Mass spectral databases for LC/MS- and GC/MS-based metabolomics: State of the field and future prospects

Mass spectrometry-based analysis of gut microbial metabolites of aromatic amino acids

Small molecules derived from gut microbiota have been increasingly investigated to better understand the functional roles of the human gut microbiome. Microbial metabolites of aromatic amino acids (AAA) have been linked to many diseases, such as metabolic disorders, chronic kidney diseases, inflammatory bowel disease, diabetes, and cancer. Important microbial AAA metabolites are often discovered via global metabolite profiling of biological specimens collected from humans or animal models. Subsequent metabolite identity confirmation and absolute quantification using targeted analysis enable comparisons across different studies, which can lead to the establishment of threshold concentrations of potential metabolite biomarkers. Owing to their excellent selectivity and sensitivity, hyphenated mass spectrometry (MS) techniques are often employed to identify and quantify AAA metabolites in various biological matrices. Here, we summarize the developments over the past five years in MS-based methodology for analyzing gut microbiota-derived AAA. Sample preparation, method validation, analytical performance, and statistical methods for correlation analysis are discussed, along with future perspectives.

Novel Peak Shift Correction Method Based on the Retention Index for Peak Alignment in Untargeted Metabolomics

Peak alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based large-scale untargeted metabolomics workflows, as it enables the integration of metabolite peaks across multiple samples, which is essential for accurate data interpretation. Slight differences or fluctuations in chromatographic separation conditions, however, can cause the chromatographic retention time (RT) shift between consecutive analyses, ultimately affecting the accuracy of peak alignment between samples. Here, we introduce a novel RT shift correction method based on the retention index (RI) and apply it to peak alignment. We synthesized a series of N-acyl glycine (C2-C23) homologues via the amidation reaction between glycine with normal saturated fatty acids (C2-C23) as calibrants able to respond proficiently in both mass spectrometric positive- and negative-ion modes. Using these calibrants, we established an N-acyl glycine RI system. This RI system is capable of covering a broad chromatographic space and addressing chromatographic RT shift caused by variations in flow rate, gradient elution, instrument systems, and LC separation columns. Moreover, based on the RI system, we developed a peak shift correction model to enhance peak alignment accuracy. Applying the model resulted in a significant improvement in the accuracy of peak alignment from 15.5 to 80.9% across long-term data spanning a period of 157 days. To facilitate practical application, we developed a Python-based program, which is freely available at https://github.com/WHU-Fenglab/RI-based-CPSC.

Comprehensive comparison of different parts of Paeonia ostii, a food-medicine plant, based on untargeted metabolomics, quantitative analysis, and bioactivity analysis

Introduction

Paeonia ostii T. Hong & J.X. Zhang (s.s.) (Chinese name, Fengdan) is a widely cultivated food-medicine plant in China, in which root bark, seed kernels, and flowers are utilized for their medicinal and edible values. However, other parts of the plant are not used efficiently, in part due to a poor understanding of their chemical composition and potential biological activity.

Methods

Untargeted ultra-performance liquid chromatography-quadrupole time of flight-mass spectrometry (UPLC-Q-TOF-MS) metabolomics was applied to characterize the metabolic profiles of 10 different parts of P. ostii.

Results and discussion

A total of 160 metabolites were alternatively identified definitely or tentatively, which were significantly different in various plant parts by multivariate statistical analysis. Quantitative analysis showed that underutilized plant parts also contain many active ingredients. Compared with the medicinal part of root bark, the root core part still contains a higher content of paeoniflorin (17.60 ± 0.06 mg/g) and PGG (15.50 ± 2.00 mg/g). Petals, as an edible part, contain high levels of quercitrin, and stamens have higher methyl gallate and PGG. Unexpectedly, the ovary has the highest content of methyl gallate and rather high levels of PGG (38.14 ± 1.27 mg/g), and it also contains surprisingly high concentrations of floralalbiflorin I. Paeoniflorin (38.68 ± 0.76 mg/g) is the most abundant in leaves, and the content is even higher than in the root bark. Branches are also rich in a variety of catechin derivatives and active ingredients such as hydrolyzable tannins. Seed kernels also contain fairly high levels of paeoniflorin and albiflorin. Fruit shells still contain a variety of components, although not at high levels. Seed coats, as by-products removed from peony seeds before oil extraction, have high contents of stilbenes, such as trans-gnetin H and suffruticosol B, showing significant potential for exploitation. Except for the seed kernels, extracts obtained from other parts exhibited good antioxidant activity in DPPH, ABTS, and ferric ion reducing antioxidant power (FRAP) assays (0.09-1.52 mmol TE/g). Five compounds (gallic acid, PGG, trans-resveratrol, kaempferol, and quercitrin) were important ingredients that contributed to their antioxidant activities. Furthermore, P. ostii seed cakes were first reported to possess agonistic activity toward CB1/CB2 receptors. This study provides a scientific basis for the further development and utilization of P. ostii plant resources.

Hollow Crystallization COF Capsuled MOF Hybrids Depict Serum Metabolic Profiling for Precise Early Diagnosis and Risk Stratification of Acute Coronary Syndrome

Acute coronary syndrome (ACS), comprising unstable angina (UA) and acute myocardial infarction (AMI), is the leading cause of death worldwide. Currently, lacking effective strategies for classifying ACS hinders the prognosis improvement of ACS patients. Disclosing the nature of metabolic disorders holds the potential to reflect disease progress and high-throughput mass spectrometry-based metabolic analysis is a promising tool for large-scale screening. Herein, a hollow crystallization COF capsuled MOF hybrids (UiO-66@HCOF) assisted serum metabolic analysis is developed for the early diagnosis and risk stratification of ACS. UiO-66@HCOF exhibits unrivaled chemical and structural stability as well as endowing satisfying desorption/ionization efficiency in the detection of metabolites. Paired with machine learning algorithms, early diagnosis of ACS is achieved with the area under the curve (AUC) value of 0.945 for validation sets. Besides, a comprehensive ACS risk stratification method is established, and the AUC value for the discrimination of ACS from healthy controls, and AMI from UA are 0.890, and 0.928. Moreover, the AUC value of the subtyping of AMI is 0.964. Finally, the potential biomarkers exhibit high sensitivity and specificity. This study makes metabolic molecular diagnosis a reality and provided new insight into the progress of ACS.

Metabolomics as a tool to elucidate biochemical cold adaptation in insects

Metabolomics is an incredibly valuable tool in helping understand insect responses to cold. It not only characterizes how low temperature disrupts metabolic homeostasis, but also how it triggers fundamental adaptive responses, for example, homeoviscous adaptation and cryoprotectant accumulation. This review outlines the advantages and disadvantages of different metabolomic technologies (nuclear magnetic resonance- versus mass spectrometry-based) and screening approaches (targeted versus untargeted). We emphasize the importance of time-series and tissue-specific data, as well as the challenges of disentangling insect versus microbiome responses. In addition, we set out the need to move beyond simple correlations between metabolite abundance and tolerance phenotypes by undertaking functional assessments, for example, using dietary supplementation or injections. We highlight studies at the vanguard of employing these approaches, and where key knowledge gaps remain.

