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4-2016 : Identification, Management, and Population Diversity Martha P. Romero Luna Purdue University

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PURDUE UNIVERSITY GRADUATE SCHOOL Thesis/Dissertation Acceptance

This is to certify that the thesis/dissertation prepared

By Martha Patricia Romero Luna

Entitled STENOCARPELLA MAYDIS: IDENTIFICATION, MANAGEMENT, AND POPULATION DIVERSITY

For the degree of Doctor of Philosophy

Is approved by the final examining committee:

Kiersten A. Wise Chair Charles Woloshuk

Mary. C. Aime

James J. Camberato

To the best of my knowledge and as understood by the student in the Thesis/Dissertation Agreement, Publication Delay, and Certification Disclaimer (Graduate School Form 32), this thesis/dissertation adheres to the provisions of Purdue University’s “Policy of Integrity in Research” and the use of copyright material.

Approved by Major Professor(s): Kiersten A. Wise

Approved by: Peter B. Goldsbrough 4/11/2016 Head of the Departmental Graduate Program Date

i

STENOCARPELLA MAYDIS: IDENTIFICATION, MANAGEMENT, AND

POPULATION DIVERSITY

A Dissertation

Submitted to the Faculty

of

Purdue University

by

Martha P. Romero Luna

In Partial Fulfillment of the

Requirements for the Degree

of

Doctor of Philosophy

May 2016

Purdue University

West Lafayette, Indiana

ii

Dedico esta tesis a mi familia. A mamá, mi calma y luz en la tempestad. Gracias por enseñarme a luchar por mis sueños y a no rendirme ante la adversidad. Sin tu amor, paciencia, apoyo incondicional y sobre todo fe en mí, yo no estaría terminando este capítulo en mi vida. A mis hermanos, Ana y Rafa. Por ser mis ejemplos y guías a seguir. Gracias por ser cómplices en cada aventura de mi vida. En sus consejos siempre encontré las palabras de aliento que necesitaba para continuar. A Zack, mi balance. Gracias por caminar esta etapa de mi vida junto a mí. Por compartir alegrías y tristezas, por hacerme saber q estabas a mi lado sin importar los miles de km que había de por medio. Y por seguir junto a mí en el siguiente capítulo que nos aguarda.

"La felicidad no es cuestión de intensidad si no de balance, orden, ritmo y armonía. Thomas Merton, (1915-1968).

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ACKNOWLEDGEMENTS

I would like to give special thanks to my advisor, Dr. Kiersten Wise. More than advisor, across these years she became a mentor and an example to look after. Thank you for your trust, support, and guidance across these years. I also would like to acknowledge and give thanks to my committee members, to Dr. Aime, Dr. Camberato and Dr.

Woloshuk for providing perspective, advice and support through my degree. Special thanks to Dr. Latin for his valuable comments and suggestions on my manuscripts. It was a pleasure to work with you during my prelims. I would also like to thank all the members of the Wise lab. I feel so lucky that I had the opportunity to work with such extraordinary people who helped me with my research across the years. Special thanks to

Nolan Anderson for his suggestions in all aspects of my research and to Jeffrey Ravellette for his field technical support over the years.

Special thanks to Esther. Girl you are amazing! My time here would not have been the same without you. Thank you for keeping me sane, for those long talks, for many coffees and many shared laughs. Also, special thanks to Rachel, my roommate, friend and huge support over the years. I would also like to thank Anna and Nicolette, it was a pleasure to meet you both, I’m so glad that I was able to share with you two the bitter sweet of graduate school.

iv

To my aunts Hilda, Quily, Gaby and Bety, and my uncle Luis. I know that every

Saturday morning my mom shared with you my adventures and her worries. Thank you for always been there with the most lovely and supportive words. I would also want to thank to all the members of the Botany and Plant Pathology department, including staff, technician, secretaries, professors and graduate students, for all their help through the years.

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TABLE OF CONTENTS

Page LIST OF TABLES ...... viii LIST OF FIGURES ...... xi ABSTRACT ...... xvii CHAPTER 1. INTRODUCTION ...... 1 1.1 The Origin of Corn and Corn Production ...... 1 1.1.1 United States Corn Production ...... 2 1.2 Diseases of Corn ...... 2 1.3 Stenocarpella spp...... 3 1.3.1 of S. maydis and S. macrospora ...... 4 1.3.2 Stenocarpella spp. as corn pathogens ...... 5 1.3.3 production ...... 6 1.4 History of ear rot in the United States ...... 7 1.5 Management of Diplodia ear rot ...... 8 1.5.1 Genetic resistance for Diplodia ear rot ...... 8 1.5.2 Chemical control for Diplodia ear rot ...... 9 1.5.3 Cultural practices for Diplodia ear rot ...... 10 1.6 Variability within S. maydis ...... 11 1.7 Study Objectives ...... 12 1.8 List of References ...... 14 CHAPTER 2. DEVELOPMENT OF MOLECULAR ASSAYS FOR DETECTION OF STENOCARPELLA MAYDIS AND STENOCARPELLA MACROSPORA IN CORN ...... 29 2.1 Abstract ...... 29

vi

Page 2.2 Introduction ...... 30 2.3 Materials and Methods ...... 33 2.3.1 Fungal isolates and growth conditions ...... 33 2.3.2 DNA extraction ...... 34 2.3.3 ITS PCR and sequencing ...... 35 2.3.4 Conventional PCR for the diagnosis of Stenocarpella spp...... 36 2.4 Results ...... 38 2.5 Discussion ...... 40 2.6 Literature cited ...... 45 CHAPTER 3. EFFECT OF CROP ROTATION AND TILLAGE SYSTEMS ON DIPLODIA EAR ROT SEVERITY ...... 64 3.1 Introduction ...... 64 3.2 Materials and Methods ...... 66 3.2.1 Data analysis ...... 68 3.3 Results ...... 69 3.3.1 Weather conditions ...... 69 3.3.2 Experimental results ...... 69 3.4 Discussion ...... 70 3.5 Literature cited ...... 75 CHAPTER 4. SURVIVAL OF STENOCARPELLA MAYDIS ON CORN RESIDUE IN INDIANA ...... 87 4.1 Introduction ...... 87 at different soil depths in a corn field in Indiana...... 88 4.2 Materials and Methods ...... 89 4.2.1 Survival of Stenocarpella maydis ...... 89 4.2.2 Decomposition rate of corn residue ...... 92 4.2.3 Pathogenicity test ...... 93

vii

Page 4.2.4 Data analysis ...... 93 4.3 Results ...... 94 4.3.1 Weather conditions ...... 94 4.3.2 Survival of Stenocarpella maydis ...... 95 4.3.3 Decomposition rate ...... 96 4.3.4 Pathogenicity test ...... 97 4.4 Discussion ...... 97 4.5 Literature cited ...... 102 CHAPTER 5. GENETIC DIVERSITY OF STENOCARPELLA MAYDIS IN THE MAJOR CORN PRODUCTION AREAS OF THE UNITED STATES ...... 117 5.1 Introduction ...... 117 5.2 Materials and Methods ...... 119 5.2.1 Fungal isolates and growth conditions ...... 119 5.2.2 DNA extraction ...... 120 5.2.3 Development of microsatellite markers, primer design, and screening for polymorphism...... 120 5.2.4 Data analysis ...... 121 5.2.5 Screening for mating type genes ...... 124 5.3 Results ...... 125 5.3.1 Variation among S. maydis isolates in a fine-scale study ...... 125 5.3.2 Population diversity of S. maydis national population ...... 126 5.3.3 Population structure ...... 127 5.3.4 Screening for mating types ...... 128 5.4 Discussion ...... 128 5.5 Literature cited ...... 132 VITA ...... 159

viii

LIST OF TABLES

Table ...... Page Table 2.1. Isolate code, year, and collection site of Stenocarpella spp. and other

pathogenic species used in this study...... 53

Table 2.2. Primer sequence used to identify isolates of Stenocarpella spp...... 58

Table 3.1. Year, soil classification, previous crop history and tillage system, planting,

harvest and disease rating date for each experiment to test the effect of rotation and

tillage on Diplodia ear rot severity at the Agronomy Center for Research and Education

(ACRE) in Tippecanoe County, Indiana...... 82

Table 3.2. Summary average for daily average of air temperatures (ºC) and precipitation

(mm) from May through October in 2014 and 2015 at the Agronomy Center for Research

and Education (ACRE) in Tippecanoe County, Indiana...... 83

Table 3.3. Detailed statistical analysis from general linear model analysis of data from

field experiments at the Agronomy Center for Research and Education (ACRE) in

Tippecanoe County, Indiana in 2014 and 2015 to evaluate the effect of crop rotation and

tillage on Diplodia ear rot severity and yield under inoculated and non-inoculated

conditions...... 84 ix

Table ...... Page Table 4.1. Mean monthly temperatures and soil moisture at the soil surface (0 cm), and

at 10 cm, and 20 cm soil depths at the experimental site located at the Agronomy Center

for Research and Education (ACRE) in which Stenocarpella maydis survival on corn

residue was evaluated...... 110

Table 4.2. Stenocarpella maydis pycnidia and conidia production on corn residue placed

in field experiment in November 2014, and retrieved four months later (March), 7 months

later (June), 11 months later (October), and 12 months later (November, 2015)...... 111

Table 4.3. Percentage of remaining biomass and decomposition rates (k) of corn kernels

after 4, 7, 11, and 12 months at the soil surface (0 cm), or buried at 10 and 20 cm at the

Agronomy Center of Research and Education (ACRE) in Tippecanoe County, IN...... 114

Table 4.4. Percentage of remaining biomass and decomposition rates (k) of corn stalks

after 4, 7, 11, and 12 months at the soil surface (0 cm), or buried at 10 and 10 cm at the

Agronomy Center of Research and Education (ACRE) in Tippecanoe County, IN...... 115

Table 5.1. Summary information and number of isolates for the Stenocarpella maydis

population analyzed in this study...... 140

Table 5.2. Stenocarpella maydis microsatellite characteristics, including sequence

identification, location, repeat motif, forward and reverse primers, product size in number

of base pairs (bp), and number of allele observed...... 143

Table 5.3. Primer sequence used to identify mating type genes on Stenocarpella maydis isolates...... 144

Table 5.4. Statistics of genetic and genotypic diversity of Stenocarpella maydis from 41

isolated obtained from three infected corn ears...... 145 x

Table ...... Page Table 5.5. Statistics of genetic and genotypic diversity of Stenocarpella maydis from 59 isolated obtained in 2010, and from 2012 to 2014...... 146

Table 5.6. Statistics of genetic and genotypic diversity of Stenocarpella maydis from

Midwestern and Southern populations...... 147

Table 5.7. Details of the microsatellite markers allele sizes (bp) detected for each of the multilocus genotype (MLG), including number of isolates, isolate code, and their geographical region of origin for each MLG...... 148

Table 5.8. Indices of association (IA) and its standardized form (ṝd) for nonclone-corrected and clone-corrected data from Midwest and Southern population of Stenocarpella maydis...... 156 xi

LIST OF FIGURES

Figure ...... Page Figure 2.1. Agarose gel electrophoresis of polymerase chain reaction products. A.

Products of S. maydis obtained with primers Smay.F/Smay.R. Lanes 1 to 5: (isolates 3-

10-1, 3a.1, 4a.1.2, 7.1, 10a.2), Lane 6 to 8: S. macrospora isolates (isolates 1a.1.1, 1a and

4b). Lane 9: Control (nuclease-free sterile water). Lane M: molecular size marker (Life

Technologies, Grand Island, NY). B. Products of other fungal and bacteria species

obtained with primers Smay.F/SmayR. Lanes 1 to 9: (Phoma spp., Diplodia pinea,

Fusarium graminearum, , Clavibacter michiganensis subsp.

nebraskensis, Exserohilum turcicum, Colletotrichum graminicola, Macrophomina

phaseolina and verticillioides. Lane 10: Control (nuclease-free sterile water).

Lane M: molecular size marker (Life Technologies, Grand Island, NY)...... 59 xii

Figure ...... Page Figure 2.2. Agarose gel electrophoresis of polymerase chain reaction products. A.

Products of S. macrospora obtained with primers Smac.F/Smac.R. Lanes 1 to 5: (isolates

1b, 4a.2, 4b, 1a.1.1, 1a (2013) Lane 6 to 8: S. maydis (isolates 2-2-1, 7.1, and 3a.1), Lane

9: Control (nuclease-free sterile water). Lane M: molecular size marker (Life

Technologies, Grand Island, NY). B. Products of other fungal and bacteria species

obtained with primers Smac.F/Smac.R. Lanes 1 to 9: (Phoma spp., Diplodia pinea,

Fusarium graminearum, Aspergillus flavus, Clavibacter michiganensis subsp.

nebraskensis, Exserohilum turcicum, Colletotrichum graminicola, Macrophomina

phaseolina and . Lane 10: Control (nuclease-free sterile water).

Lane M: molecular size marker (Life Technologies, Grand Island, NY)...... 60

Figure 2.3. Melting curve analysis for Stenocarpella maydis using real-time PCR assay.

A. Amplification curves of S. maydis isolates and controls obtained with primers

RT.Smay.F/RT.Smay.R. B. Melting curve analysis of the same samples shows the

presence of the specific PCR product. C. Comparison chart with Ct values and melting

temperatures. a DNA extracted for other fungi isolated that are not the target of this assay

and water are identified as negative controls. b Ct values and ± standard deviation...... 61 xiii

Figure ...... Page Figure 2.4. Melting curve analysis for Stenocarpella macrospora using real-time PCR assay. A. Amplification curves of S. macrospora isolates and controls obtained with primers RT.Smac.F/RT.Smac.R. B. Melting curve analysis of the same samples shows the presence of the specific PCR product. C. Comparison chart with Ct values and melting temperatures. a DNA extracted for other fungi isolated that are not the target of this assay and water are identified as negative controls. b Ct values and ± standard deviation. c Cmn: Clavibacter michiganensis subsp. nebraskensis ...... 62

Figure 2.5. Dilution series for A. Stenocarpella maydis real-time (top image) and conventional (bottom/gel image) PCR assays. Ten-fold dilutions were performed in each assay with an initial concentration of 10 ng/µl to a final concentration of 0.00001 ng/µl.

Gel image includes Lane M: molecular size marker (Life Technologies, Grand Island,

NY) and nuclease-free sterile water as control. B. Stenocarpella macrospora real-time

(top image) and conventional (bottom/gel image) PCR assays. Ten-fold dilutions were performed in each assay with and initial concentration of 10 ng/µl to a final concentration of 0.01 ng/µl. Gel image includes Lane M: molecular size marker (Life Technologies,

Grand Island, NY) and nuclease-free sterile water as control...... 63

Figure 3.1. Effect of inoculation treatment (inoculated, non-inoculated) on disease severity (A) and corn yield (B), and effect of crop rotation (corn, soybean) and tillage

(no-tillage, chisel plow) on corn yield (C) of Diplodia ear rot in 2014. Values with the same letter are not significantly different based on least square (LS) means test (P=0.05).

...... 85 xiv

Figure ...... Page Figure 3.2. Effect of inoculation treatment (inoculated, non-inoculated) on disease

severity (A) and effect of crop rotation (corn, soybean) and tillage (no-tillage, chisel plow) on corn yield (B) of Diplodia ear rot in 2015. Values with the same letter are not significantly different based on least square (LS) means test (P=0.05)...... 86

Figure 4.1. Diagrammatic scale (1 to 6) of colonized corn stalks after being artificially

inoculated with Stenocarpella maydis...... 108

Figure 4.2. Minimum (min), maximum (max), and average air temperatures (°C)

collected every 30 min with Watchdog 1000 series loggers from November 2014 to

November 2015 at the Agronomy Center for Research and Education, Tippecanoe

County, IN...... 109

Figure 4.3. Effect of burial depth on viability of Stenocarpella maydis, expressed as

germination percentage. Experiment was established at the Agronomy Center for

Research and Education (ACRE) and it was initiated in November 2014. Values with the

same letter are not significantly different based on least square (LS) means test (P=0.05).

Burial depth at 20 cm was not included because no germination was observed at any

sampling time after experiment initiation...... 112

Figure 4.4. Effect of sampling time on viability of Stenocarpella maydis, expressed as

germination percentage. Experiment was established at the Agronomy Center for

Research and Education (ACRE) and it was initiated in November 2014. Values with the

same letter are not significantly different based on least square (LS) means test (P=0.05).

Data from sampling time of 12 months after experiment initiation was not included because no germination was observed...... 113 xv

Figure ...... Page

Figure 4.5. Pathogenicity test on Stenocarpella maydis isolates obtained from retrieved corn tissue. (A) Control (water-inoculated) ears showing only a slight discoloration where the pin-bar was inserted. No white mycelium representative of Stenocarpella maydis was observed. (B) Kernel water-inoculated showing a slight discoloration. The color of the rest of the kernels and cob did not showed any level of discoloration. No pycnidia were observed. (C) Inoculated ears showing brown discoloration on kernels beyond inoculated area, and slight white mycelium representative of Stenocarpella maydis within kernels.

(D) Inoculated corn ear showing discoloration on the cob beyond the inoculated area.

Presence of pycnidia on kernels representative of Stenocarpella maydis...... 116

Figure 5.1. Genotype accumulation curve for Stenocarpella maydis isolates collected from Midwestern and Southern regions in the US. The vertical axis denotes the number of observed multilocus genotype (MLG), from 0 to the observed number of MLGS. The horizontal axis, denotes the number of loci. When 8 microsatellite loci are used in genotyping, 90% of the unique multilocus genotypes can be detected. The horizontal red dashed line represents 90% of the unique multilocus genotype...... 155

Figure 5.2. Estimated population structure for K = 2 using microsatellite genotyping.

Analysis was performed using admixture model in Structure v2.3. Each color represents one cluster, and each bar represents one isolate...... 157 xvi

Figure ...... Page

Figure 5.3. Agarose gel electrophoresis of polymerase chain reaction products. A.

Examples of PCR amplification of MAT1-1 specific-sequence from Stenocarpella maydis (203 base pairs). B. Examples of PCR amplification of MAT1-2 specific- sequence from Stenocarpella maydis (369 base pairs)...... 158 xvii

ABSTRACT

Romero Luna, Martha P. Ph.D., Purdue University, May 2016. Stenocarpella Maydis: Identification, Management, and Population Diversity. Major Professor: Kiersten Wise.

Diplodia ear rot (DER) has been a persistent corn disease across the Midwest, and in

recent years it has become an annual problem. The objectives of this study were to i)

develop a molecular assay for the identification of Stenocarpella maydis, causal agent of

DER, ii) evaluate the effect of crop rotation and tillage on DER severity, iii) determine

the survival period of S. maydis in corn residue at different soil depths in a corn field, and

iv) identify genetic diversity among S. maydis isolates collected from the Midwestern and

Southern United States. The genus Stenocarpella contains two species, S. maydis and S.

macrospora, both able to infect corn plants. Previously, identification of the two species

was based only on time consuming morphological methods. An accurate molecular assay

was developed that can be performed under conventional or real-time PCR, and both are

able to distinguish between S. maydis and S. macrospora from pure culture or infected tissue. Field experiments were conducted at the Agronomy Center for Research and

Education (ACRE) in Tippecanoe County, Indiana. From 2013 to 2014, four corn

production systems were evaluated for DER control under non-inoculated and inoculated xviii conditions. Stenocarpella maydis survival was assessed over 12 months at the soil surface, and at 10 and 20 cm burial depths. Results indicate that crop rotation with soybean and tillage practices did not eliminate Diplodia ear rot, and in certain years did not reduce DER severity. Stenocarpella maydis was able to survive on the soil surface in corn residue for at least 11 months. However, S. maydis conidia recovered from samples were not able to infect corn ears under controlled conditions after 7 months. These results indicate that at least a one year rotation away from corn is necessary in order to eliminate inoculum. Tillage operations must reduce surface residue to a minimal percentage to reduce inoculum. High genotypic diversity was observed among S. maydis isolates in the

US. Genotypic diversity was higher within sample regions and similar between the

Midwestern and Southern US S. maydis populations. This indicates that recombination occurs among S. maydis isolates. Although no sexual reproduction has been reported, both mating type genes, Mat1-1 and Mat1-2 were identified and amplified across 60 selected isolates. 1

CHAPTER 1. INTRODUCTION

1.1 The Origin of Corn and Corn Production

Corn ( mays L.) is one of the most important field crops worldwide, and is the

dominant feed grain traded in international markets (Food and Agricultural Organization

(FAO; 26). More corn is produced worldwide than any other grain crop, including rice

and (26). The origin of corn was debated by two opposite theories (4, 5).

Mangelsdorf and Reeves (1939) proposed the “tripartite hypothesis”, where an extinct

wild corn is designated as the ancestor, and Beadle (1939) proposed the “teosinte

hypothesis”, where teosinte (Zea mexicana) is named as the wild ancestor of corn.

Recently, based on molecular evidence, it has been suggested that Z. mays subsp.

parviglumis was the ancestor of corn (19).

Since the first recorded evidence of corn 7,000 years ago in Mexico’s Tehuacan

valley (5), production has remained important. World-wide corn production in 2014 and

2015 was estimated in 1,007 million metric tons (99; Table 1). The United States is the largest corn producing country, followed by China (99). Corn production plays a primary role in agricultural and food production areas, and is used mainly for ethanol production, livestock feed, and within the processed food industry (96). 2

1.1.1 United States Corn Production

Approximately 90% of corn produced in the US is for grain, while the remaining

10% is produced for silage (35, 51). Most of the states in the US grow corn, but the

largest land areas of production are located in the Corn Belt: Illinois, Indiana, Iowa,

Kentucky, Michigan, Minnesota, Missouri, Nebraska, North and South Dakota, and

Wisconsin are recognized as the major corn producers (5, 32, 101). Increased demands

for ethanol production and increased human food consumption have resulted in an

expansion of corn acreage and a demand for higher yields (23, 53, 101, 107). For

example, in 1941, corn production totaled 2.3 million metric tons (102), whereas, in

2014-2015 the preliminary report indicated a production of 361 million metric tons (99).

Continuous improvement in corn breeding programs, new farm technology, higher plant

densities, and improved crop management practices have contributed with this growth

(24).

1.2 Diseases of Corn

Corn diseases are capable of reducing the plant population and compromising yield (41, 89). According to Mueller and Wise (2012, 2013), combined yield loss from

2012 and 2013, due to corn diseases in the US and Canada, was estimated at approximately 61 million metric tons. Of these diseases, ear and stalk rots are listed as the most important diseases in corn (105). Ear rots are commonly caused by fungal pathogens, including Fusarium graminearum ( ear rot), Aspergillus flavus

(Aspergillus ear rot), F. verticillioides (Fusarium ear rot), and Stenocarpella maydis and

S. macrospora (Diplodia ear rot) (9, 10, 66, 77, 86). Ear rot symptoms and damage can be 3

severe in humid and warm locations, and when an excess of rainfall occurs during the

reproductive stages of corn (66).

1.3 Stenocarpella spp.

The genus Stenocarpella contains two different species, S. maydis (Berk) Sutton,

and S. macrospora (Earle) Sutton (15). Both species have corn as the only commercial

host, and both are able to infect ears, leaves, and stalks (49). Despite the fact that these

pathogens can be found almost everywhere corn is produced (Fig. 1; 25, 94, 95),

incidence of S. macrospora has been higher and more persistent in Africa, India and

Latin America (50, 56, 60). The southern part of the US has identified S. macrospora as a minor disease in corn (105). Stenocarpella maydis has been identified as the predominant causal agent of Diplodia ear rot across the Midwest in the US (50). The negative impact of this pathogen is also observed in other parts of the world. Recently, Brazil listed S. maydis as a non-regulated quarantine pathogen (RNQP; 1), and for seed commercialization, the Ministry of Agriculture (MAPA) has limited its presence to 2% in imported seed lots (Administrative Regulation No. 47 February 19, 2009; 1).

Identification and diagnosis of these two pathogens is still primarily based on standard methods, including microscopic observation, moist chamber incubation, and isolation on artificial nutrient media (61, 65, 76). Regulatory seed agencies require a quick and accurate method for their detection (1). The use of morphological characteristics is time consuming, and although a conventional polymerase chain reaction

(PCR) method has been developed, differentiation between the two Stenocarpella species was not accomplished (109). 4

1.3.1 Taxonomy of S. maydis and S. macrospora

Originally, Stenocarpella spp. belonged to the genus Diplodia (92), however in

1980, Stenocarpella was moved to its own genus based on observed differences in conidiogenesis (93). In 2006, Crous et al. generated molecular evidence that supported placing Stenocarpella in the . Based on this the taxonomy of S. maydis is as

follows:

Superkingdom: Eukaryota

Kingdom: Fungi

Phylum:

Subphylum: Pezizomycotina

Class:

Subclass: Sordariomycetidae

Order: Diaporthales

Family:

Genus: Stenocarpella

Species: maydis (Berk) Sutton

Species: macrospora (Earle) Sutton

In 1822, Schweinitz observed S. maydis for the first time on old corn stalks. He named it Diplodia zeae. It was re-described as Sphaeria maydis Berk. (1847), Diplodia zeae Lev. (1848), Diplodia maydis (Berk.) Sacc. (1884), and finally as Stenocarpella

maydis (Berk.) B. Sutton (1980). Stenocarpella macrospora was first observed in the US

in 1897 by Earle on corn samples from Auburn, Alabama (22).

5

Both fungi develop brown to black fruiting bodies, called pycnidia. The conidia are pale brown, curved or irregular, with a size of 15 to 34 x 5 to 8 µm for S. maydis, and

44 to 82 x 7.5 to 11.5 µm for S. macrospora. Generally only one septum is visible for S. maydis, and up to 3 for S. macrospora (93- 95).

1.3.2 Stenocarpella spp. as corn pathogens

Corn and its ancestor, teosinte, are hosts of both Stenocarpella spp.; S. maydis has also bamboo ( spp.) as a host (14, 27, 94, 95). Stenocarpella spp. overwinter on corn debris, and to some extent in the soil, until the environment is favorable for plant infection (103). Both fungi are necrotrophic, with a saprobic phase occurring when they overwinter in corn residue, and a parasitic phase when they infect corn plants (11, 13).

Temperatures between 23 and 30°C stimulate pycnidia to release the conidia (65) by way of cirri (66). The conidia function as the primary source of inoculum, and are dispersed by wind and rain splash (12). Symptoms may differ depending on the time of infection. If the infection occurs after pollination, ears will be colonized by white fungal mycelia, and ears will have a mummified appearance (37, 82). Ears are colonized typically from the base of the cob to the tip, with white mycelium growing up in the cob and into the kernels

(103). However, physical damage to the ear, and insect or bird feeding can lead to infection in the middle or the tips of the ears. Symptoms on leaves differ by their size.

Both produce elliptical chlorotic lesions with irregular margins, and a gray center that indicates the point of infection. However, S. macrospora usually has elongated lesions that expand up to 10 cm (13, 50).

6

1.3.3 Mycotoxin production

In the past few years the concern for ear rots caused by both Stenocarpella spp.

has increased. In some corn production areas, cases of diplodiosis, a neuromycotoxicosis of livestock, have been reported and associated with S. maydis (42, 59, 64, 75). The causal agent(s) responsible for this mycotoxicosis is still unknown; currently diplodiatoxin and diplonine have been described and associated with this disease, but these are not able to recreate all the diplodiosis symptoms by themselves (63,

90, 91, 106).

Field outbreaks of diplodiosis were reported only in South Africa (43) until 1964 when Darvall (1964) reported a possible outbreak in Australia. Recently, Argentina (71) and Brazil (78) reported cases of poisoned animals by diplodiosis. Many of these reports happened after the animals grazed on harvested lands during the winter (64, 72). To corroborate the relationship between S. maydis and diplodiosis, many experiments have been performed. In 1918 and 1919, Mitchell reproduced the symptoms by feeding animals with corn ears naturally infected (63, 64) while the rest of the experiments used inoculated kernels (42, 43, 72-74, 88). Kellerman et al. (1985) and Rabie et al. (1985,

1987) were able to demonstrate under laboratory conditions that isolates from the US and

Argentina also produced compounds toxic to animals. Despite these results no cases of diplodiosis have been reported in the US (59). Environmental conditions and harvest systems are possible reasons for why this mycotoxicosis is not uniformly present in all corn production areas (59, 71).

Stenocarpella macrospora produces diplodiol (Syn. Diplosporin), and

Chaetoglobosins K and L (16, 17). Cutler et al. (1980) confirmed the toxicity of these

7 mycotoxins by feeding chicks with corn ears infected S. macrospora. He emphasizes that further studies are needed to establish the impact of this mycotoxins on livestock.

1.4 History of Diplodia ear rot in the United States

In 1909, S. maydis was recognized as a corn pathogen (9). There are several names that are used to identify both the pathogen causing ear rot, and the resulting disease (2, 9, 37, 70, 84, 94, 95). In order to be consistent, we will refer to S. maydis as the causal agent and Diplodia ear rot as the disease. Throughout the history of corn production in the US, Diplodia ear rot has persisted as an important disease, especially in the Corn Belt (41). In the early 1900’s, Diplodia ear rot was considered the most important ear rot disease (9, 89), and reports continue to confirm its prevalence (47, 48).

In 1906, 4.5% of the Illinois corn crop was destroyed by this disease (9). However, between 1960 and 1970, disease incidence was reduced. This decline in disease prevalence may be attributed to increased adoption of crop rotations with soybeans (103), and breeding efforts for improved disease resistance (13, 83).

More recently, epidemics of Diplodia ear rot have been reported in important areas of corn production including South Africa in 1985 to 1988 (82), Nigeria in 1999

(62), Iowa in 1993 (69), across the Corn Belt from Iowa to Ohio in 2000 (57), and finally

Nebraska and Illinois in 2009 (8).

