Altered microRNA Transcriptome in Cultured Human Liver Cells upon Infection with Ebola Virus
Bibliographic record
Abstract
Ebola virus (EBOV) is a virulent pathogen, notorious for inducing life-threatening hemorrhagic fever, that has been responsible for several outbreaks in Africa and remains a public health threat. Yet, its pathogenesis is still not completely understood. Although there have been numerous studies on host transcriptional response to EBOV, with an emphasis on the clinical features, the impact of EBOV infection on post-transcriptional regulatory elements, such as microRNAs (miRNAs), remains largely unexplored. MiRNAs are involved in inflammation and immunity and are believed to be important modulators of the host response to viral infection. Here, we have used small RNA sequencing (sRNA-Seq), qPCR and functional analyses to obtain the first comparative miRNA transcriptome (miRNome) of a human liver cell line (Huh7) infected with one of the following three EBOV strains: Mayinga (responsible for the first Zaire outbreak in 1976), Makona (responsible for the West Africa outbreak in 2013-2016) and the epizootic Reston (presumably innocuous to humans). Our results highlight specific miRNA-based immunity pathways and substantial differences between the strains beyond their clinical manifestation and pathogenicity. These analyses shed new light into the molecular signature of liver cells upon EBOV infection and reveal new insights into miRNA-based virus attack and host defense strategy.
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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) | 0.000 | 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 | 0.001 |
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".