The ATP-binding cassette (ABC) transporter superfamily consists of 49 members, to which both Multidrug Resistance Protein 1 (MRP1/gene symbol: ABCC1) and MRP4 (ABCC4) belong. Single nucleotide polymorphisms (SNPs) in drug metabolizing genes have been shown to affect individual responses to drugs and toxins. However, the role of SNPs in modulating the activity of drug transporters, such as MRP1 and MRP4, is poorly characterized. The overall goal of my thesis was to determine the effects of SNPs in the promoter regions of human ABCC1 and ABCC4. For MRP1/ABCC1, two proximal promoter SNPs (-275A>G, -260G>C) were identified in the literature and recreated in vitro, and the activity of the mutant ABCC1 promoter constructs was measured in five human cell lines using a dual luciferase assay. The activity of the -275A>G promoter was comparable to the wild-type ABCC1 promoter. On the other hand, the -260G>C substitution decreased ABCC1 promoter activity in HepG2, MCF-7 and HeLa (40 - 60%) cells. A 1706 bp fragment containing the 5’-flanking and untranslated regions of ABCC4 were isolated from two bacterial artificial chromosome clones and six serially deleted ABCC4 promoter reporter constructs generated. Luciferase assays of the basal promoter constructs of ABCC4 in HEK293T cells revealed the presence of one or more negative regulatory regions between -1706 and -876, between -876 and -641, and one or more positive regulatory regions between -641 and -356, and between -356 and -17. Also, the ABCC4 promoter displayed differential activity in MDCKI and LLC-PK1 cells than in HEK293T cells. One SNP (-523G>C) was identified from an online database and its activity tested. However, -523G>C SNP did not cause any significant change in the ABCC4 promoter activity in both HEK293T and HepG2 cells (80 – 130%). In summary, the data obtained suggest that the promoter SNPs studied may affect the transcriptional activity of ABCC1 or ABCC4, but it seems likely that this is not true in all cell types. / Thesis (Master, Pathology & Molecular Medicine) -- Queen's University, 2007-09-28 10:03:16.119

<p>Single nucleotide polymorphisms (SNP) in leptin (LEP) and pro-opiomelanocortin (POMC) have been associated with beef carcass quality and yield respectively. Both hormones also play a role in immune performance. Since both of these genes are pleiotrophic, it was important to determine whether selection based on these SNPs would negatively affect immune cell numbers. A SNP in each of these hormones was assessed for effects on immune cell counts and antibody titres in twenty-seven beef cattle herds (n = 556). A commercial rabies vaccine was administered to these animals. Prior to being vaccinated, the types of lymphocytes evaluated included B cells, gamma delta cells, regular and activated CD4 and CD8 cells and numbers of lymphocytes as well as baseline serum antibody titres. On day 21, antibody titres were measured and a booster vaccine was administered. Finally on day 42, antibody titres and lymphocyte types were again counted. Several cell types were significantly associated with the LEP genotype however, no consistent pattern of correlation was observed between LEP genotype (TT, CT or CC) and peripheral blood lymphocyte populations. The number of different lymphocytes significantly associated with LEP genotype increased from two on day 0 to four on day 42. Animals with CT and CC genotypes had significantly higher increased rabies antibody titres in the first 21 days after vaccination than those with TT genotypes. The POMC SNP also did not show a clear pattern of association between lymphocyte subtypes and genotype. There was no difference in response to the rabies vaccination associated with the POMC genotype. Our results suggested that selection at either of the SNPs examined in this research would not detrimentally impact immune function in beef cattle.</p>

<p>Single nucleotide polymorphisms (SNP) in leptin (LEP) and pro-opiomelanocortin (POMC) have been associated with beef carcass quality and yield respectively. Both hormones also play a role in immune performance. Since both of these genes are pleiotrophic, it was important to determine whether selection based on these SNPs would negatively affect immune cell numbers. A SNP in each of these hormones was assessed for effects on immune cell counts and antibody titres in twenty-seven beef cattle herds (n = 556). A commercial rabies vaccine was administered to these animals. Prior to being vaccinated, the types of lymphocytes evaluated included B cells, gamma delta cells, regular and activated CD4 and CD8 cells and numbers of lymphocytes as well as baseline serum antibody titres. On day 21, antibody titres were measured and a booster vaccine was administered. Finally on day 42, antibody titres and lymphocyte types were again counted. Several cell types were significantly associated with the LEP genotype however, no consistent pattern of correlation was observed between LEP genotype (TT, CT or CC) and peripheral blood lymphocyte populations. The number of different lymphocytes significantly associated with LEP genotype increased from two on day 0 to four on day 42. Animals with CT and CC genotypes had significantly higher increased rabies antibody titres in the first 21 days after vaccination than those with TT genotypes. The POMC SNP also did not show a clear pattern of association between lymphocyte subtypes and genotype. There was no difference in response to the rabies vaccination associated with the POMC genotype. Our results suggested that selection at either of the SNPs examined in this research would not detrimentally impact immune function in beef cattle.</p>

