Souvik Mukherjee Assistant Professor National Institute of Biomedical Genomics West Bengal, India

Transcription

1 82 nd Annual Meeting of Indian Academy of Sciences Venue: IISER, Bhopal Souvik Mukherjee Assistant Professor National Institute of Biomedical Genomics West Bengal, India

2 The Skin and Its Commensal Microbiome The Skin (1.8m 2 of body surface) Nat Rev Microbiol. Grice and Segre, 2011 Largest Organ of the Human Body & First Line of Host Defense Innate Immunity Genes, Defensins, Cell Surface Toll-Like Receptors (TLRs) A diverse community of microorganisms, collectively known as the Skin Microbiome protects from infections by outcompeting pathogens for resources or by priming the immune response to invaders 2

3 Why study Skin Microbiome? The Skin encounters continuous assault by potentially pathogenic organisms (Nat Rev Microbiol., Grice & Segre 2011) Exhibits significant topological, temporal and inter-individual variations (Science, Grice et al 2009; Science, Costello et al 2009) Resists colonisation/invasion of opportunistic or pathogenic microbiota (Science, Naik et al 2012) Regulates expression of Complement system genes that in turn regulates its composition and diversity (PNAS, Chehoud et al 2013) Differential Skin Microbiome profile in Caesarean babies is associated with risk of immune/metabolic disorders(nature Medicine, Dominguez-Bello et al 2016) Dysbiosis (microbial imbalance) is associated with Atopic dermatitis, Psoriasis etc. (Genome Res., Kong et al 2012; Microbiome, Alekseyenko et al 2013 etc.) Skin Microbiome is important in understanding the functional significance of changing microbiota with respect to Wound healing (mbio, Grice et al 2010) 3

4 Studying the Microbiome in Healthy Individuals For establishing healthy baselines from which to detect differences associated with diseases (Costello et al. Science 2009) Healthy Microbiome is not same for all (Schnorr et al. Nat. Commun.2013) Gut Microbiome Associated in Europeans Italians (n=16) African Tribe (n=27) Bifidobacterium Healthy Gut Higher Abundance Almost absent Treponema Crohn s disease & IBD Almost Absent Higher Abundance The healthy skin microbiome is well characterised in Western populations (Grice et al. Science 2009, Zeeuwen et al. Genome Biol. 2012, Flores et al. Genome Biol. 2014, Oh et al. Nature 2014) No study had been performed on Asian populations 4

5 Characterisation of Skin Surface Microbiome across different Skin Types in Humans The human skin is a natural habitat to large number of microorganisms collectively termed as Skin Microbiome that shows considerable Topographical and Temporal variations as well as wide Inter-individual variability The causes of these variations and their interplay with host factors are inadequately understood Based on Sebum and Hydration levels, human skin can be classified into: a) Oily, b) Normal and c) Dry skin individuals Objectives of the Present Study To characterize the Facial Skin Microbiome profiles across different skin types in Healthy Humans To investigate whether inter-individual and temporal variations in human facial skin microbiome can be significantly explained by variation in sebum and hydration levels in specific facial regions 5

6 Study Design Study Subjects and Measurement of Sebum and Hydration Levels Healthy Female Volunteers (n=30) Mean Age: years Used soap without any antibacterial active in it for last 14 days Not taken a bath 12 hrs before sample collection Not taken any antibiotic for last 6 months Not Pregnant in the last one year No skin-care product on face for last 14 days Forehead Region Facial Skin Cheek Region Sebumeter Reading Corneometer Reading Sebumeter Reading Corneometer Reading Sebum Concn. ( g/cm 2 ) Hydration Level (a.u.) Sebum Concn. ( g/cm 2 ) Hydration Level (a.u.) 6

7 Sample Collection & Microbiome Sequencing Faces washed hdwith sterile water and individuals id kept in a controlled temperature of 24 C and 45% relative humidity for 4 hours before swabbing Facial Skin Swab was collected with sterile cotton pads moistened in 1x PBS ph-7.0+tween 80 Microbial DNA was isolated from microbial pellet using MO BIO BiOstic Bacteremia DNA Isolation Kit V3-V5 region (570 bp) of 16S rrna Gene was sequenced using GS-FLX Roche Platform (n=29) 357F: 5'-CCTACGGGAGGCAGCAG-3' 926R: 5'-CCGTCAATTCMTTTRAGT-3 Temporal Variability in Skin Microbiome Profile: A subset (n=24) of individuals were followed up after 60 days Ion Torrent PGM Platform was used for sequencing 12 individuals of the second batch for validation purposes 7

