Biomedical subjects

Jun Sese

Publications and source records attributed to Jun Sese.

9 recordsLinked to original sources

PubMed2006

A large-scale full-length cDNA analysis to explore the budding yeast transcriptome.

We performed a large-scale cDNA analysis to explore the transcriptome of the budding yeast Saccharomyces cerevisiae. We sequenced two cDNA libraries, one from the cells exponentially growing in a minimal medium and the other from meiotic cells. Both libraries were generated by using a vector-capping method that allows the accurate mapping of transcription start sites (TSSs). Consequently, we identified 11,575 TSSs associated with 3,638 annotated genomic features, including 3,599 ORFs, to suggest that most yeast genes have two or more TSSs. In addition, we identified 45 previously undescribed introns, including those affecting current ORF annotations and those spliced alternatively. Furthermore, the analysis revealed 667 transcription units in the intergenic regions and transcripts derived from antisense strands of 367 known features. We also found that 348 ORFs carry TSSs in their 3'-halves to generate sense transcripts starting from inside the ORFs. These results indicate that the budding yeast transcriptome is considerably more complex than previously thought, and it shares many recently revealed characteristics with the transcriptomes of mammals and other higher eukaryotes. Thus, the genome-wide active transcription that generates novel classes of transcripts appears to be an intrinsic feature of the eukaryotic cells. The budding yeast will serve as a versatile model for the studies on these aspects of transcriptome, and the full-length cDNA clones can function as an invaluable resource in such studies.

5' Untranslated Regions↗

PubMed2006

Expression profiling of muscles from Fukuyama-type congenital muscular dystrophy and laminin-alpha 2 deficient congenital muscular dystrophy; is congenital muscular dystrophy a primary fibrotic disease?

Fukuyama-type congenital muscular dystrophy (FCMD) and laminin-alpha2 deficient congenital muscular dystrophy (MDC1A) are congenital muscular dystrophies (CMDs) and they both are categorized into the same clinical entity of muscular dystrophy as Duchenne muscular dystrophy (DMD). All three disorders share a common etiologic defect in the dystrophin-glycoprotein complex, which connects muscle structural proteins with the extracellular basement membrane. To investigate the pathophysiology of these CMDs, we generated microarray gene expression profiles of skeletal muscle from patients in various clinical stages. Despite diverse pathological changes, the correlation coefficient of overall gene expression among these samples was considerably high. We performed a multi-dimensional statistical analysis, the Distillation, to extract determinant genes that distinguish CMD muscle from normal controls. Up-regulated genes were primarily extracellular matrix (ECM) components, whereas down-regulated genes included structural components of mature muscle. These observations reflect active interstitial fibrosis with less active regeneration of muscle cell components in the CMDs, characteristics that are clearly distinct from those of DMD. Although the severity of fibrosis varied among the specimens tested, ECM gene expression was consistently high without substantial changes through the clinical course. Further, in situ hybridization showed more prominent ECM gene expression on muscle cells than on interstitial tissue cells, suggesting that ECM components are induced by regeneration process rather than by 'dystrophy.' These data imply that the etiology of FCMD and MDC1A differs from that of the chronic phase of classical muscular dystrophy, and the major pathophysiologic change in CMDs might instead result from primary active fibrosis.

Child↗

PubMed2006

Evaluation of image processing programs for accurate measurement of budding and fission yeast morphology.

To study the cellular functions of gene products, various yeast morphological mutants have been investigated. To describe yeast morphology objectively, we have developed image processing programs for budding and fission yeast. The programs, named CalMorph for budding yeast and F-CalMorph for fission yeast, directly process microscopic images and generate quantitative data about yeast cell shape, nuclear shape and location, and actin distribution. Using CalMorph, we can easily and quickly obtain various quantitative data reproducibly. To study the utility and reliability of CalMorph, we evaluated its data in three ways: (1) The programs extracted three-dimensional bud information from two-dimensional digital images with a low error rate (<1%). (2) The absolute values of the diameters of manufactured fluorescent beads calculated with CalMorph were very close to those given in the manufacturer's data sheet. (3) The programs generated reproducible data consistent with that obtained by hand. Based on these results, we determined that CalMorph could monitor yeast morphological changes accompanied by the progression of the cell cycle. We discuss the potential of the CalMorph series as a novel tool for the analysis of yeast cell morphology.

Cell Division↗

PubMed2005

High-dimensional and large-scale phenotyping of yeast mutants.

One of the most powerful techniques for attributing functions to genes in uni- and multicellular organisms is comprehensive analysis of mutant traits. In this study, systematic and quantitative analyses of mutant traits are achieved in the budding yeast Saccharomyces cerevisiae by investigating morphological phenotypes. Analysis of fluorescent microscopic images of triple-stained cells makes it possible to treat morphological variations as quantitative traits. Deletion of nearly half of the yeast genes not essential for growth affects these morphological traits. Similar morphological phenotypes are caused by deletions of functionally related genes, enabling a functional assignment of a locus to a specific cellular pathway. The high-dimensional phenotypic analysis of defined yeast mutant strains provides another step toward attributing gene function to all of the genes in the yeast genome.

