Acute IL-6 exposure triggers canonical IL-6R signalling in hiPSC microglia, but not neural progenitor cells
Bibliographic record
Abstract
Abstract Background Exposure to elevated interleukin (IL)-6 levels in utero is consistently associated with increased risk for psychiatric disorders with a putative neurodevelopmental origin, such as schizophrenia (SZ) and autism spectrum condition (ASC). Although rodent models provide causal evidence for this association, we lack a detailed understanding of the cellular and molecular mechanisms in human model systems. To close this gap, we characterised the response of hiPSC-derived microglia-like cells (MGL) and neural progenitor cells (NPCs) to IL-6 in monoculture. Results We observed that human forebrain NPCs did not respond to acute IL-6 exposure in monoculture at both a protein and transcript level due to the absence of IL-6Ra expression and sIL-6Ra secretion. By contrast, acute IL-6 exposure resulted in STAT3 phosphorylation and increased IL-6, JMJD3 and IL-10 expression in MGL, confirming activation of canonical IL-6R signalling. Bulk RNAseq identified 156 upregulated genes (FDR <0.05) in MGL following acute IL-6 exposure, including IRF8, REL, HSPA1A/B and OXTR , which significantly overlapped with an upregulated gene set from post-mortem brain tissue from individuals with schizophrenia. Acute IL-6 stimulation significantly increased MGL motility suggestive of a gain of surveillance function, consistent with gene ontology pathways highlighted from the RNAseq data. Finally, MGLs displayed elevated CCL1, CXCL1, MIP-1A/B, IL-8, IL-13, IL-16, IL-18, MIF and Serpin-E1 secretion post 3h and 24h IL-6 exposure. Conclusion Our data provide evidence for cell specific effects of acute IL-6 exposure in a human model system and strongly suggest microglia-NPC co-culture models are required to study how IL-6 influences human cortical neural progenitor cell development in vitro .
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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 | 0.000 | 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".