Entropy Based Approach to Finding Interacting Genes Responsible for Complex Human Disease

2000 Mathematics Subject Classification: 62P10, 92D10, 92D30, 94A17, 62L10.

DATA DRIVEN APPROACHES TO IDENTIFY DETERMINANTS OF HEART DISEASES AND CANCER RESISTANCE

Cancer and cardio-vascular diseases are the leading causes of death world-wide. Caused by systemic genetic and molecular disruptions in cells, these disorders are the manifestation of profound disturbance of normal cellular homeostasis. People suffering or at high risk for these disorders need early diagnosis and personalized therapeutic intervention. Successful implementation of such clinical measures can significantly improve global health. However, development of effective therapies is hindered by the challenges in identifying genetic and molecular determinants of the onset of diseases; and in cases where therapies already exist, the main challenge is to identify molecular determinants that drive resistance to the therapies. Due to the progress in sequencing technologies, the access to a large genome-wide biological data is now extended far beyond few experimental labs to the global research community. The unprecedented availability of the data has revolutionized the capabilities of computational researchers, enabling them to collaboratively address the long standing problems from many different perspectives. Likewise, this thesis tackles the two main public health related challenges using data driven approaches. Numerous association studies have been proposed to identify genomic variants that determine disease. However, their clinical utility remains limited due to their inability to distinguish causal variants from associated variants. In the presented thesis, we first propose a simple scheme that improves association studies in supervised fashion and has shown its applicability in identifying genomic regulatory variants associated with hypertension. Next, we propose a coupled Bayesian regression approach -- eQTeL, which leverages epigenetic data to estimate regulatory and gene interaction potential, and identifies combinations of regulatory genomic variants that explain the gene expression variance. On human heart data, eQTeL not only explains a significantly greater proportion of expression variance in samples, but also predicts gene expression more accurately than other methods. We demonstrate that eQTeL accurately detects causal regulatory SNPs by simulation, particularly those with small effect sizes. Using various functional data, we show that SNPs detected by eQTeL are enriched for allele-specific protein binding and histone modifications, which potentially disrupt binding of core cardiac transcription factors and are spatially proximal to their target. eQTeL SNPs capture a substantial proportion of genetic determinants of expression variance and we estimate that 58% of these SNPs are putatively causal. The challenge of identifying molecular determinants of cancer resistance so far could only be dealt with labor intensive and costly experimental studies, and in case of experimental drugs such studies are infeasible. Here we take a fundamentally different data driven approach to understand the evolving landscape of emerging resistance. We introduce a novel class of genetic interactions termed synthetic rescues (SR) in cancer, which denotes a functional interaction between two genes where a change in the activity of one vulnerable gene (which may be a target of a cancer drug) is lethal, but subsequently altered activity of its partner rescuer gene restores cell viability. Next we describe a comprehensive computational framework --termed INCISOR-- for identifying SR underlying cancer resistance. Applying INCISOR to mine The Cancer Genome Atlas (TCGA), a large collection of cancer patient data, we identified the first pan-cancer SR networks, composed of interactions common to many cancer types. We experimentally test and validate a subset of these interactions involving the master regulator gene mTOR. We find that rescuer genes become increasingly activated as breast cancer progresses, testifying to pervasive ongoing rescue processes. We show that SRs can be utilized to successfully predict patients' survival and response to the majority of current cancer drugs, and importantly, for predicting the emergence of drug resistance from the initial tumor biopsy. Our analysis suggests a potential new strategy for enhancing the effectiveness of existing cancer therapies by targeting their rescuer genes to counteract resistance. The thesis provides statistical frameworks that can harness ever increasing high throughput genomic data to address challenges in determining the molecular underpinnings of hypertension, cardiovascular disease and cancer resistance. We discover novel molecular mechanistic insights that will advance the progress in early disease prevention and personalized therapeutics. Our analyses sheds light on the fundamental biological understanding of gene regulation and interaction, and opens up exciting avenues of translational applications in risk prediction and therapeutics.

