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.

The aim of this study was to examine the changes and relationships of immune and stress parameters of basketball players during a basketball season. Eight members of National Taichung University basketball team volunteered to participate. Saliva samples were collected at rest and before the start of practice or competition at seven time points during the intense training, competition and recovery period. Salivary immunoglobulin A (sIgA), cortisol, and lactoferrin were measured during training and competition period and compared with those measured at the fourth recovery week. Relationships among immune and stress parameters were evaluated. Compared with those detected at the fourth recovery week, significant decreases in secretion rates and absolute concentrations of sIgA and lactoferrin were observed at times of intense training and competition. In addition, significant increases in secretion rates and absolute concentrations of salivary cortisol were observed during intense training and competition period and the first week of recovery. Moreover, a significant inverse correlation (r = -0.28; P < 0.05) that existed between secretion rates of sIgA and cortisol as well as a positive correlation (r = 0.32; P < 0.05) that existed between secretion rates of sIgA and lactoferrin was measured. Our results demonstrated that the secreted cortisol was induced and the mucosal immunity of the participants was suppressed during the basketball season. The inverse correlation existed between secretion rates of sIgA and cortisol may indicate a possible role of cortisol in the strenuous exercise-induced immunosuppression. Our results also suggest that a delicate balance may exist between mucosal innate and adaptive immune responses.

Our results demonstrated that mucosal immunity in elite male taekwondo athletes is modulated by exercise and rapid weight reduction during the training, competition and recovery period. Cumulative effects of prolonged intensive training and rapid weight reduction suppressed mucosal immunity. Furthermore, because of the "open window" of impaired immunity during the precompetition period, the incidence of upper respiratory tract infection was significantly increased after the competition.

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.

Our data suggest that multiple tumors in some patients with prostate cancer have independent origin.

BackgroundIn the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data.ResultsTo achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets.ConclusionBased on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification.

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.

Honokiol (HNK) is a phenolic compound isolated from the bark of houpu (Magnolia officinalis), a plant widely used in traditional Chinese and Japanese medicine. While substantial evidence indicates that HNK possesses anti-inflammatory activity, its effect on dendritic cells (DCs) during the inflammatory reaction remains unclear. The present study investigates how HNK affects lipopolysaccharide (LPS)-stimulated human monocyte-derived DCs. Our experimental results show that HNK inhibits the inflammatory response of LPS-induced DCs by (1) suppressing the expression of CD11c, CD40, CD80, CD83, CD86, and MHC-II on LPS-activated DCs, (2) reducing the production of TNF-α, IL-1β, IL-6, and IL-12p70 but increasing the production of IL-10 and TGF-β1 by LPS-activated DCs, (3) inhibiting the LPS-induced DC-elicited allogeneic T-cell proliferation, and (4) shifting the LPS-induced DC-driven Th1 response toward a Th2 response. Further, our results show that HNK inhibits the phosphorylation levels of ERK1/2, p38, JNK1/2, IKKα, and IκBα in LPS-activated DCs. Collectively, the findings show that the anti-inflammatory actions of HNK on LPS-induced DCs are associated with the NF-κB and mitogen-activated protein kinase (MAPK) signaling pathways.