Gene Architectures that Minimize Cost of Gene Expression

Concentration-Dependent CsrA Regulation of the uxuB Transcript Leads to Development of a Post-Transcriptional Bandpass Filter

Post-transcriptional control systems offer new avenues for designing synthetic circuits that provide reduced burden and fewer synthetic regulatory components compared to transcriptionally based tools. Herein, we repurpose a newly identified post-transcriptional interaction between the uxuB mRNA transcript, specifically the 5' UTR + 100 nucleotides of coding sequence (100 nt CDS), and the E. coli Carbon Storage Regulatory A (CsrA) protein to design a biological post-transcriptional bandpass filter. In this work, we characterize the uxuB mRNA as a heterogeneous target of CsrA, where the protein can both activate and repress uxuB activity depending on its intracellular concentration. We leverage this interaction to implement a novel strategy of regulation within the 5' UTR of an mRNA. Specifically, we report a hierarchical binding strategy that may be leveraged by CsrA within uxuB to produce a dose-dependent response in regulatory outcomes. In our semisynthetic circuit, the uxuB 5' UTR + 100 nt CDS sequence is used as a scaffold that is fused to a gene of interest, which allows the circuit to transition between ON/OFF states based on the concentration range of free natively expressed CsrA. Notably, this system exerts regulation comparable to previously developed transcriptional bandpass filters while reducing the number of synthetic circuit components and can be used in concert with additional post-transcriptionally controlled circuits to achieve complex multi-signal control. We anticipate that future characterization of native regulatory RNA-protein systems will enable the development of more complex RNP-based circuits for synthetic biology applications.

Mechanistic Modeling of In Vivo Translation in Escherichia coli Reliably Identifies Well-Adapted and Optimized RNA Sequences

Translation elongation is a multifaceted process that intricately links translational resource availability to the biophysical effects arising from the interaction of mRNA sequences, ribosomes, and nascent polypeptide chains. Optimizing (heterologous) gene expression via codon usage or tRNA preference alone may yield suboptimal outcomes. In this study, we present a comprehensive mechanistic model that accounts for the competition of tRNAs at the ribosomal A-site, internal Shine-Dalgarno sequence interactions, and the decelerating effects of positively charged peptide patches. Our model offers a holistic perspective on the effects of translational elongation, including growth rate-dependent variation in translational rates by 22 to 25% between slow- and fast-growing Escherichia coli cells. We emphasize that endogenous E. coli sequences typically adapt to these effects, particularly in highly expressed genes, where adaptation ensures efficient translation. Conversely, heterologous gene sequences from Saccharomyces cerevisiae are predicted to exhibit lower translational elongation rates by 14 to 70% compared to the homologous isoform. Simulated elongation profiles not only underscore potential sites for translation engineering but also suggest feasible synonymous codon exchanges. The implications of our model extend beyond mere codon usage adaptation and shed light on the key factors influencing translation efficiency (e.g., codons for positively charged amino acids reduced elongation rates by ∼6%). This study provides a nuanced understanding of the intricate dynamics governing translation in E. coli.

Metabolic analyses of Yarrowia lipolytica for biopolymer production reveals roadblocks and strategies for microbial utilizing volatile fatty acids as sustainable feedstocks

This study quantifies metabolic features of engineered Yarrowia lipolytica strains for converting volatile fatty acids (VFAs) into poly-3-hydroxybutyrate (PHB) via 13C-metabolic flux analysis and RNA-Seq. Yarrowia lipolytica is unable to grow with C4 ∼ C6 VFAs due to substrate toxicity, while propionate (C3) metabolism leads to slow growth and minimal PHB production due to enzymatic limitations in substrate assimilation pathways. Acetate is a viable but challenging VFA feedstock. Comparing to glucose, acetate catabolism results in low ATP/ADP ratios, high enzyme usage, substantial CO2 release (>50 % of input carbon), and limited NADPH. Several strategies may overcome these roadblocks: 1) glucose-VFA co-catabolism improves energy charge, alleviates metabolic imbalances, reduces flux rigidity, and lowers the enzyme expression burden; 2) overexpressing acetyl-CoA synthetase and nitrogen limitation increase acetate uptake and PHB synthesis during glucose-acetate co-utilization; and 3) repression of oxidase facilitates fluxes towards PHB synthesis. The results provide insights into efficient utilization of acetate as feedstock.

Improving the generalization of protein expression models with mechanistic sequence information

The growing demand for biological products drives many efforts to maximize expression of heterologous proteins. Advances in high-throughput sequencing can produce data suitable for building sequence-to-expression models with machine learning. The most accurate models have been trained on one-hot encodings, a mechanism-agnostic representation of nucleotide sequences. Moreover, studies have consistently shown that training on mechanistic sequence features leads to much poorer predictions, even with features that are known to correlate with expression, such as DNA sequence motifs, codon usage, or properties of mRNA secondary structures. However, despite their excellent local accuracy, current sequence-to-expression models can fail to generalize predictions far away from the training data. Through a comparative study across datasets in Escherichia coli and Saccharomyces cerevisiae, here we show that mechanistic sequence features can provide gains on model generalization, and thus improve their utility for predictive sequence design. We explore several strategies to integrate one-hot encodings and mechanistic features into a single predictive model, including feature stacking, ensemble model stacking, and geometric stacking, a novel architecture based on graph convolutional neural networks. Our work casts new light on mechanistic sequence features, underscoring the importance of domain-knowledge and feature engineering for accurate prediction of protein expression levels.

Restructuring a Complex Genetic Function on Episomal Vectors in Escherichia coli

Genetic functions have evolved over long timescales and can be encoded by multiple genes dispersed in different locations in genomes, and although contemporary molecular biology enables control over single genes, more complex genetic functions remain challenging. Here, we study the restructuring and mobilization of a complex genetic function encoded by 10 genes, originally expressed from four operons and two loci on the Escherichia coli genome. We observe subtle phenotypic differences and reduced fitness when expressed from episomal DNA and demonstrate that mutations in the transcriptional machinery are necessary for successful implementation in different bacteria. The work provides new approaches for advanced genome editing and constitutes a first step toward modularization and genome-level engineering of complex genetic functions.

