The Urinary Metabolome of Newborns with Perinatal Complications.

Maternal pathological conditions such as infections and chronic diseases, along with unexpected events during labor, can lead to life-threatening perinatal outcomes. These outcomes can have irreversible consequences throughout an individual's entire life. Urinary metabolomics can provide valuable insights into early physiological adaptations in healthy newborns, as well as metabolic disturbances in premature infants or infants with birth complications. In the present study, we measured 180 metabolites and metabolite ratios in the urine of 13 healthy (hospital-discharged) and 38 critically ill newborns (admitted to the neonatal intensive care unit (NICU)). We used an in-house-developed targeted tandem mass spectrometry (MS/MS)-based metabolomic assay (TMIC Mega) combining liquid chromatography (LC-MS/MS) and flow injection analysis (FIA-MS/MS) to quantitatively analyze up to 26 classes of compounds. Average urinary concentrations (and ranges) for 167 different metabolites from 38 critically ill NICU newborns during their first 24 h of life were determined. Similar sets of urinary values were determined for the 13 healthy newborns. These reference data have been uploaded to the Human Metabolome Database. Urinary concentrations and ranges of 37 metabolites are reported for the first time for newborns. Significant differences were found in the urinary levels of 44 metabolites between healthy newborns and those admitted at the NICU. Metabolites such as acylcarnitines, amino acids and derivatives, biogenic amines, sugars, and organic acids are dysregulated in newborns with bronchopulmonary dysplasia (BPD), asphyxia, or newborns exposed to SARS-CoV-2 during the intrauterine period. Urine can serve as a valuable source of information for understanding metabolic alterations associated with life-threatening perinatal outcomes.

Urinary angiotensin-converting enzyme 2 and metabolomics in COVID-19-mediated kidney injury.

Background: Angiotensin-converting enzyme 2 (ACE2), the receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is highly expressed in the kidneys. Beyond serving as a crucial endogenous regulator of the renin-angiotensin system, ACE2 also possess a unique function to facilitate amino acid absorption. Our observational study sought to explore the relationship between urine ACE2 (uACE2) and renal outcomes in coronavirus disease 2019 (COVID-19). Methods: In a cohort of 104 patients with COVID-19 without acute kidney injury (AKI), 43 patients with COVID-19-mediated AKI and 36 non-COVID-19 controls, we measured uACE2, urine tumour necrosis factor receptors I and II (uTNF-RI and uTNF-RII) and neutrophil gelatinase-associated lipocalin (uNGAL). We also assessed ACE2 staining in autopsy kidney samples and generated a propensity score-matched subgroup of patients to perform a targeted urine metabolomic study to describe the characteristic signature of COVID-19. Results: uACE2 is increased in patients with COVID-19 and further increased in those that developed AKI. After adjusting uACE2 levels for age, sex and previous comorbidities, increased uACE2 was independently associated with a >3-fold higher risk of developing AKI [odds ratio 3.05 (95% confidence interval 1.23‒7.58), P = .017]. Increased uACE2 corresponded to a tubular loss of ACE2 in kidney sections and strongly correlated with uTNF-RI and uTNF-RII. Urine quantitative metabolome analysis revealed an increased excretion of essential amino acids in patients with COVID-19, including leucine, isoleucine, tryptophan and phenylalanine. Additionally, a strong correlation was observed between urine amino acids and uACE2. Conclusions: Elevated uACE2 is related to AKI in patients with COVID-19. The loss of tubular ACE2 during SARS-CoV-2 infection demonstrates a potential link between aminoaciduria and proximal tubular injury.

Maternal and child biomonitoring strategies and levels of exposure in western Canada during the past seventeen years: The Alberta Biomonitoring Program: 2005-2021.

