Literature DB >> 29686057

Identification of Functional Single-Nucleotide Polymorphisms Affecting Leaf Hair Number in Brassica rapa.

Wenting Zhang1,2, Shirin Mirlohi1,2, Xiaorong Li1,2, Yuke He3,4.   

Abstract

Leaf traits affect plant agronomic performance; for example, leaf hair number provides a morphological indicator of drought and insect resistance. Brassica rapa crops have diverse phenotypes, and many B. rapa single-nucleotide polymorphisms (SNPs) have been identified and used as molecular markers for plant breeding. However, which SNPs are functional for leaf hair traits and, therefore, effective for breeding purposes remains unknown. Here, we identify a set of SNPs in the B. rapa ssp. pekinenesis candidate gene BrpHAIRY LEAVES1 (BrpHL1) and a number of SNPs of BrpHL1 in a natural population of 210 B. rapa accessions that have hairy, margin-only hairy, and hairless leaves. BrpHL1 genes and their orthologs and paralogs have many SNPs. By intensive mutagenesis and genetic transformation, we selected the functional SNPs for leaf hairs by the exclusion of nonfunctional SNPs and the orthologous and paralogous genes. The residue tryptophan-92 of BrpHL1a was essential for direct interaction with GLABROUS3 and, thus, necessary for the formation of leaf hairs. The accessions with the functional SNP leading to substitution of the tryptophan-92 residue had hairless leaves. The orthologous BrcHL1b from B. rapa ssp. chinensis regulates hair formation on leaf margins rather than leaf surfaces. The selected SNP for the hairy phenotype could be adopted as a molecular marker for insect resistance in Brassica spp. crops. Moreover, the procedures optimized here can be used to explain the molecular mechanisms of natural variation and to facilitate the molecular breeding of many crops.
© 2018 American Society of Plant Biologists. All rights reserved.

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Year:  2018        PMID: 29686057      PMCID: PMC6001316          DOI: 10.1104/pp.18.00025

Source DB:  PubMed          Journal:  Plant Physiol        ISSN: 0032-0889            Impact factor:   8.340


  36 in total

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