8 Microbiome Data Analysis SFF files Sequence Flowgram Format FASTQ and FASTA files Operational Taxonomic Units (OTUs) Initial QA/QC using RDP and QIIME QV/base >20-25, Ambiguous Base(N)>2-6, 200<Seq. Length> , Homopolymers>4-6 Removal of Chimeric Sequences Clustering of Sequence Reads with > 97% pairwise similarity Singleton OTUs were removed (1 read in only 1 sample) Taxonomic Classifications (phylotyping) Representative sequences aligned to SILVA and SILVA Gold by Mothur and QIIME Alpha Diversity Shannon Index (Diversity) Chao Index (Richness) Rarefaction plots Beta Diversity Jaccard Index (community membership) Bray-Curtis Dissimilarity (community structure) 8

9 Statistical Analysis of Microbiome data Percentage Proportions = Sequence Reads/Taxon/Individual X 100 Total No. of reads/individual Percentage Proportion was calculated from Phylum to Genus Only those taxa (phyla and genera) with relative abundance > 1% in at least one individual were included for further analysis. All the other taxa were pooled into a separate group called Other Stepwise Multiple Regression Analysis was performed to identify the significant predictors of Facial Microbiome Composition and Diversity from among the Sebum and Hydration levels of Forehead and Cheek Regions For examining the temporal differences in facial microbiome, paired T-test (cross-validated by the non-parametric Wilcoxon Signed Rank Test, data not shown) were done 9

10 Sebum Concn. ( g/ml) 300 Sebumeter and Corneometer Readings )Sebum Concentration in Forehead and Cheek Regions Individual Number Forehead Sebum Cheek Sebum Forehead Sebum was significantly higher than Cheek Sebum (p= ) Hydration Level (a.u.) Hydration Level in Forehead and Cheek Regions Individual Number Forehead Hydration Cheek Hydration Forehead (p=0.022) and Cheek (p=0.042) Hydration varied significantly with age 10

11 QA/QC Results and Rarefaction Plots Total Sequence reads generated = 1,700,608 After initial QA/QC and Chimera Removal = 1,532,131 Total number of OTUs = 60997; After Singleton Removal = X-axis: Sequence Reads/sample; Y-axis: Chao Index or Shannon Index All the samples reached a plateau even on subsampling for (~20000) reads 11

12 Inter-Individual Variability at Phylum/Genera Level 34 Phyla were identified The 5 Major Phyla were: Actinobacteria (66.3%) Proteobacteria (13.1%) Firmicutes (17.7%) Bacteroidetes (1.4%) Fusobacteria (0.4%) The 5 Major Genera were: Propionibacterium (58.6%) Staphylococcus (8.6%) Streptococcus (4.0%) Corynebacterium (3.6%) Paracoccus (3.3%) 914 Genera were identified 12

13 Diversity: Jaccard and Bray-Curtis Indices Community membership is high for all individuals, Jaccard Index range: Community structure shows wide inter-individual variations Bray-Curtis Index ranges from

14 Sebum and Hydration predict Microbial Composition and Diversity Phylum/ Diversity Indicator DIVERSITY INDICATORS Genus Cheek Sebum ( g/cm 2 ) p-value Forehead Hydration [Age adjusted] (a.u.) p-value Actinobacteria Propionibacterium Proteobacteria NS Haemophilus Enhydrobacter NS Granulicatella NS Firmicutes Streptococcus Veillonella NS Bacillus NS Bacteroidetes Prevotella NS Shannon Index ( ) No. of OTUs ( ) NS Total No. of Genera ( ) Cheek Sebum is the most significant predictor of microbiome composition and diversity followed by Forehead Hydration; Forehead Sebum and Cheek Hydration levels were not significant predictors 14

