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A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection

Author

Listed:
  • Akul Singhania

    (The Francis Crick Institute)

  • Raman Verma

    (University of Leicester)

  • Christine M. Graham

    (The Francis Crick Institute)

  • Jo Lee

    (University of Leicester)

  • Trang Tran

    (BIOASTER Microbiology Technology Institute)

  • Matthew Richardson

    (University of Leicester)

  • Patrick Lecine

    (BIOASTER Microbiology Technology Institute)

  • Philippe Leissner

    (BIOASTER Microbiology Technology Institute)

  • Matthew P. R. Berry

    (Imperial College Healthcare NHS Trust, St Mary’s Hospital)

  • Robert J. Wilkinson

    (University of Cape Town
    Imperial College London
    The Francis Crick Institute)

  • Karine Kaiser

    (bioMérieux SA)

  • Marc Rodrigue

    (bioMérieux SA)

  • Gerrit Woltmann

    (University of Leicester)

  • Pranabashis Haldar

    (University of Leicester)

  • Anne O’Garra

    (The Francis Crick Institute
    National Heart and Lung Institute, Imperial College London)

Abstract

Whole blood transcriptional signatures distinguishing active tuberculosis patients from asymptomatic latently infected individuals exist. Consensus has not been achieved regarding the optimal reduced gene sets as diagnostic biomarkers that also achieve discrimination from other diseases. Here we show a blood transcriptional signature of active tuberculosis using RNA-Seq, confirming microarray results, that discriminates active tuberculosis from latently infected and healthy individuals, validating this signature in an independent cohort. Using an advanced modular approach, we utilise the information from the entire transcriptome, which includes overabundance of type I interferon-inducible genes and underabundance of IFNG and TBX21, to develop a signature that discriminates active tuberculosis patients from latently infected individuals or those with acute viral and bacterial infections. We suggest that methods targeting gene selection across multiple discriminant modules can improve the development of diagnostic biomarkers with improved performance. Finally, utilising the modular approach, we demonstrate dynamic heterogeneity in a longitudinal study of recent tuberculosis contacts.

Suggested Citation

  • Akul Singhania & Raman Verma & Christine M. Graham & Jo Lee & Trang Tran & Matthew Richardson & Patrick Lecine & Philippe Leissner & Matthew P. R. Berry & Robert J. Wilkinson & Karine Kaiser & Marc Ro, 2018. "A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection," Nature Communications, Nature, vol. 9(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-04579-w
    DOI: 10.1038/s41467-018-04579-w
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    Cited by:

    1. Nguyen Phuoc Long & Da Young Heo & Seongoh Park & Nguyen Thi Hai Yen & Yong-Soon Cho & Jae-Gook Shin & Jee Youn Oh & Dong-Hyun Kim, 2022. "Molecular perturbations in pulmonary tuberculosis patients identified by pathway-level analysis of plasma metabolic features," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-13, January.
    2. Tae Gun Kang & Kee Woong Kwon & Kyungsoo Kim & Insuk Lee & Myeong Joon Kim & Sang-Jun Ha & Sung Jae Shin, 2022. "Viral coinfection promotes tuberculosis immunopathogenesis by type I IFN signaling-dependent impediment of Th1 cell pulmonary influx," Nature Communications, Nature, vol. 13(1), pages 1-19, December.

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