{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T16:28:23Z","timestamp":1787243303257,"version":"build-2736575974"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T00:00:00Z","timestamp":1652486400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T00:00:00Z","timestamp":1652486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100005230","name":"natural science foundation of chongqing","doi-asserted-by":"publisher","award":["cstc2019jcyj-msxmX0028"],"award-info":[{"award-number":["cstc2019jcyj-msxmX0028"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"name":"scientific research foundation of science and health joint medicine of chongqing","award":["2019QNXM038"],"award-info":[{"award-number":["2019QNXM038"]}]},{"name":"youth innovation fund of chongqing public health medical center","award":["2019QNKYXM01"],"award-info":[{"award-number":["2019QNKYXM01"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Respir Res"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Blood transcriptomics can be used for confirmation of tuberculosis diagnosis or sputumless triage, and a comparison of their practical diagnostic accuracy is needed to assess their usefulness. In this study, we investigated potential biomarkers to improve our understanding of the pathogenesis of active pulmonary tuberculosis (PTB) using bioinformatics methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Differentially expressed genes (DEGs) were analyzed between PTB and healthy controls (HCs) based on two microarray datasets. Pathways and functional annotation of DEGs were identified and ten hub genes were selected. They were further analyzed and selected, then verified with an independent sample set. Finally, their diagnostic power was further evaluated between PTB and HCs or other diseases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      62 DEGs mostly related to type I IFN pathway, IFN-\u03b3-mediated pathway, etc. in GO term and immune process, and especially RIG-I-like receptor pathway were acquired. Among them,\n                      <jats:italic>OAS1<\/jats:italic>\n                      ,\n                      <jats:italic>IFIT1<\/jats:italic>\n                      and\n                      <jats:italic>IFIT3<\/jats:italic>\n                      were upregulated and were the main risk factors for predicting PTB, with adjusted risk ratios of 1.36, 3.10, and 1.32, respectively. These results further verified that peripheral blood mRNA expression levels of\n                      <jats:italic>OAS1<\/jats:italic>\n                      ,\n                      <jats:italic>IFIT1<\/jats:italic>\n                      and\n                      <jats:italic>IFIT3<\/jats:italic>\n                      were significantly higher in PTB patients than HCs (all\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.01). The performance of a\u00a0combination of these three genes (three-gene set) had exceeded that of all pairwise combinations of them in discriminating TB from HCs, with mean AUC reaching as high as 0.975 with a sensitivity of 94.4% and a specificity of 100%. The good discernibility capacity was evaluated d via 7 independent datasets with an AUC of 0.902, as well as mean sensitivity of 87.9% and mean specificity of 90.2%. In regards to discriminating PTB from other diseases (i.e., initially considered to be possible TB, but rejected in differential diagnosis), the three-gene set equally exhibited an overall strong ability to separate PTB from other diseases with an AUC of 0.999 (sensitivity: 99.0%; specificity: 100%) in the training set, and 0.974 with a sensitivity of 96.4% and a specificity of 98.6% in the test set.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The described commonalities and unique signatures in the blood profiles of PTB and the other control samples have considerable implications for PTB biosignature design and future diagnosis, and provide insights into the biological processes underlying PTB.