For Michael Lutz, PhD, a typical day straddles the line between the “drier” computer- and statistic-focused and the “wetter” genetic/genomic- and biochemistry-focused sides of Alzheimer’s disease and other neurodegenerative diseases. In this Faculty Spotlight interview, Lutz talks to us about where these two areas intersect, working on early versions of electronic medical records in the 1980s, and how Duke has changed physically and in spirit over the past 30 years.
What are your responsibilities within the Department?
My responsibilities within the department are to initiate, lead and participate in sponsored research programs that have substantial genetics, computational biology, or bioinformatics components. My clinical areas of focus are Alzheimer’s disease and other neurodegenerative diseases. The focus of my work is to more clearly understand the genetic and biological mechanisms involved in these diseases. Therefore, my studies include clinical trials, animal model work, and biochemistry/molecular genetics. Identification and characterization of biomarkers for disease is also an important focus.
What does a typical day for you look like?
One aspect of my job that I like is that there isn’t a typical day! I spend a good part of each day analyzing data: clinical data, data from laboratory experiments and public domain datasets (e.g. large genetic, genomic, metabolomic datasets). Another part of each day involves writing: grants and publications. Collaboration, consulting and teaching are other activities that round out my day. I work with collaborators at Duke and around the world on projects that involve Alzheimer’s disease, neurodegenerative diseases and age-related diseases. I work with collaborators and students to design studies and analyze data from a genetics and bioinformatics perspective. An important part of my day involves working with collaborators in the Neurology Department, especially Dr. Ornit Chiba-Falek, Dr. Kirby Gottschalk and Dr. Allen Roses.
Our research is multidisciplinary and I contribute expertise in the “drier sides” (on the computer) of science (math, statistics, computer science) with their experience in the “wetter sides” (e.g. on the lab bench) including genetics/genomics, biochemistry and molecular genetics as well as the clinical domain.
You earned your undergraduate and doctoral degrees in biomedical engineering. How did you get interested in this field? What led you to study Alzheimer’s disease?
Growing up, I had interests in math, electronics, biology and chemistry. At the time (late 70s), Duke was one of few places that offered a degree in biomedical engineering. The idea of using the tools of math and computers to solve problems in medicine was extremely appealing to me. In the long term, I thought that I would design medical instrumentation. But, in my senior year of college, I started working with Dr. Ed Hammond (Biomedical Engineering) and really enjoyed the projects that I worked on with him for an independent research study that were based on medical records and mathematical algorithms to help physicians and researchers better understand the basis of disease and contribute to better outcomes for patients.
I worked with Dr. Hammond and Dr. William Stead during graduate school continuing to develop “The Medical Record,” (TMR) which at the time was a pioneering approach to electronic medical records. Ed emphasized clinical knowledge in additional to computer science and math, so my first years of graduate school were largely spent in the School of Medicine taking classes and some introductory clinical work with medical students. This helped me develop computer software and algorithms that were most useful for clinical practice.
My interest in Alzheimer’s disease is, in large part, a consequence of collaboration with Dr. Allen Roses. Before coming to Duke, I had a long career at GlaxoSmithKline (GSK) where I led the computational biology group at Research Triangle Park. Working with Allen, who was leading Genetics Research at GSK, I clearly saw the impact of genetics research on major public health problems, including Alzheimer’s disease. Allen emphasized innovative, new approaches to understand human medical genetics, and I was thrilled when Allen encouraged me to join his group at Duke.
You were the author of a recent article that evaluated a genetics-based biomarker risk algorithm for Alzheimer’s disease. How useful with this algorithm be for identifying Alzheimer’s disease? How does this method compare to other biomarkers?
A key aspect of this work is that the biomarker risk algorithm (GBRA) is a simple but accurate prognostic measure based on age and two genotypes that can be assayed from a simple blood sample instead of imaging measures or cerebrospinal fluid which involves considerably more expense or invasive procedures. For clinical trial enrichment, the approach can be considered “fit for purpose”. We evaluated a classification of risk as high or low, based on the likelihood of phenoconversion to MCI or AD within a 5 year time-frame, typical for a delay of onset clinical trial, based on age, APOE genotype and TOMM40’523 genotype. A retrospective analysis was completed of the GBRA in two cohorts with a relatively high prevalence of phenoconversion from normal cognition to MCI or AD (40%, Bryan-ADRC cohort, 69% ADNI cohort) to compare performance of the GBRA and blood-, CSF- and imaging-based biomarkers.
