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MicroRNAs

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...

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Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay

Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay

Published on: May 25, 2015

Functional microRNA-targeting drug discovery by graph-based deep learning.

Arash Keshavarzi Arshadi^1,2,3,4, Milad Salem⁵, Heather Karner^1,2,3,4

¹Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA.

Patterns (New York, N.Y.)

|January 24, 2024

Summary

This summary is machine-generated.

RiboStrike, a deep learning tool, identifies small molecules targeting cancer-driving microRNAs (miRNAs). It successfully found compounds inhibiting miR-21, reducing breast cancer metastasis in mice.

Keywords:

RNA-targeting drug discovery artificial intelligence deep learning drug toxicity evaluation graph convolutional neural network in silico drug screening microRNA inhibition microRNA-21

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Identifying Targets of Human microRNAs with the LightSwitch Luciferase Assay System using 3'UTR-reporter Constructs and a microRNA Mimic in Adherent Cells

Published on: September 28, 2011

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Last Updated: Jul 5, 2025

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Published on: May 25, 2015

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library

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Published on: April 6, 2012

Identifying Targets of Human microRNAs with the LightSwitch Luciferase Assay System using 3'UTR-reporter Constructs and a microRNA Mimic in Adherent Cells

Identifying Targets of Human microRNAs with the LightSwitch Luciferase Assay System using 3'UTR-reporter Constructs and a microRNA Mimic in Adherent Cells

Published on: September 28, 2011

Area of Science:

Oncology
Bioinformatics
Molecular Biology

Background:

MicroRNAs (miRNAs) are crucial in cancer development, but targeting them with small molecules is difficult.
Specific miRNAs, like miR-21, are implicated as drivers in various cancers, including breast cancer.

Purpose of the Study:

To introduce RiboStrike, a novel deep-learning framework for identifying small molecules against specific microRNAs.
To validate RiboStrike's efficacy by targeting miR-21 in breast cancer models.

Main Methods:

Screened nine million molecules using RiboStrike, incorporating counter-screens for selectivity against miR-122 and DICER.
Utilized auxiliary models for toxicity assessment and candidate ranking.
Validated anti-miR-21 activity and target selectivity through reporter assays, RNA sequencing, and microRNA profiling.

Main Results:

Identified eight candidate molecules, with three demonstrating significant anti-miR-21 activity.
The top candidate compound effectively reduced lung metastases in a preclinical breast cancer xenograft mouse model.
Confirmed target selectivity of the identified compounds.

Conclusions:

RiboStrike is a powerful deep-learning framework capable of nominating effective small molecules targeting microRNA activity in cancer.
The identified compounds show promise for therapeutic intervention in miR-21-driven cancers, particularly breast cancer metastasis.