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

Transcriptomic fingerprinting of Pseudomonas putida under alternative physiological regimes

Pseudomonas putida KT2440 is a metabolically versatile soil bacterium useful both as a model biodegradative organism and as a host of catalytic activities of biotechnological interest. In this report, we present the high-resolution transcriptome of P. putida cultured on different carbon sources as revealed by deep sequencing of the corresponding RNA pools. Examination of the data from growth on substrates that are processed through distinct pathways (glucose, fructose, succinate and glycerol) revealed that ≥ 20% of the P. putida genome is differentially expressed depending on the ensuing physiological regime. Changes affected not only metabolic genes but also a suite of global regulators, e.g. the rpoS sigma subunit of RNA polymerase, various cold-shock proteins and the three HU histone-like proteins. Specifically, the genes encoding HU subunit variants hupA, hupB and hupN drastically altered their expression levels (and thus their ability to form heterodimeric combinations) under the diverse growth conditions. Furthermore, we found that two small RNAs, crcZ and crcY, known to inhibit the Crc protein that mediates catabolite repression in P. putida, were both down-regulated by glucose. The raw transcriptomic data generated in this work is made available to the community through the Gene Expression Omnibus database.

SHARP: genome-scale identification of gene-protein-reaction associations in cyanobacteria

Genome scale metabolic model provides an overview of an organism's metabolic capability. These genome-specific metabolic reconstructions are based on identification of gene to protein to reaction (GPR) associations and, in turn, on homology with annotated genes from other organisms. Cyanobacteria are photosynthetic prokaryotes which have diverged appreciably from their nonphotosynthetic counterparts. They also show significant evolutionary divergence from plants, which are well studied for their photosynthetic apparatus. We argue that context-specific sequence and domain similarity can add to the repertoire of the GPR associations and significantly expand our view of the metabolic capability of cyanobacteria. We took an approach that combines the results of context-specific sequence-to-sequence similarity search with those of sequence-to-profile searches. We employ PSI-BLAST for the former, and CDD, Pfam, and COG for the latter. An optimization algorithm was devised to arrive at a weighting scheme to combine the different evidences with KEGG-annotated GPRs as training data. We present the algorithm in the form of software "Systematic, Homology-based Automated Re-annotation for Prokaryotes (SHARP)." We predicted 3,781 new GPR associations for the 10 prokaryotes considered of which eight are cyanobacteria species. These new GPR associations fall in several metabolic pathways and were used to annotate 7,718 gaps in the metabolic network. These new annotations led to discovery of several pathways that may be active and thereby providing new directions for metabolic engineering of these species for production of useful products. Metabolic model developed on such a reconstructed network is likely to give better phenotypic predictions.

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 g_DCW · g_glc^-1, the maintenance coefficient of 0.037 g_glc · g_DCW^-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 mmol_ATP · g_DCW^-1 and the Non Growth-Associated Maintenance (NGAM) of 3.96 mmol_ATP · g_DCW^-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.

Swimming in light: a large-scale computational analysis of the metabolism of Dinoroseobacter shibae

The Roseobacter clade is a ubiquitous group of marine α-proteobacteria. To gain insight into the versatile metabolism of this clade, we took a constraint-based approach and created a genome-scale metabolic model (iDsh827) of Dinoroseobacter shibae DFL12T. Our model is the first accounting for the energy demand of motility, the light-driven ATP generation and experimentally determined specific biomass composition. To cover a large variety of environmental conditions, as well as plasmid and single gene knock-out mutants, we simulated 391,560 different physiological states using flux balance analysis. We analyzed our results with regard to energy metabolism, validated them experimentally, and revealed a pronounced metabolic response to the availability of light. Furthermore, we introduced the energy demand of motility as an important parameter in genome-scale metabolic models. The results of our simulations also gave insight into the changing usage of the two degradation routes for dimethylsulfoniopropionate, an abundant compound in the ocean. A side product of dimethylsulfoniopropionate degradation is dimethyl sulfide, which seeds cloud formation and thus enhances the reflection of sunlight. By our exhaustive simulations, we were able to identify single-gene knock-out mutants, which show an increased production of dimethyl sulfide. In addition to the single-gene knock-out simulations we studied the effect of plasmid loss on the metabolism. Moreover, we explored the possible use of a functioning phosphofructokinase for D. shibae.

The oceans are home to a large variety of microorganisms, which interact in several ways with world-wide metabolic cycles. A representative of an important group of marine bacteria called the Roseobacter clade is Dinoroseobacter shibae. This organism is known to use a variant of photosynthesis to obtain energy from light. Another feature of D. shibae and many other Roseobacters is the ability to degrade an abundant compound in the ocean called dimethylsulfoniopropionate. Importantly, one degradation pathway of dimethylsulfoniopropionate releases a side product, which affects climate by seeding cloud formation. In this work, we constructed a genome-scale metabolic model of D. shibae and carried out a detailed computational analysis of its metabolism. Our model simulates the light-harvesting capabilities of D. shibae and also accounts for the energy needed to swim. Thanks to our exhaustive simulations we were able to elucidate the effect of light on the growth of D. shibae, to study the consequences of genetic perturbations, and to identify mutants which produce more cloud-seeding compounds. Foremost, our computational results help to understand an important part of the complex processes in the ocean in greater detail. Besides, they can be a valuable guide for future wet-lab experiments.

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

A new genome-scale metabolic reconstruction of M. pneumonia is used in combination with external metabolite measurement and protein abundance measurements to quantitatively explore the energy metabolism of this genome-reduce human pathogen.

We established a detailed biomass composition for M. pneumoniae, thus allowing for growth simulations.

Using our metabolic model, we corrected the metabolic network topology and the functional annotation of key metabolic enzymes.

M. pneumoniae, unlike other laboratory-grown bacteria, uses a high fraction of energy (up to 89%) for cellular maintenance and not for growth.

Simulating different growth conditions as well as single and double mutant phenotypes, we analyzed pathway connectivity and the impact of gene deletions on the growth performance of M. pneumoniae, highlighting the limited adaptive capabilities of this minimal model organism.

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.

The biology of habitat dominance; can microbes behave as weeds?

