A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory

Physiology, fast and slow: bacterial response to variable resource stoichiometry and dilution rate

Microorganisms grow despite imbalances in the availability of nutrients and energy. The biochemical and elemental adjustments that bacteria employ to sustain growth when these resources are suboptimal are not well understood. We assessed how Pseudomonas putida KT2440 adjusts its physiology at differing dilution rates (to approximate growth rates) in response to carbon (C), nitrogen (N), and phosphorus (P) stress using chemostats. Cellular elemental and biomolecular pools were variable in response to different limiting resources at a slow dilution rate of 0.12 h-1, but these pools were more similar across treatments at a faster rate of 0.48 h-1. At slow dilution rates, limitation by P and C appeared to alter cell growth efficiencies as reflected by changes in cellular C quotas and rates of oxygen consumption, both of which were highest under P- and lowest under C- stress. Underlying these phenotypic changes was differential gene expression of terminal oxidases used for ATP generation that allows for increased energy generation efficiency. In all treatments under fast dilution rates, KT2440 formed aggregates and biofilms, a physiological response that hindered an accurate assessment of growth rate, but which could serve as a mechanism that allows cells to remain in conditions where growth is favorable. Our findings highlight the ways that microorganisms dynamically adjust their physiology under different resource supply conditions, with distinct mechanisms depending on the limiting resource at slow growth and convergence toward an aggregative phenotype with similar compositions under conditions that attempt to force fast growth.

IMPORTANCE

All organisms experience suboptimal growth conditions due to low nutrient and energy availability. Their ability to survive and reproduce under such conditions determines their evolutionary fitness. By imposing suboptimal resource ratios under different dilution rates on the model organism Pseudomonas putida KT2440, we show that this bacterium dynamically adjusts its elemental composition, morphology, pools of biomolecules, and levels of gene expression. By examining the ability of bacteria to respond to C:N:P imbalance, we can begin to understand how stoichiometric flexibility manifests at the cellular level and impacts the flow of energy and elements through ecosystems.

Pseudomonas putida KT2440: the long journey of a soil-dweller to become a synthetic biology chassis

Although members of the genus Pseudomonas share specific morphological, metabolic, and genomic traits, the diversity of niches and lifestyles adopted by the family members is vast. One species of the group, Pseudomonas putida, thrives as a colonizer of plant roots and frequently inhabits soils polluted with various types of chemical waste. Owing to a combination of historical contingencies and inherent qualities, a particular strain, P. putida KT2440, emerged time ago as an archetype of an environmental microorganism amenable to recombinant DNA technologies, which was also capable of catabolizing chemical pollutants. Later, the same bacterium progressed as a reliable platform for programming traits and activities in various biotechnological applications. This article summarizes the stepwise upgrading of P. putida KT2440 from being a system for fundamental studies on the biodegradation of aromatic compounds (especially when harboring the TOL plasmid pWW0) to its adoption as a chassis of choice in metabolic engineering and synthetic biology. Although there are remaining uncertainties about the taxonomic classification of KT2440, advanced genome editing capabilities allow us to tailor its genetic makeup to meet specific needs. This makes its traditional categorization somewhat less important, while also increasing the strain's overall value for contemporary industrial and environmental uses.

Pseudomonas putida as a platform for medium-chain length α,ω-diol production: Opportunities and challenges

Medium-chain-length α,ω-diols (mcl-diols) play an important role in polymer production, traditionally depending on energy-intensive chemical processes. Microbial cell factories offer an alternative, but conventional strains like Escherichia coli and Saccharomyces cerevisiae face challenges in mcl-diol production due to the toxicity of intermediates such as alcohols and acids. Metabolic engineering and synthetic biology enable the engineering of non-model strains for such purposes with P. putida emerging as a promising microbial platform. This study reviews the advancement in diol production using P. putida and proposes a four-module approach for the sustainable production of diols. Despite progress, challenges persist, and this study discusses current obstacles and future opportunities for leveraging P. putida as a microbial cell factory for mcl-diol production. Furthermore, this study highlights the potential of using P. putida as an efficient chassis for diol synthesis.

mergem: merging, comparing, and translating genome-scale metabolic models using universal identifiers

Numerous methods exist to produce and refine genome-scale metabolic models. However, due to the use of incompatible identifier systems for metabolites and reactions, computing and visualizing the metabolic differences and similarities of such models is a current challenge. Furthermore, there is a lack of automated tools that can combine the strengths of multiple reconstruction pipelines into a curated single comprehensive model by merging different drafts, which possibly use incompatible namespaces. Here we present mergem, a novel method to compare, merge, and translate two or more metabolic models. Using a universal metabolic identifier mapping system constructed from multiple metabolic databases, mergem robustly can compare models from different pipelines, merge their common elements, and translate their identifiers to other database systems. mergem is implemented as a command line tool, a Python package, and on the web-application Fluxer, which allows simulating and visually comparing multiple models with different interactive flux graphs. The ability to merge, compare, and translate diverse genome scale metabolic models can facilitate the curation of comprehensive reconstructions and the discovery of unique and common metabolic features among different organisms.

FastKnock: an efficient next-generation approach to identify all knockout strategies for strain optimization

Overproduction of desired native or nonnative biochemical(s) in (micro)organisms can be achieved through metabolic engineering. Appropriate rewiring of cell metabolism is performed by making rational changes such as insertion, up-/down-regulation and knockout of genes and consequently metabolic reactions. Finding appropriate targets (including proper sets of reactions to be knocked out) for metabolic engineering to design optimal production strains has been the goal of a number of computational algorithms. We developed FastKnock, an efficient next-generation algorithm for identifying all possible knockout strategies (with a predefined maximum number of reaction deletions) for the growth-coupled overproduction of biochemical(s) of interest. We achieve this by developing a special depth-first traversal algorithm that allows us to prune the search space significantly. This leads to a drastic reduction in execution time. We evaluate the performance of the FastKnock algorithm using various Escherichia coli genome-scale metabolic models in different conditions (minimal and rich mediums) for the overproduction of a number of desired metabolites. FastKnock efficiently prunes the search space to less than 0.2% for quadruple- and 0.02% for quintuple-reaction knockouts. Compared to the classic approaches such as OptKnock and the state-of-the-art techniques such as MCSEnumerator methods, FastKnock found many more beneficial and important practical solutions. The availability of all the solutions provides the opportunity to further characterize, rank and select the most appropriate intervention strategy based on any desired evaluation index. Our implementation of the FastKnock method in Python is publicly available at https://github.com/leilahsn/FastKnock .

Genome-scale metabolic network model and phenome of solvent-tolerant Pseudomonas putida S12

BACKGROUND

Pseudomonas putida S12 is a gram-negative bacterium renowned for its high tolerance to organic solvents and metabolic versatility, making it attractive for various applications, including bioremediation and the production of aromatic compounds, bioplastics, biofuels, and value-added compounds. However, a metabolic model of S12 has yet to be developed.

RESULTS

In this study, we present a comprehensive and highly curated genome-scale metabolic network model of S12 (iSH1474), containing 1,474 genes, 1,436 unique metabolites, and 2,938 metabolic reactions. The model was constructed by leveraging existing metabolic models and conducting comparative analyses of genomes and phenomes. Approximately 2,000 different phenotypes were measured for S12 and its closely related KT2440 strain under various nutritional and environmental conditions. These phenotypic data, combined with the reported experimental data, were used to refine and validate the reconstruction. Model predictions quantitatively agreed well with in vivo flux measurements and the batch cultivation of S12, which demonstrated that iSH1474 accurately represents the metabolic capabilities of S12. Furthermore, the model was simulated to investigate the maximum theoretical metabolic capacity of S12 growing on toxic organic solvents.

