Untargeted Liquid Chromatography-mass Spectrometry Research Articles

Untargeted LC-MS metabolomics reveals the metabolic responses in olive flounder subjected to hirame rhabdovirus infection.

Hirame novirhabdovirus (HIRRV), which mainly infects the olive flounder (Paralichthys olivaceus), is considered to be one of the most serious viral pathogens threatening the global fish culture industry. However, little is known about the mechanism of host-pathogen interactions at the metabolomic level. In this study, in order to explore the metabolic response of olive flounder to HIRRV infection, liquid chromatography mass spectrometry (LC-MS) was used to detect the changes of endogenous compounds of the olive flounder after HIRRV infection. A total of 954 unique masses were obtained, including 495 metabolites and 459 lipids. Among them, 7 and 173 qualified differential metabolites were identified at 2 days and 7 days post-infection, respectively. Distinct metabolic profiles were observed along with viral infection. At the early stage of infection, only a few metabolites were perturbed. Among them, the level of inosine and carnosine were increased and the potential antiviral ability of these two metabolites was further confirmed by exogenous addition experiment. At the late stage of HIRRV infection, the metabolic profiles changed remarkably. The changes in amino acids and nucleotides especially the 7-methylguanine also accelerated the amplification of viral particles. And the down-regulation of glutathione (GSH) implied an elevated level of ROS (reactive oxygen species) that attenuated the immune system of flounders. HIRRV also induced the accumulation of purine and reduction of pyrimidine, and elevated LPC and LPE levels. The unbalanced purine/pyrimidine and altered lipid profile may be beneficial for the replication and infection of HIRRV at the late stage of infection. These findings provide new insights into the pathogenic mechanism of HIRRV infection in olive flounder.

Carnitine metabolites and cognitive improvement in patients with schizophrenia treated with olanzapine: a prospective longitudinal study.

Objective: Cognitive impairment is one of the core symptoms of schizophrenia, which is stable and lifelong. L-carnitine has been shown to improve cognitive function and decrease the rate of cognitive deterioration in patients with Alzheimer's disease. However, it remains unclear regarding the role of L-carnitine and its metabolites in cognitive functions in schizophrenia after treatment with olanzapine. The purpose of this study was to evaluate the relationship between changes in plasma levels of L-carnitine metabolites and cognitive improvement after olanzapine treatment. Methods: This was a prospective longitudinal study. In this study, we recruited 25 female patients with first episode schizophrenia (FES) who were drug naïve at baseline and received 4 weeks of olanzapine monotherapy. Cognitive function was assessed at baseline and 4-week follow-up using the RBANS. Plasma L-carnitine metabolite levels were determined by a metabolomics technology based on untargeted ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). Results: We found that the immediate memory index, delayed memory index and RBANS composite score were significantly increased at the 4-week follow-up after treatment. A total of 7 differential L-carnitine metabolites were identified in FES patients after olanzapine monotherapy. In addition, we found that changes in butyrylcarnitine were positively correlated with improvements in language index and RBANS composite score. Further regression analyses confirmed the association between reduced butyrylcarnitine levels and cognitive improvement after olanzapine monotherapy in FES patients. Conclusion: Our study shows that cognitive improvement after olanzapine treatment was associated with changes in L-carnitine metabolite levels in patients with FES, suggesting a key role of L-carnitine in cognition in schizophrenia.

Metabolomics Profiling of White Button, Crimini, Portabella, Lion's Mane, Maitake, Oyster, and Shiitake Mushrooms Using Untargeted Metabolomics and Targeted Amino Acid Analysis.

Mushrooms contain multiple essential nutrients and health-promoting bioactive compounds, including the amino acid L-ergothioneine. Knowledge of the chemical composition of different mushroom varieties will aid research on their health-promoting properties. We compared the metabolomes of fresh raw white button, crimini, portabella, lion's mane, maitake, oyster, and shiitake mushrooms using untargeted liquid chromatography mass spectrometry (LC/MS)-based metabolomics. We also quantified amino acid concentrations, including L-ergothioneine, a potential antioxidant which is not synthesized by plants or animals. Among the seven mushroom varieties, more than 10,000 compounds were detected. Principal Component Analysis indicated mushrooms of the same species, Agaricus Bisporus (white button, portabella, crimini), group similarly. The other varieties formed individual, distinct clusters. A total of 1344 (520 annotated) compounds were detected in all seven mushroom varieties. Each variety had tens-to-hundreds of unique-to-mushroom-variety compounds. These ranged from 29 for crimini to 854 for lion's mane. All three Agaricus bisporus varieties had similar amino acid profiles (including detection of all nine essential amino acids), while other varieties had less methionine and tryptophan. Lion's mane and oyster mushrooms had the highest concentrations of L-ergothioneine. The detection of hundreds of unique-to-mushroom-variety compounds emphasizes the differences in chemical composition of these varieties of edible fungi.

