PubMed

Chin Herb Med. 2023 Dec 1;16(1):132-142. doi: 10.1016/j.chmed.2023.09.004. eCollection 2024 Jan.ABSTRACTOBJECTIVE: This study aimed to investigate the therapeutic effects of Xiaoyao San (XYS), a herbal medicine formula, on exercise capacity and liver mitochondrial metabolomics in a rat model of depression induced by chronic unpredictable mild stress (CUMS).METHODS: A total of 24 male SD rats were randomly divided into four groups: control group (C), CUMS control group (M), Venlafaxine positive treatment group (V), and XYS treatment group (X). Depressive behaviour and exercise capacity of rats were assessed by body weight, sugar-water preference test, open field test, pole test, and rotarod test. The liver mitochondria metabolomics were analyzed by using liquid chromatography-mass spectrometry (LC-MS) method. TCMSP database and GeneCards database were used to screen XYS for potential targets for depression, and GO and KEGG enrichment analyses were performed.RESULTS: Compared with C group, rats in M group showed significantly lower body weight, sugar water preference rate, number of crossing and rearing in the open field test, climbing down time in the pole test, and retention time on the rotarod test (P < 0.01). The above behaviors and exercise capacity indices were significantly modulated in rats in V and X groups compared with M group (P < 0.05, 0.01). Compared with C group, a total of 18 different metabolites were changed in the liver mitochondria of rats in M group. Nine different metabolites and six metabolic pathways were regulated in the liver mitochondria of rats in X group compared with M group. The results of network pharmacology showed that 88 intersecting targets for depression and XYS were obtained, among which 15 key targets such as IL-1β, IL-6, and TNF were predicted to be the main differential targets for the treatment of depression. Additionally, a total of 1 553 GO signaling pathways and 181 KEGG signaling pathways were identified, and the main biological pathways were AGE-RAGE signaling pathway, HIF-1 signaling pathway, and calcium signaling pathway.CONCLUSION: XYS treatment could improve depressive symptoms, enhance exercise capacity, positively regulate the changes of mitochondrial metabolites and improve energy metabolism in the liver of depressed rats. These findings suggest that XYS exerts antidepressant effects through multi-target and multi-pathway.PMID:38375048 | PMC:PMC10874765 | DOI:10.1016/j.chmed.2023.09.004

Chin Herb Med. 2023 Dec 18;16(1):121-131. doi: 10.1016/j.chmed.2023.10.002. eCollection 2024 Jan.ABSTRACTOBJECTIVE: Based on metabonomics technology of high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS) and hydrogen nuclear magnetic resonance spectroscopy (1H NMR), the pharmacokinetic characteristics and therapeutic mechanism of Rhei Radix et Rhizoma (RhRR, Dahuang in Chinese), Eupolyphaga Steleophaga (EuS, Tubiechong in Chinese) combined with RhRR acting on acute liver injury were explored.METHODS: Models of acute liver injury were established, and the pharmacokinetic methods of five components of RhRR-EuS in rats were found by HPLC-MS/MS. The liver tissues of different groups of mice were analyzed by 1H NMR spectroscopy combined with multivariate statistical analysis to investigate the metabolomics of RhRR-EuS and RhRR.RESULTS: Pharmacokinetic results showed there were different levels of bimodal phenomenon in different groups, and the absorption of free anthraquinone in RhRR increased after compatibility with EuS. In addition, the pathological state of acute liver injury in rats can selectively promote the absorption of emodin, chrysophanol, physcion and aloe emodin. Through 15 differential metabolites in the liver tissue of acute liver injury mice, it was revealed that RhRR-EuS and RhRR could protect the liver injury by regulating the metabolism of glutamine and glutamic acid, alanine, aspartic acid and glutamic acid, and phosphoinositide. However, the regulation of RhRR was weaker than that of RhRR-EuS.CONCLUSION: For the first time, we studied the pharmacokinetics and metabolomics differences of RhRR-EuS and RhRR in rats and mice with acute liver injury, in order to provide theoretical reference for clinical treatment of liver disease by DHZCP.PMID:38375045 | PMC:PMC10874764 | DOI:10.1016/j.chmed.2023.10.002

