Catherine Lozupone
CPBS 7711
September 19, 2013

• “The ecological community of commensal, symbiotic, and pathogenic microorganisms that share our body space”
• Microbiota: “collection of organisms”
Microbiome: “collection of genes”
• Bacteria, Archaea, microbial eukaryotes (e.g. fungi or protists) and viruses.
• Body Sites
– Important roles in health and disease: Gut, Mouth,
Vagina, Skin (diverse sites:Nasal epithelial)
– Important roles in disease: Lung, blood, liver, urine

• Majority of life ’ s diversity is microbial
• Majority of microbial life cannot be grown in pure culture
Pace, N.R.,The Universal
Nature of Biochemistry. PNAS
Vol 98(3) pp 805-808.

• 100 trillion microbial cells: outnumber human cells 10 to 1!
• Most gut microbes are harmless or beneficial.
– Protect against enteropathogens
– Extract dietary calories and vitamins
– Prevent immune disorders
• List of diseases associated with dysbiosis ever growing
– Inflammatory Diseases: IBD, IBS
– Metabolic Diseases: Obesity, Malnutrition
– Neurological Disorders
– Cancer

• What does a healthy microbiome look like?
– How diverse is it?
– What types of bacteria are there?
– What is their function?
• How variable is the microbiome?
– Over time within an individual?
– Across individuals?
– Functionally?
• What are driving factors of variability?
– Age, culture, physiological state (pregnancy)
• How do changes affect disease?
– What properties (taxa, amount of diversity) change with disease?
– Cause or affect?
– Functional consequences of dysbiosis
• Host Interactions
– Evolution/adaptation to the host over time.
– Immune system

• Culture-based studies over-emphasized the importance of easily culturable organisms (e.g. E. coli).
Culture-independent surveys
1.
Extract DNA from environmental samples.
2.
 PCR amplify SSU rRNA gene (which species?)
 Sequence random fragments (which function?)
3. Evaluate
Sequences

Data from: Yatsunenko et. al. 2012. Nature .
Different phyla: Animals and plants

• Each person harbors > 1000 species.
• Some species are unique ( red and blue )
• Some shared
( purple )
• We know very little about what most of these species do!

Sequencing technology renaissance enabled more complex study designs
• Sanger Sequencing (thousands)
• Pyrosequencing (millions)
• Illumina (billions!)

• The study of metagenomes, genetic material recovered directly from environmental samples.
• Marker gene
– PCR amplify a gene of interest
– Tells you what types of organisms are there
– Bacteria/Archaea (16S rRNA), Microbial Euks (18S rRNA), Fungi (ITS), Virus (no good marker)
• Shotgun
– Fragment DNA and sequence randomly.
– Tells you what kind of functions are there.

• Present in all known life forms
• Highly conserved
• Resistant to horizontal transfer events
16S rRNA secondary structure

• MetaTranscriptomics (sequence version of microarray)
– Isolate all RNA
– Deplete rRNA
– Sequence all transcripts
– Sometimes phenotype only seen in activity of the microbiota
• Metabolomics
– What metabolites does a community produce?
– E.g. in feces or urine
• MetaProteomics
– What proteins does a community produce?

• 16S rRNA -> shotgun metagenomics
– What gene differences cannot be explained by
16S?
– Selection by HGT
• 16S/ genomics -> transcriptomics-> metabolomics
– What species or genes (or combination of species or genes), when expressed, are responsible for producing a given metabolite?

• Sanger -> 454 Pyrosequencing -> Illumina

• UW UniFrac clustering with Arb parsimony insertion of 100 bp reads extending from primer R357.
• Assignment of short reads to an existing phylogeny
(e.g. greengenes coreset) allows for the analysis of very large datasets.
Liu Z, Lozupone C, Hamady M, Bushman FD & Knight R (2007) Short pyrosequencing reads suffice for accurate microbial community analysis. Nucleic Acids Res 35: e120.

