Significance estimation for large scale metabolomics annotations by spectral matching

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.

Metabolomic biomarkers for benign conditions and malignant ovarian cancer: Advancing early diagnosis

BACKGROUND

Ovarian cancer (OC) is a major global cause of death among gynecological cancers, with a high mortality rate. Early diagnosis, distinguishing between benign conditions and early malignant OC forms, is vital for successful treatment. This research investigates serum metabolites to find diagnostic biomarkers for early OC identification.

METHODS

Metabolomic profiles derived from the serum of 60 patients with benign conditions and 60 patients with malignant OC were examined using ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS). Comparative analysis revealed differential metabolites linked to OC, aiding biomarker identification for early-diagnosis of OC via machine learning features. The predictive ability of these biomarkers was evaluated against the traditional biomarker, cancer antigen 125 (CA125).

RESULTS

84 differential metabolites were identified, including 2-Thiothiazolidine-4-carboxylic acid (TTCA), Methionyl-Cysteine, and Citrulline that could serve as potential biomarkers to identify benign conditions and malignant OC. In the diagnosis of early-stage OC, the area under the curve (AUC) for Citrulline was 0.847 (95 % Confidence Interval (CI): 0.719-0.974), compared to 0.770 (95 % CI: 0.596-0.944) for TTCA, and 0.754 for Methionine-Cysteine (95 % CI: 0.589-0.919). These metabolites demonstrate a superior diagnostic capability relative to CA125, which has an AUC of 0.689 (95 % CI: 0.448-0.931). Among these biomarkers, Citrulline stands out as the most promising. Additionally, in the diagnosis of benign conditions and malignant OC, using logistic regression to combine potential biomarkers with CA125 has an AUC of 0.987 (95 % CI: 0.9708-1) has been proven to be more effective than relying solely on the traditional biomarker CA125 with an AUC of 0.933 (95 % CI: 0.870-0.996). Furthermore, among all the differential metabolites, lipid metabolites dominate, significantly impacting glycerophospholipid metabolism pathway.

CONCLUSION

The discovered serum metabolite biomarkers demonstrate excellent diagnostic performance for distinguishing between benign conditions and malignant OC and for early diagnosis of malignant OC.

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.

An assessment of AcquireX and Compound Discoverer software 3.3 for non-targeted metabolomics

We used the Exploris 240 mass spectrometer for non-targeted metabolomics on Saccharomyces cerevisiae strain BY4741 and tested AcquireX software for increasing the number of detectable compounds and Compound Discoverer 3.3 software for identifying compounds by MS2 spectral library matching. AcquireX increased the number of potentially identifiable compounds by 50% through six iterations of MS2 acquisition. On the basis of high-scoring MS2 matches made by Compound Discoverer, there were 483 compounds putatively identified from nearly 8000 candidate spectra. Comparisons to 20 amino acid standards, however, revealed instances whereby compound matches could be incorrect despite strong scores. Situations included the candidate with the top score not being the correct compound, matching the same compound at two different chromatographic peaks, assigning the highest score to a library compound much heavier than the mass for the parent ion, and grouping MS2 isomers to a single parent ion. Because the software does not calculate false positive and false discovery rates at these multiple levels where such errors can propagate, we conclude that manual examination of findings will be required post software analysis. These results will interest scientists who may use this platform for metabolomics research in diverse disciplines including medical science, environmental science, and agriculture.

Discovery of Glycation Products: Unraveling the Unknown Glycation Space Using a Mass Spectral Library from In Vitro Model Systems

The nonenzymatic reaction between amino acids (AAs) and reducing sugars, also known as the Maillard reaction, is the primary source of free glycation products (GPs) in vivo and in vitro. The limited number of MS/MS records for GPs in public libraries hinders the annotation and investigation of nonenzymatic glycation. To address this issue, we present a mass spectral library containing the experimental MS/MS spectra of diverse GPs from model systems. Based on the conceptional reaction processes and structural characteristics of products, we classified GPs into common GPs (CGPs) and modified AAs (MAAs). A workflow for annotating GPs was established based on the structural and fragmentation patterns of each GP type. The final spectral library contains 157 CGPs, 499 MAAs, and 2426 GP spectra with synthetic model system information, retention time, precursor m/z, MS/MS, and annotations. As a proof-of-concept, we demonstrated the use of the library for screening GPs in unidentified spectra of human plasma and urine. The AAs with the C6H10O5 modification, fructosylation from Amadori rearrangement, were the most found GPs. With the help of the model system, we confirmed the existence of C6H10O5-modified Valine in human plasma by matching both retention time, MS1, and MS/MS without reference standards. In summary, our GP library can serve as an online resource to quickly screen possible GPs in an untargeted metabolomics workflow, furthermore with the model system as a practical synthesis method to confirm their identity.

Reverse metabolomics for the discovery of chemical structures from humans

Determining the structure and phenotypic context of molecules detected in untargeted metabolomics experiments remains challenging. Here we present reverse metabolomics as a discovery strategy, whereby tandem mass spectrometry spectra acquired from newly synthesized compounds are searched for in public metabolomics datasets to uncover phenotypic associations. To demonstrate the concept, we broadly synthesized and explored multiple classes of metabolites in humans, including N-acyl amides, fatty acid esters of hydroxy fatty acids, bile acid esters and conjugated bile acids. Using repository-scale analysis1,2, we discovered that some conjugated bile acids are associated with inflammatory bowel disease (IBD). Validation using four distinct human IBD cohorts showed that cholic acids conjugated to Glu, Ile/Leu, Phe, Thr, Trp or Tyr are increased in Crohn's disease. Several of these compounds and related structures affected pathways associated with IBD, such as interferon-γ production in CD4+ T cells3 and agonism of the pregnane X receptor4. Culture of bacteria belonging to the Bifidobacterium, Clostridium and Enterococcus genera produced these bile amidates. Because searching repositories with tandem mass spectrometry spectra has only recently become possible, this reverse metabolomics approach can now be used as a general strategy to discover other molecules from human and animal ecosystems.

