Analysis of galectin expression and identification of endogenous ligands in a human colon carcinoma cell line

The 14.5 kDa (galectin-1) and 31 kDa (galectin-3) lectins are the most well characterized members of a family of vertebrate carbohydrate-binding proteins known as the galectins. Evidence has been obtained implicating these galectins in events as diverse as cell-cell and cell-extracellular matrix interactions, growth regulation, transformation, differentiation, and programmed cell death. In the present study, sodium butyrate was found to be a potent inducer of galectin-1 in the KM12 human colon carcinoma cell line. Prior to treatment with butyrate this cell line expresses only galectin-3. These cells were utilized as an in vitro model system to study galectin expression as well as that of their endogenous ligands. The initial phase of this project involved the examination of the induction of galectin-1 by butyrate at the protein level. These studies indicated that galectin-1 induction by butyrate was relatively rapid reaching nearly maximal levels after only 24 hours. Additionally, the induction was found to be reversible upon the removal of butyrate and to precede the increase in expression of the well characterized differentiation marker, carcinoembryonic antigen (CEA). The second phase of this project involved the characterization of potential glycoprotein ligands for galectin-1 and galectin-3. This work demonstrated that the polylactosaminoglycan-containing glycoproteins laminin, CEA, and the lysosome-associated glycoproteins-1 and -2 (LAMPs-1 and -2) are capable of serving as ligands for both galectin-1 and -3. The third phase of this project involved the analysis of the induction of the galectin-1 promoter by butyrate. Through the analysis of deletion constructs transiently transfected into KM12 cells, the region of the galectin-1 promoter mediating a high level of induction by butyrate was localized primarily within a proximal portion of the promoter containing a CCAAT element and an Sp1 binding site. The CCAAT-binding activity in the KM12 nuclear extracts was subsequently dentified as NF-Y by gel shift analysis. These studies suggest that: (1) the galectins may be involved in modulating adhesive interactions in human colon carcinoma cells through the binding of several polylactosaminoglycans shown to play a role in adhesion and (2) high level induction of the galectin-1 promoter by butyrate can proceed through a discreet, proximal element containing an NF-Y-binding CCAAT box and an Sp1 site. ^

Nonlinear tests for genetic studies of complex disease

Linkage and association studies are major analytical tools to search for susceptibility genes for complex diseases. With the availability of large collection of single nucleotide polymorphisms (SNPs) and the rapid progresses for high throughput genotyping technologies, together with the ambitious goals of the International HapMap Project, genetic markers covering the whole genome will be available for genome-wide linkage and association studies. In order not to inflate the type I error rate in performing genome-wide linkage and association studies, multiple adjustment for the significant level for each independent linkage and/or association test is required, and this has led to the suggestion of genome-wide significant cut-off as low as 5 × 10 −7. Almost no linkage and/or association study can meet such a stringent threshold by the standard statistical methods. Developing new statistics with high power is urgently needed to tackle this problem. This dissertation proposes and explores a class of novel test statistics that can be used in both population-based and family-based genetic data by employing a completely new strategy, which uses nonlinear transformation of the sample means to construct test statistics for linkage and association studies. Extensive simulation studies are used to illustrate the properties of the nonlinear test statistics. Power calculations are performed using both analytical and empirical methods. Finally, real data sets are analyzed with the nonlinear test statistics. Results show that the nonlinear test statistics have correct type I error rates, and most of the studied nonlinear test statistics have higher power than the standard chi-square test. This dissertation introduces a new idea to design novel test statistics with high power and might open new ways to mapping susceptibility genes for complex diseases. ^

Detecting genetic and nutritional lung cancer risk factors related to folate metabolism using Bayesian generalized linear models

Complex diseases, such as cancer, are caused by various genetic and environmental factors, and their interactions. Joint analysis of these factors and their interactions would increase the power to detect risk factors but is statistically. Bayesian generalized linear models using student-t prior distributions on coefficients, is a novel method to simultaneously analyze genetic factors, environmental factors, and interactions. I performed simulation studies using three different disease models and demonstrated that the variable selection performance of Bayesian generalized linear models is comparable to that of Bayesian stochastic search variable selection, an improved method for variable selection when compared to standard methods. I further evaluated the variable selection performance of Bayesian generalized linear models using different numbers of candidate covariates and different sample sizes, and provided a guideline for required sample size to achieve a high power of variable selection using Bayesian generalize linear models, considering different scales of number of candidate covariates. ^ Polymorphisms in folate metabolism genes and nutritional factors have been previously associated with lung cancer risk. In this study, I simultaneously analyzed 115 tag SNPs in folate metabolism genes, 14 nutritional factors, and all possible genetic-nutritional interactions from 1239 lung cancer cases and 1692 controls using Bayesian generalized linear models stratified by never, former, and current smoking status. SNPs in MTRR were significantly associated with lung cancer risk across never, former, and current smokers. In never smokers, three SNPs in TYMS and three gene-nutrient interactions, including an interaction between SHMT1 and vitamin B12, an interaction between MTRR and total fat intake, and an interaction between MTR and alcohol use, were also identified as associated with lung cancer risk. These lung cancer risk factors are worthy of further investigation.^