Dark matter in host-microbiome metabolomics: Tackling the unknowns-A review

Microbiota, natural products, and human health: exploring interactions for therapeutic insights

The symbiotic relationship between the human digestive system and its intricate microbiota is a captivating field of study that continues to unfold. Comprising predominantly anaerobic bacteria, this complex microbial ecosystem, teeming with trillions of organisms, plays a crucial role in various physiological processes. Beyond its primary function in breaking down indigestible dietary components, this microbial community significantly influences immune system modulation, central nervous system function, and disease prevention. Despite the strides made in microbiome research, the precise mechanisms underlying how bacterial effector functions impact mammalian and microbiome physiology remain elusive. Unlike the traditional DNA-RNA-protein paradigm, bacteria often communicate through small molecules, underscoring the imperative to identify compounds produced by human-associated bacteria. The gut microbiome emerges as a linchpin in the transformation of natural products, generating metabolites with distinct physiological functions. Unraveling these microbial transformations holds the key to understanding the pharmacological activities and metabolic mechanisms of natural products. Notably, the potential to leverage gut microorganisms for large-scale synthesis of bioactive compounds remains an underexplored frontier with promising implications. This review serves as a synthesis of current knowledge, shedding light on the dynamic interplay between natural products, bacteria, and human health. In doing so, it contributes to our evolving comprehension of microbiome dynamics, opening avenues for innovative applications in medicine and therapeutics. As we delve deeper into this intricate web of interactions, the prospect of harnessing the power of the gut microbiome for transformative medical interventions becomes increasingly tantalizing.

Identification of biological signatures of cruciferous vegetable consumption utilizing machine learning-based global untargeted stable isotope traced metabolomics

In recent years there has been increased interest in identifying biological signatures of food consumption for use as biomarkers. Traditional metabolomics-based biomarker discovery approaches rely on multivariate statistics which cannot differentiate between host- and food-derived compounds, thus novel approaches to biomarker discovery are required to advance the field. To this aim, we have developed a new method that combines global untargeted stable isotope traced metabolomics and a machine learning approach to identify biological signatures of cruciferous vegetable consumption. Participants consumed a single serving of broccoli (n = 16), alfalfa sprouts (n = 16) or collard greens (n = 26) which contained either control unlabeled metabolites, or that were grown in the presence of deuterium-labeled water to intrinsically label metabolites. Mass spectrometry analysis indicated 133 metabolites in broccoli sprouts and 139 metabolites in the alfalfa sprouts were labeled with deuterium isotopes. Urine and plasma were collected and analyzed using untargeted metabolomics on an AB SCIEX TripleTOF 5,600 mass spectrometer. Global untargeted stable isotope tracing was completed using openly available software and a novel random forest machine learning based classifier. Among participants who consumed labeled broccoli sprouts or collard greens, 13 deuterium-incorporated metabolomic features were detected in urine representing 8 urine metabolites. Plasma was analyzed among collard green consumers and 11 labeled features were detected representing 5 plasma metabolites. These deuterium-labeled metabolites represent potential biological signatures of cruciferous vegetables consumption. Isoleucine, indole-3-acetic acid-N-O-glucuronide, dihydrosinapic acid were annotated as labeled compounds but other labeled metabolites could not be annotated. This work presents a novel framework for identifying biological signatures of food consumption for biomarker discovery. Additionally, this work presents novel applications of metabolomics and machine learning in the life sciences.

Implementation of multiomic mass spectrometry approaches for the evaluation of human health following environmental exposure

Omics analyses collectively refer to the possibility of profiling genetic variants, RNA, epigenetic markers, proteins, lipids, and metabolites. The most common analytical approaches used for detecting molecules present within biofluids related to metabolism are vibrational spectroscopy techniques, represented by infrared, Raman, and nuclear magnetic resonance (NMR) spectroscopies and mass spectrometry (MS). Omics-based assessments utilizing MS are rapidly expanding and being applied to various scientific disciplines and clinical settings. Most of the omics instruments are operated by specialists in dedicated laboratories; however, the development of miniature portable omics has made the technology more available to users for field applications. Variations in molecular information gained from omics approaches are useful for evaluating human health following environmental exposure and the development and progression of numerous diseases. As MS technology develops so do statistical and machine learning methods for the detection of molecular deviations from personalized metabolism, which are correlated to altered health conditions, and they are intended to provide a multi-disciplinary overview for researchers interested in adding multiomic analysis to their current efforts. This includes an introduction to mass spectrometry-based omics technologies, current state-of-the-art capabilities and their respective strengths and limitations for surveying molecular information. Furthermore, we describe how knowledge gained from these assessments can be applied to personalized medicine and diagnostic strategies.

Natural products from the human microbiome: an emergent frontier in organic synthesis and drug discovery

Often referred to as the "second genome", the human microbiome is at the epicenter of complex inter-habitat biochemical networks like the "gut-brain axis", which has emerged as a significant determinant of cognition, overall health and well-being, as well as resistance to antibiotics and susceptibility to diseases. As part of a broader understanding of the nexus between the human microbiome, diseases and microbial interactions, whether encoded secondary metabolites (natural products) play crucial signalling roles has been the subject of intense scrutiny in the recent past. A major focus of these activities involves harvesting the genomic potential of the human microbiome via bioinformatics guided genome mining and culturomics. Through these efforts, an impressive number of structurally intriguing antibiotics, with enhanced chemical diversity vis-à-vis conventional antibiotics have been isolated from human commensal bacteria, thereby generating considerable interest in their total synthesis and expanding their therapeutic space for drug discovery. These developments augur well for the discovery of new drugs and antibiotics, particularly in the context of challenges posed by mycobacterial resistance and emerging new diseases. The current landscape of various synthetic campaigns and drug discovery initiatives on antibacterial natural products from the human microbiome is captured in this review with an intent to stimulate further activities in this interdisciplinary arena among the new generation.

Untargeted metabolomics and metagenomics reveal signatures for intramammary ceftiofur treatment and lactation stage in the cattle hindgut

The gut microbiota in cattle is essential for protein, energy, and vitamin production and hence, microbiota perturbations can affect cattle performance. This study evaluated the effect of intramammary (IMM) ceftiofur treatment and lactation stage on the functional gut microbiome and metabolome. Forty dairy cows were enrolled at dry-off. Half received IMM ceftiofur and a non-antibiotic teat sealant containing bismuth subnitrate (cases), while the other half received the teat sealant (controls). Fecal samples were collected before treatment at dry off, during the dry period (weeks 1 and 5) and the first week after calving (week 9). Shotgun metagenomic sequencing was applied to predict microbial metabolic pathways whereas untargeted metabolomics was used identify polar and nonpolar metabolites. Compared to controls, long-term changes were observed in the cows given ceftiofur, including a lower abundance of microbial pathways linked to energy production, amino acid biosynthesis, and other vital molecules. The metabolome of treated cows had elevated levels of stachyose, phosphatidylethanolamine diacylglycerol (PE-DAG), and inosine a week after the IMM ceftiofur application, indicating alterations in microbial fermentation, lipid metabolism, energy, and cellular signaling. Differences were also observed by sampling, with cows in late lactation having more diverse metabolic pathways and a unique metabolome containing higher levels of histamine and histamine-producing bacteria. These data illustrate how IMM ceftiofur treatment can alter the functionality of the hindgut metabolome and microbiome. Understanding how antibiotics and lactation stages, which are each characterized by unique diets and physiology, impact the function of resident microbes is critical to define normal gut function in dairy cattle.

