Differential Effect of Non-Purified and Semi-Purified Standard Diets on Kynurenine and Peripheral Metabolites in Male C57BL/6J Mice

The kynurenine (Kyn) pathway plays crucial roles in several inflammation-induced disorders such as depression. In this study, we measured Kyn and other related molecules in the blood plasma, brain, and urine of male C57BL/6J mice (B6) fed non-purified (MF) and semi-purified (AIN-93G and AIN-93M) standard rodent diets. Mice fed MF had increased plasma Kyn levels compared with those on AIN93-based diets, as well as decreased hippocampal Kyn levels compared with those fed AIN-93G. Previous studies showed that branched chain amino acids (BCAAs) suppress peripheral blood Kyn transportation to the brain, but plasma BCAA levels were not significantly different between the diet groups in our study. Urine metabolome analysis revealed that feed ingredients affected the excretion of many metabolites, and MF-fed mice had elevated excretion of kynurenic and quinolinic acids, pivotal metabolites in the Kyn pathway. Collectively, the level of critical metabolites in the Kyn pathway in the central and peripheral tissues was strongly affected by feed ingredients. Therefore, feed selection is a critical factor to ensure the reproducibility of experimental data in studies involving rodent models.

Intestinal Gluconeogenesis Shapes Gut Microbiota, Fecal and Urine Metabolome in Mice with Gastric Bypass Surgery

Background&Aims: Intestinal gluconeogenesis (IGN), gastric bypass (GBP) and gut microbiota positively regulate glucose homeostasis and diet-induced dysmetabolism. GBP modulates gut microbiota but whether IGN intensity could shape it has not been investigated. Methods: To this aim, we studied gut microbiota and microbiome in wild-type and IGN-deficient mice which underwent GBP and were fed on either a normal chow (NC) or a high-fat/high-sucrose (HFHS) diet. We also studied fecal and urine metabolome in NC-fed mice. Results: IGN and GBP had a peculiar effect on both gut microbiota and microbiome, on NC and HFHS diet. IGN inactivation induced Deltaproteobacteria on NC and higher Proteobacteria such as Helicobacter on HFHS diet. GBP induced higher Firmicutes and Proteobacteria on NC-fed WT mice and Firmicutes, Bacteroidetes and Proteobacteria on HFHS-fed WT mice. The combined effect of IGN inactivation and GBP induced higher Actinobacteria on NC and higher Enterococcaceae and Enterobacteriaceae on HFHS diet. A reduction was observed in short-chain fatty acids in fecal (by GBP) and in both fecal and urine (by IGN inactivation) metabolome. Conclusions: IGN and GBP, alone and in combination, shape gut microbiota and microbiome on NC- and HFHS-fed mice, together with a change in fecal and urine metabolome.

Network of Interactions Between Gut Microbiome, Host Biomarkers, and Urine Metabolome in Carotid Atherosclerosis

Comprehensive analyses of multi-omics data may provide insights into interactions between different biological layers concerning distinct clinical features. We integrated data on the gut microbiota, blood parameters and urine metabolites of treatment-naive individuals presenting a wide range of metabolic disease phenotypes to delineate clinically meaningful associations. Trans-omics correlation networks revealed that candidate gut microbial biomarkers and urine metabolite feature were covaried with distinct clinical phenotypes. Integration of the gut microbiome, the urine metabolome and the phenome revealed that variations in one of these three systems correlated with changes in the other two. In a specific note about clinical parameters of liver function, we identified Eubacteriumeligens, Faecalibacteriumprausnitzii and Ruminococcuslactaris to be associated with a healthy liver function, whereas Clostridium bolteae, Tyzzerellanexills, Ruminococcusgnavus, Blautiahansenii, and Atopobiumparvulum were associated with blood biomarkers for liver diseases. Variations in these microbiota features paralleled changes in specific urine metabolites. Network modeling yielded two core clusters including one large gut microbe-urine metabolite close-knit cluster and one triangular cluster composed of a gut microbe-blood-urine network, demonstrating close inter-system crosstalk especially between the gut microbiome and the urine metabolome. Distinct clinical phenotypes are manifested in both the gut microbiome and the urine metabolome, and inter-domain connectivity takes the form of high-dimensional networks. Such networks may further our understanding of complex biological systems, and may provide a basis for identifying biomarkers for diseases. Deciphering the complexity of human physiology and disease requires a holistic and trans-omics approach integrating multi-layer data sets, including the gut microbiome and profiles of biological fluids. By studying the gut microbiome on carotid atherosclerosis, we identified microbial features associated with clinical parameters, and we observed that groups of urine metabolites correlated with groups of clinical parameters. Combining the three data sets, we revealed correlations of entities across the three systems, suggesting that physiological changes are reflected in each of the omics. Our findings provided insights into the interactive network between the gut microbiome, blood clinical parameters and the urine metabolome concerning physiological variations, and showed the promise of trans-omics study for biomarker discovery.

