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1.  Chronic Exercise Increases Plasma Brain-Derived Neurotrophic Factor Levels, Pancreatic Islet Size, and Insulin Tolerance in a TrkB-Dependent Manner 
PLoS ONE  2014;9(12):e115177.
Physical exercise improves glucose metabolism and insulin sensitivity. Brain-derived neurotrophic factor (BDNF) enhances insulin activity in diabetic rodents. Because physical exercise modifies BDNF production, this study aimed to investigate the effects of chronic exercise on plasma BDNF levels and the possible effects on insulin tolerance modification in healthy rats.
Wistar rats were divided into five groups: control (sedentary, C); moderate- intensity training (MIT); MIT plus K252A TrkB blocker (MITK); high-intensity training (HIT); and HIT plus K252a (HITK). Training comprised 8 weeks of treadmill running. Plasma BDNF levels (ELISA assay), glucose tolerance, insulin tolerance, and immunohistochemistry for insulin and the pancreatic islet area were evaluated in all groups. In addition, Bdnf mRNA expression in the skeletal muscle was measured.
Principal Findings
Chronic treadmill exercise significantly increased plasma BDNF levels and insulin tolerance, and both effects were attenuated by TrkB blocking. In the MIT and HIT groups, a significant TrkB-dependent pancreatic islet enlargement was observed. MIT rats exhibited increased liver glycogen levels following insulin administration in a TrkB-independent manner.
Chronic physical exercise exerted remarkable effects on insulin regulation by inducing significant increases in the pancreatic islet size and insulin sensitivity in a TrkB-dependent manner. A threshold for the induction of BNDF in response to physical exercise exists in certain muscle groups. To the best of our knowledge, these are the first results to reveal a role for TrkB in the chronic exercise-mediated insulin regulation in healthy rats.
PMCID: PMC4274083  PMID: 25531651
2.  Flower Development as an Interplay between Dynamical Physical Fields and Genetic Networks 
PLoS ONE  2010;5(10):e13523.
In this paper we propose a model to describe the mechanisms by which undifferentiated cells attain gene configurations underlying cell fate determination during morphogenesis. Despite the complicated mechanisms that surely intervene in this process, it is clear that the fundamental fact is that cells obtain spatial and temporal information that bias their destiny. Our main hypothesis assumes that there is at least one macroscopic field that breaks the symmetry of space at a given time. This field provides the information required for the process of cell differentiation to occur by being dynamically coupled to a signal transduction mechanism that, in turn, acts directly upon the gene regulatory network (GRN) underlying cell-fate decisions within cells. We illustrate and test our proposal with a GRN model grounded on experimental data for cell fate specification during organ formation in early Arabidopsis thaliana flower development. We show that our model is able to recover the multigene configurations characteristic of sepal, petal, stamen and carpel primordial cells arranged in concentric rings, in a similar pattern to that observed during actual floral organ determination. Such pattern is robust to alterations of the model parameters and simulated failures predict altered spatio-temporal patterns that mimic those described for several mutants. Furthermore, simulated alterations in the physical fields predict a pattern equivalent to that found in Lacandonia schismatica, the only flowering species with central stamens surrounded by carpels.
PMCID: PMC2965087  PMID: 21048956
3.  Floral Morphogenesis: Stochastic Explorations of a Gene Network Epigenetic Landscape 
PLoS ONE  2008;3(11):e3626.
In contrast to the classical view of development as a preprogrammed and deterministic process, recent studies have demonstrated that stochastic perturbations of highly non-linear systems may underlie the emergence and stability of biological patterns. Herein, we address the question of whether noise contributes to the generation of the stereotypical temporal pattern in gene expression during flower development. We modeled the regulatory network of organ identity genes in the Arabidopsis thaliana flower as a stochastic system. This network has previously been shown to converge to ten fixed-point attractors, each with gene expression arrays that characterize inflorescence cells and primordial cells of sepals, petals, stamens, and carpels. The network used is binary, and the logical rules that govern its dynamics are grounded in experimental evidence. We introduced different levels of uncertainty in the updating rules of the network. Interestingly, for a level of noise of around 0.5–10%, the system exhibited a sequence of transitions among attractors that mimics the sequence of gene activation configurations observed in real flowers. We also implemented the gene regulatory network as a continuous system using the Glass model of differential equations, that can be considered as a first approximation of kinetic-reaction equations, but which are not necessarily equivalent to the Boolean model. Interestingly, the Glass dynamics recover a temporal sequence of attractors, that is qualitatively similar, although not identical, to that obtained using the Boolean model. Thus, time ordering in the emergence of cell-fate patterns is not an artifact of synchronous updating in the Boolean model. Therefore, our model provides a novel explanation for the emergence and robustness of the ubiquitous temporal pattern of floral organ specification. It also constitutes a new approach to understanding morphogenesis, providing predictions on the population dynamics of cells with different genetic configurations during development.
PMCID: PMC2572848  PMID: 18978941

Results 1-3 (3)