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Record W2921395718 · doi:10.1101/580076

Adenovirus serotype 26 utilises sialic acid bearing glycans as a primary cell entry receptor

2019· preprint· en· W2921395718 on OpenAlexaff
Alexander T. Baker, Rosie M. Mundy, James A. Davies, P.J. Rizkallah, Alan L. Parker

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsInstitute of Infection and Immunity
FundersTenovusHigher Education Funding Council for WalesCancer Research UKDiamond Light Source
KeywordsSialic acidVirologyBiologySerotypeGenetics

Abstract

fetched live from OpenAlex

Abstract Adenoviruses are clinically important agents. They cause respiratory distress, gastroenteritis, and epidemic keratoconjunctivitis (EKC). As non-enveloped, double stranded DNA viruses, they are easily manipulated, making them popular vectors for therapeutic applications, including vaccines. Species D adenovirus serotype 26 (HAdV-D26) is both a cause of EKC and other disease, and a promising vaccine vector. HAdV-D26 derived vaccines are under investigation as protective platforms against HIV, Zika, RSV infections and are in Phase-III clinical trials for Ebola. We recently demonstrated that HAdV-D26 does not utilise CD46 or desmoglein 2 as entry receptors, whilst the putative interaction with Coxsackie and Adenovirus Receptor (CAR) is low affinity and unlikely to represent the primary cell receptor. Here, we definitively establish sialic acid as the primary entry receptor utilised by HAdV-D26. We demonstrate removal of cell surface sialic acid inhibits HAdV-D26 infection and provide a high-resolution crystal structure of HAdV-D26 fiber-knob in complex with sialic acid.

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 score0.002
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.245
Teacher spread0.231 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicVirus-based gene therapy research→French-language works237,207→