Gene expression across mammalian organ development


The evolution of gene expression in mammalian organ development remains largely uncharacterized. Here we report the transcriptomes of seven organs (cerebrum, cerebellum, heart, kidney, liver, ovary and testis) across developmental time points from early organogenesis to adulthood for human, rhesus macaque, mouse, rat, rabbit, opossum and chicken. Comparisons of gene expression patterns identified correspondences of developmental stages across species, and differences in the timing of key events during the development of the gonads. We found that the breadth of gene expression and the extent of purifying selection gradually decrease during development, whereas the amount of positive selection and expression of new genes increase. We identified differences in the temporal trajectories of expression of individual genes across species, with brain tissues showing the smallest percentage of trajectory changes, and the liver and testis showing the largest. Our work provides a resource of developmental transcriptomes of seven organs across seven species, and comparative analyses that characterize the development and evolution of mammalian organs.

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Fig. 1: Organ developmental transcriptomes.
Fig. 2: Developmental correspondences.
Fig. 3: Relationships between evolution and development.
Fig. 4: Evolution of developmental trajectories.

Data availability

Raw and processed RNA-seq data have been deposited in ArrayExpress with the accession codes: E-MTAB-6769 (chicken), E-MTAB-6782 (rabbit), E-MTAB-6798 (mouse), E-MTAB-6811 (rat), E-MTAB-6813 (rhesus macaque), E-MTAB-6814 (human) and E-MTAB-6833 (opossum) ( The temporal profiles of individual genes across organs and species can be visualized and downloaded using the web-based application:


  1. 1.

    Pantalacci, S. & Semon, M. Transcriptomics of developing embryos and organs: a raising tool for evo–devo. J. Exp. Zool. Mol. Dev. Evol. 324, 363–371 (2015).

    CAS  Google Scholar 

  2. 2.

    Silbereis, J. C., Pochareddy, S., Zhu, Y., Li, M. & Sestan, N. The cellular and molecular landscapes of the developing human central nervous system. Neuron 89, 248–268 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    DeFalco, T. & Capel, B. Gonad morphogenesis in vertebrates: divergent means to a convergent end. Annu. Rev. Cell Dev. Biol. 25, 457–482 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Abzhanov, A. von Baer’s law for the ages: lost and found principles of developmental evolution. Trends Genet. 29, 712–722 (2013).

    CAS  PubMed  Google Scholar 

  5. 5.

    Kalinka, A. T. & Tomancak, P. The evolution of early animal embryos: conservation or divergence? Trends Ecol. Evol. 27, 385–393 (2012).

    PubMed  Google Scholar 

  6. 6.

    Ferner, K., Schultz, J. A. & Zeller, U. Comparative anatomy of neonates of the three major mammalian groups (monotremes, marsupials, placentals) and implications for the ancestral mammalian neonate morphotype. J. Anat. 231, 798–822 (2017).

    PubMed  Google Scholar 

  7. 7.

    Dickinson, M. E. et al. High-throughput discovery of novel developmental phenotypes. Nature 537, 508–514 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Petrovski, S., Wang, Q., Heinzen, E. L., Allen, A. S. & Goldstein, D. B. Genic intolerance to functional variation and the interpretation of personal genomes. PLoS Genet. 9, e1003709 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Cassa, C. A. et al. Estimating the selective effects of heterozygous protein-truncating variants from human exome data. Nat. Genet. 49, 806–810 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Ruderfer, D. M. et al. Patterns of genic intolerance of rare copy number variation in 59,898 human exomes. Nat. Genet. 48, 1107–1111 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Hill, M. A. Embryology Carnegie Stage Comparison (2017).

  13. 13.

    de Bakker, B. S. et al. An interactive three-dimensional digital atlas and quantitative database of human development. Science 354, aag0053 (2016).

    PubMed  Google Scholar 

  14. 14.

    Kerwin, J. et al. The HUDSEN Atlas: a three-dimensional (3D) spatial framework for studying gene expression in the developing human brain. J. Anat. 217, 289–299 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Butler, H. & Juurlink, B. H. J. An Atlas for Staging Mammalian and Chick Embryos (CRC Press, 1987).

  16. 16.

