Mass spectral molecular networking of living microbial colonies

Introducing Molecular Hypernetworks for Discovery in Multidimensional Metabolomics Data

Orthogonal separations of data from high-resolution mass spectrometry can provide insight into sample composition and address challenges of complete annotation of molecules in untargeted metabolomics. "Molecular networks" (MNs), as used in the Global Natural Products Social Molecular Networking platform, are a prominent strategy for exploring and visualizing molecular relationships and improving annotation. MNs are mathematical graphs showing the relationships between measured multidimensional data features. MNs also show promise for using network science algorithms to automatically identify targets for annotation candidates and to dereplicate features associated with a single molecular identity. This paper introduces "molecular hypernetworks" (MHNs) as more complex MN models able to natively represent multiway relationships among observations. Compared to MNs, MHNs can more parsimoniously represent the inherent complexity present among groups of observations, initially supporting improved exploratory data analysis and visualization. MHNs also promise to increase confidence in annotation propagation, for both human and analytical processing. We first illustrate MHNs with simple examples, and build them from liquid chromatography- and ion mobility spectrometry-separated MS data. We then describe a method to construct MHNs directly from existing MNs as their "clique reconstructions", demonstrating their utility by comparing examples of previously published graph-based MNs to their respective MHNs.

Nanospray Desorption Electrospray Ionization Mass Spectrometry Imaging (nano-DESI MSI): A Tutorial Review

Nanospray desorption electrospray ionization (nano-DESI) is a liquid-based ambient mass spectrometry imaging (MSI) technique that enables visualization of analyte distributions in biological samples down to cellular-level spatial resolution. Since its inception, significant advancements have been made to the nano-DESI experimental platform to facilitate molecular imaging with high throughput, deep molecular coverage, and spatial resolution better than 10 μm. The molecular selectivity of nano-DESI MSI has been enhanced using new data acquisition strategies, the development of separation and online derivatization approaches for isobar separation and isomer-selective imaging, and the optimization of the working solvent composition to improve analyte extraction and ionization efficiency. Furthermore, nano-DESI MSI research has underscored the importance of matrix effects and established normalization methods for accurately measuring concentration gradients in complex biological samples. This tutorial offers a comprehensive guide to nano-DESI experiments, detailing fundamental principles and data acquisition and processing methods and discussing essential operational parameters.

The Comprehensive Profiling of the Chemical Components in the Raw and Processed Roots of Scrophularia ningpoensis by Combining UPLC-Q-TOF-MS Coupled with MS/MS-Based Molecular Networking

Scrophulariae Radix (SR), the dried root of Scrophularia ningpoensis Hemsl (S. ningpoensis), has been extensively used as traditional Chinese medicine for thousands of years. However, since the mid-20th century, the traditional processing technology of S. ningpoensis has been interrupted. Therefore, ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry technology, together with a Global Natural Product Social Molecular Networking (GNPS) method, was applied to comprehensively analyze the characteristic changes and mutual transformation of chemical constituents in the differently processed roots of S. ningpoensis, as well as to scientifically elucidate the processing mechanism of differently processed SR. As a result, a total of 149 components were identified. Notably, with the help of the GNPS data platform and MS2 fragment ions, the possible structures of four new compounds (47, 48, 50, and 73) were deduced in differently processed SR samples, in which 47, 48, and 50 are iridoid glycosides, and 73 is a phenylpropanoid glycoside. Five cyclopeptides (78, 86, 97, 99, and 104) derived from leucine (isoleucine) were identified in SR for the first time. The heatmaps analysis results indicated that leucine or isoleucine may be converted to cyclopeptides under the prolonged high-temperature conditions. Moreover, it is found that short-time steaming can effectively prevent the degradation of glycosides by inactivating enzymes. This study provides a new and efficient technical strategy for systematically identifying the chemical components, rapidly discovering the components, and preliminarily clarifying the processing mechanism of S. ningpoensis, as well as also providing a scientific basis for the improvement of the quality standards and field processing of S. ningpoensis.

Identifying sesterterpenoids via feature-based molecular networking and small-scale fermentation

Terpenoids are known for their diverse structures and broad bioactivities with significant potential in pharmaceutical applications. However, natural products with low yields are usually ignored in traditional chemical analysis. Feature-based molecular networking (FBMN) was developed recently to cluster compounds with similar skeletons, which can highlight trace amounts of unknown compounds. Fusoxypene A is a sesterterpene synthesized by Fusarium oxysporum fusoxypene synthase (FoFS) with a unique 5/6/7/3/5 ring system. In this study, the FoFS-containing biosynthetic gene cluster was identified from F. oxysporum FO14005, and an efficient FBMN-based strategy was established to characterize four new sesterterpenoids, fusoxyordienoid A-D (1-4), based on a small-scale fermentation strategy. A cytochrome P450 monooxygenase, FusB, was found to be involved in the functionalization of fusoxypene A at C-17 and C-24 and responsible for the hydroxylation of fusoxyordienoid A at C-1 and C-8. This study highlights the potential of FBMN as a powerful tool for the discovery and characterization of natural compounds with low abundance. KEY POINTS: Combined small-scale fermentation and FBMN for rapid discovery of fusoxyordienoids Characterization of four new fusoxyordienoids with 5/6/7/3/5 ring system Biosynthetic pathway elucidation via tandem expression and substrate feeding.

LC-MS coupled with diagnostic ion strategy facilitated the discovery of 5-methylcoumarin meroterpenoids from Gerbera piloselloides

Plant-derived natural products remain crucial in drug development. However, the identification of undescribed natural products is becoming increasingly challenging. A comprehensive strategy combining LC-MS with diagnostic ions was proposed for the discovery of undescribed 5-methylcoumarin meroterpenoids. Thirteen undescribed 5-methylcoumarin meroterpenoids, including five pairs of enantiomers (1a/1b and 5a/5b-8a/8b), were isolated from the whole plant of Gerbera piloselloides. Their structures and absolute configurations were unambiguously determined based on their spectroscopic data, calculated and experimental ECD data and X-ray diffraction analysis. Bioassays conducted on scopolamine-induced injury PC12 cells revealed that compounds 5a/5b, 7a/7b and 8a/8b possessed mild protective effects. Additionally, compounds 2 and 8 showed notable IL-6 inhibition in lipopolysaccharide-induced BEAS-2B cells.

MSIGen: An Open-Source Python Package for Processing and Visualizing Mass Spectrometry Imaging Data

Mass spectrometry imaging (MSI) provides information about the spatial localization of molecules in complex samples with high sensitivity and molecular selectivity. Although point-wise data acquisition, in which mass spectra are acquired at predefined points in a grid pattern, is common in MSI, several MSI techniques use line-wise data acquisition. In line-wise mode, the imaged surface is continuously sampled along consecutive parallel lines and MSI data are acquired as a collection of line scans across the sample. Furthermore, aside from the standard imaging mode in which full mass spectra are acquired, other acquisition modes have been developed to enhance molecular specificity, enable separation of isobaric and isomeric species, and improve sensitivity to facilitate the imaging of low abundance species. These methods, including MS/MS-MSI in both MS2 and MS3 modes, multiple-reaction monitoring (MRM)-MSI, and ion mobility spectrometry (IMS)-MSI have all demonstrated their capabilities, but their broader implementation is limited by the existing MSI analysis software. Here, we present MSIGen, an open-source Python package for the visualization of MSI experiments performed in line-wise acquisition mode containing MS1, MS2, MRM, and IMS data, which is available at https://github.com/LabLaskin/MSIGen. The package supports multiple vendor-specific and open-source data formats and contains tools for targeted extraction of ion images, normalization, and exportation as images, arrays, or publication-style images. MSIGen offers multiple interfaces, allowing for accessibility and easy integration with other workflows. Considering its support for a wide variety of MSI imaging modes and vendor formats, MSIGen is a valuable tool for the visualization and analysis of MSI data.

