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Summary To unlock the full potential of genome data and to enhance data interoperability and reusability of genome annotations we have developed SAPP, a Semantic Annotation Platform with Provenance. SAPP is designed as an infrastructure supporting FAIR de novo computational genomics but can also be used to process and analyze existing genome annotations. SAPP automatically predicts, tracks and stores structural and functional annotations and associated dataset- and element-wise provenance in a Linked Data format, thereby enabling information mining and retrieval with Semantic Web technologies. This greatly reduces the administrative burden of handling multiple analysis tools and versions thereof and facilitates multi-level large scale comparative analysis. Availability and implementation SAPP is written in JAVA and freely available at https://gitlab.com/sapp and runs on Unix-like operating systems. The documentation, examples and a tutorial are available at https://sapp.gitlab.io.

Here, we present systems metabolic engineering driven by in-silico modeling to tailor Pseudomonas putida for synthesis of medium chain length PHAs on glucose. Using physiological properties of the parent wild type as constraints, elementary flux mode analysis of a large-scale model of the metabolism of P. putida was used to predict genetic targets for strain engineering. Among a set of priority ranked targets, glucose dehydrogenase (encoded by gcd) was predicted as most promising deletion target. The mutant P. putida Δgcd, generated on basis of the computational design, exhibited 100% increased PHA accumulation as compared to the parent wild type, maintained a high specific growth rate and exhibited an almost unaffected gene expression profile, which excluded detrimental side effects of the modification. A second mutant strain, P. putida Δpgl, that lacked 6-phosphogluconolactonase, exhibited a substantially decreased PHA synthesis, as was also predicted by the model. The production potential of P. putida Δgcd was assessed in batch bioreactors. The novel strain showed an increase of the PHA yield (+80%), the PHA titer (+100%) and cellular PHA content (+50%) and revealed almost unaffected growth and diminished by-product formation. It was thus found superior in all relevant criteria towards industrial production. Beyond the contribution to more efficient PHA production processes at reduced costs that might replace petrochemical plastics in the future, the study illustrates the power of computational prediction to tailor microbial strains for enhanced biosynthesis of added-value compounds.

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.

The study of plant biomass utilization by fungi is a research field of great interest due to its many implications in ecology, agriculture and biotechnology. Most of the efforts done to increase the understanding of the use of plant cell walls by fungi have been focused on the degradation of cellulose and hemicellulose, and transport and metabolism of their constituent monosaccharides. Pectin is another important constituent of plant cell walls, but has received less attention. In relation to the uptake of pectic building blocks, fungal transporters for the uptake of galacturonic acid recently have been reported in Aspergillus niger and Neurospora crassa. However, not a single L-rhamnose (6-deoxy-L-mannose) transporter has been identified yet in fungi or in other eukaryotic organisms. L-rhamnose is a deoxy-sugar present in plant cell wall pectic polysaccharides (mainly rhamnogalacturonan I and rhamnogalacturonan II), but is also found in diverse plant secondary metabolites (e.g. anthocyanins, flavonoids and triterpenoids), in the green seaweed sulfated polysaccharide ulvan, and in glycan structures from viruses and bacteria. Here, a comparative plasmalemma proteomic analysis was used to identify candidate L-rhamnose transporters in A. niger. Further analysis was focused on protein ID 1119135 (RhtA) (JGI A. niger ATCC 1015 genome database). RhtA was classified as a Family 7 Fucose: H+ Symporter (FHS) within the Major Facilitator Superfamily. Family 7 currently includes exclusively bacterial transporters able to use different sugars. Strong indications for its role in L-rhamnose transport were obtained by functional complementation of the Saccharomyces cerevisiae EBY.VW.4000 strain in growth studies with a range of potential substrates. Biochemical analysis using L-[3H(G)]-rhamnose confirmed that RhtA is a L-rhamnose transporter. The RhtA gene is located in tandem with a hypothetical alpha-L-rhamnosidase gene (rhaB). Transcriptional analysis of rhtA and rhaB confirmed that both genes have a coordinated expression, being strongly and specifically induced by L-rhamnose, and controlled by RhaR, a transcriptional regulator involved in the release and catabolism of the methyl-pentose. RhtA is the first eukaryotic L-rhamnose transporter identified and functionally validated to date., Author Summary The growth of filamentous fungi on plant biomass, which occurs through the utilization of its components (e.g. D-glucose, D-xylose, L-arabinose, L-rhamnose) as carbon sources, is a highly regulated event. L-rhamnose (6-deoxy-L-mannose) is a deoxy-sugar present in plant cell wall pectic polysaccharides (mainly rhamnogalacturonan I and rhamnogalacturonan II), but also in diverse plant secondary metabolites, ulvan from green seaweeds and glycan structures from virus and bacteria. The utilization, transformation or detoxification of this monosaccharide by fungi involves a first step of chemical hydrolysis, performed by alpha-L-rhamnosidases, and a second step of transport into the cell, prior to its metabolization. While many rhamnosidases have been identified, not a single eukaryotic plasma membrane L-rhamnose transporter is known to date. In this study we identified and characterized, for the first time, a fungal L-rhamnose transporter (RhtA), from the industrial workhorse Aspergillus niger. We also found that RhtA putative orthologs are conserved throughout different fungal orders, opening the possibility of identifying new transporters of its kind.

