Bioinformatics For Translational Spinal Cord Injury Research

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PROJECT SUMMARY/ABSTRACT Spinal cord injury (SCI) produces a multifaceted syndrome characterized by loss of mobility, loss of bladder, bowel and sexual function, pathological pain, and a loss of autonomy. The past 20 years have seen significant progress in our ability to emulate many features of human SCI in animal models, yet few experimental therapies have translated from the laboratory to human patients. One major obstacle to translation is the lack of information about which outcome metrics for SCI are comparable across different laboratories, strains, types of injuries, and species. Identification of these important common outcome metrics is a major goal of the proposed project. We hypothesize that experimental SCI produces a bio-behavioral syndrome that is reflected not by only one outcome, but rather consistent patterns across many different outcomes. This view is implicitly assumed by many experimental SCI researchers when they evaluate experimental therapeutics using several outcomes from the same experimental subjects. However these data have not been analyzed using sophisticated multivariate information processing procedures which are designed to detect, measure, and quantify disease patterns in complex datasets. By pooling data from several laboratories and making cross- species comparisons, we will leverage existing experimental data to identify common metrics of SCI that can be used for evaluating mechanism of SCI that translate across species. We propose the following Aims: 1) Build a pooled database of existing experimental rodent and primate SCI research data to provide a platform for knowledge discovery and multivariate quantification across diverse outcomes and experimental models. We will start with data from 5 major SCI research centers to provide a framework for later contributions from other research groups. 2) Identify syndrome measures in rodent SCI models, using the same multivariate techniques often used by clinical researchers to define and measure complex disease states. 3) Identify which multivariate outcome patterns in rodent models are most sensitive to the effects of graded injury and which are most sensitive to change over time, with the goal of improving sensitivity and streamlining testing of therapeutic interventions. 4) Identify which multivariate outcome patterns in non-human primates are most sensitive to the effects of SCI and recovery over time, providing important information about the most sensitive outcomes for therapeutic testing in this valuable preclinical model. 5) Make translational multivariate comparisons of rodent and primate SCI data to identify which outcome patterns best translate across experimental models and which are species- and model-specific, setting the stage for future multivariate comparisons to human data. This represents a new direction for the field of experimental SCI and we expect this approach to help define outcome metrics that are comparable across species, facilitating translational SCI research.