Eukaryotic signaling networks are composed of interconnected systems of regulatory proteins and are involved in controlling nearly all biological processes. To study these networks, our collaborative team is working toward combining phosphoproteomic and transcriptomic data with genetic-algorithm programming to build dynamic mathematical models. As a model system, we are studying the fungal Cell Wall Integrity Signaling (CWIS) pathway, which is activated upon wall stress, leading to expression of several wall-repair genes. To assess system-wide dynamic response to stress, we perturbed A. nidulans cultures with CWIS activating agent micafungin, collected 13 phosphoproteomic and transcriptomic samples over 10 and 120 min respectively and found over 1800 genes and 430 phosphorylation sites showed significant dynamic change. Omics-based hypotheses were confirmed with experiments, and our collective data indicate previously unknown connections with (i) actin regulation, (ii) endocytosis, and (iii) septum formation as critical cellular processes responding to activation of CWIS. While the CWIS pathway induces expression of some wall repair genes, it is unclear how numerous others are regulated. To address this question we used a strain lacking the last kinase in the CWIS pathway, MpkA, rendering the pathway inactive. To begin, we compared MpkA+/- strains, at a single time point, in the absence of wall stress. Over 2500 genes and 200 phosphorylation sites showed differential expression and occupancy respectively. Omics-based hypotheses were confirmed with experiments, and our collective data indicate previously unknown connections with wall maintenance, branching regulation and iron metabolism. Seeking alternate regulatory pathways, we carried out dynamic experiments with the MpkA- strain taking phosphoproteomic and transcriptomic samples over 10 and 120 min respectively. We will report on our findings from these experiments and how we are using these data, along with a genetic algorithm programming approach, to develop models to describe signal transduction and resultant changes in gene expression.

Using a multi-omic approach to understand and model eukaryotic signal transduction

ACS Fall 2021

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