Oomycete Community Diversity and Pathogenicity Associated with Soybean in Ohio DISSERTATION Presented in Partial Fulfillment Of

Home , Pythiaceae, Pythium, Pythium acanthicum, Pythium arrhenomanes, Pythium graminicola, Pythium heterothallicum

Oomycete Community Diversity and Pathogenicity Associated with Soybean in Ohio

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Krystel A. Navarro-Acevedo

Graduate Program in Plant Pathology

The Ohio State University

2019

Dissertation Committee:

Dr. Anne E. Dorrance, Advisor

Dr. Jason Slot, Member

Dr. Maria Soledad Benitez-Ponce, Member

Dr. Feng Qu, Member

Dr. Andrew Michel, Member Copyrighted by

Krystel A. Navarro-Acevedo

2019

Abstract

The oomycetes, Phytophthora (Ph.), Phytopythium (Pp.), and Pythium (Py.) cause

detrimental effects to soybean yields when susceptible cultivars are planted, and conducive

conditions occur. Soils with high clay content found in many soybean production areas of

Ohio paired with heavy rainfall, allows for water retention for longer periods of time. This

enables the germination of oospores and mobility of zoospores towards soybean roots.

Subsequently, infections occur and early symptoms include pre-and post- emergence damping-off of soybean seed and seedlings. In addition, mid-season infections lead to root rots but one species, Phytophthora sojae causes stem rot which results in plant death.

Within the genus Phytophthora, only Ph. sojae and Ph. sansomeana have been recovered

from soybean seedlings and reported as pathogens. In contrast, a vast diversity of Pythium

species have been recovered from soybean seedlings across major soybean producing states

in the U.S. and Canada. Pythium sylvaticum, Py. irregulare, Py. ultimum var. ultimum, Py.

ultimum var. sporangiiferum and Py. heterothallicum are among the most frequently recovered species. More importantly, more than one species has been recovered from symptomatic seedlings or roots, suggesting that these occur as species complexes.

To better manage this root pathogen complex, an integrated disease management approach is recommended. This includes a combination of fungicide seed treatments paired with host resistance. Fungicide efficacy is variable among species and host resistance has shown to be the best means of management in regions with high levels of inoculum and

conducive environments. The distribution and species diversity of Phytophthora,

Phytopythium and Pythium among soybean and corn producing states have been

documented in several previous surveys that used different isolation techniques. These

surveys suggested that soil edaphic factors may play a role in species distribution and

diversity. However, how community composition and species diversity change when soil

edaphic factors remain constant was unexplored. Thus, the first objective of this study was

to test the effects of temperature and agronomic practices on the community composition

and species diversity of Phytophthora, Phytopythium and Pythium. A soil baiting technique

was used to identify the pathogen complex in soils from five fields with different rotation

schemes and tillage practices but with similar soil edaphic factors. Soils were incubated at

15 and 25oC and seed of the susceptible cultivar Sloan was used as bait. Symptomatic seedlings were collected for direct isolation of pathogens onto oomycete selective media.

Additionally, rhizosphere soil was collected for metabarcoding approach in which DNA

was extracted, followed by amplification with oomycete primers of the ITS1 region and sequencing. Regardless of temperature or agronomic practice, Py. sylvaticum, and Py. ultimum were isolated through the baiting procedure and these along with Py. acrogynum,

Py. attrantheridium, and Py. heterothallicum were detected with amplicon sequencing at

all temperatures and all fields and were considered as the core species associated with

soybean. More importantly, there were distinct communities between fields with different

agronomic practices. Pythium arrhenomanes and Py. inflatum, were found in greater abundance in soil from the field under continuous corn while Ph. sojae was higher in soils from fields planted to a soybean corn rotation. There were also differences in community composition due to temperature, six Pythium species were abundant at 15oC while another

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six were favored by 25oC. These findings demonstrated that the diversity of Phytophthora,

Phytopythium, and Pythium, associated with soybean roots can be influenced by

temperature and potentially production practices which may explain in part the differences

in species composition among the many surveys that have taken place as well as the

importance previous production practices may be having on the community composition

within a field.

The second objective was to evaluate if soybean genotype could impact the

abundance of Phytophthora, Phytopythium, and Pythium. Here, three cultivars (Kottman,

Lorain, and Sloan) with different levels and types of resistance towards these three genera were planted across eleven environments with high disease pressure in Ohio over a two- year period. At soybean growth stage V1-V3, seedlings from each environment were collected for direct isolation, and rhizosphere soil used for a metabarcoding approach. In addition, data for early plant population and yield was collected from each environment to determine the performance of the cultivars under natural field conditions. Based on both a metabarcoding approach and direct isolation, the environment played a significant role on the Phytophthora, Phytopythium, and Pythium communities and these were also influenced by different soybean growth stages. In addition, cultivar significantly affected the abundance of Phytophthora species in the rhizosphere of soybean seedlings. Here the greater number of Ph. sojae reads were recovered from rhizosphere soil and symptomatic seedlings of the moderately susceptible cultivar Sloan compared to Kottman with race- specific resistance towards Ph. sojae. Two species, Py. periilum and Pythium sp. CAL, not reported previously in Ohio, were detected in the soybean rhizosphere using a

metabarcoding approach. Pythium periilum was only detected in the rhizosphere while the

undescribed Pythium sp. CAL. was only recovered from seedlings retrieved from one environment although it was detected in 9 environments. When tested against different soybean cultivars this species was highly pathogenic and optimal growth was approximately 25oC. In culture, oospores and sporangia were observed however, zoospores

were not produced. This species was highly abundant across soybean fields in Ohio and

further studies should be conducted to classify this species among the Pythium clades.

Furthermore, Py. periilum was not isolated from seedlings suggesting that temperature

during isolation or type of media should be modified to recover this species from the field.

These results provide evidence that disease, when observed in the field, is often

caused by more than one pathogen. In addition, the effect of cultivar was observed for Ph.

sojae providing further evidence that host resistance is still an effective management

strategy in environments conducive for disease development. These results will also allow

for the development of targeted disease management approaches for fields in which

conducive conditions for disease development are often encountered. Finally, species of

Phytophthora, Phytopythium, and Pythium should be continuously monitored since

populations can change and novel species can emerge that can be detrimental to soybean

production.

Dedication

Dedicate to my family; my father Victor Navarro and my mother Carmen

Acevedo for their support throughout my life and academic career. My grandmother

Ana M. Villanueva for her never-ending love and advice. To my brother Yarom

Navarro and sister Surhail Navarro for their mentorship and love. I would also dedicate this thesis to my husband Alexander Orellana for his support and understanding in this journey from Puerto Rico to Ohio. To my mother in law Mercedes Sanchez for her unconditional support and willingness to help when most needed. And to my lovely daughter Alejandra K. Orellana that came to this world in 2016, this work is for you.

Thank you all for believing in me.

Acknowledgments

I would like to first thank my advisors Dr. Anne E. Dorrance for her support and

opportunity to develop my research skills. Also, for her advises, guidance and

encouragement to become a better professional and person. To Dr. Jason Slot and Dr. Maria

Soledad Benitez-Ponce for their guidance and for providing critical comments and

suggestions to my research work. To Dr. Andrew Michel, Dr. Feng Qu for the open-door policy always encouraged and for guidance and advise. I will also like to thank the present and past members of the Dorrance lab: Jonell Winger, Amilcar Vargas, Kelsey Scott, Linda

Hebb, Felipe Sartori and Carlos Bolanos. To all thanks for your constant support, technical assistance and knowledge taught. Special thanks to Deloris Veney for the many technical guidance, advises and for making this journey enjoyable. To my friends Saranga Wijeratne,

Jaqueline Huzar Novakowiski, Cassidy Gedling, Francesca Rotondo and Amine Batnini for their constant support, friendship, brainstorming talks, advice, and for always pushing me forward in the process. Finally, to my husband for always believing in me and for your unconditional love. Salaries and research support were provided in part by State and

Federal Funds appropriated to the Ohio Agricultural Research and Development Center,

The Ohio State University. This project was funded in part by the Ohio’s Soybean

Producers’ check- off dollars through the Ohio Soybean Council. Additional funding was

also provided by The Ohio State Graduate School thorough the Summer Research

Opportunity Program, Syngenta Crop Protection and OARDC Seed Matching Grant.

vii

Vita

September 1991 ...... Born- Moca, Puerto Rico

2013...... B.S. Agronomy, University of Puerto Rico

2016...... M.S. Plant Pathology, The Ohio State

University

2017- present ...... Graduate Research Fellow, The Ohio State

University

Publications

Krystel Navarro, Dana Martin and Anne E. Dorrance (2018). Bacterial Blight and

Bacterial Pustule of Soybean. Fact Sheet PLPATH-SOY-F03-18.

Meredith Eyre, Krystel Navarro and Anne E. Dorrance (2018). Sudden Death Syndrome

of Soybean. Fact Sheet PLPATH-SOY-R02-18.

Navarro-Acevedo, K. (2017). Development and Testing of Harpin Based Products for the

Control of Nematodes and Fungal Plant Pathogens. Thesis. The Ohio State University.

Posters

Navarro-Acevedo, K., Wijeratne, S., and Dorrance, A.E. (2019). Exploring the effects of

viii soybean genotype and environment on oomycete populations in Ohio using a microbiome approach. Abstract. Congress on Molecular Plant-Microbe Interactions XVIII. Glasgow,

Scotland UK.

Navarro-Acevedo, K., Batnini, B., Wickramasinghe, D., Robertson, A., Dorrance, A.E.

(2019). Characterization of Avr1a /Avr1c locus among Phytophthora sojae isolates from

Ohio and Iowa. Abstract. Oomycete Molecular Genetics Network Annual Meeting. Oban,

Scotland UK.

Navarro-Acevedo, K., Wijeratne, S., Culman, S.W., Benitez, M.S., and Dorrance, A.E.

(2018). Influence of temperature on the isolation of water molds using a soil bating technique. Abstract. International Plant Pathology Congress. Boston, MA.

Navarro-Acevedo, K., Wijeratne, S., and Dorrance, A.E. (2018). Comparison of water- mold populations in Ohio using traditional isolation methods and an amplicon-based metagenomics approach. Abstract. American Phytopathological Society Pacific Division

Meeting and the Soilborne Conference. Portland, OR.

Navarro-Acevedo, K., and Taylor, C.G. (2016). Development and Testing of Harpin Based

Products for the Control of Nematodes and Fungal Plant Pathogens. Thesis. The Ohio State

University.

Navarro-Acevedo, K., Gedling, C., Dorrance, A.E., and Taylor, C. G. (2016). The influence of temperature and inert ingredients on harpin seed treatments towards

Fusarium graminearum. Abstract. American Phytopathological Society Annual Meeting.

Tampa, FL.

Navarro-Acevedo, K., and Taylor, C.G. (2015). Examining the use of Harpin proteins for the control of plant parasitic nematodes. Center for Applied Plant Sciences Retreat.

Abstract. Cambridge, OH.

Navarro-Acevedo, K., and Taylor, C.G. (2015). The use of biological control for plant parasitic nematodes in tomatoes and soybean. Ohio Agricultural Research and

Development Center Conference. Abstract. Wooster, OH.

Navarro-Acevedo, K., and Taylor, C.G. (2014). Harpin-Ea protein from Erwinia

amylovora as a biocontrol for plant parasitic nematodes. Department of Agricultural

Communication, Education and Leadership Conference. Abstract. Columbus, OH.

Oral Presentations

Navarro-Acevedo, K., Wijeratne, S., Culman, S.W., Benitez, M.S., and Dorrance, A.E.

(2019). Influence of temperature and production practices on the watermolds

x communities associated with soybean. American Phytopathological Society Meeting.

Cleveland, OH.

Navarro-Acevedo, K., and Dorrance, A.E. (2019). Microbiome Approaches to Improve

Crop Productivity. OSU Plant Pathology Seminar Series. Columbus, OH.

Navarro-Acevedo, K., and Taylor, C.G. (2015). Can plant parasitic nematodes be controlled? Exploring biocontrol agents and harpin proteins to increase disease resistance in agronomic crops. Wooster Area Molecular Biology Seminar Series. Wooster, OH.

Navarro-Acevedo, K., and Taylor, C.G. (2015). Harpin-Ea protein from Erwinia amylovora as a biocontrol for plant parasitic nematodes. OSU Plant Pathology Spring

Symposium. Columbus, OH.

Navarro-Acevedo, K., Frey, T., and Taylor, C.G. (2013). Composite plant system to investigate the interaction between sweet potato (Ipomea batatas) roots and plant parasitic root-knot nematode (Meloidogyne incognita). Summer Research Opportunity Program

Symposium. Columbus, OH.

Fields of Study

Major Field: Plant Pathology

Table of Contents

Abstract ...... ii

Dedication ...... vi

Acknowledgments...... vii

Vita ...... viii

Publications ...... viii

Fields of Study ...... xi

List of Tables ...... xv

List of Figures ...... xvii

1. Chapter 1: Literature Review ...... 1

1.1. Oomycete overview: Phytophthora, Pythium and Phytopythium ...... 2

1.2. Distribution of Phytophthora, Pythium and Phytopythium ...... 4

1.3. Factors affecting species distribution ...... 6

1.4. Disease management for Phytophthora and Pythium ...... 9

1.5. Deployment of “Rps” genes in the United States ...... 11

1.6. Pathogenicity mechanisms of Phytophthora sojae ...... 13

1.7. Community characterization using a metabarcoding approach ...... 14

References ...... 17

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2. Chapter 2. The Effects of Incubation Temperature and Production Practices on

Phytophthora, Phytopythium, and Pythium Communities of Soybean ...... 27

2.1. Abstract ...... 27

2.2. Introduction ...... 28

2.3. Materials and Methods ...... 33

2.4. Results ...... 42

2.5. Discussion ...... 47

References ...... 70

3. Chapter 3. The Effects of Soybean Genotype and Environment on Phytophthora,

Pythium and Phytopythium species in High Disease Environments in Ohio ...... 78

3.1. Abstract ...... 78

3.2. Introduction ...... 79

3.3. Materials and Methods ...... 84

3.4. Results ...... 93

3.5. Discussion ...... 100

References ...... 124

Chapter 4. Summary and future directions ...... 137

Appendix A: Codes use for Miseq data processing ...... 141

Appendix B. Data processing and statistical analysis of metabarcoding data ...... 146

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Appendix C: Internal transcribed spacer 1 sequences generated from isolates recovered from fields in Ohio. Sequences were added to the database for taxonomic identification using a metabarcoding approach...... 167

References: Chapter 1 to 5 ...... 175

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List of Tables

Table 2.1.Description of agronomic practices and soil physical and chemical properties of

soils collected from the Ohio State University Northwest Agricultural Research Station in

2017. Soil physical and chemical properties were calculated as the mean from 2012 to

2017...... 54

Table 2.2. Shannon’s diversity index for oomycete communities found in pools of 3, 5

and 10 DNA extractions of soils retrieved from the rhizosphere of soybean seedling when

using a soil baiting technique and an incubation temperature of 15°C. The index was

calcul ated using the Vegan package in R (Dixon 2003)...... 55

Table 2.3. Total abundance of the oomycetes Pythium, Phytophthora and Phytopythium from DNA extraction pools found in the rhizospheric soil of soybean seedlings following incubation at 15 °C from five fields using a soil baiting technique. Abundances are based on cumulative sum scaled normalized data...... 56

Table 2.4. Shannon’s diversity index from Phytophthora, Phytopythium and Pythium

communities from rhizospheric soil of soybean seedlings from five fields following

incubation at 15 and 25°C using a baiting technique...... 57

Table 2.5. Total abundance of the oomycetes Pythium, Phytophthora and Phytopythium

in the rhizosphere soil of soybean seedlings from five fields following incubation at 15

and 25°C using a soil baiting technique. Abundances are based on cumulative sum scaled normalized data...... 58

Table 2.6. Resume of species of Pythium sharing identical ITS1 sequences based on

sequence pairwise analysis. Species sharing identical ITS1 sequences were grouped

together and the clade letter to which they belong was provided...... 60

Table 3.1. Summary of accumulated precipitation and temperature each environment in

Ohio where seedlings were sampled and total number of isolates of Phytophthora,

Phytopythium and Pythium recovered from cultivars Kottman, Lorain and Sloan with different levels and types of resistance, using direct isolation methods...... 109

Table 3.2. Summary of the total number of isolates of Phytophthora, Phytopythium, and

Pythium recovered using direct isolation methods from seedlings of the cultivars

Kottman, Lorain and Sloan with different types and levels of resistance, across 11

environments in Ohio...... 110

Table 3.3. Analysis of variance significance values of the effects of environment and

cultivar for the abundance of species of Phytophthora, Phytopythium and Pythium using

a metabarcodng approach. Species highlighted in yellow were significant at P = 0.05. 111

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List of Figures

Figure 2.1. Nonmetric multidimensional scaling (NMDS) plots using Bray-Curtis

dissimilarity of oomycete communities from three (P3), five (P5) and ten (P10) DNA

extraction pools from soil samples from five different fields following incubation at 15°C

using a soil baiting technique (n=5). Colors represent number of DNA extraction pools.

Stress values for NMDS are shown in the bottom left. DNA pool were not significantly

different (PERMANOVA; P = 0.92, R2= 0.01). Communities of oomycetes varied by field (PERMANOVA; P = 0.0001, R2= 0.33)...... 61

Figure 2.2. Proportion of abundance of Phytophthora, Phytopythium and Pythium from

amplicon sequencing of soil samples from five fields were 3 (P3), 5(P5) or 10(P10) DNA

extractions were pooled. Soils were retrieved from the rhizosphere of seedlings after

incubation at 15 °C. Abundance was normalized using the cumulative-sum scaling approach and then expressed as a proportion of total abundance per sample...... 62

Figure 2.3. Nonmetric multidimensional scaling (NMDS) plots using Bray-Curtis dissimilarity of Phytophthora, Phytopythium and Pythium community data from five soils following incubation at 15 and 25°C (n=10). Stress values for NMDS are shown in the bottom left of the plot. The effect of field (PERMANOVA; P= 0.001; R2 = 0.23), temperature (PERMANOVA; P= 0.001; R2 =0.04) and the interaction of field and temperature (PERMANOVA; P= 0.001; R2 = 0.10) were significantly different.

Replicates and run were not statistically different (PERMNOVA; P= 1.0; R2 =0.004). .. 63

Figure 2.4. Nonmetric multidimensional scaling plots (NMDS) using Bray-Curtis

dissimilarity of Phytophthora, Phytopythium and Pythium community data from five xvii fields following incubation at 15 and 25 °C (n=10). NMDS plots are shown for field D15

(CSF-Nt) (A); Field B1 (SS-Till) (B); Field B6 (CSF-Till) (C), Field TA4 (CC-Nt) (D)

and, Field D3 (CSWF-Till) (E). Stress values for NMDS are shown in the bottom left.

Lines are depicting convex hulls enclosing all samples pertaining to the two temperature of incubation used for the baiting technique...... 64

Figure 2.5. Relative abundance based on cumulative sum scaling normalized counts

(n=10) of Phytophthora, Phytopythium and Pythium species present in the rhizosphere

soil of soybean seedlings following incubation at 15 and 25°C when using a baiting

technique...... 65

Figure 2.6. Distribution of Pythium sp. recovered using a culture depended approach

from five research plots after incubation at 15 and 25 °C. Isolates were recovered from

seedlings three days after flooding for 24 hours and after introduction of the susceptible

soybean cultivar Sloan. Identification of species was performed using the ITS6 and ITS7

primers which only amplified the ITS1 region of the rRNA gene. A total of five

symptomatic seedlings per field was collected for each temperature...... 66

Figure 2.7. Aerial picture of the 200-acre research farm located in Wood county

northwest Ohio...... 67

Figure 2.8. Prevalence of taxa versus total counts. Each dot represents one OTU

belonging to different Phyla after normalization using the cumulative sum scaling

approach...... 68

Figure 2.9. Species of Phytophthora, Phytopythium, and Pythium that have been found pathogenic in soybean and corn in the United States...... 69

xviii

Figure 3.1. The counties (environments) sampled in Ohio during this study that were

selected based on reported seedling disease incidence. Counties with black circle were

sampled both years; yellow triangles only in 2017; and green squares only in 2018. .... 112

Figure 3.2. Example of soils found in Ohio with high clay content. Soils after heavy

rainfall, retain water and enable disease development caused by Phytophthora,

Phytopythium and Pythium...... 113

Figure 3.3. Field assessment of the soybean cultivars Kottman, Lorain and Sloan, with different levels and types of resistance, across eleven environments in Ohio. (A) Early plant population was obtained at V1-V3 soybean growth stages while (B) yield was measured at soybean growth stage R8. Analysis of variance showed significant differences for variety, location and the interaction of variety*location (P-values <0.001).

Means followed by the same letter are not significantly different based on the Fisher’s protected LSD test. Bars represent standard deviation of the mean...... 114

Figure 3.4.Number of isolates of Phytophthora, Phytopythium and Pythium recovered from soybean seedlings of the cultivars with different levels and types of resitance

(Kottman, Lorian and Sloan) using a direct isolation technique. Seedlings were collected at V1-V3 growth stage across the eleven environments in Ohio during 2017 and 2018.

Plates were incubated at 20 oC, and species were identified by amplifying the rRNA gene

with primers ITS1 and ITS4...... 115

Figure 3.5. Pathogenicity assay of Phytophthora, Phytopythium, and Pythium isolates

recovered from soybean seedlings in 2017. The soybean cultivars Kottman, Lorain and

Sloan, with different levels and types of resitance were tested using the root cup assay

xix

method. Asterisk represents a significant reduction in root weight (P<0.05) when

compared to the non-inoculated control. Bars represent standard deviation from the mean.

...... 116

Figure 3.6. Pathogenicity assay of Pythium sp. CAL-2011f, isolate recovered from

soybean seedlings in 2018. The soybean cultivars Conrad, Kottman, Williams and Sloan,

with different levels and types of resitance were tested using the root cup assay method.

Asterisk represents a significant reduction in root weight (P<0.05) when compared to the

non-inoculated control. Bars represent standard deviation from the mean...... 117

Figure 3.7. Disease developed on soybean cultivars Sloan, Conrad, Williams and

Kottman fourteen days after planting using a root cup assay method. Roots are showing

rotting symptoms compared to the not inoculated control (NT-Control)...... 118

Figure 3.8. Optimal temperature for growth of Pythium sp. CAL. The optimal

temperature for mycelia growth was reached at 25 o C (n=6). Temperature of 37 o C was

tested but plates did not grow...... 119

Figure 3.9. Shannon’s diversity index for the species Phytophthora, Phytopythium and

Pythium detected in the rhizosphere of the cultivars Kottman, Lorain and Sloan, with

different levels and types of resistance, across 11 field environments in Ohio. Shannon’s

diversity index was calculated using the VEGAN package in R from non-normalized

data. Environments DEF25dap and VW25dap were sampled at growth stage V3-V5. . 120

xx across 11 field environments in Ohio. Colors represent environments sampled and shapes represent cultivars. Environments DEF25dap and VW25dap were sampled at growth stage V3-V5. Permutation analysis showed environments significantly contributing to the community composition. Lines are depicting convex hulls enclosing all samples pertaining to the same environment...... 121

Figure 3.11. Relative abundance based on cumulative sum scaling normalized counts

(n=8) of the species Phytophthora, Phytopythium and Pythium detected in the rhizosphere of the cultivars Kottman, Lorain and Sloan with different levels and types of resistance, across 11 field environments in Ohio (n=8)...... 122

Figure 3.12. Prevalence of taxa versus total counts. Each dot represents one OTU belonging to different Phyla after normalization using the cummulative sum scaling appraoch...... 123

xxi

1. Chapter 1: Literature Review

Soybean (Glycine max (L.) Merr.) belongs to the Fabaceae family of legumes. Native

to East Asia, soybean is among the most important food crops produced worldwide due to high levels of protein (36%), lipids (19%), and dietary fiber (9.3%) (USDA 2016).

Soybean meal is primarily used for animal feed while soybean oil is used for food, biodiesel

and industrial uses. Soybeans are produced in the tropics, subtropics and temperate areas

due to its wide range of optimal temperature for growth between 20 to 28oC. In the

Americas, soybean was introduced in the early 1800s (Singh 2010) and since then, it has

become one of the most economically important crops. Currently, the United States (U.S.),

Brazil, and Argentina are the leading producing countries while China ranks as the number

one importer of soybean (FAOSTAT 2016; USDA 2016b). In the U.S. 123 M metric tons were produced in 2018 (USDA-NASS 2019) making it a leading commodity. The North

Central Region is responsible for 80% of the soybean production and the state of Ohio produces 17% of the nation’s soybean with a total of 90.1 M acres planted in 2017 and contributing 107 B dollars to the state’s economy (Ohio Soybean Council, 2019).

Soybean production in Ohio is affected by a variety of factors including weeds, insect pest, nutrient deficiencies and pre and post-harvest diseases (Beuerlein and Dorrance,

2004; Allen et al. 2017). Of all these, diseases caused by bacteria, nematodes, fungi, oomycetes, and viruses have been reported affecting soybean in Ohio. However, not all cause detrimental effects to yield. Some diseases observed at lower frequency include those caused by bacterial and viral pathogens. In Ohio, Pseudomonas syringae and Xanthomonas

axonopodis pv. glycines which cause bacterial spot and bacterial speck, respectively have

been reported. Although not prevalent, yield losses in the U.S. due to bacterial infections

reached up to 463 K metric tons from 2010 to 2014 (Allen et al. 2017). Similarly, Alfalfa

mosaic virus, Bean yellow mosaic virus, Soybean vein necrosis virus, Tobacco ringspot

virus and Tobacco streak virus have been found widespread in Ohio (Han et al. 2016). If

present in the field, symptoms may include seed coat mottling, stunting, leaf distortion, and

characteristic browning and curling of the terminal branch. Among the most yield limiting

diseases affecting soybean, the soybean cyst nematode (SCN) (Heterodera glycines) and seedling diseases are of major importance. From 2010 to 2014 yield losses due to SCN in the US reached 16 M metric tons while seed and seedling diseases caused by Rhizoctonia,

Pythium, Fusarium and or Phomopsis caused yield losses of 6.5 M metric tons (Allen et al. 2017). In Ohio, SCN has been reported at high levels (>5,000 eggs/ 100 cc of soil) in approximately 6 to 10% of the fields sampled (Lopez-Nicora et al. 2016). Seedling diseases

are also prevalent in Ohio and species of Pythium, Phytopythium, and Phytophthora

predominated across several surveys (Broders et al. 2007, 2009; Eyre 2016; Vargas- Loyo

2018, Navarro 2019 (Chapter 3)).

1.1. Oomycete overview: Phytophthora, Pythium and Phytopythium

Oomycetes encompass a wide range of organisms that are found in all landscapes

including agricultural fields (Fry and Grünwald 2010; Martin and Loper 1999; Robideau

et al. 2011; Schroeder et al. 2013). They are closely related to brown algae and diatoms,

but their filamentous growth resembles that of true fungi. Many members have saprophytic

lifestyles, yet some are very important plant pathogens. Species of the genus Phytophthora

(Ph), Pythium (Py), Phytopythium (Pp) and Globisporangium, often referred to as watermolds, are among the most important members of oomycetes due to their pathogenicity and economic impact on a diversity of crops including soybean (Hendrix and

Campbell 1973; Lévesque & de Cock 2004; Robideau et al. 2011; Uzuhashi et al. 2010).

Taxonomic classification of oomycetes species is still under revision and some members

have been recently reclassified. For example, the genus Globisporangium encompasses

species with globose or round sporangia previously classified as Pythium (Uzuhashi et al.

2010). Similarly, Pythium Clade K has been reclassified as Phytopythium since they are

phylogenetically divergent to other members of Pythium and exhibit morphological

features like both Phytophthora and Pythium (de Cock et al. 2015).

Phytophthora, Phytopythium and Pythium produce overwintering structures known as oospores. In heterothallic or homothallic species, oospores are produced when the oogonia is fertilized by the antheridia. Oospores have thick cell walls that can form as early as 3 to

4 days for some species in culture (Erwin and Ribeiro 1996). In the soil they can survive harsh environments, but germination is greatly affected by high temperatures (Fry and

Grunwald 2010). Oospores will form inside the host tissue, which then get incorporated into the soil after decomposition. When conditions are favorable, mainly driven by temperature and moisture levels, oospores break dormancy and germinate (Erwin and

Ribeiro 1996). Mycelia can directly infect root tissue or develop into sporangia. Sporangia will then produce zoospores, which are characterized by the presence of a flagellum that

3 allows them to move in water films. Plant chemical signals such as isoflavones, sucrose and amino acids are involved in zoospore chemotaxis (Hosseini et al. 2014), encystment and germ tube orientation (Morris et al. 1998). Not all species of oomycetes will produce zoospores and in those cases sporangia will directly produce the germ tube. Others produce chlamydospores which in the absence of overwintering structures will allow them to survive long periods of time (Fry and Grunwald 2010).

