Genome-scale reconstruction and analysis of the Pseudomonas putida KT2440 metabolic network facilitates applications in biotechnology

Using metabolic networks to predict cross-feeding and competition interactions between microorganisms

Understanding the interactions between microorganisms and their impact on bacterial behavior at the community level is a key research topic in microbiology. Different methods, relying on experimental or mathematical approaches based on the diverse properties of bacteria, are currently employed to study these interactions. Recently, the use of metabolic networks to understand the interactions between bacterial pairs has increased, highlighting the relevance of this approach in characterizing bacteria. In this study, we leverage the representation of bacteria through their metabolic networks to build a predictive model aimed at reducing the number of experimental assays required for designing bacterial consortia with specific behaviors. Our novel method for predicting cross-feeding or competition interactions between pairs of microorganisms utilizes metabolic network features. Machine learning classifiers are employed to determine the type of interaction from automatically reconstructed metabolic networks. Several algorithms were assessed and selected based on comprehensive testing and careful separation of manually compiled data sets obtained from literature sources. We used different classification algorithms, including K Nearest Neighbors, XGBoost, Support Vector Machine, and Random Forest, tested different parameter values, and implemented several data curation approaches to reduce the biological bias associated with our data set, ultimately achieving an accuracy of over 0.9. Our method holds substantial potential to advance the understanding of community behavior and contribute to the development of more effective approaches for consortia design.IMPORTANCEUnderstanding bacterial interactions at the community level is critical for microbiology, and leveraging metabolic networks presents an efficient and effective approach. The introduction of this novel method for predicting interactions through machine learning classifiers has the potential to advance the field by reducing the number of experimental assays required and contributing to the development of more effective bacterial consortia.

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.

Reconstruction and metabolic profiling of the genome-scale metabolic network model of Pseudomonas stutzeri A1501

Pseudomonas stutzeri A1501 is a non-fluorescent denitrifying bacteria that belongs to the gram-negative bacterial group. As a prominent strain in the fields of agriculture and bioengineering, there is still a lack of comprehensive understanding regarding its metabolic capabilities, specifically in terms of central metabolism and substrate utilization. Therefore, further exploration and extensive studies are required to gain a detailed insight into these aspects. This study reconstructed a genome-scale metabolic network model for P. stutzeri A1501 and conducted extensive curations, including correcting energy generation cycles, respiratory chains, and biomass composition. The final model, iQY1018, was successfully developed, covering more genes and reactions and having higher prediction accuracy compared with the previously published model iPB890. The substrate utilization ability of 71 carbon sources was investigated by BIOLOG experiment and was utilized to validate the model quality. The model prediction accuracy of substrate utilization for P. stutzeri A1501 reached 90 %. The model analysis revealed its new ability in central metabolism and predicted that the strain is a suitable chassis for the production of Acetyl CoA-derived products. This work provides an updated, high-quality model of P. stutzeri A1501for further research and will further enhance our understanding of the metabolic capabilities.

Development of genetic tools for heterologous protein expression in a pentose-utilizing environmental isolate of Pseudomonas putida

Pseudomonas putida has emerged as a promising host for the conversion of biomass-derived sugars and aromatic intermediates into commercially relevant biofuels and bioproducts. Most of the strain development studies previously published have focused on P. putida KT2440, which has been engineered to produce a variety of non-native bioproducts. However, P. putida is not capable of metabolizing pentose sugars, which can constitute up to 25% of biomass hydrolysates. Related P. putida isolates that metabolize a larger fraction of biomass-derived carbon may be attractive as complementary hosts to P. putida KT2440. Here we describe genetic tool development for P. putida M2, a soil isolate that can metabolize pentose sugars. The functionality of five inducible promoter systems and 12 ribosome binding sites was assessed to regulate gene expression. The utility of these expression systems was confirmed by the production of indigoidine from C6 and C5 sugars. Chromosomal integration and expression of non-native genes was achieved by using chassis-independent recombinase-assisted genome engineering (CRAGE) for single-step gene integration of biosynthetic pathways directly into the genome of P. putida M2. These genetic tools provide a foundation to develop hosts complementary to P. putida KT2440 and expand the ability of this versatile microbial group to convert biomass to bioproducts.

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.

