1. Canadian Bioinformatics Workshops

2. Module #: Title of Module2

3. Module 7 Microbiome biomarker discovery
Fiona Brinkman
Analysis of Metagenomic Data
June 22-24, 2016
Thank you! Anamaria Crisan, Mike Peabody, Thea Van Rossum, John Parkinson

4. Learning objectives of the module
Appreciate what biomarkers are and their utility
Know the basics of how one can identify biomarkers from microbiome data
Be aware of examples, including a case study, of biomarkers identified from microbiome data
Appreciate the importance of careful, conservative microbiome/biomarker analysis

5. What are biomarkers? bi·o·mark·er Functional biomarkers
/ˈbīōˌmärkər/
Measureable biological property that can be indicative of some phenomena, such as an infection, disease, or environmental disturbance
Functional biomarkers
Biological functions (genes, proteins, metabolites…) specific to a single organism or shared among multiple organisms
Taxonomic biomarkers
Can be a specific species, or a category of organisms – including an Operational Taxonomic Unit (OTU).

6. Why identify biomarkers?
Detect/diagnose a phenotype more quickly, cheaply and/or accurately versus whole metagenomics sequencing.
“Bugs as Drugs”
Growing success stories…

7. Biomarker success stories - Asthma

8. Biomarker applications are many…
Bowel: Differentiating Inflammatory Bowel Disease from related diseases and detecting increasing disease severity; Detecting colorectal cancer
Lung: Progression of chronic obstructive pulmonary disease (COPD)
Breast: Human milk microbiome protecting against mastitis
Environmental biomarkers (pollution, ecosystem health, …)

9. What is biomarker selection?
The process of removing non-informative or redundant OTUs/taxa/gene sequence – identifying sequences that are differential between two conditions

10. DISCOVERY VALIDATION How do we find biomarkers?
Bioinformatics Software - Takes the raw digitized genomic data and performs QC and quantification of different sequences (based on taxa or genes)
Use math – Applied statistical methods can help us find useful biomarkers
VALIDATION
Design Primers – biological “hooks” that pick out our biomarker sequence of interest from a sample
qPCR – measures how many times primers (hooks) manage to snag our biomarker of interest

11. What are biomarkers and how do we find them?1
Plan Analysis: “bio”? “mark”? 2
Obtain Biological Samples 3
Extract & Sequence DNA 4
Identify Potential Biomarkers (Taxa, OTUs or Functional Genes more/less abundant in test versus control condition)
What bio? what mark? 5
Validate Potential Biomarkers (In silico then in vitro) 6
Iteratively Further Optimize Biomarkers

12. Microbiome analyses options for biomarker ID
A) Bacteria
Shotgun or 16S amplicon
Best studied, most methods developed
B) Viruses
Shotgun or amplicon (RdRp, g23)
Can be challenging to get enough DNA
Host-specificity and population “bursts” hold promise
C) Eukaryotes
Combinations!
Amplicon (18S, ITS) (large genomes make shotgun difficult)
Well studied, many methods developed

13. A) TAXONOMIC B) GENE-BASED C) OTHER?
What kind of biomarker do we want?
A) TAXONOMIC
Can use amplicon or shotgun data
Strain-level diversity can lead to false positives/negatives
More variable across environments (for better or worse)
B) GENE-BASED
Requires shotgun data (DNA or RNA)
Need good sequencing depth to reach specialised genes
Domain-based gene architecture can be tricky
C) OTHER?
Combinations!
Diversity metrics, using microbiome analysis to suggest other metabolic markers, etc

14. OTU1
OTU2
Sample frequency
Sample frequency
Abundance
Abundance

15. What makes a good biomarker?
In this example we want to find biomarkers that separate between the red and blue class labels.

16. Biomarker selection relies on statistical techniques
Range from the very simple (like a t-test) to more complex…
You can write your own statistical analysis
using R or equivalent OR
You can use more complex methods developed by others
LEfSE – implemented as a convenient Galaxy workflow (Segata et al 2010 PMID )
MetagenomeSeq – implemented using R
(Paulsen et al 2013 PMID: )

17. LEfSE - LDA Effect Size for biomarker selection
High-dimensional biomarker discovery and explanation. IDs genomic features (genes, pathways, taxa), characterizing the differences between two or more biological conditions/classes.
First: IDs statistically different features among biological classes (non-parametric factorial KW sum-rank test). Second: Performs pairwise tests among subclasses using (unpaired) Wilcoxon rank-sum test - to assess whether the differences are consistent with respect to expected biological behavior. Third: Uses Linear Discriminant Analysis to est. effect size of each differentially abundant feature (& dimensional reduction if desired)

18. https://huttenhower. sph. harvard. edu/galaxy/tool_runner

19. Biomarker selection relies on statistical techniques
KEY: Understand the statistical methods especially
Your assumptions about the data
The statistical method’s assumptions about the data
The statistical method’s limitations
How to interpret your results from the output

20. The best approach to use depends on your research question

21. Considerations for your statistical analysis …
Discrete/categorical or continuous variables?
Do your samples involve known classes or do you not know how many classes there are?

