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• The molecular networking and in silico annotation methods were applied for the variables annotation in Zanthoxlyum samples. • Two in silico annotation tools, NAP and ClassyFire, helped the global classification of metabolites. • Hesperidin and poncirin were tentatively annotated as the discriminant markers between Zanthoxlyum samples. • Z. bungeanum and Z. piperitum were more closely related than Z. schinifolium. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, data matrices with up to thousands of variables for each ion peak are subjected to multivariate analysis (MVA) to assess the homogeneity between samples. The large dimensions of LC/MS datasets hinder the identification of the discriminant or the metabolic markers. In the present study, the molecular network (MN) approach and two in silico annotation tools, network annotation propagation (NAP) and the hierarchical chemical classification method, ClassyFire, were used to annotate the metabolites of three Zanthoxylum species, Z. bungeanum , Z. schinifolium and Z. piperitum. The in silico annotation results of the MN nodes and the MVA variables were combined and visualized in loading plots. This approach helped intuitive detection of the variables that greatly contributed to the separation of the samples in the score plot as discriminant or metabolic markers, thereby allowing rapid annotation of two flavanone derivatives. [ABSTRACT FROM AUTHOR]

Polymers are a common component of chemical background which complicates data analysis and can impair interpretation. Undesired chemical background cannot always be addressed via pre-analytical methods, chromatography, or existing data processing methods. The Kendrick mass filter (KMF) is presented for the computational removal of undesired signals present in MS1 spectra. The KMF is analogous to mass defect filtering but utilizes homology information via Kendrick mass scaling in combination with chromatographic retention time and the number of observed signals. The KMF is intended to assist in situations in which current data processing methods to remove background, e.g., blank subtraction, are either not possible or effective. The major parameters affecting KMF were investigated using PEG 400 and NIST standard reference material 1950 (metabolites in human plasma). Further exploration of the KMF performance was tested using an extract of a swab known to contain polymers. An illustrative real-world example of skin analysis with polymeric signal is discussed. The KMF is also able to provide a high-level view of the compositionality of data regarding the presence of signals with repeat units and indicate the presence of different polymers.ᅟ [ABSTRACT FROM AUTHOR]

Increasing appreciation of the gut microbiome's role in health motivates understanding the molecular composition of human feces. To analyze such complex samples, we developed a platform coupling targeted and untargeted metabolomics. The approach is facilitated through split flow from one UPLC, joint timing triggered by contact closure relays, and a script to retrieve the data. It is designed to detect specific metabolites of interest with high sensitivity, allows for correction of targeted information, enables better quantitation thus providing an advanced analytical tool for exploratory studies. Procrustes analysis revealed that untargeted approach provides a better correlation to microbiome data, associating specific metabolites with microbes that produce or process them. With the subset of over one hundred human fecal samples from the American Gut project, the implementation of the described coupled workflow revealed that targeted analysis using combination of single transition per compound with retention time misidentifies 30% of the targeted data and could lead to incorrect interpretations. At the same time, the targeted analysis extends detection limits and dynamic range, depending on the compounds, by orders of magnitude. A software application has been developed as a part of the workflow to allows for quantitative assessments based on calibration curves. Using this approach, we detect expected microbially modified molecules such as secondary bile acids and unexpected microbial molecules including Pseudomonas-associated quinolones and rhamnolipids in feces, setting the stage for metabolome-microbiome-wide association studies (MMWAS). [ABSTRACT FROM AUTHOR]

