Research progress on blood transcriptomic biomarkers in the diagnosis of tuberculosis

doi:10.19982/j.issn.1000-6621.20230182

Abstract

Abstract:

Development of convenient, rapid and accurate tuberculosis (TB) diagnostic assays is an important part of ending TB. Current detection assays have drawbacks such as difficulties in sputum collection, which are expected to be overcome by methods based on blood transcripomic biomarkers. We conducted a review about the progress, limitations, and prospects of blood-based transcripomic biomarkers in the diagnosis of TB.

Key words: Tuberculosis, Diagnosis, Transcription factors, Biological markers

CLC Number:

R52
R852.2

Bei Cheng, Li Meng, Gao Qian. Research progress on blood transcriptomic biomarkers in the diagnosis of tuberculosis[J]. Chinese Journal of Antituberculosis, 2023, 45(8): 801-807. doi: 10.19982/j.issn.1000-6621.20230182

Figures/Tables 1

References 69

[1]	World Health Organization.Global tuberculosis report 2022. Geneva: World Health Organization, 2022.
[2]	Drain PK, Gardiner J, Hannah H, et al. Guidance for Studies Evaluating the Accuracy of Biomarker-Based Nonsputum Tests to Diagnose Tuberculosis. J Infect Dis, 2019, 220(220 Suppl 3): S108-S115. doi:10.1093/infdis/jiz356. doi: 10.1093/infdis/jiz356
[3]	Getahun H, Harrington M, O’Brien R, et al. Diagnosis of smear-negative pulmonary tuberculosis in people with HIV infection or AIDS in resource-constrained settings: informing urgent policy changes. Lancet, 2007, 369(9578): 2042-2049. doi:10.1016/S0140-6736(07)60284-0. doi: S0140-6736(07)60284-0 pmid: 17574096
[4]	Hong JM, Lee H, Menon NV, et al. Point-of-care diagnostic tests for tuberculosis disease. Sci Transl Med, 2022, 14(639): eabj4124. doi:10.1126/scitranslmed.abj4124. doi: 10.1126/scitranslmed.abj4124 URL
[5]	Kohli M, Schiller I, Dendukuri N, et al. Xpert MTB/RIF assay for extrapulmonary tuberculosis and rifampicin resis-tance. Cochrane Database Syst Rev, 2018, 8(8): CD012768. doi:10.1002/14651858.CD012768.pub2. doi: 10.1002/14651858.CD012768.pub2
[6]	World Health Organization. High priority target product profiles for new tuberculosis diagnostics: report of a consensus meeting, 28-29 April 2014. Geneva: World Health Organization, 2014.
[7]	李蒙, 高谦. 结核病自然史的阶段划分及其诊断的现状与展望. 中国防痨杂志, 2021, 43(11): 1125-1131. doi:10.3969/j.issn.1000-6621.2021.11.005. doi: 10.3969/j.issn.1000-6621.2021.11.005
[8]	Södersten E, Ongarello S, Mantsoki A, et al. Diagnostic Accuracy Study of a Novel Blood-Based Assay for Identification of Tuberculosis in People Living with HIV. J Clin Microbiol, 2021, 59(3): e01643-20. doi:10.1128/JCM.01643-20. doi: 10.1128/JCM.01643-20
[9]	Singhania A, Wilkinson RJ, Rodrigue M, et al. The value of transcriptomics in advancing knowledge of the immune response and diagnosis in tuberculosis. Nat Immunol, 2018, 19(11): 1159-1168. doi:10.1038/s41590-018-0225-9. doi: 10.1038/s41590-018-0225-9 pmid: 30333612
