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PLOS One logoLink to PLOS One
. 2015 Jun 25;10(6):e0130500. doi: 10.1371/journal.pone.0130500

A de novo Assembly of the Common Frog (Rana temporaria) Transcriptome and Comparison of Transcription Following Exposure to Ranavirus and Batrachochytrium dendrobatidis

Stephen J Price 1,2,*, Trenton W J Garner 2, Francois Balloux 1, Chris Ruis 1, Konrad H Paszkiewicz 3, Karen Moore 3, Amber G F Griffiths 4,¤,*
Editor: Jacob Lawrence Kerby5
PMCID: PMC4481470  PMID: 26111016

Abstract

Amphibians are experiencing global declines and extinctions, with infectious diseases representing a major factor. In this study we examined the transcriptional response of metamorphic hosts (common frog, Rana temporaria) to the two most important amphibian pathogens: Batrachochytrium dendrobatidis (Bd) and Ranavirus. We found strong up-regulation of a gene involved in the adaptive immune response (AP4S1) at four days post-exposure to both pathogens. We detected a significant transcriptional response to Bd, covering the immune response (innate and adaptive immunity, complement activation, and general inflammatory responses), but relatively little transcriptional response to Ranavirus. This may reflect the higher mortality rates found in wild common frogs infected with Ranavirus as opposed to Bd. These data provide a valuable genomic resource for the amphibians, contribute insight into gene expression changes after pathogen exposure, and suggest potential candidate genes for future host-pathogen research.

Introduction

Amphibians are currently undergoing a mass extinction event [1]. Two key pathogens are known to be contributing to amphibian population declines and species extinctions: the fungus Batrachochytrium dendrobatidis (Bd) which causes chytridiomycosis, and the Iridoviridae genus Ranavirus [25]. Bd is a non-hyphal zoosporic fungus which causes mortalities on every continent except Antarctica (http://www.bd-maps.net/) and is thought to have caused multiple species extinctions [6]. Ranaviruses are large double-stranded DNA viruses, capable of crossing poikilothermic class boundaries, and implicated in mass die-off events and population declines [2,5,7]. Ranavirus and Bd are both noted for their virulence across a broad range of hosts but previous research on wild and captive animals points to contrasting levels of pathogenicity in European common frogs (Rana temporaria). Common frogs are highly susceptible to Ranavirus infection in the UK [5,8] and Spain [7] but seem relatively resistant to Bd [9]. It is unknown whether this difference in susceptibility reflects differences in the host’s immune response to each pathogen. De novo RNAseq offers an ideal opportunity to further our understanding of the host response to Bd and Ranavirus, allowing the identification of genes and pathways involved in the response to infection.

Bd is thought to kill its host by disrupting the cutaneous integrity and function of amphibian skin [10]. Transcriptome sequencing has suggested that more resistant hosts may increase expression of genes involved in skin structure [11] and dramatic up and down-regulation of pathways relating to collagen, fibrinogen, elastin and keratin have been reported in the skin of adult amphibians experimentally infected with Bd [12]. Enrichment of inflammatory responses when challenged with Bd may be a general response regardless of susceptibility [11,13]. Increased expression of microbial peptides in response to Bd infection has also been identified in several species (Rana sierrae, Rana muscosa adults [12]; Xenopus tropicalis adults [14]), indicating that innate immunity is a component of host defence. Robust adaptive immunogenetic responses to Bd infection have in general not been observed, but components of innate and adaptive immunity have been shown to operate even in species that are highly susceptible [13]. In addition significant up and down regulation of adaptive immune genes (including the Major Histocompatibility Complex (MHC) class I and II in particular) have been shown in experimentally infected adult ranids [12,14] and a comparison of responses to Bd in hosts of varying susceptibility suggested that the ability to escape immunosuppression by mounting T-cell mediated responses may determine resistance [11]. Our understanding of the response of juveniles to Bd remains limited.

