1. The Microbiome and Metagenomics
Catherine Lozupone
CPBS 7711
September 19, 2013

2. What is the microbiome?
“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

3. The big tree Majority of life’s diversity is microbial
Majority of microbial life cannot be grown in pure culture
Vastly different metabolic capabilities, tolerance to temperature, depth, salinity
Pace, N.R.,The Universal
Nature of Biochemistry. PNAS
Vol 98(3) pp

4. The Human Gut Microbiota
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

6. What do we want to understand?
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

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

8. Gut microbiota has simple composition at the phylum level
Different phyla: Animals
and plants
Data from: Yatsunenko et. al Nature.

9. Diversity of Firmicutes in 2 healthy adults
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!
1.8 million sequences per person.

10. Sequencing technology renaissance enabled more complex study designs
Sanger Sequencing (thousands)
Pyrosequencing (millions)
Illumina (billions!)
Illumina (100 million)
Pyrosequencing

11. Metagenomics
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.

12. Small Subunit Ribosomal RNA
Present in all known life forms
Highly conserved
Resistant to horizontal transfer events
16S rRNA secondary structure

13. Other ‘Omics 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?

14. Integrating Data Types
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?

17. Sequencing Technologies
Sanger -> 454 Pyrosequencing -> Illumina

18. Short reads (pyrosequencing) can recapture the result.
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.

19. My presentation is going to cover analysis of data with QIIME
My presentation is going to cover analysis of data with QIIME. This shows many of the steps within QIIME. I am going to discuss certain steps in some detail, and cover the workflow scripts that automate many of the internal steps.

20. Preprocessing pyrosequencing datasets
Quality filtering: Discard sequences that:
Are too short and too long ( 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

21. Defining species: OTU picking
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

22. Comparing Diversity
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

23. How do we describe and compare 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

24. Quantitative versus Qualitative measures
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

25. What is a phylogenetic diversity measure?
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) B

26. 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.

27. Diversity Measures Diversity  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

28. Phylogenetic Diversity (PD)
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.

29. PD Rarefaction
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,

30. 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.

31. Phylogenetic  diversity: How is diversity partitioned across samples?
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?

32. Unique Fraction (UniFrac) metric
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

33. Clustering with the UniFrac Algorithm
Can we see broad trends that relate many samples and explain them in terms of environmental factors?

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

35. Salinity is the most important factor
PCoA of
UniFrac
Distance
Matrix

36. Hierarchical clustering (UPGMA)
of the same UniFrac distance matrix

37. Qualitative vs Quantitative measures of Phylogenetic  Diversity
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.

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

39. 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

40. So how about the obese mice?
Mice cluster perfectly by mother
Ley et al., (2005)Obesity Alters Gut Microbiota, PNAS Vol 102: pp

41. Stronger clustering with obesity with Weighted UniFrac

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

43. Measures in the same class cluster the data similarly
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.

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

45. Avoiding bias
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 ISME. 5:169-72

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

48. Study effects drive clustering of Western adults
Lozupone et al. Genome Research. 2013

49. Age and culture drive differences

50. Supervised Learning, classical statistics, taxonomic classification, and phylogenetic trees; How can we use these tools to understand which microbial taxa change across treatments?

51. Identifying compositional changes that drive diversity patterns
Histograms

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

53. Identifying taxonomic determinants
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.

54. Defining Taxa
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.

55. At what level should I classify?
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
More powerful for conserved traits
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!)

56. When ill-defined systematics can cause trouble
Clostridium cluster XIVa
Lachnospiraceae
Clostridium
Lozupone et al 2012
Genome Research
Ruminococcus
Ruminococcus
Blautia
Ruminococcus
Ruminococcus
Blautia
Clostridium
Eubacterium
Clostridium
Eubacterium
Clostridium
Eubacterium
Clostridium

57. PCoA Bi-plots
Allows visualization of taxa and samples in the same PCoA space

58. Finding discriminative OTUs
2 methods
Supervised learning
Classical statistics
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)

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

60. Classical Statistics Tests in QIIME
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.

61. Assign statistical significance values to bar charts

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

64. Are discriminatory OTUs related to each other and to type strains?
Relate them in a tree.
ARB to make the tree using parsimony insertion.
Topiary explorer to visualize/color the tree and make publication quality graphics

66. Sometimes associations are phylogenetically shallow
Erysipelotrichales with HIV infection

67. Genomics
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)
Comparative genomics
(complete genomes)
Experimental
Confirmation
(anaerobic culture)

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

69. Databases for functional assignments
COGs (Clusters of Orthologous Groups;
KEGG (Kyoto Encyclopedia of Genes and Genomes;
CAZy (Carbohydrate Active Enzymes database;
pFAM (protein family database;

70. COG database 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.

71. KEGG database 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).

72. Shotgun metagenomics

73. KEGG pathway Ontology

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

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

76. Annotating genes to taxonomic groups
Based on similarity to genes in a database of reference genomes.
Mg-RAST uses best BLAST hit: M5N4

77. Annotating metagenomes
MgRAST
Produces Table mapping samples to annotations that can be further processed in QIIME