Key biomarkers and latent pathways of dysferlinopathy: Bioinformatics analysis and in vivo validation

Pilot investigations into the mechanistic basis for adverse effects of glucocorticoids in dysferlinopathy

BACKGROUND

Dysferlinopathies are a clinically heterogeneous group of muscular dystrophies caused by gene mutations resulting in deficiency of the membrane-associated protein dysferlin. They manifest post-growth and are characterised by muscle wasting (primarily in the limb and limb-gridle muscles), inflammation, and replacement of myofibres with adipose tissue. The precise pathomechanism for dysferlinopathy is currently unclear; as such there are no treatments currently available. Glucocorticoids (GCs) are widely used to reduce inflammation and treat muscular dystrophies, but when administered to patients with dysferlinopathy, they have unexpected adverse effects, with accelerated loss of muscle strength.

METHODS

To investigate the mechanistic basis for the adverse effects of GCs in dysferlinopathy, the potent GC dexamethasone (Dex) was administered for 4-5 weeks (0.5-0.75 µg/mL in drinking water) to dysferlin-deficient BLA/J and normal wild-type (WT) male mice, sampled at 5 (Study 1) or 10 months (Study 2) of age. A wide range of analyses were conducted. Metabolism- and immune-related gene expression was assessed in psoas muscles at both ages and in quadriceps at 10 months of age. For the 10-month-old mice, quadriceps and psoas muscle histology was assessed. Additionally, we investigated the impact of Dex on the predominantly slow and fast-twitch soleus and extensor digitorum longus (EDL) muscles (respectively) in terms of contractile function, myofibre-type composition, and levels of proteins related to contractile function and metabolism, plus glycogen.

RESULTS

At both ages, many complement-related genes were highly expressed in BLA/J muscles, and WT mice were generally more responsive to Dex than BLA/J. The effects of Dex on BLA/J mice included (i) increased expression of inflammasome-related genes in muscles (at 5 months) and (ii) exacerbated histopathology of quadriceps and psoas muscles at 10 months. A novel observation was pronounced staining for glycogen in many myofibres of the damaged quadriceps muscles, with large pale vacuolated myofibres, suggesting possible myofibre death by oncosis.

CONCLUSION

These pilot studies provide a new focus for further investigation into the adverse effects of GCs on dysferlinopathic muscles.

Mitochondrial-related hub genes in dermatomyositis: muscle and skin datasets-based identification and in vivo validation

Background: Mitochondrial dysfunction has been implicated in the pathogenesis of dermatomyositis (DM), a rare autoimmune disease affecting the skin and muscles. However, the genetic basis underlying dysfunctional mitochondria and the development of DM remains incomplete. Methods: The datasets of DM muscle and skin tissues were retrieved from the Gene Expression Omnibus database. The mitochondrial related genes (MRGs) were retrieved from MitoCarta. DM-related modules in muscle and skin tissues were identified with the analysis of weighted gene co-expression network (WGCNA), and then compared with the MRGs to obtain the overlapping mitochondrial related module genes (mito-MGs). Subsequently, differential expression genes (DEGs) obtained from muscle and skin datasets were overlapped with MRGs to identify mitochondrial related DEGs (mito-DEGs). Next, functional enrichment analysis was applied to analyze possible relevant biological pathways. We used the Jvenn online tool to intersect mito-MGs with mito-DEGs to identify hub genes and validate them using reverse transcription quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry staining. In addition, we evaluated immune infiltration in muscle and skin tissues of DM patients using the one-sample gene set enrichment analysis (ssGSEA) algorithm and predicted potential transcription factor (TF) -gene network by NetworkAnalyst. Results: The WGCNA analysis revealed 105 mito-MGs, while the DEG analysis identified 3 mito-DEGs. These genes showed functional enrichment for amino acid metabolism, energy metabolism and oxidative phosphorylation. Through the intersection analysis of the mito-MGs from the WGCNA analysis and the mito-DEGs from the DEG set, three DM mito-hub genes (IFI27, CMPK2, and LAP3) were identified and validated by RT-qPCR and immunohistochemistry analysis. Additionally, positive correlations were observed between hub genes and immune cell abundance. The TF-hub gene regulatory network revealed significant interactions involving ERG, VDR, and ZFX with CMPK2 and LAP3, as well as SOX2 with LAP3 and IFI27, and AR with IFI27 and CMPK2. Conclusion: The mito-hub genes (IFI27, CMPK2, and LAP3) are identified in both muscles and skin tissues from DM patients. These genes may be associated with immune infiltration in DM, providing a new entry point for the pathogenesis of DM.

