The effect of genotype-by-environment interaction on longitudinal triglyceride profiles in the Bogalusa Heart Study

Triglyceride levels are a component of plasma lipids that are thought to be an important risk factor for coronary heart disease and are influenced by genetic and environmental factors, such as single nucleotide polymorphisms (SNPs), alcohol intake, and smoking. This study used longitudinal data from the Bogalusa Heart Study, a biracial community-based survey of cardiovascular disease risk factors. A sample of 1191 individuals, 4 to 38 years of age, was measured multiple times from 1973 to 2000. The study sample consisted of 730 white and 461 African American participants. Individual growth models were developed in order to assess gene-environment interactions affecting plasma triglycerides over time. After testing for inclusion of significant covariates and interactions, final models, each accounting for the effects of a different SNP, were assessed for fit and normality. After adjustment for all other covariates and interactions, LIPC -514C/T was found to interact with age3, age2, and age and a non-significant interaction of CETP -971G/A genotype with smoking status was found (p = 0.0812). Ever-smokers had higher triglyceride levels than never smokers, but persons heterozygous at this locus, about half of both races, had higher triglyceride levels after smoking cessation compared to current smokers. Since tobacco products increase free fatty acids circulating in the bloodstream, smoking cessation programs have the potential to ultimately reduce triglyceride levels for many persons. However, due to the effect of smoking cessation on the triglyceride levels of CETP -971G/A heterozygotes, the need for smoking prevention programs is also demonstrated. Both smoking cessation and prevention programs would have a great public health impact on minimizing triglyceride levels and ultimately reducing heart disease. ^

Gene by smoking interaction in blood pressure

Hypertension (HT) is mediated by the interaction of many genetic and environmental factors. Previous genome-wide linkage analysis studies have found many loci that show linkage to HT or blood pressure (BP) regulation, but the results were generally inconsistent. Gene by environment interaction is among the reasons that potentially explain these inconsistencies between studies. Here we investigate influences of gene by smoking (GxS) interaction on HT and BP in European American (EA), African American (AA) and Mexican American (MA) families from the GENOA study. A variance component-based method was utilized to perform genome-wide linkage analysis of systolic blood pressure (SBP), diastolic blood pressure (DBP), and HT status, as well as bivariate analysis for SBP and DBP for smokers, non-smokers, and combined groups. The most significant results were found for SBP in MA. The strongest signal was for chromosome 17q24 (LOD = 4.2), increased to (LOD = 4.7) in bivariate analysis but there was no evidence of GxS interaction at this locus (p = 0.48). Two signals were identified only in one group: on chromosome 15q26.2 (LOD = 3.37) in non-smokers and chromosome 7q21.11 (LOD = 1.4) in smokers, both of which had strong evidence for GxS interaction (p = 0.00039 and 0.009 respectively). There were also two other signals, one on chromosome 20q12 (LOD = 2.45) in smokers, which became much higher in the combined sample (LOD = 3.53), and one on chromosome 6p22.2 (LOD = 2.06) in non-smokers. Neither peak had very strong evidence for GxS interaction (p = 0.08 and 0.06 respectively). A fine mapping association study was performed using 200 SNPs in 30 genes located under the linkage signals on chromosomes 15 and 17. Under the chromosome 15 peak, the association analysis identified 6 SNPs accounting for a 7 mmHg increase in SBP in MA non-smokers. For the chromosome 17 linkage peak, the association analysis identified 3 SNPs accounting for a 6 mmHg increase in SBP in MA. However, none of these SNPs was significant after correcting for multiple testing, and accounting for them in the linkage analysis produced very small reductions in the linkage signal. ^ The linkage analysis of BP traits considering the smoking status produced very interesting signals for SBP in the MA population. The fine mapping association analysis gave some insight into the contribution of some SNPs to two of the identified signals, but since these SNPs did not remain significant after multiple testing correction and did not explain the linkage peaks, more work is needed to confirm these exploratory results and identify the culprit variations under these linkage peaks. ^

Mutual Information for Testing Gene-Environment Interaction

Despite current enthusiasm for investigation of gene-gene interactions and gene-environment interactions, the essential issue of how to define and detect gene-environment interactions remains unresolved. In this report, we define gene-environment interactions as a stochastic dependence in the context of the effects of the genetic and environmental risk factors on the cause of phenotypic variation among individuals. We use mutual information that is widely used in communication and complex system analysis to measure gene-environment interactions. We investigate how gene-environment interactions generate the large difference in the information measure of gene-environment interactions between the general population and a diseased population, which motives us to develop mutual information-based statistics for testing gene-environment interactions. We validated the null distribution and calculated the type 1 error rates for the mutual information-based statistics to test gene-environment interactions using extensive simulation studies. We found that the new test statistics were more powerful than the traditional logistic regression under several disease models. Finally, in order to further evaluate the performance of our new method, we applied the mutual information-based statistics to three real examples. Our results showed that P-values for the mutual information-based statistics were much smaller than that obtained by other approaches including logistic regression models.

