Programmable Cleavage of Double-stranded DNA by Combined Action of Argonaute CbAgo from <i>Clostridium butyricum</i> and Nuclease Deficient RecBC Helicase from <i>E.coli</i>
Bibliographic record
Abstract
ABSTRACT Prokaryotic Argonautes (pAgos) use small nucleic acids as specificity guides to cleave single-stranded DNA at complementary sequences. DNA targeting function of pAgos creates attractive opportunities for DNA manipulations that require programmable DNA cleavage. Discovery of mesophilic Argonautes active at physiological temperature places pAgos closer to their possible application for genome editing as a simpler alternative to CRISPR/Cas nucleases. Currently, the use of mesophilic pAgos as programmable DNA endonucleases is hampered by their poor action on double-stranded DNA (dsDNA), mainly due to their inability to invade the DNA duplex. The present study demonstrates that efficient in vitro cleavage of double-stranded DNA by mesophilic Argonaute CbAgo from Clostridium butyricum can be activated via the DNA strand unwinding activity of nuclease deficient mutant of RecBC DNA helicase from Escherichia coli (referred to as RecB exo- C). Properties of CbAgo and characteristics of simultaneous cleavage of complementary DNA strands in concurrence with DNA strand unwinding by RecB exo- C were thoroughly explored using 0.3-25 kb DNA substrates. When combined with RecB exo- C helicase, CbAgo was capable of cleaving target sequences located 11-12.5 kb from the ends of linear dsDNA at 37ºC. Our study demonstrates that CbAgo with RecB exo- C can be programmed to generate dsDNA fragments flanked with custom-designed single-stranded overhangs suitable for ligation with compatible DNA fragments. At present, the combination of CbAgo and RecB exo- C represents the most efficient mesophilic DNA-guided DNA-cleaving programmable endonuclease for use in diagnostic and synthetic biology methods that require sequence-specific nicking/cleavage of dsDNA at any desired location.
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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 | 0.000 | 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) | 0.001 | 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 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".