Biomedical subjects

Lue Ping Zhao

Publications and source records attributed to Lue Ping Zhao.

10 recordsLinked to original sources

PubMed2004

Gene expression analyzed by high-resolution state array analysis and quantitative proteomics: response of yeast to mating pheromone.

The transcriptome provides the database from which a cell assembles its collection of proteins. Translation of individual mRNA species into their encoded proteins is regulated, producing discrepancies between mRNA and protein levels. Using a new modeling approach to data analysis, a striking diversity is revealed in association of the transcriptome with the translational machinery. Each mRNA has its own pattern of ribosome loading, a circumstance that provides an extraordinary dynamic range of regulation, above and beyond actual transcript levels. Using this approach together with quantitative proteomics, we explored the immediate changes in gene expression in response to activation of a mitogen-activated protein kinase pathway in yeast by mating pheromone. Interestingly, in 26% of those transcripts where the predicted protein synthesis rate changed by at least 3-fold, more than half of these changes resulted from altered translational efficiencies. These observations underscore that analysis of transcript level, albeit extremely important, is insufficient by itself to describe completely the phenotypes of cells under different conditions.

Computational Biology↗

PubMed2004

Single-stranded linear amplification protocol results in reproducible and reliable microarray data from nanogram amounts of starting RNA.

The range of scientific questions utilizing DNA microarray techniques is limited by the fact that these methods require 5-40 microg of high-quality total RNA. Thus, methods that reliably amplify the starting RNA amount could expand the applicability of DNA microarray technology. We developed a single-stranded linear amplification protocol (SLAP) that combines the reproducibility of in vitro transcription and the amplification robustness of polymerase chain reactions. We compared SLAP to the NIH-IVT amplification protocol. SLAP displayed excellent conservation of the 5'/3' signal and demonstrated the most robust amplification, producing the recommended amounts of biotin-labeled RNA with as little as 0.002 microg of starting RNA. Both SLAP and NIH-IVT methods demonstrated good reproducibility, but SLAP maintained the highest level of reliability with RNA starting amounts of <0.05 microg. These results suggest that SLAP is an excellent alternative to IVT-based amplification protocols when RNA is limited by small sample size.

Biotin↗

PubMed2003

A common dominant TLR5 stop codon polymorphism abolishes flagellin signaling and is associated with susceptibility to legionnaires' disease.

Although Toll-like receptors (TLRs) are critical mediators of the immune response to pathogens, the influence of polymorphisms in this gene family on human susceptibility to infection is poorly understood. We demonstrated recently that TLR5 recognizes flagellin, a potent inflammatory stimulus present in the flagellar structure of many bacteria. Here, we show that a common stop codon polymorphism in the ligand-binding domain of TLR5 (TLR5392STOP) is unable to mediate flagellin signaling, acts in a dominant fashion, and is associated with susceptibility to pneumonia caused by Legionella pneumophila, a flagellated bacterium. We also show that flagellin is a principal stimulant of proinflammatory cytokine production in lung epithelial cells. Together, these observations suggest that TLR5392STOP increases human susceptibility to infection through an unusual dominant mechanism that compromises TLR5's essential role as a regulator of the lung epithelial innate immune response.

Codon, Terminator↗

PubMed2003

A method for the assessment of disease associations with single-nucleotide polymorphism haplotypes and environmental variables in case-control studies.

The rough draft of the human genome map has been used to identify most of the functional genes in the human genome, as well as to identify nucleotide variations, known as "single-nucleotide polymorphisms" (SNPs), in these genes. By use of advanced biotechnologies, researchers are beginning to genotype thousands of SNPs from biological samples. Among the many possible applications, one of them is the study of SNP associations with complex human diseases, such as cancers or coronary heart diseases, by using a case-control study design. Through the gathering of environmental risk factors and other lifestyle factors, such a study can be effectively used to investigate interactions between genes and environmental factors in their associations with disease phenotype. Earlier, we developed a method to statistically construct individuals' haplotypes and to estimate the distribution of haplotypes of multiple SNPs in a defined population, by use of estimating-equation techniques. Extending this idea, we describe here an analytic method for assessing the association between the constructed haplotypes along with environmental factors and the disease phenotype. This method is also robust to the model assumptions and is scalable to a large number of SNPs. Asymptotic properties of estimations in the method are proved theoretically and are tested for finite sample sizes by use of simulations. To demonstrate the use of the method, we applied it to assess the possible association between apolipoprotein CIII (six coding SNPs) and restenosis by using a case-control data set. Our analysis revealed two haplotypes that may reduce the risk of restenosis.

