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Record W2746831066 · doi:10.1021/acs.accounts.7b00262

Discovery and Biosensing Applications of Diverse RNA-Cleaving DNAzymes

2017· article· en· W2746831066 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAccounts of Chemical Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDeoxyribozymeDNARNARibozymeCleaveChemistrySequence (biology)Computational biologyBiosensorCleavage (geology)BiochemistryCombinatorial chemistryNanotechnologyBiologyGeneMaterials science

Abstract

fetched live from OpenAlex

Conspectus DNA-based enzymes, or DNAzymes, are not known to exist in Nature but can be isolated from random-sequence DNA pools using test tube selection techniques. Since the report of the first DNAzyme in 1994, many catalytic DNA molecules for catalyzing wide-ranging chemical transformations have been isolated and studied. Our laboratory has a keen interest in searching for diverse DNAzymes capable of cleaving RNA-containing substrates, determining their sequence requirements and structural properties, and examining their potential as biosensors. This Account begins with the description of an accidental discovery on the sequence adaptability of a small DNAzyme known as “8-17”, when we performed 16 parallel selections to search for DNAzymes that targeted each and every possible dinucleotide junction of RNA for cleavage. DNAzyme 8-17 dominated all the selection pools targeting purine-containing junctions. In-depth sequence analysis revealed that 8-17 could manifest itself in many sequence options defined by the requirement of four absolutely conserved nucleotides. This study also exposed the fact that 8-17 had poor activity toward pyrimidine–pyrimidine junctions. With this information in hand, we proceeded to the discovery of diverse non-8-17 DNAzymes that exhibited robust catalytic activity under physiological conditions. These DNAzymes were found to universally interact with their substrates through two Watson–Crick binding arms and have a catalytic core of varying length and secondary-structure complexity. RNA-cleaving DNAzymes were also isolated to function at acidic conditions (pH 3–5), and these molecules exhibited intriguing pH profiles, with the highest activity precisely matching the pH used for their selection. Interestingly, these DNAzymes appear to use non-Watson–Crick interactions in defining their structures. More recently, we have embarked on the development of ligand-responsive RNA-cleaving fluorogenic DNAzymes that can recognize specific bacterial pathogens, such as Escherichia coli and Clostridium difficile, using a method that does not require a priori identification of a specific biomarker. Instead, the crude extracellular mixture as a whole is used as the target to drive the DNAzyme isolation. High recognition specificity can be achieved with a double-selection approach in which a DNA library is negatively selected against the cellular mixture prepared from unintended bacteria, followed by positive selection against the same mixture derived from a specific species or strain of bacterial pathogen. Finally, we have shown that DNAzymes’ compatibility with DNA replication can benefit the design of amplification mechanisms that uniquely link the action of RNA-cleaving DNAzymes to rolling circle amplification, an isothermal DNA amplification technique. These methods are well suited for translating the target-binding and cleavage activity of an analyte-activated RNA-cleaving DNAzyme into the production of massive amounts of DNA amplicons to achieve ultrahigh detection sensitivity. Given the high chemical stability of DNA, our ability to discover catalytic DNA sequences by simultaneously evaluating as many as 10 16 different DNA sequences, the accessibility to diverse RNA-cleaving DNAzymes in a single DNA pool, and the availability of methods for designing simple biosensors that incorporate RNA-cleaving DNAzymes, we believe we are moving closer to employing RNA-cleaving DNAzymes for exciting applications, such as point of care diagnostics or field detection of environmental toxins.

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.

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)0.001
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 score0.014
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.038
GPT teacher head0.394
Teacher spread0.355 · 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