Flux balance analysis of metabolic networks for efficient engineering of microbial cell factories

Metabolic engineering principles have long been applied to explore the metabolic networks of complex microbial cell factories under a variety of environmental constraints for effective deployment of the microorganisms in the optimal production of biochemicals like biofuels, polymers, amino acids, recombinant proteins. One of the methodologies used for analyzing microbial metabolic networks is the Flux Balance Analysis (FBA), which employs applications of optimization techniques for forecasting biomass growth and metabolic flux distribution of industrially important products under specified environmental conditions. The in silico flux simulations are instrumental for designing the production-specific microbial cell factories. However, FBA has some inherent limitations. The present review emphasizes how the incorporation of additional kinetic, thermodynamic, expression and regulatory constraints and integration of omics data into the classical FBA platform improve the prediction accuracy of FBA. A programmed comparison of the simulated data with the experimental observations is presented for supporting the claim. The review further accounts for the successful implementation of classical FBA in biotechnological applications and identifies areas in which classical FBA fails to make correct predictions. The analysis of the predictive capabilities of the different FBA strategies presented here is expected to help researchers in finding new avenues in engineering highly efficient microbial metabolic pathways and identify the key metabolic bottlenecks during the process. Based on the appropriate metabolic network design, fermentation engineers will be able to effectively design the bioreactors and optimize large-scale biochemical production through suitable pathway modifications.

Enhancement of MBBR reactor efficiency using effective microorganism for treatment of wastewater containing diazinon by engineered Pseudomonas putida KT2440 with manganese peroxidase 2 gene

Pesticides not only are harmful to humans but they are noxious for water reservoirs, soil, and air quality as well. In this research, diazinon was removed from aqueous solutions by Moving Bed Biofilm Reactor (MBBR). The MBBR was spiked with transgenic Pseudomonas putida KT2440 with Pleurotus ostreatus fungus manganese peroxidase 2 gene to enhance the capabilities of Pseudomonas putida KT2440 in the degradation of diazinon. Although the amount of diazinon and COD and diazinon removal in the reactor including transgenic P. putida KT2440 was 95.46% and 97.47% and they were greater than the control and wild type (non-modified) P. putida KT2440 reactors, the surprising result was related to the adaptation pace of transgenic P. putida KT2440. The produced metabolites and the quantity of diazinon were assessed by HPLC and LC/MS. The metabolite hydroxyisopropyl diazinon was not found in the transgenic P. putida KT2440 reactor. Furthermore, a new sequence of cloned manganese peroxidase 2 gene has been recorded in GenBank with the accession number MT185558. According to bacterial identification of provided sludge the most frequent genus belonged to Aeromonas. Therefore, it seems that the MBBR in the presence of transgenic P. putida KT2440 with manganese peroxidase 2 gene can effectively remove the diazinon.

Experimental evolution of Pseudomonas putida under silver ion versus nanoparticle stress

Whether the antibacterial properties of silver nanoparticles (AgNPs) are simply due to the release of silver ions (Ag⁺ ) or, additionally, nanoparticle-specific effects, is not clear. We used experimental evolution of the model environmental bacterium Pseudomonas putida to ask whether bacteria respond differently to Ag⁺ or AgNP treatment. We pre-evolved five cultures of strain KT2440 for 70 days without Ag to reduce confounding adaptations before dividing the fittest pre-evolved culture into five cultures each, evolving in the presence of low concentrations of Ag⁺ , well-defined AgNPs or Ag-free controls for a further 75 days. The mutations in the Ag⁺ or AgNP evolved populations displayed different patterns that were statistically significant. The non-synonymous mutations in AgNP-treated populations were mostly associated with cell surface proteins, including cytoskeletal membrane protein (FtsZ), membrane sensor and regulator (EnvZ and GacS) and periplasmic protein (PP_2758). In contrast, Ag⁺ treatment was selected for mutations linked to cytoplasmic proteins, including metal ion transporter (TauB) and those with metal-binding domains (ThiL and PP_2397). These results suggest the existence of AgNP-specific effects, either caused by sustained delivery of Ag⁺ from AgNP dissolution, more proximate delivery from cell-surface bound AgNPs, or by direct AgNP action on the cell's outer membrane.

When metabolic prowess is too much of a good thing: how carbon catabolite repression and metabolic versatility impede production of esterified α,ω-diols in Pseudomonas putida KT2440

BACKGROUND

Medium-chain-length α,ω-diols (mcl-diols) are important building blocks in polymer production. Recently, microbial mcl-diol production from alkanes was achieved in E. coli (albeit at low rates) using the alkane monooxygenase system AlkBGTL and esterification module Atf1. Owing to its remarkable versatility and conversion capabilities and hence potential for enabling an economically viable process, we assessed whether the industrially robust P. putida can be a suitable production organism of mcl-diols.