The strong advances in synthetic biology enable the engineering of novel functions and complex biological features in unprecedented ways, such as implementing synthetic autotrophic metabolism into heterotrophic hosts. A key challenge for the sustainable production of fuels and chemicals entails the engineering of synthetic autotrophic organisms that can effectively and efficiently fix carbon dioxide by using sustainable energy sources. This challenge involves the integration of carbon fixation and energy uptake systems. A variety of carbon fixation pathways and several types of photosystems and other energy uptake systems can be chosen and, potentially, modularly combined to design synthetic autotrophic metabolism. Prior to implementation, these designs can be evaluated by the combination of several computational pathway analysis techniques. Here we present a systematic, integrated in silico analysis of photo-electro-autotrophic pathway designs, consisting of natural and synthetic carbon fixation pathways, a proton-pumping rhodopsin photosystem for ATP regeneration and an electron uptake pathway. We integrated Flux Balance Analysis of the heterotrophic chassis Escherichia coli with kinetic pathway analysis and thermodynamic pathway analysis (Max-min Driving Force). The photo-electro-autotrophic designs are predicted to have a limited potential for anaerobic, autotrophic growth of E. coli, given the relatively low ATP regenerating capacity of the proton pumping rhodopsin photosystems and the high ATP maintenance of E. coli. If these factors can be tackled, our analysis indicates the highest growth potential for the natural reductive tricarboxylic acid cycle and the synthetic pyruvate synthase–pyruvate carboxylate -glyoxylate bicycle. Both carbon fixation cycles are very ATP efficient, while maintaining fast kinetics, which also results in relatively low estimated protein costs for these pathways. Furthermore, the synthetic bicycles are highly thermodynamic favorable under conditions analysed. However, the most important challenge identified for improving photo-electro-autotrophic growth is increasing the proton-pumping rate of the rhodopsin photosystems, allowing for higher ATP regeneration. Alternatively, other designs of autotrophy may be considered, therefore the herein presented integrated modeling approach allows synthetic biologists to evaluate and compare complex pathway designs before experimental implementation., PLoS ONE, 11 (6), ISSN:1932-6203

Systems Biology is an approach to biology and medicine that has the potential to lead to a better understanding of how biological properties emerge from the interaction of genes, proteins, molecules, cells and organisms. The approach aims at elucidating how these interactions govern biological function by employing experimental data, mathematical models and computational simulations. As Systems Biology is inherently multidisciplinary, education within this field meets numerous hurdles including departmental barriers, availability of all required expertise locally, appropriate teaching material and example curricula. As university education at the Bachelor’s level is traditionally built upon disciplinary degrees, we believe that the most effective way to implement education in Systems Biology would be at the Master’s level, as it offers a more flexible framework. Our team of experts and active performers of Systems Biology education suggest here (i) a definition of the skills that students should acquire within a Master’s programme in Systems Biology, (ii) a possible basic educational curriculum with flexibility to adjust to different application areas and local research strengths, (iii) a description of possible career paths for students who undergo such an education, (iv) conditions that should improve the recruitment of students to such programmes and (v) mechanisms for collaboration and excellence spreading among education professionals. With the growing interest of industry in applying Systems Biology approaches in their fields, a concerted action between academia and industry is needed to build this expertise. Here we present a reflection of the European situation and expertise, where most of the challenges we discuss are universal, anticipating that our suggestions will be useful internationally. We believe that one of the overriding goals of any Systems Biology education should be a student’s ability to phrase and communicate research questions in such a manner that they can be solved by the integration of experiments and modelling, as well as to communicate and collaborate productively across different experimental and theoretical disciplines in research and development., npj Systems Biology and Applications, 2, ISSN:2056-7189

The opportunistic bacterium Pseudomonas aeruginosa is a major nosocomial pathogen causing both devastating acute and chronic persistent infections. During the course of an infection, P. aeruginosa rapidly adapts to the specific conditions within the host. In the present study, we aimed at the identification of genes that are highly expressed during biofilm infections such as in chronically infected lungs of patients with cystic fibrosis (CF), burn wounds and subcutaneous mouse tumours. We found a common subset of differentially regulated genes in all three in vivo habitats and evaluated whether their inactivation impacts on the bacterial capability to form biofilms in vitro and to establish biofilm-associated infections in a murine model. Additive effects on biofilm formation and host colonization were discovered by the combined inactivation of several highly expressed genes. However, even combined inactivation was not sufficient to abolish the establishment of an infection completely. These findings can be interpreted as evidence that either redundant traits encode functions that are essential for in vivo survival and chronic biofilm infections and/or bacterial adaptation is considerably achieved independently of transcription levels. Supplemental screens, will have to be applied in order to identify the minimal set of key genes essential for the establishment of chronic infectious diseases.

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.

Many Gram-negative bacteria employ cell-to-cell communication mediated by N-acyl homoserine lactones (quorum sensing) to control expression of a wide range of genes including, but not limited to, genes encoding virulence factors. Outside the laboratory, the bacteria live in complex communities where signals may be perceived across species. We here present a newly found natural quorum sensing inhibitor, produced by the pseudomonads Pseudomonas sp. B13 and Pseudomonas reinekei MT1 as a blind end in the biodegradation of organochloride xenobiotics, which inhibits quorum sensing in P. aeruginosa in naturally occurring concentrations. This catabolite, 4-methylenebut-2-en-4-olide, also known as protoanemonin, has been reported to possess antibacterial properties, but seems to have dual functions. Using transcriptomics and proteomics, we found that protoanemonin significantly reduced expression of genes and secretion of proteins known to be under control of quorum sensing in P. aeruginosa. Moreover, we found activation of genes and gene products involved in iron starvation response. It is thus likely that inhibition of quorum sensing, as the production of antibiotics, is a phenomenon found in complex bacterial communities.

Summary 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 char- acteristics (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 proce- dure demonstrates a new strategy for rapid genome streamlining and gain of new insights into the molecu- lar interactions and regulations.

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. © 2011 American Institute of Chemical Engineers (AIChE).

