Additional file 3 of Microbiota long-term dynamics and prediction of acute graft-versus-host disease in pediatric allogeneic stem cell transplantation
Creators: name: Ingham, Anna Cäcilia
name: Kielsen, Katrine
name: Mordhorst, Hanne
name: Ifversen, Marianne
name: Aarestrup, Frank M.
name: Müller, Klaus Gottlob
name: Pamp, Sünje Johanna
Publisher: name: figshare
Year: 2021
Subjects: subject: Medicine
subject: Microbiology
subject: FOS: Biological sciences
subject: Cell Biology
subject: Genetics
subject: 39999 Chemical Sciences not elsewhere classified
subject: FOS: Chemical sciences
subject: Ecology
subject: Cancer
subject: Hematology
subject: 110309 Infectious Diseases
subject: FOS: Health sciences

description: Additional file 2 Figure S1. The gut microbiota in the HSCT patients at pre-exam differs from the gut microbiota of age-matched healthy children. A) Fecal bacterial alpha diversity (inverse Simpson index) was 2.4-fold higher in healthy children (n=18) compared to children at pre-examination before HSCT (n=15). B) Fecal bacterial composition was significantly different between the two groups (anosim, p=0.001, R=0.44), and within-group variance was significantly greater in the HSCT group (betadisper, p&lt;0.001). C) The taxa which best explain differences in community structure between HSCT patients at preexamination and healthy children were identified by analysis of LEfSe (Linear discriminant analysis Effect Size). LefSe accounts for the hierarchical structure of bacterial phylogeny, thereby allowing identification of differentially abundant taxa on several taxonomic levels (here: kingdom to genus). Count data was centered-log ratio (CLR) transformed within the LEfSe analysis. The higher the LDA score (log10), the higher the effect size of the respective taxon in explaining group difference. Here, we show taxa with an LDA score &gt;4. D) Differentially abundant genera between the two groups were additionally identified by DESeq2. Of the top 100 most abundant genera (of the whole gut microbiota data set), eighteen genera were significantly more abundant in healthy children (yellow), and 15 genera were significantly more abundant in the patients at preexam (purple). Differences in median proportions of these genera (and their supertaxa) are displayed in a heat tree. See also additional information at https://doi.org/10.6084/m9.figshare.13614230 . Figure S2. Most abundant taxonomic families in the gut, oral cavity, and nasal cavity in allo-HSCT patients. Rank abundance curves displaying the proportions of the 12 most abundant taxonomic families at each body site (gut, oral cavity, and nasal cavity). Figure S3. Tree-based sparse linear discriminant analysis revealing nasal ASVs that distinguish time points from each other in relation to HSCT. A) Relative abundances over time of the 12 most abundant families in the nasal cavity. B) Coefficients of discriminating clades of ASVs on the first LDA axis, colored by taxonomic family, and plotted along the phylogenetic tree. C) Trajectories of ASVs in one discriminating group, affiliated with the family Corynebacteriaceae, with decreasing abundances after HSCT and recovery at late follow-up time points. The most abundant discriminating ASV is indicated. Detailed taxonomic information and LDA-coefficients of the displayed ASVs are listed in Table S2. D) PCA-biplots with the top 100 predictors (ASVs) identified by PCA (left) and the top predictors (ASVs) identified by sparse LDA (right). The time points are indicated in the same color as in Figure 1C (phase I: yellow colors; phase II: red colors; phase III: blue colors). The PCA plots with dimensions 1 and 2 are displayed here and additional PCA plots are available from https://doi.org/10.6084/m9.figshare.14510661 . Figure S4. Machine learning-based prediction of aGvHD severity from nasal microbial abundances pre-HSCT. A) Relative abundances of the 12 most abundant families over time in the nasal cavity in patients with aGvHD grade 0-I versus II-IV. B) Importance plot of top 20 predictive nasal ASVs identified by the svmLinear model with importance scores indicating the mean decrease in prediction accuracy in case the respective ASV would be excluded from the model. The final cross-validated svmLinear model predicted aGvHD (0-I versus II-IV) from the abundances of nasal ASVs pre-HSCT with 76% accuracy (95% CI: 56% to 90%). The ASVs that were also confirmed by Boruta feature selection are indicated with asterisk. C) Conditional inference tree (CTREE) displaying ASVs identified as significant split nodes by nonparametric regression for prediction of aGvHD. Numbers along the branches indicate split values of variance stabilized bacterial abundances. The terminal nodes show the proportion of samples originating from patients with aGvHD grade 0-I vs II-IV (n = number of samples). D) Boxplots depict the log transformed relative abundances of the predictive ASVs at time points up to the transplantation in aGvHD grade 0-I compared with grade II-IV patients. Figure S5. Multivariate associations of the oral microbiota with immune and clinical parameters in HSCT. A) Clustered image map (CIM) based on sparse partial least squares (sPLS) regression analysis dimensions 1, 2, and 3, displaying pairwise correlations &gt;0.2/0.2/0.3/0.2/0.2/0.3/