Variable phenotypic expressivity in inbred retinal degeneration mouse lines: A comparative study of C3H/HeOu and FVB/N rd1 mice

PURPOSE Recent advances in optogenetics and gene therapy have led to promising new treatment strategies for blindness caused by retinal photoreceptor loss. Preclinical studies often rely on the retinal degeneration 1 (rd1 or Pde6b(rd1)) retinitis pigmentosa (RP) mouse model. The rd1 founder mutation is present in more than 100 actively used mouse lines. Since secondary genetic traits are well-known to modify the phenotypic progression of photoreceptor degeneration in animal models and human patients with RP, negligence of the genetic background in the rd1 mouse model is unwarranted. Moreover, the success of various potential therapies, including optogenetic gene therapy and prosthetic implants, depends on the progress of retinal degeneration, which might differ between rd1 mice. To examine the prospect of phenotypic expressivity in the rd1 mouse model, we compared the progress of retinal degeneration in two common rd1 lines, C3H/HeOu and FVB/N. METHODS We followed retinal degeneration over 24 weeks in FVB/N, C3H/HeOu, and congenic Pde6b(+) seeing mouse lines, using a range of experimental techniques including extracellular recordings from retinal ganglion cells, PCR quantification of cone opsin and Pde6b transcripts, in vivo flash electroretinogram (ERG), and behavioral optokinetic reflex (OKR) recordings. RESULTS We demonstrated a substantial difference in the speed of retinal degeneration and accompanying loss of visual function between the two rd1 lines. Photoreceptor degeneration and loss of vision were faster with an earlier onset in the FVB/N mice compared to C3H/HeOu mice, whereas the performance of the Pde6b(+) mice did not differ significantly in any of the tests. By postnatal week 4, the FVB/N mice expressed significantly less cone opsin and Pde6b mRNA and had neither ERG nor OKR responses. At 12 weeks of age, the retinal ganglion cells of the FVB/N mice had lost all light responses. In contrast, 4-week-old C3H/HeOu mice still had ERG and OKR responses, and we still recorded light responses from C3H/HeOu retinal ganglion cells until the age of 24 weeks. These results show that genetic background plays an important role in the rd1 mouse pathology. CONCLUSIONS Analogous to human RP, the mouse genetic background strongly influences the rd1 phenotype. Thus, different rd1 mouse lines may follow different timelines of retinal degeneration, making exact knowledge of genetic background imperative in all studies that use rd1 models.

Variable effects of the conserved RNA hairpin element upon 3' end processing of histone pre-mRNA in vitro.

We have studied the requirements for efficient histone-specific RNA 3' processing in nuclear extract from mammalian tissue culture cells. Processing is strongly impaired by mutations in the pre-mRNA spacer element that reduce the base-pairing potential with U7 RNA. Moreover, by exchanging the hairpin and spacer elements of two differently processed H4 genes, we find that this difference is exclusively due to the spacer element. Finally, processing is inhibited by the addition of competitor RNAs, if these contain a wild-type spacer sequence, but not if their spacer element is mutated. Conversely, the importance of the hairpin for histone RNA 3' processing is highly variable: A hairpin mutant of the H4-12 gene is processed with almost wild-type efficiency in extract from K21 mouse mastocytoma cells but is strongly affected in HeLa cell extract, whereas an identical hairpin mutant of the H4-1 gene is affected in both extracts. The hairpin defect of H4-12-specific RNA in HeLa cells can be overcome by a compensatory mutation that increases the base complementarity to U7 snRNA. Very similar results were also obtained in RNA competition experiments: processing of H4-12-specific RNA can be competed by RNA carrying a wild-type hairpin element in extract from HeLa, but not K21 cells, whereas processing of H4-1-specific RNA can be competed in both extracts. With two additional histone genes we obtained results that were in one case intermediate and in the other similar to those obtained with H4-1. These results suggest that hairpin binding factor(s) can cooperatively support the ability of U7 snRNPs to form an active processing complex, but is(are) not directly involved in the processing mechanism.

Increased spread and replication efficiency of Listeria monocytogenes in organotypic brain-slices is related to multilocus variable number of tandem repeat analysis (MLVA) complex.

