Proteomic Analysis of Rta2p-Dependent Raft-Association of Detergent-Resistant Membranes in Candida albicans

In Candida albicans, lipid rafts (also called detergent-resistant membranes, DRMs) are involved in many cellular processes and contain many important proteins. In our previous study, we demonstrated that Rta2p was required for calcineurin-mediated azole resistance and sphingoid long-chain base release in C. albicans. Here, we found that Rta2p was co-localized with raft-constituted ergosterol on the plasma membrane of C. albicans. Furthermore, this membrane expression pattern was totally disturbed by inhibitors of either ergosterol or sphingolipid synthesis. Biochemical fractionation of DRMs together with immunoblot uncovered that Rta2p, along with well-known DRM-associated proteins (Pma1p and Gas1p homologue), was associated with DRMs and their associations were blocked by inhibitors of either ergosterol or sphingolipid synthesis. Finally, we used the proteomic analysis together with immunoblot and identified that Rta2p was required for the association of 10 proteins with DRMs. These 5 proteins (Pma1p, Gas1p homologue, Erg11p, Pmt2p and Ali1p) have been reported to be DRM-associated and also that Erg11p is a well-known target of azoles in C. albicans. In conclusion, our results showed that Rta2p was predominantly localized in lipid rafts and was required for the association of certain membrane proteins with lipid rafts in C. albicans

Gene regulatory network inference from systems genetics data using tree-based methods

peer reviewedOne of the pressing open problems of computational systems biology is the elucidation of the topology of gene regulatory networks (GRNs). In an attempt to solve this problem, the idea of systems genetics is to exploit the natural variations that exist between the DNA sequences of related individuals and that can represent the randomized and multifactorial perturbations necessary to recover GRNs. In this chapter, we present new methods, called GENIE3-SG-joint and GENIE3- SG-sep, for the inference of GRNs from systems genetics data. Experiments on the artificial data of the StatSeq benchmark and of the DREAM5 Systems Genetics challenge show that exploiting jointly expression and genetic data is very helpful for recovering GRNs, and one of our methods outperforms by a large extent the official best performing method of the DREAM5 challenge

CyNetworkBMA: a Cytoscape app for inferring gene regulatory networks

BACKGROUND: Inference of gene networks from expression data is an important problem in computational biology. Many algorithms have been proposed for solving the problem efficiently. However, many of the available implementations are programming libraries that require users to write code, which limits their accessibility. RESULTS: We have developed a tool called CyNetworkBMA for inferring gene networks from expression data that integrates with Cytoscape. Our application offers a graphical user interface for networkBMA, an efficient implementation of Bayesian Model Averaging methods for network construction. The client-server architecture of CyNetworkBMA makes it possible to distribute or centralize computation depending on user needs. CONCLUSIONS: CyNetworkBMA is an easy-to-use tool that makes network inference accessible to non-programmers through seamless integration with Cytoscape. CyNetworkBMA is available on the Cytoscape App Store at http://apps.cytoscape.org/apps/cynetworkbma. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13029-015-0043-5) contains supplementary material, which is available to authorized users

Stability in GRN inference

Reconstructing a gene regulatory network from one or more sets of omics measurements has been a major task of computational biology in the last twenty years. Despite an overwhelming number of algorithms proposed to solve the network inference problem either in the general scenario or in a ad-hoc tailored situation, assessing the stability of reconstruction is still an uncharted territory and exploratory studies mainly tackled theoretical aspects. We introduce here empirical stability, which is induced by variability of reconstruction as a function of data subsampling. By evaluating differences between networks that are inferred using different subsets of the same data we obtain quantitative indicators of the robustness of the algorithm, of the noise level affecting the data, and, overall, of the reliability of the reconstructed graph. We show that empirical stability can be used whenever no ground truth is available to compute a direct measure of the similarity between the inferred structure and the true network. The main ingredient here is a suite of indicators, called NetSI, providing statistics of distances between graphs generated by a given algorithm fed with different data subsets, where the chosen metric is the Hamming-Ipsen-Mikhailov (HIM) distance evaluating dissimilarity of graph topologies with shared nodes. Operatively, the NetSI family is demonstrated here on synthetic and high-throughput datasets, inferring graphs at different resolution levels (topology, direction, weight), showing how the stability indicators can be effectively used for the quantitative comparison of the stability of different reconstruction algorithms