Transcriptome analysis of skeletal muscle in dermatomyositis, polymyositis, and dysferlinopathy, using a bioinformatics approach

Background

Polymyositis (PM) and dermatomyositis (DM) are two distinct subgroups of idiopathic inflammatory myopathies. Dysferlinopathy, caused by a dysferlin gene mutation, usually presents in late adolescence with muscle weakness, degenerative muscle changes are often accompanied by inflammatory infiltrates, often resulting in a misdiagnosis as polymyositis.

Objective

To identify differential biological pathways and hub genes related to polymyositis, dermatomyositis and dysferlinopathy using bioinformatics analysis for understanding the pathomechanisms and providing guidance for therapy development.

Methods

We analyzed intramuscular ribonucleic acid (RNA) sequencing data from seven dermatomyositis, eight polymyositis, eight dysferlinopathy and five control subjects. Differentially expressed genes (DEGs) were identified by using DESeq2. Enrichment analyses were performed to understand the functions and enriched pathways of DEGs. A protein-protein interaction (PPI) network was constructed, and clarified the gene cluster using the molecular complex detection tool (MCODE) analysis to identify hub genes.

Results

A total of 1,048, 179 and 3,807 DEGs were detected in DM, PM and dysferlinopathy, respectively. Enrichment analyses revealed that upregulated DEGs were involved in type 1 interferon (IFN1) signaling pathway in DM, antigen processing and presentation of peptide antigen in PM, and cellular response to stimuli in dysferlinopathy. The PPI network and MCODE cluster identified 23 genes related to type 1 interferon signaling pathway in DM, 4 genes (PDIA3, HLA-C, B2M, and TAP1) related to MHC class 1 formation and quality control in PM, and 7 genes (HSPA9, RPTOR, MTOR, LAMTOR1, LAMTOR5, ATP6V0D1, and ATP6V0B) related to cellular response to stress in dysferliniopathy.

Conclusion

Overexpression of genes related to the IFN1 signaling pathway and major histocompatibility complex (MHC) class I formation was identified in DM and PM, respectively. In dysferlinopathy, overexpression of HSPA9 and the mTORC1 signaling pathway genes was detected.

Identification of Feature Genes and Key Biological Pathways in Immune-Mediated Necrotizing Myopathy: High-Throughput Sequencing and Bioinformatics Analysis

Background

Immune-mediated necrotizing myopathy (IMNM), a subgroup of idiopathic inflammatory myopathies (IIMs), is characterized by severe proximal muscle weakness and prominent necrotic fibers but no infiltration of inflammatory cells. IMNM pathogenesis is unclear. This study investigated key biomarkers and potential pathways for IMNM using high-throughput sequencing and bioinformatics technology.

Methods

RNA sequencing was conducted in 18 IMNM patients and 10 controls. A combination of weighted gene coexpression network analysis (WGCNA) and differentially expressed gene (DEG) analysis was conducted to identify IMNM-related DEGs. Feature genes were screened out by employing the protein-protein interaction (PPI) network, support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage selection operator (LASSO). Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to verify their differential expression, and the receiver operating characteristic curve (ROC) was used to evaluate their diagnostic efficiency. Functional enrichment analysis was applied to reveal the hidden functions of feature genes. Furthermore, 28 immune cell abundance patterns in IMNM samples were measured.

Results

We identified 193 IMNM-related DEGs that were aberrantly upregulated in the IMNM population and were closely associated with immune-inflammatory responses, regulation of skeletal and cardiac muscle contraction, and lipoprotein metabolism. With the help of the PPI network and the LASSO and SVM-RFE algorithms, three feature genes, LTK, MYBPH, and MYL4, were identified and further confirmed by qRT-PCR. ROC curves among IMNM, dermatomyositis (DM), inclusion body myositis (IBM), and polymyositis (PM) samples validated the LTK and MYL4 genes as IMNM-specific feature markers. In addition, all three genes had a notable association with the autophagy-lysosome pathway and immune-inflammatory responses. Ultimately, IMNM displayed a marked immune-cell infiltrative microenvironment. The most significant correlation was found between CD4 T cells, CD8 T cells, macrophages, natural killer (NK) cells, and dendritic cells (DCs).

Conclusions

LTK, MYBPH, and MYL4 were identified as potential key molecules for IMNM and are believed to play a role in the autophagy-lysosome pathway and muscle inflammation.