Growth Pattern of Kidney Cyst Number and Volume in Autosomal Dominant Polycystic Kidney Disease

The need for clinical, genetic and radiological characterization of atypical polycystic kidney disease

BACKGROUND

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a monogenic disease having a prevalence of 1:400-1000 live births. Depending on kidney imaging, patients can be subdivided into Class 1 (typical) and Class 2 (atypical). The present study aims to provide better assessment of Class 2 patients to help define their family history, together with their clinical and radiological characteristics.

METHODS

One hundred twenty-four PKD patients with abdominal Magnetic Resonance Imaging (MRI) for the staging of ADPKD, were retrospectively analyzed, aiming to focus on Class 2 ADPKD patients. Total kidney volume and total cyst volume were evaluated, while also assessing their clinical and genetic characteristics.

RESULTS

Twelve patients fulfilled the Mayo criteria for Class 2 ADPKD (two Class 2B and ten Class 2A). Extrarenal involvement was observed in 66.7% of cases, but only two subjects presented an estimated Glomerular Filtration Rate (eGFR) < 60 mL/min/1.73 m2. A positive family history for cystic disease was more frequent compared to other published cohorts. Only 8.3% tested positive for a likely pathogenic mutation in the PKD1 gene. Class 2B patients showed a lower height-adjusted total kidney volume, with a lower percentage of total cyst volume.

CONCLUSION

Based on our results, atypical ADPKD does not represent an uncommon condition, being present in about 10% of MRI-evaluated patients diagnosed with ADPKD. Genetic tests are frequently negative for PKD1/PKD2, and total cyst volume and residual tissue volume do not increase the prognostic value of MRI in patients with these radiological characteristics. Other tools are needed to better characterize their kidney prognosis.

Deep learning-based segmentation of kidneys and renal cysts on T2-weighted MRI from patients with autosomal dominant polycystic kidney disease

BACKGROUND

Our aim was to train and test a deep learning-based algorithm for automatically segmenting kidneys and renal cysts in patients with autosomal dominant polycystic kidney disease (ADPKD).

METHODS

We retrospectively selected all ADPKD patients who underwent renal MRI with coronal T2-weighted imaging at our institution from 2008 to 2022. The 20 most recent examinations constituted the test dataset, to mimic pseudoprospective enrolment. The remaining ones constituted the training dataset to which eight normal renal MRIs were added. Kidneys and cysts ground truth segmentations were performed on coronal T2-weighted images by a junior radiologist supervised by an experienced radiologist. Kidneys and cysts of the 20 test MRIs were segmented by the algorithm and three independent human raters. Segmentations were compared using overlap metrics. The total kidney volume (TKV), total cystic volume (TCV), and cystic index (TCV divided by TKV) were compared using Bland-Altman analysis.

RESULTS

We included 164 ADPKD patients. Dice similarity coefficients ranged from 85.9% to 87.4% between the algorithms and the raters' segmentations and from 84.2% to 86.2% across raters' segmentations. For TCV assessment, the biases ± standard deviations (SD) were 3-19 ± 137-151 mL between the algorithm and the raters, and 22-45 ± 49-57 mL across raters. The algorithm underestimated TKV and TCV in two outliers with TCV > 2800 mL. For cystic index assessment, the biases ± SD were 2.5-6.9% ± 6.7-8.3% between the algorithm and the raters, and 2.1-9.4 ± 7.4-11.6% across raters.

CONCLUSION

The algorithm's performance fell within the range of inter-rater variability, but large TKV and TCV were underestimated.

RELEVANCE STATEMENT

Accurate automated segmentation of the renal cysts will enable the large-scale evaluation of the prognostic value of TCV and cystic index in ADPKD patients. If these biomarkers are prognostic, then automated segmentation will facilitate their use in daily routine.

KEY POINTS

Cystic volume is an emerging biomarker in ADPKD. The algorithm's performance in segmenting kidneys and cysts fell within interrater variability. The segmentation of very large cysts, under-represented in the training dataset, needs improvement.

Visceral Adiposity and Progression of ADPKD: A Cohort Study of Patients From the TEMPO 3:4 Trial

RATIONALE & OBJECTIVE

Body mass index (BMI) is an independent predictor of kidney disease progression in individuals with autosomal dominant polycystic kidney disease (ADPKD). Adipocytes do not simply act as a fat reservoir but are active endocrine organs. We hypothesized that greater visceral abdominal adiposity would associate with more rapid kidney growth in ADPKD and influence the efficacy of tolvaptan.

