Chaang-Ray Chen

Identification of Human Housekeeping Genes and Tissue-Selective Genes by Microarray Meta-Analysis

Background Categorizing protein-encoding transcriptomes of normal tissues into housekeeping genes and tissue-selective genes is a fundamental step toward studies of genetic functions and genetic associations to tissue-specific diseases. Previous studies have been mainly based on a few data sets with limited samples in each tissue, which restrained the representativeness of their identified genes, and resulted in low consensus among them. Results This study compiled 1,431 samples in 43 normal human tissues from 104 microarray data sets. We developed a new method to improve gene expression assessment, and showed that more than ten samples are needed to robustly identify the protein-encoding transcriptome of a tissue. We identified 2,064 housekeeping genes and 2,293 tissue-selective genes, and analyzed gene lists by functional enrichment analysis. The housekeeping genes are mainly involved in fundamental cellular functions, and the tissue-selective genes are strikingly related to functions and diseases corresponding to tissue-origin. We also compared agreements and related functions among our housekeeping genes and those of previous studies, and pointed out some reasons for the low consensuses. Conclusions The results indicate that sufficient samples have improved the identification of protein-encoding transcriptome of a tissue. Comprehensive meta-analysis has proved the high quality of our identified HK and TS genes. These results could offer a useful resource for future research on functional and genomic features of HK and TS genes.

Identification of Reference Genes across Physiological States for qRT-PCR through Microarray Meta-Analysis

Background The accuracy of quantitative real-time PCR (qRT-PCR) is highly dependent on reliable reference gene(s). Some housekeeping genes which are commonly used for normalization are widely recognized as inappropriate in many experimental conditions. This study aimed to identify reference genes for clinical studies through microarray meta-analysis of human clinical samples. Methodology/Principal Findings After uniform data preprocessing and data quality control, 4,804 Affymetrix HU-133A arrays performed by clinical samples were classified into four physiological states with 13 organ/tissue types. We identified a list of reference genes for each organ/tissue types which exhibited stable expression across physiological states. Furthermore, 102 genes identified as reference gene candidates in multiple organ/tissue types were selected for further analysis. These genes have been frequently identified as housekeeping genes in previous studies, and approximately 71% of them fall into Gene Expression (GO:0010467) category in Gene Ontology. Conclusions/Significance Based on microarray meta-analysis of human clinical sample arrays, we identified sets of reference gene candidates for various organ/tissue types and then examined the functions of these genes. Additionally, we found that many of the reference genes are functionally related to transcription, RNA processing and translation. According to our results, researchers could select single or multiple reference gene(s) for normalization of qRT-PCR in clinical studies.

Microarray meta-analysis database (M 2 DB): a uniformly pre-processed, quality controlled, and manually curated human clinical microarray database

BackgroundOver the past decade, gene expression microarray studies have greatly expanded our knowledge of genetic mechanisms of human diseases. Meta-analysis of substantial amounts of accumulated data, by integrating valuable information from multiple studies, is becoming more important in microarray research. However, collecting data of special interest from public microarray repositories often present major practical problems. Moreover, including low-quality data may significantly reduce meta-analysis efficiency.ResultsM2DB is a human curated microarray database designed for easy querying, based on clinical information and for interactive retrieval of either raw or uniformly pre-processed data, along with a set of quality-control metrics. The database contains more than 10,000 previously published Affymetrix GeneChip arrays, performed using human clinical specimens. M2DB allows online querying according to a flexible combination of five clinical annotations describing disease state and sampling location. These annotations were manually curated by controlled vocabularies, based on information obtained from GEO, ArrayExpress, and published papers. For array-based assessment control, the online query provides sets of QC metrics, generated using three available QC algorithms. Arrays with poor data quality can easily be excluded from the query interface. The query provides values from two algorithms for gene-based filtering, and raw data and three kinds of pre-processed data for downloading.ConclusionM2DB utilizes a user-friendly interface for QC parameters, sample clinical annotations, and data formats to help users obtain clinical metadata. This database provides a lower entry threshold and an integrated process of meta-analysis. We hope that this research will promote further evolution of microarray meta-analysis.

Extremely low-frequency electromagnetic fields cause G1 phase arrest through the activation of the ATM-Chk2-p21 pathway.

