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Gene expression across mammalian organ development

Nature volume 571, pages 505–509 (2019)Cite this article

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Abstract

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.

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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) (https://www.ebi.ac.uk/arrayexpress/). The temporal profiles of individual genes across organs and species can be visualized and downloaded using the web-based application: http://evodevoapp.kaessmannlab.org.

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Acknowledgements

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

Authors and Affiliations

  1. Center for Molecular Biology of Heidelberg University (ZMBH), Heidelberg, Germany

    Margarida Cardoso-Moreira, Svetlana Ovchinnikova, Simon Anders & Henrik Kaessmann

  2. Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland

    Margarida Cardoso-Moreira, Jean Halbert, Delphine Valloton, Angélica Liechti, Kelly Ascenção, Coralie Rummel, Ioannis Xenarios & Keith Harshman

  3. Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany

    Britta Velten & Wolfgang Huber

  4. Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Chunyan Chen, Yi Shao & Yong E. Zhang

  5. State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Chunyan Chen, Yi Shao & Yong E. Zhang

  6. University of Chinese Academy of Sciences, Beijing, China

    Chunyan Chen & Yi Shao

  7. Center for Neurobiology and Brain Restoration, Skolkovo Institute of Science and Technology, Moscow, Russia

    Pavel V. Mazin & Philipp Khaitovich

  8. Institute for Information Transmission Problems (Kharkevich Institute) RAS, Moscow, Russia

    Pavel V. Mazin

  9. Faculty of Computer Science, HSE University, Moscow, Russia

    Pavel V. Mazin

  10. Institute of Medical Genetics, Cardiff University, Cardiff, UK

    Matthew Mort & David N. Cooper

  11. Laboratory of Behavioral Genetics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Carmen Sandi

  12. Institute for Reproduction and Perinatal Research, Departments of Pathology and Laboratory Medicine and Pediatrics, University of Kansas Medical Center, Kansas City, MO, USA

    Michael J. Soares

  13. Center for Perinatal Research, Children’s Research Institute, Children’s Mercy, Kansas City, MO, USA

    Michael J. Soares

  14. Departamento de Anatomia, Universidade do Porto, Porto, Portugal

    Paula G. Ferreira

  15. ICBAS (Instituto de Ciências Biomédicas Abel Salazar), UMIB (Unidade Multidisciplinar de Investigação Biomédica), Universidade do Porto, Porto, Portugal

    Paula G. Ferreira

  16. CIBIO/InBIO, Centro de Investigacão em Biodiversidade e Recursos Genéticos, Universidade do Porto, Porto, Portugal

    Sandra Afonso & Miguel Carneiro

  17. Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Porto, Portugal

    Miguel Carneiro

  18. Sex Chromosome Biology Laboratory, The Francis Crick Institute, London, UK

    James M. A. Turner

  19. South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, Harlingen and Edinburg, TX, USA

    John L. VandeBerg

  20. The Department of Human Genetics, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, Harlingen and Edinburg, TX, USA

    John L. VandeBerg

  21. AVIAN Behavioural Genomics and Physiology Group, IFM Biology, Linköping University, Linköping, Sweden

    Amir Fallahshahroudi & Per Jensen

  22. Platform Degenerative Diseases, German Primate Center, Leibniz Institute for Primate Research (DPZ), Göttingen, Germany

    Rüdiger Behr

  23. DZHK (German Center for Cardiovascular Research), Partner Site Göttingen, Göttingen, Germany

    Rüdiger Behr

  24. Human Developmental Biology Resource, Institute of Genetic Medicine, Newcastle University, Newcastle, UK

    Steven Lisgo & Susan Lindsay

  25. Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China

    Philipp Khaitovich & Yong E. Zhang

  26. CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China

    Philipp Khaitovich

  27. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

    Julie Baker

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  1. Margarida Cardoso-Moreira
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  2. Jean Halbert
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  14. Matthew Mort
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  31. Simon Anders
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  33. Henrik Kaessmann
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Contributions

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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Competing interests

The authors declare no competing interests.

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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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This file contains Supplementary Tables 1-20 and a guide for the supplementary tables.

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Cardoso-Moreira, M., Halbert, J., Valloton, D. et al. Gene expression across mammalian organ development. Nature 571, 505–509 (2019). https://doi.org/10.1038/s41586-019-1338-5

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