Optimization of large-scale pseudotargeted metabolomics method based on liquid chromatography-mass spectrometry

Metabolomics-driven investigation of plant defense response against pest and pathogen attack

The advancement of metabolomics has assisted in the identification of various bewildering characteristics of the biological system. Metabolomics is a standard approach, facilitating crucial aspects of system biology with absolute quantification of metabolites using minimum samples, based on liquid/gas chromatography, mass spectrometry and nuclear magnetic resonance. The metabolome profiling has narrowed the wide gaps of missing information and has enhanced the understanding of a wide spectrum of plant-environment interactions by highlighting the complex pathways regulating biochemical reactions and cellular physiology under a particular set of conditions. This high throughput technique also plays a prominent role in combined analyses of plant metabolomics and other omics datasets. Plant metabolomics has opened a wide paradigm of opportunities for developing stress-tolerant plants, ensuring better food quality and quantity. However, despite advantageous methods and databases, the technique has a few limitations, such as ineffective 3D capturing of metabolites, low comprehensiveness, and lack of cell-based sampling. In the future, an expansion of plant-pathogen and plant-pest response towards the metabolite architecture is necessary to understand the intricacies of plant defence against invaders, elucidation of metabolic pathway operational during defence and developing a direct correlation between metabolites and biotic stresses. Our aim is to provide an overview of metabolomics and its utilities for the identification of biomarkers or key metabolites associated with biotic stress, devising improved diagnostic methods to efficiently assess pest and pathogen attack and generating improved crop varieties with the help of combined application of analytical and molecular tools.

Evaluation of the effects of three different processing methods of aconite on rat metabolites based on high-coverage pseudotargeted metabolomics

Aconite is the processed product of the seed root of Aconitum carmichaelii Debx. Aconite is a commonly used traditional Chinese medicine, which is generally used after processing. Black aconite, light aconite, and salted aconite are three different processed aconite products. They have the effects of restoring yang and saving energy enemy, dispersing cold, and relieving pain. However, clinical aconite poisoning cases have frequently been reported. In our study, we investigated the effects of three different processed aconite products on the changes of metabolites in vivo. A total of 42 rats were randomly divided into seven groups with six rats in each group. After three consecutive days of intragastric administration of 2.7 g/kg of the aconite-processed product, rat serums were obtained. The rat metabolites were detected using liquid chromatography-tandem mass spectrometry. The altered metabolites related to aconite-processed products were discovered by statistical analysis using metaboanalyst software. Our study is the first time to comprehensively evaluate the effects of three different processed aconite products on rat metabolites based on pseudotargeted metabolomics.

Revealing oxidative degradation of lipids and screening potential markers of four vegetable oils during thermal processing by pseudotargeted oxidative lipidomics

The oxidative degradation of lipids in vegetable oils during thermal processing may present a risk to human health. However, not much is known about the evolution of lipids and their non-volatile derivatives in vegetable oils under different thermal processing conditions. In the present study, a pseudotargeted oxidative lipidomics approach was developed and the evolution of lipids and their non-volatile derivatives in palm oil, rapeseed oil, soybean oil, and flaxseed oil under different thermal processing conditions was investigated. The results showed that thermal processing resulted in the oxidative degradation of TGs in vegetable oils, which generated oxTGs, DGs, and FFAs, as well as TGs with smaller molecular weights. The lower the fatty acid saturation, the more severe the oxidative degradation of vegetable oils and thermal processing at high temperatures should be avoided if possible. From the accumulation of oxTGs concentrations, the hazards during thermal processing at high temperatures were, in descending order, soybean oil, rapeseed oil, flaxseed oil, and palm oil. The non-volatile potential markers were screened in palm oil, rapeseed oil, soybean oil, and flaxseed oil for 1, 7, 5, and 2 markers related to thermal processing time, respectively. The study provided suggestions for the consumption of vegetable oils from multiple perspectives and identified markers for monitored oxidative degradation of vegetable oils.

Investigation of the response of Platycodongrandiflorus (Jacq.) A. DC to salt stress using combined transcriptomics and metabolomics

BACKGROUND

Platycodon grandiflorus (Jacq.) A. DC is a famous traditional Chinese medicine in China and an authentic medicine in Inner Mongolia. It has been traditionally used as an expectorant in cough and also has anti-inflammatory and other pharmacological effects. As a homologous plant of medicine and food, P. grandiflorus is widely planted in Northeast China. Soil salinity isa limiting factor for its cultivation. In this study, we comprehensively described the physiological characteristics of P. grandiflorus and combined transcriptomics and metabolomics to study the response of roots of P. grandiflorus to salt stress.

RESULTS

Overall, 8,988 differentially expressed genes were activated and significantly altered the metabolic processes. In total, 428 differentially abundant metabolites were affected by salt stress. After moderate and severe salt stress, most of the differentially abundant metabolites were enriched in the L-phenylalanine metabolic pathway. Through the comprehensive analysis of the interaction between key genes and metabolites, the main pathways such as lignin compound biosynthesis and triterpene saponin biosynthesis were completed. The relative content of compounds related to lignin biosynthesis, such as caffeic acid, coniferin, and syringing, increased under salt stress, and the related genes such as PAL, C4H, and the key enzyme gene UGT72E2 were activated to adapt to the salt stress. Platycodon saponin is one of the major triterpene saponins in P. grandiflorus, and Platycodin D is its most abundant major bioactive component. Under severe salt stress, Platycodin D level increased by nearly 1.77-fold compared with the control group. Most of the genes involved insynthetic pathway of Platycodin D, such as HMGCR, GGPS, SE, and LUP, were upregulated under salt stress.

