bioRxivDevelopmental BiologyPreprint
Comparative transcriptome analysis by deep RNA sequencing at early stage of skin pigmentation in goats (Capra hircus)
Hangxing ReniD, Gaofu WangiD, Jing JiangiD, Liangjia LiuiD, Nianfu Li, Jie LiiD, Lin Na FuiD, Haiyan Zhang, Risu NaiD, Yongfu HuangiD, Li Qin ZhangiD, Lei CheniD, Yong HuangiD, Peng ZhouiD
Abstract
Although specific genes have been found to be associated with skin pigmentation, the global gene expression profile for the early stage of skin pigmentation and development in mammals is still not well understood. Here we reported a rare natural group of goat (Youzhou dark goat) featuring the dark skin of body including the visible mucous membranes, which may be an exclusive kind of large mammalian species with this special phenotype so far. In the present study, we characterized the 100-day-old fetal skin transcriptome in hyperpigmented (dark-skinned) and wild-type (white-skinned) goats using deep RNA-sequencing. A total of 923,013,870 raw reads from 6 libraries were obtained, and a large number of alternative splicing events were identified in the transcriptome of fetal skin, including the well-known melanogenic genes ASIP, TYRP1, and DCT, which were differentially expressed in the skin between the dark-skinned and white-skinned goats. Further analysis demonstrated that differential genes including ASIP, TYRP1, DCT, WNT2, RAB27A, FZD4, and CREB3L1 were significantly overrepresented in the melanogenesis pathway and several biological process associated with pigmentation. On the other hand, we identified 1616 novel transcripts in goat skin based on the characteristics of their expression level and gene composition. These novel transcripts may represent two distinct groups of nucleic acid molecules. Our findings contribute to the understanding of the characteristics of global gene expression at early stages of skin pigmentation and development, as well as describe an animal model for human diseases associated with pigmentation.
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: database / resource
- approach: statistical model
- Citations
- 0
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