Microbial Genomics, Pan-genomics, and Metagenomics in disease and in

health

Talk dedicated to Francis Ouellette and all the VanBUG organizers/volunteers current and past!

William Hsiao BCCDC Public Health Labs william.hsiao@bccdc.ca

Microbes Germs

Microbes – learn to love them

§ Microbes harbour much higher genetic diversity than eukaryotic organisms

§  Less than 0.5% of the microbial species have been identified – huge potential for discovery of new genes and new functions

§ Most Microbes (>99.9%) do not cause diseases in human

§ Microbes can be engineered to act as little factories for energy production, drug production, immune system booster (probiotics), pollution clean up, and environmental sensors

The Bacterial/Archaeal Genome

§  Typically contained within a single large, circular chromosome (some are linear)

§  Haploid genomes

§ May contain plasmids (extrachromosomal DNA)

§ No introns in the genes

§  Genome size range from 0.5Mb to ~10Mb (average is about 3 - 5Mb and contain about 3000-5000 genes)

§ Much easier than eukaryotic genomes to assemble and to annotate

§  First free-living organism sequenced is a bacterium – Haemophilus influenzae in 1995

Shotgun Sequencing – 90s style

Fraser et al 2000, Nature

contigs

Current Stats on Published Bacterial Genomes

§  Around 3000 published genomes in 17 years (thousands more sequenced)

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ER Mardis. Nature 470, 198-203 (2011) doi:10.1038/nature09796

DNA Sequencing Technologies

$10K per human genome or $10 per bacterial genome

$100M for the first human genome

Computing Improvements are “slower”

Cloud Computing

Cluster Computing

Next Generation Shotgun Sequencing

In

Most microbial genomes are not finished anymore

http://www.genomesonline.org/

Improving Assembly –paired-end and optical map

§  With high depth coverage from next generation sequencers, the gaps in unfinished genomes are usually due to unresolved repeats. So by incorporating long range information, we can order the contigs better and close the gaps

§  One of my post-doc projects involves in sequencing several bacterial genomes and since we got back incomplete genomes with a few hundred contigs, we explored other way to improve the genome assembly

§  We decided to use optical maps (high density, whole genome restriction maps aka fingerprints) to help us assemble the genome

Theodore Assembler

Hsiao et al, in preparation

Improved Assembly Results

Strain assembly method # of contigs total base placed n50

PBT16 Theodore 30 6661776 6566608

PBT21 Theodore 51 6927391 6739126

PBT91 Theodore 58 6927423 6738230

PBT16 Newbler 133 6564894 147681

PBT21 Newbler 204 6749359 124004

PBT91 Newbler 160 6900498 172612

Automated Genome Annotation

§  Several systems available to public, each with sophisticated approaches to assign functions to predicted genes / proteins

§  BASys (http://basys.ca)

§  Prok-annotation pipeline (http://ae.igs.umaryland.edu/cgi/intro_info.cgi)

§  IMG-ER (https://img.jgi.doe.gov/cgi-bin/er/main.cgi)

§  RAST (http://rast.nmpdr.org/rast.cgi)

§  Most of the systems run on large clusters of computers and take less than a day to annotated a genome

BASys Annotation Overview

Contigs

Regional Annotation

Non-protein encoding genes

Protein Encoding Genes

rRNA tRNA others

Manual Annotation

Functional Annotation

Automated Annotation

Intergenic Scan

Annotated Genome

Van Domselaar et al NAR 2005

Extremely time consuming!

Genome Projects – then and now

Conditions for one genome

Then Now

Sequence Time Months to a year to sequence one genome

Days to sequence several genomes

Cost of sequencing

$10,000 – 100,000

$10-100

Annotation Time A year of manual curation by multiple people

Automated annotation + spot inspection

Finish status Mostly complete fragmented

Publication Nature + Science SIGS

Comparative Genomics

§  First Comparative Genomic paper published in 1999

§  2 Helicobacter pylori genomes isolated 7 years apart were compared

Found more than half of the strain specific genes are clustered in hyper variable regions This observation soon was consistently observed in many other species

