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An inferred fitness consequence map of the rice genome

Nature Plants volume 6, pages 119–130 (2020)Cite this article

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Abstract

The extent to which sequence variation impacts plant fitness is poorly understood. High-resolution maps detailing the constraint acting on the genome, especially in regulatory sites, would be beneficial as functional annotation of noncoding sequences remains sparse. Here, we present a fitness consequence (fitCons) map for rice (Oryza sativa). We inferred fitCons scores (ρ) for 246 inferred genome classes derived from nine functional genomic and epigenomic datasets, including chromatin accessibility, messenger RNA/small RNA transcription, DNA methylation, histone modifications and engaged RNA polymerase activity. These were integrated with genome-wide polymorphism and divergence data from 1,477 rice accessions and 11 reference genome sequences in the Oryzeae. We found ρ to be multimodal, with ~9% of the rice genome falling into classes where more than half of the bases would probably have a fitness consequence if mutated. Around 2% of the rice genome showed evidence of weak negative selection, frequently at candidate regulatory sites, including a novel set of 1,000 potentially active enhancer elements. This fitCons map provides perspective on the evolutionary forces associated with genome diversity, aids in genome annotation and can guide crop breeding programs.

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Fig. 1: greenINSIGHT scores across different genomic annotations in rice.
Fig. 2: Partitioning and scoring the rice genome for selection (ρ).
Fig. 3: Properties of a subset of the 246 fitCons genome classes.
Fig. 4: Distribution of ρ across the rice genome.
Fig. 5: Proximal upstream chromatin class distribution correlates with downstream gene expression.
Fig. 6: Characterization of three categories of intergenic fitCons classes.

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Data availability

The read data used to generate the ChromHMM model and genomic classes have been deposited at the NCBI SRA (https://www.ncbi.nlm.nih.gov/sra) and can be accessed through BioProject ID PRJNA586887. Genome assemblies of O. officinalis and O. australiensis are available from the CoGe CyVerse website (https://genomevolution.org/coge/) with genome IDs id56031 and id56030, respectively. Access to genomic class annotation and INSIGHT scoring of the rice genome is available via a genome browser linked from the project’s website (http://purugganan-genomebrowser.bio.nyu.edu/insightJuly2018/greenInsight.html). All epigenomic data tracks, genome annotations, multiple alignments, conservation scores, fitCons scores and site classes are available for visualization and download on a local installation on the USCSC Genome Browser at http://purugganan-genomebrowser.bio.nyu.edu/cgi-bin/hgTracks?db=Osaj&position=Osaj.1%3A166356–178595, and are also available for download from the NCBI SRA (PRJNA586887). The greenINSIGHT-specific data used to generate the greenINSIGHT online tool are available in the “Additional information, scripts & data” section at http://purugganan-genomebrowser.bio.nyu.edu/insightJuly2018/greenInsight.html. The greenINSIGHT-specific code used to generate the greenINSIGHT online tool, as well as the code described in the Methods, are available in the “Additional information, scripts & data” section at http://purugganan-genomebrowser.bio.nyu.edu/insightJuly2018/greenInsight.html.

Code availability

The greenINSIGHT-specific data used to generate the greenINSIGHT online tool are available in the “Additional information, scripts & data” section at http://purugganan-genomebrowser.bio.nyu.edu/insightJuly2018/greenInsight.html. The greenINSIGHT-specific code used to generate the greenINSIGHT online tool, as well as the code described in the Methods, are available in the “Additional information, scripts & data” section at http://purugganan-genomebrowser.bio.nyu.edu/insightJuly2018/greenInsight.html.

References

  1. Siepel, A. et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 15, 1034–1050 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Schrider, D. R. & Kern, A. D. Inferring selective constraint from population genomic data suggests recent regulatory turnover in the human brain. Genome Biol. Evol. 7, 3511–3528 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Gronau, I., Arbiza, L., Mohammed, J. & Siepel, A. Inference of natural selection from interspersed genomic elements based on polymorphism and divergence. Mol. Biol. Evol. 30, 1159–1171 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. McDonald, J. H. & Kreitman, M. Adaptive protein evolution at the Adh locus in Drosophila. Nature 351, 652–654 (1991).

    Article  CAS  PubMed  Google Scholar 

  5. Sawyer, S. A. & Hartl, D. L. Population genetics of polymorphism and divergence. Genetics 132, 1161–1176 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Bustamante, C. D. et al. Natural selection on protein-coding genes in the human genome. Nature 437, 1153–1157 (2005).

