Toshimichi Ikemura

Substrate-induced gene-expression screening of environmental metagenome libraries for isolation of catabolic genes

Recent awareness that most microorganisms in the environment are resistant to cultivation has prompted scientists to directly clone useful genes from environmental metagenomes. Two screening methods are currently available for the metagenome approach, namely, nucleotide sequence–based screening and enzyme activity–based screening. Here we have introduced and optimized a third option for the isolation of novel catabolic operons, that is, substrate-induced gene expression screening (SIGEX). This method is based on the knowledge that catabolic-gene expression is generally induced by relevant substrates and, in many cases, controlled by regulatory elements situated proximate to catabolic genes. For SIGEX to be high throughput, we constructed an operon-trap gfp-expression vector available for shotgun cloning that allows for the selection of positive clones in liquid cultures by fluorescence-activated cell sorting. The utility of SIGEX was demonstrated by the cloning of aromatic hydrocarbon–induced genes from a groundwater metagenome library and subsequent genome-informatics analysis.

Rice Annotation Project Database (RAP-DB): an integrative and interactive database for rice genomics.

The Rice Annotation Project Database (RAP-DB, http://rapdb.dna.affrc.go.jp/) has been providing a comprehensive set of gene annotations for the genome sequence of rice, Oryza sativa (japonica group) cv. Nipponbare. Since the first release in 2005, RAP-DB has been updated several times along with the genome assembly updates. Here, we present our newest RAP-DB based on the latest genome assembly, Os-Nipponbare-Reference-IRGSP-1.0 (IRGSP-1.0), which was released in 2011. We detected 37,869 loci by mapping transcript and protein sequences of 150 monocot species. To provide plant researchers with highly reliable and up to date rice gene annotations, we have been incorporating literature-based manually curated data, and 1,626 loci currently incorporate literature-based annotation data, including commonly used gene names or gene symbols. Transcriptional activities are shown at the nucleotide level by mapping RNA-Seq reads derived from 27 samples. We also mapped the Illumina reads of a Japanese leading japonica cultivar, Koshihikari, and a Chinese indica cultivar, Guangluai-4, to the genome and show alignments together with the single nucleotide polymorphisms (SNPs) and gene functional annotations through a newly developed browser, Short-Read Assembly Browser (S-RAB). We have developed two satellite databases, Plant Gene Family Database (PGFD) and Integrative Database of Cereal Gene Phylogeny (IDCGP), which display gene family and homologous gene relationships among diverse plant species. RAP-DB and the satellite databases offer simple and user-friendly web interfaces, enabling plant and genome researchers to access the data easily and facilitating a broad range of plant research topics.

Bisulfite sequencing and dinucleotide content analysis of 15 imprinted mouse differentially methylated regions (DMRs): paternally methylated DMRs contain less CpGs than maternally methylated DMRs

Imprinted genes in mammals show monoallelic expression dependent on parental origin and are often associated with differentially methylated regions (DMRs). There are two classes of DMR: primary DMRs acquire gamete-specific methylation in either spermatogenesis or oogenesis and maintain the allelic methylation differences throughout development; secondary DMRs establish differential methylation patterns after fertilization. Targeted disruption of some primary DMRs showed that they dictate the allelic expression of nearby imprinted genes and the establishment of the allelic methylation of secondary DMRs. However, how primary DMRs are recognized by the imprinting machinery is unknown. As a step toward elucidating the sequence features of the primary DMRs, we have determined the extents and boundaries of 15 primary mouse DMRs (including 12 maternally methylated and three paternally methylated DMRs) in 12.5-dpc embryos by bisulfite sequencing. We found that the average size of the DMRs was 3.2 kb and that their average G+C content was 54%. Dinucleotide content analysis of the DMR sequences revealed that, although they are generally CpG rich, the paternally methylated DMRs contain less CpGs than the maternally methylated DMRs. Our findings provide a basis for the further characterization of DMRs.

tRNADB-CE 2011: tRNA gene database curated manually by experts

We updated the tRNADB-CE by analyzing 939 complete and 1301 draft genomes of prokaryotes and eukaryotes, 171 complete virus genomes, 121 complete chloroplast genomes and approximately 230 million sequences obtained by metagenome analyses of 210 environmental samples. The 287 102 tRNA genes in total, and thus two times of the tRNA genes compiled previously, are compiled, in which sequence information, clover-leaf structure and results of sequence similarity and oligonucleotide-pattern search can be browsed. In order to pool collective knowledge with help from any experts in the tRNA research field, we included a column to which comments can be added on each tRNA gene. By compiling tRNAs of known prokaryotes with identical sequences, we found high phylogenetic preservation of tRNA sequences, especially at a phylum level. Furthermore, a large number of tRNAs obtained by metagenome analyses of environmental samples had sequences identical to those found in known prokaryotes. The identical sequence group, therefore, can be used as phylogenetic markers to clarify the microbial community structure of an ecosystem. The updated tRNADB-CE provided functions, with which users can obtain the phylotype-specific markers (e.g. genus-specific markers) by themselves and clarify microbial community structures of ecosystems in detail. tRNADB-CE can be accessed freely at http://trna.nagahama-i-bio.ac.jp.