RESULTS

AlkBGTL and Atf1 were successfully expressed as was shown by oxidation of alkanes to alkanols, and esterification to alkyl acetates. However, the conversion rate was lower than that by E. coli, and not fully to diols. The conversion was improved by using citrate instead of glucose as energy source, indicating that carbon catabolite repression plays a role. By overexpressing the activator of AlkBGTL-Atf1, AlkS and deleting Crc or CyoB, key genes in carbon catabolite repression of P. putida increased diacetoxyhexane production by 76% and 65%, respectively. Removing Crc/Hfq attachment sites of mRNAs resulted in the highest diacetoxyhexane production. When the intermediate hexyl acetate was used as substrate, hexanol was detected. This indicated that P. putida expressed esterases, hampering accumulation of the corresponding esters and diesters. Sixteen putative esterase genes present in P. putida were screened and tested. Among them, Est12/K was proven to be the dominant one. Deletion of Est12/K halted hydrolysis of hexyl acetate and diacetoxyhexane. As a result of relieving catabolite repression and preventing the hydrolysis of ester, the optimal strain produced 3.7 mM hexyl acetate from hexane and 6.9 mM 6-hydroxy hexyl acetate and diacetoxyhexane from hexyl acetate, increased by 12.7- and 4.2-fold, respectively, as compared to the starting strain.

CONCLUSIONS

This study shows that the metabolic versatility of P. putida, and the associated carbon catabolite repression, can hinder production of diols and related esters. Growth on mcl-alcohol and diol esters could be prevented by deleting the dominant esterase. Carbon catabolite repression could be relieved by removing the Crc/Hfq attachment sites. This strategy can be used for efficient expression of other genes regulated by Crc/Hfq in Pseudomonas and related species to steer bioconversion processes.

ReScribe: An Unrestrained Tool Combining Multiplex Recombineering and Minimal-PAM ScCas9 for Genome Recoding Pseudomonas putida

Genome recoding enables incorporating new functions into the DNA of microorganisms. By reassigning codons to noncanonical amino acids, the generation of new-to-nature proteins offers countless opportunities for bioproduction and biocontainment in industrial chassis. A key bottleneck in genome recoding efforts, however, is the low efficiency of recombineering, which hinders large-scale applications at acceptable speed and cost. To relieve this bottleneck, we developed ReScribe, a highly optimized recombineering tool enhanced by CRISPR-Cas9-mediated counterselection built upon the minimal PAM 5'-NNG-3' of the Streptococcus canis Cas9 (ScCas9). As a proof of concept, we used ReScribe to generate a minimally recoded strain of the industrial chassis Pseudomonas putida by replacing TAG stop codons (functioning as PAMs) of essential metabolic genes with the synonymous TAA. We showed that ReScribe enables nearly 100% engineering efficiency of multiple loci in P. putida, opening promising avenues for genome editing and applications thereof in this bacterium and beyond.

Genetic mapping of highly versatile and solvent-tolerant Pseudomonas putida B6-2 (ATCC BAA-2545) as a 'superstar' for mineralization of PAHs and dioxin-like compounds

Polycyclic aromatic hydrocarbons (PAHs) and dioxin-like compounds, including sulfur, nitrogen and oxygen heterocycles, are widespread and toxic environmental pollutants. A wide variety of microorganisms capable of growing with aromatic polycyclic compounds are essential for bioremediation of the contaminated sites and the Earth's carbon cycle. Here, cells of Pseudomonas putida B6-2 (ATCC BAA-2545) grown in the presence of biphenyl (BP) are able to simultaneously degrade PAHs and their derivatives, even when they are present as mixtures, and tolerate high concentrations of extremely toxic solvents. Genetic analysis of the 6.37 Mb genome of strain B6-2 reveals coexistence of gene clusters responsible for central catabolic systems of aromatic compounds and for solvent tolerance. We used functional transcriptomics and proteomics to identify the candidate genes associated with catabolism of BP and a mixture of BP, dibenzofuran, dibenzothiophene and carbazole. Moreover, we observed dynamic changes in transcriptional levels with BP, including in metabolic pathways of aromatic compounds, chemotaxis, efflux pumps and transporters potentially involved in adaptation to PAHs. This study on the highly versatile activities of strain B6-2 suggests it to be a potentially useful model for bioremediation of polluted sites and for investigation of biochemical, genetic and evolutionary aspects of Pseudomonas.

