bioRxivPlant BiologyPreprint
Balancing Growth: GROOT Genes Link Plant Biomass and Temperature Adaptation in Arabidopsis
Samsad RazzaqueiD, Ling ZhangiD, Ana Páez-GarcíaiD, Yosemite Caputi, Wolfgang BuschiD
Abstract
Root systems take up water and nutrients from the soil and thereby underpin all essential plant functions. The size of the root system affects the ability of roots to capture these resources and determine the amount of carbon that roots transfer into the soil. Understanding the genetic basis of root biomass is therefore very important for enhancing crop resilience and productivity, especially in the face of climate change, as for soil carbon sequestration efforts. In our study, we catalogued root biomass of a diverse set of Arabidopsis accessions mainly derived from Sweden and Spain and utilized GWAS to identify loci associated with root biomass in Arabidopsis thaliana. We found that genetic variants associated with high biomass are enriched in accessions originating from distinct biogeographic regions in Spain. Among the most significant SNPs, one SNP on chromosome 3, was closely linked to three genes for which loss of function mutations caused significant increases in root, shoot and seed biomass and that we named GROOT genes. These genes act as general growth limiters. Additionally, our analysis revealed that the GROOT genes are in strong linkage disequilibrium, implying a potential coordinated function in regulating growth. Furthermore, accessions carrying the non-reference allele at this SNP showed markedly higher biomass under elevated temperatures, suggesting that these genes may also play a role in temperature adaptation. The discovery that the GROOT genes can enhance biomass without negative trade-offs across multiple traits opens new avenues for further research aimed at understanding genetically determined growth limitations, and for improving crop resilience and adaptability, as well as carbon sequestration.
- organism: Arabidopsis
- assay: proteomics
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 1
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