PubMed

Digestion. 2022 Nov 18:1-12. doi: 10.1159/000526864. Online ahead of print.ABSTRACTINTRODUCTION: As the high mortality rate of gastric cancer (GC) is due to delayed diagnosis, early detection is vital for improved patient outcomes. Metabolic deregulation plays an important role in GC. Although various metabolite-level biomarkers for early detection have been assessed, there is still no unified early detection method. We conducted a plasma metabolome study to assess metabolites that may distinguish GC samples from non-GC samples.METHODS: Blood samples were collected from 72 GC patients and 29 control participants (non-GC group) at the Tokyo Medical University Hospital between March 2020 and November 2020. Hydrophilic metabolites were identified and quantified using liquid chromatography-time-of-flight mass spectrometry. Differences in metabolite concentrations between the GC and non-GC groups were evaluated using the Mann-Whitney test. The discrimination ability of each metabolite was evaluated by the area under the receiver operating characteristic curve. A radial basis function (RBF) kernel-based support vector machine (SVM) model was developed to assess the discrimination ability of multiple metabolites. The selection of variables used for the SVM utilized a step-wise regression method.RESULTS: Of the 96 quantified metabolites, 8 were significantly different between the GC and non-GC groups. Of these, N1-acetylspermine, succinate, and histidine were used in the RBF-SVM model to discriminate GC samples from non-GC samples. The area under the curve (AUC) of the RBF-SVM model was higher (0.915; 95% CI: 0.865-0.965, p < 0.0001), indicating good performance of the RBF-SVM model. The application of this RBF-SVM to the validation dataset resulted from the AUC of the RBF-SVM model was (0.885; 95% CI: 0.797-0.973, p < 0.0001), indicating the good performance of the RBF-SVM model. The sensitivity of the RBF-SVM model was better (69.0%) than those of the common tumor markers carcinoembryonic antigen (CEA) (10.5%) and carbohydrate antigen 19-9 (CA19-9) (2.86%). The RBF-SVM showed a low correlation with CEA and CA19-9, indicating its independence.CONCLUSION: We analyzed plasma metabolomics, and a combination of the quantified metabolites showed high sensitivity for the detection of GC. The independence of the RBF-SVM from tumor markers suggested that their complementary use would be helpful for GC screening.PMID:36404717 | DOI:10.1159/000526864

Sci Rep. 2022 Nov 20;12(1):19977. doi: 10.1038/s41598-022-24170-0.ABSTRACTMetabolomic analysis of blood plasma samples from COVID-19 patients is a promising approach allowing for the evaluation of disease progression. We performed the metabolomic analysis of plasma samples of 30 COVID-19 patients and the 19 controls using the high-performance liquid chromatography (HPLC) coupled with tandem mass spectrometric detection (LC-MS/MS). In our analysis, we identified 103 metabolites enriched in KEGG metabolic pathways such as amino acid metabolism and the biosynthesis of aminoacyl-tRNAs, which differed significantly between the COVID-19 patients and the controls. Using ANDSystem software, we performed the reconstruction of gene networks describing the potential genetic regulation of metabolic pathways perturbed in COVID-19 patients by SARS-CoV-2 proteins. The nonstructural proteins of SARS-CoV-2 (orf8 and nsp5) and structural protein E were involved in the greater number of regulatory pathways. The reconstructed gene networks suggest the hypotheses on the molecular mechanisms of virus-host interactions in COVID-19 pathology and provide a basis for the further experimental and computer studies of the regulation of metabolic pathways by SARS-CoV-2 proteins. Our metabolomic analysis suggests the need for nonstructural protein-based vaccines and the control strategy to reduce the disease progression of COVID-19.PMID:36404352 | DOI:10.1038/s41598-022-24170-0