THE EFFECT OF PERSON-ENVIRONMENT INTERACTION ON ANXIETY, DEPRESSION, AND SUBJECTIVE DISTRESS OF OPTOMETRY STUDENTS

The purpose of this study was to investigate whether an incongruence between personality characteristics of individuals and concomitant charcteristics of health professional training environments on salient dimensions contributes to aspects of mental health. The dimensions examined were practical-theoretical orientation and the degree of structure-unstructure. They were selected for study as they are particularly important attributes of students and of learning environments. It was proposed that when the demand of the environment is disparate from the proclivities of the individual, strain arises. This strain was hypothesized to contribute to anxiety, depression, and subjective distress.^ Select subscales on the Omnibus Personality Inventory (OPI) were the operationalized measures for the personality component of the dimensions studied. An environmental index was developed to assess students' perceptions of the learning environment on these same dimensions. The Beck Depression Inventory, State-Trait Anxiety Inventory and General Well-Being schedule measured the outcome variables.^ A congruence model was employed to determine person-environment (P-E) interaction. Scores on the scales of the OPI and the environmental index were divided into high, medium, and low based on the range of scores. Congruence was defined as a match between the level of personality need and the complementary level of the perception of the environment. Alternatively, incongruence was defined as a mismatch between the person and the environment. The consistent category was compared to the inconsistent categories by an analysis of variance procedure. Furthermore, analyses of covariance were conducted with perceived supportiveness of the learning environment and life events external to the learning environment as the covariates. These factors were considered critical influences affecting the outcome measures.^ One hundred and eighty-five students (49% of the population) at the College of Optometry at the University of Houston participated in the study. Students in all four years of the program were equally represented in the study. However, the sample differed from the total population on representation by sex, marital status, and undergraduate major.^ The results of the study did not support the hypotheses. Further, after having adjusted for perceived supportiveness and life events external to the learning environment, there were no statistically significant differences between the congruent category and incongruent categories. Means indicated than the study sample experienced significantly lower depression and subjective distress than the normative samples.^ Results are interpreted in light of their utility for future study design in the investigation of the effects of P-E interaction. Emphasized is the question of the feasibility of testing a P-E interaction model with extant groups. Recommendations for subsequent research are proposed in light of the exploratory nature of the methodology. ^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Genetics and epidemiology of gallbladder disease in New World native peoples.

Native peoples of the New World, including Amerindians and admixed Latin Americans such as Mexican-Americans, are highly susceptible to diseases of the gallbladder. These include cholesterol cholelithiasis (gallstones) and its complications, as well as cancer of the gallbladder. Although there is clearly some necessary dietary or other environmental risk factor involved, the pattern of disease prevalence is geographically associated with the distribution of genes of aboriginal Amerindian origin, and levels of risk generally correspond to the degree of Amerindian admixture. This pattern differs from that generally associated with Westernization, which suggests a gene-environment interaction, and that within an admixed population there is a subset whose risk is underestimated when admixture is ignored. The risk that an individual of a susceptible New World genotype will undergo a cholecystectomy by age 85 can approach 40% in Mexican-American females, and their risk of gallbladder cancer can reach several percent. These are heretofore unrecognized levels of risk, especially of the latter, because previous studies have not accounted for admixture or for the loss of at-risk individuals due to cholecystectomy. A genetic susceptibility may, thus, be as "carcinogenic" in New World peoples as any known major environmental exposure; yet, while the risk has a genetic basis, its expression as gallbladder cancer is so delayed as to lead only very rarely to multiply-affected families. Estimates in this paper are derived in part from two studies of Mexican-Americans in Starr County and Laredo, Texas.

