Identifying collateral and synthetic lethal vulnerabilities within the DNA-damage response.

BackgroundA pair of genes is defined as synthetically lethal if defects on both cause the death of the cell but a defect in only one of the two is compatible with cell viability. Ideally, if A and B are two synthetic lethal genes, inhibiting B should kill cancer cells with a defect on A, and should have no effects on normal cells. Thus, synthetic lethality can be exploited for highly selective cancer therapies, which need to exploit differences between normal and cancer cells.ResultsIn this paper, we present a new method for predicting synthetic lethal (SL) gene pairs. As neighbouring genes in the genome have highly correlated profiles of copy number variations (CNAs), our method clusters proximal genes with a similar CNA profile, then predicts mutually exclusive group pairs, and finally identifies the SL gene pairs within each group pairs. For mutual-exclusion testing we use a graph-based method which takes into account the mutation frequencies of different subjects and genes. We use two different methods for selecting the pair of SL genes; the first is based on the gene essentiality measured in various conditions by means of the "Gene Activity Ranking Profile" GARP score; the second leverages the annotations of gene to biological pathways.ConclusionsThis method is unique among current SL prediction approaches, it reduces false-positive SL predictions compared to previous methods, and it allows establishing explicit collateral lethality relationship of gene pairs within mutually exclusive group pairs

The Effect of Teacher Candidates’ Perceptions of Their Initial Teacher Education Program on Teaching Anxiety, Efficacy, and Commitment

In a novel approach to initial teacher education, we combined a program review perspective and a psychological perspective. First, we assessed the extent to which student teachers (n=137) felt that components of their BEd program prepared them for teaching and whether these components represented meaningful program dimensions. Through content analysis, exploratory factor analysis, and teacher candidates’ own words, five dimensions emerged: classroom dynamics; curriculum, instruction, and assessment; intrapersonal reflection; ethics of teaching; and professional learning community. Second, we tested how each of these dimensions predicted teaching anxiety, efficacy, and commitment. The results from regression analyses showed that ethics of teaching was the most influential dimension by decreasing anxiety and increasing efficacy and commitment. Results are discussed in terms of the effect of initial teacher education program dimensions on the psychosocial development of teacher candidates.Adoptant une approche novatrice à la formation initiale des enseignants, nous avons combiné une perspective visant l'examen des programmes avec une perspective psychologique. Nous avons d'abord évalué la mesure dans laquelle les stagiaires (n=137) estimaient que  des composantes de leur programme d'études (B.Ed.) les avaient préparés pour l'enseignement et à quel point ces composantes représentaient des dimensions significatives de leurs programmes. Cinq dimensions ont découlé des analyses du contenu, des facteurs exploratoires et des propres paroles des stagiaires : la dynamique en salle de classe; le programme d’étude, l’enseignement et l’évaluation; la réflexion intrapersonnelle; l’éthique et l’enseignement; et les communautés professionnelles d’enseignement. Nous avons ensuite évalué dans quelle mesure chacune de ces dimensions constituait une variable explicative de l’anxiété, l’efficacité et l’engagement en enseignement. Les résultats sont présentés en fonction de l’effet des dimensions du programme de formation initiale des enseignants sur le développement psychosocial des stagiaires

Effect of single versus antibiotic combinations on Staphylococcus epidermidis biofilm viability and on genetic expression of some virulence genes

In this study five clinical isolates strains were used, and nine antibiotics at breakpoint concentrations: vancomycin, tetracycline, rifampicin, gentamicin, cefazolin, cephalothin, levofloxacine, daptomycin and clindamycin were tested. 48 hours biofilms were grown on Calgary Biofilm Device (CBD) and challenged overnight with antibiotics alone and in combination. Biofilm cells viability was determined by colony forming units (cfu). Afterwards, the effect of the most active antibiotics combinations against S. epidermidis biofilm on genetic expression of some genes of interest such as: icaA, icaR, sarA and rsbU was determined by real-time PCR. Although biofilms were generally insensitive to individual antibiotics, they were more susceptible to combinations. Levofloxacine was a constituent of almost all the combinations active against S. epidermidis biofilm pointing to be part of any antibiotic therapy directed against biofilms of these organisms

Virulence gene expression by staphylococcus epidermidis biofilm cells exposed to antibiotics

