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My research project is about understanding the evolutionary relationship, biogeography, and character evolution of leafy liverworts. Liverworts are a group of non-vascular plants that belong to the bryophyte lineage, which also includes mosses and hornworts. Specifically, research focuses on Neolepidozia, a group of liverworts in the family Lepidoziaceae, which is one of the most widely distributed liverwort families with representatives occurring on all continents. The principal objectives of this study are to infer the diversification patterns and biogeography of Neolepidozia. I am using a target enrichment approach with the GoFlag 408 probe set in combination with high throughput sequencing. GoFlag 408 is a set of specific DNA sequences generated from several non-flowering plants, including bryophytes, lycophytes, ferns, and conifers. This study will broaden our understanding of how the bryophytes respond the changing environment. It will also enable us to compare and contrast the diversification patterns between spore-dispersed plants and seed-dispersed plants. liverworts. Additionally, the phylogeny generated by this research will serve as the foundation for future chapters of my dissertation that will explore the population structure at the species level of Neolepidozia on both sides of famous barrier in the tropics, the Wallace Line. Using the same approach of target capture, the objective of the next chapter is to identify the genetic structure of species complex as well as to identify the different lineages between east and west side of Wallace‚ Line.
Accurately characterizing feed intake and efficiency in grazing beef cattle remains a major challenge. Although substantial progress has been made in developing selection tools for feed efficiency during the postweaning growth phase, comparable phenotypes are difficult and expensive to collect in mature cattle under pasture-based conditions. Mature body weight has historically been used as a practical indicator of cow efficiency; however, body size alone does not fully describe differences in feed intake, maintenance requirements, or lifetime productivity. Recent research has demonstrated that larger cows do not necessarily consume more feed than smaller cows, highlighting the need for additional phenotypes that better characterize biological differences in energy use. Enteric gas emissions may provide an opportunity to characterize these differences while generating phenotypes that can be incorporated into genomic analyses. Methane (CH₄) is a natural byproduct of ruminant fermentation and represents a loss of dietary energy that can exceed 10% of gross energy intake. Previous research has identified genetic relationships between methane production, dry matter intake, and residual feed intake, suggesting that variation in CH₄ production may reflect differences in both feed consumption and energetic efficiency. Carbon dioxide (CO₂) production provides complementary information related to metabolic activity and energy expenditure and may therefore contribute to the characterization of maintenance energy requirements. Together, CH₄ and CO₂ phenotypes may provide additional insight into the biological and genetic factors underlying efficiency in grazing beef cattle. Previous work within this project evaluated AgScent “sniffer” systems as a pasture-based method for collecting CH₄ and CO₂ phenotypes, using GreenFeed systems as a reference technology. Establishing the ability to collect emissions records in grazing environments provides an opportunity to move beyond technology validation and investigate the genetic architecture underlying variation in these traits. The objective of this study is to integrate pasture based CH₄ and CO₂ phenotypes with genomics and performance data to characterize genetic variation in enteric emissions and their relationships with economically relevant beef cattle traits. Analyses will evaluate the genetic contribution to variation in emissions phenotypes and estimate relationships among CH₄ and CO₂ production, measures of growth and feed efficiency, body weight, maintenance requirements, and available indicators of productivity. Particular emphasis will be placed on determining whether emissions phenotypes provide information about energetic efficiency that is not adequately captured by mature body size alone. Understanding the genetic architecture of enteric emissions and their relationships with economically important traits is necessary before these phenotypes can be effectively incorporated into beef cattle breeding programs. Integration of scalable pasture based emissions phenotypes with genomic information could support the development of genetic selection tools that balance reduced methane production with feed intake, maintenance requirements, growth, productivity, and beef quality. Ultimately, this research may provide a more comprehensive framework for identifying cattle that efficiently convert forage resources into economically valuable production while minimizing energy lost through enteric methane emissions.
Hops (Humulus lupulus L.) are a highly valued specialty crop cultivated for the unique alpha acids, beta acids, and essential oils produced within the lupulin glands of the female plant's inflorescences. These compounds impart the bitterness and aroma profiles for craft beer brewing and related specialty products. While commercial production has historically been concentrated in arid, high-latitude regions such as the Pacific Northwest, market demand for locally sourced ingredients has driven expansion in to the southeastern United States States. However, the expansion of hop production at these lower latitudes presents significant challenges due to warmer temperatures, altered daylengths, higher relative humidity, and frequent rainfall. These environmental factors create ideal conditions for the development of plant diseases, particularly hop downy mildew caused by Pseudoperonospora humuli. Currently, the majority of integrated pest management (IPM) recommendations are specific to higher latitude climates, requiring a modified disease management approach for the high-humidity environments in the southeastern region. This research evaluates a multi-tiered IPM framework that combines molecular diagnostics, cultural practices, canopy management, and non-chemical disease management.