CONCLUSIONS

iSH1474 represents a significant advancement in our understanding of the cellular metabolism of P. putida S12. The combined results of metabolic simulation and comparative genome and phenome analyses identified the genetic and metabolic determinants of the characteristic phenotypes of S12. This study could accelerate the development of this versatile organism as an efficient cell factory for various biotechnological applications.

Synthetic biology tools for environmental protection

Synthetic biology transforms the way we perceive biological systems. Emerging technologies in this field affect many disciplines of science and engineering. Traditionally, synthetic biology approaches were commonly aimed at developing cost-effective microbial cell factories to produce chemicals from renewable sources. Based on this, the immediate beneficial impact of synthetic biology on the environment came from reducing our oil dependency. However, synthetic biology is starting to play a more direct role in environmental protection. Toxic chemicals released by industries and agriculture endanger the environment, disrupting ecosystem balance and biodiversity loss. This review highlights synthetic biology approaches that can help environmental protection by providing remediation systems capable of sensing and responding to specific pollutants. Remediation strategies based on genetically engineered microbes and plants are discussed. Further, an overview of computational approaches that facilitate the design and application of synthetic biology tools in environmental protection is presented.

The effective deficiency of biochemical networks

The deficiency of a (bio)chemical reaction network can be conceptually interpreted as a measure of its ability to support exotic dynamical behavior and/or multistationarity. The classical definition of deficiency relates to the capacity of a network to permit variations of the complex formation rate vector at steady state, irrespective of the network kinetics. However, the deficiency is by definition completely insensitive to the fine details of the directionality of reactions as well as bounds on reaction fluxes. While the classical definition of deficiency can be readily applied in the analysis of unconstrained, weakly reversible networks, it only provides an upper bound in the cases where relevant constraints on reaction fluxes are imposed. Here we propose the concept of effective deficiency, which provides a more accurate assessment of the network's capacity to permit steady state variations at the complex level for constrained networks of any reversibility patterns. The effective deficiency relies on the concept of nonstoichiometric balanced complexes, which we have already shown to be present in real-world biochemical networks operating under flux constraints. Our results demonstrate that the effective deficiency of real-world biochemical networks is smaller than the classical deficiency, indicating the effects of reaction directionality and flux bounds on the variation of the complex formation rate vector at steady state.

Synthetic Biology Toolkit for a New Species of Pseudomonas Promissory for Electricity Generation in Microbial Fuel Cells

Microbial fuel cells (MFCs) offer sustainable solutions for various biotechnological applications and are a crucial area of research in biotechnology. MFCs can effectively treat various refuse, such as wastewater and biodiesel waste by decomposing organic matter and generating electricity. Certain Pseudomonas species possess extracellular electron transfer (EET) pathways, enabling them to transfer electrons from organic compounds to the MFC's anode. Moreover, Pseudomonas species can grow under low-oxygen conditions, which is advantageous considering that the electron transfer process in an MFC typically leads to reduced oxygen levels at the anode. This study focuses on evaluating MFCs inoculated with a new Pseudomonas species grown with 1 g.L-1 glycerol, a common byproduct of biodiesel production. Pseudomonas sp. BJa5 exhibited a maximum power density of 39 mW.m-2. Also, the observed voltammograms and genome analysis indicate the potential production of novel redox mediators by BJa5. Additionally, we investigated the bacterium's potential as a synthetic biology non-model chassis. Through testing various genetic parts, including constitutive promoters, replication origins and cargos using pSEVA vectors as a scaffold, we assessed the bacterium's suitability. Overall, our findings offer valuable insights into utilizing Pseudomonas spp. BJa5 as a novel chassis for MFCs. Synthetic biology approaches can further enhance the performance of this bacterium in MFCs, providing avenues for improvement.

FastKnock: An efficient next-generation approach to identify all knockout strategies for strain optimization

Overproduction of desired native or nonnative biochemical(s) in (micro)organisms can be achieved through metabolic engineering. Appropriate rewiring of cell metabolism is performed making rational changes such as insertion, up-/down-regulation and knockout of genes and consequently metabolic reactions. Finding appropriate targets (including proper sets of reactions to be knocked out) for metabolic engineering to design optimal production strains has been the goal of a number of computational algorithms. We developed FastKnock, an efficient next-generation algorithm for identifying all possible knockout strategies for the growth-coupled overproduction of biochemical(s) of interest. We achieve this by developing a special depth-first traversal algorithm that allows us to prune the search space significantly. This leads to a drastic reduction in execution time. We evaluate the performance of the FastKnock algorithm using three Escherichia coli genome-scale metabolic models in different conditions (minimal and rich mediums) for the overproduction of a number of desired metabolites. FastKnock efficiently prunes the search space to less than 0.2% for quadruple and 0.02% for quintuple-reaction knockouts. Compared to the classic approaches such as OptKnock and the state-of-the-art techniques such as MCSEnumerator methods, FastKnock found many more useful and important practical solutions. The availability of all the solutions provides the opportunity to further characterize and select the most appropriate intervention strategy based on any desired evaluation index. Our implementation of the FastKnock method in Python is publicly available at https://github.com/leilahsn/FastKnock.

Approaches in Sustainable, Biobased Multilayer Packaging Solutions

The depletion of fossil resources and the growing demand for plastic waste reduction has put industries and academic researchers under pressure to develop increasingly sustainable packaging solutions that are both functional and circularly designed. In this review, we provide an overview of the fundamentals and recent advances in biobased packaging materials, including new materials and techniques for their modification as well as their end-of-life scenarios. We also discuss the composition and modification of biobased films and multilayer structures, with particular attention to readily available drop-in solutions, as well as coating techniques. Moreover, we discuss end-of-life factors, including sorting systems, detection methods, composting options, and recycling and upcycling possibilities. Finally, regulatory aspects are pointed out for each application scenario and end-of-life option. Moreover, we discuss the human factor in terms of consumer perception and acceptance of upcycling.

Systematizing Microbial Bioplastic Production for Developing Sustainable Bioeconomy: Metabolic Nexus Modeling, Economic and Environmental Technologies Assessment

The excessive usage of non-renewable resources to produce plastic commodities has incongruously influenced the environment's health. Especially in the times of COVID-19, the need for plastic-based health products has increased predominantly. Given the rise in global warming and greenhouse gas emissions, the lifecycle of plastic has been established to contribute to it significantly. Bioplastics such as polyhydroxy alkanoates, polylactic acid, etc. derived from renewable energy origin have been a magnificent alternative to conventional plastics and reconnoitered exclusively for combating the environmental footprint of petrochemical plastic. However, the economically reasonable and environmentally friendly procedure of microbial bioplastic production has been a hard nut to crack due to less scouted and inefficient process optimization and downstream processing methodologies. Thereby, meticulous employment of computational tools such as genome-scale metabolic modeling and flux balance analysis has been practiced in recent times to understand the effect of genomic and environmental perturbations on the phenotype of the microorganism. In-silico results not only aid us in determining the biorefinery abilities of the model microorganism but also curb our reliance on equipment, raw materials, and capital investment for optimizing the best conditions. Additionally, to accomplish sustainable large-scale production of microbial bioplastic in a circular bioeconomy, extraction, and refinement of bioplastic needs to be investigated extensively by practicing techno-economic analysis and life cycle assessment. This review put forth state-of-the-art know-how on the proficiency of these computational techniques in laying the foundation of an efficient bioplastic manufacturing blueprint, chiefly focusing on microbial polyhydroxy alkanoates (PHA) production and its efficacy in outplacing fossil based plastic products.