STUDY DESIGN

A retrospective cohort study.

SETTING & PARTICIPANTS

1,053 patients enrolled in the TEMPO 3:4 tolvaptan trial with ADPKD and at high risk of rapid disease progression.

PREDICTOR

Estimates of visceral adiposity extracted from coronal plane magnetic resonance imaging (MRI) scans using deep learning.

OUTCOME

Annual change in total kidney volume (TKV) and effect of tolvaptan on kidney growth.

ANALYTICAL APPROACH

Multinomial logistic regression and linear mixed models.

RESULTS

In fully adjusted models, the highest tertile of visceral adiposity was associated with greater odds of annual change in TKV of≥7% versus<5% (odds ratio [OR], 4.78 [95% CI, 3.03-7.47]). The association was stronger in women than men (interaction P<0.01). In linear mixed models with an outcome of percent change in TKV per year, tolvaptan efficacy (% change in TKV) was reduced with higher visceral adiposity (3-way interaction of treatment ∗ time ∗ visceral adiposity, P=0.002). Visceral adiposity significantly improved classification performance of predicting rapid annual percent change in TKV for individuals with a normal BMI (DeLong's test z score: -2.03; P=0.04). Greater visceral adiposity was not associated with estimated glomerular filtration rate (eGFR) slope in the overall cohort; however, visceral adiposity was associated with more rapid decline in eGFR slope (below the median) in women (fully adjusted OR, 1.06 [95% CI, 1.01-1.11] per 10 unit increase in visceral adiposity) but not men (OR, 0.98 [95% CI, 0.95-1.02]).

LIMITATIONS

Retrospective; rapid progressors; computational demand of deep learning.

CONCLUSIONS

Visceral adiposity that can be quantified by MRI in the coronal plane using a deep learning segmentation model independently associates with more rapid kidney growth and improves classification of rapid progression in individuals with a normal BMI. Tolvaptan efficacy decreases with increasing visceral adiposity.

PLAIN-LANGUAGE SUMMARY

We analyzed images from a previous study with the drug tolvaptan conducted in patients with autosomal dominant polycystic kidney disease (ADPKD) to measure the amount of fat tissue surrounding the kidneys (visceral fat). We had previously shown body mass index can predict kidney growth in this population; now we determined whether visceral fat was an important factor associated with kidney growth. Using a machine learning tool to automate measurement of fat in images, we observed that visceral fat was independently associated with kidney growth, that it was a better predictor of faster kidney growth in lean patients than body mass index, and that having more visceral fat made treatment of ADPKD with tolvaptan less effective.

AI models for automated segmentation of engineered polycystic kidney tubules

Autosomal dominant polycystic kidney disease (ADPKD) is a monogenic, rare disease, characterized by the formation of multiple cysts that grow out of the renal tubules. Despite intensive attempts to develop new drugs or repurpose existing ones, there is currently no definitive cure for ADPKD. This is primarily due to the complex and variable pathogenesis of the disease and the lack of models that can faithfully reproduce the human phenotype. Therefore, the development of models that allow automated detection of cysts' growth directly on human kidney tissue is a crucial step in the search for efficient therapeutic solutions. Artificial Intelligence methods, and deep learning algorithms in particular, can provide powerful and effective solutions to such tasks, and indeed various architectures have been proposed in the literature in recent years. Here, we comparatively review state-of-the-art deep learning segmentation models, using as a testbed a set of sequential RGB immunofluorescence images from 4 in vitro experiments with 32 engineered polycystic kidney tubules. To gain a deeper understanding of the detection process, we implemented both pixel-wise and cyst-wise performance metrics to evaluate the algorithms. Overall, two models stand out as the best performing, namely UNet++ and UACANet: the latter uses a self-attention mechanism introducing some explainability aspects that can be further exploited in future developments, thus making it the most promising algorithm to build upon towards a more refined cyst-detection platform. UACANet model achieves a cyst-wise Intersection over Union of 0.83, 0.91 for Recall, and 0.92 for Precision when applied to detect large-size cysts. On all-size cysts, UACANet averages at 0.624 pixel-wise Intersection over Union. The code to reproduce all results is freely available in a public GitHub repository.

