Reference Profile Correlation Reveals Estrogen-like Trancriptional Activity of Curcumin

Background: Several secondary metabolites from herbal nutrient products act as weak estrogens (phytoestrogens), competing with endogenous estrogen for binding to the estrogen receptors and inhibiting steroid converting enzymes. However, it is still unclear whether these compounds elicit estrogen dependent transcription of genes at physiological concentrations. Methods: We compare the effects of physiological concentrations (100 nM) of the two phytoestrogens Enterolactone and Quercetin and the suspected phytoestrogen Curcumin on gene expression in the breast cancer cell line MCF7 with the effects elicited by 17-beta-estradiol (E2). Results: All three phytocompounds have weak effects on gene transcription; most of the E2 genes respond to the phytoestrogens in the same direction though to a much lesser extent and in the order Curcumin > Quercetin > Enterolactone. Gene regulation induced by these compounds was low for genes strongly induced by E2 and similar to the latter for genes only weakly regulated by the classic estrogen. Of interest with regard to the treatment of menopausal symptoms, the survival factor Birc5/survivin and the oncogene MYBL1 are strongly induced by E2 but only marginally by phytoestrogens. Conclusion: This approach demonstrates estrogenic effects of putative phytoestrogens at physiological concentrations and shows, for the first time, estrogenic effects of Curcumin. Copyright (C) 2010 S. Karger AG, Base

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

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

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

Use of Attribute Driven Incremental Discretization and Logic Learning Machine to build a prognostic classifier for neuroblastoma patients

Cancer patient's outcome is written, in part, in the gene expression profile of the tumor. We previously identified a 62-probe sets signature (NB-hypo) to identify tissue hypoxia in neuroblastoma tumors and showed that NB-hypo stratified neuroblastoma patients in good and poor outcome 1. It was important to develop a prognostic classifier to cluster patients into risk groups benefiting of defined therapeutic approaches. Novel classification and data discretization approaches can be instrumental for the generation of accurate predictors and robust tools for clinical decision support. We explored the application to gene expression data of Rulex, a novel software suite including the Attribute Driven Incremental Discretization technique for transforming continuous variables into simplified discrete ones and the Logic Learning Machine model for intelligible rule generation. We applied Rulex components to the problem of predicting the outcome of neuroblastoma patients on the bases of 62 probe sets NB-hypo gene expression signature. The resulting classifier consisted in 9 rules utilizing mainly two conditions of the relative expression of 11 probe sets. These rules were very effective predictors, as shown in an independent validation set, demonstrating the validity of the LLM algorithm applied to microarray data and patients' classification. The LLM performed as efficiently as Prediction Analysis of Microarray and Support Vector Machine, and outperformed other learning algorithms such as C4.5. Rulex carried out a feature selection by selecting a new signature (NB-hypo-II) of 11 probe sets that turned out to be the most relevant in predicting outcome among the 62 of the NB-hypo signature. Rules are easily interpretable as they involve only few conditions. Our findings provided evidence that the application of Rulex to the expression values of NB-hypo signature created a set of accurate, high quality, consistent and interpretable rules for the prediction of neuroblastoma patients' outcome. We identified the Rulex weighted classification as a flexible tool that can support clinical decisions. For these reasons, we consider Rulex to be a useful tool for cancer classification from microarray gene expression dat

Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients

Neuroblastoma is the most common pediatric solid tumor. About fifty percent of high risk patients die despite treatment making the exploration of new and more effective strategies for improving stratification mandatory. Hypoxia is a condition of low oxygen tension occurring in poorly vascularized areas of the tumor associated with poor prognosis. We had previously defined a robust gene expression signature measuring the hypoxic component of neuroblastoma tumors (NB-hypo) which is a molecular risk factor. We wanted to develop a prognostic classifier of neuroblastoma patients' outcome blending existing knowledge on clinical and molecular risk factors with the prognostic NB-hypo signature. Furthermore, we were interested in classifiers outputting explicit rules that could be easily translated into the clinical setting. Shadow Clustering (SC) technique, which leads to final models called Logic Learning Machine (LLM), exhibits a good accuracy and promises to fulfill the aims of the work. We utilized this algorithm to classify NB-patients on the bases of the following risk factors: Age at diagnosis, INSS stage, MYCN amplification and NB-hypo. The algorithm generated explicit classification rules in good agreement with existing clinical knowledge. Through an iterative procedure we identified and removed from the dataset those examples which caused instability in the rules. This workflow generated a stable classifier very accurate in predicting good and poor outcome patients. The good performance of the classifier was validated in an independent dataset. NB-hypo was an important component of the rules with a strength similar to that of tumor staging. The novelty of our work is to identify stability, explicit rules and blending of molecular and clinical risk factors as the key features to generate classification rules for NB patients to be conveyed to the clinic and to be used to design new therapies. We derived, through LLM, a set of four stable rules identifying a new class of poor outcome patients that could benefit from new therapies potentially targeting tumor hypoxia or its consequence

Identification of early gene expression profiles associated with long-lasting antibody responses to the Ebola vaccine Ad26.ZEBOV/MVA-BN-Filo

Summary: Ebola virus disease is a severe hemorrhagic fever with a high fatality rate. We investigate transcriptome profiles at 3 h, 1 day, and 7 days after vaccination with Ad26.ZEBOV and MVA-BN-Filo. 3 h after Ad26.ZEBOV injection, we observe an increase in genes related to antigen presentation, sensing, and T and B cell receptors. The highest response occurs 1 day after Ad26.ZEBOV injection, with an increase of the gene expression of interferon-induced antiviral molecules, monocyte activation, and sensing receptors. This response is regulated by the HESX1, ATF3, ANKRD22, and ETV7 transcription factors. A plasma cell signature is observed on day 7 post-Ad26.ZEBOV vaccination, with an increase of CD138, MZB1, CD38, CD79A, and immunoglobulin genes. We have identified early expressed genes correlated with the magnitude of the antibody response 21 days after the MVA-BN-Filo and 364 days after Ad26.ZEBOV vaccinations. Our results provide early gene signatures that correlate with vaccine-induced Ebola virus glycoprotein-specific antibodies

Identification of a novel mouse Dbl proto-oncogene splice variant: Evidence that SEC14 domain is involved in GEF activity regulation

The Rho guanine nucleotide exchange factor protoDbl is involved in different biochemical pathways affecting cell proliferation and migration. The N-terminal sequence of protoDbl contains negative regulatory elements that restrict the catalytic activity of the DH-PH module. Here, we report the identification of a new mouse protoDbl splice variant lacking exon 3. We found that the splice variant mRNA is expressed in the spleen and bone marrow lymphocytes, adrenal gland, gonads and brain. The protoDbl variant protein was detectable in the brain. The newly identified variant displays the disruption of the SEC14 domain, positioned on exons 2 and 3 in the protoDbl N-terminal region. We show here that an altered SEC14 sequence leads to enhanced Dbl translocation to the plasma membrane and to augmented transforming and exchange activity. (C) 2014 Elsevier B.V. All rights reserved

CD177, a specific marker of neutrophil activation, is associated with coronavirus disease 2019 severity and death

International audienceThe identification of patients with coronavirus disease 2019 and high risk of severe disease is a challenge in routine care. We performed cell phenotypic, serum, and RNA sequencing gene expression analyses in severe hospitalized patients (n = 61). Relative to healthy donors, results showed abnormalities of 27 cell populations and an elevation of 42 cytokines, neutrophil chemo-attractants, and inflammatory components in patients. Supervised and unsupervised analyses revealed a high abundance of CD177, a specific neutrophil activation marker, contributing to the clustering of severe patients. Gene abundance correlated with high serum levels of CD177 in severe patients. Higher levels were confirmed in a second cohort and in intensive care unit (ICU) than non-ICU patients (P < 0.001). Longitudinal measurements discriminated between patients with the worst prognosis, leading to death, and those who recovered (P = 0.01). These results highlight neutrophil activation as a hallmark of severe disease and CD177 assessment as a reliable prognostic marker for routine care