PubMed

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

Urol Oncol. 2022 Nov 16:S1078-1439(22)00344-1. doi: 10.1016/j.urolonc.2022.09.018. Online ahead of print.ABSTRACTINTRODUCTION: Human urine microbiota (UM) research has uncovered associations between composition of microbial communities of the lower urinary tract and various disease states including several reports on the putative link between UM and bladder cancer (BC). The aim of this study was to investigate male UM in patients with BC and controls using catheterised urine specimens unlike in previous studies.METHODS: Urine samples were obtained in theatre after surgical prepping and draping using aseptic catheterisation. DNA was extracted and hypervariable region V4 of the 16S rRNA gene was amplified using 515F and 806R primers. Sequencing was performed on Illumina MiSeq platform. Sequencing data were processed using appropriate software tools. Alpha diversity measures were calculated and compared between groups. Prevalence Interval for Microbiome Evaluation was used to test differences in beta diversity.RESULTS: A total of 63 samples were included in the analysis. Mean age of study subjects was 65.1 years (SD 12.5). Thirty-four men had bladder cancer and 29 participants were undergoing interventions for benign conditions (benign prostate hyperplasia or upper urinary tract stone disease). BC patients had lower UM richness and diversity than controls (83 vs. 139 operational taxonomic units, P = 0.015; Shannon index: 2.46 vs. 2.94, P = 0.049). There were specific taxa enriched in cancer (Veillonella, Varibaculum, Methylobacterium-Methylorubrum) and control groups (Pasteurella, Corynebacterium, Acinetobacter), respectively.CONCLUSION: BC patients had lower bladder microbiota richness and diversity than controls. Specific genera were enriched in cancer and control groups, respectively. These results corroborate some of previous reports while contradicting others. Future microbiota research would benefit from parallel transcriptomic/metabolomic analysis.PMID:36402713 | DOI:10.1016/j.urolonc.2022.09.018

J Anim Sci. 2022 Nov 19:skac386. doi: 10.1093/jas/skac386. Online ahead of print.ABSTRACTThe objective of this study was to characterize developmental differences in low birth weight (LBW) and normal birth weight (NBW) piglets with or without pre-weaning nutrient restriction using serum metabolomic profile analysis. At farrowing, 112 piglets were identified as LBW (1.22 ± 0.28 kg) or NBW (1.70 ± 0.27 kg) and were randomly assigned to receive normal nutrition (NN) or restricted nutrition (RN) (6 h/day no suckling) from d 2 to 28 post-farrow (n=8pigs/group). On d 28, piglets were weaned onto a common diet. Fasted blood samples were obtained on d 28 and 56 (n=8pigs/group) and were analyzed using quantitative metabolomics via a combination of direct injection mass spectrometry with a reverse-phase LC-MS/MS custom assay. Data were normalized using logarithmic transformation and auto-scaling. Partial least squares discriminant analysis (PLS-DA) was carried out to further explore the differential metabolites among the groups (metaboanalyst.ca) with an integrated enrichment and pathway topography analysis. On d 28, LBW piglets had lower levels of essential amino acids as well as reduced metabolites associated with fatty acid oxidation, glycolysis, and the TCA cycle compared to the NBW group. The overall reduction of metabolites associated with energy production and regulation suggests that LBW vs. NBW are in an energy-survival state. On d 56, LBW pigs had increased utilization of fatty acids and resultant ketone production, evident by increased carnitines, acetoacetate, β-hydroxybutyrate, and glycerol compared to NBW pigs. Additionally, compared to the NBW pigs LBW pigs had a consistent decrease in serum glucose and lactate as well as reduced TCA cycle metabolites: pyruvate, succinate, citrate, and α-ketoglutaric acid similar to d 28. Low reliance on glycolysis and the TCA cycle and higher glycerol production in the LBW pigs may indicate impairments in glucose tolerance at 56 d. In summary, LBW piglets appear to have more metabolic alterations in early life, which is not resolved with adequate nutrition or refeeding and may elucidate physiological and metabolic mechanisms of poor growth and life performance compared to NBW pigs later in life.PMID:36402552 | DOI:10.1093/jas/skac386