Evaluation of advanced imaging biomarkers at kidney failure in patients with ADPKD: a pilot study

Background

Autosomal dominant polycystic kidney disease (ADPKD) presents with variable disease severity and progression. Advanced imaging biomarkers may provide insights into cystic and non-cystic processes leading to kidney failure in different age groups.

Methods

This pilot study included 39 ADPKD patients with kidney failure, stratified into three age groups (<46, 46-56, >56 years old). Advanced imaging biomarkers were assessed using an automated instance cyst segmentation tool. The biomarkers were compared with an age- and sex-matched ADPKD cohort in early chronic kidney disease (CKD).

Results

Ht-total parenchymal volume correlated negatively with age at kidney failure. The median Ht-total parenchymal volume was significantly lower in patients older than 56 years. Cystic burden was significantly higher at time of kidney failure, especially in patients who reached it before age 46 years. The cyst index at kidney failure was comparable across age groups and Mayo Imaging Classes. Advanced imaging biomarkers showed higher correlation with Ht-total kidney volume in early CKD than at kidney failure. Cyst index and parenchymal index were relatively stable over 5 years prior to kidney failure, whereas Ht-total cyst volume and cyst parenchymal surface area increased significantly.

Conclusion

Age-related differences in advanced imaging biomarkers suggest variable pathophysiological mechanisms in ADPKD patients with kidney failure. Further studies are needed to validate the utility of these biomarkers in predicting disease progression and guiding treatment strategies.

Total and Extracellular Vesicle cAMP Contents in Urine Are Associated with Autosomal Dominant Polycystic Kidney Disease (ADPKD) Progression

ADPKD is the most common genetic renal disease, characterized by the presence of multiple cysts which, through slow and gradual growth, lead to glomerular filtration rate (GFR) decline and end-stage renal disease. Cystic growth is associated with increased intracellular levels of 3',5'-cyclic adenosine monophosphate (cAMP). Extracellular vesicles (EVs) are proposed to participate in "remote sensing" by transporting different cargoes, but their relevance to ADPKD progression is poorly understood. This study aimed to determine whether cAMP is contained in urinary EVs and, if so, how total and/or EV cAMP contents participate in disease progression. Fourteen ADPKD patients, naïve for V2 receptor antagonism treatment, and seven controls were studied. Progression was evaluated by estimating GFR (eGFR) and height-adjusted total kidney volume (htTKV). Fresh morning urine was collected to determine cAMP by the competitive radioligand assay. Urine EVs were isolated using an adapted centrifugation method and characterized by electron microscopy, dynamic light scanning, flow cytometry with FITC CD63 labeling, protein and RNA content, and AQP2 and GAPDH mRNA detection. Total and EV cAMP was measurable in both control and patient urine samples. Total cAMP was significantly correlated with eGFR and its annual change but inversely correlated with htTKV. The cAMP-EVs showed a bimodal pattern with htTKV, increasing to ~1 L/m and falling at larger sizes. Our results demonstrate that urine cAMP correlates with ADPKD progression markers, and that its extracellular delivery by EVs could reflect the architectural disturbances of the organ.

Utility of new image-derived biomarkers for autosomal dominant polycystic kidney disease prognosis using automated instance cyst segmentation

New image-derived biomarkers for patients affected by autosomal dominant polycystic kidney disease are needed to improve current clinical management. The measurement of total kidney volume (TKV) provides critical information for clinicians to drive care decisions. However, patients with similar TKV may present with very different phenotypes, often requiring subjective decisions based on other factors (e.g., appearance of healthy kidney parenchyma, a few cysts contributing significantly to overall TKV, etc.). In this study, we describe a new technique to individually segment cysts and quantify biometric parameters including cyst volume, cyst number, parenchyma volume, and cyst parenchyma surface area. Using data from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) study the utility of these new parameters was explored, both quantitatively as well as visually. Total cyst number and cyst parenchyma surface area showed superior prediction of the slope of estimated glomerular filtration rate decline, kidney failure and chronic kidney disease stages 3A, 3B, and 4, compared to TKV. In addition, presentations such as a few large cysts contributing significantly to overall kidney volume were shown to be much better stratified in terms of outcome predictions. Thus, these new image biomarkers, which can be obtained automatically, will have great utility in future studies and clinical care for patients affected by autosomal dominant polycystic kidney disease.

