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Human–chimpanzee fused cells reveal cis-regulatory divergence underlying skeletal evolution

Nature Genetics volume 53, pages 467–476 (2021)Cite this article

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A Publisher Correction to this article was published on 24 March 2021

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Abstract

Gene regulatory divergence is thought to play a central role in determining human-specific traits. However, our ability to link divergent regulation to divergent phenotypes is limited. Here, we utilized human–chimpanzee hybrid induced pluripotent stem cells to study gene expression separating these species. The tetraploid hybrid cells allowed us to separate cis- from trans-regulatory effects, and to control for nongenetic confounding factors. We differentiated these cells into cranial neural crest cells, the primary cell type giving rise to the face. We discovered evidence of lineage-specific selection on the hedgehog signaling pathway, including a human-specific sixfold down-regulation of EVC2 (LIMBIN), a key hedgehog gene. Inducing a similar down-regulation of EVC2 substantially reduced hedgehog signaling output. Mice and humans lacking functional EVC2 show striking phenotypic parallels to human–chimpanzee craniofacial differences, suggesting that the regulatory divergence of hedgehog signaling may have contributed to the unique craniofacial morphology of humans.

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Fig. 1: Human–chimpanzee hybrid cells capture interspecific cis-expression changes.
Fig. 2: More divergent expression is more tightly associated with divergent traits.
Fig. 3: EVC2 down-regulation is likely to have reduced Hh signaling output in humans.
Fig. 4: EVC2 down-regulation in humans is likely to have reduced Hh signaling output.
Fig. 5: Phenotypes driven by EVC2 KO are observed between humans and chimpanzees.
Fig. 6: Phenotypes driven by reduced levels of functional EVC2 are observed between humans and chimpanzees.

Data availability

Data were deposited in GEO under accession numbers GSE144825 and GSE146481. Source data are provided with this paper.

Code availability

Code used in this study is available at https://github.com/TheFraserLab/ASEr, https://github.com/TheFraserLab/Agoglia_HumanChimpanzee2020 and https://github.com/TheFraserLab/Hornet/tree/master.

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References

  1. Aiello, L. & Dean, C. An Introduction to Human Evolutionary Anatomy (Elsevier, 2002).

  2. King, M. C. & Wilson, A. C. Evolution at two levels in humans and chimpanzees. Science 188, 107–116 (1975).

    Article  CAS  Google Scholar 

  3. Enard, D., Messer, P. W. & Petrov, D. A. Genome-wide signals of positive selection in human evolution. Genome Res. https://doi.org/10.1101/gr.164822.113 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Fraser, H. B. Gene expression drives local adaptation in humans. Genome Res. 23, 1089–1096 (2013).

    Article  CAS  Google Scholar 

  5. Wittkopp, P. J. & Kalay, G. Cis-regulatory elements: molecular mechanisms and evolutionary processes underlying divergence. Nat. Rev. Genet. https://doi.org/10.1038/nrg3095 (2012).

    Article  Google Scholar 

  6. Tirosh, I., Reikhav, S., Levy, A. A. & Barkai, N. A yeast hybrid provides insight into the evolution of gene expression regulation. Science https://doi.org/10.1126/science.1169766 (2009).

    Article  PubMed  Google Scholar 

  7. Wittkopp, P. J., Haerum, B. K. & Clark, A. G. Evolutionary changes in cis and trans gene regulation. Nature https://doi.org/10.1038/nature02698 (2004).

    Article  PubMed  Google Scholar 

  8. Pastinen, T. Genome-wide allele-specific analysis: insights into regulatory variation. Nat. Rev. Genet. https://doi.org/10.1038/nrg2815 (2010).

    Article  PubMed  Google Scholar 

  9. Fraser, H. B. Genome-wide approaches to the study of adaptive gene expression evolution. BioEssays https://doi.org/10.1002/bies.201000094 (2011).

    Article  PubMed  Google Scholar 

  10. Combs, P. A. et al. Tissue-specific cis-regulatory divergence implicates eloF in inhibiting interspecies mating in Drosophila. Curr. Biol. https://doi.org/10.1016/j.cub.2018.10.036 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Wang, X., Soloway, P. D. & Clark, A. G. Paternally biased X inactivation in mouse neonatal brain. Genome Biol. https://doi.org/10.1186/gb-2010-11-7-r79 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Agoglia, A. et al. Generation of human–chimpanzee hybrid stem cell-derived organoids to investigate cis-regulatory evolution of the cerebral cortex. Nature (in the press).