Identification of altered metabolic functional components using metabolomics to analyze the different ages of fruiting bodies of Sanghuangporus vaninii cultivated on cut log substrates.

Sanghuangporus vaninii is a profitable traditional and medicinal edible fungus with uncommon therapeutic properties and medicinal value. The accumulation of active ingredients in this fungus that is used in traditional Chinese medicine is affected by its years of growth, and their pharmacological activities are also affected. However, the effects of age on the medicinal value of fruiting bodies of S. vaninii cultivated on cut log substrate remain unclear. In this study, an untargeted liquid chromatography mass spectrometry (LC-MS)-based metabolomics approach was performed to characterize the profiles of metabolites from 1-, 2- and 3-year-old fruiting bodies of S. vaninii. A total of, 156 differentially accumulated metabolites (DAMs) were screened based on the criterion of a variable importance projection greater than 1.0 and p < 0.01, including 75% up regulated and 25% down regulated. The results of enrichment of metabolic pathways showed that the metabolites involved the biosynthesis of plant secondary metabolites, biosynthesis of amino acids, central carbon metabolism in cancer, steroid hormone biosynthesis, linoleic acid metabolism, prolactin signaling pathway, and arginine biosynthesis, and so on. The biosynthesis of plant secondary metabolites pathway was significantly activated. Five metabolites were significantly elevated within the growth of fruiting bodies, including 15-keto-prostaglandin F2a, (4S, 5R)-4,5,6-trihydroxy-2-iminohexanoate, adenylsuccinic acid, piplartine, and chenodeoxycholic acid. 15-keto-prostaglandin F2a is related to the pathway of arachidonic acid metabolism and was significantly increased up to 1,320- and 535-fold in the 2- and 3-year-old fruiting bodies, respectively, compared with those in the 1-year-old group. The presence of these bioactive natural products in S. vaninii is consistent with the traditional use of Sanghuang, which prompted an exploration of its use as a source of natural prostaglandin in the form of foods and nutraceuticals. These findings may provide insight into the functional components of S. vaninii to develop therapeutic strategies.

Predicting lameness in dairy cattle using untargeted liquid chromatography–mass spectrometry-based metabolomics and machine learning

Lameness in dairy cattle is a highly prevalent condition that impacts on the health and welfare of dairy cows. Prompt detection and implementation of effective treatment is important for managing lameness. However, major limitations are associated with visual assessment of lameness, which is the most commonly used method to detect lameness. The aims of this study were to investigate the use of metabolomics and machine learning to develop novel methods to detect lameness. Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) alongside machine learning models and a stability selection method were utilized to evaluate the predictive accuracy of differences in the metabolomics profile of first-lactation dairy cows before (during the transition period) and at the time of lameness (based on visual assessment using the 0-3 scale of the Agriculture and Horticulture Development Board). Urine samples were collected from 2 cohorts of dairy heifers and stored at -86°C before analysis using LC-MS. Cohort 1 (n = 90) cows were recruited as current first-lactation cows with weekly mobility scores recorded over a 4-mo timeframe, from which newly lame and nonlame cows were identified. Cohort 2 (n = 30) cows were recruited within 3 wk before calving, and lameness events (based on mobility score) were recorded through lactation until a minimum of 70 d in milk (DIM). All cows were matched paired by DIM ± 14 d. The median DIM at lameness identification was 187.5 and 28.5 for cohort 1 and 2, respectively. The best performing machine learning models predicted lameness at the time of lameness with an accuracy of between 81 and 82%. Using stability selection, the prediction accuracy at the time of lameness was 80 to 81%. For samples collected before and after calving, the best performing machine learning model predicted lameness with an accuracy of 71 and 75%, respectively. The findings from this study demonstrate that untargeted LC-MS profiling combined with machine learning methods can be used to predict lameness as early as before calving and before observable changes in gait in first-lactation dairy cows. The methods also provide accuracies for detecting lameness at the time of observable changes in gait of up to 82%. The findings demonstrate that these methods could provide substantial advancements in the early prediction and prevention of lameness risk. Further external validation work is required to confirm these findings are generalizable; however, this study provides the basis from which future work can be conducted.