    Smith, K. K. Early development of the neural plate, neural crest and facial region of marsupials. J. Anat. 199, 121–131 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Dillman, A. A. et al. mRNA expression, splicing and editing in the embryonic and adult mouse cerebral cortex. Nat. Neurosci. 16, 499–506 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Glucksmann, A. Sexual dimorphism in mammals. Biol. Rev. Camb. Philos. Soc. 49, 423–475 (1974).

    CAS  PubMed  Google Scholar 

  19. 19.

    Feng, C. W., Bowles, J. & Koopman, P. Control of mammalian germ cell entry into meiosis. Mol. Cell. Endocrinol. 382, 488–497 (2014).

    CAS  PubMed  Google Scholar 

  20. 20.

    Soumillon, M. et al. Cellular source and mechanisms of high transcriptome complexity in the mammalian testis. Cell Rep. 3, 2179–2190 (2013).

    CAS  PubMed  Google Scholar 

  21. 21.

    Ungewitter, E. K. & Yao, H. H. How to make a gonad: cellular mechanisms governing formation of the testes and ovaries. Sex Dev. 7, 7–20 (2013).

    CAS  PubMed  Google Scholar 

  22. 22.

    Roux, J. & Robinson-Rechavi, M. Developmental constraints on vertebrate genome evolution. PLoS Genet. 4, e1000311 (2008).

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Kalinka, A. T. et al. Gene expression divergence recapitulates the developmental hourglass model. Nature 468, 811–814 (2010).

    ADS  CAS  PubMed  Google Scholar 

  24. 24.

    Hu, H. et al. Constrained vertebrate evolution by pleiotropic genes. Nat. Ecol. Evol. 1, 1722–1730 (2017).

    PubMed  Google Scholar 

  25. 25.

    Hazkani-Covo, E., Wool, D. & Graur, D. In search of the vertebrate phylotypic stage: a molecular examination of the developmental hourglass model and von Baer’s third law. J. Exp. Zool. B Mol. Dev. Evol. 304B, 150–158 (2005).

    Google Scholar 

  26. 26.

    Garfield, D. A. & Wray, G. A. Comparative embryology without a microscope: using genomic approaches to understand the evolution of development. J. Biol. 8, 65 (2009).

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Koscielny, G. et al. The International Mouse Phenotyping Consortium Web Portal, a unified point of access for knockout mice and related phenotyping data. Nucleic Acids Res. 42, D802–D809 (2014).

    CAS  PubMed  Google Scholar 

  28. 28.

    Kosiol, C. et al. Patterns of positive selection in six mammalian genomes. PLoS Genet. 4, e1000144 (2008).

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Kaessmann, H. Origins, evolution, and phenotypic impact of new genes. Genome Res. 20, 1313–1326 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Stern, D. L. Evolutionary developmental biology and the problem of variation. Evolution 54, 1079–1091 (2000).

    CAS  PubMed  Google Scholar 

  31. 31.

    Carroll, S. B. Evolution at two levels: on genes and form. PLoS Biol. 3, e245 (2005).

    PubMed  PubMed Central  Google Scholar 

  32. 32.

    Duret, L. & Mouchiroud, D. Determinants of substitution rates in mammalian genes: expression pattern affects selection intensity but not mutation rate. Mol. Biol. Evol. 17, 68–74 (2000).

    CAS  PubMed  Google Scholar 

  33. 33.

    Winter, E. E., Goodstadt, L. & Ponting, C. P. Elevated rates of protein secretion, evolution, and disease among tissue-specific genes. Genome Res. 14, 54–61 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Galis, F. & Metz, J. A. Testing the vulnerability of the phylotypic stage: on modularity and evolutionary conservation. J. Exp. Zool. 291, 195–204 (2001).

    CAS  PubMed  Google Scholar 

  35. 35.

    Sears, K., Maier, J. A., Sadier, A., Sorensen, D. & Urban, D. J. Timing the developmental origins of mammalian limb diversity. Genesis 56, e23079 (2018).

    Google Scholar 

  36. 36.

    Plant, T. M., Ramaswamy, S., Simorangkir, D. & Marshall, G. R. Postnatal and pubertal development of the rhesus monkey (Macaca mulatta) testis. Ann. NY Acad. Sci. 1061, 149–162 (2005).

    ADS  CAS  PubMed  Google Scholar 

  37. 37.