  13. Shakhova, O. & Sommer, L.. Neural crest-derived stem cells. StemBook https://doi.org/10.3824/stembook.1.51.1 (2010).

  14. Øvrebø, J. I. & Edgar, B. A. Polyploidy in tissue homeostasis and regeneration. Development https://doi.org/10.1242/dev.156034 (2018).

    Article  PubMed  Google Scholar 

  15. Shin, D.-H. et al. Characterization of tetraploid somatic cell nuclear transfer-derived human embryonic stem cells. Dev. Reprod. 21, 425–434 (2017).

    Article  Google Scholar 

  16. Cowan, C. A., Atienza, J., Melton, D. A. & Eggan, K. Nuclear reprogramming of somatic cells after fusion with human embryonic stem cells. Science 309, 1369–1373 (2005).

    Article  CAS  Google Scholar 

  17. Broughton, K. M. et al. Cardiac interstitial tetraploid cells can escape replicative senescence in rodents but not large mammals. Commun. Biol. https://doi.org/10.1038/s42003-019-0453-z (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  18. International Stem Cell Initiative et al. Screening ethnically diverse human embryonic stem cells identifies a chromosome 20 minimal amplicon conferring growth advantage. Nat. Biotechnol. 29, 1132–1144 (2011).

  19. Van De Geijn, B., Mcvicker, G., Gilad, Y. & Pritchard, J. K. WASP: allele-specific software for robust molecular quantitative trait locus discovery. Nat. Methods https://doi.org/10.1038/nmeth.3582 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. https://doi.org/10.1186/s13059-014-0550-8 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Liu, X., Li, Y. I. & Pritchard, J. K. Trans effects on gene expression can drive omnigenic inheritance. Cell https://doi.org/10.1016/j.cell.2019.04.014 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Wittkopp, P. J., Haerum, B. K. & Clark, A. G. Regulatory changes underlying expression differences within and between Drosophila species. Nat. Genet. https://doi.org/10.1038/ng.77 (2008).

    Article  PubMed  Google Scholar 

  23. Peyrégne, S., Boyle, M. J., Dannemann, M. & Prüfer, K. Detecting ancient positive selection in humans using extended lineage sorting. Genome Res. 27, 1563–1572 (2017).

    Article  Google Scholar 

  24. Racimo, F., Kuhlwilm, M. & Slatkin, M. A test for ancient selective sweeps and an application to candidate sites in modern humans. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msu255 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Kronenberg, Z. N. et al. High-resolution comparative analysis of great ape genomes. Science https://doi.org/10.1126/science.aar6343 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Prüfer, K. et al. A high-coverage Neandertal genome from Vindija Cave in Croatia. Science 358, 655–658 (2017).

    Article  Google Scholar 

  27. Prabhakar, S., Noonan, J. P., Pääbo, S. & Rubin, E. M. Accelerated evolution of conserved noncoding sequences in humans. Science https://doi.org/10.1126/science.1130738 (2006).

    Article  PubMed  Google Scholar 

  28. Lindblad-Toh, K. et al. A high-resolution map of human evolutionary constraint using 29 mammals. Nature 478, 476–482 (2011).

    Article  CAS  Google Scholar 

  29. Kostka, D., Holloway, A. K. & Pollard, K. S. Developmental loci harbor clusters of accelerated regions that evolved independently in ape lineages. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msy109 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  30. McLean, C. Y. et al. Human-specific loss of regulatory DNA and the evolution of human-specific traits. Nature https://doi.org/10.1038/nature09774 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Gittelman, R. M. et al. Comprehensive identification and analysis of human accelerated regulatory DNA. Genome Res. https://doi.org/10.1101/gr.192591.115 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Marnetto, D., Molineris, I., Grassi, E. & Provero, P. Genome-wide identification and characterization of fixed human-specific regulatory regions. Am. J. Hum. Genet. https://doi.org/10.1016/j.ajhg.2014.05.011 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature https://doi.org/10.1038/nature19057 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Gayà-Vidal, M. & Albà, M. M. Uncovering adaptive evolution in the human lineage. BMC Genomics https://doi.org/10.1186/1471-2164-15-599 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Glinsky, G. V. Transposable elements and DNA methylation create in embryonic stem cells human-specific regulatory sequences associated with distal enhancers and noncoding RNAs. Genome Biol. Evol. https://doi.org/10.1093/gbe/evv081 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  Google Scholar 

