Donkervoort, S. | Search bioRxiv

bioRxiv2024

Effects of HMGCR deficiency on skeletal muscle development

Pathogenic variants in HMGCR were recently linked to a limb-girdle muscular dystrophy (LGMD) phenotype. The protein product HMG CoA reductase (HMGCR) catalyzes a key component of the cholesterol synthesis pathway. The two other muscle diseases associated with HMGCR, statin-associated myopathy (SAM) and autoimmune anti-HMGCR myopathy, are not inherited in a Mendelian pattern. The mechanism linking pathogenic variants in HMGCR with skeletal muscle dysfunction is unclear. We knocked down Hmgcr in mouse skeletal myoblasts, knocked down hmgcr in Drosophila, and expressed three pathogenic HMGCR variants (c.1327C>T, p.Arg443Trp; c.1522_1524delTCT, p.Ser508del; and c.1621G>A, p.Ala541Thr) in Hmgcr knockdown mouse myoblasts. Hmgcr deficiency was associated with decreased proliferation, increased apoptosis, and impaired myotube fusion. Transcriptome sequencing of Hmgcr knockdown versus control myoblasts revealed differential expression involving mitochondrial function, with corresponding differences in cellular oxygen consumption rates. Both ubiquitous and muscle-specific knockdown of hmgcr in Drosophila led to lethality. Overexpression of reference HMGCR cDNA rescued myotube fusion in knockdown cells, whereas overexpression of the pathogenic variants of HMGCR cDNA did not. These results suggest that the three HMGCR-related muscle diseases share disease mechanisms related to skeletal muscle development.

developmental biology↗

bioRxiv2019

Quantifying genetic regulatory variation in human populations improves transcriptome analysis in rare disease patients

Transcriptome data holds substantial promise for better interpretation of rare genetic variants in basic research and clinical settings. Here, we introduce ANalysis of Expression VAriation (ANEVA) to quantify genetic variation in gene dosage from allelic expression (AE) data in a population. Application to GTEx data showed that this variance estimate is robust across datasets and is correlated with selective constraint in a gene. We next used ANEVA variance estimates in a Dosage Outlier Test (ANEVA-DOT) to identify genes in an individual that are affected by a rare regulatory variant with an unusually strong effect. Applying ANEVA-DOT to AE data form 70 Mendelian muscular disease patients showed high accuracy in detecting genes with pathogenic variants in previously resolved cases, and lead to one confirmed and several potential new diagnoses in cases previously unresolved. Using our reference estimates from GTEx data, ANEVA-DOT can be readily incorporated in rare disease diagnostic pipelines to better utilize RNA-seq data.\n\nOne Sentence SummaryNew statistical framework for modelling allelic expression characterizes genetic regulatory variation in populations and informs diagnosis in rare disease patients

genomics↗