Application of molecular networking to improve the compound annotation in liquid chromatography-mass spectrometry-based metabolomics analysis: A case study of Bupleuri radix

INTRODUCTION

Compound annotation is always a challenging step in metabolomics studies. The molecular networking strategy has been developed recently to organize the relationship between compounds as a network based on their tandem mass (MS2) spectra similarity, which can be used to improve compound annotation in metabolomics analysis.

OBJECTIVE

This study used Bupleuri Radix from different geographic areas to evaluate the performance of molecular networking strategy for compound annotation in liquid chromatography-mass spectrometry (LC-MS)-based metabolomics.

METHODOLOGY

The Bupleuri Radix extract was analyzed by LC-quadrupole time-of-flight MS under MSe acquisition mode. After raw data preprocessing, the resulting dataset was used for statistical analysis, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The chemical makers related to the sample growth place were selected using variable importance in projection (VIP) > 2, fold change (FC) > 2, and p < 0.05. The molecular networking analysis was applied to conduct the compound annotation.

RESULTS

The score plots of PCA showed that the samples were classified into two clusters depending on their growth place. Then, the PLS-DA model was constructed to explore the chemical changes of the samples further. Sixteen compounds were selected as chemical makers and tentatively annotated by the feature-based molecular networking (FBMN) analysis.

CONCLUSION

The results showed that the molecular networking method fully exploits the MS information and is a promising tool for facilitating compound annotation in metabolomics studies. However, the software used for feature extraction influenced the results of library searching and molecular network construction, which need to be taken into account in future studies.

Combined LC-MS/MS feature grouping, statistical prioritization, and interactive networking in msFeaST

SUMMARY

Computational metabolomics workflows have revolutionized the untargeted metabolomics field. However, the organization and prioritization of metabolite features remains a laborious process. Organizing metabolomics data is often done through mass fragmentation-based spectral similarity grouping, resulting in feature sets that also represent an intuitive and scientifically meaningful first stage of analysis in untargeted metabolomics. Exploiting such feature sets, feature-set testing has emerged as an approach that is widely used in genomics and targeted metabolomics pathway enrichment analyses. It allows for formally combining groupings with statistical testing into more meaningful pathway enrichment conclusions. Here, we present msFeaST (mass spectral Feature Set Testing), a feature-set testing and visualization workflow for LC-MS/MS untargeted metabolomics data. Feature-set testing involves statistically assessing differential abundance patterns for groups of features across experimental conditions. We developed msFeaST to make use of spectral similarity-based feature groupings generated using k-medoids clustering, where the resulting clusters serve as a proxy for grouping structurally similar features with potential biosynthesis pathway relationships. Spectral clustering done in this way allows for feature group-wise statistical testing using the globaltest package, which provides high power to detect small concordant effects via joint modeling and reduced multiplicity adjustment penalties. Hence, msFeaST provides interactive integration of the semi-quantitative experimental information with mass-spectral structural similarity information, enhancing the prioritization of features and feature sets during exploratory data analysis.

AVAILABILITY AND IMPLEMENTATION

The msFeaST workflow is freely available through https://github.com/kevinmildau/msFeaST and built to work on MacOS and Linux systems.

In Situ Raman Hyperspectral Analysis of Microbial Colonies for Secondary Metabolites Screening

Since the discovery of penicillin, a vast array of microbial antibiotics has been identified and applied in the medical field. Globally, the search for drug candidates via microbial screening is ongoing. Traditional screening methods, however, are time-consuming and require labor-intensive sample processing, significantly reducing throughput. This research introduces a Raman spectroscopy-based screening system tailored to the in situ analysis of microbial colonies on solid culture media. Employing multivariate curve resolution-alternating least-squares (MCR-ALS) for spectral decomposition, our approach reveals the production of secondary metabolites at the single colony level. We enhanced the microbial culture method, enabling direct, high signal-to-noise (S/N) ratio Raman spectroscopic measurements of colonies of Escherichia coli and actinomycetes species. Through semisupervised MCR analysis using the known spectra of actinorhodin and undecylprodigiosin as references, we accurately assessed the production of these compounds by Streptomyces coelicolor A3(2). Furthermore, we herein successfully detected the production of amphotericin B by Streptomyces nodosus, even in the absence of prior spectral information. This demonstrates the potential of our technique in the discovery of secondary metabolites. In addition to enabling the detection of the above-mentioned compounds, this analysis revealed the heterogeneity of the spatial distribution of their production in each colony. Our technique makes a significant contribution to the advancement of microbial screening, offering a rapid, efficient alternative to conventional methods and opening avenues for secondary metabolites discovery.

Microbiome multi-omics can accelerate human excrement composting research

In this editorial, we discuss the need for a new, long-term strategy for managing human excrement (feces and urine) to facilitate health equity and promote environmental sustainability. Human excrement composting (HEC), a human-directed process driven by highly variable and diverse microbiomes, provides a means to advance this need and we discuss how microbiome science can help to advance HEC research. We argue that the technological advancements that have driven the growth of microbiome science, including microbiome and untargeted metabolome profiling, can be leveraged to enhance our understanding of safe and efficient HEC. We conclude by presenting our perspective on how we can begin applying these technologies to develop accessible procedures for safe HEC. Video Abstract.

Network Topology Evaluation and Transitive Alignments for Molecular Networking

Untargeted tandem mass spectrometry (MS/MS) is an essential technique in modern analytical chemistry, providing a comprehensive snapshot of chemical entities in complex samples and identifying unknowns through their fragmentation patterns. This high-throughput approach generates large data sets that can be challenging to interpret. Molecular Networks (MNs) have been developed as a computational tool to aid in the organization and visualization of complex chemical space in untargeted mass spectrometry data, thereby supporting comprehensive data analysis and interpretation. MNs group related compounds with potentially similar structures from MS/MS data by calculating all pairwise MS/MS similarities and filtering these connections to produce a MN. Such networks are instrumental in metabolomics for identifying novel metabolites, elucidating metabolic pathways, and even discovering biomarkers for disease. While MS/MS similarity metrics have been explored in the literature, the influence of network topology approaches on MN construction remains unexplored. This manuscript introduces metrics for evaluating MN construction, benchmarks state-of-the-art approaches, and proposes the Transitive Alignments approach to improve MN construction. The Transitive Alignment technique leverages the MN topology to realign MS/MS spectra of related compounds that differ by multiple structural modifications. Combining this Transitive Alignments approach with pseudoclique finding, a method for identifying highly connected groups of nodes in a network, resulted in more complete and higher-quality molecular families. Finally, we also introduce a targeted network construction technique called induced transitive alignments where we demonstrate effectiveness on a real world natural product discovery application. We release this transitive alignment technique as a high-throughput workflow that can be used by the wider research community.

Spatial chemistry of citrus reveals molecules bactericidal to Candidatus Liberibacter asiaticus

Huanglongbing (HLB), associated with the psyllid-vectored phloem-limited bacterium, Candidatus Liberibacter asiaticus (CLas), is a disease threat to all citrus production worldwide. Currently, there are no sustainable curative or prophylactic treatments available. In this study, we utilized mass spectrometry (MS)-based metabolomics in combination with 3D molecular mapping to visualize complex chemistries within plant tissues to explore how these chemistries change in vivo in HLB-infected trees. We demonstrate how spatial information from molecular maps of branches and single leaves yields insight into the biology not accessible otherwise. In particular, we found evidence that flavonoid biosynthesis is disrupted in HLB-infected trees, and an increase in the polyamine, feruloylputrescine, is highly correlated with an increase in disease severity. Based on mechanistic details revealed by these molecular maps, followed by metabolic modeling, we formulated and tested the hypothesis that CLas infection either directly or indirectly converts the precursor compound, ferulic acid, to feruloylputrescine to suppress the antimicrobial effects of ferulic acid and biosynthetically downstream flavonoids. Using in vitro bioassays, we demonstrated that ferulic acid and bioflavonoids are indeed highly bactericidal to CLas, with the activity on par with a reference antibiotic, oxytetracycline, recently approved for HLB management. We propose these compounds should be evaluated as therapeutics alternatives to the antibiotics for HLB treatment. Overall, the utilized 3D metabolic mapping approach provides a promising methodological framework to identify pathogen-specific inhibitory compounds in planta for potential prophylactic or therapeutic applications.

