Jeffrey B. Hodgin, Ph.D. - US grants

DESCRIPTION (provided by applicant): Project Aims The Rho GTPases Cdc42 and Rac1 are intracellular signaling molecules best known for regulating the actin cytoskeleton. However, additional roles modulating cellular responses have also been described. Rac1 is important for the activation of NADPH oxidase in nonphagocytic cells and Cdc42 appears to inhibit the generation of reactive oxygen species (ROS) by NADPH oxidase through competitive inhibition. Numerous lines of evidence reveal NADPH oxidase as a major contributor to diabetes/glucose-induced ROS-mediated injury and recent publications indicate excessive NADPH-mediated ROS production in the podocyte results in podocyte apoptosis, depletion, and disease progression in diabetic nephropathy. Thus, investigating mechanisms that regulate NADPH oxidase activation in podocytes could unveil new molecular targets and improved therapies in diabetes-induced injury. This proposal includes preliminary data showing that Cdc42 expression is reduced in the glomeruli of diabetic humans and in two mouse models of diabetic nephropathy. Constructing a conserved glomerular transcriptional network using genome-wide gene expression profiling from humans and mice, we find Cdc42 to be one of the most important regulatory nodes. Furthermore, we have generated and characterized mice with conditional deletions of either Cdc42 or Rac1 in the podocyte. Findings in these mice underscore the importance of Rho GTPases in podocyte biology and glomerular health. We hypothesize that a loss of Rac1 in podocytes will reduce ROS generation and apoptosis in the podocyte and result in amelioration of murine diabetic nephropathy. In contrast, a loss of Cdc42 will exacerbate podocyte apoptosis and diabetic nephropathy. Specifically we will: (1) Investigate the role Cdc42 and Rac1 on ROS induction and apoptosis in human podocytes IN VITRO in a high glucose environment using RNAi technology, and (2) Investigate the role of the Rho GTPases Cdc42 and Rac1 in podocytes IN VIVO in a mouse model of diabetic nephropathy with podocyte specific inactivation of Cdc42 or Rac1. Our third (3) aim will be to use the Systems Biology expertise of the Kretzler laboratory to define the glomerular transcriptional networks in diabetic mice with podocyte specific inactivation of Cdc42 or Rac1, and compare to glomerular transcriptional networks in wild-type diabetic mice and humans. Diabetic nephropathy is the leading cause of end-stage renal disease in the United States and has a large impact on human suffering and a substantial economic burden. Providing a mechanistic understanding of the role of Cdc42 and Rac1 in diabetic nephropathy will reveal novel disease pathways and therapeutic targets. PUBLIC HEALTH RELEVANCE: The Rho GTPase Rac1 is a required component of NADPH oxidase, and Cdc42 can competitively inhibit Rac1-mediated NADPH oxidase activation. We will investigate the role of Cdc42 and Rac1 in the mechanistic link of hyperglycemia, NADPH oxidase, oxidative injury, podocyte death, and the progression of diabetic nephropathy using in vitro, in vivo, and systems biology approaches. Diabetic nephropathy is the leading cause of end-stage renal disease in the United States, and providing a mechanistic understanding of the role of Cdc42 and Rac1 in diabetic nephropathy will reveal novel disease pathways and therapeutic targets.

ABSTRACT The kidney has developed a complex, three-dimensional architecture to serve its key functions, including excretion of waste substances, maintenance of the internal balance for fluid and salt, blood pressure control, and hormonal function. Understanding the roles of the individual renal cell types in these processes in health and disease is critical to develop novel targeted therapies. Extensive studies lead by this investigative group and others have started to identify molecular disease mechanism in renal biopsy tissues and helped to develop novel disease markers and therapies. However, up to now these studies were limited using biopsy tissue homogenates, making it difficult to discern the specific pathways activated in cell types. The PREcision Medicine through IntErrogation of Rna in the kidnEy (PREMIERE) Network will bring investigators from three leading biomedical research institutions with diverse, complementary expertise together. Our team has an established track record in working jointly to develop state of the art approaches in molecular analysis of renal disease. We will set out to mine our existing compendium of thousands of gene expression profiles from renal biopsy tissues to extract single cell signatures using advanced data mining tools. In parallel we will develop technologies in our laboratories towards single cell analysis of renal tissues and scale them down so that they can work on single cells extracted from small renal biopsies. These single cell profiles will be linked to the disease states of the patients. Key signatures associated with specific cells and diseases will be extracted and localized in the three dimensional context of the kidney using specific RNA staining techniques in the first phase of the project. In the second phase, the analytical strategies will be scaled up so that single cell profiles from specific groups of patients can be obtained in a robust and reproducible manner. To this end the PREMIERE investigators will work closely with the tissue procurement sites and the Central Hub of the KPMP, using their 20 years of experience in team science, so that at the end of the first KPMP funding cycle novel, cell type specific treatment targets are identified fueling the therapeutic pipelines of the future.

