My lab has repeatedly made the discoveries on fundamental pathways that underlie familial and sporadic ALS including: excitotoxicity, astroglial dysfunction, oligodendroglial dysfunction and most recently, the role of nuclear pore complex and nucleocytoplasmic transport as one of the earliest pathophysiological event in familial ALS and likely sporadic ALS cases. I am affiliated with two graduate programs (Neuroscience and Cellular and molecular Medicine). I have trained more than 60 clinician scientists, basic scientists, and graduate students who now occupy positions in USA and foreign government, universities and pharmaceutical companies.
In addition, I developed the Robert Packard Center for ALS in 2000 as a model organization of mandatory academic collaboration and data sharing, the development of preclinical models and the free exchange of cutting edge ALS research.
Jeffrey Rothstein, M.D., Ph.D.
There are over 1.5 billion websites out there, but your story is what’s going to separate this one from the rest. If you read the words back and don’t hear your own voice in your head, that’s a good sign you still have more work to do.My career has been highly focused on the identification of biological pathways that underlie and contribute to neurodegeneration in ALS and the development of model systems to identify, test and validate therapies. I have over 30 years’ experience as a clinician scientist studying neurodegenerative disease/ALS pathophysiology, glial biology, therapy discovery and collaborative science.
I am the Director of the Pedersen Brain Science institute, which coordinates interdisciplinary clinical and basic research among 450 Johns Hopkins University neuroscientists. I run an ALS clinic at Johns Hopkins that evaluates and manages over 450 ALS patients per year. I founded and run Answer ALS, a national consortium of >100 individuals working as a collaborative team to generate comprehensive omics (whole genome, epigenome, RNA seq, proteome) from >1000 ALS/FTD patients and their IPS motor neurons, employing deep machine learning to subgroup patients based on their CNS biology for future trials, biomarkers and drug optimization.