Ultra-deep sequencing of VHSV isolates contributes to understanding the role of viral quasispecies

Abstract

The high mutation rate of RNA viruses enables the generation of a genetically diverse viral population, termed a quasispecies, within a single infected host. This high in-host genetic diversity enables an RNA virus to adapt to a diverse array of selective pressures such as host immune response and switching between host species. The negative-sense, single-stranded RNA virus, viral haemorrhagic septicaemia virus (VHSV), was originally considered an epidemic virus of cultured rainbow trout in Europe, but was later proved to be endemic among a range of marine fish species in the Northern hemisphere. To better understand the nature of a virus quasispecies related to the evolutionary potential of VHSV, a deep-sequencing protocol specific to VHSV was established and applied to 4 VHSV isolates, 2 originating from rainbow trout and 2 from Atlantic herring. Each isolate was subjected to Illumina paired end shotgun sequencing after PCR amplification and the 11.1 kb genome was successfully sequenced with an average coverage of 0.5-1.9 × 106 sequenced copies. Differences in single nucleotide polymorphism (SNP) frequency were detected both within and between isolates, possibly related to their stage of adaptation to host species and host immune reactions. The N, M, P and Nv genes appeared nearly fixed, while genetic variation in the G and L genes demonstrated presence of diverse genetic populations particularly in two isolates. The results demonstrate that deep sequencing and analysis methodologies can be useful for future in vivo host adaption studies of VHSV.

Full text

Schönherz et al. Vet Res (2016) 47:10
DOI 10.1186/s13567-015-0298-5
RESEARCH ARTICLE
Ultra-deep sequencing ofVHSV isolates
contributes tounderstanding the role ofviral
quasispecies
Anna A. Schönherz1, Niels Lorenzen2, Bernt Guldbrandtsen1, Bart Buitenhuis1 and Katja Einer‑Jensen3*
Abstract
The high mutation rate of RNA viruses enables the generation of a genetically diverse viral population, termed a qua‑
sispecies, within a single infected host. This high in‑host genetic diversity enables an RNA virus to adapt to a diverse
array of selective pressures such as host immune response and switching between host species. The negative‑sense,
single‑stranded RNA virus, viral haemorrhagic septicaemia virus (VHSV), was originally considered an epidemic virus
of cultured rainbow trout in Europe, but was later proved to be endemic among a range of marine fish species in the
Northern hemisphere. To better understand the nature of a virus quasispecies related to the evolutionary potential
of VHSV, a deep‑sequencing protocol specific to VHSV was established and applied to 4 VHSV isolates, 2 originating
from rainbow trout and 2 from Atlantic herring. Each isolate was subjected to Illumina paired end shotgun sequenc‑
ing after PCR amplification and the 11.1 kb genome was successfully sequenced with an average coverage of
0.5–1.9 × 106 sequenced copies. Differences in single nucleotide polymorphism (SNP) frequency were detected both
within and between isolates, possibly related to their stage of adaptation to host species and host immune reactions.
The N, M, P and Nv genes appeared nearly fixed, while genetic variation in the G and L genes demonstrated presence
of diverse genetic populations particularly in two isolates. The results demonstrate that deep sequencing and analysis
methodologies can be useful for future in vivo host adaption studies of VHSV.
© 2016 Schönherz et al. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
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and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/
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Introduction
Viral haemorrhagic septicaemia virus (VHSV) is an RNA
virus endemic to marine and freshwater fish species. It
represents one of the most important viral pathogens
in salmonid fish in continental Europe where it heavily
affects cultured rainbow trout, causing a severe systemic
disease with mortality rates as high as 90% [1] and thus
resulting in extensive economical loses to the aquaculture
industry [2, 3].
VHSV is a single-stranded RNA virus of negative polar-
ity that belongs to the genus Novirhabdovirus, within the
family Rhabdoviridae. Genetic analyses show that the
virus clusters into four major phylogenetic clades classi-
fied as genotypes I–IV with further subdivision of geno-
types I and IV. Genotypes are correlated to geographic
regions rather than host species with genotype I–III cir-
culating in Europe and genotype IV circulating in North
America and Asia [4–7]. Although these phylogenetic
analyses cannot define host species specificity, they do
demonstrate that isolates of VHSV from rainbow trout
are principally members of genotypes Ia, Ic and Id, and
the isolates adapted to marine host species are members
of genotypes Ib, Ie and II–IV. e genetic differentiation
of rainbow trout and marine adapted isolates is also phe-
notypically manifested in different pathogenicity patterns
towards the two host groups [8]. Rainbow trout adapted
isolates are highly pathogenic to rainbow trout but show
very low or no pathogenicity in marine fish species and
vice versa.
Moreover, it has been revealed that rainbow trout
adapted isolates have evolved from the marine environ-
ment and are the result of cross-species transmission
followed by subsequent host adaptation [5, 9, 10]. Cross-
species transmission from marine hosts to cultured
Open Access
Veterinary Research
*Correspondence: katja.einer@qiagen.com
3 QIAGEN AAR, 8000 Århus, Denmark
Full list of author information is available at the end of the article