Accelerating the discovery of alkyl halide-derived natural products using halide depletion

Even in the genomic era, microbial natural product discovery workflows can be laborious and limited in their ability to target molecules with specific structural features. Here we leverage an understanding of biosynthesis to develop a workflow that targets the discovery of alkyl halide-derived natural products by depleting halide anions, a key biosynthetic substrate for enzymatic halogenation, from microbial growth media. By comparing the metabolomes of bacterial cultures grown in halide-replete and deficient media, we rapidly discovered the nostochlorosides, the products of an orphan halogenase-encoding gene cluster from Nostoc punctiforme ATCC 29133. We further found that these products, a family of unusual chlorinated glycolipids featuring the rare sugar gulose, are polymerized via an unprecedented enzymatic etherification reaction. Together, our results highlight the power of leveraging an understanding of biosynthetic logic to streamline natural product discovery.

Microbiome metabolite quantification methods enabling insights into human health and disease

Many of the health-associated impacts of the microbiome are mediated by its chemical activity, producing and modifying small molecules (metabolites). Thus, microbiome metabolite quantification has a central role in efforts to elucidate and measure microbiome function. In this review, we cover general considerations when designing experiments to quantify microbiome metabolites, including sample preparation, data acquisition and data processing, since these are critical to downstream data quality. We then discuss data analysis and experimental steps to demonstrate that a given metabolite feature is of microbial origin. We further discuss techniques used to quantify common microbial metabolites, including short-chain fatty acids (SCFA), secondary bile acids (BAs), tryptophan derivatives, N-acyl amides and trimethylamine N-oxide (TMAO). Lastly, we conclude with challenges and future directions for the field.

Mathematical modeling of fluid dynamics in in vitro gut fermentation systems: A new tool to improve the interpretation of microbial metabolism

In vitro systems are widely employed to assess the impact of dietary compounds on the gut microbiota and their conversion into beneficial bacterial metabolites. However, the complex fluid dynamics and multi-segmented nature of these systems can complicate the comprehensive analysis of dietary compound fate, potentially confounding physical dilution or washout with microbial catabolism. In this study, we developed fluid dynamics models based on sets of ordinary differential equations to simulate the behavior of an inert compound within two commonly used in vitro systems: the continuous two-stage PolyFermS system and the semi-continuous multi-segmented SHIME® system as well as into various declinations of those systems. The models were validated by investigating the fate of blue dextran, demonstrating excellent agreement between experimental and modeling data (with r2 values ranging from 0.996 to 0.86 for different approaches). As a proof of concept for the utility of fluid dynamics models in in vitro system, we applied generated models to interpret metabolomic data of procyanidin A2 (ProA2) generated from the addition of proanthocyanidin (PAC)-rich cranberry extract to both the PolyFermS and SHIME® systems. The results suggested ProA2 degradation by the gut microbiota when compared to the modeling of an inert compound. Models of fluid dynamics developed in this study provide a foundation for comprehensive analysis of gut metabolic data in commonly utilized in vitro PolyFermS and SHIME® bioreactor systems and can enable a more accurate understanding of the contribution of bacterial metabolism to the variability in the concentration of target metabolites.

Integration of multiple flexible electrodes for real-time detection of barrier formation with spatial resolution in a gut-on-chip system

In healthy individuals, the intestinal epithelium forms a tight barrier to prevent gut bacteria from reaching blood circulation. To study the effect of probiotics, dietary compounds and drugs on gut barrier formation and disruption, human gut epithelial and bacterial cells can be cocultured in an in vitro model called the human microbial crosstalk (HuMiX) gut-on-a-chip system. Here, we present the design, fabrication and integration of thin-film electrodes into the HuMiX platform to measure transepithelial electrical resistance (TEER) as a direct readout on barrier tightness in real-time. As various aspects of the HuMiX platform have already been set in their design, such as multiple compressible layers, uneven surfaces and nontransparent materials, a novel fabrication method was developed whereby thin-film metal electrodes were first deposited on flexible substrates and sequentially integrated with the HuMiX system via a transfer-tape approach. Moreover, to measure localized TEER along the cell culture chamber, we integrated multiple electrodes that were connected to an impedance analyzer via a multiplexer. We further developed a dynamic normalization method because the active measurement area depends on the measured TEER levels. The fabrication process and system setup can be applicable to other barrier-on-chip systems. As a proof-of-concept, we measured the barrier formation of a cancerous Caco-2 cell line in real-time, which was mapped at four spatially separated positions along the HuMiX culture area.

Combining Experimental with Computational Infrared and Mass Spectra for High-Throughput Nontargeted Chemical Structure Identification

The inability to identify the structures of most metabolites detected in environmental or biological samples limits the utility of nontargeted metabolomics. The most widely used analytical approaches combine mass spectrometry and machine learning methods to rank candidate structures contained in large chemical databases. Given the large chemical space typically searched, the use of additional orthogonal data may improve the identification rates and reliability. Here, we present results of combining experimental and computational mass and IR spectral data for high-throughput nontargeted chemical structure identification. Experimental MS/MS and gas-phase IR data for 148 test compounds were obtained from NIST. Candidate structures for each of the test compounds were obtained from PubChem (mean = 4444 candidate structures per test compound). Our workflow used CSI:FingerID to initially score and rank the candidate structures. The top 1000 ranked candidates were subsequently used for IR spectra prediction, scoring, and ranking using density functional theory (DFT-IR). Final ranking of the candidates was based on a composite score calculated as the average of the CSI:FingerID and DFT-IR rankings. This approach resulted in the correct identification of 88 of the 148 test compounds (59%). 129 of the 148 test compounds (87%) were ranked within the top 20 candidates. These identification rates are the highest yet reported when candidate structures are used from PubChem. Combining experimental and computational MS/MS and IR spectral data is a potentially powerful option for prioritizing candidates for final structure verification.

Uncovering the Carboxylated Metabolome in Gut Microbiota-Host Co-metabolism: A Chemical Derivatization-Molecular Networking Approach

Gut microbiota-host co-metabolites serve as essential mediators of communication between the host and gut microbiota. They provide nutrient sources for host cells and regulate gut microenvironment, which are associated with a variety of diseases. Analysis of gut microbiota-host co-metabolites is of great significance to explore the host-gut microbiota interaction. In this study, we integrated chemical derivatization, liquid chromatography-mass spectrometry, and molecular networking (MN) to establish a novel CD-MN strategy for the analysis of carboxylated metabolites in gut microbial-host co-metabolism. Using this strategy, 261 carboxylated metabolites from mouse feces were detected, which grouped to various classes including fatty acids, bile acids, N-acyl amino acids, benzoheterocyclic acids, aromatic acids, and other unknown small-scale molecular clusters in MN. Based on the interpretation of the bile acid cluster, a novel type of phenylacetylated conjugates of host bile acids was identified, which were mediated by gut microbiota and exhibited a strong binding ability to Farnesoid X receptor and Takeda G protein-coupled receptor 5. Our proposed strategy offers a promising platform for uncovering carboxylated metabolites in gut microbial-host co-metabolism.

