Reproducible mass spectrometry data processing and compound annotation in MZmine 3.

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

Habitat conditions and not moss composition mediate microbial community structure in Swiss peatlands.

Peatlands, one of the oldest ecosystems, globally store significant amounts of carbon and freshwater. However, they are under severe threat from human activities, leading to changes in water, nutrient and temperature regimes in these delicate systems. Such shifts can trigger a substantial carbon flux into the atmosphere and diminish the water-holding capacity of peatlands. Microbes associated with moss in peatlands play a crucial role in providing these ecosystem services, which are at risk due to global change. Therefore, understanding the factors influencing microbial composition and function is vital. Our study focused on five peatlands along an altitudinal gradient in Switzerland, where we sampled moss on hummocks containing Sarracenia purpurea. Structural equation modelling revealed that habitat condition was the primary predictor of community structure and directly influenced other environmental variables. Interestingly, the microbial composition was not linked to the local moss species identity. Instead, microbial communities varied significantly between sites due to differences in acidity levels and nitrogen availability. This finding was also mirrored in a co-occurrence network analysis, which displayed a distinct distribution of indicator species for acidity and nitrogen availability. Therefore, peatland conservation should take into account the critical habitat characteristics of moss-associated microbial communities.

Comprehensive mass spectrometric metabolomic profiling of a chemically diverse collection of plants of the Celastraceae family.

Natural products exhibit interesting structural features and significant biological activities. The discovery of new bioactive molecules is a complex process that requires high-quality metabolite profiling data to properly target the isolation of compounds of interest and enable their complete structural characterization. The same metabolite profiling data can also be used to better understand chemotaxonomic links between species. This Data Descriptor details a dataset resulting from the untargeted liquid chromatography-mass spectrometry metabolite profiling of 76 natural extracts of the Celastraceae family. The spectral annotation results and related chemical and taxonomic metadata are shared, along with proposed examples of data reuse. This data can be further studied by researchers exploring the chemical diversity of natural products. This can serve as a reference sample set for deep metabolome investigation of this chemically rich plant family.

Shrinking Alpine chamois: higher spring temperatures over the last 27 years in Switzerland are linked to a 3 kg reduction in body mass of yearlings.

Although climate change is considered to be partly responsible for the size change observed in numerous species, the relevance of this hypothesis for ungulates remains debated. We used body mass measurements of 5635 yearlings (i.e. 1.5 years old) of Alpine chamois (Rupicapra rupicapra) harvested in September in the Swiss Alps (Ticino canton) from 1992 to 2018. In our study area, during this period, yearlings shrank by ca 3 kg while temperatures between May and July rose by 1.7°C. We identified that warmer temperatures during birth and the early suckling period (9 May to 2 July in the year of birth) had the strongest impact on yearling mass. Further analyses of year-detrended mass and temperature data indicate that this result was not simply due to changes in both variables over years, but that increases in temperature during this particularly sensitive time window for development and growth are responsible for the decrease in body mass of yearling chamois. Altogether, our results suggest that rising temperatures in the Alpine regions could significantly affect the ecology and evolution of this wild ungulate.

Functional metabolomics of the human scalp: a metabolic niche for Staphylococcus epidermidis.

