Schneider, A.-F. | Search bioRxiv

bioRxiv2026

The D2-mdx mouse as a preclinical model for Duchenne muscular dystrophy: a natural history study across two independent sites

IntroductionThe quality of preclinical studies for rare diseases, such as Duchenne muscular dystrophy (DMD), relies on the availability of comprehensive natural disease history data. In addition to the classic BL10-mdx mouse, in recent years, the D2-mdx model has increasingly been used as an alternative model due to its reportedly more severely impaired phenotype. To improve our understanding of disease progression in these two DMD models, we conducted a comprehensive natural history study. Materials and MethodsThis involved a cross-sectional analysis of key in vivo and ex vivo outcome measures performed in two independent laboratories, using the same study setup in compliance with TREAT-NMD Standard Operating Procedures (SOPs), while also taking advantage of site-specific expertise. Globally, largely comparable results were obtained across the two study sites. ResultsBody composition showed pronounced differences between the strains, with BL10-mdx mice displaying a hypertrophic and D2-mdx mice displaying an atrophic phenotype. Dystrophic mice of each strain exhibited significant alterations of disease-relevant indices related to muscle functionality and integrity, mostly worsening with age, in comparison to their wildtypes. Cardiac function was affected earlier and more severely in D2-mdx mice. DiscussionNotably, for some parameters, genetic-background related differences were observed, emphasizing the need to include control groups with matching genetic backgrounds in experimental designs. ConclusionsCollectively, our natural history study provides benchmark data for these two mdx mouse strains to guide model selection for preclinical DMD studies, allowing accurate data interpretation. HighlightsO_LIDistinct body composition phenotypes: BL10-mdx mice exhibit pseudohypertrophy while D2-mdx mice display pronounced atrophy. C_LIO_LIEarlier cardiac dysfunction in D2-mdx: D2-mdx mice develop reduced ejection fraction and stroke volume from 28 weeks, while BL10-mdx only at 52 weeks. C_LIO_LIGenetic background-dependent variations: Intrinsic deficits in wildtype D2 mice demonstrate that genetic background influences outcome measures independent of dystrophic pathology. C_LIO_LIComparable ex vivo muscle physiology: Despite divergent in vivo phenotypes, isolated muscle contractile parameters show similar impairment in both dystrophic models. C_LIO_LIMulti-site standardized validation: Cross-sectional study at two independent laboratories following harmonized TREAT-NMD Standard Operating Procedures. C_LI

pharmacology and toxicology↗

bioRxiv2024

Characterising tandem repeat complexities across long-read sequencing platforms with TREAT

Tandem repeats (TR) play important roles in genomic variation and disease risk in humans. Long-read sequencing allows for the accurate characterisation of TRs, however, the underlying bioinformatics perspectives remain challenging. We present otter and TREAT: otter is a fast targeted local assembler, cross-compatible across different sequencing platforms. It is integrated in TREAT, an end-to-end workflow for TR characterisation, visualisation and analysis across multiple genomes. In a comparison with existing tools based on long-read sequencing data from both Oxford Nanopore Technology (ONT, Simplex and Duplex) and PacBio (Sequel 2 and Revio), otter and TREAT achieved state-of-the-art genotyping and motif characterisation accuracy. Applied to clinically relevant TRs, TREAT/otter significantly identified individuals with pathogenic TR expansions. When applied to a case-control setting, we significantly replicated previously reported associations of TRs with Alzheimers Disease, including those near or within APOC1 (p=2.63x10-9), SPI1 (p=6.5x10-3) and ABCA7 (p=0.04) genes. We finally used TREAT/otter to systematically evaluate potential biases when genotyping TRs using diverse ONT and PacBio long-read sequencing datasets. We showed that, in rare cases (0.06%), long-read sequencing suffers from coverage drops in TRs, including the disease-associated TRs in ABCA7 and RFC1 genes. Such coverage drops can lead to TR mis-genotyping, hampering the accurate characterisation of TR alleles. Taken together, our tools can accurately genotype TR across different sequencing technologies and with minimal requirements, allowing end-to-end analysis and comparisons of TR in human genomes, with broad applications in research and clinical fields.

bioinformatics↗