Biomedical subjects

Andrea S Foulkes

Publications and source records attributed to Andrea S Foulkes.

5 recordsLinked to original sources

PubMed2006

Treatment of Brucella-susceptible mice with IL-12 increases primary and secondary immunity.

Brucella spp. cause disease in humans and livestock and are potential biowarfare agents. Defining the protective immune response is necessary to design vaccines. This has largely been done with mice, brucella-susceptible BALB/c and resistant C57BL strains. Since interferon-gamma is key to brucella resistance, contrary to expectations, we found that ex vivo splenocytes from naïve BALB/c mice produced IL-12 and interferon-gamma in cultures with brucellae at levels comparable to those of splenocytes from the more resistant C57BL/10 mice. Moreover, both IL-12 and interferon-gamma were produced in the first week following infection of BALB/c mice. However, by the third week of infection we found decreased IL-12Rbeta2 expression by BABL/c splenocytes, corresponding to their inability to produce interferon-gamma in Brucella recall responses at this time as reported previously. Administering recombinant IL-12 to these mice ameliorated the interferon-gamma hiatus, resulted in a 1000-fold reduction in CFU during primary infection and increased survival following secondary challenge.

Animals↗

PubMed2006

Associations among race/ethnicity, ApoC-III genotypes, and lipids in HIV-1-infected individuals on antiretroviral therapy.

BACKGROUND: Protease inhibitors (PIs) are associated with hypertriglyceridemia and atherogenic dyslipidemia. Identifying HIV-1-infected individuals who are at increased risk of PI-related dyslipidemia will facilitate therapeutic choices that maintain viral suppression while reducing risk of atherosclerotic diseases. Apolipoprotein C-III (apoC-III) gene variants, which vary by race/ethnicity, have been associated with a lipid profile that resembles PI-induced dyslipidemia. However, the association of race/ethnicity, or candidate gene effects across race/ethnicity, with plasma lipid levels in HIV-1-infected individuals, has not been reported. METHODS AND FINDINGS: A cross-sectional analysis of race/ethnicity, apoC-III/apoA-I genotypes, and PI exposure on plasma lipids was performed in AIDS Clinical Trial Group studies (n = 626). Race/ethnicity was a highly significant predictor of plasma lipids in fully adjusted models. Furthermore, in stratified analyses, the effect of PI exposure appeared to differ across race/ethnicity. Black/non-Hispanic, compared with White/non-Hispanics and Hispanics, had lower plasma triglyceride (TG) levels overall, but the greatest increase in TG levels when exposed to PIs. In Hispanics, current PI antiretroviral therapy (ART) exposure was associated with a significantly smaller increase in TGs among patients with variant alleles at apoC-III-482, -455, and Intron 1, or at a composite apoC-III genotype, compared with patients with the wild-type genotypes. CONCLUSIONS: In the first pharmacogenetic study of its kind in HIV-1 disease, we found race/ethnic-specific differences in plasma lipid levels on ART, as well as differences in the influence of the apoC-III gene on the development of PI-related hypertriglyceridemia. Given the multi-ethnic distribution of HIV-1 infection, our findings underscore the need for future studies of metabolic and cardiovascular complications of ART that specifically account for racial/ethnic heterogeneity, particularly when assessing candidate gene effects.

Black or African American↗

PubMed2005

Higher order lipase gene association with plasma triglycerides.

Lipoprotein lipase, HL, and endothelial lipase (EL) are proteoglycan-bound enzymes that regulate plasma lipoprotein levels through coordinated triglyceride (TG) lipase and phospholipase activity. We hypothesized that single nucleotide polymorphisms (SNPs) in lipase genes would have higher order impact on plasma lipoproteins beyond the influence of individual SNPs. In a sample of asymptomatic Caucasian subjects (n = 738), we used a two-stage approach, first identifying groups of subjects with similar multilocus lipase genotypes and then characterizing the relationships between genotype groups and plasma lipids. Using complementary methods, including a permutation test procedure and a mixed-effects modeling approach, we found a higher order interaction between four SNPs in three lipase genes (EL 2,237 3' untranslated region, EL Thr111Ile, HL -514C/T, and LPL HindIII) and plasma TG levels. Subjects who were heterozygous for all four lipase SNPs had significantly higher plasma TG levels beyond the effect of individual lipase SNPs and environmental factors, even after correcting for multiple comparisons. In conclusion, lipase genes had synergistic association with plasma TG beyond individual gene effects. Higher order multilocus genotype contributions to dyslipidemia and atherosclerotic cardiovascular disease need to be considered a priori because they may have an important effect even in the absence of significant main effects of the individual genes.

Adult↗

PubMed2005

Exploratory Bayesian model selection for serial genetics data.

Characterizing the process by which molecular and cellular level changes occur over time will have broad implications for clinical decision making and help further our knowledge of disease etiology across many complex diseases. However, this presents an analytic challenge due to the large number of potentially relevant biomarkers and the complex, uncharacterized relationships among them. We propose an exploratory Bayesian model selection procedure that searches for model simplicity through independence testing of multiple discrete biomarkers measured over time. Bayes factor calculations are used to identify and compare models that are best supported by the data. For large model spaces, i.e., a large number of multi-leveled biomarkers, we propose a Markov chain Monte Carlo (MCMC) stochastic search algorithm for finding promising models. We apply our procedure to explore the extent to which HIV-1 genetic changes occur independently over time.

Algorithms↗

PubMed2004

Validation and discovery in Markov models of genetics data.

Markov models provide a natural framework for modeling cellular and molecular level changes over time. Kalbfleisch and Lawless propose using a Chi-squared statistic for assessing the appropriateness of assuming a first-order, homogeneous Markov process. While this statistic provides a global test of the Markov assumption, it does not permit identification of individual departures. We consider two approaches for discovering specific departures from the Markov assumption. First, we propose a diagnostic that tests whether the number of observed transitions out of a given state at a given time point is different than expected. Second, we construct statistics based on the number of observations in each state at each time point. In both cases, we construct multiple correlated statistics and testing is achieved through simulations. These approaches are applied to HIV genetics sequences measured over time.

Journal Article↗