Use of <i>tuf</i> Sequences for Genus-Specific PCR Detection and Phylogenetic Analysis of 28 Streptococcal Species
Bibliographic record
Abstract
A 761-bp portion of the tuf gene (encoding the elongation factor Tu) from 28 clinically relevant streptococcal species was obtained by sequencing amplicons generated using broad-range PCR primers. These tuf sequences were used to select Streptococcus-specific PCR primers and to perform phylogenetic analysis. The specificity of the PCR assay was verified using 102 different bacterial species, including the 28 streptococcal species. Genomic DNA purified from all streptococcal species was efficiently detected, whereas there was no amplification with DNA from 72 of the 74 nonstreptococcal bacterial species tested. There was cross-amplification with DNAs from Enterococcus durans and Lactococcus lactis. However, the 15 to 31% nucleotide sequence divergence in the 761-bp tuf portion of these two species compared to any streptococcal tuf sequence provides ample sequence divergence to allow the development of internal probes specific to streptococci. The Streptococcus-specific assay was highly sensitive for all 28 streptococcal species tested (i.e., detection limit of 1 to 10 genome copies per PCR). The tuf sequence data was also used to perform extensive phylogenetic analysis, which was generally in agreement with phylogeny determined on the basis of 16S rRNA gene data. However, the tuf gene provided a better discrimination at the streptococcal species level that should be particularly useful for the identification of very closely related species. In conclusion, tuf appears more suitable than the 16S ribosomal RNA gene for the development of diagnostic assays for the detection and identification of streptococcal species because of its higher level of species-specific genetic divergence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".