Cost of Unneeded Proteins in E. coli Is Reduced after Several Generations in Exponential Growth

Evaluation of vectors for gene expression in Pseudovibrio marine bacteria

α-Proteobacteria belonging to the Pseudovibrio genus have been isolated from different marine organisms including marine sponges, corals, and algae. This genus was first described in 2004 and has since garnered attention due to the potential ecological relevance and biotechnological application of its metabolites. For instance, we recently reported specialized metabolites that we named pseudovibriamides from Pseudovibrio brasiliensis Ab134. The pseudovibriamide encoding ppp gene cluster is found in two-thirds of Pseudovibrio genomes. Pseudovibriamides coordinate motility and biofilm formation, behaviors that are known to be important for host colonization. Although we previously established reverse genetics methods to delete genes via homologous recombination, no self-replicative vectors have been reported for Pseudovibrio. We show that plasmid vectors containing two different broad-host-range replicons, RSF1010 and pBBR1, can be used in P. brasiliensis. The efficiency of vector transfer by electroporation averaged ~3 × 103 CFU/µg plasmid DNA, whereas the conjugation frequency from Escherichia coli ranged from 10-3 to 10-6. We then tested the vectors for fluorescent protein expression and consequent labeling, which allowed us to observe their effects on swarming motility and to compare plasmid stability. This study expands the genetic toolbox available for Pseudovibrio, which is expected to enable future ecological and biotechnological studies.IMPORTANCEThe genus Pseudovibrio of α-Proteobacteria has consistently been isolated from marine sponges and other marine organisms such as corals and algae. Pseudovibrio bacteria are a source of antibiotics and other secondary metabolites with the potential to be developed into pharmaceuticals. Moreover, the secondary metabolites they produce are important for their physiology and for interactions with other organisms. Here we expand the genetic toolbox available for Pseudovibrio bacteria by establishing self-replicative vectors that can be used for the expression of, for example, fluorescent proteins. The availability of genetic tools is important to enable us to explore the emerging ecological and biotechnological potentials of Pseudovibrio bacteria.

Incoherent feedback from coupled amino acids and ribosome pools generates damped oscillations in growing E. coli

Current theories of bacterial growth physiology demonstrate impressive predictive power but are often phenomenological, lacking mechanistic detail. Incorporating such details would significantly enhance our ability to predict and control bacterial growth under varying environmental conditions. The "Flux Controlled Regulation" (FCR) model serves as a reference framework, linking ribosome allocation to translation efficiency through a steady-state assumption. However, it neglects ppGpp-mediated nutrient sensing and transcriptional regulation of ribosomal operons. Here, we propose a mechanistic model that extends the FCR framework by incorporating three key components: (i) the amino acid pool, (ii) ppGpp sensing of translation elongation rate, and (iii) transcriptional regulation of protein allocation by ppGpp-sensitive promoters. Our model aligns with observed steady-state growth laws and makes testable predictions for unobserved quantities. We show that during environmental changes, the incoherent feedback between sensing and regulation generates oscillatory relaxation dynamics, a behavior that we support by new and existing experimental data.

Measuring the burden of hundreds of BioBricks defines an evolutionary limit on constructability in synthetic biology

Engineered DNA will slow the growth of a host cell if it redirects limiting resources or otherwise interferes with homeostasis. Escape mutants that alleviate this burden can rapidly evolve and take over cell populations, making genetic engineering less reliable and predictable. Synthetic biologists often use genetic parts encoded on plasmids, but their burden is rarely characterized. We measured how 301 BioBrick plasmids affected Escherichia coli growth and found that 59 (19.6%) were burdensome, primarily because they depleted the limited gene expression resources of host cells. Overall, no BioBricks reduced the growth rate of E. coli by >45%, which agreed with a population genetic model that predicts such plasmids should be unclonable. We made this model available online for education ( https://barricklab.org/burden-model ) and added our burden measurements to the iGEM Registry. Our results establish a fundamental limit on what DNA constructs and genetic modifications can be successfully engineered into cells.

How total mRNA influences cell growth

Experimental observations tracing back to the 1960s imply that ribosome quantities play a prominent role in determining a cell's growth. Nevertheless, in biologically relevant scenarios, growth can also be influenced by the levels of mRNA and RNA polymerase. Here, we construct a quantitative model of biosynthesis providing testable scenarios for these situations. The model explores a theoretically motivated regime where RNA polymerases compete for genes and ribosomes for transcripts and gives general expressions relating growth rate, mRNA concentrations, ribosome, and RNA polymerase levels. On general grounds, the model predicts how the fraction of ribosomes in the proteome depends on total mRNA concentration and inspects an underexplored regime in which the trade-off between transcript levels and ribosome abundances sets the cellular growth rate. In particular, we show that the model predicts and clarifies three important experimental observations, in budding yeast and Escherichia coli bacteria: i) that the growth-rate cost of unneeded protein expression can be affected by mRNA levels, ii) that resource optimization leads to decreasing trends in mRNA levels at slow growth, and iii) that ribosome allocation may increase, stay constant, or decrease, in response to transcription-inhibiting antibiotics. Since the data indicate that a regime of joint limitation may apply in physiological conditions and not only to perturbations, we speculate that this regime is likely self-imposed.

Measuring the burden of hundreds of BioBricks defines an evolutionary limit on constructability in synthetic biology

Engineered DNA will slow the growth of a host cell if it redirects limiting resources or otherwise interferes with homeostasis. Populations of engineered cells can rapidly become dominated by "escape mutants" that evolve to alleviate this burden by inactivating the intended function. Synthetic biologists working with bacteria rely on genetic parts and devices encoded on plasmids, but the burden of different engineered DNA sequences is rarely characterized. We measured how 301 BioBricks on high-copy plasmids affected the growth rate of Escherichia coli. Of these, 59 (19.6%) negatively impacted growth. The burden imposed by engineered DNA is commonly associated with diverting ribosomes or other gene expression factors away from producing endogenous genes that are essential for cellular replication. In line with this expectation, BioBricks exhibiting burden were more likely to contain highly active constitutive promoters and strong ribosome binding sites. By monitoring how much each BioBrick reduced expression of a chromosomal GFP reporter, we found that the burden of most, but not all, BioBricks could be wholly explained by diversion of gene expression resources. Overall, no BioBricks reduced the growth rate of E. coli by >45%, which agreed with a population genetic model that predicts such plasmids should be "unclonable" because escape mutants will take over during growth of a bacterial colony or small laboratory culture from a transformed cell. We made this model available as an interactive web tool for synthetic biology education and added our burden measurements to the iGEM Registry descriptions of each BioBrick.

