Evidence map›Paper›PMID 25380655›Full record

ArticleGenome biology2014

Mapping and manipulating the Mycobacterium tuberculosis transcriptome using a transcription factor overexpression-derived regulatory network.

Tige R Rustad, Kyle J Minch, Shuyi Ma, Jessica K Winkler, Samuel Hobbs, Mark Hickey, William Brabant, Serdar Turkarslan, Nathan D Price, Nitin S Baliga and 1 more

Open access · goldAbstract read
In one paragraph

Article in Genome biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 108 papers.

0numbers the graph read from it
0cells of the map it votes in
108citing papers in PubMed
7.2field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

108 citing papers in PubMed, 156 citations in OpenAlex.

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  18. Predicting fitness inFrontiers in tuberculosis · 2025
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48 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 2 institutions in 1 country.

Tige R Rustad
Kyle J Minch
Shuyi Ma
Jessica K Winkler
Samuel Hobbs
Mark Hickey
William Brabant
Serdar Turkarslan
Nathan D Price
Nitin S Baliga
David R Sherman
Center for Infectious Disease Research · USInstitute for Systems Biology · US

Funding

Quantitative Systems BiologyP50GM076547 · NIGMS · INSTITUTE FOR SYSTEMS BIOLOGY · PI DUDLEY, AIMEE M · 2006 to 2016
$30.6M
Technology CoreU19AI106761 · NIAID · SEATTLE BIOMEDICAL RESEARCH INSTITUTE · PI ADEREM, ALAN A, SHERMAN, DAVID R · 2013 to 2017
$18.9M
Diseases of Public Health Importance Training GrantT32AI007509 · NIAID · UNIVERSITY OF WASHINGTON · PI LUND, JENNIFER M · 1997 to 2024
$6.3M
NIAID NIH HHS HHSN272200800059CNIAID NIH HHS T32 AI007509NIAID NIH HHS T32AI007509.NIAID NIH HHS U19 AI106761NIGMS NIH HHS 2P50GM076547NIGMS NIH HHS P50 GM076547
6 · The paper itself

Abstract

backgroundMycobacterium tuberculosis senses and responds to the shifting and hostile landscape of the host. To characterize the underlying intertwined gene regulatory network governed by approximately 200 transcription factors of M. tuberculosis, we have assayed the global transcriptional consequences of overexpressing each transcription factor from an inducible promoter.

resultsWe cloned and overexpressed 206 transcription factors in M. tuberculosis to identify the regulatory signature of each. We identified 9,335 regulatory consequences of overexpressing each of 183 transcription factors, providing evidence of regulation for 70% of the M. tuberculosis genome. These transcriptional signatures agree well with previously described M. tuberculosis regulons. The number of genes differentially regulated by transcription factor overexpression varied from hundreds of genes to none, with the majority of expression changes repressing basal transcription. Exploring the global transcriptional maps of transcription factor overexpressing (TFOE) strains, we predicted and validated the phenotype of a regulator that reduces susceptibility to a first line anti-tubercular drug, isoniazid. We also combined the TFOE data with an existing model of M. tuberculosis metabolism to predict the growth rates of individual TFOE strains with high fidelity.

conclusionThis work has led to a systems-level framework describing the transcriptome of a devastating bacterial pathogen, characterized the transcriptional influence of nearly all individual transcription factors in M. tuberculosis, and demonstrated the utility of this resource. These results will stimulate additional systems-level and hypothesis-driven efforts to understand M. tuberculosis adaptations that promote disease.

Indexed as

Gene Regulatory NetworksCloning, MolecularGene Expression Regulation, BacterialHumansIsoniazidMycobacterium tuberculosisPromoter Regions, GeneticRegulonTranscription FactorsTranscription, GeneticTranscriptomeTuberculosisIsoniazidTranscription Factors

Identifiers

PMID25380655
PMCPMC4249609
OpenAlexW2152080623

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.