Artificial Intelligence (AI) has unveiled a groundbreaking method to decipher the intricate ‘grammar’ of DNA, potentially transforming how genes are edited and controlled.
This remarkable advancement, achieved by researchers at The Jackson Laboratory, the Broad Institute of MIT and Harvard, and Yale University, introduces a pioneering approach in the field of genetic engineering.
The key innovation involves using AI to design synthetic DNA switches known as cis-regulatory elements (CREs).
These switches can precisely control gene expression, functioning as exact on-off regulators in specific tissues or cell types.
Such precision in controlling genetic activity addresses one of the most significant challenges in gene editing, where ensuring gene modifications affect only targeted cells has been problematic.
Ryan Tewhey, co-senior author of the study, emphasizes the specificity of these synthetically designed elements, stating, “This creates the opportunity for us to turn the expression of a gene up or down in just one tissue without affecting the rest of the body.”
This capability opens the door to more controlled genetic interventions, minimizing unintended effects across the organism.
Understanding the ‘grammar’ of CREs is crucial in gene regulation.
Although every cell contains the same genetic code, CREs act as control switches, determining which genes are activated at precise times and locations within the body.
The challenge lies in deciphering how these DNA sequences operate, a task made feasible by AI-driven deep learning.
The research team employed deep learning to train models capable of predicting the activity of CREs by analyzing vast amounts of DNA data.
This analysis allowed the team to uncover patterns not easily identifiable by humans, thus enabling the design of synthetic CREs with tailored functions.
Utilizing the CODA (Computational Optimization of DNA Activity) platform, researchers successfully created thousands of novel CREs capable of regulating gene expression in selected cell types.
Validation of these synthetic CREs took place through experiments in animal models such as zebrafish and mice, demonstrating impressive precision in activating genes exclusively in intended tissues.
For instance, a synthetic CRE managed to activate a fluorescent protein in zebrafish livers without turning it on elsewhere in the organism.
This precision heralds significant possibilities for specific therapeutic applications, such as treating genetic diseases and optimizing tissue regeneration, by offering more controlled gene activation.
Consequently, researchers foresee vast applications for these AI-designed CREs in fields like advanced medical treatments and biomanufacturing.
Such advancements not only enhance basic scientific research but also promise substantial biomedical implications.
By unlocking the potential to write new regulatory elements with pre-defined functions, these tools offer the promise of precisely controlling gene expression in very specific cell types, revolutionizing therapy and research practices.