Transcriptome-wide measurement of translation by ribosome profiling.

Recent findings coming from human proteome research employing mass-spectrometry and ribosomal profiling methods have provided evidence for the translation of non-annotated coding sequence (CDSs) into alternative proteins (APs). The presence of APs in many human tissues and cell lines may become an important issue in genome sciences, especially in cancer genomics where the frequency of alternative proteins seems to be 10-fold higher than normal tissues. Finding new proteins can impact medical research by filling gaps in known molecular pathways or revealing new molecular markers and therapeutic targets. Among the cellular processes possibly involved in protein diversity, alternative splicing (AS) is the most cited, and it consists of an often-regulated mechanism that generates different mRNAs from the same gene, contributing to the functional diversity of mammalian cells. In the past, evidence for AS from multi-exon genes have come mainly from expression sequence tag (EST) data; only recently has mass-spectrometry (MS) been used to investigate the translation of alternative transcripts. Exploration of human MS data has detected tens to hundreds of alternative proteins in normal tissues, and thousands in cancer cell lines, suggesting that alternative proteins may have an important role in cancer.Analysis of MS data has revealed a vastly diverse AP repertoire, with some of this diversity being exclusively detected in cancer cells. Proteomic characterization of 20 breast cancer cell lines revealed a surprising 1,860 protein variants resulting from AS. Among these, 4 AP are clearly involved in cancer. A truncated variant of the NF- kB p65 subunit, a truncated form of the focal adhesion kinase PTK2 and two CD47 transmembrane receptor protein variants. Until now, little is known about the functional differences between these variants. Another cellular mechanism that possibly creates protein diversity is the alternative usage of translation initiation site (TIS). Detection of TIS is made possible by the Ribosome Profiling (RP) method. The principle of this technique is to capture mRNA translation by freezing the actively translating ribosomes onto transcripts, and then separating them by ultracentrifugation. Recently, RP was applied to mouse embryonic fibroblast cells and human HEK293 cells. The results revealed that the majority of mRNAs contain more than one translation initiation site (TIS), with more than 50% of the detected TISs mapping to alternative ORFs. In this review, we present a list of human alternative proteins validated by small and large-scale experimental methods. We also highlight that APs are probably not a secondary product of inaccurate splicing or translational process and most likely play an important role in the tumorigenic process. Thus, APs constitutes a promising research line for basic and clinical aspects of cancer.

The preparation of small RNA cDNA sequencing libraries depends on the unbiased ligation of adapters to the RNA ends. Small RNA with 5’ recessed ends are poor substrates for enzymatic adapter ligation, but this 5’ adapter ligation problem can go undetected if the library preparation steps are not monitored. Here we illustrate the severity of the 5’ RNA end ligation problem using several pre-miRNA-like hairpins that allow us to expand the definition of the problem to include 5’ ends close to a hairpin stem, whether recessed or in a short extension. The ribosome profiling method can avoid a difficult 5’ adapter ligation, but the enzyme typically used to circularize the cDNA has been reported to be biased, calling into question the benefit of this workaround. Using the TS2126 RNA ligase 1 (a.k.a. CircLigase) as the circularizing enzyme, we devised a bias test for the circularization of first strand cDNA. All possible dinucleotides were circle-ligated with similar efficiency. To re-linearize the first strand cDNA in the ribosome profiling approach, we introduce an improved method wherein a single ribonucleotide is placed between the sequencing primer binding sites in the reverse transcriptase primer, which later serves as the point of re-linearization by RNase A. We incorporate this step into the ribosomal profiling method and describe a complete improved library preparation method, Coligo-seq, for the sequencing of small RNA with secondary structure close to the 5’ end. This method accepts a variety of 5’ modified RNA, including 5’ monophosphorylated RNA, as demonstrated by the construction of a HeLa cell microRNA cDNA library.

