Infectious Disease Ontology

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain

Environmental contamination of pathogenic and intermediate pathogenic <em>Leptospira</em> spp in two divisional secretariats of Kandy district

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A dynamical model reveals gene co-localizations in nucleus

Co-localization of networks of genes in the nucleus is thought to play an important role in determining gene expression patterns. Based upon experimental data, we built a dynamical model to test whether pure diffusion could account for the observed co-localization of genes within a defined subnuclear region. A simple standard Brownian motion model in two and three dimensions shows that preferential co-localization is possible for co-regulated genes without any direct interaction, and suggests the occurrence may be due to a limitation in the number of available transcription factors. Experimental data of chromatin movements demonstrates that fractional rather than standard Brownian motion is more appropriate to model gene mobilizations, and we tested our dynamical model against recent static experimental data, using a sub-diffusion process by which the genes tend to colocalize more easily. Moreover, in order to compare our model with recently obtained experimental data, we studied the association level between genes and factors, and presented data supporting the validation of this dynamic model. As further applications of our model, we applied it to test against more biological observations. We found that increasing transcription factor number, rather than factory number and nucleus size, might be the reason for decreasing gene co-localization. In the scenario of frequency-or amplitude-modulation of transcription factors, our model predicted that frequency-modulation may increase the co-localization between its targeted genes

Rosa26-GFP Direct Repeat (RaDR-GFP) Mice Reveal Tissue- and Age-Dependence of Homologous Recombination in Mammals In Vivo

Homologous recombination (HR) is critical for the repair of double strand breaks and broken replication forks. Although HR is mostly error free, inherent or environmental conditions that either suppress or induce HR cause genomic instability. Despite its importance in carcinogenesis, due to limitations in our ability to detect HR in vivo, little is known about HR in mammalian tissues. Here, we describe a mouse model in which a direct repeat HR substrate is targeted to the ubiquitously expressed Rosa26 locus. In the Rosa26 Direct Repeat-GFP (RaDR-GFP) mice, HR between two truncated EGFP expression cassettes can yield a fluorescent signal. In-house image analysis software provides a rapid method for quantifying recombination events within intact tissues, and the frequency of recombinant cells can be evaluated by flow cytometry. A comparison among 11 tissues shows that the frequency of recombinant cells varies by more than two orders of magnitude among tissues, wherein HR in the brain is the lowest. Additionally, de novo recombination events accumulate with age in the colon, showing that this mouse model can be used to study the impact of chronic exposures on genomic stability. Exposure to N-methyl-N-nitrosourea, an alkylating agent similar to the cancer chemotherapeutic temozolomide, shows that the colon, liver and pancreas are susceptible to DNA damage-induced HR. Finally, histological analysis of the underlying cell types reveals that pancreatic acinar cells and liver hepatocytes undergo HR and also that HR can be specifically detected in colonic somatic stem cells. Taken together, the RaDR-GFP mouse model provides new understanding of how tissue and age impact susceptibility to HR, and enables future studies of genetic, environmental and physiological factors that modulate HR in mammals.National Institutes of Health (U.S.) (Program Project Grant P01-CA026731)National Institutes of Health (U.S.) (R33-CA112151)National Institute of Environmental Health Sciences (P30-ES002109)Singapore-MIT Alliance for Research and Technology CenterNational Institutes of Health (U.S.) (P41-EB015871)National Cancer Institute (U.S.) (P30-CA014051

Computationally-Optimized Bone Mechanical Modeling from High-Resolution Structural Images

