Data processing solutions to render metabolomics more quantitative: case studies in food and clinical metabolomics using Metabox 2.0.

In classic semiquantitative metabolomics, metabolite intensities are affected by biological factors and other unwanted variations. A systematic evaluation of the data processing methods is crucial to identify adequate processing procedures for a given experimental setup. Current comparative studies are mostly focused on peak area data but not on absolute concentrations. In this study, we evaluated data processing methods to produce outputs that were most similar to the corresponding absolute quantified data. We examined the data distribution characteristics, fold difference patterns between 2 metabolites, and sample variance. We used 2 metabolomic datasets from a retail milk study and a lupus nephritis cohort as test cases. When studying the impact of data normalization, transformation, scaling, and combinations of these methods, we found that the cross-contribution compensating multiple standard normalization (ccmn) method, followed by square root data transformation, was most appropriate for a well-controlled study such as the milk study dataset. Regarding the lupus nephritis cohort study, only ccmn normalization could slightly improve the data quality of the noisy cohort. Since the assessment accounted for the resemblance between processed data and the corresponding absolute quantified data, our results denote a helpful guideline for processing metabolomic datasets within a similar context (food and clinical metabolomics). Finally, we introduce Metabox 2.0, which enables thorough analysis of metabolomic data, including data processing, biomarker analysis, integrative analysis, and data interpretation. It was successfully used to process and analyze the data in this study. An online web version is available at http://metsysbio.com/metabox.

Lipid metabolism drives allele-specific early-stage hypertrophic cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) results from pathogenic variants in sarcomeric protein genes, that increase myocyte energy demand and lead to cardiac hypertrophy. But it is unknown whether a common metabolic trait underlies the cardiac phenotype at early disease stage. This study characterized two HCM mouse models (R92W-TnT, R403Q-MyHC) that demonstrate differences in mitochondrial function at early disease stage. Using a combination of cardiac phenotyping, transcriptomics, mass spectrometry-based metabolomics and computational modeling, we discovered allele-specific differences in cardiac structure/function and metabolic changes. TnT-mutant hearts had impaired energy substrate metabolism and increased phospholipid remodeling compared to MyHC-mutants. TnT-mutants showed increased incorporation of saturated fatty acid residues into ceramides, cardiolipin, and increased lipid peroxidation, that could underlie allele-specific differences in mitochondrial function and cardiomyopathy.

Denoising Autoencoder Normalization for Large-Scale Untargeted Metabolomics by Gas Chromatography-Mass Spectrometry.

Large-scale metabolomics assays are widely used in epidemiology for biomarker discovery and risk assessments. However, systematic errors introduced by instrumental signal drifting pose a big challenge in large-scale assays, especially for derivatization-based gas chromatography-mass spectrometry (GC-MS). Here, we compare the results of different normalization methods for a study with more than 4000 human plasma samples involved in a type 2 diabetes cohort study, in addition to 413 pooled quality control (QC) samples, 413 commercial pooled plasma samples, and a set of 25 stable isotope-labeled internal standards used for every sample. Data acquisition was conducted across 1.2 years, including seven column changes. In total, 413 pooled QC (training) and 413 BioIVT samples (validation) were used for normalization comparisons. Surprisingly, neither internal standards nor sum-based normalizations yielded median precision of less than 30% across all 563 metabolite annotations. While the machine-learning-based SERRF algorithm gave 19% median precision based on the pooled quality control samples, external cross-validation with BioIVT plasma pools yielded a median 34% relative standard deviation (RSD). We developed a new method: systematic error reduction by denoising autoencoder (SERDA). SERDA lowered the median standard deviations of the training QC samples down to 16% RSD, yielding an overall error of 19% RSD when applied to the independent BioIVT validation QC samples. This is the largest study on GC-MS metabolomics ever reported, demonstrating that technical errors can be normalized and handled effectively for this assay. SERDA was further validated on two additional large-scale GC-MS-based human plasma metabolomics studies, confirming the superior performance of SERDA over SERRF or sum normalizations.

Sexual Dimorphism of the Mouse Plasma Metabolome Is Associated with Phenotypes of 30 Gene Knockout Lines.

