Syntaphilin Ubiquitination Regulates Mitochondrial Dynamics and Tumor Cell Movements.

Syntaphilin (SNPH) inhibits the movement of mitochondria in tumor cells, preventing their accumulation at the cortical cytoskeleton and limiting the bioenergetics of cell motility and invasion. Although this may suppress metastasis, the regulation of the SNPH pathway is not well understood. Using a global proteomics screen, we show that SNPH associates with multiple regulators of ubiquitin-dependent responses and is ubiquitinated by the E3 ligase CHIP (or STUB1) on Lys111 and Lys153 in the microtubule-binding domain. SNPH ubiquitination did not result in protein degradation, but instead anchored SNPH on tubulin to inhibit mitochondrial motility and cycles of organelle fusion and fission, that is dynamics. Expression of ubiquitination-defective SNPH mutant Lys111!Arg or Lys153!Arg increased the speed and distance traveled by mitochondria, repositioned mitochondria to the cortical cytoskeleton, and supported heightened tumor chemotaxis, invasion, and metastasis in vivo. Interference with SNPH ubiquitination activated mitochondrial dynamics, resulting in increased recruitment of the fission regulator dynamin-related protein-1 (Drp1) to mitochondria and Drp1-dependent tumor cell motility. These data uncover nondegradative ubiquitination of SNPH as a key regulator of mitochondrial trafficking and tumor cell motility and invasion. In this way, SNPH may function as a unique, ubiquitination-regulated suppressor of metastasis

A chest wall model based on rib kinematics

International audienceThe success of radiotherapy treatment could be compromised by motion. Lung tumours are particularly concerned by this problem because their positions are subject to breathing motion. To reduce the uncertainty on the position of pulmonary tumours during breathing cycle, we propose to develop a complete thoracic biomechanical model. This model will be monitored through the measurement of external parameters (thorax outer-surface motion, air flow...) and should predict in real-time the location of lung tumour. In this paper, we expose a biomechanical model of the lung environment, based on anatomical and physiological knowledge. The model includes the skin, the ribs, the pleura and the soft tissue between the skin and the ribcage. Motions and deformations are computed with the Finite Element Method. The ribcage direct kinematics model, permits to compute the skin position from the ribs motion. Conversely, the inverse kinematics provides rib motion and consequently lung motion. It can be computed from the outer-surface motion. With regards to available clinical data the results are promising. In particular, the average error is lower than the resolution of the CT-scan images used as input data.Le succès du traitement par radiothérapie pourrait être compromis par le mouvement. Les tumeurs pulmonaires sont particulièrement concernées par ce problème, parce que leurs positions sont soumises à la respiration. Pour réduire l'incertitude sur la position des tumeurs pulmonaires au cours de la respiration, nous proposons de développer un modèle biomécanique de la cage thoracique. Ce modèle sera suivi par la mesure des paramètres externes (mouvement de la surface du thorax extérieur, quantité d'air inspirée et expirée ...) et devrait prévoir en temps réel la localisation de la tumeur du poumon. Dans ce document, nous exposons un modèle biomécanique de l'appareil respiratoire, fondé sur les connaissances anatomiques et physiologiques. Le modèle comprend la peau, les côtes, la plèvre et les tissus mous entre la peau et la cage thoracique. Les mouvements et les déformations sont calculées avec la méthode des éléments finis. Le modèle cinématique direct de la cage thoracique permet de calculer la position de la peau à partir du mouvement des côtes. Inversement, la cinématique inverse permet de déduire le mouvement des côtes et des poumons à partir du mouvement externe de la peau. Les résultats obtenus par ce modèle sont satisfaisants surtout que l’erreur moyenne est inférieure à la résolution des images CT-scan utilisées comme données d’entrée

4D-CT Lung Registration and its Application for Lung Radiation Therapy

Radiation therapy has been successful in treating lung cancer patients, but its efficacy is limited by the inability to account for the respiratory motion during treatment planning and radiation dose delivery. Physics-based lung deformation models facilitate the motion computation of both tumor and local lung tissue during radiation therapy. In this dissertation, a novel method is discussed to accurately register 3D lungs across the respiratory phases from 4D-CT datasets, which facilitates the estimation of the volumetric lung deformation models. This method uses multi-level and multi-resolution optical flow registration coupled with thin plate splines (TPS), to address registration issue of inconsistent intensity across respiratory phases. It achieves higher accuracy as compared to multi-resolution optical flow registration and other commonly used registration methods. Results of validation show that the lung registration is computed with 3 mm Target Registration Error (TRE) and approximately 3 mm Inverse Consistency Error (ICE). This registration method is further implemented in GPU based real time dose delivery simulation to assist radiation therapy planning

Modeling Real-Time 3-D Lung Deformations for Medical Visualization

In this paper, we propose a physics-based and physiology-based approach for modeling real-time deformations of 3-D high-resolution polygonal lung models obtained from high-resolution computed tomography (HRCT) images of normal human subjects. The physics-based deformation operator is nonsymmetric, which accounts for the heterogeneous elastic properties of the lung tissue and spatial-dynamic flow properties of the air. An iterative approach is used to estimate the deformation with the deformation operator initialized based on the regional alveolar expandability, a key physiology-based parameter. The force applied on each surface node is based on the airflow pattern inside the lungs, which is known to be based on the orientation of the human subject. The validation of lung dynamics is done by resimulating the lung deformation and comparing it with HRCT data and computing force applied on each node derived from a 4-D HRCT dataset of a normal human subject using the proposed deformation operator and verifying its gradient with the orientation

Integrin-mediated traction force enhances paxillin molecular associations and adhesion dynamics that increase the invasiveness of tumor cells into a three-dimensional extracellular matrix.

