In vivo proton magnetic resonance spectroscopy ((1) H MRS) of outbred stock ICR male mice (originating from the Institute of Cancer Research) was used to study the brain (hippocampus) metabolic response to the pro-inflammatory stimulus and to the acute deficiency of the available energy, which was confirmed by measuring the maximum oxygen consumption. Inhibition of glycolysis by means of an injection with 2-deoxy-d-glucose (2DG) reduced the levels of gamma-aminobutyric acid (GABA, p < 0.05, in comparison with control, least significant difference (LSD) test), N-acetylaspartate (NAA, p < 0.05, LSD test) and choline compounds, and at the same time increased the levels of glutamate and glutamine. An opposite effect was found after injection with bacterial lipopolysaccharide (LPS) - a very common pro-inflammatory inducer. An increase in the amounts of GABA, NAA and choline compounds in the brain occurred in mice treated with LPS. Different metabolic responses to the energy deficiency and the pro-inflammatory stimuli can explain the contradictory results of the brain (1) H MRS studies under neurodegenerative pathology, which is accompanied by both mitochondrial dysfunction and inflammation. The prevalence of the excitatory metabolites such as glutamate and glutamine in 2DG treated mice is in good agreement with excitation observed during temporary reduction of the available energy under acute hypoxia or starvation. In turn, LPS, as an inducer of the sickness behavior, which was manifested as depression, sleepiness, loss of appetite etc., shifts the brain metabolic pattern toward the prevalence of the inhibitory neurotransmitter GABA.

Network mechanisms of depression development and especially of improvement from nonpharmacological treatment remain understudied. The current study is aimed at examining brain networks functional connectivity in depressed patients and its dynamics in nonpharmacological treatment. Resting state fMRI data of 21 healthy adults and 51 patients with mild or moderate depression were analyzed with spatial independent component analysis; then, correlations between time series of the components were calculated and compared between-group (study 1). Baseline and repeated-measure data of 14 treated (psychotherapy or fMRI neurofeedback) and 15 untreated depressed participants were similarly analyzed and correlated with changes in depression scores (study 2). Aside from diverse findings, studies 1 and 2 both revealed changes in within-default mode network (DMN) and DMN to executive control network (ECN) connections. Connectivity in one pair, initially lower in depression, decreased in no treatment group and was inversely correlated with Montgomery-Asberg depression score change in treatment group. Weak baseline connectivity in this pair also predicted improvement on Montgomery-Asberg scale in both treatment and no treatment groups. Coupling of another pair, initially stronger in depression, increased in therapy though was unrelated to improvement. The results demonstrate possible role of within-DMN and DMN-ECN functional connectivity in depression treatment and suggest that neural mechanisms of nonpharmacological treatment action may be unrelated to normalization of initially disrupted connectivity.

BackgroundAn important issue in the target identification for the drug design is the tissue-specific effect of inhibition of target genes. The task of assessing the tissue-specific effect in suppressing gene activity is especially relevant in the studies of the brain, because a significant variability in gene expression levels among different areas of the brain was well documented.ResultsA method is proposed for constructing statistical models to predict the potential effect of the knockout of target genes on the expression of genes involved in the regulation of apoptosis in various brain regions. The model connects the expression of the objective group of genes with expression of the target gene by means of machine learning models trained on available expression data. Information about the interactions between target and objective genes is determined by reconstruction of target-centric gene network. STRING and ANDSystem databases are used for the reconstruction of gene networks. The developed models have been used to analyse gene knockout effects of more than 7,500 target genes on the expression of 1,900 objective genes associated with the Gene Ontology category "apoptotic process". The tissue-specific effect was calculated for 12 main anatomical structures of the human brain. Initial values of gene expression in these anatomical structures were taken from the Allen Brain Atlas database. The results of the predictions of the effect of suppressing the activity of target genes on apoptosis, calculated on average for all brain structures, were in good agreement with experimental data on siRNA-inhibition.ConclusionsThis theoretical paper presents an approach that can be used to assess tissue-specific gene knockout effect on gene expression of the studied biological process in various structures of the brain. Genes that, according to the predictions of the model, have the highest values of tissue-specific effects on the apoptosis network can be considered as potential pharmacological targets for the development of drugs that would potentially have strong effect on the specific area of the brain and a much weaker effect on other brain structures. Further experiments should be provided in order to confirm the potential findings of the method.