Translational Selection for Speed Is Not Sufficient to Explain Variation in Bacterial Codon Usage Bias

Synonymous codon substitutions modulate transcription and translation of a divergent upstream gene by modulating antisense RNA production

Synonymous codons were originally viewed as interchangeable, with no phenotypic consequences. However, substantial evidence has now demonstrated that synonymous substitutions can perturb a variety of gene expression and protein homeostasis mechanisms, including translational efficiency, translational fidelity, and cotranslational folding of the encoded protein. To date, most studies of synonymous codon-derived perturbations have focused on effects within a single gene. Here, we show that synonymous codon substitutions made far within the coding sequence of Escherichia coli plasmid-encoded chloramphenicol acetyltransferase (cat) can significantly increase expression of the divergent upstream tetracycline resistance gene, tetR. In four out of nine synonymously recoded cat sequences tested, expression of the upstream tetR gene was significantly elevated due to transcription of a long antisense RNA (asRNA) originating from a transcription start site within cat. Surprisingly, transcription of this asRNA readily bypassed the native tet transcriptional repression mechanism. Even more surprisingly, accumulation of the TetR protein correlated with the level of asRNA, rather than total tetR RNA. These effects of synonymous codon substitutions on transcription and translation of a neighboring gene suggest that synonymous codon usage in bacteria may be under selection to both preserve the amino acid sequence of the encoded gene and avoid DNA sequence elements that can significantly perturb expression of neighboring genes. Avoiding such sequences may be especially important in plasmids and prokaryotic genomes, where genes and regulatory elements are often densely packed. Similar considerations may apply to the design of genetic circuits for synthetic biology applications.

The layered costs and benefits of translational redundancy

The rate and accuracy of translation hinges upon multiple components - including transfer RNA (tRNA) pools, tRNA modifying enzymes, and rRNA molecules - many of which are redundant in terms of gene copy number or function. It has been hypothesized that the redundancy evolves under selection, driven by its impacts on growth rate. However, we lack empirical measurements of the fitness costs and benefits of redundancy, and we have poor a understanding of how this redundancy is organized across components. We manipulated redundancy in multiple translation components of Escherichia coli by deleting 28 tRNA genes, 3 tRNA modifying systems, and 4 rRNA operons in various combinations. We find that redundancy in tRNA pools is beneficial when nutrients are plentiful and costly under nutrient limitation. This nutrient-dependent cost of redundant tRNA genes stems from upper limits to translation capacity and growth rate, and therefore varies as a function of the maximum growth rate attainable in a given nutrient niche. The loss of redundancy in rRNA genes and tRNA modifying enzymes had similar nutrient-dependent fitness consequences. Importantly, these effects are also contingent upon interactions across translation components, indicating a layered hierarchy from copy number of tRNA and rRNA genes to their expression and downstream processing. Overall, our results indicate both positive and negative selection on redundancy in translation components, depending on a species' evolutionary history with feasts and famines.

Intragenomic variation in non-adaptive nucleotide biases causes underestimation of selection on synonymous codon usage

Patterns of non-uniform usage of synonymous codons vary across genes in an organism and between species across all domains of life. This codon usage bias (CUB) is due to a combination of non-adaptive (e.g. mutation biases) and adaptive (e.g. natural selection for translation efficiency/accuracy) evolutionary forces. Most models quantify the effects of mutation bias and selection on CUB assuming uniform mutational and other non-adaptive forces across the genome. However, non-adaptive nucleotide biases can vary within a genome due to processes such as biased gene conversion (BGC), potentially obfuscating signals of selection on codon usage. Moreover, genome-wide estimates of non-adaptive nucleotide biases are lacking for non-model organisms. We combine an unsupervised learning method with a population genetics model of synonymous coding sequence evolution to assess the impact of intragenomic variation in non-adaptive nucleotide bias on quantification of natural selection on synonymous codon usage across 49 Saccharomycotina yeasts. We find that in the absence of a priori information, unsupervised learning can be used to identify genes evolving under different non-adaptive nucleotide biases. We find that the impact of intragenomic variation in non-adaptive nucleotide bias varies widely, even among closely-related species. We show that the overall strength and direction of translational selection can be underestimated by failing to account for intragenomic variation in non-adaptive nucleotide biases. Interestingly, genes falling into clusters identified by machine learning are also physically clustered across chromosomes. Our results indicate the need for more nuanced models of sequence evolution that systematically incorporate the effects of variable non-adaptive nucleotide biases on codon frequencies.

