bioRxivGenomicsPreprint
Allele-specific expression reveals complex cis- and trans-regulatory divergence underlying fruit phenotypic differences between cultivated and wild tomato species
Jiantao ZhaoiD, Philippe NicolasiD, Yimin XuiD, Julia VrebaloviD, James J. GiovannoniiD, Zhangjun FeiiD, Carmen CataláiD
Abstract
Tomato (Solanum lycopersicum) is one of the most important agricultural crops and serves as a model system for fleshy fruit biology. However, domestication bottlenecks have led to limited genetic diversity for further crop improvement. Wild relatives represent a rich reservoir of phenotypic diversity, yet the molecular basis underlying such phenotypic diversity remains largely unknown. As gene expression variation is a major driver of phenotypic diversity, we performed genome-wide allele-specific expression analyses in F1 hybrids between cultivated tomato and three wild relatives spanning a range of evolutionary distances, across three fruit tissues and multiple developmental stages. Our analysis generated a multi-species map of cis - and trans-regulatory variation, revealing a predominant role of cis-regulatory effects in expression divergence across species, tissues, and stages. The majority of cis effects were tissue- and/or stage-specific, underscoring the importance of tissue context in regulatory variation. Regulatory mechanisms and inheritance patterns shifted with evolutionary distance, with more distantly related species showing increased cis-regulatory contributions. Finally, we identified extensive regulatory divergence in biosynthetic pathways related to fruit nutritional value and flavor quality, including carotenoids, flavonoids, alkaloids, sugars, and volatiles. This study presents a high-resolution map of regulatory variation underlying tomato fruit development and provides evolutionary insights into the regulation of fruit nutrition and flavor traits.
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
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