Systematic evaluation of genome-wide metabolic landscapes in lactic acid bacteria reveals diet- and strain-specific probiotic idiosyncrasies

Lactic acid bacteria (LAB) are well known to elicit health benefits in humans, but their functional metabolic landscapes remain unexplored. Here, we analyze differences in growth, intestinal persistence, and postbiotic biosynthesis of six representative LAB and their interactions with 15 gut bacteria under 11 dietary regimes by combining multi-omics and in silico modeling. We confirmed predictions on short-term persistence of LAB and their interactions with commensals using cecal microbiome abundance and spent-medium experiments. Our analyses indicate that probiotic attributes are both diet and species specific and cannot be solely explained using genomics. For example, although both Lacticaseibacillus casei and Lactiplantibacillus plantarum encode similarly sized genomes with diverse capabilities, L. casei exhibits a more desirable phenotype. In addition, "high-fat/low-carb" diets more likely lead to detrimental outcomes for most LAB. Collectively, our results highlight that probiotics are not "one size fits all" health supplements and lay the foundation for personalized probiotic design.

The hidden simplicity of metabolic networks is revealed by multireaction dependencies

Understanding the complexity of metabolic networks has implications for manipulation of their functions. The complexity of metabolic networks can be characterized by identifying multireaction dependencies that are challenging to determine due to the sheer number of combinations to consider. Here, we propose the concept of concordant complexes that captures multireaction dependencies and can be efficiently determined from the algebraic structure and operational constraints of metabolic networks. The concordant complexes imply the existence of concordance modules based on which the apparent complexity of 12 metabolic networks of organisms from all kingdoms of life can be reduced by at least 78%. A comparative analysis against an ensemble of randomized metabolic networks shows that the metabolic network of Escherichia coli contains fewer concordance modules and is, therefore, more tightly coordinated than expected by chance. Together, our findings demonstrate that metabolic networks are considerably simpler than what can be perceived from their structure alone.

Nitrogen Metabolism in Pseudomonas putida: Functional Analysis Using Random Barcode Transposon Sequencing

Pseudomonas putida KT2440 has long been studied for its diverse and robust metabolisms, yet many genes and proteins imparting these growth capacities remain uncharacterized. Using pooled mutant fitness assays, we identified genes and proteins involved in the assimilation of 52 different nitrogen containing compounds. To assay amino acid biosynthesis, 19 amino acid drop-out conditions were also tested. From these 71 conditions, significant fitness phenotypes were elicited in 672 different genes including 100 transcriptional regulators and 112 transport-related proteins. We divide these conditions into 6 classes, and propose assimilatory pathways for the compounds based on this wealth of genetic data. To complement these data, we characterize the substrate range of three promiscuous aminotransferases relevant to metabolic engineering efforts in vitro. Furthermore, we examine the specificity of five transcriptional regulators, explaining some fitness data results and exploring their potential to be developed into useful synthetic biology tools. In addition, we use manifold learning to create an interactive visualization tool for interpreting our BarSeq data, which will improve the accessibility and utility of this work to other researchers. IMPORTANCE Understanding the genetic basis of P. putida's diverse metabolism is imperative for us to reach its full potential as a host for metabolic engineering. Many target molecules of the bioeconomy and their precursors contain nitrogen. This study provides functional evidence linking hundreds of genes to their roles in the metabolism of nitrogenous compounds, and provides an interactive tool for visualizing these data. We further characterize several aminotransferases, lactamases, and regulators, which are of particular interest for metabolic engineering.

Path to improving the life cycle and quality of genome-scale models of metabolism

Genome-scale models of metabolism (GEMs) are key computational tools for the systems-level study of metabolic networks. Here, we describe the "GEM life cycle," which we subdivide into four stages: inception, maturation, specialization, and amalgamation. We show how different types of GEM reconstruction workflows fit in each stage and proceed to highlight two fundamental bottlenecks for GEM quality improvement: GEM maturation and content removal. We identify common characteristics contributing to increasing quality of maturing GEMs drawing from past independent GEM maturation efforts. We then shed some much-needed light on the latent and unrecognized but pervasive issue of content removal, demonstrating the substantial effects of model pruning on its solution space. Finally, we propose a novel framework for content removal and associated confidence-level assignment which will help guide future GEM development efforts, reduce duplication of effort across groups, potentially aid automated reconstruction platforms, and boost the reproducibility of model development.

A polyhydroxyalkanoates bioprocess improvement case study based on four fed-batch feeding strategies

The modelling and optimization of a process for the production of the medium chain length polyhydroxyalkanoate (mcl-PHA) by the bacterium Pseudomonas putida KT2440 when fed a synthetic fatty acid mixture (SFAM) was investigated. Four novel feeding strategies were developed and tested using a constructed model and the optimum one implemented in further experiments. This strategy yielded a cell dry weight of 70.6 g l^-1 in 25 h containing 38% PHA using SFAM at 5 l scale. A phosphate starvation strategy was implemented to improve PHA content, and this yielded 94.1 g l^-1 in 25 h containing 56% PHA using SFAM at 5 l scale. The process was successfully operated at 20 l resulting in a cell dry weight of 91.2 g l^-1 containing 65% PHA at the end of a 25-h incubation.

A structural property for reduction of biochemical networks

Large-scale biochemical models are of increasing sizes due to the consideration of interacting organisms and tissues. Model reduction approaches that preserve the flux phenotypes can simplify the analysis and predictions of steady-state metabolic phenotypes. However, existing approaches either restrict functionality of reduced models or do not lead to significant decreases in the number of modelled metabolites. Here, we introduce an approach for model reduction based on the structural property of balancing of complexes that preserves the steady-state fluxes supported by the network and can be efficiently determined at genome scale. Using two large-scale mass-action kinetic models of Escherichia coli, we show that our approach results in a substantial reduction of 99% of metabolites. Applications to genome-scale metabolic models across kingdoms of life result in up to 55% and 85% reduction in the number of metabolites when arbitrary and mass-action kinetics is assumed, respectively. We also show that predictions of the specific growth rate from the reduced models match those based on the original models. Since steady-state flux phenotypes from the original model are preserved in the reduced, the approach paves the way for analysing other metabolic phenotypes in large-scale biochemical networks.

Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition

Genome-scale metabolic modeling is an important tool in the study of metabolism by enhancing the collation of knowledge, interpretation of data, and prediction of metabolic capabilities. A frequent assumption in the use of genome-scale models is that the in vivo organism is evolved for optimal growth, where growth is represented by flux through a biomass objective function (BOF). While the specific composition of the BOF is crucial, its formulation is often inherited from similar organisms due to the experimental challenges associated with its proper determination.

A cell’s macro-molecular composition is not fixed and it responds to changes in environmental conditions. As a consequence, initiatives for the high-fidelity determination of cellular biomass composition have been launched. Thus, there is a need for a mathematical and computational framework capable of using multiple measurements of cellular biomass composition in different environments. Here, we propose two different computational approaches for directly addressing this challenge: Biomass Trade-off Weighting (BTW) and Higher-dimensional-plane InterPolation (HIP).

In lieu of experimental data on biomass composition-variation in response to changing nutrient environment, we assess the properties of BTW and HIP using three hypothetical, yet biologically plausible, BOFs for the Escherichia coli genome-scale metabolic model iML1515. We find that the BTW and HIP formulations have a significant impact on model performance and phenotypes. Furthermore, the BTW method generates larger growth rates in all environments when compared to HIP. Using acetate secretion and the respiratory quotient as proxies for phenotypic changes, we find marked differences between the methods as HIP generates BOFs more similar to a reference BOF than BTW. We conclude that the presented methods constitute a conceptual step in developing genome-scale metabolic modelling approaches capable of addressing the inherent dependence of cellular biomass composition on nutrient environments.

Changes in the environment promote changes in an organism’s metabolism. To achieve balanced growth states for near-optimal function, cells respond through metabolic rearrangements, which may influence the biosynthesis of metabolic precursors for building a cell’s molecular constituents. Therefore, it is necessary to take the dependence of biomass composition on environmental conditions into consideration. While measuring the biomass composition for some environments is possible, and should be done, it cannot be completed for all possible environments.

