Glycogen is typically thought of as a reserve energy source stored in the liver and muscles. While small amounts also exist in the brain, particularly in support cells called astrocytes, its role in neurons has long been dismissed as negligible. "This new study challenges that view, and it does so with striking implications," says Professor Pankaj Kapahi, PhD, senior scientist on the study. "Stored glycogen doesn't just sit there in the brain; it is involved in pathology."

The research team, led by postdoc Sudipta Bar, PhD, discovered that in both fly and human models of tauopathy (a group of neurodegenerative diseases including Alzheimer's), neurons accumulate excessive glycogen. More importantly, this buildup appears to contribute to disease progression. Bar says tau, the infamous protein that clumps into tangles in Alzheimer's patients, appears to physically bind to glycogen, trapping it and preventing its breakdown.

When glycogen can't be broken down, the neurons lose an essential mechanism for managing oxidative stress, a key feature in aging and neurodegeneration. By restoring the activity of an enzyme called glycogen phosphorylase (GlyP) -- which kicks off the process of glycogen breakdown -- the researchers found they could reduce tau-related damage in fruit flies and human stem cell-derived neurons.

Rather than using glycogen as a fuel for energy production, these enzyme-supported neurons rerouted the sugar molecules into the pentose phosphate pathway (PPP) -- a critical route for generating NADPH (nicotinamide adenine dinucleotide phosphate) and Glutathione, molecules that protect against oxidative stress. "By increasing GlyP activity, the brain cells could better detoxify harmful reactive oxygen species, thereby reducing damage and even extending the lifespan of tauopathy model flies," said Bar.

Even more promising, the team demonstrated that dietary restriction (DR) -- a well-known intervention to extend lifespan -- naturally enhanced GlyP activity and improved tau-related outcomes in flies. They further mimicked these effects pharmacologically using a molecule called 8-Br-cAMP, showing that the benefits of DR might be reproduced through drug-based activation of this sugar-clearing system. "This work could explain why GLP-1 drugs, now widely used for weight loss, show promise against dementia, potentially by mimicking dietary restriction," said Kapahi.

Researchers also confirmed similar glycogen accumulation and protective effects of GlyP in human neurons derived from patients with frontotemporal dementia (FTD), strengthening the potential for translational therapies. Kapahi says the study emphasizes the power of the fly as a model system in uncovering how metabolic dysregulation impacts neurodegeneration. "Work in this simple animal allowed us to move into human neurons in a much more targeted way," he said.

Kapahi also acknowledges the Buck's highly collaborative atmosphere as a major factor in the work. His lab, with expertise in fly aging and neurodegeneration, took advantage of proteomics expertise in the Schilling lab and the Seyfried lab (at Emory University) as well as the Ellerby lab which has expertise in human iPSCs and neurodegeneration.

Kapahi says this study not only highlights glycogen metabolism as an unexpected hero in the brain but also opens up a new direction in the search for treatments against Alzheimer's and related diseases. "By discovering how neurons manage sugar, we may have unearthed a novel therapeutic strategy: one that targets the cell's inner chemistry to fight age-related decline," he says. "As we continue to age as a society, findings like these offer hope that better understanding -- and perhaps rebalancing -- our brain's hidden sugar code could unlock powerful tools for combating dementia."

Coauthors: Additional Buck collaborators include Kenneth A. Wilson, Tyler A.U. Hilsabeck, Sydney Alderfer, Jordan B Burton, Samah Shah, Anja Holtz, Enrique M. Carrera, Jennifer N. Beck, Jackson H Chen, Grant Kauwe, Tara E. Tracy, Birgit Schilling, and Lisa M. Ellerby. Other collaborators include Eric B. Dammer, Fatemeh Seifar and Nicholas T. Seyfried, Emory Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA as well as Ananth Shantaraman, Department of Biochemistry, Emory University School of Medicine, Atlanta, GA

Acknowledgments: The work was supported by NIH grants R01AG038688, R21AG054121, AG045835, R01AG071995, R01AG070193, T32AG000266-23, R01AG061879, P01AG066591 and 1S10 OD016281. Other support came from the Hevolution Foundation, American Federation of Aging Research, the Larry L. Hillblom Foundation and the Catalyst^X award from Alex and Bob Griswold

Now, a team of Northwestern Medicine scientists has developed an AI tool called iSeg that not only matches doctors in accurately outlining lung tumors on CT scans but can also identify areas that some doctors may miss, reports a large new study.

Unlike earlier AI tools that focused on static images, iSeg is the first 3D deep learning tool shown to segment tumors as they move with each breath -- a critical factor in planning radiation treatment, which half of all cancer patients in the U.S. receive during their illness.

"We're one step closer to cancer treatments that are even more precise than any of us imagined just a decade ago," said senior author Dr. Mohamed Abazeed, chair and professor of radiation oncology at Northwestern University Feinberg School of Medicine.

"The goal of this technology is to give our doctors better tools," added Abazeed, who leads a research team developing data-driven tools to personalize and improve cancer treatment and is a member of the Robert H. Lurie Comprehensive Cancer Center of Northwestern University.

The study was published today (June 30) in the journal npj Precision Oncology.

How iSeg was built and tested

The Northwestern scientists trained iSeg using CT scans and doctor-drawn tumor outlines from hundreds of lung cancer patients treated at nine clinics within the Northwestern Medicine and Cleveland Clinic health systems. That's far beyond the small, single-hospital datasets used in many past studies.

After training, the AI was tested on patient scans it hadn't seen before. Its tumor outlines were then compared to those drawn by physicians. The study found that iSeg consistently matched expert outlines across hospitals and scan types. It also flagged additional areas that some doctors missed -- and those missed areas were linked to worse outcomes if left untreated. This suggests iSeg may help catch high-risk regions that often go unnoticed.

"Accurate tumor targeting is the foundation of safe and effective radiation therapy, where even small errors in targeting can impact tumor control or cause unnecessary toxicity," Abazeed said.

"By automating and standardizing tumor contouring, our AI tool can help reduce delays, ensure fairness across hospitals and potentially identify areas that doctors might miss -- ultimately improving patient care and clinical outcomes," added first author Sagnik Sarkar, a senior research technologist at Feinberg who holds a Master of Science in artificial intelligence from Northwestern.

Clinical deployment possible 'within a couple years'

The research team is now testing iSeg in clinical settings, comparing its performance to physicians in real time. They are also integrating features like user feedback and working to expand the technology to other tumor types, such as liver, brain and prostate cancers. The team also plans to adapt iSeg to other imaging methods, including MRI and PET scans.

"We envision this as a foundational tool that could standardize and enhance how tumors are targeted in radiation oncology, especially in settings where access to subspecialty expertise is limited," said co- author Troy Teo, instructor of radiation oncology at Feinberg.

"This technology can help support more consistent care across institutions, and we believe clinical deployment could be possible within a couple of years," Teo added.

This study is titled "Deep learning for automated, motion- resolved tumor segmentation in radiotherapy."