Loss of NLRX1 Exacerbates Neural Tissue Damage and NF-κB Signaling following Brain Injury
Bibliographic record
Abstract
Abstract Traumatic and nontraumatic brain injury results from severe disruptions in the cellular microenvironment leading to massive loss of neuronal populations and increased neuroinflammation. The progressive cascade of secondary events, including ischemia, inflammation, excitotoxicity, and free-radical release, contribute to neural tissue damage. NLRX1 is a member of the NLR family of pattern recognition receptors and is a potent negative regulator of several pathways that significantly modulate many of these events. Thus, we hypothesized that NLRX1 limits immune system signaling in the brain following trauma. To evaluate this hypothesis, we used Nlrx1−/− mice in a controlled cortical impact (CCI) injury murine model of traumatic brain injury (TBI). In this article, we show that Nlrx1−/− mice exhibited significantly larger brain lesions and increased motor deficits following CCI injury. Mechanistically, our data indicate that the NF-κB signaling cascade is significantly upregulated in Nlrx1−/− animals. This upregulation is associated with increased microglia and macrophage populations in the cortical lesion. Using a mouse neuroblastoma cell line (N2A), we also found that NLRX1 significantly reduced apoptosis under hypoxic conditions. In human patients, we identify 15 NLRs that are significantly dysregulated, including significant downregulation of NLRX1 in brain injury following aneurysm. We further demonstrate a concurrent increase in NF-κB signaling that is correlated with aneurysm severity in these human subjects. Together, our data extend the function of NLRX1 beyond its currently characterized role in host–pathogen defense and identify this highly novel NLR as a significant modulator of brain injury progression.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".