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1.  Genome-wide study of DNA methylation alterations in response to Diazinon exposure in vitro 
Pesticide exposure has repeatedly been associated with cancers. However, molecular mechanisms are largely undetermined. In this study, we examined whether exposure to diazinon, a common organophosphate that has been associated with cancers, could induce DNA methylation alterations. We conducted genome-wide DNA methylation analyses on DNA samples obtained from human hematopoietic K562 cell exposed to diazinon and ethanol using the Illumina Infinium HumanMethylation27 BeadChip. Bayesian-adjusted t-tests were used to identify differentially methylated gene promoter CpG sites. We identified 1069 CpG sites in 984 genes with significant methylation changes in diazinon-treated cells. Gene ontology analysis demonstrated that some genes are tumor suppressor genes, such as TP53INP1 (3.0-fold, q-value<0.001) and PTEN (2.6-fold, q-value<0.001), some genes are in cancer-related pathways, such as HDAC3 (2.2-fold, q-value=0.002), and some remain functionally unknown. Our results provided direct experimental evidence that diazinon may modify gene promoter DNA methylation levels, which may play a pathological role in cancer development.
doi:10.1016/j.etap.2012.07.012
PMCID: PMC3514648  PMID: 22964155
Diazinon exposure; DNA methylation alteration; carcinogenesis
3.  DNA methylation alterations in response to pesticide exposure in vitro 
Although pesticides are subject to extensive carcinogenicity testing before regulatory approval, pesticide exposure has repeatedly been associated with various cancers. This suggests that pesticides may cause cancer via non-mutagenicity mechanisms. The present study provides evidence to support the hypothesis that pesticide-induced cancer may be mediated in part by epigenetic mechanisms. We examined whether exposure to 7 commonly used pesticides (i.e., fonofos, parathion, terbufos, chlorpyrifos, diazinon, malathion, and phorate) induces DNA methylation alterations in vitro. We conducted genome-wide DNA methylation analyses on DNA samples obtained from the human hematopoietic K562 cell line exposed to ethanol (control) and several OPs using the Illumina Infinium HumanMethylation27 BeadChip. Bayesian-adjusted t-tests were used to identify differentially methylated gene promoter CpG sites. In this report, we present our results on three pesticides (fonofos, parathion, and terbufos) that clustered together based on principle component analysis and hierarchical clustering. These three pesticides induced similar methylation changes in the promoter regions of 712 genes, while also exhibiting their own OP-specific methylation alterations. Functional analysis of methylation changes specific to each OP, or common to all three OPs, revealed that differential methylation was associated with numerous genes that are involved in carcinogenesis-related processes. Our results provide experimental evidence that pesticides may modify gene promoter DNA methylation levels, suggesting that epigenetic mechanisms may contribute to pesticide-induced carcinogenesis. Further studies in other cell types and human samples are required, as well as determining the impact of these methylation changes on gene expression.
doi:10.1002/em.21718
PMCID: PMC3753688  PMID: 22847954
Pesticide exposure; DNA methylation alteration; Carcinogenesis
4.  Systems medicine and integrated care to combat chronic noncommunicable diseases 
Genome Medicine  2011;3(7):43.
We propose an innovative, integrated, cost-effective health system to combat major non-communicable diseases (NCDs), including cardiovascular, chronic respiratory, metabolic, rheumatologic and neurologic disorders and cancers, which together are the predominant health problem of the 21st century. This proposed holistic strategy involves comprehensive patient-centered integrated care and multi-scale, multi-modal and multi-level systems approaches to tackle NCDs as a common group of diseases. Rather than studying each disease individually, it will take into account their intertwined gene-environment, socio-economic interactions and co-morbidities that lead to individual-specific complex phenotypes. It will implement a road map for predictive, preventive, personalized and participatory (P4) medicine based on a robust and extensive knowledge management infrastructure that contains individual patient information. It will be supported by strategic partnerships involving all stakeholders, including general practitioners associated with patient-centered care. This systems medicine strategy, which will take a holistic approach to disease, is designed to allow the results to be used globally, taking into account the needs and specificities of local economies and health systems.
doi:10.1186/gm259
PMCID: PMC3221551  PMID: 21745417
5.  BTECH: A Platform to Integrate Genomic, Transcriptomic and Epigenomic Alterations in Brain Tumors 
Neuroinformatics  2011;9(1):59-67.
