ipaPy2: Integrated Probabilistic Annotation (IPA) 2.0-an improved Bayesian-based method for the annotation of LC-MS/MS untargeted metabolomics data.

SUMMARY: The Integrated Probabilistic Annotation (IPA) is an automated annotation method for LC-MS-based untargeted metabolomics experiments that provides statistically rigorous estimates of the probabilities associated with each annotation. Here, we introduce ipaPy2, a substantially improved and completely refactored Python implementation of the IPA method. The revised method is now able to integrate tandem MS fragmentation data, which increases the accuracy of the identifications. Moreover, ipaPy2 provides a much more user-friendly interface, and isotope peaks are no longer treated as individual features but integrated into isotope fingerprints, greatly speeding up the calculations. The method has also been fully integrated with the mzMatch pipeline, so that the results of the annotation can be explored through the newly developed PeakMLViewerPy tool available at https://github.com/UoMMIB/PeakMLViewerPy. AVAILABILITY AND IMPLEMENTATION: The source code, extensive documentation, and tutorials are freely available on GitHub at https://github.com/francescodc87/ipaPy2.

Carboxylic acid reductase-dependent biosynthesis of eugenol and related allylphenols.

BACKGROUND: (Hydroxy)cinnamyl alcohols and allylphenols, including coniferyl alcohol and eugenol, are naturally occurring aromatic compounds widely utilised in pharmaceuticals, flavours, and fragrances. Traditionally, the heterologous biosynthesis of (hydroxy)cinnamyl alcohols from (hydroxy)cinnamic acids involved CoA-dependent activation of the substrate. However, a recently explored alternative pathway involving carboxylic acid reductase (CAR) has proven efficient in generating the (hydroxy)cinnamyl aldehyde intermediate without the need for CoA activation. In this study, we investigated the application of the CAR pathway for whole-cell bioconversion of a range of (hydroxy)cinnamic acids into their corresponding (hydroxy)cinnamyl alcohols. Furthermore, we sought to extend the pathway to enable the production of a variety of allylphenols and allylbenzene. RESULTS: By screening the activity of several heterologously expressed enzymes in crude cell lysates, we identified the combination of Segniliparus rugosus CAR (SrCAR) and Medicago sativa cinnamyl alcohol dehydrogenase (MsCAD2) as the most efficient enzymatic cascade for the two-step reduction of ferulic acid to coniferyl alcohol. To optimise the whole-cell bioconversion in Escherichia coli, we implemented a combinatorial approach to balance the gene expression levels of SrCAR and MsCAD2. This optimisation resulted in a coniferyl alcohol yield of almost 100%. Furthermore, we extended the pathway by incorporating coniferyl alcohol acyltransferase and eugenol synthase, which allowed for the production of eugenol with a titre of up to 1.61 mM (264 mg/L) from 3 mM ferulic acid. This improvement in titre surpasses previous achievements in the field employing a CoA-dependent coniferyl alcohol biosynthesis pathway. Our study not only demonstrated the successful utilisation of the CAR pathway for the biosynthesis of diverse (hydroxy)cinnamyl alcohols, such as p-coumaryl alcohol, caffeyl alcohol, cinnamyl alcohol, and sinapyl alcohol, from their corresponding (hydroxy)cinnamic acid precursors but also extended the pathway to produce allylphenols, including chavicol, hydroxychavicol, and methoxyeugenol. Notably, the microbial production of methoxyeugenol from sinapic acid represents a novel achievement. CONCLUSION: The combination of SrCAR and MsCAD2 enzymes offers an efficient enzymatic cascade for the production of a wide array of (hydroxy)cinnamyl alcohols and, ultimately, allylphenols from their respective (hydroxy)cinnamic acids. This expands the range of value-added molecules that can be generated using microbial cell factories and creates new possibilities for applications in industries such as pharmaceuticals, flavours, and fragrances. These findings underscore the versatility of the CAR pathway, emphasising its potential in various biotechnological applications.

Harnessing intercellular signals to engineer the soil microbiome.

