High-Throughput and Multiplexed LC/MS/MRM Method for Targeted Metabolomics

Integration of untargeted and pseudotargeted metabolomics reveals specific markers for authentication and adulteration detection of Fritillariae Bulbus using tandem mass spectrometry and chemometrics

Authentication and adulteration detection of closely related herbal medicines is a thorny issue in the quality control and market standardization of traditional Chinese medicine. Taking Fritillariae Bulbus (FB) as a case study, we herein proposed a three-step strategy that integrates mass spectrometry-based metabolomics and multivariate statistical analysis to identify specific markers, thereby accurately identifying FBs and determining the adulteration level. First, an ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based untargeted metabolomics method was employed to profile steroid alkaloids in five sorts of FB and screen potential differential markers. Then, the reliability of the screened markers was further verified by the distribution in different FB groups acquired from ultra-high performance liquid chromatography triple quadrupole mass spectrometry-based pseudotargeted metabolomics analysis. As a result, a total of 16 compounds were screened out to be the specific markers, which were successfully applied to distinguish five FBs by using discriminant analysis model. Besides, partial least squares regression models based on specific markers allowed accurate prediction of three sets of adulterated FBs. All the models afforded good linearity and good predictive ability with regression coefficient of prediction (R2p) > 0.9 and root mean square error of prediction (RMSEP) < 0.1. The reliable results of discriminant and quantitative analysis revealed that this proposed strategy could be potentially used to identify specific markers, which contributes to rapid chemical discrimination and adulteration detection of herbal medicines with close genetic relationship.

Metabolic imprinting in beef calves supplemented with creep feeding on performance, reproductive efficiency and metabolome profile

This experiment evaluated the influence of creep feeding supplementation on productive and reproductive performance and on serum metabolome profile in Nelore (Bos indicus) heifers. Female calves were assigned to treatments: Creep (n = 190), with ad libitum access to a nutritional supplement from 70 to 220 days after birth, or Control (n = 140), without supplementation. After weaning (Day 220), both groups followed the same pasture and nutritional management. Body weight (BW) and backfat thickness (BFAT) were measured over time. Blood samples were collected at 220 and 360 days for LC-MS/MS targeted metabolomics. On day 408, during the synchronization timed artificial insemination (TAI) protocol, reproductive status (RS: diameter of uterine horn and largest follicle, and presence of CL) was assessed. Creep feeding increased BW and BFAT at weaning, but no differences in BW, BFAT, or RS after weaning were observed. Nonetheless, the pregnancy per AI (P/AI) for 1st service was 28.9% higher in the Creep group. On day 220, 11 significant metabolites influenced five metabolic pathways: Glucose-alanine cycle, alanine, glutathione, phenylalanine and tyrosine metabolism, and urea cycle. On day 360, 14 significant metabolites influenced eight metabolic pathways: Malate-aspartate shuttle, arginine and proline metabolism, urea cycle, aspartate, beta-alanine, glutamate metabolism, ammonia recycling and citric acid cycle. In conclusion, creep feeding supplementation improved calf performance and induced metabolic changes at weaning and 360 days of age. Although heifers had similar productive performance and reproductive status, when submitted to TAI, those supplemented with creep feeding had greater P/AI.

Lipidomics reveals new lipid-based lung adenocarcinoma early diagnosis model

Lung adenocarcinoma (LUAD) continues to pose a significant mortality risk with a lack of dependable biomarkers for early noninvasive cancer detection. Here, we find that aberrant lipid metabolism is significantly enriched in lung cancer cells. Further, we identified four signature lipids highly associated with LUAD and developed a lipid signature-based scoring model (LSRscore). Evaluation of LSRscore in a discovery cohort reveals a robust predictive capability for LUAD (AUC: 0.972), a result further validated in an independent cohort (AUC: 0.92). We highlight one lipid signature biomarker, PE(18:0/18:1), consistently exhibiting altered levels both in cancer tissue and in plasma of LUAD patients, demonstrating significant predictive power for early-stage LUAD. Transcriptome analysis reveals an association between increased PE(18:0/18:1) levels and dysregulated glycerophospholipid metabolism, which consistently displays strong prognostic value across two LUAD cohorts. The combined utility of LSRscore and PE(18:0/18:1) holds promise for early-stage diagnosis and prognosis of LUAD.

Exploring the Significance, Extraction, and Characterization of Plant-Derived Secondary Metabolites in Complex Mixtures

Secondary metabolites are essential components for the survival of plants. Secondary metabolites in complex mixtures from plants have been adopted and documented by different traditional medicinal systems worldwide for the treatment of various human diseases. The extraction strategies are the key components for therapeutic development from natural sources. Polarity-dependent solvent-selective extraction, acidic and basic solution-based extraction, and microwave- and ultrasound-assisted extraction are some of the most important strategies for the extraction of natural products from plants. The method needs to be optimized to isolate a specific class of compounds. Therefore, to establish the mechanism of action, the characterization of the secondary metabolites, in a mixture or in their pure forms, is equally important. LC-MS, GC-MS, and extensive NMR spectroscopic strategies are established techniques for the profiling of metabolites in crude extracts. Various protocols for the extraction and characterization of a wide range of classes of compounds have been developed by various research groups and are described in this review. Additionally, the possible means of characterizing the compounds in the mixture and their uniqueness are also discussed. Hyphenated techniques are crucial for profiling because of their ability to analyze a vast range of compounds. In contrast, inherent chemical shifts make NMR an indispensable tool for structure elucidation in complex mixtures.

Accelerating Pharmaceutical Process Development with an Acoustic Droplet Ejection-Multiple Reaction Monitoring-Mass Spectrometry Workflow

Fast-paced pharmaceutical process developments (e.g., high-throughput experimentation, directed evolution, and machine learning) involve the introduction of fast, sensitive, and accurate analytical assays using limited sample volumes. In recent years, acoustic droplet ejection (ADE) coupled with an open port interface has been invented as a sampling technology for mass spectrometry, providing high-throughput nanoliter analytical measurements directly from the standard microplates. Herein, we introduce an ADE-multiple reaction monitoring-mass spectrometry (ADE-MRM-MS) workflow to accelerate pharmaceutical process research and development (PR&D). This systematic workflow outlines the selection of MRM transitions and optimization of assay parameters in a data-driven manner using rapid measurements (1 sample/s). The synergy between ADE sampling and MRM analysis enables analytical assays with excellent sensitivity, selectivity, and speed for PR&D reaction screenings. This workflow was utilized to develop new ADE-MRM-MS assays guiding a variety of industrial processes, including (1) screening of Ni-based catalysts for C-N cross-coupling reaction at 1 Hz and (2) high-throughput regioisomer analysis-enabled enzyme library screening for peptide ligation reaction. ADE-MRM-MS assays were demonstrated to deliver accurate results that are comparable to conventional liquid chromatography (LC) experiments while providing >100-fold throughput enhancement.

