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A phenomenon observed earlier in the development of metabolomics as a systems biology methodology, consists of a small but significant number of metabolites whose levels are highly correlated between biological replicates. Contrary to initial interpretations, these correlations are not necessarily only between neighboring metabolites in the metabolic network. Most metabolites that participate in common reactions are not correlated in this way, while some non-neighboring metabolites are highly correlated. Here we investigate the origin of such correlations using metabolic control analysis and computer simulation of biochemical networks. A series of cases is identified which lead to high correlation between metabolite pairs in replicate measurement. These are (1) chemical equilibrium, (2) mass conservation, (3) asymmetric control distribution, and (4) unusually high variance in the expression of a single gene. The importance of identifying metabolite correlations within a physiological state and changes of correlation between different states is discussed in the context of systems biology.

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DNA methylation is one of several epigenetic mechanisms that contribute to the regulation of gene expression; however, the extent to which methylation of CpG dinucleotides correlates with gene expression at the genome-wide level is still largely unknown. Using purified primary monocytes from subjects in a large community-based cohort (n = 1264), we characterized methylation (>485 000 CpG sites) and mRNA expression (>48K transcripts) and carried out genome-wide association analyses of 8370 expression phenotypes. We identified 11 203 potential cis-acting CpG loci whose degree of methylation was associated with gene expression (eMS) at a false discovery rate threshold of 0.001. Most of the associations were consistent in effect size and direction of effect across sex and three ethnicities. Contrary to expectation, these eMS were not predominately enriched in promoter regions, or CpG islands, but rather in the 3' UTR, gene bodies, CpG shores or 'offshore' sites, and both positive and negative correlations between methylation and expression were observed across all locations. eMS were enriched for regions predicted to be regulatory by ENCODE (Encyclopedia of DNA Elements) data in multiple cell types, particularly enhancers. One of the strongest association signals detected (P < 2.2 × 10(-308)) was a methylation probe (cg17005068) in the promoter/enhancer region of the glutathione S-transferase theta 1 gene (GSTT1, encoding the detoxification enzyme) with GSTT1 mRNA expression. Our study provides a detailed description of the epigenetic architecture in human monocytes and its relationship to gene expression. These data may help prioritize interrogation of biologically relevant methylation loci and provide new insights into the epigenetic basis of human health and diseases.

BackgroundReverse-engineering gene networks from expression profiles is a difficult problem for which a multitude of techniques have been developed over the last decade. The yearly organized DREAM challenges allow for a fair evaluation and unbiased comparison of these methods.ResultsWe propose an inference algorithm that combines confidence matrices, computed as the standard scores from single-gene knockout data, with the down-ranking of feed-forward edges. Substantial improvements on the predictions can be obtained after the execution of this second step.ConclusionsOur algorithm was awarded the best overall performance at the DREAM4 In Silico 100-gene network sub-challenge, proving to be effective in inferring medium-size gene regulatory networks. This success demonstrates once again the decisive importance of gene expression data obtained after systematic gene perturbations and highlights the usefulness of graph analysis to increase the reliability of inference.

Our goal is gene network inference in genetical genomics or systems genetics experiments. For species where sequence information is available, we first perform expression quantitative trait locus (eQTL) mapping by jointly utilizing cis-, cis-trans-, and trans-regulation. After using local structural models to identify regulator-target pairs for each eQTL, we construct an encompassing directed network (EDN) by assembling all retained regulator-target relationships. The EDN has nodes corresponding to expressed genes and eQTL and directed edges from eQTL to cis-regulated target genes, from cis-regulated genes to cis-trans-regulated target genes, from trans-regulator genes to target genes, and from trans-eQTL to target genes. For network inference within the strongly constrained search space defined by the EDN, we propose structural equation modeling (SEM), because it can model cyclic networks and the EDN indeed contains feedback relationships. On the basis of a factorization of the likelihood and the constrained search space, our SEM algorithm infers networks involving several hundred genes and eQTL. Structure inference is based on a penalized likelihood ratio and an adaptation of Occam's window model selection. The SEM algorithm was evaluated using data simulated with nonlinear ordinary differential equations and known cyclic network topologies and was applied to a real yeast data set.

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