Improving annotation propagation on molecular networks through random walks: introducing ChemWalker.

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.

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.

Combination of GC-MS Molecular Networking and Larvicidal Effect against Aedes aegypti for the Discovery of Bioactive Substances in Commercial Essential Oils.

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).

Comprehensive mass spectrometry-guided phenotyping of plant specialized metabolites reveals metabolic diversity in the cosmopolitan plant family Rhamnaceae.

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.

Initial Development toward Non-Invasive Drug Monitoring via Untargeted Mass Spectrometric Analysis of Human Skin.

Drug monitoring is crucial for providing accurate and effective care; however, current methods (e.g., blood draws) are inconvenient and unpleasant. We aim to develop a non-invasive method for the detection and monitoring of drugs via human skin. The initial development toward this aim required information about which drugs, taken orally, can be detected via the skin. Untargeted liquid chromatography-mass spectrometry (LC-MS) was used as it was unclear if drugs, known drug metabolites, or other transformation products were detectable. In accomplishing our aim, we analyzed samples obtained by swabbing the skin of 15 kidney transplant recipients in five locations (forehead, nasolabial area, axillary, backhand, and palm), bilaterally, on two different clinical visits. Untargeted LC-MS data were processed using molecular networking via the Global Natural Products Social Molecular Networking platform. Herein, we report the qualitative detection and location of drugs and drug metabolites. For example, escitalopram/citalopram and diphenhydramine, taken orally, were detected in forehead, nasolabial, and hand samples, whereas N-acetyl-sulfamethoxazole, a drug metabolite, was detected in axillary samples. In addition, chemicals associated with environmental exposure were also detected from the skin, which provides insight into the multifaceted chemical influences on our health. The proof-of-concept results presented support the finding that the LC-MS and data analysis methodology is currently capable of the qualitative assessment of the presence of drugs directly via human skin.

Propagating annotations of molecular networks using in silico fragmentation.

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 https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp.

Investigation of Premyrsinane and Myrsinane Esters in Euphorbia cupanii and Euphobia pithyusa with MS2LDA and Combinatorial Molecular Network Annotation Propagation.

The species Euphorbia pithyusa and Euphorbia cupanii are two closely related Mediterranean spurges for which their taxonomic relationships are still being debated. Herein, the diterpene ester content of E. cupanii was investigated using liquid chromatography coupled to tandem mass spectrometry. The use of molecular networking coupled to unsupervised substructure annotation ( MS2LDA) indicated the presence of new premyrsinane/myrsinane diterpene esters in the E. cupanii fractions. A structure-guided isolation procedure yielded 16 myrsinane (11a-h, 12, and 13) and premyrsinane esters (14a-c and 15a-c), along with four 4ß-phorbol esters (16a-c and 17) that showed inhibitory activity against chikungunya virus replication. The structures of the 16 new compounds (11a-c, 11h, 12, 13, 14a-c, 15a-c, 16a-c, and 17) were characterized by NMR spectroscopy and X-ray crystallography. To further uncover the diterpene ester content of these two species, the concept of combinatorial network annotation propagation (C-NAP) was developed. By leveraging the fact that the diterpene esters of Euphorbia species are made up of limited building blocks, a combinatorial database of theoretical structures was created and used for C-NAP that made possible the annotation of 123 premyrsinane or myrsinane esters, from which 74% are not found in any compound database.

Lifestyle chemistries from phones for individual profiling.

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.

Targeted Isolation of Neuroprotective Dicoumaroyl Neolignans and Lignans from Sageretia theezans Using in Silico Molecular Network Annotation Propagation-Based Dereplication.

The integration of LC-MS/MS molecular networking and in silico MS/MS fragmentation is an emerging method for dereplication of natural products. In the present study, a targeted isolation of natural products using a new in silico-based annotation tool named Network Annotation Propagation (NAP) is described. NAP improves accuracy of in silico fragmentation analyses by reranking candidate structures based on the network topology from MS/MS-based molecular networking. Annotation for the MS/MS spectral network of the Sageratia theezans twig extract was performed using NAP, and most molecular families within the network, including the known triterpenoids 1-7, could be putatively annotated, without relying on any previous reports of molecules from this species. Based on the in silico dereplication results, molecules were prioritized for isolation. In total, six dicoumaroyl 8- O-4' neolignans (8-13) and three dicoumaroyl lignans (14-16) were isolated from the twigs of S. theezans and structurally characterized by spectroscopic analyses. Isolates were evaluated for their neuroprotective activity, and compounds 14-16 showed potent protective effects against glutamate-induced oxidative stress in mouse HT22 cells at a concentration of 12.5 µM.

Coupling Targeted and Untargeted Mass Spectrometry for Metabolome-Microbiome-Wide Association Studies of Human Fecal Samples.

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).

Prioritizing Natural Product Diversity in a Collection of 146 Bacterial Strains Based on Growth and Extraction Protocols.

