A Case Report of SYNE1 Deficiency-Mimicking Mitochondrial Disease and the Value of Pangenomic Investigations.

Mitochondrial disorders are characterized by a huge clinical, biochemical, and genetic heterogeneity, which poses significant diagnostic challenges. Several studies report that more than 50% of patients with suspected mitochondrial disease could have a non-mitochondrial disorder. Thus, only the identification of the causative pathogenic variant can confirm the diagnosis. Herein, we describe the diagnostic journey of a family suspected of having a mitochondrial disorder who were referred to our Genetics Department. The proband presented with the association of cerebellar ataxia, COX-negative fibers on muscle histology, and mtDNA deletions. Whole exome sequencing (WES), supplemented by a high-resolution array, comparative genomic hybridization (array-CGH), allowed us to identify two pathogenic variants in the non-mitochondrial SYNE1 gene. The proband and her affected sister were found to be compound heterozygous for a known nonsense variant (c.13258C>T, p.(Arg4420Ter)), and a large intragenic deletion that was predicted to result in a loss of function. To our knowledge, this is the first report of a large intragenic deletion of SYNE1 in patients with cerebellar ataxia (ARCA1). This report highlights the interest in a pangenomic approach to identify the genetic basis in heterogeneous neuromuscular patients with the possible cause of mitochondrial disease. Moreover, even rare copy number variations should be considered in patients with a phenotype suggestive of SYNE1 deficiency.

Autosomal recessive pathogenic MSTO1 variants in hereditary optic atrophy.

Gerber et al report 2 autosomal recessive pathogenic Misato homolog 1 (MSTO1) variants causing hereditary optic atrophy and raise concerns about a previously identified dominant variant of MSTO1 by Gal et al (2017).

UQCRC2-related mitochondrial complex III deficiency, about 7 patients.

Isolated complex III defect is a relatively rare cause of mitochondrial disorder. New genes involved were identified in the last two decades, with only a few cases described for each deficiency. UQCRC2, which encodes ubiquinol-cytochrome c reductase core protein 2, is one of the eleven structural subunits of complex III. We report seven French patients with UQCRC2 deficiency to complete the phenotype reported so far. We highlight the similarities with neoglucogenesis defect during decompensations - hypoglycaemias, liver failure and lactic acidosis - and point out the rapid improvement with glucose fluid infusion, which is a remarkable feature for a mitochondrial disorder. Finally, we discuss the relevance of coenzyme Q10 supplementation in this defect.

Splicing variants in NARS2 are associated with milder phenotypes and intra-familial variability.

Biallelic rare variants in NARS2 that encode the mitochondrial asparaginyl-tRNA synthetase are associated with a wide spectrum of clinical phenotypes ranging from severe neurodegenerative disorders to isolated mitochondrial myopathy or deafness. To date, only a small number of patients with NARS2 variants have been reported, and possible genotype-phenotype correlations are still lacking. Here, we present three siblings who had an early-onset hearing loss, while one developed severe symptoms in adulthood associated with early intellectual impairment, refractory seizures, moderate axonal sensorimotor neuropathy, and atypical psychiatric symptoms. Biochemical analysis revealed impairment of the activity and assembly of the respiratory chain complexes in this patient's muscle and fibroblasts. Whole Exome Sequencing allowed identification of a heterozygous variant NM_024678.5(NARS2):c.822G > C (p.Gln274His) that is known to be pathogenic and to affect splicing of the NARS2 gene, but was unable to detect a second variant in this gene. Coverage analysis and Sanger sequencing led to identification of a novel intronic deletion NM_024678.5(NARS2):c.922-21_922-19del in the three siblings in trans with the c.822G > C. Functional analysis by RT-PCR showed that this deletion was causing aberrant splicing and led to exon 9 skipping in NARS2 mRNA in patient fibroblasts. Our work expands the phenotype and genotype spectrum of NARS2-related disorders. We provide evidence of the pathogenic effect of a novel intronic deletion in the NARS2 gene and report on additional adult patients with a large intrafamilial variability associated with splice variants in this gene. More specifically, we detail the phenotype of the oldest living patient to date with NARS2 variants and, for the first time, we report the psychiatric symptoms associated with this gene. Our work confirms the complexity of genotype-phenotype correlation in patients with pathogenic NARS2 variants.

ABEILLE: a novel method for ABerrant Expression Identification empLoying machine LEarning from RNA-sequencing data.

MOTIVATION: Current advances in omics technologies are paving the diagnosis of rare diseases proposing a complementary assay to identify the responsible gene. The use of transcriptomic data to identify aberrant gene expression (AGE) has demonstrated to yield potential pathogenic events. However, popular approaches for AGE identification are limited by the use of statistical tests that imply the choice of arbitrary cut-off for significance assessment and the availability of several replicates not always possible in clinical contexts. RESULTS: Hence, we developed ABerrant Expression Identification empLoying machine LEarning from sequencing data (ABEILLE) a variational autoencoder (VAE)-based method for the identification of AGEs from the analysis of RNA-seq data without the need for replicates or a control group. ABEILLE combines the use of a VAE, able to model any data without specific assumptions on their distribution, and a decision tree to classify genes as AGE or non-AGE. An anomaly score is associated with each gene in order to stratify AGE by the severity of aberration. We tested ABEILLE on a semi-synthetic and an experimental dataset demonstrating the importance of the flexibility of the VAE configuration to identify potential pathogenic candidates. AVAILABILITY AND IMPLEMENTATION: ABEILLE source code is freely available at: https://github.com/UCA-MSI/ABEILLE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases.

Rare diseases (RDs) concern a broad range of disorders and can result from various origins. For a long time, the scientific community was unaware of RDs. Impressive progress has already been made for certain RDs; however, due to the lack of sufficient knowledge, many patients are not diagnosed. Nowadays, the advances in high-throughput sequencing technologies such as whole genome sequencing, single-cell and others, have boosted the understanding of RDs. To extract biological meaning using the data generated by these methods, different analysis techniques have been proposed, including machine learning algorithms. These methods have recently proven to be valuable in the medical field. Among such approaches, unsupervised learning methods via neural networks including autoencoders (AEs) or variational autoencoders (VAEs) have shown promising performances with applications on various type of data and in different contexts, from cancer to healthy patient tissues. In this review, we discuss how AEs and VAEs have been used in biomedical settings. Specifically, we discuss their current applications and the improvements achieved in diagnostic and survival of patients. We focus on the applications in the field of RDs, and we discuss how the employment of AEs and VAEs would enhance RD understanding and diagnosis.