PubMed

Sci Rep. 2023 Jun 27;13(1):10407. doi: 10.1038/s41598-023-34598-7.ABSTRACTWhilst most individuals with SARS-CoV-2 infection have relatively mild disease, managed in the community, it was noted early in the pandemic that individuals with cardiovascular risk factors were more likely to experience severe acute disease, requiring hospitalisation. As the pandemic has progressed, increasing concern has also developed over long symptom duration in many individuals after SARS-CoV-2 infection, including among the majority who are managed acutely in the community. Risk factors for long symptom duration, including biological variables, are still poorly defined. Here, we examine post-illness metabolomic profiles, using nuclear magnetic resonance (Nightingale Health Oyj), and gut-microbiome profiles, using shotgun metagenomic sequencing (Illumina Inc), in 2561 community-dwelling participants with SARS-CoV-2. Illness duration ranged from asymptomatic (n = 307) to Post-COVID Syndrome (n = 180), and included participants with prolonged non-COVID-19 illnesses (n = 287). We also assess a pre-established metabolomic biomarker score, previously associated with hospitalisation for both acute pneumonia and severe acute COVID-19 illness, for its association with illness duration. We found an atherogenic-dyslipidaemic metabolic profile, including biomarkers such as fatty acids and cholesterol, was associated with longer duration of illness, both in individuals with and without SARS-CoV-2 infection. Greater values of a pre-existing metabolomic biomarker score also associated with longer duration of illness, regardless of SARS-CoV-2 infection. We found no association between illness duration and gut microbiome profiles in convalescence. This highlights the potential role of cardiometabolic dysfunction in relation to the experience of long duration symptoms after symptoms of acute infection, both COVID-19 as well as other illnesses.PMID:37369825 | DOI:10.1038/s41598-023-34598-7

Brief Bioinform. 2023 Jun 27:bbad244. doi: 10.1093/bib/bbad244. Online ahead of print.ABSTRACTUntargeted metabolomics is gaining widespread applications. The key aspects of the data analysis include modeling complex activities of the metabolic network, selecting metabolites associated with clinical outcome and finding critical metabolic pathways to reveal biological mechanisms. One of the key roadblocks in data analysis is not well-addressed, which is the problem of matching uncertainty between data features and known metabolites. Given the limitations of the experimental technology, the identities of data features cannot be directly revealed in the data. The predominant approach for mapping features to metabolites is to match the mass-to-charge ratio (m/z) of data features to those derived from theoretical values of known metabolites. The relationship between features and metabolites is not one-to-one since some metabolites share molecular composition, and various adduct ions can be derived from the same metabolite. This matching uncertainty causes unreliable metabolite selection and functional analysis results. Here we introduce an integrated deep learning framework for metabolomics data that take matching uncertainty into consideration. The model is devised with a gradual sparsification neural network based on the known metabolic network and the annotation relationship between features and metabolites. This architecture characterizes metabolomics data and reflects the modular structure of biological system. Three goals can be achieved simultaneously without requiring much complex inference and additional assumptions: (1) evaluate metabolite importance, (2) infer feature-metabolite matching likelihood and (3) select disease sub-networks. When applied to a COVID metabolomics dataset and an aging mouse brain dataset, our method found metabolic sub-networks that were easily interpretable.PMID:37369636 | DOI:10.1093/bib/bbad244

Hum Mol Genet. 2023 Jun 27:ddad097. doi: 10.1093/hmg/ddad097. Online ahead of print.ABSTRACTRecently a nonparametric method has been proposed to impute the genetic component of a trait for a large set of genotyped individuals based on a separate GWAS summary dataset of the same trait (from the same population). The imputed trait may contain linear, non-linear and epistatic effects of genetic variants, thus can be used for downstream linear or non-linear association analyses and machine learning tasks. Here we propose an extension of the method to impute both genetic and environmental components of a trait using both SNP-trait and omics-trait association summary data. We illustrate an application to a UK Biobank subset of individuals (n ≈ 80 K) with both body mass index (BMI) GWAS data and metabolomic data. We divided the whole dataset into two equally sized and non-overlapping training and test datasets; we used the training data to build SNP- and metabolite-BMI association summary data and impute BMI on the test data. We compared the performance of the original and new imputation methods. As by the original method, the imputed BMI values by the new method largely retained SNP-BMI association information; however, the latter retained more information about BMI-environment associations, and were more highly correlated with the original observed BMI values.PMID:37369060 | DOI:10.1093/hmg/ddad097

