SWATHtoMRM: Development of High-Coverage Targeted Metabolomics Method Using SWATH Technology for Biomarker Discovery

Metabolomic analysis reveals the positive effects of Rhizopus oryzae fermentation on the nutritional and functional constituents of adlay millet seeds

Adlay millet seeds are well known for excellent health benefits. However, using fungal fermentation to improve their nutritional and functional constituents and the underlying mechanisms has not been thoroughly investigated. Herein, we used Rhizopus oryzae as starter and applied metabolomics combining with quantitative verification to understand the changes of the nutritional and functional profiles of adlay millet seeds. Results showed that a total of 718 metabolites from 18 compound classes were identified. The fermentation with R. oryzae varied 203 differential metabolites, of which 184 became more abundant and 19 got less abundant, and many components such as amino acids, nucleotides, vitamins, flavonoids, terpenoids, and phenols significantly increased after the fermentation process. Interestingly, we found that R. oryzae synthesized high levels of two important beneficial compounds, S-adenosylmethionine (SAMe) and β-Nicotinamide mononucleotide (β-NMN), with their contents increased from 0.56 to 370.26 μg/g and 0.55 to 8.32 μg/g, respectively. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of enriched metabolites revealed the amino acid metabolic pathways were important for conversion of the primary and secondary metabolites. Specifically, aspartate can up-regulate the biosynthesis of SAMe and β-NMN. These findings improved our understanding into the effects of R. oryzae fermentation on enhancing the nutritional and functional values of cereal foods.

MS based foodomics: An edge tool integrated metabolomics and proteomics for food science

Foodomics has become a popular methodology in food science studies. Mass spectrometry (MS) based metabolomics and proteomics analysis played indispensable roles in foodomics research. So far, several methodologies have been developed to detect the metabolites and proteins in diets and consumers, including sample preparation, MS data acquisition, annotation and interpretation. Moreover, multiomics analysis integrated metabolomics and proteomics have received considerable attentions in the field of food safety and nutrition, because of more comprehensive and deeply. In this context, we intended to review the emerging strategies and their applications in MS-based foodomics, as well as future challenges and trends. The principle and application of multiomics were also discussed, such as the optimization of data acquisition, development of analysis algorithm and exploration of systems biology.

Non-targeted mass spectrometry and feature-based molecular networking for determination of branched fatty acid esters of hydroxy fatty acids in milk

Branched fatty acid esters of hydroxy fatty acids (FAHFAs) represent trace lipids with significant natural biological functions. While exogenous FAHFAs have been extensively studied, research on FAHFAs in milk remains limited, constraining our grasp of their nutritional roles. This study introduces a non-targeted mass spectrometry approach combined with chemical networking of spectral fragmentation patterns to uncover FAHFAs. Through meticulous sample handling and comparisons of various data acquisition and processing modes, we validate the method's superiority, identifying twice as many FAHFAs compared to alternative techniques. This validated method was then applied to different milk samples, revealing 45 chemical signals associated with known and potential FAHFAs, alongside findings of 66 ceramide/hexosylceramide (Cer/HexCer), 48 phosphatidyl ethanolamine/lyso phosphatidyl ethanolamine (PE/LPE), 21 phosphatidylcholine/lysophosphatidylcholine (PC/LPC), 16 phosphatidylinositol (PI), 7 phosphatidylserine (PS), and 11 sphingomyelin (SM) compounds. This study expands our understanding of the FAHFA family in milk and provides a fast and convenient method for identifying FAHFAs.

Metabolomics for searching validated biomarkers in cancer studies: a decade in review

INTRODUCTION

In the dynamic landscape of modern healthcare, the ability to anticipate and diagnose diseases, particularly in cases where early treatment significantly impacts outcomes, is paramount. Cancer, a complex and heterogeneous disease, underscores the critical importance of early diagnosis for patient survival. The integration of metabolomics information has emerged as a crucial tool, complementing the genotype-phenotype landscape and providing insights into active metabolic mechanisms and disease-induced dysregulated pathways.

AREAS COVERED

This review explores a decade of developments in the search for biomarkers validated within the realm of cancer studies. By critically assessing a diverse array of research articles, clinical trials, and studies, this review aims to present an overview of the methodologies employed and the progress achieved in identifying and validating biomarkers in metabolomics results for various cancer types.

EXPERT OPINION

Through an exploration of more than 800 studies, this review has allowed to establish a general idea about state-of-art in the search of biomarkers in metabolomics studies involving cancer which include certain level of results validation. The potential for metabolites as diagnostic markers to reach the clinic and make a real difference in patient health is substantial, but challenges remain to be explored.

SCALiR: A Web Application for Automating Absolute Quantification of Mass Spectrometry-Based Metabolomics Data

Quantitative liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is becoming an important approach for studying complex biological systems but presents several technical challenges that limit its widespread use. Computing metabolite concentrations using standard curves generated from standard mixtures of known concentrations is a labor-intensive process that is often performed manually. Currently, there are few options for open-source software tools that can automatically calculate metabolite concentrations. Herein, we introduce SCALiR (standard curve application for determining linear ranges), a new web-based software tool specifically built for this task, which allows users to automatically transform LC-MS signals into absolute quantitative data (https://www.lewisresearchgroup.org/software). SCALiR uses an algorithm that automatically finds the equation of the line of best fit for each standard curve and uses this equation to calculate compound concentrations from the LC-MS signal. Using a standard mix containing 77 metabolites, we show a close correlation between the concentrations calculated by SCALiR and the expected concentrations of each compound (R2 = 0.99 for a y = x curve fitting). Moreover, we demonstrate that SCALiR reproducibly calculates concentrations of midrange standards across ten analytical batches (average coefficient of variation 0.091). SCALiR can be used to calculate metabolite concentrations either using external calibration curves or by using internal standards to correct for matrix effects. This open-source and vendor agnostic software offers users several advantages in that (1) it requires only 10 s of analysis time to compute concentrations of >75 compounds, (2) it facilitates automation of quantitative workflows, and (3) it performs deterministic evaluations of compound quantification limits. SCALiR therefore provides the metabolomics community with a simple and rapid tool that enables rigorous and reproducible quantitative metabolomics studies.

