Human ectoenzyme-expressing ILC3: immunosuppressive innate cells that are depleted in graft-versus-host disease
Bibliographic record
Abstract
Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is often associated with chemotherapy- and radiotherapy-induced host tissue damage, leading to graft-versus-host disease (GVHD). Innate lymphoid cells (ILC) have an essential role in tissue homeostasis and tissue repair via their production of interleukin (IL)-22, which acts on intestinal stem cells. The tissue healing capacities of ILC via IL-22 in the context of allo-HSCT and GVHD has previously been demonstrated in a mouse model for acute GVHD. We investigated potential other ways of ILC-mediated tissue protection against GVHD. Tissue injury leads to the release of danger-associated molecular patterns (DAMPs). DAMPs interact with purinergic receptors and ectoenzymes on immune cells and induce pleiotropic effects, including activation of proinflammatory antigen-presenting cells and immunosuppressive effects via the generation of adenosine. Here, we report a novel subset of human ILC3 that coexpress the ectoenzymes CD39 and CD73 (ecto+ ILC3). Ecto+ ILC3 express RORγt and were present in the oral-gastrointestinal tract and bone marrow. ILC3 ectoenzyme expression is modulated by the proinflammatory cytokine IL-1β. Extracellular adenosine triphosphate (eATP) stimulated ecto+ ILC3 to produce IL-22 and adenosine. Activated ecto+ ILC3 suppressed autologous T-cell proliferation in coculture experiments via the production of adenosine. In allo-HSCT recipients, intestinal GVHD was associated with reduced proportions of ecto+ ILC3 and decreased levels of adenosine and its metabolite inosine. Taken together, ecto+ ILC3 have immunosuppressive properties, but in patients with GVHD, ecto+ ILC3 are depleted. A lack of ecto+ ILC3 and subsequent reduced capacity to neutralize DAMPs may contribute to the development of GVHD.
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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".