Temporal and tissue-specific activation of aryl hydrocarbon receptor in discrete mouse models of kidney disease.

Emerging evidence in animal models of chronic kidney disease (CKD) implicates Aryl Hydrocarbon Receptor (AHR) signaling as a mediator of uremic toxicity. However, details about its tissue-specific and time-dependent activation in response to various renal pathologies remain poorly defined. Here, a comprehensive analysis of AHR induction was conducted in response to discrete models of kidney diseases using a transgenic mouse line expressing the AHR responsive-promoter tethered to a ß-galactosidase reporter gene. Following validation using a canonical AHR ligand (a dioxin derivative), the transgenic mice were subjected to adenine-induced and ischemia/reperfusion-induced injury models representing CKD and acute kidney injury (AKI), respectively, in humans. Indoxyl sulfate was artificially increased in mice through the drinking water and by inhibiting its excretion into the urine. Adenine-fed mice showed a distinct and significant increase in ß-galactosidase in the proximal and distal renal tubules, cardiac myocytes, hepatocytes, and microvasculature in the cerebral cortex. The pattern of ß-galactosidase increase coincided with the changes in serum indoxyl sulfate levels. Machine-learning-based image quantification revealed positive correlations between indoxyl sulfate levels and ß-galactosidase expression in various tissues. This pattern of ß-galactosidase expression was recapitulated in the indoxyl sulfate-specific model. The ischemia/reperfusion injury model showed increase in ß-galactosidase in renal tubules that persisted despite reduction in serum indoxyl sulfate and blood urea nitrogen levels. Thus, our results demonstrate a relationship between AHR activation in various tissues of mice with CKD or AKI and the levels of indoxyl sulfate. This study demonstrates the use of a reporter gene mouse to probe tissue-specific manifestations of uremia in translationally relevant animal models and provide hypothesis-generating insights into the mechanism of uremic toxicity that warrant further investigation.

Segmentation of Glomeruli Within Trichrome Images Using Deep Learning.

INTRODUCTION: The number of glomeruli and glomerulosclerosis evaluated on kidney biopsy slides constitute standard components of a renal pathology report. Prevailing methods for glomerular assessment remain manual, labor intensive, and nonstandardized. We developed a deep learning framework to accurately identify and segment glomeruli from digitized images of human kidney biopsies. METHODS: Trichrome-stained images (n = 275) from renal biopsies of 171 patients with chronic kidney disease treated at the Boston Medical Center from 2009 to 2012 were analyzed. A sliding window operation was defined to crop each original image to smaller images. Each cropped image was then evaluated by at least 3 experts into 3 categories: (i) no glomerulus, (ii) normal or partially sclerosed (NPS) glomerulus, and (iii) globally sclerosed (GS) glomerulus. This led to identification of 751 unique images representing nonglomerular regions, 611 images with NPS glomeruli, and 134 images with GS glomeruli. A convolutional neural network (CNN) was trained with cropped images as inputs and corresponding labels as output. Using this model, an image processing routine was developed to scan the test images to segment the GS glomeruli. RESULTS: The CNN model was able to accurately discriminate nonglomerular images from NPS and GS images (performance on test data: Accuracy: 92.67% ± 2.02% and Kappa: 0.8681 ± 0.0392). The segmentation model that was based on the CNN multilabel classifier accurately marked the GS glomeruli on the test data (Matthews correlation coefficient = 0.628). CONCLUSION: This work demonstrates the power of deep learning for assessing complex histologic structures from digitized human kidney biopsies.

Towards minimally-invasive, quantitative assessment of chronic kidney disease using optical spectroscopy.

The universal pathologic features implicated in the progression of chronic kidney disease (CKD) are interstitial fibrosis and tubular atrophy (IFTA). Current methods of estimating IFTA are slow, labor-intensive and fraught with variability and sampling error, and are not quantitative. As such, there is pressing clinical need for a less-invasive and faster method that can quantitatively assess the degree of IFTA. We propose a minimally-invasive optical method to assess the macro-architecture of kidney tissue, as an objective, quantitative assessment of IFTA, as an indicator of the degree of kidney disease. The method of elastic-scattering spectroscopy (ESS) measures backscattered light over the spectral range 320-900 nm and is highly sensitive to micromorphological changes in tissues. Using two discrete mouse models of CKD, we observed spectral trends of increased scattering intensity in the near-UV to short-visible region (350-450 nm), relative to longer wavelengths, for fibrotic kidneys compared to normal kidney, with a quasi-linear correlation between the ESS changes and the histopathology-determined degree of IFTA. These results suggest the potential of ESS as an objective, quantitative and faster assessment of IFTA for the management of CKD patients and in the allocation of organs for kidney transplantation.

Association of Pathological Fibrosis With Renal Survival Using Deep Neural Networks.

