Alfredo Ferro

miRandola: Extracellular Circulating MicroRNAs Database

MicroRNAs are small noncoding RNAs that play an important role in the regulation of various biological processes through their interaction with cellular messenger RNAs. They are frequently dysregulated in cancer and have shown great potential as tissue-based markers for cancer classification and prognostication. microRNAs are also present in extracellular human body fluids such as serum, plasma, saliva, and urine. Most of circulating microRNAs are present in human plasma and serum cofractionate with the Argonaute2 (Ago2) protein. However, circulating microRNAs have been also found in membrane-bound vesicles such as exosomes. Since microRNAs circulate in the bloodstream in a highly stable, extracellular form, they may be used as blood-based biomarkers for cancer and other diseases. A knowledge base of extracellular circulating miRNAs is a fundamental tool for biomedical research. In this work, we present miRandola, a comprehensive manually curated classification of extracellular circulating miRNAs. miRandola is connected to miRò, the miRNA knowledge base, allowing users to infer the potential biological functions of circulating miRNAs and their connections with phenotypes. The miRandola database contains 2132 entries, with 581 unique mature miRNAs and 21 types of samples. miRNAs are classified into four categories, based on their extracellular form: miRNA-Ago2 (173 entries), miRNA-exosome (856 entries), miRNA-HDL (20 entries) and miRNA-circulating (1083 entries). miRandola is available online at: http://atlas.dmi.unict.it/mirandola/index.html.

Integrated microRNA and mRNA signatures associated with survival in triple negative breast cancer.

Triple negative breast cancer (TNBC) is a heterogeneous disease at the molecular, pathologic and clinical levels. To stratify TNBCs, we determined microRNA (miRNA) expression profiles, as well as expression profiles of a cancer-focused mRNA panel, in tumor, adjacent non-tumor (normal) and lymph node metastatic lesion (mets) tissues, from 173 women with TNBCs; we linked specific miRNA signatures to patient survival and used miRNA/mRNA anti-correlations to identify clinically and genetically different TNBC subclasses. We also assessed miRNA signatures as potential regulators of TNBC subclass-specific gene expression networks defined by expression of canonical signal pathways. Tissue specific miRNAs and mRNAs were identified for normal vs tumor vs mets comparisons. miRNA signatures correlated with prognosis were identified and predicted anti-correlated targets within the mRNA profile were defined. Two miRNA signatures (miR-16, 155, 125b, 374a and miR-16, 125b, 374a, 374b, 421, 655, 497) predictive of overall survival (P = 0.05) and distant-disease free survival (P = 0.009), respectively, were identified for patients 50 yrs of age or younger. By multivariate analysis the risk signatures were independent predictors for overall survival and distant-disease free survival. mRNA expression profiling, using the cancer-focused mRNA panel, resulted in clustering of TNBCs into 4 molecular subclasses with different expression signatures anti-correlated with the prognostic miRNAs. Our findings suggest that miRNAs play a key role in triple negative breast cancer through their ability to regulate fundamental pathways such as: cellular growth and proliferation, cellular movement and migration, Extra Cellular Matrix degradation. The results define miRNA expression signatures that characterize and contribute to the phenotypic diversity of TNBC and its metastasis.

miRò: a miRNA knowledge base

miRò is a web-based knowledge base that provides users with miRNA–phenotype associations in humans. It integrates data from various online sources, such as databases of miRNAs, ontologies, diseases and targets, into a unified database equipped with an intuitive and flexible query interface and data mining facilities. The main goal of miRò is the establishment of a knowledge base which allows non-trivial analysis through sophisticated mining techniques and the introduction of a new layer of associations between genes and phenotypes inferred based on miRNAs annotations. Furthermore, a specificity function applied to validated data highlights the most significant associations. The miRò web site is available at: http://ferrolab.dmi.unict.it/miro. Database URL: http://ferrolab.dmi.unict.it/miro

Drug-target interaction prediction through domain-tuned network-based inference.

