PYGM expression analysis in white blood cells: a complementary tool for diagnosing McArdle disease?

Analysis and validation of biomarkers of immune cell-related genes in postmenopausal osteoporosis: An observational study

Postmenopausal osteoporosis (PMOP) is a common metabolic inflammatory disease. In conditions of estrogen deficiency, chronic activation of the immune system leads to a hypo-inflammatory phenotype and alterations in its cytokine and immune cell profile, although immune cells play an important role in the pathology of osteoporosis, studies on this have been rare. Therefore, it is important to investigate the role of immune cell-related genes in PMOP. PMOP-related datasets were downloaded from the Gene Expression Omnibus database. Immune cells scores between high bone mineral density (BMD) and low BMD samples were assessed based on the single sample gene set enrichment analysis method. Subsequently, weighted gene co-expression network analysis was performed to identify modules highly associated with immune cells and obtain module genes. Differential analysis between high BMD and low BMD was also performed to obtain differentially expressed genes. Module genes are intersected with differentially expressed genes to obtain candidate genes, and functional enrichment analysis was performed. Machine learning methods were used to filter out the signature genes. The receiver operating characteristic (ROC) curves of the signature genes and the nomogram were plotted to determine whether the signature genes can be used as a molecular marker. Gene set enrichment analysis was also performed to explore the potential mechanism of the signature genes. Finally, RNA expression of signature genes was validated in blood samples from PMOP patients and normal control by real-time quantitative polymerase chain reaction. Our study of PMOP patients identified differences in immune cells (activated dendritic cell, CD56 bright natural killer cell, Central memory CD4 T cell, Effector memory CD4 T cell, Mast cell, Natural killer T cell, T follicular helper cell, Type 1 T-helper cell, and Type 17 T-helper cell) between high and low BMD patients. We obtained a total of 73 candidate genes based on modular genes and differential genes, and obtained 5 signature genes by least absolute shrinkage and selection operator and random forest model screening. ROC, principal component analysis, and t-distributed stochastic neighbor embedding down scaling analysis revealed that the 5 signature genes had good discriminatory ability between high and low BMD samples. A logistic regression model was constructed based on 5 signature genes, and both ROC and column line plots indicated that the model accuracy and applicability were good. Five signature genes were found to be associated with proteasome, mitochondria, and lysosome by gene set enrichment analysis. The real-time quantitative polymerase chain reaction results showed that the expression of the signature genes was significantly different between the 2 groups. HIST1H2AG, PYGM, NCKAP1, POMP, and LYPLA1 might play key roles in PMOP and be served as the biomarkers of PMOP.

Biomolecules of Muscle Fatigue in Metabolic Myopathies

Metabolic myopathies are a group of genetic disorders that affect the normal functioning of muscles due to abnormalities in metabolic pathways. These conditions result in impaired energy production and utilization within muscle cells, leading to limitations in muscle function with concomitant occurrence of related signs and symptoms, among which fatigue is one of the most frequently reported. Understanding the underlying molecular mechanisms of muscle fatigue in these conditions is challenging for the development of an effective diagnostic and prognostic approach to test targeted therapeutic interventions. This paper outlines the key biomolecules involved in muscle fatigue in metabolic myopathies, including energy substrates, enzymes, ion channels, and signaling molecules. Potential future research directions in this field are also discussed.

A novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs

Background

Polycystic ovary syndrome (PCOS), a common endocrine and reproductive disorder, lacks precise diagnostic strategies. Necroptosis was found to be crucial in reproductive and endocrine disorders, but its function in PCOS remains unclear. We aimed to identify differentially diagnostic genes for necroptosis (NDDGs), construct a diagnostic model to assess the progression of PCOS and explore the potential therapeutic drugs.

Methods

Gene expression datasets were combined with weighted gene co-expression network analysis (WGCNA) and necroptosis gene sets to screen the differentially expressed genes for PCOS. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a necroptosis-related gene signatures. Independent risk analyses were performed using nomograms. Pathway enrichment of NDDGs was conducted with the GeneMANIA database and gene set enrichment analysis (GSEA). Immune microenvironment analysis was estimated based on ssGSEA algorithm analysis. The Comparative Toxicogenomics Database (CTD) was used to explore potential therapeutic drugs for NDDGs. The expression of NDDGs was validated in GSE84958, mouse model and clinical samples.

Results

Four necroptosis-related signature genes, IL33, TNFSF10, BCL2 and PYGM, were identified to define necroptosis for PCOS. The areas under curve (AUC) of receiver operating characteristic curve (ROC) for training set and validation in diagnostic risk model were 0.940 and 0.788, respectively. Enrichment analysis showed that NDDGs were enriched in immune-related signaling pathways such as B cells, T cells, and natural killer cells. Immune microenvironment analysis revealed that NDDGs were significantly correlated with 13 markedly different immune cells. A nomogram was constructed based on features that would benefit patients clinically. Several compounds, such as resveratrol, tretinoin, quercetin, curcumin, etc., were mined as therapeutic drugs for PCOS. The expression of the NDDGs in the validated set, animal model and clinical samples was consistent with the results of the training sets.

