Metabolomics Data Research Articles

Integrated metabolomics and transcriptomics reveal metabolic alterations of Ophiocordyceps sinensis from different geographical regions

Ophiocordyceps sinensis is a prized edible and medicinal fungus due to its high nutritional value and broad pharmacological properties. The quality of O. sinensis from Nagqu in Tibet has been regarded as superior to those from other regions. However, the mechanisms underlying these differences remain unclear. This study investigated the metabolic profiles and gene expression patterns of O. sinensis from five major production areas on the Tibetan Plateau in China using metabolomics and transcriptomics. A total of 349 differentially accumulated metabolites (DAMs) and 2983 differentially expressed genes (DEGs) were identified. O. sinensis from Nagqu was characterized by 42 DAMs, primarily including amino acids, nucleosides, fatty acids, and vitamins. Integrated metabolomic and transcriptomic data indicated that DAMs and DEGs were co-enriched in pathways associated with amino acid metabolism, carbohydrate metabolism, lipid metabolism, and nucleotide metabolism. Additionally, the genes ADE5, pgm, purC, IMPDH, ADSS, AK, CarA, CarB, and OMPD were suggested to play critical roles in the biosynthesis of nucleosides, while the genes LSS, CYP51-1, and ERG25 were implicated in steroid biosynthesis. These findings enhance our understanding of the regulatory mechanisms of bioactive compound synthesis in O. sinensis.

P63 affects distinct metabolic pathways during keratinocyte senescence, evaluated by metabolomic profile and gene expression analysis.

Unraveling the molecular nature of skin aging and keratinocyte senescence represents a challenging research project in epithelial biology. In this regard, depletion of p63, a p53 family transcription factor prominently expressed in human and mouse epidermis, accelerates both aging and the onset of senescence markers in vivo animal models as well as in ex vivo keratinocytes. Nonetheless, the biochemical link between p63 action and senescence phenotype remains largely unexplored. In the present study, through ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) and gas chromatography/mass spectrometry (GC/MS) metabolomic analysis, we uncover interesting pathways linking replicative senescence to metabolic alterations during p63 silencing in human keratinocytes. Integration of our metabolomic profiling data with targeted transcriptomic investigation empowered us to demonstrate that absence of p63 and senescence share similar modulation profiles of oxidative stress markers, pentose phosphate pathway metabolites and lyso-glycerophospholipids, the latter due to enhanced phospholipases gene expression profile often under p63 direct/indirect gene control. Additional biochemical features identified in deranged keratinocytes include a relevant increase in lipids production, glucose and pyruvate levels as confirmed by upregulation of gene expression of key lipid synthesis and glycolytic enzymes, which, together with improved vitamins uptake, characterize senescence phenotype. Silencing of p63 in keratinocytes instead, translates into a blunted flux of metabolites through both glycolysis and the Krebs cycle, likely due to a p63-dependent reduction of hexokinase 2 and citrate synthase gene expression. Our findings highlight the potential role of p63 in counteracting keratinocyte senescence also through fine regulation of metabolite levels and relevant biochemical pathways. We believe that our research might contribute significantly to the discovery of new implications of p63 in keratinocyte senescence and related diseases.

