ApoE4-Driven Accumulation of Intraneuronal Oligomerized Aβ42 following Activation of the Amyloid Cascade In Vivo Is Mediated by a Gain of Function

Activating the amyloid cascade by inhibiting the Aβ-degrading enzyme neprilysin in targeted replacement mice, which express either apoE4 or apoE3, results in the specific accumulation of oligomerized Aβ42 in hippocampal CA1 neurons of the apoE4 mice. We presently investigated the extent to which the apoE4-driven accumulation of Aβ42 and the resulting mitochondrial pathology are due to either gain or loss of function. This revealed that inhibition of neprilysin for one week triggers the accumulation of Aβ42 in hippocampal CA1 neurons of the apoE4 mice but not of either the corresponding apoE3 mice or apoE-deficient mice. At 10 days, Aβ42 also accumulated in the CA1 neurons of the apoE-deficient mice but not in those of the apoE3 mice. Mitochondrial pathology, which in the apoE4 mice is an early pathological consequence following inhibition of neprilyisn, also occurs in the apoE-deficient but not in the apoE3 mice and the magnitude of this effect correlates with the levels of accumulated Aβ42 and oligomerized Aβ42 in these mice. These findings suggest that the rate-limiting step in the pathological effects of apoE4 on CA1 neurons is the accumulation of intracellular oligomerized Aβ42 which is mediated via a gain of function property of apoE4

Loss of Dishevelleds Disrupts Planar Polarity in Ependymal Motile Cilia and Results in Hydrocephalus

Defects in ependymal (E) cells, which line the ventricle and generate cerebrospinal fluid flow through ciliary beating, can cause hydrocephalus. Dishevelled genes (Dvls) are essential for Wnt signaling and Dvl2 has been shown to localize to the rootlet of motile cilia. Using the hGFAP-Cre;Dvl1−/−;2flox/flox;3+/− mouse, we show that compound genetic ablation of Dvls causes hydrocephalus. In hGFAP-Cre;Dvl1−/−;2flox/flox;3+/− mutants, E cells differentiated normally, but the intracellular and intercellular rotational alignments of ependymal motile cilia were disrupted. As a consequence, the fluid flow generated by the hGFAP-Cre;Dvl1−/−;2flox/flox;3+/− E cells was significantly slower than that observed in control mice. Dvls were also required for the proper positioning of motile cilia on the apical surface. Tamoxifen-induced conditional removal of Dvls in adult mice also resulted in defects in intracellular rotational alignment and positioning of ependymal motile cilia. These results suggest that Dvls are continuously required for E cell planar polarity and may prevent hydrocephalus

Dual epithelial and immune cell function of Dvl1 regulates gut microbiota composition and intestinal homeostasis

Homeostasis of the gastrointestinal (GI) tract is controlled by complex interactions between epithelial and immune cells and the resident microbiota. Here, we studied the role of Wnt signaling in GI homeostasis using Disheveled 1 knockout (Dvl1-/-) mice, which display an increase in whole gut transit time. This phenotype is associated with a reduction and mislocalization of Paneth cells and an increase in CD8+ T cells in the lamina propria. Bone marrow chimera experiments demonstrated that GI dysfunction requires abnormalities in both epithelial and immune cells. Dvl1-/- mice exhibit a significantly distinct GI microbiota, and manipulation of the gut microbiota in mutant mice rescued the GI transit abnormality without correcting the Paneth and CD8+ T cell abnormalities. Moreover, manipulation of the gut microbiota in wild-type mice induced a GI transit abnormality akin to that seen in Dvl1-/- mice. Together, these data indicate that microbiota manipulation can overcome host dysfunction to correct GI transit abnormalities. Our findings illustrate a mechanism by which the epithelium and immune system coregulate gut microbiota composition to promote normal GI function

Age-dependent brain gene expression and copy number anomalies in autism suggest distinct pathological processes at young versus mature ages.