Chemical component of differences in the endosperm of Gleditsia species seeds revealed based on comparative metabolomics

To investigate the chemical composition and interfunctional differences among the endosperm of Gleditsia species seeds (EGS), this study was conducted to determine the metabolic profiles in three EGSs based on the metabolomics approach of UPLC-ESI-MS/MS. A total of 505 metabolites were identified, of which 156 metabolites of EGS were annotated as pharmaceutical ingredients for six human diseases. A total of 110, 146, and 104 metabolites showed different accumulation patterns in the three control groups, LEGS vs. MEGS, LEGS vs. SEGS, and MEGS vs. SEGS, respectively. The metabolic profiles of EGSs differed significantly, and KEGG annotation and enrichment analyses indicated aminoacyl-tRNA biosynthesis as the key metabolic pathway of EGSs. This study enriches the understanding of the chemical composition of EGSs and provides theoretical support for the development and application of EGSs.

The arachidonic acid metabolome reveals elevation of prostaglandin E2 biosynthesis in colorectal cancer

Arachidonic acid metabolites are a family of bioactive lipids derived from membrane phospholipids. They are involved in cancer progression, but arachidonic acid metabolite profiles and their related biosynthetic pathways remain uncertain in colorectal cancer (CRC). To compare the arachidonic acid metabolite profiles between CRC patients and healthy controls, quantification was performed using a liquid chromatography-mass spectrometry-based analysis of serum and tissue samples. Metabolomics analysis delineated the distinct oxidized lipids in CRC patients and healthy controls. Prostaglandin (PGE2)-derived metabolites were increased, suggesting that the PGE2 biosynthetic pathway was upregulated in CRC. The qRT-PCR and immunohistochemistry analyses showed that the expression level of PGE2 synthases, the key protein of PGE2 biosynthesis, was upregulated in CRC and positively correlated with the CD68+ macrophage density and CRC development. Our study indicates that the PGE2 biosynthetic pathway is associated with macrophage infiltration and progression of CRC tumors.

Environmental chemical TCPOBOP exposure alters milk liposomes and offspring growth trajectories in mice

Exposure to environmental endocrine disruptors (EEDs) has become a global health concern, and EEDs are known to be potent inducers of constitutive androstane receptor (CAR). 1,4-bis [2-(3,5-dichloropyridyloxy)] benzene (TCPOBOP, hereafter abbreviated as TC), a specific ligand for CAR, has been considered as a potential EED. Here, we analyzed the effect of TC exposure to female mice on the histological morphology of their alveoli in the basic unit of lactation. We quantified differences in the milk metabolome of the control and TC-exposed group while assessing the correlations between metabolites and neonatal growth. Mammary histological results showed that TC exposure inhibited alveolar development. Based on the milk metabolomic data, we identified a total of 1505 differential metabolites in both the positive and negative ion mode, which indicated that TC exposure affected milk composition. As expected, the differential metabolites were significantly enriched in the drug metabolism pathway. Further analyses revealed that differential metabolites were significantly enriched in multiple lipid metabolic pathways, such as fatty acid biosynthesis, suggesting that most differential metabolites were concentrated in lipids. Simultaneously, a quantitative analysis showed that TC exposure led to a decrease in the relative abundance of total milk lipids, affecting the proportion of some lipid subclasses. Notably, a portion of lipid metabolites were associated with neonatal growth. Taken together, these findings suggest that TC exposure may affect milk lipidomes, resulting in the inability of mothers to provide adequate nutrients, ultimately affecting the growth and health of their offspring.

SpecLipIDA: a pseudotargeted lipidomics approach for polyunsaturated fatty acids in milk

Polyunsaturated fatty acids (PUFAs), such as arachidonic acid (ARA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA), play an important role in the nutritional value of milk lipids. However, a comprehensive analysis of PUFAs and their esters in milk is still scarce. In this study, we developed a novel pseudotargeted lipidomics approach, named SpecLipIDA, for determining PUFA lipids in milk. Triglycerides (TGs) and phospholipids (PLs) were separated using NH2 cartridges, and mass spectrometry data in the information-dependent acquisition (IDA) mode were preprocessed by MS-DIAL, leading to improved identification in subsequent targeted analysis. The target matching algorithm, based on specific lipid cleavage patterns, demonstrated enhanced identification of PUFA lipids compared to the lipid annotations provided by MS-DIAL and GNPS. The approach was applied to identify PUFA lipids in various milk samples, resulting in the detection of a total of 115 PUFA lipids. The results revealed distinct differences in PUFA lipids among different samples, with 44 PUFA lipids significantly contributing to these differences. Our study indicated that SpecLipIDA is an efficient method for rapidly and specifically screening PUFA lipids.

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.

Recent advances in LC-MS-based metabolomics for clinical biomarker discovery

The employment of liquid chromatography-mass spectrometry (LC-MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC-MS-untargeted and targeted metabolomics. To improve the sensitivity of low-abundance or poor-ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC-MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC-MS metabolomics to accelerate metabolite identification and data-processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC-MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.

Traceability Research on Dendrobium devonianum Based on SWATHtoMRM

SWATHtoMRM technology was used in this experiment to further identify and trace the sources of Dendrobium devonianum and Dendrobium officinale produced in the same area using TOF and MS-MRM. After the conversion of the R package of SWATHtoMRM, 191 MRM pairs of positive ions and 96 pairs of negative ions were obtained. Dendrobium devonianum and Dendrobium officinale can be separated very well using the PCA and PLS-DA analysis of MRM ion pairs; this shows that there are obvious differences in chemical composition between Dendrobium devonianum and Dendrobium officinale, which clearly proves that the pseudotargeted metabolomics method based on SWATHtoMRM can be used for traceability identification research. A total of 146 characteristic compounds were obtained, with 20 characteristic compounds in Dendrobium devonianum. The enrichment pathways of the characteristic compounds were mainly concentrated in lipids and atherosclerosis, chagas disease, fluid shear stress and atherosclerosis, proteoglycans in cancer, the IL-17 signaling pathway, the sphingolipid signaling pathway, diabetic cardiomyopathy, arginine and proline metabolism, etc., among which the lipid and atherosclerosis pathways were more enriched, and 11 characteristic compounds affected the expression levels of IL-1, TNFα, CD36, IL-1β, etc. These can be used as a reference for research on variety improvement and active substance accumulation in Dendrobium devonianum and Dendrobium officinale.