  37. Shibata, Y. et al. Extensive evolutionary changes in regulatory element activity during human origins are associated with altered gene expression and positive selection. PLoS Genet. https://doi.org/10.1371/journal.pgen.1002789 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Swain-Lenz, D. et al. Comparative analyses of chromatin landscape in white adipose tissue suggest humans may have less beigeing potential than other primates. Genome Biol. Evol. https://doi.org/10.1093/gbe/evz134 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Edsall, L. E. et al. Evaluating chromatin accessibility differences across multiple primate species using a joint modelling approach. Genome Biol. Evol. https://doi.org/10.1093/gbe/evz218 (2019).

  40. Romero, I. G., Gopalakrishnan, S. & Gilad, Y. Widespread conservation of chromatin accessibility patterns and transcription factor binding in human and chimpanzee induced pluripotent stem cells. Preprint at bioRxiv https://doi.org/10.1101/466631 (2018).

  41. Glinsky, G. V. Mechanistically distinct pathways of divergent regulatory DNA creation contribute to evolution of human-specific genomic regulatory networks driving phenotypic divergence of homo sapiens. Genome Biol. Evol. https://doi.org/10.1093/gbe/evw185 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Eres, I. E., Luo, K., Hsiao, C. J., Blake, L. E. & Gilad, Y. Reorganization of 3D genome structure may contribute to gene regulatory evolution in primates. PLoS Genet. https://doi.org/10.1371/journal.pgen.1008278 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Prescott, S. L. et al. Enhancer divergence and cis-regulatory evolution in the human and chimp neural crest. Cell 163, 68–84 (2015).

    Article  CAS  Google Scholar 

  44. Reilly, S. K. & Noonan, J. P. Evolution of gene regulation in humans. Annu. Rev. Genomics Hum. Genet. https://doi.org/10.1146/annurev-genom-090314-045935 (2016).

    Article  PubMed  Google Scholar 

  45. Cotney, J. et al. The evolution of lineage-specific regulatory activities in the human embryonic limb. Cell 154, 185–196 (2013).

    Article  CAS  Google Scholar 

  46. Gokhman, D. et al. Gene ORGANizer: linking genes to the organs they affect. Nucleic Acids Res. 45, W138–W145 (2017).

    Article  CAS  Google Scholar 

  47. Köhler, S. et al. The human phenotype ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res. 42, D966–D974 (2014).

    Article  Google Scholar 

  48. Gokhman, D. et al. Reconstructing Denisovan anatomy using DNA methylation maps. Cell 179, 180–192.e10 (2019).

    Article  CAS  Google Scholar 

  49. Orr, H. A. Testing natural selection vs. genetic drift in phenotypic evolution using quantitative trait locus data. Genetics 149, 2099–2104 (1998).

    Article  CAS  Google Scholar 

  50. Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).

    Article  CAS  Google Scholar 

  51. Xavier, G. M. et al. Hedgehog receptor function during craniofacial development. Dev. Biol. https://doi.org/10.1016/j.ydbio.2016.02.009 (2016).

    Article  PubMed  Google Scholar 

  52. Ramsbottom, S. A. & Pownall, M. E. Regulation of hedgehog signalling inside and outside the cell. J. Dev. Biol. https://doi.org/10.3390/jdb4030023 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Wang, S. H., Hsiao, C. J., Khan, Z. & Pritchard, J. K. Post-translational buffering leads to convergent protein expression levels between primates. Genome Biol. https://doi.org/10.1186/s13059-018-1451-z (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Dorn, K. V., Hughes, C. E. & Rohatgi, R. A Smoothened-Evc2 complex transduces the hedgehog signal at primary cilia. Dev. Cell https://doi.org/10.1016/j.devcel.2012.07.004 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Zhang, H. et al. Elevated fibroblast growth factor signaling is critical for the pathogenesis of the dwarfism in Evc2/Limbin mutant mice. PLoS Genet. https://doi.org/10.1371/journal.pgen.1006510 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Pipes, L. et al. The non-human primate reference transcriptome resource (NHPRTR) for comparative functional genomics. Nucleic Acids Res. https://doi.org/10.1093/nar/gks1268 (2013).