In this work, we propose two main approaches, BTW and HIP, for addressing the challenge of estimating biomass composition in response to environmental changes. We evaluate the phenotypic consequences of BTW and HIP by characterizing their effect on growth, secretion potential, respiratory efficiency, and gene essentiality of a cell.

Our work constitutes a first conceptual step in accounting for the influence of growth conditions on biomass composition, and in turn the biomass composition’s effect on metabolic phenotypic traits, within constraint-based modelling. As such, we believe it will improve the relevance of constraint-based methods in metabolic engineering and drug discovery, since the biosynthetic potential of microbes for generating industrially relevant products or drugs often is closely linked to their biomass composition.

A navigation guide of synthetic biology tools for Pseudomonas putida

Pseudomonas putida is a microbial chassis of huge potential for industrial and environmental biotechnology, owing to its remarkable metabolic versatility and ability to sustain difficult redox reactions and operational stresses, among other attractive characteristics. A wealth of genetic and in silico tools have been developed to enable the unravelling of its physiology and improvement of its performance. However, the rise of this microbe as a promising platform for biotechnological applications has resulted in diversification of tools and methods rather than standardization and convergence. As a consequence, multiple tools for the same purpose have been generated, whilst most of them have not been embraced by the scientific community, which has led to compartmentalization and inefficient use of resources. Inspired by this and by the substantial increase in popularity of P. putida, we aim herein to bring together and assess all currently available (wet and dry) synthetic biology tools specific for this microbe, focusing on the last 5 years. We provide information on the principles, functionality, advantages and limitations, with special focus on their use in metabolic engineering. Additionally, we compare the tool portfolio for P. putida with those for other bacterial chassis and discuss potential future directions for tool development. Therefore, this review is intended as a reference guide for experts and new 'users' of this promising chassis.

Multi-Omics Driven Metabolic Network Reconstruction and Analysis of Lignocellulosic Carbon Utilization in Rhodosporidium toruloides

An oleaginous yeast Rhodosporidium toruloides is a promising host for converting lignocellulosic biomass to bioproducts and biofuels. In this work, we performed multi-omics analysis of lignocellulosic carbon utilization in R. toruloides and reconstructed the genome-scale metabolic network of R. toruloides. High-quality metabolic network models for model organisms and orthologous protein mapping were used to build a draft metabolic network reconstruction. The reconstruction was manually curated to build a metabolic model using functional annotation and multi-omics data including transcriptomics, proteomics, metabolomics, and RB-TDNA sequencing. The multi-omics data and metabolic model were used to investigate R. toruloides metabolism including lipid accumulation and lignocellulosic carbon utilization. The developed metabolic model was validated against high-throughput growth phenotyping and gene fitness data, and further refined to resolve the inconsistencies between prediction and data. We believe that this is the most complete and accurate metabolic network model available for R. toruloides to date.

A metabolic and physiological design study of Pseudomonas putida KT2440 capable of anaerobic respiration

BACKGROUND

Pseudomonas putida KT2440 is a metabolically versatile, HV1-certified, genetically accessible, and thus interesting microbial chassis for biotechnological applications. However, its obligate aerobic nature hampers production of oxygen sensitive products and drives up costs in large scale fermentation. The inability to perform anaerobic fermentation has been attributed to insufficient ATP production and an inability to produce pyrimidines under these conditions. Addressing these bottlenecks enabled growth under micro-oxic conditions but does not lead to growth or survival under anoxic conditions.

RESULTS

Here, a data-driven approach was used to develop a rational design for a P. putida KT2440 derivative strain capable of anaerobic respiration. To come to the design, data derived from a genome comparison of 1628 Pseudomonas strains was combined with genome-scale metabolic modelling simulations and a transcriptome dataset of 47 samples representing 14 environmental conditions from the facultative anaerobe Pseudomonas aeruginosa.

CONCLUSIONS

The results indicate that the implementation of anaerobic respiration in P. putida KT2440 would require at least 49 additional genes of known function, at least 8 genes encoding proteins of unknown function, and 3 externally added vitamins.

Diamine Biosynthesis: Research Progress and Application Prospects

Diamines are important monomers for polyamide plastics; they include 1,3-diaminopropane, 1,4-diaminobutane, 1,5-diaminopentane, and 1,6-diaminohexane, among others. With increasing attention on environmental problems and green sustainable development, utilizing renewable raw materials for the synthesis of diamines is crucial for the establishment of a sustainable plastics industry. Recently, high-performance microbial factories, such as Escherichia coli and Corynebacterium glutamicum, have been widely used in the production of diamines. In particular, several synthetic pathways of 1,6-diaminohexane have been proposed based on glutamate or adipic acid. Here, we reviewed approaches for the biosynthesis of diamines, including metabolic engineering and biocatalysis, and the application of bio-based diamines in nylon materials. The related challenges and opportunities in the development of renewable bio-based diamines and nylon materials are also discussed.

Engineering Native and Synthetic Pathways in Pseudomonas putida for the Production of Tailored Polyhydroxyalkanoates

Growing environmental concern sparks renewed interest in the sustainable production of (bio)materials that can replace oil-derived goods. Polyhydroxyalkanoates (PHAs) are isotactic polymers that play a critical role in the central metabolism of producer bacteria, as they act as dynamic reservoirs of carbon and reducing equivalents. PHAs continue to attract industrial attention as a starting point toward renewable, biodegradable, biocompatible, and versatile thermoplastic and elastomeric materials. Pseudomonas species have been known for long as efficient biopolymer producers, especially for medium-chain-length PHAs. The surge of synthetic biology and metabolic engineering approaches in recent years offers the possibility of exploiting the untapped potential of Pseudomonas cell factories for the production of tailored PHAs. In this article, an overview of the metabolic and regulatory circuits that rule PHA accumulation in Pseudomonas putida is provided, and approaches leading to the biosynthesis of novel polymers (e.g., PHAs including nonbiological chemical elements in their structures) are discussed. The potential of novel PHAs to disrupt existing and future market segments is closer to realization than ever before. The review is concluded by pinpointing challenges that currently hinder the wide adoption of bio-based PHAs, and strategies toward programmable polymer biosynthesis from alternative substrates in engineered P. putida strains are proposed.

VFFVA: dynamic load balancing enables large-scale flux variability analysis

BACKGROUND

Genome-scale metabolic models are increasingly employed to predict the phenotype of various biological systems pertaining to healthcare and bioengineering. To characterize the full metabolic spectrum of such systems, Fast Flux Variability Analysis (FFVA) is commonly used in parallel with static load balancing. This approach assigns to each core an equal number of biochemical reactions without consideration of their solution complexity.

RESULTS

Here, we present Very Fast Flux Variability Analysis (VFFVA) as a parallel implementation that dynamically balances the computation load between the cores in runtime which guarantees equal convergence time between them. VFFVA allowed to gain a threefold speedup factor with coupled models and up to 100 with ill-conditioned models along with a 14-fold decrease in memory usage.

CONCLUSIONS

VFFVA exploits the parallel capabilities of modern machines to enable biological insights through optimizing systems biology modeling. VFFVA is available in C, MATLAB, and Python at https://github.com/marouenbg/VFFVA .

