1. Metagenomics and the microbiome

2. What is metagenomics?
Looking at microorganisms via genomic sequencing rather than culturing
Environmental use case: ag, biofuels, pollution monitoring
Health use case: The human microbiome
Most microbes are anaerobic and therefore difficult to culture

3. Why care about microbiome?
You = 1013 your cells bacterial cells
More actionable genomics
Why care about microbiome?
Why should we have a microbiome at all? Why do some bugs get a pass from our immune system? Not just commensal but symbiotic – leveraged adaptation (10^5-10^6 generations of bacteria per generation of human)?
An ancient adaptation, animals have had residential microbes helping with metabolism for at least 500MM years.
“comparing germ-free and normal mice indicates that microbiota are responsible for most of the metabolites that are detected in plasma “
Responsive to and productive of environmental factors
23and me in the economist -- C. Diff
Environmental microbiome: Sushi and Japanese gut
Your pets and your microbiome
People living together
Vaginal microbiome and preterm birth: Lactobacilus
Oral microbiome and dental applications
Big thing to keep in mind is looking at this ecologically: much more than the sum of its parts in both health and disease – for instance two entirely different oral microbe populations were found to break down sugars in the same way. “One microbe, one disease” doesn’t quite work
Source:

4. Why care about microbiome?
Diagnostic or modulatory implications in:
Obesity, Diabetes, Fatigue, Pain disorders
Anxiety, Depression, Autism
Antibiotic resistant bacteria
IBD and other gut disorders
Cardiac function, cancer
Classic example is H Pylori and ulcers
As on the last slide, one microbe, one disease appears to be the wrong framework here as well. The limited association studies that have been done make it appear that the situation is like that of the GWAS and common diseases. Small effect sizes, non-additive interactions at play. We’ll see a bit more about this later when we look at composition.
Common among these diseases is that they have a somewhat nebulous, chronic character and leave sufferers trying multiple options many of which don’t work that well

5. Diseases and the microbiome
Source: The human microbiome: at the interface of health and disease. Nature reviews genetics

6. Why care about microbiome?
Science direct, papers containing ‘microbiome’
Why care about microbiome?
Publications containing ‘microbiome’ by date on Science Direct

7. 500-1000 species of bacteria in the human gut
species of bacteria in the human gut. More and more is being discovered about how composition associates with disease. A “virtual organ”
Many ways of looking at diversity
Goal 1: Composition
Source: The human microbiome: at the interface of health and disease, Nature Reviews Genetics

8. Diversity measures
Alpha diversity: how diverse is this population? Simpson’s index, Shannon’s index, etc
Difference in alpha diversity before and after antibiotics
Beta diversity: Taxonomical similarity between 2 samples
Finding compositional associations between disease cohort and microbial makeup

9. Sequencing for diversity
Pyrosequencing the 16s ribosomal RNA subunit
< 10 taxa appear in > 95% of people in HMP
Recall the implicated diseases. Looks like GWAS common disease, small effect size + common disease, rare variant
16s gene coding regions are highly conserved among bacteria. Other internal re- gions of the gene are highly variable, possessing almost entirely unique sequences in most bacterial clades.
PCR amplified, sanger-sequenced
CNV and primer biases
16s often fails to distinguish to the species level – genus and family resolution only. E.g. C. diff is hard for 16s to distinguish from other benign Clostridium species – a very important distinction!
About 30 species make up 99% of the bacteria, but the low abundance ones might still be important. More on this problem later.