These resurgences in disease can be attributed to several factors. Environmental conditions vary and can influence disease development in a given year, but common production practices such as reduced tillage, and the increase in continuous corn production in recent years could be contributing factors in the re-emergence of this

8 disease. Reduced availability of hybrids with strong genetic resistance to this disease may also contribute to disease development. The impact that Diplodia ear rot is having on corn production was demonstrated by a combined report in 2012 and 2013 that included

22 states in the US and Canada, and which estimated a yield loss of 1.36 million metric tons due to this disease (67, 68).

1.5 Management of Diplodia ear rot

Although the preferred way to manage Diplodia ear rot is by planting resistant hybrids (38), fungicide applications, crop rotation and tillage practices are also suggested as measures for disease control (82). Management of both Stenocarpella spp. is similar, however the majority of the research has been focused on the understanding and management on S. maydis, principally related with corn ears.

1.5.1 Genetic resistance for Diplodia ear rot

The incorporation of resistant germplasm into corn breeding programs is the ideal way to control Diplodia ear rot (38, 39, 44), and breeders in the public and private sector are facing a significant challenge to develop resistant materials to this disease (82).

Hybrids rated for Diplodia ear rot resistance may be commercially available from some seed companies, and ratings indicate that there are less susceptible hybrids available.

However, the genetic basic of resistance is not publically known. This pathogen requires specific environmental conditions to cause infection, and since these conditions vary from season to season, the occurrence of S. maydis can be sporadic. Many studies have concluded that inheritance for resistance to Diplodia ear rot is complex (33, 38, 108).

9

Inbred corn lines represent an important source of genetic material in hybrid corn

breeding programs (55). The amplification of popular recombinant inbred lines (RIL)

populations has provided new opportunities for extensive genetic mapping work. Three studies on mapping QTL on Diplodia ear rot have been performed. In 2008, Gutierrez identified 3 significant QTL in chromosomes 2, 3, and 5. In 2009, Rossouw identified 24

QTL across the 10 chromosomes, except for chromosomes 2 and 9. In 2012, Romero identified two QTL, in chromosome 1 and 9, respectively. The absence of consistency among the three studies corroborated the complexity of breeding for Diplodia ear rot resistance. Further investigations on mapping QTL for Diplodia ear rot resistance in different environments are required to generate consistent results (80).

1.5.2 Chemical control for Diplodia ear rot

Fungicides have also been used for chemical control of S. maydis. Flett and

Beukes (1992) assessed a flutriafol/carbendazim mixture and benomyl for management

of Diplodia ear rot. Warren and Von Qualen (1986) applied benomyl and maneb before

anthesis; and Marley and Gbenga (2004) tested efficacy of benomyl, mancozeb and luxan

(local fungicide from Nigeria) in the laboratory for in-vitro experiments. However,

diverse groups of fungi have developed resistance to benomyl, and for that reason, this

fungicide is no longer available for use (34). Kleinschmidit and White (2001 and 2002) evaluated two fungicides with different mode of actions (azoxystrobin and propiconazole) and found that ears in fungicide-treated plots had less Diplodia ear rot symptoms. In 2008, Lee et al. (2009) found that pyraclostrobin had no effect on reducing

Diplodia ear rot on treated ears. Quilt Xcel™ (azoxystrobin + propiconazole) a Syngenta

Crop Protection fungicide, is the only fungicide currently labeled for Diplodia ear rot

10

control. In 2015, Romero and Wise (2015) evaluated selected fungicides, including Quilt

Xcel™, from 2011 to 2013 in Indiana at three different corn growth stages. These results

indicated that fungicide applications did not have a consistent effect on Diplodia ear rot

among years.

1.5.3 Cultural practices for Diplodia ear rot

Stenocarpella maydis is a residue-borne pathogen (21). Disease incidence depends upon the proximity between the source and the inoculum (30, 98). Flett et al.

(2001) reported that after rotating corn with soybean, peanut or wheat, inoculum of S. maydis was reduced. Despite this, some farmers have been reported that even with crop rotation, Diplodia ear rot infections are recurrent in their fields (Dr. Kiersten Wise, pers. comm., Purdue University).

Many studies outside the US have demonstrated that the use of conventional

tillage practices will reduce the incidence of this pathogen (28-30). Flett et al. (1992) and

Casa et al. (2003) observed that S. maydis is able to survive and retain its ability to infect

on corn residue for approximately 11 to 12 months, respectively. These conclusions were

based on studies established outside the US. The ethanol production and higher food

demands in the US require new improvements to accomplish the goal of better yields, which has resulted in no-tillage practices and monoculture cropping systems in some

areas (101). Additional research is needed to analyze the interaction between the current

corn production system in the US, and S. maydis.

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1.6 Variability within S. maydis

No literature was found that reported presence of races among S. maydis. There

are a few studies that described fungal differences based on morphology in cultures, or

aggressiveness on inoculated ears. Aversion between pure cultures of S. maydis was

described by Hoppe (1936), who tested twenty-one isolates of S. maydis and reported aversion between the isolates after placing them on the same plate. The aversion was described as a black line that formed between two colonies. After this line was visible, the colonies stopped growing. This aversion was maintained even after propagating the isolates under different techniques. Within the same paper, he reported differences in pathogenicity under field conditions. Three individual strains and a mix of the same three strains were used to inoculate two inbred lines. When the infected kernels were collected to re-isolate the strains, each of the three strains were recovered in the individual inoculations, but in the mixed strain only one strain was recovered. In another experiment, Ullstrup (1958) found aversion on 85% of 100 S. maydis isolates. He described this event as a type of antibiosis where the two cultures are sensitive to a toxin.

Additional field trials concluded that variability in pathogenicity occurs among S. maydis isolates. Young et al. (1959) observed that isolates can modify their pathogenicity and become more virulent at their place of origin. He also reported a temperature influence on mycelia growth. Van Rensburg et al. (1997) concluded that environmental conditions have a strong influence on virulence and infection levels. The last paper where phenotype and genotype traits were analyzed to compare S. maydis isolates was published in 1999 by Dorrance et al. In this study, a low level of isozyme polymorphisms was observed after comparing 46 isolates from the US and South Africa. It was suggested that an

12

expanded study with more isolates and the incorporation of a genetic analysis might

determine if genetic diversity exists among S. maydis isolates (20).

1.7 Study Objectives

Stenocarpella maydis, the causal organism of Diplodia ear rot (DER), was a well-

studied fungal pathogen at the beginning of the 19th century. Many studies were

established with the focus only on understanding factors involving infection and disease

management. The results were effective; the infection process was extensively described,

and the disease incidence was minimized. However, after this, minimal research has been

performed in the US. The current information that we have for this pathogen, and the

disease management recommendations are based on research conducted outside the US in

different environments and crop production systems.

The first objective of my research is to develop a molecular assay for the

identification of Stenocarpella spp. The genus Stenocarpella involves two species, both

able to infect corn plants, and S. maydis has been the most predominant in the Midwest.

Under field conditions is almost impossible to distinguish between the two species, and previously, identification was based only on time-consuming morphological methods.

Our goal is to develop a conventional or real-time PCR.

Our second objective is to evaluate the efficacy of cultural disease management

methods and establish accurate information on the survival period of S. maydis in the US.

The third objective is to determine if genetic diversity exists among S. maydis

isolates collected from the major corn production areas in the US. Based on the

exclusively asexual reproduction, we hypothesize that S. maydis will have low diversity

13

in its population. A better understanding of the genotypic diversity of a pathogen population is essential to understand pathogen evolution, plant-pathogen interaction, and to improve disease breeding strategies.

14

1.8 List of References

1. Barrocas, E.N., Machado, J.C., Almeida, M.F., Botelho, L.S., and Pinho, É.V.R.V.

2012. Sensibility of the PCR technique in the detection of Stenocarpella sp.

associated with seeds. Rev. Bras. Sementes 34:218-224.

2. Barros, E., Crampton, M., Marais, G., and Lezar, S. 2008. A DNA-based method to

quantity Stenocarpella maydis in maize. Maydica 53:125-129.

3. Beadle, G.W. 1939. Teosinte and the origin of maize. Journal of Hered. 30:245-247.

4. Bennetzen, J., Buckler, E., Chandler, V., Doebley, J., Dorweiler, J., Brandon, G.,

Freeling, M., Hake, S., Kellogg, E., Poethig, R.S., Walbot, V., and Wessler, S. 2001.

Genetic evidence and the origin of maize. Latin Am. Antiq. 12:84-86.

5. Benson, G.O., and Pearce, R.B. 1987. Perspective and Culture. Pages 1-30 In: Corn:

Chemistry and Technology. S.A. Watson and P. E. Ramstad, eds. American

Association of Cereal Chemists, Inc., St. Paul, MN.

6. Berkeley, M.J. 1847. Decades of fungi. Dec. XII-XIV. London J. Bot. 6.

7. Beukes, H.M.B., and Flett, B.C. 1992. Efficacy of three fungicides in the control of

Stenocarpella maydis ear rot of maize. Phytophylactica 24:106.

8. Bradley, C.A. 2009. Diplodia ear rot causing problems in corn across the state.

Online. The Bulletin. Univ of Illinois Extension. No. 23, article 6.

http://bulletin.ipm.illinois.edu/print.php?id=1233. (Accessed: 27 September 2015).

9. Burrill, T.J., and Barrett, J.T. 1909. Ear rots of corn. Agric. Exp. Sta. Bull. 133:64-

109. Univ. of Illinois.

15

10. Campbell, K.W., and White, D.G. 1995. Evaluation of corn genotypes for resistance

to Aspergillus ear rot, kernel infection, and aflatoxin production. Plant Dis. 79:1039-

1045.

11. Casa, R.T., Reis, E.M., and Zambolim, L. 2003. Decomposição dos restos culturais

do milho e sobrevivência saprofítica de Stenocarpella macrospora e S. maydis.

Fitopatol. Bras. 28:355-361.

12. Casa, R.T., Reis, E.M., and Zambolim, L. 2004. Dispersao vertical e horizontal de

conidios de Stenocarpella macrospora e Stenocarpella maydis. Fitopatol. Bras.

24:141-147.

13. Casa, R.T., Reis, E.M., and Zambolim, L. 2006. Doenças do milho causadas por

fungos do gênero Stenocarpella. Fitopatol. Bras. 31:427-439.

14. Christensen, J.J., and Wilcoxson, R.D. 1966. Stalk Rot of Corn. Monogr. No. 3. APS

Press, St. Paul, MN.

15. Crous, P.W., Slippers, B., Wingfield, M.J., Rheeder, J., Marasas, W.F.O., Philips,

A.J.L., Alves, A., Burgess, T., Barber, P., and Groenewald, J.Z. 2006. Phylogenetic

lineages in the Botryosphaeriaceae. Stud. Mycol. 55:235-253.

16. Cutler, H.G., Crumley, F.G., Cox, R.H., Cole, R.J., Dorner, J.W., Latterell, F.M., and

Rossi, A.E. 1980. Diplodiol: A new toxin from Diplodia macrospora. J. Agric. Food

Chem. 28:135-138.

17. Cutler, H.G., Crumley, F.G., Cox, R.H., Cole, R.J., Dorner, J.W., Springer, J.P.,

Latterell, F.M., Thean, J.E., and Rossi, A.E. 1980. Chaetoglobosin K: a new plant

growth inhibitor and toxin from Diplodia macrospora. J. Agric. Food Chem. 28:139-

142.

16

18. Darvall, P.M. 1964. Mouldy corn cobs, a danger to cows. Queensl. Agric. J. 90:692-

693.

19. Doebley, J. 1990. Molecular evidence and the evolution of maize. Econ. Bot. 44:6-

27.

20. Dorrance, A.E., Miller, O.K., and Warren, H.L. 1999. Comparison of Stenocarpella

maydis isolates for isozyme and cultural characteristics. Plant Dis. 83:675-680.

21. Durrell, L.W. 1923. Dry rot of corn. Iowa State Agric. Exp. Sta. Bull. Ames, IA.

22. Earle, F.S. 1897. New species of fungi imperfecti from Alabama. Bull. Torrey Bot.

Club 24:28-32.

23. Edgerton, M.D. 2009. Increasing crop productivity to meet global needs for feed,

food, and fuel. Plant Physiol. 149:7-13.

24. Edmeades, G.O., Tollenaar, M., Sinha, S.K., Sane, P.V., Bhargava, S.C., Agrawal, P.

K., 1990. Genetic and cultural improvements in maize production. Pages 164-180. In:

Proceedings of the international congress of plant physiology, New Delhi, India,

25. European and Mediterranean Plant Protection Organization (EPPO). 2015.

Stenocarpella maydis. Online. Global Database. Distribution.

https://gd.eppo.int/taxon/DIPDMA/distribution. (Accessed: 27 September 2015).

26. Food and Agriculture Organization of the United Nations (FAO). 2015. Wheat

Production, World Maize Production, World Rice Production.

http://faostat.fao.org/site/339/default.aspx. (Accessed: 27 September 2015).

27. Flett, B.C. 1993. Crop plants as host and non-host of Stenocarpella maydis.

Phytophylactica 23:237-238.

17

28. Flett, B.C., and Wehner, F.C. 1990. Incidence of cob rot pathogens on maize grown

under different tillage systems. Phytophylactica 22:148.

29. Flett, B.C., and Wehner, F.C. 1991. Incidence of Stenocarpella and Fusarium cob

rots in monoculture maize under different tillage systems. J. Phytopathol. 133:327-

333.

30. Flett, B.C., Wehner, F.C., and Smith, M.F. 1992. Relationship between maize stubble

placement in soil and survival of Stenocarpella-maydis (Diplodia-maydis). J.

Phytopathol. 134:33-38.

31. Flett, B.C., McLaren, N.W., and Wehner, F.C. 2001. Incidence of Stenocarpella

maydis ear rot of corn under crop rotation systems. Plant Dis. 85:92-94.

32. Foreman, L.F. 2006. Characteristics and Production Costs of U.S. Corn Farms, 2001.

Online. An Electronic Report from the Economic Research Service, USDA. Bulletin

No. 7. http://www.ers.usda.gov/media/758353/eib7_1_.pdf. (Accessed: 27 September

2015).

33. Gevers, H.O., Lake, J.K., and McNab, H.J. 1992. An analysis of ear rot and leaf

blight resistance in departmental maize breeding material. Proc. 9th S. A. Maize

Breeding Symposium. pp. 41-46. Pretoria.

34. Gezondheidsraad. 2004. Benomyl. Health Council of the Netherlands. Health-based

Reassessment of Administrative Occupational Exposure Limits, Netherlands.

35. Gibson, L., and Benson, G. 2002. Origin, History and uses of corn. Online. Iowa

State University.

http://www.agron.iastate.edu/Courses/agron212/readings/corn_history.htm.

(Accessed: 27 September 2015).

18

36. Gutierrez, H.I. 2008. Mapeamento de QTLs para resistencia a graos ardidos causados

por diplodia (Stenocarpella sp.) en milho (Zea Mays L.). M.S. Thesis. Universidade

Federal de Ubelandia, Brazil.

37. Heald, F.D., Wildcox, E.M., and V.W., P. 1909. The life history and parasitism of

Diplodia zeae. Pages 1-7 in: Annual report of the Agricultural Experiment Station of

Nebraska.

38. Hooker, A.L. 1956. Association of resistance to several seedling, root, stalk, and ear

diseases in corn. Phytopathology 46:379-384.

39. Hooker, A.L., and White, D.G. 1976. Prevalence of corn stalk rot fungi in Illinois.

Plant Dis. Rep. 60:1032-1034.

40. Hoppe, P.E. 1936. Intraspecific and interspecific aversion in Diplodia. J. Agric. Res.

53:671-680.

41. Jugenheimer, R.W. 1985. Corn: Improvement, seed production, and uses. Robert E.

Krieger Publishing. Malabar, Fl.

42. Kellerman, T.S., Rabie, C.J., van der Westhuizen, G.C., Kriek, N.P., and Prozesky,

L. 1985. Induction of diplodiosis, a neuromycotoxicosis, in domestic ruminants with

cultures of indigenous and exotic isolates of Diplodia maydis. Onderstepoort J. Vet.

Res. 52:35-42.

43. Kellerman, T.S., Prozesky, L., Schultz, R.A., Rabie, C.J., van Ark, H., Maartens,

B.P., and Lübben, A. 1991. Perinatal mortality in lambs of ewes exposed to cultures

of Diplodia maydis (= Stenocarpella maydis) during gestation. Onderstepoort J. Vet.

Res. 58:297-308.

19

44. Klapproth, J.C., and Hawk, J.A. 1991. Evaluation of four inoculation techniques for

infecting corn ears with Stenocarpella-maydis. Plant Dis. 75:1057-1059.

45. Kleinschmidt, C., and White, D. 2002. Evaluation of quadris for control of Diplodia

ear rot in corn, 2001. Fungic. Nematicide Tests 57:FC05.

46. Kleinschmidt, C., and White, D. 2003. Evaluation of quadris and tilt for control of

Diplodia ear rot in corn. 2002. Fungic. Nematicide Tests 58:FC046.

47. Koehler, B. 1959. Corn ear rot in Illinois. Agric. Exp. Sta. Bull. 639:85, Univ. of

Illinois.

48. Koehler, B., and Holbert, J. 1930. Corn Diseases in Illinois. Their extent, nature, and

control. Univ. of Illinois Agric. Exp. Sta. Bull. 354:1-164.

49. Lamprecht, S.C., Crous, P.W., Groenewald, J Z., Tewoldemedhin, Y.T., and

Marasas, W.F. O. 2011. Diaporthaceae associated with root and crown rot of maize.

IMA 2:13.

50. Latterell, F.M., and Rossi, A.E. 1983. Stenocarpella macrospora (=Diplodia

macrospora) and S. maydis (=D.maydis) compared as pathogens of corn. Plant Dis.

67:725-729.

51. Leath, M.N., and Hill, L.D. 1987. Economics of production, marketing, and

utilization. Pages 201-249 In: Corn: Chemistry and Technology. S. A. Watson and P.

E. Ramstad, eds. American Association of Cereal Chemists, Inc., St. Paul, MN.

52. Lee, C., Vincelli, P., and Dixon, E. 2009. Evaluation of fungicides for control of gray

leaf spot of corn, 2007. Plant Dis. Manag. Rep. 2:FC096.

20

53. Leibtaq, E. 2008. Corn prices near record high, but what about food costs? Online.

Amber Waves USDA, Economic Research Service. http://www.ers.usda.gov/amber-

waves/2008-february/corn-prices-near-record-high,-but-what-about-food-

costs.aspx#.VgidPHpViko. (Accessed: 27 September 2015).

54. Léveillé, J.H. 1848. Fragmentes mycologiques. Ann. Sci. Nat. III. Page 258.

55. Liu, K., Goodman, M., Muse, S., Smith, J.S., and Buckler, E., and Doebley, J. 2003.

Genetic structure and diversity among maize inbred lines as inferred from DNA

microsatellites. Genetics 165:2117.

56. Llano, A., and Schieber, E. 1980. Diplodia macrospora of corn in Nicaragua. Plant

Dis. 64:797.

57. Malvick, D. 2001. Corn Diseases in 2000. Extension and Oureach Report. University

of Illinois.

58. Mangelsdorf, P.C., and Reeves, R.G. 1939. The origin of Indian corn and its

relatives. Tex. Agr. Expt. Sta. Bull. 574:315.

59. Marasas, W. 1977. The genus Diplodia. Pages 119-128 in: Mycotoxic fungi,

mycotoxins, mycotoxicoses. An enciclopedic handbook., vol. 1. M.L. Wyllie TD, ed.

Marcel Decker, Inc. NY.

60. Marasas, W.F.O., Kriek, N.P.J., Steyn, M., van Rensburg, S.J., and van Schalkwyk,

D.J. 1978. Mycotoxicological investigations on Zambian maize. Food Chem.

Toxicol. 16:39-45.

61. Mario, J.L., and Reis, E.M. 2001. Método simples para diferenciar Diplodia

macrospora de D. maydis em testes de patologia de sementes de milho. Fitopatol.

Bras. 26:670-672.

21

62. Marley, P.S., and Gbenga, O. 2004. Fungicide control of Stenocarpella maydis in the

Nigerian Savanna. Arch. Phytopathology Plant Protect. 37:19-28.

63. Mitchell, D.T. 1918. A condition produced, in cattle feeding on maize infected with

Diplodia zeae. Pages 427-437 in: Directory of Veterinary Research. Onderstepoort

Annual Report, Pretoria, Union of South Africa.

64. Mitchell, D.T. 1919. Poisoning of cattle by Diplodia-infected maize. S. Afr. J. Sci.

16:446-452.

65. Morant, M.A., Warren, H.L., and Vonqualen, S.K. 1993. A synthetic medium for

mass-production of pycnidiospores of Stenocarpella maydis. Plant Dis. 77:424-426.

66. Moremoholo, L., and Shimelis, H. 2009. Stability analysis for grain yield and

Stenocarpella maydis ear rot resistance in maize. Afr. Crop. Sci. J. 9:425-433.

67. Mueller, D., and Wise, K.A. 2012. Diseases of corn. Corn disease loss estimates from

the United States and Ontario, Canada. Purdue Univ. Ext. Publ. BP-96-12-W.

68. Mueller, D., and Wise, K.A. 2013. Diseases of corn. Corn disease loss estimates from

the United States and Ontario, Canada. Purdue Univ. Ext. Publ. BP-96-13-W.

69. Munkvold, G.P., and Yang, X.B. 1995. Crop damage and epidemics associated with

1993 floods in Iowa. Plant Dis. 79:95-101.

70. Naumann, T.A., and Wicklow, D.T. 2010. Allozyme-specific modification of a maize

seed chitinase by a protein xecreted by the fungal pathogen Stenocarpella maydis.

Phytopathology 100:645-654.

71. Odriozola, E., Odeon, A., Canton, G., Clemente, G., and Escande, A. 2005. Diplodia

maydis: a cause of death of cattle in Argentina. New Zeal. Vet. J. 53:160-161.

22

72. Prozesky, L., Kellerman, T.S., Swart, D.P., Maartens, B.P., and Schultz, R.A. 1994.

Perinatal mortality in lambs of ewes exposed to cultures of Diplodia maydis (=

Stenocarpella maydis) during gestation. A study of the central-nervous-system

lesions. Onderstepoort J. Vet. Res. 61:247-253.

73. Rabie, C.J., Du Preez, J.J., and Hayes, J.P. 1987. Toxicity of Diplodia maydis to

broilers, ducklings, and laying chicken hens. Poult. Sci. 66:1123-1128.

74. Rabie, C.J., Kellerman, T.S., Kriek, N.P.J., Van Der Westhuizen, G.C.A., and De

Wet, P.J. 1985. Toxicity of Diplodia maydis in farm and laboratory animals. Food

Chem. Toxicol. 23:349-353.

75. Rao, S.K., and Achar, P N. 2001. Screening and in vitro production of diplodiatoxin

from the isolates of Stenocarpella maydis and its toxigenic effect on bacterial strains.

Indian J. Exp. Biol 39:1243-1248.

76. Reis, E.M., Casa, R.T., and and Bresolin, A.C.R. 2004. Manual de diagnose e

controle de 479 doenças do milho. 144 pp. 2nd ed. Graphel Gráfica e Editora Lages.

Santa Catarina, 480 Brazil.

77. Rheeder, J.P., and Marasas, W.F.O. 1994. Comparison of three seasons data

regarding the incidence of Fusarium and Diplodia species in grain of South African

maize cultivars. Department of Agriculture, South Africa.

78. Riet-Correa, F., Schild, A.L., and Fernandes, C.G. 1998. Enfermidades do sistema

nervoso dos ruminantes no sul do Rio Grande do Sul. Ciênc. Rural 28:341-348.

79. Romero Luna, M.P. 2012. Managing Diplodia ear rot in corn: Short and long-term

solutions. M.S. Purdue University, West Lafayette, IN.

23

80. Romero Luna, M.P., and Wise, K.A. 2015. Timing and efficacy of fungicide

applications for Diplodia ear rot management in corn. Plant Health Prog. 16:123-131.

81. Rossouw, J.D., Van Rensburg, J.B.J., and Van Deventer, C. S. 2002. Breeding for

resistance to ear rot of maize, caused by Stenocarpella maydis (Berk) Sutton. 1.

Evaluation of Selection criteria. S. Afr. J. Plant & Soil 19:182-187.

82. Rossouw, J.D., Pretorius, Z.A., Silva, H.D., and Lamkey, K R. 2009. Breeding for

resistance to Stenocarpella ear rot in maize. John Wiley & Sons, Inc. NJ.

83. Rossouw, J.D., Silva, H.D., Pozar, G., Kerns, M.R., Gutierrez, H., and Mario, J.

2009. Methods and compositions for selecting corn plants resistant to diplodia ear

rot. Google Patents. http://www.google.com/patents/US8222481. (Accessed: 28

October 2015).

84. Saccardo, P.A. 1884. Sylloge fungorum. Page 373.

85. Schweinitz, L.D., and Schwaegrichen, C.F. 1822. Synopsis fungorum Carolinae

superioris secundum observationes Ludovici Davidis de Schweinitz.

86. Scott, D.B. 1992. Soil-borne diseases of wheat and maize in South Africa: etiological

and epidemiological aspects. App. Plant Sci. 7:53-61.

87. Sharda, D.P., Wilson, R.F., Williams, L.E., and Swiger, L.A. 1972. Effect of feeding

corn inoculated with Diplodia zeae in laboratory animals. J. Anim. Sci. 34:225-229.

88. Smith, D.R., and White, D.G. 1988. Diseases of Corn. in: Corn and Corn

Improvement. S. H. Mickelson, G. H. Heichel, C. W. Stuber and D. E. Kissel, eds.

American Society of Agronomy, Inc. Crop Science Society of America, Inc. Soil

Science Society of America, Inc., Madison, Wisconsin.

24

89. Smith, G.M., and Trost, J.F. 1934. Diplodia ear rot in inbred and hybrid strains of

sweet corn. Phytopathology 24:151-157.

90. Snyman, L.D., Kellerman, T.S., Vleggaar, R., Flett, B.C., Basson, K.M., and Schultz,

R.A. 2011. Diplonine, a neurotoxin isolated from cultures of the fungus

Stenocarpella maydis (Berk.) Sacc. that induces Diplodiosis. J. Food Chem. Toxicol.

59:9039-9044.

91. Steyn, P.S., Wessels, P.L., Holzapfel, C.W., Potgieter, D.J.J., and Louw, W.K.A.

1972. The isolation and structure of a toxic metabolite from Diplodia maydis (Berk.)

Sacc. Tetrahedron 28:4775-4785.

92. Sutton, B.C. 1964. Coelomycetes III. Annellolacinia Gen. Nov., Aristastoma,

Phaeocytostroma, Seimatosporium, etc. Commonwealth Mycological Institute.

93. Sutton, B.C. 1980. The Coelomycetes : fungi imperfecti with pycnidia, acervuli, and

stromata. Commonwealth Mycological Institute, Kew, Surrey, England.

94. Sutton, B.C., and Waterston, J.M. 1966a. Diplodia macrospora. CMI Descriptions of

522.

pathogenic fungi and bacteria. Set 9. No. 83. CAB International, Willingford, UK.

95. Sutton, B.C., and Waterston, J.M. 1966b. Diplodia maydis. CMI Descriptions of 525

pathogenic fungi and bacteria. Set 9. No. 84. CAB International, Willingford, UK.

96. Tang, O. 2012. Striking gold: Mandates for biofuel and steady global demand will

benefit corn farmers. IBISWorld Industry Report 11115.

http://www.ibisworld.com/industry/default.aspx?indid=8. (Accessed: 28 October

2015).

25

97. Ullstrup, A.J. 1958. A study of the nature of intraspecific aversion in Diplodia

maydis. Bull. Torrey Bot. Club 85:397-404.

98. Ullstrup, A.J. 1964. Observations on two ephiphytotics of Diplodia ear rot of corn in

Indiana. Plant Dis. 48:414-415.

99. USDA. 2015. World Agricultural Production. In: Foreign Agricultural Service.

United States Department of Agriculture, Washington, D.C. WAP 10 -15.

(http://apps.fas.usda.gov/psdonline/circulars/production.pdf). (Accessed: 10 October

2015).

100. USDA. 1941. Acreage, Yield, and Production of Principal Crops. in: National

Agricultural Statistics Service (NASS) United States Department of Agriculture,

Washington, D.C.

101. USDA. 2012. Corn. in: Economic Research Service. United States Department of

Agriculture, Washington, D.C.

102. Van Rensburg, J.B.J., and Ferreira, M.J. 1997. Resistance of elite maize inbred

lines to isolates of Stenocarpella maydis. S. Afr. J. Plant & Soil 14:89-92.

103. Vincelli, P. 1997. Ear rot of corn caused by Stenocarpella maydis (=Diplodia

maydis). Online. University of Kentucky. Cooperative Extension Service. PPA-43.

Lexington, KY. http://www2.ca.uky.edu/agc/pubs/ppa/ppa43/ppa43.htm. (Accessed:

28 September 2015).

104. Warren, H.L., and Von Qualen, S.K. 1986. Effect of fungicides on ear and stalk

rot of maize. Phytopathology 76:1106.

105. White, D.G. 1999. Compendium of corn diseases. 3rd ed. APS Press, St. Paul,

Minn.

26

106. Wicklow, D.T., Rogers, K.D., Dowd, P.F., and Gloer, J.B. 2011. Bioactive

metabolites from Stenocarpella maydis, a stalk and ear rot pathogen of maize. Fungal

Biol. 115:133-142.

