INVESTIGATING MANAGEMENT AND GENETICS OF SOYBEAN SUDDEN DEATH 

SYNDROME PATHOGENS FUSARIUM VIRGULIFORME AND F. BRASILIENSE 

 
By 
 

Mitchell G. Roth 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

 

 

 

 

A DISSERTATION 

Submitted to  

Michigan State University 

in partial fulfillment of the requirements 

for the degree of  

Genetics – Doctor of Philosophy 
Plant Pathology – Dual Major 

 

2019 

ABSTRACT 

INVESTIGATING MANAGEMENT AND GENETICS OF SOYBEAN SUDDEN DEATH 

SYNDROME PATHOGENS FUSARIUM VIRGULIFORME AND F. BRASILIENSE 

 
By 

Mitchell G. Roth 

 

Annual soybean production in the U.S. is worth nearly $40 billion, valued for its oils and 

protein content. Many pathogens and pests cause significant soybean yield losses each year, but 

one of the top threats is sudden death syndrome (SDS). At least five fungal species cause 

soybean SDS globally, but only two have been found in the U.S.; Fusarium virguliforme and F. 

brasiliense. These soil-borne pathogens infect root tissues and cause root rot, with continued 

infection leading to foliar interveinal chlorosis, interveinal necrosis, leaf drop, and yield loss. 

The pathogens are strong saprophytes that can overwinter in soybean and corn residue, so 

successful management is difficult. Long-term crop rotations and seed treatments with fungicides 

show some efficacy, but these strategies can be costly for growers. Growers desire genetic 

resistance to SDS, but no soybean germplasm has shown 100% resistance to SDS to date. 

Therefore, the overall goals of projects presented in this dissertation were to help improve SDS 

management and explore the biology and genetics of F. virguliforme and F. brasiliense.  

 

To achieve these goals, I developed a risk prediction tool for integration with current 

SDS management strategies (Chapter 2). This study revealed that pathogen data collected from 

soil at-planting can be used to accurately model spatial distributions pathogens and model future 

SDS development and yield loss at a field level. This risk prediction study used a qPCR assay 

specific for F. virguliforme, but a similar qPCR assay for F. brasiliense did not exist. Therefore, 

I developed a qPCR assay that can distinguish F. brasiliense from close relatives (Chapter 3). 

 

 

This tool that can be used to generate SDS-prediction models for F. brasiliense and I predict will 

be valuable in diagnostic labs across the country to distinguish between these two species. 

 

To advance our understanding of the biology and genetics of these pathogens, I 

developed a new protoplast generation and transformation method to generate fluorescent strains 

of each pathogen (Chapter 4). This chapter is the first to report genetic transformation in F. 

brasiliense. Furthermore, I used the fluorescent strains to investigate the synergistic role of soil-

borne nematodes in SDS (Chapter 5). The interactions between these fungal pathogens and 

nematodes in vitro show that F. virguliforme and F. brasiliense can colonize immobile 

nematodes, but suggest that they are not actively vectored into soybean roots by nematodes.  

 

The genetic mechanisms of SDS development are poorly understood, so I developed high 

quality genome sequences for F. virguliforme and F. brasiliense (Chapter 6) and investigated 

two recognized effector proteins; FvTox1 and FvNIS1 (Chapter 7). The genome assemblies 

developed here have significantly improved continuity, with improved genome assembly metrics 

like contig length (N50) and contig number. However, whole-genome alignments between F. 

virguliforme and F. brasiliense from soybean (Glycine max) or dry bean (Phaseolus vulgaris) did 

not reveal obvious mobile pathogenicity chromosomes that have been observed in the close 

relative F. oxysporum. However, these genome resources should facilitate discovery of new 

fungal effector proteins like FvTox1 and FvNIS1. Interestingly, my results show that FvNIS1 is 

able to induce a hypersensitive response in tobacco, while FvTox1 is not, suggesting a conserved 

mechanism between soybean and tobacco for FvNIS1 recognition. 

 

Overall, this work provides valuable tools for managing and studying SDS-causing fungi, 

while also revealing insights into the genetics and genomics of the SDS-causing pathogens F. 

virguliforme and F. brasiliense.  

 

iii 

 

Copyright by 
MITCHELL G. ROTH 
2019

 

iv 

I first want to thank my Lord and Savior Jesus Christ for the courage, persistence, and patience 

You provided me throughout my many failures during graduate school. 

I dedicate this dissertation to my father, who was the first to show me what a passion for 

agriculture looks like. You also showed me how to work hard, acknowledge a job well done, and 
have fun all along the way. I also dedicate this dissertation to my mother. You are a marvelous 
teacher, and I have you to thank for my teaching skills today. You showed me how to approach 
complex problems with a clear mind and a full effort, skills that are critical to success in graduate 
school. I also dedicate this dissertation to my sister. You were the first person to truly inspire me 
to avoid excuses and always put in the effort to be the best possible version of myself. You have 
always been my biggest fan, the first to celebrate with me and the first to hear my complaints. 
Finally, I also dedicate this dissertation to my beautiful wife. You have helped shoulder my 

graduate school burden and supported me with three different titles; girlfriend, fiancé, and wife. 
Your endless encouragement, fresh perspectives, and supreme confidence in me are the biggest 

reasons for my success in graduate school. 

Each of you have fueled my desire to push the boundaries of my understanding and become the 
best scientist I can be. Without the love, support, and prayers from all of you, I would not have 
become the person I am today, nor a successful scientist. My gratitude to all of you is beyond 

measure. 

 

 

 
v 

ACKNOWLEDGEMENTS 

 

I would like to acknowledge my advisor, Dr. Martin I. Chilvers, for his patience, 

understanding, and guidance throughout all of the uncertainty that comes with earning a Ph.D. 

Though things did not always go as planned, you always kept a level head and provided sensible 

suggestions for future directions.  

I would like to thank my committee members Drs. Brad Day, Frances Trail, Jonathan 

Walton, and Jaime Willbur for guidance and mentorship throughout my research. Without your 

expertise and helpful discussions, the quality of my work would be significantly lower. As I 

progressed through graduate school, I came to realize how valuable time is to a scientist, and I 

truly appreciate the time that you sacrificed to help me achieve my goals.  

I would like to thank all of my fellow lab members, past and present. In particular, I want 

to thank Dr. Alejandro Rojas and Dr. Jie Wang for their endless patience with my naïve 

questions, which made my transition into graduate school very smooth. I want to thank Zachary 

Noel for the professional and personal support throughout the countless hours we have spent 

together. I value the friendships that I have with each of you and am thankful that our common 

research interests brought us together.  

I would like to acknowledge the Genetics Graduate Program and its director Dr. Cathy 

Ernst for constant support in scientific and personal endeavors. The time and effort put into each 

student has made being part of this program an incredible experience.  

Finally, I want to thank the Michigan Soybean Promotion Committee for funding a large 

portion of my work. I hope that my research will always have a beneficial impact on growers and 

commodity groups.  

 

vi 
 

 

TABLE OF CONTENTS 

 

LIST OF TABLES ........................................................................................................... ix 
 
LIST OF FIGURES ...........................................................................................................x 
 
CHAPTER 1: LITERATURE REVIEW ........................................................................1 
Introduction .................................................................................................................1 
 
SDS Management Strategies .......................................................................................3 
 
 
Fusarium virguliforme infection and interaction with nematodes ..............................6 
Fusarium virguliforme effectors .................................................................................8 
 
Fusarium genomics ...................................................................................................12 
 
 
Conclusions ...............................................................................................................15 
 
CHAPTER 2: Predicting Soybean Yield and Sudden Death Syndrome 

Development using At-planting Risk Factors ......................................................17 
Abstract .....................................................................................................................18 
Introduction ...............................................................................................................19 
Materials and Methods ..............................................................................................23 
Results .......................................................................................................................31 
Discussion .................................................................................................................39 
Acknowledgements ...................................................................................................43 

 
 
 
 
 
 
 
CHAPTER 3: Diagnostic qPCR Assay to Detect Fusarium brasiliense, a Causal 

vii 

Agent of Soybean Sudden Death Syndrome and Root Rot of Dry Bean ...........44 
Abstract .....................................................................................................................45 
Introduction ...............................................................................................................45 
Materials and Methods ..............................................................................................48 
Results .......................................................................................................................59 
Discussion .................................................................................................................64 
Acknowledgements ...................................................................................................68 

 
 
 
 
 
 
 
CHAPTER 4: A Protoplast Generation and Transformation Method for 

Soybean Sudden Death Syndrome Causal Agents Fusarium virguliforme 
and F. brasiliense ....................................................................................................69 
Abstract .....................................................................................................................70 
Introduction ...............................................................................................................71 
Materials and Methods ..............................................................................................73 
Results .......................................................................................................................80 
Discussion .................................................................................................................83 
Conclusions ...............................................................................................................86 
Acknowledgements ...................................................................................................86 

 
 
 
 
 
 
 
 

 

 

CHAPTER 5: The role of soil-borne nematodes in soybean sudden death 

syndrome caused by Fusarium virguliforme ........................................................87 
Abstract .....................................................................................................................88 
Introduction ...............................................................................................................88 
Materials and Methods ..............................................................................................91 
Results .......................................................................................................................96 
Discussion ...............................................................................................................103 
Acknowledgements .................................................................................................105 

 
 
 
 
 
 
 
CHAPTER 6: Investigating legume host preference among Fusarium 

virguliforme and F. brasiliense using factorial inoculations and whole-
genome sequencing ...............................................................................................106 
Abstract ...................................................................................................................107 
Introduction .............................................................................................................108 
Materials and Methods ............................................................................................110 
Results .....................................................................................................................118 
Discussion ...............................................................................................................120 
Acknowledgements .................................................................................................125 

 
 
 
 
 
 
 
CHAPTER 7: Towards a mechanistic understanding of two SDS effector 

proteins: FvTox1 and FvNIS1 .............................................................................126 
Abstract ...................................................................................................................127 
Introduction .............................................................................................................127 
Materials and Methods ............................................................................................130 
Results .....................................................................................................................136 
Discussion ...............................................................................................................146 
Acknowledgements .................................................................................................149 

 
 
 
 
 
 
 
APPENDIX .....................................................................................................................150 
 
REFERENCES ...............................................................................................................155 
 

 

viii 

 

LIST OF TABLES 

 

Table 2-1. Lloyd’s index of patchiness (LIP) and correlation coefficients (r) to 
end-of-season SDS disease index (DX) and yield for variables collected 
throughout the growing season .......................................................................................... 32 
 
Table 2-2. Prediction models and cross validations for yield, soybean sudden 
death syndrome (SDS) disease index (DX), and root dry weight models ......................... 35 
 
Table 3-1. Primers and probes used in this study .............................................................. 49 
 
Table 3-2. Panel of isolates used to determine specificity of F. brasiliense qPCR 
assay ................................................................................................................................... 53 
 
Table 3-3. Serial dilutions for four F. brasiliense isolates used to determine assay 
sensitivity ........................................................................................................................... 56 
 
Table 3-4. Amplification of F. brasiliense from soils across Michigan ............................ 59 
 
Table 3-5. Amplification of serial dilutions of F. brasiliense and/or F. 
virguliforme DNA .............................................................................................................. 63 
 
Table 4-1. Primer pairs designed and used in this study for cloning and assembly 
into plasmids ...................................................................................................................... 75 
 
Table 6-1. Isolation frequencies of F. virguliforme and F. brasiliense in Michigan 
from soybean and dry bean hosts ..................................................................................... 110 
 
Table 6-2. Isolate information, DNA extraction metadata, and assembly 
assessments for isolates sequenced in this study ............................................................. 117 
 

 

 

ix 

 

LIST OF FIGURES 

 

Figure 1-1. Soybean sudden death syndrome (SDS) disease cycle caused by the 
fungal pathogen Fusarium virguliforme .............................................................................. 2 
 
Figure 1-2. Schematic model of four potential modes of resistance to F. 
virguliforme effectors in other hosts .................................................................................... 5 
 
Figure 2-1. Soybean sudden death syndrome (SDS) disease severity rating scale 
used in this study ................................................................................................................ 27 
 
Figure 2-2. Contour plots determined via kriging of the grid-sampled data 
representing Fusarium virguliforme (Fv) DNA quantities, soybean cyst nematode 
egg quantities, soybean growth stage R5 foliar SDS disease index ratings, and 
soybean yield ..................................................................................................................... 33 
 
Figure 2-3. Pathway analysis from at-planting risk factors to R5 plant health 
variables, to yield ............................................................................................................... 36 
 
Figure 2-4. Multivariate analyses of sudden death syndrome (SDS) in terms of 
Fusarium virguliforme and nematode pressure ................................................................. 38 
 
Figure 3-1. Sequence alignment of 149 base pairs of the intergenic spacer (IGS) 
of species within clade II of the Fusarium solani species complex .................................. 50 
 
Figure 3-2. Amplification results of 5 ng DNA of select isolates across different 
species within the Fusarium solani species complex ........................................................ 52 
 
Figure 3-3. Detection of serial dilutions of 4 F. brasiliense isolates ................................ 57 
 
Figure 3-4. Representative isolates of (A) F. brasiliense and (B) Fusarium solani 
complex clade III isolates plated on potato dextrose agar, 7 days post inoculation .......... 61 
 
Figure 3-5. Detection of F. brasiliense within root tissues of dry bean cultivar (A) 
Zenith or (B) Red Hawk .................................................................................................... 62 
 
Figure 4-1. Plasmid maps used to generate transgenic Fusarium strains ......................... 76 
 
Figure 4-2. Overview of the protoplasting method ........................................................... 78 
 
Figure 4-3. Wild type F. virguliforme and F. brasiliense strains are sensitive to 
hygromycin (100 µg / mL) and nourseothricin (100 µg / mL) .......................................... 81 
 
Figure 4-4. Representative protoplasts seven days post-transformation ........................... 82 

 

x 

 

Figure 4-5. Microscopic features of wild type and representative mutant strains of 
F. virguliforme (A) and F. brasiliense (B) ........................................................................ 83 
 
Figure 4-6. Mixed culture of Fusarium spores, distinguished by fluorescent 
reporter gene expression .................................................................................................... 83 
 
Figure 5-1. Fusarium virguliforme interactions with Caenorhabditis elegans ................ 97 
 
Figure 5-2. Fusarium brasiliense interactions with Caenorhabditis elegans ................... 98 
 
Figure 5-3. Linear unmixing of green C. elegans autofluorescence and GFP 
fluorescence from transgenic F. virguliforme strains ........................................................ 99 
 
Figure 5-4. Z-stack images (slices) of C. elegans colonized by a transgenic F. 
virguliforme strain expressing GFP ................................................................................. 100 
 
Figure 5-5. Fusarium virguliforme colonization of an L4 C. elegans nematode 
over time .......................................................................................................................... 101 
 
Figure 5-6. Results of growth chamber factorial inoculation experiment ...................... 102 
 
Figure 6-1. Soybean and dry bean production acreage in Michigan, and 
geographic distribution of F. virguliforme and F. brasiliense ......................................... 116 
 
Figure 6-2. Results of factorial inoculation experiment with F. virguliforme and 
F. brasiliense on soybean and dry bean ........................................................................... 118 
 
Figure 6-3. High molecular weight DNA extractions for F. virguliforme and F. 
brasiliense used in whole-genome sequencing with long-read Nanopore 
technology ........................................................................................................................ 119 
 
Figure 6-4. Visual representation of a F. virguliforme and F. brasiliense genome 
assembly in this study using BANDAGE ........................................................................ 120 
 
Figure 6-5. Whole genome alignments using the ‘minimap2’ algorithm, plotted 
with D-GENIES ............................................................................................................... 124 
 
Figure 7-1. Gel electrophoresis of DNA amplified from representative Pichia 
pastoris strains transformed with linearized pPICZ-A constructs containing one of 
three gene constructs of interest ....................................................................................... 135 
 
Figure 7-2. Gel electrophoresis of protein extracted from the supernatant of 
methanol-induced Pichia pastoris strains after 96 hours ................................................. 137 
 
Figure 7-3. Gel electrophoresis of protein extracted from the cell pellets of 
methanol-induced Pichia pastoris strain cFvTox1-1 over time ...................................... 137 

 

xi 

 

 
Figure 7-4. Gel electrophoresis of amplified cDNA after RNA extraction from the 
cell pellets of methanol-induced Pichia pastoris strains after 96 hours .......................... 138 
 
Figure 7-5. Gel electrophoresis of amplified DNA from representative E. coli 
cells transformed with pET11-b plasmids containing one of four gene constructs ......... 139 
 
Figure 7-6. Gel electrophoresis of protein extracted from IPTG-induced E. coli 
cells transformed with pET11-b plasmids ....................................................................... 140 
 
Figure 7-7. Gel electrophoresis of protein samples after purification via affinity 
chromatography ............................................................................................................... 141 
 
Figure 7-8. Gel electrophoresis of protein samples extracted from IPTG-induced 
E. coli cells and after exchange into water (de-salting) ................................................... 141 
 
Figure 7-9. Soybean trifoliate leaves 7 days after inoculation with protein samples 
in stem-cut assays ............................................................................................................ 142 
 
Figure 7-10. Tobacco leaves infiltrated with Agrobacterium tumefaciens strain 
GV3101 ............................................................................................................................ 144 
 
Figure 7-11. Co-immunoprecipitation (co-IP) of proteins 48 hours post A. 
tumefaciens infiltration of N. benthamiana leaves .......................................................... 145 
 

 

xii 

 

Introduction 

CHAPTER 1: LITERATURE REVIEW 

By 

Mitchell G. Roth 

 

The causal agent of soybean sudden death syndrome (SDS) in the U.S. was identified in 

1971 in Arkansas as Fusarium solani f. sp. glycines (Rupe 1989; Roy et al., 1989, 1997). 

Historically, soybean SDS has also been a problem in the South American soybean producing 

countries Brazil and Argentina. Formal genetic and morphological studies have shown that the 

forma speciales is composed of multiple distinct species. To date, six named and one un-named 

SDS-causing species have been identified within clade II of the Fusarium solani species 

complex (FSSC); Fusarium virguliforme (Aoki et al., 2003), F. tucumaniae (Aoki et al., 2003), 

F. brasiliense (Aoki et al., 2005), F. cuneirostrum (Aoki et al., 2005), F. crassistipitatum (Aoki 

et al., 2012a), F. azukicola (Aoki et al., 2012b), and an unnamed species in South Africa 

(Tewoldemedhin et al., 2017). Another FSSC clade II species is described as causing root rot; F. 

phaseoli (Aoki et al., 2003). Many of these species had been found in countries outside of the 

U.S., so it was previously thought that only F. virguliforme was present in the U.S. However, 

recent surveys have identified F. brasiliense and F. cuneirostrum in Michigan, although F. 

virguliforme remains the most prominent species (Jacobs et al., 2018; Wang et al., 2018).  

 

In the U.S., soybean SDS caused an average 27.7 million bushel yield loss between 2004 

and 2009, and an average 41.9 million bushel yield loss between 2010 and 2014 (Wrather and 

Koenning 2009; Koenning and Wrather 2010; Allen et al., 2017). With current soybean values 

worth approximately $9 per bushel, the economic loss due to SDS between 2010 and 2014 is 

estimated at $377 million. SDS-causing pathogens cause soybean yield loss through two-phases 

 

1 

 

of symptom development. First, initial symptoms of SDS appear in at the site of infection in the 

roots as discoloration and rotting (Fig. 1-1). Second, foliar symptoms develop as infection 

progresses which is marked by interveinal chlorosis that turns to interveinal necrosis, which 

leads to premature leaf drop and pod abortion (Fig. 1-1) (Hartman et al., 2015). These foliar 

symptoms typically occur much later in the growing season around flowering (Hartman et al., 

2015), though they can develop prior to flowering.  

 

Figure 1-1. Soybean sudden death syndrome (SDS) disease cycle caused by the fungal 
pathogen Fusarium virguliforme. The pathogen grows in culture producing sporodochia with 
blue pigmentation (A) full of asexually produced conidia (B). The conidia germinate in the soil 
and infect roots causing root rot (C, from Navi and Yang 2008), with a potential interaction with 
the soybean cyst nematode. After colonization of the xylem tissues, foliar symptoms of 
interveinal chlorosis and necrosis can develop (D). However, F. virguliforme remains in the root 
system where it can overwinter in fungal masses on plant residue (E, white arrows).  
 

 

 

 

2 

 

SDS Management Strategies 

Since its discovery in Arkansas in 1971, SDS has spread to all soybean producing states 

in the U.S. (Hartman et al., 2015). To date, all studies investigating management strategies for 

soybean SDS have focused solely on F. virguliforme. To manage F. virguliforme, researchers 

have examined soil tillage, crop rotations, fungicide seed treatments, and genetically resistant 

cultivars. Investigating soil tillage practices revealed that soil compaction with no-till strategies 

significantly enhances SDS severity, while subsoil tillage between 25-45 cm significantly 

reduced SDS severity (Vick et al., 2003, 2006). However, no-till strategies have become very 

popular among growers because it minimizes erosion and fuel costs while enhancing soil 

moisture and microbial communities (Uri 2000).  

Long-term and diverse crop rotations (3 to 4 years) can reduce F. virguliforme 

populations five-fold compared to the commonly used 2-year corn-soybean rotation used by 

growers (Leandro et al., 2018). However, this reduction in pathogen population is dependent on 

the crops used in the rotation and weed pressure, as F. virguliforme has been shown to colonize 

at least 15 other crop and weed species including dry bean, alfalfa, pea, sugar beet, canola, and 

wheat (Kolander et al. 2012), and can survive long-term in corn residue (Navi and Yang 2016). 

Due to the high value of corn and soybean, growers do not commonly adopt long-term crop 

rotations with diverse crops.  

Fungicide seed treatments and resistant cultivars have been the primary management 

strategies that growers have relied upon to manage SDS. Fungicide chemistries classified as 

succinate dehydrogenase inhibitors (SDHI), like fluopyram, have shown efficacy against SDS-

pathogens in vitro (Wang et al., 2017; Sang et al., 2018). When formulated as a seed treatment, 

fluopyram has shown reductions in F. virguliforme and SDS while also providing yield increases 

 

3 

 

(Kandel et al., 2016, 2017, 2018), but other chemistries formulated as seed treatments have not 

shown the same benefit (Weems et al., 2015).  

Genetic resistance to SDS is highly desirable, yet many studies have determined that 

resistance to SDS is complex and controlled by quantitative trait loci (QTLs) (Chang et al., 

2018). In addition, each QTL can contribute to separate aspects of SDS-development; e.g., root 

rot or foliar symptoms (Kazi et al., 2008; Luckew et al., 2013). Lastly, some SDS QTLs overlap 

with other QTLs known for soybean resistance to other diseases, so understanding the full 

contribution of each QTL to SDS-specific resistance is challenging (Hughes et al., 2004; 

Meksem et al., 1999; Srour et al., 2012; Swaminathan et al., 2018). However, progress towards 

developing genetically resistant varieties has been successful, as certain varieties do tolerate SDS 

better than others in field settings (Kandel et al., 2016, 2017).  

Foliar SDS symptoms are hypothesized to contribute primarily to yield loss (Luo et al., 

2000), and no other hosts of F. virguliforme display true SDS foliar symptoms (Kolander et al., 

2012). Therefore, these alternate hosts may employ some mechanism of resistance that could be 

transferred to soybean. Ideally, these alternate hosts employ mechanisms that could be 

transferred to soybean to prevent F. virguliforme infection of the roots altogether. Since F. 

virguliforme can cause root rot on many of these alternate hosts, this type of resistance 

mechanism is not likely (Kolander et al., 2012). Theoretically, there are at least four other 

possible resistance mechanisms (Fig. 1-2); expression of F. virguliforme effectors could be 

suppressed by alternate hosts, F. virguliforme is not able to penetrate to the xylem tissue to 

release effectors as it does in soybean (Abeysekara and Bhattacharyya 2014; Navi and Yang 

2008), effectors are degraded by enzymes in the xylem, or effectors targets in foliar tissues are 

absent or highly diverged, rendering the effectors ineffective. Each mechanism could be a 

 

4 

 

potential avenue for intervention through transgenic approaches. Other transgenic approaches 

have shown some success in limiting F. virguliforme abundance and SDS development, 

including transgenic expression of killer proteins (Lightfoot 2015) and receptor-like kinases 

(Srour et al., 2012) in soybean. Further understanding of the mechanistic methods of SDS 

effectors should provide additional insights for transgenic approaches to control SDS.  

4

3

Effector interacting protein
Fungal Effector
Fusarium virguliforme conidia
F. virguliforme invading mycelia

1

2

Figure 1-2. Schematic model of four potential modes of resistance to F. virguliforme 
effectors in other hosts. 1) Some F. virguliforme effectors may not be expressed in other hosts, 
or expression is suppressed. 2) F. virguliforme may not penetrate to the xylem tissue in non-
soybean hosts, which is necessary for effector translocation. 3) Effectors are degraded by 
enzymes in the xylem of alternate hosts. 4) Effectors reach foliar tissues in alternate hosts, but 
the effector target is absent or too highly diverged to induce SDS-like symptoms. 
 
 

 

 

5 

 

Risk prediction is not commonly used for most soybean diseases. Many states encourage 

growers to submit soil samples to test for SCN population levels, which can assist in the 

selection of SCN resistant soybean germplasm, if necessary. In fact, some states provide free 

SCN testing through checkoff dollars. However, SCN was the only pathogen for which risk 

prediction was commonly used until recent studies on white mold caused by Sclerotinia 

sclerotiorum (Willbur et al., 2018a, 2018b). Other regional risk prediction models have been 

developed for Asian soybean rust and soybean SDS (Del Ponte et al., 2006; Scherm and Yang 

1999), but they have not been widely implemented as disease management strategies.  

 

Fusarium virguliforme infection and interaction with nematodes 

 

To enter plant roots, many fungal pathogens generate specialized hyphae called an 

appressorium to build up turgor pressure, forcing a hyphal penetration peg between the cells of a 

plant host (Howard et al., 1991; Howard and Valent 1996). Once inside the host, fungal 

pathogens typically do one of three things; develop hyphae that remain in the extracellular 

apoplast, develop intracellular invasive hyphae, or develop specialized feeding structures called 

haustoria that also invade plant cells (Giraldo and Valent 2013). Scanning electron microscopy 

of F. virguliforme indicates appressoria formation on soybean root surfaces (Navi and Yang 

2008), though the appressorium does not appear as large and distinct as those produced by aerial 

pathogens (Howard and Valent 1996). Indeed, appressorium development in Fusarium relatives 

(F. oxysporum, F. graminearum, and F. verticillioides) either develop intermittent appressorium-

like structures (Czymmek et al., 2007), lobate appressoria (Boenisch and Schäfer 2011) or no 

appressoria at all (Bluhm et al., 2007; Zhang et al., 2011).  

 

6 

 

 

Genomic analysis and gene prediction algorithms of the F. virguliforme genome have 

also indicated a significant arsenal of cell-wall degrading enzymes (Chang et al., 2016b). These 

enzymes are likely expressed and used in addition to appressoria to gain entry into soybean root 

tissues. Inside the root system, F. virguliforme colonization can reach the vascular tissues, as 

supported by cross sections of infected soybean roots (Islam et al., 2017a, 2017b; Navi and Yang 

2008). It is thought that colonization of the vascular tissues is associated with the development of 

foliar symptoms, where effector proteins can be released into the xylem and translocated to the 

foliar tissues. Fungal-derived proteins have been identified in soybean xylem sap of F. 

virguliforme infected soybeans, as has the plant defense response compound pipecolic acid 

(Abeysekara and Bhattacharyya 2014; Abeysekara et al., 2016).  

 

A more efficient mode of entry into soybean roots would be through a mobile vector such 

as the soybean cyst nematode (SCN) (Fig. 1-1). Independently, SCN is a major threat to soybean 

production throughout the U.S., but F. virguliforme and SCN are commonly found in the same 

fields (Rupe et al., 1993). Soybean fields with both pathogens often develop significantly worse 

SDS than fields with F. virguliforme only (Brzostowski et al., 2018; McLean and Lawrence 

1995; Westphal et al., 2014), leading to the suggestion that the two act synergistically to enhance 

SDS severity (Xing and Westphal 2013). However, other studies have failed to find this 

association between the presence of SCN and enhanced SDS severity (Hershman et al., 1990; 

Marburger et al., 2014; Rupe et al., 1993). There are multiple hypotheses surrounding the 

interactions between F. virguliforme and SCN that could enhance SDS severity. First, the two 

pathogens may be able to directly interact with one another prior to reaching a soybean root. 

Second, interactions within the soybean root could facilitate unique physiological responses by 

the soybean that increases susceptibility to SDS. Both hypotheses surrounding F. virguliforme 

 

7 

 

and SCN interactions could be synergistic or antagonistic. Early studies suggested an 

antagonistic relationship because F. virguliforme was isolated from infected and parasitized SCN 

cysts and eggs (Donald et al., 1993; McLean and Lawrence 1995). Some Fusarium species have 

been shown to colonize and kill the model nematode Caenorhabditis elegans (Muhammed et al., 

2012b); hence, it is reasonable to hypothesize that F. virguliforme may be able to colonize and 

kill SCN, increasing in abundance and leading to increased risk of SDS. Additional studies have 

shown that the presence of F. virguliforme decreases SCN reproduction, which also suggests an 

antagonistic relationship (Gao et al., 2006). However, SDS caused by F. virguliforme was only 

enhanced by SCN, and was not enhanced in the presence of the root knot nematode Meloidogyne 

incognita (Xing and Westphal 2013), suggesting a synergistic effect on SDS due to the presence 

of SCN. The discrepancies regarding the role of SCN on SDS development could be due to 

differences in F. virguliforme isolates, SCN races, and soybean cultivars used in each study.  

 

Fusarium virguliforme effectors 

 

The mechanisms employed by F. virguliforme to cause root rot symptoms are unknown 

and have not been studied at a molecular level. In silico analyses and QTL mapping studies have 

revealed many xylanases, polygalacturonases, and hydrolases in the genome of F. virguliforme, 

which likely play a role in root entry (Chang et al., 2016b; Sahu et al., 2017). These proteins are 

likely involved in root entry and root rot symptom development, though classical genetic 

evidence of this is still missing. In contrast, classical genetic approaches have been used to 

identify specific effector proteins involved in foliar SDS development. The first report providing 

evidence that F. virguliforme secreted toxic compounds and proteins came in the late 1990’s, 

where a pair of publications showed that spent F. virguliforme broth, or “cell-free culture 

 

8 

 

filtrate” caused browning of soybean callus tissue (Jin et al., 1996b), and that a single partially-

purified protein from this culture filtrate was sufficient to cause necrosis on detached soybean 

leaves (Jin et al., 1996a). These studies were also the first to propose the hypothesis that F. 

virguliforme secretes proteins that may translocate into the xylem to the foliar tissues (Jin et al., 

1996b). Though these findings were significant, but the gene(s) encoding this protein has not 

been identified, and thus, characterization of this process is incomplete. A subsequent study has 

demonstrated that light is required for SDS symptom development, and symptoms are associated 

with the degradation of the large subunit of Rubisco (Ji et al., 2006). 

