Systemic inhibition of the membrane attack complex impedes neuroinflammation in chronic relapsing experimental autoimmune encephalomyelitis
Bibliographic record
Abstract
The complement system is a key driver of neuroinflammation. Activation of complement by all pathways, results in the formation of the anaphylatoxin C5a and the membrane attack complex (MAC). Both initiate pro-inflammatory responses which can contribute to neurological disease. In this study, we delineate the specific roles of C5a receptor signaling and MAC formation during the progression of experimental autoimmune encephalomyelitis (EAE)-mediated neuroinflammation. MAC inhibition was achieved by subcutaneous administration of an antisense oligonucleotide specifically targeting murine C6 mRNA (5 mg/kg). The C5a receptor 1 (C5aR1) was inhibited with the C5a receptor antagonist PMX205 (1.5 mg/kg). Both treatments were administered systemically and started after disease onset, at the symptomatic phase when lymphocytes are activated. We found that antisense-mediated knockdown of C6 expression outside the central nervous system prevented relapse of disease by impeding the activation of parenchymal neuroinflammatory responses, including the Nod-like receptor protein 3 (NLRP3) inflammasome. Furthermore, C6 antisense-mediated MAC inhibition protected from relapse-induced axonal and synaptic damage. In contrast, inhibition of C5aR1-mediated inflammation diminished expression of major pro-inflammatory mediators, but unlike C6 inhibition, it did not stop progression of neurological disability completely. Our study suggests that MAC is a key driver of neuroinflammation in this model, thereby MAC inhibition might be a relevant treatment for chronic neuroinflammatory diseases.
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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 | 0.000 |
| 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) | 0.001 | 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".