BACKGROUND Listeria (L.) monocytogenes causes fatal infections in many species including ruminants and humans. In ruminants, rhombencephalitis is the most prevalent form of listeriosis. Using multilocus variable number tandem repeat analysis (MLVA) we recently showed that L. monocytogenes isolates from ruminant rhombencephalitis cases are distributed over three genetic complexes (designated A, B and C). However, the majority of rhombencephalitis strains and virtually all those isolated from cattle cluster in MLVA complex A, indicating that strains of this complex may have increased neurotropism and neurovirulence. The aim of this study was to investigate whether ruminant rhombencephalitis strains have an increased ability to propagate in the bovine hippocampal brain-slice model and can be discriminated from strains of other sources. For this study, forty-seven strains were selected and assayed on brain-slice cultures, a bovine macrophage cell line (BoMac) and a human colorectal adenocarcinoma cell line (Caco-2). They were isolated from ruminant rhombencephalitis cases (n = 21) and other sources including the environment, food, human neurolisteriosis cases and ruminant/human non-encephalitic infection cases (n = 26). RESULTS All but one L. monocytogenes strain replicated in brain slices, irrespectively of the source of the isolate or MLVA complex. The replication of strains from MLVA complex A was increased in hippocampal brain-slice cultures compared to complex C. Immunofluorescence revealed that microglia are the main target cells for L. monocytogenes and that strains from MLVA complex A caused larger infection foci than strains from MLVA complex C. Additionally, they caused larger plaques in BoMac cells, but not CaCo-2 cells. CONCLUSIONS Our brain slice model data shows that all L. monocytogenes strains should be considered potentially neurovirulent. Secondly, encephalitis strains cannot be conclusively discriminated from non-encephalitis strains with the bovine organotypic brain slice model. The data indicates that MLVA complex A strains are particularly adept at establishing encephalitis possibly by virtue of their higher resistance to antibacterial defense mechanisms in microglia cells, the main target of L. monocytogenes.

Revisiting the ISN Flow Parameters, using a variable IBEX pointing strategy

The Interstellar Boundary Explorer (IBEX) has observed the interstellar neutral (ISN) gas flow over the past 6 yr during winter/spring when the Earth's motion opposes the ISN flow. Since IBEX observes the interstellar atom trajectories near their perihelion, we can use an analytical model based upon orbital mechanics to determine the interstellar parameters. Interstellar flow latitude, velocity, and temperature are coupled to the flow longitude and are restricted by the IBEX observations to a narrow tube in this parameter space. In our original analysis we found that pointing the spacecraft spin axis slightly out of the ecliptic plane significantly influences the ISN flow vector determination. Introducing the spacecraft spin axis tilt into the analytical model has shown that IBEX observations with various spin axis tilt orientations can substantially reduce the range of acceptable solutions to the ISN flow parameters as a function of flow longitude. The IBEX operations team pointed the IBEX spin axis almost exactly within the ecliptic plane during the 2012-2014 seasons, and about 5° below the ecliptic for half of the 2014 season. In its current implementation the analytical model describes the ISN flow most precisely for the spin axis orientation exactly in the ecliptic. This analysis refines the derived ISN flow parameters with a possible reconciliation between velocity vectors found with IBEX and Ulysses, resulting in a flow longitude lambda∞ = 74.°5 ± 1.°7 and latitude beta∞ = -5.°2 ± 0.°3, but at a substantially higher ISN temperature than previously reported.

Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

An empirical evaluation of the Random Forests classifier models for variable selection in a large-scale lung cancer case-control study

Random Forests™ is reported to be one of the most accurate classification algorithms in complex data analysis. It shows excellent performance even when most predictors are noisy and the number of variables is much larger than the number of observations. In this thesis Random Forests was applied to a large-scale lung cancer case-control study. A novel way of automatically selecting prognostic factors was proposed. Also, synthetic positive control was used to validate Random Forests method. Throughout this study we showed that Random Forests can deal with large number of weak input variables without overfitting. It can account for non-additive interactions between these input variables. Random Forests can also be used for variable selection without being adversely affected by collinearities. ^ Random Forests can deal with the large-scale data sets without rigorous data preprocessing. It has robust variable importance ranking measure. Proposed is a novel variable selection method in context of Random Forests that uses the data noise level as the cut-off value to determine the subset of the important predictors. This new approach enhanced the ability of the Random Forests algorithm to automatically identify important predictors for complex data. The cut-off value can also be adjusted based on the results of the synthetic positive control experiments. ^ When the data set had high variables to observations ratio, Random Forests complemented the established logistic regression. This study suggested that Random Forests is recommended for such high dimensionality data. One can use Random Forests to select the important variables and then use logistic regression or Random Forests itself to estimate the effect size of the predictors and to classify new observations. ^ We also found that the mean decrease of accuracy is a more reliable variable ranking measurement than mean decrease of Gini. ^

