Browsing by Author "Greg Gibson, Committee Member"

Now showing 1 - 8 of 8

Carbohydrate Utilization Pathway Analysis in the Hyperthermophile Thermotoga maritima
(2006-03-01) Conners, Shannon Burns; Todd Klaenhammer, Committee Member; Robert Kelly, Committee Chair; Greg Gibson, Committee Member; Bruce Weir, Committee Member; Jason Osborne, Committee Member
Carbohydrate utilization and production pathways identified in Thermotoga species likely contribute to their ubiquity in hydrothermal environments. Many carbohydrate-active enzymes from Thermotoga maritima have been characterized biochemically; however, sugar uptake systems and regulatory mechanisms that control them have not been well defined. Transcriptional data from cDNA microarrays were examined using mixed effects statistical models to predict candidate sugar substrates for ABC (ATP-binding cassette) transporters in T. maritima. Genes encoding proteins previously annotated as oligopeptide/dipeptide ABC transporters responded transcriptionally to various carbohydrates. This finding was consistent with protein sequence comparisons that revealed closer relationships to archaeal sugar transporters than to bacterial peptide transporters. In many cases, glycosyl hydrolases, co-localized with these transporters, also responded to the same sugars. Putative transcriptional repressors of the LacI, XylR, and DeoR families were likely involved in regulating genomic units for beta-1,4-glucan, beta-1,3-glucan, beta-1,4-mannan, ribose, and rhamnose metabolism and transport. Carbohydrate utilization pathways in T. maritima may be related to ecological interactions within cell communities. Exopolysaccharide-based biofilms composed primarily of β-linked glucose, with small amounts of mannose and ribose, formed under certain conditions in both pure T. maritima cultures and mixed cultures of T. maritima and M. jannaschii. Further examination of transcriptional differences between biofilm-bound sessile cells and planktonic cells revealed differential expression of beta-glucan-specific degradation enzymes, even though maltose, an alpha-1,4 linked glucose disaccharide, was used as a growth substrate. Higher transcripts of genes encoding iron and sulfur compound transport, iron-sulfur cluster chaperones, and iron-sulfur cluster proteins suggest altered redox environments in biofilm cells. Further direct comparisons between cellobiose and maltose-grown cells suggested that transcription of cellobiose utilization genes is highly sensitive to the presence of cellobiose, or a cellobiose-maltose mixture. Increased transcripts of genes related to polysulfide reductases in cellobiose-grown cells and biofilm cells suggested that T. maritima cells in pure culture biofilms escaped hydrogen inhibition by preferentially reducing sulfur compounds, while cells in mixed culture biofilms form close associations with hydrogen-utilizing methanogens. In addition to probing issues related to the microbial physiology and ecology of T. maritima, this work illustrates the strategic use of DNA microarray-based transcriptional analysis for functional genomics studies.
Functional Genomics Analyses of Carbohydrate Utilization by Lactobacillus acidopohilus
(2006-11-17) Barrangou, Rodolphe; Dahlia Nielsen, Committee Member; Greg Gibson, Committee Member; Robert M. Kelly, Committee Member; Todd Klaenhammer, Committee Chair
Carbohydrates are a primary source of energy for microbes. Specifically, lactic acid bacteria have the ability to utilize a variety of nutrients available in their respective habitats. For probiotic microbes inhabiting the human gastrointestinal tract, the ability to utilize sugars non-digested by the host plays an important role in their survival. Lactobacillus acidophilus is a probiotic organism which can utilize a variety of mono-, di- and poly-saccharides, including prebiotic compounds such as fructooligosaccharides and raffinose. However, little information is available about the mechanisms and genes involved in carbohydrate utilization by lactobacilli. The transport and catabolic machinery involved in utilization of glucose, fructose, sucrose, FOS, raffinose, lactose, galactose and trehalose was characterized using global transcriptional profiling. Microarray hybridizations were carried out using a round-robin design and data analyzed using a two-stage mixed model ANOVA. Genes differentially expressed between treatments were visualized by hierarchical clustering, volcano plots, and 3-way contour plots. Globally, a small number of genes were highly induced, including a variety of carbohydrate transporters and sugar hydrolases. Members of the phosphoenolpyruvate sugar phosphotransferase system (PTS) family of transporters were identified for uptake of glucose, fructose, sucrose and trehalose. In contrast, transporters of the ATP binding cassette (ABC) family were identified for uptake of FOS and raffinose. A member of the LacS family of galactoside-pentose-hexuronide (GPH) translocators was identified for uptake of galactose and lactose. Saccharolytic enzymes likely involved in the metabolism of mono-, di- and poly- saccharides were also identified, including the enzymatic machinery of the Leloir pathway. Insertional inactivation of genes encoding sugar transporters and hydrolases confirmed microarray results. Quantitative RT-PCR was also used to confirm differential gene expression. Additional transcription experiments showed specific induction of genes encoding sugar transporters and hydrolases, and transcriptional repression by glucose. Collectively, microarray data revealed coordinated and regulated transcription of genes involved in sugar utilization based on carbohydrate availability, likely via carbon catabolite repression. The relationships between gene expression level, codon usage, chromosomal location and intrinsic gene parameters were investigated globally. Gene expression levels correlated