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.

Dysregulated lipid metabolism in TMZ-resistant glioblastoma: pathways, proteins, metabolites and therapeutic opportunities

Glioblastoma (GBM) is a highly aggressive and lethal brain tumor with limited treatment options, such as the chemotherapeutic agent, temozolomide (TMZ). However, many GBM tumors develop resistance to TMZ, which is a major obstacle to effective therapy. Recently, dysregulated lipid metabolism has emerged as an important factor contributing to TMZ resistance in GBM. The dysregulation of lipid metabolism is a hallmark of cancer and alterations in lipid metabolism have been linked to multiple aspects of tumor biology, including proliferation, migration, and resistance to therapy. In this review, we aimed to summarize current knowledge on lipid metabolism in TMZ-resistant GBM, including key metabolites and proteins involved in lipid synthesis, uptake, and utilization, and recent advances in the application of metabolomics to study lipid metabolism in GBM. We also discussed the potential of lipid metabolism as a target for novel therapeutic interventions. Finally, we highlighted the challenges and opportunities associated with developing these interventions for clinical use, and the need for further research to fully understand the role of lipid metabolism in TMZ resistance in GBM. Our review suggests that targeting dysregulated lipid metabolism may be a promising approach to overcome TMZ resistance and improve outcomes in patients with GBM.

Flavonoid profile of Anoectochilus roxburghii (Wall.) Lindl. Under short-term heat stress revealed by integrated metabolome, transcriptome, and biochemical analyses

Global warming severely threatens plant growth, and could lead to yield reduction. Although findings suggest that flavonoids play important roles in biological process in plants, their response to heat stress in Anoectochilus roxburghii (Wall.) Lindl. remains unclear. Here, we aimed to examine the flavonoid profile of A. roxburghii under heat stress and assess the effect of exogenous application of quercetin on heat stress tolerance. Metabolome analysis showed that quercetin, tricetin, isorhamnetin, scutellarein, and 4',7-Isoflavandiol were the main upregulated flavonoids in A. roxburghii, based on variable importance in the projection >1 and with fold change >2. Determination of the concentrations of the flavonoids using a standard curve revealed that quercetin, kaempferol, and isorhamnetin contents increased by 8.24-, 7.55-, and 5.01-fold, respectively, during heat stress, whereas rutin concentration decreased from 83.04 to 80.89 mg/kg (dry weight). Additionally, transcriptome analysis indicated increased expression of several genes in flavonoid biosynthesis pathways, including phenylalanine ammonia-lyase and chalcone synthase. Moreover, exogenous application of quercetin improved the antioxidant capacity and physiological parameters, including photosynthetic rate and chlorophyll content, of A. roxburghii under heat stress. Overall, the flavonoid profile of A. roxburghii under short-term heat stress was characterized based on integrated metabolomic, transcriptomic, and biochemical analyses, providing new insights for improving the biological value of A. roxburghii.

MetaboMSDIA: A tool for implementing data-independent acquisition in metabolomic-based mass spectrometry analysis

Data-dependent acquisition (DDA) is the most widely used mode in untargeted metabolomic analysis despite its limited tandem mass spectrometry (MS2) detection coverage. We present MetaboMSDIA for complete processing of data-independent acquisition (DIA) files by the extraction of multiplexed MS2 spectra and further identification of metabolites in open libraries. In the analysis of polar extracts from lemon and olive fruits, DIA allows one to obtain multiplexed MS2 spectra for 100% of precursor ions compared to 64% of precursor ions from average MS2 acquisition in DDA. MetaboMSDIA is compatible with MS2 repositories and homemade libraries prepared by analysis of standards. An additional option is based on filtering molecular entities by searching for selective fragmentation patterns according to selective neutral losses or product ions to target the annotation of families of metabolites. Combining both options, the applicability of MetaboMSDIA was tested by annotating 50 and 35 metabolites in polar extracts from lemon and olive fruit, respectively. MetaboMSDIA is particularly proposed to increase the acquisition coverage in untargeted metabolomics and to improve spectral quality, which are two critical pillars for the tentative annotation of metabolites. The R script used in MetaboMSDIA workflow is available at github repository (https://github.com/MonicaCalSan/MetaboMSDIA).

Identification of Metabolite Interference Is Necessary for Accurate LC-MS Targeted Metabolomics Analysis

Targeted metabolomics has been broadly used for metabolite measurement due to its good quantitative linearity and simple metabolite annotation workflow. However, metabolite interference, the phenomenon where one metabolite generates a peak in another metabolite's MRM setting (Q1/Q3) with a close retention time (RT), may lead to inaccurate metabolite annotation and quantification. Besides isomeric metabolites having the same precursor and product ions that may interfere with each other, we found other metabolite interferences as the result of inadequate mass resolution of triple-quadruple mass spectrometry and in-source fragmentation of metabolite ions. Characterizing the targeted metabolomics data using 334 metabolite standards revealed that about 75% of the metabolites generated measurable signals in at least one other metabolite's MRM setting. Different chromatography techniques can resolve 65-85% of these interfering signals among standards. Metabolite interference analysis combined with the manual inspection of cell lysate and serum data suggested that about 10% out of ∼180 annotated metabolites were mis-annotated or mis-quantified. These results highlight that a thorough investigation of metabolite interference is necessary for accurate metabolite measurement in targeted metabolomics.

Serum metabolic profile and metabolome genome-wide association study in chicken

BACKGROUND

Chickens provide globally important livestock products. Understanding the genetic and molecular mechanisms underpinning chicken economic traits is crucial for improving their selective breeding. Influenced by a combination of genetic and environmental factors, metabolites are the ultimate expression of physiological processes and can provide key insights into livestock economic traits. However, the serum metabolite profile and genetic architecture of the metabolome in chickens have not been well studied.

RESULTS

Here, comprehensive metabolome detection was performed using non-targeted LC-MS/MS on serum from a chicken advanced intercross line (AIL). In total, 7,191 metabolites were used to construct a chicken serum metabolomics dataset and to comprehensively characterize the serum metabolism of the chicken AIL population. Regulatory loci affecting metabolites were identified in a metabolome genome-wide association study (mGWAS). There were 10,061 significant SNPs associated with 253 metabolites that were widely distributed across the entire chicken genome. Many functional genes affect metabolite synthesis, metabolism, and regulation. We highlight the key roles of TDH and AASS in amino acids, and ABCB1 and CD36 in lipids.

