Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
Profiling the epigenomic landscape of late embryonic and adult mouse hind limb muscles
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 17 February 2026

Profiling the epigenomic landscape of late embryonic and adult mouse hind limb muscles

  • Samantha R. Queeno1 na1,
  • Alexander S. Okamoto2 na1,
  • Damien M. Callahan3,
  • Matthew C. O’Neill4,
  • Terence D. Capellini2,5 &
  • …
  • Kirstin N. Sterner1 

Scientific Reports , Article number:  (2026) Cite this article

  • 501 Accesses

  • Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Cell biology
  • Developmental biology
  • Genetics
  • Molecular biology
  • Physiology

Abstract

Skeletal muscles are essential for movement, supporting a wide range of locomotor behaviors. Muscle tissue is composed of multiple cell types including “fast” and “slow” myofibers, whose contractile properties are largely influenced by selective expression of myosin heavy chain (MyHC) isoforms. While ‘super-enhancers’ regulating MyHC gene clusters have been identified, the cis-regulatory elements (CREs) controlling non-MyHC genes important to myofiber physiology remain less defined. Here, we profile the regulatory landscape of two pairs of mouse hind limb muscles differing in MyHC expression at a late embryonic (E18.5) and adult time point to identify candidate CREs that may regulate genes important to myofiber type. Gene expression and chromatin accessibility analyses revealed that epigenetic differences at E18.5 largely reflect limb patterning, whereas adult differences reflect myofiber differentiation. We identified thousands of differentially accessible regions that may regulate genes important for muscle development, muscle biology, and myofiber identity. Among these, twelve conserved, muscle-specific CREs associated with myofiber type were tested for regulatory activity. Nine enhanced and three reduced gene activity in vitro, although their phenotypic effects remain unknown. By profiling multiple muscles across two time points, our study extends current understanding of conserved, muscle-specific CREs that regulate gene expression during myogenesis.

Similar content being viewed by others

A fast Myosin super enhancer dictates muscle fiber phenotype through competitive interactions with Myosin genes

Article Open access 24 February 2022

Impaired myogenesis in limb girdle muscular dystrophy type 2B

Article Open access 30 September 2025

Single-cell transcriptomics reveal mechanisms of skeletal muscle differentiation across duck embryonic development

Article Open access 11 February 2026

Data availability

All ATAC-seq and RNA-seq datasets generated as part of this study have been deposited to the NCBI GEO repository under accession number GSE292230 and GSE292232. All data included in this study are available upon reasonable request by contact with the corresponding author.

References

  1. Janssen, I., Heymsfield, S. B., Wang, Z. & Ross, R. Skeletal muscle mass and distribution in 468 men and women aged 18–88 year. J. Appl. Physiol. 89, 81–88 (2000).

    Google Scholar 

  2. Zihlman, A. L. & Bolter, D. R. Body composition in Pan Paniscus compared with homo sapiens has implications for changes during human evolution. Proc. Natl. Acad. Sci. 112, 7466–7471 (2015).

    Google Scholar 

  3. O’Neill, M. C., Umberger, B. R., Holowka, N. B., Larson, S. G. & Reiser, P. J. Chimpanzee super strength and human skeletal muscle evolution. In: Proceedings of the National Academy of Sciences. 114 7343–7348 (2017).

  4. King, A. M., Loiselle, D. S. & Kohl, P. Force generation for locomotion of vertebrates: skeletal muscle overview. IEEE J. Oceanic Eng. 29, 684–691 (2004).

    Google Scholar 

  5. Brooks, S. V. Current topics for teaching skeletal muscle physiology. Adv. Physiol. Educ. 27, 171–182 (2003).

    Google Scholar 

  6. Periasamy, M., Herrera, J. L. & Reis, F. C. G. Skeletal muscle thermogenesis and its role in whole body energy metabolism. Diabetes Metab. J. 41, 327 (2017).

    Google Scholar 

  7. Bourey, R. E., Koranyi, L., James, D. E., Mueckler, M. & Permutt, M. A. Effects of altered glucose homeostasis on glucose transporter expression in skeletal muscle of the rat. J. Clin. Invest. 86, 542–547 (1990).

    Google Scholar 

  8. Wolfe, R. R. The underappreciated role of muscle in health and disease. Am. J. Clin. Nutr. 84, 475–482 (2006).

    Google Scholar 

  9. Moreno-Justicia, R. et al. Human skeletal muscle fiber heterogeneity beyond myosin heavy chains. Nat. Commun. 16, 1764 (2025).

    Google Scholar 

  10. Murgia, M. et al. Protein profile of fiber types in human skeletal muscle: a single-fiber proteomics study. Skelet. Muscle. 11, 24 (2021).

    Google Scholar 

  11. Stuart, C. A. et al. Myosin content of individual human muscle fibers isolated by laser capture microdissection. Am. J. Physiology-Cell Physiol. 310, C381–C389 (2016).

    Google Scholar 

  12. Petrany, M. J. et al. Single-nucleus RNA-seq identifies transcriptional heterogeneity in multinucleated skeletal myofibers. Nat. Commun. 11, 6374 (2020).

    Google Scholar 

  13. Howald, H., Hoppeler, H., Claassen, H., Mathieu, O. & Straub, R. Influences of endurance training on the ultrastructural composition of the different muscle fiber types in humans. Pflügers Archive Eur. J. Physiol. 403, 369–376 (1985).

    Google Scholar 

  14. Bárány, M. ATPase activity of myosin correlated with speed of muscle shortening. J. Gen. Physiol. 50, 197–218 (1967).

    Google Scholar 

  15. Schiaffino, S. & Reggiani, C. Fiber types in mammalian skeletal muscles. Physiol. Rev. 91, 1447–1531 (2011).

    Google Scholar 

  16. Schiaffino, S., Reggiani, C., Kostrominova, T. Y., Mann, M. & Murgia, M. Mitochondrial specialization revealed by single muscle fiber proteomics: focus on the Krebs cycle. Scand. J. Med. Sci. Sports. 25, 41–48 (2015).

    Google Scholar 

  17. Edman, S., Flockhart, M., Larsen, F. J. & Apró, W. Need for speed: human fast-twitch mitochondria favor power over efficiency. Mol. Metab. 79, 101854 (2024).

    Google Scholar 

  18. Schiaffino, S., Rossi, A. C., Smerdu, V., Leinwand, L. A. & Reggiani, C. Developmental myosins: expression patterns and functional significance. Skelet. Muscle. 5, 22 (2015).

