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      Probability of outbreaks and cross-border dissemination of the emerging pathogen: a genomic survey of Elizabethkingia meningoseptica

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          ABSTRACT

          The emerging infectious agent Elizabethkingia meningoseptica is associated with life-threatening infections in immunocompromised individuals. However, there are limited data on its geographic distribution, phylogenetic evolution, pathogenesis, and transmission. In this study, we comprehensively analyze and compare the genomic features, evolutionary history, emergence date, and transmission networks of global E. meningoseptica. Geographical distribution reveals the presence of the emerging bacteria in Asia, Europe, and North America, three continents with similar latitudes. Phylogenetic analyses show no relationship between the strain’s evolutionary history and its location, origin, or source, despite the presence of genetic diversity. Analysis of the emergence timeline suggests that America is the most likely source of E. meningoseptica with the common ancestor of this pathogen dating back 90 years. Putative transmission networks indicate that E. meningoseptica bacteria can spread within the same hospital and even across borders. Minor variations in resistance genotypes and virulence genes are observed, supporting existing evidence of inherent resistance and pathogenicity in E. meningoseptica. Additionally, minocycline and doxycycline demonstrate potent antimicrobial activity against this pathogen, making them promising candidates for treating E. meningoseptica infections. Our research highlights the potential for severe nosocomial outbreaks caused by E. meningoseptica with horizontal transmission occurring between countries worldwide. To prevent future outbreak infections, increased genomic surveillance of global E. meningoseptica populations is necessary.

          IMPORTANCE

          Elizabethkingia meningoseptica is an emerging infectious agent associated with life-threatening infections in immunocompromised individuals. However, there are limited data available on the genomic features of E. meningoseptica. This study aims to characterize the geographical distribution, phylogenetic evolution, pathogenesis, and transmission of this bacterium. A systematic analysis of the E. meningoseptica genome revealed that a common ancestor of this bacterium existed 90 years ago. The evolutionary history showed no significant relationship with the sample source, origin, or region, despite the presence of genetic diversity. Whole genome sequencing data also demonstrated that E. meningoseptica bacteria possess inherent resistance and pathogenicity, enabling them to spread within the same hospital and even across borders. This study highlights the potential for E. meningoseptica to cause severe nosocomial outbreaks and horizontal transmission between countries worldwide. The available evidence is crucial for the development of evidence-based public health policies to prevent global outbreaks caused by emerging pathogens.

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          Most cited references59

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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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            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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              A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood.

              The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximum- likelihood principle, which clearly satisfies these requirements. The core of this method is a simple hill-climbing algorithm that adjusts tree topology and branch lengths simultaneously. This algorithm starts from an initial tree built by a fast distance-based method and modifies this tree to improve its likelihood at each iteration. Due to this simultaneous adjustment of the topology and branch lengths, only a few iterations are sufficient to reach an optimum. We used extensive and realistic computer simulations to show that the topological accuracy of this new method is at least as high as that of the existing maximum-likelihood programs and much higher than the performance of distance-based and parsimony approaches. The reduction of computing time is dramatic in comparison with other maximum-likelihood packages, while the likelihood maximization ability tends to be higher. For example, only 12 min were required on a standard personal computer to analyze a data set consisting of 500 rbcL sequences with 1,428 base pairs from plant plastids, thus reaching a speed of the same order as some popular distance-based and parsimony algorithms. This new method is implemented in the PHYML program, which is freely available on our web page: http://www.lirmm.fr/w3ifa/MAAS/.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                Microbiol Spectr
                Microbiol Spectr
                spectrum
                Microbiology Spectrum
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2165-0497
                Nov-Dec 2023
                10 October 2023
                10 October 2023
                : 11
                : 6
                : e01602-23
                Affiliations
                [1 ] State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine; , Hangzhou, Zhejiang, China
                [2 ] Department of Neurosurgery, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine); , Shaoxing, Zhejiang, China
                [3 ] Data Resource Development Department, Hangzhou Matridx Biotechnology Co., Ltd.; , Hangzhou, Zhejiang, China
                [4 ] Department of Pathology, Zhejiang Provincial Hospital of Chinese Medicine; , Hangzhou, Zhejiang, China
                [5 ] Department of Structure and Morphology, Jinan Microecological Biomedicine Shandong Laboratory; , Jinan, Shandong, China
                [6 ] Research Units of Infectious Diseases and Microecology, Chinese Academy of Medical Sciences; , Beijing, Hebei, China
                Institut National de Santé Publique du Québec, Sainte-Anne-de-Bellevue; , Québec, Canada
                Author notes
                Address correspondence to Yonghong Xiao, xiaoyonghong@ 123456zju.edu.cn
                Address correspondence to Beiwen Zheng, zhengbw@ 123456zju.edu.cn

                Shaohua Hu and Yingying Chen contributed equally to this article. Author order was determined by friendly negotiation.

                The authors declare no conflict of interest.

                Author information
                https://orcid.org/0000-0002-8687-6905
                https://orcid.org/0000-0002-1135-722X
                https://orcid.org/0000-0002-7690-372X
                Article
                01602-23 spectrum.01602-23
                10.1128/spectrum.01602-23
                10714787
                37815354
                a700a6eb-32e4-444e-b2be-87ce6852fbb5
                Copyright © 2023 Hu et al.

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

                History
                : 16 April 2023
                : 14 August 2023
                Page count
                supplementary-material: 3, authors: 9, Figures: 7, References: 59, Pages: 15, Words: 7707
                Funding
                Funded by: MOST | Natural Science Foundation of Zhejiang Province (ZJNSF);
                Award ID: LQ21H190002
                Award Recipient :
                Funded by: State Key Laboratory for Diagnosis and Treatment of Infectious Diseases (SKL-DTID);
                Award Recipient :
                Funded by: MOST | National Natural Science Foundation of China (NSFC);
                Award ID: 82072314
                Award Recipient :
                Funded by: Research Project of Jinan Microecological Biomedicine Shangdong Laboratory;
                Award ID: JNL-2022006B, JNL-2022011B
                Award Recipient :
                Funded by: MOE | Fundamental Research Funds for the Central Universities (Fundamental Research Fund for the Central Universities);
                Award ID: 2022ZFJH003
                Award Recipient :
                Funded by: CAMS Innovation Fund for Medical Sciences;
                Award ID: 2019-I2M-5-045
                Award Recipient :
                Categories
                Research Article
                clinical-microbiology, Clinical Microbiology
                Custom metadata
                November/December 2023

                elizabethkingia meningoseptica,genome sequencing,transmission distribution,phylogenetic structure,system genomics,nosocomial outbreak

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