Combined metabolomic and lipidomic analysis uncovers metabolic profile and biomarkers for papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is the most common endocrine malignancy with a rapidly increasing incidence. The pathogenesis of PTC is unclear, but metabolic and lipidomic reprogramming may play a role in tumor growth. We applied ultra-performance liquid chromatography-tandem mass spectrometry to perform widely targeted metabolomics and lipidomics on plasma samples from 94 patients with PTC and 100 healthy controls. We identified 113 differential metabolites and 236 differential lipids, mainly involved in branched-chain amino acid metabolism, glutamate and glutamine metabolism, tricarboxylic acid cycle, and lipid metabolism. We also screened three potential metabolite biomarkers: sebacic acid, L-glutamine, and indole-3-carboxaldehyde. These biomarkers showed excellent diagnostic performance for PTC in both discovery and validation cohorts, with areas under the receiver operating characteristic curves of 0.994 and 0.925, respectively. Our findings reveal distinct metabolic and lipidomic features of PTC and provide novel targets for diagnosis and treatment.

Can plant hormonomics be built on simple analysis? A review

The field of plant hormonomics focuses on the qualitative and quantitative analysis of the hormone complement in plant samples, akin to other omics sciences. Plant hormones, alongside primary and secondary metabolites, govern vital processes throughout a plant's lifecycle. While active hormones have received significant attention, studying all related compounds provides valuable insights into internal processes. Conventional single-class plant hormone analysis employs thorough sample purification, short analysis and triple quadrupole tandem mass spectrometry. Conversely, comprehensive hormonomics analysis necessitates minimal purification, robust and efficient separation and better-performing mass spectrometry instruments. This review summarizes the current status of plant hormone analysis methods, focusing on sample preparation, advances in chromatographic separation and mass spectrometric detection, including a discussion on internal standard selection and the potential of derivatization. Moreover, current approaches for assessing the spatiotemporal distribution are evaluated. The review touches on the legitimacy of the term plant hormonomics by exploring the current status of methods and outlining possible future trends.

A targeted liquid chromatography mass spectrometry method for routine monitoring of cell culture media components for bioprocess development

The analysis of cell culture media (CCM) components is critical for understanding cell growth kinetics and overall product quality during biomanufacturing. Given the diverse physical and chemical nature of CCM compounds present at a wide range of concentrations, there is an increasing demand for single-platform analytical assays with exceptional specificity and sensitivity. This study presents a targeted LC-MS/MS method for the identification and quantitation of 110 CCM analytes is presented, where target metabolites are monitored over an 20-min gradient. The analyte panel constitutes amino acids, vitamins, organic acids, nucleic acids, carbohydrates, and lipids. The method employs isotopically labeled standards to enable specific and accurate relative quantitation of CCM compounds based on physicochemical properties and retention time. Quantitation is performed on a triple quadrupole mass spectrometer operated in multiple reaction monitoring (MRM) mode. The method demonstrates strong linearity with an R2 of ≥0.99 with three orders of linear dynamic range and inter-day and intra-day precision with a%CV of <10% for spiked-in quality control samples. We also present three case studies to demonstrate method applicability in the bioprocessing space for developing vaccines and biologics.

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.

Fecal Metabolomics Reveals the Foraging Strategies of Giant Pandas for Different Parts of Bamboo

Climate change-induced food shortages pose major threats to wildlife conservation, and the exclusive reliance of giant pandas on bamboo makes them particularly vulnerable. The aim of this study was to provide insight into the reasons for the foraging strategies of giant pandas to selectively forage for different bamboo parts (bamboo shoot, culm, and leaf) during different seasons. This study used a metabolomic approach to analyze the fecal metabolites of giant pandas and conducted a correlation analysis with their gut microbiota. The results indicate that the fecal metabolites of giant pandas differ significantly depending on the bamboo parts they forage on, with higher sugar content observed when they consume bamboo culm with high fiber content. By functional annotation, culm group metabolites were enriched in the galactose metabolic pathway, while shoot group metabolites were enriched in the phenylalanine, tyrosine and tryptophan biosynthesis pathways. Moreover, Streptococcus showed a significant positive correlation with glucose and acetic acid content. Therefore, the foraging strategy of giant pandas is based on the ability to utilize the nutrient content of different bamboo parts. Captive feeding and habitat construction should enrich bamboo species to allow them to express their natural foraging strategies and improve their welfare and reproductive status.

Comprehensive characterization of amino acids and water-soluble vitamins in a pentylenetetrazole-induced seizures rat model

Epilepsy is a complex neurological disease characterized by spontaneous recurrent seizures that affect around 1% of the global population. Despite the significant progress in the mechanisms of epileptogenesis, there is still about 60% of cases in which the cause is unknown. Thus, revealing the molecular mechanisms of epileptogenesis will greatly improve the development of epilepsy treatment. Since the comprehensive characterization of amino acids and water-soluble vitamins is important in understanding the underlying mechanisms of epilepsy or seizures, we developed two liquid chromatography-tandem mass spectrometry methods to quantify 17 water-soluble vitamins and 46 amino acids and applied them to our pentylenetetrazole-induced kindling rat model. All water-soluble vitamins were detected with a linearity of r > 0.992 and limits of quantitation between 0.1 and 5 ng/ml except for nicotinic acid. For amino acids, the linearities obtained were good with correlation coefficients higher than 0.99, and matrix effects were between 85.3% and 110%. To handle the multidimensional data more effectively, multivariate statistical analysis approaches used in non-targeted metabolomics were creatively exploited in the visualization, interpretation, and exploration of the results.

A proposed framework to evaluate the quality and reliability of targeted metabolomics assays from the UK Consortium on Metabolic Phenotyping (MAP/UK)

Targeted metabolite assays that measure tens or hundreds of pre-selected metabolites, typically using liquid chromatography-mass spectrometry, are increasingly being developed and applied to metabolic phenotyping studies. These are used both as standalone phenotyping methods and for the validation of putative metabolic biomarkers obtained from untargeted metabolomics studies. However, there are no widely accepted standards in the scientific community for ensuring reliability of the development and validation of targeted metabolite assays (referred to here as 'targeted metabolomics'). Most current practices attempt to adopt, with modifications, the strict guidance provided by drug regulatory authorities for analytical methods designed largely for measuring drugs and other xenobiotic analytes. Here, the regulatory guidance provided by the European Medicines Agency, US Food and Drug Administration and International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use are summarized. In this Perspective, we have adapted these guidelines and propose a less onerous 'tiered' approach to evaluate the reliability of a wide range of metabolomics analyses, addressing the need for community-accepted, harmonized guidelines for tiers other than full validation. This 'fit-for-purpose' tiered approach comprises four levels-discovery, screening, qualification and validation-and is discussed in the context of a range of targeted and untargeted metabolomics assays. Issues arising with targeted multiplexed metabolomics assays, and how these might be addressed, are considered. Furthermore, guidance is provided to assist the community with selecting the appropriate degree of reliability for a series of well-defined applications of metabolomics.

