Top

Published in:

Open Access 01-12-2021 | Tuberculosis | Research article

Comparing tuberculosis gene signatures in malnourished individuals using the TBSignatureProfiler

Authors: W. Evan Johnson, Aubrey Odom, Chelsie Cintron, Mutharaj Muthaiah, Selby Knudsen, Noyal Joseph, Senbagavalli Babu, Subitha Lakshminarayanan, David F. Jenkins, Yue Zhao, Ethel Nankya, C. Robert Horsburgh, Gautam Roy, Jerrold Ellner, Sonali Sarkar, Padmini Salgame, Natasha S. Hochberg

Published in: BMC Infectious Diseases | Issue 1/2021

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.

Available only for authorised users

World Health Organization. Global tuberculosis report 2018: WHO press; 2018. Available at: http://www.who.int/tb/publications/global_report/en/. Accessed 4 Jan 2021.

World Health Organization Executive Board. Global strategy and targets for tuberculosis prevention , care and control after 2015, November 2013, pp. 1–23, 2015. 2015; November 2013:1–23.

Detjen AK, DiNardo AR, Leyden J, Steingart KR, Menzies D, Schiller I, et al. Xpert MTB/RIF assay for the diagnosis of pulmonary tuberculosis in children: a systematic review and meta-analysis. Lancet Respir Med. 2015;3:451–61.CrossRefPubMedPubMedCentral

Zeka AN, Tasbakan S, Cavusoglu C. Evaluation of the GeneXpert MTB/RIF assay for rapid diagnosis of tuberculosis and detection of rifampin resistance in pulmonary and extrapulmonary specimens. J Clin Microbiol. 2011;49:4138–41. https://doi.org/10.1128/JCM.05434-11.CrossRefPubMedPubMedCentral

Hillemann D, Rüsch-Gerdes S, Boehme C, Richter E. Rapid molecular detection of extrapulmonary tuberculosis by the automated genexpert MTB/RIF system. J Clin Microbiol. 2011;49:1202–5. https://doi.org/10.1128/JCM.02268-10.CrossRefPubMedPubMedCentral

Thomas TA. Tuberculosis in children. Pediatr Clin N Am. 2017;64:893–909.CrossRef

Denkinger CM, Kik SV, Cirillo DM, Casenghi M, Shinnick T, Weyer K, et al. Defining the needs for next generation assays for tuberculosis. J Infect Dis. 2015;211(Suppl 2):S29–38.CrossRefPubMedPubMedCentral

Sloot R, van der Loeff MF S, van Zwet EW, Haks MC, Keizer ST, Scholing M, et al. Biomarkers Can Identify Pulmonary Tuberculosis in HIV-infected Drug Users Months Prior to Clinical Diagnosis. EBioMedicine. 2015;2:172–9.CrossRefPubMed

Esmail H, Lai RP, Lesosky M, Wilkinson KA, Graham CM, Horswell S, et al. Complement pathway gene activation and rising circulating immune complexes characterize early disease in HIV-associated tuberculosis. Proc Natl Acad Sci U S A. 2018;115:E964–73.CrossRefPubMedPubMedCentral

10.

Berry MPR, Berry M, Graham CM, McNab FW, Xu Z, Bloch SAA, et al. An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature. 2010;466:973–7.CrossRefPubMedPubMedCentral

11.

Walter ND, Miller MA, Vasquez J, Weiner M, Chapman A, Engle M, et al. Blood transcriptional biomarkers for active tuberculosis among patients in the United States: a case-control study with systematic cross-classifier evaluation. J Clin Microbiol. 2016;54:274–82.CrossRefPubMedPubMedCentral

12.

Kaforou M, Wright VJ, Oni T, French N, Anderson ST, Bangani N, et al. Detection of tuberculosis in HIV-infected and -uninfected African adults using whole blood RNA expression signatures: a case-control study. PLoS Med. 2013;10:e1001538. https://doi.org/10.1371/journal.pmed.1001538.CrossRefPubMedPubMedCentral

13.

Zak DE, Penn-Nicholson A, Scriba TJ, Thompson E, Suliman S, Amon LM, et al. A blood RNA signature for tuberculosis disease risk: a prospective cohort study. Lancet. 2016;387:2312–22. https://doi.org/10.1016/S0140-6736(15)01316-1.CrossRefPubMedPubMedCentral

14.

Suliman S, Thompson E, Sutherland J, Weiner Rd J, Ota MOC, Shankar S, et al. Four-gene pan-African blood signature predicts progression to tuberculosis. Am J Respir Crit Care Med. 2018;197(9):1198–208.

15.

