Designing, building, and implementing an architecture for distributed knowledge discovery.

The integration of proteomics data with biological knowledge is a recent trend in bioinformatics. A lot of biological information is available and is spread on different sources and encoded in different ontologies (e.g. Gene Ontology). Annotating existing protein data with biological information may enable the use (and the development) of algorithms that use biological ontologies as framework to mine annotated data. Recently many methodologies and algorithms that use ontologies to extract knowledge from data, as well as to analyse ontologies themselves have been proposed and applied to other fields. Conversely, the use of such annotations for the analysis of protein data is a relatively novel research area that is currently becoming more and more central in research. Existing approaches span from the definition of the similarity among genes and proteins on the basis of the annotating terms, to the definition of novel algorithms that use such similarities for mining protein data on a proteome-wide scale. This work, after the definition of main concept of such analysis, presents a systematic discussion and comparison of main approaches. Finally, remaining challenges, as well as possible future directions of research are presented.

A rising body of evidence suggests that silencing microRNAs (miRNAs) with oncogenic potential may represent a successful therapeutic strategy for human cancer. We investigated the therapeutic activity of miR-221/222 inhibitors against human multiple myeloma (MM) cells. Enforced expression of miR-221/222 inhibitors triggered in vitro anti-proliferative effects and up-regulation of canonic miR-221/222 targets, including p27Kip1, PUMA, PTEN and p57Kip2, in MM cells highly expressing miR-221/222. Conversely, transfection of miR-221/222 mimics increased S-phase and down-regulated p27Kip1 protein expression in MM with low basal miR-221/222 levels. The effects of miR-221/222 inhibitors was also evaluated in MM xenografts in SCID/NOD mice. Significant anti-tumor activity was achieved in xenografted mice by the treatment with miR-221/222 inhibitors, together with up-regulation of canonic protein targets in tumors retrieved from animals. These findings provide proof of principle that silencing the miR-221/222 cluster exerts significant therapeutic activity in MM cells with high miR-221/222 level of expression, which mostly occurs in TC2 and TC4 MM groups. These findings suggest that MM genotyping may predict the therapeutic response. All together our results support a framework for clinical development of miR-221/222 inhibitors-based therapeutic strategy in this still incurable disease.

Multiple myeloma (MM) is a disease with an adverse outcome and new therapeutic strategies are urgently awaited. A rising body of evidence supports the notion that microRNAs (miRNAs), master regulators of eukaryotic gene expression, may exert anti-MM activity. Here, we evaluated the activity of synthetic miR-34a in MM cells. We found that transfection of miR-34a mimics in MM cells induces a significant change of gene expression with relevant effects on multiple signal transduction pathways. We detected early inactivation of pro-survival and proliferative kinases Erk-2 and Akt followed at later time points by caspase-6 and -3 activation and apoptosis induction. To improve the in vivo delivery, we encapsulated miR-34a mimics in stable nucleic acid lipid particles (SNALPs). We found that SNALPs miR-34a were highly efficient in vitro in inhibiting growth of MM cells. Then, we investigated the activity of the SNALPs miR-34a against MM xenografts in SCID mice. We observed significant tumor growth inhibition (p<0.05) which translated in mice survival benefits (p = 0.0047). Analysis of miR-34a and NOTCH1 expression in tumor retrieved from animal demonstrated efficient delivery and gene modulation induced by SNALPs miR-34a in the absence of systemic toxicity. We here therefore provide evidence that SNALPs miR-34a may represent a promising tool for miRNA-therapeutics in MM.