Liquid Chromatography-high Resolution Mass Spectrometry Research Articles

A Simple, Cost-Effective, and Sustainable Method for Extraction and Quantification of Chebulagic Acid from Terminalia bellirica (Gaertn.) Roxb. Using RP-HPLC: A Comparative Study

Chebulagic acid (CA), a hydrolysable tannin found in Terminalia bellirica (T. bellirica) (Gaertn.) Roxb., has gained prominence as a significant component in Ayurvedic medicine. This study optimized methods for determining CA in T. bellirica, focusing on fruit samples and prioritizing simplicity, cost-effectiveness, less time duration, and sustainability. We evaluated various extraction techniques, including continuous shaking, microwave assisted extraction, and Soxhlet extraction using green solvents. Quantification of CA was conducted using Reverse Phase High-Performance Liquid Chromatography (RP-HPLC), with confirmation achieved via UV-Visible (UV-Vis) spectroscopy, Thin Layer Chromatography (TLC), and Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS). Results indicated that Soxhlet and microwave significant extraction methods, particularly with ethanol as the green solvent. Soxhlet extraction yielded 49607.73±18.82 μg/mL for fresh fruit and 42952.53±19.09 μg/mL for dry fruit, while microwave extraction resulted in 43627.56±113.48 μg/mL for fresh fruit and 40466.33±130.90 μg/mL for dry fruit respectively. These findings conclude that ethanol as a green solvent in Soxhlet or microwave assisted extraction emerge as most efficient, straightforward, and effective approach for detecting and screening CA in T. bellirica samples.

Effect-directed analysis of genotoxicants in food packaging based on HPTLC fractionation, bioassays, and toxicity prediction with machine learning.

Many chemicals in food packaging can leach as complex mixtures to food, potentially including substances hazardous to consumer health. Detecting and identifying all of the leachable chemicals are impractical with current analytical instrumentation and data processing methods. Therefore, our work aims to expand the analytical toolset for prioritizing and identifying chemical hazards in food packaging. We used a high-performance thin-layer chromatography (HPTLC)-based bioassay to detect genotoxic fractions in paperboard packaging. These fractions were then processed with non-targeted liquid chromatography high-resolution mass spectrometry (LC-HRMS/MS) and machine learning-based toxicity prediction (MLinvitroTox). The HPTLC bioassay detected four genotoxic zones in extracts of the paperboard. One-dimensional HPTLC separation and targeted fraction collection reduced the number of chemical features extracted from paperboard and detected with LC-HRMS by at least 98% (from 1695-2693 to 14-50). The entire process was successful for spiked genotoxic chemicals, which were correctly prioritized in the fractionation and non-target analysis workflow. The native chemical with the strongest genotoxicity signal was identified with a suspect list as 5-chloro-2-methyl-4-isothiazolin-3-one and confirmed with LC-HRMS/MS and HPTLC bioassay. Toward identification of the remaining unknown genotoxicants, two-dimensional HPTLC further reduced the number of chemical features. Genotoxicity predictions with MLinvitroTox based on molecular fingerprints of the unknown signals derived from their MS2 fragmentation spectra helped prioritize two chemical features and suggested candidate structures. This work demonstrates strategies for using HPTLC, HRMS, and toxicity prediction to help identify toxicants in food packaging.

Elucidating the Metabolism of Chiral PCB95 in Wildtype and Transgenic Mouse Models with Altered Cytochrome P450 Enzymes Using Intestinal Content Screening.

