Harnessing Artificial Intelligence for Enhanced Renal Analysis: Automated Detection of Hydronephrosis and Precise Kidney Segmentation.

Background and objective: Hydronephrosis is essential in the diagnosis of renal colic. We automated the detection of hydronephrosis from ultrasound images to standardize the therapy and reduce the misdiagnosis of renal colic. Methods: Anonymously collected ultrasound images of human kidneys, both normal and hydronephrotic, were preprocessed for neural networks. Six "state of the art" models were trained and cross-validated for the detection of hydronephrosis, and two convolutional networks were used for kidney segmentation. In the testing phase, performance metrics included true positives, true negatives, false positives, false negatives, accuracy, and F1 score, while the evaluation of the segmentation task involved accuracy, precision, dice, jaccard, recall, and ASSD. Key findings and limitations: A total of 523 sonographic kidney images (423 nonhydronephrotic and 100 hydronephrotic) were collected from three different ultrasound devices. After training on this dataset, all models were used to evaluate 200 new ultrasound kidney images (142 nonhydronephrotic and 58 hydronephrotic kidneys). The highest validation accuracy (98.5%) was achieved by the AlexNet model (GoogLeNet 97%, AlexNet_v2 96%, ResNet50 96%, ResNet101 97.5%, and ResNet152 95%). The deeplabv3_resnet50 and deeplabv3_resnet101 reached a dice coefficient of 94.74% and 94.48%, respectively, on the task of automated kidney segmentation. The study is limited by analyzing only hydronephrosis, but this specific focus enabled high detection accuracy. Conclusions and clinical implications: We show that our automated ultrasound deep learning model can be trained and used to interpret and segmentate ultrasound images from different sources with high accuracy. This method will serve as an automated tool in the diagnostic algorithm of acute renal failure in the future. Patient summary: Hydronephrosis is crucial in the diagnosis of renal colic. Recent advances in artificial intelligence allow automated detection of hydronephrosis in ultrasound images with high accuracy. These methods will help standardize the diagnosis and treatment renal colic.

Lineage tracing reveals transient phenotypic adaptation of tubular cells during acute kidney injury.

Tubular injury is the hallmark of acute kidney injury (AKI) with a tremendous impact on patients and health-care systems. During injury, any differentiated proximal tubular cell (PT) may transition into a specific injured phenotype, so-called "scattered tubular cell" (STC)-phenotype. To understand the fate of this specific phenotype, we generated transgenic mice allowing inducible, reversible, and irreversible tagging of these cells in a murine AKI model, the unilateral ischemia-reperfusion injury (IRI). For lineage tracing, we analyzed the kidneys using single-cell profiling during disease development at various time points. Labeled cells, which we defined by established endogenous markers, already appeared 8 h after injury and showed a distinct expression set of genes. We show that STCs re-differentiate back into fully differentiated PTs upon the resolution of the injury. In summary, we show the dynamics of the phenotypic transition of PTs during injury, revealing a reversible transcriptional program as an adaptive response during disease.

The role of desmoglein-2 in kidney disease.

Desmosomes are multi-protein cell-cell adhesion structures supporting cell stability and mechanical stress resilience of tissues, best described in skin and heart. The kidney is exposed to various mechanical stimuli and stress, yet little is known about kidney desmosomes. In healthy kidneys, we found desmosomal proteins located at the apical-junctional complex in tubular epithelial cells. In four different animal models and patient biopsies with various kidney diseases, desmosomal components were significantly upregulated and partly miss-localized outside of the apical-junctional complexes along the whole lateral tubular epithelial cell membrane. The most upregulated component was desmoglein-2 (Dsg2). Mice with constitutive tubular epithelial cell-specific deletion of Dsg2 developed normally, and other desmosomal components were not altered in these mice. When challenged with different types of tubular epithelial cell injury (unilateral ureteral obstruction, ischemia-reperfusion, and 2,8-dihydroxyadenine crystal nephropathy), we found increased tubular epithelial cell apoptosis, proliferation, tubular atrophy, and inflammation compared to wild-type mice in all models and time points. In vitro, silencing DSG2 via siRNA weakened cell-cell adhesion in HK-2 cells and increased cell death. Thus, our data show a prominent upregulation of desmosomal components in tubular cells across species and diseases and suggest a protective role of Dsg2 against various injurious stimuli.

