A novel analytical brain block tool to enable functional annotation of discriminatory transcript biomarkers among discrete regions of the fronto-limbic circuit in primate brain

Aberrant topology of white matter networks in patients with methamphetamine dependence and its application in support vector machine-based classification

Brain white matter (WM) networks have been widely studied in neuropsychiatric disorders. However, few studies have evaluated alterations in WM network topological organization in patients with methamphetamine (MA) dependence. Therefore, using machine learning classification methods to analyze WM network topological attributes may give new insights into patients with MA dependence. In the study, diffusion tensor imaging-based probabilistic tractography was used to map the weighted WM networks in 46 MA-dependent patients and 46 control subjects. Using graph-theoretical analyses, the global and regional topological attributes of WM networks for both groups were calculated and compared to determine inter-group differences using a permutation-based general linear model. In addition, the study used a support vector machine (SVM) learning approach to construct a classifier for discriminating subjects with MA dependence from control subjects. Relative to the control group, the MA-dependent group exhibited abnormal topological organization, as evidenced by decreased small-worldness and modularity, and increased nodal efficiency in the right medial superior temporal gyrus, right pallidum, and right ventromedial putamen; the MA-dependent group had the higher hubness scores in 25 regions, which were mainly located in the default mode network. An SVM trained with topological attributes achieved classification accuracy, sensitivity, specificity, and kappa values of 98.09% ± 2.59%, 98.24% ± 4.00%, 97.94% ± 4.26%, and 96.18% ± 5.19% for patients with MA dependence. Our results may suggest altered global WM structural networks in MA-dependent patients. Furthermore, the abnormal WM network topological attributes may provide promising features for the construction of high-efficacy classification models.

RNA-sequencing of AVPV and ARH reveals vastly different temporal and transcriptomic responses to estradiol in the female rat hypothalamus

In females, estrogens have two main modes of action relating to gonadotropin secretion: positive feedback and negative feedback. Estrogen positive and negative feedback are controlled by different regions of the hypothalamus: the preoptic area/anterior portion (mainly the anteroventral periventricular nucleus, AVPV) of the hypothalamus is associated with estrogen positive feedback while the mediobasal hypothalamus (mainly the arcuate nucleus of the hypothalamus, ARH), is associated with estrogen negative feedback. In this study, we examined the temporal pattern of gene transcription in these two regions following estrogen treatment. Adult, ovariectomized, Long Evans rats received doses of estradiol benzoate (EB) or oil every 4 days for 3 cycles. On the last EB priming cycle, hypothalamic tissues were dissected into the AVPV+ and ARH+ at 0 hrs (baseline/oil control), 6 hrs, or 24 hrs after EB treatment. RNA was extracted and sequenced using bulk RNA sequencing. Differential gene analysis, gene ontology, and weighted correlation network analysis (WGCNA) was performed. Overall, we found that the AVPV+ and ARH+ respond differently to estradiol stimulation. In both regions, estradiol treatment resulted in more gene up-regulation than down-regulation. S100g was very strongly up-regulated by estradiol in both regions at 6 and 24 hrs after EB treatment. In the AVPV+ the highest number of differentially expressed genes occurred 24 hrs after EB. In the ARH+, the highest number of genes differentially expressed by EB occurred between 6 and 24 hrs after EB, while in the AVPV+, the fewest genes changed their expression between these time points, demonstrating a temporal difference in the way that EB regulates transcription these two areas. Several genes strongly implicated in gonadotropin release were differentially affected by estradiol including Esr1, encoding estrogen receptor-α and Kiss1, encoding kisspeptin. As an internal validation, Kiss1 was up-regulated in the AVPV+ and down-regulated in the ARH+. Gene network analysis revealed the vastly different clustering of genes modulated by estradiol in the AVPV+ compared with the ARH+. These results indicate that gene expression in these two hypothalamic regions have specific responses to estradiol in timing and direction.

Sleep Deprivation Alters the Pituitary Stress Transcriptome in Male and Female Mice

Poor sleep hygiene is a growing problem, with detrimental effects on many biological systems. The pituitary gland plays a crucial role in the regulation of sleep and the stress response, and its dysfunction leads to sleep-related disorders. However, the interaction between these critical functions remains unclear. Thus, we performed a comparative, whole-transcriptome, analysis to identify stress-induced genes and relevant pathways that may be affected by sleep deprivation. One day following 12 h of Paradoxical Sleep Deprivation (PSD), mice were restrained for 20 min. Gene expression changes in the pituitary were assessed via RNA-Seq and Gene Ontology in PSD and/or restrained groups compared to controls. We show that restraint triggers transcriptional responses involved in hormone secretion, the glucocorticoid response, and apoptosis in both sexes, with 285 differentially expressed genes in females and 93 in males. When PSD preceded restraint stress, the numbers of differentially expressed genes increased to 613 in females and 580 in males. The pituitary transcriptome of restraint+PSD animals was enriched for microglia and macrophage proliferation, cellular response to corticosteroids, and apoptosis, among others. Finally, we identify sex-specific differences in restraint-induced genes following PSD. These findings provide genetic targets to consider when studying sleep and the response to stress.

"Soldier's Heart": A Genetic Basis for Elevated Cardiovascular Disease Risk Associated with Post-traumatic Stress Disorder

"Soldier's Heart," is an American Civil War term linking post-traumatic stress disorder (PTSD) with increased propensity for cardiovascular disease (CVD). We have hypothesized that there might be a quantifiable genetic basis for this linkage. To test this hypothesis we identified a comprehensive set of candidate risk genes for PTSD, and tested whether any were also independent risk genes for CVD. A functional analysis algorithm was used to identify associated signaling networks. We identified 106 PTSD studies that report one or more polymorphic variants in 87 candidate genes in 83,463 subjects and controls. The top upstream drivers for these PTSD risk genes are predicted to be the glucocorticoid receptor (NR3C1) and Tumor Necrosis Factor alpha (TNFA). We find that 37 of the PTSD candidate risk genes are also candidate independent risk genes for CVD. The association between PTSD and CVD is significant by Fisher's Exact Test (P = 3 × 10-54). We also find 15 PTSD risk genes that are independently associated with Type 2 Diabetes Mellitus (T2DM; also significant by Fisher's Exact Test (P = 1.8 × 10-16). Our findings offer quantitative evidence for a genetic link between post-traumatic stress and cardiovascular disease, Computationally, the common mechanism for this linkage between PTSD and CVD is innate immunity and NFκB-mediated inflammation.

Targeting Retinoid Receptors to Treat Schizophrenia: Rationale and Progress to Date

This review provides the rationale and reports on the progress to date regarding the targeting of retinoid receptors for the treatment of schizophrenia and schizoaffective disorder and the role of retinoic acid in functions of the normal brain, and in psychotic states. After a brief introduction, we describe the normal function of retinoic acid in the brain. We then examine the evidence regarding retinoid dysregulation in schizophrenia. Finally, findings from two add-on clinical trials with a retinoid (bexarotene) are discussed. The authors of this review suggest that targeting retinoid receptors may be a novel approach to treat schizophrenia and schizoaffective disorder. Further studies are warranted.