most highly with GC content, codon adaptation index and gene size. In contrast, gene expression levels did not correlate with GC content at the third codon position. Perhaps the high correlation between GC content and gene expression is due to the low genomic GC composition of L. acidophilus. Analysis of variance was used to investigate the impact of chromosomal location on gene expression after data was segregated into four groups, by strand and orientation relative to the origin and terminus of replication. Results showed genes on the leading strand were more highly expressed. Also, genes pointing toward the terminus of replication showed higher expression levels. This preference allows for co-directional replication and transcription. Collectively, results showed a strong influence of chromosomal architecture, GC content and codon usage on gene transcription. Globally, analysis of gene expression in Lactobacillus acidophilus revealed orchestrated transcription, and adaptation to environmental conditions. Specifically, dynamic adaptation to carbohydrate sources available in the environment might contribute to competition with other commensal microbes for the limited nutrient sources available in the human gastrointestinal tract.
Methods Evaluation and Application in Complex Human Genetic Disease
(2008-08-04) Lou, Xuemei; Greg Gibson, Committee Member; Eric Stone, Committee Member; Elizabeth R. Hauser, Committee Member; Zhao-Bang Zeng, Committee Chair
Novel Methods for Mapping Complex Disease
(2008-06-05) Mei, Hao; Margaret A. Pericak-Vance, Committee Member; Greg Gibson, Committee Member; Eden R. Martin, Committee Co-Chair; Zhao-Bang Zeng, Committee Chair; Jung-Ying Tzeng, Committee Member
Probe Design and Data Analysis for Gene Expression Microarrays
(2003-04-13) Warren, Liling Li; Greg Gibson, Committee Member; Spencer Muse, Committee Member; Ben Liu, Committee Chair; Bruce Weir, Committee Member
This thesis work focuses on several bioinformatics aspects of DNA microarray experiments. DNA microarrays are breakthrough technologies for large scale gene expression profiling. Instead of measuring transcription levels one gene at a time, expression levels for many thousands of genes can be quantified simultaneously on one microarray. Depending on the array format, cDNA or pre-synthesized oligo nucleotides can be deposited as probes onto the array. Oligo probes can also be synthesized on the array. During the complete process of a DNA microarray experiment, many steps involve bioinformatics tasks; from probe design, image analysis, data normalization to data analysis and data mining. This thesis deals with oligo probe design issues and comparisons of data normalization methods. Methods on how to select a relatively small number of short probes and use them in a combinatorial fashion to quantify large scale expression levels are also explored. In Chapter one, a novel algorithm to design gene specific probes is described. When gene specific oligos are used as probes, it is crucial to select a set of probes that have desirable properties in order for many hybridization reactions to take place in parallel on an array. Given a set of sequences, the algorithm works by finding the range of melting temperatures for all possible probe choices. Then for each possible melting temperature within the range, one probe having the closest melting temperature is picked from each sequence to form a probe set. Among all the probe sets, the one that has the most homogeneous melting temperatures is the optimized choice. The major significance of our approach is the reduction of computation amount, which increases linearly as the number of genes increases rather than exponentially. Detailed steps on how to implement the algorithm are outlined and examples are given. With some modifications, the algorithm can also be applied to design allele specific probes for SNP genotyping or point mutation detections. In Chapter two, five normalization methods are compared with each other and also compared with analysis skipping the normalization step. Overall, performing normalization can reduce systematic variations and identify more genes as differentially expressed than without the normalization step. Among different normalization methods being compared, ANOVA based normalization method has the most power to detect differentially expressed genes. When the same normalization and analysis methods are used, ratio based method has more power than the one based on absolute signal intensity values. When different number of genes are detected by different normalization methods, one way to plan for future experiment is to use the set of genes that have been detected by all methods. Alternatively, one can use all the genes that have been identified to be differentially expressed regardless which method was used to design further experiments. Insights from this study on how to incorporate biological variation into future experimental designs are also discussed. In Chapter three, we present methods to choose a set of short oligos to design a genome or tissue specific biochip and then to solve a set of equations for gene expression levels to determine genes that are differentially expressed between samples. The methods have been tested to define a set of 4000 8mers as probes to identify genes that have fold changes for more than 6000 identified yeast ORFs. These methods can also be expanded to design genome specific or tissue specific biochips for other organisms with full gene sequence information. The major advantages of using our methods is to significantly reduce overall cost in array fabrication and oligo synthesis. The process of mining probe sets depends on knowing gene sequence information in a specific genome or tissue. As more genomes are being sequenced, this method holds great promise towards enabling more accurate and less expensive microarray experiments.