Ion entropy and accurate entropy-based FDR estimation in metabolomics

Accurate metabolite annotation and false discovery rate (FDR) control remain challenging in large-scale metabolomics. Recent progress leveraging proteomics experiences and interdisciplinary inspirations has provided valuable insights. While target-decoy strategies have been introduced, generating reliable decoy libraries is difficult due to metabolite complexity. Moreover, continuous bioinformatics innovation is imperative to improve the utilization of expanding spectral resources while reducing false annotations. Here, we introduce the concept of ion entropy for metabolomics and propose two entropy-based decoy generation approaches. Assessment of public databases validates ion entropy as an effective metric to quantify ion information in massive metabolomics datasets. Our entropy-based decoy strategies outperform current representative methods in metabolomics and achieve superior FDR estimation accuracy. Analysis of 46 public datasets provides instructive recommendations for practical application.

Identification of Novel Biomarkers for Early Diagnosis of Atherosclerosis Using High-Resolution Metabolomics

Atherosclerosis (AS) is a metabolic disorder and the pre-stage of several cardiovascular diseases, including myocardial infarction, stroke, and angina pectoris. Early detection of AS can provide the opportunity for effective management and better clinical results, along with the prevention of further progression of the disease. In the current study, an untargeted and targeted metabolomic approach was used to identify possible metabolic signatures that have altered levels in AS patients. A total of 200 serum samples from individuals with AS and normal were analyzed via liquid chromatography-high-resolution mass spectrometry. Univariate and multivariate analysis approaches were used to identify differential metabolites. A group of metabolites associated with bile acids, amino acids, steroid hormones, and purine metabolism were identified that are capable of distinguishing AS-risk sera from normal. Further, the targeted metabolomics approach confirmed that six metabolites, namely taurocholic acid, cholic acid, cortisol, hypoxanthine, trimethylamine N-oxide (TMAO), and isoleucine, were found to be significantly upregulated, while the concentrations of glycoursodeoxycholic acid, glycocholic acid, testosterone, leucine, methionine, phenylalanine, tyrosine, and valine were found to be significantly downregulated in the AS-risk sera. The receiver operating characteristic curves of three metabolites, including cortisol, hypoxanthine, and isoleucine, showed high sensitivity and specificity. Taken together, these findings suggest cortisol, hypoxanthine, and isoleucine as novel biomarkers for the early and non-invasive detection of AS. Thus, this study provides new insights for further investigations into the prevention and management of AS.

PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements

Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.

Gaussian Mixture Modeling Extensions for Improved False Discovery Rate Estimation in GC-MS Metabolomics

The ability to reliably identify small molecules (e.g., metabolites) is key toward driving scientific advancement in metabolomics. Gas chromatography-mass spectrometry (GC-MS) is an analytic method that may be applied to facilitate this process. The typical GC-MS identification workflow involves quantifying the similarity of an observed sample spectrum and other features (e.g., retention index) to that of several references, noting the compound of the best-matching reference spectrum as the identified metabolite. While a deluge of similarity metrics exist, none quantify the error rate of generated identifications, thereby presenting an unknown risk of false identification or discovery. To quantify this unknown risk, we propose a model-based framework for estimating the false discovery rate (FDR) among a set of identifications. Extending a traditional mixture modeling framework, our method incorporates both similarity score and experimental information in estimating the FDR. We apply these models to identification lists derived from across 548 samples of varying complexity and sample type (e.g., fungal species, standard mixtures, etc.), comparing their performance to that of the traditional Gaussian mixture model (GMM). Through simulation, we additionally assess the impact of reference library size on the accuracy of FDR estimates. In comparing the best performing model extensions to the GMM, our results indicate relative decreases in median absolute estimation error (MAE) ranging from 12% to 70%, based on comparisons of the median MAEs across all hit-lists. Results indicate that these relative performance improvements generally hold despite library size; however FDR estimation error typically worsens as the set of reference compounds diminishes.

BUDDY: molecular formula discovery via bottom-up MS/MS interrogation

A substantial fraction of metabolic features remains undetermined in mass spectrometry (MS)-based metabolomics, and molecular formula annotation is the starting point for unraveling their chemical identities. Here we present bottom-up tandem MS (MS/MS) interrogation, a method for de novo formula annotation. Our approach prioritizes MS/MS-explainable formula candidates, implements machine-learned ranking and offers false discovery rate estimation. Compared with the mathematically exhaustive formula enumeration, our approach shrinks the formula candidate space by 42.8% on average. Method benchmarking on annotation accuracy was systematically carried out on reference MS/MS libraries and real metabolomics datasets. Applied on 155,321 recurrent unidentified spectra, our approach confidently annotated >5,000 novel molecular formulae absent from chemical databases. Beyond the level of individual metabolic features, we combined bottom-up MS/MS interrogation with global optimization to refine formula annotations while revealing peak interrelationships. This approach allowed the systematic annotation of 37 fatty acid amide molecules in human fecal data. All bioinformatics pipelines are available in a standalone software, BUDDY ( https://github.com/HuanLab/BUDDY ).

Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics

The metabolome is composed of a vast array of molecules, including endogenous metabolites and lipids, diet- and microbiome-derived substances, pharmaceuticals and supplements, and exposome chemicals. Correct identification of compounds from this diversity of classes is essential to derive biologically relevant insights from metabolomics data. In this chapter, we aim to provide a practical overview of compound identification strategies for mass spectrometry-based metabolomics, with a particular eye toward pharmacologically-relevant studies. First, we describe routine compound identification strategies applicable to targeted metabolomics. Next, we discuss both experimental (data acquisition-focused) and computational (software-focused) strategies used to identify unknown compounds in untargeted metabolomics data. We then discuss the importance of, and methods for, assessing and reporting the level of confidence of compound identifications. Throughout the chapter, we discuss how these steps can be implemented using today's technology, but also highlight research underway to further improve accuracy and certainty of compound identification. For readers interested in interpreting metabolomics data already collected, this chapter will supply important context regarding the origin of the metabolite names assigned to features in the data and help them assess the certainty of the identifications. For those planning new data acquisition, the chapter supplies guidance for designing experiments and selecting analysis methods to enable accurate compound identification, and it will point the reader toward best-practice data analysis and reporting strategies to allow sound biological and pharmacological interpretation.

