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Genome-wide mutational biases fuel transcriptional diversity in the Mycobacterium tuberculosis complex.

Álvaro Chiner-Oms | Michael Berney | Christine Boinett | Fernando González-Candelas | Douglas B Young | Sebastien Gagneux | William R Jacobs | Julian Parkhill | Teresa Cortes | Iñaki Comas
Nature communications | 2019

The Mycobacterium tuberculosis complex (MTBC) members display different host-specificities and virulence phenotypes. Here, we have performed a comprehensive RNAseq and methylome analysis of the main clades of the MTBC and discovered unique transcriptional profiles. The majority of genes differentially expressed between the clades encode proteins involved in host interaction and metabolic functions. A significant fraction of changes in gene expression can be explained by positive selection on single mutations that either create or disrupt transcriptional start sites (TSS). Furthermore, we show that clinical strains have different methyltransferases inactivated and thus different methylation patterns. Under the tested conditions, differential methylation has a minor direct role on transcriptomic differences between strains. However, disruption of a methyltransferase in one clinical strain revealed important expression differences suggesting indirect mechanisms of expression regulation. Our study demonstrates that variation in transcriptional profiles are mainly due to TSS mutations and have likely evolved due to differences in host characteristics.

Pubmed ID: 31488832

Research resources used in this publication

None found

Antibodies used in this publication

None found

Associated grants

  • Agency: Wellcome Trust, United Kingdom
  • Agency: NIAID NIH HHS, United States
    Id: R01 AI026170
  • Agency: NIAID NIH HHS, United States
    Id: R21 AI119573
  • Agency: NIAID NIH HHS, United States
    Id: R37 AI026170

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This is a list of tools and resources that we have found mentioned in this publication.


BiNGO: A Biological Networks Gene Ontology tool (tool)

RRID:SCR_005736

The Biological Networks Gene Ontology tool (BiNGO) is an open-source Java tool to determine which Gene Ontology (GO) terms are significantly overrepresented in a set of genes. BiNGO can be used either on a list of genes, pasted as text, or interactively on subgraphs of biological networks visualized in Cytoscape. BiNGO maps the predominant functional themes of the tested gene set on the GO hierarchy, and takes advantage of Cytoscape''''s versatile visualization environment to produce an intuitive and customizable visual representation of the results. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

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Picard (tool)

RRID:SCR_006525

Java toolset for working with next generation sequencing data in the BAM format.

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EMBOSS (tool)

RRID:SCR_008493

Software analysis package for molecular biology community. Automatically copes with data in variety of formats and allows transparent retrieval of sequence data from web. Libraries are provided with package. Provides toolkit for creating bioinformatics applications or workflows. Provides set of sequence analysis programs. Provided programs cover areas such as sequence alignment, rapid database searching with sequence patterns, protein motif identification, nucleotide sequence pattern analysis, codon usage analysis for small genomes, rapid identification of sequence patterns in large scale sequence sets, and presentation tools for publication.

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Suite of Nucleotide Analysis Programs (tool)

RRID:SCR_009399

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone., documented September 29, 2016. A workbench tool to make existing population genetic software more accessible and to facilitate the integration of new tools for analyzing patterns of DNA sequence variation, within a phylogenetic context. Collectively, SNAP tools can serve as a bridge between theoretical and applied population genetic analysis. The exploration of DNA sequence variation for making inferences on evolutionary processes in populations requires the coordinated implementation of a Suite of Nucleotide Analysis Programs (SNAP), each bound by specific assumptions and limitations.

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DESeq2 (tool)

RRID:SCR_015687

Software package for differential gene expression analysis based on the negative binomial distribution. Used for analyzing RNA-seq data for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates.

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