Comparison of three creatinine-based equations to predict adverse outcome in a cardiovascular high-risk cohort: an investigation using the SPRINT research materials.

Background: Novel creatinine-based equations have recently been proposed but their predictive performance for cardiovascular outcomes in participants at high cardiovascular risk in comparison to the established CKD-EPI 2009 equation is unknown. Method: In 9361 participants from the United States included in the randomized controlled SPRINT trial, we calculated baseline estimated glomerular filtration rate (eGFR) using the CKD-EPI 2009, CKD-EPI 2021, and EKFC equations and compared their predictive value of cardiovascular events. The statistical metric used is the net reclassification improvement (NRI) presented separately for those with and those without events. Results: During a mean follow-up of 3.1 ± 0.9 years, the primary endpoint occurred in 559 participants (6.0%). When using the CKD-EPI 2009, the CKD-EPI 2021, and the EKFC equations, the prevalence of CKD (eGFR <60 ml/min/1.73 m2 or >60 ml/min/1.73 m2 with an ACR ≥30 mg/g) was 37% vs. 35.3% (P = 0.02) vs. 46.4% (P < 0.001), respectively. The corresponding mean eGFR was 72.5 ± 20.1 ml/min/1.73 m2 vs. 73.2 ± 19.4 ml/min/1.73 m2 (P < 0.001) vs. 64.6 ± 17.4 ml/min/1.73 m2 (P < 0.001). Neither reclassification according to the CKD-EPI 2021 equation [CKD-EPI 2021 vs. CKD-EPI 2009: NRIevents: -9.5% (95% confidence interval (CI) -13.0% to -5.9%); NRInonevents: 4.8% (95% CI 3.9% to 5.7%)], nor reclassification according to the EKFC equation allowed better prediction of cardiovascular events compared to the CKD-EPI 2009 equation (EKFC vs. CKD-EPI 2009: NRIevents: 31.2% (95% CI 27.5% to 35.0%); NRInonevents: -31.1% (95% CI -32.1% to -30.1%)). Conclusion: Substituting the CKD-EPI 2009 with the CKD-EPI 2021 or the EKFC equation for calculation of eGFR in participants with high cardiovascular risk without diabetes changed the prevalence of CKD but was not associated with improved risk prediction of cardiovascular events for both those with and without the event.

Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT).

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.

Mechanisms of kidney fibrosis and routes towards therapy.

Kidney fibrosis is the final common pathway of virtually all chronic kidney diseases (CKDs) and is therefore considered to be a promising therapeutic target for these conditions. However, despite great progress in recent years, no targeted antifibrotic therapies for the kidney have been approved, likely because the complex mechanisms that initiate and drive fibrosis are not yet completely understood. Recent single-cell genomic approaches have allowed novel insights into kidney fibrosis mechanisms in mouse and human, particularly the heterogeneity and differentiation processes of myofibroblasts, the role of injured epithelial cells and immune cells, and their crosstalk mechanisms. In this review we summarize the key mechanisms that drive kidney fibrosis, including recent advances in understanding the mechanisms, as well as potential routes for developing novel targeted antifibrotic therapeutics.

Matrix Metalloproteinase-7 in Urinary Extracellular Vesicles Identifies Rapid Disease Progression in Autosomal Dominant Polycystic Kidney Disease.

