Psychological stress triggers the accumulation of myeloid-derived suppressor cells in the liver through adrenergic receptor signaling
Bibliographic record
Abstract
Abstract Chronic physiological stress affects tightly regulated crosstalk between the nervous and the immune system. However, the exact mechanism(s) of stress-induced immunomodulation is far from clear. Immature myeloid cells such as myeloid-derived suppressor cells (MDSCs) impair immune responses, which may in turn increase susceptibility to infections and cancer or their complications. In a mouse model of chronic psychological stress through physical restraint, we found mononuclear-MDSC (Mo-MDSC) cells, defined as CD11b+Ly6ChighLy6Ghigh cells, to accumulate in the liver. These cells expressed high levels of Mo-MDSC markers such as CD14, TLR4, IL-4Rα as well as markers indicative of anti-apoptotic and immunosuppressive functions (HIF-1α, G-CSFR, Bcl2, Arg2, iNOS). In addition, FACS sorted MDSCs suppressed production of IFN-ɣ by T cells in vitro. Increased levels of proinflammatory mediators were detectable in serum samples of stressed mice, with circulating levels of IL-6 and G-CSF being the most pronounced mediators. The observed intrahepatic accumulation of Mo-MDSC-like cells was mediated by IL-6 and G-CSF and could be reversed by treatment with corresponding receptor-blocking monoclonal antibodies. In addition, this phenomenon was driven by norepinephrine (NE), the main neurotransmitter released from sympathetic neurons, and could be recapitulated by in vivo administration of NE to non-stressed mice. Our work reveals a novel mechanism of immunosuppression that is governed by adrenergic and inflammatory cues and alters the cellular landscape of the liver with implications for immune surveillance against microbes and cancer. This work was funded by NSERC
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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".