BMC Cardiovasc Disord. 2022 Nov 20;22(1):495. doi: 10.1186/s12872-022-02912-2.ABSTRACTINTRODUCTION: Congenital heart disease (CHD) is one of the most prevalent birth defects in the world. The pathogenesis of CHD is complex and unclear. With the development of metabolomics technology, variations in metabolites may provide new clues about the causes of CHD and may serve as a biomarker during pregnancy.METHODS: Sixty-five amniotic fluid samples (28 cases and 37 controls) during the second and third trimesters were utilized in this study. The metabolomics of CHD and normal fetuses were analyzed by untargeted metabolomics technology. Differential comparison and randomForest were used to screen metabolic biomarkers.RESULTS: A total of 2472 metabolites were detected, and they were distributed differentially between the cases and controls. Setting the selection criteria of fold change (FC) ≥ 2, P value < 0.01 and variable importance for the projection (VIP) ≥ 1.5, we screened 118 differential metabolites. Within the prediction model by random forest, PE(MonoMe(11,5)/MonoMe(13,5)), N-feruloylserotonin and 2,6-di-tert-butylbenzoquinone showed good prediction effects. Differential metabolites were mainly concentrated in aldosterone synthesis and secretion, drug metabolism, nicotinate and nicotinamide metabolism pathways, which may be related to the occurrence and development of CHD.CONCLUSION: This study provides a new database of CHD metabolic biomarkers and mechanistic research. These results need to be further verified in larger samples.PMID:36404327 | DOI:10.1186/s12872-022-02912-2

Anim Microbiome. 2022 Nov 20;4(1):58. doi: 10.1186/s42523-022-00209-5.ABSTRACTBACKGROUND: Prebiotics are known to have a positive impact on fish health and growth rate, and β-glucans are among the most used prebiotics on the market. In this study, rainbow trout (Oncorhynchus mykiss) were treated with a β-1,3;1,6-glucan dietary supplement (at a dose of 0 g, 1 g, 10 g, and 50 g β-glucan per kg of feed). After 6 weeks, the effect of the β-glucan was evaluated by determining the changes in the microbiota and the blood serum metabolites in the fish. The impact of β-glucan on the immune system was evaluated through a challenge experiment with the bacterial fish pathogen Yersinia ruckeri.RESULTS: The microbiota showed a significant change in terms of composition following β-glucan treatment, notably an increase in the relative abundance of members of the genus Aurantimicrobium, associated with a decreased abundance of the genera Carnobacterium and Deefgea. Furthermore, analysis of more than 200 metabolites revealed that the relative levels of 53 metabolites, in particular compounds related to phosphatidylcholines, were up- or downregulated in response to the dietary supplementation, this included the amino acid alanine that was significantly upregulated in the fish that had received the highest dose of β-glucan. Meanwhile, no strong effect could be detected on the resistance of the fish to the bacterial infection.CONCLUSIONS: The present study illustrates the ability of β-glucans to modify the gut microbiota of fish, resulting in alteration of the metabolome and affecting fish health through the lipidome of rainbow trout.PMID:36404315 | DOI:10.1186/s42523-022-00209-5

Environ Pollut. 2022 Nov 17:120678. doi: 10.1016/j.envpol.2022.120678. Online ahead of print.ABSTRACTDEET is one of the most frequently detected insect repellents in the environment reaching concentrations of several μg L-1 in surface water. There is scarce information available regarding its mode of action in non-target organisms. Here, we have used an integrated metabolomic and transcriptomic approach to elucidate the possible adverse effects of DEET exposure in the marine fish gilthead sea bream (Sparus aurata). Individuals were exposed at an environmentally relevant concentration of DEET (10 μg L-1) for 22 days in a continuous flow-through system. Transcriptomic analysis revealed 250 differentially expressed genes in liver, while metabolomic analysis identified 190 differentially modulated features in liver and 98 in plasma. Multi-omic data integration and visualization allowed elucidation of the modes of action of DEET exposure, including: energy depletion through the disruption of carbohydrate and amino acids metabolisms, oxidative stress leading to DNA damage, lipid peroxidation, and damage to cell membrane and apoptosis. Activation of xenobiotic pathway as well as the inmune-inflammatory reaction was evidenced in the present work.PMID:36403875 | DOI:10.1016/j.envpol.2022.120678

