Biology subjects

van der Hooft, J. J. J.

Publications and source records attributed to van der Hooft, J. J. J..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Charting the Cannabis plant chemical space with computational metabolomics

IntroductionThe chemical classification of Cannabis is typically confined to the cannabinoid content, whilst Cannabis encompasses diverse chemical classes that vary in abundance among all its varieties. Hence, neglecting other chemical classes within Cannabis strains results in a restricted and biased comprehension of elements that may contribute to chemical intricacy and the resultant medicinal qualities of the plant. ObjectivesThus, herein, we report a computational metabolomics study to elucidate the Cannabis metabolic map beyond the cannabinoids. MethodsMass spectrometry-based computational tools were used to mine and evaluate the methanolic leaf and flower extracts of two Cannabis cultivars: Amnesia haze (AMNH) and Royal dutch cheese (RDC). ResultsThe results revealed the presence of different chemical compound classes including cannabinoids, but extending it to flavonoids, polyketides, and phospholipids at varying distributions across the cultivar plant tissues. Therefore, the two cultivars were differentiated based on the overall chemical content of their plant tissues where AMNH was observed to be more dominant in the flavonoid content while RDC was more dominant in the lipid-like molecules. Additionally, in silico molecular docking studies in combination with biological assay studies indicated the potentially differing anti-cancer properties of the two cultivars resulting from the elucidated chemical profiles. ConclusionThese findings highlight distinctive chemical profiles beyond cannabinoids in Cannabis strains. This novel mapping of the metabolomic landscape of Cannabis provides actionable insights into plant biochemistry and justifies selecting certain varieties for medicinal use.

systems biology↗

bioRxiv2024

Olive mill solid waste induces beneficial mushroom-specialized metabolite diversity: a computational metabolomics study.

IntroductionMushrooms contain besides proteins a diverse pallet of specialized metabolites bioactive in either beneficial or harmful manner. Therefore, mushrooms have been exploited by humans for centuries for dietary or medical purposes. For example, the edible and medicinal mushrooms Hericium erinaceus and Pleurotus eryngii are grown commercially around the world. In nature, H. erinaceus grows on old or dead tree trunks, and P. eryngii grows on Apiaceae plant roots, whereas in cultivation, they grow on substrates mainly consisting of dry wood chips, straw, and cereals. To make their farming more sustainable, supplements such as olive mill solid waste (OMSW) have been added to support mushroom development. However, so far, the impact of substrate additives on the edible mushroom metabolic content has not been assessed. MethodsHere, we examined the effect of different proportions of OMSW added to the substrate on the metabolic profiles of the fruiting body (FB) and mycelium of H. erinaceus and P. eryngii mushrooms. We used computational metabolomics strategies including GNPS molecular networking, MS2Query, and the FERMO dashboard, to organize, annotate, and prioritize metabolite features from the untargeted Q-Exactive Plus HR-LC-MS/MS metabolomics data. Following chromatography-based fractionation, the metabolite annotation of four metabolite features was further validated or fine-tuned using 1H-NMR, to resolve structural isomers. Results & DiscussionOur computational metabolomics strategies showed several annotated metabolite features to be affected by OSMW concentration. In general, the methanolic extracts of H. erinaceus FB and mycelium were more highly enriched with specialized metabolites than those of P. eryngii. Interestingly, OMSW increased several hericenone analogues in the H. erinaceus FB, to which beneficial properties, such as anti-inflammatory, anticancer and neuroprotective properties are assigned, as well as several erinacerin metabolites from the mycelium. In addition, high concentrations of OMSW decreased the toxic enniatin metabolite abundance. In conclusion, we demonstrate how a change in substrate composition affects the mushrooms specialized metabolome and can induce beneficial mushroom metabolite diversity. These results highlight the importance of including computational metabolomic strategies to investigate new sustainable growth options for edible mushrooms and other natural foods.

