Nontargeted modification-specific metabolomics study based on liquid chromatography-high-resolution mass spectrometry

The Y-ome Conundrum: Insights into Uncharacterized Genes and Approaches for Functional Annotation

The ever-increasing availability of genome sequencing data has revealed a substantial number of uncharacterized genes without known functions across various organisms. The first comprehensive genome sequencing of E. coli K12 revealed that more than 50% of its open reading frames corresponded to transcripts with no known functions. The group of protein-coding genes without a functional description and/or a recognized pathway, beginning with the letter "Y", is classified as the "y-ome". Several efforts have been made to elucidate the functions of these genes and to recognize their role in biological processes. This review provides a brief update on various strategies employed when studying the y-ome, such as high-throughput experimental approaches, comparative omics, metabolic engineering, gene expression analysis, and data integration techniques. Additionally, we highlight recent advancements in functional annotation methods, including the use of machine learning, network analysis, and functional genomics approaches. Novel approaches are required to produce more precise functional annotations across the genome to reduce the number of genes with unknown functions.

Differential protein expression and metabolite profiling in glaucoma: Insights from a multi-omics analysis

Various substances within the aqueous humor (AH) can directly or indirectly impact intraocular tissues associated with intraocular pressure (IOP), a critical factor in glaucoma development. This study aims to investigate individual changes in these AH substances and the interactions among altered components through a multi-omics approach. LC/MS analysis was conducted on AH samples from patients with exfoliation syndrome (XFS, n = 5), exfoliation glaucoma (XFG, n = 4), primary open-angle glaucoma (POAG, n = 11), and cataracts (control group, n = 7). Subsequently, differentially expressed proteins and metabolites among groups, alterations in their network interactions, and their biological functions were examined. Both data-independent acquisition and data-dependent acquisition methods were employed to analyze the AH proteome and metabolome, and the results were integrated for a comprehensive analysis. In the proteomics analysis, proteins upregulated in both the XFG and POAG groups were associated with lipid metabolism, complement activation, and extracellular matrix regulation. Metabolomic analysis highlighted significant changes in amino acids related to antioxidant processes in the glaucoma groups. Notably, VTN, APOA1, C6, and L-phenylalanine exhibited significant alterations in the glaucoma groups. Integration of individual omics analyses demonstrated that substances associated with inflammation and lipid metabolism, altered in the glaucoma groups, showed robust interactions within a complex network involving PLG, APOA1, and L-phenylalanine or C3, APOD, and L-valine. These findings offer valuable insights into the molecular mechanisms governing IOP regulation and may contribute to the development of new biomarkers for managing glaucoma.

A widely targeted metabolite modificomics strategy for modified metabolites identification in tomato

The structural and functional diversity of plant metabolites is largely created via chemical modification of a basic backbone. However, metabolite modifications in plants have still not been thoroughly investigated by metabolomics approaches. In this study, a widely targeted metabolite modificomics (WTMM) strategy was developed based on ultra-high performance liquid chromatography-quadrupole-linear ion trap (UHPLC-Q-Trap) and UHPLC-Q-Exactive-Orbitrap (UHPLC-QE-Orbitrap), which greatly improved the detection sensitivity and the efficiency of identification of modified metabolites. A metabolite modificomics study was carried out using tomato as a model, and over 34,000 signals with MS2 information were obtained from approximately 232 neutral loss transitions. Unbiased metabolite profiling was also performed by utilizing high-resolution mass spectrometry data to annotate a total of 2,118 metabolites with 125 modification types; of these, 165 modified metabolites were identified in this study. Next, the WTMM database was used to assess diseased tomato tissues and 29 biomarkers were analyzed. In summary, the WTMM strategy is not only capable of large-scale detection and quantitative analysis of plant-modified metabolites in plants, but also can be used for plant biomarker development.

Chemical-tag-based semi-annotated metabolomics facilitates gene identification and specialized metabolic pathway elucidation in wheat

The importance of metabolite modification and species-specific metabolic pathways has long been recognized. However, linking the chemical structure of metabolites to gene function in order to explore the genetic and biochemical basis of metabolism has not yet been reported in wheat (Triticum aestivum). Here, we profiled metabolic fragment enrichment in wheat leaves and consequently applied chemical-tag-based semi-annotated metabolomics in a genome-wide association study in accessions of wheat. The studies revealed that all 1,483 quantified metabolites have at least one known functional group whose modification is tailored in an enzyme-catalyzed manner and eventually allows efficient candidate gene mining. A Triticeae crop-specific flavonoid pathway and its underlying metabolic gene cluster were elucidated in further functional studies. Additionally, upon overexpressing the major effect gene of the cluster TraesCS2B01G460000 (TaOMT24), the pathway was reconstructed in rice (Oryza sativa), which lacks this pathway. The reported workflow represents an efficient and unbiased approach for gene mining using forward genetics in hexaploid wheat. The resultant candidate gene list contains vast molecular resources for decoding the genetic architecture of complex traits and identifying valuable breeding targets and will ultimately aid in achieving wheat crop improvement.

Discovery of Modified Metabolites, Secondary Metabolites, and Xenobiotics by Structure-Oriented LC-MS/MS

Exogenous compounds and metabolites derived from therapeutics, microbiota, or environmental exposures directly interact with endogenous metabolic pathways, influencing disease pathogenesis and modulating outcomes of clinical interventions. With few spectral library references, the identification of covalently modified biomolecules, secondary metabolites, and xenobiotics is a challenging task using global metabolomics profiling approaches. Numerous liquid chromatography-coupled mass spectrometry (LC-MS) small molecule analytical workflows have been developed to curate global profiling experiments for specific compound groups of interest. These workflows exploit shared structural moiety, functional groups, or elemental composition to discover novel and undescribed compounds through nontargeted small molecule discovery pipelines. This Review introduces the concept of structure-oriented LC-MS discovery methodology and aims to highlight common approaches employed for the detection and characterization of covalently modified biomolecules, secondary metabolites, and xenobiotics. These approaches represent a combination of instrument-dependent and computational techniques to rapidly curate global profiling experiments to detect putative ions of interest based on fragmentation patterns, predictable phase I or phase II metabolic transformations, or rare elemental composition. Application of these methods is explored for the detection and identification of novel and undescribed biomolecules relevant to the fields of toxicology, pharmacology, and drug discovery. Continued advances in these methods expand the capacity for selective compound discovery and characterization that promise remarkable insights into the molecular interactions of exogenous chemicals with host biochemical pathways.

