DBNet, a tool to convert Dynamic Fault Trees into Dynamic Bayesian Networks

The unreliability evaluation of a system including dependencies involving the state of components or the failure events, can be performed by modelling the system as a Dynamic Fault Tree (DFT). The combinatorial technique used to solve standard Fault Trees is not suitable for the analysis of a DFT. The conversion into a Dynamic Bayesian Network (DBN) is a way to analyze a DFT. This paper presents a software tool allowing the automatic analysis of a DFTexploiting its conversion to a DBN. First, the architecture of the tool is described, together with the rules implemented in the tool, to convert dynamic gates in DBNs. Then, the tool is tested on a case of system: its DFT model and the corresponding DBN are provided and analyzed by means of the tool. The obtained unreliability results are compared with those returned by other tools, in order to verify their correctness. Moreover, the use of DBNs allows to compute further results on the model, such as diagnostic and sensitivity indices

The specific inhibitor of protein kinase C, 1-(5-isoquinolinylsulfonyl)-2-methylpiperazine (H7), induces morphological change and cell differentiation of human neural crest-derived cell lineages

AbstractIt has been proved that inhibition of protein kinase C by 1-(5-isoquinolinylsulfonyl)-1-methylpiperazine (H7) induces morphological differentiation in murine neuroblastoma (nb) cell. Here we report that H7 is also active on human nb cell lines. The human nb cell had originally neuroblast-like (N) or intermediate (I) morphology. N and I type are thought to represent different stages of neuroblastoma differentiation. Neurite outgrowth was observed in both N and I type morphology treating the cells with 7, 14 or 28 μM of H7. The results confirm previous observations and show that inhibition of PKC by H7 also promotes neuronal differentiation in human cell line variants

A digital repository with an extensible data model for biobanking and genomic analysis management

Motivation: Molecular biology laboratories require extensive metadata to improve data collection and analysis. The heterogeneity of the collected metadata grows as research is evolving in to international multi-disciplinary collaborations and increasing data sharing among institutions. Single standardization is not feasible and it becomes crucial to develop digital repositories with flexible and extensible data models, as in the case of modern integrated biobanks management. Results: We developed a novel data model in JSON format to describe heterogeneous data in a generic biomedical science scenario. The model is built on two hierarchical entities: processes and events, roughly corresponding to research studies and analysis steps within a single study. A number of sequential events can be grouped in a process building up a hierarchical structure to track patient and sample history. Each event can produce new data. Data is described by a set of user-defined metadata, and may have one or more associated files. We integrated the model in a web based digital repository with a data grid storage to manage large data sets located in geographically distinct areas. We built a graphical interface that allows authorized users to define new data types dynamically, according to their requirements. Operators compose queries on metadata fields using a flexible search interface and run them on the database and on the grid. We applied the digital repository to the integrated management of samples, patients and medical history in the BIT-Gaslini biobank. The platform currently manages 1800 samples of over 900 patients. Microarray data from 150 analyses are stored on the grid storage and replicated on two physical resources for preservation. The system is equipped with data integration capabilities with other biobanks for worldwide information sharing. Conclusions: Our data model enables users to continuously define flexible, ad hoc, and loosely structured metadata, for information sharing in specific research projects and purposes. This approach can improve sensitively interdisciplinary research collaboration and allows to track patients' clinical records, sample management information, and genomic data. The web interface allows the operators to easily manage, query, and annotate the files, without dealing with the technicalities of the data grid.Peer reviewe

Design of a multi-signature ensemble classifier predicting neuroblastoma patients' outcome

