Transcriptional Correlates of Disease Outcome in Anticoagulant-Treated Non-Human Primates Infected with Ebolavirus
Bibliographic record
Abstract
Ebola virus (EBOV) infection in humans and non-human primates (NHPs) is highly lethal, and there is limited understanding of the mechanisms associated with pathogenesis and survival. Here, we describe a transcriptomic analysis of NHPs that survived lethal EBOV infection, compared to NHPs that did not survive. It has been previously demonstrated that anticoagulant therapeutics increase the survival rate in EBOV-infected NHPs, and that the characteristic transcriptional profile of immune response changes in anticoagulant-treated NHPs. In order to identify transcriptional signatures that correlate with survival following EBOV infection, we compared the mRNA expression profile in peripheral blood mononuclear cells from EBOV-infected NHPs that received anticoagulant treatment, to those that did not receive treatment. We identified a small set of 20 genes that are highly confident predictors and can accurately distinguish between surviving and non-surviving animals. In addition, we identified a larger predictive signature of 238 genes that correlated with disease outcome and treatment; this latter signature was associated with a variety of host responses, such as the inflammatory response, T cell death, and inhibition of viral replication. Notably, among survival-associated genes were subsets of genes that are transcriptionally regulated by (1) CCAAT/enhancer-binding protein alpha, (2) tumor protein 53, and (3) megakaryoblastic leukemia 1 and myocardin-like protein 2. These pathways merit further investigation as potential transcriptional signatures of host immune response to EBOV 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 | 0.000 | 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 | 0.000 |
| 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".