2.7. Gut microbiome analysis by 16S rRNA sequencing

Fecal samples were collected from five randomly selective C57 female offspring mice at 8 weeks of age after 4 weeks HFD treatment. Total genomic DNA was extracted and purified from fecal samples using Fecal DNA Isolation Kit (Zymo Research, Irvine, CA, USA) according to the manufacturer’s instructions. The concentration and purification of all DNA samples were determined by Qubit 4 Fluorometer (Thermo Fisher). Fecal DNAs were used to perform PCR to amplify the V3-V4 regions of the bacterial 16S rRNA gene using the unique barcoded primers 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and the reverse primer 806R (5′-GGACTACHVGGGTWTCTAAT-3′). Amplicons were purified using the QIAquick Gel Extraction Kit (Qiagen, Germantown, MD, USA). Microbial 16S rDNA was sequenced on NextGen sequencing Illumina MiSeq platform as done previously [29]. The raw sequencing data were de-multiplexed, quality-filtered, clustered and analyzed using Quantitative Insight into Microbial Ecology data analysis package (version 1.7.0). The reads were assigned to operational taxonomic units (OTUs) of representative sequences at the 97% similarity using UPARSE version 7.1. The relative abundance of each OTU was analyzed at the phylum, class, order, family, genus and species levels. We performed the microbial community-level analyses and the single-taxon analyses. Bacterial taxonomy assignment and diversity analysis were calculated using R package phyloseq [30] to compare the bacterial species richness between different treatment groups (HFD vs. Maternal-GE HFD). Chao1, Simpson and the Shannon index were analyzed for alpha diversity and the Bray-Curtis dissimilarity index for the beta diversity analysis. Linear regression models were used to analyze alpha diversity indices for each sample. Differences in beta-diversity were tested using Bray-Curtis distance matrix and permutational multivariate analysis of variance. To identify individual taxa that are significantly associated with different treatment, single-taxon analysis was performed using the negative binomial model. We used the false-discovery rate (FDR) method to adjust the P values for multiple tests that controls the FDR, the expected proportion of false discoveries amongst the rejected hypotheses. The raw data of alpha-diversity metrices and OTU abundance was provided in Supplementary Data 2. Principal component analysis (PCA) was performed to determine the overall microbiota composition in different groups. PCA plots were visualized using EMPeror version 0.9.3-dev. Hierarchical clustering heat-map based on significant OTU abundance was prepared in RStudio. This work utilized the UAB Microbiome/Gnotobiotics shared facility, which is supported by the O’Neal Comprehensive Cancer Center (P30 CA013148).