To establish precise diagnostic and field parameters for this IPM framework, symptomatic tissue was collected from 'Cascade' hops at the University of TN Research Hopyard. Pathogen identity was confirmed as P. humuli using PCR targeting the internal transcribed region (ITS) and mitochondiral cox2 loci. Complementary field trials demonstrated that organic and conventional fungicide applications reduced disease severity, while wider intra row spacing nearly doubled yields by improving canopy microclimates. To address the economic costs and resistance risks associated with chemical reliance, this study incorporates high throughput RNA sequencing (RNA-Seq) to evaluate nighttime ultraviolet (UV-B and UV-C) flashes as a non-chemical disease management approach. Bioinformatic analysis of these transcriptomic libraries clarifies whether UV treatments act through direct pathogen inhibition or host defense priming via the UVR8 photoreceptor pathway. Combining these molecular, field, and bioinformatic approaches delivers scalable integrated pest management strategies tailored for high humidity growing environments.
The rapid dissemination of plant viruses drives global agricultural instability and crop loss, with whitefly-transmitted geminiviruses—including Tomato yellow leaf curl virus (TYLCV) and emerging cucurbit viruses- Cucurbit leaf crumple virus (CuLCrV), Squash leaf curl virus (SLCV), and Watermelon chlorotic stunt virus (WmCSV) which pose as an economic threat to tomato and cucurbit production. Because these pathogens rely on strict virus-vector interactions, successful transmission is mediated exclusively by specific cryptic biotypes within the Bemisia tabaci (silverleaf whitefly) species complex that can transmit over 100 viruses. Although TYLCV was recently confirmed in Tennessee from a single isolate detected in 2024, its broader regional distribution, presence of emerging geminiviruses, and the B. tabaci cryptic biotypes driving local transmission remain unknown. To address this critical gap, this proposed research will develop a serological assay to screen commercial tomato and cucurbit production regions across Tennessee to evaluate geminivirus prevalence and molecularly biotype local B. tabaci populations. This approach will map TYLCV distribution, detect emerging viral threats, and clarify regional transmission risks to establish essential baseline data for proactive vector management.
Grape production is increasingly threatened by downy mildew, caused by the oomycete Plasmopara viticola. While North American wild Vitis species serve as critical reservoirs for resistance genes, the rapid evolution of P. viticola virulence has eroded the durability of resistance previously introgressed into hybrid cultivars. The Blue Ridge Mountains(BRM) represent a biodiversity hotspot where rugose topography and microclimatic heterogeneity have likely fostered exceptional allelic diversity for stress adaptation. My study investigates the coevolutionary dynamics of host and pathogen populations shaped by the variable stressors of the BRM. To investigate this, about 650 wild grapes leaves and 150 P. viticola isolates were collected in national forests of Tennessee, Virginia, and West Virginia. Host genomic DNA will be mapped to Vitis super-pangenome graph to accurately call single nucleotide and structural variations. P. viticola genomes was mapped to a high-quality linear reference. In order to understand the diversity, population structure and genetic diversity indices will be quantified for both organisms. Secondly, candidate immune loci across hosts and pathogens will be compared to pinpoint specific genomic signatures of coevolution and local adaptation. Simultaneously, high-throughput leaf disc phenotyping is conducted to map the molecular geographic mosaic of coevolution across spatial gradients. This research will identify populations enriched in putative resistance and virulence alleles. These findings are expected to reveal how environmental heterogeneity and constant arms race maintain allelic diversity, providing actionable insights for breeders and conservation planning to ensure these natural genetic reservoirs are preserved.
