Exploring Ribosome Positioning on Translating Transcripts with Ribosome Profiling

Post-transcriptional mechanisms modulate the consequences of adaptive copy number variation

Copy-number variants (CNVs) are large-scale amplifications or deletions of DNA that can drive rapid adaptive evolution and result in large-scale changes in gene expression. Whereas alterations in the copy number of one or more genes within a CNV can confer a selective advantage, other genes within a CNV can decrease fitness when their dosage is changed. Dosage compensation - in which the gene expression output from multiple gene copies is less than expected - is one means by which an organism can mitigate the fitness costs of deleterious gene amplification. Previous research has shown evidence for dosage compensation at both the transcriptional level and at the level of protein expression; however, the extent of compensation differs substantially between genes, strains, and studies. Here, we investigated sources of dosage compensation at multiple levels of gene expression regulation by defining the transcriptome, translatome and proteome of experimentally evolved yeast (Saccharomyces cerevisiae) strains containing adaptive CNVs. We quantified the gene expression output at each step and found evidence of widespread dosage compensation at the protein abundance (~47%) level. By contrast we find only limited evidence for dosage compensation at the transcriptional (~8%) and translational (~3%) level. We also find substantial divergence in the expression of unamplified genes in evolved strains that could be due to either the presence of a CNV or adaptation to the environment. Detailed analysis of 82 amplified and 411 unamplified genes with significantly discrepant relationships between RNA and protein abundances identified enrichment for upstream open reading frames (uORFs). These uORFs are enriched for binding site motifs for SSD1, an RNA binding protein that has previously been associated with tolerance of aneuploidy. Our findings suggest that, in the presence of CNVs, SSD1 may act to alter the expression of specific genes by potentiating uORF mediated translational regulation.

Translating Ribosome Affinity Purification (TRAP) of Cell Type-specific mRNA from Mouse Brain Lysates

Mammalian tissues are highly heterogenous and complex, posing a challenge in understanding the molecular mechanisms regulating protein expression within various tissues. Recent studies have shown that translation at the level of the ribosome is highly regulated, and can vary independently of gene expression observed at a transcriptome level, as well as between cell populations, contributing to the diversity of mammalian tissues. Earlier methods that analyzed gene expression at the level of translation, such as polysomal- or ribosomal-profiling, required large amounts of starting material to isolate enough RNA for analysis by microarray or RNA-sequencing. Thus, rare or less abundant cell types within tissues were not able to be properly studied with these methods. Translating ribosome affinity purification (TRAP) utilizes the incorporation of an eGFP-affinity tag on the large ribosome subunit, driven by expression of cell-type specific Cre-lox promoters, to allow for identification and capture of transcripts from actively translating ribosomes in a cell-specific manner. As a result, TRAP offers a unique opportunity to evaluate the entire mRNA translation profile within a specific cell type, and increase our understanding regarding the cellular complexity of mammalian tissues. Graphical abstract: Schematic demonstrating TRAP protocol for identifying ribosome-bound transcripts specifically within cerebellar Purkinje cells.

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

Protein production from mRNA is one of the fundamental molecular processes in a cell. Accurate genome-wide information on the levels of translation and ribosome distribution on mRNA can be gathered by carrying out ribosome footprinting, aka Ribo-seq. Herein, we present a detailed protocol describing the construction of parallel Ribo-seq and RNA-seq libraries from Arabidopsis seedlings treated with the plant hormone auxin. The improved protocol for ribosome footprint library generation can be easily adapted to analyzing the effects on translation of genetic perturbations and various abiotic and biotic factors to shed the much-needed light on translational regulation in plants.