CONCLUSION

Salt stress led to a decrease in the biomass and affected the activities of antioxidant enzymes and contents of osmotic regulators in the plant. These results provided not only novel insights into the underlying mechanisms of response of P. grandiflorus to salt stress but also a foundation for future studies on the function of genes related to salt tolerance in the triterpenoid saponin biosynthesis pathway.

Metabolic responses of tea (Camellia sinensis L.) to the insecticide thiamethoxam

BACKGROUND

Thiamethoxam (TMX) is insecticidal, but also can trigger physiological and metabolic reactions of plant cycles. The objective of this work was to evaluate the physiological and metabolic effect of TMX on tea plants and its potential benefits.

RESULTS

In this study, dose of TMX (0.09, 0.135 and 0.18 kg a.i./ha) were tested. Except for peroxidase (POD) and glutathione S-transferase (GST), chlorophyll, carotenoid, catalase (CAT) and malondialdehyde (MDA) were significantly affected compared with the controls. The CAT activity was increased by 3.38, 1.71, 2.91 times, respectively, under three doses of TMX treatment. The metabolic response between TMX treatment and control groups on the third day was compared using a widely targeted metabolomics. A total of 97 different metabolites were identified, including benzenoids, flavonoids, lipids and lipid-like molecules, organic acids and derivatives, organic nitrogen compounds, organic oxygen compounds, organoheterocyclic compounds, phenylpropanoids and polyketides, and others. Those metabolites were mapped on the perturbed metabolic pathways. The results demonstrated that the most perturbation occurred in flavone and flavonol biosynthesis. The beneficial secondary metabolites luteolin and kaempferol were upregulated 1.46 and 1.31 times respectively, which protect plants from biotic and abiotic stresses. Molecular docking models suggest interactions between TMX and flavonoid 3-O-glucosyltransferase.

CONCLUSION

Thiamethoxam spray positively promoted the physiological and metabolic response of tea plants. And this work also provided the useful information of TMX metabolism in tea plants as well as rational application of insecticides. © 2023 Society of Chemical Industry.

Metabolomic and transcriptomic analyses of rice plant interaction with invasive weed Leptochloa chinensis

Introduction

Leptochloa chinensis is an annual weed in paddy fields, which can engage in competition with rice, leading to a severe yield reduction. However, theunderlying mechanism governing this interaction remain unknown.

Methods

In this study, we investigated the mutual inhibition between rice and the weed undermono-culture and co-culture conditions. We found that the root exudates of both species played essential roles in mediating the mutual inhibition. Further metabolomic analysis identified a significant number of differential metabolites. These metabolites were predominantly enriched in the phenylpropanoid and flavonoid biosynthesis pathways in weed and rice. Transcriptomic analysis revealed that the differentially expressed genes responding to the interaction were also enriched in these pathways.

Results

Phenylpropanoid and flavonoid biosynthesis pathways are associated with allelopathy, indicating their pivotal role in the response of rice-weed mutual inhibition.

Discussion

Our findings shed light on the conserved molecular responses of rice and L. chinensis during theirinteraction, provide evidence to dissect the mechanisms underlying the allelopathic interaction and offer potential strategies for weed management in rice paddies.

Recent Review on Selected Xenobiotics and Their Impacts on Gut Microbiome and Metabolome

As it is well known, the gut is one of the primary sites in any host for xenobiotics, and the many microbial metabolites responsible for the interactions between the gut microbiome and the host. However, there is a growing concern about the negative impacts on human health induced by toxic xenobiotics. Metabolomics, broadly including lipidomics, is an emerging approach to studying thousands of metabolites in parallel. In this review, we summarized recent advancements in mass spectrometry (MS) technologies in metabolomics. In addition, we reviewed recent applications of MS-based metabolomics for the investigation of toxic effects of xenobiotics on microbial and host metabolism. It was demonstrated that metabolomics, gut microbiome profiling, and their combination have a high potential to identify metabolic and microbial markers of xenobiotic exposure and determine its mechanism. Further, there is increasing evidence supporting that reprogramming the gut microbiome could be a promising approach to the intervention of xenobiotic toxicity.

Quality Control of Targeted Plasma Lipids in a Large-Scale Cohort Study Using Liquid Chromatography-Tandem Mass Spectrometry

High-throughput metabolomics has enabled the development of large-scale cohort studies. Long-term studies require multiple batch-based measurements, which require sophisticated quality control (QC) to eliminate unexpected bias to obtain biologically meaningful quantified metabolomic profiles. Liquid chromatography-mass spectrometry was used to analyze 10,833 samples in 279 batch measurements. The quantified profile included 147 lipids including acylcarnitine, fatty acids, glucosylceramide, lactosylceramide, lysophosphatidic acid, and progesterone. Each batch included 40 samples, and 5 QC samples were measured for 10 samples of each. The quantified data from the QC samples were used to normalize the quantified profiles of the sample data. The intra- and inter-batch median coefficients of variation (CV) among the 147 lipids were 44.3% and 20.8%, respectively. After normalization, the CV values decreased by 42.0% and 14.7%, respectively. The effect of this normalization on the subsequent analyses was also evaluated. The demonstrated analyses will contribute to obtaining unbiased, quantified data for large-scale metabolomics.

