What Influences The Male Urogenital Tract Microbiome? Kirsty Lee - PowerPoint PPT Presentation

What Influences The Male Urogenital Tract Microbiome? Kirsty Lee Garson Supervisor: Prof Nicola Mulder

What influences the male urogenital tract microbiome? Circumcision Status STIs Testosterone Levels Male Urogenital Tract Microbiome Age Mucosal Sexual Immune Behaviour Responses

Collection of Penile Swabs, Samples and Particpant Data Visit 2 Visit 4 Visit 4 2 weeks after 24 weeks after 24 weeks after circumcision circumcision circumcision Visit 1 Visit 3 Circumcision 12 weeks after circumcision

Overview Edendale Chris Hani Hospital, 81 participants Baragwanath Hospital, KZN Gauteng 13–24 years old (n=48 ) (n=33)

Overview Edendale Chris Hani Hospital, 81 participants Baragwanath Hospital, KZN Gauteng 13–24 years old (n=48 ) (n=33) 90 samples Circumcision Status Sexual History STI Status Ethnicity

Overview Circumcision Status 81 participants, 90 samples Pre-circumcision (n=70) Post-circumcision (n=20) STI Status CT+MG+ CT+ Sexual History CT+HPV+ CT+MG+HPV+ Sexually-inexperienced (n = 17) CT+TV+HPV CT+MG+NG+HPV+ Sexually-experienced (n = 45) HPV+ HSV2+HPV+ TV+HPV+ Ethnicity HPV: Human papillomavirus (n=37) Swati (n=1) Coloured (n=5) Sotho (n=4) MG: Mycoplasma genitalium (n=4) TV: Trichomonas vaginalis (n=2) Zulu (n=36) Tsonga (n=1) Tswana (n=4) CT: Chlamydia trachomatis (n=8) NG: Neisseria gonorrhoeae (n=1) Xhosa (n=3) HSV-2: Herpes simplex virus 2 (n=1) Neg: None of the above (n=13)

Aims What is the composition of these bacterial communities? Which factors influence its composition? Can we train a model to detect patterns in the microbiome?

Aim One: Derive sequence data from clinical samples Collect samples Extract DNA Amplify and sequence a marker gene GATACAGAGATGCAT Group 1 GTATACAGAGATGCAT GGATACAGAGATGCAT GATCACAGAGATGCAT Group sequences by 97% similarity ATAGATACAGAGATCAT Group 2 ATAGTATACAGAGACAT TATGATACAGAGACAT Group 3 TATGTATACAGAACAT TAGGGATACAGACAT Assign taxonomy TATGATACAGAGAC using reference databases Bacteria_Firmicutes_... Align sequences and create a phylogeny

Aim One: Derive sequence data from clinical samples Kingdom Phylum Class Order Family Genus Species "Bacteria" "Firmicutes" "Bacilli" "Lactobacillales" "Lactobacillaceae" "Lactobacillus" "iners"

Aim Two: Analyse the microbial composition of samples What is the composition of these bacterial communities? How does it differ between healthy individuals and those with disease? (marker gene analysis) Exploratory Analysis (alpha & beta diversity, heatmaps, etc. ) Microbiome Pattern Recognition (phyloseq, labdsv, vegan, ape, ggplots) (random forest classifier) (scikit-learn) Marker Discovery/Statistical Testing (differential abundance testing/indicator species analysis) (metagenomeSeq, labdsv)

2.1 Measure diversity within samples Observed Richness Chao1 Shannon Index Simpson Index (inverted) Circumcision Before After n 1 2 S Chao 1 = S obs + 2 n 2 2

2.2 Measure diversity across samples Before Circumcision After Circumcision PCoA.2(8.7%) PCoA.1(21.3%)

2.2 Measure diversity across samples Before Circumcision After Circumcision Species Name of sample

2.2 Measure diversity across samples Before Circumcision After Circumcision Species Name of sample

2.3 Assess sample composition Relative abundance Name of sample Family

2.4 Compare sample composition across groups Before Genus Circumcision After Circumcision

2.5 Compare the number of times microbes of interest occur across groups Pre-circumcision Post-circumcision samples samples Genus No. of reads

2.6 Pattern recognition Explore potential underlying structures within these bacterial communities by grouping them into subcommunities: - certain OTUs vary together - functional redundancy exists - many zero values in microbiome data makes analysis more challenging - random forest classifier assumes independence of features

2.6 Pattern recognition: predicting group membership using a random forest classifier Predictions Before Circumcision After Circumcision Before Circumcision 15 1 Actual After Circumcision 2 3

2.6 Pattern recognition: predicting group membership using a random forest classifier Which species are most influential in making predictions? OTU 011 Peptoniphilus species OTU 065 Escherichia coli OTU 150 Lactobacillus species OTU 064 Lactobacilus iners OTU 039 Clostridiales order [Mogibacteriaceae] family OTU 159 Enterococcus species Relative Importance

Acknowledgements Computational Biology Division, Professor Nicola Mulder University of Cape Town Professor Heather Jaspan Division of Immunology, Dr. Pierre-Yves Lablanche (AIMS) University of Cape Town Dr. Arun Aniyan (SKA) Dr. Michelle Lochner (AIMS) Study participants Department of Medicine, Perinatal HIV Research Unit, Edendale Hospital, Kwa-Zulu Natal Soweto, Gauteng Clive Gray, Jo-Ann Passmore, Rushil Harrayparsad, Hoyam Gamieldien, Selena Ferrian, Abraham Olivier, Heather Jaspan, Katie Viljoen, Dirk Lang, Susan Cooper, Nyari Chigorimbo, Lungile Mayiza, Doug Wilson (Edendale hospital), Marcus McGilvray (WizzKids), Rusha Govender (WizzKids), Hillary Mukudu, Janan Dietrich, Neil Martinson (PHRU); Nono Mkhize, Raveshni Durgiah, Lynn Morris (NICD), Tom Hope, Minh Dinh, Gianguido Cianci, Francesca Chiodi, Sylvie Amu