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Label-free quantitative proteomics reveals differentially regulated proteins influencing urolithiasis.

ABSTRACT: Urinary proteins have been implicated as inhibitors of kidney stone formation (urolithiasis). As a proximal fluid, prefiltered by the kidneys, urine is an attractive biofluid for proteomic analysis in urologic conditions. However, it is necessary to correct for variations in urinary concentration. In our study, individual urine samples were normalized for this variation by using a total protein to creatinine ratio. Pooled urine samples were compared in two independent experiments. Differences between the urinary proteome of stone formers and nonstone-forming controls were characterized and quantified using label-free nano-ultraperformance liquid chromatography high/low collision energy switching analysis. There were 1063 proteins identified, of which 367 were unique to the stone former groups, 408 proteins were unique to the control pools, and 288 proteins were identified for comparative quantification. Proteins found to be unique in stone-formers were involved in carbohydrate metabolism pathways and associated with disease states. Thirty-four proteins demonstrated a consistent >twofold change between stone formers and controls. For ceruloplasmin, one of the proteins was shown to be more than twofold up-regulated in the stone-former pools, this observation was validated in individuals by enzyme-linked immunosorbent assay. Moreover, in vitro crystallization assays demonstrated ceruloplasmin had a dose-dependent increase on calcium oxalate crystal formation. Taken together, these results may suggest a functional role for ceruloplasmin in urolithiasis.

SUBMITTER: Wright CA

PROVIDER: S-EPMC3149087 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature

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Label-free quantitative proteomics reveals differentially regulated proteins influencing urolithiasis.

Wright C A CA Howles S S Trudgian D C DC Kessler B M BM Reynard J M JM Noble J G JG Hamdy F C FC Turney B W BW

Molecular & cellular proteomics : MCP 20110407 8

Urinary proteins have been implicated as inhibitors of kidney stone formation (urolithiasis). As a proximal fluid, prefiltered by the kidneys, urine is an attractive biofluid for proteomic analysis in urologic conditions. However, it is necessary to correct for variations in urinary concentration. In our study, individual urine samples were normalized for this variation by using a total protein to creatinine ratio. Pooled urine samples were compared in two independent experiments. Differences be ...[more]

PMID: 21474797

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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:Black pepper (Piper nigrum L.), a tropical spice crop of global acclaim, is susceptible to Phytophthora capsici, an oomycete pathogen which causes the highly destructive foot rot disease. A systematic understanding of this phytopathosystem has not been possible owing to lack of genome or proteome information. In this study, we explain an integrated transcriptome-assisted label-free quantitative proteomics pipeline to study the basal immune components of black pepper when challenged with P. capsici. We report a global identification of 532 novel leaf proteins from black pepper, of which 518 proteins were functionally annotated using BLAST2GO tool. A label-free quantitation of the protein datasets revealed 194 proteins common to diseased and control protein datasets of which 22 proteins showed significant up-regulation and 134 showed significant down-regulation. Ninety-three proteins were identified exclusively on P. capsici infected leaf tissues and 245 were expressed only in mock (control) infected samples. In-depth analysis of our data gives novel insights into the regulatory pathways of black pepper which are compromised during the infection. Differential down-regulation was observed in a number of critical pathways like carbon fixation in photosynthetic organism, cyano-amino acid metabolism, fructose, and mannose metabolism, glutathione metabolism, and phenylpropanoid biosynthesis. The proteomics results were validated with real-time qRT-PCR analysis. We were also able to identify the complete coding sequences for all the proteins of which few selected genes were cloned and sequence characterized for further confirmation. Our study is the first report of a quantitative proteomics dataset in black pepper which provides convincing evidence on the effectiveness of a transcriptome-based label-free proteomics approach for elucidating the host response to biotic stress in a non-model spice crop like P. nigrum, for which genome information is unavailable. Our dataset will serve as a useful resource for future studies in this plant. Data are available via ProteomeXchange with identifier PXD003887.

Dataset Information

Label-free quantitative proteomics reveals differentially regulated proteins influencing urolithiasis.

Publications

Label-free quantitative proteomics reveals differentially regulated proteins influencing urolithiasis.

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