Metabolomics-centered mining of plant metabolic diversity and function: Past decade and future perspectives

Plants are natural experts in organic synthesis, being able to generate large numbers of specific metabolites with widely varying structures that help them adapt to variable survival challenges. Metabolomics is a research discipline that integrates the capabilities of several types of research including analytical chemistry, statistics, and biochemistry. Its ongoing development provides strategies for gaining a systematic understanding of quantitative changes in the levels of metabolites. Metabolomics is usually performed by targeting either a specific cell, a specific tissue, or the entire organism. Considerable advances in science and technology over the last three decades have propelled us into the era of multi-omics, in which metabolomics, despite at an earlier developmental stage than genomics, transcriptomics, and proteomics, offers the distinct advantage of studying the cellular entities that have the greatest influence on end phenotype. Here, we summarize the state of the art of metabolite detection and identification, and illustrate these techniques with four case study applications: (i) comparing metabolite composition within and between species, (ii) assessing spatio-temporal metabolic changes during plant development, (iii) mining characteristic metabolites of plants in different ecological environments and upon exposure to various stresses, and (iv) assessing the performance of metabolomics as a means of functional gene identification , metabolic pathway elucidation, and metabolomics-assisted breeding through analyzing plant populations with diverse genetic variations. In addition, we highlight the prominent contributions of joint analyses of plant metabolomics and other omics datasets, including those from genomics, transcriptomics, proteomics, epigenomics, phenomics, microbiomes, and ion-omics studies. Finally, we discuss future directions and challenges exploiting metabolomics-centered approaches in understanding plant metabolic diversity.

Comparison of the Main Metabolites in Different Maturation Stages of Camelliavietnamensis Huang Seeds

Camellia vietnamensis Huang is an important woody oil crop in China, which has attracted much attention because of its abundant nutritional components and pharmaceutical value. Its seeds undergo a complex series of physiological and biochemical changes during maturation, with consequent alterations in metabolites. In order to investigate the endogenous metabolism of C. vietnamensis on Hainan Island during seed development, in this study, ultra-high-performance liquid tandem chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC/Q-TOF-MS) and multivariate statistical analysis (MSA) were used to analyze the differences in the chemical compounds of C. vietnamensis seeds among the four maturation stages. A total of 293 metabolites were identified from the methanol extract of the seeds of C. vietnamensis. Five metabolites, belonging to benzene and substituted derivatives, 5′-deoxyribonucleosides and linear 1,3-diarylpropanoids, were found in all three comparison groups, with consistently down-regulated trends. The Kyoto Encyclopedia of Genes and Genomes (KEGG) results showed that phloretin and 5′-methylthioadenosine were the differentially expressed metabolites when seeds were in the growth periods of S2 and S3, and indole and L-tryptophan were the differentially expressed metabolites when seeds were in the growth periods of S3 and S4. In addition, 34 flavonoid metabolites were detected, of which 4 were differentially expressed. It was indicated that flavonoids dynamically change during all the oil-tea camellia seed development stages. The findings provide data for the better understanding of endogenous metabolic pathways during C. vietnamensis seed development.

Metabolomics-based investigation of SARS-CoV-2 vaccination (Sinovac) reveals an immune-dependent metabolite biomarker

SARS-CoV-2 and its mutant strains continue to rapidly spread with high infection and fatality. Large-scale SARS-CoV-2 vaccination provides an important guarantee for effective resistance to existing or mutated SARS-CoV-2 virus infection. However, whether the host metabolite levels respond to SARS-CoV-2 vaccine-influenced host immunity remains unclear. To help delineate the serum metabolome profile of SARS-CoV-2 vaccinated volunteers and determine that the metabolites tightly respond to host immune antibodies and cytokines, in this study, a total of 59 sera samples were collected from 30 individuals before SARS-CoV-2 vaccination and from 29 COVID-19 vaccines 2 weeks after the two-dose vaccination. Next, untargeted metabolomics was performed and a distinct metabolic composition was revealed between the pre-vaccination (VB) group and two-dose vaccination (SV) group by partial least squares-discriminant and principal component analyses. Based on the criteria: FDR < 0.05, absolute log2 fold change greater than 0.25, and VIP >1, we found that L-glutamic acid, gamma-aminobutyric acid (GABA), succinic acid, and taurine showed increasing trends from SV to VB. Furthermore, SV-associated metabolites were mainly annotated to butanoate metabolism and glutamate metabolism pathways. Moreover, two metabolite biomarkers classified SV from VB individuals with an area under the curve (AUC) of 0.96. Correlation analysis identified a positive association between four metabolites enriched in glutamate metabolism and serum antibodies in relation to IgG, IgM, and IgA. These results suggest that the contents of gamma-aminobutyric acid and indole in serum could be applied as biomarkers in distinguishing vaccinated volunteers from the unvaccinated. What’s more, metabolites such as GABA and taurine may serve as a metabolic target for adjuvant vaccines to boost the ability of the individuals to improve immunity.

