The Journal of PhysiologyPhysiologyPeer-reviewed journal article
Metabolite-sensitive cross-bridge models of human atria reveal the impact of diabetes on muscle mechanoenergetics
Julia H. MusgraveiD, June‐Chiew HaniD, Marie‐Louise WardiD, Andrew James TaberneriD, Kenneth TraniD
Abstract
Type 2 diabetes is associated with a range of adverse health outcomes, including metabolic dysfunction and increased risk of heart failure. Although the interactions between diabetes and heart disease are complex and incompletely understood, they can be more effectively investigated using multiscale, biophysically-based models of the underlying physiological processes. In this study, experimental data from non-diabetic and diabetic human atrial muscles were used to develop metabolite-sensitive cross-bridge models representing each group. The parameter-isation of these cross-bridge models revealed that reduced muscle stress development and a leftward shift of the complex modulus measured in the diabetic muscles could be attributed to reduced cross-bridge stiffness and slower cross-bridge detachment rates, respectively. These cross-bridge models were also integrated into muscle models to investigate the effects of diabetic cross-bridge function, Ca2+ handling and altered metabolite availability on isometric and physiological work-loop contractions. The diabetic model produced isometric twitches with lower amplitude and prolonged duration and, in response to lowered ATP concentration, the diastolic stress increased notably. In work-loop simulations, the diabetic model exhibited slower shortening, reduced work output and lower power of shortening. However, it was also more efficient and had a less pronounced negative response to increases in Pi concentration. These simulations demonstrate that while experimentally measured differences in diabetic cardiac tissues can lead to impaired function during physiological contractions, they may also offer compensatory advantages. The insights of this study offer clear mechanisms of mechanoenergetic dysfunction in diabetic heart muscle, identifying potential therapeutic targets to improve cardiac outcomes for individuals with diabetes.
- organism: human
- assay: electrophysiology
- research focus: cell signaling & trafficking
- approach: deep learning
- approach: statistical model
- Citations
- 1
- FWCI
- 0.69
- Field percentile
- top 27%
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