Additional file 2 of The association between obesity and weight loss after bariatric surgery on the vaginal microbiota
Creators: name: Raglan, Olivia
name: MacIntyre, David A.
name: Mitra, Anita
name: Lee, Yun S.
name: Smith, Ann
name: Assi, Nada
name: Nautiyal, Jaya
name: Purkayastha, Sanjay
name: Gunter, Marc J.
name: Gabra, Hani
name: Marchesi, Julian R.
name: Bennett, Phillip R.
name: Kyrgiou, Maria
Publisher: name: figshare
Year: 2021
Subjects: subject: Microbiology
subject: FOS: Biological sciences
subject: Cell Biology
subject: Genetics
subject: Physiology
subject: Ecology
subject: Immunology
subject: FOS: Clinical medicine
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics
subject: Cancer
subject: 110309 Infectious Diseases
subject: FOS: Health sciences

description: Additional file 1: Supplementary Table 1. Patient characteristics of the bariatric surgery cohort at baseline (n=27), and where indicated, for serial sample collection timepoints: 3 months post-surgery (n=22) and 6 months post-surgery (n=19). Supplementary Table 2. Prevalence of genus group according to BMI subcategories, diabetes and insulin resistance status in the total population at baseline sampling (n=109). Supplementary Table 3. Sensitivity analyses of the prevalence of each genus group present in the total population at baseline sampling according to obesity status: excluding women who had sexual intercourse less than 48 hours prior to sample collection; who had taken antibiotics within the 2 weeks prior to sample collection; premenopausal women only; premenopausal women excluding those taking OCP; postmenopausal women only; postmenopausal women excluding those taking HRT. Supplementary Table 4. Richness and Diversity Indices for whole patient cohort. Supplementary Table 5. Cytokine expression level according to obesity status of women in the total population, taken at baseline sampling. Supplementary Table 6. Summary of weight loss and serum marker changes from baseline (day of surgery) to 6-month follow-up in bariatric surgery cohort, with sensitivity analysis which excludes premenopausal women using the oral contraceptive pill and postmenopausal women using hormone replacement therapy. Supplementary Figure 1. Linear discriminant analysis effect size (LefSe) modelling identified vaginal microbiota biomarkers based on difference in obesity status, according to relative taxonomic abundance through all taxonomic levels. a) Cladogram representing taxa with different abundance according to obesity status in samples collected at baseline (n=109). The size of the circle is proportionate to the abundance of taxon present, yellow circles characterise non-significant differences in abundance at each taxonomic level, red/green coloured circles represent significant differences in abundance between non-obese and obese categories. b) Histogram of linear discriminant analysis (LDA) scores computed for features differentially abundant between non-obese and obese women. Relative abundance counts of Actinobacteria, Bacteroidetes and Prevotella were found to be significantly over-represented in obese women, whereas Bacilli (Lactobacillales) were enriched in non-obese samples (n=109, Welch’s t-test, LDA score greater than 2 used to determine discriminative features). Key: LDA score; linear discriminant analysis score. Supplementary Figure 2. Local cervicovaginal cytokine levels in the bariatric surgery cohort at baseline sampling (n=27) and 6 months post-surgery (n=21) did not show any significant changes. Supplementary Figure 3. Individual longitudinal profiling of Lactobacillus presence in the bariatric cohort (n=27) (P1 = patient number 1), according to menopause status. Each longitudinal sample was assigned to a genus group, either Lactobacillus-dominant vaginal microbiome (VMB), Gardnerella-dominant VMB, or high diversity VMB, as indicated by the colour-coded rectangle. Supplementary Figure 4. Data collection protocol for participants undergoing bariatric surgery. S1. Illumina MiSeq sequencing of 16S rRNA gene amplicons and data processing. S2. Quantification of serum markers using ELISA.