Motivation: The identification of drug–target interaction (DTI) represents a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Recently, recommendation methods relying on network-based inference (NBI) have been proposed. However, such approaches implement naive topology-based inference and do not take into account important features within the drug–target domain. Results: In this article, we present a new NBI method, called domain tuned-hybrid (DT-Hybrid), which extends a well-established recommendation technique by domain-based knowledge including drug and target similarity. DT-Hybrid has been extensively tested using the last version of an experimentally validated DTI database obtained from DrugBank. Comparison with other recently proposed NBI methods clearly shows that DT-Hybrid is capable of predicting more reliable DTIs. Availability: DT-Hybrid has been developed in R and it is available, along with all the results on the predictions, through an R package at the following URL: http://sites.google.com/site/ehybridalgo/. Contact: apulvirenti@dmi.unict.it Supplementary information: Supplementary data are available at Bioinformatics online.

Cellular and molecular effects of protons: Apoptosis induction and potential implications for cancer therapy

Due to their ballistic precision, apoptosis induction by protons could be a strategy to specifically eliminate neoplastic cells. To characterize the cellular and molecular effects of these hadrons, we performed dose-response and time-course experiments by exposing different cell lines (PC3, Ca301D, MCF7) to increasing doses of protons and examining them with FACS, RT-PCR, and electron spin resonance (ESR). Irradiation with a dose of 10 Gy of a 26,7 Mev proton beam altered cell structures such as membranes, caused DNA double strand breaks, and significantly increased intracellular levels of hydroxyl ions, are active oxygen species (ROS). This modified the transcriptome of irradiated cells, activated the mitochondrial (intrinsic) pathway of apoptosis, and resulted in cycle arrest at the G2/M boundary. The number of necrotic cells within the irradiated cell population did not significantly increase with respect to the controls. The effects of irradiation with 20 Gy were qualitatively as well as quantitatively similar, but exposure to 40 Gy caused massive necrosis. Similar experiments with photons demonstrated that they induce apoptosis in a significantly lower number of cells and in a temporally delayed manner. These data advance our knowledge on the cellular and molecular effects of proton irradiation and could be useful for improving current hadrontherapy protocols.

SIGMA: A SET-COVER-BASED INEXACT GRAPH MATCHING ALGORITHM ∗

Network querying is a growing domain with vast applications ranging from screening compounds against a database of known molecules to matching sub-networks across species. Graph indexing is a powerful method for searching a large database of graphs. Most graph indexing methods to date tackle the exact matching (isomorphism) problem, limiting their applicability to specific instances in which such matches exist. Here we provide a novel graph indexing method to cope with the more general, inexact matching problem. Our method, SIGMA, builds on approximating a variant of the set-cover problem that concerns overlapping multi-sets. We extensively test our method and compare it to a baseline method and to the state-of-the-art Grafil. We show that SIGMA outperforms both, providing higher pruning power in all the tested scenarios.

A subgraph isomorphism algorithm and its application to biochemical data.

BackgroundGraphs can represent biological networks at the molecular, protein, or species level. An important query is to find all matches of a pattern graph to a target graph. Accomplishing this is inherently difficult (NP-complete) and the efficiency of heuristic algorithms for the problem may depend upon the input graphs. The common aim of existing algorithms is to eliminate unsuccessful mappings as early as and as inexpensively as possible.ResultsWe propose a new subgraph isomorphism algorithm which applies a search strategy to significantly reduce the search space without using any complex pruning rules or domain reduction procedures. We compare our method with the most recent and efficient subgraph isomorphism algorithms (VFlib, LAD, and our C++ implementation of FocusSearch which was originally distributed in Modula2) on synthetic, molecules, and interaction networks data. We show a significant reduction in the running time of our approach compared with these other excellent methods and show that our algorithm scales well as memory demands increase.ConclusionsSubgraph isomorphism algorithms are intensively used by biochemical tools. Our analysis gives a comprehensive comparison of different software approaches to subgraph isomorphism highlighting their weaknesses and strengths. This will help researchers make a rational choice among methods depending on their application. We also distribute an open-source package including our system and our own C++ implementation of FocusSearch together with all the used datasets (http://ferrolab.dmi.unict.it/ri.html). In future work, our findings may be extended to approximate subgraph isomorphism algorithms.

