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.

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.

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.

ReDU: a framework to find and reanalyze public mass spectrometry data.

We present ReDU ( https://redu.ucsd.edu/ ), a system for metadata capture of public mass spectrometry-based metabolomics data, with validated controlled vocabularies. Systematic capture of knowledge enables the reanalysis of public data and/or co-analysis of one's own data. ReDU enables multiple types of analyses, including finding chemicals and associated metadata, comparing the shared and different chemicals between groups of samples, and metadata-filtered, repository-scale molecular networking.

Feature-based molecular networking in the GNPS analysis environment.

Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry.

Chemically informed analyses of metabolomics mass spectrometry data with Qemistree.

Untargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on the hierarchical organization of molecular fingerprints predicted from fragmentation spectra. Qemistree allows mass spectrometry data to be represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools that are designed to analyze and visualize the relatedness of DNA sequences to metabolomics data. Here we demonstrate the use of tree-guided data exploration tools to compare metabolomics samples across different experimental conditions such as chromatographic shifts. Additionally, we leverage a tree representation to visualize chemical diversity in a heterogeneous collection of samples. The Qemistree software pipeline is freely available to the microbiome and metabolomics communities in the form of a QIIME2 plugin, and a global natural products social molecular networking workflow.

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.

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.

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.

Learning representations of microbe-metabolite interactions.

Integrating multiomics datasets is critical for microbiome research; however, inferring interactions across omics datasets has multiple statistical challenges. We solve this problem by using neural networks (https://github.com/biocore/mmvec) to estimate the conditional probability that each molecule is present given the presence of a specific microorganism. We show with known environmental (desert soil biocrust wetting) and clinical (cystic fibrosis lung) examples, our ability to recover microbe-metabolite relationships, and demonstrate how the method can discover relationships between microbially produced metabolites and inflammatory bowel disease.

MS2Planner: improved fragmentation spectra coverage in untargeted mass spectrometry by iterative optimized data acquisition.

MOTIVATION: Untargeted mass spectrometry experiments enable the profiling of metabolites in complex biological samples. The collected fragmentation spectra are the metabolite's fingerprints that are used for molecule identification and discovery. Two main mass spectrometry strategies exist for the collection of fragmentation spectra: data-dependent acquisition (DDA) and data-independent acquisition (DIA). In the DIA strategy, all the metabolites ions in predefined mass-to-charge ratio ranges are co-isolated and co-fragmented, resulting in multiplexed fragmentation spectra that are challenging to annotate. In contrast, in the DDA strategy, fragmentation spectra are dynamically and specifically collected for the most abundant ions observed, causing redundancy and sub-optimal fragmentation spectra collection. Yet, DDA results in less multiplexed fragmentation spectra that can be readily annotated. RESULTS: We introduce the MS2Planner workflow, an Iterative Optimized Data Acquisition strategy that optimizes the number of high-quality fragmentation spectra over multiple experimental acquisitions using topological sorting. Our results showed that MS2Planner increases the annotation rate by 38.6% and is 62.5% more sensitive and 9.4% more specific compared to DDA. AVAILABILITY AND IMPLEMENTATION: MS2Planner code is available at https://github.com/mohimanilab/MS2Planner. The generation of the inclusion list from MS2Planner was performed with python scripts available at https://github.com/lfnothias/IODA_MS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products.

Computational approaches such as genome and metabolome mining are becoming essential to natural products (NPs) research. Consequently, a need exists for an automated structure-type classification system to handle the massive amounts of data appearing for NP structures. An ideal semantic ontology for the classification of NPs should go beyond the simple presence/absence of chemical substructures, but also include the taxonomy of the producing organism, the nature of the biosynthetic pathway, and/or their biological properties. Thus, a holistic and automatic NP classification framework could have considerable value to comprehensively navigate the relatedness of NPs, and especially so when analyzing large numbers of NPs. Here, we introduce NPClassifier, a deep-learning tool for the automated structural classification of NPs from their counted Morgan fingerprints. NPClassifier is expected to accelerate and enhance NP discovery by linking NP structures to their underlying properties.

An evaluation of the representativeness of a national spinal cord injury registry: a population-based cohort study.

