Modeling alternative translation initiation sites in plants reveals evolutionarily conserved cis-regulatory codes in eukaryotes [RESEARCH]

Ting-Ying Wu1, Ya-Ru Li2, Kai-Jyun Chang2,3, Jhen-Cheng Fang2, Daisuke Urano4,5 and Ming-Jung Liu2,3,6 1Institute of Plant and Microbial Biology, Academia Sinica, Taipei 11529, Taiwan; 2Biotechnology Center in Southern Taiwan, Academia Sinica, Tainan 711, Taiwan; 3Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 701, Taiwan; 4Temasek Life Sciences Laboratory, Singapore 117604, Singapore; 5Department of Biological Sciences, National University of Singapore, Singapore 117558, Singapore; 6Agricultural Biotechnology Research Center, Academia Sinica, Taipei 115, Taiwan Corresponding authors: mjliugate.sinica.edu.tw, tingyinggate.sinica.edu.tw Abstract

mRNA translation relies on identifying translation initiation sites (TISs) in mRNAs. Alternative TISs are prevalent across plant transcriptomes, but the mechanisms for their recognition are unclear. Using ribosome profiling and machine learning, we developed models for predicting alternative TISs in the tomato (Solanum lycopersicum). Distinct feature sets were predictive of AUG and nonAUG TISs in 5′ untranslated regions and coding sequences, including a novel CU-rich sequence that promoted plant TIS activity, a translational enhancer found across dicots and monocots, and humans and viruses. Our results elucidate the mechanistic and evolutionary basis of TIS recognition, whereby cis-regulatory RNA signatures affect start site selection. The TIS prediction model provides global estimates of TISs to discover neglected protein-coding genes across plant genomes. The prevalence of cis-regulatory signatures across plant species, humans, and viruses suggests their broad and critical roles in reprogramming the translational landscape.

Footnotes

[Supplemental material is available for this article.]

Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.278100.123.

Freely available online through the Genome Research Open Access option.

Received May 15, 2023. Accepted February 15, 2024.

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