A new DNA sensor system for specific and quantitative detection of mycobacteria
Bibliographic record
Abstract
In the current study, we describe a novel DNA sensor system for specific and quantitative detection of mycobacteria, which is the causative agent of tuberculosis. Detection is achieved by using the enzymatic activity of the mycobacterial encoded enzyme topoisomerase IA (TOP1A) as a biomarker. The presented work is the first to describe how the catalytic activities of a member of the type IA family of topoisomerases can be exploited for specific detection of bacteria. The principle for detection relies on a solid support anchored DNA substrate with dual functions namely: (1) the ability to isolate mycobacterial TOP1A from crude samples and (2) the ability to be converted into a closed DNA circle upon reaction with the isolated enzyme. The DNA circle can act as a template for rolling circle amplification generating a tandem repeat product that can be visualized at the single molecule level by fluorescent labelling. This reaction scheme ensures specific, sensitive, and quantitative detection of the mycobacteria TOP1A biomarker as demonstrated by the use of purified mycobacterial TOP1A and extracts from an array of non-mycobacteria and mycobacteria species. When combined with mycobacteriophage induced lysis as a novel way of effective yet gentle extraction of the cellular content from the model Mycobacterium smegmatis, the DNA sensor system allowed detection of mycobacteria in small volumes of cell suspensions. Moreover, it was possible to detect M. smegmatis added to human saliva. Depending on the composition of the sample, we were able to detect 0.6 or 0.9 million colony forming units (CFU) per mL of mycobacteria, which is within the range of clinically relevant infection numbers. We, therefore, believe that the presented assay, which relies on techniques that can be adapted to limited resource settings, may be the first step towards the development of a new point-of-care diagnostic test for tuberculosis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".