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Wei Pan

Publications and source records attributed to Wei Pan.

13 recordsLinked to original sources

ApoA-I structure on discs and spheres. Variable helix registry and conformational states.

Apolipoprotein A-I (apoA-I) readily forms discoidal high density lipoprotein (HDL) particles with phospholipids serving as an ideal transporter of plasma cholesterol. In the lipid-bound conformation, apoA-I activates the enzyme lecithin:cholesterol acyltransferase stimulating the formation of cholesterol esters from free cholesterol. As esterification proceeds cholesterol esters accumulate within the hydrophobic core of the discoidal phospholipid bilayer transforming it into a spherical HDL particle. To investigate the change in apoA-I conformation as it adapts to a spherical surface, fluorescence resonance energy transfer studies were performed. Discoidal rHDL particles containing two lipid-bound apoA-I molecules were prepared with acceptor and donor fluorescent probes attached to cysteine residues located at specific positions. Fluorescence quenching was measured for probe combinations located within repeats 5 and 5 (residue 132), repeats 5 and 6 (residues 132 and 154), and repeats 6 and 6 (residue 154). Results from these experiments indicated that each of the 2 molecules of discoidal bound apoA-I exists in multiple conformations and support the concept of a "variable registry" rather than a "fixed helix-helix registry." Additionally, discoidal rHDL were transformed in vitro to core-containing particles by incubation with lecithin:cholesterol acyltransferase. Compositional analysis showed that core-containing particles contained 11% less phospholipid and 633% more cholesterol ester and a total of 3 apoA-I molecules per particle. Spherical particles showed a lowering of acceptor to donor probe quenching when compared with starting rHDL. Therefore, we conclude that as lipid-bound apoA-I adjusts from a discoidal to a spherical surface its intermolecular interactions are significantly reduced presumably to cover the increased surface area of the particle.

Apolipoprotein A-I↗

Comparing three methods for variance estimation with duplicated high density oligonucleotide arrays.

Microarray experiments are being increasingly used in molecular biology. A common task is to detect genes with differential expression across two experimental conditions, such as two different tissues or the same tissue at two time points of biological development. To take proper account of statistical variability, some statistical approaches based on the t-statistic have been proposed. In constructing the t-statistic, one needs to estimate the variance of gene expression levels. With a small number of replicated array experiments, the variance estimation can be challenging. For instance, although the sample variance is unbiased, it may have large variability, leading to a large mean squared error. For duplicated array experiments, a new approach based on simple averaging has recently been proposed in the literature. Here we consider two more general approaches based on nonparametric smoothing. Our goal is to assess the performance of each method empirically. The three methods are applied to a colon cancer data set containing 2,000 genes. Using two arrays, we compare the variance estimates obtained from the three methods. We also consider their impact on the t-statistics. Our results indicate that the three methods give variance estimates close to each other. Due to its simplicity and generality, we recommend the use of the smoothed sample variance for data with a small number of replicates.

Algorithms↗

Small-sample adjustments in using the sandwich variance estimator in generalized estimating equations.

The generalized estimating equation (GEE) approach is widely used in regression analyses with correlated response data. Under mild conditions, the resulting regression coefficient estimator is consistent and asymptotically normal with its variance being consistently estimated by the so-called sandwich estimator. Statistical inference is thus accomplished by using the asymptotic Wald chi-squared test. However, it has been noted in the literature that for small samples the sandwich estimator may not perform well and may lead to much inflated type I errors for the Wald chi-squared test. Here we propose using an approximate t- or F-test that takes account of the variability of the sandwich estimator. The level of type I error of the proposed t- or F-test is guaranteed to be no larger than that of the Wald chi-squared test. The satisfactory performance of the proposed new tests is confirmed in a simulation study. Our proposal also has some advantages when compared with other new approaches based on direct modifications of the sandwich estimator, including the one that corrects the downward bias of the sandwich estimator. In addition to hypothesis testing, our result has a clear implication on constructing Wald-type confidence intervals or regions.

Adult↗

How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach.