Alm et al, Nature 1999

Tools to detect Genomic Islands

§  In Fiona’s Lab, we developed several tools to aid the identification of genomic islands (genomic regions that are likely to be horizontally acquired from another species) §  IslandPath – based on DNA signatures

of the genomes and other features associated with islands (Hsiao et al Bioinformatics, 2003)

§  IslandPick – based on comparative genomics (Langille et al BMC Bioinformatics, 2008)

§  IslandViewer – integrated approach to identify and view genomic islands (Langille et al Bioinformatics, 2009)

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Proportions of Genes with no COG Assignment in Islands vs. Outside

OUTSIDE

ISLAND

Paired-t-test P value: 1.27E-18

More novel genes inside of islands

Hsiao et al. PLOS Genetics e62, Nov. 2005

% no assig

Organisms

Pan-genomes

§  Comparative Genomics and gene-gain and gene-loss in microbes lead to the idea of pan-genomes

§  The term first coined in 2005 in a paper by Tettelin et al., in which they compared sequenced genomes from six S. agalactiae.

§  Pan-genome consists of the core (shared) genes of a species + its strain-specific (dispensable) genes

§  Pan-genome calculation extrapolates observations based on a limited number of strains to come up with the theoretical number of genomes required to fully capture the pan-genome of a species

Open vs. Closed pan-genome

SNP-phylogeny for very closely related genomes

§  For very closely related isolates or very slowly evolving species, sometimes there is very little gene-gain and gene-loss.

§  In these cases, SNPs detected by aligning these genomes can be used as basis for comparison and phylogenetic tree reconstruction of the evolutionary history of the species

§ Whole Genome SNPs and Social Network Questionnaire used to reconstruct a TB outbreak in BC

Pangenome + Metadata!

§  A TB outbreak occurred in a BC community over a 3 year period

§ Molecular marker suggests that the outbreak is clonal but traditional contact tracing couldn’t identify a source

§ Whole genome sequencing and social network questionnaires (include location information) provide higher resolution data to allow a reconstruction of a likely scenario for the outbreak events.

§  Further epidemiological investigation point to increased crack cocaine usage (common locations) in the community

Gardy, Johnston, Ho Sui et al NEJM 2011

Putative Transmission Networks

Pangenome + Metadata!

§  This paper really demonstrated the power of whole genome sequencing

§  But, the availability of the metadata (disease conditions, locations, contacts, dates, etc) that facilitated the interpretation of the whole genome data

Biodiversity §  In a recent global ocean survey study, ~4000 novel

protein families were detected, a significant addition to ~13,000 known protein families (Yooseph et al, PLoS Biology, 03/2007)

§  Sampling human gut, >3 million non-redundant bacterial genes and >1000 prevalent species identified (Qin et al, Nature, 03/2010)

§  In environmental surveys to date, 30% - 70% of the genes identified in the samples are novel

§  >90% of all genetic diversity comes from non-eukaryotic organisms

§  How can we begin to study this diversity and identify important microorganisms?

What is Metagenomics?

§ Meta = beyond

§  Coined by Jo Handelsman (environmental microbiologist) in 1998

§  Has taken a more precise definition as studies to analyze genetic material from a mixed population living in the same environment

§ Who’s there? What do they do?

§  How do they interact with each other and with the environment?

Typical Experimental Protocols

Samples from Environment or hosts Enriched for

microbes

Extract DNA or RNA from mixed population (no culturing & cloning!)

Targeted Sequencing •  Use PCR primers to target specific

regions of genome •  E.g. 16S rRNA, capsid,18S •  Able to sequence deeper and broader •  No metabolic functional information •  Good for finding out “Who’s there”

Shotgun Sequencing •  Sequence randomly all the DNA that are

in the sample (RNA is reverse transcribed first)

•  Obtain functional information •  Don’t know the exact host of each gene •  Good for finding out “What is the

community doing”

Taxonomic Binning

§  After obtaining the 16S or other amplicon sequences, taxonomic binning based on sequence similarity or based on k-mer frequency similarity is carried out to assign a read to a taxon

§  Alternatively, reads are clusters to form OTUs (operational taxonomic unit) since many reads can not be assigned to a taxon