107. Wise, K.A., and Mueller, D. 2011. Are fungicides no longer just for fungi? An

analysis of foliar fungicide use in corn. APSnet Features.

Doi:10.1094/APSnetFeature-2011-0531.

108. Wiser, W.J., Kramer, H.H. and Ullstrup, J. 1960. Evaluating inbred lines of corn

for resistance to Diplodia ear rot. Agron. J. 52:624-626.

109. Xia, Z., and Achar, P.N. 2001. Random amplified polymorphic DNA and

polymerase chain feaction markers for the differentiation and detection of

Stenocarpella maydis in maize seeds. J. Phytopath. 149:35-44.

110. Young, H.C.J., Wilcoxson, R.D., Whitehead, M.D., Devay, J.E., Grogan, C.O.,

and Zuber, M.S. 1959. An ecological study of the pathogenicity of Diplodia maydis

isolates inciting stalk rot on corn. Plant Dis. Rep. 43:1124-1129.

27

Table 1. Production of grain in the United States and worldwide in 2013-2014 and

estimation for 2014-2015a

Wheat Rice Millet Corn Marketing Year 2013-14 2014-15 2013-14 2014-15 2013-14 2014-15 Production (Million metric tons) United States 58.1 55.1 6.1 7.1 351.27 361.09 All Other Countries 657 670.3 472.3 471.7 640.14 647.59

World Total 715.1 725.5 1,281.1 1,297.4 991.42 1,008.68 a Data was obtained from the Foreign Agricultural Service/USDA. http://apps.fas.usda.gov/psdonline/circulars/production.pdf

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Figure 1. Worldwide distribution of Stenocarpella maydis in 2014 (25).

29

CHAPTER 2. DEVELOPMENT OF MOLECULAR ASSAYS FOR DETECTION OF STENOCARPELLA MAYDIS AND STENOCARPELLA MACROSPORA IN CORN

Reproduced, by permission, from Romero, M. P., and Wise, K. A. 2015. Development of molecular assays for detection of Stenocarpella maydis and Stenocarpella macrospora in corn. Plant Dis. 99:761-769. http://dx.doi.org/10.1094/PDIS-09-14-0917-RE

2.1 Abstract

The causal agents of Diplodia ear rot are two species of the Stenocarpella genus, S.

macrospora and S. maydis. In addition to ears, both pathogens can infect leaves and

stalks, and both are present in most corn production regions around the world. It is

difficult to visually distinguish between the two pathogens based on plant symptoms and

fungal signs. To facilitate accurate and rapid pathogen identification, polymerase chain

reaction (PCR) assays were developed for identification of each species. Species- specific primers of 18 to 20 nucleotides in length were designed, targeting a portion of

the internal transcribed spacer (ITS) region of the fungal genome for conventional and real-time PCR assays. The conventional PCR method successfully amplified a single

1.7-kb and 800-bp fragment for each S. maydis and S. macrospora isolate, respectively.

The real-time method was performed using SYBR green dye, and detection of each specific target pathogen was successfully obtained. In total, 82 S. maydis and 15

S. macrospora isolates were tested to evaluate the reproducibility of these primers. Both methods provide a rapid and specific tool for the detection of Stenocarpella spp.

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2.2 Introduction

Stenocarpella macrospora (Earle) B. Sutton and S. maydis (Berk.) B. Sutton, the

only two species included in the Stenocarpella genus (9, 12, 47, 48), have been

identified as causal agents of Diplodia ear rot, Diplodia leaf streak, and Diplodia stalk

rot (53). Both species are able to infect ears, stalks, and leaves (47, 48) and, under

specific conditions, these fungi can produce mycotoxins (10, 45, 55). Stenocarpella

maydis is recognized as one of the most persistent and important ear rot pathogens in the

world (41). Typically, S. macrospora has been more significant in tropical and

subtropical areas (15, 20, 24); however, the disease is commonly present in the United

States, and increases in disease incidence have been observed across the Midwest in recent

years (3, 52).

Visually distinguishing between symptoms caused by these two species is

challenging (6). S. macrospora produces long elliptical gray-green lesions with

yellowish margins on leaves. Concentric zones are evident in the lesion around the point

of infection origin. Pycnidia are typically observed in the older part of the lesion (19, 24,

53). Foliar lesions caused by S. maydis are oval and smaller than those caused by S. macrospora (19) and pycnidia production is less abundant across the lesion. Both diseases

may be confused with other common corn foliar diseases, including northern corn leaf blight

(caused by Exserohilum turcicum; 1) and Goss’s bacterial leaf blight (Clavibacter

michiganensis subsp. nebraskensis; 56). When ears are infected, both species can colonize

the ear with white, cottony mycelia. Black pycnidia can be observed on the husk or on the kernel surface (6, 13, 24). Crop rotation and tillage are currently the only

31

reliable methods for disease management. Hybrids with complete resistance to these

diseases are not known, although some hybrids may be less susceptible than others.

In 2012, Diplodia ear rot, leaf streak, and stalk rot resulted in a combined loss of 0.97 million metric tons in the US and Ontario, Canada (31). In addition to yield loss, in some subtropical and tropical corn production areas, cases of diplodiosis, a neuromycotoxicosis of livestock, have been reported and associated with S. maydis (17,

23, 28, 35, 37). Diplodiatoxin and diplonine have also been associated with diplodiosis but these mycotoxins do not always cause the neurological signs associated with

diplodiosis symptoms in livestock animals and, therefore, it is believed that the main causal agents responsible for this mycotoxicosis are still unknown (45, 46, 55).

Stenocarpella macrospora produces diplosporin and diplodiol, two mycotoxins that

affect small animals (10, 18). Field outbreaks were reported only in South Africa (16,

28) until 1964, when Darvall reported a possible outbreak in Australia. Recently,

Argentina (32) and Brazil (38) reported cases of animal poisoning by diplodiosis.

Although there have been no reports of diplodiosis in the US (22, 51), corn grain is

tested for the presence of these pathogens when exported to other countries. S. maydis is considered a regulated nonquarantine pest (RNQP) in Brazil, due to the negative impact it

has on corn production (2, 44).

Currently, S. maydis is considered a major pathogen throughout the corn

producing regions in the US and, although S. macrospora is currently considered a

minor pathogen in the US, Latterell et al. (19) concluded that this pathogen could compromise corn production in humid areas of the US. The distribution and prevalence of S. macrospora in the US is not well known. Previous studies have focused on

32

determining morphological differences between these two pathogens. There are clear differences in conidial dimensions and number of septa between the two species.

Stenocarpella maydis conidia are 15 to 34 by 5 to 8 µm, with 0 to 2 septae.

Stenocarpella macrospora conidia are almost triple the size of S. maydis conidia, being 55 to 106 by 6 to 14 µm and with 0 to 3 septae (12, 24, 47, 48). There are also color variances

between young and old cultures of each species when grown on potato dextrose agar (PDA;

8, 25, 36). Initially, both species have a white, cottony appearance on the media; however,

after 2 weeks, S. maydis cultures turn grayish to dark brown. Cultures of S.

macrospora maintain the original white appearance. Another distinguishable characteristic is that S. maydis reportedly produces higher numbers of pycnidia in culture

(30). Pycnidia production of S. macrospora in culture increases when mycelium is removed from surface of culture. However, S. macrospora requires more time to develop pycnidia and, frequently, pycnidia are embedded in the media or at the edge of the plate

(15, 19, 29), making it difficult to obtain conidia for identification (2).

Previous studies described the use of the polymerase chain reaction (PCR) method to identify Stenocarpella spp. (33, 57). Primers designed for these assays were based on

the internal transcribed spacer (ITS) region for Stenocarpella spp., and these assays

were able to differentiate Stenocarpella spp. from other fungal species but were not

specific enough to differentiate between the two Stenocarpella spp. (2).

According to Lievens et al. (21), the absence of an effective and accurate method

to detect and identify pathogens is one of the major limitations of plant disease management. Although Stenocarpella spp. can be distinguished based on a combination of morphological and cultural factors, these techniques are time consuming and are not

33

conducive to the accurate and rapid diagnosis required for regulatory agencies that

monitor corn seed pathogens to prevent pathogen establishment in new areas and to

monitor grain for the presence of pathogens that produce mycotoxins. Stenocarpella

maydis is considered an RNQP in Brazil, due to the negative impact it has on corn

production, and corn is routinely screened for presence of the pathogen (2, 44). In

fact, Casa et al. (6) reported that seed infected with Stenocarpella spp. was

responsible for introducing the pathogen into areas in Brazil where it was not previously established. Currently, molecular assays for detecting and distinguishing Stenocarpella spp. are lacking. The objective of this study was to design and validate conventional

and real-time PCR assays for Stenocarpella spp.

2.3 Materials and Methods

2.3.1 Fungal isolates and growth conditions

In total, 82 S. maydis isolates and 15 S. macrospora isolates collected in the US

were included in this study (Table 2.1). Other pathogenic species that commonly infect

corn or are closely related to the Stenocarpella genus were included to assess assay

validity (Table 2.1). Stenocarpella spp. isolates were obtained from corn kernels and

corn leaves collected between 2010 and 2014 from fields in Illinois, Indiana, Missouri, New

York, Tennessee, and Wisconsin. Isolates were obtained from plant tissue by disinfecting leaves with 5% commercial bleach (a.i. 5.05% sodium hypochlorite) and Tween 20 (Bio-

Rad Laboratories, Hercules, CA) for 10 s. Tissue was washed twice with water, dried

for 4 h, and transferred to PDA. Cultures were stored at 28°C under 12-h light and dark cycles until pycnidia were visible (approximately 7 days). A single pycnidium was

34

hand-picked with a sterile needle and placed into a 1.5-ml Flex-Eppendorf tube

(Eppendorf AG, Hamburg, Germany) with 150 µl of double distilled H2O. After

agitation, four drops of the suspension were transferred with a sterilized pipette to water

agar, containing Bacto agar (Becton, Dickinson and Company, Sparks, MD) at 20 g/liter. Plates were incubated at room temperature for 24 h under dark conditions. Single germinated spores were picked and transferred to natural oatmeal agar (NOA) (40).

Plates were stored at 28°C under 12-h light and dark cycles until the plate was fully

colonized (approximately 7 days). Once a single-spore culture was obtained, a 5-mm

plug was transferred to a new NOA plate already containing sterilized filter paper (8

by 8 cm). Plates were placed again at 28°C under 12-h light and dark cycles until the filter paper was fully colonized and pycnidia production was visible (approximately 15 days). Filter paper was removed from the plate with a sterile forceps and transferred to new, empty petri dish. The filter paper was completely dried, placed inside an envelope covered with foil paper and stored at -20°C.

2.3.2 DNA extraction

Pure cultures from storage were subculutred on fresh NOA plates. Plates were incubated at 28°C under 12-h light and dark cycles for 7 days, after which approximately 40 mg of fresh mycelium was collected by scraping from the colony surface. DNA was extracted from each isolate by a cetyltrimethyl ammonium bromide

(CTAB) protocol derived from Saghai-Maroof et al. (42) and stored in nuclease-free sterile water at -20°C. DNA also was extracted from corn kernels and stalks infected by

S. maydis and from kernels and leaves infected by S. macrospora in 2014. DNA extraction from corn plant tissue also followed the CTAB protocol. DNA concentration

35

was measured with a Nanodrop 1000 spectrophotometer (Thermo Fisher Scientific,

Wilmington, DE). For conventional and real-time PCR assays, DNA concentration

were adjusted to 10 ng/µl.

2.3.3 ITS PCR and sequencing

The ITS regions of Stenocarpella spp. were amplified with ITS1F

(CTTGGTCATTTACAGGAAGTAA; 14) and ITS4 (TCCTCCGCTTATTGATATGC;

54) primers. PCR amplifications were carried out in a 25-µl reaction volume

containing 1 µl of template DNA (various DNA concentrations), 0.20 mM each

deoxynucleoside triphosphate (Promega Corp., Madison WI), 4 U of Taq DNA polymerase (New England BioLabs Inc., Ipswich, MA), 1× PCR buffer (New England

BioLabs Inc.), 0.60 mM each forward and reverse primer, and 16 µl of nuclease-free

sterile water. PCR was conducted with a AB 2720 Thermal Cycler (Applied

Biosystems, Foster City, CA) with the following program: an initial heating cycle of

95°C for 2 min; 30 cycles of denaturation at 95°C for 15 s, annealing at 55°C for 30 s, and extension at 72°C for 1.5 min; and a 10-min final extension at 72°C. Each total

product volume (10 µl) was loaded on a 1% (wt/vol) agarose gel in 150 1× Tris-acetate-

EDTA buffer and stained with ethidium bromide. The gels were run at 50 V for 40 min and visualized using a UV-transilluminator FBTI-88 (Thermo Fisher Scientific Inc.,

Waltham, MA). PCR products were extracted from gels, purified, and sent to the

Purdue Genomics Facility for sequencing. DNA sequences were BLAST analyzed to a database constructed from a partial genome assembly. The ITS region was located on a

7.8-kb was aligned with the S. maydis sequence, and a single set of primers for this species was designed to use in both conventional and real-time PCR assays. Both

36

species-specific sets of primers (Table 2.2) were synthesized by Integrated DNA

Technologies (Coralville, IA).

2.3.4 Conventional PCR for the diagnosis of Stenocarpella spp.

To determine the optimal annealing temperature of each primer set, temperature

gradient PCR was carried out using a Mastercycler pro S (Eppendorf AG) with an

annealing temperature range of 52 to 63°C for S. macrospora and 54 to 65°C for S. maydis.

The optimal annealing temperatures for S. macrospora and S. maydis were 57 and 60°C,

respectively. One negative control, containing nuclease-free sterile water instead of DNA,

was included with each species assay.

For S. maydis, PCR were conducted in a 20-µl reaction volume containing

template DNA at 10 ng/µl, 0.25 mM each deoxynucleoside triphosphate (Promega Corp.),

Taq DNA polymerase (New England BioLabs Inc.) at 4 U/µl, 1× PCR buffer (New England

BioLabs Inc.), 0.75 mM each forward and reverse primer (Table 2), and 11 µl of nuclease-

free sterile water (Thermo Fisher Scientific Inc., Waltham, MA). Fragments were

amplified using the Smay.F/Smay.R primers (Table 2) under the following program: an initial heating cycle of 95°C for 2 min; 35 cycles of denaturation at 95°C for 30 s,

annealing at 60°C for 30 s, and extension at 72°C for 1 min; and a 10-min final extension at 72°C.

For S. macrospora, PCR were conducted in a 20-µl reaction volume containing template DNA at 10 ng/µl, 0.25 mM each deoxynucleoside triphosphate (Promega Corp.),

Taq DNA polymerase (New England BioLabs Inc.) at 4 U/µl, 1× PCR buffer (New

England BioLabs Inc.), 0.25 mM forward primer (Table 2), 0.75 mM reverse primer

(Table 2.2), and 12 µl of nuclease-free sterile water (Thermo Fisher Scientific Inc.,

37

Waltham, MA). Fragments were amplified using the Smac.F/Smac.R primers (Table 2.2) under the following program: an initial heating cycle of 95°C for 2 min; 30 cycles of denaturation at 95°C for 30 s, annealing at 57°C for 30 s, and extension at 72°C for 1 min; and a 10-min final extension at 72°C.

Following PCR, amplified products were separated via gel electrophoresis and visualized as described above. Each reaction was performed three times. All amplified

PCR products were resequenced for verification.

Real-time PCR for the diagnosis of Stenocarpella spp. PCR assays were performed using a LightCycler 480 Instrument (Roche Diagnostics Corporation, Hague

Road, IN). For S. maydis, real-time PCR was conducted in a 20-µl volume reaction mix which included template DNA at 10 ng/µl, 1 x iTaq Universal SYBR Green Supermix

(Bio-Rad Laboratories), 0.10 µM forward primer, 0.60 µM reverse primer, and 8.6 µl of nuclease-free sterile water (Thermo Fisher Scientific Inc., Waltham, MA). Isolates tested using the RT.Smay.F/RT.Smay.R primers were run as follows: of 95°C for 2 min; 35 cycles of 95°C for 30 s, annealing at 60°C for 30 s, and 72°C for 1 min. The thermal profile for melting curve analysis consisted of a denaturation for 5 s at 95°C, lowered to

55°C for 1 min, then increased to 97°C with continuous fluorescent readings.

In total, 15 isolates of S. macrospora were amplified in a reaction volume of 20 µl which included template DNA at 10 ng/µl, 1× iTaq Universal SYBR Green Supermix

(Bio-Rad Laboratories), 0.25 mM forward primer, 0.75 mM reverse primer, and 7 µl of nuclease-free sterile water (Thermo Fisher Scientific Inc., Waltham, MA). These 15 isolates were tested with the same primers (RT.Smac.F/RT.Smac.R) and thermal cycling

38

settings as those for the conventional PCR. The thermal profile for melting curve analysis was the same as for S. maydis.

Water control (nuclease-free sterile water) was included in each run for both assays. To evaluate specificity of each assay, the heterologous Stenocarpella spp., six fungal pathogens of corn, one bacterial pathogen of corn, and two pathogenic fungi not known to attack corn were also included in each run (Table 2.1). Each reaction was

performed three times.

Detection limit for conventional and real-time PCR. The detection limit for each

assay was determined by serial 10-fold dilution of genomic DNA from pure fungal cultures and from corn seed. Dilutions ranged from 10 to 10 × 106 ng/µl for S. maydis and 10 to 10 × 103 ng/µl for S. macrospora. The PCR conditions for both assays described previously were used. Amplification was confirmed on a 1% (wt/vol) agarose gel and by melting curve profile for conventional and real-time PCR, respectively.

2.4 Results

Fragments of the ITS region were successfully isolated from the two Stenocarpella

spp. using primers ITS1F and ITS4 (gel not shown). The primer set Smay.F/Smay.R,

specific for S. maydis, generated a single and specific PCR product of 1.7 kb (Fig. 2.1A).

This PCR product was observed in all 82 S. maydis isolates tested and in none of the S. macrospora isolates tested. No cross-amplification product was obtained when DNA

from other fungal or bacterial pathogens was used (Fig. 2.1B). When DNA extracted directly from infected corn kernels and stalks (not pure culture) was tested using the

39

conventional PCR set of primers, a specific PCR product of 1.7 kb was obtained (gel

not shown).

The S. macrospora-specific primers Smac.F/Smac.R generated a single PCR

product of 800 bp (Fig. 2.2A). This PCR product was observed only in the 15 S. macrospora isolates tested, and DNA extracted from other fungal or bacterial isolates did not generate a PCR product (Fig. 2.2B). When DNA extracted directly from infected

corn kernels and leaves (not pure culture) was tested using the conventional PCR set of

primers, a specific PCR product of 800 bp was observed on the corn kernels DNA;

however, for the DNA extracted from corn leaves, a faint band was obtained (gel not

shown).

Primers designed for the real-time assay were tested first in a conventional

PCR assay. Two PCR products of 500 and 800 bp were generated by the primer sets

RT.Smay.F/RT.Smay.R and RT.Smac.F/RT.Smac.R, respectively, for each target

species (gel not shown). No cross-amplification product was obtained when DNA

from other fungal or bacterial isolates was tested. The real-time PCR assay for S. maydis

specifically amplified DNA from all S. maydis isolates tested (Fig. 2.3A). Positive S.

maydis isolates had a range of melting peaks of 88.54 to 88.94°C (Fig. 2.3B and C)

and cycle threshold (Ct) values of 23.18 to 26.16 (Fig. 2. 3C). DNA extracted from

corn kernels and stalks infected with S. maydis were tested with RT.Smay.F/RT.Smay.R

primers, melting peaks were 88.74 and 88.94°C, respectively (Fig. 2.3B and C), and Ct values were 28.55 and 30.00, respectively (Fig. 2.3C). The S. maydis specific real-time PCR assay did not amplify DNA of any of the S. macrospora isolates. Negative control samples showed a different range of melting peaks (64.00 to 78.01°C; (Fig. 2.3B and C)

40

compared with the S. maydis isolates. Ct values from most of the negative controls were

≥30; however, DNA of the nontarget species Aspergillus flavus and E. turcicum, reported

Ct values of 26.08 and 27.21, respectively. The specific primers RT.Smac.F/RT.Smac.R produced an amplicon of the expected size for all S. macrospora isolates (Fig. 2.4A). S.

macrospora isolates had a melting peak range of 89.39 to 89.80°C (Fig. 2.4B and C),

and Ct values of 18.31 to 21.69 (Fig. 2.4C). DNA extracted from infected corn kernels and leaves with S. macrospora were tested with RT.Smac.F/RT.Smac.R primers, melting peaks were 89.18 and 88.25°C, respectively, and Ct values were 22.31 and

23.89, respectively (data not shown). The S. macrospora specific assay did not amplify

DNA from any S. maydis isolates or negative controls included in the assay (melting

peak range of 76.74 to 83.28°C and Ct values = 25.00; Fig. 2.4B and C).

Detection limits of S. maydis in conventional and real-time PCR were 0.01 ng/µl with

Smay.F/SmayR primers (Fig. 2.5A) and 0.001 ng/µl with RT.Smay.F/RT.Smay.R primers

(Fig. 2.5A). The detection limits of S. macrospora in conventional and real-time assays

were 0.1 ng/µl with Smac.F/Smac.R primers (Fig. 2.5B), and 1 ng/µl with RT.Smac.F/RT.Smac

primers (Fig. 2.5B). Detection limits of DNA extracted from infected corn seed were 10 ng/µl for S. maydis and S. macrospora in conventional PCR, and 1 ng/µl for S. maydis and

S. macrospora in real-time PCR (data not shown).

2.5 Discussion

This is the first study to successfully develop multiple PCR assays with the

specificity to distinguish S. maydis from S. macrospora. These pathogens are difficult to distinguish based on pathogen signs and disease symptoms, and previous diagnostic

41

assays have been time consuming or nonspecific. Both conventional and real-time

assays were able to distinguish between S. maydis and S. macrospora isolates collected

from several states in the US Corn Belt. Real-time assays developed and validated here

demonstrate that these techniques can be used to assess S. macrospora and S. maydis

presence in both pure culture and in infected plant tissue. In this assay, two corn pathogens, A. flavus and E. turcicum, had slightly higher Ct values (26.08 and 27.21, respectively) than the range of S. maydis isolates (23.18 to 26.16). In the S. macrospora

assay, Phoma spp. had a Ct value of 19.95, which is in the Ct range of S. macrospora isolates (18.31 to 21.69). However, in both assays, the identity of each respective organism was easily differentiated from nontarget pathogens by examining the melting

profile of the amplified products. When SYBR Green is used for detection, it is difficult to determine whether the Ct value obtained is due to nontarget amplicons or to nonspecific products. Because of this, identification of isolates using these protocols should be based only on the analysis of the melting curves. Melting curves and peak

locations clarify target species as positive samples and nontarget species as negative

samples. For instance, melting curve peaks for A. flavus and E. turcicum are 64.25 and

77.73°C, respectively, which are lower than the range of S. maydis isolates (88.54 to

89.94°C). Phoma spp. had a melting curve peak of 83.28°C, compared with the range of

89.39 to 89.80°C for S. macrospora isolates. Schroeder et al. (43) described the advantage

that real-time PCR offers by observing the melting curves of each product. Melting curves are based on the GC amount, length, and sequence that each sample contains

and, for that reason, melting curve peaks for the target Stenocarpella spp. may vary slightly

when implemented in other laboratories (39).

42

SYBR Green was selected for use in the real-time assays due to the relative

simplicity and low cost of the product (4, 5, 34). Buh Gasˇparicˇ et al. (4) compared nine different real-time chemistries and concluded that SYBR Green is suitable for use in both

qualitative and quantitative detection assays. However, SYBR Green binds with all

double stranded DNA, including nontarget amplicons as well as nonspecific products. This

can result in florescence in samples that are free of the target pathogen (39). This could

explain why florescence was observed in certain real-time PCR assays of nontarget organisms for which no amplicon was detected in conventional PCR assays. This potential

disadvantage is remedied by examining the melting profile at the end of the real-time

assay (39, 43). Other assays using real-time PCR employing the fluorescent dye SYBR

Green for the detection and diagnosis of plant pathogens have shown that the analysis of the melting profile allowed differentiation of the target with the nontarget species. Tessitori

et al. (49) was able to confirm and differentiate two different citrus viroids, Citrus exocortis

viroid and Citrus viroid-IIb, by the use of melting temperatures and their melting peaks.

Torres et al. (50) also described the advantage of using the melting curve analysis to

differentiate three different apple proliferation phytoplasmas (‘Candidatus

Phytoplasma pyri’, ‘Ca. P. pronorum’, and ‘Ca. P. mali’) by their melting peaks.

Therefore, we concluded that the use of SYBR Green and the analysis of the melting

profile using real-time PCR is a reliable tool for the diagnosis of Stenocarpella spp.

Stenocarpella maydis and S. macrospora were identified directly from corn

kernels with both conventional and real-time PCR assays. Amplified bands were reliable

enough to confirm the presence of both Stenocarpella spp. Positive confirmation in

infected corn seed using the real-time assay was based on the melting peak observed in

43

the melting curve analysis. Samples were concluded to be positive for S. maydis and S.

macrospora if the melting peak was located at 88 and 89°C, respectively. The use of both assays on DNA samples extracted from infected corn seed simplifies the identification

of this pathogen and increases the utility of these assays in seed monitoring programs. However, weak bands were observed in the conventional PCR assay

when DNA was extracted directly from corn leaves infected with S. macrospora.

Because these amplified bands were not as strong as those observed when S

macrospora DNA was amplified directly from corn kernels or from fungal cultures, we cannot reliably confirm S. macrospora directly from leaf tissue with the conventional

PCR assay. However, both conventional and real-time PCR assays were able to reliably

detect S. macrospora fungal presence at a detection limit of 0.1 and 1 ng/µl,

respectively, from fungal culture, and 10 and 1 ng/µl, respectively, from infected corn

seed. Detection of fungal presence in corn seed using DNA extracted from cultures or

directly from infected tissue would be the primary use of these assays in regulatory

agencies testing for seed quality, and incubation of infected tissue to increase the

amount of DNA extracted from the pathogen may improve sensitivity in the assay (2).

Further testing is needed to determine whether amplification of S. macrospora from infected leaves can be achieved with a conventional PCR assay.

Infected corn seed has been identified as an important source of inoculum for

both Stenocarpella spp. (7, 26, 27), and can be regulated due to the potential for

importing mycotoxin-affected grain into an area (11, 16, 28, 32, 38). Barrocas et al.

(2012) described the necessity of having a specific and accurate method for the

identification of both Stenocarpella spp. for the Brazilian seed certification program,

44

where S. maydis is listed as an RNQP and the Ministry of Agriculture has proposed

that imported seed lots of certified seed may not contain more than 2% S. maydis

(Administrative Regulation Number 47 of 19 February 2009). In accurate or unreliable diagnostic methods can negatively impact seed commercialization. The assays developed in this study can be useful tools in areas where S. maydis has been categorized as an RNQP.

45

2.6 Literature cited

1. Adipala, E., Lipps, P.E., and Madden, L.V. 1993. Reaction of maize cultivars from

Uganda to Exserohilum turcicum. Phytopathology 83:217-223.

2. Barrocas, E.N., Machado, J.C., Almeida, M.F., Botelho, L.S., and Von Pinho, É.V.R.

2012. Sensibility of the PCR technique in the detection of Stenocarpella sp.

associated with maize seeds. Rev. Bras. Sementes 34:218-224.

3. Bradley, C.A., Pedersen, D.K., Zhang, G.R., and Pataky, N.R. 2010. Occurrences of

diplodia leaf streak caused by Stenocarpella macrospora on corn (Zea mays) in

Illinois. Plant Dis. 94:1262-1263.

4. Buh Gašparič, M., Tengs, T., La Paz, J., Holst-Jensen, A., Pla, M., Esteve, T., Žel, J.,

and Gruden, K. 2010. Comparison of nine different real-time PCR chemistries for

qualitative and quantitative applications in GMO detection. Anal. Bioanal. Chem.

396:2023-2029.

5. Cao, H., and Shockey, J.M. 2012. Comparison of TaqMan and SYBR Green qPCR

methods for quantitative gene expression in tung tree tissues. J. Agric. Food Chem.

60:12296-12303.

6. Casa, R.T., Reis, E.M., and Zambolim, L. 2006. Doenças do Milho Causadas por

Fungos do Gênero Stenocarpella. Fitopatol. Bras. 31:427-439.

7. Casa, R.T., Reis, E.M., and Zambolim, L. 1988. Fungos associados a semente de

milho produzida nas Regioes Sul e sudesde do Brasil. Fitopatol. Bras. 23:370-373.

8. Casa, R.T., and Zambolim, L. 1997. Diplodia maydis e Diplodia macrospora

associados à semente de milho. Dissertação de Mestrado. 71 pp. Universidade

Federal de Viçosa. Viçosa, Brazil.

46

9. Crous, P.W., Slippers, B., Wingfield, M.J., Rheeder, J., Marasas, W.F.O., Philips,

A.J.L., Alves, A., Burgess, T., Barber, P., and Groenewald, J.Z. 2006. Phylogenetic

lineages in the Botryosphaeriaceae. Stud. Mycol. 55:235-253.

10. Cutler, H.G., Crumley, F.G., Cox, R.H., Cole, R.J., Dorner, J.W., Latterell, F.M., and

Rossi, A.E. 1980. Diplodiol: A new toxin from Diplodia macrospora. J. Agric. Food

Chem. 28:135-138.

11. Darvall, P.M. 1964. Mouldy corn cobs, a danger to cows. Queensl. J. Agric. J.

90:692-693.

12. Earle, F.S. 1897. New species of fungi imperfecti from Alabama. Bull. Torrey bot.

Club. 24:28-32.