RNA cis-regulators are important for Streptococcus pneumoniae in vivo success

Bacteria have evolved complex transcriptional regulatory networks, as well as many diverse regulatory strategies at the RNA level, to enable more efficient use of metabolic resources and a rapid response to changing conditions. However, most RNA-based regulatory mechanisms are not well conserved across different bacterial species despite controlling genes important for virulence or essential biosynthetic processes. Here, we characterize the activity of, and assess the fitness benefit conferred by, twelve cis-acting regulatory RNAs (including several riboswitches and a T-box), in the opportunistic pathogen Streptococcus pneumoniae TIGR4. By evaluating native locus mutants of each regulator that result in constitutively active or repressed expression, we establish that growth defects in planktonic culture are associated with constitutive repression of gene expression, while constitutive activation of gene expression is rarely deleterious. In contrast, in mouse nasal carriage and pneumonia models, strains with either constitutively active and repressed gene expression are significantly less fit than matched control strains. Furthermore, two RNA-regulated pathways, FMN synthesis/transport and pyrimidine synthesis/transport display exceptional sensitivity to mis-regulation or constitutive gene repression in both planktonic culture and in vivo environments. Thus, despite lack of obvious phenotypes associated with constitutive gene expression in vitro, the fitness benefit conferred on bacteria via fine-tuned metabolic regulation through cis-acting regulatory RNAs is substantial in vivo, and therefore easily sufficient to drive the evolution and maintenance of diverse RNA regulatory mechanisms.

Transcriptional biomarker discovery toward building a load stress reporting system for engineered Escherichia coli strains

Foreign proteins are produced by introducing synthetic constructs into host bacteria for biotechnology applications. This process can cause resource competition between synthetic circuits and host cells, placing a metabolic burden on the host cells which may result in load stress and detrimental physiological changes. Consequently, the host bacteria can experience slow growth, and the synthetic system may suffer from suboptimal function. To help in the detection of bacterial load stress, we developed machine-learning strategies to select a minimal number of genes that could serve as biomarkers for the design of load stress reporters. We identified pairs of biomarkers that showed discriminative capacity to detect the load stress states induced in 41 engineered Escherichia coli strains.

Selective RNA Processing and Stabilization are Multi-Layer and Stoichiometric Regulators of Gene Expression in Escherichia coli

Selective RNA processing and stabilization (SRPS) facilitates the differential expression of multiple genes in polycistronic operons. However, how the coordinated actions of SRPS-related enzymes affect stoichiometric regulation remains unclear. In the present study, the first genome-wide targetome analysis is reported of these enzymes in Escherichia coli, at a single-nucleotide resolution. A strictly linear relationship is observed between the RNA pyrophosphohydrolase processing ratio and scores assigned to the first three nucleotides of the primary transcript. Stem-loops associated with PNPase targetomes exhibit a folding free energy that is negatively correlated with the termination ratio of PNPase at the 3' end. More than one-tenth of the RNase E processing sites in the 5'-untranslated regions（UTR） form different stem-loops that affect ribosome-binding and translation efficiency. The effectiveness of the SRPS elements is validated using a dual-fluorescence reporter system. The findings highlight a multi-layer and quantitative regulatory method for optimizing the stoichiometric expression of genes in bacteria and promoting the application of SRPS in synthetic biology.

Conditionally unutilized proteins and their profound effects on growth and adaptation across microbial species

Protein synthesis is an important determinant of microbial growth and response that demands a high amount of metabolic and biosynthetic resources. Despite these costs, microbial species from different taxa and habitats massively synthesize proteins that are not utilized in the conditions they currently experience. Based on resource allocation models, recent studies have begun to reconcile the costs and benefits of these conditionally unutilized proteins (CUPs) in the context of varying environmental conditions. Such massive synthesis of CUPs is crucial to consider in different areas of modern microbiology, from the systematic investigation of cell physiology, via the prediction of evolution in laboratory and natural environments, to the rational design of strains in biotechnology applications.

Optimization of whole-cell bacterial bioreporter immobilization on electrospun cellulose acetate (CA) and polycaprolactone (PCL) fibers for arsenic detection

Arsenic contamination is a critical global problem, and its widespread environmental detection is becoming a prominent issue. Herein, electrospun fibers of cellulose acetate (CA) and polycaprolactone (PCL) were successfully fabricated and used as the support material for immobilization of arsenic-sensing bacterial bioreporter for the first time. To date, no attempt has been made to immobilize fluorescent whole-cell bioreporter cells on electrospun fibers for arsenic detection. CA and PCL electrospun fibers were fabricated via traditional electrospinning technique and characterized by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and contact angle meter. Following immobilization of the bacterial bioreporter cells, the immobilized bacteria were also characterized by viability assay using AlamarBlue. The effects of growth phase and cell concentration on the fluorescence response of fiber-immobilized arsenic bioreporters to arsenic were also investigated. After immobilization of arsenic bioreporters on 10 wt% PCL fiber, 91% of bacterial cells remained viable, while this value was 55.4% for cells immobilized on 12.5 wt% CA fiber. Bioreporter cells in the exponential growth phase were shown to be more sensitive to arsenic compared to aged cells. While both the electropsun PCL- and CA-immobilized bioreporters successfully detected 50 and 100 µg/L of arsenite (As (III)) concentrations, the PCL-immobilized bioreporter showed better fluorescence performance which should be investigated in future studies. This study helps to fill some gaps in the literature and demonstrates the potential for using electrospun fiber-immobilized arsenic whole-cell bioreporter for arsenic detection in water.

Feedforward growth rate control mitigates gene activation burden

Heterologous gene activation causes non-physiological burden on cellular resources that cells are unable to adjust to. Here, we introduce a feedforward controller that actuates growth rate upon activation of a gene of interest (GOI) to compensate for such a burden. The controller achieves this by activating a modified SpoT enzyme (SpoTH) with sole hydrolysis activity, which lowers ppGpp level and thus increases growth rate. An inducible RelA+ expression cassette further allows to precisely set the basal level of ppGpp, and thus nominal growth rate, in any bacterial strain. Without the controller, activation of the GOI decreased growth rate by more than 50%. With the controller, we could activate the GOI to the same level without growth rate defect. A cell strain armed with the controller in co-culture enabled persistent population-level activation of a GOI, which could not be achieved by a strain devoid of the controller. The feedforward controller is a tunable, modular, and portable tool that allows dynamic gene activation without growth rate defects for bacterial synthetic biology applications.

Understanding the Impacts of Bacteriophage Viruses: From Laboratory Evolution to Natural Ecosystems

Viruses of bacteriophages (phages) have broad effects on bacterial ecology and evolution in nature that mediate microbial interactions, shape bacterial diversity, and influence nutrient cycling and ecosystem function. The unrelenting impact of phages within the microbial realm is the result, in large part, of their ability to rapidly evolve in response to bacterial host dynamics. The knowledge gained from laboratory systems, typically using pairwise interactions between single-host and single-phage systems, has made clear that phages coevolve with their bacterial hosts rapidly, somewhat predictably, and primarily by counteradapting to host resistance. Recent advancement in metagenomics approaches, as well as a shifting focus toward natural microbial communities and host-associated microbiomes, is beginning to uncover the full picture of phage evolution and ecology within more complex settings. As these data reach their full potential, it will be critical to ask when and how insights gained from studies of phage evolution in vitro can be meaningfully applied to understanding bacteria-phage interactions in nature. In this review, we explore the myriad ways that phages shape and are themselves shaped by bacterial host populations and communities, with a particular focus on observed and predicted differences between the laboratory and complex microbial communities. Expected final online publication date for the Annual Review of Virology, Volume 9 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Improved furfural tolerance in Escherichia coli mediated by heterologous NADH-dependent benzyl alcohol dehydrogenases

While lignocellulose is a promising source of renewable sugars for microbial fermentations, the presence of inhibitory compounds in typical lignocellulosic feedstocks, such as furfural, has hindered their utilisation. In Escherichia coli, a major route of furfural toxicity is the depletion of NADPH pools due to its use as a substrate by the YqhD enzyme that reduces furfural to its less toxic alcohol form. Here, we examine the potential of exploiting benzyl alcohol dehydrogenases as an alternative means to provide this same catalytic function but using the more abundant reductant NADH, as a strategy to increase the capacity for furfural removal. We determine the biochemical properties of three of these enzymes, from Pseudomonas putida, Acinetobacter calcoaceticus, and Burkholderia ambifaria, which all demonstrate furfural reductase activity. Furthermore, we show that the P. putida and B. ambifaria enzymes are able to provide substantial increases in furfural tolerance in vivo, by allowing more rapid conversion to furfuryl alcohol and resumption of growth. The study demonstrates that methods to seek alternative cofactor dependent enzymes can improve the intrinsic robustness of microbial chassis to feedstock inhibitors.

