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1.  A simple method for assessing the strength of evidence for association at the level of the whole gene 
It is expected that different markers may show different patterns of association with different pathogenic variants within a given gene. It would be helpful to combine the evidence implicating association at the level of the whole gene rather than just for individual markers or haplotypes. Doing this is complicated by the fact that different markers do not represent independent sources of information.
We propose combining the p values from all single locus and/or multilocus analyses of different markers according to the formula of Fisher, X = ∑(−2ln(pi)), and then assessing the empirical significance of this statistic using permutation testing. We present an example application to 19 markers around the HTRA2 gene in a case-control study of Parkinson’s disease.
Applying our approach shows that, although some individual tests produce low p values, overall association at the level of the gene is not supported.
Approaches such as this should be more widely used in assimilating the overall evidence supporting involvement of a gene in a particular disease. Information can be combined from biallelic and multiallelic markers and from single markers along with multimarker analyses. Single genes can be tested or results from groups of genes involved in the same pathway could be combined in order to test biologically relevant hypotheses. The approach has been implemented in a computer program called COMBASSOC which is made available for downloading.
PMCID: PMC3169937  PMID: 21918610
Fisher; significance; genetic marker
2.  Discrimination between biological interfaces and crystal-packing contacts 
A discrimination method between biologically relevant interfaces and artificial crystal-packing contacts in crystal structures was constructed. The method evaluates protein-protein interfaces in terms of complementarities for hydrophobicity, electrostatic potential and shape on the protein surfaces, and chooses the most probable biological interfaces among all possible contacts in the crystal. The method uses a discriminator named as “COMP”, which is a linear combination of the complementarities for the above three surface features and does not correlate with the contact area. The discrimination of homo-dimer interfaces from symmetry-related crystal-packing contacts based on the COMP value achieved the modest success rate. Subsequent detailed review of the discrimination results raised the success rate to about 88.8%. In addition, our discrimination method yielded some clues for understanding the interaction patterns in several examples in the PDB. Thus, the COMP discriminator can also be used as an indicator of the “biological-ness” of protein-protein interfaces.
PMCID: PMC3169932  PMID: 21918609
protein-protein interaction; complementarity analysis; homo-dimer interface; crystal-packing contact; biological interfaces
3.  A network biology approach evaluating the anticancer effects of bortezomib identifies SPARC as a therapeutic target in adult T-cell leukemia cells 
There is a need to identify the regulatory gene interaction of anticancer drugs on target cancer cells. Whole genome expression profiling offers promise in this regard, but can be complicated by the challenge of identifying the genes affected by hundreds to thousands of genes that induce changes in expression. A proteasome inhibitor, bortezomib, could be a potential therapeutic agent in treating adult T-cell leukemia (ATL) patients, however, the underlying mechanism by which bortezomib induces cell death in ATL cells via gene regulatory network has not been fully elucidated. Here we show that a Bayesian statistical framework by VoyaGene® identified a secreted protein acidic and rich in cysteine (SPARC) gene, a tumor-invasiveness related gene, as a possible modulator of bortezomib-induced cell death in ATL cells. Functional analysis using RNAi experiments revealed that inhibition of the expression SPARC by siRNA enhanced the apoptotic effect of bortezomib on ATL cells in accordance with an increase of cleaved caspase 3. Targeting SPARC may help to treat ATL patients in combination with bortezomib. This work shows that a network biology approach can be used advantageously to identify the genetic interaction related to anticancer effects.
PMCID: PMC3169936  PMID: 21918608
network biology; adult T cell leukemia; bortezomib; SPARC
4.  Is gene activity in plant cells affected by UMTS-irradiation? A whole genome approach 
Mobile phone technology makes use of radio frequency (RF) electromagnetic fields transmitted through a dense network of base stations in Europe. Possible harmful effects of RF fields on humans and animals are discussed, but their effect on plants has received little attention. In search for physiological processes of plant cells sensitive to RF fields, cell suspension cultures of Arabidopsis thaliana were exposed for 24 h to a RF field protocol representing typical microwave exposition in an urban environment. mRNA of exposed cultures and controls was used to hybridize Affymetrix-ATH1 whole genome microarrays. Differential expression analysis revealed significant changes in transcription of 10 genes, but they did not exceed a fold change of 2.5. Besides that 3 of them are dark-inducible, their functions do not point to any known responses of plants to environmental stimuli. The changes in transcription of these genes were compared with published microarray datasets and revealed a weak similarity of the microwave to light treatment experiments. Considering the large changes described in published experiments, it is questionable if the small alterations caused by a 24 h continuous microwave exposure would have any impact on the growth and reproduction of whole plants.
