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Record W2001270849 · doi:10.1021/pr400464p

Influenza A Infection of Primary Human Airway Epithelial Cells Up-Regulates Proteins Related to Purine Metabolism and Ubiquitin-Related Signaling

2013· article· en· W2001270849 on OpenAlexafffund
Andrea Kroeker, Peyman Ezzati, Kevin M. Coombs, Andrew J. Halayko

Bibliographic record

VenueJournal of Proteome Research · 2013
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsUniversity of WinnipegUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
FundersCanadian Institutes of Health Research
KeywordsUbiquitinPurineCell biologySignal transductionMetabolismPrimary (astronomy)BiologyPurine metabolismAirwayMicrobiologyImmunologyBiochemistryMedicineEnzymeGene

Abstract

fetched live from OpenAlex

Virus-host interactions are important determinants of virus replication and immune responses, but they are not well-defined. In this study we analyzed quantitative host protein alterations in nuclei-enriched fractions from multiple primary human bronchial airway epithelial (HBAE) cells infected by an H1N1 influenza A virus (A/PR/8/34). We first developed an effective detergent-free nuclear lysis method that was coupled with in-solution digestion and LC-MS/MS. Using SILAC, we identified 837 HBAE nuclear proteins in three different donors and compared their responses to infection at 24 h. Some proteins were altered in all three donors, of which 94 were up-regulated and 13 were down-regulated by at least 1.5-fold. Many of these up-regulated proteins clustered into purine biosynthesis, carbohydrate metabolism, and protein modification. Down-regulated proteins were not associated with any specific pathways or processes. These findings further our understanding of cellular processes that are altered in response to influenza in primary epithelial cells and may be beneficial in the search for host proteins that may be targeted for antiviral therapy.

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)0.006
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.080
GPT teacher head0.393
Teacher spread0.313 · 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

Citations34
Published2013
Admission routes2
Has abstractyes

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