Wall Tension and Tubular Resistance in Kidney Cystic Conditions

The progressive formation of single or multiple cysts accompanies several renal diseases. Specifically, (i) genetic forms, such as adult dominant polycystic kidney disease (ADPKD), and (ii) acquired cystic kidney disease (ACKD) are probably the most frequent forms of cystic diseases. Adult dominant polycystic kidney disease (ADPKD) is a genetic disorder characterized by multiple kidney cysts and systemic alterations. The genes responsible for the condition are known, and a large amount of literature focuses on the molecular description of the mechanism. The present manuscript shows that a multiscale approach that considers supramolecular physical phenomena captures the characteristics of both ADPKD and acquired cystic kidney disease (ACKD) from the pathogenetic and therapeutical point of view, potentially suggesting future treatments. We first review the hypothesis of cystogenesis in ADPKD and then focus on ACKD, showing that they share essential pathogenetic features, which can be explained by a localized obstruction of a tubule and/or an alteration of the tubular wall tension. The consequent tubular aneurysms (cysts) follow Laplace's law. Reviewing the public databases, we show that ADPKD genes are widely expressed in various organs, and these proteins interact with the extracellular matrix, thus potentially modifying wall tension. At the kidney and liver level, the authors suggest that altered cell polarity/secretion/proliferation produce tubular regions of high resistance to the urine/bile flow. The increased intratubular pressure upstream increases the difference between the inside (Pi) and the outside (Pe) of the tubules (∆P) and is counterbalanced by lower wall tension by a factor depending on the radius. The latter is a function of tubule length. In adult dominant polycystic kidney disease (ADPKD), a minimal reduction in the wall tension may lead to a dilatation in the tubular segments along the nephron over the years. The initial increase in the tubule radius would then facilitate the progressive expansion of the cysts. In this regard, tubular cell proliferation may be, at least partially, a consequence of the progressive cysts' expansion. This theory is discussed in view of other diseases with reduced wall tension and with cysts and the therapeutic effects of vaptans, somatostatin, SGLT2 inhibitors, and potentially other therapeutic targets.

How to Estimate Kidney Growth in Patients with Autosomal Dominant Polycystic Kidney Disease

Autosomal dominant polycystic kidney disease (ADPKD) is a disease characterized by a progressive kidney growth due to the development of cysts that lead to gradual destruction of the surrounding parenchyma. In the first stage, the estimated GFR will remain stable despite the reduction of the renal parenchyma because of an increase in glomerular hyperfiltration. The total kidney volume (TKV) measured with computed tomography or magnetic resonance imaging is related to the future GFR decline. Thus, TKV has become an early marker to be analyzed in all patients with ADPKD. In addition, in recent years, it has been pointed out that kidney growth rate estimated with a single TKV measurement can be a clear prognostic marker for future glomerular filtration decline. However, there is no consensus on how to measure kidney volume growth in ADPKD, so each author has used different models that, not having the same meaning, have been handled as if they produced similar values. This may lead to erroneous estimates of kidney growth rate with the consequent prognostic error. The Mayo Clinic classification is now the most widely accepted prognostic model in clinical practice to predict patients who will deteriorate faster and to decide what patients should be treated with tolvaptan. However, some aspects of this model have not been discussed in depth. Our aim in this review was to present the models that can be used to estimate kidney volume growth rate in ADPKD, to facilitate their applicability in daily clinical practice.

Predicting the Risk of Progression in Indian ADPKD Cohort using PROPKD Score - A Single-Center Retrospective Study

Background

With the variable genotype-phenotype expression of autosomal dominant polycystic kidney disease (ADPKD) and availability of novel targeted therapies, it is important to find predictors for rapid progression. The PROPKD score, consisting of genetic and clinical parameters like sex, hypertension, and urological events, is a useful tool in predicting the risk of progression. This study was aimed to determine the risk of ADPKD progression in Indian patients using the PROPKD score.