    Article  CAS  PubMed  Google Scholar 

  7. Smith, N. G. C. & Eyre-Walker, A. Adaptive protein evolution in Drosophila. Nature 415, 1022–1024 (2002).

    Article  CAS  PubMed  Google Scholar 

  8. Gulko, B., Hubisz, M. J., Gronau, I. & Siepel, A. A method for calculating probabilities of fitness consequences for point mutations across the human genome. Nat. Genet. 47, 276–283 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Gulko, B. & Siepel, A. An evolutionary framework for measuring epigenomic information and estimating cell-type-specific fitness consequences. Nat. Genet. 51, 335–342 (2019).

    Article  CAS  PubMed  Google Scholar 

  10. Wing, R. A., Purugganan, M. D. & Zhang, Q. The rice genome revolution: from an ancient grain to Green Super Rice. Nat. Rev. Genet. 19, 505–517 (2018).

    Article  CAS  PubMed  Google Scholar 

  11. Wang, W. et al. Genomic variation in 3,010 diverse accessions of Asian cultivated rice. Nature 557, 43–49 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Stein, J. C. et al. Genomes of 13 domesticated and wild rice relatives highlight genetic conservation, turnover and innovation across the genus Oryza. Nat. Genet. 50, 285–296 (2018).

    Article  CAS  PubMed  Google Scholar 

  13. Cao, J. et al. Whole-genome sequencing of multiple Arabidopsis thaliana populations. Nat. Genet. 43, 956–963 (2011).

    Article  CAS  PubMed  Google Scholar 

  14. Haudry, A. et al. An atlas of over 90,000 conserved noncoding sequences provides insight into crucifer regulatory regions. Nat. Genet. 45, 891–898 (2013).

    Article  CAS  PubMed  Google Scholar 

  15. Huang, X. et al. A map of rice genome variation reveals the origin of cultivated rice. Nature 490, 497–501 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Gutaker, R. M. et al. Genomic history and ecology of the geographic spread of rice. Preprint at https://www.biorxiv.org/content/10.1101/748178v1 (2019).

  17. Josephs, E. B., Lee, Y. W., Stinchcombe, J. R. & Wright, S. I. Association mapping reveals the role of purifying selection in the maintenance of genomic variation in gene expression. Proc. Natl Acad. Sci. USA 112, 15390–15395 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Flowers, J. M. et al. Natural selection in gene-dense regions shapes the genomic pattern of polymorphism in wild and domesticated rice. Mol. Biol. Evol. 29, 675–687 (2012).

    Article  CAS  PubMed  Google Scholar 

  19. Caicedo, A. L. et al. Genome-wide patterns of nucleotide polymorphism in domesticated rice. PLoS Genet. 3, 1745–1756 (2007).

    Article  CAS  PubMed  Google Scholar 

  20. Bradnam, K. R. & Korf, I. Longer first introns are a general property of eukaryotic gene structure. PLoS ONE 3, e3093 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Rigau, M., Juan, D., Valencia, A. & Rico, D. Intronic CNVs and gene expression variation in human populations. PLoS Genet. 15, e1007902 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Berendzen, K. W. et al. Bioinformatic cis-element analyses performed in Arabidopsis and rice disclose bZIP- and MYB-related binding sites as potential AuxRE-coupling elements in auxin-mediated transcription. BMC Plant Biol. 12, 125 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Freeling, M., Rapaka, L., Lyons, E., Pedersen, B. & Thomas, B. C. G-boxes, bigfoot genes, and environmental response: characterization of intragenomic conserved noncoding sequences in Arabidopsis. Plant Cell 19, 1441–1457 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Choi, H. I., Hong, J. H., Ha, J. O., Kang, J. Y. & Kim, S. Y. ABFs, a family of ABA-responsive element binding factors. J. Biol. Chem. 275, 1723–1730 (2000).

    Article  CAS  PubMed  Google Scholar 

  25. Lu, T. et al. Function annotation of the rice transcriptome at single-nucleotide resolution by RNA-Seq. Genome Res. 20, 1238–1249 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Peng, T. et al. Differentially expressed microRNA cohorts in seed development may contribute to poor grain filling of inferior spikelets in rice. BMC Plant Biol. 14, 196 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 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, 21.29.1–21.29.9 (2015).

    Article  Google Scholar 

  29. Feng, S. et al. Conservation and divergence of methylation patterning in plants and animals. Proc. Natl Acad. Sci. USA 107, 8689–8694 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Mahat, D. B. et al. Base-pair-resolution genome-wide mapping of active RNA polymerases using precision nuclear run-on (PRO-Seq). Nat. Protoc. 11, 1455–1476 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Kwak, H., Fuda, N. J., Core, L. J. & Lis, J. T. Precise maps of RNA polymerase reveal how promoters direct initiation and pausing. Science 339, 950–953 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Liu, Y. et al. PCSD: a plant chromatin state database. Nucleic Acids Res. 46, D1157–D1167 (2018).

