Regulatory T Cells Restrain CD4+ T Cells from Causing Unregulated Immune Activation and Hypersensitivity to Lipopolysaccharide Challenge
Bibliographic record
Abstract
Regulatory T cells (Tregs) are essential for maintenance of peripheral tolerance, and defects in Treg function have been linked to several autoimmune diseases. We previously reported that depletion of Tregs resulted in mortality to an otherwise nonlethal dose of LPS or Escherichia coli challenge. In this study, we investigated the mechanism by which Treg depletion leads to enhanced susceptibility to LPS. Using different murine lymphocyte gene knockout models, we show that the enhanced sensitivity to LPS following Treg depletion is mediated by T cells. SCID or RAG1-deficient mice, which lack T and B cells, do not show enhanced susceptibility to LPS. However, reconstitution of SCID mice with wild-type CD4(+) T cells restored Treg depletion-induced sensitivity to LPS. This CD4(+) T cell-mediated hypersensitivity to LPS challenge in the absence of Tregs was also observed upon reconstitution of SCID mice with CD4(+) T cells from CD25 knockout mice (which lack functional Tregs). Additionally, depletion of Tregs leads to increased CD4(+) T cell proliferation and proinflammatory cytokine production in response to LPS challenge. Some CD4(+) T cells express TLR4, and pretreatment of CD4(+) T cells with LPS dramatically enhanced their ability to induce inflammatory cytokine production by macrophages. Collectively, our results indicate that in the absence of functional Tregs, CD4(+) T cells are pathologic and contribute to exaggerated immune activation that is detrimental for survival in LPS-induced acute inflammation. Our data also provide evidence for direct activation of CD4(+) T cells by LPS through TLR4.
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) | 0.000 | 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) | 0.001 | 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".