Influence of the interaction between nodal fibroblast and breast cancer cells on gene expression

Our aim was to evaluate the interaction between breast cancer cells and nodal fibroblasts, by means of their gene expression profile. Fibroblast primary cultures were established from negative and positive lymph nodes from breast cancer patients and a similar gene expression pattern was identified, following cell culture. Fibroblasts and breast cancer cells (MDA-MB231, MDA-MB435, and MCF7) were cultured alone or co-cultured separated by a porous membrane (which allows passage of soluble factors) for comparison. Each breast cancer lineage exerted a particular effect on fibroblasts viability and transcriptional profile. However, fibroblasts from positive and negative nodes had a parallel transcriptional behavior when co-cultured with a specific breast cancer cell line. The effects of nodal fibroblasts on breast cancer cells were also investigated. MDA MB-231 cells viability and migration were enhanced by the presence of fibroblasts and accordingly, MDA-MB435 and MCF7 cells viability followed a similar pattern. MDA-MB231 gene expression profile, as evaluated by cDNA microarray, was influenced by the fibroblasts presence, and HNMT, COMT, FN3K, and SOD2 were confirmed downregulated in MDA-MB231 co-cultured cells with fibroblasts from both negative and positive nodes, in a new series of RT-PCR assays. In summary, transcriptional changes induced in breast cancer cells by fibroblasts from positive as well as negative nodes are very much alike in a specific lineage. However, fibroblasts effects are distinct in each one of the breast cancer lineages, suggesting that the inter-relationships between stromal and malignant cells are dependent on the intrinsic subtype of the tumor.

Cultural Consonance, a 5HT2A Receptor Polymorphism, and Depressive Symptoms: A Longitudinal Study of Gene x Culture Interaction in Urban Brazil

In this study in urban Brazil we examine, as a predictor of depressive symptoms, the interaction between a single nucleotide polymorphism in the 2A receptor in the serotonin system (-1438G/A) and cultural consonance in family life, a measure of the degree to which an individual perceives her family as corresponding to a widely shared cultural model of the prototypical family. A community sample of 144 adults was followed over a 2-year-period. Cultural consonance in family life was assessed by linking individuals` perceptions of their own families with a shared cultural model of the family derived from cultural consensus analysis. The -1438G/A polymorphism in the 2A serotonin receptor was genotyped using a standard protocol for DNA extracted from leukocytes. Covariates included age, sex, socioeconomic status, and stressful life events. Cultural consonance in family life was prospectively associated with depressive symptoms. In addition, the interaction between genotype and cultural consonance in family life was significant. For individuals with the A/A variant of the -1438G/A polymorphism of the 2A receptor gene, the effect of cultural consonance in family life on depressive symptoms over a 2-year-period was larger (beta = -0.533, P < 0.01) than those effects for individuals with either the G/A (beta = -0.280, P < 0.10) or G/G (beta = -0.272, P < 0.05) variants. These results are consistent with a process in which genotype moderates the effects of culturally meaningful social experience on depressive symptoms. Am. J. Hum. Biol. 21:91-97, 2009. (C) 2008 Wiley-Liss, Inc.

Gene-Environment Interaction Effects on Behavioral Variation and Risk of Complex Disorders: The Example of Alcoholism and Other Psychiatric Disorders

There have been few replicated examples of genotype x environment interaction effects on behavioral variation or risk of psychiatric disorder. We review some of the factors that have made detection of genotype x environment interaction effects difficult, and show how genotype x shared environment interaction (GxSE) effects are commonly confounded with genetic parameters in data from twin pairs reared together. Historic data on twin pairs reared apart can in principle be used to estimate such GxSE effects, but have rarely been used for this purpose. We illustrate this using previously published data from the Swedish Adoption Twin Study of Aging (SATSA), which suggest that GxSE effects could account for as much as 25% of the total variance in risk of becoming a regular smoker. Since few separated twin pairs will be available for study in the future, we also consider methods for modifying variance components linkage analysis to allow for environmental interactions with linked loci.

Gene-lifestyle interaction and type 2 diabetes: the EPIC interact case-cohort study.