Leveraging glycoside-targeted metabolomics to gain insight into biological function

In plants, uridine diphosphate (UDP)-dependent glycosyltransferases (UGTs) catalyze glycosylation of secondary metabolites, but assigning physiological functions to UGTs is still a daunting task. The recent study of Wu et al. presents a useful method to resolve this problem by elegantly combining modification-specific metabolomics with isotope tracing.

Metabolomics and network analysis uncovered profound inflammation-associated alterations in hepatitis B virus-related cirrhosis patients with early hepatocellular carcinoma

Patients with hepatitis B virus (HBV)-related liver cirrhosis (LC) are at high risk for hepatocellular carcinoma (HCC). Limitations in the early detection of HCC give rise to poor survival in this high-risk population. Here, we performed comprehensive metabolomics on health individuals and HBV-related LC patients with and without early HCC. Compared to non-HCC patients (N = 108) and health controls (N = 80), we found that patients with early HCC (N = 224) exhibited a specific plasma metabolome map dominated by lipid alterations, including lysophosphatidylcholines, lysophosphatidic acids and bile acids. Pathway and function network analyses indicated that these metabolite alterations were closely associated with inflammation responses. Using multivariate regression and machine learning approaches, we identified a five-metabolite combination that showed significant performances in differentiating early-HCC from non-HCC than α-fetoprotein (area under the curve values, 0.981 versus 0.613). At metabolomic levels, this work provides additional insights of metabolic dysfunction related to HCC progressions and demonstrates the plasma metabolites might be measured to identify early HCC in patients with HBV-related LC.

Comprehensive Profiling of Phosphomonoester Metabolites in Saccharomyces cerevisiae by the Chemical Isotope Labeling-LC-MS Method

Phosphomonoesters are important biosynthetic and energy metabolism intermediates in microorganisms. A comprehensive analysis of phosphomonoester metabolites is of great significance for the understanding of their metabolic phosphorylation process and inner mechanism. In this study, we established a pair of isotope reagent d₀/d₅-2-diazomethyl-N-methyl-phenyl benzamide-labeling-based LC-MS method for the comprehensive analysis of phosphomonoester metabolites. By this method, the labeled phosphomonoester metabolites specifically produced characteristic isotope paired peaks with an m/z difference of 5.0314 in the MS¹ spectra and a pair of diagnostic ions (m/z 320.0693/325.1077) in the MS² spectra. Based on this, a diagnostic ion-based strategy was established for the rapid screening, identification, and relative quantification of phosphomonoester metabolites. Using this strategy, 42 phosphomonoester metabolites were highly accurately identified fromSaccharomyces cerevisiae (S. cerevisiae). Notably, two phosphomonoesters were first detected fromS. cerevisiae. The relative quantification results indicated that the contents of nine phosphomonoester metabolites including two intermediates (Ru5P and S7P) in the pentose phosphate pathway (PPP) were significantly different between lycopene-producible and wild-type S. cerevisiae. A further enzyme assay indicated that the activity of the PPP was closely related to the production of lycopene. Our findings provide new perspectives for the related mechanism study and valuable references for making informed microbial engineering decisions.

Untargeted MSn-Based Monitoring of Glucuronides in Fish: Screening Complex Mixtures for Contaminants with Biological Relevance

The complexity of contaminant mixtures in surface waters has presented long-standing challenges to the assessment of risks to human health and the environment. As a result, novel strategies for both identifying contaminants that have not been routinely monitored through targeted methods and prioritizing detected compounds with respect to their biological relevance are needed. Tracking biotransformation products in biofluids and tissues in an untargeted fashion facilitates the identification of chemicals taken up by the resident species (e.g., fish), so by default ensuring that detected compounds are biologically relevant in terms of exposure. In this study, we investigated xenobiotic glucuronidation, which is arguably the most important phase II metabolism pathway for many pharmaceuticals, pesticides, and other environmental contaminants. The application of an untargeted high-resolution mass spectrometry-based approach tentatively revealed the presence of over 70 biologically relevant xenobiotics in bile collected from male and female fathead minnows exposed to wastewater treatment plant effluents. The majority of these were not targets of conventional contaminant monitoring. These results highlight the utility of biologically based untargeted screening methods when evaluating chemical contaminants in complex environmental mixtures.

Glycoside-specific metabolomics combined with precursor isotopic labeling for characterizing plant glycosyltransferases

Glycosylation by uridine diphosphate-dependent glycosyltransferases (UGTs) in plants contributes to the complexity and diversity of secondary metabolites. UGTs are generally promiscuous in their use of acceptors, making it challenging to reveal the function of UGTs in vivo. Here, we described an approach that combined glycoside-specific metabolomics and precursor isotopic labeling analysis to characterize UGTs in Arabidopsis. We revisited the UGT72E cluster, which has been reported to catalyze the glycosylation of monolignols. Glycoside-specific metabolomics analysis reduced the number of differentially accumulated metabolites in the ugt72e1e2e3 mutant by at least 90% compared with that from traditional untargeted metabolomics analysis. In addition to the two previously reported monolignol glycosides, a total of 62 glycosides showed reduced accumulation in the ugt72e1e2e3 mutant, 22 of which were phenylalanine-derived glycosides, including 5-OH coniferyl alcohol-derived and lignan-derived glycosides, as confirmed by isotopic tracing of [¹³C₆]-phenylalanine precursor. Our method revealed that UGT72Es could use coumarins as substrates, and genetic evidence showed that UGT72Es endowed plants with enhanced tolerance to low iron availability under alkaline conditions. Using the newly developed method, the function of UGT78D2 was also evaluated. These case studies suggest that this method can substantially contribute to the characterization of UGTs and efficiently investigate glycosylation processes, the complexity of which have been highly underestimated.

