Unraveling the influences of sequence and position on yeast uORF activity using massively parallel reporter systems and machine learning.

Upstream open-reading frames (uORFs) are potent cis-acting regulators of mRNA translation and nonsense-mediated decay (NMD). While both AUG- and non-AUG initiated uORFs are ubiquitous in ribosome profiling studies, few uORFs have been experimentally tested. Consequently, the relative influences of sequence, structural, and positional features on uORF activity have not been determined. We quantified thousands of yeast uORFs using massively parallel reporter assays in wildtype and ∆upf1 yeast. While nearly all AUG uORFs were robust repressors, most non-AUG uORFs had relatively weak impacts on expression. Machine learning regression modeling revealed that both uORF sequences and locations within transcript leaders predict their effect on gene expression. Indeed, alternative transcription start sites highly influenced uORF activity. These results define the scope of natural uORF activity, identify features associated with translational repression and NMD, and suggest that the locations of uORFs in transcript leaders are nearly as predictive as uORF sequences.

Quantitative mapping of mRNA 3' ends in Pseudomonas aeruginosa reveals a pervasive role for premature 3' end formation in response to azithromycin.

Pseudomonas aeruginosa produces serious chronic infections in hospitalized patients and immunocompromised individuals, including patients with cystic fibrosis. The molecular mechanisms by which P. aeruginosa responds to antibiotics and other stresses to promote persistent infections may provide new avenues for therapeutic intervention. Azithromycin (AZM), an antibiotic frequently used in cystic fibrosis treatment, is thought to improve clinical outcomes through a number of mechanisms including impaired biofilm growth and quorum sensing (QS). The mechanisms underlying the transcriptional response to AZM remain unclear. Here, we interrogated the P. aeruginosa transcriptional response to AZM using a fast, cost-effective genome-wide approach to quantitate RNA 3' ends (3pMap). We also identified hundreds of P. aeruginosa genes with high incidence of premature 3' end formation indicative of riboregulation in their transcript leaders using 3pMap. AZM treatment of planktonic and biofilm cultures alters the expression of hundreds of genes, including those involved in QS, biofilm formation, and virulence. Strikingly, most genes downregulated by AZM in biofilms had increased levels of intragenic 3' ends indicating premature transcription termination, transcriptional pausing, or accumulation of stable intermediates resulting from the action of nucleases. Reciprocally, AZM reduced premature intragenic 3' end termini in many upregulated genes. Most notably, reduced termination accompanied robust induction of obgE, a GTPase involved in persister formation in P. aeruginosa. Our results support a model in which AZM-induced changes in 3' end formation alter the expression of central regulators which in turn impairs the expression of QS, biofilm formation and stress response genes, while upregulating genes associated with persistence.

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.

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.

Accurate Recovery of Ribosome Positions Reveals Slow Translation of Wobble-Pairing Codons in Yeast.

Ribosome profiling quantitatively captures ribosome locations during translation. The resulting profiles of ribosome locations are widely used to study translational speed. However, an accurate estimation of the ribosome location depends on identifying the A-site from ribosome profiling reads, a problem that was previously unsolved. Here, we propose a novel method to estimate the ribosome A-site positions from high-coverage ribosome profiling reads. Our model allows more reads to be used, accurately explains the 3-nt periodicity of ribosome profiling reads from various lengths, and recovers consistent ribosome positions across different lengths. Our recovered ribosome positions are correctly highly skewed toward a single frame within a codon. They retain subcodon resolution and enable detection of off-frame translational events, such as frameshifts. Our method improves the correlation with other estimates of codon decoding time. Furthermore, the refined profiles show that yeast wobble-pairing codons are translated slower than their synonymous Watson-Crick-pairing codons. These results provide evidence that protein synthetic rate can be tuned by codon usage bias.

Isoform-level ribosome occupancy estimation guided by transcript abundance with Ribomap.

UNLABELLED: : Ribosome profiling is a recently developed high-throughput sequencing technique that captures approximately 30 bp long ribosome-protected mRNA fragments during translation. Because of alternative splicing and repetitive sequences, a ribosome-protected read may map to many places in the transcriptome, leading to discarded or arbitrary mappings when standard approaches are used. We present a technique and software that addresses this problem by assigning reads to potential origins proportional to estimated transcript abundance. This yields a more accurate estimate of ribosome profiles compared with a naïve mapping. AVAILABILITY AND IMPLEMENTATION: Ribomap is available as open source at http://www.cs.cmu.edu/∼ckingsf/software/ribomap CONTACT: carlk@cs.cmu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Next-generation analysis of gene expression regulation--comparing the roles of synthesis and degradation.

Technological advances now enable routine measurement of mRNA and protein abundances, and estimates of their rates of synthesis and degradation that inform on their values and the degree of change in response to stimuli. Importantly, more and more data on time-series experiments are emerging, e.g. of cells responding to stress, enabling first insights into a new dimension of gene expression regulation - its dynamics and how it allows for very different response signals across genes. This review discusses recently published methods and datasets, their impact on what we now know about the relationships between concentrations and synthesis rates of mRNAs and proteins in yeast and mammalian cells, their evolution, and new hypotheses on translation regulatory mechanisms generated by approaches that involve ribosome footprinting.