Protein degradation sets the fraction of active ribosomes at vanishing growth

Growing cells adopt common basic strategies to achieve optimal resource allocation under limited resource availability. Our current understanding of such “growth laws” neglects degradation, assuming that it occurs slowly compared to the cell cycle duration. Here we argue that this assumption cannot hold at slow growth, leading to important consequences. We propose a simple framework showing that at slow growth protein degradation is balanced by a fraction of “maintenance” ribosomes. Consequently, active ribosomes do not drop to zero at vanishing growth, but as growth rate diminishes, an increasing fraction of active ribosomes performs maintenance. Through a detailed analysis of compiled data, we show that the predictions of this model agree with data from E. coli and S. cerevisiae. Intriguingly, we also find that protein degradation increases at slow growth, which we interpret as a consequence of active waste management and/or recycling. Our results highlight protein turnover as an underrated factor for our understanding of growth laws across kingdoms.

The idea that simple quantitative relationships relate cell physiology to cellular composition dates back to the 1950s, but the recent years saw a leap in our understanding of such “growth laws”, with relevant implications regarding the interdependence between growth, metabolism and biochemical networks. However, recent works on nutrient-limited growth mainly focused on laboratory conditions that are favourable to growth. Thus, our current mathematical understanding of the growth laws neglects protein degradation, under the argument that it occurs slowly compared to the timescale of the cell cycle. Instead, at slow growth the timescales of mass loss from protein degradation and dilution become comparable. In this work, we propose that protein degradation shapes the quantitative relationships between ribosome allocation and growth rate, and determines a fraction of ribosomes that do not contribute to growth and need to remain active to balance degradation.

Cytolysin A-mediated protein exportation efficiency and its role in enhancing the fitness of live recombinant Salmonella Typhi vaccine strain

The genetic fusion of cytolysin A (ClyA) to heterologous antigen expressed in live Salmonella vector demonstrated efficient translocation into periplasmic space and extracellular medium. Accumulating evidence has shown that clyA-mediated antigen delivery improved growth fitness and enhanced immunogenicity of live vector vaccine, but the factors influencing this protein exportation has not been investigated. In this study, Toxoplasma gondii antigen fused at C-terminal of clyA protein was expressed in live S. Typhi vector via both plasmid and chromosomal-based expressions. The bivalent strains showed comparable growth rates as monovalent strains, but in varies antigen exportation efficiency. ClyA-fusion antigen with positive charges were translocated to the extracellular spaces, whereas those with negative charges were retained in the cytoplasm. Furthermore, excessive cellular resources expenditure on antigen expression, especially antigen with larger size, could limit the clyA-fusion antigen exportation, resulting in undesirable metabolic burden that eventually affects the growth fitness. Altogether, the present work indicates potential linkage of factors mainly on antigen properties and expression platforms that may affect clyA-mediated antigen delivery to enhance the growth fitness of live vector strain.

Translational demand is not a major source of plasmid-associated fitness costs

Plasmids are key drivers of bacterial evolution because they are crucial agents for the horizontal transfer of adaptive traits, such as antibiotic resistance. Most plasmids entail a metabolic burden that reduces the fitness of their host if there is no selection for plasmid-encoded genes. It has been hypothesized that the translational demand imposed by plasmid-encoded genes is a major mechanism driving the fitness cost of plasmids. Plasmid-encoded genes typically present a different codon usage from host chromosomal genes. As a consequence, the translation of plasmid-encoded genes might sequestrate ribosomes on plasmid transcripts, overwhelming the translation machinery of the cell. However, the pervasiveness and origins of the translation-derived costs of plasmids are yet to be assessed. Here, we systematically altered translation efficiency in the host cell to disentangle the fitness effects produced by six natural antibiotic resistance plasmids. We show that limiting translation efficiency either by reducing the number of available ribosomes or their processivity does not increase plasmid costs. Overall, our results suggest that ribosomal paucity is not a major contributor to plasmid fitness costs. This article is part of the theme issue 'The secret lives of microbial mobile genetic elements'.

Determinants of efficient modulation of ribosomal traffic jams

mRNA translation is the process which consumes most of the cellular energy. Thus, this process is under strong evolutionary selection for its optimization and rational optimization or reduction of the translation efficiency can impact the cell growth rate. Algorithms for modulating cell growth rate can have various applications in biotechnology, medicine, and agriculture. In this study, we demonstrate that the analysis of these algorithms can also be used for understanding translation.

We specifically describe and analyze various generic algorithms, based on comprehensive computational models and whole cell simulations of translation, for introducing silent mutations that can either reduce or increase ribosomal traffic jams along the mRNA. As a result, more or less resources are available, for the cell, promoting improved or reduced cells growth-rate, respectively. We then explore the cost of these algorithms' performance, in terms of their computational time, the number of mutations they introduce, the modified genomic region, the effect on local translation rates, and the properties of the modified genes.

Among others, we show that mRNA levels of a gene are much stronger predictors for the effect of its engineering on the ribosomal pool than the ribosomal density of the gene. We also demonstrate that the mutations at the ends of the coding regions have a stronger effect on the ribosomal pool. Furthermore, we report two optimization algorithms that exhibit a tread-off between the number of mutations they introduce and their executing time.

The reported results here are fundamental both for understanding the biophysics and evolution of translation, as well as for developing efficient approaches for its engineering.

Context-Dependent Stability and Robustness of Genetic Toggle Switches with Leaky Promoters

Multistable switches are ubiquitous building blocks in both systems and synthetic biology. Given their central role, it is thus imperative to understand how their fundamental properties depend not only on the tunable biophysical properties of the switches themselves, but also on their genetic context. To this end, we reveal in this article how these factors shape the essential characteristics of toggle switches implemented using leaky promoters such as their stability and robustness to noise, both at single-cell and population levels. In particular, our results expose the roles that competition for scarce transcriptional and translational resources, promoter leakiness, and cell-to-cell heterogeneity collectively play. For instance, the interplay between protein expression from leaky promoters and the associated cost of relying on shared cellular resources can give rise to tristable dynamics even in the absence of positive feedback. Similarly, we demonstrate that while promoter leakiness always acts against multistability, resource competition can be leveraged to counteract this undesirable phenomenon. Underpinned by a mechanistic model, our results thus enable the context-aware rational design of multistable genetic switches that are directly translatable to experimental considerations, and can be further leveraged during the synthesis of large-scale genetic systems using computer-aided biodesign automation platforms.

