Rapid and local autoregulation of cerebrovascular blood flow: a deep-brain imaging study in the mouse.

The brain obtains energy by keeping the cerebral blood flow constant against unexpected changes in systemic blood pressure. Although this homeostatic mechanism is widely known as cerebrovascular autoregulation, it is not understood how widely and how robustly it works in the brain. Using a needle-like objective lens designed for deep-tissue imaging, we quantified the degree of autoregulation in the mouse hippocampus with single-capillary resolution. On average, hippocampal blood flow exhibited autoregulation over a comparatively broad range of arterial blood pressure and did not significantly respond to pressure changes induced by the pharmacological activation of autonomic nervous system receptors, whereas peripheral tissues showed linear blood flow changes. At the level of individual capillaries, however, about 40% of hippocampal capillaries did not undergo rapid autoregulation. This heterogeneity suggests the presence of a local baroreflex system to implement cerebral autoregulation.

Statistical significance of precisely repeated intracellular synaptic patterns.

Can neuronal networks produce patterns of activity with millisecond accuracy? It may seem unlikely, considering the probabilistic nature of synaptic transmission. However, some theories of brain function predict that such precision is feasible and can emerge from the non-linearity of the action potential generation in circuits of connected neurons. Several studies have presented evidence for and against this hypothesis. Our earlier work supported the precision hypothesis, based on results demonstrating that precise patterns of synaptic inputs could be found in intracellular recordings from neurons in brain slices and in vivo. To test this hypothesis, we devised a method for finding precise repeats of activity and compared repeats found in the data to those found in surrogate datasets made by shuffling the original data. Because more repeats were found in the original data than in the surrogate data sets, we argued that repeats were not due to chance occurrence. Mokeichev et al. (2007) challenged these conclusions, arguing that the generation of surrogate data was insufficiently rigorous. We have now reanalyzed our previous data with the methods introduced from Mokeichev et al. (2007). Our reanalysis reveals that repeats are statistically significant, thus supporting our earlier conclusions, while also supporting many conclusions that Mokeichev et al. (2007) drew from their recent in vivo recordings. Moreover, we also show that the conditions under which the membrane potential is recorded contributes significantly to the ability to detect repeats and may explain conflicting results. In conclusion, our reevaluation resolves the methodological contradictions between Ikegaya et al. (2004) and Mokeichev et al. (2007), but demonstrates the validity of our previous conclusion that spontaneous network activity is non-randomly organized.

Activin A induces erythroid gene expressions and inhibits mitogenic cytokine-mediated K562 colony formation by activating p38 MAPK.

Activin A, a member of the transforming growth factor (TGF)-beta superfamily, is involved in the regulation of erythroid differentiation. Previous studies have shown that activin A inhibited the colony-forming activity of mouse Friend erythroleukemia cells, however, the mechanism remains unknown. First, we show herein that activin A induced the expression and activated the promoters of alpha-globin and zeta-globin in K562 cells, confirming that activin A induces erythroid differentiation in K562 cells. The p38 mitogen activated protein kinase (MAPK) inhibitor, SB203580, inhibited and the extracellular signal regulated kinase (ERK) inhibitor, PD98059, enhanced the expression and promoter activities of alpha-globin and zeta-globin by activin A, indicating that p38 MAPK and ERK are crucial for activin A-induced erythroid genes expression. Second, SB203580 inhibited the inhibitory effect of activin A on the colony-forming activity of K562 cells using the methylcellulose colony assay, indicating that activin A inhibits K562 colony formation by activating p38 MAPK. In addition, mitogenic cytokines SCF, IL-3, and GM-CSF induced colony formation of K562 cells that could be inhibited by PD98059 or enhanced by SB203580, respectively, indicating that these mitogenic cytokines induce K562 colony formation by activating ERK and inactivating p38 MAPK. Furthermore, activin A reduced the induction effect of these mitogenic cytokines on K562 colony formation in a dose-dependent manner. The inhibition of p38 MAPK reverted the inhibitory effect of activin A on mitogenic cytokine-mediated K562 colony formation. We conclude that activin A can regulate the same pathway via p38 MAPK to coordinate cell proliferation and differentiation of K562 cells.