Speed-Energy-Efficiency Trade-off in Hsp70 Chaperone System

Proteins must fold into their native structure to carry out cellular functions. However, they can sometimes misfold into non-native structures, leading to reduced efficiency or malfunction. Chaperones help prevent misfolding by guiding proteins to their active state using energy from ATP hydrolysis. Experiments have revealed numerous kinetic and structural aspects of how various chaperones facilitate the folding of proteins into their native structure. However, what remains missing is a fundamental theoretical understanding of their operational mechanisms, especially the limits and constraints imposed on their efficiency by energy flow and dissipation. To address this, we built a kinetic model of the Hsp70 chaperone system by incorporating all key structural and kinetic details. Then, using the chemical kinetic equations, we investigate how energy expenditure shapes the efficiency of Hsp70 chaperones in the proper folding of misfolded proteins. We show that ATP consumption by chaperones significantly enhances the folding of proteins into their native states. Our investigations reveal that a chaperone achieves optimal efficiency when its binding to misfolded proteins is much faster than the misfolding kinetics of that protein. We also demonstrate the presence of an upper bound on a chaperone's efficiency of protein folding and its overall rescue rate. This upper bound increases with energy dissipation until it reaches a saturation point. Furthermore, we show a speed-energy-efficiency trade-off in chaperone action, demonstrating that it is impossible to simultaneously optimize the efficiency of chaperone-assisted protein folding and the energy efficiency of the process.

Tissue-specific, temporal, and core gene-dependent expression patterns of Hsp70s reveal functional allocation in Chlamys farreri under heat stress

Heat shock proteins 70 KDa (Hsp70s) engage in a broad spectrum of cellular functions in response to various stressors. Marine bivalves face substantial threats from the rising seawater temperature attributed to global warming. In the present study, expression patterns of Hsp70s in Zhikong scallop Chlamys farreri (CfHsp70s) were determined in embryos and larvae at all developmental stages, in healthy adult tissues, and across four various tissues exposed to high temperature for acute and chronic periods through in silico analysis. Spatiotemporal expressions suggested CfHsp70s performed specific functional differentiations in scallop's development and growth. Regulatory expression patterns of CfHsp70s, characterized by predominant down-regulation in the mantle, gill and hemocytes, as well as contrasting up-regulation in the heart, suggest differential functional allocation of CfHsp70s among tissues in response to heat stress. Particularly, a core set of 14 CfHsp70s, especially the nine members of the Hsp70B2s, characterized by gene expansion, intron-less structure, shorter gene length, preference for hydrophilic amino acids, and coordinated expression profiles, was predominantly responsible for the inducible up-regulations observed across all four tissue types. Collectively, the tissue-specific, temporal and core gene-dependent expression patterns of CfHsp70s illustrate the functional allocation and molecular evolution of Hsp70 family members in Zhikong scallops.

Rewiring native post-transcriptional global regulators to achieve designer, multi-layered genetic circuits

As synthetic biology expands, creating "drag-and-drop" regulatory tools that can achieve diverse regulatory outcomes are paramount. Herein, we develop a approach for engineering complex post-transcriptional control by rewiring the Carbon Storage Regulatory (Csr) Network of Escherichia coli. We co-opt native interactions of the Csr Network to establish post-transcriptional logic gates and achieve complex bacterial regulation. First, we rationally engineer RNA-protein interactions to create a genetic toolbox of 12 BUFFER Gates that achieves a 15-fold range of expression. Subsequently, we develop a Csr-regulated NOT Gate by integrating a cognate 5' UTR that is natively Csr-activated into our platform. We then deploy the BUFFER and NOT gates to build a bi-directional regulator, two input Boolean Logic gates OR, NOR, AND and NAND and a pulse-generating circuit. Last, we port our Csr-regulated BUFFER Gate into three industrially relevant bacteria simply by leveraging the conserved Csr Network in each species.

Bixafen, Prothioconazole, and Trifloxystrobin Alone or in Combination Have a Greater Effect on Health Related Gene Expression in Honey Bees from Nutritionally Deprived than from Protein Supplemented Colonies

The aim of this study was to evaluate whether alterations in food availability compromise the metabolic homeostasis of honey bees exposed to three fungicides alone or together. Ten honey bee colonies were used, with half receiving carbohydrate-protein supplementation for 15 weeks while another five colonies had their protein supply reduced with pollen traps. Subsequently, forager bees were collected and exposed by contact to 1 or 7 µg of bixafen, prothioconazole, or trifloxystrobin, either individually or in combination. After 48 h, bee abdomens without the intestine were used for the analysis of expression of antioxidant genes (SOD-1, CAT, and GPX-1), detoxification genes (GST-1 and CYP306A1), the storage protein gene vitellogenin, and immune system antimicrobial peptide genes (defensin-1, abaecin, hymenoptaecin, and apidaecin), through real-time PCR. All fungicide treatments induced changes in gene expression, with bixafen showing the most prominent upregulation. Exposure to 1 µg of each of the three pesticides resulted in upregulation of genes associated with detoxification and nutrition processes, and downregulation of immune system genes. When the three pesticides were combined at a dose of 7 µg each, there was a pronounced downregulation of all genes. Food availability in the colonies affected the impact of fungicides on the expression of the studied genes in forager bees.

Targeting imidazole-glycerol phosphate dehydratase in plants: novel approach for structural and functional studies, and inhibitor blueprinting

The histidine biosynthetic pathway (HBP) is targeted for herbicide design with preliminary success only regarding imidazole-glycerol phosphate dehydratase (IGPD, EC 4.2.1.19), or HISN5, as referred to in plants. HISN5 catalyzes the sixth step of the HBP, in which imidazole-glycerol phosphate (IGP) is dehydrated to imidazole-acetol phosphate. In this work, we present high-resolution cryoEM and crystal structures of Medicago truncatula HISN5 (MtHISN5) in complexes with an inactive IGP diastereoisomer and with various other ligands. MtHISN5 can serve as a new model for plant HISN5 structural studies, as it enables resolving protein-ligand interactions at high (2.2 Å) resolution using cryoEM. We identified ligand-binding hotspots and characterized the features of plant HISN5 enzymes in the context of the HISN5-targeted inhibitor design. Virtual screening performed against millions of small molecules not only revealed candidate molecules but also identified linkers for fragments that were experimentally confirmed to bind. Based on experimental and computational approaches, this study provides guidelines for designing symmetric HISN5 inhibitors that can reach two neighboring active sites. Finally, we conducted analyses of sequence similarity networks revealing that plant HISN5 enzymes derive from cyanobacteria. We also adopted a new approach to measure MtHISN5 enzymatic activity using isothermal titration calorimetry and enzymatically synthesized IGP.