An ordinal logistic regression model with misclassification of the outcome variable and categorical covariate

Ordinal outcomes are frequently employed in diagnosis and clinical trials. Clinical trials of Alzheimer's disease (AD) treatments are a case in point using the status of mild, moderate or severe disease as outcome measures. As in many other outcome oriented studies, the disease status may be misclassified. This study estimates the extent of misclassification in an ordinal outcome such as disease status. Also, this study estimates the extent of misclassification of a predictor variable such as genotype status. An ordinal logistic regression model is commonly used to model the relationship between disease status, the effect of treatment, and other predictive factors. A simulation study was done. First, data based on a set of hypothetical parameters and hypothetical rates of misclassification was created. Next, the maximum likelihood method was employed to generate likelihood equations accounting for misclassification. The Nelder-Mead Simplex method was used to solve for the misclassification and model parameters. Finally, this method was applied to an AD dataset to detect the amount of misclassification present. The estimates of the ordinal regression model parameters were close to the hypothetical parameters. β1 was hypothesized at 0.50 and the mean estimate was 0.488, β2 was hypothesized at 0.04 and the mean of the estimates was 0.04. Although the estimates for the rates of misclassification of X1 were not as close as β1 and β2, they validate this method. X 1 0-1 misclassification was hypothesized as 2.98% and the mean of the simulated estimates was 1.54% and, in the best case, the misclassification of k from high to medium was hypothesized at 4.87% and had a sample mean of 3.62%. In the AD dataset, the estimate for the odds ratio of X 1 of having both copies of the APOE 4 allele changed from an estimate of 1.377 to an estimate 1.418, demonstrating that the estimates of the odds ratio changed when the analysis includes adjustment for misclassification. ^

The application of latent variable models to the assessment of determinants of HIV risk behavior

Studies on the relationship between psychosocial determinants and HIV risk behaviors have produced little evidence to support hypotheses based on theoretical relationships. One limitation inherent in many articles in the literature is the method of measurement of the determinants and the analytic approach selected. ^ To reduce the misclassification associated with unit scaling of measures specific to internalized homonegativity, I evaluated the psychometric properties of the Reactions to Homosexuality scale in a confirmatory factor analytic framework. In addition, I assessed the measurement invariance of the scale across racial/ethnic classifications in a sample of men who have sex with men. The resulting measure contained eight items loading on three first-order factors. Invariance assessment identified metric and partial strong invariance between racial/ethnic groups in the sample. ^ Application of the updated measure to a structural model allowed for the exploration of direct and indirect effects of internalized homonegativity on unprotected anal intercourse. Pathways identified in the model show that drug and alcohol use at last sexual encounter, the number of sexual partners in the previous three months and sexual compulsivity all contribute directly to risk behavior. Internalized homonegativity reduced the likelihood of exposure to drugs, alcohol or higher numbers of partners. For men who developed compulsive sexual behavior as a coping strategy for internalized homonegativity, there was an increase in the prevalence odds of risk behavior. ^ In the final stage of the analysis, I conducted a latent profile analysis of the items in the updated Reactions to Homosexuality scale. This analysis identified five distinct profiles, which suggested that the construct was not homogeneous in samples of men who have sex with men. Lack of prior consideration of these distinct manifestations of internalized homonegativity may have contributed to the analytic difficulty in identifying a relationship between the trait and high-risk sexual practices. ^

Instrumental variable method for the causal relationship between soluble receptor for advanced glycation end-products (sRAGE) and risk of pancreatic cancer