    Article  CAS  PubMed  Google Scholar 

  33. Yan, W. et al. Dynamic control of enhancer activity drives stage-specific gene expression during flower morphogenesis. Nat. Commun. 10, 1705 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Wen, M. et al. Expression variations of miRNAs and mRNAs in rice (Oryza sativa). Genome Biol. Evol. 8, 3529–3544 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zong, W., Zhong, X., You, J. & Xiong, L. Genome-wide profiling of histone H3K4-tri-methylation and gene expression in rice under drought stress. Plant Mol. Biol. 81, 175–188 (2013).

    Article  CAS  PubMed  Google Scholar 

  36. Lozano, R. et al. RNA polymerase mapping in plants identifies enhancers enriched in causal variants. Preprint at https://www.biorxiv.org/content/10.1101/376640v1 (2018).

  37. Xia, J. et al. Detecting and characterizing microRNAs of diverse genomic origins via miRvial. Nucleic Acids Res. 45, e176 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Wilkins, O. et al. EGRINs (environmental gene regulatory influence networks) in rice that function in the response to water deficit, high temperature, and agricultural environments. Plant Cell 28, 2365–2384 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Tan, F. et al. Analysis of chromatin regulators reveals specific features of rice DNA methylation pathways. Plant Physiol. 171, 2041–2054 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Liu, C., Lu, F., Cui, X. & Cao, X. Histone methylation in higher plants. Annu. Rev. Plant Biol. 61, 395–420 (2010).

    Article  CAS  PubMed  Google Scholar 

  41. Liu, N., Fromm, M. & Avramova, Z. H3K27me3 and H3K4me3 chromatin environment at super-induced dehydration stress memory genes of Arabidopsis thaliana. Mol. Plant 7, 502–513 (2014).

    Article  CAS  PubMed  Google Scholar 

  42. Fang, H., Liu, X., Thorn, G., Duan, J. & Tian, L. Expression analysis of histone acetyltransferases in rice under drought stress. Biochem. Biophys. Res. Commun. 443, 400–405 (2014).

    Article  CAS  PubMed  Google Scholar 

  43. Du, Z. et al. Genome-wide analysis of histone modifications: H3K4me2, H3K4me3, H3K9ac, and H3K27ac in Oryza sativa L. Japonica. Mol. Plant 6, 1463–1472 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Lee, T., Zhai, J. & Meyers, B. C. Conservation and divergence in eukaryotic DNA methylation. Proc. Natl Acad. Sci. USA 107, 9027–9028 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Ernst, J. & Kellis, M. Chromatin-state discovery and genome annotation with ChromHMM. Nat. Protoc. 12, 2478–1492 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Roudier, F. et al. Integrative epigenomic mapping defines four main chromatin states in Arabidopsis. EMBO J. 30, 1928–1938 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Sequeira-Mendes, J. et al. The functional topography of the Arabidopsis genome is organized in a reduced number of linear motifs of chromatin states. Plant Cell 26, 2351–2366 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Liu, C. et al. Genome-wide analysis of chromatin packing in Arabidopsis thaliana at single-gene resolution. Genome Res. 26, 1057–1068 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Guo, H. & Moose, S. P. Conserved noncoding sequences among cultivated cereal genomes identify candidate regulatory sequence elements and patterns of promoter evolution. Plant Cell 15, 1143–1158 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Liu, L., Xu, W., Hu, X., Liu, H. & Lin, Y. W-box and G-box elements play important roles in early senescence of rice flag leaf. Sci. Rep. 6, 20881 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Ding, M. et al. Enhancer RNAs (eRNAs): new insights into gene transcription and disease treatment. J. Cancer 9, 2334–2340 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Wang, Z., Chu, T., Choate, L. A. & Danko, C. G. Identification of regulatory elements from nascent transcription using dREG. Genome Res. 29, 293–303 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Danko, C. G. et al. Dynamic evolution of regulatory element ensembles in primate CD4+ T cells. Nat. Ecol. Evol. 2, 537–548 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Savisaar, R. & Hurst, L. D. Exonic splice regulation imposes strong selection at synonymous sites. Genome Res. 28, 1442–1454 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Cannavò, E. et al. Shadow enhancers are pervasive features of developmental regulatory networks. Curr. Biol. 26, 38–51 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Prescott, S. L. et al. Enhancer divergence and cis-regulatory evolution in the human and chimp neural crest. Cell 163, 68–83 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Mezmouk, S. & Ross-Ibarra, J. The pattern and distribution of deleterious mutations in maize. G3 (Bethesda) 4, 163–171 (2014).