Specific codons control cellular resources and fitness

As cellular engineering progresses from simply overexpressing proteins to imparting complex phenotypes through multigene expression, judicious appropriation of cellular resources is essential. Since codon use is degenerate and biased, codons may control cellular resources at a translational level. We investigate how partitioning transfer RNA (tRNA) resources by incorporating dissimilar codon usage can drastically alter interdependence of expression level and burden on the host. By isolating the effect of individual codons' use during translation elongation while eliminating confounding factors, we show that codon choice can trans-regulate fitness of the host and expression of other heterologous or native genes. We correlate specific codon usage patterns with host fitness and derive a coding scheme for multigene expression called the Codon Health Index (CHI, χ). This empirically derived coding scheme (χ) enables the design of multigene expression systems that avoid catastrophic cellular burden and is robust across several proteins and conditions.

Fit-Seq2.0: An Improved Software for High-Throughput Fitness Measurements Using Pooled Competition Assays

The fitness of a genotype is defined as its lifetime reproductive success, with fitness itself being a composite trait likely dependent on many underlying phenotypes. Measuring fitness is important for understanding how alteration of different cellular components affects a cell's ability to reproduce. Here, we describe an improved approach, implemented in Python, for estimating fitness in high throughput via pooled competition assays.

Revealing determinants of translation efficiency via whole-gene codon randomization and machine learning

It has been known for decades that codon usage contributes to translation efficiency and hence to protein production levels. However, its role in protein synthesis is still only partly understood. This lack of understanding hampers the design of synthetic genes for efficient protein production. In this study, we generated a synonymous codon-randomized library of the complete coding sequence of red fluorescent protein. Protein production levels and the full coding sequences were determined for 1459 gene variants in Escherichia coli. Using different machine learning approaches, these data were used to reveal correlations between codon usage and protein production. Interestingly, protein production levels can be relatively accurately predicted (Pearson correlation of 0.762) by a Random Forest model that only relies on the sequence information of the first eight codons. In this region, close to the translation initiation site, mRNA secondary structure rather than Codon Adaptation Index (CAI) is the key determinant of protein production. This study clearly demonstrates the key role of codons at the start of the coding sequence. Furthermore, these results imply that commonly used CAI-based codon optimization of the full coding sequence is not a very effective strategy. One should rather focus on optimizing protein production via reducing mRNA secondary structure formation with the first few codons.

The Molecular Mechanisms of Defective Copper Metabolism in Diabetic Cardiomyopathy

Copper is an essential trace metal element that significantly affects human physiology and pathology by regulating various important biological processes, including mitochondrial oxidative phosphorylation, connective tissue crosslinking, and antioxidant defense. Copper level has been proved to be closely related to the morbidity and mortality of cardiovascular diseases such as atherosclerosis, heart failure, and diabetic cardiomyopathy (DCM). Copper deficiency can induce cardiac hypertrophy and aggravate cardiomyopathy, while copper excess can mediate various types of cell death, such as autophagy, apoptosis, cuproptosis, pyroptosis, and cardiac hypertrophy and fibrosis. Both copper excess and copper deficiency lead to redox imbalance, activate inflammatory response, and aggravate diabetic cardiomyopathy. This defective copper metabolism suggests a specific metabolic pattern of copper in diabetes and a specific role in the pathogenesis and progression of DCM. This review is aimed at providing a timely summary of the effects of defective copper homeostasis on DCM and discussing potential underlying molecular mechanisms.

Random peptides rich in small and disorder-promoting amino acids are less likely to be harmful

Proteins are the workhorses of the cell, yet they carry great potential for harm via misfolding and aggregation. Despite the dangers, proteins are sometimes born de novo from non-coding DNA. Proteins are more likely to be born from non-coding regions that produce peptides that do little to no harm when translated than from regions that produce harmful peptides. To investigate which newborn proteins are most likely to "first, do no harm", we estimate fitnesses from an experiment that competed Escherichia coli lineages that each expressed a unique random peptide. A variety of peptide metrics significantly predict lineage fitness, but this predictive power stems from simple amino acid frequencies rather than the ordering of amino acids. Amino acids that are smaller and that promote intrinsic structural disorder have more benign fitness effects. We validate that the amino acids that indicate benign effects in random peptides expressed in E. coli also do so in an independent dataset of random N-terminal tags in which it is possible to control for expression level. The same amino acids are also enriched in young animal proteins.

Precipitous Increase of Bacterial CRISPR-Cas Abundance at Around 45°C

Although performing adaptive immunity, CRISPR-Cas systems are present in only 40% of bacterial genomes. We observed an abrupt increase of bacterial CRISPR-Cas abundance at around 45°C. Phylogenetic comparative analyses confirmed that the abundance correlates with growth temperature only at the temperature range around 45°C. From the literature, we noticed that the diversities of cellular predators (like protozoa, nematodes, and myxobacteria) have a steep decline at this temperature range. The grazing risk faced by bacteria reduces substantially at around 45°C and almost disappears above 60°C. We propose that viral lysis would become the dominating factor of bacterial mortality, and antivirus immunity has a higher priority at higher temperatures. In temperature ranges where the abundance of cellular predators does not change with temperature, the growth temperatures of bacteria would not significantly affect their CRISPR-Cas contents. The hypothesis predicts that bacteria should also be rich in CRISPR-Cas systems if they live in other extreme conditions inaccessible to grazing predators.

The Dynamic Codon Biaser: calculating prokaryotic codon usage biases

Bacterial genomes often reflect a bias in the usage of codons. These biases are often most notable within highly expressed genes. While deviations in codon usage can be attributed to selection or mutational biases, they can also be functional, for example controlling gene expression or guiding protein structure. Several different metrics have been developed to identify biases in codon usage. Previously we released a database, CBDB: The Codon Bias Database, in which users could retrieve precalculated codon bias data for bacterial RefSeq genomes. With the increase of bacterial genome sequence data since its release a new tool was needed. Here we present the Dynamic Codon Biaser (DCB) tool, a web application that dynamically calculates the codon usage bias statistics of prokaryotic genomes. DCB bases these calculations on 40 different highly expressed genes (HEGs) that are highly conserved across different prokaryotic species. A user can either specify an NCBI accession number or upload their own sequence. DCB returns both the bias statistics and the genome’s HEG sequences. These calculations have several downstream applications, such as evolutionary studies and phage–host predictions. The source code is freely available, and the website is hosted at www.cbdb.info.

