bioRxivGenomicsPreprint
Transcriptome analysis revealed the possible contribution of chromosome introgression fragments from Sea Island cotton (Gossypium barbadense) to fiber strength in Upland cotton (Gossypium hirsutum)
Quanwei Lu, Yuzhen Shi, Xianghui Xiao, Pengtao Li, Juwu Gong, Aiying Liu, Haihong Shang, Wankui Gong, Tingting Chen, Qun Ge, Junwen Li, Wei Su, Shaoqi Li, Zhen Zhang, Youlu Yuan, Jinling Huang
Abstract
Cotton fiber strength is a critical property determining fiber qualities, and determined by the secondary cell wall development. Understanding the mechanism of fiber development will provide a way to improvement of fiber strength. In this study, the introgression lines of upland and sea island cotton, and have experience of four generations of backcross with upland parent, and have significant higher fiber strength than their upland parent, and the transcriptome were analyzed and compared between the introgression lines and their upland parent. There were 2201 differentially expressed genes (DEG) identified by comparing two introgression lines with their recurrent parent CCRI45, in different development stages from 15 days post-anthesis (DPA) to 28 DPA. The up-regulated genes regulated the polysaccharide metabolic process, single-organism localization, cell wall organization or biogenesis and so on. The down-regulated genes involved in the microtubule-based process, cellular response to stress, cell cycle process and so on. Further functional analysis revealed three significant functional genes, XLOC_036333 (mannosyl-oligosaccharide-alpha-mannosidase mns1), XLOC_029945 (FLA8) and XLOC_075372 (snakin-1), playing important roles in the regulation of cotton fiber strength. Our results provide important candidates genes and inspirations for the future investigation of the molecular mechanism of fiber quality formation, and improvement of cotton fiber quality in breeding.
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: database / resource
- approach: statistical model
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