This thesis project is motivated by the potential problem of using observational data to draw inferences about a causal relationship in observational epidemiology research when controlled randomization is not applicable. Instrumental variable (IV) method is one of the statistical tools to overcome this problem. Mendelian randomization study uses genetic variants as IVs in genetic association study. In this thesis, the IV method, as well as standard logistic and linear regression models, is used to investigate the causal association between risk of pancreatic cancer and the circulating levels of soluble receptor for advanced glycation end-products (sRAGE). Higher levels of serum sRAGE were found to be associated with a lower risk of pancreatic cancer in a previous observational study (255 cases and 485 controls). However, such a novel association may be biased by unknown confounding factors. In a case-control study, we aimed to use the IV approach to confirm or refute this observation in a subset of study subjects for whom the genotyping data were available (178 cases and 177 controls). Two-stage IV method using generalized method of moments-structural mean models (GMM-SMM) was conducted and the relative risk (RR) was calculated. In the first stage analysis, we found that the single nucleotide polymorphism (SNP) rs2070600 of the receptor for advanced glycation end-products (AGER) gene meets all three general assumptions for a genetic IV in examining the causal association between sRAGE and risk of pancreatic cancer. The variant allele of SNP rs2070600 of the AGER gene was associated with lower levels of sRAGE, and it was neither associated with risk of pancreatic cancer, nor with the confounding factors. It was a potential strong IV (F statistic = 29.2). However, in the second stage analysis, the GMM-SMM model failed to converge due to non- concaveness probably because of the small sample size. Therefore, the IV analysis could not support the causality of the association between serum sRAGE levels and risk of pancreatic cancer. Nevertheless, these analyses suggest that rs2070600 was a potentially good genetic IV for testing the causality between the risk of pancreatic cancer and sRAGE levels. A larger sample size is required to conduct a credible IV analysis.^

Evaluation of the Hosmer -Lemeshow global goodness -of-fit statistic for mixed variable logistic regression models

The performance of the Hosmer-Lemeshow global goodness-of-fit statistic for logistic regression models was explored in a wide variety of conditions not previously fully investigated. Computer simulations, each consisting of 500 regression models, were run to assess the statistic in 23 different situations. The items which varied among the situations included the number of observations used in each regression, the number of covariates, the degree of dependence among the covariates, the combinations of continuous and discrete variables, and the generation of the values of the dependent variable for model fit or lack of fit.^ The study found that the $\rm\ C$g* statistic was adequate in tests of significance for most situations. However, when testing data which deviate from a logistic model, the statistic has low power to detect such deviation. Although grouping of the estimated probabilities into quantiles from 8 to 30 was studied, the deciles of risk approach was generally sufficient. Subdividing the estimated probabilities into more than 10 quantiles when there are many covariates in the model is not necessary, despite theoretical reasons which suggest otherwise. Because it does not follow a X$\sp2$ distribution, the statistic is not recommended for use in models containing only categorical variables with a limited number of covariate patterns.^ The statistic performed adequately when there were at least 10 observations per quantile. Large numbers of observations per quantile did not lead to incorrect conclusions that the model did not fit the data when it actually did. However, the statistic failed to detect lack of fit when it existed and should be supplemented with further tests for the influence of individual observations. Careful examination of the parameter estimates is also essential since the statistic did not perform as desired when there was moderate to severe collinearity among covariates.^ Two methods studied for handling tied values of the estimated probabilities made only a slight difference in conclusions about model fit. Neither method split observations with identical probabilities into different quantiles. Approaches which create equal size groups by separating ties should be avoided. ^

"Nos gritaron que nos fuéramos" (¿o que nos vayamos?) : El uso variable de los tiempos verbales como estrategia etnopragmática

Nuestro objetivo es analizar el uso alternante del Presente y el Pretérito Imperfecto del Modo Subjuntivo en un corpus constituido por emisiones pertenecientes a artículos periodísticos. El problema propuesto está vinculado a la evidencialidad como sustancia semántica que subyace a la selección de las formas. Esto es, en este caso, la posibilidad de determinar la evaluación y el grado de compromiso que cada sujeto establece con la fuente de información y la evaluación que hace de esta última. Algunas lenguas poseen morfemas específicos con los que se indica dicha función, otras, en cambio, no disponen de tales morfemas -tal es el caso del español- razón por la cual sus usuarios echan mano a ciertos recursos que la lengua les provee. Desde el enfoque de la Etnopragmática, intentaremos explicar en qué consisten los usos alternantes que los sujetos realizan en la conformación de enunciados en los que los verbos principal y dependiente se hallan en correlación temporal, descubrir a qué factores responde la selección de los verbos dependientes y relacionar tales factores con la motivación que da lugar a la selección de las formas en variación.