Purpose Tyrosine kinase with immunoglobulin-like and EGF-like domains 2 (Tie2) activation in Schlemm's canal (SC) endothelium is required for the maintenance of IOP, making the angiopoietin/Tie2 pathway a target for new and potentially disease modifying glaucoma therapies. The goal of the present study was to examine the effects of a Tie2 activator, AKB-9778, on IOP and outflow function. Methods AKB-9778 effects on IOP was evaluated in humans, rabbits, and mice. Localization studies of vascular endothelial protein tyrosine phosphatase (VE-PTP), the target of AKB-9778 and a negative regulator of Tie2, were performed in human and mouse eyes. Mechanistic studies were carried out in mice, monitoring AKB-9778 effects on outflow facility, Tie2 phosphorylation, and filtration area of SC. Results AKB-9778 lowered IOP in patients treated subcutaneously for diabetic eye disease. In addition to efficacious, dose-dependent IOP lowering in rabbit eyes, topical ocular AKB-9778 increased Tie2 activation in SC endothelium, reduced IOP, and increased outflow facility in mouse eyes. VE-PTP was localized to SC endothelial cells in human and mouse eyes. Mechanistically, AKB-9778 increased the filtration area of SC for aqueous humor efflux in both wild type and in Tie2 +/− mice. Conclusions This is the first report of IOP lowering in humans with a Tie2 activator and functional demonstration of its action in remodeling SC to increase outflow facility and lower IOP in fully developed mice. Based on these studies, a phase II clinical trial is in progress to advance topical ocular AKB-9778 as a first in class, Tie2 activator for treatment for ocular hypertension and glaucoma.

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Metastatic spread of the cancer is usually the consequence of the activation of signaling pathways that generate cell motility and tissue invasion. Metastasis involves the reorganization of cytoskeleton and cell shape for the swift movement of the cells through extracellular matrix. Previously, we have described the invasive and metastatic role played by one of the members (Toca-1) of CIP4 subfamily of F-BAR proteins. In the present study, we address the role of another member (FBP17) of same family in the invasion breast cancer cells. Here, we report that the formin-binding protein 17 (FBP17) is highly expressed at both mRNA and protein levels in breast cancer cells. The study showed the association of FBP17 with cytoskeletal actin regulatory proteins like dynamin and cortactin. To determine its role in extracellular matrix (ECM) degradation, we achieved stable knockdown of FBP17 in MDA-MB-231 cells. FBP17 knockdown cells showed a defect and were found to be compromised in the degradation of ECM indicating the role of FBP17 in the invasion of breast cancer cells. Our results suggest that FBP17 is highly expressed in breast cancer cells and facilitates the invasion of breast cancer cells.

Regulatory elements play a critical role in development process of eukaryotic organisms by controlling the spatio-temporal pattern of gene expression. Enhancer is one of these elements which contributes to the regulation of gene expression through chromatin loop or eRNA expression. Experimental identification of a novel enhancer is a costly exercise, due to which there is an interest in computational approaches to predict enhancer regions in a genome. Existing computational approaches to achieve this goal have primarily been based on training of high-throughput data such as transcription factor binding sites (TFBS), DNA methylation, and histone modification marks etc. On the other hand, purely sequence based approaches to predict enhancer regions are promising as they are not biased by the complexity or context specificity of such datasets. In sequence based approaches, machine learning models are either directly trained on sequences or sequence features, to classify sequences as enhancers or non-enhancers. In this paper, we derived statistical and nonlinear dynamic features along with k-mer features from experimentally validated sequences taken from Vista Enhancer Browser through random walk model and applied different machine learning based methods to predict whether an input test sequence is enhancer or not. Experimental results demonstrate the success of proposed model based on Ensemble method with area under curve (AUC) 0.86, 0.89, and 0.87 in B cells, T cells, and Natural killer cells for histone marks dataset.

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