CONCLUSIONS

We constructed a chicken serum metabolite dataset containing 7,191 metabolites to provide a reference for future chicken metabolome characterization work. Meanwhile, we used mGWAS to analyze the genetic basis of chicken metabolic traits and metabolites and to improve chicken breeding.

Serum lipidomic profiling by UHPLC-MS/MS may be able to detect early-stage endometrial cancer

Nowadays, screening for endometrial cancer (EC) primarily relies on clinical symptoms and imaging, which makes it difficult to detect early-stage disease. Here, we conducted a widely targeted lipidomic analysis of 38 human serum samples in a discovery set and 40 human serum samples in a validation set to profile the dysregulated lipid species and establish lipid biomarkers for early-stage EC. This comprehensive lipidomic determination of 616 serum lipids indicated significant differences between early-stage EC patients and healthy controls. Three phases of lipid biomarker investigation (discovery, validation, and determination of the lipid biomarker panel) were performed, which revealed the upregulation of some sphingolipid, glycerophospholipid, and glycerolipids and downregulation of some carnitine. Consistently, the perturbation of sphingolipid and glycerophospholipid metabolism was also observed from pathway enrichment analysis. Moreover, a lipid biomarker panel, including ursodeoxycholic acid, PC(O-14:0_20:4), and Cer(d18:1/18:0), was established. This panel was assessed as an effective diagnostic model to distinguish early-stage EC patients from healthy controls and atypical endometrial hyperplasia patients within the area under the receiver operating characteristic curve (AUC) reaching 0.903 and 0.928, respectively. In particular, the comparison results of the diagnostic efficacy indicated that the lipid biomarker panel was superior to clinically established indicators for EC diagnosis, including HE4, CA125, CA153, and CA199, suggesting that it could be used as an excellent supplementary method for the diagnosis of early-stage EC. In conclusion, we established a novel and non-invasive lipid biomarker for early-stage EC detection and these findings may provide new insight into the pathological mechanisms of EC.

Comprehensive review of composition distribution and advances in profiling of phenolic compounds in oilseeds

A wide range of phenolic compounds participate in oilseed growth, regulate oxidative stability of corresponding vegetable oil, and serve as important minor food components with health-promoting effects. Composition distribution of phenolic compounds varied in oilseeds. Isoflavones, sinapic acid derivatives, catechin and epicatechin, phenolic alcohols, chlorogenic acid, and lignans were the main phenolic compounds in soybean, rapeseed, peanut skin, olive, sunflower seed, sesame and flaxseed, respectively. Among which, the total isoflavones content in soybean seeds reached from 1,431 to 2,130 mg/100 g; the main phenolic compound in rapeseed was sinapine, representing 70–90%; chlorogenic acid as the predominant phenolic compound in sunflower kernels, represented around 77% of the total phenolic content. With the rapid development of analytical techniques, it is becoming possible for the comprehensive profiling of these phenolic compounds from oilseeds. This review aims to provide recently developments about the composition distribution of phenolic compounds in common oilseeds, advanced technologies for profiling of phenolic compounds by the metabolomics approaches based on mass spectrometry. As there is still limited research focused on the comprehensive extraction and determination of phenolics with different bound-forms, future efforts should take into account the non-targeted, pseudo-targeted, and spatial metabolomic profiling of phenolic compounds, and the construction of phenolic compound database for identifying and quantifying new types of phenolic compounds in oilseeds and their derived products.

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.

Influence of ripening stage and meteorological parameters on the accumulation pattern of polyphenols in greengages (Prunus mume Sieb. Et Zucc) by widely targeted metabolomic

Greengage is a Chinese traditional medicine food homology plant that contains abundant polyphenols. Greengages with different levels of maturity have different medicinal functions and applications. Therefore, this study comprehensively analysed the phenolic compounds in greengage of whole mature stage. Notably, the influence of climate conditions on greengage polyphenol synthesis was deeply explored. The polyphenols of greengages were profiled based on the widely targeted metabolomic technology using ultra-performance liquid chromatography-electrospray ionisation triple quadrupole mass spectrometry (UPLC-QTRAP-MS/MS). A total of 214 polyphenols were detected, covering 11 subclasses. During the ripening of greengages, these polyphenols first increased and then decreased, peaking during the swell stage. Multivariate statistical methods, including redundancy analysis (RDA), random forest analysis, Mantel test, and Spearman's correlation indicated that temperature, sunshine hours, humidity, and radiation were important factors driving the formation and changes in the polyphenols of greengages. In particular, flavanones and flavonols, showed a structure-dependent response to temperature and radiation.

A serum metabolomics study based on LC-MS: Chemosensitization effects of Rauvolfia vomitoria Afzel. combined with 5- fluorouracil on colorectal cancer mice

Colorectal cancer (CRC) is one of the malignant tumors with high incidence, and is mainly treated by chemotherapy at present. However, during CRC treatment, long-term use of traditional chemotherapeutic drugs will reduce the sensitivity of chemotherapy. Our previous studies have shown that Rauvolfia vomitoria total alkaloids (RVA) played an important role in 5-fluorouracil (5-FU) chemosensitization in CRC therapy, but its intervention mechanism has not been clarified completely in the metabolic level. Therefore, in this study, LC-MS based metabolomics was employed to explore the mechanism of 5-FU chemosensitization in CRC induced by the combination of RVA and conventional chemotherapeutic with 5-FU. The results showed that the final tumor weight of the high-dose combined group was significantly different from that of the 5-FU alone group. To evaluate the chemosensitization effects of RVA, serum samples collected from six groups (six mice in each group) with different administration methods were analyzed by HPLC-Q-Exactive Orbitrap/MS. After multivariate statistical analysis and metabolites identification, 25 different metabolites were identified between the 5-FU treatment group and combined high-dose treatment group, among which lipid and fatty acid metabolism pathways were mostly affected. These results suggest that RVA may sensitize traditional chemotherapeutic drug 5-FU and exert anti-tumor activity through influencing lipid metabolism and cell energy metabolism. Metabolomics provided a new insight into estimate of the therapeutic effect and dissection of the potential mechanisms of traditional Chinese medicine in treating colorectal cancer.