Quantitative Molecular Genetics of Longevity in Drosophila melanogaster.
(2004-08-18) Thornsberry, Gretchen Lindsay Geiger; Bruce Weir, Committee Member; Trudy F. C. Mackay, Committee Chair; Greg Gibson, Committee Member; Michael Purugganan, Committee Member
Limited life span and senescence are universal phenomena, controlled by genetic and environmental factors whose interactions both limit life span and generate variation in life span between individuals, populations and species. To understand the genetic architecture of aging it is necessary to know what loci affect variation in life span, what are the allelic effects at these loci and what molecular polymorphisms define quantitative trait locus (QTL) alleles. Here, quantitative complementation tests were used to determine whether candidate life span genes such as Superoxide dismutase (Sod), Catalase (Cat), heat shock proteins, DNA repair enzymes, glucose metabolism or male accessory gland proteins interact genetically with naturally occurring QTL affecting variation in life span in Drosophila melanogaster. Inbred strains derived from a natural population were crossed to stocks containing null mutations or deficiencies uncovering the above genes. Life span of the heterozygous progeny was assayed. A significant cross (mutant versus wild-type allele of the candidate gene) by inbred line interaction term from analysis of variance of the life span data indicates a genetic interaction between the candidate gene allele and the naturally occurring life span QTL. Of the sixteen candidate regions and genes tested, Df(2L)cl7, Df(3L)Ly, Df(3L)AC1, Df(3R)e-BS2, and α-Glycerol phosphate dehydrogenase showed significant failure to complement wild-type alleles in both sexes, and an Alcohol dehydrogenase mutant failed to complement in females. Several genes known to regulate life span (Sod, Cat, and rosy) complemented the life span effects of alleles, suggesting little natural variation affecting longevity at these loci, at least in this sample of alleles. Quantitative complementation tests are therefore useful for identifying candidate genes contributing to segregating genetic variation in life span in nature. Mutations in most vital genes can potentially affect life history traits, but it is not known what subset of these loci harbor naturally occurring variation affecting the rate of aging and the ability to resist stress. While the gene Punch (Pu) was not significant in the quantitative complementation test, it has been implicated in starvation resistance. As there is a direct relationship between stress resistance and longevity, Pu, which encodes GTP cyclohydrolase (GTPCH), is a candidate gene for associating molecular variation and variation in life pan. GTPCH regulates the catecholamine biosynthesis pathway by catalyzing the formation of tetrahydrobiopterin, the rate-limiting molecule, and by regulating tyrosine hydroxylase, a key enzyme in the pathway. The extent to which molecular variation at Pu contributes to phenotypic variation was assessed by associating single nucleotide polymorphisms (SNPs) at Pu with longevity. Nucleotide variation was determined for ten Pu alleles. Genotypes of 28 SNPs were determined on a sample of 178 isogenic second chromosomes sampled from the Raleigh, USA population and substituted into the highly inbred Samarkand background. Life span was determined for the chromosome substitution lines and the association between longevity phenotype and SNP genotype was assessed for each polymorphic marker. Three SNPs were significantly associated with life span (C6291A, P = 0.0183; A6389T, P = 0.0466; G6894C, P = 0.0024). None of these SNPs was significant individually following a permutation test accounting for multiple tests and partially correlated markers. However, the three SNPs associated with life span were in global linkage disequilibrium. Haplotypes of these SNPs were highly significantly associated with variation in longevity (P < 0.0001), and accounted for 13.5 % of the genetic variance and 1.86 % of the phenotypic variance in longevity attributable to chromosomes 2. As Pu is a regulator of the catecholamine biosynthetic pathway, these findings suggest the importance of the production of biogenic amines in determining variation for longevity.