Although metabolomics data acquisition and analysis technologies have become increasingly sophisticated over the past 5-10 years, deciphering a metabolite's function from a description of its structure and its abundance in a given experimental setting is still a major scientific and intellectual challenge. To point out ways to address this "data to knowledge" challenge, we developed a functional metabolomics strategy that combines state-of-the-art data analysis tools and applied it to a human scalp metabolomics data set: skin swabs from healthy volunteers with normal or oily scalp (Sebumeter score 60-120, n = 33; Sebumeter score > 120, n = 41) were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS), yielding four metabolomics data sets for reversed phase chromatography (C18) or hydrophilic interaction chromatography (HILIC) separation in electrospray ionization (ESI) + or - ionization mode. Following our data analysis strategy, we were able to obtain increasingly comprehensive structural and functional annotations, by applying the Global Natural Product Social Networking (M. Wang, J. J. Carver, V. V. Phelan, L. M. Sanchez, et al., Nat Biotechnol 34:828-837, 2016, https://doi.org/10.1038/nbt.3597), SIRIUS (K. Dührkop, M. Fleischauer, M. Ludwig, A. A. Aksenov, et al., Nat Methods 16:299-302, 2019, https://doi.org/10.1038/s41592-019-0344-8), and MicrobeMASST (S. ZuffaS, R. Schmid, A. Bauermeister, P. W, P. Gomes, et al., bioRxiv:rs.3.rs-3189768, 2023, https://doi.org/10.21203/rs.3.rs-3189768/v1) tools. We finally combined the metabolomics data with a corresponding metagenomic sequencing data set using MMvec (J. T. Morton, A. A. Aksenov, L. F. Nothias, J. R. Foulds, et. al., Nat Methods 16:1306-1314, 2019, https://doi.org/10.1038/s41592-019-0616-3), gaining insights into the metabolic niche of one of the most prominent microbes on the human skin, Staphylococcus epidermidis.IMPORTANCESystems biology research on host-associated microbiota focuses on two fundamental questions: which microbes are present and how do they interact with each other, their host, and the broader host environment? Metagenomics provides us with a direct answer to the first part of the question: it unveils the microbial inhabitants, e.g., on our skin, and can provide insight into their functional potential. Yet, it falls short in revealing their active role. Metabolomics shows us the chemical composition of the environment in which microbes thrive and the transformation products they produce. In particular, untargeted metabolomics has the potential to observe a diverse set of metabolites and is thus an ideal complement to metagenomics. However, this potential often remains underexplored due to the low annotation rates in MS-based metabolomics and the necessity for multiple experimental chromatographic and mass spectrometric conditions. Beyond detection, prospecting metabolites' functional role in the host/microbiome metabolome requires identifying the biological processes and entities involved in their production and biotransformations. In the present study of the human scalp, we developed a strategy to achieve comprehensive structural and functional annotation of the metabolites in the human scalp environment, thus diving one step deeper into the interpretation of "omics" data. Leveraging a collection of openly accessible software tools and integrating microbiome data as a source of functional metabolite annotations, we finally identified the specific metabolic niche of Staphylococcus epidermidis, one of the key players of the human skin microbiome.

Aspirin exposure and its association with metal stent patency in malignant distal biliary obstruction: a large international multicenter propensity score-matched study.

BACKGROUND AND AIMS: Stent dysfunction is common after ERCP with self-expandable metal stent (SEMS) insertion for malignant distal biliary obstruction (MDBO). Chronic aspirin (acetylsalicylic acid; ASA) exposure has been previously shown to potentially decrease this risk. We aim to further ascertain the protective effect of ASA and to identify other predictors of stent dysfunction. METHODS: This multicenter retrospective cohort study was conducted at 9 sites in Canada and 1 in the United States. Patients with MDBO who underwent ERCP with SEMS placement between January 2014 and December 2019 were included and divided into 2 cohorts: ASA exposed (ASA-E) and ASA unexposed (ASA-U). Propensity-score matching (PSM) was performed to limit selection bias. Matched variables were age, sex, tumor stage, and type of metal stent. The primary outcome was the hazard rate of stent dysfunction. A multivariable Cox proportional hazards model was used to identify independent predictors of stent dysfunction. RESULTS: Of 1396 patients assessed, after PSM 496 patients were analyzed (248 ASA-E and 248 ASA-U). ERCP with SEMS placement was associated with a high clinical success of 82.2% in ASA-E and 81.2% in ASA-U cohorts (P = .80). One hundred eighty-four patients had stent dysfunction with a mean stent patency time of 229.9 ± 306.2 days and 245.4 ± 241.4 days in ASA-E and ASA-U groups, respectively (P = .52). On multivariable analysis, ASA exposure did not protect against stent dysfunction (hazard ratio [HR], 1.25; 95% confidence interval [CI], .96-1.63). An etiology of pancreatic cancer (HR, 1.36; 95% CI, 1.15-1.61) predicted stent dysfunction, whereas cancer therapy was protective (HR, .73; 95% CI, .55-.96). Chronic ASA use was not associated with an increased risk for adverse events including bleeding, post-ERCP pancreatitis, and perforation. CONCLUSIONS: In this large, multicenter study using PSM, chronic exposure to ASA did not protect against stent dysfunction in MDBO. Instead, the analysis revealed that the etiology of pancreatic cancer was an independent predictor of stent dysfunction and cancer therapy was protective.

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 MS/MS spectra originating from published untargeted metabolomics experiments. Entries in this library, or "suspects," were derived from unannotated spectra that could be linked in a molecular network to an annotated spectrum. Annotations were propagated to unknowns 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 Alzheimer's brain phenotype. The nearest neighbor suspect spectral library is openly available for download or for data analysis through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

DeepSAT: Learning Molecular Structures from Nuclear Magnetic Resonance Data.

