Nascent shifts in renal cellular metabolism, structure, and function due to chronic empagliflozin in prediabetic mice.

Sodium-glucose cotransporter, type 2 inhibitors (SGLT2i) are emerging as the gold standard for treatment of type 2 diabetes (T2D) with renal protective benefits independent of glucose lowering. We took a high-level approach to evaluate the effects of the SGLT2i, empagliflozin (EMPA) on renal metabolism and function in a prediabetic model of metabolic syndrome. Male and female 12-wk-old TallyHo (TH) mice, and their closest genetic lean strain (Swiss-Webster, SW) were treated with a high-milk-fat diet (HMFD) plus/minus EMPA (@0.01%) for 12-wk. Kidney weights and glomerular filtration rate were slightly increased by EMPA in the TH mice. Glomerular feature analysis by unsupervised clustering revealed sexually dimorphic clustering, and one unique cluster relating to EMPA. Periodic acid Schiff (PAS) positive areas, reflecting basement membranes and mesangium were slightly reduced by EMPA. Phasor-fluorescent life-time imaging (FLIM) of free-to-protein bound NADH in cortex showed a marginally greater reliance on oxidative phosphorylation with EMPA. Overall, net urine sodium, glucose, and albumin were slightly increased by EMPA. In TH, EMPA reduced the sodium phosphate cotransporter, type 2 (NaPi-2), but increased sodium hydrogen exchanger, type 3 (NHE3). These changes were absent or blunted in SW. EMPA led to changes in urine exosomal microRNA profile including, in females, enhanced levels of miRs 27a-3p, 190a-5p, and 196b-5p. Network analysis revealed "cancer pathways" and "FOXO signaling" as the major regulated pathways. Overall, EMPA treatment to prediabetic mice with limited renal disease resulted in modifications in renal metabolism, structure, and transport, which may preclude and underlie protection against kidney disease with developing T2D.NEW & NOTEWORTHY Renal protection afforded by sodium glucose transporter, type 2 inhibitors (SGLT2i), e.g., empagliflozin (EMPA) involves complex intertwined mechanisms. Using a novel mouse model of obesity with insulin resistance, the TallyHo/Jng (TH) mouse on a high-milk-fat diet (HMFD), we found subtle changes in metabolism including altered regulation of sodium transporters that line the renal tubule. New potential epigenetic determinants of metabolic changes relating to FOXO and cancer signaling pathways were elucidated from an altered urine exosomal microRNA signature.

NAD metabolism modulates inflammation and mitochondria function in diabetic kidney disease.

Diabetes mellitus is the leading cause of cardiovascular and renal disease in the United -States. Despite the beneficial interventions available for patients with diabetes, there remains a need for additional therapeutic targets and therapies in diabetic kidney disease (DKD). Inflammation and oxidative stress are increasingly recognized as important causes of renal diseases. Inflammation is closely associated with mitochondrial damage. The molecular connection between inflammation and mitochondrial metabolism remains to be elucidated. Recently, nicotinamide adenine nucleotide (NAD+) metabolism has been found to regulate immune function and inflammation. In the present studies, we tested the hypothesis that enhancing NAD metabolism could prevent inflammation in and progression of DKD. We found that treatment of db/db mice with type 2 diabetes with nicotinamide riboside (NR) prevented several manifestations of kidney dysfunction (i.e., albuminuria, increased urinary kidney injury marker-1 (KIM1) excretion, and pathologic changes). These effects were associated with decreased inflammation, at least in part via inhibiting the activation of the cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) signaling pathway. An antagonist of the serum stimulator of interferon genes (STING) and whole-body STING deletion in diabetic mice showed similar renoprotection. Further analysis found that NR increased SIRT3 activity and improved mitochondrial function, which led to decreased mitochondrial DNA damage, a trigger for mitochondrial DNA leakage which activates the cGAS-STING pathway. Overall, these data show that NR supplementation boosted NAD metabolism to augment mitochondrial function, reducing inflammation and thereby preventing the progression of diabetic kidney disease.

NAD deficiency contributes to progressive kidney disease in HIV-nephropathy mice.