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

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

Targeted Metabolomics Provide Chemotaxonomic Insights of Medicago ruthenica, with Coupled Transcriptomics Elucidating the Mechanism Underlying Floral Coloration

Medicago ruthenica, a wild legume forage widely distributed in the Eurasian steppe, demonstrates high genetic and phenotypic variation. M. ruthenica with a purely yellow flower (YFM), differing from the general phenotype of M. ruthenica with a purple flower (PFM), was recently discovered. The similar characteristics of YFM with Medicago falcata have led to conflicting opinions on its taxonomy using traditional morphological methods. The lack of chemotaxonomy information about M. ruthenica species and the unclear flower coloration mechanisms have hampered their study. Here, we investigated M. ruthenica using targeted metabolomics based on the chemotaxonomy method and elaborated the floral coloration mechanisms using transcriptomics. The identified flavonoids were the same types, but there were different contents in YFM and PFM, especially the contents of cyanidin-3-O-glucoside (C3G), an anthocyanin that causes the purple-reddish color of flowers. The over-accumulation of C3G in PFM was 1,770 times more than YFM. Nineteen anthocyanin-related genes were downregulated in YFM compared with their expression in PFM. Thus, YFM could be defined as a variety of M. ruthenica rather than a different species. The loss of purple flower coloration in YFM was attributed to the downregulation of these genes, resulting in reduced C3G accumulation. The taxonomic characteristics and molecular and physiological characteristics of this species will contribute to further research on other species with similar external morphologies.

Plasm Metabolomics Study in Pulmonary Metastatic Carcinoma

Background

The lung is one of the most common metastatic sites of malignant tumors. Early detection of pulmonary metastatic carcinoma can effectively reduce relative cancer mortality. Human metabolomics is a qualitative and quantitative study of low-molecular metabolites in the body. By studying the plasm metabolomics of patients with pulmonary metastatic carcinoma or other lung diseases, we can find the difference in plasm levels of low-molecular metabolites among them. These metabolites have the potential to become biomarkers of lung metastases.

Methods

Patients with pulmonary nodules admitted to our department from February 1, 2019, to May 31, 2019, were collected. According to the postoperative pathological results, they were divided into three groups: pulmonary metastatic carcinoma (PMC), benign pulmonary nodules (BPN), and primary lung cancer (PLC). Moreover, healthy people who underwent physical examination were enrolled as the healthy population group (HPG) during the same period. On the one hand, to study lung metastases screening in healthy people, PMC was compared with HPG. The multivariate statistical analysis method was used to find the significant low-molecular metabolites between the two groups, and their discriminating ability was verified by the ROC curve. On the other hand, from the perspective of differential diagnosis of lung metastases, three groups with different pulmonary lesions (PMC, BPN, and PLC) were compared as a whole, and then the other two groups were compared with PMC, respectively. The main low-molecular metabolites were selected, and their discriminating ability was verified.

Results

In terms of lung metastases screening for healthy people, four significant low-molecular metabolites were found by comparison of PMC and HPG. They were O-arachidonoyl ethanolamine, adrenoyl ethanolamide, tricin 7-diglucuronoside, and p-coumaroyl vitisin A. In terms of the differential diagnosis of pulmonary nodules, the significant low-molecular metabolites selected by the comparison of the three groups as a whole were anabasine, octanoylcarnitine, 2-methoxyestrone, retinol, decanoylcarnitine, calcitroic acid, glycogen, and austalide L. For the comparison of PMC and BPN, L-tyrosine, indoleacrylic acid, and lysoPC (16 : 0) were selected, while L-octanoylcarnitine, retinol, and decanoylcarnitine were selected for the comparison of PMC and PLC. Their AUCs of ROC are all greater than 0.80. It indicates that these substances have a strong ability to differentiate between pulmonary metastatic carcinoma and other pulmonary nodule lesions.

Conclusion

Through the research of plasm metabolomics, it is possible to effectively detect the changes in some low-molecular metabolites among primary lung cancer, pulmonary metastatic carcinoma, and benign pulmonary nodule patients and healthy people. These significant metabolites have the potential to be biomarkers for screening and differential diagnosis of lung metastases.

Sensitive Detection of Pharmaceutical Drugs and Metabolites in Serum Using Data-Independent Acquisition Mass Spectrometry and Open-Access Data Acquisition Tools

Data-independent acquisition (DIA) based strategies have been explored in recent years for improving quantitative analysis of metabolites. However, the data analysis is challenging for DIA methods as the resulting spectra are highly multiplexed. Thus, the DIA mode requires advanced software analysis to facilitate the data deconvolution process. We proposed a pipeline for quantitative profiling of pharmaceutical drugs and serum metabolites in DIA mode after comparing the results obtained from full-scan, Data-dependent acquisition (DDA) and DIA modes. using open-access software. Pharmaceutical drugs (10) were pooled in healthy human serum and analysed by LC-ESI-QTOF-MS. MS1 full-scan and Data-dependent (MS2) results were used for identification using MS-DIAL software while deconvolution of MS1/MS2 spectra in DIA mode was achieved by using Skyline software. The results of acquisition methods for quantitative analysis validated the remarkable analytical performance of the constructed workflow, proving it to be a sensitive and reproducible pipeline for biological complex fluids.

Effects of Storage Temperature on Indica-Japonica Hybrid Rice Metabolites, Analyzed Using Liquid Chromatography and Mass Spectrometry

The Yongyou series of indica-japonica hybrid rice has excellent production potential and storage performance. However, little is known about the underlying mechanism of its storage resistance. In this study, Yongyou 1540 rice (Oryza sativa cv. yongyou 1540) was stored at different temperatures, and the storability was validated though measuring nutritional components and apparent change. In addition, a broad-targeted metabolomic approach coupled with liquid chromatography-mass spectrometry was applied to analyze the metabolite changes. The study found that under high temperature storage conditions (35 °C), Yongyou 1540 was not significantly worse in terms of fatty acid value, whiteness value, and changes in electron microscope profile. A total of 19 key differential metabolites were screened, and lipid metabolites related to palmitoleic acid were found to affect the aging of rice. At the same time, two substances, guanosine 3′,5′-cyclophosphate and pipecolic acid, were beneficial to enhance the resistance of rice under harsh storage conditions, thereby delaying the deterioration of its quality and maintaining its quality. Significant regulation of galactose metabolism, alanine, aspartate and glutamate metabolism, butyrate metabolism, and arginine and proline metabolism pathways were probably responsible for the good storage capacity of Yongyou 1540.

