British "Nature" magazine published an artificial intelligence and biotechnology research online on the 8th: Scientists reported a method to achieve accurate and predictable editing of disease-causing gene mutations through machine learning. This achievement offers new possibilities for the study of genetic diseases and the development of potential therapies.
Although CRISPR-Cas9 has revolutionized the genome editing technology used for research, it is currently considered to be particularly important to ensure the accuracy of this technology.
The CRISPR-Cas9 genome edits commonly used DNA "templates" to ensure the accuracy of DNA repair or to import specific DNA sequences into the genome. Therefore, DNA repairs that lack these templates are often considered inaccurate.
Now, Richard Schulwood, a scientist at the American Brigham and Women's Hospital and Harvard Medical School, and colleagues have invented a method for predicting the results of genome repair using machine learning, enabling accurate templateless Cas9 editing. The team trained a machine learning model called inDelphi with a database of nearly 2,000 pairs of Cas9 guide RNA (gRNA) and human DNA targets.
The model recognizes that 5%-11% of Cas9 guide RNA targeting the human genome can produce single and predictable repair results in more than 50% of cases (referred to as "Precision-50"). inDelphi also uses the template-free Cas9 editor to identify and predict appropriate target genes for disease-causing gene mutations, including targets that were previously considered unusable by this method.
Finally, the research team proved through experiments that human cells are associated with Hermansky-Pradak syndrome (HPS, a syndrome of albinism syndrome), Menkes disease (also known as hair gray matter malnutrition), and familial hypercholesterolemia. Nearly 200 kinds of pathogenic mutations related to these three diseases, the accuracy of editing and repair can reach the accuracy of -50 standard.
The researchers said the results established a way to achieve accurate, template-free genome editing.
Editor-in-chief
We can regard the CRISPR-Cas9 gene editing technology as a nano-scale "toolkit". It is best to cut and modify DNA at specific locations according to our needs. But in practice, this guy may have an accident - on the wrong gene - that is, the "off-target effect", which has some serious consequences. Now researchers have developed this machine learning algorithm, which not only improves the efficiency and specificity of the genome editing of the technology, but also makes the technology more accurate and minimizes the risk. (Reporter Zhang Mengran)
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