    Article  PubMed  Google Scholar 

  57. Wunderlich, S. et al. Primate iPS cells as tools for evolutionary analyses. Stem Cell Res. https://doi.org/10.1016/j.scr.2014.02.001 (2014).

    Article  PubMed  Google Scholar 

  58. Briscoe, J. & Small, S. Morphogen rules: design principles of gradient-mediated embryo patterning. Development https://doi.org/10.1242/dev.129452 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Young, N. M., Chong, H. J., Hu, D., Hallgrímsson, B. & Marcucio, R. S. Quantitative analyses link modulation of Sonic hedgehog signaling to continuous variation in facial growth and shape. Development https://doi.org/10.1242/dev.052340 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Hu, D. & Helms, J. A. The role of Sonic hedgehog in normal and abnormal craniofacial morphogenesis. Development 126, 4873–4884 (1999).

    Article  CAS  Google Scholar 

  61. Pan, A., Chang, L., Nguyen, A. & James, A. W. A review of hedgehog signaling in cranial bone development. Front. Physiol. https://doi.org/10.3389/fphys.2013.00061 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Jeong, J., Mao, J., Tenzen, T., Kottmann, A. H. & McMahon, A. P. Hedgehog signaling in the neural crest cells regulates the patterning and growth of facial primordia. Genes Dev. https://doi.org/10.1101/gad.1190304 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Zhang, H. et al. Generation of Evc2/Limbin global and conditional KO mice and its roles during mineralized tissue formation. Genesis https://doi.org/10.1002/dvg.22879 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Badri, M. K. et al. Expression of Evc2 in craniofacial tissues and craniofacial bone defects in Evc2 knockout mouse. Arch. Oral Biol. https://doi.org/10.1016/j.archoralbio.2016.05.002 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Badri, M. K. et al. Ellis van Creveld2 is required for postnatal craniofacial bone development. Anat. Rec. https://doi.org/10.1002/ar.23353 (2016).

    Article  Google Scholar 

  66. Takeda, H. et al. Positional cloning of the gene LIMBIN responsible for bovine chondrodysplastic dwarfism. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.152337899 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Caparrós-Martín, J. A. et al. The ciliary EVC/EVC2 complex interacts with Smo and controls hedgehog pathway activity in chondrocytes by regulating Sufu/Gli3 dissociation and Gli3 trafficking in primary cilia. Hum. Mol. Genet. https://doi.org/10.1093/hmg/dds409 (2013).

    Article  PubMed  Google Scholar 

  68. Kulkarni, A. K. et al. A ciliary protein EVC2/LIMBIN plays a critical role in the skull base for mid-facial development. Front. Physiol. 9, 1484 (2018).

    Article  Google Scholar 

  69. Pusapati, G. V. et al. EFCAB7 and IQCE regulate hedgehog signaling by tethering the EVC-EVC2 complex to the base of primary cilia. Dev. Cell https://doi.org/10.1016/j.devcel.2014.01.021 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Li, X. et al. Genome-wide linkage study suggests a susceptibility locus for isolated bilateral microtia on 4p15.32-4p16.2. PLoS ONE https://doi.org/10.1371/journal.pone.0101152 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Claes, P. et al. Modeling 3D facial shape from DNA. PLoS Genet. 10, e1004224 (2014).

    Article  Google Scholar 

  72. Lieberman, D. E. & McCarthy, R. C. The ontogeny of cranial base angulation in humans and chimpanzees and its implications for reconstructing pharyngeal dimensions. J. Hum. Evol. https://doi.org/10.1006/jhev.1998.0287 (1999).

    Article  PubMed  Google Scholar 

  73. Pilot, M. et al. Diversifying selection between pure-breed and free-breeding dogs inferred from genome-wide SNP analysis. G3 (Bethesda) https://doi.org/10.1534/g3.116.029678 (2016).