Competition between microbial species is a product of, yet can lead to a reduction in, the microbial diversity of specific habitats. Microbial habitats can resemble ecological battlefields where microbial cells struggle to dominate and/or annihilate each other and we explore the hypothesis that (like plant weeds) some microbes are genetically hard-wired to behave in a vigorous and ecologically aggressive manner. These 'microbial weeds' are able to dominate the communities that develop in fertile but uncolonized--or at least partially vacant--habitats via traits enabling them to out-grow competitors; robust tolerances to habitat-relevant stress parameters and highly efficient energy-generation systems; avoidance of or resistance to viral infection, predation and grazers; potent antimicrobial systems; and exceptional abilities to sequester and store resources. In addition, those associated with nutritionally complex habitats are extraordinarily versatile in their utilization of diverse substrates. Weed species typically deploy multiple types of antimicrobial including toxins; volatile organic compounds that act as either hydrophobic or highly chaotropic stressors; biosurfactants; organic acids; and moderately chaotropic solutes that are produced in bulk quantities (e.g. acetone, ethanol). Whereas ability to dominate communities is habitat-specific we suggest that some microbial species are archetypal weeds including generalists such as: Pichia anomala, Acinetobacter spp. and Pseudomonas putida; specialists such as Dunaliella salina, Saccharomyces cerevisiae, Lactobacillus spp. and other lactic acid bacteria; freshwater autotrophs Gonyostomum semen and Microcystis aeruginosa; obligate anaerobes such as Clostridium acetobutylicum; facultative pathogens such as Rhodotorula mucilaginosa, Pantoea ananatis and Pseudomonas aeruginosa; and other extremotolerant and extremophilic microbes such as Aspergillus spp., Salinibacter ruber and Haloquadratum walsbyi. Some microbes, such as Escherichia coli, Mycobacterium smegmatis and Pseudoxylaria spp., exhibit characteristics of both weed and non-weed species. We propose that the concept of nonweeds represents a 'dustbin' group that includes species such as Synodropsis spp., Polypaecilum pisce, Metschnikowia orientalis, Salmonella spp., and Caulobacter crescentus. We show that microbial weeds are conceptually distinct from plant weeds, microbial copiotrophs, r-strategists, and other ecophysiological groups of microorganism. Microbial weed species are unlikely to emerge from stationary-phase or other types of closed communities; it is open habitats that select for weed phenotypes. Specific characteristics that are common to diverse types of open habitat are identified, and implications of weed biology and open-habitat ecology are discussed in the context of further studies needed in the fields of environmental and applied microbiology.

Inferring ancient metabolism using ancestral core metabolic models of enterobacteria

Background

Enterobacteriaceae diversified from an ancestral lineage ~300-500 million years ago (mya) into a wide variety of free-living and host-associated lifestyles. Nutrient availability varies across niches, and evolution of metabolic networks likely played a key role in adaptation.

Results

Here we use a paleo systems biology approach to reconstruct and model metabolic networks of ancestral nodes of the enterobacteria phylogeny to investigate metabolism of ancient microorganisms and evolution of the networks. Specifically, we identified orthologous genes across genomes of 72 free-living enterobacteria (16 genera), and constructed core metabolic networks capturing conserved components for ancestral lineages leading to E. coli/Shigella (~10 mya), E. coli/Shigella/Salmonella (~100 mya), and all enterobacteria (~300-500 mya). Using these models we analyzed the capacity for carbon, nitrogen, phosphorous, sulfur, and iron utilization in aerobic and anaerobic conditions, identified conserved and differentiating catabolic phenotypes, and validated predictions by comparison to experimental data from extant organisms.

Conclusions

This is a novel approach using quantitative ancestral models to study metabolic network evolution and may be useful for identification of new targets to control infectious diseases caused by enterobacteria.

Mapping global effects of the anti-sigma factor MucA in Pseudomonas fluorescens SBW25 through genome-scale metabolic modeling

Background

Alginate is an industrially important polysaccharide, currently produced commercially by harvesting of marine brown sea-weeds. The polymer is also synthesized as an exo-polysaccharide by bacteria belonging to the genera Pseudomonas and Azotobacter, and these organisms may represent an alternative alginate source in the future. The current work describes an attempt to rationally develop a biological system tuned for very high levels of alginate production, based on a fundamental understanding of the system through metabolic modeling supported by transcriptomics studies and carefully controlled fermentations.

Results

Alginate biosynthesis in Pseudomonas fluorescens was studied in a genomics perspective, using an alginate over-producing strain carrying a mutation in the anti-sigma factor gene mucA. Cells were cultivated in chemostats under nitrogen limitation on fructose or glycerol as carbon sources, and cell mass, growth rate, sugar uptake, alginate and CO₂ production were monitored. In addition a genome scale metabolic model was constructed and samples were collected for transcriptome analyses. The analyses show that polymer production operates in a close to optimal way with respect to stoichiometric utilization of the carbon source and that the cells increase the uptake of carbon source to compensate for the additional needs following from alginate synthesis. The transcriptome studies show that in the presence of the mucA mutation, the alg operon is upregulated together with genes involved in energy generation, genes on both sides of the succinate node of the TCA cycle and genes encoding ribosomal and other translation-related proteins. Strains expressing a functional MucA protein (no alginate production) synthesize cellular biomass in an inefficient way, apparently due to a cycle that involves oxidation of NADPH without ATP production. The results of this study indicate that the most efficient way of using a mucA mutant as a cell factory for alginate production would be to use non-growing conditions and nitrogen deprivation.

Conclusions

The insights gained in this study should be very useful for a future efficient production of microbial alginates.

The Entner-Doudoroff pathway empowers Pseudomonas putida KT2440 with a high tolerance to oxidative stress

Glucose catabolism of Pseudomonas putida is carried out exclusively through the Entner-Doudoroff (ED) pathway due to the absence of 6-phosphofructokinase. In order to activate the Embden-Meyerhof-Parnas (EMP) route we transferred the pfkA gene from Escherichia coli to a P. putida wild-type strain as well as to an eda mutant, i.e. lacking 2-keto-3-deoxy-6-phosphogluconate aldolase. PfkA(E. coli) failed to redirect the carbon flow from the ED route towards the EMP pathway, suggesting that ED was essential for sugar catabolism. The presence of PfkA(E. coli) was detrimental for growth, which could be traced to the reduction of ATP and NAD(P)H pools along with alteration of the NAD(P)H/NADP(+) ratio. Pseudomonas putida cells carrying PfkA(E. coli) became highly sensitive to diamide and hydrogen peroxide, the response to which is very demanding of NADPH. The inhibitory effect of PfkA(E. coli) could in part be relieved by methionine, the synthesis of which relies much on NADPH. These results expose the role of the ED pathway for generating the redox currency (NADPH) that is required for counteracting oxidative stress. It is thus likely that environmental bacteria that favour the ED pathway over the EMP pathway do so in order to gear their aerobic metabolism to endure oxidative-related insults.

Modeling and analysis of flux distributions in the two branches of the phosphotransferase system in Pseudomonas putida

Background

Signal transduction plays a fundamental role in the understanding of cellular physiology. The bacterial phosphotransferase system (PTS) together with the PEP/pyruvate node in central metabolism represents a signaling unit that acts as a sensory element and measures the activity of the central metabolism. Pseudomonas putida possesses two PTS branches, the C-branch (PTS^Fru) and a second branch (PTS^Ntr), which communicate with each other by phosphate exchange. Recent experimental results showed a cross talk between the two branches. However, the functional role of the crosstalk remains open.

Results

A mathematical model was set up to describe the available data of the state of phosphorylation of PtsN, one of the PTS proteins, for different environmental conditions and different strain variants. Additionally, data from flux balance analysis was used to determine some of the kinetic parameters of the involved reactions. Based on the calculated and estimated parameters, the flux distribution during growth of the wild type strain on fructose could be determined.

