Additional file 1 of Longitudinal host-microbiome dynamics of metatranscription identify hallmarks of progression in periodontitis

Additional file 1. Figure S1.Experimental design. 15 participants were selected from a total cohort of 415 participants. These patients had the clinal conditions we wanted: progressing sites where CAL increased steadily and significantly during the study and stable sites where CAL values remained significantly identical. Genetic background should have a minimal effect on the outcome of individual sites. At baseline, all teeth used were clinically identical. Subgingival plaque samples were taken every 2 months for 1 year, after which all patients underwent scaling and root planing as treatment. After 3 months for a visual check-up and again after 6 months when they were monitored, all returned to the clinic, and samples were also taken. Figure S2. Phylogenetic assignment and relative quantification of microbiome metatranscriptome. a) Combined clusters with peaks before the change point. b) Venn diagram of the combined species from the stable and progressing clusters. c) List of the species in the three sections of the Venn diagram. Figure S3. Gene set enrichment analysis of the overall changes in the study. We performed gene set enrichment analysis of GO terms and KEGG pathways using clusterProfiler [49] on the DE from the host and the microbiome. In red activated enriched activities. In green suppressed enriched activities. Figure S4. Microbiome GO ontology and KEGG pathways enrichment analysis of differentially expressed (DE) gene clusters in stable sites. Clusters of DE genes were obtained by determining an optimal number of clusters using fviz_nbclust from the ‘factoextra’ package with the gap statistic method and performing clustering using tsclust with shape-based distance (SBD), which makes the clustering particularly useful for time series where the shape matters more than exact numerical values. Clusters are standardized to log2 fold-change of abundance. Colors and cluster numbers are arbitrary. The actual composition of the different clusters is presented in Table S2. Enrichment of gene sets was performed using the Cytoscape app ClueGO with the GO biological process, KEGG pathways, and KEGG-human disease ontologies. In red, metabolic activities were activated (enriched), and in blue, metabolic activities were repressed. Figure S5. Microbiome GO ontology and KEGG pathways enrichment analysis of differentially expressed (DE) gene clusters in progressing sites. Clusters of DE genes were obtained by determining an optimal number of clusters using fviz_nbclust from the ‘factoextra’ package with the gap statistic method and performing clustering using tsclust with shape-based distance (SBD), which makes the clustering particularly useful for time series where the shape matters more than exact numerical values. Clusters are standardized to log2 fold-change of abundance. Colors and cluster numbers are arbitrary. The actual composition of the different clusters is presented in Table S5. Enrichment of gene sets was performed using the Cytoscape app ClueGO with the GO biological process, KEGG pathways, and KEGG-human disease ontologies. In red, metabolic activities were activated (enriched), and in blue, metabolic activities were repressed. Figure S6. CAL-host and microbiome delay correlation analysis, GO ontology, and KEGG pathways enrichment analysis in stable sites. Using the R package dynOmics [73] we measured the correlation between the two time-series (CAL and host or microbiome genes) at different time lags. We identified the delay (positive or negative lag) at which two variables correlate most strongly. a) CAL profile preceded human activities by 2 months. b) Microbiome activities that preceded CAL by 2 months. c) CAL profile preceded microbiome activities by 2 months. The percentages represent the %terms/group, that is, the proportion of terms within each functional group or category relative to the total number of terms in your analysis. It shows how many enriched terms are associated with each functional group, showing their relative importance. n = number of nodes in the network. In red, metabolic activities were activated (enriched), and in blue, suppressed activities were suppressed. Figure S7. Host and microbiome-CAL delay correlation analysis, GO ontology, and KEGG pathways enrichment analysis in progressing sites. Using the R package dynOmics [73] we measured the correlation between the two time-series (CAL and host or microbiome genes) at different time lags. We identified the delay (positive or negative lag) at which two variables correlate most strongly. a) Human activities that preceded CAL by 2 months. b) Microbiome activities that preceded CAL by 2 months. The percentages represent the %terms/group, that is, the proportion of terms within each functional group or category relative to the total number of terms in your analysis. It shows how many enriched terms are associated with each functional group, showing their relative importance. n = number of nodes in the network. In red, metabolic activities were activated (enriched), and in blue, suppressed activities were suppressed. Figure S8. CAL-host and microbiome delay correlation analysis, GO ontology, and KEGG pathways enrichment analysis in progressing sites. Using the R package dynOmics [73] we measured the correlation between the two time-series (CAL and host or microbiome genes) at different time lags. We identified the delay (positive or negative lag) at which two variables correlate most strongly. a) CAL profile preceded human activities by 2 months. b) CAL profile preceded microbiome activities by 2 months. The percentages represent the %terms/group, that is, the proportion of terms within each functional group or category relative to the total number of terms in your analysis. It shows how many enriched terms are associated with each functional group, showing their relative importance. n = number of nodes in the network. In red, metabolic activities were activated (enriched), and in blue, suppressed activities were suppressed.