Making Order from Chaos: Using Metagenome Data as Traits in Individuals and as Markers in Entire Ecosystems“ Andrew K Benson W.W. Marshall Distinguished Professor of Biotechnology Director, Core for Applied Genomics and Ecology Professor, Dept. of Food Science University of Nebraska
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Making Order from Chaos: Using Metagenome Data as Traits in Individuals and as Markers in Entire Ecosystems“ Andrew K Benson W.W. Marshall Distinguished.
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Making Order from Chaos: Using Metagenome Data as Traits in Individuals and as Markers in Entire Ecosystems“
Andrew K BensonW.W. Marshall Distinguished Professor of
BiotechnologyDirector, Core for Applied Genomics and Ecology
Professor, Dept. of Food Science University of Nebraska
We Live in a World That is Numerically Dominated by Microorganisms
Oceanic
Soil
Rumen
Gastrointestinal
Oral
Organisms in these microbiomes contribute significantly to characteristics of these ecosystems
Phylloplane
Rhizosphere
N2 Fixation
Disease Resistance
Obesity
Inflammatory Bowel Disease
Diabetes
Gastric and Colon cancers
Significant variation in complexity of microbiomes from different ecosystems
Most of our understanding of these microbial ecosystemshas relied on culture-based approaches to cultivate, differentiate
and enumerate different species of microorganisms
Community composition 16S Microbiome
Community Genetic Potential Metagenomics and Metaproteomics
Community Physiology Metabolomics (nanoscale??)
Community Dynamics Microbiomics + FISH
Community interactions Microbiomics + FISH
High-throughput DNA sequencing technologies combined with other “omics” now allow systematic analysis of complex microbial
communities
PCR amplifyTag gene (16S rRNA)
ShotgunLibrary
Microbiome
Total genomic DNA
454 Pyrosequencing 454 Pyrosequencing
Metagenome
The 16S rRNA: the structural component of the Small Subunit and the most widely used molecular clock for bacteria
V6
V1- V2V3
V7
V5V4V8
Noller et al. 2001 Science ~54 recognized Phyla
Lyse bacteria By homogenizationWith glass beads
High throughput fecal DNA extraction
Attach gDNA To magnetic particles
Centrifuge to Remove debris
Robotic extraction
A16S 8F R357 B
gDNA from a sample
PCR amplify 16S rRNA gene
Sample-specific barcodes
Pool from 96 samples and sequence
TCTGCATG
TCTGCATG
GGAACTAA
TCCTTAGG
Quality Filtering
Length >200 bases
Barcode present
5’ 16S primer present
Average Q = 20
Trimming
Remove barcode
Remove 5’ primer
Remove 3’ primer
Remove 3’ adapter
Sample 1 2 3Barcode TCTGCATG GGAACTAA TCCTTAGG
Reads
Strategies for data analysis
1. Define species composition and abundance in each sample
2. Define phylogenetic content (genetic diversity) in each sample
3. Quantitative analysis of the distribution of species abundances and genetic diversity between two environments or through a “gradient” of environments or in multiple environments
Sequences
Kmer-based approaches
Kmer distributionKmer-based Distances
CD-Hit RDP Classifier
Multiple Sequence Alignment BLAST
Phylogenetic treeNearest neighbor(bit score)
Last common ancestorWith control sequences
Search representativeSequence against database
Amenable for high-throughput
All 8 base words from training set of known taxa is calculated and The probability of these words occurring in a query sequence is calculated
subset of words is used for probability calculation confidence of assignment is estimated by 100 reps of subsets (bootstrapping) ranking at higher order achieved by summing results from all taxon at lower level
Quantifying abundance of ecological characteristics
From guts to greens Applications
Within this same complex of host tissues, a huge mass of microbes thrive. This massis referred to as the microbiome
The Gastrointestinal tract ecosystem: the next frontier in
biology
Specialized cells and tissues for: Nutrient breakdown and adsorption Flow (peristalsis) Immune surveillance Neural connectivity
How complex is the microbiome
Population density: 106 cells/ml in the ileum 1013 cells/gram in the colon
Species richness: 5 major phyla, 1,800 genera, 2,000-10,000 species of bacteria
Genetic coding content: 20-30 billion bases (10 times the human content)
Highly variable between individuals: extensive variation at the species/strain level
The microbiome essentially acts as a metabolic organ,encoding pathways for:
Nutrient breakdown, adsorption, utilizationSignaling within the microbiota and to the host Immune stimulation/suppression…just to name a few
Fundamental questions about composition of the gut microbiome
What factors influence composition—how much “G” and how much “E”? Are there Keystone species? Mutualists? Engineers? How do aberrations arise in composition? What is more important, species composition or function?
