Analysis workflows.

<p>Metabox supports in-depth analysis of metabolomic data by including four analysis modules: data normalization (red), statistical analysis (blue), network construction (green) and functional analysis (purple). Outputs from each module are in red, blue, green and purple circles respectively. The tool accepts external inputs on each analysis level. Within metabox, the output from an analysis module can be used for subsequent analyses in the other modules denoted as a colored circle inside a box.</p

Partial visualization of the entire ‘chemical structure similarity’ network of metabolites in a lung adenocarcinoma study.

<p>Chemical similarities between all identified metabolites was calculated from PubChem substructure fingerprints. Network nodes are connected by correlation coefficients using edge thickess for correlations <i>r</i>_xy>0.7. Metabox functional class scoring was applied to estimate significantly enriched pathways (<i>p</i><0.05), yielding arginine/proline metabolism, arginine biosynthesis and pentose/glucuronate interconversions among other pathways. Pathway enrichments are given by color in node pie charts.</p

List of statistical analysis procedures in metabox.

<p>List of statistical analysis procedures in metabox.</p

Biological network integrating significant differences in gene and metabolite regulation in lung adenocarcinoma compared to paired control tissues.

<p>Significantly different genes and metabolites were mapped onto the metabox internal graph database using enzymes as linking nodes (grey). The resulting network was downloaded and mapped relative changes between tumor and non-tumor tissues using Cytoscape. Graph relationships CONTROL, CONVERSION, and CATALYSIS are labeled by colored edges. The network shows metabolic links between glycerol, palmitic acid, sphinganine, glutamic acid, glyceric acid, UDP-glucuronic acid and UDP-GlcNAc.</p

Type 2 Diabetes Associated Changes in the Plasma Non-Esterified Fatty Acids, Oxylipins and Endocannabinoids

<div><p>Type 2 diabetes has profound effects on metabolism that can be detected in plasma. While increases in circulating non-esterified fatty acids (NEFA) are well-described in diabetes, effects on signaling lipids have received little attention. Oxylipins and endocannabinoids are classes of bioactive fatty acid metabolites with many structural members that influence insulin signaling, adipose function and inflammation through autocrine, paracrine and endocrine mechanisms. To link diabetes-associated changes in plasma NEFA and signaling lipids, we quantitatively targeted >150 plasma lipidome components in age- and body mass index-matched, overweight to obese, non-diabetic (n = 12) and type 2 diabetic (n = 43) African-American women. Diabetes related NEFA patterns indicated ∼60% increase in steroyl-CoA desaturase activity and ∼40% decrease in very long chain polyunsaturated fatty acid chain shortening, patterns previously associated with the development of nonalcoholic fatty liver disease. Further, epoxides and ketones of eighteen carbon polyunsaturated fatty acids were elevated >80% in diabetes and strongly correlated with changes in NEFA, consistent with their liberation during adipose lipolysis. Endocannabinoid behavior differed by class with diabetes increasing an array of N-acylethanolamides which were positively correlated with pro-inflammatory 5-lipooxygenase-derived metabolites, while monoacylglycerols were negatively correlated with body mass. These results clearly show that diabetes not only results in an increase in plasma NEFA, but shifts the plasma lipidomic profiles in ways that reflect the biochemical and physiological changes of this pathological state which are independent of obesity associated changes.</p> </div

Characteristics of overweight diabetic and non-diabetic African American Gullah-speaking female study participants.

*<p>p<0.0001 by Mann-Whitney U-test.</p

The type 2 diabetes-associated lipidomic changes projected in context of their biological relationships in obese African-American women.

<p>Metabolites are represented by circular “nodes” linked by “edges” with arrows designating the direction of the biosynthetic gradient (i.e. substrate to product). Some metabolites are linked by more than one enzymatic step. Node sizes represent magnitudes of differences in plasma metabolite geometric means (ΔGM). Arrow widths represent magnitudes of changes in product over substrate ratios (ΔP:S). Colors of node borders and arrows represent the significance and direction of changes relative to non-diabetics as per the figure legend. Differences are significant at p<0.05 by Mann-Whitney U test adjusted for FDR (q = 0.1).</p

An OPLS-DA model built from 15 plasma lipids discriminates non-diabetic and diabetic cohorts.

<p>Horizontal scatter plots of the log transformed concentrations for each model variable are shown. The horizontal arrangement of metabolite scatter plots is scaled to their loading in the discriminant model. A given species importance in the classification increases with increasing displacement from the origin (broken line). The direction of the displacement, left or right, designates whether the species was decreased (left) or increased (right) in the diabetic relative to the non-diabetic patients. The overall model discrimination performance is presented as a scatter plot of subject model scores (inset).</p

Concentrations of selected plasma oxylipins (nM) in BMI-matched obese non-diabetic and type 2 diabetic African-American women.^*.

*<p>Values are reported as geometric means [ranges] if changes in geometric means between groups are significant (Mann-Whitney U-test, p<0.05 with FDR adjustment at q = 0.1). For remaining measurements see <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048852#pone.0048852.s006" target="_blank">Tables S6</a></b> and <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048852#pone.0048852.s007" target="_blank">Table S7</a></b>.</p

Analysis of correlations among all measured variables and estimated enzyme activities in non-diabetic and type 2 diabetic African-American women.

<p>Significant (p<0.05) non-parametric Spearman’s correlations for non-diabetic (top left triangle) and type 2 diabetic (bottom right triangle) subjects are indicated by orange (positive) and blue (negative) intersections.</p