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Label-Free Quantitative Proteomics in a Methylmalonyl-CoA Mutase-Silenced Neuroblastoma Cell Line.

ABSTRACT: Methylmalonic acidemias (MMAs) are inborn errors of metabolism due to the deficient activity of methylmalonyl-CoA mutase (MUT). MUT catalyzes the formation of succinyl-CoA from methylmalonyl-CoA, produced from propionyl-CoA catabolism and derived from odd chain fatty acids ?-oxidation, cholesterol, and branched-chain amino acids degradation. Increased methylmalonyl-CoA levels allow for the presymptomatic diagnosis of the disease, even though no approved therapies exist. MMA patients show hyperammonemia, ketoacidosis, lethargy, respiratory distress, cognitive impairment, and hepatomegaly. The long-term consequences concern neurologic damage and terminal kidney failure, with little chance of survival. The cellular pathways affected by MUT deficiency were investigated using a quantitative proteomics approach on a cellular model of MUT knockdown. Currently, a consistent reduction of the MUT protein expression was obtained in the neuroblastoma cell line (SH-SY5Y) by using small-interfering RNA (siRNA) directed against an MUT transcript (MUT siRNA). The MUT absence did not affect the cell viability and apoptotic process in SH-SY5Y. In the present study, we evaluate and quantify the alterations in the protein expression profile as a consequence of MUT-silencing by a mass spectrometry-based label-free quantitative analysis, using two different quantitative strategies. Both quantitative methods allowed us to observe that the expression of the proteins involved in mitochondrial oxido-reductive homeostasis balance was affected by MUT deficiency. The alterated functional mitochondrial activity was observed in siRNA_MUT cells cultured with a propionate-supplemented medium. Finally, alterations in the levels of proteins involved in the metabolic pathways, like carbohydrate metabolism and lipid metabolism, were found.

SUBMITTER: Costanzo M

PROVIDER: S-EPMC6275031 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Label-Free Quantitative Proteomics in a Methylmalonyl-CoA Mutase-Silenced Neuroblastoma Cell Line.

Costanzo Michele M Cevenini Armando A Marchese Emanuela E Imperlini Esther E Raia Maddalena M Del Vecchio Luigi L Caterino Marianna M Ruoppolo Margherita M

International journal of molecular sciences 20181113 11

Methylmalonic acidemias (MMAs) are inborn errors of metabolism due to the deficient activity of methylmalonyl-CoA mutase (MUT). MUT catalyzes the formation of succinyl-CoA from methylmalonyl-CoA, produced from propionyl-CoA catabolism and derived from odd chain fatty acids β-oxidation, cholesterol, and branched-chain amino acids degradation. Increased methylmalonyl-CoA levels allow for the presymptomatic diagnosis of the disease, even though no approved therapies exist. MMA patients show hyperam ...[more]

PMID: 30428564

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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.

Dataset Information

Label-Free Quantitative Proteomics in a Methylmalonyl-CoA Mutase-Silenced Neuroblastoma Cell Line.

Publications

Label-Free Quantitative Proteomics in a Methylmalonyl-CoA Mutase-Silenced Neuroblastoma Cell Line.

Similar Datasets

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