My goal is to discover the cellular and molecular mechanisms by which protein aggregates contribute to neurodegeneration and to harness these mechanisms to devise novel therapeutic strategies. We use the baker’s yeast, Saccharomyces cerevisiae, as a simple, yet powerful, model system to study the cell biology underpinning protein-misfolding diseases, which include Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis (ALS). We are focusing on the ALS disease proteins TDP-43 and FUS/TLS and have generated yeast models to define mechanisms by which these proteins cause ALS. Because these proteins aggregate and are toxic in yeast, we have used these yeast models to perform high-throughput genomewide modifier screens to discover suppressors and enhancers of toxicity. Launching from the studies in yeast, we have extended our findings into animal models and even recently into human patients. For example, we discovered mutations in one of the human homologs of a hit from our yeast TDP-43 modifier screen in ALS patients. Mutations in this gene are relatively common (~5% of cases) making it one of the most common genetic risk factors for ALS discovered to date. These screens are also providing new and completely unexpected potential drug targets, underscoring the power of such simple model systems to help reveal novel insight into human disease.
Dr. Aaron Gitler majored in Biochemistry and Molecular Biology at Penn State University and received a BS degree in 2000. He earned a PhD in Cell and Molecular Biology at the University of Pennsylvania in 2004, where his thesis project involved the discovery of a novel signaling pathway, involving sempahorin ligands and plexin receptors, that function in endothelial cells to guide blood vessel and heart patterning. Disrupting this signaling pathway in mouse resulted in cardiac anomalies similar to those seen in human congenital heart disease.
For his postdoctoral studies, he changed fields completely and joined the laboratory of Dr. Susan Lindquist at the Whitehead Institute for Biomedical Research. Here he used yeast as a model system to study mechanisms of human neurodegenerative diseases that are associated with protein misfolding, such as Parkinson’s disease. He performed high-throughput yeast genetic screens to identify modifiers of toxicity associated with the accumulation of misfolded human disease proteins.
In 2007, he joined the faculty of the University of Pennsylvania, as an Assistant Professor in the Department of Cell and Developmental Biology. In 2012 he moved to Stanford where is an Associate Professor in the Department of Genetics. His research uses a combination of yeast and human genetics to define mechanisms of neurodegenerative disease and has focused on Parkinson’s disease and ALS (also known as Lou Gehrig’s disease). His group has made several fundamental discoveries into mechanisms of ALS. These discoveries include the discovery of mutations in the ataxin 2 gene as one of the most common genetic risk factors for ALS. His work has also helped to uncover an unexpected and novel therapeutic target for ALS. At Stanford, Dr. Gitler is the co-director of the Stanford Neurosciences Institute Brain Rejuvenation Project, which aims to create a campus wide interdisciplinary center for neurodegeneration research.
Modern neuroimaging techniques are providing revolutionary insights into the human brain connectome. We are now able to study—in living people—large-scale brain networks predicted from non-human primate tract tracing investigations and lesion neuropsychology. I will review knowledge of the localization and function of brain networks in the healthy brain, and evidence that measures of these networks illuminate individual differences in cognition, affect, and sensorimotor function. The modulation of network connectivity in relation to task performance, pharmacologic manipulation, or brain stimulation is providing new insights into neuroplasticity. Age-related cognitive decline may be explained in part by a “compromised connectome”; older adults lucky enough to be “superagers”—with youthful cognitive function—have preserved anatomy in key nodes of large-scale cognitive-affective brain networks. Patients with neurodegenerative diseases develop disconnection, dysfunction, and atrophy within brain networks subserving cognitive, affective, and sensorimotor function related to symptoms of their illness. Neurodegenerative diseases appear to progress in part by following the pathways of the brain’s connectome. As a result of attending this lecture, the participant will increase their knowledge of 1) healthy human brain networks subserving normal brain function, and 2) impaired network connectomics in aging and neurodegenerative disease.
Brad Dickerson, M.D., is the Director of the Massachusetts General Hospital Frontotemporal Disorders Unit and Neuroimaging Lab in Boston. He is also a staff behavioral neurologist in the MGH Memory Disorders Unit and co-investigator on the Neuroimaging Core of the Alzheimer’s Disease Research Center. He is an Associate Professor of Neurology at Harvard Medical School.
Dr. Dickerson runs a busy weekly clinic caring for patients with various forms of cognitive impairment and dementia, as well as providing training for clinical and research fellows. His research employs quantitative structural, functional, and molecular neuroimaging techniques to investigate dementias as well as normal aging. He has published more than 100 articles in peer-reviewed scientific journals as well as many book chapters, and has edited two books on dementia. He has won a number of awards, including the American Academy of Neurology’s Norman Geschwind Award in Behavioral Neurology.
Over the last few decades, the tractable response properties of medial entorhinal neurons have provided a new access key to understanding the cognitive process of self-localization: the ability to know where you are currently located in space. Defined by functionally discrete response properties, neurons in the medial entorhinal cortex are proposed to provide the basis for an internal neural map of space and enable animals to perform path-integration based spatial navigation. My lab focuses on leveraging this system to understand how external sensory inputs meld with internal computations to generate neural codes capable of supporting spatial cognition. In this talk, I’ll discuss ongoing work aimed at discovering how sensory inputs calibrate self-motion signals, as well as work that has recently revealed how behavioral state adaptively changes the previously proposed static path-integration mode of medial entorhinal cortex.
Lisa M Giocomo worked with Dr. Michael Hasselmo for her graduate studies and received her PhD in Neuroscience from Boston University in 2008. She was then a postdoctoral fellow with Edvard and May-Britt Moser at the Kavli Institute for Systems Neuroscience at the Norwegian University of Science and Technology from 2009-2012. In 2013, she started as an Assistant Professor of Neurobiology at Stanford University School of Medicine. Her lab integrates electrophysiology, behavior, gene manipulations, two photon imaging and computational modeling to study how single-cell biophysics and network dynamics interact to mediate spatial memory and navigation.
Lisa serves as an advisor as part of the Allen Institute for Brain Science Next Generation Leaders and was named a Gabilan Fellow at Stanford University in 2013. She received the Peter and Patricia Gruber International Research Award in 2012, was named a Sloan Research Fellow in 2013, a Klingenstein-Simons Fellow in 2014, New York Stem Cell Foundation Investigator in 2014, and a James S McDonnell Scholar in 2016.
Dr. Purdon’s research integrates neuroimaging, biomedical signal processing, and the systems neuroscience of general anesthesia and sedation.
His group conducts human studies of anesthesia-induced unconsciousness, using a variety of techniques including multimodal neuroimaging, high-density EEG, and invasive neurophysiological recordings used to diagnose medically refractory epilepsy. They also develop novel methods in neuroimaging and biomedical signal processing to support these studies, as well as methods for monitoring level of consciousness under general anesthesia using EEG.