The Pseudotargeted Metabolomics Study on the Toxicity of Fuzi Using Ultraperformance Liquid Chromatography Tandem Mass Spectrometry

Fuzi is commonly used in traditional Chinese medicine. Clinical Fuzi poisoning cases have frequently been reported. Glycyrrhizae Radix is often used to alleviate Fuzi’s toxicity. However, the poisoning mechanism of Fuzi and the detoxication mechanism of Glycyrrhizae Radix are still not clear. We identified the chemical components of Fuzi at different decoction times (0.5, 1, 2, 4, and 6 h) using ultrahigh performance liquid chromatography quadrupole time-of-flight mass spectrometry. A total of 35 compounds were detected in the Fuzi decoction, including diester alkaloids, monoester alkaloids, amino acids, phenolic acids, organic acids, glycosides, and sugars among others. The content of diester alkaloids (i.e., subaconitine, neoaconitine, and aconitine) in the Fuzi decoction decreased after 2 h of decoction time, while the content of monoester alkaloids (i.e., benzoyl aconitine and benzoyl subaconitine) reached a peak at 2 h. A total of 32 rats were randomly divided into four groups, including 8 cases in the low-dosage Fuzi decoction group A, 8 cases in the high-dosage Fuzi decoction group B, 8 cases in the Fuzi and glycyrrhizae decoction group C, and 8 cases in the control group D. The decoction was administered orally for 7 days. Then, a serum was obtained. The metabolites’ changes were analyzed in serum metabolomics using liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Statistical analysis and pathway analysis were used to assess the effects of glycyrrhizae on the metabolic changes induced by Fuzi. The behavioral and biochemical characteristics indicated that Fuzi exhibited toxic effects on rats and their metabolic profiles changed. However, the metabolic profiles of the glycyrrhizae group became similar to those of the control group. These profiles showed that glycyrrhizae can effectively improve Fuzi poisoning rats. Our study demonstrated that the established pseudotargeted metabolomics is a powerful approach for investigating the mechanisms of herbal toxicity.

Discrimination of Geographical Origin of Agricultural Products From Small-Scale Districts by Widely Targeted Metabolomics With a Case Study on Pinggu Peach

Geographical indications of agricultural products are characterized by high quality and regional attributes, while they are more likely to be counterfeited by similar products from nearby regions. Accurate discrimination of origin on small geographical scales is extremely important for geographical indications of agricultural products to avoid food fraud. In this study, a widely targeted metabolomics based on ultra-high-performance liquid chromatography-tandem mass spectrometry combined with multivariate statistical analysis was used to distinguish the geographical origin of Pinggu Peach of Beijing and its two surrounding areas in Heibei province (China). Orthogonal partial least squares-discriminant analysis (OPLS-DA) based on 159 identified metabolites showed significant separation from Pinggu and the other adjacent regions. The number of the most important discriminant variables (VIP value >1) was up to 62, which contributed to the differentiation model. The results demonstrated that the metabolic fingerprinting combined with OPLS-DA could be successfully implemented to differentiate the geographical origin of peach from small-scale origins, thus providing technical support to further ensure the authenticity of geographical indication products. The greenness of the developed method was assessed using the Analytical GREEnness Metric Approach and Software (ARGEE) tool. It was a relatively green analytical method with room for improvement.

Distinct metabolic profiling is correlated with bisexual flowers formation resulting from exogenous ethephon induction in melon (Cucumis melo L.)

Melon (Cucumis melo L.) is an agronomically important vegetable. Most cultivars of melon are andromonoecious and bisexual flowers only emerged from the leaf axil of lateral branches. However, the regulatory mechanism contributing to the occurrence of bisexual flowers were still obscure. In this study, ethephon was applied in two common cultivars of melon. In control without ethephon treatment, no bisexual flower was made in the main stem. However, 6.56 ± 1.42 and 6.63 ± 0.55 bisexual flowers were respectively induced in main stem of 'Yangjiaocui-QX' and 'Lvbao' after ethephon treatment, and induced bisexual flowers distributed in 12-20 nodes of main stem. During the formation of bisexual flowers, 41 metabolites were significantly up-regulated and 98 metabolites were significantly down-regulated. According to the KEGG enrichment analysis of 139 different metabolites, a total of 30 pathways were mapped and KEGG terms of "Phenylalanine, tyrosine and tryptophan biosynthesis", "Phenylalanine metabolism" and "Flavone and flavonol biosynthesis" were significantly enriched. In three significantly enriched KEGG terms, shikimic acid, L-tryptophan, L-phenylalanine, and kaempferol were significantly up-regulated while L-tyrosine, 4-hydroxycinnami acid and luteolin were significantly down-regulated in ET compared to CK. Different metabolites were also classified depend on major class features and 14 classes were acquired. The results of metabonomics and endogenous hormone identification indicated that ethylene could enhance the concentration of salicylic acid, methyl jasmonate, ABA and IAA. This study provided an important theoretical foundation for inducing bisexual flowers in main stem and breeding new varieties of melon in future.

Data analysis methods for defining biomarkers from omics data

Omics mainly includes genomics, epigenomics, transcriptomics, proteomics and metabolomics. The rapid development of omics technology has opened up new ways to study disease diagnosis and prognosis and to define prospective information of complex diseases. Since omics data are usually large and complex, the method used to analyze the data and to define important information is crucial in omics study. In this review, we focus on advances in biomarker discovery methods based on omics data in the last decade, and categorize them as individual feature analysis, combinatorial feature analysis and network analysis. We also discuss the challenges and perspectives in this field.

Alternations in the gut microbiota and metabolome with newly diagnosed unstable angina

Gut microbiota plays an important role in coronary heart disease, but its compositional and functional changes in unstable angina (UA) remain unexplored. We performed metagenomic sequencing of 133 newly diagnosed UA patients and 133 sex- and age-matched controls, and profiled the fecal and plasma metabolomes in thirty case-control pairs. The alpha diversity of gut microbiota was increased in UA patients: the adjusted odds ratios (ORs) per standard deviation increase in Shannon and Simpson indices were 1.30 (95% confidence interval, 1.01-1.70) and 1.36 (1.05-1.81), respectively. Two common species (depleted Klebsiella pneumoniae and enriched Streptococcus parasanguinis; P ≤ 0.002) and three rare species (depleted Weissella confusa, enriched Granulicatella adiacens and Erysipelotrichaceae bacterium 6_1_45; P ≤ 0.005) were associated with UA. The UA-associated gut microbiota was depleted in the pathway of L-phenylalanine degradation (P = 0.001), primarily contributed by Klebsiella pneumoniae. Consistently, we found increased circulating phenylalanine in UA patients (OR = 2.76 [1.17-8.16]). Moreover, S. parasanguinis was negatively correlated with fecal citrulline (Spearman's r_s = -0.470, P = 0.009), a metabolite depleted in UA patients (OR = 0.26 [0.08-0.63]). These findings are informative to help understand the metabolic connection between gut microbiota and UA.