Trends Psychiatry Psychother. 2023 Jun 26. doi: 10.47626/2237-6089-2022-0599. Online ahead of print.ABSTRACTINTRODUCTION: There has been growing concern about the long-term effects of COVID-19 on mental health. The biological factors common to psychiatric conditions and COVID-19 are not yet fully understood.METHODOLOGY: We narratively reviewed prospective longitudinal studies that measured metabolic or inflammatory markers and assessed psychiatric sequalae and cognitive impairment in individuals with COVID-19 at least 3 months after the infection. A literature search identified three relevant cohort studies.RESULTS: Overall, depressive symptomatology and cognitive deficits persisted for up to one year after COVID-19; depression and cognitive changes were predicted by acute inflammatory markers, and changes in these markers correlated with changes in depressive symptomatology; female sex, obesity, and the presence of inflammatory markers were associated with more severe clusters of physical and mental health status in patients' self-perceived recovery; and plasma metabolic profiles of patients continued to differ from those of healthy controls three months after hospital discharge, which were associated with widespread alterations in neuroimaging, reflecting issues with white matter integrity. This is a non-systematic review and cautions should be made while interpreting the conclusions.CONCLUSION: In individuals affected by the COVID-19, prolonged exposure to stress and alterations in metabolic and inflammatory markers plays a central role in psychiatric sequalae and cognitive deficits in the long term.PMID:37368949 | DOI:10.47626/2237-6089-2022-0599

Toxins (Basel). 2023 Jun 15;15(6):397. doi: 10.3390/toxins15060397.ABSTRACTZearalenone (ZEA) is a mycotoxin, commonly found in agricultural products, linked to adverse health impacts in humans and livestock. However, less is known regarding effects on fish as both ecological receptors and economically relevant "receptors" through contamination of aquaculture feeds. In the present study, a metabolomics approach utilizing high-resolution magic angle spinning nuclear magnetic resonance (HRMAS NMR) was applied to intact embryos of zebrafish (Danio rerio), and two marine fish species, olive flounder (Paralichthys olivaceus) and yellowtail snapper (Ocyurus chrysurus), to investigate the biochemical pathways altered by ZEA exposure. Following the assessment of embryotoxicity, metabolic profiling of embryos exposed to sub-lethal concentrations showed significant overlap between the three species and, specifically, identified metabolites linked to hepatocytes, oxidative stress, membrane disruption, mitochondrial dysfunction, and impaired energy metabolism. These findings were further supported by analyses of tissue-specific production of reactive oxygen species (ROS) and lipidomics profiling and enabled an integrated model of ZEA toxicity in the early life stages of marine and freshwater fish species. The metabolic pathways and targets identified may, furthermore, serve as potential biomarkers for monitoring ZEA exposure and effects in fish in relation to ecotoxicology and aquaculture.PMID:37368698 | DOI:10.3390/toxins15060397

Toxins (Basel). 2023 Jun 1;15(6):370. doi: 10.3390/toxins15060370.ABSTRACTPhenyllactic acid (PLA), a promising food preservative, is safe and effective against a broad spectrum of food-borne pathogens. However, its mechanisms against toxigenic fungi are still poorly understood. In this study, we applied physicochemical, morphological, metabolomics, and transcriptomics analyses to investigate the activity and mechanism of PLA inhibition of a typical food-contaminating mold, Aspergillus flavus. The results showed that PLA effectively inhibited the growth of A. flavus spores and reduced aflatoxin B1 (AFB1) production by downregulating key genes associated with AFB1 biosynthesis. Propidium iodide staining and transmission electron microscopy analysis demonstrated a dose-dependent disruption of the integrity and morphology of the A. flavus spore cell membrane by PLA. Multi-omics analyses showed that subinhibitory concentrations of PLA induced significant changes in A. flavus spores at the transcriptional and metabolic levels, as 980 genes and 30 metabolites were differentially expressed. Moreover, KEGG pathway enrichment analysis indicated PLA-induced cell membrane damage, energy-metabolism disruption, and central-dogma abnormality in A. flavus spores. The results provided new insights into the anti-A. flavus and -AFB1 mechanisms of PLA.PMID:37368671 | DOI:10.3390/toxins15060370

