Gabriele Sales

Identification of microRNA expression patterns and definition of a microRNA/mRNA regulatory network in distinct molecular groups of multiple myeloma

To date, little evidence of miRNA expression/deregulation in multiple myeloma has been reported. To characterize miRNA in the context of the major multiple myeloma molecular types, we generated miRNA expression profiles of highly purified malignant plasma cells from 40 primary tumors. Furthermore, transcriptional profiles, available for all patients, were used to investigate the occurrence of miRNA/predicted target mRNA pair anticorrelations, and the miRNA and genome-wide DNA data were integrated in a subset of patients to evaluate the influence of allelic imbalances on miRNA expression. Differential miRNA expression patterns were identified, which were mainly associated with the major IGH translocations; particularly, t(4;14) patients showed specific overexpression of let-7e, miR-125a-5p, and miR-99b belonging to a cluster at 19q13.33. The occurrence of other lesions (ie, 1q gain, 13q and 17p deletions, and hyperdiploidy) was slightly characterized by specific miRNA signatures. Furthermore, the occurrence of several allelic imbalances or loss of heterozygosity was found significantly associated with the altered expression of miRNAs located in the involved regions, such as let-7b at 22q13.31 or miR-140-3p at 16q22. Finally, the integrative analysis based on computational target prediction and miRNA/mRNA profiling defined a network of putative functional miRNA-target regulatory relations supported by expression data.

MAGIA, a web-based tool for miRNA and Genes Integrated Analysis

MAGIA (miRNA and genes integrated analysis) is a novel web tool for the integrative analysis of target predictions, miRNA and gene expression data. MAGIA is divided into two parts: the query section allows the user to retrieve and browse updated miRNA target predictions computed with a number of different algorithms (PITA, miRanda and Target Scan) and Boolean combinations thereof. The analysis section comprises a multistep procedure for (i) direct integration through different functional measures (parametric and non-parametric correlation indexes, a variational Bayesian model, mutual information and a meta-analysis approach based on P-value combination) of mRNA and miRNA expression data, (ii) construction of bipartite regulatory network of the best miRNA and mRNA putative interactions and (iii) retrieval of information available in several public databases of genes, miRNAs and diseases and via scientific literature text-mining. MAGIA is freely available for Academic users at http://gencomp.bio.unipd.it/magia.

MAGIA2: from miRNA and genes expression data integrative analysis to microRNA–transcription factor mixed regulatory circuits (2012 update)

MAGIA2 (http://gencomp.bio.unipd.it/magia2) is an update, extension and evolution of the MAGIA web tool. It is dedicated to the integrated analysis of in silico target prediction, microRNA (miRNA) and gene expression data for the reconstruction of post-transcriptional regulatory networks. miRNAs are fundamental post-transcriptional regulators of several key biological and pathological processes. As miRNAs act prevalently through target degradation, their expression profiles are expected to be inversely correlated to those of the target genes. Low specificity of target prediction algorithms makes integration approaches an interesting solution for target prediction refinement. MAGIA2 performs this integrative approach supporting different association measures, multiple organisms and almost all target predictions algorithms. Nevertheless, miRNAs activity should be viewed as part of a more complex scenario where regulatory elements and their interactors generate a highly connected network and where gene expression profiles are the result of different levels of regulation. The updated MAGIA2 tries to dissect this complexity by reconstructing mixed regulatory circuits involving either miRNA or transcription factor (TF) as regulators. Two types of circuits are identified: (i) a TF that regulates both a miRNA and its target and (ii) a miRNA that regulates both a TF and its target.