Plasma Metabolomic Profiles in Recovered COVID-19 Patients without Previous Underlying Diseases 3 Months After Discharge

BACKGROUND

It remains unclear whether discharged COVID-19 patients have fully recovered from severe complications, including the differences in the post-infection metabolomic profiles of patients with different disease severities.

METHODS

COVID-19-recovered patients, who had no previous underlying diseases and were discharged from Wuhan Union Hospital for 3 months, and matched healthy controls (HCs) were recruited in this prospective cohort study. We examined the blood biochemical indicators, cytokines, lung computed tomography scans, including 39 HCs, 18 recovered asymptomatic (RAs), 34 recovered moderate (RMs), and 44 recovered severe/ critical patients (RCs). A liquid chromatography-mass spectrometry-based metabolomics approach was employed to profile the global metabolites of fasting plasma of these participants.

RESULTS

Clinical data and metabolomic profiles suggested that RAs recovered well, but some clinical indicators and plasma metabolites in RMs and RCs were still abnormal as compared with HCs, such as decreased taurine, succinic acid, hippuric acid, some indoles, and lipid species. The disturbed metabolic pathway mainly involved the tricarboxylic cycle, purine, and glycerophospholipid metabolism. Moreover, metabolite alterations differ between RMs and RCs when compared with HCs. Correlation analysis revealed that many differential metabolites were closely associated with inflammation and the renal, pulmonary, heart, hepatic, and coagulation system functions.

CONCLUSION

We uncovered metabolite clusters pathologically relevant to the recovery state in discharged COVID-19 patients which may provide new insights into the pathogenesis of potential organ damage in recovered patients.

Determination of amino acids in human biological fluids by high-performance liquid chromatography: critical review

The quantitation and qualification of amino acids are most commonly used in clinical and epidemiological studies, and provide an excellent way of monitoring compounds in human fluids which have not been monitored previously, to prevent some diseases. Because of this, it is not surprising that scientific interest in evaluating these compounds has resurfaced in recent years and has precipitated the development of a multitude of new analytical techniques. This review considers recent developments in HPLC analytics on the basis of publications from the last few years. It helps to update and systematize knowledge in this area. Particular attention is paid to the progress of analytical methods, pointing out the advantages and drawbacks of the various techniques used for the preparation, separation and determination of amino acids. Depending on the type of sample, the preparation conditions for HPLC analysis change. For this reason, the review has focused on three types of samples, namely urine, blood and cerebrospinal fluid. Despite time-consuming sample preparation before HPLC analysis, an additional derivatization technique should be used, depending on the detection technique used. There are proposals for columns that are specially modified for amino acid separation without derivatization, but the limit of detection of the substance is less beneficial. In view of the fact that amino acid analyses have been performed for years and new solutions may generate increased costs, it may turn out that older proposals are much more advantageous.

Methods of Lipidomic Analysis: Extraction, Derivatization, Separation, and Identification of Lipids

Lipidomics refers to the large-scale study of pathways and networks of cellular lipids in biological systems. A lipidomic analysis often involves the identification and quantification of the thousands of cellular lipid molecular species within a complex biological sample and therefore requires a well optimized method for lipid profiling. In this chapter, the methods for lipidomic analysis, including sample collection and preparation, lipid derivatization and separation, mass spectrometric identification of lipids, data processing and interpretation, and quality control, are overviewed.

Linking Plant Secondary Metabolites and Plant Microbiomes: A Review

Plant secondary metabolites (PSMs) play many roles including defense against pathogens, pests, and herbivores; response to environmental stresses, and mediating organismal interactions. Similarly, plant microbiomes participate in many of the above-mentioned processes directly or indirectly by regulating plant metabolism. Studies have shown that plants can influence their microbiome by secreting various metabolites and, in turn, the microbiome may also impact the metabolome of the host plant. However, not much is known about the communications between the interacting partners to impact their phenotypic changes. In this article, we review the patterns and potential underlying mechanisms of interactions between PSMs and plant microbiomes. We describe the recent developments in analytical approaches and methods in this field. The applications of these new methods and approaches have increased our understanding of the relationships between PSMs and plant microbiomes. Though the current studies have primarily focused on model organisms, the methods and results obtained so far should help future studies of agriculturally important plants and facilitate the development of methods to manipulate PSMs-microbiome interactions with predictive outcomes for sustainable crop productions.