The Alberta Biomonitoring Program (ABP) was created in 2005 with the initial goal of establishing baseline levels of exposure to environmental chemicals in specific populations in the province of Alberta, Canada, and was later expanded to include multiple phases. The first two phases focused on evaluating exposure in pregnant women (Phase One, 2005) and children (Phase Two, 2004-2006) by analyzing residual serum specimens. Phase Three (2013-2016) employed active recruitment techniques to evaluate environmental exposures using a revised list of chemicals in paired serum pools from pregnant women and umbilical cord blood. These three phases of the program monitored a total of 226 chemicals in 285 pooled serum samples representing 31,529 individuals. Phase Four (2017-2020) of the ABP has taken a more targeted approach, focusing on the impact of the federal legalization of cannabis on the exposure of pregnant women in Alberta to cannabis, as well as tobacco and alcohol using residual prenatal screening serum specimens. Chemicals monitored in the first three phases include herbicides, neutral pesticides, metals, metalloids, and micronutrients, methylmercury, organochlorine pesticides, organophosphate pesticides, parabens, phthalate metabolites, perfluoroalkyl substances (PFAS), phenols, phytoestrogens, polybrominated compounds, polychlorinated biphenyls (PCBs), dioxins and furans, polycyclic aromatic hydrocarbons (PAHs), and tobacco biomarkers. Phase Four monitored six biomarkers of tobacco, alcohol, and cannabis. All serum samples were pooled. Mean concentrations and 95% confidence intervals (CIs) were calculated for the chemicals detected in ≥25% of the sample pools. cross the first three phases, the data from the ABP has provided baseline exposure levels for the chemicals in pregnant women, children, and newborns across the province. Comparison within and among the phases has highlighted differences in exposure levels with age, geography, seasonality, sample type, and time. The strategies employed throughout the program phases have been demonstrated to provide effective models for population biomonitoring.

Identification of Serum-Predictive Biomarkers for Subclinical Mastitis in Dairy Cows and New Insights into the Pathobiology of the Disease.

Targeted direct injection/liquid chromatography coupled to tandem mass spectrometry-based metabolomics was employed to identify metabolite alterations that could differentiate subclinical mastitis (SCM) from control (CON) dairy cows at -8, -4, disease diagnosis, +4 and +8 wks relative to parturition. We identified and measured 128 metabolites in the serum. Univariate analysis revealed significant alterations of serum metabolites at all five time points studied. By applying multivariate analyses including principle component analysis and partial least squares-discriminant analysis, some of the metabolites were found to have the strongest power for discriminating the SCM from CON cows. The top five metabolites with the greatest variable importance in projection values were selected as potential biomarkers for SCM. A set of five serum metabolites including lysine, ornithine, isoleucine, LysoPC a C17:0, and leucine at -8 wks and five other metabolites including lysine, leucine, isoleucine, kynurenine, and sphingomyelin (SM) C26:0 at -4 wks prepartum were determined as predictive biomarkers for SCM, which provided highly predictive capabilities with AUC (area under the curve) at 1.00. Five metabolites including lysine, leucine, isoleucine, kynurenine, and SM C26:1 in the serum were identified as diagnostic biomarkers for SCM with the AUC of 1.00. Moreover, we observed that distinct metabolic pathways were affected in SCM cows including lysine degradation, biotin, cysteine, methionine, and glutathione metabolism, valine, leucine, and isoleucine biosynthesis and degradation, and aminoacyl-tRNA biosynthesis prior to and during the occurrence of the disease. Results of this study showed that metabolomics analyses can be used to identify susceptible cows to SCM starting from -8 and -4 wks prepartum and that blood can be used to diagnose cows with SCM.

Gut Microenvironment and Bacterial Invasion in Paediatric Inflammatory Bowel Diseases.

