De novo pre-miRNA prediction

Putative pre-miRNA-coding regions on viral genomes were predicted using VMir Analyser (version 2.3) [49]. The specific prediction parameters used were as follows: maximum size for calculation of score—100; maximum size for assigning assign subsidiary hairpins—50; maximum size for assigning repeat hairpins—50; minimum hairpin size—any; maximum hairpin size—any; minimum hairpin score—any.

Cell culture, virus infections and fish

The common carp brain (CCB) cell line [50] was cultured at 20°C in 75 cm² cell culture flasks using Eagle’s Minimum Essential Medium with Earle’s salts, supplemented with 25 nM NaHCO₃, 25 mM HEPES, 10% v/v Foetal Bovine Serum, 2 mM GlutaMAX Supplement, 1 x Non-essential amino acids and 250 units/ mL Penicillin/250 μg/ mL Streptomycin (Pen/Strep). Human Embryonic Kidney (HEK293) cells were grown in Dulbecco’s Modified Eagle medium (DMEM) supplemented with 10% FBS and Pen/Strep as above. CCBs were used for the culture of two separate CyHV-3 isolates, namely H361 and N076 (which were KHV-U/I and KHV-U/I/TUMST1 strain intermediates, respectively). To establish non-synchronous lytic infections, flasks freshly seeded with CCB cells were incubated for 24 hours (h) at 27°C (~70% confluent or 5.7–6.9 x10⁶ cells) and inoculated with 1 mL preparations of either H361 (2.7 x 10⁴ TCID₅₀/mL) or N076 (5 x 10⁵ TCID₅₀/mL) isolates and maintained at 22°C, thus allowing the virus to enter the lytic cycle.

All carp tissue samples used had been previously submitted to the Cefas laboratory (Weymouth, UK) for diagnostic investigation purposes. Fish that displayed clinical symptoms typical of KHVD were obtained from sites of suspected KHVD outbreaks in the UK during 2014. Gill tissue samples from uninfected common carp were obtained from a coarse fish farm in Southern England (see Dataset T in S1 File for more details relating to the origin of all carp samples). The facilities at Cefas where the uninfected fish were euthanized holds an establishment license under the Animal Scientific Procedures Act (ASPA) and are therefore licensed to carry out schedule 1 euthanasia. Carp were sampled following terminal anaesthesia with ethyl 4-aminobenzoate (Benzocaine, 150 mg/L) and destruction of the brain. Samples were screened for CyHV-3 using susceptible cell lines. Briefly, gill tissue samples (not exceeding 1g in total) from individual fish were placed in 9 mL of viral transport medium consisting of Glasgow modification of minimal essential medium (GMEM) supplemented with 10% new-born calf serum, 2 mM L-glutamine and 1% antibiotic + antimycotic solution (all from Sigma) at a temperature not exceeding 8°C. Tissues were homogenised with a pestle, mortar and sterile sand and re-suspended in the original transport medium. Gill homogenates were clarified by centrifugation at 2000 x g for 20 minutes (min), clarified supernatants were filtered through a 0.45 mm disposable cellulose acetate filter unit (Minisart, Sartorius) and 200 μL aliquots of filtrate were used to inoculate koi fin (cultured as per [39]) and CCB cell monolayers in 25 cm² tissue culture flasks (Falcon). All tissue homogenates, including those prepared in the same way from uninfected control fish, were screened for CyHV-1, CyHV-2 and CyHV-3 using a PCR targeting the DNA polymerase gene in all 3 viruses [51], followed by sequencing of PCR products. This confirmed the presence of CyHV-3 in samples from KHVD fish, its absence in negative control fish and the absence of CyHV-1 and CyHV-2 in all samples.

Nucleic acid isolation and size fractionation

DNA was extracted from all samples using the DNeasy Tissue Kit (Qiagen) as per manufacturer’s instructions. RNA was extracted from CCB cells (infected/non-infected) using the miRNeasy Mini Kit (Qiagen) following the manufacturer’s protocol. Total RNA was prepared from carp gill tissue homogenates by adding 250 μL of homogenate to 1 mL Trizol (Life Technologies) followed by extraction as per manufacturer’s protocol. Total RNA samples (or ligation products; see cDNA library preparation below) were fractionated by polyacrylamide gel electrophoresis (PAGE; 15% Polyacrylamide/7M Urea). Molecular weight estimations were made with the aid of pre-stained RNA size markers (Small RNA Plus, Biodynamics Laboratory Inc.) and gel sections containing short RNAs (or ligation products) were excised accordingly. RNA was extracted from gel slices as described elsewhere [52] and eluted in 100 μL of 10 mM Tris-HCl pH 7.5. Extracted RNA was concentrated as required by ethanol (EtOH) precipitation by adjusting to 0.3 M NaOAc, supplemented with 5 μg/ mL linear acrylamide (Ambion) followed by addition of MBG EtOH to 75% v/v and incubation at -80°C for ≥2 h. RNA pellets were then collected by centrifugation, washed with 80% MBG EtOH, air dried and resuspended in 10 mM Tris-HCl pH 7.5.