Summary The regulatory and metabolic networks that rule bio- degradation of pollutants by environmental bacteria are wired to the rest of the cellular physiology through both transcriptional factors and intermediary signal molecules. In this review, we examine some formalisms for describing catalytic/regulatory circuits of this sort and advocate the adoption of Boolean logic for combining transcriptional and enzymatic occurrences in the same biological system. As an example, we show how known regulatory and meta- bolic actions that bring about biodegradation of m-xylene by Pseudomonas putida mt-2 can be repre- sented as clusters of binary operations and then reconstructed as a digital network. Despite the many simplifications, Boolean tools still capture the gross behaviour of the system even in the absence of kinetic constants determined experimentally. On this basis, we argue that still with a limited volume of data binary formalisms allow us to penetrate the raison d'etre of extant regulatory and metabolic architectures.

The majority of models describing the kinetic properties of a microorganism for a given substrate are unstructured and empirical. They are formulated in this manner so that the complex mechanism of cell growth is simplified. Herein, a novel approach for modelling microbial growth kinetics is proposed, linking biomass growth and substrate consumption rates to the gene regulatory programmes that control these processes. A dynamic model of the TOL (pWW0) plasmid of Pseudomonas putida mt-2 has been developed, describing the molecular interactions that lead to the transcription of the upper and meta operons, known to produce the enzymes for the oxidative catabolism of m-xylene. The genetic circuit model was combined with a growth kinetic model decoupling biomass growth and substrate consumption rates, which are expressed as independent functions of the rate-limiting enzymes produced by the operons. Estimation of model parameters and validation of the model's predictive capability were successfully performed in batch cultures of mt-2 fed with different concentrations of m-xylene, as confirmed by relative mRNA concentration measurements of the promoters encoded in TOL. The growth formation and substrate utilisation patterns could not be accurately described by traditional Monod-type models for a wide range of conditions, demonstrating the critical importance of gene regulation for the development of advanced models closely predicting complex bioprocesses. In contrast, the proposed strategy, which utilises quantitative information pertaining to upstream molecular events that control the production of rate-limiting enzymes, predicts the catabolism of a substrate and biomass formation and could be of central importance for the design of optimal bioprocesses.

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.

D-amino acid oxidase (DAAO) degrades D-amino acids to produce α-ketoacids, hydrogen peroxide and ammonia. DAAO has often been investigated and engineered for industrial and clinical applications. We combined information from literature with a detailed analysis of the structure to engineer mammalian DAAOs. The structural analysis was complemented with molecular dynamics simulations to characterize solvent accessibility and product release mechanisms. We identified non-obvious residues located on the loops on the border between the active site and the secondary binding pocket essential for pig and human DAAO substrate specificity and activity. We engineered DAAOs by mutating such critical residues and characterised the biochemical activity of the resulting variants. The results highlight the importance of the selected residues in modulating substrate specificity, product egress and enzyme activity, suggesting further steps of DAAO re-engineering towards desired clinical and industrial applications.

The high complexity of naturally occurring microbial communities is the major drawback limiting the study of these important biological systems. In this study, a comparison between pure cultures of Pseudomonas reinekei sp. strain MT1 and stable community cultures composed of MT1 plus the addition of Achromobacter xylosoxidans strain MT3 (in a steady-state proportion 9:1) was used as a model system to study bacterial interactions that take place under simultaneous chemical and oxidative stress. Both are members of a real community isolated from a polluted sediment by enrichment in 4-chlorosalicylate (4CS). The analysis of dynamic states was carried out at the proteome, metabolic profile and population dynamic level. Differential protein expression was evaluated under exposure to 4CS and high concentrations of toxic intermediates (4-chlorocatechol and protoanemonin), including proteins from several functional groups and particularly enzymes of aromatic degradation pathways and outer membrane proteins. Remarkably, 4CS addition generated a strong oxidative stress response in pure strain MT1 culture led by alkyl hydroperoxide reductase, while the community showed an enhanced central metabolism response, where A. xylosoxidans MT3 helped to prevent toxic intermediate accumulation. A significant change in the outer membrane composition of P. reinekei MT1 was observed during the chemical stress caused by 4CS and in the presence of A. xylosoxidans MT3, highlighting the expression of the major outer membrane protein OprF, tightly correlated to 4CC concentration profile and its potential detoxification role.

No abstract supplied

P>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.

Type I IFNs play a key role in linking the innate and adaptive arms of the immune system. Although produced rapidly in response to pathogens, IFNs are also produced at low levels in the absence of infection. In the present study, we demonstrate that constitutively produced IFNs are necessary in vivo to maintain dendritic cells in an “Ag presentation-competent” state. Conventional dendritic cells (cDCs) isolated from spleens of IFN-β or IFNAR-deficient mice exhibit a highly impaired ability to present Ag and activate naive T cells. Microarray analysis of mRNA isolated from IFN-β−/− and IFNAR−/− cDCs revealed diminished expression of two genes that encoded members of the heat shock protein 70 (Hsp70) family. Consistent with this observation, pharmacological inhibition of Hsp70 in cDCs from wild-type mice impaired their T cell stimulatory capacity. Similarly, the Ag presentation ability of splenic cDCs isolated from Hsp70.1/3−/− mice was also severely impaired in comparison to wild-type cDCs. Thus, constitutive IFN-β expression regulates Hsp70 levels to help maintain dendritic cells in a competent state for efficient priming of effector T cells in vivo.

Pseudomonas aeruginosa is a key opportunistic pathogen causing severe acute and chronic nosocomial infections in immunocompromised or catheterized patients. It is prevalent in burn wound infections and it is generally multi-drug resistant. Understanding the genetic programs underlying infection is essential to develop highly needed new strategies for prevention and therapy. This work reviews expression profiling efforts conducted worldwide towards gaining insights into pathogenesis by P. aeruginosa, in particular in burn wounds. Work on various infection models, including the burned mouse model, has identified several direct virulence factors and elucidated their mode of action. In vivo gene expression experiments using In vivo Expression Technology (IVET) ascertained distinct regulatory circuits and traits that have helped explain P. aeruginosa s success as a general pathogen. The sequencing of the whole genome from a number of P. aeruginosa strains and the construction of genome-wide microarrays have paved the road to the several insightful studies on the (interacting) traits underlying infection. A series of in vitro and initial in vivo gene expression studies revealed specific traits pivotal for infection, such as quorum sensing systems, iron acquisition and oxidative stress responses, and toxin production among others. The data sets obtained from global transcriptional profiling provide insights that will be essential for the development of new targets and options for prevention and intervention.

