Identifying Genes Devoted to the Cell Death Process in the Gene Regulatory Network of Ustilago maydis

Abstract

Cell death is a process that can be divided into three morphological patterns: apoptosis, autophagy and necrosis. In fungi, cell death is induced in response to intracellular and extracellular perturbations, such as plant defense molecules, toxins and fungicides, among others. Ustilago maydis is a dimorphic fungus used as a model for pathogenic fungi of animals, including humans, and plants. Here, we reconstructed the transcriptional regulatory network of U. maydis, through homology inferences by using as templates the well-known gene regulatory networks (GRNs) of Saccharomyces cerevisiae, Aspergillus nidulans and Neurospora crassa. Based on this GRN, we identified transcription factors (TFs) as hubs and functional modules and calculated diverse topological metrics. In addition, we analyzed exhaustively the module related to cell death, with 60 TFs and 108 genes, where diverse cell proliferation, mating-type switching and meiosis, among other functions, were identified. To determine the role of some of these genes, we selected a set of 11 genes for expression analysis by qRT-PCR (sin3, rlm1, aif1, tdh3 [isoform A], tdh3 [isoform B], ald4, mca1, nuc1, tor1, ras1, and atg8) whose homologues in other fungi have been described as central in cell death. These genes were identified as downregulated at 72 h, in agreement with the beginning of the cell death process. Our results can serve as the basis for the study of transcriptional regulation, not only of the cell death process but also of all the cellular processes of U. maydis.

Gene Regulatory Network Inference and Gene Module Regulating Virulence in Fusarium oxysporum

“…In this regard, the highest clustering coefficient, or 1, indicates that nodes whose neighbors are connected between them form complete graphs. In the GRN of F. oxysporum , we identified 50 nodes (representing 0.49% of the network) with a clustering coefficient of 1, indicating that there are substructures such as triangles or more complex motifs; this is consistent with previous reconstructed networks ( Lenz et al, 2020 ; Soberanes-Gutiérrez et al, 2021 ). On the other hand, 4,542 nodes (44.84% of nodes in the network) have a clustering coefficient equal to 0, whereas 1791 of the nodes in the network have a degree of 1 and 2.…”

“…This inference is based on the hypothesis that if the sequences corresponding to TFs and TGs are conserved in the model organisms and in F. oxysporum , then regulatory interactions are also conserved. Similar approaches have been proposed for Penicillium echinulatum 2HH, Penicillium oxalicum 114–2, and Ustilago maydis ( Lenz et al, 2020 ; Soberanes-Gutiérrez et al, 2021 ).…”

Homology-based reconstruction of regulatory networks for bacterial and archaeal genomes

“…In this work, we have expanded the GRNs for six model organisms, by considering orthologous inference and TU assignments. This inference is based on the assumption that orthologous TFs generally regulate the expression of orthologous TGs ( Yu et al, 2004 ; Galán-Vásquez et al, 2011 ; Lenz et al, 2020 ; Soberanes-Gutiérrez et al, 2021 ). The inferred interactions were included in the GRN, and their topological properties were calculated.…”

Construction and analysis of gene co-expression network in the pathogenic fungus Ustilago maydis

“…The identification of TFs was achieved by scanning protein sequences of whole proteomes using InterProScan (v5.25–64.0), and hmmsearch (v3.1b2), by default parameters, and selecting those proteins with domains associated to TFs as anticipated by Soberanes-Gutiérrez et al (2021) . An incidence rate for the genome, and a heatmap for each genome were calculated to identify and quantify each dataset.…”