Rectal Cancer Tissue Lipidome Differs According to Response to Neoadjuvant Therapy.

Rectal cancer (RC) is a gastrointestinal cancer with a poor prognosis. While some studies have shown metabolic reprogramming to be linked to RC development, it is difficult to define biomolecules, like lipids, that help to understand cancer progression and response to therapy. The present study investigated the relative lipid abundance in tumoral tissue associated with neoadjuvant therapy response using untargeted liquid chromatography-mass spectrometry lipidomics. Locally advanced rectal cancer (LARC) patients (n = 13), clinically staged as T3-4 were biopsied before neoadjuvant chemoradiotherapy (nCRT). Tissue samples collected before nCRT (staging) and afterwards (restaging) were analyzed to discover lipidomic differences in RC cancerous tissue from Responders (n = 7) and Non-responders (n = 6) to nCRT. The limma method was used to test differences between groups and to select relevant feature lipids from tissue samples. Simple glycosphingolipids and differences in some residues of glycerophospholipids were more abundant in the Non-responder group before and after nCRT. Oxidized glycerophospholipids were more abundant in samples of Non-responders, especially those collected after nCRT. This work identified potential lipids in tissue samples that take part in, or may explain, nCRT failure. These results could potentially provide a lipid-based explanation for nCRT response and also help in understanding the molecular basis of RC and nCRT effects on the tissue matrix.

209-LB: Discovery Metabolomics across a Population Scale Study Identifies a Community of Glycerolipids Associated with Incident Type 2 Diabetes

It is well appreciated that changes in metabolic pathways precede the development of overt type 2 diabetes (T2D), but a systematic and unbiased evaluation of such metabolic changes is lacking. Here, we applied a novel high-throughput untargeted liquid chromatography-mass spectrometry (LC-MS) method to the FINRISK 2002 cohort (n=8014) to identify plasma metabolite predictors of incident T2D. We analyzed LC-MS data consisting of 14,615 metabolite features from individuals without prevalent T2D to discover 171 that predicted T2D incidence at a threshold of P&amp;lt;10-6 (Cox regression). To identify discrete molecular pathways, we performed Gaussian graphical modeling to create a network of all detected metabolite features finding that the T2D predictor metabolites were enriched in 11 distinct metabolite communities containing between 6 and 48 interconnected metabolites. Here we highlight one community consisting of 26 plasma diacylglycerols (DAGs) and closely related metabolites. One member of this community, a DAG with carbon:unsaturated bond sum composition of 36:4, predicted T2D incidence (HR = 1.22, p = 7.6×10-10) independently of insulin, glucose and HbA1c, but not 2-hour glucose as measured by OGTT. Thus, this metabolite is a novel predictor of incident T2D that can capture the metabolic dysregulation identifiable by OGTT. In summary, we have identified multiple communities of T2D metabolite biomarkers, representative of distinct pathways that are dysregulated preceding T2D, with potentially clinical useful properties. Disclosure A. Begzati: None. K. P. Godinez Macias: None. J. Watrous: None. T. Long: None. E. Kantz: None. J. Tuomilehto: Stock/Shareholder; Orion pharma. A. S. Havulinna: None. T. Niiranen: Speaker's Bureau; AstraZeneca, Servier Laboratories. P. Jousilahti: None. V. Salomaa: Research Support; Bayer Inc. T. Ideker: None. M. Jain: Employee; Sapient Bioanalytics. A. R. Majithia: None. Funding National Institutes of Health (5R01DK129840-02)

Antioxidants Discovery for Differentiation of Monofloral Stingless Bee Honeys Using Ambient Mass Spectrometry and Metabolomics Approaches.

