Enhanced production of heterologous proteins by a synthetic microbial community: Conditions and trade-offs

TIR predictor and optimizer: Web-tools for accurate prediction of translation initiation rate and precision gene design in Saccharomyces cerevisiae

Translation initiation is the primary determinant of the rate of protein production. The variation in the rate with which this step occurs can cause up to three orders of magnitude differences in cellular protein levels. Several mRNA features, including mRNA stability in proximity to the start codon, coding sequence length, and presence of specific motifs in the mRNA molecule, have been shown to influence the translation initiation rate. These molecular factors acting at different strengths allow precise control of in vivo translation initiation rate and thus the rate of protein synthesis. However, despite the paramount importance of translation initiation rate in protein synthesis, accurate prediction of the absolute values of initiation rate remains a challenge. In fact, as of now, there is no available model for predicting the initiation rate in Saccharomyces cerevisiae. To address this, we train a machine learning model for predicting the in vivo initiation rate in S. cerevisiae transcripts. The model is trained using a diverse set of mRNA transcripts, enabling the comparison of initiation rates across different transcripts. Our model exhibited excellent accuracy in predicting the translation initiation rate and demonstrated its effectiveness with both endogenous and exogenous transcripts. Then, by combining the machine learning model with the Monte-Carlo search algorithm, we have also devised a method to optimize the nucleotide sequence of any gene to achieve a specific target initiation rate. The machine learning model we've developed for predicting translation initiation rates, along with the gene optimization method, are deployed as a web server. Both web servers are accessible for free at the following link: ajeetsharmalab.com/TIRPredictor. Thus, this research advances our fundamental understanding of translation initiation processes, with direct applications in biotechnology.

Controllability and accessibility analysis of nonlinear biosystems

BACKGROUND

We address the problem of determining the controllability and accessibility of nonlinear biosystems. We consider models described by affine-in-inputs ordinary differential equations, which are adequate for a wide array of biological processes. Roughly speaking, the controllability of a dynamical system determines the possibility of steering it from an initial state to any point in its neighbourhood; accessibility is a weaker form of controllability.

METHODS

While the methodology for analysing the controllability of linear systems is well established, its generalization to the nonlinear case has proven elusive. Thus, a number of related but different properties - including different versions of accessibility, reachability or weak local controllability - have been defined to approach its study, and several partial results exist in lieu of a general test. Here, leveraging the applicable results from differential geometric control theory, we source sufficient conditions to assess nonlinear controllability, as well as a necessary and sufficient condition for accessibility.

RESULTS

We develop an algorithmic procedure to evaluate these conditions efficiently, and we provide its open source implementation. Using this software tool, we analyse the accessibility and controllability of a number of models of biomedical interest. While some of them are fully controllable, we find others that are not, as is the case of some models of EGF and NFκB signalling networks.

CONCLUSIONS

The contributions in this paper facilitate the accessibility and controllability analysis of nonlinear models, not only in biomedicine but also in other areas in which they have been rarely performed to date.

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 mathematical model of such a synthetic community in a chemostat 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.

Mathematical modeling of regulatory networks of intracellular processes - Aims and selected methods

Regulatory networks structure and signaling pathways dynamics are uncovered in time- and resource consuming experimental work. However, it is increasingly supported by modeling, analytical and computational techniques as well as discrete mathematics and artificial intelligence applied to to extract knowledge from existing databases. This review is focused on mathematical modeling used to analyze dynamics and robustness of these networks. This paper presents a review of selected modeling methods that facilitate advances in molecular biology.