Depletion by Hemodialysis of the Antioxidant Ergothioneine

Background: Hemodialysis may excessively remove valuable solutes. Untargeted metabolomics data from a prior study suggested that ergothioneine was depleted in the plasma of hemodialysis subjects. Ergothioneine is a dietary-derived solute with antioxidant properties. The presence of a highly specific ergothioneine uptake transporter suggests that it is valuable. Ergothioneine levels are high in tissues susceptible to oxidative stress, particularly erythrocytes. We compared erythrocyte and plasma ergothioneine levels in subjects receiving hemodialysis to those in subjects with advanced chronic kidney disease (CKD) and subjects without known kidney disease (controls). We further examined the extent to which indiscriminate removal by hemodialysis could contribute to ergothioneine depletion. Methods: Liquid chromatography tandem mass spectrometry with stable isotope dilution was used to measure the erythrocyte and plasma levels of ergothioneine in 12 control, 12 CKD, and 11 hemodialysis subjects. We also measured the urinary excretion of ergothioneine in control and CKD subjects, and the dialytic removal of ergothioneine in hemodialysis subjects. Results: Erythrocyte ergothioneine levels were markedly reduced in CKD and hemodialysis subjects. Erythrocyte levels in CKD subjects were on average 24% of the levels in control subjects and were even lower in hemodialysis subjects, averaging 8% of control subjects. Plasma ergothioneine levels were also reduced in CKD and hemodialysis subjects but to a lesser extent than the erythrocyte levels. Kidney tubular reabsorption of ergothioneine was avid. In contrast, hemodialysis cleared ergothioneine at a rate of 146±36 ml/min so that removal of ergothioneine by hemodialysis greatly exceeded the amount excreted in the urine in both CKD and control subjects. Conclusions: Ergothioneine, a potentially valuable antioxidant, is severely depleted in people maintained on hemodialysis. Future studies are required to assess the consequences of ergothioneine depletion.

Integration of metabolomics and transcriptomics to reveal anti-immunosuppression mechanism of Lycium barbarum polysaccharide

It is well documented that immunosuppression in chickens increases the risk of secondary infections and immunodeficiencies, resulting in significant financial setbacks for the poultry sector. It is crucial to determine if Lycium barbarum polysaccharide (LBP) can counteract immune suppression in young chickens, considering its known ability to modulate immune responses. The aim of this study was to investigate the antagonistic effect and mechanism of LBP on immunosuppression in chicks. A total of 200 seven-day-old Hyland Brown laying hens were used to develop an immunosuppression model and to investigate the optimal time of use and optimal dosage of LBP. A further 120 seven-day-old Hyland Brown laying hens were used to investigate the mechanism of antagonism of LBP against immunosuppression at the optimal time and dosage. The results demonstrated that LBP significantly elevated body weight, spleen index, and peripheral lymphocyte transformation rate, and ameliorated pathological spleen damage in immunosuppressed chickens. A total of 178 differential genes were significantly upregulated following LBP intervention, with a significant enrichment in immune-related pathways, including the chemokine signalling pathway, the C-type lectin receptor signalling pathway, the B-cell receptor signalling pathway, platelet activation, natural killer cell-mediated cytotoxicity, and Th1 and Th2 cell differentiation. A total of 20 different metabolites were identified by metabolomics, which were mainly involved in vitamin metabolism, lipid metabolism, nucleic acid metabolism and amino acid metabolism. The integrated examination of transcriptomic and metabolomic data revealed that the glycerophospholipid metabolic pathway stands out as the most significant among all metabolic pathways. The results demonstrated that LBP regulate the immune system in a multi-pathway and multi-target way.

Comparative transcriptomic and metabolomic analysis of FTO knockout and wild-type porcine iliac artery endothelial cells.

The fat mass and obesity associated (FTO) gene, previously identified as a pivotal genetic locus associated with adiposity, has recently been linked to various cancers. In this study, we established an FTO knockout (KO) cell line in porcine iliac artery endothelial cells (PIECs) utilizing CRISPR/Cas9 technology to systematically investigate the gene's function and effect through transcriptomic and metabolomic analysis. Our results revealed significant gene expression and metabolic profiles differences between the FTO KO and wild-type (WT) cells. Furthermore, enrichment analysis highlighted the involvement of differentially expressed genes in metabolic processes, cellular components, and molecular functions, as well as in complement and coagulation cascades, mineral absorption, glutathione metabolism, insulin signaling, fluid shear stress, and atherosclerosis pathways. The metabolomic profiling revealed clear distinctions between the FTO KO and WT cells, indicating profound modifications in cellular metabolism. Correlation analysis of transcriptomic and metabolomic data revealed a significant association between six metabolites and twenty genes, with melatonin showing specific correlations with the expression of several genes, indicating a complex regulatory network between gene expression and metabolic changes. This study provides a foundation for further research on the FTO gene's role in cellular processes and molecular mechanisms underlying physiological and pathological conditions.

Study on the molecular mechanism of atopic dermatitis in mice based on skin and serum metabolomic analysis.