Preprocessing pyrosequencing datasets
• Quality filtering: Discard sequences that:
– Are too short and too long (200-1000 range)
– With low quality scores
– With long homopolymers
– Can trim poor quality regions from the ends
• PyroNoise and Chimeras
– Can greatly inflate OTU counts
– Pyronoise algorithm uses SFF files to fix noisy sequences
• Use barcodes to assign sequences to samples

• Cluster sequences based on % identity
– 97% id typical for species
– CD-HIT, UCLUST
• For Phylogenetic diversity measures need to make a tree
– Align sequences: NAST, PyNAST
– Denovo tree building: FastTree
– Assign reads to sequences in a pre-defined reference tree

• Overview of methods for evaluating/comparing microbial diversity across samples using 16S rRNA
  diversity: Measures how much is there?
  diversity: How much is shared?
• Phylogenetic verses taxon based diversity.
• Quantitative verses Qualitative diversity.
• What types of taxa are driving the patterns? Which species are associated with measured properties?
• Tools: UniFrac/QIIME/Topiary Explorer
• Lozupone, C.A. and R. Knight (2008) Species divergence and the measurement of microbial diversity. FEMS Microbiol Rev. 1-22.

How do we describe and compare diversity?
 
Diversity:
– “ How many species are in a sample?
”
• (e.g. 6 colors in A and 6 in B)
– e.g.: Are polluted environments less diverse than pristine?
 
Diversity:
– “ How many species are shared between samples?
”
• (e.g. 2 shared colors between A and B)
– e.g.: Does the microbiota differ with different disease states?
A
B

Quantitative versus Qualitative measures
A • Qualitative:
Considers presence absence only
– 
: How many species are in a sample?
• e.g.: 6 colors in both A and B.
– 
How many species are shared between samples?
• e.g.: A and B are identical because the same colors are present in both.
• Quantitative:
Also considers relative abundance.
– 
: Accounts for “ evenness ” :
• e.g. B, where the population is evenly distributed across the 6 species, is more diverse than A, where all species are present but red dominates.
– 
Samples will be considered more similar if the same species are numerically dominant versus rare.
• e.g. B and A no longer look identical because of differences in abundance.
B

What is a phylogenetic diversity measure?
 
Diversity:
– Taxon: “ How many species are in a sample?
”
– Phylogenetic: “ How much phylogenetic divergence is in a sample?
”
• (e.g. B more individually diverse than A - more divergent colors)
 
Diversity:
– Taxon: “ How many species are shared between samples?
”
– Phylogenetic: “ How much phylogenetic distance is shared between samples?
”
• (only related colors from B are in A)
A
B

Advantages of phylogenetic techniques.
• Phylogenetically related organisms are more likely to have similar roles in a community.
• Taxon-based methods assume a “ star phylogeny, ” where all relationships between taxa are ignored.
• Phylogeny and Taxon-based methods can be complementary.

• 
Diversity
– Phylogenetic Diversity: PD
– Taxon-based:
• observed # species (richness)
• Correct for undersampling (Chao1, Ace)
• Richness + evenness (Shannon-Weaver index)
• 
Diversity
– Test if samples have significantly different membership.
• UniFrac Significance, P test, Libshuff (Phylogenetic)
– Identify environmental variables associated with differences between many samples.
• Phylogenetic
– Unweighted and Weighted UniFrac
– DPCoA
• Taxon-based: Jaccard/Sorenson indices

• Sum of branches leading to sequences in a sample.
• Sample with taxa spanning the most branch length in this tree represents the most phylogenetically and perhaps functionally divergent community.
Faith, D.P. (1992) Conservation evaluation and phylogenetic diversity.
Biological Conservation 61, 1-10.

• Plot the amount of branch length against the # of observations.
• Shape of curve allows for estimating how far we are from sampling all of the phylogenetic diversity.
• Allows for comparison of phylogenetic diversity between samples.
Eckburg, P.B., et al. (2005) Diversity of the human intestinal microbial flora. Science 308,
1635-1638.

Phylogenetic and OTU based techniques can be complementary
• Results of analyzing the same data with Chao1 and PD.
• Samples from stool, mouth, lung, plasma, and negative controls.
• Differentiation between the stool/mouth and negative controls greater with Chao1 than with PD
• The negative controls have few OTUs but they are phylogenetically diverse
• Chao1 estimates go up with sampling effort.


• Do two samples contain significantly different microbial populations?
• Can we see broad trends that relate many samples and explain them in terms of environmental factors?

• Qualitative phylogenetic
 diversity.
• Distance = fraction of the total branch length that is unique to any particular environment.
Lozupone and Knight, 2005, Appl Environ Microbiol 71:8228

Can we see broad trends that relate many samples and explain them in terms of environmental factors?

