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1.  Gold Nanocage-Photosensitizer Conjugates for Dual-Modal Image-Guided Enhanced Photodynamic Therapy 
Theranostics  2014;4(2):163-174.
We have demonstrated that gold nanocage-photosensitizer conjugates can enable dual image-guided delivery of photosensitizer and significantly improve the efficacy of photodynamic therapy in a murine model. The photosensitizer, 3-devinyl-3-(1'-hexyloxyethyl)pyropheophorbide (HPPH), was noncovalently entrapped in the poly(ethylene glycol) monolayer coated on the surface of gold nanocages. The conjugate is stable in saline solutions, while incubation in protein rich solutions leads to gradual unloading of the HPPH, which can be monitored optically by fluorescence and photoacoustic imaging. The slow nature of the release in turn results in an increase in accumulation of the drug within implanted tumors due to the passive delivery of gold nanocages. Furthermore, the conjugate is found to generate more therapeutic singlet oxygen and have a lower IC50 value than the free drug alone. Thus the conjugate shows significant suppression of tumor growth as compared to the free drug in vivo. Short-term study showed neither toxicity nor phenotypical changes in mice at therapeutic dose of the conjugates or even at 100-fold higher than therapeutic dose of gold nanocages.
doi:10.7150/thno.7064
PMCID: PMC3900801  PMID: 24465274
Gold Nanostructures; Drug Delivery; Fluorescence Imaging; Photoacoustic Imaging; Cancer Treatment.
2.  Sequence Quality Analysis Tool for HIV Type 1 Protease and Reverse Transcriptase 
Abstract
Access to antiretroviral therapy is increasing globally and drug resistance evolution is anticipated. Currently, protease (PR) and reverse transcriptase (RT) sequence generation is increasing, including the use of in-house sequencing assays, and quality assessment prior to sequence analysis is essential. We created a computational HIV PR/RT Sequence Quality Analysis Tool (SQUAT) that runs in the R statistical environment. Sequence quality thresholds are calculated from a large dataset (46,802 PR and 44,432 RT sequences) from the published literature (http://hivdb.Stanford.edu). Nucleic acid sequences are read into SQUAT, identified, aligned, and translated. Nucleic acid sequences are flagged if with >five 1–2-base insertions; >one 3-base insertion; >one deletion; >six PR or >18 RT ambiguous bases; >three consecutive PR or >four RT nucleic acid mutations; >zero stop codons; >three PR or >six RT ambiguous amino acids; >three consecutive PR or >four RT amino acid mutations; >zero unique amino acids; or <0.5% or >15% genetic distance from another submitted sequence. Thresholds are user modifiable. SQUAT output includes a summary report with detailed comments for troubleshooting of flagged sequences, histograms of pairwise genetic distances, neighbor joining phylogenetic trees, and aligned nucleic and amino acid sequences. SQUAT is a stand-alone, free, web-independent tool to ensure use of high-quality HIV PR/RT sequences in interpretation and reporting of drug resistance, while increasing awareness and expertise and facilitating troubleshooting of potentially problematic sequences.
doi:10.1089/aid.2011.0120
PMCID: PMC3399557  PMID: 21916749
3.  Molecular Alterations Underlying the Enhanced Disruption of Spermatogenesis by 2,5-Hexanedione and Carbendazim Co-exposure 
The current study investigated the co-exposure effects of 2,5-hexanedione (HD) and carbendazim (CBZ) on gene expression underlying the enhanced pathology previously observed. Adult male rats were exposed to HD (0.33 or 1%) followed by CBZ (67 or 200 mg/kg), and testis samples were collected after and 24 h. Microarray analysis at 3 h revealed that CBZ and HD interact in an agonistic, or synergistic, way at the gene level. Further analysis of candidate genes by qRT-PCR at both 3 and 24 h after co-exposure, revealed that Loxl1 and Clca2/Clca4l were both decreased in expression. Immunohistochemical analysis of Loxl1 at 24 h revealed that Loxl1 is localized to the seminiferous tubules, with the most intense staining in the basement membrane, blood vessels, and acrosomes, with the relative intensity reflecting the gene level changes at 3 h. These findings provide candidate genes for further investigation of the testicular response to damage.
doi:10.1016/j.reprotox.2012.01.014
PMCID: PMC3327768  PMID: 22382377
testis; carbendazim; 2,5-hexanedione; microarray; co-exposure; Loxl1; Clca2/Clca4l
4.  A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data 
Biostatistics (Oxford, England)  2012;14(2):232-243.
