Browsing by Author "Barisoni, Laura"

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    APOL1 variants change C-terminal conformational dynamics and binding to SNARE protein VAMP8.
    (JCI insight, 2017-07-20) Madhavan, Sethu M; O'Toole, John F; Konieczkowski, Martha; Barisoni, Laura; Thomas, David B; Ganesan, Santhi; Bruggeman, Leslie A; Buck, Matthias; Sedor, John R
    APOL1 variants in African populations mediate resistance to trypanosomal infection but increase risk for kidney diseases through unknown mechanisms. APOL1 is expressed in glomerular podocytes and does not vary with underlying kidney disease diagnoses or APOL1 genotypes, suggesting that the kidney disease-associated variants dysregulate its function rather than its localization or abundance. Structural homology searches identified vesicle-associated membrane protein 8 (VAMP8) as an APOL1 protein interactor. VAMP8 colocalizes with APOL1 in the podocyte, and the APOL1:VAMP8 interaction was confirmed biochemically and with surface plasmon resonance. APOL1 variants attenuate this interaction. Computational modeling of APOL1's 3-dimensional structure, followed by molecular dynamics simulations, revealed increased motion of the C-terminal domain of reference APOL1 compared with either variant, suggesting that the variants stabilize a closed or autoinhibited state that diminishes protein interactions with VAMP8. Changes in ellipticity with increasing urea concentrations, as assessed by circular dichroism spectroscopy, showed higher conformational stability of the C-terminal helix of the variants compared with the reference protein. These results suggest that reference APOL1 interacts with VAMP8-coated vesicles, a process attenuated by variant-induced reduction in local dynamics of the C-terminal. Disordered vesicular trafficking in the podocyte may cause injury and progressive chronic kidney diseases in susceptible African Americans subjects.
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    APOL1-G0 or APOL1-G2 Transgenic Models Develop Preeclampsia but Not Kidney Disease.
    (Journal of the American Society of Nephrology : JASN, 2016-12) Bruggeman, Leslie A; Wu, Zhenzhen; Luo, Liping; Madhavan, Sethu M; Konieczkowski, Martha; Drawz, Paul E; Thomas, David B; Barisoni, Laura; Sedor, John R; O'Toole, John F
    APOL1 risk variants are associated with kidney disease in blacks, but the mechanisms of renal injury associated with APOL1 risk variants are unknown. Because APOL1 is unique to humans and some primates, we created transgenic (Tg) mice using the promoter of nephrin-encoding Nphs1 to express the APOL1 reference sequence (G0) or the G2 risk variant in podocytes, establishing Tg lines with a spectrum of APOL1 expression levels. Podocytes from Tg-G0 and Tg-G2 mice did not undergo necrosis, apoptosis, or autophagic cell death in vivo, even in lines with highly expressed transgenes. Further, Tg-G0 and Tg-G2 mice did not develop kidney pathology, proteinuria, or azotemia as of 300 days of age. However, by 200 days of age, Tg-G2 mice had significantly lower podocyte density than age-matched WT and Tg-G0 mice had, a difference that was not evident at weaning. Notably, a pregnancy-associated phenotype that encompassed eclampsia, preeclampsia, fetal/neonatal deaths, and small litter sizes occurred in some Tg-G0 mice and more severely in Tg-G2 mice. Similar to human placenta, placentas of Tg mice expressed APOL1. Overall, these results suggest podocyte depletion could predispose individuals with APOL1 risk genotypes to kidney disease in response to a second stressor, and add to other published evidence associating APOL1 expression with preeclampsia.
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    APOL1-G0 protects podocytes in a mouse model of HIV-associated nephropathy.