<p>Abstract</p> <p>Background</p> <p>Neuroblastoma is the most common pediatric solid tumor of the sympathetic nervous system. Development of improved predictive tools for patients stratification is a crucial requirement for neuroblastoma therapy. Several studies utilized gene expression-based signatures to stratify neuroblastoma patients and demonstrated a clear advantage of adding genomic analysis to risk assessment. There is little overlapping among signatures and merging their prognostic potential would be advantageous. Here, we describe a new strategy to merge published neuroblastoma related gene signatures into a single, highly accurate, Multi-Signature Ensemble (MuSE)-classifier of neuroblastoma (NB) patients outcome.</p> <p>Methods</p> <p>Gene expression profiles of 182 neuroblastoma tumors, subdivided into three independent datasets, were used in the various phases of development and validation of neuroblastoma NB-MuSE-classifier. Thirty three signatures were evaluated for patients' outcome prediction using 22 classification algorithms each and generating 726 classifiers and prediction results. The best-performing algorithm for each signature was selected, validated on an independent dataset and the 20 signatures performing with an accuracy > = 80% were retained.</p> <p>Results</p> <p>We combined the 20 predictions associated to the corresponding signatures through the selection of the best performing algorithm into a single outcome predictor. The best performance was obtained by the Decision Table algorithm that produced the NB-MuSE-classifier characterized by an external validation accuracy of 94%. Kaplan-Meier curves and log-rank test demonstrated that patients with good and poor outcome prediction by the NB-MuSE-classifier have a significantly different survival (p < 0.0001). Survival curves constructed on subgroups of patients divided on the bases of known prognostic marker suggested an excellent stratification of localized and stage 4s tumors but more data are needed to prove this point.</p> <p>Conclusions</p> <p>The NB-MuSE-classifier is based on an ensemble approach that merges twenty heterogeneous, neuroblastoma-related gene signatures to blend their discriminating power, rather than numeric values, into a single, highly accurate patients' outcome predictor. The novelty of our approach derives from the way to integrate the gene expression signatures, by optimally associating them with a single paradigm ultimately integrated into a single classifier. This model can be exported to other types of cancer and to diseases for which dedicated databases exist.</p