Conclusion

Our calculations show that during growth of the wild type strain on the PTS substrate fructose, the major part of the phosphoryl groups is provided by the second branch of the PTS. This theoretical finding indicates a new role of the second branch of the PTS and will serve as a basis for further experimental studies.

Genome-level transcription data of Yersinia pestis analyzed with a new metabolic constraint-based approach

Background

Constraint-based computational approaches, such as flux balance analysis (FBA), have proven successful in modeling genome-level metabolic behavior for conditions where a set of simple cellular objectives can be clearly articulated. Recently, the necessity to expand the current range of constraint-based methods to incorporate high-throughput experimental data has been acknowledged by the proposal of several methods. However, these methods have rarely been used to address cellular metabolic responses to some relevant perturbations such as antimicrobial or temperature-induced stress. Here, we present a new method for combining gene-expression data with FBA (GX-FBA) that allows modeling of genome-level metabolic response to a broad range of environmental perturbations within a constraint-based framework. The method uses mRNA expression data to guide hierarchical regulation of cellular metabolism subject to the interconnectivity of the metabolic network.

Results

We applied GX-FBA to a genome-scale model of metabolism in the gram negative bacterium Yersinia pestis and analyzed its metabolic response to (i) variations in temperature known to induce virulence, and (ii) antibiotic stress. Without imposition of any a priori behavioral constraints, our results show strong agreement with reported phenotypes. Our analyses also lead to novel insights into how Y. pestis uses metabolic adjustments to counter different forms of stress.

Conclusions

Comparisons of GX-FBA predicted metabolic states with fluxomic measurements and different reported post-stress phenotypes suggest that mass conservation constraints and network connectivity can be an effective representative of metabolic flux regulation in constraint-based models. We believe that our approach will be of aid in the in silico evaluation of cellular goals under different conditions and can be used for a variety of analyses such as identification of potential drug targets and their action.

Reconstruction and comparison of the metabolic potential of cyanobacteria Cyanothece sp. ATCC 51142 and Synechocystis sp. PCC 6803

Cyanobacteria are an important group of photoautotrophic organisms that can synthesize valuable bio-products by harnessing solar energy. They are endowed with high photosynthetic efficiencies and diverse metabolic capabilities that confer the ability to convert solar energy into a variety of biofuels and their precursors. However, less well studied are the similarities and differences in metabolism of different species of cyanobacteria as they pertain to their suitability as microbial production chassis. Here we assemble, update and compare genome-scale models (iCyt773 and iSyn731) for two phylogenetically related cyanobacterial species, namely Cyanothece sp. ATCC 51142 and Synechocystis sp. PCC 6803. All reactions are elementally and charge balanced and localized into four different intracellular compartments (i.e., periplasm, cytosol, carboxysome and thylakoid lumen) and biomass descriptions are derived based on experimental measurements. Newly added reactions absent in earlier models (266 and 322, respectively) span most metabolic pathways with an emphasis on lipid biosynthesis. All thermodynamically infeasible loops are identified and eliminated from both models. Comparisons of model predictions against gene essentiality data reveal a specificity of 0.94 (94/100) and a sensitivity of 1 (19/19) for the Synechocystis iSyn731 model. The diurnal rhythm of Cyanothece 51142 metabolism is modeled by constructing separate (light/dark) biomass equations and introducing regulatory restrictions over light and dark phases. Specific metabolic pathway differences between the two cyanobacteria alluding to different bio-production potentials are reflected in both models.

Gene function hypotheses for the Campylobacter jejuni glycome generated by a logic-based approach

Increasingly, experimental data on biological systems are obtained from several sources and computational approaches are required to integrate this information and derive models for the function of the system. Here, we demonstrate the power of a logic-based machine learning approach to propose hypotheses for gene function integrating information from two diverse experimental approaches. Specifically, we use inductive logic programming that automatically proposes hypotheses explaining the empirical data with respect to logically encoded background knowledge. We study the capsular polysaccharide biosynthetic pathway of the major human gastrointestinal pathogen Campylobacter jejuni. We consider several key steps in the formation of capsular polysaccharide consisting of 15 genes of which 8 have assigned function, and we explore the extent to which functions can be hypothesised for the remaining 7. Two sources of experimental data provide the information for learning—the results of knockout experiments on the genes involved in capsule formation and the absence/presence of capsule genes in a multitude of strains of different serotypes. The machine learning uses the pathway structure as background knowledge. We propose assignments of specific genes to five previously unassigned reaction steps. For four of these steps, there was an unambiguous optimal assignment of gene to reaction, and to the fifth, there were three candidate genes. Several of these assignments were consistent with additional experimental results. We therefore show that the logic-based methodology provides a robust strategy to integrate results from different experimental approaches and propose hypotheses for the behaviour of a biological system.

The translational repressor Crc controls the Pseudomonas putida benzoate and alkane catabolic pathways using a multi-tier regulation strategy

Metabolically versatile bacteria usually perceive aromatic compounds and hydrocarbons as non-preferred carbon sources, and their assimilation is inhibited if more preferable substrates are available. This is achieved via catabolite repression. In Pseudomonas putida, the expression of the genes allowing the assimilation of benzoate and n-alkanes is strongly inhibited by catabolite repression, a process controlled by the translational repressor Crc. Crc binds to and inhibits the translation of benR and alkS mRNAs, which encode the transcriptional activators that induce the expression of the benzoate and alkane degradation genes respectively. However, sequences similar to those recognized by Crc in benR and alkS mRNAs exist as well in the translation initiation regions of the mRNA of several structural genes of the benzoate and alkane pathways, which suggests that Crc may also regulate their translation. The present results show that some of these sites are functional, and that Crc inhibits the induction of both pathways by limiting not only the translation of their transcriptional activators, but also that of genes coding for the first enzyme in each pathway. Crc may also inhibit the translation of a gene involved in benzoate uptake. This multi-tier approach probably ensures the rapid regulation of pathway genes, minimizing the assimilation of non-preferred substrates when better options are available. A survey of possible Crc sites in the mRNAs of genes associated with other catabolic pathways suggested that targeting substrate uptake, pathway induction and/or pathway enzymes may be a common strategy to control the assimilation of non-preferred compounds.

Flux variability scanning based on enforced objective flux for identifying gene amplification targets

BACKGROUND

In order to reduce time and efforts to develop microbial strains with better capability of producing desired bioproducts, genome-scale metabolic simulations have proven useful in identifying gene knockout and amplification targets. Constraints-based flux analysis has successfully been employed for such simulation, but is limited in its ability to properly describe the complex nature of biological systems. Gene knockout simulations are relatively straightforward to implement, simply by constraining the flux values of the target reaction to zero, but the identification of reliable gene amplification targets is rather difficult. Here, we report a new algorithm which incorporates physiological data into a model to improve the model's prediction capabilities and to capitalize on the relationships between genes and metabolic fluxes.