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12931-022-02035-4","type":"journal-article","created":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T06:16:04Z","timestamp":1652508964000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Diagnosis of pulmonary tuberculosis via identification of core genes and pathways utilizing blood transcriptional signatures: a multicohort analysis"],"prefix":"10.1186","volume":"23","author":[{"given":"Qian","family":"Qiu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anzhou","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanlin","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongxin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunfa","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shi","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongguang","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3229-0108","authenticated-orcid":false,"given":"Yaokai","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,5,14]]},"reference":[{"key":"2035_CR1","first-page":"2020","volume-title":"Global tuberculosis report 2019","author":"WH Organization","year":"2020","unstructured":"Organization WH. Global tuberculosis report 2019. Geneva: World Health Organization; 2020. p. 2020."},{"key":"2035_CR2","doi-asserted-by":"publisher","first-page":"323","DOI":"10.4103\/ijmm.IJMM_16_204","volume":"35","author":"S Oommen","year":"2017","unstructured":"Oommen S, Banaji N. Laboratory diagnosis of tuberculosis: advances in technology and drug susceptibility testing. Indian J Med Microbiol. 2017;35:323\u201331.","journal-title":"Indian J Med Microbiol"},{"key":"2035_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpath.2019.103563","volume":"134","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Zhang X, Zhao Z, Zheng Y, Xiao Z, Li F. Integrated bioinformatics analysis and validation revealed potential immune-regulatory miR-892b, miR-199b-5p and miR-582-5p as diagnostic biomarkers in active tuberculosis. Microb Pathog. 2019;134: 103563.","journal-title":"Microb Pathog"},{"key":"2035_CR4","doi-asserted-by":"publisher","first-page":"340","DOI":"10.3390\/ijms20020340","volume":"20","author":"W Zhai","year":"2019","unstructured":"Zhai W, Wu F, Zhang Y, Fu Y, Liu Z. The immune escape mechanisms of Mycobacterium tuberculosis. Int J Mol Sci. 2019;20:340.","journal-title":"Int J Mol Sci"},{"key":"2035_CR5","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0073230","volume":"8","author":"SA Joosten","year":"2013","unstructured":"Joosten SA, Fletcher HA, Ottenhoff TH. A helicopter perspective on TB biomarkers: pathway and process based analysis of gene expression data provides new insight into TB pathogenesis. PLoS ONE. 2013;8: e73230.","journal-title":"PLoS ONE"},{"key":"2035_CR6","doi-asserted-by":"publisher","first-page":"321","DOI":"10.2147\/IDR.S184640","volume":"12","author":"M Deng","year":"2019","unstructured":"Deng M, Lv XD, Fang ZX, Xie XS, Chen WY. The blood transcriptional signature for active and latent tuberculosis. Infect Drug Resist. 2019;12:321\u20138.","journal-title":"Infect Drug Resist"},{"key":"2035_CR7","first-page":"3126","volume":"7","author":"X Gui","year":"2014","unstructured":"Gui X, Xiao H. Diagnosis of tuberculosis pleurisy with adenosine deaminase (ADA): a systematic review and meta-analysis. Int J Clin Exp Med. 2014;7:3126\u201335.","journal-title":"Int J Clin Exp Med"},{"key":"2035_CR8","doi-asserted-by":"publisher","DOI":"10.1101\/cshperspect.a018531","volume":"5","author":"J Maertzdorf","year":"2014","unstructured":"Maertzdorf J, Kaufmann SH, Weiner J 3rd. Toward a unified biosignature for tuberculosis. Cold Spring Harb Perspect Med. 2014;5: a018531.","journal-title":"Cold Spring Harb Perspect Med"},{"key":"2035_CR9","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.ebiom.2016.12.009","volume":"15","author":"A Sambarey","year":"2017","unstructured":"Sambarey A, Devaprasad A, Mohan A, Ahmed A, Nayak S, Swaminathan S, D\u2019Souza G, Jesuraj A, Dhar C, Babu S, et al. Unbiased identification of blood-based biomarkers for pulmonary tuberculosis by modeling and mining molecular interaction networks. EBioMedicine. 