Clint W. Magill

· ProfessorVerified

Texas A&M University · Pathology

Active 1972–2026

h-index28

Citations2.3k

Papers15042 last 5y

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About

Clint W. Magill is a professor in the Texas A&M Department of Plant Pathology and Microbiology, with faculty affiliations in Genetics and Molecular and Environmental Plant Sciences. His research focuses on interactions between fungal pathogens and sorghum, specifically aiming to identify genes in sorghum that confer resistance to diseases such as anthracnose, head smut, and downy mildew. He also investigates the basis for race specificity in the causal pathogens. Magill's work involves understanding disease resistance mechanisms in plants and applying this knowledge to improve sorghum resilience against various fungal diseases.

Research topics

Agronomy
Botany
Biology
Horticulture
Genetics
Medicine
Ecology
Veterinary medicine

Selected publications

Functional Network Analysis of Fungal Pathogen Colletotrichum Sublineola Effectors in Sorghum Anthracnose - Data Tables
Figshare · 2026-01-01
datasetOpen access
Table 1. Colletotrichum sublineola effectors primary data. Here we list the identified Cs effectors and describe their properties, which include amino acid sequences, architectures and initial classification according to their domains’ basic functions. These effectors are distributed in the table in three different tabs depending on their destination in the host cell as apoplastic, cytoplasmic and dual location. Table 2. A closer look at some major groups of Colletotrichum sublineola apoplastic effectors and their functional roles. This table has four Tabs: A) CFEM Tab, where we describe the CFEM proteins (effectors and non-effectors) properties inferred from their amino acid sequences, such as their domain content, probability of them being membrane proteins (according to THMM), and their predicted location in the host using the Localizer and WoLFPSORT programs. B) Glycosyl hydrolases Tab. Here we classify the GH effectors in protein families, provide their EC numbers, and predicted enzymatic activity. C) Other apoplastic enzymes Tab. We list here some CAZymes and other enzymes which are important for the digestion of the plant cell wall and other processes in the initial invasion of the host. D) Binding functions Tab. We describe here six groups of effectors with different structures and their binding ability to various substrates.Table 3. Common reactions in hydrogen peroxide metabolism. This table shows a summary of reactions listed in the BRENDA Database and is divided into two Tabs: A) H2O2 as substrate Tab, where we list enzymes that catalyze H2O2 consumption. B) H2O2 as product Tab, where we list enzymatic reactions that either the pathogen, or the host enzymes, can use to generate H2O2. Table 4. Comparative genomics of Colletotrichum sublineola proteins related to fungal pathogenicity. We used a literature search, combined with comparative genomics methodology to predict Cs proteins functions in the context of fungal pathogenicity mechanisms. We show our results in five Tabs: A) Oxidative stress response Tab, where we give examples of homologs of Cs effectors and their oxidative stress regulation functions. B) Protein modifications Tab, where we predict Cs effectors functions based on studies of their homologs in other biological systems. C) CFEM Tab, where we compare Cs CFEM protein properties with those of other organisms CFEM effectors described in the literature. D) Glycosyl hydrolases other roles Tab, where we give examples of other organisms glycosyl hydrolases with elicitor activities that do not require enzymatic activity on the cell wall. E) Protein glycosylation Tab, where we do comparative genomics of fungal enzymes related to protein glycosylation and analyze their role in fungal pathogenesis. 
Publisher DOI
Additional file 1 of A machine learning approach to genome-wide association mapping of disease resistance and geographic origin in sorghum
Figshare · 2026-02-28
otherOpen accessSenior author
Supplementary Material 1. Fig. S1 Genetic distance among sorghum mini core accessions based on SNP data. The bar chart shows the average genetic distance among sorghum mini core accessions, calculated from 297,876 SNP markers using the IBS method. Distances are grouped by resistance or susceptibility to three diseases (anthracnose, head smut, and downy mildew), by country of origin, and by broader geographic region. Error bars indicate the standard error of the mean.
Publisher DOI
A haplotype-layered GWAS identifies a multi-trait grain mold resistance hub on sorghum chromosome 5
Theoretical and Applied Genetics · 2026-04-01
articleSenior author
Publisher DOI