The identification of molecular structure is essential for understanding chemical diversity and for developing drug leads from small molecules. Nevertheless, the structure elucidation of small molecules by Nuclear Magnetic Resonance (NMR) experiments is often a long and non-trivial process that relies on years of training. To achieve this process efficiently, several spectral databases have been established to retrieve reference NMR spectra. However, the number of reference NMR spectra available is limited and has mostly facilitated annotation of commercially available derivatives. Here, we introduce DeepSAT, a neural network-based structure annotation and scaffold prediction system that directly extracts the chemical features associated with molecular structures from their NMR spectra. Using only the 1H-13C HSQC spectrum, DeepSAT identifies related known compounds and thus efficiently assists in the identification of molecular structures. DeepSAT is expected to accelerate chemical and biomedical research by accelerating the identification of molecular structures.

Theoretical investigation of the dependence of population parameters on initial population size.

In a series of experiments with yeast, classical dynamical models were fitted to populations that differed only in their initial population size (Pylvänäinen 2005). The results revealed a surprising dependence between estimated growth rate and initial population size. Perceived as an artefact, this undesired relationship was tentatively removed by an ad-hoc procedure. This strategy reflects the usual approach of population models in which parameters are not considered to depend on initial conditions. However, our analysis reveals that the observed relationship between estimated growth rate and initial population size is unavoidable when the dimension of a system is reduced. For the present case, the two-dimensional food-yeast system was reduced to a model for yeast only. The consequence of system reduction questions our conception of one-dimensional population models.

Online Hemodiafiltration Compared to Conventional Hemodialysis in Critically Ill Patients.

Introduction: Online hemodiafiltration (HDF) has been increasingly used for improved clearance of middle molecular weight toxins. The impact of this mode of clearance is unknown in critically ill patients. We aimed to determine whether the use of HDF in acute kidney injury (AKI) is associated with lower mortality and improved kidney recovery up to 90 days after initiation of therapy. Methods: Single-center retrospective cohort study using data from 2017 to 2020 of adults with AKI who initiated intermittent renal replacement therapy (IRRT) in the intensive care unit (ICU), using either hemodialysis (HD) or HDF depending on the maintenance status of the water system without regards for patient characteristics. We assessed association with patient-events and session-events using time-dependent Cox models and general estimating equations models, respectively. Results: We included 182 adults with AKI for whom 848 IRRT sessions were performed in the ICU. The 90-day mortality rate was 43 of 182 (24.6%). There was no significant association with the use of HDF and mortality (adjusted hazard ratio [aHR]: 0.85 (0.43; 1.67) P = 0.64), kidney recovery (aHR: 1.18 (0.76; 1.84) P = 0.47), or intradialytic hypotension (adjusted odds ratio [aOR]: 0.91 confidence interval [CI]: 0.64-1.28 P = 0.58). HDF treatment was associated with a lower rate of subsequent vasopressor use (aOR: 0.60 CI: 0.36-0.99 P = 0.047) and a greater reduction of the neutrophil-to-lymphocyte ratio (NLR) following the first session (-15.0% vs. +5.1%, P = 0.047) but was also associated with increased risk of filter thrombosis during treatment (aOR: 2.42 CI: 1.67-3.50 P < 0.001). Conclusion: The use of HDF in the setting of AKI was not associated with a differential risk of mortality or kidney recovery.

Standardized multi-omics of Earth's microbiomes reveals microbial and metabolite diversity.

Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth's environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from diverse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment.

Inventa: A computational tool to discover structural novelty in natural extracts libraries.

Collections of natural extracts hold potential for the discovery of novel natural products with original modes of action. The prioritization of extracts from collections remains challenging due to the lack of a workflow that combines multiple-source information to facilitate the data interpretation. Results from different analytical techniques and literature reports need to be organized, processed, and interpreted to enable optimal decision-making for extracts prioritization. Here, we introduce Inventa, a computational tool that highlights the structural novelty potential within extracts, considering untargeted mass spectrometry data, spectral annotation, and literature reports. Based on this information, Inventa calculates multiple scores that inform their structural potential. Thus, Inventa has the potential to accelerate new natural products discovery. Inventa was applied to a set of plants from the Celastraceae family as a proof of concept. The Pristimera indica (Willd.) A.C.Sm roots extract was highlighted as a promising source of potentially novel compounds. Its phytochemical investigation resulted in the isolation and de novo characterization of thirteen new dihydro-ß-agarofuran sesquiterpenes, five of them presenting a new 9-oxodihydro-ß-agarofuran base scaffold.

Illuminating the dark metabolome of Pseudo-nitzschia-microbiome associations.

