Liquid Chromatography-high Resolution Mass Spectrometry Research Articles

Discovery of novel metabolic biomarkers in blood serum for diagnosis of Alzheimer's disease.

Blood metabolites have emerged as promising candidates in the search for biomarkers for Alzheimer's disease (AD), as evidence shows that various metabolic derangements contribute to neurodegeneration in AD. We aim to identify metabolic biomarkers for AD diagnosis. We conducted an in-depth analysis of the serum metabolome of AD patients and age, sex-matched cognitively unimpaired older adults using ultra-high-performance liquid chromatography-high resolution mass spectrometry. The biomarkers associated with AD were identified using machine learning algorithms. Using the discovery dataset and support vector machine (SVM) algorithm, we identified a panel of 14 metabolites predicting AD with a 1.00 area under the curve (AUC) of receiver operating characteristic (ROC). The SVM model was tested against the verification dataset using an independent cohort and retained high predictive accuracy with a 0.97 AUC. Using the random forest (RF) algorithm, we identified a panel of 13 metabolites that predicted AD with a 0.96 AUC when tested against the verification dataset. These findings pave the way for an efficient, blood-based diagnostic test for AD, holding promise for clinical screenings and diagnostic procedures.

Analysis of the relationship between neonatal birth weight and meconium metabolites based on birth cohort metabolomics

Neonatal birth weight is a crucial indicator of intrauterine growth and development with important implications for child development and adult health. The birth weight of a newborn is closely linked to the nutrition and health of the mother during pregnancy as well as genetic factors. Therefore, assessing the metabolic status of the fetus in utero is greatly significant for understanding the mechanisms responsible for abnormal birth weight. While previous studies often analyzed the impact of maternal metabolism on fetal development using umbilical cord blood from pregnant women, such blood may not accurately reflect the actual intrauterine environment owing to the barrier function of the placenta; moreover, obtaining biological samples during the fetal period is challenging. Meconium, the first feces excreted by a newborn, provides ideal biological material for studying maternal and infant health. Metabolomics can reveal metabolic changes in living organisms by analyzing small molecules in biological samples; hence studying meconium samples using metabolomics technology is expected to reveal fetal metabolic changes during pregnancy, thereby providing new insights into fetal nutritional intake, growth, and development, as well as metabolic pathways related to birth weight. To gain a deeper understanding of the metabolic changes associated with birth weight, this study collected metabolomic data from the meconium of 484 newborns in the established Xiaogan birth cohort using an untargeted metabolomics technique based on liquid chromatography-high resolution mass spectrometry (LC-HRMS) and analyzed the association between meconium metabolites and birth weight. This cohort exhibited incidence rates of low birth weight (<2500 g) and macrosomia (>4000 g) of 3.3% and 7.2%, respectively, which were roughly equivalent to the national average. Orthogonal partial least squares discriminant analysis revealed significant differences between the meconium metabolomes of the low birth weight and macrosomic groups when compared to the normal weight group. We discovered significant distinctions between the differential metabolites of newborns of low birth weight and those of normal weight, as well as between macrosomic and normal weight newborns that point to disparate biological pathways. Newborns with low birth weight exhibited significantly lower levels of critical amino acids, such as glutamate and proline, compared to the normal weight group, which may be associated with placental dysfunction and maternal nutritional deficiencies. Conversely, the meconium of macrosomic newborns contained significantly elevated levels of hormone metabolites such as estrone that reflected the pathophysiological state associated with maternal metabolic diseases or excessive placental hormone levels. Our study suggests that the metabolomic profile of the meconium reflects the metabolic pathways and regulatory mechanisms at play during fetal growth and development, and offers potential metabolic biomarkers and directions for future in-depth research into diseases related to fetal development. However, this study was based solely on the Xiaogan birth cohort, which was limited to specific regions and populations. A multicenter, multiethnic, and multiregional study is expected to help validate the universality of our research findings.