HPLC-based metabolomics of Dendrobium officinale revealing its antioxidant ability

Dendrobium officinale is an orchid with medicinal and nutritional properties that has received increasing attention because of its health benefits; however, there is limited information about the metabolic basis of these properties. In this report, secondary metabolites and the antioxidant activity of D. officinale stem samples from three provenances were analyzed, using a UHPLC-QqQ-MS/MS-based metabolomics approach. In total, 411 metabolites were identified including 8 categories such as flavonoids and phenolic acids, 136 of which were differential metabolites. These differentially accumulated metabolites (DAMs) were mainly enriched in secondary metabolic pathways such as flavone, flavonol, tropane, piperidine, pyridine, isoquinoline alkaloid biosynthesis and tyrosine metabolism. The metabolomic profiling suggested that the quantity and content of flavonoid compounds accounted for the highest proportion of total metabolites. Hierarchical cluster analysis (HCA) showed that the marker metabolites of D. officinale from the three provenances were mainly flavonoids, alkaloids and phenolic acids. Correlation analysis identified that 48 differential metabolites showed a significant positive correlation with antioxidant capacity (r ³ 0.8 and p < 0.0092), and flavonoids were the main factors affecting the different antioxidant activities. It is worth noting that quercetin-3-O-sophoroside-7-O-rhamnoside and dihydropinosylvin methyl ether might be the main compounds causing the differences in antioxidant capacity of Yunnan provenance (YN), Zhejiang provenance (ZJ), and Guizhou provenance (GZ). These finding provides valuable information for screening varieties, quality control and product development of D. officinale.

Seaweed metabolomics: A review on its nutrients, bioactive compounds and changes in climate change

Seaweed, an important food resource in several Asian countries, contains various metabolites, including sugars, organic acids, and amino acids; however, their content is affected by prevailing environmental conditions. This review discusses seaweed metabolomics, especially the distribution of primary and functional secondary metabolites (e.g., carotenoids, polyphenols) in seaweed. Additionally, the effects of global warming on seaweed metabolite profile changes are discussed. For example, high temperatures can increase amino acid levels in seaweeds. Overall, understanding the effects of global warming on seaweed metabolite profiles can be useful for evaluating the nutritional composition of seaweeds as food. This review provides an overview of recent applications of metabolomics in seaweed research as well as a perspective on the nutrient content and cultivation of seaweeds under climate change scenarios.

Comparison analysis of metabolite profiling in seeds and bark of Ulmus parvifolia, a Chinese medicine species

Ulmus parvifolia (U. parvifolia) is a Chinese medicine plant whose bark and leaves are used in the treatment of some diseases such as inflammation, diarrhea and fever. However, metabolic signatures of seeds have not been studied. The seeds and bark of U. parvifolia collected at the seed ripening stage were used for metabolite profiling analysis through the untargeted metabolomics approach. A total of 2,578 and 2,207 metabolites, while 503 and 132 unique metabolites were identified in seeds and bark, respectively. Additionally, 574 differential metabolites (DEMs) were detected in the two different organs of U. parvifolia, which were grouped into 52 classes. Most kinds of metabolites classed into prenol lipids class. The relative content of flavonoids class was the highest. DEMs contained some bioactive compounds (e.g., flavonoids, terpene glycosides, triterpenoids, sesquiterpenoids) with antioxidant, anti-inflammatory, and anti-cancer activities. Most kinds of flavonoids and sesquiterpenes were up-regulated in seeds. There were more varieties of terpene glycosides and triterpenoids showing up-regulated in bark. The pathway enrichment was performed, while flavonoid biosynthesis, flavone and flavonol biosynthesis were worthy of attention. This study identified DEMs with pharmaceutical value between seeds and bark during seed maturation and offered a molecular basis for alternative or complementary use of seeds and bark of U. parvifolia as a Chinese medicinal material.

Combining multidimensional chromatography-mass spectrometry and feature-based molecular networking methods for the systematic characterization of compounds in the supercritical fluid extract of Tripterygium wilfordii Hook F

Tripterygium wilfordii Hook F from the family Celastraceae is a traditional Chinese medicine (TCM) whose principal chemical constituents are terpenoids, including sesquiterpene alkaloids and diterpenoids, which have unique and diverse structures and remarkable biological activities. In order to advance pharmacological research and guide the preparation of monomer compounds derived from T. wilfordii, a systematic approach to efficiently discover new compounds or their derivatives is needed. Herein, compound separation and identification were performed by offline reversed-phase × supercritical fluid chromatography coupled mass spectrometry (RP × SFC-Q-TOF-MS/MS) and Global Natural Product Social (GNPS) molecular networking. The 2D chromatography system exhibited a high degree of orthogonality and significant peak capacity, and SFC has an advantage during the separation of sesquiterpene alkaloid isomers. Feature-based molecular networking offers the great advantage of quickly detecting and clustering unknown compounds, which greatly assists in intuitively judging the type of compound, and this networking technique has the potential to dramatically accelerate the identification and characterization of compounds from natural sources. A total of 324 compounds were identified and quantitated, including 284 alkaloids, 22 diterpenoids and 18 triterpenoids, which means that there are numerous potential new compounds with novel structures to be further explored. Overall, feature-based molecular networking provides an effective method for discovering and characterizing novel compounds and guides the separation and preparation of targeted natural products.

Resistant starch type-4 intake alters circulating bile acids in human subjects

Background

Resistant starch (RS) type 4 (RS4) is a type of RS, a class of non-digestible prebiotic dietary fibers with a range of demonstrated metabolic health benefits to the host. On the other hand, bile acids (BA) have recently emerged as an important class of metabolic function mediators that involve host-microbiota interactions. RS consumption alters fecal and cecal BA in humans and rodents, respectively. The effect of RS intake on circulating BA concentrations remains unexplored in humans.

Methods and results

Using available plasma and stool samples from our previously reported double-blind, controlled, 2-arm crossover nutrition intervention trial (Clinicaltrials.gov: NCT01887964), a liquid-chromatography/mass-spectrometry-based targeted multiple reaction monitoring, and absolute quantifications, we assessed BA changes after 12 weeks of an average 12 g/day RS4-intake. Stool BA concentrations were lower post RS4 compared to the control, the two groups consuming similar macronutrients (n = 14/group). Partial least squares-discriminant analysis revealed distinct BA signatures in stool and plasma post interventions. The increased circulating BA concentrations were further investigated using linear mixed-effect modeling that controlled for potential confounders. A higher plasma abundance of several BA species post RS4 was observed (fold increase compared to control in parenthesis): taurocholic acid (1.92), taurodeoxycholic acid (1.60), glycochenodeoxycholic acid (1.58), glycodeoxycholic acid (1.79), and deoxycholic acid (1.77) (all, p < 0.05). Distinct microbiome ortholog-signatures were observed between RS4 and control groups (95% CI), derived using the Piphillin function-prediction algorithm and principal component analysis (PCA) of pre-existing 16S rRNA gene sequences. Association of Bifidobacterium adolescentis with secondary BA such as, deoxycholic acid (rho = 0.55, p = 0.05), glycodeoxycholic acid (rho = 0.65, p = 0.02), and taurodeoxycholic acid (rho = 0.56, p = 0.04) were observed in the RS4-group, but not in the control group (all, p > 0.05).

