English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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.

How much can we learn about the function of bacterial rRNA modification by mining large-scale experimental datasets?