Conclusion

In this study, 4 NDDGs were identified to be highly effective in assessing the progression and prognosis of PCOS and exploring potential targets for PCOS treatment.

Molecular diagnosis of McArdle disease using whole-exome sequencing

Whole-exome sequencing (WES) analysis has been used recently as a diagnostic tool for finding molecular defects. In the present study, researchers attempted to analyze molecular defects through WES in a 13-year-old female patient who had not been diagnosed through a conventional genetic approach. DNA was extracted and subjected to WES analysis to identify the genetic defect. A total of 106,728 exons and splicing variants were selected, and synonymous single nucleotide variants (SNVs) and general single nucleotide polymorphisms (SNPs) were filtered out. Finally, nonsynonymous SNVs (c.C415T and c.C389T) of the PYGM gene were identified in nine compound heterozygous mutations. PYGM encodes myophosphorylase and degrades glycogen in the muscle to supply energy to muscle cells. The present study revealed that the patient's father had a c.C389T mutation and the mother had a c.C415T mutation, resulting in A130V and R139W missense mutations, respectively. To the best of our knowledge, the A130V variant in PYGM has not been reported in the common variant databases. All variations of the patient's family detected using WES were verified by Sanger sequencing. Because the patient had compound heterozygous mutations in the PYGM gene, the patient was presumed to exhibit markedly decreased muscle phosphorylase activity. To assess the function of myophosphorylase, an ischemic forearm exercise test was performed. The blood ammonia level sharply increased and the lactate level maintained a flat curve shape similar to the typical pattern of McArdle disease. Therefore, the diagnosis of the patient was confirmed to be McArdle disease, a glycogen storage disease. Through WES analysis, accurate and early diagnosis could be made in the present study. This report describes a novel compound heterozygous mutation of the PYGM gene in a Korean patient.

Muscle Glycogen Phosphorylase and Its Functional Partners in Health and Disease

Glycogen phosphorylase (PG) is a key enzyme taking part in the first step of glycogenolysis. Muscle glycogen phosphorylase (PYGM) differs from other PG isoforms in expression pattern and biochemical properties. The main role of PYGM is providing sufficient energy for muscle contraction. However, it is expressed in tissues other than muscle, such as the brain, lymphoid tissues, and blood. PYGM is important not only in glycogen metabolism, but also in such diverse processes as the insulin and glucagon signaling pathway, insulin resistance, necroptosis, immune response, and phototransduction. PYGM is implicated in several pathological states, such as muscle glycogen phosphorylase deficiency (McArdle disease), schizophrenia, and cancer. Here we attempt to analyze the available data regarding the protein partners of PYGM to shed light on its possible interactions and functions. We also underline the potential for zebrafish to become a convenient and applicable model to study PYGM functions, especially because of its unique features that can complement data obtained from other approaches.

McArdle Disease: New Insights into Its Underlying Molecular Mechanisms

McArdle disease, also known as glycogen storage disease type V (GSDV), is characterized by exercise intolerance, the second wind phenomenon, and high serum creatine kinase activity. Here, we recapitulate PYGM mutations in the population responsible for this disease. Traditionally, McArdle disease has been considered a metabolic myopathy caused by the lack of expression of the muscle isoform of the glycogen phosphorylase (PYGM). However, recent findings challenge this view, since it has been shown that PYGM is present in other tissues than the skeletal muscle. We review the latest studies about the molecular mechanism involved in glycogen phosphorylase activity regulation. Further, we summarize the expression and functional significance of PYGM in other tissues than skeletal muscle both in health and McArdle disease. Furthermore, we examine the different animal models that have served as the knowledge base for better understanding of McArdle disease. Finally, we give an overview of the latest state-of-the-art clinical trials currently being carried out and present an updated view of the current therapies.

Epidermal growth factor receptor controls glycogen phosphorylase in T cells through small GTPases of the RAS family

We recently uncovered a regulatory pathway of the muscle isoform of glycogen phosphorylase (PYGM) that plays an important role in regulating immune function in T cells. Here, using various enzymatic, pulldown, and immunoprecipitation assays, we describe signaling cross-talk between the small GTPases RAS and RAP1A, member of RAS oncogene family (RAP1) in human Kit 225 lymphoid cells, which, in turn, is regulated by the epidermal growth factor receptor (EGFR). We found that this communication bridge is essential for glycogen phosphorylase (PYG) activation through the canonical pathway because this enzyme is inactive in the absence of adenylyl cyclase type 6 (ADCY6). PYG activation required stimulation of both exchange protein directly activated by cAMP 2 (EPAC2) and RAP1 via RAS and ADCY6 phosphorylation, with the latter being mediated by Raf-1 proto-oncogene, Ser/Thr kinase (RAF1). Consistent with this model, PYG activation was EGFR-dependent and may be initiated by the constitutively active form of RAS. Consequently, PYG activation in Kit 225 T cells could be blocked with specific inhibitors of RAS, EPAC, RAP1, RAF1, ADCY6, and cAMP-dependent protein kinase. Our results establish a new paradigm for the mechanism of PYG activation, which depends on the type of receptor involved.