1.2. Distribution of Phytophthora, Pythium and Phytopythium

Surveys of seedling pathogens in the U.S. have been critical for the identification of pathogenic species within Phytophthora, Phytopythium and Pythium. These surveys have identified the most common and aggressive species to assist with germplasm and fungicide screening, and the development of management practices. In the genus Phytophthora, two species are pathogens of soybean. Phytophthora sojae (Kaufmann and Gerdemann 1958), was first observed in Indiana in 1948 and in Ohio in 1951 causing Phytophthora root and stem rot of soybean. Now, many different pathotypes have been reported across Iowa,

Indiana, Illinois, Kentucky, Michigan, Missouri, Nebraska, New York, Ohio and South

Dakota (Dorrance et al. 2016), which represent a major problem when trying to manage this disease. The second, Phytophthora sansomeana was reported in Oregon and Indiana affecting several hosts (Hansen et al. 2009) and reported affecting corn and soybean in

Ohio (Zeleya-Molina et al. 2010), Minnesota (Radmer et al. 2017) and across the North

Central Region (Rojas et al. 2017). In the genus Phytopythium, the species Pp. delawarense

(Broders et al. 2009; De Cock et al. 2015), Pp. helicoides, , Pp. litorale (Broders et al.

2007, 2009a, b; Dorrance et al., 2004) and most recently Pp. mercuriales (Eyre 2016;

Vargas-Loyo 2018) have been reported as pathogens of soybean and have been recovered

from numerous fields in Ohio. Similarly, Pp. boreales, Pp. litorale, Pp. megacarpum, as

well as one unknown Phytopythium spp. were recovered from Minnesota (Radmer et al.

2017). Other species isolated from soybean seedlings were Pp. chamaehyphon and Pp. aff.

vexans (Rojas et al 2017). In the genus Pythium, more species has been reported in the

U.S. For example, Dorrance et al. (2004) recovered Py. catenulatum, Py. irregulare, Py.

paroecandrum, Py. splendens and Py. torulosum from soybean and corn seedling across three fields in Ohio. Pythium splendens was recovered more frequently across all fields, while Py. catenulatum was the most abundant species in two out of the three fields. In a later survey in Ohio from both soybean and corn, Broders et al. (2007) recovered a total of

124 isolates across 11 different species of Pythium. The species Py. echinalatum, Py. ultimum var. sporangiiferum, and Py. helicoides, were only recovered from soybean while,

Py. graminicola was only recovered from corn. The remaining of the species including Py. attrantheridium, Py. dissotocum, Py. inflatum, Py. sylvaticum, and Py. torulosum, and Py. ultimum var. ultimum were recovered from both soybean and corn. However, in a survey conducted in 2009, a greater number of species were recovered when using a soil baiting technique (Broders et al. 2009). The species recovered using this technique included Py. pleroticum, Py. perplexum, Py. longandrum, Py. aphanidermatum, Py. arrhenomanes, Py. middletonii, Py. oligandrum, Py. orthogonon, Py. hypogynum, Py. parvum and Py. vanterpolii. A similar survey in Minnesota identified Py. aristosporum, Py.

attrantheridium, Py. coloratum, Py. dissotocum, Py. heterothallicum, Py. intermedium, Py. irregulare, Py. lutarium, Py. minus, Py. oopopillum, Py. perplexum, Py. sylvaticum, and

Py. ultimum (Radmer et al. 2017). Similarly, in North Dakota, Zitnick-Anderson and

Nelson (2014) reported the same species in addition to Py. arrhenomanes, Py. debaryanum,

Py. diclinum, Py. hypogynum, Py. inflatum, Py. kashmirense, Py. nunn, Py. orthogonon,

Py. periilum, Py. rostratifingens, Py. terrestris, Py. viniferum, and Py. viola. The species,

Py. sylvaticum, Py. attrantheridium, Py. irregulare, Py. heterothallicum, Py. oopapillum

and Py. ultimum var. ultimum have been the species recovered consistently across all

surveys (Broders et al. 2007, 2009; Radmer et al. 2017; Rojas et al. 2017; Zitnick-Anderson

and Nelson 2014) suggesting that these may be core pathogens of soybean. In Minnesota,

Py. ultimum var. ultimum was the most aggressive in both soybean and corn and Py.

sylvaticum and Py. irregulare were the most aggressive in soybean while Py. irregulare

was the second most aggressive in corn (Radmer at al. 2017). This was also reported by

Schlub and Schmitthenner (1978) and Schlub and Lockwood (1981) in earlier studies of

the causes of pre-emergence seedling rot by these pathogens. Based on these results,

species distribution and diversity are variable among soybean producing states with

aggressiveness towards soybean varying at a species level.

1.3. Factors affecting species distribution

The species diversity and distribution of Phytophthora, Phytopythium and Pythium has

been suggested to be influenced by soil edaphic factors and environmental conditions. In

the survey conducted in Ohio which used a high throughput baiting process more than

5,000 isolates were identified representing 21 species of Pythium. From this, five distinct

communities were observed, and the establishment of these communities was significantly

influenced by the clay and silt content in the soils. Additionally, other components such as

pH, calcium, magnesium and organic matter content, and cation exchange capacity of the

soils were also significantly associated with the community composition of Pythium. In the

2011 and 2012 survey of fields in the North Central Region of the U.S. as well as Canada,

similar results were observed (Rojas et al. 2017a). Here, community composition of

oomycetes was affected by latitude, precipitation, temperature, clay content, soil pH, and

soil water content. In addition, higher levels of zinc in the soils were reported to favor Py.

ultimum, higher cation exchange capacity (CEC) to favor Py. kashmirense and both CEC

and carbonate exchange to favor Py. heterothallicum and Py. irregulare (Zitnick-Anderson et al. 2017).

Agronomic practices, including tillage and crop rotation, can also play a role in

species abundance and prevalence. For example, in fields with history of seedling disease

caused by Phytophthora sojae and Pythium spp.; Schmitthenner and VanDoren (1985)

reported that tillage practices that increased soil drainage and decreased soil moisture, can

lower disease incidence. Similarly, crop rotation has also been reported to influence

propagule density of Pythium in wheat fields (Pankhurst et al. 1995) by changing nutrient

levels in the soil and exposing the pathogens to different root exudates. Similarly, Zhang

et al. (1998) compared fields with different rotations of continuous corn, continuous

soybean and, corn-soybean and found that the incidence of Pythium significantly increased

7 in fields that had corn-soybean or continuous soybean rotations. However, in fields where continuous corn was planted, lower levels of Pythium were found potentially due to a switch to a saprophytic lifestyle when the host was not encountered (Zhang et al. 1998).

Nonetheless, some species of Pythium have been reported as pathogens of corn (Broders et al. 2007; Dorrance et al. 2004; Matthiesen et al. 2016; Rojas et al. 2019).

Although soil edaphic factors and latitude may contribute to oomycete community composition, pathogenicity or aggressiveness are not necessarily affected. Hendrix and

Campbell (1973) reported that temperature played an important role during disease development and has been reported in other studies as the major factor influencing pathogenicity and aggressiveness. The influence of temperature was studied using isolates of Pythium recovered from fields in Minnesota (Radmer et al. 2017). Here some species were less aggressive towards soybean when exposed to high temperatures. Similarly, disease development in soybean for the species Py. debaryanum and Py. ultimum, was favored by lower temperatures of 15 to 20°C (Thomson et al. 1971). However, for the species Py. aphanidermatum, warmer temperatures (between 24 and 36°C) increased disease severity towards soybean (Thomson et al. 1971). In Iowa, temperatures of 13,18 and 23°C, were used assess aggressiveness of 21 isolates of Pythium towards soybean and corn (Matthiesen et al. 2016). Lower temperatures (13°C) increased the aggressiveness of

Py. torulosum while higher temperatures (18 and 23°C) increased aggressiveness of Py. sylvaticum. In a similar study, an increased susceptibility to damping- off was observed in soybean plants inoculated with Py. sylvaticum when the cold stress period began 2 or 4 days after planting (Serrano and Robertson 2016). In addition, in the presence of root

exudates, Py. sylvaticum had higher numbers of germinated sporangia at 18°C, suggesting that lower temperatures affect reproduction and growth of the pathogens and enables it to better infect the host (Serrano and Robertson 2016).

1.4. Disease management for Phytophthora and Pythium

With more than 50 races of Ph. sojae described and over 200 pathotypes identified

(Dorrance et al. 2004; 2016) in addition to the vast diversity of species of Pythium

encountered in the U.S., the development of effective and long-lasting management

methods becomes challenging. Fungicide seed treatment, primarily, mefenoxam (Apron

XL; Syngenta Crop Protection, Greensboro, NC), and ethoboxam (Intego Solo; Valent,

Walnut Creek, CA), had shown to protect seedlings at early stages, (Broders et al. 2007;

Radmer et al. 2017). However, specificity of the active ingredient to certain species, and

the diversity found in the field, minimizes the efficacy of these fungicides (Ellis et al.

2013). To better manage diseases caused by Phytophthora, Phytopythium and Pythium an

integrated disease management approach is recommended. This includes a combination of

fungicides seed treatment paired with host resistance (Anderson and Buzzell 1982; Bradley

2008; Dorrance et al. 2003, 2009; Urrea 2013). In soybean, three types of resistance have been used to manage Ph. sojae and defined as single dominant R genes (Rps, resistance to

Ph. sojae), root resistance and, partial resistance (Mideros et al. 2007). The R -mediated resistance interact directly with the Ph. sojae effector proteins (Avr) following the gene for gene theory (Flor 1971). As for root resistance and partial resistance they are both

expressed in the roots and are inherited quantitatively (several genes contributing to the

resistance phenotype). While partial resistance is expressed as reduced colonization, root resistance will exhibit almost complete resistance (Dorrance et al. 2003; Mideros et al.

2007). Partial resistance has been found effective in managing Pythium spp. (Klepadlo et al. 2019; Rod et al. 2018; Rosso et al. 2008; Rupe et al. 2011; Scott et al. 2019; Urrea et al.

2017) while both partial and complete resistance are used to manage Ph. sojae.

In soybean, quantitative trait loci (QTL), are regions in the genome that are associated with a specific phenotype. In soybean, many QTL’s have been found responsible for the resistance phenotype against Phytophthora and Pyhtium. For example, Schneider at al.

(2016), identified QTL’s towards Ph. sojae from 1,395 PIs originated from South Korea using a genome wide association mapping approach. Based on gene annotation, candidate genes found in this region are putatively involved in pathogen morphology and development (Schneider et al. 2016). Similarly, Ellis et al. (2013) evaluated 65 different soybean genotypes in order to identify and characterize sources of resistance towards Py. irregulare. Here one-third of the genotypes tested exhibited moderate to high levels of resistance from which PI424354 showed the highest level of resistance and two QTL were mapped to chromosome 1 and 6. A more advanced ‘Conrad’ x ‘Sloan’ F9:11 recombinant

inbred lines population was also evaluated for resistance towards Py. irregulare and 2 QTL

were found that confer resistance to Py. irregulare (Stasko et al. 2016). In another study,

approximately 300 genotypes of soybean from a nested association mapping population and other genotypes from OSU, Missouri and Virginia were screened for resistance towards Py. ultimum var. ultimum and Py. ultimum var. sporangiiferum (Balk 2014).

Within the genotypes tested, Dennison and Hutcheson among others exhibited high level of resistance to Py. ultimum var. sporangiiferum, while Williams, Kottman and Wyandot had high levels of resistance towards Py. ultimum var. ultimum. Additionally, two QTL’s were mapped from a recombinant inbred lines population derived from a cross of

‘Magellan’ and ‘PI 438493B’ towards Pythium ultimum var. ultimum (Klepadlo et al.

2018). Scott et al. (2018) also identified a QTL conferring resistance to Py. ultimum var. ultimum in addition to others conferring resistance towards Py. irregulare and Py. ultimum var. sporangiiferum from a nested association mapping population. The partial resistance that has been identified is only effective towards some Pythium species but not all.

Nonetheless since many genes contribute to resistance in a QTL, this lowers the risks of pathogen adaptation (Dorrance 2016).

1.5. Deployment of “Rps” genes in the United States

The cultivars Arksoy, Blackhawk, CNS, Dorman, Harlon, Illini, Monroe, and Mukden were the first ones identified to manage Ph. sojae (Bernard 1997). Following inoculation with Ph. sojae a hypersensitive response develops, which is often observed in monogenic race-specific resistance (complete resistance). To date, more than 30 Rps genes have been identified in soybean across 10 chromosomes. However, only Rps1a, 1b, 1c, 1k, 3a, 6 and

7 have been deployed commercially (Dorrance 2018; Grau et al. 2004; Slaminko et al.

2010). The Rps1 locus on chromosome 3, has five different alleles that were designated with a letter following the locus number (Rps1a, Rps1b, Rps1c, Rps1d and Rps1k). The

allele Rps1a was the first R gene discovered against Ph. sojae (Bernard et al. 1957) and during the 1960’s cultivars containing Rps1a were planted across the north central U.S.

(Grau et al. 2004). By the 1980’s Rps1a in addition to Rps1c were the two genes most commonly incorporated into soybean cultivars (Grau et al. 2004). However, isolates with a virulent response towards these two genes were commonly identified by the 1990’s

(Abney et al. 1997; Dorrance et al. 2003; Kaitany et al. 2001; Schmitthenner et al. 1994).

When studying the population diversity and structure of Ph. sojae in over 200 isolates collected from fields across Iowa, Ohio, South Dakota and Missouri, pathotype variability was high and none of the Rps genes or gene combinations provided control to all of the different populations found at this four states (Dorrance et al. 2016; Stewart et al., 2016). Through these finding, the authors suggested that numerous sub-populations may exist in the field or region and that each will presumably have different effectors that could lead to a poor management of the disease. Thus, understanding of the molecular

mechanism used to adapt to the deployed Rps genes becomes a crucial need to develop effective practices for farmer to manage the disease. Some of the first proposed factors driving adaptation included mutations, infrequent outcrossing of the pathogen (Goodwin

1997), and or the presence of a mixed reproductive system which ensures recombination of alleles (McDonald and Linde 2002). However, most recent studies have shown that copy number variation, gene- silencing, sequence polymorphism, presence of transposable elements close to effector regions and the presence of Avh genes, are some of the mechanisms used by Ph. sojae to adapt to specific Rps genes (Dong et al. 2009, 2010,

2011a, b; Na et al. 2014; Qutob et al. 2009, 2013; Shan et al. 2004; Song et al. 2013; Yin

et al. 2013). In addition, evidence exist that these mechanisms are controlled by epigenetic factors and inherited in a non-Mendelian way (Qutob et al. 2013). To date, cultivars with

Rps1a are still effective in Iowa while more than 90% of the isolates from Ohio have

virulence towards this gene.

1.6. Pathogenicity mechanisms of Phytophthora sojae

Mechanism used by this pathogen to evade Rps1a response have been previously

studied. Qutob et al. (2009) reported that strain P6492 (race 2) had two copies of Avr1a,

and one copy of the pseudogene Avh72 in the Avr1a locus, in tandem repeats of 5 kb.

Additionally, in isolate P6492, the nucleotide sequence for Avh72, Avr1c and Avr1a share similarities among them specially in the 5’ end of the gene. However, Avr1a differs at the

3’ end of the gene thus distinguishing it from the other two genes in the cluster (Na et al.

2014; Qutob et al. 2009). Transient expression of Avr1c alleles from strains P6497, and

P7064 triggered cell death in leaves of the isoline L75-3735 carrying Rps1c (Na et al.

2014). However, transient expression of Avr1c from strain P7074 did not caused cell death

on the isoline L75-3735. In addition, transient expression of Avr1a into isoline L75-3735,

exhibited a hypersensitive response, but these did not occur when Avr1c were expressed in leaves carrying Rps1a. This finding suggests that gain of virulence can be mediated by gene-silencing of effector genes and copy number variation (Qutob et al. 2009; Na et al.

2014).

1.7. Community characterization using a metabarcoding approach

Large scale surveys are important to assess which pathogenic species are most

prevalent across a region. This information has then been utilized for the establishment of

disease management practices that can limit losses. However, many samples must be

collected to truly determine species diversity. In order to be able to recover a greater

diversity of species, numerous isolation conditions (ie: temperature and media) are needed

to meet the different growth needs of the species. With advances in sequencing technology, detection of microorganism from soils can now be evaluated in a high throughput manner.

One approach, referred to as metabarcoding or amplicon sequencing, has been beneficial in the profiling of microbial communities in general, specifically those associated with

disease development (Bakker et al. 2017; Pérez-Jaramillo et al. 2019; Redekar et al. 2018;

Rojas et al. 2019; Shi et al. 2019). For example, Pérez-Jaramillo et al. (2019) studied the

rhizobacterial communities of eight accessions of common bean including both wild and

modern cultivars (Phaseolus vulgaris L.) grown under native and agricultural soils from

Colombia. Regardless of the soil type, Proteobacteria were commonly associated to

common bean representing 68.8% of the mean relative abundance. Also, co-occurrence

network indicated that some taxa in the rhizosphere of common bean accessions were more

complex when grown under native soil compared to the agriculturally managed soils

(Pérez-Jaramillo et al. 2019). In another study, the metabarcoding approach was used to

profile the microbial communities associated with soybean and rice rhizosphere planted in

soils with high levels of organic matter in China. This study found that plant genotype had

a greater effect on the fungal community composition than to the bacterial communities.

Wild accessions had a higher abundance of beneficial symbionts and lower abundance of

pathogens (Shi et al. 2009). In addition, metabarcoding approach was used to test the

effects of genotype on the bacterial microbial community associated with Arabidopsis

(Micallef et al. 2009), barley (Hordeum vulgare L.) (Bulgarelli et al. 2015), wild mustard

(Boechera stricta) (Wagner at al. 2015), sweet potato (Ipomoea batatas L.) (Marques et al.

2014) and potatoes (Solanum tuberosum L) (Inceoglu et al. 2010).

Metabarcoding has also been used for detecting and characterizing oomycete

communities. For example, Bakker et al. (2017) used the metabarcoding approach to enable

the detection of two previously uncultivated species, Py. volutum and Pythium sp. F86, that were associated with rye cover crops. Further validation using quantitative PCR and modified culture-based methods confirmed that this species was highly abundant on corn seedlings (Bakker et al. 2017). Similarly, Rojas et al. (2019) used a metabarcoding approach, with the coxI gene of the mtDNA to determine oomycete species associate with corn fields in Michigan. Sequences of the genera Phytophthora and Pythium were detected with both metabarcoding approach and direct isolation; however, Saprolegnia,

Aphanomycetes and Achlya were only detected in metabarcoding data but were not isolated from the corn seedlings. Metabarcoding can also be used to determine the spatial and temporal dynamics of oomycetes. For instance, Redekar et al. (2018) detected

Phytophthora, Phytopythium and Pythium in recycled irrigation water of a commercial container nursery in Oregon over the course of 1 year. Phytophthora ramorum was detected using a metabarcoding approach and its presence confirmed using quantitative real-time

PCR with specific probes. Phytophthora kernoviae was also detected from metabarcoding data, but was negative when using species specific probes, most likely due to the presence of unknown but closely related species (Redekar et al. 2018). Finally, Larousse and Galiana

(2017) proposed that functional characterization of the microbial network coupled with phylogenetic and ecological studies of the microbial communities in the rhizosphere using a metabarcoding approach can be used to better understand disease epidemiology in these systems.

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2. Chapter 2. The Effects of Incubation Temperature and Production Practices on Phytophthora, Phytopythium, and Pythium Communities of Soybean

2.1. Abstract

Phytophthora, Phytopythium, and Pythium cause detrimental effects to soybean yields when favorable conditions occur. More importantly, more than one species has been recovered from symptomatic seedlings or roots, suggesting that these occur as species complexes. The community diversity of Phytophthora, Phytopythium, and Pythium was compared from soils collected from the same 81 ha farm, but with a different history of production practices. A soil baiting technique at 15 or 25oC was used to assess the effect

of temperature on the community composition with a metabarcoding approach paired with

direct isolation methods. Regardless of temperature or agronomic practice, the species Py.

sylvaticum, and Py. ultimum were isolated through the baiting procedure and these along

with Py. acrogynum, Py. attrantheridium, and Py. heterothallicum were detected with

amplicon sequencing. There were also distinct communities among the fields with different

agronomic practices. Pythium arrhenomanes and Py. inflatum, were found in greater

abundance under continuous corn while Ph. sojae was higher in the field planted to soybean corn rotation. There were also differences in community composition due to temperature, six Pythium species were abundant at 15oC while another six were favored by 25oC. This

study demonstrated that the diversity of Phytophthora, Phytopythium, and Pythium,

associated with soybean roots can be influenced by temperature and potentially production 27 practices which may explain in part the differences among the many surveys that have taken place as well as the importance previous production practices may be having on the community composition within a field.

2.2. Introduction

Oomycetes encompass a wide range of organisms that are found in many niches including agricultural fields (Fry and Grünwald 2010; Martin and Loper 1999; Robideau et al. 2011; Schroeder et al. 2013). They are closely related to brown algae and diatoms, but their filamentous growth resembles that of true fungi. Many members have saprophytic lifestyles, yet some are very important plant pathogens. Species of the genus Phytophthora

1973; Lévesque & de Cock 2004; Robideau et al. 2011; Uzuhashi et al. 2010). The genus

Globisporangium encompasses the species with globose or round sporangia previously classified as Pythium (Uzuhashi et al. 2010). This study relies on older databases for a metabarcoding approach for identification prior to the change of the genus name; thus, we will continue to refer to members of this genus as Pythium.

In the North Central Region of the United States, more than 25 species of Pythium,

Ph. sansomeana and Ph. sojae have been reported as important pathogens of soybean and corn (Broders et al. 2007, 2009; Dorrance 2018; Jiang et al. 2012; Radmer et al. 2007;

Rojas et al. 2017a; Zitnick-Anderson and Nelson 2015). Early season symptoms include

pre- and post- emergence damping-off of soybean seed and seedlings, while root rot can

occur through the entire season, with reported losses from 2010 to 2014 of 6.5 B metric

tons in the U.S. (Allen et al. 2017). Management of seed and seedling diseases of soybean

includes fungicide seed treatment, resistant cultivars, as well as agricultural practices such

as crop rotation to prevent rapid buildup of inoculum and tillage to increase soil drainage

(Broders et al. 2007; Dorrance et al. 2004, 2008; Zhang et al. 1998).

Pathogenicity and aggressiveness of several species of Phytophthora,

Phytopythium, and Pythium are known to be greatly influenced by temperature (Hendrix

and Campbell 1973). For example, Thomson et al. (1971) reported that disease development in soybean during infection by Py. debaryanum and Py. ultimum, was favored

by lower temperatures (15- 20°C) while for Py. aphanidermatum, warmer temperatures

(between 24 and 36°C) increased disease severity. Additionally, aggressiveness towards

soybean from isolates of Py. sylvaticum retrieved from fields in Iowa increased at 18 and

23°C, while lower temperatures (13°C) increased aggressiveness of Py. torulosum, and Py.

lutarium (Matthiesen et al. 2016). Similarly, in a study of Pythium isolates recovered from

fields in Minnesota, Radmer et al. (2017) reported that some species were less aggressive

towards soybean when exposed to high temperatures. Pathogenicity and aggressiveness are

also affected by temperature in other genera such as Phytophthora as Ph. sojae’s optimal

growth and pathogenicity is approximately 25°C (Kaufmann and Gerdemann, 1958).

Several traditional surveys of soybean seedling pathogens have evaluated the

association of soil edaphic factors soil pH, calcium, magnesium, organic matter content, or cation exchange capacity with species distribution and community composition of these soybean seedling pathogens (Broders et al. 2009; Rojas et al. 2017b; Zitnick-Anderson et

al. 2017). From a survey in Ohio, 5,000 isolates of Pythium were identified from 88 fields,

and five distinct communities were defined using cluster analysis, and these were

significantly influenced by the clay and silt content of the soils (Broders et al. 2009). Other

components such as pH, calcium, magnesium, organic matter content, and cation exchange

capacity of the soils were also significantly associated with the community composition of

Pythium (Broders et al. 2009). Similar results were reported from 3,418 isolates recovered

from diseased soybean seedlings in a survey of 125 fields in the North Central Region of

the U.S. and Canada (Rojas et al. 2017a). They reported that latitude, precipitation,

temperature, soil clay content, and soil water content were significantly associated with

species abundance and composition (Rojas et al. 2017b). Additionally, Zitnick-Anderson

et al. (2017) reported higher levels of zinc favored Py. ultimum, higher cation exchange

capacity (CEC) favored Py. kashmirense and both CEC and calcium carbonate exchange

favored Py. heterothallicum and Py. irregulare. In media, zinc has been shown to be an

essential component in the development of Pythium oogonia and vegetative growth

(Lenney and Klemmer 1966) but in higher levels of zinc has shown to inhibit the antagonistic effect of Trichoderma spp. (Naar 2006) and zoospore activity (Donaldson and

Deacon 1993).

Agronomic practices, including tillage and crop rotation, can also affect

Phytophthora, Phytopythium, and Pythium species abundance and prevalence. For example, in fields with history of seedling disease caused by Ph. sojae and Pythium spp.

Schmitthenner and VanDoren (1985) reported that tillage practices that increased soil

drainage and decreased soil moisture, can lower disease incidence caused by these

pathogens. Similarly, the recovery of Ph. sojae was greater from fields under conservational till (including no-till, mulch-till, and ridge-till) practices compared to fields with conventional-till in Illinois, Minnesota, Missouri and Ohio (Workneh at al. 1999).

Conservation tillage maintains more than 30% of the crop residue thus, incorporating more inoculum back into the soil after decomposition (Workneh et al. 1998,1999) In addition, compacted no- till fields will retain moisture for longer periods of time which allows oospores to break dormancy and germinate. Crop rotation has also been reported to influence propagule density of Pythium in wheat fields (Pankhurst et al. 1995) by changing nutrient levels in the soil and exposing the pathogens to different root exudates. In addition, crop rotation is also an important factor affecting distribution of these pathogens. For example, the incidence of Pythium significantly increases in fields under corn-soybean or continuous soybean rotations compared to fields planted under continuous corn (Zhang et al. 1998).

Direct isolation or recovery of Phytophthora, Phytopythium and Pythium from soils can be challenging, and a soil baiting method (Erwin and Ribeiro 1996) is often used to assess inter- and intraspecies diversity and abundance as reported in many recent surveys

(Broders et al. 2007; Dorrance et al. 2004; Jiang et al. 2012; Radmer et al. 2017; Zitnick-

Anderson and Nelson 2015). This method combines incubation periods to break dormancy

of the oospores, flooding events to enhance production of asexual structures (sporangia),

and incorporation of a susceptible host to bait pathogenic species (reviewed in Erwin and

Ribeiro 1996). However, many samples must be collected to truly assess the species

diversity of Phytophthora, Phytopythium, and Pythium and the use of this technique alone

can limit recovery, especially if multiple species are present. With advances in sequencing

technologies, detection of microorganism from soils can now be evaluated in a high

throughput manner. One approach, referred to as metabarcoding, has been beneficial in the

detection of oomycete species associated with disease in wheat (Bakker et al. 2017), those

associated with corn (Rojas et al. 2019) and to determine the spatial and temporal

dynamics of oomycetes in recycled irrigation water of a commercial container nursery

(Redekar et al. 2018).

The influence of temperature on pathogenicity and aggressiveness have been

evaluated in controlled greenhouse studies. However, what the effects of temperature and

agronomic practices have on the community composition of Phytophthora, Phytopythium

and Pythium when soil edaphic factors remain constant has not been explored. Thus, the

objectives of this study were to assess the effect of (i) temperature during soil incubation

when using a soil baiting technique and (ii) agricultural practices on the community

composition of Phytophthora, Phytopythium and Pythium. To accomplish these objectives, soils were collected from an 81-ha research farm with similar physical and chemical properties and a metabarcoding approach was used paired with direct isolation methods. In addition, to determine the number of independent DNA extractions necessary to

characterize the communities using a metabarcoding approach, soils baited with a

susceptible soybean cultivar at 15oC were also used to sequence DNA pools of 3, 5 and 10

DNA extractions. The following hypotheses were tested for their impact towards soybean seedling pathogens: (i) temperature will influence the species complexes that are detected;

(ii) species diversity of Phytophthora, Phytopythium and Pythium will be greater in fields with continuous soybean whereas fields under continuous corn will exhibit lower diversity; and (iii) no-till fields will exhibit greater abundance of species of all there genera.

2.3. Materials and Methods

Site description and sample collection. Five separate fields at The Ohio State University

Northwest Agricultural Research Station located in Wood County, were selected for this

study (Figure 2.7). This research farm is located on 81 ha and has predominantly silty clay

and silty clay loam which is very prone to soil compaction and poor drainage with slopes

of 0 to 1 percent. The fields that were sampled were selected based on long term studies

conducted at this location that included soybean breeding plots, and management of Ph.

sojae, in addition to history of tillage, and crop rotation since 2012 (Table 2.1). The

physical and chemical properties were assessed for each field in years 2015, 2016, or 2017

(Table 2.1). The soils from each field all had similar levels of organic matter, phosphorous,

potassium, magnesium and calcium. The cation exchange capacity levels were also very

similar and pH values ranged from 6.3 to 7.1.

From each field, five soil samples of approximately 5.7 L from the top 20 cm were

collected with a shovel during January 2017. The samples were collected randomly from

33 each field and spaced at least 7.6 m apart. The soils were transported to the laboratory and stored at 4oC until processed. Soils were air dried for 24 hours in the greenhouse and then ground into fine particles using the IER Improved Soil Grinder (The Fen Machine Co.,

Cleveland, OH) to homogenize samples and to make the heavy clay soil easier to handle for baiting. After this process, approximately 60% of the particles were less than 2 mm.

Field names were abbreviated to reflect the crop rotation scheme and tillage practice as follow: field TA4 with continuous corn and no till (CC-Nt), field B1 with continuous soybean and tilled (SS-Till), field B6 with corn-soybean-fallow rotation and tilled (CSF-

Till), field D3 with corn-soybean-wheat-fallow rotation and tilled (CSWF-Till) and D15 with corn-soybean-fallow and no till (CSF-Nt).