To date, there has been one study of the transcriptional response of amphibians to Ranavirus. Ambystoma mexicanum (axolotl) showed a significant immunological response when experimentally challenged with the Ranavirus Ambystoma tigrinum virus (ATV) [15]. Wild ambystomatid salamanders are highly susceptible to ATV, however experimental animals appeared to mount an immune response to infection within 24 hours of exposure. Using spleen tissues processed through microarrays, the authors demonstrated changes in the expression of innate immunity genes and the transcriptional response increased through time [15]. Experimental infection of adult Xenopus laevis with a strain of Ranavirus (FV3) has demonstrated increased expression of pro-inflammatory cytokines e.g. tumour necrosis factor alpha (TNF-α) and interleukin-1β (IL-1β), indicating that (like for Bd) the innate immune response is activated following infection [16]. Tadpoles show weaker and more delayed up-regulation of these genes [17]. Specific MHC class Ia gene supertypes have been found to be associated with infection status of adult wild common frog (R. temporaria) populations, and diseased populations are characterized by more similar supertype frequencies (lower F ST) than infected populations, indicating pathogen-driven selection on the MHC [18]. This implies that an adaptive immune response to Ranavirus occurs in R. temporaria, and may be important for survival after infection. While adult Xenopus laevis are able to clear FV3 infections, tadpoles do not appear to mount an adaptive immune response to FV3, and succumb to infection within a month of inoculation [17]. Again, little is known about the immune response of juveniles. In general, metamorphosis is a critical point in amphibian immunity–the adaptive immune response appears to be limited pre-metamorphosis, the innate immune response is transformed at metamorphosis, and during metamorphosis individuals are thought to experience temporary immunosuppression [19].

In order to better understand the host response to Bd and Ranavirus, in this study we (i) used RNAseq to generate an annotated de-novo transcriptome for the common frog (R. temporaria) (ii) conducted comparative expression profiling of the early responses of metamorphic frogs exposed to Bd or Ranavirus relative to control animals, and (iii) identified candidate genes for future studies on the population-level impacts of these pathogens.

Methods

Experimental treatments

Ranavirus (RUK13 isolate; [20]) was cultured at 24ºC in Fathead Minnow cells (FHM, Epithelial-like cells from the posterior anal tissue, obtained from the European Collection of Cell Cultures catalogue number 88102401) in maintenance media (EMEM + 10% FBS + 1% L-Glutamine) and quantified on 96 well flat-bottomed cell culture plates using the TCID50 method [21]. Bd inoculum (Isolate IA 042, BdGPL; [22]) was prepared by culture in mTGhL medium at 18ºC for four days before counting zoospores using a haemocytometer.

R. temporaria eggs were obtained from a private garden pond in Chessington in the UK with the permission of the landowner [5]. This site has a known history of Ranavirus infection, but an unknown history of Bd infection. The eggs were hatched and reared through metamorphosis under controlled conditions, showing good survival and no signs of infection. Metamorphs (n = 45) were moved into an experimental room (18-21ºC, 33–46% humidity, full spectrum UV light) for acclimatisation one week prior to exposure, and were placed in individual boxes and fed on crickets ad libitum.

This work was carried out under Home Office license (Project Licence numbers PPL 80/2214 and PPL 80/2466) and was approved by the Institute of Zoology Ethics Committee and the University of Exeter Ethical Review Board. In total 45 frogs were used—15 animals for each of three exposure treatments (Ranavirus, Bd, Control). Animals were checked, cleaned and given fresh water frequently whilst being mindful of causing them distress through unnecessary handling. Exposure was performed in individual tubes with 29ml aged tap water with 1ml of inoculum (Ranavirus at 1k TCID50/ml or Bd at 100k active zoospores/ml), or 30ml tap water for the control treatment, for four hours. Following exposure, the frogs were returned to their individual boxes and examined daily for signs of Ranavirus infection (oedema of the eye, skin ulceration, bleeding), which would have served as an endpoint for the experiment but none were observed. Frogs were euthanized according to Schedule 1 to the Animals (Scientific Procedures) Act 1986 four days post-exposure by immersion in fresh 5g/L Tricaine methane sulphonate (MS222, Pharmaq Ltd.) solution neutralized with sodium bicarbonate in accordance with Universities Federation for Animal Welfare guidance to ameliorate suffering. We sampled livers because they are large, easily targeted organs which are important for immunity and are a target organ in ranavirus disease [23]. Livers were immediately dissected, preserved individually in RNAlater solution (Sigma Aldrich), and stored at -80ºC.

