POPULATION GENETICS, INTRASPECIFIC GROUPS, AND HISTOPATHOLOGY  
OF RHIZOCTONIA SOLANI AG 2-2 ON SUGAR BEET 

By 

Douglas H. Minier 

A DISSERTATION 

Submitted to 
Michigan State University 
in partial fulfillment of the requirements 
for the degree of 

Plant Pathology – Doctor of Philosophy 

2023 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ABSTRACT 

Sugar beet is grown in temperate regions around the world for the sucrose that accumulates 

in its root tissues and accounts for about 12% of global sugar production. In the United States, 

sugar beet accounts for roughly half of domestic sugar production with an estimated value of 

around $1.9 billion annually. However, Rhizoctonia root and crown rot is a persistent problem in 

growing regions around the world and is one of the most important soil-borne diseases of sugar 

beets. Losses to Rhizoctonia root and crown rot in the U.S. are estimated to exceed $38 million 

annually affecting harvestability, processing quality, and storability. The causal agent of 

Rhizoctonia root and crown rot is Rhizoctonia solani AG 2-2 (Kühn), a soil-borne fungus in the 

Basidiomycota. Strains of R. solani AG 2-2 that affect sugar beet have traditionally been 

separated into intraspecific groups (ISGs), known as AG 2-2IIIB and AG 2-2IV, that commonly 

are classified by their ability to grow at 35°C. It has been evident for some time now that, based 

on ITS sequences, these subgroups are polyphyletic. In the current study, a multigene 

phylogenetic analysis was used to clarify the relationship between the subgroups and the results 

indicated that the subgroups 2-2IIIB and 2-2IV are indeed artificial. Therefore, the subgroups of 

AG 2-2 were redefined to represent a more natural classification. These new subgroups, referred 

to as AG 2-2BR and AG 2-2PR in the current work, each consisted of two genetic clusters that 

all have unique genetic characteristics.  

To examine the characteristics of these newly identified genetic clusters and to address some 

open questions regarding the population biology of R. solani AG 2-2, a set of microsatellite 

markers was developed and utilized to genotype 164 isolates from eight growing regions around 

the world. Sexual reproduction in AG 2-2 has been controversial, but evidence provided by the 

microsatellite analysis supports sexual reproduction occurring in natural populations, although, it 

 
is likely restricted to members of one genetic cluster within AG 2-2BR. In addition, evidence of 

hybridization between the subgroups 2-2BR and 2-2PR is presented and it appears this 

hybridization can occur in natural populations. These life-cycle processes have important 

implications in the generation of genetic diversity in populations. Population studies using the 

newly developed set of microsatellite markers also revealed evidence of long-distance dispersal 

that appears to occur across continents and across oceans. These observations highlight the 

importance of sanitation in managing Rhizoctonia root and crown rot to limit or prevent the 

movement of inoculum on equipment, crop residues, or personal apparel.  

The current research project also examines the infection process for Rhizoctonia root and 

crown rot of sugar beet. Observations provide evidence for the involvement of cell wall 

degrading enzymes, including lignin degrading enzymes, pectin lyase, and polygalacturonase/ 

polygalacturonase-inhibiting proteins, in the invasion and colonization of sugar beet root tissue. 

The involvement of these enzymes has been previously reported in sugar beet, but they have not 

received a lot of attention since their original reporting. It is anticipated that this work may renew 

interest in the enzymes involved in the invasion of sugar beet roots and the development of 

Rhizoctonia root and crown rot and provide additional targets for resistance breeding. 

Additionally, this dissertation provides a novel perspective on the generation of genetic diversity 

in R. solani AG 2-2 which is expected to inspire innovative hypotheses regarding strategies of 

resistance breeding in sugar beet.  

 
To my Mom  

iv 

 
 
 
 
 
 
 
 
 
 
 
 
ACKNOWLEDGMENTS 

I would like to take this opportunity to thank the individuals who have helped me achieve this 

major milestone in my career. First, I would like to thank my major advisor, Dr. Linda Hanson, 

for her patience and mentorship. This has been a challenging journey for me, and I have 

experienced many personal difficulties during my graduate studies. Dr. Hanson was very 

understanding and allowed me the time I needed to work through all these issues. I believe that 

under Dr. Hanson mentoring, I have become a well-rounded pathologist and have really 

developed the ability to think about problems in much more than a superficial way. I would also 

like to thank my other committee members, Dr. Ray Hammerschmidt, Dr. Greg Bonito, and Dr. 

Jaime Willbur, for their support, advice, and willingness to help me through this process. There 

have been numerous individuals that I have met during my graduate studies that have encouraged 

and supported me, including Tom Goodwill, Qianwei Jiang, Daniel Bublitz, and Celeste 

Dmytryszyn. I would like to extend a special thanks to Dr. Camen Medina-Mora who was the 

most supportive and encouraging person during this process. I do not know how I could have 

made it through without you.  

v 

 
TABLE OF CONTENTS 

CHAPTER 1: REVIEW OF LITERATURE ............................................................................ 1 
  REFERENCES  ...........................................................................................................41  

CHAPTER 2: DEVELOPMENT OF MICROSATELLITE MARKERS AND THE 
PHYLOGENETIC ANALYSIS OF RHIZOCTONIA SOLANI AG 2-2 ................................ 60  
  REFERENCES  .........................................................................................................108  

CHAPTER 3: POPULATION STRUCTURE OF RHIZOCTONIA SOLANI AG 2-2 
REVEALS CRYPTIC SOURCES OF GENETIC DIVERSITY ..........................................116 
  REFERENCES  .........................................................................................................168  

CHAPTER 4: HISTOPATHOLOGY OF RHIZOCTONIA ROOT AND CROWN ROT 
OF SUGAR BEET .................................................................................................................174 
  REFERENCES  .........................................................................................................212 

CHAPTER 5: CONCLUSIONS  ...........................................................................................221 
  REFERENCES  .........................................................................................................229 

vi 

 
 
 
 
 
 
 
 
 
CHAPTER 1: 

REVIEW OF LITERATURE  

1 

 
 
 
 
 
 
 
 
 
 
 
 
 
Introduction to sugar beets 

Sugar beet (Beta vulgaris subsp. vulgaris L.) is grown in temperate regions throughout the 

world for the sucrose that accumulates in its root. Worldwide, about 4.4 million hectares of sugar 

beet are planted annually, yielding around 270 million tons of white sugar (FAO 2022). Sucrose 

from sugar beets accounts for about 12% of global sugar production, with the remainder coming 

primarily from sugar cane. The Russian Federation led global sugar beet production in 2021 with 

41 million metric tons grown on 994,000 hectares followed by France with 34 million metric 

tons on 402,000 hectares (FAO 2022). The United States was the third largest producer of sugar 

beets in 2021 with 33 million metric tons on 448,000 hectares. Total value in 2021 for world 

production of sugar beet was approximately $12.5 billion (FAO 2022) and about $1.9 billion in 

the United States (US Department of Agriculture; National Agricultural Statistics Services).  

Cultivated beets were likely domesticated from the wild relative Beta vulgaris subsp. 

maritima that is native to the Mediterranean basin and western Europe (Biancardi & Lewellen 

2012). Cultivar groups related to sugar beet such as table beets, chards, and fodder beets are 

included in Beta vulgaris subsp. vulgaris. Each of these cultivar lineages has been adapted to 

specific end uses and exhibit distinctive phenotypic differences (Winner 1993). Table beets have 

been selected for an expanded hypocotyl and crown tissue and the accumulation of betanin, the 

pigment that gives many table beets their red color (Lange et al. 1999). Chards were developed 

for their edible leaves. While the leaves of all beet types are edible, chards have larger, nutritious 

leaves that can be green or reddish (Ninfali & Angelino 2013). Fodder beet was developed 

primarily as feed for cattle and other livestock (Winner 1993). It is the most similar cultivar 

group to sugar beet with an expanded root and crown and higher levels of sucrose accumulation 

2 

than table beets or chards. It is likely the ancestral group from which sugar beet was developed 

(Biancardi & Tamada 2016; Fasahat et al. 2018). 

Sugar beets are the most economically important cultivar group of B. vulgaris subsp. vulgaris 

and have been adapted for a greatly expanded root and crown with high levels of sucrose 

accumulation (Winner 1993). Unlike sugar cane, sugar beets are grown primarily in temperate 

regions, where planting usually occurs as early in the spring as feasible to maximize the growing 

season and biomass accumulation (Scott & Jaggard 1993). Sucrose yield has steadily increased 

since domestication through selective breeding programs and improved agronomic practices 

(Fasahat et al. 2018; Hoffmann 2010; McGrath & Panella 2018). Dry matter makes up about 23-

24% of the sugar beet root, the remainder being water, and sucrose accounts for about 18% of 

the total dry mass (Hoffmann et al. 2005). Thus, a typical ton of sugar beets will yield about 180 

kg of sugar, 150 kg of which will be recovered as marketable sugar, the rest being lost to 

molasses or during storage (Campbell 2002). 

The sugar beet crop is susceptible to a number of diseases that can reduce yield and constrain 

production. Major soil-borne diseases include Fusarium yellows (Fusarium oxysporum f.sp. 

betae; Hanson & Jacobsen 2009; Harveson 2008a), Aphanomyces root rot (Aphanomyces 

cochlioides; Poindexter 2014; Windels & Harveson 2009), Rhizomania (Beet necrotic yellow 

vein virus (vector Polymyxa betae); Biancardi & Tamada 2016; Rush 2009) and Rhizoctonia root 

and crown rot (Rhizoctonia solani AG 2-2; Khan & Bolton 2021; Schneider & Whitney 1986). 

Important foliar diseases include Cercospora leaf spot (Cercospora beticola; Harveson 2013; 

Jacobsen & Franc 2009a), Phoma leaf spot (Phoma betae; Jacobsen & Franc 2009b; Koenick et 

al. 2019), Alternaria leaf spot (Alternaria alternata; Cortes et al. 2022; Franc 2009), beet curly 

top (Beet curly top virus (vector Circulifer tenellus); Harveson 2015; Wintermantel 2009), and 

3 

powdery mildew (Erysiphe polygoni; Francis 2002; Hanson 2009). There are also several 

arthropods and nematodes that can cause substantial economic damage to sugar beet crops. 

These include the sugar beet root aphid (Pemphigus betae; Hein et al. 2009b; Pretorius et al. 

2016), the root maggot (Tetanops myopaeformis; Hein et al. 2009a; Wenninger et al. 2019), cyst 

nematode (Heterodera schachtii; Gary 2009a; Khan & Arabiat 2021) and the root-knot nematode 

(Meloidogyne spp.; Gary 2009b; Westerdahl & Becker 2016). 

In addition to losses that occur in the field, severe losses can occur during storage, prior to 

roots being processed in the sugar factories, that can reduce recoverable sucrose. In many 

regions, harvested sugar beets are placed in large storage piles to await processing (Bugbee 

1993). Exposed to ambient weather conditions, these piles can incur substantial annual losses 

(Stausbaugh 2018; Van Driessche 2012), with the majority of loss occurring from respiration 

(Campbell & Klotz 2006). However, there are a number of fungi and bacteria that are 

responsible for storage rots including Phoma betae (Bugbee & Cole 1981), Botrytis cinerea 

(Isaksson 1942), Fusarium spp. (Christ et al. 2011), Pennicillium spp. (Strausbaugh & Dugan 

2017), and Leuconostoc spp. (Strausbaugh 2016). Additionally, primary diseases such as 

Rhizoctonia root and crown rot may predispose the sugar beet roots to storage rots and increase 

their severity (Kusstatscher et al. 2019; Strausbaugh et al. 2011b, 2013). 

Rhizoctonia root and crown rot 

Rhizoctonia root and crown rot (RRCR) is one of the most serious and prevalent soil-borne 

diseases in most growing regions around the world (Buhre et al. 2009; Harveson 2008b; Khan & 

Bolton 2021; Strausbaugh et al. 2011a). The disease has economic impact on an estimated 25% 

of the sugar beet production area in the United States with yield losses amounting to about 2% 

4 

 
annually (Harveson 2008b; Kiewnick et al. 2001). The losses in individual fields can vary 

greatly, from negligible to over 50% depending on field history and environmental conditions 

(Harveson 2008b; Kiewnick et al. 2001).  

The causal agent of Rhizoctonia root and crown rot is Rhizoctonia solani Kühn anastomosis 

group (AG) 2-2, a fungal pathogen in the family Ceratobasidiaceae (phylum: Basidiomycota, 

order: Cantharellales). Rhizoctonia solani is a large species complex that has proved challenging 

to classify and accurately define relationships within the group (Cubeta & Vilgalys 1997). 

Current classification within the genus is based on the anastomosis group (AG) concept which 

groups individuals based on the ability of their hyphae to fuse, or anastomose, with other 

members of the same AG (Parmeter et al. 1969; Sneh et al. 1991). To date, there have been at 

least 13 AG identified, each representing an independent evolutionary lineage within R. solani 

(Salazar et al. 2000; Sharon et al. 2008; Sneh et al. 1991). Although anastomosis groups can 

sometimes be challenging to determine and are occasionally imprecise due to the ability of some 

AG to fuse with other AG at low levels (Sneh et al. 1991), the anastomosis group currently 

represents our best understanding of relationships with the R. solani complex (Carling et al. 

2002; Sharon et al. 2008; Sneh et al. 1991).  

Several AG have been further subdivided into intraspecific groups (ISG), based on 

characteristics such as DNA hybridization, sclerotia size, zymography patterns, and temperature 

tolerance (Sharon et al. 2008; Sneh et al. 1991; Cubeta & Vilgalys 1994).  Rhizoctonia solani 

AG 2-2 is currently separated into at least three subgroups, AG 2-2IIIB, AG 2-2IV, and 

AG 2-2LP. Originally, the subgroups were separated by host range, with AG 2-2IIIB affecting 

mat rush (Juncus effusus), AG 2-2IV affecting sugar beet (Ogoshi 1987), and AG 2-2LP 

affecting Zoysia spp. (Hyakumachi et al. 1998). However, AG 2-2IIIB and AG 2-2IV are now 

5 

known to both affect sugar beet and are no longer separated by host. Instead, the two subgroups 

are currently distinguished by the ability to grow at 35°C, where AG 2-2IIIB grows at 35°C 

while AG 2-2IV does not (Sneh et al. 1991).  

In addition to a lack of distinction in host range, the monophylly of the subgroups 

AG 2-2IIIB and AG 2-2IV is questionable. Carling et al. (2002) showed that subgroup AG 2-2IV 

was polyphyletic with at least two clusters of AG 2-2IV isolates surrounding a cluster of 

AG 2-2IIIB isolates. Strausbaugh et al. (2011a) reported similar results with isolates of 

AG 2-2IIIB being paraphyletic. Because of the irregularities, Martin et al. (2014) analyzed the 

relationship of 64 AG 2-2 isolates using a multigene phylogeny and confirmed that the 

subgroups AG 2-2IIIB and AG 2-2IV were not supported. Instead, Martin et al. (2014) suggested 

the presence of at least three genetic groups that contained a mix of AG 2-2IIIB and AG 2-2IV 

isolates. Reported differences in virulence between the subgroups (Carling et al. 2002; Engelkes 

& Windels 1996; Strausbaugh et al. 2011a) has maintained interest in retaining them, despite 

their clear lack of phylogenetic support. Clarification of the relationships and subgroups within 

AG 2-2 is needed to better predict the functional characteristics of a given population and the 

potential effects on agronomical practices.  

Anatomy and development of the sugar beet root 

The anatomy of the sugar beet was described by Artschwager (1926) and later reviewed by 

Elliot & Weston (1993). The body of the sugar beet root consists of three regions with distinctive 

morphology. These regions are referred to as the crown, the neck, and the root. The crown forms 

a dome shape from which a tuft of leaves develops. The neck forms a smooth region between the 

crown and the root and is ontologically derived from the hypocotyl. This region is highly 

6 

 
expanded and forms the broadest part of the beet, although it may be difficult to distinguish from 

the crown and root in mature beets. The root region forms the bulk of the beet tissues and is 

slender and tapering with somewhat flattened grooves on two sides. These depressions form a 

shallow spiral that extends downward to a slender taproot and contain irregularly arranged lateral 

roots. The surface of the mature beet is covered by a thin corky layer rather than an epidermis 

that covers most herbaceous roots (Scheres et al. 2002).  

Sugar beet is a biennial plant with seed borne on stalks that develop from the previous year’s 

growth after sufficient vernalization (Cooke & Scott 1993). Clusters of two to seven flowers 

form at each node of the inflorescence and the perianth of adjoining flowers fuse to produce a 

mass of cork 3-5 mm in diameter that contains multiple germ. Planting these ‘multigerm’ 

clusters results in the germination of multiple seedlings from each ‘seed’ that will require hand-

thinning to limit overcrowding (Smith & Fehr 1987). Breeding efforts have produced germline 

that form only a single flower at an inflorescence node, producing a ‘monogerm’ seed that can be 

directly planted, minimizing thinning (Savitsky 1950). Modern commercial varieties of sugar 

beet are primarily monogerm (Biancardi et al. 2010). 

Seedlings emerge by epigeal germination with two cotyledons that are long and thin (Elliot & 

Weston 1993). Very young seedlings, less than 10 days after germination, consist of a tap root, a 

hypocotyl and a pair of cotyledons and are about 2 to 3 mm in diameter. In cross section, the root 

consists of primary vascular tissue at the core surrounded by an endodermis. Inside of the 

endodermis is a layer of pericycle and outside of the endodermis are a couple rows of 

parenchyma cells that form the primary cortex. The cortex is surrounded by a row of cutinized 

epidermal cells that are somewhat elongated (Artschwager 1926). 

7 

About 10 days after germination, vascular tissue in the central stele differentiates and the 

primary xylem forms in a plate that is oriented in line with what will eventually become the root 

grooves (Artschwager 1926). Primary phloem forms adjacent to the pericycle. Normal primary 

cambium forms between the primary xylem and the primary phloem and produces xylem to the 

inside and phloem to the outside. Primary growth concludes with the appearance of the second 

pair of leaflets, which takes between 10 and 12 days after germination (Artschwager 1926).  

After emergence of the second leaflets, the parenchyma cells between the primary xylem and 

primary phloem begin to elongate axially and become meristematic to form the primary 

cambium and the development of xylem and phloem proceeds in the normal manner with xylem 

developing to the inside of the primary cambium and phloem developing to the outside of the 

primary cambium. This primary cambium development gives rise to the innermost vascular ring 

(Artschwager 1926).  

When the primary cambium initials first divide, the outer cells become the initials of a new 

meristematic tissue, the secondary cambium. The inner cambium cells continue to divide and 

produce xylem and phloem normally. This process is repeated until all supernumerary cambia 

are formed. At each division, the inner-most cells produce xylem and phloem while the outer 

cells remain meristematic and produce a new cambium annular ring. Mature beet roots typically 

have between 7 and 11 annular rings (Artschwager 1952). Each secondary cambium is a direct 

descendant of the next inner ring, resulting in the inner rings maturing earlier than the outer 

rings. The outmost rings never fully mature and the tissue remains in the process of 

differentiation where occasionally cells will mature into small groups of sieve tubes and 

companion cells (Artschwager 1926).  

8 

Each annular ring matures a wide band of parenchyma cells which forces the vascular bands 

apart. Since the supernumerary cambia form in rapid succession, young plants will essentially 

have all cambia formed when they are still less than 12 mm in diameter, about 9 to 11 weeks old, 

at the 6 to 8 leaf growth stage. At this stage, new supernumerary cambiums stop developing and 

tissue expansion occurs by cell division and enlargement that occurs in all annular rings 

simultaneously (Artschwager 1926).  

Because the development of supernumerary vascular rings occurs inside of the endodermis, 

tissue expansion forces the endodermis, primary cortex, and epidermis outward where they are 

eventually sloughed off. The pericycle divides tangentially into a band of meristematic tissue that 

establishes the phellogen or cork cambium. A thin covering, 5 to 8 cells wide, develops from the 

cork cambium consisting of phelloderm inside of the phellogen and cork cells on the outside. 

The cells of the periderm are thin and suberized and act as a protective layer since the epidermis 

is lost when the seedling is 6 to 8 weeks old (Artschwager 1926).  

The transition between seedling beets and mature beets has been reported to occur about the 

same time that supernumerary vascular rings are formed and the periderm matures. The pattern 

of gene expression makes a dramatic change during this period of growth as well (Trebbi & 

McGrath 2009). This transition from seedling to mature beet supports the observation of limited 

reports of AG 4 and AG 5 on adult beets while AG 4 and AG 5 historically predominate on 

seedlings (Windels & Nabben 1989). While isolates of AG 4 were the most aggressive on 

seedlings, when inoculated on 8 to 9-week-old plants these AG 4 isolates caused only superficial 

lesions. In contrast, isolates of AG 2-2 were most aggressive on older plants, although they could 

still cause damage to seedlings, just not quite as severe as isolates of AG 4 (Windels & Nabben 

1989). Gaskill (1968) and Liu et al (2019) have reported that no appreciable resistance to 

9 

Rhizoctonia root and crown rot developed until the 6 to 8 leaf growth stage, which occurs about 

4 weeks after emergence. These observations are consistent with patterns of gene expression that 

change dramatically during this period and represent a transition in functional development from 

the seedling to the adult stage (Trebbi & McGrath 2009). 

Crop rotation 

Crop rotations can be effective at reducing disease severity of many plant diseases (Sumner 

et al. 1981; Wilson 2013). Recommendations for management of Rhizoctonia root and crown 

include a minimum of three years between sugar beet crops to prevent the build-up of inoculum 

(Harveson 2008b; Windels et al. 2009). Ideally, rotations should include non-host crops, 

particularly immediately preceding a sugar beet crop (Ruppel 1985; Windels et al. 2009). 

Unfortunately, many of the crops commonly grown in rotation with sugar beet are susceptible to 

the same strains that cause Rhizoctonia root and crown rot (Engelkes & Windels 1996; Herr 

1987; Ruppel 1985; Sneh et al. 1991).  

Engelkes & Windels (1996) showed that all strains of R. solani AG 2-2IIIB and AG 2-2IV 

tested caused disease on dry beans. Isolates of AG 2-2IIB from Ohio that were virulent on dry 

bean roots failed to cause foliar symptoms on dry beans or soybeans (Muyolo et al. 1993). 

Godoy-Lutz et al. (2008) identified isolates of AG 2-2 that caused foliar web blight on common 

beans, but they found these isolates clustered together in a genetic clade independent from 

AG 2-2IIIB or AG 2-2IV and identified them as subgroup AG 2-2WB. Further reports of isolates 

related to the AG 2-2WB clade are unknown. Minier (2019) found significant differences in 

virulence on dry beans and sugar beet seedlings between the clades of AG 2-2 identified by 

Martin et al. (2014) that were unrelated to the subgroups AG 2-2IIIB and AG 2-2IV. This raises 

10 

 
the possibility that strains that are more virulent on dry beans could increase in prevalence when 

dry beans are included in rotation. Recommendations have been to avoid close rotation between 

sugar beet and dry beans (Engelkes & Windels 1996). 

Soybean is another common rotation crop grown in many regions that is also susceptible to 

R. solani AG 2-2 (Windels & Brantner 2005). To the best of my knowledge, only R. solani 

AG 2-2IIIB strains have been reported from soybean (Ajayi-Oyetunde et al. 2019; Dorrance et 

al. 2003; Fenille et al. 2002; Muyolo et al. 1993), although some reports do not indicate subgroup 

of the AG 2-2 isolates examined. One of the only studies to examine population structure of R. 

solani AG 2-2, especially in regard to the potential of sexual reproduction, was conducted using 

isolates recovered from soybean (Ajayi-Oyetunde et al. 2019). The researchers concluded a 

mixed reproduction strategy for the population from Ontario and primarily clonal reproduction in 

Ohio and Illinois. While the sexual stage is rarely reported from R. solani AG 2-2, results of the 

population genetics work by Ajayi-Oyetunde et al. (2019) indicate the potential of sexual 

reproduction, at least in limited situations or regions.  

Corn is often grown in rotation with sugar beet but has a controversial history in regard to the 

effect it has on a subsequent beet crop. Coons and Kotila (1935) showed that corn decreased 

disease severity in a following sugar beet crop as did Maxson (1938), who recommended corn 

for rotation with sugar beet. Ruppel (1985) determined that corn was a host but states personal 

observation and the experiences of growers throughout the Plains region indicated that disease 

severity was reduced following corn. In the early 1980’s, R. solani AG 2-2 was reported causing 

a brace root rot of corn in the southern United States (Sumner & Bell 1982). By the early 2000’s, 

R. solani AG 2-2IIIB was reported causing disease on corn in northern growing areas such as 

Minnesota (Windels & Brantner 2005) and Germany (Buddemeyer et al. 2004; Ithurrart et al. 

11 

2004). Close rotations with corn were then shown to increase disease severity in a subsequent 

sugar beet crop (Windels & Brantner 2008). Similar results were reported for table beet in New 

York (Ohkura et al. 2009). Whether the warming climate allowed the disease to develop in more 

northern areas or rotations with corn selected for the more virulent R. solani AG 2-2 strains is 

uncertain.  

Testing alfalfa and wheat as rotational crops has also resulted in contradictory results. 

Maxson (1938) determined small grains, such as wheat, were non-hosts and effective at reducing 

disease in a following sugar beet crop. Götze et al. (2017) determined that including alfalfa in 

rotation enabled shorter rotation intervals without negative yield affects. Alfalfa was also 

considered to be a non-host by Ruppel (1985) and Schuster and Harris (1960), but Coons and 

Kotila (1935) reported an increase in damping-off of sugar beet associated with Rhizoctonia 

solani when following alfalfa. Personal observations reported by Ruppel (1985) also suggest that 

alfalfa may increase disease in a following sugar beet crop. Field studies by Rush and Winter 

(1990) supported these observations that disease could increase following alfalfa. Some of the 

inconsistency between susceptibility and affect in field studies were attributed to residual soil 

NO3-N and the colonization of crop residues (Ruppel 1985; Rush & Winter 1990).  

Additional crops have been reported as being susceptible to R. solani AG 2-2, including 

clover (Hwang et al. 1996), canola seedlings (Verma 1996), radish, carrot (Grisham & Anderson 

1983), sunflower (Rush & Winter 1990), tobacco (Gonzalez et al. 2011) and other brassicas 

(Cappelli et al. 1999). Although reported much less frequently than the primary potato type R. 

solani AG 3, AG 2-2 was reported to cause stem canker on potato (Muzhinji et al. 2015; Yanar et 

al. 2005). While not a crop commonly grown in rotation with sugar beet, R. solani AG 2-2IIIB 

12 

causes brown patch of creeping bentgrass (Agrostis pulustris; Blazier & Conway 2004) and other 

cool-season grasses (Burpee & Martin 1996).  

When determining what crops to include in rotation, more than just susceptibility of the crop 

must be considered. Sugar beet cultivar had by far the largest effect on disease severity in a study 

by Buhre et al. (2009) followed by the interaction between environment and cultivar. 

Rhizoctonia-resistant cultivars are commercially available but may have lower yields compared 

to susceptible varieties, often making their use disagreeable to growers (Khan et al. 2017; Panella 

& Hanson 2006; Panella & Ruppel 1996).  

Many environmental factors are involved in disease development including nitrogen levels 

and distribution, organic residues (Rush & Winter 1990), temperature, and moisture effects 

(Bolton et al. 2010). Essentially, environmental conditions that favor the pathogen over the plant 

lead to higher levels of disease (Baker & Martinson 1970; Leach 1947). Planting timing could be 

a factor in disease development as well, with planting into soils that favor the plant over the 

pathogen recommended (Leach 1947; Ruppel 1985).  

Ruppel (1985) proposed that the colonization of plant residues could account for the increase 

in disease severity when following what appeared to be a non-host, such as alfalfa. However, 

increased populations of R. solani surviving in residues does not always result in increased 

disease severity in the following crop (Herr 1987). Even without direct influence of pathogen 

survival, crop residues can influence soil nitrogen levels and soil moisture that could affect 

pathogen populations (Rush & Winter 1990). The effects of crop rotation are likely to be 

somewhat specific to the conditions within local areas depending on the composition of the 

pathogen population, soil type, climate, and sugar beet cultivar. This is consistent with varied 

13 

reports of the effect of crop rotation on disease, such as those reported by Rush and Winter 

(1990) versus those reported by Ruppel (1985).  

Despite the issues described above, crop rotation remains an important aspect for managing 

disease, particularly for those diseases whose inoculum levels can build up in the soil. Effective 

crop rotation has been shown to increase sugar yield (Buhre et al. 2009) and overall, promotes 

more stable yields than monoculture (Götze et al. 2017). Despite being susceptible to R. solani 

AG 2-2, intercropping sugar beet with Raphanus sativus (radish) or Brassica rapa (field 

mustard/turnip) had a positive effect on white sugar yield compared to fallow (Kluth et al. 2010). 

This could be due to other factors such as the effect on other pathogens like the sugar beet cyst 

nematode (Smith et al. 2004) or the local soil structure (Allmaras et al. 1988).  

Thus, selecting appropriate crops for rotation schemes should be carefully considered by 

employing experiments that examine the effects of specific rotational crops, not just on the 

subsequent crops, but also on the pathogen populations. The most critical aspects of crop rotation 

choices seem to be the crop immediately preceding the sugar beet crop and the amount of time 

between sugar beet crops with longer time out of sugar beet reducing disease severity and 

improving yield (Götze et al. 2017; Kluth et al. 2010; Schuster & Harris 1960). 

Basidiospore production / heterokaryon formation 

The sexual stage of R. solani is Thanatephorus cucumeris (A.B. Frank) Donk, although it has 

rarely been observed in AG 2-2 (Ajayi-Oyetunde et al. 2019; Qu et al. 2013). The sexual stage, if 

formed, may develop in the form of a greyish white to pale brown hymenial layer on above-

ground plant parts or at the soil surface (Naito 1996). Basidiospores are small (< 9 µm), ovate, 

14 

 
single-celled and borne on thin sterigmata (Talbot 1970). The number of basidiospores per 

basidium is variable but typically averages four. 

Other anastomosis groups of R. solani such as AG 3, AG 4, and AG 1, readily form a sexual 

stage (Adams & Butler 1983; Anderson 1982) but the sexual stage for AG 2-2 is rarely observed. 

It is uncertain as to why this is given that isolates of AG 2-2 have been observed producing 

hymenium in the lab (Kiyoshi et al. 2014). Curiously, only isolates identified as AG 2-2IV have 

been reported producing a sexual stage and the sexual stage has not been reported in AG 2-2IIIB. 

Most, if not all, reports of the sexual stage in AG 2-2IV have come out of Japan (Kiyoshi et al. 

2014; Toda & Hyakumachi 2006). In addition, AG 2-2IV has been associated with foliar blight 

of sugar beet, presumably initiated by basidiospores (Naito 1990). Since basidiospores can be 

dispersed aerially, connecting foliar blight to isolates that can form the sexual stage would be a 

reasonable conclusion (Kiyoshi et al. 2014; Naito 1996).  

Rhizoctonia solani AG 2-2 is heterokaryotic and multinucleate with between 4 and 13 nuclei 

per cell (Sneh et al. 1991). Single-basidiospore isolates derived from multinucleate individuals 

can all be in the same somatic compatibility group (homogeneous) or in multiple somatic 

compatibility groups (heterogeneous; Kiyoshi et al. 2014). Consequently, homogeneous isolates 

generate progeny with the same genotypes while heterogeneous isolates generate progeny with 

different genotypes.  

Somatic compatibility is considered a separate genetic phenomenon from mating 

compatibility (Julián et al. 1996). Somatic compatibility describes the ability of hyphae to 

distinguish self from non-self (Worral 1997). In R. solani, different anastomosis groups are, for 

the most part, somatically incompatible, although a few AG can form bridging reactions that 

exhibit low levels of somatic compatibility (Sneh et al. 1991). Within AG 2-2, isolates can have 

15 

differing levels of compatibility and the terminology used to describe these interactions between 

hyphae was outlined by Parmeter et al. (1969) and Carling (1996). When isolates are somatically 

compatible, or very closely related, their hyphae can fuse completely, sharing membranes and 

cytoplasm. Isolates that are in closely related but different somatic compatibility groups do not 

fuse completely. Cell walls may fuse but membrane fusion is absent, or uncertain, and cytoplasm 

is not shared between hyphae. Adjacent cells typically die within a couple hours of fusion. Very 

different somatic groups, such as those of different anastomosis groups, do not fuse at all. 

Because anastomosis represents a continuum, Carling et al. (2002) and Todo and Hyakumachi 

(2006) introduced additional categories for anastomosis reactions that were intermediate or 

distinct from categories defined by Carling (1996) and Parmeter et al. (1969).  

It is unclear whether genetic material is exchanged between isolates that are somatically 

incompatible but can still fuse to some extent. In R. solani, Todo and Hyakumachi (2006) 

observed heterokaryon formation between incompatible isolates and hypothesized nuclei could 

migrate more rapidly than cell death occurs. Studies concerning heterokaryon formation in 

AG 2-2 have only involved subgroup AG 2-2IV (Kiyoshi et al. 2014; Toda & Hyakumachi 2006) 

and reports of AG 2-2IIIB forming heterokaryons are unknown.  

Since basidiospore production has only been reported for AG 2-2IV, it is unclear whether 

AG 2-2IIIB isolates have a functioning sexual mating system. Both heterothallic and homothallic 

mating systems have been observed in AG 2-2IV (Todo & Hyakumachi 2006), but it is unknown 

whether this also applies to AG 2-2IIIB. Given the extent of diversity present within AG 2-2IIIB 

populations (Ajayi-Oyetunde et al. 2019; Liu & Sinclair 1992; Strausbaugh et al. 2011a), a 

mechanism for the generation of genetic diversity must exist within AG 2-2IIIB even if the 

sexual stage is nonfunctional. Isolates within AG 2-2IIIB anastomose freely and so could 

16 

presumably exchange nuclei. However, to the best of my knowledge empirical demonstration of 

this phenomenon is very limited or nonexistent.  

Infection process of Rhizoctonia solani 

The infection process for Rhizoctonia solani follows a generalized progression that includes 

recognition of a potential host, attachment to the host surface, development of an infection 

structure, penetration of the outer cell layers, colonization of host tissue and modulating host 

response (Ferreira et al. 2006). Much of the work investigating the infection process of R. solani, 

as a whole, focuses on infections that affect above-ground parts of the plant. In addition, most of 

the work has examined the infection process of AG 1 and AG 4 (Weinhold & Sinclair 1996), 

with limited work considering AG 2-2 on roots. Never-the-less, some generalizations of the 

infection process can be made. 

Rhizoctonia solani survives in the soil primarily as sclerotia, which are highly melanized 

masses of barrel-shaped hyphae called monilioid cells (Sumner 1996). When moisture and 

temperature conditions are suitable, the sclerotia can germinate and mycelial threads grow 

towards the plant roots, presumably as a result of an attraction to exudates produced by the plant 

(Badri & Vivanco 2009; Bongard 2012; Lombardi et al. 2018; Narula et al. 2009). Root exudates 

such as amino acids, carbohydrates, phenols, and organic acids have been shown to stimulate 

growth, influence inoculum density, and promote disease formation (Keijer 1996; Reddy 1980). 

Whether this enhanced growth and attraction is due to an increase in available nutrients, such as 

carbohydrates, or a direct response to a chemical signal is still not completely resolved and the 

specifics may depend on the precise pathosystem under consideration.  

17 

 
Once the hyphae come in contact with the root surface of a suitable host, they begin to grow 

over the plant surface (Keijer 1996). Initially, the hyphae do not attach to the surface, but form 

rounded, “runner” hyphae that spread out over the surface (Keijer 1996). These rounded hyphae 

eventually become flattened and adhere to the epidermal cells. A mucilaginous sheath has been 

observed surrounding the appressed hyphae that is apparently responsible for attachment (Flentje 

1956; Keijer 1996; Matsuura 1986). Since attachment appears to be a prerequisite for infection 

(Keijer 1996), identifying the factors that regulate attachment could be beneficial to further 

understanding of the infection process.  

Once the hyphae are firmly attached, a process known as directed growth begins, where the 

growth of the hyphae follows the anticlinal epidermal cell walls (Armentrout & Downer 1987; 

Keijer 1996). Surface topology is thought to play a role in directed growth as artificial surfaces 

have been created that mimic directed growth of the hyphae (Armentrout et al. 1987; Kou & 

Naqvi 2016; Łaźniewska et al. 2012). Additionally, the contact between epidermal cells could 

result in the leakage of stimulatory compounds that direct hyphal growth (Marshall & Rush 

1980). The hyphae then begin to form T-shaped branches of swollen hyphal tips that might be 

considered appressoria (Chethana et al. 2021). This branching process can continue, forming 

more complex aggregates that may originate from several parental hyphae (Hofman & 

Jongebloed 1988; Keijer 1996). These aggregates can develop into larger structures known as 

infection cushions that form more-or-less dome-shaped structures of densely packed hyphae 

(Armentrout & Downer 1987; Marshall & Rush 1980).  

The process of infection cushion development has been studied in several pathosystems and 

some generalizations can be made from these studies. In cotton seedlings, infection cushions 

were well-formed 21 hours after inoculation (Armentrout & Downer 1987). Prior to cushion 

18 

formation, hyphae were observed shifting from relatively unbranched hyphae growing on the 

surface to hyphae with restricted branching in limited areas and then to more dense branching 

with accumulations of hyphae. This shift in growth patterns occurred in about 9 to 10 hours 

(Armentrout & Downer 1987). Mucilage-like material accumulated among the hyphae and 

appeared to function to adhere the hyphae to the plant surface. Formation of the cushions 

followed a predictable pattern, with repeated branching and t-shaped foot cells resulting in a 

cushion composed of axial hyphae with lateral connections. Once the cushion consisted of a 

sufficient size and density of hyphae, bulbous cells on the underside of the cushion formed 

penetration tips and penetrated the plant surface in large numbers (Armentrout & Downer 1987). 

The defining characteristic of infection cushions on cotton seedlings was described as a well-

defined pattern of interwoven hyphae rather than an amorphous hyphal aggregation. However, 

although Armentrout and Downer (1987) describe infection cushions as the ”typical” infection 

structure for members of the R. solani species complex, they warn against attempts to draw 

generalizations with regard to cushion formation on other crops citing observed variations in 

cushion formation reported by other authors.  

Observations have implicated surface patterns as an inducer of infection cushion formation 

with hyphae following anticlinal epidermal walls and t-shaped branches developing that follow 

cell junctions (Armentrout & Downer 1987; Keijer 1996; Yang et al. 1992). However, this does 

not appear to always be the case. Marshall and Rush (1980) reported that fungal development on 

polystyrene replicas of rice sheaths did not follow junctions of the epidermal cells. While 

susceptible varieties had little or no cuticular wax deposits, resistant cultivars had an abundance 

of wax deposits on the outer sheath surface and infection cushion formation was limited or 

nonexistent. When the wax deposits were removed using chloroform, lobate appressoria and 

19 

infection cushions were formed, similar to those on susceptible cultivars. The implication then 

would be that infection cushion formation may be stimulated by exudates resulting from cell 

leakage and that cuticular waxes either reduce leakage, affect surface topology, or have some 

other inhibitory effect on infection cushion formation.  

Infection cushions are not always necessary for infection. Hyphae have been observed 

entering through stomatal openings on leaves of rice (Manian & Manibhushanrao 1982), soybean 

(Zheng & Wang 2011) and potato (Zhang et al. 2016). Hyphae have also been observed directly 

penetrating the epidermis without an infection cushion developing or through natural openings 

(Manian & Manibhushanrao 1982; Zhang et al. 2016; Zheng & Wang 2011). These direct-

penetrating hyphae have been described as appressoria with various associated shapes or 

morphologies attributed to their formation (Zhang et al. 2016). In Rhizoctonia species, 

appressoria tend to be lobate, flattened and strongly adhered to the plant surface (Dodman & 

Flentje 1970; Flentje 1957). In many fungi, appressoria are highly melanized, which reduces 

porosity and allows internal hydrostatic pressure to increase to levels that allow enough force to 

be generated to penetrate the plant cuticle and outer epidermal cells (Howard & Ferrari 1989). 

Glycerol provides an osmotic gradient, drawing water into the cell (de Jong et al. 1997). As 

melanin is impermeable to glycerol, internal pressure increases to levels as high as 8.0 MPa 

(Bechinger et al. 1999; Money 1995; Wang et al. 2005). A thin infection peg emerges from the 

appressorium and is pushed by pressure generated in the appressorium through the cuticle and 

into the underlying epidermal cells.  

