Biomarkers for prognosis of closed-mechanism nerve injuries

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ABSTRACT / PROJECT SUMMARY Closed-mechanism peripheral nerve injuries are among the most devastating neurologic injuries, often with complete loss of functional use of a limb. Nerve regeneration, i.e., the cascade of regenerative changes after injury, commonly fails in rapid-stretch injuries. Instead, a neuroma forms – where abundant scar tissue replaces the normal pathway for nerve regeneration. Unfortunately, in closed-mechanism injuries, there are few diagnostic clues to identify when neuromas will form – and thus, clinical management is based on waiting until failure is manifest – and the surgical outcomes are, consequentially, impoverished due to regenerative senescence. The goal of this project is to test the hypotheses that there are biomarkers in peripheral blood to provide prognosis and guide the management of closed mechanism nerve injuries. Neurological outcomes are clearly related to the 1) severity of nerve injury and to 2) healing response, whether regeneration or neuroma formation. We have established a rapid-stretch nerve injury model that mimics, along with crush injuries, the clinically relevant closed-mechanism nerve injuries. Our model matches the injury types/grades and histology seen in human nerve injuries. We propose utilizing both circulating protein and RNA molecules, to remain unbiased in selecting the optimal diagnostic tool for the future. Machine learning algorithms will be applied to the large dataset to improve diagnostic accuracy. If successful, this project will provide preliminary data for designing future human trials aimed at evaluating nerve injuries (R61/R33). Specifically, we will need to know whether proteomics or transcriptomics or a combination will provide greater diagnostic accuracy, as well as developing workflow for machine learning algorithms. The proposed evaluation will likely provide significant clinical utility in proximal nerve injuries, where the prognosis for recovery is currently poor due to the prolonged time required to detect failed regeneration.