Arp2/3 complex inhibition radically alters lamellipodial actin architecture, suspended cell shape, and the cell spreading process

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Molecular Biology of the Cell 26 (2015): 887-900, doi:10.1091/mbc.E14-07-1244.Recent studies have investigated the dendritic actin cytoskeleton of the cell edge's lamellipodial (LP) region by experimentally decreasing the activity of the actin filament nucleator and branch former, the Arp2/3 complex. Here we extend these studies via pharmacological inhibition of the Arp2/3 complex in sea urchin coelomocytes, cells that possess an unusually broad LP region and display correspondingly exaggerated centripetal flow. Using light and electron microscopy, we demonstrate that Arp2/3 complex inhibition via the drug CK666 dramatically altered LP actin architecture, slowed centripetal flow, drove a lamellipodial-to-filopodial shape change in suspended cells, and induced a novel actin structural organization during cell spreading. A general feature of the CK666 phenotype in coelomocytes was transverse actin arcs, and arc generation was arrested by a formin inhibitor. We also demonstrate that CK666 treatment produces actin arcs in other cells with broad LP regions, namely fish keratocytes and Drosophila S2 cells. We hypothesize that the actin arcs made visible by Arp2/3 complex inhibition in coelomocytes may represent an exaggerated manifestation of the elongate mother filaments that could possibly serve as the scaffold for the production of the dendritic actin network.This research was supported by National Science Foundation STEP grant 0856704 to Dickinson College, student/faculty summer research grants from the Dickinson College Research and Development Committee, Laura and Arthur Colwin Summer Research Fellowships from the Marine Biological Laboratory to J.H.H. and C.B.S., National Institutes of Health Grant EB002583 to R.O., and National Science Foundation collaborative research grants to J.H.H. (MCB-1412688) and C.B.S. (MCB-1412734)

Automatic ROI Selection in Structural Brain MRI Using SOM 3D Projection

This paper presents a method for selecting Regions of Interest (ROI) in brain Magnetic Resonance Imaging (MRI) for diagnostic purposes, using statistical learning and vector quantization techniques. The proposed method models the distribution of GM and WM tissues grouping the voxels belonging to each tissue in ROIs associated to a specific neurological disorder. Tissue distribution of normal and abnormal images is modelled by a Self-Organizing map (SOM), generating a set of representative prototypes, and the receptive field (RF) of each SOM prototype defines a ROI. Moreover, the proposed method computes the relative importance of each ROI by means of its discriminative power. The devised method has been assessed using 818 images from the Alzheimer's disease Neuroimaging Initiative (ADNI) which were previously segmented through Statistical Parametric Mapping (SPM). The proposed algorithm was used over these images to parcel ROIs associated to the Alzheimer's Disease (AD). Additionally, this method can be used to extract a reduced set of discriminative features for classification, since it compresses discriminative information contained in the brain. Voxels marked by ROIs which were computed using the proposed method, yield classification results up to 90% of accuracy for controls (CN) and Alzheimer's disease (AD) patients, and 84% of accuracy for Mild Cognitive Impairment (MCI) and AD patients.This work was partly supported by the MICINN under the TEC2012-34306 project and the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía, Spain) under the Excellence Projects P09-TIC-4530 and P11-TIC-7103. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer's Association; Alzheimer's Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRxResearch; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California

Severe axial muscular involvement in Laing distal myopathy with a thumbprint finding on MRI

International audienceMutations in the MYH7 gene are implicated in a heterogeneous group of diseases including Laing distal myopathy. We present 8 patients (M:F ratio = 5:3) with different mutations in the MYH7 gene with early-onset distal myopathy, typical abnormal gait and severe axial muscular involvement without cardiomyopathy. All patients had normal developmental milestones then started having gait abnormalities. 7 of them were ambulant at last follow up (two at 12 years and the others at adulthood) and one wheel chair bound at 33 years. Spinal deformity was always present and was characterized by scoliosis, cervical hyperextension, and spine bending forward thus giving them the “sphinx” phenotype. Clear weakness and muscle atrophy affected predominantly distal portions of limbs and cervical flexors, wrist and finger extensors in the upper limb, and hip extensors, foot dorsiflexors in the lower limbs muscles.8 MRIs were performed, 6 of them were whole body muscular MRIs and showed atrophy and fatty infiltration in thoracic and lumbar erector spinae in all. Fatty infiltration was also seen mainly in gluteus minimus, short head of the biceps femoris, tibialis anterior, soleus, and extensor digitorum longus. The most important finding on WBMRI was the presence of an abnormal signal at the central part of the muscles with “in bands” intramuscular striation and relative sparing of the periphery (inverted collagen pattern) seen in the soleus mainly but also in more muscles as the disease progressed. Muscle biopsy findings were variable showing occasionally cores, fiber size disproportion (could be type 1 or type 2), neurogenic signs, or normal aspect. Myogenic pattern was seen on EMG in all cases, however in 2 patients previous EMG that was done in early childhood showed a neurogenic pattern. Our series extends the spectrum of MYH7-related phenotypes to add the “sphinx” phenotype. WBMRI showed a characteristic finding with inverted collagen sign that seem specific for this condition

Support Vector Machines for neuroimage analysis: Interpretation from discrimination

Support vector machines (SVMs) have been widely used in neuroimage analysis as an effective multivariate analysis tool for group comparison. As neuroimage analysis is often an exploratory research, it is an important issue to characterize the group difference captured by SVM with anatomically interpretable patterns, which provides insights into the unknown mechanism of the brain. In this chapter, SVM-based methods and pplications are introduced for neuroimage analyis from this point of view. The discriminative patterns are decoded from SVMs through distinctive feature selection, SVM decision boundary interpretation, and discriminative learning of generative models