Quantitative Analytical and Computational Workflow for Large-Scale Targeted Plasma Metabolomics

Quantifying metabolites from various biological samples is necessary for the clinical and biomedical translation of metabolomics research. One of the ongoing challenges in biomedical metabolomics studies is the large-scale quantification of targeted metabolites, mainly due to the complexity of biological sample matrices. Furthermore, in LC-MS analysis, the response of compounds is influenced by their physicochemical properties, chromatographic conditions, eluent composition, sample preparation, type of MS ionization source, and analyzer used. To facilitate large-scale metabolite quantification, we evaluated the relative response factor (RRF) approach combined with an integrated analytical and computational workflow. This approach considers a compound's individual response in LC-MS analysis relative to that of a non-endogenous reference compound to correct matrix effects. We created a quantitative LC-MS library using the Skyline/Panorama web platform for data processing and public sharing of data. In this study, we developed and validated a metabolomics method for over 280 standard metabolites and quantified over 90 metabolites. The RRF quantification was validated and compared with conventional external calibration approaches as well as literature reports. The Skyline software environment was adapted for processing such metabolomics data, and the results are shared as a "quantitative chromatogram library" with the Panorama web application. This new workflow was found to be suitable for large-scale quantification of metabolites in human plasma samples. In conclusion, we report a novel quantitative chromatogram library with a targeted data analysis workflow for biomedical metabolomic applications.

Exploration of Potential Breath Biomarkers of Chronic Kidney Disease through Thermal Desorption-Gas Chromatography/Mass Spectrometry

Breath volatile organic compound (VOC) analysis is a non-invasive tool for assessing health status; the compositional profile of these compounds in the breath of patients with chronic kidney disease is believed to change with decreasing renal function. We aimed to identify breath VOCs for recognizing patients with chronic kidney disease. Using thermal desorption-gas chromatography/mass spectrometry, untargeted analysis of breath markers was performed using breath samples of healthy controls (n = 18) versus non-dialysis (n = 21) and hemodialysis (n = 12) patients with chronic kidney disease in this cross-sectional study. A total of 303 VOCs alongside 12 clinical variables were used to determine the breath VOC profile. Metabolomic analysis revealed that age, systolic blood pressure, and fifty-eight breath VOCs differed significantly between the chronic kidney disease group (non-dialysis + hemodialysis) and healthy controls. Thirty-six VOCs and two clinical variables that showed significant associations with chronic kidney disease in the univariate analysis were further analyzed. Different spectra of breath volatile organic compounds between the control and chronic kidney disease groups were obtained. A multivariate model incorporating age, 2-methyl-pentane, and cyclohexanone showed high performance (accuracy, 86%) in identifying patients with chronic kidney disease with odds ratios of 0.18 (95% CI, 0.07-2.49, p = 0.013); 2.10 (0.94-2.24, p = 0.025); and 2.31 (0.88-2.64, p = 0.008), respectively. Hence, this study showed that renal dysfunction induces a characteristic profile of breath VOCs that can be used as non-invasive potential biomarkers in screening tests for CKD.

Deep Characterization of Serum Metabolome Based on the Segment-Optimized Spectral-Stitching Direct-Infusion Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Approach

Direct-infusion Fourier transform ion cyclotron resonance mass spectrometry (DI-FTICR MS) shows great promise for metabolomic analysis due to ultrahigh mass accuracy and resolution. However, most of the DI-FTICR MS approaches focused on high-throughput metabolomics analysis at the expense of sensitivity and resolution and the potential for metabolome characterization has not been fully explored. Here, we proposed a novel deep characterization approach of serum metabolome using a segment-optimized spectral-stitching DI-FTICR MS method integrated with high-confidence and database-independent formula assignments. With varied acquisition parameters for each segment, a highly efficient acquisition was achieved for the whole mass range with sub-ppm mass accuracy. In a pooled human serum sample, thousands of features were assigned with unambiguous formulas and possible candidates based on highly accurate mass measurements. Furthermore, a reaction network was used to select confidently unique formulas from possible candidates, which was constructed by unambiguous formulas and possible candidates connected by the formula differences resulting from biochemical and MS transformation. Compared with full-range and conventional segment acquisition, 8- and 1.2-fold increases in observed features were achieved, respectively. Assignment accuracy was 93-94% for both a standard mixture containing 190 metabolites and a spiked serum sample with the root mean square mass error of 0.15-0.16 ppm. In total, 3534 unequivocal neutral molecular formulas were assigned in the pooled serum sample, 35% of which are contained in the HMDB. This method offers great enhancement in the deep characterization of serum metabolome by DI-FTICR MS.

Mass spectrometry-based metabolomic analysis as a tool for quality control of natural complex products

Metabolomics is an area of intriguing and growing interest. Since the late 1990s, when the first Omic applications appeared to study metabolite's pool ("metabolome"), to understand new aspects of the global regulation of cellular metabolism in biology, there have been many evolutions. Currently, there are many applications in different fields such as clinical, medical, agricultural, and food. In our opinion, it is clear that developments in metabolomics analysis have also been driven by advances in mass spectrometry (MS) technology. As natural complex products (NCPs) are increasingly used around the world as medicines, food supplements, and substance-based medical devices, their analysis using metabolomic approaches will help to bring more and more rigor to scientific studies and industrial production monitoring. This review is intended to emphasize the importance of metabolomics as a powerful tool for studying NCPs, by which significant advantages can be obtained in terms of elucidation of their composition, biological effects, and quality control. The different approaches of metabolomic analysis, the main and basic techniques of multivariate statistical analysis are also briefly illustrated, to allow an overview of the workflow associated with the metabolomic studies of NCPs. Therefore, various articles and reviews are illustrated and commented as examples of the application of MS-based metabolomics to NCPs.

An end-to-end deep learning framework for translating mass spectra to de-novo molecules

Elucidating the structure of a chemical compound is a fundamental task in chemistry with applications in multiple domains including drug discovery, precision medicine, and biomarker discovery. The common practice for elucidating the structure of a compound is to obtain a mass spectrum and subsequently retrieve its structure from spectral databases. However, these methods fail for novel molecules that are not present in the reference database. We propose Spec2Mol, a deep learning architecture for molecular structure recommendation given mass spectra alone. Spec2Mol is inspired by the Speech2Text deep learning architectures for translating audio signals into text. Our approach is based on an encoder-decoder architecture. The encoder learns the spectra embeddings, while the decoder, pre-trained on a massive dataset of chemical structures for translating between different molecular representations, reconstructs SMILES sequences of the recommended chemical structures. We have evaluated Spec2Mol by assessing the molecular similarity between the recommended structures and the original structure. Our analysis showed that Spec2Mol is able to identify the presence of key molecular substructures from its mass spectrum, and shows on par performance, when compared to existing fragmentation tree methods particularly when test structure information is not available during training or present in the reference database.