[10]	Wang S, He L, Wu J, et al. Transcriptional Profiling of Human Peripheral Blood Mononuclear Cells Identifies Diagnostic Biomarkers That Distinguish Active and Latent Tuberculosis. Front Immunol, 2019, 10: 2948. doi:10.3389/fimmu.2019.02948. doi: 10.3389/fimmu.2019.02948 pmid: 31921195
[11]	Mistry R, Cliff JM, Clayton CL, et al. Gene-expression patterns in whole blood identify subjects at risk for recurrent tuberculosis. J Infect Dis, 2007, 195(3): 357-365. doi:10.1086/510397. doi: 10.1086/510397 pmid: 17205474
[12]	Anderson ST, Kaforou M, Brent AJ, et al. Diagnosis of childhood tuberculosis and host RNA expression in Africa. N Engl J Med, 2014, 370(18): 1712-1723. doi:10.1056/NEJMoa1303657. doi: 10.1056/NEJMoa1303657 URL
[13]	Duffy FJ, Thompson EG, Scriba TJ, et al. Multinomial modelling of TB/HIV co-infection yields a robust predictive signature and generates hypotheses about the HIV⁺TB⁺disease state. PLoS One, 2019, 14(7): e0219322. doi:10.1371/journal.pone.0219322. doi: 10.1371/journal.pone.0219322
[14]	Gliddon HD, Kaforou M, Alikian M, et al. Identification of Reduced Host Transcriptomic Signatures for Tuberculosis Disease and Digital PCR-Based Validation and Quantification. Front Immunol, 2021, 12: 637164. doi:10.3389/fimmu.2021.637164. doi: 10.3389/fimmu.2021.637164
[15]	Kaforou M, Wright VJ, Oni T, 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(10): e1001538. doi:10.1371/journal.pmed.1001538. doi: 10.1371/journal.pmed.1001538
[16]	Penn-Nicholson A, Mbandi SK, Thompson E, et al. RISK6, a 6-gene transcriptomic signature of TB disease risk, diagnosis and treatment response. Sci Rep, 2020, 10(1): 8629. doi:10.1038/s41598-020-65043-8. doi: 10.1038/s41598-020-65043-8 pmid: 32451443
[17]	Singhania A, Verma R, Graham CM, et al. A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection. Nat Commun, 2018, 9(1): 2308. doi:10.1038/s41467-018-04579-w. doi: 10.1038/s41467-018-04579-w pmid: 29921861
[18]	Suliman S, Thompson EG, Sutherland J, et al. Four-Gene Pan-African Blood Signature Predicts Progression to Tuberculosis. Am J Respir Crit Care Med, 2018, 197(9): 1198-1208. doi:10.1164/rccm.201711-2340OC. doi: 10.1164/rccm.201711-2340OC URL
[19]	Sweeney TE, Braviak L, Tato CM, et al. Genome-wide expression for diagnosis of pulmonary tuberculosis: a multicohort analysis. Lancet Respir Med, 2016, 4(3): 213-224. doi:10.1016/S2213-2600(16)00048-5. doi: 10.1016/S2213-2600(16)00048-5 pmid: 26907218
[20]	Zak DE, Penn-Nicholson A, Scriba TJ, et al. A blood RNA signature for tuberculosis disease risk: a prospective cohort study. Lancet, 2016, 387(10035): 2312-2322. doi:10.1016/S0140-6736(15)01316-1. doi: S0140-6736(15)01316-1 pmid: 27017310
[21]	Walter ND, Miller MA, Vasquez J, 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(2): 274-282. doi:10.1128/JCM.01990-15. doi: 10.1128/JCM.01990-15 pmid: 26582831
[22]	刘艳华, 王若, 安红娟, 等. FCGR1B基因转录水平检测对活动性结核病诊断的价值研究. 中华结核和呼吸杂志, 2022, 45(4): 373-378. doi:10.3760/cma.j.cn112147-20211213-00878. doi: 10.3760/cma.j.cn112147-20211213-00878