Urine metabolome of tuberculosis patients receiving intensive phase of treatment show diurnal variations.

Diurnal variation in biofluid metabolome as observed in a healthy human may alter in perturbed conditions. Biofluids like urine are rich in molecular constituents including metabolites, and infectious disease conditions like tuberculosis (TB) may influence diurnal differences for which limited reports are available in the literature. In this study, we present an optimized gas chromatography coupled to a quadrupole mass spectrometry (GC-MS) method to analyze processed and trimethylsilyl (TMS) derivatized urine metabolites. Urine samples were collected at four time points (0, 6, 12 and 24 hours) of study subjects [n=15; mean age 37 (24-70) in years] including controls [n=7; mean age 29.3 (24-35) years] and culture-confirmed active TB patients [ATB; n=8; mean age 43.7 (25-70) years] receiving treatment in the intensive phase. Global urine metabolite profiling was carried out using the optimized GC-MS method. Higher urine analyte diversity was observed in ATB patients (74) than in controls (36) during the day. Diurnal variations of the parent anti-TB drugs and their breakdown products (pyrazinamide, pyrazinoic acid, 5-hydroxy pyrazinoic acid, isonicotinic acid and alpha amino butyric acid) were observed with maximum abundance at 6 h. Interestingly, urine of ATB subjects at 6 h showed the highest metabolic diversity, whereas it was at 12 h in controls. Many analytes including glycine and alanine amino-acids showed diurnal variation in ATB and controls. These changes could be attributed to the altered host metabolic activities due to disease, treatment-associated decrease in total body bacterial burden and gut microbiota dysbiosis. And the optimized spiked-in internal standard, urine sample volume and GC-MS method could be used for global urine metabolome analysis in healthy and different perturbed conditions.

Effect of dapagliflozin on urine metabolome in patients with type 2 diabetes

Context Inhibitors of sodium-glucose cotransporters-2 have cardio- and renoprotective properties. However, the underlying mechanisms remain indeterminate. Objective To evaluate the effect of dapagliflozin on renal metabolism assessed by urine metabolome analysis in patients with type 2 diabetes. Design Prospective cohort study. Setting Outpatient diabetes clinic of a tertiary academic centre. Patients Eighty patients with HbA1c> 7% on metformin monotherapy were prospectively enrolled. Intervention Fifty patients were treated with dapagliflozin for 3 months. To exclude that the changes observed in urine metabolome were merely the result of the improvement in glycemia, 30 patients treated with insulin degludec were used for comparison. Main Outcome Measure: Changes in urine metabolic profile before and after the administration of dapagliflozin and insulin degludec were assessed by Proton-Nuclear Magnetic Resonance spectroscopy (1H-NMR). Results In multivariate analysis urine metabolome was significantly altered by dapagliflozin (R 2X=0.819, R 2Y=0.627, Q 2Y=0.362, and CV-ANOVA, p<0.001) but not insulin. After dapagliflozin the urine concentrations of ketone bodies, lactate, branched chain amino acids (p<0.001), betaine, myo-inositol (p<0001) and N-Methylhydantoin (p< 0.005) were significantly increased. Additionally, the urine levels of alanine, creatine, sarcosine and citrate were also increased (p<0001, <0.0001 and <0.0005, respectively) whereas anserine decreased (p<0005). Conclusions Dapagliflozin significantly affects urine metabolome in patients with type 2 diabetes in a glucose lowering-independent way. Most of the observed changes can be considered beneficial and may contribute to the renoprotective properties of dapagliflozin.