Recent developments in RNA-sequencing (RNA-seq) technology have led to a rapid increase in gene expression data in the form of counts. RNA-seq can be used for a variety of applications, however, identifying differential expression (DE) remains a key task in functional genomics. There have been a number of statistical methods for DE detection for RNA-seq data. One common feature of several leading methods is the use of the negative binomial (Gamma–Poisson mixture) model. That is, the unobserved gene expression is modeled by a gamma random variable and, given the expression, the sequencing read counts are modeled as Poisson. The distinct feature in various methods is how the variance, or dispersion, in the Gamma distribution is modeled and estimated. We evaluate several large public RNA-seq datasets and find that the estimated dispersion in existing methods does not adequately capture the heterogeneity of biological variance among samples. We present a new empirical Bayes shrinkage estimate of the dispersion parameters and demonstrate improved DE detection.
doi:10.1093/biostatistics/kxs033
PMCID: PMC3590927  PMID: 23001152
Differential expression; Empirical Bayes; RNA sequencing; Shrinkage estimator
5.  Accurate genome-scale percentage DNA methylation estimates from microarray data 
Biostatistics (Oxford, England)  2010;12(2):197-210.
DNA methylation is a key regulator of gene function in a multitude of both normal and abnormal biological processes, but tools to elucidate its roles on a genome-wide scale are still in their infancy. Methylation sensitive restriction enzymes and microarrays provide a potential high-throughput, low-cost platform to allow methylation profiling. However, accurate absolute methylation estimates have been elusive due to systematic errors and unwanted variability. Previous microarray preprocessing procedures, mostly developed for expression arrays, fail to adequately normalize methylation-related data since they rely on key assumptions that are violated in the case of DNA methylation. We develop a normalization strategy tailored to DNA methylation data and an empirical Bayes percentage methylation estimator that together yield accurate absolute methylation estimates that can be compared across samples. We illustrate the method on data generated to detect methylation differences between tissues and between normal and tumor colon samples.
doi:10.1093/biostatistics/kxq055
PMCID: PMC3062148  PMID: 20858772
DNA methylation; Epigenetics; Microarray
6.  The Transcriptome and Proteome of the Diatom Thalassiosira pseudonana Reveal a Diverse Phosphorus Stress Response 
PLoS ONE  2012;7(3):e33768.
Phosphorus (P) is a critical driver of phytoplankton growth and ecosystem function in the ocean. Diatoms are an abundant class of marine phytoplankton that are responsible for significant amounts of primary production. With the control they exert on the oceanic carbon cycle, there have been a number of studies focused on how diatoms respond to limiting macro and micronutrients such as iron and nitrogen. However, diatom physiological responses to P deficiency are poorly understood. Here, we couple deep sequencing of transcript tags and quantitative proteomics to analyze the diatom Thalassiosira pseudonana grown under P-replete and P-deficient conditions. A total of 318 transcripts were differentially regulated with a false discovery rate of <0.05, and a total of 136 proteins were differentially abundant (p<0.05). Significant changes in the abundance of transcripts and proteins were observed and coordinated for multiple biochemical pathways, including glycolysis and translation. Patterns in transcript and protein abundance were also linked to physiological changes in cellular P distributions, and enzyme activities. These data demonstrate that diatom P deficiency results in changes in cellular P allocation through polyphosphate production, increased P transport, a switch to utilization of dissolved organic P through increased production of metalloenzymes, and a remodeling of the cell surface through production of sulfolipids. Together, these findings reveal that T. pseudonana has evolved a sophisticated response to P deficiency involving multiple biochemical strategies that are likely critical to its ability to respond to variations in environmental P availability.
doi:10.1371/journal.pone.0033768
PMCID: PMC3315573  PMID: 22479440
7.  Removing technical variability in RNA-seq data using conditional quantile normalization 
Biostatistics (Oxford, England)  2012;13(2):204-216.