Simultaneous Quantification of the Concentration and Carbon Isotopologue Distribution of Polar Metabolites in a Single Analysis by Gas Chromatography and Mass Spectrometry

¹³C-isotope tracing is a frequently employed approach to study metabolic pathway activity. When combined with the subsequent quantification of absolute metabolite concentrations, this enables detailed characterization of the metabolome in biological specimens and facilitates computational time-resolved flux quantification. Classically, a ¹³C-isotopically labeled sample is required to quantify ¹³C-isotope enrichments and a second unlabeled sample for the quantification of metabolite concentrations. The rationale for a second unlabeled sample is that the current methods for metabolite quantification rely mostly on isotope dilution mass spectrometry (IDMS) and thus isotopically labeled internal standards are added to the unlabeled sample. This excludes the absolute quantification of metabolite concentrations in ¹³C-isotopically labeled samples. To address this issue, we have developed and validated a new strategy using an unlabeled internal standard to simultaneously quantify metabolite concentrations and ¹³C-isotope enrichments in a single ¹³C-labeled sample based on gas chromatography-mass spectrometry (GC/MS). The method was optimized for amino acids and citric acid cycle intermediates and was shown to have high analytical precision and accuracy. Metabolite concentrations could be quantified in small tissue samples (≥20 mg). Also, we applied the method on ¹³C-isotopically labeled mammalian cells treated with and without a metabolic inhibitor. We proved that we can quantify absolute metabolite concentrations and ¹³C-isotope enrichments in a single ¹³C-isotopically labeled sample.

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.

Addressing uncertainty in genome-scale metabolic model reconstruction and analysis

The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.

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.

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.

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.

Modeling the metabolic interplay between a parasitic worm and its bacterial endosymbiont allows the identification of novel drug targets

The filarial nematode Brugia malayi represents a leading cause of disability in the developing world, causing lymphatic filariasis in nearly 40 million people. Currently available drugs are not well-suited to mass drug administration efforts, so new treatments are urgently required. One potential vulnerability is the endosymbiotic bacteria Wolbachia-present in many filariae-which is vital to the worm. Genome scale metabolic networks have been used to study prokaryotes and protists and have proven valuable in identifying therapeutic targets, but have only been applied to multicellular eukaryotic organisms more recently. Here, we present iDC625, the first compartmentalized metabolic model of a parasitic worm. We used this model to show how metabolic pathway usage allows the worm to adapt to different environments, and predict a set of 102 reactions essential to the survival of B. malayi. We validated three of those reactions with drug tests and demonstrated novel antifilarial properties for all three compounds.

Nitrate removal characteristics and 13C metabolic pathways of aerobic denitrifying bacterium Paracoccus denitrificans Z195

Strain Z195 was isolated and identified as Paracoccus denitrificans. Z195 exhibited efficient aerobic denitrification and carbon removal abilities, and removed 93.74% of total nitrogen (TN) and 97.81% of total organic carbon.71.88% of nitrogen was lost as gaseous products.¹³C-metabolic flux analysis revealed that 95% and 132% of the carbon fluxes entered the Entner-Doudoroff (ED) pathway and tricarboxylic acid (TCA) cycle, respectively. Electrons produced by carbon metabolism markedly promoted the processes of nitrogen metabolism process and aerobic respiration. A response surface methodology model demonstrated that the optimal conditions for the maximum TN removal were a C/N ratio of 7.47, shaking speed of 108 rpm, temperature of 31 °C and initial pH of 8.02. Additionally, the average TN and chemical oxygen demand removal efficiencies of raw wastewater were 89% and 91%, respectively. The results give new insight for understanding metabolic flux analysis of aerobic denitrifying bacteria.

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.

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.

Engineered Pseudomonas putida KT2440 co-utilizes galactose and glucose

BACKGROUND

Efficient conversion of plant biomass to commodity chemicals is an important challenge that needs to be solved to enable a sustainable bioeconomy. Deconstruction of biomass to sugars and lignin yields a wide variety of low molecular weight carbon substrates that need to be funneled to product. Pseudomonas putida KT2440 has emerged as a potential platform for bioconversion of lignin and the other components of plant biomass. However, P. putida is unable to natively utilize several of the common sugars in hydrolysate streams, including galactose.

RESULTS

In this work, we integrated a De Ley-Doudoroff catabolic pathway for galactose catabolism into the chromosome of P. putida KT2440, using genes from several different organisms. We found that the galactonate catabolic pathway alone (DgoKAD) supported slow growth of P. putida on galactose. Further integration of genes to convert galactose to galactonate and to optimize the transporter expression level resulted in a growth rate of 0.371 h^-1. Additionally, the best-performing strain was demonstrated to co-utilize galactose with glucose.

CONCLUSIONS

We have engineered P. putida to catabolize galactose, which will allow future engineered strains to convert more plant biomass carbon to products of interest. Further, by demonstrating co-utilization of glucose and galactose, continuous bioconversion processes for mixed sugar streams are now possible.