Page 2 of 12
Schönherz et al. Vet Res (2016) 47:10
rainbow trout has occurred several times. Recently,
cross-species transmission events have been reported in
rainbow trout cultured in marine waters of Finland [11,
12], Norway [13] and Sweden [14, 15].
e repeated emergence of VHSV into cultured rain-
bow trout illustrates the high evolutionary potential of
the virus. is is a feature common to all RNA viruses
[16, 17]. RNA viruses have high replication rates, large
population sizes, and exceptionally high mutation rates
commonly ranging from 10−3 to 10−5 mutations per
nucleotide, per replication [16–18]. e high mutation
rates result from an error-prone RNA polymerase lacking
proofreading abilities [18, 19]. As a consequence, RNA
virus populations sustain high genetic diversity, most
likely resulting in the formation of a quasispecies. A qua-
sispecies is composed of a dominant nucleotide sequence
and a spectrum of low frequency variants arising from
mutations during virus replication [18]. While appear-
ing a wasteful strategy, this feature enables an RNA virus
to rapidly adapt to environmental changes or new hosts.
However, very little is known about viral population
structures and dynamics, especially the low-frequency
mutant spectrum.
Until recently, viral evolutionary dynamics were inves-
tigated based on consensus genome sequencing, ignoring
that viral samples actually represent complex, heteroge-
neous populations, of non-identical genome sequences.
Consensus sequencing, however, only identifies the dom-
inant or major viral sequence present in a sample but is
uninformative about the mutant spectrum of minority
variants present in the population [20, 21]. Consequently,
consensus genome sequences provide an incomplete
picture of the within- and between-host viral evolu-
tionary diversity during cross-species transmission and
adaptation.
In contrast Next-Generation Sequencing (NGS) tech-
niques provide a rapid and cost-effective analysis of viral
population diversity at an unprecedented level of detail
[20, 22–24]. e great depth of resolution and high-
throughput nature of NGS platforms allows investigation
of viral evolution at the within- and between-host scale.
e main focus of this study was to develop a VHSV
specific NGS protocol applicable across viral genotypes
with the aim of understanding the evolutionary potential
of VHSV through characterization of its viral quasispe-
cies. An overlapping long range PCR amplification pro-
tocol specific to VHSV was developed targeting isolates
of genotype I–III. e nucleotide sequences of four cell
culture passaged VHSV isolates were analysed using the
Illumina HiSeq sequencing platform and coverage rates
between 0.5 and 1.9×106 were achieved allowing char-
acterization of the viral population diversity in all four
isolates of VHSV.
Materials andmethods
Virus isolates andviral propagation
Deep sequencing of viral populations was conducted
using four VHSV isolates (Table1): two isolates origi-
nating from freshwater cultured rainbow trout (DK-
3592b, DK-9895174), and two isolates originating from
Atlantic herring (4p168, 1p49). Both rainbow trout iso-
lates belong to genotype I whereas the two marine iso-
lates belong to genotypes II (1p49) and III (4p168). All
four viral stocks were propagated in bluegill fry cells
(BF-2; [25]) as described earlier [26]. When complete
cytopathic effect (CPE) was observed, cell medium
was harvested, filtered through a 0.2-µm Minisart fil-
ter (Bie & Bernsten) and the filtrate was centrifuged
at low speed (5000 RPM) for 30min at 4°C to remove
cell debris. Subsequently, the supernatant was recov-
ered and subjected to ultracentrifugation at 86000×g
for 2h at 4°C to pellet viral particles. e pellet was
harvested and stored at −80°C or directly subjected to
RNA extraction.
RNA extraction
Total RNA was extracted from replicate samples for each
isolate using the RNeasy Mini kit (Qiagen) following
manufacturer’s recommendations for extraction of RNA
from cell culture. Total RNA from each replicate was
eluted in 30 µL nuclease-free water that was treated with
DEPC (Qiagen) and finally pooled. Two microliters were
used to quantify the concentration of RNA; the remain-
der was stored at −80°C. e concentration of extracted
RNA was determined using a spectrophotometer (Nan-
oDrop, ermo Scientific) and the final concentration
of the pooled samples was in range of 16–40ng/µL per
isolate.
Table 1 Data related tothe four viral haemorrhagic septicaemia virus isolates used inthis study.
Isolate Genotype Host Geographic origin Year ofisolation Original sample type References Cell culture passage
DK‑3592b Ia Rainbow trout Denmark 1985 Tissue pool, fish pool [28, 39, 40] 8 Pass BF2
DK‑9895174 Ia Rainbow trout Denmark 1998 Tissue pool, fish pool [5] 5 Pass BF2
1p49 II Atlantic herring Baltic Sea 1996 Tissue pool, fish pool [8] 1 Pass EPC, 5 pass BF2
4p168 III Atlantic herring Skagerrak 1997 Tissue pool, fish pool [41] 8 Pass BF2

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Schönherz et al. Vet Res (2016) 47:10
Reverse transcription
Reverse transcription (RT) of the full-length VHSV
genome was performed using the SuperScript III First-
Strand Synthesis System for RT-PCR (Invitrogen) and
a VHSV genome specific primer (Table2). RT was per-
formed following manufacturer’s recommendations.
Briefly, 1µL cDNA primer (0.01mM) and 1µL dNTPs
(10 mM) were added to 8 µL total RNA, incubated at
65°C for 5min and placed on ice. Subsequently, 10µL
cDNA synthesis mix (2µL 10× buffer, 4µL MgCl2, 2µL
DTT, 1µL RNase OUT, 1µL SuperScript III reverse tran-
scriptase) were added and incubated at 50°C for 50min,
85°C for 5min, and placed on ice. Finally, 1µL RNase H
was added followed by incubation at 37°C for 20min to
remove the original viral RNA from the new synthesized
cDNA. In total, 20µL of full VHSV genome length cDNA
was synthesized and either stored at −80°C or immedi-
ately subjected to PCR amplification.
Polymerase chain reaction (PCR) andDNA purication
PCR amplification of the full length VHSV genome was
performed using the Platinum® Taq DNA Polymer-
ase High Fidelity kit (Invitrogen) and single primer set
amplifying a 11,014bp region covering all 6 open read-
ing frames, all intergenic regions and partial regions of
the leader and trailer sequence (sense primer VHSV_
Frag1I_nt18_+s: 5′-GAG TTA TGT TAC ARG GGA
CAG G-3′; antisense primer VHSV_Frag4I_nt11032_-s:
3′-TCT CCA AAT GGA AAG AAG GAC T-5′). Ampli-
fication was performed for all four isolates but full-length
genome amplification could only be established for 3 of
the isolates (DK-3592b, DK-9895174, 1p49) and with
unwanted smaller fragments (Figure 1). To maximize
coverage depth, the genome was divided into four ampli-
cons that were numbered sequentially as amplicon 1–4
starting from the 5′ end of the genome with amplicons
ranging from 2797 to 3709bp in length and overlapping
with the adjacent amplicons by 274–790bp (Table2).
Primers were designed to target conserved regions of the
VHSV genome irrespective of host origin and genotype
(Table 2). PCR amplification was performed for each
fragment and isolate separately using the Platinum® Taq
DNA Polymerase High Fidelity kit (Invitrogen) and the
corresponding primer sets. Amplification was conducted
in a total volume of 50µL in a MX Pro-Mx3005P ther-
mocycler. Reactions contained 2µL cDNA, 5µL 10x high
Fidelity PCR buffer, 1µL dNTPs (10mM), 2µL MgSO4
(50mM), 0.2 µL Platinum4 Taq High Fidelity Polymer-
ase, 1µL sense primer (0,01mM), 1µL antisense primer
(0.01 mM), and 37 µL nuclease free water. Amplicons
were produced using the following cycling program:
94°C for 1min, followed by 25 cycles of 94°C for 30s,
58°C for 30s, and 68°C for 4min, with a final step of
68°C for 5min. Individual PCR products were visualized
using agarose gel electrophoresis running 6µL on a 1%
agarose gel. A total of 30µL of the remaining amplified
DNA was purified using the QIAquick PCR Purification
kit (Qiagen) following manufacturer’s recommendations.
Purified DNA was eluted in 50 µL EB buffer (10 mM
Tris·Cl, pH 8.5). DNA concentration was determined by
fluorescence detection using a Qubit® Fluorometer (Inv-
itrogen) and the Quant-iT™ dsDNA BR Assay kit (Life
Technologies). Subsequently, amplicons of the same iso-
late were adjusted to equivalent concentrations, com-
bined to one sample and stored at −20°C.
Library preparation andsequencing
e paired end library preparation, as well as Illumina
HiSeq (2×100) sequencing of the four samples, was per-
formed on contract basis by Beckman Coulter Genomics.
Briefly, library construction was performed using sheared
DNA as input to the SPRI-TE system and LC cartridges
(Beckman Coulter, Inc.). TruSeq PE indexes (Illumina)
were added by ligation, the libraries were amplified by
PCR, and purified with AMPure XP beads (Beckman
Coulter, Inc.). e libraries were clustered on Illumina
Table 2 Primers used forRT-PCR amplication.
Names include +s or −s, which reects the positive or negative sense orientation, respectively.
* Primer used for reverse transcription of the genomes.
Name Sequence Amplicon (length inbp) Overlapping region
VHSV_Frag1I_nt18_+s* GAGTTATGTTACARGGGACAGG
VHSV_Frag1_nt2815_−s CGATTGTAGYAGTCCTTCGC Amplicon 1 (2797 bp) Amplicon 1–2 = 274 bp
VHSV_Frag2_nt2541_+s GAGAAGATTGACTTCGGGAC
VHSV_Frag2_nt6250_−s CGTAGGTAGGAACCCTGTC Amplicon 2 (3709 bp) Amplicon 2–3 = 790 bp
VHSV_Frag3_nt5460_+s TACTGGAACTTGGCCTCACA
VHSV_Frag3_nt8287_−s TGTGTCCGCCAAATGGTGTA Amplicon 3 (2827 bp) Amplicon 3–4 = 431 bp
VHSV_Frag4_nt7856_+s GATGATTGTCTCCACCATGAA
VHSV_Frag4I_nt11032_−s TCTCCAAATGGAAAGAAGGACT Amplicon 4 (3176 bp)

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Schönherz et al. Vet Res (2016) 47:10
High-Output v3 Flowcells and sequenced using a HiSeq
model 2500 sequencer (Illumina). Beckman Coulter
Genomics performed the primary software analysis using
the Illumina instrument application Real-Time Analy-
sis software, and hereby converted raw images into base
calls. Demultiplexing was then performed resulting in
FASTQ-formatted read-groups using the CASAVA soft-
ware package.
Bioinformatics analysis
e demultiplexed FASTQ files provided by Beckman
Coulter Genomics were analysed in a single workflow
using the specified tools (subsequently marked with
“”) which all are available in the CLC Genomics Work-
bench V7.5.1. e paired reads were trimmed using
the “Trim sequence” tool 4 times for removal gen-
eral adapters, PCR adaptors, PCR primers, and finally
ambiguous nucleotides (maximum number of ambi-
guities = 2) as well as nucleotides with low quality
scores (limit=0.05). e virus isolates DK-3592b and
DK-9895174 were mapped against a reference sequence
of DK-3592b (NCBI Accession number KC778774),
while the isolates 1p49 and 4p168 obtained from her-
ring were mapped against a VHSV isolate from sea-
reared rainbow trout (strain FA281107; NCBI Accession
number No EU481506). e following default param-
eters were used during mapping of reads to their refer-
ence sequence: mismatch cost=2; insertion cost= 3;
deletion cost = 3; length fraction = 0.5; similarity
fraction = 0.8. e mapped reads were subsequently
realigned and explored using the “InDel and Struc-
tural Variation Detection” tool. e hereby-generated
guidance variant track was subsequently used dur-
ing an additional realignment of the reads. Consensus
sequence and alignment of these against their respec-
tive reference sequences were performed using the
“Extract Consensus sequence” and “Create Alignment”
tools, respectively.
AB
CD
Figure1 Generation of the four overlapping amplicons (1–4) to cover the entire viral genomes of each isolate. The VHSV isolates, are
here abbreviated a DK‑3592b; b DK‑9895174; c 1p49; d 4p168. The individual RT‑PCR reactions are shown in A; B re‑amplified Amplicon 3 of sample
b*; C amplification of the whole genome RT‑PCR reaction; D pooled amplicon samples (1:1:1:1) for each of the VHSV samples used for library prepa‑
ration.

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Schönherz et al. Vet Res (2016) 47:10
Mapping efficiency and coverage of the individual sam-
ples were determined using the tools “Create Detailed
Mapping Report” and “Create Statistics for Target
Regions”, respectively.
Finally, single- and multiple nucleotide variants and
short insertions and deletions were called using the “Low
Frequency Variant Detector” tool where broken pairs and
non-specific read matches were ignored. e minimum
coverage, count and frequency were set to 1000, 200
and 1%, respectively, while the required significance was
0.01% and the relative read direction filter significance
was set to 10−19.
To reduce the number of false positives due to PCR
errors, quality filtering was performed using the fol-
lowing conservative thresholds: equality in forward and
reverse reads at least 0.35, average quality at least 30,
and statistical testing of read position and read direc-
tion probability should exceed 10−12. Since marginal
variants could still be related to cell culture passaging,
a final frequency-filtering criterion was applied includ-
ing polymorphic sites with intermediate allele frequency
(5–95%) but excluding polymorphic sites with minor allel
frequency below 5%. For each of these filtering steps, the
functional consequence was determined by translating
the encoded CDS’s using the “Amino Acid Changes” tool.
Statistical analysis
Patterns of genetic variation within and between isolates
were investigated using the statistical program R (version
3.1.0 R Core Team, 2014). All analyses conducted were
based on variant calls that passed quality control criteria.
Only polymorphisms with allele frequencies between 5
and 95% were retained for analysis. e frequency filter
was applied to ensure that variants investigated repre-
sented polymorphisms of the original isolate instead of
variants introduced during cell culture, RT-PCR ampli-
fication or sequencing and to ensure that those variants
represented genetic variation present within the original
viral population.
To identify whether genetic variation differed between
genes or isolates, a generalized linear model compari-
son analysis was conducted assuming that the number
of substitutions, including both synonymous- and non-
synonymous substitutions, as the response variable fol-
lowed a Poisson distribution. e explanatory variables
were gene (
gi
: 6 levels representing genes encoding the
nucleoprotein (N), phosphoprotein (P), matrix protein
(M), glycoprotein (G), non-structural protein (NV), and
the RNA-dependent RNA polymerase (L)), and isolate (
ij
:
4 levels representing the isolates DK-3592b, DK-9895174,
1p49, 4p168), as well as a regression coefficient taking
into account the effect of the length of the gene in nucle-
otides. e full linear model was:
where
ij
is the parameter of the Poisson distribution, gi
is the effect of gene i, sj is the effect of isolate j, log (li)
is the logarithm of the length of gene i. e coefficient
of the logarithm of the gene length was fixed to 1. ree
models were investigated: (1) the full model including all
explanatory variables; (2) a reduced model without gene
as explanatory variable; and (3) a reduced model with-
out isolate as explanatory variable. e generalized linear
models were fitted using the “glm” function from the R
package “stats”. Models were compared using the “anova”
function in R.
To identify contrasts of genetic variation between
genes, genes were divided into two groups: (1) genes
with low genetic variation (N, P, M, NV); (2) genes with
high genetic variation (G, L). e generalized linear
model comparison analysis was repeated, but with gene
grouped into 2 levels (high genetic variation, low genetic
variation), isolate (4 levels) and gene length included
as explanatory variables, investigating whether genetic
variation in the G and L gene is higher compared to the
remaining genes. A Bonferroni correction was applied to
account for all possible groupings of 6 genes. e total
number of possible groupings is given by the sixth Bell
number minus 2, B6= 203, that is correcting for 201
simultaneous (implicit) tests. e two cases that are sub-
tracted are the case of each gene having a separate effect
and the case of all genes having the same effect. When
looking at contrasts among genes, this is the most con-
servative choice.
To identify contrasts of genetic variation between iso-
lates, isolates were divided into two groups: (1) isolates
with low genetic variation (DK-3592b, DK-9895174,
4p168); (2) isolates with high genetic variation (1p49).
e generalized linear model comparison analysis was
repeated, but with isolate grouped into 2 levels (high
genetic variation, low genetic variation), gene (6 levels)
and gene length included as explanatory variables, inves-
tigating whether genetic variation in 1p49 is higher com-
pared to the reaming isolates. e Bonferroni correction
was adjusted to correct for all possible groupings of 4 iso-
lates (B4=15).
Results
Amplicon amplication
Based on agarose gel electrophoresis, amplicons of
expected size were synthesized by RT-PCR. However,
small amounts of shorter amplicons were also observed
(Figure 1A). Attempts to extract the DNA bands with
the expected amplicon size resulted in too low a con-
centration of purified DNA. Instead, a 1:1:1:1 amplicon
mixture was made from purified, but not gel-extracted
log 

