Koji Kadota

Functional annotation of a full-length mouse cDNA collection

The RIKEN Mouse Gene Encyclopaedia Project, a systematic approach to determining the full coding potential of the mouse genome, involves collection and sequencing of full-length complementary DNAs and physical mapping of the corresponding genes to the mouse genome. We organized an international functional annotation meeting (FANTOM) to annotate the first 21,076 cDNAs to be analysed in this project. Here we describe the first RIKEN clone collection, which is one of the largest described for any organism. Analysis of these cDNAs extends known gene families and identifies new ones.The RIKEN Mouse Gene Encyclopaedia Project, a systematic approach to determining the full coding potential of the mouse genome, involves collection and sequencing of full-length complementary DNAs and physical mapping of the corresponding genes to the mouse genome. We organized an international functional annotation meeting (FANTOM) to annotate the first 21,076 cDNAs to be analysed in this project. Here we describe the first RIKEN clone collection, which is one of the largest described for any organism. Analysis of these cDNAs extends known gene families and identifies new ones.

Genome-wide Expression Analysis of Mouse Liver Reveals CLOCK-regulated Circadian Output Genes

CLOCK is a positive component of a transcription/translation-based negative feedback loop of the central circadian oscillator in the suprachiasmatic nucleus in mammals. To examine CLOCK-regulated circadian transcription in peripheral tissues, we performed microarray analyses using liver RNA isolated from Clock mutant mice. We also compared expression profiles with those of Cryptochromes (Cry1 and Cry2) double knockout mice. We identified more than 100 genes that fluctuated from day to night and of which expression levels were decreased in Clock mutant mice. In Cry-deficient mice, the expression levels of most CLOCK-regulated genes were elevated to the upper range of normal oscillation. Most of the screened genes had a CLOCK/BMAL1 binding site (E box) in the 5′-flanking region. We found that CLOCK was absolutely concerned with the circadian transcription of one type of liver genes (such as DBP, TEF, and Usp2) and partially with another (such as mPer1, mPer2, mDec1, Nocturnin, P450 oxidoreductase, and FKBP51) because the latter were damped but remained rhythmic in the mutant mice. Our results showed that CLOCK and CRY proteins are involved in the transcriptional regulation of many circadian output genes in the mouse liver. In addition to being a core component of the negative feedback loop that drives the circadian oscillator, CLOCK also appears to be involved in various physiological functions such as cell cycle, lipid metabolism, immune functions, and proteolysis in peripheral tissues.

TCC: an R package for comparing tag count data with robust normalization strategies

BackgroundDifferential expression analysis based on “next-generation” sequencing technologies is a fundamental means of studying RNA expression. We recently developed a multi-step normalization method (called TbT) for two-group RNA-seq data with replicates and demonstrated that the statistical methods available in four R packages (edgeR, DESeq, baySeq, and NBPSeq) together with TbT can produce a well-ranked gene list in which true differentially expressed genes (DEGs) are top-ranked and non-DEGs are bottom ranked. However, the advantages of the current TbT method come at the cost of a huge computation time. Moreover, the R packages did not have normalization methods based on such a multi-step strategy.ResultsTCC (an acronym for Tag Count Comparison) is an R package that provides a series of functions for differential expression analysis of tag count data. The package incorporates multi-step normalization methods, whose strategy is to remove potential DEGs before performing the data normalization. The normalization function based on this DEG elimination strategy (DEGES) includes (i) the original TbT method based on DEGES for two-group data with or without replicates, (ii) much faster methods for two-group data with or without replicates, and (iii) methods for multi-group comparison. TCC provides a simple unified interface to perform such analyses with combinations of functions provided by edgeR, DESeq, and baySeq. Additionally, a function for generating simulation data under various conditions and alternative DEGES procedures consisting of functions in the existing packages are provided. Bioinformatics scientists can use TCC to evaluate their methods, and biologists familiar with other R packages can easily learn what is done in TCC.ConclusionDEGES in TCC is essential for accurate normalization of tag count data, especially when up- and down-regulated DEGs in one of the samples are extremely biased in their number. TCC is useful for analyzing tag count data in various scenarios ranging from unbiased to extremely biased differential expression. TCC is available at http://www.iu.a.u-tokyo.ac.jp/~kadota/TCC/ and will appear in Bioconductor (http://bioconductor.org/) from ver. 2.13.

