Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics

Feature-Based Molecular Network for New Psychoactive Substance Identification: The Case of Synthetic Cannabinoids in a Seized e-Liquid and Biological Samples

The comprehensive detection of new psychoactive substances, including synthetic cannabinoids along with their associated metabolites in biological samples, remains an analytical challenge. To detect these chemicals, untargeted approaches using appropriate bioinformatic tools such as molecular networks are useful, albeit it necessitates as a prerequisite the identification of a node of interest within the cluster. To illustrate it, we reported in this study the identification of synthetic cannabinoids and some of their metabolites in seized e-liquid, urine, and hair collected from an 18-year-old poisoned patient hospitalized for neuropsychiatric disorders. A comprehensive analysis of the seized e-liquid was performed using gas chromatography coupled with electron ionization mass spectrometry, 1H NMR, and liquid chromatography coupled with high resolution tandem mass spectrometry combined with data processing based on molecular network strategy. It allowed researchers to detect in the e-liquid known synthetic cannabinoids including MDMB-4en-PINACA, EDMB-4en-PINACA, MMB-4en-PINACA, and MDMB-5F-PICA. Compounds corresponding to transesterification of MDMB-4en-PINACA with pentenol, glycerol, and propylene glycol were also identified. Regarding the urine sample of the patient, metabolites of MDMB-4en-PINACA were detected, including MDMB-4en-PINACA butanoic acid, dihydroxylated MDMB-4en-PINACA butanoic acid, and glucurono-conjugated MDMB-4en-PINACA butanoic acid. Hair analysis of the patient allowed the detection of MDMB-4en-PINACA and MDMB-5F-PICA in the two investigated hair segments. This untargeted analysis of seized materials and biological samples demonstrates the utility of the molecular network strategy in identifying closely related compounds and metabolites of synthetic cannabinoids. It also emphasizes the need for developing strategies to anchor molecular networks, especially for new psychoactive substances.

Impact of diet change on the gut microbiome of common marmosets (Callithrix jacchus)

Gastrointestinal diseases are the most frequently reported clinical problems in captive common marmosets (Callithrix jacchus), often affecting the health and welfare of the animal and ultimately their use as a research subject. The microbiome has been shown to be intimately connected to diet and gastrointestinal health. Here, we use shotgun metagenomics and untargeted metabolomics in fecal samples of common marmosets collected before, during, and after a dietary transition from a biscuit to a gel diet. The overall health of marmosets, measured as weight recovery and reproductive outcome, improved after the diet transition. Moreover, each marmoset pair had significant shifts in the microbiome and metabolome after the diet transition. In general, we saw a decrease in Escherichia coli and Prevotella species and an increase in Bifidobacterium species. Untargeted metabolic profiles indicated that polyamine levels, specifically cadaverine and putrescine, were high after diet transition, suggesting either an increase in excretion or a decrease in intestinal reabsorption at the intestinal level. In conclusion, our data suggest that Bifidobacterium species could potentially be useful as probiotic supplements to the laboratory marmoset diet. Future studies with a larger sample size will be beneficial to show that this is consistent with the diet change.

IMPORTANCE

Appropriate diet and health of the common marmoset in captivity are essential both for the welfare of the animal and to improve experimental outcomes. Our study shows that a gel diet compared to a biscuit diet improves the health of a marmoset colony, is linked to increases in Bifidobacterium species, and increases the removal of molecules associated with disease. The diet transition had an influence on the molecular changes at both the pair and time point group levels, but only at the pair level for the microbial changes. It appears to be more important which genes and functions present changed rather than specific microbes. Further studies are needed to identify specific components that should be considered when choosing an appropriate diet and additional supplementary foods, as well as to validate the benefits of providing probiotics. Probiotics containing Bifidobacterium species appear to be useful as probiotic supplements to the laboratory marmoset diet, but additional work is needed to validate these findings.

