Optimal protein production by a synthetic microbial consortium: Coexistence, distribution of labor, and syntrophy

The bacterium E. coli is widely used to produce recombinant proteins such as growth hormone and insulin. One inconvenience with E. coli cultures is the secretion of acetate through overflow metabolism. Acetate inhibits cell growth and represents a carbon diversion, which results in several negative effects on protein production. One way to overcome this problem is the use of a synthetic consortium of two different E. coli strains, one producing recombinant proteins and one reducing the acetate concentration. In this paper, we study a chemostat model of such a synthetic community where both strains are allowed to produce recombinant proteins. We give necessary and sufficient conditions for the existence of a coexistence equilibrium and show that it is unique. Based on this equilibrium, we define a multi-objective optimization problem for the maximization of two important bioprocess performance metrics, process yield and productivity. Solving numerically this problem, we find the best available trade-offs between the metrics. Under optimal operation of the mixed community, both strains must produce the protein of interest, and not only one (distribution instead of division of labor). Moreover, in this regime acetate secretion by one strain is necessary for the survival of the other (syntrophy). The results thus illustrate how complex multi-level dynamics shape the optimal production of recombinant proteins by synthetic microbial consortia.

ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model

Genome-scale metabolic models (GEMs) have been widely used for the phenotypic prediction of microorganisms. However, the lack of other constraints in the stoichiometric model often leads to a large metabolic solution space being inaccessible. Inspired by previous studies that take an allocation of macromolecule resources into account, we developed a simplified Python-based workflow for constructing enzymatic constrained metabolic network model (ECMpy) and constructed an enzyme-constrained model for Escherichia coli (eciML1515) by directly adding a total enzyme amount constraint in the latest version of GEM for E. coli (iML1515), considering the protein subunit composition in the reaction, and automated calibration of enzyme kinetic parameters. Using eciML1515, we predicted the overflow metabolism of E. coli and revealed that redox balance was the key reason for the difference between E. coli and Saccharomyces cerevisiae in overflow metabolism. The growth rate predictions on 24 single-carbon sources were improved significantly when compared with other enzyme-constrained models of E. coli. Finally, we revealed the tradeoff between enzyme usage efficiency and biomass yield by exploring the metabolic behaviours under different substrate consumption rates. Enzyme-constrained models can improve simulation accuracy and thus can predict cellular phenotypes under various genetic perturbations more precisely, providing reliable guidance for metabolic engineering.

Vitreoscilla Haemoglobin: A Tool to Reduce Overflow Metabolism

Overflow metabolism is a phenomenon extended in nature, ranging from microbial to cancer cells. Accumulation of overflow metabolites pose a challenge for large-scale bioprocesses. Yet, the causes of overflow metabolism are not fully clarified. In this work, the underlying mechanisms, reasons and consequences of overflow metabolism in different organisms have been summarized. The reported effect of aerobic expression of Vitreoscilla haemoglobin (VHb) in different organisms are revised. The use of VHb to reduce overflow metabolism is proposed and studied through flux balance analysis in E. coli at a fixed maximum substrate and oxygen uptake rates. Simulations showed that the presence of VHb increases the growth rate, while decreasing acetate production, in line with the experimental measurements. Therefore, aerobic VHb expression is considered a potential tool to reduce overflow metabolism in cells.

Emergent Diversity and Persistent Turnover in Evolving Microbial Cross-Feeding Networks

A distinguishing feature of many ecological networks in the microbial realm is the diversity of substrates that could potentially serve as energy sources for microbial consumers. The microorganisms are themselves the agents of compound diversification via metabolite excretion or overflow metabolism. It has been suggested that the emerging richness of different substrates is an important condition for the immense biological diversity in microbial ecosystems. In this work, we study how complex cross-feeding networks (CFN) of microbial species may develop from a simple initial community given some elemental evolutionary mechanisms of resource-dependent speciation and extinctions using a network flow model. We report results of several numerical experiments and report an in-depth analysis of the evolutionary dynamics. We find that even in stable environments, the system is subject to persisting turnover, indicating an ongoing co-evolution. Further, we compare the impact of different parameters, such as the ratio of mineralization, as well as the metabolic versatility and variability on the evolving community structure. The results imply that high microbial and molecular diversity is an emergent property of evolution in cross-feeding networks, which affects transformation and accumulation of substrates in natural systems, such as soils and oceans, with potential relevance to biotechnological applications.

