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5e72450500004019 SARS COV2 HOST

Based on my review of the GO-CAM model and supporting information, I'll now provide a comprehensive assessment.

GO-CAM Model Review Summary - gomodel:5e72450500004019

General Assessment

This model titled "SARS-COV2/HOST" provides a detailed representation of interactions between SARS-CoV-2 viral proteins and the host innate immune response system, particularly focusing on the RIG-I-like receptor (RLR) signaling pathway. The model effectively depicts how various SARS-CoV-2 proteins antagonize key components of the human antiviral signaling cascade.

Strengths

  1. Comprehensive coverage: The model successfully captures multiple aspects of the host-pathogen interaction involving SARS-CoV-2 proteins and their targets in the human innate immune system.

  2. Evidence-based assertions: Most activities and causal associations are well-supported by primary literature references, with appropriate evidence codes (primarily ECO:0000314 - direct assay evidence used in manual assertion).

  3. Biological accuracy: The representation of RLR signaling components (IFIH1/MDA5, RIG-I, MAVS) and their interactions is consistent with current scientific knowledge as verified through UniProt entries and primary literature.

  4. Clear mechanistic view: The model provides a clear view of how SARS-CoV-2 proteins target different stages of the antiviral signaling pathway, providing a coherent picture of viral immune evasion strategies.

  5. Precise cellular context: Activities are properly annotated with subcellular locations (cytoplasm, mitochondria, nucleus), which is crucial for understanding compartmentalized signaling.

Areas for Improvement

  1. Missing evidence for some causal associations: Several causal relationships lack evidence annotations, including:
  2. The connection between IRF3 (Q14653-5) and nsp16 (P0DTD1-PRO_0000449633)

  3. The connection between nsp16 (P0DTD1-PRO_0000449633) and nsp1 (P0DTD1-PRO_0000449619)

  4. Missing "has input" annotations: According to GO-CAM best practices for:

  5. Protein sequestering activity: Several activities with GO:0140311 (protein sequestering activity) lack "has input" relations to specify which protein is being sequestered

  6. E3 ubiquitin ligase activity: Activities with GO:0061630 (ubiquitin protein ligase activity) lack "has input" annotations to indicate the ubiquitination targets

  7. Specific activity branches could be expanded:

  8. The IRF3 transcription factor activity could be enhanced by specifying its gene targets with "has input" relations

  9. For several proteins, more specific ubiquitination process terms could be provided (e.g., K48-linked vs. K63-linked)

  10. Causal relationship annotations: Some relationships use RO:0002411 (concurrence with) instead of more specific causal relationships like indirect positive/negative regulation

  11. Lack of cellular response outcomes: The model could be extended to more clearly indicate downstream effects of the innate immune suppression on cellular outcomes or viral replication.

Biological Content Accuracy

The model accurately represents the current understanding of SARS-CoV-2 interference with the innate immune system. The key components include:

  1. RIG-I/MDA5 pathway: Correctly represented as cytoplasmic PRRs that recognize viral RNA and signal through MAVS.

  2. MAVS as a central hub: The model properly captures MAVS as the central adaptor for signaling through the TRAF3/TBK1/IRF3 cascade.

  3. Viral antagonism: The model correctly represents multiple SARS-CoV-2 proteins interfering with signaling at different levels:

  4. M protein inhibits MAVS (consistent with PMID:33110251)
  5. nsp3 deISGylates MDA5 to prevent its activation
  6. ORF9b targets mitochondria and affects MAVS signaling
  7. nsp1 suppresses host translation

  8. Various proteins target IRF3 activity

  9. Downstream effects: The model adequately shows the disruption of both IRF3 and NF-κB activation by viral proteins.

Conformance to GO-CAM Best Practices

The model generally follows GO-CAM best practices but has some areas for improvement:

  1. Protein sequestering activities: While correctly using GO:0140311 and RO:0002630 (directly negatively regulates) to connect to downstream activities, it's missing the required "has input" relation to specify the target protein.

  2. E3 ubiquitin ligase activities: The model correctly uses GO:0061630 for TRIM25, TRAF3, and TRAF6, but lacks "has input" relations and specific ubiquitination process terms.

  3. DNA-binding transcription factor activity: The IRF3 activity is correctly located in the nucleus (GO:0005634) and part of mRNA transcription (GO:0009299), but misses specific gene targets with "has input" and uses RO:0002411 instead of indirect regulatory relationships.

Specific Recommendations

  1. Add missing evidence annotations: Provide evidence for all causal associations.

  2. Add "has input" relations: Add these for:

  3. Protein sequestering activities (GO:0140311) to specify which proteins are being sequestered
  4. E3 ubiquitin ligase activities (GO:0061630) to indicate ubiquitination targets

  5. IRF3 transcription factor activity to specify gene targets

  6. Enhance IRF3 activity representation: Use appropriate causal relations like "indirectly positively regulates" instead of "concurrence with" when connecting IRF3 to downstream effects.

  7. Add specific ubiquitination terms: Where known, specify the type of ubiquitination (K48-linked vs. K63-linked) to provide more precise annotation.

  8. Add cellular outcomes: Consider extending the model to show the ultimate effects of these pathways on viral replication or cellular outcomes.

Conclusion

Overall, this GO-CAM model provides a valuable representation of SARS-CoV-2 interactions with the human innate immune system, particularly the RLR signaling pathway. The model is scientifically accurate, well-supported by evidence, and generally follows GO-CAM best practices. With some modest improvements to add missing annotations and relations, it would fully conform to best practices while enhancing its biological clarity and comprehensiveness.

The model represents an important contribution to understanding SARS-CoV-2 pathogenesis and immune evasion strategies, which is particularly valuable in the context of the COVID-19 pandemic.