cDNA library preparation and small RNA deep sequencing

Two separate CyHV-3 in vitro lytic infections were prepared using the H361 and N076 isolates and total RNA was extracted at 18 and 14 days post-infection (dpi), respectively, followed by RNA size fractionation and isolation of 17–25 nt RNA. Two small RNA deep sequencing libraries were prepared from these miRNA-enriched samples, using an adaptation of the Illumina Small RNA v1.5 protocol (see Dataset A in S1 File for adaptors and primers used). There were four stages to this (i) 3′ Adaptor ligation: For each sample, 70–200 ng of 17–25 nt RNA was mixed with 10 pMol of 3′ adaptor in a total volume of 10 μL, the mixture was heated to 70°C for 2 min and placed on ice for 1 min. This was then added to 10 μL 3′ end ligation reaction mix consisting of 20 U/ μL T4 Ligase 2 (NEB), 2x T4 Ligase 2 Buffer, 8mM MgCl₂, 20% v/v DMSO, 14.5% v/v PEG 8000 (Sigma) and 4 U/ μL RNasin (Promega) (giving a total reaction volume of 20 μL), followed by incubation of the ligation reaction at 22°C for 2 h. The resulting 3′ adaptor ligation products (~34–42 nt) were PAGE purified and resuspended in 8 μL of 10 mM Tris-HCl. (ii) 5′ Adaptor ligation: The 3′ adaptor ligation products were then mixed with 10 pMol of 5′ adaptor in a total volume of 10 μL, the mixture was heated to 70°C for 2 min and placed on ice for a further 1 min. This was then added to 10 μL 5′ end ligation reaction mix consisting of 2 U/ μL T4 Ligase 1 (NEB), 2x T4 Ligase I Buffer, 20% v/v DMSO, 22.5% v/v PEG 8000 (Sigma), 4 U/ μL RNasin (Promega) (giving a total reaction volume of 20 μL), followed by incubation at 37°C for 1 h. The resulting 5′ adaptor ligation products (~60–68 nt) were isolated and resuspended as described for the 3′ ligation products above. (iii) Reverse transcription (RT): The 5′/3′ adaptor ligation products were used as templates for RT. To this end, these were mixed with 10 pMol of Library prep. RT Primer in a total volume of 10 μL and the mixture was heated to 70°C for 2 min and placed on ice for a further 1 min. This was added to 10 μL RT mix consisting of 2 mM dNTPs, 20 U/ μL SuperScript III RT (Invitrogen), 2x SS III RT Buffer, 0.10 M DTT (Promega), 4 U/ μL RNasin (Promega) (giving a total reaction volume of 20 μL). The RT reaction was carried out using the following thermocycler programme: 55°C for 90 min and 70°C for 15 min. (iv) PCR: 20 μL of RT product was used as input for PCR. This was made up to 25 μL with H₂O and added to 25 μL RT mix consisting of 2X Phusion HF Buffer, 0.4 μM each of Forward Primer and Reverse Primer, 500 uM dNTPs 0.04 U/ μL Phusion DNA Polymerase (Finnzymes) (giving a total reaction volume of 50 μL). Thermocycling conditions were: 98°C for 30 seconds (s) followed by 12 cycles of 98°C for 10 s, 60°C for 30 s, 72°C for 15 s and a final elongation step of 72°C for 10 min. The resulting dsDNA libraries were sequenced using an Illumina Genome Analyser in the TrinSeq core facility (Trinity College Institute for Molecular Medicine, St James’s Hospital, Dublin, Ireland).

Analysis of small RNA deep sequencing data and pre-miRNA identification

Fastaq files were collapsed into sequence tag files and intact adaptor sequences were trimmed from the 3′ ends. Pre-miRNA candidates were then identified using two different methods. Firstly, a ‘non-automated approach’ was taken as follows: collapsed sequence tags were given unique IDs (incorporating read count information), converted into FASTA format using tabtofasta script (tabtofasta.pl http://nebc.nerc.ac.uk/tools/code-corner/scripts/sequence-formatting-and-other-text-manipulation) and mapped to the CyHV-3 genome (GeneBank Accession No. DQ657948.1) and CyHV-3 open reading frame (ORF) sequences separately using SeqMap [53] (version 1.0.12). Mapped transcript reads were arranged in an Excel file according to DNA strand and genome position and details of any ORF overlap were noted. The same mapped data was then un-collapsed based on read count information using the FASTX uncollapser script (FASTX-Toolkit version 0.0.13 [http://hannonlab.cshl.edu/fastx_toolkit/index.html]) and visualised using SeqMonk (version 0.16.0 [http://www.bioinformatics.babraham.ac.uk/projects/seqmonk]). ‘Excel-arranged’ and ‘SeqMonk-visualised’ mapped data were used in unison to identify loci that were specifically enriched for small RNA reads. Within such loci, viral transcripts showing no ORF overlap and read counts ≥10 were selected for cross-referencing with VMir-predicted pre-miRNA loci. High-probability pre-miRNA candidates were then identified based on compliance with all of the following nine criteria: (i) presence of small RNAs (i.e. putative miRNAs) mapping to the upper stem of the pre-miRNA candidate hairpin structure (either 5′ or 3′ arm) adjacent to the terminal loop (additional transcripts mapping to the stem but adjacent to putative miRNAs were recorded as moRNAs); (ii) at least one of the putative miRNAs mapping to a given pre-miRNA candidate had a read-count of ≥10 in at least one infection experiment; (iii) the extended pre-miRNA candidate sequence did not overlap with any known CyHV-3 ORFs; (iv) each putative miRNA was accompanied by miRNA/miRNA* reads from the opposite arm of the pre-miRNA in both infection experiments; (v) on each pre-miRNA arm, the same read (or its isomiR, differing by a maximum of +/- 1 nt at the 5′ end) was identified as the putative miRNA (the dominant read at that locus) in both infection experiments; (vi) the pre-miRNA candidate itself was classified as a real pre-miRNA by both MiPred [54] and CSHMM [55]; (vii) the pre-miRNA candidate structure had a minimum free energy (MFE) < -25 kcal/mol that was statistically significant, as determined by MiPred; (viii) the alignment signatures of putative miRNAs/moRNAs and isomiRs/isomoRs to pre-miRNA candidates was consistent with the currently accepted model of miRNA biogenesis outlined elsewhere [56] and (ix) reads mapping to pre-miRNA candidate loci collectively formed discrete stacks and discrete regions of read enrichment relative to flanking genomic loci. A second ‘automated approach’ was also taken to identify pre-miRNA candidates: the same collapsed sequence tag and mapped data was reformatted appropriately for use as input for the miRDeep2 pipeline (version 0.0.5) [57,58] and MIREAP (version 0.2) [59]. High-probability pre-miRNA candidates were only described as such if either identified independently by both programmes or multiple times by the same program. Further to the above ‘automated’ and ‘non automated’ approaches, the complete set of isomiRs associated with each high-probability CyHV-3 miRNA were analysed in terms of 5′ and 3′ end heterogeneity (with isomiR sets from different infection experiments analysed separately). Individual isomiR sets were compared using three different ratios, Ratio-1 (ratio of the number of unique isomiRs offset at the 3′ end to those offset at the 5′ end), Ratio-2 (ratio of the average 3′ offset for each isomiR to the average 5′ offset) and Ratio-3 (ratio of combined read count of all isomiRs offset at the 3′ end to that of all isomiRs offset at the 5′ end)

