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Time-course RNA-seq analysis provides an improved understanding of gene regulation during the formation of nodule-like structures in rice

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Using a time-course RNA-seq analysis we identified transcriptomic changes during formation of nodule-like structures (NLS) in rice and compared rice RNA-seq dataset with a nodule transcriptome dataset in Medicago truncatula.

Abstract

Plant hormones can induce the formation of nodule-like structures (NLS) in plant roots even in the absence of bacteria. These structures can be induced in roots of both legumes and non-legumes. Moreover, nitrogen-fixing bacteria can recognize and colonize these root structures. Therefore, identifying the genetic switches controlling the NLS organogenesis program in crops, especially cereals, can have important agricultural implications. Our recent study evaluated the transcriptomic response occurring in rice roots during NLS formation, 7 days post-treatment (dpt) with auxin, 2,4-D. In this current study, we investigated the regulation of gene expression occurring in rice roots at different stages of NLS formation: early (1-dpt) and late (14-dpt). At 1-dpt and 14-dpt, we identified 1662 and 1986 differentially expressed genes (DEGs), respectively. Gene ontology enrichment analysis revealed that the dataset was enriched with genes involved in auxin response and signaling; and in anatomical structure development and morphogenesis. Next, we compared the gene expression profiles across the three time points (1-, 7-, and 14-dpt) and identified genes that were uniquely or commonly differentially expressed at all three time points. We compared our rice RNA-seq dataset with a nodule transcriptome dataset in Medicago truncatula. This analysis revealed there is some amount of overlap between the molecular mechanisms governing nodulation and NLS formation. We also identified that some key nodulation genes were not expressed in rice roots during NLS formation. We validated the expression pattern of several genes via reverse transcriptase polymerase chain reaction (RT-PCR). The DEGs identified in this dataset may serve as a useful resource for future studies to characterize the genetic pathways controlling NLS formation in cereals.

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Acknowledgements

This work was financially supported by a University Research Council award, University of Central Arkansas, AR. This work was also supported by the Arkansas Center for Plant-Powered Production, as part of the National Science Foundation’s Research Infrastructure Improvement Award EPS-1003970, and by the Arkansas INBRE program, supported by a grant from the National Institute of General Medical Sciences, (NIGMS), P20 GM103429 from the National Institutes of Health. The authors would also like to thank Ashley Spurr, Hannah McCarthy, Hamilton Newhart, Randall Rainwater, and David Zimulinda for their assistance with the experiments. The authors wish to thank USDA Dale Bumpers National Rice Research Center, Stuttgart, Arkansas, for providing rice seeds Oryza sativa (cv. Nipponbare).

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Authors and Affiliations

  1. Department of Biology, University of Central Arkansas, Conway, AR, 72035, USA

    Jacklyn Thomas, Ryan Hiltenbrand, Ha Ram Kim & Arijit Mukherjee

  2. Bioinformatics and Biostatistics Core, Van Andel Research Institute, Grand Rapids, MI, 49503, USA

    Megan J. Bowman & Mary E. Winn

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  1. Jacklyn Thomas
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  2. Ryan Hiltenbrand
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  3. Megan J. Bowman
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  4. Ha Ram Kim
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Contributions

Conceived and designed the experiments: AM. Performed the experiments: JT, RH, and HRK. Analyzed the data: JT, RH, MB, HRK, MW, and AM. Contributed reagents/materials/analysis tools: AM. Wrote the paper: AM, MW, MB, and JT.

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Correspondence to Arijit Mukherjee.