Horizontal gene transfer drives the evolution of dependencies in bacteria

Many naturally occurring bacteria lead a lifestyle of metabolic dependency for crucial resources. We do not understand what factors drive bacteria toward this lifestyle and how. Here, we systematically show the crucial role of horizontal gene transfer (HGT) in dependency evolution in bacteria. Across 835 bacterial species, we map gene gain-loss dynamics on a deep evolutionary tree and assess the impact of HGT and gene loss on metabolic networks. Our analyses suggest that HGT-enabled gene gains can affect which genes are later lost. HGT typically adds new catabolic routes to bacterial metabolic networks, leading to new metabolic interactions between bacteria. We also find that gaining new routes can promote the loss of ancestral routes (”coupled gains and losses”, CGLs). Phylogenetic patterns indicate that both dependencies—mediated by CGLs and those purely by gene loss—are equally likely. Our results highlight HGT as an important driver of metabolic dependency evolution in bacteria.

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Metabolic dependencies are widespread across bacterial genomes

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New genes expand bacterial catabolism via the process of horizontal gene transfer

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During evolution, efficient pathways are gained, whereas redundant pathways are lost

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Gained pathways often depend on the metabolic byproducts of the surrounding community

The Key Parameters that Govern Translation Efficiency

Translation of mRNA into protein is a fundamental yet complex biological process with multiple factors that can potentially affect its efficiency. Here, we study a stochastic model describing the traffic flow of ribosomes along the mRNA and identify the key parameters that govern the overall rate of protein synthesis, sensitivity to initiation rate changes, and efficiency of ribosome usage. By analyzing a continuum limit of the model, we obtain closed-form expressions for stationary currents and ribosomal densities, which agree well with Monte Carlo simulations. Furthermore, we completely characterize the phase transitions in the system, and by applying our theoretical results, we formulate design principles that detail how to tune the key parameters we identified to optimize translation efficiency. Using ribosome profiling data from S. cerevisiae, we show that its translation system is generally consistent with these principles. Our theoretical results have implications for evolutionary biology, as well as for synthetic biology.

Analysis of Nipah Virus Codon Usage and Adaptation to Hosts

A recent outbreak of Nipah virus (NiV) in India has caused 17 deaths in people living in districts of Kerala state. Its zoonotic nature, as well as high rate of human-to-human transmission, has led researchers worldwide to work toward understanding the different aspects of the NiV. We performed a codon usage analysis, based on publicly available nucleotide sequences of NiV and its host adaptation, along with other members of the Henipavirus genus in ten hosts. NiV genome encodes nine open reading frames; and overall, no significant bias in codon usage was observed. Aromaticity of proteins had no impact on codon usage. An analysis of preferred codons used by NiV and the tRNA pool in human cells indicated that NiV prefers codons from a suboptimal anticodon tRNA pool. We observed that codon usage by NiV is mainly constrained by compositional and selection pressures, not by mutational forces. Parameters that define NiV and host relatedness in terms of codon usage were analyzed, with a codon adaptation index (CAI), relative codon deoptimization index (RCDI), and similarity index calculations; which indicated that, of all hosts analyzed, NiV was best adapted to African green monkeys. A comparative analysis based on the relative codon deoptimization index (RCDI) for host adaptation of NiV, Hendra virus (HeV), Cedar virus (CedV), and Hendra like Mojiang virus (MojV) revealed that except for dogs and ferrets, all evaluated hosts were more susceptible to HeV than NiV.

Can Protein Expression Be Regulated by Modulation of tRNA Modification Profiles?

tRNAs are the central adaptor molecules in translation. Their decoding properties are influenced by post-transcriptional modifications, particularly in the critical anticodon-stem-loop (ASL) region. Synonymous codon choice, also called codon usage bias, affects both translation efficiency and accuracy, and ASL modifications play key roles in both of these processes. In combination with a handful of historical examples, recent studies integrating ribosome profiling, proteomics, codon-usage analyses, and modification quantifications show that levels of tRNA modifications can change under stress, during development, or under specific metabolic conditions and can modulate the expression of specific genes. Deconvoluting the different responses (global or specific) to tRNA modification deficiencies can be difficult because of pleiotropic effects, but, as more cases emerge, it does seem that tRNA modification changes could add another layer of regulation in the transfer of information from DNA to protein.