RESULTS

We developed an algorithm, flux variability scanning based on enforced objective flux (FVSEOF) with grouping reaction (GR) constraints, in an effort to identify gene amplification targets by considering reactions that co-carry flux values based on physiological omics data via "GR constraints". This method scans changes in the variabilities of metabolic fluxes in response to an artificially enforced objective flux of product formation. The gene amplification targets predicted using this method were validated by comparing the predicted effects with the previous experimental results obtained for the production of shikimic acid and putrescine in Escherichia coli. Moreover, new gene amplification targets for further enhancing putrescine production were validated through experiments involving the overexpression of each identified targeted gene under condition-controlled batch cultivation.

CONCLUSIONS

FVSEOF with GR constraints allows identification of gene amplification targets for metabolic engineering of microbial strains in order to enhance the production of desired bioproducts. The algorithm was validated through the experiments on the enhanced production of putrescine in E. coli, in addition to the comparison with the previously reported experimental data. The FVSEOF strategy with GR constraints will be generally useful for developing industrially important microbial strains having enhanced capabilities of producing chemicals of interest.

Structural and dynamical analysis of biological networks

Biological networks are currently being studied with approaches derived from the mathematical and physical sciences. Their structural analysis enables to highlight nodes with special properties that have sometimes been correlated with the biological importance of a gene or a protein. However, biological networks are dynamic both on the evolutionary time-scale, and on the much shorter time-scale of physiological processes. There is therefore no unique network for a given cellular process, but potentially many realizations, each with different properties as a consequence of regulatory mechanisms. Such realizations provide snapshots of a same network in different conditions, enabling the study of condition-dependent structural properties. True dynamical analysis can be obtained through detailed mathematical modeling techniques that are not easily scalable to full network models.

Metaproteogenomic insights beyond bacterial response to naphthalene exposure and bio-stimulation

Microbial metabolism in aromatic-contaminated environments has important ecological implications, and obtaining a complete understanding of this process remains a relevant goal. To understand the roles of biodiversity and aromatic-mediated genetic and metabolic rearrangements, we conducted ‘OMIC' investigations in an anthropogenically influenced and polyaromatic hydrocarbon (PAH)-contaminated soil with (Nbs) or without (N) bio-stimulation with calcium ammonia nitrate, NH₄NO₃ and KH₂PO₄ and the commercial surfactant Iveysol, plus two naphthalene-enriched communities derived from both soils (CN2 and CN1, respectively). Using a metagenomic approach, a total of 52, 53, 14 and 12 distinct species (according to operational phylogenetic units (OPU) in our work equivalent to taxonomic species) were identified in the N, Nbs, CN1 and CN2 communities, respectively. Approximately 10 out of 95 distinct species and 238 out of 3293 clusters of orthologous groups (COGs) protein families identified were clearly stimulated under the assayed conditions, whereas only two species and 1465 COGs conformed to the common set in all of the mesocosms. Results indicated distinct biodegradation capabilities for the utilisation of potential growth-supporting aromatics, which results in bio-stimulated communities being extremely fit to naphthalene utilisation and non-stimulated communities exhibiting a greater metabolic window than previously predicted. On the basis of comparing protein expression profiles and metagenome data sets, inter-alia interactions among members were hypothesised. The utilisation of curated databases is discussed and used for first time to reconstruct ‘presumptive' degradation networks for complex microbial communities.

Cra regulates the cross-talk between the two branches of the phosphoenolpyruvate : phosphotransferase system of Pseudomonas putida

The gene that encodes the catabolite repressor/activator, Cra (FruR), of Pseudomonas putida is divergent from the fruBKA operon for the uptake of fructose via the phosphoenolpyruvate : carbohydrate phosphotransferase system (PTS(Fru)). The expression of the fru cluster has been studied in cells growing on substrates that change the intracellular concentrations of fructose-1-P (F1P), the principal metabolic intermediate that counteracts the DNA-binding ability of Cra on an upstream operator. While the levels of the regulator were not affected by any of the growth conditions tested, the transcription of fruB was stimulated by fructose but not by the gluconeogenic substrate, succinate. The analysis of the P(fruB) promoter activity in a strain lacking the Cra protein and the determination of key metabolites revealed that this regulator represses the expression of PTS(Fru) in a fashion that is dependent on the endogenous concentrations of F1P. Because FruB (i.e. the EI-HPr-EIIA(Fru) polyprotein) can deliver a high-energy phosphate to the EIIA(Ntr) (PtsN) enzyme of the PTS(Ntr) branch, the cross-talk between the two phosphotransferase systems was examined under metabolic regimes that allowed for the high or low transcription of the fruBKA operon. While fructose caused cross-talk, succinate prevented it almost completely. Furthermore, PtsN phosphorylation by FruB occurred in a Δcra mutant regardless of growth conditions. These results traced the occurrence of the cross-talk to intracellular pools of Cra effectors, in particular F1P. The Cra/F1P duo seems to not only control the expression of the PTS(Fru) but also checks the activity of the PTS(Ntr) in vivo.

Phenotypic and transcriptomic response of auxotrophic Mycobacterium avium subsp. paratuberculosis leuD mutant under environmental stress

Mycobacterium avium subsp. paratuberculosis (MAP) is the causative agent of severe gastroenteritis in cattle. To gain a better understanding of MAP virulence, we investigated the role of leuD gene in MAP metabolism and stress response. For this, we have constructed an auxotrophic strain of MAP by deleting the leuD gene using allelic exchange. The wildtype and mutant strains were then compared for metabolic phenotypic changes using Biolog phenotype microarrays. The responses of both strains to physiologically relevant stress conditions were assessed using DNA microarrays. Transcriptomic data was then analyzed in the context of cellular metabolic pathways and gene networks. Our results showed that deletion of leuD gene has a global effect on both MAP phenotypic and transcriptome response. At the metabolic level, the mutant strain lost the ability to utilize most of the carbon, nitrogen, sulphur, phosphorus and nutrient supplements as energy source. At the transcriptome level, more than 100 genes were differentially expressed in each of the stress condition tested. Systems level network analysis revealed that the differentially expressed genes were distributed throughout the gene network, thus explaining the global impact of leuD deletion in metabolic phenotype. Further, we find that leuD deletion impacted metabolic pathways associated with fatty acids. We verified this by experimentally estimating the total fatty acid content of both mutant and wildtype. The mutant strain had 30% less fatty acid content when compared to wildtype, thus supporting the results from transcriptional and computational analyses. Our results therefore reveal the intricate connection between the metabolism and virulence in MAP.

GEMSiRV: a software platform for GEnome-scale metabolic model simulation, reconstruction and visualization

MOTIVATION

Genome-scale metabolic network models have become an indispensable part of the increasingly important field of systems biology. Metabolic systems biology studies usually include three major components-network model construction, objective- and experiment-guided model editing and visualization, and simulation studies based mainly on flux balance analyses. Bioinformatics tools are required to facilitate these complicated analyses. Although some of the required functions have been served separately by existing tools, a free software resource that simultaneously serves the needs of the three major components is not yet available.