Materials and Methods

A retrospective study was done from 2006 to 2021. ADPKD patients with ESRD were included in the study. Scoring was done as per the PROPKD score as follows: male sex: 1, onset of hypertension before 35 years: 2, first urological event before 35 years: 2, PKD1 truncating mutation: 4, PKD1 non-truncating mutation: 2, and PKD2 mutation: 0. Two types of risk classifications were done as follows: (a) considering the clinical variables in all 73 patients (male sex, onset of hypertension before 35 years, and first urological event before 35 years), they were classified into three risk groups: low-risk group (0-1), intermediate-risk group (2-3), and high-risk group (4-5) and (b) considering the clinical variables and type of mutation in 39 patients, they were classified into three risk groups: low-risk group (0-3), intermediate-risk group (4-6), and high-risk group (7-9).

Results

Total number of patients included was 73, with the median age at ESRD being 54 years. High-risk group of clinical variables with hazard ratio (HR) of 4.570 (2.302-9.075, P < 0.001) and high-risk group of the PROPKD score with HR of 6.594 (1.868-23.284, P = 0.003) were associated with early ESRD. High-risk groups of both classifications were associated with early ESRD.

Conclusion

High-risk groups based on the PROPKD scoring and clinical variables were associated with early progression to ESRD.

Cyst Fraction as a Biomarker in Autosomal Dominant Polycystic Kidney Disease

Autosomal dominant polycystic kidney disease (ADPKD) is the most common monogenic kidney disease. Patients at high risk of severe disease progression should be identified early in order to intervene with supportive and therapeutic measures. However, the glomerular filtration rate (GFR) may remain within normal limits for decades until decline begins, making it a late indicator of rapid progression. Kidney volumetry is frequently used in clinical practice to allow for an assessment of disease severity. Due to limited prognostic accuracy, additional imaging markers are of high interest to improve outcome prediction in ADPKD, but data from clinical cohorts are still limited. In this study, we examined cyst fraction as one of these parameters in a cohort of 142 ADPKD patients. A subset of 61 patients received MRIs in two consecutive years to assess longitudinal changes. All MRIs were analyzed by segmentation and volumetry of the kidneys followed by determination of cyst fraction. As expected, both total kidney volume (TKV) and cyst fraction correlated with estimated GFR (eGFR), but cyst fraction showed a higher R² in a univariate linear regression. Besides, only cyst fraction remained statistically significant in a multiple linear regression including both htTKV and cyst fraction to predict eGFR. Consequently, this study underlines the potential of cyst fraction in ADPKD and encourages prospective clinical trials examining its predictive value in combination with other biomarkers to predict future eGFR decline.

Polycystic Kidney Disease Drug Development: A Conference Report

Autosomal dominant polycystic kidney disease (ADPKD) is part of a spectrum of inherited diseases that also includes autosomal recessive polycystic kidney disease, autosomal dominant polycystic liver disease, and an expanding group of recessively inherited disorders collectively termed hepatorenal fibrocystic disorders. ADPKD is the most common monogenic disorder frequently leading to chronic kidney failure with an estimated prevalence of 12 million people worldwide. Currently, only one drug (tolvaptan) has been approved by regulatory agencies as disease-modifying therapy for ADPKD, but, given its mechanism of action and side effect profile, the need for an improved therapy for ADPKD remains a priority. Although significant regulatory progress has been made, with qualification of total kidney volume as a prognostic enrichment biomarker and its later designation as a reasonably likely surrogate endpoint for progression of ADPKD within clinical trials, further work is needed to accelerate drug development efforts for all forms of PKD. In May 2021, the PKD Outcomes Consortium at the Critical Path Institute and the PKD Foundation organized a PKD Regulatory Summit to spur conversations among patients, industry, academic, and regulatory stakeholders regarding future development of tools and drugs for ADPKD and autosomal recessive polycystic kidney disease. This Special Report reviews the key points discussed during the summit and provides future direction related to PKD drug development tools.

The STAGED-PKD 2-Stage Adaptive Study With a Patient Enrichment Strategy and Treatment Effect Modeling for Improved Study Design Efficiency in Patients With ADPKD

Rationale & Objective

Venglustat, a glucosylceramide synthase inhibitor, inhibits cyst growth and reduces kidney failure in mouse models of autosomal dominant polycystic kidney disease (ADPKD). STAGED-PKD aims to determine the safety and efficacy of venglustat and was designed using patient enrichment for progression to end-stage kidney disease and modeling from prior ADPKD trials.