biochemistry↗

bioRxiv2023

Tailored mass spectral data exploration using the specXplore interactive dashboard

Untargeted metabolomics promises comprehensive characterization of small molecules in biological samples. However, the field is hampered by low annotation rates and abstract spectral data. Despite recent advances in computational metabolomics, manual annotations and manual confirmation of in-silico annotations remain important in the field. Here, exploratory data analysis methods for mass spectral data provide overviews, prioritization, and structural hypothesis starting points to researchers facing large quantities of spectral data. In this research, we propose a fluid means of dealing with mass spectral data using specXplore, an interactive python dashboard providing interactive and complementary visualizations facilitating mass spectral similarity matrix exploration. Specifically, specXplore provides a two dimensional t-SNE embedding as a jumping board for local connectivity exploration using complementary interactive visualizations in the form of partial network drawings, similarity heatmaps, and fragmentation overview maps. SpecXplore makes use of of state of the art ms2deepscore pairwise spectral similarities as a quantitative backbone, while allowing fast changes of threshold and connectivity limitation settings, providing flexibility in adjusting settings to suit the localized node environment being explored. We believe that specXplore can become an integral part in mass spectral data exploration efforts and assist users in the generation of structural hypotheses for compounds of interest. Technical TermsO_LIA network is a collection of connected features. In our case, a network consists of MS/MS spectral features connected provided their spectral similarity is high. Networks are represented using node-link-diagrams. C_LIO_LINode-link diagram -a term commonly used to refer to the graphical representation of a network via nodes and links (i.e. edges). In this paper, we use node-link diagram and network-view interchangeably. C_LIO_LIA node is a feature in a network that can be connected to other features via edges. An alternative term for node is vertex. C_LIO_LIAn edge is a connection between two nodes. Other terms for edges are links or vertices. C_LIO_LINetwork layout refers to the spatial arrangement of nodes and edges on an usually two dimensional plotting surface. Network layout is also sometimes referred to as embedding. This term is avoided in this paper to avoid confusion with embedding in the machine learning sense. C_LIO_LIGiven a network G(V, E), where V denotes its nodes and E its (weighted) edges, we define its topology as the relationships between individual (groups of) nodes and edges or the network as a whole, irrespective of the networks layout. C_LIO_LIMolecular Networking (MN) is an exploratory data analysis technique merging spectral similarity-based topological clustering and visualization as node-link diagrams. C_LIO_LIThe plain English words group/grouping are wherever appropriate to avoid jargon terms such as clustering (as in k-medoid or k-means clustering), embedding (as in projection of groups of features into a close-by lower dimensional space), or molecular families. The latter are groups of spectral data features clustered and visualized as network-views via traditional MN or feature based molecular networking (FBMN). Molecular families, usually represent smaller, disconnected networks that are part of a larger dataset. When we refer to this disconnected nature, we use the phrasing disjoint sub-network for emphasis. C_LI

bioinformatics↗

bioRxiv2023

Simulated-to-real Benchmarking of Acquisition Methods in Metabolomics

Data-Dependent and Data-Independent Acquisition modes (DDA and DIA, respectively) are both widely used to acquire MS2 spectra in untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolomics analyses. Despite their wide use, little work has been attempted to systematically compare their performance due to the difficulty and cost of performing comparisons with real experimental data due to the lack of ground truth and the costs involved in running large number of acquisitions. Here, we present a systematic in-silico comparison of these two acquisition methods. To do so, we extended our Virtual Metabolomics Mass Spectrometer (ViMMS) framework with a DIA module. Our results show that the performance of these methods varies with the average number of co-eluting ions as the most important factor. At low numbers, DIA outperforms DDA, but at higher numbers, DDA has an advantage as DIA can no longer deal with the large amount of overlapping ion chromatograms. Results from simulation were further validated on an actual mass spectrometer, demonstrating that using ViMMS we can draw conclusions from simulation that translate well into the real world. Embedding this work within ViMMS also allows for researchers to easily simulate DDA and DIA LC-MS/MS runs, validate them on actual instrument, and potentially prototype novel methods that best combine the characteristics of both approaches. We believe that this work provides a useful guide to the choice of DIA or DDA for scientists involved in metabolomics data acquisition.