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.

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.

By the time the complete genome sequence of the soil bacterium Pseudomonas putida KT2440 was published in 2002 (Nelson et al., ) this bacterium was considered a potential agent for environmental bioremediation of industrial waste and a good colonizer of the rhizosphere. However, neither the annotation tools available at that time nor the scarcely available omics data-let alone metabolic modeling and other nowadays common systems biology approaches-allowed them to anticipate the astonishing capacities that are encoded in the genetic complement of this unique microorganism. In this work we have adopted a suite of state-of-the-art genomic analysis tools to revisit the functional and metabolic information encoded in the chromosomal sequence of strain KT2440. We identified 242 new protein-coding genes and re-annotated the functions of 1548 genes, which are linked to almost 4900 PubMed references. Catabolic pathways for 92 compounds (carbon, nitrogen and phosphorus sources) that could not be accommodated by the previously constructed metabolic models were also predicted. The resulting examination not only accounts for some of the known stress tolerance traits known in P. putida but also recognizes the capacity of this bacterium to perform difficult redox reactions, thereby multiplying its value as a platform microorganism for industrial biotechnology.

A metagenome expression library of bulk DNA extracted from the rumen content of a dairy cow was established in a phage lambda vector and activity-based screening employed to explore the functional diversity of the microbial flora. Twenty-two clones specifying distinct hydrolytic activities (12 esterases, nine endo-beta-1,4-glucanases and one cyclodextrinase) were identified in the library and characterized. Sequence analysis of the retrieved enzymes revealed that eight (36%) were entirely new and formed deep-branched phylogenetic lineages with no close relatives among known ester- and glycosyl-hydrolases. Bioinformatic analyses of the hydrolase gene sequences, and the sequences and contexts of neighbouring genes, suggested tentative phylogenetic assignments of the rumen organisms producing the retrieved enzymes. The phylogenetic novelty of the hydrolases suggests that some of them may have potential for new applications in biocatalysis.

SummaryWe created a metagenome expression library from the brine:seawater interface of the Urania hypersaline basin, screened it for esterases, and characterized five of these. Two had no significant sequence homology to known esterases, hydrolyzed both carboxylesters and thioesters, and exhibited unusual, habitat-specific characteristics (preference for high hydrostatic pressure and salinity). One has an unusual structural signature incorporating three catalytic active centers mediating distinct hydrolytic activities and an adaptive tertiary-quaternary structure that alters between three molecular states, according to the prevailing physicochemical conditions. Some of the esterases have high activities, specificities, enantioselectivities, and exceptional stability in polar solvents, and they are therefore potentially useful for industrial biotransformations. One possesses the highest enantioselectivity toward an ester of the important chiral synthon solketal (E: 126[S]; 98%ee).

In this paper, we provide background to the genome sequencing project of Alcanivorax borkumensis, which is a marine bacterium that uses exclusively petroleum oil hydrocarbons as sources of carbon and energy (therefore designated "hydrocarbonoclastic"). It is found in low numbers in all oceans of the world and in high numbers in oil-contaminated waters. Its ubiquity and unusual physiology suggest it is globally important in the removal of hydrocarbons from polluted marine systems. A functional genomics analysis of Alcanivorax borkumensis strain SK2 was recently initiated, and its genome sequence has just been completed. Annotation of the genome, metabolome modelling, and functional genomics, will soon reveal important insights into the genomic basis of the properties and physiology of this fascinating and globally important bacterium. (C) 2003 Elsevier B.V. All rights reserved.

Understanding the diversity and robustness of the metabolism of bacteria is fundamental for understanding how bacteria evolve and adapt to different environments. In this study, we characterised 121 Streptococcus strains and studied metabolic diversity from a protein domain perspective. Metabolic pathways were described in terms of the promiscuity of domains participating in metabolic pathways that were inferred to be functional. Promiscuity was defined by adapting existing measures based on domain abundance and versatility. The approach proved to be successful in capturing bacterial metabolic flexibility and species diversity, indicating that it can be described in terms of reuse and sharing functional domains in different proteins involved in metabolic activity. Additionally, we showed striking differences among metabolic organisation of the pathogenic serotype 2 Streptococcus suis and other strains.

Background Semantic web technologies have a tremendous potential for the integration of heterogeneous data sets. Therefore, an increasing number of widely used biological resources are becoming available in the RDF data model. There are however, no tools available that provide structural overviews of these resources. Such structural overviews are essential to efficiently query these resources and to assess their structural integrity and design, thereby strengthening their use and potential. Results Here we present RDF2Graph, a tool that automatically recovers the structure of an RDF resource. The generated overview allows to create complex queries on these resources and to structurally validate newly created resources. Conclusion RDF2Graph facilitates the creation of complex queries thereby enabling access to knowledge stored across multiple RDF resources. RDF2Graph facilitates creation of high quality resources and resource descriptions, which in turn increases usability of the semantic web technologies. Electronic supplementary material The online version of this article (doi:10.1186/s13326-015-0038-9) contains supplementary material, which is available to authorized users.

