Most common genetic disorders have a complex inheritance and may result from variants in many genes, each contributing only weak effects to the disease. Pinpointing these disease genes within the myriad of susceptibility loci identified in linkage studies is difficult because these loci may contain hundreds of genes. However, in any disorder, most of the disease genes will be involved in only a few different molecular pathways. If we know something about the relationships between the genes, we can assess whether some genes (which may reside in different loci) functionally interact with each other, indicating a joint basis for the disease etiology. There are various repositories of information on pathway relationships. To consolidate this information, we developed a functional human gene network that integrates information on genes and the functional relationships between genes, based on data from the Kyoto Encyclopedia of Genes and Genomes, the Biomolecular Interaction Network Database, Reactome, the Human Protein Reference Database, the Gene Ontology database, predicted protein-protein interactions, human yeast two-hybrid interactions, and microarray co-expressions. We applied this network to interrelate positional candidate genes from different disease loci and then tested 96 heritable disorders for which the Online Mendelian Inheritance in Man database reported at least three disease genes. Artificial susceptibility loci, each containing 100 genes, were constructed around each disease gene, and we used the network to rank these genes on the basis of their functional interactions. By following up the top five genes per artificial locus, we were able to detect at least one known disease gene in 54% of the loci studied, representing a 2.8-fold increase over random selection. This suggests that our method can significantly reduce the cost and effort of pinpointing true disease genes in analyses of disorders for which numerous loci have been reported but for which most of the genes are unknown.
Arts syndrome is an X-linked disorder characterized by mental retardation, early-onset hypotonia, ataxia, delayed motor development, hearing impairment, and optic atrophy. Linkage analysis in a Dutch family and an Australian family suggested that the candidate gene maps to Xq22.1-q24. Oligonucleotide microarray expression profiling of fibroblasts from two probands of the Dutch family revealed reduced expression levels of the phosphoribosyl pyrophosphate synthetase 1 gene (PRPS1). Subsequent sequencing of PRPS1 led to the identification of two different missense mutations, c.455T-->C (p.L152P) in the Dutch family and c.398A-->C (p.Q133P) in the Australian family. Both mutations result in a loss of phosphoribosyl pyrophosphate synthetase 1 activity, as was shown in silico by molecular modeling and was shown in vitro by phosphoribosyl pyrophosphate synthetase activity assays in erythrocytes and fibroblasts from patients. This is in contrast to the gain-of-function mutations in PRPS1 that were identified previously in PRPS-related gout. The loss-of-function mutations of PRPS1 likely result in impaired purine biosynthesis, which is supported by the undetectable hypoxanthine in urine and the reduced uric acid levels in serum from patients. To replenish low levels of purines, treatment with S-adenosylmethionine theoretically could have therapeutic efficacy, and a clinical trial involving the two affected Australian brothers is currently underway.
Recently, comparative genomic hybridization onto bacterial artificial chromosome (BAC) arrays (array-based comparative genomic hybridization) has proved to be successful for the detection of submicroscopic DNA copy-number variations in health and disease. Technological improvements to achieve a higher resolution have resulted in the generation of additional microarray platforms encompassing larger numbers of shorter DNA targets (oligonucleotides). Here, we present a novel method to estimate the ability of a microarray to detect genomic copy-number variations of different sizes and types (i.e. deletions or duplications). We applied our method, which is based on statistical power analysis, to four widely used high-density genomic microarray platforms. By doing so, we found that the high-density oligonucleotide platforms are superior to the BAC platform for the genome-wide detection of copy-number variations smaller than 1 Mb. The capacity to reliably detect single copy-number variations below 100 kb, however, appeared to be limited for all platforms tested. In addition, our analysis revealed an unexpected platform-dependent difference in sensitivity to detect a single copy-number loss and a single copy-number gain. These analyses provide a first objective insight into the true capacities and limitations of different genomic microarrays to detect and define DNA copy-number variations.
Infection of the human host by Streptococcus pneumoniae begins with colonization of the nasopharynx, which is mediated by the adherence of bacteria to the respiratory epithelium. Several studies have indicated an important role for the pneumococcal capsule in this process. Here, we used microarrays to characterize the in vitro transcriptional response of human pharyngeal epithelial Detroit 562 cells to the adherence of serotype 2 encapsulated strain D39, serotype 19F encapsulated strain G54, serotype 4 encapsulated strain TIGR4, and their nonencapsulated derivatives (⌬cps). In total, 322 genes were found to be upregulated in response to adherent pneumococci. Twenty-two genes were commonly induced, including those encoding several cytokines (e.g., interleukin 1 [IL-1] and IL-6), chemokines (e.g., IL-8 and CXCL1/2), and transcriptional regulators (e.g., FOS), consistent with an innate immune response mediated by Toll-like receptor signaling. Interestingly, 85% of genes were induced specifically by one or more encapsulated strains, suggestive of a capsule-dependent response. Importantly, purified capsular polysaccharides alone had no effect. Over a third of these loci encoded products predicted to be involved in transcriptional regulation and signal transduction, in particular mitogenactivated protein kinase signaling pathways. Real-time PCR of a subset of 10 genes confirmed the microarray data and showed a time-dependent upregulation of, especially, innate immunity genes. The downregulation of epithelial genes was most pronounced upon adherence of D39⌬cps, as 68% of the 161 genes identified were repressed only by this nonencapsulated strain. In conclusion, we identified a subset of host genes specifically induced by encapsulated strains during in vitro adherence and have demonstrated the complexity of interactions occurring during the initial stages of pneumococcal infection.
Undoubtedly, customer relationship management has gained its importance through the statement that acquiring a new customer is several times more costly than retaining and selling additional products to existing customers. Consequently, marketing practitioners are currently often focusing on retaining customers for as long as possible. However, recent findings in relationship marketing literature have shown that large differences exist within the group of long-life customers in terms of spending and spending evolution. Therefore, this paper focuses on introducing a measure of a customerÕs future spending evolution that might improve relationship marketing decision making. In this study, from a marketing point of view, we focus on predicting whether a newly acquired customer will increase or decrease his/her future spending from initial purchase information. This is essentially a classification task. The main contribution of this study lies in comparing and evaluating several Bayesian network classifiers with statistical and other artificial intelligence techniques for the purpose of classifying customers in the binary classification problem at hand. Certain Bayesian network classifiers have been recently proposed in the artificial intelligence literature as probabilistic white-box classifiers which allow to give a clear insight into the relationships between the variables of the domain under study. We discuss and evaluate several types of Bayesian network classifiers and their corresponding structure learning algorithms. We contribute to the literature by providing experimental evidence that: (1) Bayesian network classifiers offer an interesting and viable alternative for our customer lifecycle slope estimation problem; (2) the Markov Blanket concept allows for a natural form of attribute selection that was very effective for the application at hand; (3) the sign of the slope can be predicted with a powerful and parsimonious general, unrestricted Bayesian network classifier; (4) a set of three variables measuring the volume of initial purchases and the degree to which customers originally buy in different categories, are powerful predictors for estimating the sign of the slope, and might therefore provide desirable additional information for relationship marketing decision making.
Accurate planning of radiation therapy entails the definition of treatment volumes and a clear delimitation of normal tissue of which unnecessary exposure should be prevented. The spinal cord is a radiosensitive organ, which should be precisely identified because an overexposure to radiation may lead to undesired complications for the patient such as neuronal disfunction or paralysis. In this paper, a knowledge-based approach to identifying the spinal cord in computed tomography images of the thorax is presented. The approach relies on a knowledge-base which consists of a so-called anatomical structures map (ASM) and a task-oriented architecture called the plan solver. The ASM contains a frame-like knowledge representation of the macro-anatomy in the human thorax. The plan solver is responsible for determining the position, orientation and size of the structures of interest to radiation therapy. The plan solver relies on a number of image processing operators. Some are so-called atomic (e.g., thresholding and snakes) whereas others are composite. The whole system has been implemented on a standard PC. Experiments performed on the image material from 23 patients show that the approach results in a reliable recognition of the spinal cord (92% accuracy) and the spinal canal (85% accuracy). The lamina is more problematic to locate correctly (accuracy 72%). The position of the outer thorax is always determined correctly.
Leukocytes play an important role in the host defense as they may travel from the blood stream into the tissue in reacting to inflammatory stimuli. The leukocyte-vessel wall interactions are studied in post capillary vessels by intravital video microscopy during in vivo animal experiments. Sequences of video images are obtained and digitized with a frame grabber. A method for automatic detection and characterization of leukocytes in the video images is developed. Individual leukocytes are detected using a neural network that is trained with synthetic leukocyte images generated using a novel stochastic model. This model makes it feasible to generate images of leukocytes with different shapes and sizes under various lighting conditions. Experiments indicate that neural networks trained with the synthetic leukocyte images perform better than networks trained with images of manually detected leukocytes. The best performing neural network trained with synthetic leukocyte images resulted in an 18% larger area under the ROC curve than the best performing neural network trained with manually detected leukocytes.
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