2017;15:112\u201326.","journal-title":"EBioMedicine"},{"key":"2035_CR10","doi-asserted-by":"publisher","first-page":"4027","DOI":"10.1038\/s41598-019-40814-0","volume":"9","author":"J Lee","year":"2019","unstructured":"Lee J, Lee SG, Kim KK, Lim YJ, Choi JA, Cho SN, Park C, Song CH. Characterisation of genes differentially expressed in macrophages by virulent and attenuated Mycobacterium tuberculosis through RNA-Seq analysis. Sci Rep. 2019;9:4027.","journal-title":"Sci Rep"},{"key":"2035_CR11","doi-asserted-by":"publisher","first-page":"5702","DOI":"10.1038\/s41598-017-05878-w","volume":"7","author":"JD Blischak","year":"2017","unstructured":"Blischak JD, Tailleux L, Myrthil M, Charlois C, Bergot E, Dinh A, Morizot G, Cheny O, Platen CV, Herrmann JL, et al. Predicting susceptibility to tuberculosis based on gene expression profiling in dendritic cells. Sci Rep. 2017;7:5702.","journal-title":"Sci Rep"},{"key":"2035_CR12","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1001538","volume":"10","author":"M Kaforou","year":"2013","unstructured":"Kaforou M, Wright VJ, Oni T, French N, Anderson ST, Bangani N, Banwell CM, Brent AJ, Crampin AC, Dockrell HM, 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.","journal-title":"PLoS Med"},{"key":"2035_CR13","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1016\/j.tube.2015.04.008","volume":"95","author":"L Laux da Costa","year":"2015","unstructured":"Laux da Costa L, Delcroix M, Dalla Costa ER, Prestes IV, Milano M, Francis SS, Unis G, Silva DR, Riley LW, Rossetti ML. A real-time PCR signature to discriminate between tuberculosis and other pulmonary diseases. Tuberculosis. 2015;95:421\u20135.","journal-title":"Tuberculosis"},{"key":"2035_CR14","doi-asserted-by":"publisher","first-page":"86","DOI":"10.15252\/emmm.201505790","volume":"8","author":"J Maertzdorf","year":"2016","unstructured":"Maertzdorf J, McEwen G, Weiner J 3rd, Tian S, Lader E, Schriek U, Mayanja-Kizza H, Ota M, Kenneth J, Kaufmann SH. Concise gene signature for point-of-care classification of tuberculosis. EMBO Mol Med. 2016;8:86\u201395.","journal-title":"EMBO Mol Med"},{"key":"2035_CR15","doi-asserted-by":"publisher","DOI":"10.3389\/fcimb.2021.585919","volume":"11","author":"Y Chen","year":"2021","unstructured":"Chen Y, Wang Q, Lin S, Lai J, Lin J, Ao W, Han X, Ye H. 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.","journal-title":"Front Cell Infect Microbiol"},{"key":"2035_CR16","doi-asserted-by":"publisher","first-page":"38411","DOI":"10.1038\/srep38411","volume":"6","author":"M Zhao","year":"2016","unstructured":"Zhao M, Qin J, Yin H, Tan Y, Liao W, Liu Q, Luo S, He M, Liang G, Shi Y, et al. Distinct epigenomes in CD4(+) T cells of newborns, middle-ages and centenarians. Sci Rep. 2016;6:38411.","journal-title":"Sci Rep"},{"key":"2035_CR17","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/75556","volume":"25","author":"M Ashburner","year":"2000","unstructured":"Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25\u20139.","journal-title":"Nat Genet"},{"key":"2035_CR18","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1093\/nar\/27.1.29","volume":"27","author":"H Ogata","year":"1999","unstructured":"Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 1999;27:29\u201334.","journal-title":"Nucleic Acids Res"},{"key":"2035_CR19","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/nprot.2008.211","volume":"4","author":"W da Huang","year":"2009","unstructured":"da Huang W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4:44\u201357.","journal-title":"Nat