13. Eddins, A.H. 1930. Dry rot of corn caused by Diplodia macrospora Earle.

Phytopathology 20:439-447.

14. Gardes, M., and Bruns, T.D. 1993. ITS primers with enhanced specificity for

basidiomycetes - application to the identification of mycorrhizae and rusts. Mol.

Ecol. 2:113-118.

15. Johann, H. 1935. Diplodia macrospora on corn in Brazil. Plant Dis. Rep. 19:9-10.

16. Kellerman, T.S., Prozesky, L., Schultz, R.A., Rabie, C.J., Van Ark, H., Maartens,

B.P., and Lübben, A. 1991. Perinatal mortality in lambs of ewes exposed to cultures

of Diplodia maydis (= Stenocarpella maydis) during gestation. Onderstepoort J. Vet.

Res. 58:297-308.

47

17. Kellerman, T.S., Rabie, C.J., Van der Westhuizen, G.C., Kriek, N.P., and Prozesky,

L. 1985. Induction of diplodiosis, a neuromycotoxicosis, in domestic ruminants with

cultures of indigenous and exotic isolates of Diplodia maydis. Onderstepoort J. Vet.

Res. 52:35-42.

18. Kriek, N.P.J., and Marasas, W.F.O. 1979. Toxicity of Diplodia macrospora to

laboratory animals. Food Cosmet. Toxicol. 17:233-236.

19. Latterell, F.M., and Rossi, A.E. 1983. Stenocarpella macrospora (=Diplodia

macrospora) and S. maydis (=D.maydis) compared as pathogens of corn. Plant Dis.

67:725-729.

20. Latterell, F.M., Rossi, A.E., and Moreno, R. 1976. Diplodia macrospora: A

potentially serious pathogen of corn in the U.S.? (Abstract). Proc. Am. Phytopathol.

Soc. 3:28.

21. Lievens, B., and Thomma, B.P.H.J. 2005. Recent developments in pathogen

detection arrays: Implications for fungal plant pathogens and use in practice.

Phytopathology 95:1374-1380.

22. Lipps, P.E., and Mills, D.R. 2002. Diplodia ear rot of corn. Online. The Ohio State

University. Extension Fact Sheet: AC-0046-01. Columbus, OH.

http://ohioline.osu.edu/ac-fact/0046.html. (Accessed: 11 August 2014).

23. Marasas, W. 1977. The genus Diplodia. Pages 119-128 In: Mycotoxic Fungi,

Mycotoxins, Mycotoxicoses. An Enciclopedic Handbook., Vol. 1. T. D. Wyllie and

L. G. Morehouse, eds. Marcel Decker, Inc.NY.

48

24. Marasas, W.F.O., and Van der Westhuizen, G.C.A. 1979. Diplodia macrospora: the

cause of a leaf blight and cob rot of maize (Zea mays) in South Africa.

Phylophylactica 11:61-64.

25. Mario, J.L., and Reis, E.M. 2001. Método simples para diferenciar Diplodia

macrospora de D. maydis em testes de patologia de sementes de milho. Fitopatol.

Bras. 26:670-672.

26. McGee, D.C. 1988. Maize diseases: a reference source for seed technologists. 150

pp. APS Press. St. Paul, MN.

27. McNew, G.L. 1937. Crown infection of corn by Diplodia zeae. Iowa Agric. Exp. Stn.

Res. Bull. 216. Ames, Iowa.

28. Mitchell, D.T. 1919. Poisoning of cattle by Diplodia-infected maize. S. Afr. J. Sc.

16:446-452.

29. Morant, M.A. 1988. Stenocarpella (Diplodia) maydis: Influence of nitrogen and

inoculation on biochemical components of maize stalk. PhD dissertation. Botany and

Plant Pathology Department.. Purdue University. West Lafayette, IN.

30. Morant, M.A., Warren, H.L., and Vonqualen, S.K. 1993. A synthetic medium for

mass-production of pycnidiospores of Stenocarpella maydis. Plant Dis. 77:424-426.

31. Mueller, D., and Wise, K.A. 2012. Corn disease loss estimates from the United States

and Ontario, Canada. Online. Purdue University. Purdue Extension Publication: BP-

96-12-W. West Lafayeete, IN. (https://www.extension.purdue.edu/extmedia/BP/BP-

96-12-W.pdf). (Accessed: 11 August 2014).

32. Odriozola, E., Odeon, A., Canton, G., Clemente, G., and Escande, A. 2005. Diplodia

maydis: a cause of death of cattle in Argentina. New Zeal. Vet. J. 53:160-161.

49

33. Pascual, C.B., and Relevante, C.A. 2003. Cultural and molecular characterization of

Stenocarpella macrospora (Earle) sutton causing leaf blight, ear rot and stalk rot in

maize. J. Trop. Plant Pathol. 39:36-42.

34. Ponchel, F., Toomes, C., Bransfield, K., Leong, F.T., Douglas, S.H., Field, S.L., Bell,

S.M., Combaret, V., Puisieux, A., and Mighell, A.J. 2003. Real-time PCR based on

SYBR-Green I fluorescence: an alternative to the TaqMan assay for a relative

quantification of gene rearrangements, gene amplifications and micro gene deletions.

BMC Biotechnol. 3:18.

35. Rao, S.K., and Achar, P.N. 2001. Screening and in vitro production of diplodiatoxin

from the isolates of Stenocarpella maydis and its toxigenic effect on bacterial strains.

Indian J. Exp. Biol. 39:1243-1248.

36. Reis, E.M., Casa, R.T., and Bresolin, A.C.R. 2004. Manual de diagnose e controle de

doenças do milho. 144 pp. 2nd ed. Graphel Gráfica e Editora Lages. Santa Catarina,

Brazil.

37. Riet-Correa, F., Mendez, M.C., and Schild, A.L. 1984. Laboratório

Regional de Diagnóstico. Doenças. Diagnosticadas no ano 1983. Page 35. Editora da

Universidade, Pelotas, Brazil.

38. Riet-Correa, F., Schild, A.L., and Fernandes, C.G. 1998. Enfermidades do sistema

nervoso dos ruminantes no sul do Rio Grande do Sul. Ciência Rural 28:341-348.

39. Ririe, K.M., Rasmussen, R.P., and Wittwer, C.T. 1997. Product differentiation by

analysis of DNA melting curves during the polymerase chain reaction. Anal.

Biochem. 245:154-160.

50

40. Romero Luna, M.P. 2012. Managing Diplodia ear rot in corn: Short and long-term

solutions. M.S. Thesis. 95 pp. Botany and Plant Pathology Department. Purdue

University, West Lafayette, IN.

41. Rossouw, J.D., Pretorius, Z.A., Silva, H.D., and Lamkey, K.R. 2009. Breeding for

resistance to Stenocarpella ear rot in maize. Plant Breed. Rev. 31:223-246. John

Wiley & Sons, Inc.

42. Saghai-Maroof, M.A., Soliman, K.M., Jorgensen, R.A., and Allard, R.A. 1984.

Ribosomal DNA spacer length in : Mendelian inheritance, chromosomal

location, and population dynamics. Proc. Natl. Acad. Sci. 81:8014-8018.

43. Schroeder, K.L., Okubara, P.A., Tambong, J.T., Lévesque, C.A., and Paulitz, T.C.

2006. Identification and quantification of pathogenic Pythium spp. from soils in

Eastern Washington using real-time polymerase chain reaction. Phytopathology

96:637-647.

44. Siqueira, C.S., Barrocas, E.N., Machado, J.C., Silva, U.A., and Dias, I.E. 2014.

Effects of Stenocarpella maydis in seeds and in the initial development of corn. J.

Seed Sci. 36:79-86.

45. Snyman, L.D., Kellerman, T.S., Vleggaar, R., Flett, B.C., Basson, K.M., and Schultz,

R.A. 2011. Diplonine, a neurotoxin isolated from cultures of the fungus

Stenocarpella maydis (Berk.) Sacc. that induces diplodiosis. J. Agric. Food Chem.

59:9039-9044.

46. Steyn, P.S., Wessels, P.L., Holzapfel, C.W., Potgieter, D.J.J., and Louw, W.K.A.

1972. The isolation and structure of a toxic metabolite from Diplodia maydis (Berk.)

SACC. Tetrahedron 28:4775-4785.

51

47. Sutton, B.C., and Waterston, J.M. 1966a. Diplodia macrospora. CMI Descriptions of

pathogenic fungi and bacteria. Set 9. No. 83. CAB International, Willingford, UK.

48. Sutton, B.C., and Waterston, J.M. 1966b. Diplodia maydis. CMI Descriptions of

pathogenic fungi and bacteria. Set 9. No. 84. CAB International, Willingford, UK..

49. Tessitori, M., Rizza, S., Reina, A., and Catara, V. 2005. Real-time RT-PCR based on

SYBR-Green I for the detection of citrus exocortis and citrus cachexia diseases. In:

HilfME, Duran-VilaN, Rocha-PeñaMA, eds. Proceedings of the 16th Conference of

the International Organization of Citrus Virologists, 2004. USA: IOCV, 456–9.

Riverside, CA

50. Torres, E., Bertolini, E., Cambra, M., Montón, C., and Martín, M.P. 2005. Real-time

PCR for simultaneous and quantitative detection of quarantine phytoplasmas from

apple proliferation (16SrX) group. Mol. Cell Probes 19:334-340.

51. Vincelli, P. 2011. Diplodia leaf streak of corn. Pages 2-3. Online. University of

Kentucky Cooperative Extension service. Kentycky Pest News. No. 1277.,

Lexington, KY.

http://www2.ca.uky.edu/agcollege/plantpathology/extension/KPN%20Site%20Files/p

df/kpn1277.pdf. (Accessed: 14 August 2014).

52. Vincelli, P. 1997. Ear rot of corn caused by Stenocarpella maydis (=Diplodia

maydis). Online. University of Kentucky. Cooperative Extension Service. PPA-

43.Lexington, KY. http://www2.ca.uky.edu/agc/pubs/ppa/ppa43/ppa43.htm.

(Accessed: 11 August 2014).

53. White, D.G. 1999. Compendium of Corn Diseases. Page 44. 3rd ed. APS Press, St.

Paul, MN.

52

54. White, T., Bruns, T., Lee, S., and Taylor, J. 1990. Amplification and direct

sequencing of fungal ribosomal RNA genes for phylogenetics. Pages 315-322. In:

PCR Protocols: A Guide toMethods and Applications. M. Innis, D. Gelfand, J.

Shinsky and T. White, eds. Academic Press. NY.

55. Wicklow, D.T., Rogers, K.D., Dowd, P.F., and Gloer, J.B. 2011. Bioactive

metabolites from Stenocarpella maydis, a stalk and ear rot pathogen of maize. Fungal

Biol. 115:133-142.

56. Wise, K.A. 2011. Northern corn leaf blight. Diseases of corn. Online. Purdue

University. Purdue Extension: BP-84-W. Purdue University. West Lafayettte, IN.

https://www.extension.purdue.edu/extmedia/BP/BP-84-W.pdf. (Accessed: 11 August

2014.

57. Xia, Z., and Achar, P.N. 2001. Random amplified polymorphic DNA and

polymerase chain reaction markers for the differentiation and detection of

Stenocarpella maydis in maize seeds. J. Phytopath. 149:35-44.

53

Table 2.1. Isolate code, year, and collection site of Stenocarpella spp. and other pathogenic species used in this study.

Isolate Pathogen Year County, State code 1-1-1 S. maydis 2010 Tippecanoe, IN 1-1-3 S. maydis 2010 Tippecanoe, IN 1-2-4 S. maydis 2010 Tippecanoe, IN 1-3-3 S. maydis 2010 Tippecanoe, IN 1-6-2 S. maydis 2010 Tippecanoe, IN 1-11-2 S. maydis 2010 Tippecanoe, IN 1-11-4 S. maydis 2010 Tippecanoe, IN 1-13-2 S. maydis 2010 Tippecanoe, IN 1-14-4 S. maydis 2010 Tippecanoe, IN 1-18-1 S. maydis 2010 Tippecanoe, IN 1-19-1 S. maydis 2010 Tippecanoe, IN 1-20-4 S. maydis 2010 Tippecanoe, IN 2-2-1 S. maydis 2010 Tippecanoe, IN 2-3-1 S. maydis 2010 Tippecanoe, IN 2-4-1 S. maydis 2010 Tippecanoe, IN 2-6-2 S. maydis 2010 Tippecanoe, IN 2-7-4 S. maydis 2010 Tippecanoe, IN 2-8-1 S. maydis 2010 Tippecanoe, IN 2-13-1 S. maydis 2010 Tippecanoe, IN 2-14-1 S. maydis 2010 Tippecanoe, IN 2-16-1 S. maydis 2010 Tippecanoe, IN 2-20-1 S. maydis 2010 Tippecanoe, IN

54

Table 2.1. continued 3-1-1 S. maydis 2010 Tippecanoe, IN 3-2-2 S. maydis 2010 Tippecanoe, IN 3-3-1 S. maydis 2010 Tippecanoe, IN 3-3-2 S. maydis 2010 Tippecanoe, IN 3-4-4 S. maydis 2010 Tippecanoe, IN 3-5-2 S. maydis 2010 Tippecanoe, IN 3-6-4 S. maydis 2010 Tippecanoe, IN 3-8-2 S. maydis 2010 Tippecanoe, IN 3-9-1 S. maydis 2010 Tippecanoe, IN 3-9-3 S. maydis 2010 Tippecanoe, IN 3-9-4 S. maydis 2010 Tippecanoe, IN 3-10-1d S. maydis 2010 Tippecanoe, IN 3-10-2 S. maydis 2010 Tippecanoe, IN 3-11-1 S. maydis 2010 Tippecanoe, IN 3-11-2 S. maydis 2010 Tippecanoe, IN 3-12-3 S. maydis 2010 Tippecanoe, IN 3-12-4 S. maydis 2010 Tippecanoe, IN 1b.1 S. maydis 2011 Tippecanoe, IN 1b.2 S. maydis 2011 Tippecanoe, IN 2b.2 S. maydis 2011 Tippecanoe, IN 3a.1 S. maydis 2011 Tippecanoe, IN 5b.1 S. maydis 2011 Tippecanoe, IN 7a.1 S. maydis 2011 Tippecanoe, IN 8b.1 S. maydis 2011 Tippecanoe, IN 8b.2 S. maydis 2011 Tippecanoe, IN

55

Table 2.1. continued 9b.1 S. maydis 2011 Tippecanoe, IN 10a.1 S. maydis 2011 Tippecanoe, IN 10a.2 S. maydis 2011 Tippecanoe, IN 4a.1.1 S. maydis 2011 Indiana 4a.1.2 S. maydis 2011 Indiana 3b.1 S. maydis 2011 Indiana 1.2 S. maydis 2012 Tippecanoe, IN 1.4 S. maydis 2012 Tippecanoe, IN 2 S. maydis 2012 Tippecanoe, IN 2.1 S. maydis 2012 Tippecanoe, IN 3.1 S. maydis 2012 Tippecanoe, IN 3.2 S. maydis 2012 Tippecanoe, IN 3.3 S. maydis 2012 Tippecanoe, IN 3.4 S. maydis 2012 Tippecanoe, IN 4.1 S. maydis 2012 Tippecanoe, IN 5.1 S. maydis 2012 Tippecanoe, IN 5.2 S. maydis 2012 Tippecanoe, IN 6 S. maydis 2012 Tippecanoe, IN 7.1 S. maydis 2012 Tippecanoe, IN 8 S. maydis 2012 Tippecanoe, IN 9 S. maydis 2012 Tippecanoe, IN 5 S. maydis 2013 Tippecanoe, IN 8 S. maydis 2013 Tippecanoe, IN 10 S. maydis 2013 Knox, IN 13 S. maydis 2013 Knox, IN

56

Table 2.1. continued 15.1 S. maydis 2013 Howard, MO 15.2 S. maydis 2013 Howard, MO 44 S. maydis 2013 Cayugo, NY 45 S. maydis 2013 Tippecanoe, IN 1 S. maydis 2014 Gibson, TN 2 S. maydis 2014 Gibson, TN 3 S. maydis 2014 Gibson, TN 5 S. maydis 2014 Gibson, TN 6 S. maydis 2014 Gibson, TN 7 S. maydis 2014 Gibson, TN 1a.1.1 S. macrospora 2011 Indiana 1a.1.2 S. macrospora 2011 Indiana P11-2.1a.1 S. macrospora 2011 Kosciusko, IN P11-2.1a.2 S. macrospora 2011 Kosciusko, IN 4a.2a S. macrospora 2011 Indiana 4c.1 S. macrospora 2011 Indiana 10a.2 S. macrospora 2011 Indiana 10b.1 S. macrospora 2011 Indiana 1a S. macrospora 2012 Tippecanoe, IN 1b S. macrospora 2012 Tippecanoe, IN 4b S. macrospora 2012 Tippecanoe, IN 1 S. macrospora 2013 Indiana 2.1 S. macrospora 2013 Jennings, IN 2.2 S. macrospora 2013 Jennings, IN 4 S. macrospora 2014 Gibson, TN

57

Table 2.1. continued ET Exserohilum turcicum 2012 Tippecanoe, IN Fv Fusarium verticillioides 2012 Indiana Fg Fusarium graminearum 2012 Indiana Af Aspergillus flavus Urbana, IL Clavibacter michiganensis Cmn 2008 Pulaski, IN subsp. Nebraskensis Mp Macrophomina phaseolina 2011 Grant, WI Cg Colletotrichum graminicola 2012 Illinois Ps Phoma spp. 2012 Hamilton, IN Dp Diplodia pinea 2012 Tippecanoe, IN

58

Table 2.2. Primer sequence used to identify isolates of Stenocarpella spp. Annealing Product melt Product size Primer Sequence 5’ – 3’ temperature curve peak (bp) (°C) (°C)

Smay.F CCTGCTATGCATAGGTCG 52.6 … …

Smay.R CAC CAG GCC GTT AAG CCT TA 57.4 1700 …

RT.Smay.F GTT TCC ATG ACC TGC TCA CG 56.3 … …

RT.Smay.R TGT TGC TCG GTT TCA GGC TTG 58.4 500 88

Smac.F GGG CAA ATT TTC TCG GAG G 54 … …

Smac.R GCA GCT ATT CAG CGT TCA TC 54.1 800 …

RT.Smac.F GGG CAA ATT TTC TCG GAG G 54 … …

RT.Smac.R GCA GCT ATT CAG CGT TCA TC 54.1 800 89

58

58

59

Figure 2.1. Agarose gel electrophoresis of polymerase chain reaction products. A.

Products of S. maydis obtained with primers Smay.F/Smay.R. Lanes 1 to 5: (isolates 3-

10-1, 3a.1, 4a.1.2, 7.1, 10a.2), Lane 6 to 8: S. macrospora isolates (isolates 1a.1.1, 1a and

4b). Lane 9: Control (nuclease-free sterile water). Lane M: molecular size marker (Life

Technologies, Grand Island, NY). B. Products of other fungal and bacteria species

obtained with primers Smay.F/SmayR. Lanes 1 to 9: (Phoma spp., Diplodia pinea,

Fusarium graminearum, Aspergillus flavus, Clavibacter michiganensis subsp.

nebraskensis, Exserohilum turcicum, Colletotrichum graminicola, Macrophomina

phaseolina and Fusarium verticillioides. Lane 10: Control (nuclease-free sterile water).

Lane M: molecular size marker (Life Technologies, Grand Island, NY).

60

Figure 2.2. Agarose gel electrophoresis of polymerase chain reaction products. A.

Products of S. macrospora obtained with primers Smac.F/Smac.R. Lanes 1 to 5: (isolates

1b, 4a.2, 4b, 1a.1.1, 1a (2013) Lane 6 to 8: S. maydis (isolates 2-2-1, 7.1, and 3a.1), Lane

9: Control (nuclease-free sterile water). Lane M: molecular size marker (Life

Technologies, Grand Island, NY). B. Products of other fungal and bacteria species

obtained with primers Smac.F/Smac.R. Lanes 1 to 9: (Phoma spp., Diplodia pinea,

Fusarium graminearum, Aspergillus flavus, Clavibacter michiganensis subsp.

nebraskensis, Exserohilum turcicum, Colletotrichum graminicola, Macrophomina

phaseolina and Fusarium verticillioides. Lane 10: Control (nuclease-free sterile water).

Lane M: molecular size marker (Life Technologies, Grand Island, NY).

61

Figure 2.3. Melting curve analysis for Stenocarpella maydis using real-time PCR assay.

A. Amplification curves of S. maydis isolates and controls obtained with primers

RT.Smay.F/RT.Smay.R. B. Melting curve analysis of the same samples shows the presence of the specific PCR product. C. Comparison chart with Ct values and melting temperatures. a DNA extracted for other fungi isolated that are not the target of this assay and water are identified as negative controls. b Ct values and ± standard deviation. c Cmn: Clavibacter michiganensis subsp. Nebraskensis.

62

Figure 2.4. Melting curve analysis for Stenocarpella macrospora using real-time PCR assay. A. Amplification curves of S. macrospora isolates and controls obtained with primers RT.Smac.F/RT.Smac.R. B. Melting curve analysis of the same samples shows the presence of the specific PCR product. C. Comparison chart with Ct values and melting temperatures. a DNA extracted for other fungi isolated that are not the target of this assay and water are identified as negative controls. b Ct values and ± standard deviation. c Cmn: Clavibacter michiganensis subsp. nebraskensis

63

Figure 2.5. Dilution series for A. Stenocarpella maydis real-time (top image) and conventional (bottom/gel image) PCR assays. Ten-fold dilutions were performed in each assay with an initial concentration of 10 ng/µl to a final concentration of 0.00001 ng/µl. Gel image includes Lane M: molecular size marker (Life Technologies, Grand

Island, NY) and nuclease-free sterile water as control. B. Stenocarpella macrospora real-time (top image) and conventional (bottom/gel image) PCR assays. Ten-fold dilutions were performed in each assay with and initial concentration of 10 ng/µl to a final concentration of 0.01 ng/µl. Gel image includes Lane M: molecular size marker

(Life Technologies, Grand Island, NY) and nuclease-free sterile water as control.

64

CHAPTER 3. EFFECT OF CROP ROTATION AND TILLAGE SYSTEMS ON DIPLODIA EAR ROT SEVERITY

3.1 Introduction

Corn (Zea mays L.) is one of the most important crops produced in the United

States (53). Over half of its production is for exportation, making the US the largest corn

exporter. The remaining production is for domestic consumption, which has a wide range

of uses, including food, ethanol production, and livestock feed (7). The total estimated

area planted in the US in 2014-2015 was 33 million ha with a production estimated at 246

million metric tons (52).

Corn production has changed in recent years, and more hectarage is devoted to a

corn monoculture system (13). Conservation tillage has also increased among corn

farmers in order to maintain soil moisture and reduce soil erosion (4). However, conservation tillage leaves at least 30% of crop residue on the topsoil (10 to 15 cm), which facilitates the overwintering and survival of many corn pathogens (4, 48). Among these pathogens, the fungus Stenocarpella maydis (Berk.) B. Sutton, causal organism of

Diplodia ear rot, has become a persistent and important pathogen in the Midwest (15, 44,

55). In 2012 and 2013, a combined yield loss of approximately 1.36 million metric tons was estimated for 22 states and provinces in the US and Canada (35, 36). In 2014, yield loss increased to 1.7 million metric tons (37).

65

Research conducted in South Africa and Brazil determined that a continuous corn cropping systems and conservation tillage practices increase S. maydis incidence and survival (8, 18, 34). These studies determined that annual moldboard plowing was the most effective tillage method for Diplodia ear rot control in South Africa and a single rotation away from corn with wheat (Triticum spp.), soybean (Glycine max L. Merril), or peanut (Arachis hypogaea L.) were most effective for reducing Diplodia ear rot incidence

(17, 19, 20). Therefore, based on these South African studies, crop rotation and tillage have been suggested as the most reliable measures for Diplodia ear rot control. These practices are also recommended for Diplodia ear rot management in the US, based on the assumption that reducing the initial inoculum source of corn residue will result in less disease. However, there is no research examining the impact of these crop production practices on Diplodia ear rot in the US.

Options for managing Diplodia ear rot in the US are limited. Foliar fungicide applications have not consistently reduced disease severity (26, 31. 42), and although planting resistant hybrids is the ideal way to control Diplodia ear rot, currently no hybrids are completely resistant to the disease (23, 25, 44). Therefore, farmers rely on crop rotation as a primary management method, but in contrast to South African research, farmers in the Midwestern US have observed Diplodia ear rot even after one year of rotation with soybean (Dr. Kiersten Wise, personal communication). Tillage systems in the US also differ from areas where previous research has been conducted. Since 1950, the use of moldboard plow, which inverts the top soil to a depth of 17 to 25 cm, has decreased in the US (30) due to cost of fuel, labor limitations, soil erosion, land degradation, and government regulations (5). For the last decade, no-tillage, strip-tillage

66

and chisel plow have been adopted as the preferred tillage systems among corn farmers in

the Midwest (9). Although most of the corn production practices in the US and South

Africa are similar (47), winter temperatures significantly differ, which could influence

survival of S. maydis and ultimately affect the amount of initial inoculum available to

infect corn. The winter average temperatures in South Africa ranges from 6 to 20°C compared to -10 to -1°C in the Midwest (3, 28).

Because Diplodia ear rot can reduce yield and grain quality, and the current

management options are limited and not completely effective, it is important to evaluate

the efficacy of cultural practices for Diplodia ear rot control and to understand how

environment, crop rotation and tillage influence the occurrence of Diplodia ear rot in

Indiana.

3.2 Materials and Methods

The effect of crop rotation and tillage on Diplodia ear rot was evaluated in research trials conducted in 2014 and 2015 at Agronomic Center for Research and

Education (ACRE) in West Lafayette, Indiana. Year, soil classification, planting, harvest and rating dates are listed in Table 3.1. In both years, experiments were arranged as a split-plot with four replications. The whole plot was a rotation × tillage factorial assigned

one of 4 treatments: corn following soybean with tillage, corn following soybean with no- tillage, continuous corn with tillage, and continuous corn with no-tillage. Tillage consisted of one pass with a DMI Colter – Champ II chisel plow to a depth of approximately 25 cm following harvest in the fall, and a pass with a John Deere field cultivator to a depth of approximately 12 cm followed by a pass with a rolling harrow in

67

spring. This tillage treatment left approximately 35% of corn residue on the soil surface.

No–tillage fields received no pre-planting tillage operations, and the crop residue from the previous season was left undisturbed covering approximately 80% of the soil surface.

The sub-plot was inoculation treatment, and consisted of S. maydis inoculated and non-

inoculated treatments each crop rotation × tillage treatment. Each experimental plot

consisted of 4 rows trimmed to a length of 9.14 m, with 0.5 m wide alleys between both

plot ends. Inoculation occurred only in the inner 2 rows of each plot, and the outer rows

served as borders for experimental plots. In both years, Pioneer P1352AMXT (114-day maturity), was planted with a John Deere 1700 6-row planter at a seeding density of

84,016 viable seed/ha, with the exception in 2014, where the tillage whole plot was planted with a Haldrup SP-35 planter (Haldrup USA Corp, Poneto, IN). The hybrid ratings for ear rot diseases were 4 for Fusarium ear rot, and 5 for Gibberella ear rot and 5 for Diplodia ear rot on a rating scale of 1 to 9, where 9 represents the highest level of resistance (40). In 2014 and 2015, soybean varieties for fields under no-tillage were

Asgrow AG2431 and Pioneer P93Y84, respectively. Soybean seeds were planted with a

John Deere drill with a 72,842 viable seed/ha. For fields under tillage, soybean varieties in 2014 and 2015 were Valent and Asgrow AG2931. Soybean seeds were planted with a

John Deere 1700 6-row planter at a seeding density of 56,655 viable seed/ha. For both years, all trials were conducted with standard agricultural practices for the area.

Anhydrous ammonia was applied on corn fields before planting at a rate of 201.6 kg N/ha in all years. Weed management consisted of a pre-emergence application of S- metolachlor (Syngenta, Crop Protection, Greensboro, NC) at 5.9 l/ha, and glyphosate at

1.8 kg/ha as a post-emergence application.

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In both years, sub-plots assigned inoculation treatments were inoculated with

sorghum seed colonized with a mixture of 10 S. maydis isolates previously collected and

stored as described in Romero and Wise (2015b). Inoculum production was based on a previous protocol established in Romero and Wise (2015a). Field inoculations consisted of placing 5 g of sterilized sorghum seed previously colonized with S. maydis in the whorl of each plant per row at the V7 growth stage (7 visible leaf collar; 41). Five ml of water was injected into each whorl with a BioLogic backpack sprayer equipped with adjustable brass cone nozzle (Mossy Oak, MS) before inoculum was placed into each plant. Plots that did not receive inoculum were treated as the non-inoculated treatments.

Ears in the inner two rows of each plot were visually scored for Diplodia ear rot severity at maturity (approximately 100 to 120 ears per plot). Percent disease severity was based on the estimation of ear exhibiting symptoms or signs of Diplodia ear rot (41).

The same two inner rows of each experimental plot were harvested with a Kincaid 8-XP

(Kincaid Equipment Manufacturing, Haven, KS) small plot research combine. The total seed weight and seed moisture were determined, and converted to yield in kilograms per hectare. Yield data were calculated with corrections for moisture content (standardized to

15% moisture). Harvesting and rating dates are listed in Table 3.1.

