RiboA: a web application to identify ribosome A-site locations in ribosome profiling data

eIF4F complex dynamics are important for the activation of the integrated stress response

In response to stress, eukaryotes activate the integrated stress response (ISR) via phosphorylation of eIF2α to promote the translation of pro-survival effector genes, such as GCN4 in yeast. Complementing the ISR is the target of rapamycin (TOR) pathway, which regulates eIF4E function. Here, we probe translational control in the absence of eIF4E in Saccharomyces cerevisiae. Intriguingly, we find that loss of eIF4E leads to de-repression of GCN4 translation. In addition, we find that de-repression of GCN4 translation is accompanied by neither eIF2α phosphorylation nor reduction in initiator ternary complex (TC). Our data suggest that when eIF4E levels are depleted, GCN4 translation is de-repressed via a unique mechanism that may involve faster scanning by the small ribosome subunit due to increased local concentration of eIF4A. Overall, our findings suggest that relative levels of eIF4F components are key to ribosome dynamics and may play important roles in translational control of gene expression.

Tracing Translational Footprint by Ribo-Seq: Principle, Workflow, and Applications to Understand the Mechanism of Human Diseases

RNA-seq has been widely used as a high-throughput method to characterize transcript dynamic changes in a broad context, such as development and diseases. However, whether RNA-seq-estimated transcriptional dynamics can be translated into protein level changes is largely unknown. Ribo-seq (Ribosome profiling) is an emerging technology that allows for the investigation of the translational footprint via profiling ribosome-bounded mRNA fragments. Ribo-seq coupled with RNA-seq will allow us to understand the transcriptional and translational control of the fundamental biological process and human diseases. This review focuses on discussing the principle, workflow, and applications of Ribo-seq to study human diseases.

RiboNT: A Noise-Tolerant Predictor of Open Reading Frames from Ribosome-Protected Footprints

Ribo-seq, also known as ribosome profiling, refers to the sequencing of ribosome-protected mRNA fragments (RPFs). This technique has greatly advanced our understanding of translation and facilitated the identification of novel open reading frames (ORFs) within untranslated regions or non-coding sequences as well as the identification of non-canonical start codons. However, the widespread application of Ribo-seq has been hindered because obtaining periodic RPFs requires a highly optimized protocol, which may be difficult to achieve, particularly in non-model organisms. Furthermore, the periodic RPFs are too short (28 nt) for accurate mapping to polyploid genomes, but longer RPFs are usually produced with a compromise in periodicity. Here we present RiboNT, a noise-tolerant ORF predictor that can utilize RPFs with poor periodicity. It evaluates RPF periodicity and automatically weighs the support from RPFs and codon usage before combining their contributions to identify translated ORFs. The results demonstrate the utility of RiboNT for identifying both long and small ORFs using RPFs with either good or poor periodicity. We implemented the pipeline on a dataset of RPFs with poor periodicity derived from membrane-bound polysomes of Arabidopsis thaliana seedlings and identified several small ORFs (sORFs) evolutionarily conserved in diverse plant species. RiboNT should greatly broaden the application of Ribo-seq by minimizing the requirement of RPF quality and allowing the use of longer RPFs, which is critical for organisms with complex genomes because these RPFs can be more accurately mapped to the position from which they were derived.