Modifier genes and susceptibility to colorectal cancer in individuals with Lynch syndrome

Lynch syndrome, is caused by inherited germ-line mutations in the DNA mismatch repair genes resulting in cancers at an early age, predominantly colorectal (CRC) and endometrial cancers. Though the median age at onset for CRC is about 45 years, disease penetrance varies suggesting that cancer susceptibility may be modified by environmental or other low-penetrance genes. Genetic variation due to polymorphisms in genes encoding metabolic enzymes can influence carcinogenesis by alterations in the expression and activity level of the enzymes. Variation in MTHFR, an important folate metabolizing enzyme can affect DNA methylation and DNA synthesis and variation in xenobiotic-metabolizing enzymes can affect the metabolism and clearance of carcinogens, thus modifying cancer risk. ^ This study examined a retrospective cohort of 257 individuals with Lynch syndrome, for polymorphisms in genes encoding xenobiotic-metabolizing enzymes-- CYP1A1 (I462V and MspI), EPHX1 (H139R and Y113H), GSTP1 (I105V and A114V), GSTM1 and GSTT1 (deletions) and folate metabolizing enzyme--MTHFR (C677T and A1298C). In addition, a series of 786 cases of sporadic CRC were genotyped for CYP1A1 I462V and EPHX1 Y113H to assess gene-gene interaction and gene-environment interaction with smoking in a case-only analysis. ^ Prominent findings of this study were that the presence of an MTHFR C677T variant allele was associated with a 4 year later age at onset for CRC on average and a reduced age-associated risk for developing CRC (Hazard ratio: 0.55; 95% confidence interval: 0.36–0.85) compared to the absence of any variant allele in individuals with Lynch syndrome. Similarly, Lynch syndrome individuals heterozygous for CYP1A1 I462V A>G polymorphism developed CRC an average of 4 years earlier and were at a 78% increased age-associated risk (Hazard ratio for AG relative to AA: 1.78; 95% confidence interval: 1.16-2.74) than those with the homozygous wild-type genotype. Therefore these two polymorphisms may be additional susceptibility factors for CRC in Lynch syndrome. In the case-only analysis, evidence of gene-gene interaction was seen between CYP1A1 I462V and EPHX1 Y113H and between EPHX1 Y113H and smoking suggesting that genetic and environmental factors may interact to increase sporadic CRC risk. Implications of these findings are the ability to identify subsets of high-risk individuals for targeted prevention and intervention. ^

Gene by BMI Interactions Influencing C-Reactive Protein Levels in European-Americans

C-Reactive Protein (CRP) is a biomarker indicating tissue damage, inflammation, and infection. High-sensitivity CRP (hsCRP) is an emerging biomarker often used to estimate an individual’s risk for future coronary heart disease (CHD). hsCRP levels falling below 1.00 mg/l indicate a low risk for developing CHD, levels ranging between 1.00 mg/l and 3.00 mg/l indicate an elevated risk, and levels exceeding 3.00 mg/l indicate high risk. Multiple Genome-Wide Association Studies (GWAS) have identified a number of genetic polymorphisms which influence CRP levels. SNPs implicated in such studies have been found in or near genes of interest including: CRP, APOE, APOC, IL-6, HNF1A, LEPR, and GCKR. A strong positive correlation has also been found to exist between CRP levels and BMI, a known risk factor for CHD and a state of chronic inflammation. We conducted a series of analyses designed to identify loci which interact with BMI to influence CRP levels in a subsample of European-Americans in the ARIC cohort. In a stratified GWA analysis, 15 genetic regions were identified as having significantly (p-value < 2.00*10-3) distinct effects on hsCRP levels between the two obesity strata: lean (18.50 kg/m2 < BMI < 24.99 kg/m2) and obese (BMI ≥ 30.00 kg/m2). A GWA analysis performed on all individuals combined (i.e. not a priori stratified for obesity status) with the inclusion of an additional parameter for BMI by gene interaction, identified 11 regions which interact with BMI to influence hsCRP levels. Two regions containing the genes GJA5 and GJA8 (on chromosome 1) and FBXO11 (on chromosome 2) were identified in both methods of analysis suggesting that these genes possibly interact with BMI to influence hsCRP levels. We speculate that atrial fibrillation (AF), age-related cataracts and the TGF-β pathway may be the biological processes influenced by the interaction of GJA5, GJA8 and FBXO11, respectively, with BMI to cause changes in hsCRP levels. Future studies should focus on the influence of gene x bmi interaction on AF, age-related cataracts and TGF-β.