Stingless bee honey (SBH) is a natural, sweet product produced by stingless bees (Meliponini tribe) that has been used as a traditional medicine to treat various illnesses. It has been shown that SBH has high nutritional value and health-promoting properties due to the presence of plant bioactive compounds from different botanical flora of the foraged nectar. In this study, the antioxidant activities of seven monofloral honeys from acacia, agarwood, coconut, dwarf mountain pine (DMP), Mexican creeper (MC), rubber, and starfruit botanical origins were investigated. The antioxidant properties of SBH studied had a range from 19.7 to 31.4 mM TE/mg for DPPH assays, 16.1 to 29.9 mM TE/mg for ABTS assays, 69.0 to 167.6 mM TE/mg for ORAC assays, and 45.5 to 89.3 mM Fe2+/mg for FRAP assays. Acacia honey showed the highest level of antioxidant properties. The models built from mass spectral fingerprints from direct ambient mass spectrometry showed distinct clusters of SBH by botanical origin and correlated with the antioxidant properties. An untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics approach was undertaken to identify the antioxidant compounds that could explain the unique antioxidant and compositional profiles of the monofloral SBH by its botanical origin. The antioxidants that were identified predominantly consisted of alkaloids and flavonoids. Flavonoid derivatives, which are potent antioxidants, were found to be key markers of acacia honey. This work provides the fundamental basis for the identification of potential antioxidant markers in SBH associated with the botanical origin of the foraged nectar.

Altered fecal microbiome and metabolome profiles in rat models of short bowel syndrome.

Short bowel syndrome (SBS) is featured by impaired nutrients and fluids absorption due to massive small intestine resection. Gut dysbiosis has been implicated in SBS, this study aimed to characterize the metagenomic and metabolomic profiles of SBS and identify potential therapeutic targets. Fecal samples from SBS and Sham rats (n = 8 per group) were collected for high-throughput metagenomic sequencing. Fecal metabolomics was measured by untargeted liquid chromatography-mass spectrometry. We found that the species-level α-diversity significantly decreased in SBS rats, accompanied by altered microbiome compositions. The beneficial anaerobes from Firmicutes and Bacteroidetes were depleted while microorganisms from Lactobacillus, Escherichia, Enterococcus, and Streptococcus were enriched in faces from SBS rats. LEfSe analysis identified 17 microbial species and 38 KEGG modules that were remarkably distinct between SBS and Sham rats. In total, 1,577 metabolites with known chemical identity were detected from all samples, among them, 276 metabolites were down-regulated and 224 metabolites were up-regulated in SBS group. The typical signatures of SBS fecal metabolome comprised reduced short-chain fatty acids and products of amino acid metabolism (indole derivatives and p-cresol), as well as altered bile acid spectrum. We revealed 215 robust associations between representative differentially abundant microbial species and metabolites, the species with the same changing trend tended to have a similar correlation with some certain metabolites. The fecal microbiome and metabolome significantly altered in SBS. Our findings may lay the foundation for developing new strategies to facilitate intestinal adaptation in SBS patients.

Impact of environmental characteristics on children's gut microbiota – A pilot study in assessing the role of indoor microbiome and metabolites

BackgroundA diverse and balanced human gut microbiota is crucial for maintaining normal human physiological functions. However, the impact of indoor microbiome and metabolites on gut microbiota is not well understood. MethodsA self-administered questionnaire was used to collect information on more than 40 personal and environmental characteristics and dietary habits from 56 children in Shanghai, China. Shotgun metagenomics and untargeted liquid chromatography-mass spectrometry (LC-MS) were used to characterize the indoor microbiome and metabolomic/chemical exposure in children's living rooms. PacBio full-length 16 S rRNA sequencing was used to characterize children's gut microbiota. Associations between environmental characteristics and gut microbiota diversity/composition were assessed using PERMANOVA and regression. ResultsIn total, 6247 and 318 indoor and gut microbial species and 1442 indoor metabolites were characterized. Age of children (R2 = 0.033, p = 0.008), age start kindergarten (R2 = 0.029, p = 0.03), living adjacent to heavy traffic (R2 = 0.031, p = 0.01) and drinking soft drinks (R2 = 0.028, p = 0.04) significantly impacted overall gut microbial composition, consistent with previous studies. Having pets/plants and frequent vegetable intake were positively associated with gut microbiota diversity and the Gut Microbiome Health Index (GMHI), while frequent juice and fries intake decreased gut microbiota diversity (p < 0.05). The abundance of indoor Clostridia and Bacilli was positively associated with gut microbial diversity and GMHI (p < 0.01). Total indoor indole derivatives and 6 indole metabolites (L-tryptophan, indole, 3-methylindole, indole-3-acetate, 5-hydroxy-L-tryptophan and indolelactic acid, p < 0.05) were positively associated with the abundance of total protective gut bacteria, suggesting a potential role in promoting gut health. Neural network analysis revealed that these indole derivatives were derived from indoor microorganisms. ConclusionsThe study is the first to report associations between indoor microbiome/metabolites and gut microbiota, highlighting the potential role of indoor microbiome in shaping human gut microbiota.

