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Microbial cell factories are instrumental in transitioning towards a sustainable bio-based economy, offering alternatives to conventional chemical processes. However, fulfilling their potential requires simultaneous screening for optimal media composition, process and genetic factors, acknowledging the complex interplay between the organism's genotype and its environment. This study employs statistical design of experiments to systematically explore these relationships and optimize the production of p-coumaric acid (pCA) in Saccharomyces cerevisiae. Two rounds of fractional factorial designs were used to identify factors with a significant effect on pCA production, which resulted in a 168-fold variation in pCA titre. Moreover, a significant interaction between the culture temperature and expression of ARO4 highlighted the importance of simultaneous process and strain optimization. The presented approach leverages the strengths of experimental design and statistical analysis and could be systematically applied during strain and bioprocess design efforts to unlock the full potential of microbial cell factories.

Industrial biotechnology uses Design–Build–Test–Learn (DBTL) cycles to accelerate the development of microbial cell factories, required for the transition to a biobased economy. To use them effectively, appropriate connections between the phases of the cycle are crucial. Using p-coumaric acid (pCA) production in Saccharomyces cerevisiae as a case study, we propose the use of one-pot library generation, random screening, targeted sequencing, and machine learning (ML) as links during DBTL cycles. We showed that the robustness and flexibility of the ML models strongly enable pathway optimization and propose feature importance and Shapley additive explanation values as a guide to expand the design space of original libraries. This approach allowed a 68% increased production of pCA within two DBTL cycles, leading to a 0.52 g/L titer and a 0.03 g/g yield on glucose.

The DIAGONAL project advances safe by design knowledge and tools to a ready-for-implementation stage in industries related to multicomponent nanomaterials (MCNMs) and high-aspect ratio nanomaterials (HARNs). Assessing the hazard and exposure properties of a variety of MCNMs and HARNs may contribute to adapted or novel risk management guidelines. Experimental risk assessment of these hazard and exposure properties is required for evaluating the implications for living organisms and their cells exposed to MCNMs and HARNs. A human hazard and ecotoxicology evaluation were performed using lung cell lines, epidermis, intestine and immune system cells from humans along with earthworms from the soil, biofilm from sediment, and daphnids and algae from freshwater. The most up to date validated protocols, or protocols that are pending validation were selected for the human hazard and ecotoxicology toxicity testing of MCNMs and HARNs. The present deliverable report highlights various final and draft methodologies and standard operating procedures (SOP) proposed including specific adaptations of these methodologies and SOPs for toxicity testing of MCNMs and HARNs using human cells, algae, bacteria and selected animal species as biological model systems.

Centered at the Jožef Stefan Institute, Ljubljana, Slovenia, five top research & innovation partners across Europe are creating an innovation hub to study microbe-colloid–surface interactions using high-tech methodologies and equipment. The SURFBIO Innovation Hub aims to provide biotechnology researchers, academic institutions, industry and policy makers with training services and assessments to optimize novel materials for a variety of applications and will offer new, industry-oriented, research services opened to industry and institutions, covering all the needs in only one Hub, and collecting the activities together. This will lead to the founding of a SURFBIO professional society to act as a network center with the goal of fostering advanced microbial materials applications throughout Europe. Understanding the interactions of colloids (microorganisms, nanoparticles and biomolecules) with surfaces and between themselves is a key factor that can lead to improvements of advanced materials. As such the emerging field of Colloid Biology is positioned on the intersection between material science and molecular microbiology, dealing with artificial multispecies bio-aggregates, bio- films and bio-nano-constructs of bacteria and nanoparticles, to create novel advanced materials. The colloid-biological interactions can be studied and analyzed by applying different tools and techniques. Impacts of the networking activities will be: • high-impact research results on surface and colloid biology • improved knowledge transfer • increased patenting • increased peer-reviewed publications on the topic • expanded range of testable samples • contract research for industry • boosted interest on surface and colloid biology • standardization of methodologies • new possibilities in analytical testing So far, two public handbooks, two webinars and two MOOCs have been prepared and are available freely via the SURFBIO website (https://surfbio.eu/) providing information and training on the