Image-based mechanical modeling of the complex micro-structure of human bone has shown promise as a non-invasive method for characterizing bone strength and fracture risk in vivo. In particular, elastic moduli obtained from image-derived micro-finite element (μFE) simulations have been shown to correlate well with results obtained by mechanical testing of cadaveric bone. However, most existing large-scale finite-element simulation programs require significant computing resources, which hamper their use in common laboratory and clinical environments. In this work, we theoretically derive and computationally evaluate the resources needed to perform such simulations (in terms of computer memory and computation time), which are dependent on the number of finite elements in the image-derived bone model. A detailed description of our approach is provided, which is specifically optimized for μFE modeling of the complex three-dimensional architecture of trabecular bone. Our implementation includes domain decomposition for parallel computing, a novel stopping criterion, and a system for speeding up convergence by pre-iterating on coarser grids. The performance of the system is demonstrated on a dual quad-core Xeon 3.16 GHz CPUs equipped with 40 GB of RAM. Models of distal tibia derived from 3D in-vivo MR images in a patient comprising 200,000 elements required less than 30 seconds to converge (and 40 MB RAM). To illustrate the system's potential for large-scale μFE simulations, axial stiffness was estimated from high-resolution micro-CT images of a voxel array of 90 million elements comprising the human proximal femur in seven hours CPU time. In conclusion, the system described should enable image-based finite-element bone simulations in practical computation times on high-end desktop computers with applications to laboratory studies and clinical imaging

Sub-population analysis based on temporal features of high content images

Background: High content screening techniques are increasingly used to understand the regulation and progression of cell motility. The demand of new platforms, coupled with availability of terabytes of data has challenged the traditional technique of identifying cell populations by manual methods and resulted in development of high-dimensional analytical methods. Results: In this paper, we present sub-populations analysis of cells at the tissue level by using dynamic features of the cells. We used active contour without edges for segmentation of cells, which preserves the cell morphology, and autoregressive modeling to model cell trajectories. The sub-populations were obtained by clustering static, dynamic and a combination of both features. We were able to identify three unique sub-populations in combined clustering. Conclusion: We report a novel method to identify sub-populations using kinetic features and demonstrate that these features improve sub-population analysis at the tissue level. These advances will facilitate the application of high content screening data analysis to new and complex biological problems.Computation and Systems Biology Programme of Singapore--Massachusetts Institute of Technology Allianc

RedundancyMiner: De-replication of redundant GO categories in microarray and proteomics analysis

<p>Abstract</p> <p>Background</p> <p>The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process, molecular function and subcellular localization. Tools such as GoMiner can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Two or more of the categories are often redundant, in the sense that identical or nearly-identical sets of genes map to the categories. The redundancy might typically inflate the report of significant categories by a factor of three-fold, create an illusion of an overly long list of significant categories, and obscure the relevant biological interpretation.</p> <p>Results</p> <p>We now introduce a new resource, RedundancyMiner, that de-replicates the redundant and nearly-redundant GO categories that had been determined by first running GoMiner. The main algorithm of RedundancyMiner, MultiClust, performs a novel form of cluster analysis in which a GO category might belong to several category clusters. Each category cluster follows a "complete linkage" paradigm. The metric is a similarity measure that captures the overlap in gene mapping between pairs of categories.</p> <p>Conclusions</p> <p>RedundancyMiner effectively eliminated redundancies from a set of GO categories. For illustration, we have applied it to the clarification of the results arising from two current studies: (1) assessment of the gene expression profiles obtained by laser capture microdissection (LCM) of serial cryosections of the retina at the site of final optic fissure closure in the mouse embryos at specific embryonic stages, and (2) analysis of a conceptual data set obtained by examining a list of genes deemed to be "kinetochore" genes.</p

Fast splice site detection using information content and feature reduction

Background: Accurate identification of splice sites in DNA sequences plays a key role in the prediction of gene structure in eukaryotes. Already many computational methods have been proposed for the detection of splice sites and some of them showed high prediction accuracy. However, most of these methods are limited in terms of their long computation time when applied to whole genome sequence data. Results: In this paper we propose a hybrid algorithm which combines several effective and informative input features with the state of the art support vector machine (SVM). To obtain the input features we employ information content method based on Shannon\u27s information theory, Shapiro\u27s score scheme, and Markovian probabilities. We also use a feature elimination scheme to reduce the less informative features from the input data. Conclusion: In this study we propose a new feature based splice site detection method that shows improved acceptor and donor splice site detection in DNA sequences when the performance is compared with various state of the art and well known method