DNA Microarray analysis

DNA oligonucleotide probes were designed using the VMir probe design tool. Each probe was complementary to a region spanning 30 nt of the corresponding stem starting from the loop apex (i.e. complementary to putative miRNAs). Probes targeting low and high probability CyHV-3 pre-miRNA candidates identified using the ‘non-automated’ approach and the 2,893 highest-scoring VMir predicted CyHV-3 pre-miRNAs were included on the array. This strategy facilitated the probing of (i) highly abundant small RNAs (read count >10) mapping to either high or low-probability pre-miRNAs (ii) small RNAs that also mapped to either high or low-probability pre-miRNAs but were disregarded due to low abundance and (iii) theoretical miRNAs from other pre-miRNA predictions that were not represented in the sequencing data. The VMir probe design tool was also used to design associated scrambled and mismatched (by 1 nt every 5th nt) control probes for all miRNA probes. Each pre-miRNA also had associated flanking controls (targeting sequences 25 nt upstream and 25 nt downstream of the 5′ and 3′ miRNA probe target sequences respectively). Probes were also designed to target an exogenous spike-in control (a synthetic RNA oligonucleotide based on Arabidopsis thaliana miRNA ath-miR15.6g, miRBase accession MIMAT0001012) and several endogenous positive controls (host miRNA sequences ccr-miR-21, ccr-miR-22a, ccr-let-7a and ccr-et-7b; miRBase accessions MIMAT0026267, MIMAT0026275, MIMAT0026189 and MIMAT0026190 respectively). Custom oligonucleotide arrays (30K format) were synthesised by MYcroarray (Ann Arbor, MI) with array positions customised for use with a Tecan HS 400 Pro Hybridization Station. Each probe was also synthesized with a 30 nt spacer (Poly Ts) between the 3′ end and the array surface in order to eliminate the potential for reduced hybridization accessibility at extreme 3′ ends [60]. Two separate RNA size fractions, F1 (17–25 nt) and F2 (26–35 nt), were isolated from total RNA derived from CyHV-3 infected CCB cells, in this case the total RNA used was from the same H361 sample that was also used for small RNA deep sequencing. An additional RNA size fraction, F3 (17–25 nt), was isolated from non-infected CCB cells. Approximately 400 ng RNA from each sample (along with 200 aMol of the exogenous control) was non-enzymatically labelled and purified using the Label IT miRNA Labeling Kit Version 2 (Mirus Bio) according to the standard manufacturer’s protocol with modifications: 5x final concentration of Label IT Reagent was used so as to achieve a maximal labelling density and labelled RNA was eluted in 45 μL Elution buffer following purification. Forty microlitres of labelled RNA was added to 100 μL of hybridization buffer (final concentration of 2.5x SSPE, 20% Formamide, 0.01% Tween-20, 0.01 mg/mL Acetylated BSA, 6.5% w/v Dextran Sulphate and 2% SDS), this was then heated to 65°C for 5 min, kept on ice for 5 min and hybridized to the array at 42°C for 8 h in a Tecan HS 400 Pro Hybridization Station. Arrays were subsequently washed therein with 2x SSPE, 0.1% SDS at 30°C for 5 min, 1x SSPE at 22°C for 5 min and finally with 0.1x SSPE at 22°C for 3 min followed by Nitrogen drying. Arrays were scanned using a NovaRay Scanner (Alpha Innotech) with 8 s exposures at 5 μm resolution. Raw fluorescence data was imported into VMir and background values were established for each hybridization based on the median signals obtained from control probes. VMir was also used to process signals from probes targeting miRNAs and to correct them for potential non-specific hybridization where applicable using signals from corresponding control probes. Cut-off values for corrected miRNA probe signals [expressed as multiples of background (MOB)] were established for each hybridization based on the intrinsic variability of the signals from the control probes. This was done independently of VMir by calculating their average absolute deviation from the median value (background) and setting the cut-off values for corrected miRNA probe signals as one absolute deviation above the background in each hybridization. Only corrected miRNA probe signals that were above these cut-off values were scored as positive. MiRNA probes displaying positive signals using labelled RNA from F1 were provisionally attributed to CyHV-3 miRNAs if the same probes showed no corresponding positive signals with the F2 and F3 samples.

Northern Blotting

Ten microgram samples of total RNA were fractionated by PAGE as described above, transferred to a BrightStar-Plus Positively Charged Nylon Membrane (Ambion) by electroblotting and crosslinked in an oven at 80°C for 4 h. Probes consisted of custom 5′ biotin labelled DNA oligos (IDT) complementary to high probability CyHV-3 miRNAs MR5057-miR-3p, MD11776-miR-3p, MD1111-miR-3p and MR5075-miR-3p (based on sequences from the H361 infection) and the host miRNA ccr-let-7a-5p. Lyophilised probes were resuspended at 1 nmol/μL. Membranes were pre-hybridized in 9 mL ULTRAhyb-Oligo buffer (Ambion) at 37°C for 30 min with gentle rocking. Prior to hybridization, 1 μL of each probe stock was diluted in 1 mL of pre-hybridization buffer, added to the blot and incubated at 37°C for 8–12 h with gentle rocking. This was followed by two 5 min washes with 20 mL 2X SSPE/0.1% SDS at room temperature and one 2 min wash with the same buffer at 37°C. Hybridized biotin labelled probes were detected using the BrightStar BioDetect Kit (Ambion) according to manufacturer’s protocol. Chemiluminescent signals were visualised using a BioLuminizer (CAMAG).

Stem-loop reverse-transcription real-time PCR (RT-qPCR) and qPCR assays

Custom stem-loop RT primers and corresponding PCR primers/probes were designed using the Custom TaqMan Small RNA Assay Design Tool (Life Technologies). The CyHV-3 high probability miRNA sequences targeted were based on those identified in the H361 infection. The C. carpio miRNA ccr-let-7a-5p was detected using a hsa-let-7a-5p TaqMan MicroRNA Assay (Life Technologies, Assay ID 000377). RT and PCR steps for all TaqMan Small RNA Assays were done using MultiScribe RT and TaqMan Universal PCR Master mixes, respectively, according to the manufacturer’s protocol (TaqMan Small RNA Assay Protocol, Life Technologies, Rev. E01/2011). PCR was done using an Applied Biosystems 7500 instrument and associated software (version 2.0.1). The resulting normalised raw florescence data (Rn values) was imported into LinReg-PCR [61] for calculation of PCR efficiency and subsequent calculation of expression levels. Only samples showing signals in all replicates were considered valid. Where appropriate, CyHV-3 miRNA expression levels were normalised to levels of ccr-let-7a-5p in the same samples. QPCR assays used to target both CyHV-3 and carp genomic DNA were carried out as described elsewhere [45]. Viral DNA levels were expressed as linear scale values (using the formula 2^-Ct) and normalised to corresponding levels of carp genomic DNA in the same samples, allowing comparison of relative levels between samples.

Identification of pre-miRNA/miRNA homologues and miRNA target sites

CyHV-3 pre-miRNA sequences were aligned to the CyHV-1 (GeneBank Accession No. JQ815364.1) and CyHV-2 (GeneBank Accession No. JQ815363.1) genomes using the Basic Local Alignment Search Tool (BLAST) [62]. CyHV-3 pre-miRNA input sequences did not extend beyond the boundaries of the miRNAs (as only these regions are likely to be conserved [63]), except where moRNAs were present on the same pre-miRNA. All C. carpio and viral miRNAs were retrieved from miRBase (Release 19) in FASTA format, converted to tab delimited format using the fastatotab script (fastatotab.pl http://nebc.nerc.ac.uk/tools/code-corner/scripts/sequence-formatting-and-other-text-manipulation), and seeds (positions 2–7) were isolated using Excel by inserting column breaks between characters 1–2 and 8–9. Identifiers and seeds were converted back to FASTA using the tabtofasta script and aligned to CyHV-3 miRNA seeds using SeqMap (max. 1 mismatch). CyHV-3 3′ untranslated regions (UTRs) downstream of each CyHV-3 ORF were individually retrieved following importation of the CyHV-3 genome (GeneBank Acession No. DQ657948.1) into SnapGene Viewer (v2.3). Candidate CyHV-3 mRNA targets were identified using the TargetScan pipeline (version 6.0) [64–66] and PITA (version 6) [67].

