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Mutations in the Melanoma-Associated Antigen D2 (MAGED2) cause antenatal Bartter syndrome type 5 (BARTS5). This rare disease is characterized by perinatal loss of urinary concentration capability and large urine volumes. The underlying molecular mechanisms of this disease are largely unclear. Here, we study the effect of MAGED2 knockdown on kidney cell cultures using proteomic and phosphoproteomic analyses. In HEK293T cells, MAGED2 knockdown induces prominent changes in protein phosphorylation rather than changes in protein abundance. MAGED2 is expressed in mouse embryonic kidneys and its expression declines during development. MAGED2 interacts with G-protein alpha subunit (GNAS), suggesting a role in G-protein coupled receptors (GPCR) signalling. In kidney collecting duct cell lines, Maged2 knockdown subtly modulated vasopressin type 2 receptor (V2R)-induced cAMP-generation kinetics, rewired phosphorylation-dependent signalling, and phosphorylation of CREB. Maged2 knockdown resulted in a large increase in aquaporin-2 abundance during long-term V2R activation. The increase in aquaporin-2 protein was mediated transcriptionally. Taken together, we link MAGED2 function to cellular signalling as a desensitizer of V2R-induced aquaporin-2 expression. SIGNIFICANCE: In most forms of Bartter Syndrome, the underlying cause of the disease is well understood. In contrast, the role of MAGED2 mutations in a newly discovered form of Bartter Syndrome (BARTS5) is unknown. In our manuscript we could show that MAGED2 modulates vasopressin-induced protein and phosphorylation patterns in kidney cells, providing a broad basis for further studies of MAGED2 function in development and disease., (Copyright © 2021. Published by Elsevier B.V.)

In recent years, network-based methods have become an attractive analytical approach for toxicogenomics studies. They can capture not only the global changes of regulatory gene networks but also the relationships between their components. Among them, a causal reasoning approach depicts the mechanisms of regulation that connect upstream regulators in signaling networks to their downstream gene targets. In this work, we applied CARNIVAL, a causal network contextualisation tool, to infer upstream signaling networks deregulated in drug-induced liver injury (DILI) from gene expression microarray data from the TG-GATEs database. We focussed on six compounds that induce observable histopathologies linked to DILI from repeated dosing experiments in rats. We compared responses in vitro and in vivo to identify potential cross-platform concordances in rats as well as network preservations between rat and human. Our results showed similarities of enriched pathways and network motifs between compounds. These pathways and motifs induced the same pathology in rats but not in humans. In particular, the causal interactions "LCK activates SOCS3, which in turn inhibits TFDP1" was commonly identified as a regulatory path among the fibrosis-inducing compounds. This potential pathology-inducing regulation illustrates the value of our approach to generate hypotheses that can be further validated experimentally., Competing Interests: Declaration of Competing Interest JSR received funding from GSK and Sanofi and consultant fees from Travere Therapeutics., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Understanding why certain patients with IgA nephropathy progress to kidney failure while others maintain normal kidney function remains a major unanswered question. To help answer this, we performed miRNome profiling by next generation sequencing of kidney biopsies in order to identify microRNAs specifically associated with the risk of IgA nephropathy progression. Following sequencing and validation in independent cohorts, four microRNAs (-150-5p, -155-5p, -146b-5p, -135a-5p) were found to be differentially expressed in IgA nephropathy progressors compared to non-progressors, and patients with thin membrane nephropathy, lupus nephritis and membranous nephropathy, and correlated with estimated glomerular filtration rate, proteinuria, and the Oxford MEST-C scores (five histological features that are independent predictors of clinical outcome). Each individual microRNA increased the discrimination score of the International IgAN Prediction Tool, although due to the small number of samples the results did not reach statistical significance. miR-150-5p exhibited the largest amplitude of expression between cohorts and displayed the best discrimination between IgA nephropathy progressors and non-progressors by receiver operating curve analysis (AUC: 0.8). However, expression was similarly upregulated in kidneys with established fibrosis and low estimated glomerular filtration rates at the time of biopsy. Consistent with a more generic role in kidney fibrosis, in situ hybridization revealed that miR-150-5p was found in lymphoid infiltrates, and areas of proliferation and fibrosis consistent with the known drivers of progression. Thus, miR-150-5p may be a potential functional mediator of kidney fibrosis that may add value in predicting risk of progression in IgA nephropathy and other kidney diseases., (Copyright © 2021 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.)

