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Doctors newspaper online, 07.09.2019
Troubleshooting DNA
BERLIN. Scientists from the Berlin Institute of Health (BIH) and the Charité – Universitätsmedizin Berlin have changed the tax fields of 20 disease-relevant genes together with colleagues from the USA.
This allowed them to identify those changes that have the greatest impact on the disease ( Nat Commun 2019; online August 8 ).
The results of this study have helped to predict which changes in the genetic material found in patients are really responsible for the course of the disease and are therefore suitable for targeted therapy.
base changed for base
Researchers engineered the promoters and enhancers of 20 disease-relevant genes and changed this base to base of the DNA. For this purpose, they developed a method that could generate the changes in high throughput and test them in parallel.
In cell culture, they examined how each change affected protein production. "About 85 percent of the changes have no measurable effect, of the remaining 15 percent reduce about two-thirds of the amount of protein produced," said first author Dr. Martin Kircher cited in the BIH communication.
It depends greatly on the individual mutation and the examined tax area itself, how intensively it influences the happening in the cell: "If one base is exchanged against another, that usually has less influence than a complete absence."
Results Free on the Net
Controlled controls are controls of genes altered in patients with cancer, congestive heart failure, hereditary high cholesterol, or various rare diseases. The researchers have put the results of the more than 30,000 mutation analyzes freely available on the Internet.
Kircher now hopes that this dataset will also be used: "It would be great if doctors who have analyzed the genetic material of their patients look in our database to see which effect the found mutation is likely to have and thus can estimate, whether the change found in the patient may be suitable for targeted therapy. "
Better Prediction Programs
Such an effort for 20 out of possibly hundreds of thousands to millions of tax areas raises the question of whether the prediction of which mutation produces which effect could not be predicted with machine learning or artificial intelligence. There are already several computer programs that are trying to do just that.
Kircher and his colleagues also investigated how well various programs could predict the changes observed in cell culture. "Unfortunately, that was disappointing," reports the bioinformatics editor, "the predictions rarely matched our observations. Sometimes they even predicted the exact opposite. "The scientists now hope that their dataset can also be used to improve the predictive programs. (eb)
Results of the mutation analysis at: https: //mpra.gs.washington. edu / satMutMPRA
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