Hyperactive S6K1 Mediates Oxidative Stress and Endothelial Dysfunction in Aging: Inhibition by Resveratrol

Mammalian target of rapamycin (mTOR)/S6K1 signalling emerges as a critical regulator of aging. Yet, a role of mTOR/S6K1 in aging-associated vascular endothelial dysfunction remains unknown. In this study, we investigated the role of S6K1 in aging-associated endothelial dysfunction and effects of the polyphenol resveratrol on S6K1 in aging endothelial cells. We show here that senescent endothelial cells displayed higher S6K1 activity, increased superoxide production and decreased bioactive nitric oxide (NO) levels than young endothelial cells, which is contributed by eNOS uncoupling. Silencing S6K1 in senescent cells reduced superoxide generation and enhanced NO production. Conversely, over-expression of a constitutively active S6K1 mutant in young endothelial cells mimicked endothelial dysfunction of the senescent cells through eNOS uncoupling and induced premature cellular senescence. Like the mTOR/S6K1 inhibitor rapamycin, resveratrol inhibited S6K1 signalling, resulting in decreased superoxide generation and enhanced NO levels in the senescent cells. Consistent with the data from cultured cells, an enhanced S6K1 activity, increased superoxide generation, and decreased bioactive NO levels associated with eNOS uncoupling were also detected in aortas of old WKY rats (aged 20–24 months) as compared to the young animals (1–3 months). Treatment of aortas of old rats with resveratrol or rapamycin inhibited S6K1 activity, oxidative stress, and improved endothelial NO production. Our data demonstrate a causal role of the hyperactive S6K1 in eNOS uncoupling leading to endothelial dysfunction and vascular aging. Resveratrol improves endothelial function in aging, at least in part, through inhibition of S6K1. Targeting S6K1 may thus represent a novel therapeutic approach for aging-associated vascular disease

Assessment of xenoestrogenic exposure by a biomarker approach: application of the E-Screen bioassay to determine estrogenic response of serum extracts

BACKGROUND: Epidemiological documentation of endocrine disruption is complicated by imprecise exposure assessment, especially when exposures are mixed. Even if the estrogenic activity of all compounds were known, the combined effect of possible additive and/or inhibiting interaction of xenoestrogens in a biological sample may be difficult to predict from chemical analysis of single compounds alone. Thus, analysis of mixtures allows evaluation of combined effects of chemicals each present at low concentrations. METHODS: We have developed an optimized in vitro E-Screen test to assess the combined functional estrogenic response of human serum. The xenoestrogens in serum were separated from endogenous steroids and pharmaceuticals by solid-phase extraction followed by fractionation by high-performance liquid chromatography. After dissolution of the isolated fraction in ethanol-DMSO, the reconstituted extract was added with estrogen-depleted fetal calf serum to MCF-7 cells, the growth of which is stimulated by estrogen. After a 6-day incubation on a microwell plate, cell proliferation was assessed and compared with the effect of a 17-beta-estradiol standard. RESULTS AND CONCLUSIONS: To determine the applicability of this approach, we assessed the estrogenicity of serum samples from 30 pregnant and 60 non-pregnant Danish women thought to be exposed only to low levels of endocrine disruptors. We also studied 211 serum samples from pregnant Faroese women, whose marine diet included whale blubber that contain a high concentration of persistent halogenated pollutants. The estrogenicity of the serum from Danish controls exceeded the background in 22.7 % of the cases, while the same was true for 68.1 % of the Faroese samples. The increased estrogenicity response did not correlate with the lipid-based concentrations of individual suspected endocrine disruptors in the Faroese samples. When added along with the estradiol standard, an indication of an enhanced estrogenic response was found in most cases. Thus, the in vitro estrogenicity response offers a promising and feasible approach for an aggregated exposure assessment for xenoestrogens in serum