Metabolic reconstruction of Pseudomonas chlororaphis ATCC 9446 to understand its metabolic potential as a phenazine-1-carboxamide-producing strain

Pseudomonas chlororaphis is a plant-associated bacterium with reported antagonistic activity against different organisms and plant growth-promoting properties. P. chlororaphis possesses exciting biotechnological features shared with another Pseudomonas with a nonpathogenic phenotype. Part of the antagonistic role of P. chlororaphis is due to its production of a wide variety of phenazines. To expand the knowledge of the metabolic traits of this organism, we constructed the first experimentally validated genome-scale model of P. chlororaphis ATCC 9446, containing 1267 genes and 2289 reactions, and analyzed strategies to maximize its potential for the production of phenazine-1-carboxamide (PCN). The resulting model also describes the capability of P. chlororaphis to carry out the denitrification process and its ability to consume sucrose (Scr), trehalose, mannose, and galactose as carbon sources. Additionally, metabolic network analysis suggested fatty acids as the best carbon source for PCN production. Moreover, the optimization of PCN production was performed with glucose and glycerol. The optimal PCN production phenotype requires an increased carbon flux in TCA and glutamine synthesis. Our simulations highlight the intrinsic H₂O₂ flux associated with PCN production, which may generate cellular stress in an overproducing strain. These results suggest that an improved antioxidative strategy could lead to optimal performance of phenazine-producing strains of P. chlororaphis. KEY POINTS : • This is the first publication of a metabolic model for a strain of P. chlororaphis. • Genome-scale model is worthy tool to increase the knowledge of a non model organism. • Fluxes simulations indicate a possible effect of H₂O₂ on phenazines production. • P. chlororaphis can be a suitable model for a wide variety of compounds.

Microbial Polyhydroxyalkanoates and Nonnatural Polyesters

Microorganisms produce diverse polymers for various purposes such as storing genetic information, energy, and reducing power, and serving as structural materials and scaffolds. Among these polymers, polyhydroxyalkanoates (PHAs) are microbial polyesters synthesized and accumulated intracellularly as a storage material of carbon, energy, and reducing power under unfavorable growth conditions in the presence of excess carbon source. PHAs have attracted considerable attention for their wide range of applications in industrial and medical fields. Since the first discovery of PHA accumulating bacteria about 100 years ago, remarkable advances have been made in the understanding of PHA biosynthesis and metabolic engineering of microorganisms toward developing efficient PHA producers. Recently, nonnatural polyesters have also been synthesized by metabolically engineered microorganisms, which opened a new avenue toward sustainable production of more diverse plastics. Herein, the current state of PHAs and nonnatural polyesters is reviewed, covering mechanisms of microbial polyester biosynthesis, metabolic pathways, and enzymes involved in biosynthesis of short-chain-length PHAs, medium-chain-length PHAs, and nonnatural polyesters, especially 2-hydroxyacid-containing polyesters, metabolic engineering strategies to produce novel polymers and enhance production capabilities and fermentation, and downstream processing strategies for cost-effective production of these microbial polyesters. In addition, the applications of PHAs and prospects are discussed.

Industrial biotechnology of Pseudomonas putida: advances and prospects

Pseudomonas putida is a Gram-negative, rod-shaped bacterium that can be encountered in diverse ecological habitats. This ubiquity is traced to its remarkably versatile metabolism, adapted to withstand physicochemical stress, and the capacity to thrive in harsh environments. Owing to these characteristics, there is a growing interest in this microbe for industrial use, and the corresponding research has made rapid progress in recent years. Hereby, strong drivers are the exploitation of cheap renewable feedstocks and waste streams to produce value-added chemicals and the steady progress in genetic strain engineering and systems biology understanding of this bacterium. Here, we summarize the recent advances and prospects in genetic engineering, systems and synthetic biology, and applications of P. putida as a cell factory. KEY POINTS: • Pseudomonas putida advances to a global industrial cell factory. • Novel tools enable system-wide understanding and streamlined genomic engineering. • Applications of P. putida range from bioeconomy chemicals to biosynthetic drugs.

Linking metabolic phenotypes to pathogenic traits among "Candidatus Liberibacter asiaticus" and its hosts

Candidatus Liberibacter asiaticus (CLas) has been associated with Huanglongbing, a lethal vector-borne disease affecting citrus crops worldwide. While comparative genomics has provided preliminary insights into the metabolic capabilities of this uncultured microorganism, a comprehensive functional characterization is currently lacking. Here, we reconstructed and manually curated genome-scale metabolic models for the six CLas strains A4, FL17, gxpsy, Ishi-1, psy62, and YCPsy, in addition to a model of the closest related culturable microorganism, L. crescens BT-1. Predictions about nutrient requirements and changes in growth phenotypes of CLas were confirmed using in vitro hairy root-based assays, while the L. crescens BT-1 model was validated using cultivation assays. Host-dependent metabolic phenotypes were revealed using expression data obtained from CLas-infected citrus trees and from the CLas-harboring psyllid Diaphorina citri Kuwayama. These results identified conserved and unique metabolic traits, as well as strain-specific interactions between CLas and its hosts, laying the foundation for the development of model-driven Huanglongbing management strategies.

Pseudomonas putida KT2440 is naturally endowed to withstand industrial-scale stress conditions

Pseudomonas putida is recognized as a very promising strain for industrial application due to its high redox capacity and frequently observed tolerance towards organic solvents. In this research, we studied the metabolic and transcriptional response of P. putida KT2440 exposed to large-scale heterogeneous mixing conditions in the form of repeated glucose shortage. Cellular responses were mimicked in an experimental setup comprising a stirred tank reactor and a connected plug flow reactor. We deciphered that a stringent response-like transcriptional regulation programme is frequently induced, which seems to be linked to the intracellular pool of 3-hydroxyalkanoates (3-HA) that are known to serve as precursors for polyhydroxyalkanoates (PHA). To be precise, P. putida is endowed with a survival strategy likely to access cellular PHA, amino acids and glycogen in few seconds under glucose starvation to obtain ATP from respiration, thereby replenishing the reduced ATP levels and the adenylate energy charge. Notably, cells only need 0.4% of glucose uptake to build those 3-HA-based energy buffers. Concomitantly, genes that are related to amino acid catabolism and β-oxidation are upregulated during the transient absence of glucose. Furthermore, we provide a detailed list of transcriptional short- and long-term responses that increase the cellular maintenance by about 17% under the industrial-like conditions tested.

An extended and generalized framework for the calculation of metabolic intervention strategies based on minimal cut sets

The concept of minimal cut sets (MCS) provides a flexible framework for analyzing properties of metabolic networks and for computing metabolic intervention strategies. In particular, it has been used to support the targeted design of microbial strains for bio-based production processes. Herein we present a number of major extensions that generalize the existing MCS approach and broaden its scope for applications in metabolic engineering. We first introduce a modified approach to integrate gene-protein-reaction associations (GPR) in the metabolic network structure for the computation of gene-based intervention strategies. In particular, we present a set of novel compression rules for GPR associations, which effectively speedup the computation of gene-based MCS by a factor of up to one order of magnitude. These rules are not specific for MCS and as well applicable to other computational strain design methods. Second, we enhance the MCS framework by allowing the definition of multiple target (undesired) and multiple protected (desired) regions. This enables precise tailoring of the metabolic solution space of the designed strain with unlimited flexibility. Together with further generalizations such as individual cost factors for each intervention, direct combinations of reaction/gene deletions and additions as well as the possibility to search for substrate co-feeding strategies, the scope of the MCS framework could be broadly extended. We demonstrate the applicability and performance benefits of the described developments by computing (gene-based) Escherichia coli strain designs for the bio-based production of 2,3-butanediol, a chemical, that has recently received much attention in the field of metabolic engineering. With our extended framework, we could identify promising strain designs that were formerly unpredictable, including those based on substrate co-feeding.

