Identification of miR-145 targets through an integrated omics analysis.

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression and protein synthesis. To characterize functions of miRNAs and to assess their potential applications, we carried out an integrated multi-omics analysis to study miR-145, a miRNA that has been shown to suppress tumor growth. We employed gene expression profiling, miRNA profiling and quantitative proteomic analysis of a pancreatic cancer cell line. In our transcriptomic analysis, overexpression of miR-145 was found to suppress the expression of genes that are implicated in development of cancer such as ITGA11 and MAGEA4 in addition to previously described targets such as FSCN1, YES1 and PODXL. Based on miRNA profiling, overexpression of miR-145 also upregulated other miRNAs including miR-124, miR-133b and miR-125a-3p, all of which are implicated in suppression of tumors and are generally co-regulated with miR-145 in other cancers. Using the SILAC system, we identified miR-145-induced downregulation of several oncoproteins/cancer biomarkers including SET, RPA1, MCM2, ABCC1, SPTBN1 and SPTLC1. Luciferase assay validation carried out on a subset of downregulated candidate targets confirmed them to be novel direct targets of miR-145. Overall, this multi-omics approach provided insights into miR-145-mediated tumor suppression and could be used as a general strategy to study the targets of individual miRNAs.

Correlated miR-mRNA expression signatures of mouse hematopoietic stem and progenitor cell subsets predict "Stemness" and "Myeloid" interaction networks.

Several individual miRNAs (miRs) have been implicated as potent regulators of important processes during normal and malignant hematopoiesis. In addition, many miRs have been shown to fine-tune intricate molecular networks, in concert with other regulatory elements. In order to study hematopoietic networks as a whole, we first created a map of global miR expression during early murine hematopoiesis. Next, we determined the copy number per cell for each miR in each of the examined stem and progenitor cell types. As data is emerging indicating that miRs function robustly mainly when they are expressed above a certain threshold (∼100 copies per cell), our database provides a resource for determining which miRs are expressed at a potentially functional level in each cell type. Finally, we combine our miR expression map with matched mRNA expression data and external prediction algorithms, using a Bayesian modeling approach to create a global landscape of predicted miR-mRNA interactions within each of these hematopoietic stem and progenitor cell subsets. This approach implicates several interaction networks comprising a "stemness" signature in the most primitive hematopoietic stem cell (HSC) populations, as well as "myeloid" patterns associated with two branches of myeloid development.

Improved inference of gene regulatory networks through integrated Bayesian clustering and dynamic modeling of time-course expression data.

Inferring gene regulatory networks from expression data is difficult, but it is common and often useful. Most network problems are under-determined--there are more parameters than data points--and therefore data or parameter set reduction is often necessary. Correlation between variables in the model also contributes to confound network coefficient inference. In this paper, we present an algorithm that uses integrated, probabilistic clustering to ease the problems of under-determination and correlated variables within a fully Bayesian framework. Specifically, ours is a dynamic Bayesian network with integrated Gaussian mixture clustering, which we fit using variational Bayesian methods. We show, using public, simulated time-course data sets from the DREAM4 Challenge, that our algorithm outperforms non-clustering methods in many cases (7 out of 25) with fewer samples, rarely underperforming (1 out of 25), and often selects a non-clustering model if it better describes the data. Source code (GNU Octave) for BAyesian Clustering Over Networks (BACON) and sample data are available at: http://code.google.com/p/bacon-for-genetic-networks.

Discovery of miR-mRNA interactions via simultaneous Bayesian inference of gene networks and clusters using sequence-based predictions and expression data.