Designing a synthetic microbial community devoted to biological control: The case study of Fusarium wilt of banana

Fusarium oxysporum f. sp. cubense (Foc) tropical race 4 (TR4) is threatening banana production because of its increasing spread. Biological control approaches have been widely studied and constitute interesting complementary measures to integrated disease management strategies. They have been based mainly on the use of single biological control agents (BCAs). In this study, we moved a step forward by designing a synthetic microbial community (SynCom) for the control of Fusarium wilt of banana (FWB). Ninety-six isolates of Pseudomonas spp., Bacillus spp., Streptomyces spp., and Trichoderma spp. were obtained from the banana rhizosphere and selected in vitro for the antagonism against Foc TR4. In pot experiments, a large community such as SynCom 1.0 (44 isolates with moderate to high antagonistic activity) or a small one such as SynCom 1.1 (seven highly effective isolates) provided similar disease control (35% symptom severity reduction). An in vitro study of the interactions among SynCom 1.1 isolates and between them and Foc revealed that beneficial microorganisms not only antagonized the pathogen but also some of the SynCom constituents. Furthermore, Foc defended itself by antagonizing the beneficial microbes. We also demonstrated that fusaric acid, known as one of the secondary metabolites of Fusarium species, might be involved in such an interaction. With this knowledge, SynCom 1.2 was then designed with three isolates: Pseudomonas chlororaphis subsp. piscium PS5, Bacillus velezensis BN8.2, and Trichoderma virens T2C1.4. A non-simultaneous soil application of these isolates (to diminish cross-inhibition) delayed FWB progress over time, with significant reductions in incidence and severity. SynCom 1.2 also performed better than two commercial BCAs, BioPak^® and T-Gro. Eventually, SynCom 1.2 isolates were characterized for several biocontrol traits and their genome was sequenced. Our data showed that assembling a SynCom for biocontrol is not an easy task. The mere mixtures of antagonists (e.g., SynCom 1.0 and 1.1) might provide effective biocontrol, but an accurate investigation of the interactions among beneficial microorganisms is needed to improve the results (e.g., SynCom 1.2). SynCom 1.2 is a valuable tool to be further developed for the biological control of FWB.

Ratiometric control of cell phenotypes in monostrain microbial consortia

We address the problem of regulating and keeping at a desired balance the relative numbers between cells exhibiting a different phenotype within a monostrain microbial consortium. We propose a strategy based on the use of external control inputs, assuming each cell in the community is endowed with a reversible, bistable memory mechanism. Specifically, we provide a general analytical framework to guide the design of external feedback control strategies aimed at balancing the ratio between cells whose memory is stabilized at either one of two equilibria associated with different cell phenotypes. We demonstrate the stability and robustness properties of the control laws proposed and validate them in silico, implementing the memory element via a genetic toggle-switch. The proposed control framework may be used to allow long-term coexistence of different populations, with both industrial and biotechnological applications. As a representative example, we consider the realistic agent-based implementation of our control strategy to enable cooperative bioproduction of a dimer in a monostrain microbial consortium.

Resource-aware whole-cell model of division of labour in a microbial consortium for complex-substrate degradation

BACKGROUND

Low-cost sustainable feedstocks are essential for commercially viable biotechnologies. These feedstocks, often derived from plant or food waste, contain a multitude of different complex biomolecules which require multiple enzymes to hydrolyse and metabolise. Current standard biotechnology uses monocultures in which a single host expresses all the proteins required for the consolidated bioprocess. However, these hosts have limited capacity for expressing proteins before growth is impacted. This limitation may be overcome by utilising division of labour (DOL) in a consortium, where each member expresses a single protein of a longer degradation pathway.

RESULTS

Here, we model a two-strain consortium, with one strain expressing an endohydrolase and a second strain expressing an exohydrolase, for cooperative degradation of a complex substrate. Our results suggest that there is a balance between increasing expression to enhance degradation versus the burden that higher expression causes. Once a threshold of burden is reached, the consortium will consistently perform better than an equivalent single-cell monoculture.

CONCLUSIONS

We demonstrate that resource-aware whole-cell models can be used to predict the benefits and limitations of using consortia systems to overcome burden. Our model predicts the region of expression where DOL would be beneficial for growth on starch, which will assist in making informed design choices for this, and other, complex-substrate degradation pathways.

