Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Co-Expression Networks for Causal Gene Identification Based on RNA-seq Data of Corynebacterium Pseudotuberculosis

Version 1 : Received: 10 June 2020 / Approved: 12 June 2020 / Online: 12 June 2020 (08:46:02 CEST)

A peer-reviewed article of this Preprint also exists.

Franco, E.F.; Rana, P.; Queiroz Cavalcante, A.L.; da Silva, A.L.; Cybelle Pinto Gomide, A.; Carneiro Folador, A.R.; Azevedo, V.; Ghosh, P.; T. J. Ramos, R. Co-Expression Networks for Causal Gene Identification Based on RNA-Seq Data of Corynebacterium pseudotuberculosis. Genes 2020, 11, 794. Franco, E.F.; Rana, P.; Queiroz Cavalcante, A.L.; da Silva, A.L.; Cybelle Pinto Gomide, A.; Carneiro Folador, A.R.; Azevedo, V.; Ghosh, P.; T. J. Ramos, R. Co-Expression Networks for Causal Gene Identification Based on RNA-Seq Data of Corynebacterium pseudotuberculosis. Genes 2020, 11, 794.

Journal reference: Genes 2020, 11, 794
DOI: 10.3390/genes11070794

Abstract

Corynebacterium pseudotuberculosis is a Gram-positive bacterium that causes caseous lymphadenitis, a disease that predominantly affects sheep, goat, cattle, buffalo, and horses, but has also been recognized in other animals. This bacterium generates a severe economic impact on countries producing meat. Gene expression studies using RNA-seq is one of the most commonly used techniques to perform transcriptional experiments. Computational analysis on such data through reverse-engineering algorithms leads to a better understanding of the genome-wide complexity of gene interactomes, enabling the identification of genes having the most significant functions inferred by the activated stress response pathways. In this study, we identified the influential or causal genes from four RNA-seq data-sets from different stress conditions (high iron, low iron, acid, osmosis, and PH) in C. pseudotuberculosis, using a consensus-based network inference algorithm called miRsig and identified the causal genes in the network using the miRinfluence tool, which is based on the influence diffusion model. We found that over 50\% of the genes identified as influential have some essential cellular functions in the genomes. In the strains analyzed, most of the causal genes have crucial roles or participate in processes associated with response to extracellular stresses, pathogenicity, membrane components, and essential genes. This research brings new insight into the understanding of virulence and infection by C. pseudotuberculosis.

Subject Areas

Corynebacterium pseudotuberculosis; RNA-Seq; co-expression networks; influence genes; stress condition; causal genes

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