A Novel DNA Chromatography Method to Distinguish <i>M. abscessus</i> Subspecies and Macrolide Susceptibility
Bibliographic record
Abstract
Abstract Rationale The clinical impact of infection with Mycobacterium abscessus complex (MABC), a group of emerging non-tuberculosis mycobacteria (NTM), is increasing. Mycobacterium abscessus subsp. abscessus / bolletii frequently shows natural resistance to macrolide antibiotics, whereas Mycobacterium abscessus subsp. massiliense is generally susceptible. Therefore, rapid and accurate discrimination of macrolide-susceptible MABC subgroups is required for effective clinical decisions about macrolide treatments for MABC infection. Objectives To develop a simple and rapid diagnostic that can identify MABC isolates showing macrolide susceptibility. Methods Whole genome sequencing (WGS) was performed for 148 clinical or environmental MABC isolates from Japan to identify genetic markers that can discriminate three MABC subspecies and the macrolide-susceptible erm (41) T28C sequevar. Using the identified genetic markers, we established PCR based- or DNA chromatography-based assays. Validation testing was performed using MABC isolates from Taiwan. Measurements and Main Results We identified unique sequence regions that could be used to differentiate the three subspecies. Our WGS-based phylogenetic analysis indicated that M. abscessus carrying the macrolide-susceptible erm (41) T28C sequevar were tightly clustered, and identified 11 genes that were significantly associated with the lineage for use as genetic markers. To detect these genetic markers and the erm (41) locus, we developed a DNA chromatography method that identified three subspecies, the erm (41) T28C sequevar and intact erm (41) for MABC in a single assay within one hour. The agreement rate between the DNA chromatography-based and WGS-based identification was 99.7%. Conclusions We developed a novel, rapid and simple DNA chromatography method for identification of MABC macrolide susceptibility with high accuracy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".