Se Pu. 2024 Feb;42(2):176-184. doi: 10.3724/SP.J.1123.2023.10037.ABSTRACTShort-chain chlorinated paraffins (SCCPs) are an emerging class of persistent organic pollutants (POPs) that are widely detected in environmental matrices and human samples. Because of their environmental persistence, long-range transport potential, bioaccumulation potential, and biotoxicity, SCCPs pose a significant threat to human health. In this study, metabolomics technology was applied to reveal the metabolomic interference in human normal hepatic (L02) cells after exposure to low (1 μg/L), moderate (10 μg/L), and high (100 μg/L) doses of SCCPs. Principal component analysis (PCA) and metabolic effect level index (MELI) values showed that all three SCCP doses caused notable metabolic perturbations in L02 cells. A total of 72 metabolites that were annotated by MS/MS and matched with the experimental spectra in the Human Metabolome Database (HMDB) or validated by commercially available standards were selected as differential metabolites (DMs) across all groups. The low-dose exposure group shared 33 and 36 DMs with the moderate- and high-dose exposure groups, respectively. The moderate-dose exposure group shared 46 DMs with the high-dose exposure group. In addition, 33 DMs were shared among the three exposure groups. Among the 72 DMs, 9, 9, and 45 metabolites participated in the amino acid, nucleotide, and lipid metabolism pathways, respectively. The results of pathway enrichment analysis showed that the most relevant metabolic pathways affected by SCCPs were the lipid metabolism, fatty acid β-oxidation, and nucleotide metabolism pathways, and that compared with low-dose exposure, moderate- and high-dose SCCP exposures caused more notable perturbations of these metabolic pathways in L02 cells. Exposure to SCCPs perturbed glycerophospholipid and sphingolipid metabolism. Significant alterations in the levels of phosphatidylcholines, phosphatidylethanolamines, and sphingomyelins indicated SCCP-induced biomembrane damage. SCCPs inhibited fatty acid β-oxidation by decreasing the levels of short- and medium-chain acylcarnitines in L02 cells, indicating that the energy supplied by fatty acid oxidation was reduced in these cells. Furthermore, compared with low- and moderate-dose SCCPs, high-dose SCCPs produced a significantly stronger inhibition of fatty acid β-oxidation. In addition, SCCPs perturbed nucleotide metabolism. The higher hypoxanthine levels observed in L02 cells after SCCP exposures indicate that SCCPs may induce several adverse effects, including hypoxia, reactive oxygen species production, and mutagenesis in L02 cells.PMID:38374598 | DOI:10.3724/SP.J.1123.2023.10037

Se Pu. 2024 Feb;42(2):164-175. doi: 10.3724/SP.J.1123.2023.12014.ABSTRACTMetabolic associated fatty liver disease (MAFLD) is a common liver disease with a prevalence of up to 25%; it not only adversely affects human health but also aggravates the economic burden of society. An increasing number of studies have suggested that the occurrence of chronic noncommunicable diseases is affected by both environmental exposures and genetic factors. Research has also shown that environmental pollution may increase the risk of MAFLD and promote its occurrence and development. However, the relationship between these concepts, as well as the underlying exposure effects and mechanism, remains incompletely understood. Lipidomics, a branch of metabolomics that studies lipid disorders, can help researchers investigate abnormal lipid metabolites in various disease states. Lipidome-exposome wide association studies are a promising paradigm for investigating the health effects of cumulative environmental exposures on biological responses, and could provide new ideas for determining the associations between metabolic and lipid changes and disease risk caused by chemical-pollutant exposure. Hence, in this study, targeted exposomics and nontargeted lipidomics studies based on ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) and ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) were used to characterize exogenous chemical pollutants and endogenous lipid metabolites in the sera of patients with MAFLD and healthy subjects. The results demonstrated that fipronil sulfone, malathion dicarboxylic acid, and monocyclohexyl phthalate may be positively associated with the disease risk of patients diagnosed as simple fatty liver disease (hereafter referred to as MAFLD(0)). Moreover, fipronil sulfone, acesulfame potassium, perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), perfluoroundecanoic acid (PFUnDA), 4-hydroxybenzophenone, and 3,5-di-tert-butyl-4-hydroxybenzoic acid (DBPOB) may be positively associated with the disease risk of patients diagnosed as fatty liver complicated by single or multiple metabolic disorders. Association analysis was carried out to explore the lipid metabolites induced by chemical residues. Triglyceride (TG) and diglyceride (DG) were significantly increased in MAFLD and MAFLD(0). The numbers of carbons of significantly changed DGs and TGs were mainly in the ranges of 32-40 and 35-60, respectively, and both were mainly characterized by changes in polyunsaturated lipids. Most of the lipid-effect markers were positively correlated with chemical residues and associated with increased disease risk. Our research provides a scientific basis for studies on the association and mechanism between serum chemical-pollutant residues and disease outcomes.PMID:38374597 | DOI:10.3724/SP.J.1123.2023.12014