Polychlorinated biphenyls (PCBs), such as 2,2',3,5',6-pentachlorobiphenyl (PCB95), are persistent organic pollutants associated with adverse health outcomes, including developmental neurotoxicity. PCB95 is a chiral neurotoxic PCB congener atropselectively metabolized to potentially neurotoxic metabolites in vivo. However, the metabolic pathways of most PCB congeners, including PCB95, remain unknown. To address this knowledge gap, we analyzed the intestinal contents of mice exposed to PCB95 to elucidate the PCB95 metabolism pathway and assess if genetic manipulation of hepatic drug-metabolizing enzymes affects PCB95 metabolism. Our study exposed male and female wildtype (WT), Cyp2abfgs-null (KO), and CYP2A6-transgenic/Cyp2abfgs-null (KI) mice orally to 1.0 mg/kg body weight of PCB95. Intestinal content was collected 24 h after PCB administration. aS-PCB95 was enriched in all intestinal content samples, irrespective of sex and genotype. Gas chromatography-tandem mass spectrometry (GC-MS/MS) analyses identified 5 mono- (OH-PCB95) and 4 dihydroxylated PCB (diOH-PCB95) metabolites. Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) identified 15 polar hydroxylated, methoxylated, and sulfated PCB95 metabolites, including 3 dechlorinated metabolites. A sex difference in the relative OH-PCB95 levels was observed only for KO in the LC-HRMS analysis. Genotype-dependent differences were observed for female, but not male, mice, with OH-PCB95 levels in female KO (FKO) mice tending to be lower than those in female WT (FWT) and KI (FKI) mice. Based on the GC-MS/MS analysis, these differences are due to the unknown PCB95 metabolites, X1-95 and Y1-95. These findings demonstrate that combining GC-MS/MS analyses and LC-HRMS subject screening of the intestinal content of PCB95-exposed mice can significantly advance our understanding of PCB95 metabolism in vivo.

Characterization of poly(A) and poly(T) tail lengths in plasmid DNA by liquid chromatography high-resolution mass spectrometry.

Rapid advances in messenger RNA (mRNA) technology necessitate effective analytical methods. This study describes the development of a novel in vitro method using ion-pair reversed-phase liquid chromatography coupled to high-resolution mass spectrometry (IP-RP-LC-HRMS) for assessing the poly(A) and complementary poly(T) tail lengths directly into the DNA template used to manufacture mRNA. Briefly, after the validation of poly(A) tail length in the plasmid by Sanger sequencing, double-stranded DNA fragments containing these tails in the plasmid of interest were amplified by thepolymerase chain reaction (PCR), purified on silica column, and digested with restriction enzymes ClaI and HindIII. Gel or capillary electrophoresis confirmed sample quality and enzymatic digestion efficiency. Subsequently, poly(A) and complementary poly(T) tails were extracted and analyzed by LC-MS to determine their length and heterogeneity at a single-nucleotide resolution. Three DNA templates containing poly(A) tail lengths of 60A-G, 95A, or 108A were studied. LC-MS results correlated well with Sanger sequencing, identifying major populations of 60A-G, 95A, or 108A. Surprisingly, unlike Sanger sequencing, LC-MS analysis revealed minor poly(A) populations with lengths longer or shorter than the theoretically encoded poly(A) tail length. This finding could be explained by (i) the slippage of bacterial DNA polymerase I during plasmid replication in bacterial culture, which occurs on repeat mononucleotide sequences, or (ii) the slippage of Q5® High-Fidelity DNA Polymerase during PCR amplification. In conclusion, the method is easy, rapid, and accurate and could replace Sanger sequencing to assess the poly(A) and complementary poly(T) tail lengths in plasmid DNA.

Sumac liposomes/mesenchymal stem cells fight methotrexate-induced nephrotoxicity in rats via regulating Nrf-2/Keap-1/HO-1 and apoptotic signaling pathways.

