RNA interactome of hypervirulent <i>Klebsiella pneumoniae</i> reveals a small RNA inhibitor of capsular mucoviscosity and virulence
Bibliographic record
Abstract
ABSTRACT Hypervirulent Klebsiella pneumoniae (HvKP) is an emerging bacterial pathogen causing invasive infection in immune-competent humans. The hypervirulence is strongly linked to the overproduction of hypermucovisous capsule, but the underlining regulatory mechanism of hypermucoviscosity (HMV) has been elusive, especially at the post-transcriptional level mediated by small noncoding RNAs (sRNAs). Using a recently developed RNA interactome profiling approach, we interrogate the Hfq-associated sRNA regulatory network and establish the intracellular RNA-RNA interactome in HvKP. Our data reveal numerous interactions between sRNAs and HMV-related mRNAs, and identify a plethora of sRNAs that repress or promote HMV. One of the strongest repressors of HMV is ArcZ, which is activated by catabolite regulator CRP and targets many HMV-related genes including mlaA and fbp . We discover that MlaA and its function in phospholipid transport is crucial for capsule retention and HMV, inactivation of which abolished Klebsiella virulence in mice. ArcZ overexpression significantly reduced bacterial burden in mice and reduced HMV in multiple carbapenem-resistant and hypervirulent clinical isolates with diverse genetic background, indicating it is a potent RNA inhibitor of bacterial pneumonia with therapeutic potential. In summary, our work unravels a comprehensive map of the RNA-RNA interaction network of HvKP and identifies a novel CRP-ArcZ-MlaA regulatory circuit of HMV, providing mechanistic insights into the posttranscriptional virulence control in a superbug of global concern. HIGHLIGHTS Global RNA-RNA interactome map in hypervirulent Klebsiella pneumoniae Hfq and multiple small RNAs regulate capsular hypermucoviscosity ArcZ targets mlaA that is required for lipid transport, hypermucoviscosity and virulence Crp is a transcriptional activator of ArcZ governing a new virulence circuit
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.000 |
| 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 source (direct Gemma or distilled Codex), 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".