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Record W2985049695 · doi:10.7554/elife.11184.018

Author response: Huntingtin’s spherical solenoid structure enables polyglutamine tract-dependent modulation of its structure and function

2015· peer-review· en· W2985049695 on OpenAlexaff
Ravi Vijayvargia, Raquel F. Epand, Alexander Leitner, Tae‐Yang Jung, Baehyun Shin, Roy Jung, Alejandro Lloret, Randy Singh Atwal, Hyeongseok Lee, Jong‐Min Lee, Ruedi Aebersold, Hans Hebert, Ji‐Joon Song, Ihn Sik Seong

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHuntingtinPolyglutamine tractModulation (music)SolenoidFunction (biology)PhysicsCell biologyComputational biologyBiologyChemistryBiophysicsGeneticsQuantum mechanicsMutantGene

Abstract

fetched live from OpenAlex

Huntington’s disease is an inherited disorder that occurs in adulthood and sometimes in children. It causes progressive damage to the brain and people with the condition develop memory loss, movement difficulties, confusion, and other symptoms of mental decline. Eventually, the disease leads to death. Mutations in the gene that encodes a protein called huntingtin cause Huntington’s disease. Individuals who inherit just one copy of the mutated gene develop the condition. No treatments currently exist that can slow or stop disease progression. Genetic and molecular studies are beginning to shed light on how mutations in the gene encoding huntingtin cause the disease. Normally, the protein has a section near its tail end made up of the amino acid glutamine repeated around 23 times. Mutations that increase the number of glutamines to more than 38 cause Huntington’s disease. The more extra glutamines there are in this region of the protein, the earlier in life the disease symptoms begin. But it was not clear how these extra glutamines near the tail of huntingtin affect the structure and behavior of a protein that is more than 3,000 amino acids long. Now, Vijayvargia et al. have revealed why the tail end of huntingtin is so important. Several biophysical methods were used to determine the three-dimensional structure of the huntingtin protein. These methods revealed that the protein folds up into a hollow sphere and that its tail end is able to interact with the entire length of the protein and physically touches its opposite end. To see this in more detail, Vijayvargia et al. used another experimental technique called crosslinking mass spectrometry to confirm which parts of the huntingtin protein are in close contact with each other. Together with the structural data, these experiments suggest that the stretch of glutamines is in the position to bring about subtle, but widespread, changes throughout the huntingtin protein. That is to say, that having more glutamines slightly changes the curve of the sphere and alters the way different parts of the protein interact. Together the new findings explain why mutations that alter the tail of huntingtin affect the rest of the protein. Further work will now aim to provide a more-detailed structure of the huntingtin protein and to investigate what other roles of huntingtin are affected by the increased number of glutamines in the protein’s tail. These insights may help scientists understand how the mutated protein causes brain decline.

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)0.002
metaresearch head score (Gemma)0.005
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: Other · Consensus signal: none
Teacher disagreement score0.195
Threshold uncertainty score0.654

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.1950.079

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 head0.050
GPT teacher head0.312
Teacher spread0.263 · 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
GenreOther

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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