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.

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.

A LITTLE LEARNING IS A DANGEROUS THING – ON THE USEFULNESS OF BARCODE DATA FOR GENUS-LEVEL TAXONOMY

The paper comments the usefulness of barcode data for genus-level taxonomy.

SelProm: A Queryable and Predictive Expression Vector Selection Tool for Escherichia coli

The rapid prototyping and optimization of plasmid-based recombinant gene expression is one of the key steps in the development of bioengineered bacterial systems. Often, multiple genes or gene modules need to be coexpressed, and for this purpose compatible, inducible plasmid systems have been developed. However, inducible expression systems are not favored in industrial processes, due to their prohibitive cost, and consequently the conversion to constitutive expression systems is often desired. Here we present a set of constitutive-expression plasmids for this purpose, which were benchmarked using fluorescent reporter genes. To further facilitate the conversion between inducible and constitutive expression systems, we developed SelProm, a design tool that serves as a parts repository of plasmid expression strength and predicts portability rules between constitutive and inducible plasmids through model comparison and machine learning. The SelProm tool is freely available at http://selprom.synbiochem.co.uk .

Synthetic biology for fibres, adhesives and active camouflage materials in protection and aerospace

Synthetic biology has huge potential to produce the next generation of advanced materials by accessing previously unreachable (bio)chemical space. In this prospective review, we take a snapshot of current activity in this rapidly developing area, focussing on prominent examples for high-performance applications such as those required for protective materials and the aerospace sector. The continued growth of this emerging field will be facilitated by the convergence of expertise from a range of diverse disciplines, including molecular biology, polymer chemistry, materials science and process engineering. This review highlights the most significant recent advances and address the cross-disciplinary challenges currently being faced.

HOW NOT TO CONDUCT A SCIENTIFIC DEBATE: A COUNTERPOINT TO THE RECENT CRITIQUE OF THE “PRAGMATIC CLASSIFICATION” OF JUMPING SPIDERS (ARTHROPODA: ARACHNIDA: ARANEAE: SALTICIDAE)

The paper gives a counterpoint to the recent critique of the “pragmatic classification” of jumping spiders (Arthropoda: Arachnida: Araneae: Salticidae).

Defining informative priors for ensemble modeling in systems biology

Ensemble modeling in molecular systems biology requires the reproducible translation of kinetic parameter data into informative probability distributions (priors), as well as approaches that sample parameters from these distributions without violating the thermodynamic consistency of the overall model. Although a number of pioneering frameworks for ensemble modeling have been published, the issue of generating informative priors has not yet been addressed. Here, we present a protocol that aims to fill this gap. This protocol discusses the collection of parameter values from a diverse range of sources (literature, databases and experiments), assessment of their plausibility, and creation of log-normal probability distributions that can be used as informative priors in ensemble modeling. Furthermore, the protocol enables sampling from the generated distributions while maintaining thermodynamic consistency. Once all parameter values have been retrieved from literature and databases, the protocol can be implemented within ~5-10 min per parameter. The aim of this protocol is to facilitate the design and use of informative distributions for ensemble modeling, especially in fields such as synthetic biology and systems medicine.

BARCODE TAXONOMY AT THE GENUS LEVEL

DNA barcode sequencing has rapidly become one of the most powerful tools for biodiversity assessments. Beyond its original uses for the identification of animal species, including the discovery of cryptic diversity in difficult taxonomic groups, the growing public sequence datasets also offer opportunities for more wide-ranging applications. This contribution shows how barcode data can provide useful complementary information to assist taxonomic decision making at the genus level. An analysis of public barcode datasets for 10 diverse spider families, covering more than 3400 species and morphospecies, reveals numerous examples where sequence similarities either strongly support or convincingly refute recent controversial genus assignments. The following nomenclatorial changes are suggested based on a combined assessment of morphological evidence and the barcode analysis: Acantholycosa = Pardosa (syn. nov.); Piratula = Pirata (syn. nov.); Pulchellodromus, Philodromimus, Tibellomimus, Artanes, and Emargidromus = subgenera of Philodromus (stat. nov.); Cryptachaea riparia = Parasteatoda riparia (comb. nov.); Ohlertidion = Heterotheridion (syn. nov.); Saaristoa = Aphileta (syn. nov.); Aphileta microtarsa = Eulaira microtarsa (comb. conf.); Centromerita and Tallusia = Centromerus (syn. conf.); Obscuriphantes, Agnyphantes, and Acanthoneta = Poeciloneta (syn. nov.); Bolyphantes bipartitus = Poeciloneta bipartita (comb. nov.); Anguliphantes, Improphantes, Piniphantes, and Mansuphantes = Oryphantes (syn. nov.), Palliduphantes antroniensis = Oryphantes antroniensis (comb. nov.), Lepthyphantes nodifer = Oryphantes nodifer (comb. nov.), Hypositticus, Sittipub, Calositticus, Sittisax, Sittiflor, and Attulus = Sitticus (syn. nov.).