Ribosome-footprint profiling provides genome-wide snapshots of translation, but technical challenges can confound its analysis. Here, we use improved methods to obtain ribosome-footprint profiles and mRNA abundances that more faithfully reflect geneexpression in Saccharomyces cerevisiae. Our results support proposals that both the beginning of coding regions and codons matching rare tRNAs are more slowly translated. They also indicate that emergent polypeptides with as few as three basic residues within a ten-residue window tend to slow translation. With the improved mRNA measurements, the variation attributable to translational control in exponentially growing yeast was less than previously reported, and most of this variation could be predicted with a simple model that considered mRNA abundance, upstream open reading frames, cap-proximal structure and nucleotide composition, and lengths of the coding and 5' UTRs. Collectively, our results provide a framework for executing and interpreting ribosome-profiling studies and reveal key features of translational control in yeast.

To synthesize proteins, cells must first transcribe an mRNA which specifies the sequence of amino acids, the building blocks of proteins. The next step involves translation of this mRNA into protein using the cell's protein-synthesizing machinery called ribosomes. Many gene expression studies rely solely on RNA-seq, which provides information of relative abundances of mRNAs in a cell; however, RNA-seq data ignore gene regulation at the translational level. Developed by Nicholas Ingolia and Jonathan Weissman, ribosome profiling (Ribo-seq) is a technique that provides a genome-wide view of in vivo translation. Ribo-seq is based on the principle that a translating ribosome protects a short stretch of mRNA within its structure. Once ribosomes are 'frozen' in the act of translation using translation elongation inhibitors, RNA-digesting enzymes known as RNases can be added to destroy any mRNA that is unprotected by the arrested ribosomes. After RNase digestion, ribosomes are enriched and the ribosome-protected mRNA is then isolated and converted into Illumina-compatible cDNA libraries. These ribosome-protected mRNA fragments are commonly called RPFs or ribosome footprints. Mapping these sequenced RPFs to the transcriptome provides a 'snapshot' of translation that reveals the positions and densities of ribosomes on individual mRNAs transcriptome-wide. This snapshot can help determine which proteins were being synthesized in the cell at the time of the experiment. Ribo-seq enables the identification of alternative mRNA translation start sites, the confirmation of annotated open reading frames (ORFs) as well as upstream (uORFs) that may be involved in the regulation of translation, the distribution of ribosomes on an mRNA and the rate at which ribosomes decode codons.Ribosome profiling protocols have been developed for budding yeast, mammalian cell lines, tissue samples, a range of bacterial species, plant and archaea. Each protocol follows a series of steps that are outlined in Figure 1. The first step is the lysis of cultured cells or collected tissue samples. These samples could be pre-treated with drugs or subjected to external stress conditions to investigate how these external factors impact the cell or tissue translationally. Lysis preparation is done in two parts, harvesting of cells/tissue and subsequent mechanical and chemical breakdown.Harvesting is an important consideration due to the speed of translation (e.g., yeast ribosomes decode 9.6 codons a second), therefore the ribosomes must be harvested and flash-frozen immediately to preserve the position of the ribosome on mRNA. Harvesting can be done in many ways. One approach is vacuum filtration, in which a liquid culture is poured into a filter attached to a vacuum pump. As the cells remain on the filter they can then be scraped into a tube containing liquid nitrogen to flash-freeze the samples.Samples are resuspended in an enriched polysome lysis buffer containing the following components:Organisms that contain a cell wall need to be first cryo-mechanically broken down using a cryogenic mixer mill or bead beater. Samples are clarified through centrifugation and the supernatant is recovered.Ribonucleases are enzymes that break down and destroy RNA. Here, they are employed to cleave regions of mRNA that reside outside of the ribosome, ideally leaving only the fragments of mRNA that are stored within and protected by the ribosome. The choice of ribonuclease is important as some ribonucleases are not compatible with certain species. For example, RNase I is a robust ribonuclease that is capable of providing good digestion in human cell types and yeast cell types but cannot be used for digestion in bacterial cell samples as it is capable of damaging the ribosome. Therefore, a different ribonuclease must be used (such as micrococcal nuclease, which offers a much more subtle digestion) to digest the mRNA outside of the ribosome. Each ribonuclease has its own strengths and weaknesses which should be taken into account when performing ribosome profiling.The quality of the ribonucleic digestion can be visualized using plots such as a triplet periodicity plot or a metagene profile (such as the example shown in Figure 2) as they can show the triplet decoding of the ribosome. As codons are encoded in groups of three nucleotides, a strong digestion on a metagene profile would give a very clear repeating pattern where the number of RPFs in one frame would be much higher than in the other two frames.After nuclease digestion, the samples undergo T4 PNK end repair (which tailors the ends of RNA by removing the 3′-phosphoryl groups generated from RNase I cleavage and adds a hydroxyl group to prepare them for subsequent linker ligation), followed by fractionation on a 15% PAGE-urea gel. (Urea is added to denature the RNA, thereby preventing any secondary structures from forming.) The use of a size selection marker allows for the determination of bands that are within a specific size. RPFs or footprints should be approximately 28 nucleotides in length (although this depends on the cell type). Therefore the size selection marker should contain bands that are just above and below the desired footprint size (e.g., 24 and 32 nucleotides in length). Figure 3 is an example of a gel photo which highlights the region where the