Leong S, Zhao Y, Ribeiro-Rodrigues R, Jones-López EC, Acuña-Villaorduña C, Rodrigues PM, et al. Cross-validation of existing signatures and derivation of a novel 29-gene transcriptomic signature predictive of progression to TB in a Brazilian cohort of household contacts of pulmonary TB. Tuberculosis (Edinb). 2020;120:101898. https://doi.org/10.1016/j.tube.2020.101898.CrossRef

16.

Thompson EG, Du Y, Malherbe ST, Shankar S, Braun J, Valvo J, et al. Host blood RNA signatures predict the outcome of tuberculosis treatment. Tuberculosis (Edinb). 2017;107:48–58. https://doi.org/10.1016/j.tube.2017.08.004.CrossRef

17.

Sweeney TE, Braviak L, Tato CM, Khatri P. Genome-wide expression for diagnosis of pulmonary tuberculosis: a multicohort analysis. Lancet Respir Med. 2016;4:213–24. https://doi.org/10.1016/S2213-2600(16)00048-5.CrossRefPubMedPubMedCentral

18.

Bloom CI, Graham CM, Berry MPR, Rozakeas F, Redford PS, Wang Y, et al. Transcriptional blood signatures distinguish pulmonary tuberculosis, pulmonary Sarcoidosis, Pneumonias and Lung Cancers. PLoS One. 2013;8:e70630. https://doi.org/10.1371/journal.pone.0070630.CrossRefPubMedPubMedCentral

19.

Leong S, Zhao Y, Joseph NMNM, Hochberg NSNS, Sarkar S, Pleskunas J, et al. Existing blood transcriptional classifiers accurately discriminate active tuberculosis from latent infection in individuals from South India. Tuberculosis. 2018;109:41–51. https://doi.org/10.1016/j.tube.2018.01.002.CrossRefPubMed

20.

Warsinske H, Vashisht R, Khatri P. Host-response-based gene signatures for tuberculosis diagnosis: a systematic comparison of 16 signatures. PLoS Med. 2019;16:1–19.CrossRef

21.

Gupta RK, Turner CT, Venturini C, Esmail H, Rangaka MX, Copas A, et al. Concise whole blood transcriptional signatures for incipient tuberculosis: a systematic review and patient-level pooled meta-analysis. Lancet Respir Med. 2020;8:395–406. https://doi.org/10.1016/S2213-2600(19)30282-6.CrossRefPubMedPubMedCentral

22.

Lönnroth K, Castro KG, Chakaya JM, Chauhan LS, Floyd K, Glaziou P, et al. Tuberculosis control and elimination 2010–50: cure, care, and social development. Lancet. 2010;375:1814–29.CrossRefPubMed

23.

Jasenosky LD, Scriba TJ, Hanekom WA, Goldfeld AE. T cells and adaptive immunity to mycobacterium tuberculosis in humans. Immunol Rev. 2015;264:74–87.CrossRefPubMed

24.

Prada-Medina CA, Fukutani KF, Pavan Kumar N, Gil-Santana L, Babu S, Lichtenstein F, et al. Systems immunology of diabetes-tuberculosis comorbidity reveals signatures of disease complications. Sci Rep. 2017;7:1999.CrossRefPubMedPubMedCentral

25.

Jiao H, Kulyte A, Naslund E, Thorell A, Gerdhem P, Kere J, et al. Whole-exome sequencing suggests LAMB3 as a susceptibility gene for morbid obesity. Diabetes. 2016;65:2980–9.CrossRefPubMed

26.

Lopes LL. Peluzio M do CG, Hermsdorff HHM. Ingestão de ácidos graxos monoinsaturados e metabolismo lipídico. J Vasc Bras. 2016;15:52–60.CrossRef

27.

Bhargava A. Undernutrition, nutritionally acquired immunodeficiency, and tuberculosis control. BMJ. 2016;355. https://doi.org/10.1136/bmj.i5407.

28.

Beisel WR. Nutrition and immune function: overview. J Nutr Am Soc Nutr. 1996:2611S. https://doi.org/10.1093/jn/126.suppl_10.2611s.

29.

Sinha P, Davis J, Saag L, Wanke C, Salgame P, Mesick J, et al. Undernutrition and tuberculosis: public health implications. J Infect Dis. 2019;219:1356–63. https://doi.org/10.1093/infdis/jiy675.CrossRefPubMed

30.

Rodríguez L, González C, Flores L, Jiménez-Zamudio L, Graniel J, Ortiz R. Assessment by flow cytometry of cytokine production in malnourished children. Clin Diagn Lab Immunol. 2005;12:502–7. https://doi.org/10.1128/CDLI.12.4.502-507.2005.CrossRefPubMedPubMedCentral

31.