Ranking differentially expressed genes from Affymetrix gene expression data: methods with reproducibility, sensitivity, and specificity

BackgroundTo identify differentially expressed genes (DEGs) from microarray data, users of the Affymetrix GeneChip system need to select both a preprocessing algorithm to obtain expression-level measurements and a way of ranking genes to obtain the most plausible candidates. We recently recommended suitable combinations of a preprocessing algorithm and gene ranking method that can be used to identify DEGs with a higher level of sensitivity and specificity. However, in addition to these recommendations, researchers also want to know which combinations enhance reproducibility.ResultsWe compared eight conventional methods for ranking genes: weighted average difference (WAD), average difference (AD), fold change (FC), rank products (RP), moderated t statistic (modT), significance analysis of microarrays (samT), shrinkage t statistic (shrinkT), and intensity-based moderated t statistic (ibmT) with six preprocessing algorithms (PLIER, VSN, FARMS, multi-mgMOS (mmgMOS), MBEI, and GCRMA). A total of 36 real experimental datasets was evaluated on the basis of the area under the receiver operating characteristic curve (AUC) as a measure for both sensitivity and specificity. We found that the RP method performed well for VSN-, FARMS-, MBEI-, and GCRMA-preprocessed data, and the WAD method performed well for mmgMOS-preprocessed data. Our analysis of the MicroArray Quality Control (MAQC) projects datasets showed that the FC-based gene ranking methods (WAD, AD, FC, and RP) had a higher level of reproducibility: The percentages of overlapping genes (POGs) across different sites for the FC-based methods were higher overall than those for the t-statistic-based methods (modT, samT, shrinkT, and ibmT). In particular, POG values for WAD were the highest overall among the FC-based methods irrespective of the choice of preprocessing algorithm.ConclusionOur results demonstrate that to increase sensitivity, specificity, and reproducibility in microarray analyses, we need to select suitable combinations of preprocessing algorithms and gene ranking methods. We recommend the use of FC-based methods, in particular RP or WAD.

ROKU: a novel method for identification of tissue-specific genes

BackgroundOne of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. We would like to have ways of identifying such tissue-specific genes.ResultsWe describe a method, ROKU, which selects tissue-specific patterns from gene expression data for many tissues and thousands of genes. ROKU ranks genes according to their overall tissue specificity using Shannon entropy and detects tissues specific to each gene if any exist using an outlier detection method. We evaluated the capacity for the detection of various specific expression patterns using synthetic and real data. We observed that ROKU was superior to a conventional entropy-based method in its ability to rank genes according to overall tissue specificity and to detect genes whose expression pattern are specific only to objective tissues.ConclusionROKU is useful for the detection of various tissue-specific expression patterns. The framework is also directly applicable to the selection of diagnostic markers for molecular classification of multiple classes.

Clock mutation affects circadian regulation of circulating blood cells

Background Although the number of circulating immune cells is subject to high-amplitude circadian rhythms, the underlying mechanisms are not fully understood. Methods To determine whether intact CLOCK protein is required for the circadian changes in peripheral blood cells, we examined circulating white (WBC) and red (RBC) blood cells in homozygous Clock mutant mice. Results Daytime increases in total WBC and lymphocytes were suppressed and slightly phase-delayed along with plasma corticosterone levels in Clock mutant mice. The peak RBC rhythm was significantly reduced and phase-advanced in the Clock mutants. Anatomical examination revealed hemoglobin-rich, swollen red spleens in Clock mutant mice, suggesting RBC accumulation. Conclusion Our results suggest that endogenous clock-regulated circadian corticosterone secretion from the adrenal gland is involved in the effect of a Clock mutation on daily profiles of circulating WBC. However, intact CLOCK seems unnecessary for generating the rhythm of corticosterone secretion in mice. Our results also suggest that CLOCK is involved in discharge of RBC from the spleen.

Analysis of Gene Expression Involved in Brain Metastasis from Breast Cancer Using cDNA Microarray

BackgroundBrain metastases occur in 15% to 30% of breast cancer patients, usually as a late event. The patterns of metastases to different organs are determined by the tumor cell phenotype and interactions between the tumor cells and the organ environment.MethodsWe investigated the gene expression profile occurring in brain metastases from a breast cancer cell line. We used cDNA microarrays to compare patterns of gene expression between the mouse breast cancer cell line Jyg MC (A) and a subline that often metastasis to brain, (B).ResultsBy Microarray analysis about 350 of 21,000 genes were significantly up-regulated in Jyg MC (B). Many candidate genes that may be associated with the establishment of brain metastasis from breast cancer were included. Interestingly, we found that the expression of astrocyte derived cytokine receptors (IL-6 receptor, TGF-beta receptor and IGF receptor) were significantly increased in Jyg MC (B) cells. These results were confirmed by RT-PCR.ConclusionThese results suggest that cytokines produced by glial cellsin vivo may contribute, in a paracrine manner, to the development of brain metastases from breast cancer cells.

