Composability of regulatory sequences controlling transcription and translation in Escherichia coli

Speeding up directed evolution: Combining the advantages of solid-phase combinatorial gene synthesis with statistically guided reduction of screening effort

Efficient and economic methods in directed evolution at the protein, metabolic, and genome level are needed for biocatalyst development and the success of synthetic biology. In contrast to random strategies, semirational approaches such as saturation mutagenesis explore the sequence space in a focused manner. Although several combinatorial libraries based on saturation mutagenesis have been reported using solid-phase gene synthesis, direct comparison with traditional PCR-based methods is currently lacking. In this work, we compare combinatorial protein libraries created in-house via PCR versus those generated by commercial solid-phase gene synthesis. Using descriptive statistics and probabilistic distributions on amino acid occurrence frequencies, the quality of the libraries was assessed and compared, revealing that the outsourced libraries are characterized by less bias and outliers than the PCR-based ones. Afterward, we screened all libraries following a traditional algorithm for almost complete library coverage and compared this approach with an emergent statistical concept suggesting screening a lower portion of the protein sequence space. Upon analyzing the biocatalytic landscapes and best hits of all combinatorial libraries, we show that the screening effort could have been reduced in all cases by more than 50%, while still finding at least one of the best mutants.

A method for multiplex gene synthesis employing error correction based on expression

Our ability to engineer organisms with new biosynthetic pathways and genetic circuits is limited by the availability of protein characterization data and the cost of synthetic DNA. With new tools for reading and writing DNA, there are opportunities for scalable assays that more efficiently and cost effectively mine for biochemical protein characteristics. To that end, we have developed the Multiplex Library Synthesis and Expression Correction (MuLSEC) method for rapid assembly, error correction, and expression characterization of many genes as a pooled library. This methodology enables gene synthesis from microarray-synthesized oligonucleotide pools with a one-pot technique, eliminating the need for robotic liquid handling. Post assembly, the gene library is subjected to an ampicillin based quality control selection, which serves as both an error correction step and a selection for proteins that are properly expressed and folded in E. coli. Next generation sequencing of post selection DNA enables quantitative analysis of gene expression characteristics. We demonstrate the feasibility of this approach by building and testing over 90 genes for empirical evidence of soluble expression. This technique reduces the problem of part characterization to multiplex oligonucleotide synthesis and deep sequencing, two technologies under extensive development with projected cost reduction.

Analysis of genetic variation and potential applications in genome-scale metabolic modeling

Genetic variation is the motor of evolution and allows organisms to overcome the environmental challenges they encounter. It can be both beneficial and harmful in the process of engineering cell factories for the production of proteins and chemicals. Throughout the history of biotechnology, there have been efforts to exploit genetic variation in our favor to create strains with favorable phenotypes. Genetic variation can either be present in natural populations or it can be artificially created by mutagenesis and selection or adaptive laboratory evolution. On the other hand, unintended genetic variation during a long term production process may lead to significant economic losses and it is important to understand how to control this type of variation. With the emergence of next-generation sequencing technologies, genetic variation in microbial strains can now be determined on an unprecedented scale and resolution by re-sequencing thousands of strains systematically. In this article, we review challenges in the integration and analysis of large-scale re-sequencing data, present an extensive overview of bioinformatics methods for predicting the effects of genetic variants on protein function, and discuss approaches for interfacing existing bioinformatics approaches with genome-scale models of cellular processes in order to predict effects of sequence variation on cellular phenotypes.

Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues

RNA-sequencing (RNA-seq) measures the quantitative change in gene expression over the whole transcriptome, but it lacks spatial context. In contrast, in situ hybridization provides the location of gene expression, but only for a small number of genes. Here we detail a protocol for genome-wide profiling of gene expression in situ in fixed cells and tissues, in which RNA is converted into cross-linked cDNA amplicons and sequenced manually on a confocal microscope. Unlike traditional RNA-seq, our method enriches for context-specific transcripts over housekeeping and/or structural RNA, and it preserves the tissue architecture for RNA localization studies. Our protocol is written for researchers experienced in cell microscopy with minimal computing skills. Library construction and sequencing can be completed within 14 d, with image analysis requiring an additional 2 d.

How do bacteria tune translation efficiency?

