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Genetic diversity and evolution of rice centromeres

Nature Genetics volume 57, pages 2808–2818 (2025)Cite this article

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Abstract

Understanding the driving force of centromere dynamics is crucial for deciphering the complexity of eukaryotic evolution and speciation. Here we assembled 67 rice genomes from the Oryza AA group and analyzed >800 nearly complete centromeres. Through de novo annotation of centromeric satellite CEN155 sequences and employing a progressive compression strategy, we quantified the local homogenization and multilayer structures of rice satellite arrays. Our results indicate that genetic innovations in rice centromeres primarily arise from structural variations and centrophilic retrotransposon insertions. The single-base substitution rate in rice centromeres appears to be lower relative to that in chromosome arms. Comparisons of CEN155 arrays, retrotransposons and functional centromeres highlight their dynamic but correlated interplay. Contrary to the KARMA model for Arabidopsis centromere evolution, we propose a hypothesis that retrotransposon invasion probably contributes to the decline of progenitor centromeric satellite arrays and promotes centromere repositioning, as evidenced by extended CENH3 chromatin immunoprecipitation sequencing enrichment beyond the native satellite arrays.

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Fig. 1: Centromere diversity of Oryza AA genomes.
Fig. 2: Centromere haplotype, introgression and splitting.
Fig. 3: Genetic variation in CEN155 satellite sequences and centrophilic TEs in rice genomes.
Fig. 4: Multilayer nested structure of rice CEN155 satellite arrays.
Fig. 5: Sequence divergence and mutation rate in rice centromeres.
Fig. 6: Epigenetic profiling and centromere positioning.

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Data availability

Raw PacBio HiFi reads for 46 rice accessions, raw ONT sequencing reads for 10 accessions and CENH3 ChIP–seq NGS reads for 10 accessions generated in this study have been deposited in the National Genomics Data Center under BioProject, accession no. PRJCA025388 (https://ngdc.cncb.ac.cn/bioproject/browse/PRJCA025388), with the Genome Sequence Archive nos. CRA016014 (https://ngdc.cncb.ac.cn/gsa/browse/CRA016014), CRA017638 (https://ngdc.cncb.ac.cn/gsa/browse/CRA017638) and CRA017653 (https://ngdc.cncb.ac.cn/gsa/browse/CRA017653), respectively. The newly generated genome assemblies in this study are available in the NCBI (BioProject, accession no. PRJNA1276249) and via Zenodo at https://doi.org/10.5281/zenodo.12770803 (ref. 81). The genome assemblies of NIP, MH63 and ZS97 are available at the RiceSuperPIRdb (http://ricesuperpir.com/) and the Rice Information GateWay (http://rice.hzau.edu.cn/). TE and gene annotation files of 70 rice genomes are available via Zenodo at https://doi.org/10.5281/zenodo.12698984 (ref. 82). Supporting materials for centromere assembly quality control for each sample are available via Zenodo at https://doi.org/10.5281/zenodo.14286880 (ref. 83), including read-mapping coverage plots, NucFreq plots, GCI coverage plots and VerityMap plots. Rice centromere annotation and comparison plots for all accessions and chromosomes are available via Zenodo at https://doi.org/10.5281/zenodo.12702715 (ref. 84), including similarity heatmap plots generated by StainedGlass for each centromere, whole-genome synteny to the NIP reference assembly, centromere synteny against NIP and NJ11 assemblies and centromere composition for all chromosomes. Source data are provided with this paper.

Code availability

The SynPan-CEN code is available via GitHub at https://github.com/Darlene1997/SynPan-CEN (ref. 85) and the scripts for the progressive compression strategy in deciphering the satellite organization and additional in-house codes associated with this study (including assembly, annotation and visualization) are available via GitHub at https://github.com/dongyawu/CenTools (ref. 86). The code and scripts are also available via Zenodo at https://doi.org/10.5281/zenodo.16990314 (ref. 87). The visualization of centromere annotation and synteny tracks was performed using ggplot2 in R (v.4.3.1, https://www.r-project.org/).

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Acknowledgements

This work was supported by Biological Breeding-Major Projects (grant no. 2023ZD04076), China National Postdoctoral Program for Innovative Talents (grant no. BX20220269), China Postdoctoral Science Foundation (grant no. 2023M743045), Young Scientists Fund of the National Natural Science Foundation of China (grant no. 32300490), National Key Research and Development Program of China (grant no. 2019YFA0903904) and CIC MIC. We thank G. Zhang (Zhejiang University), Y. Mao (Shanghai Jiao Tong University), B. Wu (Sun Yat-sen University) and K. Wu (Zhejiang University) for constructive suggestions.