    (PloS one, 2019-01) Bruggeman, Leslie A; Wu, Zhenzhen; Luo, Liping; Madhavan, Sethu; Drawz, Paul E; Thomas, David B; Barisoni, Laura; O'Toole, John F; Sedor, John R
    African polymorphisms in the gene for Apolipoprotein L1 (APOL1) confer a survival advantage against lethal trypanosomiasis but also an increased risk for several chronic kidney diseases (CKD) including HIV-associated nephropathy (HIVAN). APOL1 is expressed in renal cells, however, the pathogenic events that lead to renal cell damage and kidney disease are not fully understood. The podocyte function of APOL1-G0 versus APOL1-G2 in the setting of a known disease stressor was assessed using transgenic mouse models. Transgene expression, survival, renal pathology and function, and podocyte density were assessed in an intercross of a mouse model of HIVAN (Tg26) with two mouse models that express either APOL1-G0 or APOL1-G2 in podocytes. Mice that expressed HIV genes developed heavy proteinuria and glomerulosclerosis, and had significant losses in podocyte numbers and reductions in podocyte densities. Mice that co-expressed APOL1-G0 and HIV had preserved podocyte numbers and densities, with fewer morphologic manifestations typical of HIVAN pathology. Podocyte losses and pathology in mice co-expressing APOL1-G2 and HIV were not significantly different from mice expressing only HIV. Podocyte hypertrophy, a known compensatory event to stress, was increased in the mice co-expressing HIV and APOL1-G0, but absent in the mice co-expressing HIV and APOL1-G2. Mortality and renal function tests were not significantly different between groups. APOL1-G0 expressed in podocytes may have a protective function against podocyte loss or injury when exposed to an environmental stressor. This was absent with APOL1-G2 expression, suggesting APOL1-G2 may have lost this protective function.
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    ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology
    (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2024-01-01) Mimar, Sayat; Paul, Anindya S; Lucarelli, Nicholas; Border, Samuel; Naglah, Ahmed; Barisoni, Laura; Hodgin, Jeffrey; Rosenberg, Avi Z; Clapp, William; Sarder, Pinaki
    Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles1 with the goal of building a modernized biomedical data resource ecosystem to establish collaborative research communities. In line with this mission and to democratize AI-based image analysis in digital pathology, we propose ComPRePS: an end-to-end automated Computational Renal Pathology Suite which combines massive scalability, on-demand cloud computing, and an easy-to-use web-based user interface for data upload, storage, management, slide-level visualization, and domain expert interaction. Moreover, our platform is equipped with both in-house and collaborator developed sophisticated AI algorithms in the back-end server for image analysis to identify clinically relevant micro-anatomic functional tissue units (FTU) and to extract image features.
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    ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology.
    (bioRxiv, 2024-04-05) Mimar, Sayat; Paul, Anindya S; Lucarelli, Nicholas; Border, Samuel; Santo, Briana A; Naglah, Ahmed; Barisoni, Laura; Hodgin, Jeffrey; Rosenberg, Avi Z; Clapp, William; Sarder, Pinaki; Kidney Precision Medicine Project
    Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles1 with the goal of building a modernized biomedical data resource ecosystem to establish collaborative research communities. In line with this mission and to democratize AI-based image analysis in digital pathology, we propose ComPRePS: an end-to-end automated Computational Renal Pathology Suite which combines massive scalability, on-demand cloud computing, and an easy-to-use web-based user interface for data upload, storage, management, slide-level visualization, and domain expert interaction. Moreover, our platform is equipped with both in-house and collaborator developed sophisticated AI algorithms in the back-end server for image analysis to identify clinically relevant micro-anatomic functional tissue units (FTU) and to extract image features.
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    Diabetic nephropathy: Is it time yet for routine kidney biopsy?
