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.
Abstract Background: The importance of gene inactivation by promoter CpG island hypermethylation has encouraged the unearthing of silenced tumor suppressor genes (TSGs) in various cancers. We aimed to identify and examine novel methylation silenced TSGs in colorectal cancer (CRC). Method: We employed Oligonucleotide microarray to find changes in global gene expression of five colon cancer cell lines (HCT116, RKO, Colo320, SW480, and HT29) that were analyzed before and after treatment with the demethylating agent 5-aza-2′-Deoxycitidine (5-aza-dC), expression of the responding genes was compared with microarray expression data of CRC tissue samples. Five genes (ASPP1, GADD45B, LIFR, SCARA5, and THSD1), which had a putative 5′ CpG islands in their promoter, were subjected to methylation-specific PCR (MSP) using 13 colon cell lines and 23 paired tumor and normal CRC tissues. Result: Seventy-two genes were up-regulated in demethylated cell lines and were simultaneously down-regulated in primary colorectal carcinomas, including genes that were known to be frequently hypermethylated and silenced in CRC. Among 13 colon cancer cell lines, hypermethylation was identified for three of five candidate genes, LIFR (61%), SCARA5 (15%), and THSD1 (23%). The methylation status of LIFR, SCARA5, and THSD1 was subsequently investigated in 23 colorectal tumors, hypermethylation was detected in (87%), (17%), and (9%), respectively. Data from normal colorectal mucosa demonstrated that the observed promoter hypermethylation was cancer-specific in the latter two genes. Conclusion: The use of a refined microarray screening led to the identification of two TSGs affected by hypermethylation with a cancer-specific manner in CRC. SCARA5 and THSD1 may have a role in CRC tumorigenesis. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the Second AACR International Conference on Frontiers in Basic Cancer Research; 2011 Sep 14-18; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2011;71(18 Suppl):Abstract nr C18.
Colorectal cancer (CRC) is one of the most frequently occurring cancers in Japan, and thus a wide range of methods have been deployed to study the molecular mechanisms of CRC. In this study, we performed a comprehensive analysis of CRC, incorporating copy number aberration (CRC) and gene expression data. For the last four years, we have been collecting data from CRC cases and organizing the information as an "omics" study by integrating many kinds of analysis into a single comprehensive investigation. In our previous studies, we had experienced difficulty in finding genes related to CRC, as we observed higher noise levels in the expression data than in the data for other cancers. Because chromosomal aberrations are often observed in CRC, here, we have performed a combination of CNA analysis and expression analysis in order to identify some new genes responsible for CRC. This study was performed as part of the Clinical Omics Database Project at Tokyo Medical and Dental University. The purpose of this study was to investigate the mechanism of genetic instability in CRC by this combination of expression analysis and CNA, and to establish a new method for the diagnosis and treatment of CRC.Comprehensive gene expression analysis was performed on 79 CRC cases using an Affymetrix Gene Chip, and comprehensive CNA analysis was performed using an Affymetrix DNA Sty array. To avoid the contamination of cancer tissue with normal cells, laser micro-dissection was performed before DNA/RNA extraction. Data analysis was performed using original software written in the R language.We observed a high percentage of CNA in colorectal cancer, including copy number gains at 7, 8q, 13 and 20q, and copy number losses at 8p, 17p and 18. Gene expression analysis provided many candidates for CRC-related genes, but their association with CRC did not reach the level of statistical significance. The combination of CNA and gene expression analysis, together with the clinical information, suggested UGT2B28, LOC440995, CXCL6, SULT1B1, RALBP1, TYMS, RAB12, RNMT, ARHGDIB, S1000A2, ABHD2, OIT3 and ABHD12 as genes that are possibly associated with CRC. Some of these genes have already been reported as being related to CRC. TYMS has been reported as being associated with resistance to the anti-cancer drug 5-fluorouracil, and we observed a copy number increase for this gene. RALBP1, ARHGDIB and S100A2 have been reported as oncogenes, and we observed copy number increases in each. ARHGDIB has been reported as a metastasis-related gene, and our data also showed copy number increases of this gene in cases with metastasis.The combination of CNA analysis and gene expression analysis was a more effective method for finding genes associated with the clinicopathological classification of CRC than either analysis alone. Using this combination of methods, we were able to detect genes that have already been associated with CRC. We also identified additional candidate genes that may be new markers or targets for this form of cancer.
