No abstract

No abstract

Autophagy activation has the potential to ameliorate neurodegenerative disease phenotypes, including protein aggregation, lipid level perturbations and axonal trafficking defects. We performed a high content imaging-based screen assessing 940,000 small molecules to identify those that accelerate lipid droplet clearance. Hits were validated in diverse cell lines and by counter-screening. Of the 77 structurally diverse validated hits, 24 increase autophagy flux. Herein, we highlight CCT020312 as a mammalian target of rapamycin (mTOR) inhibitor-independent autophagy activator, which should function without compromising human immune function. CCT020312 dose-dependently reduces cytotoxic axonal mutant prion protein aggregate levels within endosomes of primary murine hippocampal neurons and normalizes axonal trafficking deficiencies. Moreover, CCT020312 robustly clears phosphorylated insoluble tau, while reducing tau-mediated neuronal stress vulnerability in patient-derived neuronal models. CCT020312 also restores lysosomal function in neurons differentiated from sporadic Alzheimer's patients' fibroblasts bearing epigenetic marks of aging. Taken together, we describe a promising strategy to uncover novel pharmacological agents that normalize cellular neurodegenerative disease pathology.

to become a significantly more valuable asset that can attract investment, partnership, licensing, and acquisition opportunities.Becoming well acquainted early on with the intricacies of the IND application and the resources available boosts the chances for authorization by building a compelling application in a time-and cost-efficient manner.

Many traditional methods for analyzing gene-gene relationships focus on positive and negative correlations, both of which are a kind of 'symmetric' relationship. However, genes can also exhibit 'asymmetric' relationships, such as 'if-then' relationships used in boolean circuits. In this paper we develop a very general method that can be used to detect biclusters within gene-expression data that involve subsets of genes which are enriched for these 'boolean-asymmetric' relationships (BARs). These BAR-biclusters can correspond to heterogeneity that is driven by asymmetric gene-gene interactions, rather than more standard symmetric interactions. We apply our method to a single-cell RNA-sequencing data-set, demonstrating that the statistically-significant BAR-biclusters indeed contain additional information not present within more traditional 'boolean-symmetric'-biclusters. For example, the BAR-biclusters involve different subsets of cells, and highlight different gene-pathways within the data-set. Moreover, by combining the boolean-asymmetric- and boolean-symmetric-signals, one can build linear classifiers which outperform those built using only traditional boolean-symmetric signals.