    Bruneau, B. G. Signaling and transcriptional networks in heart development and regeneration. Cold Spring Harb. Perspect. Biol. 5, a008292 (2013).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Carelli, F. N., Liechti, A., Halbert, J., Warnefors, M. & Kaessmann, H. Repurposing of promoters and enhancers during mammalian evolution. Nat. Commun. 9, 4066 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Marin, R. et al. Convergent origination of a Drosophila-like dosage compensation mechanism in a reptile lineage. Genome Res. 27, 1974–1987 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Wu, T. D. & Nacu, S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26, 873–881 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Anders, S., Pyl, P. T. & Huber, W. HTSeqa Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  Google Scholar 

  43. 43.

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

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Picard. (2015).

  45. 45.

    Lê, S., Josse, J. & Husson, F. FactoMineR: an R package for multivariate analysis. J. Stat. Softw. 25, 1–18 (2008).

    Google Scholar 

  46. 46.

    R Core Team. R: A Language and Environment for Statistical Computing (2014).

  47. 47.

    Anavy, L. et al. BLIND ordering of large-scale transcriptomic developmental timecourses. Development 141, 1161–1166 (2014).

    CAS  PubMed  Google Scholar 

  48. 48.

    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).

    PubMed  PubMed Central  Google Scholar 

  49. 49.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Nueda, M. J., Tarazona, S. & Conesa, A. Next maSigPro: updating maSigPro Bioconductor package for RNA-seq time series. Bioinformatics 30, 2598–2602 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Conesa, A., Nueda, M. J., Ferrer, A. & Talón, M. maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics 22, 1096–1102 (2006).

    CAS  PubMed  Google Scholar 

  52. 52.

    Zhang, H. M. et al. AnimalTFDB 2.0: a resource for expression, prediction and functional study of animal transcription factors. Nucleic Acids Res. 43, D76–D81 (2015).

    CAS  PubMed  Google Scholar 

  53. 53.

    Giorgino, T. Computing and visualizing dynamic time warping alignments in R: the dtw package. J. Stat. Softw. 31, 1–24 (2009).

    Google Scholar 

  54. 54.

    Clancy, B., Darlington, R. B. & Finlay, B. L. Translating developmental time across mammalian species. Neuroscience 105, 7–17 (2001).

    CAS  PubMed  Google Scholar 

  55. 55.

    Smith, K. K. Craniofacial development in marsupial mammals: developmental origins of evolutionary change. Dev. Dyn. 235, 1181–1193 (2006).

    PubMed  Google Scholar 

  56. 56.

    Futschik, M. E. & Carlisle, B. Noise-robust soft clustering of gene expression time-course data. J. Bioinform. Comput. Biol. 3, 965–988 (2005).

    CAS  PubMed  Google Scholar 

  57. 57.

    Kumar, L. & E Futschik, M. Mfuzz: a software package for soft clustering of microarray data. Bioinformation 2, 5–7 (2007).

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    Eisenberg, E. & Levanon, E. Y. Human housekeeping genes, revisited. Trends Genet. 29, 569–574 (2013).

    CAS  PubMed  Google Scholar 

  59. 59.

    Domazet-Lošo, T. & Tautz, D. A phylogenetically based transcriptome age index mirrors ontogenetic divergence patterns. Nature 468, 815–818 (2010).

    ADS  PubMed  Google Scholar 

  60. 60.

    Zhang, Y. E., Vibranovski, M. D., Landback, P., Marais, G. A. & Long, M. Chromosomal redistribution of male-biased genes in mammalian evolution with two bursts of gene gain on the X chromosome. PLoS Biol. 8, e1000494 (2010).

    PubMed  PubMed Central  Google Scholar 

  61. 61.

    Yanai, I. et al. Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification. Bioinformatics 21, 650–659 (2005).

    CAS  PubMed  Google Scholar 

  62. 62.

    Hensman, J., Lawrence, N. D. & Rattray, M. Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters. BMC Bioinformatics 14, 252 (2013).

    PubMed  PubMed Central  Google Scholar 

  63. 63.

    Hensman, J., Rattray, M. & Lawrence, N. D. Fast nonparametric clustering of structured time-series. IEEE Trans. Pattern Anal. Mach. Intell. 37, 383–393 (2015).

    PubMed  Google Scholar 

  64. 64.

    Hensman, J., Rattray, M. & Lawrence, N. D. Fast variational inference in the conjugate exponential family. In Proc. NIPS’12 Proceedings 25th International Conference on Neural Information Processing Systems Vol. 2, 2888–2896 (2014).

