Skip to main content
Dryad logo

16S rRNA sequences of gut microbiome and meta data

Citation

Pandit, Lekha (2021), 16S rRNA sequences of gut microbiome and meta data, Dryad, Dataset, https://doi.org/10.5061/dryad.jsxksn075

Abstract

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 <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 <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.

Methods

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 ο C until DNA extraction.

16S rRNA sequencing of the gut microbiota

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 Project14.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 analysis8. 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 distances16.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 controls17.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.

Usage Notes

Is identical to PRJNA662563