HIV disease remains prevalent in the United States and is particularly prevalent in sub-Saharan Africa. Recent investigations revealed that mitochondrial dysfunction in kidney contributes to HIV-associated nephropathy (HIVAN) in Tg26 transgenic mice. We hypothesized that nicotinamide adenine dinucleotide (NAD) deficiency contributes to energetic dysfunction and progressive tubular injury. We investigated metabolomic mechanisms of HIVAN tubulopathy. Tg26 and wild-type (WT) mice were treated with the farnesoid X receptor (FXR) agonist INT-747 or nicotinamide riboside (NR) from 6 to 12 wk of age. Multiomic approaches were used to characterize kidney tissue transcriptomes and metabolomes. Treatment with INT-747 or NR ameliorated kidney tubular injury, as shown by serum creatinine, the tubular injury marker urinary neutrophil-associated lipocalin, and tubular morphometry. Integrated analysis of metabolomic and transcriptomic measurements showed that NAD levels and production were globally downregulated in Tg26 mouse kidneys, especially nicotinamide phosphoribosyltransferase (NAMPT), the rate-limiting enzyme in the NAD salvage pathway. Furthermore, NAD-dependent deacetylase sirtuin3 activity and mitochondrial oxidative phosphorylation activity were lower in ex vivo proximal tubules from Tg26 mouse kidneys compared with those of WT mice. Restoration of NAD levels in the kidney improved these abnormalities. These data suggest that NAD deficiency might be a treatable target for HIVAN.NEW & NOTEWORTHY The study describes a novel investigation that identified nicotinamide adenine dinucleotide (NAD) deficiency in a widely used HIV-associated nephropathy (HIVAN) transgenic mouse model. We show that INT-747, a farnesoid X receptor agonist, and nicotinamide riboside (NR), a precursor of nicotinamide, each ameliorated HIVAN tubulopathy. Multiomic analysis of mouse kidneys revealed that NAD deficiency was an upstream metabolomic mechanism contributing to HIVAN tubulopathy.

The Banff 2022 Kidney Meeting Work Plan: Data-driven refinement of the Banff Classification for renal allografts.

The XVIth Banff Meeting for Allograft Pathology was held in Banff, Alberta, Canada, from September 19 to 23, 2022, as a joint meeting with the Canadian Society of Transplantation. In addition to a key focus on the impact of microvascular inflammation and biopsy-based transcript analysis on the Banff Classification, further sessions were devoted to other aspects of kidney transplant pathology, in particular T cell-mediated rejection, activity and chronicity indices, digital pathology, xenotransplantation, clinical trials, and surrogate endpoints. Although the output of these sessions has not led to any changes in the classification, the key role of Banff Working Groups in phrasing unanswered questions, and coordinating and disseminating results of investigations addressing these unanswered questions was emphasized. This paper summarizes the key Banff Meeting 2022 sessions not covered in the Banff Kidney Meeting 2022 Report paper and also provides an update on other Banff Working Group activities relevant to kidney allografts.

Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments.

The Banff Digital Pathology Working Group (DPWG) was established with the goal to establish a digital pathology repository; develop, validate, and share models for image analysis; and foster collaborations using regular videoconferencing. During the calls, a variety of artificial intelligence (AI)-based support systems for transplantation pathology were presented. Potential collaborations in a competition/trial on AI applied to kidney transplant specimens, including the DIAGGRAFT challenge (staining of biopsies at multiple institutions, pathologists' visual assessment, and development and validation of new and pre-existing Banff scoring algorithms), were also discussed. To determine the next steps, a survey was conducted, primarily focusing on the feasibility of establishing a digital pathology repository and identifying potential hosts. Sixteen of the 35 respondents (46%) had access to a server hosting a digital pathology repository, with 2 respondents that could serve as a potential host at no cost to the DPWG. The 16 digital pathology repositories collected specimens from various organs, with the largest constituent being kidney (n = 12,870 specimens). A DPWG pilot digital pathology repository was established, and there are plans for a competition/trial with the DIAGGRAFT project. Utilizing existing resources and previously established models, the Banff DPWG is establishing new resources for the Banff community.

PodoSighter: A Cloud-Based Tool for Label-Free Podocyte Detection in Kidney Whole-Slide Images.

