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      Potential for and Distribution of Enzymatic Biodegradation of Polystyrene by Environmental Microorganisms

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          Abstract

          Polystyrene (PS) is one of the main polymer types of plastic wastes and is known to be resistant to biodegradation, resulting in PS waste persistence in the environment. Although previous studies have reported that some microorganisms can degrade PS, enzymes and mechanisms of microorganism PS biodegradation are still unknown. In this study, we summarized microbial species that have been identified to degrade PS. By screening the available genome information of microorganisms that have been reported to degrade PS for enzymes with functional potential to depolymerize PS, we predicted target PS-degrading enzymes. We found that cytochrome P4500s, alkane hydroxylases and monooxygenases ranked as the top potential enzyme classes that can degrade PS since they can break C–C bonds. Ring-hydroxylating dioxygenases may be able to break the side-chain of PS and oxidize the aromatic ring compounds generated from the decomposition of PS. These target enzymes were distributed in Proteobacteria, Actinobacteria, Bacteroidetes, and Firmicutes, suggesting a broad potential for PS biodegradation in various earth environments and microbiomes. Our results provide insight into the enzymatic degradation of PS and suggestions for realizing the biodegradation of this recalcitrant plastic.

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          Most cited references73

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          MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

          The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.
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            The neighbor-joining method: a new method for reconstructing phylogenetic trees.

            N Saitou, M Nei (1987)
            A new method called the neighbor-joining method is proposed for reconstructing phylogenetic trees from evolutionary distance data. The principle of this method is to find pairs of operational taxonomic units (OTUs [= neighbors]) that minimize the total branch length at each stage of clustering of OTUs starting with a starlike tree. The branch lengths as well as the topology of a parsimonious tree can quickly be obtained by using this method. Using computer simulation, we studied the efficiency of this method in obtaining the correct unrooted tree in comparison with that of five other tree-making methods: the unweighted pair group method of analysis, Farris's method, Sattath and Tversky's method, Li's method, and Tateno et al.'s modified Farris method. The new, neighbor-joining method and Sattath and Tversky's method are shown to be generally better than the other methods.
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              Prospects for inferring very large phylogenies by using the neighbor-joining method.

              Current efforts to reconstruct the tree of life and histories of multigene families demand the inference of phylogenies consisting of thousands of gene sequences. However, for such large data sets even a moderate exploration of the tree space needed to identify the optimal tree is virtually impossible. For these cases the neighbor-joining (NJ) method is frequently used because of its demonstrated accuracy for smaller data sets and its computational speed. As data sets grow, however, the fraction of the tree space examined by the NJ algorithm becomes minuscule. Here, we report the results of our computer simulation for examining the accuracy of NJ trees for inferring very large phylogenies. First we present a likelihood method for the simultaneous estimation of all pairwise distances by using biologically realistic models of nucleotide substitution. Use of this method corrects up to 60% of NJ tree errors. Our simulation results show that the accuracy of NJ trees decline only by approximately 5% when the number of sequences used increases from 32 to 4,096 (128 times) even in the presence of extensive variation in the evolutionary rate among lineages or significant biases in the nucleotide composition and transition/transversion ratio. Our results encourage the use of complex models of nucleotide substitution for estimating evolutionary distances and hint at bright prospects for the application of the NJ and related methods in inferring large phylogenies.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Materials (Basel)
                Materials (Basel)
                materials
                Materials
                MDPI
                1996-1944
                21 January 2021
                February 2021
                : 14
                : 3
                : 503
                Affiliations
                Department of Chemistry, SUNY College of Environmental Science and Forestry, Syracuse, NY 13210, USA; lhou02@ 123456esf.edu
                Author notes
                [* ]Correspondence: elmajumd@ 123456esf.edu or emajumder@ 123456wisc.edu ; Tel.: +1-3154706854
                Author information
                https://orcid.org/0000-0003-2337-9175
                https://orcid.org/0000-0003-3444-3881
                Article
                materials-14-00503
                10.3390/ma14030503
                7864516
                33494256
                8820c457-ff08-40b1-9552-79719c67b38e
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 18 December 2020
                : 16 January 2021
                Categories
                Communication

                plastics,polystyrene biodegradation,enzymatic biodegradation,monooxygenase,alkane hydroxylase,cytochrome p450

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