Quantitative Interactor Screening with next-generation Sequencing (QIS-Seq) identifies Arabidopsis thaliana MLO2 as a target of the Pseudomonas syringae type III effector HopZ2
Bibliographic record
Abstract
BACKGROUND: Identification of protein-protein interactions is a fundamental aspect of understanding protein function. A commonly used method for identifying protein interactions is the yeast two-hybrid system. RESULTS: Here we describe the application of next-generation sequencing to yeast two-hybrid interaction screens and develop Quantitative Interactor Screen Sequencing (QIS-Seq). QIS-Seq provides a quantitative measurement of enrichment for each interactor relative to its frequency in the library as well as its general stickiness (non-specific binding). The QIS-Seq approach is scalable and can be used with any yeast two-hybrid screen and with any next-generation sequencing platform. The quantitative nature of QIS-Seq data make it amenable to statistical evaluation, and importantly, facilitates the standardization of experimental design, data collection, and data analysis. We applied QIS-Seq to identify the Arabidopsis thaliana MLO2 protein as a target of the Pseudomonas syringae type III secreted effector protein HopZ2. We validate the interaction between HopZ2 and MLO2 in planta and show that the interaction is required for HopZ2-associated virulence. CONCLUSIONS: We demonstrate that QIS-Seq is a high-throughput quantitative interactor screen and validate MLO2 as an interactor and novel virulence target of the P. syringae type III secreted effector HopZ2.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".