5-HT1A autoreceptor modulation of locomotor activity induced by nitric oxide in the rat dorsal raphe nucleus

The dorsal raphe nucleus (DRN) is the origin of ascending serotonergic projections and is considered to be an important component of the brain circuit that mediates anxiety- and depression-related behaviors. A large fraction of DRN serotonin-positive neurons contain nitric oxide (NO). Disruption of NO-mediated neurotransmission in the DRN by NO synthase inhibitors produces anxiolytic- and antidepressant-like effects in rats and also induces nonspecific interference with locomotor activity. We investigated the involvement of the 5-HT1A autoreceptor in the locomotor effects induced by NO in the DRN of male Wistar rats (280-310 g, N = 9-10 per group). The NO donor 3-morpholinosylnomine hydrochloride (SIN-1, 150, and 300 nmol) and the NO scavenger S-3-carboxy-4-hydroxyphenylglycine (carboxy-PTIO, 0.1-3.0 nmol) were injected into the DRN of rats immediately before they were exposed to the open field for 10 min. To evaluate the involvement of the 5-HT1A receptor and the N-methyl-D-aspartate (NMDA) glutamate receptor in the locomotor effects of NO, animals were pretreated with the 5-HT1A receptor agonist 8-hydroxy-2-(di-n-propylamino)tetralin (8-OH-DPAT, 8 nmol), the 5-HT1A receptor antagonist N-(2-[4-(2-methoxyphenyl)-1-piperazinyl]ethyl)-N-2-pyridinyl-cyclohexanecarboxamide maleate (WAY-100635, 0.37 nmol), and the NMDA receptor antagonist DL-2-amino-7-phosphonoheptanoic acid (AP7, 1 nmol), followed by microinjection of SIN-1 into the DRN. SIN-1 increased the distance traveled (mean ± SEM) in the open-field test (4431 ± 306.1 cm; F7,63 = 2.44, P = 0.028) and this effect was blocked by previous 8-OH-DPAT (2885 ± 490.4 cm) or AP7 (3335 ± 283.5 cm) administration (P < 0.05, Duncan test). These results indicate that 5-HT1A receptor activation and/or facilitation of glutamate neurotransmission can modulate the locomotor effects induced by NO in the DRN.

Arginine induces GH gene expression by activating NOS/NO signaling in rat isolated hemi-pituitaries

The amino acid arginine (Arg) is a recognized secretagogue of growth hormone (GH), and has been shown to induce GH gene expression. Arg is the natural precursor of nitric oxide (NO), which is known to mediate many of the effects of Arg, such as GH secretion. Arg was also shown to increase calcium influx in pituitary cells, which might contribute to its effects on GH secretion. Although the mechanisms involved in the effects of Arg on GH secretion are well established, little is known about them regarding the control of GH gene expression. We investigated whether the NO pathway and/or calcium are involved in the effects of Arg on GH gene expression in rat isolated pituitaries. To this end, pituitaries from approximately 170 male Wistar rats (~250 g) were removed, divided into two halves, pooled (three hemi-pituitaries) and incubated or not with Arg, as well as with different pharmacological agents. Arg (71 mM), the NO donor sodium nitroprusside (SNP, 1 and 0.1 mM) and a cyclic guanosine monophosphate (cGMP) analogue (8-Br-cGMP, 1 mM) increased GH mRNA expression 60 min later. The NO acceptor hemoglobin (0.3 µM) blunted the effect of SNP, and the combined treatment with Arg and L-NAME (a NO synthase (NOS) inhibitor, 55 mM) abolished the stimulatory effect of Arg on GH gene expression. The calcium channel inhibitor nifedipine (3 µM) also abolished Arg-induced GH gene expression. The present study shows that Arg directly induces GH gene expression in hemi-pituitaries isolated from rats, excluding interference from somatostatinergic neurons, which are supposed to be inhibited by Arg. Moreover, the data demonstrate that the NOS/NO signaling pathway and calcium mediate the Arg effects on GH gene expression.

Artificial neural networks (ANN): prediction of sensory measurements from instrumental data

The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.