§  In the end, we obtain a matrix of count data associated with each taxa/OTU

Taxon E. coli OTU 1 B. theta P. aeruginosa

Sample 1 5 8 77 23

Sample 2 11 34 3 12

International Human Microbiome Consortium

§  International efforts to characterize the bacteria associated with human body sites

§  Systemic survey of the bacteria found in each site in healthy individuals – metagenomics

§  Sequencing of reference genomes of bacteria isolated from human – genomics and pangenome

§  Targeted study of microbiomes associated with various diseases

§  More information: http://www.hmpdacc.org and http://commonfund.nih.gov/hmp/

Endodontics 16S Microbiome

§  Root canal infections are a leading cause of oro-facial pain and tooth loss in western countries

§  No clear etiology; polymicrobial factors

§  Patients with root canal infections and periapical abscess were studied for the transition of microbiota from healthy oral sites to root canal and abscess

§  3 samples (normal oral, infected root canal, and abscess) were obtained from each of 8 individuals undergone treatment

§  First study we know to sample healthy and diseased oral microbiota from the same individuals

Hsiao et al, submitted

Abundant taxa show different distributions

Diseased sites have lower diversity

More abundant taxa were found in all 3 sites

All OTUs

Abundant OTUs

Differentially Distributed Bacteria may be associated with disease

§  We were interested to know which organisms are found differentially distributed in healthy vs. diseased sites

§  So after adjusting the count data for variance and sampling depth, we used paired-t tests and ANOVA tests to identify OTUs that are differentially distributed

§  We are especially interested in organisms that are found more abundant in diseased samples

§  In short, we were able to identify specific bacteria (some known opportunistic pathogens) to have higher relative abundance in diseased samples

§  These include: Granulicatella adiacens, Eubacterium yurii, Prevotella melaninogenica, Prevotella salivae, Streptococcus mitis, and Atopobium rimae

Watershed Microbiome

§  Genome BC project

§  Project leaders Dr. Patrick Tang and Dr. Judith Isaac-Renton

§  Two major goals are

§  1) To use metagenomics to identify novel microbial biomarkers of watershed health

§  2) Develop tools to match the microbial fingerprint of a contaminated watershed to the specific source of pollution

Current Water Quality Monitoring Problems

“The most significant problems associated with pathogen

measurement are the lag time involved in testing and… the large

number of false results… The absence of E. coli does not assure

the absence of more resistant fecal pathogens… source protection

planning must be carried out on an ecologically meaningful scale – that

is, at the watershed level.” The Honourable Dennis R. O’Connor

Walkerton Inquiry Commissioner 38

1. We need better tests §  Water quality test: Is fecal pollution present? §  Pollution attribution test: Which species is the cause?

2. The tests need better indicators §  New bacterial, viral, and potentially protozoan markers

3. An environmental survey is needed to find these novel indicators §  Metagenomics is the only tool that can do this survey

Metagenomics will Provide the Solutions “DNA analysis offers promise for the future” Walkerton Inquiry Report

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Pilot study looking at 16S microbiome at different sites under wet and dry conditions

§  Two Watershed sites

§  Two different conditions (wet day vs. dry day)

§  Multiple different time points throughout a day

§  Two replicated samples per sampling event

§  16S sequences from the samples amplified and sequenced

§  Microbiome profile generated based on 16S sequences

§  Clustering of the samples based on relative abundance of the species (OTUs)

Hierarchical Clustering of samples based on 16S relative abundance

Systems Approach – Mouse Gut Model

§  Host is a dynamic system just like the microbiota and it’s the interaction between host and microbes that really produce the observed outcome.

§  So, we want to be able to study the host gene expression changes and the microbiota changes simultaneously.