BACKGROUND Understanding of the genetic basis of type 2 diabetes (T2D) has progressed rapidly, but the interactions between common genetic variants and lifestyle risk factors have not been systematically investigated in studies with adequate statistical power. Therefore, we aimed to quantify the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention. METHODS AND FINDINGS The InterAct study includes 12,403 incident T2D cases and a representative sub-cohort of 16,154 individuals from a cohort of 340,234 European participants with 3.99 million person-years of follow-up. We studied the combined effects of an additive genetic T2D risk score and modifiable and non-modifiable risk factors using Prentice-weighted Cox regression and random effects meta-analysis methods. The effect of the genetic score was significantly greater in younger individuals (p for interaction  = 1.20×10-4). Relative genetic risk (per standard deviation [4.4 risk alleles]) was also larger in participants who were leaner, both in terms of body mass index (p for interaction  = 1.50×10-3) and waist circumference (p for interaction  = 7.49×10-9). Examination of absolute risks by strata showed the importance of obesity for T2D risk. The 10-y cumulative incidence of T2D rose from 0.25% to 0.89% across extreme quartiles of the genetic score in normal weight individuals, compared to 4.22% to 7.99% in obese individuals. We detected no significant interactions between the genetic score and sex, diabetes family history, physical activity, or dietary habits assessed by a Mediterranean diet score. CONCLUSIONS The relative effect of a T2D genetic risk score is greater in younger and leaner participants. However, this sub-group is at low absolute risk and would not be a logical target for preventive interventions. The high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.

Interaction of GAG trinucleotide repeat and C-129T polymorphisms impairs expression of the glutamate-cysteine ligase catalytic subunit gene.

Glutamate cysteine ligase (GCL) catalyzes the rate-limiting step in the de novo synthesis of glutathione (GSH). The catalytic subunit (GCLC) of GCL contains a GAG trinucleotide-repeat (TNR) polymorphism within the 5'-untranslated region (5'-UTR) that has been associated with various human disorders. Although several studies suggest that this variation influences GSH content, its implication for GCLC expression remains unknown. To better characterize its functional significance, we performed reporter gene assays with constructs containing the complete GCLC 5'-UTR upstream of a luciferase gene. Transfection of these vectors into various human cell lines did not reveal any significant differences between 7, 8, 9, or 10 GAG repeats, under either basal or oxidative stress conditions. To correlate these results with the previously described down-regulation induced by the C-129T GCLC promoter polymorphism, combinations of both variations were tested. Interestingly, the -129T allele down-regulates gene expression when combined with 7 GAG but not with 8, 9, or 10 GAG TNRs. This observation was confirmed in primary fibroblast cells, in which the combination of GAG TNR 7/7 and -129C/T genotypes decreased the GCLC protein level. These results provide evidence that interaction of the two variations can efficiently impair GCLC expression and thus suggest its involvement in the pathogenesis of diseases related to GSH metabolism.

Functional interaction between the estrogen receptor and CTF1: analysis of the vitellogenin gene B1 promoter in yeast.

Eukaryotic gene expression depends on a complex interplay between the transcriptional apparatus and chromatin structure. We report here a yeast model system for investigating the functional interaction between the human estrogen receptor (hER) and CTF1, a member of the CTF/NFI transcription factor family. We show that a CTF1-fusion protein and the hER transactivate a synthetic promoter in yeast in a synergistic manner. This interaction requires the proline-rich transactivation domain of CTF1. When the natural estrogen-dependent vitellogenin B1 promoter is tested in yeast, CTF1 and CTF1-fusion proteins are unable to activate transcription, and no synergy is observed between hER, which activates the B1 promoter, and these factors. Chromatin structure analysis on this promoter reveals positioned nucleosomes at -430 to -270 (+/-20 bp) and at -270 to - 100 (+/-20 bp) relative to the start site of transcription. The positions of the nucleosomes remain unchanged upon hormone-dependent transcriptional activation of the promoter, and the more proximal nucleosome appears to mask the CTF/NFI site located at - 101 to -114. We conclude that a functional interaction of hER with the estrogen response element located upstream of a basal promoter occurs in yeast despite the nucleosomal organization of this promoter, whereas the interaction of CTF1 with its target site is apparently precluded by a nucleosome.