graphite - a Bioconductor package to convert pathway topology to gene network

BackgroundGene set analysis is moving towards considering pathway topology as a crucial feature. Pathway elements are complex entities such as protein complexes, gene family members and chemical compounds. The conversion of pathway topology to a gene/protein networks (where nodes are a simple element like a gene/protein) is a critical and challenging task that enables topology-based gene set analyses.Unfortunately, currently available R/Bioconductor packages provide pathway networks only from single databases. They do not propagate signals through chemical compounds and do not differentiate between complexes and gene families.ResultsHere we present graphite, a Bioconductor package addressing these issues. Pathway information from four different databases is interpreted following specific biologically-driven rules that allow the reconstruction of gene-gene networks taking into account protein complexes, gene families and sensibly removing chemical compounds from the final graphs. The resulting networks represent a uniform resource for pathway analyses. Indeed, graphite provides easy access to three recently proposed topological methods. The graphite package is available as part of the Bioconductor software suite.Conclusionsgraphite is an innovative package able to gather and make easily available the contents of the four major pathway databases. In the field of topological analysis graphite acts as a provider of biological information by reducing the pathway complexity considering the biological meaning of the pathway elements.

Analysis of miRNA and mRNA Expression Profiles Highlights Alterations in Ionizing Radiation Response of Human Lymphocytes under Modeled Microgravity

Background Ionizing radiation (IR) can be extremely harmful for human cells since an improper DNA-damage response (DDR) to IR can contribute to carcinogenesis initiation. Perturbations in DDR pathway can originate from alteration in the functionality of the microRNA-mediated gene regulation, being microRNAs (miRNAs) small noncoding RNA that act as post-transcriptional regulators of gene expression. In this study we gained insight into the role of miRNAs in the regulation of DDR to IR under microgravity, a condition of weightlessness experienced by astronauts during space missions, which could have a synergistic action on cells, increasing the risk of radiation exposure. Methodology/Principal Findings We analyzed miRNA expression profile of human peripheral blood lymphocytes (PBL) incubated for 4 and 24 h in normal gravity (1 g) and in modeled microgravity (MMG) during the repair time after irradiation with 0.2 and 2Gy of γ-rays. Our results show that MMG alters miRNA expression signature of irradiated PBL by decreasing the number of radio-responsive miRNAs. Moreover, let-7i*, miR-7, miR-7-1*, miR-27a, miR-144, miR-200a, miR-598, miR-650 are deregulated by the combined action of radiation and MMG. Integrated analyses of miRNA and mRNA expression profiles, carried out on PBL of the same donors, identified significant miRNA-mRNA anti-correlations of DDR pathway. Gene Ontology analysis reports that the biological category of “Response to DNA damage” is enriched when PBL are incubated in 1 g but not in MMG. Moreover, some anti-correlated genes of p53-pathway show a different expression level between 1 g and MMG. Functional validation assays using luciferase reporter constructs confirmed miRNA-mRNA interactions derived from target prediction analyses. Conclusions/Significance On the whole, by integrating the transcriptome and microRNome, we provide evidence that modeled microgravity can affects the DNA-damage response to IR in human PBL.

parmigene—a parallel R package for mutual information estimation and gene network reconstruction

MOTIVATION Inferring large transcriptional networks using mutual information has been shown to be effective in several experimental setup. Unfortunately, this approach has two main drawbacks: (i) several mutual information estimators are prone to biases and (ii) available software still has large computational costs when processing thousand of genes. RESULTS Here, we present parmigene (PARallel Mutual Information estimation for GEne NEtwork reconstruction), an R package that tries to fill the above gaps. It implements a mutual information estimator based on k-nearest neighbor distances that is minimally biased with respect to the other methods and uses a parallel computing paradigm to reconstruct gene regulatory networks. We test parmigene on in silico and real data. We show that parmigene gives more precise results than existing softwares with strikingly less computational costs. AVAILABILITY AND IMPLEMENTATION The parmigene package is available on the CRAN network at http://cran.r-project.org/web/packages/. CONTACT chiara.romualdi@unipd.it

