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Biomedical subjects

Adam Gates

Publications and source records attributed to Adam Gates.

4 recordsLinked to original sources

LRRTM3 promotes processing of amyloid-precursor protein by BACE1 and is a positional candidate gene for late-onset Alzheimer's disease.

Rare familial forms of Alzheimer's disease (AD) are thought to be caused by elevated proteolytic production of the Abeta42 peptide from the beta-amyloid-precursor protein (APP). Although the pathogenesis of the more common late-onset AD (LOAD) is not understood, BACE1, the protease that cleaves APP to generate the N terminus of Abeta42, is more active in patients with LOAD, suggesting that increased amyloid production processing might also contribute to the sporadic disease. Using high-throughput siRNA screening technology, we assessed 15,200 genes for their role in Abeta42 secretion and identified leucine-rich repeat transmembrane 3 (LRRTM3) as a neuronal gene that promotes APP processing by BACE1. siRNAs targeting LRRTM3 inhibit the secretion of Abeta40, Abeta42, and sAPPbeta, the N-terminal APP fragment produced by BACE1 cleavage, from cultured cells and primary neurons by up to 60%, whereas overexpression increases Abeta secretion. LRRTM3 is expressed nearly exclusively in the nervous system, including regions affected during AD, such as the dentate gyrus. Furthermore, LRRTM3 maps to a region of chromosome 10 linked to both LOAD and elevated plasma Abeta42, and is structurally similar to a family of neuronal receptors that includes the NOGO receptor, an inhibitor of neuronal regeneration and APP processing. Thus, LRRTM3 is a functional and positional candidate gene for AD, and, given its receptor-like structure and restricted expression, a potential therapeutic target.

Alzheimer Disease↗

A genome wide analysis of ubiquitin ligases in APP processing identifies a novel regulator of BACE1 mRNA levels.

Proteolysis of beta-amyloid precursor protein (APP) into amyloid beta peptide (Abeta) by beta- and gamma-secretases is a critical step in the pathogenesis of Alzheimer's Disease (AD), but the pathways regulating secretases are not fully characterized. Ubiquitinylation, which is dysregulated in AD, may affect APP processing. Here, we describe a screen for APP processing modulators using an siRNA library targeting 532 predicted ubiquitin ligases. Seven siRNA pools diminished Abeta production. Of these, siRNAs targeting PPIL2 (hCyp-60) suppressed beta-site cleavage. Knockdown of PPIL2 mRNA decreased BACE1 mRNA, while overexpression of PPIL2 cDNA enhanced BACE1 mRNA levels. Microarray analysis of PPIL2 or BACE1 knockdown indicated that genes affected by BACE1 knockdown are a subset of those dependent upon PPIL2; suggesting that BACE1 expression is downstream of PPIL2. The association of PPIL2 with BACE expression and its requirement for Abeta production suggests new approaches to discover disease modifying agents for AD.

Amyloid Precursor Protein Secretases↗

3-(1,1-dioxo-2H-(1,2,4)-benzothiadiazin-3-yl)-4-hydroxy-2(1H)-quinolinones, potent inhibitors of hepatitis C virus RNA-dependent RNA polymerase.

Recently, we disclosed a new class of HCV polymerase inhibitors discovered through high-throughput screening (HTS) of the GlaxoSmithKline proprietary compound collection. This interesting class of 3-(1,1-dioxo-2H-1,2,4-benzothiadiazin-3-yl)-4-hydroxy-2(1H)-quinolinones potently inhibits HCV polymerase enzymatic activity and inhibits the ability of the subgenomic HCV replicon to replicate in Huh-7 cells. This report will focus on the structure-activity relationships (SAR) of substituents on the quinolinone ring, culminating in the discovery of 1-(2-cyclopropylethyl)-3-(1,1-dioxo-2H-1,2,4-benzothiadiazin-3-yl)-6-fluoro-4-hydroxy-2(1H)-quinolinone (130), an inhibitor with excellent potency in biochemical and cellular assays possessing attractive molecular properties for advancement as a clinical candidate. The potential for development and safety assessment profile of compound 130 will also be discussed.

Animals↗

Robust statistical methods for hit selection in RNA interference high-throughput screening experiments.

RNA interference (RNAi) high-throughput screening (HTS) experiments carried out using large (>5000 short interfering [si]RNA) libraries generate a huge amount of data. In order to use these data to identify the most effective siRNAs tested, it is critical to adopt and develop appropriate statistical methods. To address the questions in hit selection of RNAi HTS, we proposed a quartile-based method which is robust to outliers, true hits and nonsymmetrical data. We compared it with the more traditional tests, mean +/- k standard deviation (SD) and median +/- 3 median of absolute deviation (MAD). The results suggested that the quartile-based method selected more hits than mean +/- k SD under the same preset error rate. The number of hits selected by median +/- k MAD was close to that by the quartile-based method. Further analysis suggested that the quartile-based method had the greatest power in detecting true hits, especially weak or moderate true hits. Our investigation also suggested that platewise analysis (determining effective siRNAs on a plate-by-plate basis) can adjust for systematic errors in different plates, while an experimentwise analysis, in which effective siRNAs are identified in an analysis of the entire experiment, cannot. However, experimentwise analysis may detect a cluster of true positive hits placed together in one or several plates, while platewise analysis may not. To display hit selection results, we designed a specific figure called a plate-well series plot. We thus suggest the following strategy for hit selection in RNAi HTS experiments. First, choose the quartile-based method, or median +/- k MAD, for identifying effective siRNAs. Second, perform the chosen method experimentwise on transformed/normalized data, such as percentage inhibition, to check the possibility of hit clusters. If a cluster of selected hits are observed, repeat the analysis based on untransformed data to determine whether the cluster is due to an artifact in the data. If no clusters of hits are observed, select hits by performing platewise analysis on transformed data. Third, adopt the plate-well series plot to visualize both the data and the hit selection results, as well as to check for artifacts.

Cluster Analysis↗