Deciphering transcript architectural complexity in bacteria and archaea

• Published in: mBio Vol. 15; no. 10; p. e0235924
• Main Authors: Mattick, John S A, Bromley, Robin E, Watson, Kaylee J, Adkins, Ricky S, Holt, Christopher I, Lebov, Jarrett F, Sparklin, Benjamin C, Tyson, Tyonna S, Rasko, David A, Dunning Hotopp, Julie C
• Format: Journal Article
• Language: English
• Published: United States American Society for Microbiology 16-10-2024
• Subjects: Algorithms; Archaea - genetics; archaeal transcripts; Bacteria - classification; Bacteria - genetics; bacterial transcripts; Computational Biology - methods; direct RNA sequencing; Escherichia coli K12 - genetics; Genomics and Proteomics; Haloferax volcanii - genetics; Listeria monocytogenes - genetics; non-coding RNA (ncRNA); Pseudomonas aeruginosa - genetics; Research Article; RNA, Archaeal - genetics; RNA, Bacterial - genetics; RNA, Messenger - genetics; Sequence Analysis, RNA - methods; small RNAs; transcriptomics; archaeal transcripts; bacterial transcripts; transcriptomics; non-coding RNA (ncRNA); direct RNA sequencing; small RNAs
• ISSN: 2150-7511; 2150-7511
• DOI: 10.1128/mbio.02359-24

RNA transcripts are potential therapeutic targets, yet bacterial transcripts have uncharacterized biodiversity. We developed an algorithm for transcript prediction called tp.py using it to predict transcripts (mRNA and other RNAs) in
K12 and E2348/69 strains (Bacteria:gamma-Proteobacteria),
strains Scott A and RO15 (Bacteria:Firmicute),
strains SG17M and NN2 strains (Bacteria:gamma-Proteobacteria), and
(Archaea:Halobacteria). From >5 million
K12 and >3 million
E2348/69 newly generated Oxford Nanopore Technologies direct RNA sequencing reads, 2,487 K12 mRNAs and 1,844 E2348/69 mRNAs were predicted, with the K12 mRNAs containing more than half of the predicted
K12 proteins. While the number of predicted transcripts varied by strain based on the amount of sequence data used, across all strains examined, the predicted average size of the mRNAs was 1.6-1.7 kbp, while the median size of the 5'- and 3'-untranslated regions (UTRs) were 30-90 bp. Given the lack of bacterial and archaeal transcript annotation, most predictions were of novel transcripts, but we also predicted many previously characterized mRNAs and ncRNAs, including post-transcriptionally generated transcripts and small RNAs associated with pathogenesis in the
E2348/69
pathogenicity islands. We predicted small transcripts in the 100-200 bp range as well as >10 kbp transcripts for all strains, with the longest transcript for two of the seven strains being the
operon transcript, and for another two strains it was a phage/prophage transcript. This quick, easy, and reproducible method will facilitate the presentation of transcripts, and UTR predictions alongside coding sequences and protein predictions in bacterial genome annotation as important resources for the research community.IMPORTANCEOur understanding of bacterial and archaeal genes and genomes is largely focused on proteins since there have only been limited efforts to describe bacterial/archaeal RNA diversity. This contrasts with studies on the human genome, where transcripts were sequenced prior to the release of the human genome over two decades ago. We developed software for the quick, easy, and reproducible prediction of bacterial and archaeal transcripts from Oxford Nanopore Technologies direct RNA sequencing data. These predictions are urgently needed for more accurate studies examining bacterial/archaeal gene regulation, including regulation of virulence factors, and for the development of novel RNA-based therapeutics and diagnostics to combat bacterial pathogens, like those with extreme antimicrobial resistance.