The role of enzymes in penetration remains somewhat controversial. Infection pegs have 

been shown to penetrate paraffin wax, collodion membranes and even gold leaf, which are 

substances not likely to be degraded by enzymatic action and require mechanical force to 

20 

penetrate (Brown & Harvey 1927; Talbot 2019). It is therefore often presumed that the presence 

of cell wall degrading enzymes is not a requirement for penetration. However, studies have 

shown that impaired penetration occurred in isolates of Pyricularia oryzae (syn: Magnaporthe 

oryzae) as a result of the knock down of xylanase or cellulase genes (Nguyen et al. 2011; Vu et 

al. 2012). Inhibition of cutinase also prevented infection of Pisum sativum by Fusarium solani f. 

sp. pisi (Köller et al. 1982) and of Carica papaya by Colletotrichum gloeosporioides (Dickman 

et al. 1982). However, this was not the case of infection of cucumber by Gloeosporium 

orbiculare (syn: Colletotrichum lagenarium) where inhibition of cutinase did not reduce 

penetration (Bonnen & Hammerschmidt 1989). Infection of barley by Blumeria graminis f. sp. 

hordei (syn: Erysiphe graminis f. sp. hordei) begins with enzymatic digestion of the epidermal 

wall prior to mechanical penetration (Edwards & Allen 1970; Pryce-Jones et al. 1999). These 

studies suggest that the role that enzymatic versus mechanical factors play in plant penetration 

varies based on the fungus and host involved. Fungi that produce highly melanized appressoria 

may rely more on mechanical force while enzymes may play a larger role in those that produce 

less melanized appressoria (Talbot 2019).  

While infection cushions may not be required for infection by R. solani, several researchers 

have noted differences in the formation of infection cushions on resistant and susceptible 

varieties (Bashyal et al. 2018; Bassi et al. 1979; Pannecoucque & Höfte 2009; Yang et al. 1992; 

Zhang et al 2016). The correlation between infection cushion formation and disease severity is 

well established. However, the factors that contribute to variability in infection cushion 

formation are not as clear-cut. In potato, reduced infection cushion formation on a resistant 

variety was attributed to physical factors such as a thicker cuticle (Zhang et al. 2016). The 

thicker cuticle may result in less leakage of infection cushion-stimulating compounds. One 

21 

function of the periderm is to reduce dehydration (Campilho et al. 2020), which supports the 

hypothesis of reduced leakage of stimulating compounds. However, the presence of a thickened 

cuticle was inconsistent in a study that noted differences in infection cushion formation between 

resistant and susceptible tomato fruit (Bassi et al. 1979). Epidermal and subepidermal cells were 

smaller and more densely packed in the resistant variety indicating that the structure of the 

epidermis may play a role in resistance. Alternatively, it could be viewed that infection cushion 

formation was stimulated in the susceptible variety, which may be due to exudates, a thin 

cuticular layer, or surface cues that are unique to the susceptible varieties.    

Reduced disease severity in cauliflower has also been associated with reduced infection 

cushion formation (Pannecoucque & Höfte 2009). In addition, reduced disease severity was 

associated with a reduction in pectin degradation. The ability of R. solani AG 2-2 isolates to 

produce pectin degrading enzymes is well established (Barker & Walker 1962; Bateman 1963; 

Sherwood 1966) but in cauliflower, pectin degradation was not observed for the AG 2-2 isolate 

tested (Pannecoucque & Höfte 2009). The reason for this may be due to incompatible specificity 

of the cell wall degradation enzymes involved or production in insufficient quantities. 

Alterations to pectic structure of the cell wall, methylation patterns, or inhibitor proteins 

produced by the plant could reduce the effectiveness of a specific enzyme to degrade the pectin 

matrix (Bellicampi et al. 2104; D’Ovidio et al. 2004; Daher & Braybrook 2015). 

Several cues have been identified that induce infection structures including topographic 

signals, surface hardness, hydrophobicity, surface waxes and cutin, ethylene, and secreted 

enzymes (Kou & Naqvi 2016). While these induction clues have been demonstrated in several 

systems (Armentrout et al. 1987; Badri & Vivanco 2009; Bellincampi et al. 2014; Kou & Naqvi 

2016; Yang et al. 1992), the specific mechanisms depend on the particular system involved and 

22 

the precise role of each signal is complicated and not universal. The lack of pectin degradation in 

cauliflower could be due to a lack of appropriate signals which induce cell wall degrading 

enzyme production. This lack of signal initiation could also relate to infection cushion formation 

(Pannecoucque & Höfte 2009). 

Cell wall components and structure  

Plant cell walls not only provide rigidity and structure to the cell but also serve as a primary 

line of defense against plant pathogens (Albersheim et al. 2011; Bellincampi et al. 2014). 

Composed primarily of polysaccharides, the cell wall consists of a network of microfibrils that 

allow for diverse shapes and properties that fulfill various roles in different organs and at 

different stages of development. Because of this need for diverse structures, the cell wall must be 

dynamic and adaptable. During growth, development, and movement, it may be necessary to 

reshape and remodel the cell wall to fit the changing needs of the plant (Daher & Braybrook 

2015; Wu et al. 2018). Enzymes play a primary role in this process by altering cell wall 

elasticity, porosity, and integrity (Ene et al. 2015; Tenhaken 2015).  

Many of the same types of enzymes that plants use to modify the cell wall are also utilized by 

plant pathogens to compromise the integrity of the cell wall and gain access to plant tissues. 

These enzymes include polygalacturonases, pectin lyases, pectin methylesterases, cellulases, and 

hemicellulases (Bellincampi et al. 2014). Plants employ sophisticated regulatory controls over 

cell wall modifications to manage the effects of cell wall degrading enzymes (Ene et al. 2015; 

Wolf & Greiner 2012) and in order for a plant pathogen to successfully invade, it must either a) 

degrade tissue at a faster rate than the plant can respond (ie. necrotrophs) or b) interfere with the 

signaling networks that regulate cell wall modifications (ie. biotrophs). In addition, plants have 

23 

 
evolved a sophisticated defense system that monitors cell wall integrity through the detection of 

microbial/pathogen-associated molecular patterns (MAMPS/PAMPs) and damage-associated 

molecular patterns (DAMPs; Bellincampi et al. 2014; Ferrari et al. 2013; Hou et al. 2019). Thus, 

the cell wall serves as a key battleground between pathogen and host, with a constant struggle 

between detection, cell wall integrity, response intensity, and the timing of interactions.  

One of the primary constituents of plant cell walls is cellulose which provides the major 

load-bearing component of the cell wall (Albersheim et al. 2011). Cellulose is a polysaccharide 

consisting of hundreds or thousands of β-1,4-linked D-glucose units. The β-1,4 linkages of the 

glucosyl chain require that alternate residues be oriented 180° relative to one another, thus the 

cellulose backbone consists of disaccharide repeating units and the resulting polymer is both 

simple and stable in terms of its organization (Albersheim et al. 2011). Much of the stability and 

stiffness of cellulose comes from the ability of the β-1,4-linked glucan chains to form intra- and 

intermolecular hydrogen bonds, resulting in between 30 and 50 chains oriented in the same 

direction forming a cellulose microfibril (Klemm et al. 2005). These microfibrils are arranged in 

loose parallel sheets that are interconnected by hemicellulose.  

Hemicellulose consists of a backbone similar to cellulose, but with xyloglucan being the 

principal component with β-1,4-linked glucosyl chains with the hydroxyl group at C-6 

substituted with α-D-xylosyl residues at approximately 75% of the glycosyl resides (Albersheim 

et al. 2011). Some of these xylosyl residues have additional saccharide residues attached at C-2, 

such as β-D-galactosyl or α-L-fucosyl, which results in a highly branched polymer that adheres 

strongly to cellulose microfibrils through hydrogen bonding. These hemicellulose networks form 

a framework of cellulose microfibrils around which other wall polysaccharides are organized 

(Albersheim et al. 2011; Rao et al. 2023).  

24 

Pectic polysaccharide polymers comprise the largest share of non-cellulosic polysaccharides 

in the cell wall, and in some cases, pectin is the most prevalent component of the cell wall 

making up approximately 35% of the composition (Voragen et al. 2009). Pectin is primarily 

composed of α-1,4-linked galacturonic acid residues with various structural and chemical 

modifications such as neutral sugar side chains and varying levels of methyl esterification 

(Mohnen 2008). These pectin polymers connect and anchor the hemicellulose-cellulose network 

and form a water-retentive matrix that provides the cell wall with resistance to compressive and 

shearing forces (Albersheim et al. 2011).  

Several types of pectin polymers have been described based on the composition of the 

backbone and the side chain unit substitutions, the simplest and most common being 

homogalacturonan (Voragen et al. 2009). Consisting exclusively of β-1,4-linked galacturonic 

acid residues, homogalacturonan is the gel-forming polysaccharide of the cell wall (Voragen et 

al. 2009). The degree of methyl esterification varies depending on plant species and tissue type 

with up to 70% of carboxyl groups methyl esterified. Not only the degree, but the pattern of 

methylation is important as well (Willats et al. 2001). Regions of unesterified galacturonic acid 

residues form calcium cross-links between adjacent chains. These regions of low or unesterified 

homogalacturonan are found in the middle lamella and cell junctions and are responsible for cell-

to-cell adhesion (Daher & Braybrook 2015). Treatment with calcium chelators such as 

ethylenediaminetetraacetic acid (EDTA) and 1,2-Diaminocyclohexanetetraacetic (CDTA) results 

in cell separation in some plants (Dahar & Braybrook 2015; McCartney & Knox 2001; Tibbits et 

al. 1998) highlighting the role methylation plays in cell adhesion.  

Rhamnogalacturonan I (RG-I) is a pectic polysaccharide closely related to homogalacturonan 

that consists of repeated disaccharide units of α-D-galacturonic acid and α-L-rhamnose (Willats 

25 

et al. 2001). Many rhamnose units have side branches made up of neutral sugars such as D-

galactose, L-arabinose and D-xylose, the types and proportions varying with different sources. 

The precise organization of the side chains is highly variable with roughly 40 structurally 

different side chains known (Ridley et al. 2001; Voragen et al. 2009). Arabinosyl and galactosyl 

residues are the most common components of the side chains, but fucosyl, glucosyluronic acid 

and 4-O-methyl glucosyluronic acid residues can also be present. Side chains are attached to the 

C-4 or the rhamnosyl residues of the rhamnogalacturonan backbone. The nature of the 

relationship between homogalacturonan and rhamnogalacturonan-I is uncertain, but it is thought 

that RG-I and HGA are covalently attached through glycosidic linkages, connecting HG and RG-

I into larger units (Kaczmarska et al. 2022; Willats et al. 2001). Thus RG-I may serve as a 

scaffold to which other pectic polysaccharides attach to form the pectic matrix and which 

determines key characteristics such as cell wall strength, elasticity, and flexibility (Yapo 2011). 

The diversity of structure of RG-I is indicative of diverse functional specialization (Mohnen 

2008). 

Rhamnogalacturonan II (RG-II) is the third type of pectic polysaccharide present in plant cell 

walls and is considered to be the most complex polysaccharide known (Willats et al. 2001). 

While consisting of only about 30 residues, there have been at least 11 different 

monosaccharides identified as components of RG-II, organized into a backbone of seven to nine 

α-1,4-linked D-galacturonic acid residues bearing four oligosaccharide side chains (Pérez et al. 

2003). The structure of RG-II is highly conserved across disparate plant types despite such a 

large number of different sugars linked with more than 20 different glycosidic linkages 

(Albersheim et al. 2011; Bar-Peled et al. 2012), which likely indicates a conserved function or 

role in plant cell wall structure.  

26 

The complexity and conservation of RG-II appears to be related to the capacity to form 

dimers through borate cross-linking, but whether there is some other function that constrains 

variation is unknown (Bar-Peled et al. 2012). Borate cross-linking is associated with several 

physical properties of the cell wall including pore size and wall strength. Deficiencies in borate 

result in reduced plant growth, thickened, brittle cell walls, and the inability to form borate cross-

links has been shown to be deleterious to plant growth and development (Ahn et al. 2006; 

Fleischer et al. 1999; Voxeur et al. 2011).  

Evidence indicates that HG, RG-I and RG-II are covalently linked in a pectin network, 

although it is not yet established that this is always the case (Pérez et al. 2003). While the precise 

arrangement of the different types of pectin is uncertain, there are a couple generalities that have 

been identified. Based on studies with antibodies, RG-II is widely present in primary cell walls 

but appears to be absent from the middle lamella (Matoh et al. 1998), while nonesterified HG is 

found mostly in the middle lamella and esterified HG is found in the primary cell wall 

(Albersheim et al. 2011).  

Composition of sugar beet cell walls 

Sugar beet cell walls are rather unique when compared to most other crops having very low 

levels of xyloglucans and high levels of pectin (McGrath & Townsend 2015). Sugar beet pectin 

has an abundance of neutral sugar side chains, particularly arabinose (Marry et al. 2000). These 

neutral sugars directly link the pectin network with the cellulosic material which directly 

influences cell wall properties (Zykwinska et al. 2005). Sugar beet homogalacturonan is highly 

acetylated (Dea & Madden 1986) making it particularly resistant to degradation by enzymes 

(Volpi et al. 2011; Yadav et al. 2009). Consequently, cell wall modification that reduces 

27 

 
acetylation levels may be necessary prior to degradation by pectin degrading enzymes (Karr & 

Albersheim 1970; Wu et al. 2018).  

Calcium cross-linking is important to cell-cell adhesion in most higher plants (Daher & 

Braybrook 2015), but in addition to calcium cross-linking, sugar beet cell walls rely on ester 

bonds to maintain cell wall integrity (Marry et al. 2006). Substantial levels of ferulic acid are 

cross-linked to pectic arabinosyl and galactosyl residues and the combination of these linkages 

influences such properties as intercellular adhesion, extensibility, enzymatic digestion, texture, 

and lignification (Guillon & Thibault 1989; Waldron et al. 1999). Ferulic cross-links also occur 

in grasses (Anders et al. 2012), but in contrast to grasses, sugar beet cell walls contain relatively 

low levels of lignin (Dinand et al. 1999; McGrath & Townsend 2015). Phenolic cross-links, such 

as occur with ferulic acid, hinder degradation by reducing access to hydrolytic enzymes (Hartley 

et al. 1992).  

Cell wall degrading enzymes 

Because the plant cell wall is an important barrier to pathogen penetration, pathogens have 

evolved several strategies to overcome this obstacle. Some fungi, such as the Pucciniales (rust 

fungi), use appressoria to directly penetrate the cell wall using mechanical pressure (Talbot 2019; 

Wang et al. 2005). Other plant pathogenic fungi penetrate through stomata or other natural 

openings, such as wounds (Latunde-Dada et al. 1999; Misaghi 1982). However, the majority of 

fungi rely on cell wall degrading enzymes to not only penetrate cells and colonize tissues, but 

also to break down cell wall polymers into usable nutrients (Annis & Goodwin 1997). The cell 

wall is complex and heterogeneous in its construction and the arsenal of cell wall degrading 

enzymes produced by plant pathogens mirrors this diversity (Kubicek et al. 2014). Major classes 

28 

 
of cell wall-degrading enzymes produced by pathogenic fungi include pectinases, cellulases, 

hemicellulases, cutinases, and polysaccharide lyases (Kubicek et al. 2014).  

Glycoside hydrolase (GH) is the general term for enzymes that cleave glycosidic bonds in 

oligo- or polysaccharides using hydrolysis (EC 3.2.1.-). Glycoside hydrolases are an extensive 

group of enzymes grouped into roughly 173 families based on amino acid sequence and folding 

similarity (Carbohydrate Active Enzyme database, http://www.cazy.org; Drula et al. 2022). 

While members of this large family differ in many aspects, most have a common hydrolytic 

mechanism. Hydrolysis of the glycosidic bond is catalyzed by two amino acid residues, one that 

acts as a proton donor and the other as a nucleophile. Depending on the position of the 

nucleophile, the reaction will result in either a retention or an inversion of the anomeric carbon 

(ie. inversion would result in β-GalA → α-GalA). The catalytic residues in retaining enzymes are 

~5.5Å as opposed to ~10Å for inverting enzymes (Davies & Henrissat 1995; McCarter & 

Withers 1994).  

Cellulases are characterized by the ability to hydrolyze the β-1,4-glycosidic bonds in 

cellulose.  Three main types of glycosidic enzymes are known: endoglucanases (EC 3.2.1.4), 

exoglucanases, including glucohydrolases (EC 3.2.1.74) and cellobiohydrolases (EC 3.2.1.91), 

and β-glucosidases (EC 3.2.1.21; Teeri 1997). Endoglucanases randomly cut within the 

disordered, non-crystalline regions of the cellulose chains, resulting in oligosaccharides of 

various lengths. Exoglucanases act progressively on the free ends to release monosaccharide 

units. Glucohydrolases cleave 1 or 2 units from the reducing end producing glucose or 

cellodextrins (disaccharide of glucose) and cellobiohydrolases cleave from the non-reducing end 

producing cellobiose. β-glucosidases hydrolyze cellodextrins and cellobiose into glucose. 

29 

Collectively, this combination of enzymes can reduce the cellulosic network of the cell wall 

completely to glucose and thus a readily available source of energy (Mota et al. 2018).  

Because hemicellulose cross-links the cellulose microfibrils, much of the cellulose network is 

inaccessible to cellulases. Thus, enzymes are needed that can degrade the hemicellulose network. 

Hemicellulose is a branched polymer made up of a number of different saccharide bonds, 

particularly a xylanglucan backbone (Scheller & Ulvskov 2010). Major types of enzymes with 

activity against hemicellulose include xylanases (EC 3.2.1.8), xyloglucanases (EC 3.2.1.151), 

α-galactosidases (EC 3.2.1.22), α-arabinosidases (EC 3.2.1.55) and β-galactosidases  

(EC 3.2.1.23). Several additional enzymes are involved in the disassembly of hemicellulose that 

can affect the various sugars and bonds involved (Shallom & Shoham 2003).  

Pectic polymers fill in the voids between the cellulose-hemicellulose network, reducing 

access to the cellulosic chains. In order to access the cellulose, the network of pectin molecules 

needs to be disassembled. The major enzymes involved in degredation of pectin include: 

endopolygalacturonase (EC 3.2.1.15), exopolygalacturonase (EC 3.2.1.67), and 

rhamnogalacturonase (EC 3.2.1.171), all three types are accommodated in glycoside hydrolase 

(GH) family 28 (Kubicek et al. 2014). Endopolygalactuonases (Endo-PG) hydrolyze the α-1,4-

D-galactosiduronic bonds of the homogalacturonan backbone randomly yielding 

oligogalacturonides of various lengths. Endo-PGs typically have a preference for unesterified 

substrates, but different forms of the enzyme have varying tolerance for esterification. 

Exopolygalacturonases (Exo-PG) cleave a single galacturonic residue from the non-reducing end 

of the homogalacturonan polymer and are unable to degrade esterified substrates (Albersheim et 

al. 2011; Kubicek et al. 2014). Rhamnogalacturonan hydrolyzes the bond between 

α-D-galacturonic acid and α-L-rhamnose in rhamnogalacturonan I. Additional enzymes are 

30 

involved in degradation of side chains of RG-I as well as for RG-II such as α-L-rhammosidase 

(EC 3.2.1.40), β-L-rhammosidase (EC 3.2.1.43), and α-L-fucosidase (EC 3.2.1.51). Because 

there are more than 20 distinct glycosidic linkages between RG-I and RG-II, each bond requires 

a different enzyme to catalyze the various linkages (Cook et al. 1999; Voragen et al. 2009).  

Another family of enzymes that play a prominent role in cell wall deconstruction are 

pectinolytic lyases. These enzymes break the α-1,4-D-galacturonan bonds by means of β-

elimination rather than hydrolysis (Zheng et al. 2021). This cleavage mechanism typically relies 

on an arginine or lysine residue that functions as a Brønsted base with a water molecule acting as 

a Brønsted acid. Transition metal ions or Ca2+ ions can assist in catalyzing the reaction by 

assisting in binding the substrate and affecting the charge of target protons. The pectinolytic 

lyases are differentiated by their affinity for methylated or demethylated pectin with pectin 

lyases (EC 4.2.2.10) favoring methylated pectin and pectate lyases (EC 4.2.2.2) favoring 

demethylated pectate (Zheng et al. 2021).  

Pectin is synthesized in the Golgi apparatus and deposited in the cell wall in its 

methylesterified form with 70-80% of the GalA residues methylesterified (Mohnen 2008). 

During cell wall construction, plant pectin methylesterases (EC 3.1.1.11) facilitate the removal of 

the methyl group from the esterified carboxyl at C-6 by transfer of the methyl group to a water 

molecule producing de-esterified pectate and methanol (Daher & Braybrook 2015; Mohnen 

2008; Kohll et al. 2015). The de-esterified HG can then be readily cross-linked with Ca2+ ions, 

which occurs primarily in the middle lamella and contributes to cell-to-cell adhesion (Daher & 

Braybrook 2015). When the cell wall needs modification, methylesterases regulate access of the 

pectic substrate to various enzymes allowing the regulation of construction and deconstruction of 

31 

the cell wall (Daher & Braybrook 2015; Wu et al. 2018). This process has implications in 

abscission, fruit ripening and softening, cell growth and dehiscence.   

Evolution of gene families 

A gene family is a set of functionally related genes that are thought to have been formed by 

the duplication and diversification of a single original gene (Ohta 2000). Gene families range 

greatly in dimension and heterogeneity from families with a small number of closely related gene 

copies to families with thousands of copies of transposable genetic components that have no 

identified function. Family members can be located in a cluster on one chromosome or scattered 

throughout the entire genome (Ohta 2000; Panchy et al. 2016). The concept of gene families 

applies not only to genes within a single genome (paralogs), but also to related genes between 

genomes (orthologs) that have arisen by duplication from an ancestral gene. The result is a set of 

related genes that can function at different stages of development, in various tissues, or on 

diverse substrates, thus potentially providing an adaptive advantage (Lažetić & Troemel 2021).  

Gene families can be organized into several types (Ohta 2000). Tandemly arrayed gene 

families are typically associated with large gene families that require substantial quantities of 

gene products, such as ribosomal RNA. Pseudo gene families have members that resemble 

functional genes but contain errors or only partial copies exist such that a functional product is 

not produced (Zhang & Gerstein 2003). Gene families originating from “selfish” genetic 

elements can propagate within the genome despite their being neutral or even detrimental to the 

organism (Doolittle & Sapienza 1980). Often these elements have the capacity to be self-

replicating and exist primarily because of this capacity to duplicate (Muñoz-López & Garcia-

Pérez 2010). Diverse multigene families contain large numbers of genes with diverse functions 

32 

 
(Ohta 1991). Gene families are an important feature of organismal genomes that provide genetic 

diversity and present opportunities for the evolution of new genes and functions.  

 Duplication events that result in the formation and expansion of gene families can occur at 

four structural levels: 1) exon duplication and shuffling, 2) entire gene duplication, 3) multigene 

duplication, and 4) whole genome duplication (Panchy et al. 2016). These genomic events result 

in structural variations that have been associated with fitness benefits such as fungicide 

resistance (Jones et al. 2014). For example, in Erysiphe necator, structural variation resulting in 

copy number variations, increased the prevalence of a fungicide tolerant allele (Jones et al. 

2014). The struggle between host and pathogen is a strong driver of diversification and 

expansion of gene families, especially in situations where there is direct contact between host 

and pathogen proteins (Lažetić & Troemel 2021). Expansion of gene families in pathogens have 

been associated with cell wall degradation gene families, which, based on the host/pathogen 

struggle concept, implicates cell wall degradation as a substantial factor in virulence and possibly 

for pathogenicity (Morales-Cruz et al. 2015). 

Prior to 1970, the accepted model for the evolution of gene families was based on the 

sequence patterns observed in hemoglobin α, β, γ, and δ chains and myoglobin (Ingram 1961). In 

this model, divergence of family members is gradual with each gene copy evolving 

independently after the duplication. Thus, orthologous genes are more closely related than 

paralogous genes. This model is known as divergent evolution and has been considered to be the 

predominant mode of evolution for gene families, especially when member genes are separated 

in the genome (Ohta 2000). However, during the 1970’s, it was determined that not all gene 

families evolved in the same manner as the globin protein families. Notably, sequence patterns of 

the ribosomal RNAs (rRNA) in Xenopus could not be explained using the divergent evolution 

33 

model (Nei & Rooney 2005). Ribosomal RNA is encoded by a large number of tandemly 

repeated genes separated by intergenic regions. Brown et al. (1972) showed that, in Xenopus, 

nucleotide sequences of these intergenic regions were more similar within a species than they 

were between species. This observation becomes even more difficult to explain using the 

divergent evolution model when it is noted that the coding regions for the 18S and 28S ribosomal 

subunits are highly conserved and are very similar even among distantly related species.  

In order to explain the observed patterns in rRNA genes, Brown et al. (1972) proposed the 

model of concerted evolution. According to this model, member genes evolve together, in a 

concerted manner rather than independently and mutations that occur in individual repeat units 

spread through all member genes within a species (Liao 2003). This process has the effect of 

homogenizing member genes, and the result is that paralogous genes are more similar than 

orthologous genes. Two key mechanisms that appear to be responsible for the molecular pattern 

attributed to concerted evolution are repeated unequal crossing over and gene conversion. 

Unequal crossing over occurs when homologous sequences are not paired precisely and results in 

the deletion of a sequence in one strand and replacement with a duplication from its sister 

chromatid. Repeated occurrence of unequal crossing over has the tendency to homogenize the 

gene family (Panchy et al. 2016). Gene conversion involves the unidirectional transfer of genetic 

material during homologous recombination and can occur between sister chromatids or between 

homologous sequences on either the same or different chromosome (Chen et al. 2007). The 

exchange is initiated by double strand breaks and subsequent mismatch repairs that occur during 

DNA replication. With the intact strand used as a template to repair the broken strand, an allele 

at one locus is changed by copying a sequence form a different locus, thus resulting in multiple 

copies of the same allele.  

34 

A third model of gene family evolution is known as the birth-and-death model of evolution 

which was first proposed to explain the pattern of evolution of the major histocompatibility 

complex (MHC) gene family of mammals (Hughes & Nei 1989; Nei et al. 1997). This model 

also recognizes the creation of new genes through duplication, but proposes some duplicates are 

maintained in the genome for long periods of time but others are lost due to deletions or 

deleterious mutations (Demuth & Hahn 2009; Elrin- López et al. 2012). Multigene families such 

as MHC and immunoglobin (Ig), known for having large copy numbers, were found to be 

inconsistent with concerted evolution since member genes from the same species were not 

necessarily more closely related to each other rather than to member genes of other species (Nei 

et al. 1997).  

Purifying selection has also been proposed as an alternative to the concerted model as an 

explanation for highly conserved gene families (Nei & Rooney 2005). Gene families responsible 

for essential cell processes, such as ribosomal RNA, tend to expand and contract under purifying 

selection that produces many members with redundant functions. While in some gene families, 

duplication allows for diversification, purifying selection removes gene copies that function at 

less than optimal capacity. In the case of rRNA, cells can require a large number of ribosomes to 

efficiently translate the proteins necessary to carry out cellular functions. Multiple copies of 

rRNA genes help to produce the necessary amount of ribosomal materials, however, divergence 

in rRNA sequence can reduce or impair translation efficiency. Additional evidence of purifying 

selection comes from the example of histone genes where researchers determined that the 

number of differences in synonymous sites was greater than differences in nonsynonymous sites 

for member genes (Rooney et al. 2002). The reasoning for this being that under purifying 

35 

selection, synonymous substitutions are expected to accumulate continuously while 

nonsynonymous substitutions should not.  

Baroncelli et al. (2016) examined genomic content of 10 Colletotrichum species, as well as 

six additional members of the Sordariomycetes, in order to better understand the molecular 

determinants of host range.  They found that gene content was closely associated with host 

range, particularly for carbohydrate active enzymes (CAZymes) and peptidases, which appear to 

be an important controlling factor regarding host range. Interestingly, it was not only expansion 

of these gene families that influenced host range, but also gene family contraction or loss seemed 

to play a major role in host range. Thus, the authors concluded that the factors that influenced 

host range in Colletotrichum have evolved according to the birth-and-death model of gene family 

evolution. Indeed, many gene families involved in production of secondary metabolites are 

thought to evolve by the birth-and-death model (Morales-Cruz et al. 2015). 

Further evidence of the role of expansion and contraction in the evolution of gene families, 

particularly those associated with secondary metabolism, comes from Morales-Cruz et al. (2015). 

In a study of 10 fungal genomes, 114 gene families were found with higher-than-expected rates 

of gains/losses. Gene families identified as overrepresented included pathogen-host interaction 

genes, secreted carbohydrate-active genes (CAZymes), and P450 enzymes (Morales-Cruz et al. 

2015). These functional groups are often associated with fungal virulence (Morales-Cruz et al. 

2015) and the expectation is that expansion of these gene families could provide an adaptive 

advantage. Notably, species that were associated with similar symptoms on their respective host 

had similar repertoires within these expanded gene families even though there may not be a 

correlation with phylogenetic relationship.  

36 

Expansion and contraction of gene families can be expected to reflect the strength and type 

of selection forces. Gene families that interact with substrates that are highly diverse or can 

evolve rapidly would be expected to maintain a diverse repertoire of member genes under 

positive selection. These types of families typically involve proteins with direct interactions 

between host and pathogen and these interactions can create some of the strongest drivers for 

diversification and expansion (Lažetić & Troemel 2020).  

Polygalacturonase gene families provide an example of the type of genes that would be 

expected to be maintained under positive selection due to direct interactions between host and 

pathogens. The genome repertoire of polygalacturonase genes has been associated with host 

range (King et al. 2011) and ecological strategy (Sprockett et al. 2011). Pathogens with a large 

host range face increased challenges related to virulence, cell wall degradation, and plant defense 

responses compared to pathogens with a narrower host range (King et al. 2011; Park et al. 2008; 

Sprockett et al. 2011). The diversity of polygalacturonase genes is expected to reflect the variety 

of pectic substrates present across the various host species susceptible to the pathogen (Cook et 

al. 1999; King et al. 2011; Park et al. 2008). Likewise, necrotrophic fungi have an expanded 

repertoire of polygalacturonase genes compared to biotrophs and saprophytes, possibly related to 

interactions with polygalacturonase-inhibiting proteins (PGIP) and directed by diversifying 

selection (Sprockett et al. 2011).  

Rhizoctonia solani AG 2-2 is an aggressive necrotroph with a fairly broad host range, 

affecting at least 20 crop species (Sneh et al. 1991). As predicted based on host range and 

ecological strategy, the draft genome of R. solani AG 2-2 reported by Wibberg et al. (2016) 

encodes large numbers of secreted proteins and in particular, glycoside hydrolases, the gene 

family that includes polygalacturonase. The contribution of polygalacturonase (PG) to disease 

37 

development for R. solani has been well established (Barker & Walker 1962; Bateman 1963; 

Sherwood 1966). Multiple forms of PG, having varied activity, have been characterized from R. 

solani strains with the number of isoenzymes per isolate significantly correlated to host range 

(Scala et al. 1980). Minier & Hanson (2020) analyzed the genomes of nine R. solani AG 2-2 

isolates and identified 151 putative polygalacturonase genes (average of 16.8 genes per isolate) 

with the most aggressive isolates having the greatest number of PG genes. Future work 

examining the distribution and variability of polygalacturonase genes in R. solani AG 2-2 could 

provide targets for novel polygalacturonase-inhibitor proteins that may help breeders develop 

better disease resistant varieties (Li & Smigocki 2018).  

Conclusions 

Interactions between pathogens and their host are complex and function at multiple levels. 

Not only is it difficult to identify the range of interactions within a single host-pathogen system, 

but these interactions can vary greatly even between different hosts of the same pathogen 

(Albersheim et al. 2011; Bellincampi et al. 2014; Dodman & Flentje 1970; Weinhold & Sinclair 

1996). Thus, generalization of host-pathogen interactions, especially of specific details, can be 

unreliable.  

Much work has been done examining the effects of Rhizoctonia solani AG 2-2 on sugar beet 

and other hosts grown in rotation. However, many of these studies relied on a small number of 

isolates from which to draw conclusions and, in my opinion, were overly dependent on 

generalizations. For example, much of the work on the infection process of R. solani has been 

done using R. solani AG 1 or AG 4 on rice and other foliar blight systems (Keijer 1996; Marshall 

& Rush 1980; Weinhold & Sinclair 1996; Zheng & Wang 2011). Much less work has been done 

38 

 
examining the infection process of roots by AG 2-2, or other AG, compelling a reliance on the 

assumption that foliar and root pathogens behave similarly.  

In addition, the subgroups of R. solani AG 2-2 that form the basis of behavioral distinction 

have come into question as to the reliability of their relationships. The ascription of behavioral 

characteristics to a group that may not reflect natural relationships is likely to present 

inconsistencies. Evolutionary relationships form the backbone for the study of any biological 

organism and are an essential tool for examining behavior, function, and inherited traits (Smith et 

al. 2020). This is especially true in predator/prey systems where the struggle to survive drives an 

arms race, with the predator developing new strategies to overcome prey defenses and the prey 

adopting new defenses to prevent the predator from robbing it of resources.  

With these principals in mind, this dissertation project had three key objectives:  

1) Generate a more natural classification scheme within R. solani AG 2-2 that does 

not rely on criteria that defined previous subgroups, namely growth at 35°C. A 

classification scheme that was based on evolutionary relationships was expected to 

provide novel insights into the population biology, behavior, and life history 

strategy of this complex group.  

2) Provide an overview of the population structure and distribution of R. solani 

AG 2-2 at multiple scales, including global, regional, and local levels. In order to 

accomplish this objective, we developed and utilized a set of microsatellite markers 

to genotype individuals from sugar beet growing regions around the world. These 

populations included isolates recovered from several crops in addition to sugar beet, 

such as soybean and dry bean, with the goal of providing growers and fellow 

researchers insight into the potential effects of including these crops in rotation.  

39 

3) Present an updated histopathological examination of the infection process of R. 

solani AG 2-2 using modern microscopy techniques. A comprehensive 

histopathological examination of sugar beet has not been performed since Rupple 

(1972). Open questions regarding the symptomology of Rhizoctonia root and crown 

rot still remain and this histopathological assessment was conducted with a focus on 

determining physiological factors that influence the development of the 

characteristic symptoms of Rhizoctonia root and crown rot. The project was also 

expected to provide insights into the mechanisms of both the infection process and 

the host defense response during plant-pathogen interaction.  

40 

 
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59 

 
 
CHAPTER 2: 

DEVELOPMENT OF MICROSATELLITE MARKERS AND THE PHYLOGENETIC 

ANALYSIS OF RHIZOCTONIA SOLANI AG 2-2 

60 

 
 
 
 
 
 
 
 
 
 
 
 
Introduction 

Rhizoctonia root and crown rot (RRCR), caused by Rhizoctonia solani Kühn (teleomorph: 

Thanatephorus cucumeris Donk), is a major soil-borne disease of sugar beet (Beta vulgaris) that 

occurs in growing regions throughout the world (Windels et al. 2009). The disease is 

characterized by dark, shallow lesions on the root with sharply defined margins (Neher & 

Gallian 2011; Windels et al. 2009). As the disease progresses, the lesions expand and can 

eventually involve the entire root causing yield loss, reduced sucrose content, and increased 

susceptibility to storage rots (Strausbaugh et al. 2011b; Windels et al. 2009). In addition to 

variety selection, cultivation practices, and fungicide applications, rotation with non-host crops is 

one of the most important management strategies for controlling losses to RRCR (Kirk et al. 

2008; Neher & Gallien 2011). Unfortunately, many of the crops commonly grown in rotation 

with sugar beet are also susceptible to many of the same strains that cause RRCR, which makes 

rotation choices challenging.  

Rhizoctonia solani is a fungal species complex whose members can be distinguished by their 

ability to anastomose, or fuse, with other members of the same anastomosis group (AG; Cubeta 

& Vigalys 1997; Parmeter et al. 1969). Anastomosis groups within R. solani represent 

independent evolutionary lineages that are reproductively isolated but have yet to be separated 

into taxonomic species (Cubeta et al. 1991; Hanson & Minier 2016; McCabe et al. 1999). The 

major challenge in resolving R. solani taxonomy lies in the lack of consistent morphological 

characters, particularly the lack of asexual conidia and in several AG, the lack of, or at best, a 

very rarely observed sexual stage (Thanatephorus cucumeris). Since anastomosis groups are 

defined by the ability of members to anastomose with one another and not with members of other 

AG, the most reliable method of determining the AG of an individual isolate is to pair the 

61 

unknown isolate with a tester isolate of known AG and determine hyphal fusion frequency 

(Adams 1988; Carling 1996; Parmeter et al. 1969). However, interpreting anastomosis reactions 

is not always straightforward due to the challenge of reproducibility and the rather subjective 

nature of analyzing hyphal behavior (Cubeta & Vigalys 1997). In addition, some AG can 

anastomose with members of another AG at a low frequency (Sneh et al. 1991), creating 

additional uncertainty in the assignment of AG. 

Several AGs contain well-defined cultural types, referred to as intraspecific groups (ISG), 

that have been separated based on a variety of characteristics such as DNA hybridization, 

sclerotia size and shape, pectic zymography patterns, or temperature tolerance (MacNish & 

Sweetingham 1993; Sharon et al. 2008; Sneh et al. 1991; Vilgalys & Cubeta 1994). Accurately 

determining ISG can be difficult especially with techniques such as DNA hybridization and 

zymography. While genetic data has been effective at separating the ISGs of some AGs, such as 

those of AG 4 and AG 1 (Kuninaga & Yokosawa 1985; Liu & Sinclair 1993; Sharon et al. 2008), 

there has been inconsistencies in other AG, such as AG 2-2, the primary causal agent of RRCR 

(Minier 2019; Sharon et al. 2008; Strausbaugh et al. 2011a).  

Traditionally AG 2-2 has been separated into three ISGs; AG 2-2IIIB, AG 2-2IV, and 

AG 2-2LP. These groups were originally separated based on host range with AG 2-2IIIB 

affecting mat rush (Juncus effusus), AG 2-2IV affecting sugar beet (Ogoshi 1987), and 

AG 2-2LP affecting zoysia grass (Zoysia spp.; Hyakumachi et al. 1998). While AG 2-2LP has 

been relatively consistent in relation to host, AG 2-2IIIB and AG 2-2IV are both known to affect 

sugar beet and host preference no longer defines or distinguishes the two groups (Sneh et al. 

1991). Instead, AG 2-2IIIB and AG 2-2IV are commonly separated based on growth at 35°C, 

where AG 2-2IIIB grows at 35°C and AG 2-2IV does not (Sneh et al. 1991). While DNA 

62 

homology has been reported to separate these groups (Sneh et al. 1991), the genetic relationship 

based on rRNA-ITS has been inconsistent and the monophyly of these subgroups is questionable 

(Carling et al. 2002; Sharon et al. 2008; Strausbaugh et al. 2011a). 

Despite the inconsistencies, interest in retaining the subgroups of AG 2-2 as ‘IIIB’ and ‘IV’ 

has remained high, primarily due to evidence of variation in virulence and host interactions 

related to the subgroups (Cappelli et al. 1999; Engelkes & Windels 1996; Strausbaugh et al. 

2011a). For example, corn has been shown to increase the prevalence of more aggressive strains, 

particularly those in AG 2-2IIIB, resulting in increased damage to sugar beet after rotation with 

corn (Ithurrart et al. 2004; Windels & Brantner 2004; Windels & Brantner, 2006). Since crop 

rotation is an important component in the management of RRCR, understanding how fungal 

populations are affected by crop rotation strategies is essential for making effective disease 

management decisions (Windels et al. 2009). 

Little is known about the population biology of R. solani AG 2-2 primarily due to its 

complicated taxonomy and the limited range of reliable vegetative characteristics that can be 

used for identification and classification (Ajayi-Oyetunde & Bradley 2018; Vilgalys & Cubeta 

1994). Consequently, the development of efficient molecular methods for evaluating R. solani 

AG 2-2 populations is a necessity. Several molecular tools have been developed for use in R. 

solani population biology, including inter-simple sequence repeats (ISSR; Zheng et al. 2013), 

allozymes (Liu et al. 1990; Pannecoucque et al. 2008), restriction fragment length 

polymorphisms (RFLP; Hyakumachi et al. 1998; O’Brien 1994), and genotyping by sequencing 

(GBS; Ajayi-Oyetunde et al. 2019). ISSR markers are dominant and so can lack information on 

heterozygosity and there is concern about reproducibility between labs, as the results can be 

difficult to interpret (Grover & Sharma 2016; Ng & Tan 2015). Allozyme and RFLP techniques 

63 

rely on the analysis of electrophoresis gels and the interpretation of complex banding patterns, 

which requires experienced personnel (Parker et al. 1998). Besides being laborious, allozymes 

and RFLPs may lack sufficient polymorphism to make identification of individuals practical in 

most systems and developing large enough libraries to address this would be cost prohibitive 

(Parker et al. 1998). RFLPs do not provide information about heterozygosity and allozymes may 

not be selectively neutral making both markers less than idea for population genetics especially 

when markers exist that can overcome these disadvantages. 

