PlexinD1 Deficiency in Lung Interstitial Macrophages Exacerbates House Dust Mite–Induced Allergic Asthma
Bibliographic record
Abstract
Abstract Interstitial macrophages (IMs) are key regulators of allergic inflammation. We previously showed that the absence of semaphorin 3E (Sema3E) exacerbates asthma features in both acute and chronic asthma models. However, it has not been studied whether Sema3E, via its receptor plexinD1, regulates IM function in allergic asthma. Therefore, we investigated the role of plexinD1 deficiency on IMs in allergic asthma. We found that the absence of plexinD1 in IMs increased airway hyperresponsiveness, airway leukocyte numbers, allergen-specific IgE, goblet cell hyperplasia, and Th2/Th17 cytokine response in the house dust mite (HDM)–induced allergic asthma model. Muc5ac, Muc5b, and α-SMA genes were increased in mice with Plxnd1-deficient IMs compared with wild-type mice. Furthermore, plexinD1-deficient bone marrow–derived macrophages displayed reduced IL-10 mRNA expression, at both the baseline and following HDM challenge, compared with their wild-type counterpart mice. Our data suggest that Sema3E/plexinD1 signaling in IMs is a critical pathway that modulates airway inflammation, airway resistance, and tissue remodeling in the HDM murine model of allergic asthma. Reduced IL-10 expression by plexinD1-deficient macrophages may account for these enhanced allergic asthma features.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".