Towards a Genome-wide Fingerprint of Antibiotic Resistance Determinants in the Cystic Fibrosis Pathogen Burkholderia cenocepacia K56-2
Bibliographic record
Abstract
People with cystic fibrosis experience re-occurring polymicrobial pulmonary infections that are a leading cause of mortality. Among the infecting organisms is the Gram-negative Burkholderia cenocepacia, which causes a rapidly progressing form of necrotizing pneumonia and bacteremia known as “cepacia syndrome”, with few treatment options due to high intrinsic antibiotic resistance. The application of next-generation sequencing has powered recent genome-wide explorations into antibiotic resistance. Here, we lay the foundation for such an exploration into the epidemic clinical isolate B. cenocepacia K56-2 with an in-depth survey into its resistance arsenal. We have characterized growth dose-response curves for a panel of 87 antimicrobials from over 30 diverse classes, including clinically relevant antibiotics. Despite many not causing full growth inhibition, we observed important differences and similarities in and among structural classes. For example, tetracyclines and quinolones were up to two orders of magnitude more potent than aminoglycosides and cationic peptides. Within the cephalosporins, while ceftazidime by itself has poor activity, the related siderophore conjugate, cefiderocol, possessed a 512-fold lower MIC. Furthermore, cross-reference to the Comprehensive Antibiotic Resistance Database (CARD 2020) explains many of the observed trends in antimicrobial activity. K56-2 putatively encodes over 250 unique known resistance genes, including carbapenemases, broad-spectrum efflux pumps, and outer membrane modification systems. Furthermore, we have constructed and validated a high-density barcoded transposon mutagenesis scheme to quantitatively profile genomic contributions to antimicrobial resistance. At sub-inhibitory concentrations of antibiotics, hypersusceptible mutants will be selectively killed, which can be monitored by next-generation sequencing and enumeration of unique DNA barcodes in each mutant. Current efforts are focused on cefiderocol, ceftazidime-avibactam, meropenem, and aztreonam; however, our high-throughput platform, will allow us to profile all 87 antimicrobials in the panel. We expect these studies to yield valuable insight into novel therapeutic avenues for treating infections caused by B. cenocepacia and related bacteria.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".