Evidence map›Paper›PMID 30837989›Full record

ReviewFrontiers in immunology2019

Host Transcriptomics as a Tool to Identify Diagnostic and Mechanistic Immune Signatures of Tuberculosis.

Julie G Burel, Mariana Babor, Mikhail Pomaznoy, Cecilia S Lindestam Arlehamn, Nabeela Khan, Alessandro Sette, Bjoern Peters

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

  1. Trial
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Model systems to studyFrontiers in cellular and infection microbiology · 2025
    Review
  8. Article
  9. Article
  10. Article
  11. Human transcriptional signature of protection afterHuman vaccines & immunotherapeutics · 2023
    Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Review
  17. Using Omics to Study Leprosy, Tuberculosis, and Other Mycobacterial Diseases.Frontiers in cellular and infection microbiology · 2022
    Review
  18. Review
  19. Article
  20. Article
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

7 authors.

Julie G BurelDepartment of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, United States.
Mariana BaborDepartment of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, United States.
Mikhail PomaznoyDepartment of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, United States.
Cecilia S Lindestam ArlehamnDepartment of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, United States.
Nabeela KhanDepartment of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, United States.
Alessandro SetteDepartment of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, United States.
Bjoern PetersDepartment of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, United States.

Funding

Single-cell atlas of lung tissue-resident memory T cells reactive to upper and lower respiratory tract pathogensU19AI118626 · NIAID · LA JOLLA INSTITUTE FOR IMMUNOLOGY · PI Pandurangan Vijayanand · 2015 to 2026
$36.7M
NIAID NIH HHS U19 AI118626
6 · The paper itself

Abstract

Tuberculosis (TB) is a major infectious disease worldwide, and is associated with several challenges for control and eradication. First, more accurate diagnostic tools that better represent the spectrum of infection states are required; in particular, identify the latent TB infected individuals with high risk of developing active TB. Second, we need to better understand, from a mechanistic point of view, why the immune system is unsuccessful in some cases for control and elimination of the pathogen. Host transcriptomics is a powerful approach to identify both diagnostic and mechanistic immune signatures of diseases. We have recently reported that optimal study design for these two purposes should be guided by different sets of criteria. Here, based on already published transcriptomics signatures of tuberculosis, we further develop these guidelines and identify additional factors to consider for obtaining diagnostic vs. mechanistic signatures in terms of cohorts, samples, data generation and analysis. Diagnostic studies should aim to identify small disease signatures with high discriminatory power across all affected populations, and against similar pathologies to TB. Specific focus should be made on improving the diagnosis of infected individuals at risk of developing active disease. Conversely, mechanistic studies should focus on tissues biopsies, immune relevant cell subsets, state of the art transcriptomic techniques and bioinformatics tools to understand the biological meaning of identified gene signatures that could facilitate therapeutic interventions. Finally, investigators should ensure their data are made publicly available along with complete annotations to facilitate metadata and cross-study analyses.

Indexed as

AnimalsComputational BiologyGene Expression ProfilingHumansTranscriptomeTuberculosishost immune responsehuman immunologyinfectious diseasestranscriptomicstuberculosis

Identifiers

PMID30837989
PMCPMC6389658

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.