Vectors, western blots and luciferase reporter assays

The vector pCyHV-3-rdUTPase was designed to encode a recombinant N-terminally epitope-tagged CyHV-3 deoxyuridine triphosphatase (rdUTPase) expressed from ORF123 and linked to its own 3′ UTR. To this end a DNA sequence was designed with the following layout: Kozak sequence/Hexahistidine tag/spacer/MYC tag/spacer/ORF123/3′ UTR. Thus, the tag/spacer-coding sequence 5′-GCCACCATGGGCCATCACCATCACCATCACAATAGCGCCGTCGACGAACAAAAACTCATCTCAGAAGAGGATCTGAATAGCCATACCGGT3′ was synthesized as an in-frame fusion with a sequence corresponding to CyHV-3 genome co-ordinates 216,593–217,478 inclusive (i.e. from the second codon of ORF123 to the base immediately upstream of the transcription termination signal). The above DNA sequence was directionally cloned between the HindIII and Xho1 sites of the expression vector pcDNA3.1(+) (Life Technologies). pLS-123-3′ UTR contained the 3′ UTR from CyHV-3 ORF122/ORF123 inserted immediately downstream of the luciferase coding sequence in the 3′ UTR reporter vector pLightSwitch-3′ UTR (SwitchGear Genomics). This construct was generated by synthesizing and directionally cloning a 67 bp fragment (corresponding to CyHV-3 genome co-ordinates 217,412–217,478 inclusive) between the Xba1 and Xho1 sites of pLightSwitch-3′ UTR. A corresponding plasmid, pLS-123-3′ UTRmut, was also made in which the MR5057-miR-3p seed region match of the predicted miRNA target site (5′GCAATT3′) had been eliminated (changed to 5′CGTACA3′). Constructs were verified by DNA sequencing. Transfections were done using Lipofectamine 2000 (Life Technologies) according to the manufacturer. A custom synthesised MR5057-miR-3p mimic [5′-AAAUUGCGGCCGCUGUCGACGA-3′; Qiagen (miScript miRNA Mimics)] and the Sigma MISSION miRNA Negative Control 1 (HMC0002) were used in transfections (Lipofectamine 2000; Life Technologies). Following transfection, HEK293 cells were processed and analysed for luciferase activity using LightSwitch Luciferase Assay Reagent (Switchgear Genomics, LS010) according to the manufacturer. Statistical analysis was done using the two-tailed Welch's t-test [68]. Western blots were performed as described elsewhere [69] using Anti-MYC tag antibody (Origene Technologies, TA150121) and anti-β-actin antibody (Abcam, A1978).

De Novo prediction of pre-miRNA-coding sequences on the CyHV-3 genome

In order to investigate its miRNA-coding potential, the CyHV-3 genome (Gen Bank Ac. DQ657948.1) was first analysed using VMir. Six other viral genomes known to collectively encode 94 pre-miRNAs were also analysed so as to establish a set of relevant minimum cut-off values to be used for filtering out predictions that were less likely to represent genuine CyHV-3 pre-miRNAs. Main-Hairpins (MHPs) corresponding to 92 of these known pre-miRNAs were identified using this approach (Dataset B in S1 File). Prediction statistics from these six viral genomes were comparable to those from the CyHV-3 genome and two other viral genomes that have been deemed unlikely to encode pre-miRNAs (Dataset C in S1 File and Dataset D in S1 File). The application of relevant filtering criteria (Minimum MHP size: 50 nt, Minimum Score: 123 and Minimum Window Count: 39; see Dataset E in S1 File) resulted in the elimination of >99% of all predictions from viral genomes analysed, although it still permitted the correct identification of 71/92 known pre-miRNAs. Consequently, 236 CyHV-3 pre-miRNA predictions (excluding repeats) remained after filtering (Table 1). Taking CyHV-3 genome size into account, this represented 0.80 CyHV-3 MHPs per kilobase (kb), this was similar to that of the 6 other viral genomes known to encode pre-miRNAs which had a mean value of 0.76 MHPs per kb post-filtering (Table 1). These values were notably different to those from two other viral genomes predicted to lack pre-miRNAs, which showed a mean value of 0.47 MHPs per kb when analysed using the same criteria (Table 1). Furthermore, out of the remaining 226 CyHV-3 pre-miRNA predictions, 164 (69.5%) of them occurred outside protein-coding regions (i.e. excluding introns) (Dataset F in S1 File) where pre-miRNA genes are more likely to reside [5,6]. In summary, this data gave a strong indication that the CyHV-3 genome had the potential to encode pre-miRNAs.

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Table 1. VMir pre-miRNA predictions made from herpesvirus genomes.

https://doi.org/10.1371/journal.pone.0125434.t001

Identification of high-probability pre-miRNAs following deep sequencing of small RNAs from in vitro CyHV-3 infections

In order to experimentally determine if CyHV-3 encoded its own miRNAs, productive/lytic infections of CCB cells were first set up using the two CyHV-3 isolates H361 and N076. Size-selected small RNAs (17–25 nt) were then prepared from infected cells and analysed by deep sequencing, yielding a total of 12,016,246 (H361) and 23,903,786 (N076) small RNA sequence reads. All unique sequence tags obtained from both infections are available for download via the Dryad Digital Repository (http://dx.doi.org/10.5061/dryad.3c7m4). CyHV-3-derived reads accounted for 1.8% (212,399) and 6.7% (1,601,110) of the total from the H361 and N076 infections, respectively. SeqMonk coverage plots of all reads mapping to the CyHV-3 genome showed that most of the CyHV-3 genome was transcribed and that there was a high degree of congruency between the two infections in terms of both the qualitative and quantitative profiles observed (S1 Fig). The 17–25 nt RNA samples were expected to be specifically enriched for miRNAs and indeed the most abundant small RNAs present were found to be evolutionarily conserved host miRNAs. Similarly, CyHV-3 encoded miRNAs were expected to be among the most abundant viral reads and thus those with read counts ≥10 (median read count was 1) were classified as highly abundant and therefore of particular interest. As CyHV-3 pre-miRNAs were more likely to be derived from non-protein-coding regions, only abundant reads that mapped to such loci were considered initially. Although, such reads only represented a small subset of unique CyHV-3 reads (Fig 1A and 1B) they actually accounted for the vast majority of CyHV-3 reads, in terms of total read counts (Fig 1C and 1D).