Narrative of a versatile and adept species Pseudomonas putida

Pseudomonas putidais a fast-growing bacterium found mostly in temperate soil and water habitats. The metabolic versatility ofP. putidamakes this organism attractive for biotechnological applications such as biodegradation of environmental pollutants and synthesis of added-value chemicals (biocatalysis). This organism has been extensively studied in respect to various stress responses, mechanisms of genetic plasticity and transcriptional regulation of catabolic genes.P. putidais able to colonize the surface of living organisms, but is generally considered to be of low virulence. A number ofP. putidastrains are able to promote plant growth. The aim of this review is to give historical overview of the discovery of the speciesP. putidaand isolation and characterization ofP. putidastrains displaying potential for biotechnological applications. This review also discusses some major findings inP. putidaresearch encompassing regulation of catabolic operons, stress-tolerance mechanisms and mechanisms affecting evolvability of bacteria under conditions of environmental stress.

Large-scale kinetic metabolic models of Pseudomonas putida KT2440 for consistent design of metabolic engineering strategies

BACKGROUND

Pseudomonas putida is a promising candidate for the industrial production of biofuels and biochemicals because of its high tolerance to toxic compounds and its ability to grow on a wide variety of substrates. Engineering this organism for improved performances and predicting metabolic responses upon genetic perturbations requires reliable descriptions of its metabolism in the form of stoichiometric and kinetic models.

RESULTS

In this work, we developed kinetic models of P. putida to predict the metabolic phenotypes and design metabolic engineering interventions for the production of biochemicals. The developed kinetic models contain 775 reactions and 245 metabolites. Furthermore, we introduce here a novel set of constraints within thermodynamics-based flux analysis that allow for considering concentrations of metabolites that exist in several compartments as separate entities. We started by a gap-filling and thermodynamic curation of iJN1411, the genome-scale model of P. putida KT2440. We then systematically reduced the curated iJN1411 model, and we created three core stoichiometric models of different complexity that describe the central carbon metabolism of P. putida. Using the medium complexity core model as a scaffold, we generated populations of large-scale kinetic models for two studies. In the first study, the developed kinetic models successfully captured the experimentally observed metabolic responses to several single-gene knockouts of a wild-type strain of P. putida KT2440 growing on glucose. In the second study, we used the developed models to propose metabolic engineering interventions for improved robustness of this organism to the stress condition of increased ATP demand.

CONCLUSIONS

The study demonstrates the potential and predictive capabilities of the kinetic models that allow for rational design and optimization of recombinant P. putida strains for improved production of biofuels and biochemicals. The curated genome-scale model of P. putida together with the developed large-scale stoichiometric and kinetic models represents a significant resource for researchers in industry and academia.

Pseudomonad reverse carbon catabolite repression, interspecies metabolite exchange, and consortial division of labor

Microorganisms acquire energy and nutrients from dynamic environments, where substrates vary in both type and abundance. The regulatory system responsible for prioritizing preferred substrates is known as carbon catabolite repression (CCR). Two broad classes of CCR have been documented in the literature. The best described CCR strategy, referred to here as classic CCR (cCCR), has been experimentally and theoretically studied using model organisms such as Escherichia coli. cCCR phenotypes are often used to generalize universal strategies for fitness, sometimes incorrectly. For instance, extremely competitive microorganisms, such as Pseudomonads, which arguably have broader global distributions than E. coli, have achieved their success using metabolic strategies that are nearly opposite of cCCR. These organisms utilize a CCR strategy termed 'reverse CCR' (rCCR), because the order of preferred substrates is nearly reverse that of cCCR. rCCR phenotypes prefer organic acids over glucose, may or may not select preferred substrates to optimize growth rates, and do not allocate intracellular resources in a manner that produces an overflow metabolism. cCCR and rCCR have traditionally been interpreted from the perspective of monocultures, even though most microorganisms live in consortia. Here, we review the basic tenets of the two CCR strategies and consider these phenotypes from the perspective of resource acquisition in consortia, a scenario that surely influenced the evolution of cCCR and rCCR. For instance, cCCR and rCCR metabolism are near mirror images of each other; when considered from a consortium basis, the complementary properties of the two strategies can mitigate direct competition for energy and nutrients and instead establish cooperative division of labor.

Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models

Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems.

Metabolite profiling of the cold adaptation of Pseudomonas putida KT2440 and cold-sensitive mutants

Free-living bacteria such as Pseudomonas putida are frequently exposed to temperature shifts and non-optimal growth conditions. We compared the transcriptome and metabolome of the cold adaptation of P. putida KT2440 and isogenic cold-sensitive transposon mutants carrying transposons in their cbrA, cbrB, pcnB, vacB, and bipA genes. Pseudomonas putida changes the mRNA expression of about 43% of all annotated open reading frames during this initial phase of cold adaptation, but only a small number of 6-93 genes were differentially expressed at 10°C between the wild-type strain and the individual mutants. The spectrum of metabolites underwent major changes during cold adaptation particularly in the mutants. Both the KT2440 strain and the mutants increased the levels of the most abundant sugars and amino acids which were more pronounced in the cold-sensitive mutants. All mutants depleted their pools for core metabolites of aromatic and sugar metabolism, but increased their pool of polar amino acids which should be advantageous to cope with the cold stress.

High-quality genome-scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities

Genome-scale reconstructions of metabolism are computational species-specific knowledge bases able to compute systemic metabolic properties. We present a comprehensive and validated reconstruction of the biotechnologically relevant bacterium Pseudomonas putida KT2440 that greatly expands computable predictions of its metabolic states. The reconstruction represents a significant reactome expansion over available reconstructed bacterial metabolic networks. Specifically, iJN1462 (i) incorporates several hundred additional genes and associated reactions resulting in new predictive capabilities, including new nutrients supporting growth; (ii) was validated by in vivo growth screens that included previously untested carbon (48) and nitrogen (41) sources; (iii) yielded gene essentiality predictions showing large accuracy when compared with a knock-out library and Bar-seq data; and (iv) allowed mapping of its network to 82 P. putida sequenced strains revealing functional core that reflect the large metabolic versatility of this species, including aromatic compounds derived from lignin. Thus, this study provides a thoroughly updated metabolic reconstruction and new computable phenotypes for P. putida, which can be leveraged as a first step toward understanding the pan metabolic capabilities of Pseudomonas.

Influence of the Crc global regulator on substrate uptake rates and the distribution of metabolic fluxes in Pseudomonas putida KT2440 growing in a complete medium

When the soil bacterium Pseudomonas putida grows in a complete medium, it prioritizes the assimilation of preferred carbon sources, optimizing its metabolism and growth. This regulatory process is orchestrated by the Crc and Hfq proteins. The present work examines the changes that occur in metabolic fluxes when the crc gene is inactivated and cells grow exponentially in LB complete medium. Analyses were performed at three different moments during exponential growth, examining the assimilation rates for the compounds present in LB, changes in the proteome, and the changes in metabolic fluxes predicted by the iJN1411 metabolic model for P. putida KT2440. During the early exponential phase, consumption rates for sugars, many organic acids and most amino acids were higher in a Crc-null strain than in the wild type, leading to an overflow of the metabolic pathways and the leakage of pyruvate and acetate. These accelerated consumption rates decreased during the mid-exponential phase, when cells mostly used sugars and alanine. At later times, pyruvate was recovered from the medium and utilized. The higher consumption rates of the Crc-null strain reduced the growth rate. The lack of the Crc/Hfq regulatory system thus led to unbalanced metabolism with poorly optimized metabolic fluxes.

In silico-guided engineering of Pseudomonas putida towards growth under micro-oxic conditions

BACKGROUND

Pseudomonas putida is a metabolically versatile, genetically accessible, and stress-robust species with outstanding potential to be used as a workhorse for industrial applications. While industry recognises the importance of robustness under micro-oxic conditions for a stable production process, the obligate aerobic nature of P. putida, attributed to its inability to produce sufficient ATP and maintain its redox balance without molecular oxygen, severely limits its use for biotechnology applications.

