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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:Cells of the vascular system release spherical vesicles, called microparticles, in the size range of 0.1-1 ?m induced by a variety of stress factors resulting in variable concentrations between health and disease. Furthermore, microparticles have intercellular communication/signaling properties and interfere with inflammation and coagulation pathways. Today's most used analytical technology for microparticle characterization, flow cytometry, is lacking sensitivity and specificity, which might have led to the publication of contradicting results in the past.We propose the use of nano-liquid chromatography two-stage mass spectrometry as a nonbiased tool for quantitative MP proteome analysis.For this, we developed an improved microparticle isolation protocol and quantified the microparticle protein composition of twelve healthy volunteers with a label-free, data-dependent and independent proteomics approach on a quadrupole orbitrap instrument.Using aliquots of 250 ?l platelet-free plasma from one individual donor, we achieved excellent reproducibility with an interassay coefficient of variation of 2.7 ± 1.7% (mean ± 1 standard deviation) on individual peptide intensities across 27 acquisitions performed over a period of 3.5 months. We show that the microparticle proteome between twelve healthy volunteers were remarkably similar, and that it is clearly distinguishable from whole cell and platelet lysates. We propose the use of the proteome profile shown in this work as a quality criterion for microparticle purity in proteomics studies. Furthermore, one freeze thaw cycle damaged the microparticle integrity, articulated by a loss of cytoplasm proteins, encompassing a specific set of proteins involved in regulating dynamic structures of the cytoskeleton, and thrombin activation leading to MP clotting. On the other hand, plasma membrane protein composition was unaffected. Finally, we show that multiplexed data-independent acquisition can be used for relative quantification of target proteins using Skyline software. Mass spectrometry data are available via ProteomeXchange (identifier PXD003935) and panoramaweb.org (https://panoramaweb.org/labkey/N1OHMk.url).

Project description:BackgroundMaize is one of the most important food crops worldwide. Roots play important role in maize productivity through water and nutrient uptake from the soil. Improving maize root traits for efficient water uptake will help to optimize irrigation and contribute to sustainable maize production. Therefore, we investigated the protein profiles of maize cv. Anyu308 root system divided into Upper root zone (UR), Middle root (MR), and Lower root (LR), by label free quantitative shotgun proteomic approach (LFQ). The aim of our study was to identify proteins and mechanisms associated with enhanced water uptake in different maize root zones under automatic irrigation system.ResultsAt field capacity, MR had the highest water uptake than the UR and LR. We identified a total of 489 differentially abundant proteins (DAPs) by pairwise comparison of MR, LR, and UR. Cluster analysis of DAPs revealed MR and UR had similar protein abundance patterns different from LR. More proteins were differentially abundant in MR/UR compared to LR/MR and LR/UR. Comparisons of protein profiles indicate that the DAPs in MR increased in abundance, compared to UR and LR which had more downregulated DAPs. The abundance patterns, functional category, and pathway enrichment analyses highlight chromatin structure and dynamics, ribosomal structures, polysaccharide metabolism, energy metabolism and transport, induction of water channels, inorganic ion transport, intracellular trafficking, and vesicular transport, and posttranslational modification as primary biological processes related to enhanced root water uptake in maize. Specifically, the abundance of histones, ribosomal proteins, and aquaporins, including mitochondrion electron transport proteins and the TCA cycle, underpinned MR's enhanced water uptake. Furthermore, proteins involved in folding and vascular transport supported the radial transport of solute across cell membranes in UR and MR. Parallel reaction monitoring analysis was used to confirmed profile of the DAPs obtained by LFQ-based proteomics.ConclusionThe list of differentially abundant proteins identified in MR are interesting candidates for further elucidation of their role in enhanced water uptake in maize root. Overall, the current results provided an insight into the mechanisms of maize root water uptake.

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.

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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