Metabolomics Strategy Assisted by Transcriptomics Analysis to Identify Potential Biomarkers Associated with Tuberculosis
Jiayan Jiang,1 Zhipeng Li,1 Cheng Chen,2 Weili Jiang,1 Biao Xu,1 Qi Zhao1,3,4 1School of Public Health and Key Laboratory of Public Health Safety, Fudan University, Shanghai, People’s Republic of China; 2Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu...
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| Formato: | article |
| Lenguaje: | EN |
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Dove Medical Press
2021
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| Acceso en línea: | https://doaj.org/article/1bcb1b4e9478453798ee2f64f9c8159b |
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| Sumario: | Jiayan Jiang,1 Zhipeng Li,1 Cheng Chen,2 Weili Jiang,1 Biao Xu,1 Qi Zhao1,3,4 1School of Public Health and Key Laboratory of Public Health Safety, Fudan University, Shanghai, People’s Republic of China; 2Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu Province, Nanjing, Jiangsu, People’s Republic of China; 3NHC Key Laboratory of Health Technology Assessment,Fudan University, Shanghai, People’s Republic of China; 4Shanghai Clinical Research Center for infectious disease (Tuberculosis), Shanghai, People’s Republic of ChinaCorrespondence: Qi Zhao Tel/Fax +86-21-5423-7335Email zhaoqi@shmu.edu.cnPurpose: To investigate the dysregulated pathways and identify reliable diagnostic biomarkers for tuberculosis using integrated analysis of metabolomics and transcriptomics.Methods: Three groups of samples, untargeted metabolomics analysis of healthy controls (HC), latent tuberculosis infection patients (LTBI), and active tuberculosis patients (TB), were analyzed using gas chromatography time-of-flight mass spectrometry (GC-TOF MS) and ultra-high performance liquid chromatography-quantitative mass spectrometry (UHPLC-QE-MS). Both univariate and multivariate and statistical analyses were used to select differential metabolites (DMs) among group comparison, and LASSO regression analysis was employed to discover potential diagnostic biomarkers. Metabolite set enrichment analysis was performed to identify the altered metabolic pathways specifically in patients with TB. Meanwhile, a transcriptomic dataset GSEG4992 was downloaded from the GEO database to explore the differentially expressed genes (DEGs) between TB and HC identified in significantly enriched pathways. Finally, an integrative analysis of DMs and DEGs was performed to investigate the possible molecular mechanisms of TB.Results: Thirty-three specific metabolites were significantly different between TB and HC, of which 7 (5-hydroxyindoleacetic acid, isoleucyl-isoleucine, heptadecanoic acid, indole acetaldehyde, 5-ethyl-2,4-dimethyloxazole, and 2-hydroxycaproic acid, unknown 71) were chosen as combinational potential biomarkers for TB. The area under the curve (AUC) value of these biomarkers was 0.97 (95% CI: 0.92– 1.00). Metabolites set enrichment analysis (MSEA) displayed that there were 3 significantly enriched pathways among all. The genes in 3 significantly enriched pathways were further analyzed, of which 9(ALDH3B1, BCAT1, BCAT2, GLYAT, GOT1, IL4I1, MIF, SDS, SDSL) were expressed differentially. The area under the curve (AUC) values of these DEGs enriched in pathways mostly were greater than 0.8. As a result, a connected network of metabolites and genes in the pathways were established, which provides insights into the credibility of selected metabolites.Conclusion: The newly identified metabolic biomarkers display a high potential to be developed into a promising tool for TB screening, diagnosis, and therapeutic effect monitoring.Keywords: biomarker, diagnostic, metabolites, genes, multi-omics |
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