Genotype by sequence (GBS) is one of the more modern genotyping techniques that relies on 

restriction enzymes to reduce genomic complexity and next-generation sequencing to identify 

large numbers of single nucleotide polymorphisms (SNPs). While GBS can be a powerful tool 

for SNP discovery and genotyping, analysis of data can require a considerable investment of 

time for the evaluation of large populations (Hodel et al. 2016). There is also a concern for large 

amounts of missing data and therefore, transferability between studies (Hodel et al. 2016). With 

the strengths and limitations in currently available genetic tools, our goal for the current study 

was to develop a marker set that was robust, reproducible, and co-dominant while remaining 

inexpensive and easy to analyze, even for large studies.  

Microsatellites, or simple sequence repeats (SSR), are widely accepted as an effective 

molecular tool for examining intraspecific variation and fulfill the objectives for a suitable 

marker type (Ellegren 1991; Ellegren 2004; Jefferies et al. 1985; Schlötterer et al. 1991; Tautz 

1989). Numerous studies have used microsatellites to investigate important factors such as 

mating type (Biasi et al. 2016), disease dynamics (dos Santos Pereira et al. 2017) and population 

structure (Vaghefi et al. 2017; Wang & Chilvers 2016). Microsatellite marker sets have been 

developed for R. solani AG 1-1A (Zala et al. 2008), AG 3 (Ferrucho et al. 2009), and AG 4 

64 

(Haratian et al. 2013) and have been used successfully to examine R. solani populations on 

potato (Ferruch et al. 2013; Muzhinji et al. 2016), soybean (Ciampi et al. 2008) and on various 

crops in Iran (Haratian et al. 2013). Microsatellite markers have yet to be developed for use on 

AG 2-2 populations and those published for use in other AGs were not effective for the AG 2-2 

isolates screened (Frank Martin, personal communication). 

Traditionally, the identification of candidate microsatellite loci relies on constructing 

genomic DNA libraries that have been enriched for SSR sequences (Edwards et al. 1996; Hodel 

et al. 2016; Novelli et al. 2013; Selkoe & Toonen 2006; Zane et al. 2002). In the current study, 

we utilized NextGen sequencing (NGS) to identify candidate microsatellite loci, screened those 

loci for suitability, and developed a high-throughput screening protocol using automatic 

fragment size detection. In addition, we developed a marker set for phylogenetic analysis in 

order to clarify the relationship of the subgroups within AG 2-2. Results of the current study 

provide much-needed tools for improving our understanding of the structure, distribution, and 

dynamics of R. solani AG 2-2 populations. A more thorough and accurate assessment of R. 

solani AG 2-2 populations may prompt the recognition and development of novel management 

strategies that reduce the impact of RRCR of sugar beet and rhizoctonia root rot of other crops.  

Materials and Methods 

Fungal material  

Fungal isolates (Table 2.1) were obtained from the collection of Dr. Linda Hanson (USDA-

ARS, East Lansing, MI) which had been stored on dried barley grains at -20°C (Naito et al. 

1993). Infested barley grains were recovered from storage, placed on potato dextrose agar (PDA, 

65 

 
Sigma-Aldrich, St. Louis, MO) and allowed to grow for 5 to 7 days at room temperature. 

Cultures were inspected for contamination prior to transfer or use in subsequent procedures.  

DNA extraction 

Isolates were grown in static culture on malt-extract broth (MEB; Sigma-Aldrich, St. Louis, 

MO) in a petri dish for 5 to 7 days at room temperature (20 – 22°C). The mycelial mat was 

harvested using forceps, placed in a sterile 50 mL centrifuge tube, and rinsed twice with 

phosphate buffered saline (PBS; Cold Spring Harb Protocol 2006, doi:10.1101/pdb.rec8247). 

Fungal tissue was lyophilized (VirTis Genesis, SP Scientific, Warminster, PA) and ground in a 

modified paint shaker using 6 mm ceramic beads (Zircoa, Inc., Solon, OH).  

DNA extractions from R. solani proved challenging due to the presence of what appeared to 

be exopolysaccharides that clogged column-based kits and inhibited PCR. A modified protocol 

based on the OmniPrep for Fungi kit (G-Biosciences, St. Louis, MO) was developed for 

extracting genomic DNA from lyophilized, ground fungal tissue that yielded DNA suitable for 

use in PCR amplification and for Illumina sequencing. Approximately 20 - 25 mg of ground, 

lyophilized tissue was added to a 2 mL microcentrifuge tube, rinsed twice with 1 mL PBS and 

centrifuged at 6,500 x g for 10 min. to remove soluble contaminants. After extraction with 500 

µL chloroform/isoamyl alcohol (24:1), samples were treated with 5 µl RNase-A (5 µg/µl) for 30 

minutes at room temperature and extracted a second time with 500 µL chloroform/isoamyl 

alcohol (24:1). The aqueous phase was treated with Stripping Solution and Precipitation Solution 

provided with the OmniPrep for Fungi kit as described in the manufacturer’s instructions. 

Genomic DNA was precipitated with 500 µl of 100% isopropyl alcohol, pelletized by 

centrifugation at 12,000 x g for 5 min, and suspended in 450 µL Tris-EDTA (TE) buffer (Cold  

66 

 
Table 2.1 Rhizoctonia solani isolates used in the current study including for which aspects of the 
study the isolate was used.  

Isolate 

Original Name 

AG 

Proposed 
subgroup 

Host 

Origina 

Collector 

Seqc 

Phylogd 

Genoe 

Rs850 

Rs866 

C-116S 

F30 

Italien 

R1 

R4 

R9 

Rs 07-102-2 850 

Rs 07-110-2 866 

C-116S 

F30 

Italien 

R1 

R4 

R9 

Rs1012 

Rs 08-16-1 1012 

Rs1146 

Rs 08-87-1 1146 

Slovakia 

Slovakia 

W-22 

F321 

W-22 

F321 

Rickard 

Rickard 

2C1 

F508 

F517 

F521 

F551 

Rs255 

Rs331 

Rs890 

Rs588 

2C13 

R-164S 

Rs200 

Rs496 

Rs296 

5C5 

Rs300 

Rs393 

RH188 

RH193 

Rs106 

Rs481 

2C1 

F508 

F517 

F521 

F551 

Rs 06-28-3 255 

Rs 06-64-4 331 

Rs 07-122-1 890 

Rs 60-245-1 588 

2C13 

R-164S 

Rs 05-132-2 200 

Rs 06-221-3 496 

Rs 06-49-4 296 

5C5 

Rs 06-54-3 300 

Rs 06-79-1 393 

RH188 

RH193 

Rs 05-43-1 106 

Rs 06-218-2 481 

2-2IIIB 

2-2IIIB 

2-2IIIB 

2-2IIIB 

2-2IIIB 

2-2IIIB 

2-2IIIB 

2-2IIIB 

2-2IIIB 

2-2IIIB 

2-2IIIB 

2-2IIIB 

2-2IIIB 

2-2IIIB 

2-2IIIB 

2-2IIIB 

2-2IIIB 

2-2IIIB 

2-2IIIB 

2-2IIIB 

2-2IIIB 

2-2IIIB 

2-2IV 

2-2IV 

2-2IV 

2-2IV 

2-2IV 

2-2IV 

2-2IV 

2-2IV 

2-2IV 

2-2IV 

2-2IV 

2-2IV 

2-2IV 

PR 

BR 

PR 

PR 

BR 

BR 

BR 

BR 

PR 

PR 

PR 

BR 

PR 

PR 

PR 

BR 

PR 

BR 

PR 

PR 

PR 

BR 

BR 

BR 

BR 

BR 

BR 

BR 

BR 

BR 

PR 

BR 

BR 

BR 

BR 

Sugar beet 

Sugar beet 

MN 

MN 

C. Windels 

C. Windels 

Sugar beet 

Japan 

A. Ogoshi 

Sugar beet 

Sugar beet 

Sugar beet 

Sugar beet 

Sugar beet 

Sugar beet 

Sugar beet 

ID 

C. Strausbaugh 
Europeb  B. Holtschulte 

CO 

TX 

CO 

MN 

MN 

E. Ruppel 

C. Rush 

E. Ruppel 

C. Windels 

C. Windels 

Sugar beet 

Europeb  B. Holtschulte 

Bean root 

WI 

R.T. Sherwood 

Sugar beet 

Sugar beet 

ID 

C. Strausbaugh 
Europeb  B. Holtschulte 

- 

MT 

B. Bugbee 

Sugar beet 

Sugar beet 

Sugar beet 

Sugar beet 

Sugar beet 

Sugar beet 

Sugar beet 

Sugar beet 

- 

ID 

ID 

ID 

ID 

MN 

MN 

MN 

MN 

MT 

C. Strausbaugh 

C. Strausbaugh 

C. Strausbaugh 

C. Strausbaugh 

C. Windels 

C. Windels 

C. Windels 

C. Windels 

B. Bugbee 

Sugar beet 

Japan 

A. Ogoshi 

Sugar beet 

Sugar beet 

Sugar beet 

Sugar beet 

Sugar beet 

Sugar beet 

Sugar beet 

Sugar beet 

Sugar beet 

Sugar beet 

MN 

MN 

MN 

MN 

MN 

MN 

Japan 

Japan 

MN 

MN 

C. Windels 

C. Windels 

C. Windels 

B. Bugbee 

C. Windels 

C. Windels 

H. Obihiro 

H. Obihiro 

C. Windels 

C. Windels 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

S 

S 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

S 

X 

X 

X 

X 

X 

X 

X 

X 

X 

a. Locale of isolation, abbreviations are standard for US states 
b. Precise location is unknown – name of isolates from Europe does not necessarily reflect region from which they were isolated 
c. Isolate genome was sequenced and assembled from Illumina paired-end reads  
d. Isolate was included in the multi-gene phylogenetic analysis  
e. Isolate was genotyped using 13 microsatellite markers. ‘S’ indicates isolate was used for initial identification of microsatellite loci 

67 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 2.1 Con’t  

Isolate 

Original Name 

AG 

Proposed 
subgroup 

Host 

Origina 

Collector 

Seqc 

Phylogd 

Genoe 

Rs542 

Rs 06-231-1 542 

ACC3-LP 

ACC3 2-2LP 

R09-23 

R09-25 

24BR 

39AR 

R-5 

Rs14-2 

ST6-1 

R09-23 

R09-25 

24BR 

39AR 

R-5 

Rs_14-2 

ST6-1 

2-2IV 

2-2LP 

2-2 (Int) 

2-2 (Int) 

2-2 (Int) 

2-2 (Int) 

4 

5 

5 

BR 

LP 

PR 

BR 

PR 

PR 

- 

- 

- 

Sugar beet 

MN 

C. Windels 

- 

Sugar beet 

Sugar beet 

- 

MI 

MI 

- 

L. Hanson 

L. Hanson 

Sugar beet 

Canada 

C. Truman 

Sugar beet 

Canada 

C. Truman 

Sugar beet 

Dry bean 

CO 

MI 

E, Ruppel 

J. Jacobs 

Sugar beet 

Japan 

A, Ogoshi 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

Spring Harb Protocol 2009, doi:10.1101/pdb.rec11601). A second DNA precipitation was 

performed by adding 45 µL 5M sodium acetate (pH 5.2) and 1 mL 100% ethanol and incubating 

at 4°C overnight. DNA was pelletized by centrifugation at 12,000 x g for 5 min. and then washed 

twice with 70% ethanol. The pellet was air dried and suspended in 50 µL TE buffer. 

In order to minimize shearing of the DNA, samples were mixed by inversion at all steps 

except the initial PBS rinses where samples were mixed by vortexing to facilitate removal of 

soluble compounds. Transfers of sample volumes containing genomic DNA were performed 

using large-orifice pipette tips. While the OmniPrep for Fungi kit recommends only a single 

chloroform extraction, a second chloroform extraction proved beneficial as it reduced 

contamination as determined by spectrophotometry (Nanodrop ND-8000, Thermo Fisher 

Scientific, Waltham, MA). DNA quantities were assessed using a Qubit 3.0 fluorometer (Thermo 

Fisher Scientific). 

Microsatellite marker identification and evaluation 

Prospective microsatellite loci were identified in-silico from the genome sequences of three 

R. solani AG 2-2 isolates (Table 2.1) selected to represent each of the three genetic groups 

identified in the preliminary work reported by Martin et al. (2014). The genomic libraries of 

68 

 
 
 
 
 
 
 
 
 
 
 
isolates ‘Rs850’ (Martin et al. group 1), ‘Rs866’ (Martin et al. group 2A) and ‘Rs588’ (Martin et 

al. group 2B) were indexed and combined in a single run on an Illumina HiSeq4000 (Illumina 

Inc., San Diego, CA) at the Michigan State University Genomics Core (MSU-GC; East Lansing, 

MI). Raw sequences were assembled using CLC Genomic Workbench v.9 (Qiagen, Redwood 

City, CA) using default settings. Since isolate ‘Rs850’ was used for the initial sequence for 

marker selection, additional efforts were made to improve assembly quality. After the first 

assembly in CLC Genomics Workbench, the ‘Rs850’ assembly was filtered to discard contigs 

with less than 15X coverage. Filtered contigs were exported to SeqMan NGen v.15 (DNASTAR, 

Inc., Madison, WI) and Illumina reads mapped to assembled contigs in four cycles to extend to 

ends. Resulting contigs were imported back into CLC Genomics Workbench where a final de 

novo assembly with all contigs and Illumina reads was performed using high stringency (96% 

identity).  

Initial marker selection was accomplished using BatchPrimer3 (You et al. 2008) to identify 

simple sequence repeats in isolate ‘Rs850’. Search parameters were set to exclude dinucleotide 

repeats and limit putative fragment sizes to between 100 and 250 bp. Potential marker loci for 

‘Rs850’ were compared to the assemblies of the other two isolates to determine suitability based 

on differences in the number of repeat units, indels in the flanking regions and that primer design 

was appropriate for all three lineages. Loci with problems based on these evaluations were 

discarded, and the next potential marker on the contig was evaluated until 33 putative markers 

were catalogued. Only one locus was selected from an assembled contig to minimize linkage.  

Annealing temperature (Ta) for the thirty-three potential markers was predicted with Thermo 

Fisher’s online Tm calculator (https://www.thermofisher.com) using Phusion polymerase 

parameters. Primer pairs with a predicted Ta value between 58°C and 61°C were selected and 

69 

amplification conditions were optimized using isolates ‘Rs850’, ‘Rs866’ and ‘Rs588’. Four 

MgCl2 concentrations (1.5, 2.0, 2.5 and 3.0 mM) were examined with no noticeable differences 

in amplification quality observed. Therefore, a MgCl2 concentration of 2.5 mM was used for all 

subsequent reactions.  

Multiplex PCR conditions and analysis 

Six additional isolates were evaluated for: amplification across all lineages, band intensity, 

noticeable size differences between isolates, and suitability for multiplexing. Sixteen 

polymorphic loci that amplified for all nine isolates were selected and fluorescently labeled for 

automatic fragment sizing. Forward primers were labeled with either 6-FAM or HEX fluorescent 

dyes (Integrated DNA Technologies, Coralville, IA) for use in duplex analysis (Table 2.2). 

Reverse primers were assessed with and without a 5’ GTTT- PIG tail (Brownstein et al. 1996) to 

evaluate effectiveness at reducing the incidence of stutter peaks. 

Fluorescently labeled PCR products were evaluated on a total of 23 isolates using an Applied 

Biosystems 3130 genetic analyzer (Applied Biosystems; Foster City, CA) by the MSU-GC. 

GeneScan 400HD-ROX (Applied Biosystems) was used as the size standard. Loci were 

evaluated for: consistency of the fragment length patterns with repeat unit, the presence of stutter 

peaks, failure to amplify, and potential overlap in allele sizes of duplexed loci. Primer 

concentrations were adjusted to provide similar levels of fluorescent signal for all loci and 

fluorescent dyes. 

70 

 
Table 2.2 Microsatellite loci evaluated in the current study for use on Rhizoctonia solani 
AG 2-2. The 5’ end of the forward primers were labeled with either HEX or 6-FAM 
fluorophores for automatic sizing. Amplicon length indicates the range of fragment lengths 
across all 13 loci. Primer concentrations were adjusted to the values in the ‘conc. μM’ 
column to yield similar fluorescent peak levels in multiplexed reactions. 

Locus a 

Repeat 
motif 

Amplicon 
length 

Dye 

Forward  

Reverse  

Primer sequences 5’ ->  3’ 

2547 (a) 

AACA 

214-222 

6-FAM  AATCRCTCGAATCGGTAATT 

ATCGGGAATCATACTACCGG 

4660 (a) 

5583 (b) 

5487 (b) 

759 (c) 

6145 (c) 

8703 (d) 

CGA 

AGA 

ACG 

CAG 

CAG 

GTT 

132-159 

HEX 

GTRATGGTGAGAGTGAGAGAA  CTCSTCGTCTGAAGAGTCATA 

182-200 

6-FAM  CGTCGAGGATCTCAAATATGT  TTGCTAATGGTTCCTTTACTG 

132-141 

HEX 

ATACCGAGAGTGTCTTTACSC  AAAACGACTGGGGAGGAA 

131-170 

6-FAM  CAACAGCACGCCMTYATG 

CAGAGGGYAATTGTTGTTGAA 

146-161 

HEX 

ATGCAGATGGTTTTGTACG 

CTAGAGATCGATGCTGTGTCT 

203-221 

6-FAM 

TGRGGTGGKGGATGTATTG 

TCTCGGTCRAGTTACAATGG 

6150 (d) 

TTTC 

130-166 

HEX 

TGATATCACCACATTCTTTSA 

CRATTGACGGTCTACTGTTGY 

5402 

7420 

2893 

1656 

TCG 

CGA 

138-156 

HEX 

CCATACGCTCATACTTGAGAC  CGTAGACGAAAGTGGAMRTAG 

170-176 

6-FAM 

TATCARGCAAACTTRACCAAT  AGACCACTCTACGAACCTTGY 

GGTGTT 

116-143 

HEX 

CAGCTGGYGTAGTAGAAGTGG  GAATCRACRCCRGCAGTAGA 

CAT 

131-152 

6-FAM  ATTCRGAACACTGGTTTGARC  CCTAACTTGAACCAGACGAY 

7676 
  a. Loci followed by the same letter were paired in a duplex reaction for 8 locus genotyping  

6-FAM  GAAYGGCGAGTCGTAGTG 

176-188 

GTT 

GTGGAACAAGTAYCAAACGTC 

conc 
(μM) 

0.1 

0.45 

0.1 

0.3 

0.35 

0.3 

0.2 

0.25 

0.3 

0.2 

0.45 

0.3 

0.3 

Final reactions were performed in 20μl volumes with 15ng of DNA template, 1 x Phusion II 

HF buffer, 200µM each dNTP, 2.5mM total MgCl2 and 1 unit of Phusion II HF polymerase 

(Thermo Fisher). Final primer concentrations used for each marker are shown in Table 2.2. PIG-

tailed reverse primers were unnecessary for reducing stuttering and unmodified reverse primers 

were used for all subsequent runs. Final PCR conditions consisted of 1 cycle of 98˚C for 2 min. 

followed by 27 cycles of 98˚C for 20 s, 57˚C for 20 s, 72˚C for 12 s and a final extension cycle 

of 72˚C for 5 min. Samples were diluted 1:50 with sterile distilled water prior to submission to 

MSU-GC . 

71 

 
 
 
 
Cloning 

Marker quality was assessed by cloning ten loci from seven isolates. Cloned loci were 

sequenced, examined for errors, and compared to data generated by automatic fragment sizing.  

Ten additional isolates that had potential issues at specific loci that had been noted during the 

examination of chromatograms, were cloned, sequenced, and assessed for errors. All loci were 

amplified using unlabeled primers under the conditions described above for SSR marker 

evaluation. PCR products were cloned using a Zero Blunt TOPO PCR cloning kit for sequencing 

(Thermo Fisher Scientific) following manufacturer’s instructions. DNA was extracted from 

colonies containing the vector (pCR-4Blunt-TOPO; Invitrogen, Carlsbad, CA) and PCR product 

using a GeneJET plasmid miniprep kit (Thermo Fisher Scientific) following manufacturer’s 

instructions. Vector products were sequenced by the MSU-GC using the primer set 

M13Forward(-20) (5’– GTAAAACGACGGCCAG) and M13Reverse                                      

(5’–CAGGAAACAGCTATGAC). Results were analyzed using Geneious Prime 2021.2.2 

(Biomatters Ltd., Auckland, New Zealand).  

Microsatellite data analysis 

Chromatograms from the microsatellite fragments were analyzed with Geneious Prime 

2021.2.2 microsatellite plugin 1.4.4 (Biomatters, Inc., Newark, NJ). Peaks were called using the 

Third Order Least Squares sizing algorithm. Bin sizes were predicted using 12 isolates and 

additional bin sizes were added as needed as further samples were analyzed. Allele calls were 

examined manually for potential errors and miscalls.  

Allelic and genotypic statistics were determined using MSAnalyzer v. 4.05 (Dieringer & 

Schlötterer 2003). Hardy-Weinberg equilibrium tests, allele frequency-based correlations (FIS), 

72 

 
null allele frequency and population differentiation statistics (FST) were generated in Genepop v. 

4.5.1 (Rousset 2008). Multi-locus genotypes and pairwise distances were determined with 

GenoType 1.2 (Meirmans & Van Tienderen, 2004) using the stepwise mutation model with 

missing data counted as one mutational step. A genotype accumulation curve (GAC) was 

generated in R v.4.2.1 (R Core Team 2022) using the package ‘poppr’ v.2.9.3 (Kamar et al. 

2014; Kamar et al. 2015) to determine the minimum number of loci needed to identify 90% of 

the genotypes in our dataset.  

The dataset was clone-corrected using ‘poppr’ and samples were assigned to clusters using 

untrained clustering (‘find.clusters’) where number of PCA variables (‘n.pca’) was set to 10 and 

number of clusters (‘choose.n.clust’) was set to 4. Cluster assignment for each isolate was added 

to the database and a minimum spanning network (MSN) generated using Bruvo’s distances 

(‘bruvo.msn’). An index of association plot was generated using the clone-corrected dataset to 

provide evidence for the predominant reproductive strategy within the population.  

Polymorphism information content (PIC) was calculated according to Anderson et al. (1993), 

where PIC values greater than 0.500 were considered highly informative. The equation used is 

listed below, where p is the proportion of the jth allele at locus i. 

2
𝑃𝐼𝐶𝑖 =  1 −  ∑ 𝑝𝑖𝑗

𝑗

In addition to cluster analysis performed in ‘poppr’, population structure was investigated 

using the program STRUCTURE v.2.3.4 (Pritchard et al. 2000). The admixture model was 

employed using sampling location as prior information about population structure. Allele 

frequencies were set as independent among populations and ‘alpha’ and ‘lambda’ were inferred. 

Analysis was run for 500,000 reps after a burn-in period of 50,000 reps. The simulation was run 

at five values for k (2,3,4,5,6) and the mean(ln) and Pr(X|k) of the runs were compared, with 

73 

 
higher values indicating a better fit for the model. Population differentiation analyses were 

performed in R v.4.2.1 (R Core Team 2022) using the package ‘mmod’ (Winter 2012). AMOVA 

was conducted using ‘poppr’ with 999 repeats and correction for clones. Discriminant analysis of 

principal components (DAPC) was performed using the package ‘adegenet’ v.2.1.8 (Jombart 

2008; Jombart & Ahmed 2011) with five principal components and two discriminant analysis 

categories.  

Illumina sequencing of representative isolates  

Twenty-two R. solani isolates (Table 2.1) were indexed and shotgun sequenced on an 

Illumina HiSeq4000 (Illumina Inc., San Diego, CA) by the MSU-GC. Raw sequences were 

assembled using CLC Genomics Workbench v.9 using default settings. Raw reads and 

assembled contigs were imported into SPAdes v.3.11.0 (Nurk et al. 2013) and assembled to 

contigs with k-mer selection set to ‘automatic’, error correction ‘on’, and contigs considered 

‘untrusted’.  

Assembled genomes were evaluated with QUAST v.5.0.2 (Gurevich et al. 2013) to determine 

number of contigs, total assembly length and N50 value. Haploid genome size, repetitive 

proportion of the genome, heterozygosity and sequencing coverage was estimated using Jellyfish 

v.2.3 (Marcais & Kingsford 2011) and GenomeScope (Vurture et al. 2017) using a k-mer setting 

of 21. Completeness of finished genomes were assessed using BUSCO v.5.3 (Waterhouse et al. 

2017) set to genome mode with ‘agaricomycetes_odb10’ as the database model. The R. solani 

AG 2-2IIIB draft genome (accession no. CYGV01000000), published by Wibberg et al. (2016), 

was retrieved from GenBank and analyzed using the same settings to serve as a benchmark for 

comparison.  

74 

 
Phylogenetic analysis 

Gene sequences for calmodulin, 25S-rRNA, rpb1, rpb2, tef1a, and β-tubulin were identified 

from annotations associated with the draft genome of R. solani AG2-2IIIB accession number 

CYGV01000000. (NCBI; Wibberg et al., 2016). Homologous sequences were identified in the 

Illumina sequenced isolates using the BLAST function within Geneious Prime and primer pairs 

were designed using Primer3 (Table 2.3; You et al. 2008). Additional sequences were identified 

using the BLAST function for the remaining isolates and all sequences were trimmed using the 

associated primers.  

Table 2.3 Primer sequences used for phylogenetic analysis of  Rhizoctonia 
solani AG 2-2 isolates. 

gene 

rpb2 

tef1 

calmod 

Btub 

25S 

rpb1 

primer name 

sequence (5’ → 3’)  

RPB2-980modF  
RPB2-7modR 

GARACYCCGRAAGGACAAG 
CCCATTGCTTGTTTRCCCATG 

TEF 457F 
TEF 1142R 

Cal291F 
Cal704R 

Btub256F 
Btub968R 

R25S368F 
R25S1064R 

RPB1F 
RPB1R 

GATTTCATCAAGAACATGAT 
ACTTGACTTCAGTGGTCA 

CACCACCAAGGAACTMGGCAC 
TCGTAGTTGATCTGRCCATCGC 

CTACTACAACACYGTAGGAG 
GRAGATCAGAGTTGAGCT 

GCCTACGATTCAGAGTCCGA 
AGCCGTTCTTCGATRTTCGTAGC 

CACGCCATGGCYGGTCGAGA 
CACCGAGCGTRACGTTCTTA 

Tm

60 

60 

64 

60 

64 

64 

Primer sequences were synthesized by Integrated DNA Technologies, (Coralville, IA) and 12 

additional isolates, including two that were duplicates of those sequenced in silico, were 

amplified using PCR. Reactions were performed in 50μl volumes with 20ng of DNA template, 1 

x Phusion II HF buffer, 1.5 mM MgCl2, 0.5 µM of each primer, 200 µM each dNTP, and 1 unit 

of Phusion II HF polymerase. Final PCR conditions consisted of 1 cycle of 98˚C for 1 min. 

followed by 34 cycles of 98˚C for 10 s, Ta for 10 s, 72˚C for 15 s and a final extension cycle of 

75 

 
 
 
 
72˚C for 5 min. Ta (annealing temperature) values for each primer set are listed in Table 2.3. 

Samples were cleaned on gel filtration columns (Sephadex G-50 superfine; GE Healthcare Life 

Sciences; Pittsburg, PA) and submitted to MSU-GC to be sequenced using the Sanger 

sequencing method according to manufacturer’s specifications. 

Gene sequences for individual loci were aligned in Mega11 v.11.0.13 (Tamura et al. 2021) 

using the muscle algorithm with default settings. The ends of aligned sequences were manually 

trimmed so all sequences were of the same length. Trimmed sequences for all six loci were 

concatenated in Geneious Prime 2021.2.2 with a spacer of 5 Ns between each gene sequence. 

Concatenated sequences were exported in FASTA format, and a Neighbor-joining tree (Saitou & 

Nei 1987) was inferred in MEGA11 v.11.0.13 using the Tamura-Nei substitution model with 

uniform rates among sites. A maximum-likelihood tree was generated using RAxML v.8.2.11 

(Stamatakis 2014). Nucleotide data was partitioned by gene (calmodulin, 25S-rRNA, rpb1, rpb2, 

tef1a, and β-tubulin) and a tree was built using the GTR GAMMA I (general time reversable 

with estimated proportion of invariable sites with remaining sites gamma distributed) using the 

rapid bootstrapping algorithm with 1,000 replicates.  

Bayesian analysis was conducted using Mr Bayes v.3.2.7a (Ronquist et al. 2012). 

Concatenated sequences were partitioned into six-character sets by gene (calmodulin, 25S-rRNA, 

rpb1, rpb2, tef1a, and β-tubulin) and the rates, state frequency, and shape of each partition were 

allowed to vary independently. Rates for all partitions were set to a proportion invariant and the 

remaining gamma distributed (‘rates=invgamma’) with 6 possible states (‘nst=6’). The analysis 

was conducted in two runs for 1,100,000 generations with a sampling frequency of 1,000 and a 

25% burn-in value. Phylogenetic trees were prepared for publication using FigTree v.1.4.4 

(https://github.com/rambaut/figtree). 

76 

Results 

Microsatellite marker identification and evaluation 

The genome assemblies of isolates ‘Rs850’, ‘Rs688’ and ‘Rs588’ (Table 2.4) were examined 

for potential microsatellite loci and 33 putative markers loci were identified (data not shown). 

The most abundant microsatellites were trinucleotide (18), followed by tetra- (8), hexa- (6) and 

penta- (1). Of the thirty-three potential marker pairs originally identified in-silico, six loci were 

eliminated because of a 5°C difference in predicted annealing temperature compared to other 

primer sets, which would make them unsuitable for multiplexing. Another eight loci were 

eliminated due to the failure to amplify in one or more isolates and six loci were eliminated 

because observed fragment sizes were inconsistent with repeat unit size indicating probable 

indels in flanking region.  

Table 2.4 Results of NextGen sequencing and assembly 
for three isolates of Rhizoctonia solani AG 2-2. Data is 
shown for isolate ‘Rs850’ before (raw) and after (final) 
the additional assembly efforts. 

Isolate 

Rs850 (raw) 
Rs850 (final) 
Rs866 
Rs588 

# reads 
(million) 

# contigs 

N50 (bp) 

236 
- 
246 
265 

13,926 
13,792 
23,128 
18,179 

15.9 kb 
16.6 kb 
6.1 kb 
4.2 kb 

Avg. 
length 

7 kb 
- 
4.1 kb 
3.1 kb 

Typical stutter patterns (one or more peaks that are shorter than the main peak by an interval 

equal to the repeat unit) were not observed. There were, however, several instances of peaks that 

were 1 bp shorter than the major peak. These minor peaks generally had a peak intensity of less 

than 10% of the main peak and false allele calls were minimized by raising the peak threshold. 

PIG-tailed reverse primers showed no improvement over standard primers and were not used in 

the final analysis. Overall, stutter was not an issue and did not complicate analysis. 

77 

 
 
Data analysis 

Thirty multi-locus genotypes (MLG) were identified among the 36 isolates evaluated using 

the set of 13 microsatellite loci (Table 2.5). MLG14 and MLG16 both consisted of two isolates 

each while MLG2 consisted of five isolates. Each of the remaining 27 MLGs contained a single 

isolate.  

The number of alleles per locus ranged from 3 to 9 with an average of 5.62 alleles per locus. 

(Table 2.6). Unique genotypes per locus ranged from 3 to 12 with an average of 8.15 genotypes 

per locus. There was evidence that seven of the 13 loci had a deficit of heterozygotes and an 

estimated null frequency greater than 0.10. However, PIC values were greater than 0.500 in all 

but two loci.  

The genotype accumulation curve indicated that 90% of the genotypes identified with 13 loci 

could be detected with only 8 loci (Figure 2.1). By duplexing the eight most informative 

markers, the genotype for a sample could be generated in only four reactions, allowing up to 24 

samples to be genotyped on a single 96-well plate. Repeating the analysis using the eight loci 

identified 27 MLGs (Table 2.5), which was consistent with the expectation based on the 

genotype accumulation curve. With this reduced set of markers, the average number of alleles 

per locus increased from 5.62 to 6.38, observed heterozygosity increased from 0.45 to 0.50, and 

the average PIC value increased from 0.62 to 0.71 (Table 2.6). While the additional five loci did 

permit the discrimination of additional genotypes, the contribution was minimal, adding only an 

additional 3 MLGs (8%). 

78 

 
 
 
Table 2.5 Microsatellite alleles and multi-locus genotypes for 36 Rhizoctonia solani 
AG 2-2 isolates based on either 8 or 13 loci.  

13 loci 

8 loci 

Isolate  MLGa  Na 

b  MLGa  Na 

b 

759 c 

1656 c 

6145 c 

C116S 
R-164S 
RH188 
RH193 
Rs542 
Rs106 
Rs481 
Rs496 
Rs200 
Rs331 
2C13 
Rs588 
ACC3-LP 
F521 
R1 
Rs1012 
R9 
R4 
2C1 
F30 
F551 
R09-23 
Rs1146 
F508 
Rs890 
W-22 
Italien 
Rs255 
Rs866 
Slovakia 
Rs393 
24BR 
39AR 
F517 
R09-25 
Rs850 

5 
12 
14 
14 
18 
16 
16 
17 
15 
21 
3 
19 
29 
8 
11 
25 
13 
26 
2 
2 
2 
2 
2 
6 
24 
28 
9 
20 
23 
27 
30 
1 
4 
7 
10 
22 

13 
13 
12 
13 
14 
14 
14 
15 
16 
16 
17 
17 
16 
19 
19 
19 
20 
20 
21 
21 
21 
21 
21 
21 
21 
21 
22 
22 
22 
22 
22 
23 
23 
23 
23 
23 

5 
11 
13 
13 
17 
15 
15 
16 
14 
20 
3 
18 
27 
8 
8 
24 
12 
12 
2 
2 
2 
2 
2 
6 
23 
26 
9 
19 
22 
25 
2 
1 
4 
7 
10 
21 

8 
8 
7 
8 
9 
9 
9 
10 
11 
10 
11 
12 
11 
11 
11 
12 
12 
12 
13 
13 
13 
13 
13 
14 
13 
14 
15 
15 
14 
15 
14 
16 
16 
15 
15 
13 

131 
140 
140 
140 
140 
140 
140 
140 
140/152 
131 
140/149 
140 
131/140 
152 
152 
131/140 
152 
152 
131/137 
131/137 
131/137 
131/137 
131/137 
152/158 
152/161 
149/158 
152/161 
131/140 
152/170 
131/140 
131/137 
131/140 
131/140 
131/140 
152/161 
131/167 

137 
131 
131 
131 
146 
131 
131 
146/149 
149 
146/152 
146/146 
144/149 
134 
131 
131 
137 
131 
131 
134/140 
134/140 
134/140 
134/140 
134/140 
131/134 
131/134 
131/134 
131/134 
134/137 
131/134 
131/134 
134/140 
131/134 
131/134 
134/140 
131/134 
134 

146 
152 
152 
152 
152/158 
152/158 
152/158 
152/158 
152/158 
149 
158 
152/158 
152/161 
152/155 
152/155 
149 
152/155 
152/155 
149 
149 
149 
149 
149 
152/158 
149/155 
152/155 
149/158 
146/149 
149/158 
149/158 
149 
149/158 
149/158 
146/149 
149/158 
149/152 

Locus 

5583 c 

182 
191 
191 
191 
191 
191 
191 
191 
191 
185 
191 
191 
- 
188 
188 
188 
188 
188 
185 
185 
185 
185 
185 
188/200 
188 
200 
188/200 
185 
188/200 
185/191 
185 
185/191 
185/191 
185 
188/200 
185/200 

8703 c 

6150 c 

212 
209 
209 
209 
209 
209 
209 
209 
209 
215/221 
209 
209 
218/218 
203/209 
203/209 
206/218 
203/209 
203/209 
206/218 
206/218 
206/218 
206/218 
206/218 
203/209 
206/206 
206/209 
203/209 
206/218 
203/209 
209/212 
206/218 
209/212 
209/215 
206/212 
203/209 
209/215 

134 
138 
138 
138 
138 
138 
138 
138 
138 
134 
138 
138 
150/166 
138/142 
138/142 
130/134 
138/142 
138/142 
130/138 
130/138 
130/138 
130/138 
130/138 
134/142 
134/142 
134/142 
142/158 
130/142 
142 
138 
130/138 
134/138 
134/138 
130/142 
134/142 
134 

a. multi-locus genotype 
b. number of alleles  
c. indicates locus was used in the 8-loci marker set 

79 

 
 
 
 
  
 
 
 
 
 
Table 2.5 (con’t) 

Isolate 

4660 c 

5487 c 

2893 

C116S 
R-164S 
RH188 
RH193 
Rs542 
Rs106 
Rs481 
Rs496 
Rs200 
Rs331 
2C13 
Rs588 
ACC3-LP 
F521 
R1 
Rs1012 
R9 
R4 
2C1 
F30 
F551 
R09-23 
Rs1146 
F508 
Rs890 
W-22 
Italien 
Rs255 
Rs866 
Slovakia 
Rs393 
24BR 
39AR 
F517 
R09-25 
Rs850 

147 
144 
- 
132 
132 
132 
132 
144 
144 
150 
144 
132/144 
144 
144 
144 
147/150 
144 
144 
147/150 
147/150 
147/150 
147/150 
147/150 
144 
144 
144 
144 
147/159 
144 
144/156 
147/150 
144/156 
144/150 
150/159 
144 
144/150 

132 
135 
138 
138 
135 
135 
135 
135 
132/135 
132 
135/138 
132/135 
138 
135 
135 
132 
132/135 
132/135 
132 
132 
132 
132 
132 
135 
135 
138/141 
135/138 
132/135 
135/138 
135/138 
132/132 
135/138 
135/138 
132/135 
135/138 
135 

122 
116 
116 
116 
116 
116 
116 
116 
116 
122 
116 
116 
146 
116 
116 
122 
116 
116 
128/140 
128/140 
128/140 
128/140 
128/140 
116 
116 
116 
116 
122 
116 
116/122 
128/140 
116/122 
116/128 
122/128 
116 
116/134 

Locus 

7676 

176 
179 
179 
179 
179 
179 
179 
179 
179 
179 
179 
179 
185 
182/188 
182/188 
179 
182/188 
182/188 
179 
179 
179 
179 
179 
182 
182/185 
182/185 
182 
179 
182/188 
179 
179 
179 
179 
179 
182/188 
179/182 

2547 

5402 

7420 

222 
218 
218 
218 
218 
218 
218 
218 
218 
214/218 
218 
218 
218 
222 
222 
218/222 
222 
222 
218/222 
218/222 
218/222 
218/222 
218/222 
222 
222 
222 
222 
218/222 
222 
218/222 
218/222 
218/222 
214/218 
218/222 
222 
218/222 

141 
144 
144 
144 
144 
144 
144 
144 
144 
144 
144 
144 
144 
141/144 
138/144 
144/156 
141/144 
138/144 
144/156 
144/156 
144/156 
144/156 
144/156 
141/144 
141/144 
141/144 
141/144 
144/156 
141/144 
144 
144/156 
144 
144 
144/156 
141/144 
144/147 

170 
170 
170 
170 
170 
170 
170 
170 
170 
170 
170/173 
170 
170 
170/176 
170/176 
170 
170/176 
170/176 
170 
170 
170 
170 
170 
170/173 
170/176 
170 
170/176 
170 
170/173 
170 
170 
170 
170 
170 
170/176 
170/173 

80 

 
 
 
 
 
 
 
 
 
 
Table 2.6 Summary of allele statistics for 13 microsatellite loci based on the genotypes of 36 
Rhizoctonia solani AG 2-2 isolates.

