Biomedical subjects

Grier P Page

Publications and source records attributed to Grier P Page.

At least 19 recordsLinked to original sources

PubMed2025

Aberrant DNA methylation of genes regulating CD4+ T cell HIV-1 reservoir in women with HIV.

BACKGROUND: The HIV-1 reservoir in CD4+ T cells (HRCD4) pose a major challenge to curing HIV, with many of its mechanisms still unclear. HIV-1 DNA integration and immune responses may alter the host's epigenetic landscape, potentially silencing HIV-1 replication. METHODS: This study used bisulphite capture DNA methylation sequencing in CD4+ T cells from the blood of 427 virally suppressed women with HIV to identify differentially methylated sites and regions associated with HRCD4. RESULTS: The average total HRCD4 size was 1409 copies per million cells, with most proviruses defective and only a small proportion intact. The study identified 245 differentially methylated CpG sites and 85 regions linked to HRCD4 size, with 52% of significant sites in intronic regions. Genes associated with HRCD4 were involved in viral replication, HIV-1 latency and cell growth and apoptosis. HRCD4 size was inversely related to DNA methylation of interferon signalling genes and positively associated with methylation at known HIV-1 integration sites. HRCD4-associated genes were enriched on the pathways related to immune defence, transcription repression and host-virus interactions. CONCLUSIONS: These findings suggest that HIV-1 reservoir is linked to aberrant DNA methylation in CD4+ T cells, offering new insights into epigenetic mechanisms of HIV-1 latency and potential molecular targets for eradication strategies. KEY POINTS: Study involved 427 women with HIV. Identified 245 aberrant DNA methylation sites and 85 methylation regions in CD4+ T cells linked to the HIV-1 reservoir. Highlighted genes are involved in viral replication, immune defence, and host genome integration. Findings suggest potential molecular targets for eradication strategies.

Humans↗

PubMed2006

The PowerAtlas: a power and sample size atlas for microarray experimental design and research.

BACKGROUND: Microarrays permit biologists to simultaneously measure the mRNA abundance of thousands of genes. An important issue facing investigators planning microarray experiments is how to estimate the sample size required for good statistical power. What is the projected sample size or number of replicate chips needed to address the multiple hypotheses with acceptable accuracy? Statistical methods exist for calculating power based upon a single hypothesis, using estimates of the variability in data from pilot studies. There is, however, a need for methods to estimate power and/or required sample sizes in situations where multiple hypotheses are being tested, such as in microarray experiments. In addition, investigators frequently do not have pilot data to estimate the sample sizes required for microarray studies. RESULTS: To address this challenge, we have developed a Microrarray PowerAtlas. The atlas enables estimation of statistical power by allowing investigators to appropriately plan studies by building upon previous studies that have similar experimental characteristics. Currently, there are sample sizes and power estimates based on 632 experiments from Gene Expression Omnibus (GEO). The PowerAtlas also permits investigators to upload their own pilot data and derive power and sample size estimates from these data. This resource will be updated regularly with new datasets from GEO and other databases such as The Nottingham Arabidopsis Stock Center (NASC). CONCLUSION: This resource provides a valuable tool for investigators who are planning efficient microarray studies and estimating required sample sizes.

Algorithms↗

PubMed2006

Genome scan for loci predisposing to anxiety disorders using a novel multivariate approach: strong evidence for a chromosome 4 risk locus.