Geroscience. 2023 Jun 27. doi: 10.1007/s11357-023-00855-w. Online ahead of print.ABSTRACTMitochondrial improvements resulting from behavioral interventions, such as diet and exercise, are systemic and apparent across multiple tissues. Here, we test the hypothesis that factors present in serum, and therefore circulating throughout the body, can mediate changes in mitochondrial function in response to intervention. To investigate this, we used stored serum from a clinical trial comparing resistance training (RT) and RT plus caloric restriction (RT + CR) to examine effects of blood borne circulating factors on myoblasts in vitro. We report that exposure to dilute serum is sufficient to mediate bioenergetic benefits of these interventions. Additionally, serum-mediated bioenergetic changes can differentiate between interventions, recapitulate sex differences in bioenergetic responses, and is linked to improvements in physical function and inflammation. Using metabolomics, we identified circulating factors associated with changes in mitochondrial bioenergetics and the effects of interventions. This study provides new evidence that circulating factors play a role in the beneficial effects of interventions that improve healthspan among older adults. Understanding the factors that drive improvements in mitochondrial function is a key step towards predicting intervention outcomes and developing strategies to countermand systemic age-related bioenergetic decline.PMID:37368157 | DOI:10.1007/s11357-023-00855-w

Metabolites. 2023 Jun 20;13(6):770. doi: 10.3390/metabo13060770.ABSTRACTAluminum (Al) toxicity is a major threat to global crop production in acidic soils, which can be mitigated by natural substances such as pyroligneous acid (PA). However, the effect of PA in regulating plant central carbon metabolism (CCM) under Al stress is unknown. In this study, we investigated the effects of varying PA concentrations (0, 0.25 and 1% PA/ddH2O (v/v)) on intermediate metabolites involved in CCM in tomato (Solanum lycopersicum L., 'Scotia') seedlings under varying Al concentrations (0, 1 and 4 mM AlCl3). A total of 48 differentially expressed metabolites of CCM were identified in the leaves of both control and PA-treated plants under Al stress. Calvin-Benson cycle (CBC) and pentose phosphate pathway (PPP) metabolites were considerably reduced under 4 mM Al stress, irrespective of the PA treatment. Conversely, the PA treatment markedly increased glycolysis and tricarboxylic acid cycle (TCA) metabolites compared to the control. Although glycolysis metabolites in the 0.25% PA-treated plants under Al stress were comparable to the control, the 1% PA-treated plants exhibited the highest accumulation of glycolysis metabolites. Furthermore, all PA treatments increased TCA metabolites under Al stress. Electron transport chain (ETC) metabolites were higher in PA-treated plants alone and under 1 mM, Al but were reduced under a higher Al treatment of 4 mM. Pearson correlation analysis revealed that CBC metabolites had a significantly strong positive (r = 0.99; p < 0.001) association with PPP metabolites. Additionally, glycolysis metabolites showed a significantly moderate positive association (r = 0.76; p < 0.05) with TCA metabolites, while ETC metabolites exhibited no association with any of the determined pathways. The coordinated association between CCM pathway metabolites suggests that PA can stimulate changes in plant metabolism to modulate energy production and biosynthesis of organic acids under Al stress conditions.PMID:37367927 | DOI:10.3390/metabo13060770

Metabolites. 2023 Jun 19;13(6):769. doi: 10.3390/metabo13060769.ABSTRACTThe identification of metabolomic biomarkers relies on the analysis of large cohorts of patients compared to healthy controls followed by the validation of markers in an independent sample set. Indeed, circulating biomarkers should be causally linked to pathology to ensure that changes in the marker precede changes in the disease. However, this approach becomes unfeasible in rare diseases due to the paucity of samples, necessitating the development of new methods for biomarker identification. The present study describes a novel approach that combines samples from both mouse models and human patients to identify biomarkers of OPMD. We initially identified a pathology-specific metabolic fingerprint in murine dystrophic muscle. This metabolic fingerprint was then translated into (paired) murine serum samples and then to human plasma samples. This study identified a panel of nine candidate biomarkers that could predict muscle pathology with a sensitivity of 74.3% and specificity of 100% in a random forest model. These findings demonstrate that the proposed approach can identify biomarkers with good predictive performance and a higher degree of confidence in their relevance to pathology than markers identified in a small cohort of human samples alone. Therefore, this approach has a high potential utility for identifying circulating biomarkers in rare diseases.PMID:37367926 | DOI:10.3390/metabo13060769

