Yasunobu Okamura

ATTED-II in 2016: A Plant Coexpression Database Towards Lineage-Specific Coexpression

ATTED-II (http://atted.jp) is a coexpression database for plant species with parallel views of multiple coexpression data sets and network analysis tools. The user can efficiently find functional gene relationships and design experiments to identify gene functions by reverse genetics and general molecular biology techniques. Here, we report updates to ATTED-II (version 8.0), including new and updated coexpression data and analysis tools. ATTED-II now includes eight microarray- and six RNA sequencing-based coexpression data sets for seven dicot species (Arabidopsis, field mustard, soybean, barrel medick, poplar, tomato and grape) and two monocot species (rice and maize). Stand-alone coexpression analyses tend to have low reliability. Therefore, examining evolutionarily conserved coexpression is a more effective approach from the viewpoints of reliability and evolutionary importance. In contrast, the reliability of species-specific coexpression data remains poor. Our assessment scores for individual coexpression data sets indicated that the quality of the new coexpression data sets in ATTED-II is higher than for any previous coexpression data set. In addition, five species (Arabidopsis, soybean, tomato, rice and maize) in ATTED-II are now supported by both microarray- and RNA sequencing-based coexpression data, which has increased the reliability. Consequently, ATTED-II can now provide lineage-specific coexpression information. As an example of the use of ATTED-II to explore lineage-specific coexpression, we demonstrate monocot- and dicot-specific coexpression of cell wall genes. With the expanded coexpression data for multilevel evaluation, ATTED-II provides new opportunities to investigate lineage-specific evolution in plants.

ATTED-II in 2014: Evaluation of Gene Coexpression in Agriculturally Important Plants

ATTED-II (http://atted.jp) is a database of coexpressed genes that was originally developed to identify functionally related genes in Arabidopsis and rice. Herein, we describe an updated version of ATTED-II, which expands this resource to include additional agriculturally important plants. To improve the quality of the coexpression data for Arabidopsis and rice, we included more gene expression data from microarray and RNA sequencing studies. The RNA sequencing-based coexpression data now cover 94% of the Arabidopsis protein-encoding genes, representing a substantial increase from previously available microarray-based coexpression data (76% coverage). We also generated coexpression data for four dicots (soybean, poplar, grape and alfalfa) and one monocot (maize). As both the quantity and quality of expression data for the non-model species are generally poorer than for the model species, we verified coexpression data associated with these new species using multiple methods. First, the overall performance of the coexpression data was evaluated using gene ontology annotations and the coincidence of a genomic feature. Secondly, the reliability of each guide gene was determined by comparing coexpressed gene lists between platforms. With the expanded and newly evaluated coexpression data, ATTED-II represents an important resource for identifying functionally related genes in agriculturally important plants.

COXPRESdb in 2015: coexpression database for animal species by DNA-microarray and RNAseq-based expression data with multiple quality assessment systems

The COXPRESdb (http://coxpresdb.jp) provides gene coexpression relationships for animal species. Here, we report the updates of the database, mainly focusing on the following two points. For the first point, we added RNAseq-based gene coexpression data for three species (human, mouse and fly), and largely increased the number of microarray experiments to nine species. The increase of the number of expression data with multiple platforms could enhance the reliability of coexpression data. For the second point, we refined the data assessment procedures, for each coexpressed gene list and for the total performance of a platform. The assessment of coexpressed gene list now uses more reasonable P-values derived from platform-specific null distribution. These developments greatly reduced pseudo-predictions for directly associated genes, thus expanding the reliability of coexpression data to design new experiments and to discuss experimental results.

COXPRESdb: a database of comparative gene coexpression networks of eleven species for mammals

Coexpressed gene databases are valuable resources for identifying new gene functions or functional modules in metabolic pathways and signaling pathways. Although coexpressed gene databases are a fundamental platform in the field of plant biology, their use in animal studies is relatively limited. The COXPRESdb (http://coxpresdb.jp) provides coexpression relationships for multiple animal species, as comparisons of coexpressed gene lists can enhance the reliability of gene coexpression determinations. Here, we report the updates of the database, mainly focusing on the following two points. First, we updated our coexpression data by including recent microarray data for the previous seven species (human, mouse, rat, chicken, fly, zebrafish and nematode) and adding four new species (monkey, dog, budding yeast and fission yeast), along with a new human microarray platform. A reliability scoring function was also implemented, based on coexpression conservation to filter out coexpression with low reliability. Second, the network drawing function was updated, to implement automatic cluster analyses with enrichment analyses in Gene Ontology and in cis elements, along with interactive network analyses with Cytoscape Web. With these updates, COXPRESdb will become a more powerful tool for analyses of functional and regulatory networks of genes in a variety of animal species.