Quantitative proteomics analysis to identify biomarkers of chronic myofascial pain and therapeutic targets of dry needling in a rat model of myofascial trigger points

Background

Proteomics analysis may provide important information regarding the pathogenesis of chronic myofascial pain and the mechanisms underlying the treatment effects of dry needling.

Materials and methods

This study used a rat model of myofascial trigger points (MTrPs) to perform a proteomics analysis. Three biological replicate experiments were used to compare the proteomes of healthy control rats, a rat model of MTrP, MTrP model rats following dry needling of MTrPs, and MTrP model rats following dry needling of non-MTrPs. Tandem mass tag (TMT) labeling technology based on nanoscale liquid chromatography-tandem mass spectrometry was used. Hierarchical clustering, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and protein–protein interaction network analysis were performed to characterize the proteins. To validate the TMT results, three candidate biomarker proteins were verified using parallel reaction monitoring and Western blot analysis.

Results

A total of 2,635 proteins were identified. GO and KEGG enrichment analyses showed that the glycolysis/gluconeogenesis pathways played dominant roles in the pathogenesis of chronic myofascial pain. The three candidate biomarker proteins were the pyruvate kinase muscle isozyme (encoded by the PKM gene), the muscle isoform of glycogen phosphorylase (encoded by the PYGM gene), and myozenin 2 (encoded by the MYOZ2 gene). The validation results were consistent with the TMT results.

Conclusion

This is the first proteomics study that has investigated the pathogenesis of chronic myofascial pain and the mechanisms underlying the treatment effects of dry needling in an in vivo rat model of MTrPs, which might promote our understanding of the molecular mechanisms underlying chronic myofascial pain.

Taking advantage of an old concept, "illegitimate transcription", for a proposed novel method of genetic diagnosis of McArdle disease

PURPOSE

McArdle disease is a metabolic disorder caused by pathogenic mutations in the PYGM gene. Timely diagnosis can sometimes be difficult with direct genomic analysis, which requires additional studies of cDNA from muscle transcripts. Although the "nonsense-mediated mRNA decay" (NMD) eliminates tissue-specific aberrant transcripts, there is some residual transcription of tissue-specific genes in virtually all cells, such as peripheral blood mononuclear cells (PBMCs).

METHODS

We studied a subset of the main types of PYGM mutations (deletions, missense, nonsense, silent, or splicing mutations) in cDNA from easily accessible cells (PBMCs) in 12 McArdle patients.

RESULTS

Analysis of cDNA from PBMCs allowed detection of all mutations. Importantly, the effects of mutations with unknown pathogenicity (silent and splicing mutations) were characterized in PBMCs. Because the NMD mechanism does not seem to operate in nonspecific cells, PBMCs were more suitable than muscle biopsies for detecting the pathogenicity of some PYGM mutations, notably the silent mutation c.645G>A (p.K215=), whose effect in the splicing of intron 6 was unnoticed in previous muscle transcriptomic studies.

CONCLUSION

We propose considering the use of PBMCs for detecting mutations that are thought to cause McArdle disease, particularly for studying their actual pathogenicity.Genet Med 18 11, 1128-1135.

Determining the prevalence of McArdle disease from gene frequency by analysis of next-generation sequencing data

Purpose

McArdle disease is one of the most common glycogen storage disorders. Although the exact prevalence is not known, it has been estimated to be 1 in 100,000 patients in the United States. More than 100 mutations in PYGM have been associated with this disorder. McArdle disease has significant clinical variability with some patients presenting with severe muscle pain and weakness while others have only mild, exercise-related symptoms.

Methods

Next-Generation sequencing data allow estimation of disease prevalence with minimal ascertainment bias. We analyzed gene frequencies in two cohorts of patients from exome sequencing results. We categorized variants into three groups: a curated set of published mutations, variants of uncertain significance, and likely benign variants.

Results

An initial estimate based on the frequency of six common mutations predicts a disease prevalence of 1/7,650 (95% CI 1/5,362 to 1/11,108), which greatly deviates from published estimates. A second method using the two most common mutations predicts a prevalence of 1/42,355 (95% CI 1/24,536 - 1/76,310) in Caucasians.

Conclusions

These results suggest that the currently accepted prevalence of McArdle disease is an underestimate and that some of the currently considered pathogenic variants are likely benign.