Unveiling combinatorial regulation through the combination of ChIP information and in silico cis-regulatory module detection

Computationally retrieving biologically relevant cis-regulatory modules (CRMs) is not straightforward. Because of the large number of candidates and the imperfection of the screening methods, many spurious CRMs are detected that are as high scoring as the biologically true ones. Using ChIP-information allows not only to reduce the regions in which the binding sites of the assayed transcription factor (TF) should be located, but also allows restricting the valid CRMs to those that contain the assayed TF (here referred to as applying CRM detection in a query-based mode). In this study, we show that exploiting ChIP-information in a query-based way makes in silico CRM detection a much more feasible endeavor. To be able to handle the large datasets, the query-based setting and other specificities proper to CRM detection on ChIP-Seq based data, we developed a novel powerful CRM detection method 'CPModule'. By applying it on a well-studied ChIP-Seq data set involved in self-renewal of mouse embryonic stem cells, we demonstrate how our tool can recover combinatorial regulation of five known TFs that are key in the self-renewal of mouse embryonic stem cells. Additionally, we make a number of new predictions on combinatorial regulation of these five key TFs with other TFs documented in TRANSFAC

SomInaClust: detection of cancer genes based on somatic mutation patterns of inactivation and clustering

Background: With the advances in high throughput technologies, increasing amounts of cancer somatic mutation data are being generated and made available. Only a small number of (driver) mutations occur in driver genes and are responsible for carcinogenesis, while the majority of (passenger) mutations do not influence tumour biology. In this study, SomInaClust is introduced, a method that accurately identifies driver genes based on their mutation pattern across tumour samples and then classifies them into oncogenes or tumour suppressor genes respectively. Results: SomInaClust starts from the observation that oncogenes mainly contain mutations that, due to positive selection, cluster at similar positions in a gene across patient samples, whereas tumour suppressor genes contain a high number of protein-truncating mutations throughout the entire gene length. The method was shown to prioritize driver genes in 9 different solid cancers. Furthermore it was found to be complementary to existing similar-purpose methods with the additional advantages that it has a higher sensitivity, also for rare mutations (occurring in less than 1% of all samples), and it accurately classifies candidate driver genes in putative oncogenes and tumour suppressor genes. Pathway enrichment analysis showed that the identified genes belong to known cancer signalling pathways, and that the distinction between oncogenes and tumour suppressor genes is biologically relevant. Conclusions: SomInaClust was shown to detect candidate driver genes based on somatic mutation patterns of inactivation and clustering and to distinguish oncogenes from tumour suppressor genes. The method could be used for the identification of new cancer genes or to filter mutation data for further data-integration purposes

Organellar carbon metabolism is co-ordinated with distinct developmental phases of secondary xylem

Subcellular compartmentation of plant biosynthetic pathways in the mitochondria and plastids requires coordinated regulation of nuclear encoded genes, and the role of these genes has been largely ignored by wood researchers. In this study, we constructed a targeted systems genetics coexpression network of xylogenesis in Eucalyptus using plastid and mitochondrial carbon metabolic genes and compared the resulting clusters to the aspen xylem developmental series. The constructed network clusters reveal the organization of transcriptional modules regulating subcellular metabolic functions in plastids and mitochondria. Overlapping genes between the plastid and mitochondrial networks implicate the common transcriptional regulation of carbon metabolism during xylem secondary growth. We show that the central processes of organellar carbon metabolism are distinctly coordinated across the developmental stages of wood formation and are specifically associated with primary growth and secondary cell wall deposition. We also demonstrate that, during xylogenesis, plastid-targeted carbon metabolism is partially regulated by the central clock for carbon allocation towards primary and secondary xylem growth, and we discuss these networks in the context of previously established associations with wood-related complex traits. This study provides a new resolution into the integration and transcriptional regulation of plastid- and mitochondrial-localized carbon metabolism during xylogenesis

Pathway relevance ranking for tumor samples through network-based data integration

The study of cancer, a highly heterogeneous disease with different causes and clinical outcomes, requires a multi-angle approach and the collection of large multi-omics datasets that, ideally, should be analyzed simultaneously. We present a new pathway relevance ranking method that is able to prioritize pathways according to the information contained in any combination of tumor related omics datasets. Key to the method is the conversion of all available data into a single comprehensive network representation containing not only genes but also individual patient samples. Additionally, all data are linked through a network of previously identified molecular interactions. We demonstrate the performance of the new method by applying it to breast and ovarian cancer datasets from The Cancer Genome Atlas. By integrating gene expression, copy number, mutation and methylation data, the method's potential to identify key pathways involved in breast cancer development shared by different molecular subtypes is illustrated. Interestingly, certain pathways were ranked equally important for different subtypes, even when the underlying (epi)-genetic disturbances were diverse. Next to prioritizing universally high-scoring pathways, the pathway ranking method was able to identify subtype-specific pathways. Often the score of a pathway could not be motivated by a single mutation, copy number or methylation alteration, but rather by a combination of genetic and epi-genetic disturbances, stressing the need for a network-based data integration approach. The analysis of ovarian tumors, as a function of survival-based subtypes, demonstrated the method's ability to correctly identify key pathways, irrespective of tumor subtype. A differential analysis of survival-based subtypes revealed several pathways with higher importance for the bad-outcome patient group than for the good-outcome patient group. Many of the pathways exhibiting higher importance for the bad-outcome patient group could be related to ovarian tumor proliferation and survival