Covering: Focus on 2015 to 2020Plant and soil microbiomes consist of diverse communities of organisms from across kingdoms and can profoundly affect plant growth and health. Natural product-based intercellular signals govern important interactions between microbiome members that ultimately regulate their beneficial or harmful impacts on the plant. Exploiting these evolved signalling circuits to engineer microbiomes towards beneficial interactions with crops is an attractive goal. There are few reports thus far of engineering the intercellular signalling of microbiomes, but this article argues that it represents a tremendous opportunity for advancing the field of microbiome engineering. This could be achieved through the selection of synergistic consortia in combination with genetic engineering of signal pathways to realise an optimised microbiome.

Unravelling the γ-butyrolactone network in Streptomyces coelicolor by computational ensemble modelling.

Antibiotic production is coordinated in the Streptomyces coelicolor population through the use of diffusible signaling molecules of the γ-butyrolactone (GBL) family. The GBL regulatory system involves a small, and not completely defined two-gene network which governs a potentially bi-stable switch between the "on" and "off" states of antibiotic production. The use of this circuit as a tool for synthetic biology has been hampered by a lack of mechanistic understanding of its functionality. We here present the creation and analysis of a versatile and adaptable ensemble model of the Streptomyces GBL system (detailed information on all model mechanisms and parameters is documented in http://www.systemsbiology.ls.manchester.ac.uk/wiki/index.php/Main_Page). We use the model to explore a range of previously proposed mechanistic hypotheses, including transcriptional interference, antisense RNA interactions between the mRNAs of the two genes, and various alternative regulatory activities. Our results suggest that transcriptional interference alone is not sufficient to explain the system's behavior. Instead, antisense RNA interactions seem to be the system's driving force, combined with an aggressive scbR promoter. The computational model can be used to further challenge and refine our understanding of the system's activity and guide future experimentation.

Rapid prototyping of microbial production strains for the biomanufacture of potential materials monomers.

Bio-based production of industrial chemicals using synthetic biology can provide alternative green routes from renewable resources, allowing for cleaner production processes. To efficiently produce chemicals on-demand through microbial strain engineering, biomanufacturing foundries have developed automated pipelines that are largely compound agnostic in their time to delivery. Here we benchmark the capabilities of a biomanufacturing pipeline to enable rapid prototyping of microbial cell factories for the production of chemically diverse industrially relevant material building blocks. Over 85 days the pipeline was able to produce 17 potential material monomers and key intermediates by combining 160 genetic parts into 115 unique biosynthetic pathways. To explore the scale-up potential of our prototype production strains, we optimized the enantioselective production of mandelic acid and hydroxymandelic acid, achieving gram-scale production in fed-batch fermenters. The high success rate in the rapid design and prototyping of microbially-produced material building blocks reveals the potential role of biofoundries in leading the transition to sustainable materials production.

Dynamics of the human skin mediator lipidome in response to dietary ω-3 fatty acid supplementation.

Nutritional supplementation with fish oil or ω-3 (n-3) polyunsaturated fatty acids (PUFAs) has potential benefits for skin inflammation. Although the differential metabolism of the main n-3PUFA eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) could lead to distinct activities, there are no clinical studies comparing their relative efficacy in human skin. Following a 10-wk oral supplementation of healthy volunteers and using mass spectrometry-based lipidomics, we found that n-3PUFA mainly affected the epidermal mediator lipidome. EPA was more efficient than DHA in reducing production of arachidonic acid-derived lipids, and both n-3PUFA lowered N-acyl ethanolamines. In UV radiation-challenged skin (3 times the minimum erythemal dose), EPA attenuated the production of proinflammatory lipids, whereas DHA abrogated the migration of Langerhans cells, as assessed by immunohistochemistry. Interestingly, n-3PUFA increased the infiltration of CD4+ and CD8+ T cells but did not alter the erythemal response, either the sunburn threshold or the resolution of erythema, as assessed by spectrophotometric hemoglobin index readings. As EPA and DHA differentially impact cutaneous inflammation through changes in the network of epidermal lipids and dendritic and infiltrating immune cells, they should be considered separately when designing interventions for cutaneous disease.-Kendall, A. C., Pilkington, S. M., Murphy, S. A., Del Carratore, F., Sunarwidhi, A. L., Kiezel-Tsugunova, M., Urquhart, P., Watson, R. E. B., Breitling, R., Rhodes, L. E., Nicolaou, A. Dynamics of the human skin mediator lipidome in response to dietary ω-3 fatty acid supplementation.