Background The human pathogen Mycobacterium tuberculosis has the capacity to escape eradication by professional phagocytes. During infection, M. tuberculosis resists the harsh environment of phagosomes and actively manipulates macrophages and dendritic cells to ensure prolonged intracellular survival. In contrast to other intracellular pathogens, it has remained difficult to capture the transcriptome of mycobacteria during infection due to an unfavorable host-to-pathogen ratio. Results We infected the human macrophage-like cell line THP-1 with the attenuated M. tuberculosis surrogate M. bovis Bacillus Calmette–Guérin (M. bovis BCG). Mycobacterial RNA was up to 1000-fold underrepresented in total RNA preparations of infected host cells. We employed microbial enrichment combined with specific ribosomal RNA depletion to simultaneously analyze the transcriptional responses of host and pathogen during infection by dual RNA sequencing. Our results confirm that mycobacterial pathways for cholesterol degradation and iron acquisition are upregulated during infection. In addition, genes involved in the methylcitrate cycle, aspartate metabolism and recycling of mycolic acids were induced. In response to M. bovis BCG infection, host cells upregulated de novo cholesterol biosynthesis presumably to compensate for the loss of this metabolite by bacterial catabolism. Conclusions Dual RNA sequencing allows simultaneous capture of the global transcriptome of host and pathogen, during infection. However, mycobacteria remained problematic due to their relatively low number per host cell resulting in an unfavorable bacterium-to-host RNA ratio. Here, we use a strategy that combines enrichment for bacterial transcripts and dual RNA sequencing to provide the most comprehensive transcriptome of intracellular mycobacteria to date. The knowledge acquired into the pathogen and host pathways regulated during infection may contribute to a solid basis for the deployment of novel intervention strategies to tackle infection. Electronic supplementary material The online version of this article (doi:10.1186/s12864-014-1197-2) contains supplementary material, which is available to authorized users.

Background Evidence is accumulating that perturbation of early life microbial colonization of the gut induces long-lasting adverse health effects in individuals. Understanding the mechanisms behind these effects will facilitate modulation of intestinal health. The objective of this study was to identify biological processes involved in these long lasting effects and the (molecular) factors that regulate them. We used an antibiotic and the same antibiotic in combination with stress on piglets as an early life perturbation. Then we used host gene expression data from the gut (jejunum) tissue and community-scale analysis of gut microbiota from the same location of the gut, at three different time-points to gauge the reaction to the perturbation. We analysed the data by a new combination of existing tools. First, we analysed the data in two dimensions, treatment and time, with quadratic regression analysis. Then we applied network-based data integration approaches to find correlations between host gene expression and the resident microbial species. Results The use of a new combination of data analysis tools allowed us to identify significant long-lasting differences in jejunal gene expression patterns resulting from the early life perturbations. In addition, we were able to identify potential key gene regulators (hubs) for these long-lasting effects. Furthermore, data integration also showed that there are a handful of bacterial groups that were associated with temporal changes in gene expression. Conclusion The applied systems-biology approach allowed us to take the first steps in unravelling biological processes involved in long lasting effects in the gut due to early life perturbations. The observed data are consistent with the hypothesis that these long lasting effects are due to differences in the programming of the gut immune system as induced by the temporary early life changes in the composition and/or diversity of microbiota in the gut. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1733-8) contains supplementary material, which is available to authorized users.

Background Different methods have been developed to infer regulatory networks from heterogeneous omics datasets and to construct co-expression networks. Each algorithm produces different networks and efforts have been devoted to automatically integrate them into consensus sets. However each separate set has an intrinsic value that is diluted and partly lost when building a consensus network. Here we present a methodology to generate co-expression networks and, instead of a consensus network, we propose an integration framework where the different networks are kept and analysed with additional tools to efficiently combine the information extracted from each network. Results We developed a workflow to efficiently analyse information generated by different inference and prediction methods. Our methodology relies on providing the user the means to simultaneously visualise and analyse the coexisting networks generated by different algorithms, heterogeneous datasets, and a suite of analysis tools. As a show case, we have analysed the gene co-expression networks of Mycobacterium tuberculosis generated using over 600 expression experiments. Regarding DNA damage repair, we identified SigC as a key control element, 12 new targets for LexA, an updated LexA binding motif, and a potential mismatch repair system. We expanded the DevR regulon with 27 genes while identifying 9 targets wrongly assigned to this regulon. We discovered 10 new genes linked to zinc uptake and a new regulatory mechanism for ZuR. The use of co-expression networks to perform system level analysis allows the development of custom made methodologies. As show cases we implemented a pipeline to integrate ChIP-seq data and another method to uncover multiple regulatory layers. Conclusions Our workflow is based on representing the multiple types of information as network representations and presenting these networks in a synchronous framework that allows their simultaneous visualization while keeping specific associations from the different networks. By simultaneously exploring these networks and metadata, we gained insights into regulatory mechanisms in M. tuberculosis that could not be obtained through the separate analysis of each data type. Electronic supplementary material The online version of this article (doi:10.1186/s12918-014-0111-5) contains supplementary material, which is available to authorized users.

Titania (TiO2)-based nanocomposites subjected to light excitation are remarkably effective in eliciting microbial death. However, the mechanism by which these materials induce microbial death and the effects that they have on microbes are poorly understood. Here, we assess the low dose radical-mediated TiO2 photocatalytic action of such nanocomposites and evaluate the genome/proteome-wide expression profiles of Pseudomonas aeruginosa PAO1 cells after two minutes of intervention. The results indicate that the impact on the gene-wide flux distribution and metabolism is moderate in the analysed time span. Rather, the photocatalytic action triggers the decreased expression of a large array of genes/proteins specific for regulatory, signalling and growth functions in parallel with subsequent selective effects on ion homeostasis, coenzyme-independent respiration and cell wall structure. The present work provides the first solid foundation for the biocidal action of titania and may have an impact on the design of highly active photobiocidal nanomaterials., The authors gratefully acknowledge the financial support provided by grants CTQ2010-14872/BQU, PRI-PIBJP-2011-0914 and BIO2011-25012. A.K. thanks the “Ministerio de Economia y Competitividad” MINECO (Spain) for a Ramon y Cajal Fellowship. D.R. received a fellowship from the Spanish Ministry of Economy and Competitiveness (formerly MICINN).

