Background: Meat and fish intakes have been associated with various chronic diseases. The use of specific biomarkers may help to assess meat and fish intake and improve subject classification according to the amount and type of meat or fish consumed. Objective: A metabolomic approach was applied to search for biomarkers of meat and fish intake in a dietary intervention study and in free-living subjects from the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Design: In the dietary intervention study, 4 groups of 10 subjects consumed increasing quantities of chicken, red meat, processed meat, and fish over 3 successive weeks. Twenty-four-hour urine samples were collected during each period and analyzed by high-resolution liquid chromatography–mass spectrometry. Signals characteristic of meat or fish intake were replicated in 50 EPIC subjects for whom a 24-h urine sample and 24-h dietary recall were available and who were selected for their exclusive intake or no intake of any of the 4 same foods. Results: A total of 249 mass spectrometric features showed a positive dose-dependent response to meat or fish intake in the intervention study. Eighteen of these features best predicted intake of the 4 food groups in the EPIC urine samples on the basis of partial receiver operator curve analyses with permutation testing (areas under the curve ranging between 0.61 and 1.0). Of these signals, 8 metabolites were identified. Anserine was found to be specific for chicken intake, whereas trimethylamine-N-oxide showed good specificity for fish. Carnosine and 3 acylcarnitines (acetylcarnitine, propionylcarnitine, and 2-methylbutyrylcarnitine) appeared to be more generic indicators of meat and meat and fish intake, respectively. Conclusion: The meat and fish biomarkers identified in this work may be used to study associations between meat and fish intake and disease risk in epidemiologic studies. This trial was registered at clinicaltrials.gov as NCT01684917

(no more than 200 words)1 Scope: Classification of subjects into dietary patterns generally relies on self-reporting 2 dietary data which are prone to error. The aim of the present study was to develop a model 3 for objective classification of people into dietary patterns based on metabolomic data. 4Methods and results: Dietary and urinary metabolomic data from the National Adult 5 Nutrition Survey (NANS) was used in the analysis (n=567). Two-step cluster analysis was 6 applied to the urinary data to identify clusters. The subsequent model was used in an 7 independent cohort to classify people into dietary patterns. Two distinct dietary patterns were 8 identified. Cluster 1 was characterized by significantly higher intakes of breakfast cereals, 9 low fat and skimmed milks, potatoes, fruit and fish, fish dishes (P<0.05) representing a 10 "healthy" cluster. Cluster 2 had significantly higher intakes of chips/processed potatoes, meat 11 products, savory snacks and high-energy beverages (P<0.05) representing an "unhealthy 12 cluster". Classification was supported by significant differences in nutrient status (P<0.05). 13Validation in an independent group revealed that 94% of subjects were correctly classified.

BackgroundVisceral obesity has a strong association with both the incidence and mortality of esophageal adenocarcinoma (EAC). Alterations in mitochondrial function and energy metabolism is an emerging hallmark of cancer, however, the potential role that obesity plays in driving these alterations in EAC is currently unknown.MethodsAdipose conditioned media (ACM) was prepared from visceral adipose tissue taken from computed tomography-determined viscerally-obese and non-obese EAC patients. Mitochondrial function in EAC cell lines was assessed using fluorescent probes, mitochondrial gene expression was assessed using qPCR-based gene arrays and intracellular ATP levels were determined using a luminescence-based kit. Glycolysis and oxidative phosphophorylation was measured using Seahorse XF technology and metabolomic analysis was performed using 1H NMR. Expression of metabolic markers was assessed in EAC tumor biopsies by qPCR.ResultsACM from obese EAC patients significantly increased mitochondrial mass and mitochondrial membrane potential in EAC cells, which was significantly associated with visceral fat area, and was coupled with a significant decrease in reactive oxygen species. This mitochondrial dysfunction was accompanied by altered expression of 19 mitochondrial-associated genes and significantly reduced intracellular ATP levels. ACM from obese EAC patients induced a metabolic shift to glycolysis in EAC cells, which was coupled with significantly increased sensitivity to the glycolytic inhibitor 2-deoxyglucose. Metabolomic profiling demonstrated an altered glycolysis and amino acid-related signature in ACM from obese patients. In EAC tumors, expression of the glycolytic marker PKM2 was significantly positively associated with obesity.ConclusionThis study demonstrates for the first time that ACM from viscerally-obese EAC patients elicits an altered metabolic profile and can drive mitochondrial dysfunction and altered energy metabolism in EAC cells in vitro. In vivo, in EAC patient tumors, expression of the glycolytic enzyme PKM2 is positively associated with obesity.

