Increasing cellular fitness and product yields in Pseudomonas putida through an engineered phosphoketolase shunt.

BACKGROUND: Pseudomonas putida has received increasing interest as a cell factory due to its remarkable features such as fast growth, a versatile and robust metabolism, an extensive genetic toolbox and its high tolerance to oxidative stress and toxic compounds. This interest is driven by the need to improve microbial performance to a level that enables biologically possible processes to become economically feasible, thereby fostering the transition from an oil-based economy to a more sustainable bio-based one. To this end, one of the current strategies is to maximize the product-substrate yield of an aerobic biocatalyst such as P. putida during growth on glycolytic carbon sources, such as glycerol and xylose. We demonstrate that this can be achieved by implementing the phosphoketolase shunt, through which pyruvate decarboxylation is prevented, and thus carbon loss is minimized. RESULTS: In this study, we introduced the phosphoketolase shunt in the metabolism of P. putida KT2440. To maximize the effect of this pathway, we first tested and selected a phosphoketolase (Xfpk) enzyme with high activity in P. putida. Results of the enzymatic assays revealed that the most efficient Xfpk was the one isolated from Bifidobacterium breve. Using this enzyme, we improved the P. putida growth rate on glycerol and xylose by 44 and 167%, respectively, as well as the biomass yield quantified by OD600 by 50 and 30%, respectively. Finally, we demonstrated the impact on product formation and achieved a 38.5% increase in mevalonate and a 25.9% increase in flaviolin yield from glycerol. A similar effect was observed on the mevalonate-xylose and flaviolin-xylose yields, which increased by 48.7 and 49.4%, respectively. CONCLUSIONS: Pseudomonas putida with the implemented Xfpk shunt grew faster, reached a higher final OD600nm and provided better product-substrate yields than the wild type. By reducing the pyruvate decarboxylation flux, we significantly improved the performance of this important workhorse for industrial applications. This work encompasses the first steps towards full implementation of the non-oxidative glycolysis (NOG) or the glycolysis alternative high carbon yield cycle (GATCHYC), in which a substrate is converted into products without CO2 loss These enhanced properties of P. putida will be crucial for its subsequent use in a range of industrial processes.

Phosphite synthetic auxotrophy as an effective biocontainment strategy for the industrial chassis Pseudomonas putida.

The inclusion of biosafety strategies into strain engineering pipelines is crucial for safe-by-design biobased processes. This in turn might enable a more rapid regulatory acceptance of bioengineered organisms in both industrial and environmental applications. For this reason, we equipped the industrially relevant microbial chassis Pseudomonas putida KT2440 with an effective biocontainment strategy based on a synthetic dependency on phosphite, which is generally not readily available in the environment. The produced PSAG-9 strain was first engineered to assimilate phosphite through the genome-integration of a phosphite dehydrogenase and a phosphite-specific transport complex. Subsequently, to deter the strain from growing on naturally assimilated phosphate, all native genes related to its transport were identified and deleted generating a strain unable to grow on media containing any phosphorous source other than phosphite. PSAG-9 exhibited fitness levels with phosphite similar to those of the wild type with phosphate, and low levels of escape frequency. Beyond biosafety, this strategy endowed P. putida with the capacity to be cultured under non-sterile conditions using phosphite as the sole phosphorous source with a reduced risk of contamination by other microbes, while displaying enhanced NADH regenerative capacity. These industrially beneficial features complement the metabolic advantages for which this species is known for, thereby strengthening it as a synthetic biology chassis with potential uses in industry, with suitability towards environmental release.

A genetic toolkit and gene switches to limit Mycoplasma growth for biosafety applications.

Mycoplasmas have exceptionally streamlined genomes and are strongly adapted to their many hosts, which provide them with essential nutrients. Owing to their relative genomic simplicity, Mycoplasmas have been used to develop chassis for biotechnological applications. However, the dearth of robust and precise toolkits for genomic manipulation and tight regulation has hindered any substantial advance. Herein we describe the construction of a robust genetic toolkit for M. pneumoniae, and its successful deployment to engineer synthetic gene switches that control and limit Mycoplasma growth, for biosafety containment applications. We found these synthetic gene circuits to be stable and robust in the long-term, in the context of a minimal cell. With this work, we lay a foundation to develop viable and robust biosafety systems to exploit a synthetic Mycoplasma chassis for live attenuated vectors for therapeutic applications.

