The rapid increase of global temperature is thought to be correlated with the increase of greenhouse gas emissions.
Rice paddy fields (RPF) are one of the major sources of the greenhouse gas methane, which play a role in global warming.
Greenhouse gas methane emissions from RPF were estimated to account for 10-25% of the total methane emissions.
Cultivation-independent molecular studies suggested that a specific group of methanogenic Archaea (named Rice Cluster I (RC-I)) are mainly responsible for methane production from RPF.
Such a focus of ecological attention resulted in a construction of genome sequence of an RC-I methanogen RC-IMRE50 from an enrichment culture by metagenomic approach.
However, a pure culture of RC-I group archaebacteria had been necessary for understanding of life style such as identification of methane source.
Sakai et al. for the first time succeed to isolate archaea belonging to RC-I, by developing a co-culture method with bacteria which continuously provide H2.
Methanocella paludicola SANAE is mesophilic, hydrogenotrophic methanogen which utilize H2/CO2 and formate for growth and methane production.
Sequencing and annotation of the genome of M. paludicola SANAET (= NBRC 101707) revealed a single circular chromosome (2,957,635 bp; G+C content of 54.92%)
containing 3,004 predicted protein-coding genes. The genome had a full set of genes involved in methanogenesis from H2/CO2, being in consistent with phenotypic analyses.
About two-thirds of the predicted genes were shared with RC-IMRE50 genome, while 40-45% of genes were shared with other methanogenic lineages such as Methanomicrobiales and Methanodsrcinales.
Further studies cross-linking and compareing both RC-IMRE50 metagenomic infromation and M. paludicola genome would provide better understanding how RC-I methanogens contributes
global methane emmission from RPF environments.
2010-01-04 ..... 1
Release of the Methanocella paludicola SANAET genomic sequence data
We published the genomic data of Methanocella paludicola SANAET (= NBRC 101707) including the information of DNA clones distributed from the NBRC.
Summary of the genomic data
Number of ORFs assigned
Percentage of the coding regions
Percentage of the intronic regions
Number of rRNA genes
Number of tRNA genes
Number of other features (misc_RNA,misc_feature,repeat)
The nucleotide sequence of the M. paludicola SANAE genome was determined by the whole genome shotgun sequencing method as in the case of other organisms analyzed at NITE-DOB.
DNA shotgun library
DNA shotgun library with inserts of 1-8 kb in pUC118 vector (TAKARA) was constructed.
A Fosmid library with inserts of 40 kb in the pCC1FOS fosmid vector was constructed using the CopyControl Fosmid Library Production Kit (Epicentre).
Plasmid clones were end-sequenced using dye-terminator chemistry on an ABI PRISM3700 sequencer (ABI).
Fosmid DNA was extracted from E. coli transformants using the Montage BAC96 MiniPrep Kit (Millipore) and end-sequencing was carried out using dye-terminator chemistry on ABI PRISM3700.
Raw sequence data corresponding to approximately 10-fold coverage were assembled using PHRED/PHRAP/CONSED software (http://www.phrap.org).
Fosmid end sequences were mapped onto the assembled sequence.
Fosmid clones that link two contigs were selected and sequenced by primer walking to close gaps.
Validation of the assembled sequence data
From the final nucleotide sequence, PCR primer sequences were generated at appropriate intervals throughout the genome which were then used to amplify the corresponding genomic regions. The restriction enzyme digestion patterns of each of the PCR fragments thus obtained were accordingly compared with those deduced from the sequence data of the regions to validate the correctness of the assembled sequence data.
Genome analysis and annotation
Putative nontranslated genes were identified using the Rfam, tRNAscan-SE and ARAGORN programs, whereas rRNA genes were identified using the BLASTN program.
For the prediction of protein-coding genes, Glimmer3, GeneMarkS, Prokov programs were used.
The potential protein sequences were compared with the UniProt databases using the BLASTP program.
Potential protein sequences that showed significant similarities to known protein sequences in the database were selected.
The start sites were manually inspected and altered in comparison to the known protein sequences which shows significant similarities.
The translated sequences of the predicted protein-coding genes were searched against the nonredundant UniProt database (version 15.0) and the protein signature database, InterPro version 18.0.
The KEGG database was used for pathway reconstruction.
Signal peptides in proteins were predicted using SignalP, whereas transmembrane helices were predicted using TMHMM.