Motivation: With the exponential growth of expression and protein–protein interaction (PPI) data, the frontier of research in systems biology shifts more and more to the integrated analysis of these large datasets. Of particular interest is the identification of functional modules in PPI networks, sharing common cellular function beyond the scope of classical pathways, by means of detecting differentially expressed regions in PPI networks. This requires on the one hand an adequate scoring of the nodes in the network to be identified and on the other hand the availability of an effective algorithm to find the maximally scoring network regions. Various heuristic approaches have been proposed in the literature.Results: Here we present the first exact solution for this problem, which is based on integer-linear programming and its connection to the well-known prize-collecting Steiner tree problem from Operations Research. Despite the NP-hardness of the underlying combinatorial problem, our method typically computes provably optimal subnetworks in large PPI networks in a few minutes. An essential ingredient of our approach is a scoring function defined on network nodes. We propose a new additive score with two desirable properties: (i) it is scalable by a statistically interpretable parameter and (ii) it allows a smooth integration of data from various sources.We apply our method to a well-established lymphoma microarray dataset in combination with associated survival data and the large interaction network of HPRD to identify functional modules by computing optimal-scoring subnetworks. In particular, we find a functional interaction module associated with proliferation over-expressed in the aggressive ABC subtype as well as modules derived from non-malignant by-stander cells.Availability: Our software is available freely for non-commercial purposes at http://www.planet-lisa.net.Contact: tobias.mueller@biozentrum.uni-wuerzburg.de

Recent studies highlighted long noncoding RNAs (lncRNAs) to play an important role in cardiac development. However, understanding of lncRNAs in cardiac diseases is still limited. Global lncRNA expression profiling indicated that several lncRNA transcripts are deregulated during pressure overload-induced cardiac hypertrophy in mice. Using stringent selection criteria, we identified Chast (cardiac hypertrophy-associated transcript) as a potential lncRNA candidate that influences cardiomyocyte hypertrophy. Cell fractionation experiments indicated that Chast is specifically up-regulated in cardiomyocytes in vivo in transverse aortic constriction (TAC)-operated mice. In accordance, CHAST homolog in humans was significantly up-regulated in hypertrophic heart tissue from aortic stenosis patients and in human embryonic stem cell-derived cardiomyocytes upon hypertrophic stimuli. Viral-based overexpression of Chast was sufficient to induce cardiomyocyte hypertrophy in vitro and in vivo. GapmeR-mediated silencing of Chast both prevented and attenuated TAC-induced pathological cardiac remodeling with no early signs on toxicological side effects. Mechanistically, Chast negatively regulated Pleckstrin homology domain-containing protein family M member 1 (opposite strand of Chast), impeding cardiomyocyte autophagy and driving hypertrophy. These results indicate that Chast can be a potential target to prevent cardiac remodeling and highlight a general role of lncRNAs in heart diseases.

Epigenetic processes are primary candidates when searching for mechanisms that can stably modulate gene expression and metabolic pathways according to early life conditions. To test the effects of gestational diabetes mellitus (GDM) on the epigenome of the next generation, cord blood and placenta tissue were obtained from 88 newborns of mothers with dietetically treated GDM, 98 with insulin-dependent GDM, and 65 without GDM. Bisulfite pyrosequencing was used to compare the methylation levels of seven imprinted genes involved in prenatal and postnatal growth, four genes involved in energy metabolism, one anti-inflammatory gene, one tumor suppressor gene, one pluripotency gene, and two repetitive DNA families. The maternally imprinted MEST gene, the nonimprinted glucocorticoid receptor NR3C1 gene, and interspersed ALU repeats showed significantly decreased methylation levels (4–7 percentage points for MEST, 1–2 for NR3C1, and one for ALUs) in both GDM groups, compared with controls, in both analyzed tissues. Significantly decreased blood MEST methylation (3 percentage points) also was observed in adults with morbid obesity compared with normal-weight controls. Our results support the idea that intrauterine exposure to GDM has long-lasting effects on the epigenome of the offspring. Specifically, epigenetic malprogramming of MEST may contribute to obesity predisposition throughout life.

The BioNet package and a tutorial are available from http://bionet.bioapps.biozentrum.uni-wuerzburg.de.