a grid enabled web platform for integrated digital biobanking in paediatrics

Motivation and Objectives A solid and integrated biobanking framework is an absolute requirement for high quality investigation in paediatric tumours. The overall goal of our activity is to design and develop a centralized Digital Biobank prototype able to integrate and interconnect an increasing number of local biobanks situated in various centres across Europe. As a first step, we are designing a web-based repository to store all tissue and genomic data from paediatric tumours collected by the G. Gaslini Children's Hospital, in Genoa. The repository satisfies flexibility and extensibility criteria, and is being deployed on a data Grid architecture (Bote-Lorenzo et al., 2004). Methods The repository is designed to contain data from all the tissue and blood samples obtained from infants and children affected by paediatric tumours, such as primary bone tumour and neuroblastoma. Many samples may be extracted from the same patient in a single visit or surgical operation; moreover from a single sample, nucleic acids (i.e. DNA and RNA) may be extracted for further analysis. These extractions could happen more than once, even at a distance of months or even years, if required. In order to satisfy the strict requirements above and ensure the extensibility of the repository, we have adopted a process/event model, already used for designing data and image repositories in Neuroscience (Corradi et al., 2012). The process/event model is a multipurpose taxonomic schema composed by two main generic objects: processes and events. An event can be any 'atomic' operation that is performed on patients or samples, or any processing of data or everything else related to the repository administration and management. A process is defined as a group of sequential events or sub-processes related to an activity, allowing the creation of a sort of hierarchical structure. As an example, the storage of a DNA sample in a specified location within a -80°C freezer and a post-processing step (such as differential expression, survival or correlation/anti-correlation analysis on microarray data) are single events, pertaining respectively to the more general 'Nucleic Acid Extraction' and 'Data Mining' processes. Platform Architecture The repository has a client-server architecture and it is composed by three main components, as shown in Figure 1: Repository portal Database Grid storage The repository portal is designed to make the storage and the navigation of data and information easy, through a simple and transparent web interface. It is a Java Enterprise Edition web application based on several existing open source tools for the development of web applications. The basis of the portal consists in a framework that relies on an Apache Tomcat web application container. It incorporates a database interface layer built through MyBatis, a persistence framework that automates the mapping between SQL databases and objects in Java. To provide users with highly interactive interfaces, some components are designed using the Asynchronous Javascript and XML (AJAX) programming technique. Wherever possible information is exchanged in XML or JavaScript Object Notation (JSON) format. The web portal represents the main access point to all the functionalities available through the overall integration platform, and exposes both user and administrator interfaces. T he repository itself is based on a MySQL database. The database design is fundamental in order to make the repository highly flexible and easily extensible. The core of the database is formed by the two previously described entities: processes and events and their relationships to data and metadata. Existing processes and events are contained in two homonymous tables. Each element in the event table refers to an element in the data table. The information inside the latter represents all the data inserted in the repository. These data can be associated with one or more files accordingly to their data type. The file table contains the logical path of all the stored files. The repository can be configured to store the metadata totally or partially within the database. In this latter case, the metadata are stored as XML descriptions inside the data table, to display the data in a rapid and dynamic way using XSL Transformations, and as records of specific metadata tables, to perform complex queries in an easier way. All data files are contained in the Grid storage, so the database doesn't really have to deal with hundreds of GB of data. Moreover, the number of operators should be quite small, thus making MySQL a reasonable choice as a database. The storage subsystem has been built around the iRODS data grid software (Rajasketar et al., 2010), chosen among others because it allows building a federated and distributed data storage system without the need of central components. Being able to deal with a huge amount of metadata, iRODS is widely used by the research community, also for Next Generation Sequencing Projects (Chiang et al., 2011). Careful attention has been given to security and privacy issues. All data are anonymised and cannot be linked in any way to patients' names, since the connection between clinical and personal data is done using unique identifiers managed exclusively by clinicians. Administrators are able to control users' access by creating groups and their association with pages and functions, define processes, events and all their relationships, define new data types and related metadata, associate them with the related events and manage available ontologies. Normal users, according to their assigned permissions, can insert new data, retrieve patients' information and view all the related data, download stored information, explore processes together with all the related events, data and metadata to have a global picture. The integrated system we envision at a European level will take advantage of the data Grid features provided by iRODS. Each hospital or biobank involved in the virtual community may have a local database and a dedicated separated iRODS system (called iRODS zone) where its own metadata and files can be saved. All the iRODS zones in the community will be federated. Federated iRODS zones are administered separately, but the users in the multiple zones, if given permission, will be able to access data stored in the other zones. If more hospital or research groups are working on the same project or using the same data structure, they may share a single iRODS zone and database. To provide access to the various local databases, federated database systems will be taken into account. Results and Discussion A first prototype of the repository is currently being tested at the Giannina Gaslini Institute, in Genoa. Information on over 1300 tissue samples, with their related DNA and RNA purified samples, have been stored together with administrative and clinical data from more than 700 patients. Three kinds of genomic analyses (i.e. event types) are currently provided, two for DNA samples - Comparative Genomic Hybridization (CGH) array and Multiplex Ligation-dependent Probe Amplification (MLPA) - and one for RNA - microarray analysis. For each analysis it is possible to store one or more files and user customized metadata. New data types can be configured via administrator interface, without additional programming, when new types of analyses or processing are required. The extensibility of our data model with user-defined data types and metadata is a crucial aspect of our implementation. As mentioned before, future developments will comprise the integration of our local biobank at the Gaslini Institute, with similar digital structures located across Europe. We are currently testing a distributed storage configuration, implementing data management policies expressed as rules that are interpreted by the iRODS Rule Engine. Acknowledgements Our research activity is performed in the framework of the 'European Network for Cancer Research in Children and Adolescents' (ENCCA) European project. References Bote-Lorenzo ML, Dimitriadis YA and Gomez-Sanchez E (2004) Grid characteristics and uses: a grid definition, Proceedings of the First European Across Grids Conference, ACG'03, Springer-Verlag, LNCS 2970, 291-298. doi:10.1007/978-3-540-24689-3_36 Chiang GT, Clapham P, Qi G, Sale K and Coates G (2011) Implementing a genomic data management system using iRODS in the Wellcome Trust Sanger Institute BMC Bioinformatics 2011, 12:361. doi:10.1186/1471-2105-12-361 Corradi L, Porro I, Schenone A, Momeni P, Ferrari , Nobili F, Ferrara M, Arnulfo G and Fato MM (2012) A repository based on a dynamically extensible data model supporting multidisciplinary research in neuroscience, BMC Medical Informatics and Decision Making (in press). JSON (JavaScript Object Notation), [online], http://www.json.org/. MyBatis, [online], http://www.mybatis.org. Rajasketar A, Moore R, Hou C et al. (2010) iRODS Primer: Integrated Rule-Oriented Data Systems. Morgan & Claypool. doi:10.2200/S00233ED1V01Y200912ICR012 XSL Transformations [online], http://www.w3.org/TR/xslt. Note: Figures and tables are available in PDF version only