The objective of my research project is to evaluate whether imputed sequencing-level density data improves the genomic prediction of available growth traits in Beefmaster cattle, relative to medium- and high-density SNP arrays. Details of my dataset are from the Beefmaster breed, which is a composite breed with contributions from Bos taurus and Bos indicus. Its heterogeneous linkage-disequilibrium patterns and Brahman-derived genomic segments make it a relevant test case for sequence-level prediction. A large, multibreed imputation reference will be constructed using the available Beefmaster sequence data, together with the 1000 Bull Genomes resource. Imputation was performed using an in-house Nextflow pipeline developed by members of Troy's lab at UTK. Haplotype phasing was carried out with SHAPEIT5. Imputation proceeded in two rounds: the first imputed the observed genotypes against an 850K cross-imputed reference panel and the second used the output from the first round against a whole-genome sequence-based reference panel aligned to UMAG2. Chromosome-specific genetic maps were applied in each round. The quality of the imputation will be assessed using masked-genotype validation and summarised by genotype concordance and dosage correlation. Genomic prediction will compare GBLUP and Bayesian models using data for growth traits including birth, weaning, and yearling weight, obtained from medium-density arrays, high-density arrays (round 1), and imputed WGS (round 2). Additional analyses will evaluate informative marker sets and weighting strategies based on functional genomics resources, including FAANG, CattleGTEx, coding annotations and regulatory regions. Prediction accuracy will be evaluated using forward or partial versus whole data validation based on the LR method of Legarra and Reverter (2018). This will enable the estimation of population accuracy, bias and dispersion from successive evaluations, which will be reported as complementary metrics.
My graduate research develops computational methods for prioritizing predicted human protein interactions in specific biological contexts. Large computational screens can nominate thousands of protein pairs, but they generally do not identify the tissue, organ or cell type in which a pair should be investigated.
I analyze 17,849 predicted interactions using 336 expression networks constructed from human brain RNA measurements. Each network represents one anatomical region and cell type. For each candidate pair, I start from the gene encoding one protein and use random walk with restart to rank potential partners. I compare rankings from individual context-specific networks, multiplexes that connect only networks in which both genes were measured, and a multiplex containing all brain networks.
Predicted interactions with prior support from protein structures or interaction databases provide positive references for comparing these approaches. I will apply approaches that rank supported partners above comparable genes to predictions that lack prior support.
The goal is to help researchers choose which predicted interactions should receive additional sequence analysis, structure modeling, or laboratory testing. The rankings do not prove that an interaction occurs in a specific brain context. They identify the brain region and cell type networks in which a candidate pair receives the strongest network support.
The uneven distribution of species on Earth is a fascinating aspect of our planet that has attracted attention from biologists for centuries. The patterns of species distribution were crucial in developing the foundation of modern evolutionary and ecological thought and still continues to inspire research at the cutting edge of these fields. Of all the possible distribution patterns, I am most interested in those of wide ranging species. My dissertation explores the evolution of wide ranging species, using ferns as a study system. My first chapter, recently published in the Journal of Biogeography, investigates the broadscale phylogeographic patterns of range sizes in ferns using phylogenetic comparative methods and digitized, global occurrences records. From this work we found that ferns have a nuanced pattern of range sizes across the phylogeny, influenced by factors such as the structure of the fern phylogeny, species richness of clades, and local geography. My remaining chapters focus on one wide-spread fern species, Onoclea sensibilis- the sensitive fern, to explore the different physiological and molecular processes that have contributed to its current distribution. To do this, I am combining Double Digest Restriction-Site Associated DNA sequencing (ddRAD-seq) and a common garden experiment. With this data, I will quantify the population structure, gene flow, and variation of ecophysiological tolerance across the range of O. sensibilis. Together, my research brings insight to the broad scale patterns of fern biogeography and the mechanisms that have and continue to produce those patterns.
The investment in the proper development of replacement heifers and reproductive management in cow-calf operations is vital to its economic success in the future, being at an upwards of $1500. After development of heifers,it is important to ensure that heifers are bred in an efficient manner and calve right at 2 years of age, which is critical to driving the profitability of cow-calf operations. Fertility traits can be described as lowly heritable due to environmental variation and are difficult to measure as they are often not expressed until later in life. Despite being lowly heritable traits, fertility traits represent a major avenue for improving producer revenue through increased calves and improved genetics. By combining 3' RNA sequencing as a biological intermediate, phenotypic information, and machine learning, we hypothisize that using gene expression data in machine learning models will improve predictions of first service conception in heifers relative to phenotypic information alone. Through this, we will Implement added RNA data and differentiate expressed genes in developing heifers and recognize patterns within these data sets to predict fertility using machine learning.
