Transcriptional signatures of human peripheral blood mononuclear cells can identify the risk of tuberculosis progression from latent infection among individuals with silicosis

Qiao-Ling Ruan, Qing-Luan Yang, Yi-Xin Gao, Jing Wu, Si-Ran Lin, Jing-Yu Zhou, Ling-Yun Shao, Sen Wang, Qian-Qian Liu, Yan Gao, Ning Jiang, Wen-Hong Zhang, Qiao-Ling Ruan, Qing-Luan Yang, Yi-Xin Gao, Jing Wu, Si-Ran Lin, Jing-Yu Zhou, Ling-Yun Shao, Sen Wang, Qian-Qian Liu, Yan Gao, Ning Jiang, Wen-Hong Zhang

Abstract

www.clinicaltrials.gov (NCT02430259).

Keywords: RNA sequencing; Tuberculosis; biomarker; interferon-gamma; latent tuberculosis infection; peripheral blood mononuclear cell.

Conflict of interest statement

No potential conflict of interest was reported by the author(s).

Figures

Figure 1.
Figure 1.
Flow diagram of data collection and analysis. PBMC: peripheral blood cells; GO analysis: Gene Ontology analysis; KEGG analysis: Kyoto Encyclopedia of Genes and Genomes analysis; WGCNA: Weighted correlation network analysis; GSEA: Gene-set enrichment analysis.
Figure 1.
Figure 1.
Flow diagram of data collection and analysis. PBMC: peripheral blood cells; GO analysis: Gene Ontology analysis; KEGG analysis: Kyoto Encyclopedia of Genes and Genomes analysis; WGCNA: Weighted correlation network analysis; GSEA: Gene-set enrichment analysis.
Figure 2.
Figure 2.
Transcriptional patterns of peripheral blood mononuclear cells defining stimulated and un-stimulated, TB progressors and non-progressors. (A) Principal component analysis diagram. (B) Unsupervised hierarchical clustering of transcribed genes and differentially expressed over 1.5-fold for 12 PBMC samples. (C) Volcano plot shows the upregulated and downregulated transcribed genes between TB progressors and non-progressors. (D) Venn diagrams showing overlaps of TB-specific genes changes between TB progressors and non-progressors. TB progressors: P107 and P129; Non-progressors: P102, P105, P170 and P173. A: PBMCs stimulated by TB antigen, N: PBMCs incubated without TB antigen.
Figure 2.
Figure 2.
Transcriptional patterns of peripheral blood mononuclear cells defining stimulated and un-stimulated, TB progressors and non-progressors. (A) Principal component analysis diagram. (B) Unsupervised hierarchical clustering of transcribed genes and differentially expressed over 1.5-fold for 12 PBMC samples. (C) Volcano plot shows the upregulated and downregulated transcribed genes between TB progressors and non-progressors. (D) Venn diagrams showing overlaps of TB-specific genes changes between TB progressors and non-progressors. TB progressors: P107 and P129; Non-progressors: P102, P105, P170 and P173. A: PBMCs stimulated by TB antigen, N: PBMCs incubated without TB antigen.
Figure 3.
Figure 3.
Different gene expression and functional enrichment analysis of TB progressors and non-progressors using un-stimulated (left) and stimulated (right) samples. (A–B) DEGs was applied to GO analysis in BP, CC and MF. (C–D) DEGs was applied to KEGG analysis.
Figure 3.
Figure 3.
Different gene expression and functional enrichment analysis of TB progressors and non-progressors using un-stimulated (left) and stimulated (right) samples. (A–B) DEGs was applied to GO analysis in BP, CC and MF. (C–D) DEGs was applied to KEGG analysis.
Figure 4.
Figure 4.
Modular transcriptional signatures of TB progressors compared to non-progressors. Fold enrichment scores derived using QuSAGE are depicted, with red and blue indicating modules over- or under-expressed. Colour intensity and size represent the degree of enrichment.
Figure 4.
Figure 4.
Modular transcriptional signatures of TB progressors compared to non-progressors. Fold enrichment scores derived using QuSAGE are depicted, with red and blue indicating modules over- or under-expressed. Colour intensity and size represent the degree of enrichment.
Figure 5.
Figure 5.
Normalized expression value of twenty discriminatively expressed TB-specific genes related with type II interferon between TB progressors (red) and non-progressors (blue). Kruskal–Wallis tests were used to compare the differences among the two groups. **Significant difference: 0.001 P < 0.01; ***Significant difference: P < 0.0001. Log2 fold changes and P value were listed on the right.
Figure 5.
Figure 5.
Normalized expression value of twenty discriminatively expressed TB-specific genes related with type II interferon between TB progressors (red) and non-progressors (blue). Kruskal–Wallis tests were used to compare the differences among the two groups. **Significant difference: 0.001 P < 0.01; ***Significant difference: P < 0.0001. Log2 fold changes and P value were listed on the right.