Macroscopic growths at geographically separated acid mine drainages (AMDs) exhibit distinct populations. Yet, local heterogeneities are poorly understood. To gain novel mechanistic insights into this, we used OMICs tools to profile microbial populations coexisting in a single pyrite gallery AMD (pH ∼2) in three distinct compartments: two from a stratified streamer (uppermost oxic and lowermost anoxic sediment-attached strata) and one from a submerged anoxic non-stratified mat biofilm. The communities colonising pyrite and those in the mature formations appear to be populated by the greatest diversity of bacteria and archaea (including ‘ARMAN’ (archaeal Richmond Mine acidophilic nano-organisms)-related), as compared with the known AMD, with ∼44.9% unclassified sequences. We propose that the thick polymeric matrix may provide a safety shield against the prevailing extreme condition and also a massive carbon source, enabling non-typical acidophiles to develop more easily. Only 1 of 39 species were shared, suggesting a high metabolic heterogeneity in local microenvironments, defined by the O2 concentration, spatial location and biofilm architecture. The suboxic mats, compositionally most similar to each other, are more diverse and active for S, CO2, CH4, fatty acid and lipopolysaccharide metabolism. The oxic stratum of the streamer, displaying a higher diversity of the so-called ‘ARMAN’-related Euryarchaeota, shows a higher expression level of proteins involved in signal transduction, cell growth and N, H2, Fe, aromatic amino acids, sphingolipid and peptidoglycan metabolism. Our study is the first to highlight profound taxonomic and functional shifts in single AMD formations, as well as new microbial species and the importance of H2 in acidic suboxic macroscopic growths., We gratefully acknowledge the financial support provided by the Spanish Ministry of Economy and Competitiveness (Projects CSD2007-00005, BIO2010-16303 and BIO2011-25012) and the European Regional Development Fund (ERDF). Partial support by the FP7-KBBE program through the projects Microme (Grant No. 222886-2) and MAGICPAH (FP7-KBBE-2009-245226) is also gratefully acknowledged. Proteomics analysis performed in the ONJ laboratory was supported by a research grant (FNU 09-072287) and an EliteForsk award to ONJ from the Danish Council for Independent Research.

Synthetic biology draws on the understanding from genetics, biology, chemistry, physics, engineering, and computational sciences to (re-)design and (re-)engineer biological functions. Here we address how synthetic biology can be possibly deployed to promote health and tackle disease. We discuss how drugs can be produced in more affordable ways, how new medicines can be developed, how the re-design of cellular pathways can correct endogenous malfunctioning in a series of diseases, how bacteria can be engineered to kill tumors, and how bacterial communities in the intestine can be modulated to restore gut homeostasis and prevent metabolic diseases. We indicate how new biomedical materials can be synthetized to replace tissues, how new biosensors can assist in diagnosis and prognosis, and how synthetic biology can help preventing the onset of disease in those cases in which until now only diagnosis was possible. On the basis of this, we discuss towards what directions synthetic biology in health and disease may develop in the future.

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.

Clostridium difficile infections are an emerging health problem in the modern hospital environment. Severe alterations of the gut microbiome with loss of resistance to colonization against C. difficile are thought to be the major trigger, but there is no clear concept of how C. difficile infection evolves and which microbiological factors are involved. We sequenced 16S rRNA amplicons generated from DNA and RNA/cDNA of fecal samples from three groups of individuals by FLX technology: (i) healthy controls (no antibiotic therapy); (ii) individuals receiving antibiotic therapy (Ampicillin/Sulbactam, cephalosporins, and fluoroquinolones with subsequent development of C. difficile infection or (iii) individuals receiving antibiotic therapy without C. difficile infection. We compared the effects of the three different antibiotic classes on the intestinal microbiome and the effects of alterations of the gut microbiome on C. difficile infection at the DNA (total microbiota) and rRNA (potentially active) levels. A comparison of antibiotic classes showed significant differences at DNA level, but not at RNA level. Among individuals that developed or did not develop a C. difficile infection under antibiotics we found no significant differences. We identified single species that were up- or down regulated in individuals receiving antibiotics who developed the infection compared to non-infected individuals. We found no significant differences in the global composition of the transcriptionally active gut microbiome associated with C. difficile infections. We suggest that up- and down regulation of specific bacterial species may be involved in colonization resistance against C. difficile providing a potential therapeutic approach through specific manipulation of the intestinal microbiome., This work was supported by the ERANET Project PathoGenoMics program grant number 0315441A.

Systems-level metabolic network reconstructions and the derived constraint-based (CB) mathematical models are efficient tools to explore bacterial metabolism. Approximately one-fourth of the Mycobacterium tuberculosis (Mtb) genome contains genes that encode proteins directly involved in its metabolism. These represent potential drug targets that can be systematically probed with CB models through the prediction of genes essential (or the combination thereof) for the pathogen to grow. However, gene essentiality depends on the growth conditions and, so far, no in vitro model precisely mimics the host at the different stages of mycobacterial infection, limiting model predictions. These limitations can be circumvented by combining expression data from in vivo samples with a validated CB model, creating an accurate description of pathogen metabolism in the host. To this end, we present here a thoroughly curated and extended genome-scale CB metabolic model of Mtb quantitatively validated using 13C measurements. We describe some of the efforts made in integrating CB models and high-throughput data to generate condition specific models, and we will discuss challenges ahead. This knowledge and the framework herein presented will enable to identify potential new drug targets, and will foster the development of optimal therapeutic strategies.

