A comprehensive two-hybrid analysis to explore the <i>Legionella pneumophila</i> effector–effector interactome
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Legionella pneumophila uses over 300 translocated effector proteins to rewire host cells during infection and create a replicative niche for intracellular growth. To date, several studies have identified L. pneumophila effectors that indirectly and directly regulate the activity of other effectors, providing an additional layer of regulatory complexity. Among these are “metaeffectors,” a special class of effectors that regulate the activity of other effectors once inside the host. A defining feature of metaeffectors is direct, physical interaction with a target effector. Metaeffector identification, to date, has depended on phenotypes in heterologous systems and experimental serendipity. Using a multiplexed, recombinant barcode-based yeast two-hybrid technology we screened for protein–protein interactions among all L. pneumophila effectors and 28 components of the Dot/Icm type IV secretion system (>167,000 protein combinations). Of the 52 protein interactions identified by this approach, 44 are novel protein interactions, including 10 novel effector–effector interactions (doubling the number of known effector–effector interactions). IMPORTANCE Secreted bacterial effector proteins are typically viewed as modulators of host activity, entering the host cytosol to physically interact with and modify the activity of one or more host proteins in support of infection. A growing body of evidence suggests that a subset of effectors primarily function to modify the activities of other effectors inside the host. These “effectors of effectors” or metaeffectors are often identified through experimental serendipity during the study of canonical effector function against the host. We previously performed the first global effector-wide genetic interaction screen for metaeffectors within the arsenal of Legionella pneumophila , an intracellular bacterial pathogen with over 300 effectors. Here, using a high-throughput, scalable methodology, we present the first global interaction network of physical interactions between L. pneumophila effectors. This data set serves as a complementary resource to identify and understand both the scope and nature of non-canonical effector activity within this important human pathogen.
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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 | 0.001 |
| 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 | 0.001 |
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 it