[23]	Hoang LT, Jain P, Pillay TD, et al. Transcriptomic signatures for diagnosing tuberculosis in clinical practice: a prospective, multicentre cohort study. Lancet Infect Dis, 2021, 21(3): 366-375. doi:10.1016/S1473-3099(20)30928-2. doi: 10.1016/S1473-3099(20)30928-2 pmid: 33508221
[24]	de Araujo LS, Vaas LA, Ribeiro-Alves M, et al. Transcriptomic Biomarkers for Tuberculosis: Evaluation of DOCK9. EPHA4, and NPC2 mRNA Expression in Peripheral Blood. Front Microbiol, 2016, 7: 1586. doi:10.3389/fmicb.2016.01586. doi: 10.3389/fmicb.2016.01586
[25]	Gao J, Li C, Li W, et al. Increased UBE2L6 regulated by type 1 interferon as potential marker in TB. J Cell Mol Med, 2021, 25(24): 11232-11243. doi:10.1111/jcmm.17046. doi: 10.1111/jcmm.17046 pmid: 34773365
[26]	Xu Y, Tan Y, Zhang X, et al. Comprehensive identification of immuno-related transcriptional signature for active pulmonary tuberculosis by integrated analysis of array and single cell RNA-seq. J Infect, 2022, 85(5): 534-544. doi:10.1016/j.jinf.2022.08.017. doi: 10.1016/j.jinf.2022.08.017 URL
[27]	Roe J, Venturini C, Gupta RK, et al. Blood Transcriptomic Stratification of Short-term Risk in Contacts of Tuberculosis. Clin Infect Dis, 2020, 70(5): 731-737. doi:10.1093/cid/ciz252. doi: 10.1093/cid/ciz252 pmid: 30919880
[28]	Murphy TL, Tussiwand R, Murphy KM. Specificity through cooperation: BATF-IRF interactions control immune-regulatory networks. Nat Rev Immunol, 2013, 13(7): 499-509. doi:10.1038/nri3470. doi: 10.1038/nri3470 pmid: 23787991
[29]	Li P, Jiang W, Yu Q, et al. Ubiquitination and degradation of GBPs by a Shigella effector to suppress host defence. Nature, 2017, 551(7680): 378-383. doi:10.1038/nature24467. doi: 10.1038/nature24467 URL
[30]	Patten DA. SCARF1: a multifaceted, yet largely understudied, scavenger receptor. Inflamm Res, 2018, 67(8): 627-632. doi:10.1007/s00011-018-1154-7. doi: 10.1007/s00011-018-1154-7 pmid: 29725698
[31]	Gjøen JE, Jenum S, Sivakumaran D, et al. Novel transcriptional signatures for sputum-independent diagnostics of tuberculosis in children. Sci Rep, 2017, 7(1): 5839. doi:10.1038/s41598-017-05057-x. doi: 10.1038/s41598-017-05057-x pmid: 28724962
[32]	Maertzdorf J, McEwen G, Weiner J 3rd, et al. Concise gene signature for point-of-care classification of tuberculosis. EMBO Mol Med, 2016, 8(2): 86-95. doi:10.15252/emmm.201505790. doi: 10.15252/emmm.201505790 pmid: 26682570
[33]	Qian Z, Lv J, Kelly GT, et al. Expression of nuclear factor, erythroid 2-like 2-mediated genes differentiates tuberculosis. Tuberculosis (Edinb), 2016, 99: 56-62. doi:10.1016/j.tube.2016.04.008. doi: 10.1016/j.tube.2016.04.008 URL
[34]	Chen Y, Wang Q, Lin S, et al. Meta-Analysis of Peripheral Blood Transcriptome Datasets Reveals a Biomarker Panel for Tuberculosis in Patients Infected With HIV . Front Cell Infect Microbiol, 2021, 11: 585919. doi:10.3389/fcimb.2021.585919. doi: 10.3389/fcimb.2021.585919 URL
[35]	Sivakumaran D, Ritz C, Gjøen JE, et al. Host Blood RNA Transcript and Protein Signatures for Sputum-Independent Diagnostics of Tuberculosis in Adults. Front Immunol, 2020, 11: 626049. doi:10.3389/fimmu.2020.626049. doi: 10.3389/fimmu.2020.626049 URL