Bacterial proteins are translated with precisely determined rates to meet cellular demand. In contrast, efforts to express recombinant proteins in bacteria are often met with large unpredictability in their levels of translation. The disconnect between translation of natural and synthetic mRNA stems from the lack of understanding of the strategy used by bacteria to tune translation efficiency (TE). The development of array-based oligonucleotide synthesis and ribosome profiling provides new approaches to address this issue. Although the major determinant for TE is still unknown, these high-throughput studies point out a statistically significant but mild contribution from the mRNA secondary structure around the start codon. Here I summarize those findings and provide a theoretical framework for measuring TE.

Use of plant colonizing bacteria as chassis for transfer of N₂-fixation to cereals

Engineering cereal crops that are self-supported by nitrogen fixation has been a dream since the 1970s when nitrogenase was transferred from Klebsiella pneumoniae to Escherichia coli. A renewed interest in this area has generated several new approaches with the common aim of transferring nitrogen fixation to cereal crops. Advances in synthetic biology have afforded the tools to rationally engineer microorganisms with traits of interest. Nitrogenase biosynthesis has been a recent target for the application of new synthetic engineering tools. Early successes in this area suggest that the transfer of nitrogenase and other supporting traits to microorganisms that already closely associate with cereal crops is a logical approach to deliver nitrogen to cereal crops.

Bridging the gap: a roadmap to breaking the biological design barrier

This paper presents an analysis of an emerging bottleneck in organism engineering, and paths by which it may be overcome. Recent years have seen the development of a profusion of synthetic biology tools, largely falling into two categories: high-level “design” tools aimed at mapping from organism specifications to nucleic acid sequences implementing those specifications, and low-level “build and test” tools aimed at faster, cheaper, and more reliable fabrication of those sequences and assays of their behavior in engineered biological organisms. Between the two families, however, there is a major gap: we still largely lack the predictive models and component characterization data required to effectively determine which of the many possible candidate sequences considered in the design phase are the most likely to produce useful results when built and tested. As low-level tools continue to mature, the bottleneck in biological systems engineering is shifting to be dominated by design, making this gap a critical barrier to progress. Considering how to address this gap, we find that widespread adoption of readily available analytic and assay methods is likely to lead to rapid improvement in available predictive models and component characterization models, as evidenced by a number of recent results. Such an enabling development is, in turn, likely to allow high-level tools to break the design barrier and support rapid development of transformative biological applications.

Computing the functional proteome: recent progress and future prospects for genome-scale models

Constraint-based models enable the computation of feasible, optimal, and realized biological phenotypes from reaction network reconstructions and constraints on their operation. To date, stoichiometric reconstructions have largely focused on metabolism, resulting in genome-scale metabolic models (M-Models). Recent expansions in network content to encompass proteome synthesis have resulted in models of metabolism and protein expression (ME-Models). ME-Models advance the predictions possible with constraint-based models from network flux states to the spatially resolved molecular composition of a cell. Specifically, ME-Models enable the prediction of transcriptome and proteome allocation and limitations, and basal expression states and regulatory needs. Continued expansion in reconstruction content and constraints will result in an increasingly refined representation of cellular composition and behavior.

Evolution-guided optimization of biosynthetic pathways

Engineering biosynthetic pathways for chemical production requires extensive optimization of the host cellular metabolic machinery. Because it is challenging to specify a priori an optimal design, metabolic engineers often need to construct and evaluate a large number of variants of the pathway. We report a general strategy that combines targeted genome-wide mutagenesis to generate pathway variants with evolution to enrich for rare high producers. We convert the intracellular presence of the target chemical into a fitness advantage for the cell by using a sensor domain responsive to the chemical to control a reporter gene necessary for survival under selective conditions. Because artificial selection tends to amplify unproductive cheaters, we devised a negative selection scheme to eliminate cheaters while preserving library diversity. This scheme allows us to perform multiple rounds of evolution (addressing ∼10(9) cells per round) with minimal carryover of cheaters after each round. Based on candidate genes identified by flux balance analysis, we used targeted genome-wide mutagenesis to vary the expression of pathway genes involved in the production of naringenin and glucaric acid. Through up to four rounds of evolution, we increased production of naringenin and glucaric acid by 36- and 22-fold, respectively. Naringenin production (61 mg/L) from glucose was more than double the previous highest titer reported. Whole-genome sequencing of evolved strains revealed additional untargeted mutations that likely benefit production, suggesting new routes for optimization.

