Phylogeny and Identification of <i>Nocardia</i> Species on the Basis of Multilocus Sequence Analysis
Bibliographic record
Abstract
Nocardia species identification is difficult due to a complex and rapidly changing taxonomy, the failure of 16S rRNA and cellular fatty acid analysis to discriminate many species, and the unreliability of biochemical testing. Here, Nocardia species identification was achieved through multilocus sequence analysis (MLSA) of gyrase B of the β subunit of DNA topoisomerase (gyrB), 16S rRNA (16S), subunit A of SecA preprotein translocase (secA1), the 65-kDa heat shock protein (hsp65), and RNA polymerase (rpoB) applied to 190 clinical, 36 type, and 11 reference strains. Phylogenetic analysis resolved 30 sequence clusters with high (>85%) bootstrap support. Since most clusters contained a single type strain and the analysis corroborated current knowledge of Nocardia taxonomy, the sequence clusters were equated with species clusters and MLSA was deemed appropriate for species identification. By comparison, single-locus analysis was inadequate because it failed to resolve species clusters, partly due to the presence of foreign alleles in 22.1% of isolates. While MLSA identified the species of the majority (71.3%) of strains, it also identified clusters that may correspond to new species. The correlation of the identities by MLSA with those determined on the basis of microscopic examination, biochemical testing, and fatty acid analysis was 95%; however, MLSA was more discriminatory. Nocardia cyriacigeorgica (21.58%) and N. farcinica (14.74%) were the most frequently encountered species among clinical isolates. In summary, five-locus MLSA is a reliable method of elucidating taxonomic data to inform Nocardia species identification; however, three-locus (gyrB-16S-secA1) or four-locus (gyrB-16S-secA1-hsp65) MLSA was nearly as reliable, correctly identifying 98.5% and 99.5% of isolates, respectively, and would be more feasible for routine use in a clinical reference microbiology laboratory.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".