A novel method for efficient screening and annotation of important pathway-associated metabolites based on the modified metabolome and probe molecules

植物次生代谢物在抵御生物/非生物胁迫、生物间互作以及信息传递等方面发挥重要作用,次生代谢途径解析对植物分子育种、天然产物合成等方面具有重要意义。液相色谱-高分辨串联质谱(LC-HRMS/MS)为次生代谢物鉴定及途径表征提供了技术手段。非靶向LC-HRMS/MS方法可获得丰富的质谱信号,包括一级质谱和二级质谱(MS, MS/MS),但受质谱数据库规模以及次生代谢物复杂性的制约,次生代谢物注释十分困难。该研究以玉米叶片中苯丙烷途径代谢物为例,发展用于非靶向代谢组数据中重要途径代谢物的高效筛选和注释新方法。首先,利用公共代谢途径数据库及文献获取参与苯丙烷代谢途径的61种修饰反应类型,进而从非靶向实验数据中筛选出修饰代谢组。其次,获取开源串联质谱数据中的苯丙烷类化合物作为探针分子,构建探针分子质谱数据库。将探针分子与修饰代谢组共建分子网络,锁定目标途径代谢物并注释结构。该方法在正、负离子模式下分别筛选出玉米叶片中392个和417个苯丙烷途径候选代谢物,去冗余后共注释出129个代谢物,涉及苯丙烷代谢的主要分支途径,如黄酮途径的8个类黄酮、19个氧苷类黄酮和32个碳苷类黄酮,31个羟基肉桂酸途径代谢物以及22个木脂素途径代谢物;其中26个在PubChem和SciFinder数据库中未见收录。该研究利用探针分子结合修饰组可快速锁定途径代谢物,且有助于快速、准确的网络传播注释,可显著提高目标途径代谢物筛选与注释效率,为植物次生代谢途径的深入解析提供分析手段。

Post-Deconvolution MS/MS Spectra Extraction with Data-Independent Acquisition for Comprehensive Profiling of Urinary Glucuronide-Conjugated Metabolome

Conjugation reactions are of critical significance in human metabolism. Identification of these conjugated metabolites is still challenging. Here, we propose a strategy, post-deconvolution MS/MS spectra extraction with data-independent acquisition (PDMS2E-DIA), to comprehensively profile the glucuronide-conjugated metabolome. PDMS2E-DIA enables the identification of conjugated and unconjugated metabolite pairs through neutral loss filtering combined with a significant change in abundance after the deconjugation reaction. Purified DIA MS/MS spectra were constructed by extracting MS/MS fragments shared between spectra derived from conjugated and unconjugated metabolites. The feasibility of this approach was first demonstrated by the identification of two glucuronide-conjugated metabolite standards spiked in urine samples. For human urine samples, 479 features were structurally annotated as potential glucuronide-conjugated metabolites, resulting in the identification of 211 metabolites. Fragment peaks derived from interferents were found to be removed by PDMS2E-DIA, which increased about 6 times the number of identified urine metabolites compared with those calculated from raw DIA deconvoluted MS/MS spectra. This approach was found to have great potential for identifying glucuronide-conjugated metabolites, as well as other kinds of chemical conjugations.

Diagnostic Performance of Sex-Specific Modified Metabolite Patterns in Urine for Screening of Prediabetes

AIMS/HYPOTHESIS

Large-scale prediabetes screening is still a challenge since fasting blood glucose and HbA_1c as the long-standing, recommended analytes have only moderate diagnostic sensitivity, and the practicability of the oral glucose tolerance test for population-based strategies is limited. To tackle this issue and to identify reliable diagnostic patterns, we developed an innovative metabolomics-based strategy deviating from common concepts by employing urine instead of blood samples, searching for sex-specific biomarkers, and focusing on modified metabolites.

METHODS

Non-targeted, modification group-assisted metabolomics by liquid chromatography-mass spectrometry (LC-MS) was applied to second morning urine samples of 340 individuals from a prediabetes cohort. Normal (n = 208) and impaired glucose-tolerant (IGT; n = 132) individuals, matched for age and BMI, were randomly divided in discovery and validation cohorts. ReliefF, a feature selection algorithm, was used to extract sex-specific diagnostic patterns of modified metabolites for the detection of IGT. The diagnostic performance was compared with conventional screening parameters fasting plasma glucose (FPG), HbA_1c, and fasting insulin.

RESULTS

Female- and male-specific diagnostic patterns were identified in urine. Only three biomarkers were identical in both. The patterns showed better AUC and diagnostic sensitivity for prediabetes screening of IGT than FPG, HbA_1c, insulin, or a combination of FPG and HbA_1c. The AUC of the male-specific pattern in the validation cohort was 0.889 with a diagnostic sensitivity of 92.6% and increased to an AUC of 0.977 in combination with HbA_1c. In comparison, the AUCs of FPG, HbA_1c, and insulin alone reached 0.573, 0.668, and 0.571, respectively. Validation of the diagnostic pattern of female subjects showed an AUC of 0.722, which still exceeded the AUCs of FPG, HbA_1c, and insulin (0.595, 0.604, and 0.634, respectively). Modified metabolites in the urinary patterns include advanced glycation end products (pentosidine-glucuronide and glutamyl-lysine-sulfate) and microbiota-associated compounds (indoxyl sulfate and dihydroxyphenyl-gamma-valerolactone-glucuronide).