15 Temporal Stability of Facial Skin Microbiome Phylum Genus p-value p-value (Paired T test) FDR corrected Actinobacteria Propionibacterium Corynebacterium Actinomyces Micrococcus f_micrococcaceae_g Brevibacterium Kocuria Proteobacteria Paracoccus f_comamonadaceae_g Tepidimonas Neisseria Haemophilus Acinetobacter Lautropia f_neisseriaceae_g f_enterobacteriaceae_g f_moraxellaceae_g Roseomonas f.aeromonadaceae_g Actinobacillus Enhydrobacter Pseudomonadaceae_g Pseudomonas Temporal variability was not observed for the taxa with relative Abundance >1% in at least one individual, indicative of their functional importance in the Core Microbiome 15

16 OTU Heatmap with Representative Sequences 34,564 OTUs were observed after removing singletons The OTUs with >1000 sequence reads are shown in this Heatmap 16

17 Comparison with Recent Studies Tax. Level Name of fbacterial Taxa Zeeuwen en et al 2012 Our Study Phylum Proteobacteria Firmicutes Actinobacteria Genus Finegoldia Streptococcus Staphylococcus Propionibacterium Corynebacterium Forehead OTUs Leung et al 2015 Our Study No. of unique OTUs (%) 6,435 (22.6) 7352 (21.3%) Zeeuwen et al. 2012: Microbiome dynamics of human epidermis following skin barrier disruption (Genome Biol. 2012) (n=5) Grice et al. 2009: The three most abundant bacteria in Sebaceous sites are Propionibacterium, Staphylococcus and Corynebacterium (n=10) Leung et al. 2015: Skin microbial communities of Chinese individuals differ from other racial groups (n=40) Total OTU count (%) 28,471 (100.0) (100.0) All major phyla and genera were commonly shared with our study Phyla- Actinobacteria, Proteobacteria, Firmicutes and Bacteroidetes Genera- Propionibacterium, Staphylococcus, Acinetobacter, Streptococcus, Enhydrobacter and Corynebacterium 17

18 Key Inferences Major Highlights Differential Predictive potential of Sebum and Hydration Levels of Forehead and Cheek regions w.r.t. Composition and Diversity of Resident Skin Microbiome Cheek sebum is the best predictor of microbiome composition and diversity Significant association of Streptococcus (p=0.003; p=0.03) and Haemophilus (p=0.004; p=0.008) with Cheek Sebum and Forehead Hydration Levels No significant correlation of Corynebacterium & Staphylococcus with Sebum or Hydration levels (abundant in Sebaceous and Moist sites in Western data) The Healthy Skin Microbiome in India shows considerable variations with Western data in Composition on, Diversity and Temporal stability 18

19 Comparison of Our Study with Ganju et al 2016 Our Study Ganju et al 2016 Wide inter-individual variability as estimated by Bray-Curtis Index V3-V5 region of 16S rrna gene using GS- FLX (n=29). Longitudinal study 34 Phyla comprise upto 95% of sequence reads: Actinobacteria (66%), Firmicutes, Proteobacteria and Bacteroidetes Seven genera comprise of <80% sequence reads: Propionibacterium, Staphylococcus, Streptococcus, Corynebacterium, Paracoccus, Neisseria and Acinetobacter Core Taxa: Relative abundance >1% in at least 1 individual. 42 Core Genera Individual specific signature is dominant over Vitiligo-specific microbiota V1-V2 region of 16S rrna gene using GS-FLX (n=10, paired sites). Cross Sectional Study 21 Phyla comprise upto 85% of sequence reads: Actinobacteria (45%), Proteobacteria, Firmicutes and Bacteroidetes Eight major genera are: Corynebacterium, Staphylococcus, Propionibacterium, Micrococcus, Kocuria, Acinetobacter, Streptococcus and Paracoccus Core Taxa: Minimum abundance of 0.1% in at least 80% of samples. 23 Core Genera 19

20 Temporal Stability of Skin Microbiome after 1 year Based on Sebum and Hydration levels, 29 samples were classified into quartiles using SPSS 10 samples each from 1 st and 4 th Quartiles (extreme groups) were chosen for follow-up Only 10 could be re-sampled after 1 year: (1 st Quartile:4 samples & 4 th Quartile:6 samples) Microbial Taxa Batch-I (n=29) Batch-II (n=24) Batch-III (n=10) Phyla Genera Phyla >1% Genera > 1% Microbial Taxa Phyla >1% B1-B2 (n=24) None [Proteobacteria (p=0.012) before FDR corrections] B2-B3 (n=8) None [Firmicutes (p=0.018) before FDR corrections] Genera >1% Paracoccus (p<0.001) None [Staphylococcus (p=0.040) before FDR corrections] B1-B3 (n=10) None None [Acinetobacter (p=0.032) before FDR corrections] 20