Suspect and nontarget screening of pesticides and their transformation products in agricultural products using liquid chromatography–high-resolution mass spectrometry

Suspect and nontarget screening of pesticides and their transformation products in agricultural products using liquid chromatography–high-resolution mass spectrometry

7Method for Detection of Naturally Occurring Toxins in Human Urine Using Liquid Chromatography High Resolution Mass Spectrometry.

Natural toxins present an ongoing risk for human exposure that requires a rapid, accurate diagnosis for proper response. In this study, a qualitative liquid chromatography high resolution mass spectrometry (LC-HRMS) method was developed and validated for the detection of a large, diverse selection of natural toxins. Data-dependent acquisition was performed to identify compounds with an in-house mass spectral library of 129 hazardous toxins that originate from plants, animals, and fungi. All 129 compounds were spiked into human urine, extracted, and evaluated for spectral library matching. Of these, 92 toxins met the quality criteria and underwent validation in urine matrix based on American National Standards Institute (ANSI) guidelines. A generalized workflow for method expansion was developed and enables the rapid addition of relevant compounds to the established method. This LC-HRMS method achieves efficient detection of natural toxins in urine, and the created workflow can rapidly increase compound coverage via method expansion.

Rapid Convolutional Algorithm for the Discovery of Blueberry Honey Authenticity Markers via Nontargeted LC-MS Analysis.

Bees produce honey through the collection and transformation of nectar, whose botanical origin impacts the taste, nutritional value, and, therefore, the market price of the resulting honey. This phenomenon has led some to mislabel their honey so that it can be sold at a higher price. Metabolomics has been gaining popularity in food authentication, but rapid data mining algorithms are needed to facilitate the discovery of new authenticity markers. A nontargeted high-resolution liquid chromatography-mass spectrometry (HR/LC-MS) analysis of 262 monofloral honey samples, of which 50 were blueberry honey, was performed. Data mining methods were demonstrated for the discovery of binary single-markers (compound was only detected in blueberry honey), threshold single-markers (compound had the highest concentration in blueberry honey), and interval ratio-markers (the ratio of two compounds was within a unique interval in blueberry honey). A novel convolutional algorithm was developed for the discovery of interval ratio-markers, which trained 14× faster and achieved a 0.2 Matthews correlation coefficient (MCC) units higher classification score than existing open-source implementations. The convolutional algorithm also had classification performance similar to that of a brute-force search but trained 1521× faster. A pipeline for shortlisting candidate authenticity markers from the LC-MS spectra that may be suitable for chemical structure identification was also demonstrated and led to the identification of niacin as a blueberry honey threshold single-marker. This work demonstrates an end-to-end approach to mine the honey metabolome for novel authenticity markers and can readily be applied to other types of food and analytical chemistry instruments.

Metabolic Characterization of Vamorolone in Human Liver Microsomes: Implications for Anti-Doping.

Vamorolone, a potential alternative to conventional glucocorticoids, shows significant promise in sports medicine due to its reduced side effects and superior pharmacodynamic properties. This study aims to investigate the metabolic characteristics of this novel synthetic cyclodextrin-steroid anti-inflammatory drug and elucidate the metabolic pathways in human liver microsomes (HLMs) invitro, thereby providing a scientific basis for assessing its potential risks for athletes. All compounds are detected by liquid chromatography-high resolution mass spectrometry (LC-HRMS) and metabolite identification was performed using Compound Discoverer 3.3 software. In the HLMs model, 12 metabolites of vamorolone are successfully identified, including 10 phase I metabolites and 2 phase II metabolites. Among these, the reduction metabolite M1 exhibited the highest peak area, indicating it as one of the primary metabolic pathways. The dehydrogenated compound M2 had the second highest peak area, further elucidating the metabolic characteristics of vamorolone. This study systematically identifies the metabolite structures of vamorolone in HLMs and provide crucial data for the pharmacokinetics and biomarker research of this drug. The findings not only enhance the understanding of its metabolic mechanisms but also offer a scientific basis for evaluating its safety and efficacy in sports medicine. Meanwhile, these discoveries can contribute to better regulation and control of Vamorolone's use in competitive sports, ensuring fairness in competitions.