Graphite Web: web tool for gene set analysis exploiting pathway topology

Graphite web is a novel web tool for pathway analyses and network visualization for gene expression data of both microarray and RNA-seq experiments. Several pathway analyses have been proposed either in the univariate or in the global and multivariate context to tackle the complexity and the interpretation of expression results. These methods can be further divided into ‘topological’ and ‘non-topological’ methods according to their ability to gain power from pathway topology. Biological pathways are, in fact, not only gene lists but can be represented through a network where genes and connections are, respectively, nodes and edges. To this day, the most used approaches are non-topological and univariate although they miss the relationship among genes. On the contrary, topological and multivariate approaches are more powerful, but difficult to be used by researchers without bioinformatic skills. Here we present Graphite web, the first public web server for pathway analysis on gene expression data that combines topological and multivariate pathway analyses with an efficient system of interactive network visualizations for easy results interpretation. Specifically, Graphite web implements five different gene set analyses on three model organisms and two pathway databases. Graphite Web is freely available at http://graphiteweb.bio.unipd.it/.

miRNA Landscape in Stage I Epithelial Ovarian Cancer Defines the Histotype Specificities

Purpose: Epithelial ovarian cancer (EOC) is one of the most lethal gynecologic diseases, with survival rate virtually unchanged for the past 30 years. EOC comprises different histotypes with molecular and clinical heterogeneity, but up till now the present gold standard platinum-based treatment has been conducted without any patient stratification. The aim of the present study is to generate microRNA (miRNA) profiles characteristic of each stage I EOC histotype, to identify subtype-specific biomarkers to improve our understanding underlying the tumor mechanisms. Experimental Design: A collection of 257 snap-frozen stage I EOC tumor biopsies was gathered together from three tumor tissue collections and stratified into independent training (n = 183) and validation sets (n = 74). Microarray and quantitative real-time PCR (qRT-PCR) were used to generate and validate the histotype-specific markers. A novel dedicated resampling inferential strategy was developed and applied to identify the highest reproducible results. mRNA and miRNA profiles were integrated to identify novel regulatory circuits. Results: Robust miRNA markers for clear cell and mucinous histotypes were found. Specifically, the clear cell histotype is characterized by a five-fold (log scale) higher expression of miR-30a and miR-30a*, whereas mucinous histotype has five-fold (log scale) higher levels of miR-192/194. Furthermore, a mucinous-specific regulatory loop involving miR-192/194 cluster and a differential regulation of E2F3 in clear cell histotype were identified. Conclusions: Our findings showed that stage I EOC histotypes have their own characteristic miRNA expression and specific regulatory circuits. Clin Cancer Res; 19(15); 4114–23. ©2013 AACR.

Impact of host genes and strand selection on miRNA and miRNA* expression.

Dysregulation of miRNAs expression plays a critical role in the pathogenesis of genetic, multifactorial disorders and in human cancers. We exploited sequence, genomic and expression information to investigate two main aspects of post-transcriptional regulation in miRNA biogenesis, namely strand selection regulation and expression relationships between intragenic miRNAs and host genes. We considered miRNAs expression profiles, measured in five sizeable microarray datasets, including samples from different normal cell types and tissues, as well as different tumours and disease states. First, the study of expression profiles of “sister” miRNA pairs (miRNA/miRNA*, 5′ and 3′ strands of the same hairpin precursor) showed that the strand selection is highly regulated since it shows tissue-/cell-/condition-specific modulation. We used information about the direction and the strength of the strand selection bias to perform an unsupervised cluster analysis for the sample classification evidencing that is able to distinguish among different tissues, and sometimes between normal and malignant cells. Then, considering a minimum expression threshold, in few miRNA pairs only one mature miRNA is always present in all considered cell types, whereas the majority of pairs were concurrently expressed in some cell types and alternatively in others. In a significant fraction of concurrently expressed pairs, the major and the minor forms found at comparable levels may contribute to post-transcriptional gene silencing, possibly in a coordinate way. In the second part of the study, the behaved tendency to co-expression of intragenic miRNAs and their “host” mRNA genes was confuted by expression profiles examination, suggesting that the expression profile of a given host gene can hardly be a good estimator of co-transcribed miRNA(s) for post-transcriptional regulatory networks inference. Our results point out the regulatory importance of post-transcriptional phases of miRNAs biogenesis, reinforcing the role of such layer of miRNA biogenesis in miRNA-based regulation of cell activities.

Characterizing genomic alterations in cancer by complementary functional associations

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes.