SummaryPodocytes, highly specialized epithelial cells, build the outer part of the kidney filtration barrier and withstand high mechanical forces through a complex network of cellular protrusions. Here, we show that Arp2/3-dependent actin polymerization controls actomyosin contractility and focal adhesion maturation of podocyte protrusions and thereby regulates formation, maintenance, and capacity to adapt to mechanical requirements of the filtration barrier. We find that N-WASP-Arp2/3 define the development of complex arborized podocyte protrusions in vitro and in vivo. Loss of dendritic actin networks results in a pronounced activation of the actomyosin cytoskeleton and the generation of over-maturated but less efficient adhesion, leading to detachment of podocytes. Our data provide a model to explain podocyte protrusion morphology and their mechanical stability based on a tripartite relationship between actin polymerization, contractility, and adhesion.

Background The Prostate Imaging Reporting and Data System, version 2.1 (PI-RADSv2.1) standardizes reporting of multiparametric MRI of the prostate. Assigned assessment categories are a risk stratification algorithm, higher categories indicate a higher probability of clinically significant cancer compared to lower categories. PI-RADSv2.1 does not define these probabilities numerically. We conduct a systematic review and meta-analysis to determine the cancer detection rates (CDR) of the PI-RADSv2.1 assessment categories on lesion level and patient level. Methods Two independent reviewers screen a systematic PubMed and Cochrane CENTRAL search for relevant articles (primary outcome: clinically significant cancer, index test: prostate MRI reading according to PI-RADSv2.1, reference standard: histopathology). We perform meta-analyses of proportions with random-effects models for the CDR of the PI-RADSv2.1 assessment categories for clinically significant cancer. We perform subgroup analysis according to lesion localization to test for differences of CDR between peripheral zone lesions and transition zone lesions. Results A total of 17 articles meet the inclusion criteria and data is independently extracted by two reviewers. Lesion level analysis includes 1946 lesions, patient level analysis includes 1268 patients. On lesion level analysis, CDR are 2% (95% confidence interval: 0–8%) for PI-RADS 1, 4% (1–9%) for PI-RADS 2, 20% (13–27%) for PI-RADS 3, 52% (43–61%) for PI-RADS 4, 89% (76–97%) for PI-RADS 5. On patient level analysis, CDR are 6% (0–20%) for PI-RADS 1, 9% (5–13%) for PI-RADS 2, 16% (7–27%) for PI-RADS 3, 59% (39–78%) for PI-RADS 4, 85% (73–94%) for PI-RADS 5. Higher categories are significantly associated with higher CDR (P < 0.001, univariate meta-regression), no systematic difference of CDR between peripheral zone lesions and transition zone lesions is identified in subgroup analysis. Conclusions Our estimates of CDR demonstrate that PI-RADSv2.1 stratifies lesions and patients as intended. Our results might serve as an initial evidence base to discuss management strategies linked to assessment categories.

BackgroundPrevious research demonstrated that small Rho GTPases, modulators of the actin cytoskeleton, are drivers of podocyte foot-process effacement in glomerular diseases, such as FSGS. However, a comprehensive understanding of the regulatory networks of small Rho GTPases in podocytes is lacking.MethodsWe conducted an analysis of podocyte transcriptome and proteome datasets for Rho GTPases; mapped in vivo, podocyte-specific Rho GTPase affinity networks; and examined conditional knockout mice and murine disease models targeting Srgap1. To evaluate podocyte foot-process morphology, we used super-resolution microscopy and electron microscopy; in situ proximity ligation assays were used to determine the subcellular localization of the small GTPase-activating protein SRGAP1. We performed functional analysis of CRISPR/Cas9-generated SRGAP1 knockout podocytes in two-dimensional and three-dimensional cultures and quantitative interaction proteomics.ResultsWe demonstrated SRGAP1 localization to podocyte foot processes in vivo and to cellular protrusions in vitro. Srgap1fl/fl*Six2Cre but not Srgap1fl/fl*hNPHS2Cre knockout mice developed an FSGS-like phenotype at adulthood. Podocyte-specific deletion of Srgap1 by hNPHS2Cre resulted in increased susceptibility to doxorubicin-induced nephropathy. Detailed analysis demonstrated significant effacement of podocyte foot processes. Furthermore, SRGAP1-knockout podocytes showed excessive protrusion formation and disinhibition of the small Rho GTPase machinery in vitro. Evaluation of a SRGAP1-dependent interactome revealed the involvement of SRGAP1 with protrusive and contractile actin networks. Analysis of glomerular biopsy specimens translated these findings toward human disease by displaying a pronounced redistribution of SRGAP1 in FSGS.ConclusionsSRGAP1, a podocyte-specific RhoGAP, controls podocyte foot-process architecture by limiting the activity of protrusive, branched actin networks. Therefore, elucidating the complex regulatory small Rho GTPase affinity network points to novel targets for potentially precise intervention in glomerular diseases.

Accurate delineation of the intraprostatic gross tumour volume (GTV) is a prerequisite for treatment approaches in patients with primary prostate cancer (PCa). Prostate-specific membrane antigen positron emission tomography (PSMA-PET) may outperform MRI in GTV detection. However, visual GTV delineation underlies interobserver heterogeneity and is time consuming. The aim of this study was to develop a convolutional neural network (CNN) for automated segmentation of intraprostatic tumour (GTV-CNN) in PSMA-PET. Methods:The CNN (3D U-Net) was trained on 68Ga-PSMA-PET images of 152 patients from two different institutions and the training labels were generated manually using a validated technique. The CNN was tested on two independent internal (cohort 1: 68Ga-PSMA-PET, n=18 and cohort 2: 18F-PSMA-PET, n=19) and one external (cohort 3: 68Ga-PSMA-PET, n=20) testdatasets. Accordance between manual contours and GTV-CNN was assessed with Dice-Sørensen coefficient (DSC). Sensitivity and specificity were calculated for the two internal testdatasets (cohort 1: n=18, cohort 2: n=11) by using whole-mount histology.Results: Median DSCs for cohorts 1-3 were 0.84 (range: 0.32-0.95), 0.81 (range: 0.28-0.93) and 0.83 (range: 0.32-0.93), respectively. Sensitivities and specificities for GTV-CNN were comparable with manual expert contours: 0.98 and 0.76 (cohort 1) and 1 and 0.57 (cohort 2), respectively. Computation time was around 6 seconds for a standard dataset. Conclusion:The application of a CNN for automated contouring of intraprostatic GTV in 68Ga-PSMA-and 18F-PSMA-PET images resulted in a high concordance with expert contours and in high sensitivities and specificities in comparison with histology reference. This robust, accurate and fast technique may be implemented for treatment concepts in primary PCa. The trained model and the study's source code are available in an open source repository.