ij
=g
i
+s
j
+log(l
i)

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Schönherz et al. Vet Res (2016) 47:10
samples, and therefore included some amplicons that
were shorter than desired (Figure 1D). e obtained
coverage data (Figure 2) showed unexpected increase
in coverage (up to 20×) at the first nucleotide upstream
of primer 3′ end and at the sequences in the overlap-
ping regions of amplicon 1 and 2, amplicon 2 and 3,
and amplicon 3 and 4, respectively. is finding was
unexpected since the performed PCR amplifications
were performed in individual tubes, and not as multi-
plex reactions. Whether a minor cross contamination of
primer pairs may have occurred either during synthesis
at the producer or during handling of the primers in the
lab remains to be determined.
Coverage andmapping ecacy
e number of generated raw paired-end reads for DK-
3592b, DK-9895174, 1p49 and 4p168 was 266.4× 106,
127.6×106, 76.4 ×106 and 224.4 × 106, respectively.
Trimming removed between 3.2 and 6% of the reads
resulting in 260.6 × 106, 120.2× 106, 71.8 × 106 and
212.9×106 reads, respectively.
Based on a BLAST search two different reference
genomes were identified. Isolate DK-3592b (genotype I)
was identified as an appropriate reference genome for
isolates DK-3592b and DK-9895174 (both genotype I),
whereas isolate FA281107 (genotype III) was identified as
the best reference genome for isolates 1p49 (genotype II)
and 4p168 (genotype III).
Mapping efficacy of isolate DK-3592b, DK-9895174,
4p168 to their respective reference genomes was above
97%) and contained comparable fractions of paired reads
(above 88%). e fourth isolate (1p49) was approximately
10% lower with respect to the number of generated reads
as well as the mapping efficiency (Table3).
e average coverage was very high for all four isolates
but with some variations. e average coverage depths of
isolate 1p49 (5.28×105) was approximately 72% that of
the isolate with the highest coverage (isolate DK-3592b
with 1.9×106 coverage). However, the coverage across
the genomes appears more even for 1p49 than for the
other 3 samples due to less PCR contamination/artefacts
as discussed above (Figure2).
Alignment of mapped consensus sequences to their
respective reference genome revealed 99.93, 99.03 and
98.34% sequence identity for isolates DK-3592b, 4p168,
and DK-9895174, respectively, but only 90.09% sequence
identity for isolate 1p49.
Variant detection
In the analysis performed, we mainly focus on polymor-
phisms with frequencies between 5 and 95% (Table4),
which likely reflect polymorphisms established in vivo
(before cell culture propagation). Variants at population
frequencies below 1% were assumed to represent poly-
morphisms established invivo, variants that were estab-
lished during cell culture, as well as artefacts of RT-PCR
amplification and NGS sequencing [27]. e experimen-
tal design could not provide a method to distinguish
between the different sources of observed polymor-
phisms; therefore, a relatively conservative frequency fil-
ter was applied, excluding variants below a 5% frequency
threshold.
Following quality- and frequency filtering, the highest
genetic variation at intermediate allele frequencies was
detected for isolate 1p49 with 100 variants, followed by
DK-3592b with 10 variants, DK-9895174 with 9 variants,
and 4p168 with 3 variants (Table4). Variants recorded
included the minor and the major allele detected for each
Figure2 Coverage of mapped reads across the whole VHSV genomes for all four isolates. The four overlapping amplicons (1–4) and CDS
regions are visualized according to the genome numbers of Acc No KC778774. The obtained mapping coverage for each nucleotide position of the
individual virus isolates is shown using a logarithmic scale.