RESULTS

Here we present a software platform, GEMSiRV (GEnome-scale Metabolic model Simulation, Reconstruction and Visualization), to provide functionalities of easy metabolic network drafting and editing, amenable network visualization for experimental data integration and flux balance analysis tools for simulation studies. GEMSiRV comes with downloadable, ready-to-use public-domain metabolic models, reference metabolite/reaction databases and metabolic network maps, all of which can be input into GEMSiRV as the starting materials for network construction or simulation analyses. Furthermore, all of the GEMSiRV-generated metabolic models and analysis results, including projects in progress, can be easily exchanged in the research community. GEMSiRV is a powerful integrative resource that may facilitate the development of systems biology studies.

AVAILABILITY

The software is freely available on the web at http://sb.nhri.org.tw/GEMSiRV.

Visualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics

Background

The Phenotype MicroArray (OmniLog® PM) system is able to simultaneously capture a large number of phenotypes by recording an organism's respiration over time on distinct substrates. This technique targets the object of natural selection itself, the phenotype, whereas previously addressed ‘-omics’ techniques merely study components that finally contribute to it. The recording of respiration over time, however, adds a longitudinal dimension to the data. To optimally exploit this information, it must be extracted from the shapes of the recorded curves and displayed in analogy to conventional growth curves.

Methodology

The free software environment R was explored for both visualizing and fitting of PM respiration curves. Approaches using either a model fit (and commonly applied growth models) or a smoothing spline were evaluated. Their reliability in inferring curve parameters and confidence intervals was compared to the native OmniLog® PM analysis software. We consider the post-processing of the estimated parameters, the optimal classification of curve shapes and the detection of significant differences between them, as well as practically relevant questions such as detecting the impact of cultivation times and the minimum required number of experimental repeats.

Conclusions

We provide a comprehensive framework for data visualization and parameter estimation according to user choices. A flexible graphical representation strategy for displaying the results is proposed, including 95% confidence intervals for the estimated parameters. The spline approach is less prone to irregular curve shapes than fitting any of the considered models or using the native PM software for calculating both point estimates and confidence intervals. These can serve as a starting point for the automated post-processing of PM data, providing much more information than the strict dichotomization into positive and negative reactions. Our results form the basis for a freely available R package for the analysis of PM data.

The IHF regulon of exponentially growing Pseudomonas putida cells

Integration host factor (IHF) sites are largely absent from intergenic regions of ORFs encoding central metabolic functions in Pseudomonas putida mt-2. To gain an insight into this unequal distribution of otherwise abundant IHF-binding sequences, the transcriptome of IHF-plus and IHF-minus cells growing exponentially on glucose as sole carbon source was examined. In parallel, the cognate metabolic fluxes of the wild-type P. putida strain and its ihfA derivative were determined by culturing cells to a steady-state physiological regime with (13)C-labelled glucose. While expression of many transcripts was altered by the lack of IHF, flux balance analysis revealed that the ihfA mutation did not influence central carbon metabolism. Identification of multiple IHF sites adjacent to genes responsive to the factor allowed a refinement of the consensus and the mapping of the preferred binding positions for activation or repression of associated promoters. That few (if any) of the genes affected by IHF involved core pathways suggested that the central carbon metabolism tolerates the loss of the factor. Instead, IHF controlled various cell surface-related functions and downregulated genes encoding ribosomal proteins, the alpha subunit of RNA polymerase and components of the ATP synthase. These results were confirmed with lacZ fusions to a suite of promoters detected in the transcriptome as affected by IHF. Taken together, the data suggest that IHF plays a role in the physiological shift that sets P. putida for entering stationary phase.

Regulatory tasks of the phosphoenolpyruvate-phosphotransferase system of Pseudomonas putida in central carbon metabolism

Two branches of the phosphoenolpyruvate-phosphotransferase system (PTS) operate in the soil bacterium Pseudomonas putida KT2440. One branch encompasses a complete set of enzymes for fructose intake (PTS^Fru), while the other (N-related PTS, or PTS^Ntr) controls various cellular functions unrelated to the transport of carbohydrates. The potential of these two systems for regulating central carbon catabolism has been investigated by measuring the metabolic fluxes of isogenic strains bearing nonpolar mutations in PTS^Fru or PTS^Ntr genes and grown on either fructose (a PTS substrate) or glucose, the transport of which is not governed by the PTS in this bacterium. The flow of carbon from each sugar was distinctly split between the Entner-Doudoroff, pentose phosphate, and Embden-Meyerhof-Parnas pathways in a ratio that was maintained in each of the PTS mutants examined. However, strains lacking PtsN (EIIA^Ntr) displayed significantly higher fluxes in the reactions of the pyruvate shunt, which bypasses malate dehydrogenase in the TCA cycle. This was consistent with the increased activity of the malic enzyme and the pyruvate carboxylase found in the corresponding PTS mutants. Genetic evidence suggested that such a metabolic effect of PtsN required the transfer of high-energy phosphate through the system. The EIIA^Ntr protein of the PTS^Ntr thus helps adjust central metabolic fluxes to satisfy the anabolic and energetic demands of the overall cell physiology.

This study demonstrates that EIIA^Ntr influences the biochemical reactions that deliver carbon between the upper and lower central metabolic domains for the consumption of sugars by P. putida. These findings indicate that the EIIA^Ntr protein is a key player for orchestrating the fate of carbon in various physiological destinations in this bacterium. Additionally, these results highlight the importance of the posttranslational regulation of extant enzymatic complexes for increasing the robustness of the corresponding metabolic networks.

The metabolic response of P. putida KT2442 producing high levels of polyhydroxyalkanoate under single- and multiple-nutrient-limited growth: highlights from a multi-level omics approach

Background

Pseudomonas putida KT2442 is a natural producer of polyhydroxyalkanoates (PHAs), which can substitute petroleum-based non-renewable plastics and form the basis for the production of tailor-made biopolymers. However, despite the substantial body of work on PHA production by P. putida strains, it is not yet clear how the bacterium re-arranges its whole metabolism when it senses the limitation of nitrogen and the excess of fatty acids as carbon source, to result in a large accumulation of PHAs within the cell. In the present study we investigated the metabolic response of KT2442 using a systems biology approach to highlight the differences between single- and multiple-nutrient-limited growth in chemostat cultures.

Results

We found that 26, 62, and 81% of the cell dry weight consist of PHA under conditions of carbon, dual, and nitrogen limitation, respectively. Under nitrogen limitation a specific PHA production rate of 0.43 (g·(g·h)^-1) was obtained. The residual biomass was not constant for dual- and strict nitrogen-limiting growth, showing a different feature in comparison to other P. putida strains. Dual limitation resulted in patterns of gene expression, protein level, and metabolite concentrations that substantially differ from those observed under exclusive carbon or nitrogen limitation. The most pronounced differences were found in the energy metabolism, fatty acid metabolism, as well as stress proteins and enzymes belonging to the transport system.

Conclusion

This is the first study where the interrelationship between nutrient limitations and PHA synthesis has been investigated under well-controlled conditions using a system level approach. The knowledge generated will be of great assistance for the development of bioprocesses and further metabolic engineering work in this versatile organism to both enhance and diversify the industrial production of PHAs.