Res Microbiol. 2022 Nov 17:104009. doi: 10.1016/j.resmic.2022.104009. Online ahead of print.ABSTRACTCandida albicans is a predominant species causing candidemia in hospitalized patients. This study aimed to investigate the association of culture medium metabolomic profiles with biofilm formation and invasion properties of clinical bloodstream-isolated C. albicans. A total of twelve isolates and two reference strains were identified by virulent phenotypes. Their susceptibility was determined by the microdilution method, following EUCAST guidelines. Biofilm formation was evaluated with metabolic activity, morphology and agglutinin-like sequence 3 (ALS3) mRNA expression. Invasion into the vascular endothelial EA.hy926 cells was determined by lactate dehydrogenase release and internalization assay. Their metabolomic profiles were assessed by high-resolution accurate-mass spectrometry (HRAMS). The results showed four different phenotypes of C. albicans: high-biofilm/invasive (50%), high-biofilm/non-invasive (7%), low-biofilm/invasive (36%) and low-biofilm/non-invasive (7%). The metabolomic profiles of the culture medium determined strong correlation of the virulent phenotypes and the alteration of metabolites in the methionine metabolism pathway, such as homocysteine, 5-methyltetrahydrofolate and S-adenosylmethioninamine. Moreover, thiamine and biotin levels were significantly increased in Isolate03, representative of a high-biofilm/invasive phenotype. These results suggest that methionine and vitamin B metabolism pathways might be influenced by their virulent phenotypes and pathogenic traits. Therefore, their metabolism pathways might be a potential target for reducing virulence of C. albicans bloodstream infections.PMID:36403754 | DOI:10.1016/j.resmic.2022.104009

J Ethnopharmacol. 2022 Nov 17:115932. doi: 10.1016/j.jep.2022.115932. Online ahead of print.ABSTRACTETHNOPHARMACOLOGICAL RELEVANCE: Artemisia vestita Wall. ex Besser is wildly distributed in the western high-altitude area of China and has been used as a Tibetan medicine to treat inflammatory diseases. We previously demonstrated the total flavonoids of Artemisia vestita Wall. ex Besser (TFA) showed obvious anti-inflammatory effects and its content was 276.62 mg/g. However, the chemical profile, active ingredients, and anti-inflammatory mechanisms of TFA are not clear.AIM OF THE STUDY: This study aimed to study the components of TFA, evaluate the anti-inflammatory effects of TFA, and preliminarily predict the anti-inflammatory mechanism of TFA.MATERIALS AND METHODS: TFA was prepared by the semi-biomimetic extraction method and purified by macroporous resin. The components of TFA were analyzed based on LC-MS combined with the targeted metabolomics method. The anti-inflammatory activity of TFA was evaluated using CuSO4-induced and tail cutting-induced zebrafish inflammation models. Based on the network pharmacology method, the anti-inflammatory mechanism of the main components of TFA was preliminarily predicted.RESULTS: A total of 185 components were identified in TFA. TFA showed significant anti-inflammatory effects on CuSO4-induced and tail cutting-induced zebrafish inflammation models. According to network pharmacology prediction and experimental verification, 10 compounds were identified as the main active ingredients, including 3,7-di-O-methylquercetin, Hesperetin 5-O-glucoside, Myricitrin, et al. Twenty key targets were recognized, such as TNF, AKT1, VEGFA, MMP9, EGFR, PTGS2 et al. Moreover, the TNF signaling pathway and NOD-like receptor signaling pathway were identified to play vital roles in the anti-inflammatory effects of TFA.CONCLUSIONS: This study revealed the chemical profile of TFA and identified the main active ingredients, key targets, and pathways of TFA in anti-inflammatory effects, which is helpful to elucidate the pharmacodynamic substances and action mechanisms of Artemisia vestita Wall. ex Besser, to promote its clinical rational application.PMID:36403745 | DOI:10.1016/j.jep.2022.115932