Gene by environment interaction: effects of a single-gene and social-environment on reproductive phenotypes of fire ant queens

1. The gene Pgm-3 (or a closely linked gene) influences the phenotype and reproductive success of queens in multiple-queen (polygynous) colonies but not single-queen (monogynous) colonies of the Fire Ant Solenopsis invicta. 2. We investigated the mechanisms of differential phenotypic expression of Pgm-3 in these alternate social forms. Mature winged queens with the homozygous genotype Pgm-3(a/a) averaged 26% heavier than queens with the genotypes Pgm-3(a/b) and Pgm 3(b/b) in the polygynous form. Heterozygotes were slightly heavier (2%) than Pgm-3(b/b) queens in this form, demonstrating that the allele Pgm-3(a) is not completely recessive in its effects on weight. 3. There was no significant difference in weight among queens of the three Pgm-3 genotypes in the monogynous form, with the mean weight of monogynous queens slightly greater than that of polygynous Pgm-3(a/a) queens. Differences in weight between queens of the two social forms and among queens of the three genotypes in the polygynous form are not evident at the pupal stage and thus appear to develop during sexual maturation of the adults. This suggests that some component of the social environment of polygynous colonies inhibits weight gains during queen maturation and that Pgm-(3a/a) queens are relatively less sensitive to this factor. 4. To test whether the high cumulative queen pheromone level characteristic of polygynous colonies is the factor responsible for the differential queen maturation, we compared phenotypes of winged queens reared in split colonies in which pheromone levels were manipulated by adjusting queen number. Queens produced in colony fragments made monogynous were heavier than those produced in polygynous fragments, a finding consistent with the hypothesis that pheromone level affects the reproductive development of queens. However, genotype-specific differences in weights of queens were similar between the two treatments, suggesting that pheromone level was not the key factor of the social environment responsible for the gene-environment interaction. 5. To test whether limited food availability to winged queens associated with the high brood/worker ratios in polygynous colonies is the factor responsible for this interaction, similar split-colony experiments were performed. Elevated brood/worker ratios decreased the weight of winged queens but there was no evidence that this treatment intensified differential weight gains among queens with different Pgm-3 genotypes. Manipulation of the amount of food provided to colonies had no effect on queen weight. 6. The combined data indicate that cumulative pheromone level and brood/worker ratio are two of the factors responsible for the differences in reproductive phenotypes between monogynous and polygynous winged queens but that these factors are not directly responsible for inducing the phenotypic effects of Pgm-3 in polygynous colonies.

Functional interaction between peroxisome proliferator-activated receptors-alpha and Mef-2C on human carnitine palmitoyltransferase 1beta (CPT1beta) gene activation

Muscle-type carnitine palmitoyltransferase 1 (CPT1β) is considered to be the gene that controls fatty acid mitochondrial β-oxidation. A functional peroxisome proliferator-activated receptor (PPAR) responsive element (PPRE) and a myocite-specific (MEF2) site that binds MEF2A and MEF2C in the promoter of this gene had been previously identified. We investigated the roles of the PPRE and the MEF2 binding sites and the potential interaction between PPARα and MEF2C regulating the CPT1β gene promoter. Mutation analysis indicated that the MEF2 site contributed to the activation of the CPT1β promoter by PPAR in C2C12 cells. The reporter construct containing the PPRE and the MEF2C site was synergistically activated by co-expression of PPAR, retinoid X receptor (RXR) and MEF2C in non-muscle cells. Moreover, protein-binding assays demonstrated that MEF2C and PPAR specifically bound to one another in vitro. Also for the synergistic activation of the CPT1β gene promoter by MEF2C and PPARα-RXRα, a precise arrangement of its binding sites was essential.

Gene Promoter Evolution Targets the Center of the Human Protein Interaction Network