A biology-driven approach identifies the hypoxia gene signature as a predictor of the outcome of neuroblastoma patients

Background Hypoxia is a condition of low oxygen tension occurring in the tumor microenvironment and it is related to poor prognosis in human cancer. To examine the relationship between hypoxia and neuroblastoma, we generated and tested an in vitro derived hypoxia gene signature for its ability to predict patients' outcome. Results We obtained the gene expression profile of 11 hypoxic neuroblastoma cell lines and we derived a robust 62 probesets signature (NB-hypo) taking advantage of the strong discriminating power of the l1-l2 feature selection technique combined with the analysis of differential gene expression. We profiled gene expression of the tumors of 88 neuroblastoma patients and divided them according to the NB-hypo expression values by K-means clustering. The NB-hypo successfully stratifies the neuroblastoma patients into good and poor prognosis groups. Multivariate Cox analysis revealed that the NB-hypo is a significant independent predictor after controlling for commonly used risk factors including the amplification of MYCN oncogene. NB-hypo increases the resolution of the MYCN stratification by dividing patients with MYCN not amplified tumors in good and poor outcome suggesting that hypoxia is associated with the aggressiveness of neuroblastoma tumor independently from MYCN amplification. Conclusions Our results demonstrate that the NB-hypo is a novel and independent prognostic factor for neuroblastoma and support the view that hypoxia is negatively correlated with tumors' outcome. We show the power of the biology-driven approach in defining hypoxia as a critical molecular program in neuroblastoma and the potential for improvement in the current criteria for risk stratification.Foundation KiKaChildren's Neuroblastoma Cancer FoundationSKK FoundationDutch Cancer Societ

Robust selection of cancer survival signatures from high-throughput genomic data using two-fold subsampling

Identifying relevant signatures for clinical patient outcome is a fundamental task in high-throughput studies. Signatures, composed of features such as mRNAs, miRNAs, SNPs or other molecular variables, are often non-overlapping, even though they have been identified from similar experiments considering samples with the same type of disease. The lack of a consensus is mostly due to the fact that sample sizes are far smaller than the numbers of candidate features to be considered, and therefore signature selection suffers from large variation. We propose a robust signature selection method that enhances the selection stability of penalized regression algorithms for predicting survival risk. Our method is based on an aggregation of multiple, possibly unstable, signatures obtained with the preconditioned lasso algorithm applied to random (internal) subsamples of a given cohort data, where the aggregated signature is shrunken by a simple thresholding strategy. The resulting method, RS-PL, is conceptually simple and easy to apply, relying on parameters automatically tuned by cross validation. Robust signature selection using RS-PL operates within an (external) subsampling framework to estimate the selection probabilities of features in multiple trials of RS-PL. These probabilities are used for identifying reliable features to be included in a signature. Our method was evaluated on microarray data sets from neuroblastoma, lung adenocarcinoma, and breast cancer patients, extracting robust and relevant signatures for predicting survival risk. Signatures obtained by our method achieved high prediction performance and robustness, consistently over the three data sets. Genes with high selection probability in our robust signatures have been reported as cancer-relevant. The ordering of predictor coefficients associated with signatures was well-preserved across multiple trials of RS-PL, demonstrating the capability of our method for identifying a transferable consensus signature. The software is available as an R package rsig at CRAN (http://cran.r-project.org)

