A KLHL40 3' UTR splice-altering variant causes milder NEM8, an under-appreciated disease mechanism

HMGCS1 variants cause rigid spine syndrome amenable to mevalonic acid treatment in an animal model

Rigid spine syndrome is a rare childhood-onset myopathy characterized by slowly progressive or non-progressive scoliosis, neck and spine contractures, hypotonia and respiratory insufficiency. Biallelic variants in SELENON account for most cases of rigid spine syndrome, however, the underlying genetic cause in some patients remains unexplained. We used exome and genome sequencing to investigate the genetic basis of rigid spine syndrome in patients without a genetic diagnosis. In five patients from four unrelated families, we identified biallelic variants in HMGCS1 (3-hydroxy-3-methylglutaryl-coenzyme A synthase). These included six missense variants and one frameshift variant distributed throughout HMGCS1. All patients presented with spinal rigidity primarily affecting the cervical and dorso-lumbar regions, scoliosis and respiratory insufficiency. Creatine kinase levels were variably elevated. The clinical course worsened with intercurrent disease or certain drugs in some patients; one patient died from respiratory failure following infection. Muscle biopsies revealed irregularities in oxidative enzyme staining with occasional internal nuclei and rimmed vacuoles. HMGCS1 encodes a critical enzyme of the mevalonate pathway and has not yet been associated with disease. Notably, biallelic hypomorphic variants in downstream enzymes including HMGCR and GGPS1 are associated with muscular dystrophy resembling our cohort's presentation. Analyses of recombinant human HMGCS1 protein and four variants (p.S447P, p.Q29L, p.M70T, p.C268S) showed that all mutants maintained their dimerization state. Three of the four mutants exhibited reduced thermal stability, and two mutants showed subtle changes in enzymatic activity compared to the wildtype. Hmgcs1 mutant zebrafish displayed severe early defects, including immobility at 2 days and death by Day 3 post-fertilisation and were rescued by HMGCS1 mRNA. We demonstrate that the four variants tested (S447P, Q29L, M70T and C268S) have reduced function compared to wild-type HMGCS1 in zebrafish rescue assays. Additionally, we demonstrate the potential for mevalonic acid supplementation to reduce phenotypic severity in mutant zebrafish. Overall, our analyses suggest that these missense variants in HMGCS1 act through a hypomorphic mechanism. Here, we report an additional component of the mevalonate pathway associated with disease and suggest biallelic variants in HMGCS1 should be considered in patients presenting with an unresolved rigid spine myopathy phenotype. Additionally, we highlight mevalonoic acid supplementation as a potential treatment for patients with HMGCS1-related disease.

The widening genetic and myopathologic spectrum of congenital myopathies (CMYOs): a narrative review

Congenital myopathies (CMYOs) represent a genetically and clinically heterogeneous group of disorders characterized by early-onset muscle weakness and distinct myopathologic features. The advent of next-generation sequencing (NGS) has accelerated the identification of causative genes, leading to the discovery of novel CMYOs and thereby challenging the traditional classification. In this comprehensive review, we focus on the clinical, myopathologic, molecular and pathophysiological features of 33 newly identified CMYOs.

A Novel Splice Site Variant in KLHL40 Gene in Multiple Affected NEM8 Family Members Who Present Phenotypic Variability

Introduction

Nemaline myopathy (NEM) is a heterogeneous muscle disease, which usually presents with hypotonia and muscle weakness. Biallelic pathogenic variants of KLHL40 gene cause severe form of NEM (NEM8), which leads to a wide range of symptoms, including hypotonia, muscle weakness, joint contractures and fractures. Nemaline bodies in muscle fiber are characteristic findings of the disease.

Case Presentation

Here, we presented three affected individuals in a family with variable phenotypes, in whom the same novel splice-site variant in KLHL40 gene (c.1607+3A>T) was detected.

Discussion

This study expanded the spectrum of genotype and phenotype of NEM8, and emphasized that molecular genetic tests are highly valuable in diagnosis of patients with inconclusive muscle biopsy results.

Dinucleotide composition representation -based deep learning to predict scoliosis-associated Fibrillin-1 genotypes

Introduction

Scoliosis is a pathological spine structure deformation, predominantly classified as "idiopathic" due to its unknown etiology. However, it has been suggested that scoliosis may be linked to polygenic backgrounds. It is crucial to identify potential Adolescent Idiopathic Scoliosis (AIS)-related genetic backgrounds before scoliosis onset.

Methods

The present study was designed to intelligently parse, decompose and predict AIS-related variants in ClinVar database. Possible AIS-related variant records downloaded from ClinVar were parsed for various labels, decomposed for Dinucleotide Compositional Representation (DCR) and other traits, screened for high-risk genes with statistical analysis, and then learned intelligently with deep learning to predict high-risk AIS genotypes.