Methotrexate (MTX) is commonly employed in cancer treatment, but its clinical use is restricted due to the MTX-associated renal injury. This study investigates the combined potential of Rhus coriaria (sumac) and bone marrow mesenchymal stem cells (BMMSCs) against MTX-induced nephrotoxicity in rats. The high-resolution-liquid chromatography-mass spectrometry (HR-LC-MS) of sumac extract tentatively identified 22 phytochemicals, mostly flavonoids, anthocyanins, and steroids. Preparation of sumac liposomes attained a suitable particle size of 3041.33 ± 339.42 nm, a polydispersity index of 0.208 ± 0.086, and an encapsulation efficiency of 84.92 ± 3.47%. Rat BMMSCs were injected into the tail vein of the experimental rats (0.5 × 106 cells, intravenous [iv]) of seven treated groups. The experimental design relies on either pre- or posttreatment of rats with intraperitoneal (IP) sumac liposomes (SL) (200 mg/kg, daily with a dose of MTX (300 µg/kg/14 days). The histopathological examination and serum analysis of creatinine and urea revealed good results, besides regulating levels of oxidative stress and inflammatory markers. Additionally, a significant decrease in the gene expression levels of B-Cell Lymphoma 2 (Bcl-2) and caspases-3 and -9, a remarkable increase in the Bcl-2 Associated X-Protein (Bax), nuclear factor erythroid 2-related factor 2 (Nrf2), and heme-oxygenase 1 expression, and a downregulation of Kelch-like ECH-associated protein 1 (Keap1). Collectively, the coadministration of SL with BMMSCs might be a potent therapeutic strategy for attenuation of MTX-induced renal damage. The network pharmacology analysis identified the involved key hub genes as KEAP1, Nrf2, HMOX1, mitogen-activated protein kinase (MAPK1), nuclear factor-kappa B (NF-KB), interleukin-1 beta (IL-1B), and caspase-3. The docking results revealed strong binding affinities of 7-O-methyl-cyanidin-3-O-(2″-galloyl)-galactoside with Keap1 and amentoflavone with MAPK. These insights pave the way for future experimental validation and therapeutic development of sumac-based phytoconstituents against MTX-induced nephrotoxicity.

Fast DPPH antioxidant activity analysis by UHPLC-HRMS combined with chemometrics of tempeh during food processing.

Tempeh is an antioxidant-rich soybean fermentation product from Java, Indonesia. Cooking methods have an impact on the nutritional value and bioactivity of food. This study aims to investigate how the cooking process affects the metabolites and antioxidant activity in tempeh using ultra-high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). A nontargeted UHPLC-HRMS metabolomics and chemometric analysis were used to evaluate metabolite profiles and antioxidant activity changes because of food processing in tempeh. The score plots of tempeh produced by boiling and frying methods displayed a distinct separation from raw tempeh, revealing that the cooking process altered the metabolite composition of tempeh. Due to processing, L-glutamic acid, L-pyroglutamic acid, DL-glutamine, and D-( +)-proline became the most affected metabolites on tempeh. There were 70 metabolites that showed antioxidant activity using the DPPH assay; 23 metabolites significantly differ from DPPH and control for antioxidant activity for all processing tempeh. Metabolites with significantly different antioxidant activity in raw and processed tempeh were dominated by flavonoids, vitamin E, and bioactive lipids. The DPPH antioxidant assay using UHPLC-HRMS is promising as a fast antioxidant assay by simplifying the conventional DPPH antioxidant assay. Further, it can be used to identify the name of metabolites responsible for its antioxidant activity.

Fatty Acid Profiling in Greek Wines by Liquid Chromatography–High-Resolution Mass Spectrometry (LC-HRMS)

In recent years, the interest in lipids present in wines has increased, because these natural components, even at low or very low concentrations, play an important role in wine evolution and quality and contribute substantially to the taste and mouthfeel of wines. Herein, we present a liquid chromatography–high-resolution mass spectrometry (LC-HRMS) method for the profiling of free fatty acids (FFAs) in wines. The method is fast and allows the simultaneous determination of twenty-seven saturated and unsaturated FFAs in wine samples, avoiding any prior derivatization step. After validation, a variety of white and rose commercial wine samples from the Greek market, either sparkling or non-sparkling, were analyzed by the present method. The majority of wine FFAs are saturated long aliphatic, in particular palmitic (C16:0) and stearic (C18:0) acids, followed by myristic (C14:0) and pentadecanoic (C15:0) acids, while oleic (C18:1), palmitoleic (C16:1) and linoleic (C18:2) acids were quantified among the unsaturated FAs. The medium-chain C6:0 and the unsaturated C16:1 and C18:2 acids were found at higher concentrations in rose wines compared to white.