    Google Scholar 

  19. Pette, D. & Staron, R. S. Myosin isoforms, muscle fiber types, and transitions. Microsc Res. Tech. 50, 500–509 (2000).

    Google Scholar 

  20. Burke, R. E., Levine, D. N., Zajac, F. E., Tsairis, P. & Engel, W. K. Mammalian motor units: Physiological-histochemical correlation in three types in Cat gastrocnemius. Sci. (1979). 174, 709–712 (1971).

    Google Scholar 

  21. Bottinelli, R. & Reggiani, C. Human skeletal muscle fibres: molecular and functional diversity. Prog Biophys. Mol. Biol. 73, 195–262 (2000).

    Google Scholar 

  22. Resnicow, D. I., Deacon, J. C., Warrick, H. M., Spudich, J. A. & Leinwand, L. A. Functional diversity among a family of human skeletal muscle myosin motors. In: Proceedings of the National Academy of Sciences. 107 1053–1058 (2010).

  23. Hagiwara, N., Yeh, M. & Liu, A. Sox6 is required for normal fiber type differentiation of fetal skeletal muscle in mice. Dev. Dyn. 236, 2062–2076 (2007).

    Google Scholar 

  24. Hennebry, A. et al. Myostatin regulates fiber-type composition of skeletal muscle by regulating MEF2 and myod gene expression. Am. J. Physiology-Cell Physiol. 296, C525–C534 (2009).

    Google Scholar 

  25. Chin, E. R. et al. A calcineurin-dependent transcriptional pathway controls skeletal muscle fiber type. Genes Dev. 12, 2499–2509 (1998).

    Google Scholar 

  26. Buller, A. J., Eccles, J. C. & Eccles, R. M. Differentiation of fast and slow muscles in the Cat Hind limb. J. Physiol. 150, 399–416 (1960).

    Google Scholar 

  27. Agbulut, O., Noirez, P. & Beaumont, F. Butler-Browne, G. Myosin heavy chain isoforms in postnatal muscle development of mice. Biol. Cell. 95, 399–406 (2003).

    Google Scholar 

  28. Arakelian, C. et al. Myosin S2 origins track evolution of strong binding on actin by azimuthal rolling of motor domain. Biophys. J. 108, 1495–1502 (2015).

    Google Scholar 

  29. Mascarello, F., Toniolo, L., Cancellara, P., Reggiani, C. & Maccatrozzo, L. Expression and identification of 10 sarcomeric MyHC isoforms in human skeletal muscles of different embryological origin. Diversity and similarity in mammalian species. Annals Anat. - Anatomischer Anzeiger. 207, 9–20 (2016).

    Google Scholar 

  30. Mishra, P., Varuzhanyan, G., Pham, A. H. & Chan, D. C. Mitochondrial dynamics is a distinguishing feature of skeletal muscle fiber types and regulates organellar compartmentalization. Cell. Metab. 22, 1033–1044 (2015).

    Google Scholar 

  31. Callahan, D. M., Umberger, B. R. & Kent, J. A. Mechanisms of in vivo muscle fatigue in humans: investigating age-related fatigue resistance with a computational model. J. Physiol. 594, 3407–3421 (2016).

    Google Scholar 

  32. Cooke, R., Franks, K., Luciani, G. B. & Pate, E. The Inhibition of rabbit skeletal muscle contraction by hydrogen ions and phosphate. J. Physiol. 395, 77–97 (1988).

    Google Scholar 

  33. Debold, E. P., Dave, H. & Fitts, R. H. Fiber type and temperature dependence of inorganic phosphate: implications for fatigue. Am. J. Physiology-Cell Physiol. 287, C673–C681 (2004).

    Google Scholar 

  34. Debold, E. P., Beck, S. E. & Warshaw, D. M. Effect of low pH on single skeletal muscle myosin mechanics and kinetics. Am. J. Physiology-Cell Physiol. 295, C173–C179 (2008).

    Google Scholar 

  35. Barclay, C. J., Constable, J. K. & Gibbs, C. L. Energetics of fast- and slow‐twitch muscles of the mouse. J. Physiol. 472, 61–80 (1993).

    Google Scholar 

  36. Barclay, C. J. Efficiency of Fast- and Slow-Twitch muscles of the mouse performing Cyclic contractions. J. Exp. Biol. 193, 65–78 (1994).

    Google Scholar 

  37. Queeno, S. R. et al. Human and African ape myosin heavy chain content and the evolution of hominin skeletal muscle. Comp. Biochem. Physiol. Mol. Integr. Physiol. 281, 111415 (2023).

    Google Scholar 

  38. Spainhower, K. B. et al. Coming to grips with life upside down: how myosin fiber type and metabolic properties of sloth hindlimb muscles contribute to suspensory function. J. Comp. Physiol. B. 191, 207–224 (2021).

    Google Scholar 

  39. Kimura, T., Kumakura, H., Inokuchi, S. & Ishida, H. Composition of muscle fibers in the slow loris, using the m. biceps brachii as an example. Primates 28, 525–532 (1987).

    Google Scholar 

  40. Okerblom, J. et al. Human-like Cmah inactivation in mice increases running endurance and decreases muscle fatigability: implications for human evolution. Proc. Royal Soc. B: Biol. Sci. 285, 20181656 (2018).

    Google Scholar 

  41. Poole, D. C. & Erickson, H. H. Highly Athletic Terrestrial Mammals: Horses and Dogs. In Comprehensive Physiology (Wiley, 2011). https://doi.org/10.1002/cphy.c091001.

    Google Scholar 

  42. LaPotin, S. et al. Divergent cis-regulatory evolution underlies the convergent loss of sodium channel expression in electric fish. Sci Adv 8, https://doi.org/10.1126/sciadv.abm2970 (2022).

    Google Scholar 

  43. Röckel, F. et al. Color intensity of the Red-Fleshed berry phenotype of vitis vinifera teinturier grapes varies due to a 408 bp duplication in the promoter of VvmybA1. Genes (Basel). 11, 891 (2020).

    Google Scholar 

  44. Aldea, D. et al. Repeated mutation of a developmental enhancer contributed to human thermoregulatory evolution. In: Proceedings of the National Academy of Sciences. 118 (2021).

  45. Gokhman, D. et al. Differential DNA methylation of vocal and facial anatomy genes in modern humans. Nat. Commun. 11, 1189 (2020).