High-level integration of murine intestinal transcriptomics data highlights the importance of the complement system in mucosal homeostasis.

BACKGROUND: The mammalian intestine is a complex biological system that exhibits functional plasticity in its response to diverse stimuli to maintain homeostasis. To improve our understanding of this plasticity, we performed a high-level data integration of 14 whole-genome transcriptomics datasets from samples of intestinal mouse mucosa. We used the tool Centrality based Pathway Analysis (CePa), along with information from the Reactome database. RESULTS: The results show an integrated response of the mouse intestinal mucosa to challenges with agents introduced orally that were expected to perturb homeostasis. We observed that a common set of pathways respond to different stimuli, of which the most reactive was the Regulation of Complement Cascade pathway. Altered expression of the Regulation of Complement Cascade pathway was verified in mouse organoids challenged with different stimuli in vitro. CONCLUSIONS: Results of the integrated transcriptomics analysis and data driven experiment suggest an important role of epithelial production of complement and host complement defence factors in the maintenance of homeostasis.

Genome sequence of two members of the chloroaromatic-degrading MT community: Pseudomonas reinekei MT1 and Achromobacter xylosoxidans MT3.

We describe the genome sequence of Pseudomonas reinekei MT1 and Achromobacter xylosoxidans MT3, the most abundant members of a bacterial community capable of degrading chloroaromatic compounds. The MT1 genome contains open reading frames encoding enzymes responsible for the catabolism of chlorosalicylate, methylsalicylate, chlorophenols, phenol, benzoate, p-coumarate, phenylalanine, and phenylacetate. On the other hand, the MT3 strain genome possesses no ORFs to metabolize chlorosalicylates; instead the bacterium is capable of metabolizing nitro-phenolic and phenolic compounds, which can be used as the only carbon and energy source by MT3. We also confirmed that MT3 displays the genetic machinery for the metabolism of chlorocathecols and chloromuconates, where the latter are toxic compounds secreted by MT1 when degrading chlorosalicylates. Altogether, this work will advance our fundamental understanding of bacterial interactions.

Recommended strategies for spectral processing and post-processing of 1D 1H-NMR data of biofluids with a particular focus on urine.

1H NMR spectra from urine can yield information-rich data sets that offer important insights into many biological and biochemical phenomena. However, the quality and utility of these insights can be profoundly affected by how the NMR spectra are processed and interpreted. For instance, if the NMR spectra are incorrectly referenced or inconsistently aligned, the identification of many compounds will be incorrect. If the NMR spectra are mis-phased or if the baseline correction is flawed, the estimated concentrations of many compounds will be systematically biased. Furthermore, because NMR permits the measurement of concentrations spanning up to five orders of magnitude, several problems can arise with data analysis. For instance, signals originating from the most abundant metabolites may prove to be the least biologically relevant while signals arising from the least abundant metabolites may prove to be the most important but hardest to accurately and precisely measure. As a result, a number of data processing techniques such as scaling, transformation and normalization are often required to address these issues. Therefore, proper processing of NMR data is a critical step to correctly extract useful information in any NMR-based metabolomic study. In this review we highlight the significance, advantages and disadvantages of different NMR spectral processing steps that are common to most NMR-based metabolomic studies of urine. These include: chemical shift referencing, phase and baseline correction, spectral alignment, spectral binning, scaling and normalization. We also provide a set of recommendations for best practices regarding spectral and data processing for NMR-based metabolomic studies of biofluids, with a particular focus on urine.

Comparison of 432 Pseudomonas strains through integration of genomic, functional, metabolic and expression data.

Pseudomonas is a highly versatile genus containing species that can be harmful to humans and plants while others are widely used for bioengineering and bioremediation. We analysed 432 sequenced Pseudomonas strains by integrating results from a large scale functional comparison using protein domains with data from six metabolic models, nearly a thousand transcriptome measurements and four large scale transposon mutagenesis experiments. Through heterogeneous data integration we linked gene essentiality, persistence and expression variability. The pan-genome of Pseudomonas is closed indicating a limited role of horizontal gene transfer in the evolutionary history of this genus. A large fraction of essential genes are highly persistent, still non essential genes represent a considerable fraction of the core-genome. Our results emphasize the power of integrating large scale comparative functional genomics with heterogeneous data for exploring bacterial diversity and versatility.