Emergent correlations in gene expression dynamics as footprints of resource competition

Genetic circuits need a cellular environment to operate in, which naturally couples the circuit function with the overall functionality of gene regulatory network. To execute their functions, all gene circuits draw resources in the form of RNA polymerases, ribosomes, and tRNAs. Recent experiments pointed out that the role of resource competition on synthetic circuit outputs could be immense. However, the effect of complexity of the circuit architecture on resource sharing dynamics is yet unexplored. In this paper, we employ mathematical modelling and in-silico experiments to identify the sources of resource trade-off and to quantify its impact on the function of a genetic circuit, keeping our focus on regulation of immediate downstream proteins, which are often used as protein read-outs. We show that estimating gene expression dynamics from readings of downstream protein data might be unreliable when the resource is limited and ribosome affinities are asymmetric. We focus on the impact of mRNA copy number and ribosome binding site (RBS) strength on the nonlinear isocline that emerges with two regimes, prominently separated by a tipping point, and study how correlation and competition dominate each other depending on various circuit parameters. Focusing further on genetic toggle circuit, we have identified major effects of resource competition in this model motif and quantified the observations. The observations are testable in wet-lab experiments, as all the parameters chosen are experimentally relevant.

Analysis of 11,430 recombinant protein production experiments reveals that protein yield is tunable by synonymous codon changes of translation initiation sites

Recombinant protein production is a key process in generating proteins of interest in the pharmaceutical industry and biomedical research. However, about 50% of recombinant proteins fail to be expressed in a variety of host cells. Here we show that the accessibility of translation initiation sites modelled using the mRNA base-unpairing across the Boltzmann's ensemble significantly outperforms alternative features. This approach accurately predicts the successes or failures of expression experiments, which utilised Escherichia coli cells to express 11,430 recombinant proteins from over 189 diverse species. On this basis, we develop TIsigner that uses simulated annealing to modify up to the first nine codons of mRNAs with synonymous substitutions. We show that accessibility captures the key propensity beyond the target region (initiation sites in this case), as a modest number of synonymous changes is sufficient to tune the recombinant protein expression levels. We build a stochastic simulation model and show that higher accessibility leads to higher protein production and slower cell growth, supporting the idea of protein cost, where cell growth is constrained by protein circuits during overexpression.

Technological tools and strategies for culturing human gut microbiota in engineered in vitro models

The gut microbiota directly impacts the pathophysiology of different human body districts. Consequently, microbiota investigation is an hot topic of research and its in vitro culture has gained extreme interest in different fields. However, the high sensitivity of microbiota to external stimuli, such as sampling procedure, and the physicochemical complexity of the gut environment make its in vitro culture a challenging task. New engineered microfluidic gut-on-a-chip devices have the potential to model some important features of the intestinal structure, but they are usually unable to sustain culture of microbiota over an extended period of time. The integration of gut-on-a-chip devices with bioreactors for continuous bacterial culture would lead to fast advances in the study of microbiota-host crosstalk. In this review, we summarize the main technologies for the continuous culture of microbiota as upstream systems to be coupled with microfluidic devices to study bacteria-host cells communication. The engineering of integrated microfluidic platforms, capable of sustaining both anaerobic and aerobic cultures, would be the starting point to unveil complex biological phenomena proper of the microbiota-host crosstalks, paving to way to multiple research and technological applications.

Properties affecting transfer and expression of degradative plasmids for the purpose of bioremediation

Plasmids, circular DNA that exist and replicate outside of the host chromosome, have been important in the spread of non-essential genes as well as the rapid evolution of prokaryotes. Recent advances in environmental engineering have aimed to utilize the mobility of plasmids carrying degradative genes to disseminate them into the environment for cost-effective and environmentally friendly remediation of harmful contaminants. Here, we review the knowledge surrounding plasmid transfer and the conditions needed for successful transfer and expression of degradative plasmids. Both abiotic and biotic factors have a great impact on the success of degradative plasmid transfer and expression of the degradative genes of interest. Properties such as ecological growth strategies of bacteria may also contribute to plasmid transfer and may be an important consideration for bioremediation applications. Finally, the methods for detection of conjugation events have greatly improved and the application of these tools can help improve our understanding of conjugation in complex communities. However, it remains clear that more methods for in situ detection of plasmid transfer are needed to help detangle the complexities of conjugation in natural environments to better promote a framework for precision bioremediation.

Emergent Damped Oscillation Induced by Nutrient-Modulating Growth Feedback

Growth feedback, the inherent coupling between the synthetic gene circuit and the host cell growth, could significantly change the circuit behaviors. Previously, a diverse array of emergent behaviors, such as growth bistability, enhanced ultrasensitivity, and topology-dependent memory loss, were reported to be induced by growth feedback. However, the influence of the growth feedback on the circuit functions remains underexplored. Here, we reported an unexpected damped oscillatory behavior of a self-activation gene circuit induced by nutrient-modulating growth feedback. Specifically, after dilution of the activated self-activation switch into the fresh medium with moderate nutrients, its gene expression first decreases as the cell grows and then shows a significant overshoot before it reaches the steady state, leading to damped oscillation dynamics. Fitting the data with a coarse-grained model suggests a nonmonotonic growth-rate regulation on gene production rate. The underlying mechanism of the oscillation was demonstrated by a molecular mathematical model, which includes the ribosome allocation toward gene production, cell growth, and cell maintenance. Interestingly, the model predicted a counterintuitive dependence of oscillation amplitude on the nutrition level, where the highest peak was found in the medium with moderate nutrients, but was not observed in rich nutrients. We experimentally verified this prediction by tuning the nutrient level in the culture medium. We did not observe significant oscillatory behavior for the toggle switch, suggesting that the emergence of damped oscillatory behavior depends on circuit network topology. Our results demonstrated a new nonlinear emergent behavior mediated by growth feedback, which depends on the ribosome allocation between gene circuit and cell growth.

Recent advances in tuning the expression and regulation of genes for constructing microbial cell factories

To overcome environmental problems caused by the use of fossil resources, microbial cell factories have become a promising technique for the sustainable and eco-friendly development of valuable products from renewable resources. Constructing microbial cell factories with high titers, yields, and productivity requires a balance between growth and production; to this end, tuning gene expression and regulation is necessary to optimise and precisely control complicated metabolic fluxes. In this article, we review the current trends and advances in tuning gene expression and regulation and consider their engineering at each of the three stages of gene regulation: genomic, mRNA, and protein. In particular, the technological approaches utilised in a diverse range of genetic-engineering-based tools for the construction of microbial cell factories are reviewed and representative applications of these strategies are presented. Finally, the prospects for strategies and systems for tuning gene expression and regulation are discussed.

