Patient-derived microphysiological model identifies the therapeutic potential of metformin for thoracic aortic aneurysm

Thoracic aortic aneurysm (TAA) is the permanent dilation of the thoracic aortic wall that predisposes patients to lethal events such as aortic dissection or rupture, for which effective medical therapy remains scarce. Human-relevant microphysiological models serve as a promising tool in drug screening and discovery.We developed a dynamic, rhythmically stretching, three-dimensional microphysiological model. Using patient-derived human aortic smooth muscle cells (HAoSMCs), we tested the biological features of the model and compared them with native aortic tissues. Drug testing was performed on the individualized TAA models, and the potentially effective drug was further tested using β-aminopropionitrile-treated mice and retrospective clinical data.The HAoSMCs on the model recapitulated the expressions of many TAA-related genes in tissue. Phenotypic switching and mitochondrial dysfunction, two disease hallmarks of TAA, were highlighted on the microphysiological model: the TAA-derived HAoSMCs exhibited lower alpha-smooth muscle actin expression, lower mitochondrial membrane potential, lower oxygen consumption rate and higher superoxide accumulation than control cells, while these differences were not evidently reflected in two-dimensional culture flasks. Model-based drug testing demonstrated that metformin partially recovered contractile phenotype and mitochondrial function in TAA patients' cells. Mouse experiment and clinical investigations also demonstrated better preserved aortic microstructure, higher nicotinamide adenine dinucleotide level and lower aortic diameter with metformin treatment.These findings support the application of this human-relevant microphysiological model in studying personalized disease characteristics and facilitating drug discovery for TAA. Metformin may regulate contractile phenotypes and metabolic dysfunctions in diseased HAoSMCs and limit aortic dilation.This work was supported by grants from National Key R&D Program of China (2018YFC1005002), National Natural Science Foundation of China (82070482, 81771971, 81772007, 51927805, and 21734003), the Science and Technology Commission of Shanghai Municipality (20ZR1411700, 18ZR1407000, 17JC1400200, and 20YF1406900), Shanghai Municipal Science and Technology Major Project (2017SHZDZX01), and Shanghai Municipal Education Commission (Innovation Program 2017-01-07-00-07-E00027). Y.S.Z. was not supported by any of these funds; instead, the Brigham Research Institute is acknowledged.