The vaginal bacterial meta-transcriptome
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1 The vaginal bacterial meta-transcriptome Advisors: Dr. Greg Gloor Dr. Gregor Reid Jean Macklaim The University of Western Ontario
2 The vaginal microbiota Normal Lactobacilli-dominated Bacterial vaginosis Lack of lactobacilli + Gardnerella, Atopobium, Prevotella... Bacterial vaginosis (BV) is the most common vaginal disorder of women of child-bearing age:10-29% of women are affected 1 No single disease-causing microbe, but a shift in the microbial community leading to a dysbiosis Malodor, discharge, irritation BV leads to increased risk of: Acquisition and transmission of STIs and HIV Urinary tract infections Complications during pregnancy, and pre-term labour 1. Allsworth JE & Peipert JF (2007) Prevalence of bacterial vaginosis: national health and nutrition examination survey data. Obstetrics & Gynecology 109:
3 Ranking the vaginal microbiota Nugent scoring Presence/abundance of Lactobacillus, Gardnerella, Mobiluncus morphotypes on slide Amsel ( symptoms ) Discharge Maldor ph > 4.5 Clue cells Normal Bacterial vaginosis Verhelst, et al. (2005) BMC Microbiology
4 Bacterial enumeration/composition does not tell us the whole story What about functional differences? Hummelen et al. (2010) PLoS One Gloor et al. (2010) PLoS One
5 Meta-transcriptomics Organism-centric Gene-centric
6 Why is the vaginal microbiota a good system for metatranscriptomics? Relatively low diversity Large phylogenetic separation between organisms (sequence variation for mapping)
7 Vaginal bacterial meta-transcriptional profiling 2x N 2x BV Map sequenced transcripts to reference genomes (Bowtie: colorspace aware) Vaginal swabs Extract bacterial RNA Deplete rrna (95%+ of sample) to enrich mrna Sequence mrna (ABI SOLiD 4) Compare samples 1. DESeq R package (negative binomial distribution) 2. RPKM (reads per kilobase per millions of mapped reads) 50,000,000 x50bp reads/sample
8 How do the bacterial transcriptional profiles differ during bacterial vaginosis and how does this relate to health outcome?
9 Meta-transcriptome gene-centric Collect CDS sequences for vaginal strains (N=90)
10 Meta-transcriptome gene-centric Collect CDS sequences for vaginal strains (N=90) Cluster by 95% seq ID to make nonredundant sequence database (135,201 non-redundant CDS)
11 Meta-transcriptome gene-centric Collect CDS sequences for vaginal strains (N=90) Cluster by 95% seq ID to make nonredundant sequence database (135,201 non-redundant CDS) Mapping Functional assignment/annotation
12 Meta-transcriptome gene-centric Collect CDS sequences for vaginal strains (N=90) Cluster by 95% seq ID to make nonredundant sequence database (135,201 non-redundant CDS) Mapping 20-55% of raw reads mapped per sample: 11mil to 20mil reads N4 N30 BV27 BV31 2,528 3,234 5,135 5,992 Number of non-redundant CDS mapped at RPKM 10 Functional assignment/annotation
13 Functional assignment of the meta-transcriptome At broad functional level the communities are similar Step 2: consider individual genes and organisms Fraction of CDS assigned COGs
14 What organisms are present? Map to non-redundant cpn60 database* Choose genomes with abundant cpn60 *Janet Hill lab
15 What organisms are present? Map to non-redundant cpn60 database* Choose genomes with abundant cpn60 Map to reference genomes *Janet Hill lab
16 What organisms are present? Map to non-redundant cpn60 database* Choose genomes with abundant cpn60 Map to reference genomes Evaluate mapping: 1. Mapping coverage (is it real?) 2. Variants in mapped transcripts (what strain) *Janet Hill lab
17 What organisms are present? Map to non-redundant cpn60 database* Choose genomes with abundant cpn60 Map to reference genomes Choose reference genome(s) best matching variants Evaluate mapping: 1. Mapping coverage (is it real?) 2. Variants in mapped transcripts (what strain) *Janet Hill lab Extract sequence regions with high numbers of variants Compare (BLAST) to database of sequenced organisms
18 What organisms are present? Represents transcriptionally active organisms Eight abundant organisms plus 3 BVassociated organisms (Atopobium vaginae, Clostridiales BVAB3, Peptoniphilus lacrimalis) chosen for organism-specific transcriptome mapping
19 Mapping outcome N4 N30 BV27 BV31 Raw reads 47,634,967 46,943,184 48,832,687 53,655,422 Mapped reads 23,450,946 (49%) 16,545,092 (35%) 16,625,363 (34%) 14,075,880 (26%) Unique reads mapped to CDS 10,896,193 (46%) 8,541,748 (52%) 8,378,653 (50%) 5,908,471 (42%) 1-9% of reads mapped to human genome Of mapped reads, ~50% are 23s or 5s (incomplete/biased rrna depletion) Few mapping outside predicted CDS The rest are inaccessible in colorspace...
