SmartR: An open-source platform for interactive visual analytics for translational research data.

In translational research, efficient knowledge exchange between the different fields of expertise is crucial. An open platform that is capable of storing a multitude of data types such as clinical, pre-clinical, or OMICS data combined with strong visual analytical capabilities will significantly accelerate the scientific progress by making data more accessible and hypothesis generation easier. The open data warehouse tranSMART is capable of storing a variety of data types and has a growing user community including both academic institutions and pharmaceutical companies. tranSMART, however, currently lacks interactive and dynamic visual analytics and does not permit any post-processing interaction or exploration. For this reason, we developed SmartR , a plugin for tranSMART, that equips the platform not only with several dynamic visual analytical workflows, but also provides its own framework for the addition of new custom workflows. Modern web technologies such as D3.js or AngularJS were used to build a set of standard visualizations that were heavily improved with dynamic elements. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online. Availability: : The source code is licensed under the Apache 2.0 License and is freely available on GitHub: https://github.com/transmart/SmartR

Curcumin―The Paradigm of a Multi-Target Natural Compound with Applications in Cancer Prevention and Treatment

As cancer is a multifactor disease, it may require treatment with compounds able to target multiple intracellular components. We summarize here how curcumin is able to modulate many components of intracellular signaling pathways implicated in inflammation, cell proliferation and invasion and to induce genetic modulations eventually leading to tumor cell death. Clinical applications of this natural compound were initially limited by its low solubility and bioavailability in both plasma and tissues but combination with adjuvant and delivery vehicles was reported to largely improve bio-availability of curcumin. Moreover, curcumin was reported to act in synergism with several natural compounds or synthetic agents commonly used in chemotherapy. Based on this, curcumin could thus be considered as a good candidate for cancer prevention and treatment when used alone or in combination with other conventional treatments

Evolutionary conservation and network structure characterize genes of phenotypic relevance for mitosis in human

The impact of gene silencing on cellular phenotypes is difficult to establish due to the complexity of interactions in the associated biological processes and pathways. A recent genome-wide RNA knock-down study both identified and phenotypically characterized a set of important genes for the cell cycle in HeLa cells. Here, we combine a molecular interaction network analysis, based on physical and functional protein interactions, in conjunction with evolutionary information, to elucidate the common biological and topological properties of these key genes. Our results show that these genes tend to be conserved with their corresponding protein interactions across several species and are key constituents of the evolutionary conserved molecular interaction network. Moreover, a group of bistable network motifs is found to be conserved within this network, which are likely to influence the network stability and therefore the robustness of cellular functioning. They form a cluster, which displays functional homogeneity and this cluster is significantly enriched in genes phenotypically relevant for mitosis. Additional results reveal a relationship between specific cellular processes and the phenotypic outcomes induced by gene silencing. This study introduces new ideas regarding the relationship between genotype and phenotype in the context of the cell cycle. We show that the analysis of molecular interaction networks can result in the identification of genes relevant to cellular processes, which is a promising avenue for future research

Evolutionary conservation of MH genes and interactions.

<p>Human genes and interactions are classified as evolutionary conserved or non-conserved using inter-species conservation cutoff of 70% (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036488#s4" target="_blank">Materials and Methods</a>) for human protein-coding genes (HomoloGene), protein and gene regulatory interactions (KEGG and Reactome). The p-values are based on Fisher exact test for enrichment of MH compared to non-MH genes or interactions. Enrichment is tested for all available protein-coding genes and/or protein interactions (All); for cell division and mitosis-focused genes and/or interactions from KEGG MIN (KEGG MIN); and for the regulatory genes and/or interactions from KEGG MIN (Regulatory KEGG MIN).</p

Mirrored trees.

<p>Comparison of similarity of mitotic hit genes regarding their location in KEGG MIN and the phenoprints observed after gene silencing. Left side tree represents shortest path between MH genes in the KEGG MIN; right side tree represents similarity measure for phenoprints of MH genes based on Hausdorff distance. A line connects a gene in a pair of corresponding clusters of both trees. Thickness of the line reflects the depth in the tree, on which considered gene remains within corresponding clusters on both sides.</p

Evolutionary conserved bistable motifs.

1<p>p-value is based on Fisher exact test for enrichment of MH genes within bistable motifs.</p

Relation between network and phenotypic distances in the KEGG MIN.

<p>Network and phenotypic distances for all pairs of MH genes in KEGG MIN were calculated (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036488#s4" target="_blank">Materials and Methods</a>). The range of values for MH network distances is depicted on x-axis (Network distance). For each element <i>d</i> of this range the set of phenotypic distances was split into two parts: group I consists of phenotypic distances with corresponding network distance lower or equal to <i>d</i>; and group II consists of phenotypic distances with corresponding network distance greater than <i>d</i>. A) One-sided Wilcoxon rank-sum test was performed to determine whether the values of phenotypic distances in the first group are lower than in the second group. P-values are shown on the y-axis (p-value). The range of statistically significant values (***: p-value<0.001) is depicted by a horizontal bar. The dotted vertical bar indicates the median of observed network distances for MH genes. B) Table containing p-values displayed above and the number of MH genes in group I, absolute, and relative to the total number of genes in group I and II, respectively, for all analyzed values of network distance.</p

Modulation of anti-apoptotic and survival pathways by curcumin as a strategy to induce apoptosis in cancer cells

Apoptosis is a highly regulated mechanism by which cells undergo cell death in an active way. As one of the most challenging tasks concerning cancer is to induce apoptosis in malignant cells, researchers increasingly focus on natural products to modulate apoptotic signaling pathways. Curcumin, a natural compound isolated from the plant Curcuma longa, has chemopreventive properties, which are mainly due to its ability to arrest cell cycle and to induce apoptosis. This article reviews the main effects of curcumin on the different apoptotic signaling pathways involved in curcumin-induced apoptosis of cancer cells, including the intrinsic and extrinsic apoptosis pathways, the NF-kappaB-mediated pathway as well as the PI3K/Akt signaling pathway. This review also focuses on the sensitization of cells to TRAIL-induced apoptosis after curcumin treatment and shows that curcumin enhances the capacity to induce cell death of different chemotherapeutical drugs

Concept of evolutionary motif conservation.

<p>Phylogenetic tree for different species and corresponding species-specific networks of conserved gene regulatory interactions compared to Homo sapiens. A network motif shared between regulatory networks in human and other species is highlighted in red.</p