Abstract P1163: Identifying Novel Intracellular Interactions Driving Membrane-bound Tumour Necrosis Factor-mediated Signalling In The Microvasculature
Bibliographic record
Abstract
Elevated total peripheral resistance (TPR) is a hallmark of many cardiovascular diseases and furthers disease progression. We previously demonstrated that myogenic tone, regulated by membrane-bound tumour necrosis factor (mTNF) reverse signalling, is a primary modulator of TPR. Thus, inhibiting mTNF reverse signalling should reduce TPR with the potential to improve disease outcome. However, since mTNF is a ubiquitous protein with critical biological functions, its indiscriminate inhibition can cause widespread side effects. Therefore, therapeutics aiming to reduce myogenic tone and TPR must target other elements of mTNF reverse signalling. To identify these unknown participants of mTNF reverse signalling, we used proximity-dependent biotinylation coupled to mass spectrometry (BioID). Our search identified 42 high confidence hits that interact with and/or are proximal to mTNF’s cytoplasmic domain. Gene ontology (GO) analysis revealed that molecularly, these proteins are structural constituents of the cytoskeleton involved in actin and cell adhesion molecule binding. This aligns with our underlying hypothesis that mTNF functions as a mechanosensor that tethers to its receptors and initiates myogenic vasoconstriction in response to changes in transmural pressure. GO also identified proteins involved in MAPK and RhoA signalling, which are critically involved in myogenic vasoconstriction. Our most abundant hit (fold change = 12.6, p-value = 0.0023) is a cytoskeletal protein that plays a role in maintaining the structural integrity of cells. This protein also interacts with proteins involved in various signalling cascades, namely, RhoA and calcium-dependent signalling pathways. Thus, we hypothesize that this protein is essential for formation of signalling complexes with mTNF and interacts with other proteins to mediate reverse signalling . We will employ a bioluminescence-based approach to quantify the strength of all interactions detected by BioID and identify small molecule inhibitors that can disrupt these interactions. These newly discovered inhibitors could potentially be used as novel therapeutics targeting myogenic tone and hence, elevated TPR to improve disease management and clinical outcome of cardiovascular diseases.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".