Study Design

STAGED-PKD is a 2-stage, international, double-blind, randomized, placebo-controlled trial in adults with ADPKD (Mayo Class 1C-1E) and estimated glomerular filtration rate (eGFR) 45-<90 mL/min/1.73 m² at risk of rapidly progressive disease. Enrichment for rapidly progressing patients was identified based on retrospective analysis of total kidney volume (TKV) and eGFR slope from the combined Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease and HALT Progression of Polycystic Kidney Disease A studies.

Setting & Participants

Target enrollment in stages 1 and 2 was 240 and 320 patients, respectively.

Interventions

Stage 1 randomizes patients 1:1:1 to venglustat 8 mg or 15 mg once daily or placebo. Stage 2 randomizes patients 1:1 to placebo or venglustat, with the preferred dose based on stage 1 safety data.

Outcomes

Primary endpoints are TKV growth rate over 18 months in stage 1 and eGFR slope over 24 months in stage 2. Secondary endpoints include: annualized rate of change in eGFR from baseline to 18 months (stage 1); annualized rate of change in TKV based on magnetic resonance imaging from baseline to 18 months (stage 2); and safety, tolerability, pain, and fatigue (stages 1 and 2).

Limitations

If stage 1 is unsuccessful, patients enrolled in the trial may develop drug-related adverse events that can have long-lasting effects.

Conclusions

Modeling allows the design and powering of a 2-stage combined study to assess venglustat’s impact on TKV growth and eGFR slope. Stage 1 TKV assessment via a nested approach allows early evaluation of efficacy and increased efficiency of the trial design by reducing patient numbers and trial duration.

Funding

This study was funded by Sanofi.

Trial registration

STAGED-PKD has been registered at ClinicalTrials.gov with study number NCT03523728.

Long-Term Outcomes of Longitudinal Efficacy Study With Tolvaptan in ADPKD

Introduction

The effects of long-term and uninterrupted tolvaptan treatment on autosomal dominant polycystic kidney disease (ADPKD) are unclear. Therefore, a more than 3-year continuous treatment study was performed.

Methods

From the Kyorin University cohort, 299 patients were surveyed and 179 patients were indicated for tolvaptan having a total kidney volume (TKV) ≥750 ml, TKV slope ≥5%/yr, and estimated glomerular filtration rate (eGFR) ≥15 ml/min per 1.73 m². Among 179 patients, 118 patients consented to the study.

Results

Retrospective pretreatment and prospective on-treatment periods had a median of 1.8 and 4.0 years, respectively. During the 5 treatment-years, the log₁₀(TKV) slope/yr decreased from the pretreatment period (P < 0.0001) and the estimated height-adjusted TKV growth rate α (eHTKV-α, %/yr) decreased from baseline (P < 0.0001). The decline in eGFR improved in female patients (P < 0.0001), but not in males (P = 0.6321). Furthermore, during the 5 treatment-years, eGFR remained significantly better in the group with a percent decrease in eHTKV-α from baseline to the first treatment-year ≥ the median (2.94%) than in the group with a decrease <2.94%. The free-water clearance was higher in males than in females irrespective of treatment.

Conclusion

The TKV growth rate decreased in 4 years with tolvaptan in both sexes. The insignificant effects of tolvaptan on the eGFR slope in males were likely due to androgen stimulation of cystogenesis and analytical difficulty of longitudinal changes in nonlinear trajectories of eGFR. The larger decrease in eHTKV-α in the first year was related to a better renal prognosis. The vasopressin-mediated water reabsorption was activated more in females than males irrespective of tolvaptan administration.

Functional megalin is expressed in renal cysts in a mouse model of adult polycystic kidney disease

Background

Autosomal dominant polycystic kidney disease (ADPKD) is characterized by the progressive growth of cysts and a decline of renal function. The clinical feasibility of the number of potential disease-modifying drugs is limited by systemic adverse effects. We hypothesize that megalin, a multiligand endocytic receptor expressed in the proximal tubule, may be used to facilitate drug uptake into cysts, thereby allowing for greater efficacy and fewer side effects.