    (World journal of diabetes, 2013-12) Gonzalez Suarez, Maria L; Thomas, David B; Barisoni, Laura; Fornoni, Alessia
    Diabetic nephropathy (DN) is one of the most important long-term complications of diabetes. Patients with diabetes and chronic kidney disease have an increased risk of all-cause mortality, cardiovascular mortality, and kidney failure. The clinical diagnosis of DN depends on the detection of microalbuminuria. This usually occurs after the first five years from the onset of diabetes, and predictors of DN development and progression are being studied but are not yet implemented into clinical practice. Diagnostic tests are useful tools to recognize onset, progression and response to therapeutic interventions. Microalbuminuria is an indicator of DN, and it is considered the only noninvasive marker of early onset. However, up to now there is no diagnostic tool that can predict which patients will develop DN before any damage is present. Pathological renal injury is hard to predict only with clinical and laboratory findings. An accurate estimate of damage in DN can only be achieved by the histological analysis of tissue samples. At the present time, renal biopsy is indicated on patients with diabetes under the suspicion of the presence of nephropathies other than DN. Results from renal biopsies in patients with diabetes had made possible the classification of renal biopsies in three major groups associated with different prognostic features: diabetic nephropathy, non-diabetic renal disease (NDRD), and a superimposed non-diabetic condition on underlying diabetic nephropathy. In patients with type 2 diabetes with a higher degree of suspicion for NDRD, it is granted the need of a renal biopsy. It is important to identify and differentiate these pathologies at an early stage in order to prevent progression and potential complications. Therefore, a more extensive use of biopsy is advisable.
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    Ferroptotic stress promotes the accumulation of pro-inflammatory proximal tubular cells in maladaptive renal repair.
    (eLife, 2021-07-19) Ide, Shintaro; Kobayashi, Yoshihiko; Ide, Kana; Strausser, Sarah A; Abe, Koki; Herbek, Savannah; O'Brien, Lori L; Crowley, Steven D; Barisoni, Laura; Tata, Aleksandra; Tata, Purushothama Rao; Souma, Tomokazu
    Overwhelming lipid peroxidation induces ferroptotic stress and ferroptosis, a non-apoptotic form of regulated cell death that has been implicated in maladaptive renal repair in mice and humans. Using single-cell transcriptomic and mouse genetic approaches, we show that proximal tubular (PT) cells develop a molecularly distinct, pro-inflammatory state following injury. While these inflammatory PT cells transiently appear after mild injury and return to their original state without inducing fibrosis, after severe injury they accumulate and contribute to persistent inflammation. This transient inflammatory PT state significantly downregulates glutathione metabolism genes, making the cells vulnerable to ferroptotic stress. Genetic induction of high ferroptotic stress in these cells after mild injury leads to the accumulation of the inflammatory PT cells, enhancing inflammation and fibrosis. Our study broadens the roles of ferroptotic stress from being a trigger of regulated cell death to include the promotion and accumulation of proinflammatory cells that underlie maladaptive repair.
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    Rationale and design of the Nephrotic Syndrome Study Network (NEPTUNE) Match in glomerular diseases: designing the right trial for the right patient, today.
    (Kidney international, 2024-02) Trachtman, Howard; Desmond, Hailey; Williams, Amanda L; Mariani, Laura H; Eddy, Sean; Ju, Wenjun; Barisoni, Laura; Ascani, Heather K; Uhlmann, Wendy R; Spino, Cathie; Holzman, Lawrence B; Sedor, John R; Gadegbeku, Crystal; Subramanian, Lalita; Lienczewski, Chrysta C; Manieri, Tina; Roberts, Scott J; Gipson, Debbie S; Kretzler, Matthias; NEPTUNE investigators
    Glomerular diseases are classified using a descriptive taxonomy that is not reflective of the heterogeneous underlying molecular drivers. This limits not only diagnostic and therapeutic patient management, but also impacts clinical trials evaluating targeted interventions. The Nephrotic Syndrome Study Network (NEPTUNE) is poised to address these challenges. The study has enrolled >850 pediatric and adult patients with proteinuric glomerular diseases who have contributed to deep clinical, histologic, genetic, and molecular profiles linked to long-term outcomes. The NEPTUNE Knowledge Network, comprising combined, multiscalar data sets, captures each participant's molecular disease processes at the time of kidney biopsy. In this editorial, we describe the design and implementation of NEPTUNE Match, which bridges a basic science discovery pipeline with targeted clinical trials. Noninvasive biomarkers have been developed for real-time pathway analyses. A Molecular Nephrology Board reviews the pathway maps together with clinical, laboratory, and histopathologic data assembled for each patient to compile a Match report that estimates the fit between the specific molecular disease pathway(s) identified in an individual patient and proposed clinical trials. The NEPTUNE Match report is communicated using established protocols to the patient and the attending nephrologist for use in their selection of available clinical trials. NEPTUNE Match represents the first application of precision medicine in nephrology with the aim of developing targeted therapies and providing the right medication for each patient with primary glomerular disease.