3.2.1 Data analysis

All data were tested for normality by using PROC UNIVARIATE in SAS 9.4

(SAS Institute, Cary, NC), and transformed (arcsine-square-root transformed) to meet normality assumptions. Back-transformed means were presented after analysis. Fixed effects were year, crop rotation × tillage, and inoculation treatment. Replication was the only random effect. Analysis of variance with the GLIMMIX model procedure (PROC

69

GLIMMIX; 33) were conducted for each year on the effect of crop rotation × tillage on

disease severity and yield under inoculated and non-inoculated conditions in SAS 9.4.

Mean separations were based on least square (LS) means test at the P ≤ 0.05 level.

3.3 Results

3.3.1 Weather conditions

Weather conditions varied between the two years (Table 3.2). June 2014 was characterized by warm and wet conditions compared with cooler conditions in July.

August started as a cold month, but ended warm and wet, and these conditions continued until the end of September (45).

In 2015, June and July were unusually wet and cold. Although August was cooler

than average, it was also a dry month. Temperatures increased in September, and dry

conditions continued until harvest (46).

3.3.2 Experimental results

The effect of year on disease severity and yield for 2014 and 2015 was highly

significant (P<0.0001, P=0.0002, respectively), therefore experiments were analyzed separately by year. No significant interaction of crop rotation × tillage by inoculation treatment was observed for disease severity or yield in 2014 and 2015 (Table 3.3).

3.3.2.1 Experimental Year 2014 Results

Inoculation treatment significantly affected disease severity (Table 3.3). Diplodia

ear rot severity averaged 28% in inoculated treatments compared with 3% in non-

inoculated treatments (Fig. 3.1A). However, neither crop rotation nor tillage significantly

70

affected disease severity (Table 3.3). Inoculation treatment and crop rotation × tillage did

have a significant effect on yield (Table 3.3). Higher yields were observed in non-

inoculated treatments compared with inoculated treatments (Fig. 3.1B). Soybean

following corn with either tillage system had significantly higher yields than continuous

corn with or without tillage (Fig. 3.1C).

3.3.2.2 Experimental Year 2015 Results

Although disease severity was lower in 2015 compared with 2014, inoculation

significantly increased disease compared to the non-inoculated treatment. Disease

severity in inoculated treatments was 8% compared with 1% in non-inoculated treatments

(Table 3.3; Fig. 3.2A). Crop rotation and tillage did not reduce disease severity (Table

3.3). Inoculation did not have a significant effect on yield (Table 3.3). However, higher

yields were observed when soybean followed corn and corn was tilled, compared with

continuous corn with or without tillage (Table 3.3; Fig. 3.2B).

3.4 Discussion

Crop rotation and tillage are commonly recommended to manage corn diseases

since the removal of residue reduces the amount of primary inoculum for the next host

crop (12, 39). Our research indicates that more than one year away from corn may be

needed to reduce risk of Diplodia ear rot since one year of crop rotation and tillage did

not reduce disease severity in either year of the study. This research allows us to

understand why farmers may observe the disease even after a one-year rotation to soybean.

71

In this study, inoculated and non-inoculated treatments were included to test the

effects of Diplodia ear rot under low and high disease pressure. Because the inoculation

occurred after tillage and rotation treatments were established, it is not possible to

observe the effect of tillage and rotation on disease severity in the inoculated treatments.

In 2014 and 2015, Diplodia ear rot severity was significantly higher in inoculated

treatments compared with non-inoculated treatments. As expected, crop rotation and tillage did not reduce disease severity in inoculated treatments. However, disease severity was not reduced in non-inoculated treatments either, indicating that under natural

conditions crop rotation and the tillage methods tested in this study did not reduce

primary inoculum to a level that impacted disease. Differences in disease levels between

years were observed, and Diplodia ear rot severity in 2014 was 20% higher than in 2015.

According to Van Rensburg and Ferreira (1997) higher Diplodia ear rot severity levels

have been observed when a combination of high temperatures during the reproductive

corn stages and late-season rains occurred. July and August were warmer in 2015 than in

2014, however, in 2015, when corn plants were at the reproductive stage, it was much

drier than 2014. Warm temperatures and wet conditions after silking and during the

reproductive stages have also been reported to be favorable for Gibberella and Fusarium

ear rot (27, 50). Therefore, we hypothesize that the dry conditions during the months of

August and September of 2015 may have resulted in lower Diplodia ear rot severity

levels.

Flett and Wehner (1991), and Flett el al. (1998) reported a positive linear

relationship between amount of corn residue left on the field and S. maydis incidence.

They concluded that moldboard plowing could reduce Diplodia ear rot since the majority

72 of residue is buried with this practice. However, moldboard plowing is not used frequently in the US, and conservation tillage programs are more popular. Conservation tillage benefits corn production by reducing energy and costs for seedbed preparation, increasing soil organic matter levels, and decreasing soil erosion (29, 39, 56). The tillage program used in this study leaves approximately 35% corn residue on the surface (24), which appears to be enough residue to support the overwinter and survival of S. maydis and result in detectable disease the following year, as evidenced in our non-inoculated treatments. The effect of conservation tillage on corn pathogens has been variable. Root infection caused by Pythium spp. and Rhizoctonia solani was higher under conservation tillage on corn and other legumes. However, no differences in root infection was observed on soybean, sorghum and other vegetables like squash and cucumber under either conservation or conventional tillage systems (49). The impact of tillage on pathogen survival will also be influenced by the length of time that the fungus can survive on residue. Studies in the US indicate that Cercospora zeae-maydis (14) and

Colletotrichum graminicola (32) remained viable in corn residue for almost a year, and

Fusarium moniliforme, F. proliferatum, and F. subglutinans (10) for almost two years.

Based on studies in South Africa and Brazil, S. maydis remains viable for almost 11 months (8, 18). Therefore factors like previous crop, environmental conditions, and the pathogen itself could influence the effect of tillage practices on disease severity (1, 49).

Although farmers that till corn fields with the methods tested in this study may not see Diplodia ear rot reduced through tillage alone, this practice still offers other benefits like weed control, reduction of soil compaction, uniformity in seedbeds that facilitate crop establishment (51), and the reduction of potential primary inoculum for

73

Diplodia ear rot development. Diplodia ear rot was not eliminated in our study, but could

be reduced in years with environmental conditions less favorable for disease like 2015.

While environmental conditions could determine the disease severity levels, our research

indicates that tillage practices aimed to reduce Diplodia ear rot must reduce corn residue

levels below 35%, since this level was enough to cause detectable disease in both years of

our study.

In our study, higher yields were observed when soybean followed corn regardless

of tillage treatment. Our results indicated that yield improvement due to rotation cannot

be attributed with Diplodia ear rot control since rotation did not impact disease severity.

Therefore, yield increases are likely due to the nitrogen benefit that comes from legumes

(2). The corn-soybean rotation system has been widely used in the Midwest, however

there are areas in the Midwest where continuous corn is planted (6, 11, 13, 22). The trend

to plant corn-on-corn was influenced in part by the use of corn for ethanol, a renewable

energy source, which increased corn production demands (7). However, our study

indicates there can be a yield penalty to continuous corn, and others have indicated the

disadvantages of this system. In 2008, Erickson (2008) reported that yield loss due to the

establishment of continuous corn could range between 2% to 19%. This range was based in the summary of 28 studies on corn cropping systems from 1988 to 2008 (16).

According to Nielsen et al. (2007), additional nitrogen applications of 45 to 56 kg/ha are necessary under a continuous corn system, which results in higher production costs (21).

In our study, all treatments received the same nitrogen rate (201.6 kg N/ha) and not

adding additional nitrogen may have influenced yield results. Different weed and disease

management practices are also needed in continuous corn, since this system favors

74 growth and reproduction of weeds, as well as pathogen survival (6). Farmers should consider the yield disadvantages and costs to remedy these disadvantages when deciding to produce corn in a continuous system. Further research is needed to determine the survival period of S. maydis under Indiana conditions in order to fully understand how crop rotation influences Diplodia ear rot.

75

3.5 Literature cited

1. Bailey, K.L., Gossen, B.D., Lafond, G.P., Watson, P.R., and Derksen, D.A. 2001.

Effect of tillage and crop rotation on root and foliar diseases of wheat and pea in

Saskatchewan from 1991 to 1998: Univariate and multivariate analyses. Can. J. Plant

Sci. 81:789-803.

2. Baldock, J.O., Higgs, R.L., Paulson, W.H., Jackobs, J.A., and Shrader, W.D. 1981.

Legume and mineral N effects on crop yields in several crop sequences in the Upper

Mississippi Valley. Agron. J. 73:885-890.

3. Behnin, J.K. 2006. Climate change and South African agriculture: impacts and

adaptation options, PhD. Thesis. University of Pretoria, Pretoria South Africa.

4. Bockus, W.W., and Shroyer, J.P. 1998. The impact of reduced tillage on soilborne

plant pathogens. Annu. Rev. Phytopathol. 36:485-500.

5. Bredford, J.M., and Peterson, G.A. 2000. Conservation tillage. Pages 247-270. In:

Handbook of soil science. M.E. Summer, ed. CRC Press, Boca Raton, FL.

6. Bullock, D.G. 1992. Crop rotation. Crit. Rev. Plant. Sci.11:309-326.

7. Capehart, T. 2015. Corn. USDA, Washington, DC. Online Publication.

http://www.ers.usda.gov/data-products/feed-grains-database/feed-grains-yearbook-

tables.aspx (Accesed: 15 October 2015).

8. Casa, R.T., Reis, E.M., and Zambolim, L. 2003. Decomposição dos restos culturais

do milho e sobrevivência saprofítica de Stenocarpella macrospora e S. maydis.

Fitopatol. Bras. 28:355-361.

9. Clay, D.E., and Shanahan, J.F. 2011. GIS Applications in Agriculture, Volume Two:

nutrient management for energy efficiency. CRC Press.

76

10. Cotten, T.K., and Munkvold, G.P. 1998. Survival of Fusarium moniliforme, F.

proliferatum, and F. subglutinans in maize stalk residue. Phytopathology 88:550-555.

11. Crookston, R.K., Kurle, J.E., Copeland, P.J., Ford, J.H., and Lueschen, W.E. 1991.

Rotational cropping sequence affects yield of corn and soybean. Agron. J. 83:108-

113.

12. Curl, E.A. 1963. Control of plant diseases by crop rotation. Bot. Rev. 29:413-479.

13. Daberkow, S.G., Payne, J., and Schepers, J. 2008. Comparing continuous corn and

corn-soybean cropping systems. Western Economic Forum. Vol 7. No. 1. Online

Publication. http://purl.umn.edu/92853 (Accessed: 15 October 2015).

14. De Nazareno, N.R.X., Lipps, P.E., and Madden, L.V. 1992. Survival of Cercospora

zeae-maydis in corn residue in Ohio. Plant Dis. 76:560-563.

15. Durrell, L.W. 1923. Dry rot of corn. Iowa State Agric. Exp. Sta. Bull. 77. Ames, IA.

16. Erickson, B. 2008. Corn/soybean rotation literature summary. Purdue University,

West Lafayette, IN. Online Publication.

http://www.agecon.purdue.edu/pdf/Crop_Rotation_Lit_Review.pdf (Accesed: 2

March 2016).

17. Flett, B.C., and Wehner, F.C. 1991. Incidence of Stenocarpella and Fusarium cob

rots in monoculture maize under different tillage systems. J. Phytopathol. 133:327-

333.

18. Flett, B.C., Wehner, F.C., and Smith, M.F. 1992. Relationship between maize stubble

placement in soil and survival of Stenocarpella maydis (Diplodia maydis). J.

Phytopathol. 134:33-38.

77

19. Flett, B.C., McLaren, N.W., and Wehner, F.C. 1998. Incidence of ear rot pathogens

under alternating corn tillage practices. Plant Dis. 82:781-784.

20. Flett, B.C., McLaren, N.W., and Wehner, F.C. 2001. Incidence of Stenocarpella

maydis ear rot of corn under crop rotation systems. Plant Dis. 85:92-94.

21. Gentry, L.F., Ruffo, M.L., and Below, F.E. 2013. Identifying factors controlling the

continuous corn yield penalty. Agron. J. 105:295-303.

22. Hamed, K.A., Nacer, B., Robert, M.Z., Bruns, H.A., and Anne, M.G. 2012. Corn-

soybean rotation systems in the Mississippi Delta: implications on mycotoxin

contamination and soil populations of Aspergillus flavus. Intl. J. Agron. 2012.

23. Hooker, A.L. 1956. Association of resistance to several seedling, root, stalk, and ear

diseases in corn. Phytopathology 46:379-384.

24. Kladivko, E.J., Griffith, D.R., and Mannering, J.V. 1986. Conservation tillage effects

on soil properties and yield of corn and soybeans in Indiana. Soil Till. Res. 8:277-

287.

25. Klapproth, J.C., and Hawk, J.A. 1991. Evaluation of four inoculation techniques for

infecting corn ears with Stenocarpella-maydis. Plant Dis. 75:1057-1059.

26. Kleinschmidt, C., and White, D. 2003. Evaluation of Quadris and Tilt for control of

Diplodia ear rot in corn. 2002. Fungicide and Nematicide Tests 58:FC046.

27. Koehler, B. 1959. Corn ear rot in Illinois. Agric. Exp. Sta. Bull. 639:85, Univ. of

Illinois.

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28. Kunkel, K.E., Stevens, L.E., Sun, L., Janssen, E., Wuebbles, S.D., Hilberg, M.S.,

Tim-lin, L., Westcott, N.E., and Dobson, J.G. 2013. Regional climate trends and

scenarios for the U.S. National Climate Assessment. Part 3. Climate of the Midwest

U.S. NOAA Technical Report NESDIS 142-3. U.S. Department of Commerce.

Online Publication.

http://www.nesdis.noaa.gov/technical_reports/NOAA_NESDIS_Tech_Report_142-

3-Climate_of_the_Midwest_U.S.pdf (Accesed: 18 November 2015).

29. Lal, R. 1991. Tillage and agricultural sustainability. Soil Till. Res. 20:133-146.

30. Lal, R. 2009. The plow and agricultural sustainability. J. Sustainable Agric. 33:66-84.

31. Lee, C., Vincelli, P., and Dixon, E. 2008. Evaluation of fungicides for control of gray

leaf spot of corn, 2007. Plant Disease Management Reports 2:FC096.

32. Lipps, P.E. 1985. Influence of inoculum from buried and surface corn residues on the

incidence of corn anthracnose. Phytopathology 75:1212-1216.

33. Littell, R.C., Stroup, W.W., Milliken, G.A., Wolfinger, R.D., and Schabenberger, O.

2006. SAS for Mixed Models, Second Edition. SAS Institute.

34. Mario, J.L., and Reis, E.M. 2001. Método simples para diferenciar Diplodia

macrospora de D. maydis em testes de patologia de sementes de milho. Fitopatol.

Bras. 26:670-672.

35. Mueller, D., and Wise, K.A. 2012. Diseases of corn. Corn disease loss estimates from

the United States and Ontario, Canada. Purdue Univ. Ext. Publ. BP-96-12-W.

36. Mueller, D., and Wise, K.A. 2013. Diseases of corn. Corn disease loss estimates from

the United States and Ontario, Canada. Purdue Univ. Ext. Publ. BP-96-13-W.

79

37. Mueller, D., and Wise, K.A. 2014. Diseases of corn. Corn disease loss estimates from

the United States and Ontario, Canada. Purdue Univ. Ext. Publ. BP-96-14-W.

38. Nielsen, R.L., Johnson, B., Krupke, C., and Shaner, G. 2007. Mitigate the downside

risks of corn following corn. Purdue University, West Lafayette, IN. Online

Publication. https://www.agry.purdue.edu/ext/corn/news/articles.06/CornAfterCorn-

1121.pdf (Accesed: 2 March 2016).

39. Peters, R.D., Sturz, A.V., Carter, M.R., and Sanderson, J.B. 2003. Developing

disease-suppressive soils through crop rotation and tillage management practices.

Soil Till. Res. 72:181-192.

40. Pioneer – Dupont. 2015. Corn Grain: P1352AMXT. Online.

https://www.pioneer.com/home/site/us/products/profileperf?

smo=LWE&productLine=010&language=01&productCode=P1352AMXT&ts=AM

XT,LL,RR2 (Accesed: 15 October 2015).

41. Romero Luna, M.P. 2012. Managing Diplodia ear rot in corn: Short and long-term

solutions. M.S. thesis, Botany and Plant Pathology Department, Purdue University,

West Lafayette, IN.

42. Romero Luna, M.P., and Wise, K.A. 2015a. Timing and efficacy of fungicide

applications for Diplodia ear rot management in corn. Plant Health Prog. 16:123-131.

43. Romero, M.P., and Wise, K.A. 2015b. Development of molecular assays for

detection of Stenocarpella maydis and Stenocarpella macrospora in corn. Plant Dis.

99:761-769.

44. Rossouw, J.D., Pretorius, Z.A., Silva, H.D., and Lamkey, K.R. 2009. Breeding for

resistance to Stenocarpella ear rot in maize. Plant Breed. Rev. 31:223-246.

80

45. Scheeringa K. 2014. Daily Data-Purdue Automated in: Montly weather reports

Indiana State Climate Office. Purdue University, West Lafayette, IN. Online

Publication http://iclimate.org/data_archive_v3.asp?rdatatype=pdf (Accesed: 18

November 2015).

46. Scheeringa, K. 2015. Daily Data-Purdue Automated in: Montly weather reports

Indiana State Climate Office. Purdue University, West Lafayette, IN. Online

Publication. http://iclimate.org/data_archive_v3.asp?rdatatype=pdf (Accesed: 18

November 2015).

47. Smith, C.W., Betrán, J., and Runge, E.C.A. 2004. Corn: origin, history, technology,

and production. John Wiley, Hoboken, N.J.

48. Sturz, A.V., Carter, M.R., and Johnston, H.W. 1997. A review of plant disease,

pathogen interactions and microbial antagonism under conservation tillage in

temperate humid agriculture. Soil and Till. Res. 41:169-189.

49. Sumner, D.R., Doupnik, B., and Boosalis, M.G. 1981. Effects of reduced tillage and

multiple cropping on plant diseases. Annu. Rev. Phytopathol. 19:167-187.

50. Sutton, J.C. 1982. Epidemiology of wheat head blight and maize ear rot caused by

Fusarium graminearum. Can. J. Plant Pathol. 4:195-209.

51. Triplett, G.B., and Dick, W.A. 2008. No-tillage crop production: a revolution in

agriculture! Agron. J. 100:S-153.

52. United States Department of Agriculture. 2016. Crop Production. in: 2015 Summary,

Washington, DC. Online Publication.

http://www.usda.gov/nass/PUBS/TODAYRPT/cropan16.pdf (Accesed: 15 January

2016).

81

53. United States Department of Agriculture. 2012. Corn. in: Economic Research

Service. United States Department of Agriculture, Washington, D.C. Online

Publication. http://www.ers.usda.gov/topics/crops/corn.aspx (Accesed: 15 October

2015).

54. Van Rensburg, J.B.J., and Ferreira, M.J. 1997. Resistance of elite maize inbred lines

to isolates of Stenocarpella maydis. S. Afr. J. Plant & Soil. 14:89-92.

55. White, D.G. 1999. Page 44 in: Compendium of corn diseases. 3rd ed. APS Press, St.

Paul, MN.

56. Wittmus, H., Olson, L., and Lane, D. 1975. Energy requirements for conventional

versus minimum tillage. J. Soils Water Conserv. 30:72-75.

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Table 3.1. Year, soil classification, previous crop history and tillage system, planting, harvest and disease rating date for each experiment to test the effect of rotation and tillage on Diplodia ear rot severity at the Agronomy Center for Research and Education

(ACRE) in Tippecanoe County, Indiana.

Harvest Year Previous Tillage Planting Soil classificationa and crop system date rating date Chalmers silty clay loam in association with Toronto- Soybean NTb May-8 Oct-21 Millbrook complex with a slope of 0 to 2% 2014 Raub-Brenton complex with a Soybean Tc May-7 Oct-21 slope of 0 to 1% slopes

Corn T May-7 Oct-21 Drummer soils

Corn NT May-8 Oct-21 Drummer soils

Chalmers silty clay loam in association with Toronto- Corn NT May-22 Oct-6 Millbrook complex with a slope of 0 to 2% Raub-Brenton complex with a Corn T May-22 Oct-6 2015 slope of 0 to 1% slopes

Toronto-Millbrook complex Soybean T May-22 Oct-6 with a slope of 0 to 2%

Soybean NT May-22 Oct-6 Drummer soils a Soil data obtained from Soil Survey Staff, Natural Resources Conservation Service, USDA. http://websoilsurvey.nrcs.usda.gov. b NT =No-tillage, fields did not receive a pre-planting tillage operation. c T =Tillage, fields received a fall pass with a chisel plow, and a spring pass with a field cultivator.

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Table 3.2. Summary average for daily average of air temperatures (ºC) and precipitation

(mm) from May through October in 2014 and 2015 at the Agronomy Center for Research and Education (ACRE) in Tippecanoe County, Indiana.

Year Weather variable May June July Aug. Sept. Oct.

Temperature 17.0 22.5 20.4 21.6 17.3 12.1 2014 Precipitation 2.6 2.7 2.5 5.5 4.3 3.3

Temperature 18.2 21.3 22.3 21.5 20.4 13.0 2015 Precipitation 2.8 6.6 3.9 0.9 2.6 0.8 a Weather data were obtained from the Indiana State Climate Office. http://climate.agry.purdue.edu/climate/index.asp

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Table 3.3. Detailed statistical analysis from general linear model analysis of data from field experiments at the Agronomy Center for

Research and Education (ACRE) in Tippecanoe County, Indiana in 2014 and 2015 to evaluate the effect of crop rotation and tillage on

Diplodia ear rot severity and yield under inoculated and non-inoculated conditions.

Disease severity Yield

Year Source dfa F valueb Pb df F value P

Crop rotation × tillagec 3 2.01 0.1441 3 5.32 0.0070

2014 Inoculation treatmentd 1 282.00 <0.0001 1 23.66 <0.0001

Crop rotation × tillage * Inoculation treatment 1 1.03 0.4008 3 1.18 0.3397

Crop rotation × tillage 3 0.82 0.4980 3 5.40 0.0065

2015 Inoculation treatment 1 87.84 <0.0001 1 1.13 0.3004

Crop rotation × tillage * Inoculation treatment 3 1.21 0.3302 3 1.32 0.2957 a Degrees of freedom. b F statistic and P values based on general linear model analysis of arcsine-transformed disease severity and yield. c Experiment consisted of two cropping systems: corn following soybean and continuous corn with tillage or no-tillage. d Inoculation treatment consisted of plots artificially inoculated at growth stage V7 (seven visible leaf collars) with Stenocarpella maydis or plots not receiving any artificial inoculation. 84

84

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Figure 3.1. Effect of inoculation treatment (inoculated, non-inoculated) on disease severity (A) and corn yield (B), and effect of crop rotation (corn, soybean) and tillage

(no-tillage, chisel plow) on corn yield (C) of Diplodia ear rot in 2014. Values with the same letter are not significantly different based on least square (LS) means test (P=0.05).

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Figure 3.2. Effect of inoculation treatment (inoculated, non-inoculated) on disease severity (A) and effect of crop rotation (corn, soybean) and tillage (no-tillage, chisel plow) on corn yield (B) of Diplodia ear rot in 2015. Values with the same letter are not significantly different based on least square (LS) means test (P=0.05).

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CHAPTER 4. SURVIVAL OF STENOCARPELLA MAYDIS ON CORN RESIDUE IN INDIANA

4.1 Introduction

Corn yield is influenced by many factors, including weather, pests, and

management practices (46). Many pathogens can infect corn (49) and among the resulting

diseases, ear rots are some of the most important. Ear rot diseases occur wherever corn is

produced, reducing yield and compromising grain quality (49).

Diplodia ear rot, a monocyclic disease caused by the fungus Stenocarpella maydis

(Berk.) B. Sutton, is a predominant ear rot in the Midwest (39, 49). Diplodia ear rot occurs almost everywhere corn is produced, however, its spread is limited to the proximity of the corn plants to the inoculum (48).

Planting resistant hybrids is the most reliable way to control Diplodia ear rot (17,

21); however, no current hybrid is completely resistant to the disease (39). Therefore, crop rotation and tillage have been suggested as reliable short-term strategies for disease control. Stenocarpella maydis is a residue-born pathogen with corn as the only commercial crop host (12, 43), and rotation with any other crop could be potentially used to reduce inoculum production.

Several studies in South Africa evaluated crop rotation and tillage for Diplodia ear rot control. Flett et al. (2001) evaluated Diplodia ear rot incidence under different crop

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rotation systems and observed that wheat (Triticum spp.), soybean (Glycine max L.

Merril), and peanut (Arachis hypogaea L.) reduced Diplodia ear rot when rotated with corn. A linear relationship between disease incidence and amount of corn residue on the surface was observed, and higher levels of Diplodia ear rot were present under conservation tillage systems (13, 15). The effects of crop rotation and tillage practices on

Diplodia ear rot incidence were evaluated in the US considering that preferred tillage systems in the Midwest are no-tillage, strip tillage and chisel tillage (4, 26), which differs from the moldboard plow tillage system recommended for Diplodia ear rot control in

South Africa. Results of this research can be read in Chapter 3. Since the impact of these practices is also influenced by pathogen survival in residue, S. maydis survival should be also evaluated under local conditions. Flett et al. (1992) determined that S. maydis was able to survive on surface residue for almost 11 months in South Africa, which corroborates the positive linear relationship between residue and Diplodia ear rot incidence. However, the survival period of the fungus has yet to be determined in the US.

This is important because average winter temperatures in South Africa are 6 to 20°C compared to -10 to -1°C in the Midwest (3, 25), meaning that the results observed in

South Africa may not be applicable to the US.

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Research on the biology of S. maydis has demonstrated that ideal conditions for conidia germination under in vitro conditions are temperatures between 27-30°C under a

12 h light:dark cycle (42, 23). However, it is unknown how germination is affected by field conditions and what the impact of these conditions may be on the viability and longevity of S. maydis. The objective of this study is to determine the survival period of

S. maydis in corn stalk and kernel residue at different soil depths in a corn field in

Indiana.

4.2 Materials and Methods

4.2.1 Survival of Stenocarpella maydis

Ears and stalks from a commercial corn hybrid (P34F97, Pioneer Hi-Bred

International, Inc., Des Moines, IA) were hand-harvested in 2014 from the Northeast

Purdue Agricultural Center (NEPAC) Whitley County, IN at milk stage (R3, 1) to

establish survival of S. maydis. Ears were shelled, and stalks were cut into 8 to 22 cm

sections, consisting of one node and one complete internode. Kernels and stalks were

placed into clear autoclave bags (60.96 x 76.2 cm), with 1 L deionized water. Each bag

contained either 100 stalk pieces or 800 kernels, was soaked for 12 h then autoclaved for

1 h, allowed to cool and autoclaved for an additional hour. After sterilization, bagged

stalks and kernels were inoculated with 13 mg of wet mycelium from a single isolate of

S. maydis, and incubated at 28°C until fully colonized (approximately 15 days). Pure

cultures were obtained from ears naturally infected with S. maydis at the Agronomy

Center for Research and Education (ACRE) in Tippecanoe County, Indiana, and were

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grown for 15 days on natural oatmeal media (NOA; 36). Pure cultures were prepared and stored prior inoculation as described in Romero and Wise (2015b).

Colonized tissue was mixed and arbitrarily separated into sets containing 50 kernels and 5 stalk pieces to be placed in bags for the experiment. All colonized kernels and stalks pieces were separately weighed, and air-dried in the greenhouse for 7 days.

Dried weights and fungal colonization ratings were recorded. The average dried weight was 10 g for kernels, and 23 g for stalks. Kernels and stalks were rated to determine the level of S. maydis colonization prior to starting the experiment. Ratings were based on percentage of mycelia covering the tissue and pith degradation (Fig. 4.1). Dried and rated tissue (5 pieces) was placed in 24 x 26 cm charcoal fiberglass bags (New York Wire,

Hanover, PA), and 50 kernels were placed in similar bags sized 9 x 11 cm, and then both bags were placed in 28.5 x 29 cm aluminum screening bags (New York Wire, Hanover,

PA). Bags were stored at 4°C until buried. Final tissue weights were recorded again before bags were placed in the field.

The field experiment was established in November 2014 at ACRE. The experimental design was a split-plot with four replications, where the whole plot was residue burial depth (surface (0), 10 and 20 cm), and the sub-plots were sampling times of 4, 7, 11, and 12 months after initial placement. An experimental unit was a single plot.

Plots consisted of one row 9 m long, divided and marked every 3 m where bags were buried. Treatments were replicated 4 times in the experiment, and each replication contained a sample from each of the 4 sampling times at each burial depth. Bags were laid flat to ensure that bagged tissue had equal soil exposure. Pins with dimensions of

15.24 x 2.54 x 15.24 cm (DeWitt, Sikeston, MO) were used to anchor bags to the soil.

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Watchdog 1000 series temperature and moisture sensors were placed within the

experiment (Watchdog Spectrum Technologies, Inc. Plainfield, IL), and Decagon 5TM

digital soil sensors (Decagon Devices Inc. Pullman, WA) were placed at the soil surface,

10, and 20 cm below the soil surface at 5 different locations in the trial. All sensors

recorded data every 30 min for the duration of the experiment.