STUDY DESIGN: Population-based cohort study for the western part of Quebec. OBJECTIVES: To determine the impact of declining to participate in a national spinal cord injury (SCI) registry on patient outcomes and continuum of care. SETTING: Level-1 trauma center specialized in SCI care in Montreal, Canada. METHODS: This cohort study compared the outcomes of 444 patients who were enrolled in the Rick Hansen SCI registry and 140 patients who refused. Logistic regression analyses were performed to assess the association between voluntary participation and the outcomes, while adjusting for confounding factors. The main outcomes were: attendance to follow-up 6- to 12-month post injury, 1-year mortality, and the occurrence of pressure injury during acute care. RESULTS: Declining to be enrolled in the registry was a significant predictor of lower attendance to specialized follow-up (adjusted odds ratio [OR] 0.04, 95% confidence interval [CI] 0.02-0.08). It was also associated with a higher 1-year mortality rate (OR 12.50, CI 4.50-33.30) and higher occurrence of pressure injury (OR 2.56, CI 1.56-4.17). CONCLUSIONS: This study sheds invaluable insight on individuals that researchers and clinicians are usually blind to in SCI cohort studies. This study suggests that decline to participate in a registry during the care hospitalization may be associated with worsened health, poorer outcomes, and reduced follow-up to specialized care. Declining the enrollment to voluntary registry could represent a potential prognostic factor for future research.

A Convolutional Neural Network-Based Approach for the Rapid Annotation of Molecularly Diverse Natural Products.

This report describes the first application of the novel NMR-based machine learning tool "Small Molecule Accurate Recognition Technology" (SMART 2.0) for mixture analysis and subsequent accelerated discovery and characterization of new natural products. The concept was applied to the extract of a filamentous marine cyanobacterium known to be a prolific producer of cytotoxic natural products. This environmental Symploca extract was roughly fractionated, and then prioritized and guided by cancer cell cytotoxicity, NMR-based SMART 2.0, and MS2-based molecular networking. This led to the isolation and rapid identification of a new chimeric swinholide-like macrolide, symplocolide A, as well as the annotation of swinholide A, samholides A-I, and several new derivatives. The planar structure of symplocolide A was confirmed to be a structural hybrid between swinholide A and luminaolide B by 1D/2D NMR and LC-MS2 analysis. A second example applies SMART 2.0 to the characterization of structurally novel cyclic peptides, and compares this approach to the recently appearing "atomic sort" method. This study exemplifies the revolutionary potential of combined traditional and deep learning-assisted analytical approaches to overcome longstanding challenges in natural products drug discovery.

Increased Temperature Disrupts the Biodiversity-Ecosystem Functioning Relationship.

Gaining knowledge of how ecosystems provide essential services to humans is of primary importance, especially with the current threat of climate change. Yet little is known about how increased temperature will impact the biodiversity-ecosystem functioning (BEF) relationship. We tackled this subject theoretically and experimentally. We developed a BEF theory based on mechanistic population dynamic models, which allows the inclusion of the effect of temperature. Using experimentally established relationships between attack rate and temperature, the model predicts that temperature increase will intensify competition, and consequently the BEF relationship will flatten or even become negative. We conducted a laboratory experiment with natural microbial microcosms, and the results were in agreement with the model predictions. The experimental results also revealed that an increase in both temperature average and variation had a more intense effect than an increase in temperature average alone. Our results indicate that under climate change, high diversity may not guarantee high ecosystem functioning.

Dereplication of peptidic natural products through database search of mass spectra.

Peptidic natural products (PNPs) are widely used compounds that include many antibiotics and a variety of other bioactive peptides. Although recent breakthroughs in PNP discovery raised the challenge of developing new algorithms for their analysis, identification of PNPs via database search of tandem mass spectra remains an open problem. To address this problem, natural product researchers use dereplication strategies that identify known PNPs and lead to the discovery of new ones, even in cases when the reference spectra are not present in existing spectral libraries. DEREPLICATOR is a new dereplication algorithm that enables high-throughput PNP identification and that is compatible with large-scale mass-spectrometry-based screening platforms for natural product discovery. After searching nearly one hundred million tandem mass spectra in the Global Natural Products Social (GNPS) molecular networking infrastructure, DEREPLICATOR identified an order of magnitude more PNPs (and their new variants) than any previous dereplication efforts.

The feasibility of equilibria in large ecosystems: A primary but neglected concept in the complexity-stability debate.

The consensus that complexity begets stability in ecosystems was challenged in the seventies, a result recently extended to ecologically-inspired networks. The approaches assume the existence of a feasible equilibrium, i.e. with positive abundances. However, this key assumption has not been tested. We provide analytical results complemented by simulations which show that equilibrium feasibility vanishes in species rich systems. This result leaves us in the uncomfortable situation in which the existence of a feasible equilibrium assumed in local stability criteria is far from granted. We extend our analyses by changing interaction structure and intensity, and find that feasibility and stability is warranted irrespective of species richness with weak interactions. Interestingly, we find that the dynamical behaviour of ecologically inspired architectures is very different and richer than that of unstructured systems. Our results suggest that a general understanding of ecosystem dynamics requires focusing on the interplay between interaction strength and network architecture.