Juvenile myelomonocytic leukemia with prominent CD141+ myeloid dendritic cell differentiation

Validation and classification of RNA binding proteins identified by mRNA interactome capture

RNA binding proteins (RBPs) take part in all steps of the RNA life cycle and are often essential for cell viability. Most RBPs have a modular organization and comprise a set of canonical RNA binding domains. However, in recent years a number of high-throughput mRNA interactome studies on yeast, mammalian cell lines, and whole organisms have uncovered a multitude of novel mRNA interacting proteins that lack classical RNA binding domains. Whereas a few have been confirmed to be direct and functionally relevant RNA binders, biochemical and functional validation of RNA binding of most others is lacking. In this study, we used a combination of NMR spectroscopy and biochemical studies to test the RNA binding properties of six putative RBPs. Half of the analyzed proteins showed no interaction, whereas the other half displayed weak chemical shift perturbations upon titration with RNA. One of the candidates we found to interact weakly with RNA in vitro is Drosophila melanogaster end binding protein 1 (EB1), a master regulator of microtubule plus-end dynamics. Further analysis showed that EB1's RNA binding occurs on the same surface as that with which EB1 interacts with microtubules. RNA immunoprecipitation and colocalization experiments suggest that EB1 is a rather nonspecific, opportunistic RNA binder. Our data suggest that care should be taken when embarking on an RNA binding study involving these unconventional, novel RBPs, and we recommend initial and simple in vitro RNA binding experiments.

SurfaceGenie: a web-based application for prioritizing cell-type-specific marker candidates

MOTIVATION

Cell-type-specific surface proteins can be exploited as valuable markers for a range of applications including immunophenotyping live cells, targeted drug delivery and in vivo imaging. Despite their utility and relevance, the unique combination of molecules present at the cell surface are not yet described for most cell types. A significant challenge in analyzing 'omic' discovery datasets is the selection of candidate markers that are most applicable for downstream applications.

RESULTS

Here, we developed GenieScore, a prioritization metric that integrates a consensus-based prediction of cell surface localization with user-input data to rank-order candidate cell-type-specific surface markers. In this report, we demonstrate the utility of GenieScore for analyzing human and rodent data from proteomic and transcriptomic experiments in the areas of cancer, stem cell and islet biology. We also demonstrate that permutations of GenieScore, termed IsoGenieScore and OmniGenieScore, can efficiently prioritize co-expressed and intracellular cell-type-specific markers, respectively.

AVAILABILITY AND IMPLEMENTATION

Calculation of GenieScores and lookup of SPC scores is made freely accessible via the SurfaceGenie web application: www.cellsurfer.net/surfacegenie.

CONTACT

Rebekah.gundry@unmc.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.