Single nucleotide polymorphisms (SNPs) have been increasingly popular for a wide range of genetic studies. A high-throughput genotyping technologies usually involves a statistical genotype calling algorithm. Most calling algorithms in the literature, using methods such as k-means and mixturemodels, rely on elliptical structures of the genotyping data; they may fail when the minor allele homozygous cluster is small or absent, or when the data have extreme tails or linear patterns. We propose an automatic genotype calling algorithm by further developing a linear grouping algorithm (Van Aelst et al., 2006). The proposed algorithm clusters unnormalized data points around lines as against around centroids. In addition, we associate a quality value, silhouette width, with each DNA sample and a whole plate as well. This algorithm shows promise for genotyping data generated from TaqMan technology (Applied Biosystems). A key feature of the proposed algorithm is that it applies to unnormalized fluorescent signals when the TaqMan SNP assay is used. The algorithm could also be potentially adapted to other fluorescence-based SNP genotyping technologies such as Invader Assay. Motivated by the SNP genotyping problem, we propose a partial likelihood approach to linear clustering which explores potential linear clusters in a data set. Instead of fully modelling the data, we assume only the signed orthogonal distance from each data point to a hyperplane is normally distributed. Its relationships with several existing clustering methods are discussed. Some existing methods to determine the number of components in a data set are adapted to this linear clustering setting. Several simulated and real data sets are analyzed for comparison and illustration purpose. We also investigate some asymptotic properties of the partial likelihood approach. A Bayesian version of this methodology is helpful if some clusters are sparse but there is strong prior information about their approximate locations or properties. We propose a Bayesian hierarchical approach which is particularly appropriate for identifying sparse linear clusters. We show that the sparse cluster in SNP genotyping datasets can be successfully identified after a careful specification of the prior distributions.

Single nucleotide polymorphisms (SNPs) have been increasingly popular for a wide range of genetic studies. A high-throughput genotyping technologies usually involves a statistical genotype calling algorithm. Most calling algorithms in the literature, using methods such as k-means and mixturemodels, rely on elliptical structures of the genotyping data; they may fail when the minor allele homozygous cluster is small or absent, or when the data have extreme tails or linear patterns. We propose an automatic genotype calling algorithm by further developing a linear grouping algorithm (Van Aelst et al., 2006). The proposed algorithm clusters unnormalized data points around lines as against around centroids. In addition, we associate a quality value, silhouette width, with each DNA sample and a whole plate as well. This algorithm shows promise for genotyping data generated from TaqMan technology (Applied Biosystems). A key feature of the proposed algorithm is that it applies to unnormalized fluorescent signals when the TaqMan SNP assay is used. The algorithm could also be potentially adapted to other fluorescence-based SNP genotyping technologies such as Invader Assay. Motivated by the SNP genotyping problem, we propose a partial likelihood approach to linear clustering which explores potential linear clusters in a data set. Instead of fully modelling the data, we assume only the signed orthogonal distance from each data point to a hyperplane is normally distributed. Its relationships with several existing clustering methods are discussed. Some existing methods to determine the number of components in a data set are adapted to this linear clustering setting. Several simulated and real data sets are analyzed for comparison and illustration purpose. We also investigate some asymptotic properties of the partial likelihood approach. A Bayesian version of this methodology is helpful if some clusters are sparse but there is strong prior information about their approximate locations or properties. We propose a Bayesian hierarchical approach which is particularly appropriate for identifying sparse linear clusters. We show that the sparse cluster in SNP genotyping datasets can be successfully identified after a careful specification of the prior distributions.

