bioRxivPlant BiologyPreprint
Grain number and genotype drive nitrogen-dependent yield response in the C4 model Setaria italica (L.) P. Beauv.
T. Bandyopadhyay, S. M. Swarbreck, V. Jaiswal, R. Gupta, A. R. Bentley, H. Griffiths, M. Prasad
Abstract
Fertiliser nitrogen (N) drives crop yields and requires the breeding and selection of cultivars that are inherently highly N responsive. For major cereal crops such as wheat (Triticum aestivum L.) breeding over time has led to enhanced N use in modern cultivars however there remains a gap in understanding the N responsiveness of minor cereals grains, many of which are highly relevant to global food security. Here we investigate response to increasing N availability in a diverse population of Setaria italica (L., foxtail millet) accessions demonstrating that N-driven yield increase is dependent on grain number rather than individual grain weight. Within the population, some accessions responded strongly to increased N availability while others show little yield improvement under high N. Genetic markers were generated to enable investigation of N responsiveness at a genome-wide level, highlighting likely underlying causal loci, especially for grains per plant. Despite the lack of response in terms of yield increase, a non-responsive accession shows a strong transcriptional response suggesting different metabolic functioning under high vs low N. Our results show major differences in N responsiveness in S. italica and provide novel insight into the genetic and molecular basis for this variation. One sentence summaryNitrogen dependent yield response in Setaria italica L. is driven by grain number and genotypes with low N yield responsive genotypes being more transcriptionally dynamic under varied N levels post-flowering compared to high N yield responsive genotypes.
- organism: plant (other)
- assay: DNA methylation
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: cell type annotation
- approach: genetic perturbation / CRISPR
- approach: statistical model
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