Metabolic engineering is a cornerstone of the bio-based economy, which aims to replace fossil-fuel based processes with biological systems. In this thesis, I develop and apply computational methods for model-driven engineering of microbial metabolism. Due to the complexity of biological systems, it is often unclear beforehand exactly which aspects of the systems will be limiting when engineering metabolism. By using the data coming from experiments to inform metabolic models, these models can be used to drive decision-making for the subsequent set of experiments, thus solving obstacles one at a time with the most information possible; a process known as the Design-Build-Test-Learn cycle. Models are thus an important part of metabolic engineering and in this thesis I use several modelling frameworks: constraint-based optimization, flux sampling, as well as kinetic modelling. Each modelling method has its strengths and in chapter 2 I review the use of models in metabolic engineering, consolidating the lessons I learned by applying different modelling methods and working together with experimental partners. We discuss the various questions that should be posed before deciding on a modelling strategy, as well as the strengths and weaknesses of the most common frameworks used for metabolic modelling. In addition, we discuss the different sources of experimental data and their suitability for using them in the different modelling frameworks.In chapter 3, we apply constraint-based modelling to Pseudomonas putida to support the development of a metabolic valve enabling control over the growth versus production trade-off. Using an existing constraint-based genome-scale model for P. putida, we evaluate the potential of the PDH reaction, producing acetyl-CoA from pyruvate, to serve as this metabolic valve. However, the current model fails to recognize the experimentally confirmed essentiality of this reaction. Thus, we search for alternative pathways in the model that can caus

The rhizosphere, the region of soil surrounding roots of plants, is colonized by a unique population of Plant Growth Promoting Rhizobacteria (PGPR). Many important PGPR as well as plant pathogens belong to the genus Pseudomonas. There is, however, uncertainty on the divide between beneficial and pathogenic strains as previously thought to be signifying genomic features have limited power to separate these strains. Here we used the Genome properties (GP) common biological pathways annotation system and Machine Learning (ML) to establish the relationship between the genome wide GP composition and the plant-associated lifestyle of 91 Pseudomonas strains isolated from the rhizosphere and the phyllosphere representing both plant-associated phenotypes. GP enrichment analysis, Random Forest model fitting and feature selection revealed 28 discriminating features. A test set of 75 new strains confirmed the importance of the selected features for classification. The results suggest that GP annotations provide a promising computational tool to better classify the plant-associated lifestyle.

Plant Growth Promoting Rhizobacteria (PGPR) dwell in the rhizosphere, the area surrounding the root of plants, and enhance growth of the host through different mechanisms: they can protect plants against pathogens, assist in nutrient gathering, and in increasing stress tolerance. Hence, developing strategies to enhance their performance is important to increase crop productivity. Specific solutions are necessary to enhance the performance of the beneficials while simultaneously avoiding nurturing of pathogens. This requires insights into the mechanisms underlying these microbials interactions. Pseudomonas is one of the most studied genera and contains both beneficials and pathogenic species. Hence, we used comparative genome-scale constraint-based metabolic modeling to reveal key features of both classes of Pseudomonads and which can provide leads for the possible interventions regarding these solutions. Models of 75 plant-growth promoting rhizosphere and 33 epiphytic pathogenic Pseudomonas strains were automatically reconstructed and validated using phenotype microarray (Biolog) data. The models were used for compositional analysis and 12 representative strains, 6 of each group, were further selected for extensive simulation. The analyses reveal differences in the potential for metabolite uptake and transport between these two distinct classes that suggest their nutrient preferences and their differences in, among other, D-ornithine acquisition mechanisms. The models enable simulation of metabolic state of root exudates. Simulations highlighted and summarized the differences in pathway utilization and intracellular states between two groups. The insights obtained will be very valuable to broader such studies of rhizobiome and to possibly develop strategies to improve crop productivity by supporting the beneficial microbiome while reducing pathogen activities.

