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Piezo1 and 2 are evolutionarily conserved mechanosensory cation channels known to function on the cell surface by responding to external pressure and transducing a mechanically activated Ca2+ current. Here we show that both Piezo1 and 2 also exhibit concentrated intracellular localization at centrosomes. Both Piezo1 and 2 loss-of-function and Piezo1 activation by the small molecule Yoda1 produced supernumerary centrosomes due to inappropriate centriole disengagement. Using a centrosome-localized GCaMP Ca2+-sensitive reporter, we show that perturbations in Piezo modulate Ca2+ local concentration at centrosomes. We designed a photoactivable Yoda1 analog (caged-Yoda1) and revealed that its photoactivation specifically at centrosomes leads to rapid premature centriole disengagement within minutes. We identified the sorting nexin Snx5, which is involved in endocytic uptake and trafficking, as a protein-interactor with the conserved Piezo C-terminal domain, and Snx5 also co-localizes with Piezo1 and 2 at centrosomes. Moreover, inhibition of Polo-like-kinase 1 (PLK1) abolishes Yoda1-induced centriole disengagement. Collectively, these data suggest that Piezo1 and 2 in pericentrosomal endosomes control centrosome integrity, likely by maintaining local Ca2+ within a defined range through mechanotransduction of cell intrinsic forces from microtubules.

Roundabout guidance receptor 2 (ROBO2) plays an important role during early kidney development. ROBO2 is expressed in podocytes, inhibits nephrin-induced actin polymerization, down-regulates nonmuscle myosin IIA activity, and destabilizes kidney podocyte adhesion. However, the role of ROBO2 during kidney injury, particularly in mature podocytes, is not known. Herein, we report that loss of ROBO2 in podocytes [Robo2 conditional knockout (cKO) mouse] is protective from glomerular injuries. Ultrastructural analysis reveals that Robo2 cKO mice display less foot process effacement and better-preserved slit-diaphragm density compared with wild-type littermates injured by either protamine sulfate or nephrotoxic serum (NTS). The Robo2 cKO mice also develop less proteinuria after NTS injury. Further studies reveal that ROBO2 expression in podocytes is up-regulated after glomerular injury because its expression levels are higher in the glomeruli of NTS injured mice and passive Heymann membranous nephropathy rats. Moreover, the amount of ROBO2 in the glomeruli is also elevated in patients with membranous nephropathy. Finally, overexpression of ROBO2 in cultured mouse podocytes compromises cell adhesion. Taken together, these findings suggest that kidney injury increases glomerular ROBO2 expression that might compromise podocyte adhesion and, thus, loss of Robo2 in podocytes could protect from glomerular injury by enhancing podocyte adhesion that helps maintain foot process structure. Our findings also suggest that ROBO2 is a therapeutic target for podocyte injury and podocytopathy.

Wireless capsule endoscopy (WCE) has become an irreplaceable tool for diagnosing small intestinal diseases, and detecting the outliers in WCE images automatically remains as a hot research topic. Considering the difficulties in obtaining sufficient labeled WCE data, it is necessary to develop the diagnosis model which works well with only little labeled or even unlabeled training samples. In this paper, a novel semi-supervised deep-structured framework is introduced to solve the problem of outlier detection in WCE images. The key idea of our model is to mine the anomalous graphical patterns existed in the image by analyzing the spatial-scale trends of sequential image regions. Three main contributions are concluded: 1) we integrate a convolutional neural network into long short term memory network, so that the intrinsic differences between outliers and normal instances could be captured. Besides, 2) a assessment model is built by using various signs of anomaly occurrence and fake outliers knowledge learned during the training stage, which enhances the outlier alarm accuracy significantly. Furthermore, 3) a nest-structured training method is proposed, which helps our model achieving efficient training process. Experimental results on the real WCE images demonstrate the effectiveness of our model.