Free Radic Biol Med. 2022 Nov 17:S0891-5849(22)00978-9. doi: 10.1016/j.freeradbiomed.2022.11.015. Online ahead of print.ABSTRACTPeroxiredoxin 6 (Prdx6) is a multifunctional enzyme, a unique member of the peroxiredoxin family, with an important role in antioxidant defense. Moreover, it has also been linked with the biosynthesis of anti-inflammatory and anti-diabetic lipids called fatty acid esters of hydroxy fatty acids (FAHFAs) and many diseases, including cancer, inflammation, and metabolic disorders. Here, we performed metabolomic and lipidomic profiling of subcutaneous adipose tissue from mouse models with genetically modified Prdx6. Deletion of Prdx6 resulted in reduced levels of FAHFAs containing 13-hydroxylinoleic acid (13-HLA). Mutation of Prdx6 C47S impaired the glutathione peroxidase activity and reduced FAHFA levels, while D140A mutation, responsible for phospholipase A2 activity, showed only minor effects. Targeted analysis of oxidized phospholipids and triacylglycerols in adipocytes highlighted a correlation between FAHFA and hydroxy fatty acid production by Prdx6 or glutathione peroxidase 4. FAHFA regioisomer abundance was negatively affected by the Prdx6 deletion, and this effect was more pronounced in longer and more unsaturated FAHFAs. The predicted protein model of Prdx6 suggested that the monomer-dimer transition mechanism might be involved in the repair of longer-chain peroxidized phospholipids bound over two monomers and that the role of Prdx6 in FAHFA synthesis might be restricted to branching positions further from carbon 9. In conclusion, our work linked the peroxidase activity of Prdx6 with the levels of FAHFAs in adipose tissue.PMID:36403738 | DOI:10.1016/j.freeradbiomed.2022.11.015

Insect Biochem Mol Biol. 2022 Nov 17:103877. doi: 10.1016/j.ibmb.2022.103877. Online ahead of print.ABSTRACTThe extensive annual loss of honey bees (Apis mellifera L.) represents a global problem affecting agriculture and biodiversity. The parasitic mite Varroa destructor, associated with viral co-infections, plays a key role in this loss. Despite years of intensive research, the complex mechanisms of Varroa - honey bee interaction are still not fully defined. Therefore, this study employed a unique combination of transcriptomic, proteomic, metabolomic, and functional analyses to reveal new details about the effect of Varroa mites and naturally associated factors, including viruses, on honey bees. We focused on the differences between Varroa parasitised and unparasitised ten-day-old worker bees collected before overwintering from the same set of colonies reared without anti-mite treatment. Supplementary comparison to honey bees collected from colonies with standard anti-Varroa treatment can provide further insights into the effect of a pyrethroid flumethrin. Analysis of the honey bees exposed to mite parasitisation revealed alterations in the transcriptome and proteome related to immunity, oxidative stress, olfactory recognition, metabolism of sphingolipids, and RNA regulatory mechanisms. The immune response and sphingolipid metabolism were strongly activated, whereas olfactory recognition and oxidative stress pathways were inhibited in Varroa parasitised honey bees compared to unparasitised ones. Moreover, metabolomic analysis confirmed the depletion of nutrients and energy stores, resulting in a generally disrupted metabolism in the parasitised workers. The combined omics-based analysis conducted on strictly parasitised bees revealed the key molecular components and mechanisms underlying the detrimental effects of Varroa sp. and its associated pathogens. This study provides the theoretical basis and interlinked datasets for further research on honey bee response to biological threats and the development of efficient control strategies against Varroa mites.PMID:36403678 | DOI:10.1016/j.ibmb.2022.103877