References

    1. Houben R, Dodd P.. The global burden of latent tuberculosis infection: a re-estimation using mathematical modelling. PLoS Med. 2016;13(10):e1002152.
    1. Leung CC, Yu IT, Chen W.. Silicosis. Lancet. 2012;379(9830):2008–2018.
    1. Targeted tuberculin testing and treatment of latent tuberculosis infection. American Thoracic Society. MMWR Recomm Rep. 2000;49(RR-6):1–51.
    1. Rangaka MX, Wilkinson KA, Glynn JR, et al. . Predictive value of interferon-gamma release assays for incident active tuberculosis: a systematic review and meta-analysis. Lancet Infect Dis. 2012;12(1):45–55.
    1. Diel R, Loddenkemper R, Niemann S, et al. . Negative and positive predictive value of a whole-blood interferon-gamma release assay for developing active tuberculosis: an update. Am J Respir Crit Care Med. 2011;183(1):88–95.
    1. Diel R, Loddenkemper R, Meywald-Walter K, et al. . Predictive value of a whole blood IFN-gamma assay for the development of active tuberculosis disease after recent infection with Mycobacterium tuberculosis. Am J Respir Crit Care Med. 2008;177(10):1164–1170.
    1. Getahun H, Matteelli A, Chaisson RE, et al. . Latent Mycobacterium tuberculosis infection. N Engl J Med. 2015;372(22):2127–2135.
    1. 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.
    1. Suliman S, Thompson E, 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.
    1. Ruan QL, Huang XT, Yang QL, et al. . Efficacy and safety of weekly rifapentine and isoniazid for tuberculosis prevention in Chinese silicosis patients: a randomized controlled trial. Clin Microbiol Infect. 2021;27:576–582.
    1. Andrews S. FastQC: a quality control tool for high throughput sequence data 2010. Available from: .
    1. Kim D, Pertea G, Trapnell C, et al. . Tophat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013;14(4):R36.
    1. Liao Y, Smyth GK, Shi W.. Featurecounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923–930.
    1. Love MI, Huber W, Anders S.. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550–570.
    1. Ashburner M, Ball CA, Blake JA, et al. . Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet. 2000;25(1):25–29.
    1. Kanehisa M, Goto S.. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.
    1. Yu G, Wang LG, Han Y, et al. . Clusterprofiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–287.
    1. Szklarczyk D, Gable AL, Lyon D, et al. . STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607–D613.
    1. Gideon HP, Skinner JA, Baldwin N, et al. . Early whole blood transcriptional signatures Are associated with severity of lung inflammation in cynomolgus macaques with Mycobacterium tuberculosis infection. J Immunol. 2016;197(12):4817–4828. Baltimore, 1950.
    1. Lin PL, Rodgers M, Smith L, et al. . Quantitative comparison of active and latent tuberculosis in the cynomolgus macaque model. Infect Immun. 2009;77(10):4631–4642.
    1. Singhania A, Verma R, Graham CM, et al. . A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection. Nat Commun. 2018;9(1):2308–2324.
    1. Berry MP, Graham CM, McNab FW, et al. . An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature. 2010;466(7309):973–977.
    1. 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.
    1. Cooper AM.Cell-mediated immune responses in tuberculosis. Annu Rev Immunol. 2009;27:393–422.
    1. Lee J, Kornfeld H.. Interferon-gamma regulates the death of M. tuberculosis-infected macrophages. J Cell Death. 2010;3:1–11.
    1. Schoenborn JR, Wilson CB.. Regulation of interferon-gamma during innate and adaptive immune responses. Adv Immunol. 2007;96:41–101.
    1. Cooper AM, Dalton DK, Stewart TA, et al. . Disseminated tuberculosis in interferon gamma gene-disrupted mice. J Exp Med. 1993;178(6):2243–2247.
    1. Dupuis S, Doffinger R, Picard C, et al. . Human interferon-gamma-mediated immunity is a genetically controlled continuous trait that determines the outcome of mycobacterial invasion. Immunol Rev. 2000;178:129–137.
    1. Correa AF, Bailao AM, Bastos IM, et al. . The endothelin system has a significant role in the pathogenesis and progression of Mycobacterium tuberculosis infection. Infect Immun. 2014;82(12):5154–5165.
    1. Denisenko E, Guler R, Mhlanga M, et al. . Transcriptionally induced enhancers in the macrophage immune response to Mycobacterium tuberculosis infection. BMC Genomics. 2019;20(1):71–86.
    1. Cai L, Li Z, Guan X, et al. . The Research progress of host genes and tuberculosis susceptibility. Oxid Med Cell Longev. 2019;2019:9273056.
    1. Alvarez IB, Pasquinelli V, Jurado JO, et al. . Role played by the programmed death-1-programmed death ligand pathway during innate immunity against Mycobacterium tuberculosis. J Infect Dis. 2010;202(4):524–532.
    1. Bhalla K, Chugh M, Mehrotra S, et al. . Host ICAMs play a role in cell invasion by Mycobacterium tuberculosis and plasmodium falciparum. Nature Communication. 2015;6:6049–6061.
    1. Verway M, Bouttier M, Wang TT, et al. . Vitamin D induces interleukin-1beta expression: paracrine macrophage epithelial signaling controls M. tuberculosis infection. PLoS Pathog. 2013;9(6):e1003407.

Source: PubMed

3
Abonnere