Overlapping stimulons arising in response to divergent stresses in Escherichia coli

Cellular responses to stress can cause a similar change in some facets of fitness even if the stresses are different. Lactose as a sole carbon source for Escherichia coli is an established example: too little causes starvation while excessive lactose import causes toxicity as a side-effect. In an E. coli strain that is robust to osmotic and ionic differences in growth media, B REL606, the rate of antibiotic-tolerant persister formation is elevated in both starvation-inducing and toxicity-inducing concentrations of lactose in comparison to less stressful intermediate concentrations. Such similarities between starvation and toxification raise the question of how much the global stress response stimulon differs between them. We hypothesized that a common stress response is conserved between the two conditions, but that a previously shown threshold driving growth rate heterogeneity in a lactose-toxifying medium would reveal that the growing fraction of cells in that medium to be missing key stress responses that curb growth. To test this, we performed RNA-seq in three representative conditions for differential expression analysis. In comparison to nominally unstressed cultures, both stress conditions showed global shifts in gene expression, with informative similarities and differences. Functional analysis of pathways, gene ontology terms, and clusters of orthogonal groups revealed signatures of overflow metabolism, membrane component shifts, and altered cytosolic and periplasmic contents in toxified cultures. Starving cultures showed an increased tendency toward stringent response-like regulatory signatures. Along with other emerging evidence, our results show multiple possible pathways to stress responses, persistence, and possibly other phenotypes. These results suggest a set of overlapping responses that drives emergence of stress-tolerant phenotypes in diverse conditions.

ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model

Genome-scale metabolic models (GEMs) have been widely used for phenotypic prediction of microorganisms. However, the lack of other constraints in the stoichiometric model often leads to a large metabolic solution space inaccessible. Inspired by previous studies that take allocation of macromolecule resources into account, we developed a simplified Python-based workflow for constructing enzymatic constrained metabolic network model (ECMpy) and constructed an enzyme-constrained model for Escherichia coli (eciML1515) by directly adding a total enzyme amount constraint in the latest version of GEM for E. coli (iML1515), considering the protein subunit composition in the reaction, and automated calibration of enzyme kinetic parameters. Using eciML1515, we predicted the overflow metabolism of E. coli and revealed that redox balance was the key reason for the difference between E. coli and Saccharomyces cerevisiae in overflow metabolism. The growth rate predictions on 24 single-carbon sources were improved significantly when compared with other enzyme-constrained models of E. coli. Finally, we revealed the tradeoff between enzyme usage efficiency and biomass yield by exploring the metabolic behaviors under different substrate consumption rates. Enzyme-constrained models can improve simulation accuracy and thus can predict cellular phenotypes under various genetic perturbations more precisely, providing reliable guidance for metabolic engineering.

Loss of the acetate switch in Vibrio vulnificus enhances predation defence against Tetrahymena pyriformis

