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REVIEW

Omics Biomarkers for Monitoring Tuberculosis Treatment: A Mini-Review of Recent Insights and Future Approaches

, , ORCID Icon &
Pages 2703-2711 | Published online: 28 May 2022

References

  • Padayatchi N, Daftary A, Naidu N, Naidoo K, Pai M. Tuberculosis: treatment failure, or failure to treat? Lessons from India and South Africa. BMJ Glob Health. 2019;4(1):e001097. doi:10.1136/bmjgh-2018-001097
  • Nezenega ZS, Perimal-Lewis L, Maeder AJ. Factors influencing patient adherence to tuberculosis treatment in Ethiopia: a literature review. Int J Environ Res Public Health. 2020;17(15):5626. doi:10.3390/ijerph17155626
  • World Health Organization. Tuberculosis (TB) | WHO; 2021. Available from: https://www.who.int/news-room/fact-sheets/detail/tuberculosis. Accessed September 14, 2021.
  • Magombedze G, Pasipanodya JG, Gumbo T. Bacterial load slopes represent biomarkers of tuberculosis therapy success, failure, and relapse. Commun Biol. 2021;4(1):664. doi:10.1038/s42003-021-02184-0
  • Rockwood N, du Bruyn E, Morris T, Wilkinson RJ. Assessment of treatment response in tuberculosis. Expert Rev Respir Med. 2016;10(6):643–654. doi:10.1586/17476348.2016.1166960
  • World Health Organization. High-priority target product profiles for new tuberculosis diagnostics | WHO; 2014. Available from: https://www.who.int/publications-detail-redirect/WHO-HTM-TB-2014.18. Accessed September 11, 2021.
  • Namuganga AR, Chegou NN, Mayanja-Kizza H. Past and present approaches to diagnosis of active pulmonary tuberculosis. Front Med. 2021;8:709793. doi:10.3389/fmed.2021.709793
  • Subramanian I, Verma S, Kumar S, Jere A, Anamika K. Multi-omics data integration, interpretation, and its application. Bioinform Biol Insights. 2020;14:1177932219899051. doi:10.1177/1177932219899051
  • Cliff JM, Lee JS, Constantinou N, et al. Distinct phases of blood gene expression pattern through tuberculosis treatment reflect modulation of the humoral immune response. J Infect Dis. 2012;207(1):18–29. doi:10.1093/infdis/jis499
  • Cliff JM, Cho J-E, Lee J-S, et al. Excessive cytolytic responses predict tuberculosis relapse after apparently successful treatment. J Infect Dis. 2016;213(3):485–495. doi:10.1093/infdis/jiv447
  • Bloom CI, Graham CM, Beery MPR, et al. Detectable changes in the blood transcriptome are present after two weeks of antituberculosis therapy. PLoS One. 2012;7(10):e46191. doi:10.1371/journal.pone.0046191
  • Thompson EG, Du Y, Malherbe ST, et al. Host blood RNA signatures predict the outcome of tuberculosis treatment. Tuberculosis. 2017;107:48–58. doi:10.1016/j.tube.2017.08.004
  • Ottenhoff THM, Dass RH, Yang N, Zhang MM, Wong HEE, Sahiratmadja E. Genome-wide expression profiling identifies type 1 interferon response pathways in active tuberculosis. PLoS One. 2012;7(9):e45839. doi:10.1371/journal.pone.0045839
  • Sweeney TE, Braviak L, Tato CM, Khatri P. 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
  • Warsinske HC, Rao AM, Moreira FM, et al. Assessment of validity of a blood-based 3-gene signature score for progression and diagnosis of tuberculosis, disease severity, and treatment response. JAMA Netw Open. 2018;1(6):e183779. doi:10.1001/jamanetworkopen.2018.3779
  • Wang C, Yang S, Liu C-M, et al. Screening and identification of four serum miRNAs as novel potential biomarkers for cured pulmonary tuberculosis. Tuberculosis. 2018;108:26–34. doi:10.1016/j.tube.2017.08.010
  • Jiang -T-T, Shi L-Y, Chen J, et al. Screening and identification of potential protein biomarkers for evaluating the efficacy of intensive therapy in pulmonary tuberculosis. Biochem Biophys Res Commun. 2018;503(4):2263–2270. doi:10.1016/j.bbrc.2018.06.147
  • Kaewseekhao B, Roytrakul S, Yingchutrakul Y, Salao K, Reechaipichitkul W, Faksri K. Proteomic analysis of infected primary human leucocytes revealed PSTK as potential treatment-monitoring marker for active and latent tuberculosis. PLoS One. 2020;15(4):e0231834. doi:10.1371/journal.pone.0231834
