmark E-mail: [email protected]; [email protected] bCentro de Investigaciones en Ecosistemas, Universidad Nacional Autónoma de México, C.P. 58190 Morelia, Michoacán, México E-mail: [email protected] cDepartment of Plant and Environmental Sciences, University of Gothenburg, Box 461, 405 30 Gothenburg, Sweden E-mail: [email protected]

*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: [email protected]

(Accepted by Soil Biology & Biochemistry)

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

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,

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?

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 Latitude, Soil Yield Nutrient (mg/100g soil) Pre-crop pH field longitude type (kg/ha) P K Mg 55°01'N, Heavy 1 2,461 Beet 6.5 2.5 13.5 10 12°17'E clay 55°01'N, Heavy 2 2,461 Beet 6.5 2.5 13.5 10 12°17'E clay 54°59'N, Heavy Spring 3 2,482 6.4 3.1 10.5 7.0 12°18'E Clay barley 54°58'N, Spring 4 Clay 976 7.1 2.6 10.1 4.8 12°25'E barley 54°57'N, Spring 5 Clay 2,330 7.4 5.4 11.2 4.6 12°30'E barley 54°56'N, Heavy Not Spring 6 6.9 3.4 11.7 7.4 12°30'E clay harvested barley 54°58'N, Heavy Winter 7 3,558 7.3 4.2 12.5 6.1 12°24'E clay wheat 54°58'N, Heavy Not 8 Pea 7.0 3.4 11.9 9.8 12°20'E clay harvested 54°59'N, Spring 9 Clay 2,070 6.6 4.2 9.9 5.1 11°59'E barley

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,

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 prechilled 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

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

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.

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.

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 Shoot dry Root dry Root length AM root Disease field weight weight (m) colonization severity (g) (g) (%) 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).

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).

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 Query Similarity Number Relative accession Coverage of abundance number sequences (%) (%) (%) 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

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%).

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

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,

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.

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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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 Ravnskov1, John Larsen2, Mogens Nicolaisen1

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: [email protected]

(Manuscript in preparation)

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.

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).

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

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

& 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

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 nonparametric 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

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.

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 Shoot dry Root fresh DSI DSI status weight (g) weight (g) Root 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 ***

DSI: Disease severity index; ***, P ≤ 0.001

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.

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 ANOVA, Analysis of variance; E, Environment; H, Health status *, P < 0.05; **, P < 0.01; ***, P < 0.001

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.

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).

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

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 Health F x H (F) (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

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.

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 Asco- Basidio- Chytridio- Glomero- Zygo- Status mycota mycota mycota mycota 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 *** 0.37 - *** ** (H) 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 0.82 0.36 0.63 0.68 0.94 (H) 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 0.20 0.27 0.75 0.83 0.34 (H) F x H *** ** 0.21 0.92 0.87 ANOVA, Analysis of variance; *, P < 0.05; **, P < 0.01; ***, P < 0.001

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).

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).

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).

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

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.

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.

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

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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Figure S1. Examples of pea roots from three fields. D: diseased roots; H: healthy roots

101

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

102

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

103

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

104

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

105

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

106

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

107

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

108

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

109

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

110

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

111

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

112

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

113

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

114

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

115

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

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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

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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

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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

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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

120

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

121

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

122

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

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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

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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

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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

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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

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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

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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.

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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.

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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 13C-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

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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.

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150 Soil fungi play extremely important roles in plant root health. Soil fungal communities associated 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.