Atopic dermatitis (AD) is a common chronic inflammatory dermatosis. However, the exact molecular mechanism underlying the development of AD remain largely unclear. To investigate comprehensive metabolomic alterations in serum and skin tissue between 2,4-dinitrofluorobenzene (DNFB)-induced AD-like mice and healthy controls, aiming to identify the potential disease biomarkers and explore the molecular mechanisms of AD. In this study, Untargeted metabolomics analysis was used to investigate both skin and serum metabolic abnormalities of 2,4-dinitrofluorobenzene (DNFB)-induced AD-like mice. Then, the metabolic differences among the groups were determined through the application of multivariate analysis. Additionally, the selection of predictive biomarkers was accomplished using the receiver operating characteristic (ROC) module. Our findings showed that levels of 220 metabolites in the skin and 94 metabolites in the serum were different in AD-like mice that were treated with DNFB compared to control mice. Uracil, N-Acetyl-L-methionine, deoxyadenosine monophoosphate, 2-acetyl-l-alkyl-sn-glycero-3-phosphcholine, and prostaglandin D2 are considered potential biomarkers of AD as obtained by integrating skin and serum differential metabolite results. Metabolomic data analysis showed that the metabolic pathways in which skin and serum are involved together include histidine metabolism, pyrimidine metabolism, alanine, aspartate, and glutamate metabolism. Our research explained the possible molecular mechanism of AD at the metabolite level and provided potential targets for the development of clinical drugs for AD.

Omics Studies in CKD: Diagnostic Opportunities and Therapeutic Potential.

Omics technologies have significantly advanced the prediction and therapeutic approaches for chronic kidney disease (CKD) by providing comprehensive molecular insights. This is a review of the current state and future prospects of integrating biomarkers into the clinical practice for CKD, aiming to improve patient outcomes by targeted therapeutic interventions. In fact, the integration of genomic, transcriptomic, proteomic, and metabolomic data has enhanced our understanding of CKD pathogenesis and identified novel biomarkers for an early diagnosis and targeted treatment. Advanced computational methods and artificial intelligence (AI) have further refined multi-omics data analysis, leading to more accurate prediction models for disease progression and therapeutic responses. These developments highlight the potential to improve CKD patient care with a precise and individualized treatment plan .

Enhancing type 2 diabetes mellitus prediction by integrating metabolomics and tree-based boosting approaches

BackgroundType 2 diabetes mellitus (T2DM) is a global health problem characterized by insulin resistance and hyperglycemia. Early detection and accurate prediction of T2DM is crucial for effective management and prevention. This study explores the integration of machine learning (ML) and explainable artificial intelligence (XAI) approaches based on metabolomics panel data to identify biomarkers and develop predictive models for T2DM.MethodsMetabolomics data from T2DM (n = 31) and healthy controls (n = 34) were analyzed for biomarker discovery (mostly amino acids, fatty acids, and purines) and T2DM prediction. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) regression to enhance the model’s accuracy and interpretability. Advanced three tree-based ML algorithms (KTBoost: Kernel-Tree Boosting; XGBoost: eXtreme Gradient Boosting; NGBoost: Natural Gradient Boosting) were employed to predict T2DM using these biomarkers. The SHapley Additive exPlanations (SHAP) method was used to explain the effects of metabolomics biomarkers on the prediction of the model.ResultsThe study identified multiple metabolites associated with T2DM, where LASSO feature selection highlighted important biomarkers. KTBoost [Accuracy: 0.938; CI: (0.880-0.997), Sensitivity: 0.971; CI: (0.847-0.999), Area under the Curve (AUC): 0.965; CI: (0.937-0.994)] demonstrated its effectiveness in using complex metabolomics data for T2DM prediction and achieved better performance than other models. According to KTBoost’s SHAP, high levels of phenylactate (pla) and taurine metabolites, as well as low concentrations of cysteine, laspartate, and lcysteate, are strongly associated with the presence of T2DM.ConclusionThe integration of metabolomics profiling and XAI offers a promising approach to predicting T2DM. The use of tree-based algorithms, in particular KTBoost, provides a robust framework for analyzing complex datasets and improves the prediction accuracy of T2DM onset. Future research should focus on validating these biomarkers and models in larger, more diverse populations to solidify their clinical utility.