Statistical methods for the analysis of genetics marker and microarray data
(2004-05-18) Yu, Xiang; Bruce S. Weir, Committee Chair; Dahlia M. Nielsen, Committee Co-Chair; Greg Gibson, Committee Member; Russell D. Wolfinger, Committee Member
With the advent of high-throughput technologies in genomics study, a large volume of data has been accumulated, leaving the challenge for bioinformaticists on how to manage, analyze, and interpret the data. Analysis of genetic marker and microarray data are two important aspects in current bioinformatics studies. In this dissertation work, we tend to explore some statistical issues for such problems. We discuss two extensions of the EM algorithm to infer haplotypes from genotype data, each for a particular sampling scenario. The first one applies to a random sample of both diploid and haploid individuals from the population, in which the haplotype information from the haploid individuals is incorporated into the estimation process. The second one applies to a sample of parent-offspring trios, in which the dependencies between the parental and the offspring genotypes are correctly handled in the analysis. We show that these two modified EM algorithms perform better than the usual one when applied to their corresponding specific samples, respectively. We study the experimental designs in two-color microarray experiments and resolve some of the outstanding issues that are controversial on the use of different experiment designs. We show that the loop and balanced block designs analyzed in a mixed model are more efficient that the reference designs from a statistical point of view. We also provide general guidelines on how to optimize experimental resources to get maximal efficiency using these designs. We present an application of the mixed model to identify transcription factor-gene interactions and to infer transcriptional regulatory structures in Sacchromyces cerevisiae using microarray experiments. We demonstrate the mixed model that pools the observations across all experiments to be a powerful approach.
Statistical Topics in Disease Gene Mapping
(2003-04-14) Meng, Zhaoling; Bruce S. Weir, Committee Chair; Margaret G. Ehm, Committee Co-Chair; Zhao-Bang Zeng, Committee Member; Russ Wolfinger, Committee Member; Greg Gibson, Committee Member; Jonathan Allen, Committee Member
Efforts in disease gene mapping have achieved a great deal of success in mendelain diseases, but made slower progress in common disease studies because of their complexity. The rapid development of genetics and molecular technologies provides an immense amount of DNA data; developing powerful and efficient statistical methodologies is under high demand. This dissertation explored some aspects of the problem. The power of two genome-wide disease gene mapping strategies is investigated. One applies linkage analysis and then linkage disequilibrium (LD) tests to markers within linked regions. The other looks for LD with disease using all markers. The results showed that the genome-wide association based tests are much more likely to identify genes. Genotyping closely spaced Single Nucleotide Polymorphisms (SNPs) frequently yields highly correlated data due to extensive LD, and gives association studies unnecessary and unaffordable burden when these markers don't yield significantly different information. Two procedures are developed to select an optimum subset of SNPs that could be efficiently genotyped on larger numbers of samples while retaining most of the information based on genotypes of a large initial set of SNPs on a small number of samples. One utilizes a spectral decomposition method based on matrices of pair-wise LD, and the other extends David Clayton's htSNP selection method. Properties of the procedures are studied; minimum sample sizes needed for achieving consistent results are recommended; the procedures are evaluated using experimental data. Studying gene-treatment interaction is a long desired problem. When the genetic variation that is being tested is not specific functional sites but randomly selected polymorphisms, a source of randomness is introduced. A mixed effect model is developed to fit fixed treatment effects, random haplotypic effects, and random gene-treatment interactions in this scenario; likelihood ratio tests are applied for testing the random effects. Our simulation results showed that the mixed effect model is valid and generally behaves better than the fixed haplotypic effects model in the exploratory phase of a study.