High-throughput Saccharomyces cerevisiae cultivation method for credentialing-based untargeted metabolomics

Identifying metabolites in model organisms is critical for many areas of biology, including unravelling disease aetiology or elucidating functions of putative enzymes. Even now, hundreds of predicted metabolic genes in Saccharomyces cerevisiae remain uncharacterized, indicating that our understanding of metabolism is far from complete even in well-characterized organisms. While untargeted high-resolution mass spectrometry (HRMS) enables the detection of thousands of features per analysis, many of these have a non-biological origin. Stable isotope labelling (SIL) approaches can serve as credentialing strategies to distinguish biologically relevant features from background signals, but implementing these experiments at large scale remains challenging. Here, we developed a SIL-based approach for high-throughput untargeted metabolomics in S. cerevisiae, including deep-48 well format-based cultivation and metabolite extraction, building on the peak annotation and verification engine (PAVE) tool. Aqueous and nonpolar extracts were analysed using HILIC and RP liquid chromatography, respectively, coupled to Orbitrap Q Exactive HF mass spectrometry. Of the approximately 37,000 total detected features, only 3-7% of the features were credentialed and used for data analysis with open-source software such as MS-DIAL, MetFrag, Shinyscreen, SIRIUS CSI:FingerID, and MetaboAnalyst, leading to the successful annotation of 198 metabolites using MS² database matching. Comparable metabolic profiles were observed for wild-type and sdh1Δ yeast strains grown in deep-48 well plates versus the classical shake flask format, including the expected increase in intracellular succinate concentration in the sdh1Δ strain. The described approach enables high-throughput yeast cultivation and credentialing-based untargeted metabolomics, providing a means to efficiently perform molecular phenotypic screens and help complete metabolic networks.

NMR as a tool for compound identification in mixtures

INTRODUCTION

Natural products and metabolomics are intrinsically linked through efforts to analyze complex mixtures for compound annotation. Although most studies that aim for compound identification in mixtures use MS as the main analysis technique, NMR has complementary advances that are worth exploring for enhanced structural confidence.

OBJECTIVE

This review aimed to showcase a portfolio of the main tools available for compound identification using NMR.

MATERIALS AND METHODS

COLMAR, SMART-NMR, MADByTE, and NMRfilter are presented using examples collected from real samples from the perspective of a natural product chemist. Data are also made available through Zenodo so that readers can test each case presented here.

CONCLUSION

The acquisition of ¹ H NMR, HSQC, TOCSY, HSQC-TOCSY, and HMBC data for all samples and fractions from a natural products study is strongly suggested. The same is valid for MS analysis to create a bridged analysis between both techniques in a complementary manner. The use of NOAH supersequences has also been suggested and demonstrated to save NMR time.

Comprehensive Two-Dimensional Gas Chromatography as a Bioanalytical Platform for Drug Discovery and Analysis

Over the last decades, comprehensive two-dimensional gas chromatography (GC×GC) has emerged as a significant separation tool for high-resolution analysis of disease-associated metabolites and pharmaceutically relevant molecules. This review highlights recent advances of GC×GC with different detection modalities for drug discovery and analysis, which ideally improve the screening and identification of disease biomarkers, as well as monitoring of therapeutic responses to treatment in complex biological matrixes. Selected recent GC×GC applications that focus on such biomarkers and metabolite profiling of the effects of drug administration are covered. In particular, the technical overview of recent GC×GC implementation with hyphenation to the key mass spectrometry (MS) technologies that provide the benefit of enhanced separation dimension analysis with MS domain differentiation is discussed. We conclude by highlighting the challenges in GC×GC for drug discovery and development with perspectives on future trends.

Investigation of micropollutants in household waste fractions processed by anaerobic digestion: target analysis, suspect- and non-target screening

Household waste represents a major source of energy, nutrients, and recyclable material. In order to exploit benefits and avoid hazards in the context of circular economy, the risk profile towards human and the environment should be assessed. Here, we investigated the presence of micropollutants by quantitative target analysis, suspect and non-target screening and evaluated changes in the chemical fingerprint upon anaerobic digestion. Extracts were analyzed by reversed phase liquid chromatography high-resolution mass spectrometry (LC-HRMS) and gas chromatography mass spectrometry (GC-MS). Thirty-one of 51 target micropollutants were detected in low ng/mL levels except for few detections at µg/mL levels. The micropollutants quantified in this study included the following: pharmaceuticals (salicylic acid, amitriptyline, carbamazepine); biocides (triclocarban, 2-phenylphenol); industrial compounds used in, e.g., paper industry (pentachlorphenol, PFOS, PFOA, bisphenol A); aromatics, polycyclic aromatics, and heteroaromatics, and their alkylated, hydroxylated, or carboxylated analogues. Fifty of 206 compounds from the suspect screening list were tentatively identified. These included phthalates, methylparaben, phenol, benzophenone, and pharmaceuticals, e.g., ibuprofen. Most compounds detected by GC-MS decreased more than twofold in peak height or remained unaffected by the anaerobic digestion, and very few increased more than twofold, e.g., p-cresol, menthol, and octadecanal. From the LC-HRMS non-target screening analysis, 250 chemical components were resolved using the multiway curve resolution technique PARAFAC2; of these, carbidopa was the only identified unknown.

Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools

BACKGROUND

Untargeted metabolomics approaches based on mass spectrometry obtain comprehensive profiles of complex biological samples. However, on average only 10% of the molecules can be annotated. This low annotation rate hampers biochemical interpretation and effective comparison of metabolomics studies. Furthermore, de novo structural characterization of mass spectral data remains a complicated and time-intensive process. Recently, the field of computational metabolomics has gained traction and novel methods have started to enable large-scale and reliable metabolite annotation. Molecular networking and machine learning-based in-silico annotation tools have been shown to greatly assist metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery.

AIM OF REVIEW

We highlight recent advances in computational metabolite annotation workflows with a special focus on their evaluation and comparison with other tools. Whilst the progress is substantial and promising, we also argue that inconsistencies in benchmarking different tools hamper users from selecting the most appropriate and promising method for their research. We summarize benchmarking strategies of the different tools and outline several recommendations for benchmarking and comparing novel tools.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review focuses on recent advances in mass spectral library-based and machine learning-supported metabolite annotation workflows. We discuss large-scale library matching and analogue search, the current bloom of mass spectral similarity scores, and how molecular networking has changed the field. In addition, the potentials and challenges of machine learning-supported metabolite annotation workflows are highlighted. Overall, recent developments in computational metabolomics have started to fundamentally change metabolomics workflows, and we expect that as a community we will be able to overcome current method performance ambiguities and annotation bottlenecks.

Progress and opportunities in microbial community metabolomics

The metabolome lies at the interface of host-microbiome crosstalk. Previous work has established links between chemically diverse microbial metabolites and a myriad of host physiological processes and diseases. Coupled with scalable and cost-effective technologies, metabolomics is thus gaining popularity as a tool for characterization of microbial communities, particularly when combined with metagenomics as a window into microbiome function. A systematic interrogation of microbial community metabolomes can uncover key microbial compounds, metabolic capabilities of the microbiome, and also provide critical mechanistic insights into microbiome-linked host phenotypes. In this review, we discuss methods and accompanying resources that have been developed for these purposes. The accomplishments of these methods demonstrate that metabolomes can be used to functionally characterize microbial communities, and that microbial properties can be used to identify and investigate chemical compounds.

Cellular Stress-Induced Metabolites in Escherichia coli

Escherichia coli isolates commonly inhabit the human microbiota, yet the majority of E. coli's small-molecule repertoire remains uncharacterized. We previously employed erythromycin-induced translational stress to facilitate the characterization of autoinducer-3 (AI-3) and structurally related pyrazinones derived from "abortive" tRNA synthetase reactions in pathogenic, commensal, and probiotic E. coli isolates. In this study, we explored the "missing" tryptophan-derived pyrazinone reaction and characterized two other families of metabolites that were similarly upregulated under erythromycin stress. Strikingly, the abortive tryptophanyl-tRNA synthetase reaction leads to a tetracyclic indole alkaloid metabolite (1) rather than a pyrazinone. Furthermore, erythromycin induced two naphthoquinone-functionalized metabolites (MK-hCys, 2; and MK-Cys, 3) and four lumazines (7-10). Using genetic and metabolite analyses coupled with biomimetic synthesis, we provide support that the naphthoquinones are derived from 4-dihydroxy-2-naphthoic acid (DHNA), an intermediate in the menaquinone biosynthetic pathway, and the amino acids homocysteine and cysteine. In contrast, the lumazines are dependent on a flavin intermediate and α-ketoacids from the aminotransferases AspC and TyrB. We show that one of the lumazine members (9), an indole-functionalized analogue, possesses antioxidant properties, modulates the anti-inflammatory fate of isolated TH17 cells, and serves as an aryl-hydrocarbon receptor (AhR) agonist. These three systems described here serve to illustrate that new metabolic branches could be more commonly derived from well-established primary metabolic pathways.

