Repeat length and RNA expression level are not primary determinants in CUG expansion toxicity in Drosophila models.

Evidence for an RNA gain-of-function toxicity has now been provided for an increasing number of human pathologies. Myotonic dystrophies (DM) belong to a class of RNA-dominant diseases that result from RNA repeat expansion toxicity. Specifically, DM of type 1 (DM1), is caused by an expansion of CUG repeats in the 3'UTR of the DMPK protein kinase mRNA, while DM of type 2 (DM2) is linked to an expansion of CCUG repeats in an intron of the ZNF9 transcript (ZNF9 encodes a zinc finger protein). In both pathologies the mutant RNA forms nuclear foci. The mechanisms that underlie the RNA pathogenicity seem to be rather complex and not yet completely understood. Here, we describe Drosophila models that might help unravelling the molecular mechanisms of DM1-associated CUG expansion toxicity. We generated transgenic flies that express inducible repeats of different type (CUG or CAG) and length (16, 240, 480 repeats) and then analyzed transgene localization, RNA expression and toxicity as assessed by induced lethality and eye neurodegeneration. The only line that expressed a toxic RNA has a (CTG)(240) insertion. Moreover our analysis shows that its level of expression cannot account for its toxicity. In this line, (CTG)(240.4), the expansion inserted in the first intron of CG9650, a zinc finger protein encoding gene. Interestingly, CG9650 and (CUG)(240.4) expansion RNAs were found in the same nuclear foci. In conclusion, we suggest that the insertion context is the primary determinant for expansion toxicity in Drosophila models. This finding should contribute to the still open debate on the role of the expansions per se in Drosophila and in human pathogenesis of RNA-dominant diseases.

Lobe specific Ca2+-calmodulin nano-domain in neuronal spines: a single molecule level analysis.

Calmodulin (CaM) is a ubiquitous Ca(2+) buffer and second messenger that affects cellular function as diverse as cardiac excitability, synaptic plasticity, and gene transcription. In CA1 pyramidal neurons, CaM regulates two opposing Ca(2+)-dependent processes that underlie memory formation: long-term potentiation (LTP) and long-term depression (LTD). Induction of LTP and LTD require activation of Ca(2+)-CaM-dependent enzymes: Ca(2+)/CaM-dependent kinase II (CaMKII) and calcineurin, respectively. Yet, it remains unclear as to how Ca(2+) and CaM produce these two opposing effects, LTP and LTD. CaM binds 4 Ca(2+) ions: two in its N-terminal lobe and two in its C-terminal lobe. Experimental studies have shown that the N- and C-terminal lobes of CaM have different binding kinetics toward Ca(2+) and its downstream targets. This may suggest that each lobe of CaM differentially responds to Ca(2+) signal patterns. Here, we use a novel event-driven particle-based Monte Carlo simulation and statistical point pattern analysis to explore the spatial and temporal dynamics of lobe-specific Ca(2+)-CaM interaction at the single molecule level. We show that the N-lobe of CaM, but not the C-lobe, exhibits a nano-scale domain of activation that is highly sensitive to the location of Ca(2+) channels, and to the microscopic injection rate of Ca(2+) ions. We also demonstrate that Ca(2+) saturation takes place via two different pathways depending on the Ca(2+) injection rate, one dominated by the N-terminal lobe, and the other one by the C-terminal lobe. Taken together, these results suggest that the two lobes of CaM function as distinct Ca(2+) sensors that can differentially transduce Ca(2+) influx to downstream targets. We discuss a possible role of the N-terminal lobe-specific Ca(2+)-CaM nano-domain in CaMKII activation required for the induction of synaptic plasticity.

Molecular cross talk between misfolded proteins in animal models of Alzheimer's and prion diseases.