Analysis of primary language competition through ICT

Este artículo muestra el uso de las TIC para la mejora de la competencia lingüística de los estudiantes de Educación Primaria. Desde un diseño cuasi-experimental, la muestra conformada por 34 estudiantes de tercer ciclo de Educación Primaria de diferentes centros educativos en la comunidad andaluza, se ha dividido en dos grupos, grupo de control y grupo experimental, con la medición de la variable competencia lingüística a través de un cuestionario pretest-postest (? = ,821). A través del análisis estadístico para muestras independientes T de Student, los resultados muestran diferencias significativas en la variable de competencia lingüística (t=2,506; p=0.017) obteniendo un mayor valor en el grupo experimental. En conclusión, se mejora la competencia lingüística con las TIC en las rutinas aprendizaje.This article shows the use of ICTs for the improvement of Primary Education students' linguistic competence. From a quasi-experimental design, the sample made up of 34 students of the third cycle of Primary Education from different educational centers in the Andalusian community, has been divided into two groups, the control group and the experimental group, with the measurement of the linguistic competence variable through a pretest-postest questionnaire (? =, 821). Through statistical analysis for independent Student's T samples, the results showed significant differences in the linguistic competence variable (t = 2.506; p = 0.017), obtaining a higher score in the experimental group. In conclusion, the linguistic competence improves when the ICT is applied in learning routines

Redefining Single-Trial Memories in the Honeybee

Research on honeybee memory has led to a widely accepted model in which a single pairing of an odor stimulus with sucrose induces memories that are independent of protein synthesis but is unable to form protein-synthesis-dependent long-term memory (LTM). The latter is said to arise only after three or more pairings of odor and sucrose. Here, we show that this model underestimates the capacity of the bee brain to form LTMs after a unique appetitive experience. Using state-of-the art conditioning setups and individual-based analyses of conditioned responses, we found that protein-synthesis-dependent memories are formed already 4 h after the single conditioning trial and persist even 3 days later. These memories (4 h, 24 h, and 72 h) exhibit different dependencies on transcription and translation processes. Our results thus modify the traditional view of onetrial memories in an insect with a model status for memory research.Fil: Villar, María Eugenia. Université de Toulouse; Francia. Centre National de la Recherche Scientifique; Francia. Universite de la Mediterranee. Institut Universitaire de France; FranciaFil: Marchal, Paul. Université de Toulouse; Francia. Centre National de la Recherche Scientifique; Francia. Universite de la Mediterranee. Institut Universitaire de France; FranciaFil: Viola, Haydee Ana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Fisiología, Biología Molecular y Celular; ArgentinaFil: Giurfa, Martín. Université de Toulouse; Francia. Centre National de la Recherche Scientifique; Francia. Fujian Agriculture and Forestry University; Chin

Use of plant extracts to block bacterial biofilm formation

Proceedings of the I Congress PIIISA celebrado en la Estación Experimental del Zaidín (Granada), en mayo de 2013.We live surrounded by bacteria; in fact, in only one gram of soil we can find millions of bacterial cells. Our body houses more than 1014 bacteria. Even though some of these microorganisms can cause us problems, such as caries, actually most of them help in the proper functioning of our organism. Generally, bacteria coexist setting up communities associated to solid superficies, this is to which we refer as biofilms, that serve as a survival strategy. This type of formation cause serious sanitary problems for both humans and animals. Nowadays, chemical or natural compounds able to block this formation are looked for. In this project, we have set out how to use extracts of different plants with the purpose of testing their effects against biofilms of two bacterial species: Escherichia coli and Pseudomonas putida.This work was supported in part by grant BFU2010-17946 from the Plan Nacional de I+D+I.Peer reviewe

Engaging with complexity to improve the health of indigenous people: a call for the use of systems thinking to tackle health inequity

The 400 million indigenous people worldwide represent a wealth of linguistic and cultural diversity, as well as traditional knowledge and sustainable practices that are invaluable resources for human development. However, indigenous people remain on the margins of society in high, middle and low-income countries, and they bear a disproportionate burden of poverty, disease, and mortality compared to the general population. These inequalities have persisted, and in some countries have even worsened, despite the overall improvements in health indicators in relation to the 15-year push to meet the Millennium Development Goals. As we enter the Sustainable Development Goals (SDGs) era, there is growing consensus that efforts to achieve Universal Health Coverage (UHC) and promote sustainable development should be guided by the moral imperative to improve equity. To achieve this, we need to move beyond the reductionist tendency to frame indigenous health as a problem of poor health indicators to be solved through targeted service delivery tactics and move towards holistic, integrated approaches that address the causes of inequalities both inside and outside the health sector. To meet the challenge of engaging with the conditions underlying inequalities and promoting transformational change, equity-oriented research and practice in the field of indigenous health requires: engaging power, context-adapted strategies to improve service delivery, and mobilizing networks of collective action. The application of systems thinking approaches offers a pathway for the evolution of equity-oriented research and practice in collaborative, politically informed and mutually enhancing efforts to understand and transform the systems that generate and reproduce inequities in indigenous health. These approaches hold the potential to strengthen practice through the development of more nuanced, context-sensitive strategies for redressing power imbalances, reshaping the service delivery environment and fostering the dynamics of collective action for political reform.publishedVersio

Mining local staircase patterns in noisy data

Most traditional biclustering algorithms identify biclusters with no or little overlap. In this paper, we introduce the problem of identifying staircases of biclusters. Such staircases may be indicative for causal relationships between columns and can not easily be identified by existing biclustering algorithms. Our formalization relies on a scoring function based on the Minimum Description Length principle. Furthermore, we propose a first algorithm for identifying staircase biclusters, based on a combination of local search and constraint programming. Experiments show that the approach is promising