ribosomal footprints are located. This use of bracketing the samples with the size selection markers allows for easy identification of the region of the gel to be excised out. Gel slices containing the size-selected RNA of interest are excised using a scalpel and the RNA is then extracted and purified.After recovering the RNA from the gel slices, the next step is the removal of ribosomal RNA (rRNA). rRNA is the most predominant RNA (and can make up for about 80% of cellular RNA) in the cell. This is an issue, as an abundance of rRNA leads to fewer RPFs being sequenced, resulting in less mapping reads, essentially reducing the useful size of the library. Most contaminating rRNAs are generated from ribonuclease digestion of the ribosome, nicking off RPF-sized fragments. One way of depleting rRNA is using biotinylated oligos designed to hybridize to the predominant rRNAs. After mixing the samples of interest with a depletion mix containing the designed oligos, the biotinylated oligo-bound rRNA can be removed using streptavidin conjugated to magnetic beads. After this form of clean-up, the sample should be depleted of the majority of its rRNA contents, thereby allowing for more RPFs to be sequenced later on.In order to convert the ribosome footprints to DNA, they must be reverse transcribed. However, reverse transcriptase requires a primer to initiate polymerization. To overcome this, either the RPFs can be tailed with a poly A polymerase or else a single-stranded RNA linker of a known sequence can be ligated to the 3′-end of the RPF. There are several benefits of using 3′-linkers instead of polyadenylation tailing. One is the incorporation of random nucleotides at the 5′-end of the linker which can act as unique molecular identifiers (UMIs) to aid removal of PCR duplicates during subsequent analysis. Random nucleotides at the 5′-end also have the added benefit of reducing potential ligation biases. Another benefit of the addition of 3′-linkers is that they can be designed to contain unique barcodes for each linker, allowing for multiplexing (essentially combining different samples together into a single pool of samples for deep sequencing) prior to cDNA synthesis. Linker ligation can be done by using an enzyme known as T4 RNA ligase truncated K227Q in conjunction with adenylated linkers to join the ends of samples to the linker strands. The ligated product can then be isolated by either running the samples on a 15% PAGE-urea gel or via enzymatic linker digestion to cleave and remove any non-ligated linkers. Purified linker-ligated RPFs can then be converted into DNA by standard reverse transcriptase reaction.Following cDNA synthesis, libraries are amplified by polymerase chain reaction (PCR). PCR is a reaction that amplifies DNA exponentially, causing it to double in size every cycle. PCR is done in three stages (known as denaturation, annealing and extension) in which the DNA is subjected to rapid cycles of heating and cooling. These three stages are cycled through until a desired concentration of library is made. This library can then be sequenced to generate a bioinformatic library containing RPF sequenced reads.The analysis of the sequencing data mainly depends on the aims of the experiment. A number of tools and pipelines are publicly available online that allow for a multitude of different data analyses, including uORF detection, differential gene expression, global translation rates, ribosome stalling, codon decoding rates, amongst others. A general flow of a ribosome profiling mapping pipeline would typically include the use of the software FastQC to determine basic quality metrics like read lengths and sequencing quality, utilizing another software called Cutadapt to demultiplex and cut away adapter sequences added during linker ligation and PCR, and Bowtie a short read aligner to bioinformatically remove remaining rRNA contaminants. The library can then be aligned to the organism's annotated genome/transcriptome, followed by the use of Samtools to convert the aligned reads into a sorted BAM file. From this, a gene count file can be generated using software such as HT-seq to count the number of reads aligned to each gene. A number of online browser-based platforms are available for visualizing ribosome profiles showing the mRNA positions of mapped RPFs allowing for further metadata analysis, each requiring different specific file types such as GTF files or Fasta files.One thing that is changing currently because of ribosomal profiling is genome annotation, as ribosome profiling pointed out that translation can occur outside of protein coding regions and that this translation is impactful, as it does something to the cell irrespective of whether it is productive or not. This advancement in genome annotation is exciting as it allows for a more advanced and in-depth look within the 'black box' that currently exists in genomes and to figure out why a particular change in their genome causes a particular phenotype or how a certain kind of chain of events occurs, and how this particular change affects gene expression and production of a particular protein, or maybe its own sequence. Ribosome profiling can accelerate our understanding of complex biological processes happening within the cell and, in turn, can be utilized to explore new ways for industrial exploitation. One such example is the possibility of accelerating and de-risking drug discovery through monitoring the side effects or toxicity of a drug on mRNA translation.Luke Power is a research scientist at Ribomaps Ltd. He received his Masters of Research (MRes) degree in translatomics from University College Cork. While researching in the Baranov lab in UCC, he successfully generated ribosome profiles of the oleaginous yeast Yarrowia lipolytica, the first ribosome profiling data for this organism. His research interests include developing ribosome profiling to study translation in different species and optimizing the ribosome profiling protocol for both speed and data quality. Since joining Ribomaps, Luke has successfully performed ribosome profiling on cells, cell lines and tissues from many different species such as yeast, bacteria, plants, humans and other animals including rats and mice. His main duties include performing ribosome profiling on customer samples and the optimization and validation of protocols for different species. Email: Luke.Power@ribomaps.com.