In response to the escalating demand for sustainable agricultural methodologies, the utilization of microbial volatile organic compounds (VOCs) as antagonists against phytopathogens has emerged as a viable eco-friendly alternative. Microbial volatiles exhibit rapid diffusion rates, facilitating prompt chemical interactions. Moreover, microorganisms possess the capacity to emit volatiles constitutively, as well as in response to biological interactions and environmental stimuli. In addition to volatile compounds, these bacteria demonstrate the ability to produce soluble metabolites with antifungal properties, such as APE Vf, pyoverdin, and fragin. In this study, we identified two Pseudomonas strains (BJa3 and MCal1) capable of inhibiting the in vitro mycelial growth of the phytopathogenic fungus Aspergillus flavus, which serves as the causal agent of diseases in sugarcane and maize. Utilizing GC/MS analysis, we detected 47 distinct VOCs which were produced by these bacterial strains. Notably, certain volatile compounds, including 1-heptoxydecane and tridecan-2-one, emerged as primary candidates for inhibiting fungal growth. These compounds belong to essential chemical classes previously documented for their antifungal activity, while others represent novel molecules. Furthermore, examination via confocal microscopy unveiled significant morphological alterations, particularly in the cell wall, of mycelia exposed to VOCs emitted by both Pseudomonas species. These findings underscore the potential of the identified BJa3 and MCal1 Pseudomonas strains as promising agents for fungal biocontrol in agricultural crops. [ABSTRACT FROM AUTHOR]

The article discusses research that developed the computational tool, compound structure identification (CSI):FingerID which is designed to help in the annotation of chemistries that can be observed by mass spectrometry. Topics discussed include the phases of CSI-FingerID including the learning, Support Vector Machine classifier and Scoring and availability of the tool as a web server for wet laboratory scientists.

Summary: We present ProbMetab, an R package that promotes substantial improvement in automatic probabilistic liquid chromatography–mass spectrometry-based metabolome annotation. The inference engine core is based on a Bayesian model implemented to (i) allow diverse source of experimental data and metadata to be systematically incorporated into the model with alternative ways to calculate the likelihood function and (ii) allow sensitive selection of biologically meaningful biochemical reaction databases as Dirichlet-categorical prior distribution. Additionally, to ensure result interpretation by system biologists, we display the annotation in a network where observed mass peaks are connected if their candidate metabolites are substrate/product of known biochemical reactions. This graph can be overlaid with other graph-based analysis, such as partial correlation networks, in a visualization scheme exported to Cytoscape, with web and stand-alone versions.Availability and implementation: ProbMetab was implemented in a modular manner to fit together with established upstream (xcms, CAMERA, AStream, mzMatch.R, etc) and downstream R package tools (GeneNet, RCytoscape, DiffCorr, etc). ProbMetab, along with extensive documentation and case studies, is freely available under GNU license at: http://labpib.fmrp.usp.br/methods/probmetab/.Contact: rvencio@usp.brSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

The study aims to register Hannemania larvae in Leptodactylus luctator and their infestation rates in an area of the Pampa biome at the extreme south of Brazil. Forty-six specimens of L. luctator (25 females: 21 males) collected in an urban area of the state of Rio Grande do Sul were examined. The prevalence of mites was 93.5%, and 4207 larvae were collected. The mean intensity of infestation was 97.8 mites/host (6-379). There were no significant differences in the prevalence and mean intensity of infestation between male and female hosts. There was no relationship between abundance of larvae and size of anurans. The study increases knowledge of the parasitic fauna and its parasitological indices in anurans from extreme south of Brazil. [ABSTRACT FROM AUTHOR]