Bees are indispensable pollinators that support flowering plant diversity and contribute billions of dollars in pollination services. Yet, many populations are in decline due to urbanization. Habitat loss, urban heat islands, and environmental pollutants all represent threats to pollinator communities. Solitary bees, which account for the majority of wild bee diversity, are especially vulnerable to urbanization. Assessing urban risks faced by solitary bees is the first step in developing effective conservation strategies, and conservation genomics can provide novel and robust methods for understanding and combatting declines. Still, the genomic basis of solitary bee responses to urban environments remains almost entirely unexplored. To address this knowledge gap, I will assess the extent to which urban selection pressures drive genomic diversity in the native solitary bee Osmia georgica across an urban gradient in Tennessee. For field collections, I will establish sites along a gradient of urban microenvironments in Knoxville, TN, along with an unmodified semi-natural site. Additional sampling within the Oak Ridge Reservation will provide an ecological baseline with minimal urban development. I will collect ten O. georgica individuals per site for genomic DNA extraction. Using DNA from a single haploid male, I will generate a reference genome assembly for O. georgica with PacBio long read sequencing. I will also generate short-read whole-genome re-sequences for all individuals, which I will use to conduct population genomic analyses. I predict that urban environments will correlate with signs of inbreeding, reduced genetic diversity, smaller effective population size, and signatures of selection for genes with functions related to urban stressors. I will also conduct genotype-environment association analyses against a variety of environmental metrics, including floral abundance, surface temperature, and impervious surface cover. I predict that floral abundance will correlate with foraging alleles, while temperature and impervious surface cover will correlate with alleles related to stress response. My findings will inform conservation efforts by elucidating the impacts of urbanization on solitary bees, an understudied system in conservation genomics. I aim to identify strategies that best address threats revealed by my research, be it planting more flowers or promoting shaded green spaces, with the goal of making our cities as bee-friendly as possible.
Salmonella enterica is an important foodborne pathogen and causes many human infections every year. Some Salmonella strains can persist in the environment or reappear after a period of time. These strains may be difficult to identify using traditional typing methods. Whole-genome sequencing can provide a more detailed way to compare isolates and understand how they are related. My research focuses on the genomic analysis of Salmonella populations, with emphasis on identifying representative, persistent, and re-emerging strains. The main goal is to determine how closely related isolates are and to identify groups of strains that occur repeatedly across different years, environments, locations, hosts, or sample sources. Additional genomic characteristics may also be examined. These may include antimicrobial resistance genes, virulence genes, plasmids, and other mobile genetic elements. These features can provide more information about the biological differences among Salmonella lineages and may help explain why some strains persist or re-emerge. The expected outcome of this research is a clearer genomic framework for identifying important Salmonella strains in large surveillance datasets. Combining genomic relatedness with information about time, location, host, and source may improve the identification of persistent and re-emerging lineages. Whole-genome sequence data will be analyzed using bioinformatics methods. These methods will include single-nucleotide polymorphism (SNP) analysis, phylogenetic tree construction, pairwise genomic distance calculation, and population clustering. Reference-based and reference-free approaches may also be compared. The study will evaluate genomic thresholds that can separate closely related isolates from more distant strains. This will help determine which isolates may represent the same circulating lineage and which may belong to different populations and environments.
The European corn borer (Ostrinia nubilalis) is an important corn pest in North America. Bt corn has been effective in controlling this pest for many years, but resistance to Bt proteins is now becoming a concern. Understanding the genetic changes associated with resistance and being able to detect these changes in field populations is important for monitoring the continued effectiveness of Bt corn.
This research will use bioinformatics approaches to study the genetic basis of Bt resistance in European corn borer. RNA sequencing (RNA-Seq) and bulk segregant analysis (BSA) will be used to compare susceptible and resistant populations. The main goal of these analyses is to find genes and genetic variants that may be linked to resistance. RNA-Seq will also provide information on differences in gene expression between the populations. Together, these results will help identify candidate genes that can be studied further using targeted sequencing.
Targeted sequencing will allow several candidate genes to be screened across a larger number of insects. The sequence data will be examined for genetic variants associated with resistance and used to estimate how common these variants are in different populations. Field-collected European corn borer samples from different locations will also be included to study the distribution of resistance-associated alleles.
Comparing samples across locations may provide a better picture of where resistance alleles are present and how they are spreading among European corn borer populations. This information could also help identify populations where resistance may be more likely to develop.
Overall, this research will combine RNA-Seq, genomic analysis and targeted sequencing to study and monitor Bt resistance in European corn borer. The work is expected to provide genetic information that can be used for resistance monitoring and contribute to the long-term management of Bt resistance in corn.