Harnessing the power of microbial autotrophy.

Autotrophic microorganisms convert CO2 into biomass by deriving energy from light or inorganic electron donors. These CO2-fixing microorganisms have a large, but so far only partially realized, potential for the sustainable production of chemicals and biofuels. Productivities have been improved in autotrophic hosts through the introduction of production pathways and the modification of autotrophic systems by genetic engineering. In addition, approaches are emerging in which CO2 fixation pathways and energy-harvesting systems are transplanted into heterotrophic model microorganisms. Alternative promising concepts are hybrid production systems of autotrophs and heterotrophs, and bio-inorganic hybrids of autotrophic microorganisms with electrocatalysts or light-harvesting semiconductor materials. In this Review, we discuss recent advances and bottlenecks for engineering microbial autotrophy and explore novel strategies that will pave the way towards improved microbial autotrophic production platforms.

Characterizing the optimal flux space of genome-scale metabolic reconstructions through modified latin-hypercube sampling.

Genome-Scale Metabolic Reconstructions (GSMRs), along with optimization-based methods, predominantly Flux Balance Analysis (FBA) and its derivatives, are widely applied for assessing and predicting the behavior of metabolic networks upon perturbation, thereby enabling identification of potential novel drug targets and biotechnologically relevant pathways. The abundance of alternate flux profiles has led to the evolution of methods to explore the complete solution space aiming to increase the accuracy of predictions. Herein we present a novel, generic algorithm to characterize the entire flux space of GSMR upon application of FBA, leading to the optimal value of the objective (the optimal flux space). Our method employs Modified Latin-Hypercube Sampling (LHS) to effectively border the optimal space, followed by Principal Component Analysis (PCA) to identify and explain the major sources of variability within it. The approach was validated with the elementary mode analysis of a smaller network of Saccharomyces cerevisiae and applied to the GSMR of Pseudomonas aeruginosa PAO1 (iMO1086). It is shown to surpass the commonly used Monte Carlo Sampling (MCS) in providing a more uniform coverage for a much larger network in less number of samples. Results show that although many fluxes are identified as variable upon fixing the objective value, majority of the variability can be reduced to several main patterns arising from a few alternative pathways. In iMO1086, initial variability of 211 reactions could almost entirely be explained by 7 alternative pathway groups. These findings imply that the possibilities to reroute greater portions of flux may be limited within metabolic networks of bacteria. Furthermore, the optimal flux space is subject to change with environmental conditions. Our method may be a useful device to validate the predictions made by FBA-based tools, by describing the optimal flux space associated with these predictions, thus to improve them.

Genotypic and phenotypic analyses of a Pseudomonas aeruginosa chronic bronchiectasis isolate reveal differences from cystic fibrosis and laboratory strains.

BACKGROUND: Pseudomonas aeruginosa is an environmentally ubiquitous Gram-negative bacterium and important opportunistic human pathogen, causing severe chronic respiratory infections in patients with underlying conditions such as cystic fibrosis (CF) or bronchiectasis. In order to identify mechanisms responsible for adaptation during bronchiectasis infections, a bronchiectasis isolate, PAHM4, was phenotypically and genotypically characterized. RESULTS: This strain displays phenotypes that have been associated with chronic respiratory infections in CF including alginate over-production, rough lipopolysaccharide, quorum-sensing deficiency, loss of motility, decreased protease secretion, and hypermutation. Hypermutation is a key adaptation of this bacterium during the course of chronic respiratory infections and analysis indicates that PAHM4 encodes a mutated mutS gene responsible for a ~1,000-fold increase in mutation rate compared to wild-type laboratory strain P. aeruginosa PAO1. Antibiotic resistance profiles and sequence data indicate that this strain acquired numerous mutations associated with increased resistance levels to ß-lactams, aminoglycosides, and fluoroquinolones when compared to PAO1. Sequencing of PAHM4 revealed a 6.38 Mbp genome, 5.9 % of which were unrecognized in previously reported P. aeruginosa genome sequences. Transcriptome analysis suggests a general down-regulation of virulence factors, while metabolism of amino acids and lipids is up-regulated when compared to PAO1 and metabolic modeling identified further potential differences between PAO1 and PAHM4. CONCLUSIONS: This work provides insights into the potential differential adaptation of this bacterium to the lung of patients with bronchiectasis compared to other clinical settings such as cystic fibrosis, findings that should aid the development of disease-appropriate treatment strategies for P. aeruginosa infections.

