The diversity of clinical <i>Mycobacterium abscessus</i> isolates in morphology, glycopeptidolipids and infection rates in a macrophage model
Bibliographic record
Abstract
Abstract Mycobacterium abscessus (Mab) colonies adopt smooth (S) or rough (R) morphotypes, which are linked to the presence or absence of glycopeptidolipids (GPL), respectively. Though clinically relevant, the association between GPL levels, morphotype and pathogenesis are poorly understood. To investigate the degree of correlation between Mab morphology, GPL levels, and infectivity, we generated isolates from Mab-positive sputum samples from cystic fibrosis patients. Isolated strains were categorised based on their morphology, GPL profile, and replication rate in macrophages. Our findings revealed that around 50% of isolates displayed mixed morphologies and GPL analysis confirmed a consistent relationship between GPL content and morphotype was only found in smooth isolates. Across morphotype groups, no differences were observed in vitro , yet using a high-content THP-1 cell ex vivo infection model, clinical R strains were observed to replicate at higher levels. Moreover, the proportion of infected macrophages was notably higher among clinical R strains compared to their S counterparts at 72 hours post-infection. Clinical variants also infected at significantly higher rates compared to laboratory strains, highlighting the limited translatability of lab strain infection data to clinical contexts. Our study confirmed the general correlation between morphotype and GPL levels in smooth strains yet unveiled more variability within morphotype groups than previously recognised, particularly during intracellular infection. As the rough morphotype is of highest clinical concern, these findings contribute to the expanding knowledge base surrounding Mab infections, offering insights that can steer diagnostic methodologies, and treatment approaches.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".