The central event in protein misfolding disorders (PMDs) is the accumulation of a misfolded form of a naturally expressed protein. Despite the diversity of clinical symptoms associated with different PMDs, many similarities in their mechanism suggest that distinct pathologies may cross talk at the molecular level. The main goal of this study was to analyze the interaction of the protein misfolding processes implicated in Alzheimer's and prion diseases. For this purpose, we inoculated prions in an Alzheimer's transgenic mouse model that develop typical amyloid plaques and followed the progression of pathological changes over time. Our findings show a dramatic acceleration and exacerbation of both pathologies. The onset of prion disease symptoms in transgenic mice appeared significantly faster with a concomitant increase on the level of misfolded prion protein in the brain. A striking increase in amyloid plaque deposition was observed in prion-infected mice compared with their noninoculated counterparts. Histological and biochemical studies showed the association of the two misfolded proteins in the brain and in vitro experiments showed that protein misfolding can be enhanced by a cross-seeding mechanism. These results suggest a profound interaction between Alzheimer's and prion pathologies, indicating that one protein misfolding process may be an important risk factor for the development of a second one. Our findings may have important implications to understand the origin and progression of PMDs.

Design and analysis for three level hierarchical structures with count response

In numerous intervention studies and education field trials, random assignment to treatment occurs in clusters rather than at the level of observation. This departure of random assignment of units may be due to logistics, political feasibility, or ecological validity. Data within the same cluster or grouping are often correlated. Application of traditional regression techniques, which assume independence between observations, to clustered data produce consistent parameter estimates. However such estimators are often inefficient as compared to methods which incorporate the clustered nature of the data into the estimation procedure (Neuhaus 1993).1 Multilevel models, also known as random effects or random components models, can be used to account for the clustering of data by estimating higher level, or group, as well as lower level, or individual variation. Designing a study, in which the unit of observation is nested within higher level groupings, requires the determination of sample sizes at each level. This study investigates the design and analysis of various sampling strategies for a 3-level repeated measures design on the parameter estimates when the outcome variable of interest follows a Poisson distribution. ^ Results study suggest that second order PQL estimation produces the least biased estimates in the 3-level multilevel Poisson model followed by first order PQL and then second and first order MQL. The MQL estimates of both fixed and random parameters are generally satisfactory when the level 2 and level 3 variation is less than 0.10. However, as the higher level error variance increases, the MQL estimates become increasingly biased. If convergence of the estimation algorithm is not obtained by PQL procedure and higher level error variance is large, the estimates may be significantly biased. In this case bias correction techniques such as bootstrapping should be considered as an alternative procedure. For larger sample sizes, those structures with 20 or more units sampled at levels with normally distributed random errors produced more stable estimates with less sampling variance than structures with an increased number of level 1 units. For small sample sizes, sampling fewer units at the level with Poisson variation produces less sampling variation, however this criterion is no longer important when sample sizes are large. ^ 1Neuhaus J (1993). “Estimation efficiency and Tests of Covariate Effects with Clustered Binary Data”. Biometrics , 49, 989–996^

Sequential updating via dynamic hierarchical models: A Bayesian approach to longitudinal data analysis

Most statistical analysis, theory and practice, is concerned with static models; models with a proposed set of parameters whose values are fixed across observational units. Static models implicitly assume that the quantified relationships remain the same across the design space of the data. While this is reasonable under many circumstances this can be a dangerous assumption when dealing with sequentially ordered data. The mere passage of time always brings fresh considerations and the interrelationships among parameters, or subsets of parameters, may need to be continually revised. ^ When data are gathered sequentially dynamic interim monitoring may be useful as new subject-specific parameters are introduced with each new observational unit. Sequential imputation via dynamic hierarchical models is an efficient strategy for handling missing data and analyzing longitudinal studies. Dynamic conditional independence models offers a flexible framework that exploits the Bayesian updating scheme for capturing the evolution of both the population and individual effects over time. While static models often describe aggregate information well they often do not reflect conflicts in the information at the individual level. Dynamic models prove advantageous over static models in capturing both individual and aggregate trends. Computations for such models can be carried out via the Gibbs sampler. An application using a small sample repeated measures normally distributed growth curve data is presented. ^

Effect of nonnormal random components on level and power in group-randomized trials