CONCLUSIONS/INTERPRETATION

Our results demonstrate that the sex-specific search for diagnostic metabolite biomarkers can be superior to common metabolomics strategies. The diagnostic performance for IGT detection was significantly better than routinely applied blood parameters. Together with recently developed fully automatic LC-MS systems, this opens up future perspectives for the application of sex-specific diagnostic patterns for prediabetes screening in urine.

Integration of Chemical Derivatization and in-Source Fragmentation Mass Spectrometry for High-Coverage Profiling of Submetabolomes

In-source fragmentation-based high-resolution mass spectrometry (ISF-HRMS) is a potential analytical technique, which is usually used to profile some specific compounds that can generate diagnostic neutral loss (NL) or fragment ion (FI) in ion source inherently. However, the ISF-HRMS method does not work for those compounds that cannot inherently produce diagnostic NL or FI in ion source. In this study, a derivatization-based in-source fragmentation-information-dependent acquisition (DISF-IDA) strategy was proposed for profiling the metabolites with easily labeled functional groups (submetabolomes) by liquid chromatography-electrospray ionization-quadrupole time-of-flight mass spectrometry (LC-ESI-Q-TOF MS). As a proof-of-concept study, 36 carboxylated compounds labeled with N,N-dimethylethylenediamine (DMED) were selected as model compounds to examine performance of DISF-IDA strategy in screening the carboxylated metabolites and acquiring their MSⁿ spectra. In ESI source, the DEMD-derived carboxylated compounds were fragmented to produce characteristic neutral losses of 45.0578, 63.0684, and/or 88.1000 Da that were further used as diagnostic features for screening the carboxylated metabolites by DISF-IDA-based LC-Q-TOF MS. Furthermore, high-resolution MSⁿ spectra of the model compounds were also obtained within a single run of DISF-IDA-based LC-Q-TOF MS analysis, which contributed to the improvement of the annotation confidence. To further verify its applicability, DISF-IDA strategy was used for profiling carboxylated submetabolome in mice feces. Using this strategy, a total of 351 carboxylated metabolites were detected from mice feces, of which 178 metabolites (51% of the total) were positively or putatively identified. Moreover, DISF-IDA strategy was also demonstrated to be applicable for profiling other submetabolomes with easily labeled functional groups such as amino, carbonyl, and cis-diol groups. Overall, our proposed DISF-IDA strategy is a promising technique for high-coverage profiling of submetabolomes with easily labeled functional groups in biological samples.

Systematic, Modifying Group-Assisted Strategy Expanding Coverage of Metabolite Annotation in Liquid Chromatography-Mass Spectrometry-Based Nontargeted Metabolomics Studies

From microbes to human beings, nontargeted metabolic profiling by liquid chromatography (LC)-mass spectrometry (MS) has been commonly used to investigate metabolic alterations. Still, a major challenge is the annotation of metabolites from thousands of detected features. The aim of our research was to go beyond coverage of metabolite annotation in common nontargeted metabolomics studies by an integrated multistep strategy applying data-dependent acquisition (DDA)-based ultrahigh-performance liquid chromatography (UHPLC)-high-resolution mass spectrometry (HRMS) analysis followed by comprehensive neutral loss matches for characteristic metabolite modifications and database searches in a successive manner. Using pooled human urine as a model sample for method establishment, we found 22% of the detected compounds having modifying structures. Major types of metabolite modifications in urine were glucuronidation (33%), sulfation (20%), and acetylation (6%). Among the 383 annotated metabolites, 100 were confirmed by standard compounds and 50 modified metabolites not present in common databases such as human metabolite database (HMDB) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were structurally elucidated. Practicability was tested by the investigation of urines from pregnant women diagnosed with gestational diabetes mellitus vs healthy controls. Overall, 83 differential metabolites were annotated and 67% of them were modified metabolites including five previously unreported compounds. To conclude, the systematic modifying group-assisted strategy can be taken as a useful tool to extend the number of annotated metabolites in biological and biomedical nontargeted studies.

In Vitro Metabolism Study of Seongsanamide A in Human Liver Microsomes Using Non-Targeted Metabolomics and Feature-Based Molecular Networking

Seongsanamide A is a bicyclic peptide with an isodityrosine residue discovered in Bacillus safensis KCTC 12796BP which exhibits anti-allergic activity in vitro and in vivo without significant cytotoxicity. The purpose of this study was to elucidate the in vitro metabolic pathway and potential for drug interactions of seongsanamide A in human liver microsomes using non-targeted metabolomics and feature-based molecular networking (FBMN) techniques. We identified four metabolites, and their structures were elucidated by interpretation of high-resolution tandem mass spectra. The primary metabolic pathway associated with seongsanamide A metabolism was hydroxylation and oxidative hydrolysis. A reaction phenotyping study was also performed using recombinant cytochrome P450 isoforms. CYP3A4 and CYP3A5 were identified as the major metabolic enzymes responsible for metabolite formation. Seongsanamide A did not inhibit the cytochrome P450 isoforms commonly involved in drug metabolism (IC₅₀ > 10 µM). These results will contribute to further understanding the metabolism and drug interaction potential of various bicyclic peptides.

Insight into chemical basis of traditional Chinese medicine based on the state-of-the-art techniques of liquid chromatography-mass spectrometry

Traditional Chinese medicine (TCM) has been an indispensable source of drugs for curing various human diseases. However, the inherent chemical diversity and complexity of TCM restricted the safety and efficacy of its usage. Over the past few decades, the combination of liquid chromatography with mass spectrometry has contributed greatly to the TCM qualitative analysis. And novel approaches have been continuously introduced to improve the analytical performance, including both the data acquisition methods to generate a large and informative dataset, and the data post-processing tools to extract the structure-related MS information. Furthermore, the fast-developing computer techniques and big data analytics have markedly enriched the data processing tools, bringing benefits of high efficiency and accuracy. To provide an up-to-date review of the latest techniques on the TCM qualitative analysis, multiple data-independent acquisition methods and data-dependent acquisition methods (precursor ion list, dynamic exclusion, mass tag, precursor ion scan, neutral loss scan, and multiple reaction monitoring) and post-processing techniques (mass defect filtering, diagnostic ion filtering, neutral loss filtering, mass spectral trees similarity filter, molecular networking, statistical analysis, database matching, etc.) were summarized and categorized. Applications of each technique and integrated analytical strategies were highlighted, discussion and future perspectives were proposed as well.

