39
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Three-dimensional visualization and a deep-learning model reveal complex fungal parasite networks in behaviorally manipulated ants

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Significance

          Microbial parasites may behave collectively to manipulate their host’s behavior. We examine adaptations of a microbial parasite in its natural environment: the body of its coevolved and manipulated host. Electron microscopy and 3D reconstructions of host and parasite tissues reveal that this fungus invades muscle fibers throughout the ant’s body but leaves the brain intact, and that the fungal cells connect to form extensive networks. The connections are likened to structures that aid in transporting nutrients and organelles in several plant-associated fungi. These findings alter the current view of parasite-extended phenotypes by demonstrating that behavior control does not require the parasite to physically invade the host brain and that parasite cells may coordinate to change host behavior.

          Abstract

          Some microbes possess the ability to adaptively manipulate host behavior. To better understand how such microbial parasites control animal behavior, we examine the cell-level interactions between the species-specific fungal parasite Ophiocordyceps unilateralis sensu lato and its carpenter ant host ( Camponotus castaneus) at a crucial moment in the parasite’s lifecycle: when the manipulated host fixes itself permanently to a substrate by its mandibles. The fungus is known to secrete tissue-specific metabolites and cause changes in host gene expression as well as atrophy in the mandible muscles of its ant host, but it is unknown how the fungus coordinates these effects to manipulate its host’s behavior. In this study, we combine techniques in serial block-face scanning-electron microscopy and deep-learning–based image segmentation algorithms to visualize the distribution, abundance, and interactions of this fungus inside the body of its manipulated host. Fungal cells were found throughout the host body but not in the brain, implying that behavioral control of the animal body by this microbe occurs peripherally. Additionally, fungal cells invaded host muscle fibers and joined together to form networks that encircled the muscles. These networks may represent a collective foraging behavior of this parasite, which may in turn facilitate host manipulation.

          Related collections

          Most cited references34

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          U-Net: Convolutional Networks for Biomedical Image Segmentation

          There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Structures of Toxoplasma gondii tachyzoites, bradyzoites, and sporozoites and biology and development of tissue cysts.

            Infections by the protozoan parasite Toxoplasma gondii are widely prevalent world-wide in animals and humans. This paper reviews the life cycle; the structure of tachyzoites, bradyzoites, oocysts, sporocysts, sporozoites and enteroepithelial stages of T. gondii; and the mode of penetration of T. gondii. The review provides a detailed account of the biology of tissue cysts and bradyzoites including in vivo and in vitro development, methods of separation from host tissue, tissue cyst rupture, and relapse. The mechanism of in vivo and in vitro stage conversion from sporozoites to tachyzoites to bradyzoites and from bradyzoites to tachyzoites to bradyzoites is also discussed.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Lignocellulose degradation mechanisms across the Tree of Life

              Organisms use diverse mechanisms involving multiple complementary enzymes, particularly glycoside hydrolases (GHs), to deconstruct lignocellulose. Lytic polysaccharide monooxygenases (LPMOs) produced by bacteria and fungi facilitate deconstruction as does the Fenton chemistry of brown-rot fungi. Lignin depolymerisation is achieved by white-rot fungi and certain bacteria, using peroxidases and laccases. Meta-omics is now revealing the complexity of prokaryotic degradative activity in lignocellulose-rich environments. Protists from termite guts and some oomycetes produce multiple lignocellulolytic enzymes. Lignocellulose-consuming animals secrete some GHs, but most harbour a diverse enzyme-secreting gut microflora in a mutualism that is particularly complex in termites. Shipworms however, house GH-secreting and LPMO-secreting bacteria separate from the site of digestion and the isopod Limnoria relies on endogenous enzymes alone. The omics revolution is identifying many novel enzymes and paradigms for biomass deconstruction, but more emphasis on function is required, particularly for enzyme cocktails, in which LPMOs may play an important role.
                Bookmark

                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                21 November 2017
                7 November 2017
                7 November 2017
                : 114
                : 47
                : 12590-12595
                Affiliations
                [1] aDepartment of Entomology, Pennsylvania State University , University Park, PA 16802;
                [2] bDepartment of Computer Science and Engineering, University of Notre Dame , Notre Dame, IN 46556;
                [3] cHuck Institutes of the Life Sciences Microscopy and Cytometry Facility, Pennsylvania State University , University Park, PA 16802;
                [4] dCenter for Infectious Disease Dynamics, Pennsylvania State University , University Park, PA 16802;
                [5] eDepartment of Biochemistry and Molecular Biology, Pennsylvania State University , University Park, PA 16802;
                [6] fDepartment of Biology, Pennsylvania State University , University Park, PA 16802
                Author notes
                1To whom correspondence should be addressed. Email: dhughes@ 123456psu.edu .

                Edited by Joan E. Strassmann, Washington University in St. Louis, St. Louis, MO, and approved October 16, 2017 (received for review June 29, 2017)

                Author contributions: M.A.F. and D.P.H. designed research; M.A.F., Y.Z., R.G.L., C.A.M., D.Z.C., and D.P.H. performed research; Y.Z., M.L.H., and D.Z.C. contributed new reagents/analytic tools; M.A.F., Y.Z., and D.P.H. analyzed data; and M.A.F., Y.Z., C.A.M., D.Z.C., and D.P.H. wrote the paper.

                Article
                201711673
                10.1073/pnas.1711673114
                5703306
                29114054
                06c7c815-da6f-40a3-b97e-954e7b09f458
                Copyright © 2017 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 6
                Funding
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: IOS-1558062
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: CCF-1217906
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: CNS-1629914
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: CCF-1617735
                Funded by: HHS | National Institutes of Health (NIH) 100000002
                Award ID: R01-GM116927-02
                Funded by: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) 501100002322
                Award ID: 6203-10-8
                Funded by: American Heart Association (AHA) 100000968
                Award ID: 16POST29920001
                Categories
                Biological Sciences
                Microbiology

                deep learning,fungal networks,extended phenotype,behavioral manipulation,ants

                Comments

                Comment on this article