ArticleScientific reports2018
Gene expression models based on a reference laboratory strain are poor predictors of Mycobacterium tuberculosis complex transcriptional diversity.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- LysG-driven transcriptional network rewiring underlies lineage-specific phenotypes in Mycobacterium tuberculosis.Nature communications · 2026Article
- Animal models in tuberculosis metabolomics: a systematic review of current evidence and the road to translational relevance.Frontiers in molecular biosciences · 2025Review
- Clinical strains of Mycobacterium tuberculosis exhibit differential lipid metabolism-associated transcriptome changes in in vitro cholesterol and infection models.Pathogens and disease · 2023Article
- Transcriptional regulation and drug resistance inFrontiers in cellular and infection microbiology · 2022Review
- Identification of Hub Genes in Tuberculosis via Bioinformatics Analysis.Computational and mathematical methods in medicine · 2021Article
- Genetic Diversity inAnnual review of genetics · 2020Review
- Genome-wide mutational biases fuel transcriptional diversity in the Mycobacterium tuberculosis complex.Nature communications · 2019Article
- Review
- Mycobacterial SigA and SigB Cotranscribe Essential Housekeeping Genes during Exponential Growth.mBio · 2019Article
- Immunogenicity and Vaccine Potential of InsB, an ESAT-6-Like Antigen Identified in the Highly VirulentFrontiers in microbiology · 2019Article
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Authors and funding
3 authors.
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Abstract
Every year, species of the Mycobacterium tuberculosis complex (MTBC) kill more people than any other infectious disease caused by a single agent. As a consequence of its global distribution and parallel evolution with the human host the bacteria is not genetically homogeneous. The observed genetic heterogeneity has relevance at different phenotypic levels, from gene expression to epidemiological dynamics. However, current systems biology datasets have focused on the laboratory reference strain H37Rv. By using large expression datasets testing the role of almost two hundred transcription factors, we have constructed computational models to grab the expression dynamics of Mycobacterium tuberculosis H37Rv genes. However, we have found that many of those transcription factors are deleted or likely dysfunctional across strains of the MTBC. As a result, we failed to predict expression changes in strains with a different genetic background when compared with experimental data. These results highlight the importance of designing systems biology approaches that take into account the genetic diversity of tubercle bacilli, or any other pathogen, if we want to identify universal targets for vaccines, diagnostics and treatments.
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