Transcriptome-based variant calling and aberrant mRNA discovery enhance diagnostic efficiency for neuromuscular diseases

Abstract

BackgroundWhole-exome sequencing-based diagnosis of rare diseases typically yields 40%–50% of success rate. Precise diagnosis of the patients with neuromuscular disorders (NMDs) has been hampered by locus heterogeneity or phenotypic heterogeneity. We evaluated the utility of transcriptome sequencing as an independent approach in diagnosing NMDs.MethodsThe RNA sequencing (RNA-Seq) of muscle tissues from 117 Korean patients with suspected Mendelian NMD was performed to evaluate the ability to detect pathogenic variants. Aberrant splicing and CNVs were inspected to identify additional causal genetic factors for NMD. Aberrant splicing events in Dystrophin (DMD) were investigated by using antisense oligonucleotides (ASOs). A non-negative matrix factorisation analysis of the transcriptome data followed by cell type deconvolution was performed to cluster samples by expression-based signatures and identify cluster-specific gene ontologies.ResultsOur pipeline called 38.1% of pathogenic variants exclusively from the muscle transcriptomes, demonstrating a higher diagnostic rate than that achieved via exome analysis (34.9%). The discovery of variants causing aberrant splicing allowed the application of ASOs to the patient-derived cells, providing a therapeutic approach tailored to individual patients. RNA-Seq data further enabled sample clustering by distinct gene expression profiles that corresponded to clinical parameters, conferring additional advantages over exome sequencing.ConclusionThe RNA-Seq-based diagnosis of NMDs achieves an increased diagnostic rate and provided pathogenic status information, which is not easily accessible through exome analysis.

Epigenomic Approaches for the Diagnosis of Rare Diseases

“…In some scenarios, the inclusion of a reasonable number of healthy samples is possible. For example, Hong et al compared patients’ muscle biopsies with muscle control samples obtained from healthy individuals undergoing plastic surgery [ 31 ]. However, in most cases, healthy tissues are even more problematic to obtain than patients’ tissues.…”

“…In this regard, the description of detailed transcriptomic signatures associated with a disease in common tissues, such as blood or fibroblasts, might be useful for the diagnosis of future patients. In addition, Hong et al used RNA-seq data to perform clustering of patients with undiagnosed neuromuscular diseases based on their gene expression data to identify patients with similar pathologies [ 31 ]. The analysis of the pathways enriched in each cluster helped to identify common altered functions.…”

“…Hong et al, applying these methods, deconvoluted cell type abundances in muscle biopsies from patients with neuromuscular diseases and captured clinical and pathological aspects of the diseases. For example, the abundance of fibro–adipogenic progenitor cells estimated from the deconvolution of the bulk RNA-seq data correlated with muscle fibrosis in patients [ 31 ].…”

“…Overall, RNA-seq used to identify expression and/or splicing outliers has been reported to provide diagnoses for about 10–20% of undiagnosed cases with negative WES or WGS and confirmed diagnosis in around 50% of cases with a candidate variant identified by genome sequencing [ 26 , 37 , 38 ]. The highest diagnosis ratios are achieved when focusing on one particular pathology and analyzing the corresponding affected tissue, such as muscular disorders and muscle biopsies [ 31 ]. Overall, detection of splicing aberrations appears more successful than identifying causative genes by differential expression.…”

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“…Genome wide association analysis involving genotypic and phenotypic data has empowered efficient detection of significant markers associated with the trait of interest (Zhao et al ., 2011, Xiao et al ., 2017). If interest lies in the expressed part of the genome, variant calling from transcriptome sequencing can be adopted, which is comparatively easier, cost effective and time efficient than whole genome sequencing (Hong et al ., 2022, Jehl et al ., 2021). Further, transcriptome-based variant calling is expected to capture the variants in the parts of the transcripts modified during to post-transcriptional modifications and RNA editing.…”