Evidence map›Paper›PMID 38360432›Full record

ReviewTrends in microbiology2024

Exploiting cAMP signaling in Mycobacterium tuberculosis for drug discovery.

Dipak Kathayat, Brian C VanderVen

Open access · hybridAbstract readReview
In one paragraph

Review in Trends in microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
3.1field-weighted citation impact, top 9% 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

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
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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

2 authors at 1 institution in 1 country.

Dipak KathayatMicrobiology and Immunology, Cornell University, Ithaca, NY 14853, USA.
Brian C VanderVenMicrobiology and Immunology, Cornell University, Ithaca, NY 14853, USA. Electronic address: bcv8@cornell.edu.
Cornell University · US

Funding

Preclinical evaluation of compounds that inhibit cholesterol uptake in M. tuberculosis.R01AI130018 · NIAID · CORNELL UNIVERSITY · PI VANDERVEN, BRIAN C · 2017 to 2021
$2.3M
NIAID NIH HHS R01 AI130018
6 · The paper itself

Abstract

Mycobacterium tuberculosis (Mtb) replicates within host macrophages by adapting to the stressful and nutritionally constrained environments in these cells. Exploiting these adaptations for drug discovery has revealed that perturbing cAMP signaling can restrict Mtb growth in macrophages. Specifically, compounds that agonize or stimulate the bacterial enzyme, Rv1625c/Cya, induce cAMP synthesis and this interferes with the ability of Mtb to metabolize cholesterol. In murine tuberculosis (TB) infection models, Rv1625c/Cya agonists contribute to reducing relapse and shortening combination treatments, highlighting the therapeutic potential for this class of compounds. More recently, cAMP signaling has been implicated in regulating fatty acid utilization by Mtb. Thus, a new model is beginning to emerge in which cAMP regulates the utilization of host lipids by Mtb during infection, and this could provide new targets for TB drug development. Here, we summarize the current understanding of cAMP signaling in Mtb with a focus on our understanding of how cAMP signaling impacts Mtb physiology during infection. We also discuss additional cAMP-related drug targets in Mtb and other bacterial pathogens that may have therapeutic potential.

Indexed as

Antitubercular AgentsCyclic AMPDrug DiscoveryMacrophagesMycobacterium tuberculosisSignal TransductionTuberculosisAnimalsBacterial ProteinsHumansMiceAntitubercular AgentsBacterial ProteinsCyclic AMPcAMPcholesterolfatty acidsTB drug discoverytuberculosis

Identifiers

PMID38360432
PMCPMC11322422
OpenAlexW4391825581

What OpenQuestion holds

Textmetadata
LicenceTDM
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.