The exchange of metabolites mediates algal and bacterial interactions that maintain ecosystem function. Yet, while thousands of metabolites are produced, only a few molecules have been identified in these associations. Using the ubiquitous microalgae Pseudo-nitzschia sp., as a model, we employed an untargeted metabolomics strategy to assign structural characteristics to the metabolites that distinguished specific diatom-microbiome associations. We cultured five species of Pseudo-nitzschia, including two species that produced the toxin domoic acid, and examined their microbiomes and metabolomes. A total of 4826 molecular features were detected by tandem mass spectrometry. Only 229 of these could be annotated using available mass spectral libraries, but by applying new in silico annotation tools, characterization was expanded to 2710 features. The metabolomes of the Pseudo-nitzschia-microbiome associations were distinct and distinguished by structurally diverse nitrogen compounds, ranging from simple amines and amides to cyclic compounds such as imidazoles, pyrrolidines and lactams. By illuminating the dark metabolomes, this study expands our capacity to discover new chemical targets that facilitate microbial partnerships and uncovers the chemical diversity that underpins algae-bacteria interactions.

MEMO: Mass Spectrometry-Based Sample Vectorization to Explore Chemodiverse Datasets.

In natural products research, chemodiverse extracts coming from multiple organisms are explored for novel bioactive molecules, sometimes over extended periods. Samples are usually analyzed by liquid chromatography coupled with fragmentation mass spectrometry to acquire informative mass spectral ensembles. Such data is then exploited to establish relationships among analytes or samples (e.g., via molecular networking) and annotate metabolites. However, the comparison of samples profiled in different batches is challenging with current metabolomics methods since the experimental variation-changes in chromatographical or mass spectrometric conditions - hinders the direct comparison of the profiled samples. Here we introduce MEMO-MS2 BasEd SaMple VectOrization-a method allowing to cluster large amounts of chemodiverse samples based on their LC-MS/MS profiles in a retention time agnostic manner. This method is particularly suited for heterogeneous and chemodiverse sample sets. MEMO demonstrated similar clustering performance as state-of-the-art metrics considering fragmentation spectra. More importantly, such performance was achieved without the requirement of a prior feature alignment step and in a significantly shorter computational time. MEMO thus allows the comparison of vast ensembles of samples, even when analyzed over long periods of time, and on different chromatographic or mass spectrometry platforms. This new addition to the computational metabolomics toolbox should drastically expand the scope of large-scale comparative analysis.

A flavin-dependent monooxygenase produces nitrogenous tomato aroma volatiles using cysteine as a nitrogen source.

Tomato (Solanum lycopersicum) produces a wide range of volatile chemicals during fruit ripening, generating a distinct aroma and contributing to the overall flavor. Among these volatiles are several aromatic and aliphatic nitrogen-containing compounds for which the biosynthetic pathways are not known. While nitrogenous volatiles are abundant in tomato fruit, their content in fruits of the closely related species of the tomato clade is highly variable. For example, the green-fruited species Solanum pennellii are nearly devoid, while the red-fruited species S. lycopersicum and Solanum pimpinellifolium accumulate high amounts. Using an introgression population derived from S. pennellii, we identified a locus essential for the production of all the detectable nitrogenous volatiles in tomato fruit. Silencing of the underlying gene (SlTNH1;Solyc12g013690) in transgenic plants abolished production of aliphatic and aromatic nitrogenous volatiles in ripe fruit, and metabolomic analysis of these fruit revealed the accumulation of 2-isobutyl-tetrahydrothiazolidine-4-carboxylic acid, a known conjugate of cysteine and 3-methylbutanal. Biosynthetic incorporation of stable isotope-labeled precursors into 2-isobutylthiazole and 2-phenylacetonitrile confirmed that cysteine provides the nitrogen atom for all nitrogenous volatiles in tomato fruit. Nicotiana benthamiana plants expressing SlTNH1 readily transformed synthetic 2-substituted tetrahydrothiazolidine-4-carboxylic acid substrates into a mixture of the corresponding 2-substituted oxime, nitro, and nitrile volatiles. Distinct from other known flavin-dependent monooxygenase enzymes in plants, this tetrahydrothiazolidine-4-carboxylic acid N-hydroxylase catalyzes sequential hydroxylations. Elucidation of this pathway is a major step forward in understanding and ultimately improving tomato flavor quality.

Distinguishing the molecular diversity, nutrient content, and energetic potential of exometabolomes produced by macroalgae and reef-building corals.

