Biology subjects

Persyn, L.

Publications and source records attributed to Persyn, L..

3 recordsLinked to original sources

bioRxiv2025

Translational buffering tunes gene expression in mouse and human

BackgroundTranslational buffering refers to the regulation of ribosome occupancy to offset the effects of transcriptional variation. While previous studies have primarily investigated translational buffering in yeast under genetic variation or environmental stress, it remains unclear how widespread this is across mammalian genes in various cellular contexts. ResultsWe performed a uniform analysis of 1,515 matched ribosome profiling and RNA-seq datasets from humans and mice. This resource enabled us to assess translational buffering through comparative analysis of variation in ribosome occupancy and RNA expression, and by examining the relationship between mRNA abundance and translation efficiency. We found that translational buffering is partly conserved between humans and mice; homologous genes showed moderate cross-species correlation in mRNA-translation efficiency relationships and strong enrichment of shared buffered genes, particularly those encoding ribosomal, RNA-binding, and proteasomal proteins. Although identified buffered genes associate with specific sequence features, these alone are insufficient to predict translational buffering, highlighting the importance of cellular context. Genes exhibiting translational buffering show lower variation in protein abundance in cancer cell lines and tissues. We also observed that translationally buffered genes are more likely to be haploinsufficient and triplosensitive, suggesting a demand for stringent dosage limits. ConclusionsWe hypothesize two models of translational buffering, namely the "differential accessibility model" and the "translation initiation rate model", suggesting that different transcripts align with one or the other. Our study explores the translational buffering potential of genes across diverse conditions, elucidates their distinctive features, and provides insights into the mechanisms driving this effect.

bioinformatics↗

bioRxiv2024

Translation efficiency covariation across cell types is a conserved organizing principle of mammalian transcriptomes

Characterizing shared patterns of RNA expression between genes across conditions has led to the discovery of regulatory networks and novel biological functions. However, it is unclear if such coordination extends to translation. Here, we uniformly analyzed 3,819 ribosome profiling datasets from 117 human and 94 mouse tissues and cell lines. We introduce the concept of translation efficiency covariation (TEC), identifying coordinated translation patterns across cell types. We nominate candidate mechanisms driving shared patterns of translation regulation. TEC is conserved across human and mouse cells and uncovers gene functions that are not evident from RNA or protein co-expression. Moreover, our observations indicate that proteins that physically interact are highly enriched for positive covariation at both translational and transcriptional levels. Our findings establish TEC as a conserved organizing principle of mammalian transcriptomes.

bioinformatics↗

bioRxiv2024

Predicting the translation efficiency of messenger RNA in mammalian cells

The degree to which translational control is specified by mRNA sequence is poorly understood in mammalian cells. Here, we constructed and leveraged a compendium of 3,819 ribosomal profiling datasets, distilling them into a transcriptome-wide atlas of translation efficiency (TE) measurements encompassing >140 human and mouse cell types. We subsequently developed RiboNN, a multitask deep convolutional neural network, and classic machine learning models to predict TEs in hundreds of cell types from sequence-encoded mRNA features, achieving state-of-the-art performance (r=0.79 in human and r=0.78 in mouse for mean TE across cell types). While the majority of earlier models solely considered 5' UTR sequence1, RiboNN integrates contributions from the full-length mRNA sequence, learning that the 5' UTR, CDS, and 3' UTR respectively possess [~]67%, 31%, and 2% per-nucleotide information density in the specification of mammalian TEs. Interpretation of RiboNN revealed that the spatial positioning of low-level di- and tri-nucleotide features (i.e., including codons) largely explain model performance, capturing mechanistic principles such as how ribosomal processivity and tRNA abundance control translational output. RiboNN is predictive of the translational behavior of base-modified therapeutic RNA, and can explain evolutionary selection pressures in human 5' UTRs. Finally, it detects a common language governing mRNA regulatory control and highlights the interconnectedness of mRNA translation, stability, and localization in mammalian organisms.

bioinformatics↗