The Effects of Shiga Toxin 1, 2 and Their Subunits on Cytokine and Chemokine Expression by Human Macrophage-Like THP-1 Cells
Bibliographic record
Abstract
Infection by Shiga toxin (Stx)-producing enterohemorrhagic Escherichia coli (EHEC) results in severe diarrhea, hemorrhagic colitis, and, occasionally, hemolytic-uremic syndrome (HUS). HUS is associated with an increase in pro-inflammatory cytokines and chemokines, many of which are produced by macrophages in the kidneys, indicating that localized host innate immunity likely plays a role in renal pathogenesis. EHEC serotypes may express one or two classes of serologically defined but structurally and functionally-related Shiga toxins called Stx1 and Stx2. Of these, Stx2 appears to be linked to higher rates of HUS than Stx1. To investigate a possible reason for this, we exposed human macrophage-like THP-1 cells to Stx1 or Stx2 and then used the Luminex multiplex system to assess cytokine/chemokine concentrations in culture supernatant solutions. This analysis revealed that, relative to Stx1, Stx2 significantly caused increased expression of GRO, G-CSF, IL-1β, IL-8 and TNFα in macrophage-like THP-1 cells. This was determined to not be due to a difference in cytotoxicity since both Stx1 and Stx2 displayed similar cytotoxic activities on macrophage-like THP-1 cells. These observations indicate that, in vitro, Stx2 can provoke a greater pro-inflammatory response than Stx1 in macrophages and provides a possible partial explanation for higher rates of HUS in patients infected with EHEC strains expressing Stx2. To begin to determine a mechanism for Shiga toxin-mediated cytokine production, we exposed macrophage-like THP-1 cells to Stx1 or Stx2 A and B subunits. Luminex analysis of cytokines in cell culture supernatant solutions demonstrated that neither subunit alone induced a cytokine response in THP-1 cells.
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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) | 0.001 | 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".