Single nucleotide polymorphisms (SNPs) have been increasingly popular for a wide range of genetic studies. A high-throughput genotyping technologies usually involves a statistical genotype calling algorithm. Most calling algorithms in the literature, using methods such as k-means and mixturemodels, rely on elliptical structures of the genotyping data; they may fail when the minor allele homozygous cluster is small or absent, or when the data have extreme tails or linear patterns. We propose an automatic genotype calling algorithm by further developing a linear grouping algorithm (Van Aelst et al., 2006). The proposed algorithm clusters unnormalized data points around lines as against around centroids. In addition, we associate a quality value, silhouette width, with each DNA sample and a whole plate as well. This algorithm shows promise for genotyping data generated from TaqMan technology (Applied Biosystems). A key feature of the proposed algorithm is that it applies to unnormalized fluorescent signals when the TaqMan SNP assay is used. The algorithm could also be potentially adapted to other fluorescence-based SNP genotyping technologies such as Invader Assay. Motivated by the SNP genotyping problem, we propose a partial likelihood approach to linear clustering which explores potential linear clusters in a data set. Instead of fully modelling the data, we assume only the signed orthogonal distance from each data point to a hyperplane is normally distributed. Its relationships with several existing clustering methods are discussed. Some existing methods to determine the number of components in a data set are adapted to this linear clustering setting. Several simulated and real data sets are analyzed for comparison and illustration purpose. We also investigate some asymptotic properties of the partial likelihood approach. A Bayesian version of this methodology is helpful if some clusters are sparse but there is strong prior information about their approximate locations or properties. We propose a Bayesian hierarchical approach which is particularly appropriate for identifying sparse linear clusters. We show that the sparse cluster in SNP genotyping datasets can be successfully identified after a careful specification of the prior distributions. / Science, Faculty of / Statistics, Department of / Graduate

Indiana University-Purdue University Indianapolis (IUPUI) / 2022-08-31

No description available.

The human pathogenic Cryptococcus neoformans species complex are agents of a common AIDS-defining disease, which causes about 181,000 deaths each year. There are several specific features distinguishing this species from other fungi, including the presence of a polysaccharide capsule and melanin pigment production, both of which contribute to its virulence. A large number of studies about this pathogen used two model strains JEC20 and JEC21. In these studies, these two strains are assumed to be “isogenic”, differ only at the mating type region. Consequently, their phenotypic differences, including virulence, have been attributed to this region. Here, we applied second-generation sequencing and bioinformatics tools to identify sequence polymorphisms between the two genomes. Beside the Mating Type locus, two other regions were found to contain high frequencies of SNPs. To further understand the effects of these loci on the phenotypic differences, four phenotyping assays (mating ability, melanin pigment production, capsule formation, and high temperature growth ability) were conducted on the recombinant progeny obtained from the cross between JEC20 and JEC21. In addition, genomic sequences of these progeny were obtained to identify the complete distributions of other SNPs among the strains. Finally, we identified several novel SNPs contributing to virulence-related traits in this species, which suggest that caution should be placed in attributing phenotypic differences to specific genomic regions in “isogenic” strains derived from classical breeding experiments. / Thesis / Master of Science (MSc) / Cryptococcosis is a globally distributed infection that is prevalent among immune-compromised individuals, such as HIV/AIDS patients. This disease can be attributed to a group of opportunistic fungal pathogens – Cryptococcus neoformans species complex. During the past century, significant resources have been put in an effort to understand its ecology, evolution, life cycle, pathogenesis and virulence factors, and molecular and cellular processes. Most of the laboratory-based studies have relied on two model strains assumed to differ only at the mating type locus. My thesis investigated this assumption and found there are several additional significant genetic differences between these two strains and that such differences contribute to the observed phenotypic differences between them. My results highlight the complexity of genotype-phenotype relationships and the continued evolution of strains even in lab environments in C. neoformans.

Dilated cardiomyopathy (DCM), a heart disease, affects many vertebrates including humans and poultry. The disease can be either idiopathic (IDCM) or toxin-induced. Idiopathic DCM often occurs without a consensus cause. Though genetic and other studies of IDCM are extensive, the specific etiology of toxin-induced is still unknown. Here, our objective was to compare the level of mRNA expression of two candidate genes including troponin T (cTnT) and phospholamban (PLN) using quantitative reverse transcription polymerase chain reaction (RT-PCR) in toxin-induced DCM affected and unaffected turkeys. Cardiac TnT and PLN were chosen because their spontaneous expression has been reported to be associated with IDCM. We also scanned these genes for single nucleotide polymorphisms (SNPs) that could be useful in evaluating their functions in the incidence and severity of toxin-induced DCM in turkeys. There were no significant differences between affected and unaffected birds in the expression of both cTnT and PLN. A total of 12 SNPs were detected in cTnT and PLN DNA sequences. One of the seven haplotypes detected in cTnT was the most frequent. Linkage analysis showed that cTnT gene was unlinked on the current turkey genetic map. Resources developed here, including SNPs, haplotypes, cDNA sequences, and the PCR-RFLP genotype procedure will be used for future investigations involving cTnT and PLN and toxin-induced DCM. / Master of Science