Problems, principles and progress in computational annotation of NMR metabolomics data

BACKGROUND

Compound identification remains a critical bottleneck in the process of exploiting Nuclear Magnetic Resonance (NMR) metabolomics data, especially for ¹H 1-dimensional (¹H 1D) data. As databases of reference compound spectra have grown, workflows have evolved to rely heavily on their search functions to facilitate this process by generating lists of potential metabolites found in complex mixture data, facilitating annotation and identification. However, approaches for validating and communicating annotations are most often guided by expert knowledge, and therefore are highly variable despite repeated efforts to align practices and define community standards.

AIM OF REVIEW

This review is aimed at broadening the application of automated annotation tools by discussing the key ideas of spectral matching and beginning to describe a set of terms to classify this information, thus advancing standards for communicating annotation confidence. Additionally, we hope that this review will facilitate the growing collaboration between chemical data scientists, software developers and the NMR metabolomics community aiding development of long-term software solutions.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We begin with a brief discussion of the typical untargeted NMR identification workflow. We differentiate between annotation (hypothesis generation, filtering), and identification (hypothesis testing, verification), and note the utility of different NMR data features for annotation. We then touch on three parts of annotation: (1) generation of queries, (2) matching queries to reference data, and (3) scoring and confidence estimation of potential matches for verification. In doing so, we highlight existing approaches to automated and semi-automated annotation from the perspective of the structural information they utilize, as well as how this information can be represented computationally.

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.

Enhancing untargeted metabolomics using metadata-based source annotation

Human untargeted metabolomics studies annotate only ~10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data.

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.

An untargeted metabolomics analysis of exogenous chemicals in human milk and transfer to the infant

Human milk is the optimal infant nutrition. However, although human-derived metabolites (such as lipids and oligosaccharides) in human milk are regularly reported, the presence of exogenous chemicals (such as drugs, food, and synthetic compounds) are often not addressed. To understand the types of exogenous compounds that might be present, human milk (n = 996) was analyzed by untargeted metabolomics. This analysis revealed that lifestyle molecules, such as medications and their metabolites, and industrial sources, such as plasticizers, cosmetics, and other personal care products, are found in human milk. We provide further evidence that some of these lifestyle molecules are also detectable in the newborn's stool. Thus, this study gives important insight into the types of exposures infants receiving human milk might ingest due to the lifestyle choices, exposure, or medical status of the lactating parent.

A Novel Approach of SWATH-Based Metabolomics Analysis Using the Human Metabolome Database Spectral Library

Metabolomics is a potential approach to paving new avenues for clinical diagnosis, molecular medicine, and therapeutic drug monitoring and development. The conventional metabolomics analysis pipeline depends on the data-independent acquisition (DIA) technique. Although powerful, it still suffers from stochastic, non-reproducible ion selection across samples. Despite the presence of different metabolomics workbenches, metabolite identification remains a tedious and time-consuming task. Consequently, sequential windowed acquisition of all theoretical MS (SWATH) acquisition has attracted much attention to overcome this limitation. This article aims to develop a novel SWATH platform for data analysis with a generation of an accurate mass spectral library for metabolite identification using SWATH acquisition. The workflow was validated using inclusion/exclusion compound lists. The false-positive identification was 3.4% from the non-endogenous drugs with 96.6% specificity. The workflow has proven to overcome background noise despite the complexity of the SWATH sample. From the Human Metabolome Database (HMDB), 1282 compounds were tested in various biological samples to demonstrate the feasibility of the workflow. The current study identified 377 compounds in positive and 303 in negative modes with 392 unique non-redundant metabolites. Finally, a free software tool, SASA, was developed to analyze SWATH-acquired samples using the proposed pipeline.

Multi-Task Neural Networks and Molecular Fingerprints to Enhance Compound Identification from LC-MS/MS Data

Mass spectrometry (MS) is widely used for the identification of chemical compounds by matching the experimentally acquired mass spectrum against a database of reference spectra. However, this approach suffers from a limited coverage of the existing databases causing a failure in the identification of a compound not present in the database. Among the computational approaches for mining metabolite structures based on MS data, one option is to predict molecular fingerprints from the mass spectra by means of chemometric strategies and then use them to screen compound libraries. This can be carried out by calibrating multi-task artificial neural networks from large datasets of mass spectra, used as inputs, and molecular fingerprints as outputs. In this study, we prepared a large LC-MS/MS dataset from an on-line open repository. These data were used to train and evaluate deep-learning-based approaches to predict molecular fingerprints and retrieve the structure of unknown compounds from their LC-MS/MS spectra. Effects of data sparseness and the impact of different strategies of data curing and dimensionality reduction on the output accuracy have been evaluated. Moreover, extensive diagnostics have been carried out to evaluate modelling advantages and drawbacks as a function of the explored chemical space.

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.

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

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

An Organ-Specific Metabolite Annotation Approach for Ambient Mass Spectrometry Imaging Reveals Spatial Metabolic Alterations of a Whole Mouse Body

Rapid and accurate metabolite annotation in mass spectrometry imaging (MSI) can improve the efficiency of spatially resolved metabolomics studies and accelerate the discovery of reliable in situ disease biomarkers. To date, metabolite annotation tools in MSI generally utilize isotopic patterns, but high-throughput fragmentation-based identification and biological and technical factors that influence structure elucidation are active challenges. Here, we proposed an organ-specific, metabolite-database-driven approach to facilitate efficient and accurate MSI metabolite annotation. Using data-dependent acquisition (DDA) in liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) to generate high-coverage product ions, we identified 1620 unique metabolites from eight mouse organs (brain, liver, kidney, heart, spleen, lung, muscle, and pancreas) and serum. Following the evaluation of the adduct form difference of metabolite ions between LC-MS and airflow-assisted desorption electrospray ionization (AFADESI)-MSI and deciphering organ-specific metabolites, we constructed a metabolite database for MSI consisting of 27,407 adduct ions. An automated annotation tool, MSIannotator, was then created to conduct metabolite annotation in the MSI dataset with high efficiency and confidence. We applied this approach to profile the spatially resolved landscape of the whole mouse body and discovered that metabolites were distributed across the body in an organ-specific manner, which even spanned different mouse strains. Furthermore, the spatial metabolic alteration in diabetic mice was delineated across different organs, exhibiting that differentially expressed metabolites were mainly located in the liver, brain, and kidney, and the alanine, aspartate, and glutamate metabolism pathway was simultaneously altered in these three organs. This approach not only enables robust metabolite annotation and visualization on a body-wide level but also provides a valuable database resource for underlying organ-specific metabolic mechanisms.