SIGNIFICANCE STATEMENT: There is an unmet need for biomarkers of disease progression in autosomal dominant polycystic kidney disease (ADPKD). This study investigated urinary extracellular vesicles (uEVs) as a source of such biomarkers. Proteomic analysis of uEVs identified matrix metalloproteinase 7 (MMP-7) as a biomarker predictive of rapid disease progression. In validation studies, MMP-7 was predictive in uEVs but not in whole urine, possibly because uEVs are primarily secreted by tubular epithelial cells. Indeed, single-nucleus RNA sequencing showed that MMP-7 was especially increased in proximal tubule and thick ascending limb cells, which were further characterized by a profibrotic phenotype. Together, these data suggest that MMP-7 is a biologically plausible and promising uEV biomarker for rapid disease progression in ADPKD. BACKGROUND: In ADPKD, there is an unmet need for early markers of rapid disease progression to facilitate counseling and selection for kidney-protective therapy. Our aim was to identify markers for rapid disease progression in uEVs. METHODS: Six paired case-control groups ( n =10-59/group) of cases with rapid disease progression and controls with stable disease were formed from two independent ADPKD cohorts, with matching by age, sex, total kidney volume, and genetic variant. Candidate uEV biomarkers were identified by mass spectrometry and further analyzed using immunoblotting and an ELISA. Single-nucleus RNA sequencing of healthy and ADPKD tissue was used to identify the cellular origin of the uEV biomarker. RESULTS: In the discovery proteomics experiments, the protein abundance of MMP-7 was significantly higher in uEVs of patients with rapid disease progression compared with stable disease. In the validation groups, a significant >2-fold increase in uEV-MMP-7 in patients with rapid disease progression was confirmed using immunoblotting. By contrast, no significant difference in MMP-7 was found in whole urine using ELISA. Compared with healthy kidney tissue, ADPKD tissue had significantly higher MMP-7 expression in proximal tubule and thick ascending limb cells with a profibrotic phenotype. CONCLUSIONS: Among patients with ADPKD, rapid disease progressors have higher uEV-associated MMP-7. Our findings also suggest that MMP-7 is a biologically plausible biomarker for more rapid disease progression.

Multiomic profiling of transplant glomerulopathy reveals a novel T-cell dominant subclass.

Kidney transplant (KTx) biopsies showing transplant glomerulopathy (TG) (glomerular basement membrane double contours (cg) > 0) and microvascular inflammation (MVI) in the absence of C4d staining and donor-specific antibodies (DSAs) do not fulfill the criteria for chronic active antibody-mediated rejection (CA-AMR) diagnosis and do not fit into any other Banff category. To investigate this, we initiated a multicenter intercontinental study encompassing 36 cases, comparing the immunomic and transcriptomic profiles of 14 KTx biopsies classified as cg+MVI DSA-/C4d- with 22 classified as CA-AMR DSA+/C4d+ through novel transcriptomic analysis using the NanoString Banff-Human Organ Transplant (B-HOT) panel and subsequent orthogonal subset analysis using two innovative 5-marker multiplex immunofluorescent panels. Nineteen genes were differentially expressed between the two study groups. Samples diagnosed with CA-AMR DSA+/C4d+ showed a higher glomerular abundance of natural killer cells and higher transcriptomic cell type scores for macrophages in an environment characterized by increased expression of complement-related genes (i.e., C5AR1) and higher activity of angiogenesis, interstitial fibrosis tubular atrophy, CA-AMR, and DSA-related pathways when compared to samples diagnosed with cg+MVI DSA-/C4d-. Samples diagnosed with cg+MVI DSA-/C4d- displayed a higher glomerular abundance and activity of T cells (CD3+, CD3+CD8+, and CD3+CD8-). Thus, we show that using novel multiomic techniques, KTx biopsies with cg+MVI DSA-/C4d- have a prominent T-cell presence and activity, putting forward the possibility that these represent a more T-cell dominant phenotype.

Non-canonical Hedgehog signaling mediates profibrotic hematopoiesis-stroma crosstalk in myeloproliferative neoplasms.

