Diagnosis and aetiology of congenital muscular dystrophy: we are halfway there

OBJECTIVES: To evaluate the diagnostic outcomes in a large cohort of congenital muscular dystrophy (CMD) patients using traditional and Next Generation Sequencing (NGS) technologies. METHODS: 123 CMD patients were investigated using the traditional approaches of histology, immunohistochemical analysis of muscle biopsy and candidate gene sequencing. Undiagnosed patients available for further testing were investigated using NGS. RESULTS: Muscle biopsy and immunohistochemical analysis found deficiencies of laminin α2, α-dystroglycan or collagen VI in 50% of patients. Candidate gene sequencing and chromosomal microarray established a genetic diagnosis in 32% (39/123). Of 85 patients presenting in the last 20 years, 28 of 51 who lacked a confirmed genetic diagnosis (55%) consented to NGS studies, leading to confirmed diagnoses in a further 11 patients. Using the combination of approaches, a confirmed genetic diagnosis was achieved in 51% (43/85). The diagnoses within the cohort were heterogeneous. 45/59 probands with confirmed or probable diagnoses had variants in genes known to cause CMD (76%), and 11/59 (19%) had variants in genes associated with congenital myopathies, reflecting overlapping features of these conditions. One patient had a congenital myasthenic syndrome and two had microdeletions. Within the cohort, five patients had variants in novel (PIGY and GMPPB) or recently published genes (GFPT1 and MICU1) and seven had variants in TTN or RYR1; large genes that are technically difficult to Sanger sequence. INTERPRETATION: These data support NGS as a first-line tool for genetic evaluation of patients with a clinical phenotype suggestive of CMD, with muscle biopsy reserved as a second-tier investigation. This article is protected by copyright. All rights reserved

Optimization of Fluconazole Dosing for the Prevention and Treatment of Invasive Candidiasis Based on the Pharmacokinetics of Fluconazole in Critically Ill Patients

The efficacy of fluconazole is related to the area under the plasma concentration-time curve (AUC) over the MIC of the microorganism. Physiological changes in critically ill patients may affect the exposure of fluconazole, and therefore dosing adjustments might be needed. The aim of this study was to evaluate variability in fluconazole drug concentration in intensive care unit (ICU) patients and to develop a pharmacokinetic model to support personalized fluconazole dosing. A prospective observational pharmacokinetic study was performed in critically ill patients receiving fluconazole either as prophylaxis or as treatment. The association between fluconazole exposure and patient variables was studied. Pharmacokinetic modeling was performed with a nonparametric adaptive grid (NPAG) algorithm using R package Pmetrics. Data from 33 patients were available for pharmacokinetic analysis. Patients on dialysis and solid organ transplant patients had a significantly lower exposure to fluconazole. The population was best described with a one-compartment model, where the mean volume of distribution was 51.52 liters (standard deviation [SD], 19.81) and the mean clearance was 0.767 liters/h (SD, 0.46). Creatinine clearance was tested as a potential covariate in the model, but was not included in the final population model. A significant positive correlation was found between the fluconazole exposure (AUC) and the trough concentration (C-min). Substantial variability in fluconazole plasma concentrations in critically ill adults was observed, where the majority of patients were underexposed. Fluconazole C-min therapeutic drug monitoring (TDM)-guided dosing can be used to optimize therapy in critically ill patients

Reply to Van Daele et al., "Fluconazole Underexposure in Critically Ill Patients:a Matter of Using the Right Targets?"

We thank Van Daele et al (1) for their interest in our study investigating the pharmacokinetics in critically ill patients with aim to optimize fluconazole dosing for the prevention and treatment of invasive candida infections (2).…