BackgroundThe nitrogen containing aromatic compound indole is known for its floral odor typical of jasmine blossoms. Due to its characteristic scent, it is frequently used in dairy products, tea drinks and fine fragrances. The demand for natural indole by the flavor and fragrance industry is high, yet, its abundance in essential oils isolated from plants such as jasmine and narcissus is low. Thus, there is a strong demand for a sustainable method to produce food-grade indole.ResultsHere, we established the biotechnological production of indole upon L-tryptophan supplementation in the bacterial host Corynebacterium glutamicum. Heterologous expression of the tryptophanase gene from E. coli enabled the conversion of supplemented L-tryptophan to indole. Engineering of the substrate import by co-expression of the native aromatic amino acid permease gene aroP increased whole-cell biotransformation of L-tryptophan to indole by two-fold. Indole production to 0.2 g L−1 was achieved upon feeding of 1 g L−1 L-tryptophan in a bioreactor cultivation, while neither accumulation of side-products nor loss of indole were observed. To establish an efficient and robust production process, new tryptophanases were recruited by mining of bacterial sequence databases. This search retrieved more than 400 candidates and, upon screening of tryptophanase activity, nine new enzymes were identified as most promising. The highest production of indole in vivo in C. glutamicum was achieved based on the tryptophanase from Providencia rettgeri. Evaluation of several biological aspects identified the product toxicity as major bottleneck of this conversion. In situ product recovery was applied to sequester indole in a food-grade organic phase during the fermentation to avoid inhibition due to product accumulation. This process enabled complete conversion of L-tryptophan and an indole product titer of 5.7 g L−1 was reached. Indole partitioned to the organic phase which contained 28 g L−1 indole while n

Advances in genome sequencing and high-throughput technologies have boosted the development of Synthetic biology and Systems biology. Synthetic biology aims to create and reprogram natural systems.  Advances in Synthetic biology has facilitated the adoption of the design build test learn cycles into metabolic engineering. The DBTL cycles are a recursive loop that aims to optimize the development of microbial factories in a more systematic and efficient manner. Systems biology aims to study living organism at system level using holistic approaches. Among different modelling tools in Systems biology, genome-scale constraint-based metabolic model is the most successful approach to study the whole metabolic network. GEM is comprehensive knowledgebase that contain metabolic reactions that known to occur in a target organism. GEMs have been used in many applications to guide metabolic engineering and contextualizing ‘omics’ data. The objective of this thesis is to deploy GEMs for microbial cell factories and evaluate their main technical limitations.Chapter 1 describes two paradigms: reductionism and holism in life sciences, Systems biology, genome-scale constraint-based metabolic models, Synthetic biology and the design build test learn cycle. Chapter 1 provides the background for all other chapters.In Chapter 2 I constructed a GEM for Cutaneotrichosporon oleaginosus to model its lipid production. C. oleaginosus is a fast-growing oleaginous yeast that can grow in a wide range of low-cost carbon sources. I constructed a GEM to increase our understanding of this yeast and provide a knowledge base for further industrial use. A new modelling approach was introduced to account for changes in the biomass composition of this organism in conditions with high carbon to nitrogen (C/N) ratio in the media. This modelling approach is shown to better predict conditions with high lipid accumulation using glucose, fructose, sucrose, xylose, and glycerol as sole carbon source. The m

Mycoplasmas are bacteria belonging to the Mollicutes class. They infect a wide range of organisms, among which humans, farm animals, herd animals and pets. Infections by mycoplasmas are currently treated with antibiotics, although many are ineffective due to the lack of cell wall of these bacteria. Moreover, this strategy leads to the development of antibiotics-resistant bacteria. The use of vaccines against mycoplasmas may be a solution for preventing wide spreading of the chronic infections they cause.The human pathogen Mycoplasma pneumoniae, causative agent of atypical pneumonia, can be used as a universal chassis to be deployed as a single- or multi-vaccine in a range of animal hosts. However, due to its reduced genome and dearth of many biosynthetic pathways, this bacterium depends on rich, undefined medium for growth, which makes the large-scale production of the vaccine challenging and expensive. For this reason, a genome-scale, constraint-based metabolic model was deployed to design a serum-free medium that supports growth of Mycoplasma pneumoniae with a rate comparable to the one obtained in rich medium. Model simulations highlighted, among the other components, the importance of two key lipids, part of Mycoplasma pneumoniae’s membrane. The serum-free medium MCMyco was then developed including these components and tested in vitro, showing robust growth of a range of mycoplasmas. Another strategy to increase Mycoplasma pneumoniae’s growth consisted in the design of a fatty acid-prototrophic strain of Mycoplasma. Indeed, despite the inability of synthetizing fatty acids, Mycoplasma pneumoniae needs them to construct its membrane lipids. The study is performed through a combination of DNA engineering, genome-scale modelling and gap-filling algorithm approaches, revealing a bottleneck related to NADPH, one the cofactor mostly used in the fatty acid biosynthesis pathway. As a result, the computationally-designed strain implements a pathway synthetizing fatty aci