The ability to measure gene expression on a genome-wide scale is one of the most promising accomplishments in molecular biology. Microarrays, the technology that first permitted this, were riddled with problems due to unwanted sources of variability. Many of these problems are now mitigated, after a decade's worth of statistical methodology development. The recently developed RNA sequencing (RNA-seq) technology has generated much excitement in part due to claims of reduced variability in comparison to microarrays. However, we show that RNA-seq data demonstrate unwanted and obscuring variability similar to what was first observed in microarrays. In particular, we find guanine-cytosine content (GC-content) has a strong sample-specific effect on gene expression measurements that, if left uncorrected, leads to false positives in downstream results. We also report on commonly observed data distortions that demonstrate the need for data normalization. Here, we describe a statistical methodology that improves precision by 42% without loss of accuracy. Our resulting conditional quantile normalization algorithm combines robust generalized regression to remove systematic bias introduced by deterministic features such as GC-content and quantile normalization to correct for global distortions.
doi:10.1093/biostatistics/kxr054
PMCID: PMC3297825  PMID: 22285995
Gene expression; Normalization; RNA sequencing
8.  Sequencing technology does not eliminate biological variability 
Nature biotechnology  2011;29(7):572-573.
RNA sequencing has generated much excitement for the advantages offered over microarrays. This excitement has led to a barrage of publications discounting the importance of biological variability; as microarray publications did in the 1990s. By comparing microarray and sequencing data, we demonstrate that expression measurements exhibit biological variability across individuals irrespective of measurement technology. Our analysis suggests RNA-sequencing experiments designed to estimate biological variability are more likely to produce reproducible results.
doi:10.1038/nbt.1910
PMCID: PMC3137276  PMID: 21747377
9.  Suppression of Radiation-Induced Testicular Germ Cell Apoptosis by 2,5-Hexanedione Pretreatment. II. Gene Array Analysis Reveals Adaptive Changes in Cell Cycle and Cell Death Pathways 
Toxicological Sciences  2010;117(2):457-465.
Sertoli cells are essential for testicular germ cell maintenance and survival. We made the unexpected observation that x-radiation (x-ray)–induced germ cell loss is attenuated by co-exposure with the Sertoli cell toxicant 2,5-hexanedione (HD). The mechanisms underlying this attenuation of germ cell apoptosis with reduced Sertoli cell support are unknown. The current study was performed to examine alterations in testicular gene expression with co-exposure to HD and x-ray. Adult male rats were exposed to HD (0.33 or 1%) in the drinking water for 18 days followed by x-ray (2 or 5 Gy), resulting in nine treatment groups. Testis samples were collected after 3 h and total messenger RNA was analyzed using Affymetrix Rat Genome 230 2.0 arrays. Normalized log2-expression values were analyzed using LIMMA and summarized using linear contrasts designed to summarize the aggregated effect, in excess of x-ray alteration, of HD across all treatment groups. These contrasts were compared with the overall linear trend expression for x-ray, to determine whether HD effects were agonistic or antagonistic with respect to x-ray damage. Overrepresentation analysis to identify biological pathways where HD modification of gene expression was the greatest was performed. HD exerted a significant influence on genes involved in cell cycle and cell death/apoptosis. The results of this study provide insight into the mechanisms underlying attenuated germ cell toxicity following HD and x-ray co-exposure through the analysis of co-exposure effects on gene expression, and suggest that HD pre-exposure reduces Sertoli cell supported germ cell proliferation thereby reducing germ cell vulnerability to x-rays.
doi:10.1093/toxsci/kfq204
PMCID: PMC2940405  PMID: 20616210
testis; x-radiation; 2,5-hexanedione; microarray; co-exposure
10.  Subset Quantile Normalization Using Negative Control Features 
Journal of Computational Biology  2010;17(10):1385-1395.
Abstract
Normalization has been recognized as a necessary preprocessing step in a variety of high-throughput biotechnologies. A number of normalization methods have been developed specifically for microarrays, some general and others tailored for certain experimental designs. All methods rely on assumptions about data characteristics that are expected to stay constant across samples, although some make it more explicit than others. Most methods make assumptions that certain quantities related to the biological signal of interest stay the same; this is reasonable for many experiments but usually not verifiable. Recently, several platforms have begun to include a large number of negative control probes that nonetheless cover nearly the entire range of the measured signal intensity. Using these probes as a normalization basis makes it possible to normalize without making assumptions about the behavior of the biological signal. We present a subset quantile normalization (SQN) procedure that normalizes based on the distribution of non-specific control features, without restriction on the behavior of specific signals. We illustrate the performance of this method using three different platforms and experimental settings. Compared to two other leading nonlinear normalization procedures, the SQN method preserves more biological variation after normalization while reducing the noise observed on control features. Although the illustration datasets are from microarray experiments, this method is general for all high throughput technologies that include a large set of control features that have constant expectations across samples. It does not require an equal number of features in all samples and tolerates missing data. Supplementary Material is available online at www.liebertonline.com.
