PubMed

J Agric Food Chem. 2024 Feb 20. doi: 10.1021/acs.jafc.3c08437. Online ahead of print.ABSTRACTFusarium poae is commonly detected in field surveys of Fusarium head blight (FHB) of cereal crops and can produce a range of trichothecene mycotoxins. Although experimentally validated reports of F. poae strains producing T-2/HT-2 trichothecenes are rare, F. poae is frequently generalized in the literature as a producer of T-2/HT-2 toxins due to a single study from 2004 in which T-2/HT-2 toxins were detected at low levels from six out of forty-nine F. poae strains examined. To validate/substantiate the observations reported from the 2004 study, the producing strains were acquired and phylogenetically confirmed to be correctly assigned as F. poae; however, no evidence of T-2/HT-2 toxin production was observed from axenic cultures. Moreover, no evidence for a TRI16 ortholog, encoding a key acyltransferase shown to be necessary for T-2 toxin production in other Fusarium species, was observed in any of the de novo assembled genomes of the F. poae strains. Our findings corroborate multiple field-based and in vitro studies on FHB-associated Fusarium populations which also do not support the production of T-2/HT-2 toxins with F. poae and therefore conclude that F. poae should not be generalized as a T-2/HT-2 toxin producing species of Fusarium.PMID:38375818 | DOI:10.1021/acs.jafc.3c08437

J Integr Plant Biol. 2024 Feb 20. doi: 10.1111/jipb.13629. Online ahead of print.ABSTRACTThe structural and functional diversity of plant metabolites is largely created via chemical modification of a basic backbone. However, metabolite modifications in plants have still not been thoroughly investigated by metabolomics approaches. In this study, a widely targeted metabolite modificomics (WTMM) strategy was developed based on ultra-high performance liquid chromatography-quadrupole-linear ion trap (UHPLC-Q-Trap) and UHPLC-Q-Exactive-Orbitrap (UHPLC-QE-Orbitrap), which greatly improved the detection sensitivity and the efficiency of identification of modified metabolites. A metabolite modificomics study was carried out using tomato as a model, and over 34,000 signals with MS2 information were obtained from approximately 232 neutral loss transitions. Unbiased metabolite profiling was also performed by utilizing high-resolution mass spectrometry data to annotate a total of 2,118 metabolites with 125 modification types; of these, 165 modified metabolites were identified in this study. Next, the WTMM database was used to assess diseased tomato tissues and 29 biomarkers were analyzed. In summary, the WTMM strategy is not only capable of large-scale detection and quantitative analysis of plant-modified metabolites in plants, but also can be used for plant biomarker development.PMID:38375781 | DOI:10.1111/jipb.13629

Am J Physiol Gastrointest Liver Physiol. 2024 Feb 20. doi: 10.1152/ajpgi.00264.2023. Online ahead of print.ABSTRACTRecently, the development of non-alcoholic steatohepatitis (NASH) in common strains of pigs has been achieved using a diet high in saturated fat, fructose, cholesterol and cholate and deficient in choline and methionine. The aim of the present work was to characterize the hepatic and plasma lipidomic changes that accompany the progression of NASH and its reversal by switching pigs back to a chow diet. One month of this extreme steatotic diet was sufficient to induce porcine NASH. The lipidomic platform using liquid chromatography-mass spectrometry analyzed 467 lipid species. Seven hepatic phospholipids [PC(30:0), PC(32:0), PC(33:0), PC(33:1), PC(34:0), PC(34:3) and PC(36:2)] significantly discriminated the time of dietary exposure, and PC(30:0), PC(33:0), PC(33:1) and PC(34:0) showed rapid adaptation in the reversion period. Three transcripts, CS, MAT1A and SPP1, showed significant changes associated with hepatic triglycerides and PC(33:0). Plasma lipidomics revealed that these species [FA 16:0, FA 18:0, LPC(17:1), PA(40:5), PC(37:1), TG(45:0), TG(47:2) and TG(51:0)] were able to discriminate the time of dietary exposure. Among them, FA 16:0, FA 18:0, LPC(17:1) and PA(40:5) changed the trend in the reversion phase. Plasma LDL-cholesterol and IL12P40 were good parameters to study the progression of NASH, but their capacity was surpassed by hepatic [PC(33:0), PC(33:1), and PC(34:0)] or plasma lipid [FA 16:0, FA 18:0, and LPC(17:1)] species. Taken together, these lipid species can be used as biomarkers of metabolic changes in the progression and regression of NASH in this model. The lipid changes suggest that the development of NASH also affects peripheral lipid metabolism.PMID:38375587 | DOI:10.1152/ajpgi.00264.2023

