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A multi-omics integrative network map of maize

Nature Genetics volume 55, pages 144–153 (2023)Cite this article

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Abstract

Networks are powerful tools to uncover functional roles of genes in phenotypic variation at a system-wide scale. Here, we constructed a maize network map that contains the genomic, transcriptomic, translatomic and proteomic networks across maize development. This map comprises over 2.8 million edges in more than 1,400 functional subnetworks, demonstrating an extensive network divergence of duplicated genes. We applied this map to identify factors regulating flowering time and identified 2,651 genes enriched in eight subnetworks. We validated the functions of 20 genes, including 18 with previously unknown connections to flowering time in maize. Furthermore, we uncovered a flowering pathway involving histone modification. The multi-omics integrative network map illustrates the principles of how molecular networks connect different types of genes and potential pathways to map a genome-wide functional landscape in maize, which should be applicable in a wide range of species.

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Fig. 1: Different layers of genome-wide landscapes of the multi-omics network map.
Fig. 2: Network similarity and divergence between duplicated genes across different omics layers.
Fig. 3: Asymmetric network divergence indicates differences in evolutionary selection between the two maize subgenomes.
Fig. 4: The multi-omics map can predict gene function and reconstruct regulating networks underlying complex traits.
Fig. 5: The integrative functional map facilitates systematic dissection of the molecular mechanisms underlying FT in maize.

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Data availability

The ChIA–PET data were deposited in the Sequence Read Archive (SRA) of the NCBI (https://www.ncbi.nlm.nih.gov/sra) under BioProject accession no. PRJNA541043. The raw transcriptome data for 26 tissues or stages were deposited in the Gene Expression Omnibus (GEO) of the NCBI (https://www.ncbi.nlm.nih.gov/geo/) under GEO accession no. GSE199932. The remaining raw transcriptome data for the five other tissues or stages, which were generated in our previous study54, are available at the NCBI under BioProject accession no. PRJNA637713 (SRR13808023–SRR13808026, SRR13808028–SRR13808033). The raw translatome data for 16 tissues or stages were deposited in the GEO under accession no. GSE199932; the raw translatomic data for the five other tissues or stages are available at the SRA under accession no. PRJNA637713 (SRR13808051–SRR13808052, SRR13808055–SRR13808059, SRR13808061–SRR13808063). The raw interactome data were deposited in GEO under accession no. GSE199932; protein interactions were submitted to the IMEx (http://www.imexconsortium.org) consortium through IntAct82 and assigned the identifier IM-29553.

Code availability

We deposited the raw data matrix, codes and bioinformatics analyses in GitHub (https://github.com/hanlinqian/IntegrativeNetworkMap). Codes are also available at Zenodo (https://doi.org/10.5281/zenodo.7263543)83. A user-friendly website (http://minteractome.ncpgr.cn/) was developed to host all the expression and network information. This is publicly available for the replication of the whole study.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (nos. 92035302, 91735305 and 31922068), the Hainan Yazhou Bay Seed Lab (no. B21HJ8102), the HZAU-AGIS Cooperation Fund (no. SZYJY2021006), the major program of Hubei Hongshan Laboratory (no. 2021hszd008), the National Key Research and Development Program of China (no. 2016YFD0100800), the Huazhong Agricultural University Scientific & Technological Self-innovation Foundation (no. 2021ZKPY001) (all to L.L.) and the Chinese Academy of Agricultural Sciences Innovation Project (no. CAAS-ZDRW202004) (to H.Z.). We also thank the China National GeneBank and the high-performance computing platform at the National Key Laboratory of Crop Genetic Improvement at Huazhong Agricultural University.

Author information

Author notes

  1. These authors contributed equally: Linqian Han, Wanshun Zhong, Jia Qian, Minliang Jin, Peng Tian, Wanchao Zhu, Hongwei Zhang, Yonghao Sun.