We conducted a 10-centimorgan linkage autosomal genome scan in a set of 19 extended American pedigrees (219 subjects) ascertained through probands with panic disorder. Several anxiety disorders--including social phobia, agoraphobia, and simple phobia--in addition to panic disorder segregate in these families. In previous studies of this sample, linkage analyses were based separately on each of the individual categorical affection diagnoses. Given the substantial comorbidity between anxiety disorders and their probable shared genetic liability, it is clear that this method discards a considerable amount of information. In this article, we propose a new approach that considers panic disorder, simple phobia, social phobia, and agoraphobia as expressions of the same multivariate, putatively genetically influenced trait. We applied the most powerful multipoint Haseman-Elston method, using the grade of membership score generated from a fuzzy clustering of these phenotypes as the dependent variable in Haseman-Elston regression. One region on chromosome 4q31-q34, at marker D4S413 (with multipoint and single-point nominal P values < .00001), showed strong evidence of linkage (genomewide significance at P<.05). The same region is known to be the site of a neuropeptide Y receptor gene, NPY1R (4q31-q32), that was recently connected to anxiolytic-like effects in rats. Several other regions on four chromosomes (4q21.21-22.3, 5q14.2-14.3, 8p23.1, and 14q22.3-23.3) met criteria for suggestive linkage (multipoint nominal P values < .01). Family-by-family analysis did not show any strong evidence of heterogeneity. Our findings support the notion that the major anxiety disorders, including phobias and panic disorder, are complex traits that share at least one susceptibility locus. This method could be applied to other complex traits for which shared genetic-liability factors are thought to be important, such as substance dependencies.

Anxiety↗

PubMed2006

A proposed metric for assessing the measurement quality of individual microarrays.

BACKGROUND: High-density microarray technology is increasingly applied to study gene expression levels on a large scale. Microarray experiments rely on several critical steps that may introduce error and uncertainty in analyses. These steps include mRNA sample extraction, amplification and labeling, hybridization, and scanning. In some cases this may be manifested as systematic spatial variation on the surface of microarray in which expression measurements within an individual array may vary as a function of geographic position on the array surface. RESULTS: We hypothesized that an index of the degree of spatiality of gene expression measurements associated with their physical geographic locations on an array could indicate the summary of the physical reliability of the microarray. We introduced a novel way to formulate this index using a statistical analysis tool. Our approach regressed gene expression intensity measurements on a polynomial response surface of the microarray's Cartesian coordinates. We demonstrated this method using a fixed model and presented results from real and simulated datasets. CONCLUSION: We demonstrated the potential of such a quantitative metric for assessing the reliability of individual arrays. Moreover, we showed that this procedure can be incorporated into laboratory practice as a means to set quality control specifications and as a tool to determine whether an array has sufficient quality to be retained in terms of spatial correlation of gene expression measurements.

Algorithms↗

PubMed2006

Microarray data analysis: from disarray to consolidation and consensus.

In just a few years, microarrays have gone from obscurity to being almost ubiquitous in biological research. At the same time, the statistical methodology for microarray analysis has progressed from simple visual assessments of results to a weekly deluge of papers that describe purportedly novel algorithms for analysing changes in gene expression. Although the many procedures that are available might be bewildering to biologists who wish to apply them, statistical geneticists are recognizing commonalities among the different methods. Many are special cases of more general models, and points of consensus are emerging about the general approaches that warrant use and elaboration.

Algorithms↗

PubMed2006

Energy restriction lowers the expression of genes linked to inflammation, the cytoskeleton, the extracellular matrix, and angiogenesis in mouse adipose tissue.

Using high-density oligonucleotide microarrays, we examined the actions of energy restriction (ER) on the expression of >11,000 genes in epididymal white adipose tissue (WAT) of 10- to 11-mo-old male C57Bl6 mice. Four groups were studied: controls not subjected to food restriction (CO), food-restricted 18 h before being killed (FR), short-term ER for 23 d (SER), and long-term ER for 9 mo (LER). As we reported previously, compared with CO mice, FR and SER minimally influenced the gene expression profiles; however, 345 transcripts of 6,266 genes determined to be expressed in WAT were significantly altered by LER. We focus here on the 109 (31%) of these genes that were involved in either inflammation (56 genes), cytoskeleton (16 genes), extracellular matrix (23 genes), or angiogenesis (14 genes). Among these 109 genes, 104 transcripts (95%) were down regulated by LER. Western blotting for heat shock protein 47 and osteonectin, and immunohistochemical staining for hypoxia inducible factor (HIF)-1alpha), supported the microarray data that LER down regulated the expressions of these genes. Additionally, a 75% reduction in adipocyte size with LER reflected the change in the expression of genes involved in cell morphology. Our findings provide evidence that LER suppresses the expression of genes encoding inflammatory molecules in WAT while promoting structural remodeling of the cytoskeleton, extracellular matrix, and vasculature. These alterations may play an important role in the protection against WAT-derived inflammation and in lifespan extension by LER.