Developmental programming of tumor suppressor gene penetrance

Epidemiological studies have led to the hypothesis that major risk factors for developing diseases such as hypertension, cardiovascular disease and adult-onset diabetes are established during development. This developmental programming hypothesis proposes that exposure to an adverse stimulus or insult at critical, sensitive periods of development can induce permanent alterations in normal physiological processes that lead to increased disease risk later in life. For cancer, inheritance of a tumor suppressor gene defect confers a high relative risk for disease development. However, these defects are rarely 100% penetrant. Traditionally, gene-environment interactions are thought to contribute to the penetrance of tumor suppressor gene defects by facilitating or inhibiting the acquisition of additional somatic mutations required for tumorigenesis. The studies presented herein identify developmental programming as a distinctive type of gene-environment interaction that can enhance the penetrance of a tumor suppressor gene defect in adult life. Using rats predisposed to uterine leiomyoma due to a germ-line defect in one allele of the tuberous sclerosis complex 2 (Tsc-2) tumor suppressor gene, these studies show that early-life exposure to the xenoestrogen, diethylstilbestrol (DES), during development of the uterus increased tumor incidence, multiplicity and size in genetically predisposed animals, but failed to induce tumors in wild-type rats. Uterine leiomyomas are ovarian-hormone dependent tumors that develop from the uterine myometrium. DES exposure was shown to developmentally program the myometrium, causing increased expression of estrogen-responsive genes prior to the onset of tumors. Loss of function of the normal Tsc-2 allele remained the rate-limiting event for tumorigenesis; however, tumors that developed in exposed animals displayed an enhanced proliferative response to ovarian steroid hormones relative to tumors that developed in unexposed animals. Furthermore, the studies presented herein identify developmental periods during which target tissues are maximally susceptible to developmental programming. These data suggest that exposure to environmental factors during critical periods of development can permanently alter normal physiological tissue responses and thus lead to increased disease risk in genetically susceptible individuals. ^

A novel method for evaluating an interaction effect of correlated continuous covariates in a linear model

Interaction effect is an important scientific interest for many areas of research. Common approach for investigating the interaction effect of two continuous covariates on a response variable is through a cross-product term in multiple linear regression. In epidemiological studies, the two-way analysis of variance (ANOVA) type of method has also been utilized to examine the interaction effect by replacing the continuous covariates with their discretized levels. However, the implications of model assumptions of either approach have not been examined and the statistical validation has only focused on the general method, not specifically for the interaction effect.^ In this dissertation, we investigated the validity of both approaches based on the mathematical assumptions for non-skewed data. We showed that linear regression may not be an appropriate model when the interaction effect exists because it implies a highly skewed distribution for the response variable. We also showed that the normality and constant variance assumptions required by ANOVA are not satisfied in the model where the continuous covariates are replaced with their discretized levels. Therefore, naïve application of ANOVA method may lead to an incorrect conclusion. ^ Given the problems identified above, we proposed a novel method modifying from the traditional ANOVA approach to rigorously evaluate the interaction effect. The analytical expression of the interaction effect was derived based on the conditional distribution of the response variable given the discretized continuous covariates. A testing procedure that combines the p-values from each level of the discretized covariates was developed to test the overall significance of the interaction effect. According to the simulation study, the proposed method is more powerful then the least squares regression and the ANOVA method in detecting the interaction effect when data comes from a trivariate normal distribution. The proposed method was applied to a dataset from the National Institute of Neurological Disorders and Stroke (NINDS) tissue plasminogen activator (t-PA) stroke trial, and baseline age-by-weight interaction effect was found significant in predicting the change from baseline in NIHSS at Month-3 among patients received t-PA therapy.^

Assessing time-by-covariate interactions in Cox proportional hazards regression models using cubic spline functions

This dissertation develops and explores the methodology for the use of cubic spline functions in assessing time-by-covariate interactions in Cox proportional hazards regression models. These interactions indicate violations of the proportional hazards assumption of the Cox model. Use of cubic spline functions allows for the investigation of the shape of a possible covariate time-dependence without having to specify a particular functional form. Cubic spline functions yield both a graphical method and a formal test for the proportional hazards assumption as well as a test of the nonlinearity of the time-by-covariate interaction. Five existing methods for assessing violations of the proportional hazards assumption are reviewed and applied along with cubic splines to three well known two-sample datasets. An additional dataset with three covariates is used to explore the use of cubic spline functions in a more general setting. ^

Simultaneous induction of NKG2D and NKG2D ligand for promoting immune surveillance by IL-12 plus doxorubicin treatment