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Fig 1. Small RNA sequence reads derived from CyHV-3 during in vitro infection of CCB cells.

(A) and (B) The numbers and general genomic locations of low- and high abundance unique viral reads determined following deep sequencing of small RNAs isolated from CyHV-3-infected CCB cells using viral isolates H361 and N076. (C) and (D) Analysis of the combined read counts of low- and high-abundance unique CyHV-3 reads sequenced from both infections.

https://doi.org/10.1371/journal.pone.0125434.g001

Taking a non-automated approach, reads were first cross-referenced with the output from VMir. This resulted in the identification of 21 pre-miRNA-like candidates (Dataset G in S1 File) that complied with points (i)-(iv) of the pre-miRNA criteria applied in this study, however only six of these loci also complied with points (v)-(ix). The evidence suggested that these six candidates could give rise to twelve mature miRNAs, with further indications that four of them could also give rise to at least 7 potential moRNAs (Fig 2 and Table 2). With the exception of pre-miRNA candidate MD1111, the miRNA/miRNA* patterns remained the same in both H361 and N076 infections in terms of the pre-miRNA arms from which they were derived. The sequences of 9/12 of these putative miRNA/miRNAs* were identical in both infections, and of the three that differed, only one showed a change at the 5′ end (differing by 1 nt) (Table 2). In contrast only 13/30 sequences representing putative miRNAs from the 15 low-probability pre-miRNAs were identical in both infections, and 10/30 were different at their 5′ ends, in most cases differing by more than 1 nt (Dataset H in S1 File). All high-probability pre-miRNAs were classified as ‘real’ by both pre-miRNA classifiers (MiPred and CHSMM) and had MFE values <−25 kcal/mol that were statistically significant (p <0.05) (Dataset I in S1 File), a feature which is key in distinguishing between pre-miRNA-like and non-pre-miRNA-like hairpins [54,71]. In contrast only 4/15 low-probability pre-miRNAs were classified as real by both classifiers and 6/15 did not have MFE values <−25 kcal/mol (Dataset I in S1 File).

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Fig 2. Genomic locations and structures of high probability CyHV-3 pre-miRNA identified using a non-automated approach.

(A) Genomic locations of pre-miRNAs; all features on the forward strand are shown in black and those on the reverse strand are in grey. (B) Structures (predicted by VMir) of the high-probability CyHV-3 pre-miRNAs shown in (A). The 5′/3′ arm miRNAs and 5′/3′ arm moRNAs are highlighted in red/yellow and green/purple respectively and represent the dominant reads at these loci in the H361 infection, variants (if any) identified in the N076 infection are given in Table 2. MiRNA* reads are indicated with an asterisk. MiRNAs partially overlapping with moRNAs are underlined.

https://doi.org/10.1371/journal.pone.0125434.g002

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Table 2. Details of putative CyHV-3 miRNAs/moRNAs from high-probability pre-miRNAs using a non-automated approach.

https://doi.org/10.1371/journal.pone.0125434.t002

Assessment of the small RNA alignment signatures mapping to all 21 candidates revealed a stark contrast between high and low-probability pre-miRNAs. In both infections, low-probability pre-miRNAs (except MR6201) had signatures that were not consistent with the currently accepted model of miRNA biogenesis [56], and instead consisted mainly of randomly overlapping reads, offset across the predicted pre-miRNA sequence (Dataset J in S1 File). In contrast, the high-probability pre-miRNAs and MR6201 showed strong miRNA-like alignment signatures in both infections with groups of reads arranged into individual “stacks” representing either putative miRNAs or moRNAs (and associated isomiRs/isomoRs). The alignment signature for MD11776 was particularly consistent with the ideal miRNA alignment signature. In addition, transcripts were observed from five distinct regions of the MD11776 locus, representing the two miRNAs, two moRNAs and a terminal loop sequence resembling what is described as a “five-phased-precursor” [20,72,73] (Dataset J in S1 File). MR6201 was the only low-probability pre-miRNA locus to display a miRNA-like alignment signature, however reads mapping to this and other low-probability pre-miRNA loci did not form discrete read stacks or discrete regions of read enrichment relative to flanking genomic regions (Dataset K in S1 File). In contrast, such features were present in both infections at all six high-probability pre-miRNA loci (Dataset K in S1 File), with the exception of reads mapping to MD11410 in the N076 infection. However, despite not being enriched, these reads derived from MD11410 were nonetheless present in greater numbers than those derived from flanking genomic regions (Dataset K in S1 File). Although located on non-coding parts of the genome, all fifteen low-probability pre-miRNA loci in fact mapped to 3′ UTRs of upstream ORFs. This suggested that some of their associated reads, which often extended beyond the boundaries of pre-miRNA loci, were in fact derived from degraded protein-coding transcripts that were initiated further upstream (Dataset K in S1 File).

In contrast to the practical limitations associated with the non-automated approach to miRNA identification, the use of an automated approach, in this case a combination of miRDeep2 and MIREAP, facilitated the analysis of the entire deep sequencing dataset. Thus, this potentially allowed the identification of other viral pre-miRNAs that produced miRNAs of lower abundance and/or that mapped to within protein-coding regions. Out of a total of 76 potential pre-miRNAs identified using this automated approach (results detailed in: Dataset L in S1 File, Dataset M in S1 File, Dataset N in S1 File, Dataset O in S1 File, Dataset P in S1 File), only seven of them were independently identified by both programmes, and of these, only one (MR17383) was located in a protein-coding region, however it was not classified as a real pre-miRNA by either of the pre-miRNA classifiers. Interestingly, of the remaining six, five had also earlier been classified as high-probability pre-miRNAs using the non-automated approach (Table 3). The same five also represented the top ranked miRdeep2 intergenic pre-miRNA candidates from both infections, based on miRdeep2 scores (Dataset L in S1 File). The fact that both the non-automated and automated approaches were largely in agreement supports the criteria used to eliminate low-probability pre-miRNA candidates in both cases. The only other pre-miRNA candidate from a non-coding region to appear in multiple sets of results was MD11704 (Dataset M in S1 File). Although it occurs near MD11776 (<1 kb, Dataset O in S1 File) and largely complied with the pre-miRNA identification criteria, it was only identified by MIREAP alone (Table 3).