MicroRNAs (miRs) are known to interfere with mRNA expression, and much work has been put into predicting and inferring miR-mRNA interactions. Both sequence-based interaction predictions as well as interaction inference based on expression data have been proven somewhat successful; furthermore, models that combine the two methods have had even more success. In this paper, I further refine and enrich the methods of miRmRNA interaction discovery by integrating a Bayesian clustering algorithm into a model of prediction-enhanced miR-mRNA target inference, creating an algorithm called PEACOAT, which is written in the R language. I show that PEACOAT improves the inference of miR-mRNA target interactions using both simulated data and a data set of microarrays from samples of multiple myeloma patients. In simulated networks of 25 miRs and mRNAs, our methods using clustering can improve inference in roughly two-thirds of cases, and in the multiple myeloma data set, KEGG pathway enrichment was found to be more significant with clustering than without. Our findings are consistent with previous work in clustering of non-miR genetic networks and indicate that there could be a significant advantage to clustering of miR and mRNA expression data as a part of interaction inference.

Inferring microRNA regulation of mRNA with partially ordered samples of paired expression data and exogenous prediction algorithms.

MicroRNAs (miRs) are known to play an important role in mRNA regulation, often by binding to complementary sequences in "target" mRNAs. Recently, several methods have been developed by which existing sequence-based target predictions can be combined with miR and mRNA expression data to infer true miR-mRNA targeting relationships. It has been shown that the combination of these two approaches gives more reliable results than either by itself. While a few such algorithms give excellent results, none fully addresses expression data sets with a natural ordering of the samples. If the samples in an experiment can be ordered or partially ordered by their expected similarity to one another, such as for time-series or studies of development processes, stages, or types, (e.g. cell type, disease, growth, aging), there are unique opportunities to infer miR-mRNA interactions that may be specific to the underlying processes, and existing methods do not exploit this. We propose an algorithm which specifically addresses [partially] ordered expression data and takes advantage of sample similarities based on the ordering structure. This is done within a Bayesian framework which specifies posterior distributions and therefore statistical significance for each model parameter and latent variable. We apply our model to a previously published expression data set of paired miR and mRNA arrays in five partially ordered conditions, with biological replicates, related to multiple myeloma, and we show how considering potential orderings can improve the inference of miR-mRNA interactions, as measured by existing knowledge about the involved transcripts.

RNA interference in ageing research--a mini-review.

BACKGROUND: The search for genetic mechanisms affecting life-span and ageing represents an important part of ageing research, especially since the discovery of single-gene mutations with dramatic effects on these traits. Due to its relative ease of use and its power to specifically target arbitrary genes, RNA interference (RNAi) has rapidly been adopted as a technique for silencing gene expression. The feasibility of genome-wide RNAi screens potentially much simplifies the identification of novel ageing-related genes. OBJECTIVE: In a review of applications of RNAi in ageing research with a focus on the model organisms Caenorhabditis elegans and Drosophila melanogaster and discussing recent technical developments, we aim to highlight the current and future impact of this technology in the field. METHOD: We show how RNAi has successfully been used to complement classic mutant studies. Moreover, we discuss the novel opportunities and challenges of an application of RNAi in genome-wide screens in D. melanogaster, which has become possible with the recent availability of a comprehensive transgenic RNAi library for the fly. We highlight, in particular, how the flexible control of RNAi induction can support the study of dynamic processes like ageing through specific experiments and the development of matching computational methods. In an overview of complementary approaches we discuss the challenge of extracting insight from the high-dimensional measurement datasets that are required for the study of dynamic effects and interaction dependencies. CONCLUSION: RNAi has emerged as a powerful tool for the study of ageing, allowing the further characterization of the roles of specific genes in the ageing process as well as the efficient identification of new genes implicated. RNAi has contributed to our understanding of age-related diseases especially by making genes amenable to manipulation for which mutants were not easily available. Recent developments enable genome-wide screens with unprecedented temporal and spatial control of RNAi induction. Specific RNAi time-course experiments provide an opportunity for the analysis of high-resolution gene expression profiles capturing the dynamics of ageing-relevant processes and gene interactions. Research exploiting new avenues opened by the growing RNAi toolbox will considerably contribute to the next steps in researching the genetics of ageing and age-related diseases.