An antisense oligomer conjugate with unpredicted bactericidal activity against <i>Fusobacterium nucleatum</i>
Bibliographic record
Abstract
ABSTRACT Fusobacteria are commensal members of the oral microbiome that can spread from their primary niche and colonize distal sites in the human body. Their enrichment in colorectal and breast cancer tissue has been associated with tumor growth, metastasis, and chemotherapeutic resistance. The use of non-selective antibiotics to remove fusobacteria impairs tumor progression, but prolonged application causes side effects, such as gastrointestinal problems and dysbiosis. Species-specific antisense antibiotics based on peptide nucleic acid (PNA) have shown efficacy in many gram-negative species, suggesting that antisense PNAs may also enable a tailored depletion of fusobacteria. Here, we have investigated the antibacterial potential of cell-penetrating peptide (CPP)-PNA conjugates targeting the mRNA of putative essential genes in Fusobacterium nucleatum . Unexpectedly, we observed no growth inhibition with any of the target-specific PNAs but identified a non-targeting control CPP-PNA [FUS79, (RXR) 4 XB- GACATAATTGT ] as a potent growth inhibitor of F. nucleatum . Our data suggest that the CPP and specific sequence features of FUS79 are responsible for its activity, rather than an antisense effect. Interestingly, FUS79 also inhibits the growth of five additional fusobacterial strains but not of F. nucleatum subsp. vincentii (FNV). RNA-seq analysis indicates that FUS79 induces a membrane stress response in a vulnerable F. nucleatum strain but not in FNV. Collectively, our attempt at developing antisense antibiotics for fusobacteria discovers a potent growth inhibitor, whose bactericidal effect appears independent of target-specific mRNA inhibition. IMPORTANCE Enrichment of F. nucleatum at cancer sites is associated with increased tumor growth and metastasis. Antibiotic treatment to remove the bacteria was shown to change the course of cancer progression. Here, we explore first steps to establish peptide nucleic acids (PNAs) as specific antisense antibiotics, thereby laying the foundation for further development of antisense technology in fusobacteria. Although the CPP-PNA FUS79 was initially designed as a control, we observed that the compound was bactericidal for specific fusobacterial strains. Our results suggest that FUS79 might be able to selectively deplete fusobacterial strains from bacterial communities, offering a new perspective on fusobacterial removal at the tumor site.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".