The use of group-randomized trials is particularly widespread in the evaluation of health care, educational, and screening strategies. Group-randomized trials represent a subset of a larger class of designs often labeled nested, hierarchical, or multilevel and are characterized by the randomization of intact social units or groups, rather than individuals. The application of random effects models to group-randomized trials requires the specification of fixed and random components of the model. The underlying assumption is usually that these random components are normally distributed. This research is intended to determine if the Type I error rate and power are affected when the assumption of normality for the random component representing the group effect is violated. ^ In this study, simulated data are used to examine the Type I error rate, power, bias and mean squared error of the estimates of the fixed effect and the observed intraclass correlation coefficient (ICC) when the random component representing the group effect possess distributions with non-normal characteristics, such as heavy tails or severe skewness. The simulated data are generated with various characteristics (e.g. number of schools per condition, number of students per school, and several within school ICCs) observed in most small, school-based, group-randomized trials. The analysis is carried out using SAS PROC MIXED, Version 6.12, with random effects specified in a random statement and restricted maximum likelihood (REML) estimation specified. The results from the non-normally distributed data are compared to the results obtained from the analysis of data with similar design characteristics but normally distributed random effects. ^ The results suggest that the violation of the normality assumption for the group component by a skewed or heavy-tailed distribution does not appear to influence the estimation of the fixed effect, Type I error, and power. Negative biases were detected when estimating the sample ICC and dramatically increased in magnitude as the true ICC increased. These biases were not as pronounced when the true ICC was within the range observed in most group-randomized trials (i.e. 0.00 to 0.05). The normally distributed group effect also resulted in bias ICC estimates when the true ICC was greater than 0.05. However, this may be a result of higher correlation within the data. ^

Tumor heterogeneity dictates sensitivity to gefitinib (Iressa(TM)/ZD1839) in solid tumor models

The epidermal growth factor receptor (EGFR) and its ligands are overexpressed in many human tumors, including bladder and pancreas, correlating with a more aggressive tumor phenotype and poor patient prognosis. We initiated the present study to characterize the heterogeneity of gefitinib responsiveness in a panel of human bladder and pancreatic cancer cell lines in order to identify the biological characteristics of EGFR-dependent proliferation that could be used to prospectively identify drug-sensitive tumors. A second objective was to elucidate how to best exploit these results by utilizing gefitinib in combination therapy. To these ends, we examined the effects of the EGFR antagonist gefitinib on proliferation and apoptosis in a panel of 18 human bladder cancer cell lines and 9 human pancreatic cancer cell lines. Our data confirmed the existence of marked heterogeneity in Iressa responsiveness with less than half of the cell lines displaying significant growth inhibition by clinically relevant concentrations of the drug. Gefitinib responsiveness was found to be p27 kip1 dependent as DNA synthesis was restored following exposure to p27siRNA. Unfortunately, Iressa responsiveness was not closely linked to surface EGFR or TGF-α expression in the bladder cancer cells, however, cellular TGF-α expression correlated directly with Iressa sensitivity in the pancreatic cancer cell lines. These findings provide the potential for prospectively identifying patients with drug-sensitive tumors. ^ Further studies aimed at exploiting gefitinib-mediated cell cycle effects led us to investigate if gefitinib-mediated TRAIL sensitization correlated with increased p27kip1 accumulation. We observed that increased TRAIL sensitivity following gefitinib exposure was not dependent on p27 kip1 expression. Additional studies initiated to examine the role(s) of Akt and Erk signaling demonstrated that exposure to PI3K or MEK inhibitors significantly enhanced TRAIL-induced apoptosis at concentrations that block target phosphorylation. Furthermore, combinations of TRAIL and the PI3K or MEK inhibitors increased procaspase-8 processing above levels observed with TRAIL alone, indicating that the effects were exerted at the level of caspase-8 activation, considered the earliest step in the TRAIL pathway. ^

