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Beyond single-strain models: Genetic background modifies disease presentations and molecular signatures in muscular dystrophy

GSE316106 Mus musculus (mouse) RNA-seq (gene expression) 24 samples Submitted 2026/01/31 Platform GPL30172
At a glance

RNA-seq (gene expression) · mouse · 24 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
24 samples: 8 conditions, mostly in triplicateGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Summary (written by the submitters)
Genetic variation underlies much of human diversity, including differences in disease severity, progression, and response to therapy. Yet, most mouse models used to study genetic diseases and evaluate therapeutic interventions are maintained on a single genetic background, limiting their ability to reflect the genetic heterogeneity observed in patients.
Read the full summary ↓
This limitation extends even to Mendelian disorders such as LAMA2-deficient congenital muscular dystrophy (LAMA2-CMD), where individuals with the same pathogenic mutation in LAMA2 gene often exhibit variable clinical presentations. Current preclinical mouse models of LAMA2-CMD, constrained by their genetic uniformity, fail to capture this variability. Goal: In this study, we aimed to develop genetically diverse mouse models that better recapitulate the spectrum of disease phenotypes seen in LAMA2-CMD. We leveraged the BXD recombinant inbred panels, which are unique families of advanced intercrosses between C57BL/6J and DBA/2J mice with well-documented SNP profiles and phenotypes, to assess the impact of genetic background on disease expressivity. Methods: We generated LAMA2-CMD mouse models carrying the same Lama2. c.271+1 G>A mutation on C57BL/6J, DBA/2J, BXD67, and BXD87 backgrounds, referred to as CMD-B6, CMD-D2, CMD-67, and CMD-87 mice, respectively. We performed longitudinal assessments of body weight, forelimb grip strength, locomotor activity, and analyzed tibialis anterior (TA) muscle contractile force, muscle histology and transcriptomic profiles across all genotypes. Results: All CMD mice exhibited failure to thrive and reduced forelimb grip strength at 8 and 25 weeks. In contrast, reductions in locomotor activity and TA muscle force were observed only in select strains. Histological analyses revealed smaller muscle fiber diameter in CMD-B6 and CMD-87 compared to their WT littermates, while CMD-67 showed no difference relative to controls. Transcriptomic profiling revealed consistent upregulation of immune-related genes and leukocyte-associated pathways across all CMD strains. Notably, CMD-67 mice uniquely exhibited concurrent enrichment of both immune- and muscle developmental pathways. In contrast, downregulated pathways were largely strain-specific and included changes in developmental and metabolic processes. Conclusion: These findings demonstrate that while body weight and forelimb weakness are consistent across genetic backgrounds, other traits such as mobility, muscle force, and muscle growth, are strongly influenced by genetic backgrounds. Moreover, strain-dependent transcriptomic signatures highlight how genetic background can influence the molecular interpretation of disease. Overall, this work establishes a novel LAMA2-CMD mouse panel as a valuable resource for future studies, including identification of disease modifiers and evaluation of therapeutic responses, and provides evidence that preclinical models incorporating genetic diversity may better translate to human disease.
This dataset
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Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE316106_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1400607 and SRA study SRP661082. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly in triplicate
Quadriceps, B6, WT, 25 weeks old, sample ×3 Quadriceps, B6, CMD, 25 weeks old, sample ×3 Quadricep, 67, CMD, 25 weeks old, sample ×3 Quadricep, 87, WT, 25 weeks old, sample ×3 Quadricep, 87, CMD, 25 weeks old, sample ×3 Quadricep, D2, WT, 25 weeks old, sample ×3 Quadricep, D2, CMD, 25 weeks old, sample ×3 Quadriceps, 67, WT, 25 weeks old, sample ×2 +1 more

Supports a between-group comparison across 23 samples.

How we read this: 8 replicated groups read from 24 sample titles; they account for 23 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
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