The role of artificial intelligence in the development of anticancer therapeutics from natural polyphenols: Current advances and future prospects

Natural polyphenols, abundant in the human diet, are derived from a wide variety of sources. Numerous preclinical studies have demonstrated their significant anticancer properties against various malignancies, making them valuable resources for drug development. However, traditional experimental methods for developing anticancer therapies from natural polyphenols are time-consuming and labor-intensive. Recently, artificial intelligence has shown promising advancements in drug discovery. Integrating AI technologies into the development process for natural polyphenols can substantially reduce development time and enhance efficiency. In this study, we review the crucial roles of natural polyphenols in anticancer treatment and explore the potential of AI technologies to aid in drug development. Specifically, we discuss the application of AI in key stages such as drug structure prediction, virtual drug screening, prediction of biological activity, and drug-target protein interaction, highlighting the potential to revolutionize the development of natural polyphenol-based anticancer therapies.

A multi-component, multi-target, and multi-pathway prediction method for Chinese medicine based on the combination of mass spectrometry analysis and network analysis: An example using Weifuchun

Weifuchun, a Chinese medicinal prescription made from herbs of natural origin including Hongshen (Ginseng Radix et Rhizoma Rubra), Xiangchacai (Rabdosia Amethystoides), and Zhiqiao (Aurantii Fructus), has attracted increasing attention for clinically treating chronic atrophic gastritis, which is characterized by the chronic inflammation of the gastric mucosa leading to progressive loss of gastric glandular cells. To investigate the active ingredients and potential mechanisms of WFC, it was analyzed using a novel multi-component, multi-target, and multi-pathway prediction method. High/ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (HPLC/UPLC-Q-TOF-MS) was employed to separate and profile the chemical constituents of WFC with high precision and efficiency. Network analysis and molecular docking were used to predict bioactive compounds and their interactions with biological targets. The results highlight 42 significant compounds potentially contributing to the therapeutic effects of WFC by effecting on several key pathways, including proved PI3K/Akt, NF-κB, and JAK/STAT signaling pathways. This study showcases the efficacy of combining advanced chromatographic techniques with computational methods to elucidate the pharmacological mechanisms of complex botanical drugs.

Discovery of cycloheptapeptides phakefusins A-E from the marine sponge Phakellia fusca based on molecular networking

Guided by a probe-based molecular networking strategy, five undescribed cycloheptapeptides, phakefusins A-E (1-5), were isolated from the marine sponge Phakellia fusca. Compounds 1 and 2 contain the nonproteinogenic amino acid residues of dioxindolyalanine (Dioia) and β-3-oxindolylalanine (Oia), respectively. Compound 3 possesses a unique methionine sulfoxide, whereas compound 5 includes a glutamic acid ethyl ester unit. Their structures were elucidated through NMR spectroscopy, HR-MS/MS analysis, and the advanced Marfey's method. By synthesizing the (S, S/R)-Oia standard through tryptophan oxidation, we determined the configuration of this amino acid in compound 2 using the advanced Marfey's method. These cycloheptapeptides were evaluated for their antitumor, antibacterial, and antioxidant activities. Compound 1 showed moderate cytotoxicity against MCF-7 and PC9 cells, with IC50 values of 6.8 and 9.6 μM, respectively, while compounds 2-5 demonstrated potential antioxidant effects by upregulating HO-1, NQO1, and SOD2 levels, as well as inducing Nrf2 activation.

Innovative molecular networking analysis of steroids and characterisation of the urinary steroidome

Steroids are cholesterol-derived biomolecules that play an essential role in biological processes. These substances used as growth promoters in animals are strictly regulated worldwide. Targeted assays are the conventional methods of monitoring steroid abuse, with limitations: only detect known metabolites. Metabolism leads to many potential compounds (isomers), which complicates the analysis. Thus, to overcome these limitations, non-targeted analysis offers new opportunities for a deeper understanding of metabolites related to steroid metabolism. Molecular networking (MN) appears to be an innovative strategy combining high-resolution mass spectrometry and specific data processing to study metabolic pathways. In the present study, two databases and networks of steroids were constructed to lay the foundations for the implementation of the GNPS-MN approach. Steroids of the same family were grouped together, nandrolone and testosterone were linked to other analogues. This network and associated database were then applied to a few urine samples in order to demonstrate the annotation capacity in steroidome study. The results show that MN strategy could be used to study steroid metabolism and highlight biomarkers.

Plant peptides - redefining an area of ribosomally synthesized and post-translationally modified peptides

Covering 1965 to February 2024Plants are prolific peptide chemists and are known to make thousands of different peptidic molecules. These peptides vary dramatically in their size, chemistry, and bioactivity. Despite their differences, all plant peptides to date are biosynthesized as ribosomally synthesized and post-translationally modified peptides (RiPPs). Decades of research in plant RiPP biosynthesis have extended the definition and scope of RiPPs from microbial sources, establishing paradigms and discovering new families of biosynthetic enzymes. The discovery and elucidation of plant peptide pathways is challenging due to repurposing and evolution of housekeeping genes as both precursor peptides and biosynthetic enzymes and due to the low rates of gene clustering in plants. In this review, we highlight the chemistry, biosynthesis, and function of the known RiPP classes from plants and recommend a nomenclature for the recent addition of BURP-domain-derived RiPPs termed burpitides. Burpitides are an emerging family of cyclic plant RiPPs characterized by macrocyclic crosslinks between tyrosine or tryptophan side chains and other amino acid side chains or their peptide backbone that are formed by copper-dependent BURP-domain-containing proteins termed burpitide cyclases. Finally, we review the discovery of plant RiPPs through bioactivity-guided, structure-guided, and gene-guided approaches.

Progress, applications, and challenges in high-throughput effect-directed analysis for toxicity driver identification - is it time for HT-EDA?

The rapid increase in the production and global use of chemicals and their mixtures has raised concerns about their potential impact on human and environmental health. With advances in analytical techniques, in particular, high-resolution mass spectrometry (HRMS), thousands of compounds and transformation products with potential adverse effects can now be detected in environmental samples. However, identifying and prioritizing the toxicity drivers among these compounds remain a significant challenge. Effect-directed analysis (EDA) emerged as an important tool to address this challenge, combining biotesting, sample fractionation, and chemical analysis to unravel toxicity drivers in complex mixtures. Traditional EDA workflows are labor-intensive and time-consuming, hindering large-scale applications. The concept of high-throughput (HT) EDA has recently gained traction as a means of accelerating these workflows. Key features of HT-EDA include the combination of microfractionation and downscaled bioassays, automation of sample preparation and biotesting, and efficient data processing workflows supported by novel computational tools. In addition to microplate-based fractionation, high-performance thin-layer chromatography (HPTLC) offers an interesting alternative to HPLC in HT-EDA. This review provides an updated perspective on the state-of-the-art in HT-EDA, and novel methods/tools that can be incorporated into HT-EDA workflows. It also discusses recent studies on HT-EDA, HT bioassays, and computational prioritization tools, along with considerations regarding HPTLC. By identifying current gaps in HT-EDA and proposing new approaches to overcome them, this review aims to bring HT-EDA a step closer to monitoring applications.