Methods

The cyst expression of various tubular markers, including megalin and aquaporin 2 (AQP2), was analysed by immunohistochemistry (IHC) of kidney sections from the ADPKD mouse model (PKD1^RC/RC) at different post-natal ages. The endocytic function of megalin in cysts was examined by IHC of kidney tissue from mice injected with the megalin ligand aprotinin.

Results

Cyst lining epithelial cells expressing megalin were observed at all ages; however, the proportion decreased with age. Concomitantly, an increasing proportion of cysts revealed expression of AQP2, partial expression of megalin and/or AQP2 or no expression of the examined markers. Endocytic uptake of aprotinin was evident in megalin-positive cysts, but only in those that remained connected to the renal tubular system.

Conclusions

Megalin-expressing cysts were observed at all ages, but the proportion decreased with age, possibly due to a switch in tubular origin, a merging of cysts of different tubular origin and/or a change in the expression pattern of cyst lining cells. Megalin expressed in cysts was functional, suggesting that megalin-mediated endocytosis is a potential mechanism for drug targeting in ADPKD if initiated early in the disease.

Epithelial proliferation and cell cycle dysregulation in kidney injury and disease

Various cellular insults and injury to renal epithelial cells stimulate repair mechanisms to adapt and restore the organ homeostasis. Renal tubular epithelial cells are endowed with regenerative capacity, which allows for a restoration of nephron function after acute kidney injury. However, recent evidence indicates that the repair is often incomplete, leading to maladaptive responses that promote the progression to chronic kidney disease. The dysregulated cell cycle and proliferation is also a key feature of renal tubular epithelial cells in polycystic kidney disease and HIV-associated nephropathy. Therefore, in this review, we provide an overview of cell cycle regulation and the consequences of dysregulated cell proliferation in acute kidney injury, polycystic kidney disease, and HIV-associated nephropathy. An increased understanding of these processes may help define better targets for kidney repair and combat chronic kidney disease progression.

Semantic Instance Segmentation of Kidney Cysts in MR Images: A Fully Automated 3D Approach Developed Through Active Learning

Total kidney volume (TKV) is the main imaging biomarker used to monitor disease progression and to classify patients affected by autosomal dominant polycystic kidney disease (ADPKD) for clinical trials. However, patients with similar TKVs may have drastically different cystic presentations and phenotypes. In an effort to quantify these cystic differences, we developed the first 3D semantic instance cyst segmentation algorithm for kidneys in MR images. We have reformulated both the object detection/localization task and the instance-based segmentation task into a semantic segmentation task. This allowed us to solve this unique imaging problem efficiently, even for patients with thousands of cysts. To do this, a convolutional neural network (CNN) was trained to learn cyst edges and cyst cores. Images were converted from instance cyst segmentations to semantic edge-core segmentations by applying a 3D erosion morphology operator to up-sampled versions of the images. The reduced cysts were labeled as core; the eroded areas were dilated in 2D and labeled as edge. The network was trained on 30 MR images and validated on 10 MR images using a fourfold cross-validation procedure. The final ensemble model was tested on 20 MR images not seen during the initial training/validation. The results from the test set were compared to segmentations from two readers. The presented model achieved an averaged R² value of 0.94 for cyst count, 1.00 for total cyst volume, 0.94 for cystic index, and an averaged Dice coefficient of 0.85. These results demonstrate the feasibility of performing cyst segmentations automatically in ADPKD patients.

The Biology of Vasopressin

Vasopressins are evolutionarily conserved peptide hormones. Mammalian vasopressin functions systemically as an antidiuretic and regulator of blood and cardiac flow essential for adapting to terrestrial environments. Moreover, vasopressin acts centrally as a neurohormone involved in social and parental behavior and stress response. Vasopressin synthesis in several cell types, storage in intracellular vesicles, and release in response to physiological stimuli are highly regulated and mediated by three distinct G protein coupled receptors. Other receptors may bind or cross-bind vasopressin. Vasopressin is regulated spatially and temporally through transcriptional and post-transcriptional mechanisms, sex, tissue, and cell-specific receptor expression. Anomalies of vasopressin signaling have been observed in polycystic kidney disease, chronic heart failure, and neuropsychiatric conditions. Growing knowledge of the central biological roles of vasopressin has enabled pharmacological advances to treat these conditions by targeting defective systemic or central pathways utilizing specific agonists and antagonists.