Assessing the contribution of promoters and coding sequences to gene evolution is an important step toward discovering the major genetic determinants of human evolution. Many specific examples have revealed the evolutionary importance of cis-regulatory regions. However, the relative contribution of regulatory and coding regions to the evolutionary process and whether systemic factors differentially influence their evolution remains unclear. To address these questions, we carried out an analysis at the genome scale to identify signatures of positive selection in human proximal promoters. Next, we examined whether genes with positively selected promoters (Prom+ genes) show systemic differences with respect to a set of genes with positively selected protein-coding regions (Cod+ genes). We found that the number of genes in each set was not significantly different (8.1% and 8.5%, respectively). Furthermore, a functional analysis showed that, in both cases, positive selection affects almost all biological processes and only a few genes of each group are located in enriched categories, indicating that promoters and coding regions are not evolutionarily specialized with respect to gene function. On the other hand, we show that the topology of the human protein network has a different influence on the molecular evolution of proximal promoters and coding regions. Notably, Prom+ genes have an unexpectedly high centrality when compared with a reference distribution (P = 0.008, for Eigenvalue centrality). Moreover, the frequency of Prom+ genes increases from the periphery to the center of the protein network (P = 0.02, for the logistic regression coefficient). This means that gene centrality does not constrain the evolution of proximal promoters, unlike the case with coding regions, and further indicates that the evolution of proximal promoters is more efficient in the center of the protein network than in the periphery. These results show that proximal promoters have had a systemic contribution to human evolution by increasing the participation of central genes in the evolutionary process.

Estrogen receptor 1 gene polymorphisms in premenopausal women: interaction between genotype and smoking on lipid levels

Estrogen has multiple effects on lipid and lipoprotein metabolism. We investigated the association between the four common single nucleotide polymorphisms in the estrogen receptor 1 (ESR1) gene locus, -1989T>G, +261G>C, IVS1-397T>C and IVS1-351A>G, and lipid and lipoprotein levels in southern Brazilians. The sample consisted in 150 men and 187 premenopausal women. The women were considered premenopausal if they had regular menstrual bleeding within the previous 3 months and were 18-50 years of age. Exclusion criteria were pregnancy, secondary hyperlipidemia due to renal, hepatic or thyroid disease, and diabetes. Smoking status was self-reported; subjects were classified as never smoked and current smokers. DNA was amplified by PCR and was subsequently digested with the appropriate restriction enzymes. Statistical analysis was carried out for men and women separately. In the study population, major allele frequencies were _1989*T (0.83), +261*G (0.96), IVS1-397*T (0.58), and IVS1-351*A (0.65). Multiple linear regression analyses indicated that an interaction between +261G>C polymorphism and smoking was a significant factor affecting high-density lipoprotein cholesterol (HDL-C) levels (P = 0.028) in women. Nonsmoking women with genotype G/C of +261G>C polymorphism had mean HDL-C levels higher than those with G/G genotype (1.40 ± 0.33 vs 1.22 ± 0.26 mmol/L; P = 0.033). No significant associations with lipid and lipoprotein levels in women and men were detected for other polymorphisms. In conclusion, the +261G>C polymorphism might influence lipoprotein and lipid levels in premenopausal women, but these effects seem to be modulated by smoking, whereas in men ESR1 polymorphisms were not associated with high lipoprotein levels.

HCaRG "Hypertension-related Calcium Regulated Gene", un gène candidat de la réparation rénale : caractérisation de son interaction avec le cytosquelette et son expression génique

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

Sequentially testing for a gene-drug interaction in a genomewide analysis

Assaying a large number of genetic markers from patients in clinical trials is now possible in order to tailor drugs with respect to efficacy. The statistical methodology for analysing such massive data sets is challenging. The most popular type of statistical analysis is to use a univariate test for each genetic marker, once all the data from a clinical study have been collected. This paper presents a sequential method for conducting an omnibus test for detecting gene-drug interactions across the genome, thus allowing informed decisions at the earliest opportunity and overcoming the multiple testing problems from conducting many univariate tests. We first propose an omnibus test for a fixed sample size. This test is based on combining F-statistics that test for an interaction between treatment and the individual single nucleotide polymorphism (SNP). As SNPs tend to be correlated, we use permutations to calculate a global p-value. We extend our omnibus test to the sequential case. In order to control the type I error rate, we propose a sequential method that uses permutations to obtain the stopping boundaries. The results of a simulation study show that the sequential permutation method is more powerful than alternative sequential methods that control the type I error rate, such as the inverse-normal method. The proposed method is flexible as we do not need to assume a mode of inheritance and can also adjust for confounding factors. An application to real clinical data illustrates that the method is computationally feasible for a large number of SNPs. Copyright (c) 2007 John Wiley & Sons, Ltd.