Transl Oncol. 2022 Nov 17;27:101585. doi: 10.1016/j.tranon.2022.101585. Online ahead of print.ABSTRACTBACKGROUND: We previously showed that metabolomics predicts relapse in early breast cancer (eBC) patients, unselected by age. This study aims to identify a "metabolic signature" that differentiates eBC from advanced breast cancer (aBC) patients, and to investigate its potential prognostic role in an elderly population.METHODS: Serum samples from elderly breast cancer (BC) patients enrolled in 3 onco-geriatric trials, were retrospectively analyzed via proton nuclear magnetic resonance (1H NMR) spectroscopy. Three nuclear magnetic resonance (NMR) spectra were acquired for each serum sample: NOESY1D, CPMG, Diffusion-edited. Random Forest (RF) models to predict BC relapse were built on NMR spectra, and resulting RF risk scores were evaluated by Kaplan-Meier curves.RESULTS: Serum samples from 140 eBC patients and 27 aBC were retrieved. In the eBC cohort, median age was 76 years; 77% of patients had luminal, 10% HER2-positive and 13% triple negative (TN) BC. Forty-two percent of patients had tumors >2 cm, 43% had positive axillary nodes. Using NOESY1D spectra, the RF classifier discriminated free-from-recurrence eBC from aBC with sensitivity, specificity and accuracy of 81%, 67% and 70% respectively. We tested the NOESY1D spectra of each eBC patient on the RF models already calculated. We found that patients classified as "high risk" had higher risk of disease recurrence (hazard ratio (HR) 3.42, 95% confidence interval (CI) 1.58-7.37) than patients at low-risk.CONCLUSIONS: This analysis suggests that a "metabolic signature", identified employing NMR fingerprinting, is able to predict the risk of disease recurrence in elderly patients with eBC independently from standard clinicopathological features.PMID:36403505 | DOI:10.1016/j.tranon.2022.101585

Biosens Bioelectron. 2022 Nov 3;220:114862. doi: 10.1016/j.bios.2022.114862. Online ahead of print.ABSTRACTWe recently discovered that superparamagnetic iron oxide nanoparticles (SPIONs) can levitate plasma biomolecules in the magnetic levitation (MagLev) system and cause formation of ellipsoidal biomolecular bands. To better understand the composition of the levitated biomolecules in various bands, we comprehensively characterized them by multi-omics analyses. To probe whether the biomolecular composition of the levitated ellipsoidal bands correlates with the health of plasma donors, we used plasma from individuals who had various types of multiple sclerosis (MS), as a model disease with significant clinical importance. Our findings reveal that, while the composition of proteins does not show much variability, there are significant differences in the lipidome and metabolome profiles of each magnetically levitated ellipsoidal band. By comparing the lipidome and metabolome compositions of various plasma samples, we found that the levitated biomolecular ellipsoidal bands do contain information on the health status of the plasma donors. More specifically, we demonstrate that there are particular lipids and metabolites in various layers of each specific plasma pattern that significantly contribute to the discrimination of different MS subtypes, i.e., relapsing-remitting MS (RRMS), secondary-progressive MS (SPMS), and primary-progressive MS (PPMS). These findings will pave the way for utilization of MagLev of biomolecules in biomarker discovery for identification of diseases and discrimination of their subtypes.PMID:36403493 | DOI:10.1016/j.bios.2022.114862

J Diabetes Complications. 2022 Nov 14;36(12):108357. doi: 10.1016/j.jdiacomp.2022.108357. Online ahead of print.ABSTRACTDiabetic kidney disease (DKD) is the leading cause of kidney failure and is associated with substantial risk of cardiovascular disease, morbidity, and mortality. Traditionally, DKD prevention and management have focused on addressing hyperglycemia, hypertension, obesity, and renin-angiotensin system activation as important risk factors for disease. Over the last decade, sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists have been shown to meaningfully reduce risk of diabetes-related kidney and cardiovascular complications. Additional agents demonstrating benefit in DKD such as non-steroidal mineralocorticoid receptor antagonists and endothelin A receptor antagonists are further contributing to the growing arsenal of DKD therapies. With the availability of greater therapeutic options comes the opportunity to individually optimize DKD prevention and management. Novel applications of transcriptomic, proteomic, and metabolomic/lipidomic technologies, as well as use of artificial intelligence and reinforced learning methods through consortia such as the Kidney Precision Medicine Project and focused studies in established cohorts hold tremendous promise for advancing our understanding and treatment of DKD. Specifically, enhanced understanding of the molecular mechanisms underlying DKD pathophysiology may allow for the identification of new mechanism-based DKD subtypes and the development and implementation of targeted therapies. Implementation of personalized care approaches has the potential to revolutionize DKD care.PMID:36403478 | DOI:10.1016/j.jdiacomp.2022.108357

