Download Viral Metagenomics_EBI

Viral Metagenomics
Andrew Millard
Warwick Medical School
University of Warwick
Email: [email protected]
Twitter: @milja001
Overview
• Brief history
• Methods
• Analysis
• Examples
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
Collection of Viral Fraction
Concentration
Nucleic Acid Extraction
Library Preparation
Analysis
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.
Concentration - Ceasium Chloride
• Expensive
• Specialised equipment
• Ultra centrifuge
• Low throughput
• Does not remove all free
bacterial DNA
Viral band
Concentration - Columns
• Amicon columns
• Higher throughput
• Cheaper
• Less specialised equipment
Viral nucleic acid types
• dsDNA
• Linear
• Circular
• Linear with single strand breaks
• ssDNA
• Linear
• Circular
• dsRNA
• Linear, positive , negative strand, segmented
• ssRNA
• Linear
One library preparation method will NOT target all viruses
Bacterial Contamination (?)
• 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 ?
Sequencing
• Library Preparation
• 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
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
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 2004. The percentage of
each library that could be classified as Eukarya, Bacteria, Archaea, viruses or
showed no similarities (E-value >0.001) is shown.
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
Analysis Issues- Databases
• Databases are crucial
• Very small database of bacteriophage and viral genomes
• 4026 complete viral genomes
(http://www.ebi.ac.uk/genomes/virus.html)
• ~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)
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%
(16/192)
6.2%
(12/192)
Mycobacterium &
Pseudomonas
phages
100 %
(192/192)
0%
(0/192)
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
Viral Metagenomics
Lake Borgoria, Kenya
Sophie Clough & Martha Clokie
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)
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
Viral Metagenomics
•
Iron chloride concentration
•
Nextera XT library preparation
•
Analysis
•
•
•
Assembly CLCworkbench
MetaVir2 analysis
Annotation against custom viral database
N
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Diversity
Number of sequences
Sample Summary
Sample
Details
Contigs
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
17
39724
10603
26.6
c25
S/50
43624
62006
10
34821
8803
25.8
d22
C/75
59922
85208
42
47640
12082
25.3
d25
N/75
8355
12065
6
5537
2818
50.8
Genes
Predicted
Circular
Contigs
Unaffiliat
ed
Contigs
Affiliated
Contigs
% Affiliated
Pelagibacter phage HTVC008M
Mycobacterium phage
PattyP
Synechococcus phage ACG2014c
Novel phage 1
No
similarity
Phage associated
protein
Identification of multiple novel phage
assemblies
Depth Analysis
North Basin Vertical Stratification Comparisons
0
20
40
60
80
0
10
20
Depth (m)
30
40
50
60
70
80
% reads that map to assembly of Surface sample
100
•
Viral community
changes with depth
Flamingo Summary
•
Viral metagenomes are diverse
•
Majority of genes are unknown
•
Identification of multiple novel phage isolates
Grass in
Are phage a reservoir of antibiotic
resistance genes in slurry ?
70 Litres a day
~30 million tonnes of slurry per
year is spread onto farmland
Assaying for phage carriage of
antibiotic resistance genes in cattle
slurry
1. Isolate bacteriophage
•
Sequence their genomes
2. Add phage fraction to bacterial isolates
•
•
•
Assay for antibiotic resistance
Selects for temperate phage only
Adding phage increased occurrence of resistance
colonies ( 0.005 % of cells when using E. coli ) *
3. Metagenomics of viral fraction
•
Search for antibiotic genes
Phage Isolation on E.coli
• Isolated 20 phage
• Purify
• Sequence
• Annotate
14 plaques
305 pfu/ml
Diversity of phage genomes isolated on E. coli
T4-like
T4like
HK587-like
T5-like
Shivani
Seurat
Novel
Bacteriophage T4
No Antibiotic Resistance genes
Slurry Metagenome
• 2,171,116 PE reads
• ~217,735 contigs
• 56,824 genes
• 12,236 are annotated as known phage genes (BLASTp 1e-5)
Coverage
• Only 10% of reads map to any known viral genome
• BUT 1% of reads map to new novel phage genome
Kraken-Analysis
99.25 %
reads
unassigned
90
80
70
60
50
40
30
20
10
0
1
Astroviridae
Lipothrixviridae
Poxviridae
Potyviridae
Herpesviridae
Ascoviridae
Phycodnaviridae
Polydnaviridae
Baculoviridae
Microviridae
Tymoviridae
Flaviviridae
Podoviridae
Partitiviridae
Myoviridae
Siphoviridae
Analysis of assembled contigs- MetaVir
•70% contigs contigs have no “known” viral
genes
•30% contigs associated as viral contigs
Dominated by Siphoviridae
Assembled phage genomes (?)
Phage 1
Phage 2
Phage 3
Known phage protein
No similarity
Bacterial/phage protein
.............20
Antibiotic Resistance Genes
• 190 genes have similarity to known antibiotic
resistance genes
• Database from http://ardb.cbcb.umd.edu/
- non are localized on a contig with a “known” phage gene
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
Acknowledgements
Prof Martha Clokie
Sophie Clough
Becky Smith
Marie O Hara