Exploring the Protective Effect of Pyruvate Kinase M1 and M2 on Ototoxic Hearing Loss:
Hearing loss (HL) is a chronic, highly prevalent, and growing global health concern that significantly impairs quality of life. Affected individuals often face challenges in interpersonal communication, as reduced auditory input disrupts brain connectivity and processing. This can lead to social isolation and heightened risk of psychological disorders (e.g., depression) and cognitive decline (e.g., dementia or early-onset Alzheimer’s). The most common form of HL is sensorineural hearing loss (SNHL), caused by damage to or loss of cochlear hair cells, the most critical sensory structures that transduce sound vibrations into neural signals via the auditory nerve. Cochlear hair cells cannot regenerate once fully differentiated, these irreplaceable cells are highly vulnerable to damage from noise, aging, and disease, primarily through oxidative stress. While primarily reliant on mitochondrial metabolism, they also depend heavily on glycolysis for ATP production, especially under stress conditions such as chemotherapeutic drug exposure. Glycolysis culminates in pyruvate kinase (PK) activity, with cochlear hair cells uniquely expressing two isoforms: PKM1 and PKM2. Although PKM2 has been shown to promote stress adaptation beyond glycolysis in other cell types, the roles of PKM1 and PKM2 in hair cell function, homeostasis, and SNHL remain unknown. Thus, this project aims to elucidate their contributions to cochlear hair cell survival under normal and stress conditions, delineate the underlying molecular mechanisms, and evaluate their potential as pharmacological targets for HL prevention and treatment. To answer the question what are the distinct roles of PKM1 and PKM2 in cochlear hair cell differentiation, function, and stress resistance, and can their targeted inhibition mitigate SNHL? We generated shRNA-mediated knockdown on cochlear hair cells [UB-Organ of Corti-2 (UB-OC-2)] to select PKM1, PKM2, or both. Cells will be differentiated into mature, functional cells over 15 days, with assessments of differentiation efficiency, ATP production, and auditory function. Chemotherapeutic agents like cisplatin will be used to induce oxidative stress to model SNHL, evaluating PK isoforms deficiency on cell survival, apoptosis, and metabolic reprogramming. Preliminary findings reveal upregulated PKM1 and PKM2 expression during differentiation, confirming relevance in hair cell maturation. Based on preliminary data we hypothesize that PK isoforms are essential for proper hair cell development and contribute to the protection from oxidative stress.
The parasitoid wasp Fopius arisanus is a successful biocontrol agent that was imported for use against tephritid fruit fly pests in Hawai’i in 1946 and has since been used globally for fly suppression in the tropics and subtropics. This wasp targets the egg stage of flies like Bactrocera dorsalis, an economic pest capable of causing complete crop failures in serious infestations. Notably, the F. arisanus genome contains an endogenized nudivirus termed Fopius arisanus endogenized nudivirus (FaENV) that produces virus-like particles (VLPs) in the calyx of female wasp ovaries. By examining developmental stages of female wasp pupae as well as pharate and post-emergence adults, we aimed to determine the timeline and tissue localization where important FaENV genes are transcribed to better understand the dynamics of this endogenized virus. Transcription levels of the target genes lef-8 (viral transcription), vp39 (morphogenesis and structural nucleocapsid), p74 (viral envelope protein), and helicase (DNA replication) were estimated with quantitative PCR using extractions from full-body wasp samples, dissected ovaries, and other tissues. Results indicate a statistically significant difference in the expression of the viral gene helicase between pharate full-body samples and ovary-only samples, implying FaENV genes are actively transcribed elsewhere in the body of female wasps. This preliminary result is notable as it suggests that F. arisanus may not have complete control over virus activity when compared to more established parasitoid-endogenized virus relationships. Ultimately, this work will further characterize the evolutionary relationships between parasitoids and endogenous viruses, which may have important implications for their use in biocontrol and IPM methodology.