Se Pu. 2024 Feb;42(2):159-163. doi: 10.3724/SP.J.1123.2023.07015.ABSTRACTPeak alignment is a crucial data-processing step in untargeted metabolomics analysis that aims to integrate metabolite data from multiple liquid chromatography-mass spectrometry (LC-MS) batches for enhanced comparability and reliability. However, slight variations in the chromatographic separation conditions can result in retention time (RT) shifts between consecutive analyses, adversely affecting peak alignment accuracy. In this study, we present a retention index (RI)-based chromatographic peak-shift correction (CPSC) strategy to address RT shifts and align chromatographic peaks for metabolomics studies. A series of N-acyl glycine homologues (C2-C23) was synthesized as calibrants, and an LC RI system was established. This system effectively corrected RT shifts arising from variations in flow rate, gradient elution, instrument systems, and chromatographic columns. Leveraging the RI system, we successfully adjusted the RT of raw data to mitigate RT shifts and then implemented the Joint Aligner algorithm for peak alignment. We assessed the accuracy of the RI-based CPSC strategy using pooled human fecal samples as a test model. Notably, the application of the RI-based CPSC strategy to a long-term dataset spanning 157 d as an illustration revealed a significant enhancement in peak alignment accuracy from 15.5% to 80.9%, indicating its ability to substantially improve peak-alignment precision in multibatch LC-MS analyses.PMID:38374596 | DOI:10.3724/SP.J.1123.2023.07015

Se Pu. 2024 Feb;42(2):150-158. doi: 10.3724/SP.J.1123.2023.11005.ABSTRACTEnvironmental exposures have significant impacts on human health and can contribute to the occurrence and development of diseases. Pollutants can enter the body through ingestion, inhalation, dermal absorption, or mother-to-child transmission, and can metabolize and/or accumulate in different tissues and organs. These pollutants can recognize and interact with various biomolecules, including DNA, RNA, proteins, and metabolites, disrupting biological processes and leading to adverse effects in living organisms. Thus, it is crucial to analysis the exogenous pollutants in the body, identify potential biomarkers and investigate their toxic effects. Numerous studies have shown that the metabolism rate of environmental pollutants greatly differs in various tissues and organs, their accumulation is also heterogeneous and dynamically changing. Moreover, the synthesis and accumulation of endogenous metabolites exhibit precise spatial distributions in tissues and cells. Mapping the spatial distributions of both pollutants and endogenous metabolites can discover relevant exposure biomarkers and provide a better understanding of their toxic effects and molecular mechanisms. Mass spectrometry is currently the preferred method for the qualitative and quantitative analysis of various compounds, and has been extensively utilized in pollutant and metabolomics analyses. Mass spectrometry imaging (MSI) is an emerging technology for molecular imaging that combines the information obtained by mass spectrometry with the visualization of the two- and three-dimensional spatial distributions of various molecular species in thin sample sections. Unlike other molecular imaging techniques, MSI can perform the label-free and untargeted analysis of thousands of molecules, such as elements, metabolites, lipids, peptides, proteins, pollutants, and drugs, in a single experiment with high sensitivity and throughput. Different MSI technologies, such as matrix-assisted laser desorption ionization mass spectrometry imaging, secondary ion mass spectrometry imaging, desorption electrospray ionization mass spectrometry imaging, and laser ablation inductively coupled plasma mass spectrometry imaging, have been introduced for the mapping of compounds and elements in biological, medical, and clinical research. MSI technologies have recently been utilized to characterize the spatial distribution of pollutants in the whole body and specific tissues of organisms, assess the toxic effects of pollutants at the molecular level, and identify exposure biomarkers. Such developments have brought new perspectives to investigate the toxicity of environmental pollutants. In this review, we provide an overview of the principles, characteristics, mass analyzers, and workflows of different MSI techniques and introduce their latest application advances in the analysis of environmental pollutants and their toxic effects.PMID:38374595 | DOI:10.3724/SP.J.1123.2023.11005

Se Pu. 2024 Feb;42(2):142-149. doi: 10.3724/SP.J.1123.2023.12011.ABSTRACTEnvironmental pollution has become a prominent global problem, and the potential health hazards of pollutants have caused widespread concern. However, revealing the relationship between complex-pollutant exposure and disease development remains an immense challenge. The core of environmental-health research and risk assessment is the identification of contaminants and their effects. Exposomics provides a new approach in the study of the relationship between environmental factors and human health. Both "top-down" and "bottom-up" strategies are employed in exposomics research. The development of new technologies for chemical detection and "multi-omics" has greatly facilitated the implementation of these strategies. Exposomics focuses on the measurement of an individual's lifelong exposure and aims to identify the health effects of such exposure. It involves the dynamic monitoring of external and internal exposure levels at different stages of life through traditional biomonitoring and exposomic methods. It also includes the identification of biomarkers, which indicate specific environmental exposures and the adverse effects of these exposures on health. Compared with traditional environmental-health studies, exposomics can more accurately reflect the diversity of exposure factors such as pollutants, natural factors, and lifestyles in the real environment, as well as the complexity of their in vivo processes and the responses they trigger in an organism. Powerful chemical analytical tools such as high-resolution mass spectrometry (HRMS) are widely used in studies related to the field of exposomics. Liquid chromatography-mass spectrometry (LC-MS) has been applied in the detection and analysis of environmental pollutants. Proteomics and metabolomics, as two important tools for biomarker identification and effects analysis, are widely used to explore the relationship between environmental factors and diseases. Pollutants can lead to pathological changes and even toxic effects by interacting with proteins. In the case of mixed exposure, some contaminants may present joint toxicity. The interaction between contaminants may change their environmental behavior or the amount of each contaminant that enters the human body, which, in turn, affects their health effects.PMID:38374594 | DOI:10.3724/SP.J.1123.2023.12011