J Hazard Mater. 2022 Nov 14;444(Pt A):130397. doi: 10.1016/j.jhazmat.2022.130397. Online ahead of print.ABSTRACTCadmium (Cd2+) is a toxic heavy metal in the environment, posing severe damage to animal health and drinking water safety. The bacteria-algae consortium remediates environmental Cd2+ pollution by secreting chelating reagents, but the molecular mechanisms remain elusive. Here, we showed that Cellulosimicrobium sp. SH8 isolated from a Cd2+-polluted lake could interact with Synechocystis sp. PCC6803, a model species of cyanobacteria, in strengthening Cd2+ toxicity resistance, while SH8 or PCC6803 alone barely immobilized Cd2+. In addition, the SH8-PCC6803 consortium, but not SH8 alone, could grow in a carbon-free medium, suggesting that autotrophic PCC6803 enabled the growth of heterotrophic SH8. Totally, 12 metabolites were significantly changed when SH8 was added to PCC6803 culture in the presence of Cd2+ (PCC6803/Cd2+). Among them, kynurenic acid was the only metabolite that precipitated Cd2+. Remarkably, adding kynurenic acid increased the growth of PCC6803/Cd2+ by 14.1 times. Consistently, the expressions of kynA, kynB, and kynT genes, known to be essential for kynurenic acid synthesis, were considerably increased when SH8 was added to PCC6803/Cd2+. Collectively, kynurenic acid secreted by SH8 mitigates Cd2+ toxicity for algae, and algae provide organic carbon for the growth of SH8, unveiling a critical link that mediates beneficial bacteria-algae interaction to resist Cd2+.PMID:36403444 | DOI:10.1016/j.jhazmat.2022.130397

Respir Res. 2022 Nov 19;23(1):318. doi: 10.1186/s12931-022-02233-0.ABSTRACTIn the last decade, research on acute respiratory distress syndrome (ARDS) has made considerable progress. However, ARDS remains a leading cause of mortality in the intensive care unit. ARDS presents distinct subphenotypes with different clinical and biological features. The pathophysiologic mechanisms of ARDS may contribute to the biological variability and partially explain why some pharmacologic therapies for ARDS have failed to improve patient outcomes. Therefore, identifying ARDS variability and heterogeneity might be a key strategy for finding effective treatments. Research involving studies on biomarkers and genomic, metabolomic, and proteomic technologies is increasing. These new approaches, which are dedicated to the identification and quantitative analysis of components from biological matrixes, may help differentiate between different types of damage and predict clinical outcome and risk. Omics technologies offer a new opportunity for the development of diagnostic tools and personalized therapy in ARDS. This narrative review assesses recent evidence regarding genomics, proteomics, and metabolomics in ARDS research.PMID:36403043 | DOI:10.1186/s12931-022-02233-0

Respir Res. 2022 Nov 19;23(1):317. doi: 10.1186/s12931-022-02229-w.ABSTRACTBACKGROUND: Emerging experimental and epidemiological evidence highlights a crucial cross-talk between the intestinal flora and the lungs, termed the "gut-lung axis". However, the function of the gut microbiota in bronchiectasis remains undefined. In this study, we aimed to perform a multi-omics-based approach to identify the gut microbiome and metabolic profiles in patients with bronchiectasis.METHODS: Fecal samples collected from non-CF bronchiectasis patients (BE group, n = 61) and healthy volunteers (HC group, n = 37) were analyzed by 16 S ribosomal RNA (rRNA) sequencing. The BE group was divided into two groups based on their clinical status: acute exacerbation (AE group, n = 31) and stable phase (SP group, n = 30). Further, metabolome (lipid chromatography-mass spectrometry, LC-MS) analyses were conducted in randomly selected patients (n = 29) and healthy volunteers (n = 31).RESULTS: Decreased fecal microbial diversity and differential microbial and metabolic compositions were observed in bronchiectasis patients. Correlation analyses indicated associations between the differential genera and clinical parameters such as bronchiectasis severity index (BSI). Disease-associated gut microbiota was screened out, with eight genera exhibited high accuracy in distinguishing SP patients from HCs in the discovery cohort and validation cohort using a random forest model. Further correlation networks were applied to illustrate the relations connecting disease-associated genera and metabolites.CONCLUSION: The study uncovered the relationships among the decreased fecal microbial diversity, differential microbial and metabolic compositions in bronchiectasis patients by performing a multi-omics-based approach. It is the first study to characterize the gut microbiome and metabolome in bronchiectasis, and to uncover the gut microbiota's potentiality as biomarkers for bronchiectasis.TRIAL REGISTRATION: This study is registered with ClinicalTrials.gov, number NCT04490447.PMID:36403022 | DOI:10.1186/s12931-022-02229-w