    Google Scholar 

  46. Kvon, E. Z. et al. Progressive loss of function in a limb enhancer during snake evolution. Cell 167, 633–642e11 (2016).

    Google Scholar 

  47. Sicard, A. et al. Standing genetic variation in a tissue-specific enhancer underlies selfing-syndrome evolution in capsella. Proc. Natl. Acad. Sci. 113, 13911–13916 (2016).

    Google Scholar 

  48. Wang, X. et al. Genetic variation in ZmVPP1 contributes to drought tolerance in maize seedlings. Nat. Genet. 48, 1233–1241 (2016).

    Google Scholar 

  49. Jiang, P. & Rausher, M. Two genetic changes in cis-regulatory elements caused evolution of petal spot position in Clarkia. Nat. Plants. 4, 14–22 (2018).

    Google Scholar 

  50. Kratochwil, C. F. et al. Agouti-related peptide 2 facilitates convergent evolution of Stripe patterns across cichlid fish radiations. Sci. (1979). 362, 457–460 (2018).

    Google Scholar 

  51. Letelier, J. et al. A conserved Shh cis-regulatory module highlights a common developmental origin of unpaired and paired fins. Nat. Genet. 50, 504–509 (2018).

    Google Scholar 

  52. Roeske, M. J., Camino, E. M., Grover, S., Rebeiz, M. & Williams, T. M. Cis-regulatory evolution integrated the Bric-à-brac transcription factors into a novel fruit fly gene regulatory network. Elife 7, e32273 (2018).

    Google Scholar 

  53. Thompson, A. C. et al. A novel enhancer near the Pitx1 gene influences development and evolution of pelvic appendages in vertebrates. Elife 7, e38555 (2018).

    Google Scholar 

  54. Lewis, J. J. et al. Parallel evolution of ancient, pleiotropic enhancers underlies butterfly wing pattern mimicry. In: Proceedings of the National Academy of Sciences. 116 24174–24183 (2019).

  55. Dos Santos, M. et al. A fast myosin super enhancer dictates muscle fiber phenotype through competitive interactions with myosin genes. Nat. Commun. 13, 1039 (2022).

    Google Scholar 

  56. Long, K. et al. Identification of enhancers responsible for the coordinated expression of myosin heavy chain isoforms in skeletal muscle. BMC Genom. 23, 519 (2022).

    Google Scholar 

  57. Ramachandran, K. et al. Dynamic enhancers control skeletal muscle identity and reprogramming. PLoS Biol. 17, e3000467 (2019).

    Google Scholar 

  58. Belotti, E. et al. H2A.Z is dispensable for both basal and activated transcription in post-mitotic mouse muscles. Nucleic Acids Res. 48, 4601–4613 (2020).

    Google Scholar 

  59. Dos Santos, M. et al. Single-nucleus RNA-seq and FISH identify coordinated transcriptional activity in mammalian myofibers. Nat. Commun. 11, 5102 (2020).

    Google Scholar 

  60. Rovito, D. et al. Myod1 and GR coordinate myofiber-specific transcriptional enhancers. Nucleic Acids Res. 49, 4472–4492 (2021).

    Google Scholar 

  61. Sahinyan, K. et al. Application of ATAC-Seq for genome-wide analysis of the chromatin state at single myofiber resolution. Elife 11, e72792 (2022).

    Google Scholar 

  62. Lin, H. et al. Reprogramming of cis-regulatory networks during skeletal muscle atrophy in male mice. Nat. Commun. 14, 6581 (2023).

    Google Scholar 

  63. Blackburn, D. M. et al. The E3 ubiquitin ligase Nedd4L preserves skeletal muscle stem cell quiescence by inhibiting their activation. iScience 27, 110241 (2024).

    Google Scholar 

  64. Garcia, P. et al. Setdb1 protects genome integrity in murine muscle stem cells to allow for regenerative myogenesis and inflammation. Dev. Cell. 59, 2375–2392e8 (2024).

    Google Scholar 

  65. Dos Santos, M. et al. Opposing gene regulatory programs governing myofiber development and maturation revealed at single nucleus resolution. Nat. Commun. 14, 4333 (2023).

    Google Scholar 

  66. National Research Council (U.S.). Committee for the Update of the Guide for the Care and Use of Laboratory Animals., Institute for Laboratory Animal Research (U.S.) & National Academies Press (U.S.). Guide for the Care and Use of Laboratory Animals. https://doi.org/10.17226/12910 (National Academies Press, 2011).

  67. Kilkenny, C., Browne, W., Cuthill, I. C., Emerson, M. & Altman, D. G. Animal research: reporting in vivo experiments: the ARRIVE guidelines. Br. J. Pharmacol. 160, 1577–1579 (2010).

    Google Scholar 

  68. Wigmore, P. M. & Dunglison, G. F. The generation of fiber diversity during myogenesis. Int. J. Dev. Biol. 42, 117–125 (1998).

    Google Scholar 

  69. Staack, A., Donjacour, A. A., Brody, J., Cunha, G. R. & Carroll, P. Mouse urogenital development: a practical approach. Differentiation 71, 402–413 (2003).

    Google Scholar 

  70. Terry, E. E. et al. Transcriptional profiling reveals extraordinary diversity among skeletal muscle tissues. Elife 7, e34613 (2018).

    Google Scholar 

  71. Burkholder, T. J., Fingado, B., Baron, S. & Lieber, R. L. Relationship between muscle fiber types and sizes and muscle architectural properties in the mouse hindlimb. J. Morphol. 221, 177–190 (1994).

    Google Scholar 

  72. Asmussen, G. & Gaunitz, U. Temperature effects on isometric contractions of slow and fast twitch muscles of various rodents–dependence on fibre type composition: a comparative study. Biomed. Biochim. Acta. 48, S536–S541 (1989).

    Google Scholar 

  73. Augusto, V., Padovani, C. R. & Rocha Campos, G. E. Skeletal muscle fiber types in C57Bl6J mice. Brazilian J. Morphological Sci. 21, 89–94 (2004).

    Google Scholar 

  74. Bloemberg, D. & Quadrilatero, J. Rapid determination of myosin heavy chain expression in rat, mouse, and human skeletal muscle using multicolor immunofluorescence analysis. PLoS One https://doi.org/10.1371/journal.pone.0035273 (2012).