Transcripts switched off at the stop of phloem unloading highlight the energy efficiency of sugar import in the ripening V. vinifera fruit

Transcriptomic changes at the cessation of sugar accumulation in the pericarp of Vitis vinifera were addressed on single berries re-synchronised according to their individual growth patterns. The net rates of water, sugars and K⁺ accumulation inferred from individual growth and solute concentration confirmed that these inflows stopped simultaneously in the ripe berry, while the small amount of malic acid remaining at this stage was still being oxidised at low rate. Re-synchronised individual berries displayed negligible variations in gene expression among triplicates. RNA-seq studies revealed sharp reprogramming of cell-wall enzymes and structural proteins at the stop of phloem unloading, associated with an 80% repression of multiple sugar transporters and aquaporins on the plasma or tonoplast membranes, with the noticeable exception of H⁺/sugar symporters, which were rather weakly and constitutively expressed. This was verified in three genotypes placed in contrasted thermo-hydric conditions. The prevalence of SWEET suggests that electrogenic transporters would play a minor role on the plasma membranes of SE/CC complex and the one of the flesh, while sucrose/H⁺ exchangers dominate on its tonoplast. Cis-regulatory elements present in their promoters allowed to sort these transporters in different groups, also including specific TIPs and PIPs paralogs, and cohorts of cell wall-related genes. Together with simple thermodynamic considerations, these results lead to propose that H⁺/sugar exchangers at the tonoplast, associated with a considerably acidic vacuolar pH, may exhaust cytosolic sugars in the flesh and alleviate the need for supplementary energisation of sugar transport at the plasma membrane.

Natural variation in the consequences of gene overexpression and its implications for evolutionary trajectories

Copy number variation through gene or chromosome amplification provides a route for rapid phenotypic variation and supports the long-term evolution of gene functions. Although the evolutionary importance of copy-number variation is known, little is understood about how genetic background influences its tolerance. Here, we measured fitness costs of over 4000 overexpressed genes in 15 Saccharomyces cerevisiae strains representing different lineages, to explore natural variation in tolerating gene overexpression (OE). Strain-specific effects dominated the fitness costs of gene OE. We report global differences in the consequences of gene OE, independent of the amplified gene, as well as gene-specific effects that were dependent on the genetic background. Natural variation in the response to gene OE could be explained by several models, including strain-specific physiological differences, resource limitations, and regulatory sensitivities. This work provides new insight on how genetic background influences tolerance to gene amplification and the evolutionary trajectories accessible to different backgrounds.

Read between the lines: Diversity of non-translational selection pressures on local codon usage

Protein coding genes can contain specific motifs within their nucleotide sequence that function as a signal for various biological pathways. The presence of such sequence motifs within a gene can have beneficial or detrimental effects on the phenotype and fitness of an organism, and this can lead to the enrichment or avoidance of this sequence motif. The degeneracy of the genetic code allows for the existence of alternative synonymous sequences that exclude or include these motifs, while keeping the encoded amino acid sequence intact. This implies that locally, there can be a selective pressure for preferentially using a codon over its synonymous alternative in order to avoid or enrich a specific sequence motif. This selective pressure could -in addition to mutation, drift and selection for translation efficiency and accuracy- contribute to shape the codon usage bias. In this review, we discuss patterns of avoidance of (or enrichment for) the various biological signals contained in specific nucleotide sequence motifs: transcription and translation initiation and termination signals, mRNA maturation signals, and antiviral immune system targets. Experimental data on the phenotypic or fitness effects of synonymous mutations in these sequence motifs confirm that they can be targets of local selection pressures on codon usage. We also formulate the hypothesis that transposable elements could have a similar impact on codon usage through their preferred integration sequences. Overall, selection on codon usage appears to be a combination of a global selection pressure imposed by the translation machinery, and a patchwork of local selection pressures related to biological signals contained in specific sequence motifs.

The Ongoing Quest to Crack the Genetic Code for Protein Production

Understanding the genetic design principles that determine protein production remains a major challenge. Although the key principles of gene expression were discovered 50 years ago, additional factors are still being uncovered. Both protein-coding and non-coding sequences harbor elements that collectively influence the efficiency of protein production by modulating transcription, mRNA decay, and translation. The influences of many contributing elements are intertwined, which complicates a full understanding of the individual factors. In natural genes, a functional balance between these factors has been obtained in the course of evolution, whereas for genetic-engineering projects, our incomplete understanding still limits optimal design of synthetic genes. However, notable advances have recently been made, supported by high-throughput analysis of synthetic gene libraries as well as by state-of-the-art biomolecular techniques. We discuss here how these advances further strengthen understanding of the gene expression process and how they can be harnessed to optimize protein production.

Translation at first sight: the influence of leading codons

First triplets of mRNA coding region affect the yield of translation. We have applied the flowseq method to analyze >30 000 variants of the codons 2-11 of the fluorescent protein reporter to identify factors affecting the protein synthesis. While the negative influence of mRNA secondary structure on translation has been confirmed, a positive role of rare codons at the beginning of a coding sequence for gene expression has not been observed. The identity of triplets proximal to the start codon contributes more to the protein yield then more distant ones. Additional in-frame start codons enhance translation, while Shine-Dalgarno-like motifs downstream the initiation codon are inhibitory. The metabolic cost of amino acids affects the yield of protein in the poor medium. The most efficient translation was observed for variants with features resembling those of native Escherichia coli genes.

Antennal Proteome of the Solenopsis invicta (Hymenoptera: Formicidae): Caste Differences in Olfactory Receptors and Chemosensory Support Proteins

Little is known about the expression pattern of odorant and pheromone transporters, receptors, and deactivation enzymes in the antennae of ants carrying out different tasks. In order to begin filling in this information gap, we compared the proteomes of the antennae of workers and males of the red fire ant, Solenopsis invicta Buren (Hymenoptera: Formicidae). Male ants do not perform any colony work, and their only activity is to leave the nest on a mating flight. Previous studies showed that male ants express fewer types of odorant receptors than workers. Thus, we expected to find large differences between male and worker antennae for expression of receptors, transporters, and deactivators of signaling chemicals. We found that the abundance of receptors was consistent with the expected caste-specific signaling complexity, but the numbers of different antenna-specific transporters and deactivating enzymes in males and workers were similar. It is possible that some of these proteins have antenna-specific functions that are unrelated to chemosensory reception. Alternatively, the similar complexity could be a vestige of ant progenitors that had more behaviorally active males. As the reduced behavior of male ants evolved, the selection process may have favored a complex repertoire of transporters and deactivating enzymes alongside a limited repertoire of odorant receptors.