Se Pu. 2024 Feb;42(2):120-130. doi: 10.3724/SP.J.1123.2023.10001.ABSTRACTEnvironmental factors, such as environmental pollutants, behaviors, and lifestyles, are the leading causes of chronic noncommunicable diseases. Estimates indicate that approximately 50% of all deaths worldwide can be attributed to environmental factors. The exposome is defined as the totality of human environmental (i.e., all nongenetic) exposures from conception, including general external exposure (e.g., climate, education, and urban environment), specific external exposure (e.g., pollution, physical activity, and diet), and internal exposure (e.g., metabolic factors, oxidative stress, inflammation, and protein modification). As a new paradigm, this concept aims to comprehensively understand the link between human health and environmental factors. Therefore, a comprehensive measurement of the exposome, including accurate and reliable measurements of exposure to the external environment and a wide range of biological responses to the internal environment, is of great significance. The measurement of the general external exposome depends on advances in environmental sensors, personal-sensing technologies, and geographical information systems. The determination of exogenous chemicals to which individuals are exposed and endogenous chemicals that are produced or modified by external stressors relies on improvements in methodology and the development of instrumental approaches, including colorimetric, chromatographic, spectral, and mass-spectrometric methods. This article reviews the research strategies for chemical exposomes and summarizes existing exposome-measurement methods, focusing on mass spectrometry (MS)-based methods. The top-down and bottom-up approaches are commonly used in exposome studies. The bottom-up approach focuses on the identification of chemicals in the external environment (e.g., soil, water, diet, and air), whereas the top-down approach focuses on the evaluation of endogenous chemicals and biological processes in biological samples (e.g., blood, urine, and serum). Low- and high-resolution MS (LRMS and HRMS, respectively) have become the most popular methods for the direct measurement of exogenous and endogenous chemicals owing to their superior sensitivity, specificity, and dynamic range. LRMS has been widely applied in the targeted analysis of expected chemicals, whereas HRMS is a promising technique for the suspect and unknown screening of unexpected chemicals. The development of MS-based multiomics, including proteomics, metabolomics, epigenomics, and spatial omics, provides new opportunities to understand the effects of environmental exposure on human health. Metabolomics involves the sum of all low-molecular-weight metabolites in a living system. Nontargeted metabolomics can measure both endogenous and exogenous chemicals, which would directly link exposure to biological effects, internal dose, and disease pathobiology, whereas proteomics could play an important role in predicting potential adverse health outcomes and uncovering molecular mechanisms. MS imaging (MSI) is an emerging technique that provides unlabeled in-depth measurements of endogenous and exogenous molecules directly from tissue and cell sections without changing their spatial information. MSI-based spatial omics, which has been widely applied in biomarker discovery for clinical diagnosis, as well as drug and pollutant monitoring, is expected to become an effective method for exposome measurement. Integrating these response measurements from metabolomics, proteomics, spatial omics, and epigenomics will enable the generation of new hypotheses to discover the etiology of diseases caused by chemical exposure. Finally, we highlight the major challenges in achieving chemical exposome measurements.PMID:38374592 | DOI:10.3724/SP.J.1123.2023.10001

Se Pu. 2024 Feb;42(2):109-119. doi: 10.3724/SP.J.1123.2023.12001.ABSTRACTThe occurrence and development of human diseases are influenced by both genetic and environmental factors. Research models that describe disease occurrence only from the perspective of genetics present certain limitations. In recent years, effects of environment factors on the occurrence and development of diseases have attracted extensive attentions. Exposomics focuses on the measurement of all exposure factors in an individual's life and how these factors are related to disease development. Exposomics provides new ideas to promote studies on the relationship between human health and environmental factors. Environmental exposures are characterized with different physical and chemical properties, as well as very low concentrations in vivo, which contribute great challenges in the comprehensive measurement of chemical residues in the human body. Chromatography-mass spectrometry-based technologies combine the high-efficiency separation ability of chromatography with the high resolution and sensitive detection characteristics of mass spectrometry; the combination of these techniques can achieve the high-coverage, high-throughput, and sensitive detection of environmental exposures, thus providing a powerful tool for measuring chemical exposures. Exposomics-analysis methods based on chromatography-mass spectrometry mainly include targeted quantitative analysis, suspect screening, and non-targeted screening. To explore the relationship between environmental exposure and the occurrence and development of diseases, researchers have developed research paradigms, including exposome wide association study, mixed-exposure study, exposomics and multi-omics (genome, transcriptome, proteome, metabolome)-association study, and so on. The emergence of these methods has brought about unprecedented developments in exposomics studies. In this manuscript, analytical methods based on chromatography-mass spectrometry, exposomics research paradigms, and their relevant prospects are reviewed.PMID:38374591 | DOI:10.3724/SP.J.1123.2023.12001