Recent advances in analytical strategies for mass spectrometry-based lipidomics

Lipids are vital biological molecules and play multiple roles in cellular function of mammalian organisms such as cellular membrane anchoring, signal transduction, material trafficking and energy storage. Driven by the biological significance of lipids, lipidomics has become an emerging science in the field of omics. Lipidome in biological systems consists of hundreds of thousands of individual lipid molecules that possess complex structures, multiple categories, and diverse physicochemical properties assembled by different combinations of polar headgroups and hydrophobic fatty acyl chains. Such structural complexity poses a huge challenge for comprehensive lipidome analysis. Thanks to the great innovations in chromatographic separation techniques and the continuous advances in mass spectrometric detection tools, analytical strategies for lipidomics have been highly diversified so that the depth and breadth of lipidomics have been greatly enhanced. This review will present the current state of mass spectrometry-based analytical strategies including untargeted, targeted and pseudotargeted lipidomics. Recent typical applications of lipidomics in biomarker discovery, pathogenic mechanism and therapeutic strategy are summarized, and the challenges facing to the field of lipidomics are also discussed.

The Mechanisms of Improving IVF Outcomes of Liu-Wei-Di-Huang Pill Acting on DOR Patients

Diminished ovarian reserve (DOR) is the weakening of ovarian oocyte production and quality. It will further become premature ovarian failure without timely cure. However, disease pathology and diagnostic markers are still incompletely understood. Liu-Wei-Di-Huang (LWDH) pill, a traditional Chinese medicine formula, is commonly used in the treatment of DOR in China. To explore the mechanism of the effect of LWDH on in vitro fertilization (IVF) outcomes in patients with DOR, a pseudotargeted metabolomics study combined with multivariate data processing strategy was carried out. A liquid chromatography tandem mass spectrometry-based metabolomics approach was applied to characterize metabolic biomarker candidates. Multiple pattern recognition was used to determine groups and confirm important variables. A total of 21 potential biomarkers were characterized, and related metabolic pathways were identified. The study displayed that the established pseudotargeted metabolomics strategy is a powerful approach for investigating the mechanism of DOR and LWDH. In addition, the approach may highlight biomarkers and metabolic pathways and can capture subtle metabolite changes from headache, which may lead to an improved mechanism understanding of DOR diseases and LWDH treatment.

MMEASE: Online meta-analysis of metabolomic data by enhanced metabolite annotation, marker selection and enrichment analysis

Large-scale and long-term metabolomic studies have attracted widespread attention in the biomedical studies yet remain challenging despite recent technique progresses. In particular, the ineffective way of experiment integration and limited capacity in metabolite annotation are known issues. Herein, we constructed an online tool MMEASE enabling the integration of multiple analytical experiments with an enhanced metabolite annotation and enrichment analysis (https://idrblab.org/mmease/). MMEASE was unique in capable of (1) integrating multiple analytical blocks; (2) providing enriched annotation for >330 thousands of metabolites; (3) conducting enrichment analysis using various categories/sub-categories. All in all, MMEASE aimed at supplying a comprehensive service for large-scale and long-term metabolomics, which might provide valuable guidance to current biomedical studies. SIGNIFICANCE: To facilitate the studies of large-scale and long-term metabolomic analysis, MMEASE was developed to (1) achieve the online integration of multiple datasets from different analytical experiments, (2) provide the most diverse strategies for marker discovery, enabling performance assessment and (3) significantly amplify metabolite annotation and subsequent enrichment analysis. MMEASE aimed at supplying a comprehensive service for long-term and large-scale metabolomics, which might provide valuable guidance to current biomedical studies.

Development of a plasma pseudotargeted metabolomics method based on ultra-high-performance liquid chromatography-mass spectrometry

Untargeted methods are typically used in the detection and discovery of small organic compounds in metabolomics research, and ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) is one of the most commonly used platforms for untargeted metabolomics. Although they are non-biased and have high coverage, untargeted approaches suffer from unsatisfying repeatability and a requirement for complex data processing. Targeted metabolomics based on triple-quadrupole mass spectrometry (TQMS) could be a complementary tool because of its high sensitivity, high specificity and excellent quantification ability. However, it is usually applicable to known compounds: compounds whose identities are known and/or are expected to be present in the analyzed samples. Pseudotargeted metabolomics merges the advantages of untargeted and targeted metabolomics and can act as an alternative to the untargeted method. Here, we describe a detailed protocol of pseudotargeted metabolomics using UHPLC-TQMS. An in-depth, untargeted metabolomics experiment involving multiple UHPLC-HRMS runs with MS at different collision energies (both positive and negative) is performed using a mixture obtained using small amounts of the analyzed samples. XCMS, CAMERA and Multiple Reaction Monitoring (MRM)-Ion Pair Finder are used to find and annotate peaks and choose transitions that will be used to analyze the real samples. A set of internal standards is used to correct for variations in retention time. High coverage and high-performance quantitative analysis can be realized. The entire protocol takes ~5 d to complete and enables the simultaneously semiquantitative analysis of 800-1,300 metabolites.

Metabolic characteristics of large and small extracellular vesicles from pleural effusion reveal biomarker candidates for the diagnosis of tuberculosis and malignancy

