β-Catenin is central to DUX4-driven network rewiring in facioscapulohumeral muscular dystrophy

Abstract

Facioscapulohumeral muscular dystrophy (FSHD) is an incurable disease, characterized by skeletal muscle weakness and wasting. Genetically, FSHD is characterized by contraction or hypomethylation of repeat D4Z4 units on chromosome 4, which causes aberrant expression of the transcription factor DUX4 from the last repeat. Many genes have been implicated in FSHD pathophysiology, but an integrated molecular model is currently lacking. We developed a novel differential network methodology, Interactome Sparsification and Rewiring (InSpiRe), which detects network rewiring between phenotypes by integrating gene expression data with known protein interactions. Using InSpiRe, we performed a meta-analysis of multiple microarray datasets from FSHD muscle biopsies, then removed secondary rewiring using non-FSHD datasets, to construct a unified network of rewired interactions. Our analysis identified β-catenin as the main coordinator of FSHD-associated protein interaction signalling, with pathways including canonical Wnt, HIF1-α and TNF-α clearly perturbed. To detect transcriptional changes directly elicited by DUX4, gene expression profiling was performed using microarrays on murine myoblasts. This revealed that DUX4 significantly modified expression of the genes in our FSHD network. Furthermore, we experimentally confirmed that Wnt/β-catenin signalling is affected by DUX4 in murine myoblasts. Thus, we provide the first unified molecular map of FSHD signalling, capable of uncovering pathomechanisms and guiding therapeutic development.

Figure 1.

An overview of the InSpiRe algorithm. Public databases are interrogated for expression data and protein–protein interaction data, as inputs to the algorithm. (a) Step 1 of InSpiRe: integration of expression data with the protein interaction network, via Pearson correlations, results in a weighted network for each phenotype. (b) Step 2 of InSpiRe: information theoretic measures detect rewiring hotspots between the two phenotypes. Differential (local) flux entropy detects shifts in active pathway regimes, Kullback–Leibler (KL) divergence detects rewiring events to which differential entropy is blind. (c) Step 3 of InSpiRe: sparsification of the neighbourhood of rewiring proteins, through consideration of differential correlations across phenotypes, results in a sparse network subset, containing proteins and interactions that are significantly rewiring between the two phenotypes.

Figure 2.

The neighbourhood of CTNNB1 in the FSHD network. Interactions are coloured proportional to the Pearson correlation in gene expression between connected genes across control samples (a) and FSHD samples (b). Red edges are negatively correlated, grey edges uncorrelated and green edges positively correlated. The thickness of edges is proportional to 1 − p, where is the p-value of the statistical analysis performed to determine whether correlation in gene expression between connected edges is different between FSHD and control. Large nodes belong to the core set of 164 high confidence FSHD-specific rewiring genes. Large circles indicate proteins significantly rewiring between FSHD and control phenotypes, identified in the second stage of InSpiRe. There is a clear shift from predominantly uncorrelated to highly correlated between FSHD and controls, with an increased correlation between CTNNB1 and its interaction partners across FSHD samples as compared with controls. This is indicative of increased β-catenin activity. Note the increased positive correlation between CTNNB1 and HIF1A, CASP3 and CASP8.

Figure 3.

The Wnt pathway in the FSHD network. Interactions are coloured proportional to the Pearson correlation in gene expression between connected genes across control samples (a) and FSHD samples (b). Note the shift from predominantly uncorrelated links in controls to highly correlated links in FSHD, implying an activation of this Wnt pathway in FSHD.

Figure 4.

Clustering of the DUX4 construct expressing mouse satellite cell-derived myoblast samples. PCA (a) and hierarchical clustering (b) on the 1000 most variable probes across the five DUX4 constructs and control RV show clustering of technical replicates, demonstrating reproducibility. Note also clustering of tMALDUX4-VP16 (black) with DUX4 (red); DUX4c (green) with tMALDUX4 (yellow) but separation of tMALDUX4-ERD (purple) and control (blue) from other DUX4 constructs.

Figure 5.

Enrichment analysis of genes perturbed by DUX4 constructs in mouse satellite cell-derived myobalsts. (a) Biological processes enriched among genes perturbed by DUX4 constructs. (b) Biological pathways enriched among genes perturbed by DUX4 constructs. Note the significant enrichment for Wnt/β-catenin signalling among genes perturbed by DUX4 constructs.

Figure 6.

DUX4 perturbs Wnt/β-catenin signalling. (a) QPCR of downstream targets of Wnt/β-catenin signalling Lef1, Tcf3, Tcf4, Lgr5, Lgr6 and Myf5 in mouse satellite cell-derived myoblasts, 24 or 48 h after infection with either DUX4 or control retroviral constructs. Data are mean ± s.e.m. from three mice, where an asterisk denotes a significant difference (p < 0.05) from infection with control retrovirus using a paired two-tailed Student's t-test. (b) TopFLASH/FopFLASH assay of immortalized C2C12 murine myoblasts transfected with either DUX4 or control plasmid, shows a significant increase in TopFLASH/FopFLASH luciferase activity with DUX4 expression (n = 4). Data are mean ± s.e.m. from four independent experiments where an asterisk denotes significance (p < 0.05) from control using a two-tailed Student's t-test.