High-Throughput Mining of Novel Compounds from Known Microbes: A Boost to Natural Product Screening

Advanced techniques can accelerate the pace of natural product discovery from microbes, which has been lagging behind the drug discovery era. Therefore, the present review article discusses the various interdisciplinary and cutting-edge techniques to present a concrete strategy that enables the high-throughput screening of novel natural compounds (NCs) from known microbes. Recent bioinformatics methods revealed that the microbial genome contains a huge untapped reservoir of silent biosynthetic gene clusters (BGC). This article describes several methods to identify the microbial strains with hidden mines of silent BGCs. Moreover, antiSMASH 5.0 is a free, accurate, and highly reliable bioinformatics tool discussed in detail to identify silent BGCs in the microbial genome. Further, the latest microbial culture technique, HiTES (high-throughput elicitor screening), has been detailed for the expression of silent BGCs using 500-1000 different growth conditions at a time. Following the expression of silent BGCs, the latest mass spectrometry methods are highlighted to identify the NCs. The recently emerged LAESI-IMS (laser ablation electrospray ionization-imaging mass spectrometry) technique, which enables the rapid identification of novel NCs directly from microtiter plates, is presented in detail. Finally, various trending 'dereplication' strategies are emphasized to increase the effectiveness of NC screening.

Machine learning of cellular metabolic rewiring

Metabolic rewiring allows cells to adapt their metabolism in response to evolving environmental conditions. Traditional metabolomics techniques, whether targeted or untargeted, often struggle to interpret these adaptive shifts. Here, we introduce MetaboLiteLearner, a lightweight machine learning framework that harnesses the detailed fragmentation patterns from electron ionization (EI) collected in scan mode during gas chromatography/mass spectrometry to predict changes in the metabolite composition of metabolically adapted cells. When tested on breast cancer cells with different preferences to metastasize to specific organs, MetaboLiteLearner predicted the impact of metabolic rewiring on metabolites withheld from the training dataset using only the EI spectra, without metabolite identification or pre-existing knowledge of metabolic networks. Despite its simplicity, the model learned captured shared and unique metabolomic shifts between brain- and lung-homing metastatic lineages, suggesting cellular adaptations associated with metastasis to specific organs. Integrating machine learning and metabolomics paves the way for new insights into complex cellular adaptations.

The changing metabolic landscape of bile acids - keys to metabolism and immune regulation

Bile acids regulate nutrient absorption and mitochondrial function, they establish and maintain gut microbial community composition and mediate inflammation, and they serve as signalling molecules that regulate appetite and energy homeostasis. The observation that there are hundreds of bile acids, especially many amidated bile acids, necessitates a revision of many of the classical descriptions of bile acids and bile acid enzyme functions. For example, bile salt hydrolases also have transferase activity. There are now hundreds of known modifications to bile acids and thousands of bile acid-associated genes, especially when including the microbiome, distributed throughout the human body (for example, there are >2,400 bile salt hydrolases alone). The fact that so much of our genetic and small-molecule repertoire, in both amount and diversity, is dedicated to bile acid function highlights the centrality of bile acids as key regulators of metabolism and immune homeostasis, which is, in large part, communicated via the gut microbiome.

Hardware and software solutions for implementing nanospray desorption electrospray ionization (nano-DESI) sources on commercial mass spectrometers

Nanospray desorption electrospray ionization (nano-DESI) is an ambient ionization mass spectrometry imaging (MSI) approach that enables spatial mapping of biological and environmental samples with high spatial resolution and throughput. Because nano-DESI has not yet been commercialized, researchers develop their own sources and interface them with different commercial mass spectrometers. Previously, several protocols focusing on the fabrication of nano-DESI probes have been reported. In this tutorial, we discuss different hardware requirements for coupling the nano-DESI source to commercial mass spectrometers, such as the safety interlock, inlet extension, and contact closure. In addition, we describe the structure of our custom software for controlling the nano-DESI MSI platform and provide detailed instructions for its usage. With this tutorial, interested researchers should be able to implement nano-DESI experiments in their labs.

Targeted isolation of prenylated indole alkaloids from the marine-derived fungus Penicillium janthinellum HK1‑6 using molecular networking

Four prenylated indole alkaloids (1-4) were targeted isolated from the mangrove rhizosphere soil-derived fungus Penicillium janthinellum HK1-6 by using molecular networking strategies. Among them, the planar structure and relative configuration of notoamide X (1) were elucidated by detailed analysis of the spectroscopic data especially the NOESY spectrum for the first time and its absolute configuration was determined by ECD spectrum. Furthermore, curated molecular networks of MS/MS data were generated with GNPS which allowed highlighting six prenylated indole alkaloids (5, 6, 8, 9, 11, 12) that had not previously been identified in this fungus and two (7, 10) that had never been observed in any fungus. The MS/MS fragmentation pathway of these prenylated indole alkaloids was summarized.

A Snapshot, Using a Multi-Omic Approach, of the Metabolic Cross-Talk and the Dynamics of the Resident Microbiota in Ripening Cheese Inoculated with Listeria innocua

Raw milk cheeses harbor complex microbial communities. Some of these microorganisms are technologically essential, but undesirable microorganisms can also be present. While most of the microbial dynamics and cross-talking studies involving interaction between food-derived bacteria have been carried out on agar plates in laboratory-controlled conditions, the present study evaluated the modulation of the resident microbiota and the changes of metabolite production directly in ripening raw milk cheese inoculated with Listeria innocua strains. Using a proxy of the pathogenic Listeria monocytogenes, we aimed to establish the key microbiota players and chemical signals that characterize Latteria raw milk cheese over 60 days of ripening time. The microbiota of both the control and Listeria-inoculated cheeses was analyzed using 16S rRNA targeted amplicon sequencing, while direct analysis in real time mass spectrometry (DART-HRMS) was applied to investigate the differences in the metabolic profiles of the cheeses. The diversity analysis showed the same microbial diversity trend in both the control cheese and the inoculated cheese, while the taxonomic analysis highlighted the most representative genera of bacteria in both the control and inoculated cheese: Lactobacillus and Streptococcus. On the other hand, the metabolic fingerprints revealed that the complex interactions between resident microbiota and L. innocua were governed by continuously changing chemical signals. Changes in the amounts of small organic acids, hydroxyl fatty acids, and antimicrobial compounds, including pyroglutamic acid, hydroxy-isocaproic acid, malic acid, phenyllactic acid, and lactic acid, were observed over time in the L. innocua-inoculated cheese. In cheese that was inoculated with L. innocua, Streptococcus was significantly correlated with the volatile compounds carboxylbenzaldheyde and cyclohexanecarboxylic acid, while Lactobacillus was positively correlated with some volatile and flavor compounds (cyclohexanecarboxylic acid, pyroxidal acid, aminobenzoic acid, and vanillic acid). Therefore, we determined the metabolic markers that characterize a raw milk cheese inoculated with L. innocua, the changes in these markers with the ripening time, and the positive correlation of flavor and volatile compounds with the resident microbiota. This multi-omics approach could suggest innovative food safety strategies based on the enhanced management of undesirable microorganisms by means of strain selection in raw matrices and the addition of specific antimicrobial metabolites to prevent the growth of undesirable microorganisms.