doi:10.1089/cmb.2010.0049
PMCID: PMC3122888  PMID: 20976876
DNA arrays; functional genomics; genes chips; gene expression
11.  A Review of Statistical Methods for Preprocessing Oligonucleotide Microarrays 
Microarrays have become an indispensable tool in biomedical research. This powerful technology makes it possible to quantify a large number of nucleic acid molecules simultaneously, but also produces data with many sources of noise. A number of preprocessing steps are therefore necessary to convert the raw data, usually in the form of hybridization images, to measures of biological meaning that can be used in further statistical analysis. Preprocessing of oligonucleotide arrays includes image processing, background adjustment, data normalization/transformation and sometimes summarization when multiple probes are used to target one genomic unit. In this paper we review the issues encountered in each preprocessing step and introduce the statistical models and methods in preprocessing.
doi:10.1177/0962280209351924
PMCID: PMC3152825  PMID: 20048383
12.  Chromosome-wide mapping of DNA methylation patterns in normal and malignant prostate cells reveals pervasive methylation of gene-associated and conserved intergenic sequences 
BMC Genomics  2011;12:313.
Background
DNA methylation has been linked to genome regulation and dysregulation in health and disease respectively, and methods for characterizing genomic DNA methylation patterns are rapidly emerging. We have developed/refined methods for enrichment of methylated genomic fragments using the methyl-binding domain of the human MBD2 protein (MBD2-MBD) followed by analysis with high-density tiling microarrays. This MBD-chip approach was used to characterize DNA methylation patterns across all non-repetitive sequences of human chromosomes 21 and 22 at high-resolution in normal and malignant prostate cells.
Results
Examining this data using computational methods that were designed specifically for DNA methylation tiling array data revealed widespread methylation of both gene promoter and non-promoter regions in cancer and normal cells. In addition to identifying several novel cancer hypermethylated 5' gene upstream regions that mediated epigenetic gene silencing, we also found several hypermethylated 3' gene downstream, intragenic and intergenic regions. The hypermethylated intragenic regions were highly enriched for overlap with intron-exon boundaries, suggesting a possible role in regulation of alternative transcriptional start sites, exon usage and/or splicing. The hypermethylated intergenic regions showed significant enrichment for conservation across vertebrate species. A sampling of these newly identified promoter (ADAMTS1 and SCARF2 genes) and non-promoter (downstream or within DSCR9, C21orf57 and HLCS genes) hypermethylated regions were effective in distinguishing malignant from normal prostate tissues and/or cell lines.
Conclusions
Comparison of chromosome-wide DNA methylation patterns in normal and malignant prostate cells revealed significant methylation of gene-proximal and conserved intergenic sequences. Such analyses can be easily extended for genome-wide methylation analysis in health and disease.
doi:10.1186/1471-2164-12-313
PMCID: PMC3124442  PMID: 21669002
DNA methylation; prostate cancer; tiling microarray; epigenetics; methylated DNA binding domain; MBD-chip; ADAMTS1; SCARF2; DSCR9; HLCS
13.  Empirical bayes analysis of sequencing-based transcriptional profiling without replicates 
BMC Bioinformatics  2010;11:564.
Background
Recent technological advancements have made high throughput sequencing an increasingly popular approach for transcriptome analysis. Advantages of sequencing-based transcriptional profiling over microarrays have been reported, including lower technical variability. However, advances in technology do not remove biological variation between replicates and this variation is often neglected in many analyses.
Results
We propose an empirical Bayes method, titled Analysis of Sequence Counts (ASC), to detect differential expression based on sequencing technology. ASC borrows information across sequences to establish prior distribution of sample variation, so that biological variation can be accounted for even when replicates are not available. Compared to current approaches that simply tests for equality of proportions in two samples, ASC is less biased towards highly expressed sequences and can identify more genes with a greater log fold change at lower overall abundance.
Conclusions
ASC unifies the biological and statistical significance of differential expression by estimating the posterior mean of log fold change and estimating false discovery rates based on the posterior mean. The implementation in R is available at http://www.stat.brown.edu/Zwu/research.aspx.
doi:10.1186/1471-2105-11-564
PMCID: PMC3098101  PMID: 21080965
14.  Background Adjustment for DNA Microarrays Using a Database of Microarray Experiments 
Journal of Computational Biology  2009;16(11):1501-1515.