Metastasis requires tumor cells to navigate through a stiff stroma and squeeze through confined microenvironments. Whether tumors exploit unique biophysical properties to metastasize remains unclear. Data show that invading mammary tumor cells, when cultured in a stiffened three-dimensional extracellular matrix that recapitulates the primary tumor stroma, adopt a basal-like phenotype. Metastatic tumor cells and basal-like tumor cells exert higher integrin-mediated traction forces at the bulk and molecular levels, consistent with a motor-clutch model in which motors and clutches are both increased. Basal-like nonmalignant mammary epithelial cells also display an altered integrin adhesion molecular organization at the nanoscale and recruit a suite of paxillin-associated proteins implicated in invasion and metastasis. Phosphorylation of paxillin by Src family kinases, which regulates adhesion turnover, is similarly enhanced in the metastatic and basal-like tumor cells, fostered by a stiff matrix, and critical for tumor cell invasion in our assays. Bioinformatics reveals an unappreciated relationship between Src kinases, paxillin, and survival of breast cancer patients. Thus adoption of the basal-like adhesion phenotype may favor the recruitment of molecules that facilitate tumor metastasis to integrin-based adhesions. Analysis of the physical properties of tumor cells and integrin adhesion composition in biopsies may be predictive of patient outcome

Modeling, Simulation, And Visualization Of 3d Lung Dynamics

Medical simulation has facilitated the understanding of complex biological phenomenon through its inherent explanatory power. It is a critical component for planning clinical interventions and analyzing its effect on a human subject. The success of medical simulation is evidenced by the fact that over one third of all medical schools in the United States augment their teaching curricula using patient simulators. Medical simulators present combat medics and emergency providers with video-based descriptions of patient symptoms along with step-by-step instructions on clinical procedures that alleviate the patient\u27s condition. Recent advances in clinical imaging technology have led to an effective medical visualization by coupling medical simulations with patient-specific anatomical models and their physically and physiologically realistic organ deformation. 3D physically-based deformable lung models obtained from a human subject are tools for representing regional lung structure and function analysis. Static imaging techniques such as Magnetic Resonance Imaging (MRI), Chest x-rays, and Computed Tomography (CT) are conventionally used to estimate the extent of pulmonary disease and to establish available courses for clinical intervention. The predictive accuracy and evaluative strength of the static imaging techniques may be augmented by improved computer technologies and graphical rendering techniques that can transform these static images into dynamic representations of subject specific organ deformations. By creating physically based 3D simulation and visualization, 3D deformable models obtained from subject-specific lung images will better represent lung structure and function. Variations in overall lung deformations may indicate tissue pathologies, thus 3D visualization of functioning lungs may also provide a visual tool to current diagnostic methods. The feasibility of medical visualization using static 3D lungs as an effective tool for endotracheal intubation was previously shown using Augmented Reality (AR) based techniques in one of the several research efforts at the Optical Diagnostics and Applications Laboratory (ODALAB). This research effort also shed light on the potential usage of coupling such medical visualization with dynamic 3D lungs. The purpose of this dissertation is to develop 3D deformable lung models, which are developed from subject-specific high resolution CT data and can be visualized using the AR based environment. A review of the literature illustrates that the techniques for modeling real-time 3D lung dynamics can be roughly grouped into two categories: Geometrically-based and Physically-based. Additional classifications would include considering a 3D lung model as either a volumetric or surface model, modeling the lungs as either a single-compartment or a multi-compartment, modeling either the air-blood interaction or the air-blood-tissue interaction, and considering either a normal or pathophysical behavior of lungs. Validating the simulated lung dynamics is a complex problem and has been previously approached by tracking a set of landmarks on the CT images. An area that needs to be explored is the relationship between the choice of the deformation method for the 3D lung dynamics and its visualization framework. Constraints on the choice of the deformation method and the 3D model resolution arise from the visualization framework. Such constraints of our interest are the real-time requirement and the level of interaction required with the 3D lung models. The work presented here discusses a framework that facilitates a physics-based and physiology-based deformation of a single-compartment surface lung model that maintains the frame-rate requirements of the visualization system. The framework presented here is part of several research efforts at ODALab for developing an AR based medical visualization framework. The framework consists of 3 components, (i) modeling the Pressure-Volume (PV) relation, (ii) modeling the lung deformation using a Green\u27s function based deformation operator, and (iii) optimizing the deformation using state-of-art Graphics Processing Units (GPU). The validation of the results obtained in the first two modeling steps is also discussed for normal human subjects. Disease states such as Pneumothorax and lung tumors are modeled using the proposed deformation method. Additionally, a method to synchronize the instantiations of the deformation across a network is also discussed