Presentation at Taconic Labs Microbiome Research Symposium (http://info.taconic.com/microbiome-symposium-chicago)

1. “We made too many wrong mistakes.”
- Yogi Berra
Rob Beiko
November 3, 2016

2. 2

3. An illustrative example – frailty,
aging and the microbiome
Assisted-care facility, Halifax, NS, Canada
45 subjects, age 65-98
Weekly fecal samples x 5 weeks
Frailty Index: 54 health deficits
Relationship with the microbiome?
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4. Objectives of the study:
• Identify significant relationships between age, frailty and the
microbiome
• Other factors: diet, medication, residence time
• Latent pathogens, Enterobacteraceae?
Data collected:
• 205 x 16S samples (45 individuals, 4-5 weekly time points)
• Patient data (frailty index / comprehensive geriatric
assessment, food intake, medication)
• 45 metagenomes
Mouse models of aging and frailty:
• Correlations with certain taxa and functions (creatine
degradation, vitamin biosynthesis, …)
• Langille et al., Microbiome (2014)
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5. 1: The Operational Taxonomic Unit
97% sequence identity
99%97%
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6. Problems
• 16S sequences do not constitute natural clusters!!
• Sequencing error, mutation, other processes
• Different OTU clustering methods
• What is the ecological meaning?
• “Species”: not really. Strain-specific variations
• Depends on what V regions you sequence
• Often an unholy mess of conflicting signals
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7. Oh, behave
Temporal dynamics of sequence clusters within ONE OTU
assigned to Akkermansia muciniphila, in 14 patients
Ananke (time-series clustering):
Michael Hall, Jonathan Perrie
https://github.com/beiko-lab/ananke 7

8. Alternatives to OTUs
Clade-based strategy
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Differentiating clades –
supragingival vs. subgingival plaque
• Still similarity-based:
• Oligotyping (Murat Eren et al., Meth Ecol Evol, 2013)
• SWARM (Mahé et al., PeerJ, 2015)
• Tree-based (Ning and Beiko, Microbiome, 2015)

9. 2: Taxonomy
Assign marker-gene sequences to a taxonomic group
(RDP Classifier, phylogenetic placement, …)
Abundance versus residence time
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10. Diversity and stability
Akkermansia
Pseudomonas
Bacteriodes
Parabacteroides
Patient 16:
88 years old
FI = 0.415
1 ½ years at Northwood

11. Eubacterium
*
*
*
*
*
*
*
* 279 genomes
Conserved marker-gene tree
Ben Wright
X
X X
X
X
Ruminococcus
#
#
#
#
#
Roseburia
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12. OTU co-occurrence network from nursing-home study
Circle diameter: significance of OTU relationship with age
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13. Vexonomy
• Alternative proposals in the literature, most notably
genomic taxonomy
• Still doesn’t address the question of ecological
boundaries
• Phylogenetic revisions: Peptoclostridium difficile
• Cross-referencing with other work: tread carefully!!
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14. 3: Function
C Huttenhower et al. Nature 486, 207-214 (2012) doi:10.1038/nature11234
Look at those categories!!
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15. Shotgun metagenomics
• Good:
• “What are they doing”, rather than highly indirect
inference from taxonomic profiles
• Free from primer bias
• Bad:
• Potentially poor sampling of rare genomes
• Strain-specific resolution can be very difficult
• Annotation errors, overprediction
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16. Schnoes et al. (2009) PLoS Comp Biol
Do you want COVERAGE
- or -
Do you want ACCURACY
?
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17. Radivojac et al. (2013) Nat Meth
Functional predictions: CAFA
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18. PICRUSt
Langille et al. (2013) Nat Meth
Keys to success:
- Phylogenetic conservation of trait
- Good sampling from reference databases
- Outperforms metagenomics in some special cases
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19. Functions in aging and frailty
• Frailty:
• Clp protease subunits
• Oxygen two-component sensor protein
• Competence proteins
• Age:
• Type IV Secretion system, restriction system, pilins
• Many proteins of unknown function
• Iron transport
• Residence time:
• nonribosomal peptide synthetase VibF (putative iron
transport)
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20. Ooookay…
We need:
• NEW UNIT DEFINITIONS – sequence similarity, but also
time, co-occurrence, function
• DIFFERENT FUNCTIONAL PERSPECTIVES – different levels
of resolution
• MICROBIOMIC MYSTERY MEAT – homologous sets of
genes with no known function, good ways to deal with
unknown diversity groups
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21. The lightning round
• Primer bias can miss key taxonomic groups (e.g.,
Tremblay et al. (2015) Front Microbiol)
• V1-V3 favours Prevotella, Fusobacterium, Streptococcus,
Granulicatella, Bacteroides, Porphyromonas and Treponema
• V4-V6 failed to detect Fusobacterium
• V7-V9 failed to detect Selenomonas, TM7 and Mycoplasma
• Do we discard unknown taxonomic groups and
hypothetical proteins?
• Rarefaction
• Loss of statistical power
• Random subsampling can increase false-positive differences
(see McMurdie and Holmes (2014) PLoS Comp Biol)
• Choice of dissimilarity measures
• Parks and Beiko, ISME J, 2013: 39 different measures, almost
39 different answers!
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22. Acknowledgments
Dalhousie
Akhilesh Dhanani
Ken Rockwood
Michael Hall
Sherri Fay
Emily Byrne
Kayla Mallery
Olga Theou
Jie Ning
Donovan Parks
Nursing staff &
study participants
Northwood care
facility
Josie Ryan
John O’Keefe
Karie Raymond
Cathy Misener
Kathryn Graves
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23. FIN
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