Soil baiting. Each of the five soil samples from each of the five fields were divided in two and placed into 0.09 L pots. Pots were placed inside long polyvinyl trays and flooded by adding deionized water for 24 h. Water was then drained, and soils were left to dry until moisture levels reached approximately 300 mb matric potential. Pots were then placed in plastic trays for ease of handling, covered with a polyethylene bag to maintain moisture and incubated inside a growth chamber (CMP6010 Adaptis, Conviron, Winnipeg, MB,

Canada) at 15°C or 25°C. Each field was represented by 5 pots at each temperature. After two weeks, a total of 10 seeds of the moderately susceptible soybean cultivar Sloan were placed on the surface of the soil of each pot and covered with wet coarse vermiculite and incubated for three more days. After seed germinated, the pots were moved to the greenhouse, flooded a second time for 24 h, and placed on a greenhouse bench to drain excess water. After 3 days, a total of 5 seedlings were collected from each pot for direct

isolation of pathogens. In addition, the corresponding rhizospheric soil of the 5 seedlings

was collected by shaking the seedlings inside a manila envelope. Seedlings were process

immediately and rhizospheric soils stored at -20oC until processing.

Pathogen direct isolation. Pathogen isolation was performed as previously described by

Dorrance et al. (2007). Briefly; seedling roots were first washed with tap water to remove

debris and then taken to a laminar flow hood. Root tissue was cut into pieces and placed in

70% ethanol for 10 s followed by a 30 s rinse with sterile distilled water. Roots were blotted

dried in sterile paper towel and sections from the edge of the lesions were placed under

PBNIC (V8-media+ pentachloronitrobenzene, iprodione, benlate, neomycin sulfate, and

chloramphenicol) selective media in a 60 x 15 mm petri dish. After 3 days of incubation at

20oC, cultures were examined for growth of coenocytic mycelia, and those without this

characteristic were discarded. Hyphal tips of oomycete-like cultures were transferred to

potato carrot agar (PCA) plates amended with rifampicin antibiotic (100 µg/mL) to inhibit

bacterial growth. Pure cultures were transferred to Whatman vials with PCA for long term

storage at 15oC.

Direct colony PCR and Sanger sequencing. Direct colony PCR was adapted from

Broders et al. (2009) and Kong et al.(2005). Briefly, the internal transcribed spacer (ITS)

1 of the ribosomal RNA gene was amplified using the primers ITS6 (Cook and Duncan,

1997) and ITS7 (Cook et al 2000) from each individual isolate. A master mix solution was

prepared using the Promega GoTaq Polymerase Kit (Promega Corp, Madison, WI). The

solution consisted of 5 µl of 5X colorless reaction buffer, 1.5 µl of MgCl2 (25 mM), 1µl of

dNTP’s (10µM), 1 µl of primers ITS6 (10µl) 5’-GAAGGTGAAGTCGTAACAAGG-3’,

35 and ITS7 (10 µl) 5’-AGCGTTCTTCATCGATGTGC-3’ (Cooke et al. 2000), 0.25 µl of

GoTaq DNA Polymerase (5u/µl), and 13.25 µl of ultra-pure water. Using a 96 well plate,

23 µl of the master mix was aliquoted into each well, and with a sterile pipette tip, approximately 1 mm3 of mycelia from the edge of the plate of each isolate was transferred to individual wells and macerated with the same sterile pipette tips. The plate was immediately placed in a PCR machine with the following parameters: 95oC for 5 min; followed by 30 cycles of 94oC for 1 min; 53oC for 1 min; 72oC for 1 min; and completed with 72oC for 5 min. Quality and quantity of the amplicon was obtained by using both the

A260/A280 and A260/A230 ratios with a spectrophotometer (Nanodrop 3300, Thermo

Scientific, Vernon Hills, IL) and electrophoresis of 2 µl of the PCR product on a 1% (w/v) agarose gel for 1 hour at 90V. The gel was stained with GelRed (Biotum, Fremont, CA), and product visualized using a UV light transluminator.

Enzymatic cleanup of the amplicon was done by mixing 2 µl of ExoSAP-IT™

(Thermo-Fisher, Waltham, MA) with 5 µl of the PCR product followed by a 5 min incubation at 37oC and 15 min incubation at 80oC. After amplicon purification, a total of 3

µl of each individual primer at a concentration of 2 pmol was mixed with 6 µl of 20 ng/µl of the purified product. This mix was then submitted to the Molecular and Cellular Imaging

Center (MCIC) at the Ohio Agricultural Research and Development Center (OARDC) for

Sanger sequencing using both forward and reverse primers. Sequences from both primers were quality filtered and assembled using Codon Code Aligner (Codon Code Corporation,

Centerville, MA). Sequences were then compared to voucher specimens deposited at the

National Center for Biotechnology Information (NCBI) nucleotide non-redundant database

(Levesque and Cock, 2004; Robideau et al. 2011; and Hyde et al. 2004).

Rhizosphere soil DNA extraction. Rhizosphere soil collected from 5 seedlings from each

individual pot, was first ground using a blender (Grainger Inc., Lake Forest, IL) to make samples homogenous and for easier handling. The DNeasy Power Lyzer Power Soil Kit

(Qiagen, Carlsbad, CA) was used to extract the DNA following manufacture’s protocol

with some modifications. Modifications included: the Power Lyzer Homogenizer (Qiagen,

Carlsbad,CA) was set at 4,000 rpm for 45 sec, the incubation of soil at 2°C for 5 min and

the dilution of DNA with 50 µl of solution C6 from the kit. Quality and quantity of DNA

was assessed as described above.

Sequencing of rhizosphere DNA pools from soils incubated at 15oC. To determine how

many DNA extractions should be performed to assess species diversity a total of 10 independent DNA extractions were performed using seedling rhizospheric soils collected

from each pot incubated at 15°C alone. An equimolar pool of 3, 5, and 10 DNA extractions

was then prepared for a final concentration of 5ng/µl. Each sample consisted of two

technical PCR replicates. Pooled DNA was then amplified using the Phusion High Fidelity

DNA Polymerase (New England BioLabs, Ipswich, MA) which minimizes PCR errors.

The reaction consisted of 5 µl of 5X High Fidelity Buffer, 0.5 µl of nucleotide mix (10

µM), 1 µl of each primer ITS6 and ITS7 (2 µM) containing Illumina adapters, 5 µl of

template (5 ng/µl), 0.2 µl of Phusion Taq (1.0 units/50 µl PCR), and 9.3 µl of ultra- pure

water. PCR was performed using the following parameters: 96 °C for 3 min, followed by

25 cycles of 96 °C for 30 s; 55 °C for 30 s; 72 °C for 30 s; and completed with 72°C for 5

min. PCR products were then submitted to the MCIC for library preparation and

sequencing. Based on this result a total of 3 independent DNA extractions was used as a

standard number of extractions. Sequences have been deposited at the NCBI Sequence

Read Archive as experiment SRR10479281.

Sequencing of rhizosphere DNA from soils incubated at 15oC and 25oC. A total of three DNA extraction were performed again using rhizospheric soil collected from seedlings retrieved from pots incubated at 15oC and 25oC. The three DNA extractions were

then pooled equimolarly at a final concentration of 5ng/µl. This was followed by PCR

amplification with primers ITS6 and ITS7 and submitted to the MCIC for a second MiSeq

run. Each sample consisted of two technical PCR replicates. Sequences have been

deposited at the NCBI Sequence Read Archive as experiment SRR10492905.

Library preparation and Illumina MiSeq amplicon sequencing. A second round of

PCR was performed to add the Illumina adapter sequence which contains a unique dual

combination of the Nextera indices for individual barcodes for each sample. A total of 3 µl

of the first PCR product was then used as input for the second PCR reaction. The PCR

products were purified after each PCR amplification using the Agencourt AMPure XP

beads (Beckman Coulter Life Sciences). All the steps for library preparation and cleaning

were carried out on the epMotion5075 automated liquid handler (Eppendorf). The

amplicon libraries were quantified and pooled at equimolar ratios before sequencing. The final pool was purified using the Pippin Prep size selection system (Sage Science) to discard the presence of any primer dimers. The MiSeq sequencing platform (Illumina) was

used for amplicon sequencing at a final concentration of 14.3 pM. Amplicon libraries were

spiked with PhiX libraries (approximately 20%) to allow a more heterogeneous sample and

reduce error in the run introduced by the high levels of similar nucleotides among

oomycetes. The run was clustered to a density of 905 k/mm2 and the libraries were

sequenced using a 300 PE MiSeq sequencing kit with the standard Illumina sequencing

primers. Image analysis, base calling and data quality assessment was performed on the

MiSeq platform.

Metabarcoding data processing. The ITS1 sequence data generated from rhizospheric

soil were processed using the USEARCH pipeline (Bakker et al. 2017; Edgar 2010) and

custom scripts (Appendix C). The removal of the Illumina barcodes as well as merging of

the short pair-end reads was done using BBMerge in BBTools suite version 3 (Bushnell et

al. 2017). Quality filtering was done using the –fastq_filter command in USEARCH with

a threshold of 1% expected number of errors (Edgar and Flyvbjerg 2015). After quality

filtering, the ITS1 region was extracted using the ITSx (Bengtsson-Palme et al. 2013)

software version 1.0.11. De-replication was performed using the fastx_uniques command

to find sets of unique sequences and remove duplicate shorts reads from initial reads set.

De-replicated short reads were then analyzed using the operational taxonomic unit (OTU) approach. For OTU clustering, reads were clustered de novo at the 97% similarity level using the UPARSE distance based greedy-approach algorithm implemented in the - cluster_otus command (Edgar 2013). In order to correct sequencing errors and remove chimeric reads the UNOISE algorithm as implemented in the -unoise3 command was run.

The de-noised sequences were then used to create the OTU table using the –otutab

command. Finally, taxonomy was assigned to OTUs using the –sintax command with

100% cutoff with a custom-made database. The database was custom made and composed

of full ITS sequence accessions retrieved from Robideau et al. (2011), Hyde et al. (2014),

and Levesque and Cock (2004), in addition to internal sequences generated from the

laboratory during previous surveys (Appendix C). Database was trimmed to only the ITS1 region using the ITSx (Bengtsson-Palme et al. 2013) software version 1.0.11. Further filtering steps and data analysis was carried out using the phyloseq (McMurdie and Holmes

2013), MetagenomeSeq (Paulson et al. 2013) and Vegan (Okasen 2019) packages in R

version 3.5.0.

Phylogenetic analysis of the ITS1 among Pythium species. To assess how many of the

species shared identical ITS1 sequences, accession of the ITS (1and 2) and the 5.8 gene of

the nuclear rRNA of voucher specimens used by Levesque and Cock (2004) were retrieved

from NCBI nucleotide database. From these accessions the ITS1 region was extracted

using the ITSx (Bengtsson-Palme et al. 2013) software version 1.0.11. Extracted sequences

were then aligned using ClustalW in MEGA version 7.1. After alignment, pairwise distance

analysis was conducted using the Maximum Composite Likelihood model (Tamura et al.

2004).

Metabarcoding and direct isolation data analysis. Species richness for the isolates recovered through direct isolation was visualized as a proportion of isolates recovered compared to the total number of isolates from each sample. Due to the low number of isolates recovered from each sample, diversity indices were not calculated. Statistical analysis for the two MiSeq runs were analyzed independently. For each, OTU tables were

first subset for Phytophthora, Phytopythium and Pythium only. To assess species richness

and evenness from non-normalized data, the Shannon’s diversity index was calculated using the Vegan package (Oksanen, 2019) as implemented in R (R Core Team, 2019).

Shannon’s diversity index for the different DNA extractions was analyzed using the non- parametric Kruskal-Wallis due to violation of analysis of variance (ANOVA) assumptions.

Main effects were DNA pool, field and incubation temperature. To test if the Shannon’s

index was different between samples and between the incubation temperatures a two-way

ANOVA was done. Temperature and field were used as main effects and technical

replicates were nested inside biological replicates.

Prior to calculating beta diversity, metabarcoding data was normalized to

cumulative-sum scaling (CSS) using the package Metagenomeseq in R (Paulson et al

2013). Normalized data was then converted to relative abundances by dividing the absolute

abundance of each OTU by the total number of sequences per sample. A Bray-Curtis

dissimilarity matrix was then calculated from relative abundance data and the output matrix

used as input data for the nonmetric multidimensional scaling (NMDS) ordination plots. A

permutation analysis of variance (perMANOVA) was conducted with the adonis function

to assess significance of temperature, field, and the interaction of field and temperature in

the community composition (Anderson, 2001). Due to significant interactions of field and

incubation temperature the data was subset and analyzed by field using temperature as the

main effect. Finally, the species richness from the metabarcoding sequencing approach was

visualized as a proportion of reads compared to the total reads found on each sample.

2.4. Results

Comparison and interpretation of species designation based on the ITS1 region of

common Pythium spp. The sequence of 250- 300 bp of the ITS1 region of commonly found Pythium spp. were aligned to compare which species were identical, which is critical

to assess the limits of taxonomy classification of the OTUs from the metabarcoding data

(Table 2.6). Using pairwise comparison of the ITS1 it was found that within Pythium Clade

A, five species share identical sequences. In clade B, which groups many of the reported

pathogens of soybean, complete sequence similarity was also observed. From this clade

Py. folliculosum shares identical sequence with Py. torulosum, and Py. catenulatum.

However, Py. folliculosum is rarely found in Ohio but Py. torulosum has been frequently

recovered. Similarly, Py. arrhenomanes shares identical sequence with Py. aristosporum

the causal agent of root dysfunction in creeping bentgrass and Pythium root rot on wheat

(Chamswarng and Cook 1985). In Pythium clade D the species Py. amasculinum, Py.

oligandrum and Py. aff. hydnosporum shared identical sequences. The sequence of this

ITS1 region is also identical between Py. ultimum var. sporangiiferum and Py. ultimum

var. ultimum which belong to Clade I. From Clade E, Py. acrogynum and Py. hypogynum

share identical sequences. Other species found in this study had more than 1% sequence

difference and were detected by downstream analysis. For those species that have identical

sequences, the name of the first species in the clade was used in the figures and tables.

Effect of the number of DNA extractions on assessment of diversity. The

metabarcoding data of the pool of 3, 5 and 10 DNA extractions of rhizospheric soils

collected from soybean seedling baited in soils incubated at 15 oC consisted of 14,049,548

total reads of the ITS1 region. Total reads for the 3, 5 and 10 pools of DNA consisted of

468,4408, 523,6742 and, 3,520,956 reads respectively. A total of 6,416 OTU's were

obtained from raw data and 6,409 were kept after data normalization. From this total, 120

OTU’s were classified as oomycetes and used for downstream analysis. There were no

significant differences in the Shannon’s diversity index between pools of 3, 5, and 10 DNA

extractions that belonged to the same samples (Kruskal Wallis; P=0.92). However, when

comparing diversity indexes among the different fields these were significantly different

.The Shannon’s diversity index was higher for Fields D3 (CSWF-Till), D15 (CSF-Nt) and

B6 (CSF-Till) compared to B1 (SS-Till) and TA4 (CC-Nt) (Table 2.6).

Based on NMDS plots and statistical analysis (PERMANOVA; P> 0.001) the community composition between the 3, 5, and 10 DNA extraction pools from each sample also did not differ from one another. Again, communities were very different between fields with different rotations and tillage practices (Figure 2.1 and 2.2). In this first sequencing run the species Py. attrantheridium, Py. heterothallicum, Py. sylvaticum and

Py. ultimum where highly abundant (>1,000 reads) in 4 or 5 fields (Table 2.3) and represented 9, 10, 27, and 7% of the total abundance of species, respectively.

The effect of soil baiting temperature and agriculture practices on oomycete

communities. The final dataset for the oomycete metabarcoding approach of rhizospheric

soil collected from soybean seedlings planted in soils incubated at 15 and 25°C consisted

of 1,343,151 ITS1 sequence reads. Read depth for each sample ranged from 232,787 to

294,648 sequence reads. A total of 5,948 OTU’s were first obtained and only 3,563 were

kept after normalization. The primers ITS6/ITS 7 also captured OTUs representing, 13

Phyla including Ascomycota and Basidiomycota (Figure 2.8). Data was further subset for

selected oomycetes, leaving a total of 101 OTU’s classified within Phytophthora,

Phytopythium, and Pythium. This subset of the data was then used for further analysis and

comparisons. A minimum of 100 reads were used as a threshold to determine if a species was present in the sample.

A significant increase (ANOVA; P < 0.001) of 22% in species diversity was

observed from the rhizosphere soils collected from soybean seedlings planted in soils

incubated at 15°C, compared to 25°C (Table 2.4) across all fields. Similarly, when the main effect of field was tested for the diversity index, these were also significantly different

(ANOVA; P < 0.001). Fields with continuous soybean and tilled (B1- SS-Till) and a

corn/soybean/fallow with tillage (B6- CSF-Till) had higher diversity index compared to a

field with corn/soybean/fallow – no till (D15- CSF-Nt), corn/soybean/fallow/wheat – tilled

(D3- CSWF-Till) and continuous corn – no till (TA4- CC-Nt). In the field with

corn/soybean/fallow with tillage (B6- CSF-Till), incubation temperatures of 15°C had the

highest Shannon’s index. Field and temperature also played a role in beta diversity. Based

on the results of PERMANOVA a significant difference in the community structure was

observed between the two temperatures (P = 0.001), among the five fields (P=0.001) and

more importantly for the interaction between temperature and field (P= 0.001) (Figure 2.3).

Due to significant interaction between temperature and field the effect of temperature

individually for each field was plotted (Figure 2.4, A-E). A separation of communities

based on temperature for all fields was observed (PERMANOVA; P< 0.01). Regardless of

the field and incubation temperature, the species Py. acrogynum, Py. attrantheridium, Py.

heterothallicum, Py. sylvaticum and Py. ultimum, were highly abundant. Pythium sylvaticum and Py. ultimum were also recovered from symptomatic seedlings collected from soils incubated at both temperatures and all fields.

Communities associated with agronomic practices using a metabarcoding approach.

The abundance of Phytopythium species was highly impacted by the agronomic practice

used in the field. In fields B1 (SS-Till), D3 (CSWF-Till), D15 (CSF-Nt), and TA4 (CC-Nt) the species Pp. aff. vexans and Pp. mercuriales were not detected (Table 2.5). However, in

field B6 (CSF-Till) these two species were present and favored by incubation temperatures

of 25°C (Figure 2.5). Similarly, Ph. sojae was highly abundant in fields B1 (SS-Till) and

B6-( CSF-Till) across both temperatures and had very low abundance in field TA4- CC-Nt

(Table 2.5).

Communities associated with temperature using a metabarcoding approach.

Temperature also affected the abundance of certain species of Pythium, Phytophthora, and

Phytopythium. For example, soils incubated at 15°C, the species Py. acanthicum, Py.

aphanidermatum, Py. aff. hydnosporum, Py. adhaerens, Py. marsipium, and Py. nodosum

were more abundant compared to soils incubated at 25°C (Figure 2.5, Table 2.5). In

contrast, Pp. aff. vexans, Py. debaryanum, Py. irregulare, Py. monospermum, Py.

perplexum, and Py. violae were more abundant at 25°C than at 15oC. Interestingly, for

some Pythium species temperature affected the abundance depending on the field. For

fields D15- CSF-Nt, D3- CSWF-Till and TA4- CC-Nt, Py. folliculosum (or other species

in Clade B), was more abundant at 15 °C (Figure 2.5, Table 2.5). In contrast, in field B6-

CSF-Till this species was more abundant at 25 °C. Pythium longandrum abundance was

greater in soils incubated at 25°C in field B6- CSF-Till, while more abundance was

observed in fields D3- CSWF-Till and TA4- CC-Nt when incubated at 15°C. In fields B6-

CSF-Till and D3- CSWF-Till, Py. middletonii was more abundant at 25°C and in field

D15- CSF-Nt at 15°C (Figure 2.5, Table 2.5). For Py. minus, soils from fields B1- SS-Till,

D3- CSWF-Till and TA4- CC-Nt was in greater abundance when incubated at soils at 15°C

and greater abundance in field B6- CSF-Till when incubated at 25°C (Figure 2.5, Table

2.5). The species Py. selbyi was first described in Ohio and has been recovered in many

fields across the state. In this study, a greater abundance was observed in soils from fields

B6- CSF-Till and TA4- CC-Nt when incubated at 25°C. However, in fields B1- SS-Till

and D15- CSF-Nt there was greater abundance when soils were incubated at 15°C (Figure

2.5, Table 2.5). The unclassified Pythium species Pythium sp. CAL 2011f, was also present

in different levels across fields depending on the temperature. In field B1- SS-Till, D15-

CSF-Nt and TA4- CC-Nt, this specie was more abundant at 15°C and in fields B6- CSF-

Till and D3- CSWF-Till at 25°C (Figure 2.5, Table 2.5). Pythium insidiosum, although found at a low level, was also influenced by temperature. In fields B6-CSF-Till and D15-

CSF-Nt this species was found above the threshold level when soils were incubated at 25°C

compared to 15°C. In the other fields this species did not reach the threshold level in either

temperature of incubation.

Oomycete communities identified using a culture dependent method. A total of 108 isolates of Pythium representing ten different species were isolated from symptomatic root tissue sampled only three days after planting (Figure 2.6). Based on the ITS1 sequence, Py. sylvaticum from clade F and Py. ultimum from clade I were the most prevalent representing

48 and 36 percent of the total isolates recovered, respectively. Other species recovered at

a lower frequency included: Py. attrantheridum, Py. heterothallicum, Py. hypogynum, Py.

inflatum, Py. middletoni, Py. perplexum, Py. torulosum, and Py. oopapillum. The

temperature that soils were incubated also impacted which species were recovered via

direct isolation. Pythium attrantheridium, Py. oopapillum and Py. torulosum were only

isolated at 15°C while Py. middletoni, Py. heterothallicum and Py. inflatum were only

isolated at 25°C. Agricultural practices also influenced the number of isolates recovered

and changed based on the soil incubation temperature. In field B1-SS-Till, 6 species of

Pythium were isolated from the seedling bait when soils were incubated at 15 °C while,

only 4 species were isolated when incubated at 25°C. A similar effect was observed for

field TA4-CC-Nt where a greater number of Pythium species and total number of isolates

were isolated from the seedling bait in soil incubated at 25°C (Figure 2.6).

2.5. Discussion

Many species of Phytophthora, Phytopythium, and Pythium have been recovered

during surveys from soybean and corn in the North Central Region of the United States

(Broders et al. 2007, 2009; Dorrance et al. 2004; Jiang et al. 2012; Radmer et al. 2017;

Rojas et al. 2017a; Zitnick-Anderson and Nelson, 2015; and Zhang et al. 1998). Using

traditional culturing methods directly from field collected samples or soil baiting these

studies attributed diversity and disease development to numerous factors that included

rainfall after planting, soil edaphic factors and latitude. Often, the effect of these factors on community composition are explored in surveys that cover a wide geographical region,

different environmental conditions prior to sample collection, and from fields that often

have very different soil characteristics. In this study, soil composition was very similar

among the five separate fields allowing us to evaluate the effects of incubation temperature

and agronomic practices on the Phytophthora, Phytopythium and Pythium communities

affecting soybean.

Temperature of soil incubation during baiting affected the community composition

of Phytophthora, Phytopythium and Pythium. Based on NMDS plots, communities within

each field were significantly different between the two incubation temperatures favoring

some species over another. For example, species affected by temperature included Py.

acanthicum, Py. aphanidermatum, Py. aff. hydnosporum, Py. adhaerens, Py. marsipium,

and Py. nodosum which were favored in soils incubated at 15 oC. In comparison, species

Pp. aff. vexans, Py. debaryanum, Py. irregulare, Py. monospermum, Py. perplexum, and

Py. violae were more abundant at soils incubated at 25°C. These communities significantly

affected by temperature could be part of the species complexes affecting soybean and

provide further evidence that temperature at planting time plays a pivotal role in disease

development. The understanding of how this complex form or how they are affected by

other environmental factors should be explored to be able to provide a more targeted disease management practices that could include delay planting.

Agricultural practices including include crop rotation and tillage are often

recommended to manage seedlings diseases of soybean caused by Phytophthora,

Phytopythium and Pythium. These practices are used to reduce inoculum in the field and increase soil drainage, respectively. In this study, we were able to provide further evidence

48 on how these practices affect oomycete communities. For example, it was hypothesized that fields with a continuous corn rotation will exhibit lower diversity levels since the primary host soybean had not been planted. In this study, field TA4 with a continuous corn rotation had the lowest diversity across all the sampled fields. In addition, based on the metabarcoding data, Py. arrhenomanes was more abundant in the continuous corn field compared to other fields. This species has been previously reported at this research farm from which samples were collected for this study. It was reported in association with lower yields of corn in fields with poorly drained soils, continuous cropping, and no tillage (Deep and Lipps 1996). More recently, Rojas et al. (2019) also identified Py. arrhenomanes in soils collected from corn fields in Michigan providing further evidence that pathogenic species of corn are enhanced when fields are under continiuos corn.

It was also hypothesized that fields under continuous soybean will exhibit greater diversity due to presence of the primary host of these pathogens. In addition, this field were expected to have greater diversity due to their history of disease pressure reported across soybean disease management studies and since these fields are often flooded to create conducive environments for these species to proliferate. In this study, fields B1 and B6 which were under a continuous soybean or a corn-soybean rotation with no till, had the greatest diversity. In field B1- SS-Till, Ph. sojae was highly abundant. Additionally, Ph. sojae was also present in fields with a continuous corn, corn-soybean and corn-soybean- wheat rotation, indicating that this species can survive long periods of time even when the host is not encountered. Similarly, species of Phytopythium were only observed in fields

B1- SS-Till and B6- CSF-Till but in very low abundance. This may indicate that crop

rotations schemes that incorporate wheat and with no till can reduce the prevalence of

Phytopythium species. Similarly, Py. inflatum was highly abundant in field B6- CSF-Till

with a corn-soybean rotation and tillage. In 2007, Py. inflatum was recovered in Ohio but

also considered a mild pathogen of soybean and corn (Broders et al. 2007). Since some

species of Pythium can be pathognic to both soybean and corn, this result suggests that

some species could not be managed by using a soybean corn crop rotation alone and that

species may be surviving on the other hosts or as saprophytes in soils. This and other

studies confirm that although continuous cropping can favor a reduction in diversity, it can

also favor one species over another. Thus, the rapid buildup of inoculum of pathogenic

species which can be enhanced by continuous croping, could result in greater disease

pressure overtime (Martin and Loper 1999; Zhang et al. 1998) and lead to significant yield

lossess.

Temperature of incubation during the soil baiting technique also significantly

affected the community composition of Phytophthora, Phytopythium and Pythium. Based

on NMDS plots, communities within each field were significantly different between the

two temperatures of incubation thus favoring some species over another. For example,

species affected by temperature included the Py. acanthicum, Py. aphanidermatum, Py. aff.

hydnosporum, Py. adhaerens, Py. marsipium, and Py. nodosum which were favored in soils incubated at 15 oC. In comparison, species Pp. aff. vexans, Py. debaryanum, Py. irregulare,

Py. monospermum, Py. perplexum, and Py. violae were more abundant at soils incubated

at 25°C. These communities were significantly affected by temperature could be part of

the species complexes affecting soybean. Thus, temperature at planting time plays a pivotal

role in disease development. However, the understanding of how this complex form or how

they are affected by other environmental factors should be explored to be able to provide a

more targeted disease management practices that could include delay planting.

Although soil incubation temperatures and field agronomic practices affected the

species abundance and community composition of Phytophthora, Phytopythium and

Pythium in this study, some species were not affected. For example, among the species

detected from soybean rhizospheric soils and through direct isolation from seedlings, the

species Py. sylvaticum, Py. ultimum, Py. attrantheridium and Py. heterothallicum were

highly abundant across all samples using a metabarcoding approach and direct isolation methods. Among these species, Py. heterothallicum is considered a common soilborne saprophyte (Van der Plaats-Niterink 1981) and in cases where pathogenicity has been

observed (Coffua et al. 2016; Chamswarng and Cook 1985; Gan et al. 2010; Ingram and

Cook 1990; Zitnick- Anderson and Nelson 2015) is hypothesized to be part of a species

complex with Py. glomeratum (Robideau et al. 2011). In this study, Py. glomeratum was

not detected however, the high levels detected in this study suggests that Py.

heterothallicum may be involved in other species complex. These species that were not

affected by the soil incubation temperatures or agronomic practices, have been frequently

recovered in previous surveys across several of the soybean producing states (Broders et

al. 2007; Radmer et al. 2017; Rojas et al. 2017a; Dorrance 2004) and some have also been

reported as highly pathogenic towards soybean at different temperatures (Radmer et al.

2017). The high level of recovery from seedling and the abundance detected using a

metabarcoding approach suggests that these species are core oomycetes species responsible

for seedling disease in soybean and should be the focus for management.

There were sequences of two species, Py. insidiosum and the undescribed Pythium

sp. CAL, detected using a metabarcoding approach that were not isolated from plant tissue

after the soil baiting in this study. Pythium insidiosum known to cause pythiosis in

mammals (De Cook et al 1987), was detected in fields with continuous soybean and is and

is usually found in tropical and sub-tropical climates. It has been also previously reported

in agricultural irrigation water and soil in Thailand (Supadandhu et al 2008). In the U.S., it

has been reported in Southeastern states, in the North and irrigated dry regions in the West

(Mendoza 2008; Presser and Goss 2015). Recently, in a study of warm standing water in

lakes and ponds in North Central Florida, this species was found in 11 of the 19 lakes and

ponds sampled (Presser and Goss 2015. In Ohio, this study is the first to report Pythium

insidiosum from rhizospheric soils of soybean seedlings using a soil baiting technique.

