Identification of prognostic and diagnostic biomarkers of glucose intolerance in ApoE3Leiden mice.

The prevalence of diabetes mellitus Type 2 could be significantly reduced by early identification of subjects at risk, allowing for better prevention and earlier treatment. Glucose intolerance (GI) is a hallmark of the prediabetic stage. This study aims at identifying 1) prognostic biomarkers predicting the risk of developing GI later in life and 2) diagnostic biomarkers reflecting the degree of already manifest GI. To this end, disease development was followed over time in mice, and biomarkers were identified using lipidomics and transcriptomics. Young adult ApoE3Leiden mice were treated a high-fat diet for 12 wk to induce GI. Blood was collected before and during disease development. The individual extent of GI was determined with a glucose tolerance test and the area under the curve (AUC) was calculated for each animal. Subject-specific AUC values were correlated to the plasma lipidome (t = 0) and the white blood cell (WBC) transcriptome (t = 0, 6, and 12 wk) to identify prognostic and diagnostic biomarkers, respectively. The plasma ratio of specific free fatty acids prior to high-fat feeding (C16:1/C16:0, C18:1/C18:0 and C18:2/C22:6) was significantly correlated with the AUC and predictive for future GI. Subsequently, the expression level of specific WBC genes (Acss2, Arfgap1, Tfrc, Cox6b2, Barhl2, Abcb4, Cyp4b1, Sars2, Fgf16, and Tceal8) reflected the individual degree of GI during disease progression. Specific plasma free fatty acids as well as their ratio can be used to predict future GI. The expression levels of specific WBC genes can serve as easy accessible markers to diagnose and monitor already existing GI.

Transcriptome analysis provides new insights into liver changes induced in the rat upon dietary administration of the food additives butylated hydroxytoluene, curcumin, propyl gallate and thiabendazole.

Transcriptomics was performed to gain insight into mechanisms of food additives butylated hydroxytoluene (BHT), curcumin (CC), propyl gallate (PG), and thiabendazole (TB), additives for which interactions in the liver can not be excluded. Additives were administered in diets for 28 days to Sprague-Dawley rats and cDNA microarray experiments were performed on hepatic RNA. BHT induced changes in the expression of 10 genes, including phase I (CYP2B1/2; CYP3A9; CYP2C6) and phase II metabolism (GST mu2). The CYP2B1/2 and GST expression findings were confirmed by real time RT-PCR, western blotting, and increased GST activity towards DCNB. CC altered the expression of 12 genes. Three out of these were related to peroxisomes (phytanoyl-CoA dioxygenase, enoyl-CoA hydratase; CYP4A3). Increased cyanide insensitive palmitoyl-CoA oxidation was observed, suggesting that CC is a weak peroxisome proliferator. TB changed the expression of 12 genes, including CYP1A2. In line, CYP1A2 protein expression was increased. The expression level of five genes, associated with p53 was found to change upon TB treatment, including p53 itself, GADD45alpha, DN-7, protein kinase C beta and serum albumin. These array experiments led to the novel finding that TB is capable of inducing p53 at the protein level, at least at the highest dose levels employed above the current NOAEL. The expression of eight genes changed upon PG administration. This study shows the value of gene expression profiling in food toxicology in terms of generating novel hypotheses on the mechanisms of action of food additives in relation to pathology.

Visualization and identification of health space, based on personalized molecular phenotype and treatment response to relevant underlying biological processes.

BACKGROUND: Being able to visualize multivariate biological treatment effects can be insightful. However the axes in visualizations are often solely defined by variation and thus have no biological meaning. This makes the effects of treatment difficult to interpret. METHODS: A statistical visualization method is presented, which analyses and visualizes the effects of treatment in individual subjects. The visualization is based on predefined biological processes as determined by systems-biological datasets (metabolomics proteomics and transcriptomics). This allows one to evaluate biological effects depending on shifts of either groups or subjects in the space predefined by the axes, which illustrate specific biological processes. We built validated multivariate models for each axis to represent several biological processes. In this space each subject has his or her own score on each axis/process, indicating to which extent the treatment affects the related process. RESULTS: The health space model was applied to visualize the effects of a nutritional intervention, with the goal of applying diet to improve health. The model was therefore named the 'health space' model. The 36 study subjects received a 5-week dietary intervention containing several anti-inflammatory ingredients. Plasma concentrations of 79 proteins and 145 metabolites were quantified prior to and after treatment. The principal processes modulated by the intervention were oxidative stress, inflammation, and metabolism. These processes formed the axes of the 'health space'. The approach distinguished the treated and untreated groups, as well as two different response subgroups. One subgroup reacted mainly by modulating its metabolic stress profile, while a second subgroup showed a specific inflammatory and oxidative response to treatment. CONCLUSIONS: The 'health space' model allows visualization of multiple results and to interpret them. The model presents treatment group effects, subgroups and individual responses.