Machine Learning of All Mycobacterium tuberculosis H37Rv RNA-seq Data Reveals a Structured Interplay between Metabolism, Stress Response, and Infection

Abstract

Mycobacterium tuberculosis H37Rv is one of the world's most impactful pathogens, and a large part of the success of the organism relies on the differential expression of its genes to adapt to its environment. The expression of the organism's genes is driven primarily by its transcriptional regulatory network, and most research on the TRN focuses on identifying and quantifying clusters of coregulated genes known as regulons.

A multi-scale transcriptional regulatory network knowledge base forEscherichia coli

“…Taken together, PRECISE-1K and iModulons extracted from it highlight the central role that top-down, data-driven methods must take in transcriptional regulatory network discovery across organisms. Indeed, iModulons have already successfully generated top-down regulatory networks for other organisms (Chauhan et al, 2021; Lim et al, 2022; Poudel et al, 2020; Rajput et al, 2022; Rychel et al, 2020; Sastry et al, 2019; Yoo et al, 2022). The success of PRECISE-1K serves to further cement both the importance of pursuing such efforts and the reliability of the results.…”

“…Independent component analysis (ICA) (Comon, 1994) is a signal processing algorithm that outperforms other methods for the extraction of biologically meaningful regulatory modules from gene expression data (Saelens et al, 2018). Application of this method to publicly-available prokaryotic expression data has consistently recovered TRN modules across organisms (Chauhan et al, 2021; Poudel et al, 2020; Rajput et al, 2022; Rychel et al, 2020; Sastry et al, 2019; Yoo et al, 2022). ICA’s effectiveness results from its ability to identify independent groups of genes that vary consistently across samples, regardless of group size or overlapping membership.…”

A comprehensive update to theMycobacterium tuberculosisH37Rv reference genome

“…Accession numbers for all M . tuberculosis H37Rv RNA-sequencing experiments publicly available on the NCBI Sequence Read Archive (SRA) as of October 1, 2021 were downloaded using the NCBI’s esearch and efetch utilities, similar to Yoo et al 56 . Sequencing runs were filtered to include only those performed with Illumina short read sequencing, and runs were processed as described in Ma et al 2021 57 .…”

“…were downloaded using the NCBI's esearch and efetch utilities, similar to Yoo et al 56 . Sequencing runs were filtered to include only those performed with Illumina short read sequencing, and runs were processed as described in Ma et al 2021 57 .…”

Using a Whole Genome Co-expression Network to Inform the Functional Characterisation of Predicted Genomic Elements fromMycobacterium tuberculosisTranscriptomic Data

“…Mtb microarray data have been used to cluster protein-coding genes that show differential expression among clinical isoloates (Puniya et al, 2013) and in response to two different hypoxic models to identify potential transcription factors (Jiang et al, 2016). Another recent network analysis, using a matrix deconvolution method followed by module clustering, uses a large number of RNA-seq samples including deletion mutants, infection models and antibiotic-treated samples as well as restricted media and culture conditions (Yoo, et al, 2022). Here the authors identify 80 modules of protein-coding genes that each approximate an isolated source of variance, together estimated to account for 61% of the total variance seen in in the dataset.…”