Results

Results demonstrated that the present framework is composed of all technical sections of data parsing, scoliosis genotyping, genome encoding, machine learning (ML)/deep learning (DL) and scoliosis genotype predicting. 58,000 scoliosis-related records were automatically parsed and statistically analyzed for high-risk genes and genotypes, such as FBN1, LAMA2 and SPG11. All variant genes were decomposed for DCR and other traits. Unsupervised ML indicated marked inter-group separation and intra-group clustering of the DCR of FBN1, LAMA2 or SPG11 for the five types of variants (Pathogenic, Pathogeniclikely, Benign, Benignlikely and Uncertain). A FBN1 DCR-based Convolutional Neural Network (CNN) was trained for Pathogenic and Benign/ Benignlikely variants performed accurately on validation data and predicted 179 high-risk scoliosis variants. The trained predictor was interpretable for the similar distribution of variant types and variant locations within 2D structure units in the predicted 3D structure of FBN1.

Discussion

In summary, scoliosis risk is predictable by deep learning based on genomic decomposed features of DCR. DCR-based classifier has predicted more scoliosis risk FBN1 variants in ClinVar database. DCR-based models would be promising for genotype-to-phenotype prediction for more disease types.

KLHL40-Related Myopathy: A Systematic Review and Insight into a Follow-up Biomarker via a New Case Report

BACKGROUND

Mutations in the KLHL40 gene are a common cause of severe or even lethal nemaline myopathy. Some cases with mild forms have been described, although the cases are still anecdotal. The aim of this paper was to systematically review the cases described in the literature and to describe a 12-year clinical and imaging follow-up in an Italian patient with KLHL40- related myopathy in order to suggest possible follow-up measurements.

METHODS

Having searched through three electronic databases (PubMed, Scopus, and EBSCO), 18 articles describing 65 patients with homozygous or compound heterozygous KLHL40 mutations were selected. A patient with a KLHL40 homozygous mutation (c.1582G>A/p.E528K) was added and clinical and genetic data were collected.

RESULTS

The most common mutation identified in our systematic review was the (c.1516A>C) followed by the (c.1582G>A). In our review, 60% percent of the patients died within the first 4 years of life. Clinical features were similar across the sample. Unfortunately, however, there is no record of the natural history data in the surviving patients. The 12-year follow-up of our patient revealed a slow improvement in her clinical course, identifying muscle MRI as the only possible marker of disease progression.

CONCLUSIONS

Due to its clinical and genotype homogeneity, KLHL40-related myopathy may be a condition that would greatly benefit from the development of new gene therapies; muscle MRI could be a good biomarker to monitor disease progression.

Ablation of the carboxy-terminal end of MAMDC2 causes a distinct muscular dystrophy

The extracellular matrix (ECM) has an important role in the development and maintenance of skeletal muscle, and several muscle diseases are associated with the dysfunction of ECM elements. MAMDC2 is a putative ECM protein and its role in cell proliferation has been investigated in certain cancer types. However, its participation in skeletal muscle physiology has not been previously studied. We describe 17 individuals with an autosomal dominant muscular dystrophy belonging to two unrelated families in which different heterozygous truncating variants in the last exon of MAMDC2 co-segregate correctly with the disease. The radiological aspect of muscle involvement resembles that of COL6 myopathies with fat replacement at the peripheral rim of vastii muscles. In this cohort, a subfascial and peri-tendinous pattern is observed in upper and lower limb muscles. Here we show that MAMDC2 is expressed in adult skeletal muscle and differentiating muscle cells, where it appears to localize to the sarcoplasm and myonuclei. In addition, we show it is secreted by myoblasts and differentiating myotubes into to the extracellular compartment. The last exon encodes a disordered region with a polar residue compositional bias loss of which likely induces a toxic effect of the mutant protein. The precise mechanisms by which the altered MAMDC2 proteins cause disease remains to be determined. MAMDC2 is a skeletal muscle disease-associated protein. Its role in muscle development and ECM-muscle communication remains to be fully elucidated. Screening of the last exon of MAMDC2 should be considered in patients presenting with autosomal dominant muscular dystrophy, particularly in those with a subfascial radiological pattern of muscle involvement.

Splicing defects in rare diseases: transcriptomics and machine learning strategies towards genetic diagnosis

Genomic variants affecting pre-messenger RNA splicing and its regulation are known to underlie many rare genetic diseases. However, common workflows for genetic diagnosis and clinical variant interpretation frequently overlook splice-altering variants. To better serve patient populations and advance biomedical knowledge, it has become increasingly important to develop and refine approaches for detecting and interpreting pathogenic splicing variants. In this review, we will summarize a few recent developments and challenges in using RNA sequencing technologies for rare disease investigation. Moreover, we will discuss how recent computational splicing prediction tools have emerged as complementary approaches for revealing disease-causing variants underlying splicing defects. We speculate that continuous improvements to sequencing technologies and predictive modeling will not only expand our understanding of splicing regulation but also bring us closer to filling the diagnostic gap for rare disease patients.

Introme accurately predicts the impact of coding and noncoding variants on gene splicing, with clinical applications

Predicting the impact of coding and noncoding variants on splicing is challenging, particularly in non-canonical splice sites, leading to missed diagnoses in patients. Existing splice prediction tools are complementary but knowing which to use for each splicing context remains difficult. Here, we describe Introme, which uses machine learning to integrate predictions from several splice detection tools, additional splicing rules, and gene architecture features to comprehensively evaluate the likelihood of a variant impacting splicing. Through extensive benchmarking across 21,000 splice-altering variants, Introme outperformed all tools (auPRC: 0.98) for the detection of clinically significant splice variants. Introme is available at https://github.com/CCICB/introme .