A number of enzymes, targeting factors and chaperones engage ribosomes to support fundamental steps of nascent protein maturation, including enzymatic processing, membrane targeting and co-translational folding. The selective ribosome profiling (SeRP) method is a new tool for studying the co-translational activity of maturation factors that provides proteome-wide information on a factor's nascent interactome, the onset and duration of binding and the mechanisms controlling factor engagement. SeRP is based on the combination of two ribosome-profiling (RP) experiments, sequencing the ribosome-protected mRNA fragments from all ribosomes (total translatome) and the ribosome subpopulation engaged by the factor of interest (factor-bound translatome). We provide a detailed SeRP protocol, exemplified for the yeast Hsp70 chaperone Ssb (stress 70 B), for studying factor interactions with nascent proteins that is readily adaptable to identifying nascent interactomes of other co-translationally acting eukaryotic factors. The protocol provides general guidance for experimental design and optimization, as well as detailed instructions for cell growth and harvest, the isolation of (factor-engaged) monosomes, the generation of a cDNA library and data analysis. Experience in biochemistry and RNA handling, as well as basic programing knowledge, is necessary to perform SeRP. Execution of a SeRP experiment takes 8-10 working days, and initial data analysis can be completed within 1-2 d. This protocol is an extension of the originally developed protocol describing SeRP in bacteria.

Simple and inexpensive ribosome profiling analysis of mRNA translation

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.

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.