Anuradha R, Munisankar S, Bhootra Y, Kumar NP, Dolla C, Kumaran P, et al. Coexistent malnutrition is associated with perturbations in systemic and antigen-specific cytokine responses in latent tuberculosis infection. Clin Vaccine Immunol. 2016;23:339–45. https://doi.org/10.1128/CVI.00009-16.CrossRefPubMedPubMedCentral

32.

Bhargava A, Sharma A, Oxlade O, Menzies DPM. Undernutrition and the incidence of tuberculosis in India: national and subnational estimates of the population-attributable fraction related to undernutrition. Natl Med J India. 2014;27:128–33.PubMed

33.

Roe JK, Thomas N, Gil E, Best K, Tsaliki E, Morris-Jones S, et al. Blood transcriptomic diagnosis of pulmonary and extrapulmonary tuberculosis. JCI Insight. 2016;1. https://doi.org/10.1172/jci.insight.87238.

34.

de Araujo LS, Vaas LAI, Ribeiro-Alves M, Geffers R, Mello FCQ, de Almeida AS, et al. Transcriptomic biomarkers for tuberculosis: Evaluation of DOCK9, EPHA4, and NPC2 mRNA expression in peripheral blood. Front Microbiol. 2016;7. https://doi.org/10.3389/fmicb.2016.01586.

35.

Verma S, Du P, Nakanjako D, Hermans S, Briggs J, Nakiyingi L, et al. Tuberculosis in advanced HIV infection is associated with increased expression of IFNγ and its downstream targets. BMC Infect Dis. 2018;18. https://doi.org/10.1186/s12879-018-3127-4.

36.

Shen Y, Rahman M, Piccolo SRSRR, Gusenleitner D, El-Chaar NNNN, Cheng L, et al. ASSIGN: context-specific genomic profiling of multiple heterogeneous biological pathways. Bioinformatics. 2015;31:1745–53. https://doi.org/10.1093/bioinformatics/btv031.CrossRefPubMedPubMedCentral

37.

Barbie DA, Tamayo P, Boehm JS, Kim SY, Moody SE, Dunn IF, et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature. 2009;462:108–12.CrossRefPubMedPubMedCentral

38.

Tomfohr J, Lu J, Kepler TB. Pathway level analysis of gene expression using singular value decomposition. BMC Bioinformatics. 2005;6:225.CrossRefPubMedPubMedCentral

39.

Lee E, Chuang HY, Kim JW, Ideker T, Lee D. Inferring pathway activity toward precise disease classification. PLoS Comput Biol. 2008;4.

40.

Foroutan M, Bhuva DD, Lyu R, Horan K, Cursons J, Davis MJ. Single sample scoring of molecular phenotypes. BMC Bioinformatics. 2018;19(1):404. https://doi.org/10.1186/s12859-018-2435-4.

41.

Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics. 2013;14:7.CrossRefPubMedPubMedCentral

42.

Hochberg NS, Sarkar S, Horsburgh CRJ, Knudsen S, Pleskunas J, Sahu S, et al. Comorbidities in pulmonary tuberculosis cases in Puducherry and Tamil Nadu, India: Opportunities for intervention. PLoS One. 2017;12:e0183195.CrossRefPubMedPubMedCentral

43.

Kubiak RW, Sarkar S, Horsburgh CR, Roy G, Kratz M, Reshma A, et al. Interaction of nutritional status and diabetes on active and latent tuberculosis: a cross-sectional analysis. BMC Infect Dis. 2019;19:627.CrossRefPubMedPubMedCentral

44.

Van Ness SE, Chandra A, Sarkar S, Pleskunas J, Ellner JJ, Roy G, et al. Predictors of delayed care seeking for tuberculosis in southern India: an observational study. BMC Infect Dis. 2017;17:567.CrossRefPubMedPubMedCentral

45.

Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KA. The AUDIT alcohol consumption questions (AUDIT-C): an effective brief screening test for problem drinking. Ambulatory care quality improvement project (ACQUIP). Alcohol use disorders identification test. Arch Intern Med. 1998;158:1789–95 http://www.ncbi.nlm.nih.gov/pubmed/9738608. Accessed 13 Feb 2020.CrossRefPubMed

46.

WHO. Growth reference data for 5–19 years. Geneva: WHO; 2013. https://www.who.int/tools/growth-reference-data-for-5to19-years.

47.

Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8:118–27. https://doi.org/10.1093/biostatistics/kxj037.CrossRefPubMed

48.

