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Record W4390577205 · doi:10.1016/j.isci.2024.108801

Multi-targeted loss of the antigen presentation molecule MR1 during HSV-1 and HSV-2 infection

2024· article· en· W4390577205 on OpenAlexaff
Carolyn Samer, Hamish E. G. McWilliam, Brian P. McSharry, Thilaga Velusamy, James G. Burchfield, Richard J. Stanton, David C. Tscharke, Jamie Rossjohn, Jóse A. Villadangos, Allison Abendroth, Barry Slobedman

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldMedicine
TopicHerpesvirus Infections and Treatments
Canadian institutionsInstitute of Infection and Immunity
FundersNational Institute of Allergy and Infectious DiseasesNational Health and Medical Research CouncilWellcome TrustAustralian Research CouncilMedical Research CouncilNational Institutes of HealthUniversity of Sydney
KeywordsHSL and HSVPresentation (obstetrics)Antigen presentationAntigenVirologyCross-presentationMedicineImmunologyChemistryComputational biologyBiologyImmune systemVirusT cell

Abstract

fetched live from OpenAlex

The major histocompatibility complex (MHC), Class-I-related (MR1) molecule presents microbiome-synthesized metabolites to Mucosal-associated invariant T (MAIT) cells, present at sites of herpes simplex virus (HSV) infection. During HSV type 1 (HSV-1) infection there is a profound and rapid loss of MR1, in part due to expression of unique short 3 protein. Here we show that virion host shutoff RNase protein downregulates MR1 protein, through loss of MR1 transcripts. Furthermore, a third viral protein, infected cell protein 22, also downregulates MR1, but not classical MHC-I molecules. This occurs early in the MR1 trafficking pathway through proteasomal degradation. Finally, HSV-2 infection results in the loss of MR1 transcripts, and intracellular and surface MR1 protein, comparable to that seen during HSV-1 infection. Thus HSV coordinates a multifaceted attack on the MR1 antigen presentation pathway, potentially protecting infected cells from MAIT cell T cell receptor-mediated detection at sites of primary infection and reactivation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.134

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.018
GPT teacher head0.313
Teacher spread0.295 · 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 designObservational
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

Citations7
Published2024
Admission routes1
Has abstractyes

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