Additional file 1: of Epigenetic changes in blood leukocytes following an omega-3 fatty acid supplementation

Differentially methylated probes after the n-3 FA supplementation. Description of data: List of all 308 differentially methylated probes (FDR-corrected DiffScore ≥│13│~ P ≤ 0.05). (XLSX 42 kb

Additional file 2: of Epigenetic changes in blood leukocytes following an omega-3 fatty acid supplementation

Overrepresented pathways identified from differential methylation analysis following an n-3 FA supplementation. Description of data: Table describing all 55 significant overrepresented pathways identified from methylation analysis (IPA canonical pathways, associated P value, and list of differentially methylated genes). (DOCX 21 kb

Variations in HDL-carried miR-223 and miR-135a concentrations after consumption of dietary trans fat are associated with changes in blood lipid and inflammatory markers in healthy men - an exploratory study

<p>A high consumption of trans fatty acids (TFAs) is associated with an increased risk of cardiovascular diseases (CVDs). High-density lipoproteins (HDLs) have many cardioprotective properties and transport functional microRNAs (miRNAs) to recipient cells. We hypothesized that dietary TFAs modify the HDL-carried miRNA profile, therefore modulating its cardioprotective properties. We assessed whether consumption of dietary TFAs modifies HDL-carried miR-223-3p and miR-135a-3p concentration and the inter-relationship between diet-induced changes in HDL-carried miRNA concentration and CVD risk markers. In a double blind, randomized, crossover, controlled study, 9 men were fed each of 3 experimental isoenergetic diets: 1) High in industrial TFA (iTFA; 3.7% energy); 2) High in TFA from ruminants (rTFA; 3.7% energy); 3) Low in TFA (control; 0.8% energy) for 4 weeks each. HDLs were isolated by ultracentrifugation and miRNAs were quantified by RT-qPCR. Variations in HDL-miR-223-3p concentration were negatively correlated with variations in HDL-cholesterol after the iTFA diet (r_s= 0.82; <i>P = </i>0.007), and positively correlated with variations in C-reactive protein concentration after the rTFA diet (r_s = 0.75; <i>P</i> = 0.020). Variations in HDL-miR-135a-3p concentration were positively correlated with variations in total triglyceride (TG) concentration following the iTFA diet (r_s = −0.82; <i>P</i> = 0.007), and with variations in low-density lipoprotein (LDL)-TG concentration following the rTFA diet (r_s = 0.83; <i>P</i> = 0.005), compared to the control diet. However, the consumption of dietary TFAs has no significant unidirectional impact on HDL-carried miR-223-3p and miR-135a-3p concentrations. Our results suggest that the variability in the HDL-carried miRNAs response to TFA intake, by being associated with variations in CVD risk factors, might reflect physiological changes in HDL functions.</p

Evaluation of iTRAQ and SWATH-MS for the Quantification of Proteins Associated with Insulin Resistance in Human Duodenal Biopsy Samples

<div><p>Insulin resistance (IR) is associated with increased production of triglyceride-rich lipoproteins of intestinal origin. In order to assess whether insulin resistance affects the proteins involved in lipid metabolism, we used two mass spectrometry based quantitative proteomics techniques to compare the intestinal proteome of 14 IR patients to that of 15 insulin sensitive (IS) control patients matched for age and waist circumference. A total of 3886 proteins were identified by the iTRAQ (Isobaric Tags for Relative and Absolute Quantitation) mass spectrometry approach and 2290 by the SWATH-MS strategy (Serial Window Acquisition of Theoretical Spectra). Using these two methods, 208 common proteins were identified with a confidence corresponding to FDR < 1%, and quantified with p-value < 0.05. The quantification of those 208 proteins has a Pearson correlation coefficient (r²) of 0.728 across the two techniques. Gene Ontology analyses of the differentially expressed proteins revealed that annotations related to lipid metabolic process and oxidation reduction process are overly represented in the set of under-expressed proteins in IR subjects. Furthermore, both methods quantified proteins of relevance to IR. These data also showed that SWATH-MS is a promising and compelling alternative to iTRAQ for protein quantitation of complex mixtures.</p></div

Comparison of iTRAQ and SWATH using different approaches

<p>A) Scatter plot analysis of the 208 quantified proteins by iTRAQ and SWATH B) Volcano plot of SWATH ratios vs iTRAQ C) Comparison between SWATH and iTRAQ ratios for the 208 quantified proteins D) Density plot of iTRAQ and SWATH ratios for all differential ratios with p-value <0.05.</p

SWATH workflow.

<p>Each condition is injected separately in a DDA mode to build the spectral library and in a SWATH mode. The spectral library built from the DDA runs is then used by PeakView and MarkerView to extract the peptide and the quantification information in each of the SWATH runs. BSA 1 fmol and 50 fmol was spiked in each SWATH run as an internal standard.</p

Workflow for discovery (iTRAQ) experiment.

<p>Duodenum biopsy samples were obtained from 15 IS volunteers and 14 IR patients. Samples were pooled and two technical replicates were generated.</p

Venn diagram showing the comparison between iTRAQ and SWATH.

<p>(A) Comparison in the number of protein identification at FDR<1% (B) Comparison in the number of proteins quantified at p-value <0.05.</p

Functional analyses.

<p>Bioinformatic analyses reveal that proteins involved in lipid metabolic process and oxydation-reduction processed are mostly underexpressed in insulin resistant patients.</p

Image_2_A discriminant analysis of plasma metabolomics for the assessment of metabolic responsiveness to red raspberry consumption.JPEG

BackgroundMany studies show that the intake of raspberries is beneficial to immune-metabolic health, but the responses of individuals are heterogeneous and not fully understood.MethodsIn a two-arm parallel-group, randomized, controlled trial, immune-metabolic outcomes and plasma metabolite levels were analyzed before and after an 8-week red raspberry consumption. Based on partial least squares discriminant analysis (PLS-DA) on plasma xenobiotic levels, adherence to the intervention was first evaluated. A second PLS-DA followed by hierarchical clustering was used to classify individuals into response subgroups. Clinical immune and metabolic outcomes, including insulin resistance (HOMA-IR) and sensitivity (Matsuda, QUICKI) indices, during the intervention were assessed and compared between response subgroups.ResultsTwo subgroups of participants, type 1 responders (n = 17) and type 2 responders (n = 5), were identified based on plasma metabolite levels measured during the intervention. Type 1 responders showed neutral to negative effects on immune-metabolic clinical parameters after raspberry consumption, and type 2 responders showed positive effects on the same parameters. Changes in waist circumference, waist-to-hip ratio, fasting plasma apolipoprotein B, C-reactive protein and insulin levels as well as Matsuda, HOMA-IR and QUICKI were significantly different between the two response subgroups. A deleterious effect of two carotenoid metabolites was also observed in type 1 responders but these variables were significantly associated with beneficial changes in the QUICKI index and in fasting insulin levels in type 2 responders. Increased 3-ureidopropionate levels were associated with a decrease in the Matsuda index in type 2 responders, suggesting that this metabolite is associated with a decrease in insulin sensitivity for those subjects, whereas the opposite was observed for type 1 responders.ConclusionThe beneficial effects associated with red raspberry consumption are subject to inter-individual variability. Metabolomics-based clustering appears to be an effective way to assess adherence to a nutritional intervention and to classify individuals according to their immune-metabolic responsiveness to the intervention. This approach may be replicated in future studies to provide a better understanding of how interindividual variability impacts the effects of nutritional interventions on immune-metabolic health.</p