Researchers Use Machine Learning To Repair Genetic Damage

DNA damage is constantly occurring in cells, either due to external sources or as a result of internal cellular metabolic reactions and physiological activities. Accurate repair of such DNA damages is critical to avoid mutations and chromosomal rearrangements linked to diseases including cancer, immunodeficiencies, neurodegeneration, and premature aging. 

A team of researchers at Massachusetts General Hospital and the National Cancer Research Centre have identified a way to repair genetic damage and prevent DNA alterations using machine learning techniques. 

The researchers state that it is possible to learn more about how cancer develops and how to fight it if we understand how DNA lesions originate and repair. Therefore, they hope that their discovery will help create better cancer treatments while also protecting our healthy cells.

To combat challenges to DNA integrity, cells have evolved systems that detect DNA lesions and initiate a signaling cascade that promotes DNA repair, referred to as the DNA damage response (DDR).

Until now, one of the biggest roadblocks to measuring DNA repair kinetics was the inability to interpret and understand the massive amounts of data provided by microscope images. Researchers employed high-throughput microscopy to take millions of photos of cells after they had been genetically damaged. 

In the first phase, they introduced over 300 different proteins into the cells and tested whether they interfered with DNA repair over time in a single experiment. Their findings show that nine novel proteins were involved in DNA repair. Further, they expanded their research by using a traditional DNA micro-irradiation approach to examine 300 proteins after they had caused genetic damage visually.

The researchers observed that many proteins get attached to damaged DNA, while others migrate away from the DNA defects. A common feature of DNA repair proteins is that they either bind to or remove themselves from damaged DNA to allow repair proteins to be recruited to the lesion. 

The team believes that the study will open up new avenues for studying and manipulating DNA repair. This offers a further advantage as both platforms are pretty adaptable and may be used to uncover new genes or chemical compounds that affect DNA repair. 

Paper: https://www.cell.com/action/showPdf?pii=S2211-1247%2821%2901676-4

Reference: https://healthitanalytics.com/news/machine-learninghelps-repair-genetic-damage

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