Conclusion

Our observations indicate a previously unknown in humans- RS4-associated systemic alteration of microbiota-derived secondary BA. Follow-up investigations of BA biosynthesis in the context of RS4 may provide molecular targets to understand and manipulate microbiome-host interactions.

An Endemic Plant of the Mediterranean Area: Phytochemical Characterization of Strawberry Tree (Arbutus unedo L.) Fruits Extracts at Different Ripening Stages

This work focused on the extraction, quantification, and characterization of bioactive compounds of Arbutus unedo L. fruits, comparing the results obtained from the different ripening states. Extractions were performed by different methods (such as maceration extraction and ultrasonic extraction) and food grade solvents (aqueous and hydroalcoholic solvents) in each of the all ripening states (four states considered, associated with four different colors, i.e., green, yellow, orange, and red). The presence of (poly)phenols was quantified and characterized, and scavenging activity was determined by the Folin–Ciocâlteu reagent and the DPPH method, respectively. The content of bioactive compounds was characterized by LC-MS/MS, such as multiple reaction monitoring (MRM) mass spectrometry. The results showed that ultrasound-assisted extraction (UAE) performed better than maceration extraction; ethanol–water mixture extracts showed a more positive effect than the use of aqueous extracts regarding the content of total phenolic compounds. Overall, the total phenolic compounds in the EtOH:H₂O mixture at a ratio of 7:3 (v:v) were higher than that of the other solvents for both extraction methods. Some bioactive molecules were characterized for the first time in the extracts of A. unedo. The chemical profile of the strawberry tree extracts depended on the degree of fruit ripeness. The results suggest that A. unedo fruits may be of great interest for food and nutraceutical applications.

Comprehensive pseudotargeted metabolomics analysis based on two-phase liquid extraction-UHPLC-MS/MS for the investigation of depressive rats

Pseudotargeted analysis combines the advantages of untargeted and targeted metabolomics methods. This study proposed a comprehensive pseudotargeted metabolomics method based on two-phase liquid extraction using ultra-high-performance liquid chromatography-tandem mass spectrometry. Two-phase liquid extraction, composed of both aqueous and organic phases, extracted a wide range of metabolites from polar to nonpolar in plasma samples. Besides, the two phases were combined and detected in a single injection to save analytical time. A total of 486 potential metabolites were detected by the developed approach. Compared with the conventional methanol-based protein precipitation method, the two-phase liquid extraction method significantly increased the metabolite coverage by 20.29%. Besides, the proposed pseudotargeted metabolomics method exhibited higher sensitivity and better repeatability than the untargeted method. Finally, we applied the established pseudotargeted method to the metabolomics study of depressive rats and screened 53 differential variables. Sixteen determined differential metabolites were mainly in four metabolic pathways, including glycerophospholipid, arachidonic acid, sphingolipid metabolisms, pentose and glucuronate interconversions. The results indicated that the pseudotargeted method based on two-phase liquid extraction broadened the metabolite coverage with good sensitivity and repeatability, exhibiting significant potential for discovering differential metabolites in metabolomics studies. This article is protected by copyright. All rights reserved.

Comparative metabolomics analysis revealed biomarkers and distinct flavonoid biosynthesis regulation in Chrysanthemum mongolicum and C. rhombifolium

INTRODUCTION

Chrysanthemums are traditional flowers that originated in China and have high ornamental, economic and medicinal value. They are widely used as herbal remedies and consumed as food or beverages in folk medicine. However, little is known about their metabolic composition.

OBJECTIVES

The aims of this work were to determine the metabolic composition of and natural variation among different species of Chrysanthemum and to explore new potential resources for drug discovery and sustainable utilisation of wild Chrysanthemum.

METHODS

The metabolomes of Chrysanthemum mongolicum (Ling) Tzvel. and Chrysanthemum rhombifolium H. Ohashi & Yonek. were compared using a widely targeted metabolomics approach based on liquid chromatography-tandem mass spectrometry (LC-MS/MS).

RESULTS

In total, 477 metabolites were identified, of which 72 showed significant differences in expression between C. mongolicum and C. rhombifolium, mainly in flavonoids, organic acids and nucleotides. The flavone and flavonol biosynthesis pathway showed significant enrichment among the differentially expressed metabolites. The contents of genkwanin, trigonelline, diosmin, narcissoside, 3,4-dihydroxyphenylacetic acid, linarin, N',N'-p-coumarin, C-hexosyl-tricetin O-pentoside, chrysoeriol, acacetin and kaempferol-3-O-gentiobioside were significantly different between the two species and represent potential biomarkers.

CONCLUSION

The types of flavonoid-related metabolites in the flavonoid biosynthesis pathway differed between C. mongolicum and C. rhombifolium. The mechanisms underlying the unique adaptations of these two species to their environments may involve variations in the composition and abundance of flavonoids, organic acids, and nucleotides. These methods are promising to identify functional compounds in Chrysanthemum species and can provide potential resources for drug discovery and the sustainable utilisation of Chrysanthemum plants.

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

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

Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis

Lung cancer is the leading cause of cancer mortality, and early detection is key to improving survival. However, there are no reliable blood-based tests currently available for early-stage lung cancer diagnosis. Here, we performed single-cell RNA sequencing of different early-stage lung cancers and found that lipid metabolism was broadly dysregulated in different cell types, with glycerophospholipid metabolism as the most altered lipid metabolism-related pathway. Untargeted lipidomics was carried out in an exploratory cohort of 311 participants. Through support vector machine algorithm-based and mass spectrum-based feature selection, we identified nine lipids (lysophosphatidylcholines 16:0, 18:0, and 20:4; phosphatidylcholines 16:0-18:1, 16:0-18:2, 18:0-18:1, 18:0-18:2, and 16:0-22:6; and triglycerides 16:0-18:1-18:1) as the features most important for early-stage cancer detection. Using these nine features, we developed a liquid chromatography-mass spectrometry (MS)-based targeted assay using multiple reaction monitoring. This target assay achieved 100.00% specificity on an independent validation cohort. In a hospital-based lung cancer screening cohort of 1036 participants examined by low-dose computed tomography and a prospective clinical cohort containing 109 participants, the assay reached more than 90.00% sensitivity and 92.00% specificity. Accordingly, matrix-assisted laser desorption/ionization MS imaging confirmed that the selected lipids were differentially expressed in early-stage lung cancer tissues in situ. This method, designated as Lung Cancer Artificial Intelligence Detector, may be useful for early detection of lung cancer or large-scale screening of high-risk populations for cancer prevention.