Environ Sci Pollut Res Int. 2024 Feb 20. doi: 10.1007/s11356-024-32507-7. Online ahead of print.ABSTRACTFenoxaprop-p-ethyl (FEN) is an aryloxy phenoxy propionate herbicide that has been widely used in paddy fields. Previous studies have indicated that FEN is highly toxic to aquatic organisms, but little is known about the developmental effects of FEN. This study investigated acute and developmental toxicity, malondialdehyde (MDA) levels, superoxide dismutase (SOD) and catalase (CAT) activities, and metabolomic analyses in zebrafish embryos after 96 h of exposure. FEN exhibited high acute toxicity to zebrafish embryos and larvae. Exposure to FEN could reduce heartbeat and hatching rates and increase malformation rates in embryos. Oxidative damage was also caused in embryos. The results of metabolomics analysis showed that 102 differentially abundant metabolites were found in zebrafish embryos in the 0.05 mg/L FEN treatment group, and 60 differentially abundant metabolites were found in the 0.20 mg/L FEN treatment group. These differentially abundant metabolites mainly belonged to 9 metabolic pathways, of which folate pathways and ABC transport protein pathways had the greatest impact. These results suggested that FEN induced high acute and developmental toxicity in zebrafish embryos.PMID:38374504 | DOI:10.1007/s11356-024-32507-7

Diabetologia. 2024 Feb 19. doi: 10.1007/s00125-024-06105-8. Online ahead of print.ABSTRACTAIMS/HYPOTHESIS: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value.METHODS: In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA1c, HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel's C statistic.RESULTS: Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0-11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3-11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA1c) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]). A more extensive model including HDL-cholesterol and C-peptide performed better in both cohorts (DCS, C 0.74 [95% CI 0.67, 0.81]; GoDARTS, C 0.73 [95% CI 0.69, 0.77]). Two proteins, lactadherin and proto-oncogene tyrosine-protein kinase receptor, were most consistently selected and slightly improved model performance.CONCLUSIONS/INTERPRETATION: Using machine learning approaches, we show that insulin requirement risk can be modestly well predicted by predominantly clinical variables. Inclusion of molecular markers improves the prognostic performance beyond that of clinical variables by up to 5%. Such prognostic models could be useful for identifying people with diabetes at high risk of progressing quickly to treatment intensification.DATA AVAILABILITY: Summary statistics of lipidomic, proteomic and metabolomic data are available from a Shiny dashboard at https://rhapdata-app.vital-it.ch .PMID:38374450 | DOI:10.1007/s00125-024-06105-8

Nat Med. 2024 Feb 19. doi: 10.1038/s41591-023-02793-8. Online ahead of print.ABSTRACTDespite intensive preventive cardiovascular disease (CVD) efforts, substantial residual CVD risk remains even for individuals receiving all guideline-recommended interventions. Niacin is an essential micronutrient fortified in food staples, but its role in CVD is not well understood. In this study, untargeted metabolomics analysis of fasting plasma from stable cardiac patients in a prospective discovery cohort (n = 1,162 total, n = 422 females) suggested that niacin metabolism was associated with incident major adverse cardiovascular events (MACE). Serum levels of the terminal metabolites of excess niacin, N1-methyl-2-pyridone-5-carboxamide (2PY) and N1-methyl-4-pyridone-3-carboxamide (4PY), were associated with increased 3-year MACE risk in two validation cohorts (US n = 2,331 total, n = 774 females; European n = 832 total, n = 249 females) (adjusted hazard ratio (HR) (95% confidence interval) for 2PY: 1.64 (1.10-2.42) and 2.02 (1.29-3.18), respectively; for 4PY: 1.89 (1.26-2.84) and 1.99 (1.26-3.14), respectively). Phenome-wide association analysis of the genetic variant rs10496731, which was significantly associated with both 2PY and 4PY levels, revealed an association of this variant with levels of soluble vascular adhesion molecule 1 (sVCAM-1). Further meta-analysis confirmed association of rs10496731 with sVCAM-1 (n = 106,000 total, n = 53,075 females, P = 3.6 × 10-18). Moreover, sVCAM-1 levels were significantly correlated with both 2PY and 4PY in a validation cohort (n = 974 total, n = 333 females) (2PY: rho = 0.13, P = 7.7 × 10-5; 4PY: rho = 0.18, P = 1.1 × 10-8). Lastly, treatment with physiological levels of 4PY, but not its structural isomer 2PY, induced expression of VCAM-1 and leukocyte adherence to vascular endothelium in mice. Collectively, these results indicate that the terminal breakdown products of excess niacin, 2PY and 4PY, are both associated with residual CVD risk. They also suggest an inflammation-dependent mechanism underlying the clinical association between 4PY and MACE.PMID:38374343 | DOI:10.1038/s41591-023-02793-8

