Dataset of a comparative proteomics experiment in a methylmalonyl-CoA mutase knockout HEK 293 cell model

• Main Authors: Michele Costanzo, Marianna Caterino, Armando Cevenini, Vincent Jung, Cerina Chhuon, Joanna Lipecka, Roberta Fedele, Ida Chiara Guerrera, Margherita Ruoppolo
• Format: Article
• Language: English
• Published: Elsevier 2020-12-01
• Series: Data in Brief
• Subjects: Proteomics; Label-free quantification; Methylmalonyl-CoA mutase; Methylmalonic acidemia; LC-MS/MS; S-Trap digestion
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352340920313354

Methylmalonic acidemia is a rare inborn error of metabolism with severe clinical complications and poor outcome. The present data article is related to a proteomic investigation conducted on a HEK 293 cell line which has been genetically modified using CRISPR-CAS9 system to knockout the methylmalonyl-CoA mutase enzyme (MUT-KO). Thus, the generated cell model for methylmalonic acidemia was used for a proteomic comparison with respect to HEK 293 wild type cells performing a label-free quantification (LFQ) experiment. A comparison between FASP and S-Trap digestion methods was performed on protein extracts before to proceed with the proteomic analysis of the samples. Four biological replicates were employed for LC-MS/MS analysis and each was run in technical triplicates. MaxQuant and Perseus platforms were used to perform the LFQ of the proteomes and carry out statistical analysis, respectively. Globally, 4341 proteins were identified, and 243 as differentially regulated, of which 150 down-regulated and 93 up-regulated in the MUT-KO condition. MS proteomics data have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD017977. The information provided in this dataset shed new light on the cellular mechanisms altered in this rare metabolic disorder, highlighting quantitative unbalances in proteins acting in cell structure and architecture organization and response to the stress. This article can be used as a new source of protein actors to be validated and a starting point for the identification of clinically relevant therapeutic targets.