International audience; Congenital anomalies of the kidney and urinary tract (CAKUT) constitute one of the most frequent birth defects and represent the most common cause of chronic kidney disease in the first three decades of life. Despite the discovery of dozens of monogenic causes of CA-KUT, most pathogenic pathways remain elusive. We performed whole-exome sequencing (WES) in 551 individuals with CAKUT and identified a heterozygous de novo stop-gain variant in ZMYM2 in two different families with CAKUT. Through collaboration, we identified in total 14 different heterozygous loss-of-function mutations in ZMYM2 in 15 unrelated families. Most mutations occurred de novo, indicating possible interference with reproductive function. Human disease features are replicated in X. tropicalis larvae with morpho-lino knockdowns, in which expression of truncated ZMYM2 proteins, based on individual mutations, failed to rescue renal and cranio-facial defects. Moreover, heterozygous Zmym2-deficient mice recapitulated features of CAKUT with high penetrance. The ZMYM2 protein is a component of a transcriptional corepressor complex recently linked to the silencing of developmentally regulated endoge-nous retrovirus elements. Using protein-protein interaction assays, we show that ZMYM2 interacts with additional epigenetic silencing complexes, as well as confirming that it binds to FOXP1, a transcription factor that has also been linked to CAKUT. In summary, our findings establish that loss-of-function mutations of ZMYM2, and potentially that of other proteins in its interactome, as causes of human CAKUT, offering new routes for studying the pathogenesis of the disorder.

Background. Pan Ji Sheng Formula is a Chinese medicine formula that enables heat-free detoxification as well as anti-inflammatory and immune-boosting properties. This formula contains eight herbs. Its underlying mechanism is unknown. The bioactive ingredients were screened in our work, and the mechanism of this formula was investigated. Methods. Using traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP), ingredients in Pan Ji Sheng Chinese medicine formula were screened, and we selected the main bioactive ingredients for web-based research. The targets of bioactive ingredients are primarily obtained from the SwissTargetPrediction and TCMSP databases, and the text mining method is used. STRING and Cytoscape were then used to examine the protein-protein interaction (PPI) networks. To explore the biological function and related pathways, functional annotation and pathway analysis were performed. Results. This research discovered 96 bioactive ingredients. Then, 215 potential targets of bioactive ingredients were screened. Through the analysis of the PPI network, we discovered 25 key target genes, which can be described as hub target genes regulated by bioactive ingredients. Bioactive ingredients primarily regulate CASP3, AKT1, JUN, and other proteins. The formula works synergistically to enhance immune response and antiinfection by regulating immune-related pathways, TNF signaling pathways, and apoptosis. Conclusions. A variety of bioactive ingredients in the formula could play roles in regulating CASP3, AKT1, and other genes in immune, infection, apoptosis, and tumor-related signaling pathways. Our data point the way forward for future studies on the mechanism of action of this formula.

Introduction Focal segmental glomerulosclerosis (FSGS) is characterized by proteinuria and a histologic pattern of glomerular lesions of diverse etiology that share features including glomerular scarring and podocyte foot process effacement. Roundabout guidance receptor 2 (ROBO2)/slit guidance ligand 2 (SLIT2) signaling destabilizes the slit diaphragm and reduces podocyte adhesion to the glomerular basement membrane (GBM). Preclinical studies suggest that inhibition of glomerular ROBO2/SLIT2 signaling can stabilize podocyte adhesion and reduce proteinuria. This clinical trial evaluates the preliminary efficacy and safety of ROBO2/SLIT2 inhibition with the ROBO2 fusion protein PF-06730512 in patients with FSGS. Methods The Study to Evaluate PF-06730512 in Adults With FSGS (PODO; ClinicalTrials.gov identifier NCT03448692), an open-label, phase 2a, multicenter trial in adults with FSGS, will enroll patients into 2 cohorts (n = 22 per cohort) to receive either high- or low-dose PF-06730512 (intravenous) every 2 weeks for 12 weeks. Key inclusion criteria include a confirmed biopsy diagnosis of FSGS, an estimated glomerular filtration rate (eGFR) ≥45 ml/min/1.73 m2 based on the Chronic Kidney Disease Epidemiology Collaboration formula (30–45 with a recent biopsy), and urinary protein-to-creatinine ratio (UPCR) >1.5 g/g. Key exclusion criteria include collapsing FSGS, serious/active infection, ≥50% tubulointerstitial fibrosis on biopsy, and organ transplantation. The primary endpoint is change from baseline to week 13 in UPCR; secondary endpoints include safety, changes in eGFR, and PF-06730512 serum concentration. Results This ongoing trial will report the efficacy, safety, pharmacokinetics, and biomarker results of PF-06730512 for patients with FSGS. Conclusion Findings from this proof-of-concept study may support further development and evaluation of PF-06730512 to treat FSGS and warrant assessment in phase 3 clinical trials., Graphical abstract