CystAnalyser: A new software tool for the automatic detection and quantification of cysts in Polycystic Kidney and Liver Disease, and other cystic disorders

The Polycystic Kidney Disease (PKD) is characterized by progressive renal cyst development and other extrarenal manifestation including Polycystic Liver Disease (PLD). Phenotypical characterization of animal models mimicking human diseases are commonly used, in order to, study new molecular mechanisms and identify new therapeutic approaches. The main biomarker of disease progression is total volume of kidney and liver in both human and mouse, which correlates with organ function. For this reason, the estimation of the number and area of the tissue occupied by cysts, is critical for the understanding of physiological mechanisms underlying the disease. In this regard, cystic index is a robust parameter commonly used to quantify the severity of the disease. To date, the vast majority of biomedical researchers use ImageJ as a software tool to estimate the cystic index by quantifying the cystic areas of histological images after thresholding. This tool has imitations of being inaccurate, largely due to incorrectly identifying non-cystic regions. We have developed a new software, named CystAnalyser (register by Universidade de Santiago de Compostela–USC, and Fundación Investigación Sanitaria de Santiago—FIDIS), that combines automatic image processing with a graphical user friendly interface that allows investigators to oversee and easily correct the image processing before quantification. CystAnalyser was able to generate a cystic profile including cystic index, number of cysts and cyst size. In order to test the CystAnalyser software, 795 cystic kidney, and liver histological images were analyzed. Using CystAnalyser there were no differences calculating cystic index automatically versus user input, except in specific circumstances where it was necessary for the user to distinguish between mildly cystic from non-cystic regions. The sensitivity and specificity of the number of cysts detected by the automatic quantification depends on the type of organ and cystic severity, with values 76.84–78.59% and 76.96–89.66% for the kidney and 87.29–93.80% and 63.42–86.07% for the liver. CystAnalyser, in addition, provides a new tool for estimating the number of cysts and a more specific measure of the cystic index than ImageJ. This study proposes CystAnalyser is a new robust and freely downloadable software tool for analyzing the severity of disease by quantifying histological images of cystic organs for routine biomedical research. CystAnalyser can be downloaded from https://citius.usc.es/transferencia/software/cystanalyser (for Windows and Linux) for research purposes.

This work suggests CystAnalyser is the most reliable software tool currently available for the assessment of cystic pathologies including Polycystic Kidney Disease (PKD) and Polycystic Liver Disease (PLD). CystAnalyser combines automatic cyst recognition with a friendly graphical user interface, allowing user input prior to histological image quantification. CystAnalyser responds to the need to obtain reliable measurements of the universal biomarker for PKD and PLD disease progression, the Cystic index (area of cysts within the total area of tissue). This software tool is also able to calculate the number and size of cysts from the histological images. In summary, our results show that CystAnalyser overcomes the precision issues detected using the most commonly used software to date (ImageJ) for Cystic index quantification, offering users a reliable tool to easily characterize the phenotype and the pathophysiology of PKD and PLD in pre-clinical studies using animal models.

The role of DNA damage as a therapeutic target in autosomal dominant polycystic kidney disease

Autosomal dominant polycystic kidney disease (ADPKD) is the most common monogenic kidney disease and is caused by heterozygous germ-line mutations in either PKD1 (85%) or PKD2 (15%). It is characterised by the formation of numerous fluid-filled renal cysts and leads to adult-onset kidney failure in ~50% of patients by 60 years. Kidney cysts in ADPKD are focal and sporadic, arising from the clonal proliferation of collecting-duct principal cells, but in only 1-2% of nephrons for reasons that are not clear. Previous studies have demonstrated that further postnatal reductions in PKD1 (or PKD2) dose are required for kidney cyst formation, but the exact triggering factors are not clear. A growing body of evidence suggests that DNA damage, and activation of the DNA damage response pathway, are altered in ciliopathies. The aims of this review are to: (i) analyse the evidence linking DNA damage and renal cyst formation in ADPKD; (ii) evaluate the advantages and disadvantages of biomarkers to assess DNA damage in ADPKD and finally, (iii) evaluate the potential effects of current clinical treatments on modifying DNA damage in ADPKD. These studies will address the significance of DNA damage and may lead to a new therapeutic approach in ADPKD.