Lipopolysaccharide impacts murine CD103 <sup>+</sup> DC differentiation, altering the lung DC population balance
Bibliographic record
Abstract
Abstract Conventional DCs are a heterogeneous population that bridge the innate and adaptive immune systems. The lung DC population comprises CD103 + XCR1 + DC1s and CD11b + DC2s; their various combined functions cover the whole spectrum of immune responses needed to maintain homeostasis. Here, we report that in vivo exposure to LPS leads to profound alterations in the proportions of CD103 + XCR1 + DCs in the lung. Using ex vivo LPS and TNF stimulations of murine lung and spleen‐isolated DCs, we show that this is partly due to a direct downregulation of the GM‐CSF‐induced DC CD103 expression. Furthermore, we demonstrate that LPS‐induced systemic inflammation alters the transcriptional signature of DC precursors toward a lower capacity to differentiate into XCR1 + DCs. Also, we report that TNF prevents the capacity of pre‐DCs to express CD103 upon maturation. Overall, our results indicate that exposure to LPS directly impacts the capacity of pre‐DCs to differentiate into XCR1 + DCs, in addition to lowering their capacity to express CD103. This leads to decreased proportions of CD103 + XCR1 + DCs in the lung, favoring CD11b + DCs, which likely plays a role in the break in homeostasis following LPS exposure, and in determining the nature of the immune response to LPS.
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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".