Multi-input CRISPR/Cas genetic circuits that interface host regulatory networks

Genetic circuits require many regulatory parts in order to implement signal processing or execute algorithms in cells. A potentially scalable approach is to use dCas9, which employs small guide RNAs (sgRNAs) to repress genetic loci via the programmability of RNA:DNA base pairing. To this end, we use dCas9 and designed sgRNAs to build transcriptional logic gates and connect them to perform computation in living cells. We constructed a set of NOT gates by designing five synthetic Escherichia coli σ70 promoters that are repressed by corresponding sgRNAs, and these interactions do not exhibit crosstalk between each other. These sgRNAs exhibit high on-target repression (56- to 440-fold) and negligible off-target interactions (< 1.3-fold). These gates were connected to build larger circuits, including the Boolean-complete NOR gate and a 3-gate circuit consisting of four layered sgRNAs. The synthetic circuits were connected to the native E. coli regulatory network by designing output sgRNAs to target an E. coli transcription factor (malT). This converts the output of a synthetic circuit to a switch in cellular phenotype (sugar utilization, chemotaxis, phage resistance).

Transfer of noncoding DNA drives regulatory rewiring in bacteria

Understanding the mechanisms that generate variation is a common pursuit unifying the life sciences. Bacteria represent an especially striking puzzle, because closely related strains possess radically different metabolic and ecological capabilities. Differences in protein repertoire arising from gene transfer are currently considered the primary mechanism underlying phenotypic plasticity in bacteria. Although bacterial coding plasticity has been extensively studied in previous decades, little is known about the role that regulatory plasticity plays in bacterial evolution. Here, we show that bacterial genes can rapidly shift between multiple regulatory modes by acquiring functionally divergent nonhomologous promoter regions. Through analysis of 270,000 regulatory regions across 247 genomes, we demonstrate that regulatory "switching" to nonhomologous alternatives is ubiquitous, occurring across the bacterial domain. Using comparative transcriptomics, we show that at least 16% of the expression divergence between Escherichia coli strains can be explained by this regulatory switching. Further, using an oligonucleotide regulatory library, we establish that switching affects bacterial promoter architecture. We provide evidence that regulatory switching can occur through horizontal regulatory transfer, which allows regulatory regions to move across strains, and even genera, independently from the genes they regulate. Finally, by experimentally characterizing the fitness effect of a regulatory transfer on a pathogenic E. coli strain, we demonstrate that regulatory switching elicits important phenotypic consequences. Taken together, our findings expose previously unappreciated regulatory plasticity in bacteria and provide a gateway for understanding bacterial phenotypic variation and adaptation.

Large-scale mapping of sequence-function relations in small regulatory RNAs reveals plasticity and modularity

Two decades into the genomics era the question of mapping sequence to function has evolved from identifying functional elements to characterizing their quantitative properties including, in particular, their specificity and efficiency. Here, we use a large-scale approach to establish a quantitative map between the sequence of a bacterial regulatory RNA and its efficiency in modulating the expression of its targets. Our approach generalizes the sort-seq method, introduced recently to analyze promoter sequences, in order to accurately quantify the efficiency of a large library of sequence variants. We focus on two small RNAs (sRNAs) in E. coli, DsrA and RyhB, and their regulation of both repressed and activated targets. In addition to precisely identifying functional elements in the sRNAs, our data establish quantitative relationships between structural and energetic features of the sRNAs and their regulatory activity, and characterize a large set of direct and indirect interactions between nucleotides. A core of these interactions supports a model where specificity can be enhanced by a rigid molecular structure. Both sRNAs exhibit a modular design with limited cross-interactions, dividing the requirements for structural stability and target binding among modules.

