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Label-Free Quantitative Proteomics Identifies Novel Plasma Biomarkers for Distinguishing Pulmonary Tuberculosis and Latent Infection.

ABSTRACT: The lack of effective differential diagnostic methods for active tuberculosis (TB) and latent infection (LTBI) is still an obstacle for TB control. Furthermore, the molecular mechanism behind the progression from LTBI to active TB has been not elucidated. Therefore, we performed label-free quantitative proteomics to identify plasma biomarkers for discriminating pulmonary TB (PTB) from LTBI. A total of 31 overlapping proteins with significant difference in expression level were identified in PTB patients (n = 15), compared with LTBI individuals (n = 15) and healthy controls (HCs, n = 15). Eight differentially expressed proteins were verified using western blot analysis, which was 100% consistent with the proteomics results. Statistically significant differences of six proteins were further validated in the PTB group compared with the LTBI and HC groups in the training set (n = 240), using ELISA. Classification and regression tree (CART) analysis was employed to determine the ideal protein combination for discriminating PTB from LTBI and HC. A diagnostic model consisting of alpha-1-antichymotrypsin (ACT), alpha-1-acid glycoprotein 1 (AGP1), and E-cadherin (CDH1) was established and presented a sensitivity of 81.2% (69/85) and a specificity of 95.2% (80/84) in discriminating PTB from LTBI, and a sensitivity of 81.2% (69/85) and a specificity of 90.1% (64/81) in discriminating PTB from HCs. Additional validation was performed by evaluating the diagnostic model in blind testing set (n = 113), which yielded a sensitivity of 75.0% (21/28) and specificity of 96.1% (25/26) in PTB vs. LTBI, 75.0% (21/28) and 92.3% (24/26) in PTB vs. HCs, and 75.0% (21/28) and 81.8% (27/33) in PTB vs. lung cancer (LC), respectively. This study obtained the plasma proteomic profiles of different M.TB infection statuses, which contribute to a better understanding of the pathogenesis involved in the transition from latent infection to TB activation and provide new potential diagnostic biomarkers for distinguishing PTB and LTBI.

SUBMITTER: Sun H

PROVIDER: S-EPMC6008387 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Label-Free Quantitative Proteomics Identifies Novel Plasma Biomarkers for Distinguishing Pulmonary Tuberculosis and Latent Infection.

Sun Huishan H Pan Liping L Jia Hongyan H Zhang Zhiguo Z Gao Mengqiu M Huang Mailing M Wang Jinghui J Sun Qi Q Wei Rongrong R Du Boping B Xing Aiying A Zhang Zongde Z

Frontiers in microbiology 20180613

The lack of effective differential diagnostic methods for active tuberculosis (TB) and latent infection (LTBI) is still an obstacle for TB control. Furthermore, the molecular mechanism behind the progression from LTBI to active TB has been not elucidated. Therefore, we performed label-free quantitative proteomics to identify plasma biomarkers for discriminating pulmonary TB (PTB) from LTBI. A total of 31 overlapping proteins with significant difference in expression level were identified in PTB ...[more]

PMID: 29951049

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Project description:Lack of effective differential diagnostic methods between active tuberculosis (TB) and latent infection (LTBI) is still an obstacle for TB control. Furthermore, the molecular mechanism behind the progression from LTBI to active TB has been not elucidated until now. Therefore, we performed label-free quantitative proteomics to identify plasma biomarkers for discriminating pulmonary TB (PTB) from LTBI individuals. Atotal of 3 biological replicates for each of the groups were used for proteomic analysis. Each biological sample was composed of 5 individual plasma samples which were equally mixed. Mass spectrometry (MS)-based proteomics technologies are powerful tools used for large-scale protein identification and quantization. Among these techniques, label-free shotgun proteomics is highly effective for the identification of peptides and, subsequently, to obtain a global protein profile of a sample. Label-free shotgun proteomics provides a unique opportunity to measure peptides present in a sample and subsequently determine the abundance of the proteins across various samples. Notably, label-free shotgun proteomics is suitable for applications in complex biological systems and generates faster, cleaner and simpler results . Consequently, numerous researchers are employing label-free shotgun proteomics techniques for discovery studies. In total, 229 non-redundant proteins were identified based on the identification of one or more unique peptides. Differentially expressed proteins were defined as those that showed a fold change of greater than 2.0 or less than 0.5 in relative abundance and a P-value < 0.05. Based on these criteria, there were 59 differentially expressed proteins between PTB group and LTBI group, and 56 differentially expressed proteins between PTB group and HC group, respectively. A total of 31 overlapping proteins with significantly different expression level were identified in PTB patients, compared with LTBI individualsand healthy controls.