The microbiomes in the gastrointestinal tract (GIT) of individuals receiving antibiotics and those in obese subjects undergo compositional shifts, the metabolic effects and linkages of which are not clearly understood. Herein, we set to gain insight into these effects, particularly with regard to carbohydrate metabolism, and to contribute to unravel the underlying mechanisms and consequences for health conditions. We measured the activity level of GIT carbohydrate-active enzymes toward 23 distinct sugars in adults patients (n = 2) receiving 14-d β-lactam therapy and in obese (n = 7) and lean (n = 5) adolescents. We observed that both 14 d antibiotic-treated and obese subjects showed higher and less balanced sugar anabolic capacities, with 40% carbohydrates being preferentially processed as compared with non-treated and lean patients. Metaproteome-wide metabolic reconstructions confirmed that the impaired utilization of sugars propagated throughout the pentose phosphate metabolism, which had adverse consequences for the metabolic status of the GIT microbiota. The results point to an age-independent positive association between GIT glycosidase activity and the body mass index, fasting blood glucose and insulin resistance (r2 ≥ 0.95). Moreover, antibiotics altered the active fraction of enzymes controlling the thickness, composition and consistency of the mucin glycans. Our data and analyses provide biochemical insights into the effects of antibiotic usage on the dynamics of the GIT microbiota and pin-point presumptive links to obesity. The knowledge and the hypotheses generated herein lay a foundation for subsequent, systematic research that will be paramount for the design of “smart” dietary and therapeutic interventions to modulate host-microbe metabolic co-regulation in intestinal homeostasis., The whole consortium was funded by the Spanish Ministry of Economy and Competitiveness and the Federal Ministry of Education and Research (BMBF) within the ERA NET PathoGenoMics2 program, grant number 0315441A. This work was further funded by grants BFU2008–04501-E, SAF2009–13032-C02–01, SAF2012–31187 and CSD2007–00005 from the Spanish Ministry of Economy and Competitiveness, Prometeo/2009/092 from Generalitat Valenciana (Spain) and AGL2006–11697/ALI. M.v.B. was partially funded by DFG priority program 1656. The authors gratefully acknowledge the financial support provided by the European Regional Development Fund (ERDF). This work has been partially supported by EVASYON study funded by the Spanish Ministry of Health and Consumption (Carlos III Institute of Health. FIS Grant PI 051579). VAPMdS gratefully acknowledges the financial support provided by the European Union (FP7 project systems medicine of chronic inflammatory bowel diseases, Grant Agreement no. 305564).

Lactococcus lactis subsp. cremoris MG1363 is a paradigm strain for lactococci used in industrial dairy fermentations. However, despite of its importance for process development, no genome-scale metabolic model has been reported thus far. Moreover, current models for other lactococci only focus on growth and sugar degradation. A metabolic model that includes nitrogen metabolism and flavor-forming pathways is instrumental for the understanding and designing new industrial applications of these lactic acid bacteria. A genome-scale, constraint-based model of the metabolism and transport in L. lactis MG1363, accounting for 518 genes, 754 reactions, and 650 metabolites, was developed and experimentally validated. Fifty-nine reactions are directly or indirectly involved in flavor formation. Flux Balance Analysis and Flux Variability Analysis were used to investigate flux distributions within the whole metabolic network. Anaerobic carbon-limited continuous cultures were used for estimating the energetic parameters. A thorough model-driven analysis showing a highly flexible nitrogen metabolism, e.g., branched-chain amino acid catabolism which coupled with the redox balance, is pivotal for the prediction of the formation of different flavor compounds. Furthermore, the model predicted the formation of volatile sulfur compounds as a result of the fermentation. These products were subsequently identified in the experimental fermentations carried out. Thus, the genome-scale metabolic model couples the carbon and nitrogen metabolism in L. lactis MG1363 with complete known catabolic pathways leading to flavor formation. The model provided valuable insights into the metabolic networks underlying flavor formation and has the potential to contribute to new developments in dairy industries and cheese-flavor research.

Nature and physiological status of antigen-presenting cells, such as dendritic cells DCs, are decisive for the immune reactions elicited. Multiple factors and cell interactions have been described that affect maturation of DCs. Here, we show that DCs arising in the absence of immunoglobulins (Ig) in vivo are impaired in cross-presentation of soluble antigen. This deficiency was due to aberrant cellular targeting of antigen to lysosomes and its rapid degradation. Function of DCs could be restored by transfer of Ig irrespective of antigen specificity and isotype. Modulation of cross-presentation by Ig was inhibited by coapplication of mannan and, thus, likely to be mediated by C-type lectin receptors. This unexpected dependency of splenic DCs on Ig to cross-present antigen provides insights into the interplay between cellular and humoral immunity and the immunomodulatory capacity of Ig.

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.

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

It is known that the gastrointestinal tract (GIT) microbiota responds to different antibiotics in different ways and that while some antibiotics do not induce disturbances of the community, others drastically influence the richness, diversity, and prevalence of bacterial taxa. However, the metabolic consequences thereof, independent of the degree of the community shifts, are not clearly understood. In a recent article, we used an integrative OMICS approach to provide new insights into the metabolic shifts caused by antibiotic disturbance. The study presented here further suggests that specific bacterial lineage blooms occurring at defined stages of antibiotic intervention are mostly associated with organisms that possess improved survival and colonization mechanisms, such as those of the Enterococcus, Blautia, Faecalibacterium, and Akkermansia genera. The study also provides an overview of the most variable metabolic functions affected as a consequence of a β-lactam antibiotic intervention. Thus, we observed that anabolic sugar metabolism, the production of acetyl donors and the synthesis and degradation of intestinal/colonic epithelium components were among the most variable functions during the intervention. We are aware that these results have been established with a single patient and will require further confirmation with a larger group of individuals and with other antibiotics. Future directions for exploration of the effects of antibiotic interventions are discussed., The whole consortium was funded by the Spanish Ministry of Economy and Competitiveness and the Federal Ministry of Education and Research (BMBF) within the ERA NET PathoGenoMics2 program, grant number 0315441A. This work was further funded by grants BFU2008-04501-E, SAF2009-13032-C02–01, SAF2012-31187, and CSD2007-00005 from the Spanish Ministry of Economy and Competitiveness, Prometeo/2009/092 from Generalitat Valenciana (Spain), and AGL2006-11697/ALI. The authors gratefully acknowledge the financial support provided by the European Regional Development Fund (ERDF) and the European Union (FP7 project Systems medicine of chronic inflammatory bowel disease, Grant Agreement no. 305564). This work has been partially supported by the EVASYON study funded by the Spanish Ministry of Health and Consumption (Carlos III Institute of Health. FIS Grant PI 051579).

