Dual RNA-Seq Profiling Unveils Mycoparasitic Activities of Trichoderma atroviride against Haploid Armillaria ostoyae in Antagonistic Interaction Assays

Abstract

Armillaria species can survive for decades in the soil on dead woody debris, develop rapidly under favorable conditions, and harmfully infect newly planted forests. Our previous study found Trichoderma atroviride to be highly effective in controlling Armillaria growth; therefore, our current work explored the molecular mechanisms that might play a key role in Trichoderma-Armillaria interactions.

Recent advances in the use of Trichoderma-containing multicomponent microbial inoculants for pathogen control and plant growth promotion

“…Trichoderma species exhibit a wide range of antifungal properties effective against fungal plant pathogens (Druzhinina et al 2011 ; Chen et al 2023 ). They secrete hydrolytic enzymes including chitinases, glucanases, lipases, and endo- and exopeptidases targeting various cell wall components of the interacting fungal partners.…”

“…Advances in metatranscriptomic and metabolomic analysis and other important molecular tools will provide insights in the interaction network to help researchers in reaching the full biocontrol and plant growth promoting potential of Trichoderma species and other plant-beneficial microorganisms. Recent findings regarding the upregulation of apoptotic genes in the target organism may offer a promising monitoring option to follow up the survival and contribution of various components of the microbial consortium (Chen et al 2023 ). Multi-RNA-seq profiling of the apoptotic genes and the followup expression analysis of gene families significantly contributing to the biocontrol impact might be a highly informative source to identify and optimize the necessary partakers for the biocontrol intervention.…”

“…It leverages the power of genomics, transcriptomics, proteomics, and metabolomics to identify promising candidates for improving soil health, plant growth, and disease management (Dutta et al 2023;Mukherjee et al 2013). The integration of metabolomics data with genomics, transcriptomics, and proteomics data enables to gain a comprehensive understanding of biological processes (Lorito et al 2010;Zhang et al 2016;Chen et al 2023).…”