Abstract Background Identification of novel therapeutic targets is a key for successful drug development. However, the cost to experimentally identify therapeutic targets is huge and only 400 genes are targets for FDA-approved drugs. Therefore, it is inevitable to develop powerful computational tools to identify potential novel therapeutic targets. Because proteins make their functions together with their interacting partners, a protein-protein interaction network (PIN) in human could be a useful resource to build computational tools to investigate potential targets for therapeutic drugs. Network embedding methods, especially deep-learning based methods would be useful tools to extract an informative low-dimensional latent space that contains enough information required to fully represent original high-dimensional non-linear data of PINs. Results In this study, we developed a deep learning based computational framework that extracts low-dimensional latent space embedded in high-dimensional data of the human PIN and uses the features in the latent space (latent features) to infer potential novel targets for therapeutic drugs. We examined the relationships between the latent features and the representative network metrics and found that the network metrics can explain a large number of the latent features, while several latent features do not correlate with all the network metrics. The results indicate that the features are likely to capture information that the representative network metrics can not capture, while the latent features also can capture information obtained from the network metrics. Our computational framework uses the latent features together with state-of-the-art machine learning techniques to infer potential drug target genes. We applied our computational framework to prioritized novel putative target genes for Alzheimer’s disease and successfully identified key genes for potential novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we inferred repositionable candidate-compounds for the disease (e.g., Tamoxifen, Bosutinib, and Dasatinib) Discussions Our computational framework could be powerful computational tools to efficiently prioritize new therapeutic targets and drug repositioning. It is pertinent to note here that our computational platform is easily applicable to investigate novel potential targets and repositionable compounds for any diseases, especially for rare diseases.
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.
Understanding anatomical structures and biological functions based on gene expression is critical in a systemic approach to address the complexity of the mammalian brain, where >25 000 genes are expressed in a precise manner. Co-expressed genes are thought to regulate cell type- or region-specific brain functions. Thus, well-designed data acquisition and visualization systems for profiling combinatorial gene expression in relation to anatomical structures are crucial. To this purpose, using our techniques of microtomy-based gene expression measurements and WebGL-based visualization programs, we mapped spatial expression densities of genome-wide transcripts to the 3D coordinates of mouse brains at four post-natal stages, and built a database, ViBrism DB (http://vibrism.neuroinf.jp/). With the DB platform, users can access a total of 172 022 expression maps of transcripts, including coding, non-coding and lncRNAs in the whole context of 3D magnetic resonance (MR) images. Co-expression of transcripts is represented in the image space and in topological network graphs. In situ hybridization images and anatomical area maps are browsable in the same space of 3D expression maps using a new browser-based 2D/3D viewer, BAH viewer. Created images are shareable using URLs, including scene-setting parameters. The DB has multiple links and is expandable by community activity.
Abstract Background Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective. Methods In this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes. Results We applied our computational framework to prioritize novel putative target genes for Alzheimer’s disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib). Conclusions Our deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.
To reveal gene-environment interactions underlying common diseases and estimate the risk for common diseases, the Tohoku Medical Megabank (TMM) project has conducted prospective cohort studies and genomic and multiomics analyses. To establish an integrated biobank, we developed an integrated database called "dbTMM" that incorporates both the individual cohort/clinical data and the genome/multiomics data of 157,191 participants in the Tohoku Medical Megabank project. To our knowledge, dbTMM is the first database to store individual whole-genome data on a variant-by-variant basis as well as cohort/clinical data for over one hundred thousand participants in a prospective cohort study. dbTMM enables us to stratify our cohort by both genome-wide genetic factors and environmental factors, and it provides a research and development platform that enables prospective analysis of large-scale data from genome cohorts.