Independent component analysis of E. coli's transcriptome reveals the cellular processes that respond to heterologous gene expression

Achieving the predictable expression of heterologous genes in a production host has proven difficult. Each heterologous gene expressed in the same host seems to elicit a different host response governed by unknown mechanisms. Historically, most studies have approached this challenge by manipulating the properties of the heterologous gene through methods like codon optimization. Here we approach this challenge from the host side. We express a set of 45 heterologous genes in the same Escherichia coli strain, using the same expression system and culture conditions. We collect a comprehensive RNAseq set to characterize the host's transcriptional response. Independent Component Analysis of the RNAseq data set reveals independently modulated gene sets (iModulons) that characterize the host response to heterologous gene expression. We relate 55% of variation of the host response to: Fear vs Greed (16.5%), Metal Homeostasis (19.0%), Respiration (6.0%), Protein folding (4.5%), and Amino acid and nucleotide biosynthesis (9.0%). If these responses can be controlled, then the success rate with predicting heterologous gene expression should increase.

Widespread Transcriptional Scanning in the Testis Modulates Gene Evolution Rates

The testis expresses the largest number of genes of any mammalian organ, a finding that has long puzzled molecular biologists. Our single-cell transcriptomic data of human and mouse spermatogenesis provide evidence that this widespread transcription maintains DNA sequence integrity in the male germline by correcting DNA damage through a mechanism we term transcriptional scanning. We find that genes expressed during spermatogenesis display lower mutation rates on the transcribed strand and have low diversity in the population. Moreover, this effect is fine-tuned by the level of gene expression during spermatogenesis. The unexpressed genes, which in our model do not benefit from transcriptional scanning, diverge faster over evolutionary timescales and are enriched for sensory and immune-defense functions. Collectively, we propose that transcriptional scanning shapes germline mutation signatures and modulates mutation rates in a gene-specific manner, maintaining DNA sequence integrity for the bulk of genes but allowing for faster evolution in a specific subset.

Bacteriophage Inspired Growth-Decoupled Recombinant Protein Production in Escherichia coli

Modulating resource allocation in bacteria to redirect metabolic building blocks to the formation of recombinant proteins rather than biomass formation remains a grand challenge in biotechnology. Here, we present a novel approach for improved recombinant protein production (RPP) using Escherichia coli (E. coli) by decoupling recombinant protein synthesis from cell growth. We show that cell division and host mRNA transcription can be successfully inhibited by coexpression of a bacteriophage-derived E. coli RNA polymerase (RNAP) inhibitor peptide and that genes overtranscribed by the orthogonal T7 RNAP can finally account to >55% of cell dry mass (CDM). This RNAP inhibitor peptide binds the E. coli RNAP and therefore prevents σ-factor 70 mediated formation of transcriptional qualified open promoter complexes. Thereby, the transcription of σ-factor 70 driven host genes is inhibited, and metabolic resources can be exclusively utilized for synthesis of the protein of interest (POI). Here, we mimic the late phase of bacteriophage infection by coexpressing a phage-derived xenogeneic regulator that reprograms the host cell and thereby are able to significantly improve RPP under industrial relevant fed-batch process conditions at bioreactor scale. We have evaluated production of several different recombinant proteins at different scales (from microscale to 20 L fed-batch scale) and have been able to improve total and soluble proteins yields up to 3.4-fold in comparison to the reference expression system E. coli BL21(DE3). This novel approach for growth-decoupled RPP has profound implications for biotechnology and bioengineering and helps to establish more cost-effective and generic manufacturing processes for biologics and biomaterials.

N-terminal deletion of Swi3 created by the deletion of a dubious ORF YJL175W mitigates protein burden effect in S. cerevisiae

Extreme overproduction of gratuitous proteins can overload cellular protein production resources, leading to growth defects, a phenomenon known as the protein burden/cost effect. Genetic screening in the budding yeast Saccharomyces cerevisiae has isolated several dubious ORFs whose deletions mitigated the protein burden effect, but individual characterization thereof has yet to be delineated. We found that deletion of the YJL175W ORF yielded an N-terminal deletion of Swi3, a subunit of the SWI/SNF chromatin remodeling complex, and partial loss of function of Swi3. The deletion mutant showed a reduction in transcription of genes encoding highly expressed, secreted proteins and an overall reduction in translation. Mutations in the chromatin remodeling complex could thus mitigate the protein burden effect, likely by reallocating residual cellular resources used to overproduce proteins. This cellular state might also be related to cancer cells, as they frequently harbor mutations in the SWI/SNF complex.

Synonymous genome recoding: a tool to explore microbial biology and new therapeutic strategies

Synthetic genome recoding is a new means of generating designed organisms with altered phenotypes. Synonymous mutations introduced into the protein coding region tolerate modifications in DNA or mRNA without modifying the encoded proteins. Synonymous genome-wide recoding has allowed the synthetic generation of different small-genome viruses with modified phenotypes and biological properties. Recently, a decreased cost of chemically synthesizing DNA and improved methods for assembling DNA fragments (e.g. lambda red recombination and CRISPR-based editing) have enabled the construction of an Escherichia coli variant with a 4-Mb synthetic synonymously recoded genome with a reduced number of sense codons (n = 59) encoding the 20 canonical amino acids. Synonymous genome recoding is increasing our knowledge of microbial interactions with innate immune responses, identifying functional genome structures, and strategically ameliorating cis-inhibitory signaling sequences related to splicing, replication (in eukaryotes), and complex microbe functions, unraveling the relevance of codon usage for the temporal regulation of gene expression and the microbe mutant spectrum and adaptability. New biotechnological and therapeutic applications of this methodology can easily be envisaged. In this review, we discuss how synonymous genome recoding may impact our knowledge of microbial biology and the development of new and better therapeutic methodologies.

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.

Diversity in lac Operon Regulation among Diverse Escherichia coli Isolates Depends on the Broader Genetic Background but Is Not Explained by Genetic Relatedness

The lac operon of Escherichia coli is a classic model for studying gene regulation. This study has uncovered features such as the environmental input logic controlling gene expression, as well as gene expression bistability and hysteresis. Most lac operon studies have focused on a few lab strains, and it is not known how generally those findings apply to the diversity of E. coli strains. We examined the environmental dependence of lac gene regulation in 20 natural isolates of E. coli and found a wide range of regulatory responses. By transferring lac genes from natural isolate strains into a common reference strain, we found that regulation depends on both the lac genes themselves and on the broader genetic background, indicating potential for still-greater regulatory diversity following horizontal gene transfer. Our results reveal that there is substantial natural variation in the regulation of the lac operon and indicate that this variation can be ecologically meaningful.