Nitrogen and sulfur for phosphorus: Lipidome adaptation of anaerobic sulfate-reducing bacteria in phosphorus-deprived conditions

Understanding how microbial lipidomes adapt to environmental and nutrient stress is crucial for comprehending microbial survival and functionality. Certain anaerobic bacteria can synthesize glycerolipids with ether/ester bonds, yet the complexities of their lipidome remodeling under varying physicochemical and nutritional conditions remain largely unexplored. In this study, we thoroughly examined the lipidome adaptations of Desulfatibacillum alkenivorans strain PF2803T, a mesophilic anaerobic sulfate-reducing bacterium known for its high proportions of alkylglycerol ether lipids in its membrane, under various cultivation conditions including temperature, pH, salinity, and ammonium and phosphorous concentrations. Employing an extensive analytical and computational lipidomic methodology, we identified an assemblage of nearly 400 distinct lipids, including a range of glycerol ether/ester lipids with various polar head groups. Information theory-based analysis revealed that temperature fluctuations and phosphate scarcity profoundly influenced the lipidome's composition, leading to an enhanced diversity and specificity of novel lipids. Notably, phosphorous limitation led to the biosynthesis of novel glucuronosylglycerols and sulfur-containing aminolipids, termed butyramide cysteine glycerols, featuring various ether/ester bonds. This suggests a novel adaptive strategy for anaerobic heterotrophs to thrive under phosphorus-depleted conditions, characterized by a diverse array of nitrogen- and sulfur-containing polar head groups, moving beyond a reliance on conventional nonphospholipid types.

ModiFinder: Tandem Mass Spectral Alignment Enables Structural Modification Site Localization

Untargeted tandem mass spectrometry (MS/MS) has become a high-throughput method to measure small molecules in complex samples. One key goal is the transformation of these MS/MS spectra into chemical structures. Computational techniques such as MS/MS library search have enabled the reidentification of known compounds. Analog library search and molecular networking extend this identification to unknown compounds. While there have been advancements in metrics for the similarity of MS/MS spectra of structurally similar compounds, there is still a lack of automated methods to provide site specific information about structural modifications. Here we introduce ModiFinder which leverages the alignment of peaks in MS/MS spectra between structurally related known and unknown small molecules. Specifically, ModiFinder focuses on shifted MS/MS fragment peaks in the MS/MS alignment. These shifted peaks putatively represent substructures of the known molecule that contain the site of the modification. ModiFinder synthesizes this information together and scores the likelihood for each atom in the known molecule to be the modification site. We demonstrate in this manuscript how ModiFinder can effectively localize modifications which extends the capabilities of MS/MS analog searching and molecular networking to accelerate the discovery of novel compounds.

TransExION: a transformer based explainable similarity metric for comparing IONS in tandem mass spectrometry

Small molecule identification is a crucial task in analytical chemistry and life sciences. One of the most commonly used technologies to elucidate small molecule structures is mass spectrometry. Spectral library search of product ion spectra (MS/MS) is a popular strategy to identify or find structural analogues. This approach relies on the assumption that spectral similarity and structural similarity are correlated. However, popular spectral similarity measures, usually calculated based on identical fragment matches between the MS/MS spectra, do not always accurately reflect the structural similarity. In this study, we propose TransExION, a Transformer based Explainable similarity metric for IONS. TransExION detects related fragments between MS/MS spectra through their mass difference and uses these to estimate spectral similarity. These related fragments can be nearly identical, but can also share a substructure. TransExION also provides a post-hoc explanation of its estimation, which can be used to support scientists in evaluating the spectral library search results and thus in structure elucidation of unknown molecules. Our model has a Transformer based architecture and it is trained on the data derived from GNPS MS/MS libraries. The experimental results show that it improves existing spectral similarity measures in searching and interpreting structural analogues as well as in molecular networking. SCIENTIFIC CONTRIBUTION: We propose a transformer-based spectral similarity metrics that improves the comparison of small molecule tandem mass spectra. We provide a post hoc explanation that can serve as a good starting point for unknown spectra annotation based on database spectra.

Machine learning-enhanced molecular network reveals global exposure to hundreds of unknown PFAS

Unknown forever chemicals like per- and polyfluoroalkyl substances (PFASs) are difficult to identify. Current platforms designed for metabolites and natural products cannot capture the diverse structural characteristics of PFAS. Here, we report an automatic PFAS identification platform (APP-ID) that screens for PFAS in environmental samples using an enhanced molecular network and identifies unknown PFAS structures using machine learning. Our networking algorithm, which enhances characteristic fragment matches, has lower false-positive rate (0.7%) than current algorithms (2.4 to 46%). Our support vector machine model identified unknown PFAS in test set with 58.3% accuracy, surpassing current software. Further, APP-ID detected 733 PFASs in real fluorochemical wastewater, 39 of which are previously unreported in environmental media. Retrospective screening of 126 PFASs against public data repository from 20 countries show PFAS substitutes are prevalent worldwide.

Genomic Analysis of Kitasatospora setae to Explore Its Biosynthetic Potential Regarding Secondary Metabolites

Actinomycetes have long been recognized as important sources of clinical antibiotics. However, the exploration of rare actinomycetes, despite their potential for producing bioactive molecules, has remained relatively limited compared to the extensively studied Streptomyces genus. The extensive investigation of Streptomyces species and their natural products has led to a diminished probability of discovering novel bioactive compounds from this group. Consequently, our research focus has shifted towards less explored actinomycetes, beyond Streptomyces, with particular emphasis on Kitasatospora setae (K. setae). The genome of K. setae was annotated and analyzed through whole-genome sequencing using multiple bio-informatics tools, revealing an 8.6 Mbp genome with a 74.42% G + C content. AntiSMASH analysis identified 40 putative biosynthetic gene clusters (BGCs), approximately half of which were recessive and unknown. Additionally, metabolomic mining utilizing mass spectrometry demonstrated the potential for this rare actinomycete to generate numerous bioactive compounds such as glycosides and macrolides, with bafilomycin being the major compound produced. Collectively, genomics- and metabolomics-based techniques confirmed K. setae's potential as a bioactive secondary metabolite producer that is worthy of further exploration.

B. Trivella D. NP3 MS Workflow: An Open-Source Software System to Empower Natural Product-Based Drug Discovery Using Untargeted Metabolomics

Natural products (or specialized metabolites) are historically the main source of new drugs. However, the current drug discovery pipelines require miniaturization and speeds that are incompatible with traditional natural product research methods, especially in the early stages of the research. This article introduces the NP3 MS Workflow, a robust open-source software system for liquid chromatography-tandem mass spectrometry (LC-MS/MS) untargeted metabolomic data processing and analysis, designed to rank bioactive natural products directly from complex mixtures of compounds, such as bioactive biota samples. NP3 MS Workflow allows minimal user intervention as well as customization of each step of LC-MS/MS data processing, with diagnostic statistics to allow interpretation and optimization of LC-MS/MS data processing by the user. NP3 MS Workflow adds improved computing of the MS2 spectra in an LC-MS/MS data set and provides tools for automatic [M + H]+ ion deconvolution using fragmentation rules; chemical structural annotation against MS2 databases; and relative quantification of the precursor ions for bioactivity correlation scoring. The software will be presented with case studies and comparisons with equivalent tools currently available. NP3 MS Workflow shows a robust and useful approach to select bioactive natural products from complex mixtures, improving the set of tools available for untargeted metabolomics. It can be easily integrated into natural product-based drug-discovery pipelines and to other fields of research at the interface of chemistry and biology.

Computational methods for processing and interpreting mass spectrometry-based metabolomics

Metabolomics has emerged as an indispensable tool for exploring complex biological questions, providing the ability to investigate a substantial portion of the metabolome. However, the vast complexity and structural diversity intrinsic to metabolites imposes a great challenge for data analysis and interpretation. Liquid chromatography mass spectrometry (LC-MS) stands out as a versatile technique offering extensive metabolite coverage. In this mini-review, we address some of the hurdles posed by the complex nature of LC-MS data, providing a brief overview of computational tools designed to help tackling these challenges. Our focus centers on two major steps that are essential to most metabolomics investigations: the translation of raw data into quantifiable features, and the extraction of structural insights from mass spectra to facilitate metabolite identification. By exploring current computational solutions, we aim at providing a critical overview of the capabilities and constraints of mass spectrometry-based metabolomics, while introduce some of the most recent trends in data processing and analysis within the field.