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

Intensive agricultural production involves the extensive use of chemicals, leading to the presence of pesticides and their transformation products (TPs) in agricultural products. Our study developed a high-coverage method to map the occurrence of pesticides and their transformation products in agricultural products using liquid chromatography–high-resolution mass spectrometry (LC-HRMS). Initially, a suspect list of 1265 pesticides was compiled based on in-house standards and online databases to identify potential parent pesticides. Besides, the reported and predicted TPs, as well as the multi-class characteristic fragment ions (CFIs) of pesticides, were summarized. Subsequently, nontarget features were identified by matching with 10226 TPs and 39-classes of CFIs. Both known and unknown parent pesticides and their TPs can be identified via suspect and nontarget screening procedures. Ultimately, the proposed method was applied to strawberry samples to demonstrate its effectiveness. We identified 67 parent pesticides and 57 TPs in 107 samples, with the majority at low concentrations, and preliminary traceability suggesting they may migrate from soil. The findings suggest that our method can enable suspect and nontarget screening of pesticides and their TPs, and it is also applicable to other food matrices. This method may facilitate regulatory agencies in strengthening the supervision of unknown risk substances or TPs, thereby comprehensively safeguarding consumer health.

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.

An integrated approach to evaluating water contaminants and evaporation in agricultural water distribution systems

This study presents an innovative approach for assessing water quality in agricultural irrigation networks, integrating stable isotope analysis, in vivo zebrafish screening, and comprehensive chemical profiling to investigate the occurrence, transformation, and potential toxicity of organic contaminants. Stable isotope analysis was used to measure evaporation as a proxy for water residence time in the canal, while liquid chromatography-high resolution mass spectrometry (LC-HRMS) identified a range of organic compounds in water samples collected from both the irrigation canal and its source river. Results indicated a reduction in contaminant levels in the canal compared to the river, with the most significant evaporation and concentration changes occurring at a holding reservoir, suggesting that managing residence time could help reduce water loss in arid irrigation networks. The data also highlighted how evaporation, particularly during the dry, hot season, influences contaminant dynamics. Hierarchical clustering of LC-HRMS results showed notable differences between the chemical profiles of canal and river samples, indicating that irrigation systems may contribute to the degradation or removal of certain compounds. Over 60 % of detected compounds were naturally derived, with anthropogenic contaminants like pesticides and personal care products further highlighting human impacts. Priority contaminants, including DEET and 2-naphthalene sulfonic acid, likely originated from urban activities upstream. Initial screening using zebrafish embryos showed bioactivity across sites, confirming the presence of contaminants needing further examination. Correlation analysis linked natural compounds to evaporation rates, suggesting that flora and fauna play significant roles in the chemical makeup of canal water. Overall, this approach provides a comprehensive framework for monitoring irrigation water, offering insights into contaminant behavior and supporting the development of standardized methods for assessing chemical fate and ecological risks in agricultural irrigation systems.

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.

Identification and differentiation of somapacitan, a long-acting growth hormone derivative, and recombinant human growth hormone in equine plasma by LC-HRMS for doping control purpose

Somapacitan, a long-acting growth hormone derivative, and recombinant human growth hormone (rhGH) are protein-based drugs generally used to treat growth disorders and GH deficiency in humans. Due to their potential to enhance the horse performance, the use of these drugs is prohibited at-all-times by the International Federation of Horseracing Authorities for horseracing and the Fédération Equestre Internationale for equestrian sports. In this study, we developed a test method for the identification and differentiation of somapacitan and rhGH in equine plasma by using liquid chromatography high-resolution mass spectrometry (LC–HRMS). The method involved C4 solid-phase extraction after ammonium sulfate precipitation, followed by chloroform/methanol precipitation, trypsin digestion, and analysis by LC-HRMS. The discriminative identification of somapacitan and rhGH was successfully achieved through the detection of their respective unique T10 peptide fragments. Noteworthy, the T10 peptide fragment of somapacitan was detected with its side chain structure remaining intact. The limit of identification (confirmation) (LOI) was determined to be 5 ng/mL for somapacitan and 2 ng/mL for rhGH in equine plasma, while the limit of detection (LOD) was 5 ng/mL for somapacitan and 1 ng/mL for rhGH. Furthermore, using the peptide fragments T1, T8, and T9 (which are shared between somapacitan and rhGH), the presence of somapacitan in equine plasma could be confirmed with higher sensitivity, achieving down to LOIs of 2 ng/mL and LODs of 1 ng/mL. The method was validated with respect to specificity, identification capability, robustness, precision, and reproducibility, paving the way for the analysis of post-administration samples from our already planned administration trials and future potential positive cases.