Variability in the Incidence of miRNAs and Genes in Fragile Sites and the Role of Repeats and CpG Islands in the Distribution of Genetic Material

Background Chromosomal fragile sites are heritable specific loci especially prone to breakage. Some of them are associated with human genetic disorders and several studies have demonstrated their importance in genome instability in cancer. MicroRNAs (miRNAs) are small non-coding RNAs responsible of post-transcriptional gene regulation and their involvement in several diseases such as cancer has been widely demonstrated. The altered expression of miRNAs is sometimes due to chromosomal rearrangements and epigenetic events, thus it is essential to study miRNAs in the context of their genomic locations, in order to find significant correlations between their aberrant expression and the phenotype. Principal Findings Here we use statistical models to study the incidence of human miRNA genes on fragile sites and their association with cancer-specific translocation breakpoints, repetitive elements, and CpG islands. Our results show that, on average, fragile sites are denser in miRNAs and also in protein coding genes. However, the distribution of miRNAs and protein coding genes in fragile versus non-fragile sites depends on chromosome. We find also a positive correlation between fragility and repeats, and between miRNAs and CpG islands. Conclusion Our results show that the relationship between site fragility and miRNA density is far more complex than previously thought. For example, we find that protein coding genes seem to be following similar patterns as miRNAs, if considered their overall distribution. However, once we allow for differences at the chromosome level in our statistical analysis, we find that distribution of miRNA and protein coding genes in fragile sites is very different from that of miRNA. This is a novel result that we believe may help discover new potential correlations between the localization of miRNAs and their crucial role in biological processes and in the development of diseases.

Acute Disseminated Encephalomyelitis: A Long-Term Prospective Study and Meta-Analysis

BACKGROUND There are only a few series in the literature on acute disseminated encephalomyelitis (ADEM) in children. OBJECTIVES AND METHODS the aims of this study were to perform (i) a prospective clinical/imaging study (1992-2009) on ADEM in children consecutively referred to our institution in Catania, Italy, and (ii) to undertake a systematic review and meta-analysis of published ADEM pediatric cohorts (>10 cases). RESULTS We identified 17 patients with ADEM (incidence <10 years of age=1.1 per 100 000 person-years). 15 previously published cohorts were compared with our cohort: (i) systematically reviewed (750 cases); and (ii) meta-analyzed (492/750 cases). The 17 patients had the following characteristics: (a) male-to-female ratio, 1.4 (vs. 1.2-1.3 in previous cohorts); (b) mean age at presentation, 3.6 years (vs. 7.1 years in previous cohorts); (c) specific preceding triggering factor, 88% (vs. 69-79% in previous cohorts); (d) the most common initial signs were ataxia, seizures, headache, and thalamic syndrome; (e) brain imaging revealed >3 lesions in 100% (vs. 92% in previous cohorts); (f) the outcome was good in 94% (vs. 70-75% in previous cohorts); and (g) 12% relapsed once (vs. 18% in previous cohorts). CONCLUSIONS ADEM is generally a benign condition that mosly affects boys more than girls and rarely recurs.

SING: Subgraph search In Non-homogeneous Graphs

BackgroundFinding the subgraphs of a graph database that are isomorphic to a given query graph has practical applications in several fields, from cheminformatics to image understanding. Since subgraph isomorphism is a computationally hard problem, indexing techniques have been intensively exploited to speed up the process. Such systems filter out those graphs which cannot contain the query, and apply a subgraph isomorphism algorithm to each residual candidate graph. The applicability of such systems is limited to databases of small graphs, because their filtering power degrades on large graphs.ResultsIn this paper, SING (Subgraph search In Non-homogeneous Graphs), a novel indexing system able to cope with large graphs, is presented. The method uses the notion of feature, which can be a small subgraph, subtree or path. Each graph in the database is annotated with the set of all its features. The key point is to make use of feature locality information. This idea is used to both improve the filtering performance and speed up the subgraph isomorphism task.ConclusionsExtensive tests on chemical compounds, biological networks and synthetic graphs show that the proposed system outperforms the most popular systems in query time over databases of medium and large graphs. Other specific tests show that the proposed system is effective for single large graphs.