§  Immunity vs. metabolism in the gut: a trialogue between B lymphocytes, microbiota and the intestinal epithelium

Shulzenko, Morgun, Hsiao et al, Nat. Med, 2011

Overview of the Systems

T cell

B  cell  

Epithelial  cells  

Microbiota  

? modified from Lora V. Hooper Nature Reviews Microbiology 7, 367-374; 2009

Immune  cells  

Prepared by N. Shulzenko

BALB/c JH-/-

10 pairs (non-littermates)

Mice    –  B  lymphocyte  knockout  and  control  

For all mice: Take jejunum → Isolate RNA → gene expression by microarrays

Jejunum contents -> Isolate DNA -> 16S microbiota analysis

BALB/c WT B10.A WT

17 pairs (littermates and non-littermates)

B10.AµMT-/-

1. Comparing gene expression in the jejunum of µMT vs. heterozygous littermates

3. Validating on non-littermates (µMT and Jh-/- vs. WT)

2. Excluding B-cell origin genes (microarrays on separated B lymphocytes)

Analysis  of  microarrays  

Final  list  of  genes:  B-­‐cell  KO  profile  Prepared by N. Shulzenko

B  

GATA4

Dietary  lipids  

Microbiota  

IgA  

Epithelial    cells  

Normal host B lymphocyte/antibody-deficient host

GATA4

Microbiota  

Epithelial    cells  

T   metabolic  funcDon  

immune  funcDon    

absorption

deposition

absorption

deposition Adipose  

Adipose  

Prepared by N. Shulzenko

What happens when the B cells are knocked-out?

Clostridiaceae (family) Paracoccus (genus) Lactococcus subgroup (genus)

0.001 0.01 0.1 1 10 1000.001

0.01

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Changes in commensal microbes in the small intestine of B-cell KO mice Few significant differences detected by paired comparison of absolute amounts

All three are minor members of the microbiota (<0.4%)

B-cell KO

Con

trol

Ø  sequencing of

DNA coding for

16S rRNA

Do microbiota really play a role in the changes?

Germ-free vs. conventional B-cell KO

Microbiota  has  a  major  role  in  “B-­‐cell  KO”  intesDnal  profile  No  difference  in  gene  expression  between  BcKO  and  control  mice  under  germ-­‐free  condiDons  

Prepared  by  N.  Shulzenko  

T cell

B  cell  

Epithelial  cells  

Microbiota  

In  this  trialogue,  the  adap=ve  immune  system,  the  intes=ne,  and  the  microbiota  combine  to  influence  a  homeosta=c  metabolic  func=on,  in  mice  and  in  humans.  

metabolic  funcDon  

immune  funcDon    

Prepared  by  N.  Shulzenko  

Trans-­‐kingdom  Cross  Talk  (phylochip  +  an=bio=c  treatment)  

Red  =  host  genes  that  are  differen=ally  expressed    Blue  =  microbes  that  have  different  rela=ve  abundance    Lines  connects  nodes  that  are  correlated  across  samples  (yellow  =  posi=ve;  black=nega=ve)  

Prepared  by  A.  Morgun  

Future and Wishes

§ Microbial genomics with its rapid advances in the past two decades has a bright future in helping us to understand the world’s most dominating life forms better!

§ Many diseases and health issues have polymicrobial origins and pan-genome and metagenomics can help us solve these mysteries

§  Combination of different data types is key to interpret genomic data

§  “World Peace” – combating microbes with broad-spectrum antibiotics = last resort and is often counter-productive (we need our microbiota for health)

§  With increasing number of genomes available, tools for comparative microbial genomics and good comparative genome browser capable of handling hundreds of incomplete genomes will be very useful

§  Better statistical tools to integrate the data and to help interpret the results are also needed

Acknowledgements

§  Claire Fraser-Liggett

§  Art Delcher

§  Elliott Drábek

§  Zhenqiu Liu

§  Cheron Jones

§  Brandi Cantarel

§  Institute for Genome Sciences (sequencing, annotation)

§  Ashraf Fouad

§  Andrey Morgun

§  Natalia Shulzhenko

§  Jeffrey Gordon (and his lab)

§  Patrick Tang

§  Judy Isaac-Renton

§  Fiona Brinkman

§  Natalie Prystajecky

§  Miguel Uyaguari

§  Jennifer Gardy

§  Michael Chan

§  Stephen Pleasance

Outline

§  Progression from Microbial Genomics, Pangenomics, and Metagenomics

§  Bioinformatics tools used for these analyses

§  My own projects and HMPs as examples §  Health §  Diseases

§  Tool developments §  Database management §  Assemblers

§  Classifier

§  Future of the field and Wish list for tools