Meta-analysis Towards FSHD Reveals Misregulation of Neuromuscular Junction, Nuclear Envelope, and Spliceosome

Overview

Journal Commun Biol

Specialty Biology

Date 2024 May 25

PMID 38796645

Authors

Teresa Schatzl

Vanessa Todorow

Lars Kaiser

Helga Weinschrott

Benedikt Schoser

Hans-Peter Deigner

Peter Meinke

Matthias Kohl

Affiliations

Soon will be listed here.

Abstract

Facioscapulohumeral muscular dystrophy (FSHD) is one of the most common autosomal dominant muscle disorders, yet no cure or amelioration exists. The clinical presentation is diverse, making it difficult to identify the actual driving pathomechanism among many downstream events. To unravel this complexity, we performed a meta-analysis of 13 original omics datasets (in total 171 FSHD and 129 control samples). Our approach confirmed previous findings about the disease pathology and specified them further. We confirmed increased expression of former proposed DUX4 biomarkers, and furthermore impairment of the respiratory chain. Notably, the meta-analysis provides insights about so far not reported pathways, including misregulation of neuromuscular junction protein encoding genes, downregulation of the spliceosome, and extensive alterations of nuclear envelope protein expression. Finally, we developed a publicly available shiny app to provide a platform for researchers who want to search our analysis for genes of interest in the future.

References

Bonett D . Meta-analytic interval estimation for standardized and unstandardized mean differences. Psychol Methods. 2009; 14(3):225-38. DOI: 10.1037/a0016619. View

Ashburner M, Ball C, Blake J, Botstein D, Butler H, Cherry J . Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000; 25(1):25-9. PMC: 3037419. DOI: 10.1038/75556. View

Bosnakovski D, Gearhart M, Toso E, Ener E, Choi S, Kyba M . Low level DUX4 expression disrupts myogenesis through deregulation of myogenic gene expression. Sci Rep. 2018; 8(1):16957. PMC: 6240038. DOI: 10.1038/s41598-018-35150-8. View

Randazzo D, Khalique U, Belanto J, Kenea A, Talsness D, Olthoff J . Persistent upregulation of the β-tubulin tubb6, linked to muscle regeneration, is a source of microtubule disorganization in dystrophic muscle. Hum Mol Genet. 2018; 28(7):1117-1135. PMC: 6423419. DOI: 10.1093/hmg/ddy418. View

Tassin A, Laoudj-Chenivesse D, Vanderplanck C, Barro M, Charron S, Ansseau E . DUX4 expression in FSHD muscle cells: how could such a rare protein cause a myopathy?. J Cell Mol Med. 2012; 17(1):76-89. PMC: 3823138. DOI: 10.1111/j.1582-4934.2012.01647.x. View

van Iterson M, Boer J, Menezes R . Filtering, FDR and power. BMC Bioinformatics. 2010; 11:450. PMC: 2949886. DOI: 10.1186/1471-2105-11-450. View

Higgins J, Thompson S . Quantifying heterogeneity in a meta-analysis. Stat Med. 2002; 21(11):1539-58. DOI: 10.1002/sim.1186. View

Kolberg L, Raudvere U, Kuzmin I, Vilo J, Peterson H . gprofiler2 -- an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler. F1000Res. 2021; 9. PMC: 7859841. DOI: 10.12688/f1000research.24956.2. View

Zhang W, Liu Y, Zhang H . Extracellular matrix: an important regulator of cell functions and skeletal muscle development. Cell Biosci. 2021; 11(1):65. PMC: 8011170. DOI: 10.1186/s13578-021-00579-4. View

10.

Choi S, Gearhart M, Cui Z, Bosnakovski D, Kim M, Schennum N . DUX4 recruits p300/CBP through its C-terminus and induces global H3K27 acetylation changes. Nucleic Acids Res. 2016; 44(11):5161-73. PMC: 4914088. DOI: 10.1093/nar/gkw141. View

11.

Gu Z, Hubschmann D . simplifyEnrichment: A Bioconductor Package for Clustering and Visualizing Functional Enrichment Results. Genomics Proteomics Bioinformatics. 2022; 21(1):190-202. PMC: 10373083. DOI: 10.1016/j.gpb.2022.04.008. View

12.

Csapo R, Gumpenberger M, Wessner B . Skeletal Muscle Extracellular Matrix - What Do We Know About Its Composition, Regulation, and Physiological Roles? A Narrative Review. Front Physiol. 2020; 11:253. PMC: 7096581. DOI: 10.3389/fphys.2020.00253. View

13.

Liao Y, Smyth G, Shi W . featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2013; 30(7):923-30. DOI: 10.1093/bioinformatics/btt656. View

14.

Turki A, Hayot M, Carnac G, Pillard F, Passerieux E, Bommart S . Functional muscle impairment in facioscapulohumeral muscular dystrophy is correlated with oxidative stress and mitochondrial dysfunction. Free Radic Biol Med. 2012; 53(5):1068-79. DOI: 10.1016/j.freeradbiomed.2012.06.041. View

15.

Wang M, Zhao Y, Zhang B . Efficient Test and Visualization of Multi-Set Intersections. Sci Rep. 2015; 5:16923. PMC: 4658477. DOI: 10.1038/srep16923. View

16.

Tawil R, van der Maarel S . Facioscapulohumeral muscular dystrophy. Muscle Nerve. 2006; 34(1):1-15. DOI: 10.1002/mus.20522. View

17.

Deenen J, Arnts H, van der Maarel S, Padberg G, Verschuuren J, Bakker E . Population-based incidence and prevalence of facioscapulohumeral dystrophy. Neurology. 2014; 83(12):1056-9. PMC: 4166358. DOI: 10.1212/WNL.0000000000000797. View

18.

Das S, Chadwick B . Influence of Repressive Histone and DNA Methylation upon D4Z4 Transcription in Non-Myogenic Cells. PLoS One. 2016; 11(7):e0160022. PMC: 4965136. DOI: 10.1371/journal.pone.0160022. View

19.

Bourgon R, Gentleman R, Huber W . Independent filtering increases detection power for high-throughput experiments. Proc Natl Acad Sci U S A. 2010; 107(21):9546-51. PMC: 2906865. DOI: 10.1073/pnas.0914005107. View

20.

Erdmann H, Scharf F, Hallermayr A, Barseghyan H, Walter M, Holinski-Feder E . Reply: An epigenetic basis for genetic anticipation in facioscapulohumeral muscular dystrophy type 1. Brain. 2023; 146(12):e111-e114. DOI: 10.1093/brain/awad216. View