INTRODUCTION: Chronic kidney damage is routinely assessed semiquantitatively by scoring the amount of fibrosis and tubular atrophy in a renal biopsy sample. Although image digitization and morphometric techniques can better quantify the extent of histologic damage, we need more widely applicable ways to stratify kidney disease severity. METHODS: We leveraged a deep learning architecture to better associate patient-specific histologic images with clinical phenotypes (training classes) including chronic kidney disease (CKD) stage, serum creatinine, and nephrotic-range proteinuria at the time of biopsy, and 1-, 3-, and 5-year renal survival. Trichrome-stained images processed from renal biopsy samples were collected on 171 patients treated at the Boston Medical Center from 2009 to 2012. Six convolutional neural network (CNN) models were trained using these images as inputs and the training classes as outputs, respectively. For comparison, we also trained separate classifiers using the pathologist-estimated fibrosis score (PEFS) as input and the training classes as outputs, respectively. RESULTS: CNN models outperformed PEFS across the classification tasks. Specifically, the CNN model predicted the CKD stage more accurately than the PEFS model (κ = 0.519 vs. 0.051). For creatinine models, the area under curve (AUC) was 0.912 (CNN) versus 0.840 (PEFS). For proteinuria models, AUC was 0.867 (CNN) versus 0.702 (PEFS). AUC values for the CNN models for 1-, 3-, and 5-year renal survival were 0.878, 0.875, and 0.904, respectively, whereas the AUC values for PEFS model were 0.811, 0.800, and 0.786, respectively. CONCLUSION: The study demonstrates a proof of principle that deep learning can be applied to routine renal biopsy images.

Uremic Solute-Aryl Hydrocarbon Receptor-Tissue Factor Axis Associates with Thrombosis after Vascular Injury in Humans.

Individuals with CKD are particularly predisposed to thrombosis after vascular injury. Using mouse models, we recently described indoxyl sulfate, a tryptophan metabolite retained in CKD and an activator of tissue factor (TF) through aryl hydrocarbon receptor (AHR) signaling, as an inducer of thrombosis across the CKD spectrum. However, the translation of findings from animal models to humans is often challenging. Here, we investigated the uremic solute-AHR-TF thrombosis axis in two human cohorts, using a targeted metabolomics approach to probe a set of tryptophan products and high-throughput assays to measure AHR and TF activity. Analysis of baseline serum samples was performed from 473 participants with advanced CKD from the Dialysis Access Consortium Clopidogrel Prevention of Early AV Fistula Thrombosis trial. Participants with subsequent arteriovenous thrombosis had significantly higher levels of indoxyl sulfate and kynurenine, another uremic solute, and greater activity of AHR and TF, than those without thrombosis. Pattern recognition analysis using the components of the thrombosis axis facilitated clustering of the thrombotic and nonthrombotic groups. We further validated these findings using 377 baseline samples from participants in the Thrombolysis in Myocardial Infarction II trial, many of whom had CKD stage 2-3. Mechanistic probing revealed that kynurenine enhances thrombosis after vascular injury in an animal model and regulates thrombosis in an AHR-dependent manner. This human validation of the solute-AHR-TF axis supports further studies probing its utility in risk stratification of patients with CKD and exploring its role in other diseases with heightened risk of thrombosis.

Targeting STUB1-tissue factor axis normalizes hyperthrombotic uremic phenotype without increasing bleeding risk.

Chronic kidney disease (CKD/uremia) remains vexing because it increases the risk of atherothrombosis and is also associated with bleeding complications on standard antithrombotic/antiplatelet therapies. Although the associations of indolic uremic solutes and vascular wall proteins [such as tissue factor (TF) and aryl hydrocarbon receptor (AHR)] are being defined, the specific mechanisms that drive the thrombotic and bleeding risks are not fully understood. We now present an indolic solute-specific animal model, which focuses on solute-protein interactions and shows that indolic solutes mediate the hyperthrombotic phenotype across all CKD stages in an AHR- and TF-dependent manner. We further demonstrate that AHR regulates TF through STIP1 homology and U-box-containing protein 1 (STUB1). As a ubiquitin ligase, STUB1 dynamically interacts with and degrades TF through ubiquitination in the uremic milieu. TF regulation by STUB1 is supported in humans by an inverse relationship of STUB1 and TF expression and reduced STUB1-TF interaction in uremic vessels. Genetic or pharmacological manipulation of STUB1 in vascular smooth muscle cells inhibited thrombosis in flow loops. STUB1 perturbations reverted the uremic hyperthrombotic phenotype without prolonging the bleeding time, in contrast to heparin, the standard-of-care antithrombotic in CKD patients. Our work refines the thrombosis axis (STUB1 is a mediator of indolic solute-AHR-TF axis) and expands the understanding of the interconnected relationships driving the fragile thrombotic state in CKD. It also establishes a means of minimizing the uremic hyperthrombotic phenotype without altering the hemostatic balance, a long-sought-after combination in CKD patients.