Selective lipid recruitment by an archaeal DPANN symbiont from its host

The symbiont Ca. Nanohaloarchaeum antarcticus is obligately dependent on its host Halorubrum lacusprofundi for lipids and other metabolites due to its lack of certain biosynthetic genes. However, it remains unclear which specific lipids or metabolites are acquired from its host, and how the host responds to infection. Here, we explored the lipidome dynamics of the Ca. Nha. antarcticus - Hrr. lacusprofundi symbiotic relationship during co-cultivation. By using a comprehensive untargeted lipidomic methodology, our study reveals that Ca. Nha. antarcticus selectively recruits 110 lipid species from its host, i.e., nearly two-thirds of the total number of host lipids. Lipid profiles of co-cultures displayed shifts in abundances of bacterioruberins and menaquinones and changes in degree of bilayer-forming glycerolipid unsaturation. This likely results in increased membrane fluidity and improved resistance to membrane disruptions, consistent with compensation for higher metabolic load and mechanical stress on host membranes when in contact with Ca. Nha. antarcticus cells. Notably, our findings differ from previous observations of other DPANN symbiont-host systems, where no differences in lipidome composition were reported. Altogether, our work emphasizes the strength of employing untargeted lipidomics approaches to provide details into the dynamics underlying a DPANN symbiont-host system.

Tailored Mass Spectral Data Exploration Using the SpecXplore Interactive Dashboard

Untargeted metabolomics promises comprehensive characterization of small molecules in biological samples. However, the field is hampered by low annotation rates and abstract spectral data. Despite recent advances in computational metabolomics, manual annotations and manual confirmation of in-silico annotations remain important in the field. Here, exploratory data analysis methods for mass spectral data provide overviews, prioritization, and structural hypothesis starting points to researchers facing large quantities of spectral data. In this research, we propose a fluid means of dealing with mass spectral data using specXplore, an interactive Python dashboard providing interactive and complementary visualizations facilitating mass spectral similarity matrix exploration. Specifically, specXplore provides a two-dimensional t-distributed stochastic neighbor embedding embedding as a jumping board for local connectivity exploration using complementary interactive visualizations in the form of partial network drawings, similarity heatmaps, and fragmentation overview maps. SpecXplore makes use of state-of-the-art ms2deepscore pairwise spectral similarities as a quantitative backbone while allowing fast changes of threshold and connectivity limitation settings, providing flexibility in adjusting settings to suit the localized node environment being explored. We believe that specXplore can become an integral part of mass spectral data exploration efforts and assist users in the generation of structural hypotheses for compounds of interest.

Metabolic insights from mass spectrometry imaging of biofilms: A perspective from model microorganisms

Biofilms are dense aggregates of bacterial colonies embedded inside a self-produced polymeric matrix. Biofilms have received increasing attention in medical, industrial, and environmental settings due to their enhanced survival. Their characterization using microscopy techniques has revealed the presence of structural and cellular heterogeneity in many bacterial systems. However, these techniques provide limited chemical detail and lack information about the molecules important for bacterial communication and virulence. Mass spectrometry imaging (MSI) bridges the gap by generating spatial chemical information with unmatched chemical detail, making it an irreplaceable analytical platform in the multi-modal imaging of biofilms. In the last two decades, over 30 species of biofilm-forming bacteria have been studied using MSI in different environments. The literature conveys both analytical advancements and an improved understanding of the effects of environmental variables such as host surface characteristics, antibiotics, and other species of microorganisms on biofilms. This review summarizes the insights from frequently studied model microorganisms. We share a detailed list of organism-wide metabolites, commonly observed mass spectral adducts, culture conditions, strains of bacteria, substrate, broad problem definition, and details of the MS instrumentation, such as ionization sources and matrix, to facilitate future studies. We also compared the spatial characteristics of the secretome under different study designs to highlight changes because of various environmental influences. In addition, we highlight the current limitations of MSI in relation to biofilm characterization to enable cross-comparison between experiments. Overall, MSI has emerged to become an important approach for the spatial/chemical characterization of bacterial biofilms and its use will continue to grow as MSI becomes more accessible.

Metabolomics-guided utilization of beneficial microbes for climate-resilient crops

In the rhizosphere, plants and microbes communicate chemically, especially under environmental stress. Over millions of years, plants and their microbiome have coevolved, sharing various chemicals, including signaling molecules. This mutual exchange impacts bacterial communication and influences plant metabolism. Inter-kingdom signal crosstalk affects bacterial colonization and plant fitness. Beneficial microbes and their metabolomes offer eco-friendly ways to enhance plant resilience and agriculture. Plant metabolites are pivotal in this dynamic interaction between host plants and their interacting beneficial microbes. Understanding these associations is key to engineering a robust microbiome for stress mitigation and improved plant growth. This review explores mechanisms behind plant-microbe interactions, the role of beneficial microbes and metabolomics, and the practical applications for addressing climate change's impact on agriculture. Integrating beneficial microbes' activities and metabolomics' application to study metabolome-driven interaction between host plants and their corresponding beneficial microbes holds promise for enhancing crop resilience and productivity.

A Sample-Centric and Knowledge-Driven Computational Framework for Natural Products Drug Discovery

The ENPKG framework organizes large heterogeneous metabolomics data sets as a knowledge graph, offering exciting opportunities for drug discovery and chemodiversity characterization.

Online and Offline Prioritization of Chemicals of Interest in Suspect Screening and Non-targeted Screening with High-Resolution Mass Spectrometry

Recent advances in high-resolution mass spectrometry (HRMS) have enabled the detection of thousands of chemicals from a single sample, while computational methods have improved the identification and quantification of these chemicals in the absence of reference standards typically required in targeted analysis. However, to determine the presence of chemicals of interest that may pose an overall impact on ecological and human health, prioritization strategies must be used to effectively and efficiently highlight chemicals for further investigation. Prioritization can be based on a chemical's physicochemical properties, structure, exposure, and toxicity, in addition to its regulatory status. This Perspective aims to provide a framework for the strategies used for chemical prioritization that can be implemented to facilitate high-quality research and communication of results. These strategies are categorized as either "online" or "offline" prioritization techniques. Online prioritization techniques trigger the isolation and fragmentation of ions from the low-energy mass spectra in real time, with user-defined parameters. Offline prioritization techniques, in contrast, highlight chemicals of interest after the data has been acquired; detected features can be filtered and ranked based on the relative abundance or the predicted structure, toxicity, and concentration imputed from the tandem mass spectrum (MS2). Here we provide an overview of these prioritization techniques and how they have been successfully implemented and reported in the literature to find chemicals of elevated risk to human and ecological environments. A complete list of software and tools is available from https://nontargetedanalysis.org/.

