Statistical modeling of RNA structure profiling experiments enables parsimonious reconstruction of structure landscapes.

RNA plays key regulatory roles in diverse cellular processes, where its functionality often derives from folding into and converting between structures. Many RNAs further rely on co-existence of alternative structures, which govern their response to cellular signals. However, characterizing heterogeneous landscapes is difficult, both experimentally and computationally. Recently, structure profiling experiments have emerged as powerful and affordable structure characterization methods, which improve computational structure prediction. To date, efforts have centered on predicting one optimal structure, with much less progress made on multiple-structure prediction. Here, we report a probabilistic modeling approach that predicts a parsimonious set of co-existing structures and estimates their abundances from structure profiling data. We demonstrate robust landscape reconstruction and quantitative insights into structural dynamics by analyzing numerous data sets. This work establishes a framework for data-directed characterization of structure landscapes to aid experimentalists in performing structure-function studies

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

The organization and mining of malaria genomic and post-genomic data is highly motivated by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should therefore be as reliable and versatile as possible. In this context, we examined five aspects of the organization and mining of malaria genomic and post-genomic data: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Progresses toward a grid-enabled chemogenomic knowledge space are discussed.Comment: 43 pages, 4 figures, to appear in Malaria Journa

Flow dynamics control the effect of sphingosine-1-phosphate on endothelial permeability in a microfluidic vessel bifurcation model

Blood vessels are lined by endothelial cells that form a semipermeable barrier to restrict fluid flow across the vessel wall. The endothelial barrier is known to respond to various molecular mechanisms, but the effects of mechanical signals that arise due to blood flow remain poorly understood. Here, we report a microfluidic model that mimics the flow conditions and endothelial/extracellular matrix (ECM) architecture of a vessel bifurcation to enable systematic investigation of how flow dynamics that arise within bifurcating vessels guides the endothelial response to biochemical signals. Applying the strengths of our system, we further investigate the endothelial response to sphingosine-1-phosphate, a bioactive lipid that has demonstrated flow-dependent regulation of vascular permeability. We demonstrate that bifurcated fluid flow (BFF) that arises at the base of vessel bifurcations and laminar shear stress (LSS) that arises along downstream vessel walls induce a decrease in endothelial permeability. Furthermore, we identify that flow-dynamics and chaperone proteins regulate the endothelial response to S1P. Through pharmacological inhibition of S1P receptors 1 and 2, we report ligand-independent mechanical activation of S1P receptors 1 and 2, providing support for the role of G protein-coupled receptors as mechanosensors. These findings introduce BFF as an important regulator of vascular permeability, and establish flow dynamics as a determinant of the endothelial response to S1P.Pelotonia Fellowship ProgramBarry M. Goldwater Excellence in Education FoundationThe Ohio State University College of EngineeringA one-year embargo was granted for this item.Academic Major: Biomedical Engineerin