In order to expedite the rapid and efficient discovery and isolation of novel specialized metabolites, while minimizing the waste of resources on rediscovery of known compounds, it is crucial to develop efficient approaches for strain prioritization, rapid dereplication, and the assessment of favored cultivation and extraction conditions. Herein we interrogated bacterial strains by systematically evaluating cultivation and extraction parameters with LC-MS/MS analysis and subsequent dereplication through the Global Natural Product Social Molecular Networking (GNPS) platform. The developed method is fast, requiring minimal time and sample material, and is compatible with high-throughput extract analysis, thereby streamlining strain prioritization and evaluation of culturing parameters. With this approach, we analyzed 146 marine Salinispora and Streptomyces strains that were grown and extracted using multiple different protocols. In total, 603 samples were analyzed, generating approximately 1.8 million mass spectra. We constructed a comprehensive molecular network and identified 15 molecular families of diverse natural products and their analogues. The size and breadth of this network shows statistically supported trends in molecular diversity when comparing growth and extraction conditions. The network provides an extensive survey of the biosynthetic capacity of the strain collection and a method to compare strains based on the variety and novelty of their metabolites. This approach allows us to quickly identify patterns in metabolite production that can be linked to taxonomy, culture conditions, and extraction methods, as well as informing the most valuable growth and extraction conditions.

ProbMetab: an R package for Bayesian probabilistic annotation of LC-MS-based metabolomics.

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.

Improved virus purification processes for vaccines and gene therapy.

The downstream processing of virus particles for vaccination or gene therapy is becoming a critical bottleneck as upstream titers keep improving. Moreover, the growing pressure to develop cost-efficient processes has brought forward new downstream trains. This review aims at analyzing the state-of-the-art in viral downstream purification processes, encompassing the classical unit operations and their recent developments. Emphasis is given to novel strategies for process intensification, such as continuous or semi-continuous systems based on multicolumn technology, opening up process efficiency. Process understanding in the light of the pharmaceutical quality by design (QbD) initiative is also discussed. Finally, an outlook of the upcoming breakthrough technologies is presented.

Novel Pseudomonas Species Prevent the Growth of the Phytopathogenic Fungus Aspergillus flavus.

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.

Rosemary (Rosmarinus officinalis L.) Glycolic Extract Protects Liver Mitochondria from Oxidative Damage and Prevents Acetaminophen-Induced Hepatotoxicity.

Rosmarinus officinalis L. (rosemary) is an aromatic culinary herb. Native to the Mediterranean region, it is currently cultivated worldwide. In addition to its use as a condiment in food preparation and in teas, rosemary has been widely employed in folk medicine and cosmetics. Several beneficial effects have been described for rosemary, including antimicrobial and antioxidant activities. Here, we investigated the mechanisms accounting for the antioxidant activity of the glycolic extract of R. officinalis (Ro) in isolated rat liver mitochondria (RLM) under oxidative stress conditions. We also investigated its protective effect against acetaminophen-induced hepatotoxicity in vivo. A crude extract was obtained by fractionated percolation, using propylene glycol as a solvent due to its polarity and cosmeceutical compatibility. The quantification of substances with recognized antioxidant action revealed the presence of phenols and flavonoids. Dereplication studies carried out through LC-MS/MS and GC-MS, supported by The Global Natural Product Social Molecular Networking (GNPS) platform, annotated several phenolic compounds, confirming the previous observation. In accordance, Ro decreased the production of reactive oxygen species (ROS) elicited by Fe2+ or t-BOOH and inhibited the lipid peroxidation of mitochondrial membranes in a concentration-dependent manner in RLM. Such an effect was also observed in liposomes as membrane models. Ro also prevented the oxidation of mitochondrial protein thiol groups and reduced glutathione (GSH). In model systems, Ro exhibited a potent scavenger activity toward 2,2'-diphenyl-1-picrylhydrazyl (DPPH) radicals and superoxide anions. It also demonstrated an Fe2+ chelating activity. Moreover, Ro did not exhibit cytotoxicity or dissipate the mitochondrial membrane potential (∆Ψ) in rat liver fibroblasts (BRL3A cells). To evaluate whether such antioxidant protective activity observed in vitro could also be achieved in vivo, a well-established model of hepatotoxicity induced by acute exposure to acetaminophen (AAP) was used. This model depletes GSH and promotes oxidative-stress-mediated tissue damage. The treatment of rats with 0.05% Ro, administered intraperitoneally for four days, resulted in inhibition of AAP-induced lipid peroxidation of the liver and the prevention of hepatotoxicity, maintaining alanine and aspartate aminotransferase (ALT/AST) levels equal to those of the normal, non-treated rats. Together, these findings highlight the potent antioxidant activity of rosemary, which is able to protect mitochondria from oxidative damage in vitro, and effects such as the antioxidant and hepatoprotective effects observed in vivo.

Metabolomics Analysis of Bacterial Pathogen Burkholderia thailandensis and Mammalian Host Cells in Co-culture.