Transcription of bacterial genes is controlled by the coordinated action of cis- and trans-acting regulators. The activity and mode of action of these regulators can reflect different requirements for gene products in different environments. A well-studied example is the regulatory function that integrates the environmental availability of glucose and lactose to control the Escherichia colilac operon. Most studies of lac operon regulation have focused on a few closely related strains. To determine the range of natural variation in lac regulatory function, we introduced a reporter construct into 23 diverse E. coli strains and measured expression with combinations of inducer concentrations. We found a wide range of regulatory functions. Several functions were similar to the one observed in a reference lab strain, whereas others depended weakly on the presence of cAMP. Some characteristics of the regulatory function were explained by the genetic relatedness of strains, indicating that differences varied on relatively short time scales. The regulatory characteristics explained by genetic relatedness were among those that best predicted the initial growth of strains following transition to a lactose environment, suggesting a role for selection. Finally, we transferred the lac operon, with the lacI regulatory gene, from five natural isolate strains into a reference lab strain. The regulatory function of these hybrid strains revealed the effect of local and global regulatory elements in controlling expression. Together, this work demonstrates that regulatory functions can be varied within a species and that there is variation within a species to best match a function to particular environments.

Contemporary Tools for Regulating Gene Expression in Bacteria

Insights from novel mechanistic paradigms in gene expression control have led to the development of new gene expression systems for bioproduction, control, and sensing applications. Coupled with a greater understanding of synthetic burden and modern creative biodesign approaches, contemporary bacterial gene expression tools and systems are emerging that permit fine-tuning of expression, enabling greater predictability and maximisation of specific productivity, while minimising deleterious effects upon cell viability. These advances have been achieved by using a plethora of regulatory tools, operating at all levels of the so-called 'central dogma' of molecular biology. In this review, we discuss these gene regulation tools in the context of their design, prototyping, integration into expression systems, and biotechnological application.

Genomic Evidence for Simultaneous Optimization of Transcription and Translation through Codon Variants in the pmoCAB Operon of Type Ia Methanotrophs

Microbial methane oxidation plays a fundamental role in the biogeochemical cycle of Earth’s system. Recent reports have provided evidence for the acquisition of methane monooxygenases by horizontal gene transfer in methane-oxidizing bacteria from different environments, but how evolution has shaped the coding sequences to execute methanotrophy efficiently remains unexplored. In this work, we provide genomic evidence that among the different types of methanotrophs, type Ia methanotrophs possess a unique coding sequence of the pmoCAB operon that is under positive selection for optimal resource allocation and efficient synthesis of transcripts and proteins. This adaptive trait possibly enables type Ia methanotrophs to respond robustly to fluctuating methane availability and explains their global prevalence.

Understanding the interplay between genotype and phenotype is a fundamental goal of functional genomics. Methane oxidation is a microbial phenotype with global-scale significance as part of the carbon biogeochemical cycle and a sink for greenhouse gas. Microorganisms that oxidize methane (methanotrophs) are taxonomically diverse and widespread around the globe. In methanotrophic bacteria, enzymes in the methane oxidation metabolic module (KEGG module M00174, conversion of methane to formaldehyde) are encoded in four operons (pmoCAB, mmoXYZBCD, mxaFI, and xoxF). Recent reports have suggested that methanotrophs in Proteobacteria acquired methane monooxygenases through horizontal gene transfer. Here, we used a genomic meta-analysis to infer the transcriptional and translational advantages of coding sequences from the methane oxidation metabolic modules of different types of methanotrophs. By analyzing isolate and metagenome-assembled genomes from phylogenetically and geographically diverse sources, we detected an anomalous nucleotide composition bias in the coding sequences of particulate methane monooxygenase genes (pmoCAB) from type Ia methanotrophs. We found that this nucleotide bias increases the level of codon bias by decreasing the GC content in the third base of codons, a strategy that contrasts with that of other coding sequences in the module. Further codon usage analyses uncovered that codon variants of the type Ia pmoCAB coding sequences deviate from the genomic signature to match ribosomal protein-coding sequences. Subsequently, computation of transcription and translation metrics revealed that the pmoCAB coding sequences of type Ia methanotrophs optimize the usage of codon variants to maximize translation efficiency and accuracy, while minimizing the synthesis cost of transcripts and proteins.

IMPORTANCE Microbial methane oxidation plays a fundamental role in the biogeochemical cycle of Earth’s system. Recent reports have provided evidence for the acquisition of methane monooxygenases by horizontal gene transfer in methane-oxidizing bacteria from different environments, but how evolution has shaped the coding sequences to execute methanotrophy efficiently remains unexplored. In this work, we provide genomic evidence that among the different types of methanotrophs, type Ia methanotrophs possess a unique coding sequence of the pmoCAB operon that is under positive selection for optimal resource allocation and efficient synthesis of transcripts and proteins. This adaptive trait possibly enables type Ia methanotrophs to respond robustly to fluctuating methane availability and explains their global prevalence.

The distribution of fitness effects among synonymous mutations in a gene under directional selection

The fitness effects of synonymous mutations, nucleotide changes that do not alter the encoded amino acid, have often been assumed to be neutral, but a growing body of evidence suggests otherwise. We used site-directed mutagenesis coupled with direct measures of competitive fitness to estimate the distribution of fitness effects among synonymous mutations for a gene under directional selection and capable of adapting via synonymous nucleotide changes. Synonymous mutations had highly variable fitness effects, both deleterious and beneficial, resembling those of nonsynonymous mutations in the same gene. This variation in fitness was underlain by changes in transcription linked to the creation of internal promoter sites. A positive correlation between fitness and the presence of synonymous substitutions across a phylogeny of related Pseudomonads suggests these mutations may be common in nature. Taken together, our results provide the most compelling evidence to date that synonymous mutations with non-neutral fitness effects may in fact be commonplace.

Pathological changes are associated with shifts in the employment of synonymous codons at the transcriptome level

BACKGROUND

The usage of different synonymous codons reflects the genome organization and has been connected to parameters such as mRNA abundance and protein folding. It is also been established that mutations targeting specific synonymous codons can trigger disease.

