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“We made too many wrong mistakes.”- Yogi Berra
Rob BeikoNovember 3, 2016
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An illustrative example – frailty, aging and the microbiome
Assisted-care facility, Halifax, NS, Canada45 subjects, age 65-98Weekly fecal samples x 5 weeksFrailty Index: 54 health deficitsRelationship with the microbiome?
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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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1: The Operational Taxonomic Unit
97% sequence identity99%97%
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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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Oh, behaveTemporal dynamics of sequence clusters within ONE OTU assigned to Akkermansia muciniphila, in 14 patients
Ananke (time-series clustering): Michael Hall, Jonathan Perriehttps://github.com/beiko-lab/ananke
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Alternatives to OTUs
Clade-based strategyDifferentiating 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)
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2: TaxonomyAssign marker-gene sequences to a taxonomic group (RDP Classifier, phylogenetic placement, …)
Abundance versus residence time
Diversity and stability
Akkermansia
Pseudomonas
BacteriodesParabacteroides
Patient 16: 88 years oldFI = 0.4151 ½ years at Northwood
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Eubacterium
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* 279 genomesConserved marker-gene tree
Ben Wright
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X X
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Ruminococcus
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Roseburia
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OTU co-occurrence network from nursing-home studyCircle diameter: significance of OTU relationship with age
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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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3: Function
C Huttenhower et al. Nature 486, 207-214 (2012) doi:10.1038/nature11234
Look at those categories!!
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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
16Schnoes et al. (2009) PLoS Comp Biol
Do you want COVERAGE
- or -
Do you want ACCURACY
?
17Radivojac et al. (2013) Nat Meth
Functional predictions: CAFA
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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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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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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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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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AcknowledgmentsDalhousieAkhilesh DhananiKen RockwoodMichael HallSherri FayEmily ByrneKayla MalleryOlga TheouJie NingDonovan Parks
Nursing staff & study participants
Northwood care facilityJosie Ryan John O’Keefe Karie Raymond Cathy MisenerKathryn Graves
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FIN