Background Several computational methods have been developed that integrate transcriptomics data with genome-scale metabolic reconstructions to increase accuracy of inferences of intracellular metabolic flux distributions. Even though existing methods use transcript abundances as a proxy for enzyme activity, each method uses a different hypothesis and assumptions. Most methods implicitly assume a proportionality between transcript levels and flux through the corresponding function, although these proportionality constant(s) are often not explicitly mentioned nor discussed in any of the published methods. E-Flux is one such method and, in this algorithm, flux bounds are related to expression data, so that reactions associated with highly expressed genes are allowed to carry higher flux values. Results Here, we extended E-Flux and systematically evaluated the impact of an assumed proportionality constant on model predictions. We used data from published experiments with Escherichia coli and Saccharomyces cerevisiae and we compared the predictions of the algorithm to measured extracellular and intracellular fluxes. Conclusion We showed that detailed modelling using a proportionality constant can greatly impact the outcome of the analysis. This increases accuracy and allows for extraction of better physiological information., Background Several computational methods have been developed that integrate transcriptomics data with genome-scale metabolic reconstructions to increase accuracy of inferences of intracellular metabolic flux distributions. Even though existing methods use transcript abundances as a proxy for enzyme activity, each method uses a different hypothesis and assumptions. Most methods implicitly assume a proportionality between transcript levels and flux through the corresponding function, although these proportionality constant(s) are often not explicitly mentioned nor discussed in any of the published methods. E-Flux is one such method and, in this algorithm, flux bounds are related to expression data, so that reactions associated with highly expressed genes are allowed to carry higher flux values. Results Here, we extended E-Flux and systematically evaluated the impact of an assumed proportionality constant on model predictions. We used data from published experiments with Escherichia coli and Saccharomyces cerevisiae and we compared the predictions of the algorithm to measured extracellular and intracellular fluxes. Conclusion We showed that detailed modelling using a proportionality constant can greatly impact the outcome of the analysis. This increases accuracy and allows for extraction of better physiological information.

Background: Clostridium difficile Infection (CDI) can lead to diarrhea and fulminant colitis. C. difficile infects the host using toxins. Recent studies report prevalence of CDI in the small intestine. Berries are known to contain antioxidants and phenolic compounds that might mitigate bacterial infection.Objective: We explored the impact of C. difficile toxins on the small intestine using an in vitro approach and used systems biology techniques together with data integration to identify food compounds that can reduce their cytopathic impact.Methods: Differentiated Caco-2 cells were exposed to C. difficile toxins and the transcriptomic changes were studied. To identify foods with potential beneficial counteracting effects, the transcriptomic profiles were integrated with transcriptomics data from Caco-2 cells exposed to various food compounds and analyzed using multivariate analysis.Results: Beneficial food candidates, selected by multivariate analysis, such as blackcurrant, strawberry and yellow onion were further examined for their potential to counteract the effect of the toxin-induced disruption of cell integrity and toxin translocation. Our results confirmed effects of food compounds, on the cytopathic effects of toxins in the small intestine.Conclusion: Blackcurrant, strawberry and yellow onion can counteract C. difficile toxins induced effects.

