Vandana Kulkarni1, Artur T L Queiroz2,3, Shashi Sangle1, Anju Kagal1, Sonali Salvi1, Amita Gupta4, Jerrold Ellner5, Dileep Kadam1, Valeria C Rolla6, Bruno B Andrade2,3, Padmini Salgame5, Vidya Mave1,4. 1. Byramjee-Jeejeebhoy Government Medical College-Johns Hopkins University Clinical Research Site (BJGMC-JHU CRS), Pune, India. 2. Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil. 3. Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil. 4. Johns Hopkins University School of Medicine, Baltimore, MD, United States. 5. Rutgers- New Jersey Medical School, Center for Emerging Pathogens, Newark, NJ, United States. 6. Instituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
Abstract
Background: 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.
Background: 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.
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