ALBERT

Description: Background: In the genome of H. pylori 26695, 149 proteins containing the CXXC motif characteristic of thioldisulfide oxidoreductases have been identified to date. However, only two of these proteins have a thioredoxin-like fold (i.e., HP0377 and HP0231) and are periplasm-located. We have previously shown that HP0231 is a dimeric oxidoreductase that catalyzes disulfide bond formation in the periplasm. Although HP0377 was originally described as DsbC homologue, its resolved structure and location of the hp0377 gene in the genome indicate that it is a counterpart of CcmG/DsbE. Results: The present work shows that HP0377 is present in H. pylori cells only in a reduced form and that absence of the main periplasmic oxidase HP0231 influences its redox state. Our biochemical analysis indicates that HP0377 is a specific reductase, as it does not reduce insulin. However, it possesses disulfide isomerase activity, as it catalyzes the refolding of scrambled RNase. Additionally, although its standard redox potential is -176 mV, it is the first described CcmG protein having an acidic pKa of the N-terminal cysteine of the CXXC motif, similar to E. coli DsbA or E. coli DsbC. The CcmG proteins that play a role in a cytochrome c-maturation, both in system I and system II, are kept in the reduced form by an integral membrane protein DsbD or its analogue, CcdA. In H. pylori HP0377 is re-reduced by CcdA (HP0265); however in E. coli it remains in the oxidized state as it does not interact with E. coli DsbD. Our in vivo work also suggests that both HP0377, which plays a role in apocytochrome reduction, and HP0378, which is involved in heme transport and its ligation into apocytochrome, provide essential functions in H. pylori. Conclusions: The present data, in combination with the resolved three-dimensional structure of the HP0377, suggest that HP0377 is an unusual, multifunctional CcmG protein.

Description: Background: Algorithms designed to detect complex genetic disease associations are initially evaluated using simulated datasets. Typical evaluations vary constraints that influence the correct detection of underlying models (i.e. number of loci, heritability, and minor allele frequency). Such studies neglect to account for model architecture (i.e. the unique specification and arrangement of penetrance values comprising the genetic model), which alone can influence the detectability of a model. In order to design a simulation study which efficiently takes architecture into account, areliable metric is needed for model selection. Results: We evaluate three metrics as predictors of relative model detection difficulty derived from previous works: (1) Penetrance table variance (PTV), (2) customized odds ratio (COR), and (3) our own Ease of Detection Measure (EDM), calculated from the penetrance values and respective genotype frequencies of each simulated genetic model. We evaluate the reliability of these metrics across three very different data search algorithms, each with the capacity to detect epistatic interactions. We find that a model's EDM and COR are each stronger predictors of model detection success than heritability. Conclusions: This study formally identifies and evaluates metrics which quantify model detection difficulty. We utilize these metrics to intelligently select models from a population of potential architectures. This allows for an improved simulation study design which accounts for differences in detection difficulty attributed to model architecture. We implement the calculation and utilization of EDM and COR into GAMETES, an algorithm which rapidly and precisely generates pure, strict, n-locus epistatic models.

Description: Background: Geneticists who look beyond single locus disease associations require additional strategies for the detection of complex multi-locus effects. Epistasis, a multi-locus masking effect, presents a particular challenge, and has been the target of bioinformatic development. Thorough evaluation of new algorithms calls for simulation studies in which known disease models are sought. To date, the best methods for generating simulated multi-locus epistatic models rely on genetic algorithms. However, such methods are computationally expensive, difficult to adapt to multiple objectives, and unlikely to yield models with a precise form of epistasis which we refer to as pure and strict. Purely and strictly epistatic models constitute the worst-case in terms of detecting disease associations, since such associations may only be observed if all n-loci are included in the disease model. This makes them an attractive gold standard for simulation studies considering complex multi-locus effects. Results: We introduce GAMETES, a user-friendly software package and algorithm which generates complex biallelic single nucleotide polymorphism (SNP) disease models for simulation studies. GAMETES rapidly and precisely generates random, pure, strict n-locus models with specified genetic constraints. These constraints include heritability, minor allele frequencies of the SNPs, and population prevalence. GAMETES also includes a simple dataset simulation strategy which may be utilized to rapidly generate an archive of simulated datasets for given genetic models. We highlight the utility and limitations of GAMETES with an example simulation study using MDR, an algorithm designed to detect epistasis. Conclusions: GAMETES is a fast, flexible, and precise tool for generating complex n-locus models with random architectures. While GAMETES has a limited ability to generate models with higher heritabilities, it is proficient at generating the lower heritability models typically used in simulation studies evaluating new algorithms. In addition, the GAMETES modeling strategy may be flexibly combined with any dataset simulation strategy. Beyond dataset simulation, GAMETES could be employed to pursue theoretical characterization of genetic models and epistasis.