Background & Aims: In chronic liver diseases, inflammation induces oxidative stress and thus may contribute to the progression of liver injury, fibrosis, and carcinogenesis. The KEAP1/NRF2 axis is a major regulator of cellular redox balance. In the present study, we investigated whether the KEAP1/NRF2 system is involved in liver disease progression in humans and mice., Methods: The clinical relevance of oxidative stress was investigated by liver RNA sequencing in a well-characterized cohort of patients with non-alcoholic fatty liver disease (n = 63) and correlated with histological and clinical parameters. For functional analysis, hepatocyte-specific Nemo knockout (NEMO Δhepa ) mice were crossed with hepatocyte-specific Keap1 knockout (KEAP1 Δhepa ) mice., Results: Immunohistochemical analysis of human liver sections showed increased oxidative stress and high NRF2 expression in patients with chronic liver disease. RNA sequencing of liver samples in a human pediatric NAFLD cohort revealed a significant increase of NRF2 activation correlating with the grade of inflammation, but not with the grade of steatosis, which could be confirmed in a second adult NASH cohort. In mice, microarray analysis revealed that Keap1 deletion induces NRF2 target genes involved in glutathione metabolism and xenobiotic stress (e.g., Nqo1). Furthermore, deficiency of one of the most important antioxidants, glutathione (GSH), in NEMO Δhepa livers was rescued after deleting Keap1. As a consequence, NEMO Δhepa /KEAP1 Δhepa livers showed reduced apoptosis compared to NEMO Δhepa livers as well as a dramatic downregulation of genes involved in cell cycle regulation and DNA replication. Consequently, NEMO Δhepa /KEAP1 Δhepa compared to NEMO Δhepa livers displayed decreased fibrogenesis, lower tumor incidence, reduced tumor number, and decreased tumor size., Conclusions: NRF2 activation in patients with non-alcoholic steatohepatitis correlates with the grade of inflammation, but not steatosis. Functional analysis in mice demonstrated that NRF2 activation in chronic liver disease is protective by ameliorating fibrogenesis, initiation and progression of hepatocellular carcinogenesis., Lay Summary: The KEAP1 (Kelch-like ECH-associated protein-1)/NRF2 (erythroid 2-related factor 2) axis has a major role in regulating cellular redox balance. Herein, we show that NRF2 activity correlates with the grade of inflammation in patients with non-alcoholic steatohepatitis. Functional studies in mice actually show that NRF2 activation, resulting from KEAP1 deletion, protects against fibrosis and cancer., Competing Interests: Conflict of interest The authors have declared that no conflict of interest exists. Please refer to the accompanying ICMJE disclosure forms for further details., (Copyright © 2020 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.)

The number of biomedical image analysis challenges organized per year is steadily increasing. These international competitions have the purpose of benchmarking algorithms on common data sets, typically to identify the best method for a given problem. Recent research, however, revealed that common practice related to challenge reporting does not allow for adequate interpretation and reproducibility of results. To address the discrepancy between the impact of challenges and the quality (control), the Biomedical Image Analysis ChallengeS (BIAS) initiative developed a set of recommendations for the reporting of challenges. The BIAS statement aims to improve the transparency of the reporting of a biomedical image analysis challenge regardless of field of application, image modality or task category assessed. This article describes how the BIAS statement was developed and presents a checklist which authors of biomedical image analysis challenges are encouraged to include in their submission when giving a paper on a challenge into review. The purpose of the checklist is to standardize and facilitate the review process and raise interpretability and reproducibility of challenge results by making relevant information explicit., Competing Interests: Declaration of Competing Interest Anne Martel is a co-founder, CSO of Pathcore, Toronto, Canada. The remaining authors declare no competing interests., (Copyright © 2020. Published by Elsevier B.V.)

Primary myelofibrosis (PMF) is a myeloproliferative neoplasm (MPN) that leads to progressive bone marrow (BM) fibrosis. Although the cellular mutations involved in the pathogenesis of PMF have been extensively investigated, the sequential events that drive stromal activation and fibrosis by hematopoietic-stromal cross-talk remain elusive. Using an unbiased approach and validation in patients with MPN, we determined that the differential spatial expression of the chemokine CXCL4/platelet factor-4 marks the progression of fibrosis. We show that the absence of hematopoietic CXCL4 ameliorates the MPN phenotype, reduces stromal cell activation and BM fibrosis, and decreases the activation of profibrotic pathways in megakaryocytes, inflammation in fibrosis-driving cells, and JAK/STAT activation in both megakaryocytes and stromal cells in 3 murine PMF models. Our data indicate that higher CXCL4 expression in MPN has profibrotic effects and is a mediator of the characteristic inflammation. Therefore, targeting CXCL4 might be a promising strategy to reduce inflammation in PMF., (© 2020 by The American Society of Hematology.)