Multiomics Study of Bacterial Growth Arrest in a Synthetic Biology Application

We investigated the scalability of a previously developed growth switch based on external control of RNA polymerase expression. Our results indicate that, in liter-scale bioreactors operating in fed-batch mode, growth-arrested Escherichia coli cells are able to convert glucose to glycerol at an increased yield. A multiomics quantification of the physiology of the cells shows that, apart from acetate production, few metabolic side effects occur. However, a number of specific responses to growth slow-down and growth arrest are launched at the transcriptional level. These notably include the downregulation of genes involved in growth-associated processes, such as amino acid and nucleotide metabolism and translation. Interestingly, the transcriptional responses are buffered at the proteome level, probably due to the strong decrease of the total mRNA concentration after the diminution of transcriptional activity and the absence of growth dilution of proteins. Growth arrest thus reduces the opportunities for dynamically adjusting the proteome composition, which poses constraints on the design of biotechnological production processes but may also avoid the initiation of deleterious stress responses.

Designing microbial communities to maximize the thermodynamic driving force for the production of chemicals

Microbial communities have become a major research focus due to their importance for biogeochemical cycles, biomedicine and biotechnological applications. While some biotechnological applications, such as anaerobic digestion, make use of naturally arising microbial communities, the rational design of microbial consortia for bio-based production processes has recently gained much interest. One class of synthetic microbial consortia is based on specifically designed strains of one species. A common design principle for these consortia is based on division of labor, where the entire production pathway is divided between the different strains to reduce the metabolic burden caused by product synthesis. We first show that classical division of labor does not automatically reduce the metabolic burden when metabolic flux per biomass is analyzed. We then present ASTHERISC (Algorithmic Search of THERmodynamic advantages in Single-species Communities), a new computational approach for designing multi-strain communities of a single-species with the aim to divide a production pathway between different strains such that the thermodynamic driving force for product synthesis is maximized. ASTHERISC exploits the fact that compartmentalization of segments of a product pathway in different strains can circumvent thermodynamic bottlenecks arising when operation of one reaction requires a metabolite with high and operation of another reaction the same metabolite with low concentration. We implemented the ASTHERISC algorithm in a dedicated program package and applied it on E. coli core and genome-scale models with different settings, for example, regarding number of strains or demanded product yield. These calculations showed that, for each scenario, many target metabolites (products) exist where a multi-strain community can provide a thermodynamic advantage compared to a single strain solution. In some cases, a production with sufficiently high yield is thermodynamically only feasible with a community. In summary, the developed ASTHERISC approach provides a promising new principle for designing microbial communities for the bio-based production of chemicals.

Communities of microbes are ubiquitous in nature and also of high relevance for industrial applications, e.g. for the production of biogas. The development and use of non-natural communities for biotechnological applications has become an important subject of research. In this work, we present a new computational method to design synthetic communities with improved capabilities for the synthesis of desired target metabolites. Our method takes a constraint-based metabolic model of an organism as input and searches for a suitable partitioning of the product pathway via different strains of the organism such that the thermodynamic driving force for product synthesis is maximized. Essentially, this approach exploits the fact that having multiple strains allows adjustment of different metabolite concentrations in the different strains by which the thermodynamic driving force for product synthesis can often be increased. We tested this approach with a core and with a genome-scale metabolic network model of Escherichia coli. We found that, for dozens of metabolites, there exist communities with specifically designed strains of E. coli where the maximal thermodynamic driving force can be increased compared to a single E. coli strain. In summary, our presented method provides a new approach, together with a new design principle, for the computational design of microbial communities.

Global dynamics of the chemostat with overflow metabolism

Fast growing E. coli cells, in glucose-aerobic conditions, excrete fermentation by-products such as acetate. This phenomenon is known as overflow metabolism and has been observed in a diverse range of microorganisms. In this paper, we study a chemostat model subject to overflow metabolism: if the substrate uptake rate (or the specific growth rate) is above a threshold rate (different from zero), then secretion of a by-product happens. We assume that the presence of the by-product has an inhibitory effect on the growth of the microorganism. The model is described by a non-smooth differential system of dimension three. We prove the existence of at most one equilibrium (or steady-state) with presence of microorganism, which is globally stable. We use these results to discuss the performance of chemostat-type systems to produce biomass or recombinant proteins.