Protoc"},{"key":"2035_CR20","doi-asserted-by":"publisher","first-page":"15545","DOI":"10.1073\/pnas.0506580102","volume":"102","author":"A Subramanian","year":"2005","unstructured":"Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545\u201350.","journal-title":"Proc Natl Acad Sci USA"},{"key":"2035_CR21","doi-asserted-by":"publisher","first-page":"2498","DOI":"10.1101\/gr.1239303","volume":"13","author":"P Shannon","year":"2003","unstructured":"Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498\u2013504.","journal-title":"Genome Res"},{"issue":"Suppl 4","key":"2035_CR22","doi-asserted-by":"publisher","first-page":"S11","DOI":"10.1186\/1752-0509-8-S4-S11","volume":"8","author":"CH Chin","year":"2014","unstructured":"Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8(Suppl 4):S11.","journal-title":"BMC Syst Biol"},{"key":"2035_CR23","doi-asserted-by":"publisher","first-page":"8825997","DOI":"10.1155\/2020\/8825997","volume":"2020","author":"H Luan","year":"2020","unstructured":"Luan H, Zhang C, Zhang T, He Y, Su Y, Zhou L. Identification of key prognostic biomarker and its correlation with immune infiltrates in pancreatic ductal adenocarcinoma. Dis Markers. 2020;2020:8825997.","journal-title":"Dis Markers"},{"key":"2035_CR24","first-page":"10330","volume":"24","author":"J Chen","year":"2020","unstructured":"Chen J, Cheng Y, Zhou M, Ye L, Wang N, Wang M, Feng Z. Machine learning prediction on number of patients due to conjunctivitis based on air pollutants: a preliminary study. Eur Rev Med Pharmacol Sci. 2020;24:10330\u20137.","journal-title":"Eur Rev Med Pharmacol Sci"},{"key":"2035_CR25","doi-asserted-by":"publisher","first-page":"1258","DOI":"10.1016\/j.cptl.2020.05.017","volume":"12","author":"AA Olsen","year":"2020","unstructured":"Olsen AA, McLaughlin JE, Harpe SE. Using multiple linear regression in pharmacy education scholarship. Curr Pharm Teach Learn. 2020;12:1258\u201368.","journal-title":"Curr Pharm Teach Learn"},{"issue":"6","key":"2035_CR26","doi-asserted-by":"publisher","first-page":"2376","DOI":"10.1007\/s00261-020-02668-3","volume":"46","author":"H Park","year":"2020","unstructured":"Park H, Qin L, Guerra P, Bay CP, Shinagare AB. Decoding incidental ovarian lesions: use of texture analysis and machine learning for characterization and detection of malignancy. Abdom Radiol. 2020;46(6):2376\u201383.","journal-title":"Abdom Radiol"},{"key":"2035_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104151","volume":"129","author":"YW Lee","year":"2021","unstructured":"Lee YW, Choi JW, Shin EH. Machine learning model for predicting malaria using clinical information. Comput Biol Med. 2021;129: 104151.","journal-title":"Comput Biol Med"},{"key":"2035_CR28","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1038\/s41580-021-00407-0","volume":"23","author":"JG Greener","year":"2022","unstructured":"Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nat Rev Mol Cell Biol. 2022;23:40\u201355.","journal-title":"Nat Rev Mol Cell Biol"},{"key":"2035_CR29","doi-asserted-by":"publisher","first-page":"E540","DOI":"10.1073\/pnas.1613254114","volume":"114","author":"A Troegeler","year":"2017","unstructured":"Troegeler A, Mercier I, Cougoule C, Pietretti D, Colom A, Duval C, Vu Manh TP, Capilla F, Poincloux R, Pingris K, et al. C-type lectin receptor DCIR modulates immunity to tuberculosis by sustaining type I interferon signaling in dendritic cells. Proc Natl Acad Sci U S A. 2017;114:E540\u20139.","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2035_CR30","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.ymeth.2012.12.012","volume":"61","author":"HS Whitworth","year":"2013","unstructured":"Whitworth HS, Scott M, Connell DW, Donges B, Lalvani A. IGRAs\u2013the gateway to T cell based TB diagnosis. Methods. 