Prokaryotes have a complex noncoding
RNA (ncRNA) based regulatory system, resembling
that of eukaryotes. Recent transcriptomics studies also point out the abundance of highly
expressed uncharacterized RNAs in archaea and bacteria. However, despite the recent advances
indicating the prevalence of ncRNAs in prokaryotes, it is still unknown to what extent these
uncharacterized transcripts are functional. Therefore, we have proposed a phylogeny informed
approach to design new RNA sequencing (RNAseq)
experiments, which increases the
information harnessed from transcriptome data for ncRNA detection.
Many regulatory ncRNAs engage in RNARNA
interactions, where RNA molecules bind to
form a duplex. Predictions of true targets for an RNA enables a successful functional
characterization, these can be estimated by bioinformatics methods. However, the algorithms
developed to date are imperfect and it is an open question as to which ones perform well and
whether these can be improved upon. Towards this goal we performed a computational
benchmark study to find reliable algorithms for RNARNA
interaction prediction. We found that
energy based methods, which include the accessibility of interaction regions, are currently the
most accurate.
Many ncRNAs, including housekeeping ncRNA genes, are highly expressed. The abundances of
interacting RNA molecules enable RNARNA
duplex formation. In chapter IV we explore the
impact of high abundance RNAs on protein expression due to crosstalk RNARNA
interactions
between mRNAs and ncRNAs. With extensive RNARNA
interaction predictions we reveal that
RNA avoidance is an evolutionarily conserved phenomenon among prokaryotes, which means
that core mRNAs have evolved to avoid crosstalk interactions with abundant ncRNAs. Our
predictions also reveal that RNA avoidance may influence protein expression. To test this, we
investigated the stability of interactions between mRNAs and core ncRNAs. These predictions
show that the RNA avoidance influences the final protein abundances.
In conclusion, the primary aims of this study are to investigate the prokaryotic transcriptome for
novel ncRNA genes and examine the effects of crosstalk RNA interactions. We present a method
to increase information gained from transcriptome in prokaryotes for ncRNA identification. We
also present the most comprehensive benchmark of RNARNA
interaction prediction algorithms
to date. Lastly, we introduce and test a ‘RNA avoidance hypothesis’ that shows the influence of
crosstalk RNA interactions on protein expression in bacteria