PMCID: PMC3169933  PMID: 21918607
suspension cultured plant cells; radio frequency electromagnetic fields; microarrays; Arabidopsis thaliana
5.  Evolution of a domain conserved in microtubule-associated proteins of eukaryotes 
The microtubule network, the major organelle of the eukaryotic cytoskeleton, is involved in cell division and differentiation but also with many other cellular functions. In plants, microtubules seem to be involved in the ordered deposition of cellulose microfibrils by a so far unknown mechanism. Microtubule-associated proteins (MAP) typically contain various domains targeting or binding proteins with different functions to microtubules. Here we have investigated a proposed microtubule-targeting domain, TPX2, first identified in the Kinesin-like protein 2 in Xenopus. A TPX2 containing microtubule binding protein, PttMAP20, has been recently identified in poplar tissues undergoing xylogenesis. Furthermore, the herbicide 2,6-dichlorobenzonitrile (DCB), which is a known inhibitor of cellulose synthesis, was shown to bind specifically to PttMAP20. It is thus possible that PttMAP20 may have a role in coupling cellulose biosynthesis and the microtubular networks in poplar secondary cell walls. In order to get more insight into the occurrence, evolution and potential functions of TPX2-containing proteins we have carried out bioinformatic analysis for all genes so far found to encode TPX2 domains with special reference to poplar PttMAP20 and its putative orthologs in other plants.
PMCID: PMC3169935  PMID: 21918606
TPX2 domain; MAP20; evolution; microtubule; cellulose; bioinformatics
6.  Prion disease induced alterations in gene expression in spleen and brain prior to clinical symptoms 
Prion diseases are fatal neurodegenerative disorders that affect animals and humans. There is a need to gain understanding of prion disease pathogenesis and to develop diagnostic assays to detect prion diseases prior to the onset of clinical symptoms. The goal of this study was to identify genes that show altered expression early in the disease process in the spleen and brain of prion disease-infected mice. Using Affymetrix microarrays, we identified 67 genes that showed increased expression in the brains of prion disease-infected mice prior to the onset of clinical symptoms. These genes function in many cellular processes including immunity, the endosome/lysosome system, hormone activity, and the cytoskeleton. We confirmed a subset of these gene expression alterations using other methods and determined the time course in which these changes occur. We also identified 14 genes showing altered expression prior to the onset of clinical symptoms in spleens of prion disease infected mice. Interestingly, four genes, Atp1b1, Gh, Anp32a, and Grn, were altered at the very early time of 46 days post-infection. These gene expression alterations provide insights into the molecular mechanisms underlying prion disease pathogenesis and may serve as surrogate markers for the early detection and diagnosis of prion disease.
PMCID: PMC3169940  PMID: 21918605
prion disease; microarrays; gene expression
7.  A method to enhance the hit ratio by a combination of structure-based drug screening and ligand-based screening 
We examined the procedures to combine two different in silico drug-screening results to achieve a high hit ratio. When the 3D structure of the target protein and some active compounds are known, both structure-based and ligand-based in silico screening methods can be applied. In the present study, the machine-learning score modification multiple target screening (MSM-MTS) method was adopted as a structure-based screening method, and the machine-learning docking score index (ML-DSI) method was adopted as a ligand-based screening method. To combine the predicted compound’s sets by these two screening methods, we examined the product of the sets (consensus set) and the sum of the sets. As a result, the consensus set achieved a higher hit ratio than the sum of the sets and than either individual predicted set. In addition, the current combination was shown to be robust enough for the structural diversities both in different crystal structure and in snapshot structures during molecular dynamics simulations.
PMCID: PMC3169939  PMID: 21918604
in silico; screening; consensus score; protein-based screening; protein-ligand docking; conformation of active site
8.  Identification of significant genes in genomics using Bayesian variable selection methods 
In the studies of genomics, it is essential to select a small number of genes that are more significant than the others for research ranging from candidate gene studies to genome-wide association studies. In this study, we proposed a Bayesian method for identifying the promising candidate genes that are significantly more influential than the others. We employed the framework of variable selection and a Gibbs sampling based technique to identify significant genes. The proposed approach was applied to a genomics study for persons with chronic fatigue syndrome. Our studies show that the proposed Bayesian methodology is effective for deriving models for genomic studies and for providing information on significant genes.
PMCID: PMC3169938  PMID: 21918603
Bayesian variable selection; genomics; Gibbs sampling; variable selection
9.  Binarization of medical images based on the recursive application of mean shift filtering : Another algorithm 
Binarization is often recognized to be one of the most important steps in most high-level image analysis systems, particularly for object recognition. Its precise functioning highly determines the performance of the entire system. According to many researchers, segmentation finishes when the observer’s goal is satisfied. Experience has shown that the most effective methods continue to be the iterative ones. However, a problem with these algorithms is the stopping criterion. In this work, entropy is used as the stopping criterion when segmenting an image by recursively applying mean shift filtering. Of this way, a new algorithm is introduced for the binarization of medical images, where the binarization is carried out after the segmented image was obtained. The good performance of the proposed method; that is, the good quality of the binarization, is illustrated with several experimental results. In this paper a comparison was carried out among the obtained results with this new algorithm with respect to another developed by the author and collaborators previously and also with Otsu’s method.
PMCID: PMC3169934  PMID: 21918602
image segmentation; mean shift; algorithm; entropy; Otsu’s method

Results 1-9 (9)