Functional Genomics Reveals the Induction of Inflammatory Response and Metalloproteinase Gene Expression during Lethal Ebola Virus Infection
Bibliographic record
Abstract
Ebola virus is the etiologic agent of a lethal hemorrhagic fever in humans and nonhuman primates with mortality rates of up to 90%. Previous studies with Zaire Ebola virus (ZEBOV), mouse-adapted virus (MA-ZEBOV), and mutant viruses (ZEBOV-NP(ma), ZEBOV-VP24(ma), and ZEBOV-NP/VP24(ma)) allowed us to identify the mutations in viral protein 24 (VP24) and nucleoprotein (NP) responsible for acquisition of high virulence in mice. To elucidate specific molecular signatures associated with lethality, we compared global gene expression profiles in spleen samples from mice infected with these viruses and performed an extensive functional analysis. Our analysis showed that the lethal viruses (MA-ZEBOV and ZEBOV-NP/VP24(ma)) elicited a strong expression of genes 72 h after infection. In addition, we found that although the host transcriptional response to ZEBOV-VP24(ma) was nearly the same as that to ZEBOV-NP/VP24(ma), the contribution of a mutation in the NP gene was required for a lethal phenotype. Further analysis indicated that one of the most relevant biological functions differentially regulated by the lethal viruses was the inflammatory response, as was the induction of specific metalloproteinases, which were present in our newly identify functional network that was associated with Ebola virus lethality. Our results suggest that this dysregulated proinflammatory response increased the severity of disease. Consequently, the newly discovered molecular signature could be used as the starting point for the development of new drugs and therapeutics. To our knowledge, this is the first study that clearly defines unique molecular signatures associated with Ebola virus lethality.
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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 | 0.000 |
| 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) | 0.000 | 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".