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.

A BARCODE-BASED PHYLOGENETIC SCAFFOLD FOR XYSTICUS AND ITS RELATIVES (ARANEAE: THOMISIDAE: CORIARACHNINI)

The phylogenetic relationships and taxonomy of the crab spider genus Xysticus and its closest relatives (i.e., the tribe Coriarachnini, also including, e.g., Ozyptila, Coriarachne and Bassaniana) have long been controversial, with several alternative classifications being proposed, none of which has gained universal acceptance. As Coriarachnini is largely confined to the Holarctic region, the main target area of recent DNA barcoding projects for spiders, a large amount of genetic data for the group is now publicly available. The results of a phylogenetic analysis of this sequence dataset are largely congruent with earlier morphology-based results regarding the evolutionary structure of the group. In particular, they highlight the fact that Xysticus s. lat. is a paraphyletic assembly and that several species groups need to be placed in separate genera to achieve monophyly of Xysticus s. str. Similarly, Coriarachne and Bassaniana appear as independent clades rather than a joined monophyletic Coriarachne s. lat. In contrast, further subdivision of Ozyptila is not supported by the genetic data. Importantly, the analysis also shows that anapophysate members of Xysticus s. lat. form two widely separated groups: a primarily anapophysate division, also including Coriarachne and Bassaniana, at the base of Xysticus s. lat., and a secondarily anapophysate clade deeply nested within Xysticus s. str. This might explain some of the earlier difficulties when trying to define generally accepted subgroups within Xysticus s. lat. The phylogenetic scaffold based on barcode sequences is sufficiently dense and well resolved to attempt the tentative and provisional placement of the majority of species in Xysticus s. lat. in the independent genera Xysticus s. str., Bassaniodes, Psammitis and Spiracme as a starting point for a future more formal revision of the group.

Steatoda nobilis, a false widow on the rise: a synthesis of past and current distribution trends

The Noble False Widow, Steatodanobilis (Thorell, 1875) (Araneae, Theridiidae), is, due to its relatively large size and potential medical importance, one of the most notable invasive spider species worldwide. Probably originating from the Canary Islands and Madeira, the species is well established in Western Europe and large parts of the Mediterranean area and has spread recently into California and South America, while Central European populations were not known until 2011.

We report on long-time observations that reveal that at least two flourishing populations in Germany (Cologne) have been present for over five years, while in Ecuador one population has been observed between 2014 and 2018 and several other records were made in other parts of the country. Data obtained from the British Spider Recording Scheme demonstrate that the species moved significantly northwards since the report of the first populations in the very South of England, after several decades of relative stasis. The sudden northward expansion highly correlates with a massive rise in press coverage of the species.

In the Americas, S.nobilis is currently known from four countries (USA, Chile, Ecuador, Colombia), and available DNA barcoding data obtained for specimens from this area suggest that multiple introductions occurred within each country. Using ecological niche modeling, we identified suitable climate regions for the species and discuss possible reasons for its current spread. We propose that seaside cities and villages with a temperate oceanic or Mediterranean climate are especially favourable potential habitats for S.nobilis and will face the highest colonization pressure in the future, while tropical upland regions with temperate climates are also vulnerable to invasion by S.nobilis.

Machine Learning of Designed Translational Control Allows Predictive Pathway Optimization in Escherichia coli

The field of synthetic biology aims to make the design of biological systems predictable, shrinking the huge design space to practical numbers for testing. When designing microbial cell factories, most optimization efforts have focused on enzyme and strain selection/engineering, pathway regulation, and process development. In silico tools for the predictive design of bacterial ribosome binding sites (RBSs) and RBS libraries now allow translational tuning of biochemical pathways; however, methods for predicting optimal RBS combinations in multigene pathways are desirable. Here we present the implementation of machine learning algorithms to model the RBS sequence-phenotype relationship from representative subsets of large combinatorial RBS libraries allowing the accurate prediction of optimal high-producers. Applied to a recombinant monoterpenoid production pathway in Escherichia coli, our approach was able to boost production titers by over 60% when screening under 3% of a library. To facilitate library screening, a multiwell plate fermentation procedure was developed, allowing increased screening throughput with sufficient resolution to discriminate between high and low producers. High producers from one library did not translate during scale-up, but the reduced screening requirements allowed rapid rescreening at the larger scale. This methodology is potentially compatible with any biochemical pathway and provides a powerful tool toward predictive design of bacterial production chassis.

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.