Locusa 

b 
NA

* 0759 

* 1656 

* 6145 

* 5583 

* 8703 

* 6150 

* 4660 

* 5487 

2893 

7676 

2547 

5402 

7420  

9 

7 

6 

5 

7 

7 

6 

4 

6 

5 

3 

5 

3 

 mean (13) j 

5.62 

  mean (8)      

 6.38 

c 
RA

1.78 

1.75 

1.75 

1.74 

1.73 

1.71 

1.68 

1.65 

1.59 

1.54 

1.53 

1.49 

1.27 

1.63 

1.72 

size range 

d 
NG

e 

HE

f 
HO

P(Hdeficit)g 

Nullh   

FIS

PICi 

131 - 170 

131 - 152 

146 - 161 

182 - 200 

203 - 221 

130 - 166 

132 - 159 

132 - 141 

116 - 146 

176 - 188 

214 - 222 

138 - 156 

170 - 176 

116 - 222 

131 - 221 

12 

11 

11 

8 

10 

11 

9 

6 

8 

7 

4 

6 

3 

8.15 

9.75 

0.779 

0.749 

0.750 

0.611 

0.639 

0.556 

0.072 

<0.001 

0.035 

0.748 

0.222 

< 0.001 

0.725 

0.667 

0.705 

0.583 

0.662 

0.361 

0.175 

0.120 

0.004 

0.650 

0.389 

< 0.001 

0.591 

0.306 

0.538 

0.250 

0.534 

0.389 

0.489 

0.556 

0.273 

0.306 

0.630 

0.449 

0.721 

0.503 

<0.001 

<0.001 

0.054 

0.714 

1.000 

- 

- 

0.086 

0.142 

0.065 

0.382 

0.049 

0.084 

0.209 

0.174 

0.194 

0.284 

0.531 

0.218 

0.769 

0.262 

0.739 

0.149 

0.739 

0.695 

0.730 

0.082 

0.715 

0.175 

0.696 

0.458 

0.671 

0.405 

0.641 

0.487 

0.583 

0.539 

0.530 

0.274 

0.526 

0.004 

-0.139 

0.482 

0.000 

-0.119 

0.270 

- 

- 

- 

- 

0.62 

0.71 

a.  Loci included in multi-locus genotype using 8 loci 
b.  Number of alleles per locus 
c.  Allelic richness 
d.  Number of genotypes per locus 
e.  Expected heterozygosity  
f.  Observed heterozygosity 
g.  P-value heterozygote deficiency  
h.  Estimated frequency of null alleles  
i.  Polymorphism Information Content 
j.  Mean values for allele statistics calculated for 13 loci and for the 8 most informative loci indicated by * 

81 

 
 
 
 
 
Figure 2.1 Genotype accumulation curve for 35 Rhizoctonia 
solani AG 2-2 isolates based on microsatellite loci. Red dashed 
line indicates 90% of the genotypes that were identified using 13 
microsatellite loci.  

Cluster analysis produced somewhat confusing results. When the number of clusters was 

determined automatically in ‘poppr’, between 3 and 12 clusters were identified depending on the 

maximum number of clusters setting. Few of these cluster assignments made sense biologically, 

particularly with respect to isolates ‘Rs850’, ‘24BR’, and ‘39AR’, which would cluster with 

isolates on different branches of the MSN despite the expectation that they would group in 

cluster 3. When the number of clusters exceeded five, multiple clusters were represented by a 

single isolate, which seemed excessively complex and uninformative. From preliminary work 

and the phylogenetic analysis conducted in the current study, the expectation was that there 

would be four clusters. By setting the number of clusters to four, cluster association was more 

consistent with biological expectations and the phylogenetic analysis. Interestingly, the isolates 

mentioned above, ‘Rs850’, ‘24BR’, and ‘39AR’, along with ‘Slovakia’ and ‘ACC3-LP’ 

(AG 2-2LP), clustered together on a branch leading to cluster 3 (Figure 2.2).  

82 

 
Figure 2.2 Minimum spanning network of 30 Rhizoctonia solani AG 2-2 isolates. 
Assignment of isolates to clusters was determined using cluster analysis in the R package 
‘poppr’ (Kamvar et al. 2014). Dataset was clone corrected and each node represents a 
single genotype. Distance between nodes is represented by lines where thick, dark lines 
indicate a close relationship and thinner, lighter lines indicate a more distant relationship.  

83 

 
 
The STRUCTURE analysis had similar confusing results. Analysis with k=6 had the highest 

mean(ln) value, but the bar plot was unnecessarily complex (Figure 2.3A). Setting k=4, as had 

been done with the cluster analysis in ‘poppr’, produced a more straightforward, interpretable 

result (Figure 2.3B). The main clusters identified by STRUCTURE analysis (when k=4) matched 

those identified by cluster analysis in ‘poppr’. Isolates ‘Rs850’, ‘24BR’, ‘39AR’, ‘Slovakia’, and 

‘ACC3-LP’ did not cluster together in the STRUCTURE plot like they did in the MSN analysis 

(Figure 2.2), but rather showed evidence of considerable admixture (Figure 2.3B). Isolate 

‘C116S’ also had a conspicuous level of admixture and shared a substantial amount of variation 

with isolate ‘ACC3-LP’.  

There was no evidence of population structure by geographical region of origin (p = 0.592; 

Table 2.7), in spite of large distances between growing regions (Figure 2.4). Much of the genetic 

variation was shared between growing regions with the Red River Valley sharing variation with 

isolates from all growing regions except Canada (Figure 2.5A). AMOVA plots indicate non-

significant variation between geographical populations which also supports the lack of 

population structure by region of isolation (Figure 2.6A; p = 0.603). Despite R. solani AG 2-2 

populations being primarily clonal (𝑟̅𝑑 = 0.288; p = 0.001; Figure 2.7), our data indicates the 

likely occurrence of long-distance, even intercontinental, dispersal. For example, one of the 

isolates from Europe, ‘Slovakia’, had a nearly identical genotype as isolate ‘24BR’ from Canada, 

varying by only 1 allele out of 26. (Table 2.5).   

84 

 
 
Figure 2.3 Structure plots for 36 Rhizoctonia solani AG 2-2 isolates inferred from 13 
microsatellite loci.  A) plot assuming 6 populations (k=6; mean(ln)= -656.6).  B) plot assuming 
4 populations (k=4; mean(ln)= -733.7). Letters in parenthesis indicate region of isolation: 
NW=Northwest U.S.; PL= Plains region, U.S.; MW = Midwest U.S.; RRV= Red River Valley, 
U.S.; TX= Texas, U.S.; EUR= Europe; JPN= Japan; CAN= Canada; LP = single isolate of 
AG2-2LP (location unknown). Color of clusters for k=4 plot is associated with clusters 
identified by minimum spanning network analysis (Figure 4.2) as such: blue = cluster 1; 
yellow = cluster 2; red = cluster 3; green = cluster associated with AG 2-2LP. 

85 

 
 
Table 2.7 Population differentiation statistics for 
Rhizoctonia solani AG 2-2 isolates estimated for three 
population substructures: geographic region of 
isolation, traditional ISGs (IIIB, IV, intermediate), and 
genetic clusters identified in the current study.  

Population structure 

Geography 

0.620 
0.632 
0.058 
0.037 

ISG 

0.551 
0.606 
0.291 
0.184 

Cluster 

0.428 
0.629 
0.673 
0.468 

29.14% 
(p =0.001) 

14.23% 
(p = 0.003) 

-3.15% 
(p =0.756) 

HS 
Ht 
G'ST 
D 
Variation between 
samples within 
population 

Variation between 
populations 

-1.44% 
(p = 0.592) 

20.20% 
(p =0.001) 

40.48% 
(p =0.001) 

Hs – heterozygosity with population substructure 
Ht – heterozygosity without population substructure 
G’ST – Hendricks G’ST  
D – Jost’s Dest  

86 

 
 
 
 
 
Figure 2.4 Distribution of 33 Rhizoctonia solani AG 2-2 isolates by growing region. Pie charts 
indicate proportion of isolates included from a particular region with color of the slices 
indicating clade/cluster. Map source: USDA-National Agricultural Statistics Services. Retrieved 
from: https://www.nass.usda.gov/Charts_and_Maps/Crops_County/su-pr.php 

87 

 
 
A 

B 

Figure 2.5 Discriminant analysis of principal components plots examining population 
structure for Rhizoctonia solani AG 2-2 isolates. Plots categorized by A) geographic 
region of isolation (n=28) and B) traditional subgroups ‘IIIB’, ‘IV’ and ‘Intermediate’ 
(n=29).  

A 

B  

C 

Figure 2.6 AMOVA plots to test for population structure of 36 Rhizoctonia solani AG 2-2 
isolates. Populations were grouped by A) geographical region of isolation; B) traditional 
ISGs, ‘IIIB’, ‘IV’ and ‘Intermediate’; and C) by the four clusters identified using cluster 
analysis. The bar plots represent the distribution of the expected values based on 999 
permutations and the mark at the diamond represents the actual value of the data. Actual 
values outside the range of expected values are significant. The greater the difference 
between ‘variations between populations’ and ‘variations between samples’, the greater the 
population structure.  

88 

 
 
𝑟̅𝑑 = 0.288 
    p = 0.001 

Figure 2.7 Index of association plot for clone 
corrected dataset of 35 Rhizoctonia solani AG 2-2 
isolates based on 13 microsatellite loci. The bar 
plots represent the distribution of the expected 
values based on 999 permutations and the dashed 
line indicates actual value of the dataset.  

Grouping populations by the ISGs ‘type IIIB’, ‘type IV’ and ‘intermediate’ provided some 

population structure with a significant p-value and about 20% of the variation occurring between 

populations (Table 2.7). Differentiation was low to moderate though, with G’ST = 0.291 and D = 

0.184 (Table 2.7). Cluster overlap occurred mainly between the 'intermediate’ isolates and the 

‘type IIIB’ isolates, with one ‘type IV’ isolate occurring in the ‘type IIIB’ cluster (Figure 2.5B). 

AMOVA plots indicate significant variation between populations (Figure 2.6B; p = 0.001) but 

variation with samples was also significant (Table 2.7; p = 0.003) indicating overlap between 

populations.  

Population differentiation became distinct when grouped by the four clusters identified using 

‘poppr’ (Figure 2.2). Values for GST and D increased to 0.673 and 0.468 respectively with about 

40% of the total variation occurring between populations (Table 2.7). AMOVA plots confirm 

89 

 
that the clusters identified using ‘poppr’ provided the strongest evidence of population structure 

with significant variance between populations (Figure 2.6C; p = 0.001) and non-significant 

variance between samples within populations (Figure 2.6C; p = 0.715).  

Cloning 

A total of 210 clones were generated from 84 individual loci using the 10 primer pairs and 17 

isolates indicated in Table 2.8. Analysis of the sequenced clones resulted in 117 unique 

sequences that were deposited to GenBank as accession numbers OR101123 to OR101084 & 

OR113391 to OR113484. 

Based on automatic fragment sizing, a total of 122 alleles were identified across the 84 

individually cloned loci with 36 loci (43%) being heterozygous (Table 2.8). Analysis of cloned 

sequences from those same 84 loci identified 133 unique alleles, which represents a 9% increase. 

The number of heterozygous loci increased to 40 (48%) based on the cloned fragments. A total 

of ten loci had more than one allele with the same fragment length, but with two or more single 

nucleotide substitutions (SNPs; Table 2.8). As many as four unique sequences were identified in 

some individual loci, but the majority of alleles (>90%) were represented by monomorphic 

sequences.  

90 

 
 
 
Table 2.8 Comparison of alleles for Rhizoctonia solani AG 2-2 isolates 
identified by automatic fragment sizing (auto) and cloning (clone). 
Allele fragment sizes are listed as length in base pairs. Numbers in 
parentheses indicate the number of unique alleles with the same 
fragment length but with varied nucleotide sequences.  

1656 

2547 

Locus 

4660 

5583 

5487 

auto 

clone 

auto 

clone 

auto 

clone 

auto 

clone 

auto 

clone 

131 

134 

134 

137 

222 

213 

144  146(2)  188 

200 

131 

134 

222  213(2)  144  146(2)  188 

190 

199 

202 

190 

135 

138 

138 

141 

135 

138 

137 

140 

222 

213 

147 

149 

182 

187 

132 

135 

Rs850 

134  137(4)  218  220(2)  144 

131 

131 

134 

134 

140 

218 

213 

144 

222  213(2)  144 

222  223(2)  150 

218 

220 

147 

222  223(2)  150 

134 

140 

134 

140 

137 

143 

137 

143 

191 

193 

135 

138 

188  192(2)  132  138(2) 

185 

200 

185 

186 

202 

186 

135 

135  138(2) 

132 

135 

146 

146 

146 

152 

149 

152 

Isolate 

Italien 

R1 

Rzc5 

Rzc6 

Rzc16 

Rs1146 

Rs393 

Rs200 

Rs255 

Rs106 

144 

147 

159 

146 

149 

161 

191 

193 

135 

138 

188 

200 

190 

202 

185 

191 

186 

193 

Rzc146 

131 

134 

Rzc133 

Rs866 

Slovakia 

Rs481 

Rs542 

Total NA 

12 

16 

Total Ho 

Total He 

6 

3 

5 

4 

9 

5 

2 

14 

12 

14 

14 

16 

10 

11 

3 

4 

6 

3 

4 

5 

6 

4 

5 

5 

6 

2 

5 

3 

NA – total number of alleles 
Ho – total number of homozygotes 
He – total number of heterozygotes 
f - false allele identified as the result of improper pairing of forward primers  

91 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 2.8 Con’t  

759 

6145 

Locus 

8703 

6150 

2893 

auto 

clone 

auto 

clone 

auto 

clone 

auto 

clone 

auto 

clone 

142 

158 

143 

159 

116 

118 

Isolate 

Italien 

R1 

Rzc5 

Rzc6 

Rzc16 

Rs850 

Rs1146 

Rs393 

Rs200 

Rs255 

Rs106 

Rzc146 

Rzc133 

Rs866 

Slovakia 

Rs481 

Rs542 

152 

161 

146 

152 

131 

140 

146 

152 

131 

167 

131 

137 

157 

166 

151 

157 

136 

145 

151 

157 

136 

172 

136 

149 

158 

152 

155 

146 

152 

152 

155 

149 

152 

149 

152 

155 

158 

149 

155 

155 

158 

152 

155 

152 

203 

209 

203 

209 

212 

209 

203 

209 

209 

215 

206 

218 

201 

201 

207 

210 

207 

201 

207 

138 

142 

134 

138 

138 

142 

134 

204 

216 

130 

138 

131 

137 

136 

142 

149 

152 

182 

181(f) 

206 

218 

204 

216 

152 

170 

157 

175 

209 

207 

209 

207 

140 

144 

137 

139 

139 

143 

137 

138 

133 

141 

116 

118 

122 

124 

116 

116 

118 

118 

116 

134 

128 

140 

118 

137 

130 

142 

122 

124 

Total NA 

16 

15 

12 

11 

17 

14 

11 

12 

Total Ho 

Total He 

2 

7 

3 

6 

4 

4 

5 

3 

4 

6 

7 

3 

3 

4 

2 

5 

9 

7 

1 

10 

6 

2 

92 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The observation of more than two alleles at a given locus was rare, despite R. solani being 

heterokaryotic and multinucleate with between 3 and 13 nuclei per cell (Sneh et al. 1991). One 

locus in which three alleles were observed during fragment analysis was locus 8703. An allele of 

length 182 bp was identified in about 60% of the isolates tested, although it was viewed 

skeptically because of its weak signal and that it was quite different in size from the other alleles. 

Upon analysis of cloned sequences from this locus, it was determined that the ‘182’ allele was 

caused by improper binding of a pair of forward primers, one in reverse orientation, at a locus 

unrelated to the target microsatellite locus. Increasing stringency of the PCR reaction to the final 

values reported in Table 2.2 reduced this mispairing to a rare occurrence on subsequent analyses 

and any ‘182’ alleles that did occur were easily removed.  

Cloning revealed additional alleles with a different fragment length in three loci that were not 

detected by automatic fragment sizing. The reason for this is uncertain but is likely to be related 

to PCR conditions. In contrast, cloning failed to identify one of the alleles at five heterozygous 

loci. This is easily explained simply as having an inadequate number of clones to detect the 

second allele. Interestingly, in all five cases it was the larger allele that failed to clone.  

Illumina sequencing of representative isolates  

Illumina sequencing produced 103.9 Gbp of sequence data in 346,344,342 pair-end reads that 

passed filter with an average estimated insert size of 427 bp and an average Q-score of 35.3. 

Average yield per isolate ranged from 3.90 to 6.05 Gbp with an average yield of 4.72 Gbp. Data 

for individual isolates is shown in Table 2.9.  

Genome assemblies ranged in total length from 39.8 to 78.1 Mbp with an average length of 

59.1 Mbp and an average of 8713 contigs and an average N50 value of 24.6 Kb (Table 2.8). 

93 

 
Haploid genome sizes were estimated to be between 37.0 to 51.6 Mbp with an average of 42.8 

Mbp. Heterozygosity ranged from 0.03 to 6.62%, with a mean of 2.70% and a median of 3.00%.  

BUSCO scores indicate genome completeness that ranged from 65.0 - 86.1%. However, 

duplicated genes represented a large portion of complete scores, with BUSCO scores for 

duplicated genes as high as 72.5%. The proportion of BUSCO duplicates significantly correlated 

with heterozygosity (P< 0.001; R2 = 0.838), providing an explanation for the high levels of 

duplication in assemblies.  

Table 2.9 Assembly statistics for 22 Rhizoctonia solani isolates assembled from Illumina 
paired-end reads. CYGV01 was included from Wibberg et al. 2016 for reference. 

Isolate 

C116S 

R-5 

ACC3-LP 

R-164S 

Rs496 

Rs200 

2C13 

Rs14-2 

ST6-1 

Rs1012 

Rs1146 

F30 

F321 

R09-23 

W-22 

Italien 

R09-25 

R1 

R4 

Slovakia 

R9 

Rs296 

CYGV01 

Total assembly 
length (Mbp)  

# contigs > 
750bp 

N50 

Coverage 

% Hetero-
zygosity 

BUSCO score (n:2898) 

C:83.3%[S:82.8%,D:0.5%],F:4.7%,M:12.0% 

C:83.2%[S:82.6%,D:0.6%],F:4.3%,M:12.5% 

C:82.4%[S:82.1%,D:0.3%],F:4.8%,M:12.8% 

C:78.8%[S:77.7%,D:1.1%],F:6.8%,M:14.4% 

C:75.7%[S:74.4%,D:1.3%],F:7.8%,M:16.5% 

C:76.5%[S:74.8%,D:1.7%],F:7.9%,M:15.6% 

C:76.2%[S:75.4%,D:0.8%],F:8.1%,M:15.7% 

C:68.8%[S:64.4%,D:4.4%],F:10.7%,M:20.5% 

C:66.7%[S:61.8%,D:4.9%],F:12.3%,M:21.0% 

C:65.4%[S:55.2%,D:10.2%],F:11.8%,M:22.8% 

C:65.0%[S:54.5%,D:10.5%],F:12.7%,M:22.3% 

C:65.3%[S:54.7%,D:10.6%],F:12.5%,M:22.2% 

C:66.1%[S:55.3%,D:10.8%],F:12.2%,M:21.7% 

C:64.9%[S:54.6%,D:10.3%],F:13.0%,M:22.1% 

C:71.8%[S:31.4%,D:40.4%],F:10.3%,M:17.9% 

C:72.7%[S:26.0%,D:46.7%],F:8.7%,M:18.6% 

C:72.6%[S:28.6%,D:44.0%],F:9.0%,M:18.4% 

C:80.3%[S:21.8%,D:58.5%],F:6.2%,M:13.5% 

C:80.6%[S:20.4%,D:60.2%],F:6.0%,M:13.4% 

C:86.1%[S:13.6%,D:72.5%],F:3.5%,M:10.4% 

C:79.6%[S:20.3%,D:59.3%],F:6.6%,M:13.8% 

C:76.4%[S:74.9%,D:1.5%],F:8.1%,M:15.5% 

0.03 

0.41 

0.66 

0.79 

1.14 

1.26 

1.29 

1.84 

1.93 

2.93 

3.07 

3.11 

3.11 

3.16 

3.66 

4.35 

4.47 

4.64 

5.21 

6.62 

4.48 

1.23 

- 

39.8 

49.0 

47.7 

45.6 

49.1 

55.7 

46.8 

50.9 

51.8 

60.8 

60.8 

60.9 

60.2 

60.6 

78.1 

71.0 

70.6 

70.4 

72.5 

78.1 

72.3 

47.5 

55.9 

2,114 

8,267 

7,131 

5,548 

9,664 

12,481 

6,932 

9,723 

10,153 

11,684 

11,791 

11,858 

11,677 

11,873 

13,275 

7,212 

7,974 

3,895 

4,080 

3,400 

4,302 

7,947 

5826 

64,451 

59,217 

20,937 

24,815 

14,489 

11,755 

17,098 

10,382 

9,942 

9,948 

9,870 

9,901 

10,018 

9,877 

15,387 

20,626 

17,846 

42,795 

40,411 

67,775 

37,410 

16,131 

80,505 

35 

40 

28 

36 

40 

41 

41 

39 

41 

41 

46 

42 

40 

34 

40 

51 

42 

46 

52 

44 

34 

34 

- 

94 

 
 
 
 
Phylogenetic analysis 

Sequences from the partial gene regions for 25S-rRNA, β-tubulin, calmodulin, RPB1, RPB2 

and TEF-1α were generated and deposited in GenBank under the accession numbers OP832036 

– OP832197. The concatenated set of sequences produced a data matrix that consisted of a total 

of 4,341 characters with 938 that were phylogenetically informative. The phylogenetic trees 

generated by Bayesian analysis (Figure 2.8), neighbor-joining (Figure 2.9), and maximum-

likelihood (Figure 2.10) showed remarkable similarity to one another.  

While isolates of ‘type IIIB’ and ‘type IV’ did tend to cluster together in the current analysis, 

none of the trees examined supported the subgroups ‘AG 2-2IIIB’ and ‘AG 2-2IV’ as 

monophyletic. In addition to the presence of two ‘type IV’ isolates in clade 2, two subclades of 

‘type IIIB’ isolates in clade 1 (cluster 1) were more closely related to a subclade of ‘type IV’ 

isolates (cluster 2) than to the ‘type IIIB’ isolates in clade 2. These observations are similar to 

those of Strausbaugh et al. (2011a) and Carling et al. (2002) that also showed ‘AG 2-2IIIB’ 

and/or ‘AG 2-2IV’ to be polyphyletic.  

Clade 2 was well supported with isolate ‘C116S’ located at the basal node. ‘C116S’ is the 

original AG 2-2IIIB isolate identified by Ogoshi from mat rush (Ogoshi 1987). The reference 

genome from Wibberg et al. (2016), which was described as AG 2-2IIIB, is also included in 

clade 2. While the majority of the isolates in clade 2 were ‘type IIIB’, two isolates, Rs393 and 

R09-23, were ‘type IV’. Clade 1 was split, with about half the isolates ‘type IIIB’ and half ‘type 

IV’, grouped into 3 subclades (Figure 2.8). All ‘type IV’ isolates in clade 1 grouped into a single 

subclade while the ‘type IIIB’ and ‘intermediate’ isolates were grouped in the other two 

subclades (Figure 2.8).  

95 

 
Figure 2.8 Six-gene phylogeny of 25 AG 2-2 isolates constructed by Bayesian Inference. 
Cluster designation identifies cluster association according to minimum spanning 
network analysis (Figure 4.2). Numbers at the nodes indicate posterior probabilities with 
values < 70% not shown. Color of the branch tip labels indicate ISG designation as 
determined by growth at 35°. Intermediate designation means growth tests at 35°C were 
inconclusive. Colored circles at the branch tips represent heterozygosity as estimated by 
k-mer counts of paired-end Illumina reads. Ceratobasidium sp. AG-I was included as the 
outgroup.  

96 

 
 
Figure 2.9 Neighbor-joining tree for 25 Rhizoctonia solani AG 2-2 isolates based on 
six gene sequences. Color of branch tip labels indicates ISG designation based on 
growth at 35°C with ‘Intermediate’ meaning growth tests were inconclusive. 
Ceratobasidium sp. AG-I was included as the outgroup.  

97 

 
 
Figure 2.10 Six-gene phylogeny of 25 AG 2-2 isolates generated using Maximum-
likelihood criterion. Color of branch tip labels indicates ISG designation based on 
growth at 35°C with ‘Intermediate’ meaning growth tests were inconclusive. Numbers at 
the nodes indicate support values based on 1000 bootstrap replicates. Ceratobasidium sp. 
AG-I was included as the outgroup.  

98 

 
 
Discussion 

Results of the current study clearly demonstrate that the subgroups AG 2-2IIIB and 

AG 2-2IV are not monophyletic and instead we interpret the phylogeny as indicating there are 

two major genetic groups that contain a mix of isolates previously identified as belonging to the 

subgroups AG 2-2IIIB and AG 2-2IV. Despite several studies showing that the monophyly of 

AG 2-2IIIB and AG 2-2IV is questionable (Carling et al. 2002; Martin et al. 2014; Strausbaugh 

et al. 2011a), reports based on the subgroups AG 2-2IIIB and AG 2-2IV are still common. Much 

of the reason for this continued usage is due to reported differences in virulence and host range 

(Cappelli et al. 1999; Carling et al. 2002; Strausbaugh et al. 2011a). However, since these 

subgroups should be considered artificial, generalization of individual behavior based on them 

should be considered cautiously. Consequently, temperature tolerance should no longer be 

considered an acceptable criterion for the separation of subgroups within AG 2-2.  

The invalidation of AG 2-2IIIB and AG 2-2IV as legitimate subgroups could help explain 

inconsistencies in experimental data over the years. For example, primers designed for specific 

amplification of AG 2-2IIIB and AG 2-2IV worked well on the set of isolates used to develop 

them (Carling et al. 2002; Salazar et al. 2000) but were inconsistent on an expanded set of 

isolates (Bolton et al. 2010; Brantner, J., Hanson, L.E. unpublished data). Similarly, designation 

of ISG based on temperature tolerance has resulted in inconsistent identification of individual 

isolates. The well characterized isolate ‘R9’ isolated from sugar beet in Colorado (Hecker & 

Ruppel 1975) has been categorized as both AG 2-2IIIB and AG 2-2IV (Carling et al. 2002; 

Engelkes & Windels 1996; Stojšin et al. 2007; Strausbaugh et al. 2011a). In addition, some 

isolates have been characterized as ‘intermediate’ where only very weak growth occurred at 

99 

35°C (Brantner & Windels 2007), which is another indication that temperature tolerance is an 

unreliable method for distinguishing subgroups.  

Host range attributed to the ISGs has also been somewhat inconsistent. In addition to the 

original indication that ‘AG 2-2IIIB’ affected mat rush and ‘AG 2-2IV’ was the causal agent of 

root rot of sugar beet, not only have both groups been shown to cause root rot of sugar beet, but 

isolates characterized as ‘AG 2-2IIIB’ are generally regarded as being more aggressive on sugar 

beet than isolates characterized as ‘AG 2-2IV’ (Engelkes & Windels 1996; Strausbaugh et al. 

2011a). Inconsistency in the reported susceptibility of a host can complicate choices of suitable 

rotational crops. For example, corn was shown to be a host for ‘AG 2-2IIIB’ classified 

populations (Sumner & Bell 1982; Sumner & Minton 1989) while ‘AG 2-2IV’ classified isolates 

caused little or no damage (Windels & Brantner 2006). However, one report showed isolates 

associated with ‘AG 2-2IV’ causing more damage on corn (Ohkura et al. 2009). An accurate 

assessment of the relationship between subgroup and host susceptibility is important to making 

suitable rotation choices. We expect the subgroup revisions proposed in the current study to 

improve our understanding of host susceptibility and therefore, disease management strategies 

related to crop rotation.  

Although the evidence is clear that the subgroups ‘IIIB’ and ‘IV’ are not phylogenetically 

supported, interpretation of how subgroups are actually organized within AG 2-2 is not all that 

straightforward. It may be tempting to consider splitting ISG ‘IIIB’ into two subgroups (perhaps 

‘IIIA’ and ‘IIIB’) and in that way retain the ISG ‘IV’. However, we contend this is difficult to 

justify given the branching patterns shown in clade 1 (Figure 2.8), the presence of ‘type IV’ 

isolates in clade 2, and the confusion it would cause with the historical literature. Clade 1 could 

alternatively be interpreted as consisting of two subclades of ‘type IIIB’ isolates and one 

100 

subclade of ‘type IV’ isolates. This arrangement, however, seems unnecessarily complex, 

especially if clade 2 is treated in the same manner based on distances and branching patterns. In 

addition, both the cluster analysis and the STRUCTURE plot group ‘type IIIB’ isolates from 

clade 1 into a single cluster (Figure 2.2 & 2.8). Perhaps analyzing additional isolates could 

provide a better perspective on the relationship of the subclades within clade 1, but based on the 

current evidence, we consider clade 1 to be a single clade that consists of both ‘type IIIB’ and 

‘type IV’ isolates.  

Collectively, we interpret these observations as support for the clusters identified by cluster 

and MSN analysis. Therefore, we propose that AG 2-2 should be subdivided into three intra-

specific groups (ISG) and that these groups should be given two-letter designations that are 

consistent with AG 2-2LP described by Hyakumachi et al. (1998). Two-letter designations would 

provide more consistent terminology, not just within AG 2-2, but also among different AG, most 

of which use two-letter designations for their ISG (Sneh et al. 1991). In addition, the designation 

‘IV’ can cause confusion as AG 2-2IV can be mistaken for AG 4, not so much in writing, but 

more so when discussed verbally with growers and other stakeholders.  

We propose designating clade 1 as AG 2-2BR (which includes genetic clusters 1 & 2; Figure 

2.8) and clade 2 as AG 2-2PR (includes genetic clusters 3 & 4; Figure 2.8). These subgroup 

designations reflect host preference based on virulence testing combined with some historical 

perspective. AG 2-2BR (clade 1) not only contained isolates that were, on average, most 

aggressive on sugar beet (Beta vulgaris; Minier & Hanson, 2022) but also included the majority 

of the ‘type IV’ isolates, which were the original ‘beet type’ as described by Ogoshi (1987). 

Isolates from AG 2-2PR (clade 2) were, on average, more aggressive on dry beans (Phaseolus 

vulgaris) than isolates from the other groups (Minier 2019).  

101 

In the current study, the AG 2-2LP isolate clusters with other isolates associated with 

proposed group AG 2-2PR (clade 2) such as ‘Rs850’, ‘24BR’ and ‘Slovakia’ (Figure 2.2), but 

phylogenetically, it is more closely related to isolates from group AG 2-2BR (Figure 2.8; clade 

1). Several studies have shown AG 2-2LP to group with isolates of AG 2-2IV (Carling et al. 

2002; Salazar et al. 2000; Sharon et al. 2008), which would be consistent with its association 

with AG 2-2BR, that contains most of the ‘type IV’ isolates. However, because of reported 

differences in host range (Aoyagi et al. 1998; Burpee & Martin 1996; Hyakumachi et al. 1998), 

we propose maintaining AG 2-2LP as a separate subgroup pending additional evidence. Another 

subgroup, AG 2-2WB, was proposed by Godoy-Lutz et al. (2008) but reports examining this 

subgroup are limited and no isolates associated with this group were included in the current 

study. Consequently, we cannot assess the relationship of AG 2-2WB as a subgroup until further 

testing can be completed.  

The relationship of AG 2-2 to other AG based on rRNA-ITS sequences has also been 

inconsistent. An analysis of rRNA-LSU and ITS by Gonzalez et al. (2001) showed AG 2-2 was 

most closely related to AG 2-1 and AG 9, both from potato in the US. In contrast, the phylogeny 

of Sharon et al. (2008), using the same loci, showed AG 2-2 most closely related to AG 11 and 

AG 2-3 or AG 3, depending on tree building algorithm used. Strausbaug et al. (2011a) showed 

AG 5 as the closest relative to AG 2-2, although they did not include as many representative AG 

as did Gonzalez et al. (2001) and Sharon et al. (2008). In the current study, we included a limited 

number of AG in addition to AG 2-2 with a Ceratobasidium sp. as the outgroup. Although the 

primers for amplification of loci used in phylogenetic analysis developed in the current study 

were not evaluated on non-AG 2-2 isolates using PCR, they were developed and evaluated in-

silico on the non-AG 2-2 isolates included in the current study plus some additional 

102 

Ceratobasidium spp. that were not included in the current report. Therefore, we expect the 

primers reported herein should be suitable for the evaluation of relationships within Rhizoctonia 

and Ceratobasidium.  

Rhizoctonia solani AG2-2 is multinucleate and heterokaryotic with between 3 and 13 haploid 

nuclei per cell (Sneh et al. 1991). Exchange of nuclei during anastomosis has been observed and 

can result in heterokaryons readily distinguishable from either parental type (Whitney & 

Parmeter 1963). Because of this, we had the expectation that isolates would have more than two 

alleles at some loci, as has been reported for the ITS region (Strausbaugh et al. 2011a). However, 

with few exceptions, we identified no more than two alleles at any individual locus, making 

these isolates effectively dikaryotic. One such exception occurred at locus 8703, in which three 

alleles were detected for 8 isolates, including one allele with a length of 182bp. Because the 

peaks for those instances were less than 20% the intensity of the other peaks and their expression 

was not consistent, we suspected that allele 182 was not a true allele. Cloning results supported 

our suspicions and showed the allele was the result of non-specific binding and amplification 

between two forward primers.  

How R. solani AG 2-2 regulates nuclei in order to maintain this pseudo-diploid status is 

unknown. One possible mechanism is nuclei exclusion or retention by somatic or vegetative 

compatibility (Leslie 1996; Puhalla 1985). Since nuclei are presumably exchanged during 

anastomosis reactions, the same compatibility factors may govern both anastomosis and nuclei 

retention/exclusion. Classification of anastomosis reactions may give an indication as to the 

extent of vegetative compatibility (Carling 1996). Anastomosis reactions that are characterized 

as ‘C3’ (perfect fusion) occur between the most closely related isolates and the cells adjacent to 

the anastomosis site remain healthy. Thus, the exchange of genetic material can presumably 

103 

occur freely in ‘C3’ fusions. In contrast, ‘C2’ (imperfect fusion) reactions result in the death of 

cells adjacent to the anastomosis site, which could hinder the exchange of nuclei. While genetic 

exchange has been observed to occur in ‘C2’ reactions (Toda & Hyakumachi 2006), it is 

uncertain that it occurs in all or even most ‘C2’ reactions. Similarity between nuclei, and in 

particular, vegetative compatibility factors, may determine which nuclei can be exchanged 

during anastomosis. Consequently, heterokaryons may appear to be homozygous due to 

restraints in the variability of nuclei that can be exchanged during anastomosis.  

Several instances of hidden allelic diversity were revealed by cloning, with some loci having 

up to four alleles identical in length but different in sequence (Table 2.8). Each of these alleles 

could potentially represent a unique nuclear type indicating a heterokaryon with four nuclear 

types. However, these alleles differed by only a couple SNPs and only two loci in the same 

isolate (‘Rs850’) had evidence of more than 2 alleles. Previous findings, regarding diversity in 

ITS sequences, showed multiple sequences present in individual isolates of R. solani AG 2-2 

(Strausbaugh et al. 2011a) and AG 2-1 (Pannecoucque & Hofte 2009), although ITS is known to 

be multicopy (Vilgalys & Gonzalez 1990) and copy status of microsatellite loci in the current 

study is unknown. The consequence being that identification of complete genetic diversity and 

the actual heterokaryotic state of an individual is especially challenging to determine. 

Sequencing using long-read technology may help resolve karyotype, especially in regard to 

isolates such as ‘Rs850’, which show evidence of admixture (Figure 2.3).  

When estimated heterozygosity is plotted on the Bayesian phylogenetic tree (Figure 2.8), 

levels of heterozygosity largely corresponded with phylogenetic clades. Cluster 2, that contains 

primarily ‘type IV’ isolates within clade AG 2-2BR, is the cluster with the lowest heterozygosity, 

with all isolates in the cluster having less than 2% heterozygosity. This is in contrast with cluster 

104 

1 within the same clade (AG 2-2BR) which consists of isolates with greater than 4% 

heterozygosity. Clade AG 2-2PR is made up of mostly isolates with intermediate heterozygosity 

(2 - 4%). Curiously, observations of these heterozygosity patterns resemble observations of 

aggressiveness, especially on sugar beet. Clusters with higher heterozygosity were, on average, 

more aggressive on sugar beet than those with lower heterozygosity (data not shown).  

BUSCO scores showed high levels of duplication in some isolates (Table 2.9). Duplication 

levels were significantly correlated with heterozygosity as predicted by k-mer counting 

(p < 0.001, R2 = 0.838). The isolate ‘Slovakia’ had the highest level of both duplication and 

heterozygosity (72.5% and 6.62% respectively) and was also one of the isolates that had 

evidence for admixture between two clusters (Figure 2.3). This observation is consistent with the 

expectations of a heterokaryon consisting of nuclei from genetically distinct parental sources 

(Todo & Hyakumachi 2006). Curiously, two of the other isolates, ‘C116S’ and ‘ACC3-LP’, that 

showed evidence of admixture had some of the lowest levels of duplication (0.5% and 0.3% 

respectively) and heterozygosity (0.03% and 0.66% respectively). These contrasting observations 

may be the result of differences in heterothallic-homothallic pairings compared to heterothallic-

heterothallic or homothallic-homothallic pairings (Toda & Hyakumachi 2006). Unfortunately, 

heterozygosity alone does not appear to be a reliable indicator of heterokaryon status.  

The type of mating system present in natural populations of AG 2-2 is still an open question 

in Rhizoctonia biology. Reports of isolates of AG 2-2 producing sexual structures are rare (Olaya 

and Abawi 1994; Toda and Hyakumachi 2006), and mostly come from Japan (Kiyoshi et al. 

2014). In addition, reports of the formation of basidiospores all involve ‘type IV’ isolates 

(Kiyoshi et al. 2014; Naito 1990; Toda & Hyakumachi 2006). We are unaware of reports that 

have observed or induced basidiospore production in ‘type IIIB’ isolates. Nevertheless, high 

105 

levels of genetic diversity within field populations have been suggested to indicate the possibility 

of populations with at least some level of sexual recombination (Ajayi-Oyetunde et al. 2019). 

Data from the current study, particularly the lack of widespread admixture and the deficit of 

heterozygosity at several loci, supports clonal reproduction as the primary mode of reproduction 

in AG 2-2. However, there are several indications in our data that sexual recombination may 

occur, at least occasionally, in natural populations. For example, Kiyoshi et al. (2014) showed 

that heterogeneous, heterokaryotic parents would produce homokaryotic progeny through 

basidiospore formation, resulting in the simplification of clonal diversity through generations. 

This “simplification” could explain why there is a cluster of homogeneous isolates nested within 

a clade of highly heterogeneous isolates. Therefore, we hypothesize that the members of cluster 2 

within clade AG 2-2BR are clonal descendants of basidiospore derived progeny.  

In contrast, the pattern of admixture observed in several isolates (Figure 2.3) could be 

interpreted to be the result of asexual reproduction through the formation of heterokaryons 

during anastomosis. The formation of heterokaryons has been reported in AG 2-2IV (Kiyoshi et 

al. 2014; Toda & Hyakumachi 2006) and the exchange of entire nuclei, as would be expected in 

anastomosis interactions, could result in a heterokaryotic progeny with approximately 50/50 

association with two genetic groups. Thirty (83%) of the isolates shown on the structure plot 

have more than 95% of their genetic variation associated with a single cluster (Figure 2.3). The 

other six isolates have more than 90% of their genetic variation associated with two clusters, 

where the association is split approximately 50/50 between the two groups. The exception to this 

is isolate ACC3-LP (the only AG 2-2LP isolate in the current study) in which about 84% of its 

genetic diversity is shared with isolate C116S (from Japan) and 12% with cluster 2 that contains 

the other three isolates from Japan.  

106 

Thirteen loci were initially identified for use as part of a microsatellite panel to genotype 

field isolates. We reduced the panel size to eight loci in order to maximize utilization of a 96-

well plate and reduce overall cost of genotyping. The genotype accumulation curve indicated that 

more than 90% of the genotypes could be distinguished with eight loci (Figure 2.1) Therefore, 

we chose the eight loci with the highest PIC values and developed conditions suitable for 

duplexing. The result was that twenty-four isolates can be genotyped in an individual run 

utilizing a single 96-well plate. It may be possible to add an additional dye color and include 

some of the extra loci in the set by triplexing the reactions, but we found this endeavor 

challenging due to fragment size overlap. Since eight loci seemed to provide sufficient genotype 

information, we did not pursue adding additional loci to our eight-loci panel. Alternatively, these 

additional loci could be analyzed separately and possibly used to provide additional 

discrimination for populations that have high numbers of unresolved individuals.   

