Additional file 1 of Distinct cervical tissue-adherent and luminal microbiome communities correlate with mucosal host gene expression and protein levels in Kenyan sex workers

Additional file 1: Supplementary Figure 1. Abundance distribution of individual taxa in the luminal and tissue microbiome data sets. Violin plots showing the distribution of relative abundance of the top 30 most abundant taxa in the luminal and tissue-adherent data sets. Supplementary Figure 2. Differential bacterial abundance across the luminal and tissue microbiome datasets. Differential bacterial abundance was compared between the luminal and tissue-adherent microbiome data sets. The results are shown as a) dot plots, and b) bar plots, respectively. Bacteria with log2FC above 0.25 and p-value < 0.01 (from the Wilcoxon’s test) were considered significantly different and were sorted by the highest expression. The color scale indicates the difference in total abundance between the datasets as a proportion, where “max” is the highest abundance of the two datasets, and the other becomes a proportion of this value. The size of the dots indicates the average abundance of the given bacteria in the given data set. Supplementary Figure 3. Summary of pairwise comparisons between the study groups for differentially expressed genes, GO and KEGG pathways as well as PPI analysis. The results are shown as: a) Summary of pairwise comparison between the luminal study groups, and for the b) tissue-based study groups. For both a) and b): The number of differentially expressed genes (DEGs) (p<0.01) are displayed in the hexagon shape, these were further used for GO (round shape) and KEGG pathways (number outside round shape) analysis (FDR<0.05), as well as for PPI analysis (square shape) (FDR<0.05). The luminal group in the middle circle represents “Group A” and the luminal group at the end of the line “Group B”, and the comparison represents Group A vs. Group B, i.e. Group A has X number of upregulated DEGs compared to Group B. Supplementary Figure 4. Functional associations of the luminal microbiome with host tissue gene expression profiles. Bacterial abundances in the luminal samples were correlated with gene expression of the top 5,000 highly variable genes from the RNAseq dataset. This generated a correlation matrix between bacteria and genes. For each bacteria, genes were ranked based on their correlation to that bacteria, followed by gene set enrichment anlaysis (GSEA) using the KEGG gene annotation database. The resulting matrix display associations between individual bacterial taxa and corresponding KEGG term as defined in the host tissue sample. The heatmap shows the normalized enrichment score (NES). Only enrichments with p-value < 0.05 are shown. Bacterium and pathways with less than 10 significant NES scores were omitted from the heatmap. Bacteria are grouped according to anatomical/functional activity and marked with different colors per category. Supplementary Figure 5. Functional associations of the tissue microbiome with host tissue gene expression profiles. Bacterial abundances in the tissue samples were correlated with the gene expression of the top 5,000 highly variable genes from the RNAseq dataset. This generated a correlation matrix between bacteria and genes. For each bacteria, genes were ranked based on their correlation to that bacteria, followed by gene set enrichment anlaysis (GSEA) using the KEGG gene annotation database. The resulting matrix display associations between individual bacterial taxa and corresponding KEGG term as defined in the host tissue sample. The heatmap shows the normalized enrichment score (NES). Only enrichments with p-value < 0.05 are shown. Bacterium and pathways with less than 10 significant NES scores were omitted from the heatmap. Bacteria are grouped according to anatomical/functional activity and marked with different colors according to category. Supplementary Figure 6. Rarefaction curves for the microbiome 16S rRNA V4 sequencing. The rarefaction curves show numbers of unique ASVs detected in each sample when simulating increasing sequencing depth. Although low abundant taxa can be undetected at low sequencing depth, they can be detected at a higher sequencing depth (x-axis). When the curve flattens out, all taxa in the sample are considered detected. a) Luminal microbiome dataset, and b) Tissue-adherent microbiome dataset. The sequencing depth was > 40,000 reads in all but nine samples for the luminal dataset, while 16 samples had fewer than 2,500 reads in the tissue-adherent microbiome dataset.

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