Evaluation of Physiological Activity of Long-Term Ripened Gouda Water Extract

This study investigated peptide changes and their bioactive functions through the long-term ripening of Gouda. Young Gouda (YG), medium Gouda (MG), and extra-sharp Gouda (EG) water extracts were prepared and functional peptides were recognized using liquid chromatography–high-resolution mass spectrometry. Two peptides with ACE-inhibitory effects (IQP and LQP) were identified in YG, while in MG and EG were identified eight (EL, IVP, VP, LPP, VIP, IPP, VPP, and VVPP) and six (EL, YL, VP, IR, YPEL, and DKIHPF) functional peptides, respectively. MG (70.26%) and EG (46.81%) showed stronger antioxidant activity than YG (25.99%) in ABTS (2,2′-azino-bis-(3-ethylbenzothiazoline-6-sulfonic) acid) inhibition, though the DPPH (2,2-diphenyl-1-picrylhydrazyl) inhibition rate decreased with ripening. The antihypertensive effect increased in MG (79.76%) and EG (94.50%) due to ACE-inhibitory peptides. Measurements of inflammatory mRNA expression levels and immunoblotting were conducted to assess the anti-inflammatory properties. MG and EG suppressed the transcription of IL-1β and IL-6 mRNA. Immunoblotting indicated that EG suppressed IκBα phosphorylation to 57%. The enhancement of bioactive function in the water-soluble part of long-term ripened Gouda cheese may have affected identified peptides as well as unknown peptides. Further studies are expected to aid in discovering these novel bioactive peptides.

A systematic evaluation of quenching, extraction and analysis procedures for metabolomics study of the mechanism of QYSLD intervention in A549 cells.

The preparation of cellular metabolomics samples and how to achieve comprehensive coverage of different polar metabolites in cell samples in the analysis pose a challenge for cellular metabolomics. In this study, we optimized a metabolomics protocol based on ultra-high-performance liquid chromatography high-resolution mass spectrometry (UPLC/HRMS) for theextraction and detection of metabolites in A549 cells and exploration of the intervention effect of Qi-Yu-San-Long decoction (QYSLD) on A549 cells. The results indicate that the lowest level of ATP leakage was observed when A549 cells were quenched under liquid nitrogen. MeOH/chloroform/H2O (1:2:1) extraction yielded more chromatographic peaks and excellent reproducibility, and the relative extraction efficiency of most target metabolites was also high. And we optimized thechromatographic separation conditions in both HILIC and RPLC modes, enabling comprehensive detection and analysis of metabolites with varying polarities. Then, we applied the optimized method to UPLC-Q-TOF/MS-based metabolomics of A549 cells to study the mechanism of QYSLD intervention in non-small cell lung cancer (NSCLC). The CCK-8, EdU staining, and cell cycle assay showed that QYSLD inhibited the proliferation of A549 cells by interfering with the cell cycle and blocking them in theG1 phase. A total of 36 differential metabolites associated with the antitumor effects of QYSLD on NSCLC were identified, mainly involving nicotinate and nicotinamide metabolism, sphingolipid metabolism, and glycerophospholipid metabolism. And western blotting confirmed that thechange in 1-methylnicotinamide levels after QYSLD intervention was associated with theinhibition of nicotinamide N-methyltransferase expression in A549 cells.

Development and application of a dual isotopic labeling method for enhanced detection and quantification of stimulants in urine samples using high-resolution mass spectrometry.