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. ^

The association of HSV 1 and 2 with atherosclerosis defined by CRP level

A study of the association of Herpes simplex virus 1 and 2 exposure to early atherosclerosis using high C-reactive protein level as a marker was carried out in US born, non-pregnant, 20-49 year olds participating in a national survey between 1999 and 2004. Participants were required to have valid results for Herpes simplex virus 1 and 2 and C-Reactive Protein for inclusion. Cases were those found to have a high C-reactive protein level of 0.3-1 mg/dL, while controls had low to normal values (0.01-0.29 mg/dL). Overall, there were 1211 cases and 2870 controls. Mexican American and non-Hispanic black women were much more likely to fall into the high cardiac risk group than the other sex race groups with proportions of 44% and 39%, respectively. ^ Herpesvirus exposure was categorized such that Herpes simplex virus 1 and 2 exposure could be studied simultaneously within the same individual and models. The HSV 1+, HSV 2- category included the highest percentage (45.63%) of participants, followed by HSV 1-, HSV 2- (30.16%); HSV 1+, HSV 2+ (15.09%); and HSV 1-, HSV 2+ (9.12%) respectively. The proportion of participants in the HSV 1+, HSV 2- category was substantially higher in Mexican Americans (63%-66%). Further, the proportion in the HSV 1+, HSV 2+ category was notably higher in the non-Hispanic black participants (23%-44%). Non-Hispanic black women also had the highest percentage of HSV 1-, HSV 2+ exposure of all the sex race groups at 17%. ^ Overall, the unadjusted odds ratios for atherosclerotic disease defined by C-reactive protein with HSV 1-, HSV 2- as the referent group was 1.62 (95% CI 1.23-2.14) for HSV 1 +, HSV 2+; 1.3 (95% CI 1.10-1.69 for HSV 1+, HSV 2-; and 1.52 (95% CI 1.14-2.01). When the study was stratified into sex-race groups, only HSV 1+, HSV 2- in the Non-Hispanic white men remained significant (OR=1.6; 95% CI 1.06-2.43). Adjustment for selected covariates was made in the multivariate model for both the overall and sex-race stratified studies. High C-reactive protein values were not associated with any of the Herpesvirus exposure levels in either the overall or stratified analyses. ^

Assessing and applying evidence-based interventions at the community level in India as a model policy to reduce neonatal mortality rates in Nigeria

Background. The United Nations' Millennium Development Goal (MDG) 4 aims for a two-thirds reduction in death rates for children under the age of five by 2015. The greatest risk of death is in the first week of life, yet most of these deaths can be prevented by such simple interventions as improved hygiene, exclusive breastfeeding, and thermal care. The percentage of deaths in Nigeria that occur in the first month of life make up 28% of all deaths under five years, a statistic that has remained unchanged despite various child health policies. This paper will address the challenges of reducing the neonatal mortality rate in Nigeria by examining the literature regarding efficacy of home-based, newborn care interventions and policies that have been implemented successfully in India. ^ Methods. I compared similarities and differences between India and Nigeria using qualitative descriptions and available quantitative data of various health indicators. The analysis included identifying policy-related factors and community approaches contributing to India's newborn survival rates. Databases and reference lists of articles were searched for randomized controlled trials of community health worker interventions shown to reduce neonatal mortality rates. ^ Results. While it appears that Nigeria spends more money than India on health per capita ($136 vs. $132, respectively) and as percent GDP (5.8% vs. 4.2%, respectively), it still lags behind India in its neonatal, infant, and under five mortality rates (40 vs. 32 deaths/1000 live births, 88 vs. 48 deaths/1000 live births, 143 vs. 63 deaths/1000 live births, respectively). Both countries have comparably low numbers of healthcare providers. Unlike their counterparts in Nigeria, Indian community health workers receive training on how to deliver postnatal care in the home setting and are monetarily compensated. Gender-related power differences still play a role in the societal structure of both countries. A search of randomized controlled trials of home-based newborn care strategies yielded three relevant articles. Community health workers trained to educate mothers and provide a preventive package of interventions involving clean cord care, thermal care, breastfeeding promotion, and danger sign recognition during multiple postnatal visits in rural India, Bangladesh, and Pakistan reduced neonatal mortality rates by 54%, 34%, and 15–20%, respectively. ^ Conclusion. Access to advanced technology is not necessary to reduce neonatal mortality rates in resource-limited countries. To address the urgency of neonatal mortality, countries with weak health systems need to start at the community level and invest in cost-effective, evidence-based newborn care interventions that utilize available human resources. While more randomized controlled studies are urgently needed, the current available evidence of models of postnatal care provision demonstrates that home-based care and health education provided by community health workers can reduce neonatal mortality rates in the immediate future.^