Pleural effusion is a common respiratory disease worldwide; however, rapid and accurate diagnoses of tuberculosis pleural effusion (TPE) and malignancy pleural effusion (MPE) remain challenging. Although extracellular vesicles (EVs) have been confirmed as promising sources of disease biomarkers, little is known about the metabolite compositions of its subpopulations and their roles in the diagnosis of pleural effusion. Here, we performed metabolomics and lipidomics analysis to investigate the metabolite characteristics of two EV subpopulations derived from pleural effusion by differential ultracentrifugation, namely large EVs (lEVs, pelleted at 20,000 × g) and small EVs (sEVs, pelleted at 110,000 × g), and assessed their metabolite differences between tuberculosis and malignancy. A total of 579 metabolites, including amino acids, acylcarnitines, organic acids, steroids, amides and various lipid species, were detected. The results showed that the metabolic profiles of lEVs and sEVs overlapped with and difference from each other but significantly differed from those of pleural effusion. Additionally, different type of vesicles and pleural effusion showed unique metabolic enrichments. Furthermore, lEVs displayed more significant and larger metabolic alterations between the tuberculosis and malignancy groups, and their differential metabolites were more closely related to clinical parameters than those of sEV. Finally, a panel of four biomarker candidates, including phenylalanine, leucine, phosphatidylcholine 35:0, and sphingomyelin 44:3, in pleural lEVs was defined based on the comprehensive discovery and validation workflow. This panel showed high performance for distinguishing TPE and MPE, particularly in patients with delayed or missed diagnosis, such as the area under the receiver-operating characteristic curve (AUC) >0.95 in both sets. We conducted comprehensive metabolic profiling analysis of EVs, and further explored the metabolic reprogramming of tuberculosis and malignancy at the level of metabolites in lEVs and sEVs, providing insight into the mechanism of pleural effusion, and identifying novel biomarkers for diagnosing TPE and MPE.

Fold-Change Compression: An Unexplored But Correctable Quantitative Bias Caused by Nonlinear Electrospray Ionization Responses in Untargeted Metabolomics

The nonlinear signal response of electrospray ionization (ESI) presents a critical limitation for mass spectrometry (MS)-based quantitative analysis. In the field of metabolomics research, this issue has largely remained unaddressed; MS signal intensities are usually directly used to calculate fold changes for quantitative comparison. In this work, we demonstrate that, due to the nonlinear ESI response, signal intensity ratios of a metabolic feature calculated between two samples may not reflect their real metabolic concentration ratios (i.e., fold-change compression), implying that conventional fold-change calculations directly using MS signal intensities can be misleading. In this regard, we developed a quality control (QC) sample-based signal calibration workflow to overcome the quantitative bias caused by the nonlinear ESI response. In this workflow, calibration curves for every metabolic feature are first established using a QC sample injected in serial injection volumes. The MS signals of each metabolic feature are then calibrated to their equivalent QC injection volumes for comparative analysis. We demonstrated this novel workflow in a targeted metabolite analysis, showing that the accuracy of fold-change calculations can be significantly improved. Furthermore, in a metabolomic comparison of the bone marrow interstitial fluid samples from leukemia patients before and after chemotherapy, an additional 59 significant metabolic features were found with fold changes larger than 1.5, and an additional 97 significant metabolic features had fold changes corrected by more than 0.1. This work enables high-quality quantitative analysis in untargeted metabolomics, thus providing more confident biological hypotheses generation.

Integrated metabolomics and network toxicology to reveal molecular mechanism of celastrol induced cardiotoxicity

Celastrol (CS), an active triterpene derived from traditional Chinese medicine Tripterygium wilfordii Hook. f, has been used to treat chronic inflammation, arthritis and other diseases. However, it has been reported that CS can trigger cardiotoxicity and the molecular mechanism of heart injury induced by CS is not clear. Considering the wide application of Tripterygium wilfordii Hook. f in clinics, it is necessary to develop an accurate and reliable method to assess the safety of CS, and to elucidate as much as possible the mechanism of cardiotoxicity induced by CS. In this study, Ultra-performance liquid chromatography coupled with quadrupole time of flight mass spectrometry (UPLC-Q-TOF/MS)-based metabolomics revealed clues to the mechanism of CS-induced heart injury. Palmitic acid significantly increased in plasma from CS-treated rats, and this increase resulted in oxidative stress response in vivo. Excessive ROS further activate TNF signaling pathway and caspase family, which were obtained from the KEGG enrichment analysis of network toxicology strategy. Protein expression level of caspase-3, caspase-8, bax were significantly increased by western blot. Q-PCR also showed the similar results as western blot. It means that apoptosis plays a key role in the process of celastrol induced cardiotoxicity. Blocking this signal axis may be a potential way to protect myocardial tissue.

Targeted UPLC-MS/MS high-throughput metabolomics approach to assess the purine and pyrimidine metabolism

Purines and pyrimidines, the important components of DNA and RNA, are closely related to metabolic syndrome and disorder, such as renal disease, gout and diabetic nephropathy etc. Given the importance of the biological significance of purines and pyrimidines, it is necessary to further develop a rapid and sensitive method for practical detection of a large-scale analyses. In this study, based on 96-well solid phase extraction plates-ultra performance liquid chromatography-triple quadrupole mass spectrometry (SPE-UPLC-QqQ-MS/MS), a novel approach for simultaneous determination of 23 purines and pyrimidines in biological samples was developed. First, plasma samples were pretreated by SPE using 96-well plates, which lead to an automated, simplified and rapid sample preparation process. In the methodology development, a large-scale test was performed to evaluate the stability and reliability of the approach. Finally, the levels of purines and pyrimidines in the biological samples were analyzed by this strategy. Experimental results showed that lowest limit of quantification (LLOQ) range from 6.678 × 10^-2 μg/mL to 4.275 × 10^-6 μg/mL; intra- and inter-day precision are <15% for all analytes. The stability and maximal capability of a single analytical batch could be extended to at least 431 injections (about 70 h). Analysis time of a single run was controlled in 10 min. Under the optimized conditions, wide linear ranges and good correlation coefficients (R² > 0.99) were acquired. The successful development of this method provides a feasible protocol for a large-scale metabolomics study and it also lays the foundation of quantitative analysis in endogenous analytes.