BACKGROUND: It has been recognized that replicates of arrays (or spots) may be necessary for reliably detecting differentially expressed genes in microarray experiments. However, the often-asked question of how many replicates are required has barely been addressed in the literature. In general, the answer depends on several factors: a given magnitude of expression change, a desired statistical power (that is, probability) to detect it, a specified Type I error rate, and the statistical method being used to detect the change. Here, we discuss how to calculate the number of replicates in the context of applying a nonparametric statistical method, the normal mixture model approach, to detect changes in gene expression. RESULTS: The methodology is applied to a data set containing expression levels of 1,176 genes in rats with and without pneumococcal middle-ear infection. We illustrate how to calculate the power functions for 2, 4, 6 and 8 replicates. CONCLUSIONS: The proposed method is potentially useful in designing microarray experiments to discover differentially expressed genes. The same idea can be applied to other statistical methods.

Animals↗

Model-based cluster analysis of microarray gene-expression data.

BACKGROUND: Microarray technologies are emerging as a promising tool for genomic studies. The challenge now is how to analyze the resulting large amounts of data. Clustering techniques have been widely applied in analyzing microarray gene-expression data. However, normal mixture model-based cluster analysis has not been widely used for such data, although it has a solid probabilistic foundation. Here, we introduce and illustrate its use in detecting differentially expressed genes. In particular, we do not cluster gene-expression patterns but a summary statistic, the t-statistic. RESULTS: The method is applied to a data set containing expression levels of 1,176 genes of rats with and without pneumococcal middle-ear infection. Three clusters were found, two of which contain more than 95% genes with almost no altered gene-expression levels, whereas the third one has 30 genes with more or less differential gene-expression levels. CONCLUSIONS: Our results indicate that model-based clustering of t-statistics (and possibly other summary statistics) can be a useful statistical tool to exploit differential gene expression for microarray data.

Animals↗

A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments.

MOTIVATION: A common task in analyzing microarray data is to determine which genes are differentially expressed across two kinds of tissue samples or samples obtained under two experimental conditions. Recently several statistical methods have been proposed to accomplish this goal when there are replicated samples under each condition. However, it may not be clear how these methods compare with each other. Our main goal here is to compare three methods, the t-test, a regression modeling approach (Thomas et al., Genome Res., 11, 1227-1236, 2001) and a mixture model approach (Pan et al., http://www.biostat.umn.edu/cgi-bin/rrs?print+2001,2001a,b) with particular attention to their different modeling assumptions. RESULTS: It is pointed out that all the three methods are based on using the two-sample t-statistic or its minor variation, but they differ in how to associate a statistical significance level to the corresponding statistic, leading to possibly large difference in the resulting significance levels and the numbers of genes detected. In particular, we give an explicit formula for the test statistic used in the regression approach. Using the leukemia data of Golub et al. (Science, 285, 531-537, 1999), we illustrate these points. We also briefly compare the results with those of several other methods, including the empirical Bayesian method of Efron et al. (J. Am. Stat. Assoc., to appear, 2001) and the Significance Analysis of Microarray (SAM) method of Tusher et al. (PROC: Natl Acad. Sci. USA, 98, 5116-5121, 2001).

Acute Disease↗

Estimation in the cox proportional hazards model with left-truncated and interval-censored data.

We show that the nonparametric maximum likelihood estimate (NPMLE) of the regression coefficient from the joint likelihood (of the regression coefficient and the baseline survival) works well for the Cox proportional hazards model with left-truncated and interval-censored data, but the NPMLE may underestimate the baseline survival. Two alternatives are also considered: first, the marginal likelihood approach by extending Satten (1996, Biometrika 83, 355-370) to truncated data, where the baseline distribution is eliminated as a nuisance parameter; and second, the monotone maximum likelihood estimate that maximizes the joint likelihood by assuming that the baseline distribution has a nondecreasing hazard function, which was originally proposed to overcome the underestimation of the survival from the NPMLE for left-truncated data without covariates (Tsai, 1988, Biometrika 75, 319-324). The bootstrap is proposed to draw inference. Simulations were conducted to assess their performance. The methods are applied to the Massachusetts Health Care Panel Study data set to compare the probabilities of losing functional independence for male and female seniors.

Activities of Daily Living↗

Effectiveness of a worksite intervention to reduce an occupational exposure: the Minnesota wood dust study.