Samples were retrieved from the field at 4, 7, 11, and 12 months after initial

placement. An additional sample was processed the same day that the experiment was

established in the field to assess pycnidia presence and germination percentage of S.

maydis on corn tissue. At each subsequent sampling time, one bag of each residue burial

depth from each replicate was retrieved. Tissue from each residue burial depth was

removed from each bag, brushed to remove adhered soil, weighed, and air-dried in the

greenhouse for 7 days. Dried tissue was weighed again, surface disinfested with 5%

sodium hypochlorite and Tween® 20 (Biorad Laboratories, Hercules, CA) for 1 min, rinsed twice with distilled water, and then dried for 4 h. A total of 3 nodes from stalk tissue and 27 kernels were arbitrarily chosen per replication and processed, incubated, and analyzed for each burial depth at each sampling time. Nodes were cut in half, placed

on NOA in a 100 x 15 mm petri dish, and incubated for 15 days under 12:12 light:dark cycles at 28°C. Kernels were placed on NOA and incubated for 10 days under the same conditions. After each incubation period, nodes and kernels were examined for S. maydis

pycnidia presence under a dissecting microscope (7.6 x magnification). Formation of

dark brown to black pycnidia indicated presence of S. maydis, which was confirmed

based upon microscopic examination of conidia first under 100x magnification, and

followed by 200x. Conidial characteristics included a pale brown , narrowly

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ellipsoid, straight or curved, with 0 to 2 septa. Corn tissue that had pycnidia but no

conidia were assigned a ‘+’; if conidia were present in the pycnidia, a ‘++’ was assigned;

tissue with no pycnidia were given a ‘-’ rating.

Viability of recovered S. maydis was determined by assessing conidial germination. After pycnidia presence was confirmed, three pycnidia from each node, and nine pycnidia from the 27 kernels were hand-picked with a sterile needle and each placed separately into a 1.5 ml disposable microcentrifuge tube (VWR International, Radnor,

PA) with 50 µl of dH2O. This step was repeated for each burial depth at each replication.

Each suspension was agitated and pipetted onto a water agar plate containing 20 g of

Bacto Agar (Becton, Dickinson and Company, Sparks, MD) per liter, and spread with a

petri disposable L-shaped spreader (USA scientific, Ocala, FL). Plates were incubated at

room temperature for 24 h in the dark. Plates were then observed for germination of 100

conidia in several arbitrarily randomly selected fields of view using a dissecting

microscope (6x magnification). A conidium was considered to have germinated if the

length of the germ tube was at least as long as the conidium (37).

4.2.2 Decomposition rate of corn residue

To determine the decomposition rate of corn residue in the experiment, a negative exponential decay function was used. The decomposition rate (k) was obtained with the following equation: k= -[Ln(remaining biomass at time/initial weight) ]/t); 18), where remaining biomass is the weight recorded after the tissue was retrieved from the field and dried for 7 days, the initial weight was recorded before infected tissue was placed in the field, and t is the time in days from the date of burial.

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4.2.3 Pathogenicity test

Two pure cultures of S. maydis per sample for each burial depth were obtained when viable conidia were produced on residue and used to determine if field experimental conditions reduced pathogenicity over time. Isolates were placed in long term storage after collection, following a previously established protocol (38).

Pathogenicity tests were conducted within 3 months after isolation. Ears of the corn

hybrid P37F97 were hand-harvested in 2015 from NEPAC, and inoculated at milk stage

(R3; 1) with a suspension of 1 x 106 conidia/ml from the selected culture (23-day-old) of

S. maydis growing on NOA following Kim and Woloshuk (2010). The inoculation

protocol consisted of cleaning the corn ears by pulling back the husk and removing the

silk. Ears were wound inoculated with a pin-bar that was dipped into the conidia

suspension. The pin-bar contained 18 pins and the resulting wound was 0.5 cm depth. A

water control was included where an ear was wounded but not inoculated with S. maydis.

Three ears were inoculated per isolate. After inoculation, each ear was individually

covered with clear plastic bags and held for 24 h in the greenhouse. After this period,

plastic bags were opened to avoid moisture accumulation. Ears were maintained in the

greenhouse at 24°C for 10 days, and then visually evaluated for signs of Diplodia ear rot.

Isolates were considered pathogenic if white mycelium representative of S. maydis was

observed on the wound-inoculated kernels. Brown discoloration of the kernels and cob,

and pycnidia presence were also observed and recorded.

4.2.4 Data analysis

Conidial germination percentage of S. maydis was tested for normality using

PROC UNIVARIATE in SAS 9.4 (SAS Institute, Cary, NC), and transformed (arcsine-

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square-root transformed) to meet normality assumptions. Back-transformed means were

presented after analysis. Conidial germination percentages data were subjected to

analysis of variance (PROC GLIMMIX) in SAS software version 9.4 to detect any effect

of sampling time, burial depth, tissue, and their interactions on S. maydis survival. Means

were compared using Least-Square means (LS) test at the P=0.05 level. The rate of

decomposition was tested for normality by using PROC UNIVARIATE in SAS 9.4. Only

decomposition rate on kernels was transformed with arcsine-square-root transformation

to meet normality assumptions. Analysis of variance (PROC GLIMMIX) were conducted

to determine the effects of sampling time, burial depth, tissue, and their interactions on

decomposition rate in SAS software version 9.4. Means were compared using Least

Square means (LS) test at the P=0.05 level. Relationships between S. maydis survival

expressed as germination percentage and soil moisture, and decomposition rate (k) of

corn tissue (kernels or stalks) and soil moisture were tested by linear regression analysis

in SAS software version 9.4.

4.3 Results

4.3.1 Weather conditions

Average monthly temperatures at the field site at ACRE from November 2014 to

November 2015 are presented in Fig 4.2. Average monthly soil temperature and soil moisture at the three burial depths (surface (0), 10 and 20 cm depth) at the trial site are

presented in Table 4.1. During the months of November 2014 to February 2015, and

October 2015 to November 2015, average soil surface (0 cm) temperatures were

generally colder (within 1.5°C) than soil temperatures at 10 and 20 cm burial depths.

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Warmer monthly temperatures (within 1.5°C ) were observed from March 2015 to

September 2015 at soil surface (0 cm) compared with 10 and 20 cm burial depths.

Average of soil moisture expressed in percent indicated that soil surface (0 cm) was generally 1 to 4% drier than 10 cm burial depth and 1% to 6% drier than 20 cm burial depth (Table 4.1) except in January, February and March 2015, where the soil surface (0 cm) was 1% to 2% wetter than the 10 cm depth.

4.3.2 Survival of Stenocarpella maydis

Pycnidia and conidia of S. maydis were observed on kernels and stalks at the soil surface after 4, 7, 11, and 12 months (Table 4.2). Pycnidia were observed on kernels buried at 10 cm after 11 months, however no conidia were detected within these pycnidia when viewed under the microscope. Pycnidia were not observed from kernels buried at

20 cm after 4 months. No corn kernels were retrieved from 10 and 20 cm burial depth after 12 months, as they had decomposed. Pycnidia were observed from stalks buried at

10 and 20 cm after 7 months, but conidia were only detected on stalks buried at 10 cm after 7 months. After 11 months, pycnidia were not observed on stalks buried at 10 or 20 cm (Table 4.2). Because no pycnidia presence was detected after 11 months on corn stalks buried at 10 and 20 cm, samples retrieved after 12 months from these burial depths were not assessed for S. maydis survival.

Survival of S. maydis was determined by the ability of conidia to germinate after being retrieved from the field after 4, 7, 11, and 12 months. The type of tissue did not significantly affect S. maydis survival (P=0.9060); therefore data from corn kernels and stalks were analyzed together. Sampling time and burial depth significantly affected conidial germination (P=0.0093, P<0.0001, respectively). No conidia germinated from

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corn residue buried at 20 cm at any sampling time after experiment was initiated.

Conidial germination from the soil surface was significantly higher than germination of

conidia retrieved from residue buried at 10 cm (Fig. 4.3), and samples retrieved after 4

and 7 months had significantly higher conidial germination compared with samples

retrieved after 11 months (Fig. 4.4). No conidial germination of S. maydis was observed

after 12 months.

The relationship between S. maydis survival expressed as germination percentage and soil moisture was significant (r = 0.38; P=0.0332), indicating that as soil moisture increased, S. maydis survival decreased.

4.3.3 Decomposition rate

The type of corn tissue (kernels, stalks) significantly affected decomposition rate

(P<0.0001), therefore kernel and tissue data were analyzed separately to determine the effect of sampling time and burial depth on decomposition rate. Sampling time, burial depth, and the interaction of sampling time×burial depth significantly affected decomposition rate on kernels (P<0.0001, P<0.0001, and P=0.0032, respectively).

Kernels buried at 10 and 20 cm had a higher decomposition rate than kernels placed on the surface (Table 4.4). After 11 months, the biomass of infected kernels on the surface decreased more than 50%, whereas the kernels buried at 10 and 20 cm were almost entirely decomposed. After 12 months, no kernels were recovered from buried samples

(Table 4.4).

Sampling time, burial depth, and the interaction of sampling time × burial depth significantly affected stalk decomposition rate (P=0.0267, P<0.001, and P=0.0471, respectively). Stalks buried at 10 and 20 cm had a higher decomposition rate than stalks

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placed on the surface (Table 4.5). After 12 months, 96% of stalk tissue remained on the

soil surface, compared with 40 and 49% stalk tissue remaining from 10 and 20 cm burial

depths (Table 4.5).

Although higher soil moistures were reported at 10 and 20 cm burial depths, the

relationship between decomposition rate in corn kernels or stalks, and soil moisture was

not significant (P=0.0559 and P=0.1043, respectively).

4.3.4 Pathogenicity test

Stenocarpella maydis isolates were obtained only from corn kernels and stalks

recovered after 4 months from the soil surface (0 cm) and 10 cm, and from kernels and

stalks recovered from the soil surface after 7 months. Conidia germination was observed

after 11 months on surface samples, however, no viable isolates were recovered from

surface tissue. All collected isolates from all sampling times were pathogenic. Disease

symptoms and signs were the white mycelium representative of S. maydis, brown discoloration of kernels and cob, and pycnidia presence on and within kernels. Control

(water-inoculated) ears exhibited a slight discoloration on the inoculated kernels due to the wound. However, control ears did not exhibit any mycelial growth or discoloration consistent with infection of S. maydis (Fig. 4.6).

4.4 Discussion

This is the first field survival study conducted on S. maydis in the US. Our findings indicate that S. maydis continues to produce conidia capable of germinating on corn residue up to 11 months, however conidia were not pathogenic after 7 months. The ability of S. maydis to germinate up to 11 months could indicate that surface corn residue

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can be an annual source of inoculum for Diplodia ear rot in continuous corn, but their ability to produce infection remains uncertain. These findings support our conclusions in

Chapter 3, where more than one-year away from corn may be necessary to reduce potential inoculum for next corn crop. Our results support Flett et al. (1991) and Flett et al. (1998), that identified a positive linear relationship between surface corn residue and

S. maydis incidence, and Flett et al. (1992) and Casa et al. (2003) that determined an 11 and 12 month survival period on the soil surface for S. maydis in South Africa and Brazil, respectively. Surface crop residue has also been identified as a potential inoculum source in other corn plant pathosystems such as Colletotrichum graminicola, causing anthracnose (27) Cercospora zeae-maydis, causing gray leaf spot (10, 34), and Fusarium spp., causing Fusarium stalk rot (7).

Burial depth influenced survival of S. maydis in our study. Conidial germination decreased significantly when corn residue was buried at 10 and 20 cm depth compared with corn residue at the soil surface. Authors in other survival studies have hypothesized that an increase in soil moisture decreases pathogen viability and survival (11, 19, 35).

Our results support this hypothesis, as S. maydis survival decreased with increasing soil moisture at depths of 10 and 20 cm. Although, based on our regression analysis, no significant relationship was observed between soil moisture and decomposition rate, we observed a tendency for the decomposition rate in the buried corn residue to be higher than in surface corn residue. Soil moisture influences residue decomposition rate by regulating microbial activity (2, 47), which is involved in crop residue degradation. There are many other factors that influence decomposition rate, like Nitrogen content,

Carbon/Nitrogen ratio (C/N; proportion of mass of Carbon to mass of Nitrogen) and plant

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chemical composition such as hemicellulose, cellulose and lignin (24, 31, 32, 33, 44, 45),

and differences in decomposition rate have been observed across crop residue types (5,

22, 32). As an example, crop residue from a legume which has higher Nitrogen content

and a lower C/N ratio will decompose faster than corn residue which has a lower

Nitrogen content and a higher C/N content (30, 32). Although the effect of each of these factors on decomposition rate was not covered in this study, we observed a higher decomposition rate on kernels than stalks, particularly when buried. Kernels are composed mainly of starch, while stalks contain high levels of cellulose, hemicellulose and lignin (41). According to Coyne (1999) starch is degraded faster than cellulose, hemicellulose or lignin, which could explain the faster decomposition rate for kernels

compared with stalks.

The effect of environmental conditions, such as temperature and rainfall have an

important influence on fungal growth, reproduction and survival (28). The decline of S. maydis survival on soil surface (0 cm) residue was evident after the 7th month of the

experiment (June 2015) where a combination of higher soil temperatures and soil

moisture were observed. According to Scheeringa and Mangan (2015), June 2015 was

one of the wettest months reported in Indiana since 1895 (40). Flett et al. (1992), observed a decline in S. maydis survival when unusual spring rains occurred during their study. Wet and warm conditions at the soil surface (0 cm) continued until the beginning of August. Rainfall decreased during the month of August, and soil moisture decreased almost 6%, but soil temperatures increased. Warmer soil conditions continued in

September and beginning of October 2015. Although high soil moisture at the soil surface (0 cm) was also observed during the winter of 2014-2015, which corresponded to

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the beginning of the experiment, soil temperatures were low during this period. These

conditions did not seem to affect S. maydis survival on corn residue at the soil surface (0

cm) as much as the warmer and wetter soil conditions observed later during the summer.

Stenocarpella maydis survival declined significantly after the 11th month of experiment,

which could be attributed to the accumulation of soil moisture and high temperatures

from the 7th month to the 11th month of experiment.

This survival study was conducted in a year with unusual environmental conditions, and conducting the same experiment in a drier year or environment could influence or extend S. maydis survival on residue. In fact, survival periods vary for plant pathogens when studies are conducted at different locations and under different environments (18, 35, 50). Zhang and Bradley (2014) reported a 24 month survival period for Cercospora sojina, causal agent of frogeye leaf spot in soybean, while Cruz and Dorrance (2009) reported a 7 month survival period in Ohio, and Ma and Li (1987)

reported a 12 month survival in China for the same pathogen on soybean. Zhang and

Bradley (2014) inferred that the warmer conditions that occurred in Illinois compared

with the other locations benefit the overwintering of this soybean pathogen. Although the

three S. maydis survival studies reported similar 11 to 12 month survival period, weather

and soil conditions should differ based on the location of the studies. However,

comparison of weather and soil conditions could not be determined, since this

information was not reported in previous studies.

Additional research is necessary for the better understanding of the mechanisms

that influence S. maydis pycnidia production and conidial germination, such as light,

which has been identified as an important factor for S. maydis pycnidia production (23,

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42), as well as how factors such as temperature and moisture influence its survival on corn residue at the soil surface. A better understanding of how light, temperature and moisture influence S. maydis survival and reproduction could help improve management strategies for Diplodia ear rot under local conditions. Based on the observed effect of burial depth on S. maydis survival, the use of a tillage system that leaves minimal corn residue on the soil surface should be recommended for Diplodia ear rot management.

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4.5 Literature cited

1. Abendroth, L.J., Elmore, R.W., Boyer, M.J., and Marlay, S. K. 2011. Corn growth

and development. Iowa State University Extension, Ames, IA.

2. Barros, N., Gomez-Orellana, I., Feijóo, S., and Balsa, R. 1995. The effect of soil

moisture on soil microbial activity studied by microcalorimetry. Thermoch. Acta

249:161-168.

3. Behnin, J.K. 2006. Climate change and South African agriculture: impacts and

adaptation options, PhD. Thesis. University of Pretoria, Pretoria South Africa.

4. Bredford, J.M., and Peterson, G.A. 2000. Conservation tillage. Page 247-270. In:

M.E. Summer ed., “Handbook of soil sciene.” CRC Press, Boca Raton, FL.

5. Broder, M.W., and Wagner, G.H. 1988. Microbial colonization and decomposition of

corn, wheat, and soybean residue. Soil Sci. Soc. Am. J. 52:112-117.

6. Casa, R.T., Reis, E.M., and Zambolim, L. 2003. Decomposição dos restos culturais

do milho e sobrevivência saprofítica de Stenocarpella macrospora e S. maydis.

Fitopatol. Bras. 28:355-361.

7. Cotten, T.K., and Munkvold, G.P. 1998. Survival of Fusarium moniliforme, F.

proliferatum, and F. subglutinans in maize stalk residue. Phytopathology 88:550-555.

8. Coyne, M. 1999. Soil microbiology: an explanatory approach, 1st ed. Delmar

Publishers, Albany, NY.

9. Cruz, C.D., and Dorrance, A.E. 2009. Characterization and survival of Cercospora

sojina in Ohio. Plant Health Prog. doi:10.1094/PHP-2009-0512-03-RS.

10. De Nazareno, N.R.X., Lipps, P.E., and Madden, L.V. 1992. Survival of Cercospora

zeae-maydis in corn residue in Ohio. Plant Dis. 76:560-563.

103

11. Eastburn, D.M., and Gubler, W.D. 1992. Effects of soil moisture and temperature on

the survival of Colletotrichum acutatum. Plant Dis. 76:841-842.

12. Flett, B.C. 1993. Crop plants as host and non-host of Stenocarpella maydis.

Phytophylactica 23:237-238.

13. Flett, B.C., and Wehner, F.C. 1991. Incidence of Stenocarpella and Fusarium cob

rots in monoculture maize under different tillage systems. J. Phytopathol. 133:327-

333.

14. Flett, B.C., Wehner, F.C., and Smith, M.F. 1992. Relationship between maize stubble

placement in soil and survival of Stenocarpella maydis (Diplodia maydis). J.

Phytopathol. 134:33-38.

15. Flett, B.C., McLaren, N.W., and Wehner, F.C. 1998. Incidence of ear rot pathogens

under alternating corn tillage practices. Plant Dis. 82:781-784.

16. Flett, B.C., McLaren, N.W., and Wehner, F.C. 2001. Incidence of Stenocarpella

maydis ear rot of corn under crop rotation systems. Plant Dis. 85:92-94.

17. Hooker, A.L. 1956. Association of resistance to several seedling, root, stalk, and ear

diseases in corn. Phytopathology 46:379-384.

18. Inch, S.A., and Gilbert, J. 2003. Survival of in Fusarium-damaged

wheat kernels. Plant Dis. 87:282-287.

19. Khan, J., del Rio, L.E., Nelson, R., Rivera-Varas, V., Secor, G.A., and Khan, M.F.R.

2008. Survival, dispersal, and primary infection site for Cercospora beticola in sugar

beet. Plant Dis. 92:741-745.

104

20. Kim, H., and Woloshuk, C.P. 2010. Functional characterization of fst1 in Fusarium

verticillioides during colonization of maize kernels. Mol. Plant-Microbe Interact.

24:18-24.

21. Klapproth, J.C., and Hawk, J.A. 1991. Evaluation of four inoculation techniques for

infecting corn ears with Stenocarpella-maydis. Plant Dis. 75:1057-1059.

22. Kriaučiūnienė, Z., Velička, R., and Raudonius, S. 1999. The influence of crop

residues type on their decomposition rate in the soil: a litterbag study. Zemdirbyste.

99(3):227-236

23. Kuhnem Júnior, P.R., Casa, R.T., Bogo, A., Agostineto, L., Bolzan, J.M., and

Miqueluti, D.J. 2012. Effects of temperature, light regime and substrates on the

production and germination of Stenocarpella maydis pycnidiospores. Acta Sci.

Agron. 34:11-16.

24. Kumar, K., and Goh, K.M. 1999. Crop residues and management practices: effects on

soil quality, soil nitrogen dynamics, crop yield, and nitrogen recovery.: Adv.Agron.

68: 197-319.

25. Kunkel, K.E., Stevens, L.E., Sun, L., Janssen, E., Wuebbles, S.D., Hilberg, M.S.,

Tim-lin, L., Westcott, N.E., and Dobson, J.G. 2013. Regional climate trends and

scenarios for the U.S. National Climate Assessment. Part 3. Climate of the Midwest

U.S. NOAA Technical Report NESDIS 142-3. U.S. Department of Commerce.

Online Publication.

http://www.nesdis.noaa.gov/technical_reports/NOAA_NESDIS_Tech_Report_142-

3-Climate_of_the_Midwest_U.S.pdf (Accesed: 18 November 2015).

105

26. Lal, R. 2009. The Plow and Agricultural Sustainability. J. Sustainable Agric. 33:66-

84.

27. Lipps, P.E. 1985. Influence of inoculum from buried and surface corn residues on the

incidence of corn anthracnose. Phytopathology 75:1212-1216.

28. Lynne, B. 2015. Fungi ecosystems and global change. Pages 361-400. in: The Fungi.

Watkinson, S.C., Boddy, L., and Money, N. ed. Elsevier Science. Waltham, MA.

29. Ma, S.M., and Li, B. 1987. Cercospora sorjina overwinter test. Soybean Sci. 25:29-

34.

30. Mazzilli, S.R., Kemanian, A.R., Ernst, O.R., Jackson, R.B., and Piñeiro, G. 2014.

Priming of soil organic carbon decomposition induced by corn compared to soybean

crops. Soil Biol.Biochem. 75:273-281.

31. Melillo, J.M., Aber, J.D., and Muratore, J.F. 1982. Nitrogen and lignin control of

hardwood leaf litter decomposition dynamics. Ecology 63:621-626.

32. Parr, J.F., and Papendick, R.I. 1978. Factors affecting the decomposition of crop

residues by microorganisms. Pages 101-129. in: Crop residue management systems.

W.R. Oschwald, ed. Am. Soc. Agron., Madison, WI.

33. Paul, E.A., Paustian, K.H., Elliott, E.T., and Cole, C.V. 1996. Soil organic matter in

temperate agroecosystems. Long term experiments in North America. vol. 1. CRC

Press. Boca Raton, FL.

34. Payne, G.A., and Waldron, J.K. 1983. Overwintering and spore release of

Cercospora zeae-maydis in corn debris in North Carolina. Plant Dis. 67:87-89.

106

35. Pryor, B.M., Strandberg, J.O., Davis, R.M., Nunez, J.J., and Gilbertson, R.L. 2002.

Survival and persistence of Alternaria dauci in carrot cropping systems. Plant Dis.

86:1115-1122.

36. Romero Luna, M.P. 2012. Managing Diplodia ear rot in corn: Short and long-term

solutions. M.S. thesis, Botany and Plant Pathology Department, Purdue University,

West Lafayette, IN.

37. Romero Luna, M.P., and Wise, K.A. 2015a. Timing and efficacy of fungicide

applications for Diplodia ear rot management in corn. Plant Health Prog. 16:123-131.

38. Romero, M.P., and Wise, K.A. 2015b. Development of molecular assays for

detection of Stenocarpella maydis and Stenocarpella macrospora in corn. Plant Dis.

99:761-769.

39. Rossouw, J.D., Pretorius, Z.A., Silva, H. D., and Lamkey, K.R. 2009. Breeding for

resistance to Stenocarpella ear rot in maize. Plant Breed. Rev. 31:223-246.

40. Scheeringa, K. and Mangan, M.R. 2015. Daily Data-Purdue Automated in: Montly

weather reports Indiana State Climate Office. Purdue University, West Lafayette, IN.

Online Publication. http://iclimate.org/data_archive_v3.asp?rdatatype=pdf (Accesed:

18 November 2015).

41. Schwietzke, S., Kim, Y., Ximenes, E., Mosier, N., and Ladisch, M. 2009. Ethanol

production from maize. Pages 347-364 in: Molecular Genetic Approaches to Maize

Improvement. Kriz, A.L. and Larkins, B.A. ed. Springer. Berlin.

42. Starzyk, M.J. 1976. Effect of temperature and light on sporulation of Diplodia-zeae.

Microbios Letters 1:143-147.

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43. Sutton, B.C., and Waterston, J.M. 1966. Diplodia maydis. CMI Descriptions of

pathogenic fungi and bacteria. Set 9. No. 84. CAB International, Wallingford, UK.

44. Swift, M.J., Heal, O.W., and Anderson, J.M. 1979. Decomposition in terrestrial

ecosystems. University of California Press.

45. Taylor, B.R., Parkinson, D., and Parsons, W F.J. 1989. Nitrogen and lignin content as

predictors of litter decay rates: a microcosm test. Ecology 70:97-104.

46. Thomison, P., Lipps, P., Hammond, R., Mullen, R., and Eisley, B. 2005. Corn

production. in: Ohio Agronomy Guide. The Ohio University. Online Publication.

http://agcrops.osu.edu/specialists/fertility-fact-sheets-and-bulletins/agron_guide.pdf

(Accesed: 15 October 2015).

47. Tiwari, S.C., Tiwari, B.K., and Mishra, R.R. 1987. The influence of moisture regimes

on the population and activity of soil microorganisms. Plant Soil 101:133-136.

48. Ullstrup, A.J. 1970. Methods for inoculating corn ears with Gibberella zeae and

Diplodia maydis. Plant Dis. Rep. 54:658-662.

49. White, D.G. 1999. Page 44 in: Compendium of corn diseases. 3rd ed. APS Press, St.

Paul, MN.

50. Zhang, G., and Bradley, C.A. 2014. Survival of Cercospora sojina on soybean leaf

debris in Illinois. Plant Health Prog. 10(3):92-96.

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Figure 4.1. Diagrammatic scale (1 to 6) of colonized corn stalks after being artificially inoculated with Stenocarpella maydis.

109

40

30 20

° C) 10

0

-10 July May April June March August -20 January October February November December November September ( Temperature -30 -40 Months

Min Temperature Max Temperature Average Temperature

Figure 4.2. Minimum (min), maximum (max), and average air temperatures (°C) collected every 30 min with Watchdog 1000 series loggers from November 2014 to

November 2015 at the Agronomy Center for Research and Education, Tippecanoe

County, IN.

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Table 4.1. Mean monthly temperatures and soil moisture at the soil surface (0 cm), and at 10 cm, and 20 cm soil depths at the experimental site located at the Agronomy Center for Research and Education (ACRE) in which Stenocarpella maydis survival on corn residue was evaluated.

Soil depths Surface (0) cm 10 cm 20 cm Soil Soil Soil Temperature Temperature Temperature Year Month Moisture Moisture Moisture (°C)a (°C) (°C) (%)a (%) (%) 2014 November 2.5 23 3.1 24 3.9 27 December 2.3 23 2.9 25 3.3 29 2015 January -0.8 17 -0.4 15 0.1 21 February -2.3 14 -1.8 13 -1.1 18 March 2.9 23 2.0 22 1.8 22 April 11.5 21 10.8 24 10.4 27 May 19.1 23 18.3 24 17.6 27 June 23.0 26 22.4 28 21.7 29 July 24.6 24 24.1 27 23.4 29 August 26.2 18 25.6 22 25.1 26 September 22.8 17 22.7 21 22.5 26 October 14.8 17 15.3 21 15.7 26 November 8.1 22 8.6 25 9.2 28 Average 11.9 21 11.8 22 11.8 26 a Temperature was recorded every 30 minutes. b Soil moisture was recorded every 30 minute. 110

110

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Table 4.2. Stenocarpella maydis pycnidia and conidia production on corn residue placed in field experiment in November 2014, and retrieved four months later (March), 7 months later (June), 11 months later (October), and 12 months later (November, 2015).

Sampling timea Corn Burial depth Controlb 4 months 7 months 11 months 12 month residue type Surface (0) ++c ++ ++ ++ ++

Kernels 10 ++ - - + NAd

20 ++ - - - NA

Surface (0) ++ ++ ++ ++ ++

Stalks 10 ++ ++ ++ - NSe

20 ++ - + - NS a Sampling time is expressed as months after experiment was initiated. b Sampling set that was processed the same day that the experiment initiated. c Rating scale: ++ = pycnidia with conidia, + = pycnidia but no conidia, - = no pycnidia. d NA = no kernels were recovered after 12 months of experiment initiation. e NS = samples not assessed. 111

111

112

Figure 4.3. Effect of burial depth on viability of Stenocarpella maydis, expressed as

germination percentage over a period of 11 months. Experiment was established at the

Agronomy Center for Research and Education (ACRE) and it was initiated in November

2014. Values with the same letter are not significantly different based on least square

(LS) means test (P=0.05). Burial depth at 20 cm was not included because no germination was observed at any sampling time after experiment initiation.

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Figure 4.4. Effect of sampling time on viability of Stenocarpella maydis, expressed as mean germination percentage. Experiment was established at the Agronomy Center for

Research and Education (ACRE) and it was initiated in November 2014. Values with the same letter are not significantly different based on least square (LS) means test (P=0.05).

Data from sampling time of 12 months after experiment initiation was not included because no germination was observed.

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Table 4.3. Percentage of remaining biomass and decomposition rates (k) of corn kernels after 4, 7, 11, and 12 months at the soil surface (0 cm), or buried at 10 and 20 cm at the

Agronomy Center of Research and Education (ACRE) in Tippecanoe County, IN.

Sampling time Depth Remaining biomass Decomposition rate (months)a (cm) (%) (k)b 0 93 0.0006 a 4 10 64 0.0037 b 20 92 0.0035 b 0 63 0.0022 ab 7 10 7 0.0128 d 20 10 0.0113 d 0 34 0.0033 b 11 10 14 0.0067 c 20 7 0.0084 c 0 29 0.0035 b 12 10 NAc NA 20 NA NA a Sampling time expressed as months after the experiment was initiated in November 2014. b Values with the same letter are not significantly different based on Least-square means (LS) test (P=0.05). c NA: no kernels were recovered after 12 months.

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Table 4.4. Percentage of remaining biomass and decomposition rates (k) of corn stalks after 4, 7, 11, and 12 months at the soil surface (0 cm), or buried at 10 and 20 cm at the

Agronomy Center of Research and Education (ACRE) in Tippecanoe County, IN.

