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      Multicellular magnetotactic bacterial consortia are metabolically differentiated and not clonal

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          Abstract

          Consortia of multicellular magnetotactic bacteria (MMB) are currently the only known example of bacteria without a unicellular stage in their life cycle. Because of their recalcitrance to cultivation, most previous studies of MMB have been limited to microscopic observations. To study the biology of these unique organisms in more detail, we use multiple culture-independent approaches to analyze the genomics and physiology of MMB consortia at single cell resolution. We separately sequenced the metagenomes of 22 individual MMB consortia, representing eight new species, and quantified the genetic diversity within each MMB consortium. This revealed that, counter to conventional views, cells within MMB consortia are not clonal. Single consortia metagenomes were then used to reconstruct the species-specific metabolic potential and infer the physiological capabilities of MMB. To validate genomic predictions, we performed stable isotope probing (SIP) experiments and interrogated MMB consortia using fluorescence in situ hybridization (FISH) combined with nano-scale secondary ion mass spectrometry (NanoSIMS). By coupling FISH with bioorthogonal non-canonical amino acid tagging (BONCAT) we explored their in situ activity as well as variation of protein synthesis within cells. We demonstrate that MMB consortia are mixotrophic sulfate reducers and that they exhibit metabolic differentiation between individual cells, suggesting that MMB consortia are more complex than previously thought. These findings expand our understanding of MMB diversity, ecology, genomics, and physiology, as well as offer insights into the mechanisms underpinning the multicellular nature of their unique lifestyle.

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          CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes

          Large-scale recovery of genomes from isolates, single cells, and metagenomic data has been made possible by advances in computational methods and substantial reductions in sequencing costs. Although this increasing breadth of draft genomes is providing key information regarding the evolutionary and functional diversity of microbial life, it has become impractical to finish all available reference genomes. Making robust biological inferences from draft genomes requires accurate estimates of their completeness and contamination. Current methods for assessing genome quality are ad hoc and generally make use of a limited number of “marker” genes conserved across all bacterial or archaeal genomes. Here we introduce CheckM, an automated method for assessing the quality of a genome using a broader set of marker genes specific to the position of a genome within a reference genome tree and information about the collocation of these genes. We demonstrate the effectiveness of CheckM using synthetic data and a wide range of isolate-, single-cell-, and metagenome-derived genomes. CheckM is shown to provide accurate estimates of genome completeness and contamination and to outperform existing approaches. Using CheckM, we identify a diverse range of errors currently impacting publicly available isolate genomes and demonstrate that genomes obtained from single cells and metagenomic data vary substantially in quality. In order to facilitate the use of draft genomes, we propose an objective measure of genome quality that can be used to select genomes suitable for specific gene- and genome-centric analyses of microbial communities.
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            High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries

            A fundamental question in microbiology is whether there is continuum of genetic diversity among genomes, or clear species boundaries prevail instead. Whole-genome similarity metrics such as Average Nucleotide Identity (ANI) help address this question by facilitating high resolution taxonomic analysis of thousands of genomes from diverse phylogenetic lineages. To scale to available genomes and beyond, we present FastANI, a new method to estimate ANI using alignment-free approximate sequence mapping. FastANI is accurate for both finished and draft genomes, and is up to three orders of magnitude faster compared to alignment-based approaches. We leverage FastANI to compute pairwise ANI values among all prokaryotic genomes available in the NCBI database. Our results reveal clear genetic discontinuity, with 99.8% of the total 8 billion genome pairs analyzed conforming to >95% intra-species and <83% inter-species ANI values. This discontinuity is manifested with or without the most frequently sequenced species, and is robust to historic additions in the genome databases.
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              MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies

              We previously reported on MetaBAT, an automated metagenome binning software tool to reconstruct single genomes from microbial communities for subsequent analyses of uncultivated microbial species. MetaBAT has become one of the most popular binning tools largely due to its computational efficiency and ease of use, especially in binning experiments with a large number of samples and a large assembly. MetaBAT requires users to choose parameters to fine-tune its sensitivity and specificity. If those parameters are not chosen properly, binning accuracy can suffer, especially on assemblies of poor quality. Here, we developed MetaBAT 2 to overcome this problem. MetaBAT 2 uses a new adaptive binning algorithm to eliminate manual parameter tuning. We also performed extensive software engineering optimization to increase both computational and memory efficiency. Comparing MetaBAT 2 to alternative software tools on over 100 real world metagenome assemblies shows superior accuracy and computing speed. Binning a typical metagenome assembly takes only a few minutes on a single commodity workstation. We therefore recommend the community adopts MetaBAT 2 for their metagenome binning experiments. MetaBAT 2 is open source software and available at https://bitbucket.org/berkeleylab/metabat.
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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                27 November 2023
                : 2023.11.27.568837
                Affiliations
                [1 ]Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT 59717
                [2 ]Center for Biofilm Engineering, Montana State University, Bozeman, MT 59717
                [3 ]Thermal Biology Institute, Montana State University, Bozeman, MT 59717
                [4 ]Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354
                [5 ]Department of Energy Joint Genome Institute, Berkeley, CA, 94720
                [6 ]Ecosystems Center and Bay Paul Center, Marine Biological Laboratory, Woods Hole, MA, 02543
                [7 ]Woods Hole Oceanographic Institution, Falmouth, MA 02543
                [8 ]Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT 59717
                Author notes
                [#]

                current address: National Security Directorate, Oak Ridge National Laboratory, Oak Ridge, TN

                [* ]Corresponding author: Roland Hatzenpichler, rolandhatzenpichler@ 123456gmail.com
                Author information
                http://orcid.org/0000-0002-1031-4682
                http://orcid.org/0000-0003-3062-4933
                http://orcid.org/0000-0002-7395-5604
                http://orcid.org/0000-0002-4932-4677
                http://orcid.org/0000-0001-7171-552X
                http://orcid.org/0000-0002-3785-032X
                http://orcid.org/0000-0002-4758-7369
                http://orcid.org/0000-0002-6872-6188
                http://orcid.org/0000-0001-6805-381X
                http://orcid.org/0000-0002-5489-3444
                Article
                10.1101/2023.11.27.568837
                10705294
                38076927
                a066df03-105a-48c7-b79a-6fb4617e316a

                This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator.

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