In daily life, humans are exposed to the extremely low-frequency electromagnetic fields (ELF-EMFs) generated by electric appliances, and public concern is increasing regarding the biological effects of such exposure. Numerous studies have yielded inconsistent results regarding the biological effects of ELF-EMF exposure. Here we show that ELF-EMFs activate the ATM-Chk2-p21 pathway in HaCaT cells, inhibiting cell proliferation. To present well-founded results, we comprehensively evaluated the biological effects of ELF-EMFs at the transcriptional, protein, and cellular levels. Human HaCaT cells from an immortalized epidermal keratinocyte cell line were exposed to a 1.5 mT, 60 Hz ELF-EMF for 144 h. The ELF-EMF could cause G1 arrest and decrease colony formation. Protein expression experiments revealed that ELF-EMFs induced the activation of the ATM/Chk2 signaling cascades. In addition, the p21 protein, a regulator of cell cycle progression at G1 and G2/M, exhibited a higher level of expression in exposed HaCaT cells compared with the expression of sham-exposed cells. The ELF-EMF-induced G1 arrest was diminished when the CHK2 gene expression (which encodes checkpoint kinase 2; Chk2) was suppressed by specific small interfering RNA (siRNA). These findings indicate that ELF-EMFs activate the ATM-Chk2-p21 pathway in HaCaT cells, resulting in cell cycle arrest at the G1 phase. Based on the precise control of the ELF-EMF exposure and rigorous sham-exposure experiments, all transcriptional, protein, and cellular level experiments consistently supported the conclusion. This is the first study to confirm that a specific pathway is triggered by ELF-EMF exposure.

Intra- and Inter-Individual Variance of Gene Expression in Clinical Studies

Background Variance in microarray studies has been widely discussed as a critical topic on the identification of differentially expressed genes; however, few studies have addressed the influence of estimating variance. Methodology/Principal Findings To break intra- and inter-individual variance in clinical studies down to three levels–technical, anatomic, and individual–we designed experiments and algorithms to investigate three forms of variances. As a case study, a group of “inter-individual variable genes” were identified to exemplify the influence of underestimated variance on the statistical and biological aspects in identification of differentially expressed genes. Our results showed that inadequate estimation of variance inevitably led to the inclusion of non-statistically significant genes into those listed as significant, thereby interfering with the correct prediction of biological functions. Applying a higher cutoff value of fold changes in the selection of significant genes reduces/eliminates the effects of underestimated variance. Conclusions/Significance Our data demonstrated that correct variance evaluation is critical in selecting significant genes. If the degree of variance is underestimated, “noisy” genes are falsely identified as differentially expressed genes. These genes are the noise associated with biological interpretation, reducing the biological significance of the gene set. Our results also indicate that applying a higher number of fold change as the selection criteria reduces/eliminates the differences between distinct estimations of variance.

Distinct Epidermal Keratinocytes Respond to Extremely Low-Frequency Electromagnetic Fields Differently

Following an increase in the use of electric appliances that can generate 50 or 60 Hz electromagnetic fields, concerns have intensified regarding the biological effects of extremely low-frequency electromagnetic fields (ELF-EMFs) on human health. Previous epidemiological studies have suggested the carcinogenic potential of environmental exposure to ELF-EMFs, specifically at 50 or 60 Hz. However, the biological mechanism facilitating the effects of ELF-EMFs remains unclear. Cellular studies have yielded inconsistent results regarding the biological effects of ELF-EMFs. The inconsistent results might have been due to diverse cell types. In our previous study, we indicated that 1.5 mT, 60 Hz ELF-EMFs will cause G1 arrest through the activation of the ATM-Chk2-p21 pathway in human keratinocyte HaCaT cells. The aim of the current study was to investigate whether ELF-EMFs cause similar effects in a distinct epidermal keratinocyte, primary normal human epidermal keratinocytes (NHEK), by using the same ELF-EMF exposure system and experimental design. We observed that ELF-EMFs exerted no effects on cell growth, cell proliferation, cell cycle distribution, and the activation of ATM signaling pathway in NHEK cells. We demonstrated that the 2 epidermal keratinocytes responded to ELF-EMFs differently. To further validate this finding, we simultaneously exposed the NHEK and HaCaT cells to ELF-EMFs in the same incubator for 168 h and observed the cell growths. The simultaneous exposure of the two cell types results showed that the NHEK and HaCaT cells exhibited distinct responses to ELF-EMFs. Thus, we confirmed that the biological effects of ELF-EMFs in epidermal keratinocytes are cell type specific. Our findings may partially explain the inconsistent results of previous studies when comparing results across various experimental models.