Effects of high-voltage electrostatic field (HVEF) on frozen shrimp (Solenocera melantho) based on UPLC-MS untargeted metabolism

Shrimp meat is prone to autolysis and decay due to the abundance of endogenous enzymes and contamination from microorganisms. HVEF freezing can slow the spoilage of shrimp, producing small and uniform ice crystals, resulting in less damage to muscle tissue. In this study, HVEF technique was used to freeze the shrimp (Solenocera melantho), and the UPLC-MS metabolic technique was used to investigate the metabolites of frozen shrimp meat. Compared with the control group, 367 differential metabolites were identified in the HVEF group. Mapping them to the KEGG database, there were 108 with KEGG ID. Purine metabolism and pyrimidine metabolism were the most enriched pathways. In addition, phosphatidylcholines (PCs), inosine (HxR), and l-valine were identified as potential biomarkers associated with lipid, nucleotide, and organic acid metabolism, respectively. Overall, HVEF can improve freezing quality of shrimp meat by slowing down the metabolism of substances in the muscle of S. melantho.

New Approach for the Identification of Isobaric and Isomeric Metabolites

The structural elucidation of metabolite molecules is important in many branches of the life sciences. However, the isomeric and isobaric complexity of metabolites makes their identification extremely challenging, and analytical standards are often required to confirm the presence of a particular compound in a sample. We present here an approach to overcome these challenges using high-resolution ion mobility spectrometry in combination with cryogenic vibrational spectroscopy for the rapid separation and identification of metabolite isomers and isobars. Ion mobility can separate isomeric metabolites in tens of milliseconds, and cryogenic IR spectroscopy provides highly structured IR fingerprints for unambiguous molecular identification. Moreover, our approach allows one to identify metabolite isomers automatically by comparing their IR fingerprints with those previously recorded in a database, obviating the need for a recurrent introduction of analytical standards. We demonstrate the principle of this approach by constructing a database composed of IR fingerprints of eight isomeric/isobaric metabolites and use it for the identification of these isomers present in mixtures. Moreover, we show how our fast IR fingerprinting technology allows to probe the IR fingerprints of molecules within just a few seconds as they elute from an LC column. This approach has the potential to greatly improve metabolomics workflows in terms of accuracy, speed, and cost.

An automated toxicity based prioritization framework for fast chemical characterization in non-targeted analysis

Identification of environmental pollutants with harmful effects is commonly conducted by non-targeted analysis (NTA) using liquid chromatography coupled with high-resolution mass spectrometry. Prioritization of possible candidates is important yet challenging because of the large number of candidates from MS acquisitions. We aimed to prioritize candidates to the exposure potential of organic chemicals by their toxicity and identification evidence in the matrix. We have developed an R package application, "NTAprioritization.R", for fast prioritization of suspect lists. In this workflow, the identification levels of candidates were first rated according to spectral matching and retention time prediction. The toxicity levels were rated according to candidates' toxicity of different endpoints or ToxPi score. Finally, the various levels of candidates were identified as Tier 1 - 5 descending in priority. For validation, we used this workflow to identify pollutants in a sludge water sample spiked with 28 environmental pollutants. The workflow reduced the candidate list of over 6,982 candidates to a final list of 2,779 compounds and prioritized them to 5 tiers (Tier 1 - 5), including 21 out of 28 spiked standards. Overall, this study shows the added value of an automated prioritization R package for the fast screening of environmental pollutants based on the NTA method.

Intelligent Workflow and Software for Non-Target Analysis of Complex Samples Using a Mixture of Toxic Transformation Products of Unsymmetrical Dimethylhydrazine as an Example

Unsymmetrical dimethylhydrazine (UDMH) is a widely used rocket propellant. Entering the environment or being stored in uncontrolled conditions, UDMH easily forms an enormous variety (at least many dozens) of transformation products. Environmental pollution by UDMH and its transformation products is a major problem in many countries and across the Arctic region. Unfortunately, previous works often use only electron ionization mass spectrometry with a library search, or they consider only the molecular formula to propose the structures of new products. This is quite an unreliable approach. It was demonstrated that a newly proposed artificial intelligence-based workflow allows for the proposal of structures of UDMH transformation products with a greater degree of certainty. The presented free and open-source software with a convenient graphical user interface facilitates the non-target analysis of industrial samples. It has bundled machine learning models for the prediction of retention indices and mass spectra. A critical analysis of whether a combination of several methods of chromatography and mass spectrometry allows us to elucidate the structure of an unknown UDMH transformation product was provided. It was demonstrated that the use of gas chromatographic retention indices for two stationary phases (polar and non-polar) allows for the rejection of false candidates in many cases when only one retention index is not enough. The structures of five previously unknown UDMH transformation products were proposed, and four previously proposed structures were refined.

Combined toxicity of acetamiprid and cadmium to larval zebrafish (Danio rerio) based on metabolomic analysis

Emerging contaminants, such as neonicotinoid pesticide acetamiprid (Ace), are frequently detected in the water environment, which can interact with existing heavy metal cadmium (Cd) to produce unpredicted influence. Limited studies have evaluated the effects of multiple pollutant exposures on aquatic animals. Here, we characterized the joint toxicity of Ace and Cd exposure to zebrafish (Danio rerio). The results revealed that Cd and its combined exposure with Ace had an inhibitory effect on the growth of larval zebrafish and induced morphological defects. Combined exposure to high doses of Ace and Cd could significantly reduce the levels of TG, glucose, and pyruvate in larval zebrafish. Untargeted metabolomics revealed that Cd treatment (285) produced more differentially expressed metabolites (DEMs) than Ace treatment (115), and combined treatment produced the most DEMs (294). The KEGG pathway enrichment analysis showed that they could disrupt riboflavin metabolism, amino acid metabolism, and glycolipid metabolism in the larvae of D. rerio. ELISA showed that VB2, FMN, and FAD levels were significantly increased. In addition, gene expression analysis exhibited that the mRNA levels of essential genes related to glycolipid metabolism were substantially affected, such as PK, PEPckc, PPAR-α, and FABP6. Furthermore, targeted amino acid metabolomics confirmed that both single exposure to Cd and combined exposure to Ace and Cd altered the levels of amino acids in larvae, including ALA, ARG, MET, PRO, TYR, VAL, GLY, ORN, and PHE. Taken together, exposure to Ace and Cd, alone or in combination, exerted harmful effects on the individual development, riboflavin metabolism, glycolipid metabolism, and amino acid metabolism disorder of D. rerio. These findings highlighted that more attention should be paid to the compound toxicity of chemical mixtures to aquatic organisms.