  65. 65.

    Kumar, S., Stecher, G., Suleski, M. & Hedges, S. B. TimeTree: a resource for timelines, timetrees, and divergence times. Mol. Biol. Evol. 34, 1812–1819 (2017).

    CAS  PubMed  Google Scholar 

  66. 66.

    Vickaryous, M. K. & Hall, B. K. Human cell type diversity, evolution, development, and classification with special reference to cells derived from the neural crest. Biol. Rev. Camb. Philos. Soc. 81, 425–455 (2006).

    PubMed  Google Scholar 

  67. 67.

    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2009).

  68. 68.

    Auguie, B. gridExtra: Miscellaneous Functions for “Grid” Graphics. v.2.2.1 (2015).

  69. 69.

    Wickham, H. Reshaping data with the reshape package. J. Stat. Softw. 21, 1–20 (2007).

    Google Scholar 

  70. 70.

    Wickham, H. The split-apply-combine strategy for data analysis. J. Stat. Softw. 40, 1–29 (2011).

    Google Scholar 

  71. 71.

    Kassambara, A. & Mundt, F. factoextra: Extract and Visualize the Results of Multivariate Data Analyses. v.1.0.4 (2017).

  72. 72.

    Wang, J., Vasaikar, S., Shi, Z., Greer, M. & Zhang, B. WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic Acids Res. 45, W130–W137 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

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We thank R. Arguello, F. Lamanna, I. Sarropoulos, M. Sepp and members of the Kaessmann group for discussion; I. Moreira for developing the Shiny application; M. Sanchez-Delgado and N. Trost for figure assistance; P. Dhakal for assistance collecting rat samples; S. Nef for assistance collecting gonadal samples; the Lausanne Genomics Technology Facility for high-throughput sequencing; the Joint MRC/Wellcome (MR/R006237/1) Human Developmental Biology Resource (HDBR), the Maryland Brain Collection Center and the Chinese Brain Bank Center for providing human samples; and the Suzhou Experimental Animal Center for providing rhesus macaque samples. Computations were performed at the Vital-IT Center of the SIB Swiss Institute of Bioinformatics and at the bwForCluster from the Heidelberg University Computational Center (supported by the state of Baden-Württemberg through bwHPC and the German Research Foundation - INST 35/1134-1 FUGG). M.J.S. was supported by the National Institutes of Health (HD020676); P.J. by the European Research Council (322206, Genewell); Y.E.Z. by the National Natural Science Foundation of China (31771410, 91731302); M.C. by Fundação para a Ciência e Tecnologia through POPH-QREN funds from the European Social Fund and Portuguese MCTES (IF/00283/2014/CP1256/CT0012); S. Anders by the German Research Foundation (SFB 1036). This research was supported by grants from the European Research Council (615253, OntoTransEvol) and Swiss National Science Foundation (146474) to H.K. and by the Marie Curie FP7-PEOPLE-2012-IIF to M.C.-M. (329902).

Peer review information

Nature thanks Pavel Tomancak and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information




M.C.-M. and H.K. conceived and organized the study based on H.K.’s original design. M.C.-M. performed all analyses. M.C.-M. and H.K. wrote the manuscript, with input from all authors. J.H., D.V., C.S., M.J.S., P.G.F., S. Afonso, M.C., J.M.A.T., J.L.V., A.F., P.J., R.B., S. Lisgo, S. Lindsay, P.K. and J.B. collected samples. J.H., D.V., A.L., K.A. and C.R. performed RNA-seq experiments. B.V. and W.H. contributed to analyses on trajectory changes. S.O., S. Anders and P.V.M. contributed to analyses on developmental correspondences. C.C., Y.S. and Y.E.Z. contributed to analyses on transcriptome age. M.M. and D.N.C. contributed to data interpretation. I.X. organized high-performance computational resources. K.H. organized high-throughput sequencing.

Corresponding authors

Correspondence to Margarida Cardoso-Moreira or Henrik Kaessmann.

