Glycogen storage disease type VI with a novel PYGL mutation: Two case reports and literature review

Broadening the Phenotype and Genotype Spectrum of Glycogen Storage Disease by Unraveling Novel Variants in an Iranian Patient Cohort

Glycogen storage diseases (GSDs) are a group of rare inherited metabolic disorders characterized by clinical, locus, and allele heterogeneity. This study aims to investigate the phenotype and genotype spectrum of GSDs in a cohort of 14 families from Iran using whole-exome sequencing (WES) and variant analysis. WES was performed on 14 patients clinically suspected of GSDs. Variant analysis was performed to identify genetic variants associated with GSDs. A total of 13 variants were identified, including six novel variants, and seven previously reported pathogenic variants in genes such as AGL, G6PC, GAA, PYGL, PYGM, GBE1, SLC37A4, and PHKA2. Most types of GSDs observed in the cohort were associated with hepatomegaly, which was the most common clinical presentation. This study provides valuable insights into the phenotype and genotype spectrum of GSDs in a cohort of Iranian patients. The identification of novel variants adds to the growing body of knowledge regarding the genetic landscape of GSDs and has implications for genetic counseling and future therapeutic interventions. The diverse nature of GSDs underscores the need for comprehensive genetic testing methods to improve diagnostic accuracy. Continued research in this field will enhance our understanding of GSDs, ultimately leading to improved management and outcomes for individuals affected by these rare metabolic disorders.

Genotypic and phenotypic features of 39 Chinese patients with glycogen storage diseases type I, VI, and IX

Glycogen storage diseases (GSDs) are abnormally inherited glycogen metabolism mainly affecting the liver, muscles, and heart. Deficiency of proteins involved in glycogen metabolism caused by genetic mutations are responsible for different subtype of GSDs. However, there are still some challenges in diagnosing GSD. This study includes 39 suspected GSDs patients from unrelated families in China. Next-generation sequencing (NGS) was used to investigate the reason for their diseases at the genetic level. Finally, all 39 patients were diagnosed with GSDs, including 20 GSD-Ia, 4 GSD-VI, and 15 GSD IX (12 GSD-IXa patients and 3 GSD-IXb patients). Thirty-two mutations in G6PC1, PYGL, PHKA2, and PHKB genes were identified, with 14 of them being novel variants. The pathogenicity of novel variants was classified according to ACMG guildlines and predicted by in slico algorithms. Mutations p.L216L and p.R83H in G6PC1 gene may be the hot spot mutation in Chinese. Hearing impairment is a rare clinical feature of GSD Ia, which has also been observed in our cohort. The severity of GSD VI and IX was indicated by our patients. Close follow-up should be applied to GSD VI and IX patients. Our findings provided evidence for building the phenotype-genotype of GSDs and expanded the mutation spectrum of related genes.

Identification and immunological characterization of endoplasmic reticulum stress-related molecular subtypes in bronchopulmonary dysplasia based on machine learning

Introduction: Bronchopulmonary dysplasia (BPD) is a life-threatening lung illness that affects premature infants and has a high incidence and mortality. Using interpretable machine learning, we aimed to investigate the involvement of endoplasmic reticulum (ER) stress-related genes (ERSGs) in BPD patients. Methods: We evaluated the expression profiles of endoplasmic reticulum stress-related genes and immune features in bronchopulmonary dysplasia using the GSE32472 dataset. The endoplasmic reticulum stress-related gene-based molecular clusters and associated immune cell infiltration were studied using 62 bronchopulmonary dysplasia samples. Cluster-specific differentially expressed genes (DEGs) were identified utilizing the WGCNA technique. The optimum machine model was applied after comparing its performance with that of the generalized linear model, the extreme Gradient Boosting, the support vector machine (SVM) model, and the random forest model. Validation of the prediction efficiency was done by the use of a calibration curve, nomogram, decision curve analysis, and an external data set. Results: The bronchopulmonary dysplasia samples were compared to the control samples, and the dysregulated endoplasmic reticulum stress-related genes and activated immunological responses were analyzed. In bronchopulmonary dysplasia, two distinct molecular clusters associated with endoplasmic reticulum stress were identified. The analysis of immune cell infiltration indicated a considerable difference in levels of immunity between the various clusters. As measured by residual and root mean square error, as well as the area under the curve, the support vector machine machine model showed the greatest discriminative capacity. In the end, an support vector machine model integrating five genes was developed, and its performance was shown to be excellent on an external validation dataset. The effectiveness in predicting bronchopulmonary dysplasia subtypes was further established by decision curves, calibration curves, and nomogram analyses. Conclusion: We developed a potential prediction model to assess the risk of endoplasmic reticulum stress subtypes and the clinical outcomes of bronchopulmonary dysplasia patients, and our work comprehensively revealed the complex association between endoplasmic reticulum stress and bronchopulmonary dysplasia.

Latent disease similarities and therapeutic repurposing possibilities uncovered by multi-modal generative topic modeling of human diseases

Motivation

Human diseases are characterized by multiple features such as their pathophysiological, molecular and genetic changes. The rapid expansion of such multi-modal disease-omics space provides an opportunity to re-classify diverse human diseases and to uncover their latent molecular similarities, which could be exploited to repurpose a therapeutic-target for one disease to another.

