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. 2025 Feb 25;20(2):e0318944.
doi: 10.1371/journal.pone.0318944. eCollection 2025.

Identification of suitable qPCR reference genes for the normalization of gene expression in the BL10-mdx and D2-mdx mouse models of Duchenne muscular dystrophy

Kayleigh Putker  1 , Anne-Fleur Schneider  1 , Davy Van De Vijver  1 , John Hildyard  2 , Annemieke Aartsma-Rus  1 , Maaike van Putten  1
Affiliations

Identification of suitable qPCR reference genes for the normalization of gene expression in the BL10-mdx and D2-mdx mouse models of Duchenne muscular dystrophy

Kayleigh Putker et al. PLoS One. .

Abstract

Duchenne muscular dystrophy (DMD) is an X-linked disorder that is caused by mutations in the DMD gene, leading to progressive muscle wasting and weakness. There is currently no cure for DMD. The BL10-mdx mouse is the most commonly used model in preclinical DMD studies, but it exhibits a mild disease phenotype compared to DMD patients, limiting research translatability. The newer D2-mdx mouse has a more severe phenotype at an early age and may better recapitulate human disease. To compare these mouse models on a transcriptional level with quantitative RT-PCR, stable and reliable reference genes are indispensable. We aimed to evaluate the stability and reliability of a panel of nine candidate reference genes (Actb, Ap3d1, Gapdh, Hmbs, Htatsf1, Pak1ip1, Rpl13a, Sdha and Zfp91) in the gastrocnemius, diaphragm and heart of mice from both strains and their corresponding wild types aged 4 to 52 weeks. Data was analyzed using geNorm, BestKeeper, deltaCt and NormFinder. We found that Htatsf1, Pak1ip1 and Zfp91 are suitable reference genes for normalization of gene expression in dystrophic and healthy mice, regardless of the tissue type or age. In our hands, Actb, Gapdh and Rpl13a were not suitable as reference genes, exhibiting tissue-, age-, or disease specific changes in expression. This study highlights the importance of the selection of suitable reference genes, as their stability can differ between specific experimental setups.

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Conflict of interest statement

I have read the journal's policy and the authors of this manuscript have the following competing interests: Annemieke Aartsma-Rus discloses being employed by LUMC which has patents on exon skipping technology, some of which has been licensed to BioMarin and subsequently sublicensed to Sarepta. As co-inventor of some of these patents AAR was entitled to a share of royalties. AAR further discloses being ad hoc consultant for PTC Therapeutics, Sarepta Therapeutics, Regenxbio, Dyne Therapeutics, Lilly, BioMarin Pharmaceuticals Inc., Eisai, Entrada, Takeda, Splicesense, Galapagos, Sapreme, Italfarmaco and Astra Zeneca. In the past 5 years ad hoc consulting has occurred for: Alpha Anomeric. AAR also reports being a member of the scientific advisory boards of Eisai, Hybridize Therapeutics, Silence Therapeutics, Sarepta therapeutics, Sapreme and Mitorx. SAB memberships in the past 5 years: ProQR. Remuneration for consulting and advising activities is paid to LUMC. In the past 5 years, LUMC also received speaker honoraria from PTC Therapeutics, Alnylam Netherlands, Italfarmaco and Pfizer and funding for contract research from Sapreme, Eisai, Galapagos, Synaffix and Alpha Anomeric. Project funding is received from Sarepta Therapeutics and Entrada via unrestricted grants. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Raw quantification cycle (Cq) values.
Cq values for each candidate reference gene in the gastrocnemius, diaphragm and heart. Each datapoint represents the mean Cq value of an individual sample for each gene. Data is separated by strain: Light blue: BL10-wt; Dark blue: BL10-mdx; Light red: D2-wt; Dark red: D2-mdx.
Fig 2
Fig 2. geNorm rankings.
Rankings of the pairwise stability value M of the nine candidate reference genes, assessed by the geNorm method, for (A) the whole dataset, (B) healthy and dystrophic subsets, and (C) tissue-specific subsets. Genes are ranked from low stability (high M value) to high stability (low M value), where M values <  0.5 indicate highly stable genes. The highest scoring genes (the ‘best pair’) are considered to be equal in M value.
Fig 3
Fig 3. BestKeeper rankings.
Rankings of the coefficient of correlation (r) of the nine candidate reference genes, assessed by the BestKeeper method, for (A) the whole dataset, (B) healthy and dystrophic subsets, and (C) tissue-specific subsets. Genes are ranked from low stability (low r) to high stability (high r).
Fig 4
Fig 4. deltaCt rankings.
Rankings of the average deltaCt standard deviation of the nine candidate reference genes, assessed by the deltaCt method, for (A) the whole dataset, (B) healthy and dystrophic subsets, and (C) tissue-specific subsets. Genes are ranked from low stability (high deltaCt score) to high stability (low deltaCt score).
Fig 5
Fig 5. NormFinder rankings (ungrouped).
Ungrouped rankings of the stability value of the nine candidate reference genes, assessed by the NormFinder method, for (A) the whole dataset, (B) healthy and dystrophic subsets, and (C) tissue-specific subsets. Genes are ranked from low stability (high stability value) to high stability (low stability value).
Fig 6
Fig 6. NormFinder rankings (grouped).
Grouped rankings of the stability value of the nine candidate reference genes, assessed by the NormFinder method. The whole dataset was grouped by (A) disease, (B) tissue, (C) strain, or (D) age. For the gastrocnemius, diaphragm and heart samples, genes were grouped by (E) disease, strain, or age. Genes are ranked from low to high stability (high to low values). For each grouping, the best pair is indicated on the plot.
Fig 7
Fig 7. Aggregate rankings of the four methods.
Rankings of the geometric mean scores of the nine candidate reference genes from all four analysis methods, for (A) the whole dataset, (B) healthy and dystrophic subsets, and (C) tissue-specific subsets. Genes are ranked from low stability (high geometric mean rank) to high stability (low geometric mean rank).
Fig 8
Fig 8. Normalization of Actb and Rpl13a.
(A) Mean raw RQ values for Actb in gastrocnemius, diaphragm and heart. (B) Normalization to the geometric mean of Htatsf1, Pak1ip1 and Zfp91 in gastrocnemius, diaphragm and heart shows reduced variation. (C) Mean raw RQ values for Rpl13a in gastrocnemius, diaphragm and heart. (D) Normalization to the geometric mean of Htatsf1, Pak1ip1 and Zfp91 in gastrocnemius, diaphragm and heart shows reduced variation. Data is shown as log10 of RQ values. CoV values represent the average of the individual CoVs per time-point, per tissue. Data is separated by strain: Light blue: BL10-wt; Dark blue: BL10-mdx; Light red: D2-wt; Dark red: D2-mdx.
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