The association of genetic groups 1, 2, and 3 of Mycobacterium tuberculosis with pulmonary and extra-pulmonary tuberculosis

Objective. To investigate the association of the three major genetic groups of Mycobacterium tuberculosis with pulmonary and extra-pulmonary tuberculosis in clustered and non-clustered TB cases in the Houston area. ^ Study design. Secondary analysis of an ambi-directional study. ^ Study population. Three hundred fifty-eight confirmed cases of tuberculosis in the Houston that occurred between October 1995 and May 1997, who had been interviewed by the Houston T13 Initiative staff at Baylor College of Medicine, and whose isolates have had their DNA fingerprint and genetic group determined. ^ Exclusions. Individuals whose mycobacterial genotype was unknown, or whose data variables were unavailable. ^ Source of data. Laboratory results, patient interviews, and medical records at clinics and hospitals of the study population. ^ Results. In clustered cases, the majority of both, pulmonary and extra-pulmonary TB cases were caused by genetic group 1. Independent factors were assessed to determine the interactions that may influence the site of infection or increase the risk for one site or another. HIV negative males were protected against extra-pulmonary TB compared to HIV negative females. Individuals ages 1–14 years were at higher risk of having extra-pulmonary TB. Group 3 organisms were found less frequently in the total population in general, especially in extra-pulmonary disease. This supports the evidence in previous studies that this group is the least virulent and genetically distinct from the other two groups. Group 1 was found more frequently among African Americans than other ethnic groups, a trend for future investigations. ^ Among the non-clustered cases, group 2 organisms were the majority of the organisms found in both sites. They were also the majority of organisms found in African Americans, Caucasians, and Hispanics causing the majority of the infections at both sites. However, group 1 organisms were the overwhelming majority found in Asian/Pacific Islander individuals, which may indicate these organisms are either endemic to that area, or that there is an ethnic biological factor involved. This may also be due to a systematic bias, since isolates from individuals from that geographic region lack adequate copies of the insertion sequence IS6110, which leads to their placement in the non-clustered population. ^ The three genetic groups of Mycobacterium tuberculosis were not found equally distributed between sites of infection in both clustered and non-clustered cases. Furthermore, these groups were not distributed in the same patterns among the clustered and non-clustered cases, but rather in distinct patterns. ^

Genetic analysis of tumor progression susceptibility in the mouse skin model

In the field of chemical carcinogenesis the use of animal models has proved to be a useful tool in dissecting the multistage process of tumor formation. In this regard the outbred SENCAR mouse has been the strain of choice in the analysis of skin carcinogenesis given its high sensitivity to the chemically induced acquisition of premalignant lesions, papillomas, and the later progression of these lesions into squamous cell carcinomas (SCC).^ The derivation of an inbred strain from the SENCAR stock called SSIN, that in spite of a high sensitivity to the development of papillomas lack the ability to transform these premalignant lesions into SCC, suggested that tumor promotion and progression were under the genetic control of different sets of genes.^ In the present study the nature of susceptibility to tumor progression was investigated. Analysis of F1 hybrids between the outbred SENCAR and SSIN mice suggested that there is at least one dominant gene responsible for susceptibility to tumor progression.^ Later development of another inbred strain from the outbred SENCAR stock, that had sensitivity to both tumor promotion and progression, allowed the formulation of a more accurate genetic model. Using this newly derived line, SENCAR B/Pt. and SSIN it was determined that there is one dominant tumor progression susceptibility gene. Linkage analysis showed that this gene maps to mouse chromosome 14 and it was possible to narrow the region to a 16 cM interval.^ In order to better characterize the nature of the progression susceptibility differences between these two strains, their proliferative pattern was investigated. It was found that SENCAR B/Pt, have an enlarged proliferative compartment with overexpression of cyclin D1, p16 and p21. Further studies showed an aberrant overexpression of TGF-$\beta$ in the susceptible strain, an increase in apoptosis, p53 protein accumulation and early loss of connexin 26. These results taken together suggest that papillomas in the SENCAR B/Pt. mice have higher proliferation and may have an increase in genomic instability, these two factors would contribute to a higher sensitivity to tumor progression. ^

