Additional file 1 of Gut microbiota composition can reflect immune responses of latent tuberculosis infection in patients with poorly controlled diabetes
Creators: name: Huang, Hung-Ling
name: Luo, Yong-Chun
name: Lu, Po-Liang
name: Huang, Cheng-Hsieh
name: Lin, Kun-Der
name: Lee, Meng-Rui
name: Cheng, Meng-Hsuan
name: Yeh, Yao-Tsung
name: Kao, Cheng-Yuan
name: Wang, Jann-Yuan
name: Yang, Jinn-Moon
name: Chong, Inn-Wen
Publisher: name: figshare
Year: 2024
Subjects: subject: Medicine
subject: Microbiology
subject: FOS: Biological sciences
subject: Chemical Sciences not elsewhere classified
subject: Ecology
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Computational Biology

description: Additional file 1: Table S1. Laboratory and cytokine analysis results for the 130 patients with poorly controlled diabetes mellitus. Table S2. Relative abundance (%) of the top 10 phyla in both the latent tuberculosis infection (LTBI) group and non-LTBI groups. Table S3. Relative abundance (%) of the top 10 genera in both latent tuberculosis infection (LTBI) group and non-LTBI group. Table S4. Relative abundance (%) of the top 10 genera only in the non-latent tuberculosis infection (non-LTBI) group. Table S5. Relative abundance (%) of the top 10 genera only in the latent tuberculosis infection (LTBI) group. Table S6. Relative abundance (%) of the 26 most differential genera between the latent tuberculosis infection (LTBI) and non-LTBI groups. Table S7. Performance of predictive models that included different numbers of the 26 most differential genera between the latent tuberculosis infection (LTBI) and non-LTBI groups. Table S8. Confusion matrix of the classifier involving 6 genera and a test set (39 samples [30%]) for differentiating between latent tuberculosis infection (LTBI) and non-LTBI groups, as determined by a random forest model. Table S9. P values for each of the 6 selected genera in linear regression models for predicting the plasma levels of individual cytokines and the proposed model performance. Table S10. Change of model performance for predicting the plasma levels of individual cytokines before and after including the 6 selected genera into the linear regression models containing 11 potential confounders (sex, body-mass index ≥ 27 kg/m2, and use of metformin, DDP4 inhibitor, SGLT2 inhibitor, sulfonylurea, thiazolidinedione, meglitinides, acarbose, and use of ≥ 3 oral antidiabetic drugs, as well as statin). Figure S1. Rarefaction curve of sequencing data from 130 fecal samples. Samples with fewer than 45,000 sequences were excluded, and the remaining samples were rarefied to 49,423 sequences (range: 32,136 to 121,226) per sample for subsequent ordinations and permutational multivariate ANOVA analysis.Abbreviations: ASVs, amplicon sequence variants. Figure S2. Biodiversity and Prevotella/Bacteroides (P/B) ratio of gut microbial communities in diabetes mellitus (DM) patients with or without latent tuberculosis infection (LTBI). (A) Beta diversity was determined by a principal coordinates analysis plot based on the unweighted UniFrac distance. Each dot represents one sample. (B) Prevotella/Bacteroides (P/B) ratio between the latent tuberculosis infection (LTBI) and non-LTBI groups. *** represents a P value of &lt; 0.001 according to a Mann–Whitney U test. Figure S3. Biodiversity of gut microbial communities in diabetes mellitus (DM) patients with or without potential confounders: (A) sex, (B) BMI ≥ 27kg/m2, (C) Metformin, (D) DDP4 inhibitor, (E) SGLT2 inhibitor, (F) Sulfonylurea, (G) Thiazolidinedione, (H) Meglitinide, (I) Acarbose, (J) Statin. Figure S4. The feature importance of the 6 selected genera and potential confounders in the predicting models for serum cytokine levels. Figure S5. Prediction of gene function of the 6 most differential genera between latent tuberculosis infection (LTBI) and non-LTBI groups using PICRUSt2.