OBJECTIVES: Host-microbial relationship is disrupted in inflammatory bowel diseases (IBD). We hypothesized that altered gut luminal microenvironment can impact microbial virulence in IBD, leading to disruption of homeostasis and disease. We investigated the relationship between gut microenvironment and microbial virulence. METHODS: Intestinal aspirates were collected from 10 non-IBD controls, 9 Crohn disease, and 10 ulcerative colitis paediatric patients during endoscopy. In vitro invasion of bacteria isolated from the duodenum and terminal ileum (TI) was quantified using gentamicin protection assays. Intestinal epithelial cells were infected in vitro by known Escherichia coli strains with patient intestinal aspirates added. Nuclear magnetic resonance spectroscopy (NMR) analysis was conducted on intestinal aspirates to identify metabolites associated with invasion; these metabolites were then introduced to the infection model. RESULTS: There was no difference in in vitro invasion of bacteria obtained from intestinal aspirates of non-IBD and IBD patients. Incubation of laboratory E coli strains with TI aspirates from IBD patients increased their invasion into epithelial cells in vitro. NMR analysis revealed intestinal metabolites that correlated with bacterial invasion; succinate present in the intestinal aspirates correlated positively, whereas acetate and formate related negatively with invasion. Addition of exogenous succinate increased invasion of E coli in vitro. CONCLUSIONS: Alterations in the gut microenvironment in IBD can affect bacterial invasion. Succinate is associated with increased bacterial invasion and can alter bacterial virulence in IBD. This highlights the interaction between specific metabolites and bacteria that could be instrumental in propagating or suppressing inflammation in paediatric IBD patients.

A High-Performing Plasma Metabolite Panel for Early-Stage Lung Cancer Detection.

The objective of this research is to use metabolomic techniques to discover and validate plasma metabolite biomarkers for the diagnosis of early-stage non-small cell lung cancer (NSCLC). The study included plasma samples from 156 patients with biopsy-confirmed NSCLC along with age and gender-matched plasma samples from 60 healthy controls. A fully quantitative targeted mass spectrometry (MS) analysis (targeting 138 metabolites) was performed on all samples. The sample set was split into a discovery set and validation set. Metabolite concentration data, clinical data, and smoking history were used to determine optimal sets of biomarkers and optimal regression models for identifying different stages of NSCLC using the discovery sets. The same biomarkers and regression models were used and assessed on the validation models. Univariate and multivariate statistical analysis identified ß-hydroxybutyric acid, LysoPC 20:3, PC ae C40:6, citric acid, and fumaric acid as being significantly different between healthy controls and stage I/II NSCLC. Robust predictive models with areas under the curve (AUC) > 0.9 were developed and validated using these metabolites and other, easily measured clinical data for detecting different stages of NSCLC. This study successfully identified and validated a simple, high-performing, metabolite-based test for detecting early stage (I/II) NSCLC patients in plasma. While promising, further validation on larger and more diverse cohorts is still required.

Impact of Bovine Diet on Metabolomic Profile of Skim Milk and Whey Protein Ingredients.

The influence of bovine diet on the metabolome of reconstituted skim milk powder (SMP) and protein ingredients produced from the milk of cows fed on pasture or concentrate-based diets was investigated. Cows were randomly assigned to diets consisting of perennial ryegrass only (GRS), perennial ryegrass/white clover sward (CLV), or indoor total mixed ration (TMR) for an entire lactation. Raw milk obtained from each group was processed at pilot scale, to produce SMP and sweet whey, and SMP was further processed at laboratory scale, to yield ideal whey and acid whey. The total amino acid composition and metabolome of each sample were analyzed, using high-performance cation exchange and a targeted combination of direct-injection mass spectrometry and reverse-phase liquid chromatography-tandem mass spectrometry (LC-MS/MS), respectively. The nitrogen composition of the products from each of the diets was similar, with one exception being the significantly higher nonprotein nitrogen content in TMR-derived skim milk powder than that from the GRS system. Total amino acid analysis showed significantly higher concentrations of glycine in GRS- and CLV-derived sweet whey and acid whey than in those from TMR. The cysteine contents of CLV-derived ideal whey and acid whey were significantly higher than for TMR, while the valine content of GRS-derived acid whey was significantly higher than TMR. The phenylalanine content of GRS-derived ideal whey was significantly higher than that from CLV. Metabolomic analysis showed significantly higher concentrations of the metabolites glutamine, valine, and phosphocreatine in each ingredient type derived from TMR than those from GRS or CLV, while the serine content of each GRS-derived ingredient type was significantly higher than that in TMR-derived ingredients. These results demonstrate that the type of bovine feeding system used can have a significant effect on the amino acid composition and metabolome of skim milk and whey powders and may aid in the selection of raw materials for product manufacture, while the clear separation between the samples gives further evidence for distinguishing milk products produced from different feeding systems based on LC-MS/MS.

