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Record W2987728506 · doi:10.7554/elife.22206.023

Author response: Control of immune ligands by members of a cytomegalovirus gene expansion suppresses natural killer cell activation

2017· peer-review· en· W2987728506 on OpenAlexaff
Ceri A. Fielding, Michael P. Weekes, Luís Nobre, Eva Růčková, Gavin S. Wilkie, João A. Paulo, Chiwen Chang, Nicolás M. Suárez, James A. Davies, Robin Antrobus, Richard J. Stanton, Rebecca Aicheler, Hester Nichols, John Trowsdale, Andrew J. Davison, Steven P. Gygi, Peter Tomašec, Paul J. Lehner, Gavin W. G. Wilkinson

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsImmune systemBiologyImmunologyCytomegalovirusHuman cytomegalovirusVirusVirologyHerpesviridaeViral disease

Abstract

fetched live from OpenAlex

Cytomegalovirus (CMV) is one of eight herpesviruses that can infect humans. Most people will at some point become infected with CMV, yet the virus tends only to cause serious disease in people whose immune system is not working properly. Individuals living with HIV/AIDS and organ transplant recipients (who have to take drugs that suppress their immune system to prevent the organ being rejected) are particularly vulnerable to CMV infections. Critically, the virus can cross the placenta to infect of the foetus. CMV infection in the womb can cause miscarriage, lead to severe developmental problems in babies and is a major cause of deafness. Herpesvirus infections are for life. While the immune system cannot eliminate CMV, it does have many systems that combine to sense and control infections. Natural killer cells are known to play a critical role in detecting and destroying cells infected with CMV. The virus, in turn, has nine genes that help to protect it against natural killer cells. This includes two genes that belong to a group of similar genes called the US12 family, but it is not clear whether other members of this gene family also provide protection against natural killer cells. Fielding et al. now show that at least four members of the US12 gene family help CMV to evade natural killer cells. For example, two members work together to target a human protein called B7-H6 that acts a sensor to alert natural killer cells if a particular cell is infected. However, the impact of the US12 family goes even wider. The whole family works together to control proteins that are found on the surface of human cells, and many of these proteins appear to be involved in regulating the immune response. The findings of Fielding et al. provide an insight into how the US12 gene family works, and how CMV has evolved to escape the human immune system. New therapies to control CMV infections are urgently needed so the next challenge is to design new antiviral agents that will target CMV’s defence systems.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.292
Threshold uncertainty score0.976

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.2920.132

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 head0.016
GPT teacher head0.286
Teacher spread0.270 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2017
Admission routes1
Has abstractyes

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