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Schönherz et al. Vet Res (2016) 47:10
polymorphic side that passed quality control require-
ments. us, called variants do not represent the number
of polymorphic sites detected, but rather the total allelic
variation. A total of 89, 8, 7 and 2 polymorphic sites with
intermediate allele frequencies were detected for 1p49,
DK-9895174, DK-3592b and 4p168, respectively. Poly-
morphic sites were located in the N gene (DK-3592b), the
G gene (all isolates), the NV gene (1p49), and the L gene
(DK-3592b, 1p49). No genetic variation at intermediate
allele frequencies was detected in the M and P gene, or in
the intergenic regions. In Figure3, positions of polymor-
phic sites across the genome are shown representing the
allele frequency of the dominant variant (50–95% allele
frequency). e frequency of dominant alleles was shown
as some variants of minor allele frequency did not pass
the quality requirements and thus were not recorded.
Based on Table4 and Figure3, the G and L genes show
the highest level of genetic variation. Furthermore, iso-
late 1p49 shows higher genetic variation than the other
three isolates. Patterns of genetic variation within and
between isolates were confirmed by generalized linear
model comparison analysis. Comparing the full model
with the two model reductions revealed a significant dif-
ference between the full- and reduced models without
gene as explanatory variable (p value =9.955e−10) and
between the full- and reduced models without isolate as
explanatory variable (p value=2.2e−16). Hence, both the
genes as well as the isolates have an effect on the num-
ber of substitutions observed and the local substitution
rate because gene length was included in each model as
a regression coefficient. Comparison between genes of
high genetic variation to genes with low genetic variation
revealed that the average number of substitutions in the
G and L gene was significantly higher compared to other
genes (p value=0.002) (Figure4). In addition, compari-
son between isolates of high genetic variation with iso-
lates of low genetic variation revealed that the average
number of substitutions in isolate 1p49 was significantly
higher compared to the other isolates (p value=2.6e−15)
(Figure 4). In terms of amino acid substitutions,
DK-9895174 and 1p49 displayed the highest variability.
is was even more evident when comparing numbers of
substitutions after quality filtration only (Table4).
Discussion
In general, resequencing projects rarely exceeds a read
depth of 100×, which barely exceeds the depth needed
in order to correct for sequencing errors. In this study,
we established a protocol which provided an average cov-
erage of the four analysed VHSV genomes of between
0.5 and 1.9 million reads, while low coverage areas were
observed only in the initial part of the leader region, as
well as in the trailer region.
e results clearly demonstrated that the four isolates
of VHSV were composed of a genetically diverse virus
population, the first requirement for the formation of
a quasispecies. Intra-host genetic variation has been
demonstrated previously for other rhabdoviruses such
as rabies virus [22] but to our knowledge, this study is
the first to describe the intra-host genetic diversity in a
novirhabdovirus using ultra deep sequencing.
Statistical comparison of local substitution rates across
genes revealed that the G and L genes comprised the
majority of the genetic variation and showed signifi-
cantly different substitution rates compared to the N, P,
M and NV genes. ese findings are likely related to the
essential functions of these proteins: e G protein is
exposed on the surface of the virus particle and involved
in receptor recognition as well as cell entry. At the same
time, G is the target of neutralizing antibodies. Selection
pressure on G thus includes both the need for ability to
adapt to variation in host cell surface molecules as well
as ability to escape from the host adaptive immune sys-
tem [28]. e L gene is responsible for genome replica-
tion (transcription and translation), hence the observed
higher genetic variation may indicate the need to be able
Table 3 Read mapping oftrimmed reads againsta reference genome froma VHSV isolate ofsame host adaption environ-
ment.
Referencegenome DK-3592b (Acc No KC778774) FA281107 (Acc No EU481506)
Virus isolate DK-3592b DK-9895174 1p49 4p168
No ofreads In % No ofreads In % No ofreads In % No ofreads In %
Mapped reads 257 431 536 98.8 117 684 334 97.8 63 880 245 89.0 209 126 393 98.2
Not mapped reads 3 179 450 1.2 2 611 384 2.2 7 936 247 11.0 3 799 011 1.8
Reads in pairs 236 329 444 90.7 106 731 672 88.7 56 208 078 78.2 197 135 466 92.6
Broken paired reads 21 102 092 8.1 10 952 662 9.1 7 672 167 10.7 11 990 927 5.6
Total reads 260 610 986 100.0 120 295 718 100.0 71 816 492 100.0 212 925 404 100.0
Average coverage 1 871 006 959 252 528 148 1 729 031