LC-HRMS based Targeted Metabolomics for High-throughput and Quantitative Analysis of 21 Growth Inhibition Related Metabolites in CHO Cell Fed-Batch Cultures

Chinese hamster ovary (CHO) cells have been widely used in the biopharmaceutical industry for production of therapeutic proteins. CHO cells in fed-batch cultures produce various amino acid-derived intermediate metabolites. These small molecule metabolic byproducts have proven to be critical to cell growth, culture performance, and more interestingly antibody drug productivity. Herein, we developed a liquid chromatography - high resolution mass spectrometry (LC-HRMS) based targeted metabolomics approach for comprehensive quantification of total 21 growth inhibition related metabolites generated from 14 different amino acids in CHO cell fed-batch cultures. High throughput derivatization procedures, matrix-matched calibration curves, stable isotope-labeled internal standards, and accurate mass Full MS scan were utilized in order to achieve our goal for wide scope of metabolite screening as well as validity and reliability of metabolite quantification. We further present a novel analytical strategy for extending the assay's dynamic range by utilizing naturally occurring isotope M+1 ion as a quantification analogue in the circumstances where the principal M ion is beyond its calibration range. The integrated method was qualified for selectivity, sensitivity, linearity, accuracy, precision, isotope analysis and other analytical aspects to demonstrate assay robustness. We then applied this metabolomics approach to characterize metabolites of interest in a CHO cell based monoclonal antibody (mAb) production process with fed-batch bioreactor culture mode. Absolute quantification combined with multivariate statistical analysis illustrated that our target analytes derived from amino acids, especially from branched-chain amino acids, closely correlated with cell viability and significantly differentiated cellular stages in production process.

Metabolic detection and systems analyses of pancreatic ductal adenocarcinoma through machine learning, lipidomics, and multi-omics

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, characterized by rapid progression, metastasis, and difficulty in diagnosis. However, there are no effective liquid-based testing methods available for PDAC detection. Here we introduce a minimally invasive approach that uses machine learning (ML) and lipidomics to detect PDAC. Through greedy algorithm and mass spectrum feature selection, we optimized 17 characteristic metabolites as detection features and developed a liquid chromatography-mass spectrometry-based targeted assay. In this study, 1033 patients with PDAC at various stages were examined. This approach has achieved 86.74% accuracy with an area under curve (AUC) of 0.9351 in the large external validation cohort and 85.00% accuracy with 0.9389 AUC in the prospective clinical cohort. Accordingly, single-cell sequencing, proteomics, and mass spectrometry imaging were applied and revealed notable alterations of selected lipids in PDAC tissues. We propose that the ML-aided lipidomics approach be used for early detection of PDAC.

Towards Higher Sensitivity of Mass Spectrometry: A Perspective From the Mass Analyzers

Mass spectrometry (MS) is one of the most widely used analytical techniques in many fields. Recent developments in chemical and biological researches have drawn much attention to the measurement of substances with low abundances in samples. Continuous efforts have been made consequently to further improve the sensitivity of MS. Modifications on the mass analyzers of mass spectrometers offer a direct, universal and practical way to obtain higher sensitivity. This review provides a comprehensive overview of the latest developments in mass analyzers for the improvement of mass spectrometers' sensitivity, including quadrupole, ion trap, time-of-flight (TOF) and Fourier transform ion cyclotron (FT-ICR), as well as different combinations of these mass analyzers. The advantages and limitations of different mass analyzers and their combinations are compared and discussed. This review provides guidance to the selection of suitable mass spectrometers in chemical and biological analytical applications. It is also beneficial to the development of novel mass spectrometers.

Mass Spectrometry-Based Targeted Serum Monomethylated Ribonucleosides Profiling for Early Detection of Breast Cancer

RNA methylation plays a significant regulatory role in various of physiological activities and it has gradually become a hotspot of epigenetics in the past decade. 2′-O-methyladenosine (Am), 2′-O-methylguanosine (Gm), 2′-O-methylcytidine (Cm), 2′-O-methyluridine (Um), N⁶-methyladenosine (m⁶A), N¹-methylguanosine (m¹G), 5-methylcytidine (m⁵C), and 5-methyluridine (m⁵U) are representative 2′-O-methylation and base-methylation modified epigenetic marks of RNA. Abnormal levels of these ribonucleosides were found to be related to various diseases including cancer. Serum is an important source of biofluid for the discovery of biomarkers, and novel tumor biomarkers can be explored by measuring these ribonucleoside modifications in human serum. Herein, we developed and applied a hydrophilic interaction liquid chromatography tandem mass spectrometry (HILIC-MS/MS) method to determine the content of monomethylated ribonucleosides in human serum. The developed method enabled sensitive and accurate determination of these monomethylated ribonucleosides. By applying this robust method, we demonstrated the presence of Gm and Um in human serum for the first time, and we successfully quantified m⁶A, Gm, m¹G, Cm, Um and m⁵U in serum samples collected from 61 patients with breast cancer and 69 healthy controls. We discovered that the levels of Gm, m¹G, Cm, Um and m⁵U in serum were all significantly decreased in breast cancer patients whereas m⁶A was increased. We performed receiver operating characteristic (ROC) curve analysis, and obtained highest area under curve (AUC) value when combining these six monomethylated ribonucleosides together. These results suggest that m⁶A, Gm, m¹G, Cm, Um and m⁵U might have great potential to be novel biomarkers for detection of breast cancer in the early stage. In addition, this study may stimulate future investigations about the regulatory roles of monomethylated ribonucleosides on the initiation and development of breast cancer.

Rapid liquid chromatography-mass spectrometry quantitation of glucose-regulating hormones from human islets of Langerhans

Glucose homeostasis is maintained through the secretion of peptide hormones, such as insulin, somatostatin, and glucagon, from islets of Langerhans, clusters of endocrine cells found in the pancreas. This report describes an LC-MS method using multiple reaction monitoring for quantitation of insulin, C-peptide, glucagon, and somatostatin secretion from human islet populations. For rapid analysis, a 5 min separation was achieved using a 2.1 × 30 mm (i.d. x length) C18 column with 2.7 µm diameter core shell particles. A sacrificial protein hydrolysate was used with the sample and found to improve signal magnitude, repeatability, and to reduce carryover between runs. At optimized gradient conditions, the gradient run time was 4.55 min producing an average peak width of 0.3 min, a minimum resolution of 1.2, and a peak capacity of 20. As a proof of concept, the method was used to measure secretions from static incubations of human islets from 2 donors. Insulin and C-peptide were quantified and matched well with literature values of these hormones. We expect that this antibody-free quantitation of multiple hormones secreted from islets will provide insights into the temporal relationships of these peptides in the future.

A Standardized Strategy for Simultaneous Quantification of Urine Metabolites to Validate Development of a Biomarker Panel Allowing Comprehensive Assessment of Dietary Exposure

SCOPE

Metabolites derived from individual foods found in human biofluids after consumption could provide objective measures of dietary intake. For comprehensive dietary assessment, quantification methods would need to manage the structurally diverse mixture of target metabolites present at wide concentration ranges.