Compound Similarity Network as a Novel Data Mining Strategy for High-Throughput Investigation of Degradation Pathways of Organic Pollutants in Industrial Wastewater Treatment

Identification of degradation products and pathways is crucial for investigating emerging pollutants and evaluation of wastewater treatment methods. Nontargeted analysis is a powerful tool to comprehensively investigate the degradation pathways of organic pollutants in real-world wastewater samples but often generates large data sets, making it difficult to effectively locate the exact information on interests. Herein, to efficiently establish the linkages among compounds in the same degradation pathways, we introduce a compound similarity network (CSN) as a novel data mining strategy for LC-MS-based nontargeted analysis of complex wastewater samples. Different from molecular networks that cluster compounds based on MS/MS spectra similarity, our CSN strategy harnesses molecular fingerprints to establish linkages among compounds and thus is spectra-independent. The effectiveness of CSN was demonstrated by nontargeted identification of degradation pathways and products of organic pollutants in leather industrial wastewater that underwent laboratory-scale activated carbon adsorption (ACD) and ozonation treatments. Utilizing CSN in interpreting nontargeted data, we tentatively annotated 4324 compounds in the untreated leather industrial wastewater, 3246 after ACD, and 3777 after ACD/ozonation. We located 145 potential degradation pathways of organic pollutants in the ACD/ozonation process using CSN and validated 7 pathways with 15 chemical standards. CSN also revealed 5 clusters of emerging pollutants, from which 3 compounds were selected for in vitro cytotoxicity study to evaluate their potential biohazards as new pollutants. As CSN offers an efficient way to connect massive compounds and to find multiple degradation pathways in a high-throughput manner, we anticipate that it will find wide applications in nontargeted analysis of diverse environmental samples.

Exploring Culture Media Diversity to Produce Fungal Secondary Metabolites and Cyborg Cells

Fungi are microorganisms of significant biotechnological importance due to their ability to provide food and produce several value-added secondary metabolites and enzymes. Its products move billions of dollars in the pharmaceutical, cosmetics, and additives sectors. These microorganisms also play a notable role in bionanotechnology, leading to the production of hybrid biological-inorganic materials (such as cyborg cells) and the use of their enzyme complex in the biosynthesis of nanoparticles. In this sense, optimizing the fungal growth process is necessary, with selecting the cultivation medium as one of the essential factors for the microorganism to reach its maximum metabolic expression. The culture medium's composition can also impact the nanomaterial's stability and prevent the incorporation of nanoparticles into fungal cells. Therefore, our main objectives are the following: (1) compile and discuss the most commonly employed culture media for the production of fungal secondary metabolites and the formation of cyborg cells, accompanied by preparation methods; (2) provide a six-step guide to investigating the fungal metabolomic profile and (3) discuss the main procedures of microbial cultivation to produce fungal cyborg cells.

Host-gut microbiota interactions shape parasite infections in farmed Atlantic salmon

Animals and their associated microbiota share long evolutionary histories. However, it is not always clear how host genotype and microbiota interact to affect phenotype. We applied a hologenomic approach to explore how host-microbiota interactions shape lifetime growth and parasite infection in farmed Atlantic salmon (Salmo salar). Multi-omics data sets were generated from the guts of 460 salmon, 82% of which were naturally infected with an intestinal cestode. A single Mycoplasma bacterial strain, MAG01, dominated the gut metagenome of large, non-parasitized fish, consistent with previous studies showing high levels of Mycoplasma in the gut microbiota of healthy salmon. While small and/or parasitized salmon also had high abundance of MAG01, we observed increased alpha diversity in these individuals, driven by increased frequency of low-abundance Vibrionaceae and other Mycoplasma species that carried known virulence genes. Colonization by one of these cestode-associated Mycoplasma strains was associated with host individual genomic variation in long non-coding RNAs. Integrating the multi-omic data sets revealed coordinated changes in the salmon gut mRNA transcriptome and metabolome that correlated with shifts in the microbiota of smaller, parasitized fish. Our results suggest that the gut microbiota of small and/or parasitized fish is in a state of dysbiosis that partly depends on the host genotype, highlighting the value of using a hologenomic approach to incorporate the microbiota into the study of host-parasite dynamics.IMPORTANCEStudying host-microbiota interactions through the perspective of the hologenome is gaining interest across all life sciences. Intestinal parasite infections are a huge burden on human and animal health; however, there are few studies investigating the role of the hologenome during parasite infections. We address this gap in the largest multi-omics fish microbiota study to date using natural cestode infection of farmed Atlantic salmon. We find a clear association between cestode infection, salmon lifetime growth, and perturbation of the salmon gut microbiota. Furthermore, we provide the first evidence that the genetic background of the host may partly determine how the gut microbiota changes during parasite-associated dysbiosis. Our study therefore highlights the value of a hologenomic approach for gaining a more in-depth understanding of parasitism.

Nontargeted screening method for detection of illicit adulterants in dietary supplements and herbal medicines using UHPLC-QTOF-MS with fine-tuned Spec2Vec-based spectral similarity and chemical classification filter

Liquid chromatography-mass spectrometry (LC-MS) is a widely utilized technique for inspecting adulteration. Unscrupulous businesses persistently introduce novel illegal adulterants, making it necessary to develop methods to screen compounds not present in the current library. Conventional cosine similarity for mass spectral libraries matching is limited in their ability to identify structurally similar compounds. In our previous study, comparison of performance among four advanced similarity algorithms revealed that Spec2Vec exhibited the best performance in terms of both detection capability and false discovery rate, making it the chosen method for identifying illegal adulterants. However, Spec2Vec still exhibited worse performance compared to MS2DeepScore and entropy similarity in the aspects of detection capability and false discovery rate, respectively. In this study, our objective was to optimize the performance of spectral similarity for a specific compound class by fine-tuning a pretrained Spec2Vec model. Additionally, we implemented the chemical classification tool CANOPUS to address the issue of similarities in backbone structures between illegal adulterants and compounds found in herbal medicine, which can lead to false positives. We utilized glucocorticoids as potentially illicit adulterants to provide a proof-of-concept, and the results demonstrated that the fine-tuned Spec2Vec model not only exhibits a significant improvement in detection ability compared to the original model but also achieves comparable performance to MS2Deepscore. Moreover, the fine-tuned Spec2Vec model shows notably fewer false positives in comparison to MS2Deepscore. Overall, this proposed pipeline demonstrates high effectiveness and competitiveness in inspecting illegal adulterants, enhancing the analysis of large-scale MS data.

Molecular structure discovery for untargeted metabolomics using biotransformation rules and global molecular networking

Although untargeted mass spectrometry-based metabolomics is crucial for understanding life's molecular underpinnings, its effectiveness is hampered by low annotation rates of the generated tandem mass spectra. To address this issue, we introduce a novel data-driven approach, Biotransformation-based Annotation Method (BAM), that leverages molecular structural similarities inherent in biochemical reactions. BAM operates by applying biotransformation rules to known 'anchor' molecules, which exhibit high spectral similarity to unknown spectra, thereby hypothesizing and ranking potential structures for the corresponding 'suspect' molecule. BAM's effectiveness is demonstrated by its success in annotating suspect spectra in a global molecular network comprising hundreds of millions of spectra. BAM was able to assign correct molecular structures to 24.2 % of examined anchor-suspect cases, thereby demonstrating remarkable advancement in metabolite annotation.

Weighting Low-Intensity MS/MS Ions and m/z Frequency for Spectral Library Annotation

Calculating spectral similarity is a fundamental step in MS/MS data analysis in untargeted metabolomics experiments, as it facilitates the identification of related spectra and the annotation of compounds. To improve matching accuracy when querying an experimental mass spectrum against a spectral library, previous approaches have proposed increasing peak intensities for high m/z ranges. These high m/z values tend to be smaller in magnitude, yet they offer more crucial information for identifying the chemical structure. Here, we evaluate the impact of using these weights for identifying structurally related compounds and mass spectral library searches. Additionally, we propose a weighting approach that (i) takes into account the frequency of the m/z values within a spectral library in order to assign higher importance to the most common peaks and (ii) increases the intensity of lower peaks, similar to previous approaches. To demonstrate our approach, we applied weighting preprocessing to modified cosine, entropy, and fidelity distance metrics and benchmarked it against previously reported weights. Our results demonstrate how weighting-based preprocessing can assist in annotating the structure of unknown spectra as well as identifying structurally similar compounds. Finally, we examined scenarios in which the utilization of weights resulted in diminished performance, pinpointing spectral features where the application of weights might be detrimental.

