BackgroundCategorizing 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.ResultsThis 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.ConclusionsThe 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.

BackgroundThe 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 FindingsAfter 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/SignificanceBased 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.

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.

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.

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.