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      Connecting structure and function from organisms to molecules in small-animal symbioses through chemo-histo-tomography

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          Metabolites mediate the establishment and persistence of most interkingdom symbioses. Still, to pinpoint the metabolites each partner displays upon interaction remains the biggest challenge in studying multiorganismal assemblages. Addressing this challenge, we developed a correlative imaging workflow to connect the in situ production of metabolites with the organ-scale and cellular three-dimensional distributions of mutualistic and pathogenic (micro)organisms in the same host animal. Combining mass spectrometry imaging and micro-computed X-ray tomography provided a culture-independent approach, which is essential to include the full spectrum of naturally occurring interactions. To introduce the potential of combining high-resolution tomography with metabolite imaging, we resolved the metabolic interactions between an invertebrate host, its symbiotic bacteria, and tissue parasites at unprecedented detail for model and nonmodel symbioses.

          Abstract

          Our understanding of metabolic interactions between small symbiotic animals and bacteria or parasitic eukaryotes that reside within their bodies is extremely limited. This gap in knowledge originates from a methodological challenge, namely to connect histological changes in host tissues induced by beneficial and parasitic (micro)organisms to the underlying metabolites. We addressed this challenge and developed chemo-histo-tomography (CHEMHIST), a culture-independent approach to connect anatomic structure and metabolic function in millimeter-sized symbiotic animals. CHEMHIST combines chemical imaging of metabolites based on mass spectrometry imaging (MSI) and microanatomy-based micro-computed X-ray tomography (micro-CT) on the same animal. Both high-resolution MSI and micro-CT allowed us to correlate the distribution of metabolites to the same animal’s three-dimensional (3D) histology down to submicrometer resolutions. Our protocol is compatible with tissue-specific DNA sequencing and fluorescence in situ hybridization for the taxonomic identification and localization of the associated micro(organisms). Building CHEMHIST upon in situ imaging, we sampled an earthworm from its natural habitat and created an interactive 3D model of its physical and chemical interactions with bacteria and parasitic nematodes in its tissues. Combining MSI and micro-CT, we present a methodological groundwork for connecting metabolic and anatomic phenotypes of small symbiotic animals that often represent keystone species for ecosystem functioning.

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          MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

          We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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            FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

            Background We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. Methodology/Principal Findings Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. Conclusions/Significance FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
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              Image Quality Assessment: From Error Visibility to Structural Similarity

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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                06 July 2021
                28 June 2021
                28 June 2021
                : 118
                : 27
                : e2023773118
                Affiliations
                [1] aMax Planck Institute for Marine Microbiology , 28359 Bremen, Germany;
                [2] bMALDI Imaging Lab, University of Bremen , 28334 Bremen, Germany;
                [3] cSNSB - The Bavarian State Collection of Zoology , 81247 Munich, Germany;
                [4] dEuropean Molecular Biology Laboratory, Hamburg Unit c/o Deutsches Elektronen-Synchrotron , 22607 Hamburg, Germany
                Author notes
                1To whom correspondence may be addressed. Email: bgeier@ 123456mpi-bremen.de or mliebeke@ 123456mpi-bremen.de .

                Edited by Margaret McFall-Ngai, University of Hawaii at Manoa, Honolulu, HI, and approved May 24, 2021 (received for review January 7, 2021)

                Author contributions: B.G. and M.L. designed research; B.G. and M.P. performed research; J.O. and B.R. contributed new reagents/analytic tools; B.G., H.R.G.-V., and M.L. analyzed data; and B.G. and M.L. wrote the paper.

                Author information
                https://orcid.org/0000-0002-2942-2624
                https://orcid.org/0000-0002-4088-5742
                https://orcid.org/0000-0002-4624-4356
                https://orcid.org/0000-0001-5819-1549
                https://orcid.org/0000-0002-2339-1409
                Article
                202023773
                10.1073/pnas.2023773118
                8300811
                34183413
                2a84eb94-d1ec-4166-b5f9-061ce18f2174
                Copyright © 2021 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: 9
                Funding
                Funded by: Gordon and Betty Moore Foundation (Gordon E. and Betty I. Moore Foundation) 100000936
                Award ID: GBMF3811
                Award Recipient : Benedikt Geier Award Recipient : Harald R. Gruber-Vodicka Award Recipient : Manuel Liebeke
                Categories
                403
                Biological Sciences
                Applied Biological Sciences

                x-ray micro-ct imaging,3d reconstruction,metabolomics,symbiosis,multimodal mass spectrometry imaging

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