Transformation of polyphenols found in pigmented gluten-free flours during in vitro large intestinal fermentation.

In this work, 18 gluten-free flours (prepared from cereals, pseudocereals and legumes), differing in pigmentation, were screened for their phenolic profiles, cooked and, then, subjected to digestion and large intestinal fermentation in vitro. A combined targeted/untargeted metabolomic approach was used to elucidate the microbial biotransformation processes of polyphenols following digestion. This preliminary work demonstrated an increase in 3,5-dihydroxybenzoic acid (on average from 0.67 up to 1.30 µmol/g dry matter) throughout large intestinal fermentation of pseudocereals (esp. quinoa), due to their high alkylresorcinol contents. Isoflavones were converted into equol- or O-desmethylangolensin- derivatives, whereas anthocyanins were degraded into lower-molecular-weight phenolics (i.e., protocatechuic aldehyde and 4-hydroxybenzoic acid, with the latter exhibiting the highest increase over time). A decreasing trend was observed for antioxidant activities (i.e., FRAP and ORAC values) moving from digested to faecal fermented samples. These findings highlight that gluten-free flours are able to deliver bioaccessible polyphenols to the colon.

Edible nuts deliver polyphenols and their transformation products to the large intestine: An in vitro fermentation model combining targeted/untargeted metabolomics.

The fate of polyphenols from edible tree nuts was investigated using a simulated in vitro intestinal fermentation system. The digested food matrix was fermented for 48 h and the changes in the phenolic profiles were evaluated by both untargeted UHPLC-QTOF and targeted UHPLC-Orbitrap mass spectrometry. The untargeted metabolomics approach allowed us to monitor the comprehensive changes in phenolic profiles from 0 up to 48 h of in vitro fermentation. Multivariate statistics (i.e., orthogonal projection to latent structures discriminant analysis) applied to this untargeted data allowed us to identify the most discriminating phenolic metabolites and to further understand the colonic transformation pathways involved. In particular, 13 putatively identified compounds derived from flavonoids, lignans and phenolic acids were found to have the highest discrimination potential. Six phenolic metabolites were then quantified by means of targeted metabolomics (using a UHPLC-Orbitrap). These metabolites included 3,4-dihydroxyphenylacetic acid, 4-hydroxybenzoic acid, hippuric acid, caffeic acid, protocatechuic acid and protocatechuic aldehyde. Using the targeted data, a clear matrix effect was observed over time, with an increase of some phenolic metabolites moving from 8 to 48 h of in vitro fermentation. Based on these data, catabolic pathways for colonic microbial degradation of flavonoids, hydroxycinnamic acids, tyrosols and lignans are proposed. Our findings show that edible tree nuts deliver polyphenols to the colon, where several microbial transformations occur that lead to smaller phenolic metabolites being observed. Furthermore, we found that the combined use of targeted and untargeted metabolomics can be particularly effective for investigating the fate of polyphenols in the large intestine.

Identification of Primary Natural Killer Cell Modulators by Chemical Library Screening with a Luciferase-Based Functional Assay.