Green genes: bioinformatics and systems-biology innovations drive algal biotechnology.

Many species of microalgae produce hydrocarbons, polysaccharides, and other valuable products in significant amounts. However, large-scale production of algal products is not yet competitive against non-renewable alternatives from fossil fuel. Metabolic engineering approaches will help to improve productivity, but the exact metabolic pathways and the identities of the majority of the genes involved remain unknown. Recent advances in bioinformatics and systems-biology modeling coupled with increasing numbers of algal genome-sequencing projects are providing the means to address this. A multidisciplinary integration of methods will provide synergy for a systems-level understanding of microalgae, and thereby accelerate the improvement of industrially valuable strains. In this review we highlight recent advances and challenges to microalgal research and discuss future potential.

Delineating the effect of host environmental signals on a fully virulent strain of Bacillus anthracis using an integrated transcriptomics and proteomics approach.

Pathogenic bacteria sense the host environment and regulate expression of virulence-related genes. Environmental signals like temperature, bicarbonate/CO2 and glucose induce toxin production in Bacillus anthracis, but the mechanisms by which these signals contribute to virulence and overall physiological adaptation remains elusive. An integrated, systems level investigation using transcriptomics and iTRAQ-based proteomics was done to assess the effect of temperature, bicarbonate/CO2 and glucose on B. anthracis. Significant changes observed in amino acid, carbohydrate, energy and nucleotide metabolism indicates events of metabolic readjustments by environmental factors. Directed induction of genes involved in polyamine biosynthesis and iron metabolism revealed the redirection of cellular metabolite pool towards iron uptake. Protein levels of glycolytic enzymes, ptsH and Ldh along with transcripts involved in immune evasion (mprF, bNOS, Phospholipases and asnA), cell surface remodeling (rfbABCD, antABCD, and cls) and utilization of lactate (lutABC) and inositol showed constant repression under environmental perturbations. Discrepancies observed in mRNA/protein level of genes involved in glycolysis, protein synthesis, stress response and nucleotide metabolism hinted at the existence of additional regulatory layers and illustrated the utility of an integrated approach. The above findings might assist in the identification of novel adaptive strategies of B. anthracis during host associated survival and pathogenesis. BIOLOGICAL SIGNIFICANCE: In this study, the changes observed at both transcript and protein level were quantified and integrated to understand the effect of host environmental factors (host temperature, bicarbonate and glucose) in shaping the physiology and adaptive strategies of a fully virulent strain of B. anthracis for efficient survival and virulence in its host. Perturbations affecting toxin production were found to concordantly affect vital metabolic pathways and several known as well as novel virulence factors. These changes act as a valuable asset for generating testable hypotheses that can be further verified by detailed molecular and mutant studies to identify novel adaptive strategies of B. anthracis during infection. Adaptation of an integrated transcriptomics and proteomics approach also led to the identification of discrepancies between mRNA/protein levels among genes across major functional categories. Few of these discrepancies have been previously reported in literature for model organisms. However their existence in B. anthracis and that too as a result of growth perturbations have not been reported till date. These findings demonstrate a substantial role of regulatory processes post mRNA synthesis via post transcriptional, translational or protein degradation mechanisms. This article is part of a Special Issue entitled: Proteomics of non-model organisms.

Potential of proton-pumping rhodopsins: engineering photosystems into microorganisms.

A wide range of proton-pumping rhodopsins (PPRs) have been discovered in recent years. Using a synthetic biology approach, PPR photosystems with different features can be easily introduced in nonphotosynthetic microbial hosts. PPRs can provide hosts with the ability to harvest light and drive the sustainable production of biochemicals or biofuels. PPRs use light energy to generate an outward proton flux, and the resulting proton motive force can subsequently power cellular processes. Recently, the introduction of PPRs in microbial production hosts has successfully led to light-driven biotechnological conversions. In this review, we discuss relevant features of natural PPRs, evaluate reported biotechnological applications of microbial production hosts equipped with PPRs, and provide an outlook on future developments.