10. Goal 2: Functional profiling
Remember: Ecological approach, not necessarily the strict composition that matters as much as what the ecosystem is doing (usually metabolically) as a whole
Goal 2: Functional profiling
Source: The human microbiome: at the interface of health and disease. Nature reviews genetics

11. Functional profiling
Current: Which genes are present and are being transcribed
In development: proteomics, metabolomics
“most genes related to amino acid biosynthesis are not expressed by the typical gut microbiome—these compounds generally are available from host diet and metabolites. Rather, the most highly transcribed genes are those related to energy production“
Archea when present are transcriptionally most active, the fermentation efficiency of the entire gut micro- biome is limited by accumulation of hydrogen, and meth- anogenesis is the most efficient means of excess hydrogen removal
E.g. IBD: whole-community shifts to amino acid transport from biosynthesis, a larger reliance on host metabolites and energy harvesting, and more genes for surviving redox stress of the inflammatory immune response. Some of these might feel roughly causal while others are probably effects

12. Sequencing for function
Whole microbiome sequencing
Avoids primer biases and is more kingdom agnostic
Assembly is hard, especially where reference genomes don’t exist
Assembly also hard due to the aforementioned abundance problems – if 30 species make up 99% and there is something really nasty lurking in that remaining 1%, how do we make sure it is covered?

13. Two big problems
Can’t understand the body without understanding the microbiome
Can’t understand the microbiome by only looking at bacteria
Read fragment assembly is very very hard in metagenomics

14. Kingdom-Agnostic Metagenomics

15. The players in your body
Your cells
Metabolites
Bacteria
Bacteriophages
Other viruses
Fungi
Metabolites: various small molecules. Fuel, structure, signaling, enzyme/catalytic activity.

16. We’ve seen these wall charts showing the signaling maps of a given cell. Imagine the complexity of the real ecosystem.
That’s not complexity
Source: A comprehensive map of the toll‐like receptor signaling network. Molecular Systems Biology

17. Prokaryotic virome: bacteriophages
Infect prokaryotic bacteria
Transfer genetic material among prokaryotic bacteria
Rapidly evolving
Put constant selection pressure on bacterial microbiome
Important in antibiotic resistance gene transfer
Potential as therapeutic agents

18. Bacteriophages: deep sequencing results
60% of sequences dissimilar from all sequence databases
More than 80% come from 3 families
Little intrapersonal variation
Large interpersonal variation, even among relatives
Diet affects community structure
Antibiotic resistance genes found in viral material

19. Bacteriophages and function
Cross the intestinal barrier possibly affecting systemic immune response
Adhere to mucin glycoproteins potentially causing immune response in gut epithelium
IBD/Chron’s: relative increase in Caudovirales bacteriophages
Affect bacterial composition and/or host directly

20. Eukaryotic virome
Fecal samples from healthy children shows complex community of typically pathogenic viruses
Includes plant RNA viruses from food
Anelloviruses and circoviruses present in nearly 100% by age 5, likely from industrial ag
These are typically viruses of livestock and plants

21. Eukaryotic viruses and function
Simian immunodeficient experiment showed enteric virome expansion
Increased gut permeability and caused intestinal lining inflammation
Acute diarrhea subjects showed novel viruses and highly divergent viruses with less than 35% similarity to catalogued viruses at amino acid level
So the immune system does hold the enteric virome at bay, but not completely

22. Meiofauna Fungi, protazoa, and helminths (worms)
No experiments conducted with sampling to saturation, much more work to be done
18S sequencing showed 66 genera of fungi in gut and fungi were found in 100% of samples
Most subjects had less than 10 genera
But high fungal diversity is bad: increases in IBD, increases with antibiotic usage
Oral Candida and antibiotics
Helminthic parasites seem to confer resistance to asthma, IBD, other autoimmune disorders

23. But it’s very hard Amplicon-based don’t work well for viruses
Heterogeneous sample-prep is required
Large differences in genome sizes from a few kb in viruses to 100+Mb in fungi
Small genomes+divergence require lots of coverage to get contigs
Viruses are highly divergent, no magic 16S like unit that works well across populations
Sample-prep: large differences in cellular integrity and nucleic acid encapsidation – nuclear versus cytoplasmic

24. Getting the whole picture
Source: Meta'omic Analytic Techniques for Studying the Intestinal Microbiome. Gastroenterology.