ABSTRACT The presently employed morphology-based classification system used to distinguish unique glomerular diseases is inadequate. This classification system is not derived from an understanding of the molecular basis of these diseases, nor does it well predict observed heterogeneous disease natural history or response to therapy. This project will apply quantitative pathology methods to assess the renal parenchyma as one approach to overcoming this critical barrier to glomerular disease clinical research and patient care. Our goal is to create a well characterized data set that extracts an array of multiple discrete morphological elements from the structural complexity inherent in glomerular diseases. We will exploit this detailed characterization?combined with renal morphometrics and transcriptional and genomic profiling?to explore the hypothesis that inherent in this structural complexity is extractable information that is predictive of underlying disease biology or clinical outcome. The proposed study will use existing detailed clinical data and biomaterials obtained by NEPTUNE, a prospective longitudinal cohort study of proteinuric patients with MCD, FSGS, or MN enrolled at the time of clinically indicated renal biopsy. As part of the NEPTUNE effort, we created the NEPTUNE Digital Pathology Protocol (NDPP), an innovative program that (1) compiled a centralized internet- based digital pathology repository of whole slide digital images, and electron and immunofluorescence micrographs from 500 NEPTUNE subjects, that (2) allows standardization by application of computer- based imaging analytical software for multi-level annotation (enumeration) of all glomeruli across all biopsy levels available, and that (3) enables detailed assessment of renal biopsies using a novel quantitative scoring system. The proposed project will be carried out in three specific aims: We will (1) compile a standardized comprehensive morphologic and morphometric profile of 500 NEPTUNE cohort renal biopsies to test whether these profiles contain reproducible descriptors or groups of descriptors that can be combined with morphometric measures to reliably assess glomerular disease pathology; (2) using a variety of statistical approaches, we will identify quantitative structural parameters that are predictive of outcomes to establish a clinically relevant categorization of proteinuric diseases; and we will (3) identify transcriptomic signatures, molecular pathways, genetic variants, and expression quantitative trait loci (eQTL) that associate with specific morphologic and morphometric profiles.

Project Summary/Abstract: Computational Pathology for Proteinuric Glomerulopathies The presently employed morphology-based classification system of focal segmental glomerulosclerosis (FSGS) and minimal change disease (MCD) does not adequately capture the clinical and molecular heterogeneity of these diseases and impairs the ability of clinicians to precisely define a patient?s disease, or predict outcome or effective intervention. The goal of this research is to advance the work of the glomerular disease research community by identifying biologically-relevant surrogates and subclasses of FSGS/MCD using computational pathology and machine learning methods. Prospective, longitudinal, multi-dimensional data sets that include digital kidney biopsies and molecular and clinical information can be analyzed by advanced ?computer vision? methods and machine learning analytical approaches. This project will employ these rich resources and methods, which offer an unprecedented opportunity to leverage information derived from kidney tissue to improve the diagnosis, outcome prediction, and identification of glomerular disease mechanisms. The interdisciplinary team assembled to conduct this study has vast experience and a long-standing history of collaboration. In our preliminary studies we have demonstrated that (i) structural changes associate with outcomes and molecular mechanisms and improve clinical outcome prediction beyond current clinical approaches, and that (ii) computer vision technology can be used to accurately and efficiently detect normal and abnormal kidney structures and quantify textural (i.e., the spatial relationship between pixel values) and morphological (i.e., shape, size) information from kidney tissue. We will test our central hypothesis that inherent in the complexity of the structural changes in the renal parenchyma is information predictive of underlying disease biology and clinical outcome. We will pursue this hypothesis (1) by testing the clinical and molecular relevance of automatic detection and quantification of known morphologic biomarkers of clinical outcomes and mechanisms and of groups of patients with similar DL-derived morphologic characteristics; (2) by extracting next-generation pathomic features from DL-derived morphologic parameters and by testing their associations?individually and combined into pathomic profiles?with clinical outcomes and gene expression; and (3) by building machine learning-based models that integrate computer vision-derived pathology data with gene expression and clinical data to predict individual patient clinical outcomes, and to assess the additive prediction value of each data domain. Finally, we will group patients using the biomarkers identified to be most predictive of clinical outcomes. Ultimately, our work will contribute to a foundation for the deployment of a comprehensive artificial intelligence-guided precision medicine program for FSGS/MCD, applying descriptive, predictive, and ultimately prescriptive analytics as support tools for practicing pathologists and nephrologists.