(620c) Coupling the Mechanisms of Diabetic Kidney Disease By Modeling the Tissues of the Glomerulus

Diabetic kidney disease (DKD) is a complication of diabetes that results in irreversible damage to the kidneys. The primary method of detecting DKD is by detecting albumin in a patient’s urine, a phenomenon known as albuminuria. The issue with this method of detection is that albuminuria occurs when damage to the kidney is already done. Improved methods of detection require a better understanding of the underlying mechanisms of DKD. We propose that building a model that captures the known individual mechanisms of DKD and coupling them together may give the insight to build a better method of detecting DKD.

In the literature, multiple physiological changes in the glomerulus are observed in DKD patients. The glomeruli are structures that perform the initial filtration step in producing urine, and the point of failure when proteinuria occurs. In this work, the primary changes of concern are the thickening of the glomerular basement membrane, swelling of the mesangium, and podocyte effacement. Evidence suggest that the renin-angiotensin system (RAS) and TGF-β are connected to the physiological changes associated with DKD. Diabetes leads to systemically elevated levels of glucose in the blood. This in turn leads to dysregulation of the RAS and, via the RAS, upregulation of TGF-β in the podocytes. Evidence further suggests that damage in the mesangium may be linked to upregulated TGF-β in podocytes.

Understanding the crosstalk between cells and the associated tissue damage is instrumental in finding new ways to detect DKD before damage to the kidneys occur. In order to better understand the crosstalk occurring between cells of the glomerulus, we are creating a model that ties together dysregulation within podocytes and mesangial cells by modeling a portion of the cross section of the mesangium. Capturing the effect of tissue structure requires spatial components in the model. To capture how the signaling proteins move through space requires multiple additional phenomena beyond biochemical networks. Several model components were developed to capture spatial effects including the structure of the tissue, fluid flow, and the movement of signaling proteins and glucose through the tissue. The structure of the tissue is modeled with Compucell3D, which uses a cellular Potts model to define how cells interact. The fluid flow is modeled using a CFD simulation in Star-CCM+. The movement of signaling proteins and glucose is modeled using a system of PDEs to solve the diffusion and convection of solutes. Biochemical reaction network models that capture the RAS in both types of cells and the regulation of TGF- β and matrix metalloproteinase-1 in podocytes are included in the simulation. The RAS model predicts the concentrations of angiotensin-I, angiotensin-II, angiotensinogen, and renin in the cells. These models are coupled to the system of PDEs as point sources within the simulation. With these phenomena coupled together, we can make predictions about the importance of cellular crosstalk in the progression of DKD. With a better understanding of the progression of DKD, we can progress towards a better method of detecting DKD.