Metabolites. 2023 Jun 19;13(6):768. doi: 10.3390/metabo13060768.ABSTRACTDetermination of chemotypes and of their role in the polymorphism of populations is an important field in the research on secondary metabolites of plants. In the present study, by gas chromatography coupled with mass spectrometry, the composition of bark extracts from rowan S. aucuparia subsp. sibirica was determined for 16 trees growing within Akademgorodok of Novosibirsk, with bark samples collected both in winter and summer. Among 101 fully or partially identified metabolites, there are alkanes, alkenes, linear alcohols, fatty acids and their derivatives, phenols and their derivatives, prunasin and its parent and derivative compounds, polyprenes and their derivatives, cyclic diterpenes, and phytosterols. These compounds were grouped according to their biosynthesis pathways. Cluster analysis revealed two groups among the bark samples collected in winter and three groups among bark samples collected in summer. The key determinants of this clustering are the biosynthesis of metabolites via the cyanogenic pathway (especially potentially toxic prunasin) and their formation via the phytosterol pathway (especially potentially pharmacologically useful lupeol). It follows from the results that the presence of chemotypes having sharply different profiles of metabolites in a population from a small geographic area invalidates the practice of general sampling to obtain averaged data when a population is described. From the standpoint of possible industrial use or plant selection based on metabolomic data, it is possible to select specific sets of samples containing a minimal amount of potentially toxic compounds and the largest amount of potentially useful substances.PMID:37367925 | DOI:10.3390/metabo13060768

Metabolites. 2023 Jun 19;13(6):763. doi: 10.3390/metabo13060763.ABSTRACTPanax vietnamensis var. vietnamensis (PVV) and Panax vietnamensis var. fuscidiscus (PVF) both belong to Panax vietnamensis species and are chemically and morphologically similar, making it hard to distinguish for the consumer. Herein, 42 PVF and 12 PVV samples were collected in Quang Nam and Lai Chau Province, respectively, and subsequently characterized by ITSr-DNA sequence data to verify their origins. Next, untargeted metabolomics combined with multivariate statistical analysis was developed to differentiate PVV and PVF. The metabolic profiles of PVV and PVF were found to be distinct and classified well using Partial Least-Squares Discriminant Analysis (PLS-DA) in the training set. Among them, seven ginsenosides were of high abundance in PVV, while six were of high abundance in PVF. Next, the test set was used to validate 13 putative differential markers found in the training set, illustrating a complete match with the expression patterns of these ginsenosides in the training set. Finally, PLS-DA and linear Support Vector Machine models both indicated distinct ginsenoside profiles of PVV and PVF without misclassification in the test set. Conclusively, the developed untargeted metabolomics approach might serve as a powerful tool for the authentication of PVV and PVF at the metabolome level.PMID:37367920 | DOI:10.3390/metabo13060763

Metabolites. 2023 Jun 17;13(6):761. doi: 10.3390/metabo13060761.ABSTRACTWe aimed to explore the differential metabolites in amniotic fluid and its cells from fetuses with fetal growth restriction (FGR). A total of 28 specimens of amniotic fluid were collected, including 18 with FGR and 10 controls. Differential metabolites in all samples were detected by chromatography-mass spectrometry. Principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used to analyze the differences in metabolic spectra between the FGR and control groups through multidimensional and single-dimensional statistical analysis. The KEGG database was used for metabolic pathway enrichment analysis. Both PCA and OPLS-DA models showed a clear separation trend between FGR and control groups. We identified 27 differentially expressed metabolites in the amniotic fluid supernatant of the two groups (p < 0.05), of which 14 metabolites were up-regulated in the FGR group, and 13 metabolites, such as glutamate, phenylalanine, valine and leucine, were down-regulated. We also identified 20 differentially expressed metabolites in the amniotic fluid cell (p < 0.05), of which 9 metabolites, including malic acid, glycolic acid and D-glycerate, were up-regulated significantly and 11 metabolites, including glyceraldehyde, were down-regulated. Pathway analysis showed that most of the identified differential metabolites were involved in tricarboxylic acid cycle (TCA cycle), ABC transport, amino acid metabolism pathways and so on. The results indicated that many metabolic changes associated with FGR, which are mainly manifested by abnormal metabolism of amino acid in amniotic fluid and abnormal glucose metabolism including TCA cycle in amniotic fluid cells, respectively. Our findings provide more data for exploring the mechanism of FGR and the potential therapy targets.PMID:37367917 | DOI:10.3390/metabo13060761