systems biology↗

bioRxiv2022

FERMO: a Dashboard for Streamlined Rationalized Prioritization of Molecular Features from Mass Spectrometry Data

Small molecules shape phenotypic variation by modulating biological processes, yet linking specific molecules to observed traits remains challenging due to the complexity of biological samples. Liquid chromatography-tandem mass spectrometry routinely detects hundreds of molecules per sample, and computational tools aid in the selection of the biologically relevant subset by organizing, annotating, and integrating orthogonal data. Existing tools typically focus on facilitating data-driven exploration to support manual interpretation, rather than more objective, data-driven prioritization and hypothesis-generation. Here, we introduce FERMO, a free online dashboard interface for prioritization of molecular features and samples associated with phenotypes of interest. FERMOs modular framework automates data processing, annotation, and integration of standardized phenotypic and other metadata. FERMO supports both exploratory and targeted analysis through efficient interactive visualization, reproducible prioritization, and data filtering. We demonstrate FERMOs utility in benchmarking studies prioritizing bioactive compounds from complex biological matrices. FERMO is freely available at https://fermo.bioinformatics.nl/.

bioinformatics↗

bioRxiv2022

Trachymyrmex septentrionalis ants promote fungus garden hygiene using Trichoderma-derived metabolite cues

Fungus-growing ants depend on a fungal mutualist that can fall prey to fungal pathogens. This mutualist is cultivated by these ants in structures called fungus gardens. Ants exhibit weeding behaviors that keep their fungus gardens healthy by physically removing compromised pieces. However, how ants detect diseases of their fungus gardens is unknown. Here, we applied the logic of Kochs postulates using environmental fungal community gene sequencing, fungal isolation, and laboratory infection experiments to establish Trichoderma spp. as previously unrecognized pathogens of Trachymyrmex septentrionalis fungus gardens. Our environmental data showed that Trichoderma are the most abundant non-cultivar fungi in wild T. septentrionalis fungus gardens. We further determined that metabolites produced by Trichoderma induce an ant weeding response that mirrors their response to live Trichoderma. Combining ant behavioral experiments with bioactivity-guided fractionation and statistical prioritization of metabolites in Trichoderma extracts demonstrated that T. septentrionalis ants weed in response to peptaibols, a specific class of secondary metabolites known to be produced by Trichoderma fungi. Similar assays conducted using purified peptaibols, including the two new peptaibols trichokindins VIII and IX, suggested that weeding is likely induced by peptaibols as a class rather than by a single peptaibol metabolite. In addition to their presence in laboratory experiments, we detected peptaibols in wild fungus gardens. Our combination of environmental data and laboratory infection experiments strongly support that peptaibols act as chemical cues of Trichoderma pathogenesis in T. septentrionalis fungus gardens. Significance StatementAn extended defense response may exist in any relationship where one partner benefits from defending a mutualistic partner. Such a response is observed in the fungus-growing ant symbiosis, where ants must identify and remove pathogens of their symbiotic fungus gardens. Here we describe the fungal pathogen Trichoderma and its associated metabolites, which induce Trachymyrmex septentrionalis ant weeding behavior. Ants removed fungus garden pieces inoculated with Trichoderma spores or peptaibol-rich Trichoderma extracts, and peptaibols as a class cued ant defensive behavior, allowing T. septentrionalis to differentiate healthy from diseased fungus gardens. Extended defense responses mediated by chemical cues may be underappreciated mechanisms that structure symbiotic interactions.

