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Review of GO-CAM Model gomodel:6348a65d00000848

Model Information

  • Title: "Regulation by IL22 and REG3A of allergic airway inflammation. (Human)"
  • Taxon: Human (NCBITaxon:9606)
  • Status: Production
  • Model ID: gomodel:6348a65d00000848

Overview of the Model

This GO-CAM model represents a pathway involved in the regulation of allergic airway inflammation in humans. It shows how IL-22 and REG3A proteins function to suppress allergic airway inflammation by inhibiting inflammatory cytokine production. The model is based primarily on a publication by Ito et al. (2017), which demonstrated that IL-22 induces REG3A (referred to as REG3γ in mice) expression from lung epithelial cells through STAT3 activation to suppress house dust mite (HDM)-induced allergic airway inflammation.

Pathway Structure

The model shows the following signaling cascade:

  1. IL22 (UniProtKB:Q9GZX6) has cytokine activity (GO:0005125) in extracellular space (GO:0005615)
  2. IL22 directly positively regulates IL22RA1 (UniProtKB:Q8N6P7) which has interleukin-22 receptor activity (GO:0042018)
  3. IL22RA1 provides input for STAT3 (UniProtKB:P40763) which has DNA-binding transcription factor activity (GO:0000981)
  4. STAT3 provides input for REG3A (UniProtKB:Q06141) which has hormone activity (GO:0005179)
  5. REG3A directly positively regulates EXTL3 (UniProtKB:O43909) which has protein-hormone receptor activity (GO:0016500)
  6. EXTL3 causally upstream of (negative effect) IL33 (UniProtKB:O95760) and TSLP (UniProtKB:Q969D9), both of which have cytokine activity (GO:0005125)

Assessment of the Model

Strengths:

  1. The model captures the main findings from the Ito et al. paper showing how IL-22 induces REG3A to suppress allergic airway inflammation.
  2. The causal relationships between the entities are properly represented.
  3. The model correctly places the proteins in their cellular locations (extracellular space, plasma membrane, nucleus, etc.)
  4. The model illustrates the negative regulatory effect of this pathway on inflammatory cytokine production (IL33 and TSLP).

Areas for Improvement:

  1. Molecular Function Assignment for REG3A:

  2. REG3A is annotated with "hormone activity" (GO:0005179), but according to UniProt and literature, it should be annotated with "cytokine activity" (GO:0005125). The paper and UniProt both characterize it as acting like a cytokine that regulates tissue responses.

  3. Biological Process Context:

  4. REG3A is part of "negative regulation of inflammatory response" (GO:0050728), which is appropriate, but it could be more specific, such as "negative regulation of cytokine production involved in inflammatory response" (GO:1900016) which is already used for EXTL3 in the model.

  5. Missing Mechanistic Detail:

  6. The model does not capture the detail that STAT3 activation in epithelial cells is dependent on IL-22 binding to its receptor (IL-22R1). The paper mentions STAT3 is activated in a pathway-specific manner.

  7. The mechanism by which EXTL3 inhibits IL33 and TSLP production isn't clearly represented (the paper indicates it likely involves PI3K-AKT signaling pathway).

  8. Receptor Ligand Modeling:

  9. According to the GO-CAM signaling receptor activity guidelines, the relation between a ligand and its target receptor should be "has input" rather than "directly positively regulates".

  10. The receptor (IL22RA1) should have its input as the effector protein it regulates (STAT3 in this case), which is correctly represented in the model.

  11. Evidence Codes and References:

  12. While many of the activities are supported by evidence from the primary publication (PMID:28811323), some parts of the model rely on sequence similarity evidence (ECO:0000250) rather than direct experimental evidence.

Recommendations for Improvement:

  1. Change the molecular function of REG3A from "hormone activity" to "cytokine activity" to better reflect its biological role.

  2. Consider adding the PI3K-AKT-STAT3 pathway components that mediate the effect of REG3A-EXTL3 on inflammatory cytokine production, which would make the mechanism clearer.

  3. Consider changing the relation between IL22 and IL22RA1 from "directly positively regulates" to "has input" to better follow GO-CAM signaling receptor activity guidelines.

  4. Add more specific biological process annotations where applicable, particularly for REG3A's role in inhibiting cytokine production.

  5. Consider adding more direct experimental evidence where available, rather than relying on sequence similarity evidence.

Conclusion

Overall, this GO-CAM model effectively captures the regulatory pathway by which IL-22 and REG3A suppress allergic airway inflammation through inhibiting inflammatory cytokine production. The model is consistent with the findings reported in the literature and follows most GO-CAM best practices. With the recommended improvements, the model could more accurately reflect the current understanding of this biological pathway and better adhere to GO-CAM curation standards.