Ribosomal profiling is an emerging experimental technology to measure protein synthesis by sequencing short mRNA fragments undergoing translation in ribosomes. Applied on the genome wide scale, this is a powerful tool to profile global protein synthesis within cell populations of interest. Such information can be utilized for biomarker discovery and detection of treatment-responsive genes. However, analysis of ribosomal profiling data requires careful preprocessing to reduce the impact of artifacts and dedicated statistical methods for visualizing and modeling the high-dimensional discrete read count data. Here we present Ribosomal Profiling Reports (RP-REP), a new open-source cloud-enabled software that allows users to execute start-to-end gene-level ribosomal profiling and RNA-Seq analysis on a pre-configured Amazon Virtual Machine Image (AMI) hosted on AWS or on the user’s own Ubuntu Linux server. The software works with FASTQ files stored locally, on AWS S3, or at the Sequence Read Archive (SRA). RP-REP automatically executes a series of customizable steps including filtering of contaminant RNA, enrichment of true ribosomal footprints, reference alignment and gene translation quantification, gene body coverage, CRAM compression, reference alignment QC, data normalization, multivariate data visualization, identification of differentially translated genes, and generation of heatmaps, co-translated gene clusters, enriched pathways, and other custom visualizations. RP-REP provides functionality to contrast RNA-SEQ and ribosomal profiling results, and calculates translational efficiency per gene. The software outputs a PDF report and publication-ready table and figure files. As a use case, we provide RP-REP results for a dengue virus study that tested cytosol and endoplasmic reticulum cellular fractions of human Huh7 cells pre-infection and at 6 h, 12 h, 24 h, and 40 h post-infection. Case study results, Ubuntu installation scripts, and the most recent RP-REP source code are accessible at GitHub. The cloud-ready AMI is available at AWS (AMI ID: RPREP RSEQREP (Ribosome Profiling and RNA-Seq Reports) v2.1 (ami-00b92f52d763145d3)).

Ribosomal profiling is an emerging experimental technology to measure protein synthesis by sequencing short mRNA fragments undergoing translation in ribosomes. Applied on the genome wide scale, this is a powerful tool to profile global protein synthesis within cell populations of interest. Such information can be utilized for biomarker discovery and detection of treatment-responsive genes. However, analysis of ribosomal profiling data requires careful preprocessing to reduce the impact of artifacts and dedicated statistical methods for visualizing and modeling the high-dimensional discrete read count data. Here we present Ribosomal Profiling Reports (RP-REP), a new open-source cloud-enabled software that allows users to execute start-to-end gene-level ribosomal profiling and RNA-Seq analysis on a pre-configured Amazon Virtual Machine Image (AMI) hosted on AWS or on the user's own Ubuntu Linux server. The software works with FASTQ files stored locally, on AWS S3, or at the Sequence Read Archive (SRA). RP-REP automatically executes a series of customizable steps including filtering of contaminant RNA, enrichment of true ribosomal footprints, reference alignment and gene translation quantification, gene body coverage, CRAM compression, reference alignment QC, data normalization, multivariate data visualization, identification of differentially translated genes, and generation of heatmaps, co-translated gene clusters, enriched pathways, and other custom visualizations. RP-REP provides functionality to contrast RNA-SEQ and ribosomal profiling results, and calculates translational efficiency per gene. The software outputs a PDF report and publication-ready table and figure files. As a use case, we provide RP-REP results for a dengue virus study that tested cytosol and endoplasmic reticulum cellular fractions of human Huh7 cells pre-infection and at 6h, 12h, 24h, and 40h post-infection. Case study results, Ubuntu installation scripts, and the most recent RP-REP source code are accessible at GitHub. The cloud-ready AMI is available at AWS (AMI ID: RPREP RSEQREP (Ribosome Profiling and RNA-Seq Reports) v2.1 (ami-00b92f52d763145d3)).

