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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".