Ask the Expert: UW Nathan Shock Center Co-Director David Marcinek, PhD, discusses the challenge of making sense of huge amounts of data generated in aging research

There are eight Nathan Shock Centers in the Biology of Aging across the country, funded by the National Institute of Aging (NIA) of the National Institutes of Health (NIH). The centers provide leadership in the pursuit of basic research into the biology of aging. Each center brings unique expertise under the leadership of respected experts in the field. AFAR, which manages the Nathan Shock Centers of Excellence in the Biology of Aging Coordinating Center (NSC3) (U24AG056053), will highlight the work of these centers through this series*.

Since 2023, Dr. David Marcinek has been the co-director with Jessica Young, PhD, of the Nathan Shock Center of Excellence in Aging at the University of Washington, which is the first of the Nathan Shock Centers established some three decades ago.

Describe the Research Cores at UW and how they support the mission of the Nathan Shock Center program.

We have four Research Cores: the Protein Phenotypes of Aging Core, the Metabolic Phenotypes of Aging Core, the Invertebrate Models of Aging Core, and the eXplainable Artificial Intelligence (AI) for Aging Core.

The Protein Phenotypes of Aging Core is led by two innovators in proteomics, Judit Villen, PhD, and Mike MacCoss, PhD. What’s unique about this Core is that they have assays that run the entire life cycle of a protein. This core provides standard expression analyses, but is also a leader in developing innovative new methods. Judit is an expert in what is called post-translational modifications to these proteins—how they’re regulated under different conditions in the cell environment and is developing technologies to look at how proteins interact with one another to affect their function in the cell. Mike is a leader analyzing expression in very small samples to help understand variation in protein aging within an individual and is developing tools to study proteostasis: protein stability, protein turnover, how proteins are regulated, when they get damaged, when they aggregate, when they cause problems.

The Metabolic Phenotypes of Aging is led by Dan Raftery, PhD. Dan is an expert in using both mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy to quantify metabolites in tissues and blood. From this large global analysis, you can look at more specific panels of things like acylcarnitines and ceramides, which are key markers of metabolism and inflammation. This team is also expert in developing analytical approaches that are designed to make sense out of hundreds of metabolites across multiple tissues and species.

The Invertebrate Models of Aging Core led by Maitreya Dunham, PhD, and Alex Mendenhall, PhD, capitalizes on the power of both yeast models and C. elegans worm models to study aging. Alex and Maitreya excel in educating and disseminating their tools to other labs. Alex is an expert in integrating genetic manipulation and in vivo imaging in the C. elegans that allows you to test the mechanistic hypotheses using imaging in these live whole animals. Maitreya is an expert in genomic evolution and yeast aging. Her lab has developed and optimized chemostat technology for separating mother and daughter yeast cells. This is an important technology because it allows the researcher to collect large numbers of cells for biochemical and -omic studies of yeast aging. Through Maitreya’s lab, people can learn how to use the chemostats and then recreate one in their own lab.

The eXplainable AI Core is led by Su-In Lee, PhD, a pioneer in this relatively new adaptation of AI. eXplainable AI is an important tool for understanding the biology of aging because while it still generates predictive models from large datasets, it goes further by identifying the key features that contribute to that prediction. For example, Su-In and her collaborators used health and medical data from large cohort studies to develop a model to predict lifespan. Using the XAI approach they were able to not only predict lifespan, but also identify the quantitative contribution of specific parameters, i.e., “this feature contributed this many years to the lifespan,” “this other feature contributed this many years,” and so on. If you apply that to lab models or across species to analyze gene expression data or proteomics data, XAI can identify which pathway or protein may be driving the underlying biology of the prediction. Knowing that, you can then set up the mechanistic hypotheses to test causality.

Beyond our own Center, we make a concerted effort to collaborate with the other Shock Centers, and that’s going to be more of a push over the next several years. There’s a lot of cool stuff being done at the Centers. To maximize the impact of what we can offer, we’re trying to partner with complementary Cores at other Centers. That’s good for the program, good for the field, and especially good for the junior scientists that are the target of these pilot studies and Cores. Having a Coordinating Center is such a cool thing—to create this nucleation center that can reach out to all the different Cores and serve as a matchmaker.

How did you become interested in aging research?

I did my graduate work in the physiological ecology of tuna muscle metabolism. After which, I came to the University of Washington to do a postdoc with Kevin Conley to study muscle metabolism. Through the UW aging program I connected with Peter Rabinovich and George Martin and I started getting excited by the biology of aging and how integrative it was. From a comparative physiology perspective, it was a neat question. Super complex, but integrated, and cuts across all systems. I came in as a mitochondrial biologist, and at that time, the study of the mitochondria was exploding. What originally fascinated me about mitochondria—especially muscle mitochondria—was how you can go from sitting in a chair, not really using your muscles very much, and then you can be up in a second, full-on, and take off running. We turn on our energy metabolism so quickly and then turn it off again. Understanding how it was regulated was what drew me into mitochondria. Then thinking about how metabolism on a broader scale is controlled became clearly relevant to the biology of aging. So here I am 25 years later.

What discoveries are you seeing that look most promising?

One of the things that is exploding is the amount of data that’s being generated: not only from the people working across these multi-omics platforms, comparing proteomics, metabolomics, and transcriptomics, but also from large cohort databases where they’re recruiting hundreds or thousands of people and collecting all this phenotypic data. The work that Su-In is doing in the eXplainable AI Core provides a pathway or an analytical tool to make sense of those not only in a predictive capacity, but actually pulling out the biology across these large data sets. It’s exciting and it’s the necessary step to go from data to insight.

Also, we need to know what to measure if we want to translate to clinical trials. The work that the Proteomics Core and the Metabolomics Core are doing with identifying biomarkers—that’s where we need to go as a field. We can’t study people for 30 years to study health span. We need to identify the meaningful biomarkers.

Aside from the sheer amount of data, what other challenges are you facing scientifically?

The one challenge is bridging the gap between lab models and human translation. A lot of things rejuvenate and cure aging in worms. Many things do it in mice. How do we figure out which of those—in an efficient way—work in humans? You can’t brute force it. This goes back to (1) we need to know what to measure and (2) we need better models that can bridge the gap between lab models and humans.

Another obstacle for the field is that technology is being developed so quickly. Access to that technology for up-and-coming investigators is a challenge. That’s where the Shock Center program comes in and has a real impact.

Where do you want the field to be in the next decade?

Where we have to go—and I think we are moving in that direction—is a more universal acceptance of this geroscience concept. Basically, studying nearly all diseases in the context of life history and that includes the biology of aging. Researchers and clinicians would understand how the main features of the hallmarks of aging are contributing to the pathology associated with heart disease, diabetes, etc. Then by extension, how events earlier in life can affect your aging trajectory, which then affects your risk for disease later.

*The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.