Sequencing, de novo assembly and abundance estimation

Total RNA was extracted from 5–20μg of liver tissue using the Qiagen RNeasy Mini Kit (Qiagen, Valencia USA) using the standard protocol. Each treatment consisted of 15 frogs; three pools were sequenced per treatment, with five individuals per pool. We therefore prepared nine samples for sequencing; 3 replicates per treatment, each consisting of pooled RNA extracted from the livers of 5 individuals. RNA concentration was estimated using a NanoDrop (NanoDrop, Wilmington USA), and concentrations of individual sample extractions were equalized within pools, resulting in a total pool volume of 80μl (concentrations varied between pools, Supporting Information S1 table). The sample concentrations were not normalized. DNase treatment, library preparation and 100 base pair paired-end sequencing were performed at the Wellcome Trust Biomedical Informatics Hub using an Illumina HiSeq 2500 with two samples per lane.

Raw reads were processed with the fastq-mcf package (ea-utils; https://code.google.com/p/ea-utils/wiki/FastqMcf; [24]) to trim low quality bases and adaptor sequences from the ends of reads and remove short reads as well as those containing non-assigned bases (Ns). The following settings were used: quality threshold of 20, minimum remaining sequence length of 35, minimum identity between adapter sequence and clipped sequence of 85%, no Ns permitted, and a minimum clip length of 3. The results were then evaluated using FastQC [25]. Reads from all samples were combined and processed through the standard Trinity pipeline (r2013-02-25 release) for de novo RNAseq assembly [26]. Assembly computation requirements were reduced by performing in silico normalization on the reads to reduce the total number of reads and remove errors whilst maintaining transcriptome complexity (maximum coverage for reads = 30; minimum kmer coverage for catalogue construction = 2). Isotigs were assembled along Trinity’s three-stage protocol (Inchworm, Chrysalis, Butterfly) with default settings including a minimum isotig length of 200bp. Transcript abundance estimates for each sample were obtained using RSEM (packaged with Trinity) [27].

Functional annotation and comparative transcription rates

The assembled transcriptome was annotated using Trinotate, a suite of programs for functional annotation of transcriptomes that is suitable for use with non-model organisms. Sequences were processed through a pipeline consisting of a homology search (NCBI-BLAST), protein domain identification (HMMER/PFAM), protein signal prediction (singalP/tmHMM), and comparison to EMBL Uniprot eggNOG and the GO Pathways annotation databases [2837].

The reference assembly was filtered prior to differential expression analysis so that the assembly used for downstream analyses might better reflect transcribed genes and to reduce the number of comparisons undertaken. Filtering was based on the number of mapped reads. Assembled isotigs were retained only if the FPKM (expected fragments per kilobase of transcript per million fragments sequenced [38]) was greater than or equal to one for all three replicates within one or more treatments (FPKM ≥ 1).

We used the CEGMA pipeline to accurately annotate Core Eukaryotic Genes (CEGs; [39]). CEGMA can be used to assess assembly completeness and the impact of downstream filtering steps via the proportion of core genes present. This proportion is calculated relative to a reference set containing 248 of the most highly conserved CEGs and analysis suggests it is a good metric to assess completeness of draft assemblies [40]. We ran CEGMA with default settings on our reference (unfiltered) and FPKM ≥ 1 filtered assemblies to obtain the number of complete [more than 70% of the protein length aligned to isotig(s)] and partial [alignment length is less than 70% but remains higher than a minimum alignment threshold] core genes that these assemblies contained.

Differential expression analysis was performed with edgeR using a Trinity packaged Perl script. Subsets of differentially expressed transcripts were compiled based on log-fold change of one and Benjamini–Hochberg adjusted p-values [41] to control for false discovery rate [FDR<0.05 to obtain the transcript list and FDR<0.10 for downstream Gene Ontology (GO) term enrichment analysis]. All pairwise comparisons were considered; Bd vs. control, Ranavirus vs. control, and Bd vs. Ranavirus. Differentially expressed transcripts in the Bd vs. Ranavirus comparison were allocated to either Bd or Ranavirus (S1 Text).

