Vascular binding of a pathogen under shear force through mechanistically distinct sequential interactions with host macromolecules
Bibliographic record
Abstract
Systemic dissemination of microbial pathogens permits microbes to spread from the initial site of infection to secondary target tissues and is responsible for most mortality due to bacterial infections. Dissemination is a critical stage of disease progression by the Lyme spirochaete, Borrelia burgdorferi. However, many mechanistic features of the process are not yet understood. A key step is adhesion of circulating microbes to vascular surfaces in the face of the shear forces present in flowing blood. Using real-time microscopic imaging of the Lyme spirochaete in living mice we previously identified the first bacterial protein (B. burgdorferi BBK32) shown to mediate vascular adhesion in vivo. Vascular adhesion is also dependent on host fibronectin (Fn) and glycosaminoglycans (GAGs). In the present study, we investigated the mechanisms of BBK32-dependent vascular adhesion in vivo. We determined that BBK32-Fn interactions (tethering) function as a molecular braking mechanism that permits the formation of more stable BBK32-GAG interactions (dragging) between circulating bacteria and vascular surfaces. Since BBK32-like proteins are expressed in a variety of pathogens we believe that the vascular adhesion mechanisms we have deciphered here may be critical for understanding the dissemination mechanisms of other bacterial pathogens.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".