 

Additional searches for F. virguliforme effectors identified two different proteins, both of 

which are small proteins with putative secretion signals. The first effector is FvTox1, a 172 

amino acid protein that has a predicted 19 amino acid N-terminal secretion signal (Brar et al., 

2011). When expressed, purified, and vacuum infiltrated into soybean leaf discs, FvTox1 caused 

a significant reduction in chlorophyll content (Brar et al., 2011). Some studies have speculated 

that FvTox1 localizes to chloroplasts, and could be associated with the degradation of Rubisco 

(Wang et al., 2015; Abeysekara et al., 2016), though a formal colocalization study has not been 

performed. Similarly, the ToxA protein produced by Pyrenophora tritici-repentis also requires 

light to cause disease symptoms in wheat and functions by direct interaction with a chloroplast 

protein, which disrupts chloroplast function (Ciuffetti et al., 2010; Manning and Ciuffetti 2005; 

Manning et al., 2007). The predicted structure of mature FvTox1 (lacking the secretion signal) 

does not show homology to ToxA, and in fact shows poor structural homology to any known 

protein models. The Phyre2 protein homology software identifies homology between FvTox1 

and a calcium binding protein, but the confidence score is only 78.7. Still, mutant fvtox1 

knockout strains are reduced in their ability to induce foliar SDS in stem-cut assays, indicating a 

 

9 

 

significant role of FvTox1 in foliar SDS development (Pudake et al., 2013). The second effector 

is FvNIS1, a 152 amino acid protein with an 18 amino acid N-terminal secretion signal 

(Armenteros et al., 2019; Chang et al., 2016a). The Phyre2 predicted structure of mature FvNIS1 

(lacking the secretion signal) shows a strong homology to the Neurospora crassa PMO-2 

protein, with 96.1% confidence (data not shown). The N. crassa PMO-2 protein is a 

polysaccharide monooxygenase that enzymatically cleaves cellulose (Li et al., 2012). Assuming 

FvNIS1 acts in a similar manner to PMO-2, one would expect a significant role for FvNIS1 in 

plant virulence. Indeed, overexpression of FvNIS1 in soybean leaves with soybean mosaic virus 

induces SDS-like symptoms, and fvnis1 knockout mutants are significantly less virulent than 

wild type F. virguliforme (Chang et al., 2016a).  

 

A large number of cysteine residues is commonly found in fungal effectors 

(Stergiopoulos and de Wit 2009). In some cases, these cysteine residues can comprise up to 16% 

of amino acid residues (Liu et al., 2012). These cysteine residues are hypothesized to promote 

disulfide bonding, a chemical feature which strengthens the tertiary structure of proteins and 

enhances protein stability (Liu et al., 2012). Given the support for the hypothesis that F. 

virguliforme secretes proteins into soybean xylem, (Abeysekara and Bhattacharyya 2014), 

cysteine residues may be important to F. virguliforme effectors. A well characterized example of 

fungal-derived proteins in plant xylem tissues are the secreted in xylem (SIX) proteins from F. 

oxysporum, which are also cysteine rich (Rep et al., 2004; Takken and Rep 2010). In addition, 

effectors from Cladosporium fulvum, Stagonospora nodorum, and Phakopsora pachyrhizi have 

also shown high cysteine content (Liu et al., 2012; Qi et al., 2016; Stergiopoulos and de Wit 

2009). Interestingly, FvTox1 is cysteine-rich (9 of 172), while FvNIS1 is relatively cysteine-poor 

(2 of 152).  

 

10 

 

 

Fungal proteins that induce plant disease symptoms are typically referred to in the 

literature as host selective toxins (HSTs), or effectors. For example, the previously mentioned 

ToxA protein from P. tritici-repentis, along with ToxB and ToxC, are reported as HSTs 

(Ciuffetti et al., 2010; Manning and Ciuffetti 2005; Manning et al., 2007). By definition, HSTs 

are fungal low-molecular-weight metabolites or proteins that play an active role in colonization 

and symptom development of host plants, and in most cases, host sensitivity to the HST is 

conferred by a single gene (Wolpert et al., 2002). However, this definition is nearly identical to 

the gene-for-gene model proposed by (Flor 1946, 1971), where a pathogen-derived and host-

derived protein act in a dominant, compatible fashion to induce susceptibility in the host 

(Ciuffetti et al., 2010; Wolpert et al., 2002). For simplicity, small fungal proteins that trigger 

either avirulence or host susceptibility should be called one name, either effectors or HSTs. 

Given the volume of work needed to determine if a single protein is specific to a given host, the 

term “effector” should be adopted over “HST” for all fungal proteins involved in plant-pathogen 

interactions (Varden et al., 2017). However, in addition to secreted, virulence-associated 

proteins, F. virguliforme is also capable of producing toxic secondary metabolites like radicicol 

(i.e., monorden), fusaric acid, and citrinin that can accumulate and cause cell death in soybean 

leaves (Chang et al., 2016a). Similarly, other fungal plant pathogens produce non-protein 

compounds that aid plant pathogenicity, like polyketides (Baker et al., 2006; Gaffoor et al., 

2005). Though they are not proteinaceous in nature, these compounds still have an effect on 

plant host immunity, so “effector” is an appropriate term over “HST” for these compounds as 

well. For simplicity, all fungal-derived compounds that effect plant pathogenicity will be called 

“effectors” in this work. To date, no studies have investigated whether the symptoms produced 

by FvTox1 and FvNIS1 effectors are soybean-specific. 

 

11 

 

 

One of the key goals of effector-based research has been to define the mechanisms by 

which effectors control plant processes. As an analogy for this work, if an effector functions as a 

key and a host susceptibility factor functions as a lock, then it follows that an in-depth 

understanding of the structure and function of the effector (key) could be used to fine-tune host 

processes (change the locks) and prevent pathogen manipulation of the plant host. While many 

other pathosystems have made progress in identifying and characterizing several “lock and key” 

mechanisms, F. virguliforme effector studies have primarily focused on developing synthetic 

peptides or mammalian-derived antibodies that bind to FvTox1 (Brar and Bhattacharyya 2012; 

B. Wang et al., 2015). However, artificially binding FvTox1 does not necessarily inhibit its 

function, and synthetic constructs need to be validated in planta before they can be deemed safe 

and effective. In addition, mammalian-derived transgenes in plant tissues make them unfit for 

human consumption under the current USDA regulations. Instead, an approach that aims to 

understand the effector mode-of-action in order to alter the target and prevent the mode-of-action 

that induces susceptibility is more intuitive and likely to have practical applications that could 

enhance disease resistance if deployed in the field.  

 

Fusarium genomics  

 

Genome sequences are available for most of the FSSC clade II species, with the 

exception of F. crassistipitatum (Huang et al., 2016). The F. virguliforme genome is quite 

fragmented with over 3,000 contigs (Srivastava et al., 2014), compared to the high-quality 

genomes for its relatives F. graminearum, F. oxysporum, F. fujikuroi, and F. verticillioides, 

which all have chromosome-level assemblies (Cuomo et al., 2007; Jeong et al., 2013; King et al., 

2015; Ma et al., 2010). However, the F. virguliforme genome is quite complete relative to the 

 

12 

 

other FSSC clade II species, which have > 15,000 contigs each (Huang et al., 2016). To date, 

neither F. virguliforme or F. brasiliense genomes have been annotated for gene content, though 

multiple RNA-seq and microarray datasets are available (Chang et al., 2016a; Marquez et al., 

2019; Radwan et al., 2011; Sahu et al., 2017). However, these non-annotated genomes facilitated 

gene prediction and benchmarking using single copy orthologs (BUSCO) that suggest both 

existing genomes are relatively complete since at least 87% of the core single-copy orthologs 

found in F. graminearum were also found in the current F. virguliforme and F. brasiliense 

genomes. To learn more about the pathogenicity mechanisms employed by F. virguliforme and 

F. brasiliense, improvements to available genomes are needed.  

 

Using chromosome level assemblies, researchers studying F. oxysporum pathogenicity 

have identified small mobile pathogenicity chromosomes associated with host range 

(Vlaardingerbroek et al., 2016a, 2016b). These chromosomes are rapidly evolving through the 

presence of many active transposons (Sperschneider et al., 2015). In addition, these 

chromosomes can be transferred from a virulent isolate to non-virulent isolate and confer 

virulence, expanding the host range of the previously non-virulent isolate (Ma et al., 2010; van 

Dam et al., 2017). A formal karyotype has never been done for F. virguliforme, though whole-

genome alignments with F. graminearum and pulsed-field gel electrophoresis of F. virguliforme 

DNA extractions have tried to determine the number of chromosomes present in F. virguliforme 

(Mansouri 2012; Shultz et al., 2007). In Michigan, F. virguliforme and F. brasiliense isolation 

frequencies indicate a host preference, where F. virguliforme preferentially colonizes soybean 

and F. brasiliense preferentially colonizes dry bean. Therefore, a chromosome-level 

understanding of these pathogens may reveal a similar trend of mobile pathogenicity 

 

13 

 

chromosomes that can boost pathogenicity of each species on each host, or help explain the wide 

host range of F. virguliforme (Kolander et al., 2012).  

 

High quality genomes for these pathogens can facilitate the development of molecular 

diagnostic tools and also reveal a lot about the biology of pathogens, including genetic potential 

for fungicide resistance, mating potential, and pathogenicity chromosomes. Two studies have 

used available FSSC clade II sequences to develop highly specific and sensitive qPCR assays for 

F. virguliforme and F. brasiliense (Roth et al., 2019c; J. Wang et al., 2015). One study has 

shown differential responses among FSSC clade II species to fluopyram, the only fungicide 

currently efficacious for these pathogens, which was caused by a single nonsynonymous 

mutation in the SdhB gene (Sang et al., 2018). Identifying genes associated with fungicide 

resistance may guide engineering of new fungicide compounds that have improved function 

against these pathogens. Curiously, PCR tests investigating mating type loci in F. virguliforme 

revealed only one mating type, while both mating types have been identified in F. brasiliense 

isolates (Hughes et al., 2014). These mating type loci within the genomes of each species 

suggests that only asexual reproduction is possible for F. virguliforme, while both sexual and 

asexual reproduction may be occurring within F. brasiliense isolates. In F. oxysporum, genome 

sequencing efforts have identified dispensable chromosomes highly associated with 

pathogenicity on specific hosts (Vlaardingerbroek et al., 2016b; van Dam et al., 2017). Genomic 

studies have already helped design diagnostic tools for F. virguliforme and F. brasiliense, and 

additional improvements to current genomes may reveal more about their biology and 

reproduction, identifying potential avenues to restrict movement and revealing genetic potential 

for fungicide resistance. 

 

 

14 

 

Conclusions 

 

Soybean SDS pathogens F. virguliforme and F. brasiliense continue to spread globally 

and are difficult to manage with a single approach. The development of more management 

approaches for integration with current strategies is needed, as is a greater understanding of the 

genetics, genomics, and biology of these pathogens. In Chapter 2 of this work, I describe an SDS 

risk prediction model that uses F. virguliforme abundance data to predict SDS development at a 

field level. I envision a tool being used by growers to guide their decisions for soybean 

germplasm selection or fungicide seed treatments, particularly in regions of a field with high 

risk. Many growers already use soil-based maps to guide planting population density, and a 

similar concept could be used automatically adjust variety or seed treatments in high-risk SDS 

regions of a field. A similar SDS risk prediction model could be made for F. brasiliense using 

the qPCR assay developed in this work, described in Chapter 3. This qPCR assay will also be 

helpful to diagnostic labs throughout the U.S. to monitor the spread of F. brasiliense. 

 

A greater understanding of pathogen biology and SDS development is also needed to 

enhance SDS management. Though genetic studies have been performed in F. virguliforme, 

none have been performed in F. brasiliense. The protoplasting and genetic transformation 

protocol described in Chapter 4 provides significant details for performing classical genetic 

experiments in these two organisms. Using this protocol to develop fluorescent strains of F. 

virguliforme and F. brasiliense allowed me to investigate the biological interaction between 

these fungi and the SCN in Chapter 5, using a new approach that has not previously been 

reported – fluorescent strains coupled with advanced microscopy. The evidence from this study 

suggests that F. virguliforme is not vectored into roots by SCN, but instead can colonize 

 

15 

 

nematodes as a saprophyte. It remains to be seen whether the presence of SCN inside the same 

soybean root as F. virguliforme induces the expression of fungal effectors.  

 

In the final two Chapters, I describe the development of high-quality long-read genomes 

for F. virguliforme and F. brasiliense. As described in Chapter 6, these genomes assembled into 

less than 150 contiguous sequences and provide an opportunity to study genome-wide structural 

variation. These genomes will be a valuable resource for discovering new effector genes that can 

be studied with classical genetic approaches. Some of these genetic approaches were used to 

study the mechanisms of FvTox1 and FvNIS1, reported in Chapter 7. The evidence suggests that 

FvNIS1 has a conserved mechanism to induce cell death in tobacco and soybean, while FvTox1 

may interact with a soybean-specific target.  

 

 

 

16 

 

 

CHAPTER 2: Predicting Soybean Yield and Sudden Death Syndrome Development using 

At-planting Risk Factors 

By 

Mitchell G. Roth, Z. A. Noel, J. Wang, F. Warner, A. M. Byrne, and M. I. Chilvers 

This chapter has been accepted for publication to the journal Phytopathology pending minor 

revisions. The manuscript was submitted under a “Public Domain” copyright option. 

 

Author Contributions: 

MGR, JW, and MIC designed the experiment, MGR, JW, FW, and AMB collected data, MGR 

and ZAN analyzed data and generated figures, MGR, ZAN, and MIC wrote the manuscript. 

 

 

 

17 

 

Abstract 

 

 

In the U.S., sudden death syndrome (SDS) of soybean is caused by the fungal pathogen 

Fusarium virguliforme and is responsible for significant yield losses each year. Understanding 

the risk of SDS development and subsequent yield loss could provide growers with valuable 

information for proper management of this challenging disease. Current management strategies 

for F. virguliforme use partially resistant varieties, fungicide seed treatments, and extended crop 

rotations with diverse crops. The aim of this study was to develop models to predict SDS 

severity and soybean yield loss using at-planting risk factors to integrate with current SDS 

management strategies. In 2014 and 2015, field studies were conducted in adjacent fields in 

Decatur, MI, which were intensively monitored for F. virguliforme and nematode quantities at-

planting, plant health throughout the growing season, end-of-season SDS severity, and yield 

using an unbiased grid sampling scheme. In both years, F. virguliforme and soybean cyst 

nematode (SCN) quantities were unevenly distributed throughout the field. The distribution of F. 

virguliforme at-planting had a significant correlation with end-of-season SDS severity in 2015, 

and a significant correlation to yield in 2014 (P < 0.05). SCN distributions at-planting were 

significantly correlated with end-of-season SDS severity and yield in 2015 (P < 0.05). Prediction 

models developed through multiple linear regression showed that F. virguliforme abundance (P 

< 0.001), SCN egg quantity (P < 0.001), and year (P < 0.01) explained the most variation in end-

of-season SDS (R2 = 0.32), while end-of-season SDS (P < 0.001) and end-of-season root dry 

weight (P < 0.001) explained the most variation in soybean yield (R2 = 0.53). Further, 

multivariate analyses support a synergistic relationship between F. virguliforme and SCN, 

enhancing the severity of foliar SDS. These models indicate that it is possible to predict patches 

of SDS severity using at-planting risk factors. Verifying these models and incorporating 

 

18 

 

additional data types may help improve SDS management and forecast soybean markets in 

response to severe SDS threats.  

 

Introduction 

 

Soybean (Glycine max [L.] Merr.) is one of the most cultivated and top grossing crops in 

the U.S. but is prone to yield losses caused by many pathogens (Allen et al., 2017; Koenning and 

Wrather 2010). In North America, the fungal pathogen Fusarium virguliforme O’Donnell & T. 

Aoki causes soybean sudden death syndrome (SDS). Fusarium virguliforme has no known 

sexual reproductive stage (Hughes et al., 2014) but can produce asexual microconidia, 

macroconidia, and overwintering chlamydospores (Aoki et al., 2003). These spores can 

germinate into mycelia in the soil, which release cell wall degrading enzymes after coming into 

contact with soybean roots (Chang et al., 2016b) and develop an appressorium (Navi and Yang 

2008) to facilitate infection. Soybean roots of any age can be infected (Gongora-Canul and 

Leandro 2011; Hartman et al., 2015). Initial symptoms develop as root discoloration and root rot, 

but eventually foliar symptoms develop as interveinal chlorosis, necrosis, and premature 

defoliation, leading to yield loss (Hartman et al., 2015). However, F. virguliforme remains in the 

root tissues throughout foliar disease development and has never been isolated from above 

ground tissues (Roy et al., 1989). SDS commonly appears in heterogeneous patches, or “hot 

spots”, within a field and differentially affects yield throughout a single field (Hartman et al., 

2015). Evidence suggests that foliar SDS symptoms are the result of F. virguliforme penetration 

to the vascular tissues (Islam et al., 2017a; 2017b; Navi and Yang 2008), where various 

secondary metabolites and protein toxins are secreted into the xylem and translocated to the 

 

19 

 

foliar tissues (Abeysekara and Bhattacharyya, 2014; Brar et al., 2011; Chang et al., 2016a; 

Pudake et al., 2013).  

Heterodera glycines Ichinohe, the soybean cyst nematode (SCN), is commonly found in 

fields throughout the U.S., and also causes significant yield loss in soybean (Allen et al., 2017; 

Koenning and Wrather, 2010). The infectious SCN juveniles (J2) pierce soybean roots with a 

stylet and burrow to the vascular tissue where they induce the formation of syncytia, specialized 

host-derived feeding structures that enable nematodes to mature and complete their life cycle 

(Endo, 1986; Hussey and Grundler, 1998; Ithal et al., 2007a). Multiple plant-parasitic nematodes 

have been implicated in disease complexes with plant pathogenic fungi and oomycetes, including 

SCN with F. virguliforme in soybean (Back et al., 2002; Rupe, 1989; Rupe et al., 1993). 

Independently, both F. virguliforme and SCN can penetrate to the vascular tissue, but it is 

unknown if the two pathogens interact before reaching the vascular tissues, or if F. virguliforme 

preferentially colonizes wounds caused by SCN or other nematodes. Previous studies have 

shown that F. virguliforme can colonize SCN eggs and cysts (Donald et al., 1993; McLean and 

Lawrence, 1995). Field studies have found that foliar SDS severity is worse when soybeans are 

inoculated with both pathogens compared to F. virguliforme alone (Brzostowski et al., 2014; 

McLean and Lawrence 1993; Scherm et al., 1998; Westphal et al., 2014; Xing and Westphal, 

2013). Another recent study showed that managing SCN through partially resistant cultivars also 

reduced SDS severity (Kandel et al., 2017). Together, these studies indicate a unique relationship 

between F. virguliforme and SCN that results in severe SDS, with strong corollary evidence 

suggesting that the relationship is synergistic (Xing and Westphal, 2013). Since SCN cannot 

cause SDS independently, the relationship is not additive, and any relationship with F. 

virguliforme that enhances SDS severity could be classified as synergistic. However, other field 

 

20 

 

studies have failed to find evidence to support a synergistic relationship leading to increased SDS 

(Hershman et al., 1990; Marburger et al., 2014; Rupe et al., 1993). A factorial greenhouse 

experiment revealed that these pathogens caused a more significant reduction in root mass 

together, compared to either pathogen alone, but there was no increase in foliar SDS due to the 

presence of SCN (Gao et al., 2006).  

 

Current management strategies for SDS use partially resistant varieties, seed treatments 

with fungicides (Kandel et al., 2018), crop rotation (Leandro et al., 2018), and soil tillage 

practices (Hartman et al., 2015). However, each strategy has drawbacks. Crop rotations may fail 

because F. virguliforme can successfully colonize other crop and weed species without causing 

visual symptoms, allowing inoculum to build up (Kolander et al., 2012). Extended crop rotations 

with diverse crops can be costly depending on crop markets and is not widely adopted by 

growers. Soil tillage may fail because F. virguliforme can overwinter in soybean and corn 

residue (Navi and Yang 2016). Seed treatments containing the fungicide fluopyram (BASF, 

Ludwigshafen, Germany) have shown efficacy in vitro and in planta (Gaspar et al., 2017; Kandel 

et al., 2016; Sang et al., 2018; Wang et al., 2016). However, few other fungicides show 

significant reduction of F. virguliforme inside plant roots (Weems et al., 2015), and reliance on a 

single mode of action fungicide is undesirable due to its potential to select for fungicide 

resistance. Identification of QTLs has led to improved genetic resistance to SDS; however, 

resistance is quantitative and complex since separate QTLs control resistance to root and foliar 

symptoms (Chang et al., 2018; de Farias Neto et al., 2007; Kazi et al., 2008; Lightfoot 2015; 

Luckew et al., 2013; Tan et al 2019; Wen et al., 2014). Data suggests that suppressing foliar SDS 

symptoms significantly improves yield (Luo et al., 2000) and some breeding efforts focus solely 

on foliar SDS symptoms (Wen et al., 2014). However, cultivars with no foliar symptoms may 

 

21 

 

still be heavily colonized by F. virguliforme (Wang et al.,, 2018). The impact of root rot on yield 

is difficult to estimate, and the impact of root rot caused by F. virguliforme in soybean is 

currently unknown. Additionally, some SDS resistance QTLs overlap with known SCN 

resistance QTLs, complicating the role of each in specific resistance to SDS or SCN (Meksem et 

al., 1999; Srour et al., 2012).  

 

Another SDS management strategy that has yet to be widely implemented is risk 

prediction. Risk prediction has been used for other soybean diseases, such as Asian soybean rust 

and Sclerotinia stem rot (Del Ponte et al., 2006; Mila et al., 2004; Willbur et al., 2018a; 2018b), 

as well as SDS at the regional level (Scherm et al., 1996; 1998; Scherm and Yang 1999). These 

previous studies modelled SDS development using environmental data such as soil temperature, 

soil water potential (Scherm and Yang 1996), cold stress response, and geographic location 

(Scherm and Yang 1999). While environment plays a key role in disease development, the 

presence of the pathogen may not be evenly distributed across a field. These climate-driven 

regional models indicated a highly unfavorable environment for SDS development in Minnesota, 

South Dakota, and Nebraska (Scherm and Yang 1999) even though F. virguliforme has been 

identified there and is consistently ranked in the top 5 most damaging soybean diseases in the 

Northern U.S. (Allen et al., 2017). Incorporating pathogen abundance data into SDS predictive 

models at the field level is essential for improved prediction since F. virguliforme is present and 

causing SDS in locations predicted to have an unfavorable environment for SDS.  

In this study, we aimed to incorporate SCN and F. virguliforme abundance data into SDS 

predictive models at the field level. We employed a high-intensity sampling method over two 

years to quantify SCN and F. virguliforme and examine their spatial distribution in a naturally 

infested field in Michigan. The goals of this study were to 1) determine the spatial distribution of 

 

22 

 

F. virguliforme and SCN in a field site naturally infested with both pathogens, 2) identify at-

planting risk factors for modelling SDS severity and yield, and 3) explore synergistic effects of 

F. virguliforme and nematodes on SDS severity. 

 

Materials and Methods 

Field and sampling design 

 

Two adjacent field sites in Decatur, MI with sandy loam soils were selected for field 

trials in 2014 and 2015. Both fields are naturally infested with both F. virguliforme and SCN and 

managed under a corn-soybean rotation with minimal-till conditions using a chisel plow. In both 

years, the field was the same size, measuring 9.1 m by 48.8 m. The fields were divided into 24 

plots, each measuring 3.0 m by 6.1 m. Each plot contained four rows of soybeans with 0.8-m row 

spacing, planted at a density of approximately 9 seeds per 0.3 m. In 2014, untreated Asgrow 

AG2534 seeds were planted, and in 2015 Asgrow AG2535 (Monsanto, St. Louis, MO) 

containing the company’s “complete” Acceleron treatment package were planted (Rossman et 

al., 2018). These seed choices represent common varieties and treatments available to Michigan 

growers. Both varieties are rated 7 out of 9 for SDS tolerance by the seed company, where 1 = 

excellent and 9 = poor. Soybeans were planted in early May of each year. Fields received natural 

rainfall with additional overhead irrigation, resulting in an average weekly water volume of 2.7 

cm in 2014 and 3.0 cm in 2015. The plots were harvested with an Almaco combine equipped 

with a 1.5-m head, and yield was determined with the HarvestMaster system (Juniper Systems, 

Logan, UT). In 2014, the center two rows of each four-row plot were harvested providing 24 

data points, while in 2015 all four rows were harvested, two rows at a time, providing 48 data 

points for yield. 

 

23 

 

A predetermined grid sampling scheme was used so that sampling locations were in the 

outside rows of the 24 plots, 1.5 m from the end of the row, for a total of 96 sampling areas. 

Sampling locations were marked with a stake to ensure future samples could be obtained from 

the same location throughout the season. Soil samples were collected in-furrow, within 3 days of 

planting and again after harvest. Soil was collected using a 2.45-cm diameter, 15.2-cm deep soil 

core. Six soil cores were collected per sampling location and homogenized in a plastic bag. 

Three soybean plants were collected at each sampling location at an early vegetative growth 

stage (V3) and a late reproductive growth (R5) stage (Fehr et al., 1971).  

 

Fusarium virguliforme and nematode detection 

 

Root samples were separated from foliar tissues, oven dried at 50°C for 48 h, and ground 

using a Christy Mill (Christy Turner Ltd, Ipswich, Suffolk, U.K.). DNA was extracted from 100 

mg of ground root tissue using an automated phenol-chloroform technique performed on the 

AutoGen 850 Alpha system (AutoGen Inc, Holliston, MA), or the same phenol-chloroform 

protocol performed manually. DNA extractions from soil were performed in triplicate using 500 

mg of homogenized soil with the FastDNA SPIN Kit for Soil (MP Biomedicals, Santa Ana, CA), 

and the DNA was quantified using a Nanodrop 1000 (Thermo Fisher Scientific, Waltham, MA). 

Two microliters of DNA, at concentrations ranging from 30 to 110 ng / µL, was used in qPCR 

reactions to determine the level of F. virguliforme using the primers, probes, and cycling 

conditions reported by Wang et al., (2015). The total DNA concentration of each extraction was 

used to normalize the results obtained by qPCR into a ratio of femtograms of F. virguliforme 

DNA detected per nanogram of total DNA. Two technical replicates of qPCR were performed on 

each DNA extract in a StepOnePlus thermal cycler (v2.3, Applied Biosystems). Quantifying F. 

 

24 

 

virguliforme from root samples was performed by taking the Ctsoy – CtFv, where Ctsoy represents 

the cycle threshold for amplification of the soybean β-tubulin reference gene (Wang et al., 2018) 

and CtFv represents the cycle threshold for amplification of the F. virguliforme qPCR target. In 

this way, samples with high levels of F. virguliforme (low Ct) have a high calculated value, 

which is intuitively interpreted as high levels of F. virguliforme.  

 

Soil samples collected at planting and after harvest were also used for classifying and 

quantifying SCN, spiral nematodes (Heliocotylenchus spp.), lesion nematodes (Pratylenchus 

spp.), and dagger nematodes (Xiphinema spp.), based on morphology. One hundred milliliters of 

homogenized soil per sampling location was submitted to the Michigan State University 

Diagnostics Services (www.pestid.msu.edu) for nematode identification and quantification. 

Nematodes were isolated from soil using a modified soil centrifugation and floatation procedure 

(Jenkins 1964) and quantified under a dissecting microscope in a counting dish.  

 

Tracking SDS development 

Three plant samples were collected at each sampling point at growth stage V3 and R5, 

placed in a cooler, and transported back to Michigan State University. Foliar SDS incidence was 

calculated on each individual plant as the percent of leaves showing foliar symptoms, and the 

severity was assessed on symptomatic plants using a scale from 0-9 with increments of 0.5 

developed at Southern Illinois University (Bond et al., unpublished). Ratings of 0 = no visible 

symptoms, 1 = 1-10% of leaf surface chlorotic and/or 1-5% necrotic, 2 = 10-20% chlorotic 

and/or 6-10% necrotic, 3 = 20-40% chlorotic and/or 11-20% necrotic, 4 = 40-60% chlorotic 

and/or 21-40% necrotic, 5 = greater than 60% chlorotic and/or greater than 40% necrotic, 6 = 

premature leaf drop up to 1/3 defoliation, 7 = premature leaf drop up to 2/3 defoliation, 8 = 

 

25 

 

premature leaf drop greater than 2/3 defoliation, 9 = premature plant death (Fig. 2-1). The 0.5 

increment was applied when symptoms exceeded the requirements of one integer but did not 

meet the full requirements of the next integer, or when averaging symptomology across plants. 

The SDS severity and incidence measurements were converted to a normalized disease index 

(DX) score on a scale from 0-100 by multiplying the incidence times severity, then dividing by 

the maximum severity score of 9 (Njiti et al., 2001). The roots of the sampled plants were 

washed with water and rated for discoloration and rotten roots, on a percentage scale from 0-100. 

The mean of the three plants was used to represent the sampling location and used for subsequent 

correlation analyses. These roots were dried as described above, and dry weight was collected. 

These roots were then processed for DNA extraction and qPCR as described above. 

 

26 

 

 
Figure 2-1. Soybean sudden death syndrome (SDS) disease severity rating scale used in this 
study. Based on Southern Illinois University, J. Bond et al,, unpublished; images courtesy of B. 
Serven, Michigan State University, unpublished. DS = disease severity. 
 
 
 

Foliar SDS incidence and severity measurements were also collected in the field at 

growth stages R5 (Fehr et al., 1971). Foliar SDS incidence was observed as the percentage of 

plants in a given region showing foliar SDS, and severity was rated using the 0-9 scale detailed 

above. These incidence and severity ratings were also transformed into a DX score, and the R5 

SDS DX represents the end-of-season SDS severity in all analyses. In 2014, SDS incidence and 

severity were collected on a plot-wide basis, providing 24 data points. However, significant 

variation was observed within each plot in 2014, where some areas of severe SDS were averaged 

 

27 

 

with areas of low SDS, which decreased spatial resolution of SDS in the field. Therefore, in 2015 

foliar SDS severity and incidence ratings were measured only on approximately 25 plants that 

were in the same row as, and within 1 m of, each sampling location. This provided 96 data 

points, decreased within-plot variability, and increased spatial resolution of SDS in the field.  

 

Data analysis 

 

The 2014 and 2015 data sets were analyzed separately for Lloyd’s index of patchiness 

(LIP), spatial interpolation, and Pearson’s correlations. The 2014 and 2015 data were combined 

prior to 3D modeling, principal coordinates analysis (PCoA), and two-way stepwise multiple 

linear regression. All data was analyzed in R version 3.2.0 (R Core Team 2015, Vienna, Austria). 

Data and R scripts for these analyses can be found online at github.com/rothmi12/SDS-Risk-

Assessment. 

The qPCR data obtained from soil samples were normalized to their respective total 

sample DNA concentration obtained to account for differences in DNA extraction efficiency per 

soil sample. This ratio represents the femtograms of F. virguliforme DNA detected per nanogram 

of total DNA extracted. This ratio normalizes the data to account for differences in DNA 

extraction efficiency per soil sample. The ratio of F. virguliforme DNA was used in all 

interpolation, correlation, linear regression, and pathway analyses. However, for LIP and PCoA, 

raw DNA quantities from qPCR (in femtograms) from soil samples were used to meet the 

assumption of count data for these analyses. Using density or proportional data instead of count 

data can cause incorrect calculations in these analyses (Bez, 2000).  

Contour plots were developed by using the ‘Krig’ and ‘surface’ functions within the 

‘fields’ R package (Nychka et al., 2017). Briefly, each data point for each variable was assigned 

 

28 

 

an X and Y coordinate (in meters) based on their location within the field. A stationary 

covariance model was applied to the data to generate predictions of values from all areas, 

sampled and not sampled. The predicted data had a significant correlation to the true values in all 

cases (adjusted R2 > 0.86, P < 0.001), suggesting the kriging models accurately display values in 

non-sampled regions. 

Pearson’s correlation analyses were done using the ‘stats’ R package (R Core Team). 

Briefly, all variables were combined into a single matrix, where each row represented the same 

sampling area described above. Then, each variable was independently correlated to all other 

variables and assigned a P-value and plotted for visualization of all pairwise correlations. P-

values were adjusted using a false discovery rate (FDR) correction with the ‘p.adjust’ function 

from the ‘stats’ package to reduce the chance of obtaining false-positive significant correlations 

associated with multiple testing (Noble, 2009). Only correlations between risk factors and end-

of-season SDS or yield are presented (Table 2-1).  

