Complement-Mediated Neutralisation Identified in Ebola Virus Disease Survivor Plasma: Implications for Protection and Pathogenesis
Bibliographic record
Abstract
The 2013–2016 Ebola virus (EBOV) epidemic in West Africa was unprecedented in case numbers and fatalities, and sporadic outbreaks continue to arise. Antibodies to the EBOV glycoprotein (GP) are strongly associated with survival and their use in immunotherapy is often initially based on their performance in neutralisation assays. Other immune effector functions also contribute to EBOV protection but are more complex to measure. Their interactions with the complement system in particular are comparatively under-researched and commonly excluded from cellular immunoassays. Using EBOV convalescent plasma samples from the 2013–2016 epidemic, we investigated antibody and complement-mediated neutralisation and how these interactions can influence immunity in response to EBOV-GP and its secreted form (EBOV-sGP). We defined two cohorts: one with low-neutralising titres in relation to EBOV-GP IgG titres (LN cohort) and the other with a direct linear relationship between neutralisation and EBOV-GP IgG titres (N cohort). Using flow cytometry antibody-dependent complement deposition (ADCD) assays, we found that the LN cohort was equally efficient at mediating ADCD in response to the EBOV-GP but was significantly lower in response to the EBOV-sGP, compared to the N cohort. Using wild-type EBOV neutralisation assays with a cohort of the LN plasma, we observed a significant increase in neutralisation associated with the addition of pooled human plasma as a source of complement. Flow cytometry ADCD was also applied using the GP of the highly virulent Sudan virus (SUDV) of theSudan ebolavirusspecies. There are no licensed vaccines or therapeutics against SUDV and it overlaps in endemicity with EBOV. We found that the LN plasma was significantly less efficient at cross-reacting and mediating ADCD. Overall, we found a differential response in ADCD between LN and N plasma in response to variousEbolavirusglycoproteins, and that these interactions could significantly improve EBOV neutralisation for selected LN plasma samples. Preservation of the complement system in immunoassays could augment our understanding of neutralisation and thus protection against infection
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".