Random and cyclical deletion of large DNA segments in the genome of Pseudomonas putida

Cumulative site-directed mutagenesis is of limited suitability for the global analysis of the gene functions in the microbe's cellular network. In order to simplify and stabilize the genome of the soil bacterium Pseudomonas putida, we developed a recyclable three-step excision method based on the combination of customized mini-transposons and the FLP-FRT site-specific recombination system. To demonstrate the powerful potential of these tools, we first established insertion mutant libraries that allow users to study gene functions with respect either to phenotypic characteristics (single insertions) or to their involvement in predicted networks (double insertions). Based on these libraries, we generated as a proof-of-principle, single-deletion mutants lacking ∼ 4.1% of the genome (∼ 3.7% of the gene repertoire). A cyclical application of the method generated four double-deletion mutants of which a maximum of ∼ 7.4% of the chromosome (∼ 6.9% of the gene count) was excised. This procedure demonstrates a new strategy for rapid genome streamlining and gain of new insights into the molecular interactions and regulations.

Reconstruction and analysis of a genome-scale metabolic model for Scheffersomyces stipitis

Background

Fermentation of xylose, the major component in hemicellulose, is essential for economic conversion of lignocellulosic biomass to fuels and chemicals. The yeast Scheffersomyces stipitis (formerly known as Pichia stipitis) has the highest known native capacity for xylose fermentation and possesses several genes for lignocellulose bioconversion in its genome. Understanding the metabolism of this yeast at a global scale, by reconstructing the genome scale metabolic model, is essential for manipulating its metabolic capabilities and for successful transfer of its capabilities to other industrial microbes.

Results

We present a genome-scale metabolic model for Scheffersomyces stipitis, a native xylose utilizing yeast. The model was reconstructed based on genome sequence annotation, detailed experimental investigation and known yeast physiology. Macromolecular composition of Scheffersomyces stipitis biomass was estimated experimentally and its ability to grow on different carbon, nitrogen, sulphur and phosphorus sources was determined by phenotype microarrays. The compartmentalized model, developed based on an iterative procedure, accounted for 814 genes, 1371 reactions, and 971 metabolites. In silico computed growth rates were compared with high-throughput phenotyping data and the model could predict the qualitative outcomes in 74% of substrates investigated. Model simulations were used to identify the biosynthetic requirements for anaerobic growth of Scheffersomyces stipitis on glucose and the results were validated with published literature. The bottlenecks in Scheffersomyces stipitis metabolic network for xylose uptake and nucleotide cofactor recycling were identified by in silico flux variability analysis. The scope of the model in enhancing the mechanistic understanding of microbial metabolism is demonstrated by identifying a mechanism for mitochondrial respiration and oxidative phosphorylation.

Conclusion

The genome-scale metabolic model developed for Scheffersomyces stipitis successfully predicted substrate utilization and anaerobic growth requirements. Useful insights were drawn on xylose metabolism, cofactor recycling and mechanism of mitochondrial respiration from model simulations. These insights can be applied for efficient xylose utilization and cofactor recycling in other industrial microorganisms. The developed model forms a basis for rational analysis and design of Scheffersomyces stipitis metabolic network for the production of fuels and chemicals from lignocellulosic biomass.

A metabolic network approach for the identification and prioritization of antimicrobial drug targets

For many infectious diseases, novel treatment options are needed in order to address problems with cost, toxicity and resistance to current drugs. Systems biology tools can be used to gain valuable insight into pathogenic processes and aid in expediting drug discovery. In the past decade, constraint-based modeling of genome-scale metabolic networks has become widely used. Focusing on pathogen metabolic networks, we review in silico strategies used to identify effective drug targets and highlight recent successes as well as limitations associated with such computational analyses. We further discuss how accounting for the host environment and even targeting the host may offer new therapeutic options. These systems-level approaches are beginning to provide novel avenues for drug targeting against infectious agents.

Metabolic stasis in an ancient symbiosis: genome-scale metabolic networks from two Blattabacterium cuenoti strains, primary endosymbionts of cockroaches

BACKGROUND

Cockroaches are terrestrial insects that strikingly eliminate waste nitrogen as ammonia instead of uric acid. Blattabacterium cuenoti (Mercier 1906) strains Bge and Pam are the obligate primary endosymbionts of the cockroaches Blattella germanica and Periplaneta americana, respectively. The genomes of both bacterial endosymbionts have recently been sequenced, making possible a genome-scale constraint-based reconstruction of their metabolic networks. The mathematical expression of a metabolic network and the subsequent quantitative studies of phenotypic features by Flux Balance Analysis (FBA) represent an efficient functional approach to these uncultivable bacteria.

RESULTS

We report the metabolic models of Blattabacterium strains Bge (iCG238) and Pam (iCG230), comprising 296 and 289 biochemical reactions, associated with 238 and 230 genes, and 364 and 358 metabolites, respectively. Both models reflect both the striking similarities and the singularities of these microorganisms. FBA was used to analyze the properties, potential and limits of the models, assuming some environmental constraints such as aerobic conditions and the net production of ammonia from these bacterial systems, as has been experimentally observed. In addition, in silico simulations with the iCG238 model have enabled a set of carbon and nitrogen sources to be defined, which would also support a viable phenotype in terms of biomass production in the strain Pam, which lacks the first three steps of the tricarboxylic acid cycle. FBA reveals a metabolic condition that renders these enzymatic steps dispensable, thus offering a possible evolutionary explanation for their elimination. We also confirm, by computational simulations, the fragility of the metabolic networks and their host dependence.

CONCLUSIONS

The minimized Blattabacterium metabolic networks are surprisingly similar in strains Bge and Pam, after 140 million years of evolution of these endosymbionts in separate cockroach lineages. FBA performed on the reconstructed networks from the two bacteria helps to refine the functional analysis of the genomes enabling us to postulate how slightly different host metabolic contexts drove their parallel evolution.

The polyhydroxyalkanoate metabolism controls carbon and energy spillage in Pseudomonas putida

The synthesis and degradation of polyhydroxyalkanoates (PHAs), the storage polymer of many bacteria, is linked to the operation of central carbon metabolism. To rationalize the impact of PHA accumulation on central carbon metabolism of the prototype bacterium Pseudomonas putida, we have revisited PHA production in quantitative physiology experiments in the wild-type strain vs. a PHA negative mutant growing under low nitrogen conditions. When octanoic acid was used as PHA precursor and as carbon and energy source, we have detected higher intracellular flux via acetyl-CoA in the mutant strain than in the wild type, which correlates with the stimulation of the TCA cycle and glyoxylate shunt observed on the transcriptional level. The mutant defective in carbon and energy storage spills the additional resources, releasing CO(2) instead of generating biomass. Hence, P. putida operates the metabolic network to optimally exploit available resources and channels excess carbon and energy to storage via PHA, without compromising growth. These findings demonstrate that the PHA metabolism plays a critical role in synchronizing global metabolism to availability of resources in PHA-producing microorganisms.