A 3D scatter plot was made using the ‘plot3D’ R package (Soetaert, 2017). A PCoA was 

performed using only the at-planting F. virguliforme quantity and at-planting nematode 

quantities to investigate potential synergism in the development of SDS. PCoA was performed 

by calculating a dissimilarity matrix of Bray-Curtis distances using the ‘vegan’ R package 

(Oksanen et al., 2018), which is commonly used for analyzing species abundance data (Bray and 

Curtis, 1957; Faith et al., 1987). The ‘cmdscale’ function of the ‘stats’ R package was then used 

to run the PCoA. Principle coordinate scores of the first two dimensions, which correspond to the 

dimensions with the highest variance, were plotted using the R package ‘ggbiplot’ (Vu, 2011), 

and points were colored by the R5 SDS DX.  

 

29 

 

Two-way stepwise multiple linear regression was implemented to develop models of best 

fit using the ‘MASS’ R package (Venables and Ripley, 2002), with cross validation performed 

using the ‘caret’ R package (Kuhn, 2018). All models initially included all variables collected 

prior to the response variable so that each model had predictive capacity. For example, variables 

used in the initial model to predict R5 SDS included at-planting risk factors and V3 plant 

variables, but not R5 plant variables. All models were evaluated using the Bayesian Information 

Criterion (BIC), which penalizes models for the number of factors included in the model to 

prevent over-fitting the observed data. Each variable was dropped from each model and re-

assessed via BIC. If the model was not significantly worse, the variable was left out of the 

model, another variable was dropped, and the model was re-assessed via BIC. If the model was 

significantly worse without the variable, the variable was placed back into the model, a different 

variable was dropped, and the model was re-assessed via BIC. This iterative process was 

repeated until the best model was identified and no more variables could be dropped without 

significantly affecting the model. Finally, each of the best models was trained with a randomly 

selected subset of the data, representing 75% of the data. The models were subjected to a ten-fold 

cross validation, then used to make predictions on the remaining 25% of the data. The training 

and prediction was repeated 1,000 times, randomly selecting the training and testing data each 

time. After each training, the predicted values were correlated to the observed data, and an R2 

value was obtained for the cross validation. Model accuracy was determined by the R2 of the 

cross validations, also called Q2 (Alexander, Tropsha, and Winkler 2015). The best models were 

formatted into a confirmatory factor analysis model for pathway analysis with the ‘lavaan’ 

(Rosseel, 2012) and ‘semPlot’ packages (Epskamp, 2017) in R. The pathway analysis was re-

created by hand using Cytoscape v3.7.1 (Shannon et al., 2003). 

 

30 

 

Results 

Distribution of F. virguliforme and H. glycines in the soil 

The field trials were in two different but adjacent fields in 2014 and 2015. Patches of 

severe SDS and yield loss were seen in the fields in both years. Many of the soil risk factors had 

a LIP greater than 1 (Table 2-1) indicating that the variance of these variables exceeds the mean, 

which can be interpreted as significant aggregation (Bez, 2000; Lloyd, 1967). Subsequently, the 

physical locations of this aggregation were determined via kriging. In 2014 and 2015, areas of 

elevated at-planting F. virguliforme and SCN eggs visually overlapped with areas that developed 

more severe SDS and had reduced soybean yield (Fig. 2-2).  

 

 

 

31 

 

 

Variable 

LIP 

2014 

2015 

At-planting  

      F. virguliforme quantitya 

      SCN cysts 

      SCN eggs 

      SCN juveniles 

      Spiral nematodes 

      Lesion nematodes 

      Dagger nematodes 

Mid-season 

     V3 root F. virguliforme quantityb 

     V3 root dry weight 

     V3 root rot  

     V3 foliar SDS of collected plants 

     R5 root F. virguliforme quantityb 

     R5 root dry weight 

     R5 root rot 

     R5 foliar SDS of collected plants 

     R5 foliar SDS disease index in field 

     Yield 

 

1.08 

1.31 

1.36 

1.58 

1.74 

n.d. 

n.d. 

 

0.87 

-0.51 

1.24 

1.16 

0.86 

0.85 

1.18 

0.90 

1.52 

1.03 

 

1.10 

1.26 

1.44 

1.46 

1.73 

4.78 

30.37 

 

1.10 

-0.60 

1.22 

9.99 

0.86 

0.89 

1.69 

1.11 

1.99 

1.02 

 

 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Yield 

2014 

2015 

 

-0.64 

-0.32 

-0.3 

-0.34 

0 

n.d. 

n.d. 

 

-0.58 

0.11 

-0.21 

-0.05 

0.28 

0.27 

-0.27 

-0.72 

-0.7 

 

-0.3 

-0.45 

-0.48 

-0.46 

0.03 

0 

0.22 

 

0.22 

0.13 

-0.5 

-0.22 

-0.03 

0.72 

-0.65 

-0.52 

-0.66 

NA 

NA 

R5 SDS 

Disease Index 
2014 
2015 

 

 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

0.51 

0.5 

0.52 

0.52 

0.03 

0.01 

0.08 

 

-0.19 

-0.01 

0.28 

0.24 

0.17 

-0.49 

0.52 

0.6 

NA 
-0.66 

 

0.41 

0.21 

0.23 

0.18 

-0.24 

n.d. 

n.d. 

 

0.44 

-0.33 

0.21 

0.09 

-0.29 

-0.54 

0.26 

0.72 

NA 

-0.7 

Table 2-1. Lloyd’s index of patchiness (LIP) and correlation coefficients (r) to end-of-
season SDS disease index (DX) and yield for variables collected throughout the growing 
season. Bolded LIP values are > 1 indicating significant aggregation. Bolded R values indicate 
FDR corrected P < 0.05. SCN = soybean cyst nematode, V3 = soybean vegetative growth stage 
V3, R5 = soybean reproductive growth stage R5, n.d. = not detected in that year, NA indicates 
uninformative autocorrelations that = 1. 
aFor LIP, femtograms of F. virguliforme DNA detected. For correlations with R5 SDS DX and 
yield, femtograms of F. virguliforme DNA per nanogram of total DNA extracted 
bCalculated as Ctsoy – CtFv, as described in the materials and methods section 

 

 

32 

 

 
Figure 2-2. Contour plots determined via kriging of the grid-sampled data representing Fusarium virguliforme (Fv) DNA 
quantities, soybean cyst nematode egg quantities, soybean growth stage R5 foliar SDS disease index ratings, and soybean yield. 
(A) Data from 2014 (B) Data from 2015. Fusarium virguliforme quantities are shown as a proportion of F. virguliforme DNA detected 
(femtograms) from total DNA extracted (nanograms). SCN eggs are shown as the quantity of eggs detected. R5 foliar SDS disease 
index is determined on a scale from 0-100. Yield is shown in kilograms per hectare. In all plots, white represents low values, and 
green represents high values. Compass rose indicates cardinal direction of the plot in each year. SDS = sudden death syndrome, Fv = 
Fusarium virguliforme, SCN = soybean cyst nematode, R5 = soybean reproductive growth stage R5.

 

33 

 

Pearson’s Correlations to end-of-season SDS DX and Yield 

Pearson’s correlation analysis was used to determine if the at-planting and mid-season 

variables were significantly correlated to end-of-season SDS and yield (Table 2-1). Nine 

variables had a significant correlation to end-of-season (R5) SDS DX or yield in at least one year 

(P < 0.05, Table 2-1). However, only root dry weight at R5 and SDS DX of collected R5 plants 

had a significant correlation to R5 SDS DX in both years. Similarly, only SDS DX of collected 

R5 plants had a significant correlation to yield in both years. Yield and R5 SDS DX had a 

significant negative correlation in both years (P < 0.05, Table 2-1).  

The at-planting F. virguliforme quantities had a positive correlation with end-of-season 

SDS DX in both years, though it was significant only in 2015 (P < 0.05, Table 2-1). 

Additionally, at-planting F. virguliforme quantities had a negative correlation with yield in both 

years, though it was significant only in 2014 (P < 0.05, Table 2-1). At-planting SCN cysts, eggs, 

and juveniles all had positive correlations with end-of-season SDS DX and negative correlations 

with yield in both years but were only significant in 2015. Mid-season plant variables like V3 

root rot and R5 root dry weight had some significant correlations to end-of-season SDS DX and 

yield, as expected (Table 2-1). 

 

Modeling Yield Loss and SDS Severity 

 

The best model to predict soybean yield, determined by two-way stepwise multiple linear 

regression and assessment via the BIC, consisted of only two factors; R5 SDS DX and R5 root 

dry weight (Table 2-2). The best model to predict R5 SDS DX consisted of three factors; year, 

at-planting F. virguliforme quantity, and at-planting SCN eggs (Table 2-2). As R5 root dry 

weight was a significant factor in the yield model, we further determined that it is significantly 

 

34 

 

impacted by two factors; V3 root rot and at-planting SCN cysts (Table 2-2). For each of these 

three models, interaction terms between the explanatory variables were added to the model and 

re-assessed. Two of the models had marginally improved R2 values, but none of them had 

improved P-values (data not shown). In addition, multicollinearity of independent factors within 

the final models were examined using variance inflation factors (VIF), and all were < 2.5. Since 

multicollinearity does not inhibit interpretation of model predictions and VIF values were < 10, 

multicollinearity was not regarded as a significant concern (Kutner et al., 2005; O’Brien 2007).  

 

Response 
variable 

Explanatory 

variables 

Parameter 
estimate 

Yield 

R5 SDS 
Disease 
Index 

R5 root 
dry 
weighta 

R5 SDS DX 
R5 root dry 
weighta 
Year 

At-planting 
Fv qPCRb  
At-planting 
SCN eggs 
V3 root rotc 

At-planting 
SCN cysts 

Parameter 
standard 

error 

P 

Adjusted 

R2 

Overall 

P 

0.563  < 0.001 
0.068  < 0.001 

0.5310 

< 0.001 

Average R 
of Predicted 
vs Actual 
0.5740 

Q2 

0.349 

3.085 

0.003 

0.3162 

< 0.001 

0.4607 

0.224 

5.935  < 0.001 

-0.351 
1.951 

-9.460 

2.506 

0.003 

0.001  < 0.001 

-0.029 

-0.026 

0.009 

0.009 

0.005 

0.001 

0.2300 

< 0.001 

0.5175 

0.274 

Table 2-2. Prediction models and cross validations for yield, soybean sudden death 
syndrome (SDS) disease index (DX), and root dry weight models. Q2 represents the average 
R2 statistic of 1000 replications of a 10-fold cross validation of each model. Fv = Fusarium 
virguliforme, SCN = soybean cyst nematode, V3 = soybean vegetative growth stage V3, R5 = 
soybean reproductive growth stage R5. 
aDry weight in grams 
bFemtograms of F. virguliforme DNA per nanogram of total DNA extracted 
cIn percentage of root system rotten 
 
 
 

The R2 values of the model cross validations (Q2) was lower than the original model R2 

value, with the exception of the R5 root dry weight model (Table 2-2). These models were 

visualized in a pathway analysis, which shows the relative contribution of each risk factor on 

 

35 

 

each dependent variable, along with covariant relationships with other risk factors (Fig. 2-3). 

Pathway analysis showed that the risk factors included in the yield, R5 SDS DX, and R5 root dry 

weight models have positive covariance, particularly between at-planting F. virguliforme and 

SCN egg and cyst quantities, which together increase R5 SDS DX and decrease R5 root dry 

weight, which both affect yield (Fig. 2-3).  

 

Figure 2-3. Pathway analysis from at-planting risk factors to R5 plant health variables, to 
yield. (A) Data from 2014. (B) Data from 2015. Numbers below arrows represent standardized 
parameter estimates, or beta estimates, which represent the change in standard deviations of the 
response variable per 1 standard deviation in the predictive variable. Green dashed lines 
represent positive covariant relationships, green solid lines inicate positive causal relationships, 
red solid lines indicate negative relationships. SDS = sudden death syndrome, Fv = Fusarium 
virguliforme, SCN = soybean cyst nematode, V3, R5 = soybean vegetative growth stage V3 or 
R5, respectively. 
 

 

 

36 

 

Multivariate Analysis of F. virguliforme and Nematode Distributions 

There was a positive correlation between at-planting F. virguliforme and at-planting SCN 

quantities in both years, but was significant only in 2015 (2014: cysts r = 0.38, eggs r = 0.29, 

juveniles r = 0.30; 2015: cysts r = 0.38, eggs r = 0.39, juveniles r = 0.38, all P < 0.001). Using a 

3D plot of at-planting F. virguliforme, at-planting SCN juveniles, and end-of-season SDS DX, 

we found that at low, non-zero F. virguliforme levels, the severity of SDS increases as SCN 

increases (Fig. 2-4 A). Similarly, with high at-planting F. virguliforme quantities, the severity of 

SDS also increases as the number of SCN eggs increases (Fig. 2-4 A). Using PCoA, we 

investigated additional nematode species and found that the first two dimensions explained 

54.0% and 45.2% of the variation, respectively (Fig. 2-4 B). Both at-planting F. virguliforme 

quantities and at-planting SCN juveniles had significant correlations with these dimensions (x 

and y axes) (P < 0.001), and the impact of year is also apparent. While both at-planting F. 

virguliforme quantity and at-planting SCN juveniles had significant positive correlation with the 

second dimension (y axis), they had significant correlations in opposite directions with the first 

dimension (x axis) (Fig. 2-4 B, vectors). None of the other nematode species explained a 

significant amount of variation observed in the data. 

 

37 

 

Figure 2-4. Multivariate analyses of sudden death syndrome (SDS) in terms of Fusarium virguliforme and nematode pressure. 
(A) 3D plot depicting the quantities of at-planting F. virguliforme (femtograms of F. virguliforme per nanogram of extracted), SCN 
juveniles, and ratings of SDS foliar symptoms at soybean growth stage R5. The grid represents the 3D trend in data, as a line of best 
fit would represent in a traditional 2D plot. (B) Principal coordinates analysis (PCoA) of at-planting F. virguliforme and multiple 
nematode species quantities. The circle represents the total calculated distance for the two dimensions represented in the plot. Length 
of vector arrows show the contribution of each variable to the variation observed in the two dimensions represented. All data points 
are colored by SDS disease index at stage R5. 

 

 

38 

 

Discussion  

Soybean SDS is commonly observed in patches with strong severity. Here, we show that 

many soil variables are significantly aggregated within fields, including the causal agent of SDS, 

F. virguliforme (Table 2-1). These patches of high F. virguliforme abundance physically 

overlapped with future patches of severe SDS and yield loss (Fig. 2-2). Since the data are 

spatially aggregated (Table 2-1), the correlations between F. virguliforme and SDS and yield 

may be driven by their geospatial location rather than their biological functions, but their 

overlapping distribution and etiology of F. virguliforme suggests a biological explanation. Other 

studies have also observed that higher F. virguliforme populations increase foliar SDS (Freed et 

al., 2017; Roy et al., 1997). 

SDS caused by F. virguliforme is difficult to manage, particularly due to a lack of 

effective in-season strategies. Therefore, knowing the risk of SDS development ahead of planting 

can influence decision making about purchasing partially resistant varieties and whether or not to 

use a seed treatment. The present study supports previous findings that F. virguliforme inoculum 

has the largest contribution to SDS development and that SCN has a minor contribution to SDS 

development (Scherm et al., 1998). By quantifying pathogen abundance at-planting, valuable 

risk information can be gained and implemented into a risk prediction model for SDS 

development. The risk prediction models developed here show promise pending further 

validation in different environments. Our initial cross-validations resulted in Q2-values, which 

can be difficult to interpret (Alexander, Tropsha, and Winkler 2015; Golbraikh and Tropsha, 

2002). All of the model Q2 values in this study were positive, indicating that the models have 

good potential, as poorly performing models yield negative Q2 values (Alexander, Tropsha, and 

Winkler 2015). However, the Q2 values were not > 0.5, and fail to meet the criteria for ‘highly 

 

39 

 

accurate’ models (Alexander, Tropsha, and Winkler 2015; Golbraikh and Tropsha, 2002). 

Therefore, we encourage that these models be applied cautiously and adapted to incorporate even 

more data, such as soil temperature, soil moisture (Scherm and Yang 1996), photosynthesis 

(Bajwa et al., 2017; Kuhlgert et al., 2016), and microbiome data (Srour et al., 2017). Combining 

these types of biotic and environmental risk factors will likely enhance SDS risk prediction 

further. 

Fusarium virguliforme is a soil-borne pathogen that infects soybean roots and remains 

there throughout foliar SDS development. Detailed mechanisms of foliar SDS development are 

complicated and largely unknown. Previous studies have shown that there is no consistent 

correlation between root F. virguliforme quantity and the amount of root rot or foliar SDS 

observed (Wang et al., 2018). In this study, we also observed inconsistent correlations between 

root F. virguliforme quantities and disease symptoms. Fusarium virguliforme quantities in V3 

roots had opposite trends with R5 SDS DX in 2014 and 2015. Similarly, it had opposite trends 

with yield in 2014 and 2015 (Table 2-1). Finally, Fusarium virguliforme quantities in R5 showed 

the exact opposite trends with R5 SDS DX and yield as Fusarium virguliforme quantities in V3 

roots (Table 2-1). In contrast to root qPCR quantities, the abundance of F. virguliforme in the 

soil at planting consistently had positive correlations to R5 SDS DX and yield (Table 2-1). These 

results support previous findings that observed that the amount of F. virguliforme inside of the 

root does not necessarily indicate current or future disease severity (Wang et al., 2018). 

Previous studies have shown that soybean vascular colonization by F. virguliforme is 

critical for severe foliar symptom development (Islam et al., 2017a; 2017b; Navi and Yang, 

2008), and the F. virguliforme qPCR assay used in this study does not provide localization 

information when used on entire roots. The combination of high F. virguliforme quantity in the 

 

40 

 

root with low foliar symptoms, or vice versa, might be explained by the location of colonization 

within the root. It has also been suggested that some F. virguliforme isolates may be good at 

colonizing root tissues but poor at secreting toxins, while others may be poor root colonizers but 

efficient toxin producers (Li et al., 2009). Regardless, the abundance of F. virguliforme in the 

soil is valuable risk information, and pathogen quantities in post-harvest soil should be examined 

to determine if these data are representative of pathogen quantities in the soil the following 

spring. If so, risk information could be obtained even earlier, allowing growers to know their risk 

of developing SDS prior to placing their variety and seed treatment orders.  

It is well established that both SDS and SCN can cause significant yield loss in soybeans 

independently (Hartman et al., 2015; Koenning and Wrather, 2010), but many reports have also 

indicated a synergism between SCN and F. virguliforme that results in more severe foliar SDS in 

the field (Back et al., 2002; Brzostowski et al., 2014; McLean and Lawrence, 1993; Scherm et 

al., 1998; Westphal et al., 2014; Xing and Westphal, 2013). The multivariate analyses in this 

study indicate that the presence of both pathogens affects the severity of SDS development (Fig. 

2-4). Interestingly, the PCoA also shows that the two pathogens have strong correlations in 

opposite directions along the x-axis (Fig. 2-4 B). This suggests that there is competition between 

the two pathogens for nutrients within the root, either directly or indirectly. In instances where 

SCN are highly abundant, F. virguliforme may not be able to thrive and co-exist with SCN 

because of competition for nutrients. Similarly, in instances where F. virguliforme is highly 

abundant, SCN may not be able to thrive and co-exist with F. virguliforme. Overall, more SCN 

juveniles were observed in 2014 than in 2015 (data not shown), but this is likely because the 

studies were done in adjacent fields. Still, it was surprising that the SCN quantities had a 

significant correlation to R5 SDS DX and yield in 2015, not 2014. This observation could be due 

 

41 

 

to the increased sampling effort and higher resolution of data collected in 2015. Taken together, 

our data support the hypothesis that F. virguliforme is primarily responsible for SDS 

development, and that the additional presence of SCN can increase the risk of developing severe 

SDS in a density dependent manner. 

It has been suggested that SCN or other nematodes may cause wounds and forge a path to 

the vascular tissue, making it easier for F. virguliforme to enter the roots (Diaz Arias 2012). Our 

analyses found that SCN had a significant association with SDS development, but other 

nematodes did not (Fig. 2-4 B). Soybean responds to SCN infections by altering expression of 

pathogenesis related genes and WRKY and MYB transcription factors (Ithal et al., 2007a). 

Infectious SCN juveniles also upregulate cell wall loosening enzymes, suppress JA in syncytia, 

and increase peroxidase expression to remove reactive oxygen species (Ithal et al., 2007b). These 

physiological responses to SCN could make soybean more susceptible to infections by F. 

virguliforme, as a reduction of JA within plants benefits necrotrophic fungi (Antico et al., 2012). 

It is also possible that a juvenile SCN infecting the same root as F. virguliforme may create an 

environmental change that stimulates more phytotoxin production. In fact, the pathway analysis 

indicates that F. virguliforme increases foliar SDS through interactions with SCN (Fig. 2-3). The 

exact mechanism by which SCN increases the amount of foliar SDS caused by F. virguliforme is 

still unknown.  

Soybean yield and disease severity are simple to measure, but difficult to predict because 

numerous environmental, edaphic, and biotic factors can affect them. Data from other locations 

should be processed through these models to further test the accuracy of each model and also 

determine if different models are better suited to other locations. However, the work here 

provides a basic framework for incorporating biotic information into risk prediction models for 

 

42 

an economically important disease of soybean. This work is an important step towards 

facilitating improved SDS management and also provides insights into potential soybean yield 

 

losses which can affect soybean markets. 

 

Acknowledgements 

The authors would like to thank Dr. D. Kramer and Dr. D. TerAvest for helpful 

discussions, Dr. H.-X. Chang, Dr. J. Willbur, and J.L. Jacobs for critical review of this 

manuscript, J.L. Jacobs for root rot disease severity ratings, and Dr. C. and Mrs. A. Druskovich 

for use of fields in Decatur, MI. This study was supported by the Michigan Soybean Promotion 

Committee, Bayer CropSciences, the Michigan State University Plant Science Fellowship, the 

Everett “Tex” Beneke Fund, and the Syngenta Agricultural Scholarship. 

 

 

 

43 

 

CHAPTER 3: Diagnostic qPCR Assay to Detect Fusarium brasiliense, a Causal Agent of 

Soybean Sudden Death Syndrome and Root Rot of Dry Bean 

Mitchell G. Roth, K. A. Oudman, A. Griffin, J. L. Jacobs, H. Sang, and M. I. Chilvers 

By 

 

This chapter submitted to the journal Plant Disease and was accepted with minor revisions. The 

manuscript was submitted under a “Public Domain” copyright option. 

 

Author Contributions: 

MGR, JLJ, and MIC formalized the objectives of the project. MGR designed primers, probes, 

and reaction conditions. MGR, KAO, AG, and HS used and validated the method. MGR 

analyzed data and generated figures. MGR and MIC wrote the manuscript. 

 

 

 

44 

 

Abstract 

Species within clade II of the Fusarium solani species complex (FSSC) are significant 

pathogens of dry bean (Phaseolus vulgaris) and soybean (Glycine max), causing root rot and/or 

sudden death syndrome (SDS). These species are morphologically difficult to distinguish, and 

often require molecular tools for proper diagnosis to a species level. Here, a TaqMan probe-

based qPCR assay was developed to distinguish Fusarium brasiliense from other closely related 

species within clade II of the FSSC. The assay displays high specificity against close relatives 

and high sensitivity, with a detection limit of 100 femtograms. This assay was able to detect F. 

brasiliense from purified mycelia, from infected dry bean roots, and from soil samples 

throughout Michigan. When multiplexed with an existing qPCR assay specific to F. 

virguliforme, accurate quantification of both F. brasiliense and F. virguliforme was obtained, 

which can facilitate accurate diagnoses and identify co-infections with a single reaction. The 

assay is compatible with multiple qPCR thermal cycling platforms and will be helpful in 

providing accurate detection of F. brasiliense. Management of root rot and SDS pathogens in 

clade II of the FSSC is challenging and must be done proactively, as no mid-season management 

strategies currently exist. However, accurate detection can facilitate management decisions for 

subsequent growing seasons to successfully manage these pathogens. 

 

Introduction 

 

The Fusarium solani species complex (FSSC) is composed of three major clades, 

designated I, II, and III (Chitrampalam and Nelson 2016; O’Donnell 2000). Fusarium species 

within clade II are soil-borne plant pathogens that cause root rot of soybean (Glycine max) and 

dry bean (Phaseolus vulgaris) (Aoki et al., 2014; Chitrampalam and Nelson 2016). Macroscopic 

 

45 

 

and microscopic morphological features of the species within clade II are very challenging to 

differentiate, as all produce similar pigments, and conidia lengths and widths overlap (Aoki et 

al., 2003). Of the eight species within this clade, six are also capable of causing sudden death 

syndrome (SDS) on soybean (Aoki et al., 2003, 2005; 2012a; 2012b; Tewoldemedhin et al., 

2016). In South America, the SDS-causing pathogens include F. virguliforme O’Donnell & T. 

Aoki, F. brasiliense T. Aoki & O’Donnell, F. tucumaniae, and F crassistipitatum, while in North 

America SDS is caused by F. virguliforme and the recently identified F. brasiliense (Wang et al., 

2018b). F. virguliforme, F. brasiliense, and a third undescribed Fusarium sp. cause soybean SDS 

in South Africa (Tewoldemedhin et al., 2014, 2016). The sixth SDS-causing species, F. 

azukicola, has only been isolated from adzuki bean (Vigna angularis), but when inoculated on 

soybeans it caused foliar SDS symptoms (Aoki et al., 2012b). 

Soybean SDS is an annual threat in both the U.S. and Brazil (Aoki et al., 2003; 

O’Donnell et al., 2010). These countries dominate soybean production in North and South 

America, respectively, both producing an estimated 120 million metric tons of soybeans in 2017-

2018 (World Agricultural Supply and Demand Estimates, 2018). Managing these SDS-causing 

FSSC clade II species is challenging, as few fungicides demonstrate efficacy (Kandel et al., 

2018; Sang et al., 2018; Wang et al., 2017), genetic resistance is partial (Chang et al., 2018), and 

crop rotation practices take up to 3 or 4 years of non-host crops to lower SDS pressure (Leandro 

et al., 2018). However, some SDS-causing species like F. tucumaniae show increased sensitivity 

to certain chemistries (Sang et al., 2018), so it is important to know the species present in order 

to recommend proper management.  

Monitoring the spread of fungal pathogens is also important to determine if management 

strategies are effective. Fungal pathogens can be spread through the transfer of infected 

 

46 

 

materials, shared use of contaminated field equipment, and through wind-blown spores. The 

mechanism of F. virguliforme spread remains unknown, though the sexual stage has never been 

observed and only one mating type has been detected (Hughes et al., 2014), so spread through 

the discharge of sexual ascospores appears unlikely. A single mating type for F. brasiliense was 

identified in South Africa (Tewoldemedhin et al., 2016), but both mating types have been found 

in South America (Hughes et al., 2014) and Michigan isolates (Oudman et al, in prep). In 

addition, F. brasiliense isolates with different mating types were identified in the same field 

(Oudman et al, in prep). This indicates that conventional fungal sexual reproduction is probable 

in F. brasiliense, providing an opportunity for the pathogen to produce perithecia and spread 

through the discharge of sexual ascospores into the air. Only one isolate of F. brasiliense had 

been identified in the U.S. prior to 2014 from an unknown host in California (Aoki et al., 2005), 

so it is unlikely that it has spread from California to Michigan without being detected in 

agricultural states in between. Population genetic studies of F. virguliforme suggest that it 

originated in South America and spread to North America (Wang 2016). It may be possible that 

F. brasiliense also originated in South America and spread to the U.S. through unknown 

mechanisms, but a native Michigan population of F. brasiliense cannot be ruled out. 

As both soybeans and dry beans are hosts for Fusarium root rot pathogens, accurate 

identification of these species in these fields is critical to prevent significant inoculum increases, 

severe yield losses, and potential spread to neighboring fields. Accurate identification of F. 

brasiliense and monitoring its spread throughout the U.S. will allow growers to implement the 

few successful management strategies available. Additionally, with consumer trends increasing 

the demand of non-genetically modified foods, many dry bean growers are beginning to rotate 

non-genetically modified soybeans with dry beans (B. Glass, personal communication), 

 

47 

 

potentially leading to even more significant inoculum increases. Our primary objective was to 

develop a highly specific and sensitive quantitative polymerase chain reaction (qPCR) assay to 

detect F. brasiliense. The second objective was to validate the performance of this qPCR assay 

in detecting F. brasiliense from purified mycelia, infected plant roots, and soil. Third, we 

examined the compatibility of this assay with a F. virguliforme qPCR assay for multiplexing 

purposes, which provides fast and efficient diagnosis of closely related species or the presence of 

co-infections.  

 

Materials and Methods 

Assay Design 

 

The intergenic spacer (IGS) region of the ribosomal RNA gene has been used to 

distinguish species within the FSSC (Wang et al., 2011), including SDS- and brown root rot-

causing fusaria (O’Donnell et al., 2010), and was used as the target locus for this assay as in 

other qPCR assays (Chilvers et al., 2007; Okubara et al., 2013; Wang et al., 2015). IGS 

sequences from species within clade II of the FSSC were obtained from National Center for 

Biotechnology Information (NCBI); F. azukicola GenBank number JQ670149, F. brasiliense 

GenBank numbers KF706657 and FJ919512, F. cuneirostrum GenBank numbers FJ919511 and 

FJ919548, F. crassistipitatum GenBank numbers FJ919521 and FJ919554, F. phaseoli GenBank 

numbers FJ919498, FJ919510, and FJ919500, F. tucumaniae GenBank numbers FJ919507 and 

FJ919515, and F. virguliforme GenBank numbers FJ919499 and FJ919557. Sequences were 

aligned in MEGA7 (Kumar et al., 2016) using the MUSCLE algorithm (Edgar 2004). 

 

 

 

48 

Primer/Probe 

Name 

Sequence (5’-3’) 

Length 
(bp) 

Tm (°C)a 

Reference 

 

F6-3 

R6 

2014 

2014 

2014 

Fb_F2 
Fb_R2 
Fb_Prb1 

GTAAGTGAGATTTAGTCTAGGGTAGGTGAC  

AGGTCAGATTTGGTATAGGGTAGGTGAGA 
CGGACCATCCGTCTGGGAATTT 
5HEX-TGGGATGCCCT+AATTTTT+ACGG-3IABkFQ 

67.4  This study 
66.3  This study 
64.7  This study 
57.8  Wang et al., 
59.6  Wang et al., 
70.0  Wang et al., 
FvPrb-3 
Table 3-1. Primers and probes used in this study. Tm = melting temperature, bp = base pairs 
a Calculated with the OligoAnalyzer tool from IDT, using the default “qPCR” parameter settings. 
 
 
 

A TaqMan probe using two locked nucleic acids was designed to capitalize on the 

6FAM-TTTGGTCTAGGGTAGGCCG-MGBNFQ  

GGGACCACCTACCCTACACCTACT 

29 
22 
22 

30 

24 

19 

presence of two single nucleotide polymorphisms (SNPs) specific to F. brasiliense within eight 

base pairs of each other (Fig. 3-1). Primer and probe sequences are reported in Table 3-1. Each 

qPCR reaction was performed with two technical replicates, and contained 10 µL of 2X TaqMan 

Universal real-time PCR master mix (1X final concentration, Applied Biosystems), 250 nM 

Fb_Prb1, 500 nM Fb_F1 primer, 500 nM Fb_F2 primer, 400 ng BSA, 2 µL DNA template, and 

molecular grade water up to a total reaction volume of 20 µL. All assays were performed on the 

StepOnePlus v2.3 (Applied Biosystems) and were set up in clear MicroAmp 96-well plates 

(Thermo-Fisher Scientific, Waltham, MA), except for multiplexed assays. Multiplexed reactions 

were performed with two technical replicates, each containing 10 µL of 2X TaqMan Universal 

real-time PCR master mix (Applied Biosystems), 250 nM Fb_Prb1, 500 nM Fb_F1 primer, 500 

nM Fb_F2 primer, 100 nM FvPrb3, 500 nM F6-3, 500 nM R6, 400 ng BSA, 2 µL of each DNA 

template, and molecular grade water up to a total reaction volume of 20 µL.  