Manually curated genome-scale reconstruction of the metabolic network of Bacillus megaterium DSM319

Bacillus megaterium is a microorganism widely used in industrial biotechnology for production of enzymes and recombinant proteins, as well as in bioleaching processes. Precise understanding of its metabolism is essential for designing engineering strategies to further optimize B. megaterium for biotechnology applications. Here, we present a genome-scale metabolic model for B. megaterium DSM319, iJA1121, which is a result of a metabolic network reconciliation process. The model includes 1709 reactions, 1349 metabolites, and 1121 genes. Based on multiple-genome alignments and available genome-scale metabolic models for other Bacillus species, we constructed a draft network using an automated approach followed by manual curation. The refinements were performed using a gap-filling process. Constraint-based modeling was used to scrutinize network features. Phenotyping assays were performed in order to validate the growth behavior of the model using different substrates. To verify the model accuracy, experimental data reported in the literature (growth behavior patterns, metabolite production capabilities, metabolic flux analysis using ¹³C glucose and formaldehyde inhibitory effect) were confronted with model predictions. This indicated a very good agreement between in silico results and experimental data. For example, our in silico study of fatty acid biosynthesis and lipid accumulation in B. megaterium highlighted the importance of adopting appropriate carbon sources for fermentation purposes. We conclude that the genome-scale metabolic model iJA1121 represents a useful tool for systems analysis and furthers our understanding of the metabolism of B. megaterium.

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.

High c-di-GMP promotes expression of fpr-1 and katE involved in oxidative stress resistance in Pseudomonas putida KT2440

Oxidative stress is an unavoidable consequence of interactions with various reactive oxygen species (ROS)-inducing agents that would damage cells or even cause cell death. Bacteria have developed defensive systems, including induction of stress-sensing proteins and detoxification enzymes, to handle oxidative stress. Cyclic diguanylate (c-di-GMP) is a ubiquitous intracellular bacterial second messenger that coordinates diverse aspects of bacterial growth and behavior. In this study, we revealed a mechanism by which c-di-GMP regulated bacterial oxidative stress resistance in Pseudomonas putida KT2440. High c-di-GMP level was found to enhance bacterial resistance towards hydrogen peroxide. Transcription assay showed that expression of two oxidative stress resistance genes, fpr-1 and katE, was promoted under high c-di-GMP level. Deletion of fpr-1 and katE both decreased bacterial tolerance to hydrogen peroxide and weakened the effect of c-di-GMP on oxidative stress resistance. The promoted expression of fpr-1 under high c-di-GMP level was caused by increased cellular ROS via a transcriptional regulator FinR. We further demonstrated that the influence of high c-di-GMP on cellular ROS depend on the existence of FleQ, a transcriptional regulatory c-di-GMP effector. Besides, the regulation of katE by c-di-GMP was also FleQ dependent in an indirect way. Our results proved a connection between c-di-GMP and oxidative stress resistance and revealed a mechanism by which c-di-GMP regulated expression of fpr-1 and katE in P. putida KT2440.

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.

Pseudomonas putida KT2440 is HV1 certified, not GRAS

Pseudomonas putida is rapidly becoming a workhorse for industrial production due to its metabolic versatility, genetic accessibility and stress-resistance properties. The P. putida strain KT2440 is often described as Generally Regarded as Safe, or GRAS, indicating the strain is safe to use as food additive. This description is incorrect. P. putida KT2440 is classified by the FDA as HV1 certified, indicating it is safe to use in a P1 or ML1 environment.

FindTargetsWEB: A User-Friendly Tool for Identification of Potential Therapeutic Targets in Metabolic Networks of Bacteria

Background: Healthcare-associated infections (HAIs) are a serious public health problem. They can be associated with morbidity and mortality and are responsible for the increase in patient hospitalization. Antimicrobial resistance among pathogens causing HAI has increased at alarming levels. In this paper, a robust method for analyzing genome-scale metabolic networks of bacteria is proposed in order to identify potential therapeutic targets, along with its corresponding web implementation, dubbed FindTargetsWEB. The proposed method assumes that every metabolic network presents fragile genes whose blockade will impair one or more metabolic functions, such as biomass accumulation. FindTargetsWEB automates the process of identification of such fragile genes using flux balance analysis (FBA), flux variability analysis (FVA), extended Systems Biology Markup Language (SBML) file parsing, and queries to three public repositories, i.e., KEGG, UniProt, and DrugBank. The web application was developed in Python using COBRApy and Django.

