Regulation of vascular leak and recovery from ischemic injury by general and VE-cadherin-restricted miRNA antagonists of miR-27.

Cellular junctions are essential to the normal functioning of the endothelium and control angiogenesis, tissue leak, and inflammation. From a screen of micro RNAs (miRNAs) altered in in vitro angiogenesis, we selected a subset predicted to target junctional molecules. MiR-27a was rapidly downregulated upon stimulation of in vitro angiogenesis, and its level of expression is reduced in neovessels in vivo. The downregulation of miR-27a was essential for angiogenesis because ectopic expression of miR-27a blocked capillary tube formation and angiogenesis. MiR-27a targets the junctional, endothelial-specific cadherin, VE-cadherin. Consistent with this, vascular permeability to vascular endothelial growth factor in mice is reduced by administration of a general miR-27 inhibitor. To determine that VE-cadherin was the dominant target of miR-27a function, we used a novel technology with "Blockmirs," inhibitors that bind to the miR-27 binding site in VE-cadherin. The Blockmir CD5-2 demonstrated specificity for VE-cadherin and inhibited vascular leak in vitro and in vivo. Furthermore, CD5-2 reduced edema, increased capillary density, and potently enhanced recovery from ischemic limb injury in mice. The Blockmir technology offers a refinement in the use of miRNAs, especially for therapy. Further, targeting of endothelial junctional molecules by miRNAs has clinical potential, especially in diseases associated with vascular leak.

Active subnetwork recovery with a mechanism-dependent scoring function; with application to angiogenesis and organogenesis studies.

BACKGROUND: The learning active subnetworks problem involves finding subnetworks of a bio-molecular network that are active in a particular condition. Many approaches integrate observation data (e.g., gene expression) with the network topology to find candidate subnetworks. Increasingly, pathway databases contain additional annotation information that can be mined to improve prediction accuracy, e.g., interaction mechanism (e.g., transcription, microRNA, cleavage) annotations. We introduce a mechanism-based approach to active subnetwork recovery which exploits such annotations. We suggest that neighboring interactions in a network tend to be co-activated in a way that depends on the "correlation" of their mechanism annotations. e.g., neighboring phosphorylation and de-phosphorylation interactions may be more likely to be co-activated than neighboring phosphorylation and covalent bonding interactions. RESULTS: Our method iteratively learns the mechanism correlations and finds the most likely active subnetwork. We use a probabilistic graphical model with a Markov Random Field component which creates dependencies between the states (active or non-active) of neighboring interactions, that incorporates a mechanism-based component to the function. We apply a heuristic-based EM-based algorithm suitable for the problem. We validated our method's performance using simulated data in networks downloaded from GeneGO against the same approach without the mechanism-based component, and two other existing methods. We validated our methods performance in correctly recovering (1) the true interaction states, and (2) global network properties of the original network against these other methods. We applied our method to networks generated from time-course gene expression studies in angiogenesis and lung organogenesis and validated the findings from a biological perspective against current literature. CONCLUSIONS: The advantage of our mechanism-based approach is best seen in networks composed of connected regions with a large number of interactions annotated with a subset of mechanisms, e.g., a regulatory region of transcription interactions, or a cleavage cascade region. When applied to real datasets, our method recovered novel and biologically meaningful putative interactions, e.g., interactions from an integrin signaling pathway using the angiogenesis dataset, and a group of regulatory microRNA interactions in an organogenesis network.