The role of hematopoietic Hedgehog signaling in myeloproliferative neoplasms (MPNs) remains incompletely understood despite data suggesting that Hedgehog (Hh) pathway inhibitors have therapeutic activity in patients. We aim to systematically interrogate the role of canonical vs. non-canonical Hh signaling in MPNs. We show that Gli1 protein levels in patient peripheral blood mononuclear cells (PBMCs) mark fibrotic progression and that, in murine MPN models, absence of hematopoietic Gli1, but not Gli2 or Smo, significantly reduces MPN phenotype and fibrosis, indicating that GLI1 in the MPN clone can be activated in a non-canonical fashion. Additionally, we establish that hematopoietic Gli1 has a significant effect on stromal cells, mediated through a druggable MIF-CD74 axis. These data highlight the complex interplay between alterations in the MPN clone and activation of stromal cells and indicate that Gli1 represents a promising therapeutic target in MPNs, particularly that Hh signaling is dispensable for normal hematopoiesis.

Mapping cardiac remodeling in chronic kidney disease.

Patients with advanced chronic kidney disease (CKD) mostly die from sudden cardiac death and recurrent heart failure. The mechanisms of cardiac remodeling are largely unclear. To dissect molecular and cellular mechanisms of cardiac remodeling in CKD in an unbiased fashion, we performed left ventricular single-nuclear RNA sequencing in two mouse models of CKD. Our data showed a hypertrophic response trajectory of cardiomyocytes with stress signaling and metabolic changes driven by soluble uremia-related factors. We mapped fibroblast to myofibroblast differentiation in this process and identified notable changes in the cardiac vasculature, suggesting inflammation and dysfunction. An integrated analysis of cardiac cellular responses to uremic toxins pointed toward endothelin-1 and methylglyoxal being involved in capillary dysfunction and TNFα driving cardiomyocyte hypertrophy in CKD, which was validated in vitro and in vivo. TNFα inhibition in vivo ameliorated the cardiac phenotype in CKD. Thus, interventional approaches directed against uremic toxins, such as TNFα, hold promise to ameliorate cardiac remodeling in CKD.

IgA nephropathy.

IgA nephropathy (IgAN), the most prevalent primary glomerulonephritis worldwide, carries a considerable lifetime risk of kidney failure. Clinical manifestations of IgAN vary from asymptomatic with microscopic or intermittent macroscopic haematuria and stable kidney function to rapidly progressive glomerulonephritis. IgAN has been proposed to develop through a 'four-hit' process, commencing with overproduction and increased systemic presence of poorly O-glycosylated galactose-deficient IgA1 (Gd-IgA1), followed by recognition of Gd-IgA1 by antiglycan autoantibodies, aggregation of Gd-IgA1 and formation of polymeric IgA1 immune complexes and, lastly, deposition of these immune complexes in the glomerular mesangium, leading to kidney inflammation and scarring. IgAN can only be diagnosed by kidney biopsy. Extensive, optimized supportive care is the mainstay of therapy for patients with IgAN. For those at high risk of disease progression, the 2021 KDIGO Clinical Practice Guideline suggests considering a 6-month course of systemic corticosteroid therapy; however, the efficacy of systemic steroid treatment is under debate and serious adverse effects are common. Advances in understanding the pathophysiology of IgAN have led to clinical trials of novel targeted therapies with acceptable safety profiles, including SGLT2 inhibitors, endothelin receptor blockers, targeted-release budesonide, B cell proliferation and differentiation inhibitors, as well as blockade of complement components.

Enhanced fatty acid oxidation through metformin and baicalin as therapy for COVID-19 and associated inflammatory states in lung and kidney.