BACKGROUND: Podocyte depletion precedes progressive glomerular damage in several kidney diseases. However, the current standard of visual detection and quantification of podocyte nuclei from brightfield microscopy images is laborious and imprecise. METHODS: We have developed PodoSighter, an online cloud-based tool, to automatically identify and quantify podocyte nuclei from giga-pixel brightfield whole-slide images (WSIs) using deep learning. Ground-truth to train the tool used immunohistochemically or immunofluorescence-labeled images from a multi-institutional cohort of 122 histologic sections from mouse, rat, and human kidneys. To demonstrate the generalizability of our tool in investigating podocyte loss in clinically relevant samples, we tested it in rodent models of glomerular diseases, including diabetic kidney disease, crescentic GN, and dose-dependent direct podocyte toxicity and depletion, and in human biopsies from steroid-resistant nephrotic syndrome and from human autopsy tissues. RESULTS: The optimal model yielded high sensitivity/specificity of 0.80/0.80, 0.81/0.86, and 0.80/0.91, in mouse, rat, and human images, respectively, from periodic acid-Schiff-stained WSIs. Furthermore, the podocyte nuclear morphometrics extracted using PodoSighter were informative in identifying diseased glomeruli. We have made PodoSighter freely available to the general public as turnkey plugins in a cloud-based web application for end users. CONCLUSIONS: Our study demonstrates an automated computational approach to detect and quantify podocyte nuclei in standard histologically stained WSIs, facilitating podocyte research, and enabling possible future clinical applications.

Automated Computational Detection of Interstitial Fibrosis, Tubular Atrophy, and Glomerulosclerosis.

BACKGROUND: Interstitial fibrosis, tubular atrophy (IFTA), and glomerulosclerosis are indicators of irrecoverable kidney injury. Modern machine learning (ML) tools have enabled robust, automated identification of image structures that can be comparable with analysis by human experts. ML algorithms were developed and tested for the ability to replicate the detection and quantification of IFTA and glomerulosclerosis that renal pathologists perform. METHODS: A renal pathologist annotated renal biopsy specimens from 116 whole-slide images (WSIs) for IFTA and glomerulosclerosis. A total of 79 WSIs were used for training different configurations of a convolutional neural network (CNN), and 17 and 20 WSIs were used as internal and external testing cases, respectively. The best model was compared against the input of four renal pathologists on 20 new testing slides. Further, for 87 testing biopsy specimens, IFTA and glomerulosclerosis measurements made by pathologists and the CNN were correlated to patient outcome using classic statistical tools. RESULTS: The best average performance across all image classes came from a DeepLab version 2 network trained at 40× magnification. IFTA and glomerulosclerosis percentages derived from this CNN achieved high levels of agreement with four renal pathologists. The pathologist- and CNN-based analyses of IFTA and glomerulosclerosis showed statistically significant and equivalent correlation with all patient-outcome variables. CONCLUSIONS: ML algorithms can be trained to replicate the IFTA and glomerulosclerosis assessment performed by renal pathologists. This suggests computational methods may be able to provide a standardized approach to evaluate the extent of chronic kidney injury in situations in which renal-pathologist time is restricted or unavailable.

SAM: Self-augmentation mechanism for COVID-19 detection using chest X-ray images.

COVID-19 is a rapidly spreading viral disease and has affected over 100 countries worldwide. The numbers of casualties and cases of infection have escalated particularly in countries with weakened healthcare systems. Recently, reverse transcription-polymerase chain reaction (RT-PCR) is the test of choice for diagnosing COVID-19. However, current evidence suggests that COVID-19 infected patients are mostly stimulated from a lung infection after coming in contact with this virus. Therefore, chest X-ray (i.e., radiography) and chest CT can be a surrogate in some countries where PCR is not readily available. This has forced the scientific community to detect COVID-19 infection from X-ray images and recently proposed machine learning methods offer great promise for fast and accurate detection. Deep learning with convolutional neural networks (CNNs) has been successfully applied to radiological imaging for improving the accuracy of diagnosis. However, the performance remains limited due to the lack of representative X-ray images available in public benchmark datasets. To alleviate this issue, we propose a self-augmentation mechanism for data augmentation in the feature space rather than in the data space using reconstruction independent component analysis (RICA). Specifically, a unified architecture is proposed which contains a deep convolutional neural network (CNN), a feature augmentation mechanism, and a bidirectional LSTM (BiLSTM). The CNN provides the high-level features extracted at the pooling layer where the augmentation mechanism chooses the most relevant features and generates low-dimensional augmented features. Finally, BiLSTM is used to classify the processed sequential information. We conducted experiments on three publicly available databases to show that the proposed approach achieves the state-of-the-art results with accuracy of 97%, 84% and 98%. Explainability analysis has been carried out using feature visualization through PCA projection and t-SNE plots.

Artificial intelligence driven next-generation renal histomorphometry.