A novel approach, based on BLSOMs (Batch Learning Self-Organizing Maps), to the microbiome analysis of ticks

Ticks transmit a variety of viral, bacterial and protozoal pathogens, which are often zoonotic. The aim of this study was to identify diverse tick microbiomes, which may contain as-yet unidentified pathogens, using a metagenomic approach. DNA prepared from bacteria/archaea-enriched fractions obtained from seven tick species, namely Amblyomma testudinarium, Amblyomma variegatum, Haemaphysalis formosensis, Haemaphysalis longicornis, Ixodes ovatus, Ixodes persulcatus and Ixodes ricinus, was subjected to pyrosequencing after whole-genome amplification. The resulting sequence reads were phylotyped using a Batch Learning Self-Organizing Map (BLSOM) program, which allowed phylogenetic estimation based on similarity of oligonucleotide frequencies, and functional annotation by BLASTX similarity searches. In addition to bacteria previously associated with human/animal diseases, such as Anaplasma, Bartonella, Borrelia, Ehrlichia, Francisella and Rickettsia, BLSOM analysis detected microorganisms belonging to the phylum Chlamydiae in some tick species. This was confirmed by pan-Chlamydia PCR and sequencing analysis. Gene sequences associated with bacterial pathogenesis were also identified, some of which were suspected to originate from horizontal gene transfer. These efforts to construct a database of tick microbes may lead to the ability to predict emerging tick-borne diseases. Furthermore, a comprehensive understanding of tick microbiomes will be useful for understanding tick biology, including vector competency and interactions with pathogens and symbionts.

8-oxoguanine causes spontaneous de novo germline mutations in mice

Spontaneous germline mutations generate genetic diversity in populations of sexually reproductive organisms, and are thus regarded as a driving force of evolution. However, the cause and mechanism remain unclear. 8-oxoguanine (8-oxoG) is a candidate molecule that causes germline mutations, because it makes DNA more prone to mutation and is constantly generated by reactive oxygen species in vivo. We show here that endogenous 8-oxoG caused de novo spontaneous and heritable G to T mutations in mice, which occurred at different stages in the germ cell lineage and were distributed throughout the chromosomes. Using exome analyses covering 40.9 Mb of mouse transcribed regions, we found increased frequencies of G to T mutations at a rate of 2 × 10−7 mutations/base/generation in offspring of Mth1/Ogg1/Mutyh triple knockout (TOY-KO) mice, which accumulate 8-oxoG in the nuclear DNA of gonadal cells. The roles of MTH1, OGG1, and MUTYH are specific for the prevention of 8-oxoG-induced mutation, and 99% of the mutations observed in TOY-KO mice were G to T transversions caused by 8-oxoG; therefore, we concluded that 8-oxoG is a causative molecule for spontaneous and inheritable mutations of the germ lineage cells.

tRNADB-CE: tRNA gene database well-timed in the era of big sequence data

The tRNA gene data base curated by experts “tRNADB-CE” (http://trna.ie.niigata-u.ac.jp) was constructed by analyzing 1,966 complete and 5,272 draft genomes of prokaryotes, 171 viruses’, 121 chloroplasts’, and 12 eukaryotes’ genomes plus fragment sequences obtained by metagenome studies of environmental samples. 595,115 tRNA genes in total, and thus two times of genes compiled previously, have been registered, for which sequence, clover-leaf structure, and results of sequence-similarity and oligonucleotide-pattern searches can be browsed. To provide collective knowledge with help from experts in tRNA researches, we added a column for enregistering comments to each tRNA. By grouping bacterial tRNAs with an identical sequence, we have found high phylogenetic preservation of tRNA sequences, especially at the phylum level. Since many species-unknown tRNAs from metagenomic sequences have sequences identical to those found in species-known prokaryotes, the identical sequence group (ISG) can provide phylogenetic markers to investigate the microbial community in an environmental ecosystem. This strategy can be applied to a huge amount of short sequences obtained from next-generation sequencers, as showing that tRNADB-CE is a well-timed database in the era of big sequence data. It is also discussed that batch-learning self-organizing-map with oligonucleotide composition is useful for efficient knowledge discovery from big sequence data.