22. Generally, statistical techniques either
try to predict labels or continuous values

23. And…they also generally belong to one of two categories
(Lied a little : Semi-supervised methods also exist)

26. How do we validate our biomarkers?
Once you ID the gene or taxonomic group you want to use as a biomarker, you need to design a test for it (q)PCR is a good option:
Identify biomarker specific sequence
Use marker-based tool (e.g. MetaPhlAn2) OR
Cluster reads or align them to find conserved sequences – verifying that the “representative” sequence is selective
2. Design primers (& probe)
PrimerProspector: Designs primers from a sequence alignment
PrimerBLAST: Designs primers specific to a clade

27. Case Study

28. Case Study Improved pollution/pathogen detection, source tracking: Using metagenomics to identify markers of water quality
Will Hsiao Patrick Tang Matt Coxen Thea Van Rossum Mike Peabody
Univ. of Sask.
Janet Hill
NRC-PBI
Sean Hemmingsen
MSFHR
Bev Holmes
McGill University
Bartha Knoppers
Vural Ozdemir
Yann Joly

29. The current emphasis of water quality testing is at the tap…
Why Watershed Metagenomics? An Ecosystem approach to water quality monitoring…
The current emphasis of water quality testing is at the tap…
…but we should be looking more at the source.
The fecal coliform test can’t ID the source and is inaccurate.
False negatives: Not all pathogens are coliforms
False positives: Not all coliforms are pathogens
Goal: Panel of qPCR assays, based on metagenomic surveys
Module 7
bioinformatics.ca

30. Case study Design bioinformatics.ca Control (Protected) Watershed
Human- Impacted Watershed
Agriculturally-Impacted Watershed
S1: Upstream
Case study
Design
S2: Pollution
S3: Downstream
STUDY DESIGN – SAMPLING YEAR ONE
96 water samples collected over 1 year, plus additional hourly time courses
Bioinformatics analysis, with metadata, modeling, biomarker identification
Illumina sequencing of microbial DNA (and viral RNA) – 16S, 18S, CPN60, shotgun seq
Water filtered for microbes 30
Module 7
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31. Case study: Identifying biomarkers of water quality
Microbiome survey
(taxon & gene profiles)
One sampling site upstream of contamination
Differential features
Two sampling sites downstream of contamination
qPCR panel test
Monthly water samples (with positive and negative controls)
Module 7
bioinformatics.ca

32. Case Study – Categorical Biomarker from Bacterial Shotgun Data
Example of fast track to marker ID and PCR test development
Bacterial shotgun data using Illumina HiSeq
Using MetaPhlAn
High precision, Low sensitivity
Based on select, clade-specific gene sequences
3000 reference genomes
Fast: 3 million reads ( bp) in 10 minutes
Metaphlan is a fast taxonomic classification software developed by Huttenhower lab at Harvard. It relies on unique, clade-specific gene sequences identified from 3,000 reference genomes. It was used in the Human Microbiome Project main paper for species-level metagenomic profiling.
Watershed Metagenomics SAB Meeting January 24, 2014

33. Case Study: Fast track approach Step 1. Process and validate data
Quality trimmed and normalised data across samples
MOCK COMMUNITY (POSITIVE CONTROL) VALIDATION
Validated with mock community: DNA-free water spiked with DNA from multiple taxa of lab-cultured bacteria
7% of reads were assigned by MetaPhlAn to a species (i.e. the expected low sensitivity for such a fast, precise approach)
Of those, 84% were correctly assigned
Low quality: sliding window with Q of at least average 20 over a sliding window of 10 bp.
Then the adapters were trimmed from the high-quality reads (allowing 20% mismatch/Ns)
Trimmed low quality reads
Trimmed adapter sequences
Filtered short reads
Discarded reverse paired reads (redundant data)
Assessed amount of data left
Positive Bacterial Control 1: 66,815 reads classified out of 901,344 (7.4%)
Positive Bacterial Control 2: 419,720 reads classified out of 5,797,824 (7.2%)
Strains in mock community:
Pseudomonas aeruginosa PAO1
Streptomyces coelicolor A3(2)
Rhodobacter capsulatus SB1003
7.3% of reads were assigned
Most common incorrect:
Genus level:
Burkholderia 2.6%
Frankia 1.8%
Thioalkalivibrio 1.6%
Species level:
Desulfovibrio desulfuricans 0.75%
Module 7
bioinformatics.ca
Watershed Metagenomics SAB Meeting January 24, 2014

