Dataset Information

Label?free quantitative proteomics and bioinformatics analyses of alcoholic liver disease in a chronic and binge mouse model.

ABSTRACT: As a significant cause of mortality and morbidity, alcoholic liver disease (ALD) has been widely investigated. However, little is known about the underlying metabolic mechanisms involved in the complicated pathological processes of ALD. The present study used label?free quantitative proteomics and bioinformatics analyses to investigate the differentially expressed proteins (DEPs) and their functions in the livers of alcohol?feed (AF) and control pair?feed (PF) mice. As a result, 87 upregulated DEPs and 133 downregulated DEPs were identified in AF liver tissues compared with PF livers. Gene ontology and Kyoto encyclopedia of genes and genomes bioinformatics analyses demonstrated that the DEPs were significantly enriched in 'protein binding', 'metabolism', 'signal conduction' and 'immune response'. The expression of several core proteins including thyroid hormone receptor interactor 12 (TRIP12), NADH dehydrogenase (ubiquinone)1 ? subcomplex, assembly factor 3 (NDUFAF3) and guanine monophosphate synthetase (GMPS) was validated by reverse transcription?quantitative polymerase chain reaction (RT?qPCR) in a larger series of samples. The RT?qPCR results confirmed that TRIP12, NDUFAF3 and GMPS genes were significantly differentially expressed in between the AF and PF samples. These results extend our understanding of the molecular mechanisms underlying the occurrence and development of ALD. The present study indicated that the majority of DEPs serve vital roles in multiple metabolic pathways and this extends our knowledge of the molecular mechanisms involved in the occurrence and progression of ALD.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC6072164 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Label‑free quantitative proteomics and bioinformatics analyses of alcoholic liver disease in a chronic and binge mouse model.

Zhang Yu Y Zhan Cheng C Chen Genwen G Sun Jianyong J

Molecular medicine reports 20180626 2

As a significant cause of mortality and morbidity, alcoholic liver disease (ALD) has been widely investigated. However, little is known about the underlying metabolic mechanisms involved in the complicated pathological processes of ALD. The present study used label‑free quantitative proteomics and bioinformatics analyses to investigate the differentially expressed proteins (DEPs) and their functions in the livers of alcohol‑feed (AF) and control pair‑feed (PF) mice. As a result, 87 upregulated D ...[more]

PMID: 29956796

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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:BACKGROUND:Although a great deal of rice proteomic research has been conducted, there are relatively few studies specifically addressing the rice grain proteome. The existing rice grain proteomic researches have focused on the identification of differentially expressed proteins or monitoring protein expression patterns during grain filling stages. RESULTS:Proteins were extracted from rice grains 10, 20, and 30 days after flowering, as well as from fully mature grains. By merging all of the identified proteins in this study, we identified 4,172 non-redundant proteins with a wide range of molecular weights (from 5.2 kDa to 611 kDa) and pI values (from pH 2.9 to pH 12.6). A Genome Ontology category enrichment analysis for the 4,172 proteins revealed that 52 categories were enriched, including the carbohydrate metabolic process, transport, localization, lipid metabolic process, and secondary metabolic process. The relative abundances of the 1,784 reproducibly identified proteins were compared to detect 484 differentially expressed proteins during rice grain development. Clustering analysis and Genome Ontology category enrichment analysis revealed that proteins involved in the metabolic process were enriched through all stages of development, suggesting that proteome changes occurred even in the desiccation phase. Interestingly, enrichments of proteins involved in protein folding were detected in the desiccation phase and in fully mature grain. CONCLUSION:This is the first report conducting comprehensive identification of rice grain proteins. With a label free shotgun proteomic approach, we identified large number of rice grain proteins and compared the expression patterns of reproducibly identified proteins during rice grain development. Clustering analysis, Genome Ontology category enrichment analysis, and the analysis of composite expression profiles revealed dynamic changes of metabolisms during rice grain development. Interestingly, we detected that proteins involved in glycolysis, TCA-cycle, lipid metabolism, and proteolysis accumulated at higher levels in fully mature grain compared to grain developing stages, suggesting that the accumulation of these proteins during the desiccation stage may be associated with the preparation of proteins required in germination.

Dataset Information

Label?free quantitative proteomics and bioinformatics analyses of alcoholic liver disease in a chronic and binge mouse model.

Publications

Label‑free quantitative proteomics and bioinformatics analyses of alcoholic liver disease in a chronic and binge mouse model.

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