Although metabolic alterations are observed in many monogenic and complex genetic disorders, the impact of most mammalian genes on cellular metabolism remains unknown. Understanding the effect of mouse gene dysfunction on metabolism can inform the functions of their human orthologues. We investigated the effect of loss-of-function mutations in 30 unique gene knockout (KO) lines on plasma metabolites, including genes coding for structural proteins (11 of 30), metabolic pathway enzymes (12 of 30) and protein kinases (7 of 30). Steroids, bile acids, oxylipins, primary metabolites, biogenic amines and complex lipids were analyzed with dedicated mass spectrometry platforms, yielding 827 identified metabolites in male and female KO mice and wildtype (WT) controls. Twenty-two percent of 23,698 KO versus WT comparison tests showed significant genotype effects on plasma metabolites. Fifty-six percent of identified metabolites were significantly different between the sexes in WT mice. Many of these metabolites were also found to have sexually dimorphic changes in KO lines. We used plasma metabolites to complement phenotype information exemplified for Dhfr, Idh1, Mfap4, Nek2, Npc2, Phyh and Sra1. The association of plasma metabolites with IMPC phenotypes showed dramatic sexual dimorphism in wildtype mice. We demonstrate how to link metabolomics to genotypes and (disease) phenotypes. Sex must be considered as critical factor in the biological interpretation of gene functions.

Randomized crossover clinical trial of coenzyme Q10 and nicotinamide riboside in chronic kidney disease.

BackgroundCurrent studies suggest mitochondrial dysfunction is a major contributor to impaired physical performance and exercise intolerance in chronic kidney disease (CKD). We conducted a clinical trial of coenzyme Q10 (CoQ10) and nicotinamide riboside (NR) to determine their impact on exercise tolerance and metabolic profile in patients with CKD.MethodsWe conducted a randomized, placebo-controlled, double-blind, crossover trial comparing CoQ10, NR, and placebo in 25 patients with an estimated glomerular filtration rate (eGFR) of less than 60mL/min/1.73 m2. Participants received NR (1,000 mg/day), CoQ10 (1,200 mg/day), or placebo for 6 weeks each. The primary outcomes were aerobic capacity measured by peak rate of oxygen consumption (VO2 peak) and work efficiency measured using graded cycle ergometry testing. We performed semitargeted plasma metabolomics and lipidomics.ResultsParticipant mean age was 61.0 ± 11.6 years and mean eGFR was 36.9 ± 9.2 mL/min/1.73 m2. Compared with placebo, we found no differences in VO2 peak (P = 0.30, 0.17), total work (P = 0.47, 0.77), and total work efficiency (P = 0.46, 0.55) after NR or CoQ10 supplementation. NR decreased submaximal VO2 at 30 W (P = 0.03) and VO2 at 60 W (P = 0.07) compared with placebo. No changes in eGFR were observed after NR or CoQ10 treatment (P = 0.14, 0.88). CoQ10 increased free fatty acids and decreased complex medium- and long-chain triglycerides. NR supplementation significantly altered TCA cycle intermediates and glutamate that were involved in reactions that exclusively use NAD+ and NADP+ as cofactors. NR decreased a broad range of lipid groups including triglycerides and ceramides.ConclusionsSix weeks of treatment with NR or CoQ10 improved markers of systemic mitochondrial metabolism and lipid profiles but did not improve VO2 peak or total work efficiency.Trial registrationClinicalTrials.gov NCT03579693.FundingNational Institutes of Diabetes and Digestive and Kidney Diseases (grants R01 DK101509, R03 DK114502, R01 DK125794, and R01 DK101509).

Statistics and Machine Learning in Mass Spectrometry-Based Metabolomics Analysis.

In this chapter, we review the cutting-edge statistical and machine learning methods for missing value imputation, normalization, and downstream analyses in mass spectrometry metabolomics studies, with illustration by example datasets. The missing peak recovery includes simple imputation by zero or limit of detection, regression-based or distribution-based imputation, and prediction by random forest. The batch effect can be removed by data-driven methods, internal standard-based, and quality control sample-based normalization. We also summarize different types of statistical analysis for metabolomics and clinical outcomes, such as inference on metabolic biomarkers, clustering of metabolomic profiles, metabolite module building, and integrative analysis with transcriptome.

Synthesis and biological assessment of indole derivatives containing penta-heterocycles scaffold as novel anticancer agents towards A549 and K562 cells.