Decoding Metabolic Reprogramming in Plants under Pathogen Attacks, a Comprehensive Review of Emerging Metabolomics Technologies to Maximize Their Applications

In their environment, plants interact with a multitude of living organisms and have to cope with a large variety of aggressions of biotic or abiotic origin. What has been known for several decades is that the extraordinary variety of chemical compounds the plants are capable of synthesizing may be estimated in the range of hundreds of thousands, but only a fraction has been fully characterized to be implicated in defense responses. Despite the vast importance of these metabolites for plants and also for human health, our knowledge about their biosynthetic pathways and functions is still fragmentary. Recent progress has been made particularly for the phenylpropanoids and oxylipids metabolism, which is more emphasized in this review. With an increasing interest in monitoring plant metabolic reprogramming, the development of advanced analysis methods should now follow. This review capitalizes on the advanced technologies used in metabolome mapping in planta, including different metabolomics approaches, imaging, flux analysis, and interpretation using bioinformatics tools. Advantages and limitations with regards to the application of each technique towards monitoring which metabolite class or type are highlighted, with special emphasis on the necessary future developments to better mirror such intricate metabolic interactions in planta.

Microbial spoilage mechanisms of vacuum-packed lamb meat: A review

Lamb meat is an important export commodity, however chilled vacuum-packed (VP) lamb has approximately half the shelf-life of beef under the same storage conditions. This makes the industry more vulnerable to financial losses due to long shipping times and unexpected spoilage. Understanding the spoilage mechanisms of chilled VP lamb in relation to VP beef is important for developing effective strategies to extend the shelf-life of lamb. This review has discussed various key factors (i.e., pH, fat, and presence of bone) that have effects on microbial spoilage of VP lamb contributing to its shorter shelf-life relative to VP beef. A range of bacterial organisms and their metabolisms in relevance to lamb spoilage are also discussed. The data gap in the literature regarding the potential mechanisms of spoilage in VP red meat is highlighted. This review has provided the current understanding of key factors affecting the shelf-life of VP lamb relative to VP beef. It has also identified key areas of research to further understand the spoilage mechanisms of VP lamb. These include investigating the potential influence of fat and bone (including bone marrow) on the shelf-life, as well as assessing changes in the meat metabolome as the spoilage microbial community is developing using an integrated approach. Such new knowledge would aid the development of effective approaches to extend the shelf-life of VP lamb.

How searching against multiple libraries can lead to biased results in GC/MS-based metabolomics

RATIONALE

Databases of electron ionization mass spectra are often used in GC/MS-based untargeted metabolomics analysis. The results of the library search depend on several factors, such as the size and quality of the database, and the library search algorithm. We found out that the list of considered m/z values is another important parameter. Unfortunately, this information is not usually specified by software developers and it is hidden from the end user.

METHODS

We created synthetic data sets and figured out how several popular software products (AMDIS, ChromaTOF, MS Search, and Xcalibur) select the list of m/z values for the library search. Moreover, we considered data sets of real mass spectra (presented in both the NIST and FiehnLib libraries) and compared the library search results obtained within different software products. All programs under consideration use the NIST MS Search binaries to perform the library search using the Identity algorithm.

RESULTS

We found that AMDIS and ChromaTOF can give biased library search results under particular conditions. In untargeted metabolomics, this can happen when NIST and FiehnLib libraries are used simultaneously, the scan range of the instrument is less than 85, and the correct answer is present only in the FiehnLib library.

CONCLUSIONS

The main reason for biased results is that the information about the scan range is not stored in the metadata of library records. As a result, in the case of AMDIS and ChromaTOF software, some unrecorded peaks are considered as missing during the library search, the respective compound is penalized, and the correct answer falls outside the top five or even top 10 hits. At the same time, the default algorithm for selecting the list of considered m/z values implemented in MS Search is free from such unexpected behavior.

Metabolomics-Based Mechanistic Insights into Revealing the Adverse Effects of Pesticides on Plants: An Interactive Review

In plant biology, metabolomics is often used to quantitatively assess small molecules, metabolites, and their intermediates in plants. Metabolomics has frequently been applied to detect metabolic alterations in plants exposed to various biotic and abiotic stresses, including pesticides. The widespread use of pesticides and agrochemicals in intensive crop production systems is a serious threat to the functionality and sustainability of agroecosystems. Pesticide accumulation in soil may disrupt soil-plant relationships, thereby posing a pollution risk to agricultural output. Application of metabolomic techniques in the assessment of the biological consequences of pesticides at the molecular level has emerged as a crucial technique in exposome investigations. State-of-the-art metabolomic approaches such as GC-MS, LC-MS/MS UHPLC, UPLC-IMS-QToF, GC/EI/MS, MALDI-TOF MS, and ¹H-HR-MAS NMR, etc., investigating the harmful effects of agricultural pesticides have been reviewed. This updated review seeks to outline the key uses of metabolomics related to the evaluation of the toxicological impacts of pesticides on agronomically important crops in exposome assays as well as bench-scale studies. Overall, this review describes the potential uses of metabolomics as a method for evaluating the safety of agricultural chemicals for regulatory applications. Additionally, the most recent developments in metabolomic tools applied to pesticide toxicology and also the difficulties in utilizing this approach are discussed.

Integrating metabolomics, bionics, and culturomics to study probiotics-driven drug metabolism

Many drugs have been shown to be metabolized by the human gut microbiome, but probiotic-driven drug-metabolizing capacity is rarely explored. Here, we developed an integrated metabolomics, culturomics, and bionics framework for systematically studying probiotics-driven drug metabolism. We discovered that 75% (27/36 of the assayed drugs) were metabolized by five selected probiotics, and drugs containing nitro or azo groups were more readily metabolized. As proof-of-principle experiments, we showed that Lacticaseibacillus casei Zhang (LCZ) could metabolize racecadotril to its active products, S-acetylthiorphan and thiorphan, in monoculture, in a near-real simulated human digestion system, and in an ex vivo fecal co-culture system. However, a personalized effect was observed in the racecadotril-metabolizing activity of L. casei Zhang, depending on the individual's host gut microbiome composition. Based on data generated by our workflow, we proposed a possible mechanism of interactions among L. casei Zhang, racecadotril, and host gut microbiome, providing practical guidance for probiotic-drug co-treatment and novel insights into precision probiotics.

