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Objective: Metabolic syndrome, obesity, and steatosis are characterized by a range of dysregulations including defects in ubiquitin ligase tagging proteins for degradation. The identification of novel hepatic genes associated with fatty liver disease and metabolic dysregulation may be relevant to unravelling new mechanisms involved in liver disease progression METHODS: Through integrative analysis of liver transcriptomic and metabolomic obtained from obese subjects with steatosis, we identified itchy E ubiquitin protein ligase (ITCH) as a gene downregulated in human hepatic tissue in relation to steatosis grade. Wild-type or ITCH knockout mouse models of non-alcoholic fatty liver disease (NAFLD) and obesity-related hepatocellular carcinoma were analyzed to dissect the causal role of ITCH in steatosis RESULTS: We show that ITCH regulation of branched-chain amino acids (BCAAs) degradation enzymes is impaired in obese women with grade 3 compared with grade 0 steatosis, and that ITCH acts as a gatekeeper whose loss results in elevation of circulating BCAAs associated with hepatic steatosis. When ITCH expression was specifically restored in the liver of ITCH knockout mice, ACADSB mRNA and protein are restored, and BCAA levels are normalized both in liver and plasma CONCLUSIONS: Our data support a novel functional role for ITCH in the hepatic regulation of BCAA metabolism and suggest that targeting ITCH in a liver-specific manner might help delay the progression of metabolic hepatic diseases and insulin resistance., (Copyright © 2022 The Authors. Published by Elsevier GmbH.. All rights reserved.)

Background: The gut microbiome and iron status are known to play a role in the pathophysiology of non-alcoholic fatty liver disease (NAFLD), although their complex interaction remains unclear., Results: Here, we applied an integrative systems medicine approach (faecal metagenomics, plasma and urine metabolomics, hepatic transcriptomics) in 2 well-characterised human cohorts of subjects with obesity (discovery n = 49 and validation n = 628) and an independent cohort formed by both individuals with and without obesity (n = 130), combined with in vitro and animal models. Serum ferritin levels, as a markers of liver iron stores, were positively associated with liver fat accumulation in parallel with lower gut microbial gene richness, composition and functionality. Specifically, ferritin had strong negative associations with the Pasteurellaceae, Leuconostocaceae and Micrococcaea families. It also had consistent negative associations with several Veillonella, Bifidobacterium and Lactobacillus species, but positive associations with Bacteroides and Prevotella spp. Notably, the ferritin-associated bacterial families had a strong correlation with iron-related liver genes. In addition, several bacterial functions related to iron metabolism (transport, chelation, heme and siderophore biosynthesis) and NAFLD (fatty acid and glutathione biosynthesis) were also associated with the host serum ferritin levels. This iron-related microbiome signature was linked to a transcriptomic and metabolomic signature associated to the degree of liver fat accumulation through hepatic glucose metabolism. In particular, we found a consistent association among serum ferritin, Pasteurellaceae and Micrococcacea families, bacterial functions involved in histidine transport, the host circulating histidine levels and the liver expression of GYS2 and SEC24B. Serum ferritin was also related to bacterial glycine transporters, the host glycine serum levels and the liver expression of glycine transporters. The transcriptomic findings were replicated in human primary hepatocytes, where iron supplementation also led to triglycerides accumulation and induced the expression of lipid and iron metabolism genes in synergy with palmitic acid. We further explored the direct impact of the microbiome on iron metabolism and liver fact accumulation through transplantation of faecal microbiota into recipient's mice. In line with the results in humans, transplantation from 'high ferritin donors' resulted in alterations in several genes related to iron metabolism and fatty acid accumulation in recipient's mice., Conclusions: Altogether, a significant interplay among the gut microbiome, iron status and liver fat accumulation is revealed, with potential significance for target therapies. Video abstract.