First Characterization of Human Amniotic Fluid Stem Cell Extracellular Vesicles as a Powerful Paracrine Tool Endowed with Regenerative Potential

Human amniotic fluid stem cells (hAFS) have shown a distinct secretory profile and significant regenerative potential in several preclinical models of disease. Nevertheless, little is known about the detailed characterization of their secretome. Herein we show for the first time that hAFS actively release extracellular vesicles (EV) endowed with significant paracrine potential and regenerative effect. c-KIT(+) hAFS were isolated from leftover samples of amniotic fluid from prenatal screening and stimulated to enhance EV release (24 hours 20% O2 versus 1% O2 preconditioning). The capacity of the c-KIT(+) hAFS-derived EV (hAFS-EV) to induce proliferation, survival, immunomodulation, and angiogenesis were investigated in vitro and in vivo. The hAFS-EV regenerative potential was also assessed in a model of skeletal muscle atrophy (HSA-Cre, Smn(F7/F7) mice), in which mouse AFS transplantation was previously shown to enhance muscle strength and survival. hAFS secreted EV ranged from 50 up to 1,000 nm in size. In vitro analysis defined their role as biological mediators of regenerative, paracrine effects while their modulatory role in decreasing skeletal muscle inflammation in vivo was shown for the first time. Hypoxic preconditioning significantly induced the enrichment of exosomes endowed with regenerative microRNAs within the hAFS-EV. In conclusion, this is the first study showing that c-KIT(+) hAFS dynamically release EV endowed with remarkable paracrine potential, thus representing an appealing tool for future regenerative therapy. Stem Cells Translational Medicine 2017;6:1340-1355

Hypoxia Modifies the Transcriptome of Human NK Cells, Modulates Their Immunoregulatory Profile, and Influences NK Cell Subset Migration

Hypoxia, which characterizes most tumor tissues, can alter the function of different immune cell types, favoring tumor escape mechanisms. In this study, we show that hypoxia profoundly acts on NK cells by influencing their transcriptome, affecting their immunoregulatory functions, and changing the chemotactic responses of different NK cell subsets. Exposure of human peripheral blood NK cells to hypoxia for 16 or 96 h caused significant changes in the expression of 729 or 1,100 genes, respectively. Gene Set Enrichment Analysis demonstrated that these changes followed a consensus hypoxia transcriptional profile. As assessed by Gene Ontology annotation, hypoxia-targeted genes were implicated in several biological processes: metabolism, cell cycle, differentiation, apoptosis, cell stress, and cytoskeleton organization. The hypoxic transcriptome also showed changes in genes with immunological relevance including those coding for proinflammatory cytokines, chemokines, and chemokine-receptors. Quantitative RT-PCR analysis confirmed the modulation of several immune-related genes, prompting further immunophenotypic and functional studies. Multiplex ELISA demonstrated that hypoxia could variably reduce NK cell ability to release IFNγ, TNFα, GM-CSF, CCL3, and CCL5 following PMA+Ionomycin or IL15+IL18 stimulation, while it poorly affected the response to IL12+IL18. Cytofluorimetric analysis showed that hypoxia could influence NK chemokine receptor pattern by sustaining the expression of CCR7 and CXCR4. Remarkably, this effect occurred selectively (CCR7) or preferentially (CXCR4) on CD56bright NK cells, which indeed showed higher chemotaxis to CCL19, CCL21, or CXCL12. Collectively, our data suggest that the hypoxic environment may profoundly influence the nature of the NK cell infiltrate and its effects on immune-mediated responses within tumor tissues