Sci Total Environ. 2022 Nov 16:160223. doi: 10.1016/j.scitotenv.2022.160223. Online ahead of print.ABSTRACTEnvironmental antibiotics raise serious health concerns due to their contribution to the obesity prevalence. Moreover, antibiotics promote antibiotic-resistance bacteria (ARB) which represent another emerging pollutant. However, the interaction between antibiotic and ARB in the obesogenic effects remained unexplored. In the present study, the obesogenic effects of tetracycline antibiotic (TCH) and ARB containing tetA were studied on C. elegans, and E. coli OP50 (OP50) was referred as a normal bacterial food. Results showed that TCH stimulated nematode triglyceride contents, while ARB alone had no significant influences. The combination of TCH and ARB showed less obesogenic effects than TCH alone, showing antagonism. Biochemical assays showed that the combination of TCH and ARB showed similar effects to ARB alone, and had less increases in lipid metabolism enzymes or metabolites than those of TCH or ARB alone, supporting the antagonism. In the nontargeted metabolomic analysis, TCH with ARB showed less significantly changed metabolites (SCMs) in the nematodes than TCH or ARB alone, partially explaining the antagonism. The metabolomic results also pointed out the significant involvement of amino acids, the carboxylic acids and derivatives, and also the benzene and substituted derivatives in the obesogenic effects of TCH and ARB. The findings of the present study provided a direct support for interaction between antibiotics and ARB underlying their health risks.PMID:36402327 | DOI:10.1016/j.scitotenv.2022.160223

J Nutr Biochem. 2022 Nov 16:109207. doi: 10.1016/j.jnutbio.2022.109207. Online ahead of print.ABSTRACTSqualene is a key minor component of virgin olive oil, the main source of fat in the Mediterranean diet, and had shown to improve the liver metabolism in rabbits and mice. The present research was carried out to find out whether this effect was conserved in a porcine model of hepatic steatohepatitis and to search for the lipidomic changes involved. The current study revealed that a 0.5% squalene supplementation to a steatotic diet for a month led to hepatic accumulation of squalene and decreased triglyceride content as well as area of hepatic lipid droplets without influencing cholesterol content or fiber areas. However, ballooning score was increased and associated with the hepatic squalene content. Of forty hepatic transcripts related to lipid metabolism and hepatic steatosis, only citrate synthase and a non-coding RNA showed decreased expressions. The hepatic lipidome, assessed by liquid chromatography-mass spectrometry in a platform able to analyze 467 lipids, revealed that squalene supplementation increased ceramide, Cer(36:2), and phosphatidylcholine [PC(32:0), PC(33:0) and PC(34:0)] species and decreased cardiolipin, CL(69:5), and triglyceride [TG(54:2), TG(55:0) and TG(55:2)] species. Plasma levels of interleukin 12p40 increased in pigs receiving the squalene diet. The latter also modified plasma lipidome by increasing TG(58:12) and decreasing non-esterified fatty acid (FA 14:0, FA 16:1 and FA 18:0) species without changes in total NEFA levels. Together this shows that squalene-induced changes in hepatic and plasma lipidomic profiles, non-coding RNA and anti-inflammatory interleukin are suggestive of an alleviation of the disease despite the increase in the ballooning score.PMID:36402249 | DOI:10.1016/j.jnutbio.2022.109207

Environ Int. 2022 Nov 13;170:107632. doi: 10.1016/j.envint.2022.107632. Online ahead of print.ABSTRACTBACKGROUND: Long-term exposure to air pollution has been associated with cardiopulmonary diseases, while the underlying mechanisms remain unclear.OBJECTIVES: To investigate changes in serum metabolites associated with long-term exposure to air pollution and explore the susceptibility characteristics.METHODS: We used data from the German population-based Cooperative Health Research in the Region of Augsburg (KORA) S4 survey (1999-2001) and two follow-up examinations (F4: 2006-08 and FF4: 2013-14). Mass-spectrometry-based targeted metabolomics was used to quantify metabolites among serum samples. Only participants with repeated metabolites measurements were included in the current analysis. Land-use regression (LUR) models were used to estimate annual average concentrations of ultrafine particles, particulate matter (PM) with an aerodynamic diameter less than 10 μm (PM10), coarse particles (PMcoarse), fine particles, PM2.5 absorbance (a proxy of elemental carbon related to traffic exhaust, PM2.5abs), nitrogen oxides (NO2, NOx), and ozone at individuals' residences. We applied confounder-adjusted mixed-effects regression models to examine the associations between long-term exposure to air pollution and metabolites.RESULTS: Among 9,620 observations from 4,261 KORA participants, we included 5,772 (60.0%) observations from 2,583 (60.6%) participants in this analysis. Out of 108 metabolites that passed stringent quality control across three study points in time, we identified nine significant negative associations between phosphatidylcholines (PCs) and ambient pollutants at a Benjamini-Hochberg false discovery rate (FDR) corrected p-value < 0.05. The strongest association was seen for an increase of 0.27 μg/m3 (interquartile range) in PM2.5abs and decreased phosphatidylcholine acyl-alkyl C36:3 (PC ae C36:3) concentrations [percent change in the geometric mean: -2.5% (95% confidence interval: -3.6%, -1.5%)].CONCLUSIONS: Our study suggested that long-term exposure to air pollution is associated with metabolic alterations, particularly in PCs with unsaturated long-chain fatty acids. These findings might provide new insights into potential mechanisms for air pollution-related adverse outcomes.PMID:36402035 | DOI:10.1016/j.envint.2022.107632