Quantitative analysis of mammalian translation initiation sites by FACS-seq

An approach combining fluorescence-activated cell sorting and high-throughput DNA sequencing (FACS-seq) was employed to determine the efficiency of start codon recognition for all possible translation initiation sites (TIS) utilizing AUG start codons. Using FACS-seq, we measured translation from a genetic reporter library representing all 65,536 possible TIS sequences spanning the −6 to +5 positions. We found that the motif RYMRMVAUGGC enhanced start codon recognition and translation efficiency. However, dinucleotide interactions, which cannot be conveyed by a single motif, were also important for modeling TIS efficiency. Our dataset combined with modeling allowed us to predict genome-wide translation initiation efficiency for all mRNA transcripts. Additionally, we screened somatic TIS mutations associated with tumorigenesis to identify candidate driver mutations consistent with known tumor expression patterns. Finally, we implemented a quantitative leaky scanning model to predict alternative initiation sites that produce truncated protein isoforms and compared predictions with ribosome footprint profiling data. The comprehensive analysis of the TIS sequence space enables quantitative predictions of translation initiation based on genome sequence.

Biological 2-input decoder circuit in human cells

Decoders are combinational circuits that convert information from n inputs to a maximum of 2ⁿ outputs. This operation is of major importance in computing systems yet it is vastly underexplored in synthetic biology. Here, we present a synthetic gene network architecture that operates as a biological decoder in human cells, converting 2 inputs to 4 outputs. As a proof-of-principle, we use small molecules to emulate the two inputs and fluorescent reporters as the corresponding four outputs. The experiments are performed using transient transfections in human kidney embryonic cells and the characterization by fluorescence microscopy and flow cytometry. We show a clear separation between the ON and OFF mean fluorescent intensity states. Additionally, we adopt the integrated mean fluorescence intensity for the characterization of the circuit and show that this metric is more robust to transfection conditions when compared to the mean fluorescent intensity. To conclude, we present the first implementation of a genetic decoder. This combinational system can be valuable toward engineering higher-order circuits as well as accommodate a multiplexed interface with endogenous cellular functions.

Optogenetic characterization methods overcome key challenges in synthetic and systems biology

Systems biologists aim to understand how organism-level processes, such as differentiation and multicellular development, are encoded in DNA. Conversely, synthetic biologists aim to program systems-level biological processes, such as engineered tissue growth, by writing artificial DNA sequences. To achieve their goals, these groups have adapted a hierarchical electrical engineering framework that can be applied in the forward direction to design complex biological systems or in the reverse direction to analyze evolved networks. Despite much progress, this framework has been limited by an inability to directly and dynamically characterize biological components in the varied contexts of living cells. Recently, two optogenetic methods for programming custom gene expression and protein localization signals have been developed and used to reveal fundamentally new information about biological components that respond to those signals. This basic dynamic characterization approach will be a major enabling technology in synthetic and systems biology.

Advances and computational tools towards predictable design in biological engineering

The design process of complex systems in all the fields of engineering requires a set of quantitatively characterized components and a method to predict the output of systems composed by such elements. This strategy relies on the modularity of the used components or the prediction of their context-dependent behaviour, when parts functioning depends on the specific context. Mathematical models usually support the whole process by guiding the selection of parts and by predicting the output of interconnected systems. Such bottom-up design process cannot be trivially adopted for biological systems engineering, since parts function is hard to predict when components are reused in different contexts. This issue and the intrinsic complexity of living systems limit the capability of synthetic biologists to predict the quantitative behaviour of biological systems. The high potential of synthetic biology strongly depends on the capability of mastering this issue. This review discusses the predictability issues of basic biological parts (promoters, ribosome binding sites, coding sequences, transcriptional terminators, and plasmids) when used to engineer simple and complex gene expression systems in Escherichia coli. A comparison between bottom-up and trial-and-error approaches is performed for all the discussed elements and mathematical models supporting the prediction of parts behaviour are illustrated.

Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria

Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs.

Principles of genetic circuit design

Cells navigate environments, communicate and build complex patterns by initiating gene expression in response to specific signals. Engineers seek to harness this capability to program cells to perform tasks or create chemicals and materials that match the complexity seen in nature. This Review describes new tools that aid the construction of genetic circuits. Circuit dynamics can be influenced by the choice of regulators and changed with expression 'tuning knobs'. We collate the failure modes encountered when assembling circuits, quantify their impact on performance and review mitigation efforts. Finally, we discuss the constraints that arise from circuits having to operate within a living cell. Collectively, better tools, well-characterized parts and a comprehensive understanding of how to compose circuits are leading to a breakthrough in the ability to program living cells for advanced applications, from living therapeutics to the atomic manufacturing of functional materials.