Vibrio vulnificus is an opportunistic human pathogen and autochthonous inhabitant of coastal marine environments, where the bacterium is under constant predation by heterotrophic protists or protozoans. As a result of this selection pressure, genetic variants with anti-predation mechanisms are selected for and persist in the environment. Such natural variants may also be pathogenic to animal or human hosts, making it important to understand these defence mechanisms. To identify anti-predator strategies, thirteen V. vulnificus strains of different genotypes isolated from diverse environments were exposed to predation by the ciliated protozoan, Tetrahymena pyriformis , and only strain ENV1 was resistant to predation. Further investigation of the cell-free supernatant showed that ENV1 acidifies the environment by the excretion of organic acids, which is toxic to T. pyriformis . As this predation resistance was dependent on the availability of iron, transcriptomes of V. vulnificus in iron-replete and iron-deplete conditions were compared. This analysis revealed that ENV1 ferments pyruvate and the resultant acetyl-CoA leads to acetate synthesis under aerobic conditions, a hallmark of overflow metabolism. The anaerobic respiration global regulator, arcA , was upregulated when iron was available. An Δ arcA deletion mutant of ENV1 accumulated less acetate and importantly, was sensitive to grazing by T. pyriformis . Based on the transcriptome response and quantification of metabolites, we conclude that ENV1 has adapted to overflow metabolism and has lost a control switch that shifts metabolism from acetate excretion to acetate assimilation, enabling it to excrete acetate continuously. We show that overflow metabolism and the acetate switch contribute to prey-predator interactions. Importance Bacteria in the environment, including Vibrio spp., interact with protozoan predators. To defend against predation, bacteria evolve anti-predator mechanisms ranging from changing morphology, biofilm formation and secretion of toxins or virulence factors. Some of these adaptations may result in strains that are pathogenic to humans. Therefore, it is important to study predator defence strategies of environmental bacteria. V. vulnificus thrives in coastal waters and infects humans. Very little is know about the defence mechanisms V. vulnificus expresses against predation. Here we show that a V. vulnificus strain (ENV1) has rewired the central carbon metabolism enabling the production of excess organic acid that is toxic to the protozoan predator, T. pyriformis . This is a previously unknown mechanism of predation defence that protects against protozoan predators.

Understanding and mathematical modelling of cellular resource allocation in microorganisms: a comparative synthesis

Background The rising consensus that the cell can dynamically allocate its resources provides an interesting angle for discovering the governing principles of cell growth and metabolism. Extensive efforts have been made in the past decade to elucidate the relationship between resource allocation and phenotypic patterns of microorganisms. Despite these exciting developments, there is still a lack of explicit comparison between potentially competing propositions and a lack of synthesis of inter-related proposals and findings. Results In this work, we have reviewed resource allocation-derived principles, hypotheses and mathematical models to recapitulate important achievements in this area. In particular, the emergence of resource allocation phenomena is deciphered by the putative tug of war between the cellular objectives, demands and the supply capability. Competing hypotheses for explaining the most-studied phenomenon arising from resource allocation, i.e. the overflow metabolism, have been re-examined towards uncovering the potential physiological root cause. The possible link between proteome fractions and the partition of the ribosomal machinery has been analysed through mathematical derivations. Finally, open questions are highlighted and an outlook on the practical applications is provided. It is the authors’ intention that this review contributes to a clearer understanding of the role of resource allocation in resolving bacterial growth strategies, one of the central questions in microbiology. Conclusions We have shown the importance of resource allocation in understanding various aspects of cellular systems. Several important questions such as the physiological root cause of overflow metabolism and the correct interpretation of ‘protein costs’ are shown to remain open. As the understanding of the mechanisms and utility of resource application in cellular systems further develops, we anticipate that mathematical modelling tools incorporating resource allocation will facilitate the circuit-host design in synthetic biology.

A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics

Eukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these industrial systems, a mathematical approach can be used to integrate the description of multiple biological networks into a single model for cell analysis and engineering. One of the most accurate models of biological systems include Expression and Thermodynamics FLux (ETFL), which efficiently integrates RNA and protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable for E. coli. To adapt this model for Saccharomyces cerevisiae, we developed yETFL, in which we augmented the original formulation with additional considerations for biomass composition, the compartmentalized cellular expression system, and the energetic costs of biological processes. We demonstrated the ability of yETFL to predict maximum growth rate, essential genes, and the phenotype of overflow metabolism. We envision that the presented formulation can be extended to a wide range of eukaryotic organisms to the benefit of academic and industrial research.