Integrated multi-omics reveals anaplerotic insufficiency in methylmalonyl-CoA mutase deficiency

MMAomics - a multi-omics study on the rare inborn error of metabolism methylmalonic aciduria (MMA)

This repository provides all R scripts used to analyse multi-omics and phenotype datasets in Forny et al. 2022. A pre-print of the publication has been deposited on the medRxiv server.

The repository is structured according to the figures as presented in the paper.

Table of contents

Figures

1. Multi-faceted omics view enabled a molecular diagnosis in 84% of individuals.
2. Phenomics analysis reveals two main surrogate markers of disease severity (clinical severity score and PI+ activity).
3. Untargeted integration of omics data layers highlights the TCA cycle and associated pathways as well as oxidative phosphorylation gene sets to be dysregulated in MMA.
4. Transcript-protein and protein-protein correlation analyses reveal coordinated relationships between MMUT and TCA genes and proteins but not their isoforms.
5. Polar metabolomics and glutamine tracing studies in CRISPR/Cas9 KO 293T cells and primary patient fibroblasts highlight differential glutamine anaplerosis.
6. MMUT interacts physically with GLUD1, DLST, and GOT2 as demonstrated by FLAG-tag pull-down.

Extended data Figures

1. Historic context of sample collection and quality control measurements of multi-omics data.
2. Biochemical assessment of MMUT activity and propionate incorporation activity supports diagnosis of affected individuals.
3. Expression outlier analysis reveals causative genes in specific disease samples.
4. The clinical severity score and propionate incorporation activity are associated with several phenotypic traits.
5. Global computational approaches to transcriptomics and proteotyping datasets were unable to stratify samples into disease and non-disease groups.
6. Transcriptomics analysis of mouse brain revealed sample clustering according to genotype.
7. Significantly dysregulated proteins were enriched for for mitochondrial localization.
8. Transcript-protein and protein-protein correlation analysis illustrates coordinated regulation of MMUT with most TCA transcripts and proteins.
9. Metabolomics investigation of a subset of patient cell lines.
10. Validation of CRISPR knock-out 293T cell lines.
11. Metabolomics and glutamine labelling in 293T cells.
12. Labelling patterns derived from [U-13C]glutamine in primary patient and control fibroblasts.
13. Treatment of primary patient and control fibroblasts.
14. Fractional labeling pattern derived from glutamine in 293T and primary fibroblast cells upon treatment.
15. MMUT-flag is enzymatically active and pulls down other propionate pathway proteins using immunoprecipitation.
16. Quantitative pull-down results following affinity purification mass spectrometry and confirmation of DLST pull-down by MMUT-flag by immunoprecipitation.

(10.-15. no data analyses. For 16. check Main Figure 6 folder)