Large-scale de novo DNA synthesis: technologies and applications

For over 60 years, the synthetic production of new DNA sequences has helped researchers understand and engineer biology. Here we summarize methods and caveats for the de novo synthesis of DNA, with particular emphasis on recent technologies that allow for large-scale and low-cost production. In addition, we discuss emerging applications enabled by large-scale de novo DNA constructs, as well as the challenges and opportunities that lie ahead.

In pursuit of design principles of regulatory sequences

Instructions for when, where and to what level each gene should be expressed are encoded within regulatory sequences. The importance of motifs recognized by DNA-binding regulators has long been known, but their extensive characterization afforded by recent technologies only partly accounts for how regulatory instructions are encoded in the genome. Here, we review recent advances in our understanding of regulatory sequences that influence transcription and go beyond the description of motifs. We discuss how understanding different aspects of the sequence-encoded regulation can help to unravel the genotype-phenotype relationship, which would lead to a more accurate and mechanistic interpretation of personal genome sequences.

Integrating biological redesign: where synthetic biology came from and where it needs to go

Synthetic biology seeks to extend approaches from engineering and computation to redesign of biology, with goals such as generating new chemicals, improving human health, and addressing environmental issues. Early on, several guiding principles of synthetic biology were articulated, including design according to specification, separation of design from fabrication, use of standardized biological parts and organisms, and abstraction. We review the utility of these principles over the past decade in light of the field's accomplishments in building complex systems based on microbial transcription and metabolism and describe the progress in mammalian cell engineering.

Integrating artificial with natural cells to translate chemical messages that direct E. coli behaviour

Previous efforts to control cellular behaviour have largely relied upon various forms of genetic engineering. Once the genetic content of a living cell is modified, the behaviour of that cell typically changes as well. However, other methods of cellular control are possible. All cells sense and respond to their environment. Therefore, artificial, non-living cellular mimics could be engineered to activate or repress already existing natural sensory pathways of living cells through chemical communication. Here we describe the construction of such a system. The artificial cells expand the senses of Escherichia coli by translating a chemical message that E. coli cannot sense on its own to a molecule that activates a natural cellular response. This methodology could open new opportunities in engineering cellular behaviour without exploiting genetically modified organisms.

The control of cellular behaviour largely relies on genetic engineering, but artificial cells could be designed to control cell processes through chemical communication. Here, the authors develop an artificial cell that is able to translate a chemical message into a signal that can be sensed by E. coli and activate a cellular response.

Quantifying position-dependent codon usage bias

Although the mapping of codon to amino acid is conserved across nearly all species, the frequency at which synonymous codons are used varies both between organisms and between genes from the same organism. This variation affects diverse cellular processes including protein expression, regulation, and folding. Here, we mathematically model an additional layer of complexity and show that individual codon usage biases follow a position-dependent exponential decay model with unique parameter fits for each codon. We use this methodology to perform an in-depth analysis on codon usage bias in the model organism Escherichia coli. Our methodology shows that lowly and highly expressed genes are more similar in their codon usage patterns in the 5′-gene regions, but that these preferences diverge at distal sites resulting in greater positional dependency (pD, which we mathematically define later) for highly expressed genes. We show that position-dependent codon usage bias is partially explained by the structural requirements of mRNAs that results in increased usage of A/T rich codons shortly after the gene start. However, we also show that the pD of 4- and 6-fold degenerate codons is partially related to the gene copy number of cognate-tRNAs supporting existing hypotheses that posit benefits to a region of slow translation in the beginning of coding sequences. Lastly, we demonstrate that viewing codon usage bias through a position-dependent framework has practical utility by improving accuracy of gene expression prediction when incorporating positional dependencies into the Codon Adaptation Index model.

