Role of the Unfolded Protein Response Induced by Macrophage-NLRP3 as a Driver of Diastolic Dysfunction
Bibliographic record
Abstract
Introduction: Diastolic dysfunction (DD) is characterized by myocardial stiffening that impairs relaxation instigated by inflammation and microvascular rarefaction, related to many cardiac diseases—notably heart failure with preserved ejection fraction (HFpEF). Key contributors to this condition include excessive extracellular matrix remodeling, myofilament dysfunction, and abnormal calcium handling. Inflammation, driven by NLRP3 inflammasome activity and prolonged endoplasmic reticulum (ER) stress, can contribute to the unfolded protein response (UPR) underlying DD pathogenesis. These processes are exacerbated by the senescence-associated secretory phenotype (SASP). Although SGLT2 inhibitors are known for their glucose-lowering effects, they also show pleiotropic promise to attenuate DD in HFpEF, but the mechanisms are unclear. Hypothesis: SGLT2 inhibitors ameliorate DD by reducing NLRP3 inflammasome activity in macrophages and decreasing UPR in cardiomyocytes. Methods: Human THP.1 monocytes are differentiated into macrophages with PMA, polarized either with IL4 and IL13(M2like) or TNFα and IFNγ (M2-like) were assessed for NLRP3 activation by qPCR and western blot. Human AC16 cardiomyocytes are treated with thapsigargin to induce ER stress and UPR. To elicit a senescent-like condition, we add d-galactose. Co-culture or conditioned media can be used to correlate an interaction between macrophage and cardiomyocyte cells treated with SGLT2 inhibitor empagliflozin to attenuate NLRP3 and UPR. Results: Macrophages were successfully differentiated and alternatively polarized, with distinct phenotypic patterns and expression of TG2, HMOX1, CD206 and STAT3. Elevated NLRP3 inflammasome activity was observed by elevated expression of IL-1b after differentiation with more significant activity in the M1 compared to the M2 polarized state (N=6). Thapsigargin significantly elevated UPR expression markers BiP (p=0.0009) and p-JNK (p=<0.0001) compared to controls in cardiomyocytes, yet treatment was comparable to tunicamycin p-JNK. Conclusion: We established cell-based models for evaluating contributing mechanisms of efficacy of SGLT2 inhibitors that could ultimately improve DD. Funding: NSERC, Research NS, Dalhousie FoM This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.
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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 | 0.000 | 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) | 0.000 | 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".