Kazuro Shimokawa

Genome-wide analysis of mammalian promoter architecture and evolution

Mammalian promoters can be separated into two classes, conserved TATA box–enriched promoters, which initiate at a well-defined site, and more plastic, broad and evolvable CpG-rich promoters. We have sequenced tags corresponding to several hundred thousand transcription start sites (TSSs) in the mouse and human genomes, allowing precise analysis of the sequence architecture and evolution of distinct promoter classes. Different tissues and families of genes differentially use distinct types of promoters. Our tagging methods allow quantitative analysis of promoter usage in different tissues and show that differentially regulated alternative TSSs are a common feature in protein-coding genes and commonly generate alternative N termini. Among the TSSs, we identified new start sites associated with the majority of exons and with 3′ UTRs. These data permit genome-scale identification of tissue-specific promoters and analysis of the cis-acting elements associated with them.

Absolute expression values for mouse transcripts: re-annotation of the READ expression database by the use of CAGE and EST sequence tags

The RIKEN expression array database (READ) provides comprehensive gene expression data for the mouse, which were obtained as relative values from microarray double‐staining experiments with E17.5 mRNA as common reference. To assign absolute expression values for mouse transcripts within READ, we applied the E17.5 reference sample to CAGE (cap analysis of gene expression) and expressed sequence tag (EST) high‐throughput tag sequencing. Newly assigned values within the READ database were validated by comparison to expression data from serial analysis of gene expression, CAGE and EST experiments. These experiments confirmed the great significance of the absolute expression values within the improved READ database. The new Absolute READ database on absolute expression data is available under http://genome.gsc.riken.jp/absolute.

Transcriptome Tomography for Brain Analysis in the Web-Accessible Anatomical Space

Increased information on the encoded mammalian genome is expected to facilitate an integrated understanding of complex anatomical structure and function based on the knowledge of gene products. Determination of gene expression-anatomy associations is crucial for this understanding. To elicit the association in the three-dimensional (3D) space, we introduce a novel technique for comprehensive mapping of endogenous gene expression into a web-accessible standard space: Transcriptome Tomography. The technique is based on conjugation of sequential tissue-block sectioning, all fractions of which are used for molecular measurements of gene expression densities, and the block- face imaging, which are used for 3D reconstruction of the fractions. To generate a 3D map, tissues are serially sectioned in each of three orthogonal planes and the expression density data are mapped using a tomographic technique. This rapid and unbiased mapping technique using a relatively small number of original data points allows researchers to create their own expression maps in the broad anatomical context of the space. In the first instance we generated a dataset of 36,000 maps, reconstructed from data of 61 fractions measured with microarray, covering the whole mouse brain (ViBrism: http://vibrism.riken.jp/3dviewer/ex/index.html) in one month. After computational estimation of the mapping accuracy we validated the dataset against existing data with respect to the expression location and density. To demonstrate the relevance of the framework, we showed disease related expression of Huntington’s disease gene and Bdnf. Our tomographic approach is applicable to analysis of any biological molecules derived from frozen tissues, organs and whole embryos, and the maps are spatially isotropic and well suited to the analysis in the standard space (e.g. Waxholm Space for brain-atlas databases). This will facilitate research creating and using open-standards for a molecular-based understanding of complex structures; and will contribute to new insights into a broad range of biological and medical questions.

Expression analysis for inverted effects of serotonin transporter inactivation

Inactivation of serotonin transporter (HTT) by pharmacologically in the neonate or genetically increases risk for depression in adulthood, whereas pharmacological inhibition of HTT ameliorates symptoms in depressed patients. The differing role of HTT function during early development and in adult brain plasticity in causing or reversing depression remains an unexplained paradox. To address this we profiled the gene expression of adult Htt knockout (Htt KO) mice and HTT inhibitor-treated mice. Inverted profile changes between the two experimental conditions were seen in 30 genes. Consistent results of the upstream regulatory element search and the co-localization search of these genes indicated that the regulation may be executed by Pax5, Pax7 and Gata3, known to be involved in the survival, proliferation, and migration of serotonergic neurons in the developing brain, and these factors are supposed to keep functioning to regulate downstream genes related to serotonin system in the adult brain.

Large-scale clustering of CAGE tag expression data

BackgroundRecent analyses have suggested that many genes possess multiple transcription start sites (TSSs) that are differentially utilized in different tissues and cell lines. We have identified a huge number of TSSs mapped onto the mouse genome using the cap analysis of gene expression (CAGE) method. The standard hierarchical clustering algorithm, which gives us easily understandable graphical tree images, has difficulties in processing such huge amounts of TSS data and a better method to calculate and display the results is needed.ResultsWe use a combination of hierarchical and non-hierarchical clustering to cluster expression profiles of TSSs based on a large amount of CAGE data to profit from the best of both methods. We processed the genome-wide expression data, including 159,075 TSSs derived from 127 RNA samples of various organs of mouse, and succeeded in categorizing them into 70–100 clusters. The clusters exhibited intriguing biological features: a cluster supergroup with a ubiquitous expression profile, tissue-specific patterns, a distinct distribution of non-coding RNA and functional TSS groups.ConclusionOur approach succeeded in greatly reducing the calculation cost, and is an appropriate solution for analyzing large-scale TSS usage data.