METHODS AND RESULTS

A strategy for selection of candidate dietary exposure biomarkers is developed. An analytical method for 62 food biomarkers is validated by extensive analysis of chromatographic and ionization behavior characteristics using triple quadrupole mass spectrometry. Urine samples from two food intervention studies are used: a controlled, inpatient study (n = 19) and a free-living study where individuals (n = 15) are provided with food as a series of menu plans. As proof-of-principle, it is demonstrated that the biomarker panel could discriminate between menu plans by detecting distinctive changes in the concentration in urine of targeted metabolites. Quantitative relationships between four biomarker concentrations in urine and dietary intake are shown.

CONCLUSION

Design concepts for an analytical strategy are demonstrated, allowing simultaneous quantification of a comprehensive panel of chemically-diverse biomarkers of a wide range of commonly-consumed foods. It is proposed that integration of self-reported dietary recording tools with biomarker approaches will provide more robust assessment of dietary exposure.

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.

Proteomics of Campylobacter jejuni Growth in Deoxycholate Reveals Cj0025c as a Cystine Transport Protein Required for Wild-type Human Infection Phenotypes

Campylobacter jejuni is a major cause of food-borne gastroenteritis. Proteomics by label-based two-dimensional liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) identified proteins associated with growth in 0.1% sodium deoxycholate (DOC, a component of gut bile salts), and system-wide validation was performed by data-independent acquisition (DIA-SWATH-MS). LC-MS/MS quantified 1326 proteins (∼82% of the predicted C. jejuni proteome), of which 1104 were validated in additional biological replicates by DIA-SWATH-MS. DOC resulted in a profound proteome shift with 512 proteins showing significantly altered abundance. Induced proteins were associated with flagellar motility and antibiotic resistance; and these correlated with increased DOC motility and resistance to polymyxin B and ciprofloxacin. DOC also increased human Caco-2 cell adherence and invasion. Abundances of proteins involved in nutrient transport were altered by DOC and aligned with intracellular changes to their respective carbon sources. DOC increased intracellular levels of sulfur-containing amino acids (cysteine and methionine) and the dipeptide cystine (Cys-Cys), which also correlated with reduced resistance to oxidative stress. A DOC induced transport protein was Cj0025c, which has sequence similarity to bacterial Cys-Cys transporters. Deletion of cj0025c (Δcj0025c) resulted in proteome changes consistent with sulfur starvation, as well as attenuated invasion, reduced motility, atypical morphology, increased antimicrobial susceptibility and poor biofilm formation. Targeted metabolomics showed Δcj0025c could use known C. jejuni amino and organic acid substrates commensurate with wild-type. Medium Cys-Cys levels however, were maintained in Δcj0025c relative to wild-type. A toxic Cys-Cys mimic (selenocystine) inhibited wild-type growth, but not Δcj0025c Provision of an alternate sulfur source (2 mm thiosulfate) restored Δcj0025c motility. Our data confirm that Cj0025c is a Cys-Cys transporter that we have named TcyP consistent with the nomenclature of homologous proteins in other species.

Comprehensive Targeted Metabolomic Assay for Urine Analysis

Among all the human biological fluids used for disease biomarker discovery or clinical chemistry, urine stands out. It can be collected easily and noninvasively, it is readily available in large volumes, it is typically free from protein contamination, and it is chemically complex-reflecting a wide range of physiological states and functions. However, the comprehensive metabolomic analysis of urine has been somewhat less studied compared to blood. Indeed, most published metabolomic assays are specifically optimized for serum or plasma. In an effort to improve this situation, we have developed a comprehensive, quantitative MS-based assay for urine analysis. The assay robustly detects and quantifies 142 urinary metabolites including 28 amino acids and derivatives, 17 organic acids, 22 biogenic amines and derivatives, 40 acylcarnitines, 34 lipids, and glucose/hexose, among which 67 metabolites are absolutely quantified and 75 metabolites are semiquantified. All the analysis methods in this assay are based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) using both positive and negative-mode multiple reaction monitoring (MRM). The recovery rates of spiked urine samples at three different concentration levels, that is, low, medium and high, are in the range of 80% to 120% with satisfactory precision values of less than 20%. This targeted metabolomic assay has been successfully applied to the analysis of large numbers of human urine samples, with results closely matching those reported in the literature as well as those obtained from orthogonal analysis via NMR spectroscopy. Moreover, the assay was specifically developed in a 96-well plate format, which enables automated, high-throughput sample analysis. The assay has already been used to analyze more than 1800 urine samples in our laboratory since early 2019.

Large-scale targeted metabolomics method for metabolite profiling of human samples

Targeted metabolomics has significant advantages for quantification but suffers from reduced metabolite coverage. In this study, we developed a large-scale targeted metabolomics method and expanded its applicability to various human samples. This approach initially involved unbiased identification of metabolites in human cells, tissues and body fluids using ultra high-performance liquid chromatography (UHPLC) coupled to high-resolution Orbitrap mass spectrometry (MS). Targeted metabolomics method was established with utility of UHPLC-triple quadrupole MS, which enables targeted profiling of over 400 biologically important metabolites (e.g., amino acids, sugars, nucleotides, dipeptides, coenzymes, and fatty acids), covering 92 metabolic pathways (e.g., Krebs cycle, glycolysis, amino acids metabolism, ammonia recycling, and one-carbon metabolism). The present method displayed better sensitivity, repeatability and linearity than the Orbitrap MS-based untargeted metabolomics approach and demonstrated excellent performance in lung cancer biomarker discovery, in which 107 differential metabolites were able to discriminate between carcinoma and adjacent normal tissues, implicating the Warburg effect, alteration of redox state, and nucleotide metabolism of lung cancer. This new method is flexible and expandable and offers many advantages for metabolomics analysis, such as wide metabolite coverage, good repeatability and linearity and excellent capability in biomarker discovery, making it useful for both basic and clinical metabolic research.

Targeted toxicometabolomics of endosulfan sulfate in adult zebrafish (Danio rerio) using GC-MS/MS in multiple reaction monitoring mode

Endosulfan sulfate is a major oxidative metabolite of the chlorinated insecticide endosulfan. In this study, a targeted metabolomics approach was used to investigate the toxic mechanisms of endosulfan sulfate in adult zebrafish using the multiple reaction monitoring mode of a GC-MS/MS. The LC₅₀ of endosulfan sulfate in adult zebrafish was determined and then zebrafish were exposed to endosulfan sulfate at one-tenth the LC₅₀ (0.1LC₅₀) or the LC₅₀ for 24 and 48 h. After exposure, the fish were extracted, derivatized and analyzed by GC-MS/MS for 379 metabolites to identify 170 metabolites. Three experimental groups (control, 0.1LC₅₀ and LC₅₀) were clearly separated in PLS-DA score plots. Based on the VIP, ANOVA, and fold change results, 40 metabolites were selected as biomarkers. Metabolic pathways associated with those metabolites were identified using MetaboAnalyst 4.0 as follows: aminoacyl-tRNA biosynthesis, valine/leucine/isoleucine biosynthesis, citrate cycle, glycerolipid metabolism, and arginine/proline metabolism. Gene expression studies confirmed the activation of citrate cycle and glycerolipids metabolism. MDA levels of the exposed group significantly increased in oxidative toxicity assay tests. Such significant perturbations of important metabolites within key biochemical pathways must result in biologically hazardous effects in zebrafish.