Adipose Tissue↗

PubMed2005

COGA phenotypes and linkages on chromosome 2.

An initial linkage analysis of the alcoholism phenotype as defined by DSM-III-R criteria and alcoholism defined by DSM-IV criteria showed many, sometimes striking, inconsistencies. These inconsistencies are greatly reduced by making the definition of alcoholism more specific. We defined new phenotypes combining the alcoholism definitions and the latent variables, defining an individual as affected if that individual is alcoholic under one of the definitions (either DSM-III-R or DSM-IV), and indicated having a symptom defined by one of the latent variables. This was done for each of the two alcoholism definitions and five latent variables, selected from a canonical discriminant analyses indicating they formed significant groupings using the electrophysiological variables. We found that linkage analyses utilizing these latent variables were much more robust and consistent than the linkage results based on DSM-III-R or DSM-IV criteria for definition of alcoholism. We also performed linkage analyses on two first principal components derived phenotypes, one derived from the electrophysiological variables, and the other derived from the latent variables. A region on chromosome 2 at 250 cM was found to be linked to both of these derived phenotypes. Further examination of the SNPs in this region identified several haplotypes strongly associated with these derived phenotypes.

Alcoholism↗

PubMed2005

DCDC2 is associated with reading disability and modulates neuronal development in the brain.

DYX2 on 6p22 is the most replicated reading disability (RD) locus. By saturating a previously identified peak of association with single nucleotide polymorphism markers, we identified a large polymorphic deletion that encodes tandem repeats of putative brain-related transcription factor binding sites in intron 2 of DCDC2. Alleles of this compound repeat are in significant disequilibrium with multiple reading traits. RT-PCR data show that DCDC2 localizes to the regions of the brain where fluent reading occurs, and RNA interference studies show that down-regulation alters neuronal migration. The statistical and functional studies are complementary and are consistent with the latest clinical imaging data for RD. Thus, we propose that DCDC2 is a candidate gene for RD.

Adult↗

PubMed2005

TDT-association analysis of EKN1 and dyslexia in a Colorado twin cohort.

A candidate gene, EKN1, was recently described in a cohort from Finland for the dyslexia locus on chromosome 15q, DYX1. This report described a (2;15) (q11;21) translocation disrupting EKN1 that cosegregated with dyslexia in a two-generation family. It also characterized a sequence polymorphism in the 5' untranslated region and a missense mutation that showed significant association in 109 dyslexics compared to 195 controls (p=0.002 and p=0.006, respectively). To confirm these results we interrogated the same polymorphisms in a cohort of 150 nuclear families with dyslexia ascertained through the Colorado Learning Disabilities Research Center. Using QTDT analysis with nine individual quantitative tasks and two composite measures of reading performance, we could not replicate the reported association. We conclude that the polymorphisms identified in the Finland sample are unlikely to be functional DNA changes contributing to dyslexia, and that if variation in EKN1 is causal such changes are more likely to be in regulatory regions that were not sequenced in this study. Alternatively, the published findings of association with markers in EKN1 may reflect linkage disequilibrium with variation in another gene(s) in the region.

Base Sequence↗

PubMed2005

Sources of variation in Affymetrix microarray experiments.

BACKGROUND: A typical microarray experiment has many sources of variation which can be attributed to biological and technical causes. Identifying sources of variation and assessing their magnitude, among other factors, are important for optimal experimental design. The objectives of this study were: (1) to estimate relative magnitudes of different sources of variation and (2) to evaluate agreement between biological and technical replicates. RESULTS: We performed a microarray experiment using a total of 24 Affymetrix GeneChip arrays. The study included 4th mammary gland samples from eight 21-day-old Sprague Dawley CD female rats exposed to genistein (soy isoflavone). RNA samples from each rat were split to assess variation arising at labeling and hybridization steps. A general linear model was used to estimate variance components. Pearson correlations were computed to evaluate agreement between technical and biological replicates. CONCLUSION: The greatest source of variation was biological variation, followed by residual error, and finally variation due to labeling when *.cel files were processed with dChip and RMA image processing algorithms. When MAS 5.0 or GCRMA-EB were used, the greatest source of variation was residual error, followed by biology and labeling. Correlations between technical replicates were consistently higher than between biological replicates.