LC-MS Based Metabolomic Profiling of Largehead Hairtail (Trichiurus japonicus) Ovary Reveals Metabolic Signatures of Ovarian Developmental Process (II–IV)

Trichiurus japonicus is an economically important fish that ranks 11th in global marine fish capture production. However, the reproductive characteristics of this fish have undergone notable changes in recent decades, potentially affecting the quality of offspring and sustainable utilization. To improve our understanding of the physiological regulation of maturation in T. japonicus, untargeted liquid chromatography mass spectrometry was utilized to identify the small molecules that characterize the comprehensive metabolic profiles of ovaries during ovary development from stage II to stage IV. According to the results of OPLS-DA, the ovarian metabolite profiles of the three developmental stages were separated. The concentrations of 124 and 100 metabolites were significantly altered between stage II vs. III and III vs. IV, respectively. Lipids and lipid-like molecules accounted for the largest proportion of the altered metabolites, followed by amino acids, peptides, and analogues. The significantly altered metabolites-enriched pathways differed slightly between stages II and III and stages III and IV. Steroid-related pathways were heavily affected during stages II to III, while significantly altered metabolites from stages III to IV were involved in oocyte-maturation-related pathways. Through metabonomics analysis, potentially important metabolic pathways and metabolites between different ovarian stages were detected, providing basic information for further investigation of maturation mechanisms in wild fish.

A cross-sectional study identifying disparities in serum metabolic profiles among hypertensive patients with ISH, IDH and SDH subtypes.

It has been well acknowledged that disordered intestinal microflora and their fermented products play crucial role during the development of hypertension (HTN). Aberrant profiles of fecal bacteria have been documented in subjects with isolated systolic HTN (ISH) and isolated diastolic HTN (IDH) previously. Nevertheless, evidence regarding the association of metabolic products in the bloodstream with ISH, IDH and combined systolic and diastolic HTN (SDH) remains scarce. We performed a cross-sectional study and conducted untargeted liquid chromatography-mass spectrometry (LC/MS) analysis on serum samples of 119 participants, including 13 subjects with normotension (SBP < 120/DBP < 80 mm Hg), 11 individuals with ISH (SBP ≥ 130/DBP < 80 mm Hg), 27 patients with IDH (SBP < 130/DBP ≥ 80 mm Hg), and 68 SDH patients (SBP ≥ 130, DBP ≥ 80 mm Hg). Here, the results showed clearly separated clusters in PLS-DA and OPLS-DA score plots for patients suffering from ISH, IDH and SDH when compared with normotension controls. The ISH group was characterized by elevated levels of 3,5-tetradecadien carnitine and notable reduction of maleic acid. While IDH patients were enriched with metabolites in L-lactic acid and depleted in citric acid. Stearoylcarnitine was identified to be specifically enriched in SDH group. The differentially abundant metabolites between ISH and controls were involved in tyrosine metabolism pathways, and in biosynthesis of phenylalanine for those between SDH and controls. Potential linkages between the gut microbial and serum metabolic signatures were detected within ISH, IDH and SDH groups. Furthermore, we found the association of discriminatory metabolites with the characteristics of patients. Our findings demonstrate disparate blood metabolomics signatures across ISH, IDH and SDH, with differentially enriched metabolites and potential functional pathways identified, reveal the underlying microbiome and metabolome network in HTN subtypes, and provide potential targets for disease classification and therapeutic strategy in clinical practice.

Linking platelet lipidome and inflammatory response in septic patients

Sepsis is characterized by major endothelial dysfunction, microvascular alterations, and coagulopathy. Platelets are important players in sepsis as they promote thrombo-inflammation and generate lipidic mediators of inflammation. Previously, we observed that platelet acetyl-CoA carboxylase 1 (ACC1), responsible for de novo lipid synthesis, is phosphorylated and thus inhibited by AMP-activated protein kinase (AMPK) upon thrombin stimulation, leading to platelet lipidome changes. The role of the AMPK-ACC signaling in the regulation of platelet lipidome during sepsis, and its potential impact on inflammation, has never been investigated. To investigate whether changes in ACC phosphorylation can influence the platelet lipidome and the inflammatory response of patients during sepsis. Platelets and plasmas were isolated from 48 septic and 48 control patients. In parallel, WT and DKI mice injected intraperitoneally with lipopolysaccharides were used as a model of endotoxemia. Platelets ACC1 phosphorylation/expression levels were measured by western blot. Lipidomics analysis was carried out by untargeted liquid chromatography–mass spectrometry or on the commercial Lipidyzer platform. Plasma factors were measured by Enzyme-linked immunosorbent assays (ELISA). Platelet ACC1 expression and phosphorylation were increased both in septic patients and in septic mice. Lipidomics analysis highlighted significant changes of lipids in human and murine platelets during sepsis, with a substantial reduction in phosphatidylcholines and phosphatidylethanolamines containing long polyunsaturated fatty acid chains (ω3 and ω6), which are key phospholipids for the generation of pro- and anti-inflammatory lipid mediators. Our preliminary data obtained in humans and mice, suggest that elevated ACC phosphorylation in sepsis is associated with a higher content of these phospholipids as well as a plasma reduction in myeloid inflammatory parameters. Our data reveal that critical changes in the platelet lipidome occur during sepsis and may contribute to the pathophysiology of the disease. Our results also suggest that ACC can influence the platelet lipid content and/or composition.

