ASCI / Emerging-Generation Awards, 2024

The Emerging Generation Awards (E-Gen Awards) recognize post-MD, pre-faculty appointment physician-scientists who are meaningfully engaged in immersive research.

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Daniel P. Cook, MD, PhD
Vanderbilt University School of Medicine
(Affiliation at the time of recognition)

About the awardee

Daniel Cook, MD, PhD is a physician-scientist and Instructor in pulmonary and critical care medicine at Vanderbilt University Medical Center. He studies the interactions between lung epithelial cells and cells of the innate and adaptive immune system in the context of allergy. He seeks to identify new therapies to modify allergic disease, with particular focus on the role of the cystic fibrosis transmembrane conductance regulator (CFTR) protein in allergy. This research is broadly relevant for many type 2 inflammatory diseases, but holds particular importance in individuals affected by cystic fibrosis lung disease where loss of CFTR leads to increased type 2 inflammation.

Dr. Cook received his BA in Chemistry and BS in Biological Sciences at the University of Missouri, where his undergraduate thesis examined the transcriptional regulation of satellite cell plasticity in skeletal muscle regeneration. Dr. Cook earned MD and PhD degrees at the University of Iowa Carver College of Medicine, researching the role of CFTR in airway hyperreactivity using a porcine model of cystic fibrosis. His long-term goal is to use clinical observations from large clinical datasets to drive basic science discoveries. This led him to pursue residency training in Internal Medicine in the Physician Scientist Training Pathway at Vanderbilt University Medical Center, where he used Vanderbilt’s large electronic health record to develop a general computational framework for transforming EHR data into meaningful clinical phenotypes. He recently finished his clinical fellowship in Pulmonary and Critical Care Medicine at Vanderbilt. Dr. Cook currently conducts research defining allergy in CF lung disease using mouse and human models of CF and type 2 inflammation, and interfacing computational analysis of EHR data with large-scale cellular multiomics approaches.