Herein, a new series of 2-chloro-N-(5-(2-oxoindolin-3-yl)-4H-pyrazol-3-yl) acetamide derivatives containing 1,3,4-thiadiazole (10a-i) and 4H-1,2,4-triazol-4-amine (11a-r) moiety was designed, synthesised as novel anticancer agents. The antiproliferative activity values indicated that compound 10 b stood as the most potent derivative with IC50 values of 12.0 nM and 10 nM against A549 and K562 cells, respectively. Mechanism investigation and docking studies of 10 b indicated that it possessed good apoptosis characteristic and dose-dependent growth arrest of A549 and K562 cells, blocked cell cycle into G2/M phase. Interestingly, 10 b suppressed the growth of A549 and K562 cells via modulation of EGFR and p53-MDM2 mediated pathway.

Impaired incretin homeostasis in non-diabetic moderate-severe CKD.

Background: Incretins are regulators of insulin secretion and glucose homeostasis that are metabolized by dipeptidyl peptidase-4 (DPP-4). Moderate-severe CKD may modify incretin release, metabolism, or response. Methods: We performed 2-hour oral glucose tolerance testing (OGTT) in 59 people with non-diabetic CKD (eGFR<60 ml/min per 1.73 m2) and 39 matched controls. We measured total (tAUC) and incremental (iAUC) area under the curve of plasma total glucagon-like peptide-1 (GLP-1) and total glucose-dependent insulinotropic polypeptide (GIP). Fasting DPP-4 levels and activity were measured. Linear regression was used to adjust for demographic, body composition, and lifestyle factors. Results: Mean eGFR was 38 ±13 and 89 ±17ml/min per 1.73 m2 in CKD and controls. GLP-1 iAUC and GIP iAUC were higher in CKD than controls with a mean of 1531 ±1452 versus 1364 ±1484 pMxmin, and 62370 ±33453 versus 42365 ±25061 pgxmin/ml, respectively. After adjustment, CKD was associated with 15271 pMxmin/ml greater GIP iAUC (95% CI 387, 30154) compared to controls. Adjustment for covariates attenuated associations of CKD with higher GLP-1 iAUC (adjusted difference, 122, 95% CI -619, 864). Plasma glucagon levels were higher at 30 minutes (mean difference, 1.6, 95% CI 0.3, 2.8 mg/dl) and 120 minutes (mean difference, 0.84, 95% CI 0.2, 1.5 mg/dl) in CKD compared to controls. There were no differences in insulin levels or plasma DPP-4 activity or levels between groups. Conclusion: Incretin response to oral glucose is preserved or augmented in moderate-severe CKD, without apparent differences in circulating DPP-4 concentration or activity. However, neither insulin secretion nor glucagon suppression are enhanced.

A Least Absolute Shrinkage and Selection Operator-Derived Predictive Model for Postoperative Respiratory Failure in a Heterogeneous Adult Elective Surgery Patient Population.

BACKGROUND: Postoperative respiratory failure (PRF) is associated with increased hospital charges and worse patient outcomes. Reliable prediction models can help to guide postoperative planning to optimize care, to guide resource allocation, and to foster shared decision-making with patients. RESEARCH QUESTION: Can a predictive model be developed to accurately identify patients at high risk of PRF? STUDY DESIGN AND METHODS: In this single-site proof-of-concept study, we used structured query language to extract, transform, and load electronic health record data from 23,999 consecutive adult patients admitted for elective surgery (2014-2021). Our primary outcome was PRF, defined as mechanical ventilation after surgery of > 48 h. Predictors of interest included demographics, comorbidities, and intraoperative factors. We used logistic regression to build a predictive model and the least absolute shrinkage and selection operator procedure to select variables and to estimate model coefficients. We evaluated model performance using optimism-corrected area under the receiver operating curve and area under the precision-recall curve and calculated sensitivity, specificity, positive and negative predictive values, and Brier scores. RESULTS: Two hundred twenty-five patients (0.94%) demonstrated PRF. The 18-variable predictive model included: operations on the cardiovascular, nervous, digestive, urinary, or musculoskeletal system; surgical specialty orthopedic (nonspine); Medicare or Medicaid (as the primary payer); race unknown; American Society of Anesthesiologists class ≥ III; BMI of 30 to 34.9 kg/m2; anesthesia duration (per hour); net fluid at end of the operation (per liter); median intraoperative FIO2, end title CO2, heart rate, and tidal volume; and intraoperative vasopressor medications. The optimism-corrected area under the receiver operating curve was 0.835 (95% CI,0.808-0.862) and the area under the precision-recall curve was 0.156 (95% CI, 0.105-0.203). INTERPRETATION: This single-center proof-of-concept study demonstrated that a structured query language extract, transform, and load process, based on readily available patient and intraoperative variables, can be used to develop a prediction model for PRF. This PRF prediction model is scalable for multicenter research. Clinical applications include decision support to guide postoperative level of care admission and treatment decisions.