In the current study, we provide evidence that the traditional subgroups of AG 2-2 are not 

phylogenetically supported and should be abandoned. Instead, we propose three genetic 

subgroups, AG 2-2PR, AG 2-2BR (that contains a mix of ‘type IIIB’ and ‘type IV’ isolates) and 

tentatively retaining AG 2-2LP as defined by Hyakumachi et al. (1998). In addition, we have 

developed a set of highly polymorphic microsatellite markers that can provide high-throughput 

analysis of R. solani AG 2-2 populations. We expect these results and supporting molecular 

markers will lead to the formation and investigation of novel hypotheses regarding the 

population biology of R. solani AG 2-2, especially regarding the formation of heterokaryons and 

the prevalence of sexual or asexual reproduction in natural populations. 

107 

 
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CHAPTER 3: 

POPULATION STRUCTURE OF RHIZOCTONIA SOLANI AG 2-2 REVEALS CRYPTIC 

SOURCES OF GENETIC DIVERSITY 

116 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Introduction 

Soil-borne fungal pathogens can be difficult to manage because of unique challenges such as 

the difficulty identifying infections in a timely manner due to the delay in above-ground 

symptom expression (Leclerc et al. 2014; Rush et al. 1992), the difficulty in delivering chemical 

controls effectively, and the longevity of many soil-borne fungal survival structures (Panth et al. 

2020; Sussman 1968). The lack of effective chemical controls has prompted a renewed focus on 

physical and cultural controls for the management of soil-borne diseases, such as adjusting 

planting dates, managing soil wetness, deep plowing, and sanitation (Katan 2000). However, 

cultural practices often have negative effects on other agronomic conditions. For example, deep 

plowing can reduce inoculum load in some cases but can also increase soil erosion, mineral 

leaching and reduced total organic carbon levels (Karlen et al. 2013). 

Some of the most effective strategies employed against soil-borne fungal diseases are host 

resistance and crop rotation (Katan 2000; Mihajlović et al. 2017). While these strategies can be 

effective, it is not always possible to develop an effective management program that utilizes 

these approaches. In particular, host resistance for many soil-borne diseases is simply not 

available. Additionally, choosing suitable crop rotation programs can be challenging, especially 

against pathogens that may have a wide host range and an aggressive, necrotrophic lifestyle 

(Okubara eet al. 2014). Populations with high levels of genetic diversity can exacerbate the 

situation and reduce the effectiveness of management strategies by overcoming host resistance 

and limiting crop rotation choices (Reeleder 2003). Thus, it is critical to understand not only the 

life history traits of plant pathogens, but also the extent and significance of the biological 

diversity of populations. 

117 

 Rhizoctonia solani AG 2-2 is one of the most important soil-borne pathogens of sugar beet, 

causing Rhizoctonia root and crown rot (Buhre et al. 2009; Neher & Gallian 2011; Windels et al. 

2009). It is a major problem in many growing regions around the world (Buhre et al. 2009; 

Strausbaugh et al. 2011a). Rhizoctonia solani is a species complex, where members are 

distinguished by their ability to anastomose with other members of the same anastomosis group 

(Carling 1996; Cubeta & Vilgalys 1997; Parmeter et al. 1969). At least 13 anastomosis groups 

(AG) have been described and the individual AG can be thought of as independently evolving 

lineages. Several AG contain well-defined cultural types, referred to as intraspecific groups 

(ISG), including R. solani AG 2-2 (Sharon et al. 2008; Sneh et al. 1991). Traditionally, AG 2-2 

has been separated into three ISGs, originally separated by host range (2-2IIIB, 2-2IV, and 

AG 2-2LP), but more commonly AG 2-2IIIB and AG 2-2IV are separated by their ability to 

grow at 35°C. Recently, these subgroups have been shown to be phylogenetically unsupported 

(Carling et al. 2002; Martin et al. 2014; Strausbaugh et al. 2011a). Instead, based on the analysis 

presented in chapter 1 (current dissertation), we have proposed reorganizing AG 2-2 into three 

subgroups, AG 2-2LP, AG 2-2BR and AG 2-2PR, which do not rely on growth at 35°C for 

categorization.  

Tentatively, subgroup AG 2-2LP retains its current designation based on the disease ‘Large 

Patch’ that it causes on warm-season grasses (Hyakumachi et al. 1998). Subgroups AG 2-2BR 

and AG 2-2PR each contain a mix of ‘type IIIB’ and 'type IV’ isolates and are proposed to be 

named based on the crop which the group is most severe (BR = Beta rot; PR = Phaseolus rot; 

Minier 2019; Minier & Hanson 2021). In addition, each of the subgroups AG 2-2BR and 

AG 2-2PR consist of two genetic clusters that do not form monophyletic groups. Clusters 1 & 2 

are contained in subgroup AG 2-2BR and clusters 3 & 4 are contained in subgroup AG 2-2PR. 

118 

Each genetic cluster has unique characteristics that are explored in more detail in the current 

study.  

In general, Rhizoctonia solani AG 2-2, is an aggressive necrotroph, and can result in losses of 

30 to 60% in sugar beet fields (Neher & Gallian 2011; Windels et al. 2009) and reduced 

storability after harvest (Strausbaugh et al. 2011b). Complete crop losses for individual fields are 

possible when disease levels are greater than 50% due to poor harvestability and associated 

problems with processing and storage (Windels et al. 2009). Host resistance to Rhizoctonia root 

and crown rot is available in commercial varieties although resistance is incomplete, and the 

varieties have yield potential below that of the best approved varieties (Jacobsen et al. 2004). 

The most effective management strategies for limiting damage from Rhizoctonia root and crown 

rot involve those that minimize introduction of the pathogen or limit the buildup of inoculum.   

Crop rotation is among the more important strategies for limiting inoculum build up in sugar 

beet production with a 3-year minimum between sugar beet crops being commonly 

recommended (Buhre et al. 2009; Windels et al. 2009). Rotation with non-host crops is crucial to 

reducing inoculum load prior to a subsequent sugar beet crop (Panth et al. 2020). However, many 

of the crops commonly grown in rotation with sugar beet are also susceptible to R. solani 

AG 2-2, including soybean (Fenille et al. 2002; Nelson et al. 1996), common bean (Muyolo et al. 

1993; Peña et al. 2013), and corn (Ithurrart et al. 2004; Sumner & Milton 1989). Furthermore, 

reports describing the contribution of rotational crops to the reduction of disease have been 

inconsistent. For example, wheat is considered a non-host, yet Rush & Winter (1990) reported 

that wheat in rotation with sugar beet increased disease severity. This increase in disease 

pressure was likely due to colonization of the stubble (Neate 1987; Rush & Winter 1990). 

Another example comes from Coons & Kotila (1935) who showed corn decreased disease 

119 

severity while Windels & Brantner (2005) found that corn increased disease severity on a 

following sugar beet crop. Ruppel (1985) determined that alfalfa was not a host to strains of R. 

solani isolated from sugar beets in contrast to previous findings by Maxson (1938).  

Some of these discrepancies may be attributed to the variability of individual strains of 

R. solani AG 2-2. The genetics of R. solani AG 2-2, that underlies variability in virulence and 

host preference, is complicated and not yet fully understood. R. solani AG 2-2 is heterokaryotic 

and multi-nucleate with between 3 and 13 nuclei per cell (Ogoshi 1987; Sneh et al. 1991). The 

sexual stage for R. solani AG 2-2 has rarely been observed, but when present, has been identified 

as Thanatephorus cucumeris (Frank)Donk and reports have been limited to one subgroup, ‘type 

IV’ (AG 2-2IV) isolates (Kiyoshi et al. 2014; Qu et al. 2013; Toda & Hyakumachi 2006). While 

there is at least one report describing the infection of sugar beet by basidiospores (Naito & 

Sugimoto 1980), much of the work with basidiospores derived from AG 2-2 involved the 

formation of new somatic compatibility groups from the formation of heterokaryons (Kiyoshi et 

al. 2014; Qu et al. 2013; Toda & Hyakumachi 2006).  

The reproductive strategy of R. solani AG 2-2 is primarily clonal, with a lack of asexual 

spores (Sneh et al. 1991) and a sexual stage that has not been observed in the field in most the 

growing regions around the world. Even so, high levels of diversity exist within R. solani AG 2-

2 populations (Ajayi-Oyetunde et al. 2019; Strausbaugh et al. 2011a; Zheng et al. 2013). The 

source of this diversity is uncertain, but we hypothesize diversity is generated through parasexual 

exchange of nuclei during anastomosis and the presence of cryptic sexual recombination in some 

growing regions.  Regardless of the source, high levels of diversity are expected to contribute to 

pathogen persistence and increase the challenges of disease management (Wang et al. 2017).  

120 

At least some individuals within R. solani AG 2-2 appear to have both heterothallic and 

homothallic mating systems and genetic exchange can occur between homothallic and 

heterothallic isolates (Toda & Hyakumachi 2006). Genetic exchange relies on the transfer of 

nuclei during anastomosis, which is regulated by vegetative compatibility and is a separate 

system from sexual compatibility (Leslie 1996). Collectively, these characteristics make the 

genetics of R. solani AG 2-2 challenging to assess.  

The objective of the current study is to demonstrate the utility of these newly identified 

subgroups to generate novel hypotheses regarding the generation and distribution of genetic 

diversity in R. solani AG 2-2. We expect these hypotheses will lead to an enhanced 

understanding of R. solani AG 2-2 genetics and improved management strategies. To this end, 

we utilized a set of eight microsatellite markers that were described in chapter 2 (current 

dissertation) to examine the distribution patterns, reproductive strategy, and diversity of R. solani 

AG 2-2 populations at multiple scales including global, state and field levels. By exploring the 

populations at several levels, we provide a survey of diversity and organization in R. solani 

AG 2-2 populations that affect sugar beet. The relationship of genotype and subgroup to sugar 

beet, dry bean, and soybean as hosts was also investigated, to assist in crop rotation selections 

that may be altered due to the presence of certain subgroups within a particular field. In order to 

facilitate such choices, we have developed a set of subgroup-specific primers that may be used to 

identify field isolates to subgroup. In the long term, we expect this information will allow for 

more informed decisions regarding crop rotation choices.  

121 

 
 
 
Methods 

Isolate collection 

A total of 147 isolates of Rhizoctonia solani AG 2-2 were genotyped using a set of eight 

microsatellite markers as described in chapter 2 (current dissertation). The samples were drawn 

from three collections (Table 3.1): 86 isolates from the collection of Dr. Linda Hanson (USDA-

ARS, East Lansing, MI), 17 isolates from the field crop pathology program of Dr. Martin 

Chilvers (MSU-PSM) and 44 isolates collected for the current study from the USDA Cercospora 

disease nursery at Saginaw Valley Research and Extension Center (SVREC).  

Isolates from the collection of Dr. Linda Hanson were recovered from storage at -20°C by 

placing a single grain of infested barley on potato dextrose agar (PDA, Sigma-Aldrich, St. Louis, 

MO) and allowing culture to grow for 5-7 days at room temperature. Isolates from Dr. Chilvers 

field crop program were recovered in 2014 from symptomatic dry bean and soybean roots in 

Michigan. Each isolate was purified by hyphal tip transfer (Leslie & Summerell 2006) and stored 

on barley grains at -20°C until use. Cultures were recovered by placing a single infested grain on 

PDA and growing for 5-7 days at room temperature.  

Isolates from the Cercospora nursery (located at the Saginaw Valley Research and Extension 

Center, Frankenmuth, MI) were collected in 2016 and 2017 from symptomatic, adult sugar beet 

roots. The nursery is separated into four quadrants, with each field approximately 4 hectares in 

size and separated by a grass strip approximately 10 meters wide. Sugar beets are grown in a 

particular quadrant every fourth year following a crop rotation schedule of sugar beet, corn, 

soybeans, and wheat with sugar beet following wheat. Collection followed nursery rotation with 

isolates from 2016 collected from the field in the northwest quadrant and from the field in the 

122 

northeast quadrant in 2017. Isolates were purified by hyphal tip transfer and stored on barley 

grains at -20°C. 

Fungal tissue was grown in petri dishes on malt extract broth (MEB; Sigma-Aldrich) without 

shaking for 5 days. Hyphal mats were harvested using forceps, placed in 50 ml centrifuge tubes, 

and rinsed with sterile phosphate buffered saline (PBS; Cold Spring Harb Protoc 2006, 

doi:10.1101/pdb.rec8247). Fungal tissue was lyophilized (VirTis Genesis, SP Scientific, 

Warminster, PA) and ground in a modified paint shaker using 6 mm ceramic beads (Zircoa, Inc., 

Solon, OH). DNA was extracted from lyophilized tissue as reported in chapter 2 (current 

dissertation) using a modified protocol based on the OmniPrep for Fungi kit (G-Biosciences, St. 

Louis, MO). 

Geographic Populations 

Isolates were organized by geographic areas representative of the major sugar beet growing 

regions throughout the world (Table 3.1). Europe was considered a single growing region 

although we recognize that there are likely distinct regions within the European continent that 

should be considered separate geographical populations, much like exist in the US. However, the 

precise origin of some of the isolates included from Europe was uncertain and our access to 

European sugar beet practices was limited so we classified ‘Europe’ as a single geographical 

region for the purposes of the current study. The ‘Midwest’ region included isolates from 

Michigan, Ohio, and Wisconsin. Isolates from the ‘West’ region were collected from Idaho, 

Montana, and Colorado. Isolates from the ‘Red River Valley’ included those isolates collected 

from Minnesota and North Dakota and isolates from the ’South’ were collected from Texas and 

Arizona (Table 3.1).  

123 

 
Table 3.1 Isolates of Rhizoctonia solani used in the current study.  

Name 
Taltfalt 
R14-12 
Vander-2 
Cookson 
R15-100 
Plattling 
Rickard 
Cavalie 
Rs331 
99-15 
F24 
39AR 
R14-10 
R14-9 
Roland 
R15-73 
R15-93 
H502 
R33 
RH188 
RH193 
7A1 
Rs106 
H549 
Bayern 
91003 
Rs481 
Rs542 
Gg670a 
Hubert 
Ifz 
R18-19 
Rs296 
H582 
Rs588 
2C13 
Rs496 
Rs200 
5C5 
R164S 
R86 

MLG a 
1 
2 
3 
4 
5 
6 
6 
7 
8 
9 
9 
10 
10 
10 
10 
11 
12 
13 
13 
13 
13 
14 
15 
16 
17 
18 
18 
18 
19 
19 
19 
19 
20 
21 
22 
23 
24 
25 
26 
27 
28 

ISG b 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 
PR 
PR 
PR 
PR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 

Cluster c 
3 
3 
3 
3 
4 
3 
3 
3 
3 
4 
4 
4 
4 
4 
4 
4 
4 
2 
2 
2 
2 
2 
2 
2 
2 
2 
2 
2 
3 
3 
3 
3 
2 
2 
2 
2 
2 
2 
2 
2 
1 

Collection 
Hanson 
Hanson 
Hanson 
Hanson 
Chilvers 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 

Original 
Collector 
L Holmquist 
CH Seed 
L Holmquist 
L Holmquist 
J Jacobs 
L Holmquist 
B Holtschulte 
B Holtschulte 
C Windels 
- 
C Strausbaugh 
C Truman 
C Truman 
C Truman 
L Holmquist 
D Minier 
D Minier 
L Herr 
C Rush 
A Ogoshi 
A Ogoshi 
B Bill 
C Windels 
L Herr 
L Holmquist 
L Holmquist 
C Windels 
C Windels 
L Holmquist 
L Holmquist 
L Holmquist 
L Holmquist 
C Windels 
L Herr 
C Windels 
B Bugbee 
C Windels 
C Windels 
B Bugbee 
A Ogoshi 
C Rush 

Crop d 
sugar beet 
spinach 
fodder beet 
fodder beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
soybean 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
- 
sugar beet 
sugar beet 
sugar beet 
- 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
- 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
wheat 

Locale e 
Sweden 
- 
N Zealand 
N Zealand 
Michigan 
Germany 
- 
- 
Minnesota 
Ohio 
Idaho 
Ontario 
Ontario 
Ontario 
Germany 
Michigan 
Michigan 
Ohio 
Texas 
Japan 
Japan 
Minnesota 
Minnesota 
Ohio 
Germany 
- 
Minnesota 
Minnesota 
Holland 
Germany 
Germany 
Germany 
Minnesota 
Ohio 
Minnesota 
Montana 
Minnesota 
Minnesota 
Minnesota 
Japan 
Texas 

Region f 
Europe 
- 
N Zealand 
N Zealand 
Midwest 
Europe 
Europe 
Europe 
RedRiver 
Midwest 
West 
Ontario 
Ontario 
Ontario 
Europe 
Midwest 
Midwest 
Midwest 
South 
Japan 
Japan 
RedRiver 
RedRiver 
Midwest 
Europe 
- 
RedRiver 
RedRiver 
Europe 
Europe 
Europe 
Europe 
RedRiver 
Midwest 
RedRiver 
West 
RedRiver 
RedRiver 
RedRiver 
Japan 
South 

County g 
- 
- 
- 
- 
Saginaw 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
Saginaw 
Saginaw 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

a) multi-locus genotype 
b) intraspecific group 
c) genetic cluster determined by popper 
d) crop type isolate was recovered from 
e) geographic location isolate was collected 
f) category used in the current study to group isolates by geographic region of isolation 
g) county which isolates was collected for those isolates recovered from Michigan. If isolate was collected from the 
Cercospora Nursery, designation indicates isolate was from Saginaw County in either 2016 (SV16) or 2017 (SV17) 

124 

 
 
 
Table 3.1 (Con’t) 

Name 
Johnson 
11-272a 
Kratzer 
99-11 
99-12 
R17-6 
R15-66 
R15-82 
R15-83 
R15-84 
Rs255 
F517 
87-36-1 
F30 
F36 
F551 
R16-20 
R16-7 
R17-31 
R15-30 
R15-1 
R15-5 
R15-15 
R15-59 
2048 SB 
2049 SB 
2C1 
87-36-2 
87-36-3 
R09-23 
R15-17 
R15-25 
R15-34 
R15-48 
R15-53 
R16-1 
R16-11 
R16-12 
R16-13 
R16-15 
R16-16 
R16-18 
R16-2 
R16-21 
R16-3 
R16-4 
R16-5 

MLG a 
29 
30 
31 
32 
32 
32 
33 
34 
34 
34 
35 
36 
37 
37 
37 
37 
37 
37 
37 
38 
39 
39 
40 
40 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 

ISG b 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 

Cluster c 
3 
3 
3 
3 
3 
3 
4 
4 
4 
4 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 

Collection 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Cercospora 
Chilvers 
Chilvers 
Chilvers 
Chilvers 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Cercospora 
Cercospora 
Cercospora 
Chilvers 
Chilvers 
Chilvers 
Chilvers 
Chilvers 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Chilvers 
Chilvers 
Chilvers 
Chilvers 
Chilvers 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 

Crop d 
fodder beet 
sugar beet 
sugar beet 
soybean 
soybean 
sugar beet 
soybean 
soybean 
soybean 
soybean 
sugar beet 
sugar beet 
dry bean 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
dry bean 
dry bean 
dry bean 
dry bean 
soybean 
sugar beet 
sugar beet 
sugar beet 
dry bean 
dry bean 
sugar beet 
dry bean 
dry bean 
dry bean 
dry bean 
dry bean 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 

Original 
Collector 
L Holmquist 
L Holmquist 
L Holmquist 
- 
- 
D Minier 
J Jacobs 
J Jacobs 
J Jacobs 
J Jacobs 
C Windels 
C Strausbaugh 
C Windels 
C Strausbaugh 
C Strausbaugh 
C Strausbaugh 
D Minier 
D Minier 
D Minier 
J Jacobs 
J Jacobs 
J Jacobs 
J Jacobs 
J Jacobs 
L. Hanson 
L. Hanson 
B Bugbee 
C Windels 
C Windels 
L. Hanson 
J Jacobs 
J Jacobs 
J Jacobs 
J Jacobs 
J Jacobs 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 

125 

Region f 

Locale e 
N Zealand  N Zealand 
Holland 
Germany 
Ohio 
Ohio 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Minnesota 
Idaho 
N Dakota 
Idaho 
Idaho 
Idaho 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Montana 
N Dakota 
N Dakota 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 

County g 
- 
- 
Europe 
- 
Europe 
- 
Midwest 
- 
Midwest 
SV17 
Midwest 
Allegan 
Midwest 
Clinton 
Midwest 
Clinton 
Midwest 
Clinton 
Midwest 
- 
RedRiver 
- 
West 
- 
West 
- 
West 
- 
West 
- 
West 
SV16 
Midwest 
SV16 
Midwest 
SV17 
Midwest 
Saginaw 
Midwest 
Saginaw 
Midwest 
Ingham 
Midwest 
Saginaw 
Midwest 
Gratiot 
Midwest 
Midland 
Midwest 
Bay 
Midwest 
- 
West 
- 
West 
- 
West 
Bay 
Midwest 
Saginaw 
Midwest 
Saginaw 
Midwest 
Midwest 
Saginaw 
Midwest  Montcalm 
Saginaw 
Midwest 
SV16 
Midwest 
SV16 
Midwest 
SV16 
Midwest 
SV16 
Midwest 
SV16 
Midwest 
SV16 
Midwest 
SV16 
Midwest 
SV16 
Midwest 
SV16 
Midwest 
SV16 
Midwest 
SV16 
Midwest 
SV16 
Midwest 

 
 
 
Table 3.1 (Con’t) 

Name 
R16-6 
R16-8 
R17-10 
R17-11 
R17-12 
R17-13 
R17-14 
R17-21 
R17-22 
R17-23 
R17-27 
R17-28 
R17-5 
R17-7 
R17-8 
R17-9 
R18-10 
R18-11 
R18-5 
R18-9 
Rs1146 
Rs393 
R15-21 
R15-6 
R15-60 
R15-61 
R15-7 
Rs1012 
R15-67 
R15-70 
Slovakia 
R15-78 
24BR 
R15-98 
R16-14 
R17-24 
R17-26 
Rs850 
R17-16 
R17-15 
R17-18 
R17-19 
R17-20 
R17-29 
R17-30 
Rs300 
C116S 

MLG a 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
41 
42 
42 
42 
42 
42 
43 
44 
44 
45 
46 
47 
47 
47 
47 
47 
48 
49 
50 
50 
50 
50 
50 
50 
51 
52 

ISG b 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
BR 
PR 

Cluster c 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
1 
3 

Collection 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Chilvers 
Chilvers 
Chilvers 
Chilvers 
Chilvers 
Hanson 
Chilvers 
Chilvers 
Hanson 
Chilvers 
Hanson 
Hanson 
Cercospora 
Cercospora 
Cercospora 
Hanson 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Cercospora 
Hanson 
Hanson 

Crop d 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
soybean 
sugar beet 
sugar beet 
dry bean 
dry bean 
soybean 
soybean 
dry bean 
sugar beet 
soybean 
soybean 
sugar beet 
soybean 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
mat rush 

Locale e 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Minnesota 
Minnesota 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Minnesota 
Michigan 
Michigan 
- 
Michigan 
Ontario 
Michigan 
Michigan 
Michigan 
Michigan 
Minnesota 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Minnesota 
Japan 

Region f 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
RedRiver 
RedRiver 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
RedRiver 
Midwest 
Midwest 
Europe 
Midwest 
Ontario 
Midwest 
Midwest 
Midwest 
Midwest 
RedRiver 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
RedRiver 
Japan 

County g 
SV16 
SV16 
SV17 
SV17 
SV17 
SV17 
SV17 
SV17 
SV17 
SV17 
SV17 
SV17 
SV17 
SV17 
SV17 
SV17 
Saginaw 
Saginaw 
Saginaw 
Saginaw 
- 
- 
Saginaw 
Ingham 
Gratiot 
Gratiot 
Ingham 
- 
Shiawassee 
Shiawassee 
- 
Allegan 
- 
Saginaw 
SV16 
SV17 
SV17 
- 
SV17 
SV17 
SV17 
SV17 
SV17 
SV17 
SV17 
- 
- 

Original 
Collector 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
J Jacobs 
D Minier 
C Windels 
C Windels 
J Jacobs 
J Jacobs 
J Jacobs 
J Jacobs 
J Jacobs 
C Windels 
J Jacobs 
J Jacobs 
B Holtschulte 
J Jacobs 
C Truman 
D Minier 
D Minier 
D Minier 
D Minier 
C Windels 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
D Minier 
C Windels 
A Ogoshi 

126 

 
 
 
Table 3.1 (Con’t) 

Name 

R15-63 

R18-32 
R15-80 
R15-74 
R15-90 
R15-86 
R15-87 
R15-72 
R15-76 
R15-88 
R15-92 
B Hill 
R14-14 
Rs890 
R18-31 
W-22 
Alburry 
R16-10 
R16-9 
Rs866 
DKL4 
Italien 
R09-25 
F16 
F508 
R15-81 
F521 
R4 
R1 
R15-95 
R-8 
R9 

MLG a 

ISG b 

Cluster c 

Collection 

53 

54 
55 
56 
57 
58 
59 
60 
60 
60 
60 
61 
62 
63 
64 
65 
66 
67 
67 
68 
69 
69 
70 
71 
72 
72 
73 
73 
74 
74 
74 
74 

PR 

PR 
BR 
PR 
BR 
BR 
PR 
PR 
PR 
PR 
PR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 
BR 

3 

3 
1 
4 
4 
4 
4 
4 
4 
4 
4 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 

Chilvers 

Hanson 
Chilvers 
Chilvers 
Chilvers 
Chilvers 
Chilvers 
Chilvers 
Chilvers 
Chilvers 
Chilvers 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Cercospora 
Cercospora 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Chilvers 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 
Hanson 

Original 
Collector 

J Jacobs 
A Stouffer-
Hopkins 
J Jacobs 
J Jacobs 
J Jacobs 
J Jacobs 
J Jacobs 
J Jacobs 
J Jacobs 
J Jacobs 
J Jacobs 
L Holmquist 
CH Seed 
C Windels 
N Lukasco 
RT Sherwood 
L Holmquist 
D Minier 
D Minier 
C Windels 
L Holmquist 
B Holtschulte 
L. Hanson 
C Strausbaugh 
C Strausbaugh 
J Jacobs 
C Strausbaugh 
C Rush 
E Ruppel 
D Minier 
E Ruppel 
E Ruppel 

Crop d 

Locale e 

Region f 

County g 

soybean 

Michigan 

Midwest 

Ithaca 

turf 
soybean 
soybean 
soybean 
soybean 
soybean 
soybean 
soybean 
soybean 
soybean 
fodder beet 
swiss chard 
sugar beet 
carrot 
dry bean 
fodder beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
soybean 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 
sugar beet 

Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
Michigan 
N Zealand 
- 
Minnesota 
Michigan 
Wisconsin 
N Zealand 
Michigan 
Michigan 
Minnesota 
Denmark 
- 
Michigan 
Idaho 
Idaho 
Michigan 
Idaho 
Texas 
Colorado 
Michigan 
Arizona 
Colorado 

Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
Midwest 
N Zealand 
- 
RedRiver 
Midwest 
Midwest 
N Zealand 
Midwest 
Midwest 
RedRiver 
Europe 
Europe 
Midwest 
West 
West 
Midwest 
West 
South 
West 
Midwest 
South 
West 

- 
Midland 
Shiawassee 
Clinton 
Clinton 
Clinton 
Shiawassee 
Ithaca 
Clinton 
Clinton 
- 
- 
- 
Oceana 
- 
- 
SV16 
SV16 
- 
- 
- 
Gratiot 
- 
- 
Clinton 
- 
- 
- 
Saginaw 
- 
- 

127 

 
 
 
Data Analysis 

Basic population statistics were determined using ‘poppr’ v.2.1.8 (Kamvar et al. 2014; 

Kamvar et al. 2015) in R v.4.2.1 (R Core Team 2022). Cluster assignment was conducted using 

the R package ‘adegenet’ v.2.1.8 (Jombart 2008) with the number of axes retained in the 

principal components analysis step set to six. Allowing the number of clusters to be determined 

automatically resulted in two clusters that corresponded to AG 2-2BR and AG 2-2PR. However, 

given that important characteristics have been shown to distinguish sub-clusters within each of 

the main clades, the number of clusters was set to four to be consistent with those described in 

chapter 2 (current dissertation).  

Discriminate analysis of principal comments (DAPC) was conducted in the R package 

‘adegenet’ v.2.1.8 on the clone-corrected datasets for crop-type and Michigan counties. Cross 

validation based on 1000 reps was used to determine appropriate values for the number of axes 

(‘n.da’) and principal components (‘n.pca’) to be retained. The clone-corrected dataset for crop-

type ‘n.pca’ was set to 15 and ‘n.da’ set to 3. The Michigan county dataset, ‘n.pca’ was set to 4 

and ‘n.da’ set to 4.  

Basic statistics such as number of alleles, allelic richness, and heterozygosity were 

determined with MSAnalyzer v.4.05 (Dieringer & Schlötterer 2003). Hardy-Weinberg exact 

tests, genotypic differentiation and pairwise FST values were determined using genepop v.4.5.1 

(Rousset 2008). Index of association plots (Brown et al. 1980) were generated using the R 

package ‘poppr’ on clone-corrected datasets with 999 permutations.  

Structure plots were generated using Structure v.2.3.4 (Pritchard et al. 2000) on clone-

corrected datasets. All simulations were conducted with a burn-in period of 12,000 reps followed 

by 120,000 MCMC reps after the burn-in period. We used the admixture model of ancestry with 

128 

sampling locations used as prior information about group membership. Alpha was inferred and 

considered to be the same for each population.  

Development of subgroup specific primers 

Lineage specific primers were identified through selective exclusion by comparing whole 

genome sequences of representative isolates from the three subgroups, AG 2-2BRa, AG 2-2BRb, 

and AG 2-2PR. Whole genomes were sequenced using Illumina and were described in chapter 2 

(current dissertation). The genome sequence of isolate ‘R1’ was used to represent subgroup 

AG 2-2BRa and was aligned to another isolate of AG 2-2BRa using minimap2 v.2.24 (Li 2018). 

Reads that mapped were retained and aligned to another isolate of AG 2-2BRa and mapped reads 

again were retained. This provided a set of reads that were shared between three isolates of 

AG 2-2BRa. These reads were then mapped to three isolates from subgroup AG 2-2BRb and 

reads that did not map were retained. The process was repeated for subgroup AG 2-2PR and the 

final set of unmapped reads was assembled using SPAdes v.3.11.0 (Nurk et al. 2013) with k-mer 

selection set to ‘automatic’ and error correction ‘on’. The resulting assemblies contained contigs 

that were presumed to be common to AG 2-2BRa but absent from AG 2-2BRb and AG 2-2PR. 

Starting with the longest contig, primer pairs were generated using Primer3 v.2.3.7 (Untergasser 

et al. 2012) and the predicted PCR product compared to the other assemblies using a BLAST 

algorithm until three primer pairs were generated that were predicted to amplify only for 

subgroup AG 2-2BRa. The entire process of selective exclusion was repeated with isolate ‘Rzc6’ 

from subgroup AG 2-2BRb and isolate ‘Rzc115’ from subgroup AG 2-2PR.  

A set of nine primers pairs, three pairs for each subgroup, were identified and oligos ordered 

from Integrated DNA Technologies (Coralville, IA). Initial testing was conducted on eight 

129 

 
isolates using the following PCR conditions: reactions were performed in 25µl volumes with 

15ng of DNA template, 1 x Phusion II HF buffer (Thermo Fisher Scientific. Waltham, MA), 1.5 

mM MgCl2, 0.5 µM of each primer, 200 µM each dNTP, and 1 unit of Phusion II HF polymerase 

(Thermo Fisher Scientific). Final PCR conditions consisted of 1 cycle of 98˚C for 1 min. 

followed by 30 cycles of 98˚C for 10 s, 58°C for 10 s, 72˚C for 15 s and a final extension cycle 

of 72˚C for 5 min. Amplicons were run on a 0.75% agarose gel, stained using RedSafe Nucleic 

Acid Staining Solution (iNtRON Biotechnology, Gyeonggi-do, Korea) and visualized on a UVP 

ChemStudio 815 gel imager (Analytik Jena US, Upland, CA). The presence of a band from a 

specific primer set indicated association with that subgroup.  

One primer pair for each genetic group amplified as predicted for all 8 initially screened 

isolates (Table 3.2) and were then tested on a total of 71 isolates, including one AG 2-2LP 

isolate, seven isolates that were not in AG 2-2, and 15 isolates of unknown subgroup. After 

preliminary identification of subgroup for these 15 unknowns using the lineage specific primers, 

they were all genotyped using the microsatellite markers and included in cluster analysis. 

Subgroup predicted by lineage-specific markers was compared to subgroups assigned by 

genotype analysis. A subset of 23 isolates was independently analyzed by Dr. Carmen Medina-

Mora (Michigan State University) using this set of lineage specific primers as confirmation of 

the process and consistency of identification across laboratories.  

130 

 
 
 
Table 3.2 Subgroup specific primers for Rhizoctonia solani AG 2-2.   

Primer 

Sequence 5’ -> 3’ 

BRa-4-3F 
BRa-491-3R 

CTCTGAGAGAAATAAGCATC 
CTGTAACGTTGTAAGTGTTC 

BRb-1164-3F 
BRb-2145-3R  CCTCCTAGTGTACTTGATT 

CAAGTCATCTAATTGCTTTA 

PR-212-3F 
PR-870-3R 

GGGCAATCCTCCTCGTTCAA 
TAACGTTCACTGAGGAGGGC 

a) annealing temperature   

Approx. 
product size 

490 

Ta a 

57°C 

980 

57°C 

660 

57°C 

Cloning and sequencing of potential hybrid from the Cercospora nursery 

Isolate ‘R17-15’ was identified from the Cercospora nursery collection as being a member of 

subgroup AG 2-2PRb and had variation that was shared with AG 2-2BRa and AG 2-2PRa (Table 

3.3; Figure 3.1). Our hypothesis was that isolate ‘R17-15’ was representative of a hybrid that 

resulted from the exchange of nuclei of isolates of subgroup AG 2-2BRa and AG 2-2PRa. To test 

this hypothesis, we compared isolate ‘R17-15’ to the isolates ‘R16-10’ and ‘R17-6’, also from 

the Cercospora nursery, that were classified as AG 2-2BRa and AG 2-2PRa respectively. Four 

genes, calmodulin, β-tubulin, rpb1, and rpb2, were amplified by PCR as described in chapter 2 

(current dissertation). PCR products were cloned using Zero Blunt TOPO PCR cloning kit for 

sequencing (Thermo Fisher Scientific) following manufacturer’s instructions. DNA was 

extracted from colonies containing the vector (pCR-4Blunt-TOPO; Invitrogen, Carlsbad, CA) 

and PCR product using a GeneJET plasmid miniprep kit (Thermo Fisher Scientific) following 

manufacturer’s instructions. Vector products were sequenced by the Michigan State University 

Genomic Core (East Lansing, MI) using the primer set M13Forward(-20) 

(5’- GTAAAACGACGGCCAG) and M13Reverse (5’- CAGGAAACAGCTATGAC). Results 

were analyzed using Geneious Prime 2021.2.2 (Biomatters Ltd., Auckland, New Zealand).  

131 

 
 
 
 
 
 
 
 
Table 3.3 Cluster association of 84 Rhizoctonia solani AG 2-2 isolates. Cluster 
designation matches those identified by cluster analysis in the R package ‘poppr’ 
v.2.1.8. Bolding indicates isolates that could not be confidently assigned to a single 
cluster with greater than 85% confidence or have more than 10% association with 
more than one cluster. 

Inferred cluster 

Inferred cluster 

Isolate 
Italian 
R18-31 
R09-25 
R15-81 
R15-95 
R16-10 
W-22 
R15-80 
Bhill 
Alburry 
Rs866 
Rs890 
Rs300 
R-8 
R86 
R4 
F508 
F521 
R1 
F16 
Bayern 
R164S 
RH193 
H502 
H582 
H549 
Rs106 
Rs200 
Rs296 
Rs481 
Rs496 
Rs588 
7A1 
5C5 
R33 
2C13 
Cavalie 
Taltfalt 
Plattling 
Kratzer 
Hubert 
11-272a 

cluster 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
2 
2 
2 
2 
2 
2 
2 
2 
2 
2 
2 
2 
2 
2 
2 
2 
3 
3 
3 
3 
3 
3 

1 
0.966 
0.981 
0.962 
0.980 
0.984 
0.980 
0.955 
0.983 
0.665 
0.793 
0.966 
0.956 
0.513 
0.980 
0.810 
0.979 
0.984 
0.984 
0.984 
0.987 
0.003 
0.006 
0.003 
0.004 
0.006 
0.096 
0.005 
0.121 
0.006 
0.006 
0.008 
0.007 
0.005 
0.012 
0.012 
0.035 
0.003 
0.002 
0.003 
0.005 
0.006 
0.004 

2 
0.006 
0.004 
0.003 
0.003 
0.004 
0.005 
0.005 
0.003 
0.206 
0.091 
0.016 
0.021 
0.472 
0.015 
0.179 
0.015 
0.005 
0.007 
0.007 
0.005 
0.984 
0.985 
0.989 
0.986 
0.982 
0.760 
0.988 
0.855 
0.848 
0.987 
0.985 
0.971 
0.987 
0.936 
0.983 
0.946 
0.004 
0.003 
0.007 
0.014 
0.005 
0.013 

3 
0.015 
0.005 
0.007 
0.004 
0.005 
0.004 
0.012 
0.004 
0.061 
0.063 
0.009 
0.012 
0.005 
0.003 
0.006 
0.003 
0.005 
0.006 
0.006 
0.005 
0.008 
0.006 
0.006 
0.004 
0.004 
0.006 
0.005 
0.018 
0.130 
0.005 
0.005 
0.018 
0.005 
0.049 
0.003 
0.014 
0.960 
0.989 
0.982 
0.935 
0.967 
0.973 

4 
0.014 
0.010 
0.028 
0.013 
0.008 
0.011 
0.027 
0.011 
0.068 
0.054 
0.009 
0.011 
0.009 
0.002 
0.005 
0.003 
0.005 
0.003 
0.003 
0.003 
0.004 
0.003 
0.002 
0.006 
0.008 
0.138 
0.002 
0.006 
0.017 
0.003 
0.003 
0.005 
0.003 
0.004 
0.002 
0.005 
0.034 
0.005 
0.008 
0.046 
0.023 
0.011 

Isolate 
  C116S 
  R09-23 
  R15-1 
  R15-15 
  R15-21 
  R15-63 
  R16-20 
  R17-6 
  R18-32 
  R15-30 
  R15-67 
  Cookson 
Johnson 
  Vandeer2 
  Rs1012 
  Rs1146 
  Rs255 
  Rs331 
2C1 
F30 
F517 
24BR 
39AR 
  Roland 
Slovakia 
  R15-100 
  R15-66 
  R15-73 
  R15-74 
  R15-86 
  R15-90 
  R15-93 
  R16-14 
  R17-15 
  R17-16 
99-15 
  R15-72 
  R15-78 
  R15-82 
  R15-87 
  Rs850 
F24 

cluster 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 

1 
0.004 
0.005 
0.008 
0.008 
0.007 
0.005 
0.004 
0.006 
0.006 
0.008 
0.006 
0.063 
0.016 
0.042 
0.006 
0.005 
0.053 
0.004 
0.008 
0.007 
0.056 
0.010 
0.007 
0.004 
0.005 
0.060 
0.011 
0.095 
0.142 
0.132 
0.132 
0.126 
0.010 
0.031 
0.017 
0.006 
0.237 
0.009 
0.095 
0.318 
0.036 
0.011 

2 
0.008 
0.002 
0.003 
0.002 
0.002 
0.002 
0.003 
0.003 
0.003 
0.002 
0.003 
0.015 
0.006 
0.004 
0.036 
0.013 
0.062 
0.011 
0.004 
0.004 
0.030 
0.199 
0.192 
0.177 
0.211 
0.002 
0.142 
0.005 
0.005 
0.003 
0.003 
0.008 
0.178 
0.004 
0.004 
0.194 
0.003 
0.243 
0.003 
0.003 
0.037 
0.204 

3 
0.965 
0.981 
0.977 
0.970 
0.978 
0.591 
0.980 
0.799 
0.591 
0.974 
0.599 
0.911 
0.967 
0.949 
0.947 
0.978 
0.856 
0.522 
0.983 
0.983 
0.858 
0.013 
0.009 
0.018 
0.033 
0.032 
0.009 
0.039 
0.141 
0.090 
0.163 
0.024 
0.012 
0.008 
0.010 
0.008 
0.195 
0.011 
0.056 
0.195 
0.016 
0.011 

4 
0.023 
0.011 
0.013 
0.019 
0.012 
0.402 
0.013 
0.192 
0.400 
0.015 
0.392 
0.011 
0.012 
0.005 
0.011 
0.004 
0.029 
0.463 
0.005 
0.005 
0.056 
0.778 
0.793 
0.801 
0.751 
0.906 
0.838 
0.860 
0.712 
0.776 
0.703 
0.842 
0.799 
0.956 
0.969 
0.791 
0.565 
0.737 
0.846 
0.483 
0.910 
0.774 

132 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.1 Structure plots for 84 Rhizoctonia solani AG 2-2 isolates from a clone corrected 
dataset. Colored bars above plots represent cluster association of individual isolates as 
determined using the R package ‘poppr’ v.2.1.8. For the upper plot, the number of populations 
was set to four (k=4) and for the lower plot number of populations was set to three (k=3). 
Arrow indicates isolate ‘R17-15’, that was used to test for potential hybridization in the 
Cercospora nursery.   

