Cell surface proteins are excellent targets for diagnostic and therapeutic interventions. By using bioinformatics tools, we generated a catalog of 3,702 transmembrane proteins located at the surface of human cells (human cell surfaceome). We explored the genetic diversity of the human cell surfaceome at different levels, including the distribution of polymorphisms, conservation among eukaryotic species, and patterns of gene expression. By integrating expression information from a variety of sources, we were able to identify surfaceome genes with a restricted expression in normal tissues and/or differential expression in tumors, important characteristics for putative tumor targets. A high-throughput and efficient quantitative real-time PCR approach was used to validate 593 surfaceome genes selected on the basis of their expression pattern in normal and tumor samples. A number of candidates were identified as potential diagnostic and therapeutic targets for colorectal tumors and glioblastoma. Several candidate genes were also identified as coding for cell surface cancer/testis antigens. The human cell surfaceome will serve as a reference for further studies aimed at characterizing tumor targets at the surface of human cells.colorectal tumors ͉ CT antigens ͉ glioblastoma ͉ transmembrane ͉ tumor cell surface antigens W ith the availability of the human genome sequence, an important goal of current biological research is a more specific and accurate annotation of human genes. One critical property is the subcellular localization of gene products, because this affects their use as potential diagnostic and therapeutic targets. In this respect, the identification of cell surface proteins is of particular interest (1-3) because these proteins represent ideal therapeutic targets. Indeed, cell surface proteins have proved to be relevant to many areas of medicine, and a number of monoclonal antibodies against them are approved for therapeutic applications by the Food and Drug Administration, particularly in cancer therapy. Furthermore, cell surface proteins are also excellent targets for diagnostic assays, especially in biological fluids. On the other hand, there are several issues that make cell surface proteins difficult to manipulate biochemically. First, their hydrophobic transmembrane (TM) domain makes them insoluble. Second, several posttranslational modifications are not executed in commonly used expression systems. Finally, interactions involving cell surface proteins usually have an extremely short half-life (on the order of milliseconds), which has an effect on the development of purification protocols. Despite these limitations, decades of intensive research of cell surface proteins have generated a significant information base. Ideally, this information should be analyzed in a genome-wide context.We generated here a catalog of more than 3,700 genes believed to encode proteins located at the surface of human cells. For the sake of simplicity, we will call this catalog the ''human cell surfaceome.'' An integrated ...

MicroRNAs (miRNAs) are a class of small (∼22 nucleotides) non-coding RNAs that post-transcriptionally regulate gene expression by interacting with target mRNAs. A majority of miRNAs is located within intronic or exonic regions of protein-coding genes (host genes), and increasing evidence suggests a functional relationship between these miRNAs and their host genes. Here, we introduce miRIAD, a web-service to facilitate the analysis of genomic and structural features of intragenic miRNAs and their host genes for five species (human, rhesus monkey, mouse, chicken and opossum). miRIAD contains the genomic classification of all miRNAs (inter- and intragenic), as well as classification of all protein-coding genes into host or non-host genes (depending on whether they contain an intragenic miRNA or not). We collected and processed public data from several sources to provide a clear visualization of relevant knowledge related to intragenic miRNAs, such as host gene function, genomic context, names of and references to intragenic miRNAs, miRNA binding sites, clusters of intragenic miRNAs, miRNA and host gene expression across different tissues and expression correlation for intragenic miRNAs and their host genes. Protein–protein interaction data are also presented for functional network analysis of host genes. In summary, miRIAD was designed to help the research community to explore, in a user-friendly environment, intragenic miRNAs, their host genes and functional annotations with minimal effort, facilitating hypothesis generation and in-silico validations.Database URL: http://www.miriad-database.org

MicroRNAs have established their role as potent regulators of the epigenome. Interestingly, most miRNAs are located within protein-coding genes with functional consequences that have yet to be fully investigated. MiRIAD is a database with an interactive and user-friendly online interface that has been facilitating research on intragenic miRNAs. In this article, we present a major update. First, data for five additional species (chimpanzee, rat, dog, cow and frog) were added to support the exploration of evolutionary aspects of the relationship between host genes and intragenic miRNAs. Moreover, we integrated data from two different sources to generate a comprehensive alternative polyadenylation dataset. The miRIAD interface was therefore redesigned and provides a completely new gene model representation, including an interactive visualization of the 3′ untranslated region (UTR) with alternative polyadenylation sites, corresponding signals and potential miRNA binding sites. Furthermore, we expanded on functional host gene network analysis. Although the previous version solely reported protein interactions, the update features a separate network analysis view that can either be accessed through the submission of a list of genes of interest or directly from a gene’s list of protein interactions. In addition to statistical properties of the submitted gene set, the interaction network graph is presented and miRNAs with seed site over- and underrepresentation are identified. In summary, the update of miRIAD provides novel datasets and bioinformatics resources with a significant increase in functionality to facilitate intragenic miRNA research in a user-friendly and interactive way. Database URL: http://www.miriad-database.org

No abstract

Nowadays, the massive amount of data generated by modern sequencing technologies provides an unprecedented opportunity to find genes associated with cancer patient prognosis, connecting basic and translational research. However, treating high dimensionality of gene expression data and integrating it with clinical variables are major challenges to perform these analyses. Here, we present Reboot, an integrative approach to find and validate genes and transcripts (splicing isoforms) associated with cancer patient prognosis from high dimensional expression datasets. Reboot innovates by using a multivariate strategy with penalized Cox regression (LASSO method) combined with a bootstrap approach, in addition to statistical tests and plots to support the findings. Applying Reboot on data from 154 glioblastoma patients, we identified a three-gene signature (IKBIP, OSMR, PODNL1) whose increased derived risk score was significantly associated with worse patients’ prognosis. Similarly, Reboot was able to find a seven-splicing isoforms signature related to worse overall survival in 177 pancreatic adenocarcinoma patients with elevated risk scores after uni- and multivariate analyses. In summary, Reboot is an efficient, intuitive and straightforward way of finding genes or splicing isoforms signatures relevant to patient prognosis, which can democratize this kind of analysis and shed light on still under-investigated cancer-related genes and splicing isoforms.