Comprehensive MS/MS profiling by UHPLC-ESI-QTOF-MS/MS using SWATH data-independent acquisition for the study of platelet lipidomes in coronary artery disease

A non-targeted lipidomics workflow based on C8 core-shell particle ultra high-performance liquid chromatography (UHPLC) hyphenated to ESI-QTOF-MS in data-independent acquisition (DIA) mode with sequential window acquisition of all theoretical fragment ion spectra (SWATH) was developed and applied to differential platelet lipidomics profiling of cardiovascular disease patients (stable angina pectoris (n = 10), ST-elevated myocardial infarction (n = 13)) against healthy controls (n = 10). DIA with SWATH generates comprehensive MS and MS/MS data throughout the entire chromatograms and all study samples. Hence, chromatograms can be extracted based on precursors or fragments which provided some benefits in terms of assay specificity in some cases. SWATH acquisition offers flexible experimental design with variable Q1 isolation windows. Liquid chromatography as well as SWATH settings were optimized to cover the lipidome of human platelets. The flexibility of the SWATH experiment design was utilized to implement target SWATH windows with narrow 5 Da Q1 precursor ion selection width (multiple reaction monitoring (MRM)-like SWATH windows) for the detection of low abundant oxidized phospholipids. Data processing was performed with MS-DIAL, and its feasibilities and caveats are discussed by illustrative examples. Thereby, identification of lipids is still a bottleneck in non-targeted lipidomics workflow. MS-DIAL, however, offers automatic identification via spectral matching using an in silico library. In total 1971 molecular features were detected cross the samples of which 611 were identified (total score >70%). The quality of the acquired data was validated with embedded quality control samples (n = 11). 80.3% of all features detected in the QC samples showed a coefficient of variation of below 30%. Multivariate statistics were used to visualize differences in the lipidome of distinct sample groups at a false discovery rate of 5%.

A Large-scale, multicenter serum metabolite biomarker identification study for the early detection of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the third most lethal cancer worldwide. The lack of effective biomarkers for the early detection of HCC results in unsatisfactory curative treatments. Here, metabolite biomarkers were identified and validated for HCC diagnosis. A total of 1,448 subjects, including healthy controls and patients with chronic hepatitis B virus infection, liver cirrhosis, and HCC, were recruited from multiple centers in China. Liquid chromatography-mass spectrometry-based metabolomics methods were used to characterize the subjects' serum metabolic profiles and to screen and validate the HCC biomarkers. A serum metabolite biomarker panel including phenylalanyl-tryptophan and glycocholate was defined. This panel had a higher diagnostic performance than did α-fetoprotein (AFP) in differentiating HCC from a high-risk population of cirrhosis, such as an area under the receiver-operating characteristic curve of 0.930, 0.892, and 0.807 for the panel versus 0.657, 0.725, and 0.650 for AFP in the discovery set, test set, and cohort 1 of the validation set, respectively. In the nested case-control study, this panel had high sensitivity (range 80.0%-70.3%) to detect preclinical HCC, and its combination with AFP provided better risk prediction of preclinical HCC before clinical diagnosis. Besides, this panel showed a larger area under the receiver-operating characteristic curve than did AFP (0.866 versus 0.682) to distinguish small HCC, and 80.6% of the AFP false-negative patients with HCC were correctly diagnosed using this panel in the test set, which was corroborated by the validation set. The specificity and biological relevance of the identified biomarkers were further evaluated using sera from another two cancers and HCC tissue specimens, respectively. Conclusion: The discovered and validated serum metabolite biomarker panel exhibits good diagnostic performance for the early detection of HCC from at-risk populations. (Hepatology 2018;67:662-675).

Review of emerging metabolomic tools and resources: 2015-2016

Data processing and analysis are major bottlenecks in high-throughput metabolomic experiments. Recent advancements in data acquisition platforms are driving trends toward increasing data size (e.g., petabyte scale) and complexity (multiple omic platforms). Improvements in data analysis software and in silico methods are similarly required to effectively utilize these advancements and link the acquired data with biological interpretations. Herein, we provide an overview of recently developed and freely available metabolomic tools, algorithms, databases, and data analysis frameworks. This overview of popular tools for MS and NMR-based metabolomics is organized into the following sections: data processing, annotation, analysis, and visualization. The following overview of newly developed tools helps to better inform researchers to support the emergence of metabolomics as an integral tool for the study of biochemistry, systems biology, environmental analysis, health, and personalized medicine.

Metabolomics, Nutrition, and Potential Biomarkers of Food Quality, Intake, and Health Status

Diet, dietary patterns, and other environmental factors such as exposure to toxins are playing an important role in the prevention/development of many diseases, like obesity, type 2 diabetes, and consequently on the health status of individuals. A major challenge nowadays is to identify novel biomarkers to detect as early as possible metabolic dysfunction and to predict evolution of health status in order to refine nutritional advices to specific population groups. Omics technologies such as genomics, transcriptomics, proteomics, and metabolomics coupled with statistical and bioinformatics tools have already shown great potential in this research field even if so far only few biomarkers have been validated. For the past two decades, important analytical techniques have been developed to detect as many metabolites as possible in human biofluids such as urine, blood, and saliva. In the field of food science and nutrition, many studies have been carried out for food authenticity, quality, and safety, as well as for food processing. Furthermore, metabolomic investigations have been carried out to discover new early biomarkers of metabolic dysfunction and predictive biomarkers of developing pathologies (obesity, metabolic syndrome, type-2 diabetes, etc.). Great emphasis is also placed in the development of methodologies to identify and validate biomarkers of nutrients exposure.