Engineering supramolecular dynamics of self-assembly and turnover of oncogenic microRNAs to drive their synergistic destruction in tumor models
Source abstract
Abstract
Rationally-engineered functional biomaterials offer the opportunity to interface with complex biology in a predictive, precise, yet dynamic way to reprogram their behaviour and correct shortcomings. Success here may lead to a desired therapeutic effect against life-threatening diseases, such as cancer. Here, we engineered "Crab"-like artificial ribonucleases through coupling of peptide and nucleic acid building blocks, capable of operating alongside and synergistically with intracellular enzymes (RNase H and AGO2) for potent destruction of oncogenic microRNAs. "Crab"-like configuration of two catalytic peptides ("pincers") flanking the recognition oligonucleotide was instrumental here in providing increased catalytic turnover, leading to ≈30-fold decrease in miRNA half-life as compared with that for "single-pincer" conjugates. Dynamic modeling of miRNA cleavage illustrated how such design enabled "Crabs" to drive catalytic turnover through simultaneous attacks at different locations of the RNA-DNA heteroduplex, presumably by producing smaller cleavage products and by providing toeholds for competitive displacement by intact miRNA strands. miRNA cleavage at the 5'-site, spreading further into double-stranded region, likely provided a synergy for RNase H1 through demolition of its loading region, thus facilitating enzyme turnover. Such synergy was critical for sustaining persistent disposal of continually-emerging oncogenic miRNAs. A single exposure to the best structural variant (Crab-p-21) prior to transplantation into mice suppressed their malignant properties and reduced primary tumor volume (by 85 %) in MCF-7 murine xenograft models.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced biosensing and bioanalysis techniques · RNA Interference and Gene Delivery · DNA and Nucleic Acid Chemistry
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (88)
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