  • Nahid P, Bliven-Sizemore E, Jarlsberg LG, et al. Aptamer-based proteomic signature of intensive phase treatment response in pulmonary tuberculosis. Tuberculosis. 2014;94(3):187–196. doi:10.1016/j.tube.2014.01.006
  • Choi R, Kim K, Kim M-J, et al. Serum inflammatory profiles in pulmonary tuberculosis and their association with treatment response. J Proteomics. 2016;149:23–30. doi:10.1016/j.jprot.2016.06.016
  • Combrink M, du Preez I, Ronacher K, Walzl G, Loots DT. Time-dependent changes in urinary metabolome before and after intensive phase tuberculosis therapy: a pharmacometabolomics study. OMICS. 2019;23(11):560–572. doi:10.1089/omi.2019.0140
  • Yi W-J, Han Y-S, Wei -L-L, et al. l-Histidine, arachidonic acid, biliverdin, and l-cysteine-glutathione disulfide as potential biomarkers for cured pulmonary tuberculosis. Biomed Pharmacother. 2019;116:108980. doi:10.1016/j.biopha.2019.108980
  • Vrieling F, Alisjahbana B, Sahiratmadja E, et al. Plasma metabolomics in tuberculosis patients with and without concurrent type 2 diabetes at diagnosis and during antibiotic treatment. Sci Rep. 2019;9(1):18669. doi:10.1038/s41598-019-54983-5
  • Dutta NK, Tornheim JA, Fukutani KF, et al. Integration of metabolomics and transcriptomics reveals novel biomarkers in the blood for tuberculosis diagnosis in children. Sci Rep. 2020;10(1):19527. doi:10.1038/s41598-020-75513-8
  • Qian X, Nguyen DTM, Li Y, Lyu J, Graviss E, Hu TY. Predictive value of serum bradykinin and desArg9-bradykinin levels for chemotherapeutic responses in active tuberculosis patients: a retrospective case series. Tuberc Tuberculosis. 2016;101S:S109-S118. doi:10.1016/j.tube.2016.09.022
  • Wu C, Zhou F, Ren J, et al. A selective review of multi-level omics data integration using variable selection. High Throughput. 2019;8(1):4. doi:10.3390/ht8010004
  • Montaner J, Ramiro L, Simats A, et al. Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke. Nat Rev Neurol. 2020;16(5):247–264. doi:10.1038/s41582-020-0350-6
  • Misra BB, Langefeld C, Olivier M, Cox LA. Integrated omics: tools, advances and future approaches. J Mol Endocrinol. 2018. doi:10.1530/JME-18-0055
  • Land WH, Ford W, Park J-W, et al. Partial Least Squares (PLS) applied to medical bioinformatics. Procedia Comput Sci. 2011;6:273–278. doi:10.1016/j.procs.2011.08.051
  • de Tayrac M, Lê S, Aubry M, Mosser J, Husson F. Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: multiple factor analysis approach. BMC Genomics. 2009;10(1):32. doi:10.1186/1471-2164-10-32
  • Klassert TE, Goyal S, Stock M, et al. AmpliSeq screening of genes encoding the C-type lectin receptors and their signaling components reveals a common variant in MASP1 associated with pulmonary tuberculosis in an Indian population. Front Immunol. 2018;9:242. doi:10.3389/fimmu.2018.00242
  • Özdemir V, Dove ES, Gürsoy UK; Özdemir V, Dov ES, Gursoy UK, Sardas S, Yildirim A, Yilmaz SG, et al. Personalized medicine beyond genomics: alternative futures in big data-proteomics, environtome and the social proteome. J. Neural Transm. Vienna Austria. 2017;124(1):25–32. doi:10.1007/s00702-015-1489-y
  • West L, Vidwans SJ, Nicholas PC, et al. A novel classification of lung cancer into molecular subtypes. PLoS One. 2012;7(2):e31906. doi:10.1371/journal.pone.0031906
  • Higdon R, Earl RK, Stanberry L, et al. The promise of multi-omics and clinical data integration to identify and target personalized healthcare approaches in autism spectrum disorders. Omics J Integr Biol. 2014;19(4):197–208. doi:10.1089/omi.2015.0020
  • Young AT, Carette X, Helmel M, et al. Multi-omic regulatory networks capture downstream effects of kinase inhibition in Mycobacterium tuberculosis. NPJ Syst Biol Appl. 2021;7(1):8. doi:10.1038/s41540-020-00164-4
  • Wei W, Yan H, Zhao J, et al. Multi-omics comparisons of p-aminosalicylic acid (PAS) resistance in folC mutated and un-mutated Mycobacterium tuberculosis strains. Emerg Microbes Infect. 2019;8(1):248–261. doi:10.1080/22221751.2019.1568179