Meta-analysis of host response networks identifies a common core in tuberculosis

npj Systems Biology and Applications

Volumn 3, Issue 1, 2017, Pages

  Sambarey, Awanti ^a   Devaprasad, Abhinandan ^a   Baloni, Priyanka ^a   Mishra, Madhulika ^a   Mohan, Abhilash ^a   Tyagi, Priyanka ^a   Singh, Amit ^a   Akshata, Js ^b   Sultana, Razia ^b   Buggi, Shashidhar ^b   Chandra, Nagasuma ^a  

^a INDIAN INSTITUTE OF SCIENCE   (India)

^b SDS Sanitorium and Rajiv Gandhi Institute of Chest Diseases   (India)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85009175912     PISSN: None     EISSN: 20567189     Source Type: Journal    
DOI: 10.1038/s41540-017-0005-4     Document Type: Article

References (56)

1. World Health Organization. Global Tuberculosis Report 2015. http://www.who.int/tb/publications/global_report/en/. Accessed 4 April (2016)
2. Gupta, A. et al. Mycobacterium tuberculosis: immune evasion, latency and reactivation. Immunobiology. 217, 363 (2012)
3. Flynn, J. L. & Chan, J. Immune evasion by Mycobacterium tuberculosis: living with the enemy. Curr. Opin. Immunol. 15(4), 450 (2003)
4. Casadevall, A. & Pirofski, L. A. Host-pathogen interactions: basic concepts of microbial commensalism, colonization, infection, and disease. Infect. Immun. 68, 6511 (2000)
5. O’Garra, A. et al. The immune response in tuberculosis. Annu. Rev. Immunol. 31, 475 (2013)
6. Apt, A. & Kondratieva, T. K. Tuberculosis: pathogenesis, immune response, and host genetics. Mol. Biol. 42, 784 (2008)
7. Zhang, X. et al. Polymorphic allele of human MRC1 confer protection against tuberculosis in a Chinese population. Int. J. Biol. Sci. 8, 375 (2012)
8. Schröder, N. W. J. & Schumann, R. R. Single nucleotide polymorphisms of Toll-like receptors and susceptibility to infectious disease. Lancet Infect. Dis. 5, 156 (2005)
9. Stein, C. M. et al. Linkage and association analysis of candidate genes for TB and TNFalpha cytokine expression: evidence for association with IFNGR1, IL-10, and TNF receptor 1 genes. Hum. Genet. 121, 663 (2007)
10. Henao, M. I., Montes, C., Parí­s, S. C. & Garcí­a, L. F. Cytokine gene polymorphisms in Colombian patients with different clinical presentations of tuberculosis. Tuberculosis 86, 11 (2006)
11. Tobin, D. M. et al. The lta4h locus modulates susceptibility to mycobacterial infection in Zebrafish and humans. Cell 140, 717 (2010)
12. Azad, A. K., Sadee, W. & Schlesinger, L. S. Innate immune gene polymorphisms in tuberculosis. Infect. Immun. 80, 3343 (2012)
13. Thye, T. et al. Common variants at 11p13 are associated with susceptibility to tuberculosis. Nat. Genet. 44, 257 (2012)
14. Cooper, A. M. Cell mediated immune responses in tuberculosis. Annu. Rev. Immunol. 27, 393 (2009)
15. Jasenosky, L. D., Scriba, T. J., Hanekom, W. A. & Goldfeld, A. E. T cells and adaptive immunity to mycobacterium tuberculosis in humans. Immunol. Rev. 264, 74 (2015)
16. Haas, C. T. et al. Diagnostic ‘omics’ for active tuberculosis. BMC Med. 14, 1 (2016)
17. Tobin, D. M. et al. Host genotype-specific therapies can optimize the inflammatory response to mycobacterial infections. Cell 148, 434 (2012)
18. Agranoff, D. et al. Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum. Lancet 368, 1012 (2006)
19. Curtis, J. et al. Susceptibility to tuberculosis is associated with variants in the ASAP1 gene encoding a regulator of dendritic cell migration. Nat. Genet. 47, 523 (2015)
20. De Groote, M. A. et al. Elucidating novel serum biomarkers associated with pulmonary tuberculosis treatment. PLoS One 8, e61002 (2013)
21. Berry, M. P. et al. An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature 466, 973 (2010)
22. Bloom, C. I. et al. Transcriptional blood signatures distinguish pulmonary tuberculosis, pulmonary sarcoidosis, pneumonias and lung cancers. PLoS One 8, e70630 (2013)
23. Maertzdorf, J. et al. Functional correlations of pathogenesis-driven gene expression signatures in tuberculosis. PLoS One 6, e26938 (2011)
24. Maertzdorf, J. et al. Common patterns and disease-related signatures in tuberculosis and sarcoidosis. Proc. Natl. Acad. Sci. USA 109, 7853 (2012)
25. Ottenhoff, T. H. et al. Genome-wide expression profiling identifies type 1 interferon response pathways in active tuberculosis. PLoS One 7, e45839 (2012)
26. Thuong, N. T. T. et al. Identification of tuberculosis susceptibility genes with human macrophage gene expression profiles. PLoS Pathog. 4, e1000229 (2008)