[Effect of mild moxibustion on SIRT1/NF-κB signaling in atherosclerotic rabbits].

OBJECTIVE: To observe the effect of mild moxibustion on blood lipid, histopathological structure of the aortic arch, thoracic aortic silent information regulator 1 (SIRT1)/nuclear factor κB (NF-κB) signaling pathway in atherosclerosis (AS) rabbits, so as to explore its underlying mechanisms in improving AS. METHODS: Sixty male rabbits were randomly divided into control group (n=12), model group(n=11), mild moxibustion group (n=11), mild moxibustion + blocker (blocker) group (n=12). The AS model was established by feeding the rabbits with high-fat forage for 8 weeks, followed by immune response damage. Mild moxibustion was applied to "Danzhong"(CV17), "Shenque"(CV8) and "Neiguan" (PC6, bilateral) and "Xuehai" (SP10, bilateral) for 30 min, once daily, 3 times a week for 4 weeks. Rabbits of the blocker group received intraperitoneal injection of EX527 (a selective inhibitor of SIRT1, 5 mg·kg-1·d-1) 30 min before moxibustion. Rabbits of the control and model groups were only grabbed and fixed without intervention. After the intervention, the contents of serum triglyceride (TG) and total cholesterol (TC) were determined by enzymatic method, and those of serum low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) were determined by colorimetric method. The Sudan Ⅳ staining was employed to observe the histopathological structure of the aortic arch, and Western blot and fluorescence quantitative real time-PCR were used to detect the expressions of SIRT1 and NF-κB proteins and mRNAs in the thoracic aorta, respectively. RESULTS: Compared with the control group, the contents of serum TG, TC and LDL-C and the expression levels of NF-κB protein and mRNA were significantly increased (P<0.01, P<0.05), whereas the content of HDL-C and the expression of SIRT1 mRNA markedly decreased in the model group (P<0.01). After mild moxibustion, the contents of serum TG, TC, and LDL-C and the expression of NF-κB protein and mRNA were significantly down-regulated (P<0.01, P<0.05), while the content of HDL-C and the expression levels of SIRT1 protein and mRNA significantly up-regulated in the mild moxibustion group (P<0.05, P<0.01). There were no significant differences between the blocker and model groups in all the indexes (P>0.05). Compared with the mild moxibustion group, the serum TG, TC, and LDL-C contents and NF-κB protein expression were significantly increased (P<0.01, P<0.05), and HDL-C content and the expression of SIRT1 protein and mRNA significantly decreased (P<0.05, P<0.01) in the blocker group. Sudan Ⅳ staining showed vague structure of the aortic arch with obvious lipid infiltration in the model group, which was relatively milder in the mild moxibustion. CONCLUSION: Mild-moxibustion can reduce blood lipid levels and endothelial damage in atherosclerotic rabbits, which may be related to its function in regulating SIRT1/NF-κB signaling pathway.

Perceived effectiveness of added-sugar warning label designs for U.S. restaurant menus: An online randomized controlled trial.

Added-sugar consumption in the U.S. exceeds recommended limits. Policymakers are considering requiring restaurants to use menu warning labels to indicate items high in added sugar. We sought to determine whether icon-only and icon-plus-text added-sugar menu labels were (1) perceived as more effective at potentially reducing consumption of items high in added sugar and (2) increased knowledge of menu items' added-sugar content relative to control labels, and if effects differed by label design. A national sample of U.S. adults (n = 1327) participated in an online randomized experiment. Participants viewed menu items with either a control label, 1 of 6 icon-only labels, or 1 of 18 icon-plus-text labels with 3 text variations. For their assigned label, participants provided ratings of perceived message effectiveness (a validated scale of a message's potential to change behavior). Participants were also asked to classify menu items by their added-sugar content. The icon-only and icon-plus-text labels were perceived as more effective than the control label (means: 3.7 and 3.7 vs. 3.1, respectively, on a 5-point scale; p < 0.001). The icon-only and icon-plus-text groups each correctly classified 71% of menu items by added-sugar content vs. 56% in the control group (p < 0.001). All icons and text variations were perceived as similarly effective. In conclusion, relative to a control label, icon-only and icon-plus-text added-sugar menu labels were perceived as effective and helped consumers identify items high in added sugar. Menu warning labels may be a promising strategy for reducing added-sugar consumption from restaurants, but research on behavioral effects in real-world settings is needed. Clinical Trials Identifier:NCT04637412.