[36]	Roe JK, Thomas N, Gil E, et al. Blood transcriptomic diagnosis of pulmonary and extrapulmonary tuberculosis. JCI Insight, 2016, 1(16): e87238. doi:10.1172/jci.insight.87238. doi: 10.1172/jci.insight.87238
[37]	Wang JB, Qiu QZ, Zheng QL, et al. Tumor Immunophenotyping-Derived Signature Identifies Prognosis and Neoadjuvant Immunotherapeutic Responsiveness in Gastric Cancer. Adv Sci (Weinh), 2023, 10(15): e2207417. doi:10.1002/advs.202207417. doi: 10.1002/advs.202207417
[38]	Elkington P, Tebruegge M, Mansour S. Tuberculosis: An Infection-Initiated Autoimmune Disease? Trends Immunol, 2016, 37(12): 815-818. doi:10.1016/j.it.2016.09.007. doi: S1471-4906(16)30141-7 pmid: 27773684
[39]	Starshinova A, Malkova A, Kudryavtsev I, et al. Tuberculosis and autoimmunity: Common features. Tuberculosis (Edinb), 2022, 134: 102202. doi:10.1016/j.tube.2022.102202. doi: 10.1016/j.tube.2022.102202 URL
[40]	Maertzdorf J, Weiner J 3rd, Mollenkopf HJ, et al. Common patterns and disease-related signatures in tuberculosis and sarcoidosis. Proc Natl Acad Sci U S A, 2012, 109(20): 7853-7858. doi:10.1073/pnas.1121072109. doi: 10.1073/pnas.1121072109 URL
[41]	Del Rosario RCH, Poschmann J, Lim C, et al. Histone acetylome-wide associations in immune cells from individuals with active Mycobacterium tuberculosis infection. Nat Microbiol, 2022, 7(2): 312-326. doi:10.1038/s41564-021-01049-w. doi: 10.1038/s41564-021-01049-w
[42]	Yue H, Hu Z, Hu R, et al. ALDH1A 1 in Cancers: Bidirectional Function, Drug Resistance, and Regulatory Mechanism. Front Oncol, 2022, 12: 918778. doi:10.3389/fonc.2022.918778. doi: 10.3389/fonc.2022.918778
[43]	Hamada Y, Penn-Nicholson A, Krishnan S, et al. Are mRNA based transcriptomic signatures ready for diagnosing tuberculosis in the clinic?-A review of evidence and the technological landscape. EBioMedicine, 2022, 82: 104174. doi:10.1016/j.ebiom.2022.104174. doi: 10.1016/j.ebiom.2022.104174 URL
[44]	Turner CT, Gupta RK, Tsaliki E, et al. Blood transcriptional biomarkers for active pulmonary tuberculosis in a high-burden setting: a prospective, observational, diagnostic accuracy study. Lancet Respir Med, 2020, 8(4): 407-419. doi:10.1016/S2213-2600(19)30469-2. doi: 10.1016/S2213-2600(19)30469-2 pmid: 32178775
[45]	Sutherland JS, van der Spuy G, Gindeh A, et al. Diagnostic Accuracy of the Cepheid 3-gene Host Response Fingerstick Blood Test in a Prospective, Multi-site Study: Interim Results. Clin Infect Dis, 2022, 74(12): 2136-2141. doi:10.1093/cid/ciab839. doi: 10.1093/cid/ciab839 URL
[46]	Moreira FMF, Verma R, Pereira Dos Santos PC, et al. Blood-based host biomarker diagnostics in active case finding for pulmonary tuberculosis: A diagnostic case-control study. EClinicalMedicine, 2021, 33: 100776. doi:10.1016/j.eclinm.2021.100776. doi: 10.1016/j.eclinm.2021.100776 URL
[47]	Dupnik KM, Bean JM, Lee MH, et al. Blood transcriptomic markers of Mycobacterium tuberculosis load in sputum. Int J Tuberc Lung Dis, 2018, 22(8): 950-958. doi:10.5588/ijtld.17.0855. doi: 10.5588/ijtld.17.0855 pmid: 29991407