microbiology↗

bioRxiv2022

MS2Query: Reliable and Scalable MS2 Mass Spectral-based Analogue Search

Metabolomics-driven discoveries of biological samples remain hampered by the grand challenge of metabolite annotation and identification. Only few metabolites have an annotated spectrum in spectral libraries; hence, searching only for exact library matches generally returns a few hits. An attractive alternative is searching for so-called analogues as a starting point for structural annotations; analogues are library molecules which are not exact matches, but display a high chemical similarity. However, current analogue search implementations are not yet very reliable and relatively slow. Here, we present MS2Query, a machine learning-based tool that integrates mass spectral embedding-based chemical similarity predictors (Spec2Vec and MS2Deepscore) as well as detected precursor masses to rank potential analogues and exact matches. Benchmarking MS2Query on reference mass spectra and experimental case studies demonstrates an improved reliability and scalability. Thereby, MS2Query offers exciting opportunities for further increasing the annotation rate of complex metabolite mixtures and for discovering new biology.

bioinformatics↗

bioRxiv2022

Homologue Series Detection and Management in LC-MS data with homologueDiscoverer

SummaryUntargeted metabolomics data analysis is highly labor intensive and can be severely frustrated by both experimental noise and redundant features. Homologous polymer series are a particular case of features that can either represent large numbers of noise features, or alternatively represent features of interest with large peak redundancy. Here we present homologueDiscoverer, an R package which allows for the targeted and untargeted detection of homologue series as well as their evaluation and management using interactive plots and simple local database functionalities. AvailabilityhomologueDiscoverer is freely available at github https://github.com/kevinmildau/homologueDiscoverer. Contactkevin.mildau@univie.ac.at, christoph.bueschl@boku.ac.at, juergen.zanghellini@univie.ac.at

bioinformatics↗

bioRxiv2022

Comparison of Cosine, Modified Cosine, and Neutral Loss Based Spectral Alignment For Discovery of Structurally Related Molecules

Spectrum alignment of tandem mass spectrometry (MS/MS) data using the modified cosine similarity and subsequent visualization as molecular networks have been demonstrated to be a useful strategy to discover analogs of molecules from untargeted MS/MS-based metabolomics experiments. Recently, a neutral loss matching approach has been introduced as an alternative to MS/MS-based molecular networking, with an implied performance advantage in finding analogs that cannot be discovered using existing MS/MS spectrum alignment strategies. To comprehensively evaluate the scoring properties of neutral loss matching, the cosine similarity, and the modified cosine similarity, similarity measures of 955,228 peptide MS/MS spectrum pairs and 10 million small molecule MS/MS spectrum pairs were compared. This comparative analysis revealed that the modified cosine similarity outperformed neutral loss matching and the cosine similarity in all cases. The data further indicated that the performance of MS/MS spectrum alignment depends on the location and type of the modification, as well as the chemical compound class of fragmented molecules.

bioinformatics↗

bioRxiv2022

Open Access Repository-Scale Propagated Nearest Neighbor Suspect Spectral Library for Untargeted Metabolomics

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of public MS/MS spectra. Annotations were propagated based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimers brain phenotype. The nearest neighbor suspect spectral library is openly available through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

bioinformatics↗

bioRxiv2021

From mass spectral features to molecules in molecular networks: a novel workflow for untargeted metabolomics.