The Association of Zoos and Aquariums (AZA) uses Species Survival Plans‚Ñ¢ (SSPs) to minimize inbreeding and maintain genetic diversity (GD) for long-term viability of ex-situ populations. Generally, SSPs assume that founders are unrelated and not already inbred. Such assumptions can lead to overestimation of GD and underestimation of inbreeding. This may be particularly important if founders are sampled from low-density wild populations, where inbreeding is likely to be high. Pyxis planicauda and Pyxis arachnoides are critically endangered tortoises with captive populations managed using SSPs. While founders are assumed to be unrelated and not inbred, their original provenance and relationships are unknown. Further, almost half of the offspring have uncertain parentage (127 individuals). To address these concerns, we will sequence whole genomes for all tortoises listed in their SSPs, allowing us to evaluate the inbreeding status of wild-caught founders and resolve pedigrees. In addition, there are three P. arachnoides subspecies that are managed independently. We have preliminary evidence of natural hybridization and 12 captive individuals are taxonomically uncertain. Genomic analysis will help ensure alignment between SSPs and natural patterns of genetic variation. Our research will (1) estimate founder relatedness and inbreeding, (2) complete Pyxis pedigrees to refine breeding recommendations, and (3) assess subspecies boundaries and taxonomically uncertain P. arachnoides.
My research focuses on identifying and evaluating virulence factors in Cercospora sojina and Septoria glycines and understanding how genetic differences among isolates influence their interactions with soybean. I use a combination of pathogen phenotyping, genome sequencing, comparative genomics, population genomics, and transcriptomics to investigate these interactions.
For C. sojina, my research examines genetic variation among isolates with different levels of virulence. I am using genome-wide association studies to identify genomic regions associated with virulence while expanding the number and diversity of isolates included in these analyses. I am also using selected virulent and avirulent isolates to investigate host-pathogen interactions through RNA sequencing and differential gene expression analysis.
For S. glycines, I generated a high-quality reference genome and am using it to investigate potential virulence factors, including secreted proteins, candidate effectors, and secondary metabolite biosynthetic gene clusters. Comparative genomic and structural analyses are being used to identify conserved and species-specific factors that may contribute to pathogenicity. One area of particular interest is a putative cercosporin biosynthetic cluster, which I am investigating through genomic, transcriptomic, and phenotypic approaches.
Overall, my research combines pathogen phenotyping with genomic and bioinformatic approaches to better understand the genetic and molecular basis of virulence in soybean fungal pathogens. By identifying candidate virulence factors and genomic regions associated with disease, this work aims to improve our understanding of soybean–pathogen interactions and provide a foundation for future research in disease resistance and management.
My graduate research focuses on developing and applying computational approaches to improve the interpretation of complex microbial community data, particularly metaproteomic data. Metaproteomics uses mass spectrometry to characterize the proteins expressed by microorganisms within a community, providing information about both the organisms that are present and the biological functions they are performing. However, analyzing these data can be challenging because closely related microorganisms often contain highly similar proteins and can produce peptides that are shared among multiple proteins or organisms. This makes it difficult to confidently determine which organisms contributed particular peptides and proteins and, ultimately, to accurately characterize community composition and function.
My research is aimed at exploring computational and bioinformatic strategies for addressing these challenges and improving biological interpretation of metaproteomic datasets. This includes analyzing protein and peptide identification data, investigating approaches for resolving ambiguous or shared peptide assignments, and integrating information from protein sequences, genome annotations, and evolutionary relationships.
Another component of my research involves investigating computational approaches for predicting the functions of poorly characterized microbial proteins. A substantial fraction of proteins identified in microbial genomes and environmental datasets remain annotated as hypothetical or have limited functional information. I am interested in evaluating how complementary sources of evidence—including sequence similarity, conserved domains and motifs, predicted protein structure, protein interactions, and emerging machine-learning approaches—can be integrated to improve functional annotation.
Ash trees (Fraxinus sp.) have been decimated by the invasion of the emerald ash borer (EAB; Agrilus planipennis) since the early- to mid-2000s. In some areas, ash mortality has been reported to be over 95%. Working with the U.S. Forest Service Northern Research Station, the goal of my PhD is to determine which genes are important for resistance to EAB in lingering ash, trees who persist despite EAB infestation. Specifically, I will focus on the section Melioides, the North American ashes. For the first two chapters, I will specifically be studying green-ish (F. pennsylvanica) and white-ish ash (F. americana). The first chapter of my PhD research will focus on identifying genes that are more prevalent in lingering ash. To do this, I will conduct a genome-wide association study (GWAS) and quantitative trait loci (QTL) analysis. I am using progeny trees created by pollination crosses in the USFS breeding program. The second chapter will take this one step further with RNA-seq and transcriptome analysis. Trees will be inoculated with EAB eggs, and RNA will be extracted at several timepoints post-inoculation to monitor the tree’s defensive response as the EAB larvae develop. Ultimately, these results will be used to inform ash breeding programs to generate new trees with better resilience to EAB infestation. With this knowledge, the USFS hopes to reintroduce ash to areas where it has become locally extinct.