Some flexible appendages, such as solar panels, communication antenna and other large structures are mounted on the base of the space robot. The structure flexibilities will cause vibration during the operation of manipulators. Due to complicated dynamic coupling among manipulators, the rigid base and flexible appendages, it is very challenging to control the end-effector to track inertial trajectories, especially when the target states are constantly changing. This paper proposes a vibration suppression method for autonomous target capturing during the preimpact phase without controlling the base. Firstly, we derive the rigid-flexible coupling dynamics of a space robot system with flexible appendages. Then, the relationship among joint rates, elastic motion and the end-effector velocities is established by using the linear momentum and angular momentum conservation equations. Secondly, a closed-loop control system is designed based on the dynamic coupling model. And the control system is composed of target motion prediction, autonomous trajectory planning, energy-based joint controllers and so on. Thirdly, the energy-based joint control is detailed, which is proved to be stable by Lyapunov direct method. Finally, simulations of a planar space robot with two flexible appendages and a 3D space robot with single flexible appendage are provided to verify the effectiveness of the presented approach. The effectiveness of energy-based joint control for vibration suppression is verified by a single-degree-of-freedom space robot experimental system. The simulation and experimental results show that the space manipulator can successfully capture the moving target while suppressing the structure vibration.

Early fault detection technique is crucial to reduce the machine downtime and has high impact on a wide variety of industrial applications. However, early fault detection is still subject to the following challenges: 1) extracting features from incipient fault signals; 2) detecting anomalies with considering sequential data correlation; and 3) enhancing the reliability of fault alarm. In this paper, we introduce a novel deep-structured framework to solve the early fault detection problem. First, the system variation is measured with the deviation value generated by a current feature extraction model using deep neural network (DNN) and a distribution estimator based on the long short-term memory (LSTM) network. DNN has the ability of representing a complicated and intrinsic distribution for data, which is suitable for handling the early fault data masked by heavy noise, and LSTM is able to discover temporal dependencies in high-dimensional sequential data, which allows distribution estimator making use of previous context information as well as makes the distribution estimator more robust to warp along the time axis. Second, a circular indirect alarm assessment strategy is designed for collecting deviation values and confirming the fault appearance only when a specified confidence level is reached. Experimental results on the typical real-world bearing data sets demonstrate the effectiveness and the reliability of our model.

Slit guidance ligand 2 (SLIT2) is a large, secreted protein that binds roundabout (ROBO) receptors on multiple cell types, including neurons and kidney podocytes. SLIT2-ROBO–mediated signaling regulates neuronal migration and ureteric bud (UB) outgrowth during kidney development as well as glomerular filtration in adult kidneys. Additionally, SLIT2 binds Gremlin, an antagonist of bone morphogenetic proteins (BMPs), and BMP–Gremlin signaling also regulates UB formation. However, direct cross-talk between the ROBO2–SLIT2 and BMP–Gremlin signaling pathways has not been established. Here, we report the discovery of negative feedback between the SLIT2 and BMP–Gremlin signaling pathways. We found that the SLIT2–Gremlin interaction inhibited both SLIT2–ROBO2 signaling in neurons and Gremlin antagonism of BMP activity in myoblasts and fibroblasts. Furthermore, BMP2 down-regulated SLIT2 expression and promoter activity through canonical BMP signaling. Gremlin treatment, BMP receptor inhibition, and SMAD family member 4 (SMAD4) knockdown rescued BMP-mediated repression of SLIT2. BMP2 treatment of nephron progenitor cells derived from human embryonic stem cells decreased SLIT2 expression, further suggesting an interaction between the BMP2–Gremlin and SLIT2 pathways in human kidney cells. In conclusion, our study has revealed direct negative cross-talk between two pathways, previously thought to be unassociated, that may regulate both kidney development and adult tissue maintenance.