Apolipoprotein C-III↗

PubMed2003

The transcriptome and its translation during recovery from cell cycle arrest in Saccharomyces cerevisiae.

Complete genome sequences together with high throughput technologies have made comprehensive characterizations of gene expression patterns possible. While genome-wide measurement of mRNA levels was one of the first applications of these advances, other important aspects of gene expression are also amenable to a genomic approach, for example, the translation of message into protein. Earlier we reported a high throughput technology for simultaneously studying mRNA level and translation, which we termed translation state array analysis, or TSAA. The current studies test the proposition that TSAA can identify novel instances of translation regulation at the genome-wide level. As a biological model, cultures of Saccharomyces cerevisiae were cell cycle-arrested using either alpha-factor or the temperature-sensitive cdc15-2 allele. Forty-eight mRNAs were found to change significantly in translation state following release from alpha-factor arrest, including genes involved in pheromone response and cell cycle arrest such as BAR1, SST2, and FAR1. After the shift of the cdc15-2 strain from 37 degrees C to 25 degrees C, 54 mRNAs were altered in translation state, including the products of the stress genes HSP82, HSC82, and SSA2. Thus, regulation at the translational level seems to play a significant role in the response of yeast cells to external physical or biological cues. In contrast, surprisingly few genes were found to be translationally controlled as cells progressed through the cell cycle. Additional refinements of TSAA should allow characterization of both transcriptional and translational regulatory networks on a genomic scale, providing an additional layer of information that can be integrated into models of system biology and function.

Cell Cycle↗

PubMed2003

Array rank order regression analysis for the detection of gene copy-number changes in human cancer.

cDNA microarray technology has been applied to the detection of DNA copy-number changes in malignant tumors. Test and control genomic DNA samples are differentially labeled and cohybridized to a spotted cDNA microarray. The ratio of test to control fluorescence intensities for each spot reflects relative gene copy number. The low signal-to-noise ratios of this assay and the variable levels of gene amplification and deletion among tumors hamper the detection of deviations from the diploid complement. We describe a regression-based statistical method to test for altered copy number on each gene and apply the technique to copy-number profiles in 10 thyroid tumors. We show that a novel transformation of fluorescence ratios into array rank order efficiently normalizes the heterogeneity among copy-number profiles and improves the reproducibility of the results. Array rank order regression analysis enhances the detection of consistent changes in gene copy number in solid tumors by cDNA microarray-based comparative genome hybridization.

Fluorescence↗

PubMed2003

Estimating haplotype frequencies and standard errors for multiple single nucleotide polymorphisms.

Estimating haplotype frequencies becomes increasingly important in the mapping of complex disease genes, as millions of single nucleotide polymorphisms (SNPs) are being identified and genotyped. When genotypes at multiple SNP loci are gathered from unrelated individuals, haplotype frequencies can be accurately estimated using expectation-maximization (EM) algorithms (Excoffier and Slatkin, 1995; Hawley and Kidd, 1995; Long et al., 1995), with standard errors estimated using bootstraps. However, because the number of possible haplotypes increases exponentially with the number of SNPs, handling data with a large number of SNPs poses a computational challenge for the EM methods and for other haplotype inference methods. To solve this problem, Niu and colleagues, in their Bayesian haplotype inference paper (Niu et al., 2002), introduced a computational algorithm called progressive ligation (PL). But their Bayesian method has a limitation on the number of subjects (no more than 100 subjects in the current implementation of the method). In this paper, we propose a new method in which we use the same likelihood formulation as in Excoffier and Slatkin's EM algorithm and apply the estimating equation idea and the PL computational algorithm with some modifications. Our proposed method can handle data sets with large number of SNPs as well as large numbers of subjects. Simultaneously, our method estimates standard errors efficiently, using the sandwich-estimate from the estimating equation, rather than the bootstrap method. Additionally, our method admits missing data and produces valid estimates of parameters and their standard errors under the assumption that the missing genotypes are missing at random in the sense defined by Rubin (1976).

