Lack of functional selectin-ligand interactions enhances innate immune resistance to systemic <i>Listeria monocytogenes</i> infection
Bibliographic record
Abstract
Selectin-ligand interactions are important for leukocyte homing and functionality. The roles of selectin-ligand interactions in modulating immunity to intracellular infections are not completely understood. Mice lacking the expression of fucosyltransferase-IV and -VII (Fucosyltransferase-IV and -VII double knockout, FtDKO) exhibit deficient functionality of selectin-ligand interactions. We addressed the kinetics of infection and immunity to Listeria monocytogenes (LM), an intracellular pathogen, in FtDKO mice. These mice exhibited enhanced ability to clear infection and increased survival to a lethal dose of LM infection relative to wild-type (WT) C57BL/6J controls. This was associated with increased levels of neutrophils, monocytes, and dendritic cells (DCs) in the blood and/or infected organs. Adoptive transfer of bone marrow (BM) cells from FtDKO mice to WT mice resulted in enhanced neutrophil numbers and improved clearance of LM bacteria in recipients. In vivo depletion of myeloid innate immune cells, particularly neutrophils, monocytes, macrophages, and DCs, using anti-Ly-6G (RB6-8C5) monoclonal antibody, reduced the ability of FtDKO mice to curtail LM infection. Nevertheless, depletion using anti-Ly-6G (1A8) known to exclusively deplete neutrophils did not abrogate increased resistance of FtDKO mice to LM infection, suggesting a role for other myeloid innate immune cells in this model. Examination of BM hematopoietic progenitors through flow cytometry and cell culture colony-forming unit assay showed increased frequencies of granulocyte-macrophage progenitors in FtDKO relative to WT mice, Overall, our results indicate that functional selectin ligand deficiency enhances innate immune-mediated resistance to systemic LM infection despite defective leukocyte migration and lymphocyte homing.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".