Metabonomic, transcriptomic, and genomic variation of a population cohort

Abstract

Comprehensive characterization of human tissues promises novel insights into the biological architecture of human diseases and traits. We assessed metabonomic, transcriptomic, and genomic variation for a large population-based cohort from the capital region of Finland. Network analyses identified a set of highly correlated genes, the lipid-leukocyte (LL) module, as having a prominent role in over 80 serum metabolites (of 134 measures quantified), including lipoprotein subclasses, lipids, and amino acids. Concurrent association with immune response markers suggested the LL module as a possible link between inflammation, metabolism, and adiposity. Further, genomic variation was used to generate a directed network and infer LL module's largely reactive nature to metabolites. Finally, gene co-expression in circulating leukocytes was shown to be dependent on serum metabolite concentrations, providing evidence for the hypothesis that the coherence of molecular networks themselves is conditional on environmental factors. These findings show the importance and opportunity of systematic molecular investigation of human population samples. To facilitate and encourage this investigation, the metabonomic, transcriptomic, and genomic data used in this study have been made available as a resource for the research community.

Figure 1

Overview of data integration and analyses. A high-level view of the study design and analysis employed.

Figure 2

Edge-directed network of LL module and serum metabolites. Genetic variation was used to infer a causal network of core LL module expression and metabolites (see Supplementary Methods and Supplementary Table 1 for abbreviations). Core LL module genes are represented by purple nodes in the upper left, whereas metabolites significantly associated with genetic variation and LL module are denoted by all other nodes. Arrows denote directed edges. (A) Shows core LL module (red) reactivity to most lipoprotein subclass levels (green); however, two components of large VLDL (blue) were predicted downstream of CPA3, SPRYD5, and HDC expression. (B) Shows that triglycerides in the small HDL subclass (red) are predicted to be largely reactive to concentrations of the larger HDL subclasses (green). Source data is available for this figure at www.nature.com/msb.

Figure 3

Conditional co-expression of LL module. Core LL module expression was partitioned into quintiles based on metabolite concentration rank. For each quintile (x axis, lowest to highest metabolite concentration), co-expression of all non-redundant pairs was calculated using Spearman's rank correlation coefficient (y axis). Fitting a linear model to all co-expression pairs suggests that if LL module expression is negatively correlated with metabolite concentration, then co-expression among genes belonging to LL module increases and vice versa.