Summary: Plants produce a myriad of specialized metabolites to overcome their sessile habit and combat biotic as well as abiotic stresses. Evolution has shaped the diversity of specialized metabolites, which then drives many other aspects of plant biodiversity. However, until recently, large‐scale studies investigating the diversity of specialized metabolites in an evolutionary context have been limited by the impossibility of identifying chemical structures of hundreds to thousands of compounds in a time‐feasible manner. Here we introduce a workflow for large‐scale, semi‐automated annotation of specialized metabolites and apply it to over 1000 metabolites of the cosmopolitan plant family Rhamnaceae. We enhance the putative annotation coverage dramatically, from 2.5% based on spectral library matches alone to 42.6% of total MS/MS molecular features, extending annotations from well‐known plant compound classes into dark plant metabolomics. To gain insights into substructural diversity within this plant family, we also extract patterns of co‐occurring fragments and neutral losses, so‐called Mass2Motifs, from the dataset; for example, only the Ziziphoid clade developed the triterpenoid biosynthetic pathway, whereas the Rhamnoid clade predominantly developed diversity in flavonoid glycosides, including 7‐O‐methyltransferase activity. Our workflow provides the foundations for the automated, high‐throughput chemical identification of massive metabolite spaces, and we expect it to revolutionize our understanding of plant chemoevolutionary mechanisms. Significance Statement: Tandem mass spectrometry (MS/MS) has become a popular tool for untargeted metabolomics study, but annotation, classification and identification of metabolites are still significant obstacles in MS/MS‐based plant metabolomics workflows. This study introduces a next‐generation data analysis strategy which provides structural insight into hundreds of specialized metabolites at the level of both chemical class and diversified scaffolds; the efficacy of the workflow was demonstrated by revealing the chemical space of the Rhamnaceae family. [ABSTRACT FROM AUTHOR]

Motivation Annotation of the mass signals is still the biggest bottleneck for the untargeted mass spectrometry analysis of complex mixtures. Molecular networks are being increasingly adopted by the mass spectrometry community as a tool to annotate large-scale experiments. We have previously shown that the process of propagating annotations from spectral library matches on molecular networks can be automated using Network Annotation Propagation (NAP). One of the limitations of NAP is that the information for the spectral matches is only propagated locally, to the first neighbor of a spectral match. Here, we show that annotation propagation can be expanded to nodes not directly connected to spectral matches using random walks on graphs, introducing the ChemWalker python library. Results Similarly to NAP, ChemWalker relies on combinatorial in silico fragmentation results, performed by MetFrag, searching biologically relevant databases. Departing from the combination of a spectral network and the structural similarity among candidate structures, we have used MetFusion Scoring function to create a weight function, producing a weighted graph. This graph was subsequently used by the random walk to calculate the probability of 'walking' through a set of candidates, departing from seed nodes (represented by spectral library matches). This approach allowed the information propagation to nodes not directly connected to the spectral library match. Compared with NAP, ChemWalker has a series of improvements, on running time, scalability and maintainability and is available as a standalone python package. Availability and implementation ChemWalker is freely available at https://github.com/computational-chemical-biology/ChemWalker Contact ridasilva@usp.br Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform . [ABSTRACT FROM AUTHOR]

Imagine a scenario where personal belongings such as pens, keys, phones, or handbags are found at an investigative site. It is often valuable to the investigative team that is trying to trace back the belongings to an individual to understand their personal habits, even when DNA evidence is also available. Here, we develop an approach to translate chemistries recovered from personal objects such as phones into a lifestyle sketch of the owner, using mass spectrometry and informatics approaches. Our results show that phones' chemistries reflect a personalized lifestyle profile. The collective repertoire of molecules found on these objects provides a sketch of the lifestyle of an individual by highlighting the type of hygiene/beauty products the person uses, diet, medical status, and even the location where this person may have been. These findings introduce an additional form of trace evidence from skin-associated lifestyle chemicals found on personal belongings. Such information could help a criminal investigator narrowing down the owner of an object found at a crime scene, such as a suspect or missing person. [ABSTRACT FROM AUTHOR]

Dengue is a neglected disease, present mainly in tropical countries, with more than 5.2 million cases reported in 2019. Vector control remains the most effective protective measure against dengue and other arboviruses. Synthetic insecticides based on organophosphates, pyrethroids, carbamates, neonicotinoids and oxadiazines are unattractive due to their high degree of toxicity to humans, animals and the environment. Conversely, natural-product-based larvicides/insecticides, such as essential oils, present high efficiency, low environmental toxicity and can be easily scaled up for industrial processes. However, essential oils are highly complex and require modern analytical and computational approaches to streamline the identification of bioactive substances. This study combined the GC-MS spectral similarity network approach with larvicidal assays as a new strategy for the discovery of potential bioactive substances in complex biological samples, enabling the systematic and simultaneous annotation of substances in 20 essential oils through LC50 larvicidal assays. This strategy allowed rapid intuitive discovery of distribution patterns between families and metabolic classes in clusters, and the prediction of larvicidal properties of acyclic monoterpene derivatives, including citral, neral, citronellal and citronellol, and their acetate forms (LC50 < 50 µg/mL). [ABSTRACT FROM AUTHOR]