Biotechnol J. 2024 Jan;19(2):e2300338. doi: 10.1002/biot.202300338.ABSTRACTChinese hamster ovary (CHO) cells are widely used to produce complex biopharmaceuticals. Improving their productivity is necessary to fulfill the growing demand for such products. One way to enhance productivity is by cultivating cells at high densities, but inhibitory by-products, such as metabolite derivatives from amino acid degradation, can hinder achieving high cell densities. This research examines the impact of these inhibitory by-products on high-density cultures. We cultured X1 and X2 CHO cell lines in a small-scale semi-perfusion system and introduced a mix of inhibitory by-products on day 10. The X1 and X2 cell lines were chosen for their varied responses to the by-products; X2 was susceptible, while X1 survived. Proteomics revealed that the X2 cell line presented changes in the proteins linked to apoptosis regulation, cell building block synthesis, cell growth, DNA repair, and energy metabolism. We later used the AB cell line, an apoptosis-resistant cell line, to validate the results. AB behaved similar to X1 under stress. We confirmed the activation of apoptosis in X2 using a caspase assay. This research provides insights into the mechanisms of cell death triggered by inhibitory by-products and can guide the optimization of CHO cell culture for biopharmaceutical manufacturing.PMID:38375561 | DOI:10.1002/biot.202300338

Heliyon. 2024 Feb 3;10(4):e25258. doi: 10.1016/j.heliyon.2024.e25258. eCollection 2024 Feb 29.ABSTRACTOBJECTIVE: "Metabolism affects function" is the consensus of researchers at present. It has potential clinical application value to study the effects of regulating glutamine (Gln) metabolism on diabetes physiology or pathology. Our research aimed to summarize the latest research progress, frontier hot topics and future development trends in this field from the perspective of scientometrics.METHODS: Relevant literatures and reviews were obtained from the Web of Science (WoS) between January 1, 2001 and May 31, 2022. An online analysis platform of bibliometrics, CiteSpace, and VOS viewer software were used to generate visual knowledge network graphs, including publication countries, institutions and authors partnership analysis, co-occurrence analysis, co-citation analysis, as well as citations and keywords burst detection to acquire research trends and hotspots.RESULTS: Our results showed that a total of 945 publications in the WoS database met the analysis requirements, with articles being the main type. The overall characteristics showed an increasing trend in the number of publications and citations. The United States was leading the way in this research and was a hub for aggregating collaborations across countries. Vanderbilt University delivered high-quality impact with the most published articles. DeBerardinis, RJ in this field was the most representative author and his main research contents were Gln metabolism and mitochondrial glutaminolysis. Significantly, there was a relative lack of collaboration between institutions and authors. In addition, "type 2 diabetes", "glutamine", "metabolism", "gene expression" and "metabolomics" were the keywords categories with high frequency in co-citation references and co-occurrence cluster keywords. Analysis of popular keywords burst detection showed that "branched chain", "oxidative phosphorylation", "kinase", "insulin sensitivity", "tca cycle", "magnetic resonance spectroscopy" and "flux analysis" were new research directions and emerging methods to explore the link between Gln metabolism and diabetes. Overall, exploring Gln metabolism showed a gradual upward trend in the field of diabetes.CONCLUSION: This comprehensive scientometric study identified the general outlook for the field and provided valuable guidance for ongoing research. Strategies to regulate Gln metabolism hold promise as a novel target to treat diabetes, as well as integration and intersection of multidisciplinary provides cooperation strategies and technical guarantees for the development of this field.PMID:38375272 | PMC:PMC10875382 | DOI:10.1016/j.heliyon.2024.e25258