The Tier 1 HHS/USDA Select Agent Burkholderia pseudomallei is a bacterial pathogen that is highly virulent when introduced into the respiratory tract and intrinsically resistant to many antibiotics. Transcriptomic- and proteomic-based methodologies have been used to investigate mechanisms of virulence employed by B. pseudomallei and Burkholderia thailandensis, a convenient surrogate; however, analysis of the pathogen and host metabolomes during infection is lacking. Changes in the metabolites produced can be a result of altered gene expression and/or post-transcriptional processes. Thus, metabolomics complements transcriptomics and proteomics by providing a chemical readout of a biological phenotype, which serves as a snapshot of an organism's physiological state. However, the poor signal from bacterial metabolites in the context of infection poses a challenge in their detection and robust annotation. In this study, we coupled mammalian cell culture-based metabolomics with feature-based molecular networking of mono- and co-cultures to annotate the pathogen's secondary metabolome during infection of mammalian cells. These methods enabled us to identify several key secondary metabolites produced by B. thailandensis during infection of airway epithelial and macrophage cell lines. Additionally, the use of in silico approaches provided insights into shifts in host biochemical pathways relevant to defense against infection. Using chemical class enrichment analysis, for example, we identified changes in a number of host-derived compounds including immune lipids such as prostaglandins, which were detected exclusively upon pathogen challenge. Taken together, our findings indicate that co-culture of B. thailandensis with mammalian cells alters the metabolome of both pathogen and host and provides a new dimension of information for in-depth analysis of the host-pathogen interactions underlying Burkholderia infection.

Metabolic Profiling of Interspecies Interactions During Sessile Bacterial Cultivation Reveals Growth and Sporulation Induction in Paenibacillus amylolyticus in Response to Xanthomonas retroflexus.

The toolbox available for microbiologists to study interspecies interactions is rapidly growing, and with continuously more advanced instruments, we are able to expand our knowledge on establishment and function of microbial communities. However, unravelling molecular interspecies interactions in complex biological systems remains a challenge, and interactions are therefore often studied in simplified communities. Here we perform an in-depth characterization of an observed interspecies interaction between two co-isolated bacteria, Xanthomonas retroflexus and Paenibacillus amylolyticus. Using microsensor measurements for mapping the chemical environment, we show how X. retroflexus promoted an alkalization of its local environment through degradation of amino acids and release of ammonia. When the two species were grown in proximity, the modified local environment induced a morphological change and growth of P. amylolyticus followed by sporulation. 2D spatial metabolomics enabled visualization and mapping of the degradation of oligopeptide structures by X. retroflexus and morphological changes of P. amylolyticus through e.g. the release of membrane-associated metabolites. Proteome analysis and microscopy were used to validate the shift from vegetative growth towards sporulation. In summary, we demonstrate how environmental profiling by combined application of microsensor, microscopy, metabolomics and proteomics approaches can reveal growth and sporulation promoting effects resulting from interspecies interactions.

Microbial and Nonvolatile Chemical Diversities of Chinese Dark Teas Are Differed by Latitude and Pile Fermentation.

Understanding the microbial and chemical diversities, as well as what affects these diversities, is important for modern manufacturing of traditional fermented foods. In this work, Chinese dark teas (CDTs) that are traditional microbial fermented beverages with relatively high sample diversity were collected. Microbial DNA amplicon sequencing and mass spectrometry-based untargeted metabolomics show that the CDT microbial ß diversity, as well as the nonvolatile chemical α and ß diversities, is determined by the primary impact factors of geography and manufacturing procedures, in particular, latitude and pile fermentation after blending. A large number of metabolites sharing between CDTs and fungi were discovered by Feature-based Molecular Networking (FBMN) on the Global Natural Products Social Molecular Networking (GNPS) web platform. These molecules, such as prenylated cyclic dipeptides and B-vitamins, are functionally important for nutrition, biofunctions, and flavor. Molecular networking has revealed patterns in metabolite profiles on a chemical family level in addition to individual structures.

Prediction of enteric methane production and yield in dairy cattle using a Latin America and Caribbean database.

Successful mitigation efforts entail accurate estimation of on-farm emission and prediction models can be an alternative to current laborious and costly in vivo CH4 measurement techniques. This study aimed to: (1) collate a database of individual dairy cattle CH4 emission data from studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH4 production (g d-1) and yield [g kg-1 of dry matter intake (DMI)]; (3) develop and cross-validate these newly-developed models; and (4) compare models' predictive ability with equations currently used to support national greenhouse gas (GHG) inventories. A total of 42 studies including 1327 individual dairy cattle records were collated. After removing outliers, the final database retained 34 studies and 610 animal records. Production and yield of CH4 were predicted by fitting mixed-effects models with a random effect of study. Evaluation of developed models and fourteen extant equations was assessed on all-data, confined, and grazing cows subsets. Feed intake was the most important predictor of CH4 production. Our best-developed CH4 production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the all-data and grazing subsets, whereas they had similar performance for confined animals. Developed CH4 production models that include milk yield can be accurate and useful when feed intake is missing. Some extant equations had similar predictive performance to our best-developed models and can be an option for predicting CH4 production from LAC dairy cows. Extant equations were not accurate in predicting CH4 yield. The use of the newly-developed models rather than extant equations based on energy conversion factors, as applied by the IPCC, can substantially improve the accuracy of GHG inventories in LAC countries.