At CPHx Jesse Stombaugh presented a talk about High-Throughput Sequencing of the human microbiome.

1. High-Throughput
Sequencing of the Human
Microbiome
Jesse Stombaugh
Biofrontiers Institute
University of Colorado at Boulder

2. Viewing the human microbiome through an
RNA lens

3. There are as many E. coli in your
gut...

4. As there are humans on the Earth!
=
However, only a tiny fraction of the gut
microbes are E.coli…

5. You are covered with microbes on and in your
body: How human are we?
Micah Hamady, PhD Thesis, 2009

6. Where do our microbes come from?

7. It’s important to remember that our
world...
NASA: Earth from Apollo

8. ...is a microbial world.
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 cost of sequencing

10. As the cost of sequencing declines...

11. …quantitative differences become qualitative
differences

12. Need to interpret vast amounts of data

13. Problem: Big trees hard 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 of Hundreds of Samples
Hamady et al. 2008 Nature Methods 5:235; Caporaso et al. 2010 Nature Methods 7:335

15. QIIME: Analysis of Hundreds of Samples
Hamady et al. 2008 Nature Methods 5:235; Caporaso et al. 2010 Nature Methods 7:335

16. QIIME: Analysis of Hundreds of Samples
Hamady et al. 2008 Nature Methods 5:235; Caporaso et al. 2010 Nature Methods 7:335

17. QIIME: Analysis of Hundreds of Samples
Hamady et al. 2008 Nature Methods 5:235; Caporaso et al. 2010 Nature Methods 7:335

18. QIIME: Analysis of Hundreds of Samples
Hamady et al. 2008 Nature Methods 5:235; Caporaso et al. 2010 Nature Methods 7:335

19. QIIME: Analysis of Hundreds 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 the microbiota 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 changes over 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 Abundances by 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 the Abundances 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 of public 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 of public 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 MIxS enable everyone to
contribute
Minimal Information about
any (x) Sequence

31. Direct environmental sequencing sees 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 sequencing sees 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 changes across time and
geography
531 Subjects and 3 Countries (USA, Malawi and Venezuala)
Yatsunenko et al. (2012) Nature

34. UPGMA Clustering of Samples 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 Abundance between 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