Project description:Tuberculosis (TB) is a serious infectious disease in that 90% of those latently infected with Mycobacterium tuberculosis present no symptoms, but possess a 10% lifetime chance of developing active TB. To prevent the spread of the disease, early diagnosis is crucial. However, current methods of detection require improvement in sensitivity, efficiency or specificity. In the present study, we conducted a microarray experiment, comparing the gene expression profiles in the peripheral blood mononuclear cells among individuals with active TB, latent infection, and healthy conditions in a Taiwanese population.Bioinformatics analysis revealed that most of the differentially expressed genes belonged to immune responses, inflammation pathways, and cell cycle control. Subsequent RT-PCR validation identified four differentially expressed genes, NEMF, ASUN, DHX29, and PTPRC, as potential biomarkers for the detection of active and latent TB infections. Receiver operating characteristic analysis showed that the expression level of PTPRC may discriminate active TB patients from healthy individuals, while ASUN could differentiate between the latent state of TB infection and healthy condidtion. In contrast, DHX29 may be used to identify latently infected individuals among active TB patients or healthy individuals. To test the concept of using these biomarkers as diagnostic support, we constructed classification models using these candidate biomarkers and found the Naïve Bayes-based model built with ASUN, DHX29, and PTPRC to yield the best performance.Our study demonstrated that gene expression profiles in the blood can be used to identify not only active TB patients, but also to differentiate latently infected patients from their healthy counterparts. Validation of the constructed computational model in a larger sample size would confirm the reliability of the biomarkers and facilitate the development of a cost-effective and sensitive molecular diagnostic platform for TB.

Project description:AbstractTuberculosis (TB) is one of the leading causes of childhood morbidity and death globally. Lack of rapid, effective non-sputum diagnosis and prediction methods for TB in children are some of the challenges currently faced. In recent years, blood transcriptional profiling has provided a fresh perspective on the diagnosis and predicting the progression of tuberculosis. Meanwhile, combined with bioinformatics analysis can help to identify the differentially expressed genes (DEGs) and functional pathways involved in the different clinical stages of TB. Therefore, this study investigated potential diagnostic markers for use in distinguishing between latent tuberculosis infection (LTBI) and active TB using children's blood transcriptome data.From the Gene Expression Omnibus database, we downloaded two gene expression profile datasets (GSE39939 and GSE39940) of whole blood-derived RNA sequencing samples, reflecting transcriptional signatures between latent and active tuberculosis in children. GEO2R tool was used to screen for DEGs in LTBI and active TB in children. Database for Annotation, Visualization and Integrated Discovery tools were used to perform Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis. STRING and Cytoscape analyzed the protein-protein interaction network and the top 15 hub genes respectively. Receiver operating characteristics curve was used to estimate the diagnostic value of the hub genes.A total of 265 DEGs were identified, including 79 upregulated and 186 downregulated DEGs. Further, 15 core genes were picked and enrichment analysis revealed that they were highly correlated with neutrophil activation and degranulation, neutrophil-mediated immunity and in defense response. Among them TLR2, FPR2, MMP9, MPO, CEACAM8, ELANE, FCGR1A, SELP, ARG1, GNG10, HP, LCN2, LTF, ADCY3 had significant discriminatory power between LTBI and active TB, with area under the curves of 0.84, 0.84, 0.84, 0.80, 0.87, 0.78, 0.88, 0.84, 0.86, 0.82, 0.85, 0.85, 0.79, and 0.88 respectively.Our research provided several genes with high potential to be candidate gene markers for developing non-sputum diagnostic tools for childhood Tuberculosis.

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Label-Free Quantitative Proteomics Identifies Novel Plasma Biomarkers for Distinguishing Pulmonary Tuberculosis and Latent Infection.

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Label-Free Quantitative Proteomics Identifies Novel Plasma Biomarkers for Distinguishing Pulmonary Tuberculosis and Latent Infection.

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