Leveraging Clinical Trancriptomic Data for Targeted Drug Interventions in Preclinical Models of SCI
Abstract
Spinal cord injury (SCI) is a devastating condition affecting millions of people worldwide, for which treatment options do not exist. In United States alone, it is estimated that 17,000 new SCIs occur every year, changing the lives of patients and their families forever. At the Brain and Spinal Injury Center (BASIC) of the University of California San Francisco (UCSF) we have assembled a multidisciplinary team of experts with the mission to better understand the biology of SCI and develop novel therapeutical strategies. Over the last 5 years at BASIC, through an ongoing, Department of Defense-funded, clinical prospective study called Transforming Research and Clinical Knowledge in Spinal Cord Injury (TRACK-SCI), we have collected a vast amount of clinical SCI data. With the use of advanced analytical approaches, we strive to better understand human SCI, propose interventions, and develop blood-based predictors of injury severity and recovery. Importantly, our team has recently reported the first proof-of-concept results that blood gene expression levels can be utilized to diagnose the severity of SCI without use of MRI or neurological examination. In parallel, we are conducting a similar experiment in a rat model of SCI. We have also identified several gene networks that are highly correlated with the SCI severity in rats. Our overarching hypothesis is based on the premise that blood gene networks, which very efficiently predict injury severity, also have a causal effect, and by modulating them pharmacologically we can change the course of the long-term recovery. Testing that hypothesis in humans though, is practically impossible. Therefore, we will utilize a rat model of SCI. A critical problem with the rat model (and by extension all animal injury models) is that more often than not, successful or partially successful interventions in animals cannot be transferred to humans. To avoid this, we will utilize sophisticated computational approaches to identify evolutionarily conserved gene networks in the blood between rats and humans from our own two datasets. Thus, it will be possible to proceed in developing pharmacological schemes in the rat model based on these conserved networks, which provides additional confidence that a positive outcome will be of high translational value. After identifying conserved gene networks, we will use them as input in a predictive tool developed by the BROAD Institute called CMap. CMap consists of a large database of more than 8,000 chemical compounds and their effects on gene expression in multiple cell types. The result of our query will be a ranked list of compounds with high probability of reversing the gene expression changes caused by the SCI. We will use the rat paradigm to examine in detail the gene expression changes over time after SCI in the various white blood cell types. This will help us determine the appropriate timing and administration route of the predicted compounds. After administration of the compounds, we will monitor the animals long-term and will be assessing their recovery weekly. Discovering a compound that leads to a significantly better recovery after SCI will be the first step toward clinical trials and a new promising therapy.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 05, 2021
- Source ID
- W81XWH2110505
Entities
People
- Nikolaos Kyritsis
Organizations
- United States Army
- University of California, San Francisco