Abstract
DNA microarrays have become an indispensable technique in biomedical research. The raw measurements from microarrays undergo a number of preprocessing steps before the data are converted to the genomic level for further analysis. Background adjustment is an important step in preprocessing. Estimating background noise has been challenging because background levels vary a lot from probe to probe, yet there are limited observations on each probe. Most current methods have used the empirical Bayes approach to borrow information across probes on the same array. These approaches shrink the background estimate for either the entire sample or probes sharing similar sequence structures. In this article, we present a solution that is truly probe specific by using a database of large number of microarray experiments. Information is borrowed across samples and background noise is estimated for each probe individually. The ability to obtain probe specific background distributions allows us to extend the dynamic range of gene expression levels. We illustrate the improvement in detecting gene expression variation on two datasets: a Latin Square spike-in experiment from Affymetrix and an Estrogen Receptor experiment with biological replicates. An R package dbRMA implementing our method can be obtained from the authors.
doi:10.1089/cmb.2009.0063
PMCID: PMC3154459  PMID: 19958080
background adjustment; gene expression; high-density oligonucleotide microarrays; preprocessing
15.  Anti-HIV Activity in Cervical-Vaginal Secretions from HIV-Positive and -Negative Women Correlate with Innate Antimicrobial Levels and IgG Antibodies 
PLoS ONE  2010;5(6):e11366.
Background
We investigated the impact of antimicrobials in cervicovaginal lavage (CVL) from HIV(+) and HIV(−) women on target cell infection with HIV. Since female reproductive tract (FRT) secretions contain a spectrum of antimicrobials, we hypothesized that CVL from healthy HIV(+) and (−) women inhibit HIV infection.
Methodology/Principal Findings
CVL from 32 HIV(+) healthy women with high CD4 counts and 15 healthy HIV(−) women were collected by gently washing the cervicovaginal area with 10 ml of sterile normal saline. Following centrifugation, anti-HIV activity in CVL was determined by incubating CVL with HIV prior to addition to TZM-bl cells. Antimicrobials and anti-gp160 HIV IgG antibodies were measured by ELISA. When CXCR4 and CCR5 tropic HIV-1 were incubated with CVL from HIV(+) women prior to addition to TZM-bl cells, anti-HIV activity in CVL ranged from none to 100% inhibition depending on the viral strains used. CVL from HIV(−) controls showed comparable anti-HIV activity. Analysis of CH077.c (clone of an R5-tropic, mucosally-transmitted founder virus) viral inhibition by CVL was comparable to laboratory strains. Measurement of CVL for antimicrobials HBD2, trappin-2/elafin, SLPI and MIP3α indicated that each was present in CVL from HIV(+) and HIV(−) women. HBD2 and MIP3α correlated with anti-HIV activity as did anti-gp160 HIV IgG antibodies in CVL from HIV(+) women.
Conclusions/Significance
These findings indicate that CVL from healthy HIV(+) and HIV(−) women contain innate and adaptive defense mechanisms that inhibit HIV infection. Our data suggest that innate endogenous antimicrobials and HIV-specific IgG in the FRT can act in concert to contribute toward the anti-HIV activity of the CVL and may play a role in inhibition of HIV transmission to women.
doi:10.1371/journal.pone.0011366
PMCID: PMC2894072  PMID: 20614007
16.  Kinetics of Host Cell Recruitment During Dissemination of Diffuse Malignant Peritoneal Mesothelioma 
Cancer Microenvironment  2010;4(1):39-50.
Diffuse malignant mesothelioma is an aggressive tumor which displays a median survival of 11.2 months and a 5-year survival of less than 5% emphasizing the need for more effective treatments. This study uses an orthotopic model of malignant mesothelioma established in syngeneic, immunocompetent C57Bl/6 mice which produce malignant ascites and solid tumors that accurately replicate the histopathology of the human disease. Host stromal and immune cell accumulation within malignant ascites and solid tumors was determined using immunofluorescent labeling with confocal microscopy and fluorescence-activated cell sorting. An expression profile of cytokines and chemokines was produced using quantitative real-time PCR arrays. Tumor spheroids and solid tumors show progressive growth and infiltration with host stromal and immune cells including macrophages, endothelial cells, CD4+ and CD8+ lymphocytes, and a novel cell type, myeloid derived suppressor cells (MDSCs). The kinetics of host cell accumulation and inflammatory mediator expression within the tumor ascites divides tumor progression into two distinct phases. The first phase is characterized by progressive macrophage and T lymphocyte recruitment, with a cytokine profile consistent with regulatory T lymphocytes differentiation and suppression of T cell function. The second phase is characterized by decreased expression of macrophage chemotactic and T-cell regulating factors, an increase in MDSCs, and increased expression of several cytokines which stimulate differentiation of MDSCs. This cellular and expression profile suggests a mechanism by which host immune cells promote diffuse malignant mesothelioma progression.
