Unbiased Long-Read Whole-Genome Sequencing Enables High-Resolution Mapping of Transgene Concatenation and Off-target Genomic Disruption in a Mouse Model.
Maanik Mehta, Kashif Ahmed, Rowaida Hussein, Erika Tavares, Zorana Berberovic, Rabiat Adele, Abigail D'Souza, Bin Gu, Michael D Wilson, Evgueni Ivakine, Philippe P Monnier, Elise Héon, Ajoy Vincent
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引用次数: 0
Abstract
Transgenic mouse models are indispensable for dissecting disease mechanisms, yet their interpretability is frequently compromised by cryptic genomic alterations introduced during transgenesis. Thus, robust quality-control strategies are needed to elucidate integration architecture and evaluate model performance when such unintended events occur. Here, we applied unbiased whole-genome long-read sequencing using the PacBio Revio to investigate a mouse model exhibiting unexpected transgene silencing, originally designed to recapitulate autosomal-dominant hereditary macular dystrophy driven by the up-regulation of a ZZEF1-ALOX15 fusion gene. Long-read sequencing analysis revealed a ≥29-kb head-to-tail concatemer containing at least 3 transgene-derived copies. Reconstruction of transgene-genome junctions revealed unintended off-target integration of the concatemer into the calcium-sensing receptor gene (Casr), along with exogenous Escherichia coli DNA, that together defined final transgene architecture. Aggregate 5-methylcytosine profiling identified hypermethylation of the transgene promoter across the concatemer, and additional phenotyping suggested disruption of endogenous CaSR function resulting from the rearrangement. Our workflow enabled direct detection of transgene concatenation and off-target mapping. These findings highlight the power of whole-genome long-read sequencing in uncovering hidden genomic complexity, resolving aberrant phenotypes, and enhancing the reliability of in vivo disease modeling, supporting its incorporation as a potentially scalable quality-control tool for genetically engineered animal models.
期刊介绍:
Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to:
Structure and function of proteins, nucleic acids and other macromolecules
Structure and function of multi-component complexes
Protein folding, processing and degradation
Enzymology
Computational and structural studies of plant systems
Microbial Informatics
Genomics
Proteomics
Metabolomics
Algorithms and Hypothesis in Bioinformatics
Mathematical and Theoretical Biology
Computational Chemistry and Drug Discovery
Microscopy and Molecular Imaging
Nanotechnology
Systems and Synthetic Biology