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Record W2988299016 · doi:10.7554/elife.10365.028

Author response: Unravelling druggable signalling networks that control F508del-CFTR proteostasis

2015· peer-review· en· W2988299016 on OpenAlexaff
Ramanath Narayana Hegde, Seetharaman Parashuraman, Francesco Iorio, Fabiana Ciciriello, Fabrizio Capuani, Annamaria Carissimo, Diego Carrella, Vincenzo Belcastro, Advait Subramanian, Laura Bounti, Maria Persico, Graeme W. Carlile, Luis J. V. Galietta, David Y. Thomas, Diego di Bernardo, Alberto Luini

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsMcGill University
Fundersnot available
KeywordsProteostasisDruggabilitySignallingComputational biologyBiologyCell biologyChemistryGeneticsGene

Abstract

fetched live from OpenAlex

Cystic fibrosis is a genetic disease that commonly affects people of European descent. The condition is caused by mutations in the gene encoding a protein called “cystic fibrosis transmembrane conductance regulator” (or CFTR for short). CFTR forms a channel in the membrane of cells in the lungs that help transport salt across the membrane. Mutated versions of the protein are not as efficient at transporting salts, and eventually this damages the lung tissue. As the damage progresses, individuals become very vulnerable to bacterial infections that further damage the lungs and may eventually lead to death. One of the reasons CFTR mutations are harmful is that they cause the protein to fold up incorrectly and remain trapped inside the cell. Cells have quality control systems that recognize and destroy poorly folded proteins, and so only a few of the mutated CFTR proteins ever make it to the membrane to move salts. New therapies have been developed that improve folding of the protein and/or help the CFTR proteins that make it to the membrane work better. But more and better treatment options are needed. Hegde, Parashuraman et al. have now tested drugs that control how proteins fold and move to the membrane to see how they affect gene expression in cells with the most common cystic fibrosis-causing mutation. These drugs are known to improve the activity of the CFTR mutant, but do so too weakly to be of clinical interest. The experiments revealed that the expression of a few hundred genes was changed in response the drugs. Many of these genes were involved in major signalling pathways that control how CFTR is folded and trafficked within cells. Next, Hegde, Parashuraman et al. tested drugs that inhibit these signalling pathways to see if they improve salt handling in the mutated cells. The experiments demonstrated that these inhibitor drugs efficiently block the breakdown of misfolded CFTR, or boost the likelihood of CFTR making it to the membrane, helping improve salt trafficking in the cells. The inhibitors produced even better results when used in combination with a known CFTR-protecting drug. The results suggest that identifying and targeting signalling pathways involved in the folding, trafficking, and breakdown of CFTR may prove a promising way to treat cystic fibrosis.

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)0.009
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: Commentary
Teacher disagreement score0.172
Threshold uncertainty score0.576

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0020.003
Research integrity0.0120.009
Insufficient payload (model declined to judge)0.1720.073

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 head0.086
GPT teacher head0.384
Teacher spread0.299 · 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".

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Citations1
Published2015
Admission routes1
Has abstractyes

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