A metabolomics approach to investigate the proceedings of mitochondrial dysfunction in rats from prediabetes to diabetes

Diabetes mellitus (DM) is a leading cause of preventable cardiovascular disease, but the metabolic changes from prediabetes to diabetes have not been fully clarified. This study implemented a metabolomics profiling platform to investigate the variations of metabolites and to elucidate their global profiling from metabolic syndrome to DM. Methods: Male Sprague-Dawley rats (n = 44) were divided into four groups. Three groups were separately fed with a normal diet, a high-fructose diet (HF), or a high-fat (HL) diet while one group was treated with streptozotocin. The HF and HL diet were meant to induce insulin resistance, obesity, and dyslipidemia, which known to induce DM. Results: The most significant metabolic variations in the DM group’s urine samples were the reduced release of citric acid cycle intermediates, the increase in acylcarnitines, and the decrease in urea excretion, all of which indicated energy metabolism abnormalities and mitochondrial dysfunction. Overall, the metabolic analysis revealed tryptophan metabolic pathway variations in the prediabetic phase, even though the mitochondrial function remains unaffected. Conclusion: This study show that widespread methylations and impaired tryptophan metabolism occur in metabolic syndrome and are then followed by a decline in citric acid cycle intermediates, indicating mitochondrial dysfunction in diabetes.

Applications of Chromatography-Ultra High-Resolution MS for Stable Isotope-Resolved Metabolomics (SIRM) Reconstruction of Metabolic Networks

Metabolism is a complex network of compartmentalized and coupled chemical reactions, which often involve transfers of substructures of biomolecules, thus requiring metabolite substructures to be tracked. Stable isotope resolved metabolomics (SIRM) enables pathways reconstruction, even among chemically identical metabolites, by tracking the provenance of stable isotope-labeled substructures using NMR and ultrahigh resolution (UHR) MS. The latter can resolve and count isotopic labels in metabolites and can identify isotopic enrichment in substructures when operated in tandem MS mode. However, MS2 is difficult to implement with chromatography-based UHR-MS due to lengthy MS1 acquisition time that is required to obtain the molecular isotopologue count, which is further exacerbated by the numerous isotopologue source ions to fragment. We review here recent developments in tandem MS applications of SIRM to obtain more detailed information about isotopologue distributions in metabolites and their substructures.

A novel neutral loss/product ion scan-incorporated integral approach for the untargeted characterization and comparison of the carboxyl-free ginsenosides from Panax ginseng, Panax quinquefolius, and Panax notoginseng

Differentiated composition in precursor ions for different subclasses of ginsenosides in the negative electrospray-ionization mode has been reported, which lays a foundation for the sorted and untargeted identification of ginsenosides. Carboxyl-free ginsenosides simultaneously from Panax ginseng, P. quinquefolius, and P. notoginseng, were comprehensively characterized and statistically compared. A neutral loss/product ion scan (NL-PIS) incorporated untargeted profiling approach, coupled to ultra-high performance liquid chromatography, was developed on a linear ion-trap/Orbitrap mass spectrometer for characterizing carboxyl-free ginsenosides. It incorporated in-source fragmentation (ISF) full scan-MS¹, mass tag-MS², and product ion scan-MS³. Sixty batches of ginseng samples were analyzed by metabolomics workflows for the discovery of ginsenoside markers. Using formic acid (FA) as the additive, carboxyl-free ginsenosides (protopanaxadiol-type, protopanaxatriol-type, and octillol-type) gave predominant FA-adducts, while rich deprotonated molecules were observed for carboxyl-containing ginsenosides (oleanolic acid-type and malonylated) when source-induced dissociation (SID) was set at 0 V. Based on the NL transition [M+FA‒H]^- > [M-H]^- and the characteristic sapogenin product ions, a NL-PIS approach was established. It took advantage of the efficient full-information acquisition of ISF-MS¹ (SID: 50 V), the high specificity of mass tag (NL: 46.0055 Da)-induced MS² fragmentation, and the substructure fragmentation of product ion scan-MS³. We could characterize 216 carboxyl-free ginsenosides, and 21 thereof were potentially diagnostic for the species differentiation. Conclusively, sorted and untargeted characterization of the carboxyl-free ginsenosides was achieved by the established NL-PIS approach. In contrast to the conventional NL or PIS-based survey scan strategies, the high-accuracy MSⁿ data obtained can enable more reliable identification of ginsenosides.

The Structure and Function of Major Plant Metabolite Modifications

Plants produce a myriad of structurally and functionally diverse metabolites that play many different roles in plant growth and development and in plant response to continually changing environmental conditions as well as abiotic and biotic stresses. This metabolic diversity is, to a large extent, due to chemical modification of the basic skeletons of metabolites. Here, we review the major known plant metabolite modifications and summarize the progress that has been achieved and the challenges we are facing in the field. We focus on discussing both technical and functional aspects in studying the influences that various modifications have on biosynthesis, degradation, transport, and storage of metabolites, as well as their bioactivity and toxicity. Finally, we discuss some emerging insights into the evolution of metabolic pathways and metabolite functionality.