A RecA-mediated exon profiling method

We have developed a RecA-mediated simple, rapid and scalable method for identifying novel alternatively spliced full-length cDNA candidates. This method is based on the principle that RecA proteins allow to carry radioisotope-labeled probe DNAs to their homologous sequences, resulting in forming triplexes. The resulting complex is easily detected by mobility difference on electrophoresis. We applied this exon profiling method to four selected mouse genes as a feasibility study. To design probes for detection, the information on known exonic regions was extracted from public database, RefSeq. Concerning the potentially transcribed novel exonic regions, RNA mapping experiment using Affymetrix tiling array was performed. As a result, we were able to identify alternative splice variants of Thioredoxin domain containing 5, Interleukin1β, Interleukin 1 family 6 and glutamine-rich hypothetical protein. In addition, full-length sequencing demonstrated that our method could profile exon structures with >90% accuracy. This reliable method can allow us to screen novel splice variants from a huge number of cDNA clone set effectively.

Broad Integration of Expression Maps and Co-Expression Networks Compassing Novel Gene Functions in the Brain

Using a recently invented technique for gene expression mapping in the whole-anatomy context, termed transcriptome tomography, we have generated a dataset of 36,000 maps of overall gene expression in the adult-mouse brain. Here, using an informatics approach, we identified a broad co-expression network that follows an inverse power law and is rich in functional interaction and gene-ontology terms. Our framework for the integrated analysis of expression maps and graphs of co-expression networks revealed that groups of combinatorially expressed genes, which regulate cell differentiation during development, were present in the adult brain and each of these groups was associated with a discrete cell types. These groups included non-coding genes of unknown function. We found that these genes specifically linked developmentally conserved groups in the network. A previously unrecognized robust expression pattern covering the whole brain was related to the molecular anatomy of key biological processes occurring in particular areas.

Calculation of Absolute Expression Values for DNA Microarray Data

INTRODUCTIONIn terms of cost per measurement, the use of DNA microarrays for comprehensive and quantitative expression measurements is vastly superior to other methods such as Northern blotting or quantitative reverse transcriptase polymerase chain reaction (QRT-PCR). However, the output values of DNA microarrays are not always highly reliable or accurate compared with other techniques, and the output data sometimes consist of measurements of relative expression (treated sample vs. untreated) rather than absolute expression values as desired. In effect, some measurements from some laboratories do not represent absolute expression values (such as the number of transcripts) and as such are experimentally deficient. To address the problem that some microarray data sets fail to reflect the number of mRNA molecules sufficiently in a given sample (i.e., fail to provide absolute expression levels), additional methods are required. The procedure described here provides a new method for converting microarray data to absolute expression values with the use of external data such as expressed sequence tags (ESTs) and cap analysis of gene expression (CAGE) tags.

Erratum: Genome-wide analysis of mammalian promoter architecture and evolution (Nature Genetics (2006) 38, (626-635))

Nat. Genet. 38, 626–635 (2006); published online 28 April 2006; corrected online 5 May 2006; corrected after print 29 August 2007 In the version of this article initially published, two of the smaller bar plots in Figure 1e were mistakenly duplicated. Specifically, the Zfp385 plot is an erroneous copy of the 137774 plot, and the Txndc7 plot is an erroneous copy of the Pik3r5 plot.

Calculation of Spot Reliability Evaluation Scores (SRED) for DNA Microarray Data

INTRODUCTIONIn terms of cost per measurement, the use of DNA microarrays for comprehensive and quantitative expression measurements is vastly superior to other methods such as Northern blotting or quantitative reverse transcriptase polymerase chain reaction (QRT-PCR). However, the output values of DNA microarrays are not always highly reliable or accurate compared with other techniques, and the output data sometimes consist of measurements of relative expression (treated sample vs. untreated) rather than absolute expression values as desired. In effect, some measurements from some laboratories do not represent absolute expression values (such as the number of transcripts) and as such are experimentally deficient. This protocol addresses one problem in some microarray data: the absence of accurate measurements. Spot reliability evaluation score for DNA microarrays (SRED) offers a reliability value for each spot in the microarray. SRED does not require an entire microarray to assess the reliability, but rather analyzes the reliability of individual spots of the microarray. The calculation of a reliability index can be used for different microarray systems, which facilitates the analysis of multiple microarray data sets from different experimental platforms.