An Amish founder population reveals rare-population genetic determinants of the human lipidome.

Identifying the genetic determinants of inter-individual variation in lipid species (lipidome) may provide deeper understanding and additional insight into the mechanistic effect of complex lipidomic pathways in CVD risk and progression beyond simple traditional lipids. Previous studies have been largely population based and thus only powered to discover associations with common genetic variants. Founder populations represent a powerful resource to accelerate discovery of previously unknown biology associated with rare population alleles that have risen to higher frequency due to genetic drift. We performed a genome-wide association scan of 355 lipid species in 650 individuals from the Amish founder population including 127 lipid species not previously tested. To the best of our knowledge, we report for the first time the lipid species associated with two rare-population but Amish-enriched lipid variants: APOB_rs5742904 and APOC3_rs76353203. We also identified novel associations for 3 rare-population Amish-enriched loci with several sphingolipids and with proposed potential functional/causal variant in each locus including GLTPD2_rs536055318, CERS5_rs771033566, and AKNA_rs531892793. We replicated 7 previously known common loci including novel associations with two sterols: androstenediol with UGT locus and estriol with SLC22A8/A24 locus. Our results show the double power of founder populations and detailed lipidome to discover novel trait-associated variants.

Untargeted Metabolomics Analysis of the Serum Metabolic Signature of Childhood Obesity.

Obesity rates among children are growing rapidly worldwide, placing massive pressure on healthcare systems. Untargeted metabolomics can expand our understanding of the pathogenesis of obesity and elucidate mechanisms related to its symptoms. However, the metabolic signatures of obesity in children have not been thoroughly investigated. Herein, we explored metabolites associated with obesity development in childhood. Untargeted metabolomic profiling was performed on fasting serum samples from 27 obese Caucasian children and adolescents and 15 sex- and age-matched normal-weight children. Three metabolomic assays were combined and yielded 726 unique identified metabolites: gas chromatography-mass spectrometry (GC-MS), hydrophilic interaction liquid chromatography coupled to mass spectrometry (HILIC LC-MS/MS), and lipidomics. Univariate and multivariate analyses showed clear discrimination between the untargeted metabolomes of obese and normal-weight children, with 162 significantly differentially expressed metabolites between groups. Children with obesity had higher concentrations of branch-chained amino acids and various lipid metabolites, including phosphatidylcholines, cholesteryl esters, triglycerides. Thus, an early manifestation of obesity pathogenesis and its metabolic consequences in the serum metabolome are correlated with altered lipid metabolism. Obesity metabolite patterns in the adult population were very similar to the metabolic signature of childhood obesity. Identified metabolites could be potential biomarkers and used to study obesity pathomechanisms.

Genomics of Postprandial Lipidomics in the Genetics of Lipid-Lowering Drugs and Diet Network Study.

Postprandial lipemia (PPL) is an important risk factor for cardiovascular disease. Inter-individual variation in the dietary response to a meal is known to be influenced by genetic factors, yet genes that dictate variation in postprandial lipids are not completely characterized. Genetic studies of the plasma lipidome can help to better understand postprandial metabolism by isolating lipid molecular species which are more closely related to the genome. We measured the plasma lipidome at fasting and 6 h after a standardized high-fat meal in 668 participants from the Genetics of Lipid-Lowering Drugs and Diet Network study (GOLDN) using ultra-performance liquid chromatography coupled to (quadrupole) time-of-flight mass spectrometry. A total of 413 unique lipids were identified. Heritable and responsive lipid species were examined for association with single-nucleotide polymorphisms (SNPs) genotyped on the Affymetrix 6.0 array. The most statistically significant SNP findings were replicated in the Amish Heredity and Phenotype Intervention (HAPI) Heart Study. We further followed up findings from GOLDN with a regional analysis of cytosine-phosphate-guanine (CpGs) sites measured on the Illumina HumanMethylation450 array. A total of 132 lipids were both responsive to the meal challenge and heritable in the GOLDN study. After correction for multiple testing of 132 lipids (α = 5 × 10-8/132 = 4 × 10-10), no SNP was statistically significantly associated with any lipid response. Four SNPs in the region of a known lipid locus (fatty acid desaturase 1 and 2/FADS1 and FADS2) on chromosome 11 had p < 8.0 × 10-7 for arachidonic acid FA(20:4). Those SNPs replicated in HAPI Heart with p < 3.3 × 10-3. CpGs around the FADS1/2 region were associated with arachidonic acid and the relationship of one SNP was partially mediated by a CpG (p = 0.005). Both SNPs and CpGs from the fatty acid desaturase region on chromosome 11 contribute jointly and independently to the diet response to a high-fat meal.