  2. These authors jointly supervised this work: Lin Li, Fang Yang, Jianbing Yan.

Authors and Affiliations

  1. National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China

    Linqian Han, Wanshun Zhong, Jia Qian, Minliang Jin, Peng Tian, Wanchao Zhu, Yonghao Sun, Jia-Wu Feng, Guo Chen, Babar Farid, Ruonan Li, Zimo Xiong, Juan Li, Zi Luo, Dengxiang Du, Sijia Chen, Qixiao Jin, Jiaxin Li, Zhao Li, Yan Liang, Xiaomeng Jin, Yong Peng, Chang Zheng, Ling-Ling Chen, Qing Li, Jianbing Yan, Fang Yang & Lin Li

  2. Hubei Hongshan Laboratory, Wuhan, China

    Linqian Han, Wanshun Zhong, Jia Qian, Minliang Jin, Peng Tian, Wanchao Zhu, Yonghao Sun, Juan Li, Zi Luo, Qixiao Jin, Zhao Li, Qing Li, Jianbing Yan, Fang Yang & Lin Li

  3. Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China

    Hongwei Zhang

  4. Institute of Agricultural Biotechnology, Jilin Academy of Agricultural Sciences, Changchun, China

    Xiangguo Liu, Xinnan Ye & Yuejia Yin

  5. Institute of Nuclear and Biological Technology, Xinjiang Academy of Agricultural Sciences, Urumqi, China

    Guo Chen

  6. Institute of Plant Breeding and Biotechnology, Muhammad Nawaz Shareef University of Agriculture Multan, Multan, Pakistan

    Babar Farid

  7. College of Science, Huazhong Agricultural University, Wuhan, China

    Zhihui Tian, Hong Chen & Weifu Li

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  1. Linqian Han
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  2. Wanshun Zhong
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Contributions

L.L., F.Y. and J.Y. designed and supervised the study. W.S.Z., J.Q., M.J., P.T., W.C.Z., H.Z. and Y.S. collected the raw data. L.H., W.S.Z., J.Q., P.T., H.Z., Y.S., J.-W.F., G.C., B.F., R.L., X.G.L., Z.X., J.L., Z. Luo, S.C., D.X.D., Q.J., J.X.L., Z. Li, Y.L., X.J., Y.P., X.Y., C.Z., Y.Y., Z.T., H.C., W.F.L., L.-L.C., Q.L., F.Y. and L.L. performed the data analysis. L.L., F.Y., J.Y. and L.H. wrote the manuscript.

Corresponding authors

Correspondence to Jianbing Yan, Fang Yang or Lin Li.

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The authors declare no competing interests.

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Nature Genetics thanks Pascal Falter-Braun, Silin Zhong and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Landscape of functional elements in maize.

a, Data collection. The numbers in the cells of the matrix indicate the number of tissues collected for each of the four omics of high-throughput data. b, Distribution of different elements in transcriptome across the maize genome. Chr, chromosome. c, Proportion of different elements in transcriptome across the maize genome. d, Numbers of different elements identified across 31 tissues and stages. From top to bottom each bar represents fusion transcripts, long non-coding RNA, protein-coding transcripts, circRNAs and sRNA clusters in each tissue or stage, respectively.

Extended Data Fig. 2 Expression-level variation across 31 different stages/tissues for all different types of transcripts.

a, Sampling of 31 stages/tissues spanning the maize whole life period. b, Transcriptome landscape of all 31 different stages/tissues for all different types of transcript isoforms. c, Stage/tissue-specific expression of protein-coding (mRNA), ribosome-imprinted transcripts (Ribo-Seq RNA), long noncoding (lncRNA), fusion, circular and small RNA.

Extended Data Fig. 3 High-throughput yeast two-hybrid analysis for the identification of protein–protein interactions.

a, The high-throughput yeast two-hybrid pipeline composed three major steps: yeast cDNA library construction, yeast mating and sequencing, and data analysis (see Methods for details). b, Number of PPIs of different confidence levels between ZmPPIs and PPIM high-confidence PPIs. HC, high confidence; MC, middle confidence; LC, low confidence (N = 1000 repeats for Random; Numbers of Random of HC = 12.505 ± 3.592; Numbers of Random of LC = 44.537 ± 6.655; Numbers of Observed of HC = 176; Numbers of Observed of LC = 372). c, Validation rates of ZmPPIs by BiFC. d, Number of saturated PPIs for each library across different numbers of matings. Saturation value is the predicted value of the specific library.

