Mechanistic underpinnings of chronic low back pain
- Institution
- UNIVERSITY OF TEXAS DALLAS, TX
- Principal investigator
- Michele Curatolo
- Funding
- $628K
- Duration
- 2022–2027
Moonbase score
Still being scored
Abstract
Chronic low back pain (CLBP) is the leading cause of disability worldwide and current treatments are mostly ineffective. The proposed project aims to use RNA-seq techniques with cellular resolution on nerve, joint, muscle, and fascia tissues from thoroughly-phenotyped individuals with CLBP undergoing spine surgery to gain new insight into CLBP. Our central hypothesis is that transcriptional changes in nerves and pathological tissues drive human CLBP. We will test this hypothesis by applying single cell RNA-seq to tissue samples of thoroughly characterized patients with CLBP undergoing surgery. In our first aim we will apply single nucleus and bulk RNA- seq to the peripheral and neural tissues outlined above. We hypothesize that peripheral and neural tissues from patients with CLBP will display shifts in cell types and ligands that give insight into distinct mechanisms of CLBP. In our second aim we will apply our computational interactome model to our RNA-seq data to identify ligand- receptor interactions between cells/tissues associated with CLBP and neuronal transcriptomes. We will associate nociceptor phenotypes assembled from our collective DRG datasets from organ donors and chronic pain patients to create interactome profiles from CLBP tissues, and associate these interactomes to pain phenotypes. Finally, in our third aim we will conduct in vitro pharmacology studies on human DRG neurons from organ donors to understand how putative pain mediators act on nociceptors. The project will lead to a new understanding of CLBP, and elucidate pain phenotype-specific therapeutic opportunities based entirely on human molecular neuroscience. The research project will serve as an example of how platforms that are based entirely on the use of human samples and tissues can be developed for discovery, target identification and clinical validation for other pain disorders.
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