BioHackathon series in 2011 and 2012: penetration of ontology and linked data in life science domains

The application of semantic technologies to the integration of biological data and the interoperability of bioinformatics analysis and visualization tools has been the common theme of a series of annual BioHackathons hosted in Japan for the past five years. Here we provide a review of the activities and outcomes from the BioHackathons held in 2011 in Kyoto and 2012 in Toyama. In order to efficiently implement semantic technologies in the life sciences, participants formed various sub-groups and worked on the following topics: Resource Description Framework (RDF) models for specific domains, text mining of the literature, ontology development, essential metadata for biological databases, platforms to enable efficient Semantic Web technology development and interoperability, and the development of applications for Semantic Web data. In this review, we briefly introduce the themes covered by these sub-groups. The observations made, conclusions drawn, and software development projects that emerged from these activities are discussed.

ALCOdb: Gene Coexpression Database for Microalgae

In the era of energy and food shortage, microalgae have gained much attention as promising sources of biofuels and food ingredients. However, only a small fraction of microalgal genes have been functionally characterized. Here, we have developed the Algae Gene Coexpression database (ALCOdb; http://alcodb.jp), which provides gene coexpression information to survey gene modules for a function of interest. ALCOdb currently supports two model algae: the green alga Chlamydomonas reinhardtii and the red alga Cyanidioschyzon merolae. Users can retrieve coexpression information for genes of interest through three unique data pages: (i) Coexpressed Gene List; (ii) Gene Information; and (iii) Coexpressed Gene Network. In addition to the basal coexpression information, ALCOdb also provides several advanced functionalities such as an expression profile viewer and a differentially expressed gene search tool. Using these user interfaces, we demonstrated that our gene coexpression data have the potential to detect functionally related genes and are useful in extrapolating the biological roles of uncharacterized genes. ALCOdb will facilitate molecular and biochemical studies of microalgal biological phenomena, such as lipid metabolism and organelle development, and promote the evolutionary understanding of plant cellular systems.

Establishment of Protocols for Global Metabolomics by LC-MS for Biomarker Discovery.

Metabolomics is a promising avenue for biomarker discovery. Although the quality of metabolomic analyses, especially global metabolomics (G-Met) using mass spectrometry (MS), largely depends on the instrumentation, potential bottlenecks still exist at several basic levels in the metabolomics workflow. Therefore, we established a precise protocol initially for the G-Met analyses of human blood plasma to overcome some these difficulties. In our protocol, samples are deproteinized in a 96-well plate using an automated liquid-handling system, and conducted either using a UHPLC-QTOF/MS system equipped with a reverse phase column or a LC-FTMS system equipped with a normal phase column. A normalization protocol of G-Met data was also developed to compensate for intra- and inter-batch differences, and the variations were significantly reduced along with our normalization, especially for the UHPLC-QTOF/MS data with a C18 reverse-phase column for positive ions. Secondly, we examined the changes in metabolomic profiles caused by the storage of EDTA-blood specimens to identify quality markers for the evaluation of the specimens’ pre-analytical conditions. Forty quality markers, including lysophospholipids, dipeptides, fatty acids, succinic acid, amino acids, glucose, and uric acid were identified by G-Met for the evaluation of plasma sample quality and established the equation of calculating the quality score. We applied our quality markers to a small-scale study to evaluate the quality of clinical samples. The G-Met protocols and quality markers established here should prove useful for the discovery and development of biomarkers for a wider range of diseases.