Expression of codon optimized genes in microbial systems: current industrial applications and perspectives

The efficient production of functional proteins in heterologous hosts is one of the major bases of modern biotechnology. Unfortunately, many genes are difficult to express outside their original context. Due to their apparent “silent” nature, synonymous codon substitutions have long been thought to be trivial. In recent years, this dogma has been refuted by evidence that codon replacement can have a significant impact on gene expression levels and protein folding. In the past decade, considerable advances in the speed and cost of gene synthesis have facilitated the complete redesign of entire gene sequences, dramatically improving the likelihood of high protein expression. This technology significantly impacts the economic feasibility of microbial-based biotechnological processes by, for example, increasing the volumetric productivities of recombinant proteins or facilitating the redesign of novel biosynthetic routes for the production of metabolites. This review discusses the current applications of this technology, particularly those regarding the production of small molecules and industrially relevant recombinant enzymes. Suggestions for future research and potential uses are provided as well.

D-Tailor: automated analysis and design of DNA sequences

Motivation: Current advances in DNA synthesis, cloning and sequencing technologies afford high-throughput implementation of artificial sequences into living cells. However, flexible computational tools for multi-objective sequence design are lacking, limiting the potential of these technologies.

Results: We developed DNA-Tailor (D-Tailor), a fully extendable software framework, for property-based design of synthetic DNA sequences. D-Tailor permits the seamless integration of multiple sequence analysis tools into a generic Monte Carlo simulation that evolves sequences toward any combination of rationally defined properties. As proof of principle, we show that D-Tailor is capable of designing sequence libraries comprising all possible combinations among three different sequence properties influencing translation efficiency in Escherichia coli. The capacity to design artificial sequences that systematically sample any given parameter space should support the implementation of more rigorous experimental designs.

Availability: Source code is available for download at https://sourceforge.net/projects/dtailor/

Contact:aparkin@lbl.gov or cambray.guillaume@gmail.com

Supplementary information:Supplementary data are available at Bioinformatics online (D-Tailor Tutorial).

A wise consistency: engineering biology for conformity, reliability, predictability

The next generation of synthetic biology applications will increasingly involve engineered organisms that exist in intimate contact with humans, animals and the rest of the environment. Examples include cellular and viral approaches for maintaining and improving health in humans and animals. The need for reliable and specific function in these environments may require more complex system designs than previously. In these cases the uncertainties in the behavior of biological building blocks, their hosts and their environments present a challenge for design of predictable and safe systems. Here, we review systematic methods for the effective characterization of these uncertainties that are lowering the barriers to predictive design of reliable complex biological systems.

Translation rate is controlled by coupled trade-offs between site accessibility, selective RNA unfolding and sliding at upstream standby sites

The ribosome's interactions with mRNA govern its translation rate and the effects of post-transcriptional regulation. Long, structured 5' untranslated regions (5' UTRs) are commonly found in bacterial mRNAs, though the physical mechanisms that determine how the ribosome binds these upstream regions remain poorly defined. Here, we systematically investigate the ribosome's interactions with structured standby sites, upstream of Shine-Dalgarno sequences, and show that these interactions can modulate translation initiation rates by over 100-fold. We find that an mRNA's translation initiation rate is controlled by the amount of single-stranded surface area, the partial unfolding of RNA structures to minimize the ribosome's binding free energy penalty, the absence of cooperative binding and the potential for ribosomal sliding. We develop a biophysical model employing thermodynamic first principles and a four-parameter free energy model to accurately predict the ribosome's translation initiation rates for 136 synthetic 5' UTRs with large structures, diverse shapes and multiple standby site modules. The model predicts and experiments confirm that the ribosome can readily bind distant standby site modules that support high translation rates, providing a physical mechanism for observed context effects and long-range post-transcriptional regulation.

Causes and effects of N-terminal codon bias in bacterial genes

Most amino acids are encoded by multiple codons, and codon choice has strong effects on protein expression. Rare codons are enriched at the N terminus of genes in most organisms, although the causes and effects of this bias are unclear. Here, we measure expression from >14,000 synthetic reporters in Escherichia coli and show that using N-terminal rare codons instead of common ones increases expression by ~14-fold (median 4-fold). We quantify how individual N-terminal codons affect expression and show that these effects shape the sequence of natural genes. Finally, we demonstrate that reduced RNA structure and not codon rarity itself is responsible for expression increases. Our observations resolve controversies over the roles of N-terminal codon bias and suggest a straightforward method for optimizing heterologous gene expression in bacteria.