Nicotinamide Inhibits Alkylating Agent-Induced Apoptotic Neurodegeneration in the Developing Rat Brain

BACKGROUND: Exposure to the chemotherapeutic alkylating agent thiotepa during brain development leads to neurological complications arising from neurodegeneration and irreversible damage to the developing central nerve system (CNS). Administration of single dose of thiotepa in 7-d postnatal (P7) rat triggers activation of apoptotic cascade and widespread neuronal death. The present study was aimed to elucidate whether nicotinamide may prevent thiotepa-induced neurodegeneration in the developing rat brain. METHODOLOGY/PRINCIPAL FINDINGS: Neuronal cell death induced by thiotepa was associated with the induction of Bax, release of cytochrome-c from mitochondria into the cytosol, activation of caspase-3 and cleavage of poly (ADP-ribose) polymerase (PARP-1). Post-treatment of developing rats with nicotinamide suppressed thiotepa-induced upregulation of Bax, reduced cytochrome-c release into the cytosol and reduced expression of activated caspase-3 and cleavage of PARP-1. Cresyl violet staining showed numerous dead cells in the cortex hippocampus and thalamus; post-treatment with nicotinamide reduced the number of dead cells in these brain regions. Terminal deoxynucleotidyl transferase (TdT)-mediated dUTP nick end-labeling (TUNEL) and immunohistochemical analysis of caspase-3 show that thiotepa-induced cell death is apoptotic and that it is inhibited by nicotinamide treatment. CONCLUSION: Nicotinamide (Nic) treatment with thiotepa significantly improved neuronal survival and alleviated neuronal cell death in the developing rat. These data demonstrate that nicotinamide shows promise as a therapeutic and neuroprotective agent for the treatment of neurodegenerative disorders in newborns and infants

Effects of Ethanol and NAP on Cerebellar Expression of the Neural Cell Adhesion Molecule L1

The neural cell adhesion molecule L1 is critical for brain development and plays a role in learning and memory in the adult. Ethanol inhibits L1-mediated cell adhesion and neurite outgrowth in cerebellar granule neurons (CGNs), and these actions might underlie the cerebellar dysmorphology of fetal alcohol spectrum disorders. The peptide NAP potently blocks ethanol inhibition of L1 adhesion and prevents ethanol teratogenesis. We used quantitative RT-PCR and Western blotting of extracts of cerebellar slices, CGNs, and astrocytes from postnatal day 7 (PD7) rats to investigate whether ethanol and NAP act in part by regulating the expression of L1. Treatment of cerebellar slices with 20 mM ethanol, 10−12 M NAP, or both for 4 hours, 24 hours, and 10 days did not significantly affect L1 mRNA and protein levels. Similar treatment for 4 or 24 hours did not regulate L1 expression in primary cultures of CGNs and astrocytes, the predominant cerebellar cell types. Because ethanol also damages the adult cerebellum, we studied the effects of chronic ethanol exposure in adult rats. One year of binge drinking did not alter L1 gene and protein expression in extracts from whole cerebellum. Thus, ethanol does not alter L1 expression in the developing or adult cerebellum; more likely, ethanol disrupts L1 function by modifying its conformation and signaling. Likewise, NAP antagonizes the actions of ethanol without altering L1 expression

DFA7, a New Method to Distinguish between Intron-Containing and Intronless Genes

Intron-containing and intronless genes have different biological properties and statistical characteristics. Here we propose a new computational method to distinguish between intron-containing and intronless gene sequences. Seven feature parameters α, β, γ, λ, θ, φ and σ based on detrended fluctuation analysis (DFA) are fully used, and thus we can compute a 7-dimensional feature vector for any given gene sequence to be discriminated. Furthermore, support vector machine (SVM) classifier with Gaussian radial basis kernel function is performed on this feature space to classify the genes into intron-containing and intronless. We investigate the performance of the proposed method in comparison with other state-of-the-art algorithms on biological datasets. The experimental results show that our new method significantly improves the accuracy over those existing techniques