    Google Scholar 

  75. Hämäläinen, N. & Pette, D. The histochemical profiles of fast fiber types IIB, IID, and IIA in skeletal muscles of mouse, rat, and rabbit. J. Histochem. Cytochemistry. 41, 733–743 (1993).

    Google Scholar 

  76. Hitomi, Y. et al. Seven skeletal muscles rich in slow muscle fibers May function to sustain neutral position in the rodent hindlimb. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 140, 45–50 (2005).

    Google Scholar 

  77. Minchew, E. C., Williamson, N. C., Readyoff, A. T., McClung, J. M. & Spangenburg, E. E. Isometric skeletal muscle contractile properties in common strains of male laboratory mice. Front. Physiol. https://doi.org/10.3389/fphys.2022.937132 (2022).

    Google Scholar 

  78. Brasseur, J. E. et al. Systematic distribution of muscle fiber types in the medical gastrocnemius of the laboratory mouse: A morphometric analysis. Anat. Rec. 218, 396–401 (1987).

    Google Scholar 

  79. Charles, J. P., Cappellari, O., Spence, A. J., Hutchinson, J. R. & Wells, D. J. Musculoskeletal Geometry, muscle architecture and functional specialisations of the mouse hindlimb. PLoS One. 11, e0147669 (2016).

    Google Scholar 

  80. Hitz, B. C. et al. The ENCODE uniform analysis pipelines. bioRxiv https://doi.org/10.1101/2023.04.04.535623 (2023).

    Google Scholar 

  81. Racine, J. S. & RStudio: A Platform-Independent IDE for R and Sweave. J. Appl. Econom. 27, 167–172 (2012).

    Google Scholar 

  82. R Core Team. R: A Language and Environment for Statistical Computing. Preprint at https://www.R-project.org/ (2024).

  83. Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data. Preprint at http://www.bioinformatics.babraham.ac.uk/projects/fastqc (2015).

  84. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Google Scholar 

  85. Deng, Z. L., Münch, P. C., Mreches, R. & McHardy, A. C. Rapid and accurate identification of ribosomal RNA sequences via deep learning. Nucleic Acids Res. 50, e60–e60 (2022).

    Google Scholar 

  86. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Google Scholar 

  87. Witten, D. M. Classification and clustering of sequencing data using a Poisson model. Ann. Appl. Stat. 5 (2011).

  88. Liao, Y., Smyth, G. K. & Shi, W. FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Google Scholar 

  89. Love, M. I., Huber, W. & Anders, S. Moderated Estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Google Scholar 

  90. Wickham, H. Ggplot2: Elegant Graphics for Data Analysis. (Springer-Verlag, 2016).

  91. Chemello, F. et al. Degenerative and regenerative pathways underlying Duchenne muscular dystrophy revealed by single-nucleus RNA sequencing. Proc. Natl. Acad. Sci. 117, 29691–29701 (2020).

    Google Scholar 

  92. Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinform. 14, 7 (2013).

    Google Scholar 

  93. Wu, T. et al. ClusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innov. 2, 100141 (2021).

    Google Scholar 

  94. Korotkevich, G. et al. Fast gene set enrichment analysis. bioRxiv https://doi.org/10.1101/060012 (2021).

    Google Scholar 

  95. Yu, G., Wang, L. G., Yan, G. R. & He, Q. Y. DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics 31, 608–609 (2015).

    Google Scholar 

  96. Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods. 10, 1213–1218 (2013).

    Google Scholar 

  97. Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: A Method for Assaying Chromatin Accessibility Genome‐Wide. Curr Protoc. Mol. Biol 109, https://doi.org/10.1002/0471142727.mb2129s109 (2015).

    Google Scholar 

  98. Young, M. et al. The developmental impacts of natural selection on human pelvic morphology. Sci. Adv. 8 (2022).

  99. Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods. 14, 959–962 (2017).

    Google Scholar 

  100. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with bowtie 2. Nat. Methods. 9, 357–359 (2012).

    Google Scholar 

  101. Li, H. et al. The sequence Alignment/Map format and samtools. Bioinformatics 25, 2078–2079 (2009).

    Google Scholar 

  102. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Google Scholar 

  103. Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).

    Google Scholar 

  104. Li, Q., Brown, J. B., Huang, H. & Bickel, P. J. Measuring reproducibility of high-throughput experiments. Ann. Appl. Stat. 5 (2011).

  105. Laiker, I. & Frankel, N. Pleiotropic enhancers are ubiquitous regulatory elements in the human genome. Genome Biol. Evol. 14 (2022).

  106. Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE blacklist: identification of problematic regions of the genome. Sci. Rep. 9, 9354 (2019).

    Google Scholar 

  107. Guo, M. et al. Epigenetic profiling of growth plate chondrocytes sheds insight into regulatory genetic variation influencing height. Elife 6 (2017).

  108. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Google Scholar 

  109. Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R. & Siepel, A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 20, 110–121 (2010).

    Google Scholar 

  110. Karolchik, D. The UCSC genome browser database. Nucleic Acids Res. 31, 51–54 (2003).

    Google Scholar 

  111. Maas, S. A. & Fallon, J. F. Single base pair change in the long-range Sonic Hedgehog limb‐specific enhancer is a genetic basis for preaxial polydactyly. Dev. Dyn. 232, 345–348 (2005).

    Google Scholar 

  112. Prabhakar, S. et al. Human-specific gain of function in a developmental enhancer. Sci. (1979). 321, 1346–1350 (2008).

    Google Scholar 

  113. Wittkopp, P. J. & Kalay, G. Cis -regulatory elements: molecular mechanisms and evolutionary processes underlying divergence. Nat. Rev. Genet. 13, 59–69 (2011).

    Google Scholar 

  114. Richard, D. et al. Evolutionary selection and constraint on human knee chondrocyte regulation impacts osteoarthritis risk. Cell 181, 362–381e28 (2020).

    Google Scholar 

  115. Yu, G., Wang, L. G. & He, Q. Y. ChIPseeker: an R/Bioconductor package for chip peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).

    Google Scholar 

  116. McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

    Google Scholar 

  117. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell. 38, 576–589 (2010).

    Google Scholar 

  118. Bengtsen, M. et al. Comparing the epigenetic landscape in myonuclei purified with a PCM1 antibody from a fast/glycolytic and a slow/oxidative muscle. PLoS Genet. 17, e1009907 (2021).

