Histamine Produced by Gram-Negative Bacteria Impairs Neutrophil’s Antimicrobial Response by Engaging the Histamine 2 Receptor
Bibliographic record
Abstract
We found that histamine (10<sup>−9</sup> M) did not have any effect on the <i>in vitro</i> capture of <i>Escherichia coli</i> by neutrophils but accelerated its intracellular killing. In contrast, histamine (10<sup>−6</sup> M) delayed the capture of <i>Escherichia coli</i> by neutrophils and reduced the amounts of pHrodo zymosan particles inside acidic mature phagosomes. Histamine acted through the H<sub>4</sub>R and the H<sub>2</sub>R, which are coupled to the Src family tyrosine kinases or the cAMP/protein kinase A pathway, respectively. The protein kinase A inhibitor H-89 abrogated the delay in bacterial capture induced by histamine (10<sup>−6</sup> M) and the Src family tyrosine kinase inhibitor PP2 blocked histamine (10<sup>−9</sup> M) induced acceleration of bacterial intracellular killing and tyrosine phosphorylation of proteins. To investigate the role of histamine in pathogenicity, we designed an <i>Acinetobacter baumannii</i> strain deficient in histamine production (hdc::TOPO). <i>Galleria mellonella</i> larvae inoculated with the wild-type <i>Acinetobacter baumannii</i> ATCC 17978 strain (1.1 × 10<sup>5</sup> CFU) died rapidly (100% death within 40 h) but not when inoculated with the <i>Acinetobacter baumannii</i> hdc::TOPO mutant (10% mortality). The concentration of histamine rose in the larval haemolymph upon inoculation of the wild type but not the <i>Acinetobacter baumannii</i> hdc::TOPO mutant, such concentration of histamine blocks the ability of hemocytes from <i>Galleria mellonella</i> to capture <i>Candida albicans</i> <i>in vitro</i>. Thus, bacteria-producing histamine, by maintaining high levels of histamine, may impair neutrophil phagocytosis by hijacking the H<sub>2</sub>R.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".