Creation of a Plant Metabolite Spectral Library for Untargeted and Targeted Metabolomics

Large-scale high throughput metabolomic technologies are indispensable components of systems biology in terms of discovering and defining the metabolite parts of the system. However, the lack of a plant metabolite spectral library limits the metabolite identification of plant metabolomic studies. Here, we have created a plant metabolite spectral library using 544 authentic standards, which increased the efficiency of identification for untargeted metabolomic studies. The process of creating the spectral library was described, and the mzVault library was deposited in the public repository for free download. Furthermore, based on the spectral library, we describe a process of creating a pseudo-targeted method, which was applied to a proof-of-concept study of Arabidopsis leaf extracts. As authentic standards become available, more metabolite spectra can be easily incorporated into the spectral library to improve the mzVault package.

Annotation of natural product compound families using molecular networking topology and structural similarity fingerprinting

Spectral matching of MS² fragmentation spectra has become a popular method for characterizing natural products libraries but identification remains challenging due to differences in MS² fragmentation properties between instruments and the low coverage of current spectral reference libraries. To address this bottleneck we present Structural similarity Network Annotation Platform for Mass Spectrometry (SNAP-MS) which matches chemical similarity grouping in the Natural Products Atlas to grouping of mass spectrometry features from molecular networking. This approach assigns compound families to molecular networking subnetworks without the need for experimental or calculated reference spectra. We demonstrate SNAP-MS can accurately annotate subnetworks built from both reference spectra and an in-house microbial extract library, and correctly predict compound families from published molecular networks acquired on a range of MS instrumentation. Compound family annotations for the microbial extract library are validated by co-injection of standards or isolation and spectroscopic analysis. SNAP-MS is freely available at www.npatlas.org/discover/snapms .

Metabolomics as a Critical Tool for Studying Clinical Surgery

Metabolomics enables the analysis of metabolites within an organism, which offers the closest direct measurement of the physiological activity of the organism, and has advanced efforts to characterize metabolic states, identify biomarkers, and investigate metabolic pathways. A high degree of innovation in analytical techniques has promoted the application of metabolomics, especially in the study of clinical surgery. Metabolomics can be employed as a clinical testing method to maximize therapeutic outcomes, and has been applied in rapid diagnosis of diseases, timely postoperative monitoring, prognostic assessment, and personalized medicine. This review focuses on the use of mass spectrometry and nuclear magnetic resonance-based metabolomics in clinical surgery, including identifying metabolic changes before and after surgery, finding disease-associated biomarkers, and exploring the potential of personalized therapy. Challenges and opportunities of metabolomics in organ transplantation are also discussed, with a particular emphasis on metabolomics in donor organ evaluation and protection, prognostic outcome prediction, as well as postoperative adverse reaction monitoring. In the end, current limitations of metabolomics in clinical surgery and future research directions are presented.

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.

Common Strategies and Factors Affecting Off-Line Breath Sampling and Volatile Organic Compounds Analysis Using Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS)

An analysis of exhaled breath enables specialists to noninvasively monitor biochemical processes and to determine any pathological state in the human body. Breath analysis holds the greatest potential to remold and personalize diagnostics; however, it requires a multidisciplinary approach and collaboration of many specialists. Despite the fact that breath is considered to be a less complex matrix than blood, it is not commonly used as a diagnostic and prognostic tool for early detection of disordered conditions due to its problematic sampling, analysis, and storage. This review is intended to determine, standardize, and marshal experimental strategies for successful, reliable, and especially, reproducible breath analysis.

Mass Spectrometric Methods for Non-Targeted Screening of Metabolites: A Future Perspective for the Identification of Unknown Compounds in Plant Extracts

Phyto products are widely used in natural products, such as medicines, cosmetics or as so-called “superfoods”. However, the exact metabolite composition of these products is still unknown, due to the time-consuming process of metabolite identification. Non-target screening by LC-HRMS/MS could be a technique to overcome these problems with its capacity to identify compounds based on their retention time, accurate mass and fragmentation pattern. In particular, the use of computational tools, such as deconvolution algorithms, retention time prediction, in silico fragmentation and sophisticated search algorithms, for comparison of spectra similarity with mass spectral databases facilitate researchers to conduct a more exhaustive profiling of metabolic contents. This review aims to provide an overview of various techniques and tools for non-target screening of phyto samples using LC-HRMS/MS.

Metabolomics of plant root exudates: From sample preparation to data analysis

Plants release a set of chemical compounds, called exudates, into the rhizosphere, under normal conditions and in response to environmental stimuli and surrounding soil organisms. Plant root exudates play indispensable roles in inhibiting the growth of harmful microorganisms, while also promoting the growth of beneficial microbes and attracting symbiotic partners. Root exudates contain a complex array of primary and specialized metabolites. Some of these chemicals are only found in certain plant species for shaping the microbial community in the rhizosphere. Comprehensive understanding of plant root exudates has numerous applications from basic sciences to enhancing crop yield, production of stress-tolerant crops, and phytoremediation. This review summarizes the metabolomics workflow for determining the composition of root exudates, from sample preparation to data acquisition and analysis. We also discuss recent advances in the existing analytical methods and future perspectives of metabolite analysis.

Nontargeted metabolomics reveals differences in the metabolite profiling among methicillin-resistant and methicillin-susceptible Staphylococcus aureus in response to antibiotics

Staphylococcus aureus (S. aureus) causes infections and can be fatal. In the long-term struggle against antibiotics, S. aureus has acquired resistance toward antibiotics and become more difficult to kill. Metabolomics could directly reflect the responses of S. aureus toward antibiotics, which is effective for studying the resistance mechanism of S. aureus. In this study, based on a nontargeted metabolic figure printing technique, the metabolome of a pair of isogenic methicillin-susceptible and resistant S. aureus strains ATCC25923 (MSSA) and ATCC43300 (MRSA) treated with or without oxacillin was characterized. 7 and 29 significantly changed metabolites in MRSA and MSSA were identified by combined accurate mass and mass fragmentation analysis. Pathway enrichment analysis suggested that DNA repair and flavin biosynthesis are the universal pathways of both MSSA and MRSA under antibiotic stress. MRSA systematically and effectively fights against oxacillin through precise control of energy production, PBP2a substrate biosynthesis and antioxidant function. In contrast, MSSA lacks effective defense pathways against oxacillin. The different metabolome responses of MSSA and MRSA toward antibiotics provide us with new insights into how S. aureus develops antibiotic resistance.

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.

Chemical compositions, antibacterial, antifungal and cytotoxic effects of Alhagi mannifera five extracts

OBJECTIVES

Plants were used as medicines thousands of years ago. Conventional medicine use is increasing and many of the currently used drugs are extracted from herbal sources. In Palestinian traditional medicine, the Alhagi mannifera plant is used for the treatment of cancer. Our study aimed to extract this plant using five solvent fractions, identifying their chemical compositions, and evaluating their antimicrobial and cytotoxic effects.