    Google Scholar 

  119. Vincze, T., Posfai, J. & Roberts, R. J. NEBcutter: a program to cleave DNA with restriction enzymes. Nucleic Acids Res. 31, 3688–3691 (2003).

    Google Scholar 

  120. Maxam, A. M. (ed, W.) A new method for sequencing DNA. Proc. Natl. Acad. Sci. 74 560–564 (1977).

    Google Scholar 

  121. Sun, C. et al. Lineage tracing of nuclei in skeletal myofibers uncovers distinct transcripts and interplay between myonuclear populations. Nat. Commun. 15, 9372 (2024).

    Google Scholar 

  122. Van den Berge, K. et al. Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects. Cell. Rep. Methods. 2, 100321 (2022).

    Google Scholar 

  123. Yamaguchi, Y., Kodama, R. & Yamada, S. Morphogenetic progression of thigh and lower leg muscles during human embryonic development. Anat. Rec. 306, 2072–2080 (2023).

    Google Scholar 

  124. Queeno, S. R., Sterner, K. N. & O’Neill, M. C. Meta-analysis data of skeletal muscle slow fiber content across mammalian species. Data Brief. 50, 109520 (2023).

    Google Scholar 

  125. Christ, B. & Brand-Saberi, B. Limb muscle development. Int. J. Dev. Biol. 46, 905–914 (2002).

    Google Scholar 

  126. Ontell, M. P., Sopper, M. M., Lyons, G., Buckingham, M. & Ontell, M. Modulation of contractile protein gene expression in fetal murine crural muscles: emergence of muscle diversity. Dev. Dyn. 198, 203–213 (1993).

    Google Scholar 

  127. Sensiate, L. A. et al. Dact gene expression profiles suggest a role for this gene family in integrating Wnt and TGF-β signaling pathways during chicken limb development. Dev. Dyn. 243, 428–439 (2014).

    Google Scholar 

  128. Hostikka, S. L. & Capecchi, M. R. The mouse Hoxc11 gene: genomic structure and expression pattern. Mech. Dev. 70, 133–145 (1998).

    Google Scholar 

  129. Steingruber, L. et al. ALDH1A1 and ALDH1A3 paralogues of aldehyde dehydrogenase 1 control myogenic differentiation of skeletal muscle satellite cells by retinoic acid-dependent and -independent mechanisms. Cell. Tissue Res. 394, 515–528 (2023).

    Google Scholar 

  130. Lin, X. et al. Hoxa11 and Hoxa13 facilitate slow-twitch muscle formation in C2C12 cells and indirectly affect the lipid deposition of 3T3‐L1 cells. Animal Sci. J. 92, (2021).

  131. de Wilde, J. et al. The embryonic genes Dkk3, Hoxd8, Hoxd9 and Tbx1 identify muscle types in a diet-independent and fiber-type unrelated way. BMC Genom. 11, 176 (2010).

    Google Scholar 

  132. Ono, Y. et al. Scleraxis-lineage cells are required for correct muscle patterning. Development https://doi.org/10.1242/dev.201101 (2023).

    Google Scholar 

  133. 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 

  134. Ataman, B. et al. Evolution of osteocrin as an activity-regulated factor in the primate brain. Nature 539, 242–247 (2016).

    Google Scholar 

  135. Zito, A. et al. Neuritin 1 promotes neuronal migration. Brain Struct. Funct. 219, 105–118 (2014).

    Google Scholar 

  136. Wang, J. et al. Long Non-coding RNA HOTAIR in Central Nervous System Disorders: New Insights in Pathogenesis, Diagnosis, and Therapeutic Potential. Front. Mol. Neurosci 15, 949095 (2022).

    Google Scholar 

  137. Li, S. M. H. et al. Skin regional specification and higher-order HoxC regulation. Sci. Adv. https://doi.org/10.1126/sciadv.ado2223 (2025).

    Google Scholar 

  138. Gibson-Brown, J. J., Agulnik, S. I., Silver, L. M., Niswander, L. & Papaioannou, V. E. Involvement of T-box genes Tbx2-Tbx5 in vertebrate limb specification and development. Development 125, 2499–2509 (1998).

    Google Scholar 

  139. Sweat, M. E. et al. Tbx5 maintains atrial identity in postnatal cardiomyocytes by regulating an atrial-specific enhancer network. Nat. Cardiovasc. Res. 2, 881–898 (2023).

    Google Scholar 

  140. Cardoso-Moreira, M. et al. Gene expression across mammalian organ development. Nature 571, 505–509 (2019).

    Google Scholar 

  141. La Manno, G. et al. Molecular architecture of the developing mouse brain. Nature 596, 92–96 (2021).

    Google Scholar 

  142. Cai, S. et al. Integrative single-cell RNA-seq and ATAC-seq analysis of myogenic differentiation in pig. BMC Biol. 21, 19 (2023).

    Google Scholar 

  143. Jin, Y. et al. Glutathione S-transferase mu 2 inhibits hepatic steatosis via ASK1 suppression. Commun. Biol. 5, 326 (2022).

    Google Scholar 

  144. O’Reilly, M. E. et al. linc-ADAIN, a human adipose lincRNA, regulates adipogenesis by modulating KLF5 and IL-8 mRNA stability. Cell. Rep. 43, 114240 (2024).

    Google Scholar 

  145. Wang, F., Liang, R., Soibam, B., Yang, J. & Liu, Y. Coregulatory long non-coding RNA and protein-coding genes in serum starved cells. Biochim. Et Biophys. Acta (BBA) - Gene Regul. Mech. 1862, 84–95 (2019).

    Google Scholar 

  146. Scott, T. A., Soemardy, C., Ray, R. M. & Morris, K. V. Targeted zinc-finger repressors to the oncogenic HBZ gene inhibit adult T-cell leukemia (ATL) proliferation. NAR Cancer https://doi.org/10.1093/narcan/zcac046 (2023).

    Google Scholar 

  147. Yamada, M., Warabi, E., Oishi, H., Lira, V. A. & Okutsu, M. Muscle p62 stimulates the expression of antioxidant proteins alleviating cancer cachexia. FASEB J. 37 (2023).

  148. Huraskin, D. et al. Wnt/β-catenin signaling via Axin2 is required for myogenesis and, together with YAP/Taz and Tead1, active in IIa/IIx muscle fibers. Development 143, 3128–3142 (2016).

