Please use this identifier to cite or link to this item: https://www.arca.fiocruz.br/handle/icict/46533
Title: A Two-Gene Signature for Tuberculosis Diagnosis in Persons With Advanced HIV
Authors: Kulkarni, Vandana
Queiroz, Artur Trancoso Lima de
Sangle, Shashi
Kagal, Anju
Salvi, Sonali
Gupta, Amita
Ellner, Jerrold
Kadam, Dileep
Rolla, Valeria C.
Andrade, Bruno de Bezerril
Salgame, Padmini
Mave, Vidya
Affilliation: Johns Hopkins University Clinical Research Site. Byramjee-Jeejeebhoy Government Medical College. Pune, India.
Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Bahia, Brasil / Multinational Organization Network Sponsoring Translational and Epidemiological Research Initiative, Salvador, BA, Brazil.
Johns Hopkins University Clinical Research Site. Byramjee-Jeejeebhoy Government Medical College. Pune, India.
Johns Hopkins University Clinical Research Site. Byramjee-Jeejeebhoy Government Medical College. Pune, India.
Johns Hopkins University Clinical Research Site. Byramjee-Jeejeebhoy Government Medical College. Pune, India.
Johns Hopkins University School of Medicine. Baltimore, MD, United States.
Rutgers- New Jersey Medical School. Center for Emerging Pathogens. Newark, NJ, United States.
Johns Hopkins University Clinical Research Site. Byramjee-Jeejeebhoy Government Medical College. Pune, India.
Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Bahia, Brasil / Multinational Organization Network Sponsoring Translational and Epidemiological Research Initiative, Salvador, BA, Brazil.
Rutgers- New Jersey Medical School. Center for Emerging Pathogens. Newark, NJ, United States.
Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Bahia, Brasil / Johns Hopkins University School of Medicine. Baltimore, MD, United States.
Abstract: Transcriptomic signatures for tuberculosis (TB) have been proposed and represent a promising diagnostic tool. Data remain limited in persons with advanced HIV. Methods: We enrolled 30 patients with advanced HIV (CD4 < 100 cells/mm3) in India; 16 with active TB and 14 without. Whole-blood RNA sequencing was performed; these data were merged with a publicly available dataset from Uganda (n = 33; 18 with TB and 15 without). Transcriptomic profiling and machine learning algorithms identified an optimal gene signature for TB classification. Receiver operating characteristic analysis was used to assess performance. Results: Among 565 differentially expressed genes identified for TB, 40 were shared across India and Uganda cohorts. Common upregulated pathways reflect Toll-like receptor cascades and neutrophil degranulation. The machine-learning decision-tree algorithm selected gene expression values from RAB20 and INSL3 as most informative for TB classification. The signature accurately classified TB in discovery cohorts (India AUC 0.95 and Uganda AUC 1.0; p < 0.001); accuracy was fair in external validation cohorts. Conclusions: Expression values of RAB20 and INSL3 genes in peripheral blood compose a biosignature that accurately classified TB status among patients with advanced HIV in two geographically distinct cohorts. The functional analysis suggests pathways previously reported in TB pathogenesis.
Keywords: HIV
Tuberculosis
Ttranscriptomics
Diagnosis
Gene signature
keywords: HIV
Tuberculose
Diagnóstico
Genes
Issue Date: 2021
Publisher: Frontiers Media
Citation: KULKARNI, Vandana et al. A Two-Gene Signature for Tuberculosis Diagnosis in Persons With Advanced HIV. Frontiers in Immunology, v. 12, 2021.
DOI: 10.3389/fimmu.2021.631165
ISSN: 1664-3224
Copyright: open access
Appears in Collections:INI - Artigos de Periódicos
BA - IGM - Artigos de Periódicos
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