Progressive respiratory failure is the primary cause of death in the coronavirus disease 2019 (COVID-19) pandemic. It is the final outcome of the acute respiratory distress syndrome (ARDS), characterized by an initial exacerbated inflammatory response, metabolic derangement and ultimate tissue scarring. A positive balance of cellular energy may result crucial for the recovery of clinical COVID-19. Hence, we asked if two key pathways involved in cellular energy generation, AMP-activated protein kinase (AMPK)/acetyl-CoA carboxylase (ACC) signaling and fatty acid oxidation (FAO) could be beneficial. We tested the drugs metformin (AMPK activator) and baicalin (CPT1A activator) in different experimental models mimicking COVID-19 associated inflammation in lung and kidney. We also studied two different cohorts of COVID-19 patients that had been previously treated with metformin. These drugs ameliorated lung damage in an ARDS animal model, while activation of AMPK/ACC signaling increased mitochondrial function and decreased TGF-ß-induced fibrosis, apoptosis and inflammation markers in lung epithelial cells. Similar results were observed with two indole derivatives, IND6 and IND8 with AMPK activating capacity. Consistently, a reduced time of hospitalization and need of intensive care was observed in COVID-19 patients previously exposed to metformin. Baicalin also mitigated the activation of pro-inflammatory bone marrow-derived macrophages (BMDMs) and reduced kidney fibrosis in two animal models of kidney injury, another key target of COVID-19. In human epithelial lung and kidney cells, both drugs improved mitochondrial function and prevented TGF-ß-induced renal epithelial cell dedifferentiation. Our results support that favoring cellular energy production through enhanced FAO may prove useful in the prevention of COVID-19-induced lung and renal damage.

The challenges of research data management in cardiovascular science: a DGK and DZHK position paper-executive summary.

The sharing and documentation of cardiovascular research data are essential for efficient use and reuse of data, thereby aiding scientific transparency, accelerating the progress of cardiovascular research and healthcare, and contributing to the reproducibility of research results. However, challenges remain. This position paper, written on behalf of and approved by the German Cardiac Society and German Centre for Cardiovascular Research, summarizes our current understanding of the challenges in cardiovascular research data management (RDM). These challenges include lack of time, awareness, incentives, and funding for implementing effective RDM; lack of standardization in RDM processes; a need to better identify meaningful and actionable data among the increasing volume and complexity of data being acquired; and a lack of understanding of the legal aspects of data sharing. While several tools exist to increase the degree to which data are findable, accessible, interoperable, and reusable (FAIR), more work is needed to lower the threshold for effective RDM not just in cardiovascular research but in all biomedical research, with data sharing and reuse being factored in at every stage of the scientific process. A culture of open science with FAIR research data should be fostered through education and training of early-career and established research professionals. Ultimately, FAIR RDM requires permanent, long-term effort at all levels. If outcomes can be shown to be superior and to promote better (and better value) science, modern RDM will make a positive difference to cardiovascular science and practice. The full position paper is available in the supplementary materials.

Association of mineral and bone biomarkers with adverse cardiovascular outcomes and mortality in the German Chronic Kidney Disease (GCKD) cohort.

Mineral and bone disorder (MBD) in chronic kidney disease (CKD) is tightly linked to cardiovascular disease (CVD). In this study, we aimed to compare the prognostic value of nine MBD biomarkers to determine those associated best with adverse cardiovascular (CV) outcomes and mortality. In 5 217 participants of the German CKD (GCKD) study enrolled with an estimated glomerular filtration rate (eGFR) between 30-60 mL·min-1 per 1.73 m2 or overt proteinuria, serum osteoprotegerin (OPG), C-terminal fibroblast growth factor-23 (FGF23), intact parathyroid hormone (iPTH), bone alkaline phosphatase (BAP), cross-linked C-telopeptide of type 1 collagen (CTX1), procollagen 1 intact N-terminal propeptide (P1NP), phosphate, calcium, and 25-OH vitamin D were measured at baseline. Participants with missing values among these parameters (n = 971) were excluded, leaving a total of 4 246 participants for analysis. During a median follow-up of 6.5 years, 387 non-CV deaths, 173 CV deaths, 645 nonfatal major adverse CV events (MACEs) and 368 hospitalizations for congestive heart failure (CHF) were observed. OPG and FGF23 were associated with all outcomes, with the highest hazard ratios (HRs) for OPG. In the final Cox regression model, adjusted for CV risk factors, including kidney function and all other investigated biomarkers, each standard deviation increase in OPG was associated with non-CV death (HR 1.76, 95% CI: 1.35-2.30), CV death (HR 2.18, 95% CI: 1.50-3.16), MACE (HR 1.38, 95% CI: 1.12-1.71) and hospitalization for CHF (HR 2.05, 95% CI: 1.56-2.69). Out of the nine biomarkers examined, stratification based on serum OPG best identified the CKD patients who were at the highest risk for any adverse CV outcome and mortality.