    Google Scholar 

  149. Hwang, M., Lee, E. J., Chung, M. J., Park, S. & Jeong, K. S. Five transcriptional factors reprogram fibroblast into myogenic lineage cells via paraxial mesoderm stage. Cell. Cycle. 19, 1804–1816 (2020).

    Google Scholar 

  150. Bonczek, O., Balcar, V. J. & Šerý, O. PAX9 gene mutations and tooth agenesis: A review. Clin. Genet. 92, 467–476 (2017).

    Google Scholar 

  151. Mita, Y. et al. R-spondin3 is a myokine that differentiates myoblasts to type I fibres. Sci. Rep. 12, 13020 (2022).

    Google Scholar 

  152. Pan, H. et al. A role for Zic1 and Zic2 in Myf5 regulation and Somite myogenesis. Dev. Biol. 351, 120–127 (2011).

    Google Scholar 

  153. Deng, C. et al. TNFRSF19 inhibits TGFβ signaling through interaction with TGFβ receptor type I to promote tumorigenesis. Cancer Res. 78, 3469–3483 (2018).

    Google Scholar 

  154. Shima, N. et al. Up-regulated expression of two-pore domain K + channels, KCNK1 and KCNK2, is involved in the proliferation and migration of pulmonary arterial smooth muscle cells in pulmonary arterial hypertension. Front Cardiovasc. Med 11, 1343804 (2024).

    Google Scholar 

  155. Jang, D. G., Kwon, K. Y., Song, E. K. & Park, T. J. Integrin β-like 1 protein (ITGBL1) promotes cell migration by preferentially inhibiting integrin-ECM binding at the trailing edge. Genes Genomics. 44, 405–413 (2022).

    Google Scholar 

  156. Lee, W. et al. Role of HIF-1α-Activated IL-22/IL-22R1/Bmi1 signaling modulates the Self-Renewal of cardiac stem cells in acute myocardial ischemia. Stem Cell. Rev. Rep. 20, 2194–2214 (2024).

    Google Scholar 

  157. Javed, A., Abbas, H. B., Ahmed, S., Ahmed, A. & Trali, G. A. In Silico analysis of molecular interactions of FZD10 in Wnt signaling pathway involved in wound healing. Pakistan J. Med. Health Sci. 15, 2841–2844 (2021).

    Google Scholar 

  158. Yu, X., Riley, T. & Levine, A. J. The regulation of the endosomal compartment by p53 the tumor suppressor gene. FEBS J. 276, 2201–2212 (2009).

    Google Scholar 

  159. Bomholt, A. B. et al. Evaluation of commercially available glucagon receptor antibodies and glucagon receptor expression. Commun. Biol. 5, 1278 (2022).

    Google Scholar 

  160. Guo, F. et al. NOTUM promotes thermogenic capacity and protects against diet-induced obesity in male mice. Sci. Rep. 11, 16409 (2021).

    Google Scholar 

  161. Lee, L. A., Karabina, A., Broadwell, L. J. & Leinwand, L. A. The ancient sarcomeric myosins found in specialized muscles. Skelet. Muscle. 9, 7 (2019).

    Google Scholar 

  162. Desjardins, P. R., Burkman, J. M., Shrager, J. B., Allmond, L. A. & Stedman, H. H. Evolutionary implications of three novel members of the human sarcomeric myosin heavy chain gene family. Mol. Biol. Evol. 19, 375–393 (2002).

    Google Scholar 

  163. Zhu, J. et al. Comparative genomics search for losses of Long-Established genes on the human lineage. PLoS Comput. Biol. 3, e247 (2007).

    Google Scholar 

  164. Hoh, J. F. Y. Myosin heavy chains in extraocular muscle fibres: Distribution, regulation and function. Acta Physiologica 231, e13535 (2021).

    Google Scholar 

  165. Klemm, S. L., Shipony, Z. & Greenleaf, W. J. Chromatin accessibility and the regulatory epigenome. Nat. Rev. Genet. 20, 207–220 (2019).

    Google Scholar 

  166. Picardi, E. & Pesole, G. Mitochondrial genomes gleaned from human whole-exome sequencing. Nat. Methods. 9, 523–524 (2012).

    Google Scholar 

  167. Montefiori, L. et al. Reducing mitochondrial reads in ATAC-seq using CRISPR/Cas9. Sci. Rep. 7, 2451 (2017).

    Google Scholar 

  168. Rhodes, C. T. et al. An epigenome atlas of neural progenitors within the embryonic mouse forebrain. Nat. Commun. 13, 4196 (2022).

    Google Scholar 

  169. Abbasova, L. et al. CUT&Tag recovers up to half of ENCODE ChIP-seq histone acetylation peaks. Nat. Commun. 16, 2993 (2025).

    Google Scholar 

  170. Asfour, H. A., Allouh, M. Z. & Said, R. S. Myogenic regulatory factors: the orchestrators of myogenesis after 30 years of discovery. Exp. Biol. Med. 243, 118–128 (2018).

    Google Scholar 

  171. Zhou, P. et al. Identification of novel transcription factors regulated by H3K27 acetylation in myogenic differentiation of porcine skeletal muscle satellite cells. The FASEB Journal 38, e70144 (2024).

    Google Scholar 

  172. Spinelli, S. et al. Estrogen-Related receptor α: A key transcription factor in the regulation of energy metabolism at an organismic level and a target of the ABA/LANCL hormone receptor system. Int. J. Mol. Sci. 25, 4796 (2024).

    Google Scholar 

  173. Lee, H. J. et al. Dysregulation of nuclear receptor COUP-TFII impairs skeletal muscle development. Sci. Rep. 7, 3136 (2017).

    Google Scholar 

  174. Tontonoz, P. et al. The orphan nuclear receptor Nur77 is a determinant of myofiber size and muscle mass in mice. Mol. Cell. Biol. 35, 1125–1138 (2015).

    Google Scholar 

  175. Liu, N. et al. Requirement of MEF2A, C, and D for skeletal muscle regeneration. In Proceedings of the National Academy of Sciences. 111 4109–4114 (2014).

  176. Sanchez, A. M. J., Candau, R. B. & Bernardi, H. FoxO transcription factors: their roles in the maintenance of skeletal muscle homeostasis. Cell. Mol. Life Sci. 71, 1657–1671 (2014).

    Google Scholar 

  177. Sousa-Victor, P., García-Prat, L. & Muñoz-Cánoves, P. Control of satellite cell function in muscle regeneration and its disruption in ageing. Nat. Rev. Mol. Cell. Biol. 23, 204–226 (2022).

