November 10th, 2024
Recent Publications Harnessing the Power of Translatomics
Every week we provide a digest of a small number of recent interesting papers in the field of translatomics.
In this week’s Sunday papers, Rangel et al. develop a machine learning approach to ribosome pausing discovery, Wang et al. present new features to the updated database RPFdb v3.0, and Wang Z. et al. find that ILF3 plays a central role in inflammation and the development of AAA.
A machine learning approach uncovers principles and determinants of eukaryotic ribosome pausing
Science Advances, 2024
Aguilar Rangel M., Stein K. and Frydman J.
This paper introduces a machine learning-based framework to dissect the intricacies of ribosome pausing during eukaryotic translation, particularly in Saccharomyces cerevisiae. Leveraging ribosome profiling data, the authors examine how factors such as tRNA availability, codon sequence, and amino acid properties regulate ribosome movement across mRNA transcripts. By treating ribosome pausing as an anomaly detection problem, the study identifies consistent pausing sites across both high and low-expression genes, overcoming limitations of conventional sequencing analyses which often favour abundant transcripts.
The study reveals a coordinated influence of tRNA availability and amino acid side chain properties on ribosome elongation rates, demonstrating that codons paired with high-abundance tRNAs are translated faster, while those with wobble codon interactions tend to slow down translation. Pausing events also facilitate specific protein-folding interactions, as shown by the link between ribosome pausing sites and chaperone binding. Furthermore, codons with pause-inducing amino acids often correlate with slower elongation, and clusters of these pauses align with critical stages in nascent chain folding and chaperone recruitment.
These findings have important implications throughout translatomics and synthetic biology, providing valuable insights for optimizing translation rates in engineered systems. Understanding how codon composition, tRNA pools, and amino acid characteristics jointly shape translation could enhance the design of synthetic genes and therapeutic proteins with minimal translation errors and efficient folding pathways. Moreover, this research offers foundational knowledge for developing treatments targeting diseases linked to protein misfolding, potentially leading to innovations in personalized medicine and protein engineering.
RPFdb v3.0: an enhanced repository for ribosome profiling data and related content
Nucleic Acid Research, 2024
Wang Y., Tang Y., Xie, Z. and Wang H.
This paper introduces RPFdb v3.0, an updated database designed to streamline access and analysis of ribosome profiling (Ribo-seq) data. RPFdb v3.0 now hosts over 5,000 Ribo-seq datasets across diverse species, incorporating matched RNA-seq data to offer insights into transcriptional and translational dynamics. Enhanced functionalities include advanced ORF annotation, pausing score estimation, and visualization tools, enabling comprehensive exploration of translational efficiency.
The database’s v3.0 key updates underscore its utility in standardizing and expanding translational data across multiple species, facilitating comparisons that reveal the regulatory complexity of translation. Updated annotations improve the accuracy of ORF identification, critical for studying protein synthesis. RPFdb v3.0 also includes advanced tools for visualizing ribosome density, aiding studies into protein folding and synthesis regulation by providing triplet-amino acid-specific pausing scores.
The utility of this resource extends to translational research and therapeutic applications, particularly RNA-based therapies, where optimizing mRNA sequences for translation is essential. By enabling easier access and standardized analysis across varied biological contexts, RPFdb v3.0 democratizes translatome data, fostering collaborations and innovations in understanding translation at cellular and tissue levels.
Macrophage ILF3 promotes abdominal aortic aneurysm by inducing inflammatory imbalance in male mice
Nature Communications, 2024
Wang Z., Cheng J., Wang Y., Yuan H., Bi S., Wang S., Hou Y., Zhang X., Xu B., Wang Z., Zhang Y., Jiang W., Chen Y. and Zhang M.
This paper explores the role of the RNA-binding protein ILF3 in promoting abdominal aortic aneurysm (AAA), a serious vascular disease marked by inflammation and aortic wall degradation. Focusing on macrophages, the study finds that elevated ILF3 levels drive AAA progression by intensifying inflammation and suppressing anti-inflammatory mechanisms, specifically by activating NF-κB while inhibiting the Keap1-Nrf2 pathway. By using gene-targeting methods and Bardoxolone methyl (BM) to reduce ILF3 levels, researchers observed significantly reduced aneurysm formation in mouse models.
Key findings reveal that ILF3 overexpression in macrophages worsens AAA symptoms, increasing pro-inflammatory responses while reducing protective anti-inflammatory actions. Mechanistically, ILF3 destabilizes p105 mRNA to activate NF-κB and augments Keap1 translation, a mechanism confirmed by ribosome profiling, which in turn limits Nrf2’s anti-inflammatory signaling. Genetic silencing of ILF3 or pharmacological intervention with BM markedly alleviated these inflammatory processes, suggesting that targeting ILF3 could offer therapeutic benefits for AAA patients.
The implications of these findings extend beyond AAA, highlighting ILF3 as a critical mediator of inflammation in vascular diseases. The study suggests a new approach to treating AAA by targeting ILF3 to restore inflammatory balance, potentially preventing aneurysm progression and rupture. The success of BM as an ILF3-targeting agent in reducing inflammation and protecting the aortic wall also points to its potential as a broader anti-inflammatory treatment in cardiovascular medicine.