20 Mapping outcome 11 reference organisms Organism CDS size (bp) N4 cov N30 cov BV27 cov BV31 cov Atopobium vaginae 1,224, , , Clostridiales BVAB3 1,589, , , Gardnerella vaginalis 1,381,069 1, , ,482, , Lactobacillus iners 1,129, , , ,789, ,644, Lactobacillus crispatus 2,030,618 10,563, ,923, , ,381, Lactobacillus jensenii 1,445, , , , , Megasphaera 1,541, , , Peptoniphilus lacrimalis 1,527, , , Prevotella amnii 2,065,134 1, , ,080, , Prevotella disiens 2,515, , , Prevotella timonensis 2,330, , , Total mapped reads 10,896,193 8,541,748 8,378,635 5,908,471 Uniquely mapped reads
21 BV organism Prevotella amnii CDS with RPKM 10 B27 B31 What transcriptionally active genes have a role in BV pathogenesis? 90% shared between 2 BV samples Represents 72% of all CDS in P. amnii Interactions with host Interactions with other members of the biota (contributions to co-occurrence and competitive exclusion)* * Andrew Fernandes (38)
22 Conserved L. iners gene expression? L. iners gene expression in normal biota cluster together as do BV biota During BV the gene expression is highly similar despite different biota composition L. iners cultured in MRS broth (D) has a very different profile than in vivo more similar less similar How does L. iners gene expression function differ in BV compared to normal?
23 An altered transcriptional profile in L. iners during BV COG categories: L. iners genes expressed in Normal COG categories: L. iners genes expressed in BV Number of genes: 60 Number of genes: 43 L. iners total CDS = 1190
24 L. iners - Normal vaginal conditions Putative cell-surface protein containing YSIRK and Gram-pos anchor (host adhesion) Iron-sulfur (Fe-S) cluster and ferritin (oxidative stress)
25 L. iners - Bacterial vaginosis Cholesterol-dependent cytolysin (acquire nutrients, invasion?) PTS transport system and related metabolic enzymes (mucin)
26 An altered transcriptional profile in L. iners during BV Constitutive expression PTS carbohydrate transporters Breakdown products of mucin glycoproteins
27 Lactobacillus crispatus under BV conditions L. iners L. crispatus
28 L. crispatus functional assignment of DE genes Fraction of CDS assigned COGs During BV Shift to carbohydrate transport and metabolism and energy production (similar to L. iners)
29 0.16 Fraction of CDS assigned COGs
30 What next? Dig deeper! Validate with qpcr In process of recruiting and collecting more samples Use RNA-seq to refine gene predictions and genomic architechture (start and stop positions, operon structure)
31 Summary Possible to get high quality data from clinical samples L. iners is able to differentially express 10% of its gene complement during BV and shows a shift towards carbohydrate utilization Carbohydrate availability driving the population changes? Organism- and gene-centric analyses paint a different picture
32 Acknowledgements Dr. Greg Gloor Russ Dickson Dr. Andrew Fernandes Kevin Chen Ryan Jung Charles Yin Dr. Gregor Reid Kingsley Anukam Jordan Bisanz Ruben Hummelen Roderick MacPhee Amy McMillan Shannon Mifflin Dr. Wayne Miller Marc Monachese Beth Radford Camilla Urbaniak
33
34 Why is vaginal microbiota a good system for metatranscriptomics? Relatively low diversity Usually 1-4 organisms dominating Normal Bacterial vaginosis Microbiota fraction Organism abundance rank Large phylogenetic separation between organisms (sequence variation for mapping) Bacterial enumeration/composition does not tell us the whole story
35 Method 16s (V6) community profiling conserved variable
36 Studies and samples Cohort Treatment Sampling time points Total no. samples Tanzania Brazil Toronto HIV + with and without BV Oral metronidzole + Oral probiotic capsule or placeobo Day 0 2 weeks 5 weeks 15 weeks 25 weeks Study 1With and without BV Study 2With and without VVC Oral antibiotic/antifungal + Oral probiotic or placebo Day 0 (before treatment) Day 28 (after treatment) Pregnant (~28 weeks) Progesterone (prevention of preterm labour) 28 weeks gestation?? London (post-menopausal) With and without VVA (dryness) Day 0 2 weeks 4 weeks 6 weeks 8 weeks 10 weeks None London (pre-menopausal) With and without BV None Single samples Total vaginal microbiota: 777 VVA Vulvovaginal atrophy (dryness) BV Bacterial vaginosis VVC Vulvovaginal candidias (yeast) Probiotic capsule: Lactobacillus rhamnosus GR-1 and Lactobacillus reuteri RC-14
37 Macklaim, et al. (2010) PNAS
38 Reduced genome Smallest Lactobacillus genome sequenced to date (reduced across 13/20 functional categories) Highest proportion of genes acquired from foreign sources (via horizontal gene transfer) L. iners clade average No. genes COG functional category
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