27. Joosten, S. A., Fletcher, H. A. & Ottenhoff, T. H. M. A helicopter perspective on TB biomarkers: pathway and process based analysis of gene expression data provides new insight into TB pathogenesis. PLoS One 8, e73230 (2013)
28. Blankley, S. et al. The application of transcriptional blood signatures to enhance our understanding of the host response to infection: the example of tuberculosis. Philos. Trans. R. Soc. Lond. B Biol. Sci. 369, 20130427 (2014)
29. Cliff, J. M. et al. The human immune response to tuberculosis and its treatment: a view from the blood. Immunol. Rev. 264, 88 (2015)
30. Binder, H., Kirsten, T., Loeffler, M. & Stadler, P. F. Sensitivity of microarray oligonucleotide probes: variability and effect of base composition. J. Phys. Chem. B. 108, 18003 (2004)
31. Bruun, G. M. et al. Improving comparability between microarray probe signals by thermodynamic intensity correction. Nucleic Acids Res. 35, e48 (2007)
32. Moreau, Y. et al. Comparison and meta-analysis of microarray data: from the bench to the computer desk. Trends Genet. 19, 570 (2003)
33. Sambarey, A., Prashanthi, K. & Chandra, N. Mining large-scale response networks reveals ‘topmost activities’ in mycobacterium tuberculosis infection. Sci. Rep. 3, 2302 (2013)
34. Sambaturu, N., Mishra, M. & Chandra, N. EpiTracer - an algorithm for identifying epicenters in condition-specific biological networks. BMC Genom. 17, 355 (2016)
35. de Padilla, C. M. L. & Niewold, T. B. The type I interferons: basic concepts and clinical relevance in immune-mediated inflammatory diseases. Gene 576, 14 (2016)
36. Dong, B. et al. Phospholipid scramblase 1 potentiates the antiviral activity of interferon. J. Virol. 78, 8983 (2004)
37. Py, B. et al. The phospholipid scramblases 1 and 4 are cellular receptors for the secretory leukocyte protease inhibitor and interact with CD4 at the plasma membrane. PLoS One 4, e5006 (2009)
38. Yen, P. H. Putative biological functions of the DAZ family. Int. J. Androl. 27, 125 (2004)
39. Amberger, J. S. et al. OMIM. org: online mendelian inheritance in man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 43, D789 (2015)
40. Bloom, C. I. et al. Detectable changes in the blood transcriptome are present after two weeks of antituberculosis therapy. PLoS One 7, e46191 (2012)
41. Chaussabel, D., Pascual, V. & Banchereau, J. Assessing the human immune system through blood transcriptomics. BMC Biol. 8, 84 (2010)
42. Deffur, A., Wilkinson, R. J., and Coussens, A. K. Tricks to translating TB transcriptomics. Ann. Transl. Med. 3, (2015)
43. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 9, 1 (2008)
44. Vidal, M., Ramana, C. V. & Dusso, A. S. Stat1-vitamin D receptor interactions antagonize 1, 25-dihydroxyvitamin D transcriptional activity and enhance stat1-mediated transcription. Mol. Cell Biol. 22, 2777 (2002)
45. Lange, C. M. et al. Vitamin D receptor and Jak-STAT signaling crosstalk results in calcitriol-mediated increase of hepatocellular response to IFN-α. J. Immunol. 192, 6037 (2014)
46. Smyth, G. K., Bioinformatics and computational biology solutions using R and Bioconductor pp. 397 (Springer, 2005)
47. Du, P., Kibbe, W. A. & Lin, S. M. Lumi: a pipeline for processing Illumina microarray. Bioinformatics 24, 1547 (2008)
48. Szklarczyk, D. et al., STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res., 43, D447–D452 (2014)
49. Fazekas, D. et al. SignaLink 2-a signaling pathway resource with multi-layered regulatory networks. BMC Syst. Biol. 7, 1 (2013)
50. Krogan, N. J. et al. The cancer cell map initiative: defining the hallmark networks of cancer. Mol. Cell 58, 690 (2015)
51. Chatr-Aryamontri, A. et al. The BioGRID interaction database: 2015 update. Nucleic Acids Res. 43, D470 (2015)
52. Khurana, E., Fu, Y., Chen, J. & Gerstein, M. Interpretation of genomic variants using a unified biological network approach. PLoS Comput. Biol. 9, e1002886 (2013)
53. Mostafavi, S. et al. GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol. 9, 1 (2008)
54. Su, G., Morris, J. H., Demchak, B. & Bader, G. D. Biological network exploration with cytoscape 3. Curr. Protoc. Bioinform. 1, 8–13 (2014)
55. Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinform. 14, 128 (2013)
56. Simpson, S. L., Lyday, R. G., Hayasaka, S., Marsh, A. P. & Laurienti, P. J. A permutation testing framework to compare groups of brain networks. Front Computat. Neurosci. 7, 171 (2013)