No abstract

The adipose tissue is the site of expression and secretion of a range of biologically active proteins, called adipokines, for example, leptin, adiponectin, and resistin. Leptin has previously been shown to be expressed in osteoblasts and to promote bone mineralization, whereas adiponectin expression is enhanced during osteoblast differentiation. In the present study we explored the possible role of resistin in bone metabolism. We found that resistin is expressed in murine preosteoclasts and preosteoblasts (RAW 264.7, MC3T3-E1), in primary human bone marrow stem cells and in mature human osteoblasts. The expression of resistin mRNA in RAW 264.7 was increased during differentiation and seemed to be regulated through PKC- and PKA-dependent mechanisms. Recombinant resistin increased the number of differentiated osteoclasts and stimulated NFkappaB promoter activity, indicating a role in osteoclastogenesis. Resistin also enhanced the proliferation of MC3T3-E1 cells in a PKA and PKC-dependent manner, but only weakly interfered with genes known to be upregulated during differentiation of MC3T3-E1 into osteoblasts. All together, our results indicate that resistin may play a role in bone remodeling.

Recent studies have proposed a role for serotonin and its transporter in regulation of bone cell function. In the present study, we examined the in vitro effects of serotonin and the serotonin transporter inhibitor fluoxetine "Prozac" on osteoblasts and osteoclasts. Human mononuclear cells were differentiated into osteoclasts in the presence of serotonin or fluoxetine. Both compounds affected the total number of differentiated osteoclasts as well as bone resorption in a bell-shaped manner. RT-PCR on the human osteoclasts demonstrated several serotonin receptors, the serotonin transporter, and the rate-limiting enzyme in serotonin synthesis, tryptophan hydroxylase 1 (Tph1). Tph1 expression was also found in murine osteoblasts and osteoclasts, indicating an ability to produce serotonin. In murine pre-osteoclasts (RAW264.7), serotonin as well as fluoxetine affected proliferation and NFkappaB activity in a biphasic manner. Proliferation of human mesenchymal stem cells (MSC) and primary osteoblasts (NHO), and 5-HT2A receptor expression was enhanced by serotonin. Fluoxetine stimulated proliferation of MSC and murine preosteoblasts (MC3T3-E1) in nM concentrations, microM concentrations were inhibitory. The effect of fluoxetine seemed direct, probably through 5-HT2 receptors. Serotonin-induced proliferation of MC3T3-E1 cells was inhibited by the PKC inhibitor (GF109203) and was also markedly reduced when antagonists of the serotonin receptors 5-HT2B/C or 5-HT2A/C were added. Serotonin increased osteoprotegerin (OPG) and decreased receptor activator of NF-kappaB ligand (RANKL) secretion from osteoblasts, suggesting a role in osteoblast-induced inhibition of osteoclast differentiation, whereas fluoxetine had the opposite effect. This study further describes possible mechanisms by which serotonin and the serotonin transporter can affect bone cell function.

Discovery of efficient anti-cancer drug combinations is a major challenge, since experimental testing of all possible combinations is clearly impossible. Recent efforts to computationally predict drug combination responses retain this experimental search space, as model definitions typically rely on extensive drug perturbation data. We developed a dynamical model representing a cell fate decision network in the AGS gastric cancer cell line, relying on background knowledge extracted from literature and databases. We defined a set of logical equations recapitulating AGS data observed in cells in their baseline proliferative state. Using the modeling software GINsim, model reduction and simulation compression techniques were applied to cope with the vast state space of large logical models and enable simulations of pairwise applications of specific signaling inhibitory chemical substances. Our simulations predicted synergistic growth inhibitory action of five combinations from a total of 21 possible pairs. Four of the predicted synergies were confirmed in AGS cell growth real-time assays, including known effects of combined MEK-AKT or MEK-PI3K inhibitions, along with novel synergistic effects of combined TAK1-AKT or TAK1-PI3K inhibitions. Our strategy reduces the dependence on a priori drug perturbation experimentation for well-characterized signaling networks, by demonstrating that a model predictive of combinatorial drug effects can be inferred from background knowledge on unperturbed and proliferating cancer cells. Our modeling approach can thus contribute to preclinical discovery of efficient anticancer drug combinations, and thereby to development of strategies to tailor treatment to individual cancer patients.