Background & Aims: Atherosclerosis is characterized by an inflammatory disease linked to excessive lipid accumulation in the artery wall. The Notch signalling pathway has been shown to play a key regulatory role in the regulation of inflammation. Recently, in vitro and pre-clinical studies have shown that apolipoprotein A-I binding protein (AIBP) regulates cholesterol metabolism (SREBP) and NOTCH signalling (haematopoiesis) and may be protective against atherosclerosis, but the evidence in humans is scarce., Methods: We evaluated the APOA1bp-SREBF-NOTCH axis in association with atherosclerosis in two well-characterized cohorts of morbidly obese patients (n = 78) within the FLORINASH study, including liver transcriptomics, 1 H NMR plasma metabolomics, high-resolution ultrasonography evaluating carotid intima-media thickness (cIMT), and haematological parameters., Results: The liver expression levels of APOA1bp were associated with lower cIMT and leukocyte counts, a better plasma lipid profile and higher circulating levels of metabolites associated with lower risk of atherosclerosis (glycine, histidine and asparagine). Conversely, liver SREBF and NOTCH mRNAs were positively associated with atherosclerosis, liver steatosis, an unfavourable lipid profile, higher leukocytes and increased levels of metabolites linked to inflammation and CVD such as branched-chain amino acids and glycoproteins. APOA1bp and NOTCH signalling also had a strong association, as revealed by the negative correlations among APOA1bp expression levels and those of all NOTCH receptors and jagged ligands., Conclusions: We here provide the first evidence in human liver of the putative APOA1bp-SREBF-NOTCH axis signalling pathway and its association with atherosclerosis and inflammation., (Copyright © 2020 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.)

Hepatic steatosis is a multifactorial condition that is often observed in obese patients and is a prelude to non-alcoholic fatty liver disease. Here, we combine shotgun sequencing of fecal metagenomes with molecular phenomics (hepatic transcriptome and plasma and urine metabolomes) in two well-characterized cohorts of morbidly obese women recruited to the FLORINASH study. We reveal molecular networks linking the gut microbiome and the host phenome to hepatic steatosis. Patients with steatosis have low microbial gene richness and increased genetic potential for the processing of dietary lipids and endotoxin biosynthesis (notably from Proteobacteria), hepatic inflammation and dysregulation of aromatic and branched-chain amino acid metabolism. We demonstrated that fecal microbiota transplants and chronic treatment with phenylacetic acid, a microbial product of aromatic amino acid metabolism, successfully trigger steatosis and branched-chain amino acid metabolism. Molecular phenomic signatures were predictive (area under the curve = 87%) and consistent with the gut microbiome having an effect on the steatosis phenome (>75% shared variation) and, therefore, actionable via microbiome-based therapies.

Scope: To examine the potential relationship among gene expression markers of adipose tissue browning, gut microbiota, and insulin sensitivity in humans., Methods and Results: Gut microbiota composition and gene markers of browning are analyzed in subcutaneous (SAT) and visceral (VAT) adipose tissue from morbidly obese subjects (n = 34). Plasma acetate is measured through 1 H NMR and insulin sensitivity using euglycemic hyperinsulinemic clamp. Subjects with insulin resistance show an increase in the relative abundance (RA) of the phyla Bacteroidetes and Proteobacteria while RA of Firmicutes is decreased. In all subjects, Firmicutes RA is negatively correlated with HbA 1c and fasting triglycerides, whereas Proteobacteria RA was negatively correlated with insulin sensitivity. Firmicutes RA is positively associated with markers of brown adipocytes (PRDM16, UCP1, and DIO2) in SAT, but not in VAT. Multivariate regression analysis indicates that Firmicutes RA contributes significantly to SAT PRDM16, UCP1, and DIO2 mRNA variance after controlling for age, BMI, HbA 1c , or insulin sensitivity. Interestingly, Firmicutes RA, specifically those bacteria belonging to the Ruminococcaceae family, is positively associated with plasma acetate levels, which are also linked to SAT PRDM16 mRNA and insulin sensitivity., Conclusion: Gut microbiota composition is linked to adipose tissue browning and insulin action in morbidly obese subjects, possibly through circulating acetate., (© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The influence of the gut microbiome on metabolic and behavioral traits is widely accepted, though the microbiome-derived metabolites involved remain unclear. We carried out untargeted urine 1 H-NMR spectroscopy-based metabolic phenotyping in an isogenic C57BL/6J mouse population (n = 50) and show that microbial-host co-metabolites are prodromal (i.e., early) markers predicting future divergence in metabolic (obesity and glucose homeostasis) and behavioral (anxiety and activity) outcomes with 94%-100% accuracy. Some of these metabolites also modulate disease phenotypes, best illustrated by trimethylamine-N-oxide (TMAO), a product of microbial-host co-metabolism predicting future obesity, impaired glucose tolerance (IGT), and behavior while reducing endoplasmic reticulum stress and lipogenesis in 3T3-L1 adipocytes. Chronic in vivo TMAO treatment limits IGT in HFD-fed mice and isolated pancreatic islets by increasing insulin secretion. We highlight the prodromal potential of microbial metabolites to predict disease outcomes and their potential in shaping mammalian phenotypic heterogeneity., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