Results: The proposed method was demonstrated to be robust enough to process even non-curated, incomplete, or imprecise metabolic networks, in addition to integrated host-pathogen models. A list of potential therapeutic targets and their putative inhibitors was generated as a result of the analysis of Pseudomonas aeruginosa metabolic networks available in the literature and a curated version of the metabolic network of a multidrug-resistant P. aeruginosa strain belonging to a clone endemic in Brazil (P. aeruginosa ST277). Genome-scale metabolic networks of other gram-positive and gram-negative bacteria, such as Staphylococcus aureus, Klebsiella pneumoniae, and Haemophilus influenzae, were also analyzed using FindTargetsWEB. Multiple potential targets have been found using the proposed method in all metabolic networks, including some overlapping between two or more pathogens. Among the potential targets, several have been previously reported in the literature as targets for antimicrobial development, and many targets have approved drugs. Despite similarities in the metabolic network structure for closely related bacteria, we show that the method is able to selectively identify targets in pathogenic versus non-pathogenic organisms.

Conclusions: This new computational system can give insights into the identification of new candidate therapeutic targets for pathogenic bacteria and discovery of new antimicrobial drugs through genome-scale metabolic network analysis and heterogeneous data integration, even for non-curated or incomplete networks.

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.

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.

Asp305Gly mutation improved the activity and stability of the styrene monooxygenase for efficient epoxide production in Pseudomonas putida KT2440

Background

Styrene monooxygenase (SMO) catalyzes the first step of aromatic alkene degradation yielding the corresponding epoxides. Because of its broad spectrum of substrates, the enzyme harbors a great potential for an application in medicine and chemical industries.

Results

In this study, we achieved higher enzymatic activity and better stability towards styrene by enlarging the ligand entrance tunnel and improving the hydrophobicity through error-prone PCR and site-saturation mutagenesis. It was found that Asp305 (D305) hindered the entrance of the FAD cofactor according to the model analysis. Therefore, substitution with amino acids possessing shorter side chains, like glycine, opened the entrance tunnel and resulted in up to 2.7 times higher activity compared to the wild-type enzyme. The half-lives of thermal inactivation for the variant D305G at 60 °C was 28.9 h compared to only 3.2 h of the wild type SMO. Moreover, overexpression of SMO in Pseudomonas putida KT2440 with NADH regeneration was carried out in order to improve biotransformation efficiency for epoxide production. A hexadecane/buffer (v/v) biphasic system was applied in order to minimize the inactivation effect of high substrate concentrations on the SMO enzyme. Finally, SMO activities of 190 U/g CDW were measured and a total amount of 20.5 mM (S)-styrene oxide were obtained after 8 h.

Conclusions

This study offers an alternative strategy for improved SMO expression and provides an efficient biocatalytic system for epoxide production via engineering the entrance tunnel of the enzyme’s active site.

Electronic supplementary material

The online version of this article (10.1186/s12934-019-1065-5) contains supplementary material, which is available to authorized users.

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.

Metabolic network reconstruction and phenome analysis of the industrial microbe, Escherichia coli BL21(DE3)

Escherichia coli BL21(DE3) is an industrial model microbe for the mass-production of bioproducts such as biofuels, biorefineries, and recombinant proteins. However, despite its important role in scientific research and biotechnological applications, a high-quality metabolic network model for metabolic engineering is yet to be developed. Here, we present the comprehensive metabolic network model of E. coli BL21(DE3), named iHK1487, based on the latest genome reannotation and phenome analysis. The metabolic model consists of 1,164 unique metabolites, 2,701 metabolic reactions, and 1,487 genes. The model was validated and improved by comparing the simulation results with phenome data from phenotype microarray tests. Previous transcriptome profile data was incorporated during model reconstruction, and flux prediction was simulated using the model. iHK1487 was simulated to explore the metabolic features of BL21(DE3) such as broad spectrum amino acid utilization and enhanced flux through the upper glycolytic pathway and TCA cycle. iHK1487 will contribute to systematic understanding of cellular physiology and metabolism of E. coli BL21(DE3) and highlight its biotechnological applications.

Using genome-scale metabolic models to compare serovars of the foodborne pathogen Listeria monocytogenes