Integrated Probabilistic Annotation: A Bayesian-Based Annotation Method for Metabolomic Profiles Integrating Biochemical Connections, Isotope Patterns, and Adduct Relationships.

In a typical untargeted metabolomics experiment, the huge amount of complex data generated by mass spectrometry necessitates automated tools for the extraction of useful biological information. Each metabolite generates numerous mass spectrometry features. The association of these experimental features to the underlying metabolites still represents one of the major bottlenecks in metabolomics data processing. While certain identification (e.g., by comparison to authentic standards) is always desirable, it is usually achievable only for a limited number of compounds, and scientists often deal with a significant amount of putatively annotated metabolites. The confidence in a specific annotation is usually assessed by considering different sources of information (e.g., isotope patterns, adduct formation, chromatographic retention times, and fragmentation patterns). IPA (integrated probabilistic annotation) offers a rigorous and reproducible method to automatically annotate metabolite profiles and evaluate the resulting confidence of the putative annotations. It is able to provide a rigorous measure of our confidence in any putative annotation and is also able to update and refine our beliefs (i.e., background prior knowledge) by incorporating different sources of information in the annotation process, such as isotope patterns, adduct formation and biochemical relations. The IPA package is freely available on GitHub ( https://github.com/francescodc87/IPA ), together with the related extensive documentation.

Selenzyme: enzyme selection tool for pathway design.

Summary: Synthetic biology applies the principles of engineering to biology in order to create biological functionalities not seen before in nature. One of the most exciting applications of synthetic biology is the design of new organisms with the ability to produce valuable chemicals including pharmaceuticals and biomaterials in a greener; sustainable fashion. Selecting the right enzymes to catalyze each reaction step in order to produce a desired target compound is, however, not trivial. Here, we present Selenzyme, a free online enzyme selection tool for metabolic pathway design. The user is guided through several decision steps in order to shortlist the best candidates for a given pathway step. The tool graphically presents key information about enzymes based on existing databases and tools such as: similarity of sequences and of catalyzed reactions; phylogenetic distance between source organism and intended host species; multiple alignment highlighting conserved regions, predicted catalytic site, and active regions and relevant properties such as predicted solubility and transmembrane regions. Selenzyme provides bespoke sequence selection for automated workflows in biofoundries. Availability and implementation: The tool is integrated as part of the pathway design stage into the design-build-test-learn SYNBIOCHEM pipeline. The Selenzyme web server is available at http://selenzyme.synbiochem.co.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

antiSMASH 4.0-improvements in chemistry prediction and gene cluster boundary identification.

Many antibiotics, chemotherapeutics, crop protection agents and food preservatives originate from molecules produced by bacteria, fungi or plants. In recent years, genome mining methodologies have been widely adopted to identify and characterize the biosynthetic gene clusters encoding the production of such compounds. Since 2011, the 'antibiotics and secondary metabolite analysis shell-antiSMASH' has assisted researchers in efficiently performing this, both as a web server and a standalone tool. Here, we present the thoroughly updated antiSMASH version 4, which adds several novel features, including prediction of gene cluster boundaries using the ClusterFinder method or the newly integrated CASSIS algorithm, improved substrate specificity prediction for non-ribosomal peptide synthetase adenylation domains based on the new SANDPUMA algorithm, improved predictions for terpene and ribosomally synthesized and post-translationally modified peptides cluster products, reporting of sequence similarity to proteins encoded in experimentally characterized gene clusters on a per-protein basis and a domain-level alignment tool for comparative analysis of trans-AT polyketide synthase assembly line architectures. Additionally, several usability features have been updated and improved. Together, these improvements make antiSMASH up-to-date with the latest developments in natural product research and will further facilitate computational genome mining for the discovery of novel bioactive molecules.

