Dissecting the Ability of Siglecs To Antagonize Fcγ Receptors
Bibliographic record
Abstract
Fcγ receptors (FcγRs) play key roles in the effector function of IgG, but their inappropriate activation plays a role in several disease etiologies. Therefore, it is critical to better understand how FcγRs are regulated. Numerous studies suggest that sialic acid-binding immunoglobulin-type lectins (Siglecs), a family of immunomodulatory receptors, modulate FcγR activity; however, it is unclear of the circumstances in which Siglecs can antagonize FcγRs and which Siglecs have this ability. Using liposomes displaying selective ligands to coengage FcγRs with a specific Siglec, we explore the ability of Siglec-3, Siglec-5, Siglec-7, and Siglec-9 to antagonize signaling downstream of FcγRs. We demonstrate that Siglec-3 and Siglec-9 can fully inhibit FcγR activation in U937 cells when coengaged with FcγRs. Cells expressing Siglec mutants reveal differential roles for the immunomodulatory tyrosine-based inhibitory motif (ITIM) and immunomodulatory tyrosine-based switch motif (ITSM) in this inhibition. Imaging flow cytometry enabled visualization of SHP-1 recruitment to Siglec-3 in an ITIM-dependent manner, while SHP-2 recruitment is more ITSM-dependent. Conversely, both cytosolic motifs of Siglec-9 contribute to SHP-1/2 recruitment. Siglec-7 poorly antagonizes FcγR activation for two reasons: masking by cis ligands and differences in its ITIM and ITSM. A chimera of the Siglec-3 extracellular domains and Siglec-5 cytosolic tail strongly inhibits FcγR when coengaged, providing evidence that Siglec-5 is more like Siglec-3 and Siglec-9 in its ability to antagonize FcγRs. Additionally, Siglec-3 and Siglec-9 inhibited FcγRs when coengaged by cells displaying ligands for both the Siglec and FcγRs. These results suggest a role for Siglecs in mediating FcγR inhibition in the context of an immunological synapse, which has important relevance to the effectiveness of immunotherapies.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".