Assessing polyomic risk to predict Alzheimer's disease using a machine learning model.

Alzheimer's disease (AD) is the most common form of dementia in the elderly. Given that AD neuropathology begins decades before symptoms, there is a dire need for effective screening tools for early detection of AD to facilitate early intervention. Here, we used tree-based and deep learning methods to train polyomic prediction models for AD affection status and age at onset, employing genomic, proteomic, metabolomic, and drug use data from UK Biobank. We used SHAP to determine the feature's importance. Our best-performing polyomic model achieved an area under the receiver operating characteristics curve (AUROC) of 0.87. We identified GFAP and CXCL17 proteins to be the strongest predictors of AD, besides apolipoprotein E (APOE) alleles. Increasing the number of cases by including "AD-by-proxy" cases did not improve AD prediction. Among the four modalities, genomics, and proteomics were the most informative modality based on AUROC (area under the receiver operating characteristic curve). Our data suggest that two blood-based biomarkers (glial fibrillary acidic protein [GFAP] and CXCL17) may be effective for early presymptomatic prediction of AD. We developed a polyomic model to predict AD and age-at-onset using omics and medication use data from EHR. We identified GFAP and CXCL17 proteins to be the strongest predictors of AD, besides APOE alleles. "AD-by-proxy" cases, if used in training, do not improve AD prediction. Proteomics was the most informative modality overall for affection status and AAO prediction.

Chronic corticosterone exposure causes anxiety- and depression-related behaviors with altered gut microbial and brain metabolomic profiles in adult male C57BL/6J mice

Chronic exposure to glucocorticoids in response to long-term stress is thought to be a risk factor for major depression. Depression is associated with disturbances in the gut microbiota composition and peripheral and central energy metabolism. However, the relationship between chronic glucocorticoid exposure, the gut microbiota, and brain metabolism remains largely unknown. In this study, we first investigated the effects of chronic corticosterone exposure on various domains of behavior in adult male C57BL/6J mice treated with the glucocorticoid corticosterone to evaluate them as an animal model of depression. We then examined the gut microbial composition and brain and plasma metabolome in corticosterone-treated mice. Chronic corticosterone treatment resulted in reduced locomotor activity, increased anxiety-like and depression-related behaviors, decreased rotarod latency, reduced acoustic startle response, decreased social behavior, working memory deficits, impaired contextual fear memory, and enhanced cued fear memory. Chronic corticosterone treatment also altered the composition of gut microbiota, which has been reported to be associated with depression, such as increased abundance of Bifidobacterium, Turicibacter, and Corynebacterium and decreased abundance of Barnesiella. Metabolomic data revealed that long-term exposure to corticosterone led to a decrease in brain neurotransmitter metabolites, such as serotonin, 5-hydroxyindoleacetic acid, acetylcholine, and gamma-aminobutyric acid, as well as changes in betaine and methionine metabolism, as indicated by decreased levels of adenosine, dimethylglycine, choline, and methionine in the brain. These results indicate that mice treated with corticosterone have good face and construct validity as an animal model for studying anxiety and depression with altered gut microbial composition and brain metabolism, offering new insights into the neurobiological basis of depression arising from gut-brain axis dysfunction caused by prolonged exposure to excessive glucocorticoids.

An Integrated Profiling of Liver Metabolome and Transcriptome of Pigs Fed Diets with Different Starch Sources

