TRPC6 channel translocation into phagosomal membrane augments phagosomal function

Defects in the innate immune system in the lung with attendant bacterial infections contribute to lung tissue damage, respiratory insufficiency, and ultimately death in the pathogenesis of cystic fibrosis (CF). Professional phagocytes, including alveolar macrophages (AMs), have specialized pathways that ensure efficient killing of pathogens in phagosomes. Phagosomal acidification facilitates the optimal functioning of degradative enzymes, ultimately contributing to bacterial killing. Generation of low organellar pH is primarily driven by the V-ATPases, proton pumps that use cytoplasmic ATP to load H(+) into the organelle. Critical to phagosomal acidification are various channels derived from the plasma membrane, including the anion channel cystic fibrosis transmembrane conductance regulator, which shunt the transmembrane potential generated by movement of protons. Here we show that the transient receptor potential canonical-6 (TRPC6) calcium-permeable channel in the AM also functions to shunt the transmembrane potential generated by proton pumping and is capable of restoring microbicidal function to compromised AMs in CF and enhancement of function in non-CF cells. TRPC6 channel activity is enhanced via translocation to the cell surface (and then ultimately to the phagosome during phagocytosis) in response to G-protein signaling activated by the small molecule (R)-roscovitine and its derivatives. These data show that enhancing vesicular insertion of the TRPC6 channel to the plasma membrane may represent a general mechanism for restoring phagosome activity in conditions, where it is lost or impaired.Fil: Riazanski, Vladimir. University of Chicago; Estados UnidosFil: Gabdoulkhakova, Aida G.. University of Chicago; Estados UnidosFil: Boynton, Lin S.. University of Chicago; Estados UnidosFil: Eguchi, Raphael R.. University of Chicago; Estados UnidosFil: Deriy, Ludmila V.. University of Chicago; Estados UnidosFil: Hogarth, D. Kyle. University of Chicago; Estados UnidosFil: Loaëc, Nadège. ManRos Therapeutics; FranciaFil: Oumata, Nassima. ManRos Therapeutics; FranciaFil: Galons, Hervé. Universite de Paris; FranciaFil: Brown, Mary E.. University of Chicago; Estados UnidosFil: Shevchenko, Pavel. University of Chicago; Estados UnidosFil: Gallan, Alexander J.. University of Chicago; Estados UnidosFil: Yoo, Sang Gune. University of Chicago; Estados UnidosFil: Naren, Anjaparavanda P.. Cincinnati Children’s Hospital Medical Center; Estados UnidosFil: Villereal, Mitchel L.. University of Chicago; Estados UnidosFil: Beacham, Daniel W.. Thermo Scientific; Estados UnidosFil: Bindokas, Vytautas P.. University of Chicago; Estados UnidosFil: Birnbaumer, Lutz. National Institute of Environmental Health Sciences; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Meijer, Laurent. ManRos Therapeutics; FranciaFil: Nelson, Deborah J.. University of Chicago; Estados Unido

Nuclear grade and necrosis predict prognosis in malignant epithelioid pleural mesothelioma: a multi-institutional study

A recently described nuclear grading system predicted survival in patients with epithelioid malignant pleural mesothelioma. The current study was undertaken to validate the grading system and to identify additional prognostic factors. We analyzed cases of epithelioid malignant pleural mesothelioma from 17 institutions across the globe from 1998 to 2014. Nuclear grade was computed combining nuclear atypia and mitotic count into a grade of I-III using the published system. Nuclear grade was assessed by one pathologist for three institutions, the remaining were scored independently. The presence or absence of necrosis and predominant growth pattern were also evaluated. Two additional scoring systems were evaluated, one combining nuclear grade and necrosis and the other mitotic count and necrosis. Median overall survival was the primary endpoint. A total of 776 cases were identified including 301 (39%) nuclear grade I tumors, 354 (45%) grade II tumors and 121 (16%) grade III tumors. The overall survival was 16 months, and correlated independently with age (P=0.006), sex (0.015), necrosis (0.030), mitotic count (0.001), nuclear atypia (0.009), nuclear grade (<0.0001), and mitosis and necrosis score (<0.0001). The addition of necrosis to nuclear grade further stratified overall survival, allowing classification of epithelioid malignant pleural mesothelioma into four distinct prognostic groups: nuclear grade I tumors without necrosis (29 months), nuclear grade I tumors with necrosis and grade II tumors without necrosis (16 months), nuclear grade II tumors with necrosis (10 months) and nuclear grade III tumors (8 months). The mitosis-necrosis score stratified patients by survival, but not as well as the combination of necrosis and nuclear grade. This study confirms that nuclear grade predicts survival in epithelioid malignant pleural mesothelioma, identifies necrosis as factor that further stratifies overall survival, and validates the grading system across multiple institutions and among both biopsy and resection specimens. An alternative scoring system, the mitosis-necrosis score is also proposed

A user-friendly tool for cloud-based whole slide image segmentation with examples from renal histopathology.