Metabolome and Transcriptome Profiling Reveal That Four Terpenoid Hormones Dominate the Growth and Development of Sanghuangporus baumii

Sanghuangporus baumii is a traditional medicinal fungus that produces pharmacological terpenoids, but natural resources are insufficient for applications, and its growth and development mechanisms are poorly understood. Combining metabolomic and transcriptomic analyses, we found four terpenoid hormones and a central gene, isopentenyl diphosphate isomerase (IDI), involved in growth and development. Additionally, an exogenous hormone test was used to further confirm the importance of the four terpenoid hormones. Finally, hormone content determination and qRT−PCR were performed to explore the growth and development mechanism; we found thatcis-zeatin (CZ) plays a major role in the mycelia stage, trans-zeatin (TZ) and gibberellin A4 (GA4) are important in the primordia stage, GA4 is crucial for the fruiting bodies stage, and abscisic acid (ABA) may be a marker of maturity. The IDI gene was also found to affectterpenoid hormone content by regulating the relative gene transcript levels, thereby controlling morphological changes in S. baumii. Our results revealthe growth and development mechanisms of S. baumii and may promote the breeding and utilisation of high-quality varieties.

Comparison of Widely Targeted Metabolomics and Untargeted Metabolomics of Wild Ophiocordyceps Sinensis

The authors of this paper conducted a comparative metabolomic analysis of Ophiocordyceps sinensis (OS), providing the metabolic profiles of the stroma (OSBSz) and sclerotia (OSBSh) of OS by widely targeted metabolomics and untargeted metabolomics. The results showed that 778 and 1449 metabolites were identified by the widely targeted metabolomics and untargeted metabolomics approaches, respectively. The metabolites in OSBSz and OSBSh are significantly differentiated; 71 and 96 differentially expressed metabolites were identified by the widely targeted metabolomics and untargeted metabolomics approaches, respectively. This suggests that these 71 metabolites (riboflavine, tripdiolide, bromocriptine, lumichrome, tetrahymanol, citrostadienol, etc.) and 96 metabolites (sancycline, vignatic acid B, pirbuterol, rubrophen, epalrestat, etc.) are potential biomarkers. 4-Hydroxybenzaldehyde, arginine, and lumichrome were common differentially expressed metabolites. Using the widely targeted metabolomics approach, the key pathways identified that are involved in creating the differentiation between OSBSz and OSBSh may be nicotinate and nicotinamide metabolism, thiamine metabolism, riboflavin metabolism, glycine, serine, and threonine metabolism, and arginine biosynthesis. The differentially expressed metabolites identified using the untargeted metabolomics approach were mainly involved in arginine biosynthesis, terpenoid backbone biosynthesis, porphyrin and chlorophyll metabolism, and cysteine and methionine metabolism. The purpose of this research was to provide support for the assessment of the differences between the stroma and sclerotia, to furnish a material basis for the evaluation of the physical effects of OS, and to provide a reference for the selection of detection methods for the metabolomics of OS.

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.

SWATH-MS2&1: Development and Validation of a Pseudotargeted Lipidomics Method for the Analysis of Glycerol Esters in Milk

Glycerol ester (GE) is a kind of important lipid in milk, which varies greatly depending on many factors. In this study, a novel pseudotargeted lipidomics strategy, named SWATH-MS2&1, was developed for the detection of GEs in milk and the Folch method was selected for the sample preparation. The developed method exhibited a competitive alternative to the acknowledged pseudotargeted strategy, including wider coverage (12 more GEs detected), higher repeatability (12 more GEs, whose coefficient of variation < 0.3), better linearity (5 more GEs, whose R² > 0.8), and similar sensitivity (only 2 GEs less than P-MRM after dilution). SWATH-MS2&1 was applied in the investigation of GEs from different milk samples. The orthogonal partial least-squares difference analysis of 219 GEs identified from SWATH-MS2&1 showed satisfying differentiation of different milk samples, and 76 GEs were screened out as potential markers. Our findings demonstrated that SWATH-MS2&1 could offer an accurate method to measure a wide spectrum of GEs in milk.

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

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

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.

Assessment of Fruit and Vegetables Intake with Biomarkers in Children and Adolescents and Their Level of Validation: A Systematic Review

Fruit and vegetables (FV) are part of a healthy diet and should be frequently consumed already at a young age. However, intake of FV is difficult to assess in children and adolescents due to various misreporting aspects. Thus, measurement of dietary biomarkers may be a promising alternative to assess FV intake more objectively at young age. To date, dietary biomarkers have been primarily studied in adults, and research focused on their usefulness in children is scarce. However, clinical studies have revealed important differences between children and adults, most importantly in their gut microbiome composition, resulting in differences in postprandial metabolism, as well as in food choices and meal compositions that may influence individual biomarker levels. Therefore, the present review aimed to identify biomarkers of FV intake (BFVI) currently available in children and adolescents and to explore whether there are any differences in the BFVI profile above between children and adolescents and adults. In addition, the current level of validation of BFVI in children and adolescents was examined. In total, 28 studies were eligible for this review, and 18 compounds were identified as potential biomarkers for FV intake in children and adolescents. Carotenoid concentration in skin was a valuable biomarker for total FV intake for both children and adult populations. Common BFVI in blood in adults (e.g., carotenoids and vitamin C) showed inconsistent results in children and adolescents. Biomarkers particularly useful in children included urinary hippuric acid as a biomarker of polyphenolic compound intake originating from FV and the combination of N-methylnicotinic acid and acetylornithine as a biomarker of bean intake. Further studies are needed to assess their kinetics, dose-response, and other validation aspects. There is limited evidence so far regarding valid BFVI in children and adolescents. Thus, to put BFVI into practice in children and adolescents, further studies, particularly based on metabolomics, are needed to identify and validate BFVI that can be used in future epidemiological studies.

