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      Complete genome analysis of pathogenic Metschnikowia bicuspidata strain MQ2101 isolated from diseased ridgetail white prawn, Exopalaemon carinicauda

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          Abstract

          Background

          Metschnikowia bicuspidata is a pathogenic yesst that can cause disease in many different economic aquatic animal species. In recent years, there was a new disease outbreak in ridgetail white prawn ( Exopalaemon carinicauda) in coastal areas of Jiangsu Province China that was referred to as zombie disease by local farmers. The pathogen was first isolated and identified as M. bicuspidata. Although the pathogenicity and pathogenesis of this pathogen in other animals have been reported in some previous studies, research on its molecular mechanisms is still very limited. Therefore, a genome-wide study is necessary to better understand the physiological and pathogenic mechanisms of M. bicuspidata.

          Result

          In this study, we obtained a pathogenic strain, MQ2101, of M. bicuspidata from diseased E. carinicauda and sequenced its whole genome. The size of the whole genome was 15.98 Mb, and it was assembled into 5 scaffolds. The genome contained 3934 coding genes, among which 3899 genes with biological functions were annotated in multiple underlying databases. In KOG database, 2627 genes were annotated, which were categorized into 25 classes including general function prediction only, posttranslational modification, protein turnover, chaperones, and signal transduction mechanisms. In KEGG database, 2493 genes were annotated, which were categorized into five classes, including cellular processes, environmental information processing, genetic information processing, metabolism and organismal systems. In GO database, 2893 genes were annotated, which were mainly classified in cell, cell part, cellular processes and metabolic processes. There were 1055 genes annotated in the PHI database, accounting for 26.81% of the total genome, among which 5 genes were directly related to pathogenicity (identity ≥ 50%), including hsp90, PacC, and PHO84. There were also some genes related to the activity of the yeast itself that could be targeted by antiyeast drugs. Analysis based on the DFVF database showed that strain MQ2101 contained 235 potential virulence genes. BLAST searches in the CAZy database showed that strain MQ2101 may have a more complex carbohydrate metabolism system than other yeasts of the same family. In addition, two gene clusters and 168 putative secretory proteins were predicted in strain MQ2101, and functional analysis showed that some of the secretory proteins may be directly involved in the pathogenesis of the strain. Gene family analysis with five other yeasts revealed that strain MQ2101 has 245 unique gene families, including 274 genes involved in pathogenicity that could serve as potential targets.

          Conclusion

          Genome-wide analysis elucidated the pathogenicity-associated genes of M. bicuspidate while also revealing a complex metabolic mechanism and providing putative targets of action for the development of antiyeast drugs for this pathogen. The obtained whole-genome sequencing data provide an important theoretical basis for transcriptomic, proteomic and metabolic studies of M. bicuspidata and lay a foundation for defining its specific mechanism of host infestation.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12866-023-02865-2.

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          Most cited references68

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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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            Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis.

            The use of some multiple-sequence alignments in phylogenetic analysis, particularly those that are not very well conserved, requires the elimination of poorly aligned positions and divergent regions, since they may not be homologous or may have been saturated by multiple substitutions. A computerized method that eliminates such positions and at the same time tries to minimize the loss of informative sites is presented here. The method is based on the selection of blocks of positions that fulfill a simple set of requirements with respect to the number of contiguous conserved positions, lack of gaps, and high conservation of flanking positions, making the final alignment more suitable for phylogenetic analysis. To illustrate the efficiency of this method, alignments of 10 mitochondrial proteins from several completely sequenced mitochondrial genomes belonging to diverse eukaryotes were used as examples. The percentages of removed positions were higher in the most divergent alignments. After removing divergent segments, the amino acid composition of the different sequences was more uniform, and pairwise distances became much smaller. Phylogenetic trees show that topologies can be different after removing conserved blocks, particularly when there are several poorly resolved nodes. Strong support was found for the grouping of animals and fungi but not for the position of more basal eukaryotes. The use of a computerized method such as the one presented here reduces to a certain extent the necessity of manually editing multiple alignments, makes the automation of phylogenetic analysis of large data sets feasible, and facilitates the reproduction of the final alignment by other researchers.
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              tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence.

              We describe a program, tRNAscan-SE, which identifies 99-100% of transfer RNA genes in DNA sequence while giving less than one false positive per 15 gigabases. Two previously described tRNA detection programs are used as fast, first-pass prefilters to identify candidate tRNAs, which are then analyzed by a highly selective tRNA covariance model. This work represents a practical application of RNA covariance models, which are general, probabilistic secondary structure profiles based on stochastic context-free grammars. tRNAscan-SE searches at approximately 30 000 bp/s. Additional extensions to tRNAscan-SE detect unusual tRNA homologues such as selenocysteine tRNAs, tRNA-derived repetitive elements and tRNA pseudogenes.
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                Author and article information

                Contributors
                wxh1708@163.com
                sqin@yic.ac.cn
                Journal
                BMC Microbiol
                BMC Microbiol
                BMC Microbiology
                BioMed Central (London )
                1471-2180
                29 April 2023
                29 April 2023
                2023
                : 23
                : 120
                Affiliations
                [1 ]GRID grid.453127.6, ISNI 0000 0004 1798 2362, Key Laboratory of Biology and Bioresource Utilization, , Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, ; No. 17, Chunhui Road, Yantai, Shandong Province 264003 People’s Republic of China
                [2 ]Institute of Oceanology & Marine Fisheries, No. 31, Jiaoyu Road, Nantong, Jiangsu 226007 People’s Republic of China
                [3 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, University of Chinese Academy of Sciences, ; Beijing, 100049 People’s Republic of China
                [4 ]GRID grid.412514.7, ISNI 0000 0000 9833 2433, National Demonstration Center for Experimental Fisheries Science Education, , Shanghai Ocean University, ; Shanghai, 201306 People’s Republic of China
                Article
                2865
                10.1186/s12866-023-02865-2
                10148492
                88788939-7f46-4bd0-9166-14b149de9d7a
                © The Author(s) 2023

                Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 1 November 2022
                : 18 April 2023
                Funding
                Funded by: This research was funded by The Jiangsu Provincial Agricultural Major New Varieties Creation Project
                Award ID: PZCZ201747
                Award Recipient :
                Funded by: Jiangsu Agricultural Industry Technology System
                Award ID: JATS[2022]417
                Award Recipient :
                Funded by: The “JBGS” Project of Seed Industry Revitalization in Jiangsu Province
                Award ID: JBGS〔2021〕122
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2023

                Microbiology & Virology
                exopalaemon carinicauda,metschnikowia bicuspidata,complete genome sequence,virulence genes,metabolic mechanism

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