Improved Yield of Recombinant Protein via Flagella Regulator Deletion in Escherichia coli

Protein production requires a significant amount of intracellular energy. Eliminating the flagella has been proposed to help Escherichia coli improve protein production by reducing energy consumption. In this study, the gene encoding a subunit of FlhC, a master regulator of flagella assembly, was deleted to reduce the expression of flagella-related genes. FlhC knockout in the ptsG-deleted strain triggered significant growth retardation with increased ATP levels and a higher NADPH/NADP⁺ ratio. Metabolic flux analysis using a ¹³C-labeled carbon substrate showed increased fluxes toward the pentose phosphate and tricarboxylic acid cycle pathways in the flhC- and ptsG-deleted strains. Introduction of a high copy number plasmid or overexpression of the recombinant protein in this strain restored growth rate without increasing glucose consumption. These results suggest that the metabolic burden caused by flhC deletion was resolved by recombinant protein production. The recombinant enhanced green fluorescent protein yield per glucose consumption increased 1.81-fold in the flhC mutant strain. Thus, our study demonstrates that high-yield production of the recombinant protein was achieved with reduced flagella formation.

Stability and Robustness of Unbalanced Genetic Toggle Switches in the Presence of Scarce Resources

While the vision of synthetic biology is to create complex genetic systems in a rational fashion, system-level behaviors are often perplexing due to the context-dependent dynamics of modules. One major source of context-dependence emerges due to the limited availability of shared resources, coupling the behavior of disconnected components. Motivated by the ubiquitous role of toggle switches in genetic circuits ranging from controlling cell fate differentiation to optimizing cellular performance, here we reveal how their fundamental dynamic properties are affected by competition for scarce resources. Combining a mechanistic model with nullcline-based stability analysis and potential landscape-based robustness analysis, we uncover not only the detrimental impacts of resource competition, but also how the unbalancedness of the switch further exacerbates them. While in general both of these factors undermine the performance of the switch (by pushing the dynamics toward monostability and increased sensitivity to noise), we also demonstrate that some of the unwanted effects can be alleviated by strategically optimized resource competition. Our results provide explicit guidelines for the context-aware rational design of toggle switches to mitigate our reliance on lengthy and expensive trial-and-error processes, and can be seamlessly integrated into the computer-aided synthesis of complex genetic systems.

Engineering Sensors for Gene Expression Burden

RNA-seq enables the analysis of gene expression profiles across different conditions and organisms. Gene expression burden slows down growth, which results in poor predictability of gene constructs and product yields. Here, we describe how we applied RNA-seq to study the transcriptional profiles of Escherichia coli when burden is elicited during heterologous gene expression. We then present how we selected early responsive promoters from our RNA-seq results to design sensors for gene expression burden. Finally, we describe how we used one of these sensors to develop a burden-driven feedback regulator to improve cellular fitness in engineered E. coli.

Algorithms for ribosome traffic engineering and their potential in improving host cells' titer and growth rate

mRNA translation is a fundamental cellular process consuming most of the intracellular energy; thus, it is under extensive evolutionary selection for optimization, and its efficiency can affect the host's growth rate. We describe a generic approach for improving the growth rate (fitness) of any organism by introducing synonymous mutations based on comprehensive computational models. The algorithms introduce silent mutations that may improve the allocation of ribosomes in the cells via the decreasing of their traffic jams during translation respectively. As a result, resources availability in the cell changes leading to improved growth-rate. We demonstrate experimentally the implementation of the method on Saccharomyces cerevisiae: we show that by introducing a few mutations in two computationally selected genes the mutant's titer increased. Our approach can be employed for improving the growth rate of any organism providing the existence of data for inferring models, and with the relevant genomic engineering tools; thus, it is expected to be extremely useful in biotechnology, medicine, and agriculture.

Integration of absolute multi-omics reveals dynamic protein-to-RNA ratios and metabolic interplay within mixed-domain microbiomes

While the field of microbiology has adapted to the study of complex microbiomes via modern meta-omics techniques, we have not updated our basic knowledge regarding the quantitative levels of DNA, RNA and protein molecules within a microbial cell, which ultimately control cellular function. Here we report the temporal measurements of absolute RNA and protein levels per gene within a mixed bacterial-archaeal consortium. Our analysis of this data reveals an absolute protein-to-RNA ratio of 10²–10⁴ for bacterial populations and 10³–10⁵ for an archaeon, which is more comparable to Eukaryotic representatives’ humans and yeast. Furthermore, we use the linearity between the metaproteome and metatranscriptome over time to identify core functional guilds, hence using a fundamental biological feature (i.e., RNA/protein levels) to highlight phenotypical complementarity. Our findings show that upgrading multi-omic toolkits with traditional absolute measurements unlocks the scaling of core biological questions to dynamic and complex microbiomes, creating a deeper insight into inter-organismal relationships that drive the greater community function.

Here, the authors perform a temporal multi-omic analysis of a minimalistic cellulose-degrading and methane-producing consortium at the strain level and estimate protein-to-RNA ratios and RNA-protein dynamics of the community simultaneously over time.

Gene Transcription as a Limiting Factor in Protein Production and Cell Growth

Cell growth is driven by the synthesis of proteins, genes, and other cellular components. Defining processes that limit biosynthesis rates is fundamental for understanding the determinants of cell physiology. Here, we analyze the consequences of engineering cells to express extremely high levels of mCherry proteins, as a tool to define limiting processes that fail to adapt upon increasing biosynthetic demands. Protein-burdened cells were transcriptionally and phenotypically similar to mutants of the Mediator, a transcription coactivator complex. However, our binding data suggest that the Mediator was not depleted from endogenous promoters. Burdened cells showed an overall increase in the abundance of the majority of endogenous transcripts, except for highly expressed genes. Our results, supported by mathematical modeling, suggest that wild-type cells transcribe highly expressed genes at the maximal possible rate, as defined by the transcription machinery’s physical properties. We discuss the possible cellular benefit of maximal transcription rates to allow a coordinated optimization of cell size and cell growth.

Verrucomicrobia use hundreds of enzymes to digest the algal polysaccharide fucoidan

Brown algae are important players in the global carbon cycle by fixing carbon dioxide into 1 Gt of biomass annually, yet the fate of fucoidan-their major cell wall polysaccharide-remains poorly understood. Microbial degradation of fucoidans is slower than that of other polysaccharides, suggesting that fucoidans are more recalcitrant and may sequester carbon in the ocean. This may be due to the complex, branched and highly sulfated structure of fucoidans, which also varies among species of brown algae. Here, we show that 'Lentimonas' sp. CC4, belonging to the Verrucomicrobia, acquired a remarkably complex machinery for the degradation of six different fucoidans. The strain accumulated 284 putative fucoidanases, including glycoside hydrolases, sulfatases and carbohydrate esterases, which are primarily located on a 0.89-megabase pair plasmid. Proteomics reveals that these enzymes assemble into substrate-specific pathways requiring about 100 enzymes per fucoidan from different species of brown algae. These enzymes depolymerize fucoidan into fucose, which is metabolized in a proteome-costly bacterial microcompartment that spatially constrains the metabolism of the toxic intermediate lactaldehyde. Marine metagenomes and microbial genomes show that Verrucomicrobia including 'Lentimonas' are abundant and highly specialized degraders of fucoidans and other complex polysaccharides. Overall, the complexity of the pathways underscores why fucoidans are probably recalcitrant and more slowly degraded, since only highly specialized organisms can effectively degrade them in the ocean.

