Transcriptional regulation of type 2 innate lymphoid cells and precursors by interleukin-7 receptor signalling
Bibliographic record
Abstract
Abstract Innate lymphoid cells (ILCs) are a rare population of innate immune cells that act as the first line of defense against pathogens. These cells arise from the lymphoid lineage that B and T cells also belong to. Type 2 ILCs (ILC2s) are a subset of ILCs that reside in the lungs, mucosal layers, and skin, and have roles in clearing helminth infections, triggering allergic responses, and promoting lung tissue repair after influenza infections. The functions of ILC2s mirror those of TH2 cells but lack antigen recognition. However, their full development background is still unknown. It is believed that ILC2s develop in the fetal liver and adult bone marrow from common lymphoid progenitors and differentiate into several intermediates including helper ILC precursors. From this, ILC2 progenitors arise and express interleukin-7 (IL-7) receptor with the aid of the transcription factor GATA3. Our lab has found that IL-7 is critical for ILC2 development as mutations to the IL-7 receptor show a reduction in the ILC2 numbers and GATA3 expression. Conversely, overexpression of IL-7 results in the expansion of ILC2s and elevated GATA3 expression. We hypothesize that IL-7 signalling dictates ILC2 development through the transcriptional regulation of lineage determining factors. We aim to identify the transcriptional and epigenetic landscape regulated by IL-7 in ILC2s and their progenitors using single cell RNA sequencing and chromatin immunoprecipitation sequencing. We will also investigate how IL-7 influences airway immune responses generated by ILC2s using flow cytometry and RNA sequencing. This study will increase our understanding of a vital cell population and contribute to the development of therapeutics for allergic asthma and viral infections.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".