Defining cardiac functional recovery in end-stage heart failure at single-cell resolution.

Recovery of cardiac function is the holy grail of heart failure therapy yet is infrequently observed and remains poorly understood. In this study, we performed single-nucleus RNA sequencing from patients with heart failure who recovered left ventricular systolic function after left ventricular assist device implantation, patients who did not recover and non-diseased donors. We identified cell-specific transcriptional signatures of recovery, most prominently in macrophages and fibroblasts. Within these cell types, inflammatory signatures were negative predictors of recovery, and downregulation of RUNX1 was associated with recovery. In silico perturbation of RUNX1 in macrophages and fibroblasts recapitulated the transcriptional state of recovery. Cardiac recovery mediated by BET inhibition in mice led to decreased macrophage and fibroblast Runx1 expression and diminished chromatin accessibility within a Runx1 intronic peak and acquisition of human recovery signatures. These findings suggest that cardiac recovery is a unique biological state and identify RUNX1 as a possible therapeutic target to facilitate cardiac recovery.

Transformative Materials for Interfacial Drug Delivery.

Drug delivery systems (DDS) are designed to temporally and spatially control drug availability and activity. They assist in improving the balance between on-target therapeutic efficacy and off-target toxic side effects. DDS aid in overcoming biological barriers encountered by drug molecules upon applying them via various routes of administration. They are furthermore increasingly explored for modulating the interface between implanted (bio)medical materials and host tissue. Herein, an overview of the biological barriers and host-material interfaces encountered by DDS upon oral, intravenous, and local administration is provided, and material engineering advances at different time and space scales to exemplify how current and future DDS can contribute to improved disease treatment are highlighted.

Transformative Materials to Create 3D Functional Human Tissue Models In Vitro in a Reproducible Manner.

Recreating human tissues and organs in the petri dish to establish models as tools in biomedical sciences has gained momentum. These models can provide insight into mechanisms of human physiology, disease onset, and progression, and improve drug target validation, as well as the development of new medical therapeutics. Transformative materials play an important role in this evolution, as they can be programmed to direct cell behavior and fate by controlling the activity of bioactive molecules and material properties. Using nature as an inspiration, scientists are creating materials that incorporate specific biological processes observed during human organogenesis and tissue regeneration. This article presents the reader with state-of-the-art developments in the field of in vitro tissue engineering and the challenges related to the design, production, and translation of these transformative materials. Advances regarding (stem) cell sources, expansion, and differentiation, and how novel responsive materials, automated and large-scale fabrication processes, culture conditions, in situ monitoring systems, and computer simulations are required to create functional human tissue models that are relevant and efficient for drug discovery, are described. This paper illustrates how these different technologies need to converge to generate in vitro life-like human tissue models that provide a platform to answer health-based scientific questions.

A previously uncharacterized Factor Associated with Metabolism and Energy (FAME/C14orf105/CCDC198/1700011H14Rik) is related to evolutionary adaptation, energy balance, and kidney physiology.

In this study we use comparative genomics to uncover a gene with uncharacterized function (1700011H14Rik/C14orf105/CCDC198), which we hereby name FAME (Factor Associated with Metabolism and Energy). We observe that FAME shows an unusually high evolutionary divergence in birds and mammals. Through the comparison of single nucleotide polymorphisms, we identify gene flow of FAME from Neandertals into modern humans. We conduct knockout experiments on animals and observe altered body weight and decreased energy expenditure in Fame knockout animals, corresponding to genome-wide association studies linking FAME with higher body mass index in humans. Gene expression and subcellular localization analyses reveal that FAME is a membrane-bound protein enriched in the kidneys. Although the gene knockout results in structurally normal kidneys, we detect higher albumin in urine and lowered ferritin in the blood. Through experimental validation, we confirm interactions between FAME and ferritin and show co-localization in vesicular and plasma membranes.