Metabolites. 2023 Jun 13;13(6):749. doi: 10.3390/metabo13060749.ABSTRACTMaternal metabolites influence the size of newborns independently of maternal body mass index (BMI) and glycemia, highlighting the importance of maternal metabolism on offspring outcomes. This study examined associations of maternal metabolites during pregnancy with childhood adiposity, and cord blood metabolites with childhood adiposity using phenotype and metabolomic data from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study and the HAPO Follow-Up Study. The maternal metabolites analyses included 2324 mother-offspring pairs, while the cord blood metabolites analyses included 937 offspring. Multiple logistic and linear regression were used to examine associations between primary predictors, maternal or cord blood metabolites, and childhood adiposity outcomes. Multiple maternal fasting and 1 hr metabolites were significantly associated with childhood adiposity outcomes in Model 1 but were no longer significant after adjusting for maternal BMI and/or maternal glycemia. In the fully adjusted model, fasting lactose levels were negatively associated with child BMI z-scores and waist circumference, while fasting urea levels were positively associated with waist circumference. One-hour methionine was positively associated with fat-free mass. There were no significant associations between cord blood metabolites and childhood adiposity outcomes. Few metabolites were associated with childhood adiposity outcomes after adjusting for maternal BMI and glucose, suggesting that maternal BMI accounts for the association between maternal metabolites and childhood adiposity.PMID:37367907 | DOI:10.3390/metabo13060749

Metabolites. 2023 Jun 12;13(6):744. doi: 10.3390/metabo13060744.ABSTRACTThe inflammatory and insulinemic potentials of diets have been associated with colorectal cancer risk. However, it is unknown whether the plasma metabolite profiles related to inflammatory diets, or to insulinemic diets, underlie this association. The aim of this study was to evaluate the association between metabolomic profile scores related to the food-based empirical dietary inflammatory patterns (EDIP), the empirical dietary index for hyperinsulinemia (EDIH), and plasma inflammation (CRP, IL-6, TNFα-R2, adiponectin) and insulin (C-peptide) biomarkers, and colorectal cancer risk. Elastic net regression was used to derive three metabolomic profile scores for each dietary pattern among 6840 participants from the Nurses' Health Study and Health Professionals Follow-up Study, and associations with CRC risk were examined using multivariable-adjusted logistic regression, in a case-control study of 524 matched pairs nested in both cohorts. Among 186 known metabolites, 27 were significantly associated with both the EDIP and inflammatory biomarkers, and 21 were significantly associated with both the EDIH and C-peptide. In men, odds ratios (ORs) of colorectal cancer, per 1 standard deviation (SD) increment in metabolomic score, were 1.91 (1.31-2.78) for the common EDIP and inflammatory-biomarker metabolome, 1.12 (0.78-1.60) for EDIP-only metabolome, and 1.65 (1.16-2.36) for the inflammatory-biomarkers-only metabolome. However, no association was found for EDIH-only, C-peptide-only, and the common metabolomic signatures in men. Moreover, the metabolomic signatures were not associated with colorectal cancer risk among women. Metabolomic profiles reflecting pro-inflammatory diets and inflammation biomarkers were associated with colorectal cancer risk in men, while no association was found in women. Larger studies are needed to confirm our findings.PMID:37367904 | DOI:10.3390/metabo13060744