Development and validation of an updated computational model of Streptomyces coelicolor primary and secondary metabolism.

BACKGROUND: Streptomyces species produce a vast diversity of secondary metabolites of clinical and biotechnological importance, in particular antibiotics. Recent developments in metabolic engineering, synthetic and systems biology have opened new opportunities to exploit Streptomyces secondary metabolism, but achieving industry-level production without time-consuming optimization has remained challenging. Genome-scale metabolic modelling has been shown to be a powerful tool to guide metabolic engineering strategies for accelerated strain optimization, and several generations of models of Streptomyces metabolism have been developed for this purpose. RESULTS: Here, we present the most recent update of a genome-scale stoichiometric constraint-based model of the metabolism of Streptomyces coelicolor, the major model organism for the production of antibiotics in the genus. We show that the updated model enables better metabolic flux and biomass predictions and facilitates the integrative analysis of multi-omics data such as transcriptomics, proteomics and metabolomics. CONCLUSIONS: The updated model presented here provides an enhanced basis for the next generation of metabolic engineering attempts in Streptomyces.

RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets.

MOTIVATION: The Rank Product (RP) is a statistical technique widely used to detect differentially expressed features in molecular profiling experiments such as transcriptomics, metabolomics and proteomics studies. An implementation of the RP and the closely related Rank Sum (RS) statistics has been available in the RankProd Bioconductor package for several years. However, several recent advances in the understanding of the statistical foundations of the method have made a complete refactoring of the existing package desirable. RESULTS: We implemented a completely refactored version of the RankProd package, which provides a more principled implementation of the statistics for unpaired datasets. Moreover, the permutation-based P -value estimation methods have been replaced by exact methods, providing faster and more accurate results. AVAILABILITY AND IMPLEMENTATION: RankProd 2.0 is available at Bioconductor ( https://www.bioconductor.org/packages/devel/bioc/html/RankProd.html ) and as part of the mzMatch pipeline ( http://www.mzmatch.sourceforge.net ). CONTACT: rainer.breitling@manchester.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mobilising ion mobility mass spectrometry for metabolomics.

Chromatography-based mass spectrometry approaches (xC-MS) are commonly used in untargeted metabolomics, providing retention time, m/z values and metabolite-specific fragments, all of which are used to identify and validate an unknown analyte. Ion mobility-mass spectrometry (IM-MS) is emerging as an enhancement to classic xC-MS strategies, by offering additional ion separation as well as collision cross section (CCS) determination. In order to apply such an approach to a metabolomics workflow, verified data from metabolite standards is necessary. In this work we present experimental DTCCSN2 values for a range of metabolites in positive and negative ionisation modes using drift tube-ion mobility-mass spectrometry (DT-IM-MS) with nitrogen as the buffer gas. The value of DTCCSN2 measurements for application in metabolite identification relies on a robust technique that acquires measurements of high reproducibility. We report that the CCS values found for 86% of metabolites measured in replicate have a relative standard deviation lower than 0.2%. Examples of metabolites with near identical mass are demonstrated to be separated by ion mobility with over 4% difference in DTCCSN2 values. We conclude that the integration of ion mobility into current LC-MS workflows can aid in small molecule identification for both targeted and untargeted metabolite screening.

Output ordering and prioritisation system (OOPS): ranking biosynthetic gene clusters to enhance bioactive metabolite discovery.

The rapid increase of publicly available microbial genome sequences has highlighted the presence of hundreds of thousands of biosynthetic gene clusters (BGCs) encoding valuable secondary metabolites. The experimental characterization of new BGCs is extremely laborious and struggles to keep pace with the in silico identification of potential BGCs. Therefore, the prioritisation of promising candidates among computationally predicted BGCs represents a pressing need. Here, we propose an output ordering and prioritisation system (OOPS) which helps sorting identified BGCs by a wide variety of custom-weighted biological and biochemical criteria in a flexible and user-friendly interface. OOPS facilitates a judicious prioritisation of BGCs using G+C content, coding sequence length, gene number, cluster self-similarity and codon bias parameters, as well as enabling the user to rank BGCs based upon BGC type, novelty, and taxonomic distribution. Effective prioritisation of BGCs will help to reduce experimental attrition rates and improve the breadth of bioactive metabolites characterized.

