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Abstract Bacterial mRNAs have short life cycles, in which transcription is rapidly followed by translation and degradation within seconds to minutes. The resulting diversity of mRNA molecules across different life-cycle stages impacts their functionality but has remained unresolved. Here we quantitatively map the 3’ status of cellular RNAs in Escherichia coli during steady-state growth and report a large fraction of molecules (median>60%) that are fragments of canonical full-length mRNAs. The majority of RNA fragments are decay intermediates, whereas nascent RNAs contribute to a smaller fraction. Despite the prevalence of decay intermediates in total cellular RNA, these intermediates are underrepresented in the pool of ribosome-associated transcripts and can thus distort quantifications and differential expression analyses for the abundance of full-length, functional mRNAs. The large heterogeneity within mRNA molecules in vivo highlights the importance in discerning functional transcripts and provides a lens for studying the dynamic life cycle of mRNAs.
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This content will become publicly available on October 16, 2025
Deciphering transcript architectural complexity in bacteria and archaea
ABSTRACT 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) inEscherichia coliK12 and E2348/69 strains (Bacteria:gamma-Proteobacteria),Listeria monocytogenesstrains Scott A and RO15 (Bacteria:Firmicute),Pseudomonas aeruginosastrains SG17M and NN2 strains (Bacteria:gamma-Proteobacteria), andHaloferax volcanii(Archaea:Halobacteria). From >5 millionE. coliK12 and >3 millionE. coliE2348/69 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 predictedE. coliK12 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 theE. coliE2348/69LEEpathogenicity 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 thenuooperon 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.
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- Award ID(s):
- 2025384
- PAR ID:
- 10569467
- Editor(s):
- Parkhill, Julian
- Publisher / Repository:
- ASM
- Date Published:
- Journal Name:
- mBio
- Volume:
- 15
- Issue:
- 10
- ISSN:
- 2150-7511
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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