We have investigated the urinary and plasma metabolic phenotype of acute pancreatitis (AP) patients presenting to the emergency room at a single center London teaching hospital with acute abdominal pain using (1)H NMR spectroscopy and multivariate modeling. Patients were allocated to either the AP (n = 15) or non-AP patients group (all other causes of abdominal pain, n = 21) on the basis of the national guidelines. Patients were assessed for three clinical outcomes: (1) diagnosis of AP, (2) etiology of AP caused by alcohol consumption and cholelithiasis, and (3) AP severity based on the Glasgow score. Samples from AP patients were characterized by high levels of urinary ketone bodies, glucose, plasma choline and lipid, and relatively low levels of urinary hippurate, creatine and plasma-branched chain amino acids. AP could be reliably identified with a high degree of sensitivity and specificity (OPLS-DA model R(2) = 0.76 and Q(2)Y = 0.59) using panel of discriminatory biomarkers consisting of guanine, hippurate and creatine (urine), and valine, alanine and lipoproteins (plasma). Metabolic phenotyping was also able to distinguish between cholelithiasis and colonic inflammation among the heterogeneous non-AP group. This work has demonstrated that combinatorial biomarkers have a strong diagnostic and prognostic potential in AP with relevance to clinical decision making in the emergency unit.

Significant advances in understanding aging have been achieved through studying model organisms with extended healthy lifespans. Employing 1H NMR spectroscopy, we characterized the plasma metabolic phenotype (metabotype) of three long-lived murine models: 30% dietary restricted (DR), insulin receptor substrate 1 null (Irs1-/-), and Ames dwarf (Prop1df/df). A panel of metabolic differences were generated for each model relative to their controls, and subsequently, the three long-lived models were compared to one another. Concentrations of mobile very low density lipoproteins, trimethylamine, and choline were significantly decreased in the plasma of all three models. Metabolites including glucose, choline, glycerophosphocholine, and various lipids were significantly reduced, while acetoacetate, d-3-hydroxybutyrate and trimethylamine-N-oxide levels were increased in DR compared to ad libitum fed controls. Plasma lipids and glycerophosphocholine were also decreased in Irs1-/- mice compared to controls, as were methionine and citrate. In contrast, high density lipoproteins and glycerophosphocholine were increased in Ames dwarf mice, as were methionine and citrate. Pairwise comparisons indicated that differences existed between the metabotypes of the different long-lived mice models. Irs1-/- mice, for example, had elevated glucose, acetate, acetone, and creatine but lower methionine relative to DR mice and Ames dwarfs. Our study identified several potential candidate biomarkers directionally altered across all three models that may be predictive of longevity but also identified differences in the metabolic signatures. This comparative approach suggests that the metabolic networks underlying lifespan extension may not be exactly the same for each model of longevity and is consistent with multifactorial control of the aging process.