The identification of molecular signatures predictive of clinical behavior and outcome in brain tumors has been the focus of many studies in the recent years. Despite the wealth of data that are available in the public domain on alterations in the genome, epigenome and transcriptome of brain tumors, the underlying molecular mechanisms leading to tumor initiation and progression remain largely unknown. Unfortunately, most of these data are scattered in multiple databases and supplementary materials of publications, thus making their retrieval, evaluation, comparison and visualization a rather arduous task. Here we report the development and implementation of an open access database (BTECH), a community resource for the deposition of a wide range of molecular data derived from brain tumor studies. This comprehensive database integrates multiple datasets, including transcript profiles, epigenomic CpG methylation data, DNA copy number alterations and structural chromosomal rearrangements, tumor-associated gene lists, SNPs, genomic features concerning Alu repeats and general genomic annotations. A genome browser has also been developed that allows for the simultaneous visualization of the different datasets and the various annotated features. Besides enabling an integrative view of diverse datasets through the genome browser, we also provide links to the original references for users to have a more accurate understanding of each specific dataset. This integrated platform will facilitate uncovering interactions among genetic and epigenetic factors associated with brain tumor development. BTECH is freely available at http://cmbteg.childrensmemorial.org/.
Electronic supplementary material
The online version of this article (doi:10.1007/s12021-010-9091-9) contains supplementary material, which is available to authorized users.
doi:10.1007/s12021-010-9091-9
PMCID: PMC3063551  PMID: 21210251
Database; Brain tumor; Genome browser; Genomics; Epigenomics; DNA methylation; Gene expression
6.  A biphasic pattern of gene expression during mouse retina development 
Background
Between embryonic day 12 and postnatal day 21, six major neuronal and one glia cell type are generated from multipotential progenitors in a characteristic sequence during mouse retina development. We investigated expression patterns of retina transcripts during the major embryonic and postnatal developmental stages to provide a systematic view of normal mouse retina development,
Results
A tissue-specific cDNA microarray was generated using a set of sequence non-redundant EST clones collected from mouse retina. Eleven stages of mouse retina, from embryonic day 12.5 (El2.5) to postnatal day 21 (PN21), were collected for RNA isolation. Non-amplified RNAs were labeled for microarray experiments and three sets of data were analyzed for significance, hierarchical relationships, and functional clustering. Six individual gene expression clusters were identified based on expression patterns of transcripts through retina development. Two developmental phases were clearly divided with postnatal day 5 (PN5) as a separate cluster. Among 4,180 transcripts that changed significantly during development, approximately 2/3 of the genes were expressed at high levels up until PN5 and then declined whereas the other 1/3 of the genes increased expression from PN5 and remained at the higher levels until at least PN21. Less than 1% of the genes observed showed a peak of expression between the two phases. Among the later increased population, only about 40% genes are correlated with rod photoreceptors, indicating that multiple cell types contributed to gene expression in this phase. Within the same functional classes, however, different gene populations were expressed in distinct developmental phases. A correlation coefficient analysis of gene expression during retina development between previous SAGE studies and this study was also carried out.
Conclusion
This study provides a complementary genome-wide view of common gene dynamics and a broad molecular classification of mouse retina development. Different genes in the same functional clusters are expressed in the different developmental stages, suggesting that cells might change gene expression profiles from differentiation to maturation stages. We propose that large-scale changes in gene regulation during development are necessary for the final maturation and function of the retina.
doi:10.1186/1471-213X-6-48
PMCID: PMC1633734  PMID: 17044933
7.  A robust two-way semi-linear model for normalization of cDNA microarray data 
BMC Bioinformatics  2005;6:14.
Background
Normalization is a basic step in microarray data analysis. A proper normalization procedure ensures that the intensity ratios provide meaningful measures of relative expression values.
Methods
We propose a robust semiparametric method in a two-way semi-linear model (TW-SLM) for normalization of cDNA microarray data. This method does not make the usual assumptions underlying some of the existing methods. For example, it does not assume that: (i) the percentage of differentially expressed genes is small; or (ii) the numbers of up- and down-regulated genes are about the same, as required in the LOWESS normalization method. We conduct simulation studies to evaluate the proposed method and use a real data set from a specially designed microarray experiment to compare the performance of the proposed method with that of the LOWESS normalization approach.
Results
The simulation results show that the proposed method performs better than the LOWESS normalization method in terms of mean square errors for estimated gene effects. The results of analysis of the real data set also show that the proposed method yields more consistent results between the direct and the indirect comparisons and also can detect more differentially expressed genes than the LOWESS method.
Conclusions
Our simulation studies and the real data example indicate that the proposed robust TW-SLM method works at least as well as the LOWESS method and works better when the underlying assumptions for the LOWESS method are not satisfied. Therefore, it is a powerful alternative to the existing normalization methods.
doi:10.1186/1471-2105-6-14
PMCID: PMC549200  PMID: 15663789

Results 1-7 (7)