DOI: 10.5061/dryad.jsxksn075
title: 16S rRNA sequences of gut microbiome and meta data

description: Objective:To understand the role of gut microbiome in influencing the
 pathogenesis of neuromyelitis optica spectrum disorders (NMOSD)among
 patients of south Indian origin. Methods:In this case control study, stool
 and blood samples were collected from 39 NMOSD patients, including 17 with
 aquaporin 4 IgG antibodies (AQP4+) and 36 matched controls. 16S rRNA
 sequencing was used to investigate the gut microbiome. Peripheral CD4+ T
 cells were sorted in 12 healthy controls & 12 AQP4+NMOSD patients,
 RNA extracted, and immune gene expression analyzed using Nanostring
 nCounter human immunology kit code set. Results: Microbiota community
 structure (beta-diversity) differed between AQP4+ NMOSD and healthy
 controls (p &lt;0.001, pairwise PERMANOVA test). Linear discriminatory
 analysis effect size (LEfSe) identified several members of the microbiota
 that were altered in NMOSD patients, including an increase in Clostridium
 bolteae (effect size 4.23, pvalue 0.00007). C.bolteae was significantly
 more prevalent (p=0.02) amongAQP4-IgG + NMOSD (n= 8/17 subjects)compared
 to seronegative patients (n= 3/22) and was absent among healthy stool
 samples.C bolteae has a highly conserved glycerol uptake facilitator and
 related aquaporin protein(p59-71) that shares sequence homology with AQP4
 peptide(p92-104), positioned within an immunodominant (AQP4specific)T cell
 epitope (p91-110).Presence of C. bolteae correlated with expression of
 inflammatory genes associated with both innate and adaptive immunity and
 particularly involved in plasma cell differentiation ,B cell chemotaxis
 and Th17 activation. Conclusion: Our study described elevated levels of C.
 bolteae associated with AQP4+ NMOSD among Indian patients. It is possible
 that this organism may be causally related to the immunopathogenesis of
 this disease in susceptible individuals. Objective:To understand the role
 of gut microbiome in influencing the pathogenesis of neuromyelitis optica
 spectrum disorders (NMOSD)among patients of south Indian origin.
 Methods:In this case control study, stool and blood samples were collected
 from 39 NMOSD patients, including 17 with aquaporin 4 IgG antibodies
 (AQP4+) and 36 matched controls. 16S rRNA sequencing was used to
 investigate the gut microbiome. Peripheral CD4+ T cells were sorted in 12
 healthy controls & 12 AQP4+NMOSD patients, RNA extracted, and
 immune gene expression analyzed using Nanostring nCounter human immunology
 kit code set. Results: Microbiota community structure (beta-diversity)
 differed between AQP4+ NMOSD and healthy controls (p &lt;0.001,
 pairwise PERMANOVA test). Linear discriminatory analysis effect size
 (LEfSe) identified several members of the microbiota that were altered in
 NMOSD patients, including an increase in Clostridium bolteae (effect size
 4.23, pvalue 0.00007). C.bolteae was significantly more prevalent (p=0.02)
 amongAQP4-IgG + NMOSD (n= 8/17 subjects)compared to seronegative patients
 (n= 3/22) and was absent among healthy stool samples.C bolteae has a
 highly conserved glycerol uptake facilitator and related aquaporin
 protein(p59-71) that shares sequence homology with AQP4 peptide(p92-104),
 positioned within an immunodominant (AQP4specific)T cell epitope
 (p91-110).Presence of C. bolteae correlated with expression of
 inflammatory genes associated with both innate and adaptive immunity and
 particularly involved in plasma cell differentiation ,B cell chemotaxis
 and Th17 activation. Conclusion: Our study described elevated levels of C.
 bolteae associated with AQP4+ NMOSD among Indian patients. It is possible
 that this organism may be causally related to the immunopathogenesis of
 this disease in susceptible individuals.
description: Seventeen AQP4 IgG positive (AQP4+) NMOSD patients,22 AQP4-IgG
 negative (AQP4-) patients and 36 healthy volunteers matched for age and
 body mass index (BMI) were included from our registry.Stool samples were
 collected in containers provided to patients. They were delivered in
 person or by relative on the same day of collection over a median period
 of 3hours (range: 1-5 hours).Stool samples were then immediately frozen at
 –80 <sup>ο</sup> C until DNA extraction.
 <b>16S rRNA sequencing of the gut
 microbiota</b> DNA was extracted from stool using
 QIAamp® DNA Stool extraction kit as per the manufacturer’s instructions.
 DNA was transported in dry ice from Nitte University to Brigham
 &Women’s Hospital,Boston, USA for further analysis. Samples were
 normalized and bacterial 16S rRNA gene was amplified using primers for
 amplification of V4 region as per protocols described in the Earth
 Microbiome Project<sup>14</sup>.Samples were sequenced by
 paired end 250base pair reads at the Harvard Medical School Biopolymer
 facility using Illumina MiSeq platform. FastQC program (www.bio
 informatics.babraham.ac.uk/projects /fastqc/)was used to evaluate sequence
 quality.We used QIIME2 (Qualitative Insights into microbial Ecology), an
 open source bioinformatics platform, for downstream
 analysis<sup>8</sup>. Sequences were de-multiplexed and
 de-noised using DADA2. Taxonomy was assigned using reference database
 SILVA (https://www.arb-silva.de). In addition, we used EZ-Taxon (www.EzTaxon.org) to identify some species that could not be annotated by SILVA.Alpha-diversity was calculated using Faith’s phylogenetic diversity (PD), observed (operational taxonomic unit (OTUs) and Shannon diversity metrics. Beta-diversity was calculated using weighted and unweighted UniFrac distances<sup>16</sup>.Relative abundance of individual species was calculated ( relative frequency in each sample / sum of relative frequency in all samples tested (n=75) and expressed as a percentage). We used linear discriminant analysis (LDA) effect size (LEfSe) on the Galaxy Browser, http://huttenhower.sph.harvard.edu/galaxy to detect compositional changes in the microbiome in NMOSDpatients and controls<sup>17</sup>.Accordingly, non-parametric factorial Wilcoxon rank-sum test was usedinitially to detect bacterial taxa with significant (a set at 0.01) differential abundance in NMOSD patients vs healthy controls.Then, estimate of the effect size of each differentially abundant feature was calculated using LDA, with a cutoff greater than 3.
description: Is identical to PRJNA662563