Diets containing higher-amylose-content starches were proved to have some beneficial effects on monogastric animals, such as promoting the proliferation of intestinal probiotics. However, current research on the effects of diets with different starch sources on animals at the extraintestinal level is still very limited. We hypothesized that diets with different starch sources may affect lipid-related gene expression and metabolism in the liver of pigs. This study aimed to use adult pig models to evaluate the effects of diets with different starch sources (tapioca starch, TS; pea starch, PS) on the liver gene expressions and metabolism. In total, 48 growing pigs were randomly assigned to the TS and PS diets with 8 replicate pens/group and 3 pigs per pen. On day 44 of the experiment, liver samples were collected for metabolome and transcriptome analysis. Metabolome data suggested that different starch sources affected (p < 0.05) the metabolic patterns of liver. Compared with the TS diet, the PS diet increased (p < 0.05) some unsaturated fatty acids and several amino acids or peptide levels in the liver of pigs. Moreover, transcriptome data indicated the PS diets elevated (p < 0.05) fatty acid β-oxidation-related gene expression in the liver of pigs, and reduced (p < 0.05) unsaturated fatty acid metabolism-related gene expression. The results of quantitative real-time PCR confirmed that the PS diet upregulated (p < 0.05) the expression of acyl-CoA dehydrogenase very long chain (ACADVL), carnitine palmitoyl transferase (CPT) 1A, and malonyl-CoA decarboxylase (MLYCD), and downregulated (p < 0.05) the expression level of cytochrome P450 2U1 (CYP2U1) and aldehyde dehydrogenase 1B1 (ALDH1B1) in the liver. In addition, the results of a Mantel test indicated the muscle fatty acids were significantly closely correlated (p < 0.05) with liver gene expressions and metabolites. In summary, these findings suggest that diets containing higher amylose starches improved the lipid degradation and the unsaturated fatty acid levels in pig livers, and thus can generate some potential beneficial effects (such as anti-inflammatory and antioxidant) on pig health.

Identification of a Novel Biomarker Panel for Breast Cancer Screening.

Breast cancer remains a major public health concern, and early detection is crucial for improving survival rates. Metabolomics offers the potential to develop non-invasive screening and diagnostic tools based on metabolic biomarkers. However, the inherent complexity of metabolomic datasets and the high dimensionality of biomarkers complicates the identification of diagnostically relevant features, with multiple studies demonstrating limited consensus on the specific metabolites involved. Unlike previous studies that rely on singular feature selection techniques such as Partial Least Square (PLS) or LASSO regression, this research combines supervised and unsupervised machine learning methods with random sampling strategies, offering a more robust and interpretable approach to feature selection. This study aimed to identify a parsimonious and robust set of biomarkers for breast cancer diagnosis using metabolomics data. Plasma samples from 185 breast cancer patients and 53 controls (from the Cooperative Human Tissue Network, USA) were analyzed. This study also overcomes the common issue of dataset imbalance by using propensity score matching (PSM), which ensures reliable comparisons between cancer and control groups. We employed Univariate Naïve Bayes, L2-regularized Support Vector Classifier (SVC), Principal Component Analysis (PCA), and feature engineering techniques to refine and select the most informative features. Our best-performing feature set comprised 11 biomarkers, including 9 metabolites (SM(OH) C22:2, SM C18:0, C0, C3OH, C14:2OH, C16:2OH, LysoPC a C18:1, PC aa C36:0 and Asparagine), a metabolite ratio (Kynurenine-to-Tryptophan), and 1 demographic variable (Age), achieving an area under the ROC curve (AUC) of 98%. These results demonstrate the potential for a robust, cost-effective, and non-invasive breast cancer screening and diagnostic tool, offering significant clinical value for early detection and personalized patient management.

Metabolite and protein associations with general health in the population-based CHRIS study

Identifying biomarkers able to discriminate individuals on different health trajectories is crucial to understand the molecular basis of age-related morbidity. We investigated multi-omics signatures of general health and organ-specific morbidity, as well as their interconnectivity. We examined cross-sectional metabolome and proteome data from 3,142 adults of the Cooperative Health Research in South Tyrol (CHRIS) study, an Alpine population study designed to investigate how human biology, environment, and lifestyle factors contribute to people’s health over time. We had 174 metabolites and 148 proteins quantified from fasting serum and plasma samples. We used the Cumulative Illness Rating Scale (CIRS) Comorbidity Index (CMI), which considers morbidity in 14 organ systems, to assess health status (any morbidity vs. healthy). Omics-signatures for health status were identified using random forest (RF) classifiers. Linear regression models were fitted to assess directionality of omics markers and health status associations, as well as to identify omics markers related to organ-specific morbidity. Next to age, we identified 21 metabolites and 10 proteins as relevant predictors of health status and results confirmed associations for serotonin and glutamate to be age-independent. Considering organ-specific morbidity, several metabolites and proteins were jointly related to endocrine, cardiovascular, and renal morbidity. To conclude, circulating serotonin was identified as a potential novel predictor for overall morbidity.

