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Dataset of quantitative proteomic analysis to understand aging processes in rabbit liver

Here, we present a proteomics dataset of liver proteins to understand aging in rabbits, which complements the publication “Quantitative proteomics to study aging in rabbit liver” [1]. This dataset was generated to understand the molecular basis and metabolic changes of aging processes in liver, whic...

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Detalles Bibliográficos
Autores principales: Amin, Bushra, Robinson, Renã A.S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7262418/
https://www.ncbi.nlm.nih.gov/pubmed/32490075
http://dx.doi.org/10.1016/j.dib.2020.105701
Descripción
Sumario:Here, we present a proteomics dataset of liver proteins to understand aging in rabbits, which complements the publication “Quantitative proteomics to study aging in rabbit liver” [1]. This dataset was generated to understand the molecular basis and metabolic changes of aging processes in liver, which is the main organ involved in metabolism, detoxification, transport, and signaling. Proteins from young, middle, and old age rabbits were extracted and digested. Generated peptides were labeled with light or heavy dimethyl groups at their N-termini, while lysine amines were labeled with TMT10-plex using a cPILOT workflow [2]. Labeled peptides were fractionated by basic pH reverse phase chromatography and analyzed with online reverse phase LC coupled with tandem mass spectrometry (MS/MS and MS(3)). The RAW files were generated using a Fusion Lumos Orbitrap mass spectrometer (Thermo Scientific) and processed with Proteome Discoverer (PD) version 2.2 to generate a list of identified and quantified proteins. Data was searched against the Rabbit UniProtKB redundant database. A total of 3,867 proteins were identified corresponding to 2,586 protein groups and 22,229 peptides. Dynamic levels of age-related proteins associated with fat metabolism, mitochondrial dysfunction, and protein degradation were detected. The mass spectrometry proteomics data (RAW files) and processed Proteome Discoverer 2.2 files (MSF files) have been deposited to the Proteomics Identification Database (PRIDE) ProteomeXchange Consortium and can be accessed with the dataset identifier PDX013220 (http://www.ebi.ac.uk/pride/archive/projects/PXD013220).