 

49 

 

 
Figure 3-1. Sequence alignment of 149 base pairs of the intergenic spacer (IGS) of species 
within clade II of the Fusarium solani species complex. Primers (blue) and probe (green) 
sequences are displayed as arrows at their annealing location within the IGS. Asterisks represent 
locked nucleic acids designed in the qPCR probe.  
 
 
qPCR Assay Specificity 

Fusarium isolates used for testing the assay specificity were either collected from 

counties throughout Michigan or requested from the USDA ARS Culture Collection (NRRL) 

(Table 3-2). Isolates collected from Michigan were obtained from root tissues of infected 

soybean or dry bean plants. Briefly, plants displaying stunting or foliar SDS symptoms from 

fields throughout Michigan were uprooted and transported to the lab where soil was thoroughly 

washed off roots. Root sections displaying severe discoloration and rot were soaked in 5% 

bleach solution (0.4% NaOCl) for 4 minutes, rinsed thoroughly in sterile deionized water for 4 

minutes, blotted dry with sterile paper towel, and plated on WMS agar (2% agar, 15µg/mL 

metalaxyl, 300µg/mL streptomycin) to select for fungal growth. Plates were incubated for seven 

days at room temperature and observed under a dissecting microscope (Leica Microsystems, 

 

50 

 

Buffalo Grove, IL, USA) for the formation of sporodochia. Using an insect pin, macroconidia 

from a sporodochium were transferred and spread on fresh WMS agar and allowed to grow for 

24 hours. Finally, a single germinating macroconidium was aseptically transferred onto full 

strength potato dextrose agar (Neogen, Lansing, MI). Isolates were stored at room temperature 

on synthetic nutrient agar slants (1 g/L KH2PO4, 1 g/L KNO3, 0.5 g/L MgSO4, 0.5 g/L KCl, 0.2 

g/L glucose, 0.2 g/L sucrose, 2% agar) in scintillation vials (Research Products International, 

Mount Prospect, IL, USA) for long-term storage.  

Macroconidia from pure cultures were inoculated into potato dextrose broth and allowed 

to grow at room temperature for 48 hours with shaking at 100 rpm. DNA was extracted with two 

similar methods. For testing specificity at a standard 5ng per reaction, mycelia were filtered from 

broth with two layers of Miracloth (Millipore Sigma, St. Louis, MO, USA) and rinsed with 200 

mL of sterile deionized water. The fresh, rinsed mycelia were immediately transferred to a 

sterile, pre-chilled mortar and ground with a pestle in the presence of liquid nitrogen. After 

approximately 1 minute of tissue disruption, the ground mycelia were immediately transferred 

into microcentrifuge tubes containing a DNA extraction buffer (200 mM Tris-HCl, 1 M NaCl, 25 

mM EDTA, 25 mM sodium dodecyl sulfate, 100 µg RNaseA) and incubated for 30 minutes at 

room temperature, with gentle inversions every 5 minutes. Proteinase K (200 µg) was added to 

each sample and incubated at room temperature for 20 minutes, with gentle inversions every 5 

minutes. The samples were chilled on ice for 5 minutes, followed by an addition of 0.2 volumes 

of ice-cold 5 M potassium acetate, another 5-minute incubation, and then centrifuged at 5000 x g 

for 12 minutes at 4°C. The supernatant was transferred to fresh tubes, and an equal volume of 

25:24:1 phenol:chloroform:isoamyl alcohol (Millipore Sigma) was added, mixed gently by 

inversion, and centrifuged. The phenol:chloroform step was repeated, followed by an additional 

 

51 

 

step using chloroform only. The DNA was precipitated by adding 0.1 volumes 5 M NaCl, 1 

volume isopropanol, incubated on ice for 10 minutes, and pelleted at 10,000 x g in a refrigerated 

centrifuge at 4°C. The pelleted DNA was washed twice with freshly prepared 80% ethanol and 

resuspended in 10 mM Tris buffer pH 8.0 overnight. The quality of the DNA was determined by 

measuring the 260/280 nm and 260/230 nm readings using a Nanodrop 1000 (Thermo-Fisher 

Scientific), and quantity was determined using the Qubit dsDNA BR Assay Kit (Thermo-Fisher 

Scientific). An aliquot of DNA from each isolate was diluted to 2.5 ng/µL, with 2 µL used in 

qPCR assays for specificity testing (Fig. 3-2). For testing specificity of other isolates at various 

starting DNA concentrations (Table 3-2), mycelia were harvested with sterilized wooden stirrers 

(STR Supplies Inc., British Colombia, Canada) into sterile 1.5 mL tubes. The tube was plugged 

with a sterile cotton ball, and mycelia were lyophilized for 48 hours. Approximately 20 mg of 

lyophilized mycelia were ground with 3-5 2 mm glass beads and subjected to the same extraction 

protocol as described above. 

Figure 3-2.  

 

52 

 

 

Figure 3-2 (cont’d). Amplification results of 5 ng DNA of select isolates across different 
species within the Fusarium solani species complex. Isolates represented are F. azukicola 
isolates NRRL 54361 and 54362, F. brasiliense isolates F-14-42, F-15-158, F-16-137, NRRL 
34938, MI-Mtc-C1, FSSC III-5 isolates MI-Mtc-B10 and F-15-118, F. crassistipitatum isolate 
NRRL 31949, no-template (water) control, F. cuneirostrum F-14-52, F. phaseoli isolates MI-
Mtc-A12 and NRRL 22411, F. tucumaniae isolates NRRL 43334 and 31777, and F. virguliforme 
isolates DB P28 R13 and Vb-2a. 

 
 

Table 3-2. 

Isolatea 

Speciesb 

Host 

Location isolated 

Assay 
Result  
– 

Ct with 5 
ng starting 
DNA 
n.d. 

– 
+ 

+ 

+ 

+ 

+ 
+ 

+ 

+ 

+ 
+ 
+ 

+ 
+ 
+ 
+ 
+ 

+ 

+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
– 

n.d. 
14.92 

 

14.21 

14.43 

 
14.71 

 

 

 
 
 

14.47 
 
 
 
 

13.94 

 
 
 
 
 
 
 
 
14.52 
n.d. 

54361 

54362 
34938 

22678 

F. azukicola 

F. azukicola 
F. brasiliense 

F. brasiliense 

MI-Mtc-C1 

F. brasiliense 

Obihiro, Hokkaido, Japan 

Vigna 
angularis 
V. angularis  Obihiro, Hokkaido, Japan 
Vila Maria, Rio Grande do 
Unknown 
Sul, Brazil 
California, USA 

Phaseolus 
vulgaris 
Glycine max  Montcalm County, MI, 

MI-Mtc-A13 

F. brasiliense 

G. max 

F-14-12 
F-14-42 

F-14-44 

F-15-33 

F-15-101 
F-15-102 
F-15-144 

F-15-158 
F-15-162 
F-15-174 
F-15-192 
F-16-21 

F-16-59 

F-16-118 
F-16-119 
F-16-124 
F-16-125 
F-16-127 
F-16-128 
F-16-131 
F-16-136 
F-16-137 
31949 

F. brasiliense 
F. brasiliense 

P. vulgaris 
P. vulgaris 

F. brasiliense 

P. vulgaris 

F. brasiliense 

P. vulgaris 

F. brasiliense 
F. brasiliense 
F. brasiliense 

F. brasiliense 
F. brasiliense 
F. brasiliense 
F. brasiliense 
F. brasiliense 

P. vulgaris 
P. vulgaris 
P. vulgaris 

P. vulgaris 
P. vulgaris 
P. vulgaris 
P. vulgaris 
G. max 

F. brasiliense 

G. max 

F. brasiliense 
F. brasiliense 
F. brasiliense 
F. brasiliense 
F. brasiliense 
F. brasiliense 
F. brasiliense 
F. brasiliense 
F. brasiliense 
F. crassistipitatum  G. max 

P. vulgaris 
P. vulgaris 
P. vulgaris 
P. vulgaris 
P. vulgaris 
P. vulgaris 
P. vulgaris 
P. vulgaris 
P. vulgaris 

USA 
Montcalm County, MI, 
USA 
Ingham County, MI, USA 
Montcalm County, MI, 
USA 
Montcalm County, MI, 
USA 
Montcalm County, MI, 
USA 
Huron County, MI, USA 
Huron County, MI, USA 
Montcalm County, MI, 
USA 
Saginaw County, MI, USA 
Saginaw County, MI, USA 
Gratiot County, MI, USA 
Michigan, USA 
Montcalm County, MI, 
USA 
Montcalm County, MI, 
USA 
Saginaw County, MI 
Saginaw County, MI 
Saginaw County, MI 
Saginaw County, MI 
Saginaw County, MI 
Saginaw County, MI 
Saginaw County, MI 
Saginaw County, MI 
Saginaw County, MI, USA 
Cristalina, Goias, Brazil 

 

53 

 

 
– 

– 

– 

– 
– 

– 

– 

– 
– 
– 

– 
– 
– 

+ 

– 

– 

– 

+ 

– 

– 

– 
– 

– 

– 
– 

– 

– 
– 
– 
– 

– 

– 

 
 

 

n.d. 

 
 

 

n.d. 

 
 
 

n.d. 
 
 

21.13 

n.d. 

 

 

30.43 

n.d. 

n.d. 

 
n.d. 

n.d. 

 
 

 

 
 
 
 

 

 

Montcalm County, MI, 
USA  
Montcalm County, MI, 
USA 
Montcalm County, MI, 
USA 
Montcalm County, MI, 
USA 
Montcalm County, MI, 
USA 
Montcalm County, MI, 
USA 
Vila Maria, Rio Grande do 
Sul, Brazil 
Armstrong, Sante Fe, 
Argentina 
Argentina 
Van Buren County, MI, 
USA 
Van Buren County, MI, 
USA 
Ingham County, MI, USA  
Van Buren County, MI, 
USA 
Van Buren County, MI, 
USA 
Illinois, USA 
Argentina 
Argentina 
Van Buren County, MI, 
USA 
 

Table 3-2 (cont’d) 
46170 

F. crassistipitatum  G. max 

 

46175 

F-14-52 

Fsp3 
F5 

F. crassistipitatum  G. max 

F. cuneirostrum 

P. vulgaris 

F. cuneirostrum 
F. oxysporum 

F-16-16 

F. phaseoli 

MI-Mtc-A12 

F. phaseoli 

 
Las Lajitas, Salta, 
Argentina 
Las Lajitas, Salta, 
Argentina 
Montcalm County, MI, 
USA 
Uganda, Africa 
Michigan, USA 

P. vulgaris 
Solanum 
tuberosum 
G. max 

G. max 

Montcalm County, MI, 
USA 
Montcalm County, MI, 
USA 
Rockville, MD, USA 
Rockville, MD, USA 
Montcalm County, MI, 
USA 
California, USA 
P. vulgaris 
Gladiolus sp.  The Netherlands 

P. vulgaris 
P. vulgaris 
G. max 

22158 
22276 
MI-Mtc-A4 

22411 
36549 
MI-Mtc-C3FS 
(Wht) 
MI-Mtc-A2FS 

F. phaseoli 
F. phaseoli 
F. phaseoli 

F. phaseoli 
F. proliferatum 
FSSC clade III-11  G. max 

FSSC clade III-11  G. max 

F-15-118 

FSSC clade III-5 

P. vulgaris 

MI-Mtc-C17FS 

FSSC clade III-5 

G. max 

MI-Mtc-B16FS 

FSSC clade III-5 

G. max 

MI-Mtc-B10 

FSSC clade III-5 

G. max 

31777 

43334 

F. tucumaniae 

F. tucumaniae 

G. max 

G. max 

31096 
DB P28 R13 

F. tucumaniae 
F. virguliforme 

Unknown 
P. vulgaris 

Vb-2a 

F. virguliforme 

G. max 

F-14-77 
DB P27 R13 

F. virguliforme 
F. virguliforme 

G. max 
P. vulgaris 

DB P30 R5 

F. virguliforme 

P. vulgaris 

G. max 
G. max 
G. max 
G. max 

G. max 

G. max 

22292 (Mont-1) 
36897 
54291 
VB-1 

F. virguliforme 
F. virguliforme 
F. virguliforme 
F. virguliforme 

Rhizoctonia solani 
(AG2-2) 
Rhizoctonia solani 
(AG4) 

 

 

 

 

54 

Table 3-2 (cont’d) 
 

 

 

 

Phialophora 
gregata (genotype 
A) 
Phialophora 
gregata (genotype 
B) 
Macrophomina 
phaseolina 
Sclerotinia 
sclerotiorum 

 
G. max 

G. max 

G. max 

G. max 

 
 

 

 

 

 

 
– 

– 

– 

– 

 
 

 

 

 

Table 3-2 (cont’d). Panel of isolates used to determine specificity of F. brasiliense qPCR 
assay. + indicates positive amplification, while – indicates no amplification. n.d. = not 
determined. 
aIsolate IDs with only numbers were obtained from the USDA ARS Culture Collection (NRRL). 
All other isolates were obtained by J. Jacobs and Dr. Martin Chilvers. 
bFSSC = Fusarium solani species complex. Clade III designation indicates phylogenetic 
placement within the FSSC, where III-5 and III-11 represent unnamed species within the FSSC 
clade III. 
 
 
qPCR Assay Sensitivity 

Extracted DNA from four F. brasiliense isolates were used to test the sensitivity of the 

assay using serial DNA dilutions. Concentrations tested were 10-fold dilutions beginning with 

1,000,000 femtograms (1 nanogram) of F. brasiliense DNA to 10 femtograms (Fig. 3-3 A). The 

Ct values for each concentration were averaged across isolates, since rDNA copy number can 

vary among strains of the same species (Balajee et al., 2009; Wang et al., 2015). The qPCR 

efficiency was calculated by finding the line of best fit on a plot with the logarithm of the DNA 

concentrations and their respective Ct value (Fig. 3-3B, Table 3-3). The limit of detection was 

determined using samples showing > 95% amplification at the lowest DNA concentration tested, 

as previously described (Bustin et al., 2009). 

 

 

 

55 

 

 

Isolatea 

F-14-42 
F-15-158 
F-16-137 
NRRL 34938 
F-14-42 
F-15-158 
F-16-137 
NRRL 34938 
F-14-42 
F-15-158 
F-16-137 
NRRL 34938 
F-14-42 
F-15-158 
F-16-137 
NRRL 34938 
F-14-42 
F-15-158 
F-16-137 
NRRL 34938 
F-14-42 
F-15-158 
F-16-137 
NRRL 34938 

DNA 

Concentrationb 
1000000 
1000000 
1000000 
1000000 
100000 
100000 
100000 
100000 
10000 
10000 
10000 
10000 
1000 
1000 
1000 
1000 
100 
100 
100 
100 
10 
10 
10 
10 

Ctc 

Mean Ct 

Standard 

Error 

17.20 
16.82 
17.37 
17.50 
20.50 
20.22 
20.57 
20.69 
24.08 
23.72 
24.33 
24.17 
27.65 
27.35 
27.92 
27.72 
30.96 
30.86 
31.28 
31.62 
35.26 
33.93 
35.36 
35.16 

  
  
  

17.22 
  
  
  
20.49 
  
  
  
24.08 
  
  
  
27.66 
  
  
  
31.18 
  
  
  
34.93 

  
  
  

0.099 
  
  
  
0.075 
  
  
  
0.097 
  
  
  
0.079 
  
  
  
0.139 
  
  
  
0.249 

Table 3-3. Serial dilutions for four F. brasiliense isolates used to determine assay sensitivity. 
aIsolate 34938 was obtained from the USDA ARS Culture Collection (NRRL). All other isolates 
were obtained by J. Jacobs and Dr. Martin Chilvers. 
bConcentration in femtograms of DNA per reaction 
cCt = cycle threshold, or cycle at which fluorescent signal crossed the detection threshold. 
 
 

 

56 

 

 

Figure 3-3. Detection of serial dilutions of 4 F. brasiliense isolates. The qPCR assay 
sensitivity reached 10 fg (A). These isolates consistently amplified at all concentrations tested, 
with an average of 99.6% efficiency (B). Fb = Fusarium brasiliense 
 
 
Assay Validation 

A field site in Ingham County, MI was planted with a randomized complete block design 

containing two dry bean cultivars (Zenith and Red Hawk), three treatments (non-inoculated, F. 

brasiliense inoculated, and F. oxysporum inoculated), with 4 plot replicates per cultivar x 

treatment combination. Each plot was 20 ft (6.1 m) long with 4 rows of plants with 76 cm (30 

inch) row spacing. The plots were planted on 8 June 2018 at 69,696 seeds/acre for Red Hawk 

and 78,408 seeds/acre for Zenith using a cone planter. Inoculum was produced on autoclaved 

sorghum grains, as described in Wang et al., 2018a. Inoculum was packaged into envelopes 

directly with seeds, and the entire contents were poured into the cone planter, sowing the seeds 

and inoculum in-furrow at an average rate of 7.1 mL of inoculum per linear foot. Three plants 

from each plot were sampled on 12 September 2018. The foliar tissues were removed from the 

roots in the field, and the roots were thoroughly washed clean of soil and allowed to dry at 55°C 

for 2 days. The dried roots from each sample were placed in a 50 mL plastic container (OPS 

Diagnostics, Lebanon, NJ, USA) with 2, 1 cm diameter steel balls and ground into a powder 

 

57 

 

using a GenoGinder 2010 (SPEX Sample Prep, Metuchen, NJ, USA) at 1500 rpm for 5 minutes. 

DNA was extracted from these roots using the same phenol:chloroform procedure described 

above. Total DNA was quantified using a Nanodrop 1000 (Thermo-Fisher Scientific).  

 

To validate the assay with soil samples, bulk soil was collected from ten soybean fields 

throughout Michigan during the spring and summer of 2018, using a 3 cm diameter, 15 cm deep 

soil core. Soil was placed into a 3.8 L (1-gallon) plastic bag, homogenized, subsampled into 0.45 

kg (5 lb) paper bags, and dried at 55°C for 2 days. Approximately 500 mg of dried soil was used 

for DNA extraction using a FastDNA Spin Kit for Soil (MP Bio, Solon, OH). Two technical 

DNA extraction replicates were performed, and a third technical replicate was extracted after 

being spiked with approximately 5000 F. brasiliense conidia. An estimated number of conidia in 

non-spiked soil samples that showed positive qPCR results was obtained by performing the 

following calculation: 5000 spores * z = x spores * y, where 5000 is the number of spores spiked 

into each soil sample, z is the Ct of the spiked sample, and y is the Ct of the non-spiked sample. 

Non-spiked samples that amplified at a Ct > 31 cannot be reliably quantified because they are 

below the limit of detection. Therefore, an estimated number of conidia was not calculated for 

these samples (Table 3-4).  

 

To test the multiplexing capacity and transferability of this assay onto other platforms, 

serial dilutions of F. virguliforme DNA and F. brasiliense DNA were amplified in a single 

reaction containing primers F6-3, R6, and probe FvPrb-3 (Wang et al., 2015) along with primers 

and probe developed in this study (Table 3-1). Reactions were set up in LightCycler 480 96-well 

plates (Roche Diagnostics, Mannheim, Germany) and performed on a CFX96 Real-Time PCR 

Detection System v3.1 (BioRad Laboratories, Hercules, CA). Multiplexed reactions were set up 

as described above. 

 

58 

 

Location in MI 

Type 

Cta 

Quantity DNA 

detectedb  

Spores / 

500 mg soilc 

Clinton County 
 

Hillsdale County 
 

Lenawee County 
 

Montcalm County 
 

Ingham County 
 

Saginaw County 
 

Sanilac County 
 

St. Joseph County 
 

Van Buren County, 
     Field 1 
Van Buren County, 
     Field 2 

Unknown 
Spiked 
Unknown 
Spiked 
Unknown 
Spiked 
Unknown 
Spiked 
Unknown 
Spiked 
Unknown 
Spiked 
Unknown 
Spiked 
Unknown 
Spiked 
Unknown 
Spiked 
Unknown 
Spiked 

– 
21.26 
– 
18.10 
36.92 
20.14 
32.03 
18.90 
34.13 
17.29 
30.83 
18.01 
29.51 
17.41 
32.95 
19.72 
– 
19.90 
– 
21.97 

– 
57083 
– 
346132 
7 
108360 
137 
226150 
39 
547582 
246 
363252 
529 
512226 
74 
137118 
– 
123579 
– 
70167 

– 
5000 
– 
5000 
n.d. 
5000 
n.d. 
5000 
n.d. 
5000 
2804* 
5000 
3052* 
5000 
n.d. 
5000 
– 
5000 
– 
5000 

Table 3-4. Amplification of F. brasiliense from soils across Michigan. Values with an asterisk 
(*) are estimates, calculated as indicated in the materials and methods. Dashes indicate no 
detection, and n.d. indicates not determined since detection was below the limit of detection. 
aCt = cycle threshold, or cycle at which fluorescent signal crossed the detection threshold. 
bQuantity in femtograms 
cEstimated by taking solving for x, using the equation: 5000 spores * z = x spores * y, where 
5000 is the number of spores spiked into each soil sample, z is the Ct of the spiked sample, and y 
is the Ct of the non-spiked sample. 
 
 
Results 

Assay Design 

 

A 149 base pair segment in the IGS was identified containing F. brasiliense specific 

SNPs, and the primers and probe were designed in this region (Fig. 3-1). The forward primer 

encompasses one SNP shared by F. brasiliense, F. cuneirostrum, F. crassistipitatum, and F. 

virguliforme, but excludes F. phaseoli and F. tucumaniae. The reverse primer is conserved 

among all species used in the IGS alignment. The probe encompasses two SNPs unique and 

 

59 

specific to F. brasiliense, containing two thymines instead of two cytosines. The probe 

associated with these residues was designed with locked nucleic acid bases to enhance specific 

binding at these sites (Fig. 3-1, asterisks). The primers and probe are not predicted to form any 

significant homo- or heterodimers (ΔG > -9), and performs at an ideal anneal and extension 

 

temperature of 60°C.  

 

qPCR Assay Specificity 

 

Genomic DNA isolated from purified mycelia was used to evaluate specificity across 

closely related species within clade II of the FSSC (Table 3-2). The only isolates detected were 

F. brasiliense and some members of FSSC clade III (Chitrampalam and Nelson 2016) (Fig. 3-2, 

Table 3-2). However, when tested with a standard 5 ng of starting DNA, F. brasiliense isolates 

consistently had Ct values within 1 Ct of each other (13.94 - 14.92, Table 3-2). In contrast, FSSC 

clade III isolates with positive reactions had higher Ct values. These results indicate that the 

assay designed here can distinguish F. brasiliense from closely related clade II species and other 

common soil-borne pathogenic fungi (Table 3-2). However, there may be some cross-reactivity 

with clade III species. With pure cultures, F. brasiliense can readily be distinguished from clade 

III species based on growth rate and morphology (Fig. 3-4).  

 

60 

 

 

Figure 3-4. Representative isolates of (A) F. brasiliense and (B) Fusarium solani complex 
clade III isolates plated on potato dextrose agar, 7 days post inoculation. F. brasiliense 
isolates represented are (i) MI-Mtc-C1, (ii) MI-Mtc-A13, (iii) F-16-137, and (iv) F-15-158. F. 
solani clade III isolates represented are (i) MI-Mtc-C17, (ii) MI-Mtc-B16, (iii) MI-Mtc-B10 (iv) 
MI-Mtc-A2. 
 
 
qPCR Assay Sensitivity 

 

The F. brasiliense qPCR assay targeting the IGS region provided high sensitivity, 

reproducibly amplifying target DNA from multiple isolates at concentrations as low as 10 

femtograms of target DNA (Fig. 3-3). Although all four isolates amplified with just 10 

femtograms of starting DNA, the standard error of detection was also highest at this 

concentration. Therefore, the limit of detection (Bustin et al., 2009) was conservatively 

determined to be 100 fg, or a Ct > 31 (Table 3-3). Although target DNA can be amplified at 

lower concentrations, quantities determined below this cycle threshold may not be reliable.  

 

 

 

 

61 

 

Assay Validation 

 

To determine the applicability of this assay to field trials, roots from artificially 

inoculated dry beans were harvested and subjected to DNA extraction and tested with the F. 

brasiliense qPCR assay developed here. F. brasiliense was detected consistently in dry bean 

plots inoculated with F. brasiliense (average Ct = 29.6), but not in samples from non-inoculated 

plots or plots inoculated with F. oxysporum (average Ct = 36.3) (Fig. 3-5).  

Figure 3-5. Detection of F. brasiliense within root tissues of dry bean cultivar (A) Zenith or 
(B) Red Hawk. Values listed over bars indicate the average femtograms of F. brasiliense DNA 
detected within roots of 12 plants per treatment. Non-inoculated and F. oxysporum inoculated 
samples were not detected above the limit of detection of 100 fg. 
 
 
 

Soil samples obtained from ten counties throughout Michigan were screened for the 

 

presence of F. brasiliense. All soil samples spiked with F. brasiliense conidia were successfully 

detected with Ct values ranging from 17.29 to 21.97 indicating relatively pure DNA samples 

 

62 

 

lacking PCR inhibitors (Table 3-4). Additionally, F. brasiliense was detected in non-spiked soil 

samples from six counties, although only two were confidently above the limit of detection of 

100 fg (Table 3-4). Using a known quantity of spores in the spiked samples, an estimated 2804 

and 3051 spores were present in Saginaw and Sanilac Counties, respectively, per 500 mg of soil.  

 

The primers and probes for the F. brasiliense assay and a F. virguliforme assay (Wang et 

al., 2015) were used simultaneously in a multiplexed reaction (Table 3-1). Amplification for each 

assay was only observed when target DNA was present, indicating no cross-reactivity between 

the assays (Table 3-5). Combinations of serial dilutions of F. brasiliense and F. virguliforme 

DNA were successfully amplified in pooled reactions, and an accurate quantification of each 

F. virguliforme 

F. brasiliense 

target was obtained (Table 3-5).  

F. virguliforme  
DNA addeda 

F. brasiliense DNA 
addeda 

1,000,000 
100,000 
10,000 
1,000 
100 
0 
0 
0 
0 
0 
1,000,000 
10,000 
10,000 
100 
1,000 
100 
0 

0 
0 
0 
0 
0 
1,000,000 
100,000 
10,000 
1,000 
100 
1,000,000 
1,000,000 
100,000 
100,000 
10,000 
1,000 
0 

17.49 
24.74 
24.52 
33.34 
28.02 
31.82 

– 

Ctb 
17.35 
20.95 
24.46 
28.10 
31.53 

– 
– 
– 
– 
– 

Ctc 
– 
– 
– 
– 
– 

17.57 
21.02 
24.17 
26.60 
31.80 
18.86 
17.26 
20.73 
20.87 
23.61 
27.00 

– 

Table 3-5. Amplification of serial dilutions of F. brasiliense and/or F. virguliforme DNA. 
Assays display specificity towards target species and can be distinguished and quantified in a 
multiplexed system. Dashes represent no detection. 
aQuantity in femtograms 
bCt = cycle threshold, or cycle at which fluorescence crossed the threshold for F. virguliforme. 
cCt = cycle threshold, or cycle at which fluorescent signal crossed the detection threshold for F. 
brasiliense. 

 

63 

 

Discussion 

Clade II species in the FSSC are economically important pathogens capable of causing 

root rot and/or soybean SDS, and significant yield losses. They are very similar morphologically 

with a slow growth rate and are taxonomically distinguished by conidia measurements and 

number of septa (Aoki et al., 2014). However, since a pure culture is required to obtain conidia, 

and conidia measurements of these species overlap (Aoki et al., 2003), it can be very difficult to 

obtain accurate identifications in a timely manner, if at all, using morphology alone. DNA 

extractions and qPCR assays can be performed in a single day, expediting accurate diagnoses. 

High quality qPCR assays have high sensitivity, capable of detecting very low quantities of 

target DNA. Designing an assay around a multi-copy target like the IGS region can increase 

sensitivity. Previous assays designed around the IGS region have provided sensitivity to as low 

as 100 femtograms of target DNA (Rojas et al., 2017; Wang et al., 2015). The assay developed 

here validates two SNPs specific to F. brasiliense compared to other species within the FSSC 

clade II (Fig. 3-1, Table 3-2). The assay performs at a high efficiency of 99.6%, and consistently 

detected 10 fg of target DNA when obtained from pure culture and has a confident limit of 

detection of 100 fg (Fig. 3-3, Table 3-3).  

Interestingly, some FSSC clade III isolates showed amplification, while others did not. 

The use of the name F. solani has been discouraged by taxonomists (Aoki et al., 2014), so 

isolates not confidently identified to a species level were listed with their phylogenetic clades. 

For instance, clade III-5 and clade III-11 isolates in this study represent unnamed species that fall 

within the FSSC clade III, though FSSC clade III-5 is sometimes considered the true F. solani 

(Coleman, 2016). Amplification was not specific to clade III-5 or clade III-11 isolates, 

suggesting some IGS sequence diversity among these unnamed species. Sanger sequencing of 

 

64 

 

the 149-base pair IGS region amplified with this qPCR assay showed that clade III-11 isolate 

MI-Mtc-C3FS (Wht) and clade III-5 isolates F-15-118 and MI-Mtc-C17FS share SNPs with 

other non-F. brasiliense species, confirming their negative reactions. Sanger sequencing of this 

region for clade III-11 isolate MI-Mtc-A2FS and clade III-5 isolate MI-Mtc-B10 IGS showed 

SNPs shared with F. brasiliense, thus explaining the positive reactions. Phylogenetic analyses of 

F. solani clade III isolates commonly use the other molecular markers such as TEF1, ITS rDNA, 

and RPB2 (Chitrampalam and Nelson, 2016, Wang et al., 2018b), perhaps due to the difficulty of 

sequencing and assembling the repetitive IGS region. However, this assay provides specificity to 

F. brasiliense within F. solani clade II, and qPCR positive FSSC clade III strains can be 

distinguished from F. brasiliense though morphology (Fig. 3-4). However, the identity of all 

isolates should be confirmed through PCR and Sanger sequencing of multiple loci (Wang et al., 

2018b).  

This assay can provide an initial screen for F. brasiliense in soil and root samples even if 

pure fungal cultures are not being obtained. The assay is not likely to produce false-positive 

results by cross-amplification of FSSC clade III species because some of these species do not 

cross react even at high concentrations, and others that do cross-react do so with much lower 

efficiency, producing significantly higher Ct values (Table 3-2, Fig. 3-2). For instance, with five 

nanograms of purified fungal DNA, cross-reactive FSSC clade III species showed higher Ct 

values by at least 6 cycles (Table 3-2). Therefore, obtaining a sufficient concentration of FSSC 

clade III DNA from root or soil samples for cross-reactivity is unlikely. In contrast, all F. 

brasiliense isolates from international locations were consistently detected with high accuracy 

and efficiency. Therefore, obtaining a sufficient concentration of F. brasiliense from root or soil 

samples for successful amplification and detection is possible, as shown in Table 3-4 and Figure 

 

65 

 

3-5. Initial screens using this qPCR assay can help guide when and where to begin isolation 

attempts for pure cultures, and also help generate an understanding of the global distribution of 

F. brasiliense. For example, the F. brasiliense assay yielded Ct values < 31 from soil samples 

from Saginaw and Sanilac, counties, where F. brasiliense has already been successfully isolated 

from plant roots (Oudman et al., in prep). The assay also gave Ct values > 31, but < 36 with 

DNA from soil samples from Montcalm and Ingham counties, where F. brasiliense has also been 

successfully isolated (Oudman et al., in prep). Finally, the assay gave Ct values > 31, but < 36, 

from Lenawee and St. Joseph soils, where F. brasiliense has not yet been identified. Therefore, 

the assay provides evidence that warrants further investigation and isolation attempts for F. 

brasiliense from these counties.  