Front Endocrinol (Lausanne). 2024 Feb 2;15:1338147. doi: 10.3389/fendo.2024.1338147. eCollection 2024.ABSTRACTBACKGROUND: The obesity epidemic has been on the rise due to changes in living standards and lifestyles. To combat this issue, sleeve gastrectomy (SG) has emerged as a prominent bariatric surgery technique, offering substantial weight reduction. Nevertheless, the mechanisms that underlie SG-related bodyweight loss are not fully understood.METHODS: In this study, we conducted a collection of preoperative and 3-month postoperative serum and fecal samples from patients who underwent laparoscopic SG at the First Affiliated Hospital of Shandong First Medical University (Jinan, China). Here, we took an unbiased approach of multi-omics to investigate the role of SG-altered gut microbiota in anti-obesity of these patients. Non-target metabolome sequencing was performed using the fecal and serum samples.RESULTS: Our data show that SG markedly increased microbiota diversity and Rikenellaceae, Alistipes, Parabacteroides, Bactreoidales, and Enterobacteraies robustly increased. These compositional changes were positively correlated with lipid metabolites, including sphingolipids, glycerophospholipids, and unsaturated fatty acids. Increases of Rikenellaceae, Alistipes, and Parabacteroide were reversely correlated with body mass index (BMI).CONCLUSION: In conclusion, our findings provide evidence that SG induces significant alterations in the abundances of Rikenellaceae, Alistipes, Parabacteroides, and Bacteroidales, as well as changes in lipid metabolism-related metabolites. Importantly, these changes were found to be closely linked to the alleviation of obesity. On the basis of these findings, we have identified a number of microbiotas that could be potential targets for treatment of obesity.PMID:38375198 | PMC:PMC10875461 | DOI:10.3389/fendo.2024.1338147

Front Oncol. 2024 Feb 5;14:1305684. doi: 10.3389/fonc.2024.1305684. eCollection 2024.ABSTRACTAIM: This research aimed to explore the causal impact of blood metabolites on oral cancer using a two-sample Mendelian randomization (MR) analysis. The study endeavored to identify potential biomarkers for oral cancer's clinical management.MATERIALS AND METHODS: Based on the large individual-level datasets from UK Biobank as well as GWAS summary datasets, we first constructed genetic risk scores (GRSs) of 486 human blood metabolites and evaluated the effect on oral cancer. Various statistical methods, including inverse variance weighted (IVW), MR-Egger, and weighted median, among others, were employed to analyze the potential causal relationship between blood metabolites and oral cancer. The sensitivity analyses were conducted using Cochran's Q tests, funnel plots, leave-one-out analyses, and MR-Egger intercept tests.RESULTS: 29 metabolites met the stringent selection criteria. Out of these, 14 metabolites demonstrated a positive association with oral cancer risk, while 15 metabolites indicated a protective effect against oral cancer. The IVW-derived estimates were significant, and the results were consistent across different statistical methodologies. Both the Cochran Q test and the MR-Egger intercept test indicated no heterogeneity and pleiotropy.CONCLUSION: This MR study offers evidence of the role specific blood metabolites play in oral cancer, pinpointing several with potential risk or protective effects. These findings could be helpful for new diagnostic tools and treatments for oral cancer. While the results are promising, additional research is necessary to fully validate and refine these conclusions. This study serves as a foundational step towards more comprehensive understandings in the future.PMID:38375154 | PMC:PMC10876297 | DOI:10.3389/fonc.2024.1305684

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