SIMILE enables alignment of tandem mass spectra with statistical significance

Interrelating small molecules according to their aligned fragmentation spectra is central to tandem mass spectrometry-based untargeted metabolomics. Current alignment algorithms do not provide statistical significance and compounds that have multiple delocalized structural differences and therefore often fail to have their fragment ions aligned. Here we align fragmentation spectra with both statistical significance and allowance for multiple chemical differences using Significant Interrelation of MS/MS Ions via Laplacian Embedding (SIMILE). SIMILE yields spectral alignment inferred structural connections in molecular networks that are not found with cosine-based scoring algorithms. In addition, it is now possible to rank spectral alignments based on p-values in the exploration of structural relationships between compounds and enhance the chemical connectivity that can be obtained with molecular networking.

Interrelating metabolites by their fragmentation spectra is central to metabolomics. Here the authors align fragmentation spectra with both statistical significance and allowance for multiple chemical differences using Significant Interrelation of MS/MS Ions via Laplacian Embedding (SIMILE).

foodMASST a mass spectrometry search tool for foods and beverages

There is a growing interest in unraveling the chemical complexity of our diets. To help the scientific community gain insight into the molecules present in foods and beverages that we ingest, we created foodMASST, a search tool for MS/MS spectra (of both known and unknown molecules) against a growing metabolomics food and beverage reference database. We envision foodMASST will become valuable for nutrition research and to assess the potential uniqueness of dietary biomarkers to represent specific foods or food classes.

Machine Learning-Based Retention Time Prediction of Trimethylsilyl Derivatives of Metabolites

In gas chromatography–mass spectrometry-based untargeted metabolomics, metabolites are identified by comparing mass spectra and chromatographic retention time with reference databases or standard materials. In that sense, machine learning has been used to predict the retention time of metabolites lacking reference data. However, the retention time prediction of trimethylsilyl derivatives of metabolites, typically analyzed in untargeted metabolomics using gas chromatography, has been poorly explored. Here, we provide a rationalized framework for machine learning-based retention time prediction of trimethylsilyl derivatives of metabolites in gas chromatography. We compared different machine learning paradigms, in addition to exploring the influence of the computational molecular structure representation to train the prediction models: fingerprint class and fingerprint calculation software. Our study challenged predicted retention time when using chemical ionization and electron impact ionization sources in simulated and real cases, demonstrating a good correct identity ranking capability by machine learning, despite observing a limited false identity filtering power in cases where a spectrum or a monoisotopic mass match to multiple candidates. Specifically, machine learning prediction yielded median absolute and relative retention index (relative retention time) errors of 37.1 retention index units and 2%, respectively. In addition, fingerprint class and fingerprint calculation software, as well as the molecular structural similarity between the training and test or real case sets, showed to be critical modulators of the prediction performance. Finally, we leveraged the structural similarity between the training and test or real case set to determine the probability that the prediction error is below a specific threshold. Overall, our study demonstrates that predicted retention time can provide insights into the true structure of unknown metabolites by ranking from the most to the least plausible molecular identity, and sets the guidelines to assess the confidence in metabolite identification using predicted retention time data.

Dereplication of Acetogenins from Annona muricata by Combining Tandem Mass Spectrometry after Lithium and Copper Postcolumn Cationization and Molecular Networks

Annonaceous acetogenins are natural products held responsible for atypical Parkinsonism due to chronic consumption in traditional medicine or as food, leading to the development of analytical strategies for their complete chemical characterization in complex mixtures. Characterization by tandem mass spectrometry (MS/MS) of acetogenins using collision-induced dissociation from lithium adducts provides additional structural information compared to protonated or sodiated species such as ketone location on the acetogenin backbone. However, very low intensity diagnostic ions together with the lack of extensive structural information regarding position of OH and THF substituents limit this approach. Copper adducts led to diagnostic fragment ions that allow us to identify the position of oxygen rings and hydroxyl substituents. Fragmentation rules were established on the basis of acetogenin standards allowing the identification of 45 over the 77 analogues observed in an extract of Annona muricata by LC-MS/MS using postcolumn infusion of copper sulfate (CuSO₄) solution. Molecular networks that were generated thanks to specific fragmentations obtained with copper led to the distinction of THF ring position or to the identification of hydroxylated lactone, for instance.

Metabolic Footprinting of Microbial Systems Based on Comprehensive In Silico Predictions of MS/MS Relevant Data

Metabolic footprinting represents a holistic approach to gathering large-scale metabolomic information of a given biological system and is, therefore, a driving force for systems biology and bioprocess development. The ongoing development of automated cultivation platforms increases the need for a comprehensive and rapid profiling tool to cope with the cultivation throughput. In this study, we implemented a workflow to provide and select relevant metabolite information from a genome-scale model to automatically build an organism-specific comprehensive metabolome analysis method. Based on in-house literature and predicted metabolite information, the deduced metabolite set was distributed in stackable methods for a chromatography-free dilute and shoot flow-injection analysis multiple-reaction monitoring profiling approach. The workflow was used to create a method specific for Saccharomyces cerevisiae, covering 252 metabolites with 7 min/sample. The method was validated with a commercially available yeast metabolome standard, identifying up to 74.2% of the listed metabolites. As a first case study, three commercially available yeast extracts were screened with 118 metabolites passing quality control thresholds for statistical analysis, allowing to identify discriminating metabolites. The presented methodology provides metabolite screening in a time-optimised way by scaling analysis time to metabolite coverage and is open to other microbial systems simply starting from genome-scale model information.

DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics

The extraction of meaningful biological knowledge from high-throughput mass spectrometry data relies on limiting false discoveries to a manageable amount. For targeted approaches in metabolomics a main challenge is the detection of false positive metabolic features in the low signal-to-noise ranges of data-independent acquisition results and their filtering. Another factor is that the creation of assay libraries for data-independent acquisition analysis and the processing of extracted ion chromatograms have not been automated in metabolomics. Here we present a fully automated open-source workflow for high-throughput metabolomics that combines data-dependent and data-independent acquisition for library generation, analysis, and statistical validation, with rigorous control of the false-discovery rate while matching manual analysis regarding quantification accuracy. Using an experimentally specific data-dependent acquisition library based on reference substances allows for accurate identification of compounds and markers from data-independent acquisition data in low concentrations, facilitating biomarker quantification.

Mass spectrometry-based metabolomics in microbiome investigations

Microbiotas are a malleable part of ecosystems, including the human ecosystem. Microorganisms affect not only the chemistry of their specific niche, such as the human gut, but also the chemistry of distant environments, such as other parts of the body. Mass spectrometry-based metabolomics is one of the key technologies to detect and identify the small molecules produced by the human microbiota, and to understand the functional role of these microbial metabolites. This Review provides a foundational introduction to common forms of untargeted mass spectrometry and the types of data that can be obtained in the context of microbiome analysis. Data analysis remains an obstacle; therefore, the emphasis is placed on data analysis approaches and integrative analysis, including the integration of microbiome sequencing data.

Chemical Characterization and Non-targeted Analysis of Medical Device Extracts: A Review of Current Approaches, Gaps, and Emerging Practices

The developers of medical devices evaluate the biocompatibility of their device prior to FDA's review and subsequent introduction to the market. Chemical characterization, described in ISO 10993-18:2020, can generate information for toxicological risk assessment and is an alternative approach for addressing some biocompatibility end points (e.g., systemic toxicity, genotoxicity, carcinogenicity, reproductive/developmental toxicity) that can reduce the time and cost of testing and the need for animal testing. Additionally, chemical characterization can be used to determine whether modifications to the materials and manufacturing processes alter the chemistry of a patient-contacting device to an extent that could impact device safety. Extractables testing is one approach to chemical characterization that employs combinations of non-targeted analysis, non-targeted screening, and/or targeted analysis to establish the identities and quantities of the various chemical constituents that can be released from a device. Due to the difficulty in obtaining a priori information on all the constituents in finished devices, information generation strategies in the form of analytical chemistry testing are often used. Identified and quantified extractables are then assessed using toxicological risk assessment approaches to determine if reported quantities are sufficiently low to overcome the need for further chemical analysis, biological evaluation of select end points, or risk control. For extractables studies to be useful as a screening tool, comprehensive and reliable non-targeted methods are needed. Although non-targeted methods have been adopted by many laboratories, they are laboratory-specific and require expensive analytical instruments and advanced technical expertise to perform. In this Perspective, we describe the elements of extractables studies and provide an overview of the current practices, identified gaps, and emerging practices that may be adopted on a wider scale in the future. This Perspective is outlined according to the steps of an extractables study: information gathering, extraction, extract sample processing, system selection, qualification, quantification, and identification.

An actionable annotation scoring framework for gas chromatography-high-resolution mass spectrometry

Omics-based technologies have enabled comprehensive characterization of our exposure to environmental chemicals (chemical exposome) as well as assessment of the corresponding biological responses at the molecular level (eg, metabolome, lipidome, proteome, and genome). By systematically measuring personal exposures and linking these stimuli to biological perturbations, researchers can determine specific chemical exposures of concern, identify mechanisms and biomarkers of toxicity, and design interventions to reduce exposures. However, further advancement of metabolomics and exposomics approaches is limited by a lack of standardization and approaches for assigning confidence to chemical annotations. While a wealth of chemical data is generated by gas chromatography high-resolution mass spectrometry (GC-HRMS), incorporating GC-HRMS data into an annotation framework and communicating confidence in these assignments is challenging. It is essential to be able to compare chemical data for exposomics studies across platforms to build upon prior knowledge and advance the technology. Here, we discuss the major pieces of evidence provided by common GC-HRMS workflows, including retention time and retention index, electron ionization, positive chemical ionization, electron capture negative ionization, and atmospheric pressure chemical ionization spectral matching, molecular ion, accurate mass, isotopic patterns, database occurrence, and occurrence in blanks. We then provide a qualitative framework for incorporating these various lines of evidence for communicating confidence in GC-HRMS data by adapting the Schymanski scoring schema developed for reporting confidence levels by liquid chromatography HRMS (LC-HRMS). Validation of our framework is presented using standards spiked in plasma, and confident annotations in outdoor and indoor air samples, showing a false-positive rate of 12% for suspect screening for chemical identifications assigned as Level 2 (when structurally similar isomers are not considered false positives). This framework is easily adaptable to various workflows and provides a concise means to communicate confidence in annotations. Further validation, refinements, and adoption of this framework will ideally lead to harmonization across the field, helping to improve the quality and interpretability of compound annotations obtained in GC-HRMS.

Impact of Citrus Pulp or Inulin on Intestinal Microbiota and Metabolites, Barrier, and Immune Function of Weaned Piglets

We investigated the use of citrus pulp (CP) as a novel prebiotic capable of exerting microbiota and immunomodulating capacities to alleviate weaning stress. Inulin (IN), a well-known prebiotic, was used for comparison. Hundred and 28 male weaned piglets of 21 days old were assigned to 32 pens of 4 piglets each. Piglets were assigned to one of the four treatments, i.e., control, IN supplemented at 0.2% (IN0.2%), and CP supplemented either at 0.2% (CP0.2%) or at 2% (CP2%). On d10–11 and d31–32 post-weaning, one pig per pen was euthanized for intestinal sampling to evaluate the growth performance, chyme characteristics, small intestinal morphology, colonic inflammatory response and barrier integrity, metabolite profiles [gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-mass spectrometry (LC-MS)], and microbial populations. The IN treatment and the two CP treatments induced higher small intestinal villus height to crypt depth ratios in comparison with the control diet at both sampling times. All treatments decreased acidic goblet cell absolute counts in the crypts in comparison to the control diet of the duodenum on d10–11 and d31–32. The gene expression of β-defensin 2 was downregulated in colonic tissues following the IN and CP2% inclusion on d31–32. On d31–32, piglets fed with IN and CP0.2% showed lower mRNA levels of occludin and claudin-3, respectively. Not surprisingly, flavonoids were observed in the colon in the CP treatments. Increased colonic acetate proportions on d10–11, at the expense of branched-chain fatty acid (BCFA) levels, were observed following the CP2% supplementation compared to the control diet, inferring a reduction of proteolytic fermentation in the hindgut. The beneficial microbial community Faecalibacterium spp. was promoted in the colon of piglets fed with CP2% on d10–11 (p = 0.04; false discovery rate (FDR) non-significant) and on d31–32 (p = 0.03; FDR non-significant) in comparison with the control diet. Additionally, on d31–32, CP2% increased the relative abundance of Megasphaera spp. compared to control values (p = 0.03; FDR non-significant). In conclusion, CP2% promoted the growth of beneficial bacterial communities in both post-weaning time points, modulating colonic fermentation patterns in the colon. The effects of CP supplementation were similar to those of IN and showed the potential as a beneficial feed supplement to alleviate weaning stress.