2013;61:52\u201362.","journal-title":"Methods"},{"key":"2035_CR31","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0004319","volume":"4","author":"F Gondois-Rey","year":"2009","unstructured":"Gondois-Rey F, Dental C, Halfon P, Baumert TF, Olive D, Hirsch I. Hepatitis C virus is a weak inducer of interferon alpha in plasmacytoid dendritic cells in comparison with influenza and human herpesvirus type-1. PLoS ONE. 2009;4: e4319.","journal-title":"PLoS ONE"},{"key":"2035_CR32","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1111\/j.0105-2896.2004.00204.x","volume":"202","author":"S Pestka","year":"2004","unstructured":"Pestka S, Krause CD, Walter MR. Interferons, interferon-like cytokines, and their receptors. Immunol Rev. 2004;202:8\u201332.","journal-title":"Immunol Rev"},{"key":"2035_CR33","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1038\/ni.2229","volume":"13","author":"N Yan","year":"2012","unstructured":"Yan N, Chen ZJ. Intrinsic antiviral immunity. Nat Immunol. 2012;13:214\u201322.","journal-title":"Nat Immunol"},{"key":"2035_CR34","doi-asserted-by":"publisher","first-page":"522","DOI":"10.4049\/jimmunol.179.1.522","volume":"179","author":"D Ordway","year":"2007","unstructured":"Ordway D, Henao-Tamayo M, Harton M, Palanisamy G, Troudt J, Shanley C, Basaraba RJ, Orme IM. The hypervirulent Mycobacterium tuberculosis strain HN878 induces a potent TH1 response followed by rapid down-regulation. J Immunol. 2007;179:522\u201331.","journal-title":"J Immunol"},{"key":"2035_CR35","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1038\/nature09247","volume":"466","author":"MP Berry","year":"2010","unstructured":"Berry MP, Graham CM, McNab FW, Xu Z, Bloch SA, Oni T, Wilkinson KA, Banchereau R, Skinner J, Wilkinson RJ, et al. An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature. 2010;466:973\u20137.","journal-title":"Nature"},{"key":"2035_CR36","doi-asserted-by":"publisher","first-page":"1684","DOI":"10.1126\/science.1224026","volume":"337","author":"D Bogunovic","year":"2012","unstructured":"Bogunovic D, Byun M, Durfee LA, Abhyankar A, Sanal O, Mansouri D, Salem S, Radovanovic I, Grant AV, Adimi P, et al. Mycobacterial disease and impaired IFN-gamma immunity in humans with inherited ISG15 deficiency. Science. 2012;337:1684\u20138.","journal-title":"Science"},{"key":"2035_CR37","doi-asserted-by":"publisher","first-page":"2316","DOI":"10.1021\/acs.jproteome.9b00895","volume":"19","author":"E Choudhary","year":"2020","unstructured":"Choudhary E, Bullen CK, Goel R, Singh AK, Praharaj M, Thakur P, Dhiman R, Bishai WR, Agarwal N. Relative and quantitative phosphoproteome analysis of macrophages in response to infection by virulent and avirulent mycobacteria reveals a distinct role of the cytosolic RNA Sensor RIG-I in Mycobacterium tuberculosis pathogenesis. J Proteome Res. 2020;19:2316\u201336.","journal-title":"J Proteome Res"},{"key":"2035_CR38","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1016\/j.isci.2019.11.001","volume":"22","author":"S Ranjbar","year":"2019","unstructured":"Ranjbar S, Haridas V, Nambu A, Jasenosky LD, Sadhukhan S, Ebert TS, Hornung V, Cassell GH, Falvo JV, Goldfeld AE. Cytoplasmic RNA sensor pathways and nitazoxanide broadly inhibit intracellular Mycobacterium tuberculosis growth. iScience. 2019;22:299\u2013313.","journal-title":"iScience"},{"key":"2035_CR39","doi-asserted-by":"publisher","first-page":"2919","DOI":"10.1084\/jem.20180508","volume":"215","author":"Y Cheng","year":"2018","unstructured":"Cheng Y, Schorey JS. Mycobacterium tuberculosis-induced IFN-beta production requires cytosolic DNA and RNA sensing pathways. J Exp Med. 2018;215:2919\u201335.","journal-title":"J Exp Med"},{"key":"2035_CR40","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0045839","volume":"7","author":"TH Ottenhoff","year":"2012","unstructured":"Ottenhoff TH, Dass RH, Yang N, Zhang MM, Wong HE, Sahiratmadja E, Khor CC, Alisjahbana B, van Crevel