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Record W1989971013 · doi:10.3791/3961

Detection of Bacteria Using Fluorogenic DNAzymes

2012· article· en· W1989971013 on OpenAlexafffund
Sergio D. Aguirre, M. Monsur Ali, Pushpinder Kanda, Yingfu Li

Bibliographic record

VenueJournal of Visualized Experiments · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDeoxyribozymeBacteriaComputational biologyBiologyMicrobiologyChemistryDNABiochemistryGenetics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.417
Teacher spread0.389 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2012
Admission routes2
Has abstractyes

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