Zhang Y, Parmigiani G, Johnson WE. ComBat-Seq: batch effect adjustment for RNA-Seq count data. bioRxiv. 2020; 2020.01.13.904730.

49.

DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837. https://doi.org/10.2307/2531595.CrossRefPubMed

50.

Sun X, Xu W, Xu W. Fast implementation of DeLong’s algorithm for comparing the areas under correlated receiver operating characteristic curves MANUSCRIPT ACCEPTED BY IEEE SIGNAL PROCESSING LETTERS 1 fast implementation of DeLong’s algorithm for comparing the areas under correlated receiver operating characteristic curves MANUSCRIPT ACCEPTED BY IEEE SIGNAL PROCESSING LETTERS 2. 2014. doi:https://doi.org/10.1109/LSP.2014.2337313.

51.

Shenoy AR, Wellington DA, Kumar P, Kassa H, Booth CJ, Cresswell P, et al. GBP5 promotes NLRP3 inflammasome assembly and immunity in mammals. Science. 2012;336:481–5.CrossRefPubMed

52.

Tretina K, Park E-S, Maminska A, MacMicking JD. Interferon-induced guanylate-binding proteins: guardians of host defense in health and disease. J Exp Med. 2019;216:482–500.CrossRefPubMedPubMedCentral

53.

Meunier E, Wallet P, Dreier RF, Costanzo S, Anton L, Ruhl S, et al. Guanylate-binding proteins promote activation of the AIM2 inflammasome during infection with Francisella novicida. Nat Immunol. 2015;16:476–84.CrossRefPubMedPubMedCentral

54.

Swisher JFA, Feldman GM. The many faces of FcgammaRI: implications for therapeutic antibody function. Immunol Rev. 2015;268:160–74.CrossRefPubMed

55.

Mortaz E, Alipoor SD, Adcock IM, Mumby S, Koenderman L. Update on neutrophil function in severe inflammation. Front Immunol. 2018;9:2171.CrossRefPubMedPubMedCentral

56.

Ramanathan K, Glaser A, Lythgoe H, Ong J, Beresford MW, Midgley A, et al. Neutrophil activation signature in juvenile idiopathic arthritis indicates the presence of low-density granulocytes. Rheumatology (Oxford). 2018;57:488–98.CrossRef

57.

Lang R, Raffi FAM. Dual-specificity phosphatases in immunity and infection: an update. Int J Mol Sci. 2019;20.

58.

Sedgwick SG, Smerdon SJ. The ankyrin repeat: a diversity of interactions on a common structural framework. Trends Biochem Sci. 1999;24:311–6.CrossRefPubMed

59.

Varnier OE, Kindt TJ. Current status of HIV-1 infection in the rabbit. AIDS Res Hum Retrovir. 1992;8:533–5.CrossRefPubMed

60.

Su ZZ, Lee SG, Emdad L, Lebdeva IV, Gupta P, Valerie K, et al. Cloning and characterization of SARI (suppressor of AP-1, regulated by IFN). Proc Natl Acad Sci U S A. 2008;105:20906–11.CrossRefPubMedPubMedCentral

61.

Roy S, Guler R, Parihar SP, Schmeier S, Kaczkowski B, Nishimura H, et al. Batf2/Irf1 induces inflammatory responses in classically activated macrophages, lipopolysaccharides, and mycobacterial infection. J Immunol. 2015;194:6035–44. https://doi.org/10.4049/jimmunol.1402521.CrossRefPubMed

62.

Infante RE, Wang ML, Radhakrishnan A, Hyock JK, Brown MS, Goldstein JL. NPC2 facilitates bidirectional transfer of cholesterol between NPC1 and lipid bilayers, a step in cholesterol egress from lysosomes. Proc Natl Acad Sci U S A. 2008;105:15287–92. https://doi.org/10.1073/pnas.0807328105.CrossRefPubMedPubMedCentral

63.

Liou HL, Dixit SS, Xu S, Tint GS, Stock AM, Lobel P. NPC2, the protein deficient in Niemann-pick C2 disease, consists of multiple glycoforms that bind a variety of sterols. J Biol Chem. 2006;281:36710–23. https://doi.org/10.1074/jbc.M608743200.CrossRefPubMed

64.

Liao Y, Wei J, Wang J, Shi X, Luo J, Song BL. The non-canonical NF-κB pathway promotes NPC2 expression and regulates intracellular cholesterol trafficking. Sci China Life Sci. 2018;61:1222–32. https://doi.org/10.1007/s11427-018-9339-0.CrossRefPubMed

65.

