Additional file 2 of Interplay between the gut microbiome and typhoid fever: insights from endemic countries and a controlled human infection model

Additional file 2. Table S1: ANOVA analysis of alpha diversity from healthy household contacts and typhoid fever patients from all three sites. Table S2: PERMANOVA analysis of beta diversity from healthy household contacts and typhoid fever patients from all three sites. Table S3: PERMANOVA analysis of beta diversity from healthy household contacts and typhoid fever patients from all Bangladesh. Table S4: PERMANOVA analysis of beta diversity from healthy household contacts and typhoid fever patients from Malawi. Table S5: PERMANOVA analysis of beta diversity from healthy household contacts and typhoid fever patients from Nepal. Table S6: Median proportion of reads assigned to different phyla from healthy household contacts and typhoid fever patients from all three sites. Table S7: Species associated with health or disease in Bangladesh only. Positive co-efficient (coef) means the species is associated with health, negative co-efficient means it’s associated with typhoid fever. Table S8: Species associated with health or disease in Malawi only. Positive co-efficient (coef) means the species is associated with health, negative co-efficient means it’s associated with typhoid fever. Table S9: Full information about Metabolic Gene Clusters associated with health in both Bangladesh and Malawi. Table S10: ANOVA analysis of alpha diversity from healthy household contacts, typhoid fever patients, and high Vi-titre participants from Malawi and Nepal. Table S11: PERMANOVA analysis of beta diversity from healthy household contacts, typhoid fever patients, and high Vi-titre participants from Malawi. Table S12: PERMANOVA analysis of beta diversity from healthy household contacts, typhoid fever patients, and high Vi-titre participants from Nepal. Table S13: Species associated with high-Vi titre compared with household controls in Malawi only. Positive coefficient (coef) is associated with health, negative co-efficient is associated with high-Vi titre. Table S14: Metagenome identification of AMR genes commonly identified in Salmonella Typhi per country. Table 15: Metagenome identification of AMR genes commonly identified in Salmonella Typhi per participant group. Table S16: The average weighted importance of species to random forest classification of samples as being from control or presumptive carrier participants. Table S17: The correlation between the proportion of S. Typhi isolates and the proportion of microbiome samples from each site with AMR genes to quinolones, sulphonamides and tetracycline. Table S18: Demographic information on CHIM participants. Table S19: Alpha diversity ANOVA results for CHIM data. Table 20: PERMANOVA analysis of beta-diversity amongst CHIM participants challenged with S. Typhi and S. Paratyphi. Table 21: Species associated with household controls vs both typhoid fever and high Vi titre from Malawi. Table S22: SGBs identified in Malawi. Table S23: Associations between MGCs belonging to classes identified associated with household controls compared with acute typhoid cases and no disease following challenge in the CHIM. Table S24: MGCs significantly different between household contacts and typhoid cases in Nepal. Table S25: MGCs significantly different between household contacts and typhoid cases in Bangladesh. Table S26: MGCs significantly different between household contacts and typhoid cases in Malawi. Table S27: Endemic country cohort participant information. Table S28: metadata on CHIM samples. Table S29: The prevalence and abundance of species in the CHIM that were associated with typhoid fever in Malawi and Bangladesh. Table S30: Combined results of maaslin analysis of MGC types in four cohorts (Bangladesh, Malawi, Nepal, CHIM). Column headings indicate which cohort-specific results are from mal is Malawi, bgd is Bangladesh, nep is Nepal, and patch is the CHIM. If a result is NA, then there was insufficient of the MGC type identified in that cohort for maaslin analysis