Soybean and dry bean are valuable crops in Michigan worth a combined $1.08 billion in 

2017 (USDA-NASS, 2017). Fusarium root rot and SDS consistently rank in the top 10 most 

destructive soybean diseases in northern states, including Michigan (Allen et al., 2017). To date, 

genetic resistance to Fusarium root rot is partial, and only few fungicides are effective against it 

(Sang et al., 2018; Wang et al., 2017). Therefore, accurate detection and integrating successful 

management practices like using seed treatments (Kandel et al., 2018), crop rotations (Leandro et 

al., 2018), tillage practices, and planting partially resistant cultivars are key to preventing yield 

loss. Further understanding of a possible host preference is also needed in order to inform 

management strategies, as F. brasiliense is more commonly found in areas of dry bean 

production while F. virguliforme is more commonly found in areas of soybean production in 

Michigan (Oudman et al., in prep). Preliminary experiments examining host preference have 

shown that both F. brasiliense and F. virguliforme cause disease on both soybean and dry bean, 

and that disease severity or root colonization is not significantly different from one another in a 

 

66 

 

growth chamber setting (Roth and Chilvers, unpublished). Symptomatic roots infected by F. 

brasiliense and F. virguliforme are indistinguishable from one another, and co-infections may 

also be possible. Therefore, the ability to multiplex the F. brasiliense and F. virguliforme assays 

can also save time and effort in accurate diagnoses.  

Partial genetic resistance will continue to be an important part of integrated management 

for Fusarium root rot pathogens. Though more research has been done with resistance to 

Fusarium spp. in soybean, resistance is complicated and partial (Chang et al., 2018). Dry beans 

(Phaseolus vulgaris) originated from Mesoamerica (Bitocchi et al., 2012), but were traded 

through Central America and experienced a bottle neck while reaching South America. 

Therefore, gene pools in current bean breeding lines originate from Mesoamerica or the northern 

Andean region of South America (Bellucci et al., 2014; Miklas et al., 2006; Singh 2001; Singh 

and Schwartz 2010). Dry bean varieties from Mesoamerican gene pools, like cultivar Zenith, are 

smaller in size and typically have higher resistance to root rot diseases (Román-Avilés and Kelly, 

2005; Schneider et al., 2001). Another Mesoamerican variety, Zorro, was shown to have more 

resistance to F. virguliforme than the Andean variety Red Hawk, a large-seeded red kidney bean 

cultivar (Dry Bean Research Report, 2017). Interestingly, more F. brasiliense was detected in 

inoculated Zenith roots than Red Hawk roots in this study. However, root colonization and 

disease severity are not always strongly correlated, particularly among the clade II FSSC species 

(Wang et al., 2018a). A formal study should be conducted to determine the inherent resistance 

levels of multiple varieties from the two different gene pools to F. brasiliense. 

 

 

 

 

67 

 

Acknowledgements 

 

The authors would like to thank Z.A. Noel, M. Breunig, V. Ortiz, and A.G. McCoy for 

helpful discussions and manuscript revisions. This work was funded in part by the Michigan 

Soybean Promotion Committee, Michigan Dry Bean Commission, Michigan Department of 

Agriculture and Rural Development, Bayer CropSciences, the Syngenta Agricultural 

Scholarship, and Project GREEEN.  

 

 

 

68 

CHAPTER 4: A Protoplast Generation and Transformation Method for Soybean Sudden 

Death Syndrome Causal Agents Fusarium virguliforme and F. brasiliense 

 

By 

Mitchell G. Roth and Martin I. Chilvers 

 

This chapter was accepted for publication pending minor revisions in the journal Fungal Biology 

and Biotechnology, an open-access journal.  

 

Author Contributions: 

MGR and MIC developed the ideas to generate this protocol. MGR designed primers and 

plasmids, performed the experiments, and acquired all images. MGR and MIC wrote and revised 

the manuscript, approving of the final draft. 

 

 

 

69 

 

Abstract 

 

Soybean production around the globe faces significant annual yield losses due to pests 

and diseases. One of the most significant causes of soybean yield loss annually in the U.S. is 

sudden death syndrome (SDS), caused by soil-borne fungi in the Fusarium solani species 

complex. Two of these species, F. virguliforme and F. brasiliense, have been discovered in the 

U.S. However, genetic mechanisms that these pathogens employ to induce root rot and SDS are 

largely unknown. Previous methods describing F. virguliforme protoplast generation and 

transformation have been used to study gene function, but these methods lack important details 

and controls. In addition, no reports of protoplast generation and genetic transformation have 

been made for F. brasiliense. We developed a new protocol for developing fungal protoplasts in 

these species and test the protoplasts for the ability to take up foreign DNA. We show that wild-

type strains of F. virguliforme and F. brasiliense are sensitive to the antibiotics hygromycin and 

nourseothricin, but mutants transformed with resistance genes displayed resistance to these 

antibiotics. In addition, integration of a reporter gene demonstrates that the foreign DNA is 

expressed and results in a functional protein, providing fluorescence to both pathogens. This 

protocol provides significant details for reproducibly producing protoplasts and transforming F. 

virguliforme and F. brasiliense. The protocol can be used to develop high quality protoplasts for 

further investigations into genetic mechanisms of growth and pathogenicity of F. virguliforme 

and F. brasiliense. Fluorescent strains developed in this study can be used to investigate 

temporal colonization and potential host preferences of these species. 

 

 

 

 

70 

 

Introduction 

 

Soybean sudden death syndrome (SDS) is an economically important disease across 

soybean growing regions around the world (Allen et al., 2017; Aoki et al., 2005; Tewoldemedhin 

et al., 2014, 2017). Causal agents of soybean SDS are soil-borne ascomycete fungi in clade II of 

the Fusarium solani species complex (Aoki et al., 2014; Chitrampalam and Nelson 2016). Two 

of the SDS-causing species, F. virguliforme and F. brasiliense, have been discovered in the U.S. 

(Roy et al., 1997; Wang et al., 2018). Although transformation of F. virguliforme has been 

demonstrated (Mansouri et al., 2009), it has not been for F. brasiliense. Due to the recent 

discovery of F. brasiliense on common bean (Phaseolus vulgaris) and soybean (Glycine max) 

roots in the U.S. (Jacobs et al., 2018; Wang et al., 2018) and the significance of soybean SDS 

caused by these pathogens, it is desirable to have a detailed protocol for efficient genetic 

manipulation that is applicable to both pathogens. Improved understanding of the genetic 

underpinnings of SDS development could lead to improved management for these two 

pathogens. 

 

Both F. virguliforme and F. brasiliense infect root tissues, with evidence suggesting that 

both appressoria and cell wall degrading enzymes are involved (Chang et al., 2016b; Navi and 

Yang, 2008). However, the genetic mechanisms of SDS development are largely unknown. Two 

effector proteins have been identified and characterized from F. virguliforme, which seem to 

play a role in SDS development (Brar et al., 2011; Chang et al., 2016a; Pudake et al., 2013). 

These two effector genes can also be found in isolates of F. brasiliense via BLAST searches, but 

the mechanism by which these effector proteins induce SDS remain unknown in both species. 

Breeding efforts have improved soybean tolerance to foliar symptom development, but no 

completely resistant lines exist to date (Hartman et al., 2015). Successful management of these 

 

71 

 

pathogens can be achieved through long-term crop rotations and with seed treatments containing 

the fungicide fluopyram (Kandel et al., 2018; Leandro et al., 2018), but long-term crop rotations 

are not widely adopted by growers, and different members of the Fusarium solani species 

complex have shown different sensitivities to fluopyram (Sang et al., 2018). Therefore, improved 

genetic resistance is desired, but requires a deeper understanding of the genetic mechanisms of 

SDS development caused by each pathogen. Genome sequences are available for both of these 

pathogens, but experimental identification and characterization of effector genes through genetic 

manipulation needs to be done in order to gain a better understanding of how these pathogens 

induce soybean SDS. 

 

One of the major challenges in genetic transformation of fungi is the development of 

high-quality protoplasts. Each fungus has unique cell wall properties, which can require different 

sets of expensive cell wall degrading enzymes to successfully remove the cell wall (Peberdy 

1979). Once the cell wall is digested to reveal the membrane bound protoplast, it is prone to 

lysing or shriveling due to osmotic stress (Peberdy 1979). One protocol for protoplasting and 

transformation of F. virguliforme has been reported and used to generate a fluorescent strain 

(Mansouri et al., 2009), with additional studies using it to knock out candidate effector genes 

(Chang et al., 2016a; Pudake et al., 2013) and genes involved in fungal development (Islam et 

al., 2017b, 2017a). However, no such protocols exist for F. brasiliense. Difficulties obtaining F. 

brasiliense and F. virguliforme protoplasts using the existing protocol led us to investigate other 

methods. The objective of this study was to develop an efficient protoplasting and transformation 

method suitable for genetic manipulation in both F. brasiliense and F. virguliforme for future 

hypothesis testing of gene function. 

 

 

72 

 

Materials and Methods  

Sensitivity to selectable antibiotics 

 

To determine an appropriate concentration to select for growth of successful 

transformants, an initial screen using amended agar plates was conducted. The wild type strain of 

F. virguliforme chosen was Mont-1 (NRRL 22292), which was isolated from soybean in Illinois, 

and the wild type strain of F. brasiliense chosen was F-16-137, which was isolated from dry 

bean in Michigan. Full strength potato dextrose agar (PDA) (Neogen Corporation, Lansing, MI) 

was amended with 0, 25, 50, 100, or 150 µg / mL of hygromycin B (Millipore-Sigma, 

Burlington, MA) or nourseothricin (Research Products International, Mt. Prospect, IL). A single 

0.5 mm PDA plug from 14-day old cultures of F. virguliforme isolate Mont-1 (NRRL 22292) or 

F. brasiliense isolate F-16-137 were added to each amended plate. After 14 days, fungal growth 

was visually assessed to determine an appropriate concentration that provides 100% growth 

inhibition. Based on a visual assessment, 100 µg / mL of each antibiotic was sufficient to prevent 

fungal growth of both isolates. 

 

Development of transformation constructs 

 

To provide evidence of genetic transformation, we constructed DNA vectors containing a 

resistance gene to a selectable antibiotic and a gene that provides a fluorescent phenotype under 

ultraviolet light. Both transformation vectors were constructed with Gibson assembly (Gibson et 

al., 2009) of four DNA fragments; a bacterial origin of replication, a bacterial selection marker, a 

fungal selection marker, and a fluorescent protein marker. A high copy origin of replication and 

a kanamycin resistance gene were cloned from a plasmid designed for the overexpression of 

MinC in cyanobacteria (MacCready et al., 2017), which we refer to as pJM2016. For F. 

 

73 

 

virguliforme, the selectable marker was the hygromycin phosphotransferase gene (hph) under the 

control of the Aspergillus nidulans TrpC promoter and terminator derived from pCB1004 

(Carroll et al., 1994) and the fluorescent protein marker was enhanced green fluorescent protein 

(eGFP) under the control of the A. nidulans gpd promoter and TrpC terminator, derived from 

pDS23-eGFP (M. Nowrousian, unpublished). For F. brasiliense, the selectable marker was the 

nourseothricin acetyltransferase gene (nat) under the control of the A. nidulans TrpC promoter 

derived from pDS23-eGFP and the fluorescent protein marker was the red fluorescent protein 

mCherry, derived from pCMB-TMEr (Ivanov and Harrison, 2014) under the control of the A. 

nidulans gpd promoter and TrpC terminator. All primers used to amplify these regions prior to 

Gibson assembly are listed in Table 4-1. Assembled plasmids were named FvGFP2 and 

pmCherry_NAT, respectively, and propagated in E. coli strain DH5α plated on LB amended 

with 50 µg / mL kanamycin. Plasmids were isolated using the GeneJET Plasmid Miniprep kit 

(Thermo Fisher Scientific, Waltham, MA) and linearized with either SacI-HF or EcoRI-HF (New 

England Biolabs, Ipswich, MA) prior to transformation, according to the manufacturer protocol. 

The plasmid pmCherry_NAT is designed such that the selectable marker can be replaced by 

restriction digest with EcoRI and NheI, and the fluorescent marker gene can be replaced by 

restriction digest with NheI and SacI. A double digest of these plasmids can separate the 

transformation construct from the bacterial DNA sequences, which can be purified via gel 

extraction. However, gel extraction decreases DNA yield and transformation efficiency (data not 

shown). Plasmid maps are presented in Figure 4-1.

 

74 

 

Template 

PCR Product 

Length 

Purpose 

pDS23-eGFP 

1333 bp 

Amplify nat gene 

pCMB-TMeR 

708 bp 

pDS23-eGFP 

2410 / 2427 bp 

pDS23-eGFP with 
eGFP replaced by 
mCherry 
Both versions of 
pDS23 (eGFP or 
mCherry) 

2410 bp 

2427 bp 

pJM2016 

655 bp 

pJM2016 

941 bp 

Cloning mCherry into 
pDS23-eGFP 

Amplifying GPD 
promoter + eGFP + 
TrpC terminator  
Amplifying GPD 
promoter + mCherry + 
TrpC terminator  
Amplifying GPD 
promoter + eGFP or 
mCherry + TrpC 
terminator  

Amplify high copy 
number OriC 

Amplify kanamycin 
resistance gene 

Primer name 

Sequence (5’-3’) 

Nat_F_NheI 

GACGACAGCTAGCATCGATCTGACGATACTTTCTAG
AGAATAGGAACTTCGG 

Nat_R_EcoRI  AGTTCTGGTGAATTCTCACCAGTGTAACTGATATTGA

AGGAGCATTTTTTGG 

HindIII-
mChF 
NotI-mChR 

GPD_F_PstI 

GTGTGAAGCTTATGGTGAGCAAGGGCGAG 

TTCTTGCGGCCGCCTACTTGTACAGCTCGTCCATGCC 

GACGACACTGCAGATCGATCTGACGGTACAGTGACC
GGTGACTCTTTCTGG 

GPD_F_SacI 

ATGCTCGATGAGTTTTTCTAAGAGCTCTGTACAGTGA
CCGGTGACT 

TrpC_R_NheI  AGATCGATGCTAGCTGTCGTCGACTTCGAGTGGAGA

TGTGGAGTG 

OriF_EcoRI 

TGGTGAGAATTCACCAGAACTGTCAAGATCAAAGGA
TCTTCTTGAGATCCTT 
TGGCTTTCCCCGCGTTGCTGGCGTT 
GCCAGCAACGCGGGGAAAGCCACGTTGTGTCTC 
TTAGAAAAACTCATCGAGCATCAAATGAAACTGCA 

Ori-F2 
KanF3 
Kan-R 
Table 4-1. Primer pairs designed and used in this study for cloning and assembly into plasmids.  
 

 

 

 

75 

 

 

Figure 4-1. Plasmid maps used to generate transgenic Fusarium strains. (A) Plasmid used to transform F. virguliforme, providing 
resistance to hygromycin and creating a GFP fluorescent phenotype. (B) Plasmid used to transform F. brasiliense, providing resistance 
to nourseothricin and creating an RFP fluorescent phenotype. 

 

 

76 

 

Protoplasting 

 

A hybrid method of two existing protocols was used for developing protoplasts of F. 

virguliforme and F. brasiliense (Hallen-Adams et al., 2011; Mansouri et al., 2009). A two to 

three-week-old PDA plate containing mature Fusarium sporodochia was flooded with 5 mL 

sterile water and scraped gently with a sterile spreader to dislodge spores. Approximately 4 mL 

of the water could be recovered from the plate and was filtered through two layers of sterile 

cheesecloth into a 15 mL conical tube to purify conidia away from mycelia. One hundred 

microliters of conidia were used to inoculate 50 mL sterilized potato dextrose broth (PDB, 

Neogen) in a 250 mL flask. The flasks were incubated for 36-48 hours at room temperature, with 

shaking at 125 rpm, to facilitate conidia germination.  

 

Mycelia were collected aseptically using a sterile funnel and Miracloth (Millipore-

Sigma), removing the spent PDB. Mycelia were transferred back into the empty 250 mL flask 

with a sterile spatula, and 30 mL of protoplasting solution was added immediately. The 

protoplasting solution was prepared one hour prior to protoplasting, composed of 1.2 M KCl, 

750 mg driselase (Millipore-Sigma), 1.5 mg chitinase from Streptomyces griseus (Millipore-

Sigma), and 150 mg lysing enzymes from Trichoderma harzianum (Millipore-Sigma). The 

protoplasting solution was stirred for at least 30 minutes prior to use and filtered through a 0.45 

µm filter just before adding to the mycelia. Once immersed in the protoplasting solution, the 

mycelia were incubated at 30°C for 3-5 hours with shaking at 75 rpm. A 10 µL aliquot of the 

protoplasting reaction was taken every 30 to 60 minutes to determine the level of protoplasts. 

After 3-5 hours, the majority of the mycelia was visually determined to be digested and many 

protoplasts were easily identified with a microscope. The protoplasting reaction was filtered 

through a 30 µm Nylon mesh filter (Millipore-Sigma), with protoplasts passing through into a 50 

 

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mL conical tube. The protoplasts were centrifuged at 3,200 x g for 5 minutes at 4°C, then gently 

resuspended in 10 mL of chilled STC buffer (1.2 M D-sorbitol, 10 mM CaCl2, 10 mM Tris-HCl, 

pH 7.5) using a wide orifice pipet tip (Mettler-Toledo Rainin, Oakland, CA). The centrifuge and 

resuspension steps were repeated twice, with the last resuspension using 1mL of STC buffer. A 

subsample of the protoplasts was diluted 1:100 and quantified using a hemocytometer to 

determine the initial quantity. The protoplasts were diluted to a final concentration of 107 per 

milliliter and transferred into aliquots of 400 µL. Dimethyl sulfoxide (DMSO) was added to a 

final concentration of 7%, and the protoplasts were stored immediately at -80°C. 

Figure 4-2. Overview of the protoplasting method. 

 

78 

 

 

Transformation 

 

Protoplasts were removed from -80°C, thawed on ice, and centrifuged at 3000 x g for 4 

minutes at 4°C. The supernatant was carefully pipetted out to remove DMSO, and the protoplasts 

were resuspended in 400 µL chilled STC buffer. The centrifugation and resuspension steps were 

repeated twice, with the final resuspension using 200 µL STC buffer. With the protoplasts on ice, 

5 µg of linearized transformation vector was added, followed by 50 µL of 30% PEG-8000 

solution (10 mM Tris-HCl, 50 mM CaCl2, 30% poly-ethylene glycol w/v), so that PEG 

comprised 20% of the transformation reaction volume. Fusarium virguliforme was transformed 

with 5 µg of a linear construct containing hph and eGFP, while F. brasiliense was transformed 

with 5 µg of a linear construct containing nat and mCherry (Fig. 4-1). The reactions were gently 

inverted 3 times, then incubated on ice for one hour. Using a wide orifice pipet tip, each reaction 

was transferred into a sterile 15 mL tube containing 2 mL 30% PEG-8000 and incubated for 15 

minutes. Four milliliters of room-temperature STC buffer was added and gently inverted three 

times to mix. The reaction was then poured into 250 mL sterile, molten regeneration medium at 

42°C (67.75 g sucrose, 0.25 g yeast extract, 0.25 g N-Z amine, 1.86 g agar). The agar was 

swirled gently to mix, then poured as thin a layer into 20, 100 x 15 mm Petri plates (VWR 

International, Radnor, PA). After 24 hours of incubation at room temperature, another layer of 

regeneration medium amended with either 100 µg / mL hygromycin B or 100 µg / mL 

nourseothricin was overlaid onto the germinating protoplasts. One plate was overlaid with non-

amended regeneration medium as a control to determine viability of protoplasts after the 

transformation and plating procedures. 

 

 

 

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Screening for stable transformants 

 

Putative transformants that grew through the layer of regeneration medium amended with 

antibiotics were transferred to PDA amended with antibiotics. After 10-14 days of growth, a 

single spore of the putative transformant was isolated and re-transferred to a new PDA plate 

amended with antibiotics. After 10-14 days of germination from this single spore, hyphae were 

scraped off the surface of the plate using a sterile inoculation loop and transferred onto a small 

pad of 2% agarose on a microscope slide, and viewed under a compound fluorescent microscope 

(Leica Microsystems, Buffalo Grove, IL). Stable transformants and wild type strains were 

viewed under bright field and fluorescent channels. To determine fluorescence of F. 

virguliforme, a GFP/FITC filter was used, and for F. brasiliense a TexasRed filter was used 

(Leica Microsystems). 

 

Results  

 

Fusarium virguliforme is sensitive to hygromycin (Chang et al., 2016a; Islam et al., 

2017b; Mansouri et al., 2009; Pudake et al., 2013; Sang et al., 2018), but sensitivity to 

hygromycin has not been reported for F. brasiliense. Medium amended with 100 µg / mL 

hygromycin prevents growth in two wild type strains of F. virguliforme and F. brasiliense (Fig. 

4-3). Another common antibiotic used for genetic selection is nourseothricin, which has been 

used in F. graminearum and F. fujikuroi transformations, but not F. virguliforme or F. 

brasiliense (Menke et al., 2013; Wiemann et al., 2009, 2010). Medium amended with 100 µg / 

mL nourseothricin prevents growth in both wild type strains of F. virguliforme and F. brasiliense 

(Fig. 4-3). However, genetic mutants containing the antibiotic resistance genes hph or nat are 

able to grow on amended medium with hygromycin and nourseothricin, respectively. 

 

80 

Representative growth of these mutants on amended medium is presented after 14 days (Fig. 4-

3). 

 

 
Figure 4-3. Wild type F. virguliforme and F. brasiliense strains are sensitive to hygromycin 
(100 µg / mL) and nourseothricin (100 µg / mL). Transgenic strains of F. virguliforme with the 
addition of the hph gene are resistant to hygromycin, while transgenic strains of F. brasiliense 
with the addition of the nat gene are resistant to nourseothricin. 
 
 
 

Following the protocol described here and outlined in Fig. 4-2, high quality protoplasts of 

F. virguliforme were produced. The protocol was also successfully applied to F. brasiliense. 

These protoplasts were stored frozen at -80°C for up to two years and still showed viability 

throughout the transformation process (Fig. 4-4). Introducing the transformation vector to viable 

protoplasts resulted in successful transformation of some individual protoplasts, which allowed 

growth on medium amended with a high concentration of the selectable antibiotic (Fig. 4-4).  

 

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Figure 4-4. Representative protoplasts seven days post-transformation. Protoplasts were 
overlaid with non-amended regeneration medium (A), regeneration medium amended with 
hygromycin (B), or nourseothricin (C). Arrows represent putative transformants that have grown 
through the entire layer of amended medium. 
 
 
 

Beginning with 107 protoplasts per transformation, up to 76 putative transformants were 

obtained in a single reaction (data not shown). Stable transformants were obtained and screened 

for fluorescence. Neither wild type strain of F. virguliforme or F. brasiliense demonstrated green 

or red autofluorescence (Fig. 4-5). However, many stable transformants displayed fluorescent 

phenotypes, and representative isolates are presented (Fig. 4-5). Phenotypic differentiation of F. 

virguliforme and F. brasiliense requires many spore measurements, which can be very 

challenging since spore lengths and widths overlap (Aoki et al., 2005). However, a mixed culture 

of spores of the transgenic strains developed here are easily distinguished based on fluorescence 

of individual spores (Fig. 4-6).  

 

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Figure 4-5. Microscopic features of wild type and representative mutant strains of F. 
virguliforme (A) and F. brasiliense (B). No autofluorescence was detected from the wild-type 
strains, but was detected in representative mutant strains transformed with a fluorescent reporter 
gene. All scale bars represent 50 µm. 
 
 

Figure 4-6. Mixed culture of Fusarium spores, distinguished by fluorescent reporter gene 
expression. Fusarium virguliforme fluoresces green and F. brasiliense fluoresces red under 
appropriate excitation spectra for eGFP and mCherry, respectively. All scale bars represent 50 
µm. 
 
 
Discussion 

 

Generating protoplasts of F. virguliforme has been successful in other labs using a 

previously published protocol studies, but was unsuccessful in our hands (Chang et al., 2016a; 

Islam et al., 2017b, 2017a; Mansouri et al., 2009; Pudake et al., 2013). Protoplast generation was 

 

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successful following the protocol developed for F. graminearum, but the transformation method 

reported therein was unsuccessful for transforming F. virguliforme and F. brasiliense (Hallen-

Adams et al., 2011). The transformation protocol for F. virguliforme, as written (Mansouri et al., 

2009), also failed to produce successful transformants for us. Therefore, we integrated 

components from both of these published protocols, resulting in the protocol described here. 

Implementing this transformation protocol with high quality protoplasts allowed for the 

generation of stable transformants of both F. virguliforme and F. brasiliense (Fig. 4-3). In 

addition, Fusarium virguliforme protoplasts developed with the method described here were 

successfully used in a targeted gene replacement study testing different fungal succinate 

dehydrogenase alleles (Sang et al., 2018). 

 

The two previously published protoplasting protocols for F. graminearum and F. 

virguliforme differ in many aspects (Hallen-Adams et al., 2011; Mansouri et al., 2009). 

Differences include buffer types, enzyme compositions, and enzyme concentrations. While the 

F. virguliforme protocol uses a sodium phosphate buffer containing β-glucuronidase and a high 

concentration of lysing enzymes (25 mg / mL), the F. graminearum protocol uses a potassium 

chloride buffer with chitinase rather than β-glucuronidase, and a lower lysing enzyme 

concentration (5 mg / mL). Given that chitin and β-glucan are the major polysaccharides found in 

Fusarium spp., the addition of chitinase likely increased our protoplasting efficiency for F. 

virguliforme and F. brasiliense (Schoffelmeer et al., 1999). It is worth noting that 3-5 hours were 

required to gain sufficient protoplasts in F. virguliforme and F. brasiliense compared to the 2 

hours reported for F. graminearum (Hallen-Adams et al., 2011). A detailed comparison between 

protoplasting buffers and enzyme compositions also concluded that potassium chloride was 

 

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optimal for protoplast generation in the fungal pathogen F. verticillioides, and the addition of 

chitinase helps generate protoplasts in other Fusarium species (Ramamoorthy et al., 2015).  

 

Fusarium brasiliense is a close relative of F. virguliforme, both members of clade II in 

the Fusarium solani species complex (Aoki et al., 2005; Chitrampalam and Nelson 2016). 

However, different members of this species complex display different sensitivities to fluopyram, 

a common fungicide used to control these species (Sang et al., 2018). Understanding the genetic 

mechanisms of fungicide sensitivity or resistance can be elucidated by testing gene function 

through gene knockout and gene replacement studies using split marker gene replacement 

constructs (Catlett et al., 2003). The genetic mechanisms of SDS development are poorly 

characterized, and whether mechanisms of SDS development are shared between F. virguliforme 

and F. brasiliense are also unknown. Though we introduced foreign DNA into F. brasiliense via 

random insertion in this study, this protocol should also facilitate knock out experiments via gene 

replacement as it has in F. virguliforme protoplasts obtained with this method (Sang et al., 2018).  

 

Both F. virguliforme and F. brasiliense are capable of infecting soybean and common 

bean (Phaseolus vulgaris) (Kolander et al., 2012; Jacobs et al., 2018; Wang et al., 2018), and 

isolation frequency data suggests that F. brasiliense may have a host preference for common 

bean (Wang et al., 2018). The development of transgenic F. virguliforme and F. brasiliense 

strains expressing different fluorescent markers may allow for studies investigating host 

preference through co-inoculations, as they can easily be distinguished under the microscope 

based on their reporter gene expression (Fig. 4-6). With this protocol and carefully designed 

experiments we can come to a greater genetic understanding of these pathogens which could lead 

to new and effective management strategies.  

 

 

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Conclusions 

 

Fusarium virguliforme and F. brasiliense are both causal agents of soybean SDS and are 

amenable to genetic manipulation for studying gene function. Confirmation of genetic 

transformation in these species requires high quality protoplasts and appropriate controls. The 

protocol developed here provides methods for producing high quality protoplasts that builds 

upon previously reported protocols and should be valuable to the soybean SDS and common 

bean root rot research community. The fluorescent strains developed here can be used to 

investigate temporal colonization and co-infections of plant roots as well as the potential host 

preferences of these species. Using this protocol to probe gene function in these pathogens will 

be critical to understanding their pathology and advancing plant genetics to improve SDS 

management. 

 

Acknowledgements 

 

The authors would like to thank R. Shay, Z. Noel, Dr. H.-X. Chang, and Dr. H. Sang for 

critical review of this manuscript prior to submission. This work was funded in part by the 

Michigan Soybean Promotion Committee, the United Soybean Board and the North Central 

Soybean Research Program.  

 

 

 

86 

CHAPTER 5: The role of soil-borne nematodes in soybean sudden death syndrome caused 

 

by Fusarium virguliforme 

By 

Mitchell G. Roth 

 

This work presented in this chapter is unpublished. 

 

 

 

87 

 

Abstract 

 

Soybean cyst nematode (SCN) and Fusarium virguliforme are major threats to soybean 

production throughout the U.S. Management strategies for both pathogens are similar and 

include using genetically resistant varieties, diverse crop rotations, and seed treatments 

containing a nematicide or fungicide. Fusarium virguliforme causes soybean sudden death 

syndrome (SDS) by infecting root tissues, colonizing vascular tissues, and releasing protein 

effectors and toxins into soybean xylem, inducing foliar SDS symptoms. Though SCN cannot 

cause SDS, many studies have identified a strong positive correlation between SDS severity and 

SCN abundance, though the role of SCN in SDS development remains unknown. In this study, 

we explored the interactions between F. virguliforme and the model nematode Caenorhabditis 

elegans and the soybean cyst nematode Heterodera glycines. Fusarium virguliforme is able to 

colonize dead and immobilized nematodes, suggesting that F. virguliforme is an excellent 

saprophyte and may be able to use nematodes as a nutrient source, which could increase F. 

virguliforme inoculum. However, colonization of living mobile nematodes was not observed, 

indicating that F. virguliforme is not vectored into soybean roots by nematodes. The presence of 

SCN in co-infected soybean roots significantly increased the level of FvTox1 expression relative 

to F. virguliforme-only inoculated plants. FvNIS1 expression was not affected by the presence of 

SCN. Overall, these results suggest that successful SCN management may also lead to improved 

SDS management. 

 

Introduction 

 

Soybeans are a global commodity known for quality protein content and valuable oils, 

but also have many industrial uses such as the production of adhesives and paints (Singh 2010). 

 

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The USDA projects an estimated 4.5 billion bushels of soybeans were produced in the U.S. 

during 2018-2019 (USDA 2019). However, soybean production faces numerous annual 

challenges including those from pests and pathogens, which lead to reduction in yields In 2010-

2014, loses of approximately 41.9 million bushels were observed throughout the U.S. and 

Ontario, Canada (Allen et al., 2017). Two significant threats to soybean production in the U.S. 

are the soybean cyst nematode, Heterodera glycines Ichinohe (SCN), and the ascomycete fungus 

Fusarium virguliforme, which causes sudden death syndrome (SDS) (Allen et al., 2017). 

 

The SCN is an obligate roundworm parasite that requires a compatible host to complete 

its life cycle (Niblack et al., 2006). Mature SCN females (cysts) can contain 600 or more eggs 

and survive in the soil for 10 years or more (Niblack et al., 2006; Sipes et al., 1992). The eggs 

can be induced to hatch into infectious juveniles (J2) by root exudates of developing soybean 

roots (Riga et al., 2001). The J2 physically pierce roots with a stylet, and their saliva contains 

cell wall degrading enzymes, together allowing the nematode to gain entry to the root system 

(Wang et al., 1999). Inside the root system, the J2 evades the plant immune system and develops 

a syncytium, or feeding site, by releasing effector proteins from their esophageal glands 

(Mitchum et al., 2013; Noon et al., 2015). Feeding off the root, J2 nematodes molt two more 

times (J3, J4) before becoming mature adults, completing their reproductive cycle in three to four 

weeks under typical field conditions (Alston and Schmitt, 1988; Anand et al., 1995). Above-

ground symptoms are not always obvious even when SCN is causing significant yield losses 

(Noel and Edwards, 1996; Wang et al., 2003; Young, 1996). Symptoms associated with SCN 

damage often develop as secondary problems like nutrient deficiencies (Niblack et al., 2006). 