Integrated multi-omics for rapid rare disease diagnosis on a national scale

Published online: 8 June 2023Critically ill infants and children with rare diseases need equitable access to rapid and accurate diagnosis to direct clinical management. Over 2 years, the Acute Care Genomics program provided whole-genome sequencing to 290 families whose critically ill infants and children were admitted to hospitals throughout Australia with suspected genetic conditions. The average time to result was 2.9 d and diagnostic yield was 47%. We performed additional bioinformatic analyses and transcriptome sequencing in all patients who remained undiagnosed. Long-read sequencing and functional assays, ranging from clinically accredited enzyme analysis to bespoke quantitative proteomics, were deployed in selected cases. This resulted in an additional 19 diagnoses and an overall diagnostic yield of 54%. Diagnostic variants ranged from structural chromosomal abnormalities through to an intronic retrotransposon, disrupting splicing. Critical care management changed in 120 diagnosed patients (77%). This included major impacts, such as informing precision treatments, surgical and transplant decisions and palliation, in 94 patients (60%). Our results provide preliminary evidence of the clinical utility of integrating multi-omic approaches into mainstream diagnostic practice to fully realize the potential of rare disease genomic testing in a timely manner.Sebastian Lunke ... Peer Arts ... Christopher P. Barnett ..., Chirag V. Patel ... Hamish S. Scott ... Karin S. Kassahn ... et al

Whole genome sequence analysis of the TALLYHO/Jng mouse

Background: The TALLYHO/Jng (TH) mouse is a polygenic model for obesity and type 2 diabetes first described in the literature in 2001. The origin of the TH strain is an outbred colony of the Theiler Original strain and mice derived from this source were selectively bred for male hyperglycemia establishing an inbred strain at The Jackson Laboratory. TH mice manifest many of the disease phenotypes observed in human obesity and type 2 diabetes. Results: We sequenced the whole genome of TH mice maintained at Marshall University to a depth of approximately 64.8X coverage using data from three next generation sequencing runs. Genome-wide, we found approximately 4.31 million homozygous single nucleotide polymorphisms (SNPs) and 1.10 million homozygous small insertions and deletions (indels) of which 98,899 SNPs and 163,720 indels were unique to the TH strain compared to 28 previously sequenced inbred mouse strains. In order to identify potentially clinically-relevant genes, we intersected our list of SNP and indel variants with human orthologous genes in which variants were associated in GWAS studies with obesity, diabetes, and metabolic syndrome, and with genes previously shown to confer a monogenic obesity phenotype in humans, and found several candidate variants that could be functionally tested using TH mice. Further, we filtered our list of variants to those occurring in an obesity quantitative trait locus, tabw2, identified in TH mice and found a missense polymorphism in the Cidec gene and characterized this variant’s effect on protein function. Conclusions: We generated a complete catalog of variants in TH mice using the data from whole genome sequencing. Our findings will facilitate the identification of causal variants that underlie metabolic diseases in TH mice and will enable identification of candidate susceptibility genes for complex human obesity and type 2 diabetes

Leiomodin-3 dysfunction results in thin filament disorganization and nemaline myopathy

Peer reviewe

Improving genetic diagnosis in Mendelian disease with transcriptome sequencing

Exome and whole-genome sequencing are becoming increasingly routine approaches in Mendelian disease diagnosis. Despite their success, the current diagnostic rate for genomic analyses across a variety of rare diseases is approximately 25 to 50%. We explore the utility of transcriptome sequencing [RNA sequencing (RNA-seq)] as a complementary diagnostic tool in a cohort of 50 patients with genetically undiagnosed rare muscle disorders. We describe an integrated approach to analyze patient muscle RNA-seq, leveraging an analysis framework focused on the detection of transcript-level changes that are unique to the patient compared to more than 180 control skeletal muscle samples. We demonstrate the power of RNA-seq to validate candidate splice-disrupting mutations and to identify splice-altering variants in both exonic and deep intronic regions, yielding an overall diagnosis rate of 35%. We also report the discovery of a highly recurrent de novo intronic mutation in COL6A1 that results in a dominantly acting splice-gain event, disrupting the critical glycine repeat motif of the triple helical domain. We identify this pathogenic variant in a total of 27 genetically unsolved patients in an external collagen VI–like dystrophy cohort, thus explaining approximately 25% of patients clinically suggestive of having collagen VI dystrophy in whom prior genetic analysis is negative. Overall, this study represents a large systematic application of transcriptome sequencing to rare disease diagnosis and highlights its utility for the detection and interpretation of variants missed by current standard diagnostic approaches