A Novel pH-Regulated, Unusual 603 bp Overlapping Protein Coding Gene pop Is Encoded Antisense to ompA in Escherichia coli O157:H7 (EHEC)

Antisense transcription is well known in bacteria. However, translation of antisense RNAs is typically not considered, as the implied overlapping coding at a DNA locus is assumed to be highly improbable. Therefore, such overlapping genes are systematically excluded in prokaryotic genome annotation. Here we report an exceptional 603 bp long open reading frame completely embedded in antisense to the gene of the outer membrane protein ompA. An active σ70 promoter, transcription start site (TSS), Shine-Dalgarno motif and rho-independent terminator were experimentally validated, providing evidence that this open reading frame has all the structural features of a functional gene. Furthermore, ribosomal profiling revealed translation of the mRNA, the protein was detected in Western blots and a pH-dependent phenotype conferred by the protein was shown in competitive overexpression growth experiments of a translationally arrested mutant versus wild type. We designate this novel gene pop (pH-regulated overlapping protein-coding gene), thus adding another example to the growing list of overlapping, protein coding genes in bacteria.

The evolutionary puzzle of Escherichia coli ST131

The abrupt expansion of Escherichia coli sequence type (ST) 131 is unmatched among Gram negative bacteria. In many ways, ST131 can be considered a real-world model for the complexities involved in the evolution of a multidrug resistant pathogen. While much progress has been made on our insights into the organism's population structure, pathogenicity and drug resistance profile, significant gaps in our knowledge remain. Whole genome studies have shed light on key mutations and genes that have been selected against the background of antibiotics, but in most cases such events are inferred and not supported by experimental data. Notable examples include the unknown fitness contribution made by specific plasmids, genomic islands and compensatory mutations. Furthermore, questions remain like why this organism in particular achieved such considerable success in such a short time span, compared to other more pathogenic and resistant clones. Herein, we document what is known regarding the genetics of this organism since its first description in 2008, but also highlight where work remains to be done for a truly comprehensive understanding of the biology of ST131, in order to account for its dramatic rise to prominence.

The relation between crosstalk and gene regulation form revisited

Genes differ in the frequency at which they are expressed and in the form of regulation used to control their activity. In particular, positive or negative regulation can lead to activation of a gene in response to an external signal. Previous works proposed that the form of regulation of a gene correlates with its frequency of usage: positive regulation when the gene is frequently expressed and negative regulation when infrequently expressed. Such network design means that, in the absence of their regulators, the genes are found in their least required activity state, hence regulatory intervention is often necessary. Due to the multitude of genes and regulators, spurious binding and unbinding events, called “crosstalk”, could occur. To determine how the form of regulation affects the global crosstalk in the network, we used a mathematical model that includes multiple regulators and multiple target genes. We found that crosstalk depends non-monotonically on the availability of regulators. Our analysis showed that excess use of regulation entailed by the formerly suggested network design caused high crosstalk levels in a large part of the parameter space. We therefore considered the opposite ‘idle’ design, where the default unregulated state of genes is their frequently required activity state. We found, that ‘idle’ design minimized the use of regulation and thus minimized crosstalk. In addition, we estimated global crosstalk of S. cerevisiae using transcription factors binding data. We demonstrated that even partial network data could suffice to estimate its global crosstalk, suggesting its applicability to additional organisms. We found that S. cerevisiae estimated crosstalk is lower than that of a random network, suggesting that natural selection reduces crosstalk. In summary, our study highlights a new type of protein production cost which is typically overlooked: that of regulatory interference caused by the presence of excess regulators in the cell. It demonstrates the importance of whole-network descriptions, which could show effects missed by single-gene models.

Genes differ in the frequency at which they are expressed and in the form of regulation used to control their activity. The basic level of regulation is mediated by different types of DNA-binding proteins, where each type regulates particular gene(s). We distinguish between two basic forms of regulation: positive—if a gene is activated by the binding of its regulatory protein, and negative—if it is active unless bound by its regulatory protein. Due to the multitude of genes and regulators, spurious binding and unbinding events, called “crosstalk”, could occur. How does the form of regulation, positive or negative, affect the extent of regulatory crosstalk? To address this question, we used a mathematical model integrating many genes and many regulators. As intuition suggests, we found that in most of the parameter space, crosstalk increased with the availability of regulators. We propose, that crosstalk is usually reduced when networks are designed such that minimal regulation is needed, which we call the ‘idle’ design. In other words: a frequently needed gene will use negative regulation and conversely, a scarcely needed gene will employ positive regulation. In both cases, the requirement for the regulators is minimized. In addition, we demonstrate how crosstalk can be calculated from available datasets and discuss the technical challenges in such calculation, specifically data incompleteness.

Yeast systems biology in understanding principles of physiology underlying complex human diseases

Complex human diseases commonly arise from deregulation of cell growth, metabolism, and/or gene expression. Yeast is a eukaryal model organism that is widely used to study these processes. Yeast systems biology benefits from the ability to exert fine experimental control over the cell growth rate and nutrient composition, which allows orthogonal experimental design and generation of multi-omics data at high resolution. This has led to several insights on the principles of cellular physiology, including many cellular processes associated with complex human diseases. Here we review these biological insights together with experimental and modeling approaches developed in yeast to study systems biology. The role of yeast systems biology to further advance systems and personalized therapies for complex diseases is discussed.

The Rocky Road From Fed-Batch to Continuous Processing With E. coli

Escherichia coli still serves as a beloved workhorse for the production of many biopharmaceuticals as it fulfills essential criteria, such as having fast doubling times, exhibiting a low risk of contamination, and being easy to upscale. Most industrial processes in E. coli are carried out in fed-batch mode. However, recent trends show that the biotech industry is moving toward time-independent processing, trying to improve the space-time yield, and especially targeting constant quality attributes. In the 1950s, the term "chemostat" was introduced for the first time by Novick and Szilard, who followed up on the previous work performed by Monod. Chemostat processing resulted in a major hype 10 years after its official introduction. However, enthusiasm decreased as experiments suffered from genetic instabilities and physiology issues. Major improvements in strain engineering and the usage of tunable promotor systems facilitated chemostat processes. In addition, critical process parameters have been identified, and the effects they have on diverse quality attributes are understood in much more depth, thereby easing process control. By pooling the knowledge gained throughout the recent years, new applications, such as parallelization, cascade processing, and population controls, are applied nowadays. However, to control the highly heterogeneous cultivation broth to achieve stable productivity throughout long-term cultivations is still tricky. Within this review, we discuss the current state of E. coli fed-batch process understanding and its tech transfer potential within continuous processing. Furthermore, the achievements in the continuous upstream applications of E. coli and the continuous downstream processing of intracellular proteins will be discussed.

A Bacterial Growth Law out of Steady State

Bacterial growth follows simple laws in constant conditions. However, bacteria in nature often face fluctuating environments. We therefore ask whether there are growth laws that apply to changing environments. We derive a law for upshifts using an optimal resource-allocation model: the post-shift growth rate equals the geometrical mean of the pre-shift growth rate and the growth rate on saturating carbon. We test this using chemostat and batch culture experiments, as well as previous data from several species. The increase in growth rate after an upshift indicates that ribosomes have spare capacity (SC). We demonstrate theoretically that SC has the cost of slow steady-state growth but is beneficial after an upshift because it prevents large overshoots in intracellular metabolites and allows rapid response to change. We also provide predictions for downshifts. The present study quantifies the optimal degree of SC, which rises the slower the growth rate, and suggests that SC can be precisely regulated.