BackgroundStaphylococcus and Streptococcus species can cause many different diseases, ranging from mild skin infections to life-threatening necrotizing fasciitis. Both genera consist of commensal species that colonize the skin and nose of humans and animals, and of which some can display a pathogenic phenotype.ResultsWe compared 235 Staphylococcus and 315 Streptococcus genomes based on their protein domain content. We show the relationships between protein persistence and essentiality by integrating essentiality predictions from two metabolic models and essentiality measurements from six large-scale transposon mutagenesis experiments. We identified clusters of strains within species based on proteins associated to similar biological processes. We built Random Forest classifiers that predicted the zoonotic potential. Furthermore, we identified shared attributes between of Staphylococcus aureus and Streptococcus pyogenes that allow them to cause necrotizing fasciitis.ConclusionsDifferences observed in clustering of strains based on functional groups of proteins correlate with phenotypes such as host tropism, capability to infect multiple hosts and drug resistance. Our method provides a solid basis towards large-scale prediction of phenotypes based on genomic information.

Infection by Mycoplasma pneumoniae has been identified as a preceding factor of Guillain–Barré–Stohl syndrome. The Guillain–Barré–Stohl syndrome is triggered by an immune reaction against the major glycolipids and it has been postulated that M. pneumoniae infection triggers this syndrome due to bacterial production of galactocerebroside. Here, we present an extensive comparison of 224 genome sequences from 104 Mycoplasma species to characterize the genetic determinants of galactocerebroside biosynthesis. Hidden Markov models were used to analyse glycosil transferases, leading to identification of a functional protein domain, termed M2000535 that appears in about a third of the studied genomes. This domain appears to be associated with a potential UDP‐glucose epimerase, which converts UDP‐glucose into UDP‐galactose, a main substrate for the biosynthesis of galactocerebroside. These findings clarify the pathogenic mechanisms underlining the triggering of Guillain–Barré–Stohl syndrome by M. pneumoniae infections.

Microbial fermentation of synthesis gas (syngas) is becoming more attractive for sustainable production of commodity chemicals. To date, syngas fermentation focuses mainly on the use of Clostridium species for the production of small organic molecules such as ethanol and acetate. The co-cultivation of syngas-fermenting microorganisms with chain-elongating bacteria can expand the range of possible products, allowing, for instance, the production of medium-chain fatty acids (MCFA) and alcohols from syngas. To explore these possibilities, we report herein a genome-scale, constraint-based metabolic model to describe growth of a co-culture of Clostridium autoethanogenum and Clostridium kluyveri on syngas for the production of valuable compounds. Community flux balance analysis was used to gain insight into the metabolism of the two strains and their interactions, and to reveal potential strategies enabling production of butyrate and hexanoate. The model suggests that one strategy to optimize the production of medium-chain fatty-acids from syngas would be the addition of succinate. According to the prediction, addition of succinate would increase the pool of crotonyl-CoA and the ethanol/acetate uptake ratio in C. kluyveri, resulting in a flux of up to 60 of electrons into hexanoate. Another potential way to further optimize butyrate and hexanoate production would be an increase of C. autoethanogenum ethanol production. Blocking either acetaldehyde dehydrogenase or formate dehydrogenase (ferredoxin) activity or formate transport, in the C. autoethanogenum metabolic model could potentially lead to an up to 150 increase in ethanol production.

Contains fulltext : 201327.pdf (publisher's version ) (Open Access), Gastrointestinal (GI) microbiota composition differs between breastfed and formula-fed infants. Today's infant formulas are often fortified with prebiotics to better mimic properties of human milk with respect to its effect on GI microbiota composition and function. We used Illumina HiSeq sequencing of PCR-amplified 16S rRNA gene fragments to investigate the composition of faecal microbiota in 2-12 week old infants receiving either breastmilk, infant formulas fortified with prebiotics, or mixed feeding. We compared these results with results from infants fed traditional formulas used in the Netherlands in 2002-2003, which contained no added prebiotics. We showed that today's formulas supplemented with either scGOS (0.24-0.50 g/100 ml) or scGOS and lcFOS (at a 9:1 ratio; total 0.6 g/100 ml) had a strong bifidogenic effect as compared to traditional formulas, and they also resulted in altered patterns of microbial colonisation within the developing infant gastrointestinal tract. We identified three microbial states (or developmental stages) in the first 12 weeks of life, with a gradual transition pattern towards a bifidobacteria dominated state. In infants receiving only fortified formulas, this transition towards the bifidobacteria dominated state was accelerated, whereas in infants receiving mixed feeding the transition was delayed, as compared to exclusively breastfed infants.

