Telomere shortening limits the proliferative capacity of a cell, but perhaps surprisingly, shortening is also known to be associated with increased rates of tumor initiation. A current hypothesis suggests that telomere dysfunction increases tumor initiation by induction of chromosomal instability, but that initiated tumors need to reactivate telomerase for genome stabilization and tumor progression. This concept has not been tested in vivo, since appropriate mouse models were lacking. Here, we analyzed hepatocarcinogenesis in a mouse model of inducible telomere dysfunction on a telomerase-proficient background, in telomerase knockout mice with chronic telomere dysfunction (G3 mTerc -/-), and in WT mice with functional telomeres and telomerase. Transient or chronic telomere dysfunction enhanced the rates of chromosomal aberrations during hepatocarcinogenesis, but only telomerase-proficient mice exhibited significantly increased rates of macroscopic tumor formation in response to telomere dysfunction. In contrast, telomere dysfunction resulted in pronounced accumulation of DNA damage, cell-cycle arrest, and apoptosis in telomerase-deficient liver tumors. Together, these data provide in vivo evidence that transient telomere dysfunction during early or late stages of tumorigenesis promotes chromosomal instability and carcinogenesis in telomerase-proficient mice.

Background: The emerging field of integrative bioinformatics provides the tools to organize and systematically analyze vast amounts of highly diverse biological data and thus allows to gain a novel understanding of complex biological systems. The data warehouse DWARF applies integrative bioinformatics approaches to the analysis of large protein families.

Gene interactions in cells can be represented by gene regulatory networks. A Boolean network models gene interactions according to rules where gene expression is represented by binary values (on / off or {1, 0}). In reality, however, the gene’s state can have multiple values due to biological properties. Furthermore, the noisy nature of the experimental design results in uncertainty about a state of the gene. Here we present a new Boolean network paradigm to allow intermediate values on the interval [0, 1]. As in the Boolean network, fixed points or attractors of such a model correspond to biological phenotypes or states. We use our new extension of the Boolean network paradigm to model gene expression in first and second heart field lineages which are cardiac progenitor cell populations involved in early vertebrate heart development. By this we are able to predict additional biological phenotypes that the Boolean model alone is not able to identify without utilizing additional biological knowledge. The additional phenotypes predicted by the model were confirmed by published biological experiments. Furthermore, the new method predicts gene expression propensities for modelled but yet to be analyzed genes.

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