RESULTS

We performed a systematic meta-analysis of transcriptome results from 75 datasets representing 40 pathologies. We found that a subset of codons was preferentially employed in abundant transcripts, while other codons were preferentially found in low-abundance transcripts. By comparing control and pathological transcriptomes, we observed a shift in the employment of synonymous codons for every analyzed disease. For example, cancerous tissue employed preferentially A- or U-ending codons, shifting from G- or C-ending codons, which were preferred by control tissues. This analysis was able to discriminate patients and controls with high specificity and sensitivity.

CONCLUSIONS

Here we show that the employment of specific synonymous codons, quantified at the whole transcriptome level, changes profoundly in many diseases. We propose that the changes in codon employment offer a novel perspective for disease studies, and could be used to design new diagnostic tools.

A Prunus persica genome-wide RNA-seq approach uncovers major differences in the transcriptome among chilling injury sensitive and non-sensitive varieties

Chilling injury represents a major constrain for crops productivity. Prunus persica, one of the most relevant rosacea crops, have early season varieties that are resistant to chilling injury, in contrast to late season varieties, which display chilling symptoms such as mealiness (dry, sandy fruit mesocarp) after prolonged storage at chilling temperatures. To uncover the molecular processes related to the ability of early varieties to withstand mealiness, postharvest and genome-wide RNA-seq assessments were performed in two early and two late varieties. Differences in juice content and ethylene biosynthesis were detected among early and late season fruits that became mealy after exposed to prolonged chilling. Principal component and data distribution analysis revealed that cold-stored late variety fruit displayed an exacerbated and unique transcriptome profile when compared to any other postharvest condition. A differential expression analysis performed using an empirical Bayes mixture modeling approach followed by co-expression and functional enrichment analysis uncover processes related to ethylene, lipids, cell wall, carotenoids and DNA metabolism, light response, and plastid homeostasis associated to the susceptibility or resistance of P. persica varieties to chilling stress. Several of the genes related to these processes are in quantitative trait loci (QTL) associated to mealiness in P. persica. Together, these analyses exemplify how P. persica can be used as a model for studying chilling stress in plants.

Within-Gene Shine-Dalgarno Sequences Are Not Selected for Function

The Shine-Dalgarno (SD) sequence motif facilitates translation initiation and is frequently found upstream of bacterial start codons. However, thousands of instances of this motif occur throughout the middle of protein coding genes in a typical bacterial genome. Here, we use comparative evolutionary analysis to test whether SD sequences located within genes are functionally constrained. We measure the conservation of SD sequences across Enterobacteriales, and find that they are significantly less conserved than expected. Further, the strongest SD sequences are the least conserved whereas we find evidence of conservation for the weakest possible SD sequences given amino acid constraints. Our findings indicate that most SD sequences within genes are likely to be deleterious and removed via selection. To illustrate the origin of these deleterious costs, we show that ATG start codons are significantly depleted downstream of SD sequences within genes, highlighting the constraint that these sequences impose on the surrounding nucleotides to minimize the potential for erroneous translation initiation.

Mitochondrial metabolism promotes adaptation to proteotoxic stress

The mechanisms by which cells adapt to proteotoxic stress are largely unknown, but key to understanding how tumor cells, particularly in vivo, are largely resistant to proteasome inhibitors. Analysis of cancer cell lines, mouse xenografts and patient-derived tumor samples all showed an association between mitochondrial metabolism and proteasome inhibitor sensitivity. When cells were forced to use oxidative phosphorylation rather than glycolysis, they became proteasome inhibitor-resistant. This mitochondrial state, however, creates a unique vulnerability: sensitivity to the small-molecule compound elesclomol. Genome-wide CRISPR/Cas9 screening showed that a single gene, encoding the mitochondrial reductase FDX1, could rescue elesclomol-induced cell death. Enzymatic function and NMR-based analyses further showed that FDX1 is the direct target of elesclomol, which promotes a unique form of copper-dependent cell death. These studies elucidate a fundamental mechanism by which cells adapt to proteotoxic stress and suggests strategies to mitigate proteasome inhibitor-resistance.

Codon Clarity or Conundrum?

Synonymous variations in protein-coding sequences alter protein expression dynamics, which has important implications for cellular physiology and evolutionary fitness, but disentangling the underlying molecular mechanisms remains challenging.

Translation affects mRNA stability in a codon-dependent manner in human cells

mRNA translation decodes nucleotide into amino acid sequences. However, translation has also been shown to affect mRNA stability depending on codon composition in model organisms, although universality of this mechanism remains unclear. Here, using three independent approaches to measure exogenous and endogenous mRNA decay, we define which codons are associated with stable or unstable mRNAs in human cells. We demonstrate that the regulatory information affecting mRNA stability is encoded in codons and not in nucleotides. Stabilizing codons tend to be associated with higher tRNA levels and higher charged/total tRNA ratios. While mRNAs enriched in destabilizing codons tend to possess shorter poly(A)-tails, the poly(A)-tail is not required for the codon-mediated mRNA stability. This mechanism depends on translation; however, the number of ribosome loads into a mRNA modulates the codon-mediated effects on gene expression. This work provides definitive evidence that translation strongly affects mRNA stability in a codon-dependent manner in human cells.

Proteins are made by joining together building blocks called amino acids into strings. The proteins are ‘translated’ from genetic sequences called mRNA molecules. These sequences can be thought of as series of ‘letters’, which are read in groups of three known as codons. Molecules called tRNAs recognize the codons and add the matching amino acids to the end of the protein. Each tRNA can recognize one or several codons, and the levels of different tRNAs inside the cell vary.

There are 61 codons that code for amino acids, but only 20 amino acids. This means that some codons produce the same amino acid. Despite this, there is evidence to suggest that not all of the codons that produce the same amino acid are exactly equivalent. In bacteria, yeast and zebrafish, some codons seem to make the mRNA molecule more stable, and others make it less stable. This might help the cell to control how many proteins it makes. It was not clear whether the same is true for humans.

To find out, Wu et al. used three separate methods to examine mRNA stability in four types of human cell. Overall, the results revealed that some codons help to stabilize the mRNA, while others make the mRNA molecule break down faster. The effect seems to depend on the supply of tRNAs that have a charged amino acid; mRNA molecules were more likely to self-destruct in cells that contained codons with low levels of the tRNA molecules.

