Accurate design of translational output by a neural network model of ribosome distribution

Synonymous codon choice can have dramatic effects on ribosome speed and protein expression. Ribosome profiling experiments have underscored that ribosomes do not move uniformly along mRNAs. Here, we have modeled this variation in translation elongation by using a feed-forward neural network to predict the ribosome density at each codon as a function of its sequence neighborhood. Our approach revealed sequence features affecting translation elongation and characterized large technical biases in ribosome profiling. We applied our model to design synonymous variants of a fluorescent protein spanning the range of translation speeds predicted with our model. Levels of the fluorescent protein in budding yeast closely tracked the predicted translation speeds across their full range. We therefore demonstrate that our model captures information determining translation dynamics in vivo; that this information can be harnessed to design coding sequences; and that control of translation elongation alone is sufficient to produce large quantitative differences in protein output

Accurate design of translational output by a neural network model of ribosome distribution

Synonymous codon choice can have dramatic effects on ribosome speed and protein expression. Ribosome profiling experiments have underscored that ribosomes do not move uniformly along mRNAs. Here, we have modeled this variation in translation elongation by using a feed-forward neural network to predict the ribosome density at each codon as a function of its sequence neighborhood. Our approach revealed sequence features affecting translation elongation and characterized large technical biases in ribosome profiling. We applied our model to design synonymous variants of a fluorescent protein spanning the range of translation speeds predicted with our model. Levels of the fluorescent protein in budding yeast closely tracked the predicted translation speeds across their full range. We therefore demonstrate that our model captures information determining translation dynamics in vivo; that this information can be harnessed to design coding sequences; and that control of translation elongation alone is sufficient to produce large quantitative differences in protein output

Expression proteomics of UPF1 knockdown in HeLa cells reveals autoregulation of hnRNP A2/B1 mediated by alternative splicing resulting in nonsense-mediated mRNA decay

BACKGROUND: In addition to acting as an RNA quality control pathway, nonsense-mediated mRNA decay (NMD) plays roles in regulating normal gene expression. In particular, the extent to which alternative splicing is coupled to NMD and the roles of NMD in regulating uORF containing transcripts have been a matter of debate. RESULTS: In order to achieve a greater understanding of NMD regulated gene expression we used 2D-DiGE proteomics technology to examine the changes in protein expression induced in HeLa cells by UPF1 knockdown. QPCR based validation of the corresponding mRNAs, in response to both UPF1 knockdown and cycloheximide treatment, identified 17 bona fide NMD targets. Most of these were associated with bioinformatically predicted NMD activating features, predominantly upstream open reading frames (uORFs). Strikingly, however, the majority of transcripts up-regulated by UPF1 knockdown were either insensitive to, or even down-regulated by, cycloheximide treatment. Furthermore, the mRNA abundance of several down-regulated proteins failed to change upon UPF1 knockdown, indicating that UPF1`s role in regulating mRNA and protein abundance is more complex than previously appreciated. Among the bona fide NMD targets, we identified a highly conserved AS-NMD event within the 3` UTR of the HNRNPA2B1 gene. Overexpression of GFP tagged hnRNP A2 resulted in a decrease in endogenous hnRNP A2 and B1 mRNA with a concurrent increase in the NMD sensitive isoforms. CONCLUSIONS: Despite the large number of changes in protein expression upon UPF1 knockdown, a relatively small fraction of them can be directly attributed to the action of NMD on the corresponding mRNA. From amongst these we have identified a conserved AS-NMD event within HNRNPA2B1 that appears to mediate autoregulation of HNRNPA2B1 expression levels

Transcriptome-wide measurement of translation by ribosome profiling

Translation is one of the fundamental processes of life. It comprises the assembly of polypeptides whose amino acid sequence corresponds to the codon sequence of an mRNA's ORF. Translation is performed by the ribosome; therefore, in order to understand translation and its regulation we must be able to determine the numbers and locations of ribosomes on mRNAs in vivo. Furthermore, we must be able to examine their redistribution in different physiological contexts and in response to experimental manipulations. The ribosome profiling method provides us with an opportunity to learn these locations, by sequencing a cDNA library derived from the short fragments of mRNA covered by the ribosome. Since its original description, the ribosome profiling method has undergone continuing development; in this article we describe the method's current state. Important improvements include: the incorporation of sample barcodes to enable library multiplexing, the incorporation of unique molecular identifiers to enable to removal of duplicated sequences, and the replacement of a gel-purification step with the enzymatic degradation of unligated linker

A genome-scale CRISPR interference guide library enables comprehensive phenotypic profiling in yeast.

BackgroundCRISPR/Cas9-mediated transcriptional interference (CRISPRi) enables programmable gene knock-down, yielding loss-of-function phenotypes for nearly any gene. Effective, inducible CRISPRi has been demonstrated in budding yeast, and genome-scale guide libraries enable systematic, genome-wide genetic analysis.ResultsWe present a comprehensive yeast CRISPRi library, based on empirical design rules, containing 10 distinct guides for most genes. Competitive growth after pooled transformation revealed strong fitness defects for most essential genes, verifying that the library provides comprehensive genome coverage. We used the relative growth defects caused by different guides targeting essential genes to further refine yeast CRISPRi design rules. In order to obtain more accurate and robust guide abundance measurements in pooled screens, we link guides with random nucleotide barcodes and carry out linear amplification by in vitro transcription.ConclusionsTaken together, we demonstrate a broadly useful platform for comprehensive, high-precision CRISPRi screening in yeast

A genome-scale CRISPR interference guide library enables comprehensive phenotypic profiling in yeast.

BackgroundCRISPR/Cas9-mediated transcriptional interference (CRISPRi) enables programmable gene knock-down, yielding loss-of-function phenotypes for nearly any gene. Effective, inducible CRISPRi has been demonstrated in budding yeast, and genome-scale guide libraries enable systematic, genome-wide genetic analysis.ResultsWe present a comprehensive yeast CRISPRi library, based on empirical design rules, containing 10 distinct guides for most genes. Competitive growth after pooled transformation revealed strong fitness defects for most essential genes, verifying that the library provides comprehensive genome coverage. We used the relative growth defects caused by different guides targeting essential genes to further refine yeast CRISPRi design rules. In order to obtain more accurate and robust guide abundance measurements in pooled screens, we link guides with random nucleotide barcodes and carry out linear amplification by in vitro transcription.ConclusionsTaken together, we demonstrate a broadly useful platform for comprehensive, high-precision CRISPRi screening in yeast

Accurate design of translational output by a neural network model of ribosome distribution.

Synonymous codon choice can have dramatic effects on ribosome speed and protein expression. Ribosome profiling experiments have underscored that ribosomes do not move uniformly along mRNAs. Here, we have modeled this variation in translation elongation by using a feed-forward neural network to predict the ribosome density at each codon as a function of its sequence neighborhood. Our approach revealed sequence features affecting translation elongation and characterized large technical biases in ribosome profiling. We applied our model to design synonymous variants of a fluorescent protein spanning the range of translation speeds predicted with our model. Levels of the fluorescent protein in budding yeast closely tracked the predicted translation speeds across their full range. We therefore demonstrate that our model captures information determining translation dynamics in vivo; that this information can be harnessed to design coding sequences; and that control of translation elongation alone is sufficient to produce large quantitative differences in protein output