MCEE Mutations in an Adult Patient with Parkinson's Disease, Dementia, Stroke and Elevated Levels of Methylmalonic Acid

Methylmalonyl Coenzyme A (CoA) Epimerase Deficiency, an Ultra-Rare Cause of Isolated Methylmalonic Aciduria With Predominant Neurological Features

Methylmalonyl coenzyme A (CoA) epimerase (MCE) converts D-methylmalonyl-CoA into L-methylmalonyl CoA in the final common degradation pathway of valine, isoleucine, methionine, threonine, odd-chain fatty acids, and cholesterol side chains. Methylmalonyl-CoA epimerase deficiency is an ultra-rare autosomal recessive disorder where methylmalonic acid, methylcitrate, 3-hydroxypropionate, and propionylcarnitine are accumulated. We describe two novel pediatric patients and review the previously reported cases of MCE deficiency. Including our two novel patients, at least 24 cases of MCE deficiency have been described, with a broad clinical spectrum ranging from asymptomatic to severely neurologically impaired patients. Our patients are siblings of Arabic origin who presented with metabolic decompensation with coma and epilepsy during infancy. Methylmalonic aciduria was disclosed, L-methylmalonyl-CoA mutase deficiency was assumed, and they were treated accordingly. When first seen in our country, aged 10 and four years, respectively, both presented severe intellectual disability and spasticity. The younger had an ataxic gait, and the older was wheelchair-ridden. The study of the MMUT, MMAA, MMAB, and MMADHC genes was normal. Subsequently, the pathogenic variant c.139C>T (p.Arg47*) in the MCEE gene was identified in homozygosity in both patients, leading to the diagnosis of MCE deficiency (Online Mendelian Inheritance in Man (OMIM®) 251120, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, MD, USA). Most patients were homozygous for that variant (83% of the alleles). Correct diagnosis allowed treatment adequacy and genetic counseling. Methylmalonyl-CoA epimerase deficiency shares a similar biochemical profile with other rare genetic disorders. Patients present with overlapping clinical features with predominant neurological manifestations; genetic testing is indispensable for diagnosis. We found no association between genotype and biochemical and clinical phenotypes.

Identification and validation of metabolism-related genes signature and immune infiltration landscape of rheumatoid arthritis based on machine learning

Rheumatoid arthritis (RA) causes irreversible joint damage, but the pathogenesis is unknown. Therefore, it is crucial to identify diagnostic biomarkers of RA metabolism-related genes (MRGs). This study obtained transcriptome data from healthy individuals (HC) and RA patients from the GEO database. Weighted gene correlation network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), and random forest (RF) algorithms were adopted to identify the diagnostic feature biomarker for RA. In addition, biomarkers were verified by qRT-PCR and Western blot analysis. We established a mouse model of collagen-induced arthritis (CIA), which was confirmed by HE staining and bone structure micro-CT analysis, and then further verified the biomarkers by immunofluorescence. In vitro NMR analysis was used to analyze and identify possible metabolites. The correlation of diagnostic feature biomarkers and immune cells was performed using the Spearman-rank correlation algorithm. In this study, a total of 434 DE-MRGs were identified. GO and KEGG enrichment analysis indicated that the DE-MRGs were significantly enriched in small molecules, catabolic process, purine metabolism, carbon metabolism, and inositol phosphate metabolism. AKR1C3, MCEE, POLE4, and PFKM were identified through WGCNA, LASSO, and RF algorithms. The nomogram result should have a significant diagnostic capacity of four biomarkers in RA. Immune infiltration landscape analysis revealed a significant difference in immune cells between HC and RA groups. Our findings suggest that AKR1C3, MCEE, POLE4, and PFKM were identified as potential diagnostic feature biomarkers associated with RA's immune cell infiltrations, providing a new perspective for future research and clinical management of RA.

Racemases and epimerases operating through a 1,1-proton transfer mechanism: reactivity, mechanism and inhibition

Racemases and epimerases catalyse changes in the stereochemical configurations of chiral centres and are of interest as model enzymes and as biotechnological tools. They also occupy pivotal positions within metabolic pathways and, hence, many of them are important drug targets. This review summarises the catalytic mechanisms of PLP-dependent, enolase family and cofactor-independent racemases and epimerases operating by a deprotonation/reprotonation (1,1-proton transfer) mechanism and methods for measuring their catalytic activity. Strategies for inhibiting these enzymes are reviewed, as are specific examples of inhibitors. Rational design of inhibitors based on substrates has been extensively explored but there is considerable scope for development of transition-state mimics and covalent inhibitors and for the identification of inhibitors by high-throughput, fragment and virtual screening approaches. The increasing availability of enzyme structures obtained using X-ray crystallography will facilitate development of inhibitors by rational design and fragment screening, whilst protein models will facilitate development of transition-state mimics.