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

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

A pseudotargeted method based on sequential window acquisition of all theoretical spectra mass spectrometry acquisition and its application in quality assessment of traditional Chinese medicine preparation-Yuanhu Zhitong tablet

Quality control plays a key role in the application of Chinese materia medica, especially in the preparation of traditional Chinese medicine. A pseudotargeted analysis method using an ultra-high-performance liquid chromatography-quadrupole-time-of-flight-mass spectrometry that was operated in the sequential window acquisition of all theoretical spectra mode was proposed to explore the chemical markers of traditional Chinese medicine preparation. Full-scan-based untargeted analysis was applied to extract the target ions. After data preprocessing, 302 target ions were extracted and used for the subsequent sequential window acquisition of all theoretical spectra analyses. The established sequential window acquisition of all theoretical spectra-based pseudotargeted approaches exhibited good repeatability and a wide linear range. The established method was successfully applied to discover analytical markers for the Yuanhu Zhitong tablet. After multivariate statistical analysis, 94 potential markers were identified. Ten markers were annotated by matching accurate m/z and product ion information obtained from previous reports. It is clearly indicated that the pseudotargeted analysis could make a great contribution to the quality assessment of traditional Chinese medicine preparation as a newly emerging technique.

Widely targeted metabolomics analysis of enriched secondary metabolites and determination of their corresponding antioxidant activities in Elaeagnus angustifolia var. orientalis (L.)Kuntze fruit juice enhanced by Bifidobacterium animalis subsp. Lactis HN-3 fermentation

Elaeagnus angustifolia var. orientalis (L.)Kuntze fruit contains a large number of naturally occurring molecules present as glycoside, methylated, and methyl ester conjugates, which should be hydolysed or transformed to become bioactive forms. For this purpose, Bifidobacterium animalis subsp. lactis HN-3 was selected to ferment Elaeagnus angustifolia var. orientalis (L.)Kuntze fruit juice (EOJ). After fermentation, the total phenolic content (TPC) and antioxidant capacity of the EOJ increased significantly compared to the non-fermented EOJ. Using widely-targeted metabolomics analysis, polyphenolic compounds involved in the flavonoid biosynthetic pathway were determined to be up-regulated in the fermented EOJ. In addition, the metabolites generated by 8 deglycosidation, 5 demethylation, 5 hydrogenation, and 28 other reactions were detected in higher concentrations in the fermented EOJ compared to the non-fermented EOJ. Interestingly, these up-regulated metabolites have higher antioxidant and other biological activities than their metabolic precursors, which provide a theoretical basis for the development of Bifidobacterium-fermented plant products with stronger functional activities.

Metabolomics meets lipidomics: Assessing the small molecule component of metabolism

Metabolomics, including lipidomics, is emerging as a quantitative biology approach for the assessment of energy flow through metabolism and information flow through metabolic signaling; thus, providing novel insights into metabolism and its regulation, in health, healthy ageing and disease. In this forward-looking review we provide an overview on the origins of metabolomics, on its role in this postgenomic era of biochemistry and its application to investigate metabolite role and (bio)activity, from model systems to human population studies. We present the challenges inherent to this analytical science, and approaches and modes of analysis that are used to resolve, characterize and measure the infinite chemical diversity contained in the metabolome (including lipidome) of complex biological matrices. In the current outbreak of metabolic diseases such as cardiometabolic disorders, cancer and neurodegenerative diseases, metabolomics appears to be ideally situated for the investigation of disease pathophysiology from a metabolite perspective.

Developing a SWATH capillary LC-MS/MS method for simultaneous therapeutic drug monitoring and untargeted metabolomics analysis of neonatal plasma

Most medications prescribed to neonatal patients are off-label uses. The pharmacokinetics and pharmacodynamics of drugs differ significantly between neonates and adults. Therefore, personalized pharmacotherapy guided by therapeutic drug monitoring (TDM) and drug response biomarkers are particularly beneficial to neonatal patients. Herein, we developed a capillary LC-MS/MS metabolomics method using a SWATH-based data-independent acquisition strategy for simultaneous targeted and untargeted metabolomics analysis of neonatal plasma samples. We applied the method to determine the global plasma metabolomics profiles and quantify the plasma concentrations of five drugs commonly used in neonatal intensive care units, including ampicillin, caffeine, fluconazole, vancomycin, and midazolam and its active metabolite α-hydroxymidazolam, in neonatal patients. The method was successfully validated and found to be suitable for the TDM of the drugs of interest. Moreover, the global metabolomics analysis revealed plasma metabolite features that could differentiate preterm and full-term neonates. This study demonstrated that the SWATH-based capillary LC-MS/MS metabolomics approach could be a powerful tool for simultaneous TDM and the discovery of neonatal plasma metabolite biomarkers.

MS2Planner: improved fragmentation spectra coverage in untargeted mass spectrometry by iterative optimized data acquisition

Motivation

Untargeted mass spectrometry experiments enable the profiling of metabolites in complex biological samples. The collected fragmentation spectra are the metabolite’s fingerprints that are used for molecule identification and discovery. Two main mass spectrometry strategies exist for the collection of fragmentation spectra: data-dependent acquisition (DDA) and data-independent acquisition (DIA). In the DIA strategy, all the metabolites ions in predefined mass-to-charge ratio ranges are co-isolated and co-fragmented, resulting in multiplexed fragmentation spectra that are challenging to annotate. In contrast, in the DDA strategy, fragmentation spectra are dynamically and specifically collected for the most abundant ions observed, causing redundancy and sub-optimal fragmentation spectra collection. Yet, DDA results in less multiplexed fragmentation spectra that can be readily annotated.

Results

We introduce the MS2Planner workflow, an Iterative Optimized Data Acquisition strategy that optimizes the number of high-quality fragmentation spectra over multiple experimental acquisitions using topological sorting. Our results showed that MS2Planner increases the annotation rate by 38.6% and is 62.5% more sensitive and 9.4% more specific compared to DDA.

Availability and implementation

MS2Planner code is available at https://github.com/mohimanilab/MS2Planner. The generation of the inclusion list from MS2Planner was performed with python scripts available at https://github.com/lfnothias/IODA_MS.

Supplementary information

Supplementary data are available at Bioinformatics online.

