Gamma-secretase modulators and inhibitors in Alzheimer's disease: influence of genetic background on efficacy and mechanism of action

According to the amyloid hypothesis, Alzheimer’s disease (AD) is caused by aberrant production or clearance of the amyloid-β (Aβ) peptides, and in particular of the longer more aggregation-prone Aβ42. The Aβ peptides are generated through successive proteolytic cleavage of the amyloid precursor protein (APP) by the β-site APP cleaving enzyme (BACE) and γ-secretase. γ-secretase produces Aβ peptides with variable C-termini ranging from Aβ34 to Aβ48, presumably by sequential trimming of longer into shorter peptides. γ-secretase is a multiprotein complex consisting of at least four different proteins and the presenilin proteins (PS1 or PS2) contain the catalytic center of the complex. In 2001 several non-steroidal anti-inflammatory drugs were identified as the founding members of a new class of γ-secretase modulators (GSMs) that can selectively reduce production of Aβ42. Concomitantly, these GSMs increase Aβ38 production indicating closely coordinated generation of Aβ42 and Aβ38 and a potential precursor-product relationship between these peptides. GSMs seem to exert their activity by direct modulation of γ-secretase. Support for this hypothesis is drawn from the finding that some PS mutations associated with early-onset familial AD (FAD) can modulate the cellular response to GSMs and to γ-secretase inhibitors (GSIs), which inhibit production of all Aβ peptides and are known to directly interact with PS. A particularly interesting FAD PS mutation is PS1-ΔExon9, a complex deletion mutant that blocks endoproteolysis of PS1 and renders cells completely non-responsive to GSMs. Studies presented in this thesis show that the diminished response of PS1-ΔExon9 to GSMs is mainly caused by its lack of endoproteolytic cleavage. Furthermore, we were able to demonstrate that a reduced response to GSMs and GSIs is not limited to PS1-ΔExon9 but is a common effect of aggressive FAD-associated PS1 mutations. Surprisingly, we also found that while the Aβ42 response to GSMs is almost completely abolished by these PS1 mutations, the accompanying Aβ38 increase was indistinguishable to wild-type PS1. Finally, the reduced response to GSIs was confirmed in a mouse model with transgenic expression of an aggressive FAD-associated PS1 mutation as a highly potent GSI failed to reduce Aβ42 levels in brain of these mice. Taken together, our findings provide clear evidence for independent generation of Aβ42 and Aβ38 peptides, and argue that the sequential cleavage model might be an oversimplification of the molecular mechanism of γ-secretase. Most importantly, our results highlight the significance of genetic background in drug discovery efforts aimed at γ-secretase, and indicate that the use of cellular models with transgenic expression of FAD-associated PS mutations might confound studies of the potency and efficacy of GSMs and GSIs. Therefore, such models should be strictly avoided in the ongoing preclinical development of these promising and potentially disease-modifying therapeutics for AD.

Biomolecular studies in Alzheimer’s Disease models: investigations in vitro and in vivo

The Alzheimer’s disease (AD), the most prevalent form of age-related dementia, is a multifactorial and heterogeneous neurodegenerative disease. The molecular mechanisms underlying the pathogenesis of AD are yet largely unknown. However, the etiopathogenesis of AD likely resides in the interaction between genetic and environmental risk factors. Among the different factors that contribute to the pathogenesis of AD, amyloid-beta peptides and the genetic risk factor apoE4 are prominent on the basis of genetic evidence and experimental data. ApoE4 transgenic mice have deficits in spatial learning and memory associated with inflammation and brain atrophy. Evidences suggest that apoE4 is implicated in amyloid-beta accumulation, imbalance of cellular antioxidant system and in apoptotic phenomena. The mechanisms by which apoE4 interacts with other AD risk factors leading to an increased susceptibility to the dementia are still unknown. The aim of this research was to provide new insights into molecular mechanisms of AD neurodegeneration, investigating the effect of amyloid-beta peptides and apoE4 genotype on the modulation of genes and proteins differently involved in cellular processes related to aging and oxidative balance such as PIN1, SIRT1, PSEN1, BDNF, TRX1 and GRX1. In particular, we used human neuroblastoma cells exposed to amyloid-beta or apoE3 and apoE4 proteins at different time-points, and selected brain regions of human apoE3 and apoE4 targeted replacement mice, as in vitro and in vivo models, respectively. All genes and proteins studied in the present investigation are modulated by amyloid-beta and apoE4 in different ways, suggesting their involvement in the neurodegenerative mechanisms underlying the AD. Finally, these proteins might represent novel potential diagnostic and therapeutic targets in AD.

