Areas of Focus:

Animal ModelsBiological & MechanisticCytokine SignalingExperimental Platforms & ModelsHuman GeneticsInnate ImmunityTherapeutic DevelopmentTranslational & ClinicalAutoimmune-Associated Interstitial Lung DiseaseCross-Cutting & Special PopulationsNeurologic DiseasesPulmonary & Cardiovascular DiseasesRare Autoimmune Diseases
  • Associate Professor, Department of Medicine (Rheumatology), Perelman School of Medicine, University of Pennsylvania

Dr. Jonathan Miner is an Associate Professor of Medicine in the Division of Rheumatology at the Perelman School of Medicine, University of Pennsylvania, where he directs the Penn Center for Retinal Vasculopathy with Cerebral Leukoencephalopathy (RVCL) and chairs the Genomics and Translational Virology Graduate Group. He received his PhD and MD from the University of Oklahoma and completed residency and rheumatology fellowship training at Washington University in St. Louis.

The Miner laboratory studies the molecular regulation of type I interferon responses by the cGAS-STING-TREX1 axis and the role of nucleic acid sensing in driving autoimmunity. His group has made fundamental contributions to understanding how dysregulation of these innate immune pathways causes monogenic interferonopathies and contributes to systemic lupus erythematosus, and his lab is actively developing therapeutic strategies that modulate these pathways.

An elected member of the American Society for Clinical Investigation, Dr. Miner anchors a growing program in interferon biology and rare autoimmune disease at Penn. He is a senior collaborator within the Penn Colton Center, the Institute for Immunology and Immune Health, and patient advocacy communities focused on RVCL and related interferon-driven syndromes.

Projects

Featured Pilot Projects

High-Throughput Center for AutoImmune Therapeutic Discovery (HIT-AI)
Project | University of Pennsylvania

High-Throughput Center for AutoImmune Therapeutic Discovery (HIT-AI)

Systematically identifying and validating repurposed FDA-approved drugs for over 160 autoimmune diseases using AI, knowledge graphs, and high-throughput experimental screening.