MetaboKit: a comprehensive data extraction tool for untargeted metabolomics

We have developed MetaboKit, a comprehensive software package for compound identification and relative quantification in mass spectrometry-based untargeted metabolomics analysis. In data dependent acquisition (DDA) analysis, MetaboKit constructs a customized spectral library with compound identities from reference spectral libraries, adducts, dimers, in-source fragments (ISF), MS/MS fragmentation spectra, and more importantly the retention time information unique to the chromatography system used in the experiment. Using the customized library, the software performs targeted peak integration for precursor ions in DDA analysis and for precursor and product ions in data independent acquisition (DIA) analysis. With its stringent identification algorithm requiring matches by both MS and MS/MS data, MetaboKit provides identification results with significantly greater specificity than the competing software packages without loss in sensitivity. The proposed MS/MS-based screening of ISFs also reduces the chance of unverifiable identification of ISFs considerably. MetaboKit's quantification module produced peak area values highly correlated with known concentrations in a DIA analysis of the metabolite standards at both MS1 and MS2 levels. Moreover, the analysis of Cdk1Liv-/- mouse livers showed that MetaboKit can identify a wide range of lipid species and their ISFs, and quantitatively reconstitute the well-characterized fatty liver phenotype in these mice. In DIA data, the MS1-level and MS2-level peak area data produced similar fold change estimates in the differential abundance analysis, and the MS2-level peak area data allowed for quantitative comparisons in compounds whose precursor ion chromatogram was too noisy for peak integration.

An improved strategy for analysis of lipid molecules utilising a reversed phase C30 UHPLC column and scheduled MS/MS acquisition

Measuring physiochemically diverse molecules (including lipids) which vary significantly in their concentrations poses a great analytical challenge. In untargeted lipidomics studies, reversed phase chromatography coupled with data-dependent MS/MS acquisition (DDA) is frequently applied. The optimal assay should deliver a high number of detected compounds with associated fragmentation data. In this work, we introduce novel 30 and 50 min UHPLC assays utilising lipid separation on a C30 stationary phase with a modified DDA strategy using smaller precursor m/z ranges scheduled for different lipid classes across the retention time range (defined as scheduled MS/MS). To evaluate the efficiency of the novel assays, mammalian tissue extracts (lamb liver, kidney and heart) were analysed and data were compared to a 15 min reversed phase C18 assay with multiple traditional DDA injections. The 30 min C30 assay detected double the number of detected compounds compared to the 15 min C18 assay. Applying the scheduled MS/MS DDA strategy with a single injection, a similar number of annotated lipids were reported compared to the traditional DDA strategy applied with five replicate injections on a C18 column. A longer 50 min C30 chromatographic assay did not result in an expected improvement in the chromatographic separation of overlapping isomer peaks compared to the 30 min method but did result in loss of accuracy of peak picking algorithms. We recommend the 30 min C30 assay with scheduled MS/MS acquisition as an efficient tool to analyse complex biological matrices and to annotate lipid species based on MS/MS data.

Effects of Dufulin on Oxidative Stress and Metabolomic Profile of Tubifex

Dufulin is a highly effective antiviral pesticide used in plants. In this study, a seven-day experiment was conducted to evaluate the effects of Dufulin at five different concentrations (1 × 10⁻⁴, 1 × 10⁻³, 1 × 10⁻², 0.1, and 1 mg/L) on Tubifex. LC-MS-based metabolome analysis detected a total of 5356 features in positive and 9110 features in negative, of which 41 showed significant changes and were identified as differential metabolites. Four metabolic pathways were selected for further study. Detailed analysis revealed that Dufulin exposure affected the urea cycle of Tubifex, probably via argininosuccinate lyase (ASL) inhibition. It also affected the fatty acid metabolism, leading to changes in the concentration of free fatty acids in Tubifex. Furthermore, the changes in metabolites after exposure to Dufulin at 1 × 10⁻² mg/L were different from those at the other concentrations.

The Study on the Pathogenesis of Pediatric Lymphoma Based on the Combination of Pseudotargeted and Targeted Metabolomics

Pediatric lymphoma is a kind of malignant tumor with high mortality. The complexity of pediatric lymphoma shows a great challenge for effective diagnosis and treatment. In order to meet the challenge, the combination of pseudotargeted and targeted metabolomics was used to analyze the serum metabolites in pediatric lymphoma patients and healthy controls for discovering the metabolites related to pediatric lymphoma. The serum samples were obtained from the treatment group (n = 43), the control group (n = 26), and the patients group (n = 18). A total of 17 serum metabolites, including carnitine, leucine, creatine, urea, (6Z,9Z,12Z)-octadecatrienoic acid, linoleate, octadecenoic acid, L-palmitoylcarnitine, hexadecanoic acid, tetradecanoic acid, (9Z)-hexadecenoic acid, uric acid, glucose, 1-methylnicotinamide, hypoxanthine, L-glutamine, and taurine, were found to be related to pediatric lymphoma. They could provide a scientific diagnostic basis and therapeutic target for pediatric lymphoma and elucidate the mechanism of pediatric lymphoma.

Quantifying Precision Loss in Targeted Metabolomics Based on Mass Spectrometry and Nonmatching Internal Standards

In mass spectrometry, reliable quantification requires correction for variations in ionization efficiency between samples. The preferred method is the addition of a stable isotope-labeled internal standard (SIL-IS). In targeted metabolomics, a dedicated SIL-IS for each metabolite of interest may not always be realized due to high cost or limited availability. We recently completed the analysis of more than 70 biomarkers, each with a matching SIL-IS, across four mass spectrometry-based platforms (one GC-MS/MS and three LC-MS/MS). Using data from calibrator and quality control samples added to 60 96-well trays (analytical runs), we calculated analytical precision (CV) retrospectively. The use of integrated peak areas for all metabolites and internal standards allowed us to calculate precision for all matching analyte (A)/SIL-IS (IS) pairs as well as for all nonmatching A/IS pairs within each platform (total n = 1442). The median between-run precision for matching A/IS across the four platforms was 2.7-5.9%. The median CV for nonmatching A/IS (corresponding to pairing analytes with a non-SIL-IS) was 2.9-10.7 percentage points higher. Across all platforms, CVs for nonmatching A/IS increased with increasing difference in retention time (Spearman's rho of 0.17-0.93). The CV difference for nonmatching vs matching A/IS was often, but not always, smaller when analytes and internal standards were close structural analogs.