Wichit S, Hamel R, Bernard E, Talignani L, Diop F, Ferraris P, et al. Imipramine inhibits Chikungunya virus replication in human skin fibroblasts through interference with intracellular cholesterol trafficking. Sci Rep. 2017;7:1–12. https://doi.org/10.1038/s41598-017-03316-5.CrossRef

66.

Schraml BU, Hildner K, Ise W, Lee WL, Smith WAE, Solomon B, et al. The AP-1 transcription factor Batf controls T H 17 differentiation. Nature. 2009;460:405–9. https://doi.org/10.1038/nature08114.CrossRefPubMedPubMedCentral

67.

Quigley M, Pereyra F, Nilsson B, Porichis F, Fonseca C, Eichbaum Q, et al. Transcriptional analysis of HIV-specific CD8+ T cells shows that PD-1 inhibits T cell function by upregulating BATF. Nat Med. 2010;16:1147–51. https://doi.org/10.1038/nm.2232.CrossRefPubMedPubMedCentral

68.

Shield KD, Parry C, Rehm J. Chronic diseases and conditions related to alcohol use. Alcohol Res Curr Rev. 2013;35:155–71.

69.

Williams EC, Hahn JA, Saitz R, Bryant K, Lira MC, Samet JH. Alcohol use and human immunodeficiency virus (HIV) infection: current knowledge, implications, and future directions. Alcohol Clin Exp Res. 2016;40:2056–72.CrossRefPubMedPubMedCentral

70.

Nouaman MN, Vinikoor M, Seydi M, Ekouevi DK, Coffie PA, Mulenga L, et al. High prevalence of binge drinking among people living with HIV in four African countries. J Int AIDS Soc. 2018;21.

71.

Saag LA, Lavalley MP, Hochberg NS, Cegielski JP, Pleskunas JA, Linas BP, et al. Low body mass index and latent tuberculous infection: a systematic review and meta-analysis. Int J Tuberc Lung Dis. 2018;22:358–65.CrossRefPubMed

72.

Anderson ST, Kaforou M, Brent AJ, Wright VJ, Banwell CM, Chagaluka G, et al. Diagnosis of childhood tuberculosis and host RNA expression in Africa. N Engl J Med. 2014;370:1712–23. https://doi.org/10.1056/NEJMoa1303657.CrossRefPubMedPubMedCentral

Title: Comparing tuberculosis gene signatures in malnourished individuals using the TBSignatureProfiler
Authors: W. Evan Johnson
Aubrey Odom
Chelsie Cintron
Mutharaj Muthaiah
Selby Knudsen
Noyal Joseph
Senbagavalli Babu
Subitha Lakshminarayanan
David F. Jenkins
Yue Zhao
Ethel Nankya
C. Robert Horsburgh
Gautam Roy
Jerrold Ellner
Sonali Sarkar
Padmini Salgame
Natasha S. Hochberg
Publication date: 01-12-2021
Publisher: BioMed Central
Keywords: Tuberculosis
Tuberculosis
Malnutrition
Biomarkers
Published in: BMC Infectious Diseases / Issue 1/2021
Electronic ISSN: 1471-2334
DOI: https://doi.org/10.1186/s12879-020-05598-z

ACC 2024 Congress Coverage

Heart Failure Video

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Comparing tuberculosis gene signatures in malnourished individuals using the TBSignatureProfiler

Abstract

Background

Methods

Results

Conclusion

ACC 2024 Congress Coverage

Year in Review: Heart failure and cardiomyopathies

Semaglutide benefits people with type 2 diabetes and obesity-related heart failure

Incentivised to exercise

New intervention for STEMI-related cardiogenic shock

» View more highlights on the ACC 2024 congress hub page

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 1/2021

“OPTIONS-DC”, a feasible discharge planning conference to expand infection treatment options for people with substance use disorder

Prevalence of oral human papillomavirus infection among Indian HIV-positive men who have sex with men: a cross-sectional study

Prevention of SARS-CoV-2 (COVID-19) transmission in residential aged care using ultraviolet light (PETRA): a two-arm crossover randomised controlled trial protocol

Decreased human respiratory syncytial virus activity during the COVID-19 pandemic in Japan: an ecological time-series analysis

Mid-term follow-up results after implementing a new strategy for the diagnosis and management of periprosthetic joint infections

First Chinese case report of Helcococcus kunzii in a patient with diabetic foot

ACC 2024 Congress Coverage

Year in Review: Heart failure and cardiomyopathies

Semaglutide benefits people with type 2 diabetes and obesity-related heart failure

Incentivised to exercise

New intervention for STEMI-related cardiogenic shock

» View more highlights on the ACC 2024 congress hub page