Bardhan, A. et al. Epidermolysis bullosa. Nat. Rev. Dis. Primers 6, 78 (2020).
Google Scholar
Has, C. et al. Consensus reclassification of inherited epidermolysis bullosa and other disorders with skin fragility. Br. J. Dermatol. 183, 614–627 (2020).
Google Scholar
Christiano, A. M. et al. Structural organization of the human type VII collagen gene (COL7A1), composed of more exons than any previously characterized gene. Genomics 21, 169–179 (1994).
Google Scholar
Varki, R., Sadowski, S., Uitto, J. & Pfendner, E. Epidermolysis bullosa. II. Type VII collagen mutations and phenotype-genotype correlations in the dystrophic subtypes. J. Med. Genet. 44, 181–192 (2007).
Google Scholar
Nystrom, A. & Bruckner-Tuderman, L. Injury- and inflammation-driven skin fibrosis: the paradigm of epidermolysis bullosa. Matrix Biol. 68-69, 547–560 (2018).
Google Scholar
Fine, J. D., Johnson, L. B., Weiner, M., Li, K. P. & Suchindran, C. Epidermolysis bullosa and the risk of life-threatening cancers: the National EB Registry experience, 1986-2006. J. Am. Acad. Dermatol. 60, 203–211 (2009).
Google Scholar
Guerra, L., Odorisio, T., Zambruno, G. & Castiglia, D. Stromal microenvironment in type VII collagen-deficient skin: the ground for squamous cell carcinoma development. Matrix Biol. 63, 1–10 (2017).
Google Scholar
Has, C., South, A. & Uitto, J. Molecular therapeutics in development for epidermolysis bullosa: update 2020. Mol. Diagn. Ther. 24, 299–309 (2020).
Google Scholar
Vandamme, T. F. Use of rodents as models of human diseases. J. Pharm. Bioallied. Sci. 6, 2–9 (2014).
Google Scholar
Heinonen, S. et al. Targeted inactivation of the type VII collagen gene (Col7a1) in mice results in severe blistering phenotype: a model for recessive dystrophic epidermolysis bullosa. J. Cell Sci. 112(Pt 21), 3641–3648 (1999).
Google Scholar
Fritsch, A. et al. A hypomorphic mouse model of dystrophic epidermolysis bullosa reveals mechanisms of disease and response to fibroblast therapy. J. Clin. Invest. 118, 1669–1679 (2008).
Google Scholar
Landrum, M. J. et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 46, D1062–D1067 (2018).
Google Scholar
Shalem, O., Sanjana, N. E. & Zhang, F. High-throughput functional genomics using CRISPR-Cas9. Nat. Rev. Genet 16, 299–311 (2015).
Google Scholar
Huijbers, I. J. Generating genetically modified mice: a decision guide. Methods Mol. Biol. 1642, 1–19 (2017).
Google Scholar
Ohtsuka, M. et al. i-GONAD: a robust method for in situ germline genome engineering using CRISPR nucleases. Genome Biol. 19, 25 (2018).
Google Scholar
Tamai, K. et al. Recurrent COL7A1 mutations in Japanese patients with dystrophic epidermolysis bullosa: positional effects of premature termination codon mutations on clinical severity. Japanese Collaborative Study Group on Epidermolysis Bullosa. J. Invest. Dermatol. 112, 991–993 (1999).
Google Scholar
Sawamura, D. et al. Genetic studies of 20 Japanese families of dystrophic epidermolysis bullosa. J. Hum. Genet 50, 543–546 (2005).
Google Scholar
Dang, N. & Murrell, D. F. Mutation analysis and characterization of COL7A1 mutations in dystrophic epidermolysis bullosa. Exp. Dermatol. 17, 553–568 (2008).
Google Scholar
Koshida, S. et al. Hallopeau-Siemens dystrophic epidermolysis bullosa due to homozygous 5818delC mutation in the COL7A gene. Pediatr. Int. 55, 234–237 (2013).
Google Scholar
Saito, M., Masunaga, T., Teraki, Y., Takamori, K. & Ishiko, A. Genotype-phenotype correlations in six Japanese patients with recessive dystrophic epidermolysis bullosa with the recurrent p.Glu2857X mutation. J. Dermatol. Sci. 52, 13–20 (2008).
Google Scholar
Gurumurthy, C. B. et al. Creation of CRISPR-based germline-genome-engineered mice without ex vivo handling of zygotes by i-GONAD. Nat. Protoc. 14, 2452–2482 (2019).
Google Scholar
Aoto, K. et al. ATP6V0A1 encoding the a1-subunit of the V0 domain of vacuolar H+-ATPases is essential for brain development in humans and mice. Nat. Commun. 12, 2107 (2021).
Google Scholar
Wasylishen, A. R. et al. Daxx maintains endogenous retroviral silencing and restricts cellular plasticity in vivo. Sci. Adv. 6, eaba8415 (2020).
Google Scholar
Ho, Y. T. et al. Longitudinal single-cell transcriptomics reveals a role for Serpina3n-mediated resolution of inflammation in a mouse colitis model. Cell Mol. Gastroenterol. Hepatol. 12, 547–566 (2021).
Google Scholar
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Google Scholar
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
Google Scholar
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Google Scholar
Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015).
Google Scholar
Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).
Google Scholar
Joost, S. et al. Single-cell transcriptomics reveals that differentiation and spatial signatures shape epidermal and hair follicle heterogeneity. Cell Syst. 3, 221–237 e229 (2016).
Google Scholar
Joost, S. et al. The molecular anatomy of mouse skin during hair growth and rest. Cell Stem Cell 26, 441–457.e447 (2020).
Google Scholar
Chacon-Solano, E. et al. Fibroblast activation and abnormal extracellular matrix remodelling as common hallmarks in three cancer-prone genodermatoses. Br. J. Dermatol. 181, 512–522 (2019).
Google Scholar
Capecchi, M. R. Gene targeting in mice: functional analysis of the mammalian genome for the twenty-first century. Nat. Rev. Genet 6, 507–512 (2005).
Google Scholar