133 

 
 
Results and Discussion 

Subgroup and cluster assignment  

In the current study, we used the conventions for identification and naming for the R. solani 

AG 2-2 subgroups that were outlined in chapter 2 of the current dissertation. The 167 isolates 

genotyped in the current study grouped into 4 clusters that were consistent with the newly 

defined subgroups, AG 2-2BR and AG 2-2PR. Clusters 1 and 2 (AG 2-2BR) consisted of 24 and 

19 isolates respectively and clusters 3 and 4 (AG 2-2PR) had 88 and 36 isolates respectively 

(Table 3.4). A total of 74 multi-locus genotypes (MLG) were identified among all isolates with 

cluster 3 (AG 2-2PRa) having 25 MLGs. Much of cluster 3 was made up of clones with a clonal 

fraction of 72% with 45 isolates represented by a single genotype (MLG50), 28 of which came 

from the Cercospora nursery collection (Table 3.1). Hardy-Weinberg tests indicated a significant 

deficit of heterozygotes in cluster 2 while cluster 4 had a significant excess of heterozygotes 

(Table 3.4).  

Table 3.4 Summary statistics for 167 Rhizoctonia solani AG 2-2 isolates based on 13 
microsatellite loci. Genetic clusters were identified by cluster analysis using the R 
package ‘poppr’ v.2.1.8. Heterozygosity and index of association statistics were 
calculated using a clone corrected dataset.  

Genetic 
cluster 
   1 
   2 
   3 
   4 

N a  MLG b 
24 
19 
88 
36 

17 
14 
25 
18 

Total 

167 

74 

Clonal 
fraction 

29% 
26% 
72% 
50% 

56% 

eMLG c 
14.4 
14.0 
8.7 
11.9 

13.7 

Evenness  Hexp

d 

0.86 
0.80 
0.35 
0.78 

0.32 

0.469 
0.304 
0.468 
0.641 

0.681 

H1 = Hdeficit
0.724 
< 0.001 
0.064 
0.992 

- 

f 
e  H1 = Hexcess

0.238 
1.000 
0.928 
0.007 

- 

g 
FIS
-
0.060 
0.517 
0.041 
-
0.272 
- 

Iah 
1.046 
0.198 
3.198 
2.058 

 i 

𝑟̅𝑑
0.158 
0.043 
0.487 
0.296 

3.377 

0.486 

   a) number of samples 

b) multi-locus genotype 
c) effective number of MLGs at lowest common sample size 
d) expected heterozygosity Nei’s gene diversity 
e) heterozygosity deficit (p value) 
f) heterozygote excess (p value) 
g) inbreeding coefficient 
h) index of association 
i) linkage disequilibrium index 

134 

 
 
 
 
The number of alleles per locus ranged from 5 to 18 with a total of 65 alleles over all loci. 

Allelic richness ranged from 1.63 to 1.83 with an average of 1.72 across all loci (Table 3.5). 

When analyzed as a single population, Hardy-Weinberg (HW) tests identified 7 loci with a 

significant deficit of heterozygotes and high positive FIS values (0.133 to 0.449). Since a 

heterozygote deficit and positive FIS values are typically interpreted as indicating an inbred 

population (Spielman et al. 1977; Wright 1965) and the biological expectation was that these 

populations were primarily clonal, we hypothesized a Wahlund effect. The analysis of samples at 

such a large scale, as was represented by the global population, may result in a deficit of 

heterozygotes due to unaccounted for population substructure (De Meeûs 2018; Waples 2015).  

Table 3.5 Allelic statistics for the 8 microsatellite loci used for genotyping 167 
Rhizoctonia solani AG 2-2 isolates. Heterozygosity and Hardy-Weinberg testing 
was performed on the clone corrected dataset.  

clone corrected population 

b 
Hobs

c 
Hdeficit

d 
Hexcess

e 
FIS

0.419 

0.459 

0.527 

0.351 

0.689 

0.622 

0.662 

0.622 

< 0.001 

< 0.001 

< 0.001 

< 0.001 

< 0.001 

< 0.001 

0.078 

0.023 

1.000 

0.999 

1.000 

1.000 

1.000 

1.000 

0.926 

0.975 

0.341 

0.337 

0.324 

0.449 

0.175 

0.133 

0.112 

0.175 

a 

Hexp

0.634 

0.692 

0.778 

0.636 

0.834 

0.716 

0.745 

0.753 

65 

1.72 

- 

- 

0.724 

0.544 

< 0.001 

1.000 

0.249 

Locus   

No. 
alleles 

Allelic  
Richness 

Min 
Allele 

Max 
Allele 

2547 

4660 

5583 

5487 

5 

8 

5 

5 

759 

18 

9 

7 

8 

6145 

8703 

6150 
value over 
all loci 

1.63 

1.69 

1.78 

1.64 

1.83 

1.72 

1.75 

1.75 

214 

132 

183 

126 

122 

123 

202 

126 

230 

159 

198 

141 

188 

159 

220 

158 

a) expected heterozygosity 
b) observed heterozygosity 
c) p-value for heterozygosity deficit 
d) p-value for heterozygosity excess 
e) inbreeding coefficient  

135 

 
 
 
 
 
When the global population was organized into subgroups based on cluster analysis and 

heterozygosity reanalyzed, results were more in line with biological expectations (Table 3.6). 

One locus (2547) showed evidence of heterozygote deficiency (p = 0.027) when calculated over 

all subgroups. FIS values were negative for most loci (Table 3.6) except for 2547 (FIS = 0.047), 

5487 (FIS = 0.069) and 5583 (FIS = 0.157). Overall, the population was in Hardy-Weinberg 

equilibrium when population substructure was considered and had an FIS value of -0.019. 

Consistent with a Wahlund effect, each subgroup had unique characteristics associated with 

heterozygosity indicating differences in life histories (Garnier-Géré & Chikhi 2013).  

Table 3.6 Population statistics for the four subgroups of Rhizoctonia 
solani AG 2-2 based on clone corrected dataset of genotypes of 167 
isolates.  

Locus 

AG2-2BRa  AG2-2BRb  AG2-2PRa 

AG2-2PRb 

c 
subgroups  Hexcess

d 
Hdeficit

FIS values for subgroups a 

global HW test b 

over all 

2547 

4660 

5583 

5487 

759 

6145 

8703 

6150 
value over      
all loci 

Hexcess 

Hdeficit 

0.652 

0.000 

0.230 

-0.077 

-0.088 

-0.176 

-0.450 

-0.045 

-0.060 

0.238 

0.724 

- 

0.874 

0.866 

0.632 

0.262 

-0.110 

- 

-0.020 

0.517 

1.000 

< 0.001 

0.007 

0.015 

0.202 

-0.029 

-0.038 

0.142 

0.013 

0.007 

0.041 

0.928 

0.064 

a) inbreeding coefficient by subgroup 
b) Hardy-Weinberg tests for all populations 
c) p-value for heterozygote excess 
d) p-value for heterozygote deficit 

-0.041 

-0.691 

-0.351 

-0.256 

-0.099 

-0.160 

-0.286 

-0.337 

-0.272 

0.007 

0.992 

0.047 

-0.007 

0.157 

0.069 

-0.036 

-0.060 

-0.185 

-0.106 

-0.019 

- 

- 

0.968 

0.372 

0.789 

0.798 

0.826 

0.843 

0.057 

0.246 

0.853 
- 
- 

0.027 

0.636 

0.191 

0.195 

0.162 

0.133 

0.944 

0.747 

0.119 
- 
- 

136 

 
 
 
 
 
 
Subgroup AG 2-2BRa (cluster 1) was in Hardy-Weinberg equilibrium (Table 3.6) with a 

slightly negative FIS value (-0.060) which is consistent with clonal reproduction (Balloux et al. 

2003; Weir & Cockerham 1984; Wright 1965). There was strong evidence of a heterozygote 

deficit in subgroup AG 2-2BRb (cluster 2) with a high FIS value (0.517) which is characteristic 

of a strongly inbred population (Spielman et al. 1977; Wright 1965). We previously 

hypothesized that sexual reproduction in R. solani AG 2-2 results in genotypic simplification due 

to haploidization of basidiospores (chapter 2, current dissertation). Our results lend support to 

this hypothesis and the implications of sexual reproduction are discussed in more detail later in 

the current manuscript. Subgroup AG 2-2PRb showed strong evidence for an excess of 

heterozygotes (p = 0.007) and a highly negative FIS value (-0.272). We interpret this data as 

being consistent with a highly clonal population (Balloux et al. 2003; Wright 1949). Overall, we 

view these observations as support for population structure as identified by cluster analysis and 

support for the subgroups AG 2-2BRa (cluster 1), AG 2-2BRb (cluster 2), AG 2-2PRa (cluster 

3), and AG 2-2PRb (cluster 4).  

STRUCTURE analysis of the clone-corrected regional dataset was able to assign 56 of 84 

(67%) genotypes to a cluster with greater than 85% confidence (Table 3.3). Twenty-nine of 36 

(81%) isolates in clusters 1 and 2 (AG 2-2BR) could be assigned to their respective group with 

greater than 85% confidence. For the other seven isolates, association was split between groups 1 

and 2 except for one isolate (‘Rs296’) where group association was split with group 3 and one 

isolate (‘H549’) where group association was split with group 4 (Table 3.3). 

 Twenty-two out of 48 (46%) isolates in clusters 3 and 4 (AG 2-2PR) could be confidently 

assigned to a single group with greater than 85% confidence (Table 3.3). The five isolates from 

cluster 3 that could not be confidently assigned to a single group had group association split 

137 

between groups 3 and 4. The 16 isolates from cluster 4 that could not be confidently assigned to 

a single group had group association split between groups 3 and 4 (5 isolates), groups 2 and 4 (8 

isolates), groups 1 and 4 (3 isolates), groups 1, 2 and 4 (1 isolate), and groups 1, 3 and 4 (3 

isolates).  

Each of the groups identified by STRUCTURE analysis corresponded to subgroups 

AG 2-2BRa (cluster 1), AG 2-2BRb (cluster 2), AG 2-2PRa (cluster 3), and AG 2-2PRb (cluster 

4). Observations of association patterns for cluster 4 support our hypothesis that AG 2-2PRb 

consists primarily of hybrids that contain nuclei from different genetic groups. This conclusion is 

even more evident when the STRUCTURE analysis is set for three populations (Figure 3.1). 

Each group of isolates that were associated with a single cluster are separated by a group of 

isolates where cluster association was split. These admixed isolates all correspond to AG 2-2PRb 

(cluster 4) as determined by cluster analysis.  

None of the isolates identified with AG 2-2PRa had more than 6.3% membership association 

with AG 2-2BRa or AG 2-2BRb, while members of AG 2-2PRb that had less than 85% 

membership had between 10% and 20% association with either cluster 1 or cluster 2 in roughly 

equal proportions. Collectively, these observations support the hypothesis that subgroup 

AG 2-2PRb is made up of what we have referred to as hybrids that result from recombination 

between AG 2-2PRa and either AG 2-2BRa or AG 2-2BRb. Nuclear exchange between 

AG 2-2BRa and AG 2-2BRb apparently does occur as indicated by several isolates that share 

group association between the groups. However, this exchange may often be masked due to the 

genetic similarity between AG 2-2BRa and AG 2-2BRb. 

138 

 
 
Distribution of global population  

A total of 164 isolates were assigned to eight geographical regions with the number of 

isolates per region ranging from 4 to 99. The region that included the highest number of isolates 

was the Midwest region with 99 samples, 92 of which were from Michigan. Despite the high 

number of isolates recovered from Michigan, there were no isolates from AG 2-2BRb (cluster 2) 

recovered from the state. There were, however, three isolates from Ohio included in the Midwest 

region that were identified as AG 2-2BRb (Figure 3.2). In contrast, the Red River Valley region 

had a large proportion of isolates identified as AG 2-2BRb (50%) and accounts for about half of 

all AG 2-2BRb isolates recovered from all regions in the current study.  

The reason for the prevalence of AG 2-2BRb isolates in the Red River Valley is uncertain, 

although our observations are consistent with previous reports from the region (Brantner & 

Windels 2007). The close relationship between the Japan population and Red River Valley 

(genotypic differentiation; p = 0.204; FST = 0.012) might suggest that the AG 2-2BRb population 

in the Red River Valley originated in Japan (or possibly visa-versa). However, there were no 

identical genotypes shared between Japan and the Red River Valley. Rather, genotypes identical 

with those from Japan were identified in the Midwest and South regions (Figure 3.2). This could 

be due to a more recent introduction of genotypes to the Midwest and South regions while 

isolates introduced to the Red River Valley have had time to undergo recombination and 

diversification.  

139 

 
 
1

3

4

8

Red River 
Valley 

1

3

4

1

West 

Ontario 

2

New Zealand 

2

3

1

2

Japan 

2

1

1

6

Europe 

15

7

3

10

1

3

Midwest 

South 

AG 2-2BRa 
(cluster 1) 

AG 2-2BRb 
(cluster 2) 

AG 2-2PRa 
(cluster 3) 

AG 2-2PRb 
(cluster 4) 

Figure 3.2 Distribution of Rhizoctonia solani AG 2-2 genotypes among 8 sugar beet 
growing regions. Pie charts indicate number of unique genotypes collected from each 
region with each slice representing the number of isolates in each subgroup. Solid lines 
connecting circles indicate a lack of genotypic differentiation (p > 0.05). Arrow heads 
indicate one or more multi-locus genotypes (MLG) are shared between regions. 
Dashed lines indicate populations are significantly differentiated but share one or more 
MLGs.  

140 

 
 
  
Genotypic differentiation tests (exact G test, Fisher’s method; Raymond & Rousset 1995) 

showed significant differentiation between the New Zealand and all other populations except the 

Japan population (p = 0.268). Moreover, the Japan population was not significantly differentiated 

from the Ontario population (p = 0.223), the Red River Valley population (p = 0.204) or the 

South population (p = 0.162). All other population pairs were significantly differentiated (exact 

G test, p < 0.05) except the Midwest and West regions (p = 0.065).  

Researchers in Europe have reported that isolates present in their growing regions were 

limited to ‘type IIIB’ (Buhre et al. 2009; Ithurrart et al. 2004). However, in the current study, we 

identified isolates recovered from Europe in each of the four genetic clusters, including one 

isolate (‘Bayern’) from subgroup AG 2-2BRb, which was the cluster that contains primarily 

‘type IV’ isolates. The population from Europe, examined in the current study, was relatively 

distinct from other populations throughout the world. While there was evidence of significant 

genotypic differentiation between European isolates and all other regions, FST values between 

Europe and the Midwest and between Europe and the West region were fairly low (0.037 and 

0.042 respectively; Table 3.7). In addition, a single genotype, MLG59, was shared between 

Europe and Ontario (Table 3.7). Overall, the Europe population appears to have remained 

relatively isolated from other growing regions throughout the world but is substantially more 

diverse than previously reported.  

Despite R. solani AG 2-2 having a worldwide distribution, it was surprising that genotypes 

were shared across considerable geographical distances. In the current study, nine genotypes 

were shared between different regions including seven from the Midwest region and six from the 

West region (Table 3.8). These genotypes are shared between regions as distant as Japan and the 

Midwest or Europe and Ontario. Since R. solani does not produce asexual spores that can be 

141 

dispersed by wind or water, we expect transport primarily occurs by moving infested soil, 

equipment, or personal items. The significant genotypic similarity between geographical distant 

regions highlights the importance of sanitation in preventing the spread of Rhizoctonia 

propagules (Rodriguez-Salamanca 2015). 

Table 3.7 Genotypic differentiation for regional populations of Rhizoctonia solani 
AG 2-2 based on 8 microsatellite loci. p-value is the result of exact G-tests performed 
in Genepop v.4.5.1. Non-significant results are bolded indicating a lack of genotypic 
differentiation between populations. FST values were also calculated using Genepop. 
Values below 0.100 are bolded indicating low levels of genetic differentiation 
between populations. Shared multi-locus genotypes (MLG) lists those genotypes 
shared between populations.   

Population pairs 

Chi2 a 

df  b 

p-value 

c 

FST

shared MLG d 

&  Europe 
Japan 
& 
& 
Japan 
&  Midwest 
&  Midwest 
&  Midwest 
&  N Zealand 
&  N Zealand 
&  N Zealand 
&  N Zealand 
&  RedRiver 
&  RedRiver 
&  RedRiver 
&  RedRiver 
&  RedRiver 
&  South 
&  South 
&  South 
&  South 
&  South 
&  South 
&  West 
&  West 
&  West 
&  West 
&  West 
&  West 
&  West 

Ontario 
Ontario 
Europe 
Ontario 
Europe 
Japan 
Ontario 
Europe 
Japan 
Midwest 
Ontario 
Europe 
Japan 
Midwest 
N Zealand 
Ontario 
Europe 
Japan 
Midwest 
N Zealand 
RedRiver 
Ontario 
Europe 
Japan 
Midwest 
N Zealand 
RedRiver 
South 
  a) Chi square value 
b) degrees of freedom 
c) fixation index 
d) multi-locus genotypes shared between regions 

37.00 
19.93 
48.69 
65.66 
Infinity 
91.01 
46.45 
36.68 
19.02 
Infinity 
37.35 
58.90 
20.38 
Infinity 
50.47 
44.32 
63.27 
21.45 
86.86 
32.40 
37.62 
62.56 
38.82 
53.98 
25.29 
44.87 
42.76 
36.40 

16 
16 
16 
16 
16 
16 
16 
16 
16 
16 
16 
16 
16 
16 
16 
16 
16 
16 
16 
16 
16 
16 
16 
16 
16 
16 
16 
16 

0.002 
0.223 
< 0.001 
< 0.001 
< 0.001 
< 0.001 
< 0.001 
0.002 
0.268 
< 0.001 
0.002 
< 0.001 
0.204 
< 0.001 
< 0.001 
< 0.001 
< 0.001 
0.162 
< 0.001 
0.009 
0.002 
< 0.001 
0.001 
< 0.001 
0.065 
< 0.001 
< 0.001 
0.003 

0.139 
0.138 
0.195 
0.168 
0.037 
0.246 
0.228 
0.104 
0.108 
0.149 
0.056 
0.109 
0.012 
0.138 
0.119 
0.282 
0.233 
0.095 
0.262 
0.129 
0.085 
0.131 
0.042 
0.174 
0.017 
0.083 
0.069 
0.128 

59 

66 

18 

50 

18 
18,17 

58,46,50,15,17 

50 
16,17 

142 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 3.8 Multi-locus genotypes (MLG)of Rhizoctonia solani AG 2-2 
isolates shared between geographical regions. Shared genotypes are 
indicated by an ‘x’.  

MLG a  Midwest  West  Ontario 

South 

Red River 

Europe 

Japan 

Growing region 

x 
x 
x 

x 
x 
x 

15 
16 
17 
18 
46 
50 
58 
59 
66 

x 

x 
x 
x 
x 
x 

x 

a) multi-locus genotype 

x 
x 
x 

x 
x 

x 

x 

x 

Relationship of genotypes to crop type 

A total of 158 samples from four host crops, including sugar beet (112), soybean (25), dry 

bean (16) and fodder beet (5), were examined for a relationship between genotype and host. 

There was a total of 66 unique genotypes across all samples with 45 from sugar beet, 17 from 

soybean, 7 from dry bean and 5 from fodder beet (Table 3.9). Isolates from sugar beet included 

representatives from all four genetic subgroups, AG 2-2BRa (14%), AG 2-2BRb (14%), 

AG 2-2PRa (52%) and AG 2-2PRb (20%). Soybean isolates were split between subgroups 

AG 2-2BRa (8%), AG 2-2PRa (36%), and AG 2-2PRb (56%). Isolates from dry bean were the 

most restricted in respect to subgroup with 15 (94%) from AG 2-2PRa and only 1 (6%) isolate 

from AG 2-2BRa (Figure 3.3). Isolates from additional crops were represented by a single isolate 

each and included carrot, swiss chard and wheat in subgroup AG 2-2BRa and isolates from 

spinach and turf in subgroup AG 2-2PRa. Further investigation of the subgroups that affect these 

additional crops and host susceptibility to them will be needed.  

143 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
 
Table 3.9 Summary statistics for 158 Rhizoctonia solani AG 2-2 
isolates by crop type.  

Clone 
fraction 

59.8% 
0.0% 
32.0% 
56.3% 

58.2% 

eMLG c 

7.22 
5 
8.44 
5.34 

7.77 

Crop 

N a  MLG b 

sugar beet 
fodder beet 
soybean 
dry bean 

Total 

112 
5 
25 
16 

158 

45 
5 
17 
7 

66 

a) number of samples 
b) multi-locus genotypes 
c) effective multi-locus genotypes 
d) expected heterozygosity 
e) index of association  
f) linkage disequilibrium index 

Clone Corrected 
Ia e 

Hexp d 

rbarD f 

0.677 
0.628 
0.647 
0.495 

0.675 

1.85 
1.923 
1.669 
5.613 

1.927 

0.266 
0.289 
0.241 
0.833 

0.277 

The DAPC plot (Figure 3.4) shows that fodder beet had a distinct distribution compared to 

the other crops. However, this distinction is likely related to geography rather than host 

specificity since the only location where isolates were included from fodder beet was New 

Zealand. Both major subgroups AG 2-2BR and AG 2-2PR were recovered from fodder beet, 

despite the small number of samples. Dry bean was the only crop with any evidence for crop 

specificity as AG 2-2PRa made up 94% of the isolates recovered from the crop. On the other 

hand, the DAPC plot did not support dry bean isolates as a separate population (Figure 3.4). In 

other words, although the group that was recovered from dry beans was primarily AG 2-2PRa, 

the same group also affects sugar beet and soybeans. This is consistent with previous findings 

where group AG 2-2PRa was more aggressive on dry beans, as a whole, than the other subgroups 

(Minier 2019). It is possible that samples were selected because of obvious or severe symptoms 

and the selection process skewed the results to favor the AG 2-2PRa isolates, which cause more 

severe symptoms on dry beans. Conversely, planting dry beans may increase the prevalence of 

AG 2-2PRa isolates due to selection. Further investigation of the effects of including specific 

crops in rotation on AG 2-2 populations is warranted.  

144 

 
 
 
 
 
 
Figure 3.3 Minimum spanning network of 74 isolates of Rhizoctonia solani 
AG 2-2 recovered from four host crops. The data set was corrected for clones and 
is represented by 64 unique genotypes. The number of isolates per genotype is 
indicated by the size of the circles and host is indicated by color. Edges connecting 
genotypes indicated genetic distance with thicker, darker lines indicating closer 
relationships and thinner, lighter lines more distant relationships. Ellipses are 
drawn around isolates associated with the indicated subgroups.  

145 

 
 
2
C
P

PC1  

Figure 3.4 Discriminant analysis of principal components for 74 
Rhizoctonia solani AG 2-2 isolates from four host crops. Fifteen principal 
components (PC) and three discriminant functions (DA) were retained 
during analysis to describe relationships between clusters. Scatter plot 
shows first two PCs of the analysis with eigenvalues of the discriminant 
functions indicated in the upper right corner.  

Isolates recovered from soybean were more diverse than those from dry bean with 

representatives from all subgroups except AG 2-2BRb. Since the majority of soybean isolates 

came from Michigan and no AG 2-2BRb isolates were detected in Michigan, this could be a 

factor in why no isolates from AG 2-2BRb were recovered from soybean. Additional sampling 

from soybean in regions that have a substantial AG 2-2BRb population could help establish the 

susceptibility of soybean to AG 2-2BRb. We hypothesize that soybeans are susceptible to all 

subgroups of AG 2-2 (except AG 2-2LP), which would be consistent with previous reports from 

the Red River Valley region (Brantner & Windels 2007). Curiously, the only crop from which 

AG 2-2BRb isolates were recovered was sugar beet. Sampling various crops from a region with 

146 

 
 
 
high levels of AG 2-2BRb would be needed to ensure that AG 2-2 being restricted to sugar beet 

is not a result of sampling bias.  

Rhizoctonia solani AG 2-2 is distributed worldwide but its occurrence in particular areas or 

even within specific agricultural fields is patchy (Anees et al. 2010; Truscott & Gilligan 2001) 

and it is likely that cultivated crops have exerted a strong influence on prevalence and 

distribution (Ogoshi 1987). Reports have indicated that crop rotations affect R. solani AG 2-2 

populations, but these reports have focused on differences between ‘type IIIB’ and ‘type IV’ 

populations (Buhre et al. 2009; Engelkes & Windels 1996; Windels & Brantner 2006). Future 

work on the effects of crop rotation on populations of AG 2-2 should focus on the newly 

identified genetic groups as outlined in the current study.  

Diversity of Michigan population 

The population from Michigan consisted of 92 individuals from 10 counties. The collection 

included 56 isolates recovered from sugar beet, 22 from soybean, 12 from dry bean and 1 each 

from carrot and turf grass. The majority of isolates from Michigan were in subgroup AG 2-2PRa 

and AG 2-2PRb with 58 (63%) and 25 (27%) isolates respectively. AG 2-2BRa also had a small 

number of representatives with 9 (10%) but no AG 2-2BRb isolates were recovered from 

Michigan despite sampling from diverse collection areas. Isolates R09-23 and R09-25 were both 

identified as ‘type IV’ based on growth at 35°C (unpublished) but were identified as AG 2-2PRa 

and AG 2-2BRa respectively. The reason for the lack of AG 2-2BRb isolates in Michigan is 

uncertain, especially since AG 2-2BRb isolates were identified from the neighboring state of 

Ohio. We expect that the lack of AG 2-2BRb isolates collected from the state is a result of a) the 

nature of historical introductions and b) the effects of crop rotation choices. There were three 

147 

 
AG 2-2PRb genotypes present in Michigan that had substantial group association with 

AG 2-2BRb (R15-66 (14%); R15-78 (24%); R16-14 (18%); Table 3.3). Based on the hypothesis 

that AG 2-2PRb is a group of hybrid individuals, this observation suggests that AG 2-2BRb does 

have a historical presence in Michigan. Therefore, crop rotation may have played a larger role in 

current population structure than historical introduction.  

Saginaw county contained the largest number of genotypes with 15 (52% of total Michigan 

genotypes) and shared one or more genotypes with five other counties, Bay, Midland, Montcalm, 

Ingham, and Gratiot (Figure 3.5). Midland and Gratiot counties also shared substantial genotypic 

connection to other counties with Midland County sharing genotypic diversity with all reported 

counties except Oceana County. Likewise, Gratiot County shared genotypic diversity with all 

other counties reported except Oceana and Allegan, the western-most counties tested.  

Because we have little historical data regarding distribution patterns of R. solani AG 2-2 in 

Michigan, it is not practical to determine migration patterns. We do, however, have concerns 

over distribution patterns that may reflect the movement of genotypes between farms. For 

example, MLG50 is shared between Saginaw County and Montcalm County but not with Gratiot 

County which is spatially intermediate between Saginaw and Montcalm counties. Ingham county 

shares diversity with Saginaw, Bay, Midland, and Gratiot counties but not the adjacent counties 

Clinton and Shiawassee. These observations should not be construed as conclusive evidence of 

the movement of isolates between farms but should serve as a reminder of the importance of 

sanitation when moving equipment, vehicles, and personnel between farm locations.  

148 

 
 
AG 2-2BRa 
(cluster 1) 

AG 2-2BRb 
(cluster 2) 

AG 2-2PRa 
(cluster 3) 

AG 2-2PRb 
(cluster 4) 

Figure 3.5 Distribution of Rhizoctonia solani AG 2-2 genotypes among 10 Michigan counties. 
Pie charts indicate number of unique genotypes collected from each county with each slice 
representing the number of isolates in each subgroup. Solid lines connecting circles indicate a 
lack of genotypic differentiation and a FST value less than 0.10. Arrow heads indicate one or 
more multi-locus genotypes (MLG) are shared between populations. Dashed lines indicate 
populations are significantly differentiated but share one or more MLGs.  

149 

 
 
  
Overall, the R. solani AG 2-2 population in Michigan is widespread and shows little evidence 

of population structure (Figure 3.6). Samples from the western counties of Oceana and Allegan 

were not genotypically differentiated from some counties on the eastern side of the state (Figure 

3.5; Table 3.10). Multi-locus genotypes are shared between numerous counties and there was no 

apparent structure based on geographic origin or crop type. There was no evidence of sexual 

reproduction in the state (𝑟̅𝑑 = 0.361, p = 0.001; Figure 3.7) which is hypothesized to be due in 

part to the lack of AG 2-2BRb isolates.  

2
C
P

PC1  

Figure 3.6 Discriminate analysis of principal components for 38 
Rhizoctonia solani AG 2-2 isolates from 10 Michigan counties. Four 
principal components (PC) and four discriminant functions (DA) 
were retained during analysis to describe relationships between 
clusters. Scatter plot shows first two PCs of the analysis with 
eigenvalues of the discriminant functions indicated in the lower left 
corner.  

150 

 
 
 
 
 
Table 3.10 Genotypic differentiation for Michigan county 
populations of Rhizoctonia solani AG 2-2. p-value is the 
result of exact G-tests performed in Genepop v.4.5.1. 
Non-significant results are bolded indicating a lack of 
genotypic differentiation between populations. FST values 
were also calculated using Genepop. Values below 0.100 
are bolded indicating low levels of genetic differentiation 
between populations. Shared multi-locus genotypes 
(MLG) lists those genotypes shared between populations.  

Population Pair 

Chi2 a 

Df b 

p-value 

Fst c 

Shared d 
MLG 

0.496 

0.000 

0.000 

0.004 

0.832 

0.000 

0.005 

0.328 

0.000 

0.941 

0.686 

1.000 

0.007 

0.990 

0.357 

0.530 

NA 

0.066 

0.999 

0.596 

1.000 

0.530 

0.349 

0.126 

0.117 

0.137 

1.000 

NA 

0.000 

1.000 

0.000 

0.442 

0.248 

0.281 

0.223 

0.006 

0.100 

0.438 

0.067 

0.287 

0.008 

0.096 

0.000 

0.069 

-0.055 

0.083 

0.380 

0.000 

0.225 

-0.144 

0.480 

-0.456 

0.365 

0.614 

0.189 

0.262 

0.587 

-0.338 

0.621 

0.369 

-0.044 

0.196 

74 

51 

50 

50 

50 

50 

Allegan 

&  Bay 

Allegan 

&  Clinton 

Bay 

&  Clinton 

Allegan 

&  Gratiot 

Bay 

&  Gratiot 

Clinton 

&  Gratiot 

Allegan 

Bay 

Clinton 

Gratiot 

& 

& 

& 

& 

Ingham 

Ingham 

Ingham 

Ingham 

Allegan 

&  Midland 

Bay 

&  Midland 

Clinton 

&  Midland 

Gratiot 

&  Midland 

Ingham 

&  Midland 

Allegan 

&  Montcalm 

15.39 

49.48 

45.29 

34.69 

10.63 

46.14 

33.98 

4.63 

60.09 

8.25 

11.00 

0.00 

33.33 

5.80 

17.44 

10.98 

16 

16 

16 

16 

16 

16 

16 

4 

16 

16 

14 

16 

16 

16 

16 

12 

Bay 

&  Montcalm 

NA 

NA 

Clinton 

&  Montcalm 

25.22 

16 

16 

4 

16 

12 

16 

16 

16 

16 

8 

NA 

16 

16 

16 

Gratiot 

&  Montcalm 

Ingham 

&  Montcalm 

Midland 

&  Montcalm 

Allegan 

&  Oceana 

Bay 

&  Oceana 

Clinton 

&  Oceana 

Gratiot 

&  Oceana 

Ingham 

&  Oceana 

Midland 

&  Oceana 

Montcalm  &  Oceana 

Allegan 

Bay 

Clinton 

& 

& 

& 

Saginaw 

Saginaw 

3.89 

2.78 

0.00 

10.99 

17.58 

22.55 

22.87 

22.19 

0.00 

NA 

61.75 

1.76 

Saginaw 

Infinity 

a) Chi square value 
b) degrees of freedom 
c) fixation index 
d) multi-locus genotypes shared between regions 

151 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 3.10 (Con’t) 

Population Pair 

Chi2 a 

Df b 

p-value 

Fst c 

Gratiot 

Ingham 

Midland 

& 

& 

& 

Saginaw 

Saginaw 

Saginaw 

Montcalm  & 

Saginaw 

32.04 

16.23 

25.58 

0.00 

42.05 

Oceana 

Allegan 

Bay 

Clinton 

Gratiot 

Ingham 

Midland 

& 

& 

& 

& 

& 

& 

& 

Saginaw 

Shiawassee 

30.90 

Shiawassee 

23.99 

Shiawassee 

37.77 

Shiawassee 

19.36 

Shiawassee 

39.79 

Shiawassee 

18.87 

Montcalm  & 

Shiawassee 

14.68 

Oceana 

Saginaw 

& 

& 

Shiawassee 

22.51 

Shiawassee 

61.44 

16 

16 

16 

16 

16 

16 

16 

16 

16 

16 

16 

16 

16 

16 

0.010 

0.437 

0.060 

1.000 

0.000 

0.014 

0.090 

0.002 

0.250 

0.001 

0.275 

0.548 

0.127 

0.000 

0.027 

0.016 

0.052 

-0.144 

0.365 

0.287 

0.235 

0.120 

0.044 

0.253 

0.085 

0.135 

0.359 

0.143 

Shared d 
MLG 

49,51 

48,51 

50 

50 

74 

74 

𝑟̅𝑑 = 0.361 
    p = 0.001 

Figure 3.7 Index of association plot for 29 multi-locus genotypes 
from a Michigan population of Rhizoctonia solani AG 2-2. 𝑟̅𝑑 
value is shown on x-axis with dotted line indicating value for the 
given dataset. Predicted distribution based on 999 permutations is 
shown as gray bars.  

152 

 
 
 
 
 
 
 
 
 
 
 
 
 
Cercospora nursery population 

Populations collected from the Saginaw Valley Research and Extension Center (SVREC) 

came from adjacent fields following sugar beet in a fixed rotation cycle. Forty-two (95%) of the 

44 isolates recovered were in subgroup AG 2-2PR and two (5%) were in subgroup AG 2-2BRa. 

These proportions are consistent with the overall Michigan population found in this study where 

AG 2-2BRa was about 9% of the population and AG 2-2PR about 91% of the population. No 

isolates from subgroup AG 2-2BRb were isolated from any Michigan location including the 

SVREC population. There were 10 (23%) isolates from subgroup AG 2-2PRb (cluster 4), which 

is slightly higher than the Michigan population at large in which there were about 16%.  

There was a total of 7 multi-locus genotypes (MLG) identified in the SVREC population, 4 

MLGs in the SV16 population and 6 MLGs in the SV17 population (Figure 3.8). A majority of 

the isolates recovered (64%) were in MLG50 (14 isolates from each subpopulation). Three 

MLGs were shared between populations (MLG49, MLG50, MLG66) and four were unique to 

the subpopulations, MLG10 in the SV16 population and MLG43, MLG68, and MLG69 in the 

SV17 population (Figure 3.8). The SV16 population contained one MLG from AG 2-2BRa 

(cluster 1), one MLG from AG 2-2PRb (cluster 4) and two MLGs from AG 2-2PGa. The SV17 

population consisted of three MLGs from each AG 2-2PRa (cluster 3) and AG 2-2PRb      

(cluster 4).  

There was a total of eight private alleles present in the population, four in each subpopulation 

(Figure 3.9). Density plot from discriminant analysis of principal components (DAPC) illustrates 

the relationship between subpopulations (Figure 3.10). While each population shared a large 

portion of the principal components, each population had independent peaks reflecting the 

unique MLGs in each subpopulation. Genotypic differentiation tests indicated no significant 

153 

difference between subpopulations (Χ2 = 6.626, df = 16,  p = 0.980) with negative a FST value 

(FST = -0.044).  

50 

10 

46 

66 

50  69  66  68  43  46 

Multi-locus genotype 

Figure 3.8 Histogram of multi-locus genotypes for 44 isolates of Rhizoctonia solani 
AG 2-2 from USDA Cercospora nursery in Michigan. Isolates were recovered from 
two adjacent fields in subsequent years, 2016 (SV16) and 2107 (SV17).  

Figure 3.9 Private alleles in two populations of Rhizoctonia solani 
AG 2-2 from the USDA Cercospora nursery in Michigan. Populations 
were collected in 2016 (SV16) and 2017 (SV17).  

154 

 
 
Figure 3.10 Density plot from discriminant analysis of principal 
components for two populations of Rhizoctonia solani AG 2-2 
from the USDA Cercospora nursery in Michigan. Distribution 
colored blue represents the 2016 population and red represents 
the 2017 population.  

There was no evidence of sexual recombination within the population. The index of 

association plot indicated a significant linkage between alleles (𝑟̅𝑑 = 0.721, p = 0.001; Figure 

3.11) which is characteristic of clonally reproducing populations (Balloux et al. 2003; Wright 

1965). Global Hardy-Weinberg tests indicate evidence of a slight heterozygote excess in the 

SV17 population (p = 0.045). Individually, one locus (2547) showed evidence of heterozygote 

excess (p = 0.041) and one locus (5487) showed evidence of a deficiency of heterozygotes (p = 

0.002). Estimates of FIS were negative for all loci except 5487 (FIS = 0.714) and 6145 (FIS = 

0.091). Estimate of FIS across all loci was FIS = -0.072. Collectively we interpret this evidence as 

supporting a population with primarily clonal reproduction (Balloux et al. 2003; Wright 1965).  

155 

 
 
 
𝑟̅𝑑 = 0.721 
    p = 0.001 

Figure 3.11 Index of association plot for 7 multi-locus genotypes 
for a population of Rhizoctonia solani AG 2-2 from the USDA 
Cercospora nursery in Michigan. 𝑟̅𝑑 value is shown on x-axis with 
dotted line indicating value for the given dataset. Predicted 
distribution based on 999 permutations is shown as gray bars. 

Prior to 2010, the field that currently contains the Cercospora nursery was not subdivided and 

the rotation at that time included corn, dry bean, soybean, and wheat with sugar beets not having 

been grown there in the previous 20 years (personal communication, former grower). The field 

took its current configuration in 2010 with the field being divided into quadrants separated by a 

grass strip 10 meters wide and beginning the current rotation of sugar beet, corn, dry beans, and 

wheat. During that time, no inoculation of Rhizoctonia spp. has been performed on this section of 

the farm, thus we consider these to be natural populations.  

Overall, the evidence supports SV16 and SV17 as a single population despite the limited 

separation for over 12 years. Of course, it is likely there has been movement of propagules from 

field to field during cultivation that would tend to homogenize the populations. Another factor 

that likely contributes to maintaining homogeneity between fields is that the rotation scheme was 

identical for each field and has remained fairly constant for almost 12 years. Despite these 

156 

 
factors that may homogenize the two field populations, there were marked differences between 

the populations. There were eight private alleles and four unique MLGs between the two 

populations. These observations are consistent with the patchy nature of RRCR (Anees et al. 

2010; Truscott & Gilligan 2001) and may indicate that individual isolates have a relatively 

limited distribution.  

Reproductive mode and presence of a cryptic sexual stage 

Previously, we hypothesized that AG 2-2BRb (cluster 2) consists of isolates that had 

undergone sexual recombination (chapter 2, current dissertation). In the current study, we 

examined 19 AG 2-2BRb isolates from six growing regions. Clonal fraction of the group was 

26.3% with 14 unique MLGs. Hardy-Weinberg tests indicated a deficit of heterozygotes with a 

FIS value of 0.517, which is consistent with a highly inbred, sexually reproducing population 

(Brzyski et al. 2018; Wright 1965). The index of association plot (Brown et al. 1980) provided 

additional evidence with support for a lack of linkage between alleles, which is also consistent 

with a sexually reproducing population (𝑟̅𝑑 = 0.027, p = 0.230; Figure 3.12). These observations 

provide support for our hypothesis that AG 2-2BRb (cluster 2) consists of isolates that have 

undergone sexual recombination despite the rarity of the teleomorph in nature.  