Delineation of a novel genetic region at 5q11 harboring tumor suppressor gene(s) and identification of the telomeric DNA as a marker for human prostate cancer

Prostate cancer is the most commonly diagnosed cancer and the second leading cause of cancer mortality in American men. The distinction between those cases of prostate cancer destined to progress rapidly to lethal metastatic disease and those with little likelihood of causing morbidity and mortality is a major goal of current research. Some type of diagnostic method is urgently needed to identify which histological prostate cancers have completed the progression to a stage that will produce a life-threatening disease, thus requiring immediate therapeutic intervention. The objectives of this dissertation are to delineate a novel genetic region harboring tumor suppressor gene(s) and to identify a marker for prostate tumorigenesis. I first established an in vitro cell model system from a human prostate epithelial cells derived from tissue fragments surrounding a prostate tumor in a patient with prostatic adenocarcinoma. Since chromosome 5 abnormality was present in early, middle and late passages of this cell model system, I examined long-term established prostate cancer cell lines for this chromosome abnormality. The results implicated the region surrounding marker D5S2068 as the locus of interest for further experimentation and location of a tumor suppressor gene in human prostate cancer. ^ Cancer is a group of complex genetic diseases with uncontrolled cell; division and prostate cancer is no exception. I determined if telomeric DNA, and telomerase activity, alone or together, could serve as biomarkers of prostate tumorigenesis. I studied three newly established human prostate cancer cell lines and three fibroblast cell cultures derived from prostate tissues. In conclusion, my data reveal that in the presence of telomerase activity, telomeric repeats are maintained at a certain optimal length, and analysis of telomeric DNA variations might serve as early diagnostic and prognostic biomarkers for prostate cancer. (Abstract shortened by UMI.)^

Disease risk factors and humoral antibody response to periodontal-associated pathogens: A model to evaluate periodontal disease in older adults

Periodontal diseases (PD) are infectious, inflammatory, and tissue destructive events which affect the periodontal ligament that surround and support the teeth. Periodontal diseases are the major cause of tooth loss after age 35, with gingivitis and periodontitis affecting 75% of the adult population. A select group of bacterial organisms are associated with periodontal pathogenesis. There is a direct association between oral hygiene and prevention of PD. The importance of genetic differences and host immune response capabilities in determining host, susceptibility or resistance to PD has not been established. This study examined the risk factors and serum (humoral) immune response to periodontal diseased-associated pathogens in a 55 to 80+ year old South Texas study sample with PD. This study sample was described by: age, sex, ethnicity, the socioeconomic factors marital status, income and occupation, IgG, IgA, IgM immunoglobulin status, and the autoimmune response markers rheumatoid factor (RF) and antinuclear antibody (ANA). These variables were used to determine the risk factors associated with development of PD. Serum IgG, IgA, IgM antibodies to bacterial antigens provided evidence for disease exposure.^ A causal model for PD was constructed from associations for risk factors (ethnicity, marital status, income, and occupation) with dental exam and periodontitis. The multiple correlation between PD and ethnicity, income and dental exam was significant. Hispanics of low income were least likely to have had a dental exam in the last year and most likely to have PD. The etiologic agents for PD, as evidenced by elevated humoral antibody responses, were the Gram negative microorganisms Bacteroides gingivalis, serotypes FDC381 and SUNYaBA7A1-28, and Wolinella recta. Recommendation for a PD prevention and control program are provided. ^

Genetic variants in the mammalian target of rapamycin (mTOR) signaling pathway as predictors of survival and clinical response in women with ovarian cancer