Functional Network Analysis of Fungal Pathogen Colletotrichum Sublineola Effectors in Sorghum Anthracnose - Data Tables
Open MIND · 2026-01-01
dataset
Table 1. Colletotrichum sublineola effectors primary data. Here we list the identified Cs effectors and describe their properties, which include amino acid sequences, architectures and initial classification according to their domains’ basic functions. These effectors are distributed in the table in three different tabs depending on their destination in the host cell as apoplastic, cytoplasmic and dual location. Table 2. A closer look at some major groups of Colletotrichum sublineola apoplastic effectors and their functional roles. This table has four Tabs: A) CFEM Tab, where we describe the CFEM proteins (effectors and non-effectors) properties inferred from their amino acid sequences, such as their domain content, probability of them being membrane proteins (according to THMM), and their predicted location in the host using the Localizer and WoLFPSORT programs. B) Glycosyl hydrolases Tab. Here we classify the GH effectors in protein families, provide their EC numbers, and predicted enzymatic activity. C) Other apoplastic enzymes Tab. We list here some CAZymes and other enzymes which are important for the digestion of the plant cell wall and other processes in the initial invasion of the host. D) Binding functions Tab. We describe here six groups of effectors with different structures and their binding ability to various substrates.Table 3. Common reactions in hydrogen peroxide metabolism. This table shows a summary of reactions listed in the BRENDA Database and is divided into two Tabs: A) H2O2 as substrate Tab, where we list enzymes that catalyze H2O2 consumption. B) H2O2 as product Tab, where we list enzymatic reactions that either the pathogen, or the host enzymes, can use to generate H2O2. Table 4. Comparative genomics of Colletotrichum sublineola proteins related to fungal pathogenicity. We used a literature search, combined with comparative genomics methodology to predict Cs proteins functions in the context of fungal pathogenicity mechanisms. We show our results in five Tabs: A) Oxidative stress response Tab, where we give examples of homologs of Cs effectors and their oxidative stress regulation functions. B) Protein modifications Tab, where we predict Cs effectors functions based on studies of their homologs in other biological systems. C) CFEM Tab, where we compare Cs CFEM protein properties with those of other organisms CFEM effectors described in the literature. D) Glycosyl hydrolases other roles Tab, where we give examples of other organisms glycosyl hydrolases with elicitor activities that do not require enzymatic activity on the cell wall. E) Protein glycosylation Tab, where we do comparative genomics of fungal enzymes related to protein glycosylation and analyze their role in fungal pathogenesis. 
DOI
Additional file 1 of A machine learning approach to genome-wide association mapping of disease resistance and geographic origin in sorghum
Figshare · 2026-02-28
otherOpen accessSenior author
Supplementary Material 1. Fig. S1 Genetic distance among sorghum mini core accessions based on SNP data. The bar chart shows the average genetic distance among sorghum mini core accessions, calculated from 297,876 SNP markers using the IBS method. Distances are grouped by resistance or susceptibility to three diseases (anthracnose, head smut, and downy mildew), by country of origin, and by broader geographic region. Error bars indicate the standard error of the mean.
Publisher DOI
Additional file 2 of A machine learning approach to genome-wide association mapping of disease resistance and geographic origin in sorghum
Figshare · 2026-02-28
datasetOpen accessSenior author
Supplementary Material 2.
Publisher DOI
Additional file 2 of A machine learning approach to genome-wide association mapping of disease resistance and geographic origin in sorghum
Figshare · 2026-02-28
datasetOpen accessSenior author
Supplementary Material 2.
Publisher DOI
A machine learning approach to genome-wide association mapping of disease resistance and geographic origin in sorghum
BMC Plant Biology · 2026-02-27
articleOpen accessSenior author
