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Record W2083435322 · doi:10.1073/pnas.111165798

Pattern of mutation in the genome of influenza A virus on adaptation to increased virulence in the mouse lung: Identification of functional themes

2001· article· en· W2083435322 on OpenAlexaff
E. G. Brown, H. Liu, L. Chang Kit, Sonya M. Baird, M. Nesrallah

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2001
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsVirulenceBiologyHemagglutinin (influenza)VirusVirologyGeneticsInfluenza A virusMutationGeneAdaptation (eye)GenomePhenotypeInfluenza A virus subtype H5N1

Abstract

fetched live from OpenAlex

The genetic basis for virulence in influenza virus is largely unknown. To explore the mutational basis for increased virulence in the lung, the H3N2 prototype clinical isolate, A/HK/1/68, was adapted to the mouse. Genomic sequencing provided the first demonstration, to our knowledge, that a group of 11 mutations can convert an avirulent virus to a virulent variant that can kill at a minimal dose. Thirteen of the 14 amino acid substitutions (93%) detected among clonal isolates were likely instrumental in adaptation because of their positive selection, location in functional regions, and/or independent occurrence in other virulent influenza viruses. Mutations in virulent variants repeatedly involved nuclear localization signals and sites of protein and RNA interaction, implicating them as novel modulators of virulence. Mouse-adapted variants with the same hemagglutinin mutations possessed different pH optima of fusion, indicating that fusion activity of hemagglutinin can be modulated by other viral genes. Experimental adaptation resulted in the selection of three mutations that were in common with the virulent human H5N1 isolate A/HK/156/97 and that may be instrumental in its extreme virulence. Analysis of viral adaptation by serial passage appears to provide the identification of biologically relevant mutations.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.137
GPT teacher head0.390
Teacher spread0.253 · 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 teacher head, 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

Citations189
Published2001
Admission routes1
Has abstractyes

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