Analysis and Interpretation of metagenomics data: an approach

Advances in next-generation sequencing technologies have accelerated the momentum of metagenomic studies, which is increasing yearly. The metagenomics field is one of the versatile applications in microbiology, where any interaction in the environment involving microorganisms can be the topic of study. Due to this versatility, the number of applications of this omics technology reached its horizons. Agriculture is a crucial sector involving crop plants and microorganisms interacting together. Hence, studying these interactions through the lenses of metagenomics would completely disclose a new meaning to crop health and development. The rhizosphere is an essential reservoir of the microbial community for agricultural soil. Hence, we focus on the R&D of metagenomic studies on the rhizosphere of crops such as rice, wheat, legumes, chickpea, and sorghum. These recent developments are impossible without the continuous advancement seen in the next-generation sequencing platforms; thus, a brief introduction and analysis of the available sequencing platforms are presented here to have a clear picture of the workflow. Concluding the topic is the discussion about different pipelines applied to analyze data produced by sequencing techniques and have a significant role in interpreting the outcome of a particular experiment. A plethora of different software and tools are incorporated in the automated pipelines or individually available to perform manual metagenomic analysis. Here we describe 8-10 advanced, efficient pipelines used for analysis that explain their respective workflows to simplify the whole analysis process.

The critical role that spectral libraries play in capturing the metabolomics community knowledge

BACKGROUND

Spectral library searching is currently the most common approach for compound annotation in untargeted metabolomics. Spectral libraries applicable to liquid chromatography mass spectrometry have grown in size over the past decade to include hundreds of thousands to millions of mass spectra and tens of thousands of compounds, forming an essential knowledge base for the interpretation of metabolomics experiments.

AIM OF REVIEW

We describe existing spectral library resources, highlight different strategies for compiling spectral libraries, and discuss quality considerations that should be taken into account when interpreting spectral library searching results. Finally, we describe how spectral libraries are empowering the next generation of machine learning tools in computational metabolomics, and discuss several opportunities for using increasingly accessible large spectral libraries.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review focuses on the current state of spectral libraries for untargeted LC-MS/MS based metabolomics. We show how the number of entries in publicly accessible spectral libraries has increased more than 60-fold in the past eight years to aid molecular interpretation and we discuss how the role of spectral libraries in untargeted metabolomics will evolve in the near future.

Microbiome and Human Health: Current Understanding, Engineering, and Enabling Technologies

The human microbiome is composed of a collection of dynamic microbial communities that inhabit various anatomical locations in the body. Accordingly, the coevolution of the microbiome with the host has resulted in these communities playing a profound role in promoting human health. Consequently, perturbations in the human microbiome can cause or exacerbate several diseases. In this Review, we present our current understanding of the relationship between human health and disease development, focusing on the microbiomes found across the digestive, respiratory, urinary, and reproductive systems as well as the skin. We further discuss various strategies by which the composition and function of the human microbiome can be modulated to exert a therapeutic effect on the host. Finally, we examine technologies such as multiomics approaches and cellular reprogramming of microbes that can enable significant advancements in microbiome research and engineering.

Differential metabolism between biofilm and suspended Pseudomonas aeruginosa cultures in bovine synovial fluid by 2D NMR-based metabolomics

Total joint arthroplasty is a common surgical procedure resulting in improved quality of life; however, a leading cause of surgery failure is infection. Periprosthetic joint infections often involve biofilms, making treatment challenging. The metabolic state of pathogens in the joint space and mechanism of their tolerance to antibiotics and host defenses are not well understood. Thus, there is a critical need for increased understanding of the physiological state of pathogens in the joint space for development of improved treatment strategies toward better patient outcomes. Here, we present a quantitative, untargeted NMR-based metabolomics strategy for Pseudomonas aeruginosa suspended culture and biofilm phenotypes grown in bovine synovial fluid as a model system. Significant differences in metabolic pathways were found between the suspended culture and biofilm phenotypes including creatine, glutathione, alanine, and choline metabolism and the tricarboxylic acid cycle. We also identified 21 unique metabolites with the presence of P. aeruginosa in synovial fluid and one uniquely present with the biofilm phenotype in synovial fluid. If translatable in vivo, these unique metabolite and pathway differences have the potential for further development to serve as targets for P. aeruginosa and biofilm control in synovial fluid.

A look beyond dietary (poly)phenols: The low molecular weight phenolic metabolites and their concentrations in human circulation

A large number of epidemiological studies have shown that consumption of fruits, vegetables, and beverages rich in (poly)phenols promote numerous health benefits from cardiovascular to neurological diseases. Evidence on (poly)phenols has been applied mainly to flavonoids, yet the role of phenolic acids has been largely overlooked. Such phenolics present in food combine with those resulting from gut microbiota catabolism of flavonoids and chlorogenic acids and those produced by endogenous pathways, resulting in large concentrations of low molecular weight phenolic metabolites in human circulation. Independently of the origin, in human intervention studies using diets rich in (poly)phenols, a total of 137 low molecular weight phenolic metabolites have been detected and quantified in human circulation with largely unknown biological function. In this review, we will pinpoint two main aspects of the low molecular weight phenolic metabolites: (i) the microbiota responsible for their generation, and (ii) the analysis (quali- and quantitative) in human circulation and their respective pharmacokinetics. In doing so, we aim to drive scientific advances regarding the ubiquitous roles of low molecular weight phenolic metabolites using physiologically relevant concentrations and under (patho)physiologically relevant conditions in humans.

Native metabolomics identifies the rivulariapeptolide family of protease inhibitors

The identity and biological activity of most metabolites still remain unknown. A bottleneck in the exploration of metabolite structures and pharmaceutical activities is the compound purification needed for bioactivity assignments and downstream structure elucidation. To enable bioactivity-focused compound identification from complex mixtures, we develop a scalable native metabolomics approach that integrates non-targeted liquid chromatography tandem mass spectrometry and detection of protein binding via native mass spectrometry. A native metabolomics screen for protease inhibitors from an environmental cyanobacteria community reveals 30 chymotrypsin-binding cyclodepsipeptides. Guided by the native metabolomics results, we select and purify five of these compounds for full structure elucidation via tandem mass spectrometry, chemical derivatization, and nuclear magnetic resonance spectroscopy as well as evaluation of their biological activities. These results identify rivulariapeptolides as a family of serine protease inhibitors with nanomolar potency, highlighting native metabolomics as a promising approach for drug discovery, chemical ecology, and chemical biology studies.

Bioactivity-guided isolation of specialized metabolites is an iterative process. Here, the authors demonstrate a native metabolomics approach that allows for fast screening of complex metabolite extracts against a protein of interest and simultaneous structure annotation.