Sorghum, the fifth most important cereal crop globally, faces persistent threats from fungal diseases that limit productivity and resilience. To investigate the genetic basis of disease resistance and geographic adaptation, we applied a machine learning-enabled genome-wide association study (GWAS) to a panel of 377 genetically diverse sorghum accessions, incorporating nearly 300,000 SNP markers and phenotypic evaluations for resistance to anthracnose, head smut, and downy mildew. While disease resistance phenotypes did not cluster strictly by geographic origin, SNP-based analyses revealed significant genetic differentiation among accessions from different regions, particularly involving a genetically distinct group from Senegal. Bootstrap Forest models highlighted candidate SNPs predictive of geographic origin, most notably on Chromosome 10, near genes encoding transcription factors (e.g., bHLH, EREBP-like) and DUF6598-domain proteins with potential roles in plant defense. For disease resistance, top-ranked SNPs were located near genes implicated in canonical immune pathways, including zinc-binding proteins (anthracnose), NB-ARC and LRR-containing proteins (head smut), and F-box proteins (downy mildew). Although exploratory in nature, these findings suggest that local adaptation to pathogen pressure may have shaped sorghum’s genomic landscape. The identified candidate genes and associated SNPs help prioritize targets for marker-assisted selection and follow-up functional validation, contributing to the development of sorghum varieties with enhanced resistance and adaptability.
Publisher DOI
Genomic Analysis of Resistance to Exserohilum turcicum in Nigerien and Senegalese Sorghum Using GWAS and Machine Learning
Pathogens · 2026-04-05
articleOpen accessSenior author
Sorghum, an essential crop in Niger, ranks second to pearl millet in importance for food, feed, and commerce. However, its yields are hindered by various factors, including diseases such as leaf blight caused by Exserohilum turcicum. In this study, field phenotypes were analyzed on 102 accessions (including checks SC748-5 and BTx623) grown and evaluated at two locations in Niger for leaf blight incidence and severity. The panel included accessions originally collected from Niger and Senegal. Genotypes were generated for 120 accessions, and GWAS/ML analyses were performed on 102 accessions due to missing phenotypic data. Among the accessions, S39, N23, and N38 exhibited mean leaf blight incidence below 50%, while S3, S43, N23, and N38 displayed the lowest severity levels, with a mean severity in Niger of 24.5 ± 0.64. Accession N23 showed relatively low incidence and severity levels across the Niger field evaluations. Using genome-wide association studies and machine learning, candidate SNPs associated with leaf blight phenotypes were identified. Genes near these SNPs were associated with functions related to plant defense mechanisms and stress responses, providing preliminary targets for future validation in sorghum leaf blight studies.
Publisher OA PDF DOI
Functional Network Analysis of Fungal Pathogen Colletotrichum sublineola Effectors in Sorghum Anthracnose
bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-10
articleOpen access
Abstract Colletotrichum sublineola (Cs) is a hemibiotrophic fungal pathogen that causes anthracnose in Sorghum bicolor , leading to significant yield losses. To enable infection, Cs secretes effectors - proteins, small RNAs, and metabolites - that damage the plant cell wall or enter the plant cell to suppress immune responses and manipulate host metabolism. Effectors can detoxify host antimicrobials, alter nutrient processing, and evade host immunity. Paradoxically, some effectors can also trigger pattern-triggered immunity (PTI), especially in biotrophic and necrotrophic fungi. More than half of fungal protein effectors lack conserved domains and functional network annotations. In this study, we identified prospective Cs effectors, separating those with non-conserved domains and classifying those with conserved domains by protein families. Comparative genomics is employed to predict effector functions and analyze their roles. Using their predicted locations and domains, we mapped the effectors into functional subsystems related to PTI. These include interactions in the apoplast, oxidative stress response, protein modification and degradation systems, and C ysteine-rich F ungus-specific E pidermal Growth Factor-like M odule (CFEM) domain proteins involved in immune regulation. Our functional network analysis advances the understanding of Cs pathogenicity and offers insights into effector infection mechanisms.
Publisher OA PDF DOI

Frequent coauthors

Louis K. Prom
Southern Plains Agricultural Research Center
60 shared
Ezekiel Ahn
Beltsville Agricultural Research Center
36 shared
Ramasamy Perumal
Fort Hays State University
31 shared
Alois A. Bell
Agricultural Research Service
26 shared
Jinggao Liu
Agricultural Research Service
26 shared
G. N. Odvody
23 shared
Thomas Isakeit
Texas A&M University
21 shared
Jane M. Magill
20 shared

Education

Other, Agriculture Science
University of Illinois
Ph.D., Genetics
Cornell University
Other, Molecular Genetics
University of Minnesota

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