Evidence map›Paper›PMID 39034171›Full record

ReviewTrends in microbiology2025

Immunological roads diverged: mapping tuberculosis outcomes in mice.

Rachel K Meade, Clare M Smith

Abstract readReview
In one paragraph

Review in Trends in microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Methods and Models for StudyingInternational journal of molecular sciences · 2024
    Review
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.

Rachel K MeadeDepartment of Molecular Genetics and Microbiology, Duke University, Durham, NC, USA; University Program in Genetics and Genomics, Duke University, Durham, NC, USA.
Clare M SmithDepartment of Molecular Genetics and Microbiology, Duke University, Durham, NC, USA; University Program in Genetics and Genomics, Duke University, Durham, NC, USA. Electronic address: clare.m.smith@duke.edu.

Funding

Dissecting the genetic basis of protective immunity to tuberculosis in diverse hostsDP2GM146458 · NIGMS · DUKE UNIVERSITY · PI SMITH, CLARE MARGARET · 2021 to 2021
$1.4M
Dissecting the genetic basis of protective immunity to tuberculosis in diverse hostsDP2AI183152 · NIAID · DUKE UNIVERSITY · PI SMITH, CLARE MARGARET · 2024 to 2024
$966k
NIAID NIH HHS DP2 AI183152NIGMS NIH HHS DP2 GM146458
6 · The paper itself

Abstract

The journey from phenotypic observation to causal genetic mechanism is a long and challenging road. For pathogens like Mycobacterium tuberculosis (Mtb), which causes tuberculosis (TB), host-pathogen coevolution has spanned millennia, costing millions of human lives. Mammalian models can systematically recapitulate host genetic variation, producing a spectrum of disease outcomes. Leveraging genome sequences and deep phenotyping data from infected mouse genetic reference populations (GRPs), quantitative trait locus (QTL) mapping approaches have successfully identified host genomic regions associated with TB phenotypes. Here, we review the ongoing optimization of QTL mapping study design alongside advances in mouse GRPs. These next-generation resources and approaches have enabled identification of novel host-pathogen interactions governing one of the most prevalent infectious diseases in the world today.

Indexed as

Disease Models, AnimalHost-Pathogen InteractionsMycobacterium tuberculosisQuantitative Trait LociTuberculosisAnimalsChromosome MappingHumansMicePhenotypecomplex traitsgenetic diversityhost–pathogen interactionsmicequantitative trait locituberculosis

Identifiers

PMID39034171
PMCPMC13527666

What OpenQuestion holds

Textmetadata
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.