FragHub: A Mass Spectral Library Data Integration Workflow

Open mass spectral libraries (OMSLs) are critical for metabolite annotation and machine learning, especially given the rising volume of untargeted metabolomic studies and the development of annotation pipelines. Despite their importance, the practical application of OMSLs is hampered by the lack of standardized file formats, metadata fields, and supporting ontology. Current libraries, often restricted to specific topics or matrices, such as natural products, lipids, or the human metabolome, may limit the discovery potential of untargeted studies. The goal of FragHub is to provide users with the capability to integrate various OMSLs into a single unified format, thereby enhancing the annotation accuracy and reliability. FragHub addresses these challenges by integrating multiple OMSLs into a single comprehensive database, supporting various data formats, and harmonizing metadata. It also proposes some generic filters for the mass spectrum using a graphical user interface. Additionally, a workflow to generate in-house libraries compatible with FragHub is proposed. FragHub dynamically segregates libraries based on ionization modes and chromatography techniques, thereby enhancing data utility in metabolomic research. The FragHub Python code is publicly available under a MIT license, at the following repository: https://github.com/eMetaboHUB/FragHub. Generated data can be accessed at 10.5281/zenodo.11057687.

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.

High-confidence structural annotation of substances via multi-layer molecular network reveals the system-wide constituent alternations in milk interfered with diphenylolpropane

The spectral database-based mass spectrometry (MS) matching strategy is versatile for structural annotating in ingredient fluctuation profiling mediated by external interferences. However, the systematic variability of MS pool attributable to aliasing peaks and inadequacy of present spectral database resulted in a substantial metabolic feature depletion. An amended procedure termed multiple-charges overlap peaks extraction algorithm (MCOP) was proposed involving identifying collision-trigged dissociation precursor ions through iteratively matching mass features of fragmentations to expand the spectral reference library. We showcased the versatility and utility of established strategy in an investigation centered on the stimulation of milk mediated by diphenylolpropane (BPA). MCOP enabled efficient unknown annotations at metabolite-lipid-protein level, which elevated the accuracy of substance annotation to 85.3% after manual validation. Arginase and α-amylase (|r| > 0.75, p < 0.05) were first identified as the crucial issues via graph neural network-based virtual screening in the abnormal metabolism of urea triggered by BPA, resulting in the accumulation of arginine (original: 1.7 μg kg-1 1.7 times) and maltodextrin (original: 6.9 μg kg-1 2.9 times) and thus, exciting the potential dietary risks. Conclusively, MCOP demonstrated generalisation and scalability and substantially advanced the discovery of unknown metabolites for complex matrix samples, thus deciphering dark matter in multi-omics.

Should Transformation Products Change the Way We Manage Chemicals?

When chemical pollutants enter the environment, they can undergo diverse transformation processes, forming a wide range of transformation products (TPs), some of them benign and others more harmful than their precursors. To date, the majority of TPs remain largely unrecognized and unregulated, particularly as TPs are generally not part of routine chemical risk or hazard assessment. Since many TPs formed from oxidative processes are more polar than their precursors, they may be especially relevant in the context of persistent, mobile, and toxic (PMT) and very persistent and very mobile (vPvM) substances, which are two new hazard classes that have recently been established on a European level. We highlight herein that as a result, TPs deserve more attention in research, chemicals regulation, and chemicals management. This perspective summarizes the main challenges preventing a better integration of TPs in these areas: (1) the lack of reliable high-throughput TP identification methods, (2) uncertainties in TP prediction, (3) inadequately considered TP formation during (advanced) water treatment, and (4) insufficient integration and harmonization of TPs in most regulatory frameworks. A way forward to tackle these challenges and integrate TPs into chemical management is proposed.

The underappreciated diversity of bile acid modifications

The repertoire of modifications to bile acids and related steroidal lipids by host and microbial metabolism remains incompletely characterized. To address this knowledge gap, we created a reusable resource of tandem mass spectrometry (MS/MS) spectra by filtering 1.2 billion publicly available MS/MS spectra for bile-acid-selective ion patterns. Thousands of modifications are distributed throughout animal and human bodies as well as microbial cultures. We employed this MS/MS library to identify polyamine bile amidates, prevalent in carnivores. They are present in humans, and their levels alter with a diet change from a Mediterranean to a typical American diet. This work highlights the existence of many more bile acid modifications than previously recognized and the value of leveraging public large-scale untargeted metabolomics data to discover metabolites. The availability of a modification-centric bile acid MS/MS library will inform future studies investigating bile acid roles in health and disease.

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.