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      Iron Oxidation by a Fused Cytochrome-Porin Common to Diverse Iron-Oxidizing Bacteria

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          ABSTRACT

          Iron (Fe) oxidation is one of Earth’s major biogeochemical processes, key to weathering, soil formation, water quality, and corrosion. However, our understanding of microbial contribution is limited by incomplete knowledge of microbial iron oxidation mechanisms, particularly in neutrophilic iron oxidizers. The genomes of many diverse iron oxidizers encode a homolog to an outer membrane cytochrome (Cyc2) shown to oxidize iron in two acidophiles. Phylogenetic analyses show Cyc2 sequences from neutrophiles cluster together, suggesting a common function, though this function has not been verified in these organisms. Therefore, we investigated the iron oxidase function of heterologously expressed Cyc2 from a neutrophilic iron oxidizer Mariprofundus ferrooxydans PV-1. Cyc2 PV-1 is capable of oxidizing iron, and its redox potential is 208 ± 20 mV, consistent with the ability to accept electrons from Fe 2+ at neutral pH. These results support the hypothesis that Cyc2 functions as an iron oxidase in neutrophilic iron-oxidizing organisms. The results of sequence analysis and modeling reveal that the entire Cyc2 family shares a unique fused cytochrome-porin structure, with a defining consensus motif in the cytochrome region. On the basis of results from structural analyses, we predict that the monoheme cytochrome Cyc2 specifically oxidizes dissolved Fe 2+, in contrast to multiheme iron oxidases, which may oxidize solid Fe(II). With our results, there is now functional validation for diverse representatives of Cyc2 sequences. We present a comprehensive Cyc2 phylogenetic tree and offer a roadmap for identifying cyc2/Cyc2 homologs and interpreting their function. The occurrence of cyc2 in many genomes beyond known iron oxidizers presents the possibility that microbial iron oxidation may be a widespread metabolism.

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          RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

          Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
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            MUSCLE: multiple sequence alignment with high accuracy and high throughput.

            We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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              BLAST+: architecture and applications

              Background Sequence similarity searching is a very important bioinformatics task. While Basic Local Alignment Search Tool (BLAST) outperforms exact methods through its use of heuristics, the speed of the current BLAST software is suboptimal for very long queries or database sequences. There are also some shortcomings in the user-interface of the current command-line applications. Results We describe features and improvements of rewritten BLAST software and introduce new command-line applications. Long query sequences are broken into chunks for processing, in some cases leading to dramatically shorter run times. For long database sequences, it is possible to retrieve only the relevant parts of the sequence, reducing CPU time and memory usage for searches of short queries against databases of contigs or chromosomes. The program can now retrieve masking information for database sequences from the BLAST databases. A new modular software library can now access subject sequence data from arbitrary data sources. We introduce several new features, including strategy files that allow a user to save and reuse their favorite set of options. The strategy files can be uploaded to and downloaded from the NCBI BLAST web site. Conclusion The new BLAST command-line applications, compared to the current BLAST tools, demonstrate substantial speed improvements for long queries as well as chromosome length database sequences. We have also improved the user interface of the command-line applications.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                mBio
                mBio
                mbio
                mBio
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2150-7511
                27 July 2021
                Jul-Aug 2021
                27 July 2021
                : 12
                : 4
                : e01074-21
                Affiliations
                [a ] Department of Earth Sciences, University of Delawaregrid.33489.35, , Newark, Delaware, USA
                [b ] School of Marine Science and Policy, University of Delawaregrid.33489.35, , Newark, Delaware, USA
                [c ] Department of Biological Sciences, University of Delawaregrid.33489.35, , Newark, Delaware, USA
                [d ] Department of Chemistry and Biochemistry, University of Delawaregrid.33489.35, , Newark, Delaware, USA
                University of California, Berkeley
                Author notes
                [*]

                Present address: Sean M. McAllister, The Cooperative Institute for Climate, Ocean, and Ecosystem Studies, University of Washington, Seattle, Washington, USA, and Pacific Marine Environmental Laboratory, National Oceanic and Atmospheric Administration, Seattle, Washington, USA; Arkadiy Garber, Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, Arizona, USA.

                Author information
                https://orcid.org/0000-0002-0302-3588
                https://orcid.org/0000-0001-6654-3495
                https://orcid.org/0000-0001-7935-0246
                https://orcid.org/0000-0002-7932-5339
                https://orcid.org/0000-0003-4902-0777
                https://orcid.org/0000-0003-1810-4994
                Article
                mBio01074-21
                10.1128/mBio.01074-21
                8406198
                34311573
                915f1b54-f0b3-4fcc-bafd-8b9bc31ec86a
                Copyright © 2021 Keffer et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 19 April 2021
                : 23 June 2021
                Page count
                supplementary-material: 10, Figures: 9, Tables: 0, Equations: 0, References: 90, Pages: 15, Words: 10855
                Funding
                Funded by: National Science Foundation (NSF), FundRef https://doi.org/10.13039/100000001;
                Award ID: EAR-1151682
                Award Recipient :
                Funded by: National Science Foundation (NSF), FundRef https://doi.org/10.13039/100000001;
                Award ID: BIO-1817651
                Award Recipient : Award Recipient : Award Recipient :
                Funded by: DOD | US Navy | Office of Naval Research (ONR), FundRef https://doi.org/10.13039/100000006;
                Award ID: N00014-17-1-2640
                Award Recipient : Award Recipient :
                Categories
                Research Article
                environmental-microbiology, Environmental Microbiology
                Custom metadata
                July/August 2021

                Life sciences
                cytochromes,environmental microbiology,iron metabolism,iron oxidizers,outer membrane proteins

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