Although this species is not pathogenic towards soybean, this could also be a related

species that shares the same identical ITS 1 sequence and thus, should be further confirmed

using species specific probes. The species Pythium sp. CAL has been previously isolated

from soybean roots grown under PBNIC media at an incubation temperature of 23°C

(Zitnick-Anderson and Nelson 2015) which suggests that warmer temperatures are required for growth. It may also require more time to grow, as our baiting procedure was limited to 3 dap, thus it may have been missed in this study when trying to recover from seedling root tissue. Due to greater abundance found across all temperatures and fields, more studies should be conducted to understand its role in disease towards soybean.

Deciphering the disease complexes that can form when different temperatures are

encountered or when different agronomic practices are used, can enable the development of more targeted management practices. However, five species were not influenced by temperature or agronomic practices and these would represent very good targets for the evaluation of seed treatment products and breeding for disease resistance. Temperature greatly influenced the communities that were identified both through direct isolation and metabarcoding. These results may explain in part the diversity of species recovered from the many surveys across the United States that have attempted to identify the cause of soybean seed and seedling damping-off. In this study, fields that were more intensively

planted with one crop or another favored some species, thus emphasizing again the benefits

that rotation can have. This suggests that different rotation schemes may be a means to avoid favoring or allowing the build-up of inoculum of one species over another but will not impact all species.

Table 2.1.Description of agronomic practices and soil physical and chemical properties of soils collected from the Ohio State University Northwest Agricultural Research Station in 2017. Soil physical and chemical properties were calculated as the mean from 2012 to 2017.

* Soil Physical and Chemical Properties

Field Rotation Tillage OM P K Mg Ca pH CEC

Continuous TA-4 Corn No till 3.5 26.2 202.0 310.0 1950.0 6.2 16.6 Disk, Continuous Moldboard B1 Soybean , Chisel 3.7 29.0 190.0 435.0 2350.0 7.1 15.9

Corn/Soybean/ Chisel B6 Fallow Plow 2.9 53.6 184.6 326.3 1768.6 6.4 13.5 Disk, Corn/Soybean/ Moldboard D3 Wheat/Fallow , Chisel 3.5 39.3 176.6 407.5 2525.0 6.6 17.5

Corn/Soybean/ D15 Fallow No till 3.3 33.5 174.0 307.5 2420.0 6.2 16.9

* Phosphorus, potassium, calcium and magnesium are measured as ppm. Cation exchange capacity (CEC) is measured as meq/100g and organic matter (OM) was measured as the %.

Table 2.2. Shannon’s diversity index for oomycete communities found in pools of 3, 5 and 10 DNA extractions of soils retrieved from the rhizosphere of soybean seedling when using a soil baiting technique and an incubation temperature of 15°C. The index was calcul ated using the Vegan package in R (Dixon 2003).

Shannon’s Diversity Index

* Agronomic Practice P3 P5 P10 Average

B1 2.97 2.67 2.59 2.74 (SS-Till)

B6 2.85 3.87 3.75 3.49 (CSF-Till)

D15 3.26 3.31 3.59 3.38 (CSF-Nt)

D3 3.45 3.42 3.88 3.58 (CSWF-Till)

TA4 3.08 3.01 2.59 2.89 (CC-Nt) * Abbreviation for rotation: (SS-Till) continuous soybean and tilled; (CSF-Till)- corn, soybean, fallow and tilled; (CSF-Nt)- corn, soybean and fallow with no till; (CSWF-Till)- corn, soybean, wheat and fallow and tilled; (CC-Nt) continuous corn with no till.

B1 B6 D15 D3 TA4 a a a a a SS-Till CSF-Till CSF-Nt C-S- No till CSWF-Till bP3 P5 P10 P3 P5 P10 P3 P5 P10 P3 P5 P10 P3 P5 P10 Ph. sansomea 0.00 0.00 0.00 0.00 0.00 0.00 3.58 1.01 0.99 0.00 0.00 0.31 0.00 0.00 0.00 Ph. sojae 886.50 1458.86 1909.59 20017.63 344.73 208.63 936.97 405.70 335.49 252.59 126.68 158.64 375.00 187.00 45.42 Pp. aff. vexans 10.76 2.92 3.26 0.00 6.58 2.27 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Pp. boreale 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.17 2.19 0.00 0.00 0.00 Pp. mercuriale 7.70 0.00 2.62 0.73 6.15 8.16 0.00 0.00 0.00 0.00 0.00 0.15 2.03 1.71 6.63 Py. acanthicum 50.39 124.71 37.95 54.64 78.95 60.49 54.55 133.59 38.84 47.71 52.96 22.44 110.19 141.34 54.80 Py. acrogynum 1238.53 2090.21 1102.77 405.13 465.19 301.10 741.65 1246.39 330.07 3740.14 4133.08 1160.79 333.77 561.50 60.73 Py. adhaerens 0.00 0.00 0.00 0.00 0.39 0.20 0.00 2.38 3.25 1449.77 569.28 217.72 0.42 12.48 0.00 Py. aff. hydnosporum 17.05 28.61 9.03 13.30 295.01 134.56 7.51 24.34 3.30 37.78 38.60 8.85 10.82 36.43 9.51 Py. aphanidermatum 519.40 604.14 186.62 0.00 0.12 1.60 2.23 21.70 2.21 0.00 0.00 0.00 69.29 152.93 75.61 Py. arrhenomanes 11.44 3.74 3.78 6.91 40.74 21.11 0.83 2.90 0.16 21.47 10.98 5.04 63.33 794.41 251.68 Py. attrantheridium 4829.73 6306.99 6020.84 109.01 1099.69 704.79 3952.53 7932.80 2524.05 568.71 569.45 175.53 2362.08 3053.58 1220.62 Py. emineosum 2.22 5.24 2.52 1.86 2.28 1.50 6.50 5.22 1.04 6.48 4.23 2.31 9.37 3.13 2.69 Py. folliculosum 14.91 43.93 16.56 803.12 1969.80 1555.28 1287.26 2686.58 522.12 393.89 506.32 57.12 409.09 717.56 352.65 Py. heterothallicum 4423.70 4135.29 3301.37 246.49 738.51 351.27 1421.62 1548.95 438.86 3486.95 2295.12 729.42 232.29 277.62 236.15 Py. inflatum 68.01 49.64 50.73 202.13 1182.77 437.15 1067.53 2319.66 464.46 27.28 56.54 21.38 273.49 771.32 35.26 Py. irregulare 2.74 0.92 2.33 3.88 5.63 5.89 0.00 0.00 0.25 1.84 6.38 0.77 0.00 0.00 0.00 Py. longandrum 71.26 87.06 102.31 610.21 1245.82 269.35 15.27 53.12 23.13 1.41 13740.35 2600.14 2165.52 3842.73 1498.46 Py. middletonii 18.84 16.65 1.05 113.40 404.53 147.70 3742.32 8980.06 2197.44 118.26 201.52 33.21 47.78 37.68 10.35 Py. minus 295.90 947.93 583.27 33.14 70.04 61.82 1255.75 1257.15 401.42 1182.72 1583.17 359.62 11161.79 13824.55 9829.55 Py. monospermum 25.79 69.50 44.26 112.95 807.68 212.01 74.93 125.87 19.57 183.82 132.02 32.77 497.91 587.44 135.88 Py. oopapillum 101.90 240.39 119.26 0.00 0.00 0.00 8.34 3.63 2.57 8.63 6.00 1.97 0.00 0.00 0.00 Py. orthogonon 920.79 699.99 496.32 5291.57 1849.53 577.31 0.00 68.55 2.31 570.57 325.22 46.84 258.87 688.11 183.10 Py. periplocum 0.00 0.00 7.71 0.00 0.00 0.61 2.04 16.92 1.54 0.00 0.00 0.00 0.00 3.65 0.00 Py. perplexum 1.65 10.45 9.09 0.00 9.65 2.70 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Py. rostratifingens 64.48 80.69 50.19 32.42 29.17 19.99 27.24 27.74 22.45 20.04 18.11 19.35 27.09 26.95 14.82 Py. selbyi 469.89 831.49 2697.90 439.43 1696.62 1231.83 224.11 445.55 108.74 212.74 531.07 114.88 167.56 225.90 127.95 Py. sp. CAL-2011f 267.20 647.35 189.76 1584.32 1539.04 3524.09 22.62 66.82 6.56 119.95 99.69 33.68 1042.42 3273.92 2217.57 Py. sylvaticum 1335.40 3293.71 1985.45 2281.82 3996.00 2273.83 6326.60 6213.62 1741.09 10634.58 10051.30 4940.35 12008.51 11774.26 11113.67 Py. ultimum 5453.97 7475.20 6260.51 552.88 1212.37 119.92 48.91 108.18 24.73 4324.68 3492.47 1189.98 4028.84 7486.06 2662.03 Py. volutum 0.00 0.00 0.00 0.00 1.37 0.63 1.45 7.63 1.57 0.00 2.33 0.00 7.17 0.00 0.00 a Abbreviation for rotation: (SS-Till) continuous soybean and tilled; (CSF-Till)- corn, soybean, fallow and tilled; (CSF-Nt)- corn, soybean and fallow with no till; (CSWF-Till)- corn, soybean, wheat and fallow and tilled; (CC-Nt) continuous corn with no till.b Abbreviation for DNA pools: three extractions pools (P3); five extractions pool (P5); ten extractions pools (P10). 56

Table 2.4. Shannon’s diversity index from Phytophthora, Phytopythium and Pythium communities from rhizospheric soil of soybean seedlings from five fields following incubation at 15 and 25°C using a baiting technique.

Field/Agronomic Soil incubation Shannon’s Tukey P-value Practice Temperature (oC) Diversity Index 15 1.99 A B1 (SS-Till) 25 1.62 A 0.08

15 2.18 A B6 (CSF-Till) 25 1.69 B 0.003

15 1.61 A D15 (CSF-Nt) 25 1.45 A 0.521

15 1.68 A D3 (CSWF-Till) 25 1.27 A 0.348

15 1.76 A TA4 (CC-Nt) 25 1.24 B 0.003

a Abbreviation for rotation: (SS-Till) continuous soybean and tilled; (CSF-Till)- corn, soybean, fallow and tilled; (CSF-Nt)- corn, soybean and fallow with no till; (CSWF-Till)- corn, soybean, wheat and fallow and tilled; (CC-Nt) continuous corn with no till.

Table 2.5. Total abundance of the oomycetes Pythium, Phytophthora and Phytopythium in the rhizosphere soil of soybean seedlings from five fields following incubation at 15 and 25°C using a soil baiting technique. Abundances are based on cumulative sum scaled normalized data.

a CS- Tilled a C-S- Tilled a C-S-W-F- Tilled a C-S- No till a CC- No till B1 B6 D3 D15 TA4

15 °C 25 °C 15 °C 25 °C 15 °C 25 °C 15 °C 25 °C 15 °C 25 °C Ph. sansomea 2.95 23.14 5.71 25.30 0.00 3.03 14.04 0.00 5.06 4.07 Ph. sojae 720.24 2525.06 1569.31 1270.89 483.23 315.99 1446.53 409.45 77.05 200.50 Pp. aff vexans 23.25 0.00 1.58 290.67 0.00 0.00 0.00 0.00 0.00 0.00 Pp. mercuriale 6.47 0.00 47.27 105.88 0.00 0.00 0.00 0.00 14.54 2.56 b Py. zeae 0.00 0.00 1.09 0.00 0.00 0.00 6.22 0.00 0.00 0.00 Py. acanthicum 138.39 8.96 268.33 42.11 72.31 4.96 84.27 88.84 302.05 109.24 Py. acrogynum 2262.67 6362.71 1198.87 6803.75 4004.20 300.47 1684.17 1076.92 1245.50 3432.67 Py. adhaerens 0.00 0.00 2.35 0.00 1034.43 0.00 0.00 0.00 3.61 0.00 Py. aff hydnosporum 34.58 82.29 189.05 0.00 45.49 2.48 38.84 0.00 43.02 0.00 Py. aff polymastum 200.70 806.15 306.77 5450.29 459.97 419.99 136.07 161.85 3174.09 821.78 Py. aff volutum 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 54.74 0.00 Py. aphanidermatum 714.29 0.00 1.33 0.00 0.00 0.00 21.77 0.00 217.89 0.00 Py. arrhenomanes 5.55 0.00 83.75 551.69 18.25 29.83 0.00 10.10 738.07 201.18 Py. attrantheridium 7929.40 3689.52 1868.41 32524.79 1018.95 502.11 5368.36 5466.18 7226.00 12324.43 Py. debaryanum 0.00 0.00 35.36 267.07 0.00 6.04 0.00 0.00 0.00 0.00 Py. emineosum 33.84 44.32 61.49 238.24 570.96 30.81 25.05 102.87 154.59 40.87 Py. folliculosum 46.22 75.97 1673.58 23086.00 149.68 107.74 2758.38 10.10 1045.52 321.96 Py. heterothallicum 7354.37 12653.17 1873.79 43031.50 3376.03 13811.87 1246.44 2254.12 1056.85 17632.31 Py. inflatum 157.49 30.79 595.64 2487.50 33.65 17.27 623.76 23.28 554.31 0.00

Py. insidiosum 42.53 31.32 63.34 172.44 68.37 71.14 65.12 172.34 44.35 77.98 Py. irregulare 0.00 0.00 17.43 233.02 8.85 0.00 1.56 0.00 0.00 0.00 Py. longandrum 133.86 132.33 1955.60 5189.34 10855.28 0.00 45.68 18.45 5190.26 834.33 Py. longisporangium 0.00 0.00 10.36 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Py. marsipium 81.68 0.00 295.13 0.00 96.44 0.00 100.90 0.00 8.03 0.00 Py. middletonii 36.61 32.25 246.66 6732.10 75.50 249.79 6937.89 13.79 78.12 0.00 Py. minus 489.08 122.19 311.06 3691.53 1310.39 42.28 2762.52 81.90 26803.68 24241.80 Py. monospermum 95.75 408.90 648.41 4293.90 206.21 346.73 126.91 79.14 953.78 1222.90 Py. nodosum 7.86 78.74 100.13 10.53 0.00 0.00 0.00 0.00 0.00 0.00 Py. orthogonon 944.11 315.10 2164.65 1559.26 297.26 94.04 0.00 175.76 670.01 3871.27 Py. periplocum 0.00 0.00 3.78 0.00 0.00 0.00 16.37 0.00 0.00 0.00 Py. perplexum 1.45 0.00 17.75 537.25 0.00 0.00 0.00 0.00 0.00 0.00 Py. rostratifingens 231.33 716.86 504.06 384.30 349.70 218.34 119.01 68.88 458.09 694.29 Py. selbyi 1237.41 798.91 3742.73 11703.50 509.31 515.94 724.55 6.93 573.46 2261.06 Py. sp AL 2010 0.00 0.00 10.70 0.00 7.06 0.00 12.56 0.00 0.00 0.00 Py. sp CAL 2011f 1421.78 468.08 4878.20 62895.20 117.54 878.53 162.55 41.70 5475.73 1504.51 Py. sylvaticum 2110.86 17537.71 12106.57 68361.16 16395.40 10396.83 10152.92 3887.05 32740.77 23878.85 Py. ultimum 9750.78 3040.19 628.68 21841.28 3817.91 172.82 127.52 681.52 15193.61 13793.51 Py. vanterpoolii 0.00 0.00 0.00 0.00 16.13 0.00 0.00 0.00 0.00 0.00 Py. violae 94.91 1605.46 68.23 736.54 182.36 943.00 72.65 182.40 726.68 2304.24 Py. volutum 0.00 0.00 0.00 0.00 0.00 6.04 0.00 0.00 5.22 0.00 a Abbreviation for rotation: (CS)- continuous soybean; (C-S)- corn and soybean; (C-S-F)- corn, soybean and fallow; (C-S-W-F)- corn, soybean, wheat and fallow. b Abbreviation for genus: Phytophthora (Ph), Pythium (Py) and Phytopythium (Pp). Row colors: Yellow: represents species abundant in rhizospheric soil across all fields and incubation temperatures. Green: represent species favored when soils are incubated at 15°C. Blue: represent species favored when soils are incubated at 25°C. Red: represent species where temperature influenced the abundance based on the field conditions.

Table 2.6. Resume of species of Pythium sharing identical ITS1 sequences based on sequence pairwise analysis. Species sharing identical ITS1 sequences were grouped together and the clade letter to which they belong was provided.

Species aPythium clade Py_porphyrae Py_chondricola Py_adhaerens CladeA

Py_aristosporum Py_arrhenomanes Py_coloratum Py_dissotocum Py_diclinum Py_lutarium Py_marinum Py_pachycaule Py_catenulatum Py_torulosum

Py_folliculosum Py_myriotylum Py_zingiberis Py_capillosum

Py_flevoense CladeB Py_amasculinum

Py_oligandrum Py_aff. hydnosporum CladeD Py_acrogynum

Py_hypogynum CladeE Py_ultimum

Py_ultimum_var._spor Py_ultimum_var._ult CladeI

Estimates of evolutionary divergence between species was done in MEGA version 7.1.. Accession of the ITS (1and 2) and the 5.8 gene of the nuclear rRNA of 113 voucher specimens used by Levesque and Cock, (2004), Robideau et al. (2011) or Hyde et al. (2004) were retrieved from the NCBI nucleotide database and the ITS1 was extracted using the ITSx (Bengtsson-Palme et al. 2013) software version 1.0.11. Extracted sequences were then aligned using ClustalW. After alignment, pairwise distance analysis was conducted using the Maximum Composite Likelihood model (Tamura et al. 2004). a Pythium clade based on taxonomic analysis of the genus Pythium by Levesque et al. 2004. 60

Figure 2.1. Nonmetric multidimensional scaling (NMDS) plots using Bray-Curtis dissimilarity of oomycete communities from three (P3), five (P5) and ten (P10) DNA extraction pools from soil samples from five different fields following incubation at 15°C using a soil baiting technique (n=5). Colors represent number of DNA extraction pools. Stress values for NMDS are shown in the bottom left. DNA pool were not significantly different (PERMANOVA; P = 0.92, R2= 0.01). Communities of oomycetes varied by field (PERMANOVA; P = 0.0001, R2= 0.33).

Figure 2.2. Proportion of abundance of Phytophthora, Phytopythium and Pythium from amplicon sequencing of soil samples from five fields were 3 (P3), 5(P5) or 10(P10) DNA extractions were pooled. Soils were retrieved from the rhizosphere of seedlings after incubation at 15 °C. Abundance was normalized using the cumulative-sum scaling approach and then expressed as a proportion of total abundance per sample.

Figure 2.4. Nonmetric multidimensional scaling plots (NMDS) using Bray-Curtis dissimilarity of Phytophthora, Phytopythium and Pythium community data from five fields following incubation at 15 and 25 °C (n=10). NMDS plots are shown for field D15 (CSF- Nt) (A); Field B1 (SS-Till) (B); Field B6 (CSF-Till) (C), Field TA4 (CC-Nt) (D) and, Field D3 (CSWF-Till) (E). Stress values for NMDS are shown in the bottom left. Lines are depicting convex hulls enclosing all samples pertaining to the two temperature of incubation used for the baiting technique.

Figure 2.5. Relative abundance based on cumulative sum scaling normalized counts (n=10) of Phytophthora, Phytopythium and Pythium species present in the rhizosphere soil of soybean seedlings following incubation at 15 and 25°C when using a baiting technique.

Figure 2.6. Distribution of Pythium sp. recovered using a culture depended approach from five research plots after incubation at 15 and 25 °C. Isolates were recovered from seedlings three days after flooding for 24 hours and after introduction of the susceptible soybean cultivar Sloan. Identification of species was performed using the ITS6 and ITS7 primers which only amplified the ITS1 region of the rRNA gene. A total of five symptomatic seedlings per field was collected for each temperature.

Figure 2.7. Aerial picture of the 200-acre research farm located in Wood county northwest Ohio.

Figure 2.8. Prevalence of taxa versus total counts. Each dot represents one OTU belonging to different Phyla after normalization using the cumulative sum scaling approach.

Pathogenicity Pathogenicity Score = 3 Species in Corn in Soybean Pythium oopapillum Pythium sylvaticum Pythium torulosum Pytium dissotocum Score = 2 Pythium ultimum var. ultimum Pythium ultimum var. sporangiorefum Pythium intermedium Pythium attrantheridium Score = 1 Pythium coloratum Pythium hypogonum Pythium heterothallicum Score = 0 Pythium irregulare Pythium inflatum Pythium acanthicum Pythium rostratifingens

Pythium perplexum Phytopythium vexans Phytopythium mercuriale

Phytopythium chamaehyphon Phytophthora sojae Phytophthora sansomeana *Data retrieved from: Broders et al. (2007), Coffua and Blair (2016), Rojas et al. (2017).

Figure 2.9. Species of Phytophthora, Phytopythium, and Pythium that have been found pathogenic in soybean and corn in the United States.

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3. Chapter 3. The Effects of Soybean Genotype and Environment on Phytophthora, Pythium and Phytopythium species in High Disease Environments in Ohio

3.1. Abstract

The oomycetes Phytophthora, Phytopythium and Pythium are a major concern in

Ohio due to losses in stand and yield when susceptible soybean cultivars are planted. In this study, the community composition and abundance of these pathogens on the rhizosphere of three different soybean cultivars was investigated across 11 different environments in Ohio. From field trials lower yields were obtained from the susceptible cultivar Sloan compared to the cultivars Kottman, with high partial resistance to Ph. sojae, and Lorain with high partial resistance to both Pythium spp. and Ph. sojae. Using traditional culturing methods, 78%, 14% and 8% of the isolates recovered from seedlings, belonged to Pythium, Phytopythium and Phytophthora, respectively. Under greenhouse conditions, these isolates were able to reduce root weight of the three soybean cultivars. Similarly, when using a metabarcoding approach from rhizopheric soil, Pythium species encompass the greatest number of counts and species diversity. Across all environments and cultivars,

Py. attrantheridium, Py. heterothallicum and Py. sylvaticum were always detected. In addition, environment affected the community composition and cultivar to a lesser extent.

Across all environments, the cultivar Sloan had an increase of 85% in abundance compared to Kottman, suggesting this cultivar can reduce inoculum levels in the field. Additionally, this is the first report of Py. perrillum from soils in Ohio as well as the isolation and detection of Pythium sp. CAL in soybean in Ohio. Interestingly, in the southern environment (CLN18), Ph. sansomeana was 84% more abundant than Ph. sojae. This 78

study provides insights on how environments and cultivars play a key role on the diversity

and abundance of Phytophthora, Phytopythium and Pythium species in Ohio as well as

highlights species for focus in the development of new cultivars or for fungicide testing.

3.2. Introduction

Within the Oomycetes, species of Phytophthora (Ph), Phytopythium (Pp) and

Pythium (Py) (including Globisporangium) cause seed rot, seedling damping-off and roots rots of a wide range of crops in the United States (U.S.) including soybean and corn

(Dorrance et al. 2004; Broders et al. 2007, 2009; Matthiesen et al. 2016; Radmer et al.

2017; Rojas et al. 2017a, b; Serrano and Robertson 2018; Zitnick-Anderson and Nelson

2015). One species, Ph. sojae has primarily one host and can also cause stem rot during mid-season (Dorrance et al. 2007). Several regional and state surveys have recovered a vast diversity of Pythium species from soybean and corn seedlings across major soybean producing states in the U.S. and Canada (Dorrance et al. 2004; Broders et al. 2007, 2009;

Radmer et al. 2017; Rojas et al. 2017a; Zitnick-Anderson and Nelson 2015). Pythium sylvaticum, Py. irregulare, Py. ultimum, and Py. heterothallicum were among the most frequently recovered species. Phytophthora sojae continues to be recovered from soybean seedlings in fields across the North Central region of the U.S. (Dorrance et al. 2016) whereas, Ph. sansomeana isolates are less common and has been recovered from a greater number of hosts including: douglas-fir, soybean and alfalfa in Oregon (Hamm and Hansen

1981), from soybean in Indiana (Reeser et al. 1991) and from corn and soybean in Ohio

(Eyre 2016; Vargas-Loyo 2018; Zelaya-Molina et al. 2010). Similarly, Phytopythium has

also been recovered from soybean seedlings in fields in Ohio (Broders 2009; Eyre 2016;

Vargas-Loyo 2018), Minnesota (Radmer et al. 2017), and other states including South

Dakota, Indiana, Nebraska, Arkansas, Illinois, Iowa, and Minnesota (Rojas et al 2017b).

Disease management of Phytophthora, Phytopythium and Pythium can be challenging due

to: i) more than one species can be recovered from the same seedling (Broders et al. 2009;

Zitnick-Anderson and Nelson 2015); ii) the diversity of pathotypes observed in

Phytophthora (Dorrance et al. 2016); and iii) due to similarities among certain clades of

Pythium classified as species complexes or cryptic species (Gárzon et al. 2005; Lévesque

and de Cock 2004; Schroeder et al. 2013).

From previous surveys in Ohio, a diversity of species was recovered with soil

baiting and direct isolation methods. Specifically, a total of 124 isolates were recovered

from symptomatic corn and soybean seedlings collected from 40 locations in Ohio in 2004

and 30 locations in Ohio and 2 locations in northeast Indiana in 2005. (Broders et al. 2007).

These belonged to eleven different Pythium species and Py. dissotocum and Py. sylvaticum

were the most prevalent representing 23% and 20% of the isolates, respectively. In a second

survey more than 7,000 isolates were baited from soils incubated at 16℃, and 21 different

species of Pythium were recovered with only 6 species recovered from more than 40% of

the fields (Broders et al. 2009). In this second survey, Py. irregulare was the most abundant

along with Py. inflatum, Py. ultimum var. ultimum, Py. ultimum var. sporangiiferum, Py. torulosum, and Py. dissotocum. More recent surveys of seedling pathogens in Ohio conducted during 2014 to 2016, recovered Py. ultimum var. ultimum, and Py. oopapillum

in a higher proportion while other species such as Py. torulosum, P. middletonii, Py.

inflatum and Py. dissotocum have also been recovered but not consistently across the years

(Eyre 2016; Vargas-Loyo 2018). The observed changes in the predominant species of oomycetes among these surveys suggests that environment may be an important factor driving community composition.

To better manage seed and seedling diseases caused by these soilborne pathogens, an

integrated disease management approach is recommended. This includes a combination of

fungicides seed treatment paired with host resistance (Anderson and Buzzell 1982; Bradley

2008; Dorrance et al. 2003, 2009; Urrea 2013). Fungicide efficacy is variable among

species and host resistance has shown to be the best means of management in regions where

there are high levels of inoculum and conducive environments (Dorrance et al. 2009;

Schmitthenner 1985). In soybean, three types of resistance have been described to control

Phytophthora sojae; single dominant R genes (Resistance to Ph. sojae; Rps), root

resistance and partial resistance (Mideros et al. 2007). In the U.S., Rps genes for Ph. sojae

are deployed providing protection against this pathogen where most of the soybean is

produced (Grau et al. 2004; Slaminko et al. 2010). However, for Pythium, few sources of

resistance have been identified (Ellis et al., 2013; Rod et al., 2018; Rupe et al., 2011; Scott

et al., 2019) but partial resistance has been found towards different species (Ellis et al.,

2013; Klepadlo et al. 2019; Rod et al. 2018; Rosso et al. 2008; Rupe et al. 2011; Scott et

al. 2019; Stasko et al. 2016; Urrea et al. 2017).

The incorporation of genes from wild relatives into adapted cultivars has enhanced the

resistance to many plant pathogens thus, increasing yield and biomass of many crops.

However, plant genotype has also been proposed to affect the microbial community

81 associated with the rhizosphere (Bulgarelli et al. 2015; Pérez-Jaramillo et al.2016, 2017,

2019). The microorganisms that colonize the rhizosphere play a pivotal role in biogeochemical cycling, plant growth and resistance to biotic and abiotic factors (Firáková et al. 2007; Mendes et al. 2011, 2013, 2014; Phillippot et al. 2013). Changes in the bacterial microbial composition may have a proportional impact on the overall health of the plant.

Advances in DNA sequencing technologies and the use of a metabarcoding approach

(amplicon sequencing), has enabled researchers to explore more in depth the microbial communities under agricultural, natural, and experimental conditions (Bakker et al. 2017;

Pérez-Jaramillo et al. 2019; Redekar et al. 2018; Rojas et al. 2019; Shi et al. 2019). For example, Pérez-Jaramillo et al. (2019), compared the rhizobacterial communities associated with eight accessions of wild and modern common bean cultivars (Phaseolus vulgaris L) grown in native and agricultural soils from Colombia. Soil type (native and agricultural soils) was the principal component affecting the rhizosphere bacterial community composition. However, the effects of bean genotype within each soil type, was able to explain 31.3% and 28.3% of the total variability in the agricultural and native soil, respectively. Regardless of the soil type, Proteobacteria represented 68.8% of the mean relative abundance. Also, co-occurrence network indicated that some taxa in the rhizosphere of common bean accessions were more complex when grown under native soils, compared to the agriculturally managed soils suggesting that soil edaphic factors are highly associated with overall bacterial species diversity and abundance (Pérez-Jaramillo et al. 2019). Similarly, in another study the effect of crop domestication on the rhizosphere microbial community structure was explored in rice (Oryza sativa L. and Oryza

82 rufipogon Griff.) and soybean (Glycine max L. and Glycine soja Sieb. et Zucc.) (Shi et al 2019). Here they found a strong association between cultivated crops and fungal and bacterial communities compared to wild relatives. However, wild relatives exhibited higher abundance of beneficial symbionts and lower abundance of pathogens (Shi et al. 2019) suggesting that plant domestication might have enhance the attraction or development of pathogenic species. The effects of host genotype on the bacterial rhizosphere community have been studied in other systems including Arabidopsis (Micallef et al. 2009), barley

(Hordeum vulgare L.) (Bulgarelli et al. 2015), wild mustard (Boechera stricta (Graham)

Al-Shehbaz) (Wagner at al. 2015), sweet potato (Ipomoea batatas L.) (Marques et al.

