MétaCan
Menu
← Back to cohort
Record W3037610980 · doi:10.1101/2020.06.26.169458

Virus-host protein-protein interactions between human papillomavirus 16 E6 A1 and D2/D3 sub-lineages: variances and similarities

2020· preprint· en· W3037610980 on OpenAlexafffund
Guillem Dayer, Mehran L. Masoom, Melissa Togtema, Ingeborg Zehbe

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsNOSM UniversityLakehead UniversityThunder Bay Regional Research Institute
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsBiologyCarcinogenesisInteractomeGeneticsVirusComputational biologyGene

Abstract

fetched live from OpenAlex

Abstract High-risk strains of human papillomavirus are causative agents for cervical and other mucosal cancers with type 16 being the most frequent. Compared to the European Prototype (A1, denoted “EP”), the Asian-American (D2/D3, denoted “AA”) sub-lineage or “variant” is reported to have increased abilities to promote carcinogenesis. Few global interactome studies have looked at protein-protein interactions (PPIs) between host proteins and variants of the key transforming E6 protein. We applied a primary human foreskin keratinocyte model transduced with EP and AA variant E6 genes and co-immunoprecipitated expressed E6 proteins along with interacting cellular proteins to detect virus-host binding partners. We reasoned that, due to single nucleotide polymorphisms, AAE6 and EPE6 may have unique PPIs with host cellular proteins—conferring gain or loss of function—resulting in varied abilities to promote carcinogenesis. Using liquid chromatography-mass spectrometry and stringent interactor selection criteria based on the number of peptides, we identified 25 candidates: 6 unique to each of AAE6 and EPE6, along with 13 E6 targets common to both AAE6 and EPE6. We also applied a more inclusive process based on pathway selection and discovered 171 target proteins: 90 unique AAE6 and 61 unique EPE6 along with 20 common E6 targets between the two sub-lineages. Interpretations for both approaches were made using databases such as UniProt, BioGRID and Reactome. Detected E6 targets are implicated in important hallmarks of cancer: deregulating Notch and other signaling, energetics and hypoxia, DNA replication and repair, and immune response. Validation experiments, such as reverse co-immunoprecipitation and RNA interference, are required to substantiate these findings. Here, we provide an unprecedented resource for new research questions in HR HPV biology. The current data also underline our lab’s driving hypothesis that E6, being a “master regulator” in HPV-positive cancers, is an excellent candidate for anti-cancer treatment strategies. Author Summary Chronic infection with high-risk human papillomavirus (HPV) type 16 is the most prevalent cause of cervical and other mucosal cancers. The E6 oncoproteins of the European Prototype (EP) and the Asian-American (AA) HPV variants differentially promote carcinogenesis. We looked at protein-protein interactions between host proteins and two key HPV variant E6 proteins of these strains to reveal how high risk HPVs cause cancer, based on the proteins they bind to in infected cells. Our methodology combined molecular biology and data mining techniques using widely available databases. We confirmed and discovered novel virus-host associations that explained how HPV AA and EP variants differ in their carcinogenic capabilities, and confirmed the candidacy of the E6 protein as a viable target for HPV therapies.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.046
GPT teacher head0.303
Teacher spread0.257 · 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

Citations3
Published2020
Admission routes2
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicCervical Cancer and HPV Research→French-language works237,207→