A metabolome atlas of the aging mouse brain.

The mammalian brain relies on neurochemistry to fulfill its functions. Yet, the complexity of the brain metabolome and its changes during diseases or aging remain poorly understood. Here, we generate a metabolome atlas of the aging wildtype mouse brain from 10 anatomical regions spanning from adolescence to old age. We combine data from three assays and structurally annotate 1,547 metabolites. Almost all metabolites significantly differ between brain regions or age groups, but not by sex. A shift in sphingolipid patterns during aging related to myelin remodeling is accompanied by large changes in other metabolic pathways. Functionally related brain regions (brain stem, cerebrum and cerebellum) are also metabolically similar. In cerebrum, metabolic correlations markedly weaken between adolescence and adulthood, whereas at old age, cross-region correlation patterns reflect decreased brain segregation. We show that metabolic changes can be mapped to existing gene and protein brain atlases. The brain metabolome atlas is publicly available ( https://mouse.atlas.metabolomics.us/ ) and serves as a foundation dataset for future metabolomic studies.

Exposure to DMSO during infancy alters neurochemistry, social interactions, and brain morphology in long-evans rats.

INTRODUCTION: Dimethyl sulfoxide (DMSO) is a widely used solvent to dissolve hydrophobic substances for clinical uses and experimental in vivo purposes. While usually regarded safe, our prior studies suggest changes to behavior following DMSO exposure. We therefore evaluated the effects of a five-day, short-term exposure to DMSO on postnatal infant rats (P6-10). METHODS: DMSO was intraperitoneally injected for five days at 0.2, 2.0, and 4.0 ml/kg body mass. One cohort of animals was sacrificed 24 hr after DMSO exposure to analyze the neurometabolic changes in four brain regions (cortex, hippocampus, basal ganglia, and cerebellum) by hydrophilic interaction liquid chromatography. A second cohort of animals was used to analyze chronic alterations to behavior and pathological changes to glia and neuronal cells later in life (P21-P40). RESULTS: 164 metabolites, including key regulatory molecules (retinoic acid, orotic acid, adrenic acid, and hypotaurine), were found significantly altered by DMSO exposure in at least one of the brain regions at P11 (p < .05). Behavioral tests showed significant hypoactive behavior and decreased social habits to the 2.0 and 4.0 ml DMSO/kg groups (p < .01). Significant increases in number of microglia and astrocytes at P40 were observed in the 4.0 ml DMSO/kg group (at p < .015.) CONCLUSIONS: Despite short-term exposure at low, putatively nontoxic concentrations, DMSO led to changes in behavior and social preferences, chronic alterations in glial cells, and changes in essential regulatory brain metabolites. The chronic neurological effects of DMSO exposure reported here raise concerns about its neurotoxicity and consequent safety in human medical applications and clinical trials.

Leucoselect Phytosome Modulates Serum Eicosapentaenoic Acid, Docosahexaenoic Acid, and Prostaglandin E3 in a Phase I Lung Cancer Chemoprevention Study.