Since the population from the Red River Valley had a high proportion of AG 2-2BRb 

genotypes (50%), we tested this population for evidence of sexual reproduction. When the entire 

population from the Red River Valley was considered, there was strong evidence of linkage 

between markers (𝑟̅𝑑 = 0.347, p = 0.001) and therefore, no evidence of sexual reproduction 

(Brown et al. 1980; Wright 1949). However, when only members of AG 2-2BRb (cluster 2) from 

the region were considered, there was evidence of a sexually recombinant population 

157 

 
(𝑟̅𝑑 = -0.026, p = 0.645). Therefore, we hypothesize a portion of the population in the Red River 

Valley may have a sexual mode of reproduction and thus we predict a mixed mode of 

reproduction in the Red River Valley population. 

𝑟̅𝑑 = 0.027 
    p = 0.230 

Figure 3.12 Index of association plot for 14 multi-locus genotypes of 
subgroup Rhizoctonia solani AG 2-2BRb. 𝑟̅𝑑 value is shown on x-axis 
with dotted line indicating value for the given dataset. Predicted 
distribution based on 999 permutations is shown as gray bars. 

The conclusion of a mixed mode of reproduction is consistent with the observations of Ajayi-

Oyetunde et al. (2019) who determined a population of AG 2-2 isolates from Ontario to have a 

mixed mode of reproduction. The Ontario population was made up entirely of ‘type IIIB’ isolates 

and to the best of our knowledge, the sexual stage has not been observed to occur in AG 2-2 

‘type IIIB’ isolates. It is possible the isolates from the Ontario population were misidentified as 

‘type IIIB’, especially if a genetic comparison was used to determine subgroup association rather 

than in-vitro growth trials. It is also possible that ‘type IIIB’ isolates can form a sexual stage but 

have not been observed doing so. Alternatively, our data suggests another explanation. All the 

isolates from Ontario in the current study were identified as AG 2-2PRb (cluster 4), and group 

association was split with about 80% association with AG 2-2PRb (cluster 4) and 20% with 

158 

 
 
AG 2-2BRb (cluster 2). In addition, there was a lack of genetic differentiation between the 

Ontario population and the Japan population, which had a high proportion of AG 2-2BRb 

isolates. Therefore, our hypothesis is that AG 2-2BRb (cluster 2) is present in Ontario, has 

undergone sexual recombination at some point, and has produced heterokaryons by asexual 

recombination with AG 2-2PRa. Examination of additional isolates from Ontario would be 

needed to test this hypothesis.  

Rhizoctonia solani AG 2-2 populations having a cryptic mode of sexual reproduction in field 

populations could have implications in management by increasing genetic diversity (Heitman 

2010), reducing deleterious mutations through purifying selection and increasing adaptability to 

environmental conditions (Brandt et al. 2017; MacPherson et al. 2021). For example, 

recombination has the potential to intensify the evolution of multiple-fungicide resistance (De 

Miccoli Angelini et al. 2015; Takahashi et al. 2021). In addition, basidiospores can be dispersed 

more readily than hyphal fragments and can even be carried by air and water currents (Naito 

1996). Foliar blight of sugar beet has been associated with basidiospore dispersal (Naito & 

Sugimoto 1980; Naito 1990) and has been observed in several states within the United States 

including: Arizona, Colorado, Michigan, Minnesota, Ohio, and Wisconsin, but can be caused by 

AG other than AG 2-2 (Windels 2009).  

Hymenia of R. solani AG 2-2 have been reported to occur on sugar beet leaf bases and at the 

soil surface (Naito 1996). However, it is uncertain what environmental conditions are necessary 

for the formation of a sexual stage in a field setting. Since the soil-over-culture method is 

preferred for inducing the sexual stage in the lab (Flentje 1956; Ogoshi 1972; Toda & 

Hyakumachi 2006), it may be that in most circumstances the hymenia are formed at the soil 

surface and are just too difficult to detect in most situations. Instead, identifying the sexual stage 

159 

in a field setting may require indirect evidence from population genetics such as we present in 

the current study. The potential for sexual reproduction in some regions highlights the need for 

experiments involving the nature of heterothallic and homothallic mating systems in AG 2-2 

(Toda & Hyakumachi 2006), the fate of basidiospore derived isolates (Kiyoshi et al. 2014) and 

the consequences of heterokaryosis in the generation of diversity (Qu et al. 2013).  

Hybridization and the generation of genetic diversity 

Observations in previous studies indicated the possibility that some isolates were hybrids, 

with genetic composition originating from two or more parental sources from different 

subgroups (chapter 2, current dissertation). These isolates showed evidence of admixture and 

clustered together separately from the main genetic clusters that corresponded with the 

subgroups. In the current study, we observed 11 additional isolates with similar characteristics 

and investigated the phenomenon further. Isolates with evidence of admixture grouped together 

according to cluster analysis regardless of whether genetic diversity was shared between clade 

AG 2-2PR and AG 2-2BRa or between AG 2-2PR and AG 2-2BRb (Figure 3.1).  

The isolate ‘slovakia’ was one of the samples that showed evidence of admixture and also 

had a whole genome sequence available. Based on structure analysis, we expected genes to be 

shared between AG 2-2PR and AG 2-2BRb (Table 3.3) by what we hypothesize to be the 

exchange of nuclei during anastomosis. To test this, we identified gene sequences for β-tubulin, 

Calmod, Rpb1, and Rpb2 from the genome assemblies of ‘slovakia’ and six additional isolates 

that were representative of subgroups AG 2-2BRa, AG 2-2BRb, and AG 2-2PRa.  The resulting 

neighbor-joining trees for all four genes showed that the genome of ‘slovakia’ contained two 

alleles of each gene, with one allele most similar to sequences from isolates in clade AG 2-2BRb 

160 

 
and the other most similar to sequences from isolates in clade AG 2-2PRa (Figure 3.13). These 

observations provide support for our hypothesis that members of AG 2-2PRb are formed from 

hybrids between AG 2-2BR and AG 2-2PR.  

To determine if the hypothesized hybridization occurs in natural populations, we examined a 

set of isolates from the SVREC population that had similar characteristics as the ‘slovakia’ 

sample. Isolate ‘R17-15’ (AG 2-2PRb) had group association split between AG 2-2PR and 

AG 2-2BRa and appeared to be a hybrid. Isolates ‘R17-6’ (AG 2-2PR) and ‘R16-10’ 

(AG 2-2BRa) were selected from the Cercospora nursery population and the β-tub, Calmod, 

Rpb1, and Rpb2 genes were cloned and sequenced for all three isolates. The results were mixed 

with two genes (Calmod and Rpb1) having alleles that were associated with both group 

AG 2-2BRa and AG 2-2PR (Table 3.11). Both alleles for the β-tub gene were similar to alleles 

from AG 2-2PR while both alleles for the Rpb2 gene were similar to alleles from AG 2-2BRa. 

While these results do not offer conclusive evidence of hybridization or asexual recombination, 

they are suggestive that such events do occur in natural populations.  

161 

 
Β-tub 

Calmod 

Rpb1 

Rpb2 

Figure 3.13 Gene trees of four gene sequences for seven Rhizoctonia solani AG 2-2 
isolates. Trees were generated using the neighbor-joining method with an AG-5 isolate 
included as an outgroup. Alleles from the suspected hybrid ‘slovakia’ are indicated by 
arrows and show the relationship between those alleles and the subgroups, which are 
indicated by colored branches (green = AG 2-2BRa, blue = AG 2-2BRb, and red = 
AG 2-2PR). 

162 

 
 
Table 3.11 Association of alleles of four genes 
from potential hybrid ‘R17-15’ with alleles 
from isolates in subgroups AG 2-2BRa and 
AG 2-2PRa.  

Number of alleles            

associated with subgroups 
BRa 

BRb 

PRa 

Btub 
Calmod 

Rpb1 
Rpb2 

0 
1 

2 
2 

0 
0 

0 
0 

2 
1 

1 
0 

Subgroup specific primers 

Fifty-eight of sixty-three isolates (92%) were correctly identified to subgroup using the group 

specific primer set (Table 3.12). Four isolates failed to amplify for any primer except for the 

AG 2-2 specific primers (Carling et al. 2002). One isolate (‘R9’) amplified with both 

AG 2-2BRa and AG 2-2PR primer pairs producing ambiguous results. Phylogenetically, isolate 

‘R9’ is clearly in subgroup AG 2-2BRa, so it is unclear why amplification occurred with the AG 

2-2PR primers. However, there have been conflicting assignments of subgroup for isolate ‘R9’ 

(Carling et al. 2002; Engelkes & Windels 1996; Stojšin et al. 2007; Strausbaugh et al. 2011a), so 

there may be a unique genetic condition in this isolate that results in conflicting subgroup 

association. None of the non-AG 2-2 isolates amplified with the group specific primers. 

Curiously, the AG 2-2LP isolate was expected to amplify with at least one primer set but did not 

(Table 3.12), providing further support that AG 2-2LP should be considered a separate subgroup 

from AG 2-2BR and AG 2-2PR as suggested in chapter 2 (current dissertation). 

163 

 
 
 
 
 
 
 
Table 3.12 Results of subgroup specific primer testing for 71 isolates of Rhizoctonia 
solani. The columns for the primer pairs shows the results of testing a specific isolate 
with the indicated primer pair, where a ‘+’ indicates a positive PCR reaction and ‘-‘ 
indicates a negative PCR reaction. The predicted subgroup is indicated in the column 
‘ID”. Cluster as determined by cluster analysis is indicated.  

Isolate 

Italian 
R09-25 
R1 
R15-80 
R16-10 
R16-9 
F16 
R9 
Rs300 
Rs866 
F508 
F521 
R86 
Bayern 
91.003 
Rs481 
Rs496 
Rs588 
2C13 
H502 
R164S 
H549 
H582 
RH188 
Cavalie 
R15-30 
R15-5 
R15-6 
R15-60 
R15-61 
R15-67 

R15-70 

R16-12 
R16-4 
R16-8 
R17-10 

Primer pairs  
BRb 

PR 

BRa 

+ 
+ 
+ 
- 
+ 
+ 
+ 
+ 
- 
+ 
+ 
+ 
+ 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 

- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
- 
+ 
+ 
- 
- 
- 
- 
- 
- 
- 

- 

- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
+ 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
+ 
+ 
+ 
+ 
+ 
+ 
+ 

+ 

+ 
+ 
+ 
+ 

ID  

BRa 
BRa 
BRa 
- 
BRa 
BRa 
BRa 
- 
- 
BRa 
BRa 
BRa 
BRa 
BRb 
BRb 
BRb 
BRb 
BRb 
BRb 
BRb 
BRb 
- 
BRb 
BRb 
PR 
PR 
PR 
PR 
PR 
PR 
PR 

PR 

PR 
PR 
PR 
PR 

Cluster  

1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
2 
2 
2 
2 
2 
2 
2 
2 
2 
2 
2 
3 
3 
3 
3 
3 
3 
3 

3 

3 
3 
3 
3 

Isolate 
  R17-31 
  Rs1012 
  Rs1146 
  Rs393 
  C116S 
24BR 
  R14-10 
  R15-72 
  R15-74 
  R15-76 
  R15-78 
  R15-82 
  R15-83 
  R15-84 
  R15-87 
  R15-88 
  R15-92 
  R15-98 
  R17-15 
  R17-19 
  R17-24 
  R17-30 
  Roland 
  F24 
  Rs850 
2C1 
  R16-14 
  R15-12 
  R15-14 
  R15-43 
  R15-51 
  ACC3-LP 
  R-5 
  R15-8 
  ST6-1 

Primer pairs  

BRa  BRb 

PR 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 

- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 

- 
- 
- 

+ 
+ 
+ 
+ 
- 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
- 
- 
- 
- 

- 

- 
- 
- 

ID  

PR 
PR 
PR 
PR 
- 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
PR 
- 
- 
- 
- 

- 

- 
- 
- 

Cluster  

3 
3 
3 
3 
3 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
4 
AG-4 
AG-5 
AG-E 
AG-5 
AG2-
2LP 
AG-4 
AG-5 
AG-5 

164 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
We were unable to develop markers that could distinguish AG 2-2PRa and AG 2-2PRb, 

presumably because they contain much of the same genomic content. We expected that 

AG 2-2PRb would amplify with both PRa and BR primers since the evidence supports 

AG 2-2PRb being hybrids of AG 2-2PR and AG 2-2BR. However, the AG 2-2PRb (cluster 4) 

isolates we tested amplified only with AG 2-2PR primers and not with either AG 2-2BR primers. 

We do not currently have an explanation for why this was the case.  

These primers provide a means for rapidly identifying these newly classified subgroups of 

AG 2-2 which could provide researchers and managers with valuable information about 

population composition. They may also provide the foundation for the development of qPCR 

probes that could allow high-throughput screening of isolates.  

Conclusions 

The heterokaryotic and multi-nucleate nature of R. solani AG 2-2 makes the genetics of the 

group especially complicated. This complexity and the inconsistent conclusions that have often 

followed have left some open questions regarding the population genetics of the group, 

particularly regarding the origin of diversity and evolutionary relationships. We expect that the 

research presented in the current study will guide future research that can lead to a better 

understanding of the biology of this important pathogen and improve our ability to control the 

diseases it incites.  

One of the more important conclusions to come from the current study is that it appears 

cryptic sexual reproduction occurs in natural populations but is limited to a single subgroup, 

AG 2-2BRb. The potential for a sexual stage has implications for future management strategies, 

especially resistance breeding and fungicide sensitivity, by introducing recombination and 

165 

 
increasing diversity. The other source of diversity that the current study substantiated is the 

hybridization or parasexual exchange of nuclei between subgroups. While genetic exchange has 

been demonstrated previously (Kiyoshi et al. 2013; Toda & Hyakumachi 2006), our observations 

demonstrate that these putative hybrids form a genetic cluster that is distinct from the parental 

isolates. The effect of this hybridization on characteristics such as virulence still needs to be 

explored.  

It is widely accepted that Rhizoctonia solani AG 2-2 has a worldwide distribution, but the 

current study provides additional information regarding the specifics of that distribution, most 

notably, the identification of all four genetic groups in Europe, the prevalence of the subgroup 

AG 2-2BRb in the Red River Valley, and the lack of the same group collected from Michigan. 

We encourage other researchers to expand on our work to develop a more complete 

representation of regional and local populations. More specifically, assessments of diversity 

should focus on relating regional diversity to those processes, such as sexual reproduction, that 

substantially contribute to the genetic diversity of populations and can potentially complicate 

management strategies.  

To the best of our knowledge, this is the first report to provide evidence of the long-range 

dispersal of R. solani AG 2-2 genotypes. In several cases, genotypes were shared between 

regions separated by an ocean, or shared between regions without being detected in regions that 

are spatially intermediate. The later condition could be explained by insufficient sampling from 

these intermediate regions, but nevertheless, the dispersal across substantial distances in clear. 

This observation highlights the necessity for the sanitation of equipment, materials and personal 

gear when moving between growing areas. One area related to R. solani AG 2-2 dispersal that 

has not been adequately explored is the role of the horticultural trade on the movement of 

166 

propagules. Horticultural plants have an enormous potential to move microbes between regions, 

either as a host or through contaminated soil, and could possibly explain the sharing of genotypes 

between areas such as Japan and the United States.  

There was little evidence of host preference related to subgroup in the current study. 

Although isolates recovered from dry bean were mostly restricted to AG 2-2PRa, previous 

studies have shown that dry beans are susceptible to all subgroups (Minier 2019). Significant 

differences in aggressiveness between groups has previously been demonstrated with subgroup 

AG 2-2PR being the most aggressive on dry bean (Minier 2019), which may result in selection 

for the more aggressive genotypes. Similar selection for more aggressive genotypes has also 

been demonstrated for corn (Windels & Brantner 2005, 2006). Thus, we expect that crop rotation 

choices can have a critical effect on Rhizoctonia solani AG 2-2 populations and could affect not 

only a following sugar beet crop, but the rotational crops as well. Crop rotation continues to be 

an important consideration in the management of RRCR and additional studies are needed to 

examine the effects of different crops on R. solani AG 2-2 populations. The markers outlined in 

the current study provide valuable tools for the evaluation of R. solani AG2-2 populations that 

can be used to improve management of Rhizoctonia root and crown rot of sugar beet.  

167 

 
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HISTOPATHOLOGY OF RHIZOCTONIA ROOT AND CROWN ROT OF SUGAR BEET  

CHAPTER 4: 

174 

 
 
 
 
 
 
 
 
 
 
 
 
Introduction  

Rhizoctonia root and crown rot (RRCR) is a major disease of sugar beets in growing regions 

throughout the world. RRCR affects approximately 25% of cultivated sugar beet area in the 

United States and 10% of the sugar beet area in Europe (Harveson 2008). Yield losses can 

exceed 50% in severe cases and epidemics are often more intense when crop rotations are 

shortened to under three years (Bühre et al. 2009). In addition, the presence of diseased beets in 

storage piles reduces storability and processing quality (Strausbaugh & Gillen 2008). 

The causal agent of RRCR is the fungal pathogen, Rhizoctonia solani AG 2-2 (Kühn). R. 

solani is a species complex consisting of several independent lineages known as anastomosis 

groups (AG), that are separated by the ability of the hyphae to fuse or anastomose (Parmeter et 

al. 1969; Sneh et al. 1991). Currently, at least 13 AGs have been identified and this classification 

system represents our best understanding of relationships within the R. solani complex 

(Gonzalez et al. 2016. Several AG have been further subdivided into intraspecific groups (ISG) 

based on factors such as DNA hybridization, sclerotia size, zymography patterns, and/or 

temperature tolerance (Sneh et al. 1991). Rhizoctonia solani AG 2-2 has traditionally been 

separated into three subgroups, AG 2-2IIIB, AG 2-2IV, and AG 2-2LP, although the legitimacy 

of these subgroups is questionable (current study; Martin et al. 2014; Strausbaugh et al. 2011).  

Above ground symptoms of RRCR include sudden, permanent collapse of the leaves with the 

petiole often becoming blackened where it attaches to the crown (Windels et al. 2009). Root rot 

symptoms begin as dark, circular to oval lesions that often develop in a ladder-like pattern. 

Lesions coalesce as the disease progresses and may eventually envelop the entire root (Harveson 

2008). Symptomatic tissue is often limited to external layers and does not generally spread into 

the internal tissues until the more advanced stages of the disease. It is not uncommon for the 

175 

entire surface of the beet to become covered with rot prior to the disease spreading into the 

interior of the root (Windels et al. 2009). In the most advanced stages of the disease, the entire 

root becomes rotten and unharvestable. Heavy presence of a brown or whitish mycelium can 

often be observed covering tissue exposed by cracking and cavity rot (Harveson 2008).  

The sugar beet (Beta vulgaris L.) is an herbaceous dicotyledonous root crop, grown for its 

ability to accumulate sucrose in the root tissues (Cooke & Scott 1993). The mature root consists 

of three morphological regions, the crown, the neck, and the root (Artschwager 1926). The 

crown is the rounded upper section of the “root” body that bears leaves in a large tuft and is stem 

tissue. The neck develops from the hypocotyl and comprises the broadest portion of the beet. The 

bulk of the beet tissue is the true root, which tapers to a slender taproot. The root is more or less 

flattened on two sides with a prominent groove that forms a shallow spiral containing the lateral 

rootlets. Rather than an epidermis that covers many herbaceous roots, the sugar beet root is 

covered by a thin corky layer called the periderm (Artschwager 1926).  

The anatomy of the sugar beet is rather unique among non-woody herbaceous plants 

(Artschwager 1926; Elliot & Weston 1993). A horizontal section through the mature root shows 

concentric growth rings that are comprised of bands of vascular tissue separated by storage 

parenchyma (Figure 4.1). These vascular rings are composed of bundles of vascular tissue that 

are widest at the area of the cambium and taper towards the phloem. Between the vascular 

bundles lies a narrow band of parenchyma that consists of elongated cells. 

The annular rings develop from secondary cambium that initiates from the pericycle 

(Artschwager 1926). When the cambial initials divide, the inner daughter cells become 

secondary xylem and phloem while the outer cells become the initials of a new supernumerary 

cambium. Since these supernumerary cambia are initiated in rapid succession, practically all 

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rings are formed within 4 to 6 weeks after germination, when the root is just a couple millimeters 

in diameter (Artschwager 1926; Trebbi & McGrath 2009).  

phelloderm 

cork cambium 

cortex 

secondary 

cambium 

vascular bundle 

Figure 4.1 Partial cross section of 8-week-old sugar beet root showing outer vascular 
rings and periderm. Section was stained with toluidine blue O and imaged on an 
Olympus BX60 microscope. Scale bar = 100 µm.  

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Expansion of the root proceeds by division of the parenchyma cells that separate the vascular 

rings. Since the pericycle originates inside of the endodermis (Beeckman & De Smet 2014), 

expansion of the secondary tissues forces the endodermis outward and, along with the primary 

cortex and the epidermis, is eventually sloughed off (Artschwager 1926). As the primary tissues 

are being sloughed off, the pericycle differentiates into a band of meristematic cells called the 

phellogen or cork cambium. The cells of the phellogen differentiate to form cork cells toward the 

outside and phelloderm toward the inside of the phellogen layer. The phellogen forms a thin 

layer five to eight cells wide that are somewhat flattened and suberized.  

The presence of an endodermis in sugar beet seedlings has been implicated in resistance to 

Rhizoctonia damping-off (Nagendran 2006; Nagendran et al. 2009). However, in mature sugar 

beet roots, the endodermis has been sloughed off and is therefore unavailable to play a role in 

adult plant resistance.  Liu et al. (2019) confirmed earlier reports (Gaskill 1968; Ruppel et al. 

1979) that a good level of resistance to Rhizoctonia AG 2-2 was not expressed in most 

commercial varieties until the six- to eight-leaf stage (approx. 4 to 5 weeks after emergence) 

which is consistent with reports that younger plants are more susceptible than older plants 

(Engelkes & Windels 1994; Ruppel & Hecker 1988). There are a small number of exceptions 

which express seedling resistance (Nagendran et al.2009, McGrath et al. 2015) but the 

mechanism is largely unknown (Galewski et al. 2022).  This shift to higher levels of resistance 

corresponds to a developmental phase change from the seedling stage to the adult stage that 

occurs between 4 and 6 weeks after emergence (Trebbi & McGrath 2009). During this transition, 

gene expression changes drastically and is generally associated with increased sucrose 

accumulation and the completion of vascular ring formation (Artschwager 1926; Trebbi & 

McGrath 2009). 

178 

Ruppel (1973) compared the reactions of sugar beet varieties that were either susceptible or 

resistant to Rhizoctonia solani AG 2-2- and noted that R. solani hyphae did not penetrate beyond 

the periderm and outer secondary cortex in the resistant roots. These observations suggest a 

physiological barrier that developed in resistant roots, but Ruppel (1973) did not observe wound 

periderm or suberization related to fungal penetration. Hyphal spread proceeded tangentially 

more rapidly than radially, and necrosis proceeded the hyphae in all roots examined.  

In a generalized model of the infection process for R. solani, the process begins when hyphae 

grow towards the plant and over the root surface (Keijer 1996). At this stage, hyphae are rounded 

and not attached to the surface. Prior to penetration into the plant tissue, the hyphae become 

flattened and adhere to the surface. The hyphae begin directed growth, following the anticlinal 

walls of the surface cells. Side branches form in a pattern that has been described as T-shaped 

branches (Keijer 1996) that aggregate and form infection cushions. Several studies have 

connected a reduction in the size and number of infection cushions to a reduction in disease 

severity (Kousik et al. 1994; Yang et al. 1992). 

Fungal hyphae detect the plant surface and establish infection using various strategies that 

fall into two general categories, surface sensing and chemical exudates (Badri & Vivanco 2009; 

Kou & Naqvi 2016). Hyphae that orient along the anticlinal walls may exhibit characteristics of 

surface sensing, but surface characteristics alone may not be sufficient to initiate infection. For 

example, some rust fungi are unable to recognize stomata from non-host plants (Wynn & Staples 

1981). Marshall and Rush (1980) noted that infection structures did not form on replicas of rice 

sheath surfaces and the hyphae did not follow the junctions of the anticlinal walls like occurred 

on rice sheaths themselves. Other physicochemical signals from the surface have been associated 

179 

with infection structure formation such as surface hardness, hydrophobicity, plant waxes, and 

cutin (Kou & Naqvi 2016).  

The typical route of penetration of root tissue is by the formation of infection pegs (Keijer 

1996). But on leaves or other above ground tissues, hyphae of R. solani have been reported as 

entering through stomata (Bashyal et al. 2018; Manian & Manibhushanrao 1982; Zheng & Wang 

2011) and forming appressoria for direct penetration (Bashyal et al. 2018; Naito & Sugimoto 

1978; Zheng & Wang 2011). Less is known regarding the infection process of roots or 

underground structures. Ruppel (1973) reported that penetration of sugar beet root by R. solani 

AG 2-2 occurred by infection cushions or directly by individual hyphae on sugar beet roots. In 

potato, R. solani AG 3 formed infection cushions, appressoria, and penetrated directly through 

lenticels and epidermal cracks on tubers (Zhang et al. 2016). While sugar beets roots do not have 

lenticles, the root surface is similar to potato tuber skin in that it consists of a periderm and an 

outer corky layer. Potato skin has been demonstrated to respond to infection by wound healing 

suberization (Lulai 2007). Thus, there is the potential that sugar beet periderm also responds to 

wounding or fungal invasion by suberization and that this could serve as a defense response, but 

this needs further study.  

Penetration of sugar beet tissue has been reported to occur inter- and intracellularly with 

hyphae closer to the site of penetration proceeding intracellularly and in the advanced portion of 

the lesions proceeding intercellularly (Ruppel 1973). Necrosis consistently precedes the hyphae 

but reports of necrosis occurring under R. solani infection cushions are inconsistent. In cotton, 

Weinhold and Motta (1973) reported the removal of pectic substances beneath infection cushions 

prior to hyphal penetration. However, it is more commonly reported that infection pegs form 

beneath the cushion and mechanical force is used to penetrate epidermal cells (Armentrout & 

180 

Downer 1987; Hofman & Jongebloed 1988; Yang et al. 1992). Appressoria also have been 

identified in R. solani on potato (Zhang et al. 2016), rice (Marshall & Rush 1980), soybean 

(Zheng & Wang 2011), and sugar beet leaves (Naito & Sugimoto 1978). Of these reports of 

appressoria formation in R. solani, only the sugar beet leaves were infected with AG 2-2 while 

the other crops were infected with other AG. Reports of infection structures on sugar beet root, 

or any roots for that matter, are limited.  

The infection process for necrotrophic pathogens often involves the utilization of cell wall 

degrading enzymes (Bellincampi et al. 2014; King et al. 2011; Kubicek et al. 2014). Several 

enzymes have been implicated in cell well degradation including cellulases, polygalacturonases, 

pectin and pectate lyases, and pectin methylesterases (Bellincampi et al. 2014; Kubicek et al. 

2014). Necrotrophic pathogens utilize a number of cell wall degrading enzymes (CWDE) to 

depolymerize the cell wall (Bellincampi et al. 2014; D’Ovidio et al. 2004; Kubieck et al. 2014). 

The diversity of cell wall degrading enzymes present in a pathogen has been associated with host 

range and virulence (Alghisi & Favaron 1995; Cook et al. 1999; King et al. 2011). Rhizoctonia 

solani isolates appear to have a diverse array of cell wall degrading enzymes (Anderson et al. 

2016; Rafiei et al. 2023; Scala et al. 1980; Wibberg et al. 2016) and several CWDEs have been 

characterized from R. solani isolates including pectic lyase (Bugbee 1990), polygalacturonase 

(Scala et al. 1980; Xue et al. 2018) and laccase (Wahleithner et al. 1996). 

There is considerable variability in the infection process among R. solani AG, crop types, 

and plant tissue. It is important to keep in mind that R. solani cannot be thought of as a 

homogenous group and behavior that is true in one system may not be true in a different system. 

Anastomosis groups can be thought of as independent species and this concept was just gaining 

traction when much of the work regarding the infection process was being done. In addition, a 

181 

comprehensive histopathological study of RRCR has not been done since Ruppel (1973). Since 

that time, many advances have been made in microscopy methodology and staining techniques. 

In addition, new RRCR resistant sugar beet varieties have been released since then, including 

SR98/2 examined in the current study (McGrath et al. 2015). SR98/2 is a smooth-rooted sugar 

beet germplasm derived from the EL51 breeding line (PI 598074) with good resistance to 

Rhizoctonia damping-off and RRCR (Nagendran et al. 2009). The SR98/2 germplasm is one of 

the few releases with good resistance to damping-off and adult plant RRCR, while retaining 

acceptable agronomic properties (Nagendran et al. 2009, McGrath et al. 2015). These 

characteristics make it an excellent choice for examination of physiological characteristics 

associated with RRCR resistance.  

Materials and Methods 

Sample preparation 

Seed for sugar beet varieties ‘C869’ (RRCR susceptible; Lewellen 2004) and ‘SR98/2’ 

(RRCR resistant; McGrath et al. 2015) were surface sanitized in 0.6% sodium hypochlorite plus 

0.1% tween 20 for 15 minutes, rinsed twice in sterile water and soaked overnight in 0.3% 

hydrogen peroxide to reduce germination variability (McGrath et al. 2000). Sanitized seed was 

treated with a 2% solution of metalaxyl (Allegiance-FL; Bayer Crop Science, Research Triangle 

Park, NC) to manage Pythium damping-off and planted in soilless potting mix (Suremix Pearlite; 

Michigan Grower Products, Galesburg, MI). Plants were cultivated in a growth chamber 

(Conviron PGW 36, Controlled Environments Inc., Pembina, ND) set to 23°C and a 14-hr 

photoperiod.  

182 

 
Plants were grown to the 8 to 10 leaf growth stage, after the seedling to adult transition, and 

inoculated with a single grain of barley infested with isolate ‘R1’, an AG 2-2BRa isolate (type 

‘IIIB’) known to be highly aggressive (Nagendran et al. 2009). Two roots of each variety were 

harvested at 3, 4, 5, 7, 9 and 12 days after inoculation and gently washed with tap water. Root 

tissue with visible symptoms was sampled by cutting into 3 to 5 mm cuboid shaped pieces and 

fixed in 10% neutral buffered formalin solution (Sigma Aldrich, St. Louis, MO) with shaking for 

4 days. Tissue samples were rinsed with 70% ethanol and stored in 70% ethanol until 

embedding. Fixed samples were embedded in paraffin wax at the Investigative HistoPathology 

Laboratory (Michigan State University, East Lansing, MI). Sections were cut 12 to 15 µm thick 

on a Reichert-Jung 820 Histocut microtome (Reichert Technologies, Buffalo, NY), transferred to 

glass slides, and dried at 57°C overnight.  

Prepared sections were dewaxed through a xylene/ethanol rehydration series (Nagy et al. 

2007) with two changes of Neo-Clear (Sigma Aldridge), one change of histological grade 

xylenes (Sigma Aldridge), two changes each of 100% and 95% ethanol, and one change each of 

70% and 50% ethanol and finally, distilled water. Each step was conducted for 8 minutes with 

samples being air dried after washing with xylenes to minimize contamination of subsequent 

ethanol washes. Dewaxed slides were dried at 35°C overnight.  

Brightfield microscopy 

General tissue staining was performed by covering sample with 0.05% (w/v) toluidine blue O 

(Sigma Aldrich) in phosphate buffered saline (PBS, pH 7.6; Cold Spring Harbor Protocols; 

2007) for 1 to 2 minutes and rinsing twice with distilled water (O’Brien et al. 1964). Samples 

were dried at 35°C for 1 to 2 hours and mounted using Eukitt quick-hardening mounting medium 

183 

 
(Sigma Aldrich). Images were collected on an Olympus BX60 microscope (Evident Corporation, 

Tokyo, Japan) fitted with a Spot Insight 12MP CMOS camera (SPOT Imaging, Sterling Heights, 

MI).  

Epi-illumination microscopy 

Epi-illumination was conducted using a Keyence VHX-6000 digital microscope (Keyence 

Corporation of America, Itasca, IL). To improve visualization of the tissues within the root 

groove, cross sections of fresh root tissue were cut free hand, stained with 0.05% w/v Ruthenium 

red (Sigma Aldrich) for 2 minutes, rinsed three times in distilled water and imaged with the 

Keyence VHX-6000 digital microscope.   

To visualize surface hyphae and the formation of infection cushions, fresh, non-inoculated 

root sections were harvested, washed under running tap water and surface disinfested with a 

0.6% sodium hypochlorite plus 0.1% tween 20 solution for 2 minutes and then rinsed in sterile 

distilled water. Sections were cut freehand with a razor blade into pieces that were about 1 cm 

square and 5 mm thick.  Four of these pieces were placed on a 2% (w/v) water agar plate so the 

periderm surface was facing up. A 4 mm plug from an actively growing culture of isolate ‘R1’ 

was placed approximately 1 cm from the sections and the hyphae were allowed to grow onto the 

root pieces. Infested pieces were removed from the agar plate 2 days after hyphae contacted the 

root surface and stained with lactophenol cotton blue solution (Sigma Aldrich). Sections were 

visualized on a Keyence VHX-6000 digital microscope on the same day as collection and 

staining. A total of 12 sections were observed for each sugar beet variety.  

184 

 
 
 
Confocal and epifluorescence microscopy 

Staining with berberine hemisulfate (Sigma-Aldrich) was performed according to Brundrett 

et al. (1988) to visualize suberin and lignin deposits. Thin sections, 12 to 15 μm thick, on glass 

slides were covered with 0.1% (w/v) berberine hemisulfate in distilled water for 30 min and then 

rinsed twice in distilled water. Samples were counter stained with 0.5% (w/v) aniline blue WS 

(Sigma-Aldrich), rinsed twice in distilled water and mounted in a drop of 0.1% FeCl3 (Sigma 

Aldrich) in 50% glycerin. A cover slip was applied and immediately sealed with clear nail 

polish. Images were collected with an Olympus BX60 fluorescent microscope (Evident 

Corporation) fitted with a SPOT Insight 12MP CMOS camera (SPOT Imaging) using a DAPI 

excitation filter (band pass 352-402 nm). At least six sections at five and seven days after 

inoculation were observed for each sugar beet variety.  

Autofluorescence was examined by imaging at least six unstained sections 12 to 15 μm thick 

using epifluorescence with a double (FITC/TxRed) excitation filter (band pass 460-490, 532-554 

nm). These unstained sections were also examined with a Nikon A1Rsi LSCM using 408 nm, 

489 nm and 561 nm diode lasers with 430-480 nm, 500-550 nm, and 570-620 nm band pass 

filters respectively.  

For the safranin O/calcofluor white staining, six samples of each sugar beet variety were 

stained with a 0.1% aqueous solution of safranin O (Sigma Aldrich) for 3-5 minutes and then de-

stained by rinsing in an ethanol series of 25, 50, 70, and 95% ethanol for 10 minutes each until 

dye no longer leached from samples. The samples were returned to distilled water by passing 

them back through the ethanol series of 70, 50, and 25% ethanol for 10 minutes each and then 

into distilled water (Kitin et al. 2020). One drop of 10% potassium hydroxide was placed on each 

sample and two drops of Calcofluor white stain (0.1% calcofluor white M2R plus 0.05% Evans 

185 

blue; Sigma Aldrich) were added. After incubating for 5 minutes at room temperature, samples 

were rinsed twice in distilled water, dried at 35°C for 1 to 2 hours, and a cover slip mounted 

using Eukitt quick-hardening mounting medium (Sigma Aldrich). Stained samples were imaged 

using epifluorescence on the Olympus BX60 fluorescence microscope with a SPOT Insight 

12MP CMOS camera using a triple (DAPI/FITC/TxRed) excitation filter (band pass 352-402, 

460-490, 532-554 nm).  

Fungal hyphae within the root tissues were visualized using the fluorescence techniques 

described by Carotenuto and Genre (2020). First, root sections were treated with 10% bleach for 

5 minutes to remove cellular and nuclear membranes. The treated samples were flooded with a 

10 mg/ml solution of wheat germ agglutinin conjugated to Alexa Fluor 488 (WGA) and 

incubated for 1 hour at room temperature. After washing three times in PBS, two drops of 

propidium iodide (100 ng/ml; Sigma-Aldrich) were added to the slide to cover the sample for 1-2 

minutes. Samples were rinsed three times with PBS and mounted with Fluoro-Gel mounting 

medium (Electron Microscopy Sciences, Hatfield, PA). Stained samples were imaged on a Nikon 

A1Rsi laser scanning confocal microscope (LSCM; Nikon Instruments Inc., Melville, NY) with 

either a PlanApo 20x VC (NA 0.75) or a PlanApo λ 10x (NA 0.45) objective. Alexa Fluor 488 

fluorophore was imaged using a 489 nm diode laser with a 500-550 nm band pass emission filter. 

Propidium iodide was imaged using a 561 nm diode laser and a 570-620 nm band pass emission 

filter. Images were z-stacked using 5 slices with a 1 µm step size, and an area scan was 

conducted with a 10% overlap. At least six sections of each sugar beet variety at each of the six 

time points that tissue was harvested was observed using this technique and representative 

samples were selected for imaging on the confocal microscope.  

186 

Sections displaying changes in autofluorescence were examined for alterations in pectin 

esterification using the rat monoclonal antibodies LM19 and LM20 (Verhertbruggen et al. 2008). 

Sequential sections were hydrated in PBS (pH 7.6) for 15 minutes and then treated with a 10% 

bleach solution for 5 minutes to remove cellular and nuclear membranes. Samples were rinsed 

twice in PBS and treated with 5% bovine serum albumin (Sigma Aldrich) to reduce non-specific 

binding of primary antibodies (Miller & Shakes 1995). Samples were rinsed twice in PBS and 

incubated with a 1:10 dilution of either LM19 or LM20 antibodies for 30 minutes. Samples were 

washed in PBS three times and incubated with a 1:100 dilution of anti-rat IgG linked to Alexa 

Fluor 488 (Thermo Fisher) for 20 minutes. Samples were rinsed three times in PBS and mounted 

with Fluoro-Gel mounting medium (Electron Microscopy Sciences). Imaging was performed on 

a Nikon A1Rsi LSCM (Nikon Instruments Inc.) using a 489 nm diode laser with a 500-550 nm 

band pass emission filter and an Apo 60x Oil λS (NA=1.40) objective. At least 10 sections for 

each treatment were observed using the Olympus BX60 fluorescent microscope and three 

representative samples were selected for imaging on the confocal microscope.  

A complete list of stains and physiological targets that were used in the current study are 

shown in Table 1.  

187 

 
 
Table 4.1 Reagents used in the current study to label cell wall components of sugar 
beet and Rhizoctonia solani AG 2-2. 

Reagent  

Safranin O 

Target 

lignin 

Excitation 
laser 

Emission 
filter 

488 

570-560 

Notes 

Toluidine blue O 

general cell walls 
stain 

Lactophenol cotton blue 

chitin 

Wheat-germ agglutinin – 
Alexa fluor 488 

chitin 

- 

- 

- 

- 

polychromatic stain - normal tissue 
stains blue/purple; lignified tissue 
stains green/blue 

general stain for fungi 

488 

500 

binds to N-acetyl-D-glucosamine 

Calcofluor white 

cellulose, chitin 

380 

475 

binds to 1-3,β- and 1-4,β-
polysaccharides in cellulose and 
chitin 

615 

preference for unesterified pectin 

580 

500 

binds to 1-3,β-glucans 

- 

- 

binds to α-1,4 galacturonic acid 
residues of unesterified pectin 

binds to α-1,4 galacturonic acid 
residues of esterified pectin 

secondary antibody binds to rat 
IgM primary antibody 

Propidium Iodide 

cell wall pectin 

Berberine hemisulfate 

lignin, suberin 

Aniline blue WS 

callose 

LM19 rat IgM monoclonal 
antibody 

unesterified pectin 

LM20 rat IgM monoclonal 
antibody 

methyl esterified 
pectin 

535 

430 

405 

- 

- 

Alexa Fluor 488 goat anti-
rat IgG (H+L)  

LM19, LM20 
primary antibody 

488 

500 

188 

 
 
 
 
 
Results 

Infection cushion formation 

The external cork cells of the root tissue were irregularly shaped and roughly arranged in 

rows (Figure 4.2). However, the hyphae on the root surface generally did not follow the anti-

clinal walls (Figure 4.2) as has been reported for other systems with a true epidermis 

(Armentrout & Downer 1987; Pannecoucque & Höfte 2009; Yang et al. 1992). Instead, runner 

hyphae crossed the tissue at oblique angles, branching at regular intervals. The frequency of 

branching increased in the side branches, creating shorter and shorter spurs developing into 

dense aggregates of hyphae. Some of these branch spurs did orient with the anti-clinal walls, but 

that behavior was inconsistent, and most did not. T-shaped branching was not observed, only 

increasingly shorter branch spurs (Figure 4.2). Three days after inoculation, hyphae were denser 

on the surface of the susceptible variety (Figure 4.2A) than on the resistant variety (Figure 4.2B).   