Given the critical nature of anti-doping efforts, the detection of stimulant substances is shifting from accurate qualitative analysis to precise quantitative analysis. Additionally, the use of liquid chromatography-high-resolution mass spectrometry (LC-HRMS) in detecting stimulants is becoming more widespread. However, the lack of isotope-labeled internal standards is causing increasing issues with quantitative accuracy. Furthermore, challenges such as the mass spectrometric response of small molecules and the separation of isomers present additional difficulties. We have developed a quantitative method for stimulant substances containing amine or phenol hydroxyl groups, using a dual-label derivatization system. This method offers a new perspective for analyzing and detecting low molecular weight substances, isomers, or those with poor LC-MS response, and proposes a solution to the problem of missing isotope-labeled internal standards. Methodological validation has shown that this approach has promising application potential.

Administration Route Differentiation of Altrenogest via the Metabolomic LC-HRMS Analysis of Equine Urine

Altrenogest, also known as allyltrenbolone, is a synthetic form of progesterone used therapeutically to suppress unwanted symptoms of estrus in female horses. Altrenogest affects the system by decreasing levels of endogenous gonadotrophin and luteinizing and follicle-stimulating hormones, which in turn decreases estrogen and mimics the increase of progesterone production. This results in more manageable mares for training and competition alongside male horses while improving the workplace safety of riders and handlers. However, when altrenogest is administered, prohibited steroid impurities such as trendione, trenbolone, and epitrenbolone can be detected. It has been assumed that greater concentrations of these steroid impurities are present in injectable preparations and, therefore, pose a greater risk of causing anabolic effects when administered. For this reason, and due to the necessity of this therapeutic substance for the safety of thoroughbred racing participants, a metabolomic approach investigating the differentiation of two main administration routes was conducted. Liquid chromatography high-resolution mass spectrometry analysis of equine urine samples found five sulfated compounds, estrone sulfate, testosterone sulfate, 2-methoxyestradiol sulfate, pregnenolone sulfate, and cortisol sulfate, with the potential to differentiate between oral and intramuscularly administered altrenogest using a random forest classification model. The best model results, comparing two horses’ administration normalized peak area datasets, gave an AUC score of 0.965 with a confidence level of 95% (between 0.931 and 0.995). Identifications of these compounds were confirmed with assistance from the Shimadzu Insight Explore Assign feature, together with MS/MS spectrum and retention time matching of purchased and synthesized reference standards. This study proposes a new potential application for metabolomic multi-tool workflows and machine learning models in a forensic toxicological context.

Chemical Profiling of Polar Lipids and the Polyphenolic Fraction of Commercial Italian Phaseolus Seeds by UHPLC-HRMS and Biological Evaluation

The common bean (Phaseolus vulgaris L.) is one of the oldest food crops in the world. In this study, the ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC-MS/MS) technique was used to characterize the polar lipid composition and polyphenolic fraction of five bean varieties commonly consumed in Italy: Cannellino (PVCA), Controne (PVCO), Borlotti (PVBO), Stregoni (PVST), and Vellutina (PVVE). Lipid content represents a minor fraction of the whole metabolome in dry beans, and little is known about their polar lipids, which could be potentially bioactive components. Thirty-three compounds were detected through UHPLC-MS/MS, including oxylipins, phospholipids, N-acyl glycerolipids, and several fatty acids. The dichloromethane extracts were subjected to principal component analysis (PCA), with the results showing greater differentiation for the Borlotti variety. Moreover, 27 components belonging to different polyphenol classes, such as phenolic acids, flavonoids, catechins, anthocyanins and their glycosides, and some saponins, were identified in the hydroalcoholic seed extracts. In addition, the mineral content of the beans was determined. Considering the high number of compounds in the five apolar seed extracts, all samples were examined to determine their in vitro inhibitory activity against the enzyme cyclooxygenase-2 (COX-2), which is inducible in inflammatory cells and mediates inflammatory responses. Only PVCO showed the best inhibition of the COX-2 enzyme with an IC50 = 31.15 ± 2.16 µg/mL. In light of these results, the potential anti-inflammatory properties of PVCO were evaluated in the LPS-stimulated murine macrophage cell line J774A.1. Herein, we demonstrate, for the first time, that PVCO at 30 µg/mL can significantly reduce the release of TNF-α, with a less significant anti-inflammatory effect being observed in terms of IL-6 release.