Reconciling in vivo and in silico key biological parameters of Pseudomonas putida KT2440 during growth on glucose under carbon-limited condition.

BACKGROUND: Genome scale metabolic reconstructions are developed to efficiently engineer biocatalysts and bioprocesses based on a rational approach. However, in most reconstructions, due to the lack of appropriate measurements, experimentally determined growth parameters are simply taken from literature including other organisms, which reduces the usefulness and suitability of these models. Pseudomonas putida KT2440 is an outstanding biocatalyst given its versatile metabolism, its ability to generate sufficient energy and turnover of NADH and NAD. To apply this strain optimally in industrial production, a previously developed genome-scale metabolic model (iJP815) was experimentally assessed and streamlined to enable accurate predictions of the outcome of metabolic engineering approaches. RESULTS: To substantially improve the accuracy of the genome scale model (iJP815), continuous bioreactor cultures on a mineral medium with glucose as a sole carbon source were carried out at different dilution rates, which covered pulling analysis of the macromolecular composition of the biomass. Besides, the maximum biomass yield (on substrate) of 0.397 gDCW · gglc-1, the maintenance coefficient of 0.037 gglc · gDCW-1 · h-1 and the maximum specific growth rate of 0.59 h-1 were determined. Only the DNA fraction increased with the specific growth rate. This resulted in reliable estimation for the Growth-Associated Maintenance (GAM) of 85 mmolATP · gDCW-1 and the Non Growth-Associated Maintenance (NGAM) of 3.96 mmolATP · gDCW-1 · h-1. Both values were found significantly different from previous assignment as a consequence of a lower yield and higher maintenance coefficient than originally assumed. Contrasting already published 13C flux measurements and the improved model allowed for constraining the solution space, by eliminating futile cycles. Furthermore, the model predictions were compared with transcriptomic data at overall good consistency, which helped to identify missing links. CONCLUSIONS: By careful interpretation of growth stoichiometry and kinetics when grown in the presence of glucose, this work reports on an accurate genome scale metabolic model of Pseudomonas putida, providing a solid basis for its use in designing superior strains for biocatalysis. By consideration of substrate specific variation in stoichiometry and kinetics, it can be extended to other substrates and new mutants.

Dissecting the energy metabolism in Mycoplasma pneumoniae through genome-scale metabolic modeling.

Mycoplasma pneumoniae, a threatening pathogen with a minimal genome, is a model organism for bacterial systems biology for which substantial experimental information is available. With the goal of understanding the complex interactions underlying its metabolism, we analyzed and characterized the metabolic network of M. pneumoniae in great detail, integrating data from different omics analyses under a range of conditions into a constraint-based model backbone. Iterating model predictions, hypothesis generation, experimental testing, and model refinement, we accurately curated the network and quantitatively explored the energy metabolism. In contrast to other bacteria, M. pneumoniae uses most of its energy for maintenance tasks instead of growth. We show that in highly linear networks the prediction of flux distributions for different growth times allows analysis of time-dependent changes, albeit using a static model. By performing an in silico knock-out study as well as analyzing flux distributions in single and double mutant phenotypes, we demonstrated that the model accurately represents the metabolism of M. pneumoniae. The experimentally validated model provides a solid basis for understanding its metabolic regulatory mechanisms.

Streamlining genomes: toward the generation of simplified and stabilized microbial systems.

At the junction between systems and synthetic biology, genome streamlining provides a solid foundation both for increased understanding of cellular circuitry, and for the tailoring of microbial chassis towards innovative biotechnological applications. Iterative genomic deletions (targeted and random) helps to generate simplified, stabilized and predictable genomes, whereas multiplexing genome engineering reveals a broad functional genetic diversity. The decrease in oligo and gene synthesis costs promises effective combinatorial tools for the generation of chassis based on streamlined and tractable genomes. Here we review recent progresses in streamlining genomes through recombineering techniques aiming to generate insights into cellular mechanisms and responses towards the design and assembly of streamlined genome chassis together with new cellular modules in diverse biotechnological applications.

pH-stat fed-batch process to enhance the production of cis, cis-muconate from benzoate by Pseudomonas putida KT2440-JD1.