Indian J Gastroenterol. 2024 Feb 20. doi: 10.1007/s12664-024-01541-1. Online ahead of print.ABSTRACTInflammatory bowel diseases (IBD) are chronic inflammatory gut disorders, majorly classified as ulcerative colitis and Crohn's disease. The complex, multifactorial etiopathogenesis of IBD involves genetic predisposition, environmental cues, aberrant mucosal immune response and a disturbed gut microbiota. Epidemiological trends, studies in gnotobiotic mice models and genome-wide association studies, identifying genes involved in microbial handling, together mount evidence in support of the gut microbiota playing a pivotal role in IBD pathogenesis. Both Crohn's disease and ulcerative colitis are characterized by severe dysbiosis of the gut microbiome, marked by an expansion of detrimental taxa and concomitant depletion of beneficial members. IBD is characterized by reduction in abundances of bacterial genera involved in production of short-chain fatty acids, bio-transformations of bile acids and synthesis of indole-based tryptophan compounds such as Faecalibacterium, Ruminococcus, Coprococcus, Dorea, Parabacteroides, Eubacterium, Oscillibacter and Prevotella and elevation in members of phyla Proteobacteria and Actinobacteria. This imbalance not only results in exaggerated immune signaling towards the microbial antigens, but also results in an altered metabolomic milieu that triggers additional inflammatory cascades. The present review provides insights into the bacterial dysbiosis observed across different intestinal sites and their metabolomic imprints participating in IBD.PMID:38374283 | DOI:10.1007/s12664-024-01541-1

Light Sci Appl. 2024 Feb 20;13(1):52. doi: 10.1038/s41377-024-01394-5.ABSTRACTRaman spectroscopy has tremendous potential for material analysis with its molecular fingerprinting capability in many branches of science and technology. It is also an emerging omics technique for metabolic profiling to shape precision medicine. However, precisely attributing vibration peaks coupled with specific environmental, instrumental, and specimen noise is problematic. Intelligent Raman spectral preprocessing to remove statistical bias noise and sample-related errors should provide a powerful tool for valuable information extraction. Here, we propose a novel Raman spectral preprocessing scheme based on self-supervised learning (RSPSSL) with high capacity and spectral fidelity. It can preprocess arbitrary Raman spectra without further training at a speed of ~1 900 spectra per second without human interference. The experimental data preprocessing trial demonstrated its excellent capacity and signal fidelity with an 88% reduction in root mean square error and a 60% reduction in infinite norm ([Formula: see text]) compared to established techniques. With this advantage, it remarkably enhanced various biomedical applications with a 400% accuracy elevation (ΔAUC) in cancer diagnosis, an average 38% (few-shot) and 242% accuracy improvement in paraquat concentration prediction, and unsealed the chemical resolution of biomedical hyperspectral images, especially in the spectral fingerprint region. It precisely preprocessed various Raman spectra from different spectroscopy devices, laboratories, and diverse applications. This scheme will enable biomedical mechanism screening with the label-free volumetric molecular imaging tool on organism and disease metabolomics profiling with a scenario of high throughput, cross-device, various analyte complexity, and diverse applications.PMID:38374161 | DOI:10.1038/s41377-024-01394-5

Nat Commun. 2024 Feb 19;15(1):1520. doi: 10.1038/s41467-024-45782-2.ABSTRACTFatty acid desaturation is central to metazoan lipid metabolism and provides building blocks of membrane lipids and precursors of diverse signaling molecules. Nutritional conditions and associated microbiota regulate desaturase expression, but the underlying mechanisms have remained unclear. Here, we show that endogenous and microbiota-dependent small molecule signals promote lipid desaturation via the nuclear receptor NHR-49/PPARα in C. elegans. Untargeted metabolomics of a β-oxidation mutant, acdh-11, in which expression of the stearoyl-CoA desaturase FAT-7/SCD1 is constitutively increased, revealed accumulation of a β-cyclopropyl fatty acid, becyp#1, that potently activates fat-7 expression via NHR-49. Biosynthesis of becyp#1 is strictly dependent on expression of cyclopropane synthase by associated bacteria, e.g., E. coli. Screening for structurally related endogenous metabolites revealed a β-methyl fatty acid, bemeth#1, which mimics the activity of microbiota-dependent becyp#1 but is derived from a methyltransferase, fcmt-1, that is conserved across Nematoda and likely originates from bacterial cyclopropane synthase via ancient horizontal gene transfer. Activation of fat-7 expression by these structurally similar metabolites is controlled by distinct mechanisms, as microbiota-dependent becyp#1 is metabolized by a dedicated β-oxidation pathway, while the endogenous bemeth#1 is metabolized via α-oxidation. Collectively, we demonstrate that evolutionarily related biosynthetic pathways in metazoan host and associated microbiota converge on NHR-49/PPARα to regulate fat desaturation.PMID:38374083 | DOI:10.1038/s41467-024-45782-2