Natural killer (NK) cells are essential players of the innate immune response that secrete cytolytic factors and cytokines such as IFN-γ when contacting virus-infected or tumor cells. They represent prime targets in immunotherapy as defects in NK cell functions are hallmarks of many pathological conditions, such as cancer and chronic infections. The functional screening of chemical libraries or biologics would greatly help identify new modulators of NK cell activity, but commonly used methods such as flow cytometry are not easily scalable to high-throughput settings. Here we describe an efficient assay to measure the natural cytotoxicity of primary NK cells where the bioluminescent enzyme NanoLuc is constitutively expressed in the cytoplasm of target cells and is released in co-culture supernatants when lysis occurs. We fully characterized this assay using either purified NK cells or total peripheral blood mononuclear cells (PBMCs), including some patient samples, as effector cells. A pilot screen was also performed on a library of 782 metabolites, xenobiotics, and common drugs, which identified dextrometorphan and diphenhydramine as novel NK cell inhibitors. Finally, this assay was further improved by developing a dual-reporter cell line to simultaneously measure NK cell cytotoxicity and IFN-γ secretion in a single well, extending the potential of this system.

Evolution of renal function and urinary biomarker indicators of inflammation on serial kidney biopsies in pediatric kidney transplant recipients with and without rejection.

Urinary CXCL10 and metabolites are biomarkers independently associated with TCMR. We sought to test whether these biomarkers fluctuate in association with histological severity of TCMR over short time frames. Forty-nine pairs of renal biopsies obtained 1-3 months apart from 40 pediatric renal transplant recipients were each scored for TCMR acuity score (i + t; Banff criteria). Urinary CXCL10:Cr and TCMR MDS were obtained at each biopsy and were tested for association with changes between biopsies in acuity, estimated GFR (ΔeGFR), and 12-month ΔeGFR. Sequential biopsies were obtained 1.8 ± 0.8 months apart. Biopsy 1 was usually obtained under protocol (75%), and 62% percent had evidence of TCMR. Using each biopsy pair for comparison, ΔeGFR did not predict change in acuity. By contrast, change in acuity was significantly correlated with change in urinary CXCL10:Cr (ρ 0.45, P = .003) and MDS (ρ 0.29, P = .04) between biopsies. The 12-month ΔeGFR was not predicted by TCMR acuity or CXCL10:Cr at Biopsy 2; however, an inverse correlation was seen with urinary MDS (ρ -0.35; P = .02). Changes in eGFR correlate poorly with evolving TCMR acuity on histology. Urinary biomarkers may be superior for non-invasive monitoring of rejection, including histological response to therapy, and may be prognostic for medium-term function.

A Distinctive Urinary Metabolomic Fingerprint Is Linked With Endoscopic Postoperative Disease Recurrence in Crohn's Disease Patients.

Background: Crohn's disease (CD) patients who undergo ileocolonic resection frequently have disease recurrence. The aim of this preliminary study was to identify urinary metabolomic profiles associated with disease recurrence in order to identify underlying mechanisms of recurrence and possible disease biomarkers. Methods: Biopsies from the neoterminal ileum were collected from CD patients (n = 38) after ileocolonic resection in order to assess mucosa-associated microbiota using 16S rRNA multitag pyrosequencing. Urine samples were collected, and metabolomic profiling was done using high-resolution nuclear magnetic resolution spectroscopy and a combined direct infusion liquid chromatography tandem mass spectrometry. The Rutgeerts scoring system was used to assess endoscopic postoperative recurrence of CD. Results: There were 28 (73.7%) patients with endoscopic CD recurrence. CD patients who were in endoscopic remission had a higher abundance of Bacteroidetes and lower abundance of Fusobacteria and Proteobacteria in comparison with CD patients who had endoscopic recurrence. In addition, metabolomic profiling could also discriminate between these 2 groups of patients. Endoscopic recurrence was associated with increased concentration of urinary levoglucosan. Rutgeerts score was positively correlated with levoglucosan and propylene glycol levels. Conclusions: CD patients who present with endoscopic disease recurrence after surgery have a unique urinary metabolomic fingerprint that can differentiate them from CD patients who are in endoscopic remission after ileocolonic resection. In addition, mucosal-associated microbiota in CD patients with or without disease recurrence after surgery differs and correlates with some urinary metabolites.

