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Genome-Wide Identification of Flowering-Time Genes in Brassica Species and Reveals a Correlation between Selective Pressure and Expression Patterns of Vernalization-Pathway Genes in Brassica napus.

Haojie LiYonghai FanJingyin YuLiang ChaiJingfang ZhangJun JiangCheng CuiBenchuan ZhengLiangcai JiangKun Lu
Published in: International journal of molecular sciences (2018)
Flowering time is a key agronomic trait, directly influencing crop yield and quality. Many flowering-time genes have been identified and characterized in the model plant Arabidopsis thaliana; however, these genes remain uncharacterized in many agronomically important Brassica crops. In this study, we identified 1064, 510, and 524 putative orthologs of A. thaliana flowering-time genes from Brassica napus, Brassica rapa, and Brassica oleracea, respectively, and found that genes involved in the aging and ambient temperature pathways were fewer than those in other flowering pathways. Flowering-time genes were distributed mostly on chromosome C03 in B. napus and B. oleracea, and on chromosome A09 in B. rapa. Calculation of non-synonymous (Ka)/synonymous substitution (Ks) ratios suggested that flowering-time genes in vernalization pathways experienced higher selection pressure than those in other pathways. Expression analysis showed that most vernalization-pathway genes were expressed in flowering organs. Approximately 40% of these genes were highly expressed in the anther, whereas flowering-time integrator genes were expressed in a highly organ-specific manner. Evolutionary selection pressures were negatively correlated with the breadth and expression levels of vernalization-pathway genes. These findings provide an integrated framework of flowering-time genes in these three Brassica crops and provide a foundation for deciphering the relationship between gene expression patterns and their evolutionary selection pressures in Brassica napus.
Keyphrases
  • genome wide identification
  • arabidopsis thaliana
  • genome wide analysis
  • transcription factor
  • genome wide
  • gene expression
  • bioinformatics analysis
  • poor prognosis
  • air pollution