Azelaic acid and guanosine in tears improve discrimination of proliferative from non-proliferative diabetic retinopathy in type-2 diabetes patients: A tear metabolomics study

BackgroundDiabetic retinopathy (DR) is the microvascular ocular complication of diabetes mellitus (DM), which can lead to irreversible blindness and visual impairment if not properly treated. Tears can be collected non-invasively, and the compositions of tears could be the potential biomarkers for ocular diseases. Here we aimed to delineate the metabolomics signature in tears collected from Chinese type-2 DM patients with DR. MethodsThe metabolomics profiles of tear samples from 41 Chinese type-2 DM patients with DR and 21 non-diabetic subjects were determined by the untargeted liquid chromatography-mass spectrometry. The associated pathways of the differentially abundant metabolites were delineated, and the receiver operating characteristic curve analysis was conducted to identify the metabolites differentiating non-proliferative DR (NPDR) from proliferative DR (PDR). ResultsTotal 14 differentially abundant metabolites were identified between total DR and non-diabetic subjects, and 17 differentially abundant metabolites were found between the NPDR and PDR subjects. Moreover, total 18 differentially abundant metabolites were identified between the NPDR and PDR subjects with stratification in DR duration and blood glucose level. d-Glutamine and d-glutamate metabolism was significantly highlighted in the PDR group as compared to the non-diabetic group. For the predictive performance, azelaic acid combined with guanosine achieved the area under receiver operating characteristic curve of 0.855 in the comparison between NPDR and PDR groups. ConclusionThis study revealed the metabolomics changes in tear samples of DR patients. The metabolites in tears could be the potential biomarkers in the DR analysis.

Potential preventive markers in the intracerebral hemorrhage process are revealed by serum untargeted metabolomics in mice using hypertensive cerebral microbleeds.

Hypertensive cerebral microbleeds (HCMB) may be the early stage of hypertensive intracerebral hemorrhage (HICH), which is a serious threat to health due to its high mortality and disability rates. The early clinical symptoms of HCMB may not be significant. Moreover, it is difficult to achieve early diagnosis and intervention for targeted prevention of HICH. Although hypertension (HTN) is a predisposition for HCMB, it remains unclear whether there is any difference between hypertensive patients with or without HCMB. Therefore, we carried out liquid chromatography-mass spectrometry (LC-MS) to analyze early biomarkers for HCMB in mice with hypertension and to lay the foundation for early prevention of HICH in hypertensive patients. In total, 18 C57 male mice were randomly divided into the HCMB (n = 6), HTN (n = 6), and control groups (CON, n = 6). Hematoxylin-eosin and diaminobenzidine staining were used to assess the reliability of the model. The metabolite expression level and sample category stability were tested using the displacement test of orthogonal partial least squares discriminant analysis (OPLS-DA). Significant differences in metabolites were screened out using variable importance in the projection (VIP > 1), which were determined using the OPLS-DA model and the P-value of the t-test (P < 0.05) combined with the nonparametric rank-sum test. With an area under the curve (AUC) > 0.85 and a P-value of 0.05, the receiver operating characteristic curve (ROC) was used to further screen the distinct metabolites of HCMB. Compared with the HTN and CON groups, the HCMB group had significantly higher blood pressure and lower average body weight (P < 0.05). Through untargeted LC-MS analysis, 93 distinct metabolites were identified in the HCMB (P < 0.05, VIP > 1) group. Among these potential biomarkers, six significantly decreased and eight significantly increased differential metabolites were found. Meanwhile, we found that the HCMB group had statistically distinct arginine and purine metabolism pathways (P < 0.05), and citrulline may be the most significant possible biomarker of HCMB (AUC > 0.85, P < 0.05). All of these potential biomarkers may serve as early biomarkers for HICH in hypertension.