Pseudomonas putida KT2440-JD1 is able to cometabolize benzoate to cis, cis-muconate in the presence of glucose as growth substrate. P. putida KT2440-JD1 was unable to grow in the presence of concentrations above 50 mM benzoate or 600 mM cis, cis-muconate. The inhibitory effects of both compounds were cumulative. The maximum specific uptake rate of benzoate was higher than the specific production rate of cis, cis-muconate during growth on glucose in the presence of benzoate, indicating that a benzoate derivative accumulated in the cells, which is likely to be catechol. Catechol was shown to reduce the expression level of the ben operon, which encodes the conversion of benzoate to cis, cis-muconate. To prevent overdoses of benzoate, a pH-stat fed-batch process for the production of cis, cis-muconate from benzoate was developed, in which the addition of benzoate was coupled to the acidification of the medium. The maximum specific production rate during the pH-stat fed-batch process was 0.6 g (4.3 mmol) g dry cell weight(-1) h(-1), whereas 18.5 g L(-1) cis, cis-muconate accumulated in the culture medium with a molar product yield of close to 100%. Proteome analysis revealed that the outer membrane protein H1 was upregulated during the pH-stat fed-batch process, whereas the expression of 10 other proteins was reduced. The identified proteins are involved in energy household, transport, translation of RNA, and motility.

In-vivo expression profiling of Pseudomonas aeruginosa infections reveals niche-specific and strain-independent transcriptional programs.

Pseudomonas aeruginosa is a threatening, opportunistic pathogen causing disease in immunocompromised individuals. The hallmark of P. aeruginosa virulence is its multi-factorial and combinatorial nature. It renders such bacteria infectious for many organisms and it is often resistant to antibiotics. To gain insights into the physiology of P. aeruginosa during infection, we assessed the transcriptional programs of three different P. aeruginosa strains directly after isolation from burn wounds of humans. We compared the programs to those of the same strains using two infection models: a plant model, which consisted of the infection of the midrib of lettuce leaves, and a murine tumor model, which was obtained by infection of mice with an induced tumor in the abdomen. All control conditions of P. aeruginosa cells growing in suspension and as a biofilm were added to the analysis. We found that these different P. aeruginosa strains express a pool of distinct genetic traits that are activated under particular infection conditions regardless of their genetic variability. The knowledge herein generated will advance our understanding of P. aeruginosa virulence and provide valuable cues for the definition of prospective targets to develop novel intervention strategies.

Redefining plant systems biology: from cell to ecosystem.

Molecular biologists typically restrict systems biology to cellular levels. By contrast, ecologists define biological systems as communities of interacting individuals at different trophic levels that process energy, nutrient and information flows. Modern plant breeding needs to increase agricultural productivity while decreasing the ecological footprint. This requires a holistic systems biology approach that couples different aggregation levels while considering the variables that affect these biological systems from cell to community. The challenge is to generate accurate experimental data that can be used together with modelling concepts and techniques that allow experimentally verifying in silico predictions. The coupling of aggregation levels in plant sciences, termed Integral Quantification of Biological Organization (IQ(BiO)), might enhance our abilities to generate new desired plant phenotypes.

The regulatory logic of m-xylene biodegradation by Pseudomonas putida mt-2 exposed by dynamic modelling of the principal node Ps/Pr of the TOL plasmid.

The structure of the extant transcriptional control network of the TOL plasmid pWW0 born by Pseudomonas putida mt-2 for biodegradation of m-xylene is far more complex than one would consider necessary from a mere engineering point of view. In order to penetrate the underlying logic of such a network, which controls a major environmental cleanup bioprocess, we have developed a dynamic model of the key regulatory node formed by the Ps/Pr promoters of pWW0, where the clustering of control elements is maximal. The model layout was validated with batch cultures estimating parameter values and its predictive capability was confirmed with independent sets of experimental data. The model revealed how regulatory outputs originated in the divergent and overlapping Ps/Pr segment, which expresses the transcription factors XylS and XylR respectively, are computed into distinct instructions to the upper and lower catabolic xyl operons for either simultaneous or stepwise consumption of m-xylene and/or succinate. In this respect, the model reveals that the architecture of the Ps/Pr is poised to discriminate the abundance of alternative and competing C sources, in particular m-xylene versus succinate. The proposed framework provides a first systemic understanding of the causality and connectivity of the regulatory elements that shape this exemplary regulatory network, facilitating the use of model analysis towards genetic circuit optimization.