Global translational landscape of the Candida albicans morphological transition

Candida albicans, a major human fungal pathogen associated with high mortality and/or morbidity rates in a wide variety of immunocompromised individuals, undergoes a reversible morphological transition from yeast to filamentous cells that is required for virulence. While previous studies have identified and characterized global transcriptional mechanisms important for driving this transition, as well as other virulence properties, in C. albicans and other pathogens, considerably little is known about the role of genome-wide translational mechanisms. Using ribosome profiling, we report the first global translational profile associated with C. albicans morphogenesis. Strikingly, many genes involved in pathogenesis, filamentation, and the response to stress show reduced translational efficiency (TE). Several of these genes are known to be strongly induced at the transcriptional level, suggesting that a translational fine-tuning mechanism is in place. We also identify potential upstream open reading frames (uORFs), associated with genes involved in pathogenesis, and novel ORFs, several of which show altered TE during filamentation. Using a novel bioinformatics method for global analysis of ribosome pausing that will be applicable to a wide variety of genetic systems, we demonstrate an enrichment of ribosome pausing sites in C. albicans genes associated with protein synthesis and cell wall functions. Altogether, our results suggest that the C. albicans morphological transition, and most likely additional virulence processes in fungal pathogens, is associated with widespread global alterations in TE that do not simply reflect changes in transcript levels. These alterations affect the expression of many genes associated with processes essential for virulence and pathogenesis.

uORF-seqr: A Machine Learning-Based Approach to the Identification of Upstream Open Reading Frames in Yeast

The identification of upstream open reading frames (uORFs) using ribosome profiling data is complicated by several factors such as the noise inherent to the procedure, the substantial increase in potential translation initiation sites (and false positives) when one includes non-canonical start codons, and the paucity of molecularly validated uORFs. Here we present uORF-seqr, a novel machine learning algorithm that uses ribosome profiling data, in conjunction with RNA-seq data, as well as transcript aware genome annotation files to identify statistically significant AUG and near-cognate codon uORFs.

XPRESSyourself: Enhancing, standardizing, and automating ribosome profiling computational analyses yields improved insight into data

Ribosome profiling, an application of nucleic acid sequencing for monitoring ribosome activity, has revolutionized our understanding of protein translation dynamics. This technique has been available for a decade, yet the current state and standardization of publicly available computational tools for these data is bleak. We introduce XPRESSyourself, an analytical toolkit that eliminates barriers and bottlenecks associated with this specialized data type by filling gaps in the computational toolset for both experts and non-experts of ribosome profiling. XPRESSyourself automates and standardizes analysis procedures, decreasing time-to-discovery and increasing reproducibility. This toolkit acts as a reference implementation of current best practices in ribosome profiling analysis. We demonstrate this toolkit’s performance on publicly available ribosome profiling data by rapidly identifying hypothetical mechanisms related to neurodegenerative phenotypes and neuroprotective mechanisms of the small-molecule ISRIB during acute cellular stress. XPRESSyourself brings robust, rapid analysis of ribosome-profiling data to a broad and ever-expanding audience and will lead to more reproducible and accessible measurements of translation regulation. XPRESSyourself software is perpetually open-source under the GPL-3.0 license and is hosted at https://github.com/XPRESSyourself, where users can access additional documentation and report software issues.

Conserved non-AUG uORFs revealed by a novel regression analysis of ribosome profiling data

Upstream open reading frames (uORFs), located in transcript leaders (5' UTRs), are potent cis-acting regulators of translation and mRNA turnover. Recent genome-wide ribosome profiling studies suggest that thousands of uORFs initiate with non-AUG start codons. Although intriguing, these non-AUG uORF predictions have been made without statistical control or validation; thus, the importance of these elements remains to be demonstrated. To address this, we took a comparative genomics approach to study AUG and non-AUG uORFs. We mapped transcription leaders in multiple Saccharomyces yeast species and applied a novel machine learning algorithm (uORF-seqr) to ribosome profiling data to identify statistically significant uORFs. We found that AUG and non-AUG uORFs are both frequently found in Saccharomyces yeasts. Although most non-AUG uORFs are found in only one species, hundreds have either conserved sequence or position within Saccharomyces uORFs initiating with UUG are particularly common and are shared between species at rates similar to that of AUG uORFs. However, non-AUG uORFs are translated less efficiently than AUG-uORFs and are less subject to removal via alternative transcription initiation under normal growth conditions. These results suggest that a subset of non-AUG uORFs may play important roles in regulating gene expression.