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The genetic associations of TREM2 loss-of-function variants with Alzheimer disease (AD) indicate the protective roles of microglia in AD pathogenesis. Functional deficiencies of TREM2 disrupt microglial clustering around amyloid β (Aβ) plaques, impair their transcriptional response to Aβ, and worsen neuritic dystrophy. However, the molecular mechanism underlying these phenotypes remains unclear. In this study, we investigated the pathological role of another AD risk gene, INPP5D, encoding a phosphoinositide PI(3,4,5)P3 phosphatase expressed in microglia. In a Tyrobp-deficient TREM2 loss-of-function mouse model, Inpp5d haplodeficiency restored the association of microglia with Aβ plaques, partially restored plaque compaction and astrogliosis, and reduced phosphorylated tau+ dystrophic neurites. RNA-sequencing of isolated microglia demonstrated that the expression of “disease-associated microglia” genes is not responsible for these beneficial effects. Our results suggest that INPP5D acts downstream of TREM2/TYROBP to regulate the microglial barrier against Aβ toxicity, thereby modulates the pathological progression from Aβ deposition to tau pathology.

Here, we review the genetic risk factors for late onset Alzheimer's disease (AD) and their role in AD pathogenesis. Recent advances in our understanding of the human genome, namely technological advances in methods to analyze millions of polymorphisms in thousands of subjects, have revealed new genes associated with AD risk: ABCA7, BIN1, CASS4, CD33, CD2AP, CELF1, CLU, CR1, DSG2, EPHA1, FERMT2, HLA-DRB5-DBR1, INPP5D, MS4A, MEF2C, NME8, PICALM, PTK2B, SLC24H4 RIN3, SORL1, ZCWPW1. Emerging technologies to analyze the entire genome in large datasets have also revealed coding variants that increase AD risk: PLD3 and TREM2. We review the relationship between these AD risk genes and the cellular and neuropathological features of AD. Together, understanding the mechanisms underlying the association of these genes with risk for disease will provide the most meaningful targets for therapeutic development to date.

The herbicide linuron (LIN) is an endocrine disruptor with an anti-androgenic mode of action. The objectives of this study were to (1) improve knowledge of androgen and anti-androgen signaling in the teleostean ovary and to (2) assess the ability of gene networks and machine learning to classify LIN as an anti-androgen using transcriptomic data. Ovarian explants from vitellogenic fathead minnows (FHMs) were exposed to three concentrations of either 5α-dihydrotestosterone (DHT), flutamide (FLUT), or LIN for 12h. Ovaries exposed to DHT showed a significant increase in 17β-estradiol (E2) production while FLUT and LIN had no effect on E2. To improve understanding of androgen receptor signaling in the ovary, a reciprocal gene expression network was constructed for DHT and FLUT using pathway analysis and these data suggested that steroid metabolism, translation, and DNA replication are processes regulated through AR signaling in the ovary. Sub-network enrichment analysis revealed that FLUT and LIN shared more regulated gene networks in common compared to DHT. Using transcriptomic datasets from different fish species, machine learning algorithms classified LIN successfully with other anti-androgens. This study advances knowledge regarding molecular signaling cascades in the ovary that are responsive to androgens and anti-androgens and provides proof of concept that gene network analysis and machine learning can classify priority chemicals using experimental transcriptomic data collected from different fish species.

The action of lipid phosphatases on inositol phosphates is thought to be one method that the cell uses to attenuate growth factor- or cytokine-induced mobilization of calcium. We have cloned a cDNA from a mouse spleen library that is virtually identical to the recently described inositol tetraphosphate and phosphatidylinositol 3,4,5-trisphosphate 5-phosphatase, SHIP. Chromosomal localization studies in human and mouse by FISH mapping and interspecies backcrossing in mice have demonstrated that human SHIP is localized to 2q36-q37.1 and that mouse SHIP is localized to 1C5.