updated data file

this data file contains the final data for the submitted manuscrip

R script for analyzing field data from experiments in Michigan

R script for analyzing field data from experiments in Michiga

The blood metabolome-transcriptome interface.

<p><b>A:</b> Distributions of correlation coefficients for metabolite-mRNA, mRNA-mRNA and metabolite-metabolite associations. <b>B:</b> Number of nodes and edges as a function of the absolute correlation coefficient. Red dotted line represents the correlation cutoff used in this study (0.01 FDR). <b>C:</b> Percentages of blood cell type specific markers contained in the BMTI. <b>D:</b> Visualization of the blood metabolome-transcriptome interface. The correlation network consists of 114 metabolites and 522 transcripts connected by 1109 edges. Edge widths represent correlation strengths.</p

Associations between DNA methylation at cg06500161 (<i>ABCG1</i>) and different phenotypes, based on quintiles of methylation level.

<p>Associations between DNA methylation at cg06500161 (<i>ABCG1</i>) and different phenotypes, based on quintiles of methylation level.</p

Model-based evaluation of metabolite-mRNA correlations.

<p><b>A:</b> Schematic representation of the mitochondrial carnitine-shuttle with an explanation of network distance calculation. Note that co-factors are only illustrated for completeness, but are not considered for the calculation of the shortest path between two compounds. Dashed circles indicate unmeasured metabolites. <b>B:</b> Spearman correlation coefficient plotted against the number of pathway steps in human Recon 2. Significant correlations, i.e. those present in the BMTI are displayed as red crosses, whereas all non-significant correlations are plotted as a distribution. NM: no mapping. A distance of infinity (Inf) was assigned if there was no connection in Recon 2. <b>C:</b> Enrichment of significant correlations as determined by Fisher’s exact test. Black bars indicate log10 p-values assessing whether we observe more significant correlations for that particular distance than expected by chance. White bars represent the same test, only for a cumulative distance (i.e. “up to a distance of x”). <b>D:</b> Functional annotations of significantly associated transcripts at distances 0 and 1. At both distances, mainly transcripts belonging to the transport, energy, lipid and amino acid subsystems were observable.</p

Transcription factor binding site analysis.

<p><b>A:</b> Heatmap of shared significantly overrepresented TFBS between metabolic <i>subpathways</i>. Upper right triangle matrix was left out. Darker colors indicate a higher number of shared TF binding sites. <b>B+C:</b> Identified network motifs of TFs and their respective target genes associated to the same metabolite. Green arrows indicate regulation. Black lines indicate positive correlation; red dotted lines indicate negative correlation.</p

Top 25 network edges.

<p>We observed particularly strong effects for lipid metabolism, especially around the mitochondrial transporter SLC25A20. Pipe symbols (|) separate alternative metabolite names.</p><p>Top 25 network edges.</p

Intermediate clinical phenotype associations.

<p>Linear regression results of TG, HDL and LDL for each metabolite and transcript <b>(A, C, E)</b>, or pathway and GO term <b>(B, D, F)</b> were projected onto the respective networks. Blue colors indicate negative associations, while red colors represent positive associations to the respective phenotype. Color strength of the nodes encodes the-log10 p-value of the respective association. <i>β</i> denotes the regression coefficient and its sign represents the direction of the associations (positive or negative correlation).</p

Pathway interaction network (PIN).

<p><b>A:</b> Bipartite correlation network, where each node represents either a metabolic pathway or a gene set summarized in a GO term, while edges between them represent the correlation of the respective aggZ-scores. <b>B+C:</b> Expanded view on exemplary pathway interactions. Note that zoomed parts correspond to subnetworks from <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005274#pgen.1005274.g002" target="_blank">Fig 2D</a> that are explained by one single link of the PIN. Edge widths represent correlation strengths.</p

Data integration and network analysis workflow for the blood metabolome-transcriptome interface (BMTI).

<p><b>A:</b> We analyzed fasting serum metabolomics and whole blood transcriptomics data from 712 samples of the KORA F4 cohort. After preprocessing and filtering, a cross-correlation matrix between 440 distinct metabolites and 16,780 unique, gene-mapped probes was calculated. The correlation matrix was transformed into a bipartite network by applying a statistical significance threshold. <b>B:</b> Scientific literature was screened for biological evidence for the strongest metabolite-mRNA associations. All correlating metabolite-mRNA pairs contained in the human metabolic model Recon2 were systematically evaluated with respect to their distance in the metabolic pathway network. <b>C:</b> Aggregated z-scores for each functional annotation were calculated. A pathway interaction network (PIN) was then constructed via cross-correlation of scores between pairs of functional annotations. <b>D:</b> For each metabolite contained in the BMTI, we investigated the promoter regions of associated transcripts for shared regulatory signatures. Similarly, shared regulatory signatures within and between metabolic pathways were examined. As a final step, we identified specific regulatory motifs in the BMTI. <b>E:</b> Both BMTI and the PIN were integrated with the results from an association analysis to the three intermediate physiological traits (HDL-C, LDL-C and TG).</p