A  

B  

Figure 4.2 Hyphae of Rhizoctonia solani AG 2-2 on the surface of an 8-week-old sugar beet 
root two days after inoculation. A) susceptible variety C869. B) resistant variety SR98/2. 
Hyphae were stained using lactophenol blue and imaged with a Keyence VHX-6000 digital 
microscope (200x). Scale bar = 250 µm.  

189 

 
 
The aggregates of hyphae, formed from branch spurs, developed into small infection 

cushions within 3 days after inoculation (Figure 4.3). Cells that made up the infection cushions 

were thick walled, had a high affinity for trypan blue and were extensively interconnected. There 

were no obvious surface features associated with infection cushion development except that 

occasionally hyphae were observed accumulating around wounds and natural cracks in the cork 

layer (Figure 4.4). Infection cushions appeared to form where two or more hyphae overlapped, 

but this attribute was difficult to qualify. But, since the hyphae originated from the same source, 

it is unclear why this interaction would be necessary for infection cushion formation.  

Regardless, the formation of infection cushions was apparently not needed for penetration of 

the outer cork cells. Hyphae was observed penetrating the outer cell wall three days after 

inoculation, without forming an infection cushion (Figure 4.5). The outer layer of the periderm 

was penetrated directly in several cases and hyphae could be observed growing within and 

among the cork cells. It was unclear how hyphae penetrated into the outer cortex, whether intra- 

or intercellularly, but the outer cortex was colonized rapidly once hyphae entered the tissue.  

A  

B  

Figure 4.3 Infection cushions of Rhizoctonia solani AG 2-2 forming on the surface of 
susceptible sugar beet variety C869 two days after inoculation. A) Hyphae were stained with 
lactophenol blue and imaged with a Keyence VHX-6000 digital microscope at 100x (scale bar 
= 250 µm). B) Infection cushion circled in image A is shown at 500x (scale bar = 50 µm).  

190 

 
 
A  

B  

Figure 4.4 Hyphae of Rhizoctonia solani AG 2-2 aggregating around natural openings in the 
cork layer of sugar beet variety C869 two days after inoculation. A) Hyphae were stained using 
lactophenol blue and imaged with a Keyence VHX-6000 digital microscope at 150x and B) at 
200x. Scale bars = 250 µm. 

Figure 4.5 Hyphae of Rhizoctonia solani AG 2-2 on the surface of root 
tissue sugar beet variety C869 two days after inoculation. Arrows 
indicate hyphae that are directly penetrating the outer cork layer. 
Hyphae were stained with lactophenol blue and imaged with a Keyence 
VHX-6000 digital microscope at 1000x. Scale bar = 50 µm.  

191 

 
 
 
 
 
Hyphae that entered through the smooth root surface were observed within the outer layers of 

cork cells by three days after inoculation (Figure 4.6). Colonization occurred rapidly within the 

outermost cortex, proceeding inter- and intracellularly between the periderm and the outer 

cambium. Hyphae breached the outermost cambium by invading the parenchyma cells between 

vascular bundles or in other areas where breaks occur in the outer cambium. The regions of 

interfascicular parenchyma increased in size in the vascular rings closer to the center of the root 

and there was evidently less restriction to fungal invasion. 

Figure 4.6 Hyphae of Rhizoctonia solani AG 2-2 within root tissue of resistant sugar beet 
variety SR98/2 three days after inoculation. Fungal hyphae were labeled using wheat germ 
agglutin conjugated with Alexa Fluor 488 and are colored green. Plant tissue was stained with 
propidium iodide and is colored red. Image was collected on a Nikon A1Rsi laser scanning 
microscope. Scale bar = 250 µm.  

192 

 
 
 
Invasion through tissue associated with lateral roots 

The tissues associated with the root groove and lateral roots form complicated folds and 

grooves that appear to provide access to fungal hyphae (Figure 4.7). Fungal hyphae were 

observed growing within these folds and penetrating tissue directly without forming an infection 

cushion. Cork cells were present on the outer surfaces of the root groove tissues, but tissue 

connections between lateral roots and the main root tissue were irregular and provided multiple 

routes for fungal hyphae to invade. Hyphae were observed in and around lateral roots and 

appeared to follow lateral root tissue into the main root and from there, spread rapidly (Figure 

4.8).  

Figure 4.7 Longitudinal section through the root groove 
region of a sugar beet root (variety C869) stained with 
ruthenium red. Arrow indicates a gap between folds of 
tissue. Scale bar = 500 µm.  

193 

 
 
 
Figure 4.8 Hyphae of Rhizoctonia solani AG 2-2 within the root groove of a sugar 
beet root (variety C869) growing among lateral roots three days after inoculation. 
Fungal hyphae were labeled using wheat germ agglutin conjugated with Alexa 
Fluor 488 and are colored green. Plant tissue was stained with propidium iodide 
and is colored red. Image was collected on a Nikon A1Rsi laser scanning 
microscope. Scale bar = 250 µm. 

Alteration of cell wall components 

Substantial maceration was observed in the cortex tissues shortly after colonization by fungal 

hyphae (Figure 4.6 & 4.9A). Prior to maceration, the tissue underwent several changes. Within 

three days after inoculation, a zone of discoloration developed in the area surrounding the 

invading hyphae that extended up to 500 µm beyond any visible hyphae. Under visible light, 

these regions were darkened and appeared brown to black and were comparable to the dark 

lesions that are characteristic of RRCR. This discolored region exhibited strong fluorescence 

when excited using the FITC/TxRed excitation filter (460-490, 532-554 nm), which occurred 

with (Figure 4.10) or without propidium iodide staining (Figure 4.11 & 4.12). When unstained 

sections were stimulated with the 561 nm (red) laser or 489 nm (green), fluorescence was 

194 

 
increased in the lesion compared to healthy tissue (Figure 4.12A & 4.12B). However, there was a 

reduction or lack of fluorescence in the lesion when stimulated with the 408 nm (blue) laser 

(Figure 4.12C). A composite RBG image clearly shows the area of cell wall changes associated 

with a lesion (Figure 4.12D). Both resistant and susceptible varieties exhibited similar levels and 

patterns of fluorescence, although lesions were generally larger in the susceptible variety. These 

fluorescent zones were so conspicuous they served as excellent predictors for the presence of 

fungal hyphae and in no case was fungal hyphae observed within the root tissue without an area 

of fluorescence enclosing it.  

A  

B  

Figure 4.9 Sugar beet tissue stained with calcofluor white and safranin O seven days after 
inoculation. A) tissue infected with Rhizoctonia solani AG 2-2. B) uninfected tissue. Images 
were taken on an Olympus BX60 fluorescence microscope using a DAPI/FITC/TxRed 
excitation filter (band pass 352-402, 460-490, 532-554 nm). Scale bar = 250 µm. 

195 

 
 
Figure 4.10 Fluorescence associated with lesion caused by Rhizoctonia solani 
AG 2-2 in sugar beet root (variety C869) three days after inoculation. Fungal 
hyphae were labeled using wheat germ agglutin conjugated with Alexa Fluor 488 
and are colored green. Plant tissue was stained with propidium iodide and is 
colored red. Image was collected on a Nikon A1Rsi laser scanning microscope. 
Scale bar = 250 µm.  

Figure 4.11 Sugar beet root tissue infected with Rhizoctonia solani 
AG 2-2 showing thickened cell walls. Tissue was immunolabeled 
with the monoclonal antibody LM19. Goat anti-rat IgG secondary 
antibody conjugated with Alexa Fluor 488 dye was used as a 
fluorescent label. Image was collected on a Nikon A1Rsi laser 
scanning microscope. Scale bar = 25 µm.  

196 

 
 
A 

C 

B 

D 

Figure 4.12 Fluorescence associated with lesion caused by Rhizoctonia solani AG 2-2 
in sugar beet root (variety C869) three days after inoculation. Tissue was unstained and 
the same sample was examined using A) 561 nm (red) laser, B) 488 nm (green) laser, C) 
408 nm (blue) laser and D) a RBG composite. Image was collected on a Nikon A1Rsi 
laser scanning microscope. Scale bar = 100 µm.  

197 

 
 
 
Cellulose was plentiful in healthy tissue as indicated by a strong fluorescent signal from 

calcofluor white staining (Figure 4.9B). As the tissue became colonized, the fluorescent signal 

from calcofluor white was obscured by red/orange fluorescence of the safranin O stain (Figure 

4.9A). The region of increased fluorescence associated with safranin O corresponded with 

alterations in fluorescence associated with the development of lesions (Figure 4.10). In addition, 

the cell walls in the area of the lesion became thickened and exhibited conspicuous 

autofluorescence as previously described (Figure 4.11 & 4.12). 

Samples that had been stained with berberine hemisulfate showed increased fluorescence at 

the margins of the lesions (Figure 4.13). Increased fluorescence associated with berberine 

hemisulfate coincided with the region of increased fluorescence associated with lesion 

development. Fluorescence associated with berberine hemisulfate was strongest at the margin of 

the lesion, but not as intense as the highly suberized cork cells or xylem. It is notable that 

increased fluorescence related to berberine hemisulfate staining was more apparent in the 

resistant variety than in the susceptible variety.   

A  

B  

Figure 4.13 Sugar beet tissue infected with Rhizoctonia solani AG 2-2 and stained with 
berberine hemisulfate.  Image was collected on an Olympus BX60 microscope using a DAPI 
excitation filter (band pass 352-402 nm). Arrows indicate regions of increased fluorescence 
that correspond to the margin of a distinct lesion. Scale bar = 200 µm.  

198 

 
 
There were minimal differences in distribution of pectic epitopes stained by immunolabeling 

by antibodies LM19 and LM20 (Figures 4.14 & 4.15). Both pectic epitopes were present in 

similar quantities in healthy tissues, although the LM20 labeled epitope may have been present 

in slightly greater quantities than the LM19 labeled epitope. In infected tissue, the LM19 labeled 

epitope was eliminated more rapidly in the susceptible variety ‘C869’ than was the LM20 

labeled epitope, which remained present in the intracellular spaces (Figure 4.14). In the resistant 

variety SR98/2, the LM19 labeled epitope was clumped into aggregates around the inside of the 

cell walls (Figure 4.15B). The LM20 labeled epitope exhibited clumping as well, but to a lesser 

extent than the LM19 labeled epitope. Overall, the prevalence of both epitopes was reduced in 

the cell walls of infected tissue compared to healthy tissue. The resistant variety retained more 

labeled material than did the susceptible variety especially for the epitope bound by LM19 

(Figure 4.15), although it appeared to have been extricated from the cell wall.  

199 

 
 
 
A 

C 

B 

D 

Figure 4.14 Root tissue of susceptible sugar beet variety C869 immunolabeled with 
the rat IgM monoclonal antibodies LM19 and LM20 four days after inoculation. 
Goat anti-rat IgG secondary antibody conjugated with Alexa Fluor 488 dye was 
used as a fluorescent label. A) LM19 healthy B) LM19 infected C) LM20 healthy 
D) LM20 infected. Images were collected on a Nikon A1Rsi laser scanning 
microscope. Scale bar = 25 µm.  

200 

 
 
  
A 

C 

B 

D 

Figure 4.15 Root tissue of resistant sugar beet variety SR98/2 immunolabeled with 
the rat IgM monoclonal antibodies LM19 and LM20 three days after inoculation. 
Goat anti-rat IgG secondary antibody conjugated with Alexa Fluor 488 dye was 
used as a fluorescent label. A) LM19 healthy B) LM19 infected C) LM20 healthy 
D) LM20 infected. Images were collected on a Nikon A1Rsi laser scanning 
microscope. Scale bar = 25 µm.  

201 

 
 
Discussion 

Colonization and infection cushion formation 

Hyphae were denser on the susceptible sugar beet variety than on the resistant variety which 

has been hypothesized to result in a higher number of infection sites and more severe disease 

symptoms (Bassi et al. 1979; Pannecoucque & Höfte 2009; Zhang et al. 2016) and something 

similar may occur on sugar beet. Much of the increase in the density of the surface hyphae was 

due to an apparent increase in branching proliferation. Denser hyphae have been associated with 

increased lesion size in tomato where lesion size was proportional to the size of the infection 

cushion (Hofman & Jongebloed 1988).  

The observation of hyphae spreading over the root surface without following the anticlinal 

walls, which is in contrast to many reports of the infection process for R. solani (Armentrout & 

Downer 1987; Yang et al. 1992). However, the observation is consistent with Ruppel’s (1973) 

original report and results from potato as reported by Zhang et al. (2016). The similarity of 

potato skin to the surface of beet root in that both consist of a periderm and a cork outer layer 

may provide an explanation for the absence of directed growth. Cork cells may lack the uniform 

cell characteristics, such as a linear orientation, that is common with epidermal cells (Albersheim 

et al. 2011).  

As well as surface topology, root exudates have been shown to have a potential role in 

hyphae recruitment, determination of invasion sites, and infection cushion formation for some R. 

solani AG (Badri & Vivanco 2009; Kou & Naqvi 2016; Lombardi et al. 2018; Marshall & Rush 

1980). Increased production of exudates could result in higher levels of hyphal colonization and 

a greater number of potential infection sites which has been shown to be positively correlated to 

disease severity for mungbean (Bashyal et al. 2018) and tomato fruit (Bassi et al. 1979) infected 

202 

with R. solani. Observations in the current study of denser hyphae on the susceptible compared 

to the resistant variety are consistent with this correlation and may indicate higher levels of 

specific types of exudates produced by the susceptible variety. The site of infection cushion 

formation coinciding with hyphal overlap may be an indication of locations with increased levels 

of exudate leakage and warrants further testing. Rather than requiring hyphal interaction, it could 

be hypothesized that different hyphae are simply attracted to the same location. The potential 

role of exudates in the recruitment of R. solani AG 2-2 hyphae and the formation of infection 

cushions needs further study.  

Shortening branch lengths leading to the formation of infection cushions has previously been 

reported (Armentrout & Downer 1987; Hofman & Jongebloed 1988; Yang et al. 1992). 

However, in the current study, the shortening hyphae did not form t-shaped branches as has been 

reported for other systems. This lack of t-shaped branching may be related to the lack of parallel 

anticlinal walls in the sugar beet surface cells that would serve to orient the branches. T-shaped 

branches do not form in potato, which has a periderm and cork cells similar to those in sugar beet 

(Demirci & Döken 1998; Zhang et al. 2016). Whether R. solani AG 2-2 forms t-shaped branches 

on hosts with an epidermis, such as dry beans, is uncertain and is worthy of investigation.  

Penetration of the periderm 

Tissue colonization was observed within three days of inoculation (Figure 4.10) and hyphae 

were observed penetrating the periderm within 2 days after hyphae came into contact with the 

root surface (Figure 4.5). Ruppel (1973) observed hyphae within the cork cells within two days 

but did not report evidence of hyphae in the outer cortex and accompanying necrosis until four 

days after inoculation. However, penetration of the periderm was reported in potato in as little as 

203 

 
8 hours after inoculation with R. solani AG 3, although most of the infection occurred through 

epidermal cracks and lenticles (Zhang et al. 2016). The periderm apparently poses little to no 

barrier to penetration by R. solani since penetration and colonization of the periderm occurs 

rapidly in both potato (AG 3) and sugar beet (AG 2-2). However, similar to the colonization of 

cork cells by Aspergillus nidulans reported by Martins et al. (2014), the cork layer remained 

largely intact indicating superficial degradation of the cork cells and/or mechanical force.  

Penetration and invasion of the cortex 

Hyphae appear to be able to penetrate the outer cells directly without requiring the formation 

of infection cushions, infection pegs or appressoria. This observation is in contrast with many 

studies of the infection process in R. solani that reported the involvement of specialized infection 

structures in penetration. Yang et al. (1992) reported direct penetration of canola hypocotyls by 

means of the anticlinal walls of the epidermal cells. Since in much of the early stages of infection 

and in younger portions of the lesions the hyphae proceeded intracellularly, hypothesizing that 

invasion occurs through the anticlinal walls would be reasonable. However, there were no 

conclusive observations in the current study that supported this hypothesis. It is possible that the 

invading hyphae penetrate the cork cells at any favorable point, but transition to intracellular 

after penetrating the periderm. This hypothesis would be challenging to prove as it would likely 

take many, many sections to capture an individual hypha making such a transition. The 

proverbial “needle in the haystack.”  

Another hypothesis would be that R. solani AG 2-2 has several strategies for entering the 

host tissue and can react to a variety of situations, essentially employing a barrage strategy in 

order to overpower cell wall barriers. The potential ability of the fungus to attack from multiple 

204 

 
routes is suggestive of substantial diversity in pathogenicity and virulence factors present in the 

genome of R. solani AG 2-2 (King et al. 2011; Wibberg et al. 2106). In the current study, 

evidence was observed for the degradation of pectin, methylated pectin, lignin, and cellulose 

reflecting a diverse arsenal of cell wall degrading enzymes produced by R. solani AG 2-2 

isolates, as has been reported previously (Bugbee 1990; Scala et al. 1980; Wahleithner et al. 

1996; Wibberg et al. 2016).  

While tissues within the root groove typically have an outer cork layer, the periderm is less 

distinct and the transition to parenchyma tissue is more rapid, with breaks in the cork layer 

occurring due to the emergence of lateral roots. In addition, the tissues within the folds of the 

root groove generally lack a layer of secondary cambium. In the smooth root section, the 

outermost secondary cambium appears to act as a temporary barrier, encouraging hyphae to 

spread laterally. This does not appear to be the case in tissues within the root groove, where 

hyphae appear less restricted in their spread (Figure 4.8). The result being that invasion can 

spread to deeper internal tissues and could damage the ability of the lateral roots to take up water 

and nutrients effectively. While damage to feeder roots often leads to wilting, this symptom is 

not typically associated with RRCR rot of sugar beet. Instead, leaves suddenly and permanently 

collapse resulting in a rosette of dead leaves and petioles (Harveson 2008). Although phytotoxins 

have been reported from R. solani infecting sugar beet (Aoki et al. 1963; Hyakumachi et al. 

1980), this sudden collapsing of leaves may be associated with the ease of hyphal spread within 

tissues of the root groove. Although hyphae were observed within feeder roots in the current 

study (Figure 4.8), the type of feeder root decay associated with some wilt pathogens is not 

typically observed in sugar beet infected with R. solani AG 2-2 (L. Hanson, personal 

communication). More study on the health of feeder roots and the effect of invasion within the 

205 

root groove is needed to assess the relationship of these observations to the symptom of sudden 

leaf collapse.  

Response of cell wall 

The presence of autofluorescence complicated the analysis. Initially, the phenomenon was 

detected during staining with propidium iodide which exhibited increased fluorescence within 

the lesion. This observation was hypothesized to indicate an abundance in demethylated pectin as 

propidium iodide has been reported to have a higher affinity for demethylated pectin than 

methylated pectin (Rounds et al. 2011). It was therefore hypothesized that R. solani utilized a 

pectin de-methylesterase to increase susceptibility of cell wall pectin to polygalacturonases 

(Baher & Braybrook 2011; Karr & Albersheim 1970). However, immunofluorescence did not 

support changes in methylation state as the reason for increased propidium iodide binding. Prior 

to cell wall fragmentation and elimination of pectin, the cell wall appeared to thicken as 

autofluorescence in the red spectra increased (Figure 4.11). This increase in autofluorescence and 

apparent cell wall thickening within the lesion is likely to be associated with as yet unknown 

physical changes in cell wall structure.  

Safranin O in combination with calcofluor white can distinguish between cellulosic and 

lignified tissue (Bond et al. 2008; Kitin et al. 2020; Sant’Anna et al. 2013). Ruppel (1973) 

reported that cell walls within a lesion were thickened and had greater affinity for safranin, 

possibly indicating a concentration or localization of suberin within infected tissues. Safranin O 

is an azo dye commonly used to stain lignified tissues such as xylem (Bond et al. 2008) and 

observations in the current study of increased safranin O staining in infected tissues could 

indicate lignification (Figure 4.9), the potential of which was not explored in Ruppel (1973). The 

206 

 
increase in fluorescence associated with berberine hemisulfate that was less intense than the 

highly suberized cork cells could also support lignification rather than suberization as the source 

of increased safranin O staining.  

Healthy sugar beet cell walls contain relatively low levels of lignin (Dinand et al. 1999; 

McGrath & Townsend 2015) but contain unique phenolic cross-links between ferulic acid and 

pectic arabinosyl and galactosyl residues that influence such properties as intercellular adhesion, 

texture and lignification (Guillon & Thibault 1989; Marry et al. 2006; Waldron et al. 1999). 

Deposition of lignin as a defense response reinforces the cellulose microfibrils and makes the 

cell wall more resistant to cell wall degrading enzymes (Bellincampi et al. 2014; Bhuiyan et al. 

2009). Increased lignification has been associated with disease resistance in cotton (Smit & 

Dubery 1996; Xu et al. 2011), Camelina sativa (Enyck et al. 2012), wheat (Dushnicky et al. 

1998; Menden et al. 2007) and has been recognized as part of a generalized response to biotic 

stresses (Bhuiyan et al. 2009; Nicholson & Hammerschmidt 1992; Xie et al. 2018). 

It is uncertain whether R. solani AG 2-2 is pathogenic on woody plants. Other R. solani AG, 

including AG 4 and others where the anastomosis group was not determined, have been reported 

from pine and other tree species (Mehrotra 1990; Starkey & Enebak 2012). Laccase genes have 

been characterized from an isolate in a closely related group, R. solani AG 6 (Wahleithner et al. 

1996). An analysis of the R. solani AG 2-2 genome revealed high numbers of secreted proteins 

including 1097 putative carbohydrate active enzymes (CAZymes) that may function as cell wall 

degrading enzymes (Wibberg et al. 2016). Fifteen instances of genes that had been putatively 

identified as ‘laccase’ were identified in the AG 2-2 draft genome published by Wibberg et al. 

(2016), indicating the potential for lignin degradation. Quantification of lignin content and 

207 

confirmation of the presence of laccase, or other lignin degrading enzymes, in infected tissue is 

needed to test this hypothesis.  

Pectin modifications 

Elimination of binding sites for LM19 and LM20 antibodies from the cell wall indicated 

degradation of both methylated and demethylated pectin epitopes (Figure 4.14 & 4.15). While 

this occurred in both the resistant and susceptible varieties, it appeared to occur more rapidly in 

the susceptible variety than the resistant, although quantification was not attempted. In the 

susceptible variety (CR869), much of the unesterified pectin was eliminated from the cell walls 

of infected tissue, while in the resistant variety (SR98/2), the unesterified pectin appeared to 

aggregate and form clumps along the inner surface of the cell wall (Figure 4.15). This 

observation may indicate the accumulation of pectin fragments liberated from the cell wall by 

enzymatic action (Cervone et al. 1989; Pontiggia et al. 2015; Ridley et al. 2001). The presence of 

pectic fragments, or oligogalacturonides (OG), has been associated with defense responses such 

as cell wall reinforcement and could be considered a damage-associated defense response 

(Bellincampi et al. 2014). Cell wall reinforcements could include deposition of callose (Flors et 

al. 2008) and lignin (Eynck et al. 2012; Xu et al. 2011), and catalyzation of cross-linkages 

between cell wall components (Deepak et al. 2010; Passardi et al. 2004). Further work is needed 

to assess the nature of the pectin accumulation and the potential responses initiated in the current 

system.   

The incomplete degradation of pectin fragments and the potential accumulation of OGs may 

be attributed to interactions between polygalacturonase and polygalacturonase-inhibiting proteins 

(Cook et al. 1991; Davidsson et al. 2017; De Lorenzo et al. 1994). Polygalacturonase enzymes 

208 

 
can exhibit differential degradation patterns based on substrate recognition (Cook et al. 1999). 

Secretion of a variety of polygalacturonase isoforms by an invading fungal pathogen can reduce 

oligogalacturonide fragments to sizes that do not elicit a defense response (Benedetti et al. 2014; 

Scala et al. 1980). Inhibition of one or more polygalacturonase isoforms could limit the 

pathogen’s capacity to avoid triggering plant defenses by increasing the accumulation of OGs of 

the size that can trigger defense responses.    

Several polygalacturonase inhibitor proteins (PGIP) have been identified that confer 

resistance to fungal pathogens in sugar beets (Li & Smigocki 2016) and other crops (Chen et al. 

2019; Kalunke et al. 2015; Liu et al. 2016). Inhibitor proteins can be induced as a response to 

wounding (Li & Smigocki 2016) and are important players in plant innate immunity (Di Matteo 

et al. 2006; Federici et al. 2006). The potential accumulation of OGs in the resistant variety could 

be a factor in triggering a defense response resulting in responses such as lignin deposition. 

Investigation of expressed PGIP genes could provide insight into defense mechanisms in sugar 

beet and provide targets for breeding programs, similar to work done with dry bean (Desiderio et 

al. 1997; D’Ovidio et al. 2004).  

The degradation of highly esterified pectin, indicated by a reduction in binding of the LM20 

antibody, implicates the presence of pectin lyase which is the only known enzyme that can 

degrade highly methylated pectin (Yadav et al. 2009). Pectin lyase (PNL; EC 4.2.2.10) has been 

isolated from R. solani AG 2-2 and was shown to have higher activity on sugar beet tissue than 

polygalacturonase in both culture and infected tissue (Bugbee 1990). Further support for the 

involvement of pectin lyase comes from the Rhizoctonia-resistant sugar beet cultivar FC712 

(Panella 2001). Bugbee 1993 showed reduced pectin lyase activity in variety FC712 compared to 

209 

susceptible varieties and attributed the reduction in activity to a proteinaceous pectin lyase 

inhibitor from the host, although this enzyme was not isolated.  

Conclusions 

Rhizoctonia solani AG 2-2 utilizes multiple routes to penetrate and invade sugar beet root 

tissue, including infection cushions, direct penetration of the periderm, colonization of natural 

openings and wounds, and through vulnerable tissues in the root groove (current study; Ruppel 

1973). This diversity in infection routes is expected to allow the fungus to be adaptable in its 

approach to infection and colonization. Although the behavior of R. solani AG 2-2 on sugar beet 

during the early stages of infection differs from reports on several other crops (Bashyal et al. 

2018; Bassi et al. 1979; Hofman & Jongebloed 1988; Marshal & Rush 1980; Pannecoucque & 

Höfte 2009), the process is similar to that of AG 3 on potato tubers (Zhang et al. 2016) and 

possibly reflects the presence of a periderm rather than an epidermis.  

Structural components of the root tissues can help explain some of the characteristic 

symptoms of Rhizoctonia root and crown rot. The outer cambium appears to act as a temporary 

barrier for hyphal colonization influencing the hyphae to spread laterally, producing shallow 

lesions in a characteristic ladder-like pattern. Necrosis precedes the hyphae in all lesions, 

indicating the likely presence of secreted CWDEs and a corresponding defense response in the 

root tissue that attempts to reinforce cell walls against degradation.  

Wibberg et al. (2016) identified almost 1100 putative carbohydrate-active genes within the 

genome of R. solani AG 2-2. Our observations support diverse enzymatic functions involved in 

the colonization of sugar beet roots. We observed evidence for the degradation of cellulose, 

pectin, methylated pectin, and lignin and degradation of these cell wall components may involve 

210 

 
the enzymes cellulase (El-Samawaty et al. 2008), polygalacturonase (Scala et al. 1980), pectin 

lyase (Bugbee 1990), and laccase (Wahleithner et al. 1996), all of which have been identified or 

characterized from R. solani isolates. Further research investigating the relationship and 

evolutionary history of cell wall degrading enzymes in R. solani AG 2-2 could provide new 

insights into pathogenesis and virulence on sugar beet. These processes also have implications in 

other hosts and research is needed to determine which enzymes are employed in common 

between the different hosts of R. solani AG 2-2 and whether any enzymes are unique to a given 

host.  

The accumulation of pectin fragments could indicate the presence of proteinaceous inhibitors 

such as polygalacturonase-inhibiting proteins (PGIP). Several enzyme-inhibiting proteins have 

been characterized from sugar beet including a pectin lyase inhibitor (Bugbee 1993) and several 

polygalacturonase-inhibiting proteins (Li & Smigocki 2016). While the current study does not 

explicitly implicate CWDE inhibitors in resistance, it is likely they play a substantial role in 

resistant varieties as demonstrated by the accumulation of pectin fragments in the resistant 

variety. Potential PGIPs have been examined in several systems and have shown promise at 

limiting disease development (Federici et al. 2006; Li & Smigocki 2018; Tundo et al. 2016). 

Since PGIPs appear to have differential specificity (Desiderio et al. 1997; Sella et al. 2004), it is 

likely that a combination of PGIPs would be required to be effective against Rhizoctonia root 

and crown rot in a broad range of situations (Federici et al. 2001).  

211 

 
 
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CHAPTER 5: 

CONCLUSIONS 

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Rhizoctonia root and crown rot is a persistent problem in sugar beet growing regions 

around the world. The disease has proved challenging to manage because of a lack of 

satisfactory resistance in commercial varieties and rotational crops that are susceptible to the 

same strains of the pathogen. Discrepancies in Rhizoctonia solani anastomosis group 

(AG) 2-2 subgroups have led to inconsistencies and unanswered questions regarding the 

population biology of AG 2-2. For example, some studies have provided evidence of possible 

sexual reproduction in natural populations (Ajayi-Oyentunde et al. 2019; Kiyoshi et al. 2014; 

Toda & Hyakumachi 2006), but little is known about conditions and limitations for the 

formation of the sexual stage and recombination in natural populations.  

To address some of the open questions regarding the population biology of R. solani 

AG 2-2, including global and regional distributions, and reproductive strategy, a major goal 

of this dissertation project was to develop a set of microsatellite markers to examine the 

population genetics of R. solani AG 2-2. This set of microsatellite markers, in combination 

with data from a multigene phylogeny, was used to redefine subgroups so they reflect a more 

natural classification. Results indicated two subgroups within AG 2-2 that were composed of 

a mix of isolates that were previously classified as AG 2-2IIIB and AG 2-2IV. These newly 

defined groups were termed AG 2-2BR (Beta rot) and AG 2-2PR (Phaseolus rot) based on 

which crop members of the group were most aggressive according to Minier (2019). Both 

subgroups contain two distinct genetic clusters, each with unique characteristics.  

The major conclusions of this dissertation research are two-fold. First, this research 

identified several mechanisms that contribute to genetic diversity within AG 2-2. While both 

the sexual stage (Thanatephorus cucumeris) and the asexual exchange of nuclei have 

previously been demonstrated in R. solani AG 2-2 (Kiyoshi et al. 2014; Toda & Hyakumachi 

222 

2006), the contribution of these processes to the diversity of field populations and their 

consequences is not well understood. Evidence for sexual reproduction was found to be 

limited to a single genetic subgroup while the other groups appear to reproduce primarily 

clonally. However, AG 2-2 isolates can exchange nuclei as well as other genetic material 

asexually through anastomosis reactions (Kiyoshi et al. 2014; Toda & Hyakumachi 2006). 

This transfer of genetic material in a horizontal manner confuses relationships within the 

group. It is likely that the characterization of isolates will require sequencing of multiple 

genes rather than relying on single genetic regions, such as ITS, for identification. The 

lineage-specific markers developed in the current study should simplify assignment of 

isolates to specific subgroups. Identification of pathogenicity or virulence factors could 

provide additional information that might help identify risks associated with a given isolate.  

Genetic exchange between isolates has previously been demonstrated in AG 2-2IV 

(Kiyoshi et al. 2014; Toda & Hyakumachi 2006) but to the best of my knowledge, has not 

been convincingly demonstrated in AG 2-2IIIB or between AG 2-2IIIB and AG 2-2IV 

isolates. The results of this dissertation project have provided evidence that genetic exchange 

does occur between the different subgroups of AG 2-2, creating hybrids that potentially have 

unique genetic composition. Further work that provides direct observations regarding genetic 

exchange, especially of nuclei, between AG 2-2 subgroups is needed as well as determination 

of the consequences of genetic exchange on features such as virulence or host preference.  

Sexual reproduction in AG 2-2 has been controversial. While the sexual stage 

(Thanatephorus cucumeris) has been reported in several other anastomosis groups, it has 

rarely been reported in AG 2-2, particularly from the field. Furthermore, reports of 

hymenium formation in AG 2-2 have mostly been from Japan in vitro from AG 2-2IV 

223 

isolates (Kiyoshi et al. 2014; Toda & Hyakumachi 2006). The results of this dissertation 

research provide evidence for the presence of sexual reproduction in natural populations. 

Evidence of sexual reproduction was limited to the subgroup AG 2-2BRb, which was the 

predominant group recovered from the Red River Valley in the current study. The results of 

the current study indicate that sexual reproduction may be occurring in the Red River Valley 

region and further scrutiny should be given to field populations in the region to determine 

conditions that contribute to the formation of the sexual stage. While the sexual stage has 

been reported on beet from other regions (Windels 2009), the hymenial stage on beets has 

sometimes been associated with other anastomosis groups (Ohkura et al. 2009; Windels et al. 

1997), including in the Red River Valley (Windels et al. 1997).  

The analysis of AG 2-2 populations revealed several interesting aspects about population 

structure. Isolates recovered from Europe were more diverse than previously reported 

(Buddemeyer et al. 2004) with representatives from all four genetic groups recovered in the 

current study. No isolates from subgroup AG 2-2BRb were recovered from Michigan in this 

study, while AG 2-2BRb was the predominant group in samples from the Red River Valley 

region. Some regions had limited representation in the current work and further examination 

is needed to determine the nature of the population structure within those regions. Also, 

additional work is needed to examine population structure and diversity within individual 

fields and from varied cropping systems.  

The current study provided evidence of long-distance dispersal with genotypes being 

shared between regions that were separated by large distances, even across oceans. For 

example, isolates from Japan shared substantial genotype diversity with many of the other 

regions around the world. In contrast, the Europe population was relatively isolated, 

224 

genetically, from other regions. While basidiospores can be dispersed aerially (Naito 1996) 

and the sexual stage has been reported in Japan, it seems unlikely that basidiospores would 

disperse on air currents to regions such as the Red River Valley but not to Europe. In 

addition, there was evidence of long-distance dispersal even within Michigan, despite the 

lack of evidence for sexual reproduction in the state. It is likely that at least some of the 

dispersal occurs through the movement of contaminated soil or equipment and more research 

is needed to determine how this movement occurs and how to better prevent it. This work 

also highlights the importance of sanitation in disease management.  

The exchange of nuclei or other genetic material between isolates of different subgroups 

apparently plays a substantial role in the generation of diversity within R. solani AG 2-2. 

How this process is regulated presumably involves somatic compatibility factors, but more 

research is needed to determine these factors and how “hybridization” generates diversity. 

The evolution of field populations, whether through sexual recombination or “hybridization”, 

could have important implications to disease management in areas such as fungicide 

resistance, host preference, and virulence.  

The second major conclusion to come from the project concerns the involvement of 

multiple cell wall degrading enzymes in disease development. It appeared that lignin 

deposition occurred within the infected tissue in both the resistant and susceptible sugar beet 

varieties. The observation that these lignin deposits were degraded as tissue maceration 

progressed supports previous indications that ligninolytic enzymes, such as laccase, are 

produced by R. solani (Wahleithner et al. 1996). Genes involved in both the lignin defense 

response by the plant and lignin degradation by the fungus need to be identified and 

characterized. 

225 

The accumulation of suspected oligogalacturonides in infected sugar beet tissue was 

observed and was most pronounced in the resistant variety. Short oligogalacturonides have 

been shown to be triggers of plant immunity (Davidson et al. 2017) and could provide a 

mechanism for some of the defense response in resistant sugar beet. This accumulation of 

pectin fragments could also indicate one or more polygalacturonase-inhibitor proteins 

produced by the plant that prevent the polygalacturonases produced by the fungus from 

completely degrading pectin. Polygalacturonase-inhibitor proteins have been identified in 

sugar beet (Li & Smigocki 2018), dry bean (D’Ovidio et al. 2004), and soybean (Favaron et 

al. 1993). Further research on these inhibitors and their performance in sugar beet could 

provide more direct approaches to disease resistance in sugar beet. 

Expectations were that there would be evidence of pectin methylesterase activity since 

highly methylated pectin is resistant to degradation by most enzymes (Yadav et al. 2009). 

However, there was no clear evidence of systematic demethylation in either the resistant or 

susceptible sugar beet varieties. Since pectin lyase is the only enzyme known to degrade 

methylated pectin, it is likely that the enzyme is involved in tissue degradation. Pectin lyase 

has previously been isolated from R. solani AG 2-2 (Bugbee 1990) and warrants further 

investigation. In addition, a pectin lyase inhibitor has also been isolated from sugar beet by 

Bugbee (1993). Further work examining pectin lyase inhibitors and their activity in sugar 

beet could identify additional resources for developing varieties with more complete 

resistance.  

Consistent with previous studies (Ruppel 1972), histopathological observations showed 

less colonization on the surface of resistant varieties than on susceptible varieties. However, 

in contrast to reports of infection cushion formation on other crops and in other anastomosis 

226 

groups of R. solani, directed growth following anticlinal walls was not observed in the 

current study. In addition, in the current study the hyphae were not observed forming 

t-shaped branches prior to infection cushion formation. These observations may implicate 

exudates or other chemical signals rather than surface cues as the primary factor that 

stimulates infection cushion formation on adult sugar beet. This hypothesis is supported by 

the observation of increased colonization around wounds or natural defects in the periderm 

that expose the underlying cortex. The premise that leads to this conclusion being that the 

periderm helps to limit the loss of water and other chemicals present in the cortex tissue 

(Campilho et al. 2020) and so, damage to the periderm would result in increased attraction 

and colonization of fungal hyphae.  

The histopathological observations outlined in this dissertation provide a description of 

some physiological features that help explain characteristic symptomology of Rhizoctonia 

root and crown rot of sugar beet. In particular, the outer cambial ring provides at least a 

temporary barrier against fungal penetration, directing hyphal growth laterally. This results in 

the characteristic symptom of shallow lesions that tend to form in a ladder-like pattern. As 

reported in previous studies (Ruppel 1972), necrosis consistently preceded the hyphae in both 

susceptible and resistant sugar beet varieties. This region of darkening tissue appears to be 

largely the result of a plant defense response rather than damage to the cell wall. The precise 

biochemical situation in this region and details of the plant-pathogen interaction still need to 

be elucidated. A transcriptome analysis of the affected region could help clarify the process 

responsible for the discolored region.  

Based on observations discussed in this dissertation project and the current understanding 

of cell wall degradation defense, some enzymes that are most likely to have an impact on 

227 

disease resistance and deserve to be a focus of future research are polygalacturonase-

inhibiting proteins and pectin lyase-inhibiting proteins. Both enzyme types have been 

isolated from sugar beet but their role in disease resistance is unknown.  

Even if inhibitor enzymes were present in a variety, they could be ineffective against 

fungal wall-degrading enzymes that escape recognition due to mutations or sequence 

variations. Polygalacturonase-inhibiting proteins exhibit substrate specificity (Desiderio et al. 

1997; Federici et al. 2001), meaning that if multiple forms of an enzyme are secreted by the 

fungus, some of them may escape recognition by the inhibitor proteins leaving the cell wall 

vulnerable to degradation. Transforming novel inhibitor proteins with appropriate 

specificities into sugar beet varieties or editing existing genes using technology such as 

CRISPR/Cas9 could provide additional specificity that may be needed to increase disease 

resistance.  

This dissertation project represents a significant advance in our understanding of 

Rhizoctonia solani AG 2-2 population genetics. Not only does this work reclassify the 

subgroups to reflect more natural relationships, but it also provides new insight into the life 

history strategy of this important fungal pathogen. In particular, this study considers sources 

of genetic variation and how that variation might explain population structure. As a part of 

the life history strategy, the fungus utilizes several cell wall degrading enzymes, and this 

study proposes potential targets for breeding efforts to improve Rhizoctonia root and crown 

rot resistant sugar beet varieties.  

228 

 
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