Growth of Malignant Non-CNS Tumors Alters Brain Metabolome.

Cancer survivors experience numerous treatment side effects that negatively affect their quality of life. Cognitive side effects are especially insidious, as they affect memory, cognition, and learning. Neurocognitive deficits occur prior to cancer treatment, arising even before cancer diagnosis, and we refer to them as "tumor brain." Metabolomics is a new area of research that focuses on metabolome profiles and provides important mechanistic insights into various human diseases, including cancer, neurodegenerative diseases, and aging. Many neurological diseases and conditions affect metabolic processes in the brain. However, the tumor brain metabolome has never been analyzed. In our study we used direct flow injection/mass spectrometry (DI-MS) analysis to establish the effects of the growth of lung cancer, pancreatic cancer, and sarcoma on the brain metabolome of TumorGraft™ mice. We found that the growth of malignant non-CNS tumors impacted metabolic processes in the brain, affecting protein biosynthesis, and amino acid and sphingolipid metabolism. The observed metabolic changes were similar to those reported for neurodegenerative diseases and brain aging, and may have potential mechanistic value for future analysis of the tumor brain phenomenon.

Dietary and metabolomic determinants of relapse in ulcerative colitis patients: A pilot prospective cohort study.

AIM: To identify demographic, clinical, metabolomic, and lifestyle related predictors of relapse in adult ulcerative colitis (UC) patients. METHODS: In this prospective pilot study, UC patients in clinical remission were recruited and followed-up at 12 mo to assess a clinical relapse, or not. At baseline information on demographic and clinical parameters was collected. Serum and urine samples were collected for analysis of metabolomic assays using a combined direct infusion/liquid chromatography tandem mass spectrometry and nuclear magnetic resolution spectroscopy. Stool samples were also collected to measure fecal calprotectin (FCP). Dietary assessment was performed using a validated self-administered food frequency questionnaire. RESULTS: Twenty patients were included (mean age: 42.7 ± 14.8 years, females: 55%). Seven patients (35%) experienced a clinical relapse during the follow-up period. While 6 patients (66.7%) with normal body weight developed a clinical relapse, 1 UC patient (9.1%) who was overweight/obese relapsed during the follow-up (P = 0.02). At baseline, poultry intake was significantly higher in patients who were still in remission during follow-up (0.9 oz vs 0.2 oz, P = 0.002). Five patients (71.4%) with FCP > 150 µg/g and 2 patients (15.4%) with normal FCP (≤ 150 µg/g) at baseline relapsed during the follow-up (P = 0.02). Interestingly, baseline urinary and serum metabolomic profiling of UC patients with or without clinical relapse within 12 mo showed a significant difference. The most important metabolites that were responsible for this discrimination were trans-aconitate, cystine and acetamide in urine, and 3-hydroxybutyrate, acetoacetate and acetone in serum. CONCLUSION: A combination of baseline dietary intake, fecal calprotectin, and metabolomic factors are associated with risk of UC clinical relapse within 12 mo.

Urinary Metabolomics for Noninvasive Detection of Antibody-Mediated Rejection in Children After Kidney Transplantation.