Identification and Assessment of Secondary Metabolites from Three Fungal Endophytes of Solanum mauritianum Against Public Health Pathogens

Fungal endophytes, symbiotic microorganisms residing within plants, are renowned for producing bioactive secondary metabolites with diverse beneficial properties. We investigated the antimicrobial potential of fungal endophytes isolated from Solanum mauritianum, an invasive weed, against clinically significant bacterial pathogens. Selected fungal endophytes (Penicillium chrysogenum, Fusarium sp., and Paracamarosporium leucadendri) were isolated from the plant’s leaves and fruits. Their crude extracts were tested against various referenced strains, such as Mycobacterium species (M. smegmatis ATCC 607 and M. bovis ATCC 27290), Staphylococcus aureus ATCC 6571, Bacillus subtilis ATCC 11774, Klebsiella species (K. pneumoniae ATCC 10031 and K. oxytoca ATCC 8724), Escherichia coli ATCC 10536, and Pseudomonas aeruginosa ATCC 10145, using the Kirby-Bauer disk diffusion method. Resazurin Microtiter Assay was used for the determination of the minimum inhibitory concentration. The chemical nature of the secondary metabolites in the crude extracts produced by fungal endophytes was evaluated using high-resolution liquid chromatography–mass spectrometry (LC-MS) using water and acetonitrile gradient. Liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS/MS) was employed for untargeted metabolomics. LC-QTOF-MS/MS identified 63 bioactive compounds across the three endophytes. P. chrysogenum had the highest activity against S. aureus and M. smegmatis (1.15 mg/mL and 0.02 mg/mL, respectively), while P. leucadendri demonstrated moderate activity against M. smegmatis (2.91 mg/mL) and E. coli (1.16 mg/mL). Fusarium sp. exhibited the broadest spectrum of antibacterial activity, with MIC values ranging from 0.03 mg/mL (B. subtilis) to 10 mg/mL (M. smegmatis). P. leucadendri produced 29 metabolites, Fusarium sp. had 23 identified metabolites, and a total of 11 metabolites were identified from P. chrysogenum. The fruits of the plant, accounting for 60%, appeared to be the most abundant in the endophyte diversity when compared to the stems and leaves. This study highlights the potential of fungal endophytes from S. mauritianum as a source of novel bioactive compounds, particularly against multidrug-resistant pathogens, contributing to the ongoing efforts to combat antimicrobial resistance.

Dachaihu decoction alleviates septic intestinal epithelial barrier disruption via PI3K/AKT pathway based on transcriptomics and network pharmacology

