Back

One pot RNA:DNA assembly for ribosomal RNA detection of pathogenic bacteria with single-molecule sensitivity

Boskovic, F. N.; Sandler, S.; Brauburger, S.; Shui, Y.; Kumar, B.; Pereira Dias, J.; Naydenova, P.; Zhu, J.; Baker, S.; Keyser, U. F.

2024-08-17 biophysics
10.1101/2024.08.15.608086 bioRxiv
Show abstract

Ribosomal RNAs (rRNAs) serve as species-defining markers and undergo processing steps such as excision of intervening sequences (IVSs). Direct analysis of native rRNAs is hampered by amplification-induced biases and by the high conservation of rRNA sequences, which complicates discrimination of closely related variants. Here, we present modular RNA:DNA nanostructures that enable direct, amplification-free identification of rRNAs and their variants. The approach employs rationally designed RNA:DNA duplexes, named RNA identifiers (IDs), assembled onto native rRNAs via short complementary oligonucleotides bearing programmable coding motifs. We demonstrate that native bacterial 16S rRNAs can be directly converted into RNA IDs and detected with solid-state nanopores. Having established direct rRNA readout, we next show that biologically encoded rRNA processing states, including serovar-specific 23S rRNA fragmentation patterns arising from IVS excision, are resolved using RNA IDs. Finally, to extend discrimination beyond processing-level differences, we incorporate catalytically inactive Cas9 ribonucleoprotein complexes to enable single-nucleotide discrimination of rRNA variants. Our modular RNA ID-nanopore system facilitates studying rRNA processing and rRNA diversity.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.