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

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

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

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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). 1.Extract DNA from environmental samples. 2.  PCR amplify SSU rRNA gene (which species?)  Sequence random fragments (which function?) 3. Evaluate Sequences Culture-independent surveys

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

9 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! Diversity of Firmicutes in 2 healthy adults

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

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?

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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.

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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?  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. A B

25 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

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 –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 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. Phylogenetic Diversity (PD) 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 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? Phylogenetic  diversity: How is diversity partitioned across samples?

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? Temperature 0-100°C pH 1-12 Nutrient Availability Oligotrophic Eutrophic Pressure atm 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 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.

38 Weighted UniFrac Lozupone et al., Appl Environ Microbiol 73:1576 QualitativeQuantitative

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

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 Computation enhancements create order of magnitude increases in speed and reduced memory requirements. Fast UniFrac 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 Lozupone et al ISME. 5: 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

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

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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 16S rRNA gene tree of OTUs prevalent in 2 studies of diet/obesity –Turnbaugh 2009 Sci Transl Med. 1:6ra14 –Ley Nature. 444: Clostridia clusters XIVa and IV are the most abundant in the healthy gut. Histograms and trees can pain a different picture Peterson 2008 Cell Host Microbe: 3: 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 – 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!)

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

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 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)

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.

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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 –

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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) Distribution (16S rRNA) Experimental Confirmation (anaerobic culture) Experimental Confirmation (anaerobic culture) Comparative genomics (complete genomes) Comparative genomics (complete genomes)

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; abases/pfam.html)

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 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.

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. – bin/GOLD/index.cgi bin/GOLD/index.cgi Mg-RAST uses best BLAST hit: M5N4

77 Annotating metagenomes MgRAST gi?page=Analysis gi?page=Analysis Produces Table mapping samples to annotations that can be further processed in QIIME


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