Metabolites exuded by primary producers comprise a significant fraction of marine dissolved organic matter, a poorly characterized, heterogenous mixture that dictates microbial metabolism and biogeochemical cycling. We present a foundational untargeted molecular analysis of exudates released by coral reef primary producers using liquid chromatography-tandem mass spectrometry to examine compounds produced by two coral species and three types of algae (macroalgae, turfing microalgae, and crustose coralline algae [CCA]) from Mo'orea, French Polynesia. Of 10,568 distinct ion features recovered from reef and mesocosm waters, 1,667 were exuded by producers; the majority (86%) were organism specific, reflecting a clear divide between coral and algal exometabolomes. These data allowed us to examine two tenets of coral reef ecology at the molecular level. First, stoichiometric analyses show a significantly reduced nominal carbon oxidation state of algal exometabolites than coral exometabolites, illustrating one ecological mechanism by which algal phase shifts engender fundamental changes in the biogeochemistry of reef biomes. Second, coral and algal exometabolomes were differentially enriched in organic macronutrients, revealing a mechanism for reef nutrient-recycling. Coral exometabolomes were enriched in diverse sources of nitrogen and phosphorus, including tyrosine derivatives, oleoyl-taurines, and acyl carnitines. Exometabolites of CCA and turf algae were significantly enriched in nitrogen with distinct signals from polyketide macrolactams and alkaloids, respectively. Macroalgal exometabolomes were dominated by nonnitrogenous compounds, including diverse prenol lipids and steroids. This study provides molecular-level insights into biogeochemical cycling on coral reefs and illustrates how changing benthic cover on reefs influences reef water chemistry with implications for microbial metabolism.

Comparative metabolomic analysis reveals shared and unique chemical interactions in sponge holobionts.

BACKGROUND: Sponges are ancient sessile metazoans, which form with their associated microbial symbionts a complex functional unit called a holobiont. Sponges are a rich source of chemical diversity; however, there is limited knowledge of which holobiont members produce certain metabolites and how they may contribute to chemical interactions. To address this issue, we applied non-targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) and gas chromatography mass spectrometry (GC-MS) to either whole sponge tissue or fractionated microbial cells from six different, co-occurring sponge species. RESULTS: Several metabolites were commonly found or enriched in whole sponge tissue, supporting the notion that sponge cells produce them. These include 2-methylbutyryl-carnitine, hexanoyl-carnitine and various carbohydrates, which may be potential food sources for microorganisms, as well as the antagonistic compounds hymenialdisine and eicosatrienoic acid methyl ester. Metabolites that were mostly observed or enriched in microbial cells include the antioxidant didodecyl 3,3'-thiodipropionate, the antagonistic compounds docosatetraenoic acid, and immune-suppressor phenylethylamide. This suggests that these compounds are mainly produced by the microbial members in the sponge holobiont, and are potentially either involved in inter-microbial competitions or in defenses against intruding organisms. CONCLUSIONS: This study shows how different chemical functionality is compartmentalized between sponge hosts and their microbial symbionts and provides new insights into how chemical interactions underpin the function of sponge holobionts. Video abstract.

Tandem Mass Spectrometry Molecular Networking as a Powerful and Efficient Tool for Drug Metabolism Studies.

Molecular networking (MN) has become a popular data analysis method for untargeted mass spectrometry (MS)/MS-based metabolomics. Recently, MN has been suggested as a powerful tool for drug metabolite identification, but its effectiveness for drug metabolism studies has not yet been benchmarked against existing strategies. In this study, we compared the performance of MN, mass defect filtering, Agilent MassHunter Metabolite ID, and Agilent Mass Profiler Professional workflows to annotate metabolites of sildenafil generated in an in vitro liver microsome-based metabolism study. Totally, 28 previously known metabolites with 15 additional unknown isomers and 25 unknown metabolites were found in this study. The comparison demonstrated that MN exhibited performances comparable or superior to those of the existing tools in terms of the number of detected metabolites (27 known metabolites and 22 unknown metabolites), ratio of false positives, and the amount of time and effort required for human labor-based postprocessing, which provided evidence of the efficiency of MN as a drug metabolite identification tool.

Metabolomics in Ecology and Bioactive Natural Products Discovery: Challenges and Prospects for a Comprehensive Study of the Specialised Metabolome.

Metabolomics is playing an increasingly prominent role in chemical ecology and in the discovery of bioactive natural products (NPs). The identification of metabolites is a common/central objective in both research fields. NPs have significant biological properties and play roles in multiple chemical-ecological interactions. Classically, in pharmacognosy, their chemical structure is determined after a complex process of isolating and interpreting spectroscopic data. With the advent of powerful analytical techniques such as liquid chromatography-mass spectrometry (LC-MS) the annotation process of the specialised metabolome of plants and microorganisms has improved considerably. In this article, we summarise the possibilities opened by these advances and illustrate how we harnessed them in our own research to automate annotations of NPs and target the isolation of key compounds. In addition, we are also discussing the analytical and computational challenges associated with these emerging approaches and their perspective.