For both cohorts, there was a strong correlation between the risk categories determined by the GBRA and neurocognitive measures (MMSE and CDR). For the ADNI cohort, where extensive data was available for CSF (Ab1-42 and phosphorylated tau ) and imaging biomarkers, a similar strong correlation between risk categories and these biomarkers of neuropathological findings was observed where the direction and magnitude of the correlation was consistent with previous publications which compared the biomarker to diagnoses of MCI, AD or healthy control.
The GBRA is being used in an innovative clinical trial, the TOMMORROW trial, which is designed to test a therapeutic approach (low dose pioglitazone) for the delay of AD onset symptoms and to qualify the biomarker risk algorithm as a tool to identify individuals at risk for developing AD onset symptoms. The genetic biomarker risk algorithm will be qualified for use as a prognostic biomarker at the end of the phase 3 trial when the performance characteristics and ROC curves can be calculated from the trial data which will provide a large (n>3,000), prospectively sampled cohort. Once qualified, the biomarker can be used as a companion pharmacogenetics test for a therapeutic to delay the onset of AD.
This year marks your 30th anniversary since you received your PhD at Duke. What’s the biggest change that has occurred here since then?
Great question, 30 years have gone really fast. It is great to see the investment that Duke has made in biomedical and clinical research over the last 30 years. The LSRC, Duke Pavillion, Trent Semans Center didn’t exist 30 years ago.
While many advances in science are incremental, looked at over the perspective of 30 years, the changes are huge. In graduate school, I was excited to work with the “MicroVAX” computer; it was a huge jump from using punch cards with the IBM monsters that served Triangle Universities. Now, a cell phone has more computing power than the MicroVAX. On the science side, the human genome project, huge repositories of genetic, genomic, proteomic data are available with a few mouse clicks.
Experiments can be completed using only data available to the public. There are also new, fundamental aspects of biology that have been uncovered: different consequences of genetic variation, microRNAs, evolutionary biology based on ancient genomes, the ability to manipulate cells (CRISPR/CAS9) , neuroimaging to understand brain biochemistry and connectivity. It is also interesting to think about what has not changed much in 30 years. The commitment to encourage and mentor students (undergraduate, graduate, medical) remains a strength of Duke.
You attended the 2016 AAIC Conference in Toronto this August. Did you present any research, abstracts or other material?
I presented some work with collaborators at Rush University. In a large, community-based cohort (ROSMAP) we showed that specific genetic variants in the TOMM40 gene had faster decline in global cognition than subjects with an alternative variant and that this relationship was independent of APOE genotype.
This result is important because it is one of the first, large cohort studies to support earlier data that we presented on this genetic variant. Additionally, it is becoming clearer that the strongest phenotypic signal for this genetic variant is longitudinal cognitive decline as suggested in a recent paper in Neurobiology of Aging.
What did you enjoy most about the conference?
Overall, the meeting was excellent. I enjoyed hearing about new models and data for Alzheimer’s Disease that challenged some of the standard thinking about the pathological progression of the disease. Three years ago, mitochondrial dysfunction and AD were covered by a cluster of three posters. This year, there were excellent platform sessions on the topics including several papers that focused on translational research and repurposing of drugs.
There were also several excellent sessions and poster presentations on metabolic pathways and AD, systems biology approaches to AD and the use of big data. Phil de Jager gave an excellent talk where he used multiple sources of data including gene co-expression and data epigenetic data to define molecular networks operating in the human cortex and to identify those elements that are involved in the pathophysiology of AD. The approach is focused on identification of possible network regulators. The role of these regulators is validated in vitro using iPSC-derived neurons and an astrocyte cell line as well as targeted proteomics in the brain samples, prioritizing a set of genes as novel AD targets for small molecule screening. As has been the case for AAIC conferences in past memory, the reports of clinical trials have been largely disappointing which point to the need for new approaches to the development of therapeutics and clearer understanding of pathobiology. The lack of clinically effective treatment for AD drives the sense of urgency of work in the basic and translational sciences for this field.
What passions or hobbies do you have outside of the Department?
It is hard to identify passions that are outside of the department, because the biggest one, science, spans both my Duke and “outside of Duke” life. I enjoy reading about science and speaking with people about it. Interestingly, a passion that has been unchanged from high school is using math, computers for problems in biology. Sharing my approaches to solve problems is a passion, as is teaching and writing. I wrote a book with a colleague about computational approaches for drug discovery and it is rewarding when people come up to me at a conference and mention that reading my book helped them with a specific difficult problem that they were working on.
Hobbies are many, but finding the time for them is challenging. I enjoying running and I try to get out 4-6 times each week for a run on the great trails that we have in the area. I enjoy gardening, hiking, and working on projects around the house. The most recent one was renovating a screened porch.