Detection of Bacteria Using Fluorogenic DNAzymes
Bibliographic record
Abstract
Outbreaks linked to food-borne and hospital-acquired pathogens account for millions of deaths and hospitalizations as well as colossal economic losses each and every year. Prevention of such outbreaks and minimization of the impact of an ongoing epidemic place an ever-increasing demand for analytical methods that can accurately identify culprit pathogens at the earliest stage. Although there is a large array of effective methods for pathogen detection, none of them can satisfy all the following five premier requirements embodied for an ideal detection method: high specificity (detecting only the bacterium of interest), high sensitivity (capable of detecting as low as a single live bacterial cell), short time-to-results (minutes to hours), great operational simplicity (no need for lengthy sampling procedures and the use of specialized equipment), and cost effectiveness. For example, classical microbiological methods are highly specific but require a long time (days to weeks) to acquire a definitive result.(1) PCR- and antibody-based techniques offer shorter waiting times (hours to days), but they require the use of expensive reagents and/or sophisticated equipment.(2-4) Consequently, there is still a great demand for scientific research towards developing innovative bacterial detection methods that offer improved characteristics in one or more of the aforementioned requirements. Our laboratory is interested in examining the potential of DNAzymes as a novel class of molecular probes for biosensing applications including bacterial detection.(5) DNAzymes (also known as deoxyribozymes or DNA enzymes) are man-made single-stranded DNA molecules with the capability of catalyzing chemical reactions.(6-8) These molecules can be isolated from a vast random-sequence DNA pool (which contains as many as 10(16) individual sequences) by a process known as "in vitro selection" or "SELEX" (systematic evolution of ligands by exponential enrichment).(9-16) These special DNA molecules have been widely examined in recent years as molecular tools for biosensing applications.(6-8) Our laboratory has established in vitro selection procedures for isolating RNA-cleaving fluorescent DNAzymes (RFDs; Fig. 1) and investigated the use of RFDs as analytical tools.(17-29) RFDs catalyze the cleavage of a DNA-RNA chimeric substrate at a single ribonucleotide junction (R) that is flanked by a fluorophore (F) and a quencher (Q). The close proximity of F and Q renders the uncleaved substrate minimal fluorescence. However, the cleavage event leads to the separation of F and Q, which is accompanied by significant increase of fluorescence intensity. More recently, we developed a method of isolating RFDs for bacterial detection.(5) These special RFDs were isolated to "light up" in the presence of the crude extracellular mixture (CEM) left behind by a specific type of bacteria in their environment or in the media they are cultured (Fig. 1). The use of crude mixture circumvents the tedious process of purifying and identifying a suitable target from the microbe of interest for biosensor development (which could take months or years to complete). The use of extracellular targets means the assaying procedure is simple because there is no need for steps to obtain intracellular targets. Using the above approach, we derived an RFD that cleaves its substrate (FS1; Fig. 2A) only in the presence of the CEM produced by E. coli (CEM-EC).(5) This E. coli-sensing RFD, named RFD-EC1 (Fig. 2A), was found to be strictly responsive to CEM-EC but nonresponsive to CEMs from a host of other bacteria (Fig. 3). Here we present the key experimental procedures for setting up E. coli detection assays using RFD-EC1 and representative results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".