THEME: A web tool for loop-design microarray data analysis

A number of recent studies have shown that loop-design is more efficient than reference control design. Data analysis for loop-design microarray experiments is commonly undertaken using linear models and statistical tests. These techniques require specialized knowledge in statistical programming. However, limited loop-design web-based tools are available. We have developed the THEME (Tsing Hua Engine of Microarray Experiment) that exploits all necessary data analysis tools for loop-design microarray studies. THEME allows users to construct linear models and to apply multiple user-defined statistical tests of hypotheses for detection of DEG (differentially expressed genes). Users can modify entries of design matrix for experimental design as well as that of contrast matrix for statistical tests of hypotheses. The output of multiple user-defined statistical tests of hypotheses, DEG lists, can be cross-validated. The web platform provides data assessment and visualization tools that significantly assist users when evaluating the performance of microarray experimental procedures. THEME is also a MIAME (Minimal Information About a Microarray Experiment) compliant system, which enables users to export formatted files for GEO (Gene Expression Omnibus) submission. THEME offers comprehensive web services to biologists for data analysis of loop-design microarray experiments. This web-based resource is especially useful for core facility service as well as collaboration projects when researchers are not at the same site. Data analysis procedures, starting from uploading raw data files to retrieving DEG lists, can be flexibly operated with natural workflows. These features make THEME a reliable and powerful on-line system for data analysis of loop-design microarrays. The THEME server is available at http://metadb.bmes.nthu.edu.tw/theme/.

Comparative transcriptome profiling of an SV40-transformed human fibroblast (MRC5CVI) and its untransformed counterpart (MRC-5) in response to UVB irradiation.

Simian virus 40 (SV40) transforms cells through the suppression of tumor-suppressive responses by large T and small t antigens; studies on the effects of these two oncoproteins have greatly improved our knowledge of tumorigenesis. Large T antigen promotes cellular transformation by binding and inactivating p53 and pRb tumor suppressor proteins. Previous studies have shown that not all of the tumor-suppressive responses were inactivated in SV40-transformed cells; however, the underlying cause is not fully studied. In this study, we investigated the UVB-responsive transcriptome of an SV40-transformed fibroblast (MRC5CVI) and that of its untransformed counterpart (MRC-5). We found that, in response to UVB irradiation, MRC-5 and MRC5CVI commonly up-regulated the expression of oxidative phosphorylation genes. MRC-5 up-regulated the expressions of chromosome condensation, DNA repair, cell cycle arrest, and apoptotic genes, but MRC5CVI did not. Further cell death assays indicated that MRC5CVI was more sensitive than MRC-5 to UVB-induced cell death with increased caspase-3 activation; combining with the transcriptomic results suggested that MRC5CVI may undergo UVB-induced cell death through mechanisms other than transcriptional regulation. Our study provides a further understanding of the effects of SV40 transformation on cellular stress responses, and emphasizes the value of SV40-transformed cells in the researches of sensitizing neoplastic cells to radiations.

Identification of Under-Detected Periodicity in Time- Series Microarray Data by Using Empirical Mode Decomposition

Detecting periodicity signals from time-series microarray data is commonly used to facilitate the understanding of the critical roles and underlying mechanisms of regulatory transcriptomes. However, time-series microarray data are noisy. How the temporal data structure affects the performance of periodicity detection has remained elusive. We present a novel method based on empirical mode decomposition (EMD) to examine this effect. We applied EMD to a yeast microarray dataset and extracted a series of intrinsic mode function (IMF) oscillations from the time-series data. Our analysis indicated that many periodically expressed genes might have been under-detected in the original analysis because of interference between decomposed IMF oscillations. By validating a protein complex coexpression analysis, we revealed that 56 genes were newly determined as periodic. We demonstrated that EMD can be used incorporating with existing periodicity detection methods to improve their performance. This approach can be applied to other time-series microarray studies.

The Classification of Sini Decoction Pattern in Traditional Chinese Medicine by Gene Expression Profiling

We investigated the syndromes of the Sini decoction pattern (SDP), a common ZHENG in traditional Chinese medicine (TCM). The syndromes of SDP were correlated with various severe Yang deficiency related symptoms. To obtain a common profile for SDP, we distributed questionnaires to 300 senior clinical TCM practitioners. According to the survey, we concluded 2 sets of symptoms for SDP: (1) pulse feels deep or faint and (2) reversal cold of the extremities. Twenty-four individuals from Taipei City Hospital, Linsen Chinese Medicine Branch, Taiwan, were recruited. We extracted the total mRNA of peripheral blood mononuclear cells from the 24 individuals for microarray experiments. Twelve individuals (including 6 SDP patients and 6 non-SDP individuals) were used as the training set to identify biomarkers for distinguishing the SDP and non-SDP groups. The remaining 12 individuals were used as the test set. The test results indicated that the gene expression profiles of the identified biomarkers could effectively distinguish the 2 groups by adopting a hierarchical clustering algorithm. Our results suggest the feasibility of using the identified biomarkers in facilitating the diagnosis of TCM ZHENGs. Furthermore, the gene expression profiles of biomarker genes could provide a molecular explanation corresponding to the ZHENG of TCM.