Novel data-driven approaches to elucidate interactions between soybean and Phytopthora sojae

Author

Thivanka Sandaruwan Ratnayake, Arkansas State UniversityFollow

Date of Award

2-9-2023

Document Type

Thesis

Degree Name

Biology, MS

First Advisor

Asela Wijeratne

Committee Members

Aaron Shew; Jason Causey; Sudeepa Bhattachrya

Call Number

LD 251 .A566t 2022 R38

Abstract

Phytophthora sojae is a soil-borne oomycete and the causal agent of Phytophthora stem and root rot (PSR) in soybean (Glycine max [L.] Merrill). Yield losses attributed to P. sojae are devastating in disease-conducive environments, with global estimates surpassing 1.1 million tonnes annually. Historically, management of PSR has entailed host genetic resistance (i.e., vertical and horizontal) complemented by disease-suppressive cultural practices (e.g., oomicide application). However, the vast expansion of complex and/or diverse P. sojae pathotypes complicated by climate instability necessitates the development of novel technologies for the attenuation of PSR in field environments. Therefore, the objective of the present study was to couple high-throughput sequencing data, bioinformatics tools, and machine learning to elucidate molecular features in soybean following infection by P. sojae as well as understand how P. sojae evolve. We identified differentially expressed genes during compatible and incompatible interactions with P. sojae and a mock inoculation. The expression data were then used to select two defense-relevant transcription factors (TFs) belonging to WRKY and RAV gene families. DNA Affinity Purification and sequencing (DAP-seq) data were obtained for each TF, providing putative DNA binding sites for each in the soybean genome. These bound sites were used to train a Deep Neural Network with convolution and recurrent layers to predict new target sites of WRKY and RAV family members in the DE gene set. Moreover, we leveraged publicly available Arabidopsis DAP-seq data for six TF families reported to function in biotic stress tolerance to train similar models. These Arabidopsis data-based models were then used for cross-species TF binding site prediction on soybean. Finally, we created a gene regulatory network depicting TF-target interactions that orchestrate an immune response against P. sojae. Information herein may prove beneficial for developing soybean cultivars with more durable resistance to P. sojae. Further, we used 47 P. sojae isolates from the host rotation study and conducted variant calling analysis. According to genome-wide SNPs, population structure analysis indicates that there is a link between host resistant and mutations patterns in the pathogen. This was further supported by the distance tree analysis of SNPs from 402 Avr genes. However, when we examined the loci of 11 essential Avr genes based on past studies, we could not find evidence to support the idea that the observed pattern arises due to variation in Rps genes in cultivars

Rights Management

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Ratnayake, Thivanka Sandaruwan, "Novel data-driven approaches to elucidate interactions between soybean and Phytopthora sojae" (2023). Student Theses and Dissertations. 221.
https://arch.astate.edu/all-etd/221