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Table 3. Summary of high-probability CyHV-3 pre-miRNAs Identified using both approaches.

https://doi.org/10.1371/journal.pone.0125434.t003

In summary, using both approaches, a total of 8 high probability pre-miRNA candidates were identified from the deep sequencing data (Table 3). Although MR6201 was independently identified by both automated methods, based on results from the non-automated approach, it remains possible that reads from this locus were in fact degraded fragments of the ORF50 mRNA 3′ UTR (Dataset K in S1 File). Notably, reads mapping to the MD11704 loci were too low in read count (Dataset O in S1 File) to be considered a high-probability candidate in this study. For these reasons, these loci are therefore classed as putative pre-miRNAs until further experimental evidence suggests otherwise. Ultimately, six of the candidates in Table 3 were scored as high-probability CyHV-3 pre-miRNAs as they fully complied with pre-miRNA identification criteria.

MiRNAs from the six high-probability pre-miRNAs were also accompanied by isomiRs (Dataset J in S1 File). As isomiRs generally display a greater degree of 3′ end heterogeneity [74–77] we therefore compared the extent of 3′ versus 5′ end heterogeneity within each isomiR. This analysis revealed that (i) most isomiR sets were comprised mainly of isomiRs offset at the 3′ end (Ratio-1), (ii) isomiR sets consisted of isomiRs that, on average, had greater 3′ end offsets (measured in nts) (Ratio-2) and (iii) most isomiR sets had greater numbers of isomiRs offset at the 3′ end, indicating that 5′ end processing was generally more consistent (Ratio-3). Overall, these results indicated a general bias towards greater 3′ end heterogeneity within individual isomiR sets (Table 4; Dataset Q in S1 File and Dataset R in S1 File).

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Table 4. IsomiR end heterogeneity analysis for miRNAs from high-probability CyHV-3 pre-miRNAs.

https://doi.org/10.1371/journal.pone.0125434.t004

Detection of CyHV-3 miRNAs by DNA microarray analysis

The same RNA from the H361 infection was used in a DNA microarray hybridization experiment. Size-selected small RNAs from both miRNA-inclusive (17–25 nt) and non-inclusive (26–35 nt) size ranges (referred to as F1 and F2 respectively) were thus analysed using probes targeting putative miRNAs from both high and low-probability pre-miRNAs. MiRNA-inclusive size selected RNA (F3) from non-infected CCB cells was also analysed as a negative control. Known host miRNAs were detected in F1 and F3 but not in F2 and an exogenous spike miRNA control was detected in all samples (Dataset S in S1 File). Putative miRNAs mapping to two out of the six high-probability pre-miRNAs (MR5057 and MD11776) gave positive signals in F1 but not in F2 or F3 (Dataset S in S1 File). MiRNAs from both arms of these two pre-miRNAs were detected and their relative levels generally concurred with those determined from deep sequencing, although this data suggested that MR5057-miR-5p was present at higher levels than MD11776-miR-5p (Fig 3). Notably, 10/15 low-probability pre-miRNAs identified using the non-automated approach gave positive signals in F1 but 12/15 showed positive signals in F2 implying that they were degradation products of overlapping transcripts, most likely 3′ UTRs (Dataset S in S1 File).

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Fig 3. Comparison of the levels of miRNAs derived from CyHV-3 pre-miRNA candidates MR5057 and MD11776.

(A) Small RNA deep sequencing read counts and (B) DNA microarray miRNA probe hybridization signal intensities corrected and expressed as multiples of background (MOB). Data from both (A) and (B) were obtained using size-selected miRNA-inclusive RNA (F1 fraction; 17–25 nt) from the same in vitro infection (CyHV-3 H361 isolate). No corresponding hybridization signals were detected in miRNA non-inclusive RNA (F2 faction; 26–35 nt) from the same infection or in miRNA inclusive RNA (F3 fraction, 17–25 nt) from non-infected cells (Dataset S in S1 File). Error bars represent the data range.

https://doi.org/10.1371/journal.pone.0125434.g003

DNA microarray analysis was also used to test if ‘highly abundant’ small RNAs were in fact merely over-represented in the data due to the enzymatic bias. The small RNAs derived from all pre-miRNA candidates (Dataset G in S1 File) were among the most abundant identified in the deep sequencing data from the H361 infection. The 12 of these that were detected in F1 also gave some of the strongest hybridization signals with an average percentile rank of 97 (Dataset S in S1 File). As the hybridization samples were processed in the absence of any enzymatic bias, this verified that most of these were indeed highly abundant within the sample. Other VMir miRNA predictions in which related small RNA read counts may have been under-represented or absent due to enzymatic bias were also examined. Although miRNA-like signals (i.e. F1 +ve, F2 −ve and F3 −ve) were identified among these, it was found that either the corresponding sequencing data and/or predicted pre-miRNAs at such loci displayed gross non-compliance with the pre-miRNA identification criteria or the signals were caused by array surface artefacts. In summary, the DNA microarray data agreed well with the deep sequencing output regarding two high-probability pre-miRNAs (MR5057 and MD11776). It also validated the criteria that were applied to eliminate low-probability pre-miRNA candidates following deep sequencing. Furthermore, deep sequencing data did not corroborate with DNA microarray evidence regarding the existence of additional potential CyHV-3 pre-miRNAs.

Detection of MR5057-miR-3p by Northern Blotting in CyHV-3-infected CCB cells

Expression of the four most abundant miRNAs was also investigated by Northern blotting. Total RNA was prepared at 12 dpi from a second in vitro lytic infection using the CyHV-3 N076 isolate and also from uninfected CCB cells as control. Hybridization using a probe for MR5057-miR-3p resulted in the detection of two discrete transcripts in RNA prepared from the CyHV-3 infection but not from the uninfected control (Fig 4). These corresponded to the expected sizes for the mature miRNA (22 nt) identified by deep sequencing and the pre-miRNA MR5057 (56 nt) produced after Drosha processing (size inferred from the positions of the 5′ end of MR5057-miR-5p and the 3′ end of MR5057-miR-3p). Detection of the host miRNA ccr-let-7a confirmed that RNA quality was comparable in both samples. This experiment verified that MR5057-miR-3p occurs as a fully processed miRNA from CyHV-3 pre-miRNA MR5057. Using the same approach, we were unable to detect MD11776-miR-3p, MD1111-miR-3p and MR5075-miR-3p or CyHV-3-specific bands corresponding to the expected size of their respective pre-miRNAs.

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Fig 4. Detection of CyHV-3 miRNA MR5057-miR-3p and its pre-miRNA by Northern blotting.

