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Characterization of plasma proteins in children of different Mycobacterium tuberculosis infection status using label-free quantitative proteomics.

ABSTRACT: Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is an infectious disease found worldwide. Children infected with MTB are more likely to progress to active TB (ATB); however, the molecular mechanism behind this process has long been a mystery. We employed the label-free quantitative proteomic technology to identify and characterize differences in plasma proteins between ATB and latent TB infection (LTBI) in children. To detect differences that are indicative of MTB infection, we first selected proteins whose expressions were markedly different between the ATB and LTBI groups and the control groups (inflammatory disease control (IDC) and healthy control (HC) groups). A total of 521 proteins differed (> 1.5-fold or < 0.6-fold) in the LTBI group, and 318 proteins in the ATB group when compared with the control groups. Of these, 49 overlapping proteins were differentially expressed between LTBI and ATB. Gene Ontology (GO) analysis revealed most proteins had a cellular and organelle distribution. The MTB infection status was mainly related to differences in binding, cellular and metabolic processes. XRCC4, PCF11, SEMA4A and ATP11A were selected and further verified by qPCR and western blot. At the mRNA level, the expression of XRCC4, PCF11and SEMA4A presented an increased trend in ATB group compare with LTBI. At the protein level, the expression of all these proteins by western blot in ATB/LTBI was consistent with the trends from proteomic detection. Our results provide important data for future mechanism studies and biomarker selection for MTB infection in children.

SUBMITTER: Li J

PROVIDER: S-EPMC5732728 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Characterization of plasma proteins in children of different <i>Mycobacterium tuberculosis</i> infection status using label-free quantitative proteomics.

Li Jieqiong J Sun Lin L Xu Fang F Xiao Jing J Jiao Weiwei W Qi Hui H Shen Chen C Shen Adong A

Oncotarget 20170923 61

Tuberculosis (TB), caused by <i>Mycobacterium tuberculosis</i> (MTB), is an infectious disease found worldwide. Children infected with MTB are more likely to progress to active TB (ATB); however, the molecular mechanism behind this process has long been a mystery. We employed the label-free quantitative proteomic technology to identify and characterize differences in plasma proteins between ATB and latent TB infection (LTBI) in children. To detect differences that are indicative of MTB infection ...[more]

PMID: 29262562

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Project description: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.

Project description:BackgroundLabel-free quantitative proteomics holds a great deal of promise for the future study of both medicine and biology. However, the data generated is extremely intricate in its correlation structure, and its proper analysis is complex. There are issues with missing identifications. There are high levels of correlation between many, but not all, of the peptides derived from the same protein. Additionally, there may be systematic shifts in the sensitivity of the machine between experiments or even through time within the duration of a single experiment.ResultsWe describe a hierarchical model for analyzing unbiased, label-free proteomics data which utilizes the covariance of peptide expression across samples as well as MS/MS-based identifications to group peptides-a strategy we call metaprotein expression modeling. Our metaprotein model acknowledges the possibility of misidentifications, post-translational modifications and systematic differences between samples due to changes in instrument sensitivity or differences in total protein concentration. In addition, our approach allows us to validate findings from unbiased, label-free proteomics experiments with further unbiased, label-free proteomics experiments. Finally, we demonstrate the clinical/translational utility of the model for building predictors capable of differentiating biological phenotypes as well as for validating those findings in the context of three novel cohorts of patients with Hepatitis C.ConclusionsMass-spectrometry proteomics is quickly becoming a powerful tool for studying biological and translational questions. Making use of all of the information contained in a particular set of data will be critical to the success of those endeavors. Our proposed model represents an advance in the ability of statistical models of proteomic data to identify and utilize correlation between features. This allows validation of predictors without translation to targeted assays in addition to informing the choice of targets when it is appropriate to generate those assays.

Project description:Rifampicin (RIF) is one of the most important first-line anti-tuberculosis (TB) drugs, and more than 90% of RIF-resistant (RR) Mycobacterium tuberculosis clinical isolates belong to multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB. In order to identify specific candidate target proteins as diagnostic markers or drug targets, differential protein expression between drug-sensitive (DS) and drug-resistant (DR) strains remains to be investigated. In the present study, a label-free, quantitative proteomics technique was performed to compare the proteome of DS, RR, MDR, and XDR clinical strains. We found iniC, Rv2141c, folB, and Rv2561 were up-regulated in both RR and MDR strains, while fadE9, espB, espL, esxK, and Rv3175 were down-regulated in the three DR strains when compared to the DS strain. In addition, lprF, mce2R, mce2B, and Rv2627c were specifically expressed in the three DR strains, and 41 proteins were not detected in the DS strain. Functional category showed that these differentially expressed proteins were mainly involved in the cell wall and cell processes. When compared to the RR strain, Rv2272, smtB, lpqB, icd1, and folK were up-regulated, while esxK, PPE19, Rv1534, rpmI, ureA, tpx, mpt64, frr, Rv3678c, esxB, esxA, and espL were down-regulated in both MDR and XDR strains. Additionally, nrp, PPE3, mntH, Rv1188, Rv1473, nadB, PPE36, and sseA were specifically expressed in both MDR and XDR strains, whereas 292 proteins were not identified when compared to the RR strain. When compared between MDR and XDR strains, 52 proteins were up-regulated, while 45 proteins were down-regulated in the XDR strain. 316 proteins were especially expressed in the XDR strain, while 92 proteins were especially detected in the MDR strain. Protein interaction networks further revealed the mechanism of their involvement in virulence and drug resistance. Therefore, these differentially expressed proteins are of great significance for exploring effective control strategies of DR-TB.

Dataset Information

Characterization of plasma proteins in children of different Mycobacterium tuberculosis infection status using label-free quantitative proteomics.

Publications

Characterization of plasma proteins in children of different <i>Mycobacterium tuberculosis</i> infection status using label-free quantitative proteomics.

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