Ethnopharmacological relevanceDachaihu decoction (DCH) is a famous and ancient TCM formula, extensively utilized for over 1800 years in treating gastrointestinal and inflammatory conditions. Our previous study showed that DCH ameliorated intestinal damage and modulated the gut microflora in septic rats. However, the material basis for these effects and the underlying mechanism of action remains ill-defined. We aimed to explore the pharmaceutical ingredients of DCH and its mechanism in mitigating sepsis-induced intestinal epithelial barrier disruption (IEBD). Materials and methodsUltra-high-performance liquid chromatography–high-resolution mass spectrometry (UHPLC–HRMS) was used to identify DCH composition. A septic rat model and Caco-2 cells were employed to investigate DCH's effects on IEBD. Transcriptomics and network pharmacology were used to predict potential mechanisms, which were further validated by molecular docking and dynamics simulations. The Modified Murine Sepsis Score (mMSS) and histological assessments were performed. Serum fluorescence intensity of FD4 and the expression of Occludin were evaluated to assess intestinal barrier integrity. And p-PI3K P85, PI3K P85, p-AKT, AKT, Bax and Bcl-2 were determined by Western blot. Cell viability was determined using CCK-8 assay, IL-6 and TNF-α by ELISA and quantitative Real-time PCR (RT-qPCR). The integrity and permeability of single layer of Caco-2 cells were assessed via transepithelial resistance (TEER), alkaline phosphatase (ALP) activity and FD4 permeability. ResultsUHPLC–HRMS identified 180 compounds in DCH. DCH significantly reduced mMSS, improved pathological conditions in the ileum, decreased FD4 serum fluorescence, and enhanced Occludin expression. Transcriptomic and network pharmacology analyses identified the PI3K/AKT pathway as a critical mechanism of action. Molecular docking and dynamics simulations confirmed strong binding of DCH components to PIK3R1. DCH upregulated p-PI3K and p-AKT in ileum tissue of septic rats. DCH improved cell viability, decreased IL-6 and TNF-α, promoted cell survival and Occludin level, and upregulated p-PI3K and p-AKT in LPS-stimulated Caco-2 cells. DCH also maintained TEER, ALP activity and decreased FD4 permeability and these effects were reversed by PI3K inhibitor, LY294002. DCH also downregulated Bax expression and increased Bcl-2 levels in both septic rats and LPS-stimulated Caco-2 cells. ConclusionDCH ameliorates sepsis-induced IEBD via PI3K/AKT pathway activation, offering a novel therapeutic perspective for sepsis-related intestinal dysfunction.

Unrevealing the Halyomorpha halys Damage Fingerprint on Hazelnut Metabolome by Multiomic Platforms and AI-Aided Strategies.

The brown marmorated stink bug (Halyomorpha halys) poses a significant threat to hazelnut crops by affecting kernel development and causing quality defects, reducing the market value. While previous studies have identified bitter-tasting compounds in affected kernels, the impact of stink bug feeding on the hazelnut metabolome, particularly concerning aroma precursors, remains underexplored. This study aims to map the nonvolatile metabolome and volatilome of hazelnut samples obtained by caging H. halys on different cultivars in two locations to identify markers for diagnosing stink bug damage. Using a multiomic approach involving headspace solid-phase microextraction (HS-SPME), comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOF MS), and liquid chromatography-high-resolution mass spectrometry (LC-HRMS), both raw and roasted hazelnuts are analyzed, with artificial intelligence (AI) and machine learning tools employed to explore data correlations. The study finds that the hazelnut metabolome and volatilome exhibit high chemical complexity with significant classes of compounds such as aldehydes, ketones, alcohols, and terpenes identified in both raw and roasted hazelnuts. Multivariate analysis indicates that the orchard location significantly impacts the metabolome, followed by damage type, with cultivar differences being less pronounced. Partial least-squares discriminant analysis (PLS-DA) models achieve high predictive accuracy for orchard location (99%) and damage type (≈80%), with the roasted volatilome showing the highest predictive accuracy. Correlation matrices reveal significant relationships between raw hazelnut metabolites and aroma compounds in roasted samples, suggesting potential markers for stink bug damage that could guide the quality assessment and mitigation strategies. Data fusion techniques further enhance classification performance, particularly in predicting damage type, underscoring the potential of integrating multiple data sets for comprehensive quality assessment.

Virgin Olive oil Authenticity Assays in a Single Run Using Two-Dimensional Liquid Chromatography-High Resolution Mass Spectrometry.