2014), and potatoes (Solanum tuberosum L.) (Inceoglu et al. 2010).

Although the effects of genotype have been explored for some plant species, these have been focused primarily on beneficial microbes, but little is known about how the abundance, diversity and community composition of pathogenic species change when different cultivars are used. To date, the community composition of Phytophthora,

Phytopythium, and Pythium among cultivars with different levels and types of resistance remains unexplored and how these genotypes affect rhizosphere community diversity remains to be elucidated. Thus, in this study a metabarcoding approach paired with direct isolation methods were used to determine the effects of cultivars Kottman, Lorain and

Sloan on the abundance, diversity and species composition of these three oomycete genera, across eleven high disease environments in Ohio. These three cultivars have very different levels of resistance and susceptibility to the predominant species of these pathogens. Thus, the hypothesis were: a) there will be a greater abundance and diversity of species of

Phytophthora, Phytopythium and Pythium recovered from seedlings and in the rhizosphere soil of the susceptible (Sloan), compared to the moderate resistance cultivars Kottman and

Lorain; b) there will be higher levels of diversity and distinct communities of

Phytophthora, Phytopythium and Pythium in the high disease environments (with higher levels of precipitation following planting; and c) soybean genotypes with combined resistance to Ph. sojae and other Pythium spp. will have higher plant populations and yield across all environments.

3.3. Materials and Methods

Field experiments. To evaluate the effects of environment and cultivar on the composition

of Phytophthora, Phytopythium, and Pythium communities, a total of six and five fields were selected in 2017 and 2018, respectively. Fields in 2017 were located in Clinton

(CLN17), Darke (DAK17), Defiance (DEF17), VanWert (VW17) Wayne (SNY17), and

Wood (NWB17) counties; and for 2018 in Clinton (CLN18), Logan (LOG18), Paulding

(PAL18), Wayne (SNY18), and Wood (NWB18) (Figure 3.1). Fields were selected based

on prior history of seedling diseases as reported by farmers. The term “environment” was

used to describe a single field site each year (e.g. Clinton field in 2017). The field located

at the OARDC Northwest Agricultural Research Station (Wood County) was used as the

“control” environment since fields, both years, were irrigated to capacity 2 to 3 days after

planting to enhance disease development. The remaining 9 environments were dependent

on natural rainfall for conducive conditions for disease development. Two environments,

Defiance (DEF25dap) and VanWert (VW25dap), were sampled in 2017 at two different

growth stages to evaluate the effect of rainfall and soybean growth stage on communities

of Phytophthora, Phytopythium, and Pythium.

Plant material. Three soybean cultivars were evaluated in each environment including

Sloan (Bahrenfus and Fehr, 1980), with moderate susceptibility to Ph. sojae and Pythium

and no Rps genes; Kottman (St. Martin et al. 2001) with Rps1k plus Rps3a genes and

moderate resistance to Ph. sojae, and moderate susceptibility to Pythium sp.; and Lorain

(Ohio Agricultural Research and Development Center, The Ohio State University) with

Rps1c and moderate resistance to both Ph. sojae and Pythium spp. (Balk 2014; Scott 2018).

For each environment, cultivars were planted in a randomized complete block design with

8 replications each. Each field plot was planted with 4 rows, spaced 1.5 meters wide and

9.1 meters long except for fields located at Wayne (SNY17, SNY18) and Wood (NWB17,

NWB18) where the plot length was 7.6 meters. Data was collected for early plant

population at soybean growth stage VC to V1; final plant population at R8 (Fehr and

Caviness 1977) and yield (kg/ha).

Seedling sampling. At soybean growth stage VC to V1, five symptomatic seedlings from each plot were collected and placed in plastic bags that were then placed in a cooler with

ice for transport. In plots where seedling didn’t exhibit symptoms, five asymptomatic seedlings were collected. Soybean seedling samples and their associated rhizosphere soils

were placed in a 4oC until processed. For processing, large clumps of soils were first

removed from the seedling and disposed. Rhizospheric soil was then collected by gently

shaking each seedling inside a manila envelope. The rhizospheric soil from each of the five

seedlings collected from each plot was pooled into one sample and a total of 0.2 g was

measured and used for DNA extraction. The remaining soil samples were then stored at -

20 oC.

Direct isolation from seedlings and species identification. Isolations of Phytophthora,

Phytopythium, and Pythium were performed as previously described by (Dorrance et al.

2007). Briefly, seedling roots were gently washed under tap water to remove debris,

wrapped in paper towel and transferred to a laminar flow hood. Roots were surface

sterilized using 70% ethanol for 10 s followed by a 30 s rinse with sterile distilled water.

Roots were blotted dried in sterile paper towel and sections from the edge of the lesions

were placed under selective media PIBNC (V8-media+ pentachloronitrobenzene, iprodione, benlate, neomycin sulfate, and chloramphenicol). Hyphal tips of coenocytic mycelia were transferred to potato carrot agar (PCA) plates amended with rifampicin

(100µg/mL). Pure cultures were transferred to Whatman vials with PCA for long term

storage at 14oC.

In addition to morphological features, each isolate was confirmed to species, based

on the full-length sequence of the ITS regions in the ribosomal gene. The DNA for each

isolate was extracted as previously described by (Zelaya-Molina et al. 2011). Briefly, a total of 6-10 mycelia plugs from each culture were transferred to 50 ml of V8 broth media in a 125 ml Erlenmeyer flask and grown at room temperature for 4 days. Mycelia was collected using a Buchner funnel, macerated in liquid nitrogen with a mortar and pestle,

and stored at -20◦C. The primers ITS1 and ITS4 (White et al. 1993) were used and the PCR

master mix was prepared using the Promega GoTaq Polymerase Kit (Promega Corp,

Madison, WI) and consisted of 5 µl of 5X colorless reaction buffer, 1.5 µl of MgCl2 (25

86 mM), 1µl of dNTP’s (10µM), 1 µl of primers ITS1 (10µl), and ITS4 (10 µl), 0.25 µl of

GoTaq DNA Polymerase (5u/µl), and 13.25 µl of ultra-pure water. The PCR parameters were: 95 oC for 5 min; followed by 30 cycles of 94 oC for 1 min; 53oC for 1 min; 72 oC for

1 min; and completed with 72oC for 5 min. Quality and quantity of the DNA was obtained by using the A260/A280 and A260/A230 ratios with a spectrophotometer (Nanodrop 3300,

Thermo Scientific, Vernon Hills, IL) and electrophoresis in a 1% (w/v) agarose gel with

Gel Red nucleic acid stain (New England Biolabs) for 1 hour at 90V.

For sequencing, amplicons were purified by mixing 2 µl of ExoSAP-IT™ (Thermo-

Fisher, Waltham, MA) with 5 µl of the PCR reaction, and incubated at 37oC for 5 min which was followed by 80oC for 15 min. A total of 3 µl of each individual primer at a concentration of 2 pmol was mixed with 6 µl of purified PCR product for a final concentration of 20 ng/µl. Purified and diluted PCR products were submitted to the

Molecular and Cellular Imaging Center (MCIC) at the Ohio Agricultural Research and

Development Center (OARDC) or the OSU James Genomic Shared Resources for Sanger sequencing using both forward and reverse primers. Sequences from both primers were quality filtered and assembled using Codon Code Aligner (Codon Code Corporation,

Centerville, MA). Sequences were then compared to voucher specimens deposited at the

National Center for Biotechnology Information (NCBI) nucleotide non-redundant database for identification.

Pathogenicity assay. To evaluate the pathogenicity of isolates recovered from the field, a root cup assay with isolates of representative species of Phytophthora, Phytopythium and

Pythium recovered from fields in 2017 was done in the growth chamber as previously

described Ellis et al. 2013). Briefly, spawn bags (Myco Supply, Pittsburgh, PA) containing

950 ml of ‘Clean Play Sand’ (Quikrete, Ravenna, Ohio), 50 ml cornmeal (Quaker Oats

Company, Chicago, IL) and 250 ml of deionized distilled water were prepared and then

autoclaved on two consecutive days. Individual sterile bags were inoculated with eight to

ten plugs (5 mm) from the edge of a 5 to 7-day old culture and sealed with a sealer-electrical impulse (Harbor Freight Tools; Calabasas, CA). Bags were incubated at room temperature for ten days and mixed manually every day. A single spawn bag was mixed with 4 L of fine vermiculite in a 1:4 ratio, and 300 ml of inoculum was placed on top of 100 ml of coarse vermiculite (Perlite Vermiculite Packing Industries, Inc., North Bloomfield, OH) in each 0.5 L styrofoam cups and watered prior to planting 3 times over 24 hours. A total of

10 seeds of a cultivar Kottman, Lorain, or Sloan were then placed on top of each individual cup and covered with an additional 100 ml of coarse vermiculite. Cups were placed in a growth chamber at 20°C, with 16 h light, and the humidity set for 20%. Data was collected for plant germination and emergence at 7 days after planting (dap). At 14 dap plants were gently removed from cups and washed under tap water. Data for root rot score, root weight, shoot weight, shoot height and final number of plants per cup was collected. The isolates

Miami 1-3-7 (P. ultimum var. ultimum) and Brown (P. irregulare) were used as control isolates. Experiment was conducted as a randomized complete block with three reps per cultivar and isolate. Experiment was run three time.

Pythium sp. CAL pathogenicity, growth, and morphological structures. The

undescribed Pythium sp. CAL was directly isolated from seedlings retrieved from the

NWB18. The pathogenicity was evaluated in a separate experiment with the cultivars

Kottman, Conrad, Sloan and Williams using the root cup assay as described above. Optimal mycelia growth was evaluated at five different temperatures. A 5 mm diameter plug from a 7-day old culture (incubated in the dark at 20 oC) was placed in the center of a PCA plate

(100 mm x 15 mm). Plates were transferred to incubators with temperature of 4, 15, 20, 25,

30 and 37oC without light. Mycelia growth (diameter of the colony) was measured three times over a 72-hour period. There were three replicates for each temperature and the experiment was repeated three times.

To induce morphological structures the sterile grass blades culture method described by Waterhouse (1967) was used. Briefly, young grass leaf blades were collected and placed in 100 ml deionized water and autoclaved for 30 min for two consecutive days.

Three to four grass blades were placed inside a petri dish containing a mix of snow water and sterile deionized water at a 1:2 ratio. A 5 mm diameter plug was added to plate incubated at 20 and 25oC in the dark. Cultures were monitored periodically over a week for the formation of oospores, sporangia and zoospore release. Images were taken using the microscope digital camera model MU503 (AmScope, Ivine, CA).

Growth was compared on four different media, clarified V8-juice agar, PCA, potato dextrose agar and lima bean agar. A 5 mm agar plug from a 7-day old culture was placed in the center, followed by incubation at 20oC. Three days later cultures were observed under a compound microscope for oospore production and colony characteristics.

To determine which species of Pythium were closely related to this isolate, three different genes were studied. First, primers ITS1 and ITS4 were used to sequence the full length ITS region of the rRNA gene. In addition, the primers FM35 and FM55 (Martin 2000) were

89 used to amplify the coxI and coxII gene cluster of the mitochondrial rDNA. Sequences of the ITS was deposited in Genebank under accessions MN512275.

Soil DNA extraction. Pooled soil collected from the rhizosphere of the seedlings from each plot, was ground in a blender for a homogenous sample. The Power Lyzer, Power

Soil Kit (Qiagen, Carlsbad, CA) was used to extract the DNA following manufacturer’s protocol with some modifications. Modifications included: PowerLyzer Homogenizer

(Qiagen, Carlsbad,CA) at 4,000 rpm for 45 sec, 5 min incubation period at 2°C and DNA was diluted with 50 µl of the solution C6 from kit. Quality and quantity of DNA was measured with the 260:280 absorbance method (Nanodrop 3300, Thermo Scientific,

Vernon Hills, IL). For each sample, there were a total of three separate DNA extractions, and these were pooled equimolar to a final concentration of 5ng/ul. Only the ITS1 of the

RNA ribosomal genes was amplified with primers ITS6/ ITS7 (Cook and Duncan, 1997; de Cook et al. 2000) using the Phusion High Fidelity DNA Polymerase (New England

BioLabs, Ipswich, MA) to reduce PCR errors. The reaction consisted of 5 µl of 5X High

Fidelity Buffer, 0.5 µl of nucleotide mix (10 µM), 1 µl of primers ITS6 and ITS7 (2 µM) containing Illumina adapters, 5 µl of template (5 ng/µl), 0.2 µl of Phusion Taq (1.0 units/50

µl PCR), and 9.3 µl of ultra- pure water. The PCR parameters were: 96 °C for 3 min, followed by 25 cycles of 96 °C for 30 s; 55 °C for 30 s; 72 °C for 30 s; and completed with

72°C for 5 min. Samples were submitted to the MCIC for library preparation and sequencing.

Illumina library preparation and sequencing. There were two Mi-seq (Illumina, U.S.A.) runs, one for environments sampled in 2017 and the second for those sampled in 2018.

Amplicons of the ITS1 region generated at the laboratory were submitted to the MCIC for

library preparation and sequencing. A total of 3 µl of the first cleaned PCR product was

used as the template for the second PCR reaction to add the Illumina adapter sequence

which contains a unique dual combination of the Nextera indices for individual tagging of

each sample. These products were then purified using the Agencourt AMPure XP beads

(Beckman Coulter Life Sciences). All the steps for library preparation and cleaning were

carried out on the epMotion5075 automated liquid handler (Eppendorf, Hauppauge, NY).

The amplicon libraries were quantified and pooled at equimolar ratios before sequencing.

The final pool was purified using the Pippin Prep size selection system (Sage Science) to

discard the presence of any primer dimers. The Mi-Seq sequencing platform (Illumina) was used for amplicon sequencing at a final concentration of 14.3 pM. Amplicon libraries were spiked with PhiX libraries to allow a more heterogeneous sample and reduce error in the run introduced by the high levels of similar nucleotides among watermolds. The run was clustered to a density of 905 k/mm2 and the libraries were sequenced using a 300 PE Mi-

Seq sequencing kit with the standard Illumina sequencing primers. Image analysis, base

calling and data quality assessment were performed on the Mi-seq platform.

Amplicon sequencing data processing. Data quality was first assessed using the FastQC

and MultiQC software (Wingett and Andrews 2018). Removal of Illumina barcodes and merging of the short pair-end reads was performed using BBMerge command in BBTools suit. Prior to data processing using the USEARCH pipeline (Edgar 2010), the two Mi-seq

runs were pooled together. Quality filtering was done using a threshold of 1% expected

errors (Edgar and Flyvbjerg 2015). The ITS1 region was trimmed from the sequences using

the ITSx (Bengtsson-Palme et al. 2013) software version 1.0.11. This was followed by de-

replication to find set of unique sequences and remove duplicated shorts reads from initial

data set. De-replicated short-reads were then cluster into operational taxonomic unit (OTU)

using a de novo approach at 97% similarity level using the UPARSE distance based

greedy-approach algorithm (Edgar 2013). Finally, taxonomy was assigned to OTUs using

the –sintax command with 100% cutoff using a custom-made database. Database was

created using accessions from Robideau et al. (2011), Hyde et al. (2014) and internal

sequences from the lab. Further filtering steps and data analysis was carried out using the

phyloseq (McMurdie and Holmes 2013), MetagenomeSeq (Paulson et al. 2013) and Vegan

(Dixon 2003) packages in R version 3.5.0.

Statistical analysis. Early plant population and yield data were first analyzed for all the

environments. Assumptions were tested using the Levene’s test for homogeneity of

variance and Shapiro-Wilk for normality in SAS (SAS version 9.4; SAS Institute, Cary,

NC). An analysis of variance (ANOVA) to test the main effect of environment, cultivar and replicate as well as the interactions of these three factors. Due to significance (P-value

<0.05) for the interaction of environment by cultivar for the early population and yield the

ANOVA was run independently for each environment to test effects of cultivar.

For the metabarcoding approach, OTU tables were first subset for species of

Phytophthora, Phytopythium, and Pythium. Several species in Pythium clades A, B, D, E, and share identical sequences when using primers ITS6 and ITS7 (Navarro, Chapter 2;

Redekar et al. 2018). When species had identical sequences, taxonomical identification was assigned in alphabetical order. The Shannon’s diversity index was calculated using the

Vegan package (Okasen 2019) as implemented in R (R Core Team) to assess species

richness and evenness from non-normalized data. Comparison of the mean diversity index

between environments was done using a two-way analysis of variance after assumptions

were tested. The main effect of environment and cultivar were used tested. To test the effect of environment and cultivar in the community composition of oomycetes OTU sequence counts were normalized using the cumulative-sum scaling (CSS) method as implemented in the package Metagenomeseq in R (Paulson et al. 2013). Data was then converted to relative abundances by dividing the absolute abundance of each OTU by the total number of sequences per sample. Bray-Curtis dissimilarity matrix was then calculated from relative abundance data and the output matrix used as input data for the nonmetric multidimensional scaling (NMDS) ordination plots. A permutation analysis of variance

(PERMANOVA) was conducted to assess significance of environment, cultivar, and the interaction of environment and cultivar in the community composition of Phytophthora,

Phytopythium, and Pythium (Anderson 2001).

3.4. Results

Environmental conditions. The amount of rainfall and air temperature from planting to

sampling date for each environment are summarized in Table 3.1. Rainfall amounts were

similar in 2017 and 2018 and ranged from 23.6 to 72.3 mm and 22.6 to 74.9 mm,

respectively. For environments VW17 and DEF17, rainfall occurred three to five days prior to sampling and this may have limited seedling disease development (Table 3.1). For

SNY18, conditions were conducive for disease based on rainfall, however the slope of the

field allowed for drainage. Flooding conditions occurred at CLN18 following the first stand

counts, stand and yield losses reached 100% in many of the plots and surviving plots were

discarded. Both The fields NWB17 and NWB18 fields were irrigated to field capacity and

these two environments had the greatest number of isolates recovered across both years.

Early plant population and yields. Reductions in plant populations and yield are symptoms of seedling blight caused by species of Phytophthora, Phytopythium, and

Pythium. There was a significant effect (P < 0.05) of environment, cultivar and the

interaction between environment and cultivar for early plant population. Due to

significance in the interaction term, the environments were analyzed individually. Plant

population and yield of the moderately susceptible cultivar Sloan was significantly (P

<0.05) lower compared to Kottman and Lorain in NWB17, DEF17 and CLN18 (Figure

3.3). Kottman (Rps1k +3a, moderate resistance to Ph. sojae) had significant higher number

of seedlings and yields across all environments. However, for the cultivar Lorain, which

has both Rps1c plus partial resistance to Ph. sojae and moderate resistance to Py. ultimum

var. ultimum and Py. ultimum var. sporangiiferum, the early plant population was

significantly higher compared to Sloan in two of eleven environments (Figure 3.3A). Yield

for Lorain were also significantly higher than Sloan in 4 environments (Figure 3.3B). These

results indicate that based on seedling loss due to lower plant populations and reductions

in yield, these 4 environments, NWB17, NWB18, PAL18 and CLN18 had the highest

disease pressure.

Direct Isolation. A total of 277 isolates were recovered from symptomatic seedlings across

the 11 environments (fields) sampled. All environments had very different seedling

pathogens based on the isolation of different species of Phytophthora, Pythopythium and

Pythium (Table 3.1, Figure 3.4). Among the isolates recovered 74, 21, and 5 percent

belonged to Pythium, Phytophthora, and Phytopythium, respectively (Table 3.4). In

addition, from all the isolates recovered, 147 were retrieved from the susceptible cultivar

Sloan while the remaining 69 and 61 isolates were recovered from Kottman and Lorain,

respectively. Certain species seem to more frequently recovered from Sloan compared to

Kottman and Lorain. For example, isolates of Ph. sojae, Py. oopapillum and Py. sylvaticum

were isolated in greater numbers in Sloan. Phytopythium vexans and Pp. mercuriales were

also only isolated from Sloan.

From one environment, NWB17, 33 out of the 51 isolates isolated were identified

as Py. oopapillum. Overall, species isolated at a high frequency were, Py. inflatum (12),

Py. ultimum var. ultimum (30), Py. sylvaticum (32), Py. dissotocum (18) and Py. torulosum

(13). For both Ph. sojae and Ph. sansomeana, similar number of isolates were recovered

(Figure 3.4). In environments sampled in 2017, Ph. sojae was only recovered from NWB17 and SNY17 while environments sampled in 2018, Ph. sansomeana was only recovered in

PAL18 and NWB18. Of the four species of Phytopythium recovered, Phytopythium mercuriales was recovered from NWB18 field located in northern Ohio, while Pp. vexans was recovered only from CLN17, located in southern Ohio (Figure 3.4).

Pathogenicity of isolates recovered from the field. Isolates recovered from the environments sampled in 2017 were tested for pathogenicity towards the soybean cultivars

Kottman, Lorain, and Sloan. The root weight following inoculation was used to compare pathogenicity among isolates. There was highly significant difference among isolates (P

<0.001), a marginally significant effect for cultivar (P = 0.06) and no significance for the interaction between isolate and cultivar for root weight (P >0.05). The three check isolates,

Py. ultimum (Miami), Py. irregulare (Brown) and Ph. sojae (R25) all had significantly lower root weight compared to the non-inoculated control (Figure 3.5). In addition, Py. perplexum had significantly lower root weight across all cultivars. Other isolates of Ph. sansomeana, Ph. sojae, Py. sylvaticum, Py. ultimum var. ultimum and Py. attrantheridium had significantly lower root weight for the cultivar Lorain. For the cultivar Kottman, only

Py. ultimum var. ultimum had significantly lower root weight compared to the non- inoculated control.

Evaluation of Pythium sp. CAL on soybean. A comparison to the GenBank non- redundant nucleotide database using BLASTn identified an undescribed species not recovered in Ohio from previous surveys. The isolate was retrieved from the NWB18 environment and was taxonomically identified as Pythium sp. CAL voucher specimen. The amplicon sequence was 823 bp in length and had 100% match to Py, species CAL

(accession: HQ643829), and Py. recalcitrans sp. nov. (accession: KJ716861.1). The closest relatives with 90% identity at the ITS level were Py. sylvaticum (accession: KU209564.1) and Py. paroecandrum (accession: HQ643734.1). For the amplicon sequence for mitochondrial rDNA cox gene cluster was 1010 bp. The sequence was closely related to

Py. sp. WL-2015 (accession: KR559729.1) with 97% similarity and to Py. sylvaticum

(accession: KR559729.1) with 97% identity.

When pathogenicity was tested using the cup assay methods, this isolate was highly pathogenic on four soybean cultivars exhibiting significantly lower root weights compared

to the non-inoculated control (Figure 3.6). The control isolates (Py. irregulare, Py.

ultimum, Ph. sojae, and Ph. sansomeana) also had significantly lower root weight

compared to the non-inoculated control. For the isolate classified as Pythium sp. CAL,

there was 48% reduction in root weight compared to the non-inoculated control for

Kottman. Although pathogenicity was observed at 20 oC, the optimal mycelia growth was

determined to be 25 oC (Figure 3.6) and this was consistent over three independent

replicates. In both agar cultures and grass blade cultures, this isolate did not form sporangia

at either 20oC or 25oC.

Diversity and community composition of Phytophthora, Pythopythium and Pythium

using a metabarcoding approach. Due to violation of normality assumptions, data for the

Shannon’s diversity index was analyzed using the non-parametric Kruskal-Wallis test

(Kruskal and Wallis 1952). Environment had a significant effect on the diversity of

Phytophthora, Phytopythium and Pythium, but cultivars were not significantly different.

Mean diversity of the communities was greater at NWB17 and NWB18 environments

(Figure 3.9). These fields were irrigated to field capacity to enhance disease which resulted in greater levels of diversity compared to the other environments. The environments

DAK17, VW17 and VW25dap, had the lowest diversity, with indices of 1.3, 1.2 and 1.7, respectively. The total rainfall at these environments were DAK17 with 2.36 cm, VW17 with 3.98, and VW25dap with 10.23 cm. In addition, the Shannon’s diversity index was similar among the two environments that were sampled at both the V1 to V3 and V3 to V5 growth stages (VW17, VW25dap and DEF17, DEF25dap).

To test if the community composition of Phytophthora, Pythopythium and Pythium

was different among environments and cultivars, permutation analysis of variance was

conducted on the OTU counts after conversion to Bray-Curtis dissimilarity matrix (Figure

3.10). There was a significant effect for both environment (Adonis; P< 0.001; R2= 0.48)

and cultivar (Adonis; P< 0.001; R2= 0.01). Distinct communities were observed at each environment and these were different even at the two sampling times for environments

DEF17, DEF25dap, and VW17, VW25dap. Environments NWB17 and NWB18, which were irrigated to field capacity had very similar communities but differed from all the other environments (Figure 3.10). However, the effect of cultivar could only explain a small amount of the variance within each environment. To test whether cultivar influenced the abundance of specific genera, data was subset to the genus level. Based on ANOVA, the abundance of Phytophthora was significantly (P< 0.001) influenced by soybean cultivar, but not Phytopythium and Pythium.

Effect of environment and soybean cultivar on the abundance of Phytophthora,

Phytopythium and Pythium using a metabarcoding approach. The species abundance

of Phytophthora, Phytopythium, and Pythium, were different among environments. To

determine is a was present an arbitrary threshold of 100 counts was used. A total of 31

species of Pythium, 2 species of Phytophthora and 1 species of Phytopythium were detected

across the eleven environments (Figure 3.11). Overall, environments DAK17 and

DEF25dap had the lowest number of species, with only 5 and 6 species of Pythium present, respectively (Figure 3.11). Some Pythium species were found in all the environments

including, Py. attrantheridium, Py. heterothallicum, and Py. sylvaticum; all known

pathogens of soybean. The undescribed Pythium sp. CAL was highly abundant across all

cultivars and environments except for DEF17. Also, Py. monospermum was present in all

environments except for NWB17 and NWB18. Similarly, Py. minus was present in all

environments except for DEF25dap. Similar communities were observed at the NWB17

and NWB18 and these environments only differed by a few species. Pythium nodosum, Py.

oopapillum, and Py. ultimum were only observed at NWB17 whereas, Pythium acanthicum, Py. orthogonon, and Py. sp. CAL. were observed only at NWB18. The two environments sampled at two different growth stages had very distinct communities. For example, at VW17 out of the five species found, the community was characterized by Ph. sojae, Py. acanthicum, and Py. perrillum while at VW25dap, the community was characterized by the presence of 3 species including Ph. sansomeana, Py. acrogynum and

Py. sp. CAL. For DEF17 OTUs representing a total of 16 species of Pythium were found while at the second sampling date (DEF25dap) only 6 species of Pythium were found.

The abundance of Phytopythium was influenced by environment while Phytophthora was influenced by both environment and cultivar, compared to Pythium (Table 3). Although not significant, greater counts of Pp. helicoides were found in CLN18 and for Pp. mercuriales only at NWB17. Similarly, at the threshold of detection, Ph. sojae and Ph. sansomeana were not present in environments DAK17, CLN17, DEF17 and DEF25dap.

However, late season stem rot caused by Ph. Sojae was widely distributed at all three environments. Interestingly, at CLN18, Ph. sansomeana was 84% more abundant than Ph. sojae, and lower stand counts were obtained from this field at early plant population. In addition, the abundance of Ph. sojae in the rhizosphere was significantly affected by

cultivars. For example, the abundance of Ph. sojae in Sloan was over three quarters (~85%) greater in abundance than the resistant cultivar Kottman (ANOVA; P = 0.001) across all environments. In contrast, the abundance of Ph. sansomeana was not affected by cultivar.

For Pythium, the abundance of Py. pachycaule was the only species significantly affected by cultivar, with a 67% increase (P=0.013) in Sloan compared to Kottman.

3.5. Discussion

The diversity of species of Phytophthora, Phytopythium and Pythium recovered from across soybean producing regions where environmental conditions and soil types differ from one another, demonstrates the adaptability of this group of oomycetes to different environments. Based on different surveys conducted in Ohio, several high disease environments have been identified which has enable the study of disease management strategies including breeding advancements. These environments were selected for this study to better understand how environmental conditions drive community composition of these pathogens in soybean in Ohio. Additionally, microbial plant engineering has been proposed as a possible breeding strategy to help select beneficial microbes or repel pathogens (Bakker et al. 2012; Quiza et al. 2015; Oger et al. 2004; Perez-Jaramillo et al.

2015; Ryan et al. 2009). One means to explore the effectiveness of this approach is by deciphering the communities enhanced or reduced in cultivars that exhibited diverse levels and types of resistance. Thus, in this study the baseline or confirmation that genotype may influence the pathogen communities was addressed within each environment.

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Environment (soil plus environmental factors) has been documented to

significantly affect bacterial communities in the rhizosphere. For example, the bacterial communities associated with the rhizosphere of strawberry (Fragaria ananassa Duch.), oilseed rape (Brassica napus L.), and potato (Solanun tuberosum L.) were highly affected by the seasonal changes that occurred during a two-year sampling period (Smalla et al.

