bioRxivGeneticsPreprint
Joint Segmental Duplication Co-option Drives Human-specific Transcriptional Readthrough and Expression Fine-tuning of NPEPPS-TBC1D3
Kaiyue MaiD, Zikun YangiD, Zhengtong LiiD, Jingyi GuoiD, Da LianiD, Ziyi WangiD, Hongyi MaiD, Shilong ZhangiD, Lianting FuiD, Hezheng LyuiD, Xinrui JiangiD, Qiujin XieiD, Guanting LiiD, Chenhao YangiD, Jieyi CheniD, Juan ZhangiD, Peng LiuiD, Xiangyu YangiD, Zhen‐Ge LuoiD, Guojie ZhangiD, Yafei MaoiD
Abstract
Segmental duplication (SD) is a major driver of functional changes in evolution and disease. Many genes embedded within SDs, such as NPEPPS and TBC1D3, display substantial copy number variation (CNV) across individuals. Yet, the precise identification of the functional copies and their transcriptional outputs remains largely unstudied. Focusing on NPEPPS and TBC1D3, we illustrate human-specific expression fine-tuning mechanisms associated with readthrough transcripts. We identified a human-specific NPEPPS-TBC1D3 digenic genomic structure that originated from a joint SD pair and became fixed across populations. Experiments demonstrate that this structure generates NPEPPS-TBC1D3 readthrough transcripts, which are the predominant isoforms of TBC1D3 expression in various cell types, fine-tuning its protein level. Furthermore, a human-specific hypomethylation signal within an upstream CpG island of NPEPPS precisely pinpoints the expressed TBC1D3 paralog. Moreover, we reveal transcriptional readthrough events are [~]3-fold enriched for joint-SD-associated transcriptional readthrough (JSDTR) and identify 109 JSDTR gene pairs, including neurodevelopmentally important pairs and clinically interesting SERF1A/B-SMN1/2. Taken together, our findings comprehensively describe an example of how a joint SD event shaped evolution and suggest that JSDTR is a broad mechanism for the emergence of new functions.
- organism: human
- assay: DNA methylation
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 0
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