MS2 and LC libraries for untargeted metabolomics: Enhancing method development and identification confidence

As part of the "omics" technologies in the life sciences, metabolomics is becoming increasingly important. In untargeted metabolomics, unambiguous metabolite identification and the inevitable coverage bias that comes with the selection of analytical conditions present major challenges. Reliable compound annotation is essential for translating metabolomics data into meaningful biological information. Here, we developed a fast and transferable method for generating in-house MS² libraries to improve metabolite identification. Using the new method we established an in-house MS² library that includes over 4,000 fragmentation spectra of 506 standard compounds for 6 different normalized collision energies (NCEs). Additionally, we generated a comprehensive liquid chromatography (LC) library by testing 57 different LC-MS conditions for 294 compounds. We used the library information to develop an untargeted metabolomics screen with maximum coverage of the metabolome that was successfully tested in a study of 360 human serum samples. The current work demonstrates a workflow for LC-MS/MS-based metabolomics, with enhanced metabolite identification confidence and the possibility to select suitable analysis conditions according to the specific research interest.

Foliar Supplied Boron Can Be Transported to Roots as a Boron-Sucrose Complex via Phloem in Citrus Trees

Although foliar boron (B) fertilization is regarded as an efficient way to remedy B deficiency, the mechanisms of foliar B transport from leaves to roots are still unclear. In this study, performed with 1-year-old "Newhall" navel orange (Citrus sinensis) grafted on trifoliate orange (Poncirus trifoliata) plants, we analyzed the B concentration in leaves and roots, B-sucrose complex in the phloem sap after foliar application of 10B, girdling, and/or shading treatments. Results indicated that 10B concentration was significantly increased in roots after foliar 10B treatment. On the other hand, both girdling the scion stem and shading over the plants with a black plastic net significantly reduced the B and 10B concentration in roots. LC-MS analysis revealed that foliar 10B-treated plants had higher concentration of sucrose and some sugar alcohols in the phloem sap as compared to foliar water-treated plants. Combining with the analysis in the artificial mixture of B and sucrose, a higher peak intensity of the 10B-sucrose complex was found in the phloem sap of foliar 10B-treated plants compared to the control plants. Taken together, it is concluded that foliar B can be long distance transported from leaves to roots via phloem, at least by forming a B-sucrose complex in citrus plants.

Analysis of Tryptophan Metabolites in Serum Using Wide-Isolation Strategies for UHPLC-HRMS/MS

Current targeted metabolomic workflows are limited by design and thus sacrifice crucial information from a profiling standpoint that could lead to a more fundamental understanding of the metabolic processes of interest. One drawback to performing targeted analysis on ion trapping instruments is the potential for increased variability in analysis when analytes and standards are isolated and trapped individually for fragmentation. In addition, this sequential isolation process increases the duty cycle of the mass spectrometer and reduces the number of points collected across a chromatographic peak. To address this, the use of a wide-isolation window (12 Da) to encompass the target analyte and the isotope standard within a single fragmentation window ensures that fragmentation is consistent when quantitation relies on the ratio of the target to the internal standard. Additionally, the preservation of a faster scan rate ensures that optimal representation of chromatographic peaks is preserved for the purposes of both quantitative and qualitative analyses that require peak integration for statistical analysis. The use of this flexible method is promising in the investigation of pathways that require multiple targets and are highly integrated within the system. Here, we demonstrate the application of this method in a fast ultra-high performance liquid chromatography (UHPLC) analysis to integrate wide-isolation quantitative strategies for high-resolution mass spectrometry (HRMS) combined with profiling qualitative metabolomics for the analysis of tryptophan degradation metabolites in mouse serum. Analysis of tryptophan-deficient states as compared to control in both germ-free or E. coli gut microbiota states was used to quantitate pathway-specific metabolites as well as obtain full profiling information. The quantitative and qualitative results revealed the preservation of the primary pathways of degradation in the kynurenine pathway to potentially produce primary products such as nicotinamide during stress-induced dietary states.

Emerging Insights into the Metabolic Alterations in Aging Using Metabolomics

Metabolomics is the latest 'omics' technology and systems biology science that allows for comprehensive profiling of small-molecule metabolites in biological systems at a specific time and condition. Metabolites are cellular intermediate products of metabolic reactions, which reflect the ultimate response to genomic, transcriptomic, proteomic, or environmental changes in a biological system. Aging is a complex biological process that is characterized by a gradual and progressive decline in molecular, cellular, tissue, organ, and organismal functions, and it is influenced by a combination of genetic, environmental, diet, and lifestyle factors. The precise biological mechanisms of aging remain unknown. Metabolomics has emerged as a powerful tool to characterize the organism phenotypes, identify altered metabolites, pathways, novel biomarkers in aging and disease, and offers wide clinical applications. Here, I will provide a comprehensive overview of our current knowledge on metabolomics led studies in aging with particular emphasis on studies leading to biomarker discovery. Based on the data obtained from model organisms and humans, it is evident that metabolites associated with amino acids, lipids, carbohydrate, and redox metabolism may serve as biomarkers of aging and/or longevity. Current challenges and key questions that should be addressed in the future to advance our understanding of the biological mechanisms of aging are discussed.

Daphnia magna metabolic profiling as a promising water quality parameter for the biological early warning system

The inclusion of omics data into water quality monitoring programs is being considered to help alleviate the growing threat to water resources and ecosystem services. Despite the increasing need, the biological early warning system (BEWS), the widely used real-time water quality monitoring system, does not currently incorporate omics information, despite that metabolomics is a highly sensitive indicator of organism health and stress. We examined Daphnia magna metabolomics, which is the analysis of small molecules in living D. magna, as a potential water quality parameter for incorporation in the BEWS. The concentrations of 24 metabolites were measured with changes in water quality and variation of metabolite abundances was compared within and between conditions. Age-dependent monitoring revealed that matured individuals older than 8 days are appropriate model organisms for monitoring based on their low metabolomic variation as compared to younger daphnids. Hourly monitoring of metabolic variability and regulation under ambient and starved conditions demonstrated the rapid and sensitive detection of nutritional changes. Moreover, the metabolomic dysregulation due to exposure to the pollutant propranolol was also observed. By integrating all the observations, we found that the D. magna metabolome is a sensitive and useful parameter for detecting water quality changes and how these alter the function of keystone organisms. As such, this metabolomics-based framework is applicable to BEWS and highlights the beneficial advantages of integrating biomolecular and apical endpoint observations for enhanced performance in biomonitoring programs.