Probing the metabolic network in bloodstream-form Trypanosoma brucei using untargeted metabolomics with stable isotope labelled glucose.

Metabolomics coupled with heavy-atom isotope-labelled glucose has been used to probe the metabolic pathways active in cultured bloodstream form trypomastigotes of Trypanosoma brucei, a parasite responsible for human African trypanosomiasis. Glucose enters many branches of metabolism beyond glycolysis, which has been widely held to be the sole route of glucose metabolism. Whilst pyruvate is the major end-product of glucose catabolism, its transamination product, alanine, is also produced in significant quantities. The oxidative branch of the pentose phosphate pathway is operative, although the non-oxidative branch is not. Ribose 5-phosphate generated through this pathway distributes widely into nucleotide synthesis and other branches of metabolism. Acetate, derived from glucose, is found associated with a range of acetylated amino acids and, to a lesser extent, fatty acids; while labelled glycerol is found in many glycerophospholipids. Glucose also enters inositol and several sugar nucleotides that serve as precursors to macromolecule biosynthesis. Although a Krebs cycle is not operative, malate, fumarate and succinate, primarily labelled in three carbons, were present, indicating an origin from phosphoenolpyruvate via oxaloacetate. Interestingly, the enzyme responsible for conversion of phosphoenolpyruvate to oxaloacetate, phosphoenolpyruvate carboxykinase, was shown to be essential to the bloodstream form trypanosomes, as demonstrated by the lethal phenotype induced by RNAi-mediated downregulation of its expression. In addition, glucose derivatives enter pyrimidine biosynthesis via oxaloacetate as a precursor to aspartate and orotate.

antiSMASH 3.0-a comprehensive resource for the genome mining of biosynthetic gene clusters.

Microbial secondary metabolism constitutes a rich source of antibiotics, chemotherapeutics, insecticides and other high-value chemicals. Genome mining of gene clusters that encode the biosynthetic pathways for these metabolites has become a key methodology for novel compound discovery. In 2011, we introduced antiSMASH, a web server and stand-alone tool for the automatic genomic identification and analysis of biosynthetic gene clusters, available at http://antismash.secondarymetabolites.org. Here, we present version 3.0 of antiSMASH, which has undergone major improvements. A full integration of the recently published ClusterFinder algorithm now allows using this probabilistic algorithm to detect putative gene clusters of unknown types. Also, a new dereplication variant of the ClusterBlast module now identifies similarities of identified clusters to any of 1172 clusters with known end products. At the enzyme level, active sites of key biosynthetic enzymes are now pinpointed through a curated pattern-matching procedure and Enzyme Commission numbers are assigned to functionally classify all enzyme-coding genes. Additionally, chemical structure prediction has been improved by incorporating polyketide reduction states. Finally, in order for users to be able to organize and analyze multiple antiSMASH outputs in a private setting, a new XML output module allows offline editing of antiSMASH annotations within the Geneious software.

TrypanoCyc: a community-led biochemical pathways database for Trypanosoma brucei.

The metabolic network of a cell represents the catabolic and anabolic reactions that interconvert small molecules (metabolites) through the activity of enzymes, transporters and non-catalyzed chemical reactions. Our understanding of individual metabolic networks is increasing as we learn more about the enzymes that are active in particular cells under particular conditions and as technologies advance to allow detailed measurements of the cellular metabolome. Metabolic network databases are of increasing importance in allowing us to contextualise data sets emerging from transcriptomic, proteomic and metabolomic experiments. Here we present a dynamic database, TrypanoCyc (http://www.metexplore.fr/trypanocyc/), which describes the generic and condition-specific metabolic network of Trypanosoma brucei, a parasitic protozoan responsible for human and animal African trypanosomiasis. In addition to enabling navigation through the BioCyc-based TrypanoCyc interface, we have also implemented a network-based representation of the information through MetExplore, yielding a novel environment in which to visualise the metabolism of this important parasite.

