Characterizing host-microbe interactions with bacterial effector proteins using proximity-dependent biotin identification (BioID)
Bibliographic record
Abstract
Bacterial pathogens have evolved diverse strategies to manipulate host cells to establish infection. At a molecular level, this is often mediated by virulence factors that are secreted into host cells (herein referred to as effectors), which target host cellular pathways by initiating host-pathogen protein-protein interactions that alter cellular function in the host. By establishing this network of host-pathogen protein-protein interactions, pathogenic bacteria modulate and hijack host cell processes for the benefit of the pathogen, ultimately promoting survival, replication, and cell-to-cell spread within the host. Effector proteins also mediate diverse host-microbe interactions in nature, contributing to symbiotic relationships spanning from mutualism to commensalism to parasitism. While effector proteins play crucial roles in nature, molecular properties such as the transient nature of the underlying protein-protein interactions and their affinity for targeting host biological membranes often presents challenges to elucidating host targets and mechanism of action. Proximity-dependent biotin identification (termed BioID) has proven to be a valuable tool in the field of cell biology to identify candidate protein-protein interactions in eukaryotic cells, yet has remained relatively underexploited by bacterial pathogenesis researchers. Here, we discuss bacterial effector function at a molecular level, and challenges presented by traditional approaches to host target identification. We highlight the BioID approach and its potential strengths in the context of identifying host-pathogen protein-protein interactions, and explore BioID's implementation to study host-microbe interactions mediated by bacteria. Collectively, BioID represents a powerful tool for the study of bacterial effector proteins, providing new insight into our understanding of pathogenesis and other symbiotic relationships, and opportunities to identify new factors that contribute to host response to infection.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".