bioRxivGenomicsPreprint
Contrasting transcriptional responses and genetic determinants underlie Zymoseptoria tritici adaptation mechanisms to simulated host defense environments
Silvia Minana-PosadaiD, Alice FeurteyiD, Bruce Alan McDonaldiD, Cécile LorrainiD
Abstract
Successful colonization of the wheat apoplast requires that Zymoseptoria tritici tolerate host-derived stresses, but the mechanisms underlying this adaptation remain poorly understood. We combined phenotypic assays, transcriptomics, and genome-wide association analyses to characterize fungal responses to acidic pH, salicylic acid, gibberellic acid, and oxidative stress. Exposure to salicylic acid inhibited in vitro growth across a global collection of 411 Z. tritici strains, whereas acidic pH promoted growth, illustrating contrasting effects on pathogen performance of environments simulating host-defense responses. At the transcriptional level, acidic pH and oxidative stress induced the strongest and most similar responses, while salicylic acid elicited a more distinct transcriptional program and gibberellic acid caused only limited transcriptional changes. Although the sets of differentially expressed genes were largely condition specific, overlapping enrichment of transport- and redox-related functions across conditions indicated shared transcriptional responses. K-mer based genome-wide association mapping identified five candidate loci associated with growth under acidic pH, gibberellic acid and salicylic acid, including four loci specific to a single growth condition. These loci colocalized with genes implicated in cell wall remodeling, nitrogen metabolite regulation, proteostasis, and ubiquitin-related processes. This study highlights the multifaceted strategies employed by Z. tritici to navigate environments simulating host-defense responses, involving shared and environment-specific adaptations. We provide new insights into the genetic and molecular basis of fungal resilience, with implications for understanding pathogen-host interactions.
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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