With the development of space technology, lighter and larger space manipulators will be born, of which flexible characteristics are more obvious. The manipulator vibration caused by the flexibility not only reduces the efficiency of the manipulator but also affects the accuracy of the operation. The flexibility of space manipulator mainly comes from structural flexibility of links and transmission flexibility of harmonic gear reducer in joints. The vibrations generated by these two kinds of flexibility are coupled and transformed mutually, making the dynamics characteristics of space manipulator system complicated. Therefore it is difficult to assess respective effects of these flexibilities on vibrations of the manipulator tip. And the characteristics of integrated vibration of manipulator tip with different link and joint stiffnesses are not very clear. In this paper, the dynamic equations of multi-link multi-DOF flexible manipulator are established. Then, vibration responses of the tip under different elastic modulus, damping and joint stiffness were studied, and vibration characteristics of the tip with both link and joint were also analyzed. Moreover, the effects of motion planning on the vibration of the tip were analyzed. Finally, the vibration characteristics of the manipulator with flexible joints and links are verified by a two-degree-of-freedom manipulator experimental system. Dynamics analysis results presented some useful rules for the path planning and control to suppress the vibration of the flexible space manipulator.

Item does not contain fulltext BACKGROUND: Congenital anomalies of the kidney and urinary tract (CAKUT) are the most prevalent cause of kidney disease in the first three decades of life. Previous gene panel studies showed monogenic causation in up to 12% of patients with CAKUT. METHODS: We applied whole-exome sequencing to analyze the genotypes of individuals from 232 families with CAKUT, evaluating for mutations in single genes known to cause human CAKUT and genes known to cause CAKUT in mice. In consanguineous or multiplex families, we additionally performed a search for novel monogenic causes of CAKUT. RESULTS: In 29 families (13%), we detected a causative mutation in a known gene for isolated or syndromic CAKUT that sufficiently explained the patient's CAKUT phenotype. In three families (1%), we detected a mutation in a gene reported to cause a phenocopy of CAKUT. In 15 of 155 families with isolated CAKUT, we detected deleterious mutations in syndromic CAKUT genes. Our additional search for novel monogenic causes of CAKUT in consanguineous and multiplex families revealed a potential single, novel monogenic CAKUT gene in 19 of 232 families (8%). CONCLUSIONS: We identified monogenic mutations in a known human CAKUT gene or CAKUT phenocopy gene as the cause of disease in 14% of the CAKUT families in this study. Whole-exome sequencing provides an etiologic diagnosis in a high fraction of patients with CAKUT and will provide a new basis for the mechanistic understanding of CAKUT. 01 september 2018

The real-world large industry has gradually become a data-rich environment with the development of information and sensor technology, making the technology of data-driven fault diagnosis acquire a thriving development and application. The success of these advanced methods depends on the assumption that enough labeled samples for each fault type are available. However, in some practical situations, it is extremely difficult to collect enough data, e.g., when the sudden catastrophic failure happens, only a few samples can be acquired before the system shuts down. This phenomenon leads to the few-shot fault diagnosis aiming at distinguishing the failure attribution accurately under very limited data conditions. In this paper, we propose a new approach, called Feature Space Metric-based Meta-learning Model (FSM3), to overcome the challenge of the few-shot fault diagnosis under multiple limited data conditions. Our method is a mixture of general supervised learning and episodic metric meta-learning, which will exploit both the attribute information from individual samples and the similarity information from sample groups. The experiment results demonstrate that our method outperforms a series of baseline methods on the 1-shot and 5-shot learning tasks of bearing and gearbox fault diagnosis across various limited data conditions. The time complexity and implementation difficulty have been analyzed to show that our method has relatively high feasibility. The feature embedding is visualized by t-SNE to investigate the effectiveness of our proposed model.

