1. Andrew Millard Warwick Medical School University of Warwick
Viral Metagenomics
Andrew Millard
Warwick Medical School
University of Warwick

2. Overview
Brief history
Methods
Analysis
Examples

3. Brief History 1St Viral Metagenome – Brietbart 2003, PNAS 2002
Marine seawater sample
Human faeces – Brietbart 2003, J Bacteriology
Four Ocean biomes – Angly 2006, PLoS Biology
Global Ocean Survey – Williamson 2008, PLOS ONE
Human faeces from monozygotic twins – Reyes 2010, Nature
Pacific Ocean Virome – Hurwitz 2013, PLOS ONE
TARA Oceans – Brum 2015 , Science

4. Collection of Viral Fraction
Concentration
Nucleic Acid Extraction
Library Preparation
Analysis

5. Virus Collection Separation from larger particulate matter
Water samples –easy
Faecal matter – less fun
Iron Chloride flocculation – (Poulos 2015)
Filtration
Tangential flow filtration
Both methods work
Depends on samples – TFF can filter from 0.5 l to 100s litres of water.

6. Concentration - Ceasium Chloride
Expensive
Specialised equipment
Ultra centrifuge
Low throughput
Does not remove all free bacterial DNA
Viral band

7. Concentration - Columns
Amicon columns
Higher throughput
Cheaper
Less specialised equipment

8. Viral nucleic acid types
dsDNA
Linear
Circular
Linear with single strand breaks
ssDNA
dsRNA
Linear, positive , negative strand, segmented
ssRNA
One library preparation method will NOT target all viruses

9. Bacterial Contamination (?)
Does it matter ?
Removal of bacterial DNA by DnaseI treatment, prior to viral lysis.
Check for contamination with PCR or qPCR – 16S primers, rpoB etc
BUT – is detection contamination ?
Transduction
Rate vary for different bacteriophages
1x109 vlp in seawater
Transduction rate of 1x10-6
1x103 particles may contain host DNA – is this contamination ?

10. Sequencing Library Preparation Options How much sequence is enough ?
Will depend on nucleic acid type
DNA –dsDNA –Nextera XT
Options
RASL, LASL , Nextera XT, TruSeq ……
Amount of DNA is dependent on method used
Amplification – phi29. Can cause 10,000 x differences in resultant population (Zhang 2006 )
How much sequence is enough ?
Depends on the question

11. To assemble or not ? Dependent on question of interest Assembly
MetaVelvet, CLC, Ray Meta , etc
Annotation
EBI, MG-RAST
PROKKA (Seeman 2014) with custom bacteriophage database

12. Analysis – The Dark Matter
Known sequence
Unknown sequence
Data will look
something like this
Viral metagenomic sequences from human faeces, a marine sediment sample and two seawater samples were compared to the GenBank non-redundant database at the date of publication and in December The percentage of each library that could be classified as Eukarya, Bacteria, Archaea, viruses or showed no similarities (E-value >0.001) is shown.

13. Viral Diversity
Majority of sequences will have no similarity to current databases
Estimates of viral protein clusters vary
~2 billion protein clusters (Rohwer 2003)
3.9 million (Espinoza, 2013)
~5,746 virus populations in the ocean
Only 39 previously identified

14. Analysis Issues- Databases
Databases are crucial
Very small database of bacteriophage and viral genomes
4026 complete viral genomes (
~96 Mb total sequence
~20 kb mean ( 7 kb median) viral genome size
In contrast >40,000 Salmonella genomes !!
1 MiSeq run V3 chemistry ~ 15 Gb
Would allow sequencing of all complete viral genomes to reasonable coverage ( in theory)

15. Analysis – Assembly
Fully assembled genomes no misassembly
Partially assembled genomes no misassembly
Genomes with assembly errors
Mycobacterium phages
88%
(169/192)
3.6 %
(7/192)
8.4%
(16/192)
Pseudomonas phages
85.5 %
(164/192)
8.3%
6.2%
(12/192)
Mycobacterium & Pseudomonas phages
100 %
(192/192)
0 %
(0/192)
Mycobacterium & Pseudomonas & Synechococcus phages
92.7%
(267/288)
3.47%
(10/288)
3.81 %
(11/288) 
Mycobacterium & Pseudomonas & Synechococcus & Bacillus phages
87.32 %
(335/384)
4.42%
(17/384)
8.33%
(32/384)
In silico modelling suggests misassembly of phage genomes will occur in mixtures of closely related phages

