Description: Alzheimer disease (AD) is a degenerative tauopathy characterized by aggregation of Tau protein through the repeat domain to form intraneuronal paired helical filaments (PHFs). We report two cell models in which we control the inherent toxicity of the core Tau fragment. These models demonstrate the properties of prion-like recruitment of full-length Tau into an aggregation pathway in which template-directed, endogenous truncation propagates aggregation through the core Tau binding domain. We use these in combination with dissolution of native PHFs to quantify the activity of Tau aggregation inhibitors (TAIs). We report the synthesis of novel stable crystalline leucomethylthioninium salts (LMTX®), which overcome the pharmacokinetic limitations of methylthioninium chloride. LMTX®, as either a dihydromesylate or a dihydrobromide salt, retains TAI activity in vitro and disrupts PHFs isolated from AD brain tissues at 0.16 μm. The Ki value for intracellular TAI activity, which we have been able to determine for the first time, is 0.12 μm. These values are close to the steady state trough brain concentration of methylthioninium ion (0.18 μm) that is required to arrest progression of AD on clinical and imaging end points and the minimum brain concentration (0.13 μm) required to reverse behavioral deficits and pathology in Tau transgenic mice.

Description: Motivation : There are a number of well-established methods such as principal component analysis (PCA) for automatically capturing systematic variation due to latent variables in large-scale genomic data. PCA and related methods may directly provide a quantitative characterization of a complex biological variable that is otherwise difficult to precisely define or model. An unsolved problem in this context is how to systematically identify the genomic variables that are drivers of systematic variation captured by PCA. Principal components (PCs) (and other estimates of systematic variation) are directly constructed from the genomic variables themselves, making measures of statistical significance artificially inflated when using conventional methods due to over-fitting. Results : We introduce a new approach called the jackstraw that allows one to accurately identify genomic variables that are statistically significantly associated with any subset or linear combination of PCs. The proposed method can greatly simplify complex significance testing problems encountered in genomics and can be used to identify the genomic variables significantly associated with latent variables. Using simulation, we demonstrate that our method attains accurate measures of statistical significance over a range of relevant scenarios. We consider yeast cell-cycle gene expression data, and show that the proposed method can be used to straightforwardly identify genes that are cell-cycle regulated with an accurate measure of statistical significance. We also analyze gene expression data from post-trauma patients, allowing the gene expression data to provide a molecularly driven phenotype. Using our method, we find a greater enrichment for inflammatory-related gene sets compared to the original analysis that uses a clinically defined, although likely imprecise, phenotype. The proposed method provides a useful bridge between large-scale quantifications of systematic variation and gene-level significance analyses. Availability and implementation : An R software package, called jackstraw , is available in CRAN. Contact : jstorey@princeton.edu

Description: Understanding the differences between microarray and RNA-Seq technologies for measuring gene expression is necessary for informed design of experiments and choice of data analysis methods. Previous comparisons have come to sometimes contradictory conclusions, which we suggest result from a lack of attention to the intensity-dependent nature of variation generated by the technologies. To examine this trend, we carried out a parallel nested experiment performed simultaneously on the two technologies that systematically split variation into four stages (treatment, biological variation, library preparation and chip/lane noise), allowing a separation and comparison of the sources of variation in a well-controlled cellular system, Saccharomyces cerevisiae . With this novel dataset, we demonstrate that power and accuracy are more dependent on per-gene read depth in RNA-Seq than they are on fluorescence intensity in microarrays. However, we carried out quantitative PCR validations which indicate that microarrays may demonstrate greater systematic bias in low-intensity genes than in RNA-seq.