    Article  Google Scholar 

  74. Hu, Y. & Albertson, R. C. Hedgehog signaling mediates adaptive variation in a dynamic functional system in the cichlid feeding apparatus. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1323154111 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Burga, A. et al. A genetic signature of the evolution of loss of flight in the Galapagos cormorant. Science https://doi.org/10.1126/science.aal3345 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Dorus, S. et al. Sonic hedgehog, a key development gene, experienced intensified molecular evolution in primates. Hum. Mol. Genet. https://doi.org/10.1093/hmg/ddl123 (2006).

    Article  PubMed  Google Scholar 

  77. Claes, P. et al. Genome-wide mapping of global-to-local genetic effects on human facial shape. Nat. Genet. 50, 414–423 (2018).

    Article  CAS  Google Scholar 

  78. Lieberman, P. The evolution of human speech: its anatomical and neural bases. Curr. Anthropol. 48, 39–66 (2007).

    Article  Google Scholar 

  79. Boë, L.-J. et al. Which way to the dawn of speech?: Reanalyzing half a century of debates and data in light of speech science. Sci. Adv. 5, eaaw3916 (2019).

    Article  Google Scholar 

  80. Rada-Iglesias, A. et al. Epigenomic annotation of enhancers predicts transcriptional regulators of human neural crest. Cell Stem Cell https://doi.org/10.1016/j.stem.2012.07.006 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Bajpai, V. K. et al. Reprogramming postnatal human epidermal keratinocytes toward functional neural crest fates. Stem Cells https://doi.org/10.1002/stem.2583 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Ward, M. C. et al. Silencing of transposable elements may not be a major driver of regulatory evolution in primate iPSCs. eLife https://doi.org/10.7554/eLife.33084 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Marchetto, M. C. N. et al. Differential L1 regulation in pluripotent stem cells of humans and apes. Nature https://doi.org/10.1038/nature12686 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics https://doi.org/10.1093/bioinformatics/bts635 (2013).

    Article  PubMed  Google Scholar 

  85. Tehranchi, A. et al. Fine-mapping cis-regulatory variants in diverse human populations. eLife https://doi.org/10.7554/elife.39595 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Romero, I. G. et al. A panel of induced pluripotent stem cells from chimpanzees: a resource for comparative functional genomics. eLife https://doi.org/10.7554/eLife.07103.001 (2015).

    Article  Google Scholar 

  87. Weissbein, U., Plotnik, O., Vershkov, D. & Benvenisty, N. Culture-induced recurrent epigenetic aberrations in human pluripotent stem cells. PLoS Genet. https://doi.org/10.1371/journal.pgen.1006979 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Piñero, J. et al. DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database 2015, bav028 (2015).

    Article  Google Scholar 

  89. Hamosh, A., Scott, A. F., Amberger, J. S., Bocchini, C. A. & McKusick, V. A. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33, D514–D517 (2005).

    Article  CAS  Google Scholar 

  90. Pizzollo, J. et al. Comparative serum challenges show divergent patterns of gene expression and open chromatin in human and chimpanzee. Genome Biol. Evol. https://doi.org/10.1093/gbe/evy041 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank S. Bar, L. Carmel and members of the Fraser, Petrov and Pritchard laboratories for critical comments, and the Gilad laboratory (Chicago University) and the You laboratory (University of Pennsylvania) for sharing data and cells. D.G. was funded by the Human Frontier, Rothschild and Zuckerman fellowships. H.B.F. is supported by National Institutes of Health (NIH) grant no. 2R01GM097171-05A1. The cells used in this study were derived from the iPSCs generated by Gallego Romero et al.86, whose study was supported by the NIH, Office of Research Infrastructure Programs/OD (grant no. P51OD011132).

Author information

Authors and Affiliations

  1. Department of Biology, Stanford University, Stanford, CA, USA

    David Gokhman, Danqiong Sun, Dmitri A. Petrov & Hunter B. Fraser

  2. Department of Genetics, Stanford University, Stanford, CA, USA

    Rachel M. Agoglia

  3. Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA

    Maia Kinnebrew & Rajat Rohatgi

  4. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA

    Wei Gordon & Nadav Ahituv

  5. Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA

    Vivek K. Bajpai, Sahin Naqvi & Joanna Wysocka

  6. Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA

    Vivek K. Bajpai, Sahin Naqvi & Joanna Wysocka

  7. Department of Biologic and Materials Sciences, School of Dentistry, University of Michigan, Ann Arbor, MI, USA

    Coral Chen, Honghao Zhang & Yuji Mishina

  8. Yerkes National Primate Research Center, Atlanta, GA, USA

    Anthony Chan

  9. Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA

    Anthony Chan

  10. Department of Oral and Maxillofacial Surgery and Pharmacology, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Chider Chen

