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Chromosome-level reference genome of Tetrastigma hemsleyanum (Vitaceae) provides insights into genomic evolution and the biosynthesis of phenylpropanoids and flavonoids.

Shanshan ZhuXinyi ZhangChaoqian RenXinhan XuHans Peter ComesWeimei JiangChengxin FuHuixia FengLiming CaiDeyuan HongKunlun LiGuoyin KaiYingxiong Qiu
Published in: The Plant journal : for cell and molecular biology (2023)
Here, we present a high-quality chromosome-scale genome assembly (2.19 Gb) and annotation of Tetrastigma hemsleyanum, a perennial herbaceous liana native to subtropical China, with diverse medicinal applications. Approximately 73% of the genome was comprised of transposable elements (TEs), of which long terminal repeat retrotransposons (LTR-RTs) were a predominant group (69%). The genome size increase of T. hemsleyanum (relative to Vitis species) was mostly due to the proliferation of LTR-RTs. Of the different modes of gene duplication identified, transposed duplication (TRD) and dispersed duplication (DSD) were the predominant ones. Genes, particularly those involved in the phenylpropanoid-flavonoid (PF) pathway and associated with therapeutic properties and environmental stress resistance, were significantly amplified through recent tandem duplications. We dated the divergence of two intraspecific lineages in Southwest (SW) vs. Central-South-East (CSE) China to the late Miocene (c. 5.2 Mya). Of those, the former showed more up-regulated genes and metabolites. Based on resequencing data of 38 individuals representing both lineages, we identified various candidate genes related to 'response to stimulus' and 'biosynthetic process', including ThFLS11, putatively involved in flavonoid accumulation. Overall, this study provides abundant genomic resources for future evolutionary, ecological and functional genomics studies in T. hemsleyanum and related species.
Keyphrases
  • genome wide
  • copy number
  • dna methylation
  • genome wide identification
  • signaling pathway
  • transcription factor
  • climate change
  • machine learning
  • gene expression
  • single cell
  • stress induced
  • deep learning