First-Ever Study of Triple Gene Combinations Could Help Predict Risk of Disease
Like a giant game of Jenga, researchers reveal critical combinations of blocks that can cause a cell system to collapse
20-Apr-2018 12:05 PM EDT
Newswise — MINNEAPOLIS/ST. PAUL (04/20/2018) — We inherit many traits from our parents including the color of our eyes and hair. Unfortunately, we also inherit risk factors for certain diseases such as cancer, heart disease, and diabetes. Understanding those genetic connections can help prevention efforts and develop new life-saving treatments to combat these diseases.
In the largest study of its kind, researchers at the University of Minnesota and University of Toronto have revealed a complex network of genes that work together. The research builds on the teams’ previous work that showed how genes combine in pairs to underpin a cell’s health. Taking it a step further, the new study examines for the first time how higher-order gene combinations—comprising three genes—help maintain normal cell physiology.
The research is published in today’s issue of Science, a premier peer-reviewed academic journal of the American Association for the Advancement of Science (AAAS).
To understand how a cell works, scientists like to take it apart. By removing genes from cells in diverse combinations using cutting-edge gene editing techniques, researchers have now uncovered how different genes work together to keep cells alive. The research will help scientists understand how faults in multiple genes combine to drive common diseases.
Researchers say it’s much like a giant game of Jenga with thousands of gene blocks. While most single blocks can be taken out without compromising the structure, when critical combinations of blocks are removed, the system collapses. Similarly, genes with different roles can combine to keep the cell alive. By unpicking such gene alliances, we hope to reveal clues about the foundations of personal health.
It’s now clear from genome sequencing studies that each person carries thousands of genetic variants—differences in genes’ DNA sequence—that could combine to impact our health. However, these studies do not have the statistical power to predict a person’s risk of disease from their unique combination of genetic variants. This poses a major obstacle for personalized medicine which seeks to use genome information to predict risk of disease and tailor treatment.
To uncover the rules of combinatorial gene function, the team previously investigated how genes work in pairs in yeast cells. Yeast is one of biologists’ favorite cell models due to its relatively small genome comprising 6,000 genes and an already existing wealth of data. Having previously removed from yeast all possible gene pairs—18 million of them—the team now went a step further to examine what happens when you remove a subset of 36 billion possible trigenic combinations.
“Only about 1,000 of the 6,000 yeast genes are essential when deleted individually. When we studied pairs of mutants, we found about 10,000 pairs of genes that were essential when deleted in combination,” said Chad Myers, a computer scientist at the University of Minnesota and one of the lead co-authors. “Our study of triple gene combinations suggests there are at least 10- to 100-fold more instances of triple mutants that are lethal, which highlights the astounding complexity of biological systems.”
They found that, similar to interactions between two genes, trigenic interactions also mainly occur between genes that are functionally related—they code for parts belonging to the same molecular machine or that exist in the same part of the cell, for example. But with trigenic interactions, the researchers also began to see more surprising partnerships between genes that have unrelated function and are involved in different bioprocesses in the cell.
“There’s a growing understanding that interactions between genes can drive inherited disease susceptibility, which is why we have to understand the general principles of these genetic interactions,” said Charles Boone, a professor in the University of Toronto’s Donnelly Centre and one of the other lead co-authors of the study.
Custom-built robots at the University of Toronto conduct the large-scale experiments in cell genetics. The team at the University of Minnesota develops sophisticated computational methods that help make interpret the resulting data.
Using computational modeling, the researchers estimated that all genes in the cell have a role to play when trigenic interactions are taken into account. This could finally explain why yeast maintains 6,000 genes when only about 15 percent of them are essential when removed individually, a rule that holds for other cell types including human cells.
“Studying genetic networks allows you to see how genes are connected, how biological processes talk to one another and how a cell deals with perturbations in multiple genes,” said Elena Kuzmin, a lead author on the paper and a previous graduate student at the University of Toronto who is now a postdoctoral researcher at McGill University in Montreal. “You get a global view of the cell,” she said.
Thanks to recent advances in gene editing, it is now possible to remove combinations of genes from human cells, which this research team is now working on to map relationships between disease genes.
“Our yeast work demonstrates how mutations in multiple genes combine to have unexpected effects and is providing a roadmap for understanding genetic interactions in much more complex cells and organisms, including humans,” said Brenda Andrews, another one of the lead co-authors and a University of Toronto professor and director of the Donnelly Centre. “Identifying combinations of genes that work together to underpin robust biological systems is important for deciphering what goes wrong with its collapse into a disease state.”
The research was funded primarily by the National Science Foundation and the National Institutes of Health with computing infrastructure support from the University of Minnesota.
To read the full research paper entitled “Systematic analysis of complex genetic interactions” and see a full list of authors, visit the Science website.