Total RNA was prepared from CCB cells following an in vitro lytic infection with the CyHV-3 N076 isolate (+) and from uninfected control cells (-). Replicate blots were probed separately for MR5057-miR-3p (left), and the host miRNA ccr-let-7a-5p which served as a loading control (right). The location of size markers (in nucleotides) is indicated on the left; black arrows point to bands corresponding to MR5057-miR-3p and ccr-let-7a-5p (both 22 nt); the open arrow points to the band corresponding to the MR5057 pre-miRNA (56 nt).

https://doi.org/10.1371/journal.pone.0125434.g004

Detection of CyHV-3 miRNAs by Stem-Loop RT-qPCR following productive CyHV-3 infections in vitro and in vivo

MiRNAs often escape detection using hybridization technologies that do not involve pre-amplification of the target sequence. Stem-loop RT-qPCR is a sensitive technique for the specific detection of mature miRNAs [78] and it was therefore used to re-test the same RNA from the H361 infection used for deep sequencing. Custom stem-loop RT-qPCR assays were designed to detect the dominant miRNAs from the 6 high-probability CyHV-3 pre-miRNAs. Using these assays, all target miRNAs were detected in the H361 sample and the hierarchy in expression levels was generally comparable to that observed in the deep sequencing data (Fig 5). The same stem-loop RT-qPCR assays were then used to analyse RNA sampled over the course of a second in vitro non-synchronous infection (N076 isolate, biological replicates representing such infections at 0–9 dpi). The levels of CyHV-3 DNA were seen to increase over the sampling period (Fig 6A) and corresponding increases in the levels of each CyHV-3 miRNA was also observed, all of which were detectable by 2 dpi (Fig 6B). The hierarchy in miRNA levels was consistent with the observations from the H361 infection (Fig 5), with the exception of MR5075-miR-3p, which was present at lower levels relative to other targets (Fig 6B). In order to investigate discrepancies between the two sequencing experiments regarding the miRNA/miRNA* pattern from MD1111 (Table 2), stem-loop RT-qPCR was used to measure the levels of both MD1111 miRNAs in the same samples described above. The data was consistent, indicating that the MD1111 miRNA* was in fact derived from the 5′ arm in accordance with the deep sequencing data from the H361 infection (Fig 7).

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Fig 5. Relative expression levels of the dominant miRNAs from six high probability CyHV-3 pre-miRNAs.

RNA prepared from CyHV-3-infected CCB cells was analysed for viral miRNA expression by (A) small RNA deep sequencing (read counts), and (B) stem-loop RT-qPCR. In (B), all input cDNA came from a common RT reaction; values shown are based on means of replicates and error bars represent the data range.

https://doi.org/10.1371/journal.pone.0125434.g005

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Fig 6. CyHV-3 miRNAs are consistently expressed during in vitro infection of CCB cells.

The numbers underneath each graph indicate days post-infection (d.p.i.) with the CyHV-3 N076 isolate. UC denotes uninfected cells. (A) Detection of CyHV-3 genomic DNA by PCR; levels were normalized to CCB cell genomic DNA in each case. (B) Relative levels of the dominant miRNAs from six high probability CyHV-3 pre-miRNAs over the course of the experiment shown in (A) as measured by stem-loop RT-qPCR and normalized to levels of the host miRNA ccr-let-7a.

https://doi.org/10.1371/journal.pone.0125434.g006

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Fig 7. Relative levels of miRNAs from both arms of CyHV-3 pre-miRNA MD1111.

(A) Small RNA deep sequencing read counts obtained following infection of CCB cells with CyHV-3 isolate H361, and (B) stem-loop RT-qPCR analysis of RNA from same in vitro infection. Assays were done in duplicate; values shown are based on the means of replicates (error bars represent the data range). (C) Stem-loop RT-qPCR analysis of total RNA from a second non-synchronous in vitro infection (CyHV-3 N076 isolate). Relative expression levels were normalized to host cell miRNA ccr-let-7a. All results are in agreement that MD1111-miR-5p is the miRNA*.

https://doi.org/10.1371/journal.pone.0125434.g007

We next used stem-loop RT-qPCR to confirm expression of MR5057-miR-3p and MD11776-miR-5p in vivo. Total RNA was extracted from carp gill tissue samples originating from sites of suspected KHVD outbreaks in the UK. All fish were shown to be CyHV-3 positive by both virus isolation and PCR (Dataset T in S1 File). MR5057-miR-3p and MD11776-miR-5p were detected in 7 and 8 out of the 9 samples, respectively (Fig 8). The target viral miRNAs were not detected in gill tissue from 5 CyHV-3-negative carp, taken from KHVD free sites and processed at the same time as positive samples (Dataset T in S1 File). MiRNA integrity was confirmed in all samples by ccr-let-7a analysis (Dataset T in S1 File). As per observations in vitro, MR5057-miR-3p was expressed at higher levels than MD11776-miR-3p, with the exception of sample 7, where MD11776-miR-3p was detected in the absence of MR5057-miR-3p (Fig 8).

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Fig 8. MiRNAs MR5057-miR-3p and MD11776-miR-3p are expressed in gill tissues from CyHV-3-infected carp.

Total RNA was extracted from samples of gill tissue taken from both healthy carp and carp with Koi Herpesvirus Associated Disease (KHVD), and were analysed for CyHV-3 miRNAs by stem-loop RT-qPCR. Relative values were normalized to levels of host cell miRNA ccr-let-7a. Assays were done in triplicate, values shown are based on means of replicates and error bars represent the standard deviation.

https://doi.org/10.1371/journal.pone.0125434.g008

Identification of CyHV-3 pre-miRNA homologues in other CyHVs and potential miRNA functional homologues

We used BLASTN [62] to align high-probability and putative CyHV-3 pre-miRNA sequences to the genomes of the closely related CyHV-1 and CyHV-2 [79,80]. Although many short alignments were identified across both genomes, the best MD1111, MD11776 and MD11704 alignments all consisted of relatively longer sequences (all of which had E-values ≤1.00^–6, Dataset U in S1 File). Interestingly, these were also the only alignments to occur within corresponding orthologous regions of each viral genome (Fig 9). In addition, the sequence similarity was greatest in the regions encoding the miRNAs and especially within the seed region of MD11776-miR-3p and MD11704-miR-3p (Fig 10). In VMir analysis of the CyHV-1 and CyHV-2 genomes, only the potential MD11776 CyHV-2 homologue corresponded to a predicted pre-miRNA, which in addition was classified as ‘Real’ by both MiPred and CSHMM (Dataset V in S1 File). We also noted that BLASTN alignment of CyHV-3 pre-miRNAs to the genomes of three fully sequenced CyHV-3 strains (KHV-U, KHV-1 and TUMST1) [81] showed that MD1111 and MR5075 are not conserved in the TUMST1 strain, with both of the TUMST1 pre-miRNA homologues containing significant insertions (9 bp in the loop for MD1111 and separate 6 bp and 4 bp insertions in the MR5075-miR-5p sequence).