Listeria monocytogenes is a microorganism of great concern for the food industry and the cause of human foodborne disease. Therefore, novel methods of control are needed, and systems biology is one such approach to identify them. Using a combination of computational techniques and laboratory methods, genome-scale metabolic models (GEMs) can be created, validated, and used to simulate growth environments and discern metabolic capabilities of microbes of interest, including L. monocytogenes. The objective of the work presented here was to generate GEMs for six different strains of L. monocytogenes, and to both qualitatively and quantitatively validate these GEMs with experimental data to examine the diversity of metabolic capabilities of numerous strains from the three different serovar groups most associated with foodborne outbreaks and human disease. Following qualitative validation, 57 of the 95 carbon sources tested experimentally were present in the GEMs, and; therefore, these were the compounds from which comparisons could be drawn. Of these 57 compounds, agreement between in silico predictions and in vitro results for carbon source utilization ranged from 80.7% to 91.2% between strains. Nutrient utilization agreement between in silico predictions and in vitro results were also conducted for numerous nitrogen, phosphorous, and sulfur sources. Additionally, quantitative validation showed that the L. monocytogenes GEMs were able to generate in silico predictions for growth rate and growth yield that were strongly and significantly (p < 0.0013 and p < 0.0015, respectively) correlated with experimental results. These findings are significant because they show that these GEMs for L. monocytogenes are comparable to published GEMs of other organisms for agreement between in silico predictions and in vitro results. Therefore, as with the other GEMs, namely those for Escherichia coli, Staphylococcus aureus, Vibrio vulnificus, and Salmonella spp., they can be used to determine new methods of growth control and disease treatment.

Regulation of carbohydrate degradation pathways in Pseudomonas involves a versatile set of transcriptional regulators

Bacteria of the genus Pseudomonas are widespread in nature. In the last decades, members of this genus, especially Pseudomonas aeruginosa and Pseudomonas putida, have acquired great interest because of their interactions with higher organisms. Pseudomonas aeruginosa is an opportunistic pathogen that colonizes the lung of cystic fibrosis patients, while P. putida is a soil bacterium able to establish a positive interaction with the plant rhizosphere. Members of Pseudomonas genus have a robust metabolism for amino acids and organic acids as well as aromatic compounds; however, these microbes metabolize a very limited number of sugars. Interestingly, they have three-pronged metabolic system to generate 6-phosphogluconate from glucose suggesting an adaptation to efficiently consume this sugar. This review focuses on the description of the regulatory network of glucose utilization in Pseudomonas, highlighting the differences between P. putida and P. aeruginosa. Most interestingly, It is highlighted a functional link between glucose assimilation and exotoxin A production in P. aeruginosa. The physiological relevance of this connection remains unclear, and it needs to be established whether a similar relationship is also found in other bacteria.

Phylogenetic MLSA and phenotypic analysis identification of three probable novel Pseudomonas species isolated on King George Island, South Shetland, Antarctica

Antarctica harbors a great diversity of microorganisms, including bacteria, archaea, microalgae and yeasts. The Pseudomonas genus is one of the most diverse and successful bacterial groups described to date, but only eight species isolated from Antarctica have been characterized. Here, we present three potentially novel species isolated on King George Island. The most abundant isolates from four different environments, were genotypically and phenotypically characterized. Multilocus sequence analysis and 16S rRNA gene analysis of a sequence concatenate for six genes (16S, aroE, glnS, gyrB, ileS and rpoD), determined one of the isolates to be a new Pseudomonas mandelii strain, while the other three are good candidates for new Pseudomonas species. Additionally, genotype analyses showed the three candidates to be part of a new subgroup within the Pseudomonas fluorescens complex, together with the Antarctic species Pseudomonas antarctica and Pseudomonas extremaustralis. We propose terming this new subgroup P. antarctica. Likewise, phenotypic analyses using API 20 NE and BIOLOG^® corroborated the genotyping results, confirming that all presented isolates form part of the P. fluorescens complex. Pseudomonas genus research on the Antarctic continent is in its infancy. To understand these microorganisms’ role in this extreme environment, the characterization and description of new species is vital.

Genome-scale metabolic models as platforms for identification of novel genes as antimicrobial drug targets

The growing number of multidrug-resistant pathogenic bacteria is becoming a world leading challenge for the scientific community and for public health. However, advances in high-throughput technologies and whole-genome sequencing of bacterial pathogens make the construction of bacterial genome-scale metabolic models (GEMs) increasingly realistic. The use of GEMs as an alternative platforms will expedite identification of novel unconditionally essential genes and enzymes of target organisms with existing and forthcoming GEMs. This approach will follow the existing protocol for construction of high-quality GEMs, which could ultimately reduce the time, cost and labor-intensive processes involved in identification of novel antimicrobial drug targets in drug discovery pipelines. We discuss the current impact of existing GEMs of selected multidrug-resistant pathogenic bacteria for identification of novel antimicrobial drug targets and the challenges of closing the gap between genome-scale metabolic modeling and conventional experimental trial-and-error approaches in drug discovery pipelines.

