Tacrolimus Impairs Kupffer Cell Capacity to Control Bacteremia: Why Transplant Recipients Are Susceptible to Infection
Bibliographic record
Abstract
BACKGROUND AND AIMS: Kupffer cells (KCs) are the resident intravascular phagocyte population of the liver and critical to the capture and killing of bacteria. Calcineurin/nuclear factor of activated T cells (NFAT) inhibitors (CNIs) such as tacrolimus are used to prevent rejection in solid organ transplant recipients. Although their effect on lymphocytes has been studied extensively, there are limited experimental data about if and how CNIs shape innate immunity, and whether this contributes to the higher rates of infection observed in patients taking CNIs. APPROACH AND RESULTS: Here, we investigated the impact of tacrolimus treatment on innate immunity and, more specifically, on the capability of Kupffer cells (KCs) to fight infections. Retrospective analysis of data of >2,700 liver transplant recipients showed that taking calcineurin inhibitors such as tacrolimus significantly increased the likelihood of Staphylococcus aureus infection. Using a mouse model of acute methicillin-resistant S. aureus (MRSA) bacteremia, most bacteria were sequestered in the liver and we found that bacteria were more likely to disseminate and kill the host in tacrolimus-treated mice. Using imaging, we unveiled the mechanism underlying this observation: the reduced capability of KCs to capture, phagocytose, and destroy bacteria in tacrolimus-treated animals. Furthermore, in a gene expression analysis of infected KCs, the triggering receptor expressed on myeloid cells 1 (TREM1) pathway was the one with the most significant down-regulation after tacrolimus treatment. TREM1 inhibition likewise inhibited KC bacteria capture. TREM1 levels on neutrophils as well as the overall neutrophil response after infection were unaffected by tacrolimus treatment. CONCLUSIONS: Our results indicate that tacrolimus treatment has a significant impact directly on KCs and on TREM1, thereby compromising their capacity to fend off infections.
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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 | 0.000 |
| 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".