A Ribo-Seq Method to Study Genome-Wide Translational Regulation in Plants

RNA-seq and Ribosome Profiling Reveal the Translational Landscape of Rice in Response to Rice Stripe Virus Infection

Rice is a crucial staple food for over half the global population, and viral infections pose significant threats to rice yields. This study focuses on the Rice Stripe Virus (RSV), which is known to drastically reduce rice productivity. We employed RNA-seq and ribosome profiling to analyze the transcriptional and translational responses of RSV-infected rice seedlings. Our results reveal that translational reprogramming is a critical aspect of the plant's defense mechanism, operating independently of transcriptional changes. Notably, less than half of the differentially expressed genes showed concordance between transcription and translation. Furthermore, RSV infection led to significant alterations in translational efficiency for numerous genes, suggesting that the virus selectively manipulates translation to enhance its pathogenicity. Our findings underscore the necessity of examining both transcriptional and translational landscapes to fully understand plant responses to viral infections.

Utilizing high-resolution ribosome profiling for the global investigation of gene expression in Chlamydomonas

Ribosome profiling (Ribo-seq) is a powerful method for the deep analysis of translation mechanisms and regulatory circuits during gene expression. Extraction and sequencing of ribosome-protected fragments (RPFs) and parallel RNA-seq yields genome-wide insight into translational dynamics and post-transcriptional control of gene expression. Here, we provide details on the Ribo-seq method and the subsequent analysis with the unicellular model alga Chlamydomonas reinhardtii (Chlamydomonas) for generating high-resolution data covering more than 10 000 different transcripts. Detailed analysis of the ribosomal offsets on transcripts uncovers presumable transition states during translocation of elongating ribosomes within the 5' and 3' sections of transcripts and characteristics of eukaryotic translation termination, which are fundamentally distinct for chloroplast translation. In chloroplasts, a heterogeneous RPF size distribution along the coding sequence indicates specific regulatory phases during protein synthesis. For example, local accumulation of small RPFs correlates with local slowdown of psbA translation, possibly uncovering an uncharacterized regulatory step during PsbA/D1 synthesis. Further analyses of RPF distribution along specific cytosolic transcripts revealed characteristic patterns of translation elongation exemplified for the major light-harvesting complex proteins, LHCs. By providing high-quality datasets for all subcellular genomes and attaching our data to the Chlamydomonas reference genome, we aim to make ribosome profiles easily accessible for the broad research community. The data can be browsed without advanced bioinformatic background knowledge for translation output levels of specific genes and their splice variants and for monitoring genome annotation.

A custom library construction method for super-resolution ribosome profiling in Arabidopsis

BACKGROUND

Ribosome profiling, also known as Ribo-seq, is a powerful technique to study genome-wide mRNA translation. It reveals the precise positions and quantification of ribosomes on mRNAs through deep sequencing of ribosome footprints. We previously optimized the resolution of this technique in plants. However, several key reagents in our original method have been discontinued, and thus, there is an urgent need to establish an alternative protocol.

RESULTS

Here we describe a step-by-step protocol that combines our optimized ribosome footprinting in plants with available custom library construction methods established in yeast and bacteria. We tested this protocol in 7-day-old Arabidopsis seedlings and evaluated the quality of the sequencing data regarding ribosome footprint length, mapped genomic features, and the periodic properties corresponding to actively translating ribosomes through open resource bioinformatic tools. We successfully generated high-quality Ribo-seq data comparable with our original method.

CONCLUSIONS

We established a custom library construction method for super-resolution Ribo-seq in Arabidopsis. The experimental protocol and bioinformatic pipeline should be readily applicable to other plant tissues and species.