2001). Similarly, oomycetes are affected by environmental changes requiring optimal temperatures and high moisture levels for growth as observed in laboratory and field experiments of several species of oomycetes (Erwin and Ribeiro 1996; Matthiesen et al.

2016). The effect of environment in the oomycete community composition was also studied

in a survey conducted across the Midwest were latitutude, longitude and precipitation were

among the most significant factor driving community composition (Rojas et al. 2017b). In

this study, further evidence that environment, and more specifically precipitation levels,

were important drivers of community composition and disease development in the field

across eleven high disease environments in Ohio was reported. High disease pressure was

observed in the environments NWB17 and NWB18 (controlled irrigated environments)

and these also exhibited a high diversity of species based on the Shannon’s diversity index.

Saturated soil conditions that occurs after heavy rainfall, lengthens the time to favor the germination of oospores, sporangia formation and the release of motile zoospores that enables the pathogen to infect (Erwin and Ribeiro 1996). In other environments where low levels of precipitation (<3.5 cm) occurred, lower number of isolates were recovered from seedlings as well as fewer OTU’s from the rhizospheric soils, again emphasizing the importance of precipitation in the proliferation of Phytophthora, Phytopythium and

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Pythium in Ohio. Therefore, these results suggest that detection of these pathogens was

primarily controlled by the environment encountered following planting.

It has been reported that plant genotype can influence communities of

Rhizobacteria (Bulgarelli et al. 2015; Inceoglu et al. 2010; Micallef et al. 2009; Peiffer et

al. 2012; Wagner at al. 2015). However, the effect of genotype was often masked by the

environment in some studies (Peiffer et al. 2012; Pérez-Jaramillo et al. 2019). In this study

which is the first to measure the effect of soybean genotype on the abundance of

Phytophthora, Phytopythium and Pythium; environment also masked the effect of soybean genotype. For example, soybean genotype explained <1% of the variance while

environment explained almost 50%, as calculated using permutation analysis of variance.

However, when the abundance of Phytophthora, Phytopythium and Pythium was analyzed

for each genotype, some effects were observed, and these varied by environment. For

instance, the susceptible cultivar Sloan had an overall higher abundance of species of all

three genera compared to Kottman and Lorain in both the metabarcoding approach and

direct isolation methods. This was expected as Sloan is moderately susceptible to all the

species. For Ph. sojae, the presence of Rps1k, Rps3a, plus high levels of partial resistance

in Kottman and Rps1c plus high levels of partial resistance in Lorain could explain the

reduced abundance in the rhizospheric soil and lower number of isolates recovered using

traditional isolation methods. For Pythium and Phytopythium, the cultivar Kottman tends

to be susceptible to more species compared to Lorain. In addition, the saprophytic lifestyle

observed in many species of Pythium will allow these group of pathogens to proliferate

regardless of the plant genotype encountered. Although unexplored, one possible

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explanation is that cultivars may have different root exudate profiles that can lead to the

recruitment of zoospores of specific species within these group of oomycetes. For instance,

in soybean, root exudates compounds such as flavonoids (Hassan and Mathesius 2012) and

the recently discovered soyasaponins (Shiraiwa and Kurosawa 2001; Tsuno et al. 2018)

can alter the rhizosphere communities. Soyasaponins, are allelochemicals, and act as

repellent for certain microbes (Wailer et al. 1999). Flavonoids are also crucial in the

development of rhizobia, as well for the interactions with plant growth promoting bacteria,

mycorrhizal fungi, pathogens and nematodes in soybean (Hassan and Mathesius 2012). In

addition, it has been reported that Ph. sojae is positively attracted to the isoflavones daidzein and genistein which are secreted by soybean into the rhizosphere (Morris and

Ward 1992; Tyler et al. 1996). However, other Phytophthora and Pythium species are not attracted to these compounds, indicating the probability that root exudates may play a role in the assembly of communities in the rhizosphere (Morris et al. 1998). Thus, the role these compounds have in the attraction or repel of oomycete species should be further explored.

Different cells and root exudates have also been reported to affect the establishment

of oomycetes. For example, Goldberg et al. (1989) showed that zoospore of Py. dissotocum

and Py. catenulatum were chemotactically attracted to border cells of cotton and cucumber,

respectively. Interestingly, zoospores of these two species were not attracted to border cells

of non-host species. Border cells which are produced on the root tip and that are released into the environment, have also been found to attract nematodes (Zhao et al. 2000), bacteria

(Hawes et al. 2016) and fungal (Gunawardena and Hawes 2002) species and are hypothesized to be a mechanism used by plants to defend against pathogens. This provides

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further evidence of the specific interactions that occurs in the rhizosphere between the plant

and microbes and that microbial plant engineer could possibly be used to manage oomycete

pathogens. Other possible explanations for the attraction or repellent of oomycetes towards specific soybean genotypes may be plant developmental stage. For example, roots of plants at the flowering stage are more heavily populated by Bradyrhizobium, Bacillus and

Stenotrophomonas compared to vegetative and mature growth stages (Sugiyama et al.

2014). In this study, there is some evidence to support this hypothesis since two very

distinct communities were observed at the two different sampling times within the same

environment (VW17/VW25dap and DEF17/DEF25dap). Additionally, since Ph. sojae has

the capability to also infect stems during mid-season greater abundance of this species compared to Pythium and Phytopythium should be observed at later growth stages.

However, more studies should be conducted to determine which communities are enhanced at different soybean growth stages and which ones are affected based on the environmental conditions found at the time of sampling. Nonetheless, soybean cultivars with R-gene

mediated resistance combined with partial resistance were demonstrated to have higher

plant population and yields across the different high disease environments studied. The cultivar Lorain was not consistent across environments, but this could be due to the different pathogen communities found at each environment. For example, although many

species of Phytophthora, Phytopythium and Pythium were recovered from seedlings or

detected using a metabarcoding approach, not all isolates seemed to affect soybean

cultivars in the same way. When isolates were tested in the greenhouse against these

genotypes, some were able to cause disease and some exhibited low to no pathogenicity.

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This could suggest that i) species are genotype specific when tested under controlled

environments, ii) that some have saprophytic lifestyles or iii) that some may need other

species to form species complexes and cause disease. Most importantly, highlights that

isolates of the same species may have different levels of aggressiveness. Thus, it is

recommended that several isolates of the same species should be used when testing efficacy

of fungicide or during germplasm screening. To this end, results from this study provides

further evidence that host resistance still is an effective disease management strategy for

these environments that are highly conducive for disease development.

Among the species of Phytophthora, Phytopythium and Pythium detected in this

study via metabarcoding and direct isolation, several have been previously reported in

Ohio. For example, Py. sylvaticum and Py. oopapillum were found in high abundance across all environments and cultivars and are also among the most recovered species across different surveys conducted in Ohio (Broders et al. 2007, 2009; Dorrance 2016, unpublished data). In addition, the high abundance of these species, suggests that they may be core species associated with soybean. Other species like Py. attrantheridium and Py. heterothallicum, were highly abundant in the metabarcoding approach but only a few isolates were recovered from seedlings. These may have been due to the different soil

temperatures among the environments at the time of planting, compared to incubation

temperatures during isolation. For instance, some studies have used temperatures of 23-

25℃ for the recovery of isolates of Py. attrantheridium and when testing for pathogenicity

against soybean (Broders et al. 2007; Radmer et al. 2017; Rojas et al. 2017a; Zitnick-

Anderson and Nelson 2017). In this study, 20oC was used for root tissue incubation during

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isolation which may have reduced the chance to recover this species since warmer

temperature seems to be optimal for growth and pathogenicity (Radmer et al. 2017; Rojas

et al. 2017a; Zitnick-Anderson and Nelson 2017). Other species, Py. ultimum, was also found in great abundance in both the metabarcoding and direct isolation methods. This species has been recovered from many soybean producing regions and used in many germplasm screenings. Pythium ultimum is highly pathogenic towards soybean and its recovery in many different surveys suggests that this species is a core pathogen of soybean.

Pythium monospermum was also highly abundant across all environments but has not been reported as a pathogen of soybean or corn. This species was originally isolated from dead

insects in Germany and recovered in many parts of the world (van der Plaats-Niterink

1981) and reported to parasitize nonstylet-bearing nematodes (Tzean and Estey 1979). The different lifestyles this species exhibits have been hypothesized to be part of the survival mechanism used when the host is not present (Tzean and Estey 1979). Interestingly, this species has also been detected from amplicon libraries developed from soils of fields with

a soybean-corn rotation scheme in Pennsylvania (Coffua et al. 2016) and isolated from

soybean seedlings in the North Central region of the U.S. (Rojas et al. 2017a).

Another important aspect of metabarcoding is the detection of low abundance taxa

and the detection of taxa that cannot be isolated when using traditional isolation methods

alone. In this study, Py. periilum was detected in some fields with metabarcoding but not

isolated directly from seedlings. This species was previously isolated from soybean

seedlings in North Dakota where it was the first report as a pathogen of soybean in the U.S.

(Zitnick-Anderson and Nelson 2017). It was isolated from soybean seedlings plated into

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PARB+B media incubated at 23oC which was different from the protocol used in this study

both in incubation temperature and isolation media. Another important species detected in

the metabarcoding approach and from seedlings is the undescribed species Pythium sp.

CAL. This species was detected in high abundance across environments and only a few

isolates directly recovered from soybean seedlings. This study is the first one to report this

species in Ohio, but it has previously been reported in other surveys including North

Dakota (Zitnick-Anderson and Nelson 2017) and across the North Central region of the

U.S. (Rojas et al. 2017a). This species is highly pathogenic and has an optimal mycelia

growth at warmer temperatures (20-30 oC). In addition, the lack of important

morphological structures makes it difficult to describe and categorize among Pythium

clades. However, due to the abundance detected in this study through a metabarcoding

approach and the pathogenicity exhibited by the isolate towards different soybean cultivars

with different levels of resistance, further studies should be conducted to classify this

species among Pythium clades.

This study characterized the community composition of Phytophthora,

Phytopythium and Pythium species from three soybean cultivars with different types and combinations of resistance across eleven high disease environments. It was shown that environment played a major role in the community composition of these pathogens and that higher diversity of species will be observed in environments with high levels of

precipitation. In addition, it was found that a greater number of isolates can be retrieved

from the moderate susceptible cultivar Sloan compared to the moderate resistant cultivars

Kottman and Lorain. Most importantly, a significant reduction in the abundance of Ph.

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sojae was observed in the rhizosphere soil Kottman compared to Sloan. We also generated a collection of isolates obtained from symptomatic and asymptomatic soybean seedlings from 11 different environments across Ohio including isolates of the undescribed Pythium

sp. CAL. These results also provide evidence that disease, when observed in the field, is

often caused by more than one pathogen and that changes in lifestyles are used by the

pathogen to survive when the host is not encountered. Further studies should focus in the

profiling of root exudates by these soybean genotypes to better characterize compounds

involved in the interaction of oomycetes with soybean. Finally, results from this study will

enable the development of more target disease management practices by selecting cultivar

that can perform better in fields where conducive conditions are encountered. Also, this

study shows that species of Phytophthora, Phytopythium, and Pythium should be monitored in a regular base since populations can change and novel species can develo

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Table 3.1. Summary of accumulated precipitation and temperature each environment in Ohio where seedlings were sampled and total number of isolates of Phytophthora, Phytopythium and Pythium recovered from cultivars Kottman, Lorain and Sloan with different levels and types of resistance, using direct isolation methods.

Environment Rain Irrigation(cm) Rain Total Average air Total isolates Total recovered by (County-year) 14dap 25dap rain temperature(oC) recovered by cultivar (cm) (cm) (cm) environment

Kottman Lorain Sloan

DAK17a 2.36 . 2.36 15 3 0 3 0

CLN17b 3.32 . 3.32 17 41 9 7 25 VW17c 3.98 . 10.23 *3.98 14 18 1 5 12 DEF17 d 7.23 . 8.58 *7.23 14 4 0 2 2

NWB17 3.83 6.14 9.398 15 74 17 20 37

SNY17e 5.20 . 5.20 14 58 12 14 32 15 3 4 8 PAL18 2.84 . 2.84 19 NWB18 2.2 3.63 5.89 16 45 10 12 23

LOG18 3.30 . 3.30 19 2 0 0 2 CLN18 5.96 . 5.96 21 13 7 2 4 SNY18 3.5 . 3.5 18 4 2 0 2

Total 277 61 69 147

σ OARDC weather station on site.Ψ www.cocorah.com station OH-DR-19. ¥ www.cocorah.com station OH-CN-16 Σww.weatherlink.com Δ www.cocorah.com station OH-DF-1 https://www.ncdc.noaa.gov/crn/station.htm?stationId=1797 *Precipitation occurred 3 days before sampling. 109

Table 3.2. Summary of the total number of isolates of Phytophthora, Phytopythium, and Pythium recovered using direct isolation methods from seedlings of the cultivars Kottman, Lorain and Sloan with different types and levels of resistance, across 11 environments in Ohio.

Kottman Lorain Sloan Total s.Pythium.oopapillum 13 14 24 51 s.Pythium.sylvaticum 7 5 20 32 s.Phytophthora.sansomena 5 5 10 20 s.Pythium dissotocum 5 5 8 18 s.Pythium.inflatum 5 2 5 12 s.Pythium.ultimum 4 7 19 30 s.Phytopythium.mercuriale 3 0 2 5 s.Pythium.torulosum 2 3 8 13 s.Phytopythium.vexans 2 3 2 7 s.Pythium.coloratum 2 1 5 8 s.Pythium.aphanidermatum 2 1 1 4 s.Pythium.sp.CAL.2011f 2 0 1 3 s.Phytophthora.sojae 1 10 26 37 s.Pythium.irregulare 1 5 0 6 s.Pythium.intermedium 1 1 2 4 s.Pythium.acanthicum 1 0 1 2 s.Pythium.aff.hydnosporum 1 0 1 2 s.Pythium.aff.pleroticum 1 0 0 1 s.Pythium.hypogynum 1 0 0 1 s.Pythium.orthogonon 1 0 0 1 s.Pythium.rostratifingens 1 0 0 1 s.Pythium.attrantheridium 0 3 4 7 s.Pythium.periplocum 0 2 0 2 s.Pythium.heterothallicum 0 1 3 4 s.Pythium.perplexum 0 1 2 3 s.Phytopythium.chamaehyphon 0 0 2 2 s.Phytopythium.helicoides 0 0 1 1 Total 61 69 147 277

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Table 3.3. Analysis of variance significance values of the effects of environment and cultivar for the abundance of species of Phytophthora, Phytopythium and Pythium using a metabarcodng approach. Species highlighted in yellow were significant at P = 0.05. Species Environment Cultivar s.Phytophthora.sojae 0.002 0.016 s.Phytophthora.sansomea 0.314 0.124 s.Phytopythium.chamaehyphon 0.376 0.242 s.Phytopythium.helicoides 0.405 0.385 s.Phytopythium.vexans 0.010 0.461 s.Phytopythium_mercuriale 0.231 0.436 s.Pythium.acanthicum 0.012 0.523 s.Pythium.acrogynum 0.000 0.382 s.Pythium.aff..hydnosporum 0.001 0.752 s.Pythium.aff..pleroticum 0.000 0.694 s.Pythium.aff..volutum 0.000 0.383 s.Pythium.aphanidermatum 0.123 0.409 s.Pythium.arrhenomanes 0.000440 0.407 s.Pythium.attrantheridium 0.000000 0.720 s.Pythium.chondricola 0.000189 0.346 s.Pythium.conidiophorum 0.000156 0.732 s.Pythium.folliculosum 0.002950 0.287 s.Pythium.heterothallicum 0.000002 0.740 s.Pythium.inflatum 0.000000 0.117 s.Pythium.irregulare 0.000010 0.470 s.Pythium.longandrum 0.268 0.382 s.Pythium.middletonii 0.444 0.428 s.Pythium.minus 0.417 0.820 s.Pythium.monospermum 0.001 0.605 s.Pythium.nodosum 0.329 0.317 s.Pythium.nunn 0.000 0.217 s.Pythium.oopapillum 0.132 0.596 s.Pythium.orthogonon 0.000 0.429 s.Pythium.pachycaule 0.000 0.013 s.Pythium.parvum 0.014 0.607 s.Pythium.periilum 0.000 0.162 s.Pythium.periplocum 0.089 0.379 s.Pythium.perplexum 0.000 0.384 s.Pythium.rostratifingens 0.001 0.573 s.Pythium.selbyi 0.004 0.559 s.Pythium.sp..CAL.2011f 0.000 0.301 s.Pythium.sylvaticum 0.000 0.345 s.Pythium.ultimum 0.000 0.562 111

Figure 3.1. The counties (environments) sampled in Ohio during this study that were selected based on reported seedling disease incidence. Counties with black circle were sampled both years; yellow triangles only in 2017; and green squares only in 2018.

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Figure 3.2. Example of soils found in Ohio with high clay content. Soils after heavy rainfall, retain water and enable disease development caused by Phytophthora, Phytopythium and Pythium.

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Figure 3.4.Number of isolates of Phytophthora, Phytopythium and Pythium recovered from soybean seedlings of the cultivars with different levels and types of resitance (Kottman, Lorian and Sloan) using a direct isolation technique. Seedlings were collected at V1-V3 growth stage across the eleven environments in Ohio during 2017 and 2018. Plates were incubated at 20 oC, and species were identified by amplifying the rRNA gene with primers ITS1 and ITS4.

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Figure 3.5. Pathogenicity assay of Phytophthora, Phytopythium, and Pythium isolates recovered from soybean seedlings in 2017. The soybean cultivars Kottman, Lorain and Sloan, with different levels and types of resitance were tested using the root cup assay method. Asterisk represents a significant reduction in root weight (P<0.05) when compared to the non-inoculated control. Bars represent standard deviation from the mean.

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Figure 3.6. Pathogenicity assay of Pythium sp. CAL-2011f, isolate recovered from soybean seedlings in 2018. The soybean cultivars Conrad, Kottman, Williams and Sloan, with different levels and types of resitance were tested using the root cup assay method. Asterisk represents a significant reduction in root weight (P<0.05) when compared to the non- inoculated control. Bars represent standard deviation from the mean.

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Figure 3.7. Disease developed on soybean cultivars Sloan, Conrad, Williams and Kottman fourteen days after planting using a root cup assay method. Roots are showing rotting symptoms compared to the not inoculated control (NT-Control).

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Figure 3.8. Optimal temperature for growth of Pythium sp. CAL. The optimal temperature for mycelia growth was reached at 25 o C (n=6). Temperature of 37 o C was tested but plates did not grow.

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Figure 3.9. Shannon’s diversity index for the species Phytophthora, Phytopythium and Pythium detected in the rhizosphere of the cultivars Kottman, Lorain and Sloan, with different levels and types of resistance, across 11 field environments in Ohio. Shannon’s diversity index was calculated using the VEGAN package in R from non-normalized data. Environments DEF25dap and VW25dap were sampled at growth stage V3-V5.

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Figure 3.10. Nonmetric multidimensional scaling (NMDS) plots using Bray-Curtis dissimilarity of Phytophthora, Phytopythium and Pythium community data retrieved from the rhizosphere of three soybean cultivars with different levels and types of resistance, across 11 field environments in Ohio. Colors represent environments sampled and shapes represent cultivars. Environments DEF25dap and VW25dap were sampled at growth stage V3-V5. Permutation analysis showed environments significantly contributing to the community composition. Lines are depicting convex hulls enclosing all samples pertaining to the same environment.

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Figure 3.11. Relative abundance based on cumulative sum scaling normalized counts (n=8) of the species Phytophthora, Phytopythium and Pythium detected in the rhizosphere of the cultivars Kottman, Lorain and Sloan with different levels and types of resistance, across 11 field environments in Ohio (n=8).

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Figure 3.12. Prevalence of taxa versus total counts. Each dot represents one OTU belonging to different Phyla after normalization using the cummulative sum scaling appraoch.

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Chapter 4. Summary and future directions

The root rhizosphere is a place where plants are in constant association with many

different microorganisms including bacteria, archea, oomycetes, fungi and other

microfauna. Soil edaphic factors and environmental conditions are important in the

establishment and growth of these microbes. Interactions among organisms within the

rhizosphere reportedly have been known to affect the overall plant health. Thus, a deeper

understanding of these dynamics is crucial to improve crop productivity. Our research goal

for this study was to understand factors affecting the community composition of

Phytophthora, Phytopythium and Pythium species that are associated with soybean in Ohio.

This understanding will then allow us to focus on those factors (ie: agronomic practices)

that can enhance the establishment of beneficial microbes or reduce pathogen inoculum

levels to help increase crop productivity in a sustainable way.

In the second chapter of this study, a metabarcoding approach as well as direct isolation from soybean seedlings enabled the characterization of communities of

Phytophthora, Phytopythium and Pythium among soils from an 81-ha research farm with

similar soil edaphic factors but where different agronomic practices where performed. The

different agronomic practices of tillage and crop rotation had differences in community

composition and these communities also differed when soils were incubated at different

temperatures of 15 and 25oC. Results from this study highlight the importance of

temperature in the development of this group of pathogens. The effects of temperature in

growth and pathogenicity have been found previously, but none of these studies have

looked at changes in the entire community. Similarly, continuous cropping is known to 137 enhance levels of pathogenic species as observed in field where a continuous corn rotation has been used (Deep and Lips 1996; Rojas et al. 2019). In this study, a field planted in continuous corn, had higher levels of of Py. arrhenomanes compared to other fields. This species is known to infect corn, serves as an example that rotation with different crops or fallow helps manage disease. Also, in soils retrieved from an irrigated plot (NWB), a greater level of diversity was observed. This result indicates that in addition to temperature, moisture levels maybe the most important factor affecting community composition and diversity. However, more experiments should be conducted to test this effect in more detail.

For example, further experiments should look at how abundance of certain species change when soils are first sterilized and known levels of inoculum added to the soil. By comparing abundance of Phytophthora, Phytopythium and Pythium between non sterilized soils and sterilized soils inoculated with these pathogens, we could determine which species are directly affected by temperature alone, and which ones are affected by the presence of other microbes in the soil. Similarly, other experiments that should be conducted to better characterize the communities present after agronomic practices are used. For example, species diversity and community composition should be explored over time. Fields should be sampled before conducting the agronomic practice and changes should be tracked over different years. This will then help understand which species are more affected by tillage and crop rotation and will help to better understand what other microbes will colonize when some species are no longer present.

In the third Chapter, the community composition of Phytophthora, Phytopythium and

Pythium associated with three soybean cultivars with different levels and types of

138 resistance across eleven high disease environments in Ohio was explored using a metabarcoding approach and direct isolation methods. Markedly, environment was the biggest factor contributing to diversity of these pathogens. From all the environmental factors, precipitation played a big role in the recovery of species from seedlings and the diversity and community composition when using a metabarcoding approach. High levels of precipitation paired with soils with high clay content will retain free water that is then used for oospore germination and zoospore motility towards soybean roots. In locations were precipitation did not occur or where the field slope helped drain the water, lower number of isolates were recovered, and lower diversity and species abundance was observed. This study also showed that rhizosphere soil from the susceptible soybean cultivar Sloan, had an increased abundance of Ph. sojae compared to the resistant cultivar

Kottman. This result highlights the importance of using resistance in the field to manage disease. In addition, it opens a new area of study for microbiome engineering. If cultivars of soybean could be bred for repelling certain pathogens, then inoculum could be lowered in the field and lower levels of disease incidence will be observed. Other significant results observed in this chapter were the recovery of Py. species CAL from seedlings of soybean and the detection of Py. periilum through the metabarcoding approach. When Py. species

CAL was tested for pathogenicity, this species was very aggressive and seemed to affect soybean cultivars with different levels of resistance and exhibited an optimal mycelia growth at 25oC. In contrast, Py. periilum was not retrieved from seedlings but was detected in the metabarcoding data. Due to the high aggressiveness exhibited by Py. species CAL, this species should be added to the list of pathogens used for fungicide and germplasm

139 screening. In addition, morphological studies should be conducted to determine to which

Pythium clade this species belongs to. In addition, the presence of Py. periilum should be confirmed with other molecular methods and modified culturing media should be used to recover this species from seedlings.

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Appendix A: Codes use for Miseq data processing

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# Step 1: Move all the fastq files into Raw_Data folder

#This will creat "Raw_Data" folder mkdir /fs/project/PAS0471/osu8900/Mi-SeqRun2/Raw_Data # Assign download folder path to variable 'fastq_download_dir'- reduce path length. fastq_download_dir=/fs/project/PAS0471/osu8900/Mi- SeqRun2/180206_AnneDorrance_KrystelNavarro-amplicons-64594557 # change the directory to 'fastq_download_dir' cd $fastq_download_dir #move fastq files inside 'fastq_download_dir' to 'Raw_Data' folder for i in */*/*;do echo $i ../Raw_Data/;done for i in */*/*;do mv $i ../Raw_Data/;done

#Samples from the Run were relabeled with original sample labels for i in $(cat filenames.txt); do samplename=$(echo $i | cut -f1 -d","); sampleID=$(echo $i | cut -f2 -d","); cp ${sampleID}_*_R1_* ../Raw_Data/${samplename}_R1.fastq.gz;done

for i in $(cat filenames.txt); do samplename=$(echo $i | cut -f1 -d","); sampleID=$(echo $i | cut -f2 -d","); cp ${sampleID}_*_R2_* ../Raw_Data/${samplename}_R2.fastq.gz;done

# Step 2: Remove Illumina barcodes from foward reads and reverse reads # We are using 'illumina_adapter_rm.sh' script which is in /fs/project/PAS0471/osu8900/Software/Scripts # Edit the paths in 'illumina_adapter_rm.sh', i.e working_dir, raw_data, ADAPTSEQ # working_dir=/fs/project/PAS0471/osu8900/Desktop/osu8900/Mi- SeqRun/Illumina_Adapter_Removed # raw_data=/fs/project/PAS0471/osu8900/Desktop/osu8900/Mi- SeqRun/Raw_Reads # ADAPTSEQ=/fs/project/PAS0471/osu8900/Desktop/osu8900/Software/bbmap/resources /nextera.fa.gz

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cd /fs/project/PAS0471/osu8900/Mi-SeqRun2/Scripts bash illumina_adapter_rm.sh

#Step 3: Merged_Pair_Ends #Edit the paths in 'merged_paires.sh', raw_data=/fs/project/PAS0471/osu8900/Mi- SeqRun2/Illumina_Adapter_Removed working_dir=/fs/project/PAS0471/osu8900/Mi-SeqRun2/Merged_Reads rm -rf $working_dir mkdir $working_dir cd $raw_data

#Files will have to be un-zip before merging gzip -d *

/users/PAS0471/osu8900/Desktop/osu8900/Software/usearch10.0.240_i86linux32 -fastq_mergepairs *_R1.fastq -reverse *_R2.fastq -relabel @ -fastq_minovlen 20 - fastqout $working_dir/merged.fq 2>$working_dir/merged.stats

#Run stats. Use stats scripts

#Step 4: Quality filtering #Edit the paths in q_filtered_pairs.shg working_dir=/fs/project/PAS0471/osu8900/Mi-SeqRun2/Q-Filtered merged_data=/fs/project/PAS0471/osu8900/Mi-SeqRun2/Merged_Reads cd $merged_data rm -rf $working_dir mkdir $working_dir

/users/PAS0471/osu8900/Desktop/osu8900/Software/usearch10.0.240_i86linux32 -fastq_filter merged.fq -fastq_maxee 1.0 -fastaout $working_dir/q-filtered.fasta -log $working_dir/q-filtered.log

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#Step 5 ITSx #make sure to select all organims in the analysis. #run following commands export PATH=/fs/project/PAS0471/osu8900/Software/ITSx_1.0.11:$PATH export PATH=/fs/project/PAS0471/osu8900/Software/hmmer-3.1b2-linux-intel- x86_64/binaries:$PATH

ITSx -i q-filtered.fasta -o q-filtered.x --reset T --cpu 25 --allow_reorder T -- allow_single_domain 1e-1,0 --only_full T --save_raw T

grep -c ">" Oom.x.ITS1.fasta #to know how many sequences you got

#Step 6 OTU clustering, and taxaonomy clasification. Steps can be found here: http://drive5.com/usearch/manual/ex_miseq_its.bash

# Find unique read sequences and abundances /users/PAS0471/osu8900/Desktop/osu8900/Software/usearch10.0.240_i86linux32 -fastx_uniques /users/PAS0471/osu8900/Desktop/osu8900/Mi-SeqRun2/ITSx/q- filtered.x.ITS1.fasta -sizeout -relabel Uniq -fastaout ITS1uniques.fa

# Run UPARSE algorithm to make 97% OTUs /users/PAS0471/osu8900/Desktop/osu8900/Software/usearch10.0.240_i86linux32 -cluster_otus /users/PAS0471/osu8900/Desktop/osu8900/Mi-SeqRun2/ITS1uniques.fa - otus ITS1OTU_2.fa

# Run UNOISE algorithm to get denoised sequences (ZOTUs) /users/PAS0471/osu8900/Desktop/osu8900/Software/usearch10.0.240_i86linux32 -unoise3 /users/PAS0471/osu8900/Desktop/osu8900/Mi- SeqRun2/OTUTable/ITS1OTU_2.fa -zotus ITS1zotus_2.fa

# Downstream analysis of OTU sequences & OTU table # Can do this for both OTUs and ZOTUs.

# Make OTU table

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/users/PAS0471/osu8900/Desktop/osu8900/Software/usearch10.0.240_i86linux32 -otutab /users/PAS0471/osu8900/Desktop/osu8900/Mi-SeqRun2/ITSx/q- filtered.x.ITS1.fasta -otus ITS1zotus_2.fa -otutabout zotutab_Run2.txt

#Taxonomy- We used a costum database from Bakker (Oomycetes) and UNITE (fungal ITS db) /users/PAS0471/osu8900/Desktop/osu8900/Software/usearch10.0.240_i86linux32 -sintax ITS1zotus_2.fa -db /users/PAS0471/osu8900/Desktop/osu8900/Mi- SeqRun2/Database/UNITE_Bakkerdb_mod.fasta -tabbedout Taxo_Run2modzotu.txt - strand both -sintax_cutoff 0.1

#Fixing the output file after taxonomy

#This was the command used and then file was imported to Excel and fixed the file so that it did not have duplicates. sed 's/\+.*//g' Taxo_Run2modzotu.txt | sed 's/\-.*//g'| sed 's/[(][^)]*[)]//g'| sed 's/[a- z]\://g' >Taxo_Run2modzotu_2.csv

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Appendix B. Data processing and statistical analysis of metabarcoding data

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#------# #------Data analysis in R------# #------#

#------Library upload------library("phyloseq") library("ggplot2") library('gridExtra') library('scales') library("RColorBrewer") library('plyr') library("colorspace") require('gdata') library('reshape2') library('vegan') library("data.table") library("edgeR") library("reshape") library("agricolae") library("cowplot") library("vegan") library("tidyverse") library("reshape") library("metagenomeSeq")

#------# #------Set Working Directory------# #------# getwd() setwd("C:/Users/navarro-acevedo.1/Box Sync/Krystal/01_21_19")

#------# #------Importing files------# #------#

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otumat<- read.csv("Temp_zotu.csv", header=TRUE, row.names = 1) otumat <- as.matrix(otumat[,])

taxmat= read.csv("Taxo_05_09_19.csv",fill =T, na.strings = "", header = TRUE) colnames(taxmat)<-c("OTU","Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species") Kingdom<-gsub("\t","", taxmat$Kingdom) taxmat$Phylum<-gsub("\t","", taxmat$Phylum) taxmat$Class<-gsub("\t","", taxmat$Class) taxmat$Order<-gsub("\t","", taxmat$Order) taxmat$Family<-gsub("\t","", taxmat$Family) taxmat$Genus<-gsub("\t","", taxmat$Genus) taxmat$Species<-gsub("\t","", taxmat$Species) rownames(taxmat)<- taxmat$OTU taxmat<-taxmat[,-1] taxmat <- as.matrix(taxmat[,])

sample_data_t <-read.csv("Mapping_Temperature.csv", header = T, row.names = 1)

#------# #------Create a phyloseq object------# #------#

OTU = otu_table(otumat, taxa_are_rows = TRUE) TAX = tax_table(taxmat) sampledata = sample_data(sample_data_t)

physeq = phyloseq(OTU,sampledata, TAX) physeq #This object has 100 samples. We need to merge technical reps to get only 50. The 50 represent samples retrieved from 5 pots from 5 fields at 15 and 25C.