METHODS

The successive technique was used to extract five solvent fractions of A. mannifera. While the spectral analysis was used to characterize quantitatively and qualitatively the chemical components of these extracts. The antimicrobial activity of plant extracts was evaluated against seven microbial strains using a broth micro-dilution assay. The cytotoxic activity was assessed using a 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) assay against cervical cancer cell line (HeLa).

RESULTS

A total of 165 compounds were identified in A. mannifera different extracts. In the petroleum ether extract were found a total of 55 compounds. The major compounds were 2,5-cyclooctadien-1-ol (9.42%), 3-chloropropionic acid, heptyl ester (9.42%), carbonic acid, ethyl nonyl ester (9.42%) and chloroacetic acid. In methylene chloride extract a total of 11 compounds were found, and the major compounds were m-ainobenzenesulfonyl fluoride (14.35%), dodecane,2,6,10-trimethyl- (14.35%) and propanoic acid,2,2-dimethyl-,2-ethylexyl ester (14.35%). In chloroform extract, a total of 23 compounds were found. The major compounds were 5-ethyl-1-nonene (21.28%), and decanedioic acid, bis(2-ethylhexyl) ester (21.28%). In acetone extract were found a total of 47 compounds and the major compound was phenol,2,4-bis(1,1-dimethylethyl)- (5.22%). In methanol extract a total of 29 compounds were found and the major compounds were 3-o-methyl-d-glucose (10.79%), myo-inositol, 2-c-methyl- (10.79%), myo-inositol, 4-c-methyl- (10.79%), and scyllo-inositol,1C-methyl- (10.79%). All extracts showed antimicrobial activity. However, the petroleum ether extract showed the most potent antimicrobial effect against Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumonia, MRSA, and Candida albicans with minimal inhibitory concentration (MIC) values of 1.25, 1.25, 6.25, 0.325, 6.25, and 1.56 μg/mL, respectively. De facto, chloroform extract followed by ether extract displayed potential cytotoxic activity with IC₅₀ values of 0.2 and 1.2 mg/mL, respectively.

CONCLUSIONS

A. mannifera was found to contain a variety of phytochemicals and its chloroform extract showed a potent cytotoxic effect on HeLa cancer cells. In addition, petroleum ether showed potent antimicrobial agents and these extracts look promising as drug candidates. Further in vivo investigations should be conducted to provide the basis for developing new cancer and microbial infections treatments.

Molecular Networking and On-Tissue Chemical Derivatization for Enhanced Identification and Visualization of Steroid Glycosides by MALDI Mass Spectrometry Imaging

Spatial metabolomics describes the spatially resolved analysis of interconnected pathways, biochemical reactions, and transport processes of small molecules in the spatial context of tissues and cells. However, a broad range of metabolite classes (e.g., steroids) show low intrinsic ionization efficiencies in mass spectrometry imaging (MSI) experiments, thus restricting the spatial characterization of metabolic networks. Additionally, decomposing complex metabolite networks into chemical compound classes and molecular annotations remains a major bottleneck due to the absence of repository-scaled databases. Here, we describe a multimodal mass-spectrometry-based method combining computational metabolome mining tools and high-resolution on-tissue chemical derivatization (OTCD) MSI for the spatially resolved analysis of metabolic networks at the low micrometer scale. Applied to plant toxin sequestration in Danaus plexippus as a model system, we first utilized liquid chromatography (LC)-MS-based molecular networking in combination with artificial intelligence (AI)-driven chemical characterization to facilitate the structural elucidation and molecular identification of 32 different steroidal glycosides for the host-plant Asclepias curassavica. These comprehensive metabolite annotations guided the subsequent matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) analysis of cardiac-glycoside sequestration in D. plexippus. We developed a spatial-context-preserving OTCD protocol, which improved cardiac glycoside ion yields by at least 1 order of magnitude compared to results with untreated samples. To illustrate the potential of this method, we visualized previously inaccessible (sub)cellular distributions (2 and 5 μm pixel size) of steroidal glycosides in D. plexippus, thereby providing a novel insight into the sequestration of toxic metabolites and guiding future metabolomics research of other complex sample systems.

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.

Suspect and non-targeted screening-based human biomonitoring identified 74 biomarkers of exposure in urine of Slovenian children

Human exposure to organic contaminants is widespread. Many of these contaminants show adverse health effects on human population. Human biomonitoring (HBM) follows the levels and the distribution of biomarkers of exposure (BoE), but it is usually done in a targeted manner. Suspect and non-targeted screening (SS/NTS) tend to find BoE in an agnostic way, without preselection of compounds, and include finding evidence of exposure to predicted, unpredicted known and unknown chemicals. This study describes the application of high-resolution mass spectrometry (HRMS)-based SS/NTS workflow for revealing organic contaminants in urine of a cohort of 200 children from Slovenia, aged 6-9 years. The children originated from two regions, urban and rural, and the latter were sampled in two time periods, summer and winter. We tentatively identified 74 BoE at the confidence levels of 2 and 3. These BoE belong to several classes of pharmaceuticals, personal care products, plasticizers and plastic related products, volatile organic compounds, nicotine, caffeine and pesticides. The risk of three pesticides, atrazine, amitraz and diazinon is of particular concern since their use was limited in the EU. Among BoE we tentatively identified compounds that have not yet been monitored in HBM schemes and demonstrate limited exposure data, such as bisphenol G, polyethylene glycols and their ethers. Furthermore, 7 compounds with unknown use and sources of exposure were tentatively identified, either indicating the entry of new chemicals into the market, or their metabolites and transformation products. Interestingly, several BoE showed location and time dependency. Globally, this study presents high-throughput approach to SS/NTS for HBM. The results shed a light on the exposure of Slovenian children and raise questions on potential adverse health effects of such mixtures on this vulnerable population.

How to employ metabolomic analysis to research on functions of prebiotics and probiotics in poultry gut health?

Gut health can be considered one of the major, manageable constituents of the animal immunity and performance. The fast spread of intestinal diseases, and increase of antimicrobial resistance have been observed, therefore the intestinal health has become not only economically relevant, but also highly important subject addressing the interest of public health. It is expected, that the strategies to control infections should be based on development of natural immunity in animals and producing resilient flocks using natural solutions, whilst eliminating antibiotics and veterinary medicinal products from action. Probiotics and prebiotics have been favored, because they have potential to directly or indirectly optimize intestinal health by manipulating the metabolism of the intestinal tract, including the microbiota. Studying the metabolome of probiotics and gut environment, both in vivo, or using the in vitro models, is required to attain the scientific understanding about the functions of bioactive compounds in development of gut health and life lasting immunity. There is a practical need to identify new metabolites being the key bioactive agents regulating biochemical pathways of systems associated with gut (gut-associated axes). Technological advancement in metabolomics studies, and increasing access to the powerful analytical platforms have paved a way to implement metabolomics in exploration of the effects of prebiotics and probiotics on the intestinal health of poultry. In this article, the basic principles of metabolomics in research involving probiotics and probiotics are introduced, together with the overview of existing strategies and suggestions of their use to study metabolome in poultry.