 

Fusarium virguliforme is the main causal agent of soybean SDS in the U.S. (Aoki et al., 

2003; Hartman et al., 2015). The soil-borne fungus infects root tissues by developing appressoria 

 

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and releasing cell wall degrading enzymes (Chang et al., 2016b; Navi and Yang 2008). Heavy 

colonization of the roots can lead to significant root rot symptoms without foliar symptom 

development (Wang et al., 2018). Root rot caused by F. virguliforme is sufficient to significantly 

reduce yield, though foliar symptoms of SDS are thought to be the primary driver of yield loss 

(Luo et al., 2000; Wang et al., 2018). Foliar symptom development is thought to require 

colonization of the vascular tissues (Islam et al., 2017a, 2017b; Navi and Yang 2008). There, F. 

virguliforme can secrete effector proteins and toxins into the xylem and translocate to the leaves, 

leading to the foliar symptoms of interveinal chlorosis and necrosis (Abeysekara and 

Bhattacharyya 2014; Brar et al., 2011; Chang et al., 2016a; Navi and Yang, 2008). F. 

virguliforme remains in the roots throughout foliar symptom development, and develops 

overwintering chlamydospores that can survive in soybean and corn debris (Aoki et al., 2003; 

Navi and Yang, 2016; Roy et al., 1989). 

Multiple disease complexes include plant pathogenic fungi and plant parasitic nematodes 

(Back et al., 2002), including Verticillium dahliae with Pratylenchus nematodes in potato early 

die (Martin et al., 1982; Rowe et al., 1985) and wilt disease of mint (Wheeler et al., 2019), 

Rhizoctonia solani and Ditylenchus dipsaci in sugar beet crown and root rot (Hillnhütter et al., 

2011), and Fusarium solani and Meloidogyne incognita in root rot of lentil (Ahmed and Shahab 

2018). Similarly, it has been suggested that SDS is a disease complex composed of F. 

virguliforme and SCN, where SCN increases SDS synergistically with F. virguliforme (Back et 

al., 2002; Roth et al., 2019a; Xing and Westphal, 2013). However, the exact role of the nematode 

in these disease complexes remain unknown. Some hypotheses include nematodes vectoring 

fungi into plants (Khan and Pathak 1993), nematodes creating wounds that are easier for fungi to 

colonize (Hillnhütter et al., 2011), and altering defense gene expression that benefits fungal 

 

90 

 

invasion (Biere and Goverse, 2016). In the case of SCN and F. virguliforme, evidence has 

suggested that F. virguliforme colonizes wounds caused by SCN (Diaz Arias 2012). However, 

soybeans undergo tremendous transcriptional reprogramming upon infection by SCN, including 

altered expression of WRKY and MYB transcription factors and pathogenesis related genes 

(Ithal et al., 2007a). SCN also upregulate cell wall loosening enzymes, suppress JA, and increase 

peroxidase expression (Ithal et al., 2007b). This suggests that the presence of SCN could 

significantly affect the ability for F. virguliforme to induce SDS. Despite the unknown role of 

SCN in SDS, many field studies have observed an increase in foliar SDS severity when SCN is 

also present (Brzostowski et al., 2018; McLean and Lawrence, 1995; Roth et al., 2019a; 

Westphal et al., 2014; Xing and Westphal, 2013), though others have not observed this effect 

(Hershman et al., 1990; Marburger et al., 2014; Rupe et al., 1993). The objectives of this study 

were to 1) determine the ability for F. virguliforme to colonize living vermiform nematodes, 2) 

examine the temporal colonization of immobile nematodes by F. virguliforme, and 3) quantify 

the expression of two F. virguliforme effector genes in planta in the presence and absence of 

SCN.  

 

Materials and Methods 

Fungal and nematode strains 

 

Fusarium virguliforme strain NRRL 22292, also called “Mont-1”, was obtained from the 

USDA Agricultural Research Service Culture Collection. Fluorescent strains were developed 

from Mont-1 by protoplast development and homologous recombination, as described in Roth et 

al., (2019b). The temperature sensitive mutant strain SS104 of Caenorhabditis elegans was 

obtained from the Caenorhabditis Genetics Center (CGC, University of Minnesota, St. Paul, 

 

91 

MN). Heterodera glycines were obtained from soil samples obtained from Decatur, MI using a 

modified soil centrifugation and floatation procedure (Jenkins 1964). The H. glycines type was 

determined to be 2.5.7 (Niblack et al., 2002; Tylka 2016), and nematodes were propagated in the 

greenhouse using the susceptible soybean line ‘Williams 82’ grown in a 2:1 soil:sand mix.  

 

 

In vitro Fusarium-nematode interactions 

 

Synchronous C. elegans cultures were obtained by harvesting adult nematodes from 

nematode growth media (NGM) plates (0.3% NaCl, 0.25% peptone, 1.7% agar, 1 mM CaCl2, 1 

mM MgSO4, 25 mM KPO4, and 5 mg/L cholesterol) containing a lawn of E. coli strain OP50. 

Briefly, five-day old NGM plates were flooded with 6 mL sterile water and the supernatant was 

re-collected into a 15 mL conical tube. The nematodes were concentrated by centrifuging at 1500 

x g for 1 minute and decanting the supernatant. Sodium hydroxide and sodium hypochlorite were 

added to the nematodes at a final concentration of 500 mM and 1% respectively, and incubated 

for 4 minutes with frequent vortexing, killing all adult nematodes and leaving only eggs. The 

eggs were centrifuged at 1,500 x g for 1 minute and washed three times with 15 mL M9 buffer 

(22 mM KH2PO4, 1 mM Na2HPO4 • 7H2O, 85.5mM NaCl, 50 µg/ml kanamycin, 5 µg/ml 

cholesterol, 2mM MgSO4). The eggs were allowed to hatch overnight in 10 mL M9 buffer with 

shaking at 75 rpm, transferred to NGM plates containing a lawn of OP50, and incubated at room 

temperature for 36-48 hours. When all nematodes reached the L4 stage, they were collected with 

10 mL M9 buffer, washed once to remove OP50, and used in interaction experiments with 

Fusarium.  

 

Fusarium isolates were cultured on half strength PDA (Neogen Corporation, Lansing, 

MI) for 10-14 days. Spores were collected by flooding the plate with 5 mL of M9 buffer and 

 

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quantified with a hemocytometer. Approximately 105 spores and 150 nematodes were used in 

each experiment. Spores and synchronized nematodes were mixed in 1 mL reactions consisting 

of 80% M9 buffer and 20% BHI broth and incubated at 25°C for four hours. While centrifuge 

and washing steps has been reported to remove spores from nematodes (Muhammed et al., 

2012b), this technique failed to separate spores from nematodes in this study. Instead, the 

reactions were centrifuged at 1,500 x g for two minutes to pellet nematodes and spores. Nine-

hundred microliters were carefully removed with a pipet and discarded. The final 100µL 

containing nematodes and spores were plated onto one side half of an NGM plate. The other half 

of the NGM plate contained a lawn of OP50 (Fig. 5-1 A). Living nematodes were drawn away 

from the spores and towards OP50, their food source. These plates were incubated at 25°C for 

three days, then all dead and living nematodes were quantified. A subset of nematodes on each 

half of the plate were picked and visually examined for fungal colonization using a fluorescent 

microscope (Leica Microsystems, Buffalo Grove, IL). All nematodes were transferred onto a 2% 

agar pad containing 1 µM tetramisole hydrochloride (Millipore-Sigma, Burlington, MA) to 

immobilize living nematodes. Z-stack imaging of nematodes was performed on a confocal laser 

scanning microscope (Olympus, Center Valley, PA).  

 

Temporal colonization of nematodes by Fusarium 

 

Nematodes or eggs were transferred onto a 2% agar pad containing 1µM tetramisole 

hydrochloride. The agar pad was surrounded by petroleum jelly to create a small chamber. M9 

buffer was added to this chamber to keep the agar pad from dehydrating. Bright-field and 

fluorescent images were captured every 20 minutes for 56 hours using a fluorescent microscope 

containing a GFP/FITC filter connected to a DFC450 C camera (Leica Microsystems). Acquired 

 

93 

images were exported to FIJI for merging channels, adding scalebars, and generating moving 

 

images (Schindelin et al., 2012). 

 

In vivo expression of Fusarium effectors in the presence of SCN 

 

Heterodera glycines were collected from greenhouse soil containing ‘Williams 82’ 

soybean roots using a modified centrifugation floatation protocol (Jenkins 1964). Briefly, 4,000 

cm3 (1 gallon) of soil was homogenized in an 18.9 L (5 gallon) bucket, filled to the top with 

water. The water was filtered through a 710 µm filter twice to remove soil aggregates, then 

filtered through a 45 µm filter (Hogentogler & Co., Columbia, MD) to collect nematodes 

(Shurtleff and Averre III 2000). Nematodes were back-washed from the sieve into 50 mL conical 

tubes and centrifuged for 5 min at 1,150 x g. Water was decanted until 10 mL remained, then 25 

mL of a sucrose solution (1,210 g / L, in water) was added and nematodes were centrifuged 

again for 1 min at 1,150 x g. Cyst and vermiform nematodes floated to the top and were decanted 

onto a 45 µm filter and washed thoroughly with water, then back-washed into a 250 mL beaker 

(Shurtleff and Averre III 2000). Fifty microliters of water containing nematodes were aliquoted 

onto a microscope slide and the number of J2 juvenile nematodes were quantified. The 

nematodes were diluted to approximately 75 J2 juveniles per milliliter, and two milliliters were 

used to inoculate soybean roots for a total inoculation with 150 J2.  

Williams 82 soybean seeds were surface sterilized by soaking in 6% bleach (0.4% 

NaOCl) for 10 minutes, rinsing in DI water for 10 minutes, then air drying on sterile paper 

towels in a laminar flow hood. A single seed was sown into a 107 mL conetainer (Greenhouse 

Megastore, Danville, IL) filled with 2:1 soil:sand mix of SureMix (Michigan Grower Products 

Inc., Galesburg, MI, USA) and washed play sand. The soybeans were allowed to germinate for 7 

 

94 

 

days in a growth chamber at 25°C and 75% relative humidity under a 14-10 h light-dark cycle. 

The conetainers were set up in a randomized complete block design with eight plants in the first 

biological replicate and seven plants in the second. A 6-cm deep hole was made next to each 

soybean using 1 mL pipet tip and was filled with one of four treatments; 2 mL sterile water and a 

single 3.4 mm non-colonized PDA plug (mock inoculation), 2 mL sterile water and a single 5 

mm PDA plug colonized with F. virguliforme, 2 mL of water containing J2 nematodes and a 

single 5 mm non-colonized PDA plug, or 2 mL water containing J2 nematodes and a single 5 

mm PDA plug colonized with F. virguliforme. The inoculated soybeans were incubated in the 

growth chamber for four weeks and watered daily to maintain adequate moisture. All samples 

were harvested at four weeks post inoculation (five weeks post planting). Plant roots were 

washed to remove attached soil, separated from foliar tissues, and fresh shoot and root weights 

was collected. Shoots were discarded and roots were snap frozen in liquid nitrogen and stored at 

-20°C for one week.  

The inoculated roots were ground in liquid nitrogen using a mortar and pestle, and the 

ground tissues were subjected to RNA extraction. Approximately 100 mg of root tissue was 

added to 1 mL TRIzol (Thermo Fisher Scientific, Waltham, MA) and 150 µL of lysing matrix D 

(MP Biomedicals, Solon, OH). The samples were brought to room temperature then ground 

again in a FastPrep machine (Thermo Fisher Scientific) for 30 seconds on speed setting 5. The 

rest of the RNA extraction was performed following the TRIzol manual. One microgram of total 

RNA from each sample was reverse transcribed to cDNA using a High Capacity cDNA Reverse 

Transcription Kit (Applied Biosystems, Foster City, CA) according to manufacturer protocol.  

Quantities of FvTox1 and FvNIS1 expression were obtained by using SYBR Green qPCR 

assays with previously reported primers (Chang et al., 2016a). Gene expression levels were 

 

95 

 

calculated relative to the F. virguliforme TEF1 reference gene by taking (CtEF1a – CtGOI) + 10, 

where CtEF1a is the Ct value for EF1a expression, CtGOI is the Ct value for a gene of interest, 

either FvTox1 or FvNIS1, and 10 being a unitless addition to remove negative values and make 

interpretation of gene expression levels intuitive. In samples where no amplification was 

detected, “NA” values were replaced with the maximum Ct value of 40 so calculations could be 

made.  

 

Results 

Fusarium virguliforme does not actively colonize vermiform nematodes 

 

The ability for F. virguliforme to colonize active nematodes was examined using C. 

elegans as a model and previously published protocols for fungal-nematode interactions 

(Muhammed et al., 2012b, 2012a). Most nematodes were found feeding on E. coli, while only 

some were found outside the E. coli lawn (Fig. 5-1 A). All nematodes feeding on E. coli were 

healthy and living, as observed by their behavior and indicated by the auto-fluorescent gut 

granules (Pincus et al., 2016) (Fig. 5-1 B). In contrast, most nematodes outside the lawn of E. 

coli were dead, and GFP signal was detected in a filamentous pattern consistent with fungal 

growth rather than gut granules (Fig. 5-1 B). Some nematodes outside the lawn of E. coli were 

alive and exploring but had no visual indication of fungal colonization. Despite being incubated 

with wild type F. virguliforme spores, GFP-expressing spores, or no spores, most nematodes 

were living and non-colonized (Fig. 5-1 C). Similar results were observed with F. brasiliense 

isolates expressing mCherry (Fig. 5-2).  

 

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Figure 5-1. Fusarium virguliforme interactions with Caenorhabditis elegans. After four hours of incubating fungal spores with L4 
nematodes, experiments were plated onto half a plate containing a lawn of E. coli OP50 on the other half and incubated for three days 
(A). Nematodes from each half of the plate were transferred and viewed under bright field and GFP channels (B). Nematodes on the 
fungal half were dead, and nematodes on the bacterial half were alive, so the percent living nematodes were quantified per treatment 
(C). 
 

 

 

 

97 

 

Figure 5-2. Fusarium brasiliense interactions with Caenorhabditis elegans. After four hours of incubating fungal spores with L4 
nematodes, experiments were plated onto half a plate containing a lawn of E. coli OP50 on the other half and incubated for three days 
(A). Nematodes from each half of the plate were transferred and viewed under bright field and GFP channels (B). Nematodes on the 
fungal half were dead, and nematodes on the bacterial half were alive, so the percent living nematodes were quantified per treatment 
(C).

 

 

98 

 

 

Fluorescent signals from dead nematodes were successfully separated to distinguish GFP 

fluorescence from transgenic F. virguliforme strains and autofluorescence from the nematode. 

These results show that dead nematodes were indeed colonized by F. virguliforme (Fig. 5-3). In 

colonized nematodes, Z-stack imaging revealed that colonization can occur inside the nematode, 

with most fluorescence being detected 16 µm from the nematode cuticle (Fig. 5-4).  

 

 
Figure 5-3. Linear unmixing of green C. elegans autofluorescence and GFP fluorescence 
from transgenic F. virguliforme strains. Autofluorescence from C. elegans was pseudo-colored 
red (left) while true GFP fluorescence was left as green (center), allowing visual discernment of 
the nematode body and fluorescent fungal hyphae (right).  
 

 

 

99 

 

 

Bright Field

GFP

Merge

Slice 2

15.9 µm

Slice 9

15.9 µm

Slice 16

Figure 5-4. Z-stack images (slices) of C. elegans colonized by a transgenic F. virguliforme 
strain expressing GFP. Image (slice) 2 displays the dorsal surface of the nematode, image 
(slice) 9 displays the nematode internal body cavity colonized by F. virguliforme, and image 
(slice) 16 displays the ventral surface of the nematode. All slices shown are 15.9 µm apart in 
depth.  
 
 
Fusarium virguliforme can colonize immobilized nematodes 

 

Some C. elegans were found dead, outside of the E. coli lawn even when no fungal 

spores were added (Fig. 5-1 C, Fig. 5-2 C) suggesting that repeated pipetting of the nematodes 

may have caused some mortality. Therefore, F. virguliforme colonization of nematodes may be 

before nematode death (pre-mortem) or after (post-mortem). We investigated whether F. 

virguliforme can colonize immobilized, living nematodes. After 12 h, germination of F. 

virguliforme spores was observed, and by 48 h F. virguliforme spores were had penetrated the 

nematode cuticle, initiating a strong burst of autofluorescence from the nematode that indicates 

nematode death (Pincus et al., 2016) (Fig. 5-5).  

 

 

 

 

100 

 

Bright Field

GFP

Merge

50 µm

50 µm

50 µm

Figure 5-5. Fusarium virguliforme colonization of an L4 C. elegans nematode over time. All 
images were taken at the same magnification, so the 50 µm scalebar at 6 hpi is representative for 
all images. hpi = hours post inoculation with transgenic F. virguliforme spores 

 

6hpi

12hpi

18hpi

24hpi

30hpi

36hpi

42hpi

48hpi

54hpi

 

101 

 

SCN does not affect F. virguliforme effector expression 

 

Biological replicate had a significant effect on root fresh weight, and therefore, were 

analyzed separately. In the first biological replicate, soybeans inoculated with F. virguliforme or 

both F. virguliforme and SCN had significantly lower root fresh weight compared to the mock-

inoculated controls (Fig. 5-6 A). FvTox1 and FvNIS1 expression was detected in all treatments 

even though the primers were designed specifically for F. virguliforme genes, indicating some 

cross-reactivity. However, there was significantly higher FvTox1 expression in soybean roots 

inoculated with F. virguliforme and SCN compared to F. virguliforme alone (Fig. 5-6 B). No 

significant differences in FvNIS1 expression were detected (Fig. 5-6 B).  

A

Biological Replicate 1

Biological Replicate 2

Both

Fv Mock

SCN

Both

Fv Mock

SCN

8

7

6

5

4

30

20

10

30

20

10

)
g
(
 
t
h
g
i
e

W

 

 
t
o
o
R
h
s
e
r
F

B

n
o
i
s
s
e
r
p
x
e
 

1
x
o
T
v
F
 
e
v
i
t
a
l
e
R

n
o
i
s
s
e
r
p
x
e
 

1
S
I
N
v
F
 
e
v
i
t
a
l
e
R

Figure 5-6. 

 

F. virguliforme +

SCN

F. virguliforme

Mock

SCN

 

102 

 

Figure 5-6 (cont’d). Results of growth chamber factorial inoculation experiment. (A) Root 
dry weight of inoculated soybeans. (B) Expression levels of F. virguliforme effectors FvTox1 
and FvNIS1, relative to the F. virguliforme housekeeping gene TEF1. 
 
 
Discussion 

 

Both SCN and F. virguliforme independently cause significant damage and yield loss in 

soybeans (Allen et al., 2017). Potential synergism between the two increases the risk of severe 

SDS symptoms and yield loss (Roth et al., 2019a; Xing and Westphal 2013). Though F. 

virguliforme is the sole cause of soybean SDS (Roy et al., 1989), it is interesting that Gao et al., 

(2006) observed some foliar necrosis in SCN-only inoculated plants. Fusarium virguliforme 

(formerly F. solani f. sp. glycines) has been isolated from SCN eggs and cysts (Donald et al., 

1993; McLean and Lawrence 1995), and Gao et al., (2006) also indicated that F. virguliforme 

decreased SCN reproduction. These reports are suggestive of nematode vectoring F. 

virguliforme. However, our results support previous findings that F. virguliforme can colonize 

nematodes (Donald et al., 1993; McLean and Lawrence 1995), but is not actively vectored by 

nematodes (Fig. 5-1, Fig. 5-2).  

 

Fungal colonization inside C. elegans adults was an interesting finding. Muhammed et al 

(2012b) reported a similar finding using clinical isolates of F. solani and suggested that this 

resulted from conidia germination inside C. elegans. For this to happen, fungal spores must get 

inside C. elegans prior to germination, likely by ingestion. However, C. elegans is reported to be 

a strict bacterial feeder, and SCN is reported to be a strict herbivore (Avery and You 2005; Davis 

and Tylka 2005). In contrast, the time-course infection presented in this work indicates that F. 

virguliforme can internally colonize C. elegans without the need to ingest spores (Fig. 5-5). 

Adult C. elegans are approximately 30-40 µm in diameter (Backholm et al., 2013), and F. 

virguliforme detection at 16 µm from the cuticle is within this range (Fig. 5-4). This colonization 

 

103 

 

of nematodes indicates the saprophytic abilities of F. virguliforme, which has been shown to 

colonize many other plant hosts outside of soybean (Kolander et al., 2012). Sufficient 

colonization of nematodes could lead to an increase in fungal inoculum, which has been shown 

to be significantly associated with increased SDS severity (Freed et al., 2017; Roth et al., 2019a; 

Roy et al., 1997). 

 

Fungal development in culture is characterized macroscopically by outward radial growth 

of hyphae. Microscopically, radial growth is an over-simplification. The time-course experiment 

in this study shows that F. virguliforme spores germinate from their terminal (foot) cells and 

continue growing in seemingly random directions (Fig. 5-5). Since initial hyphae germinate and 

travel away from C. elegans, it is possible that fungal colonization of nematodes is a random 

event. However, once F. virguliforme successfully penetrated C. elegans (approximately 35-40 

hours post inoculation), subsequent colonization of the nematode was rapid, killing the nematode 

(Fig. 5-4). Quorum sensing in fungi has been described, though is not a well-studied topic 

(Albuquerque and Casadevall 2012). In particular, some fungi have shown strong reactions to 

farnesol, an isoprenoid that inhibits mycelial development, including F. graminearum (Hornby et 

al., 2001; Albuquerque and Casadevall 2012; Semighini et al., 2008). Unlike arthropods and 

tardigrades, nematodes have retained their ability to synthesize farnesol (De Loof et al., 2015), 

which would suggest colonization of C. elegans by F. virguliforme would be inhibited. However, 

other fungal quorum sensing molecules have been identified, like tyrosol, which can induce 

fungal filamentation (Albuquerque and Casadevall 2012). It may be possible that F. virguliforme 

releases a quorum sensing molecule in the presence of nematodes, facilitating rapid colonization 

and utilization of the food source. A metabolomics study of colonized and non-colonized C. 

elegans may reveal potential quorum sensing molecules. 

 

104 

 

Root infections by SCN cause significant changes in soybean gene expression and is 

associated with increased SDS severity (Ithal et al., 2007a, 2007b; Roth et al., 2019a; Xing and 

Westphal 2013). However, F. virguliforme expression of two known effectors were not 

significantly affected by SCN in this study (Fig. 5-6 B). Fusarium virguliforme inoculations 

caused a significant reduction in root fresh weight in both biological replicates of this 

experiment, suggesting that soybeans were subjected to a sufficient level of inoculum (Fig. 5-6 

A). However, expression of both F. virguliforme effectors were also detected frequently in Mock 

and SCN-only inoculated samples, suggesting a lack of specificity of primers designed by Chang 

et al., (2016a). Therefore, the presence of SCN may alter F. virguliforme gene expression and 

will need to be investigated further, with either 1) improved primer design or 2) higher inoculum 

levels to reduce false-positive detection of F. virguliforme gene expression. 

 

Acknowledgements 

 

The authors would like to thank Dr. M. Frame for confocal and spectral imaging 

assistance, and Z. Noel, J. Jacobs, A. McCoy, M. Breunig, V. Ortiz, H.-X. Chang, and H. Sang 

for helpful discussions on interpretation of results. This work was funded in part by the Michigan 

Soybean Promotion Committee, the United Soybean Board, the North Central Soybean Research 

Program, and the Syngenta Agricultural Scholarship.  

 

 

 

105 

CHAPTER 6: Investigating legume host preference among Fusarium virguliforme and F. 

brasiliense using factorial inoculations and whole-genome sequencing 

Mitchell G. Roth, Jie Wang, Janette L. Jacobs, Kevin Childs, and Martin I. Chilvers 

By 

 

 

This work presented in this chapter is unpublished. 

 

Author Contributions: 

MGR, JW, and MIC conceptualized project goals, MGR, JW, and KC generated methods, MGR 

and JLJ worked with isolates to generate high molecular weight DNA, KC prepared all libraries 

and generated all sequences, MGR and JW analyzed all data, and MGR wrote the manuscript.  

 

 

 

106 

 

Abstract 

 

Soybean sudden death syndrome is caused by multiple species within clade II of the 

Fusarium solani species complex (FSSC). Two of these species, F. virguliforme and F. 

brasiliense, are present in Michigan. The geographic distribution of F. virguliforme aligns with 

the soybean producing regions in Michigan, while the geographic distribution of F. brasiliense 

aligns with the dry bean producing regions of Michigan. Isolation frequencies of F. virguliforme 

from soybean and F. brasiliense from dry bean led us to hypothesize that these two pathogens 

adapted to their preferred hosts, where F. virguliforme colonizes soybeans better than dry beans, 

and F. brasiliense colonizes dry beans better than soybeans. We used a factorial inoculation 

experiment in a growth chamber and whole-genome sequencing to investigate a potential host 

preference. Both F. virguliforme and F. brasiliense caused significant root rot on soybean and 

dry bean compared to the non-inoculated control. However, neither pathogen caused a significant 

reduction in dry weights of roots nor shoots during the 3-week experiment. Additionally, neither 

pathogen showed significantly more root rot symptoms than the other, on either host. Whole 

genome sequencing using long-read Nanopore technologies improved the genome assembly 

continuity with longer N50 length and a smaller number of contigs. Indeed, the current 

assemblies are an improvement over currently available genomes assembled via short-read 

sequencing technology, but the sequencing error-rate of Nanopore resulted in decreased genome 

quality with respect to gene-predictions of single copy orthologs. Whole-genome alignments 

suggest that F. virguliforme might be more clonal than F. brasiliense. These alignments also fail 

to provide evidence of lineage-specific or host-specific pathogenicity chromosomes. Both F. 

virguliforme and F. brasiliense are significant pathogens of soybean and dry bean with little 

evidence of a host preference among these legume crops. 

 

107 

 

Introduction 

 

Sudden death syndrome (SDS) is an economically important disease of soybean caused 

by some soil borne ascomycetes in clade II of the Fusarium solani species complex (Aoki et al., 

2005; Coleman 2016). In South America, SDS is caused by four species; F. virguliforme, F. 

tucumaniae, F. cuneirostrum, and F. brasiliense (Aoki et al., 2005). In North America, F. 

virguliforme was identified in 1971 in Arkansas causing SDS (Roy et al., 1989), but F. 

brasiliense isolates have also recently been reported in Michigan, capable of causing soybean 

SDS and root rot of dry bean, Phaseolus vulgaris (Jacobs et al., 2018; Wang et al., 2018b).  

 

Root rot and SDS caused by these species are difficult to manage, and there are no mid-

season management strategies currently available to growers. The fungicide fluopyram has 

shown efficacy in controlling F. virguliforme, both in vitro and in the field as a seed treatment 

(Kandel et al., 2016, 2018; Sang et al., 2018; Wang et al., 2017). However, few other fungicides 

have shown efficacy in the field (Weems et al., 2015) and different SDS-causing species respond 

differently to fluopyram (Sang et al., 2018). Genetically resistant or tolerant lines are available, 

but resistance is partial and controlled by many quantitative trait loci (QTLs) that overlap with 

QTLs for other diseases (Chang et al., 2018; Meksem et al., 1999). Crop rotations with non-hosts 

can be effective, but F. virguliforme has a broad asymptomatic host range, so rotations of up to 3 

or 4 years may be necessary to lower SDS pressure (Kolander et al., 2012; Leandro et al., 2018).  

 

Fusarium virguliforme has spread from Arkansas throughout the U.S. via unknown 

mechanisms, but spreading through aerial dispersal of ascospores appears unlikely because only 

one mating type has been detected among global isolates (Hughes et al., 2014). In contrast, both 

mating types have been found in F. brasiliense isolates (Hughes et al., 2014), including 

Michigan isolates from the same field (Oudman et al., in prep). Therefore, sexual reproduction is 

 

108 

 

likely occurring among F. brasiliense in Michigan. It is unknown if F. brasiliense was recently 

introduced to Michigan or if it was only recently detected. However, only one F. brasiliense 

isolate has been reported in the U.S. prior to 2014 from an unknown host in California (Aoki et 

al., 2005). Since F. brasiliense is a successful pathogen of soybeans and dry beans, it is unlikely 

that it has spread from California to Michigan without being detected in agricultural states in 

between.  

 

High quality genomes of plant pathogens allow for many hypotheses to be addressed. 

Recently, advances in genome quality in fungal plant pathogens has revealed a “two-speed 

genome” hypothesis, where core chromosomes contain genes critical for survival and 

reproduction, while repeat- and transposon-rich genomic regions contain rapidly evolving 

effector genes specific to adaptation on specific hosts (Dong et al., 2015). High quality genomes 

of close relatives of F. virguliforme and F. brasiliense have revealed similar concepts 

(Sperschneider et al., 2015). These repeat-rich regions containing effector genes are sometimes 

found on small DNA elements that go by many names: lineage-specific, dispensable, or mini-

chromosomes. Studies in F. oxysporum show that these small DNA elements can be transferred 

from a non-pathogenic isolate and confer virulence (Vlaardingerbroek et al., 2016b; van Dam et 

al., 2017). These small DNA elements can be identified by obtaining high-quality genome 

sequences. 

 

Recent surveys of fungal pathogens causing root rot on soybeans and dry beans identified 

F. brasiliense in Michigan, but also revealed an interesting trend. Attempting isolations from 

soybean roots commonly yielded F. virguliforme isolates and rarely yielded F. brasiliense 

isolates (Jacobs et al., in prep). Similarly, isolation attempts from dry bean roots commonly 

yielded F. brasiliense isolates and rarely yielded F. virguliforme isolates (Jacobs et al., in prep, 

 

109 

 

Table 6-1). This suggested a possible host preference, where F. virguliforme preferentially 

colonizes soybean and F. brasiliense preferentially colonizes dry bean. The objectives of this 

study were to 1) use reciprocal inoculations to examine level of colonization of each pathogen 

(F. virguliforme and F. brasiliense) on each host (soybean and dry bean) and 2) obtain high 

quality genome sequences with long-read technologies to examine overall genome structure and 

identify potential pathogenicity chromosomes. 

 

Species 

Host 

Isolates 

Frequencya 

F. virguliforme 
F. virguliforme 
F. brasiliense 
F. brasiliense 

Soybean (Glycine max) 
Dry bean (Phaseolus vulgaris) 
Soybean (Glycine max) 
Dry bean (Phaseolus vulgaris) 

12 
42 
235 
9 

23% 
77% 
96% 
4% 

Table 6-1. Isolation frequencies of F. virguliforme and F. brasiliense in Michigan from 
soybean and dry bean hosts. 
a Frequency represents percent of isolates from each host and sum to 100% for each species 
 
 
Materials and Methods 

Isolation of fungal strains 

 

Fungal strains were isolated from soybean and dry bean roots displaying root rot 

symptoms collected throughout Michigan. The roots were cut into sections soaked in 5% bleach 

(0.4% NaOCl) for 4 minutes, then rinsed thoroughly in sterile deionized water. The root sections 

were dried with sterile paper towel and plated on WMS agar (2% agar, 15µg/mL metalaxyl, 

300µg/mL streptomycin). Plates were incubated for seven days at room temperature and 

observed under a dissecting microscope sporodochium development. Macroconidia from a 

sporodochium were transferred and spread on fresh WMS agar using a flame-sterilized insect 

pin. After 24 hours, a single germinating macroconidium was aseptically transferred onto full 

 

110 

strength potato dextrose agar (Neogen, Lansing, MI). Isolates were stored at room temperature 

on synthetic nutrient agar slants (1 g/L KH2PO4, 1 g/L KNO3, 0.5 g/L MgSO4, 0.5 g/L KCl, 0.2 

g/L glucose, 0.2 g/L sucrose, 2% agar) in scintillation vials (Research Products International, 

 

Mount Prospect, IL, USA) for long-term storage. 