Event Abstract Back to Event Integrated Analysis of Anatomical Gene Expression Maps and Co-Expression Networks Using a Database, ViBrism Yuko Okamura-Oho1*, Kazuro Shimokawa2, Satoko Takemoto3, Gang Song4, James Gee4 and Hideo Yokota3 1 BReNt-Brain Research Network and Advanced Science Institute, RIKEN, Japan 2 Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Japan 3 Bio-research Infrastructure Construction Team, Advanced Science Institute, RIKEN , Japan 4 Penn Image Computing and Science Laboratory, University of Pennsylvania, United States Detection of gene expression-anatomy association in biological structure is crucial for understanding its function based on the molecular and genetic/genomic information. Particularly in the mammalian brain where there are estimated 25,000 genes expressed, systematic and comprehensive quantification of the expression densities in the whole three-dimensional (3D) anatomical context is critical. The combinatorial number of randomly selected genes is more than the cell number in the brain, which assumes that non-random combinatorial gene expression underlies the formation of a wide variety of functional brain regions composed of multiple cells. To determine the association systematically, we have introduced a novel framework, Transcriptome Tomography, for spatially integrating comprehensive endogenous gene expression within an isotropic anatomical context. Using this rapid and unbiased 3D mapping technique, in the first instance, we have generated a dataset of 36,000 maps covering the whole mouse brain (ViBrism: http://vibrism.riken.jp/3dviewer/ex/index.html ) and validated them against existing data with respect to the expression location and density (paper submitted). Here, we used an informatics approach to identify the combinatorial gene expression as a broad co-expression network. The gene network links covering the whole brain followed an inverse-power law and were rich in functional interaction and gene ontology terms. Developmentally conserved co-expression modules underlie the network structure. To demonstrate the relevance of the finding, we mined Huntington's disease gene (Htt) and found a novel disease-related co-expression network containing genes potentially co-functioning with Htt in neural differentiation and modulating the disease specific differential vulnerability in brain regions. The maps are spatially isotropic and well suited to analysis in the standard space for brain-atlas databases, e.g. Waxholm Space (PLoS Comput Biol 2011, 7[2]: e1001065) as shown in the related poster by J. Boline et., al. Our time and cost effective framework will facilitate research creating and using open-resources for a molecular-based understanding of complex structures. A part of this work was conducted within the Waxholm Space Task Force of the International Neuroinformatics Coordinating Facility (INCF) Program on Digital Brain Atlasing. We thank the program members, particularly, R. Baldock, I. Zaslavsky, L.Ibanez and J. Boline. Keywords: digital atlasing, Gene Expression, Network analysis, mammalian brain development, computational neuroscience Conference: 5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012. Presentation Type: Poster Topic: Neuroinformatics Citation: Okamura-Oho Y, Shimokawa K, Takemoto S, Song G, Gee J and Yokota H (2014). Integrated Analysis of Anatomical Gene Expression Maps and Co-Expression Networks Using a Database, ViBrism. Front. Neuroinform. Conference Abstract: 5th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2014.08.00079 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 Mar 2013; Published Online: 27 Feb 2014. * Correspondence: Dr. Yuko Okamura-Oho, BReNt-Brain Research Network and Advanced Science Institute, RIKEN, Zushi-shi, Japan, yoho-tky@umin.ac.jp Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Yuko Okamura-Oho Kazuro Shimokawa Satoko Takemoto Gang Song James Gee Hideo Yokota Google Yuko Okamura-Oho Kazuro Shimokawa Satoko Takemoto Gang Song James Gee Hideo Yokota Google Scholar Yuko Okamura-Oho Kazuro Shimokawa Satoko Takemoto Gang Song James Gee Hideo Yokota PubMed Yuko Okamura-Oho Kazuro Shimokawa Satoko Takemoto Gang Song James Gee Hideo Yokota Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. 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