Gene. 2024 Jan 30;893:147927. doi: 10.1016/j.gene.2023.147927. Epub 2023 Oct 30.ABSTRACTRecent semi-targeted metabolomics studies have highlighted a number of metabolites in wheat that associate with leaf rust resistance genes and/or rust infection. Here, we report the structural characterization of a novel glycosylated and partially saturated apocarotenoid, reminiscent of a reduced form of mycorradicin, (6E,8E,10E)-4,9-dimethyl-12-oxo-12-((3,4,5-trihydroxy-6-(2-hydroxyethoxy)tetrahydro-2H-pyran-2-yl)methoxy)-3-((3,4,5-trihydroxy-6-(hydroxymethyl)tetrahydro-2H-pyran-2-yl)oxy)dodeca-6,8,10-trienoic acid, isolated from Triticum aestivum L. (Poaceae) variety 'Thatcher' (Tc) flag leaves. While its accumulation was not associated with any of Lr34, Lr67 or Lr22a resistance genes, infection of Tc with leaf rust was found to deplete it, consistent with the idea of this metabolite being a glycosylated-storage form of an apocarotenoid of possible relevance to plant defense. A comparative analysis of wheat transcriptomic changes shows modulation of terpenoid, carotenoid, UDP-glycosyltransferase and glycosylase -related gene expression profiles, consistent with anticipated biosynthesis and degradation mechanisms. However, details of the exact nature of the relevant pathways remain to be validated in the future. Together these findings highlight another example of the breadth of unique metabolites underlying plant host-fungal pathogen interactions.PMID:38374023 | DOI:10.1016/j.gene.2023.147927

Mol Hortic. 2024 Feb 20;4(1):6. doi: 10.1186/s43897-024-00081-8.ABSTRACTPutrescine plays a role in superficial scald development during the cold storage of pear fruit. However, the molecular mechanism behind this phenomenon has not been un-fully clarified until recently. In this study, a conjoint analysis of metabolites and gene expression profiles in the putrescine-metabolic pathway of P. bretschneideri Rehd. fruit followed by experimental validation revealed that PbrADC1, forming a homodimer in the chloroplast, was involved in putrescine biosynthesis and thus fruit chilling resistance. Additionally, the substrate-binding residue Cys546 in PbrADC1, whose activity was modified by H2O2, played a crucial role in arginine decarboxylation into agmatine. Through a combined analysis of the distribution of cis-acting elements in the PbrADC1 promoter as well as the expression profiles of related transcription factors (TFs), several TFs were identified as upstream regulators of PbrADC1 gene. Further investigation revealed that the nuclear PbrWRKY62 could directly bind to the W-box elements in the PbrADC1 promoter, activate its expression, enhance putrescine accumulation, and thus increase fruit chilling tolerance. In conclusion, our results suggest that the PbrWRKY62-PbrADC1 module is involved in the development of superficial scald in P. bretschneideri Rehd. fruit via regulating putrescine biosynthesis. Consequently, these findings could serve as valuable genetic resources for breeding scald-resistant pear fruit.PMID:38373989 | DOI:10.1186/s43897-024-00081-8

Alzheimers Res Ther. 2024 Feb 19;16(1):41. doi: 10.1186/s13195-024-01402-1.ABSTRACTBACKGROUND: The relationship between periodontitis and Alzheimer's disease (AD) has attracted more attention recently, whereas profiles of subgingival microbiomes and gingival crevicular fluid (GCF) metabolic signatures in AD patients have rarely been characterized; thus, little evidence exists to support the oral-brain axis hypothesis. Therefore, our study aimed to characterize both the microbial community of subgingival plaque and the metabolomic profiles of GCF in patients with AD and amnestic mild cognitive impairment (aMCI) for the first time.METHODS: This was a cross-sectional study. Clinical examinations were performed on all participants. The microbial community of subgingival plaque and the metabolomic profiles of GCF were characterized using the 16S ribosomal RNA (rRNA) gene high-throughput sequencing and liquid chromatography linked to tandem mass spectrometry (LC-MS/MS) analysis, respectively.RESULTS: Thirty-two patients with AD, 32 patients with aMCI, and 32 cognitively normal people were enrolled. The severity of periodontitis was significantly increased in AD patients compared with aMCI patients and cognitively normal people. The 16S rRNA gene sequencing results showed that the relative abundances of 16 species in subgingival plaque were significantly correlated with cognitive function, and LC-MS/MS analysis identified a total of 165 differentially abundant metabolites in GCF. Moreover, multiomics Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO) analysis revealed that 19 differentially abundant metabolites were significantly correlated with Veillonella parvula, Dialister pneumosintes, Leptotrichia buccalis, Pseudoleptotrichia goodfellowii, and Actinomyces massiliensis, in which galactinol, sn-glycerol 3-phosphoethanolamine, D-mannitol, 1 h-indole-1-pentanoic acid, 3-(1-naphthalenylcarbonyl)- and L-iditol yielded satisfactory accuracy for the predictive diagnosis of AD progression.CONCLUSIONS: This is the first combined subgingival microbiome and GCF metabolome study in patients with AD and aMCI, which revealed that periodontal microbial dysbiosis and metabolic disorders may be involved in the etiology and progression of AD, and the differential abundance of the microbiota and metabolites may be useful as potential markers for AD in the future.PMID:38373985 | DOI:10.1186/s13195-024-01402-1