The molecular mechanism through which soybean resistance from southern root-knot nematode works, is crucial to understand. Southern root-knot nematode has a wide range of host species. This research aims to identify and characterize major RKN resistance quantitative trait loci (QTL) and candidate genes to characterize soyabean protiens which can be utilized to build resistance in susceptible soyabean species. This study will also emphasize on how RKN (Melodogyne incognita) manipulates soybean cellular processes through secreted effector proteins. Previously identified but uncharacterized nematode effectors from dorsal and subventral gland cells are under study for functional characterization. These candidate genes targets proteins associated with plant defense, hormone signaling, transcriptional regulation, chromatin remodeling and RNA processing. Bioinfromatics analysis will be used to study these aspects in more details. Protein-protein interactions will be further validated using computational studies, biomolecular fluorescence complementation and co-immunoprecipitation. Further studies will be followed by functional characterization through overexpression and gene silencing.
Together, these complementary genetics, genomics, molecular and bioinformatics approaches will provide a comprehensive understanding of plant-nematode interactions. Identification of major resistance genes and host susceptibility factors targeted by nematode effectors will provide valuable genetic resources for genome-editing strategies like CRISPR-Cas9, aimed at developing root-knot nematode-resistant soybean cultivars.
Frogeye leaf spot (FLS), caused by the fungal pathogen Cercospora sojina, is a destructive foliar disease of soybean (Glycine max L.) that causes significant yield losses in Tennessee and other soybean-producing regions worldwide. To date, three resistance genes have been identified: Rcs1, Rcs2, and Rcs3. However, C. sojina has evolved to overcome Rcs1 and Rcs2, leaving Rcs3 as the only source of reliable resistance. To identify novel sources of resistance and genomic regions associated with FLS resistance, soybean accessions representing maturity groups 0–VIII from diverse geographic origins are inoculated with a spore suspension of C. sojina. Disease is evaluated three weeks post-inoculation, using a lesion-type-based rating scale. Genotypic data for each line is retrieved from SoyBase. A Genome-Wise Association Study (GWAS) is performed to identify marker-trait associations involved in FLS-resistance to identify a significant quantitative trait loci (QTL).Once sufficient data is collected, the identified QTLs will be investigated further. Future steps include identification of a causative resistance gene within QTL, determination of inheritance patterns through cross breeding, and validation of results using RNASeq and qPCR.
AICAR transformylase/IMP cyclohydrolase (ATIC) is an enzyme which functions as a metabolic switch telling fat cells to store or burn energy. It has been shown that inhibition of ATIC promotes levels of 5-aminoimidazole-4-carboxamide ribonucleotide (ZMP) in adipocyte tissue. The increased ZMP levels allow for greater AMPK activation, metabolic reprogramming, and transdifferentiation of adipocytes from calorie storing white fat into calorie burning brown-like fat. The first focus of the project is to observe the role of ZMP in adipose stem cell fate determination. This will be done through pharmacologic modulation, use of CRISPR-engineered ATIC loss- and gain-of-function models, and multi-omic profiling. The next focus of the project is to investigate the impact of ATIC inhibition on energy utilization and metabolic stability in vivo. Selective organ-targeted lipid nanoparticles (SORT) will be used to inhibit ATIC in adipose tissue. The goal of this project is to observe the effects of metabolic signals on stem cell fate and adipocyte identity, demonstrate that targeting ATIC is a viable strategy for improving energy balance, and lay groundwork for developing safer therapies to prevent and treat obesity and related diseases such as diabetes. Ultimately, this research has the potential to advance therapeutic strategies for metabolic disorders while also informing applications in regenerative medicine and tissue engineering.
Camelina sativa is an oil-producing cover crop often cultivated by farmers in the Midwest. While it is not currently considered a cash crop, increasing global bioenergy, along with the effects of climate change, underscores the need to improve high-oil-yielding crops without expanding existing agricultural land.
Canola (Brassica napus), another member of the Brassicaceae family, is the primary model crop for oilseed and biofuel research. In contrast, its botanical cousin, Camelina, remains largely understudied. This knowlege gap warrants further investigation because recent studies suggest that Camelina may be a more viable commercial oilseed crop than canola as climate conditions change, as it has been found to be more tolerant of changes induced by climate change like tolerance to cold, limited nutrient availability, and drought (Quinlan & Goslee, 2025).
