Staphylococcal Superantigens Use LAMA2 as a Coreceptor To Activate T Cells
Bibliographic record
Abstract
Canonical Ag-dependent TCR signaling relies on activation of the src-family tyrosine kinase LCK. However, staphylococcal superantigens can trigger TCR signaling by activating an alternative pathway that is independent of LCK and utilizes a Gα11-containing G protein-coupled receptor (GPCR) leading to PLCβ activation. The molecules linking the superantigen to GPCR signaling are unknown. Using the ligand-receptor capture technology LRC-TriCEPS, we identified LAMA2, the α2 subunit of the extracellular matrix protein laminin, as the coreceptor for staphylococcal superantigens. Complementary binding assays (ELISA, pull-downs, and surface plasmon resonance) provided direct evidence of the interaction between staphylococcal enterotoxin E and LAMA2. Through its G4 domain, LAMA2 mediated the LCK-independent T cell activation by these toxins. Such a coreceptor role of LAMA2 involved a GPCR of the calcium-sensing receptor type because the selective antagonist NPS 2143 inhibited superantigen-induced T cell activation in vitro and delayed the effects of toxic shock syndrome in vivo. Collectively, our data identify LAMA2 as a target of antagonists of staphylococcal superantigens to treat toxic shock syndrome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".