The Core Human Microbiome: Does It Exist and How Can We Find It? A Critical Review of the Concept

The core microbiome, which refers to a set of consistent microbial features across populations, is of major interest in microbiome research and has been addressed by numerous studies. Understanding the core microbiome can help identify elements that lead to dysbiosis, and lead to treatments for microbiome-related health states. However, defining the core microbiome is a complex task at several levels. In this review, we consider the current state of core human microbiome research. We consider the knowledge that has been gained, the factors limiting our ability to achieve a reliable description of the core human microbiome, and the fields most likely to improve that ability. DNA sequencing technologies and the methods for analyzing metagenomics and amplicon data will most likely facilitate higher accuracy and resolution in describing the microbiome. However, more effort should be invested in characterizing the microbiome’s interactions with its human host, including the immune system and nutrition. Other components of this holobiontic system should also be emphasized, such as fungi, protists, lower eukaryotes, viruses, and phages. Most importantly, a collaborative effort of experts in microbiology, nutrition, immunology, medicine, systems biology, bioinformatics, and machine learning is probably required to identify the traits of the core human microbiome.

In Silico Structure Predictions for Non-targeted Analysis: From Physicochemical Properties to Molecular Structures

While important advances have been made in high-resolution mass spectrometry (HRMS) and its applications in non-targeted analysis (NTA), the number of identified compounds in biological and environmental samples often does not exceed 5% of the detected chemical features. Our aim was to develop a computational pipeline that leverages data from HRMS but also incorporates physicochemical properties (equilibrium partition ratios between organic solvents and water; Ksolvent-water) and can propose molecular structures for detected chemical features. As these physicochemical properties are often sufficiently different across isomers, when put together, they can form a unique profile for each isomer, which we describe as the "physicochemical fingerprint". In our study, we used a comprehensive database of compounds that have been previously reported in human blood and collected their Ksolvent-water values for 129 partitioning systems. We used RDKit to calculate the number of RDKit fragments and the number of RDKit bits per molecule. We then developed and trained an artificial neural network, which used as an input the physicochemical fingerprint of a chemical feature and predicted the number and types of RDKit fragments and RDKit bits present in that structure. These were then used to search the database and propose chemical structures. The average success rate of predicting the right chemical structure ranged from 60 to 86% for the training set and from 48 to 81% for the testing set. These observations suggest that physicochemical fingerprints can assist in the identification of compounds with NTA and substantially improve the number of identified compounds.

Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research

Microbial communities on and within the host contact environmental pollutants, toxic compounds, and other xenobiotic compounds. These communities of bacteria, fungi, viruses, and archaea possess diverse metabolic potential to catabolize compounds and produce new metabolites. Microbes alter chemical disposition thus making the microbiome a natural subject of interest for toxicology. Sequencing and metabolomics technologies permit the study of microbiomes altered by acute or long-term exposure to xenobiotics. These investigations have already contributed to and are helping to re-interpret traditional understandings of toxicology. The purpose of this review is to provide a survey of the current methods used to characterize microbes within the context of toxicology. This will include discussion of commonly used techniques for conducting omic-based experiments, their respective strengths and deficiencies, and how forward-looking techniques may address present shortcomings. Finally, a perspective will be provided regarding common assumptions that currently impede microbiome studies from producing causal explanations of toxicologic mechanisms.

CFM-ID 4.0 - a web server for accurate MS-based metabolite identification

The CFM-ID 4.0 web server (https://cfmid.wishartlab.com) is an online tool for predicting, annotating and interpreting tandem mass (MS/MS) spectra of small molecules. It is specifically designed to assist researchers pursuing studies in metabolomics, exposomics and analytical chemistry. More specifically, CFM-ID 4.0 supports the: 1) prediction of electrospray ionization quadrupole time-of-flight tandem mass spectra (ESI-QTOF-MS/MS) for small molecules over multiple collision energies (10 eV, 20 eV, and 40 eV); 2) annotation of ESI-QTOF-MS/MS spectra given the structure of the compound; and 3) identification of a small molecule that generated a given ESI-QTOF-MS/MS spectrum at one or more collision energies. The CFM-ID 4.0 web server makes use of a substantially improved MS fragmentation algorithm, a much larger database of experimental and in silico predicted MS/MS spectra and improved scoring methods to offer more accurate MS/MS spectral prediction and MS/MS-based compound identification. Compared to earlier versions of CFM-ID, this new version has an MS/MS spectral prediction performance that is ∼22% better and a compound identification accuracy that is ∼35% better on a standard (CASMI 2016) testing dataset. CFM-ID 4.0 also features a neutral loss function that allows users to identify similar or substituent compounds where no match can be found using CFM-ID’s regular MS/MS-to-compound identification utility. Finally, the CFM-ID 4.0 web server now offers a much more refined user interface that is easier to use, supports molecular formula identification (from MS/MS data), provides more interactively viewable data (including proposed fragment ion structures) and displays MS mirror plots for comparing predicted with observed MS/MS spectra. These improvements should make CFM-ID 4.0 much more useful to the community and should make small molecule identification much easier, faster, and more accurate.

PeakBot: Machine learning based chromatographic peak picking

Motivation

Chromatographic peak picking is among the first steps in data processing workflows of raw LC-HRMS datasets in untargeted metabolomics applications. Its performance is crucial for the holistic detection of all metabolic features as well as their relative quantification for statistical analysis and metabolite identification. Random noise, non-baseline separated compounds and unspecific background signals complicate this task.

Results

A machine-learning-based approach entitled PeakBot was developed for detecting chromatographic peaks in LC-HRMS profile-mode data. It first detects all local signal maxima in a chromatogram, which are then extracted as super-sampled standardized areas (retention-time versus m/z). These are subsequently inspected by a custom-trained convolutional neural network that forms the basis of PeakBot’s architecture. The model reports if the respective local maximum is the apex of a chromatographic peak or not as well as its peak center and bounding box. In training and independent validation datasets used for development, PeakBot achieved a high performance with respect to discriminating between chromatographic peaks and background signals (accuracy of 0.99). For training the machine-learning model a minimum of 100 reference features are needed to learn their characteristics to achieve high-quality peak-picking results for detecting such chromatographic peaks in an untargeted fashion. PeakBot is implemented in python (3.8) and uses the TensorFlow (2.5.0) package for machine-learning related tasks. It has been tested on Linux and Windows OSs.

Availability and implementation

The package is available free of charge for non-commercial use (CC BY-NC-SA). It is available at https://github.com/christophuv/PeakBot.

Supplementary information

Supplementary data are available at Bioinformatics online.

BioTransformer 3.0-a web server for accurately predicting metabolic transformation products

BioTransformer 3.0 (https://biotransformer.ca) is a freely available web server that supports accurate, rapid and comprehensive in silico metabolism prediction. It combines machine learning approaches with a rule-based system to predict small-molecule metabolism in human tissues, the human gut as well as the external environment (soil and water microbiota). Simply stated, BioTransformer takes a molecular structure as input (SMILES or SDF) and outputs an interactively sortable table of the predicted metabolites or transformation products (SMILES, PNG images) along with the enzymes that are predicted to be responsible for those reactions and richly annotated downloadable files (CSV and JSON). The entire process typically takes less than a minute. Previous versions of BioTransformer focused exclusively on predicting the metabolism of xenobiotics (such as plant natural products, drugs, cosmetics and other synthetic compounds) using a limited number of pre-defined steps and somewhat limited rule-based methods. BioTransformer 3.0 uses much more sophisticated methods and incorporates new databases, new constraints and new prediction modules to not only more accurately predict the metabolic transformation products of exogenous xenobiotics but also the transformation products of endogenous metabolites, such as amino acids, peptides, carbohydrates, organic acids, and lipids. BioTransformer 3.0 can also support customized sequential combinations of these transformations along with multiple iterations to simulate multi-step human biotransformation events. Performance tests indicate that BioTransformer 3.0 is 40–50% more accurate, far less prone to combinatorial ‘explosions’ and much more comprehensive in terms of metabolite coverage/capabilities than previous versions of BioTransformer.