[48]	Nahid P, Jarlsberg LG, Kato-Maeda M, et al. Interplay of strain and race/ethnicity in the innate immune response to M.tuberculosis. PLoS One, 2018, 13(5): e0195392. doi:10.1371/journal.pone.0195392. doi: 10.1371/journal.pone.0195392
[49]	Kong L, Moorlag S, Lefkovith A, et al. Single-cell transcriptomic profiles reveal changes associated with BCG-induced trained immunity and protective effects in circulating monocytes. Cell Rep, 2021, 37(7): 110028. doi:10.1016/j.celrep.2021.110028. doi: 10.1016/j.celrep.2021.110028 URL
[50]	Ferluga J, Yasmin H, Al-Ahdal MN, et al. Natural and trained innate immunity against Mycobacterium tuberculosis. Immunobiology, 2020, 225(3): 151951. doi:10.1016/j.imbio.2020.151951. doi: 10.1016/j.imbio.2020.151951 URL
[51]	Cirovic B, de Bree LCJ, Groh L, et al. BCG Vaccination in Humans Elicits Trained Immunity via the Hematopoietic Progenitor Compartment. Cell Host Microbe, 2020, 28(2): 322-334.e5. doi:10.1016/j.chom.2020.05.014. doi: S1931-3128(20)30296-1 pmid: 32544459
[52]	Parwati I,van Crevel R, van Soolingen D. Possible underlying mechanisms for successful emergence of the Mycobacterium tuberculosis Beijing genotype strains. Lancet Infect Dis, 2010, 10(2): 103-111. doi:10.1016/S1473-3099(09)70330-5. doi: 10.1016/S1473-3099(09)70330-5 pmid: 20113979
[53]	Glynn JR, Whiteley J, Bifani PJ, et al. Worldwide occurrence of Beijing/W strains of Mycobacterium tuberculosis: a systematic review. Emerg Infect Dis, 2002, 8(8): 843-849. doi:10.3201/eid0805.020002. doi: 10.3201/eid0805.020002 URL
[54]	European Concerted Action on New Generation Genetic Markers and Techniques for the Epidemiology and Control of Tuberculosis. Beijing/W genotype Mycobacterium tuberculosis and drug resistance. Emerg Infect Dis, 2006, 12(5): 736-743. doi:10.3201/eid1205.050400. doi: 10.3201/eid1205.050400 URL
[55]	Manca C, Tsenova L, Freeman S, et al. Hypervirulent M.tuberculosis W/Beijing strains upregulate type Ⅰ IFNs and increase expression of negative regulators of the Jak-Stat pathway. J Interferon Cytokine Res, 2005, 25(11): 694-701. doi:10.1089/jir.2005.25.694. doi: 10.1089/jir.2005.25.694 URL
[56]	Chacón-Salinas R, Serafín-López J, Ramos-Payán R, et al. Differential pattern of cytokine expression by macrophages infected in vitro with different Mycobacterium tuberculosis genotypes. Clin Exp Immunol, 2005, 140(3): 443-449. doi:10.1111/j.1365-2249.2005.02797.x. doi: 10.1111/j.1365-2249.2005.02797.x pmid: 15932505
[57]	Rocha-Ramírez LM, Estrada-García I, López-Marín LM, et al. Mycobacterium tuberculosis lipids regulate cytokines, TLR-2/4 and MHC class Ⅱ expression in human macrophages. Tuberculosis (Edinb), 2008, 88(3): 212-220. doi:10.1016/j.tube.2007.10.003. doi: 10.1016/j.tube.2007.10.003 URL
[58]	Sohn H, Lee KS, Kim SY, et al. Induction of cell death in human macrophages by a highly virulent Korean Isolate of Mycobacterium tuberculosis and the virulent strain H37Rv. Scand J Immunol, 2009, 69(1): 43-50. doi:10.1111/j.1365-3083.2008.02188.x. doi: 10.1111/j.1365-3083.2008.02188.x pmid: 19140876