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Fig 9. Identification of CyHV-3 pre-miRNA homologues on the genomes of CyHV-1 and -2.

Genomic locations of (A) CyHV-3 pre-miRNA MD1111 relative to its potential homologue on the CyHV-2 genome and (B) CyHV-3 pre-miRNAs MD11776 and MD11704 relative to their potential homologues on the CyHV-1 and CyHV-2 genomes.

https://doi.org/10.1371/journal.pone.0125434.g009

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Fig 10. Alignment of CyHV-3 pre-miRNAs to candidate homologues on the CyHV-1 and CyHV-2 genomes.

CyHV-1 and CyHV-2 sequences that are underlined represent homologous sequences identified by BLASTN, sequences not underlined (i.e. remainder of input sequences) were subsequently manually inspected for sequence similarity. Nucleotides shaded in grey are conserved in all viruses in the respective alignments and those in dark grey are conserved in 2/3 viruses. (A) MD1111—with the exception of the first 3 nucleotides, the MD1111-miR-5p sequence is fully conserved in CyHV-2; (B) MD11776—the seed (bases 2–7) of MD11776-miR-3p is fully conserved and the remainder of the miRNA is also conserved in all three viruses with the exception of two mismatches; (C) MD11704—the MD11704-miR-5p seed region is fully conserved, the MD11704-miR-3p seed region has one mismatch to each virus.

https://doi.org/10.1371/journal.pone.0125434.g010

The seed regions of the CyHV-3 miRNAs were also compared to those of other known viral and host miRNAs in miRBase. Six miRNAs from five of the CyHV-3 pre-miRNAs had either full or partial matches (1 mismatch) to seed regions from other viral miRNAs. Two had full seed matches and one of these (MD11776-miR-5p) had a full match to miRNAs from four different viruses (Dataset W in S1 File). Three CyHV-3 5′ arm miRNAs (all miRNAs*) had matches to multiple host miRNAs, although many multiple matches were to members of the same miRNA families that share the same seed sequence (Dataset X in S1 File).

Identification of potential mRNA targets for CyHV-3 miRNAs

CyHV-3 miRNAs that accumulate at high levels during lytic infections are likely to have roles during that stage of the viral life cycle. As the CyHV-3 miRNA MR5057-miR-3p was consistently found to be present at the highest levels in all experiments, TargetScan and PITA were therefore used to identify candidate MR5057-miR-3p target sites on CyHV-3 mRNAs. TargetScan takes many miRNA target site attributes into account, including sequence pairing within and outside of the seed, seed base-pairing stability and local sequence composition [64–66] whereas PITA ranks potential target sites based entirely on their accessibility [67]. TargetScan identified a single target site for MR5057-miR-3p situated within the 3′ UTR of ORF122/ORF123 (Context Score = -0.139) (Fig 11). The same site was also deemed to be the most accessible MR5057-miR-3p 3′ UTR target by PITA (ΔΔG = -13.65). MR5057-miR-3p also occurs antisense to CyHV-3 ORF43, with which it has full complementary, and was also ranked as the most accessible protein-coding MR5057-miR-3p target site by PITA (ΔΔG = -25.01).

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Fig 11. Identification of a potential target for CyHV-3 MR5057-miR-3p.

(A) Base pairing between MR5057-miR-3p and target site in the ORF123 3′ UTR (genomic co-ordinates in brackets). It is a 7mer-m8 site, in line with the nomenclature system outlined elsewhere [65]. Complementary nucleotides are shown in bold. Seed matches are highlighted in dark grey. An additional nucleotide representing part of the 7mer-m8 target site is underlined. (B) Genomic location of the same site described in (A) (indicated by the light grey arrow and box). It is located in the 3′ UTR of ORF123 which itself overlaps with the predicted longer 3′ UTR from ORF122. The black arrow and box show the location of the ORF122/ORF123 polyA signal.

https://doi.org/10.1371/journal.pone.0125434.g011

MR5057-miR-3p targets a sequence in the 3′ UTR of ORF123 and downregulates the level of its encoded protein

CyHV-3 ORF123 is an early gene [82] that encodes a putative deoxyuridine triphosphatase (dUTPase) [81]. In order to determine if it was a potential target for MR5057-miR-3p as predicted, we first investigated whether MR5057-miR-3p could regulate CyHV-3 dUTPase levels. As antibodies are not yet available for this protein, a vector was generated from which a recombinant N-terminally epitope-tagged dUTPase (rdUTPase) was expressed from ORF123 linked to its own 3′ UTR. This vector was then used to co-transfect HEK293 cells in conjunction with either a MR5057-miR-3p mimic or negative control miRNA mimic and rdUTPase levels were subsequently determined by Western blotting. It can be seen that rdUTPase levels were greatly reduced in the presence of MR5057-miR-3p mimic relative to when negative control mimic was used (Fig 12A). In order to confirm a role for the predicted MR5057-miR-3p target site in the ORF123 3′ UTR, the latter was inserted immediately downstream of the luciferase coding sequence in the 3′ UTR reporter vector pLightSwitch-3′ UTR. A matching plasmid was also generated in which bases complementary to the seed region of MR5057-miR-3p were eliminated from the target site by mutagenesis. These vectors were then separately used to co-transfect HEK293 cells in conjunction with either a MR5057-miR-3p mimic or negative control miRNA mimic. Ablation of the 3′ UTR target seed region led to a significant recovery of luciferase activity in the presence of MR5057-miR-3p when compared to the negative control mimic (p < 0.05) (Fig 12B). This indicated that the predicted target site was important for MR5057-miR-3p interaction with the 3′ UTR of ORF123. Together these results are evidence that MR5057-miR-3p interacts with the 3′ UTR of ORF123 and can downregulate the level of CyHV-3 dUTPase.

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Fig 12. MR5057-miR-3p interacts with the 3′ UTR of CyHV-3 ORF123 and downregulates its encoded protein.

(A) Western blot showing relative CyHV-3 rdUTPase levels (top panel) produced 48 h following transfection of CCB cells with pCyHV-3-rdUTPase in combination with miRNA mimics (as indicated above the image). Endogenous β-actin levels are also shown from the same blot (lower panel). (B) Luciferase assays showing relative values obtained from pLS-123-3′ UTR (containing predicted target seed region 5′GCAATT3′) and matched control pLS-123-3′ UTRmut (seed changed to 5′CGTACA3′) in the presence of miRNA mimics. Data are presented as the Log₂ ratios of luciferase activities of targeting to non-targeting miRNA mimics (i.e. MR5057-miR-3p to negative control). The data shown were compiled from three experiments, values represent means between experiments and error bars represent standard deviations; *indicates p < 0.05.

https://doi.org/10.1371/journal.pone.0125434.g012