Host Adaptation and Evolutionary Analysis of Zaire ebolavirus: Insights From Codon Usage Based Investigations
Bibliographic record
Abstract
Ebola virus (EBOV) has caused several outbreaks as the consequence of spillover events from zoonotic sources and has resulted in huge death tolls. In spite of considerable progress, a thorough know-how regarding EBOV adaptation in various host species and detailed information about the potential reservoirs of EBOV still remains obscure. The present study was executed to examine the patterns of codon usage and its associated influence in the adaptation of EBOV to potential hosts that dwell in Africa, the origin of the viral outbreaks. Correspondence analysis (CA) revealed that the codon usage signature in EBOV is a complex interplay of factors including compositional bias and natural selection, with the latter having a more pronounced impact. Low codon usage bias in EBOV indicates a flexibility of the viruses in adapting to diverse range of hosts with different codon usage architectures. EBOV adaptation in potential hosts, as estimated by codon adaptation index (CAI) and relative codon deoptimization index (RCDI), revealed that the viruses were relatively better adapted to African primates than other mammals examined, which might account for the high fatality rate of primates owing to EBOV infection. Bats have been speculated as natural reservoirs of EBOV. In the present analysis it was interesting to note that EBOV displayed lower degrees of adaptation, as estimated by CAI and RCDI, with bats in comparison to the primate hosts. Lower degrees of adaptation might contribute to long-term co-existence and circulation of the viral pathogens in bat populations. Codon usage patterns of EBOV isolates associated with different outbreaks varied significantly, with discrete patterns between the West and Central African isolates. Additional evolutionary analyses indicated that the West African Epidemic began with an initial spillover infection and there was more than one population of EBOV circulating in the natural reservoir in the Democratic Republic of the Congo. The present study yields valuable information regarding the possible circulation of EBOV in various African mammals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".