Autism is a highly heritable neurodevelopmental disorder, yet the genetic underpinnings of the disorder are largely unknown. Aberrant brain overgrowth is a well-replicated observation in the autism literature; but association, linkage, and expression studies have not identified genetic factors that explain this trajectory. Few studies have had sufficient statistical power to investigate whole-genome gene expression and genotypic variation in the autistic brain, especially in regions that display the greatest growth abnormality. Previous functional genomic studies have identified possible alterations in transcript levels of genes related to neurodevelopment and immune function. Thus, there is a need for genetic studies involving key brain regions to replicate these findings and solidify the role of particular functional pathways in autism pathogenesis. We therefore sought to identify abnormal brain gene expression patterns via whole-genome analysis of mRNA levels and copy number variations (CNVs) in autistic and control postmortem brain samples. We focused on prefrontal cortex tissue where excess neuron numbers and cortical overgrowth are pronounced in the majority of autism cases. We found evidence for dysregulation in pathways governing cell number, cortical patterning, and differentiation in young autistic prefrontal cortex. In contrast, adult autistic prefrontal cortex showed dysregulation of signaling and repair pathways. Genes regulating cell cycle also exhibited autism-specific CNVs in DNA derived from prefrontal cortex, and these genes were significantly associated with autism in genome-wide association study datasets. Our results suggest that CNVs and age-dependent gene expression changes in autism may reflect distinct pathological processes in the developing versus the mature autistic prefrontal cortex. Our results raise the hypothesis that genetic dysregulation in the developing brain leads to abnormal regional patterning, excess prefrontal neurons, cortical overgrowth, and neural dysfunction in autism

Dysregulated gene expression in top two MetaCore Map Folders in autism independent of age.

<p>Graph on left shows fold change of genes in DNA damage response and apoptosis and survival Map Folders. The third most significant map folder is depicted in Figure S3. Colors on fold change graph correspond to circles on the left depicting network maps in each category. From each category of differentially expressed genes in the all autistic vs. all control comparison (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002592#pgen.1002592.s009" target="_blank">Table S5</a>), networks were created using MetaCore Network Analysis.</p

Dysregulated gene expression in top three Map Folders in adult autistic prefrontal cortex and differentially affected M-Pathways of young and adult autistic cases.

<p>(A) Graph on the right shows fold change of genes in cell differentiation, mitogenic signaling, and apoptosis and survival Map Folders. Colors on fold change graph correspond to circles on the left depicting network maps in each category. From each category of differentially expressed genes in adult autistic vs. adult control posthoc comparison (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002592#pgen.1002592.s008" target="_blank">Table S4</a>), gene networks on the left were created using MetaCore Network Analysis. (B) Top differentially affected M-pathway comparisons of dysregulated genes between adult and young autistic cases. Bars represent significance of listed pathways. Orange = adult; blue = young; faded color = FDR>0.1 or p>0.05.</p

Dysregulated gene expression in developmental M-Pathways and Process Networks in young autistic prefrontal cortex.

<p>From differentially expressed genes of young autistic vs. young control cases (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002592#pgen.1002592.s006" target="_blank">Table S2</a>), networks were created using MetaCore Network Analysis. Colors on fold change graph correspond to circles on the right depicting network maps in each category. Yellow = differentially expressed genes in two or more functional domains. Overlapping circles = differentially expressed genes common to two domains.</p

MetaCore process networks and network maps in autism based on gene deletions located in CNVs from DLPFC, and genetic association results.

<p>Autistic cases had significant enrichment of MetaCore process networks in genes contained within total CNVs (A) and filtered CNVs (i.e., not present in the Database of Genomic Variants) using PennCNV (B) and CNVision (C). Blue bars = controls; red bars = autistic cases. (D) MetaCore network analysis of the top three enriched MetaCore process networks in (B). Symbols in (D) represent gene types or associations. Genes with blue circles were identified autistic CNVs; genes without circles were summoned by the database to complete network. Pink lines = canonical pathway connections. (E) Gene sets tested in set-based association analysis using PLINK in Broad/JHMI and CHOP datasets, number of genes in each set, database from which genes were taken and p-values of associations.</p

MetaCore Pathway Map Folders (left) and M-Pathways (right) in three ANOVA-based analyses.

<p>(A–C) MetaCore Map Folders and M-Pathways identified in posthoc young autism vs. young control analysis (p<0.05, FDR = 0.1; A), posthoc adult autism vs. adult control analysis (p<0.05, FDR = 0.1; B) and diagnosis main effect analyses comparing all autism and all control cases (Top 25; C). P-values and numbers of network objects/ratios of differentially expressed genes are shown.</p