Metabolomic profiling of glucose homeostasis in African Americans: the Insulin Resistance Atherosclerosis Family Study (IRAS-FS)

IntroductionAfrican Americans are at increased risk for type 2 diabetes.ObjectivesThis work aimed to examine metabolomic signature of glucose homeostasis in African Americans.MethodsWe used an untargeted liquid chromatography-mass spectrometry metabolomic approach to comprehensively profile 727 plasma metabolites among 571 African Americans from the Insulin Resistance Atherosclerosis Family Study (IRAS-FS) and investigate the associations between these metabolites and both the dynamic (SI, insulin sensitivity; AIR, acute insulin response; DI, disposition index; and SG, glucose effectiveness) and basal (HOMA-IR and HOMA-B) measures of glucose homeostasis using univariate and regularized regression models. We also compared the results with our previous findings in the IRAS-FS Mexican Americans.ResultsWe confirmed increased plasma metabolite levels of branched-chain amino acids and their metabolic derivatives, 2-aminoadipate, 2-hydroxybutyrate, glutamate, arginine and its metabolic derivatives, carbohydrate metabolites, and medium- and long-chain fatty acids were associated with insulin resistance, while increased plasma metabolite levels in the glycine, serine and threonine metabolic pathway were associated with insulin sensitivity. We also observed a differential ancestral effect of glutamate on glucose homeostasis with significantly stronger effects observed in African Americans than those previously observed in Mexican Americans.ConclusionWe extended the observations that metabolites are useful biomarkers in the identification of prediabetes in individuals at risk of type 2 diabetes in African Americans. We revealed, for the first time, differential ancestral effect of certain metabolites (i.e., glutamate) on glucose homeostasis traits. Our study highlights the need for additional comprehensive metabolomic studies in well-characterized multiethnic cohorts.

The identification of a potential plasma metabolite marker for Alzheimer’s disease by LC-MS untargeted metabolomics

Background and aimsAlzheimer's disease (AD), the most common type of dementia, is hard to recognize early, resulting in delayed treatment and poor outcome. At present, there is neither reliable, non-invasive methods to diagnose it accurately and nor effective drugs to recover it. Discovery and quantification of novel metabolite markers in plasma of AD patients and investigation of the correlation between the markers and AD assessment scores. Materials and methodsUntargeted liquid chromatography-mass spectrometry (LC-MS)-based metabolomics with LC-quadrupole- time-of-flight (Q-TOF) was performed in plasma samples of age-matched AD patients and healthy controls. The potential markers were further quantified with targeted multiple reaction monitoring (MRM) approach. ResultsAmong the candidates, progesterone, and 3-indoleacetic acid (3-IAA) were successfully identified and then validated in 50 plasma samples from 25 AD patients and 25 matched normal controls with MRM approach. As a result, 3-IAA was significantly altered in AD patients and correlated with some AD assessment scores. ConclusionBy using untargeted LC-MS metabolomic and LC-MRM approaches to analyze plasma metabolites of AD patients and normal subjects, 3-IAA was discovered and quantified to be significantly altered in AD patients and correlated with several AD assessment scores.

Delivering on sustainable development goals in wastewater reuse for agriculture: Initial prioritization of emerging pollutants in the Tula Valley, Mexico

Wastewater reuse for agricultural irrigation is a widespread beneficial practice, in line with the sustainable development goals. However, contaminants of emerging concern (CECs) present in wastewater, such as pharmaceuticals, pose an environmental risk. The Tula Valley in Mexico is one of the world's largest agricultural areas reusing wastewater for agriculture. However, no untargeted CEC monitoring has been undertaken there, limiting the information available to prioritise local environmental risk assessment. Furthermore, CEC environmental presence in the Global South remains understudied, compared to the Global North. There is a risk that current research efforts focus on CECs predominantly found in the Global North, leading to strategies that may not be appropriate for the Global South where the pollution profile may be different. To address these knowledge gaps, a sampling campaign at five key sites in the Tula Valley was undertaken and samples analysed using multi-residue targeted and untargeted liquid chromatography mass spectrometry methods. Using the targeted data, ten CECs were found to be of environmental risk for at least one sampling site: 4‑tert-octylphenol, acetaminophen, bezafibrate, diclofenac, erythromycin, levonorgestrel, simvastatin, sulfamethoxazole, trimethoprim and tramadol as well as total estrogenicity (combination of three steroid hormones). Six of these have not been previously quantified in the Tula Valley. Over one hundred pollutants never previously measured in the area were identified through untargeted analysis supported by library spectrum match. Examples include diclofenac and carbamazepine metabolites and area-specific pollutants such as the herbicide fomesafen. This research contributes to characterising the presence of CECs in the Global South, as well as providing site-specific data for the Tula Valley.