  11. Institute for Human Genetics, University of California, San Francisco, CA, USA

    Nadav Ahituv

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  1. David Gokhman
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Contributions

D.G. and H.B.F. designed experiments and analyses and wrote the manuscript with input from all authors. D.G. conducted the analyses. R.M.A. designed the ASE pipeline and generated RNA-seq data. M.K. designed and performed the EVC2 and Hedgehog signaling experiments and was supervised by R.R. W.G. designed and performed the reporter assay experiments and was supervised by N.A. D.S. generated the hybrid cells. V.K.B. and S.N. differentiated the cells and were supervised by J.W. Coral Chen, A.C. and Chider Chen contributed human and chimpanzee DPSCs. D.A.P. cosupervised D.G. H.Z. and Y.M. generated the mouse KO. H.B.F. devised the original idea and supervised the project.

Corresponding authors

Correspondence to David Gokhman or Hunter B. Fraser.

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The authors declare no competing interests.

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Peer review information Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Identifying human-chimpanzee expression changes using hybrid cells.

a, Immunostaining for CNCC markers NR2F1 and PAX3 was performed to confirm CNCC differentiation. b, Expression levels of positive and negative markers in the parental and hybrid CNCCs. c, Heatmap and dendrogram of total gene expression across iPSC and CNCC samples. d,e, Fold-change per gene for hybrid iPSCs and hybrid CNCCs when aligned to the human (GRCh38) vs chimpanzee (panTro5) genomes. Grey points are genes where the absolute difference in log2(fold-change) when aligned to the human vs. chimpanzee genome is greater than 1 (that is, genes with potential alignment bias that were excluded from the analysis). Genes with no observable alignment bias are marked with blue (significant ASE: q-value < 0.05) or yellow (non-significant ASE). f, Venn diagram of genes with significant human-chimpanzee expression changes in parental and hybrid samples. g. Parental vs hybrid iPSC expression changes. See Fig. 1d legend.

Extended Data Fig. 2 Differentially expressed genes are associated with divergent chromatin and phenotypes.

a, Overlap of ASE genes in CNCCs with loci showing divergent regulatory marks. Each of the datasets was examined twice: (1) against Ch > Hu genes (red), and (2) against Hu > Ch genes (blue). In 14 out of 16 datasets, expression differences reflect regulatory differences, that is, Hu > Ch regulatory marks show more overlap with Hu > Ch genes than with Ch > Hu genes, and vice versa. P-value shows one-tailed paired t-test for overall overlap (see Methods). Asterisks mark significant randomization test overlap (FDR < 0.05). See Supplementary Table 8. b, Mean fraction of divergent phenotypes for groups of genes with increasingly higher fold-change thresholds. c, Violin plots showing phenotype assignment accuracy in groups of genes with increasingly more divergent differential expression in parental cells. Randomization test P-values are shown for overall accuracy compared to random (PAUC), and accuracy increase compared to random (Pslope), as shown in d. See Fig. 2c legend. d, Randomization output for the phenotype assignment pipeline. Genes associated with each phenotype were randomly assigned a direction of expression change, while keeping their absolute fold-change. Randomization test P-values are shown for overall accuracy compared to random (PAUC), and accuracy increase compared to random (Pslope). e, Phenotype assignment accuracy before and after applying unidirectionality filtering, for ASE and parental differential expression with cis-contribution ≥ 90%. See Fig. 2d legend. In the unidirectionality filter, only phenotypes where all genes point in the same phenotypic direction (that is, complete agreement) are analyzed48.

