Dataset Information

A Swath Label-Free Proteomics insight into the Faah^-/- Mouse Liver.

ABSTRACT: Fatty acid amide hydrolase (FAAH) is an important enzyme for lipid metabolism and an interesting pharmacological target, given its role in anandamide breakdown. The FAAH^-/- genotype is the most widely used mouse model to investigate the effects of a complete pharmacological inhibition of this enzyme. In this paper, we explore, by means of label-free SWATH proteomics, the changes in protein expression occurring in the liver of FAAH^-/- knockout (KO) mice. We identified several altered biological processes and pathways, like fatty acid synthesis and glycolysis, which explain the observed phenotype of this mouse. We also observed the alteration of other proteins, like carboxylesterases and S-methyltransferases, apparently not immediately related to FAAH, but known to have important biological roles. Our study, reporting more than 3000 quantified proteins, offers an in-depth analysis of the liver proteome of this model.

SUBMITTER: Hamid Z

PROVIDER: S-EPMC6092373 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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A Swath Label-Free Proteomics insight into the Faah-/- Mouse Liver.

Hamid Zeeshan Z Summa Maria M Armirotti Andrea A

Scientific reports 20180814 1

Fatty acid amide hydrolase (FAAH) is an important enzyme for lipid metabolism and an interesting pharmacological target, given its role in anandamide breakdown. The FAAH-/- genotype is the most widely used mouse model to investigate the effects of a complete pharmacological inhibition of this enzyme. In this paper, we explore, by means of label-free SWATH proteomics, the changes in protein expression occurring in the liver of FAAH-/- knockout (KO) mice. We identified severa ...[more]

PMID: 30108271

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Project description:BackgroundSWATH-MS has emerged as the strategy of choice for biomarker discovery due to the proteome coverage achieved in acquisition and provision to re-interrogate the data. However, in quantitative analysis using SWATH, each sample from the comparison group is run individually in mass spectrometer and the resulting inter-run variation may influence relative quantification and identification of biomarkers. Normalization of data to diminish this variation thereby becomes an essential step in SWATH data processing. In most reported studies, data normalization methods used are those provided in instrument-based data analysis software or those used for microarray data. This study, for the first time provides an experimental evidence for selection of normalization method optimal for biomarker identification.MethodsThe efficiency of 12 normalization methods to normalize SWATH-MS data was evaluated based on statistical criteria in 'Normalyzer'-a tool which provides comparative evaluation of normalization by different methods. Further, the suitability of normalized data for biomarker discovery was assessed by evaluating the clustering efficiency of differentiators, identified from the normalized data based on p-value, fold change and both, by hierarchical clustering in Genesis software v.1.8.1.ResultsConventional statistical criteria identified VSN-G as the optimal method for normalization of SWATH data. However, differentiators identified from VSN-G normalized data failed to segregate test and control groups. We thus assessed data normalized by eleven other methods for their ability to yield differentiators which segregate the study groups. Datasets in our study demonstrated that differentiators identified based on p-value from data normalized with Loess-R stratified the study groups optimally.ConclusionThis is the first report of experimentally tested strategy for SWATH-MS data processing with an emphasis on identification of clinically relevant biomarkers. Normalization of SWATH-MS data by Loess-R method and identification of differentiators based on p-value were found to be optimal for biomarker discovery in this study. The study also demonstrates the need to base the choice of normalization method on the application of the data.

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

A Swath Label-Free Proteomics insight into the Faah^-/- Mouse Liver.

Publications

A Swath Label-Free Proteomics insight into the Faah<sup>-/-</sup> Mouse Liver.

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