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Extended data figures and tables

Extended Data Fig. 1 Organ developmental transcriptomes.

a, PC3 and PC4 of the PCA based on 7,696 1:1 orthologues depicted in Fig. 1b (each dot represents the median across replicates), and scree plot describing the amount of variance explained by the first 10 principal components. b, PCAs of individual organs (n = 7,696 1:1 orthologues). c, Correlation of expression levels throughout development between human brain and the other organs (20,345 genes) (top), and between mouse liver and the other organs (21,798 genes) (bottom). Similar patterns were observed using other organs as the focal organ, and species. For human, the prenatal data are in weeks (w) postnatally; new, newborn; sch, school age (7–9 years); ya, young adult (25–32 years); sen, senior (58–65 years).

Extended Data Fig. 2 DDGs.

a, Number of DDGs identified in each organ and species using the set of 7,696 1:1 orthologues (left) and the set of all protein-coding genes (right) in each species. The horizontal bar depicts the median. Br, brain; Cr, cerebellum; He, heart; Ki, kidney; Li, liver; Ov, ovary; Te, testis; Te*, testis pre-sexual maturity. b, Number of DDGs per species, including number of organs where they show dynamic expression. Asterisk denotes that ovary development is not covered in rhesus macaque, hence there are only 6 organs in total. c, Relationship between the number of organs in which genes show dynamic expression and the tolerance to functional variants as measured by: pLI score, RVIS and shet score (n = 13,160 genes; two-sided Wilcoxon rank-sum test). d, Relationship between the number of organs in which genes show dynamic expression and intolerance to duplication and deletion variants (CNV intolerance score; n = 15,728 genes; two-sided Wilcoxon rank-sum test). e, Percentage of organ-specific expressed DDGs at each developmental stage. Bars indicate the range between the replicates. For the brain tissues, DDGs are organ-specific in brain and/or cerebellum. Time points on the x axis are as described in Fig. 1a. f, Percentage of transcription factors (TFs) expressed at each developmental stage. Bars indicate the range between the replicates. Time points on the x axis are as described in Fig. 1a. Box plots are as in Fig. 3e.

Extended Data Fig. 3 Developmental correspondences across species.

a, Developmental stage correspondences established in this study and correspondences based on the Carnegie staging (when available)12,13,14,15. b, Using mouse as a reference, a dynamic time warping algorithm was used to select the best alignment (pink line) between the time series based on stage transcriptome correlations combining all somatic organs (n = 8,940 genes per organ combinations). New, newborn; tod, toddler (2–4 years); teen, teenager (13–19 years); yma, young middle age (39–41 years); sen, senior (58–63 years).

Extended Data Fig. 4 Periods of greater transcriptional change in mouse.

Number of genes differentially expressed between adjacent stages in each organ (log2 fold change ≥ 0.5). Solid lines refer to genes that increase in expression and dashed lines to genes that decrease. The biological processes and phenotypes enriched at the peaks of differential expression are detailed in Supplementary Table 13.

Extended Data Fig. 5 Periods of greater transcriptional change across species.

Number of genes differentially expressed between adjacent, species-matched, stages for each organ (log2 fold change ≥ 0.5). Solid lines refer to genes that increase in expression and dashed lines to genes that decrease. The vertical dotted line marks birth.

Extended Data Fig. 6 Organ-specific stage correspondences.

Comparison of the global stage correspondences (based on the combined expression of somatic organs; n = 8,940 genes per organ combinations; black line) with organ-specific correspondences (based on 2,727 genes for brain, 2,146 for cerebellum, 1,276 for heart, 1,486 for kidney, 1,305 for liver, 1,298 for ovary and 2,153 for testis; coloured lines). With the exception of early heart development in opossum and early ovary development in rabbit and human, the global correspondences are within the 98% confidence interval for predictions computed by the LOESS regression function (local polynomial regression) for each of the organ-specific correspondences (shaded grey area). The same applies to all organs in mouse–chicken and mouse–rhesus macaque comparisons (data not shown). The inset on the bottom right shows the Spearman correlation between mouse and rabbit (top) and mouse and human (bottom) for testis transcriptomes using the global stage correspondences (black line) or adjusting for the different start of meiosis across species (orange line; that is, matching a P14 mouse with a young teenager in human and a P84 rabbit).