Trade-offs between gene expression, growth and phenotypic diversity in microbial populations

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Limitations in molecular resources for gene expression influence bacterial physiology.

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Bacteria optimise trade-offs between resource allocation and growth.

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Resource allocation plays a role in the emergence of phenotypic heterogeneity.

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Trade-offs between bet-hedging and growth can be harnessed in biotechnology.

Bacterial cells have a limited number of resources that can be allocated for gene expression. The intracellular competition for these resources has an impact on the cell physiology. Bacteria have evolved mechanisms to optimize resource allocation in a variety of scenarios, showing a trade-off between the resources used to maximise growth (e.g. ribosome synthesis) and the rest of cellular functions. Limitations in gene expression also play a role in generating phenotypic diversity, which is advantageous in fluctuating environments, at the expenses of decreasing growth rates. Our current understanding of these trade-offs can be exploited for biotechnological applications benefiting from the selective manipulation of the allocation of resources.

Start-Stop Assembly: a functionally scarless DNA assembly system optimized for metabolic engineering

DNA assembly allows individual DNA constructs or libraries to be assembled quickly and reliably. Most methods are either: (i) Modular, easily scalable and suitable for combinatorial assembly, but leave undesirable 'scar' sequences; or (ii) bespoke (non-modular), scarless but less suitable for construction of combinatorial libraries. Both have limitations for metabolic engineering. To overcome this trade-off we devised Start-Stop Assembly, a multi-part, modular DNA assembly method which is both functionally scarless and suitable for combinatorial assembly. Crucially, 3 bp overhangs corresponding to start and stop codons are used to assemble coding sequences into expression units, avoiding scars at sensitive coding sequence boundaries. Building on this concept, a complete DNA assembly framework was designed and implemented, allowing assembly of up to 15 genes from up to 60 parts (or mixtures); monocistronic, operon-based or hybrid configurations; and a new streamlined assembly hierarchy minimizing the number of vectors. Only one destination vector is required per organism, reflecting our optimization of the system for metabolic engineering in diverse organisms. Metabolic engineering using Start-Stop Assembly was demonstrated by combinatorial assembly of carotenoid pathways in Escherichia coli resulting in a wide range of carotenoid production and colony size phenotypes indicating the intended exploration of design space.

Inactivation of a Mismatch-Repair System Diversifies Genotypic Landscape of Escherichia coli During Adaptive Laboratory Evolution

Adaptive laboratory evolution (ALE) is used to find causal mutations that underlie improved strain performance under the applied selection pressure. ALE studies have revealed that mutator populations tend to outcompete their non-mutator counterparts following the evolutionary trajectory. Among them, mutS-inactivated mutator cells, characterize d by a dysfunctional methyl-mismatch repair system, are frequently found in ALE experiments. Here, we examined mutS inactivation as an approach to facilitate ALE of Escherichia coli. The wild-type E. coli MG1655 and mutS knock-out derivative (ΔmutS) were evolved in parallel for 800 generations on lactate or glycerol minimal media in a serial-transfer experiment. Whole-genome re-sequencing of each lineage at 100-generation intervals revealed that (1) mutations emerge rapidly in the ΔmutS compared to in the wild-type strain; (2) mutations were more than fourfold higher in the ΔmutS strain at the end-point populations compared to the wild-type strain; and (3) a significant number of random mutations accumulated in the ΔmutS strains. We then measured the fitness of the end-point populations on an array of non-adaptive carbon sources. Interestingly, collateral fitness increases on non-adaptive carbon sources were more pronounced in the ΔmutS strains than the parental strain. Fitness measurement of single mutants revealed that the collateral fitness increase seen in the mutator lineages can be attributed to a pool of random mutations. Together, this study demonstrates that short-term mutator ALE extensively expands possible genotype space, resulting in versatile bacteria with elevated fitness levels across various carbon sources.

Just the Two of Us? A Family of Pseudomonas Megaplasmids Offers a Rare Glimpse into the Evolution of Large Mobile Elements

Pseudomonads are ubiquitous group of environmental proteobacteria, well known for their roles in biogeochemical cycling, in the breakdown of xenobiotic materials, as plant growth promoters, and as pathogens of a variety of host organisms. We have previously identified a large megaplasmid present within one isolate of the plant pathogen Pseudomonas syringae, and here we report that a second member of this megaplasmid family is found within an environmental Pseudomonad isolate most closely related to Pseudomonas putida. Many of the shared genes are involved in critical cellular processes like replication, transcription, translation, and DNA repair. We argue that presence of these shared pathways sheds new light on discussions about the types of genes that undergo horizontal gene transfer (i.e., the complexity hypothesis) as well as the evolution of pangenomes. Furthermore, although both megaplasmids display a high level of synteny, genes that are shared differ by over 50% on average at the amino acid level. This combination of conservation in gene order despite divergence in gene sequence suggests that this Pseudomonad megaplasmid family is relatively old, that gene order is under strong selection within this family, and that there are likely many more members of this megaplasmid family waiting to be found in nature.

Salicylate Increases Fitness Cost Associated with MarA-Mediated Antibiotic Resistance

Antibiotic resistance is generally associated with a fitness deficit resulting from the burden of producing and maintaining resistance machinery. This additional cost suggests that resistant bacteria will be outcompeted by susceptible bacteria in conditions without antibiotics. However, in practice, this process is slow in part because of regulation that minimizes expression of these genes in the absence of antibiotics. This suggests that if it were possible to turn on their expression, the cost would increase, thereby accelerating removal of resistant strains. Experimental and theoretical studies have shown that environmental chemicals can change the fitness cost associated with resistance and therefore have a significant impact on population dynamics. The multiple antibiotic resistance activator (MarA) is a clinically important regulator in Escherichia coli that activates downstream genes to increase resistance against multiple classes of antibiotics. Salicylate is an inducer of MarA that can be found in the environment and derepresses marA's expression. In this study, we sought to unravel the interplay between salicylate and the fitness cost of MarA-mediated antibiotic resistance. Using salicylate as an inducer of MarA, we found that a wide spectrum of concentrations can increase burden in resistant strains compared to susceptible strains. Induction resulted in rapid exclusion of resistant bacteria from mixed populations of antibiotic-resistant and susceptible cells. A mathematical model captures the process and predicts its effect in various environmental conditions. Our work provides a quantitative understanding of salicylate exposure on the fitness of different MarA variants and suggests that salicylate can lead to selection against MarA-mediated resistant strains. More generally, our findings show that natural inducers may serve to bias population membership and could impact antibiotic resistance and other important phenotypes.