In the context of untargeted metabolomics, molecular networking is a popular and efficient tool which organizes and simplifies mass spectrometry fragmentation data (LC-MS/MS), by clustering ions based on a cosine similarity score. However, the nature of the ion species is rarely taken into account, causing redundancy as a single compound may be present in different forms throughout the network. Taking advantage of the presence of such redundant ions, we developed a new method named MolNotator. Using the different ion species produced by a molecule during ionization (adducts, dimers, trimers, in-source fragments), a predicted molecule node (or neutral node) is created by triangulation, and ultimately computing the associated molecules calculated mass. These neutral nodes provide researchers with several advantages. Firstly, each molecule is then represented in its ionization context, connected to all produced ions and indirectly to some coeluted compounds, thereby also highlighting unexpected widely present adduct species. Secondly, the predicted neutrals serve as anchors to merge the complementary positive and negative ionization modes into a single network. Lastly, the dereplication is improved by the use of all available ions connected to the neutral nodes, and the computed molecular masses can be used for exact mass dereplication. MolNotator is available as a Python library and was validated using the lichen database spectra acquired on an Orbitrap, computing neutral molecules for >90% of the 156 molecules in the dataset. By focusing on actual molecules instead of ions, MolNotator greatly facilitates the selection of molecules of interest. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/473622v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@730ecaorg.highwire.dtl.DTLVardef@1d012e1org.highwire.dtl.DTLVardef@187a1e5org.highwire.dtl.DTLVardef@195c53f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2021

Boosting annotation in nutrimetabolomics by Feature-Based Molecular Networking: analytical and computational strategies applied to human urine samples from an untargeted LC-MS/MS based bilberry-blueberry intervention study.

Urine represents a challenging metabolite mixture to decipher. Yet, it contains valuable information on dietary intake patterns as typically investigated using randomized, single-blinded, intervention studies. This research demonstrates how the use of Feature-Based Molecular Networking in combination with public spectral libraries, further expanded with an "In-house" library of metabolite spectra, improved the non-trivial annotation of metabolites occurring in human urine samples following bilberry and blueberry intake. Following this approach, 65 berry-related and human endogenous metabolites were annotated, increasing the annotation coverage by 72% compared to conventional annotation approaches. Furthermore, the structures of 15 additional metabolites were hypothesized by spectral analysis. Then, by leveraging the MzMine quantitative information, several molecular families of phase II (e.g., glucuronidated phenolics) and phase I (e.g., phenylpropionic acid and hydroxybenzoic acid molecular scaffolds) metabolism were identified by correlation analysis of postprandial kinetics, and the dietary impact of endogenous and exogenous metabolites following bilberry-blueberry intake was estimated.

bioinformatics↗

bioRxiv2021

A supervised fingerprint-based strategy to connect natural product mass spectrometry fragmentation data to their biosynthetic gene clusters

Microbial specialized metabolites are an important source of and inspiration for many pharmaceutical, biotechnological products and play key roles in ecological processes. However, most bioactivity-guided isolation and identification methods widely employed in metabolite discovery programs do not explore the full biosynthetic potential of an organism. Untargeted metabolomics using liquid chromatography coupled with tandem mass spectrometry is an efficient technique to access metabolites from fractions and even environmental crude extracts. Nevertheless, metabolomics is limited in predicting structures or bioactivities for cryptic metabolites. Linking the biosynthetic potential inferred from (meta)genomics to the specialized metabolome would accelerate drug discovery programs. Here, we present a k-nearest neighbor classifier to systematically connect mass spectrometry fragmentation spectra to their corresponding biosynthetic gene clusters (independent of their chemical compound class). Our pipeline offers an efficient method to link biosynthetic genes to known, analogous, or cryptic metabolites that they encode for, as detected via mass spectrometry from bacterial cultures or environmental microbiomes. Using paired data sets that include validated genes-mass spectral links from the Paired Omics Data Platform, we demonstrate this approach by automatically linking 18 previously known mass spectra to their corresponding previously experimentally validated biosynthetic genes (i.e., via NMR or genetic engineering). Finally, we demonstrated that this new approach is a substantial step towards making in silico (and even de novo) structure predictions for peptidic metabolites and a glycosylated terpene. Altogether, we conclude that NPOmix minimizes the need for culturing and facilitates specialized metabolite isolation and structure elucidation based on integrative omics mining. SignificanceThe pace of natural product discovery has remained relatively constant over the last two decades. At the same time, there is an urgent need to find new therapeutics to fight antibiotic-resistant bacteria, cancer, tropical parasites, pathogenic viruses, and other severe diseases. Here, we introduce a new machine learning algorithm that can efficiently connect metabolites to their biosynthetic genes. Our Natural Products Mixed Omics (NPOmix) tool provides access to genomic information for bioactivity, class, (partial) structure, and stereochemistry predictions to prioritize relevant metabolite products and facilitate their structural elucidation. Our approach can be applied to biosynthetic genes from bacteria (used in this study), fungi, algae, and plants where (meta)genomes are paired with corresponding mass fragmentation data.

