W Evan Johnson1,2,3, Aubrey Odom4,5,6, Padmini Salgame7, Natasha S Hochberg8,9,10, Chelsie Cintron8, Mutharaj Muthaiah11, Selby Knudsen8, Noyal Joseph12, Senbagavalli Babu12, Subitha Lakshminarayanan12, David F Jenkins4,5,6, Yue Zhao4,5,6, Ethel Nankya4,5,6, C Robert Horsburgh9, Gautam Roy12, Jerrold Ellner7, Sonali Sarkar12. 1. Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA. wej@bu.edu. 2. Bioinformatics Program, Boston University, Boston, MA, USA. wej@bu.edu. 3. Division of Computational Biomedicine and Bioinformatics Program, Boston University, Boston, MA, USA. wej@bu.edu. 4. Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA. 5. Bioinformatics Program, Boston University, Boston, MA, USA. 6. Division of Computational Biomedicine and Bioinformatics Program, Boston University, Boston, MA, USA. 7. Department of Medicine, Center for Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, USA. 8. Boston Medical Center, Boston, MA, USA. 9. Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA. 10. Section of Infectious Diseases, Boston University School of Medicine, Boston, MA, USA. 11. Government Hospital for Chest Diseases, Puducherry, India. 12. Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India.
Abstract
BACKGROUND: Gene expression signatures have been used as biomarkers of tuberculosis (TB) risk and outcomes. Platforms are needed to simplify access to these signatures and determine their validity in the setting of comorbidities. We developed a computational profiling platform of TB signature gene sets and characterized the diagnostic ability of existing signature gene sets to differentiate active TB from LTBI in the setting of malnutrition. METHODS: We curated 45 existing TB-related signature gene sets and developed our TBSignatureProfiler software toolkit that estimates gene set activity using multiple enrichment methods and allows visualization of single- and multi-pathway results. The TBSignatureProfiler software is available through Bioconductor and on GitHub. For evaluation in malnutrition, we used whole blood gene expression profiling from 23 severely malnourished Indian individuals with TB and 15 severely malnourished household contacts with latent TB infection (LTBI). Severe malnutrition was defined as body mass index (BMI) < 16 kg/m2 in adults and based on weight-for-height Z scores in children < 18 years. Gene expression was measured using RNA-sequencing. RESULTS: The comparison and visualization functions from the TBSignatureProfiler showed that TB gene sets performed well in malnourished individuals; 40 gene sets had statistically significant discriminative power for differentiating TB from LTBI, with area under the curve ranging from 0.662-0.989. Three gene sets were not significantly predictive. CONCLUSION: Our TBSignatureProfiler is a highly effective and user-friendly platform for applying and comparing published TB signature gene sets. Using this platform, we found that existing gene sets for TB function effectively in the setting of malnutrition, although differences in gene set applicability exist. RNA-sequencing gene sets should consider comorbidities and potential effects on diagnostic performance.
BACKGROUND: Gene expression signatures have been used as biomarkers of tuberculosis (TB) risk and outcomes. Platforms are needed to simplify access to these signatures and determine their validity in the setting of comorbidities. We developed a computational profiling platform of TB signature gene sets and characterized the diagnostic ability of existing signature gene sets to differentiate active TB from LTBI in the setting of malnutrition. METHODS: We curated 45 existing TB-related signature gene sets and developed our TBSignatureProfiler software toolkit that estimates gene set activity using multiple enrichment methods and allows visualization of single- and multi-pathway results. The TBSignatureProfiler software is available through Bioconductor and on GitHub. For evaluation in malnutrition, we used whole blood gene expression profiling from 23 severely malnourished Indian individuals with TB and 15 severely malnourished household contacts with latent TB infection (LTBI). Severe malnutrition was defined as body mass index (BMI) < 16 kg/m2 in adults and based on weight-for-height Z scores in children < 18 years. Gene expression was measured using RNA-sequencing. RESULTS: The comparison and visualization functions from the TBSignatureProfiler showed that TB gene sets performed well in malnourished individuals; 40 gene sets had statistically significant discriminative power for differentiating TB from LTBI, with area under the curve ranging from 0.662-0.989. Three gene sets were not significantly predictive. CONCLUSION: Our TBSignatureProfiler is a highly effective and user-friendly platform for applying and comparing published TB signature gene sets. Using this platform, we found that existing gene sets for TB function effectively in the setting of malnutrition, although differences in gene set applicability exist. RNA-sequencing gene sets should consider comorbidities and potential effects on diagnostic performance.
Authors: Hong Jiao; Agné Kulyté; Erik Näslund; Anders Thorell; Paul Gerdhem; Juha Kere; Peter Arner; Ingrid Dahlman Journal: Diabetes Date: 2016-07-18 Impact factor: 9.461
Authors: Matthew P R Berry; Christine M Graham; Finlay W McNab; Zhaohui Xu; Susannah A A Bloch; Tolu Oni; Katalin A Wilkinson; Romain Banchereau; Jason Skinner; Robert J Wilkinson; Charles Quinn; Derek Blankenship; Ranju Dhawan; John J Cush; Asuncion Mejias; Octavio Ramilo; Onn M Kon; Virginia Pascual; Jacques Banchereau; Damien Chaussabel; Anne O'Garra Journal: Nature Date: 2010-08-19 Impact factor: 49.962