Dissecting the Ability of Siglecs To Antagonize Fcγ Receptors
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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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it