Mycobacterium tuberculosis is one of the most successful pathogens in the world,
still responsible for millions of deaths each year. Nearly half of its protein
coding genes have functions that are unknown. We are a Functional Genomics
Resource Center funded by NIAID, with the goal of defining functions for unknown ORFs,
hypothetical genes, and non-coding RNAs in Mtb. On this site you can learn more about
our project, access our data, and find information on a specific Mtb gene by using
the search box above.
Functional classes of Mtb genes
- 8-25-2022: Added links to genes with correlated TnSeq profiles (TnSeqCorr)
- 7-26-2022: Added updated annotations based on recent publications
- 10-13-2021: Updated top 10 homologs in PDB for each gene; add links to PATRIC
- 10-01-2021: Added links to Vulnerability Index and AlphaFold model for each gene;
Improved Search (so keywords work, not just ORF ids and gene names)
- 04-28-2021: Added Gene Expression data on transcriptional responses to drug treatments (Boshoff et al, 2004)
- 03-14-2018: Updated GO terms from Uniprot
- 03-14-2018: Added enzyme reactions from iSM810 metabolic model
- 09-19-2017: New TnSeq data added for PPE68, EccD1, PE35 and EspI [expand]
TnSeq data has been added for PPE68, EccD1, PE35 and EspI knockouts. Data can be found in their respective gene pages:
ABMP data has been added for Rv0480c, Rv1059, Rv2971, and Rv3368c knockouts.
Data can be found in their respective gene pages:
ORF pages now have new operon images that include ncRNAs, tRNAs, sRNAs, rRNAs, and transcriptional start sites.
You can click on any ORF arrow (blue) or name in the image to go directly to that ORF's page.
Take a look:
The following genes have new metabolomic data added to their gene pages:
|Orf ||Name ||Description ||Type
|Rv1016c ||lpqT ||Probable conserved lipoprotein LpqT ||Conditional
|Rv1130 ||prpD ||Possible methylcitrate dehydratase PrpD ||Conditional
|Rv1713 ||engA ||Probable GTP-binding protein EngA ||Essential
|Rv3244c ||lpqB ||Probable conserved lipoprotein LpqB ||Essential
|Rv3280 ||accD5 ||Probable propionyl-CoA carboxylase beta chain 5 AccD5 (pccase) (propanoyl-CoA:carbon dioxide ligase) ||Essential
|Rv3311 ||- ||hypothetical protein ||Conditional
|Rv3722c ||- ||hypothetical protein ||Essential
|Rv3801c ||fadD32 ||Fatty-acid-AMP ligase FadD32 (fatty-acid-AMP synthetase) (fatty-acid-AMP synthase) Also shown to have acyl-ACP ligase activity ||Essential
Disruption of M. tuberculosis membrane protein PerM (Rv0955) resulted in an IFN-γ-dependent persistence defect in chronic mouse infection.
Goodsmith N, Guo XV, Vandal OH1, Vaubourgeix J, Wang R, Botella H, Song S, Bhatt K, Liba A, Salgame P, Schnappinger D, Ehrt S.
Disruption of an M. tuberculosis membrane protein causes a magnesium-dependent cell division defect and failure to persist in mice.
PLoS Pathog. 2015 Feb 6;11(2):e1004645. doi: 10.1371/journal.ppat.1004645. eCollection 2015.
Ganapathy et. al. Publication to be disclosed.
GO-Term(s) Assigned: GO:0047547
Publication to be disclosed.
Individual gene pages now include a section with clustering of the Boshoff Transcriptional Response Data, showing which other genes are significantly up- or down- regulated and in which conditions.
We have added a shopping-like functionality to facilitate printing labels for samples.
1. Go to the sample list.
2. Click on the "shopping cart" icon to add samples you wish to print.
3. Go to the sample "shopping cart".
4. Add texts you wish to appear on the labels.
5. Click the "Print Labels" button to generate a PDF that can be printed on label paper.
Click "Post a comment" at the bottom of a gene page to add a message or click reply to respond to other messages.
The ability to analyze and visualize expression data is slowly being added to the website.
This is a work in progress, and will be continually improved.
You can see an example in the RNA-Seq Data section of Rv3263.
Scrolling down to the "RNA-Seq Data section" and clicking [+] to expand
the list of available data will provide options to download or analyze the data.
This is a test of the notification system.
Vast quantities of new genome sequence are added
to public databases on a daily basis. But, while we are
constantly deluged with new gene sequences we still
have a limited ability to define their functions. Almost all
functions are defined by comparison with genes from
other strains in which experimental data are available.
But these experimental data have not kept pace with
the availability of new sequence. In fact, for most
bacterial species, many genes have no known function
and even those that are annotated have only limited
information. This is particularly striking for
Mycobacterium tuberculosis (Mtb), where only 52% of
protein-coding genes have a putative function.
We plan to discover the roles of genes from Mtb,
an important and widespread human pathogen, with previously
unknown functions. Critically, we will target genes
that fulfill vital roles in bacterial growth and survival,
genes we have previously identified in genome-wide
screens. This offers three major advantages. First, we will
concentrate on only the most important unannotated
genes. Second, phenotypes allow us to use the power of
synthetic lethality to identify interactions. And
third, phenotypes provide us with a context in which we can
understand the outcome of biochemical and genetic
assays. In addition to defining the roles for Mtb genes we
aim to establish an efficient pathway for
identifying gene function that can serve as a paradigm for other
bacterial species. To accomplish this we will
undertake an ambitious program to construct large numbers of
Mtb mutants. This will be possible as we will take
advantage of substantial mycobacterial genetic expertise
among the participants. Moreover, we will use a
number of analytic modalities brought in through a set of highly
interconnected projects and cores.
This project was originally funded by the NIH as part of the NIAID Functional Genomics Program, under grant U19 AI107774 (2013-2018).
The NIAID Functional Genomics Program for understanding the functions of uncharacterized genes in infectious disease pathogens
will generate experimental data to determine the
biochemical function(s) of hypothetical genes, unknown open reading
and noncoding RNAs. The program will apply
state-of-the-art technologies to determine the biochemical and
of these gene components. Obtaining a more
comprehensive understanding of uncharacterized genes in infectious
will lead to improved genomic annotation and allow
for the development of potential new targets for medical diagnostics,
therapeutics and vaccines. The program will
distribute data, software, and reagents generated from the research
to the broader scientific community.
The successor of the original project has been funded by NIH as P01 AI143575 (Pathway Analysis in Tuberculosis; program director: S. Ehrt; 2020-2025).