MAW: the reproducible Metabolome Annotation Workflow for untargeted tandem mass spectrometry

Mapping the chemical space of compounds to chemical structures remains a challenge in metabolomics. Despite the advancements in untargeted liquid chromatography-mass spectrometry (LC–MS) to achieve a high-throughput profile of metabolites from complex biological resources, only a small fraction of these metabolites can be annotated with confidence. Many novel computational methods and tools have been developed to enable chemical structure annotation to known and unknown compounds such as in silico generated spectra and molecular networking. Here, we present an automated and reproducible Metabolome Annotation Workflow (MAW) for untargeted metabolomics data to further facilitate and automate the complex annotation by combining tandem mass spectrometry (MS2) input data pre-processing, spectral and compound database matching with computational classification, and in silico annotation. MAW takes the LC-MS2 spectra as input and generates a list of putative candidates from spectral and compound databases. The databases are integrated via the R package Spectra and the metabolite annotation tool SIRIUS as part of the R segment of the workflow (MAW-R). The final candidate selection is performed using the cheminformatics tool RDKit in the Python segment (MAW-Py). Furthermore, each feature is assigned a chemical structure and can be imported to a chemical structure similarity network. MAW is following the FAIR (Findable, Accessible, Interoperable, Reusable) principles and has been made available as the docker images, maw-r and maw-py. The source code and documentation are available on GitHub (https://github.com/zmahnoor14/MAW). The performance of MAW is evaluated on two case studies. MAW can improve candidate ranking by integrating spectral databases with annotation tools like SIRIUS which contributes to an efficient candidate selection procedure. The results from MAW are also reproducible and traceable, compliant with the FAIR guidelines. Taken together, MAW could greatly facilitate automated metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery.

Untargeted metabolomics unravel serum metabolic alterations in smokers with hypertension.

Background: Cigarette smoking is an important environmental risk factor for cardiovascular events of hypertension (HTN). Existing studies have provided evidence supporting altered gut microbiota by cigarette smoking, especially in hypertensive patients. Metabolic biomarkers play a central role in the functional potentials of the gut microbiome but are poorly characterized in hypertensive smokers. To explore whether serum metabolomics signatures and compositions of HTN patients were varied in smokers, and investigate their connecting relationship to gut microbiota, the serum metabolites were examined in untreated hypertensive patients using untargeted liquid chromatography-mass spectrometry (LC/MS) analysis. Results: A dramatic difference and clear separation in community features of circulating metabolomics members were seen in smoking HTN patients compared with the non-smoking controls, according to partial least squares discrimination analysis (PLS-DA) and orthogonal partial least squares discrimination analysis (OPLS-DA). Serum metabolic profiles and compositions of smoking patients with HTN were significantly distinct from the controls, and were characterized by enrichment of 12-HETE, 7-Ketodeoxycholic acid, Serotonin, N-Stearoyl tyrosine and Deoxycholic acid glycine conjugate, and the depletion of Tetradecanedioic acid, Hippuric acid, Glyceric acid, 20-Hydroxyeicosatetraenoic acid, Phenylpyruvic acid and Capric acid. Additionally, the metabolome displayed prominent functional signatures, with a majority proportion of the metabolites identified to be discriminating between groups distributed in Starch and sucrose metabolism, Caffeine metabolism, Pyruvate metabolism, Glycine, serine and threonine metabolism, and Phenylalanine metabolic pathways. Furthermore, the observation of alterations in metabolites associated with intestinal microbial taxonomy indicated that these metabolic members might mediate the effects of gut microbiome on the smoking host. Indeed, the metabolites specific to smoking HTNs were strongly organized into co-abundance networks, interacting with an array of clinical parameters, including uric acid (UA), low-denstiy lipoprotein cholesterol (LDLC) and smoking index. Conclusions: In conclusion, we demonstrated disparate circulating blood metabolome composition and functional potentials in hypertensive smokers, showing a linkage between specific metabolites in blood and the gut microbiome.