Sampling time Depth (cm) Remaining biomass (%) Decomposition rate (k)b (months)a 0 94 0.0005 abc 4 10 89 0.0010 bc 20 92 0.0008 abc 0 101 0.0000 a 7 10 66 0.0020 de 20 72 0.0015 cd 0 72 0.0010 bc 11 10 52 0.0020 de 20 53 0.0020 de 0 96 0.0001 ab 12 10 40 0.0026 e 20 49 0.0020 de a Sampling time expressed as months after the experiment was initiated in November 2014. b Values with the same letter are not significantly different based on Least-square means (LS) test (P=0.05).

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Figure 4.5. Pathogenicity test on Stenocarpella maydis isolates obtained from retrieved corn tissue. (A) Control (water-inoculated) ears showing only a slight discoloration where the pin-bar was inserted. No white mycelium representative of Stenocarpella maydis was observed. (B) Kernel water-inoculated showing a slight discoloration. The color of the rest of the kernels and cob did not showed any level of discoloration. No pycnidia were observed. (C) Inoculated ears showing brown discoloration on kernels beyond inoculated area, and slight white mycelium representative of Stenocarpella maydis within kernels.

(D) Inoculated corn ear showing discoloration on the cob beyond the inoculated area.

Presence of pycnidia on kernels representative of Stenocarpella maydis.

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CHAPTER 5. GENETIC DIVERSITY OF STENOCARPELLA MAYDIS IN THE MAJOR CORN PRODUCTION AREAS OF THE UNITED STATES

5.1 Introduction

Stenocarpella maydis, causal agent of Diplodia ear rot, was identified as a corn pathogen in 1909 (9), and was recognized as the most important ear rot disease at that time (30). Stenocarpella maydis is only known to reproduce asexually via conidia and

survives as mycelia or pycnidia on corn residue (11, 60, 64). Under warm and humid

conditions, pycnidia will release conidia by way of cirri (42, 43), which are disseminated

by wind and rain (10) to the base of the ear, where infection generally starts within 21

days after silking (4). Conidia dissemination is typically limited to 120 m from inoculum

source (10). Corn ears infected by S. maydis will have a bleached husk appearance and

white fungal mycelia covering the ear. There is no infection from ear to ear, therefore, S.

maydis is considered a monocyclic pathogen (63).

While the disease was prominent in the 1900s, its presence declined significantly

in the 1970s due to planting hybrids with genetic resistance to the disease (23), but increased again in the early 1980s and 1990s (64). Latterell and Rossi (1983) suggested that changes in the corn production system and the absence of truly resistant hybrids contributed to the increases in disease occurrence observed in the 1980s. Later, Dorrance et al. (1999) suggested that a change in pathogen virulence could have resulted in its

118 resurgence. Diplodia ear rot continues to be persistent, with multiple outbreaks occurring in the 2000s across the Midwest (7, 37, 65).

Despite the persistence of S. maydis in corn fields across the Midwest, limited genetic diversity studies have been conducted. The most recent study on genetic diversity among S maydis isolates was completed in 1999 by Dorrance et al. In this study, 46 S. maydis isolates from Untied States and South Africa were evaluated for morphological differences, and isoenzyme polymorphisms were examined for 10 enzymes. Although S. maydis isolates did not demonstrate significant isoenzyme variation, isolates varied in morphological characteristics, such as the abundance of pycnidia production and mycelium color. They concluded that the lack of isoenzyme variation could be due to the limited host range of S. maydis, which includes corn (Zea maydis), teosinte (Z. mays subsp. parviglumis), and bamboo (Arundinaria spp.), and the minimal selection pressure on the haploid state. Research conducted in obligate parasites (6) or pathogens with a limited host range (21) has also observed limited variation among isolates. Although little is known about the genetic diversity among S. maydis isolates, Stenocarpella isolates do vary in pathogenicity, with higher virulence observed with local isolates (27, 66).

Therefore, were are interested in determining the impact of environment on the S. maydis genetic diversity across the corn production areas in the US.

Microsatellite markers, also known as simple sequences repeats (SSR), have been widely used to study genetic diversity in other fungi because of their abundance and even distribution in the genome. They are codominant markers, highly specific, and multiallelic (18), and DNA polymorphisms can be easily detected with a specific polymerase chain reaction (PCR; 44).

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The objectives of this study were to determine if differences among S. maydis isolates exist across the major corn production areas in the US and, if so, if these differences are associated with geographic regions. Given the limited host range of S. maydis, the restricted conidia dissemination within a field (10, 62), and the current knowledge of exclusive asexual propagation (38 60), we hypothesized that S. maydis would have low genetic diversity and a defined population structure between the

Midwestern and Southern US.

5.2 Materials and Methods

5.2.1 Fungal isolates and growth conditions

Stenocarpella maydis isolates were collected from corn fields across the

Midwestern and Southern regions of the US between 2010 and 2015. Isolate background and collection locations are listed in Table 5.1. Isolates were obtained from infected corn ears or leaves based on a previous established protocol (51). Briefly, corn plant tissue was disinfected with 5% commercial bleach (a.i. 5.05% sodium hypochlorite) and Tween 20

(Bio-rad Laboratories, Hercules, CA) for 10 sec. Tissue was washed twice with water, dried for 4 hours and transferred to natural oatmeal agar (NOA; 50). Cultures were stored at 28°C under 12 h light and dark cycles until pycnidia were visible (approximately 7 days). A single pycnidium was hand-picked with a sterile needle and placed into a 1.5 ml

Flex-Eppendorf tube (Eppendorf AG, Hamburg, Germany) with 150 μl of nuclease-free sterile water. After agitation, four drops of the suspension were transferred with a sterilized pipette to water agar, containing 20 g Bacto agar (Becton, Dickinson and

Company, Sparks, MD) per liter. Plates were incubated at room temperature for 24 h

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under dark conditions. Single germinated spores were picked and transferred to natural

oatmeal agar (NOA). Plates were stored at 28°C under 12 h light and dark cycles until the

plate was fully colonized (approximately 7 days). Once a single-spore culture was

obtained, DNA extractions were performed. Stenocarpella maydis species-specific PCR

assay (51) was used to confirm S. maydis identity of all putative isolates before

microsatellite analysis. Each isolate was placed in long term storage following a

previously established protocol (51).

5.2.2 DNA extraction

DNA extraction from S. maydis isolates was performed using a previously established protocol (51). DNA extracted from each isolate was stored in nuclease-free sterile water at -20ºC. DNA was measured with a Nanodrop 1000 spectrophotometer

(Thermo Fisher Scientific, Wilmington, DE), and concentrations adjusted to 10 ng/µl for further analysis.

5.2.3 Development of microsatellite markers, primer design, and screening for

polymorphism.

A genome sequence of S. maydis strain A1-1 containing 5,678 contigs was obtained from Dr. Woloshuk (Purdue University), and submitted to the Purdue Genomic

Center for simple sequence repeat (SSR) prediction. The program Primer3 (32) was used to design 199 primer pairs flanking microsatellites located in different contigs in the S. maydis genome.

Fifty-eight microsatellite loci were initially screened for polymorphisms against 8 isolates of S. maydis collected during different years and different locations across the US

(Table 5.1). Microsatellite loci were prepared fragment analysis using a modified M13

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method (54). Each forward SSR primer had an 18 bp tail added to their 5' ends with the

universal M13 primer (5’TGTAAAACGACGGCCAGT-3’) which was previously 5'-

labeled with the specific 6-FAM fluorescent dye from the DS-33 dye set (Applied

Biosystems, Carlsbad, CA). PCR amplifications were carried out in a final volume of 20

µl containing 1 µl (10 ng) of DNA, 2.5 µl X PCR buffer, 0.5 µl of each dNTPs, 0.9375 µl

of 6-FAM, 0.3125 µl forward primer with M13 5'-tail, 1.25 µl of reverse primer, 0.5 µl

Taq DNA and 11.5 µl of nuclease-free sterile water. Thermal cycling included: 94°C, 5

min; 30 cycles of 94°C, 30 sec; (annealing temperature varies depending of primer used),

45 sec; 72°C, 45 sec; 8 cycles 94°C, 30 sec; 53°C, 45 sec, 72°C, 45 sec, and 1 cycle

72°C, 10 min. PCR products were diluted 1/50 and 2 µl of that aliquot was transferred to a plate prefilled with 10 µl of a composed solution of 15 µl of GeneScan™ -500 LIZ®

(Applied Biosystems, Carlsbad, CA) in 1.2 ml of Hi-Di™ Formamide, and separated by capillary electrophoresis on an ABI 3730XL Genetic Analyzer (Applied Biosystems) at the Purdue Genomic Facility.

After this preliminary screening, 9 microsatellite loci were used to analyze the remaining 166 isolates using the same methodology. These microsatellite marker sequences were deposited in GenBank under the accession numbers of KU588407 to

KU588415 (Table 5.2).

5.2.4 Data analysis

Allele size within microsatellites was scored using Peak Scanner software

(Applied Biosystems, Carlsbad, CA). Haploid data were converted into pseudo-diploid, and allele binning was performed with TANDEM v1.09 (39).

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5.2.4.1 Preliminary study to determine variation among S. maydis isolates under fine-

scale sampling

Two preliminary studies were conducted to understand the extent of variation among S. maydis isolates under a fine-scale sampling strategy, as well as to identify predominant clones among selected isolates. For the first study, the fine-scale included the assessment of diversity on isolates obtained from the same infected corn ears. Isolates were obtained from 3 infected corn ears collected in 2010 from a corn field located at the

Agronomy Center for Research and Education (ACRE) in Tippecanoe County, Indiana.

Each corn ear represented one population, and each population consisted of 12, 10, and

19 isolates, respectively (Table 5.1).

For the second study, the assessment of diversity was on 59 selected isolates obtained in 2010, and from 2012 to 2014 (Table 5.1) from different corn fields at ACRE in Tippecanoe County, Indiana. Each year represented one population, and each population consisted of 41, 6, 5, and 7 isolates, respectively.

In each study, all isolates were genotyped with the 9 microsatellite makers as described previously. For each study, population genetic and genotypic diversity analysis were conducted with poppr package in R (26).

5.2.4.2 Population genetic diversity

Based on results obtained from the small-scale genetic diversity study, the sampling area was extended from one location to locations across the major corn production areas of the US. Sampling was limited to presence of infection across years.

Although a larger isolate collection was obtained from the Midwest, the number of isolates collected per state was not enough to represent each state as a single population.

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Isolates were grouped into two populations depending on their geographic locations

(Table 5.1). Isolates collected from Illinois, Indiana, Iowa, Michigan, Missouri,

Nebraska, and New York were grouped into the Midwestern population. Isolates

collected from Arkansas, Kentucky, Mississippi, and Tennessee were grouped into the

Southern population. To determine if genetic diversity existed in Midwestern and

Southern populations, a population genetic analysis was done at the genotypic level. In

order to identify unique multilocus genotypes (MLG) based on microsatellite allele size

at each population, data were clone-corrected. Unless stated otherwise, clone-corrected data were used for all further analyses. Summary statistics for genetic and genotypic diversity at each population were calculated by Nei unbiased gene diversity (45) and

Shannon-Wiener index of genotypic diversity (55) with poppr package in R (26).

Additionally, and for comparative purposes based on the uneven sample size of our data,

Simpson’s index (56) was also calculated to provide an estimation of the probability that two randomly selected genotypes are different. The scale ranged from 0 (no genotypes are different) to 1 (all genotypes are different). In addition, to determine whether the number of microsatellite loci selected represented the genotypic diversity of both populations, the relationship between the number of loci and genotype diversity was

assessed using the poppr function “genotype_curve”. Evidence of recombination was

examined by calculating the index of association (IA; 8) and its standardized form (ṝd ; 1).

Both calculations were estimated from 1,000 permutations, in the full data and clone-

corrected data for both populations with poppr package in R.

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5.2.4.3 Population structure

Sources of genetic differentiation were investigated in GenAlEx v6.501 (47) using analysis of molecular variance (AMOVA) tests, between and within populations

(17). Variability components were expressed as a percentage of the entire variability.

Pairwise population comparison was calculated with the parameter PhiPT, an analogue of

Fst for haploid data, to determine significant differences between populations. Statistical significance was tested with the use of 1,000 permutations. Population structure was also investigated using the Bayesian clustering software Structure v2.3 (49). Ten runs for each

K ranging from 1 to 10 were done for 100,000 iterations after a burn-in period of 20,000.

The lambda was set to 1.0 and an admixture model was assumed. To facilitate selection of the most appropriate K, delta K (16) was calculated with the best web-based

STRUCTURE-HARVESTER (15). The 10 replicated runs of the optimal K, were combined and visualized using CLUMPAK (31). Population studies were performed with the clone-corrected data.

5.2.5 Screening for mating type genes

Preliminary results from the population genetic diversity and population structure analyses suggested the occurrence of sexual reproduction among S. maydis isolates. To verify this finding, the S. maydis genome sequence was screened for the presence of mating type genes. To identify the mating type genes in S. maydis, MAT1-1 gene encoding a protein with an alpha domain and MAT1-2 gene encoding a protein with a

HMG domain of Magnaporthe grisea (GenBank accession numbers BAC65083.1 and

XP_003720722.1, respectively) were used as a query sequence to tBLASTn searches of the S. maydis genomic sequence. The top hits of each gene were located on the same 20

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kb contig (GenBank accession number KU588406). MAT1-1 and MAT1-2 genes of S.

maydis were aligned using MAFFT (29) to choose primers for the mating-type determination for S. maydis by PCR. The primers used to amplify MAT1-1 and MAT1-2 genes are presented in Table 5.3. A total of 60 isolates (51 from Midwest and 9 from

South) that differ in their genotype were intentionally selected and used to corroborate presence of mating genes (Table 5.6). PCR was conducted in a 20-ml reaction volume containing template DNA at 10 ng/ml, 0.25mM each deoxynucleoside triphosphate

(Promega Corp.), Taq DNA polymerase (New England BioLabs Inc.) at 4 U/ml, 1× PCR buffer (New England BioLabs Inc.), 0.75 mM forward primer, 0.75 mM reverse primer, and 11 ml of nuclease-free sterile water (Thermo Fisher Scientific Inc., Waltham, MA).

Fragments were amplified under the following program: an initial heating cycle of 95°C for 2 min; 30 cycles of denaturation at 95°C for 30 s, primer annealing at 53°C

(Mat1F/Mat1r) or 55°C (Mat2F/Mat2R) for 30 s and extension at 72°C for 40 s; and a

10-min final extension at 72°C. Each total product volume (10 ml) was loaded on a 1%

(wt/vol) agarose gel with 1× Tris-acetate-EDTA buffer and stained with ethidium bromide. The gels were run at 100 V for 20 min and visualized using a UV- transilluminator FBTI-88 (Thermo Fisher Scientific Inc.,Waltham, MA).

5.3 Results

5.3.1 Variation among S. maydis isolates in a fine-scale study

Variation among S. maydis isolates was observed in isolates obtained from three different infected corn ears collected from a corn field in Tippecanoe County, Indiana in

2010. Although isolates obtained from the same corn ear had repeated genotypes, more

126 than one genotype was observed within each ear (Table 5.4). Out of the 41 selected isolates, 4 unique multilocus genotypes were observed, and two of them were represented by a single isolate (Table 5.4). Higher genetic diversity was observed in the population represented by ear number 2 (0.0593), however the highest genotypic diversity was observed on ear number 3, from which a large number of isolates was collected (0.743;

Table 5.4).

From the 59 genotyped isolates collected across four years from the same corn field location, 10 unique multilocus genotypes were identified with 7 unique multilocus genotype represented by a single isolate (Table 5.5). Although isolates obtained from each year shared genotypes, at least 3 genotypes were different within each year (Table

5.5). Despite having the highest number of isolates available, isolates from 2010 had the lowest genetic and genotypic diversity (0.0497, 0.743, respectively). Higher genetic and genotypic diversity was observed in 2014 with 0.2698, 1.475, respectively (Table 5.5).

5.3.2 Population diversity of S. maydis national population

The initial data set for this population genetic study consisted of 174 S. maydis isolates. After data was subjected to clone-correction, where repeated genotypes were only omitted within the state of origin, a total of 70 S. maydis isolates (59 from Midwest and 11 from South) were identified as unique and used for further analysis. For each population, all 9 microsatellites were polymorphic, with an average of 3.2 alleles per locus. The genetic and genotypic diversity in each population is presented in Table 5.6.

Although identical genotypes among states and between populations were still observed in the clone-corrected data, out of 70 S. maydis isolates, 55 unique multilocus genotypes were identified in the two populations (Table 5.7). As shown in Table 5.6, estimated

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genetic and genotypic diversity was higher in isolates from the Midwestern population.

The Southern population included less isolates, therefore, lower genetic diversity and

fewer genotypes were observed. When the number of expected multilocus genotypes

(eMLG based on rarefaction curve; 24) was compared based on the largest sample size

(11 isolates), the Midwestern population still had a larger number of multilocus genotypes compared with the Southern population (Table 5.6). The higher Simpson’s index of both populations indicated that both populations are composed of different genotypes. However, the Midwestern population had a higher Simpson’s index than the

Southern population, corroborating that genotypic diversity in this population was higher

(Table 5.6). The genotype accumulation curve indicated that with 8 microsatellites, 90% of the genotypes from the two S. maydis populations could be identified (Fig. 5.1).

The index of association (IA) and its standardized form (ṝd) were significant for

both populations when no clonal correction was conducted (Table 5.8), supporting the

hypothesis that alleles are effectively linked across loci by clonal reproduction. However,

when clonal correction was conducted, IA and ṝd were not significant (Table 5.8).

5.3.3 Population structure

Based on AMOVA, 94% of variance was detected within populations, while 6% was

detected between populations. Pairwise comparison showed little evidence for population

differentiation between Midwest and Southern populations (PhiPT = 0.059). Estimate of

PhiPT was close to zero, indicating only weak differentiation, even when significantly

greater than zero (P=0.031). STRUCTURE analysis provided more evidence for the lack

of population differentiation between Midwestern and Southern populations. The Evanno

delta K method, suggested the number of populations is equal to two (Fig. 5.2). Two

128 clusters were observed, however no variation in genetic structure was observed between the two populations. Clusters did not strictly represent individual populations, and except for two New York isolates, all states with isolates tested had isolates in each cluster. This analysis provides support for AMOVA analysis that the majority of the genetic diversity resides within populations and not between them.

5.3.4 Screening for mating types

Mating type genes were amplified in all S. maydis isolates tested. Amplifications are represented by a band of predicted size, 203 bp for MAT1-1 and 369 bp for MAT1-2

(Fig. 5.3A, Fig. 5.3B).

5.4 Discussion

This research is the first molecular study analyzing the genetic diversity of S. maydis isolates from the US using microsatellites. The results obtained from the genetic diversity and population structure analyses indicated that US S. maydis populations have a high genotypic diversity, and large variability within each region, but a weak genetic differentiation between the Midwestern and Southern regions. Different corn production practices, such as continuous cropping corn production system, and greater hectarage designated to corn production may explain the predominance of S maydis in the

Midwestern US compared with the Southern US (52). Although S. maydis conidia are disseminated by wind or rain, their dispersal is limited (5, 10, 62). Therefore inoculum dispersal does not explain the widespread genotypes within populations and the low population differentiation across the two geographic regions.

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We hypothesized that S. maydis has low genetic diversity based on the

assumption that organisms that are only known to reproduce asexually are expected to

have low levels of genetic diversity (41). However, different multilocus genotypes were

observed in both of our fine-scale studies. These findings were observed again when

isolates collected across Midwestern and Southern US were included in analysis. After

data were subjected to clone correction, the number of isolates decreased but high genetic

and genotypic diversity was still observed in both populations, and out of the 70 S.

maydis isolates, 55 unique multilocus genotypes were identified. These findings could

indicate that our previous assumptions of S. maydis having low genetic diversity may be incorrect.

Diverse population studies on other asexual fungal plant pathogens such as

Rhynchosporium secalis (53), Colletotrichum truncatum (13), and Alternaria alternata

(3, 48), have reported large genotypic variation, which suggests the occurrence of sexual recombination. Even though many fungi are classified as asexual, the production of the sexual stage is possible in specific environmental conditions (22, 59). It is also possible that a sexual stage has been overlooked (36).

In our study, the evidence of sexual reproduction among S. maydis isolates provided by the genetic diversity and population structure analyses was corroborated by identifying MAT1-1 and MAT1-2 genes in the same contig, and by amplifying part of each gene in 60 selected S. maydis isolates. The use of mating type gene analysis to confirm sexual recombination in asexual fungi has been conducted in other plant pathogens such as Cercospora spp., (20), Cladosporium fulvum (58), Septoria passerinii

(19), Alternaria brassicicola (35), Fusarium oxysporum and F. culmorum (25). We

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detected both mating genes in all isolates tested, which could indicate that S. maydis is a

homothallic species. Homothallic species are able to self-fertilize and complete the sexual

cycle by themselves (61). High levels of genotypic diversity, such as observed in this

study, have also been observed in Aspergillus nidulans and Sclerotia sclerotiorum, both

homothallic species (2, 46).

A previous study by Dorrance et al. (1999) used an isoenzyme analysis to assess

polymorphism among S. maydis isolates collected from US and South Africa. Based on a

simple matching coefficient they found low levels of polymorphism among S. maydis

isolates. In contrast, based on our genotypic diversity analysis, we found high levels of

diversity in S. maydis isolates sampled from Midwestern and Southern US. Dorrance et

al. (1999) assumed that the lack of polymorphism was due to the limited host range of S.

maydis and exclusively asexual reproduction. Although we are still certain of the limited

host range of S. maydis, the current knowledge of its exclusively asexual reproduction is in question. We believe that our findings differ from those of Dorrance et al. (1999)

based on the selected molecular markers. Although isoenzymes and microsatellites are

both codominant molecular markers, studies using both molecular markers have observed

a larger level of polymorphism with microsatellites than with isoenzymes (12, 28, 33,

57). Nine microsatellites were identified as highly polymorphic molecular markers

among S. maydis isolates. Overall, these new microsatellite markers were inexpensive

and reproducible among the isolates obtained from a wide sampling area across the US.

Organisms with both asexual and sexual reproduction are more challenging to

manage due to the higher evolutionary potential. Sexual reproduction will create new

genotypes which may be better adapted to survive and reproduce asexually (40). Previous

131 studies have documented differences in pathogenicity and virulence among S. maydis isolates (27, 66). This information, coupled with our findings indicate that additional sampling across the US could facilitate the identification of unique genotypes, as well as the extent of their distribution in the US corn production areas. These unique genotypes should be screened for differences in pathogenicity and virulence, which would be valuable information when breeding for disease resistance to Diplodia ear rot.

The results of this study yield new information on the population diversity of S. maydis. For the first time, evidence of sexual recombination and presence of both mating type genes were reported in this pathogen. Therefore, further studies that include a more extensive sample collection are needed to fully understand the population diversity of this pathogen and its population structure. A characterization study of the mating type genes should also be conducted to identify the mechanisms by which this fungus generates and maintains its high genotypic diversity.

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5.5 Literature cited

1. Agapow, P.M., and Burt, A. 2001. Indices of multilocus linkage disequilibrium. Mol.

Ecol. Notes 1:101-102.

2. Aldrich-Wolfe, L., Travers, S., and Nelson, B.D. 2015. Genetic variation of

Sclerotinia sclerotiorum from multiple crops in the North Central United States.

PLoS one 10:e0139188.

3. Aradhya, M.K., Chan, H.M., and Parfitt, D.E. 2001. Genetic variability in the

pistachio late blight fungus, Alternaria alternata. Mycol.l Res. 105:300-306.

4. Bensch, M.J. 1995. Stenocarpella maydis (Berk) Sutton colonization of maize ears.

Journal of Phytopathology-Phytopathologische Zeitschrift 143:597-599.

5. Bensch, M. J., Vanstaden, J., and Rijkenberg, F.H.J. 1992. Time and site of

inoculation of maize for optimum infection of ears by Stenocarpella-maydis.

Phytopathol. Z. 136:265-269.

6. Bosland, P.W., and Williams, P.H. 1987. An evaluation of Fusarium oxysporum from

crucifers based on pathogenicity, isozyme polymorphism, vegetative compatibility,

and geographic origin. Can. J. Bot. 65:2067-2073.

7. Bradley, C.A. 2009. Diplodia ear rot causing problems in corn across the state.

Online. The Bulletin. Univ. of Illinois Extension. No. 23, article 6.

8. Brown, A.H.D., Feldman, M.W., and Nevo, E. 1980. Multilocus structure of natural

populations of spontaneum. Genetics 96:523-536.

9. Burrill, T.J., and Barrett, J.T. 1909. Ear rots of corn. Agric. Exp. Sta. Bull. 133:64-

109. Univ. of Illinois.

133

10. Casa, R.T., Reis, E.M., and Zambolim, L. 2004. Dispersao vertical e horizontal de

conidios de Stenocarpella macrospora e Stenocarpella maydis. Fitopatol. Bras.

24:141-147.

11. Casa, R.T., Reis, E.M., and Zambolim, L. 2006. Doenças do milho causadas por

fungos do gênero Stenocarpella. Fitopatol. Bras. 31:427-439.

12. da Costa-Ribeiro, M. C., Lourenco-de-Oliveira, R., and Failloux, A. B. 2006. Higher

genetic variation estimated by microsatellites compared to isoenzyme markers in

Aedes aegypti from Rio de Janeiro. Mem. Inst. Oswaldo Cruz 101:917-921.

13. Diao, Y., Zhang, C., Xu, J., Lin, D., Liu, L., Mtung'e, O.G., and Liu, X. 2015.

Genetic differentiation and recombination among geographic populations of the

fungal pathogen Colletotrichum truncatum from chili peppers in China. Evol. Appl.

8:108-118.

14. Dorrance, A.E., Miller, O.K., and Warren, H.L. 1999. Comparison of Stenocarpella

maydis isolates for isozyme and cultural characteristics. Plant Dis. 83:675-680.

15. Earl, D.A. 2012. STRUCTURE HARVESTER: a website and program for

visualizing STRUCTURE output and implementing the Evanno method. Conserv.

Genet. Resour. 4:359-361.

16. Evanno, G., Regnaut, S., and Goudet, J. 2005. Detecting the number of clusters of

individuals using the software STRUCTURE: a simulation study. Mol. Ecol.

14:2611-2620.

17. Excoffier, L., Smouse, P.E., and Quattro, J.M. 1992. Analysis of molecular variance

inferred from metric distances among DNA haplotypes: application to human

mitochondrial DNA restriction data. Genetics 131:479-491.

134

18. Field, D., and Wills, C. 1996. Long, polymorphic microsatellites in simple

organisms. Proc. R. Soc. Lond. [Biol]. 263:209-215.

19. Goodwin, S.B., Waalwijk, C., Kema, G.H.J., Cavaletto, J.R., and Zhang, G. 2003.

Cloning and analysis of the mating-type idiomorphs from the barley pathogen

Septoria passerinii. Mol. Genet. Genomics 269:1-12.

20. Groenewald, M., Groenewald, J.Z., Harrington, T.C., Abeln, E.C.A., and Crous, P.W.

2006. Mating type gene analysis in apparently asexual Cercospora species is

suggestive of cryptic sex. Fungal Genet. Biol. 43:813-825.

21. Hellmann, R., and Christ, B. J. 1991. Isozyme variation of physiologic races of

Ustilago hordei. Phytopathology 81:1536-1540.

22. Holland, B. 2002. Sexual selection fails to promet adaptaion to a new environemnt.

Evolution 56:721-730.

23. Hooker, A.L., and White, D.G. 1976. Prevalence of corn stalk rot fungi in Illinois.

Plant Dis. Rep. 60:1032-1034.

24. Hurlbert, S.H. 1971. The nonconcept of species diversity: A critique and alternative

parameters. Ecology 52:577-586.

25. Irzykowska, L., and Kosiada, T. 2011. Molecular identification of mating type genes

in asexually reproducing Fusarium oxysporum and F. culmorum. J. Plant Prot. Res.

51:405-409.

26. Kamvar, Z.N., Tabima, J.F., and Grünwald, N.J. 2014. Poppr: an R package for

genetic analysis of populations with clonal, partially clonal, and/or sexual

reproduction. PeerJ. 2:e281.

135

27. Kappelman, A.J., Thompson, D.L., and Nelson, R.R. 1965. Virulence of 20 isolates

of Diplodia zeae as revealed by stalk rot development in corn. Crop Sci. 5:541-543.

28. Karasawa, M.M.G., Vencovsky, R., Silva, C.M., Cardim, D.C., Bressan, E.A.,

Oliveira, G.C.X., and Veasey, E.A. 2012. Comparison of microsatellites and

isozymes in genetic diversity studies of glumaepatula (Poaceae) populations.

Rev. Biol. Trop. 60:1463-1478.

29. Katoh, K., and Standley, D.M. 2013. MAFFT Multiple Sequence Aligment Software

Version 7: Improvement in performance and usability. Mol. Biol. Evol. 30:772-780

30. Koehler, B. 1959. Corn ear rot in Illinois. Agric. Exp. Sta. Bull. 639:85, Univ. of

Illinois.

31. Kopelman, N.M., Mayzel, J., Jakobsson, M., Rosenberg, N. A., and Mayrose, I.

2015. CLUMPAK: a program for identifying clustering modes and packaging

population structure inferences across K. Mol. Ecol. Resour. 15(5):1179-1191.

32. Koressaar, T., and Remm, M. 2007. Enhancements and modifications of primer

design program Primer3. Bioinformatics 23:1289-1291.