Generation and characterization of mouse models of Small cell lung cancer and Basal cell carcinoma for the preclinical evaluation of new therapies

Background. Human small cell lung cancer (SCLC) accounting for approximately 15-20% of all lung cancers, is an aggressive tumor with high propensity for early regional and distant metastases. Although the initial tumor rate response to chemotherapy is very high, SCLC relapses after approximately 4 months in ED and 12 months in LD. Basal cell carcinoma (BCC) is the most prevalent cancer in the western world, and its incidence is increasing worldwide. This type of cancer rarely metastasizes and the death rate is extraordinary low. Surgery is curative for most of the patients, but for those that develop locally advanced or metastatic BCC there is currently no effective treatment. Both types of cancer have been deeply investigated and genetic alterations, MYCN amplification (MA) among the most interesting, have been found. These could become targets of new pharmacological therapies. Procedures. We created and characterized novel BLI xenograft orthotopic mouse models of SCLC to evaluate the tumor onset and progression and the efficacy of new pharmacological strategies. We compared an in vitro model with a transgenic mouse model of BCC, to investigate and delineate the canonical HH signalling pathway and its connections with other molecular pathways. Results and conclusions. The orthotopic models showed latency and progression patterns similar to human disease. Chemotherapy treatments improved survival rates and validated the in vivo model. The presence of MA and overexpression were confirmed in each model and we tested the efficacy of a new MYCN inhibitor in vitro. Preliminar data of BCC models highlighted Hedgehog pathway role and underlined the importance of both in vitro and in vivo strategies to achieve a better understanding of the pathology and to evaluate the applicability of new therapeutic compounds

The genetic basis of craniofacial and dental abnormalities

The embryonic head development, including the formation of dental structures, is a complex and delicate process guided by specific genetic programs. Genetic changes and environmental factors can disturb the execution of these programs and result in abnormalities in orofacial and dental structures. Orofacial clefts and hypodontia/ oligodontia are examples of such abnormalities frequently seen in dental clinics. An insight into the mechanisms and genes involved in the formation of orofacial and dental structures has been gradually gained by genetic analysis of families and by the use of experimental vertebrate models such as the mouse and chick models. The development of novel clinical therapies for orofacial and dental pathological conditions depends very much on a detailed knowledge of the molecular and cellular processes that are involved in head formation.

Echinococcus metacestodes as laboratory models for the screening of drugs against cestodes and trematodes

Among the cestodes, Echinococcus granulosus, Echinococcus multilocularis and Taenia solium represent the most dangerous parasites. Their larval stages cause the diseases cystic echinococcosis (CE), alveolar echinococcosis (AE) and cysticercosis, respectively, which exhibit considerable medical and veterinary health concerns with a profound economic impact. Others caused by other cestodes, such as species of the genera Mesocestoides and Hymenolepis, are relatively rare in humans. In this review, we will focus on E. granulosus and E. multilocularis metacestode laboratory models and will review the use of these models in the search for novel drugs that could be employed for chemotherapeutic treatment of echinococcosis. Clearly, improved therapeutic drugs are needed for the treatment of AE and CE, and this can only be achieved through the development of medium-to-high throughput screening approaches. The most recent achievements in the in vitro culture and genetic manipulation of E. multilocularis cells and metacestodes, and the accessability of the E. multilocularis genome and EST sequence information, have rendered the E. multilocularis model uniquely suited for studies on drug-efficacy and drug target identification. This could lead to the development of novel compounds for the use in chemotherapy against echinococcosis, and possibly against diseases caused by other cestodes, and potentially also trematodes.