Electronic supplementary material
The online version of this article (doi:10.1007/s12307-010-0048-1) contains supplementary material, which is available to authorized users.
doi:10.1007/s12307-010-0048-1
PMCID: PMC3047623  PMID: 21505561
Malignant mesothelioma; Orthotopic model; Murine; Expression profile; Tumor microenvironment
17.  Kinetics of Host Cell Recruitment During Dissemination of Diffuse Malignant Peritoneal Mesothelioma 
Cancer Microenvironment  2010;4(1):39-50.
Diffuse malignant mesothelioma is an aggressive tumor which displays a median survival of 11.2 months and a 5-year survival of less than 5% emphasizing the need for more effective treatments. This study uses an orthotopic model of malignant mesothelioma established in syngeneic, immunocompetent C57Bl/6 mice which produce malignant ascites and solid tumors that accurately replicate the histopathology of the human disease. Host stromal and immune cell accumulation within malignant ascites and solid tumors was determined using immunofluorescent labeling with confocal microscopy and fluorescence-activated cell sorting. An expression profile of cytokines and chemokines was produced using quantitative real-time PCR arrays. Tumor spheroids and solid tumors show progressive growth and infiltration with host stromal and immune cells including macrophages, endothelial cells, CD4+ and CD8+ lymphocytes, and a novel cell type, myeloid derived suppressor cells (MDSCs). The kinetics of host cell accumulation and inflammatory mediator expression within the tumor ascites divides tumor progression into two distinct phases. The first phase is characterized by progressive macrophage and T lymphocyte recruitment, with a cytokine profile consistent with regulatory T lymphocytes differentiation and suppression of T cell function. The second phase is characterized by decreased expression of macrophage chemotactic and T-cell regulating factors, an increase in MDSCs, and increased expression of several cytokines which stimulate differentiation of MDSCs. This cellular and expression profile suggests a mechanism by which host immune cells promote diffuse malignant mesothelioma progression.
Electronic supplementary material
The online version of this article (doi:10.1007/s12307-010-0048-1) contains supplementary material, which is available to authorized users.
doi:10.1007/s12307-010-0048-1
PMCID: PMC3047623  PMID: 21505561
Malignant mesothelioma; Orthotopic model; Murine; Expression profile; Tumor microenvironment
18.  DNA hypomethylation arises later in prostate cancer progression than CpG island hypermethylation and contributes to metastatic tumor heterogeneity 
Cancer research  2008;68(21):8954-8967.
Hypomethylation of CpG dinucleotides in genomic DNA was one of the first somatic epigenetic alterations discovered in human cancers. DNA hypomethylation is postulated to occur very early in almost all human cancers, perhaps facilitating genetic instability and cancer initiation and progression. We therefore examined the nature, extent, and timing of DNA hypomethylation changes in human prostate cancer. Contrary to the prevailing view that global DNA hypomethylation changes occur extremely early in all human cancers, we show that reductions in 5meC content in the genome occur very late in prostate cancer progression, appearing at a significant extent only at the stage of metastatic disease. Furthermore, we found that while some LINE1 promoter hypomethylation does occur in primary prostate cancers compared to normal tissues, this LINE1 hypomethylation is significantly more pronounced in metastatic prostate cancer. Next, we carried out a tiered, gene expression microarray and bisulfite genomic sequencing based approach to identify genes that are silenced by CpG island methylation in normal prostate cells but become over-expressed in prostate cancer cells as a result of CpG island hypomethylation. Through this analysis we show that a class of cancer testis antigen genes undergoes CpG island hypomethylation and over-expression in primary prostate cancers, but more so in metastatic prostate cancers. Finally, we show that DNA hypomethylation patterns are quite heterogeneous across different metastatic sites within the same patients. These findings provide evidence that DNA hypomethylation changes occur later in prostate carcinogenesis than the CpG island , and occur heterogeneously during prostate cancer progression and metastatic dissemination.
doi:10.1158/0008-5472.CAN-07-6088
PMCID: PMC2577392  PMID: 18974140
DNA methylation; hypomethylation; prostate cancer; metastasis; epigenetics; LINE1; tumor heterogeneity; cancer testis antigens
19.  Genome-wide methylation analysis of human colon cancer reveals similar hypo- and hypermethylation at conserved tissue-specific CpG island shores 
Nature genetics  2009;41(2):178-186.