BackgroundImage-based machine learning tools hold great promise for clinical applications in pathology research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often lack the programming experience required for the setup and use of these tools which often rely on the use of command line interfaces.MethodsWe have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis.ResultsBy segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in three murine models.ConclusionsHisto-Cloud is open source, accessible over the internet, and adaptable for segmentation of any histological structure regardless of stain

A user-friendly tool for cloud-based whole slide image segmentation with examples from renal histopathology.

Background: Image-based machine learning tools hold great promise for clinical applications in pathology research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often lack the programming experience required for the setup and use of these tools which often rely on the use of command line interfaces. Methods: We have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis. Results: By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in three murine models. Conclusions: Histo-Cloud is open source, accessible over the internet, and adaptable for segmentation of any histological structure regardless of stain. Keywords: Computational biology and bioinformatics; End-stage renal disease

Disease-causing Mutations in the Cystic Fibrosis Transmembrane Conductance Regulator Determine the Functional Responses of Alveolar Macrophages*

Alveolar macrophages (AMs) play a major role in host defense against microbial infections in the lung. To perform this function, these cells must ingest and destroy pathogens, generally in phagosomes, as well as secrete a number of products that signal other immune cells to respond. Recently, we demonstrated that murine alveolar macrophages employ the cystic fibrosis transmembrane conductance regulator (CFTR) Cl− channel as a determinant in lysosomal acidification (Di, A., Brown, M. E., Deriy, L. V., Li, C., Szeto, F. L., Chen, Y., Huang, P., Tong, J., Naren, A. P., Bindokas, V., Palfrey, H. C., and Nelson, D. J. (2006) Nat. Cell Biol. 8, 933–944). Lysosomes and phagosomes in murine cftr−/− AMs failed to acidify, and the cells were deficient in bacterial killing compared with wild type controls. Cystic fibrosis is caused by mutations in CFTR and is characterized by chronic lung infections. The information about relationships between the CFTR genotype and the disease phenotype is scarce both on the organismal and cellular level. The most common disease-causing mutation, ΔF508, is found in 70% of patients with cystic fibrosis. The mutant protein fails to fold properly and is targeted for proteosomal degradation. G551D, the second most common mutation, causes loss of function of the protein at the plasma membrane. In this study, we have investigated the impact of CFTR ΔF508 and G551D on a set of core intracellular functions, including organellar acidification, granule secretion, and microbicidal activity in the AM. Utilizing primary AMs from wild type, cftr−/−, as well as mutant mice, we show a tight correlation between CFTR genotype and levels of lysosomal acidification, bacterial killing, and agonist-induced secretory responses, all of which would be expected to contribute to a significant impact on microbial clearance in the lung

COVID-19 Vaccination and New Onset Glomerular Disease : Results from the IRocGN2 International Registry

Background Patients with de novo glomerular disease (GD) with various renal histologies have been reported after vaccination against SARS-CoV-2. Causality has not been established, and the long-term outcomes are not known. To better characterize the GDs and clinical courses/outcomes, we created the International Registry of COVID-19 vaccination and Glomerulonephritis to study in aggregate patients with de novo GN suspected after COVID-19 vaccine exposure.Methods A REDCap survey was used for anonymized data collection. Detailed information on vaccination type and timing and GD histology were recorded in the registry. We collected serial information on laboratory values (before and after vaccination and during follow-up), treatments, and kidney-related outcomes.Results Ninety-eight patients with GD were entered into the registry over 11 months from 44 centers throughout the world. Median follow-up was 89 days after diagnosis. IgA nephropathy (IgAN) and minimal change disease (MCD) were the most common kidney diseases reported. Recovery of kidney function and remission of proteinuria were more likely in IgAN and MCD at 4-6 months than with pauci-immune GN/vasculitis and membranous nephropathy.Conclusions The development of GD after vaccination against SARS-CoV-2 may be a very rare adverse event. Temporal association is present for IgAN and MCD, but causality is not firmly established. Kidney outcomes for IgAN and MCD are favorable. No changes in vaccination risk-benefit assessment are recommended based on these findings.Peer reviewe