    Google Scholar 

  178. Braun, T. & Gautel, M. Transcriptional mechanisms regulating skeletal muscle differentiation, growth and homeostasis. Nat. Rev. Mol. Cell. Biol. 12, 349–361 (2011).

    Google Scholar 

  179. Doni Jayavelu, N., Jajodia, A., Mishra, A. & Hawkins, R. D. Candidate silencer elements for the human and mouse genomes. Nat. Commun. 11, 1061 (2020).

    Google Scholar 

  180. Wong, E. S. et al. Deep conservation of the enhancer regulatory code in animals. Science (1979) https://doi.org/10.1126/science.aax8137 (2020).

    Google Scholar 

  181. Orchard, P. et al. Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits. Genome Res. 31, 2258–2275 (2021).

    Google Scholar 

  182. Nord, A. S. et al. Rapid and pervasive changes in Genome-wide enhancer usage during mammalian development. Cell 155, 1521–1531 (2013).

    Google Scholar 

  183. Thomas, S. et al. Dynamic reprogramming of chromatin accessibility during drosophilaembryo development. Genome Biol. 12, R43 (2011).

    Google Scholar 

  184. Dunwell, T. L. & Holland, P. W. H. Diversity of human and mouse homeobox gene expression in development and adult tissues. BMC Dev. Biol. 16, 40 (2016).

    Google Scholar 

  185. Gluck, C. et al. RNA-seq based transcriptomic map reveals new insights into mouse salivary gland development and maturation. BMC Genom. 17, 923 (2016).

    Google Scholar 

  186. Su, X. et al. Single-cell RNA-Seq analysis reveals dynamic trajectories during mouse liver development. BMC Genom. 18, 946 (2017).

    Google Scholar 

  187. He, P. et al. The changing mouse embryo transcriptome at whole tissue and single-cell resolution. Nature 583, 760–767 (2020).

    Google Scholar 

  188. Petit, F., Sears, K. E. & Ahituv, N. Limb development: a paradigm of gene regulation. Nat. Rev. Genet. 18, 245–258 (2017).

    Google Scholar 

  189. Wong, M. K. et al. Timing of Tissue-specific cell division requires a differential onset of zygotic transcription during metazoan embryogenesis. J. Biol. Chem. 291, 12501–12513 (2016).

    Google Scholar 

  190. Lacombe, J. et al. Genetic and functional modularity of hox activities in the specification of Limb-Innervating motor neurons. PLoS Genet. 9, e1003184 (2013).

    Google Scholar 

  191. Lawrence, J. E. G. et al. HOX gene expression in the developing human spine. Nat. Commun. 15, 10023 (2024).

    Google Scholar 

  192. Capdevila, J. & Belmonte, J. C. I. Patterning mechanisms controlling vertebrate limb development. Annu. Rev. Cell. Dev. Biol. 17, 87–132 (2001).

    Google Scholar 

  193. Hnisz, D. et al. Super-Enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).

    Google Scholar 

  194. Lei, S. et al. Roles of super enhancers and enhancer RNAs in skeletal muscle development and disease. Cell. Cycle. 22, 495–505 (2023).

    Google Scholar 

  195. Zhang, S. et al. Analyzing super-enhancer Temporal dynamics reveals potential critical enhancers and their gene regulatory networks underlying skeletal muscle development. Genome Res. 34, 2190–2202 (2024).

    Google Scholar 

  196. Casanova, A., Wevers, A., Navarro-Ledesma, S. & Pruimboom, L. Mitochondria: It is all about energy. Front Physiol 14, 1114231 (2023).

    Google Scholar 

  197. Sin, J. et al. Mitophagy is required for mitochondrial biogenesis and myogenic differentiation of C2C12 myoblasts. Autophagy 12, 369–380 (2016).

    Google Scholar 

  198. Smerdu, V. & Cvetko, E. Myosin heavy chain-2b transcripts and isoform are expressed in human laryngeal muscles. Cells Tissues Organs. 198, 75–86 (2013).

    Google Scholar 

  199. Pereira Sant’Ana, J. A., Ennion, S., Sargeant, A. J., Moorman, A. F. & Goldspink, G. Comparison of the molecular, antigenic and ATPase determinants of fast myosin heavy chains in rat and human: a single-fibre study. Pflügers Archive Eur. J. Physiol. 435, 151–163 (1997).

    Google Scholar 

  200. Staron, R. S. Human skeletal muscle fiber types: Delineation, Development, and distribution. Can. J. Appl. Physiol. 22, 307–327 (1997).

    Google Scholar 

  201. Rivero, J. L., Serrano, A. L., Barrey, E., Valette, J. P. & Jouglin, M. Analysis of myosin heavy chains at the protein level in horse skeletal muscle. J. Muscle Res. Cell. Motil. 20, 211–221 (1999).

    Google Scholar 

  202. Harrison, B. C., Allen, D. L. & Leinwand, L. A. IIb or not IIb? Regulation of myosin heavy chain gene expression in mice and men. Skelet. Muscle. 1, 5 (2011).

    Google Scholar 

  203. IJkema-Paassen, J. & Gramsbergen, A. Development of postural muscles and their innervation. Neural Plast. 12, 141–151 (2005).

    Google Scholar 

  204. Pette, D. Historical perspectives: plasticity of mammalian skeletal muscle. J. Appl. Physiol. 90, 1119–1124 (2001).

    Google Scholar 

  205. Wigmore, P. M. & Evans, D. J. R. Molecular and cellular mechanisms involved in the generation of fiber diversity during myogenesis. Int. rev. cytol. 216, 175–232. https://doi.org/10.1016/S0074-7696(02)16006-2 (2002).

    Google Scholar 

  206. Lang, F. et al. Single muscle fiber proteomics reveals distinct protein changes in slow and fast fibers during muscle atrophy. J. Proteome Res. 17, 3333–3347 (2018).

    Google Scholar 

  207. Ahn, J. S. et al. Ectopic overexpression of Porcine Myh1 increased in slow muscle fibers and enhanced endurance exercise in Transgenic mice. Int. J. Mol. Sci. 19, 2959 (2018).

    Google Scholar 

  208. Smerdu, V. Expression of MyHC-15 and ‐2x in human muscle spindles: an immunohistochemical study. J. Anat. 243, 826–841 (2023).

