Dynamic transcriptomic analysis reveals suppression of PGC1α/ERRα drives perturbed myogenesis in facioscapulohumeral muscular dystrophy

Abstract

Facioscapulohumeral muscular dystrophy (FSHD) is a prevalent, incurable myopathy, linked to epigenetic derepression of D4Z4 repeats on chromosome 4q, leading to ectopic DUX4 expression. FSHD patient myoblasts have defective myogenic differentiation, forming smaller myotubes with reduced myosin content. However, molecular mechanisms driving such disrupted myogenesis in FSHD are poorly understood. We performed high-throughput morphological analysis describing FSHD and control myogenesis, revealing altered myogenic differentiation results in hypotrophic myotubes. Employing polynomial models and an empirical Bayes approach, we established eight critical time points during which human healthy and FSHD myogenesis differ. RNA-sequencing at these eight nodal time points in triplicate, provided temporal depth for a multivariate regression analysis, allowing assessment of interaction between progression of differentiation and FSHD disease status. Importantly, the unique size and structure of our data permitted identification of many novel FSHD pathomechanisms undetectable by previous approaches. For further analysis here, we selected pathways that control mitochondria: of interest considering known alterations in mitochondrial structure and function in FSHD muscle, and sensitivity of FSHD cells to oxidative stress. Notably, we identified suppression of mitochondrial biogenesis, in particular via peroxisome proliferator-activated receptor gamma coactivator 1-α (PGC1α), the cofactor and activator of oestrogen-related receptor α (ERRα). PGC1α knock-down caused hypotrophic myotubes to form from control myoblasts. Known ERRα agonists and safe food supplements biochanin A, daidzein or genistein, each rescued the hypotrophic FSHD myotube phenotype. Together our work describes transcriptomic changes in high resolution that occur during myogenesis in FSHD ex vivo, identifying suppression of the PGC1α-ERRα axis leading to perturbed myogenic differentiation, which can effectively be rescued by readily available food supplements.

Lymphocytes contribute to DUX4 target genes in FSHD muscle biopsies

“…We also performed RNA-seq on 3 primary FSHD myoblast cell lines described previously 16 , namely FSHD3 (FSHD1, 7RU, female), FSHD6 (FSHD1, 8RU, female) and FSHD9 (FSHD1, 7RU, male) alongside age and gender matched controls, in proliferation and after 3 days of differentiation into multinucleated myotubes, in singlet. This new RNA-Seq data was considered with our previously published datasets of immortalised FSHD myoblasts and myotubes in triplicate 23,36 . This data describes 3 pathological FSHD cell lines (54-12, 54-A5 and 54-2, all FSHD1, 3RU, male) alongside 2 control lines (54-A10, 54-6, 11RU) from a mosaic patient 37 as well as 2 further FSHD cell lines (16Abic, FSHD1, 7RU, female and 12Abic, FSHD1, 6RU, female) alongside sibling and gender matched controls (16Ubic and 12Ubic respectively).…”

“…A table summarises DUX4 expression in RNA-seq data corresponding to FSHD cellular models (B). Myoblasts and in vitro differentiated myotube data are either new data (primary cell lines) or data previously published by ourselves 23,36 . Single cell RNA-seq of FSHD and control myocytes was previously published by van den Heuvel et al, 2019 32 .…”

“…Data containing myoblast and myotube RNA-seq samples in triplicate from immortalised FSHD myoblast cell lines 54-2, 54-12, 54-A5, 16ABic and 16UBic and matched controls 54-A10, 54-6, 16UBic and 12UBic that we previously described 23,36 are available from the GEO database, accession numbers: GSE123468 and GSE102812. This data describes 27 (18 FSHD, 12 control) myoblast samples and 27 (18 FSHD, 12 control) myotube samples.…”

A Multi-Objective Genetic Algorithm to Find Active Modules in Multiplex Biological Networks

“…We used five Facio-Scapulo-Humeral muscular Dystrophy type 1 (FSHD1) RNA-sequencing expression datasets publicly available [32,33,34], extracted from the Gene Expression Omnibus [35]. We performed the differential expression analyses using the R package edgeR version 3.26.8 [31].…”

“…We performed the differential expression analyses using the R package edgeR version 3.26.8 [31]. As recommended in the user guide of edgeR, we performed glmQLF tests for the two datasets with samples from different batches [33,34], and Fisher Exact tests for the three datasets with samples from a single batch [32]. We considered a gene as significantly Differentially Expressed (significantly DEG) if the False Discovery Rate F DR ≤ 0.05 and the |log2(F C)| > 1, where the FC (Fold-Change) is the ratio of the difference in expression between cases and controls.…”