AI-Assisted Identification of Primary and Secondary Metabolomic Markers for Postoperative Delirium.

Despite considerable investigative efforts, the molecular mechanisms of postoperative delirium (POD) remain unresolved. The present investigation employs innovative methodologies for identifying potential primary and secondary metabolic markers of POD by analyzing serum metabolomic profiles utilizing the genetic algorithm and artificial neural networks. The primary metabolomic markers constitute a combination of metabolites that optimally distinguish between POD and non-POD groups of patients. Our analysis revealed L-lactic acid, inositol, and methylcysteine as the most salient primary markers upon which the prediction accuracy of POD manifestation achieved AUC = 99%. The secondary metabolomic markers represent metabolites that exhibit perturbed correlational patterns within the POD group. We identified 54 metabolites as the secondary markers of POD, incorporating neurotransmitters such as gamma-aminobutyric acid (GABA) and serotonin. These findings imply a systemic disruption in metabolic processes in patients with POD. The deployment of gene network reconstruction techniques facilitated the postulation of hypotheses describing the role of established genomic POD markers in the molecular-genetic mechanisms of metabolic pathways dysregulation, and involving the identified primary and secondary metabolomic markers. This study not only expands the understanding of POD pathogenesis but also introduces a novel technology for the bioinformatic analysis of metabolomic data that could aid in uncovering potential primary and secondary markers in diverse research domains.

Analysis of GCRV Pathogenesis and Therapeutic Measures Through Proteomic and Metabolomic Investigations in GCRV-Infected Tissues of Grass Carp (Ctenopharyngodon idella).

Hemorrhagic disease caused by grass carp reovirus (GCRV) infection is a major problem affecting the grass carp aquaculture industry. Therefore, inhibiting the spread of GCRV infection is of great economic significance. Herein, we sequenced five tissues (gill, liver, intestine, kidney, and muscle) from grass carp before and after GCRV infection using data-independent acquisition proteomic and untargeted metabolomic technologies, and quantitatively identified 10,808 proteins and 4040 metabolites. Then, we analyzed the differentially expressed proteins (DEPs) and metabolites (DEMs) before and after GCRV infection in the five tissues. Gene ontology analysis revealed that the five tissue DEPs were enriched in metabolic, including carbohydrate and lipid metabolic processes. Chemical taxonomy analysis showed that the categories of DEMs mainly included carbohydrates and lipids, such as fatty acids, glycerophospholipids, steroids, and their derivatives. Both the proteomic and the metabolomic data showed that GCRV affected the carbohydrate and lipid metabolism in the host. Shared pathway analysis was performed at both the protein and metabolic levels, showing significant enrichment of the glycolysis and pentose phosphate pathways (p < 0.001). Further analysis of glycolysis and pentose phosphate pathway inhibitors revealed that these two pathways are important for GCRV replication. As the kidney was the most affected among the five tissues, we analyzed the butanoate metabolism in the kidney, which revealed that most of the differentially expressed proteins and differently expressed metabolites in the butanoate metabolism were related to the TCA cycle. Further investigation showed that fumaric acid, an intermediate product in the TCA cycle, significantly inhibited GCRV replication in the CIK cells (p < 0.001), and that this inhibitory effect may be related to its induction of interferon system activation. The addition of fumaric acid to feed increased the survival rate of juvenile grass carp by 19.60% during GCRV infection, and protected the tissues of those infected with GCRV, making it a potential anti-GCRV feed additive. Our results provide new perspectives on GCRV pathogenesis and antiviral strategies for grass carp.

