Visual data mining of biological networks: one size does not fit all.

High-throughput technologies produce massive amounts of data. However, individual methods yield data specific to the technique used and biological setup. The integration of such diverse data is necessary for the qualitative analysis of information relevant to hypotheses or discoveries. It is often useful to integrate these datasets using pathways and protein interaction networks to get a broader view of the experiment. The resulting network needs to be able to focus on either the large-scale picture or on the more detailed small-scale subsets, depending on the research question and goals. In this tutorial, we illustrate a workflow useful to integrate, analyze, and visualize data from different sources, and highlight important features of tools to support such analyses.

Retraction: Circulating plant miRNAs can regulate human gene expression in vitro.

This corrects the article DOI: 10.1038/srep32773.

Circulating plant miRNAs can regulate human gene expression in vitro.

While Brassica oleracea vegetables have been linked to cancer prevention, the exact mechanism remains unknown. Regulation of gene expression by cross-species microRNAs has been previously reported; however, its link to cancer suppression remains unexplored. In this study we address both issues. We confirm plant microRNAs in human blood in a large nutrigenomics study cohort and in a randomized dose-controlled trial, finding a significant positive correlation between the daily amount of broccoli consumed and the amount of microRNA in the blood. We also demonstrate that Brassica microRNAs regulate expression of human genes and proteins in vitro, and that microRNAs cooperate with other Brassica-specific compounds in a possible cancer-preventive mechanism. Combined, we provide strong evidence and a possible multimodal mechanism for broccoli in cancer prevention.

The murine caecal microRNA signature depends on the presence of the endogenous microbiota.

The intestinal messenger RNA expression signature is affected by the presence and composition of the endogenous microbiota, with effects on host physiology. The intestine is also characterized by a distinctive micronome. However, it is not known if microbes also impact intestinal gene expression epigenetically. We investigated if the murine caecal microRNA expression signature depends on the presence of the microbiota, and the potential implications of this interaction on intestinal barrier function. Three hundred and thirty four microRNAs were detectable in the caecum of germ-free and conventional male mice and 16 were differentially expressed, with samples from the two groups clustering separately based on their expression patterns. Through a combination of computational and gene expression analyses, including the use of our curated list of 527 genes involved in intestinal barrier regulation, 2,755 putative targets of modulated microRNAs were identified, including 34 intestinal barrier-related genes encoding for junctional and mucus layer proteins and involved in immune regulation. This study shows that the endogenous microbiota influences the caecal microRNA expression signature, suggesting that microRNA modulation is another mechanism through which commensal bacteria impact the regulation of the barrier function and intestinal homeostasis. Through microRNAs, the gut microbiota may impinge a much larger number of genes than expected, particularly in diseases where its composition is altered. In this perspective, abnormally expressed microRNAs could be considered as novel therapeutic targets.

Neutropenia Prediction Based on First-Cycle Blood Counts Using a FOS-3NN Classifier.

Background. Delivery of full doses of adjuvant chemotherapy on schedule is key to optimal breast cancer outcomes. Neutropenia is a serious complication of chemotherapy and a common barrier to this goal, leading to dose reductions or delays in treatment. While past research has observed correlations between complete blood count data and neutropenic events, a reliable method of classifying breast cancer patients into low- and high-risk groups remains elusive. Patients and Methods. Thirty-five patients receiving adjuvant chemotherapy for early-stage breast cancer under the care of a single oncologist are examined in this study. FOS-3NN stratifies patient risk based on complete blood count data after the first cycle of treatment. All classifications are independent of breast cancer subtype and clinical markers, with risk level determined by the kinetics of patient blood count response to the first cycle of treatment. Results. In an independent test set of patients unseen by FOS-3NN, 19 out of 21 patients were correctly classified (Fisher's exact test probability P < 0.00023 [2 tailed], Matthews' correlation coefficient +0.83). Conclusions. We have developed a model that accurately predicts neutropenic events in a population treated with adjuvant chemotherapy in the first cycle of a 6-cycle treatment.

NAViGaTing the micronome--using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs.

BACKGROUND: MicroRNAs are a class of small RNAs known to regulate gene expression at the transcript level, the protein level, or both. Since microRNA binding is sequence-based but possibly structure-specific, work in this area has resulted in multiple databases storing predicted microRNA:target relationships computed using diverse algorithms. We integrate prediction databases, compare predictions to in vitro data, and use cross-database predictions to model the microRNA:transcript interactome--referred to as the micronome--to study microRNA involvement in well-known signalling pathways as well as associations with disease. We make this data freely available with a flexible user interface as our microRNA Data Integration Portal--mirDIP (http://ophid.utoronto.ca/mirDIP). RESULTS: mirDIP integrates prediction databases to elucidate accurate microRNA:target relationships. Using NAViGaTOR to produce interaction networks implicating microRNAs in literature-based, KEGG-based and Reactome-based pathways, we find these signalling pathway networks have significantly more microRNA involvement compared to chance (p<0.05), suggesting microRNAs co-target many genes in a given pathway. Further examination of the micronome shows two distinct classes of microRNAs; universe microRNAs, which are involved in many signalling pathways; and intra-pathway microRNAs, which target multiple genes within one signalling pathway. We find universe microRNAs to have more targets (p<0.0001), to be more studied (p<0.0002), and to have higher degree in the KEGG cancer pathway (p<0.0001), compared to intra-pathway microRNAs. CONCLUSIONS: Our pathway-based analysis of mirDIP data suggests microRNAs are involved in intra-pathway signalling. We identify two distinct classes of microRNAs, suggesting a hierarchical organization of microRNAs co-targeting genes both within and between pathways, and implying differential involvement of universe and intra-pathway microRNAs at the disease level.