Low-dose exposure of glyphosate-based herbicides disrupt the urine metabolome and its interaction with gut microbiota

Glyphosate-based herbicides (GBHs) can disrupt the host microbiota and influence human health. In this study, we explored the potential effects of GBHs on urinary metabolites and their interactions with gut microbiome using a rodent model. Glyphosate and Roundup (equal molar for glyphosate) were administered at the USA glyphosate ADI guideline (1.75 mg/kg bw/day) to the dams and their pups. The urine metabolites were profiled using non-targeted liquid chromatography—high resolution mass spectrometry (LC-HRMS). Our results found that overall urine metabolite profiles significantly differed between dams and pups and between female and male pups. Specifically, we identified a significant increase of homocysteine, a known risk factor of cardiovascular disease in both Roundup and glyphosate exposed pups, but in males only. Correlation network analysis between gut microbiome and urine metabolome pointed to Prevotella to be negatively correlated with the level of homocysteine. Our study provides initial evidence that exposures to commonly used GBH, at a currently acceptable human exposure dose, is capable of modifying urine metabolites in both rat adults and pups. The link between Prevotella-homocysteine suggests the potential role of GBHs in modifying the susceptibility of homocysteine, which is a metabolite that has been dysregulated in related diseases like cardiovascular disease or inflammation, through commensal microbiome.

Multiomics Longitudinal Modeling of Preeclamptic Pregnancies

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear and that poses a threat to both mothers and infants. Specific complex changes in women's physiology precede a diagnosis of preeclampsia. Understanding multiple aspects of such a complex changes at different levels of biology, can be enabled by simultaneous application of multiple assays. We developed prediction models for preeclampsia risk by analyzing six omics datasets from a longitudinal cohort of pregnant women. A machine learning-based multiomics model had high accuracy (area under the receiver operating characteristics curve (AUC) of 0.94, 95% confidence intervals (CI): [0.90, 0.99]). A prediction model using only ten urine metabolites provided an accuracy of the whole metabolomic dataset and was validated using an independent cohort of 16 women (AUC = 0.87, 95% CI: [0.76, 0.99]). Integration with clinical variables further improved prediction accuracy of the urine metabolome model (AUC = 0.90, 95% CI: [0.80, 0.99], urine metabolome, validated). We identified several biological pathways to be associated with preeclampsia. The findings derived from models were integrated with immune system cytometry data, confirming known physiological alterations associated with preeclampsia and suggesting novel associations between the immune and proteomic dynamics. While further validation in larger populations is necessary, these encouraging results will serve as a basis for a simple, early diagnostic test for preeclampsia.

The Urine Metabolome of Young Autistic Children Correlates with Their Clinical Profile Severity

Autism diagnosis is moving from the identification of common inherited genetic variants to a systems biology approach. The aims of the study were to explore metabolic perturbations in autism, to investigate whether the severity of autism core symptoms may be associated with specific metabolic signatures; and to examine whether the urine metabolome discriminates severe from mild-to-moderate restricted, repetitive, and stereotyped behaviors. We enrolled 57 children aged 2–11 years; thirty-one with idiopathic autism and twenty-six neurotypical (NT), matched for age and ethnicity. The urine metabolome was investigated by gas chromatography-mass spectrometry (GC-MS). The urinary metabolome of autistic children was largely distinguishable from that of NT children; food selectivity induced further significant metabolic differences. Severe autism spectrum disorder core deficits were marked by high levels of metabolites resulting from diet, gut dysbiosis, oxidative stress, tryptophan metabolism, mitochondrial dysfunction. The hierarchical clustering algorithm generated two metabolic clusters in autistic children: 85–90% of children with mild-to-moderate abnormal behaviors fell in cluster II. Our results open up new perspectives for the more general understanding of the correlation between the clinical phenotype of autistic children and their urine metabolome. Adipic acid, palmitic acid, and 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid can be proposed as candidate biomarkers of autism severity.