Unsupervised outlier detection is a vital task and has high impact on a wide variety of applications domains, such as image analysis and video surveillance. It also gains long-standing attentions and has been extensively studied in multiple research areas. Detecting and taking action on outliers as quickly as possible are imperative in order to protect network and related stakeholders or to maintain the reliability of critical systems. However, outlier detection is difficult due to the one class nature and challenges in feature construction. Sequential anomaly detection is even harder with more challenges from temporal correlation in data, as well as the presence of noise and high dimensionality. In this paper, we introduce a novel deep structured framework to solve the challenging sequential outlier detection problem. We use autoencoder models to capture the intrinsic difference between outliers and normal instances and integrate the models to recurrent neural networks that allow the learning to make use of previous context as well as make the learners more robust to warp along the time axis. Furthermore, we propose to use a layerwise training procedure, which significantly simplifies the training procedure and hence helps achieve efficient and scalable training. In addition, we investigate a fine-tuning step to update all parameters set by incorporating the temporal correlation in the sequence. We further apply our proposed models to conduct systematic experiments on five real-world benchmark data sets. Experimental results demonstrate the effectiveness of our model, compared with other state-of-the-art approaches.

Prognostics, which usually means the prediction of the field reliability or the Remaining Useful Life (RUL), is the basis of Prognostic and Health Management (PHM). Research in this paper focuses on remaining useful life prediction of aircraft engine in the same gradual degradation mode. As the gradual degradation with same failure mechanism has some regularity in macro, there would be certain relation between an arbitrary point of the degradation process and the correspondent RUL. This paper tries to learn this certain relation via neural network and the learned network, which reflects the relation, can be partly perceived as degradation pattern. The main prognostic idea of degradation pattern learning is firstly proposed and illustrated. And then an improved back propagation neural network is designed and analyzed as the implementation technique, in whose loss function an adjacent difference item is added. Next details of implementation via adjacent difference neural network are elaborated. Finally, the proposed approach is validated by two experiments respectively using different aircraft engine degradation datasets. Results of the experiments show a relatively good prediction accuracy, which verifies the correctness, effectiveness and practicability of the idea.

Protein mimotopes, or blocking peptides, are small therapeutic peptides that prevent protein-protein interactions by selectively mimicking a native binding domain. Inexpensive technology facilitates straightforward design and production of blocking peptides in sufficient quantities to allow preventive and therapeutic trials in both in vitro and in vivo experimental disease models. The kidney is an ideal peptide target, since small molecules undergo rapid filtration and efficient bulk absorption by tubular epithelial cells. Because the half-life of peptides is markedly prolonged in the kidneys compared with the bloodstream, blocking peptides are an attractive tool for treating diverse renal diseases, including ischemia, proteinuric states, such as membranous nephropathy and focal and segmental glomerulosclerosis, and renal cell carcinoma. Therapeutic peptides represent one of the fastest-growing reagent classes for novel drug development in human disease, partly because of their ease of administration, high binding affinity, and minimal off-target effects. This review introduces the concepts of blocking peptide design, production, and administration and highlights the potential use of therapeutic peptides to prevent or treat specific renal diseases.

Congenital anomalies of the kidney and urinary tract (CAKUT) are the most common cause of CKD in the first three decades of life. However, for most patients with CAKUT, the causative mutation remains unknown. We identified a kindred with an autosomal dominant form of CAKUT. By whole-exome sequencing, we identified a heterozygous truncating mutation (c.279delG, p.Trp93fs*) of the nuclear receptor interacting protein 1 gene (NRIP1) in all seven affected members. NRIP1 encodes a nuclear receptor transcriptional cofactor that directly interacts with the retinoic acid receptors (RARs) to modulate retinoic acid transcriptional activity. Unlike wild-type NRIP1, the altered NRIP1 protein did not translocate to the nucleus, did not interact with RARα, and failed to inhibit retinoic acid–dependent transcriptional activity upon expression in HEK293 cells. Notably, we also showed that treatment with retinoic acid enhanced NRIP1 binding to RARα. RNA in situ hybridization confirmed Nrip1 expression in the developing urogenital system of the mouse. In explant cultures of embryonic kidney rudiments, retinoic acid stimulated Nrip1 expression, whereas a pan-RAR antagonist strongly reduced it. Furthermore, mice heterozygous for a null allele of Nrip1 showed a CAKUT-spectrum phenotype. Finally, expression and knockdown experiments in Xenopus laevis confirmed an evolutionarily conserved role for NRIP1 in renal development. These data indicate that dominant NRIP1 mutations can cause CAKUT by interference with retinoic acid transcriptional signaling, shedding light on the well documented association between abnormal vitamin A levels and renal malformations in humans, and suggest a possible gene-environment pathomechanism in this disease.