Overview of Bioactive Fungal Secondary Metabolites: Cytotoxic and Antimicrobial Compounds

Microorganisms are known as important sources of natural compounds that have been studied and applied for different purposes in distinct areas. Specifically, in the pharmaceutical area, fungi have been explored mainly as sources of antibiotics, antiviral, anti-inflammatory, enzyme inhibitors, hypercholesteremic, antineoplastic/antitumor, immunomodulators, and immunosuppressants agents. However, historically, the high demand for new antimicrobial and antitumor agents has not been sufficiently attended by the drug discovery process, highlighting the relevance of intensifying studies to reach sustainable employment of the huge world biodiversity, including the microorganisms. Therefore, this review describes the main approaches and tools applied in the search for bioactive secondary metabolites, as well as presents several examples of compounds produced by different fungi species with proven pharmacological effects and additional examples of fungal cytotoxic and antimicrobial molecules. The review does not cover all fungal secondary metabolites already described; however, it presents some reports that can be useful at any phase of the drug discovery process, mainly for pharmaceutical applications.

The way to AI-controlled synthesis: how far do we need to go?

Chemical synthesis always plays an irreplaceable role in chemical, materials, and pharmacological fields. Meanwhile, artificial intelligence (AI) is causing a rapid technological revolution in many fields by replacing manual chemical synthesis and has exhibited a much more economical and time-efficient manner. However, the rate-determining step of AI-controlled synthesis systems is rarely mentioned, which makes it difficult to apply them in general laboratories. Here, the history of developing AI-aided synthesis has been overviewed and summarized. We propose that the hardware of AI-controlled synthesis systems should be more adaptive to execute reactions with different phase reagents and under different reaction conditions, and the software of AI-controlled synthesis systems should have richer kinds of reaction prediction modules. An updated system will better address more different kinds of syntheses. Our viewpoint could help scientists advance the revolution that combines AI and synthesis to achieve more progress in complicated systems.

Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking

Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an in vitro enzymatic reaction system and different biological samples, with ~100-300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with in silico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites for common biological samples from model organisms, towards deciphering dark matter in untargeted metabolomics.

Tapping into Plant-Microbiome Interactions through the Lens of Multi-Omics Techniques

This review highlights the pivotal role of root exudates in the rhizosphere, especially the interactions between plants and microbes and between plants and plants. Root exudates determine soil nutrient mobilization, plant nutritional status, and the communication of plant roots with microbes. Root exudates contain diverse specialized signaling metabolites (primary and secondary). The spatial behavior of these metabolites around the root zone strongly influences rhizosphere microorganisms through an intimate compatible interaction, thereby regulating complex biological and ecological mechanisms. In this context, we reviewed the current understanding of the biological phenomenon of allelopathy, which is mediated by phytotoxic compounds (called allelochemicals) released by plants into the soil that affect the growth, survival, development, ecological infestation, and intensification of other plant species and microbes in natural communities or agricultural systems. Advances in next-generation sequencing (NGS), such as metagenomics and metatranscriptomics, have opened the possibility of better understanding the effects of secreted metabolites on the composition and activity of root-associated microbial communities. Nevertheless, understanding the role of secretory metabolites in microbiome manipulation can assist in designing next-generation microbial inoculants for targeted disease mitigation and improved plant growth using the synthetic microbial communities (SynComs) tool. Besides a discussion on different approaches, we highlighted the advantages of conjugation of metabolomic approaches with genetic design (metabolite-based genome-wide association studies) in dissecting metabolome diversity and understanding the genetic components of metabolite accumulation. Recent advances in the field of metabolomics have expedited comprehensive and rapid profiling and discovery of novel bioactive compounds in root exudates. In this context, we discussed the expanding array of metabolomics platforms for metabolome profiling and their integration with multivariate data analysis, which is crucial to explore the biosynthesis pathway, as well as the regulation of associated pathways at the gene, transcript, and protein levels, and finally their role in determining and shaping the rhizomicrobiome.

Exact mass GC-MS analysis: Protocol, database, advantages and application to plant metabolic profiling

Plant metabolomics has been used widely in plant physiology, in particular to analyse metabolic responses to environmental parameters. Derivatization (via trimethylsilylation and methoximation) followed by GC-MS metabolic profiling is a major technique to quantify low molecular weight, common metabolites of primary carbon, sulphur and nitrogen metabolism. There are now excellent opportunities for new generation analyses, using high resolution, exact mass GC-MS spectrometers that are progressively becoming relatively cheap. However, exact mass GC-MS analyses for routine metabolic profiling are not common, since there is no dedicated available database. Also, exact mass GC-MS is usually dedicated to structural resolution of targeted secondary metabolites. Here, we present a curated database for exact mass metabolic profiling (made of 336 analytes, 1064 characteristic exact mass fragments) focused on molecules of primary metabolism. We show advantages of exact mass analyses, in particular to resolve isotopic patterns, localise S-containing metabolites, and avoid identification errors when analytes have common nominal mass peaks in their spectrum. We provide a practical example using leaves of different Arabidopsis ecotypes and show how exact mass GC-MS analysis can be applied to plant samples and identify metabolic profiles.

Machine learning for identification of silylated derivatives from mass spectra

Motivation

Compound structure identification is using increasingly more sophisticated computational tools, among which machine learning tools are a recent addition that quickly gains in importance. These tools, of which the method titled Compound Structure Identification:Input Output Kernel Regression (CSI:IOKR) is an excellent example, have been used to elucidate compound structure from mass spectral (MS) data with significant accuracy, confidence and speed. They have, however, largely focused on data coming from liquid chromatography coupled to tandem mass spectrometry (LC–MS).

Gas chromatography coupled to mass spectrometry (GC–MS) is an alternative which offers several advantages as compared to LC–MS, including higher data reproducibility. Of special importance is the substantial compound coverage offered by GC–MS, further expanded by derivatization procedures, such as silylation, which can improve the volatility, thermal stability and chromatographic peak shape of semi-volatile analytes. Despite these advantages and the increasing size of compound databases and MS libraries, GC–MS data have not yet been used by machine learning approaches to compound structure identification.

