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      Effect of long-term methylene blue treatment on the composition of mouse gut microbiome and its relationship with the cognitive abilities of mice

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          Abstract

          In recent years, methylene blue (MB) has attracted considerable interest as a potential drug for the treatment of methemoglobinemia and neurodegenerative diseases. MB is active against microorganisms from various taxonomic groups. However, no studies have yet been conducted on the effect of MB on the intestinal microbiome of model animals. The aim of this work was to study the effect of different concentrations of MB on the mouse gut microbiome and its relationship with the cognitive abilities of mice. We showed that a low MB concentration (15 mg/kg/day) did not cause significant changes in the microbiome composition. The Bacteroidetes/Firmicutes ratio decreased relative to the control on the 2 nd and 3 rd weeks. A slight decrease in the levels Actinobacteria was detected on the 3 rd week of the experiment. Changes in the content of Delta, Gamma, and Epsilonproteobacteria have been also observed. We did not find significant alterations in the composition of intestinal microbiome, which could be an indication of the development of dysbiosis or other gut dysfunction. At the same time, a high concentration of MB (50 mg/kg/day) led to pronounced changes, primarily an increase in the levels of Delta, Gamma and Epsilonproteobacteria. Over 4 weeks of therapy, the treatment with high MB concentration has led to an increase in the median content of Proteobacteria to 7.49% vs. 1.61% in the control group. Finally, we found that MB at a concentration of 15 mg/kg/day improved the cognitive abilities of mice, while negative correlation between the content of Deferribacteres and cognitive parameters was revealed. Our data expand the understanding of the relationship between MB, cognitive abilities, and gut microbiome in respect to the antibacterial properties of MB.

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          Search and clustering orders of magnitude faster than BLAST.

          Biological sequence data is accumulating rapidly, motivating the development of improved high-throughput methods for sequence classification. UBLAST and USEARCH are new algorithms enabling sensitive local and global search of large sequence databases at exceptionally high speeds. They are often orders of magnitude faster than BLAST in practical applications, though sensitivity to distant protein relationships is lower. UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters. UCLUST offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets. Binaries are available at no charge for non-commercial use at http://www.drive5.com/usearch.
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            Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

            The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (> or = 95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.
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              Proteobacteria: microbial signature of dysbiosis in gut microbiota.

              Recent advances in sequencing techniques, applied to the study of microbial communities, have provided compelling evidence that the mammalian intestinal tract harbors a complex microbial community whose composition is a critical determinant of host health in the context of metabolism and inflammation. Given that an imbalanced gut microbiota often arises from a sustained increase in abundance of the phylum Proteobacteria, the natural human gut flora normally contains only a minor proportion of this phylum. Here, we review studies that explored the association between an abnormal expansion of Proteobacteria and a compromised ability to maintain a balanced gut microbial community. We also propose that an increased prevalence of Proteobacteria is a potential diagnostic signature of dysbiosis and risk of disease.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: ValidationRole: Writing – original draft
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Writing – original draft
                Role: Formal analysisRole: Investigation
                Role: InvestigationRole: Visualization
                Role: Formal analysisRole: Methodology
                Role: Formal analysisRole: Methodology
                Role: Formal analysisRole: Methodology
                Role: Investigation
                Role: Funding acquisitionRole: Project administrationRole: Supervision
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                18 November 2020
                2020
                : 15
                : 11
                : e0241784
                Affiliations
                [1 ] Department of Genetics, Cytology and Bioengineering, Voronezh State University, Voronezh, Russia
                [2 ] Laboratory of Metagenomics and Food Biotechnology, Voronezh State University of Engineering Technologies, Voronezh, Russia
                [3 ] Laboratory of Innovative Recombinant Proteomics, All-Russian Veterinary Research Institute of Pathology, Pharmacology and Therapy, Voronezh, Russia
                McMaster University, CANADA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0003-3562-5329
                https://orcid.org/0000-0001-9028-0613
                https://orcid.org/0000-0003-0432-1760
                https://orcid.org/0000-0002-4626-7320
                https://orcid.org/0000-0003-2076-3868
                Article
                PONE-D-20-10817
                10.1371/journal.pone.0241784
                7673545
                33206681
                b2d4c4b0-de6d-4fd1-bb4a-25e01a242af5
                © 2020 Gureev et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 15 April 2020
                : 20 October 2020
                Page count
                Figures: 5, Tables: 2, Pages: 18
                Funding
                Funded by: Grant of the President of the Russian Federation for young scientists
                Award ID: MK-3173.2019.11
                Award Recipient :
                Funded by: RF Ministry of Science and Higher Education in the framework of the national project “Science”
                Award ID: project FZGW-2020-0001, Unique number of the register of State tasks 075001X39782002
                Award Recipient :
                This work was supported by the grant of the President of the Russian Federation for young scientists (Project MK-3173.2019.11) to M.Yu.S. and A.P.G.; by RF Ministry of Science and Higher Education in the framework of the national project “Science” (Agreement 075-03-2020-088, Unique number of the register of State tasks 075001X39782002) to V.N.P.
                Categories
                Research Article
                Biology and Life Sciences
                Microbiology
                Medical Microbiology
                Microbiome
                Biology and Life Sciences
                Genetics
                Genomics
                Microbial Genomics
                Microbiome
                Biology and Life Sciences
                Microbiology
                Microbial Genomics
                Microbiome
                Biology and Life Sciences
                Organisms
                Bacteria
                Gut Bacteria
                Physical Sciences
                Chemistry
                Chemical Compounds
                Hydrocarbons
                Methylenes
                Methylene Blue
                Biology and Life Sciences
                Organisms
                Bacteria
                Actinobacteria
                Biology and Life Sciences
                Microbiology
                Medical Microbiology
                Microbial Pathogens
                Bacterial Pathogens
                Epsilonproteobacteria
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Pathogens
                Microbial Pathogens
                Bacterial Pathogens
                Epsilonproteobacteria
                Biology and Life Sciences
                Anatomy
                Digestive System
                Gastrointestinal Tract
                Medicine and Health Sciences
                Anatomy
                Digestive System
                Gastrointestinal Tract
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Mammals
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                Mice
                Biology and Life Sciences
                Zoology
                Animals
                Vertebrates
                Amniotes
                Mammals
                Rodents
                Mice
                Medicine and Health Sciences
                Gastroenterology and Hepatology
                Inflammatory Bowel Disease
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                All relevant data are within the manuscript and its Supporting Information files.

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