Guidelines for Inferring and Characterizing a Family of Bacterial trans-Acting Small Noncoding RNAs

So far, every sequenced bacterial transcriptome encompasses hundreds of small regulatory noncoding RNAs (sRNAs). From those sRNAs that have been already characterized, we learned that their regulatory functions could span over almost every bacterial process, mostly acting at the posttranscriptional control of gene expression (Wagner and Romby, Adv Genet 90:133-208, 2015). Canonical molecular mechanisms of sRNA action have been described to rely on both sequence and/or structural traits of the RNA molecule. As for protein-coding genes, the conservation of sRNAs among species suggests conserved and adjusted functions across evolution. Knowing the phylogenetic distribution of an sRNA gene and how its functional traits have evolved may help to get a broad picture of its biological role in each single species. Here, we present a simple computational workflow to identify close and distant sRNA homologs present in sequenced bacterial genomes, which allows defining novel sRNA families. This strategy is based on the use of Covariance Models (CM) and assumes the conservation of sequence and structure of functional sRNA genes throughout evolution. Moreover, by carefully inspecting the conservation of the close genomic context of every member of the RNA family and how the patterns of microsynteny follow the path of species evolution, it is possible to define subgroups of sRNA orthologs, which in turn enables the definition of RNA subfamilies.

Predicting RNA Families in Nucleotide Sequences Using StructRNAfinder

Noncoding RNA (ncRNA) research is already a routine in every genomics or transcriptomics initiatives. According to their functions, ncRNAs can be grouped into several different RNA families, which can be represented by conserved primary sequences, secondary structures, or covariance models (CMs). CMs are very sensitive in predicting RNA families in nucleotide sequences and have been widely used in characterizing the repertoire of ncRNAs in organisms from all domains of life. However, the large-scale prediction and annotation of ncRNAs require multiple tools along the process, imposing a great obstacle for researchers with lesser computational or bioinformatics background. StructRNAfinder emerged as an automated tool to avoid these bottlenecks, by performing the automatic identification and complete annotation of regulatory RNA families derived directly from nucleotide sequences. In this chapter, we provide a complete tutorial for both stand-alone and web server versions of StructRNAfinder. This will help users to install the tool and to perform predictions of RNA families in any genome or transcriptome sequences dataset.

Functional annotation and distribution overview of RNA families in 27 Streptococcus agalactiae genomes

Background

Streptococcus agalactiae, also known as Group B Streptococcus (GBS), is a Gram-positive bacterium that colonizes the gastrointestinal and genitourinary tract of humans. This bacterium has also been isolated from various animals, such as fish and cattle. Non-coding RNAs (ncRNAs) can act as regulators of gene expression in bacteria, such as Streptococcus pneumoniae and Streptococcus pyogenes. However, little is known about the genomic distribution of ncRNAs and RNA families in S. agalactiae.

Results

Comparative genome analysis of 27 S. agalactiae strains showed more than 5 thousand genomic regions identified and classified as Core, Exclusive, and Shared genome sequences. We identified 27 to 89 RNA families per genome distributed over these regions, from these, 25 were in Core regions while Shared and Exclusive regions showed variations amongst strains. We propose that the amount and type of ncRNA present in each genome can provide a pattern to contribute in the identification of the clonal types.

Conclusions

The identification of RNA families provides an insight over ncRNAs, sRNAs and ribozymes function, that can be further explored as targets for antibiotic development or studied in gene regulation of cellular processes. RNA families could be considered as markers to determine infection capabilities of different strains. Lastly, pan-genome analysis of GBS including the full range of functional transcripts provides a broader approach in the understanding of this pathogen.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4951-z) contains supplementary material, which is available to authorized users.