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Schönherz et al. Vet Res (2016) 47:10
Table 4 Variant detection incoding andnone coding regions ofthe four analyzed VHSV genomes.
The table summarizes the distribution of nucleotide (Nt) and amino acid (Aa) variants before and after lteringprocedures. Two ltering procedures were applied based on read quality (Q) and variant frequency at
polymorphic sites (F). Read quality ltering was applied using the following parameters: equality in forward and reverse reads should be at least 0.35, average quality at least 30, while statistical testing of read position- as
well as read direction probability should exceed 10−12 . Filtering on variant frequency was conducted to distinguish true variants from errors introduced during sequence preparation and sequencing. Only polymorphic
sites with variants with intermediate allel frequencies (5–95%) were incluede whereas polymorphic sites with minor allele frequencies below 5% were excluded from analysis.
a Coverage of trailer was below 80×.
Reference genome DK-3592b (Acc No KC778774) FA281107 (Acc No EU481506)
Isolate DK-3592b DK-9895174 1p49 4p168
Filtering Nt Aa Nt, Q Aa, Q Nt, QF Aa, QF Nt Aa Nt, Q Aa, Q Nt, QF Aa, QF Nt Aa Nt, Q Aa, Q Nt, QF Aa, QF Nt Aa Nt, Q Aa, Q Nt, QF Aa, QF
Leader 4 – 0 – 0 – 7 – 0 – 0 – 12 – 0 – 0 – 0 – 0 – 0 –
N 7 1 5 0 2 0 15 3 12 1 0 0 200 33 94 15 0 0 14 8 10 7 0 0
Inter N‑P 3 – 1 – 0 – 3 – 2 – 0 – 11 – 6 – 0 – 0 – 0 – 0 –
P 2 1 3 1 0 0 17 4 17 3 0 0 72 11 48 7 0 0 5 2 5 2 0 0
Inter P‑M 2 – 0 – 0 – 3 – 1 – 0 – 26 – 8 – 0 – 6 – 5 – 0 –
M 2 1 0 0 0 0 13 5 6 2 0 0 88 18 29 5 0 0 9 3 6 1 0 0
Inter M‑G 0 – 0 – 0 – 1 – 1 – 0 – 20 – 11 – 0 – 5 – 4 – 0 –
G 22 9 17 4 5 1 60 27 48 22 9 4 286 45 161 16 7 1 32 7 15 3 2 1
Inter G‑Nv 6 – 0 – 0 – 5 – 3 – 0 – 17 – 13 – 0 – 0 – 5 – 0 –
Nv 20 9 12 1 0 0 23 12 22 11 0 0 65 33 37 18 2 0 11 4 5 3 1 0
Inter Nv‑L 4 – 0 – 0 – 6 – 5 – 0 – 11 – 8 – 0 – 2 – 1 – 0 –
L 15 1 8 0 3 0 85 10 65 5 0 0 869 73 464 32 91 4 51 12 38 5 0 0
Trailera0 – 0 – 0 – 0 – 0 – 0 – 0 – 0 – 0 – 0 – 0 – 0 –
Total 87 22 46 6 10 1 238 61 182 44 9 4 1677 213 879 93 100 5 135 36 94 21 3 1

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Schönherz et al. Vet Res (2016) 47:10
Figure3 Detected polymorphic positions with intermediate allele frequency (5–95%) representing the allele frequency of the domi-
nant allele (frequency between 50 and 95%). Colours of vertical lines indicate different genes: pink N, yellow P, red M, green G, light blue NV, blue
L, dotted vertical lines intergenic regions. Horizontal line represents the 95% frequency threshold. Red stars at top of vertical lines indicate amino acid
change in the dominant allele.
Figure4 Box plot of number of detected substitutions with intermediate allele frequencies (5–95%). Number of substitutions is
represented as a count of substitution events recorded for polymorphic sites with intermediate allele frequencies (5–95%). Accord‑
ingly, substitution events resulting into alleles with frequencies below 5% were ignored. A The substitution counts for the individual gene across all
four isolates for each of the six genes (four counts per gene). B Substitution counts for the individual isolate across all six genes for each of the four
isolates (six counts per isolate).

Page 10 of 12
Schönherz et al. Vet Res (2016) 47:10
to adapt to replication conditions in different cell types
or host species. Recently, a single amino acid substitu-
tion (I1012F) in the L protein was shown to be associated
with a change in the invitro virulence of an isolate of
VHSV obtained from marine fish. Following the substitu-
tion, this isolate was able to replicate in primary cultures
of rainbow trout gill epithelial cells. While the parental
isolate was avirulent to rainbow trout, virulence of the
recombinant variant invivo was not analysed [29]. e
present study included two isolates originating from wild
Atlantic herring (1p49 and 4p168) that are known to be
avirulent for rainbow trout [8, 30]. Sequences of these
isolates were mapped against those of an isolate of VHSV
originating from the marine environment. However, this
reference isolate (FA281107) originated from sea-reared
rainbow trout and represents the first VHSV isolate of
genotype III pathogenic to rainbow trout [13]. In both
herring isolates, isoleucine (I) was detected at amino acid
position 1012 in the L protein within the list of quality-
filtered variants at a frequency higher than 99%, while the
marine reference isolate known to be pathogenic to rain-
bow trout showed a phenylalanine (F) at this position. To
further investigate this aspect, we aligned the 23 VHSV L
protein sequences currently available at NCBI, and found
a general correlation with the presence of the I1012F
substitution when the host is rainbow trout (Figure5).
ere were, however, four exceptions which included the
virulent SVA-1033 isolate and two plaque clones of the
same isolate and a plaque clone of another mixed isolate
(SVA-14). Based on typing using monoclonal antibodies,
the Swedish isolates SVA-14 and SVA-1033 contains a
mixed virus population (N. Lorenzen unpublished data)
so the available Sanger consensus sequences might there-
fore be misleading, and the reason why plaque-cloning
attempts have been performed. Our findings show the
same trend as the invitro findings reported by Kim etal.
[29], although it remains to be proven e.g. by reverse
genetics that the single substitution I1012F facilitates
an increase in the invivo virulence of a marine strain of
VHSV for rainbow trout. Statistical comparison of local
substitution rates across isolates reveal that the 1p49 iso-
late indeed behaved differently compared to the other
three isolates. e high number of nucleotide substitu-
tions might indicate a low and unstable state within the
fitness landscape. While it cannot be excluded that the
higher stability of 4p168 isolate could be due to passage
of the latter in one cell line only (BF2) compared to two
for 1p49 (BF2 and EPC), the difference might also reflect
different host/environmental conditions at the geo-
graphical sites of virus isolation. Both marine isolates
were obtained from tissue pools of herring. However,
while isolate 4p168 was from Skagerrak, a region with
stable high salinity, limited fish species diversity and low
prevalence of VHSV occurring in only a few species, iso-
late 1p49 was from the Baltic Sea, characterized by high
fluctuations in salinity and with rather high prevalence of
VHSV in a range of different fish species [31]. In terms
of the two freshwater VHSV isolates, both derived from
serious disease outbreaks in freshwater-farmed rain-
bow trout, the observed differences in variability might
also be related to host conditions. Although isolate DK-
3592B displayed slightly higher nucleotide variability fol-
lowing frequency filtering, this was not reflected at the
amino acid level, where isolate DK-9895174 displayed the
Figure5 Alignment of all L protein sequences available in GenBank. Amino acid position 1012 is highlighted by the vertical box.