bioinformatics↗

bioRxiv2021

MS2DeepScore - a novel deep learning similarity measure for mass fragmentation spectrum comparisons

Mass spectrometry data is one of the key sources of information in many workflows in medicine and across the life sciences. Mass fragmentation spectra are considered characteristic signatures of the chemical compound they originate from, yet the chemical structure itself usually cannot be easily deduced from the spectrum. Often, spectral similarity measures are used as a proxy for structural similarity but this approach is strongly limited by a generally poor correlation between both metrics. Here, we propose MS2DeepScore: a novel Siamese neural network to predict the structural similarity between two chemical structures solely based on their MS/MS fragmentation spectra. Using a cleaned dataset of >100,000 mass spectra of about 15,000 unique known compounds, MS2DeepScore learns to predict structural similarity scores for spectrum pairs with high accuracy. In addition, sampling different model varieties through Monte-Carlo Dropout is used to further improve the predictions and assess the models prediction uncertainty. On 3,600 spectra of 500 unseen compounds, MS2DeepScore is able to identify highly-reliable structural matches and predicts Tanimoto scores with a root mean squared error of about 0.15. The prediction uncertainty estimate can be used to select a subset of predictions with a root mean squared error of about 0.1. We demonstrate that MS2DeepScore outperforms classical spectral similarity measures in retrieving chemically related compound pairs from large mass spectral datasets, thereby illustrating its potential for spectral library matching. Finally, MS2DeepScore can also be used to create chemically meaningful mass spectral embeddings that could be used to cluster large numbers of spectra. Added to the recently introduced unsupervised Spec2Vec metric, we believe that machine learning-supported mass spectral similarity metrics have great potential for a range of metabolomics data processing pipelines.

bioinformatics↗

bioRxiv2020

BiG-MAP: an automated pipeline to profile metabolic gene cluster abundance and expression in microbiomes

Microbial gene clusters encoding the biosynthesis of primary and secondary metabolites play key roles in shaping microbial ecosystems and driving microbiome-associated phenotypes. Although effective approaches exist to evaluate the metabolic potential of such bacteria through identification of metabolic gene clusters in their genomes, no automated pipelines exist to profile the abundance and expression levels of such gene clusters in microbiome samples to generate hypotheses about their functional roles and to find associations with phenotypes of interest. Here, we describe BiG-MAP, a bioinformatic tool to profile abundance and expression levels of gene clusters across metagenomic and metatranscriptomic data and evaluate their differential abundance and expression between different conditions. To illustrate its usefulness, we analyzed 47 metagenomic samples from healthy and caries-associated human oral microbiome samples and identified 58 gene clusters, including unreported ones, that were significantly more abundant in either phenotype. Among them, we found the muc operon, a gene cluster known to be associated to tooth decay. Additionally, we found a putative reuterin biosynthetic gene cluster from a Streptococcus strain to be enriched but not exclusively found in healthy samples; metabolomic data from the same samples showed masses with fragmentation patterns consistent with (poly)acrolein, which is known to spontaneously form from the products of the reuterin pathway and has been previously shown to inhibit pathogenic Streptococcus mutans strains. Thus, we show how BiG-MAP can be used to generate new hypotheses on potential drivers of microbiome-associated phenotypes and prioritize the experimental characterization of relevant gene clusters that may mediate them. ImportanceMicrobes play an increasingly recognized role in determining host-associated phenotypes by producing small molecules that interact with other microorganisms or host cells. The production of these molecules is often encoded in syntenic genomic regions, also known as gene clusters. With the increasing numbers of (multi-)omics datasets that can help understanding complex ecosystems at a much deeper level, there is a need to create tools that can automate the process of analyzing these gene clusters across omics datasets. The current study presents a new software tool called BiG-MAP, which allows assessing gene cluster abundance and expression in microbiome samples using metagenomic and metatranscriptomic data. In this manuscript, we describe the tool and its functionalities, and how it has been validated using a mock community. Finally, using an oral microbiome dataset, we show how it can be used to generate hypotheses regarding the functional roles of gene clusters in mediating host phenotypes.