A normalization strategy for comparing tag count data

BackgroundHigh-throughput sequencing, such as ribonucleic acid sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) analyses, enables various features of organisms to be compared through tag counts. Recent studies have demonstrated that the normalization step for RNA-seq data is critical for a more accurate subsequent analysis of differential gene expression. Development of a more robust normalization method is desirable for identifying the true difference in tag count data.ResultsWe describe a strategy for normalizing tag count data, focusing on RNA-seq. The key concept is to remove data assigned as potential differentially expressed genes (DEGs) before calculating the normalization factor. Several R packages for identifying DEGs are currently available, and each package uses its own normalization method and gene ranking algorithm. We compared a total of eight package combinations: four R packages (edgeR, DESeq, baySeq, and NBPSeq) with their default normalization settings and with our normalization strategy. Many synthetic datasets under various scenarios were evaluated on the basis of the area under the curve (AUC) as a measure for both sensitivity and specificity. We found that packages using our strategy in the data normalization step overall performed well. This result was also observed for a real experimental dataset.ConclusionOur results showed that the elimination of potential DEGs is essential for more accurate normalization of RNA-seq data. The concept of this normalization strategy can widely be applied to other types of tag count data and to microarray data.

Gene Expression Profile of Hyperoxic and Hypoxic Retinas in a Mouse Model of Oxygen-Induced Retinopathy

PURPOSE To determine a profile of gene expression in retinas of a murine model of oxygen-induced retinopathy (OIR). METHODS OIR was induced in C57BL/6N mice by exposing postnatal day (P)7 pups to 75% oxygen for 5 days and then returning them to room air at P12. Gene microarrays containing more than 47,000 transcripts were used to study the changes in gene expression in retinas isolated immediately (P12) and at 12 hours (P12.5) after exposure to hyperoxia. The retinas of P12 mice raised under normoxic conditions served as control subjects. Quantitative RT-PCR and multiplex ELISA were performed to validate the microarray analyses. RESULTS The expression of 83 gene transcripts was significantly altered in the hyperoxic P12 retinas. These genes were classified as cellular components or were associated with development, metabolism, transport, stress response, cell adhesion, inflammation, or vision. The genes related to retinal growth, such as Pdgfb and Robo4, which are associated with vascular development, were downregulated. In contrast, the expression levels of 95 genes were significantly altered in the hypoxic P12.5 retinas, which contained several known hypoxia-regulated genes including Vegfa and Hif1a. The differentially expressed genes were broadly clustered into the development, inflammation, metabolism, signaling, antiapoptosis, cellular component, transport, glycolysis, and vision groups. Those associated with organogenesis (e.g., Vegfa, Igfbp3, Tnfrsf12a, and Nestin) and to inflammation (e.g., Ccl3, Ccl4, and MHCs) were upregulated. The results of quantitative RT-PCR and multiplex ELISA were in agreement with the microarray data. CONCLUSIONS These alterations in gene expression may determine the hyperoxic growth retardation, postischemic inflammation, neovascularization, and remodeling in retinas of murine OIR.

Response of the Pseudomonas host chromosomal transcriptome to carriage of the IncP-7 plasmid pCAR1

Plasmid carriage requires appropriate expression of the genes on the plasmid or host chromosome through cooperative transcriptional regulation. To clarify the impact of plasmid carriage on the host chromosome, we compared the chromosomal RNA maps of plasmid-free and plasmid-containing host strains using the incompatibility group P-7 archetype plasmid pCAR1, which is involved in carbazole degradation, and three distinct Pseudomonas strains. The possession of pCAR1 altered gene expression related to the iron acquisition systems in each host. Expression of the major siderophore pyoverdine was greater in plasmid-containing P. putida KT2440 and P. aeruginosa PAO1 than in the plasmid-free host strains, in part due to the expression of carbazole-degradative genes on pCAR1. The mexEFoprN operon encoding an efflux pump of the resistance-nodulation-cell division family was specifically upregulated by the carriage of pCAR1 in P. putida KT2440, whereas the expression of orthologous genes in the other species remained unaltered. Induction of the mexEFoprN genes increased the resistance of pCAR1-containing KT2440 to chloramphenicol compared with pCAR1-free KT2440. Our findings indicate that the possession of pCAR1 altered the growth rate of the host via the expression of genes on pCAR1 and the host chromosomes.