[59]	Tsenova L, Harbacheuski R, Sung N, et al. BCG vaccination confers poor protection against M.tuberculosis HN878-induced central nervous system disease. Vaccine, 2007, 25(28): 5126-5132. doi:10.1016/j.vaccine.2006.11.024. doi: 10.1016/j.vaccine.2006.11.024 pmid: 17241704
[60]	López B, Aguilar D, Orozco H, et al. A marked difference in pathogenesis and immune response induced by different Mycobacterium tuberculosis genotypes. Clin Exp Immunol, 2003, 133(1): 30-37. doi:10.1046/j.1365-2249.2003.02171.x. doi: 10.1046/j.1365-2249.2003.02171.x pmid: 12823275
[61]	Ranaivomanana P, Rabodoarivelo MS, Ndiaye MDB, et al. Different PPD-stimulated cytokine responses from patients infected with genetically distinct Mycobacterium tuberculosis complex lineages. Int J Infect Dis, 2021, 104: 725-731. doi:10.1016/j.ijid.2021.01.073. doi: 10.1016/j.ijid.2021.01.073 pmid: 33556615
[62]	Tong J, Meng L, Bei C, et al. Modern Beijing sublineage of Mycobacterium tuberculosis shift macrophage into a hyperinflammatory status. Emerg Microbes Infect, 2022, 11(1): 715-724. doi:10.1080/22221751.2022.2037395. doi: 10.1080/22221751.2022.2037395 URL
[63]	Ribeiro SC, Gomes LL, Amaral EP, et al. Mycobacterium tuberculosis strains of the modern sublineage of the Beijing family are more likely to display increased virulence than strains of the ancient sublineage. J Clin Microbiol, 2014, 52(7): 2615-2624. doi:10.1128/JCM.00498-14. doi: 10.1128/JCM.00498-14 pmid: 24829250
[64]	Marioni JC, Mason CE, Mane SM, et al. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res, 2008, 18(9): 1509-1517. doi:10.1101/gr.079558.108. doi: 10.1101/gr.079558.108 pmid: 18550803
[65]	Rao MS, Van Vleet TR, Ciurlionis R, et al. Comparison of RNA-Seq and Microarray Gene Expression Platforms for the Toxicogenomic Evaluation of Liver From Short-Term Rat Toxicity Studies. Front Genet, 2018, 9: 636. doi:10.3389/fgene.2018.00636. doi: 10.3389/fgene.2018.00636 pmid: 30723492
[66]	Kogenaru S, Qing Y, Guo Y, et al. RNA-seq and microarray complement each other in transcriptome profiling. BMC Genomics, 2012, 13: 629. doi:10.1186/1471-2164-13-629. doi: 10.1186/1471-2164-13-629 pmid: 23153100
[67]	Walzl G, McNerney R, du Plessis N, et al. Tuberculosis: advances and challenges in development of new diagnostics and biomarkers. Lancet Infect Dis, 2018, 18(7): e199 -e210. doi:10.1016/S1473-3099(18)30111-7.
[68]	Wang X, Vanvalkenberg A, Odom-Mabey AR, et al. Comparison of gene set scoring methods for reproducible evaluation of multiple tuberculosis gene signatures. bioRxiv, 2023. doi:10.1101/2023.01.19.520627. doi: 10.1101/2023.01.19.520627
[69]	Kendall EA, Kitonsa PJ, Nalutaaya A, et al. The Spectrum of Tuberculosis Disease in an Urban Ugandan Community and Its Health Facilities. Clin Infect Dis, 2021, 72(12): e1035-e1043. doi:10.1093/cid/ciaa1824. doi: 10.1093/cid/ciaa1824

目的	敏感度	特异度
筛选(triage)
最低标准	>90%	>70%
最佳标准	>95%	>80%
确诊(confirmatory)
最低标准	(1)总体敏感度≥65%;(2)涂片与培养均为阳性的患者中敏感度>98%	≥98%
最佳标准	(1)涂片阴性且培养阳性的患者中敏感度≥68%;(2)涂片与培养均为阳性的患者中敏感度>98%	≥98%