Phosphoinositides, which are membrane-bound phospholipids, are critical signaling molecules located at the interface between the extracellular matrix, cell membrane, and cytoskeleton. Phosphoinositides are essential regulators of many biological and cellular processes, including but not limited to cell migration, proliferation, survival, and differentiation, as well as cytoskeletal rearrangements and actin dynamics. Over the years, a multitude of studies have uniquely implicated phosphoinositide signaling as being crucial in cardiovascular biology and a dominant force in the development of cardiovascular disease and its progression. Independently, the cellular transduction of mechanical forces or mechanotransduction in cardiovascular cells is widely accepted to be critical to their homeostasis and can drive aberrant cellular phenotypes and resultant cardiovascular disease. Given the versatility and diversity of phosphoinositide signaling in the cardiovascular system and the dominant regulation of cardiovascular cell functions by mechanotransduction, the molecular mechanistic overlap and extent to which these two major signaling modalities converge in cardiovascular cells remain unclear. In this review, we discuss and synthesize recent findings that rightfully connect phosphoinositide signaling to cellular mechanotransduction in the context of cardiovascular biology and disease, and we specifically focus on phosphatidylinositol-4,5-phosphate, phosphatidylinositol-4-phosphate 5-kinase, phosphatidylinositol-3,4,5-phosphate, and phosphatidylinositol 3-kinase. Throughout the review, we discuss how specific phosphoinositide subspecies have been shown to mediate biomechanically sensitive cytoskeletal remodeling in cardiovascular cells. Additionally, we discuss the direct interaction of phosphoinositides with mechanically sensitive membrane-bound ion channels in response to mechanical stimuli. Furthermore, we explore the role of phosphoinositide subspecies in association with critical downstream effectors of mechanical signaling in cardiovascular biology and disease.
Vascular dysfunction is a common cause of cardiovascular diseases characterized by the narrowing and stiffening of arteries, such as atherosclerosis, restenosis, and hypertension. Arterial narrowing results from the aberrant proliferation of vascular smooth muscle cells (VSMCs) and their increased synthesis and deposition of extracellular matrix (ECM) proteins. These, in turn, are modulated by arterial stiffness, but the mechanism for this is not fully understood. We found that survivin (an inhibitor of apoptosis) is an important regulator of stiffness-mediated ECM synthesis and intracellular stiffness in VSMCs. Whole-transcriptome analysis and cell culture experiments showed that survivin expression is upregulated in injured femoral arteries in mice and in human VSMCs cultured on stiff fibronectin-coated hydrogels. Suppressed expression of survivin in human VSMCs and mouse embryonic fibroblasts decreased the stiffness-mediated expression of ECM components implicated in arterial stiffness, namely, collagen-I, fibronectin, and lysyl oxidase. By contrast, expression of these proteins was upregulated by the overexpression of survivin in human VSMCs cultured on soft hydrogels. Atomic force microscopy analysis showed that suppressed or enhanced expression of survivin decreases or increases intracellular stiffness, respectively. These findings suggest a novel mechanism by which survivin modulates arterial stiffness.
Machine learning approaches have shown great promise in biology and medicine discovering hidden information to further understand complex biological and pathological processes. In this study, we developed a deep learning-based machine learning algorithm to meaningfully process image data and facilitate studies in vascular biology and pathology. Vascular injury and atherosclerosis are characterized by neointima formation caused by the aberrant accumulation and proliferation of vascular smooth muscle cells (VSMCs) within the vessel wall. Understanding how to control VSMC behaviors would promote the development of therapeutic targets to treat vascular diseases. However, the response to drug treatments among VSMCs with the same diseased vascular condition is often heterogeneous. Here, to identify the heterogeneous responses of drug treatments, we created an in vitro experimental model system using VSMC spheroids and developed a machine learning-based computational method called HETEROID (heterogeneous spheroid). First, we established a VSMC spheroid model that mimics neointima-like formation and the structure of arteries. Then, to identify the morphological subpopulations of drug-treated VSMC spheroids, we used a machine learning framework that combines deep learning-based spheroid segmentation and morphological clustering analysis. Our machine learning approach successfully showed that FAK, Rac, Rho, and Cdc42 inhibitors differentially affect spheroid morphology, suggesting that multiple drug responses of VSMC spheroid formation exist. Overall, our HETEROID pipeline enables detailed quantitative drug characterization of morphological changes in neointima formation, that occurs in vivo, by single-spheroid analysis.
Hyper proliferation of vascular smooth muscle cells (VSMCs) contributes to neointima formation in atherosclerosis and the response to vascular injury. Understanding how to control VSMC proliferation would advance the effort to treat vascular disease. Drug responses are often different among patients with the same vascular disease condition, making it difficult to cater patientspecific treatments (existence of heterogeneity). Thus, we examined variations in response to drug treatments using VSMC spheroids that mimic vascular disease condition in vivo. FAK and its downstream Rho GTPases (Rac, Rho, and Cdc42) control cell-cell contact and play a key role in vascular pathology. Here, we tested the importance of FAK and Rho GTPases in spheroid formation. VSMC spheroids were made using a hanging-drop method with either inhibitors or vehicle control. Changes in morphology were used as a key indicator of spheroid response to drug treatment. A machine learning (ML) image segmentation (VGG16-U-net) was used to segment the spheroid images. After the morphological features were extracted from the segmented images, a two-level cluster framework was used to cluster VSMC spheroids into different morphologies. We found that FAK and Rho GTPases are required for normal spheroid formation. Next, we analyzed the various morphologies of disrupted spheroids resulting from drug treatment using our ML pipeline. The first-level clustering analysis showed the presence of 4 clusters of spheroids with rounded and disrupted morphologies. The subsequent second-level clustering analysis identified the presence of four distinct morphological clusters among these disrupted spheroids, and they exhibited differential responses to FAK and Rho GTPases inhibition. Particularly, we found FAK and Rho specific morphological phenotypes, thus suggesting that there may be two distinct pathways governing VSMC spheroid formation.Collectively, we revealed there are significant heterogeneities in the drug responses of spheroid formation, which were overlooked in previous analyses. This is our first step towards developing the ML-based method that can be used to assess the effects of different drugs on the VSMC spheroid model for better characterization of pathologic progression of vascular disease.
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