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      The anticancer and antibacterial potential of bioactive secondary metabolites derived From bacterial endophytes in association with Artemisia absinthium

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          Abstract

          The continuous search for secondary metabolites in microorganisms isolated from untapped reservoirs is an effective prospective approach to drug discovery. In this study, an in-depth analysis was conducted to investigate the diversity of culturable bacterial endophytes present in the medicinal plant A. absinthium, as well as the antibacterial and anticancer potential of their bioactive secondary metabolites. The endophytic bacteria recovered from A. absinthium, were characterized via the implementation of suitable biochemical and molecular analyses. Agar well diffusion and broth microdilution were used to screen antibacterial activity. SEM was performed to assess the impact of the extracted metabolite on MRSA strain cell morphology. Apoptosis and cytotoxicity assays were used to evaluate anticancer activity against MCF7 and A549. The FTIR, GC–MS were used to detect bioactive compounds in the active solvent fraction. Of the various endophytic bacteria studied, P. aeruginosa SD01 showed discernible activity against both bacterial pathogens and malignancies. The crude ethyl acetate extract of P. aeruginosa SD01 showed MICs of 32 and 128 µg/mL for S. aureus and MRSA, respectively. SEM examination demonstrated MRSA bacterial cell lysis, hole development, and intracellular leaking. This study revealed that the crude bioactive secondary metabolite SD01 has potent anticancer activity. In this study, 2-aminoacetophenone, 1,2-apyrazine-1,4-dione, phenazine and 2-phenyl-4-cyanopyridine were the major bioactive secondary metabolites. In conclusion, our findings indicate that the bacteria recovered from A. absinthium plants and in particular, P. aeruginosa SD01 is a remarkable source of untapped therapeutic, i.e., antimicrobial and anticancer compounds.

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          Most cited references48

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          MEGA11: Molecular Evolutionary Genetics Analysis Version 11

          The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens, and gene families using rapid relaxed-clock methods. Methods for estimating divergence times and confidence intervals are implemented to use probability densities for calibration constraints for node-dating and sequence sampling dates for tip-dating analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor , and an extended Tree Explorer to display timetrees. Also added is a Bayesian method for estimating neutral evolutionary probabilities of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihood analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net .
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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
                shojaei@med.mui.ac.ir
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                27 October 2023
                27 October 2023
                2023
                : 13
                : 18473
                Affiliations
                Department of Microbiology, School of Medicine, Isfahan University of Medical Sciences, ( https://ror.org/04waqzz56) Isfahan, Iran
                Article
                45910
                10.1038/s41598-023-45910-w
                10611800
                37891400
                89ba20a2-668c-4008-ade7-bbe9df356a05
                © 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 July 2023
                : 25 October 2023
                Categories
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                Custom metadata
                © Springer Nature Limited 2023

                Uncategorized
                cancer therapy,antimicrobials,applied microbiology,phenotypic screening
                Uncategorized
                cancer therapy, antimicrobials, applied microbiology, phenotypic screening

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