Staphylococcus epidermidis have become important causes of nosocomial infections, as its pathogenesis is correlated with the ability to form biofilms on polymeric surfaces. Production of poly-N-acetylglucosamine (PNAG) is crucial for S. epidermidis biofilm formation and is synthesized by the gene products of the icaADBC gene cluster. Production of PNAG/polysaccharide intercellular adhesin and biofilm formation are regulated by the alternative sigma factor, σB, and is influenced by a variety of environmental conditions including disinfectants and other antimicrobial substances. The susceptibility of five S. epidermidis strains to antibiotics alone and in double combination was previously tested. Our results demonstrated that some combinations are active and present a general broad spectrum against S. epidermidis biofilms, namely rifampicin–clindamycin and rifampicin–gentamicin. In the present study, it was investigated whether the combination of rifampicin with clindamycin and gentamicin and these antibiotics alone influence the expression of specific genes (icaA and rsbU) of S. epidermidis within biofilms using real-time polymerase chain reaction. The data showed that in most cases the expression of both genes tested significantly increased after exposure to antimicrobial agents alone and in combination. Besides having a similar antimicrobial effect, rifampicin combined with clindamycin and gentamicin induced a lower expression of biofilm-related genes relatively to rifampicin alone. Associated with the advantage of combinatorial therapy in avoiding the emergence of antibiotic resistance, this study demonstrated that it can also cause a lower genetic expression of icaA and rsbU genes, which are responsible for PNAG/polysaccharide intercellular adhesin production, and consequently reduce biofilm formation recidivism, relatively to rifampicin alone.F. Gomes and P. Teixeira fully acknowledge the financial support of Fundacao para a Ciencia e Tecnologia through the grants SFRH/BD/32126/2006 and SFRH/BPD/26803/2006, respectively

ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution

Entity resolution (ER), an important and common data cleaning problem, is about detecting data duplicate representations for the same external entities, and merging them into single representations. Relatively recently, declarative rules called matching dependencies (MDs) have been proposed for specifying similarity conditions under which attribute values in database records are merged. In this work we show the process and the benefits of integrating three components of ER: (a) Classifiers for duplicate/non-duplicate record pairs built using machine learning (ML) techniques, (b) MDs for supporting both the blocking phase of ML and the merge itself; and (c) The use of the declarative language LogiQL -an extended form of Datalog supported by the LogicBlox platform- for data processing, and the specification and enforcement of MDs.Comment: To appear in Proc. SUM, 201

Empathic and Self-Regulatory Processes Governing Doping Behavior

Evidence associating doping behavior with moral disengagement (MD) has accumulated over recent years. However, to date, research examining links between MD and doping has not considered key theoretically grounded influences and outcomes of MD. As such, there is a need for quantitative research in relevant populations that purposefully examines the explanatory pathways through which MD is thought to operate. Toward this end, the current study examined a conceptually grounded model of doping behavior that incorporated empathy, doping self-regulatory efficacy (SRE), doping MD, anticipated guilt and self-reported doping/doping susceptibility. Participants were specifically recruited to represent four key physical-activity contexts and consisted of team- (n = 195) and individual- (n = 169) sport athletes and hardcore- (n = 125) and corporate- (n = 121) gym exercisers representing both genders (nmale = 371; nfemale = 239); self-reported lifetime prevalence of doping across the sample was 13.6%. Each participant completed questionnaires assessing the aforementioned variables. Structural equation modeling indicated strong support for all study hypotheses. Specifically, we established: (a) empathy and doping SRE negatively predicted reported doping; (b) the predictive effects of empathy and doping SRE on reported doping were mediated by doping MD and anticipated guilt; (c) doping MD positively predicted reported doping; (d) the predictive effects of doping MD on reported doping were partially mediated by anticipated guilt. Substituting self-reported doping for doping susceptibility, multisample analyses then demonstrated these predictive effects were largely invariant between males and females and across the four physical-activity contexts represented. These findings extend current knowledge on a number of levels, and in doing so aid our understanding of key psychosocial processes that may govern doping behavior across key physical-activity contexts

GenoMetric Query Language: A novel approach to large-scale genomic data management

Motivation: Improvement of sequencing technologies and data processing pipelines is rapidly providing sequencing data, with associated high-level features, of many individual genomes in multiple biological and clinical conditions. They allow for data-driven genomic, transcriptomic and epigenomic characterizations, but require state-of-the-art ‘big data’ computing strategies, with abstraction levels beyond available tool capabilities. Results: We propose a high-level, declarative GenoMetric Query Language (GMQL) and a toolkit for its use. GMQL operates downstream of raw data preprocessing pipelines and supports queries over thousands of heterogeneous datasets and samples; as such it is key to genomic ‘big data’ analysis. GMQL leverages a simple data model that provides both abstractions of genomic region data and associated experimental, biological and clinical metadata and interoperability between many data formats. Based on Hadoop framework and Apache Pig platform, GMQL ensures high scalability, expressivity, flexibility and simplicity of use, as demonstrated by several biological query examples on ENCODE and TCGA datasets. Availability and implementation: The GMQL toolkit is freely available for non-commercial use at http://www.bioinformatics.deib.polimi.it/GMQL/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online