Individual- versus group-optimality in the production of secreted bacterial compounds

How unicellular organisms optimize the production of compounds is a fundamental biological question. While it is typically thought that production is optimized at the individual-cell level, secreted compounds could also allow for optimization at the group level, leading to a division of labor where a subset of cells produces and shares the compound with everyone. Using mathematical modeling, we show that the evolution of such division of labor depends on the cost function of compound production. Specifically, for any trait with saturating benefits, linear costs promote the evolution of uniform production levels across cells. Conversely, production costs that diminish with higher output levels favor the evolution of specialization-especially when compound shareability is high. When experimentally testing these predictions with pyoverdine, a secreted iron-scavenging compound produced by Pseudomonas aeruginosa, we found linear costs and, consistent with our model, detected uniform pyoverdine production levels across cells. We conclude that for shared compounds with saturating benefits, the evolution of division of labor is facilitated by a diminishing cost function. More generally, we note that shifts in the level of selection from individuals to groups do not solely require cooperation, but critically depend on mechanistic factors, including the distribution of compound synthesis costs.

Added value of autoregulation and multi-step kinetics of transcription initiation

Bacterial gene expression regulation occurs mostly during transcription, which has two main rate-limiting steps: the close complex formation, when the RNA polymerase binds to an active promoter, and the subsequent open complex formation, after which it follows elongation. Tuning these steps' kinetics by the action of e.g. transcription factors, allows for a wide diversity of dynamics. For example, adding autoregulation generates single-gene circuits able to perform more complex tasks. Using stochastic models of transcription kinetics with empirically validated parameter values, we investigate how autoregulation and the multi-step transcription initiation kinetics of single-gene autoregulated circuits can be combined to fine-tune steady state mean and cell-to-cell variability in protein expression levels, as well as response times. Next, we investigate how they can be jointly tuned to control complex behaviours, namely, time counting, switching dynamics and memory storage. Overall, our finding suggests that, in bacteria, jointly regulating a single-gene circuit's topology and the transcription initiation multi-step dynamics allows enhancing complex task performance.

Adapting to stress - chaperome networks in cancer

In this Opinion article, we aim to address how cells adapt to stress and the repercussions chronic stress has on cellular function. We consider acute and chronic stress-induced changes at the cellular level, with a focus on a regulator of cellular stress, the chaperome, which is a protein assembly that encompasses molecular chaperones, co-chaperones and other co-factors. We discuss how the chaperome takes on distinct functions under conditions of stress that are executed in ways that differ from the one-on-one cyclic, dynamic functions exhibited by distinct molecular chaperones. We argue that through the formation of multimeric stable chaperome complexes, a state of chaperome hyperconnectivity, or networking, is gained. The role of these chaperome networks is to act as multimolecular scaffolds, a particularly important function in cancer, where they increase the efficacy and functional diversity of several cellular processes. We predict that these concepts will change how we develop and implement drugs targeting the chaperome to treat cancer.

Exploring of the feature space of de novo developed post-transcriptional riboregulators

Metabolic engineering increasingly depends upon RNA technology to customly rewire the metabolism to maximize production. To this end, pure riboregulators allow dynamic gene repression without the need of a potentially burdensome coexpressed protein like typical Hfq binding small RNAs and clustered regularly interspaced short palindromic repeats technology. Despite this clear advantage, no clear general design principles are available to de novo develop repressing riboregulators, limiting the availability and the reliable development of these type of riboregulators. Here, to overcome this lack of knowledge on the functionality of repressing riboregulators, translation inhibiting RNAs are developed from scratch. These de novo developed riboregulators explore features related to thermodynamical and structural factors previously attributed to translation initiation modulation. In total, 12 structural and thermodynamic features were defined of which six features were retained after removing correlations from an in silico generated riboregulator library. From this translation inhibiting RNA library, 18 riboregulators were selected using a experimental design and subsequently constructed and co-expressed with two target untranslated regions to link the translation inhibiting RNA features to functionality. The pure riboregulators in the design of experiments showed repression down to 6% of the original protein expression levels, which could only be partially explained by a ordinary least squares regression model. To allow reliable forward engineering, a partial least squares regression model was constructed and validated to link the properties of translation inhibiting RNA riboregulators to gene repression. In this model both structural and thermodynamic features were important for efficient gene repression by pure riboregulators. This approach enables a more reliable de novo forward engineering of effective pure riboregulators, which further expands the RNA toolbox for gene expression modulation.

To allow reliable forward engineering of microbial cell factories, various metabolic engineering efforts rely on RNA-based technology. As such, programmable riboregulators allow dynamic control over gene expression. However, no clear design principles exist for de novo developed repressing riboregulators, which limits their applicability. Here, various engineering principles are identified and computationally explored. Subsequently, various design criteria are used in an experimental design, which were explored in an in vivo study. This resulted in a regression model that enables a more reliable computational design of repression small RNAs.

Estimating the protein burden limit of yeast cells by measuring the expression limits of glycolytic proteins

The ultimate overexpression of a protein could cause growth defects, which are known as the protein burden. However, the expression limit at which the protein-burden effect is triggered is still unclear. To estimate this limit, we systematically measured the overexpression limits of glycolytic proteins in Saccharomyces cerevisiae. The limits of some glycolytic proteins were up to 15% of the total cellular protein. These limits were independent of the proteins’ catalytic activities, a finding that was supported by an in silico analysis. Some proteins had low expression limits that were explained by their localization and metabolic perturbations. The codon usage should be highly optimized to trigger the protein-burden effect, even under strong transcriptional induction. The S–S-bond-connected aggregation mediated by the cysteine residues of a protein might affect its expression limit. Theoretically, only non-harmful proteins could be expressed up to the protein-burden limit. Therefore, we established a framework to distinguish proteins that are harmful and non-harmful upon overexpression.

If a cell makes too much of a given protein, it can sometimes cause problems and impair the cell’s growth. Overproducing some proteins may deplete the cell’s limited resources, meaning it does not have enough to make other more essential proteins. This phenomenon is known as the protein burden effect. Theoretically, only harmless proteins can be overproduced up to a level where growth would be impaired in this way. Conversely, if an overproduced protein causes harm before it becomes a burden on resources, scientists must consider other mechanisms to explain the cell’s problems, namely that the protein itself is harmful.

Knowing the ultimate level of protein production that could cause the protein burden effect – the protein burden limit – would allow scientists to distinguish between harmful and non-harmful proteins. However, to date, this limit had not been defined for any cell.

Eguchi et al. have now tried to estimate the protein burden limit for budding yeast – one of the best-studied experimental organisms. The experiments first focused on enzymes involved in alcoholic fermentation because they were expected to be non-harmful. Some of these enzymes were overproduced to the level were the made up 15% of all the cell’s proteins before they started to cause growth defects. The same results were seen with versions of the enzymes that had been mutated to be less active, leading Eguchi et al. to conclude that this level is the protein burden limit.

In other experiments, harmful enzymes could only be overproduced to levels that were far less than this proposed protein burden limit. These enzymes caused problems for the yeast in several ways, including interfering with biochemical reactions and forming large aggregates in the cell. Lastly, Eguchi et al. looked at the yeast’s genetic code and saw that most of its genes seemed to have evolved to specifically limit the production of proteins to a level that would avoid the unwanted protein burden effect.

Together these findings establish a framework to clearly distinguish between harmful and non-harmful proteins. This framework will be useful to understand the different reasons why the overproduction of certain proteins, which is seen in neurodegenerative diseases and cancer cells, can cause problems for cells.