Integrated non-targeted metabolomics and lipidomics reveal mechanisms of fluorotelomer sulfonates-induced toxicity in human hepatocytes

Fluorotelomer sulfonates (FTSs) are widely used as novel substitutes for perfluorooctane sulfonate, inevitably leading to FTSs accumulation in various environmental media and subsequent exposure to humans. This accumulation eventually poses environmental hazards and health risks. However, their toxicity mechanisms remain unclear. Herein, the mechanisms of two FTSs (6:2 and 8:2 FTS) induced toxicity in human hepatocellular carcinoma cells were investigated via non-targeted metabolomics and lipidomics based on liquid chromatography–high resolution mass spectrometry. Our results revealed that amino acid, purine, acylcarnitine and lipid levels were significantly perturbed by 6:2 and 8:2 FTS exposure. The effects of 8:2 FTS exposure were largely characterized by up-regulation of pyruvate metabolism pathway and down-regulation of purine metabolism pathway, whereas the opposite trends were induced by 6:2 FTS exposure. The opposite trends were confirmed by the mRNA expression levels of four key genes (glyoxalase 1, adenylosuccinate lyase, inosine monophosphate dehydrogenase 1 (IMPDH1) and IMPDH2) determined by real-time PCR. Common lipid perturbations included significantly increased ceramide/sphingomyelin ratios, and obvious accumulation of hexosylceramides and lysoglycerophospholipids. 6:2 FTS exposure induced sharp accumulation of glycerides, including monoglycerides, diglycerides and triglycerides. 8:2 FTS exposure induced decreased levels of acylcarnitines and fatty acids. Both of 6:2 and 8:2 FTS exposure induced increased levels of intracellular reactive oxygen species, an imbalance in energy metabolism homeostasis, and mitochondrial dysfunction. The results of integrated omics analysis are expected to serve as valuable information for the health risk assessment of 6:2 FTS and 8:2 FTS.

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.

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.

Identification of novel microcystins in algal extracts by a liquid chromatography–high-resolution mass spectrometry data analysis pipeline

BackgroundMicrocystins are an emergent public health problem. These toxins are secondary metabolites of harmful cyanobacterial blooms, with blooms becoming more prevalent with eutrophication of water. Exposure to microcystins can result in sickness, liver damage, and even death. Over 300 microcystins have been identified to date, with differences in toxicity based on the specific amino acid composition. Because of this diversity in microcystins, as well as the likelihood of detecting as yet undiscovered microcystins, it is vital to establish a methodological workflow to identify any microcystin in a complex sample, regardless of the availability of a reference standard. Additionally, ascribing varying levels of confidence to these identifications is critical to effectively communicate discoveries. MethodsA liquid-chromatography–high-resolution mass spectrometry method was utilized to identify microcystins present in cyanobacterial extracts from a strain of Microcystis aeruginosa and an Aphanizomenon sp. First, microcystin congeners with available standards were identified in the cyanobacterial extract. These known-unknown microcystins were considered to have the highest confidence identifications due to availability of accurate masses, retention times, and library spectra for comparison. Utilizing the spectra of these microcystins, relatively high-abundance diagnostic product-ions were identified and employed to screen the data for additional candidate microcystins. Microcystins without a standard that had an exact mass matching a microcystin published in CyanoMetDB were considered semi-known-unknown microcystins. The remaining microcystins were considered unknown-unknown microcystins. The identities of the microcystins determined herein were additionally supported by product-ion analysis, thiol reactivity, esterification reactions, neutral loss analysis, and literature contextualization. ResultsIn total, utilizing the systematic workflow presented herein, 23 microcystins were identified in the M. aeruginosa culture, including two not published previously: [d-Asp3]MC-LCit and the incompletely identified MC-L(C7H11NO3).