Rapid Solution-Phase Hydrogen/Deuterium Exchange for Metabolite Compound Identification

Rapid, solution-phase hydrogen/deuterium exchange (HDX) coupled with mass spectrometry (MS) is demonstrated as a means for distinguishing small-molecule metabolites. HDX is achieved using capillary vibrating sharp-edge spray ionization (cVSSI) to allow sufficient time for reagent mixing and exchange in micrometer-sized droplets. Different compounds are observed to incorporate deuterium with varying efficiencies resulting in unique isotopic patterns as revealed in the MS spectra. Using linear regression techniques, parameters representing contribution to exchange by different hydrogen types can be computed. In this proof-of-concept study, the exchange parameters are shown to be useful in the retrodiction of the amount of deuterium incorporated within different compounds. On average, the exchange parameters retrodict the exchange level with ~ 2.2-fold greater accuracy than treating all exchangeable hydrogens equally. The parameters can be used to produce hypothetical isotopic distributions that agree (± 16% RMSD) with experimental measurements. These initial studies are discussed in light of their potential value for identifying challenging metabolite species.

High-resolution MS/MS metabolomics by data-independent acquisition reveals urinary metabolic alteration in experimental colitis

INTRODUCTION

Traditional high-resolution MS1 based untargeted metabolomics suffers from low sensitivity, while low-resolution MS/MS based multiple reaction monitoring increases sensitivity at the cost of metabolite coverage and the mass accuracy.

OBJECTIVES

To evaluate and apply the high-resolution MS/MS level untargeted metabolomics.

METHODS

SWATH based data-independent acquisition (DIA) was optimized to obtain MS/MS of all precursor ions.

RESULTS

SWATH-MS/MS could rescue MS1 obscured or saturated metabolites and potentially provide diagnostic fragments to differentiate isomers. For SWATH-MS/MS, 4944 out of 21492 (23.0%) and 2289 out of 12831 (17.8%) fragment ion features significantly changed (Fold change > 1.5, P < 0.05) between Normal and experimental acute ulcerative colitis (UC) groups in positive and negative ion mode, respectively. For SWATH-MS1, 1022 out of 4818 (21.2%) and 353 out of 2266 (15.6%) features significantly changed in positive and negative ion mode, respectively. By deciphering the metabolite profiles with high-resolution MS/MS, it allows versatile post-acquisition data mining such as open detection of different sub-metabolome. The method revealed a global urinary metabolic alteration and increased glucuronide and sulfate sub-metabolome in UC. The major limitation of untargeted SWATH-MS/MS is increased interferences derived from wider Q1 isolation window.

CONCLUSIONS

SWATH-MS/MS is a versatile metabolomics strategy, merging the coverage of high-resolution untargeted metabolomics and the sensitivity of MS/MS.

Metabolomics identifies increases in the acylcarnitine profiles in the plasma of overweight subjects in response to mild weight loss: a randomized, controlled design study

Background

Using metabolomics technique to analyze the response to a dietary intervention generates valuable information concerning the effects of the prescribed diet on metabolic regulation. To determine whether low calorie diet (LCD)-induced weight reduction causes changes in plasma metabolites and metabolic characteristics.

Methods

Overweight subjects consumed a LCD (n = 47) or a weight maintenance diet (control, n = 50) in a randomized, controlled design study with a 12-week clinical intervention period. Plasma samples were analyzed using an UPLC-LTQ-Orbitrap MS.

Results

The 12-week LCD intervention resulted in significant mild weight loss, with an 8.3% and 10.6% reduction observed in the visceral fat area (VFA) at the level of the lumbar vertebrae L1 and L4, respectively. The LCD group showed a significant increase in the mean change of serum free fatty acids compared to the control group. In the LCD group, we observed a significant increase in the acylcarnitine (AC) levels, including hexanoylcarnitine, L-octanoylcarnitine, 9-decenoylcarnitine, trans-2-dodecenoylcanitine, dodecanoylcarnitine, 3,5-tetradecadiencarnitine, cis-5-tetradecenoylcarnitine, 9,12-hexadecadienoylcarnitine, and 9-hexadecenoylcarnitne at the 12-week follow-up assessment. When the plasma metabolite changes from baseline were compared between the control and LCD groups, the LCD group showed significant increases in hexanoylcarnitine, L-octanoylcarnitine, trans-2-dodecenoylcanitine, and 3,5-tetradecadiencarnitine than the control group. Additionally, the changes in these ACs in the LCD group strongly negatively correlated with the changes in the VFA at L1 and/or L4.

Conclusion

Mild weight loss from 12-week calorie restriction increased the plasma levels of medium- and long-chain ACs. These changes were coupled with a decrease in VFA and an increase in free fatty acids.

Trial registration

NCT03135132; April 26, 2017.

Electronic supplementary material

The online version of this article (10.1186/s12944-018-0887-1) contains supplementary material, which is available to authorized users.

Comparative Metabolic Responses and Adaptive Strategies of Tea Leaves ( Camellia sinensis) to N2 and CO2 Anaerobic Treatment by a Nontargeted Metabolomics Approach

It is well-known that anaerobic treatment has been considered as a utility process to accumulate γ-aminobutyric acid (GABA) in tea leaves. In this article, the nonvolatile differential compounds in picked-tea leaves between filled-N₂ treatment and filled-CO₂ treatment were compared in metabolic profiles and dynamic changes via ultrahigh performance liquid chromatography linked to a hybrid quadrupole orthogonal time-of-flight mass spectrometer (UPLC-Q-TOF-MS). Multivariate analysis and heat map of hierarchical clustering analysis indicated that filled-N₂ treatment resulted in a wider range of metabolic perturbation than filled-CO₂ treatment, but GABA accumulates faster and more significantly under filled-CO₂ treatment than other treatment. The differential metabolites in anaerobic treatment were mainly reflected in the levels of glucose metabolism and amino acid metabolism, and the main differential pathway included the glyoxylate metabolism pathway, galactose metabolism, and phenylalanine metabolism. These metabolomic analyses were also evaluated to illuminate the physiological adaptive strategies of tea adopted to tolerate certain anaerobic stress types.

