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A proteomics approach to the identification of plasma biomarkers for latent tuberculosis infection.

ABSTRACT: A proteomic analysis was performed to screen the potential latent tuberculosis infection (LTBI) biomarkers. A training set of spectra was used to generate diagnostic models, and a blind testing set was used to determine the accuracy of the models. Candidate peptides were identified using nano-liquid chromatography-electrospray ionization-tandem mass spectrometry. Based on the training set results, 3 diagnostic models recognized LTBI subjects with good cross-validation accuracy. In the blind testing set, LTBI subjects could be identified with sensitivities and specificities of 85.20% to 88.90% and 85.7% to 100%, respectively. Additionally, 14 potential LTBI biomarkers were identified, and all proteins were identified for the first time through proteomics in the plasma of healthy, latently infected individuals. In all, proteomic pattern analyses can increase the accuracy of LTBI diagnosis, and the data presented here provide novel insights into potential mechanisms involved in LTBI.

SUBMITTER: Zhang X

PROVIDER: S-EPMC7127109 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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A proteomics approach to the identification of plasma biomarkers for latent tuberculosis infection.

Zhang Xia X Liu Fei F Li Qi Q Jia Hongyan H Pan Liping L Xing Aiying A Xu Shaofa S Zhang Zongde Z

Diagnostic microbiology and infectious disease 20140426 4

A proteomic analysis was performed to screen the potential latent tuberculosis infection (LTBI) biomarkers. A training set of spectra was used to generate diagnostic models, and a blind testing set was used to determine the accuracy of the models. Candidate peptides were identified using nano-liquid chromatography-electrospray ionization-tandem mass spectrometry. Based on the training set results, 3 diagnostic models recognized LTBI subjects with good cross-validation accuracy. In the blind test ...[more]

PMID: 24865408

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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: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.

Dataset Information

A proteomics approach to the identification of plasma biomarkers for latent tuberculosis infection.

Publications

A proteomics approach to the identification of plasma biomarkers for latent tuberculosis infection.

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