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      A case of peritoneal dialysis-associated peritonitis caused by Rhodococcus kroppenstedtii

      case-report

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          Abstract

          Background

          Rhodococcus kroppenstedtii is an aerobic, gram-positive bacterium firstly identified in the environment, which has not been reported in human-related infection. Herein, we reported the first case of peritoneal dialysis (PD)-associated peritonitis caused by R. kroppenstedtii which was identified by whole genome sequencing.

          Case presentation

          A 69-year-old man was admitted to hospital with abdominal pain and fever. Over the last 2 years, he had been undergoing continuous ambulatory peritoneal dialysis (CAPD) due to end-stage renal disease. Clinical symptom and sign in combination with laboratory examinations supported the clinical diagnosis of PD-associated peritonitis. Thus, ceftizoxime and teicoplanin were empirically used after PD effluent was collected for bacterial culture. A gram-positive bacterium was found from the PD effluent culture, which could not be identified by either Vitek 2 Compact ANC card or matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. The strain was finally confirmed to be R. kroppenstedtii by whole genome sequencing (WGS) through the average nucleotide identity (ANI) analysis. With a continuous treatment with teicoplanin and imipenem for 15 days and intraperitoneal catheter removed, the infection symptom was improved evidenced by a normal body temperature, also with white blood cell count (WBC), procalcitonin (PCT) and C-reactive protein (CRP) dropped to normal levels. Peritoneal dialysis effluent culture showed a negative result. Then, hemodialysis and arteriovenous fistula angioplasty were performed, but the patient developed a progressive blood pressure loss, accompanied by multiple organ disorder, and died on Feb 25, 2020.

          Conclusions

          To the best of our knowledge, this is the first time to report a peritoneal dialysis-associated peritonitis caused by R. kroppenstedtii which was identified by average nucleotide identity analysis based on WGS.

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          Most cited references22

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          The COG database: a tool for genome-scale analysis of protein functions and evolution.

          Rational classification of proteins encoded in sequenced genomes is critical for making the genome sequences maximally useful for functional and evolutionary studies. The database of Clusters of Orthologous Groups of proteins (COGs) is an attempt on a phylogenetic classification of the proteins encoded in 21 complete genomes of bacteria, archaea and eukaryotes (http://www. ncbi.nlm. nih.gov/COG). The COGs were constructed by applying the criterion of consistency of genome-specific best hits to the results of an exhaustive comparison of all protein sequences from these genomes. The database comprises 2091 COGs that include 56-83% of the gene products from each of the complete bacterial and archaeal genomes and approximately 35% of those from the yeast Saccharomyces cerevisiae genome. The COG database is accompanied by the COGNITOR program that is used to fit new proteins into the COGs and can be applied to functional and phylogenetic annotation of newly sequenced genomes.
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            KEGG for integration and interpretation of large-scale molecular data sets

            Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/ or http://www.kegg.jp/) is a database resource that integrates genomic, chemical and systemic functional information. In particular, gene catalogs from completely sequenced genomes are linked to higher-level systemic functions of the cell, the organism and the ecosystem. Major efforts have been undertaken to manually create a knowledge base for such systemic functions by capturing and organizing experimental knowledge in computable forms; namely, in the forms of KEGG pathway maps, BRITE functional hierarchies and KEGG modules. Continuous efforts have also been made to develop and improve the cross-species annotation procedure for linking genomes to the molecular networks through the KEGG Orthology system. Here we report KEGG Mapper, a collection of tools for KEGG PATHWAY, BRITE and MODULE mapping, enabling integration and interpretation of large-scale data sets. We also report a variant of the KEGG mapping procedure to extend the knowledge base, where different types of data and knowledge, such as disease genes and drug targets, are integrated as part of the KEGG molecular networks. Finally, we describe recent enhancements to the KEGG content, especially the incorporation of disease and drug information used in practice and in society, to support translational bioinformatics.
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              OrthoANI: An improved algorithm and software for calculating average nucleotide identity.

              Species demarcation in Bacteria and Archaea is mainly based on overall genome relatedness, which serves a framework for modern microbiology. Current practice of obtaining these measures between two strains is shifting from experimentally determined similarity obtained by DNA-DNA hybridization (DDH) to genome sequence-based similarity. Average nucleotide identity (ANI) is a simple algorithm that mimics DDH. Like DDH, ANI values between two genome sequences may be different from each other when reciprocal calculations are compared. We compared 63,690 pairs of genome sequences and found that the differences in reciprocal ANI values are significantly high, showing over 1% in some cases. To resolve this problem of not being symmetrical, new algorithm, named OrthoANI, was developed to accommodate the concept of orthology for which both genome sequences were fragmented and only orthologous fragment pairs taken into consideration for calculating nucleotide identities. OrthoANI is highly correlated with ANI (using BLASTn) and the former showed ~0.1% higher values than the latter. In conclusion, OrthoANI provides a more robust and faster means of calculating average nucleotide identity for the taxonomic purposes. The standalone software tools are freely available at http://www.ezbiocloud.net/sw/oat.
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                Author and article information

                Contributors
                zwq_096@163.com
                dr.wu@nju.edu.cn
                Journal
                BMC Infect Dis
                BMC Infect Dis
                BMC Infectious Diseases
                BioMed Central (London )
                1471-2334
                13 June 2021
                13 June 2021
                2021
                : 21
                : 565
                Affiliations
                [1 ]GRID grid.89957.3a, ISNI 0000 0000 9255 8984, Department of Infectious Diseases, Nanjing Drum Tower Hospital, , Clinical College of Nanjing Medical University, ; 321# Zhongshan Road, Gulou District, Nanjing City, Jiangsu Province 210008 P. R. China
                [2 ]GRID grid.412676.0, ISNI 0000 0004 1799 0784, Department of Laboratory Medicine, Nanjing Drum Tower Hospital, , the Affiliated Hospital of Nanjing University Medical School, ; Nanjing, Jiangsu Province 210008 P. R. China
                Author information
                http://orcid.org/0000-0003-2214-8500
                Article
                6280
                10.1186/s12879-021-06280-8
                8201711
                34120601
                4ab9bb49-5523-4ce9-aa8d-5d60315e64b0
                © The Author(s) 2021

                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
                : 2 December 2020
                : 3 June 2021
                Categories
                Case Report
                Custom metadata
                © The Author(s) 2021

                Infectious disease & Microbiology
                rhodococcus kroppenstedtii,peritoneal dialysis-associated peritonitis,whole genome sequencing,average nucleotide identity

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