Extended Data Fig. 3 EVC2 down-regulation in humans.

a, The down:up ratio of Hh signaling genes across increasingly more stringent FDR and fold-change thresholds. b, Differential expression along all of the exons of EVC2 in a CNCC hybrid (Hy1_30_rep1), showing that the majority of reads come from the chimpanzee alleles. Introns are not shown to scale. c, Violin plots of EVC2 expression across iPSC and CNCC non-hybrid samples from various sources, showing consistent EVC2 down-regulation in humans compared to chimpanzees. Diamonds show mean expression levels. DESeq2 FDR-adjusted P-values are presented for cell type. The observation that the human-chimpanzee ratios are similar to the ones observed within the hybrid cells suggests that the majority of differential expression is driven by cis changes. d, EVC2 expression across nine additional tissues for which both human and chimpanzee data are available56, showing that EVC2 down-regulation is not restricted to iPSCs and CNCCs. Dashed line shows mean expression. One-sided t-test P-values are shown. e, Gorilla vs human EVC2 expression. One-sided t-test P-values are shown. f, Western blot of EVC2 protein levels in human and chimpanzee DPSCs. The samples derive from the same experiment and blots were processed in parallel. For gel source data, see Source Data.

Source data

Extended Data Fig. 4 Differentially regulated regions in EVC2.

ATAC-seq read pileup along EVC2 and for the three loci showing species-biased peaks within EVC2. Arrows mark peaks. b,c, NR2F1 and TFAP2A ChIP-seq read pileup for loci <10 kb away from the ATAC-seq peaks. d, MUSCLE103 sequence alignment of rhesus, gorilla, chimp and human sequences. Regions with a high proportion of mismatches are colored in red. e, Reporter assay comparing relative firefly/Renilla luciferase activity for chimpanzee and human EVC2 sequences following transient transfection in human DPSCs. Empty vector (pGL4.11b) was used as negative control. Box plots show mean (center), 2nd and 3rd quartiles (box boundaries), and minima and maxima (whiskers). One-tailed t-test P-values in two independent experiments of quadruplet measurements (n = 8) are shown.

Extended Data Fig. 5 Reduced levels of EVC2 result in reduced Hedgehog signaling output and affect craniofacial phenotypes.

a, Western blot of Gli1 protein levels (a measure of Hh signaling output induced by Shh) at different Evc2 and Hh signaling input levels. EvcC2 was introduced at various levels into Evc2−/− mouse NIH/3T3 fibroblasts through retroviral infection. Cells with higher levels of Evc2 show higher Hh signaling output. p38 served as positive control. Pearson’s R and P-value are shown for 40 nM SHH. The samples derive from the same experiment and blots were processed in parallel. For gel source data, see Source Data. b, Micro-CT radiographic images of the palate bone, enamel (extra bright) and roots of the first mandibular molar in Evc2 control and Evc2 KO mice at P28. c, Diagram of the mandible indicating the landmarks for the parameters measured. d, Mean skull and mandible measurements from Evc2 control and Evc2 KO mice at P28. (n = 5 for each group, FDR-adjusted two-tailed t-test P-values are shown). Whiskers show one standard deviation in each direction. Landmarks used are shown in the titles.

Source data

Extended Data Fig. 6 No aneuploidies observed in CNCC hybrid samples.

Figure shows ASE (top), sliding window ASE median over 20 genes (middle), and Wilcoxon rank sum test P-values for each sliding window against the entire genome (bottom). Dashed line shows mean for ASE and sliding window ASE, and shows Bonferroni P-value cutoff for the Wilcoxon rank sum test. An example of data is presented for autosomal chromosomes 1 and 20, and for chromosome X from the CNCC Hy1_30_rep1 sample. No significant deviations were detected in any of the CNCC hybrid samples. See Agoglia et al. for iPSC aneuploidy analyses12.

Extended Data Fig. 7 No chromosomal duplications or losses observed in the CNCC hybrid samples.

Density plots of percentage of human-aligned reads per gene per chromosome for each of the CNCC hybrid samples. Vertical dashed lines show mean per sample.

Supplementary information

Supplementary Information

Supplementary Methods, Tables 24 and 25, and Figures

Reporting Summary

Peer Review Information

Supplementary Tables

Supplementary Tables 1–23 and 26–28

Source data

Source Data Fig. 4

Unprocessed western blots.

Source Data Extended Data Fig. 3

Unprocessed western blots.

Source Data Extended Data Fig. 5

Unprocessed western blots.

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Gokhman, D., Agoglia, R.M., Kinnebrew, M. et al. Human–chimpanzee fused cells reveal cis-regulatory divergence underlying skeletal evolution. Nat Genet 53, 467–476 (2021). https://doi.org/10.1038/s41588-021-00804-3

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