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AI System Predicts Effects of Gene Modifications

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Researchers at Gladstone Institutes, the Broad Institute of MIT and Harvard, and Dana-Farber Cancer Institute have turned to artificial intelligence (AI) to help them understand how large networks of interconnected human genes control the function of cells, and how disruptions in those networks cause disease.

Large language models, also known as foundation models, are AI systems that learn fundamental knowledge from massive amounts of general data, and then apply that knowledge to accomplish new tasks—a process called transfer learning. These systems have recently gained mainstream attention with the release of ChatGPT, a chatbot built on a model from OpenAI.

In the new work, published in the journal Nature, Gladstone Assistant Investigator Christina Theodoris, MD, PhD, developed a foundation model for understanding how genes interact. The new model, dubbed Geneformer, learns from massive amounts of data on gene interactions from a broad range of human tissues and transfers this knowledge to make predictions about how things might go wrong in disease.

In the new study, Theodoris, Ellinor, and their colleagues tackled this problem by leveraging a machine learning technique called “transfer learning” to train Geneformer as a foundational model whose core knowledge can be transferred to new tasks.

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First, they “pretrained” Geneformer to have a fundamental understanding of how genes interact by feeding it data about the activity level of genes in about 30 million cells from a broad range of human tissues.

To demonstrate that the transfer learning approach was working, the scientists then fine-tuned Geneformer to make predictions about the connections between genes, or whether reducing the levels of certain genes would cause disease. Geneformer was able to make these predictions with much higher accuracy than alternative approaches because of the fundamental knowledge it gained during the pretraining process.

In addition, Geneformer was able to make accurate predictions even when only shown a very small number of examples of relevant data.

“This means Geneformer could be applied to make predictions in diseases where research progress has been slow because we don’t have access to sufficiently large datasets, such as rare diseases and those affecting tissues that are difficult to sample in the clinic,” says Theodoris.

Lessons for heart disease

Theodoris’s team next set out to use transfer learning to advance discoveries in heart disease. They first asked Geneformer to predict which genes would have a detrimental effect on the development of cardiomyocytes, the muscle cells in the heart.

Among the top genes identified by the model, many had already been associated with heart disease.

“The fact that the model predicted genes that we already knew were really important for heart disease gave us additional confidence that it was able to make accurate predictions,” says Theodoris.

However, other potentially important genes identified by Geneformer had not been previously associated with heart disease, such as the gene TEAD4. And when the researchers removed TEAD4 from cardiomyocytes in the lab, the cells were no longer able to beat as robustly as healthy cells.

Therefore, Geneformer had used transfer learning to make a new conclusion: even though it had not been fed any information on cells lacking TEAD4, it correctly predicted the important role that TEAD4 plays in cardiomyocyte function.

“The transfer learning approach allowed us to overcome the challenge of limited patient data to efficiently identify possible proteins to target with drugs in diseased cells.” – CHRISTINA THEODORIS, MD, PHD

Finally, the group asked Geneformer to predict which genes should be targeted to make diseased cardiomyocytes resemble healthy cells at a gene network level. When the researchers tested two of the proposed targets in cells affected by cardiomyopathy (a disease of the heart muscle), they indeed found that removing the predicted genes using CRISPR gene editing technology restored the beating ability of diseased cardiomyocytes.

“In the course of learning what a normal gene network looks like and what a diseased gene network look like, Geneformer was able to figure out what features can be targeted to switch between the healthy and diseased states,” says Theodoris. “The transfer learning approach allowed us to overcome the challenge of limited patient data to efficiently identify possible proteins to target with drugs in diseased cells.”

“A benefit of using Geneformer was the ability to predict which genes could help to switch cells between healthy and disease states,” says Ellinor. “We were able to validate these predictions in cardiomyocytes in our laboratory at the Broad Institute.”

The researchers are planning to expand the number and types of cells that Geneformer has analyzed in order to keep boosting its ability to analyze gene networks. They’ve also made the model open-source so that other scientists can use it.

“With standard approaches, you have to retrain a model from scratch for every new application,” says Theodoris. “The really exciting thing about our approach is that Geneformer’s fundamental knowledge about gene networks can now be transferred to answer many biological questions, and we’re looking forward to seeing what other people do with it.”

Reference: Theodoris CV, Xiao L, Chopra A, et al. Transfer learning enables predictions in network biology. Nature. 2023:1-9. doi: 10.1038/s41586-023-06139-9

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