    Google Scholar 

  209. Smerdu, V., Ugwoke, C. K. & Šink, Ž. Co-expression of MyHC-15 with other known isoforms in rat muscle spindles. European J. Histochemistry 69, 4192 (2025).

    Google Scholar 

  210. Rossi, A. C., Mammucari, C., Argentini, C., Reggiani, C. & Schiaffino, S. Two novel/ancient myosins in mammalian skeletal muscles: MYH14/7b and MYH15 are expressed in extraocular muscles and muscle spindles. J. Physiol. 588, 353–364 (2010).

    Google Scholar 

  211. Yang, J. H. & Hansen, A. S. Enhancer selectivity in space and time: from enhancer–promoter interactions to promoter activation. Nat. Rev. Mol. Cell. Biol. 25, 574–591 (2024).

    Google Scholar 

  212. Lawrence, M., Daujat, S. & Schneider, R. Lateral thinking: how histone modifications regulate gene expression. Trends Genet. 32, 42–56 (2016).

    Google Scholar 

  213. Oe, M., Ojima, K. & Muroya, S. Difference in potential DNA methylation impact on gene expression between fast- and slow-type myofibers. Physiol. Genomics. 53, 69–83 (2021).

    Google Scholar 

  214. Wang, B., Starr, A. L. & Fraser, H. B. Cell-type-specific cis-regulatory divergence in gene expression and chromatin accessibility revealed by human-chimpanzee hybrid cells. Elife https://doi.org/10.7554/eLife.89594 (2024).

    Google Scholar 

  215. Reilly, S. K. & Noonan, J. P. Evolution of gene regulation in humans. Annu. Rev. Genomics Hum. Genet. 17, 45–67 (2016).

    Google Scholar 

  216. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods. 6, 377–382 (2009).

    Google Scholar 

  217. Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    Google Scholar 

  218. Giordani, L. et al. High-Dimensional Single-Cell cartography reveals novel skeletal Muscle-Resident cell populations. Mol. Cell. 74, 609–621e6 (2019).

    Google Scholar 

  219. Rubenstein, A. B. et al. Single-cell transcriptional profiles in human skeletal muscle. Sci. Rep. 10, 229 (2020).

    Google Scholar 

  220. Hollingsworth, E. W. et al. Rapid and quantitative functional interrogation of human enhancer variant activity in live mice. Nat. Commun. 16, 409 (2025).

    Google Scholar 

  221. Chang, T. Y. & Waxman, D. J. HDI-STARR-seq: Condition-specific enhancer discovery in mouse liver in vivo. BMC Genom. 25, 1240 (2024).

    Google Scholar 

  222. Osterwalder, M. et al. Characterization of Mammalian In Vivo Enhancers Using Mouse Transgenesis and CRISPR Genome Editing. Craniofacial Dev. Methods Protocols https://doi.org/10.1007/978-1-0716-1847-9_11 (2022).

    Google Scholar 

  223. Lambert, J. T. et al. Parallel functional testing identifies enhancers active in early postnatal mouse brain. Elife 10, e69479 (2021).

    Google Scholar 

  224. Pennacchio, L. A. et al. In vivo enhancer analysis of human conserved non-coding sequences. Nature 444, 499–502 (2006).

    Google Scholar 

Download references

Acknowledgements

This study was supported by the University of Oregon, National Science Foundation (SRQ BCS-1945809 and ASO DGE-1745303) and the Leakey Foundation. Thanks to Emmanuelle Boucicaut, Joseph Braud, Jack Chambers, Emma Freedman and Elijah Reed for their help in the cell culture lab and to Natalie Dunn, Carrie McCurdy, Kristin Kohler, Daniel Richard, Avika Gomez-Sharma, Allissa Van Steenis and Mariel Young for their expertise and technical support. This work benefited from access to the University of Oregon high performance computing cluster, Talapas. Some figures were created using BioRender.

Author information

Author notes
  1. Samantha R. Queeno and Alexander S. Okamoto contributed equally to this work.

Authors and Affiliations

  1. Department of Anthropology, University of Oregon, Eugene, OR, 97403, USA

    Samantha R. Queeno & Kirstin N. Sterner

  2. Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA

    Alexander S. Okamoto & Terence D. Capellini

  3. Department of Human Physiology, University of Oregon, Eugene, OR, 97403, USA

    Damien M. Callahan

  4. Department of Anatomy, Midwestern University, Glendale, AZ, 85309, USA

    Matthew C. O’Neill

  5. Broad Institute of MIT and Harvard, Cambridge, MA, 02138, USA

    Terence D. Capellini

Authors
  1. Samantha R. Queeno
    View author publications

    Search author on:PubMed Google Scholar

  2. Alexander S. Okamoto
    View author publications

    Search author on:PubMed Google Scholar

  3. Damien M. Callahan
    View author publications

    Search author on:PubMed Google Scholar

  4. Matthew C. O’Neill
    View author publications

    Search author on:PubMed Google Scholar

  5. Terence D. Capellini
    View author publications

    Search author on:PubMed Google Scholar

  6. Kirstin N. Sterner
    View author publications

    Search author on:PubMed Google Scholar

Contributions

SRQ, TDC, and KNS conceptualized the study; KNS and TDC supervised the project; ASO and SRQ performed the experiments; SRQ wrote the manuscript, analyzed the data, and prepared the figures; MCO and DMC substantially revised the manuscript for intellectual content, and all authors edited the manuscript. All authors approved the final version of the manuscript.

Corresponding author

Correspondence to Kirstin N. Sterner.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary Material 2

Supplementary Material 3

Supplementary Material 4

Supplementary Material 5

Supplementary Material 6

Supplementary Material 7

Supplementary Material 8

Supplementary Material 9

Supplementary Material 10

Supplementary Material 11

Supplementary Material 12

Supplementary Material 13

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Queeno, S.R., Okamoto, A.S., Callahan, D.M. et al. Profiling the epigenomic landscape of late embryonic and adult mouse hind limb muscles. Sci Rep (2026). https://doi.org/10.1038/s41598-025-32705-4

Download citation

  • Received: 27 June 2025

  • Accepted: 11 December 2025

  • Published: 17 February 2026

  • DOI: https://doi.org/10.1038/s41598-025-32705-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Development
  • Locomotion
  • Enhancer
  • Myosin heavy chain
  • Mitochondria
  • Metabolism
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on X
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com sitemap

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing