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      Asymmetric distribution and spatial switching of dynein activity generates ciliary motility

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      1 , 2 , 1 , 2 , *
      Science (New York, N.Y.)

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          Abstract

          Motile cilia and flagella are essential, highly conserved organelles, and their motility is driven by the coordinated activities of multiple dynein isoforms. The prevailing “switch-point” hypothesis posits that dyneins are asymmetrically activated to drive flagellar bending. To test this model, we applied cryo–electron tomography to visualize activity states of individual dyneins relative to their locations along beating flagella of sea urchin sperm cells. As predicted, bending was generated by the asymmetric distribution of dynein activity on opposite sides of the flagellum. However, contrary to predictions, most dyneins were in their active state, and the smaller population of conformationally inactive dyneins switched flagellar sides relative to the bending direction. Thus, our data suggest a “switch-inhibition” mechanism in which force imbalance is generated by inhibiting, rather than activating, dyneins on alternating sides of the flagellum.

          Graphical Abstract

          INTRODUCTION:

          Motile cilia and flagella are highly conserved, hairlike appendages of eukaryotic cells that propel the movement of cells or fluids. They play important roles in the normal development and health of many species, including humans. Flagellar beating is driven by the coordinated activities of multiple dynein isoforms that must be spatially and temporally regulated. Although the prevailing “switch-point” hypothesis posits that flagellar motility results from periodic switching of spatially restricted, asymmetrical activation of dyneins, no direct evidence has been reported, and how the thousands of dyneins inside a flagellum work together to generate flagellar motility remains elusive.

          RATIONALE:

          Here we rapidly froze swimming sea urchin sperm cells and used cryo–electron tomography (cryo-ET) to image their beating flagella. Subtomogram averaging and classification analyses allowed us to identify and visualize the different activity states of individual dyneins and their regulators in situ. These conformational states were then mapped to their locations along the sinusoidal wave of the beating flagellum, for example, in relation to principal bend, reverse bend, or straight regions between bends. The results allowed us to elucidate the distinct roles played by various dyneins and to propose a model for the mechanism that underlies ciliary and flagellar motility.

          RESULTS:

          The native three-dimensional structures of flagellar complexes were determined in situ with resolutions sufficient for identifying different activity states. Dyneins of immotile control flagella were found to be in post–power stroke conformations (unprimed, inactive states). By contrast, in beating flagella, most dyneins were in pre–power stroke conformations (primed, active states), with only a few dyneins in intermediate conformations. Moreover, for all outer dyneins, the intermediate and inactive conformations were only found in bent regions and were clustered on one side of the flagellum in a bend direction–dependent manner. For inner dyneins, certain isoforms (dyneins I1, a, d, and g) showed similar bend direction–dependent distribution patterns in bent regions of flagella, whereas the distribution patterns of other isoforms (dyneins b, c, and e) lacked obvious correlations with bending direction.

          Our results revealed three key tenets that are important for generating flagellar motility: (i) The asymmetric distribution of dynein activity on opposite sides of the flagellum results in unidirectional bending, and (ii) the switching of dynein conformations between opposite sides causes the undulating waveform of beating flagella, both of which directly confirmed the switching aspect of the previously proposed switch-point hypothesis. (iii) In contrast to predictions, however, the findings also suggested the paradigm-shifting model that dyneins are active by default and that the asymmetry of dynein activity is driven by spatially restricted inhibition rather than activation of dyneins on alternating sides of the flagellum. This “switch-inhibition” mechanism was further supported by our analyses of a regulation-deficient Chlamydomonas mutant, which revealed that dyneins consumed adenosine triphosphate (ATP) and adopted pre–power stroke conformations, even though flagella were paralyzed.

          CONCLUSION:

          Our comprehensive structural analysis combined with biochemical investigations provides an enhanced understanding of the distinct roles played by various dyneins and regulatory complexes in the motility of cilia and flagella and suggests critical modifications to previous hypotheses regarding robust molecular mechanisms underlying flagellar motility. Our study demonstrates that comparative cellular cryo-ET studies provide the conceptual framework and experimental tools to better understand molecular mechanisms and cellular functions.

          Asymmetric dynein activity underlies beating of cilia and flagella. Cryo-ET was used to image the active flagellum of swimming sea urchin sperm cells. Different activity states of the motility-driving dynein motors were identified. Magnified views show active (right) and inactive intermediate states (left). The distribution patterns of dynein conformations along the undulating waveform suggest a switch-inhibition mechanism for ciliary and flagellar motility.

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          Most cited references40

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          Functions and mechanics of dynein motor proteins.

          Fuelled by ATP hydrolysis, dyneins generate force and movement on microtubules in a wealth of biological processes, including ciliary beating, cell division and intracellular transport. The large mass and complexity of dynein motors have made elucidating their mechanisms a sizable task. Yet, through a combination of approaches, including X-ray crystallography, cryo-electron microscopy, single-molecule assays and biochemical experiments, important progress has been made towards understanding how these giant motor proteins work. From these studies, a model for the mechanochemical cycle of dynein is emerging, in which nucleotide-driven flexing motions within the AAA+ ring of dynein alter the affinity of its microtubule-binding stalk and reshape its mechanical element to generate movement.
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            Cryo-EM Reveals How Human Cytoplasmic Dynein Is Auto-inhibited and Activated

            Summary Cytoplasmic dynein-1 binds dynactin and cargo adaptor proteins to form a transport machine capable of long-distance processive movement along microtubules. However, it is unclear why dynein-1 moves poorly on its own or how it is activated by dynactin. Here, we present a cryoelectron microscopy structure of the complete 1.4-megadalton human dynein-1 complex in an inhibited state known as the phi-particle. We reveal the 3D structure of the cargo binding dynein tail and show how self-dimerization of the motor domains locks them in a conformation with low microtubule affinity. Disrupting motor dimerization with structure-based mutagenesis drives dynein-1 into an open form with higher affinity for both microtubules and dynactin. We find the open form is also inhibited for movement and that dynactin relieves this by reorienting the motor domains to interact correctly with microtubules. Our model explains how dynactin binding to the dynein-1 tail directly stimulates its motor activity.
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              Clustering and variance maps for cryo-electron tomography using wedge-masked differences.

              Cryo-electron tomography provides 3D imaging of frozen hydrated biological samples with nanometer resolution. Reconstructed volumes suffer from low signal-to-noise-ratio (SNR)(1) and artifacts caused by systematically missing tomographic data. Both problems can be overcome by combining multiple subvolumes with varying orientations, assuming they contain identical structures. Clustering (unsupervised classification) is required to ensure or verify population homogeneity, but this process is complicated by the problems of poor SNR and missing data, the factors that led to consideration of multiple subvolumes in the first place. Here, we describe a new approach to clustering and variance mapping in the face of these difficulties. The combined subvolume is taken as an estimate of the true subvolume, and the effect of missing data is computed for individual subvolumes. Clustering and variance mapping then proceed based on differences between expected and observed subvolumes. We show that this new method is faster and more accurate than two current, widely used techniques. Copyright © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                0404511
                7473
                Science
                Science
                Science (New York, N.Y.)
                0036-8075
                1095-9203
                14 June 2019
                27 April 2018
                19 July 2019
                : 360
                : 6387
                : eaar1968
                Affiliations
                [1 ]Departments of Cell Biology and Biophysics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
                [2 ]Department of Biology, Rosenstiel Basic Medical Sciences Research Center, Brandeis University, Waltham, MA 02454, USA.
                Author notes

                Author contributions: D.N. conceived and directed the study. J.L. performed the experiments. J.L. and D.N. analyzed the data and wrote the manuscript.

                Article
                PMC6640125 PMC6640125 6640125 nihpa1035235
                10.1126/science.aar1968
                6640125
                29700238
                c6dc2f92-2cfa-4c6a-b975-59010cbd95e0
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