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Enregistrement W1989971013 · doi:10.3791/3961

Detection of Bacteria Using Fluorogenic DNAzymes

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

Notice bibliographique

RevueJournal of Visualized Experiments · 2012
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueAdvanced biosensing and bioanalysis techniques
Établissements canadiensMcMaster University
Organismes subventionnairesNatural Sciences and Engineering Research Council of Canada
Mots-clésDeoxyribozymeBacteriaComputational biologyBiologyMicrobiologyChemistryDNABiochemistryGenetics

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,353

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,028
Tête enseignante GPT0,417
Écart entre enseignants0,389 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations17
Publié2012
Routes d'admission2
Résumé présentoui

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