Ultra-performance liquid chromatography coupled to mass spectrometry (UPLC/MS) has been used increasingly for measuring changes of low molecular weight metabolites in biofluids/tissues in response to biological challenges such as drug toxicity and disease processes. Typically samples show high variability in concentration, and the derived metabolic profiles have a heteroscedastic noise structure characterized by increasing variance as a function of increased signal intensity. These sources of experimental and instrumental noise substantially complicate information recovery when statistical tools are used. We apply and compare several preprocessing procedures and introduce a statistical error model to account for these bioanalytical complexities. In particular, the use of total intensity, median fold change, locally weighted scatter plot smoothing, and quantile normalizations to reduce extraneous variance induced by sample dilution were compared. We demonstrate that the UPLC/MS peak intensities of urine samples should respond linearly to variable sample dilution across the intensity range. While all four studied normalization methods performed reasonably well in reducing dilution-induced variation of urine samples in the absence of biological variation, the median fold change normalization is least compromised by the biologically relevant changes in mixture components and is thus preferable. Additionally, the application of a subsequent log-based transformation was successful in stabilizing the variance with respect to peak intensity, confirming the predominant influence of multiplicative noise in peak intensities from UPLC/MS-derived metabolic profile data sets. We demonstrate that variance-stabilizing transformation and normalization are critical preprocessing steps that can benefit greatly metabolic information recovery from such data sets when widely applied chemometric methods are used.

Background: Clostridium difficile is the major cause of antibiotic associated diarrhoea and in recent years its increased prevalence has been linked to the emergence of hypervirulent clones such as the PCR-ribotype 027. Characteristically, C. difficile infection (CDI) occurs after treatment with broad-spectrum antibiotics, which disrupt the normal gut microflora and allow C. difficile to flourish. One of the relatively unique features of C. difficile is its ability to ferment tyrosine to para-cresol via the intermediate para-hydroxyphenylacetate (p-HPA). P-cresol is a phenolic compound with bacteriostatic properties which C. difficile can tolerate and may provide the organism with a competitive advantage over other gut microflora, enabling it to proliferate and cause CDI. It has been proposed that the hpdBCA operon, rarely found in other gut microflora, encodes the enzymes responsible for the conversion of p-HPA to p-cresol., Results: We show that the PCR-ribotype 027 strain R20291 quantitatively produced more p-cresol in-vitro and was significantly more tolerant to p-cresol than the sequenced strain 630 (PCR-ribotype 012). Tyrosine conversion to p-HPA was only observed under certain conditions. We constructed gene inactivation mutants in the hpdBCA operon in strains R20291 and 630Δerm which curtails their ability to produce p-cresol, confirming the role of these genes in p-cresol production. The mutants were equally able to tolerate p-cresol compared to the respective parent strains, suggesting that tolerance to p-cresol is not linked to its production., Conclusions: C. difficile converts tyrosine to p-cresol, utilising the hpdBCA operon in C. difficile strains 630 and R20291. The hypervirulent strain R20291 exhibits increased production of and tolerance to p-cresol, which may be a contributory factor to the virulence of this strain and other hypervirulent PCR-ribotype 027 strains.

The metabonomic approach to biological analysis has demonstrated considerable success in obtaining and decoding metabolic signatures of health, disease and biological challenge. The rise of metabonomics to join the principal 'omics' streams in medical research has been enhanced in particular over the last 10 years by developments in modelling methods, rather than simply via advances in the supporting analytical platforms and biosampling modalities. Metabonomic analysis has been applied in a diverse range of areas from toxicology and dietary effects through to parasitology and molecular epidemiology, and promises yet further advances and wider future application. Some of the basis and methodology of this success is discussed, and some analytical sampling options, future modelling techniques and new targets, and 'blue skies' possibilities are presented in the context of personalised health and the delivery of optimised medical care to individuals. Metabonomics will continue to contribute significantly to improving our knowledge of a wide range of biological systems.