Results

This study presents a successful application of the CSI:IOKR machine learning method for the identification of environmental contaminants from GC–MS spectra. We use CSI:IOKR as an alternative to exhaustive search of MS libraries, independent of instrumental platform and data processing software. We use a comprehensive dataset of GC–MS spectra of trimethylsilyl derivatives and their molecular structures, derived from a large commercially available MS library, to train a model that maps between spectra and molecular structures. We test the learned model on a different dataset of GC–MS spectra of trimethylsilyl derivatives of environmental contaminants, generated in-house and made publicly available. The results show that 37% (resp. 50%) of the tested compounds are correctly ranked among the top 10 (resp. 20) candidate compounds suggested by the model. Even though spectral comparisons with reference standards or de novo structural elucidations are neccessary to validate the predictions, machine learning provides efficient candidate prioritization and reduction of the time spent for compound annotation.

Comparison of Cosine, Modified Cosine, and Neutral Loss Based Spectrum Alignment For Discovery of Structurally Related Molecules

Spectrum alignment of tandem mass spectrometry (MS/MS) data using the modified cosine similarity and subsequent visualization as molecular networks have been demonstrated to be a useful strategy to discover analogs of molecules from untargeted MS/MS-based metabolomics experiments. Recently, a neutral loss matching approach has been introduced as an alternative to MS/MS-based molecular networking with an implied performance advantage in finding analogs that cannot be discovered using existing MS/MS spectrum alignment strategies. To comprehensively evaluate the scoring properties of neutral loss matching, the cosine similarity, and the modified cosine similarity, similarity measures of 955 228 peptide MS/MS spectrum pairs and 10 million small molecule MS/MS spectrum pairs were compared. This comparative analysis revealed that the modified cosine similarity outperformed neutral loss matching and the cosine similarity in all cases. The data further indicated that the performance of MS/MS spectrum alignment depends on the location and type of the modification, as well as the chemical compound class of fragmented molecules.

Integrated multi-omics approaches to understand microbiome assembly in Jiuqu, a mixed-culture starter

The use of Jiuqu as a saccharifying and fermenting starter in the production of fermented foods is a very old biotechnological process that can be traced back to ancient times. Jiuqu harbors a hub of microbial communities, in which prokaryotes and eukaryotes cohabit, interact, and communicate. However, the spontaneous fermentation based on empirical processing hardly guarantees the stable assembly of the microbiome and a standardized quality of Jiuqu. This review describes the state of the art, limitations, and challenges towards the application of traditional and omics-based technology to study the Jiuqu microbiome and highlights the need for integrating meta-omics data. In addition, we review the varieties of Jiuqu and their production processes, with particular attention to factors shaping the microbiota of Jiuqu. Then, the potentials of integrated omics approaches used in Jiuqu research are examined in order to understand the assembly of the microbiome and improve the quality of the products. A variety of different approaches, including molecular and mass spectrometry-based techniques, have led to scientific advances in the analysis of the complex ecosystem of Jiuqu. To date, the extensive research on Jiuqu has mainly focused on the microbial community diversity, flavor profiles, and biochemical characteristics. An integrative approach to large-scale omics datasets and cultivated microbiota has great potential for understanding the interrelation of the Jiuqu microbiome. Further research on the Jiuqu microbiome may explain the inherent property of compositional stability and stable performance of a complex microbiota coping with environmental perturbations and provide important insights to reconstruct synthetic microbiota and develop modern intelligent manufacturing procedures for Jiuqu.

Visualizing the spatial distribution of functional metabolites in Forsythia suspensa at different harvest stages by MALDI mass spectrometry imaging

As a traditional Chinese medicine, Forsythia suspensa (F. suspensa) has attracted much attention due to its significant pharmacological activity. Revealing the spatial distribution of metabolites during F. suspensa development is important for understanding its biosynthesis rules and improving the quality of medicinal materials. However, there is currently a lack of information on the spatial distribution of F. suspensa metabolites. In this work, the spatial distribution and growth metabolism patterns of important metabolites of F. suspensa were studied for the first time using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Using 2,5-dimethylnaphthalene (DAN) as the matrix and detecting in negative ion mode, the spatial distribution and growth patterns of 11 metabolites obtained from longitudinal sections of F. suspensa included pinoresinol, phillygenin, forsythoside A, forsythoside E, rutin, caffeic acid, malic acid, citric acid, stearic acid, oleic acid, and linoleic acid. These results showed the mesocarp and endosperm tissues of F. suspensa were important for storing important functional metabolites. Changes in mesocarp and endosperm growth and development tissues caused large changes in the content of important functional metabolites in F. suspensa. These results provide a basis for understanding the spatial distribution of metabolites in F. suspensa tissues and the significant changes that occur during growth and development, exploring the mechanism of important synthesis of metabolites, regulating the harvest of F. suspensa, and improving the quality of medicinal herbs.

[The promise of omics in the precision medicine era]

The rise of omics technologies that simultaneously measure thousands of molecules in a complex biological sample represents the core of systems biology. These technologies have profoundly impacted biomarkers and therapeutic targets discovery in the precision medicine era. Systems biology aims to perform a systematic probing of complex interactions in biological systems. Powered by high-throughput omics technologies and high-performance computing, systems biology provides relevant, resolving, and multi-scale overviews from cells to populations. Precision medicine takes advantage of these conceptual and technological developments and is based on two main pillars: the generation of multimodal data and their subsequent modeling. High-throughput omics technologies enable the comprehensive and holistic extraction of biological information, while computational capabilities enable multidimensional modeling and, as a result, offer an intuitive and intelligible visualization. Despite their promise, translating these technologies into clinically actionable tools has been slow. In this contribution, we present the most recent multi-omics data generation and analysis strategies and their clinical deployment in the post-genomic era. Furthermore, medical application challenges of omics-based biomarkers are discussed.

Metabolomics-Guided Comparison of Pollen and Microalgae-Based Artificial Diets in Honey Bees

Managed honey bee colonies used for crop pollination are fed artificial diets to offset nutritional deficiencies related to land-use intensification and climate change. In this study, we formulated novel microalgae diets using Chlorella vulgaris and Arthrospira platensis (spirulina) biomass and fed them to young adult honey bee workers. Diet-induced changes in bee metabolite profiles were studied relative to a natural pollen diet using liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) metabolomics. Untargeted analyses of pollen- and microalgae-fed bees revealed significant overlap, with 248 shared features determined by LC-MS and 87 shared features determined by GC-MS. Further metabolomic commonalities were evident upon subtraction of unique diet features. Twenty-five identified metabolites were influenced by diet, which included complex lipids, essential fatty acids, vitamins, and phytochemicals. The metabolomics results are useful to understand mechanisms underlying favorable growth performance as well as increased antioxidant and heat shock protein gene expression in bees fed the microalgae diets. We conclude that the tested microalgae have potential as sustainable feed additives and as a source of bee health-modulating natural products. Metabolomics-guided diet development could eventually help tailor feed interventions to achieve precision nutrition in honey bees and other livestock animals.