ASOs for microbes: Programmable RNA antibiotics for selective targeting of bacteria and phages
Speaker: Jörg Vogel (University of Würzburg)
Host: Andreas Peschel (CMFI)
Date & Time: 17.12.2026 | 12:30–15:30 p. m.
Venue: Lecture Hall 3M07, GUZ Building, Schnarrenbergstraße 94-96, 72076 Tübingen
Public Event. No registration needed and everyone welcome. The talks are followed by a reception in the GUZ entrance hall.
Abstract:
RNA-centric antisense technologies have the potential to form a foundation for the development of a new generation of antimicrobials. Upon delivery into bacteria, short antisense oligonucleotides (ASOs) can directly modulate bacterial gene expression at the RNA level. The programmable nature of such “asobiotics”, following simple base-pairing rules, allows rational and specific drug design. This opens many applications, including the rapid development of ASOs that can kill emerging pathogens, sensitize drug-resistant strains, or block expression of key virulence factors—all while sparing the native microbiome. Mirobiome editing is another exciting application, especially by targeting non-model bacteria. This talk will discuss the challenges of progressing asobiotics from a promising area of research to an applied technology capable of addressing the growing antimicrobial resistance crisis and providing solutions for the analysis of genetically intractable microbes. We will also discuss how asobiotics can be used as a versatile tool for functional genomics of phages.
Flash Talk:
Janko Sattler (University of Tübingen) – Conserved plasmid proteins enable machine learning-based detection of pOXA-48-carrying Enterobacterales from routine MALDI-TOF mass spectra
OXA-48 carbapenemases are clinically highly relevant resistance mechanisms in Enterobacterales. However, their detection typically requires screening via antimicrobial susceptibility testing plus phenotypic or molecular confirmation once pure cultures are available. We hypothesized that conserved proteins encoded on the endemic IncL plasmid pOXA-48 generate reproducible signatures detectable in routine MALDI-TOF species-identification spectra, and, therefore, developed and biologically validated a machine learning classifier for pOXA-48 detection across species, acquisition protocols, and instrument platforms.