In recent years, machine learning techniques have been widely used to solve many problems for fault diagnosis. However, in many real-world fault diagnosis applications, the distribution of the source domain data (on which the model is trained) is different from the distribution of the target domain data (where the learned model is actually deployed), which leads to performance degradation. In this paper, we introduce domain adaptation, which can find the solution to this problem by adapting the classifier or the regression model trained in a source domain for use in a different but related target domain. In particular, we proposed a novel deep neural network model with domain adaptation for fault diagnosis. Two main contributions are concluded by comparing to the previous works: first, the proposed model can utilize domain adaptation meanwhile strengthening the representative information of the original data, so that a high classification accuracy in the target domain can be achieved, and second, we proposed several strategies to explore the optimal hyperparameters of the model. Experimental results, on several real-world datasets, demonstrate the effectiveness and the reliability of both the proposed model and the exploring strategies for the parameters.

Continuum robots composed of elastic backbones have a broad application prospect in the narrow and restricted environment because they overcome the disadvantages of traditional articulated robots, such as being bulky and inflexible. Statics plays an important role in the planning and control of the continuum robot composed of the elastic backbone. Pseudo-Rigid Body (PRB) theory has shown great potential in the description of flexible body statics. The PRB 3R model accurately describes the large deformation of the flexible body and has high computational efficiency. However, PRB 3R models mostly focus on the planar static modeling, and there are few applications in three-dimensional (3D) statics. In this paper, a 3D static modeling method of cable-driven continuum robot based on PRB 3R theory is proposed. By introducing the equilibrium constraint equations of resultant force/moment and bending plane normal of the elastic backbone, the state of the continuum robot is determined. The 3D static equations established by the proposed method take into account the comprehensive effects of the elastic force, external force, gravity and friction. A static verification experiment system of the cable-driven continuum robot is designed to verify the proposed method. The accuracy of the proposed method is verified by comparison with experimental data. The maximum position error between simulation and experimental results is 7.6%.

Deep learning has been widely developed to solve fault diagnosis issues, and it is becoming a crucial technology in the modern manufacturing industry. As an important transmission device of mechanical equipment, gearbox often runs at different speeds and loads, which may lead to changes in data distribution for the actual application. The cross-domain problem caused by the different data distribution may decline the performance of the fault diagnosis model based on deep learning. To overcome this challenge, a new domain adaptation method, named MAAN: Multilayer Adversarial Adaptation Networks, for fault diagnosis of gearbox running at multiple operating conditions. The basic framework of our MAAD is a deep convolutional neural network (CNN) and then an adversarial adaptation learning procedure is used for optimizing the basic CNN to adapt cross different domain. The results of the experiment demonstrate that MAAN has outstanding fault diagnosis and domain adaptation capacity, and it could obtain high accuracies for fault diagnosis of the gearbox with changing mode. For investigating the adaptability in this method, we use t-SNE to reduce the high dimension feature for better visualization.

In recent years, intelligent fault diagnosis technology with the deep learning algorithm has been widely used in the manufacturing industry for substituting time-consuming human analysis method to enhance the efficiency of fault diagnosis. The rolling bearing as the connection between the rotor and support is the crucial component in rotating equipment. However, the working condition of the rolling bearing is under changing with complex operation demand, which will significantly degrade the performance of the intelligent fault diagnosis method. In this paper, a new deep transfer model based on Wasserstein distance guided multi-adversarial networks (WDMAN) is proposed to address this problem. The WDMAN model exploits complex feature space structures to enable the transfer of different data distributions based on multiple domain critic networks. The essence of our method is learning the shared feature representation by minimizing the Wasserstein distance between the source domain and target domain distribution in an adversarial training way. The experiment results demonstrate that our model outperforms the state-of-the-art methods on rolling bearing fault diagnosis under different working conditions. The t-distributed stochastic neighbor embedding (t-SNE) technology is used to visualize the learned domain invariant feature and investigate the transferability behind the great performance of our proposed model.