Sterols and triterpenic alcohol analyses are one of the officially established parameters for assessing the authenticity of virgin olive oil (VOO). Most of the applications described for sterol analysis, including the official method, only allow the determination of the total sterol content but not its distribution in free or esterified form. This work proposes a two-dimensional liquid chromatography/high-resolution mass spectrometry (2D-LC-HRMS) method for the simultaneous analysis of triterpenic alcohols, free sterols and steryl esters. A reversed phase liquid chromatography (RPLC)-RPLC coupling was performed through a multiple heart-cutting interface equipped with an active solvent modulation (ASM) valve. Additionally, a selection valve was coupled to the 2D-LC system, allowing the simultaneous data acquisition from both dimensions in a single analysis. A simplified sample treatment based on solid phase extraction was also proposed to avoid tedious steps such as saponification or derivatization before gas chromatography analysis. To evaluate the content of these compounds in different olive oil categories, the proposed 2D-LC-HRMS system was applied to a set of samples from different commercial olive oil categories (extra virgin olive oil, virgin olive oil, olive oil, pomace olive oil) and sunflower oil. The results revealed significant differences in the distribution of free and esterified sterols of the analyzed samples, highlighting the free/esterified sterol ratio as a powerful tool to unveil olive oil fraud practices and fat manipulation.

Lipid and Corticosteroid Biomarkers Under the Influence of Bisphosphonates.

Detecting the use of bisphosphonates (BPs) in equine athletes is of interest to regulators and laboratories due to the threat to welfare issues for the potential to provide analgesic effects and manipulating bone structure. The detection of BPs in biological matrices is challenging due to erratic biological elimination and inconsistent analytical recoveries. Therefore, complementary approaches are needed to provide evidence of their misuse in racehorses. BPs have two sub-classes: nitrogenous and non-nitrogenous. This study investigated plasma elimination following administration of one example from each sub-class, together with changes in endogenous eicosanoid and corticosteroids. Zoledronic acid (ZA) and tiludronic acid (TA) were administered by IV infusion to 8 thoroughbred horses with an 11-month washout period between each administration. Sample preparation for quantification of BPs by liquid chromatography-tandem mass spectrometry (LC-MS/MS) utilised a two-step solid phase extraction (SPE) consisting of polymeric reversed-phase followed by weak anion exchange prior to derivatisation using trimethyl orthoacetate. Endogenous biomarkers were analysed after protein precipitation and SPE with polymeric reversed-phase prior to liquid chromatography-high resolution mass spectrometry (LC-HRMS) using data independent acquisition. The LC-MS/MS analysis showed ZA was undetectable after 8 h post-administration while TA was detected up to the final collection point of 28 days post-administration. The LC-HRMS analysis utilised targeted (i.e., prior inclusion list of compounds) approaches to monitor level changes of eicosanoid and corticosteroid biomarkers. Putative biomarkers were identified and now subject to validation for translation into routine sample analysis for improved retrospectivity to detecting BP misuse in equine plasma.

Development and Validation of an Analytical Method to Identify and Quantitate Novel Modafinil Analogs in Products Marketed as Dietary Supplements

Modafinil (brand name Provigil®) is a Schedule IV (U.S.) drug used for the treatment of narcolepsy and sleep disorders. It is also known to be used recreationally. Analogs of modafinil, including adrafinil, remain unapproved and/or unscheduled. The lack of scheduling has made these analogs a popular target for recreational use and inclusion in dietary supplements. However, the use of controlled substances (or their analogs) without the care of a physician presents a public health risk. Preliminary nontargeted analyses in our laboratory revealed the presence of adrafinil in several dietary supplements, highlighting the need for an analytical method to identify modafinil analogs in supplements. A liquid chromatography high resolution mass spectrometry (LC-HRMS) method was developed and validated to quantitate modafinil, plus four novel unscheduled modafinil analogs: adrafinil, CRL-40,940, CRL-40,941, and N-methyl-4,4-difluoromodafinil. This method was then applied to four samples of products marketed as dietary supplements collected via undercover purchase. These four products were marketed as nootropics or cognitive enhancers and labeled to contain adrafinil. Adrafinil was found in all four samples. The identification of modafinil analogs in this context is important so that consumers are not, knowingly or unknowingly, consuming these active pharmaceutical ingredients in products marketed as dietary supplements.

Untargeted Metabolomics Analysis Using FTIR and LC-HRMS for Differentiating Sonchus arvensis Plant Parts and Evaluating Their Biological Activity.

