Cellular Radiosensitivity: How much better do we understand it?

Purpose: Ionizing radiation exposure gives rise to a variety of lesions in DNA that result in genetic instability and potentially tumorigenesis or cell death. Radiation extends its effects on DNA by direct interaction or by radiolysis of H2O that generates free radicals or aqueous electrons capable of interacting with and causing indirect damage to DNA. While the various lesions arising in DNA after radiation exposure can contribute to the mutagenising effects of this agent, the potentially most damaging lesion is the DNA double strand break (DSB) that contributes to genome instability and/or cell death. Thus in many cases failure to recognise and/or repair this lesion determines the radiosensitivity status of the cell. DNA repair mechanisms including homologous recombination (HR) and non-homologous end-joining (NHEJ) have evolved to protect cells against DNA DSB. Mutations in proteins that constitute these repair pathways are characterised by radiosensitivity and genome instability. Defects in a number of these proteins also give rise to genetic disorders that feature not only genetic instability but also immunodeficiency, cancer predisposition, neurodegeneration and other pathologies. Conclusions: In the past fifty years our understanding of the cellular response to radiation damage has advanced enormously with insight being gained from a wide range of approaches extending from more basic early studies to the sophisticated approaches used today. In this review we discuss our current understanding of the impact of radiation on the cell and the organism gained from the array of past and present studies and attempt to provide an explanation for what it is that determines the response to radiation

L2-norm multiple kernel learning and its application to biomedical data fusion

<p>Abstract</p> <p>Background</p> <p>This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields different extensions of multiple kernel learning (MKL) such as <it>L</it>_∞, <it>L</it>₁, and <it>L</it>₂MKL. In particular, <it>L</it>₂MKL is a novel method that leads to non-sparse optimal kernel coefficients, which is different from the sparse kernel coefficients optimized by the existing <it>L</it>_∞MKL method. In real biomedical applications, <it>L</it>₂MKL may have more advantages over sparse integration method for thoroughly combining complementary information in heterogeneous data sources.</p> <p>Results</p> <p>We provide a theoretical analysis of the relationship between the <it>L</it>₂optimization of kernels in the dual problem with the <it>L</it>₂coefficient regularization in the primal problem. Understanding the dual <it>L</it>₂problem grants a unified view on MKL and enables us to extend the <it>L</it>₂method to a wide range of machine learning problems. We implement <it>L</it>₂MKL for ranking and classification problems and compare its performance with the sparse <it>L</it>_∞and the averaging <it>L</it>₁MKL methods. The experiments are carried out on six real biomedical data sets and two large scale UCI data sets. <it>L</it>₂MKL yields better performance on most of the benchmark data sets. In particular, we propose a novel <it>L</it>₂MKL least squares support vector machine (LSSVM) algorithm, which is shown to be an efficient and promising classifier for large scale data sets processing.</p> <p>Conclusions</p> <p>This paper extends the statistical framework of genomic data fusion based on MKL. Allowing non-sparse weights on the data sources is an attractive option in settings where we believe most data sources to be relevant to the problem at hand and want to avoid a "winner-takes-all" effect seen in <it>L</it>_∞MKL, which can be detrimental to the performance in prospective studies. The notion of optimizing <it>L</it>₂kernels can be straightforwardly extended to ranking, classification, regression, and clustering algorithms. To tackle the computational burden of MKL, this paper proposes several novel LSSVM based MKL algorithms. Systematic comparison on real data sets shows that LSSVM MKL has comparable performance as the conventional SVM MKL algorithms. Moreover, large scale numerical experiments indicate that when cast as semi-infinite programming, LSSVM MKL can be solved more efficiently than SVM MKL.</p> <p>Availability</p> <p>The MATLAB code of algorithms implemented in this paper is downloadable from <url>http://homes.esat.kuleuven.be/~sistawww/bioi/syu/l2lssvm.html</url>.</p

Gene expression profiling of monkeypox virus-infected cells reveals novel interfaces for host-virus interactions

Monkeypox virus (MPV) is a zoonotic Orthopoxvirus and a potential biothreat agent that causes human disease with varying morbidity and mortality. Members of the Orthopoxvirus genus have been shown to suppress antiviral cell defenses, exploit host cell machinery, and delay infection-induced cell death. However, a comprehensive study of all host genes and virus-targeted host networks during infection is lacking. To better understand viral strategies adopted in manipulating routine host biology on global scale, we investigated the effect of MPV infection on Macaca mulatta kidney epithelial cells (MK2) using GeneChip rhesus macaque genome microarrays. Functional analysis of genes differentially expressed at 3 and 7 hours post infection showed distinctive regulation of canonical pathways and networks. While the majority of modulated histone-encoding genes exhibited sharp copy number increases, many of its transcription regulators were substantially suppressed; suggesting involvement of unknown viral factors in host histone expression. In agreement with known viral dependence on actin in motility, egress, and infection of adjacent cells, our results showed extensive regulation of genes usually involved in controlling actin expression dynamics. Similarly, a substantial ratio of genes contributing to cell cycle checkpoints exhibited concerted regulation that favors cell cycle progression in G1, S, G2 phases, but arrest cells in G2 phase and inhibits entry into mitosis. Moreover, the data showed that large number of infection-regulated genes is involved in molecular mechanisms characteristic of cancer canonical pathways. Interestingly, ten ion channels and transporters showed progressive suppression during the course of infection. Although the outcome of this unusual channel expression on cell osmotic homeostasis remains unknown, instability of cell osmotic balance and membrane potential has been implicated in intracellular pathogens egress. Our results highlight the role of histones, actin, cell cycle regulators, and ion channels in MPV infection, and propose these host functions as attractive research focal points in identifying novel drug intervention sites

DNA damage in B and T lymphocytes of farmers during one pesticide spraying season

Purpose The effect of one pesticide spraying seasonon DNA damage was measured on B and T lymphocytesamong open-field farmers and controls.Methods At least two peripheral blood samples were collectedfrom each individual: one in a period without anypesticide application, several weeks after the last use (January,at period P0), and another in the intensive pesticidespraying period (May or June, at period P4). DNA damagewas studied by alkaline comet assay on isolated B or Tlymphocytes.Results Longitudinal comparison of DNA damageobserved at both P0 and P4 periods revealed a statisticallysignificant genotoxic effect of the pesticide spraying seasonin both B (P = 0.02) and T lymphocytes (P = 0.02) in exposed farmers. In contrast, non-farmers did not showany significant modifications. DNA damage levels in Band T lymphocytes were significantly higher in farmersthan in non-farmers during the P4 period (P = 0.003 andP = 0.001 for B and T lymphocytes, respectively) but notduring the P0 period. The seasonal effect observed amongfarmers was not correlated with either total farm area, farmarea devoted to crops or recent solar exposure. On average,farmers used pesticides for 21 days between P0 and P4.Between the two time points studied, there was a tendencyfor a potential effect of the number of days of fungicidetreatments (r2 = 0.43; P = 0.11) on T lymphocyte DNAdamage.Conclusions A genotoxic effect was found in lymphocytesof farmers exposed to pesticides, suggesting in particularthe possible implication of fungicides