NKG2D (natural killer group 2, member D) and its ligands interaction in tumor microenvironment directs tumor infiltrating immune cells to recognize tumor cells, stimulate cytotoxic effector immune cells, and therefore eradicate tumor cells. IL-12, a cytokine produced by antigen presenting cells, has remarkable antitumor effect by activating innate and adaptive immunity. Doxorubicin, a commonly used chemotherapeutic agent also boosts the host antitumor immune response to cause tumor cell death. Our previous publication suggests that IL-12 plus doxorubicin enhances NKG2D function-dependent inhibition of tumor progression and promotes CD8+T cells infiltrating into tumors. The purpose of this study is to determine the underlying mechanism. Our study reveals a novel function of doxorubicin, which is to augment IL-12–induced NKG2D expression in CD8+T cells but not in NK or CD4+T cells. This observation was further validated by NK and CD8+T cell-depletion studies, in which only depletion of CD8+T cells abolished the expression of NKG2D in lymphocytes. The induced NKG2D expression in CD8+T cells is tightly associated with tumor-specific localization of CD8+T cells and improved antitumor efficacy. The IL-12 plus doxorubicin treatment-induced antitumor efficacy is also due to NKG2D ligand Rae-1 induction in tumors. Rae-1 induction in tumors is a long term effect in multiple tumor models, but not in normal tissues. A novel CD8+T cell direct contact dependent mechanism accounts for Rae-1 induction in vivo and in vitro, and CD80 is the receptor through which CD8+T cells interplay with tumor cells to upregulate Rae-1 on tumor cells. In summary, increased NKG2D expression in CD8+T cells in response to IL-12 plus doxorubicin was closely associated with tumor-specific localization of CD8+T cells and greater antitumor efficacy of the combined regimen than either agent alone. NKG2D ligand Rae-1 induction is triggered by the interaction of CD80 on tumor cells with tumor infiltrating CD+8 T cells.

Molecular signals in B lymphocyte activation: The role of intracellular calcium ion concentration, protein kinase C activation, and autocrine interleukin-2

Calcium ionophore, ionomycin, and phorbol myristate acetate (PMA) were used to activate rabbit peripheral blood B cells to study the role of increased intracellular calcium ion concentration ( (Ca$\sp2+\rbrack\sb{\rm i}$), protein kinase C (PKC) activation, and autocrine interleukin (IL-2) in inducing cell cycle entry and maintaining activation to DNA synthesis. When stimulated with a combination of ionomycin and PMA the B cells produced a soluble factor that supported the IL-2 dependent cell line, CTLL-2. The identity of the factor was established as IL-2 and its source was proved to be B cells in further experiments. Absorption studies and limiting dilution analysis indicated that IL-2 produced by B cells can act as an autocrine growth factor. Next, the effect of complete and incomplete signalling on B lymphocyte activation leading to cell cycle entry, IL-2 production, functional IL-2 receptor (IL-2R) expression, and DNA synthesis was examined. It was observed that cell cycle entry could be induced by signals provided by each reagent alone, but IL-2 production, IL-2R expression, and progression to DNA synthesis required activation with both reagents. Incomplete activation with ionomycin or PMA alone altered the responsiveness of B cells to further stimulation only in the case of ionomycin, and the unresponsiveness of these cells was apparently due to a lack of functional IL-2R expression on these cells, even though IL-2 production was maintained. The requirement of IL-2 for maintenance of activation to DNA synthesis was then investigated. The hypothesis that IL-2, acts in late G$\sb1$ and is required for DNA synthesis in B cells was supported by comparing IL-2 production and DNA synthesis in peripheral blood cells and purified B cells, kinetic analysis of these events in B cells, effects of anti-IL-2 antibody and PKC inhibitors, and by the response of G$\sb1$ B cells. Additional signals transduced by the interaction of autocrine IL-2 and functional IL-2 receptor on rabbit B cells were found to be necessary to drive these cells to S phase, after initial activation caused by simultaneous increase in (Ca$\sp2+\rbrack\sb{\rm i}$ and PKC activation had induced cell cycle entry, IL-2 production, and functional IL-2 receptor expression. ^