bioinformatics↗

bioRxiv2020

The ant fungus garden acts as an external digestive system

Most animals digest their food within their own bodies, but some do not. Many species of ants grow fungus gardens that pre-digest food as an essential step of the ants nutrient uptake. To better understand this digestion process, we generated a 3D molecular map of an Atta texana fungus garden, revealing chemical modifications mediated by the fungus garden as plant material passes through.

systems biology↗

bioRxiv2020

Rapid Development of Improved Data-dependent Acquisition Strategies.

Tandem mass spectrometry (LC-MS/MS) is widely used to identify unknown ions in untargeted metabolomics. Data Dependent Acquisition (DDA) chooses which ions to fragment based upon intensity observed in MS1 survey scans and typically only fragment a small subset of the ions present. Despite this inefficiency, relatively little work has addressed the development of new DDA methods, partly due to the high overhead associated with running the many extracts necessary to optimise approaches in busy MS facilities. In this work, we firstly provide theoretical results that show how much improvement is possible over current DDA strategies. We then describe an in silico framework for fast and cost efficient development of new DDA acquisition strategies using a previously developed Virtual Metabolomics Mass Spectrometer (ViMMS). Additional functionality is added to ViMMS to allow methods to be used both in simulation and on real samples via an instrument application programming interface (API). We demonstrate this framework through the development and optimisation of two new DDA methods which introduce new advanced ion prioritisation strategies. Upon application of the here developed methods to two complex metabolite mixtures, our results show that they are able to fragment more unique ions than standard DDA acquisition strategies.

bioinformatics↗

bioRxiv2020

BiG-SLiCE: A Highly Scalable Tool Maps the Diversity of 1.2 Million Biosynthetic Gene Clusters

BackgroundGenome mining for Biosynthetic Gene Clusters (BGCs) has become an integral part of natural product discovery. The >200,000 microbial genomes now publicly available hold information on abundant novel chemistry. One way to navigate this vast genomic diversity is through comparative analysis of homologous BGCs, which allows identification of cross-species patterns that can be matched to the presence of metabolites or biological activities. However, current tools suffer from a bottleneck caused by the expensive network-based approach used to group these BGCs into Gene Cluster Families (GCFs). ResultsHere, we introduce BiG-SLiCE, a tool designed to cluster massive numbers of BGCs. By representing them in Euclidean space, BiG-SLiCE can group BGCs into GCFs in a non-pairwise, near-linear fashion. We used BiG-SLiCE to analyze 1,225,071 BGCs collected from 209,206 publicly available microbial genomes and metagenome-assembled genomes (MAGs) within ten days on a typical 36-cores CPU server. We demonstrate the utility of such analyses by reconstructing a global map of secondary metabolic diversity across taxonomy to identify uncharted biosynthetic potential. BiG-SLiCE also provides a "query mode" that can efficiently place newly sequenced BGCs into previously computed GCFs, plus a powerful output visualization engine that facilitates user-friendly data exploration. ConclusionsBiG-SLiCE opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global, searchable interconnected network of BGCs. As more genomes get sequenced from understudied taxa, more information can be mined to highlight their potentially novel chemistry. BiG-SLiCE is available via https://github.com/medema-group/bigslice.

bioinformatics↗