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      Aerobic bacteria produce nitric oxide via denitrification and promote algal population collapse

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          Abstract

          Microbial interactions govern marine biogeochemistry. These interactions are generally considered to rely on exchange of organic molecules. Here we report on a novel inorganic route of microbial communication, showing that algal-bacterial interactions between Phaeobacter inhibens bacteria and Gephyrocapsa huxleyi algae are mediated through inorganic nitrogen exchange. Under oxygen-rich conditions, aerobic bacteria reduce algal-secreted nitrite to nitric oxide (NO) through denitrification, a well-studied anaerobic respiratory mechanism. The bacterial NO is involved in triggering a cascade in algae akin to programmed cell death. During death, algae further generate NO, thereby propagating the signal in the algal population. Eventually, the algal population collapses, similar to the sudden demise of oceanic algal blooms. Our study suggests that the exchange of inorganic nitrogen species in oxygenated environments is a potentially significant route of microbial communication within and across kingdoms.

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          Most cited references112

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          IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

          Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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            Prokka: rapid prokaryotic genome annotation.

            T Seemann (2014)
            The multiplex capability and high yield of current day DNA-sequencing instruments has made bacterial whole genome sequencing a routine affair. The subsequent de novo assembly of reads into contigs has been well addressed. The final step of annotating all relevant genomic features on those contigs can be achieved slowly using existing web- and email-based systems, but these are not applicable for sensitive data or integrating into computational pipelines. Here we introduce Prokka, a command line software tool to fully annotate a draft bacterial genome in about 10 min on a typical desktop computer. It produces standards-compliant output files for further analysis or viewing in genome browsers. Prokka is implemented in Perl and is freely available under an open source GPLv2 license from http://vicbioinformatics.com/. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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              MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform.

              K Katoh (2002)
              A multiple sequence alignment program, MAFFT, has been developed. The CPU time is drastically reduced as compared with existing methods. MAFFT includes two novel techniques. (i) Homo logous regions are rapidly identified by the fast Fourier transform (FFT), in which an amino acid sequence is converted to a sequence composed of volume and polarity values of each amino acid residue. (ii) We propose a simplified scoring system that performs well for reducing CPU time and increasing the accuracy of alignments even for sequences having large insertions or extensions as well as distantly related sequences of similar length. Two different heuristics, the progressive method (FFT-NS-2) and the iterative refinement method (FFT-NS-i), are implemented in MAFFT. The performances of FFT-NS-2 and FFT-NS-i were compared with other methods by computer simulations and benchmark tests; the CPU time of FFT-NS-2 is drastically reduced as compared with CLUSTALW with comparable accuracy. FFT-NS-i is over 100 times faster than T-COFFEE, when the number of input sequences exceeds 60, without sacrificing the accuracy.
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                Author and article information

                Contributors
                einat.segev@weizmann.ac.il
                Journal
                ISME J
                ISME J
                The ISME Journal
                Nature Publishing Group UK (London )
                1751-7362
                1751-7370
                12 May 2023
                12 May 2023
                August 2023
                : 17
                : 8
                : 1167-1183
                Affiliations
                [1 ]GRID grid.13992.30, ISNI 0000 0004 0604 7563, Department of Plant and Environmental Sciences, , The Weizmann Institute of Science, ; Rehovot, Israel
                [2 ]GRID grid.13992.30, ISNI 0000 0004 0604 7563, Depertment of Chemical Research Support, , The Weizmann Institute of Science, ; Rehovot, Israel
                [3 ]GRID grid.13992.30, ISNI 0000 0004 0604 7563, Department of Life Science Core Facilities, , The Weizmann Institute of Science, ; Rehovot, Israel
                [4 ]GRID grid.428474.9, ISNI 0000 0004 1776 9385, CIDEA Consortium Conacyt-Centro de Investigación en Alimentación y Desarrollo, ; Hermosillo, Mexico
                [5 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Department of Earth, Atmospheric and Planetary Sciences, , Massachusetts Institute of Technology, ; Cambridge, MA USA
                Author information
                http://orcid.org/0000-0002-3694-640X
                http://orcid.org/0000-0001-8707-7761
                http://orcid.org/0000-0001-9604-1939
                http://orcid.org/0000-0002-8083-4325
                http://orcid.org/0000-0002-9118-6933
                http://orcid.org/0000-0002-5046-0609
                http://orcid.org/0000-0002-2266-1219
                Article
                1427
                10.1038/s41396-023-01427-8
                10356946
                37173383
                49cae945-b3b4-4785-ab49-bf9d4a835b85
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 22 November 2022
                : 23 April 2023
                : 28 April 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001658, Minerva Foundation (Minerva Stiftung);
                Funded by: The Angel Faivovich Foundation for Ecological Research The Weizmann SABRA - Yeda-Sela - WRC Program The Estate of Emile Mimran, and the Maurice and Vivienne Wohl Biology Endowment
                Funded by: The Dean of Faculty fellowship The Sir Charles Clore Postdoctoral Fellowship
                Funded by: The Weizmann SAERI program
                Funded by: FundRef https://doi.org/10.13039/100000893, Simons Foundation;
                Award ID: 622065
                Award ID: 622065
                Award Recipient :
                Categories
                Article
                Custom metadata
                © International Society for Microbial Ecology 2023

                Microbiology & Virology
                water microbiology,biogeochemistry
                Microbiology & Virology
                water microbiology, biogeochemistry

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