Background The use of in silico simulations as a basis for designing artificial biological systems (and experiments to characterize them) is one of the tangible differences between Synthetic Biology and “classical” Genetic Engineering. To this end, synthetic biologists have adopted approaches originating from the traditionally non-biological fields of Nonlinear Dynamics and Systems & Control Theory. However, due to the complex molecular interactions affecting the emergent properties of biological systems, mechanistic descriptions of even the simplest genetic circuits (transcriptional feedback oscillators, bi-stable switches) produced by these methods tend to be either oversimplified, or numerically intractable. More comprehensive and realistic models can be approximated by constructing “toy” genetic circuits that provide the experimenter with some degree of control over the transcriptional dynamics, and allow for experimental set-ups that generate reliable data reflecting the intracellular biochemical state in real time. To this end, we designed two genetic circuits (basic and tunable) capable of exhibiting synchronized oscillatory green fluorescent protein (GFP) expression in small populations of Escherichia coli cells. The functionality of the basic circuit was verified microscopically. High-level visualizations of computational simulations were analyzed to determine whether the reliability and utility of a synchronized transcriptional oscillator could be enhanced by the introduction of chemically inducible repressors. Results Synchronized oscillations in GFP expression were repeatedly observed in chemically linked sub-populations of cells. Computational simulations predicted that the introduction of independently inducible repressors substantially broaden the range of conditions under which oscillations could occur, in addition to allowing the frequency of the oscillation to be tuned. Conclusions The genetic circuits described here may prove to be valuable research tools for the study of synchronized transcriptional feedback loops under a variety of conditions and experimental set-ups. We further demonstrate the benefit of using abstract visualizations to discover subtle non-linear trends in complex dynamic models with large parameter spaces.

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., We acknowledge the funding by the BMBF (DE) and MICINN (ES) within the scope of the ERA-NET program on the Systems Biology of Microorganisms (Project PSYSMO: Grant No.0313980).

Since their discovery many decades ago, Pseudomonas putida and related subspecies have been intensively studied with regard to their potential application in industrial biotechnology. Today, these Gram-negative soil bacteria, traditionally known as well-performing xenobiotic degraders, are becoming efficient cell factories for various products of industrial relevance including a full range of unnatural chemicals. This development is strongly driven by systems biotechnology, integrating systems metabolic engineering approaches with novel concepts from bioprocess engineering, including novel reactor designs and renewable feedstocks.

Novel microbial cultivation platforms are of increasing interest to researchers in academia and industry. The development of materials with specialized chemical and geometric properties has opened up new possibilities in the study of previously unculturable microorganisms and has facilitated the design of elegant, high-throughput experimental set-ups. Within the context of the international Genetically Engineered Machine (iGEM) competition, we set out to design, manufacture, and implement a flow device that can accommodate multiple growth platforms, that is, a silicon nitride based microsieve and a porous aluminium oxide based microdish. It provides control over (co-)culturing conditions similar to a chemostat, while allowing organisms to be observed microscopically. The device was designed to be affordable, reusable, and above all, versatile. To test its functionality and general utility, we performed multiple experiments with Escherichia coli cells harboring synthetic gene circuits and were able to quantitatively study emerging expression dynamics in real-time via fluorescence microscopy. Furthermore, we demonstrated that the device provides a unique environment for the cultivation of nematodes, suggesting that the device could also prove useful in microscopy studies of multicellular microorganisms.

The rapid expansion of synthetic biology is due to the design and construction of synthetic gene networks that have opened many new avenues in fundamental and applied research. Synthetic Gene Networks: Methods and Protocols provides the necessary information to design and construct synthetic gene networks in different host backgrounds. Divided into four convenient sections, this volume focuses on design concepts to devise synthetic gene networks and how mathematical models can be applied to the predictable engineering of desired network features. The volume continues by highlighting the construction and validation of biologic tools, describing strategies to optimize and streamline the host cell for optimized network performance, and covering how optimally designed gene networks can be implemented in a large variety of host cells ranging from bacteria over yeast and insect cells to plant and mammalian cell culture. Written in the successful Methods in Molecular Biology™ series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible protocols, and notes on troubleshooting and avoiding known pitfalls. Authoritative and easily accessible, Synthetic Gene Networks: Methods and Protocols serves as an invaluable resource for established biologists, engineers, and computer scientists or novices just entering into the rapidly growing field of synthetic biology

Bacteria have long been used for the synthesis of a wide range of useful proteins and compounds. The developments of new bioprocesses and improvements of existing strategies for syntheses of valuable products in various bacterial cell hosts have their own challenges and limitations. The field of synthetic biology has combined knowledge from different science and engineering disciplines and facilitated the advancement of novel biological components which has inspired the design of targeted biosynthesis. Here we discuss recent advances in synthetic biology with relevance to biosynthesis in bacteria and the applications of computational algorithms and tools for manipulation of cellular components. Continuous improvements are necessary to keep up with increasing demands in terms of complexity, scale, and predictability of biosynthesis products.