Additional file 1 of Gut microbiota mediated the individualized efficacy of Temozolomide via immunomodulation in glioma
Creators: name: Hou, Xiaoying
name: Du, Hongzhi
name: Deng, Yufei
name: Wang, Haiping
name: Liu, Jinmi
name: Qiao, Jialu
name: Liu, Wei
name: Shu, Xiji
name: Sun, Binlian
name: Liu, Yuchen
Publisher: name: figshare
Year: 2024
Subjects: subject: Biochemistry
subject: Medicine
subject: Microbiology
subject: FOS: Biological sciences
subject: Genetics
subject: Biotechnology
subject: Chemical Sciences not elsewhere classified
subject: Ecology
subject: Immunology
subject: FOS: Clinical medicine
subject: Cancer
subject: Infectious Diseases
subject: FOS: Health sciences

description: Additional file 1: Figure S1. 3D images for glioma orthotopic xenograft mice. Figure S2. The α-diversity indexes of Chao1 (A), Shannon (B) and Simpson (C) of gut microbiota between Control and Model groups. Figure S3. Similarity percentage (SIMPER) analysis based on Bray-Curtis was used to study bacterial species (Top 10) contributing to the variability between Control (C) and Model (M) groups. Figure S4. Non-target metabolomics analysis of fecal samples from Control and Model mice. (A) QC samples were clustered very well in PCA score plots derived from LC-MS (+) and LC-MS (-) datasets. (B) Permutation test result (500 times) of OPLS-DA models constructed from LC-MS (+) and LC-MS (-) data. (C) Differently enrichment of KEGG pathways in Control and Model mice. Figure S5. Taxonomic distributions of bacteria before (Model) and after TMZ (TMZ) treatment at phylum level (A) and genus level (B); (C) Significantly changed bacterial genera between Model and TMZ mice evaluated by MetaStat analysis. Figure S6. Differently abundant of KEGG pathways of the gut microbiota before (Model) and after (TMZ) TMZ treatment mice. Figure S7. Non-target metabolomics analysis of fecal samples before (Model) and after (TMZ) TMZ treated mice. (A) QC samples were clustered very well in PCA score plots derived from LC-MS (+) and LC-MS (-) datasets. (B) Permutation test result (500 times) of OPLS-DA models constructed from LC-MS (+) and LC-MS (-) data. Figure S8. Taxonomic distributions of bacteria in TMZ sensitive and non-sensitive mice at phylum level (A) and genus level (B). Figure S9. Non-target metabolomics analysis of fecal samples in TMZ sensitive and non-sensitive mice. (A) QC samples were clustered very well in PCA score plots derived from LC-MS (+) and LC-MS (-) datasets. (B) Permutation test result (500 times) of OPLS-DA models constructed from LC-MS (+) and LC-MS (-) data. (C) Differently abundant of KEGG pathways of the gut microbiota metabolites in S and NS mice. Figure S10. Heatmap of Spearman correlation coefficient between the changed fecal metabolites and bacterial genera. The intensity of the colors represents the degree of association between the level of fecal metabolites and bacterial genera abundance measured by Spearman’s correlations. The p-values&lt;0.05 were considered statistically significant, *p&lt;0.05, **p&lt;0.01. Figure S11. Relative abundance of bacterial genera/metabolites that was significantly associated with the tumor inhibition rate in TMZ sensitive and non-sensitive mice measured by the Spearman’s correlations analysis. Figure S12. Relative expression of key molecules in tryptophan metabolism in the brain and tumor tissue. (A) The relative mRNA expression of AHR and IDO1 in the brain&amp;tumor tissue of Control versus Model mice, Sensitive versus Non-Sensitive mice. (B) Immunohistochemistry of AHR in Control versus Model mice, Sensitive versus Non-Sensitive mice. Table S1. Differential metabolites in fecal samples between Control (C) and Model (M) group. Only features with VIP &gt; 1, p &lt; 0.05 and fold change &gt; 2 were listed. Table S2. Differential metabolites in fecal samples before (Model, M) and after TMZ (TMZ, T) TMZ treatment. Only features with VIP &gt; 1, p &lt; 0.05 and fold change &gt; 2 were listed.