Author information

Author notes

  1. These authors contributed equally: Lingjuan Xie, Yujie Huang, Wei Huang, Lianguang Shang.

Authors and Affiliations

  1. Institute of Crop Science & Institute of Bioinformatics, Zhejiang University, Hangzhou, China

    Lingjuan Xie, Yujie Huang, Shiyu Zhang, Longjiang Fan & Dongya Wu

  2. State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, China

    Wei Huang, Shuangtian Bi & Weiwei Jin

  3. Yazhouwan National Laboratory, Sanya, China

    Lianguang Shang, Xiaoming Zheng, Qian Qian & Longjiang Fan

  4. Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China

    Lianguang Shang

  5. Center for Evolutionary & Organismal Biology, Zhejiang University, Hangzhou, China

    Yanqing Sun, Quanyu Chen, Mingyu Suo & Dongya Wu

  6. State Key Laboratory of Agricultural Genomics, BGI Research, Shenzhen, China

    Chentao Yang

  7. Tianjin Key Laboratory of Intelligent Breeding of Major Crops, Fresh Corn Research Center of BTH, Tianjin Agricultural University, Tianjin, China

    Weiwei Jin

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  1. Lingjuan Xie
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Contributions

D.W. conceived and initiated this study. L.S. collected the samples. D.W., Q.C. and M.S. performed the sequencing data quality control, centromere assembly and quality evaluation. L.X., D.W. and Y.S. performed the analysis of satellite sequence identification and clustering and satellite array organization. Y.H. and D.W. performed the annotation and centromeric insertion analysis of TEs. W.H., L.X. and S.B. conducted the CENH3 ChIP experiments. L.X., D.W. and S.Z. processed the ChIP–seq data and analyzed the epigenetic profiling of rice centromeres. L.F. and D.W. supervised all analyses. Q.Q., W.J., C.Y., L.S. and X.Z. provided suggestions on analysis, organization and writing. D.W., L.X. and Y.H. wrote the manuscripts with input from all the coauthors. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Longjiang Fan or Dongya Wu.

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Extended data

Extended Data Fig. 1 Variation in rice centromere positioning.

a, Chromosome length and the ratio of long arm length to short arm length across each chromosome. b, Relationships between chromosome size and CEN155 satellite array size across different taxonomic groups. Linear regression analyses were performed to evaluate the relationships between variables, with P values based on two-sided t test. Shaded areas represent 95% confidence intervals of the fitted regression lines. c, Two megabase-scale inversions observed in the centromere region of chromosome Chr06.

Source data

Extended Data Fig. 2 StainedGlass sequence identity heat maps of centromeres from chromosomes Chr02, Chr05 and Chr04.

Representative centromere haplotypes for each chromosome are shown.

Extended Data Fig. 3 Centromere divergence and fission on rice chromosome Chr12.

a, Centromere similarity and structural variations compared to NIP on chromosomes Chr12, showing divergent centromere haplotypes (CenHaps) and putative centromere introgression events (for example CW15, MH63). Left, a maximum-likelihood phylogenetic tree across rice accessions on chromosome Chr12. b, StainedGlass sequence similarity heat maps within and between CEN155 arrays on chromosomes Chr12 from SL044, CW09 and NJ11, and their synteny, indicating a fission from SL044-type centromere to CW09 (X3) and NJ11 (X4) type. TEs (blue) and gene-like elements (red) are shown. Their commonly shared retrotransposon RETROSAT-2C around the junction is highlighted. c, Comparison of phylogenetic trees built using upstream 500-Kbp and downstream 500-Kbp SNPs flanking the Chr12 CEN155 array. Taxonomic information is represented by colored circles, with the position of SL044 highlighted by a dashed line. d, Schematic representation of centromeric structural alterations, including introgression, duplication and fission or splitting.

Extended Data Fig. 4 Divergence sites between CEN155 superfamilies.

The CENP-B box-like and pJα-like motif regions are shown.

Extended Data Fig. 5 Inference of multimers and muHRs in rice centromeres.

The de Bruijn graphs are constructed based on the dimer-compressed satellite string of each centromere.

Extended Data Fig. 6 Structural variations in centromere regions.

a, Schematic diagram of structural variations in satellite arrays (SaSVs). A query satellite array is aligned against a reference array using satellites and TEs as markers. Based on syntenic pairing of CEN155 satellites and TEs, SVs with more than 50 copies of CEN155 satellites are defined as large expansions (LEs) or contractions (LCs). Regions with continuously poor synteny are referred to as divergent blocks, compared to the reference array. b, SaSV number and involved CEN155 satellite size (upper), and SaSV size distribution (bottom) in GJ and XI satellite arrays compared to their corresponding reference assemblies NIP and NJ11, respectively. c. SaSVs in individual genomes associated with the phylogenetic kinship.

Extended Data Fig. 7 CENH3 ChIP-seq enrichment and element annotation across Chr05 centromeres.

Top, CENH3 ChIP-seq enrichment (log2(ChIP/input), two replicates). Beneath, CEN155 superfamily, TE and sati annotation along each centromere.

Extended Data Fig. 8 A retrotransposon-induced centromere evolution model, summarized from the rice centromere analysis.

This model highlights the multi-layer structures of rice satellite arrays by local homogenization and emphasizes the triggering role of LTR invasion in initiating satellite array degeneration and centromere repositioning determined by CENH3 occupancy.

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Xie, L., Huang, Y., Huang, W. et al. Genetic diversity and evolution of rice centromeres. Nat Genet 57, 2808–2818 (2025). https://doi.org/10.1038/s41588-025-02365-1

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