21 Sebum-Hydration Levels are also stable after 1 year Biophysical Parameters of Skin Forehead Sebum Mean B1 (n=10) N Std. Dev Mean B3 (n=10) N Std. dev. T-test Value df p- value Cheek Sebum Forehead Hydration Cheek Hydration Wilcoxon-Signed Rank test also gave similar results 21

22 Ongoing Projects 1. Metagenomic profiling of Human Facial Skin by Shotgun Sequencing and its relation to changes in Skin Health Parameters like: a) Expression of Antimicrobial Peptides, b) Skin Elasticity, c) Sebum Level and d) Hydration Level (n=60) [Unileverer Research International al-funded] 2. Metagenomic Characterization of Major Immunogenetic Subtypes of Psoriasis in India [DST- Funded] 3. Diabetic Foot Ulcer Microbiome: Its Role in Biofilm formation, Antibiotic Resistance and Wound Healing [WB DBT & SciGenom Research Award Funded] In all the above studies, we have now focused on both the Host Immunity Genes and the Skin/Wound Microbiome jointly to understand the Host- Microbiome Interaction in Health and Disease 22

23 Diabetic Foot Ulcer Microbiome Study Major Objectives: 1. To detect the presence of Polymicrobial Biofilms in Diabetic Foot Ulcers (DFUs) by Microscopy and in vitro Biofilm formation assays 2. To characterise the nature and diversity of DFU Microbiome both pre and post antibiotic treatment for evaluating its association with Wound Healing and Biofilm formation treatment for evaluating its association with Wound Healing and Biofilm formation M1 Vs M2: Microbiome Pattern Associated with Non-Healing DFUs prior to Antibiotic exposure M2 Vs M4: Longitudinal shift in DFU Microbiome associated with impaired wound healing M1 Vs M3: Identification of Longitudinal shift in wound microbiome in healing Ulcers 23

24 Fluorescence In Situ Hybridisation Assay 24

25 Deep Sequencing V3-V5 region of 16S rdna (n=8) Raw Reads: 5,24,110 After QA/QC: 4,67,311 OTUs without singletons: No. of Phyla = 5, Genera = 114 [ Phyla: Proteobacteria (55.6%), Fusobacteria (17%), Firmicutes (10.2%), Actinobacteria (7.3%), Bacteroidetes (9%)] Non-healing DFUs showed increase in Anaerobes like Fusobacterium (73.5%) and Bacteroides (10.4%) post-antibiotic treatment, that are not common skin flora Microbiome Profiles in Healed DFUs Clustered both Before and After Antibiotic Treatment Commensals like Streptococcus (35.2%), Neisseriaceae (10.5%), Pseudomonas (6.3%) and Stenothrophomonas (25.2%) increased in abundance in Healed DFUs after Antibiotic Treatment 25

26 Acknowledgements Indian Academy of Sciences for giving me the opportunity to deliver my lecture Prof. Partha P. Majumder, Director, NIBMG Prof. Nitai P. Bhattacharyya, Director, BMGC Prof. (Dr.) Satinath Mukhopadhyay- Clinical Collaborator, DFU Project Prof. (Dr.) Debabrata Bandyopadhyay and Dr. Pranab Basak Clinical Collaborators, Skin Microbiome Projects My Lab Members: Mr. Badal Dey, Senior Laboratory Technician Shankha Nath, M.Sc. Genetics, Research Fellow Sayan Das, M.Sc. Bioinformatics, Data Analyst Dr. Poulami Mukherjee, MD (Microbiology), DBT-RA in DFU Project The Individuals and Patients who voluntarily provided samples for study Department of Biotechnology, Govt. of India Unilever R&D, Bangalore for funding the Skin Microbiome Research West Bengal-Dept. of Biotechnology for funding the DFU Research 26