High-pressure photon ionization time-of-flight mass spectrometry combined with dynamic purge-injection for rapid analysis of volatile metabolites in urine

Small molecule metabolites are widely used as biomarkers in the research field of metabolomics for disease diagnosis and exposure assessment. As a readily available biofluid containing plenty of volatile organic metabolites (VOMs), urine is ideal for non-invasive metabolomic analysis; however, there is still lack of rapid analysis method for VOMs in urine. Here we report a kind of rapid method for urine analysis by employing high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) combined with dynamic purge-injection. Various types of metabolites, such as ketones, alcohols, acids, sulfides, pyrroles and amines were detected directly by simple acidification or alkalization of urines. It is noteworthy that nitrogen-containing compounds, especially polar amines, could be ultrasensitively measured without any derivatization. The analytical capability of the direct HPPI-MS technique was demonstrated by analyzing five valuable metabolites, i.e., toluene, 2,5-dimethylpyrrole, trimethlyamine, styrene, and p-xylene, which exhibited relatively low limits of detection, wide linear range and satisfactory repeatability. Being highly sensitive and humidity-friendly, the whole analytical procedure is easily operated in less than 6 min. Interestingly, a new biomarker 2,5-dimethylpyrrole was exclusively found in the smoker's urine sample besides toluene. The work presents a novel tool for rapid nontarget disease biomarkers screening or target monitoring of specific compounds through the investigation of volatile metabolites in urine.

Metabolomics Investigation Reveals That 8-C N-Ethyl-2-pyrrolidinone-Substituted Flavan-3-ols Are Potential Marker Compounds of Stored White Teas

White teas of different stored ages have varied flavor, bioactivity, and commercial value. In this study, a liquid chromatography-mass spectrometry-based metabolomics investigation revealed that there are distinct differences among the compound patterns of Baihaoyinzhen (BHYZ) and Baimudan (BMD) white teas with various storage durations. The levels of flavan-3-ols, procyanidins, theasinensins, theaflavins, flavonol- O-glycosides, flavone- C-glycosides, and most of the amino acids were reduced after long-term (>4 years) storage. More importantly, 8-C N-ethyl-2-pyrrolidinone-substituted flavan-3-ols (EPSFs), including seven novel compounds discovered in white teas for the first time, were formed from theanine and flavan-3-ols during storage, and their contents were positively correlated with the storage duration. These findings were further confirmed by the linearly increasing formation of EPSFs in reaction solution and BMD white teas stored in an environment-controlled cabinet. In conclusion, EPSFs were detected in white teas for the first time and were discovered as marker compounds and potential indicators for long-term storage of white tea.

Serum and plasma amino acids as markers of prediabetes, insulin resistance, and incident diabetes

Presently, routine screening misses many cases of prediabetes and early type 2 diabetes (T2D). Therefore, better biomarkers are needed for a simple and early detection of abnormalities of glucose metabolism and prediction of future T2D. Possible candidates for this include plasma or serum amino acids because glucose and amino acid metabolism are closely connected. This review presents the available evidence of this connectivity and discusses its clinical implications. First, we examine the underlying physiological, pre-analytical, and analytical issues. Then, we summarize results of human studies that evaluate amino acid levels as markers for insulin resistance, prediabetes, and future incident T2D. Finally, we illustrate the interconnection of amino acid levels and metabolic syndrome with our own data from a deeply phenotyped human cohort. We also discuss how amino acids may contribute to the pathophysiology of T2D. We conclude that elevated branched-chain amino acids and reduced glycine are currently the most robust and consistent amino acid markers for prediabetes, insulin resistance, and future T2D. Yet, we are cautious regarding the clinical potential even of these parameters because their discriminatory power is insufficient and their levels depend not only on glycemia, but also on other components of the metabolic syndrome. The identification of more precise intermediates of amino acid metabolism or combinations with other biomarkers will, therefore, be necessary to obtain in order to develop laboratory tests that can improve T2D screening.

Data acquisition workflows in liquid chromatography coupled to high resolution mass spectrometry-based metabolomics: Where do we stand?

Typical mass spectrometry (MS) based untargeted metabolomics protocols are tedious as well as time- and sample-consuming. In particular, they often rely on "full-scan-only" analyses using liquid chromatography (LC) coupled to high resolution mass spectrometry (HRMS) from which metabolites of interest are first highlighted, and then tentatively identified by using targeted MS/MS experiments. However, this situation is evolving with the emergence of integrated HRMS based-data acquisition protocols able to perform multi-event acquisitions. Most of these protocols, referring to as data dependent and data independent acquisition (DDA and DIA, respectively), have been initially developed for proteomic applications and have recently demonstrated their applicability to biomedical studies. In this context, the aim of this article is to take stock of the progress made in the field of DDA- and DIA-based protocols, and evaluate their ability to change conventional metabolomic and lipidomic data acquisition workflows, through a review of HRMS instrumentation, DDA and DIA workflows, and also associated informatics tools.

Characterization of white tea metabolome: Comparison against green and black tea by a nontargeted metabolomics approach

White tea is considered the least processed form of tea and is reported to have a series of potent bioactivities, such as antioxidant, anti-inflammatory, anti-mutagenic, and anti-cancer activities. However, the chemical composition of white tea and the dynamic changes of the metabolites during the manufacturing process are far from clear. In this study, we applied a nontargeted metabolomics approach based on ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS) to comprehensively profile the characteristic metabolites of white tea. There were significant differences in the content of amino acids, catechins, dimeric catechins, flavonol and flavone glycosides, and aroma precursors in white tea compared with green and black teas that were manufactured from the same fresh tea leaves. Furthermore, the dynamic changes of the metabolites in the tea samples with various withering durations of 0, 4, 8, 12, 16, 20, 24, 28, and 36 h were also profiled. This study offers a comprehensive characterization of the metabolites and their changes in white tea.

Metabolomics Toward Biomarker Discovery

Metabolomics has been used as practical tool in the discovery of novel biomarkers in a broad area in the clinic. The analytical platforms including nuclear magnetic resonance (NMR) and mass spectrometry (MS) can cover thousands of metabolites. With the help of multivariate data analysis, many potential biomarkers can be defined in the studies. Since metabolites stand at the end point of metabolism, it remains difficult to find novel biomarkers with good diagnostic or prognostic performance. In this chapter, we will introduce a general protocol for biomarker discovery within the scope of metabolomics using MS.