RESULTS

Here, a combination of genome-scale metabolic modelling and comparative genomics is used to pinpoint essential [Formula: see text]-dependent processes. These explain the inability of the strain to grow under anoxic conditions: a deficient ATP generation and an inability to synthesize essential metabolites. Based on this, several P. putida recombinant strains were constructed harbouring acetate kinase from Escherichia coli for ATP production, and a class I dihydroorotate dehydrogenase and a class III anaerobic ribonucleotide triphosphate reductase from Lactobacillus lactis for the synthesis of essential metabolites. Initial computational designs were fine-tuned by means of adaptive laboratory evolution.

CONCLUSIONS

We demonstrated the value of combining in silico approaches, experimental validation and adaptive laboratory evolution for microbial design by making the strictly aerobic Pseudomonas putida able to grow under micro-oxic conditions.

Mevalonate production from ethanol by direct conversion through acetyl-CoA using recombinant Pseudomonas putida, a novel biocatalyst for terpenoid production

Background

Bioethanol is one of the most representative eco-friendly fuels developed to replace the non-renewable fossil fuels and is the most successful commercially available bio-conversion technology till date. With the availability of inexpensive carbon sources, such as cellulosic biomass, bioethanol production has become cheaper and easier to perform, which can facilitate the development of methods for converting ethanol into higher value-added biochemicals. In this study, a bioconversion process using Pseudomonas putida as a biocatalyst was established, wherein ethanol was converted to mevalonate. Since ethanol can be converted directly to acetyl-CoA, bypassing its conversion to pyruvate, there is a possibility that ethanol can be converted to mevalonate without producing pyruvate-derived by-products. Furthermore, P. putida seems to be highly resistant to the toxicity caused by terpenoids, and thus can be useful in conducting terpenoid production research.

Results

In this study, we first expressed the core genes responsible for mevalonate production (atoB, mvaS, and mvaE) in P. putida and mevalonate production was confirmed. Thereafter, through an improvement in genetic stability and ethanol metabolism manipulation, mevalonate production was enhanced up to 2.39-fold (1.70 g/L vs. 4.07 g/L) from 200 mM ethanol with an enhancement in reproducibility of mevalonate production. Following this, the metabolic characteristics related to ethanol catabolism and mevalonate production were revealed by manipulations to reduce fatty acid biosynthesis and optimize pH by batch fermentation. Finally, we reached a product yield of 0.41 g mevalonate/g ethanol in flask scale culture and 0.32 g mevalonate/g ethanol in batch fermentation. This is the highest experimental yield obtained from using carbon sources other than carbohydrates till date and it is expected that further improvements will be made through the development of fermentation methods.

Conclusion

Pseudomonas putida was investigated as a biocatalyst that can efficiently convert ethanol to mevalonate, the major precursor for terpenoid production, and this research is expected to open new avenues for the production of terpenoids using microorganisms that have not yet reached the stage of mass production.

Pseudomonas putida KT2440 metabolism undergoes sequential modifications during exponential growth in a complete medium as compounds are gradually consumed

Pseudomonas putida is a soil bacterium with a versatile and robust metabolism. When confronted with mixtures of carbon sources, it prioritizes the utilization of the preferred compounds, optimizing metabolism and growth. This response is particularly strong when growing in a complex medium such as LB. This work examines the changes occurring in P. putida KT2440 metabolic fluxes, while it grows exponentially in LB medium and sequentially consumes the compounds available. Integrating the uptake rates for each compound at three different moments during the exponential growth with the changes observed in the proteome, and with the metabolic fluxes predicted by the iJN1411 metabolic model for this strain, allowed the metabolic rearrangements that occurred to be determined. The results indicate that the bacterium changes significantly the configuration of its metabolism during the early, mid and late exponential phases of growth. Sugars served as an energy source during the early phase and later as energy and carbon source. The configuration of the tricarboxylic acids cycle varied during growth, providing no energy in the early phase, and turning to a reductive mode in the mid phase and to an oxidative mode later on. This work highlights the dynamism and flexibility of P. putida metabolism.

Modeling metabolic networks of individual bacterial agents in heterogeneous and dynamic soil habitats (IndiMeSH)

Natural soil is characterized as a complex habitat with patchy hydrated islands and spatially variable nutrients that is in a constant state of change due to wetting-drying dynamics. Soil microbial activity is often concentrated in sparsely distributed hotspots that contribute disproportionally to macroscopic biogeochemical nutrient cycling and greenhouse gas emissions. The mechanistic representation of such dynamic hotspots requires new modeling approaches capable of representing the interplay between dynamic local conditions and the versatile microbial metabolic adaptations. We have developed IndiMeSH (Individual-based Metabolic network model for Soil Habitats) as a spatially explicit model for the physical and chemical microenvironments of soil, combined with an individual-based representation of bacterial motility and growth using adaptive metabolic networks. The model uses angular pore networks and a physically based description of the aqueous phase as a backbone for nutrient diffusion and bacterial dispersal combined with dynamic flux balance analysis to calculate growth rates depending on local nutrient conditions. To maximize computational efficiency, reduced scale metabolic networks are used for the simulation scenarios and evaluated strategically to the genome scale model. IndiMeSH was compared to a well-established population-based spatiotemporal metabolic network model (COMETS) and to experimental data of bacterial spatial organization in pore networks mimicking soil aggregates. IndiMeSH was then used to strategically study dynamic response of a bacterial community to abrupt environmental perturbations and the influence of habitat geometry and hydration conditions. Results illustrate that IndiMeSH is capable of representing trophic interactions among bacterial species, predicting the spatial organization and segregation of bacterial populations due to oxygen and carbon gradients, and provides insights into dynamic community responses as a consequence of environmental changes. The modular design of IndiMeSH and its implementation are adaptable, allowing it to represent a wide variety of experimental and in silico microbial systems.

Soil bacterial communities are key players in global biogeochemical cycles and drive other soil regulatory and provisional ecosystem functions. Despite the relatively high bacterial abundance found in fertile soil, bacteria occupy only a small fraction of the soil surfaces and often form hotspots with disproportionate contributions to observed biogenic fluxes. As soil opacity and complexity limit detailed observations of such hotspots in situ, we have developed a modeling platform, IndiMeSH (Individual-based Metabolic network model for Soil Habitats), to enable systematic study of dense multispecies bacterial communities within a structured habitat resembling (but not limited) to soil. The model is capable of representing multispecies trophic interactions and spatial self-organization in response to nutrient gradients, as confirmed in comparison with published results. IndiMeSH offers new opportunities for quantifying bacterial hotspot formation and dynamics and observe their resilience and response to perturbations in hydration and nutrient conditions.

Genome-Scale Metabolic Model of a Microbial Cell Factory (Brevibacillus thermoruber 423) with Multi-Industry Potentials for Exopolysaccharide Production

Brevibacillus thermoruber 423 is a thermophilic bacterium capable of producing high levels of exopolysaccharide (EPS) that has broad applications in nutrition, feed, cosmetics, pharmaceutical, and chemical industries, not to mention in health and bionanotechnology sectors. EPS is a natural, nontoxic, and biodegradable polymer of sugar residues and plays pivotal roles in cell-to-cell interactions, adhesion, biofilm formation, and protection of cell against environmental extremes. This bacterium is a thermophilic EPS producer while exceeding other thermophilic producers by virtue of high level of polymer synthesis. Recently, B. thermoruber 423 was noted for relevance to multiple industry sectors because of its capacity to use xylose, and produce EPS, isoprenoids, ethanol/butanol, lipases, proteases, cellulase, and glucoamylase enzymes as well as its resistance to arsenic. A key step in understanding EPS production with a systems-based approach is the knowledge of microbial genome sequence. To speed biotechnology and industrial applications, this study reports on a genome-scale metabolic model (GSMM) of B. thermoruber 423, constructed using the recently available high-quality genome sequence that we have subsequently validated using physiological data on batch growth and EPS production on seven different carbon sources. The model developed contains 1454 reactions (of which 1127 are assigned an enzyme commission number) and 1410 metabolites from 925 genes. This GSMM offers the promise to enable and accelerate further systems biology and industrial scale studies, not to mention the ability to calculate metabolic flux distribution in large networks and multiomic data integration.

