Uncovering injury-specific proteomic signatures and neurodegenerative risks in single and repetitive traumatic brain injury
Bibliographic record
Abstract
Traumatic brain injury (TBI) is a major public health concern associated with an increased risk of neurodegenerative diseases including Alzheimer's disease (AD), Parkinson's disease (PD), and chronic traumatic encephalopathy, yet the underlying molecular mechanisms in repetitive TBI remain poorly defined. This study investigates proteomic and behavioral changes following single and repetitive mild TBI in a mouse model, focusing on molecular alterations in the cortex and hippocampus across acute (48 h) and subacute (1 week) stages. Using shotgun proteomics and bioinformatics approaches, including weighted gene co-expression network analysis (WGCNA) and machine learning, we analyzed the proteomic landscapes of TBI-affected brain regions including the hippocampus and the cortex. We assessed motor and cognitive outcomes at 2-, 7-, and 30-days post-injury to explore functional impairments associated with observed molecular changes. Our findings reveal spatio-temporal injury- and time-specific proteomic changes, with a single TBI promoting neuroprotective and repair mechanisms, while repetitive TBI exacerbating neuronal damage and synaptic deficits in the hippocampus. Key deregulated proteins, including Apoa1, ApoE, Cox6a1, and Snca, were linked to neurodegenerative pathways, suggesting molecular connections between TBI and diseases like AD and PD. Behavioral assessments indicated that repetitive TBI significantly impaired motor and cognitive functions, with recovery in motor function by day 30, whereas cognitive deficits persisted. This study provides a detailed analysis of the proteomic and behavioral consequences of TBI, identifying molecular networks as potential biomarkers or therapeutic targets for mitigating long-term cognitive decline associated with repetitive head trauma. These findings underscore the importance of mitochondrial and synaptic integrity in TBI response and suggest that targeting these pathways could reduce neurodegenerative risk following repetitive TBI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".