Actins↗

PubMed2005

Data mining tools for the Saccharomyces cerevisiae morphological database.

For comprehensive understanding of precise morphological changes resulting from loss-of-function mutagenesis, a large collection of 1,899,247 cell images was assembled from 91,71 micrographs of 4782 budding yeast disruptants of non-lethal genes. All the cell images were processed computationally to measure approximately 500 morphological parameters in individual mutants. We have recently made this morphological quantitative data available to the public through the Saccharomyces cerevisiae Morphological Database (SCMD). Inspecting the significance of morphological discrepancies between the wild type and the mutants is expected to provide clues to uncover genes that are relevant to the biological processes producing a particular morphology. To facilitate such intensive data mining, a suite of new software tools for visualizing parameter value distributions was developed to present mutants with significant changes in easily understandable forms. In addition, for a given group of mutants associated with a particular function, the system automatically identifies a combination of multiple morphological parameters that discriminates a mutant group from others significantly, thereby characterizing the function effectively. These data mining functions are available through the World Wide Web at http://scmd.gi.k.u-tokyo.ac.jp/.

Computer Graphics↗

PubMed2005

5'SAGE: 5'-end Serial Analysis of Gene Expression database.

To comprehensively identify transcription start sites and the frequencies of individual mRNAs in human cell libraries, a method of 5' end Serial Analysis of Gene Expression (SAGE) was developed recently, which makes it possible to collect a large amount of start site information, and subsequently, we have established a related database server called 5'SAGE. This database displays the observed frequencies of individual 5' end SAGE tags and previously unknown transcription start sites in the promoter regions, introns and intergenic regions of known genes. 5'SAGE will be useful for analyzing promoter regions and start site variation in different tissues, and is freely available at http://5sage.gi.k.u-tokyo.ac.jp/.

5' Flanking Region↗

PubMed2004

5'-end SAGE for the analysis of transcriptional start sites.

Identification of the mRNA start site is essential in establishing the full-length cDNA sequence of a gene and analyzing its promoter region, which regulates gene expression. Here we describe the development of a 5'-end serial analysis of gene expression (5' SAGE) that can be used to globally identify transcriptional start sites and the frequency of individual mRNAs. Of the 25,684 5' SAGE tags in the HEK293 human cell library, 19,893 matched to the human genome. Among 15,448 tags in one locus of the genome, 85.8%-96.1% of the 5' SAGE tags were assigned within -500 to +200 nt of mRNA start sites using the RefSeq, UniGene and DBTSS databases. This technique should facilitate 5'-end transcriptome analysis in a variety of cells and tissues.

5' Flanking Region↗

PubMed2004

Constrained clusters of gene expression profiles with pathological features.

MOTIVATION: Gene expression profiles should be useful in distinguishing variations in disease, since they reflect accurately the status of cells. The primary clustering of gene expression reveals the genotypes that are responsible for the proximity of members within each cluster, while further clustering elucidates the pathological features of the individual members of each cluster. However, since the first clustering process and the second classification step, in which the features are associated with clusters, are performed independently, the initial set of clusters may omit genes that are associated with pathologically meaningful features. Therefore, it is important to devise a way of identifying gene expression clusters that are associated with pathological features. RESULTS: We present the novel technique of 'itemset constrained clustering' (IC-Clustering), which computes the optimal cluster that maximizes the interclass variance of gene expression between groups, which are divided according to the restriction that only divisions that can be expressed using common features are allowed. This constraint automatically labels each cluster with a set of pathological features which characterize that cluster. When applied to liver cancer datasets, IC-Clustering revealed informative gene expression clusters, which could be annotated with various pathological features, such as 'tumor' and 'man', or 'except tumor' and 'normal liver function'. In contrast, the k-means method overlooked these clusters.

Algorithms↗

PubMed2003

Gene expression profile in human leukocytes.

Leukocytes are classified as myelocytic or lymphocytic, and each class of leukocytes consists of several types of cells that have different phenotypes and different roles. To define the gene expression in these cells, we have performed serial analysis of gene expression (SAGE) using human leukocytes and have provided the gene database for these cells not only at the resting stage but also at the activated stage. A total of 709,990 tags from 17 libraries were analyzed for the manifestation of gene expression profiles in various types of human leukocytes. Types of leukocytes analyzed were as follows: peripheral blood monocytes, colony-stimulating factor-induced macrophages, monocyte-derived immature dendritic cells, mature/activated dendritic cells, granulocytes, natural killer (NK) cells, resting B cells, activated B cells, naive T cells, CCR4(-) memory T cells (resting T(H)1 cells), CCR4(+) memory T cells (resting T(H)2 cells), activated T(H)1 cells, and activated T(H)2 cells. Among 38,961 distinct tags that appeared more than once in the combined total libraries, 27,323 tags were found to represent unique genes in certain type(s) of leukocytes. Using probability (P) and hierarchical clustering analysis, we identified the genes selectively expressed in each type of leukocytes. Identification of the genes specifically expressed in different types of leukocytes provides not only a novel molecular signature to define different subsets of resting and activated cells but also contributes to further understanding of the biologic function of leukocytes in the host defense system.

Databases, Factual↗