Systematization of the Protein Sequence Diversity in Enzymes Related to Secondary Metabolic Pathways in Plants, in the Context of Big Data Biology Inspired by the KNApSAcK Motorcycle Database

Biology is increasingly becoming a data-intensive science with the recent progress of the omics fields, e.g. genomics, transcriptomics, proteomics and metabolomics. The species-metabolite relationship database, KNApSAcK Core, has been widely utilized and cited in metabolomics research, and chronological analysis of that research work has helped to reveal recent trends in metabolomics research. To meet the needs of these trends, the KNApSAcK database has been extended by incorporating a secondary metabolic pathway database called Motorcycle DB. We examined the enzyme sequence diversity related to secondary metabolism by means of batch-learning self-organizing maps (BL-SOMs). Initially, we constructed a map by using a big data matrix consisting of the frequencies of all possible dipeptides in the protein sequence segments of plants and bacteria. The enzyme sequence diversity of the secondary metabolic pathways was examined by identifying clusters of segments associated with certain enzyme groups in the resulting map. The extent of diversity of 15 secondary metabolic enzyme groups is discussed. Data-intensive approaches such as BL-SOM applied to big data matrices are needed for systematizing protein sequences. Handling big data has become an inevitable part of biology.

Novel bioinformatics strategies for prediction of directional sequence changes in influenza virus genomes and for surveillance of potentially hazardous strains

BackgroundWith the remarkable increase of microbial and viral sequence data obtained from high-throughput DNA sequencers, novel tools are needed for comprehensive analysis of the big sequence data. We have developed “Batch-Learning Self-Organizing Map (BLSOM)” which can characterize very many, even millions of, genomic sequences on one plane. Influenza virus is one of zoonotic viruses and shows clear host tropism. Important issues for bioinformatics studies of influenza viruses are prediction of genomic sequence changes in the near future and surveillance of potentially hazardous strains.MethodsTo characterize sequence changes in influenza virus genomes after invasion into humans from other animal hosts, we applied BLSOMs to analyses of mono-, di-, tri-, and tetranucleotide compositions in all genome sequences of influenza A and B viruses and found clear host-dependent clustering (self-organization) of the sequences.ResultsViruses isolated from humans and birds differed in mononucleotide composition from each other. In addition, host-dependent oligonucleotide compositions that could not be explained with the host-dependent mononucleotide composition were revealed by oligonucleotide BLSOMs. Retrospective time-dependent directional changes of mono- and oligonucleotide compositions, which were visualized for human strains on BLSOMs, could provide predictive information about sequence changes in newly invaded viruses from other animal hosts (e.g. the swine-derived pandemic H1N1/09).ConclusionsBasing on the host-dependent oligonucleotide composition, we proposed a strategy for prediction of directional changes of virus sequences and for surveillance of potentially hazardous strains when introduced into human populations from non-human sources. Millions of genomic sequences from infectious microbes and viruses have become available because of their medical and social importance, and BLSOM can characterize the big data and support efficient knowledge discovery.

A Novel Bioinformatics Strategy for Function Prediction of Poorly-Characterized Protein Genes Obtained from Metagenome Analyses

As a result of remarkable progresses of DNA sequencing technology, vast quantities of genomic sequences have been decoded. Homology search for amino acid sequences, such as BLAST, has become a basic tool for assigning functions of genes/proteins when genomic sequences are decoded. Although the homology search has clearly been a powerful and irreplaceable method, the functions of only 50% or fewer of genes can be predicted when a novel genome is decoded. A prediction method independent of the homology search is urgently needed. By analyzing oligonucleotide compositions in genomic sequences, we previously developed a modified Self-Organizing Map ‘BLSOM’ that clustered genomic fragments according to phylotype with no advance knowledge of phylotype. Using BLSOM for di-, tri- and tetrapeptide compositions, we developed a system to enable separation (self-organization) of proteins by function. Analyzing oligopeptide frequencies in proteins previously classified into COGs (clusters of orthologous groups of proteins), BLSOMs could faithfully reproduce the COG classifications. This indicated that proteins, whose functions are unknown because of lack of significant sequence similarity with function-known proteins, can be related to function-known proteins based on similarity in oligopeptide composition. BLSOM was applied to predict functions of vast quantities of proteins derived from mixed genomes in environmental samples.