BACKGROUND: Biomarkers are needed that identify patients with antibody-mediated rejection (AMR). The goal of this study was to evaluate the utility of urinary metabolomics for early noninvasive detection of AMR in pediatric kidney transplant recipients. METHODS: Urine samples (n = 396) from a prospective, observational cohort of 59 renal transplant patients with surveillance or indication biopsies were assayed for 133 unique metabolites by quantitative mass spectrometry. Samples were classified according to Banff criteria for AMR and partial least squares discriminant analysis was used to identify associated changes in metabolite patterns by creating a composite index based on all 133 metabolites. RESULTS: Urine samples of patients with (n = 40) and without AMR (n = 278) were analyzed and a classifier for AMR was identified (area under receiver operating characteristic curve = 0.84; 95% confidence interval, 0.77-0.91; P = 0.006). Application of the classifier to "indeterminate" samples (samples that partially fulfilled Banff criteria for AMR; n = 65) yielded an AMR score of 0.19 ± 0.15, intermediate between scores for AMR and No AMR (0.28 ± 0.14 and 0.10 ± 0.13 respectively, P ≤ 0.001). The AMR score was associated with the presence of donor-specific antibodies, biopsy indication, Banff ct, t, ah and cg scores, and retained accuracy when applied to subclinical cases (creatinine, <25% increase from baseline) or had minimal or no transplant glomerulopathy (Banff cg0-1). Exploratory classifiers that segregated samples based on concurrent T cell-mediated rejection (TCMR) identified overlapping metabolite signatures between AMR and TCMR, suggesting similar pathophysiology of tissue injury. CONCLUSIONS: These preliminary findings identify a urine metabolic classifier for AMR. Independent validation is needed to verify its utility for accurate, noninvasive AMR detection.

Metabolomic prediction of endometrial cancer.

INTRODUCTION: Endometrial cancer (EC) is associated with metabolic disturbances including obesity, diabetes and metabolic syndrome. Identifying metabolite biomarkers for EC detection has a crucial role in reducing morbidity and mortality. OBJECTIVE: To determine whether metabolomic based biomarkers can detect EC overall and early-stage EC. METHODS: We performed NMR and mass spectrometry based metabolomic analyses of serum in EC cases versus controls. A total of 46 early-stage (FIGO stages I-II) and 10 late-stage (FIGO stages III-IV) EC cases constituted the study group. A total of 60 unaffected control samples were used. Patients and controls were divided randomly into a discovery group (n = 69) and an independent validation group (n = 47). Predictive algorithms based on biomarkers and demographic characteristics were generated using logistic regression analysis. RESULTS: A total of 181 metabolites were evaluated. Extensive changes in metabolite levels were noted in the EC versus the control group. The combination of C14:2, phosphatidylcholine with acyl-alkyl residue sum C38:1 (PCae C38:1) and 3-hydroxybutyric acid had an area under the receiver operating characteristics curve (AUC) (95% CI) = 0.826 (0.706-0.946) and a sensitivity = 82.6%, and specificity = 70.8% for EC overall. For early EC prediction: BMI, C14:2 and PC ae C40:1 had an AUC (95% CI) = 0.819 (0.689-0.95) and a sensitivity = 72.2% and specificity = 79.2% in the validation group. CONCLUSIONS: EC is characterized by significant perturbations in important cellular metabolites. Metabolites accurately detected early-stage EC cases and EC overall which could lead to the development of non-invasive biomarkers for earlier detection of EC and for monitoring disease recurrence.

Detecting Renal Allograft Inflammation Using Quantitative Urine Metabolomics and CXCL10.

BACKGROUND: The goal of this study was to characterize urinary metabolomics for the noninvasive detection of cellular inflammation and to determine if adding urinary chemokine ligand 10 (CXCL10) improves the overall diagnostic discrimination. METHODS: Urines (n = 137) were obtained before biopsy in 113 patients with no (n = 66), mild (borderline or subclinical; n = 58), or severe (clinical; n = 13) rejection from a prospective cohort of adult renal transplant patients (n = 113). Targeted, quantitative metabolomics was performed with direct flow injection tandem mass spectrometry using multiple reaction monitoring (ABI 4000 Q-Trap). Urine CXCL10 was measured by enzyme-linked immunosorbent assay. A projection on latent structures discriminant analysis was performed and validated using leave-one-out cross-validation, and an optimal 2-component model developed. Chemokine ligand 10 area under the curve (AUC) was determined and net reclassification index and integrated discrimination index analyses were performed. RESULTS: PLS2 demonstrated that urinary metabolites moderately discriminated the 3 groups (Cohen κ, 0.601; 95% confidence interval [95% CI], 0.46-0.74; P < 0.001). Using binary classifiers, urinary metabolites and CXCL10 demonstrated an AUC of 0.81 (95% CI, 0.74-0.88) and 0.76 (95% CI, 0.68-0.84), respectively, and a combined AUC of 0.84 (95% CI, 0.78-0.91) for detecting alloimmune inflammation that was improved by net reclassification index and integrated discrimination index analyses. Urinary CXCL10 was the best univariate discriminator, followed by acylcarnitines and hexose. CONCLUSIONS: Urinary metabolomics can noninvasively discriminate noninflamed renal allografts from those with subclinical and clinical inflammation, and the addition of urine CXCL10 had a modest but significant effect on overall diagnostic performance. These data suggest that urinary metabolomics and CXCL10 may be useful for noninvasive monitoring of alloimmune inflammation in renal transplant patients.

