Identifying and targeting neural subpopulations of the spinal cord that mediate chronic pain via cell-type-specific cis-regulatory elements - Project Summary/Abstract Chronic pain is a common and debilitating medical condition, yet current treatment options are often ineffective and have side effects. Recent experimental work has identified classes of neurons in the spinal cord that mediate pain, which may be novel candidates for more targeted pharmaceutical intervention. However, there are two issues limiting clinical translation. First, Genome Wide Association Studies in humans have given us genetic markers of chronic pain, but we lack a genetic characterization of which cell types are affected by this genetic variation. Second, we need a method of targeting these specific cell types in mice, non-human-primates, and humans, the typical progression of testing for new therapeutics. I will make significant progress on both problems by analyzing the genomic cis-regulatory elements (CREs) of neural subtypes in the macaque and mouse spinal cord. Recent evidence suggests risk variants for many polygenic diseases especially accumulate in CREs. I hypothesize that neuron subtypes identified as pain-mediating in anatomical studies will be genetically enriched for chronic pain markers in CREs, compared to several controls. I further hypothesize that CREs evolutionarily conserved across macaque and mouse are even more enriched relative to those not conserved, based on the idea that CREs that are particularly important to function will be conserved. To address the goal of in vivo targeting of these cell types, I will leverage the fact that CREs are highly cell-type- specific drivers of gene expression, and can control the expression of an exogenous gene that could in principle alter neuronal excitability for therapeutic benefit. I will develop a machine learning pipeline that identifies CREs predicted to have high specific activity in target cell types. I will train models on mixed mouse and macaque CRE data, and I hypothesize that cross-species models will identify more translatable CREs. This strategy may not be optimal for pain treatment given the diversity of cell types involved in pain signaling, and my strategy may require simultaneous control of complex combinations of neuron subtypes, while minimizing off-target effects such as in motoneurons. I will therefore also design synthetic regulatory elements to preferentially target combinations of pain-mediating neurons, with minimal effects in ventral horn neurons such as motoneurons. I will take the top CRE candidates from my machine learning models and validate their cell-type-specificity activity in mouse with RNAScope experiments, and collaborators will validate CREs in macaque in a related project. In summary, I will characterize the genetic risk burden of chronic pain in evolutionarily conserved neuron populations, which will inform us of genetic pain mechanisms across experimental animal models. I will computationally identify and design CREs that target these populations, and validate their function in vivo in mouse, providing a valuable tool for pain experiments, and making progress toward a new form of pain therapy.