Orthogonal Regulatory Circuits for Escherichia coli Based on the γ-Butyrolactone System of Streptomyces coelicolor

Chemically inducible transcription factors are widely used to control gene expression of synthetic devices. The bacterial quorum sensing system is a popular tool to achieve such control. However, different quorum sensing systems have been found to cross-talk, both between themselves and with the hosts of these devices, and they are leaky by nature. Here we evaluate the potential use of the γ-butyrolactone system from Streptomyces coelicolor A3(2) M145 as a complementary regulatory circuit. First, two additional genes responsible for the biosynthesis of γ-butyrolactones were identified in S. coelicolor M145 and then expressed in E. coli BL21 under various experimental conditions. Second, the γ-butyrolactone receptor ScbR was optimized for expression in E. coli BL21. Finally, signal and promoter crosstalk between the γ-butyrolactone system from S. coelicolor and quorum sensing systems from Vibrio fischeri and Pseudomonas aeruginosa was evaluated. The results show that the γ-butyrolactone system does not crosstalk with the quorum sensing systems and can be used to generate orthogonal synthetic circuits.

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).

Supplementary information

Supplementary data are available at Bioinformatics online.

Rational cell culture optimization enhances experimental reproducibility in cancer cells

Optimization of experimental conditions is critical in ensuring robust experimental reproducibility. Through detailed metabolomic analysis we found that cell culture conditions significantly impacted on glutaminase (GLS1) sensitivity resulting in variable sensitivity and irreproducibility in data. Baseline metabolite profiling highlighted that untreated cells underwent significant changes in metabolic status. Both the extracellular levels of glutamine and lactate and the intracellular levels of multiple metabolites changed drastically during the assay. We show that these changes compromise the robustness of the assay and make it difficult to reproduce. We discuss the implications of the cells' metabolic environment when studying the effects of perturbations to cell function by any type of inhibitor. We then devised 'metabolically rationalized standard' assay conditions, in which glutaminase-1 inhibition reduced glutamine metabolism differently in both cell lines assayed, and decreased the proliferation of one of them. The adoption of optimized conditions such as the ones described here should lead to an improvement in reproducibility and help eliminate false negatives as well as false positives in these assays.

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.

Detection and Quantification of Butyrolactones from Streptomyces

In Streptomyces, the onset of antibiotic production and sporulation is coordinated through small diffusible molecules known as γ-butyrolactones (GBLs). These are active in very low amounts, and their extraction and characterization are challenging. Here we describe a rapid, small-scale method for the extraction of GBL from Streptomyces coelicolor, from both solid and liquid cultures, which provides sufficient material for subsequent bioassays and partial characterization. We also present two different bioassay techniques for the detection and quantification of the GBL content in the extracts: the antibiotic bioassay and the kanamycin bioassay.

A transatlantic perspective on 20 emerging issues in biological engineering

Advances in biological engineering are likely to have substantial impacts on global society. To explore these potential impacts we ran a horizon scanning exercise to capture a range of perspectives on the opportunities and risks presented by biological engineering. We first identified 70 potential issues, and then used an iterative process to prioritise 20 issues that we considered to be emerging, to have potential global impact, and to be relatively unknown outside the field of biological engineering. The issues identified may be of interest to researchers, businesses and policy makers in sectors such as health, energy, agriculture and the environment.

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.

reGenotyper: Detecting mislabeled samples in genetic data

In high-throughput molecular profiling studies, genotype labels can be wrongly assigned at various experimental steps; the resulting mislabeled samples seriously reduce the power to detect the genetic basis of phenotypic variation. We have developed an approach to detect potential mislabeling, recover the “ideal” genotype and identify “best-matched” labels for mislabeled samples. On average, we identified 4% of samples as mislabeled in eight published datasets, highlighting the necessity of applying a “data cleaning” step before standard data analysis.

Respectful Modeling: Addressing Uncertainty in Dynamic System Models for Molecular Biology

Although there is still some skepticism in the biological community regarding the value and significance of quantitative computational modeling, important steps are continually being taken to enhance its accessibility and predictive power. We view these developments as essential components of an emerging 'respectful modeling' framework which has two key aims: (i) respecting the models themselves and facilitating the reproduction and update of modeling results by other scientists, and (ii) respecting the predictions of the models and rigorously quantifying the confidence associated with the modeling results. This respectful attitude will guide the design of higher-quality models and facilitate the use of models in modern applications such as engineering and manipulating microbial metabolism by synthetic biology.

Synthetic Biology of Natural Products

The diversity and natural modularity of their biosynthetic pathways has turned natural products into attractive, but challenging, targets for synthetic biology approaches. Here, we discuss the current state of the field, highlighting recent advances and remaining bottlenecks. Global genomic assessments of natural product biosynthetic capacities across large parts of microbial diversity provide a first survey of the available natural parts libraries and identify evolutionary design rules for further engineering. Methods for compound and pathway detection and characterization are developed increasingly on the basis of synthetic biology tools, contributing to an accelerated translation of genomic information into usable building blocks for pathway assembly. A wide range of methods is also becoming available for accessing ever larger parts of chemical space by rational diversification of natural products, guided by rapid progress in our understanding of the underlying biochemistry and enzymatic mechanisms. Enhanced genome assembly and editing tools, adapted to the needs of natural products research, facilitate the realization of ambitious engineering strategies, ranging from combinatorial library generation to high-throughput optimization of product titers. Together, these tools and concepts contribute to the emergence of a new generation of revitalized natural product research.