A Broad-Host-Range CRISPRi Toolkit for Silencing Gene Expression in <i>Burkholderia</i>
Bibliographic record
Abstract
Abstract Genetic tools are critical to dissecting the mechanisms governing cellular processes, from fundamental physiology to pathogenesis. Members of the genus Burkholderia have potential for biotechnological applications but can also cause disease in humans with a debilitated immune system. The lack of suitable genetic tools to edit Burkholderia GC-rich genomes has hampered the exploration of useful capacities and the understanding of pathogenic features. To address this, we have developed CRISPR interference (CRISPRi) technology for gene silencing in Burkholderia, testing it in B.?cenocepacia, B.?multivorans, and B.?thailandensis. Tunable expression was provided by placing a codon-optimized dcas9 from Streptococcus pyogenes under control of a rhamnose-inducible promoter. As a proof of concept, the paaABCDE operon controlling genes necessary for phenylacetic acid degradation was targeted by plasmid-borne gRNAs, resulting in near complete inhibition of growth on phenylacetic acid as the sole carbon source. This was supported by reductions in paaA mRNA expression. The utility of CRISPRi to probe other functions at the single cell level was demonstrated by knocking down phbC and fliF, which dramatically reduces polyhydroxybutyrate granule accumulation and motility, respectively. As a hallmark of the mini-CTX system is the broad host-range of integration, we putatively identified 67 genera of Proteobacteria that might be amenable to modification with our CRISPRi toolkit. Our CRISPRi toolkit provides a simple and rapid way to silence gene expression to produce an observable phenotype. Linking genes to functions with CRISPRi will facilitate genome editing with the goal of enhancing biotechnological capabilities while reducing Burkholderia?s pathogenic arsenal.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".