An Introduction to the Benchmarking and Publications for Non-Targeted Analysis Working Group

Non-targeted analysis (NTA) encompasses a rapidly evolving set of mass spectrometry techniques aimed at characterizing the chemical composition of complex samples, identifying unknown compounds, and/or classifying samples, without prior knowledge regarding the chemical content of the samples. Recent advances in NTA are the result of improved and more accessible instrumentation for data generation and analysis tools for data evaluation and interpretation. As researchers continue to develop NTA approaches in various scientific fields, there is a growing need to identify, disseminate, and adopt community-wide method reporting guidelines. In 2018, NTA researchers formed the Benchmarking and Publications for Non-Targeted Analysis Working Group (BP4NTA) to address this need. Consisting of participants from around the world and representing fields ranging from environmental science and food chemistry to 'omics and toxicology, BP4NTA provides resources addressing a variety of challenges associated with NTA. Thus far, BP4NTA group members have aimed to establish a consensus on NTA-related terms and concepts and to create consistency in reporting practices by providing resources on a public Web site, including consensus definitions, reference content, and lists of available tools. Moving forward, BP4NTA will provide a setting for NTA researchers to continue discussing emerging challenges and contribute to additional harmonization efforts.

Modifying Chromatography Conditions for Improved Unknown Feature Identification in Untargeted Metabolomics

Untargeted metabolomics is an essential component of systems biology research, but it is plagued by a high proportion of detectable features not identified with a chemical structure. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments produce spectra that can be searched against databases to help identify or classify these unknowns, but many features do not generate spectra of sufficient quality to enable successful annotation. Here, we explore alterations to gradient length, mass loading, and rolling precursor ion exclusion parameters for reversed phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) that improve compound identification performance for human plasma samples. A manual review of spectral matches from the HILIC data set was used to determine reasonable thresholds for search score and other metrics to enable semi-automated MS/MS data analysis. Compared to typical LC-MS/MS conditions, methods adapted for compound identification increased the total number of unique metabolites that could be matched to a spectral database from 214 to 2052. Following data alignment, 68.0% of newly identified features from the modified conditions could be detected and quantitated using a routine 20-min LC-MS run. Finally, a localized machine learning model was developed to classify the remaining unknowns and select a subset that shared spectral characteristics with successfully identified features. A total of 576 and 749 unidentified features in the HILIC and RPLC data sets were classified by the model as high-priority unknowns or higher-importance targets for follow-up analysis. Overall, our study presents a simple strategy to more deeply annotate untargeted metabolomics data for a modest additional investment of time and sample.

Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification

Compound identification in small-molecule research, such as untargeted metabolomics or exposome research, relies on matching tandem mass spectrometry (MS/MS) spectra against experimental or in silico mass spectral libraries. Most software programs use dot product similarity scores. Here we introduce the concept of MS/MS spectral entropy to improve scoring results in MS/MS similarity searches via library matching. Entropy similarity outperformed 42 alternative similarity algorithms, including dot product similarity, when searching 434,287 spectra against the high-quality NIST20 library. Entropy similarity scores proved to be highly robust even when we added different levels of noise ions. When we applied entropy levels to 37,299 experimental spectra of natural products, false discovery rates of less than 10% were observed at entropy similarity score 0.75. Experimental human gut metabolome data were used to confirm that entropy similarity largely improved the accuracy of MS-based annotations in small-molecule research to false discovery rates below 10%, annotated new compounds and provided the basis to automatically flag poor-quality, noisy spectra.

Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches

Covering: up to the end of 2020Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions.

High-confidence structural annotation of metabolites absent from spectral libraries

Untargeted metabolomics experiments rely on spectral libraries for structure annotation, but, typically, only a small fraction of spectra can be matched. Previous in silico methods search in structure databases but cannot distinguish between correct and incorrect annotations. Here we introduce the COSMIC workflow that combines in silico structure database generation and annotation with a confidence score consisting of kernel density P value estimation and a support vector machine with enforced directionality of features. On diverse datasets, COSMIC annotates a substantial number of hits at low false discovery rates and outperforms spectral library search. To demonstrate that COSMIC can annotate structures never reported before, we annotated 12 natural bile acids. The annotation of nine structures was confirmed by manual evaluation and two structures using synthetic standards. In human samples, we annotated and manually validated 315 molecular structures currently absent from the Human Metabolome Database. Application of COSMIC to data from 17,400 metabolomics experiments led to 1,715 high-confidence structural annotations that were absent from spectral libraries.

FT-ICR-MS-based metabolomics: A deep dive into plant metabolism

Metabolomics involves the identification and quantification of metabolites to unravel the chemical footprints behind cellular regulatory processes and to decipher metabolic networks, opening new insights to understand the correlation between genes and metabolites. In plants, it is estimated the existence of hundreds of thousands of metabolites and the majority is still unknown. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a powerful analytical technique to tackle such challenges. The resolving power and sensitivity of this ultrahigh mass accuracy mass analyzer is such that a complex mixture, such as plant extracts, can be analyzed and thousands of metabolite signals can be detected simultaneously and distinguished based on the naturally abundant elemental isotopes. In this review, FT-ICR-MS-based plant metabolomics studies are described, emphasizing FT-ICR-MS increasing applications in plant science through targeted and untargeted approaches, allowing for a better understanding of plant development, responses to biotic and abiotic stresses, and the discovery of new natural nutraceutical compounds. Improved metabolite extraction protocols compatible with FT-ICR-MS, metabolite analysis methods and metabolite identification platforms are also explored as well as new in silico approaches. Most recent advances in MS imaging are also discussed.

