Network-based analyses uncover how harmful microglia form in Alzheimer’s disease

Cleveland Clinic Genome Center researchers have unraveled how microglia, which perform key neuroprotective activities, also can transform and drive harmful processes such as inflammation in Alzheimer’s disease.

The study, published in Alzheimer’s & Dementia: The Journal of The Alzheimer’s Association, integrated drug databases with patient data to identify FDA-approved drugs that may be repurposed to target disease-associated microglia in Alzheimer’s disease. Specifically, the non-steroidal anti-inflammatory (NSAID) Ketorolac was identified as potentially helpful.

Led by corresponding author Feixiong Cheng, PhD, the researchers integrated genetic, chemical and human health data. They encourage other scientists to apply network-based methodology in their own research to fuel innovations in the discovery of AD drug therapies.

Microglia in Alzheimer’s disease

Although microglia use neuroinflammation and other mechanisms to protect the brain, new types of microglia can form that promote disease progression.

“Microglia have been implicated in Alzheimer’s disease for over a century. So far, attempts to stop disease progression with broad spectrum anti-inflammatory drugs and harmful microglial blockers have been ineffective,” says Dr. Cheng, director of the Genome Center. “We need to selectively block harmful microglia subtypes while leaving normal, healthy microglia intact.”

The challenge, he says, is that the factors that cause harmful microglia, and the specific ways some of them function, are unknown.

Network-based solutions

To develop a more specific drug that targets harmful microglia, Dr. Cheng and his lab needed to ask:

1. At the molecular level, what made harmful microglia different from their normal counterparts?
2. What drugs could target these differences to block or reverse the process that causes formation of harmful microglia?
3. If they identified more than one potential drug, which was the most promising? Was there any evidence to suggest that any drugs they identified could be helpful in humans?

Each of these questions required different types of data. To efficiently integrate the large amounts of data for computational analysis, Dr. Cheng assembled a team to take an integrative, network-based approach.

The team received help interpreting their data from collaborators including Michal Rosen-Zvi, PhD (Director, AI for Drug Discovery at IBM Research); Jianying Hu, PhD (Director, Healthcare and Life Sciences Research; Global Science Leader, AI for Health at IBM Research); Fei Wang, PhD (Director of the Institute of AI for Digital Health, Weill Cornell Medicine); James Leverenz, MD (Director, Luo Ruvo Center for Brain Health and Cleveland Alzheimer’s Disease Research Center); Andrew Pieper, MD, PhD (Case Western Reserve University, Louis Stokes Cleveland VA Medical Center, Harrington Discovery Institute); and Jeffrey Cummings, MD (Director, Chambers-Grundy Center for Transformative Neuroscience, University of Nevada Las Vegas).

Led by first author Jielin Xu, PhD, the team created an algorithm to combine and analyze:

• publicly available RNA-sequencing datasets from more than 700,000 individual Alzheimer’s-affected single cells to identify unique signatures of harmful microglia by determining which genes were molecular drivers.
• protein-protein interaction data from 18 publicly available datasets to predict how genes unique to harmful microglia affect cellular processes.
• chemical and drug databases to determine which FDA-approved drugs, if any, could block disease-specific protein-protein interactions to treat harmful processes caused by the gene activity of disease-associated microglia.
• real-world patient databases from millions of insured individuals to determine whether any drugs are associated with lower instances of Alzheimer’s disease diagnoses.

“Our study offers a powerful, deep, generative model to identify repurposable drugs from many types of Alzheimer’s disease findings, but the overall methods can be broadly applied to other diseases as well,” says Dr. Cheng.

Ketorolac holds promise as a treatment for AD

The analyses identified three microglia subtypes with distinct genetic signatures that caused them to switch from helpful to harmful and that drove behaviors to support Alzheimer’s disease, such as neuroinflammation and buildup of tau. Further study has the potential to reveal more drug targets and advance Alzheimer’s disease treatments, says Dr. Cheng.

The analyses also revealed that there were already FDA-approved drugs on the market designed to block many of these harmful transitions. Real-world data showed that the non-steroidal analgesic Ketorolac was associated with reduced incidence of Alzheimer’s disease. The team validated their computational predictions in dish experiments on microglia derived from patients affected by Alzheimer’s disease, where they showed that Ketorolac blocked type-I interferon (IFN) signaling. The next step is to design further experimental and clinical validation methods to evaluate the effects of Ketorolac on Alzheimer’s disease.

Dr. Cheng adds that even though these analyses focused primarily on Alzheimer’s disease, the team’s overall findings have wide-reaching implications in other neurogenerative diseases and age-related complex diseases.

“In the past, each of these discoveries would have needed to be made with their own extensive research project,” says Dr. Cheng. “Our advanced computing techniques allow us to make biological, chemical and patient-based discoveries with one study. We believe these types of artificial intelligence-assisted, network-based analyses represent the future of biomedical research.”

This research was supported by grants from the National Institute on Aging (NIA).