Metabolites. 2023 Jun 11;13(6):745. doi: 10.3390/metabo13060745.ABSTRACTMetabolomics is the analytical study of metabolites in biological matrices using high-throughput profiling. Traditionally, the metabolome has been studied to identify various biomarkers for the diagnosis and pathophysiology of disease. Over the last decade, metabolomic research has grown to include the identification of prognostic markers, the development of novel treatment strategies, and the prediction of disease severity. In this review, we summarized the available evidence on the use of metabolome profiling in neurocritical care populations. Specifically, we focused on aneurysmal subarachnoid hemorrhage, traumatic brain injury, and intracranial hemorrhage to identify the gaps in the current literature and to provide direction for future studies. A primary literature search of the Medline and EMBASE databases was conducted. Upon removing duplicate studies, abstract screening and full-text screening were performed. We screened 648 studies and extracted data from 17 studies. Based on the current evidence, the utility of metabolomic profiling has been limited due to inconsistencies amongst studies and a lack of reproducible data. Studies identified various biomarkers for diagnosis, prognosis, and treatment modification. However, studies evaluated and identified different metabolites, resulting in an inability to compare the study results. Future research towards addressing the gaps in the current literature, including reproducing data on the use of specific metabolite panels, is needed.PMID:37367902 | DOI:10.3390/metabo13060745

Metabolites. 2023 Jun 10;13(6):742. doi: 10.3390/metabo13060742.ABSTRACTFlower color is an important characteristic of ornamental plants and is determined by various chemical components, including anthocyanin. In the present study, combined metabolomics and transcriptomics analysis was used to explore color variations in the chrysanthemums of three cultivars, of which the color of JIN is yellow, FEN is pink, and ZSH is red. A total of 29 different metabolites, including nine anthocyanins, were identified in common in the three cultivars. Compared with the light-colored cultivars, all of the nine anthocyanin contents were found to be up-regulated in the dark-colored ones. The different contents of pelargonidin, cyanidin, and their derivates were found to be the main reason for color variations. Transcriptomic analysis showed that the color difference was closely related to anthocyanin biosynthesis. The expression level of anthocyanin structural genes, including DFR, ANS, 3GT, 3MaT1, and 3MaT2, was in accordance with the flower color depth. This finding suggests that anthocyanins may be a key factor in color variations among the studied cultivars. On this basis, two special metabolites were selected as biomarkers to assist in chrysanthemum breeding for color selection.PMID:37367900 | DOI:10.3390/metabo13060742

Metabolites. 2023 Jun 8;13(6):735. doi: 10.3390/metabo13060735.ABSTRACTInsect metabolites play vital roles in regulating the physiology, behavior, and numerous adaptations of insects, which has contributed to them becoming the largest class of Animalia. However, systematic metabolomics within the insects is still unclear. The present study performed a widely targeted metabolomics analysis based on the HPLC-MS/MS technology to construct a novel integrated metabolic database presenting comprehensive multimetabolite profiles from nine insect species across three metamorphosis types. A total of 1442 metabolites were identified, including amino acids and their metabolites, organic acids and their derivatives, fatty acids (FAs), glycerophospholipids (GPs), nucleotides and their metabolites, and benzene and its substituted derivatives. Among them, 622 metabolites were used to generate a 0 and 1 matrix based on their presence or absence, and these metabolites were enriched in arachidonic acid metabolism, tyrosine metabolism, phenylalanine metabolism, and insect hormone biosynthesis pathways. Our study revealed that there is a high coincidence between the evolutionary relationships of the species and the hierarchical cluster based on the types of metabolites, while the quantities of the metabolites show a high diversity among species. The metabolome of the nine representative insects provides an important platform for implementing the analysis of insect systemic metabolites and biological events at the metabolic level.PMID:37367893 | DOI:10.3390/metabo13060735

Metabolites. 2023 Jun 6;13(6):727. doi: 10.3390/metabo13060727.ABSTRACTThe fruit of the oil palm (Elaeis guineensis Jacq.) has fleshy mesocarpic tissue rich in lipids. This edible vegetable oil is economically and nutritionally significant across the world. The core concepts of oil biosynthesis in oil palms remain to be researched as the knowledge of oil biosynthesis in plants improves. In this study, we utilized a metabolite approach and mass spectral analysis to characterize metabolite changes and identify the sequences of protein accumulation during the physiological processes that regulate oil synthesis during oil palm fruit ripening. Here, we performed a comprehensive lipidomic data analysis in order to understand the role of lipid metabolism in oil biosynthesis mechanisms. The experimental materials were collected from the mesocarp of oil palm (Tenera) at 95 days (early accumulation of fatty acid, first stage), 125 days (rapid growth of fatty acid accumulation, second stage), and 185 days (stable period of fatty acid accumulation, third stage) after pollination. To gain a clear understanding of the lipid changes that occurred during the growth of the oil palm, the metabolome data were found using principal component analysis (PCA). Furthermore, the accumulations of diacylglycerols, ceramides, phosphatidylethanolamine, and phosphatidic acid varied between the developmental stages. Differentially expressed lipids were successfully identified and functionally classified using KEGG analysis. Proteins related to the metabolic pathway, glycerolipid metabolism, and glycerphospholipid metabolism were the most significantly changed proteins during fruit development. In this study, LC-MS analysis and evaluation of the lipid profile in different stages of oil palm were performed to gain insight into the regulatory mechanisms that enhance fruit quality and govern differences in lipid composition and biosynthesis.PMID:37367885 | DOI:10.3390/metabo13060727