Global Metabolic Profiling of Plasma Shows that Three-Year Mild-Caloric Restriction Lessens an Age-Related Increase in Sphingomyelin and Reduces L-leucine and L-phenylalanine in Overweight and Obese Subjects

The effect of weight loss from long-term, mild-calorie diets (MCD) on plasma metabolites is unknown. This study was to examine whether MCD-induced weight reduction caused changes in the extended plasma metabolites. Overweight and obese subjects aged 40-59 years consumed a MCD (approximately 100 kcal/day deficit, n=47) or a weight-maintenance diet (control, n=47) in a randomized, controlled design with a three-year clinical intervention period and plasma samples were analyzed by using UPLC-LTQ-Orbitrap mass spectrometry. The three-year MCD intervention resulted in weight loss (-8.87%) and significant decreases in HOMA-IR and TG. The three-year follow-up of the MCD group showed reductions in the following 13 metabolites: L-leucine; L-phenylalanine; 9 lysoPCs; PC (18:0/20:4); and SM (d18:0/16:1). The three-year MCD group follow-up identified increases in palmitic amide, oleamide, and PC (18:2/18:2). Considering the age-related alterations in the identified metabolites, the MCD group showed a greater decrease in L-leucine, L-phenylalanine, and SM (d18:0/16:1) compared with those of the control group. Overall, the change (Δ) in BMI positively correlated with the ΔTG, ΔHOMA-IR, ΔL-leucine, and ΔSM (d18:0/16:1). The ΔHOMA-IR positively correlated with ΔTG, ΔL-leucine, ΔL-phenylalanine, and ΔSM (d18:0/16:1). The weight loss resulting from three-year mild-caloric restriction lessens the age-related increase in SM and reduces L-leucine and L-phenylalanine in overweight and obese subjects. These changes were coupled with improved insulin resistance (ClinicalTrials.gov: NCT02081898).

Nontargeted Modification-Specific Metabolomics Investigation of Glycosylated Secondary Metabolites in Tea (Camellia sinensis L.) Based on Liquid Chromatography-High-Resolution Mass Spectrometry

Glycosylation on small molecular metabolites modulates a series of biological events in plants. However, a large number of glycosides have not been discovered and investigated using -omics approaches. Here, a general strategy named "nontargeted modification-specific metabolomics" was applied to map the glycosylation of metabolites. The key aspect of this method is to adopt in-source collision-induced dissociation to dissociate the glycosylated metabolite, causing a characteristic neutral loss pattern, which acts as an indicator for the glycosylation identification. In an exemplary application in green teas, 120 glucosylated/galactosylated, 38 rhamnosylated, 21 rutinosylated, and 23 primeverosylated metabolites were detected simultaneously. Among them, 61 glycosylated metabolites were putatively identified according to current tea metabolite databases. Thanks to the annotations of glycosyl moieties in advance, the method aids metabolite identifications. An additional 40 novel glycosylated metabolites were tentatively elucidated. This work provides a feasible strategy to discover and identify novel glycosylated metabolites in plants.

A data preprocessing strategy for metabolomics to reduce the mask effect in data analysis

Highlights

Developed a data preprocessing strategy to cope with missing values and mask effects in data analysis from high variation of abundant metabolites.

A new method- ‘x-VAST’ was developed to amend the measurement deviation enlargement.

Applying the above strategy, several low abundant masked differential metabolites were rescued.

Metabolomics is a booming research field. Its success highly relies on the discovery of differential metabolites by comparing different data sets (for example, patients vs. controls). One of the challenges is that differences of the low abundant metabolites between groups are often masked by the high variation of abundant metabolites. In order to solve this challenge, a novel data preprocessing strategy consisting of three steps was proposed in this study. In step 1, a ‘modified 80%’ rule was used to reduce effect of missing values; in step 2, unit-variance and Pareto scaling methods were used to reduce the mask effect from the abundant metabolites. In step 3, in order to fix the adverse effect of scaling, stability information of the variables deduced from intensity information and the class information, was used to assign suitable weights to the variables. When applying to an LC/MS based metabolomics dataset from chronic hepatitis B patients study and two simulated datasets, the mask effect was found to be partially eliminated and several new low abundant differential metabolites were rescued.

Current state of the art of mass spectrometry-based metabolomics studies – a review focusing on wide coverage, high throughput and easy identification

Metabolomics aims at the comprehensive assessment of a wide range of endogenous metabolites and attempts to identify and quantify the attractive metabolites in a given biological sample.

A short review of applications of liquid chromatography mass spectrometry based metabolomics techniques to the analysis of human urine

Mass spectrometry based metabolomics profiling.

Systems-based approaches to unravel multi-species microbial community functioning

Some of the most transformative discoveries promising to enable the resolution of this century's grand societal challenges will most likely arise from environmental science and particularly environmental microbiology and biotechnology. Understanding how microbes interact in situ, and how microbial communities respond to environmental changes remains an enormous challenge for science. Systems biology offers a powerful experimental strategy to tackle the exciting task of deciphering microbial interactions. In this framework, entire microbial communities are considered as metaorganisms and each level of biological information (DNA, RNA, proteins and metabolites) is investigated along with in situ environmental characteristics. In this way, systems biology can help unravel the interactions between the different parts of an ecosystem ultimately responsible for its emergent properties. Indeed each level of biological information provides a different level of characterisation of the microbial communities. Metagenomics, metatranscriptomics, metaproteomics, metabolomics and SIP-omics can be employed to investigate collectively microbial community structure, potential, function, activity and interactions. Omics approaches are enabled by high-throughput 21st century technologies and this review will discuss how their implementation has revolutionised our understanding of microbial communities.