Abstract: Semi-quantitative sampling protocols have been proposed as the most cost-effective and comprehensive way of sampling spiders in many regions of the world. In the present study, a balanced sampling design with the same number of samples per day, time of day, collector and method, was used to assess the species richness and composition of a Quercus suber woodland in Central Portugal. A total of 475 samples, each corresponding to one hour of effective fieldwork, were taken. One hundred sixty eight species were captured, of which 150 were recorded inside a delimited one-hectare plot; this number corresponds to around 90% of the estimated species richness. We tested the effect of applying different sampling approaches (sampling day, time of day, collector experience and method) on species richness, abundance, and composition. Most sampling approaches were found to influence the species measures, of which method, time of day and the respective interaction had the strongest influence. The data indicated that fauna depletion of the sampled area possibly occurred and that the inventory was reaching a plateau by the end of the sampling process. We advocate the use of the Chao estimators as best for intensive protocols limited in space and time and the use of the asymptotic properties of the Michaelis–Menten curve as a stopping or reliability rule, as it allows the investigator to know when a close-to-complete inventory has been obtained and when reliable non-parametric estimators have been achieved. [Copyright &y& Elsevier]

Alnus spp. (Betulaceae) have been used for treatments of hemorrhage, burn injuries, antipyretic fever, diarrhea, and alcoholism in traditional medicines. In this study, a digitized LC–MS/MS data analysis workflow was applied to provide an overview on chemical diversity of 15 Alnus extracts prepared from bark, twigs, leaves, and fruits of A. japonica , A. firma , A. hirsuta , and A. hirsuta var. sibirica. Most of the MS/MS spectra could be putatively annotated based on library matching, in silico fragmentation, and substructural topic modeling. The putative annotation allowed us to discriminate the extracts into three chemotypes based on dominant chemical scaffolds: diarylheptanoids, flavonoids or tannins. This high-throughput chemical annotation was correlated with α-glucosidase inhibition data of extracts, and it allowed us to identify gallic acid as the major active compound of A. firma. Image 1 • A digitized data analysis workflow for MS/MS was applied to Alnus metabolite dataset. • Most of MS/MS spectra were putatively annotated by the workflow. • Extracts were discriminated into three groups based on annotated chemical classes. • Gallic acid was identified as α-glucosidase inhibitor by integrating bioactivity. [ABSTRACT FROM AUTHOR]

• First large-scale food composition analysis using mass spectral molecular networking. • A methodological pipeline for compound discovery applicable to diverse foods was generated. • Molecular networking and statistical analyses were combined to detect chemical changes in foods. • Brewing, roasting, fermenting, spoiling, and ripening were investigated in example foods. • Time-based chemical changes were detected in yogurt, tea, beef, turkey, and tomato samples. In our daily lives, we consume foods that have been transported, stored, prepared, cooked, or otherwise processed by ourselves or others. Food storage and preparation have drastic effects on the chemical composition of foods. Untargeted mass spectrometry analysis of food samples has the potential to increase our chemical understanding of these processes by detecting a broad spectrum of chemicals. We performed a time-based analysis of the chemical changes in foods during common preparations, such as fermentation, brewing, and ripening, using untargeted mass spectrometry and molecular networking. The data analysis workflow presented implements an approach to study changes in food chemistry that can reveal global alterations in chemical profiles, identify changes in abundance, as well as identify specific chemicals and their transformation products. The data generated in this study are publicly available, enabling the replication and re-analysis of these data in isolation, and serve as a baseline dataset for future investigations. [ABSTRACT FROM AUTHOR]