Identification of Novel Pharmacological Targets For Pain Treatment Using Computational Analysis of RNA-seq Data
MetadataShow full item record
Chronic pain is a disease that impacts 1.9 billion people worldwide every year, which includes over 100 million in the United States alone. Despite the extensive efforts in preclinical research, only a few pain treatments have been approved over the past decade. And out of these approved new treatments for pain, the majority are still narcotics or NSAIDs. We are in desperate need of novel targets for developing treatments. Preclinical research in the pain field has been limited to certain established experimental approach, including but not limited to electrophysiology, immunohistochemistry, and behavioral experiments mostly in animals. These limited approaches strained researchers’ ability to identify novel pharmacological targets for the treatment of pain. In the past 2 decades, the development and advancement of RNA sequencing (RNA-seq) technology has provided a novel, highthroughput, unbiased method for investigating transcriptome landscapes in biological samples and even individual cells. This allows us to evaluate potential pharmacological targets across the whole transcriptome in different chronic pain models and in human samples. Here we applied computational methods over multiple RNA-seq datasets to demonstrate a novel approach for the identification of new pharmacological targets for developing potential chronic pain treatments. Because pain usually originates from injured or diseased tissue, we first sought to identify the ligand-receptor mediated interactions (interactome) and their corresponding signaling pathways between sensory neurons and their innervating tissue. Dorsal root ganglia (DRG), the peripheral sensory neuron cluster where chronic pain development is mediated, is widely used in experiments to investigate chronic pain. We utilized single-cell RNA-seq (scRNA-seq) datasets of DRG, and 42 cell-types identified in 20 tissues innervated by DRG to set a baseline transcriptome landscape in mouse. We then used ligand-receptor interaction lists previously reported in literature and databases to identify potential ligand-receptor mediated signaling pathways. We successfully established the interactome landscape of DRG neurons at baseline, and identified interactions specific to certain types of cells, like TNFα and interferon gamma pathways are enriched in T-cells and natural killer cells, respectively. Some of the interactions/pathways previously identified in pain models also present in naïve state suggests that the pain phenotype may be caused by the recruit of immune cells to the peripheral nerve or regulation of the gene expression level, rather than switch on/off of these important genes. After successfully performed the interactome analysis in naïve state in mouse, we then sought to apply the same analysis to disease models to identify interactions regulated in disease states. We chose a colitis model in mouse, a human rheumatoid arthritis (RA) study, and a human pancreatic cancer study, where all 3 diseases are known to be painful. These 3 studies were chosen to demonstrate our computational approach of identifying potential pharmacological targets for pain treatment works across species and diseases. We successfully identified certain ligand-receptor signaling pathways are potentially responsible for the development of chronic pain in these 3 disease models. For example, we’ve identified cytokine to cytokine receptor signaling pathways including CXCL9 signaling to CXCR3 is elevated in RA patients comparing with osteoarthritis patients. These findings demonstrate the impact of utilizing multiple RNA-seq datasets from interacting tissues on identification of novel pharmacological targets potentially available for developing new treatments. In the final part of this dissertation, we sought to understand the systematic differences between neurons in central nervous system (CNS) and peripheral nervous system (PNS). Understanding the systematic differences between CNS neurons vs. PNS neurons will greatly help the selection for PNS specific pharmacological targets. Besides well-established PNS specific genes like Scn10a and Trpv1, we identified novel ion channels, GPCRs, kinases, and cytokine related receptor genes that are enriched in CNS or PNS neurons. For example, Lpar3 is enriched in PNS sensory neurons, and has only been previously mentioned in nerve injury models related to LPA1/LPA3 pathway. These findings will systematically identify signal transduction related genes that are specific to PNS/CNS, which will guide the selection of potential pharmacological targets in drug development.