Consensus Prediction of Human Gut Microbiota-Mediated Metabolism Susceptibility for Small Molecules by Machine Learning, Structural Alerts, and Dietary Compounds-Based Average Similarity Methods

The human gut microbiota (HGM) colonizing human gastrointestinal tract (HGT) confers a repertoire of dynamic and unique metabolic capacities that are not possessed by the host and therefore is tentatively perceived as an alternative metabolic ″organ″ besides the liver in the host. Nevertheless, the significant contribution of HGM to the overall human metabolism is often overlooked in the modern drug discovery pipeline. Hence, a systematic evaluation of HGM-mediated drug metabolism is gradually important, and its computational prediction becomes increasingly necessary. In this work, a new data set containing both the HGM-mediated metabolism susceptible (HGMMS) and insusceptible (HGMMI) compounds (329 vs 320) was manually curated. Based on this data set, the first machine learning (ML) model, a new structural alerts (SA) model, and the K-nearest neighboring dietary compounds-based average similarity (AS) model were proposed to directly predict the HGM-mediated metabolism susceptibility for small molecules, and exhibit promising performance on three independent test sets. Finally, consensus prediction (ML/SA/AS) for DrugBank molecules revealed an intriguing phenomenon that a typical Michael acceptor ″α,β-unsaturated carbonyl group″ is a very common warhead for the design of covalent inhibitors and inclined to be metabolized by HGM in anaerobic HGT to generate the reduced metabolite without the reactive warhead, which could be a new concern to medicinal chemists. To the best of our knowledge, we gleaned the first HGMMS/HGMMI data set, developed the first HGMMS/HGMMI classification model, implemented a relatively comprehensive program based on ML/SA/AS approaches, and found a new phenomenon on the HGM-mediated deactivation of an extensively used warhead for covalent inhibitors.

Development of a method for dansylation of metabolites using organic solvent-compatible buffer systems for amine/phenol submetabolome analysis

Metabolomics, which serves as a readout of biological processes and diseases monitoring, is an informative research area for disease biomarker discovery and systems biology studies. In particular, reversed-phase liquid chromatography-mass spectrometry (RPLC-MS) has become a powerful and popular tool for metabolomics analysis, enabling the detection of most metabolites. Very polar and ionic metabolites, however, are less easily detected because of their poor retention in RP columns. Dansylation of metabolites simplifies the sub-metabolome analysis by reducing its complexity and increasing both hydrophobicity and ionization ability. However, the various metabolite concentrations in clinical samples have a wide dynamic range with highly individual variation in total metabolite amount, such as in saliva. The bicarbonate buffer typically used in dansylation labeling reactions induces solvent stratification, resulting in poor reproducibility, selective sample loss and an increase in false-determined metabolite peaks. In this study, we optimized the dansylation protocol for samples with wide concentration range of metabolites, utilizing diisopropylethylamine (DIPEA) or tri-ethylamine (TEA) in place of bicarbonate buffer, and presented the results of a systemic investigation of the influences of individual processes involved on the overall performance of the protocol. In addition to achieving high reproducibility, substitution of DIPEA or TEA buffer resulted in similar labeling efficiency of most metabolites and more efficient labeling of some metabolites with a higher pK_a. With this improvement, compounds that are only present in samples in trace amounts can be detected, and more comprehensive metabolomics profiles can be acquired for biomarker discovery or pathway analysis, making it possible to analyze clinical samples with limited amounts of metabolites.

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine.

Systematic characterization of human gut microbiome-secreted molecules by integrated multi-omics

The human gut microbiome produces a complex mixture of biomolecules that interact with human physiology and play essential roles in health and disease. Crosstalk between micro-organisms and host cells is enabled by different direct contacts, but also by the export of molecules through secretion systems and extracellular vesicles. The resulting molecular network, comprised of various biomolecular moieties, has so far eluded systematic study. Here we present a methodological framework, optimized for the extraction of the microbiome-derived, extracellular biomolecular complement, including nucleic acids, (poly)peptides, and metabolites, from flash-frozen stool samples of healthy human individuals. Our method allows simultaneous isolation of individual biomolecular fractions from the same original stool sample, followed by specialized omic analyses. The resulting multi-omics data enable coherent data integration for the systematic characterization of this molecular complex. Our results demonstrate the distinctiveness of the different extracellular biomolecular fractions, both in terms of their taxonomic and functional composition. This highlights the challenge of inferring the extracellular biomolecular complement of the gut microbiome based on single-omic data. The developed methodological framework provides the foundation for systematically investigating mechanistic links between microbiome-secreted molecules, including those that are typically vesicle-associated, and their impact on host physiology in health and disease.

Rheum tanguticum Alleviates Cognitive Impairment in APP/PS1 Mice by Regulating Drug-Responsive Bacteria and Their Corresponding Microbial Metabolites

Drugs targeting intestinal bacteria have shown great efficacy for alleviating symptoms of Alzheimer’s disease (AD), and microbial metabolites are important messengers. Our previous work indicated that Rheum tanguticum effectively improved cognitive function and reshaped the gut microbial homeostasis in AD rats. However, its therapeutic mechanisms remain unclear. Herein, this study aimed to elaborate the mechanisms of rhubarb for the treatment of AD by identifying effective metabolites associated with rhubarb-responsive bacteria. The results found that rhubarb reduced hippocampal inflammation and neuronal damage in APP/PS1 transgenic (Tg) mice. 16S rRNA sequencing and metabolomic analysis revealed that gut microbiota and their metabolism in Tg mice were disturbed in an age-dependent manner. Rhubarb-responsive bacteria were further identified by real-time polymerase chain reaction (RT-PCR) sequencing. Four different metabolites reversed by rhubarb were found in the position of the important nodes on rhubarb-responsive bacteria and their corresponding metabolites combined with pathological indicators co-network. Furthermore, in vitro experiments demonstrated o-tyrosine not only inhibited the viabilities of primary neurons as well as BV-2 cells, but also increased the levels of intracellular reactive oxygen species and nitric oxide. In the end, the results suggest that rhubarb ameliorates cognitive impairment in Tg mice through decreasing the abundance of o-tyrosine in the gut owing to the regulation of rhubarb-responsive bacteria. Our study provides a promising strategy for elaborating therapeutic mechanisms of bacteria-targeted drugs for AD.

Microbial metabolite delta-valerobetaine is a diet-dependent obesogen

Obesity and obesity-related metabolic disorders are linked to the intestinal microbiome. However, the causality of changes in the microbiome-host interaction affecting energy metabolism remains controversial. Here, we show the microbiome-derived metabolite δ-valerobetaine (VB) is a diet-dependent obesogen that is increased with phenotypic obesity and is correlated with visceral adipose tissue mass in humans. VB is absent in germ-free mice and their mitochondria but present in ex-germ-free conventionalized mice and their mitochondria. Mechanistic studies in vivo and in vitro show VB is produced by diverse bacterial species and inhibits mitochondrial fatty acid oxidation through decreasing cellular carnitine and mitochondrial long-chain acyl-coenzyme As. VB administration to germ-free and conventional mice increases visceral fat mass and exacerbates hepatic steatosis with a western diet but not control diet. Thus, VB provides a molecular target to understand and potentially manage microbiome-host symbiosis or dysbiosis in diet-dependent obesity.

MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra

The 'inverse problem' of mass spectrometric molecular identification ('given a mass spectrum, calculate/predict the 2D structure of the molecule whence it came') is largely unsolved, and is especially acute in metabolomics where many small molecules remain unidentified. This is largely because the number of experimentally available electrospray mass spectra of small molecules is quite limited. However, the forward problem ('calculate a small molecule's likely fragmentation and hence at least some of its mass spectrum from its structure alone') is much more tractable, because the strengths of different chemical bonds are roughly known. This kind of molecular identification problem may be cast as a language translation problem in which the source language is a list of high-resolution mass spectral peaks and the 'translation' a representation (for instance in SMILES) of the molecule. It is thus suitable for attack using the deep neural networks known as transformers. We here present MassGenie, a method that uses a transformer-based deep neural network, trained on ~6 million chemical structures with augmented SMILES encoding and their paired molecular fragments as generated in silico, explicitly including the protonated molecular ion. This architecture (containing some 400 million elements) is used to predict the structure of a molecule from the various fragments that may be expected to be observed when some of its bonds are broken. Despite being given essentially no detailed nor explicit rules about molecular fragmentation methods, isotope patterns, rearrangements, neutral losses, and the like, MassGenie learns the effective properties of the mass spectral fragment and valency space, and can generate candidate molecular structures that are very close or identical to those of the 'true' molecules. We also use VAE-Sim, a previously published variational autoencoder, to generate candidate molecules that are 'similar' to the top hit. In addition to using the 'top hits' directly, we can produce a rank order of these by 'round-tripping' candidate molecules and comparing them with the true molecules, where known. As a proof of principle, we confine ourselves to positive electrospray mass spectra from molecules with a molecular mass of 500Da or lower, including those in the last CASMI challenge (for which the results are known), getting 49/93 (53%) precisely correct. The transformer method, applied here for the first time to mass spectral interpretation, works extremely effectively both for mass spectra generated in silico and on experimentally obtained mass spectra from pure compounds. It seems to act as a Las Vegas algorithm, in that it either gives the correct answer or simply states that it cannot find one. The ability to create and to 'learn' millions of fragmentation patterns in silico, and therefrom generate candidate structures (that do not have to be in existing libraries) directly, thus opens up entirely the field of de novo small molecule structure prediction from experimental mass spectra.

Gut Microbial Metabolite-Mediated Regulation of the Intestinal Barrier in the Pathogenesis of Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is a chronic inflammatory disease. The disease has a multifactorial aetiology, involving genetic, microbial as well as environmental factors. The disease pathogenesis operates at the host-microbe interface in the gut. The intestinal epithelium plays a central role in IBD disease pathogenesis. Apart from being a physical barrier, the epithelium acts as a node that integrates environmental, dietary, and microbial cues to calibrate host immune response and maintain homeostasis in the gut. IBD patients display microbial dysbiosis in the gut, combined with an increased barrier permeability that contributes to disease pathogenesis. Metabolites produced by microbes in the gut are dynamic indicators of diet, host, and microbial interplay in the gut. Microbial metabolites are actively absorbed or diffused across the intestinal lining to affect the host response in the intestine as well as at systemic sites via the engagement of cognate receptors. In this review, we summarize insights from metabolomics studies, uncovering the dynamic changes in gut metabolite profiles in IBD and their importance as potential diagnostic and prognostic biomarkers of disease. We focus on gut microbial metabolites as key regulators of the intestinal barrier and their role in the pathogenesis of IBD.

Bridging the Gap between Analytical and Microbial Sciences in Microbiome Research

Metabolites from the microbiome influence human, animal, and environmental health, but the diversity and functional roles of these compounds have only begun to be elucidated. Comprehensively characterizing these molecules are significant challenges, as it requires expertise in analytical methods, such as mass spectrometry and nuclear magnetic resonance spectroscopy, skills that not many traditional microbiologists or microbial ecologists possess. This creates a gap between microbiome scientists that want to understand the role of microbial metabolites in microbiome systems and the skills required to generate and interpret complex metabolomics data sets. To bridge this gap, microbiome scientists should engage analytical chemists to best understand the underlying chemical principles of the data. Conversely, analytical scientists are encouraged to engage with microbiome scientists to better understand the biological questions being asked with metabolomics and to best communicate its intricacies. Better communication across the chemistry/biology disciplines will further reveal the "dark matter" within microbiomes that maintain healthy humans and environments.

Large scale enzyme based xenobiotic identification for exposomics

Advances in genomics have revealed many of the genetic underpinnings of human disease, but exposomics methods are currently inadequate to obtain a similar level of understanding of environmental contributions to human disease. Exposomics methods are limited by low abundance of xenobiotic metabolites and lack of authentic standards, which precludes identification using solely mass spectrometry-based criteria. Here, we develop and validate a method for enzymatic generation of xenobiotic metabolites for use with high-resolution mass spectrometry (HRMS) for chemical identification. Generated xenobiotic metabolites were used to confirm identities of respective metabolites in mice and human samples based upon accurate mass, retention time and co-occurrence with related xenobiotic metabolites. The results establish a generally applicable enzyme-based identification (EBI) for mass spectrometry identification of xenobiotic metabolites and could complement existing criteria for chemical identification.

Aflatoxin B1 Induces Gut-Inflammation-Associated Fecal Lipidome Changes in F344 Rats

Aflatoxin B1 (AFB1) induced intestinal epithelial damage in rodent models, which indicates that long-term exposure to AFB1 may cause chronic gut disorders. In this study we tested the hypothesis that AFB1-induced adverse effects on gut is mediated by gut-microbiota, which is partially reflected by the changes of fecal microbiome and metabolome. F344 rats were orally exposed to AFB1 of 0, 5, 25 and 75 µg kg-1 body weight for 4 weeks and fecal samples were collected. An ion-fragmentation-spectrum-based metabolomics approach was developed to investigate the fecal microbiota-associated metabolic changes in fecal samples. We found that AFB1 inhibited the hepatic and intestinal metabolism of bile constituents. As compared to the controls, bile acid synthesis-associated cholesterols in rats treated with 25 µg kg-1 (the middle-dose group) were significantly decreased in the fecal samples, e.g., lathosterol (45% reduction), cholesterol ester (21% reduction), chenodeoxycholic acid (20% reduction), dihydroxycholesterol (55% reduction), hydroxycholesterol (20% reduction), and 5-cholestene (29% reduction). While disease-associated lipids were not detectable in the feces of the control group, they were found in AFB1-treated groups, including diglyceride, monoacylglyceride, 19,20-dihydroxy-docosapentaenoic acid, and phosphatidylethanolamine. Metabolisms of carbohydrates and production of short chain fatty acids were remarkedly decreased in all treated groups. Moreover, an inflammatory-bowel-disease (IBD)-associated taxonomic structure of fecal microbiota was observed as ∼25% Lachnospiraceae, ∼25% Ruminococcaceae, < 1% Lactobacillales, which was similar to the composition pattern found in IBD patients. These results suggest that AFB1-induced disruption on gut-microbiota, partially reflected by fecal microbiome and metabolome, may play important roles in the pathogenesis of chronic gut disorders.

