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How much can we learn about the function of bacterial rRNA modification by mining large-scale experimental datasetsстатья
Статья опубликована в высокорейтинговом журнале
Информация о цитировании статьи получена из
Web of Science
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 11 сентября 2012 г.
- Авторы: Sergiev PV, Golovina AY, Sergeeva OV, Osterman IA, Nesterchuk MV, Bogdanov AA, Dontsova OA
- Журнал: Nucleic Acids Research
- Год издания: 2012
- Издательство: Oxford University Press
- Местоположение издательства: United Kingdom
- DOI: 10.1093/nar/gks219
- Аннотация: Modification of ribosomal RNA is ubiquitous among living organisms. Its functional role is well established for only a limited number of modified nucleotides. There are examples of rRNA modification involvement in the gene expression regulation in the cell. There is a need for large data set analysis in the search for potential functional partners for rRNA modification. In this study, we extracted phylogenetic profile, genome neighbourhood, co-expression and phenotype profile and co-purification data regarding Escherichia coli rRNA modification enzymes from public databases. Results were visualized as graphs using Cytoscape and analysed. Majority linked genes/proteins belong to translation apparatus. Among co-purification partners of rRNA modification enzymes are several candidates for experimental validation. Phylogenetic profiling revealed links of pseudouridine synthetases with RF2, RsmH with translation factors IF2, RF1 and LepA and RlmM with RdgC. Genome neighbourhood connections revealed several putative functionally linked genes, e.g. rlmH with genes coding for cell wall biosynthetic proteins and others. Comparative analysis of expression profiles (Gene Expression Omnibus) revealed two main associations, a group of genes expressed during fast growth and association of rrmJ with heat shock genes. This study might be used as a roadmap for further experimental verification of predicted functional interactions.
- Добавил в систему: Донцова Ольга Анатольевна