Identification of Hub Genes in Duchenne Muscular Dystrophy: Evidence from Bioinformatic Analysis

Identifying hub genes and dysregulated pathways in Duchenne muscular dystrophy

PURPOSE

The aim of this study was to identify the hub genes and dysregulated pathways in the progression of duchenne muscular dystrophy (DMD) and to unveil detailedly the cellular and molecular mechanisms associated with DMD for developing efficacious treatments in the future.

MATERIAL AND METHODS

Three mRNA microarray datasets (GSE13608, GSE38417 and GSE109178) were downloaded from Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) between DMD and normal tissues were obtained via R package. Function enrichment analyses were implemented respectively using DAVID online database. The network analysis of protein-protein interaction network (PPI) was conducted using String. Cytoscape and String were used to analyse modules and screen hub genes. The expression of the identified hub genes was confirmed in mdx mice through using qRT-PCR.

RESULTS

In total, 519 DEGs were identified, consisting of 393 upregulated genes and 126 downregulated genes. The enriched functions and pathways of the DEGs mainly involve extracellular matrix organization, collagen fibril organization, interferon-gamma-mediated signaling pathway, muscle contraction, endoplasmic reticulum lumen, MHC class II receptor activity, phagosome, graft-versus-host disease, cardiomyocytes, calcium signaling pathway. Twelve hub genes were discovered and biological process analysis proved that these genes were mainly enriched cell cycle, cell division. The result of qRT-PCR suggested that increase in expression of CD44, ECT2, TYMS, MAGEL2, HLA-DMA, SERPINH1, TNNT2 was confirmed in mdx mice and the downregulation of ASB2 and LEPREL1 was also observed.

CONCLUSION

In conclusion, DEGs and hub genes identified in the current research help us probe the molecular mechanisms underlying the pathogenesis and progression of DMD, and provide candidate targets for diagnosis and treatment of DMD.

Spatial transcriptomics reveal markers of histopathological changes in Duchenne muscular dystrophy mouse models

Duchenne muscular dystrophy is caused by mutations in the DMD gene, leading to lack of dystrophin. Chronic muscle damage eventually leads to histological alterations in skeletal muscles. The identification of genes and cell types driving tissue remodeling is a key step to developing effective therapies. Here we use spatial transcriptomics in two Duchenne muscular dystrophy mouse models differing in disease severity to identify gene expression signatures underlying skeletal muscle pathology and to directly link gene expression to muscle histology. We perform deconvolution analysis to identify cell types contributing to histological alterations. We show increased expression of specific genes in areas of muscle regeneration (Myl4, Sparc, Hspg2), fibrosis (Vim, Fn1, Thbs4) and calcification (Bgn, Ctsk, Spp1). These findings are confirmed by smFISH. Finally, we use differentiation dynamic analysis in the D2-mdx muscle to identify muscle fibers in the present state that are predicted to become affected in the future state.

Ca2+ as a coordinator of skeletal muscle differentiation, fusion and contraction

Muscle regeneration is essential for vertebrate muscle homeostasis and recovery after injury. During regeneration, muscle stem cells differentiate into myocytes, which then fuse with pre-existing muscle fibres. Hence, differentiation, fusion and contraction must be tightly regulated during regeneration to avoid the disastrous consequences of premature fusion of myocytes to actively contracting fibres. Cytosolic calcium (Ca²⁺ ), which is coupled to both induction of myogenic differentiation and contraction, has more recently been implicated in the regulation of myocyte-to-myotube fusion. In this viewpoint, we propose that Ca²⁺ -mediated coordination of differentiation, fusion and contraction is a feature selected in the amniotes to facilitate muscle regeneration.

Application of Droplet Digital PCR Technology in Muscular Dystrophies Research

Although they are considered rare disorders, muscular dystrophies have a strong impact on people’s health. Increased disease severity with age, frequently accompanied by the loss of ability to walk in some people, and the lack of treatment, have directed the researchers towards the development of more effective therapeutic strategies aimed to improve the quality of life and life expectancy, slow down the progression, and delay the onset or convert a severe phenotype into a milder one. Improved understanding of the complex pathology of these diseases together with the tremendous advances in molecular biology technologies has led to personalized therapeutic procedures. Different approaches that are currently under extensive investigation require more efficient, sensitive, and less invasive methods. Due to its remarkable analytical sensitivity, droplet digital PCR has become a promising tool for accurate measurement of biomarkers that monitor disease progression and quantification of various therapeutic efficiency and can be considered a tool for non-invasive prenatal diagnosis and newborn screening. Here, we summarize the recent applications of droplet digital PCR in muscular dystrophy research and discuss the factors that should be considered to get the best performance with this technology.

Normalizing and denoising protein expression data from droplet-based single cell profiling

Multimodal single-cell profiling methods that measure protein expression with oligo-conjugated antibodies hold promise for comprehensive dissection of cellular heterogeneity, yet the resulting protein counts have substantial technical noise that can mask biological variations. Here we integrate experiments and computational analyses to reveal two major noise sources and develop a method called "dsb" (denoised and scaled by background) to normalize and denoise droplet-based protein expression data. We discover that protein-specific noise originates from unbound antibodies encapsulated during droplet generation; this noise can thus be accurately estimated and corrected by utilizing protein levels in empty droplets. We also find that isotype control antibodies and the background protein population average in each cell exhibit significant correlations across single cells, we thus use their shared variance to correct for cell-to-cell technical noise in each cell. We validate these findings by analyzing the performance of dsb in eight independent datasets spanning multiple technologies, including CITE-seq, ASAP-seq, and TEA-seq. Compared to existing normalization methods, our approach improves downstream analyses by better unmasking biologically meaningful cell populations. Our method is available as an open-source R package that interfaces easily with existing single cell software platforms such as Seurat, Bioconductor, and Scanpy and can be accessed at "dsb [ https://cran.r-project.org/package=dsb ]".

Assessment of Weighted Gene Co-Expression Network Analysis to Explore Key Pathways and Novel Biomarkers in Muscular Dystrophy

Purpose

This study aimed to explore the key molecular pathways involved in Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) and thereby identify hub genes to be potentially used as novel biomarkers using a bioinformatics approach.

Methods

Raw GSE109178 data were collected from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was conducted on the top 50% of altered genes. The key modules associated with the clinical features of DMD and BMD were identified. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the DAVID website. A protein-protein interaction (PPI) network was constructed using the STRING website. MCODE, together with the Cytohubba plug-ins of Cytoscape, screened out the potential hub genes, which were subsequently verified via receiver operating characteristic (ROC) curves in other datasets.

Results

Among the 11 modules obtained, the black module was predominantly associated with pathology and DMD, whereas the light-green module was primarily related to age and BMD. Functional enrichment assessments indicated that the genes in the black module were primarily clustered in “immune response” and “phagosome,” whereas the ones in the light-green module were chiefly enriched in “protein polyubiquitination”. Eleven essential genes were eventually identified, including VCAM1, TYROBP, CD44, ITGB2, CSF1R, LCP2, C3AR1, CCL2, and ITGAM for DMD, along with UBA5 and UBR2 for BMD.

Conclusion

Overall, our findings may be useful for investigating the mechanisms underlying DMD and BMD. In addition, the hub genes discovered might serve as novel molecular markers correlated with dystrophinopathies.