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    Single cell transcriptomics of mouse kidney transplants reveals a myeloid cell pathway for transplant rejection.
    (JCI insight, 2020-10) Dangi, Anil; Natesh, Naveen R; Husain, Irma; Ji, Zhicheng; Barisoni, Laura; Kwun, Jean; Shen, Xiling; Thorp, Edward B; Luo, Xunrong
    Myeloid cells are increasingly recognized as major players in transplant rejection. Here, we used a murine kidney transplantation model and single cell transcriptomics to dissect the contribution of myeloid cell subsets and their potential signaling pathways to kidney transplant rejection. Using a variety of bioinformatic techniques, including machine learning, we demonstrate that kidney allograft-infiltrating myeloid cells followed a trajectory of differentiation from monocytes to proinflammatory macrophages, and they exhibited distinct interactions with kidney allograft parenchymal cells. While this process correlated with a unique pattern of myeloid cell transcripts, a top gene identified was Axl, a member of the receptor tyrosine kinase family Tyro3/Axl/Mertk (TAM). Using kidney transplant recipients with Axl gene deficiency, we further demonstrate that Axl augmented intragraft differentiation of proinflammatory macrophages, likely via its effect on the transcription factor Cebpb. This, in turn, promoted intragraft recruitment, differentiation, and proliferation of donor-specific T cells, and it enhanced early allograft inflammation evidenced by histology. We conclude that myeloid cell Axl expression identified by single cell transcriptomics of kidney allografts in our study plays a major role in promoting intragraft myeloid cell and T cell differentiation, and it presents a potentially novel therapeutic target for controlling kidney allograft rejection and improving kidney allograft survival.
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    Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine.
    (Kidney international, 2024-01) Reznichenko, Anna; Nair, Viji; Eddy, Sean; Fermin, Damian; Tomilo, Mark; Slidel, Timothy; Ju, Wenjun; Henry, Ian; Badal, Shawn S; Wesley, Johnna D; Liles, John T; Moosmang, Sven; Williams, Julie M; Quinn, Carol Moreno; Bitzer, Markus; Hodgin, Jeffrey B; Barisoni, Laura; Karihaloo, Anil; Breyer, Matthew D; Duffin, Kevin L; Patel, Uptal D; Magnone, Maria Chiara; Bhat, Ratan; Kretzler, Matthias
    Current classification of chronic kidney disease (CKD) into stages using indirect systemic measures (estimated glomerular filtration rate (eGFR) and albuminuria) is agnostic to the heterogeneity of underlying molecular processes in the kidney thereby limiting precision medicine approaches. To generate a novel CKD categorization that directly reflects within kidney disease drivers we analyzed publicly available transcriptomic data from kidney biopsy tissue. A Self-Organizing Maps unsupervised artificial neural network machine-learning algorithm was used to stratify a total of 369 patients with CKD and 46 living kidney donors as healthy controls. Unbiased stratification of the discovery cohort resulted in identification of four novel molecular categories of disease termed CKD-Blue, CKD-Gold, CKD-Olive, CKD-Plum that were replicated in independent CKD and diabetic kidney disease datasets and can be further tested on any external data at kidneyclass.org. Each molecular category spanned across CKD stages and histopathological diagnoses and represented transcriptional activation of distinct biological pathways. Disease progression rates were highly significantly different between the molecular categories. CKD-Gold displayed rapid progression, with significant eGFR-adjusted Cox regression hazard ratio of 5.6 [1.01-31.3] for kidney failure and hazard ratio of 4.7 [1.3-16.5] for composite of kidney failure or a 40% or more eGFR decline. Urine proteomics revealed distinct patterns between the molecular categories, and a 25-protein signature was identified to distinguish CKD-Gold from other molecular categories. Thus, patient stratification based on kidney tissue omics offers a gateway to non-invasive biomarker-driven categorization and the potential for future clinical implementation, as a key step towards precision medicine in CKD.