市民社会のプレーヤー交代劇と政治-仁平典宏の<贈与のパラドックス>論を読み解く-

千葉大学大学院人文社会科学研究科研究プロジェクト報告書第257集『都市コミュニティにおける相互扶助と次世代育成』水島治郎 編Sustainable Urban Communities: Communality and Generativity Report on the Research Projects No.25

Additional file 4: of Impacts of incorporating personal genome sequencing into graduate genomics education: a longitudinal study over three course years

Questionnaire administered at time point T4. (PDF 791 kb

Additional file 2: of Impacts of incorporating personal genome sequencing into graduate genomics education: a longitudinal study over three course years

Questionnaire administered at time points T1 and T2. (PDF 680 kb

Additional file 3: of Impacts of incorporating personal genome sequencing into graduate genomics education: a longitudinal study over three course years

Questionnaire administered at time point T3. (PDF 725 kb

Systems Genetic Analyses Highlight a TGFβ-FOXO3 Dependent Striatal Astrocyte Network Conserved across Species and Associated with Stress, Sleep, and Huntington’s Disease

<div><p>Recent systems-based analyses have demonstrated that sleep and stress traits emerge from shared genetic and transcriptional networks, and clinical work has elucidated the emergence of sleep dysfunction and stress susceptibility as early symptoms of Huntington's disease. Understanding the biological bases of these early non-motor symptoms may reveal therapeutic targets that prevent disease onset or slow disease progression, but the molecular mechanisms underlying this complex clinical presentation remain largely unknown. In the present work, we specifically examine the relationship between these psychiatric traits and Huntington's disease (HD) by identifying striatal transcriptional networks shared by HD, stress, and sleep phenotypes. First, we utilize a systems-based approach to examine a large publicly available human transcriptomic dataset for HD (GSE3790 from GEO) in a novel way. We use weighted gene coexpression network analysis and differential connectivity analyses to identify transcriptional networks dysregulated in HD, and we use an unbiased ranking scheme that leverages both gene- and network-level information to identify a novel astrocyte-specific network as most relevant to HD caudate. We validate this result in an independent HD cohort. Next, we computationally predict FOXO3 as a regulator of this network, and use multiple publicly available in vitro and in vivo experimental datasets to validate that this astrocyte HD network is downstream of a signaling pathway important in adult neurogenesis (TGFβ-FOXO3). We also map this HD-relevant caudate subnetwork to striatal transcriptional networks in a large (n = 100) chronically stressed (B6xA/J)F2 mouse population that has been extensively phenotyped (328 stress- and sleep-related measurements), and we show that this striatal astrocyte network is correlated to sleep and stress traits, many of which are known to be altered in HD cohorts. We identify causal regulators of this network through Bayesian network analysis, and we highlight their relevance to motor, mood, and sleep traits through multiple in silico approaches, including an examination of their protein binding partners. Finally, we show that these causal regulators may be therapeutically viable for HD because their downstream network was partially modulated by deep brain stimulation of the subthalamic nucleus, a medical intervention thought to confer some therapeutic benefit to HD patients. In conclusion, we show that an astrocyte transcriptional network is primarily associated to HD in the caudate and provide evidence for its relationship to molecular mechanisms of neural stem cell homeostasis. Furthermore, we present a unified systems-based framework for identifying gene networks that are associated with complex non-motor traits that manifest in the earliest phases of HD. By analyzing and integrating multiple independent datasets, we identify a point of molecular convergence between sleep, stress, and HD that reflects their phenotypic comorbidity and reveals a molecular pathway involved in HD progression.</p></div

Outline of the integrative analysis framework for relating sleep and stress (non-motor) traits to specific HD-associated transcriptional networks in the striatum.

<p>HD-relevant gene networks were identified in a human HD cohort (GSE3790) and unbiasedly mapped to gene networks independently identified in a (B6xA/J)F2 mouse population associated with stress susceptibility and sleep. This strategy revealed a shared gene network associated with sleep, stress, and HD.</p

Network-specific pathology and functional characterization of CN Thistle2 module.

<p>(A,B) Differential connectivity analysis reveals network-level alterations (light purple) that were not observed by previous differential expression analysis in the same cohort¹ (dark purple). (B) Venn diagrams depict the number of genes identified by differential connectivity (light purple) and differential expression analyses (dark purple), as well as their overlap. (C) CN modules showing enrichment for previously published cell-type specific gene signatures identified by FACS (F) and in situ hybridization (I) experiments. Fisher’s exact test odds ratios are plotted only for modules with P < 0.05, two-sided, Bonferroni corrected. (D) Circos plot depicting FOXO3 as the top TF associated with Thistle2 in CN; rings are numbered 1 (outermost) to 5 (innermost). TF binding site enrichment scores are depicted in rings 2, 3, and 4 (Z score, Fisher’s score, and Composite Rank, respectively). Ring 5 depicts the differential expression profile of each TF in HD (-log10(P)). Blue histogram height (ring 1) reflects the cumulative scores of each TF based upon rings 2–5, with taller heights depicts greater relevance to Thistle2.</p

Candidate causal regulators of the Blue-mmSS module are upstream of HD-relevant nodes.

<p>(A) Bayesian network reconstruction of the Blue-mmSS reveals 26 CCRs (large labeled nodes). (B) Genes in Thistle2-hsHD, especially those with FOXO3 binding sites, are significantly overrepresented in the Blue-mmSS downstream network. Significance threshold (red line): P = 0.01, two-sided. (C) Enrichment of the causal regulator PIN for huntingtin protein binding partners identified by affinity purification-mass spectrometry (AP-MS) and yeast two-hybrid (Y2H) methods, and for genes necessary for voluntary movement and affective behavior. Significance thresholds (red line): P = 0.01, two-sided; Odds Ratio (95% Confidence Interval) = 2. (D) Drugs that concordantly upregulate (Drug-Causal Regulator Association Score > 0) and downregulate (Drug-Causal Reulgator Association Score < 0) Blue-mmSS CCRs. All drugs shown have Benjamini-Hochberg adjusted P < 0.05.</p

Conservation of the HD-associated Thistle2 module in mmSS cohort.

<p>(A) Scatterplot of enrichment results for Thistle2-hsHD, depicting its conservation in the Blue module of the (B6xA/J)F2 mouse cohort. Significance thresholds (red lines): Odds Ratio = 2 and P = 0.05, two-sided, Bonferroni corrected. (B) Barplots showing module-trait associations between the Blue module and several sleep and stress traits measured in the mouse cohort. Module relevance to phenotype categories are based upon significant module-trait correlations. Module-trait associations above the horizontal red line are the top ranked relationships (belonging to the top quartile).</p

Coexpression networks in the CN, CB, and CTX of a human HD cohort.

<p>Topological overlap (TO) matrix plot depicts gene coexpression networks in (A) CN, (C) CB, and (E) CTX. (B,D,F) A comparison of TO matrices in cases (top right triangle) versus controls (bottom left triangle) for selected modules in each brain region. High TO (greater coexpression) is colored red, while low TO is colored white. Module differential connectivity (MDC) and FDR values are depicted for each module. Differential connectivity was considered significant by conservative thresholds (MDC > 2.0 or MDC < 0.5, FDR < 0.001). Three differentially connected modules and one conserved module are depicted for each brain region.</p