Ribosomal profiling is a promising approach with increasing popularity for studying translation. This approach enables monitoring the ribosomal density along genes at a resolution of single nucleotides.In this study, we focused on ribosomal density profiles of mouse embryonic stem cells. Our analysis suggests, for the first time, that even in mammals such as M. musculus the elongation speed is significantly and directly affected by determinants of the coding sequence such as: 1) the adaptation of codons to the tRNA pool; 2) the local mRNA folding of the coding sequence; 3) the local charge of amino acids encoded in the codon sequence. In addition, our analyses suggest that in general, the translation velocity of ribosomes is slower at the beginning of the coding sequence and tends to increase downstream.Finally, a comparison of these data to the expected biophysical behavior of translation suggests that it suffers from some unknown biases. Specifically, the ribosomal flux measured on the experimental data increases along the coding sequence; however, according to any biophysical model of ribosomal movement lacking internal initiation sites, the flux is expected to remain constant or decrease. Thus, developing experimental and/or statistical methods for understanding, detecting and dealing with such biases is of high importance.

Ribosome profiling provides a snapshot of ribosome positions and density across the transcriptome at a sub‐codon resolution. By sequencing the entire set of ribosome‐protected mRNA fragments, this powerful approach has been successfully used to measure ribosome dynamics and reveal the hidden coding potential of transcriptome. Since its conceptual inception, ribosome profiling has evolved into a versatile method with many innovative variations. It has been applied to study translation in diverse cell types, adapted to capture specific subsets of ribosomes and further improved to address translational regulation in multicellular organisms. The continuous development of ribosome profiling technologies over the coming decade promises a broad view of translational regulation of gene expression. Key Concepts Ribosome profiling reveals a global translation snapshot. Ribosome profiling is applicable to diverse cell types. Profiling of specific ribosomes permits analysis of nascent chains. Profiling of initiating ribosomes allows the identification of alternative start codon. Ribosome profiling can be adapted to probe translation in vivo .

Identification of the coding elements in the genome is a fundamental step to understanding the building blocks of living systems. Short peptides (< 100 aa) have emerged as important regulators of development and physiology, but their identification has been limited by their size. We have leveraged the periodicity of ribosome movement on the mRNA to define actively translated ORFs by ribosome footprinting. This approach identifies several hundred translated small ORFs in zebrafish and human. Computational prediction of small ORFs from codon conservation patterns corroborates and extends these findings and identifies conserved sequences in zebrafish and human, suggesting functional peptide products (micropeptides). These results identify micropeptide-encoding genes in vertebrates, providing an entry point to define their function in vivo.

Ribosome profiling provides quantitative, comprehensive, and high-resolution snapshots of cellular translation by the high-throughput sequencing of short mRNA fragments that are protected by ribosomes from nucleolytic digestion. While the overall principle is simple, the workflow of ribosome profiling experiments is complex and challenging, and typically requires large amounts of sample, limiting its broad applicability. Here, we present a new protocol for ultra-rapid ribosome profiling from low-input samples. It features a robust strategy for sequencing library preparation within one day that employs solid phase purification of reaction intermediates, allowing to reduce the input to as little as 0.1 pmol of ∼30 nt RNA fragments. Hence, it is particularly suited for the analyses of small samples or targeted ribosome profiling. Its high sensitivity and its ease of implementation will foster the generation of higher quality data from small samples, which opens new opportunities in applying ribosome profiling.

In eukaryotes, ribosome profiling provides insight into the mechanism of protein synthesis at the codon level. In bacteria, however, the method has been more problematic and no consensus has emerged for how to best prepare profiling samples. Here, we identify the sources of these problems and describe new solutions for arresting translation and harvesting cells in order to overcome them. These improvements remove confounding artifacts and improve the resolution to allow analyses of ribosome behavior at the codon level. With a clearer view of the translational landscape in vivo, we observe that filtering cultures leads to translational pauses at serine and glycine codons through the reduction of tRNA aminoacylation levels. This observation illustrates how bacterial ribosome profiling studies can yield insight into the mechanism of protein synthesis at the codon level and how these mechanisms are regulated in response to changes in the physiology of the cell.