Body fat distribution and serum lipids in children and adolescents

Previous studies of normal children have linked body fat but not body fat distribution (BFD), to higher blood pressures, lipids, and insulin resistance (Berenson et al., 1988) BFD is a well-established risk factor for cardiovascular disease in adults (Björntorp, 1988). This study investigates the relation of BFD and serum lipids at baseline in children from Project HeartBeat!, a study of the growth and development of cardiovascular risk factors in 678 children in three cohorts measured initially at ages 8, 11, and 14 years. Initially, two of four indices of BFD were significantly related to the lipids: ratio of upper to lower body skinfolds (ln US:LS) and conicity (C Index). A factor analysis reduced the information in the serum lipids to two vectors: (1) total cholesterol + LDL-cholesterol and (2) HDL-cholesterol − triglycerides, which together accounted for 85% of the lipid variation. Using each serum lipid and vector as separate dependent variables, linear and quadratic regression models were constructed to examine the predictive ability of the two BFD variables, controlling for total body fat, gender, ethnicity (Black, non-Black) and maturation. Linear models provided an acceptable fit. Percent body fat (%BF) was a significant predictor in each and every lipid model, independent of age, maturation, or ethnicity (p ≤ 0.05). No BFD variable entered the equation for total or LDL-cholesterol, although there was a significant maturity by BFD interaction for LDL (ln US:LS was a significant predictor in more mature individuals). Both %BF and BFD (by way of Conicity) were significant predictors of HDL-cholesterol and triglycerides (p ≤ 0.01). All models were statistically significant at a high level (p ≤ 0.01), but adjusted R 2's for all models were low (0.05–0.15). Body fat distribution is a significant predictor of lipids in normal children, but secondarily to %BF, and for LDL-cholesterol in particular, the relation is dependent on maturity status. ^

An interferon-inducible protein, p202, in cancer gene therapy

Interferons (IFNs) have been shown to exert antiviral, cell growth regulatory, and immunomodulatory effects on target cells. Both type I (α and β) and type II (γ) IFNs regulate cellular activities by specifically inducing the expression or activation of endogenous proteins that perform distinct biological functions. p202 is a 52 kDa nuclear phosphoprotein known to be induced by IFNs. p202 interacts with a variety of cellular transcription and growth regulatory factors and affects their functions. ^ In this report, we showed that the expression of p202 was associated with an anti-proliferative effect on human prostate cancer cells. Cells that expressed p202 showed reduced ability to grow in soft-agar, indicating a loss of transformation phenotype. More importantly, p202 expression reduced the tumorigenicity of human prostate cancer cells. p202-expressing cells exhibit an elevated level of hypophosphorylated form of pRb, and reduced level of cyclin B1 and p55CDC. ^ Our data suggest that p202 is a growth inhibitor gene in prostate cancer cells and its expression may also suppress transformation phenotype and tumorigenicity of prostate cancer cells. ^ In addition to inhibiting in vitro cell growth, suppressing the tumorigenicity of breast cancer cells in vivo, p202 expression could sensitize breast cancer cells to apoptosis induced by TNF-α treatment. One possible mechanism contributing to this sensitization is the inactivation of NF-κB by its interaction with p202. These results provide a scientific basis for a novel therapeutic strategy that combines p202 and TNF-α treatment against breast cancer. ^ It has been reported that NF-κB is constitutively active in human pancreatic cancer cells. Since p202 interacts with NF-κB and inhibits its activity, we examined a potential p202-mediated anti-tumor activity in pancreatic cancer. We used both ectopic and orthotopic xenograft models and demonstrated that p202 expression is associated with multiple anti-tumor activities that include inhibition of tumor growth, reduced tumorigenicity, prolonged survival, and remarkably, suppression of metastasis and angiogenesis. In vitro invasion assay also showed that p202-expressing pancreatic cancer cells are less invasive than those without p202 expression. That observation was supported by the findings that p202-expressing tumors showed reduced expression of angiogenic factors such as IL-8, and VEGF by inhibiting their transcription, and p202-expressing pancreatic cancer cells have reduced level of MAP-2 activity, a secreted protease activity important for metastasis. Together, our results strongly suggest that p202 expression mediates multiple anti-tumor activities against pancreatic cancer, and that may provide a scientific basis for developing a p202-based gene therapy in pancreatic cancer treatment. ^ Importantly, we demonstrated a treatment efficacy by using p202/SN2 liposome complex in a nude mice orthotopic breast cancer, and an ectopic pancreatic cancer xenograft model, through systemic and intra-tumor injection respectively. These results suggest a feasibility of using p202/SN2 liposome in future pre-clinical gene therapy experiments. ^