Discovery of Serotransferrin Glycoforms: Novel Markers for Diagnosis of Liver Periductal Fibrosis and Prediction of Cholangiocarcinoma

Cholangiocarcinoma (CCA) caused by chronic liver fluke infection is a major public health problem in Northeast Thailand. Identification of CCA risk groups is urgently needed for the control of CCA in this region. Periductal fibrosis (PDF) induced by chronic inflammation of bile ducts is known as a pre-neoplastic lesion of CCA. We aimed to identify the serum CCA and PDF biomarkers using mass spectrometry (UPLC-ESI-QqQ) with multiple reaction mode (MRM) analysis. Here, serum levels of serotransferrin glycoforms at the glycopeptide level were measured in the sera of CCA (n = 100), PDF (n = 50), and healthy control (n = 100) subjects. The results indicated that serotransferrin peptide levels were generally the same between the control and PDF groups, whereas CCA patients had reduced levels. Moreover, 56 serotransferrin glycoforms were detected, with nine increased in CCA compared to control subjects. Among them, the serum levels of four glycoforms were increased in PDF and CCA patients compared to control subjects. In particular, highly sialylated multi-branched glycans of serotransferrin serum were significantly correlated with poor prognosis and tumor stage in CCA patients. Taken together, these glycoforms could be used as risk biomarkers and prognosis and diagnosis markers of CCA.

Simultaneous Analysis of Twelve Bile Acids by UPLC-MS and Exploration of the Processing Mechanism of Bile Arisaema by Fermentation

Ultrahigh-performance liquid chromatography (UPLC) coupled with quadrupole time-of-flight tandem mass spectrometry (Q/TOF-MS) in the MS/MS mode and UPLC coupled with triple quadrupole mass spectrometry (QqQ-MS) using the multiple reaction monitoring (MRM) mode were used to make a qualitative and quantitative analysis of twelve bile acids in Bile Arisaema. The fragmentation pathway of twelve bile acids was proposed. The quantification method showed a good linearity over a wide concentration range (R ² > 0.99), repeatability (RSD < 4.12%), stability (RSD < 4.25%), precision (RSD < 4.06%), and recovery (95.36-102.15%). Content of twelve compounds in Bile Arisaema varied significantly depending on region. Chemometric methods, hierarchical clustering analysis (HCA), and principal components analysis (PCA) were successfully used to optimize the fermentation time of the Bile Arisaema. The results suggested that the Bile Arisaema could complete fermentation in 15 days. The possible processing mechanism of Bile Arisaema promoted the transformation of conjugated bile acids into free bile acids in fermentation.

Developing a Food Exposure and Urine Sampling Strategy for Dietary Exposure Biomarker Validation in Free-Living Individuals

SCOPE

Dietary choices modulate the risk of chronic diseases and improving diet is a central component of public health strategies. Food-derived metabolites present in urine could provide objective biomarkers of dietary exposure. To assist biomarker validation, this work aims to develop a food intervention strategy mimicking a typical annual diet over a short period of time and assesses urine sampling protocols potentially suitable for future deployment of biomarker technology in free-living populations.

METHODS AND RESULTS

Six different menu plans comprehensively represent a typical UK annual diet that is split into two dietary experimental periods. Free-living adult participants (n = 15 and n = 36, respectively) are provided with all their food, as a series of menu plans, over a period of three consecutive days. Multiple spot urine samples are collected and stored at home.

CONCLUSION

A successful food exposure strategy is established following a conventional UK eating pattern, which is suitable for biomarker validation in free-living individuals. The urine sampling procedure is acceptable for volunteers and delivered samples suitable for biomarker quantification. The study design provides scope for validation of existing biomarker candidates and potentially for discovery of new biomarker leads, and should help inform the future deployment of biomarker technology for habitual dietary exposure measurement.

Addressing the pitfalls when designing intervention studies to discover and validate biomarkers of habitual dietary intake

INTRODUCTION

Dietary exposure monitoring within populations is reliant on self-reported measures such as Food Frequency Questionnaires and diet diaries. These methods often contain inaccurate information due to participant misreporting, non-compliance and bias. Urinary metabolites derived from individual foods could provide additional objective indicators of dietary exposure. For biomarker approaches to have utility it is essential that they cover a wide-range of commonly consumed foods and the methodology works in a real-world environment.

OBJECTIVES

To test that the methodology works in a real-world environment and to consider the impact of the major sources of likely variance; particularly complex meals, different food formulations, processing and cooking methods, as well as the dynamics of biomarker duration in the body.

METHODS

We designed and tested a dietary exposure biomarker discovery and validation strategy based on a food intervention study involving free-living individuals preparing meals and collecting urine samples at home. Two experimental periods were built around three consecutive day menu plans where all foods and drinks were provided (n = 15 and n = 36).

RESULTS

The experimental design was validated by confirming known consumption biomarkers in urinary samples after the first menu plan. We tested biomarker performance with different food formulations and processing methods involving meat, wholegrain, fruits and vegetables.

CONCLUSION

It was demonstrated that spot urine samples, together with robust dietary biomarkers, despite major sources of variance, could be used successfully for dietary exposure monitoring in large epidemiological studies.

Proteomics Reveals Multiple Phenotypes Associated with N-linked Glycosylation in Campylobacter jejuni

Campylobacter jejuni is a major gastrointestinal pathogen generally acquired via consumption of poorly prepared poultry. N-linked protein glycosylation encoded by the pgl gene cluster targets >80 membrane proteins and is required for both nonsymptomatic chicken colonization and full human virulence. Despite this, the biological functions of N-glycosylation remain unknown. We examined the effects of pgl gene deletion on the C. jejuni proteome using label-based liquid chromatography/tandem mass spectrometry (LC-MS/MS) and validation using data independent acquisition (DIA-SWATH-MS). We quantified 1359 proteins corresponding to ∼84% of the C. jejuni NCTC 11168 genome, and 1080 of these were validated by DIA-SWATH-MS. Deletion of the pglB oligosaccharyltransferase (ΔpglB) resulted in a significant change in abundance of 185 proteins, 137 of which were restored to their wild-type levels by reintroduction of pglB (Δaaz.batpglB::ΔpglB). Deletion of pglB was associated with significantly reduced abundances of pgl targets and increased stress-related proteins, including ClpB, GroEL, GroES, GrpE and DnaK. pglB mutants demonstrated reduced survival following temperature (4 °C and 46 °C) and osmotic (150 mm NaCl) shock and altered biofilm phenotypes compared with wild-type C. jejuni Targeted metabolomics established that pgl negative C. jejuni switched from aspartate (Asp) to proline (Pro) uptake and accumulated intracellular succinate related to proteome changes including elevated PutP/PutA (proline transport and utilization), and reduced DctA/DcuB (aspartate import and succinate export, respectively). ΔpglB chemotaxis to some substrates (Asp, glutamate, succinate and α-ketoglutarate) was reduced and associated with altered abundance of transducer-like (Tlp) proteins. Glycosylation negative C. jejuni were depleted of all respiration-associated proteins that allow the use of alternative electron acceptors under low oxygen. We demonstrate for the first time that N-glycosylation is required for a specific enzyme activity (Nap nitrate reductase) that is associated with reduced abundance of the NapAB glycoproteins. These data indicate a multifactorial role for N-glycosylation in C. jejuni physiology.