 

Reciprocal inoculation experiment 

 

Fungal inoculum was prepared as described in Wang et al., (2018) using F. virguliforme 

strain ‘Mont-1’ and F. brasiliense strain ‘F-14-42’. Briefly, one fully colonized plate and one 

sterile plate of Nash-Snyder medium (Leslie et al., 2008) were added to a sterile blender with 

100 mL sterile water and blended on low speed for 30 s. The inoculum slurry was added to 

waterlogged, sterile sorghum seed in a spawn bag, then sealed with a heat sealer. The inoculated 

sorghum was incubated for 4 weeks at room temperature and mixed by hand every other day. 

The inoculum was poured onto sterile paper towel in a biosafety hood and allowed to dry for 3 

days. Colony forming units (CFU) were determined by adding 10 g of colonized sorghum to 100 

mL sterile water, incubating at room temperature with shaking at 150 rpm for 30 minutes, then 

performing serial dilution plating. The CFU of F. virguliforme inoculum was higher than F. 

brasiliense, so 2.0 g of F. virguliforme and 11.5 g of F. brasiliense inoculum were used, 

resulting in a standardized 1.5 x 106 CFUs per pot. 

 

Inoculum was added to 300 mL medium vermiculite in a 1064 mL (1 quart) plastic bag 

and homogenized. The inoculated vermiculite was poured into 355 mL paper cups (Solo Cup 

Company, Lake Forest, IL) containing three 7 mm holes for water drainage. Five seeds of 

soybean cultivar ‘Williams 82’ or dry bean cultivar ‘Red Hawk’ were sown on top of the 

inoculated medium vermiculite, then covered with 50 mL non-inoculated medium vermiculite. 

 

111 

 

Non-inoculated sorghum was used as a negative control. Five biological replicate cups were used 

per plant-inoculum-time point combination. Pots were watered with approximately 100 mL of 

water every other day, alternating between deionized water and half-strength Hoagland solution. 

After one, two, and three weeks of growth, all germinated plants from each pot were harvested, 

roots washed clean, and rated individually for root rot severity, as a percentage of discolored 

roots from 0-100. The average root rot severity of all plants in each pot are reported. Three plants 

were randomly selected from each pot, and the foliar and root tissues were separated, dried at 

50°C for 48 hours, and dry weights collected. For 3-week inoculated plants, these root tissues 

were ground to a fine powder in a 50 mL cone-bottom tube using two 8 mm steel balls in a 

GenoGrinder 2010 (SPEX Sample Prep, Metuchen, NJ, USA). DNA was extracted from 50 mg 

of ground root tissues and used in qPCR to detect F. virguliforme and F. brasiliense quantities 

using highly specific qPCR assays (Roth et al., 2019; Wang et al., 2015). The relative abundance 

of F. virguliforme and F. brasiliense was calculated by taking (Ctplant + 10) – Ctpathogen), where 

Ctplant represents the cycle at which amplification of the plant β-tubulin gene (Wang et al., 2018a) 

crossed the threshold, and Ctpathogen represents the cycle at which amplification of the pathogen 

crossed the threshold. This calculation can be intuitively interpreted, with high numbers 

indicative of high pathogen abundance. 

 

Genome sequencing 

 

Long-read sequencing technologies require high molecular weight DNA. Multiple 

protocols were used to extract high molecular weight DNA, though many failed to provide both 

high yield and high quality, which can be challenging for fungi (Schwessinger and Rathjen 2017; 

Kaur et al., 2017). Ultimately, the best protocol for the isolates in this study was developed by 

 

112 

 

plant researchers at Michigan State University described below (S. K. K. Raju, C. E. Niederhuth, 

personal communication).  

 

First, F. virguliforme and F. brasiliense isolates were grown on half-strength PDA 

(Neogen, Lansing, MI) for 2-3 weeks for sporodochium development. Plates were flooded with 5 

mL sterile water, and 4 mL water was re-collected into 15 mL conical tubes, now containing 

conidia. One hundred microliters of conidia were inoculated into 50 mL sterile PDB (Neogen) in 

125 mL flasks and incubated at room temperature for 3-7 days with shaking at 100 rpm. Flasks 

were examined daily to evaluate the level and age of mycelia in order to collect a high volume of 

young (non-pigmented) mycelia. Mycelia was harvested by pouring the flasks into a Büchner 

funnel containing two layers of Miracloth (Millipore-Sigma, St. Louis, MO). Mycelia were 

washed twice with ~100 mL sterile water. Mycelia was gathered into the Miracloth and wrung to 

remove as much water as possible, then immediately transferred to a ceramic mortar containing 

liquid nitrogen. The mycelia were ground to a fine powder with a pestle and transferred to a 50 

mL tube on ice using a SmartSpatula (Research Products International, Mt. Prospect, IL). Fifteen 

milliliters of DNA extraction buffer (100 mM Tris 1.4 M NaCl, 20 mM EDTA, 2% CTAB, 2% 

polyvinylpyrrolidone, 40 mg / mL Proteinase K, 0.2% β-mercaptoethanol) was added to the 

ground mycelia immediately and transferred to a 57°C water bath for 30 minutes, inverting 

frequently. The samples were brought to room temperature and 15 mL chloroform:isoamyl 

alcohol (24:1) (Millipore-Sigma) was added and incubated at room temperature for 10 minutes 

with shaking at 50 rpm. The samples were centrifuged for 30 minutes at 4,000 x g at 4°C and the 

supernatant was transferred to a new 50 mL conical tube using a wide orifice pipet tip. These 

steps were repeated with 15 mL of phenol:chloroform:isoamyl alcohol (25:24:1) (Millipore-

Sigma) once, followed by 15 mL chloroform:isoamyl alcohol (24:1) once. After the final 

 

113 

 

centrifugation, the supernatant was removed using a wide orifice tip and one-half volume of 

chilled 5M NaCl was added, followed by 2.5 volumes of chilled isopropanol. The samples were 

inverted to mix, and a final addition of 3 mL 3M sodium acetate was added and inverted again. 

The samples were stored in -20°C for a minimum of 30 minutes and the DNA was pelleted by 

centrifugation at 4,000 x g for 30 minutes at 4℃. The pelleted DNA was washed twice with 

freshly prepared, chilled 70% ethanol using a centrifugation at maximum speed for 1 minute. 

After the final wash, all ethanol was removed except for 1 mL, which was gently poured from 

the 50 mL conical tube to a 1.5 mL tube, along with the DNA pellet. The 1.5 mL tubes 

containing the DNA pellet were centrifuged at maximum speed for 1 minute and the remaining 

ethanol was decanted. The DNA pellet was resuspended in 600 µl of 10 mM Tris (pH 8.0) by 

incubating it overnight at room temperature, with gentle shaking. RNA contamination was 

removed by adding RNase A and incubating at 37℃ for 30 mins. The three chloroform:isoamyl, 

phenol:chloroform:isoamyl alcohol, and chloroform:isoamyl steps were repeated on a smaller 

scale in the 1.5 mL tubes, with a final DNA precipitation step using 0.5 volume chilled 5M NaCl 

and 1 volume chilled 100% ethanol. The DNA was precipitated at -20°C for 30 minutes, then 

centrifuged at max speed for 1 minute. The DNA pellet was washed twice with freshly prepared, 

chilled 70% ethanol. Finally, the DNA was resuspended in 200 µL 10 mM Tris (pH 8.0) and 

stored overnight at 4°C.  

 

To determine if the DNA extractions contained high molecular weight, non-fragmented 

DNA, 1 µL was loaded and separated on a 1% agarose gel using 75 V for 45 minutes, stained in 

1 µg / mL ethidium bromide for 45 minutes, and visualized on a Fotodyne FOTO/Analyst 

Investigator Eclipse (Thermo Fisher Scientific, Waltham, MA) (Fig. 6-3). Once DNA was 

determined to be high molecular weight, quantity was assessed using a Qubit fluorometer 

 

114 

 

(Thermo Fisher). To examine the presence of contaminants, 2 µL of DNA was assessed via a 

Nanodrop 1000 (Thermo Fisher) to obtain wavelength readings at 230, 260, and 280 nm (Table 

6-2). 

 

High molecular weight DNA was submitted to the MSU Research Technology Support 

Facility. Libraries for each sample were prepared using RBK004 rapid barcoding kit and 

sequenced on the GridION (Oxford Nanopore, Oxford, UK). Real-time bases were called using 

Guppy v1.4.3 and re-called using Albacore v2.3.3 (Oxford Nanopore). Genome assemblies were 

done using the canu pipeline using the ‘genomeSize=50m’ estimated from previously published 

F. virguliforme genomes (Srivastava et al., 2014) and the ‘ovlMerThreshold=500’ option to 

prevent the algorithm from setting an automatic kmer that is too high for the available data, 

causing significant increases in computation time. The N50 length for each genome was 

calculated with the Quality Assessment Tool for Genome Assemblies (QUAST) (Gurevich et al., 

2013). Genome completeness was calculated with the benchmarking universal single-copy 

orthologs (BUSCO) tool, using the F. graminearum dataset as a reference (Simão et al., 2015). 

Genome assembly graphs were visualized with BANDAGE v0.8.1 to examine connections 

between contigs that are not intuitive from sequences alone (Wick et al., 2015). Whole-genome 

alignments were done using the ‘minimap2’ algorithm on the D-GENIES platform (Cabanettes 

and Klopp 2018).  

 

 

 

 

115 

 

 

F. virguliforme
detected 
(28 counties
since 2008)

F. brasiliense
detected 
(7 counties 
since 2014)

Soybean Acres by County

Dry Bean Acres by County

< 20,000 
20,000 – 40,000
40,000 – 60,000
60,000 – 80,000
> 80,000

< 2,000 
2,000 – 8,000
8,000 – 14,000
14,000 – 20,000
> 20,000

Figure 6-1. Soybean and dry bean production acreage in Michigan, and geographic 
distribution of F. virguliforme and F. brasiliense.

 

116 

Isolatea 

Species 

Location 

Host 

VB-2a 

F-14-77 

F. 
virguliforme 
F. 
virguliforme 

DBP27R13  F. 

Van Buren 
County, MI 
Ingham  
County, MI 
Van Buren 
County, MI 
Van Buren 
County, MI 
Piatt County,  
IL 
 

County, MI 

virguliforme 
F. 
virguliforme 
F. 
virguliforme 
 
F. brasiliense  Saginaw  

F. brasiliense  Saginaw  

County, MI 
F. brasiliense  Montcalm 
County, MI 
F. brasiliense  Montcalm 
County, MI 

DBP30R5 

Mont-1 

 
F-15-158 

F-16-137 

F-16-21 

F-16-59 

 

[DNA]b  A260 / 
A280c 
1.88 

133 

A260 / 
A230c  N50d  Genome 
Size 
53 Mb 
2.38 

1.23 

Contigs  Coverage  BUSCOe  Reference 

66   19.3 X 

66.6% 

This study 

86 

 1.92 

 2.46 

4.61 

53 Mb 

22 

29.3 X 

73.3% 

This study 

143 

 1.89 

 2.48 

4.43 

53 Mb 

25 

39.1 X 

77.6% 

This study 

146 

 1.90 

 2.41 

0.66 

53 Mb 

148 

19.7 X 

63.6% 

This study 

NA 

 
73 

NA 

 
1.89 

NA 

0.07 

 
2.35 

 
4.25 

51 Mb 

 
55 Mb 

3,098 

20 X 

 
40 

 
28.7 X 

99.2% 

 
75.7% 

Srivastava 
et al., 2014 
 
This study 

192 

 1.80 

 2.28 

3.20 

53 Mb 

37 

17.6 X 

64.2% 

This study 

114 

 1.83 

 2.43 

1.20 

53 Mb 

67 

12.4 X 

57.6% 

This study 

296 

 1.74 

 2.25 

3.69 

54 Mb  

43 

26.5 X 

73.3% 

This study 

Glycine 
max 
Glycine 
max 
Phaseolus 
vulgaris 
Phaseolus 
vulgaris 
Glycine 
max 
 
Phaseolus 
vulgaris 
Phaseolus 
vulgaris 
Glycine 
max 
Glycine 
max 
Glycine 
max 

NA 

NA 

NA 

0.01 

49 Mb 

F. brasiliense  Distrito Federal, 
Brasilia, Brazil 

NRRL 
31757 
Table 6-2. Isolate information, DNA extraction metadata, and assembly assessments for isolates sequenced in this study. 
a Isolates in this study were collected by Dr. J. Wang, Dr. M. Chilvers, and J. Jacobs. 
b DNA concentration in nanograms per microliter 
c Absorbance ratios at 230, 260, and 280 nm, indicative of DNA quality and purity 
d N50 of raw corrected contigs, in Mbp, calculated with QUAST 
e Percent of universal single-copy orthologs detected in each genome

22,229 

87.4% 

150 X 

Huang et 
al., 2016 

 

117 

 

Results 

 

Both F. virguliforme and F. brasiliense caused significantly higher root rot on soybean 

and dry bean compared to the non-inoculated sorghum (Fig. 6-2 A, P < 0.10). However, neither 

pathogen caused significant reduction in dry weights of roots nor shoots (Fig 6-2 B, C). Pathogen 

quantification did not suggest a host preference, as F. brasiliense colonized both soybean and dry 

bean significantly more than F. virguliforme did (Fig. 6-2 D).  

Soybean

Dry Bean

0.6

0.4

0.2

0

Week 1 Week 2 Week 3

Week 1 Week 2 Week 3

A

Soybean

Dry Bean

a

ab

b

a

ab

b

a
a

b

60

40

20

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a
a

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Week 1 Week 2 Week 3

Week 1 Week 2 Week 3

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1.5

1.0

0.5

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Week 1 Week 2 Week 3

Dry Bean

Soybean

F. brasiliense

F. virguliforme

Non-inoculated control

F. brasiliense

F. virguliforme

Figure 6-2. Results of factorial inoculation experiment with F. virguliforme and F. 
brasiliense on soybean and dry bean. Pathogen inoculation had an overall significant effect on 
root rot (P < 0.05) and these effects are plotted over time (A). No effect of pathogen on root dry 
weight or shoot dry weight were observed (B, C). Fusarium brasiliense was significantly higher 
in abundance in both soybean and dry bean roots compared to F. virguliforme (D). 
**** = P < 0.001; * = P < 0.05 
 
 

 

 

118 

 

High quality high molecular weight DNA was successfully obtained from purified fungal 

mycelia (Fig. 6-3, Table 6-2). Compared to the best existing F. virguliforme and F. brasiliense 

genomes, the genomes developed here had lower BUSCO scores, but a larger estimated genome 

size, larger contig N50 values, and fewer contigs (Table 6-2). In addition, BANDAGE plots of 

genome assemblies developed in this study indicate near-complete genomes, with few 

problematic regions (Fig. 6-4).  

F. virguliforme

F. brasiliense

F-16-137

F-15-158

F-16-21

F-16-59

DBP27R13

DBP30R5

VB-2a

F-14-77

kb
48.5

3

0.5

Figure 6-3. High molecular weight DNA extractions for F. virguliforme and F. brasiliense 
used in whole-genome sequencing with long-read Nanopore technology. 
 
 

 

119 

 

 

 

 

Genome-wide comparisons of each species within and across hosts revealed significant 

similarities in genome structure within F. virguliforme isolates (Fig. 6-5 A). In contrast, F. 

brasiliense isolates had unique structural variations, even if they were collected from the same 

host (Fig. 6-5 B). Finally, soybean isolates of F. virguliforme and F. brasiliense seem to have 

very similar genome structures, while dry bean isolates appear more to have more structural 

variation (Fig. 6-5 C).  

F-14-77 (F. virguliforme)

F-16-137 (F. brasiliense)

 
Figure 6-4. Visual representation of a F. virguliforme and F. brasiliense genome assembly in 
this study using BANDAGE. Isolate F-14-77 consists of 22 contigs and has very few regions of 
sequence complexity that needs to be resolved. Isolate F-16-137 consists of 37 contigs and has 
one region of strong sequence complexity that could be resolved through additional sequencing 
efforts. 
 
 
Discussion 

 

Both F. virguliforme and F. brasiliense are capable of infecting soybean and dry bean, 

and the factorial growth chamber experiment in this study indicated that significant root rot 

occurred in both plants due to both pathogens (Fig. 6-1). However, isolation frequencies in 

Michigan root rot surveys suggest a potential host preference (Table 6-1). Quantification of 

pathogen DNA in root systems did not support this host preference hypothesis, as F. brasiliense 

colonized soybean and dry bean roots significantly more than F. virguliforme (Fig. 6-2 D). 

 

120 

 

Therefore, the isolation frequencies of F. virguliforme from soybean and F. brasiliense from dry 

bean is more likely due to the overlapping acreage of soybean and dry bean (Fig. 6-1, Table 6-1). 

In addition, the smaller range of F. brasiliense could be due to a more recent introduction, while 

F. virguliforme has been present and spreading for a longer period of time. 

 

Since its discovery in 1971 in Arkansas, F. virguliforme has spread to all soybean 

producing areas of the U.S. (Hartman et al., 2015). Isolates of F. brasiliense have been collected 

from symptomatic soybeans in Brazil since at least 1992, but was not formally named its own 

species since 2005 (Aoki et al., 2005). The recent discovery of F. brasiliense in Michigan 

(Jacobs et al., 2018; Wang et al., 2018b), coupled with the significant root rot and colonization in 

soybean and dry bean (Fig. 6-2) suggests that if F. brasiliense continues to spread, soybeans and 

dry beans may be faced with a significant risk of root rot and SDS. A qPCR assay for F. 

brasiliense has been developed and validated and should be widely deployed to actively monitor 

the spread of F. brasiliense (Roth et al., 2019). It remains to be seen whether F. virguliforme 

management strategies like crop rotation (Leandro et al., 2018), fungicide seed treatments 

(Kandel et al., 2016, 2018), and genetic resistance (Chang et al., 2018) will also be effective 

against F. brasiliense.  

 

Genome quality can be assessed in many ways. High-quality genomes for Fusarium plant 

pathogens like F. graminearum, F. oxysporum, F. fujikuroi, and F. verticillioides (Cuomo et al., 

2007; Jeong et al., 2013; King et al., 2015; Ma et al., 2010), but the quality of the F. virguliforme 

and F. brasiliense are lower (Huang et al., 2016; Srivastava et al., 2014). First, the number of 

contiguous sequences from current assemblies are over 3000, making it impossible to determine 

if pathogenicity chromosomes associated with host range exist as they do in F. oxysporum (Ma et 

al,. 2010; Sperschneider et al., 2015; van Dam et al., 2017; Vlaardingerbroek et al., 2016a, 

 

121 

 

2016b). Additionally, neither F. virguliforme or F. brasiliense genomes are annotated with RNA-

seq data, so gene prediction algorithms are not guided, and important genes involved in 

pathogenicity or fungicide resistance may be incorrect or missed altogether. Despite this 

drawback, benchmarking using single copy ortholog (BUSCO) analyses suggest that both 

existing genomes are relatively complete, having over 87% of the core single-copy orthologs 

found in F. graminearum (Table 6-2). The genome assemblies generated in this study have a 

significant reduction in contigs because we used Nanopore’s long-read technology (Table 6-2). 

However, a formal karyotype is needed to determine how close these assemblies are to being 

chromosome-level.  

 

The most commonly cited flaw of Nanopore sequencing are the high error rates. 

Therefore, it was interesting to see BUSCO scores that were all > 57% and as high as 77% 

(Table 6-2). The best genome assemblies often use hybrid approaches with long-read 

technologies for scaffolding and generating contigs, along with sequencing-by-synthesis 

technologies like Illumina that can correct sequence errors and allow stronger confidence in gene 

predictions and BUSCO analyses. Incorporating Illumina-type sequencing data to these genome 

assemblies should provide the best genome assemblies for these species to date.  

 

An investigation by Hughes et al. (2014) showed that global F. virguliforme isolates only 

have a single mating type, while F. brasiliense isolates have both. Therefore, it was not 

surprising to see the strong conservation of genome structure within F. virguliforme isolates that 

are likely asexually reproducing (Fig. 6-5 A). Likewise, the structural variation seen in F. 

brasiliense is not very surprising as these isolates are likely sexually reproducing (Fig. 6-5 B), 

since both mating types of F. brasiliense isolates have been identified in the same Michigan field 

(Oudman et al., in prep). However, the strong conservation of genome structure between F. 

 

122 

 

virguliforme and F. brasiliense soybean isolates is surprising, though the conservation is not seen 

in dry bean isolates (Fig 6-5 C). Without chromosome-level assemblies, lineage specific 

pathogenicity chromosomes cannot be confirmed or refuted. However, the strong conservation 

between soybean and dry bean isolates of F. virguliforme suggests that there are no soybean- or 

dry bean-specific pathogenicity chromosomes. Instead, it is more likely that both pathogens are 

equally capable of infecting both hosts given the right environment.  

 

 

123 

A

F. virguliforme (F-14-77, Soybean)

F. virguliforme (F-14-77, Soybean)

 

B

F. brasiliense (F-16-137, Dry bean)

F.
 
v
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(

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8
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)

F. brasiliense (F-16-137, Dry bean)

C

F. virguliforme (F-14-77, Soybean)

F. brasiliense (F-15-158, Dry bean)

F.
 
b
r
a
s
i
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i
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s
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(
F
-
1
6
-
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Identity

1

0.75 0.5 0.25

0

F.
 
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b
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D
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Figure 6-5. Whole genome alignments using the ‘minimap2’ algorithm, plotted with D-
GENIES. Fusarium virguliforme isolates appear clonal across hosts (A), while F. brasiliense 
shows some variation in genome structure across both hosts (B). Comparisons between F. 
virguliforme and F. brasiliense within hosts show more conservation for soybean than for dry 
bean (C). 

 

 

124 

 

Acknowledgements 

 

The authors would like to thank Z. Noel, B. Schwessinger, S. K. K. Raju, C. E. 

Niederhuth, and E. Patterson for helpful comments and suggestions regarding high molecular 

weight DNA extractions, genome sequencing, genome assemblies, and assessing genome 

quality. This work was funded in part by the Michigan Soybean Promotion Committee, the 

United Soybean Board, the North Central Soybean Research Program, the Syngenta Agricultural 

Scholarship, and the Turk Endowed Fellowship Award. 

 

 

 

 

125 

CHAPTER 7: Towards a mechanistic understanding of two SDS effector proteins: FvTox1 

 

and FvNIS1 

By 

Mitchell G. Roth 

 

This work presented in this chapter is unpublished. 

 

 

126 

 

Abstract 

 

Fusarium virguliforme causes soybean sudden death syndrome (SDS) in the U.S., but 

also establishes relationships with other agronomically important crops and common field weeds 

without causing above-ground symptoms. In soybean, the soil-borne pathogen infects roots 

causing initial root rot symptoms, later followed by foliar symptoms marked by interveinal 

chlorosis and necrosis collectively known as SDS symptoms. These SDS symptoms are thought 

to be caused by effector proteins that enter xylem during the root infection, since F. virguliforme 

remains in the roots throughout infection. Two effector proteins, FvTox1 and FvNIS1, have been 

identified as important factors in foliar SDS development, but their modes of action remain 

unknown. Here, we explored FvTox1 and FvNIS1 production in two heterologous systems to test 

their ability to induce SDS-like symptoms in stem-cut assays. Neither FvTox1 nor FvNIS1 alone 

were able to induce SDS symptoms at the protein concentrations tested. However, transient 

expression of FvTox1 and FvNIS1 in tobacco revealed that FvNIS1 can cause a hypersensitive 

response, while FvTox1 causes no reproducible symptoms. Finally, we propose multiple 

mechanisms that alternate hosts may employ to evade SDS-like symptoms. 

 

Introduction 

 

Fusarium virguliforme is a soil-borne Ascomycete that is responsible for causing sudden 

death syndrome (SDS) in soybean. With severe infections, SDS can cause 100% yield loss, and 

is considered one of the top five most yield limiting diseases of soybean in the U.S. (Allen et al., 

2017; Hartman et al., 2015; Koenning and Wrather 2010). Infection occurs in the soybean root 

and the plant symptoms it induces are two-fold, starting with root rot and later causing 

interveinal chlorosis and necrosis of the foliar tissues. To date, the pathogen has not been 

 

127 

 

isolated from above-ground tissues (Roy et al., 1989), so foliar symptoms are thought to be 

caused by pathogen secretion of phytotoxic effector proteins into the xylem. Once secreted into 

the host, these proteins translocate from the roots to the leaves and induce SDS foliar symptoms 

through currently unknown mechanisms. A study by Abeysekara and Bhattacharyya (2014) 

found fungal-derived proteins in the xylem sap of soybeans infected by F. virguliforme to 

support this hypothesis. Three effector proteins have been identified, but only two have been 

characterized (Brar et al., 2011; Chang et al., 2016a; Jin et al., 1996; Pudake et al., 2013). These 

two characterized effectors were named FvTox1 and FvNIS1. Both contain predicted N-terminal 

secretion signals, and knocking out either of these genes in F. virguliforme reduces its ability to 

induce SDS symptoms of soybeans in stem-cut and inoculation assays (Chang et al., 2016a; 

Pudake et al., 2013).  

 

Management practices for F. virguliforme include long-term crop rotations (Leandro et 

al., 2018), fungicide seed treatments (Kandel et al., 2016, 2018), and genetic resistance, though 

genetic resistance is partial and controlled by complex QTLs (Chang et al., 2018). Exploring the 

molecular mechanisms of effector-induced SDS development could reveal specific mechanisms 

that could be targeted in breeding or transgenic efforts to improve soybean genetic resistance to 

SDS. Agrobacterium-mediated transformation of soybean is historically very challenging, which 

makes transient expression assays commonly used to study effector function impossible 

(Yamada et al., 2012). Though transient expression assays in somatic soybean embryos have 

been reported (Nishizawa et al., 2006), transient expression in actively growing soybean leaves 

has not been reported. To circumvent these apparent limitations, researchers studying soybean 

SDS have developed a protocol to artificially induce foliar SDS in soybeans without live F. 

virguliforme cells. This “stem-cut” assay utilizes soybean plantlets cut just above the soil line 

 

128 

 

and spent, filter-sterilized F. virguliforme liquid cultures, called cell-free culture filtrates, which 

significantly increases soybean cell death (Li et al., 1999; Xiang et al., 2015). Cell-free culture 

filtrates from fvtox1 and fvnis1 knockout mutants show significantly less foliar SDS symptoms, 

suggesting that FvTox1 and FvNIS1 play a role in SDS development but are not necessary for 

SDS (Chang et al., 2016a; Pudake et al., 2013). If FvTox1 or FvNIS1 are sufficient to produce 

SDS symptoms, purified FvTox1 or FvNIS1 protein would induce SDS-like symptoms in stem-

cut assays. It is important to determine the mechanism by which FvTox1 and FvNIS1 trigger 

SDS symptoms so that scientists can accurately predict new ways to intervene and generate 

soybean cultivars resistant to SDS.  

 

To study pathogen effector function, the Agrobacterium-Nicotiana model system is often 

employed. Tobacco, Nicotiana benthamiana, is very susceptible to Agrobacterium tumefaciens 

transformation and therefore can be used to study the effect of pathogen gene expression in 

planta (Sparkes et al., 2006). However, non-host resistance is the most robust form of plant 

resistance to microbial pathogens because it includes a broad spectrum of genetic mechanisms 

(Senthil-Kumar and Mysore 2013). Fusarium virguliforme is capable of infecting the roots of at 

least 15 other common field crops and weed species (Kolander et al., 2012), but no reports have 

investigated whether tobacco is a host for F. virguliforme. Therefore, using tobacco to study F. 

virguliforme effector function provides convenient methods, but results may not be directly 

applicable to soybean.  

 

The major objective of this study was to investigate the molecular mechanisms of 

FvTox1- and FvNIS1-induced SDS symptoms. Three approaches were used; 1) heterologous 

expression of effectors using the model yeast Pichia pastoris for purification and use in stem-cut 

assays, 2) heterologous expression of effectors using the model bacterium E. coli for purification 

 

129 

and use in stem-cut assays, and 3) transient expression of effector genes in tobacco using A. 

 

tumefaciens-mediated transformation.  

 

Materials and Methods 

Heterologous expression in Pichia pastoris 

 

The model yeast Pichia pastoris has been used for heterologous production of proteins in 

multiple plant-pathogen related studies (Kemen et al., 2005; Lacerda et al., 2016; Liu et al., 

2016). This model yeast has a strong methanol inducible promoter, AOX1, which can be cloned 

upstream of a gene-of-interest and induced in Pichia cultures with the addition of methanol 

(Pichia Expression Kit User Guide 2014). Briefly, FvTox1 and FvNIS1 cDNAs were obtained 

using RT-PCR from isolated total RNA. The cDNA was amplified using modified primers to 

incorporate a KpnI cut site upstream of the genes and an ApaI cut site downstream of the genes. 

For full-length FvTox1 and cFvTox1, the primer sequences were 5’ CCA CCA GGT ACC GGT 

ATG AAG TCC ACA TTC ACC CTT GCG 3’ and 5’ CCA CCA GGG CCC CTG TGG GTT 

GCG CAC ACA GTT G 3’. For cFvNIS1, the primer sequences were 5’ CCA CCA GGT ACC 

GGT ATG CGC TTC TCC CTC GCC C 3’ and 5’ CCA CCA GGG CCC CTG GCT GGC CTT 

GTA CTC CTT GC 3’. All three genes were individually cloned into pPICZ-A, which contains 

the AOX-1 promoter and the BleoR gene, conferring resistance to Zeocin (Pichia expression 

vectors for selection on Zeocin and purification of recombinant proteins 2010). These constructs 

were linearized by digestion with the restriction SacI-HF (New England Biolabs, Ipswich, MA) 

and transformed into Pichia pastoris strain X-33 using electroporation according to the Pichia 

provider protocol (ThermoFisher Scientific, Waltham, MA). Transformed Pichia were incubated 

on YPDS plates containing 100 µg / µL Zeocin for 2 days at 30°C in the dark. Putative 

 

130 

 

transformants were confirmed for the presence of FvTox1 and FvNIS1 using PCR with the 

primers AOX-1-F (5’ GAC TGG TTC CAA TTG ACA AGC 3’) and AOX-1-R (5’ GCA AAT 

GGC ATT CTG ACA TCC 3’).  

 

Transformed Pichia isolates containing either FvTox1 or FvNIS1 were grown in liquid 

culture to log phase, then diluted to an OD600 of 1 in methanol medium in a baffled flask, 

according to the manufacturer protocol (Pichia Expression Kit User Guide 2014). One milliliter 

of culture was collected at regular time intervals of 0, 6, 12, 24, 36, 48, 60, 72, 84, and 96 hours, 

and methanol was added every 24 hours to maintain a final concentration of approximately 0.5% 

methanol in the culture (Pichia Expression Kit User Guide 2014). Since FvTox1 and FvNIS1 are 

predicted secreted proteins, 1 mL of collected cells were pelleted, and the cells and supernatant 

were analyzed separately. Amicon Ultra-15 spin filters were used to concentrate any proteins 

present in the supernatant according to manufacturer protocol (Millipore-Sigma, St. Louis, MO).  

