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Unleashing floret fertility in wheat through the mutation of a homeobox gene.

Shun SakumaGuy GolanZifeng GuoTaiichi OgawaAkemi TagiriKazuhiko SugimotoNadine BernhardtJonathan BrassacMartin MascherGoetz HenselShizen OhnishiHironobu JinnoYoko YamashitaIdan AyalonCurtis PozniakThorsten SchnurbuschTakao Komatsuda
Published in: Proceedings of the National Academy of Sciences of the United States of America (2019)
Floret fertility is a key determinant of the number of grains per inflorescence in cereals. During the evolution of wheat (Triticum sp.), floret fertility has increased, such that current bread wheat (Triticum aestivum) cultivars set three to five grains per spikelet. However, little is known regarding the genetic basis of floret fertility. The locus Grain Number Increase 1 (GNI1) is shown here to be an important contributor to floret fertility. GNI1 evolved in the Triticeae through gene duplication. The gene, which encodes a homeodomain leucine zipper class I (HD-Zip I) transcription factor, was expressed most abundantly in the most apical floret primordia and in parts of the rachilla, suggesting that it acts to inhibit rachilla growth and development. The level of GNI1 expression has decreased over the course of wheat evolution under domestication, leading to the production of spikes bearing more fertile florets and setting more grains per spikelet. Genetic analysis has revealed that the reduced-function allele GNI-A1 contributes to the increased number of fertile florets per spikelet. The RNAi-based knockdown of GNI1 led to an increase in the number of both fertile florets and grains in hexaploid wheat. Mutants carrying an impaired GNI-A1 allele out-yielded WT allele carriers under field conditions. The data show that gene duplication generated evolutionary novelty affecting floret fertility while mutations favoring increased grain production have been under selection during wheat evolution under domestication.
Keyphrases
  • genome wide identification
  • genome wide
  • transcription factor
  • copy number
  • childhood cancer
  • poor prognosis
  • young adults
  • machine learning
  • single cell
  • big data
  • long non coding rna
  • artificial intelligence