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Record W2987449891 · doi:10.7554/elife.32579.018

Author response: Transmission genetics of drug-resistant hepatitis C virus

2018· peer-review· en· W2987449891 on OpenAlexaff
Nicholas van Buuren, Timothy L. Tellinghuisen, Christopher D. Richardson, Karla Kirkegaard

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsNS5AHepatitis C virusNS5BBiologyVirologyGenomePhosphoproteinComputational biologyVirusGeneticsDrug resistanceNS3RNA-dependent RNA polymerasePolymeraseHepacivirusGene

Abstract

fetched live from OpenAlex

Viruses are simple organisms that consist of genetic information and a few types of proteins. They cannot replicate on their own, and instead hijack the molecular machinery of a host cell to produce more of themselves. Inside an infected cell, the genetic information of the virus is replicated and ‘read’ to create viral proteins. These components are then assembled to form a new generation of viruses. During this process, genetic errors may occur that lead to modifications in the viral proteins, and help the virus become resistant to treatment. For instance, a viral protein that used to be targeted by a drug can change slightly and not be recognized anymore. Currently, the most efficient way to fight drug resistance is to use combination therapy, where several drugs are given at the same time. This strategy is successful, for example to treat infections with the hepatitis C virus, but it is also expensive, especially for developing countries. An alternative approach is dominant-drug targeting, which exploits the fact that both drug-resistant and drug-susceptible viruses are ‘born’ in the same cell. There, the susceptible viruses can overwhelm and ‘mask’ the benefits of the resistant ones. For example, proteins from resistant strains, which are no longer detected by a treatment, can bind to proteins from susceptible viruses; drugs will still be able to recognize these resulting viral structures. The proteins that operate in such ways are potential dominant-drug targets. However, resistant and susceptible strains can also cohabit without any contacts if their proteins do not interact with each other. Now, van Buuren et al. screen several viral proteins, including one called NS5A, to test whether a dominant drug target exists for the hepatitis C virus. Only a few molecules of a drug that targets NS5A can stop the virus from growing. In theory, drug-bound NS5A proteins could block their non-drug-bound neighbors, but when these drugs have been used on their own, resistance quickly emerged. Experiments showed that NS5A is not a dominant drug target because the drug-resistant and drug-susceptible proteins do not mix. Unless ‘forced’ in the laboratory, NS5A proteins only bind to the ones produced by the same strain of virus. This explains why resistant viruses quickly take over when NS5A drugs are the sole treatment. However, other hepatitis C proteins, such as the HCV core protein, are known to mix during the assembly of the virus, and thus are likely be dominant drug targets.

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 categoriesInsufficient payload (model declined to judge)
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 score0.330
Threshold uncertainty score0.956

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 communication0.0010.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.3300.137

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 head0.072
GPT teacher head0.407
Teacher spread0.334 · 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.

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

Citations1
Published2018
Admission routes1
Has abstractyes

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