Bioinformatics for the synthetic biology of natural products: integrating across the Design-Build-Test cycle.

Covering: 2000 to 2016Progress in synthetic biology is enabled by powerful bioinformatics tools allowing the integration of the design, build and test stages of the biological engineering cycle. In this review we illustrate how this integration can be achieved, with a particular focus on natural products discovery and production. Bioinformatics tools for the DESIGN and BUILD stages include tools for the selection, synthesis, assembly and optimization of parts (enzymes and regulatory elements), devices (pathways) and systems (chassis). TEST tools include those for screening, identification and quantification of metabolites for rapid prototyping. The main advantages and limitations of these tools as well as their interoperability capabilities are highlighted.

Incorporating peak grouping information for alignment of multiple liquid chromatography-mass spectrometry datasets.

MOTIVATION: The combination of liquid chromatography and mass spectrometry (LC/MS) has been widely used for large-scale comparative studies in systems biology, including proteomics, glycomics and metabolomics. In almost all experimental design, it is necessary to compare chromatograms across biological or technical replicates and across sample groups. Central to this is the peak alignment step, which is one of the most important but challenging preprocessing steps. Existing alignment tools do not take into account the structural dependencies between related peaks that coelute and are derived from the same metabolite or peptide. We propose a direct matching peak alignment method for LC/MS data that incorporates related peaks information (within each LC/MS run) and investigate its effect on alignment performance (across runs). The groupings of related peaks necessary for our method can be obtained from any peak clustering method and are built into a pair-wise peak similarity score function. The similarity score matrix produced is used by an approximation algorithm for the weighted matching problem to produce the actual alignment result. RESULTS: We demonstrate that related peak information can improve alignment performance. The performance is evaluated on a set of benchmark datasets, where our method performs competitively compared to other popular alignment tools. AVAILABILITY: The proposed alignment method has been implemented as a stand-alone application in Python, available for download at http://github.com/joewandy/peak-grouping-alignment.

Phosphoenolpyruvate carboxylase identified as a key enzyme in erythrocytic Plasmodium falciparum carbon metabolism.

Phospoenolpyruvate carboxylase (PEPC) is absent from humans but encoded in the Plasmodium falciparum genome, suggesting that PEPC has a parasite-specific function. To investigate its importance in P. falciparum, we generated a pepc null mutant (D10(Δpepc) ), which was only achievable when malate, a reduction product of oxaloacetate, was added to the growth medium. D10(Δpepc) had a severe growth defect in vitro, which was partially reversed by addition of malate or fumarate, suggesting that pepc may be essential in vivo. Targeted metabolomics using (13)C-U-D-glucose and (13)C-bicarbonate showed that the conversion of glycolytically-derived PEP into malate, fumarate, aspartate and citrate was abolished in D10(Δpepc) and that pentose phosphate pathway metabolites and glycerol 3-phosphate were present at increased levels. In contrast, metabolism of the carbon skeleton of (13)C,(15)N-U-glutamine was similar in both parasite lines, although the flux was lower in D10(Δpepc); it also confirmed the operation of a complete forward TCA cycle in the wild type parasite. Overall, these data confirm the CO2 fixing activity of PEPC and suggest that it provides metabolites essential for TCA cycle anaplerosis and the maintenance of cytosolic and mitochondrial redox balance. Moreover, these findings imply that PEPC may be an exploitable target for future drug discovery.

SYNBIOCHEM-a SynBio foundry for the biosynthesis and sustainable production of fine and speciality chemicals.

The Manchester Synthetic Biology Research Centre (SYNBIOCHEM) is a foundry for the biosynthesis and sustainable production of fine and speciality chemicals. The Centre's integrated technology platforms provide a unique capability to facilitate predictable engineering of microbial bio-factories for chemicals production. An overview of these capabilities is described.