Preventing Type 1 diabetes by deleting a stress response gene

Diabetes blood sugar testing
The immune system's T cells stopped destroying insulin-producing beta cells in mice when a gene involved in the stress response was removed, University of Wisconsin scientists discovered. (Pixabay/stevepb)

In Type 1 diabetes, the immune system’s T cells mistakenly attack insulin-producing beta cells. Physicians can identify many patients who are at risk for developing the disease using blood tests to identify abnormal autoantibodies, but there’s nothing they can do to actually prevent the disease.

Now, scientists at the University of Wisconsin (UW)-Madison are proposing a new approach to preventing Type 1 diabetes: by deleting a gene in beta cells that controls the response to stress. When they removed the gene, called IRE1-alpha, in the beta cells of mice, the cells changed into mature insulin producers. More importantly, T cells stopped attacking them. They reported their findings in the journal Cell Metabolism.

The researchers started with mice that had been genetically altered to develop Type 1 diabetes. Just before the immune attack began, they removed IRE1-alpha from the animals’ beta cells. They expected that the cells would immediately die, but they didn’t. Instead, they “de-differentiated” into immature cells, which the T cells no longer recognized as a threat. Then they transformed back into normal beta cells, which were able to produce insulin while still being left alone by T cells.

Virtual Roundtable

ASCO Explained: Expert predictions and takeaways from the world's biggest cancer meeting

Join FiercePharma for our ASCO pre- and post-show webinar series. We'll bring together a panel of experts to preview what to watch for at ASCO. Cancer experts will highlight closely watched data sets to be unveiled at the virtual meeting--and discuss how they could change prescribing patterns. Following the meeting, we’ll do a post-show wrap up to break down the biggest data that came out over the weekend, as well as the implications they could have for prescribers, patients and drugmakers.

The T cells “don't really recognize the beta cells as a problem anymore. They don't attack," said lead author Feyza Engin, Ph.D., professor of biomolecular chemistry at UW-Madison, in a statement. The mice experienced transient high blood sugar that wasn’t dangerous, Engin’s team reported, but then their blood sugar levels normalized.

The team tracked the altered beta cells and T cells in the mice for a year and observed no destruction that would lead to diabetes.

RELATED: Gene therapy temporarily reverses diabetes in mice

Several research groups are investigating the potential of using gene therapy and gene editing against Type 1 diabetes. They include the University of Pittsburgh School of Medicine, which in 2018 reported progress in its effort to transform alpha cells from the pancreas into insulin-producing beta cells by using an adeno-associated viral vector to deliver two proteins into the pancreas. CRISPR Therapeutics is working with ViaCyte to develop gene-edited stem cell treatments for diabetes.

Last year, a research team in Korea used a technology called CRISPR interference against obesity and Type 2 diabetes, inhibiting the gene FABP4 in adipose tissue. In mice, the technique prompted a 20% loss of body weight and reduced insulin resistance.

UW’s Engin previously worked at Harvard University, where she discovered that the naturally occurring chemical tauroursodeoxycholic acid could be used to correct a stress response in cells that contributes to both Type 1 and Type 2 diabetes. She believes her latest findings related to IRE1-alpha could also be used to identify new compounds that might prevent or treat Type 1 diabetes.

Suggested Articles

The FDA named more than two dozen coronavirus antibody tests that should be taken off the market weeks after the agency clamped down on tests.

Inovio CEO J. Joseph Kim is undeterred by short sellers and other detractors who doubt his company can shuttle a COVID-19 DNA vaccine to market.

The machine-learning programs scroll through data to detect hard-to-spot patterns. Yet few have been tested against standard procedures.