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Splicing Retention and Enhancer Divergence Govern the Evolutionary Fate of Ohnologues Following Whole-Genome Duplication in Rainbow Trout

Ali, A.; Al-Tobasei, R.; Zhou, H.; Salem, M.

2025-08-07 genomics
10.1101/2025.08.06.669003 bioRxiv
Show abstract

Whole-genome duplication (WGD) produces gene duplicates--known as ohnologues--that expand evolutionary potential while posing challenges for regulatory coordination and functional balance. While divergence in gene expression has been widely studied post-WGD, the long-term evolutionary dynamics of alternative splicing (AS) and its regulatory basis remain unresolved. Here, we investigate the evolution of AS in rainbow trout (Oncorhynchus mykiss), a species that underwent a salmonid-specific WGD [~]100 million years ago. Using a high-quality genome assembly, transcriptome data across six tissues, and ChIP-seq profiling of histone modifications, we classify ohnologue pairs based on their expression divergence, splicing complexity, and epigenetic signatures. We find that most ohnologues are retained through conservation, with a gradual reduction in AS diversity over time. Contrary to earlier models, a substantial fraction of ohnologues follows an independent splicing model, maintaining splicing complexity similar to their unduplicated ancestors. Strikingly, we show that enhancer-associated histone marks, particularly H3K27ac, diverge significantly between neofunctionalized and independently splicing gene pairs, implicating enhancer rewiring as a key driver of regulatory and functional divergence. These findings reveal that AS evolution after WGD is shaped by both selective pressures and epigenetic modulation, challenging assumptions of rapid splicing loss and highlighting the independent model as a dominant long-term fate. Our results provide a unified framework for understanding how splicing and regulatory landscapes evolve following genome duplication, with broad implications for vertebrate genome evolution and functional innovation.

Published in Scientific Reports (predicted rank #17) · training set

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