Prediction of metabolic fluxes from gene expression data with Huber penalty convex optimization function

As one of the critical parameters of a metabolic pathway, the metabolic flux in a metabolic network serves as an essential role in physiology and pathology. Constraint-based metabolic models are the widely used frameworks for predicting metabolic fluxes in genome-scale metabolic networks. Integrating the transcriptomic data into the constraint-based metabolic models can effectively predict context-specific fluxes across different conditions. However, these methods always need user-defined thresholds to identify the expression levels of metabolic genes or restrain the rate of biomass production, and the predictive results are sensitive to the thresholds. In this work, we present the Huber penalty convex optimization function (HPCOF) combined with the flux minimization principle to predict metabolic fluxes. Our HPCOF method integrates gene expression profiles into the genome-scale metabolic models (GEMs) to reduce the sensitivity to outliers, and uses continuous expression data to avoid selection of arbitrary threshold parameters. In the case studies of Saccharomyces cerevisiae (S. cerevisiae) and Escherichia coli (E. coli) strains under different conditions, the results show that our HPCOF method has a better fit to the experimentally measured values, and has a higher Pearson correlation coefficient, a smaller P-value and a lower sum of squared error than other methods. The HPCOF code can be freely downloaded from for academic users.

Insights into metabolic osmoadaptation of the ectoines-producer bacterium Chromohalobacter salexigens through a high-quality genome scale metabolic model

Background

The halophilic bacterium Chromohalobacter salexigens is a natural producer of ectoines, compatible solutes with current and potential biotechnological applications. As production of ectoines is an osmoregulated process that draws away TCA intermediates, bacterial metabolism needs to be adapted to cope with salinity changes. To explore and use C. salexigens as cell factory for ectoine(s) production, a comprehensive knowledge at the systems level of its metabolism is essential. For this purpose, the construction of a robust and high-quality genome-based metabolic model of C. salexigens was approached.

Results

We generated and validated a high quality genome-based C. salexigens metabolic model (iFP764). This comprised an exhaustive reconstruction process based on experimental information, analysis of genome sequence, manual re-annotation of metabolic genes, and in-depth refinement. The model included three compartments (periplasmic, cytoplasmic and external medium), and two salinity-specific biomass compositions, partially based on experimental results from C. salexigens. Using previous metabolic data as constraints, the metabolic model allowed us to simulate and analyse the metabolic osmoadaptation of C. salexigens under conditions for low and high production of ectoines. The iFP764 model was able to reproduce the major metabolic features of C. salexigens. Flux Balance Analysis (FBA) and Monte Carlo Random sampling analysis showed salinity-specific essential metabolic genes and different distribution of fluxes and variation in the patterns of correlation of reaction sets belonging to central C and N metabolism, in response to salinity. Some of them were related to bioenergetics or production of reducing equivalents, and probably related to demand for ectoines. Ectoines metabolic reactions were distributed according to its correlation in four modules. Interestingly, the four modules were independent both at low and high salinity conditions, as they did not correlate to each other, and they were not correlated with other subsystems.

Conclusions

Our validated model is one of the most complete curated networks of halophilic bacteria. It is a powerful tool to simulate and explore C. salexigens metabolism at low and high salinity conditions, driving to low and high production of ectoines. In addition, it can be useful to optimize the metabolism of other halophilic bacteria for metabolite production.

Electronic supplementary material

The online version of this article (10.1186/s12934-017-0852-0) contains supplementary material, which is available to authorized users.

In silico engineering of Pseudomonas metabolism reveals new biomarkers for increased biosurfactant production

BACKGROUND

Rhamnolipids, biosurfactants with a wide range of biomedical applications, are amphiphilic molecules produced on the surfaces of or excreted extracellularly by bacteria including Pseudomonas aeruginosa. However, Pseudomonas putida is a non-pathogenic model organism with greater metabolic versatility and potential for industrial applications.

METHODS

We investigate in silico the metabolic capabilities of P. putida for rhamnolipids biosynthesis using statistical, metabolic and synthetic engineering approaches after introducing key genes (RhlA and RhlB) from P. aeruginosa into a genome-scale model of P. putida. This pipeline combines machine learning methods with multi-omic modelling, and drives the engineered P. putida model toward an optimal production and export of rhamnolipids out of the membrane.

RESULTS

We identify a substantial increase in synthesis of rhamnolipids by the engineered model compared to the control model. We apply statistical and machine learning techniques on the metabolic reaction rates to identify distinct features on the structure of the variables and individual components driving the variation of growth and rhamnolipids production. We finally provide a computational framework for integrating multi-omics data and identifying latent pathways and genes for the production of rhamnolipids in P. putida.

CONCLUSIONS

We anticipate that our results will provide a versatile methodology for integrating multi-omics data for topological and functional analysis of P. putida toward maximization of biosurfactant production.