Chin Med. 2022 Nov 19;17(1):130. doi: 10.1186/s13020-022-00687-4.ABSTRACTBACKGROUND: Traditional Chinese medicine (TCM) has been used to treat various diseases for thousands of years. However, the uncertainty of dosage as well as the lack of systemic evaluation of pharmacology and toxicology is one major reason why TCM remains mysterious and is not accepted worldwide. Hence, we aimed to propose an integrated dose-response metabolomics strategy based on both therapeutic effects and adverse reactions to guide the TCM dosage in treatment.METHODS: The proposed methodology of integrated dose-response metabolomics includes four steps: dose design, multiple comparison of metabolic features, response calculation and dose-response curve fitting. By comparing the changes of all metabolites under different doses and calculating these changes through superposition, it is possible to characterize the global disturbance and thus describe the overall effect and toxicity of TCM induced by different doses. Rhubarb, commonly used for constipation treatment, was selected as a representative TCM.RESULTS: This developed strategy was successfully applied to rhubarb. The dose-response curves clearly showed the efficacy and adverse reactions of rhubarb at different doses. The rhubarb dose of 0.69 g/kg (corresponding to 7.66 g in clinic) was selected as the optimal dose because it was 90% of the effective dose and three adverse reactions were acceptable in this case.CONCLUSION: An integrated dose-response metabolomics strategy reflecting both therapeutic effects and adverse reactions was established for the first time, which we believe is helpful to uncover the mysterious veil of TCM dosage. In addition, this strategy benefits the modernization and internationalization of TCM, and broadens the application of metabolomics.PMID:36403018 | DOI:10.1186/s13020-022-00687-4

BMC Genomics. 2022 Nov 19;23(1):759. doi: 10.1186/s12864-022-08981-z.ABSTRACTBACKGROUND: The cold pressor test (CPT) is a widely used pain provocation test to investigate both pain tolerance and cardiovascular responses. We hypothesize, that performing multi-omic analyses during CPT gives the opportunity to home in on molecular mechanisms involved. Twenty-two females were phenotypically assessed before and after a CPT, and blood samples were taken. RNA-Sequencing, steroid profiling and untargeted metabolomics were performed. Each 'omic level was analyzed separately at both single-feature and systems-level (principal component [PCA] and partial least squares [PLS] regression analysis) and all 'omic levels were combined using an integrative multi-omics approach, all using the paired-sample design.RESULTS: We showed that PCA was not able to discriminate time points, while PLS did significantly distinguish time points using metabolomics and/or transcriptomic data, but not using conventional physiological measures. Transcriptomic and metabolomic data revealed at feature-, systems- and integrative- level biologically relevant processes involved during CPT, e.g. lipid metabolism and stress response.CONCLUSION: Multi-omics strategies have a great potential in pain research, both at feature- and systems- level. Therefore, they should be exploited in intervention studies, such as pain provocation tests, to gain knowledge on the biological mechanisms involved in complex traits.PMID:36402977 | DOI:10.1186/s12864-022-08981-z

