New Aspects of Progesterone Interactions with the Actin Cytoskeleton and Neurosteroidogenesis in the Cerebellum and the Neuronal Growth Cone

The impact of progesterone on neuronal tissues in the central (CNS) and peripheral (PNS) nervous system is of significant scientific and therapeutic interest. Glial and neuronal cells of vertebrates express steroidogenic enzymes, and are able to synthesize progesterone de novo from cholesterol. Progesterone is described to have neuroprotective, neuroreparative, anti-degenerative, and anti-apoptotic effects in the CNS and the PNS. Thus, the first clinical studies promise new therapeutic options using progesterone in the treatment of patients with traumatic brain injury. Additionally, experimental data from different animal models suggest further positive effects of progesterone on neurological diseases such as cerebral ischemia, peripheral nerve injury and amyothropic lateral sclerosis. In regard to this future clinical use of progesterone, we discuss in this review the underlying physiological principles of progesterone effects in neuronal tissues. Mechanisms leading to morphological reorganizations of neurons in the CNS and PNS affected by progesterone are addressed, with special focus on the actin cytoskeleton. Furthermore, new aspects of a progesterone-dependent regulation of neurosteroidogenesis mediated by the recently described progesterone binding protein PGRMC1 in the nervous system are discussed.     

Ridge estimation of the VAR(1) model and its time series chain graph from multivariate time‐course omics data

Omics experiments endowed with a time‐course design may enable us to uncover the dynamic interplay among genes of cellular processes. Multivariate techniques (like VAR(1) models describing the temporal and contemporaneous relations among variates) that may facilitate this goal are hampered by the high‐dimensionality of the resulting data. This is resolved by the presented ridge regularized maximum likelihood estimation procedure for the VAR(1) model. Information on the absence of temporal and contemporaneous relations may be incorporated in this procedure. Its computational efficient implemention is discussed. The estimation procedure is accompanied with an LOOCV scheme to determine the associated penalty parameters. Downstream exploitation of the estimated VAR(1) model is outlined: an empirical Bayes procedure to identify the interesting temporal and contemporaneous relationships, impulse response analysis, mutual information analysis, and covariance decomposition into the (graphical) relations among variates. In a simulation study the presented ridge estimation procedure outperformed a sparse competitor in terms of Frobenius loss of the estimates, while their selection properties are on par. The proposed machinery is illustrated in the reconstruction of the p53 signaling pathway during HPV‐induced cellular transformation. The methodology is implemented in the ragt2ridges R‐package available from CRAN.     

Experimentell erzeugte Duplicitas cruciata bei Triton

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