The composition and concentration of compounds in medicinal plants vary based on several factors, including the specific part of the plant being used. These variations in composition and concentration lead to differences in biological activity levels. In this study, we aimed to assess the phytochemical profile of Sonchus arvensis and to investigate the biological activity of different plant parts (roots, stems, and leaves) using ametabolomics approach. We analyzed the plant extracts for total phenolic and flavonoid levels, antioxidant activity, and xanthine oxidase inhibition. We also conducted metabolite profiling using Fourier-transform infrared spectroscopy and liquid chromatography-high resolution mass spectrometry. A total of 17 metabolites were identified (13 in leaves, 10 in stems, and 9 in roots). Principal component analysis effectively differentiated S. arvensis extracts based on differences in plant parts. These findings indicate that the quantity and diversity of metabolites present in the roots, stems, and leaves influence the biological activity of S. arvensis.

Comparative analysis of features extraction protocols for LC-HRMS untargeted metabolomics in mountain cheese ‘identitation’

This study presents a comprehensive metabolomic analysis of Parmigiano Reggiano samples to differentiate between those designated as Mountain Quality Certification (QC) and conventional Protected Designation of Origin (PDO). Despite following the same production protocol, these cheese varieties differ in the cows’ feeding regimes and milk stable locations, with mountain-certified samples adhering to specific requirements regarding milk origin and feed composition. An untargeted approach with Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS) was proposed to characterize the cheese metabolome. High-resolution LC-MS data can generate gigabyte-sized files, making data compression essential for manageable multivariate analysis and noise reduction. This study employs the Region of Interest-Multivariate Curve Resolution (ROI-MCR) protocol to achieve effective data compression and chromatographic resolution, thereby extracting the most informative features. This method was compared with a classical approach for feature extraction from chromatographic data, namely Compound Discoverer (CD) software. The features extracted by both methods were analysed through Principal Component Analysis (PCA) and ASCA (ANOVA Simultaneous Component Analysis). The comparison of ROI-MCR and CD approaches demonstrated that while both methods yielded similar overall conclusions, ROI-MCR provided a more streamlined and manageable dataset, facilitating easier interpretation of the metabolic differences. Both approaches indicated that amino acids, fatty acids, and bacterial activity-related compounds played significant roles in distinguishing between the two sample types.

Clonality Determination by Detecting Unmodified Monoclonal Serum Free Light Chains Using On-Probe Extraction Coupled with Liquid Chromatography-High-Resolution Mass Spectrometry.

Serum free light chains (FLCs) are an essential clinical biomarker for the diagnosis and monitoring of patients with plasma cell neoplasms. The current widely used immunoassay methods quantify total serum FLCs, which include monoclonal FLCs as well as FLCs in the polyclonal background. Patients with chronic diseases, inflammatory disorders, or renal dysfunction can have elevated total FLCs that lead to ambiguous results. These patients may benefit from a direct measurement of monoclonal FLCs. The purpose of this study was to develop a method that couples on-probe extraction (OPEX) with liquid chromatography-high-resolution mass spectrometry (LC-HR-MS), abbreviated to OPEX-MS, to directly determine the clonality of FLCs. OPEX immunocapture was performed using microprobes loaded with anti-kappa or anti-lambda light chain antibodies. Captured proteins were separated by reversed-phase LC and analyzed by HR-MS. Four cohorts of samples from unique patients were tested based on immunoassay FLC results. The LC-HR-MS analysis in the OPEX-MS method provides both a unique retention time along with deconvoluted masses of FLC monomers and dimers for each clone. The study found that 16 out of 49 (33%) kappa FLC elevated samples as well as 83 out of 100 (83%) dual kappa and lambda FLC elevated samples did not have monoclonal FLCs, which is consistent with the knowledge that there is often no clonal population in samples with mildly elevated FLC immunoassay results. The OPEX-MS method can serve as a complementary approach to directly determine clonality in patients with difficult-to-interpret FLC immunoassay results.