The longitudinal biochemical profiling of TBI in a drop weight model of TBI

Traumatic brain injury (TBI) is a major cause of mortality and disability worldwide, particularly among individuals under the age of 45. It is a complex, and heterogeneous disease with a multifaceted pathophysiology that remains to be elucidated. Metabolomics has the potential to identify metabolic...

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Veröffentlicht in:Scientific reports 2023-12, Vol.13 (1), p.22260-22260, Article 22260
Hauptverfasser: Yilmaz, Ali, Liraz-Zaltsman, Sigal, Shohami, Esther, Gordevičius, Juozas, Kerševičiūtė, Ieva, Sherman, Eric, Bahado-Singh, Ray O., Graham, Stewart F.
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Sprache:eng
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Zusammenfassung:Traumatic brain injury (TBI) is a major cause of mortality and disability worldwide, particularly among individuals under the age of 45. It is a complex, and heterogeneous disease with a multifaceted pathophysiology that remains to be elucidated. Metabolomics has the potential to identify metabolic pathways and unique biochemical profiles associated with TBI. Herein, we employed a longitudinal metabolomics approach to study TBI in a weight drop mouse model to reveal metabolic changes associated with TBI pathogenesis, severity, and secondary injury. Using proton nuclear magnetic resonance ( 1 H NMR) spectroscopy, we biochemically profiled post-mortem brain from mice that suffered mild TBI (N = 25; 13 male and 12 female), severe TBI (N = 24; 11 male and 13 female) and sham controls (N = 16; 11 male and 5 female) at baseline, day 1 and day 7 following the injury. 1 H NMR-based metabolomics, in combination with bioinformatic analyses, highlights a few significant metabolites associated with TBI severity and perturbed metabolism related to the injury. We report that the concentrations of taurine , creatinine , adenine , dimethylamine , histidine , N-Acetyl aspartate , and glucose 1-phosphate are all associated with TBI severity. Longitudinal metabolic observation of brain tissue revealed that mild TBI and severe TBI lead distinct metabolic profile changes. A multi-class model was able to classify the severity of injury as well as time after TBI with estimated 86% accuracy. Further, we identified a high degree of correlation between respective hemisphere metabolic profiles (r > 0.84, p 
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-48539-x