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      Metagenomic Analysis of the Pediatric-Onset Multiple Sclerosis Gut Microbiome

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          Abstract

          Background and Objectives

          Little is known of the functional potential of the gut microbiome in pediatric-onset multiple sclerosis (MS). We performed metagenomic analyses using stool samples from individuals with pediatric-onset MS and unaffected controls.

          Methods

          Persons ≤21 years old enrolled in the Canadian Pediatric Demyelinating Disease Network providing a stool sample were eligible. Twenty patients with MS (McDonald criteria) with symptom onset <18 years were matched to 20 controls by sex, age (±3 years), stool consistency, and race. Microbial taxonomy and functional potentials were estimated from stool sample–derived metagenomic reads and compared by disease status (MS vs controls) and disease-modifying drug (DMD) exposure using alpha diversity, relative abundance, and prevalence using Wilcoxon rank sum, ALDEx2, and Fisher exact tests, respectively.

          Results

          Individuals with MS were aged 13.6 years (mean) at symptom onset and 8 were DMD-naive. Mean ages at stool sample were 16.1 and 15.4 years for MS and control participants, respectively; 80% were girls. Alpha diversity of enzymes and proteins did not differ by disease or DMD status ( p > 0.20), but metabolic pathways, gene annotations, and microbial taxonomy did. Individuals with MS (vs controls) exhibited higher methanogenesis prevalence (odds ratio 10, p = 0.044) and Methanobrevibacter abundance (log 2 fold change [LFC] 1.7, p = 0.0014), but lower homolactic fermentation abundance (LFC −0.48, p = 0.039). Differences by DMD status included lower phosphate butyryl transferase for DMD-naive vs exposed patients with MS (LFC −1.0, p = 0.033).

          Discussion

          The gut microbiome's functional potential and taxonomy differed between individuals with pediatric-onset MS vs controls, including higher prevalence of a methane-producing pathway from Archaea and depletion of the lactate fermentation pathway. DMD exposure was associated with butyrate-producing enzyme enrichment. Together these findings indicate that the gut microbiome of individuals with MS may have a disturbed functional potential.

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          Most cited references47

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          Is Open Access

          Improved metagenomic analysis with Kraken 2

          Although Kraken’s k-mer-based approach provides a fast taxonomic classification of metagenomic sequence data, its large memory requirements can be limiting for some applications. Kraken 2 improves upon Kraken 1 by reducing memory usage by 85%, allowing greater amounts of reference genomic data to be used, while maintaining high accuracy and increasing speed fivefold. Kraken 2 also introduces a translated search mode, providing increased sensitivity in viral metagenomics analysis.
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            Tandem repeats finder: a program to analyze DNA sequences.

            G. Benson (1999)
            A tandem repeat in DNA is two or more contiguous, approximate copies of a pattern of nucleotides. Tandem repeats have been shown to cause human disease, may play a variety of regulatory and evolutionary roles and are important laboratory and analytic tools. Extensive knowledge about pattern size, copy number, mutational history, etc. for tandem repeats has been limited by the inability to easily detect them in genomic sequence data. In this paper, we present a new algorithm for finding tandem repeats which works without the need to specify either the pattern or pattern size. We model tandem repeats by percent identity and frequency of indels between adjacent pattern copies and use statistically based recognition criteria. We demonstrate the algorithm's speed and its ability to detect tandem repeats that have undergone extensive mutational change by analyzing four sequences: the human frataxin gene, the human beta T cellreceptor locus sequence and two yeast chromosomes. These sequences range in size from 3 kb up to 700 kb. A World Wide Web server interface atc3.biomath.mssm.edu/trf.html has been established for automated use of the program.
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              Is Open Access

              KEGG as a reference resource for gene and protein annotation

              KEGG (http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an integrated database resource for biological interpretation of genome sequences and other high-throughput data. Molecular functions of genes and proteins are associated with ortholog groups and stored in the KEGG Orthology (KO) database. The KEGG pathway maps, BRITE hierarchies and KEGG modules are developed as networks of KO nodes, representing high-level functions of the cell and the organism. Currently, more than 4000 complete genomes are annotated with KOs in the KEGG GENES database, which can be used as a reference data set for KO assignment and subsequent reconstruction of KEGG pathways and other molecular networks. As an annotation resource, the following improvements have been made. First, each KO record is re-examined and associated with protein sequence data used in experiments of functional characterization. Second, the GENES database now includes viruses, plasmids, and the addendum category for functionally characterized proteins that are not represented in complete genomes. Third, new automatic annotation servers, BlastKOALA and GhostKOALA, are made available utilizing the non-redundant pangenome data set generated from the GENES database. As a resource for translational bioinformatics, various data sets are created for antimicrobial resistance and drug interaction networks.
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                Author and article information

                Contributors
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                Journal
                Neurology
                Neurology
                Ovid Technologies (Wolters Kluwer Health)
                0028-3878
                1526-632X
                March 07 2022
                March 08 2022
                March 08 2022
                December 22 2021
                : 98
                : 10
                : e1050-e1063
                Article
                10.1212/WNL.0000000000013245
                34937787
                11cad9f5-eb50-4515-8be8-0a086b13e83f
                © 2021
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