Surgical trauma initiates a complex series of metabolic host responses designed to maintain homeostasis and ensure survival. (1)H NMR spectroscopy was applied to intraoperative urine and plasma samples as part of a strategy to analyze the metabolic response of Wistar rats to a laparotomy model. Spectral data were analyzed by multivariate statistical analysis. Principal component analysis (PCA) confirmed that surgical injury is responsible for the majority of the metabolic variability demonstrated between animals (R² Urine = 81.2% R² plasma = 80%). Further statistical analysis by orthogonal projection to latent structure discriminant analysis (OPLS-DA) allowed the identification of novel urinary metabolic markers of surgical trauma. Urinary levels of taurine, glucose, urea, creatine, allantoin, and trimethylamine-N-oxide (TMAO) were significantly increased after surgery whereas citrate and 2-oxoglutarate (2-OG) negatively correlated with the intraoperative state as did plasma levels of betaine and tyrosine. Plasma levels of lipoproteins such as VLDL and LDL also rose with the duration of surgery. Moreover, the microbial cometabolites 3-hydroxyphenylpropionate, phenylacetylglycine, and hippurate correlated with the surgical insult, indicating that the gut microbiota are highly sensitive to the global homeostatic state of the host. Metabonomic profiling provides a global overview of surgical trauma that has the potential to provide novel biomarkers for personalized surgical optimization and outcome prediction.

Multicellular organisms maintain the stability of their internal environment using metabolic and physiological regulatory mechanisms that are disrupted during disease. The loss of homeostatic control results in a complex set of disordered states that may lead to metabolic network failure and irreversible system damage. We have applied a new statistical entropy-based approach to quantify temporal systemic disorder (divergence of metabolic responses) in experimental patho-physiological states, via NMR-spectroscopy generated metabolic profiles of urine. A recovery (R-) potential metric has also been developed to evaluate the relative extent to which defined metabolic processes are perturbed in the context of a global system in terms of multiple changes in concentrations of biofluid components accompanying the disrupted functional activity. This approach is sensitive to physiological as well as pathological interventions. We show that global disruptions of metabolic processes, lesion reversibility, and disorder in metabolic responses to a stressor can be visualized via metabolic entropy metrics, giving insights into biological robustness and thus providing a new tool for assessing deviation from homeostatic regulation.

The widely-used blood anticoagulants citrate and EDTA give rise to prominent peaks in (1)H NMR spectra of plasma samples collected in epidemiological and clinical studies, and these cause varying levels of interference in recovering biochemical information on endogenous metabolites. To investigate both the potential metabolic information loss caused by these substances and any possible inter-molecular interactions between the anticoagulants and endogenous components, the (1)H NMR spectra of 40 split human plasma samples collected from 20 individuals into either citrate or EDTA have been analysed. Endogenous metabolite peaks were selectively obscured by large citrate peaks or those from free EDTA and its calcium and magnesium complexes. It is shown that the endogenous metabolites that give rise to peaks obscured by those from EDTA or citrate almost invariably also have other resonances that allow their identification and potential quantitation. Also, metabolic information recovery could be maximised by use of spectral editing techniques such as spin-echo, diffusion-editing and J-resolved experiments. The NMR spectral effects of any interactions between the added citrate or EDTA and endogenous components were found to be negligible. Finally, identification of split samples was feasible using simple multivariate statistical approaches such as principal components analysis. Thus even when legacy epidemiological plasma samples have been collected using the NMR-inappropriate citrate or EDTA anticoagulants, useful biochemical information can still be recovered effectively.