OBJECTIVES: This study assessed the effectiveness of an intervention to reduce wood dust, a carcinogen, by approximately 26% in small woodworking businesses. METHODS: We randomized 48 businesses to an intervention (written recommendations, technical assistance, and worker training) or comparison (written recommendations alone) condition. Changes from baseline in dust concentration, dust control methods, and worker behavior were compared between the groups 1 year later. RESULTS: At follow-up, workers in intervention relative to comparison businesses reported greater awareness, increases in stage of readiness, and behavioral changes consistent with dust control. The median dust concentration change in the intervention group from baseline to follow-up was 10.4% (95% confidence interval = -28.8%, 12.7%) lower than the change in comparison businesses. CONCLUSIONS: We attribute the smaller-than-expected reduction in wood dust to the challenge of conducting rigorous intervention effectiveness research in occupational settings.

Adult↗

Cloning and Characterization of fup1, A Gene Highly Expressed in Hepatocellular Carcinoma.

Primary hepatocellular carcinoma(HCC) is one of the common malignant tumors in China. In our previous work, a gene named fup1(function-unknown protein 1) was isolated that was expressed differently in HCC and in normal liver. We assumed that it might be a candidate oncogene for the HCC. The fup1 gene had a ORF of 1 233 bp, encoding a protein with M(r) of 46 kD and isoelectric point of 5.48. The sequence characteristics showed its possible localization in nuclei. Northern blots showed that this gene was weakly expressed in many types of human tissues, except in the heart, implying its tissue-specific expression pattern. MTT assay of the NIH 3T3 cells transfected with this gene in the form of recombinant eukaryotic expression plasmid showed its enhancing role to cellular proliferation.

Journal Article↗

On the Mechanism of Growth Inhibition of Epiregulin in A431 Epidermal Carcinoma Cells.

Preliminary investigation on the mechanism of the growth inhibition by recombinant epiregulin(EPI)of epidermal carcinoma cell A431 is reported. Northern blotting indicated that the mRNA level of cyclin dependent kinase(CDK)inhibitor, p21(WAF1/CIP1), was increased significantly after stimulation of the recombinant epiregulin protein. Luc reporter revealed that STAT1 could bind the promoter region of p21 in response to the EPI signal. Flow cytometry assay showed that the EPI-induced growth inhibition was not related to the apoptosis. The above results indicate that the EPI-induced cell growth inhibition might result from the STAT1-stimulated expression of p21, leading to the G1 arrest.

Journal Article↗

Cloning, Expression and Biological Activity of VEGI(151), a Novel Vascular Endothelial Cell Growth Inhibitor.

VEGI(vascular endothelial cell growth inhibitor) is a novel cytokine which belongs to the TNF super-family. In this study, the VEGI gene from ECV304 cells was cloned. A truncated form of VEGI, where 23 amino acids from N-terminal were deleted and named VEGI(151), was expressed in E.coli with 25.5% of expression rate. The purity of VEGI(151) reached 92.5% after purification. VEGI(151) showed significant inhibitory effect on endothelial cells. IC(50) of VEGI151 was 10 mg/L at 24 h. At the concentration of 0.613 mg/L, VEGI(151) induced apoptosis of endothelial cells within 36 h. However, neither stimulatory effect nor inhibitory effect of VEGI(151) was detected on tumor cells(A549 HepG2 Hela)cultured in vitro. These results suggest that endothelial cells was the main target cells of VEGI(151). Our findings indicate that VEGI(151) is a potential therapeutic drug on angiogenic disease and cancer.

Journal Article↗

Expression of Human Epiregulin in E.coli.

Human epiregulin cDNA was amplified from the lung cancer cell line A549 using RT-PCR. After adding 6 His codon to its 3' end, it was cloned into a high efficient secretive Escherichia coli system with alkaline phosphatase promoter(phoA promoter)constructed in our lab and induced for expression. The product was purified one-step by Ni-NTA column. Amino acid sequence analysis revealed the identity of our product with that previously reported. The product showed strong proliferative effect on fibroblast cell line Balb/c3T3 and growth inhibitory effect on epithelial carcinoma cell line A431.

Journal Article↗