A CRISPR-Cas-associated transposon system for genome editing in <i>Burkholderia cepacia</i> complex species
Bibliographic record
Abstract
ABSTRACT Genome editing in non-model bacteria is important to understand gene-to-function links that may differ from those of model microorganisms. Although species of the Burkholderia cepacia complex (Bcc) have great biotechnological capacities, the limited genetic tools available to understand and mitigate their pathogenic potential hamper their utilization in industrial applications. To broaden the genetic tools available for Bcc species, we developed RhaCAST, a targeted DNA insertion platform based on a CRISPR-associated transposase driven by a rhamnose-inducible promoter. We demonstrated the utility of the system for targeted insertional mutagenesis in the Bcc strains B. cenocepacia K56-2 and Burkholderia multivorans ATCC17616. We showed that the RhaCAST system can be used for loss- and gain-of-function applications. Importantly, the selection marker could be excised and reused to allow iterative genetic manipulation. The RhaCAST system is faster, easier, and more adaptable than previous insertional mutagenesis tools available for Bcc species and may be used to disrupt pathogenicity elements and insert relevant genetic modules, enabling Bcc biotechnological applications. IMPORTANCE Species of the Burkholderia cepacia complex (Bcc) have great biotechnological potential but are also opportunistic pathogens. Genetic manipulation of Bcc species is necessary to understand gene-to-function links. However, limited genetic tools are available to manipulate Bcc, hindering our understanding of their pathogenic traits and their potential in biotechnological applications. We developed a genetic tool based on CRISPR-associated transposase to increase the genetic tools available for Bcc species. The genetic tool we developed in this study can be used for loss and gain of function in Bcc species. The significance of our work is in expanding currently available tools to manipulate Bcc.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".