What types of environments have similar phylogenetic diversity?
pH
Temperature
0-100°C
1-12
Pressure Nutrient
Availability
Oligotrophic
Eutrophic
1-200 atm
Lozupone CA & Knight R (2007) Global patterns in bacterial diversity. Proc
Natl Acad Sci U S A 104: 11436-11440.

PCoA of
UniFrac
Distance
Matrix

Hierarchical clustering
(UPGMA) of the same
UniFrac distance matrix

Qualitative vs Quantitative measures of
Phylogenetic

Diversity
• Qualitative:
– Unweighted UniFrac
– Detects factors restrictive for microbial growth.
– High temperature, low pH, founder effects.
• Quantitative:
– Weighted UniFrac, DPCoA.
– Detects transient changes.
– Seasonal changes, nutrient availability, response to pollution.
• Yield different, complementary results and applying both to same data can provide insight into nature of community changes.

Qualitative
Quantitative
Lozupone et al., 2007. Appl Environ Microbiol 73:1576

Obesity and Gut Microbiota
• Mice heterozygous for mutation in
Leptin gene interbreed.
• 16S gene sequenced for bacteria in gut of mothers and offspring.
Ley et al., (2005)Obesity Alters Gut Microbiota, PNAS Vol 102: pp 11070-11075

Mice cluster perfectly by mother
Ley et al., (2005)Obesity Alters Gut Microbiota, PNAS Vol 102: pp 11070-11075

Stronger clustering with obesity with
Weighted UniFrac

Unweighted UniFrac
Weighted UniFrac
Comparison of human stool and mucosal microbes
• Unweighted: all samples cluster by individual.
• Weighted: stool looks different.
Eckburg, P.B., et al. (2005) Diversity of the human intestinal microbial flora. Science 308, 1635-
1638.

• Double principal coordinates analysis (DPCoA)
– Another quantitative
 diversity measure.
– A matrix of species distances is first used to ordinate the species using
PCoA.
–
The position of the communities in coordinate space is the average position of the species that they contain, weighted by relative abundances.
• Produces same results as weighted
UniFrac.

• Computation enhancements create order of magnitude increases in speed and reduced memory requirements.
Hamady, Lozupone and Knight, The ISME Journal. 2009. Epub ahead of print.

• Pyrosequencing often produces high variability in the number of sequences per sample.
• This can introduce bias because undersampling creates inflated beta diversity values
• Randomly resampled a dataset at different depths and calculated the average UniFrac distance.
• Samples with fewer sequences look artificially different.
• Rarefaction: randomly select an even amount of sequences
Lozupone et al. 2011. ISME. 5:169-72

Web interfaces have >2200 registered users .
Unifrac papers have collectively 1250 citations.
461 citations

www.microbio.me/qiime

Lozupone et al. Genome Research. 2013

• Histograms

Histograms and trees can pain a different picture
Peterson 2008 Cell Host Microbe: 3:417-27
Cluster XIVa ~43% of the total bacteria in the stool of healthy individuals (Maukonen 2006. J Med
Microbiol. 55:625-33.)
• 16S rRNA gene tree of OTUs prevalent in 2 studies of diet/obesity
– Turnbaugh 2009 Sci Transl Med. 1:6ra14
– Ley 2006. Nature. 444:1022-3
• Clostridia clusters XIVa and IV are the most abundant in the healthy gut.

• Which taxa are significantly different between health and disease?
– Using OTUs versus classifier derived taxa.
• PCoA Biplots:Which taxa are correlated with overall clustering patterns?
• Finding discriminatory OTUs with Supervised
Learning.
• Applying classical statistical tests with out_category_significance.py
• Exploring relationships in trees.

• 2 methods
– OTUs
– Classifiers (e.g. the RDP classifier)
• For both methods phylogenetic depth of the taxa can be varied.
– OTUs – different %IDs (97%, 95%, 90%)
– Classifiers – different levels (species, genus, family)
• Advantage of using OTUs
– Can evaluate phylotypes not related to known species or in taxonomic groups with poorly defined systematics.
– Each OTU represents an equal amount of phylogenetic divergence.
• Advantage of using Classifiers
– Can more easily relate results to other published results.
– Fewer taxa than OTUs.

