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Ferrero-Serrano Á, Sylvia MM, Forstmeier PC, Olson AJ, Ware D, Bevilacqua PC, Assmann SM. Experimental demonstration and pan-structurome prediction of climate-associated riboSNitches in Arabidopsis. Genome Biol 2022; 23:101. [PMID: 35440059 PMCID: PMC9017077 DOI: 10.1186/s13059-022-02656-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 03/20/2022] [Indexed: 11/23/2022] Open
Abstract
Background Genome-wide association studies (GWAS) aim to correlate phenotypic changes with genotypic variation. Upon transcription, single nucleotide variants (SNVs) may alter mRNA structure, with potential impacts on transcript stability, macromolecular interactions, and translation. However, plant genomes have not been assessed for the presence of these structure-altering polymorphisms or “riboSNitches.” Results We experimentally demonstrate the presence of riboSNitches in transcripts of two Arabidopsis genes, ZINC RIBBON 3 (ZR3) and COTTON GOLGI-RELATED 3 (CGR3), which are associated with continentality and temperature variation in the natural environment. These riboSNitches are also associated with differences in the abundance of their respective transcripts, implying a role in regulating the gene's expression in adaptation to local climate conditions. We then computationally predict riboSNitches transcriptome-wide in mRNAs of 879 naturally inbred Arabidopsis accessions. We characterize correlations between SNPs/riboSNitches in these accessions and 434 climate descriptors of their local environments, suggesting a role of these variants in local adaptation. We integrate this information in CLIMtools V2.0 and provide a new web resource, T-CLIM, that reveals associations between transcript abundance variation and local environmental variation. Conclusion We functionally validate two plant riboSNitches and, for the first time, demonstrate riboSNitch conditionality dependent on temperature, coining the term “conditional riboSNitch.” We provide the first pan-genome-wide prediction of riboSNitches in plants. We expand our previous CLIMtools web resource with riboSNitch information and with 1868 additional Arabidopsis genomes and 269 additional climate conditions, which will greatly facilitate in silico studies of natural genetic variation, its phenotypic consequences, and its role in local adaptation. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-022-02656-4.
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Affiliation(s)
- Ángel Ferrero-Serrano
- Department of Biology, Pennsylvania State University, University Park, State College, PA, 16802, USA.
| | - Megan M Sylvia
- Department of Biology, Pennsylvania State University, University Park, State College, PA, 16802, USA
| | - Peter C Forstmeier
- Department of Biochemistry, Microbiology, and Molecular Biology, Pennsylvania State University, University Park, State College, PA, 16802, USA
| | - Andrew J Olson
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
| | - Doreen Ware
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA.,USDA ARS NAA Robert W. Holley Center for Agriculture and Health, Ithaca, NY, 14853, USA
| | - Philip C Bevilacqua
- Department of Biochemistry, Microbiology, and Molecular Biology, Pennsylvania State University, University Park, State College, PA, 16802, USA.,Department of Chemistry, Pennsylvania State University, University Park, State College, PA, 16802, USA.,Center for RNA Molecular Biology, Pennsylvania State University, University Park, State College, PA, 16802, USA
| | - Sarah M Assmann
- Department of Biology, Pennsylvania State University, University Park, State College, PA, 16802, USA. .,Center for RNA Molecular Biology, Pennsylvania State University, University Park, State College, PA, 16802, USA.
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