25. The assembly problem

26. Isn’t assembly easy?
Recall: species of bacteria in the gut, but about 30 of them make up 99% of composition
33% of bacterial microbiome not well-represented in reference databases, > 60% for bacteriophages
However, low abundance organisms can still have a large impact, so we need to know if they are there are not.
<1% of reads mapped to non-bacterial taxa in Human Microbial Consortium studies

27. Coverage Coverage: mean number of reads per base
L=read length, N=number of reads, G=genome size
Problem, with 2nd gen WMS technologies, L is low and G is astronomical or unknown
Thus, “full or sometimes even adequate coverage may be unattainable”
Source: A primer on metagenomics

28. Sequence length and discovery
Mostly stuck on the first 3 or 4 rows
Sequence length and discovery
Source: A primer on metagenomics

29. All is not lost Can use rarefaction curves to estimate our coverage
Green is well-sampled
All is not lost
Can use rarefaction curves to estimate our coverage

30. All is not lost
For composition analysis the phylogenetic marker regions (18S, 16S) work pretty well
For functional analysis: can still find ORFs fairly reliably and can be aligned to homologs in databases
Barring this, clustering and motif-finding yield some information
Open reading frames: ie sequences with no stop codon, ie genes
ORF finding is estimated at around 85% to 90% accuracy

31. Different sequencing approaches?
Single-cell microfluidics in the future
Now: hybrid long/short read approaches. “finishing” with Sanger sequencing
Pacific biosciences SMRT approach
SMRT errors are random, unbiased
De novo assembly is % concordant with reference genomes
We still haven’t addressed how to get at particularly rare or divergent sequences, what to do?
Short reads are used to correct errors in long reads
Single-molecule, real-time: only requires one library instead of 2nd gen + sanger

32. HGAP: the SMRT assembly algorithm
Select longest reads as seeds
Use seed reads to recruit short reads
Assemble using off the shelf assembly tools
Refine assembly using sequencer metadata
HGAP: the SMRT assembly algorithm
Source: Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nature Methods

33. Seed selection Order reads according to length
Considering reads above length L ~ 6kb
Rough end-pair align reads until ~20x coverage is reached
17.7k seed reads, averaging 7.2kb in length, already at 86.9% accuracy compared to reference
Average read length from SMRT is 3.2kb, 141k total continuous long reads were generated

34. Recruiting short reads
Align all reads to the seed reads
Each read can be mapped to multiple seed reads, controlled by –bestn parameter
-bestn must be chosen so that the coverage of seeds + short aligned reads is about equal to the expected coverage of the sequenced genome
Use MSA and consensus to error correct long reads
Result is 17.2k reads of length 5.7kb with 99.9% accuracy

35. Overlap layout consensus assembly
Source: Overview of Genome Assembly Algorithms. Ntino Krampis.

36. Refinement
Use Quiver algorithm which looks at raw physical data from sequencer
Uses an HMM and observed data to tell classify base calls as genuine or spurious
Do a final consensus alignment, conditioned on Quiver’s probabilities
Final result: 17.2k reads, length of 5.7kb, accuracy of %

37. Summary Most of the cells in your body aren’t yours
But looking at bacteria alone is insufficient
Expanding our view causes us to look for needles in haystacks which is beyond most conventional approaches
Motif-finding and hybrid approaches will work until 3rd gen sequencing arrives

38. References
Cho, Ilseung, and Martin J. Blaser. "The human microbiome: at the interface of health and disease." Nature Reviews Genetics 13.4 (2012):
Wooley, John C., Adam Godzik, and Iddo Friedberg. "A primer on metagenomics." PLoS computational biology 6.2 (2010): e
Chin, Chen-Shan, et al. "Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data." Nature methods 10.6 (2013):
Human Microbiome Project Consortium. "Structure, function and diversity of the healthy human microbiome." Nature (2012):
Norman, Jason M., Scott A. Handley, and Herbert W. Virgin. "Kingdom-agnostic metagenomics and the importance of complete characterization of enteric microbial communities." Gastroenterology (2014):
Morgan, X. C., and C. Huttenhower. "Meta'omic Analytic Techniques for Studying the Intestinal Microbiome." Gastroenterology (2014).