Extended Data Fig. 4 High quality transcriptomic and translatomic data across different replicates.

a, Correlation coefficients of two replicates at the transcriptome level across different tissues/stages. b, Correlation coefficients of two replicates at the translational level across different tissues/stages. c, Good 3-nt periodicity of Ribo-seq data in our study. d, Overlap for co-expression and co-translation networks constructed by two replicates.

Extended Data Fig. 5 Five regulating divergence patterns of duplicate types in different omic layers.

a–d, Enrichment and depletion of five divergence patterns in Proximal (a), Tandem (b), Transposed (c) and Dispersed (d) in transcriptome, translatome and proteome three layers. P values were calculated by Chi-square test.

Extended Data Fig. 6

Mutagenesis resulting in the zmalog1, zmalog2, zmnam1 and zmnam2 mutants.

Extended Data Fig. 7 Reconstructions of the regulating networks for well-known pathways in maize.

a, Starch synthesis pathway in maize. b, Meristem developmental pathway. P value was calculated from the frequency in 1000 simulations.

Extended Data Fig. 8 AUC and F1 values from evaluations using five classical algorithms and the expression matrix from the transcriptome (co-expression) and translatome (co-translation), network attributes from the interactome, and integrative omics integrating the attributes of each omics dataset.

Box middle line represented the median, box edges were the 25th and 75th percentiles, and further outliers were marked individually.

Extended Data Fig. 9 Predicted pathway associated with Flowering time in maize.

a, Percentage of predicted flowering time genes supported by other, related evidence for effects on flowering time. Homolog represents genes that are homologs to Arabidopsis FT genes identified in the FLOR-ID flowering time database (http://www.phytosystems.ulg.ac.be/florid/). SNPs-GWAS refers to genes with FT association signals identified in maize by Liu et al.43. Others denotes genes for which there is no known functional evidence for an effect on flowering time. b, Eight molecular pathways were predicted to be associated with flowering time in maize.

Extended Data Fig. 10 Detailed flowchart of the whole study.

The accompanied intermediate data and bioinformatics scripts were deposited in GitHub (https://github.com/hanlinqian/IntegrativeNetworkMap).

Supplementary information

Supplementary Information

Supplementary Figs. 1–34, Results and Methods.

Reporting Summary

Supplementary Tables 1–16

Supplementary Table 1. Summary of raw data collected in our study. Supplementary Table 2. Tissue information of PPI mating libraries. Supplementary Table 3. Detailed information of PPIs detected across different libraries. Supplementary Table 4. PPIs validated by bimolecular fluorescence complementation (BiFC). Supplementary Table 5. Domains detected in PPIs. Supplementary Table 6. Summary of edges and modules detected in the multi-omics functional map. Supplementary Table 7. Validation rate by BiFC of different PPI datasets. Supplementary Table 8. Detailed information of functional genes involved in kernel development. Supplementary Table 9. Genes involved in the starch synthesis-related pathway. Supplementary Table 10. Genes involved in the meristem development-related pathway. Supplementary Table 11. Detailed information of well-known FT genes. Supplementary Table 12. Information of predicted FT genes. Supplementary Table 13. Detailed summary of field phenotypic tests for the mutants and WT counterparts of predicted FT genes. Supplementary Table 14. Detailed information of 20 validated FT genes. Supplementary Table 15. Detailed information of the primers/sequences used in PCR amplification, sequencing and mapping in the RLL-Y2H assay. Supplementary Table 16. Primer sequences in the CRISPR knockout experiments.

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Han, L., Zhong, W., Qian, J. et al. A multi-omics integrative network map of maize. Nat Genet 55, 144–153 (2023). https://doi.org/10.1038/s41588-022-01262-1

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