Grape seed procyanidin extract (GSE) has been shown to exert antineoplastic properties in preclinical studies. Recently, we reported findings from a modified phase I, open-label, dose escalation clinical study conducted to evaluate the safety, tolerability, MTD, and potential chemopreventive effects of leucoselect phytosome, a standardized GSE complexed with soy phospholipids to enhance bioavailability, in heavy active and former smokers. Three months of leucoselect phytosome treatment significantly decreased bronchial Ki-67 labeling index (LI), a marker of cell proliferation on the bronchial epithelium. Because GSE is widely used as a supplement to support cardiovascular health, we evaluate the impact of oral leucoselect phytosome on the fasting serum complex lipid metabolomics profiles in our participants. One month of leucoselect phytosome treatment significantly increased eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), the omega-3 polyunsaturated fatty acids (n-3 PUFA) with well-established anticancer properties. Leucoselect phytosome also significantly increased unsaturated phosphatidylcholines (PC), likely from soy phospolipids in the phytosome and functioning as transporters for these PUFAs. Furthermore, 3-month leucoselect phytosome treatment significantly increased serum prostaglandin (PG) E3 (PGE3), a metabolite of EPA with anti-inflammatory and antineoplastic properties. Such increases in PGE3 correlated with reductions of bronchial Ki-67 LI (r = -0.9; P = 0.0374). Moreover, posttreatment plasma samples from trial participants significantly inhibited proliferation of human lung cancer cell lines A549 (adenocarcinoma), H520 (squamous cell carcinoma), DMS114 (small cell carcinoma), and 1198 (preneoplastic cell line). Our findings further support the potential utility of leucoselect phytosome in reducing cardiovascular and neoplastic risks in heavy former and active smokers. PREVENTION RELEVANCE: In this correlative study of leucoselect phytosome for lung cancer chemoprevention in heavy active and former smokers, we demonstrate for the first time, favorable modulations of n-3PUFA and downstream PGE3 in fasting serum, further supporting the chemopreventive potential of leucoselect phytosome against lung cancer.

Gastric Metabolomics Detects Helicobacter pylori Correlated Loss of Numerous Metabolites in Both the Corpus and Antrum.

Helicobacter pylori is a chronic bacterial pathogen that thrives in several regions of the stomach, causing inflammation that can vary by site and result in distinct disease outcomes. Whether the regions differ in terms of host-derived metabolites is not known. We thus characterized the regional variation of the metabolomes of mouse gastric corpus and antrum organoids and tissue. The uninfected secreted organoid metabolites differed between the corpus and antrum in only seven metabolites as follows: lactic acid, malic acid, phosphoethanolamine, alanine, uridine, glycerol, and isoleucine. Several of the secreted chemicals were depleted upon H. pylori infection in both regions, including urea, cholesterol, glutamine, fumaric acid, lactic acid, citric acid, malic acid, and multiple nonessential amino acids. These results suggest a model in which H. pylori preferentially uses carboxylic acids and amino acids in complex environments, and these are found in both the corpus and antrum. When organoid metabolites were compared to mouse tissue, there was little overlap. The tissue corpus and antrum metabolomes were distinct, including antrum-elevated 5-methoxytryptamine, lactic acid, and caprylic acid, and corpus-elevated phospholipid products. The corpus and antrum remained distinct over an 8-month infection time course. The antrum displayed no significant changes between the time points in contrast to the corpus, which exhibited metabolite changes that were consistent with stress, tissue damage, and depletion of key nutrients, such as glutamine and fructose-6-phosphate. Overall, our results suggest that the corpus and antrum have largely but not completely overlapping metabolomes that change moderately upon H. pylori infection.

Functional Microbiomics Reveals Alterations of the Gut Microbiome and Host Co-Metabolism in Patients With Alcoholic Hepatitis.

Alcohol-related liver disease is a major public health burden, and the gut microbiota is an important contributor to disease pathogenesis. The aim of the present study is to characterize functional alterations of the gut microbiota and test their performance for short-term mortality prediction in patients with alcoholic hepatitis. We integrated shotgun metagenomics with untargeted metabolomics to investigate functional alterations of the gut microbiota and host co-metabolism in a multicenter cohort of patients with alcoholic hepatitis. Profound changes were found in the gut microbial composition, functional metagenome, serum, and fecal metabolomes in patients with alcoholic hepatitis compared with nonalcoholic controls. We demonstrate that in comparison with single omics alone, the performance to predict 30-day mortality was improved when combining microbial pathways with respective serum metabolites in patients with alcoholic hepatitis. The area under the receiver operating curve was higher than 0.85 for the tryptophan, isoleucine, and methionine pathways as predictors for 30-day mortality, but achieved 0.989 for using the urea cycle pathway in combination with serum urea, with a bias-corrected prediction error of 0.083 when using leave-one-out cross validation. Conclusion: Our study reveals changes in key microbial metabolic pathways associated with disease severity that predict short-term mortality in our cohort of patients with alcoholic hepatitis.