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Record W7011591180

Modulation of Porcine Epidemic Diarrhea Virus (PEDV) RNA translation by the nucleocapsid protein

2024· dissertation· en· W7011591180 on OpenAlexfundno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2024
Typedissertation
Languageen
FieldArts and Humanities
TopicMedieval Philosophy and Theology
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaInnovation Saskatchewan
KeywordsPorcine epidemic diarrhea virusRNACoronavirusTranslation (biology)Viral structural proteinVirusRNA-binding proteinProtein biosynthesis
DOInot available

Abstract

fetched live from OpenAlex

Porcine epidemic diarrhea (PED) is a serious epidemic outbreak, characterized by vomiting, diarrhea, dehydration, and anorexia in pigs of all ages. It brings problems to pig industry and causes significant economic losses. However, according to the effectiveness of current porcine epidemic diarrhea virus (PEDV) vaccines is not very high, so it is very important to develop new and effective PEDV vaccines and therapeutics. The PEDV nucleocapsid (N) protein is a highly conserved protein, which usually be phosphorylated. It has multiple functions. For example, as a structural protein, it plays a role in the nucleocapsid formation with viral genomic RNA. Moreover, it regulates viral replication, transcription, and assembly. At the same time, the N-terminal domain and serine arginine-rich (SR) region of some coronavirus N proteins are usually modified by phosphorylation, which contributes to increasing RNA binding and is essential for replication. In addition, the C-terminal domain of N protein mediates dimerization. Although it has been shown that N protein plays an important role in both virus RNA synthesis and host cell processes regulation, the effect of PEDV N protein on viral translation is not well understood. We therefore studied the role of PEDV N protein in regulating viral RNA translation. Our results showed that N protein increases PEDV RNA translation. Furthermore, we discovered a synergistic impact of the N-terminal domain (NTD) and the linker region in increasing PEDV translation. Additionally, our research demonstrated that N protein dramatically increases PEDV RNA translation when only 3' untranslated region is present. Moreover, PEDV N protein regulates viral RNA translation through the 3' untranslated region bulged stem loop (BSL) domain.\nAkt is a serine/threonine–protein kinase which exists as three isoforms: Akt1, Akt2 and Akt3. Both Akt1 and Akt2 have been found to be activated by virus infections and play a role in regulating viral replication or translation. However, whether one or more Akt isoforms influence PEDV translation remains unclear. We performed ectopic expression and knockdown experiments to investigate the effects of Akt isoforms on viral RNA translation modulation by PEDV N protein. Results showed that Akt1 increases the enhancement of RNA translation by the N protein. Furthermore, we demonstrated that Akt1 enhancement of viral translation depends on its kinase activity and catalytic domain. We provided evidence of the interaction between PEDV N and Akt1 through GST pull-down and co-localization assays. Moreover, we revealed that the NTD and Linker region of PEDV N protein decreases its interaction with Akt1 but increases PEDV RNA translation enhancement by Akt1.\nIn summary, this study indicated PEDV N protein increases RNA translation and Akt1 enhances viral translation upregulation by N protein.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.177
Teacher spread0.162 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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