The quantitative genetic basis of offspring solicitation and parental response in a passerine bird with biparental care

The coevolution of parental investment and offspring solicitation is driven by partly different evolutionary interests of genes expressed in parents and their offspring. In species with biparental care, the outcome of this conflict ma!: be influenced by the sexual conflict over parental investment, Models for the resolution of such family conflicts have made so far untested assumptions about genetic variation and covariation in the parental resource provisioning response and the level of offspring solicitation. Using a combination of cross-fostering and begging playback experiments, we show that, in the great tit (Parus major), (i) the begging call intensity of nestlings depends on their common origin, suggesting genetic variation for this begging display, (ii) only mothers respond to begging calls by increased food provisioning, and (iii! the size of the parental response is positively related to the begging call intensity of nestlings in the maternal but not paternal line. This study indicates that genetic covariation, its differential expression in the maternal and paternal lines and/or early environmental and parental effects need to be taken into account when predicting the phenotypic outcome of the conflict over investment between genes expressed in each parent and the offspring. [References: 36]

USE OF HIDDEN MARKOV MODELS FOR QTL MAPPING

An important aspect of the QTL mapping problem is the treatment of missing genotype data. If complete genotype data were available, QTL mapping would reduce to the problem of model selection in linear regression. However, in the consideration of loci in the intervals between the available genetic markers, genotype data is inherently missing. Even at the typed genetic markers, genotype data is seldom complete, as a result of failures in the genotyping assays or for the sake of economy (for example, in the case of selective genotyping, where only individuals with extreme phenotypes are genotyped). We discuss the use of algorithms developed for hidden Markov models (HMMs) to deal with the missing genotype data problem.

Asymptotics for Marginal Generalized Linear Models With Sparse Correlations

Marginal generalized linear models can be used for clustered and longitudinal data by fitting a model as if the data were independent and using an empirical estimator of parameter standard errors. We extend this approach to data where the number of observations correlated with a given one grows with sample size and show that parameter estimates are consistent and asymptotically Normal with a slower convergence rate than for independent data, and that an information sandwich variance estimator is consistent. We present two problems that motivated this work, the modelling of patterns of HIV genetic variation and the behavior of clustered data estimators when clusters are large.

Fitting ACE Structural Equation Models to Case-Control Family Data

Investigators interested in whether a disease aggregates in families often collect case-control family data, which consist of disease status and covariate information for families selected via case or control probands. Here, we focus on the use of case-control family data to investigate the relative contributions to the disease of additive genetic effects (A), shared family environment (C), and unique environment (E). To this end, we describe a ACE model for binary family data and then introduce an approach to fitting the model to case-control family data. The structural equation model, which has been described previously, combines a general-family extension of the classic ACE twin model with a (possibly covariate-specific) liability-threshold model for binary outcomes. Our likelihood-based approach to fitting involves conditioning on the proband’s disease status, as well as setting prevalence equal to a pre-specified value that can be estimated from the data themselves if necessary. Simulation experiments suggest that our approach to fitting yields approximately unbiased estimates of the A, C, and E variance components, provided that certain commonly-made assumptions hold. These assumptions include: the usual assumptions for the classic ACE and liability-threshold models; assumptions about shared family environment for relative pairs; and assumptions about the case-control family sampling, including single ascertainment. When our approach is used to fit the ACE model to Austrian case-control family data on depression, the resulting estimate of heritability is very similar to those from previous analyses of twin data.