Theriogenology. 2022 Nov 11;196:88-96. doi: 10.1016/j.theriogenology.2022.11.013. Online ahead of print.ABSTRACTSperm cryopreservation maintains the diversities of porcine genetic resources and improves utilization efficiency of boar semen in artificial insemination practices. Freezability of boar semen presents remarkable differences among individuals. However, metabolic markers for boar semen freezability in both sperm and seminal plasma largely remain unknown. The present study thus aims to determine differences in metabolites of sperm and seminal plasma between poor (PF) and good (GF) freezability semen from a Chinese native pig and screen potential markers for semen freezability. A total of 72,048 metabolites in sperm and 66,551 metabolites in seminal plasma were identified by liquid chromatography-mass spectrometry, respectively. The proportion of lipid molecules among all metabolites in both sperm and seminal plasma was the maximum regardless of negative or positive mode. Furthermore, we identified 21 differentially expressed metabolites (DEMs) in sperm and 185 DEMs in seminal plasma between PF and GF group. Additionally, clustering analysis showed that DEMs in sperm and seminal plasma exhibited significant changes between PF and GF group. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that DEMs in sperm were mainly enriched in metabolic pathways of amino acids and caffeine. DEMs in seminal plasma were associated with AMPK and cAMP signaling pathways. Taken together, these results demonstrate that sperm and seminal plasma of native pigs present differential metabolome between PF and GF semen.PMID:36401936 | DOI:10.1016/j.theriogenology.2022.11.013

Food Chem. 2022 Nov 7;405(Pt B):134868. doi: 10.1016/j.foodchem.2022.134868. Online ahead of print.ABSTRACTIn NMR-based untargeted analysis, Fourier transformation is applied to the time-domain data to extract observables such as frequency and intensity. Despite its wide application, this approach has several limitations that can prevent NMR from reaching its highest potential. Here, we utilized Bayesian analysis through CRAFT as an alternative method, using California-style table olives as a model system. Our hypothesis was that the time-domain analysis through CRAFT will be as successful as the traditional approach. The results showed that CRAFT generated efficient unsupervised and supervised models in a robust, and rapid/automated manner. The duration of CRAFT analysis can be further reduced by using the first 14 k complex data points of the initial part of the FID, without affecting the performance of the untargeted analysis. For unsupervised analysis, CRAFT was generally more efficient, while for supervised analysis both approaches were effective. CRAFT can be also used for identifying marker compounds driving classifications.PMID:36401894 | DOI:10.1016/j.foodchem.2022.134868

Arch Pharm Res. 2022 Nov 19. doi: 10.1007/s12272-022-01416-z. Online ahead of print.ABSTRACTVarious factors related to growing conditions can influence the nutritional quality of plants, including vegetable crops, especially the contents of health-promoting phytochemicals. In this study, the phytochemical contents of spinach (Spinacia oleracea) cultivated under greenhouse and open field conditions were comparatively analyzed using a metabolomic approach with Mass Profiler Professional (MPP) software. S. oleracea, which is one of the well-known leafy vegetables belonging to the family Chenopodiaceae, is cultivated worldwide. Although the nutritional value of spinach is high, the phytochemical contents of spinach cultivated under greenhouse and open field conditions have not been comparatively analyzed. Metabolomic analysis of the methanol (MeOH) extracts of greenhouse-cultivated spinach (GS) and open field-cultivated spinach (OFS) using ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS), followed by principal component analysis (PCA) with MPP demonstrated the differential metabolite profiles of GS and OFS. The active compounds 1-3 were isolated and identified using LC-Q-TOF-MS-guided fractionation. Among these, 5,3',4'-trihydroxy-3-methoxy-6,7-methylenedioxyflavone 4'-glucuronide (2) exhibited growth-inhibitory activities against Helicobacter pylori 51. Distribution analysis of compound 2 revealed that the anti-H. pylori compound 2 is an OFS-specific bioactive phytochemical. This indicates that the phytochemical quality of OFS is better than that of GS. These findings will aid in providing vital data for vegetable processors, dieticians, nutritionists, and consumers to select optimal green leafy vegetables.PMID:36401778 | DOI:10.1007/s12272-022-01416-z