Materials are addressed as possible enablers for solutions to many global societal challenges. A foresight exercise has been conducted to identify research paths to design, with a new approach, a generation of materials which can provide multi-functionalities. These material systems have been named “stem”, in analogy to living cells where a base of primitive units can be designed and assembled for self-reacting to external inputs. These materials will embed a concept of “internet in things”, where their processing capacity will enable the systems to interact with the environment and express diverse functionalities. Stem materials do not exist yet, but many clues from different theoretical and experimental results suggest they can be developed, and because living organisms exist. This article aims at launching this new approach and promoting the structuring of a multi-disciplinary community to fill the research gaps.

Supplementary files supporting the research performed in Expected and observed genotype complexity in prokaryotes: correlation between 16S-rRNA phylogeny and protein domain content.

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.

Aspergillus niger N402 derivatives, harbouring an argB deletion and thus making them arginine auxothrophs, were grown with either 1.1 mM arginine or 5 mM citrulline added to the medium., Aspergillus niger N402 derivatives, harbouring an argB deletion and thus making them arginine auxothrophs, were grown with either 1.1 mM arginine or 5 mM citrulline added to the medium.

The gastrointestinal tract is subject of much research for its role in an organism’s health owing to its role as gatekeeper. The tissue acts as a barrier to keep out harmful substances like pathogens and toxins while absorbing nutrients that arise from the digestion of dietary components in in the lumen. There is a large population of microbiota that plays an important role in the functioning of the gut. All these sub-systems of the gastrointestinal tract contribute to the normal functioning of the gut. Due to its various functionalities, the gut is able to respond to different types of stimuli and bring the system back to homeostasis after perturbations. The work done in this thesis uses several bioinformatic tools to improve our understanding of the functioning of the gut. This was achieved with data from model animals, mice and pigs which were subjected to changing environments before their gastrointestinal response was measured. Different types of stimuli were studied (eg, antibiotic exposure, changing diets and infection with pathogens) in order to understand the response of the gut to varying environments. This data was analysed using different data integration techniques that provide a holistic view of the gut response. Vertical data integration techniques look for associations between different types of ~omics data to highlight possible interactions between the measured variables. Lateral integration techniques allow the study of one type of ~omics data over several time points or several experimental conditions. Using these techniques, we show proof of interactions between different sub-systems of the gut and the functional plasticity of the gut. Of the several hypotheses generated in this thesis we have validated several using existing literature and one using an in-vitro system. Further validation of these hypotheses will increase understanding of the responses of the gut and the interactions involved.

Transcriptome analysis comparing the gene expression of A. muciniphila grown on mucin or glucose media confirmed the activity of the genes involved in mucin degradation, and revealed most of them to be upregulated in the presence of mucin. A. muciniphila grown on glucose showed a stress response and upregulation of specific genes, such as Amuc_1094 which was identified as a glucokinase induced by glucose. The transcriptional response was confirmed by a proteome analysis, reinforcing the adaptation of A. muciniphila to the mucosal environment. These new findings provide molecular insights into the lifestyle of A. muciniphila, and further confirm its role as a mucin specialist in the gut., Transcriptome analysis comparing the gene expression of A. muciniphila grown on mucin or glucose media confirmed the activity of the genes involved in mucin degradation, and revealed most of them to be upregulated in the presence of mucin. A. muciniphila grown on glucose showed a stress response and upregulation of specific genes, such as Amuc_1094 which was identified as a glucokinase induced by glucose. The transcriptional response was confirmed by a proteome analysis, reinforcing the adaptation of A. muciniphila to the mucosal environment. These new findings provide molecular insights into the lifestyle of A. muciniphila, and further confirm its role as a mucin specialist in the gut.

Aspergillus niger N402 derivatives were grown with either no iron added to the medium, or 10g/L Fe(II)SO4 added to the medium., Aspergillus niger N402 derivatives were grown with either no iron added to the medium, or 10g/L Fe(II)SO4 added to the medium.

