Development of a Species-Specific <i>fur</i> Gene-Based Method for Identification of the <i>Burkholderia cepacia</i> Complex
Bibliographic record
Abstract
Burkholderia is an important bacterial genus with a complex taxonomy that contains species of both ecological and pathogenic importance, including nine closely related species collectively termed the Burkholderia cepacia complex (BCC). Unfortunately, 16S rRNA gene analysis has proven to be not sensitive enough to discriminate between species of the BCC. Alternative species identification strategies such as recA-based PCR followed by restriction fragment length polymorphism analysis, although initially useful, have proven to be inaccurate with the increasing species diversity of the BCC. recA gene sequence analysis is more discriminatory and corroborates other biochemical and polyphasic means of taxonomic differentiation. However, it is limited by the fact that certain BCC species are subdivided into discrete recA sequence subgroups that may confuse clinical diagnoses. In this study, an effective approach is described for the rapid differentiation of BCC species from both environmental and clinical sources by means of a single-locus sequencing and PCR assay using fur as a target gene that provides sequence phylogenies that are species specific and, with few exceptions, not divided into subspecies clusters. This assay is specific and can be used to correctly determine the species status of BCC strains tested following sequencing and amplification of the fur gene by both general and species-specific primers. Based on our results, this typing strategy is simpler than and as sensitive as established tests currently in use clinically. This assay is useful for the rapid, definitive identification of all nine current BCC species and potentially novel species groups.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".