Cancer Lett. 2024 Feb 17:216724. doi: 10.1016/j.canlet.2024.216724. Online ahead of print.ABSTRACTCD24 is a well-characterized breast cancer (BC) stem cell (BCSC) marker. Primary breast tumor cells having CD24-negativity together with CD44-positivity is known to maintain high metastatic potential. However, the functional role of CD24 gene in triple-negative BC (TNBC), an aggressive subtype of BC, is not well understood. While the significance of CD24 in regulating immune pathways is well recognized in previous studies, the significance of CD24 low expression in onco-signaling and metabolic rewiring is largely unknown. Using CD24 knock-down and over-expression TNBC models, our in vitro and in vivo analysis suggest that CD24 is a tumor suppressor in metastatic TNBC. Comprehensive in silico gene expression analysis of breast tumors followed by lipidomic and metabolomic analyses of CD24-modulated cells revealed that CD24 negativity induces mitochondrial oxidative phosphorylation and reprograms TNBC metabolism toward the fatty acid beta-oxidation (FAO) pathway. CD24 silencing activates PPARα-mediated regulation of FAO in TNBC cells. Further analysis using reverse-phase protein array and its validation using CD24-modulated TNBC cells and xenograft models nominated CD24-NF-κB-CPT1A signaling pathway as the central regulatory mechanism of CD24-mediated FAO activity. Overall, our study proposes a novel role of CD24 in metabolic reprogramming that can open new avenues for the treatment strategies for patients with metastatic TNBC.PMID:38373689 | DOI:10.1016/j.canlet.2024.216724

J Ethnopharmacol. 2024 Feb 17:117910. doi: 10.1016/j.jep.2024.117910. Online ahead of print.ABSTRACTETHNOPHARMACOLOGICAL RELEVANCE: For centuries, Shaoyao-Gancao-Fuzi decoction (SGFD) has been a reliable traditional Chinese medicine for treating rheumatoid arthritis (RA). Despite its long history of use, the specific active components and underlying mechanisms of its therapeutic effects have yet to be fully understood.AIM OF THE STUDY: The aim of this study was to investigate the active ingredients and therapeutic effects of SGFD on RA, and to further understand its underlying mechanism.MATERIALS AND METHODS: The chemical constituents in SGFD extract and in rat serum after oral administration of SGFD were identified and evaluated using ultra-performance liquid chromatography quadrupole-time-flight mass spectrometry (UPLC-Q-TOF/MS) together with various data-processing methods, respectively. The efficacy of SGFD was assessed by using an adjuvant-induced arthritis (AIA) rat model and lipopolysaccharide-stimulated RAW 264.7 cell. Subsequently, cell metabolomic was conducted to clarify the potential biomarkers and pathways. ELISA, RT-qPCR, and WB were used to verify the anti-arthritis mechanism of SGFD.RESULTS: A total of 65 chemical constituents were identified in SGFD. 17 active components were distinguished in rat serum samples, of which 13 may be the main active ingredients for SGFD treatment of RA. The remarkable efficacy of SGFD in reducing the symptoms of RA is evident through its ability to alleviate the redness and swelling of the affected paws, as well as reduce the infiltration of inflammatory cells. Cell experiments revealed that rat serum of SGFD reduced IL-1β, IL-6, and TNF-α secretion in RAW 264.7 cells. 27 potential biomarkers were identified through cell metabolomics analysis. The arachidonic acid (AA) metabolism signaling pathway was activated in RA, which could be reversed by rat serum of SGFD. SGFD effectively inhibited the expression and transformation of AA by downregulating the expression of key enzymes, including phospholipase A and cyclooxygenase.CONCLUSION: SGFD may ameliorate RA symptoms by regulating the AA-PGH2-PGE2/PGF2α pathway. The main active components include songorine, fuziline, neoline, albiflorin, paeoniflorin, liquiritin, benzoylmesaconine, isoformononetin, liquiritigenin, isoliquiritigenin, formononetin, glycyrrhizic acid, and glycyrrhetinic acid.PMID:38373664 | DOI:10.1016/j.jep.2024.117910