Background

Gene therapy has emerged as a next-generation medical technology that targets the root causes of diseases. However, precisely controlling the expression of exogenous genes within target cells is extremely challenging. Excessive gene expression in unintended tissues can trigger severe adverse effects or immune responses. Traditionally, gene switches that utilize antibiotic molecules, such as doxycycline, as regulators have been studied. However, long-term administration of antibiotics can lead to immune rejection or affect unintended tissues, resulting in safety issues. To address this, novel switch technologies that precisely control gene expression have long been of interest. Riboswitches are non-coding RNA molecules that change their structure upon binding to a specific molecule called a ligand, thereby regulating gene expression. This switch operates with RNA alone, without complex regulatory proteins, which efficiently saves the payload capacity of gene therapy vectors. However, redesigning naturally occurring switches found in bacteria for the mammalian cellular environment has been extremely difficult. Artificially engineered RNA structures have struggled to bind specifically to target molecules within complex human cells, making it difficult to achieve a clear on-off response.

Key Findings

The research team at the University of Navarra recently presented a new milestone in overcoming this impasse in a paper published in the journal 'Molecular Therapy - Nucleic Acids'. They combined artificial intelligence (AI)-based deep learning (DL) models, cutting-edge structural biology techniques such as cryo-electron microscopy (Cryo-EM), and a new high-throughput selection technology to create a platform that rapidly completes the design of customized riboswitches. This platform moves beyond traditional simple sequence analysis and predicts three-dimensional interactions between molecules through computer simulations, increasing the reliability of the switch. In fact, the research team used a DL model to screen billions of RNA library candidates and select the optimal aptamer structure that precisely binds to the target ligand. This structure is designed to operate with a clinically validated, safe ligand that is well-tolerated and easily permeates cell membranes. The research team elucidated the differences between natural switches found in bacteria and eukaryotes and designed a pathway to optimize the expression platform structure to prevent the loss of regulatory signals in mammalian cells. While previous riboswitch models had a difference of only 2-3 times in expression levels when turning genes on and off, the computer-designed next-generation switch showed a strong working efficiency, inducing more than 10 times the gene expression when the ligand was bound. In addition, the selectivity criteria were significantly improved, demonstrating high precision by not reacting at all to structurally very similar non-target substances.

Significance and Prospects

This research is considered an important milestone in advancing RNA-based therapeutic technologies, which have been limited to proof-of-concept studies at the laboratory level, towards actual clinical-grade therapies. In particular, it is expected to be useful in the next generation of immunotherapies that require precise control of the administered dose and in the treatment of rare diseases that require precise regulation of hormone secretion. However, there are still technical hurdles to overcome before it can be commercialized as a finished product that is directly administered to patients. First, it is necessary to carefully determine whether the customized ligand administered externally is safely degraded and excreted in the body and whether it causes toxicity with long-term use. Furthermore, minimizing the human immune response to the viral vector that delivers the riboswitch is also a challenge to be addressed in the future. Nevertheless, the research team is taking one step further and envisions a 'self-regulating switch' that detects disease-generated metabolites that spontaneously occur in the disease state and activates therapeutic genes without the patient having to take an inducing drug directly. There is growing anticipation that an era of intelligent, autonomous medicine, in which the body responds to internal danger signals and delivers only the necessary amount of therapeutic protein, will soon arrive.