Editorial: Molecular insights of microbiota and innate lymphoid cell interactions
Bibliographic record
Abstract
In the dynamic field of immunology, the discovery of innate lymphoid cells (ILCs) in 2008 marked a transformative milestone, as highlighted by Vivier et al. (2018). These cells, present in both humans and mice, have become central to research due to their crucial role in maintaining barrier immunity and their involvement in a range of microbiota-driven disorders.ILCs are a diverse group of cells categorized into three main subsets based on their expression of transcription factors and cytokines (Vivier et al., 2018). Group 1 ILCs (including ILC1 and NK cells) are characterized by the expression of T-bet and IFN-γ, mirroring the function of Th1 cells.Group 2 ILCs express GATA3 and cytokines such as IL-4, IL-5, and IL-13, resembling Th2 cells.Group 3 ILCs, defined by the expression of RORγt and production of are analogous to Th17 cells. Although ILCs perform functions similar to adaptive T cells, they are distinguished by the absence of antigen-specific receptors. Instead, their activation and responses are mediated through cytokine and alarmin signaling. These cells are primarily located at mucosal surfaces and other barrier sites and act as a crucial first line of defense by rapidly producing cytokines that orchestrate immune responses and maintain tissue homeostasis. Their role in health and disease is increasingly recognized, particularly in conditions such as inflammatory bowel diseases, cancer, autoimmunity, and metabolic disorders. A key aspect of their function is their interaction with the microbiota-a complex and diverse community of microorganisms essential for shaping host immunity. In particular, the gut microbiota influences immune cell development, modulates inflammatory responses, and impacts systemic immunity (Zheng et al., 2020). Positioned strategically at these barrier sites, ILCs are well-equipped to sense microbial signals and respond accordingly, helping to maintain a balanced immune environment. Disruptions in this crosstalk can lead to dysbiosis, driving the onset and progression of various diseases. Understanding these interactions holds significant potential for developing microbiota-targeted therapies that could restore immune balance and offer new approaches to disease treatment and prevention. In addition to exploring these biological interactions, the integration of multi-omics technologies and artificial intelligence (AI) has opened new avenues for investigating the complex relationships between immune cells and their environment. Xu et al. contribution within this Research Topic utilizes these advanced approaches to uncover new neutrophil clusters and potential biomarkers in sepsis, offering valuable insights into the immune landscape during severe infection. This research not only enhances our understanding of sepsis but also illustrates the potential of integrating multi-
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".