Statistical evaluation of alternative models of human evolution

An appropriate model of recent human evolution is not only important to understand our own history, but it is necessary to disentangle the effects of demography and selection on genome diversity. Although most genetic data support the view that our species originated recently in Africa, it is still unclear if it completely replaced former members of the Homo genus, or if some interbreeding occurred during its range expansion. Several scenarios of modern human evolution have been proposed on the basis of molecular and paleontological data, but their likelihood has never been statistically assessed. Using DNA data from 50 nuclear loci sequenced in African, Asian and Native American samples, we show here by extensive simulations that a simple African replacement model with exponential growth has a higher probability (78%) as compared with alternative multiregional evolution or assimilation scenarios. A Bayesian analysis of the data under this best supported model points to an origin of our species approximately 141 thousand years ago (Kya), an exit out-of-Africa approximately 51 Kya, and a recent colonization of the Americas approximately 10.5 Kya. We also find that the African replacement model explains not only the shallow ancestry of mtDNA or Y-chromosomes but also the occurrence of deep lineages at some autosomal loci, which has been formerly interpreted as a sign of interbreeding with Homo erectus.

Genetic association studies under the population stratification, family pedigree and application to genome-wide association studies

This dissertation has three separate parts: the first part deals with the general pedigree association testing incorporating continuous covariates; the second part deals with the association tests under population stratification using the conditional likelihood tests; the third part deals with the genome-wide association studies based on the real rheumatoid arthritis (RA) disease data sets from Genetic Analysis Workshop 16 (GAW16) problem 1. Many statistical tests are developed to test the linkage and association using either case-control status or phenotype covariates for family data structure, separately. Those univariate analyses might not use all the information coming from the family members in practical studies. On the other hand, the human complex disease do not have a clear inheritance pattern, there might exist the gene interactions or act independently. In part I, the new proposed approach MPDT is focused on how to use both the case control information as well as the phenotype covariates. This approach can be applied to detect multiple marker effects. Based on the two existing popular statistics in family studies for case-control and quantitative traits respectively, the new approach could be used in the simple family structure data set as well as general pedigree structure. The combined statistics are calculated using the two statistics; A permutation procedure is applied for assessing the p-value with adjustment from the Bonferroni for the multiple markers. We use simulation studies to evaluate the type I error rates and the powers of the proposed approach. Our results show that the combined test using both case-control information and phenotype covariates not only has the correct type I error rates but also is more powerful than the other existing methods. For multiple marker interactions, our proposed method is also very powerful. Selective genotyping is an economical strategy in detecting and mapping quantitative trait loci in the genetic dissection of complex disease. When the samples arise from different ethnic groups or an admixture population, all the existing selective genotyping methods may result in spurious association due to different ancestry distributions. The problem can be more serious when the sample size is large, a general requirement to obtain sufficient power to detect modest genetic effects for most complex traits. In part II, I describe a useful strategy in selective genotyping while population stratification is present. Our procedure used a principal component based approach to eliminate any effect of population stratification. The paper evaluates the performance of our procedure using both simulated data from an early study data sets and also the HapMap data sets in a variety of population admixture models generated from empirical data. There are one binary trait and two continuous traits in the rheumatoid arthritis dataset of Problem 1 in the Genetic Analysis Workshop 16 (GAW16): RA status, AntiCCP and IgM. To allow multiple traits, we suggest a set of SNP-level F statistics by the concept of multiple-correlation to measure the genetic association between multiple trait values and SNP-specific genotypic scores and obtain their null distributions. Hereby, we perform 6 genome-wide association analyses using the novel one- and two-stage approaches which are based on single, double and triple traits. Incorporating all these 6 analyses, we successfully validate the SNPs which have been identified to be responsible for rheumatoid arthritis in the literature and detect more disease susceptibility SNPs for follow-up studies in the future. Except for chromosome 13 and 18, each of the others is found to harbour susceptible genetic regions for rheumatoid arthritis or related diseases, i.e., lupus erythematosus. This topic is discussed in part III.

