Back

Sequence-specific and programmable catalytic degradation of extracellular microRNAs by membrane-anchored DNA nanomachines

Zhou, Z.; Zhang, L.; Wang, X.; Zhang, C.; Yan, Q.; Wang, S.; Liu, H.; Su, X.

2026-10-01 biochemistry
10.64898/2026.09.30.755580 bioRxiv
Show abstract

Extracellular microRNAs (miRNAs) contribute to metastasis through communication between tumour cells and their microenvironment, yet their sustained, sequence-selective depletion remains challenging. Here we report Programmable RNA-Intercepting Molecular Eraser (PRIME), a membrane-anchored DNA nanomachine that organizes miRNA recognition and catalytic degradation at the cell surface. A double-tetrahedral DNA scaffold positions a programmable recognition hairpin beside RNase H. Target binding generates an RNA/DNA hybrid for RNase H-mediated cleavage, and product release resets the hairpin for subsequent degradation cycles. Membrane-anchored PRIME depleted matched extracellular miRNAs by 70.6-89.4% across lung, breast and cervical cancer cell models. Multi-target PRIME (M-PRIME) inhibited migration by 71.3-96.0% in Transwell assays across these models. To evaluate its therapeutic potential, we established an orthotopic lung-tumour model and an early-intervention breast-cancer lung-colonization model in mice. Aptamer-directed M-PRIME combined with gemcitabine reduced pulmonary tumour burden by 74.5% in established lung tumours and 93.2% in the lung-colonization model. These effects were consistent with PRIME-mediated extracellular miRNA degradation, with circulating target miRNA levels reduced by 77.5-98.7% relative to gemcitabine alone. Together, these findings establish a programmable material strategy for sustained extracellular RNA depletion and support its potential to enhance chemotherapy in preclinical tumour models.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.