PURPOSE OF REVIEW: Successful integration of artificial intelligence into extant clinical workflows is contingent upon a number of factors including clinician comprehension and interpretation of computer vision. This article discusses how image analysis and machine learning have enabled comprehensive characterization of kidney morphology for development of automated diagnostic and prognostic renal pathology applications. RECENT FINDINGS: The primordial digital pathology informatics work employed classical image analysis and machine learning to prognosticate renal disease. Although this classical approach demonstrated tremendous potential, subsequent advancements in hardware technology rendered artificial neural networks '(ANNs) the method of choice for machine vision in computational pathology'. Offering rapid and reproducible detection, characterization and classification of kidney morphology, ANNs have facilitated the development of diagnostic and prognostic applications. In addition, modern machine learning with ANNs has revealed novel biomarkers in kidney disease, demonstrating the potential for machine vision to elucidate novel pathologic mechanisms beyond extant clinical knowledge. SUMMARY: Despite the revolutionary developments potentiated by modern machine learning, several challenges remain, including data quality control and curation, image annotation and ontology, integration of multimodal data and interpretation of machine vision or 'opening the black box'. Resolution of these challenges will not only revolutionize diagnostic pathology but also pave the way for precision medicine and integration of artificial intelligence in the process of care.

Computational Segmentation and Classification of Diabetic Glomerulosclerosis.

BACKGROUND: Pathologists use visual classification of glomerular lesions to assess samples from patients with diabetic nephropathy (DN). The results may vary among pathologists. Digital algorithms may reduce this variability and provide more consistent image structure interpretation. METHODS: We developed a digital pipeline to classify renal biopsies from patients with DN. We combined traditional image analysis with modern machine learning to efficiently capture important structures, minimize manual effort and supervision, and enforce biologic prior information onto our model. To computationally quantify glomerular structure despite its complexity, we simplified it to three components consisting of nuclei, capillary lumina and Bowman spaces; and Periodic Acid-Schiff positive structures. We detected glomerular boundaries and nuclei from whole slide images using convolutional neural networks, and the remaining glomerular structures using an unsupervised technique developed expressly for this purpose. We defined a set of digital features which quantify the structural progression of DN, and a recurrent network architecture which processes these features into a classification. RESULTS: Our digital classification agreed with a senior pathologist whose classifications were used as ground truth with moderate Cohen's kappa κ = 0.55 and 95% confidence interval [0.50, 0.60]. Two other renal pathologists agreed with the digital classification with κ1 = 0.68, 95% interval [0.50, 0.86] and κ2 = 0.48, 95% interval [0.32, 0.64]. Our results suggest computational approaches are comparable to human visual classification methods, and can offer improved precision in clinical decision workflows. We detected glomerular boundaries from whole slide images with 0.93±0.04 balanced accuracy, glomerular nuclei with 0.94 sensitivity and 0.93 specificity, and glomerular structural components with 0.95 sensitivity and 0.99 specificity. CONCLUSIONS: Computationally derived, histologic image features hold significant diagnostic information that may augment clinical diagnostics.

Molecular probes for fluorescence lifetime imaging.

Visualization of biological processes and pathologic conditions at the cellular and tissue levels largely relies on the use of fluorescence intensity signals from fluorophores or their bioconjugates. To overcome the concentration dependency of intensity measurements, evaluate subtle molecular interactions, and determine biochemical status of intracellular or extracellular microenvironments, fluorescence lifetime (FLT) imaging has emerged as a reliable imaging method complementary to intensity measurements. Driven by a wide variety of dyes exhibiting stable or environment-responsive FLTs, information multiplexing can be readily accomplished without the need for ratiometric spectral imaging. With knowledge of the fluorescent states of the molecules, it is entirely possible to predict the functional status of biomolecules or microevironment of cells. Whereas the use of FLT spectroscopy and microscopy in biological studies is now well-established, in vivo imaging of biological processes based on FLT imaging techniques is still evolving. This review summarizes recent advances in the application of the FLT of molecular probes for imaging cells and small animal models of human diseases. It also highlights some challenges that continue to limit the full realization of the potential of using FLT molecular probes to address diverse biological problems and outlines areas of potential high impact in the future.

Protonation and Trapping of a Small pH-Sensitive Near-Infrared Fluorescent Molecule in the Acidic Tumor Environment Delineate Diverse Tumors in Vivo.