Transcriptome profiling followed by differential gene expression analysis often leads to lists of genes that are hard to analyze and interpret. Functional genomics tools are powerful approaches for downstream analysis, as they summarize the large and noisy gene expression space into a smaller number of biological meaningful features. In particular, methods that estimate the activity of processes by mapping transcripts level to process members are popular. However, footprints of either a pathway or transcription factor (TF) on gene expression show superior performance over mapping-based gene sets. These footprints are largely developed for humans and their usability in the broadly-used model organism Mus musculus is uncertain. Evolutionary conservation of the gene regulatory system suggests that footprints of human pathways and TFs can functionally characterize mice data. In this paper we analyze this hypothesis. We perform a comprehensive benchmark study exploiting two state-of-the-art footprint methods, DoRothEA and an extended version of PROGENy. These methods infer TF and pathway activity, respectively. Our results show that both can recover mouse perturbations, confirming our hypothesis that footprints are conserved between mice and humans. Subsequently, we illustrate the usability of PROGENy and DoRothEA by recovering pathway/TF-disease associations from newly generated disease sets. Additionally, we provide pathway and TF activity scores for a large collection of human and mouse perturbation and disease experiments (2374). We believe that this resource, available for interactive exploration and download (https://saezlab.shinyapps.io/footprint_scores/), can have broad applications including the study of diseases and therapeutics., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Introduction: To develop effective therapies and identify novel early biomarkers for chronic kidney disease, an understanding of the molecular mechanisms orchestrating it is essential. We here set out to understand how differences in chronic kidney disease (CKD) origin are reflected in gene expression. To this end, we integrated publicly available human glomerular microarray gene expression data for 9 kidney disease entities that account for most of CKD worldwide. Our primary goal was to demonstrate the possibilities and potential on data analysis and integration to the nephrology community., Methods: We integrated data from 5 publicly available studies and compared glomerular gene expression profiles of disease with that of controls from nontumor parts of kidney cancer nephrectomy tissues. A major challenge was the integration of the data from different sources, platforms, and conditions that we mitigated with a bespoke stringent procedure., Results: We performed a global transcriptome-based delineation of different kidney disease entities, obtaining a transcriptomic diffusion map of their similarities and differences based on the genes that acquire a consistent differential expression between each kidney disease entity and nephrectomy tissue. We derived functional insights by inferring the activity of signaling pathways and transcription factors from the collected gene expression data and identified potential drug candidates based on expression signature matching. We validated representative findings by immunostaining in human kidney biopsies indicating, for example, that the transcription factor FOXM1 is significantly and specifically expressed in parietal epithelial cells in rapidly progressive glomerulonephritis (RPGN) whereas not expressed in control kidney tissue. Furthermore, we found drug candidates by matching the signature on expression of drugs to that of the CKD entities, in particular, the Food and Drug Administration-approved drug nilotinib., Conclusion: These results provide a foundation to comprehend the specific molecular mechanisms underlying different kidney disease entities that can pave the way to identify biomarkers and potential therapeutic targets. To facilitate further use, we provide our results as a free interactive Web application: https://saezlab.shinyapps.io/ckd_landscape/. However, because of the limitations of the data and the difficulties in its integration, any specific result should be considered with caution. Indeed, we consider this study rather an illustration of the value of functional genomics and integration of existing data., (© 2019 International Society of Nephrology. Published by Elsevier Inc.)

There have been tremendous advances during the last decade in methods for large-scale, high-throughput data generation and in novel computational approaches to analyze these datasets. These advances have had a profound impact on biomedical research and clinical medicine. The field of genomics is rapidly developing toward single-cell analysis, and major advances in proteomics and metabolomics have been made in recent years. The developments on wearables and electronic health records are poised to change clinical trial design. This rise of 'big data' holds the promise to transform not only research progress, but also clinical decision making towards precision medicine. To have a true impact, it requires integrative and multi-disciplinary approaches that blend experimental, clinical and computational expertise across multiple institutions. Cancer research has been at the forefront of the progress in such large-scale initiatives, so-called 'big science,' with an emphasis on precision medicine, and various other areas are quickly catching up. Nephrology is arguably lagging behind, and hence these are exciting times to start (or redirect) a research career to leverage these developments in nephrology. In this review, we summarize advances in big data generation, computational analysis, and big science initiatives, with a special focus on applications to nephrology., (Copyright © 2019 International Society of Nephrology. All rights reserved.)

Acute kidney injury (AKI) leads to significant morbidity and mortality; unfortunately, strategies to prevent or treat AKI are lacking. In recent years, several preconditioning protocols have been shown to be effective in inducing organ protection in rodent models. Here, we characterized two of these interventions-caloric restriction and hypoxic preconditioning-in a mouse model of cisplatin-induced AKI and investigated the underlying mechanisms by acquisition of multi-layered omic data (transcriptome, proteome, N-degradome) and functional parameters in the same animals. Both preconditioning protocols markedly ameliorated cisplatin-induced loss of kidney function, and caloric restriction also induced lipid synthesis. Bioinformatic analysis revealed mRNA-independent proteome alterations affecting the extracellular space, mitochondria, and transporters. Interestingly, our analyses revealed a strong dissociation of protein and RNA expression after cisplatin treatment that showed a strong correlation with the degree of damage. N-degradomic analysis revealed that most posttranscriptional changes were determined by arginine-specific proteolytic processing. This included a characteristic cisplatin-activated complement signature that was prevented by preconditioning. Amyloid and acute-phase proteins within the cortical parenchyma showed a similar response. Extensive analysis of disease-associated molecular patterns suggested that transcription-independent deposition of amyloid P-component serum protein may be a key component in the microenvironmental contribution to kidney damage. This proof-of-principle study provides new insights into the pathogenesis of cisplatin-induced AKI and the molecular mechanisms underlying organ protection by correlating phenotypic and multi-layered omics data., (Copyright © 2018 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.)