beiko taconic-nov3

“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

X

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