Algorithms↗

PubMed2002

Transcriptional expression profiles of oral squamous cell carcinomas.

BACKGROUND: Currently, the classification of oral squamous cell carcinoma (OSCC) depends heavily on the clinical and pathologic examination of tissue. This system can lead to the classification of potentially heterogeneous tumors into single groups when they may have different degrees of aggressiveness. No system to date has incorporated genetic changes as a factor by which to classify OSCC tumors. METHODS: To test the hypothesis that OSCC has a genome-wide genetic expression profile that differs from normal oral tissue and that transcriptional expression profiling can be used to characterize the heterogeneity among tumors, the authors examined the genetic expression profiles of 26 invasive squamous cell carcinomas of the oral cavity and oropharynx, 2 premalignant lesions, and 18 normal oral tissue samples using oligonucleotide arrays that contained probes representing approximately 7000 full-length human genes. RESULTS: Using hierarchical clustering analysis, the data show that oral carcinomas are distinguishable from normal oral tissue based on genome-wide transcriptional expression patterns. However, there is genetic expression profile heterogeneity among tumors of a particular histopathologic grade and stage. In addition, using a statistical approach that integrated normalization and regression analysis, the authors found 314 genes that were expressed differentially in the OSCC samples with statistical significance (P <or= 0.05). Of these, 239 genes were overexpressed in the OSCC samples, whereas 75 genes were down-regulated. CONCLUSIONS: No statistically significant differences in gene expression were found between early-stage disease and late-stage disease or between metastatic tumors and nonmetastatic tumors. The implications of these findings for the prediction of clinical outcome and for the discovery of new OSCC tumor markers are discussed.

Carcinoma, Squamous Cell↗

PubMed2002

A regression-based method to identify differentially expressed genes in microarray time course studies and its application in an inducible Huntington's disease transgenic model.

Time-course studies with microarray technologies provide enormous potential for exploring underlying mechanisms of biological phenomena in many areas of biomedical research, but the large amount of gene expression data generated by such studies also presents great challenges to data analysis. Here we introduce a regression-based statistical modeling approach that identifies differentially expressed genes in microarray time-course studies. To illustrate this method, we applied it to data generated from an inducible Huntington's disease transgenic model. The regression method accounts for the induction process, incorporates relevant experimental information, and includes parameters that specifically address the research interest: the temporal differences in gene expression profiles between the mutant and control mice over the time course, in addition to heterogeneities that commonly exist in microarray data. Least-squares and estimating equation techniques were used to estimate parameters and variances, and inferences were made based on efficient and robust Z-statistics under a set of well-defined assumptions. A permutation test was also used to estimate the number of false-positives, providing an alternative measurement of statistical significance useful for investigators to make decisions on follow-up studies.

Animals↗

PubMed2002

Combined association, segregation and aggregation analysis on case-control family data.

Recently genetic epidemiologists have begun using case-control family study designs to investigate the role of genetic and environmental risk factors in disease etiology. The objective of these studies is to assess the association of environmental factors with the disease trait; to characterize the disease genes using segregation analysis; and to quantify the residual familial aggregation after controlling for environmental and genetic factors. Typically these objectives are achieved by conducting separate studies and analysis. This paper describes an estimating equation based approach for a combined association, segregation and aggregation analysis on data from case-control family studies. Simulations indicate that the method performs well in a variety of settings. The method is illustrated using simulated family history data made available to participants in a recent Genetic Analysis Workshop.

Journal Article↗