IMN4NPD: An Integrated Molecular Networking Workflow for Natural Product Dereplication

Molecular networking has emerged as a standard approach for natural product (NP) discovery. However, the current pipeline based on molecular networks tends to prioritize larger clusters comprising multiple nodes. To address this issue, we present the integrated molecular networking workflow for NP dereplication (IMN4NPD). This approach not only expedites the rapid dereplication of extensive clusters within the molecular network but also places specific emphasis on self-looped or pairs of nodes, which are often overlooked by the current methods. By amalgamating the outputs from various computational tools, we efficiently dereplicate compounds falling into specific categories and provide annotations for both large cluster nodes and self-looped or pair of nodes within the molecular network. Furthermore, we have incorporated several fundamentally distinct similarity algorithms, namely, Spec2Vec and MS2DeepScore, for constructing the t-SNE network. Through comparison with modified cosine similarity, we have observed that integrating additional diverse spectral similarity measures, the resulting t-SNE network enhanced the ability to dereplicate NPs. Demonstrating the use case of an ethanol extract of Plumula nelumbinis, we illustrate that an integration of multiple computational solutions with IMN4NPD aids the dereplication, especially self-looped nodes, and in the discovery of novel compounds in NPs.

A high-throughput platform enables in situ screening of fatty acid-producing strains using laser ablation electrospray ionization mass spectrometry and a Python package

Microbial fatty acid-producing strains are commonly engineered to improve their performance for industrial applications. However, it is challenging to efficiently and rapidly screen target strains for engineering. This study reported an in situ analytical platform using laser ablation electrospray ionization mass spectrometry (LAESI-MS) for fast profiling of triacylglycerols in cellular lipid droplets of Aurantiochytrium sp. colonies cultured on agar plates. LAESI-MS approach allowed for the direct acquisition of a colony cell's characteristic fingerprint mass spectrum and MS/MS facilitated the identification of triacylglycerol species containing three fatty acyl groups. The fatty acid contents of colony cells were calculated based on the intensities of triacylglycerols from their characteristic fingerprint mass spectrum. A Python package called TAFA-LEMS (Triacylglycerol to Fatty Acid by LAESI-MS) was also developed to process the high-throughput MS data and extract fatty acid contents in colony cells. The results demonstrated that the LAESI-MS platform is fast, stable, and reproducible, with a data acquisition rate of ≤2 s per sampling point and ≤13.69% RSDs of the relative contents of fatty acids. In addition, LAESI-MS was successfully performed on the analysis of P. tricornutum and Y lipolytica strains. This in situ MS platform has the potential to become a common biotechnology platform for microbial strain engineering.

microbeMASST: a taxonomically informed mass spectrometry search tool for microbial metabolomics data

microbeMASST, a taxonomically informed mass spectrometry (MS) search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging a curated database of >60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns. Identification of microbe-derived metabolites and relative producers without a priori knowledge will vastly enhance the understanding of microorganisms' role in ecology and human health.

ChloroDBPFinder: Machine Learning-Guided Recognition of Chlorinated Disinfection Byproducts from Nontargeted LC-HRMS Analysis

High-resolution mass spectrometry (HRMS) is a prominent analytical tool that characterizes chlorinated disinfection byproducts (Cl-DBPs) in an unbiased manner. Due to the diversity of chemicals, complex background signals, and the inherent analytical fluctuations of HRMS, conventional isotopic pattern (37Cl/35Cl), mass defect, and direct molecular formula (MF) prediction are insufficient for accurate recognition of the diverse Cl-DBPs in real environmental samples. This work proposes a novel strategy to recognize Cl-containing chemicals based on machine learning. Our hierarchical machine learning framework has two random forest-based models: the first layer is a binary classifier to recognize Cl-containing chemicals, and the second layer is a multiclass classifier to annotate the number of Cl present. This model was trained using ∼1.4 million distinctive MFs from PubChem. Evaluated on over 14,000 unique MFs from NIST20, this machine learning model achieved 93.3% accuracy in recognizing Cl-containing MFs (Cl-MFs) and 92.9% accuracy in annotating the number of Cl for Cl-MFs. Furthermore, the trained model was integrated into ChloroDBPFinder, a standalone R package for the streamlined processing of LC-HRMS data and annotating both known and unknown Cl-containing compounds. Tested on existing Cl-DBP data sets related to aspartame chlorination in tap water, our ChloroDBPFinder efficiently extracted 159 Cl-containing DBP features and tentatively annotated the structures of 10 Cl-DBPs via molecular networking. In another application of a chlorinated humic substance, ChloroDBPFinder extracted 79 high-quality Cl-DBPs and tentatively annotated six compounds. In summary, our proposed machine learning strategy and the developed ChloroDBPFinder provide an advanced solution to identifying Cl-containing compounds in nontargeted analysis of water samples. It is freely available on GitHub (https://github.com/HuanLab/ChloroDBPFinder).

Pulmonary primary oxysterol and bile acid synthesis as a predictor of outcomes in pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a rare and fatal vascular disease with heterogeneous clinical manifestations. To date, molecular determinants underlying the development of PAH and related outcomes remain poorly understood. Herein, we identify pulmonary primary oxysterol and bile acid synthesis (PPOBAS) as a previously unrecognized pathway central to PAH pathophysiology. Mass spectrometry analysis of 2,756 individuals across five independent studies revealed 51 distinct circulating metabolites that predicted PAH-related mortality and were enriched within the PPOBAS pathway. Across independent single-center PAH studies, PPOBAS pathway metabolites were also associated with multiple cardiopulmonary measures of PAH-specific pathophysiology. Furthermore, PPOBAS metabolites were found to be increased in human and rodent PAH lung tissue and specifically produced by pulmonary endothelial cells, consistent with pulmonary origin. Finally, a poly-metabolite risk score comprising 13 PPOBAS molecules was found to not only predict PAH-related mortality but also outperform current clinical risk scores. This work identifies PPOBAS as specifically altered within PAH and establishes needed prognostic biomarkers for guiding therapy in PAH.

Production of structurally diverse sphingolipids by anaerobic marine bacteria in the euxinic Black Sea water column

Microbial lipids, used as taxonomic markers and physiological indicators, have mainly been studied through cultivation. However, this approach is limited due to the scarcity of cultures of environmental microbes, thereby restricting insights into the diversity of lipids and their ecological roles. Addressing this limitation, here we apply metalipidomics combined with metagenomics in the Black Sea, classifying and tentatively identifying 1623 lipid-like species across 18 lipid classes. We discovered over 200 novel, abundant, and structurally diverse sphingolipids in euxinic waters, including unique 1-deoxysphingolipids with long-chain fatty acids and sulfur-containing groups. Sphingolipids were thought to be rare in bacteria and their molecular and ecological functions in bacterial membranes remain elusive. However, genomic analysis focused on sphingolipid biosynthesis genes revealed that members of 38 bacterial phyla in the Black Sea can synthesize sphingolipids, representing a 4-fold increase from previously known capabilities and accounting for up to 25% of the microbial community. These sphingolipids appear to be involved in oxidative stress response, cell wall remodeling, and are associated with the metabolism of nitrogen-containing molecules. Our findings underscore the effectiveness of multi-omics approaches in exploring microbial chemical ecology.

Fast mass spectrometry search and clustering of untargeted metabolomics data

The throughput of mass spectrometers and the amount of publicly available metabolomics data are growing rapidly, but analysis tools such as molecular networking and Mass Spectrometry Search Tool do not scale to searching and clustering billions of mass spectral data in metabolomics repositories. To address this limitation, we designed MASST+ and Networking+, which can process datasets that are up to three orders of magnitude larger than those processed by state-of-the-art tools.