Nuclear and plastid markers reveal the persistence of genetic identity: A new perspective on the evolutionary history of Petunia exserta

Recently divergent species that can hybridize are ideal models for investigating the genetic exchanges that can occur while preserving the species boundaries. Petunia exserta is an endemic species from a very limited and specific area that grows exclusively in rocky shelters. These shaded spots are an inhospitable habitat for all other Petunia species, including the closely related and widely distributed species P. axillaris. Individuals with intermediate morphologic characteristics have been found near the rocky shelters and were believed to be putative hybrids between P. exserta and P. axillaris, suggesting a situation where Petunia exserta is losing its genetic identity. In the current study, we analyzed the plastid intergenic spacers trnS/trnG and trnH/psbA and six nuclear CAPS markers in a large sampling design of both species to understand the evolutionary process occurring in this biological system. Bayesian clustering methods, cpDNA haplotype networks, genetic diversity statistics, and coalescence-based analyses support a scenario where hybridization occurs while two genetic clusters corresponding to two species are maintained. Our results reinforce the importance of coupling differentially inherited markers with an extensive geographic sample to assess the evolutionary dynamics of recently diverged species that can hybridize. (C) 2013 Elsevier Inc. All rights reserved.

The Individualized Genetic Barrier Predicts Treatment Response in a Large Cohort of HIV-1 Infected Patients

The success of combination antiretroviral therapy is limited by the evolutionary escape dynamics of HIV-1. We used Isotonic Conjunctive Bayesian Networks (I-CBNs), a class of probabilistic graphical models, to describe this process. We employed partial order constraints among viral resistance mutations, which give rise to a limited set of mutational pathways, and we modeled phenotypic drug resistance as monotonically increasing along any escape pathway. Using this model, the individualized genetic barrier (IGB) to each drug is derived as the probability of the virus not acquiring additional mutations that confer resistance. Drug-specific IGBs were combined to obtain the IGB to an entire regimen, which quantifies the virus' genetic potential for developing drug resistance under combination therapy. The IGB was tested as a predictor of therapeutic outcome using between 2,185 and 2,631 treatment change episodes of subtype B infected patients from the Swiss HIV Cohort Study Database, a large observational cohort. Using logistic regression, significant univariate predictors included most of the 18 drugs and single-drug IGBs, the IGB to the entire regimen, the expert rules-based genotypic susceptibility score (GSS), several individual mutations, and the peak viral load before treatment change. In the multivariate analysis, the only genotype-derived variables that remained significantly associated with virological success were GSS and, with 10-fold stronger association, IGB to regimen. When predicting suppression of viral load below 400 cps/ml, IGB outperformed GSS and also improved GSS-containing predictors significantly, but the difference was not significant for suppression below 50 cps/ml. Thus, the IGB to regimen is a novel data-derived predictor of treatment outcome that has potential to improve the interpretation of genotypic drug resistance tests.

Multiple independent genetic factors at NOS1AP modulate the QT interval in a multi-ethnic population.

Extremes of electrocardiographic QT interval are associated with increased risk for sudden cardiac death (SCD); thus, identification and characterization of genetic variants that modulate QT interval may elucidate the underlying etiology of SCD. Previous studies have revealed an association between a common genetic variant in NOS1AP and QT interval in populations of European ancestry, but this finding has not been extended to other ethnic populations. We sought to characterize the effects of NOS1AP genetic variants on QT interval in the multi-ethnic population-based Dallas Heart Study (DHS, n = 3,072). The SNP most strongly associated with QT interval in previous samples of European ancestry, rs16847548, was the most strongly associated in White (P = 0.005) and Black (P = 3.6 x 10(-5)) participants, with the same direction of effect in Hispanics (P = 0.17), and further showed a significant SNP x sex-interaction (P = 0.03). A second SNP, rs16856785, uncorrelated with rs16847548, was also associated with QT interval in Blacks (P = 0.01), with qualitatively similar results in Whites and Hispanics. In a previously genotyped cohort of 14,107 White individuals drawn from the combined Atherosclerotic Risk in Communities (ARIC) and Cardiovascular Health Study (CHS) cohorts, we validated both the second locus at rs16856785 (P = 7.63 x 10(-8)), as well as the sex-interaction with rs16847548 (P = 8.68 x 10(-6)). These data extend the association of genetic variants in NOS1AP with QT interval to a Black population, with similar trends, though not statistically significant at P<0.05, in Hispanics. In addition, we identify a strong sex-interaction and the presence of a second independent site within NOS1AP associated with the QT interval. These results highlight the consistent and complex role of NOS1AP genetic variants in modulating QT interval.