Rhizopus delemar grown under high and low oxygen conditions.

A plasmid degradation assay with CRISPR Cas3 was performed on different plasmids to investigate the respective degradation patterns., A plasmid degradation assay with CRISPR Cas3 was performed on different plasmids to investigate the respective degradation patterns.

Biological pathways are increasingly available in the BioPAX format which uses an RDF model for data storage. One can retrieve the information in this data model in the scripting language R using the package rBiopaxParser, which converts the BioPAX format to one readable in R. It also has a function to build a regulatory network from the pathway information. Here we describe an extension of this function. The new function allows the user to build graphs of entire pathways, including regulated as well as non-regulated elements, and therefore provides a maximum of information. This function is available as part of the rBiopaxParser distribution from Bioconductor.

BackgroundThe 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.ResultsWe 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.ConclusionsDual 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 strategie

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.

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.

Item does not contain fulltext, Prokaryotes encode adaptive immune systems, called CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR associated), to provide resistance against mobile invaders, such as viruses and plasmids. Host immunity is based on incorporation of invader DNA sequences in a memory locus (CRISPR), the formation of guide RNAs from this locus, and the degradation of cognate invader DNA (protospacer). Invaders can escape type I-E CRISPR-Cas immunity in Escherichia coli K12 by making point mutations in the seed region of the protospacer or its adjacent motif (PAM), but hosts quickly restore immunity by integrating new spacers in a positive-feedback process termed "priming." Here, by using a randomized protospacer and PAM library and high-throughput plasmid loss assays, we provide a systematic analysis of the constraints of both direct interference and subsequent priming in E. coli. We have defined a high-resolution genetic map of direct interference by Cascade and Cas3, which includes five positions of the protospacer at 6-nt intervals that readily tolerate mutations. Importantly, we show that priming is an extremely robust process capable of using degenerate target regions, with up to 13 mutations throughout the PAM and protospacer region. Priming is influenced by the number of mismatches, their position, and is nucleotide dependent. Our findings imply that even outdated spacers containing many mismatches can induce a rapid primed CRISPR response against diversified or related invaders, giving microbes an advantage in the coevolutionary arms race with their invaders.

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

Pseudomonas aeruginosa is a highly versatile opportunistic pathogen capable of colonizing multiple ecological niches. This bacterium is responsible for a wide range of both acute and chronic infections in a variety of hosts. The success of this microorganism relies on its ability to adapt to environmental changes and re-program its regulatory and metabolic networks. The study of P. aeruginosa adaptation to temperature is crucial to understanding the pathogenesis upon infection of its mammalian host. We examined the effects of growth temperature on the transcriptome of the P. aeruginosa PAO1. Microarray analysis of PAO1 grown in Lysogeny broth at mid-exponential phase at 22°C and 37°C revealed that temperature changes are responsible for the differential transcriptional regulation of 6.4% of the genome. Major alterations were observed in bacterial metabolism, replication, and nutrient acquisition. Quorum-sensing and exoproteins secreted by type I, II, and III secretion systems, involved in the adaptation of P. aeruginosa to the mammalian host during infection, were up-regulated at 37°C compared to 22°C. Genes encoding arginine degradation enzymes were highly up-regulated at 22°C, together with the genes involved in the synthesis of pyoverdine. However, genes involved in pyochelin biosynthesis were up-regulated at 37°C. We observed that the changes in expression of P. aeruginosa siderophores correlated to an overall increase in Fe(2+) extracellular concentration at 37°C and a peak in Fe(3+) extracellular concentration at 22°C. This suggests a distinct change in iron acquisition strategies when the bacterium switches from the external environment to the host. Our work identifies global changes in bacterial metabolism and nutrient acquisition induced by growth at different temperatures. Overall, this study identifies factors that are regulated in genome-wide adaptation processes and discusses how this life-threatening pathogen responds to temperature

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 a

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.

We infected the human macrophage-like cell line THP-1 with the attenuated M. tuberculosis surrogate M. bovis Bacillus Calmette–Guérin (M. bovis BCG). 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.

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