A study of Correlation of gut microbiota and metabolic functions with the antibody response to the BBIBP-CorV vaccine. Tang et al.

Published: 7 September 2022| Version 3 | DOI: 10.17632/shmjgwhvsd.3
Contributor:
Bo Tang

Description

We investigate whether the human gut microbiota and metabolic function correlate with the inactivated SARS-CoV-2 vaccine (BBIBP-CorV, CNBG) response. A total of 207 participants who receive the BBIBP-CorV vaccine at Days 0 and 28 are enrolled. The gut microbiome and related metabolic functions are investigated using metagenomic sequencing and metabolomic assays. We find that BBIBP-CorV vaccination is accompanied by altered gut microbiome composition and functional pathways, and the gut microbiome and its functional profiles correlate with the vaccine response. Targeted metabolomic analysis show that the fecal and serum levels of short-chain fatty acids (SCFAs) are much higher in the high antibody response group compared to the low response group after vaccination, and several SCFAs display a positive correlation with the ACE2-RBD inhibiting antibody response. Our study highlights that gut microbiome and its function is associated with the BBIBP-CorV vaccine response, providing evidence for further exploration of microbiome modulation to improve COVID-19 vaccine efficacy. All these spreadsheets were created during the metagenomic sequencing analysis, and used for alpha, beta diversity analysis, and correlation analysis, etc.

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Differences in bacterial abundance and the Metacyc pathway were analyzed using Maaslin2. Shannon, Simpson and richness indices were calculated using the R Community Ecology Package vegan. Weighted Unifrac distance was calculated using Metaphlan3 R script “Unifrac_distance.r” and root-tree file “mpa_v30_CHOCOPhlAn_201901_species_tree.nwk”. The PCoA results were calculated and visualized using R build-in functions and the plot3D R package. The ANOSIM test was used to calculate the significance of dissimilarity using the R Community Ecology Package vegan. All machine learning classifiers were built by using the caret R package. The ROC-AUC was performed using the pROC R package. Pearson correlation and P values were evaluated using the rcorr function in the Hmisc R package.

Institutions

Xinqiao Hospital

Categories

Metagenomics

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