Background: Metabolic profiling of biofluid specimens is an established method for investigating disease states in clinical studies but is only recently being applied to large-scale human population studies. As part of protocol development for the UK Biobank study, a (1)H nuclear magnetic resonance (NMR)-based metabonomic analysis of specimen storage effects and analytical reproducibility was carried out using urine and serum specimens from 40 volunteers., Methods: Aliquots of each specimen were stored for t = 0 and t = 24 h at 4 degrees C prior to freezing, and in the case of serum samples for a further 12 h (t = 36), to determine whether the storage times affected specimen composition and quality. A blinded split-specimen matching exercise was implemented to assign candidate spectral pairs stored for different times using multivariate statistical analysis of the NMR data., Results: Using a chemometric strategy, split specimens at time t = 0 and t = 24 or 36 h after storage at 4 degrees C were easily paired and the split-specimen matching task was reduced to a workable size. (1)H NMR profiling established that the t = 24 h urine and serum groups showed no systematic metabolite changes, indicating biochemical stability. Some small differences in serum specimens stored for t = 36 h at 4 degrees C were detectable only by multivariate analysis, and were attributed to generalized alterations in proteins and protein fragments, and possibly trimethylamine-N-oxide. No other specific metabolite was implicated., Conclusions: For the purposes of NMR-based analysis, storage of urine and serum for up to t = 24 h at 4 degrees C does not detectably affect the metabolic profile and the methodology is robust. Future application of multivariate methods to data-rich studies should substantially enhance information recovery from epidemiological studies.

Insulin resistance plays a central role in type 2 diabetes and obesity, which develop as a consequence of genetic and environmental factors. Dietary changes including high fat diet (HFD) feeding promotes insulin resistance in rodent models which present useful systems for studying interactions between genetic background and environmental influences contributing to disease susceptibility and progression. We applied a combination of classical physiological, biochemical and hormonal studies and plasma (1)H NMR spectroscopy-based metabonomics to characterize the phenotypic and metabotypic consequences of HFD (40%) feeding in inbred mouse strains (C57BL/6, 129S6, BALB/c, DBA/2, C3H) frequently used in genetic studies. We showed the wide range of phenotypic and metabonomic adaptations to HFD across the five strains and the increased nutrigenomic predisposition of 129S6 and C57BL/6 to insulin resistance and obesity relative to the other strains. In contrast mice of the BALB/c and DBA/2 strains showed relative resistance to HFD-induced glucose intolerance and obesity. Hierarchical metabonomic clustering derived from (1)H NMR spectral data of the strains provided a phylometabonomic classification of strain-specific metabolic features and differential responses to HFD which closely match SNP-based phylogenetic relationships between strains. Our results support the concept of genomic clustering of functionally related genes and provide important information for defining biological markers predicting spontaneous susceptibility to insulin resistance and pathological adaptations to fat feeding.

Aims/hypothesis: Complex changes in gene expression are associated with insulin resistance and non-alcoholic fatty liver disease (NAFLD) promoted by feeding a high-fat diet (HFD). We used functional genomic technologies to document molecular mechanisms associated with diet-induced NAFLD., Materials and Methods: Male 129S6 mice were fed a diet containing 40% fat (high-fat diet, HFD) for 15 weeks. Glucose tolerance, in vivo insulin secretion, plasma lipid profile and adiposity were determined. Plasma metabonomics and liver transcriptomics were used to identify changes in gene expression associated with HFD-induced NAFLD., Results: In HFD-fed mice, NAFLD and impaired glucose and lipid homeostasis were associated with increased hepatic transcription of genes involved in fatty acid uptake, intracellular transport, modification and elongation, whilst genes involved in beta-oxidation and lipoprotein secretion were, paradoxically, also upregulated. NAFLD developed despite strong and sustained downregulation of transcription of the gene encoding stearoyl-coenzyme A desaturase 1 (Scd1) and uncoordinated regulation of transcription of Scd1 and the gene encoding sterol regulatory element binding factor 1c (Srebf1c) transcription. Inflammatory mechanisms appeared to be stimulated by HFD., Conclusions/interpretation: Our results provide an accurate representation of subtle changes in metabolic and gene expression regulation underlying disease-promoting and compensatory mechanisms, collectively contributing to diet-induced insulin resistance and NAFLD. They suggest that proposed models of NAFLD pathogenesis can be enriched with novel diet-reactive genes and disease mechanisms.