Metabolic networks of Sodalis glossinidius: a systems biology approach to reductive evolution

BACKGROUND

Genome reduction is a common evolutionary process affecting bacterial lineages that establish symbiotic or pathogenic associations with eukaryotic hosts. Such associations yield highly reduced genomes with greatly streamlined metabolic abilities shaped by the type of ecological association with the host. Sodalis glossinidius, the secondary endosymbiont of tsetse flies, represents one of the few complete genomes available of a bacterium at the initial stages of this process. In the present study, genome reduction is studied from a systems biology perspective through the reconstruction and functional analysis of genome-scale metabolic networks of S. glossinidius.

RESULTS

The functional profile of ancestral and extant metabolic networks sheds light on the evolutionary events underlying transition to a host-dependent lifestyle. Meanwhile, reductive evolution simulations on the extant metabolic network can predict possible future evolution of S. glossinidius in the context of genome reduction. Finally, knockout simulations in different metabolic systems reveal a gradual decrease in network robustness to different mutational events for bacterial endosymbionts at different stages of the symbiotic association.

CONCLUSIONS

Stoichiometric analysis reveals few gene inactivation events whose effects on the functionality of S. glossinidius metabolic systems are drastic enough to account for the ecological transition from a free-living to host-dependent lifestyle. The decrease in network robustness across different metabolic systems may be associated with the progressive integration in the more stable environment provided by the insect host. Finally, reductive evolution simulations reveal the strong influence that external conditions exert on the evolvability of metabolic systems.

Complete genome sequence of the nicotine-degrading Pseudomonas putida strain S16

Pseudomonas putida S16 is an efficient degrader of nicotine. The complete genome of strain S16 (5,984,790 bp in length) includes genes related to catabolism of aromatic and heterocyclic compounds. The genes of enzymes in the core genome and a genomic island encode the proteins responsible for nicotine catabolism.

Genome-scale consequences of cofactor balancing in engineered pentose utilization pathways in Saccharomyces cerevisiae

Biofuels derived from lignocellulosic biomass offer promising alternative renewable energy sources for transportation fuels. Significant effort has been made to engineer Saccharomyces cerevisiae to efficiently ferment pentose sugars such as D-xylose and L-arabinose into biofuels such as ethanol through heterologous expression of the fungal D-xylose and L-arabinose pathways. However, one of the major bottlenecks in these fungal pathways is that the cofactors are not balanced, which contributes to inefficient utilization of pentose sugars. We utilized a genome-scale model of S. cerevisiae to predict the maximal achievable growth rate for cofactor balanced and imbalanced D-xylose and L-arabinose utilization pathways. Dynamic flux balance analysis (DFBA) was used to simulate batch fermentation of glucose, D-xylose, and L-arabinose. The dynamic models and experimental results are in good agreement for the wild type and for the engineered D-xylose utilization pathway. Cofactor balancing the engineered D-xylose and L-arabinose utilization pathways simulated an increase in ethanol batch production of 24.7% while simultaneously reducing the predicted substrate utilization time by 70%. Furthermore, the effects of cofactor balancing the engineered pentose utilization pathways were evaluated throughout the genome-scale metabolic network. This work not only provides new insights to the global network effects of cofactor balancing but also provides useful guidelines for engineering a recombinant yeast strain with cofactor balanced engineered pathways that efficiently co-utilizes pentose and hexose sugars for biofuels production. Experimental switching of cofactor usage in enzymes has been demonstrated, but is a time-consuming effort. Therefore, systems biology models that can predict the likely outcome of such strain engineering efforts are highly useful for motivating which efforts are likely to be worth the significant time investment.

Agent-based dynamic knowledge representation of Pseudomonas aeruginosa virulence activation in the stressed gut: Towards characterizing host-pathogen interactions in gut-derived sepsis

Background

There is a growing realization that alterations in host-pathogen interactions (HPI) can generate disease phenotypes without pathogen invasion. The gut represents a prime region where such HPI can arise and manifest. Under normal conditions intestinal microbial communities maintain a stable, mutually beneficial ecosystem. However, host stress can lead to changes in environmental conditions that shift the nature of the host-microbe dialogue, resulting in escalation of virulence expression, immune activation and ultimately systemic disease. Effective modulation of these dynamics requires the ability to characterize the complexity of the HPI, and dynamic computational modeling can aid in this task. Agent-based modeling is a computational method that is suited to representing spatially diverse, dynamical systems. We propose that dynamic knowledge representation of gut HPI with agent-based modeling will aid in the investigation of the pathogenesis of gut-derived sepsis.

Methodology/Principal Findings

An agent-based model (ABM) of virulence regulation in Pseudomonas aeruginosa was developed by translating bacterial and host cell sense-and-response mechanisms into behavioral rules for computational agents and integrated into a virtual environment representing the host-microbe interface in the gut. The resulting gut milieu ABM (GMABM) was used to: 1) investigate a potential clinically relevant laboratory experimental condition not yet developed - i.e. non-lethal transient segmental intestinal ischemia, 2) examine the sufficiency of existing hypotheses to explain experimental data - i.e. lethality in a model of major surgical insult and stress, and 3) produce behavior to potentially guide future experimental design - i.e. suggested sample points for a potential laboratory model of non-lethal transient intestinal ischemia. Furthermore, hypotheses were generated to explain certain discrepancies between the behaviors of the GMABM and biological experiments, and new investigatory avenues proposed to test those hypotheses.

Conclusions/Significance

Agent-based modeling can account for the spatio-temporal dynamics of an HPI, and, even when carried out with a relatively high degree of abstraction, can be useful in the investigation of system-level consequences of putative mechanisms operating at the individual agent level. We suggest that an integrated and iterative heuristic relationship between computational modeling and more traditional laboratory and clinical investigations, with a focus on identifying useful and sufficient degrees of abstraction, will enhance the efficiency and translational productivity of biomedical research.

Reconstruction of a genome-scale metabolic network of Rhodococcus erythropolis for desulfurization studies

The remarkable catabolic diversity of Rhodococcus erythropolis makes it an interesting organism for bioremediation and fuel desulfurization. However, a model that can describe and explain the combined influence of various intracellular metabolic activities on its desulfurizing capabilities is missing from the literature. Such a model can greatly aid the development of R. erythropolis as an effective desulfurizing biocatalyst. This work reports the reconstruction of the first genome-scale metabolic model for R. erythropolis using the available genomic, experimental, and biochemical information. We have validated our in silico model by successfully predicting cell growth results and explaining several experimental observations in the literature on biodesulfurization using dibenzothiophene. We report several in silico experiments and flux balance analyses to propose minimal media, determine gene and reaction essentiality, and compare effectiveness of carbon, nitrogen, and sulfur sources. We demonstrate the usefulness of our model by studying a few in silico mutants of R. erythropolis for improved biodesulfurization, and comparing the desulfurization abilities of R. erythropolis with an in silico mutant of E. coli.