Page 11 of 12
Schönherz et al. Vet Res (2016) 47:10
highest variability. When looking at the data after qual-
ity filtering but before frequency filtering, the higher
variability of DK-9895174 became even more prominent
(Table4). While isolate DK-3592b was derived back in
1985, where almost all Danish VHSV isolates belonged to
a single serotype (based on a plaque neutralization test),
isolate DK-9895174 was isolated 14 years later, when
serotyping data revealed a higher frequency of isolates
not neutralized by reference antibodies raised against the
original VHSV F1 isolate [32, 33]. To confirm whether
our NGS data reflects viral quasispecies related adapta-
tions to selective pressures set by the host availability or
immune defence as discussed above will require more
extensive analyses.
Errors may occur at any of the many steps involved in
deep sequencing of the evolving pathogen population,
including RNA extraction; reverse transcription; PCR
amplification of target regions; library preparation and
sequencing; read quality control and filtering and map-
ping. In a deep sequencing analysis of a mixture of HIV
clones, the estimated PCR chimera rate was 1.9% [34],
while the average error rates when using the Illumina
platform have been estimated at between 0.31 and 1.66%
[35]. Estimation of the actual accumulated error rate in
the performed experimental setting is not possible at this
point, a number of internal controls (e.g. sample repli-
cates) is needed for this. Instead we filtered using con-
servative quality parameters as well as ignored variants at
frequencies below 5%.
In the present study, 0.5–1.9 million×coverage was
obtained. However, during the process of data analy-
sis, we realized that a number of control samples are
needed in order to be able to determine that mutations
detected at extremely low frequencies (under 1%) repre-
sent true polymorphisms. Such controls could consist of
plaque-cloned viral samples and control plasmids. ese
would provide the baseline for the error rate due to the
methods used. Once determined, the established deep-
sequencing methodology would prove very powerful for
analysis of tissue samples from viral adaptation studies.
With respect to the actual sample source, the use of an
enrichment approach, either by hybridization [36, 37]
or through enriching for virus-specific RNA by deplet-
ing host genomic DNA and rRNA, might be relevant to
explore as well [38]. Nevertheless, since all virus isolates
had been propagated invitro, our results may not fully
reflect how VHSV acts as a quasispecies in vivo. is
will need further deep sequencing analyses of VHSV
genomes directly derived from infected fish.
e overall aim of this study was to better understand
the potential for evolution and host adaption of VHSV
by applying a deep-sequencing approach. Based on the
multiple and unique results obtained during this study,
we find that the established deep sequencing and applied
bioinformatics- and statistical analysis methods are valu-
able, and will probably be even more when used in future
invivo host adaption studies of VHSV.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
AAS prepared the RNA samples, generated the overlapping RT‑PCR amplifica‑
tions for library preparation and sequencing, performed the statistical analysis
and wrote significant parts of the manuscript, NL contributed to planning of
experiment and discussion of the results, BG assisted with the statistical analy‑
sis and discussion of results, BB contributed to the planning of the experiment
and discussion of the results, KEJ contributed to the planning of the experi‑
ment, performed the bioinformatic analysis and wrote significant parts of the
manuscript. All authors contributed to the manuscript. All authors read and
approved the final manuscript.
Acknowledgements
The authors thank Niels Jørgen Olesen (Technical University of Denmark) for
supplying the VHSV isolates, and Hanne Buchholz and Lisbeth Troels ( Techni‑
cal University of Denmark) for their excellent technical assistance. This work
was supported by Grants from The Danish Agency for Science, Technology
and Innovation (Grant Nos 09‑066097 and 09‑065033/FTP) and EU‑FP7 (PATH‑
SEEK, project reference 304875, and TargetFish, project reference 7311993).
Author details
1 Department of Molecular Biology and Genetics, Center for Quantitative
Genetics and Genomics, Aarhus University, Blichers Allé 20, P.O. Box 50,
8830 Tjele, Denmark. 2 Department of Animal Science, Aarhus University,
Blichers Allé 20, P.O. Box 50, 8830 Tjele, Denmark. 3 QIAGEN AAR, 8000 Århus,
Denmark.
Received: 2 February 2015 Accepted: 1 June 2015
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