34. Case Study: Fast track approach 2. Identify differential taxa
Top 50 most “abundant” taxa
Taxon 1
Taxon 2
Upstream At Site & Downstream
Prioritised high abundance taxa
Use White’s non-parametric t-test with false discovery rate multiple test correction to find differentially abundant taxa (alternative: RandomForests)
Figure on left are the top 50 most “abundant” microbes. Values are proportions of reads assigned to each species
White’s non-parametric t-test: Non-parametric test proposed by White et al. for clinical
metagenomic data. This test uses a permutation procedure to remove the normality assumption of a standard t-test. In addition, it uses a heuristic to identify sparse features which are handled with Fisher’s exact test and a pooling strategy when either group consists of less than 8 samples. See White et al., 2009 for details.
Watershed Metagenomics SAB Meeting January 24, 2014

35. Case Study: Fast track approach 3
Case Study: Fast track approach 3. Identify sequences characteristic of taxa
57,016 reads assigned to Taxon 1
2,176 reads assigned to Taxon 2
Prioritised taxon 1 due to our research indicating high abundance taxa are most accurately predicted
Extracted taxon-specific sequences from MetaPhlAn database
607 sequences for Taxon 1
Aligned reads against these sequences
Chose regions of MetaPhlAn sequences with most hits
Watershed Metagenomics SAB Meeting January 24, 2014

36. Case Study: Fast track approach
3. Identify sequences characteristic of taxa
MetaPhlAn marker sequence
Our reads aligned with quite a bit of depth to the first part of the sequence, but not the second part.
This is because the marker is from Polynucleobacter necessarius subsp. asymbioticus QLW-P1DMWA-1. The second part of the sequence is different in Polynucleobacter necessarius subsp. necessarius STIR1, so this suggests that the strain present in our samples is not asymbioticus
This marker is from LSU ribosomal protein L10P
Consensus sequence
Highest Coverage =
candidate marker sequence
Module 7
bioinformatics.ca
Watershed Metagenomics SAB Meeting January 24, 2014

37. Case Study: Fast track approach 4
Case Study: Fast track approach 4. Design qPCR primers & probes from marker seq.
Used Primer3 for primer and probe design
Checked in silico relative occurrence rates of candidate primers and probes
Considered matches that are exact or have 1-2 mismatches
Chose sequences that minimize non-specific matches
Lab: Confirmed we can amplify a product of the right size
Forward primer
Probe
Reverse primer
At site & downstream
Upstream
Module 7
bioinformatics.ca
Watershed Metagenomics SAB Meeting January 24, 2014

38. “Fast track” Case Study Comments
ID’d a marker based on differential abundance of a bacterial species  being used to pilot our iterative validation process
Benefits of this approach
Fast: Sequence data to PCR primers in a couple days
Doesn’t require large amounts of processing power
Limitations of this approach
Depends on differential abundance of known bacteria (if the differential bacteria aren’t highly similar to those in the MetaPhlAn database, this approach won’t work)
Based on taxa, which have been shown to be more variable across environments than (gene) functions
A “low hanging fruit” approach; a good first step
Watershed Metagenomics SAB Meeting January 24, 2014

39. Case Study Alternative Approach: More complete analysis of bacterial shotgun seq.
More complete taxonomic analysis
Kraken, Discribinate (See Peabody et al. 2015)
Gene function analysis
MEGAN4 (using SEED, KEGG databases)
Cluster based analysis:
Get predicted proteins
Cluster
Find differential clusters
Design PCR
Watershed Metagenomics SAB Meeting January 24, 2014

40. From Biomarker to Lab Test
Identify discriminative taxa or functions
Identify informative region for primer design (CD-HIT to cluster reads by identity)
Design primers (Primer-BLAST or IDT Realtime PCR Tool)
Primer 1
Primer 2
Validate primers in silico (Primer Prospector, Primer-BLAST)
Validate primers in vitro (qPCR)

41. Remember other markers…
Community diversity as an indicator of ecosystem health
Microbiome analysis may suggest other types of screening tests… (metabolites, etc)
Markers are only as good as the data they are based on, so design experiments carefully, include +ve and -ve controls

42. Final Comments Use controls, careful experimental design
Note variation in accuracy of different metagenomics analysis methods
Appreciate biases, limitations re what’s in sequence databases
Carefully examine data, methods and biases when comparing across different datasets (i.e. from different research groups)
Careful, considerate analysis can really pay off

45. Final Comments Use controls, careful experimental design
Note variation in accuracy of different metagenomics analysis methods
Appreciate biases, limitations re what’s in sequence databases
Carefully examine data, methods and biases when comparing across different datasets (i.e. from different research groups)
Careful, considerate analysis can really pay off

46. Questions?