A virtual tool for studying gene function aims to reduce the number of animals used for genetic testing

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debt: Nucleic Acids Research (2023) DOI: 10.1093/nar/gkad450

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debt: Nucleic Acids Research (2023) DOI: 10.1093/nar/gkad450

Researchers at the Texas A&M University School of Veterinary Medicine and Biomedical Sciences (VMPS) have developed a new virtual instrument that will allow scientists to study gene function more efficiently and reduce the number of animal models used in genetic research.

A tool called gene knockout inference (GenKI) allows scientists to simulate the relationship between genes in individual cells so they can study which genes affect cellular functions. Understanding the purpose of each gene is essential to developing new treatments for everything from cancer to the common cold.

„Molecular biologists and other scientists use so-called 'knockout models' to study target genes,” said Dr. James Cai, VMBS professor in the Department of Veterinary Integrative Biology. „Scientists delete – or 'knock out' – a gene to see what its function is Published In the magazine Nucleic Acids Research, our newly developed virtual instrument, my Ph.D. student Yongjian Yang, will allow researchers to study genes without using multiple mouse models, making it easier to see how genes interact with each other.”

Looking at the bigger picture

Determining the function of every gene in an organism is challenging. Humans have 20,000 to 25,000 protein-coding genes, and some plants may have more than 40,000.

To make matters more complicated, genes often work together to govern different parts of the body, meaning that knockout studies don't always capture the full picture of gene function.

„Genes are part of a larger network,” Cai said. „They have partners, backups, and long-distance relationships with each other. Sometimes it's not easy to determine which genes are linked.

„If scientists want to study genes together, they can do what's called a 'double knockout,' but that's expensive and limited to two genes,” he said. „Theoretically, as the technology develops a virtual instrument could knock out two, three or more genes.”

Another advantage of using a virtual tool for genetic research is that researchers can study cross-tissue response, or the relationship of genes between multiple tissues, organs, and systems.

„It's common for research labs to specialize in studying one aspect of the body, such as the central nervous system or metabolism,” Cai said. „Therefore, they focused on studying tissue samples from a single location, such as the liver. But some genes may play more than one role, so we expect that using a virtual tool like GenKI, which can access multiple tissue cell types, will make it easier to see cross-tissue connections.”

Focusing on the essentials

Using a computerized tool is also beneficial because researchers can't read genes that otherwise wouldn't be able to study.

„When you're working with tissue, you can't knock out any genes that are essential for the body to function, otherwise you won't have a viable model,” Cai said. „This forces scientists to study compensatory or secondary-effects of the gene, but not the target gene.

„With GenKI, we can turn off the activity of any gene and the simulation will still work,” he explained. „All we have to do is set the gene expression level to zero or reduce it, and the tool will show us which data points are affected by the change.”

GenKI will allow scientists to get answers to questions earlier in the research process than is possible when using tissue.

„Even if you're only trying to answer preliminary questions, you can start the simulation,” Cai said. „We hope that as the technology develops, scientists will be able to rely more and more on the tool instead of animal models.”

The future of genetic research

Organisms are incredibly complex, which makes creating digital simulations challenging.

„Right now, the tool can simulate genetic relationships in individual cells from humans and mice,” Cai said. „We start with something simple to make sure the tool is accurate.”

While the tool is still in development, Cai expects that as more people send his team data on different cell types, they will be able to expand the tool's functionality while maintaining a high degree of accuracy.

„Eventually, we hope to be able to simulate other cell types and even other organisms,” he explained.

„With this technology, we can reduce animal model use by 50% in 15 years,” he said. „But we can't just do it—it's going to take a cultural shift. Researchers have to keep trying to develop the tools to believe that.”

More information:
Yongjian Yang et al, Single-Cell Gene Regulatory Networks Learning Differential Graph Autoencoder with Gene Knockout Inference, Nucleic Acids Research (2023) DOI: 10.1093/nar/gkad450

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