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Record W3009388533 · doi:10.1016/j.isci.2020.100958

Positive Selection of a Serine Residue in Bat IRF3 Confers Enhanced Antiviral Protection

2020· article· en· W3009388533 on OpenAlexafffund
Arinjay Banerjee, Xi Zhang, Alyssa Yip, Katharina S. Schulz, Aaron T. Irving, Dawn M. E. Bowdish, Brian Golding, Lin‐Fa Wang, Karen Mossman

Bibliographic record

VenueiScience · 2020
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Vectors
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche ForschungsgemeinschaftNational Research Foundation Singapore
KeywordsIRF3BiologySerineInnate immune systemVirologyImmune systemResidue (chemistry)VirusViral replicationCell biologyImmunologyPhosphorylationBiochemistry

Abstract

fetched live from OpenAlex

Compared with other mammals, bats harbor more zoonotic viruses per species and do not demonstrate signs of disease on infection with these viruses. To counteract infections with viruses, bats have evolved enhanced mechanisms to limit virus replication and immunopathology. However, molecular and cellular drivers of antiviral responses in bats largely remain an enigma. In this study, we demonstrate that a serine residue in IRF3 is positively selected for in multiple bat species. IRF3 is a central regulator of innate antiviral responses in mammals. Replacing the serine residue in bat IRF3 with the human leucine residue decreased antiviral protection in bat cells, whereas the addition of this serine residue in human IRF3 significantly enhanced antiviral protection in human cells. Our study provides genetic and functional evidence for enhanced IRF3-mediated antiviral responses in bats and adds support to speculations that bats have positively selected for multiple adaptations in their antiviral immune responses.

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 score0.003
Threshold uncertainty score0.009

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.285
Teacher spread0.261 · 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

Citations49
Published2020
Admission routes2
Has abstractyes

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