Gene-environment interaction promotes Alzheimer's risk as revealed by synergy of repeated mild traumatic brain injury and mouse App knock-in
Bibliographic record
Abstract
There is a strong unmet need for translational progress towards Alzheimer's disease (AD) modifying therapy. Unfortunately, preclinical modeling of the disease has been disappointing, relying primarily on transgenic mouse overexpression of rare dominant mutations. Clinical manifestation of AD symptoms is known to reflect interaction between environmental and genetic risks. Mild traumatic brain injury (mTBI) is an environmental risk for dementia, including Alzheimer's, but there has been limited mechanistic analysis of mTBI contribution to AD. Here, we investigate the interplay between mTBI and Aβ precursor protein gene mutation in AD pathogenesis. We employed a knock-in (KI) model of AD that expresses the Aß-containing exons from human APP bearing the Swedish and Iberian mutations, namely AppNL-F/NL-F mice. Without environmental risk, this genetic variation yields minimal mouse symptomatology. Anesthetized 4-month-old KI mice and their age-matched wild type (WT) controls were subjected to repeated mild closed head injury (rmCHI), once daily for 14 days. Anesthetized, uninjured genotype- and age-matched mice were used as sham controls. At 3- and 8-months post-injury, amyloid-β, phospho-tau and Iba1 expression in the injured KI cortices were assessed. Our data reveal that rmCHI enhances accumulation of amyloid-β and hyperphosphorylated tau inclusions, as well as neuroinflammation in AppNL-F/NL-F mice. Furthermore, novel object recognition and Morris water maze tests demonstrated that rmCHI greatly exacerbates persistent cognitive deficits in APPNL-F/NL-F mice. Therefore, study of gene-environment interaction demonstrates that combining risk factors provides a more robust model for AD, and that repeated mTBI substantially accelerates AD pathology in a genetically susceptible situation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".