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High-Throughput Sequencing of the Human Microbiome, Rob Knight Research Group, University of Colorado at Boulder, Jesse Stombaugh, Copenhagenomics 2012
The document discusses high-throughput sequencing techniques for analyzing human microbiome samples. It provides examples of studies that used these techniques to analyze microbiome samples from various body sites and populations. These studies characterized differences in microbial communities between body sites, tracked changes over time, identified correlations with disease markers, and compared communities across geographic locations and diets. Advanced analysis tools like QIIME and UniFrac were highlighted for interpreting vast amounts of sequencing data from hundreds of microbiome samples.
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High-Throughput Sequencing of the Human Microbiome, Rob Knight Research Group, University of Colorado at Boulder, Jesse Stombaugh, Copenhagenomics 2012
- 1. High-Throughput Sequencing of theHuman Microbiome Jesse Stombaugh Biofrontiers Institute University of Colorado at Boulder
- 2. Viewing the humanmicrobiome through an RNA lens
- 3. There are asmany E. coli in your gut...
- 4. As there arehumans on the Earth! = However, only a tiny fraction of the gut microbes are E.coli…
- 5. You are coveredwith microbes on and in your body: How human are we? Micah Hamady, PhD Thesis, 2009
- 6. Where do ourmicrobes come from?
- 7. It’s important toremember that our world... NASA: Earth from Apollo
- 8. ...is a microbialworld. o Multicellular lineages (red) rare, not diverse as measured by SSU rRNA o Most molecular diversity can be found in microbes o Most (99%+) microbes can’t be cultured: known only from sequences Figure adapted from Norm Pace, Science (1997) 276:734-740.
- 9. Decline in costof sequencing
- 10. As the costof sequencing declines...
- 11. …quantitative differences becomequalitative differences
- 12. Need to interpretvast amounts of data
- 13. Problem: Big treeshard to understand and analyze Example: 5088 mouse gut and 11,831 human colon bacterial sequences •See many clusters of sequences from each sample •Significance tests for differences, but no phylogenetic metric Ley et al., 2005 PNAS 102:11070
- 14. QIIME: Analysis ofHundreds of Samples Hamady et al. 2008 Nature Methods 5:235; Caporaso et al. 2010 Nature Methods 7:335
- 15. QIIME: Analysis ofHundreds of Samples Hamady et al. 2008 Nature Methods 5:235; Caporaso et al. 2010 Nature Methods 7:335
- 16. QIIME: Analysis ofHundreds of Samples Hamady et al. 2008 Nature Methods 5:235; Caporaso et al. 2010 Nature Methods 7:335
- 17. QIIME: Analysis ofHundreds of Samples Hamady et al. 2008 Nature Methods 5:235; Caporaso et al. 2010 Nature Methods 7:335
- 18. QIIME: Analysis ofHundreds of Samples Hamady et al. 2008 Nature Methods 5:235; Caporaso et al. 2010 Nature Methods 7:335
- 19. QIIME: Analysis ofHundreds of Samples Hamady et al. 2008 Nature Methods 5:235; Caporaso et al. 2010 Nature Methods 7:335
- 20. Comparing Microbial Communities •What is there? • How much is there? • α (i.e., within sample) diversity • How similar or different are samples? • β (i.e., between sample) diversity • What relationships exist between a microbial community and characteristics of the sampled environment?