Genome-scale reconstruction and in silico analysis of the Ralstonia eutropha H16 for polyhydroxyalkanoate synthesis, lithoautotrophic growth, and 2-methyl citric acid production

Background

Ralstonia eutropha H16, found in both soil and water, is a Gram-negative lithoautotrophic bacterium that can utillize CO₂ and H₂ as its sources of carbon and energy in the absence of organic substrates. R. eutropha H16 can reach high cell densities either under lithoautotrophic or heterotrophic conditions, which makes it suitable for a number of biotechnological applications. It is the best known and most promising producer of polyhydroxyalkanoates (PHAs) from various carbon substrates and is an environmentally important bacterium that can degrade aromatic compounds. In order to make R. eutropha H16 a more efficient and robust biofactory, system-wide metabolic engineering to improve its metabolic performance is essential. Thus, it is necessary to analyze its metabolic characteristics systematically and optimize the entire metabolic network at systems level.

Results

We present the lithoautotrophic genome-scale metabolic model of R. eutropha H16 based on the annotated genome with biochemical and physiological information. The stoichiometic model, RehMBEL1391, is composed of 1391 reactions including 229 transport reactions and 1171 metabolites. Constraints-based flux analyses were performed to refine and validate the genome-scale metabolic model under environmental and genetic perturbations. First, the lithoautotrophic growth characteristics of R. eutropha H16 were investigated under varying feeding ratios of gas mixture. Second, the genome-scale metabolic model was used to design the strategies for the production of poly[R-(-)-3hydroxybutyrate] (PHB) under different pH values and carbon/nitrogen source uptake ratios. It was also used to analyze the metabolic characteristics of R. eutropha when the phosphofructokinase gene was expressed. Finally, in silico gene knockout simulations were performed to identify targets for metabolic engineering essential for the production of 2-methylcitric acid in R. eutropha H16.

Conclusion

The genome-scale metabolic model, RehMBEL1391, successfully represented metabolic characteristics of R. eutropha H16 at systems level. The reconstructed genome-scale metabolic model can be employed as an useful tool for understanding its metabolic capabilities, predicting its physiological consequences in response to various environmental and genetic changes, and developing strategies for systems metabolic engineering to improve its metabolic performance.

High-resolution time series of Pseudomonas aeruginosa gene expression and rhamnolipid secretion through growth curve synchronization

Background

Online spectrophotometric measurements allow monitoring dynamic biological processes with high-time resolution. Contrastingly, numerous other methods require laborious treatment of samples and can only be carried out offline. Integrating both types of measurement would allow analyzing biological processes more comprehensively. A typical example of this problem is acquiring quantitative data on rhamnolipid secretion by the opportunistic pathogen Pseudomonas aeruginosa. P. aeruginosa cell growth can be measured by optical density (OD₆₀₀) and gene expression can be measured using reporter fusions with a fluorescent protein, allowing high time resolution monitoring. However, measuring the secreted rhamnolipid biosurfactants requires laborious sample processing, which makes this an offline measurement.

Results

Here, we propose a method to integrate growth curve data with endpoint measurements of secreted metabolites that is inspired by a model of exponential cell growth. If serial diluting an inoculum gives reproducible time series shifted in time, then time series of endpoint measurements can be reconstructed using calculated time shifts between dilutions. We illustrate the method using measured rhamnolipid secretion by P. aeruginosa as endpoint measurements and we integrate these measurements with high-resolution growth curves measured by OD₆₀₀ and expression of rhamnolipid synthesis genes monitored using a reporter fusion. Two-fold serial dilution allowed integrating rhamnolipid measurements at a ~0.4 h^-1 frequency with high-time resolved data measured at a 6 h^-1 frequency. We show how this simple method can be used in combination with mutants lacking specific genes in the rhamnolipid synthesis or quorum sensing regulation to acquire rich dynamic data on P. aeruginosa virulence regulation. Additionally, the linear relation between the ratio of inocula and the time-shift between curves produces high-precision measurements of maximum specific growth rates, which were determined with a precision of ~5.4%.

Conclusions

Growth curve synchronization allows integration of rich time-resolved data with endpoint measurements to produce time-resolved quantitative measurements. Such data can be valuable to unveil the dynamic regulation of virulence in P. aeruginosa. More generally, growth curve synchronization can be applied to many biological systems thus helping to overcome a key obstacle in dynamic regulation: the scarceness of quantitative time-resolved data.

Engineering antibiotic production and overcoming bacterial resistance

Progress in DNA technology, analytical methods and computational tools is leading to new developments in synthetic biology and metabolic engineering, enabling new ways to produce molecules of industrial and therapeutic interest. Here, we review recent progress in both antibiotic production and strategies to counteract bacterial resistance to antibiotics. Advances in sequencing and cloning are increasingly enabling the characterization of antibiotic biosynthesis pathways, and new systematic methods for de novo biosynthetic pathway prediction are allowing the exploration of the metabolic chemical space beyond metabolic engineering. Moreover, we survey the computer-assisted design of modular assembly lines in polyketide synthases and non-ribosomal peptide synthases for the development of tailor-made antibiotics. Nowadays, production of novel antibiotic can be tranferred into any chosen chassis by optimizing a host factory through specific strain modifications. These advances in metabolic engineering and synthetic biology are leading to novel strategies for engineering antimicrobial agents with desired specificities.

Growth of Pseudomonas putida at low temperature: global transcriptomic and proteomic analyses

In its natural habitats (soil, water and rhizosphere), Pseudomonas putida can suffer frequent and long-term changes in temperature that affect its growth and survival. Pseudomonas putida KT2440, a well-characterized model strain, grows optimally at 30°C but can proliferate at temperatures as low as 4°C. However, little information is available on the physiological changes that occur when P. putida grows at low temperatures. To investigate this area, the transcriptome and proteome profiles of cells exponentially growing in a complex medium at 10°C were compared with those of cells exponentially growing at 30°C. Low temperature modified the expression of at least 266 genes (some 5% of the genome). Many of the genes showing differential expression were involved in energy metabolism or in the transport and binding of substrates, although genes implicated in other cellular functions were also affected. Several changes seemed directed towards neutralizing problems created by low temperature, such as increased protein misfolding, the increased stability of DNA/RNA secondary structures, reduced membrane fluidity and a reduced growth rate. The present results improve our understanding of the P. putida lifestyle at low temperature, which may be relevant for its applications in bioremediation and in promotion of plant growth.

Comparative genomics and functional analysis of niche-specific adaptation in Pseudomonas putida

Pseudomonas putida is a gram-negative rod-shaped gammaproteobacterium that is found throughout various environments. Members of the species P. putida show a diverse spectrum of metabolic activities, which is indicative of their adaptation to various niches, which includes the ability to live in soils and sediments contaminated with high concentrations of heavy metals and organic contaminants. Pseudomonas putida strains are also found as plant growth-promoting rhizospheric and endophytic bacteria. The genome sequences of several P. putida species have become available and provide a unique tool to study the specific niche adaptation of the various P. putida strains. In this review, we compare the genomes of four P. putida strains: the rhizospheric strain KT2440, the endophytic strain W619, the aromatic hydrocarbon-degrading strain F1 and the manganese-oxidizing strain GB-1. Comparative genomics provided a powerful tool to gain new insights into the adaptation of P. putida to specific lifestyles and environmental niches, and clearly demonstrated that horizontal gene transfer played a key role in this adaptation process, as many of the niche-specific functions were found to be encoded on clearly defined genomic islands.