Congenital anomalies of the kidney and urinary tract (CAKUT) are the most common reason for chronic kidney disease in children. Although more than 30 monogenic causes have been implicated in isolated forms of human CAKUT so far, the vast majority remains elusive. To identify novel monogenic causes of CAKUT we applied homozygosity mapping, together with whole exome sequencing, in a patient from consanguineous descent with isolated CAKUT. We identified a homozygous missense mutation (p.Arg415His) of the Ets Translocation Variant Gene 4 (ETV4). The transcription factor ETV4 is a downstream target of the GDNF/RET signaling pathway that plays a crucial role in kidney development. We show by means of electrophoretic mobility shift assay that the Arg415His mutant causes loss of the DNA binding affinity of ETV4 and fails to activate transcription in a cell-based luciferase reporter assay. We furthermore investigated the impact of the mutant protein on cell migration rate. Unlike wildtype ETV4, the Arg415His mutant failed to rescue cell migration defects observed in two ETV4 knock-down cell-lines. We therefore identified and functionally characterized a recessive mutation in ETV4 in a human patient with CAKUT. We hypothesize that the pathomechanism of this mutation could be via loss of the transcriptional function of ETV4, and a resulting abrogation of GDNF/RET/ETV4 signaling pathway.

Background Necroptosis is a newly discovered cell death pathway that plays a critical role in AKI. The involvement of integrin-linked kinase (ILK) in necroptosis has not been studied. Methods We performed experiments in mice with an Ilk deletion in collecting duct (CD) principal cells (PCs), and cultured tubular epithelial cells treated with an ILK inhibitor or ILK siRNA knockdown. Results Ilk deletion in CD PCs resulted in acute tubular injury and early mortality in mice. Progressive interstitial fibrosis and inflammation associated with the activation of the canonical TGF-β signaling cascade were detected in the kidneys of the mice lacking ILK in the CD PCs. In contrast to the minimal apoptosis detected in the animals' injured CDs, widespread necroptosis was present in ILK-deficient PCs, characterized by cell swelling, deformed mitochondria, and rupture of plasma membrane. In addition, ILK deficiency resulted in increased expression and activation of necroptotic proteins MLKL and RIPK3, and membrane translocation of MLKL in CD PCs. ILK inhibition and siRNA knockdown reduced cell survival in cultured tubular cells, concomitant with increased membrane accumulation of MLKL and/or phospho-MLKL. Administration of a necroptosis inhibitor, necrostatin-1, blocked cell death in vitro and significantly attenuated inflammation, interstitial fibrosis, and renal failure in ILK-deficient mice. Conclusions The study demonstrates the critical involvement of ILK in necroptosis through modulation of the RIPK3 and MLKL pathway and highlights the contribution of CD PC injury to the development of inflammation and interstitial fibrosis of the kidney.

Congenital anomalies of the kidney and urinary tract (CAKUT) are the most common cause (40-50%) of chronic kidney disease (CKD) in children. About 40 monogenic causes of CAKUT have so far been discovered. To date less than 20% of CAKUT cases can be explained by mutations in these 40 genes. To identify additional monogenic causes of CAKUT, we performed whole exome sequencing (WES) and homozygosity mapping (HM) in a patient with CAKUT from Indian origin and consanguineous descent. We identified a homozygous missense mutation (c.1336C>T, p.Arg446Cys) in the gene Von Willebrand factor A domain containing 2 (VWA2). With immunohistochemistry studies on kidneys of newborn (P1) mice, we show that Vwa2 and Fraser extracellular matrix complex subunit 1 (Fras1) co-localize in the nephrogenic zone of the renal cortex. We identified a pronounced expression of Vwa2 in the basement membrane of the ureteric bud (UB) and derivatives of the metanephric mesenchyme (MM). By applying in vitro assays, we demonstrate that the Arg446Cys mutation decreases translocation of monomeric VWA2 protein and increases translocation of aggregated VWA2 protein into the extracellular space. This is potentially due to the additional, unpaired cysteine residue in the mutated protein that is used for intermolecular disulfide bond formation. VWA2 is a known, direct interactor of FRAS1 of the Fraser-Complex (FC). FC-encoding genes and interacting proteins have previously been implicated in the pathogenesis of syndromic and/or isolated CAKUT phenotypes in humans. VWA2 therefore constitutes a very strong candidate in the search for novel CAKUT-causing genes. Our results from in vitro experiments indicate a dose-dependent neomorphic effect of the Arg446Cys homozygous mutation in VWA2.

