RNA-seq Transcriptional Profiling of an Arbuscular Mycorrhiza Provides Insights into Regulated and Coordinated Gene Expression in Lotus japonicus and Rhizophagus irregularis

Gene expression during arbuscular mycorrhizal development is highly orchestrated in both plants and arbuscular mycorrhizal fungi. To elucidate the gene expression profiles of the symbiotic association, we performed a digital gene expression analysis of Lotus japonicus and Rhizophagus irregularis using a HiSeq 2000 next-generation sequencer with a Cufflinks assembly and de novo transcriptome assembly. There were 3,641 genes differentially expressed during arbuscular mycorrhizal development in L. japonicus, approximately 80% of which were up-regulated. The up-regulated genes included secreted proteins, transporters, proteins involved in lipid and amino acid metabolism, ribosomes and histones. We also detected many genes that were differentially expressed in small-secreted peptides and transcription factors, which may be involved in signal transduction or transcription regulation during symbiosis. Coregulated genes between arbuscular mycorrhizal and root nodule symbiosis were not particularly abundant, but transcripts encoding for membrane traffic-related proteins, transporters and iron transport-related proteins were found to be highly co-up-regulated. In transcripts of arbuscular mycorrhizal fungi, expansion of cytochrome P450 was observed, which may contribute to various metabolic pathways required to accommodate roots and soil. The comprehensive gene expression data of both plants and arbuscular mycorrhizal fungi provide a powerful platform for investigating the functional and molecular mechanisms underlying arbuscular mycorrhizal symbiosis.ArticlePLANT AND CELL PHYSIOLOGY. 56(8):1490-1511 (2015)journal articl

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

Construction of an ortholog database using the semantic web technology for integrative analysis of genomic data.

Recently, various types of biological data, including genomic sequences, have been rapidly accumulating. To discover biological knowledge from such growing heterogeneous data, a flexible framework for data integration is necessary. Ortholog information is a central resource for interlinking corresponding genes among different organisms, and the Semantic Web provides a key technology for the flexible integration of heterogeneous data. We have constructed an ortholog database using the Semantic Web technology, aiming at the integration of numerous genomic data and various types of biological information. To formalize the structure of the ortholog information in the Semantic Web, we have constructed the Ortholog Ontology (OrthO). While the OrthO is a compact ontology for general use, it is designed to be extended to the description of database-specific concepts. On the basis of OrthO, we described the ortholog information from our Microbial Genome Database for Comparative Analysis (MBGD) in the form of Resource Description Framework (RDF) and made it available through the SPARQL endpoint, which accepts arbitrary queries specified by users. In this framework based on the OrthO, the biological data of different organisms can be integrated using the ortholog information as a hub. Besides, the ortholog information from different data sources can be compared with each other using the OrthO as a shared ontology. Here we show some examples demonstrating that the ortholog information described in RDF can be used to link various biological data such as taxonomy information and Gene Ontology. Thus, the ortholog database using the Semantic Web technology can contribute to biological knowledge discovery through integrative data analysis

An example orthology relation and its RDF representation.

<p>(A) A schematic illustration of an orthology relation from the OrthoXML documentation (<a href="http://orthoxml.org/0.3/orthoxml_doc_v0.3.html#trees" target="_blank">http://orthoxml.org/0.3/orthoxml_doc_v0.3.html#trees</a>). Here, each node is assigned a URI and a class that are required for RDF representation. The filled circles representing speciation events are assigned the <i>orth</i>:<i>OrthologGroup</i> class. (B) RDF representation (Turtle format) of the example shown in A.</p

The portal page of MBGD SPARQL Search.

<p>The portal page of MBGD SPARQL Search.</p

Time required for executing SPARQL queries.

<p>Time required for executing SPARQL queries.</p

Retrieval of phylogenetic patterns of orthologs related to a specific function.

<p>(A) Schematic diagram of the RDF graph structure related to the queries in B and C. (B) SPARQL query to get MBGD clusters including members related to the GO term GO:0009288 (bacterial-type flagellum). (C) SPARQL query to obtain organisms that contain members of an ortholog group. (D) Search results of the query shown in B. (E) The results obtained from the queries shown in B and C visualized using R (the R source code is included in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122802#pone.0122802.s005" target="_blank">S1 Dataset</a>). The number of target organisms in each phylum is shown in parenthesis. After obtaining the output from R, the phyla containing gram-positive bacteria (+) and genes functioning in the flagellar export system (*) are marked, and the blue line was added to represent clusters with relatively wide organismal distribution (in at least 16 phyla).</p

List of the datasets available at the MBGD SPARQL endpoint.

<p>List of the datasets available at the MBGD SPARQL endpoint.</p

RDF model of ortholog information based on OrthO.

<p>(A) Hierarchical structure of classes and properties in OrthO. OrthO includes 12 classes (<i>owl</i>:<i>Class</i>) and 20 properties (15 of <i>owl</i>:<i>ObjectProperty</i> and 5 of <i>owl</i>:<i>DatatypeProperty</i>). (B) Schematic representation of RDF graph structure of ortholog information described using OrthO. The elliptical nodes represent instances of classes. The directed edges represent properties. The dotted lines represent possible links to other resources.</p