Enhanced glycolysis and poor perfusion in most solid malignant tumors create an acidic extracellular environment, which enhances tumor growth, invasion, and metastasis. Complex molecular systems have been explored for imaging and treating these tumors. Here, we report the development of a small molecule, LS662, that emits near-infrared (NIR) fluorescence upon protonation by the extracellular acidic pH environment of diverse solid tumors. Protonation of LS662 induces selective internalization into tumor cells and retention in the tumor microenvironment. Noninvasive NIR imaging demonstrates selective retention of the pH sensor in diverse tumors, and two-photon microscopy of ex vivo tumors reveals significant retention of LS662 in tumor cells and the acid tumor microenvironment. Passive and active internalization processes combine to enhance NIR fluorescence in tumors over time. The low background fluorescence allows tumors to be detected with high sensitivity, as well as dead or dying cells to be delineated from healthy cells. In addition to demonstrating the feasibility of using small molecule pH sensors to image multiple aggressive solid tumor types via a protonation-induced internalization and retention pathway, the study reveals the potential of using LS662 to monitor treatment response and tumor-targeted drug delivery.

ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology.

Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles1 with the goal of building a modernized biomedical data resource ecosystem to establish collaborative research communities. In line with this mission and to democratize AI-based image analysis in digital pathology, we propose ComPRePS: an end-to-end automated Computational Renal Pathology Suite which combines massive scalability, on-demand cloud computing, and an easy-to-use web-based user interface for data upload, storage, management, slide-level visualization, and domain expert interaction. Moreover, our platform is equipped with both in-house and collaborator developed sophisticated AI algorithms in the back-end server for image analysis to identify clinically relevant micro-anatomic functional tissue units (FTU) and to extract image features.

ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology.

Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles1 with the goal of building a modernized biomedical data resource ecosystem to establish collaborative research communities. In line with this mission and to democratize AI-based image analysis in digital pathology, we propose ComPRePS: an end-to-end automated Computational Renal Pathology Suite which combines massive scalability, on-demand cloud computing, and an easy-to-use web-based user interface for data upload, storage, management, slide-level visualization, and domain expert interaction. Moreover, our platform is equipped with both in-house and collaborator developed sophisticated AI algorithms in the back-end server for image analysis to identify clinically relevant micro-anatomic functional tissue units (FTU) and to extract image features.

Investigating quantitative histological characteristics in renal pathology using HistoLens.

HistoLens is an open-source graphical user interface developed using MATLAB AppDesigner for visual and quantitative analysis of histological datasets. HistoLens enables users to interrogate sets of digitally annotated whole slide images to efficiently characterize histological differences between disease and experimental groups. Users can dynamically visualize the distribution of 448 hand-engineered features quantifying color, texture, morphology, and distribution across microanatomic sub-compartments. Additionally, users can map differentially detected image features within the images by highlighting affected regions. We demonstrate the utility of HistoLens to identify hand-engineered features that correlate with pathognomonic renal glomerular characteristics distinguishing diabetic nephropathy and amyloid nephropathy from the histologically unremarkable glomeruli in minimal change disease. Additionally, we examine the use of HistoLens for glomerular feature discovery in the Tg26 mouse model of HIV-associated nephropathy. We identify numerous quantitative glomerular features distinguishing Tg26 transgenic mice from wild-type mice, corresponding to a progressive renal disease phenotype. Thus, we demonstrate an off-the-shelf and ready-to-use toolkit for quantitative renal pathology applications.

PKR activation-induced mitochondrial dysfunction in HIV-transgenic mice with nephropathy.

HIV disease remains prevalent in the USA and chronic kidney disease remains a major cause of morbidity in HIV-1-positive patients. Host double-stranded RNA (dsRNA)-activated protein kinase (PKR) is a sensor for viral dsRNA, including HIV-1. We show that PKR inhibition by compound C16 ameliorates the HIV-associated nephropathy (HIVAN) kidney phenotype in the Tg26 transgenic mouse model, with reversal of mitochondrial dysfunction. Combined analysis of single-nucleus RNA-seq and bulk RNA-seq data revealed that oxidative phosphorylation was one of the most downregulated pathways and identified signal transducer and activator of transcription (STAT3) as a potential mediating factor. We identified in Tg26 mice a novel proximal tubular cell cluster enriched in mitochondrial transcripts. Podocytes showed high levels of HIV-1 gene expression and dysregulation of cytoskeleton-related genes, and these cells dedifferentiated. In injured proximal tubules, cell-cell interaction analysis indicated activation of the pro-fibrogenic PKR-STAT3-platelet-derived growth factor (PDGF)-D pathway. These findings suggest that PKR inhibition and mitochondrial rescue are potential novel therapeutic approaches for HIVAN.

FUSION: A web-based application for in-depth exploration of multi-omics data with brightfield histology.