Background. The mTOR pathway is commonly altered in human tumors and promotes cell survival and proliferation. Preliminary evidence suggests this pathway's involvement in chemoresistance to platinum and taxanes, first line therapy for epithelial ovarian cancer. A pathway-based approach was used to identify individual germline single nucleotide polymorphisms (SNPs) and cumulative effects of multiple genetic variants in mTOR pathway genes and their association with clinical outcome in women with ovarian cancer. ^ Methods. The case-series was restricted to 319 non-Hispanic white women with high grade ovarian cancer treated with surgery and platinum-based chemotherapy. 135 SNPs in 20 representative genes in the mTOR pathway were genotyped. Hazard ratios (HRs) for death and Odds ratios (ORs) for failure to respond to primary therapy were estimated for each SNP using the multivariate Cox proportional hazards model and multivariate logistic regression model, respectively, while adjusting for age, stage, histology and treatment sequence. A survival tree analysis of SNPs with a statistically significant association (p<0.05) was performed to identify higher order gene-gene interactions and their association with overall survival. ^ Results. There was no statistically significant difference in survival by tumor histology or treatment regimen. The median survival for the cohort was 48.3 months. Seven SNPs were significantly associated with decreased survival. Compared to those with no unfavorable genotypes, the HR for death increased significantly with the increasing number of unfavorable genotypes and women in the highest risk category had HR of 4.06 (95% CI 2.29–7.21). The survival tree analysis also identified patients with different survival patterns based on their genetic profiles. 13 SNPs on five different genes were found to be significantly associated with a treatment response, defined as no evidence of disease after completion of primary therapy. Rare homozygous genotype of SNP rs6973428 showed a 5.5-fold increased risk compared to the wild type carrying genotypes. In the cumulative effect analysis, the highest risk group (individuals with ≥8 unfavorable genotypes) was significantly less likely to respond to chemotherapy (OR=8.40, 95% CI 3.10–22.75) compared to the low risk group (≤4 unfavorable genotypes). ^ Conclusions. A pathway-based approach can demonstrate cumulative effects of multiple genetic variants on clinical response to chemotherapy and survival. Therapy targeting the mTOR pathway may modify outcome in select patients.^

Robust effect sizes and their confidence intervals for group difference between trajectories in hierarchical linear growth model

Hierarchical linear growth model (HLGM), as a flexible and powerful analytic method, has played an increased important role in psychology, public health and medical sciences in recent decades. Mostly, researchers who conduct HLGM are interested in the treatment effect on individual trajectories, which can be indicated by the cross-level interaction effects. However, the statistical hypothesis test for the effect of cross-level interaction in HLGM only show us whether there is a significant group difference in the average rate of change, rate of acceleration or higher polynomial effect; it fails to convey information about the magnitude of the difference between the group trajectories at specific time point. Thus, reporting and interpreting effect sizes have been increased emphases in HLGM in recent years, due to the limitations and increased criticisms for statistical hypothesis testing. However, most researchers fail to report these model-implied effect sizes for group trajectories comparison and their corresponding confidence intervals in HLGM analysis, since lack of appropriate and standard functions to estimate effect sizes associated with the model-implied difference between grouping trajectories in HLGM, and also lack of computing packages in the popular statistical software to automatically calculate them. ^ The present project is the first to establish the appropriate computing functions to assess the standard difference between grouping trajectories in HLGM. We proposed the two functions to estimate effect sizes on model-based grouping trajectories difference at specific time, we also suggested the robust effect sizes to reduce the bias of estimated effect sizes. Then, we applied the proposed functions to estimate the population effect sizes (d ) and robust effect sizes (du) on the cross-level interaction in HLGM by using the three simulated datasets, and also we compared the three methods of constructing confidence intervals around d and du recommended the best one for application. At the end, we constructed 95% confidence intervals with the suitable method for the effect sizes what we obtained with the three simulated datasets. ^ The effect sizes between grouping trajectories for the three simulated longitudinal datasets indicated that even though the statistical hypothesis test shows no significant difference between grouping trajectories, effect sizes between these grouping trajectories can still be large at some time points. Therefore, effect sizes between grouping trajectories in HLGM analysis provide us additional and meaningful information to assess group effect on individual trajectories. In addition, we also compared the three methods to construct 95% confident intervals around corresponding effect sizes in this project, which handled with the uncertainty of effect sizes to population parameter. We suggested the noncentral t-distribution based method when the assumptions held, and the bootstrap bias-corrected and accelerated method when the assumptions are not met.^