Alterations in DNA methylation (DNAm) in cancer have been known for 25 years, including hypomethylation of oncogenes and hypermethylation of tumor suppressor genes1. However, most studies of cancer methylation have assumed that functionally important DNAm will occur in promoters, and that most DNAm changes in cancer occur in CpG islands2,3. Here we show that most methylation alterations in colon cancer occur not in promoters, and also not in CpG islands but in sequences up to 2 kb distant which we term “CpG island shores.” CpG island shore methylation was strongly related to gene expression, and it was highly conserved in mouse, discriminating tissue types regardless of species of origin. There was a surprising overlap (45-65%) of the location of colon cancer-related methylation changes with those that distinguished normal tissues, with hypermethylation enriched closer to the associated CpG islands, and hypomethylation enriched further from the associated CpG island and resembling non-colon normal tissues. Thus, methylation changes in cancer are at sites that vary normally in tissue differentiation, and they are consistent with the epigenetic progenitor model of cancer4, that epigenetic alterations affecting tissue-specific differentiation are the predominant mechanism by which epigenetic changes cause cancer.
doi:10.1038/ng.298
PMCID: PMC2729128  PMID: 19151715
20.  Drug and Cell Type-Specific Regulation of Genes with Different Classes of Estrogen Receptor β-Selective Agonists 
PLoS ONE  2009;4(7):e6271.
Estrogens produce biological effects by interacting with two estrogen receptors, ERα and ERβ. Drugs that selectively target ERα or ERβ might be safer for conditions that have been traditionally treated with non-selective estrogens. Several synthetic and natural ERβ-selective compounds have been identified. One class of ERβ-selective agonists is represented by ERB-041 (WAY-202041) which binds to ERβ much greater than ERα. A second class of ERβ-selective agonists derived from plants include MF101, nyasol and liquiritigenin that bind similarly to both ERs, but only activate transcription with ERβ. Diarylpropionitrile represents a third class of ERβ-selective compounds because its selectivity is due to a combination of greater binding to ERβ and transcriptional activity. However, it is unclear if these three classes of ERβ-selective compounds produce similar biological activities. The goals of these studies were to determine the relative ERβ selectivity and pattern of gene expression of these three classes of ERβ-selective compounds compared to estradiol (E2), which is a non-selective ER agonist. U2OS cells stably transfected with ERα or ERβ were treated with E2 or the ERβ-selective compounds for 6 h. Microarray data demonstrated that ERB-041, MF101 and liquiritigenin were the most ERβ-selective agonists compared to estradiol, followed by nyasol and then diarylpropionitrile. FRET analysis showed that all compounds induced a similar conformation of ERβ, which is consistent with the finding that most genes regulated by the ERβ-selective compounds were similar to each other and E2. However, there were some classes of genes differentially regulated by the ERβ agonists and E2. Two ERβ-selective compounds, MF101 and liquiritigenin had cell type-specific effects as they regulated different genes in HeLa, Caco-2 and Ishikawa cell lines expressing ERβ. Our gene profiling studies demonstrate that while most of the genes were commonly regulated by ERβ-selective agonists and E2, there were some genes regulated that were distinct from each other and E2, suggesting that different ERβ-selective agonists might produce distinct biological and clinical effects.
doi:10.1371/journal.pone.0006271
PMCID: PMC2707612  PMID: 19609440
21.  Feature-level exploration of a published Affymetrix GeneChip control dataset 
Genome Biology  2006;7(8):404.
A comment on Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset by SE Choe, M Boutros, AM Michelson, GM Church and MS Halfon. Genome Biology 2005, 6:R16
A comment on Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset by SE Choe, M Boutros, AM Michelson, GM Church and MS Halfon. Genome Biology 2005, 6:R16.
doi:10.1186/gb-2006-7-8-404
PMCID: PMC1779590  PMID: 16953902

Results 1-21 (21)