Animals↗

PubMed2005

Reproducible clusters from microarray research: whither?

MOTIVATION: In cluster analysis, the validity of specific solutions, algorithms, and procedures present significant challenges because there is no null hypothesis to test and no 'right answer'. It has been noted that a replicable classification is not necessarily a useful one, but a useful one that characterizes some aspect of the population must be replicable. By replicable we mean reproducible across multiple samplings from the same population. Methodologists have suggested that the validity of clustering methods should be based on classifications that yield reproducible findings beyond chance levels. We used this approach to determine the performance of commonly used clustering algorithms and the degree of replicability achieved using several microarray datasets. METHODS: We considered four commonly used iterative partitioning algorithms (Self Organizing Maps (SOM), K-means, Clutsering LARge Applications (CLARA), and Fuzzy C-means) and evaluated their performances on 37 microarray datasets, with sample sizes ranging from 12 to 172. We assessed reproducibility of the clustering algorithm by measuring the strength of relationship between clustering outputs of subsamples of 37 datasets. Cluster stability was quantified using Cramer's v2 from a kXk table. Cramer's v2 is equivalent to the squared canonical correlation coefficient between two sets of nominal variables. Potential scores range from 0 to 1, with 1 denoting perfect reproducibility. RESULTS: All four clustering routines show increased stability with larger sample sizes. K-means and SOM showed a gradual increase in stability with increasing sample size. CLARA and Fuzzy C-means, however, yielded low stability scores until sample sizes approached 30 and then gradually increased thereafter. Average stability never exceeded 0.55 for the four clustering routines, even at a sample size of 50. These findings suggest several plausible scenarios: (1) microarray datasets lack natural clustering structure thereby producing low stability scores on all four methods; (2) the algorithms studied do not produce reliable results and/or (3) sample sizes typically used in microarray research may be too small to support derivation of reliable clustering results. Further research should be directed towards evaluating stability performances of more clustering algorithms on more datasets specially having larger sample sizes with larger numbers of clusters considered.

Cluster Analysis↗

PubMed2005

Identification of genes required for cellulose synthesis by regression analysis of public microarray data sets.

Coexpression patterns of gene expression across many microarray data sets may reveal networks of genes involved in linked processes. To identify factors involved in cellulose biosynthesis, we used a regression method to analyze 408 publicly available Affymetrix Arabidopsis microarrays. Expression of genes previously implicated in cellulose synthesis, as well as several uncharacterized genes, was highly coregulated with expression of cellulose synthase (CESA) genes. Four candidate genes, which were coexpressed with CESA genes implicated in secondary cell wall synthesis, were investigated by mutant analysis. Two mutants exhibited irregular xylem phenotypes similar to those observed in mutants with defects in secondary cellulose synthesis and were designated irx8 and irx13. Thus, the general approach developed here is useful for identification of elements of multicomponent processes.

Arabidopsis↗

PubMed2005

HDBStat!: a platform-independent software suite for statistical analysis of high dimensional biology data.

BACKGROUND: Many efforts in microarray data analysis are focused on providing tools and methods for the qualitative analysis of microarray data. HDBStat! (High-Dimensional Biology-Statistics) is a software package designed for analysis of high dimensional biology data such as microarray data. It was initially developed for the analysis of microarray gene expression data, but it can also be used for some applications in proteomics and other aspects of genomics. HDBStat! provides statisticians and biologists a flexible and easy-to-use interface to analyze complex microarray data using a variety of methods for data preprocessing, quality control analysis and hypothesis testing. RESULTS: Results generated from data preprocessing methods, quality control analysis and hypothesis testing methods are output in the form of Excel CSV tables, graphs and an Html report summarizing data analysis. CONCLUSION: HDBStat! is a platform-independent software that is freely available to academic institutions and non-profit organizations. It can be downloaded from our website http://www.soph.uab.edu/ssg_content.asp?id=1164.