    Article  CAS  Google Scholar 

  59. Wallace, J. G., Rodgers-Melnick, E. & Buckler, E. S. On the road to breeding 4.0: unraveling the good, the bad, and the boring of crop quantitative genomics. Annu. Rev. Genet. 52, 421–444 (2018).

    Article  CAS  PubMed  Google Scholar 

  60. Moyers, B. T., Morrell, P. L. & McKay, J. K. Genetic costs of domestication and improvement. J. Hered. 109, 103–116 (2018).

    Article  PubMed  Google Scholar 

  61. Morrell, P. L., Buckler, E. S. & Ross-Ibarra, J. Crop genomics: advances and applications. Nat. Rev. Genet. 13, 85–96 (2012).

    Article  CAS  Google Scholar 

  62. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. 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 

  65. Kent, W. J. BLAT—the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Raurell-Vila, H., Ramos-Rodríguez, M. & Pasquali, L. in CpG Islands. Methods in Molecular Biology Vol. 1766 (eds Vavouri, T. & Peinado, M. A.) 197–208 (Humana Press, 2018).

  68. Hetzel, J., Duttke, S. H., Benner, C. & Chory, J. Nascent RNA sequencing reveals distinct features in plant transcription., Proc. Natl Acad. Sci. USA 113, 12316–12321 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Boisvert, S., Raymond, F., Godzaridis, É., Laviolette, F. & Corbeil, J. Ray Meta: scalable de novo metagenome assembly and profiling. Genome Biol. 13, R122 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Butler, J. et al. ALLPATHS: de novo assembly of whole-genome shotgun microreads. Genome Res. 18, 810–820 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Green, E. D. et al. Aligning multiple genomic sequences with the threaded blockset aligner. Genome Res. 14, 708–715 (2004).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Siepel, A. & Haussler, D. Phylogenetic estimation of context-dependent substitution rates by maximum likelihood. Mol. Biol. Evol. 21, 468–488 (2004).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank the New York University Center for Genomics and Systems Biology GenCore Facility and the Next Generation Sequencing core at Cold Spring Harbor Laboratory for sequencing support. We thank O. Wilkins and C. Danko for valuable suggestions relating to the ATAC and PRO-Seq protocols, respectively. This work was supported primarily by a grant from the Zegar Family Foundation (no. A16-0051-004), as well as some support from the National Science Foundation Plant Genome Research Program (no. IOS-1546218) and NYU Abu Dhabi Research Institute to M.D.P., the National Science Foundation CAREER award (no. MCB-1552455), the US National Institutes of Health (no. R35GM124806) and US Department of Agriculture Hatch Program (no. 1012915) to X.Z., the US National Institutes of Health (no. R35GM127070) to A.S., and fellowships from the Gordon and Betty Moore Foundation and Life Sciences Research Foundation (no. GBMF2550.06) to S.C.G. and from the Natural Sciences and Engineering Research Council of Canada (no. PDF-502464-2017) to Z.J.-L.

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Authors and Affiliations

  1. Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, USA

    Zoé Joly-Lopez, Adrian E. Platts, Jae Young Choi, Simon C. Groen & Michael D. Purugganan

  2. Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

    Adrian E. Platts, Brad Gulko & Adam Siepel

  3. Laboratory of Genetics and Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA

    Xuehua Zhong

  4. Center for Genomics and Systems Biology, NYU Abu Dhabi Research Institute, NYU Abu Dhabi, Abu Dhabi, United Arab Emirates

    Michael D. Purugganan

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  1. Zoé Joly-Lopez
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Contributions

M.D.P. conceived of the study idea. M.D.P., Z.J.-L., A.E.P. and A.S. designed the study. M.D.P. directed the study. Z.J.-L. and X.Z. collected the data, A.E.P., Z.J.-L., J.Y.C., B.G., S.C.G. and M.D.P. analysed the data. Z.J.-L., A.E.P., A.S. and M.D.P. wrote the paper.

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Correspondence to Michael D. Purugganan.

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Joly-Lopez, Z., Platts, A.E., Gulko, B. et al. An inferred fitness consequence map of the rice genome. Nat. Plants 6, 119–130 (2020). https://doi.org/10.1038/s41477-019-0589-3

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