- 21. Sampling of themicrobiota 20 minutes after birth 9 Mothers and 10 children Dominguez-Bello et al. (2010) PNAS
- 22. Phylogenetic Diversity (PD)of the infant gut microbiota over time Peas + formula introduced Antibiotics (cefdinir) Day before fever PD provides a measure of the diversity within a community based on the extent of the 16S rRNA phylogenetic tree that is represented by that community. Koenig et al. (2010) PNAS
- 23. Community composition changesover time conform to a smooth temporal gradient • Time and PC1 from a PCoA of bacterial communities determined from 16S rRNA genes are plotted. • Blue color gradient based on time (days). Mother’s sample is red. Koenig et al. (2010) PNAS
- 24. Human oral, gut,and plaque microbiota in patients with Atherosclerosis • Samples collected from 15 patients with atherosclerosis and 14 healthy patients • Bacterial diversity clustering by body habitat using unweighted UniFrac. Koren et al. (2010) PNAS
- 25. Mean Phylum Abundancesby Body Habitat for Patients and Controls • Plotted values are mean sequence abundances in each phylum for 1,700 randomly selected sequences per sample. Most Abundant Phyla • Plaque: Proteobacteria/Actinobacteria • Oral: Firmicutes/Bacteroidetes/ Actinobacteria • Gut: Firmicutes/Bacteroidetes Koren et al. (2010) PNAS
- 26. Correlations between theAbundances of Different Genera and Disease Markers Oral samples • Pearson correlation coefficients are represented by color ranging from blue, negative correlation (−1), to red, positive correlation (1). • Positive correlations between Fusobacteria with LDL and cholesterol levels • Positive correlation between Streptococcus with HDL cholesterol and Apolipoprotein A-1 (ApoA1), whereas Neisseria was negatively correlated to levels of these two disease markers • Significant correlations are noted by *P < 0.05; **P < 0.01, and ***P < 0.001. Koren et al. (2010) PNAS
- 27. Microbial biogeography ofpublic restroom surfaces 12 University of Colorado Restrooms (6 men and 6 women) Light blue indicates low abundance while dark blue indicates high abundance of taxa. B.Skin-associated taxa (Propionibacteriaceae, Corynebacteriaceae, Staphylococcaceae and Streptococcaceae) were abundant on all surfaces. C.Gut-associated taxa (Clostridiales, Clostridiales group XI, Ruminococcaceae, Lachnospiraceae, Prevotellaceae and Bacteroidaceae) were most abundant on toilet surfaces. D.soil-associated taxa (Rhodobacteraceae, Rhizobiales, Microbacteriaceae and Nocardioidaceae) were in low abundance on all restroom surfaces, they were relatively more abundant on the floor of the restrooms we surveyed. Flores et al. (2012) Plos One
- 28. Microbial biogeography ofpublic restroom surfaces Flores et al. (2012) Plos One
- 29. Principal Investigators: Rob Knight(CU) Acknowledgements Noah Fierer (CU) Ruth Ley (Cornell) Frederik Backhed (Gothenburg) Jeff Gordon (Wash U.) Support: Knight Lab: Jose Clemente Litran Doug Wendel Antonio Gonzalez-Pena Jeremy Widmann Meg Pirrung Tony Walters Daniel McDonald Cathy Lozupone Greg Caporaso -> Northern Arizona U. Justin Kuczynski -> Second Genome Jens Reeder -> Genetech Dan Knights -> Harvard Julia Goodrich -> Cornell Jesse Zaneveld -> Oregon State U. Chris Lauber Donna Berg-Lyons Jerry Kennedy Gail Ackermann Elizabeth Costello -> Stanford Micah Hamady -> world travels Other Labs: Jeremy Koenig (Cornell) Omry Koren (Cornell) Ayme Spor (Cornell)
- 30. Technologies like MIxSenable everyone to contribute Minimal Information about any (x) Sequence
- 31. Direct environmental sequencingsees the “other” 99% of microbes 1. Get samples 1. Sequence and extract DNA 1. BLAST sequences, group by similarity to GenBank 1. PCR amplify (usually SSU rRNA gene)
- 32. Direct environmental sequencingsees the “other” 99% of microbes 1. Get samples 1. Sequence 1. Align, build tree and extract DNA X 1. BLAST sequences, group by similarity to GenBank 1. PCR amplify (usually SSU rRNA gene)
- 33. Gut community changesacross time and geography 531 Subjects and 3 Countries (USA, Malawi and Venezuala) Yatsunenko et al. (2012) Nature
- 34. UPGMA Clustering ofSamples Using the Unweighted Unifrac Distances o Branches are colored by body site, and numbers in labels refer to subject numbers in the study. o All atherosclerotic plaque samples are from patients; oral and gut samples from patients are noted with an asterisk. Koren et al. (2010) PNAS
- 35. Differences in Abundancebetween Body Sites (A) Shrunken differences for the 10 genera accounting for the differences among the three body sites. • Plaque: (+) Chyrseomonas/Staphylococcus/Propionibactererineae • Oral: (+) Streptococcus • Gut: (+) Lachnospiraceae/Ruminococcus/Faecalibacterium, (-) Streptococcus (B) Heat map of the abundances of genera (i.e., those driving differences between body sites) Koren et al. (2010) PNAS
Editor's Notes
- #7 Which raises the question: where do our microbes come from?
- #11 Due to this decline, we have the opportunity to sequence more microbial communities, where in the past we could only sequence a couple dozen samples, whereas today, we can sequence thousands of samples in 1 run, which will allow quantitative differences to become qualitative differences.
- #17 Now, we can identify which sequences are associated to a particular sample by its barcodes.
- #25 It has been suggested that periodontal disease has been associated with atherosclerosis Also, that gut bacteria may contribute to obesity
- #36 For each genus listed in center, the direction of the horizontal bars indicates relative overrepresentation (Right) and underrepresentation (Left), and the length of the bar indicates the strength of the effect. Columns show, for each sample, the abundance data of genera listed in center. The abundances of the genera were clustered using unsupervised hierarchical clustering (blue, low abundance; red, high abundance). The phylum;genus of each of the classifying OTUs is noted.