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Electronic supplementary material

11103_2020_978_MOESM1_ESM.pdf

Electronic supplementary material 1 Comparison of whole roots following 2,4-D treatment at different time points. Images of whole rice roots with (B, D, F) or without (A, B, C) auxin, 2,4-D treatment at different time points. (A, B) represent images of roots at 1dpt, (C, D) represent images of roots at 7dpt, and (E, F) represent images of roots at 14dpt (PDF 5939 kb)

11103_2020_978_MOESM2_ESM.jpeg

Electronic supplementary material 2 Gene Ontology (GO) enrichment analysis of genes that are unique to each time point. Gene ontology (GO) enrichment analysis of the uniquely expressed genes at each time point, using singular enrichment analysis (SEA) in agriGO gene ontology database using default parameters. Y-axis represent –log10 of the FDR adjusted P-value. Dotted red line indicates a FDR adjusted P-value = 0.05. Biological processes = black bars, cellular components = orange bars, and molecular function = blue bars (JPEG 559 kb)

11103_2020_978_MOESM3_ESM.png

Electronic supplementary material 3 Comparison of the differentially expressed transcription factors during NLS formation at the three time points. (A) Gene ontology (GO) enrichment analysis of all the differentially expressed transcription factors at the three time points (1, 7 & 14dpt) using singular enrichment analysis (SEA) in agriGO gene ontology database using default parameters. Y-axis represent –log10 of the FDR adjusted P-value. Dotted red line indicates a FDR adjusted P-value = 0.05. Biological processes = black bars, cellular components = orange bars, and molecular function = blue bars. (B) Venn diagram analysis of the differentially expressed transcription factors at the three time points (1dpt, DEG = 148; 7dpt, DEG = 187; and 14dpt, DEG = 185) during NLS formation. Fifty-three transcription factors were differentially expressed at all time points. (C) Heat map representation of the fifty-three transcription factors that were expressed across all the time points. Color gradient indicates log2 fold change. Blue indicates down-regulation and red indicates upregulation of gene expression of auxin treated versus untreated roots (PNG 983 kb)

11103_2020_978_MOESM4_ESM.png

Electronic supplementary material 4 Comparison of the differentially expressed protein kinases during NLS formation at the three time points. (A) Gene ontology (GO) enrichment analysis of all the differentially expressed protein kinases at the three time points (1, 7 & 14dpt) using singular enrichment analysis (SEA) in agriGO gene ontology database using default parameters. Y-axis represent –log10 of the FDR adjusted P-value. Dotted red line indicates a FDR adjusted P-value = 0.05. Biological processes = black bars, cellular components = orange bars, and molecular function = blue bars. (B) Venn diagram analysis of the differentially expressed protein kinases at the three time points (1dpt, DEG = 194; 7dpt, DEG = 208; and 14 dpt, DEG = 171) during NLS formation. Fifty-four protein kinases were differentially expressed at all time points. (C) Heat map representation of the fifty-four protein kinases that were expressed across all the time points. Color gradient indicates log2 fold change. Blue indicates down-regulation and red indicates upregulation of gene expression of auxin treated versus untreated roots (PNG 1107 kb)

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Electronic supplementary material 5 Comparison of the differentially expressed transporters during NLS formation at the three time points. (A) Gene ontology (GO) enrichment analysis of all the differentially expressed transporters at the three time points (1, 7 & 14dpt) using singular enrichment analysis (SEA) in agriGO gene ontology database using default parameters. Y-axis represent –log10 of the FDR adjusted P-value. Dotted red line indicates a FDR adjusted P-value = 0.05. Biological processes = black bars, cellular components = orange bars, and molecular function = blue bars. (B) Venn diagram analysis of the differentially expressed transporters at the three time points (1dpt, DEG = 138; 7dpt, DEG = 204; and 14 dpt, DEG = 184) during NLS formation. Sixty transporters were differentially expressed at all time points. (C) Heat map representation of the sixty transporters that were expressed across all the time points. Color gradient indicates log2 fold change. Blue indicates down-regulation and red indicates upregulation of gene expression of auxin treated versus untreated roots (PNG 1075 kb)

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Thomas, J., Hiltenbrand, R., Bowman, M.J. et al. Time-course RNA-seq analysis provides an improved understanding of gene regulation during the formation of nodule-like structures in rice. Plant Mol Biol 103, 113–128 (2020). https://doi.org/10.1007/s11103-020-00978-0

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