Comparison of Gene Coexpression Profiles and Construction of Conserved Gene Networks to Find Functional Modules

Background Computational approaches toward gene annotation are a formidable challenge, now that many genome sequences have been determined. Each gene has its own function, but complicated cellular functions are achieved by sets of genes. Therefore, sets of genes with strong functional relationships must be identified. For this purpose, the similarities of gene expression patterns and gene sequences have been separately utilized, although the combined information will provide a better solution. Result & Discussion We propose a new method to find functional modules, by comparing gene coexpression profiles among species. A coexpression pattern is represented as a list of coexpressed genes with each guide gene. We compared two coexpression lists, one from a human guide gene and the other from a homologous mouse gene, and defined a measure to evaluate the similarity between the lists. Based on this coexpression similarity, we detected the highly conserved genes, and constructed human gene networks with conserved coexpression between human and mouse. Some of the tightly coupled genes (modules) showed clear functional enrichment, such as immune system and cell cycle, indicating that our method could identify functionally related genes without any prior knowledge. We also found a few functional modules without any annotations, which may be good candidates for novel functional modules. All of the comparisons are available at the http://v1.coxsimdb.info web database.

Zinc finger–IRF composite elements bound by Ikaros/IRF4 complexes function as gene repression in plasma cell

The transcription factor (TF) interferon regulatory factor-4 (IRF4) promotes both germinal center (GC) reactions and plasma cell (PC) differentiation by binding to alternative DNA motifs including AP-1-IRF composite elements, Ets-IRF composite elements (EICEs), and interferon sequence response elements (ISREs). Although all of these motifs mediate transcriptional activation by IRF4, it is still unknown how some of the IRF4 target genes are downregulated upon PC differentiation. Here, we revealed a molecular mechanism of IRF4-mediated gene downregulation during PC differentiation. By combining IRF4 chromatin immunoprecipitation sequence and gene expression analysis, we identified zinc finger-IRF composite elements (ZICEs) in IRF4 binding regions aligned with genes whose expression was downregulated in PCs. The zinc finger TFs Ikaros and Aiolos were identified as IRF4 binding partners in PCs, and Ikaros but not Aiolos was essential for IRF4 binding to the ZICE sequence and for PC differentiation. The Ebf1 gene, which positively controls B-cell activation and GC reactions, was identified as one of the Ikaros/IRF4 target genes. Importantly, while the ZICE embeds the ISRE motif, IRF4 bound the ZICE motif as heterodimers with Ikaros for repression of target genes, which include Ebf1 In contrast, if the zinc finger motif is juxtaposed to the EICE motif, the Ikaros/PU.1/IRF4 complex functioned to activate target gene expression. Our findings revealed a novel mode of IRF4 activity upon PC differentiation where upon forming an Ikaros/IRF4 DNA-bound complex, a subset of genes is repressed.

Inflammatory responses induce an identity crisis of alveolar macrophages, leading to pulmonary alveolar proteinosis

Pulmonary alveolar proteinosis (PAP) is a severe respiratory disease characterized by dyspnea caused by accumulation of surfactant protein. Dysfunction of alveolar macrophages (AMs), which regulate the homeostasis of surfactant protein, leads to the development of PAP; for example, in mice lacking BTB and CNC homology 2 (Bach2). However, how Bach2 helps prevent PAP is unknown, and the cell-specific effects of Bach2 are undefined. Using mice lacking Bach2 in specific cell types, we found that the PAP phenotype of Bach2-deficient mice is due to Bach2 deficiency in more than two types of immune cells. Depletion of hyperactivated T cells in Bach2-deficient mice restored normal function of AMs and ameliorated PAP. We also found that, in Bach2-deficient mice, hyperactivated T cells induced gene expression patterns that are specific to other tissue-resident macrophages and dendritic cells. Moreover, Bach2-deficient AMs exhibited a reduction in cell cycle progression. IFN-γ released from T cells induced Bach2 expression in AMs, in which Bach2 then bound to regulatory regions of inflammation-associated genes in myeloid cells. Of note, in AMs, Bach2 restricted aberrant responses to excessive T cell-induced inflammation, whereas, in T cells, Bach2 puts a brake on T cell activation. Moreover, Bach2 stimulated the expression of multiple histone genes in AMs, suggesting a role of Bach2 in proper histone expression. We conclude that Bach2 is critical for the maintenance of AM identity and self-renewal in inflammatory environments. Treatments targeting T cells may offer new therapeutic strategies for managing secondary PAP.