Cancer Metabolomics and the Human Metabolome Database.

The application of metabolomics towards cancer research has led to a renewed appreciation of metabolism in cancer development and progression. It has also led to the discovery of metabolite cancer biomarkers and the identification of a number of novel cancer causing metabolites. The rapid growth of metabolomics in cancer research is also leading to challenges. In particular, with so many cancer-associate metabolites being identified, it is often difficult to keep track of which compounds are associated with which cancers. It is also challenging to track down information on the specific pathways that particular metabolites, drugs or drug metabolites may be affecting. Even more frustrating are the difficulties associated with identifying metabolites from NMR or MS spectra. Fortunately, a number of metabolomics databases are emerging that are designed to address these challenges. One such database is the Human Metabolome Database (HMDB). The HMDB is currently the world's largest and most comprehensive, organism-specific metabolomics database. It contains more than 40,000 metabolite entries, thousands of metabolite concentrations, >700 metabolic and disease-associated pathways, as well as information on dozens of cancer biomarkers. This review is intended to provide a brief summary of the HMDB and to offer some guidance on how it can be used in metabolomic studies of cancer.

Clinical phenotype clustering in cardiovascular risk patients for the identification of responsive metabotypes after red wine polyphenol intake.

This study aims to evaluate the robustness of clinical and metabolic phenotyping through, for the first time, the identification of differential responsiveness to dietary strategies in the improvement of cardiometabolic risk conditions. Clinical phenotyping of 57 volunteers with cardiovascular risk factors was achieved using k-means cluster analysis based on 69 biochemical and anthropometric parameters. Cluster validation based on Dunn and Figure of Merit analysis for internal coherence and external homogeneity were employed. k-Means produced four clusters with particular clinical profiles. Differences on urine metabolomic profiles among clinical phenotypes were explored and validated by multivariate orthogonal signal correction partial least-squares discriminant analysis (OSC-PLS-DA) models. OSC-PLS-DA of (1)H-NMR data revealed that model comparing "obese and diabetic cluster" (OD-c) against "healthier cluster" (H-c) showed the best predictability and robustness in terms of explaining the pairwise differences between clusters. Considering these two clusters, distinct groups of metabolites were observed following an intervention with wine polyphenol intake (WPI; 733 equivalents of gallic acid/day) per 28days. Glucose was significantly linked to OD-c metabotype (P<.01), and lactate, betaine and dimethylamine showed a significant trend. Tartrate (P<.001) was associated with wine polyphenol intervention (OD-c_WPI and H-c_WPI), whereas mannitol, threonine methanol, fucose and 3-hydroxyphenylacetate showed a significant trend. Interestingly, 4-hydroxyphenylacetate significantly increased in H-c_WPI compared to OD-c_WPI and to basal groups (P<.05)-gut microbial-derived metabolite after polyphenol intake-, thereby exhibiting a clear metabotypic intervention effect. Results revealed gut microbiota responsive phenotypes to wine polyphenols intervention. Overall, this study illustrates a novel metabolomic strategy for characterizing interindividual responsiveness to dietary intervention and identification of health benefits.