Results

Herein, we probe this underexplored space by soft-clustering 6955 human diseases by multi-modal generative topic modeling. Focusing on chronic kidney disease and myocardial infarction, two most life-threatening diseases, unveiled are their previously underrecognized molecular similarities to neoplasia and mental/neurological-disorders, and 69 repurposable therapeutic-targets for these diseases. Using an edit-distance-based pathway-classifier, we also find molecular pathways by which these targets could elicit their clinical effects. Importantly, for the 17 targets, the evidence for their therapeutic usefulness is retrospectively found in the pre-clinical and clinical space, illustrating the effectiveness of the method, and suggesting its broader applications across diverse human diseases.

Availability and implementation

The code reported in this article is available at: https://github.com/skozawa170301ktx/MultiModalDiseaseModeling.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Genotypic and phenotypic characteristics of 12 chinese children with glycogen storage diseases

Background: Glycogen storage diseases (GSDs) are known as a group of disorders characterized by genetic errors leading to accumulation of glycogen in various tissues. Since different types of GSD can sometimes be clinically indistinguishable, next generation sequencing is becoming a powerful tool for clinical diagnosis.

Methods: 12 patients with suspected GSDs and their parents were enrolled in this study. The clinical and laboratory data of the patients were reviewed. Causative gene variants were identified in the patients using whole exome sequencing (WES) and verified by Sanger sequencing.

Results: Genetic testing and analysis showed that 7 patients were diagnosed with GSD II (Pompe disease), 2 patients with GSD III, 1 patient with GSD VI, and 2 patients with GSD IXα. A total number of 18 variants were identified in 12 patients including 11 variants in GAA gene, 3 variants in AGL gene, 2 variants in PYGL gene and 2 variants in PHKA2 gene, of which 9 variants were reported and 9 variants were novel. SIFT, Polyphen-2, Mutation Taster, and REVEL predicted the novel variants (except GAA c.1052_1075 + 47del) to be disease-causing. The 3D structures of wild/mutant type GAA protein were predicted indicating that variants p. Trp621Gly, p. Pro541Leu, p. Ser800Ile and p. Gly293Trp might affect the proteins function via destroying hydrogen bonds or conformational constraints. Neither liver size nor laboratory findings allow for a differentiation among GSD III, GSD VI and GSD IXα.

Conclusion: Our study expanded the variation spectrum of genes associated with GSDs. WES, in combination with clinical, biochemical, and pathological hallmarks, could provide accurate results for diagnosing and sub-typing GSD and related diseases in clinical setting.

Glycogen storage diseases with liver involvement: a literature review of GSD type 0, IV, VI, IX and XI

Background

Glycogen storage diseases (GSDs) with liver involvement are classified into types 0, I, III, IV, VI, IX and XI, depending on the affected enzyme. Hypoglycemia and hepatomegaly are hallmarks of disease, but muscular and renal tubular involvement, dyslipidemia and osteopenia can develop. Considering the paucity of literature available, herein we provide a narrative review of these latter forms of GSDs.

Main body

Diagnosis is based on clinical manifestations and laboratory test results, but molecular analysis is often necessary to distinguish the various forms, whose presentation can be similar. Compared to GSD type I and III, which are characterized by a more severe impact on metabolic and glycemic homeostasis, GSD type 0, VI, IX and XI are usually known to be responsive to the nutritional treatment for achieving a balanced metabolic homeostasis in the pediatric age. However, some patients can exhibit a more severe phenotype and an important progression of the liver and muscular disease. The effects of dietary adjustments in GSD type IV are encouraging, but data are limited.

Conclusions

Early diagnosis allows a good metabolic control, with improvement of quality of life and prognosis, therefore we underline the importance of building a proper knowledge among physicians about these rare conditions. Regular monitoring is necessary to restrain disease progression and complications.

The Phenotypic and Genetic Spectrum of Glycogen Storage Disease Type VI

Glycogen storage disease type VI (GSD VI) is an autosomal recessive disorder of glycogen metabolism due to mutations in the glycogen phosphorylase gene (PYGL), resulting in a deficiency of hepatic glycogen phosphorylase. We performed a systematic literature review in order to collect information on the clinical phenotypes and genotypes of all published GSD VI patients and to compare the data to those for GSD IX, a biochemically and clinically very similar disorder caused by a deficiency of phosphorylase kinase. A total of 63 genetically confirmed cases of GSD VI with clinical information were identified (median age: 5.3 years). The age at presentation ranged from 5 weeks to 38 years, with a median of 1.8 years. The main presenting symptoms were hepatomegaly and poor growth, while the most common laboratory findings at initial presentation comprised elevated activity of liver transaminases, hypertriglyceridemia, fasting hypoglycemia and postprandial hyperlactatemia. Liver biopsies (n = 37) showed an increased glycogen content in 89.2%, liver fibrosis in 32.4% and early liver cirrhosis in 10.8% of cases, respectively. No patient received a liver transplant, and one successful pregnancy was reported. Our review demonstrates that GSD VI is a disorder with broad clinical heterogeneity and a small number of patients with a severe phenotype and liver cirrhosis. Neither clinical nor laboratory findings allow for a differentiation between GSD VI and GSD IX. Early biochemical markers of disease severity or clear genotype phenotype correlations are missing. Given the overall benign and unspecific phenotype and the need for enzymatic or genetic analyses for confirmation of the diagnosis, GSD VI is likely underdiagnosed. With new treatment approaches in sight, early, pre-symptomatic diagnosis, especially with respect to hepatic cirrhosis, will become even more important.