Sci Rep. 2022 Nov 19;12(1):19948. doi: 10.1038/s41598-022-24428-7.ABSTRACTSevere malnutrition accounts for half-a-million deaths annually in children under the age of five. Despite improved WHO guidelines, inpatient mortality remains high and is associated with metabolic dysfunction. Previous studies suggest a correlation between hepatic metabolic dysfunction and impaired autophagy. We aimed to determine the role of mTORC1 inhibition in a murine model of malnutrition-induced hepatic dysfunction. Wild type weanling C57/B6 mice were fed a 18 or 1% protein diet for two weeks. A third low-protein group received daily rapamycin injections, an mTORC1 inhibitor. Hepatic metabolic function was assessed by histology, immunofluorescence, gene expression, metabolomics and protein levels. Low protein-fed mice manifested characteristics of severe malnutrition, including weight loss, hypoalbuminemia, hypoglycemia, hepatic steatosis and cholestasis. Low protein-fed mice had fewer mitochondria and showed signs of impaired mitochondrial function. Rapamycin prevented hepatic steatosis, restored ATP levels and fasted plasma glucose levels compared to untreated mice. This correlated with increased content of LC3-II, and decreased content mitochondrial damage marker, PINK1. We demonstrate that hepatic steatosis and disturbed mitochondrial function in a murine model of severe malnutrition can be partially prevented through inhibition of mTORC1. These findings suggest that stimulation of autophagy could be a novel approach to improve metabolic function in severely malnourished children.PMID:36402829 | DOI:10.1038/s41598-022-24428-7

Nat Commun. 2022 Nov 19;13(1):7121. doi: 10.1038/s41467-022-34754-z.ABSTRACTType 2 diabetes (T2D) has a heterogeneous etiology influencing its progression, treatment, and complications. A data driven cluster analysis in European individuals with T2D previously identified four subtypes: severe insulin deficient (SIDD), severe insulin resistant (SIRD), mild obesity-related (MOD), and mild age-related (MARD) diabetes. Here, the clustering approach was applied to individuals with T2D from the Qatar Biobank and validated in an independent set. Cluster-specific signatures of circulating metabolites and proteins were established, revealing subtype-specific molecular mechanisms, including activation of the complement system with features of autoimmune diabetes and reduced 1,5-anhydroglucitol in SIDD, impaired insulin signaling in SIRD, and elevated leptin and fatty acid binding protein levels in MOD. The MARD cluster was the healthiest with metabolomic and proteomic profiles most similar to the controls. We have translated the T2D subtypes to an Arab population and identified distinct molecular signatures to further our understanding of the etiology of these subtypes.PMID:36402758 | DOI:10.1038/s41467-022-34754-z

J Burn Care Res. 2022 Nov 19:irac175. doi: 10.1093/jbcr/irac175. Online ahead of print.ABSTRACTThis work aims to elucidate the molecular mechanism of Qi Wei anti-burn Tincture (QW) on wound healing in burnt mice using metabolomics and molecular biology techniques. A scald model was first established in Kunming mice. After treatment, biochemical indicators for liver function and burnt skin tissues were then evaluated via biochemical detection and HE staining respectively. Liver tissues were further analyzed for differential metabolites, inflammatory factors, and mRNA levels of cytokines using metabolomics and molecular biology techniques. Involved metabolic pathways were also identified using software. Qi Wei anti-burn Tincture treatment did promote the healing of the burn wounds on Kunming mice with a downregulation of ALP, ALT, AST to normal levels. In mouse liver tissue, the contents of glutamine, aspartic acid, succinic acid and citrulline were significantly reduced, while the contents of 5-hydroxyproline, taurine, hypotaurine and glutamic acid significantly increased. These major differential compounds are involved in the arginine metabolic pathway, nitrogen excretion, and the metabolism of taurine and hypotaurine, suggesting that Qi Wei anti-burn Tincture reprogramed the above metabolic processes in the liver. Furthermore, the application of Qi Wei anti-burn Tincture increased the expression of TGF-β1 and FGF-2, and reduced the levels of TNF-α, IL-1β, IL-6 and reactive oxygen species in the liver of mice induced by burn injury. This study found that Qi Wei anti-burn Tincture treatment promoted metabolic pathway remodeling in liver, which might be a potential mechanism for Qi Wei anti-burn Tincture to treat burn wounds.PMID:36402740 | DOI:10.1093/jbcr/irac175