R, Marzuki S, et al. Genome-wide expression profiling identifies type 1 interferon response pathways in active tuberculosis. PLoS ONE. 2012;7: e45839.","journal-title":"PLoS ONE"},{"key":"2035_CR41","doi-asserted-by":"publisher","first-page":"1593","DOI":"10.1007\/PL00000644","volume":"57","author":"J Justesen","year":"2000","unstructured":"Justesen J, Hartmann R, Kjeldgaard NO. Gene structure and function of the 2\u2019-5\u2019-oligoadenylate synthetase family. Cell Mol Life Sci. 2000;57:1593\u2013612.","journal-title":"Cell Mol Life Sci"},{"key":"2035_CR42","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1093\/infdis\/jiy084","volume":"217","author":"G Leisching","year":"2018","unstructured":"Leisching G, Wiid I, Baker B. OAS1, 2, and 3: significance during active tuberculosis? J Infect Dis. 2018;217:1517\u201321.","journal-title":"J Infect Dis"},{"key":"2035_CR43","doi-asserted-by":"publisher","first-page":"42225","DOI":"10.1038\/srep42225","volume":"7","author":"N Andreu","year":"2017","unstructured":"Andreu N, Phelan J, de Sessions PF, Cliff JM, Clark TG, Hibberd ML. Primary macrophages and J774 cells respond differently to infection with Mycobacterium tuberculosis. Sci Rep. 2017;7:42225.","journal-title":"Sci Rep"},{"key":"2035_CR44","volume-title":"Meeting report: high-priority target product profiles for new tuberculosis diagnostics","author":"WH Organization","year":"2014","unstructured":"Organization WH. Meeting report: high-priority target product profiles for new tuberculosis diagnostics. Geneva: World Health Organization; 2014."},{"key":"2035_CR45","doi-asserted-by":"publisher","first-page":"112907","DOI":"10.18632\/oncotarget.22889","volume":"8","author":"L Pan","year":"2017","unstructured":"Pan L, Wei N, Jia H, Gao M, Chen X, Wei R, Sun Q, Gu S, Du B, Xing A, Zhang Z. Genome-wide transcriptional profiling identifies potential signatures in discriminating active tuberculosis from latent infection. Oncotarget. 2017;8:112907\u201316.","journal-title":"Oncotarget"},{"key":"2035_CR46","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1136\/thoraxjnl-2018-213021","volume":"75","author":"Q Yang","year":"2020","unstructured":"Yang Q, Chen Q, Zhang M, Cai Y, Yang F, Zhang J, Deng G, Ye T, Deng Q, Li G, et al. Identification of eight-protein biosignature for diagnosis of tuberculosis. Thorax. 2020;75:576\u201383.","journal-title":"Thorax"},{"key":"2035_CR47","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/S2213-2600(16)00048-5","volume":"4","author":"TE Sweeney","year":"2016","unstructured":"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\u201324.","journal-title":"Lancet Respir Med"}],"container-title":["Respiratory Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12931-022-02035-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12931-022-02035-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12931-022-02035-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T07:05:14Z","timestamp":1652511914000},"score":1,"resource":{"primary":{"URL":"https:\/\/respiratory-research.biomedcentral.com\/articles\/10.1186\/s12931-022-02035-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,14]]},"references-count":47,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["2035"],"URL":"https:\/\/doi.org\/10.1186\/s12931-022-02035-4","relation":{},"ISSN":["1465-993X"],"issn-type":[{"value":"1465-993X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,14]]},"assertion":[{"value":"17 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 May 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was performed in accordance with the Declaration of Helsinki. This human study was approved by the Ethics Committee of the Chongqing Public Health Medical Center-approval: 2021-018-01-KY. All adult participants provided written informed consent to participate in this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"125"}}