Aging and Pathological Conditions Similarity Revealed by Meta-Analysis of Metabolomics Studies Suggests the Existence of the Health and Age-Related Metapathway

Background: The incidence of many diseases increases with age and leads to multimorbidity, characterized by the presence of multiple diseases in old age. This phenomenon is closely related to systemic metabolic changes; the most suitable way to study it is through metabolomics. The use of accumulated metabolomic data to characterize this phenomenon at the system level may provide additional insight into the nature and strength of aging–disease relationships. Methods: For this purpose, metabolic changes associated with human aging and metabolic alterations under different pathological conditions were compared. To do this, the published results of metabolomic studies on human aging were compared with data on metabolite alterations collected in the human metabolome database through metabolite set enrichment analysis (MSEA) and combinatorial analysis. Results: It was found that human aging and pathological conditions involve the set of the same metabolic pathways with a probability of 99.96%. These data show the high identity of the aging process and the development of diseases at the metabolic level and allow to identify the set of metabolic pathways reflecting age-related changes closely associated with health. Based on these pathways, a metapathway was compiled, changes in which are simultaneously associated with health and age. Conclusions: The knowledge about the strength of the convergence of aging and pathological conditions has been supplemented by the rigor evidence at the metabolome level, which also made it possible to outline the age and health-relevant place in the human metabolism.

Sex-bias metabolism of fetal organs, and their relationship to the regulation of fetal brain-placental axis.

The placenta plays influential role in the fetal development of mammals. But how the metabolic need of the fetal organs is related to that of the placenta, and whether this relationship is influenced by the sex of the fetus remain poorly understood. This study used pigs to investigate metabolomic signatures of male and female fetal organs, and determine the relevance of gene expression of the placenta and brain to the metabolism of peripheral organs. Untargeted metabolomics analysis was performed with the day-45 placenta, kidney, heart, liver, lung and brain of male and female pig fetuses to model sex differences in themetabolism of the peripheral organs relative to that of the brain and placenta. Transcriptomic analysis was performed to investigate the expression of metabolic genes in the placenta and fetal brain of both sexes. The results of this study show that the fetoplacental metabolic regulation was not only influenced by the fetal sex but also dependent on the metabolic requirement of theindividual organs of the fetus. Neural network modeling of metabolomics data revealed differential relationship of the metabolic changes of the peripheral organs with the placenta and fetal brain between males and females. RNA sequencing further showed that genes associated with themetabolism of the peripheral organs were differentially expressed in the placenta and fetal brain. The findings of this study suggest a regulatory role of the fetal brain and placenta axis in the sex-bias metabolism of the peripheral organs.

Environmental concentrations of 6PPD and 6PPD-quinone induce hepatic lipid metabolism disorders in male black-spotted frogs

Aquatic environments are generally contaminated with N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine (6PPD) and its oxidation product 6PPD-quinone (6PPD-Q). Recently, 6PPD-Q was found lethally toxic to some specific species, especially salmonid silverfish. This study investigated male black-spotted frogs (Pelophylax nigromaculatus) exposed to 6PPD and 6PPD-Q with different environmental concentrations (0, 1, and 10 μg/L) for 21 days, after which biochemical, metabolomic, gene expression analyses, and molecular docking were conducted. 6PPD and 6PPD-Q were both found to bioaccumulate in frogs’ livers in a dose-dependent manner and produce a significant reduction of the hepatosomatic index. Metabolomics data showed that 6PPD and 6PPD-Q induced distinct alterations in metabolite expression, predominantly within pathways associated with the biosynthesis of unsaturated fatty acids as well as the metabolism of arachidonic and linoleic acids. Exposure to 10 μg/L 6PPD and 6PPD-Q increased the cholesterol level by 2.22 and 4.35 folds, and the triglyceride level by 1.90 and 2.25 folds, respectively. 6PPD-Q inhibited the enzyme activity and gene expression involved in lipolysis, and promoted the lipid synthesis. Moreover, 6PPD and 6PPD-Q bound to peroxisome proliferators-activated receptors of α and γ. In conclusion, 6PPD and 6PPD-Q with environmental concentrations induced frogs’ lipid metabolism disorders. These findings contribute to our understanding of 6PPD and 6PPD-Q health risks in amphibians.