Extended Data Fig. 7 Heterochronies in gonadal development.

a, Temporal dynamics of meiotic genes during ovary development. SYCP1 is not expressed in human ovary. The genes SPO11 and STAG3 are not present in the chicken gene annotations used in this work. b, Expression of STRA8 during ovary development. The vertical bars show the range between the replicates and the horizontal dashed line marks 1 RPKM. c, Temporal dynamics of meiotic genes during testis development. The profiles of STRA8 and DMC1 are represented not by their range of expression but by their highest peak of expression. In rhesus macaque, meiosis is known to start around 3–4 years36; our data suggest it had not yet started in the 3-year-old individuals examined. STRA8 is lowly expressed in the human testis. d, Expression of STRA8 during testis development. The vertical bars show the range between the replicates and the horizontal dashed line marks 1 RPKM. e, PCA of ovary and testis development for each species (n = 21,798 protein-coding genes in mouse, 19,390 in rat, 19,271 in rabbit, 20,345 in human, 21,886 in rhesus macaque, 21,304 in opossum and 15,481 in chicken).

Extended Data Fig. 8 Relationships between evolution and development.

a, Observed relationship between evolution and development. Divergence (horizontal distance) can be morphological or molecular. b, Transcriptome similarity between three species pairs throughout development (matched stages) using 11,439 1:1 orthologues. Similar trends were obtained using all species pairs. The weighted average Spearman correlation coefficients are −0.81 (P = 1 × 10−12) for the mouse–rat comparison, −0.69 (P = 2 × 10−11) for mouse–human and −0.42 (P = 0.0004) for mouse–opossum. At the bottom are the Spearman correlations between transcriptome correlation coefficients and matched developmental time for each organ and species pair (**P < 0.02, *P < 0.05). Lines were estimated through linear regression and the 95% confidence interval is shown in the shaded areas. c, The pLI score for genes with different developmental trajectories in human (top) and mouse (bottom). Lower values mean less tolerance. The pLI scores used for mouse genes are from their human orthologues. The P values refer to early versus late comparisons, two-sided Wilcoxon rank-sum test. Box plots are as in Fig. 3e. d, Percentage of lethal and subviable genes expressed throughout development among a set of 2,686 neutrally ascertained mouse knockouts (top) and the same after excluding housekeeping genes (bottom). Spearman correlations at the bottom of each plot. Lines were estimated through linear regression and the 95% confidence interval is shown in grey.

Extended Data Fig. 9 Pleiotropy as a determinant of the evolution of development.

a, Relationship between tissue- and time-specificity. Gene developmental profiles illustrate the extremes of the indexes, which range from 0 (broad time/spatial expression) to 1 (specific time/spatial expression). In the gene plots, the x axis shows the samples ordered by stage and organ and the y axis shows expression levels. b, Functional constraints (measured by pLI score) decrease with increasing time- and tissue-specificity (n = 9,965 genes). **P < 0.01, two-sided Wilcoxon rank-sum test. c, Tissue- and time-specificity of mouse genes identified as lethal, subviable, or viable (n = 2,686; two-sided Wilcoxon rank-sum test). d, Levels of functional constraint as measured by RVIS, shet and pLI scores for the human orthologues of genes identified as lethal, subviable and viable in mouse (n = 2,408; two-sided Wilcoxon rank-sum test). e, Tissue- and time-specificity of genes with different developmental trajectories in human (top) and the same after excluding housekeeping genes (bottom). The P values refer to early versus late comparisons, two-sided Wilcoxon rank-sum test. Box plots are as in Fig. 3e.

Extended Data Fig. 10 Evolution of developmental trajectories.

a, Number of genes in each organ that evolved new trajectories across the phylogeny. Includes genes that differ between opossum and eutherians, for which the change cannot be polarized because of the lack of an outgroup. b, Distribution of trajectory changes among organs for the different species. The number of genes that changed in each organ is depicted in Fig. 4b. Humans show a relative excess of changes in brain tissues and a relative paucity in testis. **P = 2 × 10−5 for brain, P = 0.02 for cerebellum and P = 1 × 10−10 for testis (from binomial tests where the probability of success is derived from what is observed in mouse, rat and rabbit). c, Genes tested for trajectory changes (7,020 genes) in mouse (top) and human (bottom) have significantly lower tissue- and time-specificity than genes not tested for trajectory changes (13,325 genes in mouse and 14,778 in human, two-sided Wilcox rank-sum test). d, Genes with trajectory changes in mouse (top) and human (bottom) have similar or lower tissue- and time-specificity than genes with conserved trajectories (two-sided Wilcox rank-sum test). N.S., not significant. e, Number of organs in which genes evolved new trajectories in the different species. Box plots are as in Fig. 3e.

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

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