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      ACBD5 and VAPB mediate membrane associations between peroxisomes and the ER

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          Costello et al. identify ACBD5 and VAPB as key components of a peroxisome–ER tether in mammalian cells. Disruption of this tethering complex leads to reduced peroxisomal membrane expansion and increased peroxisomal movement.


          Peroxisomes (POs) and the endoplasmic reticulum (ER) cooperate in cellular lipid metabolism and form tight structural associations, which were first observed in ultrastructural studies decades ago. PO–ER associations have been suggested to impact on a diverse number of physiological processes, including lipid metabolism, phospholipid exchange, metabolite transport, signaling, and PO biogenesis. Despite their fundamental importance to cell metabolism, the mechanisms by which regions of the ER become tethered to POs are unknown, in particular in mammalian cells. Here, we identify the PO membrane protein acyl-coenzyme A–binding domain protein 5 (ACBD5) as a binding partner for the resident ER protein vesicle-associated membrane protein-associated protein B (VAPB). We show that ACBD5–VAPB interaction regulates PO–ER associations. Moreover, we demonstrate that loss of PO–ER association perturbs PO membrane expansion and increases PO movement. Our findings reveal the first molecular mechanism for establishing PO–ER associations in mammalian cells and report a new function for ACBD5 in PO–ER tethering.

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          The NumPy array: a structure for efficient numerical computation

          In the Python world, NumPy arrays are the standard representation for numerical data. Here, we show how these arrays enable efficient implementation of numerical computations in a high-level language. Overall, three techniques are applied to improve performance: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts. We first present the NumPy array structure, then show how to use it for efficient computation, and finally how to share array data with other libraries.
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            The BioPlex Network: A Systematic Exploration of the Human Interactome.

            Protein interactions form a network whose structure drives cellular function and whose organization informs biological inquiry. Using high-throughput affinity-purification mass spectrometry, we identify interacting partners for 2,594 human proteins in HEK293T cells. The resulting network (BioPlex) contains 23,744 interactions among 7,668 proteins with 86% previously undocumented. BioPlex accurately depicts known complexes, attaining 80%-100% coverage for most CORUM complexes. The network readily subdivides into communities that correspond to complexes or clusters of functionally related proteins. More generally, network architecture reflects cellular localization, biological process, and molecular function, enabling functional characterization of thousands of proteins. Network structure also reveals associations among thousands of protein domains, suggesting a basis for examining structurally related proteins. Finally, BioPlex, in combination with other approaches, can be used to reveal interactions of biological or clinical significance. For example, mutations in the membrane protein VAPB implicated in familial amyotrophic lateral sclerosis perturb a defined community of interactors.
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              scikit-image: image processing in Python

              scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image. More information can be found on the project homepage,

                Author and article information

                J Cell Biol
                J. Cell Biol
                The Journal of Cell Biology
                The Rockefeller University Press
                February 2017
                February 2017
                : 216
                : 2
                : 331-342
                [1 ]Biosciences, University of Exeter, Exeter EX4 4QD, England, UK
                [2 ]Max Planck Institute for Molecular Biomedicine, 48149 Muenster, Germany
                [3 ]Institute for Clinical Chemistry, Medical Faculty Mannheim, University of Heidelberg, 68167 Mannheim, Germany
                [4 ]Institute of Neuroanatomy, Center for Biomedicine and Medical Technology Mannheim, Medical Faculty Mannheim, University of Heidelberg, 68167 Mannheim, Germany
                Author notes
                Correspondence to Michael Schrader: M.Schrader@

                M. Islinger and M. Schrader contributed equally to this paper.

                © 2017 Costello et al.

                This article is available under a Creative Commons License (Attribution 4.0 International, as described at

                Funded by: Biotechnology and Biological Sciences Research Council, DOI;
                Award ID: BB/K006231/1
                Award ID: BB/N01541X/1
                Funded by: Wellcome Trust, DOI;
                Award ID: WT097835MF
                Award ID: WT105618MA
                Funded by: Fundação para a Ciência e a Tecnologia, DOI;
                Award ID: SFRH/BPD/90084/2012
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