Our long-term objective is to improve oil yield in Camelina while identifying the biological mechanisms that regulate oil production. We will first conduct a large-scale phenotyping study across diverse Camelina lines. This experiment will document variation in growth, architecture, reproductive development, and oil-related traits, using statistical analyses to identify individual traits and trait combinations most strongly associated with oil yield. Specifically, we will measure branch yield, pod yield, seed yield, seed oil content, days to germination (DTG), days to flowering (DTF), days to maturity (DTM), and chlorophyl content. Chrlorophyll content will be measured at DTF and DTM time points and extracted from three tissue types: leaves, pods, and seeds. Additionally, we also are growing various seasonal-types of Camelina to see how life-cycle dynamics may contribute to their growth capabilities. Winter-types and some inbred types of Camelina require a vernalization process to flower, while the spring-types do not. To this end, we aim to grow 30 spring-type lines, 5-winter lines, and 15-inbred lines of Camelina so determine the impact on development-type on oil-production capabilities. Previous studies and a preliminary run of growing spring-type Camelina lines revealed substantial variation in branch number and pod production, supporting a broader investigation of these and related traits and highlights a need to accrue more information on the all seasonal-types (Li, H., 2021; Kandel, Jinita Sthapit, 2024).
Understanding relationships among traits will help us identify useful breeding and engineering targets. For example, a trait such as branch number may not directly increase oil concentration per seed, but it could increase total oil yield per plant by increasing pod production(Sabaghnia et al., 2026). Once priority traits are identified, we will integrate phenotypic data with metabolic and publicly available genomic data (Li et al., 2021) to identify the genes underlying desirable variation. These genes can then be targeted through breeding and gene-editing approaches to develop Camelina lines with improved oil yield and resilience.
By building a detailed line-by-line Camelina dataset, this project will address a major gap in current knowledge and provide a foundation for developing a climate-resilient, high-performing oilseed crop for biofuel production.
Anaplasma phagocytophilum second most prevalent tick-borne disease in US which is transmitted by Ixodes scapularis. The vector-pathogen interaction of A. phagocytophilum and I. scapularis is not well studied. Our research is focused to identify key interactions of vector and pathogen which will help in development of transmission blocking vaccine. Currently, my graduate research focuses on how piezo-type mechanosensitive channels of I. scapularis are interacting with transmission and acquisition of A. phagocytophilum. Among different types of mechanosensory ion channels, Piezo proteins are recently identified trimeric proteins on the plasma membrane. In humans, rats, and mice, piezo proteins convert mechanical energy in the cells to chemical signals by the influx of cations. We are using different techniques like quantitative real-time PCR (qRT-PCR) analysis, ELISA, in vitro and in vivo experiments, bioinformatic and phylogenetic analysis to determine their expression and function in ticks. We are investigating the role of piezo-like proteins in tick-A.phagocytophilum interactions. We are using mouse model for in vivo experiments whereas for in vitro experiments ISE6 cells (I. scapularis embryonic cell line) are being used.
Frogeye Leaf Spot (FLS) is a foliar disease of soybean (Glycine max) caused by the fungal pathogen Cercospora sojina and is a significant threat to soybean production in areas such as the Southeastern United States. Under conditions favorable to the pathogen, yield losses from this disease can reach up to 60%. Although genetically resistant cultivars have historically been effective for managing FLS, resistance genes such as Rcs2 have been overcome by C. sojina. Despite the economic importance of this issue, the pathogen effectors that have allowed Rcs2-mediated virulence to arise remain poorly understood, representing a notable knowledge gap.
The primary objective of this study is to identify candidate effector regions in C. sojina associated with virulence on the Rcs2 representative differential line “Kent” using a genome-wide association study (GWAS). Additionally, this project aims to develop a reference-quality genome for an isolate confirmed to be virulent on Rcs2.
To accomplish these objectives, a geographically diverse population of C. sojina isolates will be developed. Genotypic data will be collected through whole-genome resequencing and SNP calling. Population structure will be assessed using principal component analysis (PCA) and linkage disequilibrium (LD). Isolates will be phenotyped through three replications of inoculation on “Kent,” as well as resistant and susceptible control lines.
This research is expected to identify genomic regions associated with Rcs2-mediated virulence. These findings will improve our understanding of host-pathogen interactions and may support future work toward rehabilitating Rcs2 resistance through selective breeding or genetic engineering of soybean cultivars. These efforts will contribute to more sustainable FLS management in soybean production."