Chemical Gradients of Plant Substrates in an Atta texana Fungus Garden

Many ant species grow fungus gardens that predigest food as an essential step of the ants' nutrient uptake. These symbiotic fungus gardens have long been studied and feature a gradient of increasing substrate degradation from top to bottom. To further facilitate the study of fungus gardens and enable the understanding of the predigestion process in more detail than currently known, we applied recent mass spectrometry-based approaches and generated a three-dimensional (3D) molecular map of an Atta texana fungus garden to reveal chemical modifications as plant substrates pass through it. The metabolomics approach presented in this study can be applied to study similar processes in natural environments to compare with lab-maintained ecosystems. IMPORTANCE The study of complex ecosystems requires an understanding of the chemical processes involving molecules from several sources. Some of the molecules present in fungus-growing ants' symbiotic system originate from plants. To facilitate the study of fungus gardens from a chemical perspective, we provide a molecular map of an Atta texana fungus garden to reveal chemical modifications as plant substrates pass through it. The metabolomics approach presented in this study can be applied to study similar processes in natural environments.

Meeting report of the fourth annual Tri-Service Microbiome Consortium symposium

The Tri-Service Microbiome Consortium (TSMC) was founded to enhance collaboration, coordination, and communication of microbiome research among U.S. Department of Defense (DoD) organizations. The annual TSMC symposium is designed to enable information sharing between DoD scientists and leaders in the field of microbiome science, thereby keeping DoD consortium members informed of the latest advances within the microbiome community and facilitating the development of new collaborative research opportunities. The 2020 annual symposium was held virtually on 24-25 September 2020. Presentations and discussions centered on microbiome-related topics within four broad thematic areas: (1) Enabling Technologies; (2) Microbiome for Health and Performance; (3) Environmental Microbiome; and (4) Microbiome Analysis and Discovery. This report summarizes the presentations and outcomes of the 4th annual TSMC symposium.

Natural products dereplication by diffusion ordered NMR spectroscopy (DOSY)

Diffusion-ordered NMR spectroscopy (DOSY) can be used to analyze mixtures of compounds since resonances deriving from different compounds are distinguished by their diffusion coefficients (D). Previously, DOSY has mostly been used for organometallic and polymer analysis, we have now applied DOSY to investigate diffusion coefficients of structurally diverse organic compounds such as natural products (NP). The experimental Ds derived from 55 diverse NPs has allowed us to establish a power law relationship between D and molecular weight (MW) and therefore predict MW from experimental D. We have shown that D is also affected by factors such as hydrogen bonding, molar density and molecular shape of the compound and we have generated new models that incorporate experimentally derived variables for these factors so that more accurate predictions of MW can be calculated from experimental D. The recognition that multiple physicochemical properties affect D has allowed us to generate a polynomial equation based on multiple linear regression analysis of eight calculated physicochemical properties from 63 compounds to accurately correlate predicted D with experimental D for any known organic compound. This equation has been used to calculate predicted D for 217 043 compounds present in a publicly available natural product database (DEREP-NP) and to dereplicate known NPs in a mixture based on matching of experimental D and structural features derived from NMR analysis with predicted D and calculated structural features in the database. These models have been validated by the dereplication of a mixture of two known sesquiterpenes obtained from Tasmannia xerophila and the identification of new alkaloids from the bryozoan Amathia lamourouxi. These new methodologies allow the MW of compounds in mixtures to be predicted without the need for MS analysis, the dereplication of known compounds and identification of new compounds based solely on parameters derived by DOSY NMR.

We report accurate DOSY NMR based molecular weight and diffusion coefficient prediction tools. These tools can be used to dereplicate known natural products from databases using structurally rich NMR data as a surrogate for mass spectrometric data.

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.

UHPLC-HRMS/MS on untargeted metabolomics: a case study with Copaifera (Fabaceae)

Untargeted metabolomics is a powerful tool in chemical fingerprinting. It can be applied in phytochemistry to aid species identification, systematic studies and quality control of bioproducts. This approach aims to produce as much chemical information as possible, without focusing on any specific chemical class, thus, requiring extensive chemometric effort. This study aimed to evaluate the feasibly of an untargeted metabolomics method in phytochemistry by a study case of the Copaifera genus (Fabaceae). This genus contains significant medicinal species used worldwidely. Copaifera exploitation issues include a lack of chemical data, ambiguous species identification methods and absence of quality control for its bioproducts. Different organs of five Copaifera species were analysed by UHPLC-HRMS/MS, GNPS platform and chemometric tools. Untargeted metabolomics enabled the identification of 19 chemical markers and 29 metabolites, distinguishing each sample by species, plant organs, and biome type. Chemical markers were classified as flavonoids, terpenoids and condensed tannins. The applied method provided reliable information about species chemodiversity using fast workflow with little sampling size. The untargeted approach by UHPLC-HRMS/MS proved to be a promising tool for species identification, pharmacological prospecting and in the future for the quality control of extracts used in the manufacture of bioproducts.

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.

Intermittent Hypoxia and Hypercapnia Alter Diurnal Rhythms of Luminal Gut Microbiome and Metabolome

