Identification of Variable-Number Tandem-Repeat (VNTR) Sequences in Acinetobacter baumannii and Interlaboratory Validation of an Optimized Multiple-Locus VNTR Analysis Typing Scheme
Bibliographic record
Abstract
Acinetobacter baumannii is an important opportunistic pathogen responsible for nosocomial outbreaks, mostly occurring in intensive care units. Due to the multiplicity of infection sources, reliable molecular fingerprinting techniques are needed to establish epidemiological correlations among A. baumannii isolates. Multiple-locus variable-number tandem-repeat analysis (MLVA) has proven to be a fast, reliable, and cost-effective typing method for several bacterial species. In this study, an MLVA assay compatible with simple PCR- and agarose gel-based electrophoresis steps as well as with high-throughput automated methods was developed for A. baumannii typing. Preliminarily, 10 potential polymorphic variable-number tandem repeats (VNTRs) were identified upon bioinformatic screening of six annotated genome sequences of A. baumannii. A collection of 7 reference strains plus 18 well-characterized isolates, including unique types and representatives of the three international A. baumannii lineages, was then evaluated in a two-center study aimed at validating the MLVA assay and comparing it with other genotyping assays, namely, macrorestriction analysis with pulsed-field gel electrophoresis (PFGE) and PCR-based sequence group (SG) profiling. The results showed that MLVA can discriminate between isolates with identical PFGE types and SG profiles. A panel of eight VNTR markers was selected, all showing the ability to be amplified and good amounts of polymorphism in the majority of strains. Independently generated MLVA profiles, composed of an ordered string of allele numbers corresponding to the number of repeats at each VNTR locus, were concordant between centers. Typeability, reproducibility, stability, discriminatory power, and epidemiological concordance were excellent. A database containing information and MLVA profiles for several A. baumannii strains is available from http://mlva.u-psud.fr/.
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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 | 0.003 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".