33. Kumar, P., Gupta, V.K., Misra, A.K., Modi, D.R., and Pandey, B.K. 2009. Potential

of molecular markers in plant biotechnology. Plant Omics. 2:141.

34. Latterell, F.M., and Rossi, A.E. 1983. Stenocarpella macrospora (=Diplodia

macrospora) and S. maydis (=D.maydis) compared as pathogens of corn. Plant Dis.

67:725-729

35. Linde, C.C., Liles, J.A., and Thrall, P.H. 2010. Expansion of genetic diversity in

randomly mating founder populations of Alternaria brassicicola infecting Cakile

maritima in Australia. Appl. Environ. Microbiol. 76:1946-1954.

136

36. Lucas, J. 2009. Plant Pathology and Plant Pathogens. John Wiley & Sons.

37. Malvick, D. 2001. Diplodia ear rot of corn. Online. The Bulletin. Univ. of Illinois

Extension. http://bulletin.ipm.illinois.edu/pastpest/articles/200120e.html. (Accesed 2

February 2016).

38. Masango, M.G., Flett, B.C., Ellis, C.E., and Botha, C.J. 2014. Stenocarpella maydis

and its toxic metabolites: a South African perspective on diplodiosis. World

Mycotoxin J. 8:341-350.

39. Matschiner, M., and Salzburger, W. 2009. TANDEM: integrating automated allele

binning into genetics and genomics workflows. Bioinformatics 25:1982-1983.

40. McDonald, B. A., and Linde, C. 2002. Pathogen population genetics, evolutionary

potential, and durable resistance. Ann. Rev. Phytopathol. 40:349-379.

41. Milgroom, M. G. 1996. Recombination and the multilocus structure of fungal

populations. Ann. Rev. Phytopathol. 34:457-477.

42. Morant, M.A., Warren, H.L., and Von Qualen, S.K. 1993. A synthetic medium for

mass production of pycnidiospores of Stenocarpella species. Plant Dis. 77:424-426.

43. Moremoholo, L., and Shimelis, H. 2009. Stability analysis for grain yield and

Stenocarpella maydis ear rot resistance in maize. Afr. Crop Sci. J. 9:425-433.

44. Morgante, M., and Olivieri, A.M. 1993. PCR‐amplified microsatellites as markers in

plant genetics. Plant J. 3:175-182.

45. Nei, M. 1978. Estimation of average heterozygosity and genetic distance from small

number of individuals. Genetics 89:583-590.

137

46. Paoletti, M., Seymour, F.A., Alcocer, M.J. C., Kaur, N., Calvo, A.M., Archer, D. B.,

and Dyer, P.S. 2007. Mating type and the genetic basis of self-fertility in the model

fungus Aspergillus nidulans. Curr. Biol. 17:1384-1389.

47. Peakall, R., and Smouse, P.E. 2012. GenAlEx 6.5: genetic analysis in Excel.

Population genetic software for teaching and research—an update. Bioinformatics

28:2537-2539.

48. Peever, T.L., Ibanez, A., Akimitsu, K., and Timmer, L.W. 2002. Worldwide

phylogeography of the citrus brown spot pathogen, Alternaria alternata.

Phytopathology 92:794-802.

49. Pritchard, J.K., Stephens, M., and Donnelly, P. 2000. Inference of population

structure using multilocus genotype data. Genetics 155:945-959.

50. Romero Luna, M.P. 2012. Managing Diplodia ear rot in corn: Short and long-term

solutions. M.S. Purdue University, West Lafayette, IN.

51. Romero, M.P., and Wise, K.A. 2015. Development of molecular assays for detection

of Stenocarpella maydis and Stenocarpella macrospora in corn. Plant Dis. 99:761-

769.

52. Rossouw, J.D., Pretorius, Z.A., Silva, H.D., and Lamkey, K R. 2009. Breeding for

resistance to Stenocarpella ear rot in maize. John Wiley & Sons, Inc. NJ.

53. Salamati, S., Zhan, J., Burdon, J.J., and McDonald, B.A. 2000. The genetic structure

of field populations of Rhynchosporium secalis from three continents suggests

moderate gene flow and regular recombination. Phytopathology 90:901-908.

54. Schuelke, M. 2000. An economic method for the fluorescent labeling of PCR

fragments. Nature Biotechnol. 18:233-234.

138

55. Shannon, C.E. 2001. A mathematical theory of communication. ACM SIGMOBILE

Mobile Comput. Commun. Rev. 5:3-55.

56. Simpson, E.H. 1949. Measurement of diversity. Nature. 163:688.

57. Solano, P., Duvallet, G., Dumas, V., Cuisance, D., and Cuny, G. 1997. Microsatellite

markers for genetic population studies in Glossina palpalis (Diptera: Glossinidae).

Acta trop. 65:175-180.

58. Stergiopoulos, I., Groenewald, M., Staats, M., Lindhout, P., Crous, P.W., and De

Wit, P.J. 2007. Mating-type genes and the genetic structure of a world-wide

collection of the tomato pathogen Cladosporium fulvum. Fungal Genet. Biol. 44:415-

429.

59. Sun, S., and Heitman, J. 2011. Is sex necessary? BMC Biology 9:56-56.

60. Sutton, B.C., and Waterston, J.M. 1966b. Diplodia maydis. CMI Descriptions of 525

pathogenic fungi and bacteria. Set 9. No. 84. CAB International, Willingford, UK.

61. Taylor, J.W., Jacobson, D.J., and Fisher, M.C. 1999. The evolution of asexual fungi:

reproduction, speciation and classification Ann. Rev. Phytopathol. 37:197-246.

62. Ullstrup, A.J. 1958. A study of the nature of intraspecific aversion in Diplodia

maydis. Bull. Torrey Bot. Club 85:397-404.

63. Van Rensburg, J.B.J., and Flett, B.C. 2010. A review of research achievements on

maize stem borer, Busseola fusca (Fuller) and Diplodia ear rot caused by

Stenocarpella maydis (Berk. Sutton). S. Afr. J. Plant & Soil 27:74-80.

139

64. Vincelli, P. 1997. Ear rot of corn caused by Stenocarpella maydis (=Diplodia

maydis). Online. University of Kentucky. Cooperative Extension Service. PPA-43.

Lexington, KY. http://www2.ca.uky.edu/agc/pubs/ppa/ppa43/ppa43.htm. (Accessed:

28 September 2015).

65. White, D., and Malvick, D. 2001. Diplodia ear rot: an old disease reborn. Online.

Univ. of Illinois, Urbana IL.

http://agronomyday.cropsci.illinois.edu/2001/tours/diplodia-ear-rot//. (Accessed: 28

Septermber 2015).

66. Young, H.C.J., Wilcoxson, R.D., Whitehead, M.D., Devay, J.E., Grogan, C.O., and

Zuber, M.S. 1959. An ecological study of the pathogenicity of Diplodia maydis

isolates inciting stalk rot on corn. Plant Dis. Rep. 43:1124-1129.

140

Table 5.1. Summary information and number of isolates for the Stenocarpella maydis population analyzed in this study.

Region Year State Location Sourceb No. Isolates (Population)a

2010 Indiana Tippecanoe Midwestern K.W. 41de

2011 Indiana Tippecanoe Midwestern M.R 10df

2012 Indiana Tippecanoe Midwestern M.R. 6de

2013 Illinois Adams Co. Midwestern C.B. 5d

2013 Illinois NAc Midwestern C.B. 5d

2013 Indiana Knox Midwestern M.R. 4d

2013 Indiana Tippecanoe Midwestern M.R. 5e

2013 Missouri Howard Midwestern L.S. 6d

2013 New York Cayuga Midwestern G.B. 5d

2014 Indiana Tippecanoe Midwestern M.R. 7e

2014 Iowa Boone Midwestern D.M. 5

2014 Mississippi Washington Southern T.A. 7

2014 Tennessee Gibson Southern H.K. 5

2014 Tennessee Madison Southern H.K. 6

2015 Arkansas NA Southern T.F. 2

2015 Illinois Champaign Midwestern C.B. 5

2015 Illinois Pope Midwestern C.B. 1

2015 Illinois Randolph Midwestern C.B. 6

141

Table 5.1. continued

2015 Illinois Sangamon Midwestern C.B. 5

2015 Indiana Benton Midwestern M.R 2

2015 Indiana Carroll Midwestern M.R 2

2015 Indiana Clay Midwestern C.N. 1

2015 Indiana Daviess Midwestern M.R 1

2015 Indiana Greene Midwestern M.R 1

2015 Indiana Jasper Midwestern M.R 2

2015 Indiana Montgomery Midwestern M.R 1

2015 Indiana Newton Midwestern M.R 1

2015 Indiana Rochester Midwestern C.N. 3

2015 Indiana Tippecanoe Midwestern M.R 8

2015 Indiana Wanatah Midwestern K.W. 1

2015 Indiana White Midwestern M.R 2

2015 Kentucky Caldwell Southern C.B. 2

2015 Iowa Boone Midwestern D.M. 1

2015 Iowa Calhoun Midwestern D.M. 1

2015 Iowa Dallas Midwestern D.M. 1

2015 Iowa Madison Midwestern D.M. 1

2015 Iowa NA Midwestern D.M. 2

2015 Michigan Isabella Midwestern M.R. 2

2015 Missouri Harrison Midwestern L.S. 2

142

Table 5.1. continued

2015 Nebraska NA Midwestern T.J. 1

a Regional designation used to group isolates by the geographical locations from where the samples were collected. Each regional designation was considered as separate population. b Samples were kindly provided by Kiersten Wise (K.W.), Department of Plant Pathology, Purdue University, West Lafayette, IN., Martha Romero, (M.P.), Department of Plant Pathology, Purdue University, West Lafayette, IN., Chenxing Niu (C.N.), Department of Plant Pathology, Purdue University, West Lafayette, IN., Carl Bradley (C.B.), Department of Crop Science, University of Illinois, Urbana-Champaign, IL – Department of Plant Pathology, University of Kentucky Research and Education Center, Princeton, KY., Laura Sweets (L.S.), Division of Plant Science, University of Missouri, Columbia, MO., Gary Bergstrom (G.B.), Plant Pathology and Plant-Microbe Section, Cornell University, Ithaca, NY., Daren Muller (D.M.), Department of Plant Pathology, Iowa State University, Ames, IA., Tamra Jackson-Ziems (T.J.), Plant Pathology Department, University of Nebraska-Lincoln, Lincoln, NE., Heather Kelly (H.K.), Entomology and Plant Pathology, University of Tennessee, Jackson, TN., Tom Allen (T.A.), Biochemistry, Molecular Biology, Entomology, and Plant Pathology, Mississippi State University, Stoneville, MS., Travis Faske (T.F.), Department of Plant Pathology, University of Arkansas, Lonoke, AR., and Carl Bradley (C.B.),. c NA: Unknown origin. d Sample from where intentionally selected isolates were used in the initial screening of microsatellite markers. e Isolates used in the fine-scale study. f Isolates obtained from infected corn leaves.

143

Table 5.2. Stenocarpella maydis microsatellite characteristics, including sequence identification, location, repeat motif, forward and reverse primers, product size in number of base pairs (bp), and number of allele observed.

Repeat Product # of alleles IDa Location Forward Primer Reverse Primer motif size (bp) observed

4 Contig_1661 (CG)10 TAAAGGATACGGGCGAACAC CACGCAATAGCGAAACTCAA 251 3

9 Contig_2935 (TG)10 TTGGGTTGCCCAACTTACTC TGACGACTTGCAGGAATCAG 218 2

10 Contig_2994 (CA)7 TGGCAACATAAGGTGTGAGC CCGTGGCAGAGTGGTCTAAT 260 3

18 Contig_6275 (CA)6 ATCTCAAAGCGAGCGATGTT AAAACCCGCGTACACAGTTC 227 6

42 Contig_463 (AGC)5 CGCAAAACAATCATGTACCG CGTGTCTCAGAGGGAAAAGC 216 3

46 Contig_5611 (TAA)6 GGGCTCACGATAGCATCAAT GTCTAGCTATGAGCGCCAGG 213 5

54 Contig_572 (CCCAG)7 GCCTCAACGCTCAAAGAAAC CTTCTCTATGGCTGGCCTTG 280 2

55 Contig_152 (CTTTT)5 CTCCTGGGAAAGTCATGGAA ACCACCAACACCTCCAAGAG 225 2

56 Contig_132 (TTTCT)8 CCCAATACAAGCCACCTGTT GCTCCGGTTTATGTTCGGTA 223 3 a Sequences of microsatellite loci were submitted to GenBank (KU588407 – KU588415). 143

143

144

Table 5.3. Primer sequence used to identify mating type genes on Stenocarpella maydis isolates.

Primer Sequence 5’ – 3’ Annealing temperature (°C) Product size (bp) Mating gene

Mat1F GAT GAT ATC GGT GGA CAA ATC 50.9 …

Mat1R TGG CTC TCC AAT GAC TTA C 51.7 203 MAT1-1

Mat2F CTT CGC CGT TGG TCA TTG TG 57 …

Mat2R GTC ATC ATA CCC GTC TTC TAC 52.1 369 MAT1-2 144

144

145

Table 5.4. Statistics of genetic and genotypic diversity of Stenocarpella maydis from 41

isolated obtained from three infected corn ears.

Number of isolates with Populationa Nb MLGc eMLGd He Hexpf unique genotypeg

Ear_1 12 3 2.67 0.566 0.0370 1

Ear_2 10 2 2.00 0.673 0.0593 0

Ear_3 19 3 2.49 0.708 0.0507 1

Total 41 4h 2.43 0.743 0.0497 2i

a For this analysis each ear was considered as a population. b Number of isolates. c Number of multilocus genotype (MLG) observed. d Expected multilocus genotype (eMLG) at the smallest sample size based ≥10 on rarefaction. e Shannon-Weiner genotypic diversity (H). f Nei’s genetic diversity corrected for sample size (Hexp).g Unique multilocus genotypes out of the total number of isolates collected in 2010 from three corn ears. g Number of isolates that did not share genotypes among populations. h Total number of unique genotypes identified among populations. i Total number of unique genotypes that were observed only in one isolate.

146

Table 5.5. Statistics of genetic and genotypic diversity of Stenocarpella maydis from 59

isolated obtained in 2010, and from 2012 to 2014.

Number of isolates with Populationa Nb MLGc eMLGd He Hexpf unique genotypeg

2010 41 4 2.43 0.743 0.0497 2

2012 6 3 3.00 1.011 0.1259 1

2013 5 4 4.00 1.332 0.1556 1

2014 7 5 5.00 1.475 0.2698 3

Total 59 10h 3.67 1.325 0.1009 7i

a For this analysis each ear was considered as a population. b Number of isolates. c Number of multilocus genotype (MLG) observed. d Expected multilocus genotype (eMLG) at the smallest sample size based ≥10 on rarefaction. e Shannon-Weiner genotypic diversity (H). f Nei’s genetic diversity corrected for sample size (Hexp). g Number of isolates that did not share genotypes among populations. h Total number of unique genotypes identified among populations. i Total number of unique genotypes that were observed only in one isolate.

147

Table 5.6. Statistics of genetic and genotypic diversity of Stenocarpella maydis from

Midwestern and Southern populations.

Population Na MLGb eMLGc Hd De Hexpf

Midwestern 59 50 10.6 3.84 0.993 0.384

Southern 11 9 9 2.15 0.964 0.267

Total 70 55 10.6 3.92 0.992 0.373

a Number of isolates b Number of multilocus genotype (MLG) observed e Expected multilocus(eMLG) at the smallest sample size based ≥10 on rarefaction d Shannon-Weiner genotypic diversity (H) e Simpson’s Index (D) f Nei’s genetic diversity corrected for sample size (Hexp)

148

Table 5.7. Details of the microsatellite markers allele sizes (bp) detected for each of the multilocus genotype (MLG), including number of isolates, isolate code, and their geographical region of origin for each MLG.

MLG No. Locus Locus Locus Locus Locus Locus Locus Locus Locus Sample codeb Populationc a Isolates 4 9 10 18 42 46 54 55 56

1 1 IN_2012TP56* MW 256 239 275 244 231 230 294 246 242

2 1 IN_2012TP53* MW 256 237 275 244 231 230 294 246 242

3 1 IN_2015WA74* MW 260 239 277 238 231 233 296 246 242

IL_2013AM01*, 4 2 MW – ST 260 239 275 240 231 230 296 246 242 TN_2014GB01*

5 1 IL_2013AM03* MW 260 239 275 242 231 230 296 246 242

6 1 IN_2011TP42* MW 260 239 275 236 231 230 296 246 242

IN_2015RO77*, 7 2 MW 260 239 275 244 231 230 296 246 242 MO_2013HW01 148

148

149

Table 5.7. continued

8 1 AR_2015NA02* ST 260 237 277 240 231 230 296 246 242

9 1 IN_2015BE88* MW 260 237 277 236 231 230 296 246 242

10 1 IN_2015TP86* MW 260 237 277 244 231 230 296 246 242

11 1 IL_2013AM04* MW 260 237 273 240 231 230 296 246 242

12 1 IN_2015TP84* MW 260 237 273 236 231 236 294 246 257

13 1 IL_2013AM05* MW 260 237 273 244 231 230 296 246 242

TN_2014GB02*, 14 2 ST 260 237 275 240 231 230 296 246 242 MS_2014SV02*

15 1 IN_2015TP82* MW 260 237 275 246 231 230 296 246 242

16 1 MS_2014SV07 ST 260 237 275 242 231 230 296 246 242

17 1 IL_2015UR11* MW 260 237 275 236 228 233 296 246 242 149

149

150

Table 5.7. continued

18 1 IN_2015GR96* MW 260 237 275 236 234 230 296 246 242

19 1 IN_2015MG98* MW 260 237 275 236 231 233 296 246 242

20 1 IN_2011TP47 MW 260 237 275 236 231 230 296 248 242

IN_2011TP43*,

IL_2015UR12*, 21 4 MW 260 237 275 236 231 230 296 246 242 MO_2013HW02*,

IA_2015MA06*

22 1 MO_2013HW06 MW 260 237 275 236 231 230 294 246 242

23 1 IN_2015WH93* MW 260 237 275 244 231 233 296 246 242

24 1 MO_2013HW04* MW 260 237 275 244 231 230 296 246 242

25 1 IL_2013NA06* MW 258 239 275 236 231 230 294 246 257 1 50

150

151

Table 5.7. continued

26 1 NY_2013CY01* MW 258 239 275 236 231 230 294 246 242

27 1 IN_2015TP83* MW 258 239 275 244 231 233 294 246 247

28 1 IN_2015RO75* MW 258 239 275 244 231 230 296 246 247

29 1 IN_2010TP02* MW 258 239 275 244 231 230 294 246 242

30 1 IN_2015JA90* MW 258 237 277 240 231 230 296 246 242

31 1 IN_2015BE89* MW 258 237 277 236 231 230 294 246 242

32 1 IN_2015CA95* MW 258 237 277 244 231 236 294 246 257

33 1 MI_2015IS01* MW 258 237 277 244 231 230 296 246 247

34 1 IN_2015CA94* MW 258 237 273 244 231 233 294 246 242

35 1 IN_2014TP73 MW 258 237 273 244 231 236 294 246 257

36 1 MO_2015HR07* MW 258 237 273 244 231 236 294 246 247 151

151

152

Table 5.7. continued

MO_2015HR08*, 37 2 MW 258 237 273 244 231 236 294 246 242 IA_2014BO02*

MS_2014SV03*, 38 2 ST 258 237 275 240 231 230 296 246 242 AR_2015NA01*

39 1 IL_2015PE22* MW 258 237 275 246 231 233 296 246 247

40 1 IN_2015TP81* MW 258 237 275 246 231 230 294 246 242

41 1 IN_2015TP79* MW 258 237 275 242 231 230 296 246 242

42 1 IL_2013NA07* MW 258 237 275 236 231 230 294 246 257

IN_2015NW87*, 43 2 MW 258 237 275 236 231 230 294 246 242 NY_2013CY02*

44 1 MS_2014SV01* ST 258 237 275 244 228 230 296 246 242 152

152

153

Table 5.7. continued

45 1 IL_2015AU18 MW 258 237 275 244 231 233 296 246 247

46 1 NE_2015SA01* MW 258 237 275 244 231 233 294 246 257

IA_2015DA09*, 47 2 ST 258 237 275 244 231 233 294 246 242 TN_2014MD07*

IN_2015CL78*, 48 2 MW 258 237 275 244 231 236 294 246 242 IA_2014BO01*

49 1 IN_2010TP36* MW 258 237 275 244 231 227 294 246 242

50 1 IN_2010TP06* MW 258 237 275 244 231 224 294 246 242

51 1 MI_2015IS02* MW 258 237 275 244 231 230 296 246 257

IN_2014TP72, 247

52 3 IL_2015PE25, MW – ST 258 237 275 244 231 230 296 246

KY_2015PR01 153

153

154

Table 5.8. continued

53 1 MO_2013HW03* MW 258 237 275 244 231 230 296 246 242

54 1 IN_2013TP58* MW 258 237 275 244 231 230 294 246 247

IN_2010TP01,

55 3 IL_2015PE21*, MW – ST 258 237 275 244 231 230 294 246 242

TN_2014MD09* a MLG: Multilocus genotype. b Isolates marked with ’*’ were used to identify mating type genes c MW: Midwestern, and ST: Southern.

154

154

155

Figure 5.1. Genotype accumulation curve for Stenocarpella maydis isolates collected from Midwestern and Southern regions in the US. The vertical axis denotes the number of observed multilocus genotype (MLG), from 0 to the observed number of MLGS. The horizontal axis, denotes the number of loci. When 8 microsatellite loci are used in genotyping, 90% of the unique multilocus genotypes can be detected. The horizontal red dashed line represents 90% of the unique multilocus genotype.

156

Table 5.9. Indices of association (IA) and its standardized form (ṝd) for nonclone-corrected and clone-corrected data from Midwest and Southern population of Stenocarpella maydis.

Population

Midwesta Southern

Sample No. 152 22

Clonally corrected (n) 59 11

IA 0.44 (P =0.0009) 0.78 (P =0.0009)

IA, clonally corrected 0.05 (P =0.2628) -0.70 (P = 0.7348)

ṝd 0.06 (P =0.0009) 0.12 (P =0.0009)

ṝd, clonally corrected 0.01 (P =0.2627) -0.03 (P=0.7343) a P values were determined by 1,000 randomizations.

157

Figure 5.2. Estimated population structure for K = 2 using microsatellite genotyping.

Analysis was performed using admixture model in Structure v2.3. Each color represents one cluster, and each bar represents one isolate.

158

Figure 5.3. Agarose gel electrophoresis of polymerase chain reaction products. A.

Examples of PCR amplification of MAT1-1 specific-sequence from Stenocarpella

maydis (203 base pairs). B. Examples of PCR amplification of MAT1-2 specific- sequence from Stenocarpella maydis (369 base pairs).

VITA

159

VITA

Martha Patricia Romero Luna

Personal Vita

I was born on February 25, 1985 in Guadalajara, Jalisco Mexico. My dad grew up

in a farm. Through the years he tried to maintain and shared with his family that part of

his life. Thanks to him, I grew up surrounded by agriculture. I decided to study biology,

despite my mom’s concern. After four years, I graduate from the University of

Guadalajara, a tradition among my mom’s family. I started my professional career at

Dupont – Pioneer Mexico and I continued until my life took a big turn and I moved to the

United States to begin my graduate school at Purdue University in the Botany and Plant

Pathology department. After over five years of graduate school, I’m ready to start the next chapter of my life and continue my professional career.

Professional Vita

EDUCATION:

Ph.D. in Plant Pathology, Purdue University, (May 2016).

M. S. in Plant Pathology, Purdue University, 2012.

B. S. in Biology, Universidad de Guadalajara, Mexico, 2007.

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PROFESSIONAL EXPERIENCE:

Graduate Research Assistant: Department of Plant Pathology, Purdue University, West Lafayette, IN, January 2013- present (research advisor: Dr. Kiersten Wise). • Develop molecular assays for the identification of Stenocarpella maydis and Stenocarpella macrospora, causal pathogens of Diplodia ear rot. • Conduct a phylogenetic investigation of S. maydis isolates. • Investigate the efficacy of crop rotation and tillage practices for Diplodia ear rot management • Determine the survival period of S. maydis on corn residue under different residue placement depths. • Train and supervise undergraduate research assistants.

Graduate Research Assistant: Department of Plant Pathology, Purdue University, West Lafayette, IN, January 2011 – December 2012 (research advisor: Dr. Kiersten Wise). • Screened a maize population developed for the purpose of mapping traits of interest in maize, for resistance to Diplodia ear rot. • Identified genetic markers and loci associated with Diplodia ear rot. • Determined the efficacy of fungicide applications for the Diplodia ear rot management. • Assessed in vitro fungicide sensitivity for three fungicides against S. maydis.

Intern Research Field Pathology Maize Product Development – Plant Pathology department, Dupont-Pioneer, Johnston, Iowa, May – December 2010 (supervisor: Dr. Scott Heuchelin) • Evaluated two inoculation methods for Gibberella ear rot under greenhouse conditions • Determined inoculation methods for Goss’s wilt under greenhouse conditions • Scored Diplodia ear rot in field research trials. • Assisted diagnostic lab with processing samples and entering data in internal data base.

Research Associate Plant Pathology Maize Product Development- Plant Pathology laboratory, Dupont- Pioneer, Mexico, January 2008 – May 2010 (supervisor: Carmen Velazquez, M.S.). • Assisted research breeding programs by preparing inoculum, performing inoculations and scoring disease reactions. • Supported research, agronomy and sales programs by diagnosing plant pathogens. • Implemented safety programs.

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Plant Pathology Intern Maize Product Development- Plant Pathology laboratory, Dupont- Pioneer, Mexico, June – December 2007 (supervisor: Carmen Velazquez, M.S.). • Supported maize breeding program by performing inoculations and characterizing disease on germplasm.

Undergraduate Research Assistant: Instituto de Ciencia y Tecnología de Semillas - Universidad de Guadalajara CUCBA, Mexico, April 2004 – June 2007 (research advisor: Dr. Elias Sandoval). • Assisted seed preparation and data collection from maize and sorghum research trials.

AWARDS AND HONORS:

• American Phytopathological Society – North Central Division travel award, 2012. • Purdue College of Agriculture Graduate Research Spotlight, 2013. • Department of Plant Pathology, Purdue University. First place poster competition 2013. • American Phytopathological Society – North Central Division travel award, 2015.

TEACHING EXPERIENCE:

Teaching Assistant: Introduction to Plant Pathology, Purdue University, West Lafayette, 2013 (Teaching professor: Dr. Charles Woloshuk). • Organized lab experiments, prepared lab lectures, review materials, prepare lab quizzes.

Purdue Zip-Trips – Plant Science: guest scientist. Purdue University, West Lafayette, 2014. • Taught middle-school audience about how diseases affect plants.

Purdue Crop Scouting Competition Judge: Corn diseases. Purdue University, West Lafayette, 2014.

Purdue Crop Scouting Competition Judge: Soybean diseases. Purdue University, West Lafayette, 2015.

Agricultural Education and Market Improvement Program (AEMIP): Introduction to Plant Pathology. I’Institut Superieur Agronomique Valery Giscard d’Estaing de Faranah. Faranah, Guinea.

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PROFESSIONALISM:

• Plant Pathology Graduate Student Organization President, Purdue University, September 2012 – September 2013. • American Phytopathological Society Member 2011-present. • North Central Division of American Phytopathological Society Member 2011- present.

SKILLS:

• Strong interpersonal and communications skills with proven experience working independently and with a culturally diverse team. • Well-organized self-starter with excellent organization and time management skills. • Routine use of Microsoft Software (Word, Excel, Power Point) and Adobe Photoshop. • Experience with statistical analysis programs such as SAS and R. • Bilingual, English (fluent) and Spanish (first language).

SCIENTIFIC PUBLICATIONS:

Refereed: Romero, M. P., and Wise, K. A. 2015. Timing and efficacy of fungicide applications for Diplodia ear rot management. Plant Health Prog. doi:10.1094/PHP-RS-15-0010

Romero, M. P., and Wise, K. A. 2014. Development of molecular assays for detection of Stenocarpella maydis and Stenocarpella macrospora in corn. Plant Dis. 99(6):761-769.

Abstracts: Romero, M.P., and Wise, K.A. 2015. Influence of cultural practices on Diplodia ear rot, and survival of fungal Stenocarpella maydis in infested corn residue. North Central Division Meeting. APS. East Lansing, MI.

Romero, M., Aime, M., Woloshuk, C., and Wise, K.A. 2014. Development of conventional PCR assays for the Diagnosis of Stenocarpella spp. North Central Division Meeting. APS. Madison, WI.

Romero, M.P., Liu, C., and Woloshuk. C. 2013. Characterization of Stenocarpella maydis mutants. Phytopathology 103:S123. APS Annual Meeting. Austin, TX.

Romero, M.P., Woloshuk, C.P., Johal, G.S., and Wise, K.A. 2012. Mapping of genes associated with Diplodia ear rot resistance in maize. Phytopathology 102:S102. APS Annual Meeting. Providence, RI

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Romero, M.P., Woloshuk, C.P., and Wise, K.A. 2012. Timing the efficacy of fungicide application for Diplodia ear rot management. Phytopathology 102:S8. North Central Division Meeting. APS. Wooster, OH.

EXTENSION PUBLICATIONS:

Romero, M. P., and Wise, K. A. 2015. Evaluation of fungicides for Diplodia ear rot. Purdue University. BP-87-W. EXTENSION PRESENTATIONS:

Romero, M. P. 2015. Diplodia ear rot in corn. (Talk presented at Pinney Purdue Field day, August, IN).