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      Predominance of Escherichia-Shigella in Gut Microbiome and Its Potential Correlation with Elevated Level of Plasma Tumor Necrosis Factor Alpha in Patients with Tuberculous Meningitis

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          ABSTRACT

          Tuberculous meningitis (TBM), the most lethal and disabling form of tuberculosis (TB), may be related to gut microbiota composition, warranting further study. Here we systematically compared gut microbiota compositions and blood cytokine profiles of TBM patients, pulmonary TB patients, and healthy controls. Notably, the significant gut microbiota dysbiosis observed in TBM patients was associated with markedly high proportions of Escherichia-Shigella species as well as increased blood levels of tumor necrosis factor alpha (TNF-α) and interleukin 6 (IL-6). Next, we obtained a fecal bacterial isolate from a TBM patient and administered it via oral gavage to mice in order to develop a murine gut microbiota dysbiosis model for use in exploring mechanisms underlying the observed relationship between gut microbial dysbiosis and TBM. Thereafter, cells of commensal Escherichia coli ( E. coli) were isolated and administered to model mice by gavage and then mice were inoculated with Mycobacterium tuberculosis ( M. tuberculosis). Subsequently, these mice exhibited increased blood TNF-α levels accompanied by downregulated expression of tight junction protein claudin-5, increased brain tissue bacterial burden, and elevated central nervous system inflammation relative to corresponding indicators in controls administered PBS by gavage. Thus, our results demonstrated that a signature dysbiotic gut microbiome profile containing a high proportion of E. coli was potentially associated with an increased circulating TNF-α level in TBM patients. Collectively, these results suggest that modulation of dysbiotic gut microbiota holds promise as a new strategy for preventing or alleviating TBM.

          IMPORTANCE As the most severe form of tuberculosis, the pathogenesis of tuberculous meningitis (TBM) is still unclear. Gut microbiota dysbiosis plays an important role in a variety of central nervous system diseases. However, the relationship between gut microbiota and TBM has not been identified. In our study, significant dysbiosis in gut microbiota composition with a high proportion of E. coli and increased levels of TNF-α in plasma was noted in TBM patients. A commensal E. coli was isolated and shown to increase the plasma level of TNF-α and downregulate brain tight junction protein claudin-5 in the murine model. Gavage administration of E. coli aggravated the bacterial burden and increased the inflammatory responses in the central nervous system after M. tuberculosis infection. Dysbiosis of gut microbiota may be a promising therapeutic target and biomarker for TBM prevention or treatment.

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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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            GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions.

            J Besemer (2001)
            Improving the accuracy of prediction of gene starts is one of a few remaining open problems in computer prediction of prokaryotic genes. Its difficulty is caused by the absence of relatively strong sequence patterns identifying true translation initiation sites. In the current paper we show that the accuracy of gene start prediction can be improved by combining models of protein-coding and non-coding regions and models of regulatory sites near gene start within an iterative Hidden Markov model based algorithm. The new gene prediction method, called GeneMarkS, utilizes a non-supervised training procedure and can be used for a newly sequenced prokaryotic genome with no prior knowledge of any protein or rRNA genes. The GeneMarkS implementation uses an improved version of the gene finding program GeneMark.hmm, heuristic Markov models of coding and non-coding regions and the Gibbs sampling multiple alignment program. GeneMarkS predicted precisely 83.2% of the translation starts of GenBank annotated Bacillus subtilis genes and 94.4% of translation starts in an experimentally validated set of Escherichia coli genes. We have also observed that GeneMarkS detects prokaryotic genes, in terms of identifying open reading frames containing real genes, with an accuracy matching the level of the best currently used gene detection methods. Accurate translation start prediction, in addition to the refinement of protein sequence N-terminal data, provides the benefit of precise positioning of the sequence region situated upstream to a gene start. Therefore, sequence motifs related to transcription and translation regulatory sites can be revealed and analyzed with higher precision. These motifs were shown to possess a significant variability, the functional and evolutionary connections of which are discussed.
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              Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour.

              Recent years have witnessed the rise of the gut microbiota as a major topic of research interest in biology. Studies are revealing how variations and changes in the composition of the gut microbiota influence normal physiology and contribute to diseases ranging from inflammation to obesity. Accumulating data now indicate that the gut microbiota also communicates with the CNS--possibly through neural, endocrine and immune pathways--and thereby influences brain function and behaviour. Studies in germ-free animals and in animals exposed to pathogenic bacterial infections, probiotic bacteria or antibiotic drugs suggest a role for the gut microbiota in the regulation of anxiety, mood, cognition and pain. Thus, the emerging concept of a microbiota-gut-brain axis suggests that modulation of the gut microbiota may be a tractable strategy for developing novel therapeutics for complex CNS disorders.
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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
                9 November 2022
                Nov-Dec 2022
                9 November 2022
                : 10
                : 6
                : e01926-22
                Affiliations
                [a ] Department of Bacteriology and Immunology, Beijing Chest Hospitalgrid.414341.7, , Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People’s Republic of China
                [b ] Department of Tuberculosis, Beijing Chest Hospitalgrid.414341.7, , Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People’s Republic of China
                [c ] Department of Infectious Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, People’s Republic of China
                [d ] Beijing Key Laboratory for Pediatric Diseases of Otolaryngology, Head and Neck Surgery, MOE Key Laboratory of Major Diseases in Children, Beijing Pediatric Research Institute, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, People’s Republic of China
                Johns Hopkins University School of Medicine
                Author notes

                Shanshan Li, Jidong Guo, and Rongmei Liu contributed equally to this article. Author order was determined by the corresponding author after negotiation.

                The authors declare no conflict of interest.

                Author information
                https://orcid.org/0000-0001-6803-9807
                Article
                01926-22 spectrum.01926-22
                10.1128/spectrum.01926-22
                9769903
                36350161
                1303ced4-99a9-47b6-94d9-d4627cd23549
                Copyright © 2022 Li et al.

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

                History
                : 26 May 2022
                : 12 October 2022
                Page count
                supplementary-material: 0, Figures: 6, Tables: 1, Equations: 0, References: 45, Pages: 14, Words: 9425
                Funding
                Funded by: National Natural Science Foundation of China (NSFC), FundRef https://doi.org/10.13039/501100001809;
                Award ID: 82072243
                Award Recipient :
                Funded by: Research Cultivation Foundation of Capital Medical University;
                Award ID: PYZ21166
                Award Recipient :
                Funded by: Translational Cultivation Project of Beijing Chest Hospital affiliated to Capital Medical University;
                Award ID: SPY2021-15
                Award Recipient :
                Categories
                Research Article
                mycobacteriology, Mycobacteriology
                Custom metadata
                November/December 2022

                tuberculous meningitis,gut microbiota,escherichia-shigella,tnf-α,claudin-5

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