Genetic landscape of an <i>in vivo</i> protein interactome
Bibliographic record
Abstract
SUMMARY Protein-protein interaction (PPI) networks accurately map environmental perturbations to their molecular consequences in cells, but effects of genome-wide genetic variation on PPIs remain unknown. We hypothesized that PPI networks integrate genetic and environmental effects, potentially defining biochemical mechanisms underlying complex polygenic traits. Here, we measured 61 PPIs in inbred strains of Saccharomyces cerevisiae with ∼12,000 single-nucleotide polymorphisms (SNPs) across the genome. Unlike mRNA expression and protein abundance that are primarily affected by SNPs local (in “cis”) to a gene, PPIs are predominantly affected by SNPs far (in “trans”) to the genomic loci of the interacting proteins. However, consistent with the PPI network’s small-world characteristic, these transacting SNPs are in neighboring genes in the network. We likewise discovered SNPs in non-coding RNAs and post-transcriptional regulators (3’ UTRs) with, counterintuitively, larger PPI-modulating effects than SNPs within protein-coding regions. Finally, we inferred known and novel mechanisms of action for yeast and human drugs. HIGHLIGHTS Protein-interaction quantitative trait locus (“piQTL”) mapping reveals sensitivity of in vivo PPIs to polymorphisms across the yeast genome Trans-piQTLs significantly outnumber and are stronger than cis-piQTLs SNPs in non-coding RNAs and 3’ UTRs have comparable effects to PPI as SNPs in coding regions piQTL mapping reveals known and novel mechanism of yeast and human drugs
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".