Space robot is one of the promising approach to perform space tasks. Modeling and controlling of space robots is complicated caused by high dynamics coupling between the manipulator and the target spacecraft. Beyond that, vibration of flexible appendages mounted on the base of space robot can be excited, which is not expected for the precise control. After capturing the target satellite, the manipulator will be controlled to move from the capturing configuration to the berthing configuration, which is conveniently servicing the target. In this paper, we propose to learn the control policy of space robot directly from observed trajectories. A Gaussian regression model fitted to observed trajectories is used to approximate dynamics of space robotic system, based on which the control policy is learned. Plane space robot systems are used to verify the effectiveness of the presented approach. The simulation results indicate that the proposed method can well achieve the coupling control of the manipulator and the base. Moreover, the space robot can successfully berth the target while suppressing the vibrations of the structure.

Congenital anomalies of the kidneys and urinary tract (CAKUT) are the most common cause of chronic kidney disease in the first three decades of life. Identification of single-gene mutations that cause CAKUT permits the first insights into related disease mechanisms. However, for most cases the underlying defect remains elusive. We identified a kindred with an autosomal-dominant form of CAKUT with predominant ureteropelvic junction obstruction. By whole exome sequencing, we identified a heterozygous truncating mutation (c.1010delG) of T-Box transcription factor 18 (TBX18) in seven affected members of the large kindred. A screen of additional families with CAKUT identified three families harboring two heterozygous TBX18 mutations (c.1570C>T and c.487A>G). TBX18 is essential for developmental specification of the ureteric mesenchyme and ureteric smooth muscle cells. We found that all three TBX18 altered proteins still dimerized with the wild-type protein but had prolonged protein half life and exhibited reduced transcriptional repression activity compared to wild-type TBX18. The p.Lys163Glu substitution altered an amino acid residue critical for TBX18-DNA interaction, resulting in impaired TBX18-DNA binding. These data indicate that dominant-negative TBX18 mutations cause human CAKUT by interference with TBX18 transcriptional repression, thus implicating ureter smooth muscle cell development in the pathogenesis of human CAKUT.

Congenital anomalies of the kidney and urinary tract (CAKUT) account for 40–50 % of chronic kidney disease that manifests in the first two decades of life. Thus far, 31 monogenic causes of isolated CAKUT have been described, explaining ~12 % of cases. To identify additional CAKUT-causing genes, we performed whole-exome sequencing followed by a genetic burden analysis in 26 genetically unsolved families with CAKUT. We identified two heterozygous mutations in SRGAP1 in 2 unrelated families. SRGAP1 is a small GTPase-activating protein in the SLIT2–ROBO2 signaling pathway, which is essential for development of the metanephric kidney. We then examined the pathway-derived candidate gene SLIT2 for mutations in cohort of 749 individuals with CAKUT and we identified 3 unrelated individuals with heterozygous mutations. The clinical phenotypes of individuals with mutations in SLIT2 or SRGAP1 were cystic dysplastic kidneys, unilateral renal agenesis, and duplicated collecting system. We show that SRGAP1 is expressed in early mouse nephrogenic mesenchyme and that it is coexpressed with ROBO2 in SIX2-positive nephron progenitor cells of the cap mesenchyme in developing rat kidney. We demonstrate that the newly identified mutations in SRGAP1 lead to an augmented inhibition of RAC1 in cultured human embryonic kidney cells and that the SLIT2 mutations compromise the ability of the SLIT2 ligand to inhibit cell migration. Thus, we report on two novel candidate genes for causing monogenic isolated CAKUT in humans.