Metabolome and transcriptome association study reveals biosynthesis of specialized benzylisoquinoline alkaloids in Phellodendron amurense

ObjectiveBenzylisoquinoline alkaloids (BIAs) have pharmacological functions and clinical use. BIAs are mainly distributed in plant species across the order Ranunculales and the genus Phellodendron from Sapindales. The BIA biosynthesis has been intensively investigated in Ranunculales species. However, the accumulation mechanism of BIAs in Phellodendron is largely unknown. The aim of this study is to unravel the biosynthetic pathways of BIAs in Phellodendron amurens. MethodsThe transcriptome and metabolome data from 18 different tissues of P. amurense were meticulously sequenced and subsequently subjected to a thorough analysis. Weighted gene co-expression network analysis (WGCNA), a powerful systems biology approach that facilitates the construction and subsequent analysis of co-expression networks, was utilized to identify candidate genes involved in BIAs biosynthesis. Following this, recombinant plasmids containing candidate genes were expressed in Escherichia coli, a widely used prokaryotic expression system. The purpose of this genetic engineering endeavor was to express the candidate genes within the bacteria, thereby enabling the assessment of the resultant enzyme activity. ResultsThe synonymous substitutions per synonymous site for paralogs indicated that at least one whole genome duplication event has occurred. The potential BIA biosynthetic pathway of P. amurense was proposed, and two PR10/Bet v1 members, 14 CYP450s, and 33 methyltransferases were selected as related to BIA biosynthesis. One PR10/Bet v1 was identified as norcoclaurine synthase, which could catalyze dopamine and 4-hydroxyphenylacetaldehyde into (S)-norcoclaurine. ConclusionOur studies provide important insights into the biosynthesis and evolution of BIAs in non-Ranunculales species.

Wen-Shen-Tong-Luo-Zhi-Tong-Decoction inhibits bone loss in senile osteoporosis model mice by promoting testosterone production

Ethnopharmacological relevanceWen-Shen-Tong-Luo-Zhi-Tong-Decoction (WSTLZTD) is a traditional Chinese medicine formula, and its effectiveness in the treatment of senile osteoporosis(SOP) has been confirmed by clinical studies. However, the underlying mechanism of WSTLZTD in SOP is unclear. Aim of the studyThis study aimed to clarify the unique effects of Wen-Shen-Tong-Luo-Zhi-Tong-Decoction(WSTLZTD) on senile osteoporosis(SOP) and its underlying mechanisms. Materials and methodsSAMP6 mice were treated with varying doses of WSTLZTD as the SOP model. Bone loss was evaluated by micro-CT, HE, OCN immunohistochemistry staining, and serum Trap level. Metabolomics studies serum metabolites. ELISA, qPCR, and immunofluorescence were utilized to measure testosterone levels in mouse testis. The effect of testosterone on the mitochondrial energy metabolism of BMSCs was investigated using ROS generation, NAD+/NADH ratio, and WB. Cell senescence was examined by β-galactosidase staining and WB. The effect of TM3 cell conditioned media (CM) on mitochondrial energy metabolism and BMSCs osteogenesis were studied using ALP, ARS, ROS staining, the NAD+/NADH, and WB. ResultsWSTLZTD effectively reversed bone loss in SOP model mice, resulting in better bone microstructure, increased BMD, BV/TV, Tb.n, Tb.Th and, and decreased Tb.Sp. WSTLZTD can increase OCN expression and decrease Trap levels. Network pharmacology data suggest that WSTLZTD regulates steroid hormone production, cellular senescence, inflammation. Metabolomic data indicate that WSTLZTD increases testosterone production or metabolism-related metabolites. WSTLZTD enhanced testosterone production and the mRNA expression of genes involved in testosterone synthesis. Testosterone inhibited the decline in osteogenic differentiation and mitochondrial energy metabolism of senescent BMSCs. The decreased testosterone production in senescent TM3 is reversed by WSTLZTD. CM derived from WSTLZTD-treated TM3 cells promoted osteogenic differentiation and mitochondrial energy metabolism of BMSCs. ConclusionsBy increasing testosterone production, WSTLZTD may promote mitochondrial energy metabolism and osteogenic differentiation of senescent BMSCs, thereby exerting its anti-SOP effect.