Metabolites. 2023 Jun 5;13(6):725. doi: 10.3390/metabo13060725.ABSTRACTVery long-chain acylcarnitine dehydrogenase deficiency (VLCADD) is a rare inherited metabolic disorder associated with fatty acid β-oxidation and characterized by genetic mutations in the ACADVL gene and accumulations of acylcarnitines. VLCADD, developed in neonates or later adults, can be diagnosed using newborn bloodspot screening (NBS) or genetic sequencing. These techniques have limitations, such as a high false discovery rate and variants of uncertain significance (VUS). As a result, an extra diagnostic tool is needed to deliver improved performance and health outcomes. As VLCADD is linked with metabolic disturbance, we postulated that newborn patients with VLCADD could display a distinct metabolomics pattern compared to healthy newborns and other disorders. Herein, we applied an untargeted metabolomics approach using liquid chromatography-high resolution mass spectrometry (LC-HRMS) to measure the global metabolites in dried blood spot (DBS) cards collected from VLCADD newborns (n = 15) and healthy controls (n = 15). Two hundred and six significantly dysregulated endogenous metabolites were identified in VLCADD, in contrast to healthy newborns. Fifty-eight and one hundred and eight up- and down-regulated endogenous metabolites were involved in several pathways such as tryptophan biosynthesis, aminoacyl-tRNA biosynthesis, amino sugar and nucleotide sugar metabolism, pyrimidine metabolism and pantothenate, and CoA biosynthesis. Furthermore, biomarker analyses identified 3,4-Dihydroxytetradecanoylcarnitine (AUC = 1), PIP (20:1)/PGF1alpha) (AUC = 0.982), and PIP2 (16:0/22:3) (AUC = 0.978) as potential metabolic biomarkers for VLCADD diagnosis. Our findings showed that compared to healthy newborns, VLCAADD newborns exhibit a distinctive metabolic profile, and identified potential biomarkers that can be used for early diagnosis, which improves the identification of the affected patients earlier. This allows for the timely administration of proper treatments, leading to improved health. However, further studies with large independent cohorts of VLCADD patients with different ages and phenotypes need to be studied to validate our potential diagnostic biomarkers and their specificity and accuracy during early life.PMID:37367883 | DOI:10.3390/metabo13060725

Metabolites. 2023 Jun 2;13(6):721. doi: 10.3390/metabo13060721.ABSTRACTThe use of sensitive animals in toxicological studies tends to be limited. Even though cell culture is an attractive alternative, it has some limitations. Therefore, we investigated the potential of the metabolomic profiling of the allantoic fluid (AF) from ex ovo chick embryos to predict the hepatotoxicity of valproate (VPA). To this end, the metabolic changes occurring during embryo development and following exposure to VPA were assessed using 1H-NMR spectroscopy. During embryonic development, our findings indicated a metabolism progressively moving from anaerobic to aerobic, mainly based on lipids as the energy source. Next, liver histopathology of VPA-exposed embryos revealed abundant microvesicles indicative of steatosis and was metabolically confirmed via the determination of lipid accumulation in AF. VPA-induced hepatotoxicity was further demonstrated by (i) lower glutamine levels, precursors of glutathione, and decreased β-hydroxybutyrate, an endogenous antioxidant; (ii) changes in lysine levels, a precursor of carnitine, which is essential in the transport of fatty acids to the mitochondria and whose synthesis is known to be reduced by VPA; and (iii) choline accumulation that promotes the export of hepatic triglycerides. In conclusion, our results support the use of the ex ovo chick embryo model combined with the metabolomic assessment of AF to rapidly predict drug-induced hepatotoxicity.PMID:37367880 | DOI:10.3390/metabo13060721