A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection
| Autor: | Christine M. Graham, Gerrit Woltmann, Anne O'Garra, Pranabashis Haldar, Akul Singhania, Trang Tran, Matthew Berry, Marc Rodrigue, Patrick Lecine, Philippe Leissner, Robert J. Wilkinson, Raman Verma, Matthew Richardson, Karine Kaiser, Jo Lee |
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| Přispěvatelé: | Wellcome Trust |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Male
0301 basic medicine TBX21 BACTERIAL BLOOD Transcription Genetic Microarray General Physics and Astronomy SUSCEPTIBILITY EXPRESSION PROFILES Cohort Studies Transcriptome 0302 clinical medicine IMMUNE-RESPONSE Longitudinal Studies 030212 general & internal medicine lcsh:Science Oligonucleotide Array Sequence Analysis 0303 health sciences Multidisciplinary GAMMA RELEASE ASSAYS Middle Aged 3. Good health Multidisciplinary Sciences Improved performance Phenotype Area Under Curve Science & Technology - Other Topics Female MYCOBACTERIAL INFECTION Immunosuppressive Agents Adult Risk Tuberculosis Science Computational biology Biology General Biochemistry Genetics and Molecular Biology Interferon-gamma 03 medical and health sciences medicine Humans Diagnostic biomarker Tuberculosis Pulmonary Gene 030304 developmental biology Aged Gene Library Science & Technology Sequence Analysis RNA Genetic heterogeneity Gene Expression Profiling Mycobacterium tuberculosis General Chemistry ACTIVE TUBERCULOSIS medicine.disease GENE Gene selection 030104 developmental biology ROC Curve Immunology lcsh:Q T-Box Domain Proteins RESISTANCE Biomarkers 030215 immunology |
| Zdroj: | Nature Communications, Vol 9, Iss 1, Pp 1-17 (2018) |
| ISSN: | 2041-1723 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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