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Record W1963682547 · doi:10.1186/gm404

A yeast phenomic model for the gene interaction network modulating CFTR-ΔF508 protein biogenesis

2012· article· en· W1963682547 on OpenAlexaff
Raymond J. Louie, Jingyu Guo, John Rodgers, Rick White, Najaf A. Shah, Silvère Pagant, Peter Kim, Michael Livstone, Kara Dolinski, Brett A. McKinney, Jeong S. Hong, Eric J. Sorscher, Jennifer Bryan, Elizabeth A. Miller, John L. Hartman

Bibliographic record

VenueGenome Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Center for Research ResourcesNational Institute of General Medical SciencesNational Institutes of Health
KeywordsΔF508Cystic fibrosis transmembrane conductance regulatorBiogenesisBiologyGeneticsGeneCystic fibrosisCell biologyMutant

Abstract

fetched live from OpenAlex

BACKGROUND: The overall influence of gene interaction in human disease is unknown. In cystic fibrosis (CF) a single allele of the cystic fibrosis transmembrane conductance regulator (CFTR-[increment]F508) accounts for most of the disease. In cell models, CFTR-[increment]F508 exhibits defective protein biogenesis and degradation rather than proper trafficking to the plasma membrane where CFTR normally functions. Numerous genes function in the biogenesis of CFTR and influence the fate of CFTR-[increment]F508. However it is not known whether genetic variation in such genes contributes to disease severity in patients. Nor is there an easy way to study how numerous gene interactions involving CFTR-[increment]F would manifest phenotypically. METHODS: To gain insight into the function and evolutionary conservation of a gene interaction network that regulates biogenesis of a misfolded ABC-transporter, we employed yeast genetics to develop a "phenomic" model, in which the CFTR-[increment]F508-equivalent residue of a yeast homolog is mutated (Yor1-[increment]F670), and where the genome is scanned quantitatively for interaction. We first confirmed that Yor1-[increment]F undergoes protein misfolding and has reduced half-life, analogous to CFTR-[increment]F. Gene interaction was then assessed quantitatively by growth curves for all ~5000 double mutants, based on alteration in the dose response to growth inhibition by oligomycin, a toxin extruded from the cell at the plasma membrane by Yor1. RESULTS: From a comparative genomic perspective, yeast gene interaction influencing Yor1-[increment]F biogenesis was representative of human homologs previously found to modulate processing of CFTR-[increment]F in mammalian cells. Additional evolutionarily conserved pathways were implicated by the study, and a [increment]F-specific pro-biogenesis function of the recently discovered ER Membrane Complex (EMC) was evident from the yeast screen. This novel function was validated biochemically by siRNA of an EMC ortholog in a human cell line expressing CFTR-[increment]F508. The precision and accuracy of quantitative high throughput cell array phenotyping (Q-HTCP), which captures tens of thousands of growth curves simultaneously, provided powerful resolution to measure gene interaction on a phenomic scale, based on discrete cell proliferation parameters. CONCLUSION: We propose phenomic analysis of Yor1-[increment]F as a model for investigating gene interaction networks that can modulate cystic fibrosis disease severity. Although the clinical relevance of the Yor1-[increment]F gene interaction network for cystic fibrosis remains to be defined, the model appears to be informative with respect to human cell models of CFTR-[increment]F. Moreover, the general strategy of yeast phenomics can be employed in a systematic manner to model gene interaction for other diseases relating to pathologies that result from protein misfolding or potentially any disease involving evolutionarily conserved genetic pathways.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.057
GPT teacher head0.333
Teacher spread0.276 · 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 designSimulation or modeling
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

Citations91
Published2012
Admission routes1
Has abstractyes

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