• Shallow
– 97% ID OTU or species-level taxonomy assignments
– Advantage
• Biological properties of taxa have the potential to be more strictly defined
– Disadvantage
• Can loose power to find associations in broader lineages in which a trait is conserved
• Broad
– 90% ID OTUs or family-level taxonomic assignments
– Advantage
• More powerful for conserved traits
– Disadvantage
• Association in a broader group is often driven by only a subset of its members (i.e. if you detect that Gamma Proteobacteria go up you cannot say that E. coli did it!)

Clostridium cluster XIVa
Lachnospiraceae
Clostridium
Lozupone et al 2012
Genome Research
Ruminococcus
Ruminococcus
Blautia
Ruminococcus
Ruminococcus
Blautia
Clostridium
Eubacterium
Clostridium
Eubacterium
Clostridium
Eubacterium
Clostridium

• Allows visualization of taxa and samples in the same PCoA space

• 2 methods
– Supervised learning
– Classical statistics
• Supervised learning
– Evaluates how well OTUs/taxa can be used to classify by treatment.
– Discriminative OTUs are those for which classification power is reduced when they are removed from the set
– Advantage:
• evaluates OTUs contextually rather than independently
– Disadvantage:
• only works with Discrete sample groupings (i.e. will not handle correlations with disease severity or changes over time)

• All OTUs with scores
> 0.001 considered
‘important’
– Yatsunenko et al
Nature 2012
• Problem: We do not know the direction of change.
• With only two categories – compare the means.

• otu_category_significance.py
– i: otu table
– m: category mapping
– c: category (e.g. health status)
– s: statistical test
• ANOVA
• Pearson correlation
• Paired T test
• G-test of independence
– f: minimum number of samples found in to be considered
– Removes OTUs that don’t pass the filter, performs a statistical test on each OTU, corrects for multiple comparisons with FDR and Bonferroni correction.
– Can also be run on Taxa Summary tables files if in BIOM format.

Assign statistical significance values to bar charts

• I use these means and their significance to assess direction of change in Supervised learning results.

• Relate them in a tree.
• ARB to make the tree using parsimony insertion.
– http://www.mpi-bremen.de/ARBSILVA.html
• Topiary explorer to visualize/color the tree and make publication quality graphics
– http://topiaryexplorer.sourceforge.net

Erysipelotrichales with HIV infection

• Genomics : Thousands of complete and draft genome sequences for human commensals publicly available
– Promise: translate 16S into functional predictions (PiCRUST)
– Challenges: no genomes for unculturable microbes
– Genes with high HGT
Distribution
(16S rRNA)
Experimental
Confirmation
(anaerobic culture)
Comparative genomics
(complete genomes)

• Based on similarity to genes of known function.
NCBI genomes have functions listed for predicted proteins

• COGs (Clusters of Orthologous Groups; http://www.ncbi.nlm.nih.gov/COG/ )
• KEGG (Kyoto Encyclopedia of Genes and Genomes; http://www.genome.jp/kegg/ )
• CAZy (Carbohydrate Active Enzymes database; http://www.cazy.org/ )
• pFAM (protein family database; http://www.sanger.ac.uk/resources/da tabases/pfam.html)

• Orthologous groups
– A group of proteins that are expected to perform the same function in the different organisms in which they are found.
– Function is inferred for the whole group based on experimental work with one of its members.
– COGs are grouped into larger functional groups.

• Orthologous groups
(assigned KO numbers)
• Metabolic pathways.
– Boxes contain enzyme commission database (EC) numbers.
• Each EC is associated with KO numbers (a protein family that is known to perform that reaction).

Glycoside Hydrolases (GH)
Degradation: hydrolyze glycosidic bonds between two carbs or between a carb and a non-carb.
Important for degradation of plant polysaccharides.
GlycosylTransferases (GT)
Biosynthesis: catalyze the transfer of sugar moeties.
Important for communication with host immune system.
• Database describing protein families predicted to be carbohydrat e active based on homology
• Uses HMMs
• Exact reaction performed does not need to be known.

• Similar to CAZy but with a broader scope.
• Hidden Markov Models that describe sequence motifs of a known function

• Based on similarity to genes in a database of reference genomes.
– http://www.genomesonline.org/cgibin/GOLD/index.cgi
• Mg-RAST uses best BLAST hit:
M5N4

• MgRAST http://metagenomics.anl.gov/metagenomics.c
gi?page=Analysis
• Produces Table mapping samples to annotations that can be further processed in
QIIME