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#------# #------Measuring Alpha diversisty ------# #------#

#----First Subset for only Oomycetes----#

oom_raw <- subset_taxa(physeq, Phylum == "p:Stramenopila")

#---Create alpha diversity measurments---#

a.div_raw <- estimate_richness(oom_raw, measures = c("Observed", "Shannon", "Simpson", "Fisher")) as.data.frame(a.div_raw) write.csv(a.div_raw, "alpha_diver_raw.csv") a.div_raw<- read.csv("alpha_diver_raw.csv", header = TRUE)#metadata was added to the file using excel

#---Check Distribution---#

par(mfrow = c(1, 2)) hist(a.div_raw$Shannon, main="Shannon diversity", xlab="", breaks=10)#normally distributed hist(a.div_raw$Simpson, main="Simpson diversity", xlab="", breaks=10) #normallly distributed

#---Check Normalicy------#

shapiro.test(a.div_raw$Shannon) shapiro.test(a.div_raw$Simpson)

#---One-way ANOVA on Temperature---#

adiverB1 <- a.div_raw[a.div_raw$Field=="B1",] adiverB6 <- a.div_raw[a.div_raw$Field=="B6",] adiverD3 <- a.div_raw[a.div_raw$Field=="D3",] adiverD15 <- a.div_raw[a.div_raw$Field=="D15",] adiverTA4 <- a.div_raw[a.div_raw$Field=="TA4",]

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#--- change the data.frame to see ANOVA results---#

aov.shannon.all = aov(Shannon ~ Field*Temperature, data=a.div_raw) summary(aov.shannon.all) #Interaction was not significant, we remove the interaction term

aov.shannon.all2 = aov(Shannon ~ Field+Temperature, data=a.div_raw) summary(aov.shannon.all2)

out_Tukey<- HSD.test(aov.shannon.all2, "Temperature", group=TRUE)

adiver_result <- aov(Shannon ~ Temperature, adiverB1) summary(adiver_result) out_Tukey<- HSD.test(adiver_result, "Temperature", group=TRUE) out_Tukey

popPalette <- c("#CC79A7", "goldenrod", "#0072B2","#009E73","grey","#F0E442","lavender", "aquamarine3","darkorchid","cadetblue3","#D55E00")

ggplot(a.div_raw,aes(x=Field,y=Shannon,color=Temperature))+ geom_point(size=4,position=position_dodge(w=0.3))+ scale_color_manual(values=c('red','blue'),name='Year')+ theme_classic()+ ggtitle("Alpha Diversity")+ ylab("Shannon's Diversity Index")+ theme(plot.title=element_text(size=36,face="bold",colour="grey"))+ theme(axis.title.x=element_text(size=36,face="bold"),axis.text.x=element_text(size=30,v just=0.8,hjust=0.9,face="bold",colour=popPalette))+ theme(axis.title.y=element_text(size=36,face="bold"),axis.text.y=element_text(size=30,f ace="bold"))+ theme(legend.title= element_text(size=30),legend.text=element_text(size=28,face="bold"))+ theme(legend.key.height=unit(1.5,"lines"),legend.key.width=unit(2.5,"lines"))+ theme(legend.background = element_rect(fill="gray90", size=.5))

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#---Make boxplot of alpha measurments---#

bp1<- ggplot(a.div_raw, aes(x=Field, y=Shannon)) + geom_boxplot(fill='#A4A4A4', color="black") + theme_classic()

#------# #------Data Normalization------# #------#

phenotypeData<-AnnotatedDataFrame(as.data.frame(sample_data(physeq))) TAXdata<-AnnotatedDataFrame(as.data.frame(tax_table(physeq))) OTUdata<-as(otu_table(physeq), "matrix") obj<-newMRexperiment(OTUdata, phenoData = phenotypeData, featureData = TAXdata)

MRcounts(obj) raw_libsize <- libSize(obj)

normalization_method<-"CSS" present<-1 depth<-1000 source('normalization_css.R') obj_normalized_flr<- normalization_filter_css(obj,present,depth,normalization_method)

normFactors(obj_normalized_flr) norm <- MRcounts(obj_normalized_flr, norm = TRUE, log=FALSE)

exportMat(norm, file = 'TempCSS_norm.csv', sep = '\t')

#Read otu table back to phyloseq object

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otumatCSS<- read.csv("TempCSS_norm.csv", header=TRUE, row.names = 1)

#otumatCSS <- as.data.frame(otumat[,])

otumat <- norm

OTU_CSS <- otu_table(otumat, taxa_are_rows = TRUE)

phyloseqCSS <- phyloseq(OTU_CSS, sampledata, TAX)

phyloseqCSS

#------# #------Prevalence of other Taxa------# #------#

rank_names(phyloseqCSS) table(tax_table(phyloseqCSS)[, "Phylum"], exclude = NULL)

# Compute prevalence of each feature, store as data.frame prevdf = apply(X = otu_table(phyloseqCSS), MARGIN = ifelse(taxa_are_rows(phyloseqCSS), yes = 1, no = 2), FUN = function(x){sum(x > 0)}) # Add taxonomy and total read counts to this data.frame. prevalence in the dataset, which we will define here as the number of samples in which a taxon appears at least once. prevdf = data.frame(Prevalence = prevdf, TotalAbundance = taxa_sums(phyloseqCSS), tax_table(phyloseqCSS))

#Are there phyla that are comprised of mostly low-prevalence features? Compute the total and average prevalences of the features in each phylum.

plyr::ddply(prevdf, "Phylum", function(df1){cbind(mean(df1$Prevalence),sum(df1$Prevalence))})

# Subset to the remaining phyla

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prevdf1 = subset(prevdf, Phylum %in% get_taxa_unique(phyloseqCSS, "Phylum")) ggplot(prevdf1, aes(TotalAbundance, Prevalence / nsamples(phyloseqCSS),color=Phylum)) + theme_bw()+

# Include a guess for parameter geom_hline(yintercept = 0.05, alpha = 0.5, linetype = 2) + geom_point(size = 2, alpha = 0.7) + scale_x_log10() + xlab("Total Abundance") + ylab("Prevalence of taxa") + facet_wrap(~Phylum) + theme(legend.position="none")+

theme(axis.text.y = element_text(color = "black"))+ theme(axis.text.x = element_text(color = "black"))+

theme(text=element_text(size=14, # family="Comic Sans MS")) # family="CM Roman")) # family="TT Times New Roman")) # family="Sans")) family="sans", face="bold"))+ theme(axis.title.y = element_text(margin = margin(t = 0, r = 20, b = 0, l = 0)))

#---Subset for only oomyctes---#

oomycetes = subset_taxa(phyloseqCSS, Phylum == "p:Stramenopila") oomycetes3 = subset_taxa(physeq, Phylum == "p:Stramenopila")

table(tax_table(oomycetes)[, "Species"], exclude = NULL)

# Compute prevalence of each feature, store as data.frame Pydf = apply(X = otu_table(oomycetes),

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MARGIN = ifelse(taxa_are_rows(oomycetes), yes = 1, no = 2), FUN = function(x){sum(x > 0)}) # Add taxonomy and total read counts to this data.frame. prevalence in the dataset, which we will define here as the number of samples in which a taxon appears at least once. Pydf2 = data.frame(Prevalence = Pydf, TotalAbundance = taxa_sums(oomycetes), tax_table(oomycetes))

write.csv(Pydf2, "prevalence.csv")

#------# #------Ordination ------# #------#

glomsp <- tax_glom(oomycetes, taxrank = 'Species') melt.glom <- psmelt(glomsp) melt.glom$Species <- as.character(melt.glom$Species) glomsp2 <- aggregate(Abundance ~ Sample+Species, melt.glom, FUN=sum) glomsp3 <- cast(glomsp2, Sample ~ Species)

drop <- apply(glomsp3_CSS, 2, function(x) { length(which(x == 0))/length(x) > .99 }) abuntable <- glomsp3_CSS[, !drop] write.csv(abuntable, "abundance_table.csv")

write.csv(glomsp3, "glomsp3_CSS.csv")

#---Dataframe was edited in excel to add the temperature, field and sample colums---#

glomsp3_CSS<- read.csv("glomsp3_CSS.csv")

#---define the data.frame---#

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df <- mutate_at(glomsp3_CSS, 1:7, as.factor) str(df) dim(df)

#--- Convert to Relative Abundance by Sample---#

#---split apart experimental factors from community matrix---#

factor.df <- df[,1:7] comm.df <- df[,8:58]

#---covert each total abundance value to a percentage of the row (sample) total sum---#

rel.df <- comm.df for(i in 1:dim(comm.df)[1]){ rel.df[i,] <- comm.df[i,]/sum(comm.df[i,]) }

#---check output. It should equal to 1 since is rel abundance---# str(rel.df) rowSums(rel.df)

#---recombine data matrix---#

rel.df <- cbind(factor.df,rel.df) str(rel.df) dim(rel.df)

##------## ##------permutational ANOVA and NMDS------## ##Do our temperature influence microbial community structure? ##------##

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#---drop any taxa that was since zeros can affect the statistics----#

dropidx <- apply(rel.df, 2, function(x) { length(which(x == 0))/length(x) > .99 }) rel.df2 <- rel.df[, !dropidx]

write.csv(rel.df2, "rel.df2.csv")

adonis(rel.df2[,8:58] ~Field*Temperature + Replicate/Run, data=rel.df2)

d1rel <- rel.df2[,1:7] #factors d2rel <- rel.df2[,8:58] #abundance matrix, relative aundance, normalized

#NMDS in vegan NMDSall <- metaMDS(d2rel, distance = "bray", k=2)

par(mfrow=c(1,1))

#VIzualization co=c("red", "blue") shape=c(18, 16, 12, 17, 13)

plot(NMDSall$points, col=co[d1rel$Temperature], pch=shape[d1rel$Field], cex=1.2, xlab= "NMDS 1", ylab="NMDS 2", font.axis=2, font.lab=2) txt <- c("B1 (SS-Till)", "B6 (CSF-Till)", "D15 (CSF-Nt)", "TA4 (CC-Nt)", "D3 (CSWF-Till)") txt2<- c("15°C", "25°C") op <- par(family = "sans", font=2) legend('topleft', txt, pch= c(18, 16, 12, 17, 13), cex=1, bty="y") legend('topright',txt2, pch=21, col=co, cex=1, bty="y") par(op)

## By each locations individually

#------

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#---B1------

B1 <- rel.df[rel.df$Field=="B1",]

## drop any OTUs that have no data in column dropidx <- apply(B1, 2, function(x) { length(which(x == 0))/length(x) > .99 }) B1 <- B1[, !dropidx] dim(B1) str(B1) adonis(B1[,8:44]~Temperature+ Replicate/Run, data= B1)

#NMDS in vegan NMDSB1 <- metaMDS(B1[,8:44], distance = "bray", k=2)

#Vizualization co=c("red", "blue") plot(NMDSB1$points, col=co[B1[,1:7]$Temperature], pch=19, cex=1.2, xlab= "NMDS 1", ylab="NMDS 2",font.axis=2, font.lab=2) ordispider(NMDSB1, groups = B1[,1:6]$Temperature, label=TRUE) txt2<- c("15°C", "25°C") op <- par(family = "sans", font=2) legend('toprigh',txt2, pch=19, col=co, cex=1, bty="y") par(op)

#------#---B6------

B6 <- rel.df[rel.df$Field=="B6",]

## drop any OTUs that have no data in column dropidxB6 <- apply(B6, 2, function(x) { length(which(x == 0))/length(x) > .99 }) B6 <- B6[, !dropidxB6] dim(B6) str(B6)

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adonis(B6[,8:51]~Temperature+Replicate/Run, data= B6)

#NMDS in vegan NMDSB6 <- metaMDS(B6[,8:51], distance = "bray", k=2)

#VIzualization co=c("red", "blue")

plot(NMDSB6$points, col=co[B6[,1:7]$Temperature], pch=19, cex=1.2, xlab= "NMDS 1", ylab="NMDS 2", font.axis=2, font.lab=2) ordispider(NMDSB6, groups = B6[,1:7]$Temperature, label=TRUE) txt2<- c("15°C", "25°C") op <- par(family = "sans", font=2) legend('toprigh',txt2, pch=19, col=co, cex=1, bty="y") par(op)

#------#---TA4------

TA4 <- rel.df[rel.df$Field=="TA4",]

## drop any OTUs that have no data in column dropidxTA4 <- apply(TA4, 2, function(x) { length(which(x == 0))/length(x) > .99 }) TA4 <- TA4[, !dropidxTA4] dim(TA4) str(TA4)

adonis(TA4[,8:42]~Temperature+Replicate/Run, data= TA4)

#NMDS in vegan NMDSTA4 <- metaMDS(TA4[,8:42], distance = "bray", k=2)

#VIzualization co=c("red", "blue") plot(NMDSTA4$points, col=co[B6[,1:7]$Temperature], pch=19, cex=1.2, xlab= "NMDS 1", ylab="NMDS 2", font.axis=2, font.lab=2)

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ordispider(NMDSTA4, groups = TA4[,1:7]$Temperature, label=TRUE) txt2<- c("15°C", "25°C") op <- par(family = "sans", font=2) legend('toprigh',txt2, pch=19, col=co, cex=1, bty="y") par(op)

#------#---D15------

D15 <- rel.df[rel.df$Field=="D15",]

## drop any OTUs that have no data in column dropidxD15 <- apply(D15, 2, function(x) { length(which(x == 0))/length(x) > .99 }) D15 <- D15[, !dropidxD15] dim(D15) str(D15)

adonis(D15[,8:43]~Temperature+Replicate/Run, data= D15)

#NMDS in vegan NMDSD15 <- metaMDS(D15[,8:42], distance = "bray", k=2)

#VIzualization co=c("red", "blue") plot(NMDSD15$points, col=co[D15[,1:7]$Temperature], pch=19, cex=1.2, xlab= "NMDS 1", ylab="NMDS 2", font.axis=2, font.lab=2) ordispider(NMDSD15, groups = D15[,1:7]$Temperature, label=TRUE) txt2<- c("15°C", "25°C") op <- par(family = "sans", font=2) legend('toprigh',txt2, pch=19, col=co, cex=1, bty="y") par(op)

#------#---D3------

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D3 <- rel.df[rel.df$Field=="D3",]

## drop any OTUs that have no data in column dropidxD3 <- apply(D3, 2, function(x) { length(which(x == 0))/length(x) > .99 }) D3 <- D3[, !dropidxD3] dim(D3) str(D3)

adonis(D3[,8:46]~Temperature+Replicate/Run, data= D3)

#NMDS in vegan NMDSD3 <- metaMDS(D3[,8:46], distance = "bray", k=2)

#VIzualization op <- par(family = "sans", font=2) co=c("red", "blue")

plot(NMDSD3$points, col=co[D3[,1:7]$Temperature], pch=19, cex=1.2, xlab= "NMDS 1", ylab="NMDS 2", font.axis=2, font.lab=2)

ordispider(NMDSD3, groups = D3[,1:7]$Temperature, label=TRUE) txt2<- c("15°C", "25°C") legend('toprigh',txt2, pch=19, col=co, cex=1, bty="y") par(op)

## ------## ## Homogeneity of Variances ## ## ------##

## to test if treatment variances are homogenous across samples ## Does (multivariate)variance differ between treatments? This assumption is needed to determine if your adonis is due to ##treatment effect or if the effect is due to differences in dispersion.

## Temperature within each location

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c <- betadisper(vegdist(rel.df[,8:58]), rel.df$Field) t<- betadisper(vegdist(rel.df[,8:58]), rel.df$Temperature)

x<- betadisper(vegdist(B1[,8:44]), B1$Temperature) y<- betadisper(vegdist(B6[,8:51]), B6$Temperature) z<- betadisper(vegdist(TA4[,8:42]), TA4$Temperature) q<- betadisper(vegdist(D15[,8:43]), D15$Temperature) v<- betadisper(vegdist(D3[,8:46]), D3$Temperature)

anova(v) plot(v)

#------Vizualization of species after converted to relative abundanc: http://deneflab.github.io/MicrobeMiseq/demos/mothur_2_phyloseq.html------#

filtergenus = c("g:Achlya", "g:Aphanomyces", "g:Saprolegnia", "g:Lagenidium", "g:Brevilegnia", "g:Lagena","g:Plectospira", "g:Protoachlya") filterspecies= c("s:Lagenidium sp. PWL-2010i", "s:Lagenidium sp. PWL-2010h", "s:Apodachlya brachynema")

# Filter entries with unidentified Phylum. oomycetes4= subset_taxa(oomycetes, !Genus %in% filtergenus) oomycetes4= subset_taxa(oomycetes4, !Species %in% filterspecies)

species_bar <- oomycetes4 %>% tax_glom(taxrank = "Species") %>% # agglomerate at phylum level transform_sample_counts(function(x) {x/sum(x)} ) %>% # Transform to rel. abundance psmelt() %>% # Melt to long format arrange(Species)

species_colors <- c(

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"#CBD588", "#5F7FC7", "orange","#DA5724", "#508578", "#CD9BCD", "#AD6F3B", "#673770","#D14285", "#652926", "#C84248", "#8569D5", "#5E738F","#D1A33D", "#8A7C64", "#599861", "violetred2", "deepskyblue1", "sienna3", "black", "orchid","navajowhite4", "mistyrose4","firebrick4" ,"mediumturquoise" , "maroon" , "olivedrab4","magenta1" , "turquoise3", "tan3", "darkolivegreen" ,"cyan1" ,"plum4" , "gray6", "ivory4" ,"red" , "pink", "coral", "aquamarine3", "blue"

)

# Plot ggplot(species_bar, aes(x = Field, y = Abundance, fill = Species)) + facet_grid(Temperature~.) + theme_bw()+ geom_bar(stat = "identity", position= "fill") + scale_fill_manual(values = species_colors) + theme(axis.text.y = element_text(color = "black"))+ theme(axis.text.x = element_text(color = "black"))+

theme(text=element_text(size=14, # family="Comic Sans MS")) # family="CM Roman")) # family="TT Times New Roman")) # family="Sans")) family="sans", face="bold"))+

# Remove x axis title theme(axis.title.x = element_blank()) + theme(axis.title.y = element_text(margin = margin(t = 0, r = 20, b = 0, l = 0)))+

# guides(fill = guide_legend(reverse = TRUE, keywidth = 1, keyheight = 1)) + theme(legend.text = element_text(face = "italic"))+ ylab("Relative abundance")

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## ------## ## ------Colony PCR--- ## ## ------##

dc_pcr = read.csv("DC_PCR.csv", fill=T, na.strings = "", header = T)

species_colors <- c( "lightcoral", "#5F7FC7", "orange","#DA5724", "#508578", "#CD9BCD", "green4", "#673770","gray20", "#652926", "#C84248", "#8569D5", "#5E738F","#D1A33D", "#599861" )

# Plot ggplot(dc_pcr, aes(x = Field, y = Abundance, fill = Species)) + facet_grid(Temperature~.) + geom_bar(stat = "identity") + scale_fill_manual(values = species_colors) + theme_bw()+ theme(text=element_text(size=14, # family="Comic Sans MS")) # family="CM Roman")) # family="TT Times New Roman")) # family="Sans")) family="sans", face="bold"))+

theme(axis.text.y = element_text(color = "black"))+ theme(axis.text.x = element_text(color = "black"))+ # Remove x axis title theme(axis.title.x = element_blank()) + theme(axis.title.y = element_text(margin = margin(t = 0, r = 20, b = 0, l = 0)))+ # guides(fill = guide_legend(reverse = TRUE, keywidth = 1, keyheight = 1)) +

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theme(legend.text = element_text(face = "italic"))+ ylab("Number of isolates recovered")

#---Box plot Shannons---#

dc_shannon = read.csv("shannons_sanger_2.csv", fill=T, na.strings = "", header = T)

#---Make boxplot of alpha measurments---#

boxplot(Shannon ~ Field, data=dc_shannon, col=(c("gold","darkgreen")), ylab="Shannon's diversity")

## ------## ## Differentially abundant taxa ## ------##

# Define phyla to filter filtergenus = c("g:Achlya", "g:Aphanomyces", "g:Saprolegnia", "g:Lagenidium", "g:Brevilegnia", "g:Lagena","g:Plectospira", "g:Protoachlya") filterspecies= c("s:Lagenidium sp. PWL-2010i", "s:Lagenidium sp. PWL-2010h", "s:Apodachlya brachynema")

# Filter entries with unidentified Phylum. oomycetes3= subset_taxa(oomycetes3, !Genus %in% filtergenus) oomycetes3= subset_taxa(oomycetes3, !Species %in% filterspecies)

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read.csv("Mapping_Temperature.csv", header = TRUE, row.names = 1) map <- sample_data_t

glom <- tax_glom(oomycetes3, taxrank = 'Species') glom <- psmelt(glom)

glom$Species <- as.character(glom$Species) glom1 <- aggregate(Abundance ~ Sample+Species, glom, FUN=sum) glom1 <- cast(glom1, Sample ~ Species) str(glom1)

dropidxglom <- apply(glom1, 2, function(x) { length(which(x == 0))/length(x) > .99 }) glom1 <- glom1[, !dropidxglom]

#glom <- read.csv("glom.csv", header = TRUE, row.names = 1) #write.csv(glom1, "glom1.csv") #write.csv(glom, "glom.csv")

countDataMatrix <- as.matrix(glom1[,-1]) row.names(countDataMatrix) <- glom1[,1] colnames(countDataMatrix) <- colnames(glom1[,-1])

#glm source("C:\\Users\\navarro-acevedo.1\\Box Sync\\Krystal\\01_21_19\\wrap.edgeR.r")

#Temperature effect by itself results <- exact.test.edgeR(x=map$Temperature, Y= countDataMatrix) topTags(results) sum(topTags(results, n=Inf)$table$FDR <= 0.05) diff_results <- topTags(results, n=Inf) write.csv(diff_results, file="toptags_results.csv")

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#Temperature effect using field as covariate results1 <- glm.edgeR(x= map$Temperature, Y=countDataMatrix, covariates=map$Field) topTags(results1) sum(topTags(results1, n=Inf)$table$FDR <= 0.05) diff_results1 <- topTags(results1, n=Inf) write.csv(diff_results1, file="toptags_results1.csv")

top1<- read.csv("toptags_results.csv") top2<- read.csv("toptags_results1.csv")

#---Vizualize differentially abundance---#

theme_set(theme_bw()) scale_fill_discrete <- function(palname = "Set1", ...) { scale_fill_brewer(palette = palname, ...) }

#---Species order---#

x = tapply(top2$logFC, top2$Species, function(x) max(x)) x = sort(x, TRUE) top2$Species = factor(as.character(top2$Species), levels=names(x))

q <- ggplot(top2, aes(x=Species, y=logFC, color=Clade)) + geom_point(shape=16, size=5) q + theme(axis.text.x = element_text(angle = -90, hjust = 0.1,vjust=0.1, size=10), plot.margin=margin(1, 1, 1, 1, "cm"), panel.grid.major = element_line(colour='grey94',size=0.1),panel.grid.minor = element_line(colour = "lightgrey",size=0.1), panel.border = element_blank(), axis.title.x = element_blank(), axis.line.x = element_line(color="black", size = 0.3), axis.line.y = element_line(color="black", size = 0.3))

166

167

>N313.2;tax=k:Eukaryota,c:Oomycetes,o:Pythiales,f:Pythiaceae,g:Phytopythium,s:Phyto pythium_mercuriale, TTGTTTTGTACACTGTTGGGTTTCGCTGGCGCGTGTTTTCTTGTCGAATGTAGT GTTTGACGGCGCGCGGCGGCTGGAGGCCATCAGGGCGTCTGTTGTTGTGATG GACGCTCTTTTTGTAAACCCCCTTTTTTTTTATTTTGTGAAACTGATTGTACTG TGGGGACGAAAGTCTCTGCTTTGAACTAGATAGCAACTTTCAGCAGTGGATG TCTAGGCTCGCACATCGATGAAGAACGCTGCGAACTGCGATACGTAATGCGA ATTGCAGGATTCAGTGAGTCATCGAAATTTTGAACGCATATTGCACTTTCGGG TTCTGCCTGGAAGTATGTCTGTATCAGTGTCCGTACACTAAAGTTGCCTTTCTT GCGTCGTGTAGTCGTCGCTTGGAACGAGCAGATGTGAGGTGTCTTGCGTGGT GGTTTTGTGGTGGCGGTTTTGTGCTGCTGGCGTAAAGTAGTCGTGCAAGTCCC TTTAAAGTCGGACGTGTTTTCTCTGTAGTGTGCTGTGGCAGCTTTGGTGGCAT GCGGGACGTAGTCTGTTGACGAGTCTGGCGACCTTTGGCGCTTGCATTGTGGG GATTCCTCGATTGGCGGTATGTTAGGCCTCGGCTTTGACAATGCAGCTTATTG TGTGTGTTCTCTGTGGCTTGTGCTGTATGGGGTGAACCGGATGGTCTTTGGGC GTTTGTTGTTTTGCGTGTGTGTCTGGAGTTGTGTTGGCGTGCTTTGGGAGGGT CACCATTTGGGAATTTTTGTGCTCTTTTGTTTGTTTTGTTGCTTCGAAAGAGTA TCTCAATTGGACCTGATATCA >N512.3;tax=k:Eukaryota,c:Oomycetes,o:Pythiales,f:Pythiaceae,g:Phytopythium,s:Phyto pythium_mercuriale, ACGTGAATTGTTTTGTACACTGTTGGGTTTCGCTGGCGCGTGTTTTCTTGTCGA ATGTAGTGTTTGACGGCGCGCGGCGGCTGGAGGCCATCAGGGCGTCTGTTGT TGTGATGGACGCTCTTTTTGTAAACCCCCTTTTTTTTTATTTTGTGAAACTGAT TGTACTGTGGGGACGAAAGTCTCTGCTTTGAACTAGATAGCAACTTTCAGCA GTGGATGTCTAGGCTCGCACATCGATGAAGAACGCTGCGAACTGCGATACGT AATGCGAATTGCAGGATTCAGTGAGTCATCGAAATTTTGAACGCATATTGCA CTTTCGGGTTCTGCCTGGAAGTATGTCTGTATCAGTGTCCGTACACTAAAGTT GCCTTTCTTGCGTCGTGTAGTCGTCGCTTGGAACGAGCAGATGTGAGGTGTCT TGCGTGGTGGTTTTGTGGTGGCGGTTTTGTGCTGCTGGCGTAAAGTAGTCGTG CAAGTCCCTTTAAAGTCGGACGTGTTTTCTCTGTAGTGTGCTGTGGCAGCTTT GGTGGCATGCGGGACGTAGTCTGTTGACGAGTCTGGCGACCTTTGGCGCTTG CATTGTGGGGATTCCTCGATTGGCGGTATGTTAGGCCTCGGCTTTGACAATGC AGCTTATTGTGTGTGTTCTCTGTGGCTTGTGCTGTATGGGGTGAACCGGATGG TCTTTGGGCGTTTGTTGTTTTGCGTGTGTGTCTGGAGTTGTGTTGGCGTGCTTT GGGAGGGTCACCATTTGGGAATTTTTGTGCTCTTTTGTTTGTTTTGTTGCTTCG AAAGAGTATCTCAATTGGACCTGATATCA >N404.1;tax=k:Eukaryota,c:Oomycetes,o:Pythiales,f:Pythiaceae,g:Phytopythium,s:Phyto pythium_mercuriale, TTGTTTTGTACACTGTTGGGTTTCGCTGGCGCGTGTTTTCTTGTCGAATGTAGT GTTTGACGGCGCGCGGCGGCTGGAGGCCATCAGGGCGTCTGTTGTTGTGATG GACGCTCTTTTTGTAAACCCCCTTTTTTTTTATTTTGTGAAACTGATTGTACTG TGGGGACGAAAGTCTCTGCTTTGAACTAGATAGCAACTTTCAGCAGTGGATG TCTAGGCTCGCACATCGATGAAGAACGCTGCGAACTGCGATACGTAATGCGA ATTGCAGGATTCAGTGAGTCATCGAAATTTTGAACGCATATTGCACTTTCGGG 168