Description: Background: The statistical genetics phenomenon of epistasis is widely acknowledged to confound diseaseetiology. In order to evaluate strategies for detecting these complex multi-locus disease associations,simulation studies are required. The development of the GAMETES software for the generation ofcomplex genetic models, has provided the means to randomly generate an architecturally diversepopulation of epistatic models that are both pure and strict, i.e. all n loci, but no fewer, are predictiveof phenotype. Previous theoretical work characterizing complex genetic models has yet to examinepure, strict, epistasis which should be the most challenging to detect. This study addresses threegoals: (1) Classify and characterize pure, strict, two-locus epistatic models, (2) Investigate the effectof model `architecture¿ on detection difficulty, and (3) Explore how adjusting GAMETES constraintsinfluences diversity in the generated models. Results: In this study we utilized a geometric approach to classify pure, strict, two-locus epistatic models by¿shape¿. In total, 33 unique shape symmetry classes were identified. Using a detection difficultymetric, we found that model shape was consistently a significant predictor of model detectiondifficulty. Additionally, after categorizing shape classes by the number of edges in their shapeprojections, we found that this edge number was also significantly predictive of detection difficulty.Analysis of constraints within GAMETES indicated that increasing model population size canexpand model class coverage but does little to change the range of observed difficulty metric scores.A variable population prevalence significantly increased the range of observed difficulty metricscores and, for certain constraints, also improved model class coverage. Conclusions: These analyses further our theoretical understanding of epistatic relationships and uncover guidelinesfor the effective generation of complex models using GAMETES. Specifically, (1) we havecharacterized 33 shape classes by edge number, detection difficulty, and observed frequency (2) ourresults support the claim that model architecture directly influences detection difficulty, and (3) wefound that GAMETES will generate a maximally diverse set of models with a variable populationprevalence and a larger model population size. However, a model population size as small as 1,000 islikely to be sufficient.

Description: Background Geneticists who look beyond single locus disease associations require additional strategies for the detection of complex multi-locus effects. Epistasis, a multi-locus masking effect, presents a particular challenge, and has been the target of bioinformatic development. Thorough evaluation of new algorithms calls for simulation studies in which known disease models are sought. To date, the best methods for generating simulated multi-locus epistatic models rely on genetic algorithms. However, such methods are computationally expensive, difficult to adapt to multiple objectives, and unlikely to yield models with a precise form of epistasis which we refer to as pure and strict. Purely and strictly epistatic models constitute the worst-case in terms of detecting disease associations, since such associations may only be observed if all n-loci are included in the disease model. This makes them an attractive gold standard for simulation studies considering complex multi-locus effects. Results We introduce GAMETES, a user-friendly software package and algorithm which generates complex biallelic single nucleotide polymorphism (SNP) disease models for simulation studies. GAMETES rapidly and precisely generates random, pure, strict n-locus models with specified genetic constraints. These constraints include heritability, minor allele frequencies of the SNPs, and population prevalence. GAMETES also includes a simple dataset simulation strategy which may be utilized to rapidly generate an archive of simulated datasets for given genetic models. We highlight the utility and limitations of GAMETES with an example simulation study using MDR, an algorithm designed to detect epistasis. Conclusions GAMETES is a fast, flexible, and precise tool for generating complex n-locus models with random architectures. While GAMETES has a limited ability to generate models with higher heritabilities, it is proficient at generating the lower heritability models typically used in simulation studies evaluating new algorithms. In addition, the GAMETES modeling strategy may be flexibly combined with any dataset simulation strategy. Beyond dataset simulation, GAMETES could be employed to pursue theoretical characterization of genetic models and epistasis.

Description: Background Algorithms designed to detect complex genetic disease associations are initially evaluated using simulated datasets. Typical evaluations vary constraints that influence the correct detection of underlying models (i.e. number of loci, heritability, and minor allele frequency). Such studies neglect to account for model architecture (i.e. the unique specification and arrangement of penetrance values comprising the genetic model), which alone can influence the detectability of a model. In order to design a simulation study which efficiently takes architecture into account, a reliable metric is needed for model selection. Results We evaluate three metrics as predictors of relative model detection difficulty derived from previous works: (1) Penetrance table variance (PTV), (2) customized odds ratio (COR), and (3) our own Ease of Detection Measure (EDM), calculated from the penetrance values and respective genotype frequencies of each simulated genetic model. We evaluate the reliability of these metrics across three very different data search algorithms, each with the capacity to detect epistatic interactions. We find that a model’s EDM and COR are each stronger predictors of model detection success than heritability. Conclusions This study formally identifies and evaluates metrics which quantify model detection difficulty. We utilize these metrics to intelligently select models from a population of potential architectures. This allows for an improved simulation study design which accounts for differences in detection difficulty attributed to model architecture. We implement the calculation and utilization of EDM and COR into GAMETES, an algorithm which rapidly and precisely generates pure, strict, n-locus epistatic models.