Spatial -OMICS technologies facilitate the interrogation of molecular profiles in the context of the underlying histopathology and tissue microenvironment. Paired analysis of histopathology and molecular data can provide pathologists with otherwise unobtainable insights into biological mechanisms. To connect the disparate molecular and histopathologic features into a single workspace, we developed FUSION (Functional Unit State IdentificatiON in WSIs [Whole Slide Images]), a web-based tool that provides users with a broad array of visualization and analytical tools including deep learning-based algorithms for in-depth interrogation of spatial -OMICS datasets and their associated high-resolution histology images. FUSION enables end-to-end analysis of functional tissue units (FTUs), automatically aggregating underlying molecular data to provide a histopathology-based medium for analyzing healthy and altered cell states and driving new discoveries using "pathomic" features. We demonstrate FUSION using 10x Visium spatial transcriptomics (ST) data from both formalin-fixed paraffin embedded (FFPE) and frozen prepared datasets consisting of healthy and diseased tissue. Through several use-cases, we demonstrate how users can identify spatial linkages between quantitative pathomics, qualitative image characteristics, and spatial --omics.

Urine complement proteins are associated with kidney disease progression of type 2 diabetes in Korean and American cohorts.

Background: Mechanisms of progression of diabetic kidney disease (DKD) are not completely understood. This study uses untargeted and targeted mass spectrometry-based proteomics in two independent cohorts on two continents to decipher the mechanisms of DKD in patients with type 2 diabetes. Methods: We conducted untargeted mass spectrometry on urine samples collected at the time of kidney biopsy from Korean patients with type 2 diabetes and biopsy-proven diabetic nephropathy at Seoul National University Hospital (SNUH-DN cohort; n = 64). These findings were validated using targeted mass spectrometry in urine samples from a Chronic Renal Insufficiency Cohort subgroup with type 2 diabetes and DKD (CRIC-T2D; n = 282). Urinary biomarkers/pathways associated with kidney disease progression (doubling of serum creatinine, ≥50% decrease in estimated glomerular filtration rates, or the development of end-stage kidney disease) were identified. Results: SNUH-DN patients had an estimated glomerular filtration rate (eGFR) of 55 mL/min/1.73 m 2 (interquartile range [IQR], 44-75) and random urine protein-to-creatinine ratio of 3.1 g/g (IQR, 1.7-7.0). Urine proteins clustered into two groups, with cluster 2 having a 4.6-fold greater hazard (95% confidence interval [CI], 1.9-11.5) of disease progression than cluster 1 in multivariable-adjusted, time-to-event analyses. Proteins in cluster 2 mapped to 10 pathways, four of the top five of which were complement or complement-related. A high complement score, constructed from urine complement protein abundance, was strongly correlated to 4 of 5 histopathologic DN features and was associated with a 2.4-fold greater hazard (95% CI, 1.0-5.4) of disease progression than a low complement score. Targeted mass spectrometry of the CRIC-T2D participants, who had an eGFR of 42 mL/min/1.73 m 2 (IQR, 37-49) and 24-hr urine protein of 0.48 g (IQR, 0.10-1.87), showed that the complement score similarly segregated them into rapid and slow DKD progression groups. In both cohorts, the complement score had a linear association with disease progression. Conclusions: Urinary proteomic profiling confirms the association between the complement pathway and rapid DKD progression in two independent cohorts. These results suggest a need to further investigate complement pathway inhibition as a novel treatment for DKD.

Ontology-based modeling, integration, and analysis of heterogeneous clinical, pathological, and molecular kidney data for precision medicine.

Many data resources generate, process, store, or provide kidney related molecular, pathological, and clinical data. Reference ontologies offer an opportunity to support knowledge and data integration. The Kidney Precision Medicine Project (KPMP) team contributed to the representation and addition of 329 kidney phenotype terms to the Human Phenotype Ontology (HPO), and identified many subcategories of acute kidney injury (AKI) or chronic kidney disease (CKD). The Kidney Tissue Atlas Ontology (KTAO) imports and integrates kidney-related terms from existing ontologies (e.g., HPO, CL, and Uberon) and represents 259 kidney-related biomarkers. We have also developed a precision medicine metadata ontology (PMMO) to integrate 50 variables from KPMP and CZ CellxGene data resources and applied PMMO for integrative kidney data analysis. The gene expression profiles of kidney gene biomarkers were specifically analyzed under healthy control or AKI/CKD disease states. This work demonstrates how ontology-based approaches support multi-domain data and knowledge integration in precision medicine.