TTCTGCCTGGAAGTATGTCTGTATCAGTGTCCGTACACTAAAGTTGCCTTTCTT GCGTCGTGTAGTCGTCGCTTGGAACGAGCAGATGTGAGGTGTCTTGCGTGGT GGTTTTGTGGTGGCGGTTTTGTGCTGCTGGCGTAAAGTAGTCGTGCAAGTCCC TTTAAAGTCGGACGTGTTTTCTCTGTAGTGTGCTGTGGCAGCTTTGGTGGCAT GCGGGACGTAGTCTGTTGACGAGTCTGGCGACCTTTGGCGCTTGCATTGTGGG GATTCCTCGATTGGCGGTATGTTAGGCCTCGGCTTTGACAATGCAGCTTATTG TGTGTGTTCTCTGTGGCTTGTGCTGTATGGGGTGAACCGGATGGTCTTTGGGC GTTTGTTGTTTTGCGTGTGTGTCTGGAGTTGTGTTGGCGTGCTTTGGGAGGGT CACCATTTGGGAATTTTTGTGCTCTTTTGTTTGTTTTGTTGCTTCGAAAGAGTA TCTCAATTGGACCTGATATCA >N313.;tax=k:Eukaryota,c:Oomycetes,o:Pythiales,f:Pythiaceae,g:Phytopythium,s:Phytop ythium_mercuriale, TCTTTCCACGTGAATTGTTTTGTACACTGTTGGGTTTCGCTGGCGCGTGTTTTC TTGTCGAATGTAGTGTTTGACGGCGCGCGGCGGCTGGAGGCCATCAGGGCGT CTGTTGTTGTGATGGACGCTCTTTTTGTAAACCCCCTTTTTTTTTATTTTGTGA AACTGATTGTACTGTGGGGACGAAAGTCTCTGCTTTGAACTAGATAGCAACTT TCAGCAGTGGATGTCTAGGCTCGCACATCGATGAAGAACGCTGCGAACTGCG ATACGTAATGCGAATTGCAGGATTCAGTGAGTCATCGAAATTTTGAACGCAT ATTGCACTTTCGGGTTCTGCCTGGAAGTATGTCTGTATCAGTGTCCGTACACT AAAGTTGCCTTTCTTGCGTCGTGTAGTCGTCGCTTGGAACGAGCAGATGTGAG GTGTCTTGCGTGGTGGTTTTGTGGTGGCGGTTTTGTGCTGCTGGCGTAAAGTA GTCGTGCAAGTCCCTTTAAAGTCGGACGTGTTTTCTCTGTAGTGTGCTGTGGC AGCTTTGGTGGCATGCGGGACGTAGTCTGTTGACGAGTCTGGCGACCTTTGGC GCTTGCATTGTGGGGATTCCTCGATTGGCGGTATGTTAGGCCTCGGCTTTGAC AATGCAGCTTATTGTGTGTGTTCTCTGTGGCTTGTGCTGTATGGGGTGAACCG GATGGTCTTTGGGCGTTTGTTGTTTTGCGTGTGTGTCTGGAGTTGTGTTGGCGT GCTTTGGGAGGGTCACCATTTGGGAATTTTTGTGCTCTTTTGTTTGTTTTGTTG CTTCGAAAGAGTATCTCAATTGGACCTGATATCA >N514.2;tax=k:Eukaryota,c:Oomycetes,o:Pythiales,f:Pythiaceae,g:Phytopythium,s:Phyto pythium_mercuriale, CGTGAATTGTTTTGTACACTGTTGGGTTTCGCTGGCGCGTGTTTTCTTGTCGAA TGTAGTGTTTGACGGCGCGCGGCGGCTGGAGGCCATCAGGGCGTCTGTTGTT GTGATGGACGCTCTTTTTGTAAACCCCCTTTTTTTTTATTTTGTGAAACTGATT GTACTGTGGGGACGAAAGTCTCTGCTTTGAACTAGATAGCAACTTTCAGCAG TGGATGTCTAGGCTCGCACATCGATGAAGAACGCTGCGAACTGCGATACGTA ATGCGAATTGCAGGATTCAGTGAGTCATCGAAATTTTGAACGCATATTGCACT TTCGGGTTCTGCCTGGAAGTATGTCTGTATCAGTGTCCGTACACTAAAGTTGC CTTTCTTGCGTCGTGTAGTCGTCGCTTGGAACGAGCAGATGTGAGGTGTCTTG CGTGGTGGTTTTGTGGTGGCGGTTTTGTGCTGCTGGCGTAAAGTAGTCGTGCA AGTCCCTTTAAAGTCGGACGTGTTTTCTCTGTAGTGTGCTGTGGCAGCTTTGG TGGCATGCGGGACGTAGTCTGTTGACGAGTCTGGCGACCTTTGGCGCTTGCAT TGTGGGGATTCCTCGATTGGCGGTATGTTAGGCCTCGGCTTTGACAATGCAGC TTATTGTGTGTGTTCTCTGTGGCTTGTGCTGTATGGGGTGAACCGGATGGTCT TTGGGCGTTTGTTGTTTTGCGTGTGTGTCTGGAGTTGTGTTGGCGTGCTTTGGG 169

AGGGTCACCATTTGGGAATTTTTGTGCTCTTTTGTTTGTTTTGTTGCTTCGAAA GAGTATCTCAATGGACCTGATATCA >MVa21.5;tax=k:Eukaryota,c:Oomycetes,o:Pythiales,f:Pythiaceae,g:Phytopythium,s:Phy topythium_helicoides, CCACGTGAACCGTTTGTGACATGGTTGGGCTTGTGCGTGTTCTCTCTCTTTTGG GGGAGGCGTGCGAGCTATCTGTAAACTTGTCAAACCCATTCTCTTTGATAACT GAAACATACTGTGGGGACGAAAGTCTCTGCTTTGAACTAGATAGCAACTTTC AGCAGTGGATGTCTAGGCTCGCACATCGATGAAGAACGCTGCGAACTGCGAT ACGTAATGCGAATTGCAGGATTCAGTGAGTCATCGAAATTTTGAACGCATAT TGCACTTTCGGGTTATGCCTGGAAGTATGTCTGTATCAGTGTCCGTACACTAA ACTTGCCTCCTTTGCGTCGTGTAGTCGTCGCGTTGGAAATTTGTGGCAGATGT GAGGTGTCTTGTTTGCTGTGTCTTTGTTGATGCGGCGGGCAAGTCCCTTGAAA GTCGGACGCGTATCTTTGCGTGCGTTGGGTGCCGGTGGGCTGTGGGACGCGT CTGTTGACGAGTCTGGCGACCTTTGGCGCGTGCATGCTTGGGCACTGTGTATT GGCGGTATGTTAGGCTGCGTTCGCGCGGCTTTGACAATGCAGCTGATGCGTGT GTTTGGGCTGTGGTGCTGTATGGGTGAACCGGATGGTCGATGGGTTTTTATAT CGCGTTTCGCGTGTCTGTTTTTATCCGGTGTTCTGTATCGTGCGTGGAGTGTGT CAGCATTTGGGAATTTGTACGTCTTTTTTTGTGGGCGTATCTCATTGGACCTG ATATCAGA >N104.2;tax=k:Eukaryota,c:Oomycetes,o:Pythiales,f:Pythiaceae,g:Phytopythium,s:Phyto pythium_mercuriale, CTGATATCAGGTCCATTGAGATACTCTTTCGAAGCAACAAAACAAACAAAAG AGCACAAAAATTCCCAAATGGTGACCCTCCCAAAGCACGCCAACACAACTCC AGACACACACGCAAAACAACAAACGCCCAAAGACCATCCGGTTCACCCCAT ACAGCACAAGCCACAGAGAACACACACAATAAGCTGCATTGTCAAAGCCGA GGCCTAACATACCGCCAATCGAGGAATCCCCACAATGCAAGCGCCAAAGGTC GCCAGACTCGTCAACAGACTACGTCCCGCATGCCACCAAAGCTGCCACAGCA CACTACAGAGAAAACACGTCCGACTTTAAAGGGACTTGCACGACTACTTTAC GCCAGCAGCACAAAACCGCCACCACAAAACCACCACGCAAGACACCTCACA TCTGCTCGTTCCAAGCGACGACTACACGACGCAAGAAAGGCAACTTTAGTGT ACGGACACTGATACAGACATACTTCCAGGCAGAACCCGAAAGTGCAATATGC GTTCAAAATTTCGATGACTCACTGAATCCTGCAATTCGCATTACGTATCGCAG TTCGCAGCGTTCTTCATCGATGTGCGAGCCAGACATCCACTGCTGAAAGTTGC TATCTAGTTCAAAGCAGAGACTTTCGTCCCCACAGTACAATCAGTTTCACAAA ATAAAAAAAAAGGGGGTTTACAAAAAGAGCGTCCATCACAACAACAGACGC CCTGATGGCCTCCAGCCGCCGCGCGCCGTCAAACACTACATTCGACAAGAAA ACACGCGCCAGCGAAACCCAACAGTGTACAAAACAATTCACG >N503.1;tax=k:Eukaryota,c:Oomycetes,o:Pythiales,f:Pythiaceae,g:Phytopythium,s:Phyto pythium_helicoides, TCTGATATCAGGTCCAAATGAGATACGCCCACAAAAAAAGACGTACAAATTC CCAAATGCTGACACACTCCACGCACGATACAGAACACCGGATAAAAACAGA CACGCGAAACGCGATATAAAAACCCATCGACCATCCGGTTCACCCATACAGC ACCACAGCCCAAACACACGCATCAGCTGCATTGTCAAAGCCGCGCGAACGCA GCCTAACATACCGCCAATACACAGTGCCCAAGCATGCACGCGCCAAAGGTCG 170

CCAGACTCGTCAACAGACGCGTCCCACAGCCCACCGGCACCCAACGCACGCA AAGATACGCGTCCGACTTTCAAGGGACTTGCCCGCCGCATCAACAAAGACAC AGCAAACAAGACACCTCACATCTGCCACAAATTTCCAACGCGACGACTACAC GACGCAAAGGAGGCAAGTTTAGTGTACGGACACTGATACAGACATACTTCCA GGCATAACCCGAAAGTGCAATATGCGTTCAAAATTTCGATGACTCACTGAAT CCTGCAATTCGCATTACGTATCGCAGTTCGCAGCGTTCTTCATCGATGTGCGA GCCTAGACATCCACTGCTGAAAGTTGCTATCTAGTTCAAAGCAGAGACTTTCG TCCCCACAGTATGTTTCAGTTATCAAAGAGAATGGGTTTGACAAGTTTACAGA TAGCTCGCACGCCTCCCCCAAAAGAGAGAGAACACGCACAAGCCCAACCAT GTCACAAACGGT >N109.1;tax=k:Eukaryota,c:Oomycetes,o:Pythiales,f:Pythiaceae,g:Phytopythium,s:Phyto pythium_mercuriale, ATTGTTTTGTACACTGTTGGGTTTCGCTGGCGCGTGTTTTCTTGTCGAATGTAG TGTTTGACGGCGCGCGGCGGCTGGAGGCCATCAGGGCGTCTGTTGTTGTGAT GGACGCTCTTTTTGTAAACCCCCTTTTTTTTTATTTTGTGAAACTGATTGTACT GTGGGGACGAAAGTCTCTGCTTTGAACTAGATAGCAACTTTCAGCAGTGGAT GTCTAGGCTCGCACATCGATGAAGAACGCTGCGAACTGCGATACGTAATGCG AATTGCAGGATTCAGTGAGTCATCGAAATTTTGAACGCATATTGCACTTTCGG GTTCTGCCTGGAAGTATGTCTGTATCAGTGTCCGTACACTAAAGTTGCCTTTC TTGCGTCGTGTAGTCGTCGCTTGGAACGAGCAGATGTGAGGTGTCTTGCGTGG TGGTTTTGTGGTGGCGGTTTTGTGCTGCTGGCGTAAAGTAGTCGTGCAAGTCC CTTTAAAGTCGGACGTGTTTTCTCTGTAGTGTGCTGTGGCAGCTTTGGTGGCA TGCGGGACGTAGTCTGTTGACGAGTCTGGCGACCTTTGGCGCTTGCATTGTGG GGATTCCTCGATTGGCGGTATGTTAGGCCTCGGCTTTGACAATGCAGCTTATT GTGTGTGTTCTCTGTGGCTTGTGCTGTATGGGGTGAACCGGATGGTCTTTGGG CGTTTGTTGTTTTGCGTGTGTGTCTGGAGTTGTGTTGGCGTGCTTTGGGAGGG TCACCATTTGGGAATTTTTGTGCTCTTTTGTTTGTTTTGTTGCTTCGAAAGAGT ATCTCAATTGGACCTGATATCAGACA >N404.2;tax=k:Eukaryota,c:Oomycetes,o:Pythiales,f:Pythiaceae,g:Phytopythium,s:Phyto pythium_mercuriale, CCGCGCGGAATGTAGTGTTTGACGGCGCGCGGCGGCTGGAGGCCATCAGGGC GTCTGTTGTTGTGATGGACGCTCTTTTTGTAAACCCCCTTTTTTTTTATTTTGTG AAACTGATTGTACTGTGGGGACGAAAGTCTCTGCTTTGAACTAGATAGCAAC TTTCAGCAGTGGATGTCTAGGCTCGCACATCGATGAAGAACGCTGCGAACTG CGATACGTAATGCGAATTGCAGGATTCAGTGAGTCATCGAAATTTTGAACGC ATATTGCACTTTCGGGTTCTGCCTGGAAGTATGTCTGTATCAGTGTCCGTACA CTAAAGTTGCCTTTCTTGCGTCGTGTAGTCGTCGCTTGGAACGAGCAGATGTG AGGTGTCTTGCGTGGTGGTTTTGTGGTGGCGGTTTTGTGCTGCTGGCGTAAAG TAGTCGTGCAAGTCCCTTTAAAGTCGGACGTGTTTTCTCTGTAGTGTGCTGTG GCAGCTTTGGTGGCATGCGGGACGTAGTCTGTTGACGAGTCTGGCGACCTTTG GCGCTTGCATTGTGGGGATTCCTCGATTGGCGGTATGTTAGGCCTCGGCTTTG ACAATGCAGCTTATTGTGTGTGTTCTCTGTGGCTTGTGCTGTATGGGGTGAAC CGGATGGTCTTTGGGCGTTTGTTGTTTTGCGTGTGTGTCTGGAGTTGTGTTGGC

171

GTGCTTTGGGAGGGTCACCATTTGGGAATTTTTGTGCTCTTTTGTTTGTTTTGT TGCTTCGAAAGAGTATCTCAATTGGACCTGATATCAGAC >N514.1;tax=k:Eukaryota,c:Oomycetes,o:Pythiales,f:Pythiaceae,g:Phytopythium,s:Phyto pythium_mercuriale, GAATGTAGTGTTTGACGGCGCGCGGCGGCTGGAGGCCATCAGGGCGTCTGTT GTTGTGATGGACGCTCTTTTTGTAAACCCCCTTTTTTTTTATTTTGTGAAACTG ATTGTACTGTGGGGACGAAAGTCTCTGCTTTGAACTAGATAGCAACTTTCAGC AGTGGATGTCTAGGCTCGCACATCGATGAAGAACGCTGCGAACTGCGATACG TAATGCGAATTGCAGGATTCAGTGAGTCATCGAAATTTTGAACGCATATTGC ACTTTCGGGTTCTGCCTGGAAGTATGTCTGTATCAGTGTCCGTACACTAAAGT TGCCTTTCTTGCGTCGTGTAGTCGTCGCTTGGAACGAGCAGATGTGAGGTGTC TTGCGTGGTGGTTTTGTGGTGGCGGTTTTGTGCTGCTGGCGTAAAGTAGTCGT GCAAGTCCCTTTAAAGTCGGACGTGTTTTCTCTGTAGTGTGCTGTGGCAGCTT TGGTGGCATGCGGGACGTAGTCTGTTGACGAGTCTGGCGACCTTTGGCGCTTG CATTGTGGGGATTCCTCGATTGGCGGTATGTTAGGCCTCGGCTTTGACAATGC AGCTTATTGTGTGTGTTCTCTGTGGCTTGTGCTGTATGGGGTGAACCGGATGG TCTTTGGGCGTTTGTTGTTTTGCGTGTGTGTCTGGAGTTGTGTTGGCGTGCTTT GGGAGGGTCACCATTTGGGAATTTTTGTGCTCTTTTGTTTGTTTTGTTGCTTCG AAAGAG >N507.3;tax=k:Eukaryota,c:Oomycetes,o:Pythiales,f:Pythiaceae,g:Phytopythium,s:Phyto pythium_mercuriale, ACGTGAATTGTTTTGTACACTGTTGGGTTTCGCTGGCGCGTGTTTTCTTGTCGA ATGTAGTGTTTGACGGCGCGCGGCGGCTGGAGGCCATCAGGGCGTCTGTTGT TGTGATGGACGCTCTTTTTGTAAACCCCCTTTTTTTTTATTTTGTGAAACTGAT TGTACTGTGGGGACGAAAGTCTCTGCTTTGAACTAGATAGCAACTTTCAGCA GTGGATGTCTAGGCTCGCACATCGATGAAGAACGCTGCGAACTGCGATACGT AATGCGAATTGCAGGATTCAGTGAGTCATCGAAATTTTGAACGCATATTGCA CTTTCGGGTTCTGCCTGGAAGTATGTCTGTATCAGTGTCCGTACACTAAAGTT GCCTTTCTTGCGTCGTGTAGTCGTCGCTTGGAACGAGCAGATGTGAGGTGTCT TGCGTGGTGGTTTTGTGGTGGCGGTTTTGTGCTGCTGGCGTAAAGTAGTCGTG CAAGTCCCTTTAAAGTCGGACGTGTTTTCTCTGTAGTGTGCTGTGGCAGCTTT GGTGGCATGCGGGACGTAGTCTGTTGACGAGTCTGGCGACCTTTGGCGCTTG CATTGTGGGGATTCCTCGATTGGCGGTATGTTAGGCCTCGGCTTTGACAATGC AGCTTATTGTGTGTGTTCTCTGTGGCTTGTGCTGTATGGGGTGAACCGGATGG TCTTTGGGCGTTTGTTGTTTTGCGTGTGTGTCTGGAGTTGTGTTGGCGTGCTTT GGGAGGGTCACCATTTGGGAATTTTTGTGCTCTTTTGTTTGTTTTGTTGCTTCG AAAGAGTATCTCAATTGGACCTGA >D502.1;tax=k:Eukaryota,c:Oomycetes,o:Pythiales,f:Pythiaceae,g:Phytopythium,s:Phyto pythium_helicoides, CCCCCCAGGCCCAACCAAGTCCCCAACCGGTTACACGGGGAAAGAAGTTTTT TAAGGTGGGGTAAAAAACCTCTCCGTAGGGGGGACCTGCGGAAGGATCATTA CCCCCCCTAAAAAATTCTTTCCCCGTGAACCGTTTGTGACATGGTTGGGCTTG TGCGTGTTCTCTCTCTTTTGGGGGAGGGGTGCGAGCTATCTGTAAACTTGTCA AACCCATTCTCTTTGATAACTGAAACATACTGGGGGGACGAAAGTCTTTGCTT 172

TGAACTAGATAGCAACTTTCAGCAGTGGATGTCTAGGCTCGCACATCGATGA AGAACGCTGCGAACTGCGATACCTAATGGGAATTGCAGGATTCAGTGAGTCA TCGAAATTTTGAACGCATATTGCACTTTCGGGTTATGCCTGGAAGTATGTCTG TATCAGTGTCCGTACACTAAACTTGCCTCCTTTGCGTCGTGKAGTCSTCGCGTT GGAAATTTGTGGCAGATGTGAGGTGTCTTGTTTGCTGTGTCTTTGTTGATGCG GCGGGCAAGTCCCTTGAAAGTCGGACGGGTATCTTTGCGTGCGTTGGGGTGC CGGTGGGCTGTGGGACGCGTCTGTTGACGAGTCTGGCGACCTTTGGCGCGTG CATGCTTGGGCACTGTGTATTGGCGGTATGTTAGGCTGCGTTCGCGCGGCTTT GACAATGCAGCTGATGCGTGTGTTTGGGCTGTGGTGCTGTATGGGTGAACCG GATGGTCGATGGGTTTTTATATCGCGTTTCGCGTGTCTGTTTTTATCCGGTGTT CTGTATCGTGCGTGGAGTGTGTCAGCATTTGGGAATTTGTACGTCTTTTTTTGT GGGCGTATCTCATTTGGACCTGATATCAG >382B;tax=k:Eukaryota,c:Oomycetes,o:Pythiales,f:Pythiaceae,g:Phytopythium,s:Phytopy thium_delawarense, GCACAAATTCCCAAATGGTGACCGTCCCCCCAACGCAGCACGCCACACCAGC AACAGACTCGCGCGCAAAACTCACAGAAACGACCGAAGACCATCCGGTTCA CCCATACAGCCACGCGCCACGAAGCCCACTCAATAAGCTGCATTGTCAAAGC CGAAGCCTAACATACCGCCAATCGAGCACACCCCGCAATGCACGAGCCAAA GGTCGCCAGACTCGTCAACAGACAAGAGAGTCTCACGCGCCGCCAGCGCCGA CACAGCACACCACAGAGAGAAAACACGTCCGACTTTAAAGGGACTTGCTCCT GCCCAAAGAATAGGAACCAAAGCAAGACAAACCTCACATCTGCGCATTCCCA GCGACGACTACACGACGCAAGAAAGCCAAGTTTAGTGTACGGACACTGATAC AGACATACTTCCAGGCAGAACCCGAAAGTGCAATATGCGTTCAAAATTTCGA TGACTCACTGAATCCTGCAATTCGCATTACGTATCGCAGTTCGCAGCGTTCTT CATCGATGTGCGAGCCTAGACATCCACTGCTGAAAGTTGCTATCTAGTTAAA AGCAGAGACTTTCGTCCCCACAGTATGATCAGTTTTTCAAAAGAAATGGGTT GACAAAAGGACGCACTCGCGCGCGAATCATAAAATCATAAAACGCACGASG AACACGCCCCTGATGGCCTCCAACCGGGTTCCCCTATTTTTTTTTGCAACAAC AAAAAAAGAGAAGAAGAAGACAACGGCGAAGCCCAAA >MVa14.4;tax=k:Eukaryota,c:Oomycetes,o:Pythiales,f:Pythiaceae,g:Phytopythium,s:Phy topythium_helicoides, CCCCCCACACGTGACCGTTTGTGACATGGTTGGGCTTGTGCGTGTTCTCTCTC TTTTGGGGGAGGCGTGCGAGCTATCTGTAAACTTGTCAAACCCATTCTCTTTG ATAACTGAAACATACTGTGGGGACGAAAGTCTCTGCTTTGAACTAGATAGCA ACTTTCAGCAGTGGATGTCTAGGCTCGCACATCGATGAAGAACGCTGCGAAC TGCGATACGTAATGCGAATTGCAGGATTCAGTGAGTCATCGAAATTTTGAAC GCATATTGCACTTTCGGGTTATGCCTGGAAGTATGTCTGTATCAGTGTCCGTA CACTAAACTTGCCTCCTTTGCGTCGTGTAGTCGTCGCGTTGGAAATTTGTGGC AGATGTGAGGTGTCTTGTTTGCTGTGTCTTTGTTGATGCGGCGGGCAAGTCCC TTGAAAGTCGGACGCGTATCTTTGCGTGCGTTGGGTGCCGGTGGGCTGTGGG ACGCGTCTGTTGACGAGTCTGGCGACCTTTGGCGCGTGCATGCTTGGGCACTG TGTATTGGCGGTATGTTAGGCTGCGTTCGCGCGGCTTTGACAATGCAGCTGAT GCGTGTGTTTGGGCTGTGGTGCTGTATGGGTGAACCGGATGGTCGATGGGTTT

173

TTATATCGCGTTTCGCGTGTCTGTTTTTATCCGGTGTTCTGTATCGTGCGTGGA GTGTGTCAGCATTTGGGAATTTGTACGTCTTTTTTTGTGGGCGTG >N101.2;tax=k:Eukaryota,c:Oomycetes,o:Pythiales,f:Pythiaceae,g:Phytopythium,s:Phyto pythium_mercuriale, TCTTTCGAAGCAACAAAACAAACAAAAGAGCACAAAAATTCCCAAATGGTG ACCCTCCCAAAGCACGCCAACACAACTCCAGACACACACGCAAAACAACAA ACGCCCAAAGACCATCCGGTTCACCCCATACAGCACAAGCCACAGAGAACAC ACACAATAAGCTGCATTGTCAAAGCCGAGGCCTAACATACCGCCAATCGAGG AATCCCCACAATGCAAGCGCCAAAGGTCGCCAGACTCGTCAACAGACTACGT CCCGCATGCCACCAAAGCTGCCACAGCACACTACAGAGAAAACACGTCCGAC TTTAAAGGGACTTGCACGACTACTTTACGCCAGCAGCACAAAACCGCCACCA CAAAACCACCACGCAAGACACCTCACATCTGCTCGTTCCAAGCGACGACTAC ACGACGCAAGAAAGGCAACTTTAGTGTACGGACACTGATACAGACATACTTC CAGGCAGAACCCGAAAGTGCAATATGCGTTCAAAATTTCGATGACTCACTGA ATCCTGCAATTCGCATTACGTATCGCAGTTCGCAGCGTTCTTCATCGATGTGC GAGCCTAGACATCCACTGCTGAAAGTTGCTATCTAGTTCAAAGCAGAGACTT TCGTCCCCACAGTACAATCAGTTTCACAAAATAAAAAAAAAGGGGGTTTACA AAAAGAGCGTCCATCACAACAACAGACGCCCTGATGGCCTCCAGCCGCCGCG CGCCGTCAAACACTACATTCGACAAGAAAACACGCGCCAGCGAAACCCAAC AGTGTACAAAACAATTCACGTG >D206.3;tax=k:Eukaryota,c:Oomycetes,o:Pythiales,f:Pythiaceae,g:Phytopythium,s:Phyto pythium_litorale, CTTTCCCGTGATTGTTTTGCTGTACCTTTGGGCTTCGCCGTTGTCTTGTTCTTTT GTAAGAGAAAGGGGGAGGCGCGGTTGGAGGCCATCAGGGGTGTGTTCGTCG CGGTTTGTTTCTTTTGTTGGAACTTGCGCGCGAATGCGTCCTTTTGTCAACCCA TTTTTTGAATGAAAAACTGATCATACTGTGGGGACGAAAGTCTCTGCTTTTAA CTAGATAGCAACTTTCAGCAGTGGATGTCTAGGCTCGCACATCGATGAAGAA CGCTGCGAACTGCGATACGTAATGCGAATTGCAGGATTCAGTGAGTCATCGA AATTTTGAACGCATATTGCACTTTCGGGTTCTGCCTGGAAGTATGTCTGTATC AGTGTCCGTACACTAAACTTGCCTTTCTTGCGTCGTGTAGTCGTCGCTGGGAA TGCGCAGATGTGAGGTTTTGTCTTGCTCTGGCTCGAATTCGTTGGGCAGGAGC AAGTCCCTTTAAAGTCGGACGTGTTTTCTCTCTGCGGTGTGCTGTGTCGGCGC TGGCGGCGCGTGGGACTCTCTTGTCTGTTGACGAGTCTGGCGACCTTTGGCTC GTGCATTGCGGGGTGTGCTCGATTGGCGGTATGTTAGGCTTCGGCTTTGACAA TGCAGCTTATTGGGTGCGCTTCGTGGCGCGTGGCTGTATGGGTGAACCGGAT GGTCTTCGGTCGTTTGTGTGAGTTTCGCGTGCGAGTCTGTTGCTGGTGTGGCG TGCTGCGTTGAGGGACGGTCACCATTTGGGAATGTTGTGCTCTTTGCGTTTTT TGTTGTTGTTGTTGTTGCTAGT

174

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