Obstructive sleep apnea (OSA), characterized by intermittent hypoxia and hypercapnia (IHC), affects the composition of the gut microbiome and metabolome. The gut microbiome has diurnal oscillations that play a crucial role in regulating circadian and overall metabolic homeostasis. Thus, we hypothesized that IHC adversely alters the gut luminal dynamics of key microbial families and metabolites. The objective of this study was to determine the diurnal dynamics of the fecal microbiome and metabolome of Apoe-/- mice after a week of IHC exposure. Individually housed, 10-week-old Apoe-/- mice on an atherogenic diet were split into two groups. One group was exposed to daily IHC conditions for 10 h (Zeitgeber time 2 [ZT2] to ZT12), while the other was maintained in room air. Six days after the initiation of the IHC conditions, fecal samples were collected every 4 h for 24 h (6 time points). We performed 16S rRNA gene amplicon sequencing and untargeted liquid chromatography-mass spectrometry (LC-MS) to assess changes in the microbiome and metabolome. IHC induced global changes in the cyclical dynamics of the gut microbiome and metabolome. Ruminococcaceae, Lachnospiraceae, S24-7, and Verrucomicrobiaceae had the greatest shifts in their diurnal oscillations. In the metabolome, bile acids, glycerolipids (phosphocholines and phosphoethanolamines), and acylcarnitines were greatly affected. Multi-omic analysis of these results demonstrated that Ruminococcaceae and tauro-β-muricholic acid (TβMCA) cooccur and are associated with IHC conditions and that Coriobacteriaceae and chenodeoxycholic acid (CDCA) cooccur and are associated with control conditions. IHC significantly change the diurnal dynamics of the fecal microbiome and metabolome, increasing members and metabolites that are proinflammatory and proatherogenic while decreasing protective ones. IMPORTANCE People with obstructive sleep apnea are at a higher risk of high blood pressure, type 2 diabetes, cardiac arrhythmias, stroke, and sudden cardiac death. We wanted to understand whether the gut microbiome changes induced by obstructive sleep apnea could potentially explain some of these medical problems. By collecting stool from a mouse model of this disease at multiple time points during the day, we studied how obstructive sleep apnea changed the day-night patterns of microbes and metabolites of the gut. Since the oscillations of the gut microbiome play a crucial role in regulating metabolism, changes in these oscillations can explain why these patients can develop so many metabolic problems. We found changes in microbial families and metabolites that regulate many metabolic pathways contributing to the increased risk for heart disease seen in patients with obstructive sleep apnea.

Non-targeted tandem mass spectrometry enables the visualization of organic matter chemotype shifts in coastal seawater

Urbanization along coastlines alters marine ecosystems including contributing molecules of anthropogenic origin to the coastal dissolved organic matter (DOM) pool. A broad assessment of the nature and extent of anthropogenic impacts on coastal ecosystems is urgently needed to inform regulatory guidelines and ecosystem management. Recently, non-targeted tandem mass spectrometry approaches are gaining momentum for the analysis of global organic matter composition (chemotypes) including a wide array of natural and anthropogenic compounds. In line with these efforts, we developed a non-targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) workflow that utilizes advanced data analysis approaches such as feature-based molecular networking and repository-scale spectrum searches. This workflow allows the scalable comparison and mapping of seawater chemotypes from large-scale spatial surveys as well as molecular family level annotation of unknown compounds. As a case study, we visualized organic matter chemotype shifts in coastal environments in northern San Diego, USA, after notable rain fall in winter 2017/2018 and highlight potential anthropogenic impacts. The observed seawater chemotype, consisting of 4384 LC-MS/MS features, shifted significantly after a major rain event. Molecular drivers of this shift could be attributed to multiple anthropogenic compounds, including pesticides (Imazapyr and Isoxaben), cleaning products (Benzyl-tetradecyl-dimethylammonium) and chemical additives (Hexa (methoxymethyl)melamine) and potential degradation products. By expanding the search of identified xenobiotics to other public tandem mass spectrometry datasets, we further contextualized their possible origin and show their importance in other ecosystems. The mass spectrometry and data analysis pipelines applied here offer a scalable framework for future molecular mapping and monitoring of marine ecosystems, which will contribute to a deliberate assessment of how chemical pollution impacts our oceans.

Non-targeted screening of trace organic contaminants in surface waters by a multi-tool approach based on combinatorial analysis of tandem mass spectra and open access databases

Non-targeted screening (NTS) in mass spectrometry (MS) helps alleviate the shortcoming of targeted analysis such as missing the presence of concerning compounds that are not monitored and its lack of retrospective analysis to subsequently look for new contaminants. Most NTS workflows include high resolution tandem mass spectrometry (HRMS²) and structure annotation with libraries which are still limited. However, in silico combinatorial fragmentation tools that simulate MS² spectra are available to help close the gap of missing compounds in empirical libraries. Three NTS tools were combined and used to detect and identify unknown contaminants at ultra-trace levels in surface waters in real samples in this qualitative study. Two of them were based on combinatorial fragmentation databases, MetFrag and the Similar Partition Searching algorithm (SPS), and the third, the Global Natural Products Social Networking (GNPS), was an ensemble of empirical databases. The three NTS tools were applied to the analysis of real samples from a local river. A total of 253 contaminants were identified by combining all three tools: 209 were assigned a probable structure and 44 were confirmed using reference standards. The two major classes of contaminants observed were pharmaceuticals and consumer product additives. Among the confirmed compounds, octylphenol ethoxylates, denatonium, irbesartan and telmisartan are reported for the first time in surface waters in Canada. The workflow presented in this work uses three highly complementary NTS tools and it is a powerful approach to help identify and strategically select contaminants and their transformation products for subsequent targeted analysis and uncover new trends in surface water contamination.

Metabolomics data complemented drug use information in epidemiological databases: pilot study of potential kidney donors

OBJECTIVE

The objective of this study was to investigate whether clinical metabolomics, which is increasingly applied in population-based and epidemiological studies, can be used to provide analytical evidence of exposures, and whether such information can be useful to strengthen and/or complement corresponding clinical database entries, taking drug use as an example.

STUDY DESIGN AND SETTING

Liquid chromatography-mass spectrometry (LC-MS) metabolomics analyses were performed on urine from 100 randomly-selected control subjects (50% females) from the TransplantLines Food and Nutrition Biobank and Cohort Study (NCT identifier 'NCT02811835'), and drugs were identified through spectral library searching and targeted signal extraction.

RESULTS

In 83 subjects for whom drug use information was available, 22 expected and 26 unexpected prescription-only drugs were identified, while 28 expected prescription-only drugs remained undetected. In addition, 7 prescription-only drugs were found in 17 subjects for whom drug use information was unavailable, and 58 over-the-counter drugs were identified in all 100 subjects.

CONCLUSION

Molecular evidence for many drugs could be retrieved from LC-MS metabolomics data, which could be useful to complement and strengthen epidemiological databases given that considerable discrepancies were found between analytically-identified drugs and drugs listed in the available clinical database.