Algorithms↗

PubMed2005

Mitochondrial DNA (mtDNA) haplotypes reveal maternal population genetic affinities of Sea Island Gullah-speaking African Americans.

To better understand the population substructure of African Americans living in coastal South Carolina, we used restriction site polymorphisms and an insertion/deletion in mitochondrial DNA (mtDNA) to construct seven-position haplotypes across 1,395 individuals from Sierra Leone, Africa, from U.S. European Americans, and from the New World African-derived populations of Jamaica, Gullah-speaking African Americans of the South Carolina Sea Islands (Gullahs), African Americans living in Charleston, South Carolina, and West Coast African Americans. Analyses showed a high degree of similarity within the New World African-derived populations, where haplotype frequencies and diversities were similar. Phi-statistics indicated that very little genetic differentiation has occurred within New World African-derived populations, but that there has been significant differentiation of these populations from Sierra Leoneans. Genetic distance estimates indicated a close relationship of Gullahs and Jamaicans with Sierra Leoneans, while African Americans living in Charleston and the West Coast were progressively more distantly related to the Sierra Leoneans. We observed low maternal European American admixture in the Jamaican and Gullah samples (m = 0.020 and 0.064, respectively) that increased sharply in a clinal pattern from Charleston African Americans to West Coast African Americans (m = 0.099 and 0.205, respectively). The appreciably reduced maternal European American admixture noted in the Gullah indicates that the Gullah may be uniquely situated to allow genetic epidemiology studies of complex diseases in African Americans with low European American admixture.

Black or African American↗

PubMed2005

Employment of microarray analysis to characterize biologic differences associated with tropism-modified adenoviral vectors: utilization of non-native cellular entry pathways.

In this study, we have applied high-density oligonucleotide microarray technology to characterize biologic changes associated with adenoviral vector-mediated target cell infection. We infected a human melanoma cell line, M21, with the tropism-modified vectors, Ad5lucRGD and Ad5/3luc1. In addition, we infected the M21 cell line with the Ad5luc1, a vector which primarily exploits the coxsackie and adenovirus receptor, as its primary native receptor. We found significant changes in gene expression of 5492 genes induced by Ad5luc1 infection, 2439 genes induced by Ad5/3luc1 infection, and 1251 genes induced by Ad5lucRGD infection, compared to uninfected cells. Among these changes in gene expression, 783 changes were common to Ad5/3luc1 and Ad5luc1 infections, 266 were common to Ad5lucRGD and Ad5luc1 infections, and 185 changes in gene expression were common to Ad5/3luc1 and Ad5lucRGD infections. Interestingly, 89 changes in gene expression were common to all the three groups, suggesting a commonly affected pathway. This analysis represents a unique application of microarray to study vector-related issues. Furthermore, these studies demonstrate the utility of microarray for characterizing the biologic sequelae of host-vector interaction.

Adenoviridae↗

PubMed2004

Differential gene expression modulated by the cytoplasmic domain of Fc gamma RIa (CD64) alpha-chain.

The cytoplasmic domain (CY) of the ligand-binding alpha-chain of the gamma-chain-associated FcRs can modulate receptor function such as phagocytosis, endocytosis, and intracellular trafficking of receptor-Ag complexes. To assess the potential role of the CY domain of human FcgammaRIa (CD64) alpha-chain in the transcriptional regulation of receptor-induced gene expression, we developed stably transfected murine macrophage cell lines expressing a full-length or a CY deletion mutant (tail-less) of human FcgammaRIa to analyze gene expression in response to receptor-specific cross-linking. Using the Affymetrix murine genome U74Av2 GeneChip array, we observed >100 candidate genes having > or =2-fold difference expression at 1.5 and 3 h after stimulation. Focusing on several immunologically related genes, we confirmed differential expression of M-CSF, macrophage inhibitory cytokine-1, leukocyte-specific protein 1, MIP-2, and IL-1R antagonist by RT-PCR and RNase protection assays. Analysis of mRNA stability indicated that the differential regulation of gene expression by the CY of the CD64 alpha-chain is at the level of gene transcription. Our results indicate that the CY of the CD64 alpha-chain modulates transcriptional activity induced by receptor-specific engagement in macrophages and provides a framework for understanding distinct expression profiles elicited by different Fc gamma-chain-associated receptors.

