Ensemble-Based Network Aggregation Improves the Accuracy of Gene Network Reconstruction

<div><p>Reverse engineering approaches to constructing gene regulatory networks (GRNs) based on genome-wide mRNA expression data have led to significant biological findings, such as the discovery of novel drug targets. However, the reliability of the reconstructed GRNs needs to be improved. Here, we propose an ensemble-based network aggregation approach to improving the accuracy of network topologies constructed from mRNA expression data. To evaluate the performances of different approaches, we created dozens of simulated networks from combinations of gene-set sizes and sample sizes and also tested our methods on three <i>Escherichia coli</i> datasets. We demonstrate that the ensemble-based network aggregation approach can be used to effectively integrate GRNs constructed from different studies – producing more accurate networks. We also apply this approach to building a network from epithelial mesenchymal transition (EMT) signature microarray data and identify hub genes that might be potential drug targets. The R code used to perform all of the analyses is available in an R package entitled “ENA”, accessible on CRAN (<a href="http://cran.r-project.org/web/packages/ENA/" target="_blank">http://cran.r-project.org/web/packages/ENA/</a>).</p></div

Receiver Operating Characteristic (ROC) curves and the Precision Recall Curve both demonstrate the performance of the SPACE algorithm on the 231-gene network with 20 samples and a noise value of 0.25 when performing a single iteration (<i>i.e.</i>, “non-bootstrapped”) or bootstrapping the dataset using the Ensemble Network Aggregation approach.

<p>In this case, the Area Under the ROC Curve (AUC) of the non-bootstrapped SPACE method was 0.748, while that of the bootstrapped SPACE method was 0.816. The Area Under the Precision-Recall (AUPR) curve also improves from 0.249 (SPACE) to 0.273 (bootstrapping).</p

Comparison of the Area Under the Curves (AUCs) of the re-constructed networks from the 231-gene network with a noise value of 0.25 and different sample sizes (20, 50 or 100) for SPACE (a.), GeneNet (b.), and WGCNA (c.).

<p>In these plots, the y-axis shows the performance of the reconstructed network, measured by the AUCs; a horizontal line is drawn to represent the AUC of the non-bootstrapped reconstruction (a single reconstruction using all available samples). The x-axis represents the number of iterations in the bootstrapping process. Points below the horizontal line represent a loss in accuracy of the reconstructed networks, and points above the horizontal line represent a gain of AUC (<i>i.e.</i>, an increase in model performance).</p

The AUCs of the generated networks when executed on the E. coli datasets.

<p>Note that the aggregating ENA networks from SPACE, WGCNA and GeneNet increase the accuracy within each individual dataset, and aggregating results from three datasets further increases the accuracy beyond that of any one dataset.</p

Network reconstruction (based on a previous epithelial-to-mesenchymal transition gene signature) [34] via ENA identifies potential drug targets for non-small-cell lung cancer (NSCLC).

<p>Microarray data from 54 NSCLC cell lines were analyzed using four different methods and the results integrated via ENA. Identified hub genes <i>ZEB1</i>, <i>MARVELD3</i> and <i>EPHA1</i> have interesting clinical implications as novel drug targets. Node color and size are proportional to the degree of connectivity (<i>i.e.</i>, the number of edges connecting each node).</p

The ROC curves of different approaches to reconstruct the gene network based on three simulated datasets.

<p>The ENA approach outperformed the alternative approach of simply combining the expression into a single dataset and individual network with increasing noise of 0.25, 1, and 2. AUCs of all five approaches are 0.98, 0.96, 0.96, 0.96, and 0.89 respectively.</p

The effect of network size on ENA performance.

<p>The y-axis represents the improvement in AUC of the bootstrapped SPACE networks vs. the non-bootstrapped SPACE networks. Different bars represent different sizes of networks in the simulation study.</p

Surface Wettability of Macroporous Anodized Aluminum Oxide

The correlation between the structural characteristics and the wetting of anodized aluminum oxide (AAO) surfaces with large pore sizes (>100 nm) is discussed. The roughness-induced wettability is systematically examined for oxide films grown by a two-step, high-field anodization in phosphoric acid of three different concentrations using a commercial aluminum alloy. This is done for the as-synthesized AAO layers, after various degrees of pore widening by a wet chemical etching in phosphoric acid solution, and upon surface modification by either Lauric acid or a silane. The as-grown AAO films feature structurally disordered pore architectures with average pore openings in the range 140–190 nm but with similar interpore distances of about 405 nm. The formation of such AAO structures induces a transition from slightly hydrophilic to moderately hydrophobic surfaces up to film thicknesses of about 6 μm. Increased hydrophobicity is obtained by pore opening and a maximum value of the water contact angle (WCA) of about 128° is measured for AAO arrays with a surface porosity close to 60%. Higher surface porosity by prolonged wet chemical etching leads to a rapid decrease in the WCA as a result of the limited pore wall thickness and partial collapse of the dead-end pore structures. Modification of the AAO surfaces by Lauric acid results in 5–30° higher WCA’s, whereas near-superhydrophobicity (WCA ∼146°) is realized through silane coating. The “rose petal effect” of strongly hydrophobic wetting with high adhesive force on the produced AAO surfaces is explained by a partial penetration of water through capillary action into the dead-end pore cavities which leads to a wetting state in-between the Wenzel and Cassie states. Moreover, practical guidelines for the synthesis of rough, highly porous AAO structures with controlled wettability are provided and the possibility of forming superhydrophobic surfaces is evaluated

The performance in aggregating different methods.

<p>A comparison of the accuracy of the reconstructed networks using the datasets containing 200 samples (left) and 1,000 samples (right) from the 83-gene network with a noise value of 0.25. As can be seen here, the ensemble network aggregation approach performs better than any of the other individual techniques on these two networks.</p

Synthesis of <i>o</i>‑Alkenylated 2‑Arylbenzoxazoles via Rh-Catalyzed Oxidative Olefination of 2‑Arylbenzoxazoles: Scope Investigation, Structural Features, and Mechanism Studies

2-Arylbenzazoles are promising molecules for potential applications in medicine and material areas. Efficient protocols for direct regioselective functionalization of 2-arylbenzoxazoles are in high demand. Herein, we disclose a general method for selective <i>ortho</i>-olefination of 2-arylbenzo­[<i>d</i>]­oxazoles with alkenes enabled by versatile Cp*Rh­(III) in high yields. This protocol features broad functional group tolerance and high regioselectivity. Intermolecular competition studies and kinetic isotope effect experiments imply that the oxidative olefination process occurs via an electrophilic C–H activation pathway. The molecular structure of the <i>m</i>-fluoro-substituted olefination product confirms regioselective C–H activation/olefination at the more hindered site in cases where the <i>meta</i> F atom or heteroatom substituent existed. Apparent torsion angles were observed in the structures of mono- and bis-olefination products, which resulted in distinct different chemical shifts of olefinic protons. Additionally, two gram-scale reactions and further transformation experiments demonstrate that this method is practical for synthesis of <i>ortho</i>-alkenylated 2-arylbenzoxazole derivatives