16. Viral Metagenomics
Lake Borgoria, Kenya
Sophie Clough & Martha Clokie

17. Background Flamingos eat Arthrospira sp blooms
High specialised diet
Crashes in blooms results in less food
Flamingos die ( Kaggwa et al, 2013)
Flamingos provide tourism to local community.
Cyanophage implicated in lysis of blooms Peduzzi et al. (2014)

19. Data Collected Viral numbers Identification of Arthrospira sp present
Abundance data of Arthrospira
Viral DNA samples
CTD, pH,
Samples are continually being collected on a monthly basis

20. Viral Metagenomics Iron chloride concentration
Nextera XT library preparation
Analysis
Assembly CLCworkbench
MetaVir2 analysis
Annotation against custom viral database

22. Number of sequence clusters
Diversity
Number of sequences

23. Sample Summary Sample Details Contigs Genes Predicted Circular Contigs
Unaffiliated Contigs
Affiliated Contigs
% Affiliated
a22
C/0
67315
88990 42
52070
15245
29.2
a23
N/0
48677
71352 21
36112
12565
34.7
a25
S/0
109987
157761 67
88809
21178
23.8
b22
C/25
79880
12744 54
59185
20695
34.9
b23
N/25
52125
73752 17
40182
11943
29.7
b25
S/25
87002
132177 53
67179
19823
29.50
c22
C/50
12936
18973 4
9265
3671
39.6
c23
N/50
50327
71111
39724
10603
26.6
c25
S/50
43624
62006 10
34821
8803
25.8
d22
C/75
59922
85208
47640
12082
25.3
d25
N/75
8355
12065 6
5537
2818
50.8

24. Pelagibacter phage HTVC008M
Mycobacterium phage PattyP
Synechococcus phage ACG-2014c

25. Novel phage 1 No similarity Phage associated protein
Identification of multiple novel phage assemblies

26. Depth Analysis Viral community changes with depth
North Basin Vertical Stratification Comparisons
Viral community changes with depth

27. Flamingo Summary Viral metagenomes are diverse
Majority of genes are unknown
Identification of multiple novel phage isolates

28. Are phage a reservoir of antibiotic resistance genes in slurry ?
Grass in
70 Litres a day
~30 million tonnes of slurry per year is spread onto farmland

29. Assaying for phage carriage of antibiotic resistance genes in cattle slurry
Isolate bacteriophage
Sequence their genomes
Add phage fraction to bacterial isolates
Assay for antibiotic resistance
Selects for temperate phage only
Adding phage increased occurrence of resistance colonies ( % of cells when using E. coli ) *
Metagenomics of viral fraction
Search for antibiotic genes

30. Phage Isolation on E.coli
Isolated 20 phage
Purify
Sequence
Annotate
Stdev 80
14 plaques
305 pfu/ml

31. Diversity of phage genomes isolated on E. coli
T4-like
T4-like
HK587-like
T5-like
Shivani
Seurat
Novel

32. No Antibiotic Resistance genes
Bacteriophage T4
Gp37 – tail fibre proteins are variable
Alt -NAD+:protein ADP-ribosyltransferase
No Antibiotic Resistance genes

33. Slurry Metagenome 2,171,116 PE reads ~217,735 contigs
56,824 genes
12,236 are annotated as known phage genes (BLASTp 1e-5)
Only 10% of reads map to any known viral genome
BUT 1% of reads map to new novel phage genome
Coverage

34. Kraken-Analysis 99.25 % reads unassigned
Partitiviridae - plant and funfal genomes

35. Analysis of assembled contigs- MetaVir
70% contigs contigs have no “known” viral genes
30% contigs associated as viral contigs
Dominated by Siphoviridae

36. Assembled phage genomes (?)
Known phage protein
No similarity
Bacterial/phage protein

37. Antibiotic Resistance Genes
190 genes have similarity to known antibiotic resistance genes
Database from
non are localized on a contig with a “known” phage gene

38. Conclusions III
Cattle slurry harbours a vast diversity of unknown bacteriophages
Just like every other viral metagenome !!
Isolation of 20 phage from single host did not isolate the “same” phage twice.
Viral fraction can transfer antibiotic resistance to E. coli
Viral metagenomics reveals the presence of antibiotic resistance genes

39. Acknowledgements Prof Martha Clokie Sophie Clough Becky Smith
Marie O Hara