High-Throughput Non-targeted Chemical Structure Identification Using Gas-Phase Infrared Spectra

The high-throughput identification of unknown metabolites in biological samples remains challenging. Most current non-targeted metabolomics studies rely on mass spectrometry, followed by computational methods that rank thousands of candidate structures based on how closely their predicted mass spectra match the experimental mass spectrum of an unknown. We reasoned that the infrared (IR) spectra could be used in an analogous manner and could add orthologous structure discrimination; however, this has never been evaluated on large data sets. Here, we present results of a high-throughput computational method for predicting IR spectra of candidate compounds obtained from the PubChem database. Predicted spectra were ranked based on their similarity to gas-phase experimental IR spectra of test compounds obtained from the NIST. Our computational workflow (IRdentify) consists of a fast semiempirical quantum mechanical method for initial IR spectra prediction, ranking, and triaging, followed by a final IR spectra prediction and ranking using density functional theory. This approach resulted in the correct identification of 47% of 258 test compounds. On average, there were 2152 candidate structures evaluated for each test compound, giving a total of approximately 555,200 candidate structures evaluated. We discuss several variables that influenced the identification accuracy and then demonstrate the potential application of this approach in three areas: (1) combining IR and mass spectra rankings into a single composite rank score, (2) identifying the precursor and fragment ions using cryogenic ion vibrational spectroscopy, and (3) the incorporation of a trimethylsilyl derivatization step to extend the method compatibility to less-volatile compounds. Overall, our results suggest that matching computational with experimental IR spectra is a potentially powerful orthogonal option for adding significant high-throughput chemical structure discrimination when used with other non-targeted chemical structure identification methods.

Chemoselective and highly sensitive quantification of gut microbiome and human metabolites

The microbiome has a fundamental impact on the human host's physiology through the production of highly reactive compounds that can lead to disease development. One class of such compounds are carbonyl‐containing metabolites, which are involved in diverse biochemical processes. Mass spectrometry is the method of choice for analysis of metabolites but carbonyls are analytically challenging. Herein, we have developed a new chemical biology tool using chemoselective modification to overcome analytical limitations. Two isotopic probes allow for the simultaneous and semi‐quantitative analysis at the femtomole level as well as qualitative analysis at attomole quantities that allows for detection of more than 200 metabolites in human fecal, urine and plasma samples. This comprehensive mass spectrometric analysis enhances the scope of metabolomics‐driven biomarker discovery. We anticipate that our chemical biology tool will be of general use in metabolomics analysis to obtain a better understanding of microbial interactions with the human host and disease development.

Genome-wide scan identifies novel genetic loci regulating salivary metabolite levels

Saliva, as a biofluid, is inexpensive and non-invasive to obtain, and provides a vital tool to investigate oral health and its interaction with systemic health conditions. There is growing interest in salivary biomarkers for systemic diseases, notably cardiovascular disease. Whereas hundreds of genetic loci have been shown to be involved in the regulation of blood metabolites, leading to significant insights into the pathogenesis of complex human diseases, little is known about the impact of host genetics on salivary metabolites. Here we report the first genome-wide association study exploring 476 salivary metabolites in 1419 subjects from the TwinsUK cohort (discovery phase), followed by replication in the Study of Health in Pomerania (SHIP-2) cohort. A total of 14 distinct locus-metabolite associations were identified in the discovery phase, most of which were replicated in SHIP-2. While only a limited number of the loci that are known to regulate blood metabolites were also associated with salivary metabolites in our study, we identified several novel saliva-specific locus-metabolite associations, including associations for the AGMAT (with the metabolites 4-guanidinobutanoate and beta-guanidinopropanoate), ATP13A5 (with the metabolite creatinine) and DPYS (with the metabolites 3-ureidopropionate and 3-ureidoisobutyrate) loci. Our study suggests that there may be regulatory pathways of particular relevance to the salivary metabolome. In addition, some of our findings may have clinical significance, such as the utility of the pyrimidine (uracil) degradation metabolites in predicting 5-fluorouracil toxicity and the role of the agmatine pathway metabolites as biomarkers of oral health.

The role of fecal sulfur metabolome in inflammatory bowel diseases

Sulfur metabolism and sulfur-containing metabolites play an important role in the human digestive system, and sulfur compounds and pathways are associated with inflammatory bowel diseases (IBD). In fact, cysteine metabolism results in the production of taurine and sulfate, and gut microbes catabolize them into hydrogen sulfide, a signaling molecule with various biological functions. Besides metabolites originating from sulfur metabolism, several other sulfur-containing metabolites of different classes were detected in human feces, consisting of non-volatile and volatile compounds. Sulfated steroids and bile acids such as taurine-conjugated bile acids are the major classes along with sulfur amino acids and sulfur-containing peptides. Indeed, sulfur-containing metabolites were described in stool samples from healthy subjects, patients suffering from colorectal cancer or IBD. In metabolomics-driven studies, around 50 known sulfur-containing metabolites were linked to IBD. Taurine, taurocholic acid, taurochenodeoxycholic acid, methionine, methanethiol and hydrogen sulfide were regularly reported in IBD studies, and most of them were elevated in stool samples from IBD patients. We summarized from this review that there is strong interplay between perturbed gut microbiota in IBD, and the consistently higher abundance of sulfur-containing metabolites, which potentially represent substrates for sulfidogenic bacteria such as Bilophila or Escherichia and promote their growth. These bacteria might shift their metabolism towards the degradation of taurine and cysteine and therefore to a higher hydrogen sulfide production.

Determination of Abundant Metabolite Matrix Adducts Illuminates the Dark Metabolome of MALDI-Mass Spectrometry Imaging Datasets

Spatial metabolomics using mass spectrometry imaging (MSI) is a powerful tool to map hundreds to thousands of metabolites in biological systems. One major challenge in MSI is the annotation of m/z values, which is substantially complicated by background ions introduced throughout the chemicals and equipment used during experimental procedures. Among many factors, the formation of adducts with sodium or potassium ions, or in case of matrix-assisted laser desorption ionization (MALDI)-MSI, the presence of abundant matrix clusters strongly increases total m/z peak counts. Currently, there is a limitation to identify the chemistry of the many unknown peaks to interpret their biological function. We took advantage of the co-localization of adducts with their parent ions and the accuracy of high mass resolution to estimate adduct abundance in 20 datasets from different vendors of mass spectrometers. Metabolites ranging from lipids to amines and amino acids form matrix adducts with the commonly used 2,5-dihydroxybenzoic acid (DHB) matrix like [M + (DHB-H₂O) + H]⁺ and [M + DHB + Na]⁺. Current data analyses neglect those matrix adducts and overestimate total metabolite numbers, thereby expanding the number of unidentified peaks. Our study demonstrates that MALDI-MSI data are strongly influenced by adduct formation across different sample types and vendor platforms and reveals a major influence of so far unrecognized metabolite–matrix adducts on total peak counts (up to one third). We developed a software package, mass2adduct, for the community for an automated putative assignment and quantification of metabolite–matrix adducts enabling users to ultimately focus on the biologically relevant portion of the MSI data.

Emulating the gut-liver axis: Dissecting the microbiome's effect on drug metabolism using multiorgan-on-chip models

The homeostatic relationship between the gut, its microbiome, and the liver is crucial for the regulation of drug metabolism processes. Gut microbes are known to influence human health and disease by enhancing food metabolism and providing a first line of defense against pathogens. In addition to this, the gut microbiome also plays a key role in the processing of exogenous pharmaceutical compounds. Modeling the highly variable luminal gut environment and understanding how gut microbes can modulate drug availability or induce liver toxicity remains a challenge. However, microfluidics-based technologies such as organ-on-chips could overcome current challenges in drug toxicity assessment assays because these technologies are able to better recapitulate complex human responses. Efforts are being made to create in vitro multiorgan platforms, tailored for an individual patient's microbial background. These platforms could be used as a tool to predict the effect of the gut microbiome on pharmacokinetics in a personalized way.

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The gut microbiome and gut barrier integrity play key roles in drug metabolism.

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Gut barrier disruption leads to inflammation and modifies drug bioavailability.

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Developing a stem cell–based gut–liver model would pave the way to personalized drug testing and toxicity assessment.