Polyhydroxybutyrate (PHB) is an important biopolymer accumulated by bacteria and associated with cell survival and stress response. Here, we make two surprising findings in the PHB-accumulating species Rhodospirillum rubrum S1. We first show that the presence of PHB promotes the increased assimilation of acetate preferentially into biomass rather than PHB. When R. rubrum is supplied with (13)C-acetate as a PHB precursor, 83.5 % of the carbon in PHB comes from acetate. However, only 15 % of the acetate ends up in PHB with the remainder assimilated as bacterial biomass. The PHB-negative mutant of R. rubrum assimilates 2-fold less acetate into biomass compared to the wild-type strain. Acetate assimilation proceeds via the ethylmalonyl-CoA pathway with (R)-3-hydroxybutyrate as a common intermediate with the PHB pathway. Secondly, we show that R. rubrum cells accumulating PHB have reduced ribulose 1,5-bisphosphate carboxylase (RuBisCO) activity. RuBisCO activity reduces 5-fold over a 36-h period after the onset of PHB. In contrast, a PHB-negative mutant maintains the same level of RuBisCO activity over the growth period. Since RuBisCO controls the redox potential in R. rubrum, PHB likely replaces RuBisCO in this role. R. rubrum is the first bacterium found to express RuBisCO under aerobic chemoheterotrophic conditions.

Oesophageal adenocarcinoma (OAC) is an exemplar model of obesity-associated cancer. Response to neoadjuvant chemoradiotherapy (NA CRT) is a clinical challenge. We examined if visceral adipose tissue and obesity status alter radiosensitivity in OAC. The radioresistant (OE33R) and radioresponsive (OE33P) OAC isogenic model was cultured with adipose tissue conditioned media from three patient cohorts: non-cancer patients, surgery only OAC patients and NA CRT OAC patients. Cell survival was characterised by clonogenic assay, metabolomic profiling by nuclear magnetic resonance spectroscopy and adipokine receptor gene expression by qPCR. A retrospective in vivo study compared tumour response to NA CRT in normal weight (n=53) versus overweight/obese patients (n=148). Adipose conditioned media (ACM) from all patient cohorts significantly increased radiosensitivity in radioresistant OE33R cells. ACM from the NA CRT OAC cohort increased radiosensitivity in OE33P cells. Metabolomic profiling demonstrated separation of the non-cancer and surgery only OAC cohorts and between the non-cancer and NA CRT OAC cohorts. Gene expression profiling of OE33P versus OE33R cells demonstrated differential expression of the adiponectin receptor-1 (AR1), adiponectin receptor-2 (AR2), leptin receptor (LepR) and neuropilin receptor-1 (NRP1) genes. In vivo overweight/obese OAC patients achieved an enhanced tumour response following NA CRT compared to normal weight patients. This study demonstrates that visceral adipose tissue modulates the cellular response to radiation in OAC.

Traditional dietary assessment methods, including food-frequency questionnaires (FFQs) and weighed food diaries, are associated with a number of errors (1) . Such errors can produce inconsistent findings in relation to food intake and disease risk. Dietary biomarkers have been suggested as an objective measure of dietary intake and the application of metabolomic technologies offers a route to the identification of new dietary biomarkers. However, dietary biomarker discovery has predominantly focused on single foods (2,3) . A novel approach, nutritypes (metabolic profiles that reflect dietary intake) has emerged facilitating a more comprehensive relationship between nutrition and disease-risk while also addressing issues of nutrient-interactions. The aim of this study is to use metabolic profiles to define dietary intake patterns and additionally link these dietary patterns with nutrient and biochemical data.Dietary intake data and urinary data from the Irish National Adult Nutrition Survey (NANS) (www.iuna.net) was used in this analysis (n = 600). Dietary intake data obtained from 4-day food diaries was reduced into 34 food groups and expressed as percentage of energy intake. Urinary data was analysed using the metabolomic technique 1 H nuclear magnetic resonance (NMR) spectroscopy. K-means cluster analysis was applied to the metabolomic data to identify clusters. Discriminatory metabolites responsible for cluster separation were identified using Chenomx Profiler (Version 7.5, Chenomx Inc.; Edmonton, Canada). Food group, nutrient and biochemical data were compared across the clusters using independent samples t-test in IBM SPSS Statistics 20. Heatmap analysis was used to link metabolites and food group intake. Participants from the NutriTech food intake study (n = 40) were used to investigate the ability of this model to classify people into different dietary patterns.Cluster analysis identified two clusters; cluster 1 (C1), the "healthy" cluster and cluster 2 (C2) the "unhealthy" cluster. C1 had a significantly higher mean intake (%E) of nutritionally desirable food groups; breakfast cereals and porridge, low fat/skimmed milks and poultry while C2 had had significantly higher intakes of chips and processed potatoes and red meat (P < 0·05). Nutrients including carbohydrates, protein, fibre, calcium, potassium, folate and vitamin C were also significantly higher in C1, while C2 had significantly higher intakes of fats (P < 0·05). Biochemical measurements; red cell folate, serum folate and 25-hydroxyvitamin D were also significantly higher in C1 compared to C2 (P < 0·05). Heatmap analysis revealed that metabolites found at higher concentrations in C1 and in C2 were positively correlated with the food groups consumed at higher percentages in C1 and C2 respectively. Validation of this model in an independent group revealed that 95 % of subjects were placed into the correct dietary pattern.The current analysis identified two distinct clusters that were reflective of a healthy and unhealthy dietary pattern intake and ...