1. Sterile at birth rapidly colonized from maternal environment 2. Successive waves of colonizationstabilizes to climax community after weaning 3. Some resistance to perturbation memory?
Then we should observe significant effects of artificial selection on microbiome composition
Multiple generations of selective mating
Host genetic diversity high decreased genetic diversity
16 animals per line (one line rep)pyrosequencing at 5,000-10,000 reads per animal
Did composition of the GI microbiome respond to selection?
UNIFRAC analysis of 16S rRNA phylotypes from MH, ML, and MC
CD-Hit and cluster analysis weighted UNIFRAC analysis
MC
MC
MH + ML MH + ML
Rarefaction curves (97% cutoff) of microbiota from data pooled by line
Number of sequences
Phyl
otyp
es
MCMHML
Selective breeding compositional changes in gut microbiome (abundance of taxa)
Compositional changes contributed to phenotype
Statistics and BioinformaticsSteve Kachman (STATS)
Etsuko Moriyama (BioSci)
Mouse GenomicsDaniel Pomp
(Univ. of North Carolina)
What about direct evidence?
If there is significant effect of host genotype, then it should behaveas a polygenic phenotype: microbiome composition should co-segregate with multiple genomic markers in breeding populations
X
F1
Genotyping SNPs
Phenotyping454 sequencing16S rRNA from poops
QTL mapping to identify genetic architecture controlling Composition of the gut microbiome
F4
What is a trait with respect to gut microbiome?
1. Relative abundance of individual taxonomic ranks
2. Groups of taxa with positive or negative correlation
7-8 weeks exercise cages Fecal samples collected at day 1 and day 6In exercise cages
Genotyping 768 fully informative markers Between ICR and B6 (present study stage at 550 QC’d Markers)
Phenotyping 10,000 454 reads from each animal using V1-V2Region, Taxonomy-assignment (RDP CLASSIFIER), normalized as proportion of total reads
QTLs mapped from 200 animals of the F4 cross
10 QTLs mapping to 7 chromosomes 4 different “compositional phenotypes”
Sometimes, you get lucky…QTLs on chromosome 15 control colonization by Helicobacter
Experiment N Sex SNPs Diet Parent Genetic of Origin Diversity
1a) C57 x HR F4 800 Both 768 Regular Y Low1b) C57 x HR F10 400 Both 50 per QTL High Fat vs. Reg Y Low2) Phenome Lines 400 Both 600,000+ Regular N Moderate3) Collaborative 1600 Both 600,000+ High Fat vs. Reg Y High Cross
Microbiome analysis (Class level) of 700 animals from the F4 mapping population
Are strong effects of host genetics conserved in plants?
Plants also susceptible to infectious disease Microbiome of phylloplane (epiphytes and endophytes) May play protective role Much more prone to environmental variation?
Maize genetic resource populations:Nested Association Mapping (NAM) RILs from crosses of B73 X 25 other Inbred lines
Preliminary evaluation: 27 Inbred lines = parental inbreds of the NAM collection
Sample unit = 3 plants per pot, 3 pots per line
Leaves harvested at 14 days post planting and phylloplane bacteria removed by soaking
What factors influence composition—how much “G” and how much “E”? Are there Keystone species? Mutualists? Engineers? Indicators? How do aberrations arise in composition? What is more important, species composition or function?
Role for Computational, Mathematical, and Statistical Modeling
1. Develop models that can predict how microbial communities will respondto perturbation