Analysis of Pseudomonas putida growth on non-trivial carbon sources using transcriptomics and genome-scale modelling

Pseudomonas putida is characterized by a versatile metabolism and stress tolerance traits that allow the bacterium to cope with different environmental conditions. In this work, the mechanisms that allow P. putida KT2440 to grow in the presence of four sole carbon sources (glucose, citrate, ferulic acid, serine) were investigated by RNA sequencing (RNA-seq) and genome-scale metabolic modelling. Transcriptomic data identified uptake systems for the four carbon sources, and candidates were subjected to preliminary experimental characterization by mutant strain growth to test their involvement in substrate assimilation. The OpdH and BenF-like porins were involved in citrate and ferulic acid uptake respectively. The citrate transporter (encoded by PP_0147) and the TctABC system were important for supporting cell growth in citrate; PcaT and VanK were associated with ferulic acid uptake; and the ABC transporter AapJPQM was involved in serine transport. A genome-scale metabolic model of P. putida KT2440 was used to integrate and analyze the transcriptomic data, identifying and confirming the active catabolic pathways for each carbon source. This study reveals novel information about transporters that are essential for understanding bacterial adaptation to different environments.

New Applications of Synthetic Biology Tools for Cyanobacterial Metabolic Engineering

Cyanobacteria are promising microorganisms for sustainable biotechnologies, yet unlocking their potential requires radical re-engineering and application of cutting-edge synthetic biology techniques. In recent years, the available devices and strategies for modifying cyanobacteria have been increasing, including advances in the design of genetic promoters, ribosome binding sites, riboswitches, reporter proteins, modular vector systems, and markerless selection systems. Because of these new toolkits, cyanobacteria have been successfully engineered to express heterologous pathways for the production of a wide variety of valuable compounds. Cyanobacterial strains with the potential to be used in real-world applications will require the refinement of genetic circuits used to express the heterologous pathways and development of accurate models that predict how these pathways can be best integrated into the larger cellular metabolic network. Herein, we review advances that have been made to translate synthetic biology tools into cyanobacterial model organisms and summarize experimental and in silico strategies that have been employed to increase their bioproduction potential. Despite the advances in synthetic biology and metabolic engineering during the last years, it is clear that still further improvements are required if cyanobacteria are to be competitive with heterotrophic microorganisms for the bioproduction of added-value compounds.

CRISPR interference-mediated gene regulation in Pseudomonas putida KT2440

Targeted gene regulation is indispensable for reprogramming a cellular network to modulate a microbial phenotype. Here, we adopted the type II CRISPR interference (CRISPRi) system for simple and efficient regulation of target genes in Pseudomonas putida KT2440. A single CRISPRi plasmid was generated to express a nuclease-deficient Cas9 gene and a designed single guide RNA, under control of l-rhamnose-inducible P_{rha BAD} and the constitutive Biobrick J23119 promoter respectively. Two target genes were selected to probe the CRISPRi-mediated gene regulation: exogenous green fluorescent protein on the multicopy plasmid and endogenous glpR on the P. putida KT2440 chromosome, encoding GlpR, a transcriptional regulator that represses expression of the glpFKRD gene cluster for glycerol utilization. The CRISPRi system successfully repressed the two target genes, as evidenced by a reduction in the fluorescence intensity and the lag phase of P. putida KT2440 cell growth on glycerol. Furthermore, CRISPRi-mediated repression of glpR improved both the cell growth and glycerol utilization, resulting in the enhanced production of mevalonate in an engineered P. putida KT2440 harbouring heterologous genes for the mevalonate pathway. CRISPRi is expected to become a robust tool to reprogram P. putida KT2440 for the development of microbial cell factories producing industrially valuable products.

Kinetic understanding of nitrogen supply condition on biosynthesis of polyhydroxyalkanoate from benzoate by Pseudomonas putida KT2440

Nitrogen supply is critical to the synthesis of intracellular PHA in various bacteria. However, the specific role of the nitrogen in synthesizing PHA from benzoate, a lignin model compound use for the study of bacteria catabolism of aromatics, is still not clear. In this study, two culture conditions were maintained for Pseudomonas putida KT2440 to produce PHA using benzoate as a carbon source. Under nitrogen-limited and surplus conditions, the accumulation of PHA was to 37.3% and 0.25% of cell dry weight, respectively. A model fit to the kinetics of biomass growth and PHA accumulation showed good agreement with data. GC-MS and NMR showed that PHA contained six hydroxyl fatty acid monomers under nitrogen-limited conditions, while two monomers were identified under nitrogen surplus conditions. The average molecular weight of PHA increased after the nitrogen source was exhausted. These results provide a promising strategy for optimization of lignin to PHA yields.

Chasing bacterial chassis for metabolic engineering: a perspective review from classical to non-traditional microorganisms

The last few years have witnessed an unprecedented increase in the number of novel bacterial species that hold potential to be used for metabolic engineering. Historically, however, only a handful of bacteria have attained the acceptance and widespread use that are needed to fulfil the needs of industrial bioproduction - and only for the synthesis of very few, structurally simple compounds. One of the reasons for this unfortunate circumstance has been the dearth of tools for targeted genome engineering of bacterial chassis, and, nowadays, synthetic biology is significantly helping to bridge such knowledge gap. Against this background, in this review, we discuss the state of the art in the rational design and construction of robust bacterial chassis for metabolic engineering, presenting key examples of bacterial species that have secured a place in industrial bioproduction. The emergence of novel bacterial chassis is also considered at the light of the unique properties of their physiology and metabolism, and the practical applications in which they are expected to outperform other microbial platforms. Emerging opportunities, essential strategies to enable successful development of industrial phenotypes, and major challenges in the field of bacterial chassis development are also discussed, outlining the solutions that contemporary synthetic biology-guided metabolic engineering offers to tackle these issues.

CiliateGEM: an open-project and a tool for predictions of ciliate metabolic variations and experimental condition design

BACKGROUND

The study of cell metabolism is becoming central in several fields such as biotechnology, evolution/adaptation and human disease investigations. Here we present CiliateGEM, the first metabolic network reconstruction draft of the freshwater ciliate Tetrahymena thermophila. We also provide the tools and resources to simulate different growth conditions and to predict metabolic variations. CiliateGEM can be extended to other ciliates in order to set up a meta-model, i.e. a metabolic network reconstruction valid for all ciliates. Ciliates are complex unicellular eukaryotes of presumably monophyletic origin, with a phylogenetic position that is equal from plants and animals. These cells represent a new concept of unicellular system with a high degree of species, population biodiversity and cell complexity. Ciliates perform in a single cell all the functions of a pluricellular organism, including locomotion, feeding, digestion, and sexual processes.

RESULTS

After generating the model, we performed an in-silico simulation with the presence and absence of glucose. The lack of this nutrient caused a 32.1% reduction rate in biomass synthesis. Despite the glucose starvation, the growth did not stop due to the use of alternative carbon sources such as amino acids.

CONCLUSIONS

The future models obtained from CiliateGEM may represent a new approach to describe the metabolism of ciliates. This tool will be a useful resource for the ciliate research community in order to extend these species as model organisms in different research fields. An improved understanding of ciliate metabolism could be relevant to elucidate the basis of biological phenomena like genotype-phenotype relationships, population genetics, and cilia-related disease mechanisms.