Animals↗

PubMed2004

Breast fibroblasts modulate epithelial cell proliferation in three-dimensional in vitro co-culture.

BACKGROUND: Stromal fibroblasts associated with in situ and invasive breast carcinoma differ phenotypically from fibroblasts associated with normal breast epithelium, and these alterations in carcinoma-associated fibroblasts (CAF) may promote breast carcinogenesis and cancer progression. A better understanding of the changes that occur in fibroblasts during carcinogenesis and their influence on epithelial cell growth and behavior could lead to novel strategies for the prevention and treatment of breast cancer. To this end, the effect of CAF and normal breast-associated fibroblasts (NAF) on the growth of epithelial cells representative of pre-neoplastic breast disease was assessed. METHODS: NAF and CAF were grown with the nontumorigenic MCF10A epithelial cells and their more transformed, tumorigenic derivative, MCF10AT cells, in direct three-dimensional co-cultures on basement membrane material. The proliferation and apoptosis of MCF10A cells and MCF10AT cells were assessed by 5-bromo-2'-deoxyuridine labeling and TUNEL assay, respectively. Additionally, NAF and CAF were compared for expression of insulin-like growth factor II as a potential mediator of their effects on epithelial cell growth, by ELISA and by quantitative, real-time PCR. RESULTS: In relatively low numbers, both NAF and CAF suppressed proliferation of MCF10A cells. However, only NAF and not CAF significantly inhibited proliferation of the more transformed MCF10AT cells. The degree of growth inhibition varied among NAF or CAF from different individuals. In greater numbers, NAF and CAF have less inhibitory effect on epithelial cell growth. The rate of epithelial cell apoptosis was not affected by NAF or CAF. Mean insulin-like growth factor II levels were not significantly different in NAF versus CAF and did not correlate with the fibroblast effect on epithelial cell proliferation. CONCLUSION: Both NAF and CAF have the ability to inhibit the growth of pre-cancerous breast epithelial cells. NAF have greater inhibitory capacity than CAF, suggesting that the ability of fibroblasts to inhibit epithelial cell proliferation is lost during breast carcinogenesis. Furthermore, as the degree of transformation of the epithelial cells increased they became resistant to the growth-inhibitory effects of CAF. Insulin-like growth factor II could not be implicated as a contributor to this differential effect of NAF and CAF on epithelial cell growth.

Breast↗

PubMed2004

Empirical Bayes estimation of gene-specific effects in micro-array research.

Micro-array technology allows investigators the opportunity to measure expression levels of thousands of genes simultaneously. However, investigators are also faced with the challenge of simultaneous estimation of gene expression differences for thousands of genes with very small sample sizes. Traditional estimators of differences between treatment means (ordinary least squares estimators or OLS) are not the best estimators if interest is in estimation of gene expression differences for an ensemble of genes. In the case that gene expression differences are regarded as exchangeable samples from a common population, estimators are available that result in much smaller average mean-square error across the population of gene expression difference estimates. We have simulated the application of such an estimator, namely an empirical Bayes (EB) estimator of random effects in a hierarchical linear model (normal-normal). Simulation results revealed mean-square error as low as 0.05 times the mean-square error of OLS estimators (i.e., the difference between treatment means). We applied the analysis to an example dataset as a demonstration of the shrinkage of EB estimators and of the reduction in mean-square error, i.e., increase in precision, associated with EB estimators in this analysis. The method described here is available in software that is available at http://www.soph.uab.edu/ssg.asp?id=1087.

Bayes Theorem↗