Developmental biology - DNA|
Algorithm Predicts How Cells Repair Broken DNA
Online tool can help correct pathogenic mutations and restore genes to healthy state...
The human genome has its own proofreaders and editors, and their handiwork is not as haphazard as we thought.
When DNA's double helix is broken following damage, perhaps from exposure to X-rays, molecular machines perform a kind of genetic "auto-correct" and put the genome back together. But! Repairs can often be imperfect. Just as smartphones might "fix" a misspelled text message into something unreadable, a cell's natural DNA repair can add or remove - at the break site - bits of DNA in a random, unpredictable manner. Although editing genes with CRISPR-Cas9 allows scientists to break DNA at specific locations, it can also create "spelling errors" that alter gene function.
The response to CRISPR-induced damage, called "end joining," is useful for disabling a gene, but researchers have deemed it too error-prone to exploit for therapeutic purposes.
A new study turns this view around. A team of researchers recently discovered cells often repair broken genes in ways that are precise and predictable, sometimes even returning mutated genes back to their healthy version. In addition, they put this predictive power to the test and successfully corrected mutations in cells taken from patients with one of two rare genetic disorders. They created a machine-learning algorithm that can predict how human and mouse cells respond to CRISPR-induced breaks in DNA.
This work suggests that a cell's genetic auto-correction could one day be combined with CRISPR-based therapies to correct gene mutations by simply cutting DNA precisely and allowing the cell to naturally heal the damage.
The study, published this week in Nature, was led by David Liu, the Richard Merkin Professor and director of the Merkin Institute of Transformative Technologies in Healthcare, as well as vice chair of the faculty at the Broad Institute; and David Gifford, professor of computer science and biological engineering at MIT; along with Richard Sherwood, assistant professor of medicine in the Division of Genetics at Brigham and Women's Hospital.
Gifford: "Machine learning offers new horizons for the development of human therapeutics. This study is an example of how combining computational experiment design and analysis with therapeutic goals can produce an unexpected therapeutic modality."
Liu: "We don't currently have an efficient way to precisely correct many human disease mutations. Using machine learning, we've shown we can often correct those mutations predictably, by simply letting the cell repair itself."
Many disease-associated mutations involve extra or missing DNA, known as insertions and deletions. Researchers have tried to correct those mutations with CRISPR-based gene editing. To do this, they cut the double helix with an enzyme and insert missing DNA, or remove extra DNA, using a template of genetic material that serves as a blueprint. The approach, however, only works in rapidly dividing cells like blood stem cells and even then is only partly effective, making it a poor choice for therapeutics aimed at the majority of cell types in the body. To restore gene function without a repair template requires knowing how the cell will fix CRISPR-induced DNA breaks - knowledge that only exists now.
Evidence of a pattern to CRISPR repair outcomes was seen before, and Gifford's lab began to think such outcomes might be enough to make accurate predictions. But, they needed more data.
Led by MIT graduate student Max Shen and Broad Institute postdoctoral researcher Mandana Arbab, researchers developed a strategy to observe how cells repair a library of 2,000 sites targeted by CRISPR in the mouse and human genomes. After observing how the cell repaired those cuts, they poured that data into a machine-learning model, inDelphi, prompting the algorithm to learn how the cell responded to cuts at each site - that is, which bits of DNA the cell added to or removed from each damaged gene.
They found that inDelphi could discern patterns at cut sites that predicted what insertions and deletions were made in the corrected gene. At many sites, the set of corrected genes did not contain a huge mixture of variations, but rather a single outcome, such as correction of a pathogenic gene. Indeed, after querying inDelphi for disease-relevant genes that could be corrected by cutting in just the right place, the researchers found nearly two hundred pathogenic genetic variants that were mostly corrected to their normal, healthy versions after being cut with CRISPR-associated enzymes. They were also able to correct mutations in cells from patients with two rare genetic disorders, Hermansky-Pudlak syndrome and Menkes disease.
"We show that the same CRISPR enzyme that has been used primarily as a sledgehammer can also act as a chisel," said Sherwood. "The ability to know the most likely outcome of your experiment before you do it will be a real advance for the many researchers using CRISPR."
"We had hoped that we would be able to repair disease-associated genes to their native forms, and it was quite rewarding to see that our hypothesis was correct," said Gifford.
More work remains before this approach can be used to correct mutations in the clinic. In cases where the predicted outcomes lead to something useful, either for research or therapeutic purposes, this study shows that triggering the cell's natural "autocorrect" can be efficient in genome editing.
Following Cas9 cleavage, DNA repair without a donor template is generally considered stochastic, heterogeneous and impractical beyond gene disruption. Here, we show that template-free Cas9 editing is predictable and capable of precise repair to a predicted genotype, enabling correction of disease-associated mutations in humans. We constructed a library of 2,000 Cas9 guide RNAs paired with DNA target sites and trained inDelphi, a machine learning model that predicts genotypes and frequencies of 1- to 60-base-pair deletions and 1-base-pair insertions with high accuracy (r = 0.87) in five human and mouse cell lines. inDelphi predicts that 5–11% of Cas9 guide RNAs targeting the human genome are ‘precise-50’, yielding a single genotype comprising greater than or equal to 50% of all major editing products. We experimentally confirmed precise-50 insertions and deletions in 195 human disease-relevant alleles, including correction in primary patient-derived fibroblasts of pathogenic alleles to wild-type genotype for Hermansky–Pudlak syndrome and Menkes disease. This study establishes an approach for precise, template-free genome editing.
Max W. Shen, Mandana Arbab, Jonathan Y. Hsu, Daniel Worstell, Sannie J. Culbertson, Olga Krabbe, Christopher A. Cassa, David R. Liu, David K. Gifford and Richard I. Sherwood.
Co-authors Mark Sansom PhD, professor of biochemistry at University of Oxford and Shanlin Rao, a graduate student in his lab, carried out molecular dynamics simulations on the different conformational states of the serotonin receptors for this study.
InDelphi is available at the site that allows academic researchers around the globe to design guide-RNAs for making precise edits. Scientists interested in repairing pathogenic mutations can query the site to see where they might be able to cut DNA and get their desired outcomes. In addition, scientists may also use the site to confirm the efficiency of DNA cuts intended to turn genes off, or to determine the end-joining byproducts of a template-driven repair.
About the Broad Institute of MIT and Harvard
Broad Institute of MIT and Harvard was launched in 2004 to empower this generation of creative scientists to transform medicine. The Broad Institute seeks to describe all the molecular components of life and their connections; discover the molecular basis of major human diseases; develop effective new approaches to diagnostics and therapeutics; and disseminate discoveries, tools, methods, and data openly to the entire scientific community.
Founded by MIT, Harvard, Harvard-affiliated hospitals, and the visionary Los Angeles philanthropists Eli and Edythe L. Broad, the Broad Institute includes faculty, professional staff, and students from throughout the MIT and Harvard biomedical research communities and beyond, with collaborations spanning over a hundred private and public institutions in more than 40 countries worldwide. For further information about the Broad Institute, go tohttp://www.broadinstitute.org.
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Cas9-expressing cells are transduced [a process that introduces foreign DNA into a cell by a virus] using pooled lentiviral [a retrovirus that can infect both dividing and nondividing cells] targeting thousands of genes with several guide sequences per gene. Credit: Authors and Nature magazine.