Using FLIM-FRET to Measure Conformational Changes of Transglutaminase Type 2 in Live Cells
Bibliographic record
Abstract
Transglutaminase type 2 (TG2) is a ubiquitously expressed member of the transglutaminase family, capable of mediating a transamidation reaction between a variety of protein substrates. TG2 also has a unique role as a G-protein with GTPase activity. In response to GDP/GTP binding and increases in intracellular calcium levels, TG2 can undergo a large conformational change that reciprocally modulates the enzymatic activities of TG2. We have generated a TG2 biosensor that allows for quantitative assessment of TG2 conformational changes in live cells using Förster resonance energy transfer (FRET), as measured by fluorescence lifetime imaging microscopy (FLIM). Quantifying FRET efficiency with this biosensor provides a robust assay to quickly measure the effects of cell stress, changes in calcium levels, point mutations and chemical inhibitors on the conformation and localization of TG2 in living cells. The TG2 FRET biosensor was validated using established TG2 conformational point mutants, as well as cell stress events known to elevate intracellular calcium levels. We demonstrate in live cells that inhibitors of TG2 transamidation activity can differentially influence the conformation of the enzyme. The irreversible inhibitor of TG2, NC9, forces the enzyme into an open conformation, whereas the reversible inhibitor CP4d traps TG2 in the closed conformation. Thus, this biosensor provides new mechanistic insights into the action of two TG2 inhibitors and defines two new classes based on ability to alter TG2 conformation in addition to inhibiting transamidation activity. Future applications of this biosensor could be to discover small molecules that specifically alter TG2 conformation to affect GDP/GTP or calcium binding.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".