The biochemical effects of acute and chronic psychological stress have been investigated in male Sprague-Dawley rats using a combination of 1H NMR spectral analysis of plasma and conventional hematological analyses. Animals were subjected to 35 consecutive days of 6-h sessions of stress, and following a 9 day break, were stressed for a further 6-h period. Plasma samples were collected at 0, 1, 3, and 6 h on days 1, 9, 21, 35, and 44, measured using 600 MHz 1H NMR spectroscopy, and analyzed by Principal Components Analysis. Time-dependent biochemical effects of psychological stress on a range of endogenous metabolites were evident and were correlated with the intensity of the stress response as defined by corticosterone and hematological parameters. Following acute stress, increases in the levels of glucose and ketone bodies, and decreases in the levels of acetate, alanine, isoleucine, lactate, leucine, valine, and lipoproteins, were observed. Chronic stress-induced increases in plasma levels of alanine, lactate (day 9), and leucine, valine, and choline (day 44) and decreases in acetate (day 9) and lipoprotein concentrations were observed. Positive correlations between plasma corticosterone level and glucose and glycerol, and between plasma lipoprotein concentrations and hemoglobin levels, were established using Projection to Latent Structures (PLS) analysis. This study indicates the potential of using NMR-based metabonomic strategies for the characterization of endogenous metabolic perturbations induced by psychological stressors and lifestyle choices.

Characterizing the relationships between genomic and phenotypic variation is essential to understanding disease etiology. Information-dense data sets derived from pathophysiological, proteomic and transcriptomic profiling have been applied to map quantitative trait loci (QTLs). Metabolic traits, already used in QTL studies in plants, are essential phenotypes in mammalian genetics to define disease biomarkers. Using a complex mammalian system, here we show chromosomal mapping of untargeted plasma metabolic fingerprints derived from NMR spectroscopic analysis in a cross between diabetic and control rats. We propose candidate metabolites for the most significant QTLs. Metabolite profiling in congenic strains provided evidence of QTL replication. Linkage to a gut microbial metabolite (benzoate) can be explained by deletion of a uridine diphosphate glucuronosyltransferase. Mapping metabotypic QTLs provides a practical approach to understanding genome-phenotype relationships in mammals and may uncover deeper biological complexity, as extended genome (microbiome) perturbations that affect disease processes through transgenomic effects may influence QTL detection.

Caloric restriction (CR) increases healthy life span in a range of organisms. The underlying mechanisms are not understood but appear to include changes in gene expression, protein function, and metabolism. Recent studies demonstrate that acute CR alters mortality rates within days in flies. Multitissue transcriptional changes and concomitant metabolic responses to acute CR have not been described. We generated whole genome RNA transcript profiles in liver, skeletal muscle, colon, and hypothalamus and simultaneously measured plasma metabolites using proton nuclear magnetic resonance in mice subjected to acute CR. Liver and muscle showed increased gene expressions associated with fatty acid metabolism and a reduction in those involved in hepatic lipid biosynthesis. Glucogenic amino acids increased in plasma, and gene expression for hepatic gluconeogenesis was enhanced. Increased expression of genes for hormone-mediated signaling and decreased expression of genes involved in protein binding and development occurred in hypothalamus. Cell proliferation genes were decreased and cellular transport genes increased in colon. Acute CR captured many, but not all, hepatic transcriptional changes of long-term CR. Our findings demonstrate a clear transcriptional response across multiple tissues during acute CR, with congruent plasma metabolite changes. Liver and muscle switched gene expression away from energetically expensive biosynthetic processes toward energy conservation and utilization processes, including fatty acid metabolism and gluconeogenesis. Both muscle and colon switched gene expression away from cellular proliferation. Mice undergoing acute CR rapidly adopt many transcriptional and metabolic changes of long-term CR, suggesting that the beneficial effects of CR may require only a short-term reduction in caloric intake.

