A multifunctional enolase mediates cytoadhesion and interaction with host plasminogen and fibronectin in Mycoplasma hyorhinis
Bibliographic record
Abstract
Abstract Mycoplasma hyorhinis may cause systemic inflammation of pigs, typically polyserositis and arthritis, and is also associated with several types of human cancer. However, the pathogenesis of M. hyorhinis colonizing and breaching the respiratory barrier to establish systemic infection is poorly understood. Glycolytic enzymes are important moonlighting proteins and virulence-related factors in various bacteria. In this study, we investigated the functions of a glycolytic critical enzyme, enolase in the infection and systemic spread of M. hyorhinis. Bacterial surface localization of enolase was confirmed by flow cytometry and colony hybridization assay. Recombinant M. hyorhinis enolase (rEno) was found to adhere to pig kidney (PK-15) cells, and anti-rEno serum significantly decreased adherence. The enzyme was also found to bind host plasminogen and fibronectin, and interactions were specific and strong, with dissociation constant (KD) values of 1.4 nM and 14.3 nM, respectively, from surface plasmon resonance analysis. Activation of rEno-bound plasminogen was confirmed by its ability to hydrolyze plasmin-specific substrates and to degrade a reconstituted extracellular matrix. To explore key sites during these interactions, C-terminal lysine residues of enolase were replaced with leucine, and the resulting single-site and double-site mutants show significantly reduced interaction with plasminogen in far-Western blotting and surface plasmon resonance tests. The binding affinities of all mutants to fibronectin were reduced as well. Collectively, these results imply that enolase moonlights as an important adhesin of M. hyorhinis, and interacts with plasminogen and fibronectin. The two lysine residues in the C-terminus are important binding sites for its multiple binding activities.
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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 | 0.000 | 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 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".