Jeffrey A Tornheim1, Anil K Madugundu2,3,4,5, Mandar Paradkar6, Kiyoshi F Fukutani7,8,9, Artur T L Queiroz7,8, Nikhil Gupte1,6, Akshay N Gupte1, Aarti Kinikar10, Vandana Kulkarni6, Usha Balasubramanian6, Sreelakshmi Sreenivasamurthy2,3,11, Remya Raja2,3,4, Neeta Pradhan6, Shri Vijay Bala Yogendra Shivakumar12, Chhaya Valvi10, Luke Elizabeth Hanna13, Bruno B Andrade7,8,9,14,15, Vidya Mave1,6, Akhilesh Pandey2,3,5,11, Amita Gupta1,16. 1. Center for Clinical Global Health Education, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. 2. Institute of Bioinformatics, Bangalore, Karnataka, India. 3. Center for Molecular Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India. 4. Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India. 5. Department of Laboratory Medicine and Pathology and Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA. 6. Byramjee Jeejeebhoy Government Medical College-Johns Hopkins University Clinical Research Site, Pune, Maharashtra, India. 7. Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil. 8. Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil. 9. Faculdade de Tecnologia e Ciências (FTC), Salvador, Brazil. 10. Byramjee Jeejeebhoy Government Medical College, Pune, Maharashtra, India. 11. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. 12. Johns Hopkins University - India office (Center for Clinical Global Health Education), Pune, Maharashtra, India. 13. National Institute for Research in Tuberculosis, Chennai, Tamil Nadu, India. 14. Universidade Salvador (UNIFACS), Laureate Universities, Salvador, Brazil. 15. Escola Bahiana de Medicina e Saúde Pública (EBMSP), Salvador, Brazil. 16. Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Abstract
BACKGROUND: Gene expression profiling is emerging as a tool for tuberculosis diagnosis and treatment response monitoring, but limited data specific to Indian children and incident tuberculosis infection (TBI) exist. METHODS: Sixteen pediatric Indian tuberculosis cases were age- and sex-matched to 32 tuberculosis-exposed controls (13 developed incident TBI without subsequent active tuberculosis). Longitudinal samples were collected for ribonucleic acid sequencing. Differential expression analysis generated gene lists that identify tuberculosis diagnosis and tuberculosis treatment response. Data were compared with published gene lists. Population-specific risk score thresholds were calculated. RESULTS: Seventy-one genes identified tuberculosis diagnosis and 25 treatment response. Within-group expression was partially explained by age, sex, and incident TBI. Transient changes in gene expression were identified after both infection and treatment. Application of 27 published gene lists to our data found variable performance for tuberculosis diagnosis (sensitivity 0.38-1.00, specificity 0.48-0.93) and treatment response (sensitivity 0.70-0.80, specificity 0.40-0.80). Our gene lists found similarly variable performance when applied to published datasets for diagnosis (sensitivity 0.56-0.85, specificity 0.50-0.85) and treatment response (sensitivity 0.49- 0.86, specificity 0.50-0.84). CONCLUSIONS: Gene expression profiles among Indian children with confirmed tuberculosis were distinct from adult-derived gene lists, highlighting the importance of including distinct populations in differential gene expression models.
BACKGROUND: Gene expression profiling is emerging as a tool for tuberculosis diagnosis and treatment response monitoring, but limited data specific to Indian children and incident tuberculosis infection (TBI) exist. METHODS: Sixteen pediatric Indian tuberculosis cases were age- and sex-matched to 32 tuberculosis-exposed controls (13 developed incident TBI without subsequent active tuberculosis). Longitudinal samples were collected for ribonucleic acid sequencing. Differential expression analysis generated gene lists that identify tuberculosis diagnosis and tuberculosis treatment response. Data were compared with published gene lists. Population-specific risk score thresholds were calculated. RESULTS: Seventy-one genes identified tuberculosis diagnosis and 25 treatment response. Within-group expression was partially explained by age, sex, and incident TBI. Transient changes in gene expression were identified after both infection and treatment. Application of 27 published gene lists to our data found variable performance for tuberculosis diagnosis (sensitivity 0.38-1.00, specificity 0.48-0.93) and treatment response (sensitivity 0.70-0.80, specificity 0.40-0.80). Our gene lists found similarly variable performance when applied to published datasets for diagnosis (sensitivity 0.56-0.85, specificity 0.50-0.85) and treatment response (sensitivity 0.49- 0.86, specificity 0.50-0.84). CONCLUSIONS: Gene expression profiles among Indian children with confirmed tuberculosis were distinct from adult-derived gene lists, highlighting the importance of including distinct populations in differential gene expression models.
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Authors: Julie G Burel; Akul Singhania; Paige Dubelko; Julius Muller; Rachel Tanner; Eneida Parizotto; Martin Dedicoat; Thomas E Fletcher; James Dunbar; Adam F Cunningham; Cecilia S Lindestam Arlehamn; Donald G Catanzaro; Antonino Catanzaro; Timothy Rodwell; Helen McShane; Matthew K O'Shea; Bjoern Peters Journal: Tuberculosis (Edinb) Date: 2021-09-14 Impact factor: 3.131