Here, we study the intricate relationship between gut microbiota and host cometabolic phenotypes associated with dietary-induced impaired glucose homeostasis and nonalcoholic fatty liver disease (NAFLD) in a mouse strain (129S6) known to be susceptible to these disease traits, using plasma and urine metabotyping, achieved by (1)H NMR spectroscopy. Multivariate statistical modeling of the spectra shows that the genetic predisposition of the 129S6 mouse to impaired glucose homeostasis and NAFLD is associated with disruptions of choline metabolism, i.e., low circulating levels of plasma phosphatidylcholine and high urinary excretion of methylamines (dimethylamine, trimethylamine, and trimethylamine-N-oxide), coprocessed by symbiotic gut microbiota and mammalian enzyme systems. Conversion of choline into methylamines by microbiota in strain 129S6 on a high-fat diet reduces the bioavailability of choline and mimics the effect of choline-deficient diets, causing NAFLD. These data also indicate that gut microbiota may play an active role in the development of insulin resistance.

We describe here the implementation of the statistical total correlation spectroscopy (STOCSY) analysis method for aiding the identification of potential biomarker molecules in metabonomic studies based on NMR spectroscopic data. STOCSY takes advantage of the multicollinearity of the intensity variables in a set of spectra (in this case 1H NMR spectra) to generate a pseudo-two-dimensional NMR spectrum that displays the correlation among the intensities of the various peaks across the whole sample. This method is not limited to the usual connectivities that are deducible from more standard two-dimensional NMR spectroscopic methods, such as TOCSY. Moreover, two or more molecules involved in the same pathway can also present high intermolecular correlations because of biological covariance or can even be anticorrelated. This combination of STOCSY with supervised pattern recognition and particularly orthogonal projection on latent structure-discriminant analysis (O-PLS-DA) offers a new powerful framework for analysis of metabonomic data. In a first step O-PLS-DA extracts the part of NMR spectra related to discrimination. This information is then cross-combined with the STOCSY results to help identify the molecules responsible for the metabolic variation. To illustrate the applicability of the method, it has been applied to 1H NMR spectra of urine from a metabonomic study of a model of insulin resistance based on the administration of a carbohydrate diet to three different mice strains (C57BL/6Oxjr, BALB/cOxjr, and 129S6/SvEvOxjr) in which a series of metabolites of biological importance can be conclusively assigned and identified by use of the STOCSY approach.

In general, applications of metabonomics using biofluid NMR spectroscopic analysis for probing abnormal biochemical profiles in disease or due to toxicity have all relied on the use of chemometric techniques for sample classification. However, the well-known variability of some chemical shifts in 1H NMR spectra of biofluids due to environmental differences such as pH variation, when coupled with the large number of variables in such spectra, has led to the situation where it is necessary to reduce the size of the spectra or to attempt to align the shifting peaks, to get more robust and interpretable chemometric models. Here, a new approach that avoids this problem is demonstrated and shows that, moreover, inclusion of variable peak position data can be beneficial and can lead to useful biochemical information. The interpretation of chemometric models using combined back-scaled loading plots and variable weights demonstrates that this peak position variation can be handled successfully and also often provides additional information on the physicochemical variations in metabonomic data sets.

1H nuclear magnetic resonance (NMR)-based metabonomics is a well-established technique used to analyse and interpret complex multiparametric metabolic data, and has a wide number of applications in the development of pharmaceuticals. However, interpretation of biological data can be confounded by extraneous variation in the data such as fluctuations in either experimental conditions or in physiological status. Here we have shown the novel application of a data filtering method, orthogonal signal correction (OSC), to biofluid NMR data to minimise the influence of inter- and intra-spectrometer variation during data acquisition, and also to minimise innate physiological variation. The removal of orthogonal variation exposed features of interest in the NMR data and facilitated interpretation of the derived multivariate models. Furthermore, analysis of the orthogonal variation provided an explanation of the systematic analytical/biological changes responsible for confounding the original NMR data.