Additional file 3: of The mysterious orphans of Mycoplasmataceae

Description of selected bacterial genomes. Difference between COGs and ORFans. (XLSX 314 kb

Additional file 2: Table S1. of The mysterious orphans of Mycoplasmataceae

Genomic GC content and genic GC3 content for annotated species of Mycoplasma, Spiroplasma, and Ureaplasma. (DOCX 22 kb

Gene connectivity in the proliferation module: highly connected genes are associated with relevant biology and poor survival prognosis.

<p>Figures A and B correspond to the GSE20685 dataset (the largest breast cancer dataset in our study); C and D–to GSE21653 (the second largest dataset). A and C: proportion of genes related to the cell cycle GO process in a 50-gene window sliding from lowly to highly connected genes. B and D: scatter plots for gene connectivity against the power of a gene to predictive survival. The gene predictive power was defined as–log(P) from Cox univariate survival regression. Spearman correlations and statistical significance values are shown.</p

Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach

<div><p>The discovery of novel drug targets is a significant challenge in drug development. Although the human genome comprises approximately 30,000 genes, proteins encoded by fewer than 400 are used as drug targets in the treatment of diseases. Therefore, novel drug targets are extremely valuable as the source for first in class drugs. On the other hand, many of the currently known drug targets are functionally pleiotropic and involved in multiple pathologies. Several of them are exploited for treating multiple diseases, which highlights the need for methods to reliably reposition drug targets to new indications. Network-based methods have been successfully applied to prioritize novel disease-associated genes. In recent years, several such algorithms have been developed, some focusing on local network properties only, and others taking the complete network topology into account. Common to all approaches is the understanding that novel disease-associated candidates are in close overall proximity to known disease genes. However, the relevance of these methods to the prediction of novel drug targets has not yet been assessed. Here, we present a network-based approach for the prediction of drug targets for a given disease. The method allows both repositioning drug targets known for other diseases to the given disease and the prediction of unexploited drug targets which are not used for treatment of any disease. Our approach takes as input a disease gene expression signature and a high-quality interaction network and outputs a prioritized list of drug targets. We demonstrate the high performance of our method and highlight the usefulness of the predictions in three case studies. We present novel drug targets for scleroderma and different types of cancer with their underlying biological processes. Furthermore, we demonstrate the ability of our method to identify non-suspected repositioning candidates using diabetes type 1 as an example.</p> </div

Modules in a GSE20865 breast cancer dataset.

<p>GSE20865 was the largest breast cancer dataset analyzed here and includes 327 patients. The coexpression network identified 50 modules in this dataset. This heatmap displays expression patterns of genes in each module: with genes in rows and patients in columns. The modules larger than 250 genes (M1—M4) are represented by only the top 250 highly connected genes (to facilitate compact visualization). For selected modules, key biological functions are specified, with corresponding enrichment P-values.</p

Drug Repositioning through Systematic Mining of Gene Coexpression Networks in Cancer

<div><p>Gene coexpression network analysis is a powerful “data-driven” approach essential for understanding cancer biology and mechanisms of tumor development. Yet, despite the completion of thousands of studies on cancer gene expression, there have been few attempts to normalize and integrate co-expression data from scattered sources in a concise “meta-analysis” framework. We generated such a resource by exploring gene coexpression networks in 82 microarray datasets from 9 major human cancer types. The analysis was conducted using an elaborate weighted gene coexpression network (WGCNA) methodology and identified over 3,000 robust gene coexpression modules. The modules covered a range of known tumor features, such as proliferation, extracellular matrix remodeling, hypoxia, inflammation, angiogenesis, tumor differentiation programs, specific signaling pathways, genomic alterations, and biomarkers of individual tumor subtypes. To prioritize genes with respect to those tumor features, we ranked genes within each module by connectivity, leading to identification of module-specific functionally prominent hub genes. To showcase the utility of this network information, we positioned known cancer drug targets within the coexpression networks and predicted that Anakinra, an anti-rheumatoid therapeutic agent, may be promising for development in colorectal cancer. We offer a comprehensive, normalized and well documented collection of >3000 gene coexpression modules in a variety of cancers as a rich data resource to facilitate further progress in cancer research.</p></div

Overview of datasets and transcriptional modules.

<p>Overview of datasets and transcriptional modules.</p

Network reconstruction for c-Myc as a common drug target in different cancers.

<p>The blue, green and magenta boxes show uniquely up-regulated genes that were predicted as drug targets (within the top 100 predictions) for the indicated cancer type and that contribute to the regulation of cell proliferation. c-Myc (in the middle) is the top drug target prediction for all three cancer types and is involved in the regulation of cell proliferation as well. Downstream targets of c-Myc are shown in the gray box below c-Myc and are uniformly up-regulated in all three cancer types. Cyan stars represent known drug targets for the respective cancer type. Purple stars correspond to drug targets that have been associated with other diseases and can be readily repositioned to the treatment of this type of cancer, while yellow stars indicate unexploited drug targets that can be used for the development of novel treatment strategies. Red thermometers show significantly up-regulated genes in (1) Thyroid Cancer, (2) Colon Cancer, and (3) Melanoma.</p

Workflow overview.

<p>In each dataset, the following workflow was applied. 1. The dataset was used as a starting point to construct a gene coexpression network based on Topological Overlap between genes. TO determines similarity between gene expression profiles taking into account a systems level context. The network was next hierarchically clustered, resulting in a cluster dendrogram. 2. Using DynamicTreeCut algorithm, branches were identified in the dendrogram, leading to identification of gene coexpression modules. 3. Genes in each module were further prioritized by intramodular connectivity, providing a distinction between lowly and highly connected genes. The entire workflow was repeated independently for 82 datasets, resulting in a set of gene coexpression modules in each of them.</p

Cross-dataset high level functional landscape.

<p>This heatmap displays associations between gene coexpression modules and biological processes across all the datasets. Color denotes enrichment of a given module with a biological process: hypergeometric log p-value after Benjamini-Hochberg adjustment. Cluster height reflects how many interrelated processes are associated with the given module set: the higher a cluster–the broader is the module-associated functional theme. Cluster width reflects how many modules are sharing this function: the wider a cluster–the more frequently this function is found in the GEO datasets. For major clusters, key biological themes are subscribed. The heatmap includes 1,240 biological processes and 668 modules, which were selected as follows. A GO process was included if it’s associated 3 or more coexpression modules (P < 0.001). A module was included if it’s enriched with 3 or more biological process terms (P < 0.001).</p