Wu et al. also found that conditions in the cell can alter how strongly the codons affect mRNA stability. For example, a cell that has been infected by a virus reduces translation. Under these conditions, the identity of the codons in the mRNA has less effect on the stability of the mRNA molecule.

Changes to protein production happen in many diseases. Understanding what controls these changes could help to reveal more about our fundamental biology, and what happens when it goes wrong.

Evolthon: A community endeavor to evolve lab evolution

In experimental evolution, scientists evolve organisms in the lab, typically by challenging them to new environmental conditions. How best to evolve a desired trait? Should the challenge be applied abruptly, gradually, periodically, sporadically? Should one apply chemical mutagenesis, and do strains with high innate mutation rate evolve faster? What are ideal population sizes of evolving populations? There are endless strategies, beyond those that can be exposed by individual labs. We therefore arranged a community challenge, Evolthon, in which students and scientists from different labs were asked to evolve Escherichia coli or Saccharomyces cerevisiae for an abiotic stress—low temperature. About 30 participants from around the world explored diverse environmental and genetic regimes of evolution. After a period of evolution in each lab, all strains of each species were competed with one another. In yeast, the most successful strategies were those that used mating, underscoring the importance of sex in evolution. In bacteria, the fittest strain used a strategy based on exploration of different mutation rates. Different strategies displayed variable levels of performance and stability across additional challenges and conditions. This study therefore uncovers principles of effective experimental evolutionary regimens and might prove useful also for biotechnological developments of new strains and for understanding natural strategies in evolutionary arms races between species. Evolthon constitutes a model for community-based scientific exploration that encourages creativity and cooperation.

This Community Page article describes Evolthon; a first-of-its-kind community-based effort, involving about 30 participant labs around the world, aiming to explore the best strategy for evolving microorganisms to cope with an environmental challenge.

Encircling the regions of the pharmacogenomic landscape that determine drug response

BACKGROUND

The integration of large-scale drug sensitivity screens and genome-wide experiments is changing the field of pharmacogenomics, revealing molecular determinants of drug response without the need for previous knowledge about drug action. In particular, transcriptional signatures of drug sensitivity may guide drug repositioning, prioritize drug combinations, and point to new therapeutic biomarkers. However, the inherent complexity of transcriptional signatures, with thousands of differentially expressed genes, makes them hard to interpret, thus giving poor mechanistic insights and hampering translation to clinics.

METHODS

To simplify drug signatures, we have developed a network-based methodology to identify functionally coherent gene modules. Our strategy starts with the calculation of drug-gene correlations and is followed by a pathway-oriented filtering and a network-diffusion analysis across the interactome.

RESULTS

We apply our approach to 189 drugs tested in 671 cancer cell lines and observe a connection between gene expression levels of the modules and mechanisms of action of the drugs. Further, we characterize multiple aspects of the modules, including their functional categories, tissue-specificity, and prevalence in clinics. Finally, we prove the predictive capability of the modules and demonstrate how they can be used as gene sets in conventional enrichment analyses.

CONCLUSIONS

Network biology strategies like module detection are able to digest the outcome of large-scale pharmacogenomic initiatives, thereby contributing to their interpretability and improving the characterization of the drugs screened.

Central dogma rates and the trade-off between precision and economy in gene expression

Steady-state protein abundance is set by four rates: transcription, translation, mRNA decay and protein decay. A given protein abundance can be obtained from infinitely many combinations of these rates. This raises the question of whether the natural rates for each gene result from historical accidents, or are there rules that give certain combinations a selective advantage? We address this question using high-throughput measurements in rapidly growing cells from diverse organisms to find that about half of the rate combinations do not exist: genes that combine high transcription with low translation are strongly depleted. This depletion is due to a trade-off between precision and economy: high transcription decreases stochastic fluctuations but increases transcription costs. Our theory quantitatively explains which rate combinations are missing, and predicts the curvature of the fitness function for each gene. It may guide the design of gene circuits with desired expression levels and noise.

Meiotic Recombination: Genetics' Good Old Scalpel

In the era of genome engineering, a new study returns to classical genetics to decipher genotype-phenotype relationships in unprecedented throughput and with unprecedented accuracy. Capitalizing on natural variation in yeast strains and frequent meiotic recombination, She and Jarosz (2018) dissect and map to nucleotide resolution, simple and complex determinants of diverse phenotypic traits.

Unbiased Fitness Estimation of Pooled Barcode or Amplicon Sequencing Studies

Standard practice for phenotyping complex cell pools is to measure the fold enrichment of genotype-specific amplicons after a period of competitive growth. Here, we show that fold-enrichment measures cannot be compared across genotype pools with different fitness distributions. We develop a method to calculate an unbiased estimate of relative fitness by tracking abundances over several time points and show how to optimize experimental protocols to minimize fitness measurement error.

Codon usage influences fitness through RNA toxicity

Many organisms are subject to selective pressure that gives rise to unequal usage of synonymous codons, known as codon bias. To experimentally dissect the mechanisms of selection on synonymous sites, we expressed several hundred synonymous variants of the GFP gene in Escherichia coli, and used quantitative growth and viability assays to estimate bacterial fitness. Unexpectedly, we found many synonymous variants whose expression was toxic to E. coli Unlike previously studied effects of synonymous mutations, the effect that we discovered is independent of translation, but it depends on the production of toxic mRNA molecules. We identified RNA sequence determinants of toxicity and evolved suppressor strains that can tolerate the expression of toxic GFP variants. Genome sequencing of these suppressor strains revealed a cluster of promoter mutations that prevented toxicity by reducing mRNA levels. We conclude that translation-independent RNA toxicity is a previously unrecognized obstacle in bacterial gene expression.

Gene expression drives the evolution of dominance

Dominance is a fundamental concept in molecular genetics and has implications for understanding patterns of genetic variation, evolution, and complex traits. However, despite its importance, the degree of dominance in natural populations is poorly quantified. Here, we leverage multiple mating systems in natural populations of Arabidopsis to co-estimate the distribution of fitness effects and dominance coefficients of new amino acid changing mutations. We find that more deleterious mutations are more likely to be recessive than less deleterious mutations. Further, this pattern holds across gene categories, but varies with the connectivity and expression patterns of genes. Our work argues that dominance arises as a consequence of the functional importance of genes and their optimal expression levels.

Dominance is difficult to measure in natural populations as it is confounded with fitness. Here, Huber et al. developed a new approach to co-estimate dominance and selection coefficients, and found that the observed relationship is best fit by a new model of dominance based on gene expression level.