A novel in silico approach for identifying defense-related biomarkers from Neurospora crassa during plant associations to understand pathogenicity mechanisms

Abstract

Neurospora crassa, an ideal eukaryotic model organism, offers key advantages for studying various biological processes. While it is predominantly known for its saprotrophic lifestyle, thriving on dead, decaying organic matter and burnt vegetation, investigations on their pathogenicity potential and the nature of its association with living plants remain largely unexplored and limited. A novel gene analysis pipeline has been proposed for the identification of defense-related biomarkers through an in silico biocomputational approach. GSE34098, a gene expression dataset from the GEO database, has been utilized as a source for our study. Deployment of a customized Python script in this pipeline has enabled gene refinement of 10,926 DEGs and in protein enrichment processes. Comprehensive parametric gene and interactome analyses identified five key proteins as potential biomarkers. These findings are substantiated by scientific literature, which contribute to validating the outcomes of the current study. The implementation of the proposed workflow will help in the identification of key biomolecules of elicitor and effector potential involved in pathogenicity mechanisms. Importantly, insights regarding the potential of the host and the corresponding pathogen’s ability to impact each other can be understood. The knowledge of the same can pave way to improve crop resilience.