 

Heterologous expression in Escherichia coli 

 

For expression of FvTox1 and FvNIS1 in E. coli, custom gene sequences were ordered 

using gBlocks Gene Fragments (Integrated DNA Technologies, Coralville, IA). Two versions of 

each cDNA was ordered, one with the secretion signal (FvTox1-full, FvNIS1-full) and one 

without the predicted secretion signal (FvTox1-noSS, FvNIS1-noSS). Each gene construct was 

codon optimized for expression in E. coli and a 6X-HIS tag and SUMO tag were fused to the N-

terminus of each gene. The 6X-HIS tag is intended for affinity chromatography purification and 

the SUMO tag was added to aid solubility (Kuo et al., 2014). After successful expression of the 

fusion protein, the HIS- and SUMO-tag can be cleaved in vivo by incubating with Ulp1 protease 

(Kuo et al., 2014). These custom gene fragments were cloned into pET11-b downstream of the 

 

131 

 

strong T7 promoter and transformed into E. coli strain BL21 DE3 using heat-shock 

transformation. Successful transformants were selected on LB agar containing 50 µg / mL 

carbenicillin (Research Products International, Mt. Prospect, IL). Transgenic E. coli cells were 

grown to an OD600 of 0.6 in 100 mL LB broth at 37°C. These cells were induced to express the 

proteins of interest by adding IPTG to a final concentration of 5 mM and transferring the cultures 

to a 15°C shaking incubator to aid solubility. After a 14 h incubation, the cells were pelleted, 

resuspended in 50 mM sodium-phosphate buffer with 50 mM NaCl (pH 8) or 50 mM HEPES 

buffer with 300 mM NaCl (pH 8), and lysed twice at 4°C in a high pressurize homogenizer 

(Constant Systems LTD, UK). The cell lysate was centrifuged at 20,000 x g for 30 minutes to 

pellet insoluble materials like DNA, lipids, and insoluble proteins. The supernatant containing 

soluble protein was mixed with SDS loading dye (250 mM Tris-HCl, 30% glycerol, 0.1 g / mL 

SDS, 0.33 mg / mL bromophenol blue, 0.05% β-mercaptoethanol), boiled at 98°C for 15 

minutes, and separated on a 4-20% gradient polyacrylamide gel (Bio-Rad Laboratories, 

Hercules, CA). The gels were stained for 1 hour in Coomassie blue (0.1 mg / mL Coomassie 

blue, 50% methanol, 40% water, 10% glacial acetic acid). The gels were destained in sterile 

water, with 2-minute microwave bursts until background Coomassie blue stain was removed. 

The predicted molecular weight of all four protein constructs of interest ranged between 26.5 and 

32.3 kDa.  

 

Attempts at purifying soluble HIS-tagged proteins were performed using Ni-NTA 

charged resin (ThermoFisher). Briefly, concentrated cell lysate was added to 1 mL Ni-NTA in a 

5 mL plastic centrifuge column (ThermoFisher). The column was washed twice with 5 mL of 50 

mM HEPES buffer containing 300 mM NaCl and 20 mM imidazole. The proteins bound to the 

Ni-NTA column were eluted with 3 mL of the same wash buffer, but now containing 300mM 

 

132 

 

imidazole. Due to low yields and non-specific binding of non-target proteins to the Ni-NTA 

column, eluted protein was not used in the stem cut assay, but rather total soluble protein from 

induced and non-induced E. coli cells.  

 

High salt conditions, like 300 mM NaCl, allowed protein solubility but can cause 

chlorosis in soybean (Pantalone et al., 1997). To ensure that soybean stem-cut phenotypes were 

due to the presence of FvTox1 or FvNIS1 instead of high salt concentrations, the 4 mL of soluble 

proteins were subjected to buffer exchange using a desalting column (Bio-Rad) and re-collected 

in 5 mL sterile water. Five milliliters of soluble protein in 300 mM salt or 5 mL of soluble 

protein in sterile water were diluted with 20 mL sterile water in a 50 mL conical tube and sealed 

with parafilm. Three-week old soybean stems (variety ‘Williams 82’) were cut at the soil line, 

and the cotyledons and primary leaves were removed, leaving a single trifoliate leaf. The stems 

were used to puncture the parafilm of the 50 mL conical tubes containing protein samples, and 

the plants were incubated in a growth chamber at 25°C. Trifoliate leaves were harvested and 

imaged after 7 days of incubation.  

 

Transient expression in Nicotiana 

 

Full-length FvTox1 cDNA was PCR amplified with the primers 5’ CAC CAT GAA GTC 

CAC ATT CAC CCT TGC G 3’ and 5’ CTG TGG GTT GCG CAC ACA G 3’, while full length 

FvNIS1 cDNA was PCR amplified with the primers 5’ CAC CAT GCG CTT CTC CCT CGC C 

3’ and 5’ CTG GCT GGC CTT GTA CTC CTT G 3’. These primers incorporated a ‘CACC’ 

motif that allowed for Gateway cloning of the blunt PCR fragments into pENTR/SD/D-TOPO 

(ThermoFisher). The pENTR plasmids containing FvTox1-full or FvNIS1-full were transformed 

into E. coli DH5α competent cells via heat-shock transformation and selected for on LB agar 

 

133 

 

containing 50 µg / mL kanamycin. Successful transformants were grown overnight in 5 mL LB 

broth, and pENTR plasmids were purified using a GeneJET Plasmid Miniprep Kit 

(ThermoFisher). pENTR vectors containing FvTox1 and FvNIS1 inserts were digested with ApoI 

to degrade the Kanamycin resistance gene. The linear pENTR construct containing the gene of 

interest was gel extracted and purified using a Wizard SV Gel and PCR Clean-up Kit (Promega 

Corporation, Madison, WI). The gene of interest in the linear pENTR construct was transferred 

into pEAQ-HT-DEST3 (Sainsbury et al., 2009) using the Gateway LR Clonase II kit 

(ThermoFisher) and transformed into E. coli DH5α cells via heat shock, and selected on LB agar 

amended with 50 µg / mL kanamycin. Successful insertion of FvTox1 or FvNIS1 into pEAQ-

HT-DEST3 was confirmed via PCR and Sanger sequencing, then transformed into 

Agrobacterium tumefaciens strain GV3101 using the freeze-thaw method (Weigel and 

Glazebrook 2006) and selected on LB agar amended with 20 µg / mL rifampicin, 10 µg / mL 

gentamycin, and 50 µg / mL kanamycin. Genes inserted into the pEAQ-HT-DEST3 incorporate a 

6X-HIS tag on the C-terminus of a protein of interest. Constructs lacking the secretion signal 

(FvTox1-noSS, FvNIS1-noSS) were not used in Agrobacterium infiltration.  

 

Agrobacterium isolates containing the desired vector were streaked onto LB agar 

amended with rifampicin, gentamycin, and kanamycin and incubated at room temperature for 2 

days to generate a lawn of bacteria. The cells were harvested by scraping the LB agar with a 1 

mL pipet tip and resuspending the cells in 900 µL infiltration buffer (10 mM MES buffer, 10 

mM MgCl2, 100 µM acetosyringone dissolved in DMSO, pH 5.8). The cells were diluted to an 

optical density at 600nm (OD600) of 0.6 with infiltration buffer and incubated in the dark for 2 h 

with gentle shaking. Three-week old N. benthamiana plants were wounded with an insect pin, 

and the A. tumefaciens cells were infiltrated into the wound using a 1 mL syringe. The plants 

 

134 

 

were incubated in a greenhouse for 7 days. Infiltration buffer with no A. tumefaciens cells was 

used as a negative control and A. tumefaciens containing the AvrRpt2 or AvrB gene was used as a 

positive control known to induce the hypersensitive response (HR). Three 4 mm diameter leaf 

punches were taken each day for 6 days for RT-PCR confirmation of transgene expression, 

which was highest at 2 days post infiltration (data not shown). Therefore, infiltrations were 

repeated and 20, 10 mm diameter leaf punches were taken 2 days post infiltration for protein 

extraction and co-immunoprecipitation (co-IP) using PureProteome Nickel Magnetic Beads or 

PureProteome Protein A/G Magnetic Beads (Millipore-Sigma) with mouse IgG1 monoclonal 

anti-6X-antibodies (ThermoFisher).  

 

pPicZ-A + 

empty

cFvNIS1

FvTox1

cFvTox1

2000
1500
1000
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650
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400
300
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100

2000
1500
1000
850
650
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Figure 7-1. Gel electrophoresis of DNA amplified from representative Pichia pastoris 
strains transformed with linearized pPICZ-A constructs containing one of three gene 
constructs of interest. 
 

 

 

 

135 

 

Results  

Heterologous expression in Pichia pastoris 

 

Pichia pastoris cells were successfully transformed with linear pPICZ-A DNA constructs 

containing full length FvTox1 or intron-less cDNA versions of FvTox1 and FvNIS1 (cFvTox1, 

cFvNIS1, Fig. 7-1). The expected sizes of the empty pPicZ-A vector, cFvNIS1, FvTox1, and 

cFvTox1 amplicons were 325, 727, 931, and 820 bp, respectively. However, a DNA fragment 

approximately 2 kb in length was observed in all Pichia transformants indicating that the genes 

of interest were inserted randomly in the Pichia genome and did not replace the native AOX1 

gene. Screening these isolates for protein expression upon induction with methanol indicated that 

neither FvTox1 nor FvNIS1 were produced in the supernatant (Fig. 7-2). In addition, no obvious 

signs of FvTox1 protein expression was observed at any time point inside cells that would have 

suggested a failure to secrete the protein (Fig. 7-3). Instead, this observation suggested that 

Pichia was not expressing these genes of interest at all. However, RT-PCR at 96 hours post-

induction did show some mRNA expression in some Pichia isolates (Fig. 7-4). However, 

significant protein production was never observed using transgenic Pichia pastoris cells, so this 

method was abandoned for further study of FvTox1 and FvNIS1. 

 

 

136 

r
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180kDa
130kDa
100kDa
70kDa
55kDa
40kDa
35kDa
25kDa

15kDa

10kDa

Figure 7-2. Gel electrophoresis of protein extracted from the supernatant of methanol-
induced Pichia pastoris strains after 96 hours. One strain of P. pastoris containing FvTox1 
was tested, while two P. pastoris strains each containing cFvTox1 or cFvNIS1 were tested for 
protein expression and are indicated by “-1” or “-2”. Left, protein from P. pastoris supernatant. 
Right, protein from P. pastoris supernatant after a 1:50 concentration with spin filters.  
 
 

 

0       6      12    18    24     36    48     60    72     84    96

70kDa
55kDa
40kDa
35kDa
25kDa

15kDa

10kDa

Figure 7-3. Gel electrophoresis of protein extracted from the cell pellets of methanol-
induced Pichia pastoris strain cFvTox1-1 over time. The protein profile does not change over 
time, suggesting no induction of heterologous protein expression due to the presence of 
methanol. 
 

 

 

137 

r
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d
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1
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2
-
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v
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2000
1500

1000
850
650
500

Figure 7-4. Gel electrophoresis of amplified cDNA after RNA extraction from the cell 
pellets of methanol-induced Pichia pastoris strains after 96 hours. Lane “Empty 1” is cDNA 
from P. pastoris containing the empty pPICZ-A vector amplified with FvTox1 primers, while 
“Empty 2” is the same cDNA amplified with FvNIS1 primers.  
 
 
Heterologous expression in Escherichia coli 

 

 

Escherichia coli cells were successfully transformed with pET11-b containing one of 

four transgenic constructs (Fig. 7-5). The addition of IPTG induced cells to express and produce 

the proteins of interest; however, these proteins were found to be insoluble in sodium phosphate 

buffer (Fig. 7-6 A). Buffer optimization suggested that three of the four proteins were highly 

soluble in 50 mM HEPES with 300 mM NaCl (Fig. 7-6 B, C). In all instances, protein 

production was specific upon induction with IPTG (Fig. 7-6 D), though in some instances the 

observed size of the protein constructs (35-40 KDa) was larger than the predicted molecular 

weight (Fig. 7-6 D).  

 

Many native E. coli proteins were soluble that were not of interest to us. Therefore, 

affinity chromatography was implemented to reduce the number of non-specific proteins and 

purify our proteins of interest. Protein yields were significantly lower than anticipated, and 

 

138 

although many soluble proteins were washed away, some proteins other than the desired protein 

product remained after affinity chromatography (Fig. 7-7). 

pET11-b +

FvTox1

full

FvTox1
noSS

FvNIS1

full

cFvNIS1
noSS

empty

 

2000
1500

1000
850
650
500

2000
1500

1000
850
650
500

 
Figure 7-5. Gel electrophoresis of amplified DNA from representative E. coli cells 
transformed with pET11-b plasmids containing one of four gene constructs. “Empty” 
indicates amplification from the empty pET11-b plasmid using FvTox1 primers. 
 
 
 

To determine if FvTox1 or FvNIS1 are sufficient to induce SDS, we wanted to use 

purified protein from induced E. coli cells in a stem-cut assay. Due to the low yields and non-

specific purification after affinity chromatograph (Fig. 7-7), we decided to use whole-protein 

extract from induced and non-induced E. coli cells (Fig. 7-6 D). We hypothesized that it is 

unlikely that any native E. coli proteins would be able to cause SDS-like symptoms in a stem-cut 

assay, while heterologously produced FvTox1 or FvNIS1 may cause SDS-like symptoms. 

However, solubility of FvTox1 and FvNIS1 was optimal in high salt concentrations, which alone 

can cause SDS-like symptoms (Pantalone et al., 1997). The solubility of FvTox1 and FvNIS1 did 

not appear to be affected by the buffer exchange process, though yield did decrease slightly (Fig. 

 

139 

7-8). However, none of the three de-salted protein constructs tested were sufficient to produce 

SDS-like symptoms, while all high-salt conditions did induce significant chlorosis (Fig. 7-9).  

 

Induced cells

Concentrated supernatant

B

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Tox1 full

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Tox1 noSS

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A

170 kDa
130 kDa
100 kDa
70 kDa
55 kDa
40 kDa
35 kDa
25 kDa

15 kDa

10 kDa

C

170 kDa
130 kDa
100 kDa
70 kDa
55 kDa
40 kDa
35 kDa
25 kDa

15 kDa

10 kDa

170 kDa
130 kDa
100 kDa
70 kDa
55 kDa
40 kDa
35 kDa
25 kDa

15 kDa

10 kDa

NIS1 full

NIS1 noSS

s
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Soluble!

Soluble!

Soluble!

Insoluble

Tox1 noSS

NIS1 full

NIS1 noSS

d
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170 kDa

130 kDa

100 kDa
70 kDa
55 kDa
40 kDa
35 kDa
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15 kDa

10 kDa

Figure 7-6. Gel electrophoresis of protein extracted from IPTG-induced E. coli cells 
transformed with pET11-b plasmids. In sodium phosphate buffer, none of the four proteins of 
interest were soluble (A). In HEPES buffer, Tox1 lacking the secretion signal (noSS) was soluble 
(B), as were both FvNIS1 constructs (C). Production of these three soluble proteins were 
specifically observed after the addition of the inducer, IPTG (D). 
 
 

 

 

 

140 

NIS1 full

NIS1 noSS

Tox1 noSS

 

h
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M
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Figure 7-7. Gel electrophoresis of protein samples after purification via affinity 
chromatography. The “flow through” lane shows proteins that had no affinity to the column 
and went straight through the column. The “Wash 20 mM” lane shows proteins that had non-
specific interaction with the column and were washed away in the presence of 20 mM imidazole. 
The “Elution 300 mM” lanes show three consecutive washes of the column with 2 mL of wash 
buffer containing 300 mM imidazole which releases proteins that had strong interactions with the 
column (i.e. 6X-HIS-tagged constructs and other proteins with strong positive charges).  
 
 

 

NIS1 noSS

 

 

+
S
E
P
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H
M
m
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5

 

l

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M
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3

r
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Tox1 noSS

NIS1 full

 

 

 

+
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5

 

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r
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170 kDa
130 kDa

100 kDa

70 kDa

55 kDa

40 kDa

35 kDa
25 kDa

15 kDa

10 kDa

Figure 7-8. Gel electrophoresis of protein samples extracted from IPTG-induced E. coli 
cells and after exchange into water (de-salting). These protein samples were used in functional 
stem-cut assays.  

 

 

141 

 

A

D

G

B

E

H

C

F

I

 
Figure 7-9. Soybean trifoliate leaves 7 days after inoculation with protein samples in stem-
cut assays. “Mont-1” is the culture filtrate positive control, while “HEPES + 300 mM NaCl” and 
“Water” were other controls. Any protein sample containing 300 mM NaCl showed interveinal 
chlorosis and some necrosis along the leaf edges. De-salted protein samples did not induce 
significant chlorosis or necrosis, similar to the water negative control.  
 
 

 

142 

 

Transient expression in Nicotiana 

 

Seven days after infiltration, obvious signs of chlorosis and necrosis were present in 

AvrRpt2 and FvNIS1 infiltrated regions of N. benthamiana leaves (Fig. 7-10 A). In contrast, very 

faint chlorosis was observed in some FvTox1 infiltrations, but not all (Fig. 7-10). No symptoms 

were observed in buffer-infiltrated regions (Fig. 7-10). The development of HR symptoms in 

FvNIS1-infiltrated leaves suggested specific interactions with host proteins that trigger 

programmed cell death. Interestingly, FvTox1 was able to induce chlorosis in N. tabacum. Co-IP 

experiments failed to purify any HIS-tagged FvTox1 or FvNIS1 from N. benthamiana using 

nickel based magnetic beads (Fig. 7-11 A) or anti-HIS antibodies and protein A magnetic beads 

(Fig. 7-11 B). Specific FvTox1 and FvNIS1 interacting proteins were not identified. 

 

143 

 

 

A

B

Figure 7-10. Tobacco leaves infiltrated with Agrobacterium tumefaciens strain GV3101. (A) Six leaf replicates from three N. 
benthamiana plants infiltrated with full-length FvTox1 (upper left), full-length FvNIS1 (upper right), AvrRpt2 (bottom left) or empty 
vector (bottom right), 10 days after infiltration. Chlorosis and spots of necrosis are frequently observed in AvrRpt2 and FvNIS1 
infiltrated leaves, while no symptoms are consistently observed in FvTox1 and empty vector infiltrations. (B) One leaf of N. tabacum 
infiltrated with the same constructs as in (A), except with AvrB replacing AvrRpt2. In N. tabacum, FvTox1 does cause chlorosis.  
 

 

 

144 

 

 

Protein not 

bound to beads
EV
NF

TF

Supernatant of 
bead wash step
EV
NF

TF

Protein eluted 
from beads

EV

TF

NF

A

kDa
180
130
100
70
55
40
35
25

15
10

Protein not 

bound to beads
EV
NF

TF

Supernatant of 
bead wash step

EV

TF

NF

Protein eluted 
from beads

EV

TF

NF

B

kDa

180
130
100
70
55
40
35
25

15
10

Figure 7-11. Co-immunoprecipitation (co-IP) of proteins 48 hours post A. tumefaciens infiltration of N. benthamiana leaves. 
(A) Co-IP using nickel-based magnetic beads, which have an affinity for 6X-HIS tags. (B) Co-IP using anti-6X-HIS antibody and 
protein A magnetic beads. In both co-IP experiments, protein samples from empty vector (EV), FvTox1 (TF), and FvNIS1 (NF) are 
identical, indicating a lack of FvTox1 or FvNIS1 expression.  
 
 

 

145 

 

Discussion 

 

Fusarium virguliforme can infect at least 15 common plants as asymptomatic hosts yet 

SDS-symptoms remain specific to soybean (Kolander et al., 2012). There are multiple 

mechanisms by which these alternate hosts may evade the FvTox1 chlorosis inducing functions. 

First, it is possible that some F. virguliforme effectors are not expressed in other hosts, or 

expression is suppressed by alternate hosts. Second, effectors might be expressed in alternate 

hosts, but F. virguliforme is not able to penetrate to the xylem tissue in other hosts and release 

effectors as it does in soybean (Abeysekara and Bhattacharyya 2014; Navi and Yang 2008). 

Third, effectors could be degraded by enzymes in the xylem of non-soybean hosts. Finally, 

effectors may enter xylem and reach foliar tissues in alternate hosts, but their targets could be 

absent or highly diverged, rendering the effectors ineffective. 

 

Multiple methods to produce large concentrations of FvTox1 and FvNIS1 were explored. 

Though P. pastoris and E. coli have been used in multiple heterologous expression experiments 

(Demain and Vaishnav, 2009; Kemen et al., 2005; Lacerda et al., 2016; Liu et al., 2016), neither 

system was highly productive for these two proteins. Pichia pastoris failed to produce any 

detectable protein (Fig. 7-2). Western blots are more sensitive than Coomassie staining, but 

western blots were not performed in this study. The Pichia strains in this study contained the 

native AOX1 gene (Fig. 7-1), which are known as “Mut+” strains, as opposed to the “MutS” 

strains where AOX1 is replaced by the gene of interest. “MutS” strains grow much slower, but are 

known to produce significantly more protein of interest than Mut+ strains and could explain the 

low protein production (Pichia Expression Kit User Guide 2014). It has also been suggested that 

FvTox1 and FvNIS1 effectors may be toxic to Pichia, which might explain a lack of production 

(J. Walton, personal communication). Escherichia coli was able to produce heterologous protein 

 

146 

 

detectable through Coomassie staining, but purification methods were insufficient and resulted in 

very low yields. True protein quantification with Bradford assays were not performed, so the 

amount of protein that soybeans were exposed to in stem-cut assays is unclear (Fig. 7-8, Fig. 7-

9). Therefore, there are two possible explanations for the lack of SDS-like symptoms in the stem-

cut assays. First, FvTox1 and FvNIS1 may be insufficient to produce SDS-like symptoms 

independently. Second, the protein concentration used in this study may not have been high 

enough to mimic the levels produced during an active F. virguliforme infection of a soybean. 

Repeated experiments with a higher range of protein concentrations could reveal if FvTox1 and 

FvNIS1 are sufficient to produce SDS-like symptoms.  

 

The Pseudomonas syringae effectors AvrRpt2 and AvrB induce HR in Nicotiana 

benthamiana (Bisgrove et al., 2007; Mackey et al., 2002; Kessens et al., 2014). Both do so by 

altering the state of RIN4, through either phosphorylation or degradation of RIN4, which is 

recognized by RPM1 and RPS2 respectively (Axtell et al., 2003; Axtell and Staskawicz, 2003; 

Mackey et al., 2002, 2003). However, many plant pathogen effectors have unknown targets and 

are not known to alter RIN4. Instead, recently identified leucine-rich repeat (LRR) receptor-like 

proteins have been implicated in recognizing effector function, though classical genetic evidence 

is still lacking (Swaminathan et al., 2019). The development of HR in FvNIS1 infiltrated leaves 

indicates that FvNIS1 interacts with or alters a specific host protein, perhaps a LRR containing 

receptor that triggers an immune response. An NIS1 ortholog in Colletotrichum orbiculare was 

also shown to trigger HR in Nicotiana, with NIS1-induced HR suppressed by C. gloeosporioides 

protein DN3 (Yoshino et al., 2012). In addition, full-length CgNIS1 was required to induce HR 

in tobacco, while CgNIS1 lacking the secretion signal failed to induce HR (Yoshino et al., 2012). 

Infiltration of FvTox1 constructs failed to produce reproducible chlorosis or necrosis in N. 

 

147 

 

benthamiana. This result is in contrast to previous findings that showed FvTox1 protein 

expressed from insect cell lines were able to cause two-fold reduction in chlorophyll in soybean 

leaf discs (Brar et al., 2011). However, FvTox1 did show chlorosis in N. tabacum leaves. This 

difference suggests that FvTox1 functions with a specific mode of action rather than a general 

toxicity, where the specific target is present in N. tabacum and absent in N. benthamiana. These 

results indicate that the target of FvNIS1 may be conserved between soybean, N. benthamiana, 

and N. tabacum, while the target of FvTox1 may be conserved only between soybean and N. 

tabacum.  

 

Fusarium virguliforme is generally considered a hemibiotrophic pathogen, infecting 

living tissue and also persisting in dead tissue. In addition, F. virguliforme is quite a successful 

saprophyte, able to grow on water agar (data not shown), overwinter in corn and soybean residue 

(Navi and Yang 2016), and colonize nematodes (Donald et al., 1993; McLean and Lawrence, 

1995). Finally, some may argue that F. virguliforme is a necrotroph because it causes cell death 

throughout soybean plants; root and foliar necrosis (Hartman et al., 2015). The life strategy of F. 

virguliforme is appears to transition between hemi-biotrophic, saprotrophic, and necrotrophic, 

which makes it a challenge to understand what roles of FvTox1 and FvNIS1 play during the 

host-pathogen interaction. To invade living root tissue, biotrophic pathogens release effectors to 

dampen plant immune signaling. To feed on dead tissues, necrotrophs purposefully induce cell 

death. Does F. virguliforme release these effectors to purposefully kill soybean plants and feed 

off the dead tissues in the soil? Does F. virguliforme release the effectors to thrive in the root 

system undetected, eventually causing the plant to succumb to the disease? Until a mechanistic 

understanding of FvTox1 and FvNIS1 exists, these questions will remain unanswered. 

 

 

148 

 

Acknowledgements 

 

The authors would like to thank H.-X. Chang, J. Walton, L. Danhoff, and J. MacCready 

for helpful suggestions regarding heterologous expression of proteins, protein purification, and 

Agrobacterium infiltration. This work was funded in part by the Michigan Soybean Promotion 

Committee, the United Soybean Board, the North Central Soybean Research Program, and the 

Syngenta Agricultural Scholarship. 

 

149 

 

APPENDIX 

 

150 

APPENDIX 

 

A list of additional manuscripts published or prepared for publication. 

 

 

Kuhlgert, S., Austic, G., Zegarac, R., Osei-Bonsu, I., Hoh, D., Chilvers, M.I., Roth, M.G., Bi, 

K., TerAvest, D., Weebadde, P., Kramer, D.M. 2016. MultispeQ Beta: a tool for large-
scale plant phenotyping connected to the open PhotosynQ network. Royal Society Open 
Science. DOI 10.1098/rsos.160592. 

 
All authors made substantial intellectual contributions to the work in this paper. S.K., G.A., R.Z., 

K.B., P.W. and D.M.K. contributed to the design, testing, programming and construction of the 

platform. All authors contributed to the design and implementation of the experiments. S.K., 

G.A., D.T., M.I.C. and D.M.K. contributed to the interpretation and analyses of the data.  

 

Chang, H.-X., Roth, M.G., Wang, D., Lightfoot, D.A., Hartman, G.L., Cianzio, S.R., Chilvers, 

M.I. 2018. Integration of Sudden Death Syndrome Resistance Loci in the Soybean 
Genome. Theoretical and Applied Genetics. https://doi.org/10.1007/s00122-018-3063-0. 

 
HXC integrated the literature, presented the data, drafted and wrote the manuscript. MGR 

reviewed the data and contributed to the manuscript. DW, SRC, DAL, GLH, and MIC reviewed 

and contributed to the manuscript.  

 

Wang, J., Jacobs, J.L., Roth, M.G., Chilvers, M.I. 2018. Temporal dynamics of Fusarium 

virguliforme colonization of soybean roots. Plant Disease. https://doi.org/10.1094/PDIS-
03-18-0384-RE. 

 
JW, JLJ, and MGR collected and processed data. JW analyzed data, generated figures, and 

drafted the manuscript. JW, JLJ, MGR, and MIC contributed to the manuscript content.  

 

 

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Sang, H., Witte, A., Jacobs, J.L., Chang, H.-X., Wang, J., Roth, M.G., and Chilvers, M.I. 2018. 

Fluopyram sensitivity and functional characterization of SdhB in the Fusarium solani 
species complex causing soybean sudden death syndrome. Frontiers in Microbiology. DOI 
10.3389/fmicb.2018.02335. 

HS, AW, JJ, H-XC, and MIC designed the research. HS and AW performed the experiments and 

analyzed the data. JJ, JW, H-XC, and MGR provided input on the experiments. HS and MIC 

wrote the manuscript. AW, JJ, JW, H-XC, and MGR edited the manuscript. 

 

Strock, C.F., Schneider, H.M., Galindo-Castañeda, T., Hall, B.T., Van Gansbeke, B., Mather, 
D.E., Roth, M.G., Chilvers, M.I., Guo, X., Brown, K., and Lynch, J.P. Laser Ablation 
Tomography for Visualization of Root Colonization by Edaphic Organisms. Journal of 
Experimental Botany. Submitted 11/14/18. 

 
Conceived the idea: JPL. Designed the experiments: CFS, TGC, KMB and JPL. Conducted 

experiments and collected data: CFS, TGC, XG, HMS. Analyzed data: CFS. Interpreted data: 

CFS, HMS, TCG, BVG, DEM, MR, MC. Wrote the manuscript: CFS, HMS and TGC with 

participation of JPL, KMB, BTH, BVG, DEM, MR, MC, XG. 

 

Roth, M.G., Sang, H., Oudman, K., Jacobs, J.L., Griffin, A., and Chilvers, M.I. Diagnostic 
qPCR Assay to Detect Fusarium brasiliense, a causal agent of soybean Sudden Death 
Syndrome and Root Rot of Dry Bean. Plant Disease. Accepted 2/27/19. 

 
MGR, JLJ, and MIC formalized the objectives of the project. MGR designed primers, probes, 

and reaction conditions. MGR, KAO, AG, and HS used and validated the method. MGR 

analyzed data and generated figures. MGR and MIC wrote the manuscript. 

 

*McCoy, A.G., *Roth, M.G., *Shay, R., *Noel, Z.A., Jayawardana, M.A., Longley, R.W., 
Bonito, G., and Chilvers, M.I. Next Generation Sequencing Identification of Fungal 
Communities Within the Tar Spot Complex of Corn in Michigan. Phytobiomes. Submitted 
3/9/19. 

 

 

152 

AGM, MGR, RS, MAJ, GB, and MIC planned experiments. AGM, MGR, RS, and MAJ and 

collected and processed samples. RWL performed PCR and constructed libraries. AGM 

submitted raw sequences to Sequence Read Archives. MGR and ZAN analyzed data and 

generated figures. AGM, RS, MGR, ZAN, and MAJ wrote the manuscript. * indicates co-first 

 

authors. 

 

Roth, M. G. and Chilvers, M. I. A Protoplast Generation and Transformation Method for 

Soybean Sudden Death Syndrome Causal Agents Fusarium virguliforme and F. 
brasiliense. Fungal Biology and Biotechnology. Accepted 4/21/19.  

 
MGR and MIC developed the ideas to generate this protocol. MGR designed primers and 

plasmids, performed the experiments, and acquired all images. MGR and MIC wrote and revised 

the manuscript, approving of the final draft. 

 

Noel, Z.A., Sang, H., Roth, M.G., and Chilvers, M.I. Convergent evolution of C239S mutation 

in Pythium spp. β-tubulin coincides with inherent insensitivity to ethaboxam. 
Phytopathology. Submitted 4/5/19. 

 
ZAN and MIC outlined objectives for this project. ZAN performed all experiments, collected 

data, and analyzed data. HS and MGR provided insight and guidance for experimental 

procedures. ZAN wrote the manuscript with contributions from HS, MGR, and MIC.  

 

Roth, M. G., Noel, Z. A., Wang, J. Warner, F. Byrne, A. M. and Chilvers, M. I. Predicting 

Soybean Yield and Sudden Death Syndrome Development using At-planting Risk Factors. 
Phytopathology. Accepted 4/28/19. 

 
MGR, JW, and MIC designed the experiment, MGR, JW, FW, and AMB collected data, MGR 

and ZAN analyzed data and generated figures, MGR, ZAN, and MIC wrote the manuscript. 

 

 

153 

Roth, M.G., Jacobs, J.L., Napieralski, S., Byrne, A.M., Warner. F., Chilvers, M.I. Effect of 

Fluopyram on Heterodera glycines development in Soybean. Submitted 4/29/19. 

 
AMB and MIC formalized the objectives of this project. JLJ, SN, AMB, and FW performed all 

 

experiments and collected all data. MGR analyzed all data and wrote the manuscript, with 

contributions from JLJ, SN, AMB, FW, and MIC.  

 

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