Analysis of Gene Ontology term enrichment

Bingo (a Cytoscape plugin; [42]) was used to search for GO term enrichment (identifying which GO terms are over or under-represented). Bingo compares the list of differentially expressed products to a user generated list of all genes/transcripts and is therefore of particular use for research on non-model organisms. EdgeR subsets of differentially expressed transcripts at FDR = 0.05 and FDR = 0.10 were analyzed.

Results

Sequencing, Assembly and annotation

A total of 1.29 x 109 reads were generated across all nine samples with a mean quality score of 35.2. All raw data is available through EMBL-EBI Array Express, accession number E-MTAB-3632. In total, 199,602 isotigs were generated with an N50 isotig length of 1,086 base pairs and 30,931 isotigs longer than 1,000bp. There were 134,080,068 bases contained in all isotigs with a GC content of 44%. Assembly summary statistics are shown in Table 1. Trinotate generated a list of 48,263 annotated isotigs from 32,486 disconnected subgraphs. These isotigs hit 17,631 genes when orthologs were included and 11,851 genes when excluding orthologs. The species with the highest number of hits were humans (5,605 genes), mouse (3,800), Xenopus laevis (1,720), rat (1,382), cow (1,154) and Xenopus tropicalis (1,035). In total, 71–76% of reads mapped back to the reference assembly from each set of sample reads (i.e. as part of RSEM analysis); control 73–76%, Ranavirus 71–74%, Bd 72–74%.

Table 1. Trinity assembly summary statistics.

Isotig lengths:
Minimum isotig length: 201
Maximum isotig length: 18,036
Mean isotig length: 672
Standard deviation of isotig length: 944
Median isotig length: 352
N50 isotig length: 1,086
Numbers of isotigs:
Number of isotigs: 199,602
Number of isotigs more than 1kb in length: 30,931
Number of isotigs in N50: 28,238
Number of bases in assembled isotigs:
Number of bases in all isotigs: 134,080,068
Number of bases in isotigs > = 1kb in length: 69,845,219
GC Content of isotigs: 43.95%

The reference assembly was almost complete when compared to CEGMA’s 248 CEG set, giving us confidence that our methods have yielded an assembly that is a good approximation to the actual transcriptome. In total, 240 (of 248; 97%) complete CEGs and an additional six partial CEGs (total = 246 of 248; 99%) were found in our reference assembly. Summary statistics measuring the completeness of the assembly (broken down by KOG group) are included in S2 Table.

Some genes were lost through our filtering operation but a large majority of CEGs remained. The FPKM ≥ 1 filtered assembly contained 215 (87%) complete genes out of the 248 most highly conserved CEGs. A further five partial genes were present giving a total of 220 CEGs (89%; S2 Table).

Differential expression

Transcriptional profiles of replicates were consistent within treatments, clustering together within treatments when expression values were compared for each pair of samples (Fig 1). Expression values were more similar within the Ranavirus and control treatments than in the more divergent Bd treatment. The total number of differentially expressed transcripts that we report was affected by the False Discovery Rate (FDR), as well as our procedure for re-allocating some differentially expressed transcripts from the Bd vs. Ranavirus comparisons to the other comparisons (Table 2; Fig 2). Increasing the FDR increased the number of differentially expressed transcripts in our output. Classifying some of the Bd vs. Ranavirus transcripts to Bd or Ranavirus vs. control (S1 Text) also increased the number of transcripts for each pathogen vs. control comparison but reduced the total number of unique differentially expressed transcripts because, for example, some of these transcripts were due to small but opposite effects of the two pathogens compared to controls.

Fig 1. Comparison of transcriptional profiles across all samples.

Fig 1

Heatmap visualizing the hierarchically clustered Spearman correlation matrix resulting from a comparison of the transcript expression values (TMM-normalized FPKM) for each pair of samples; Bd = Batrachochytrium dendrobatidis, Con = control, Rv = Ranavirus.

Table 2. Summary of the total number of differentially expressed transcripts under alternate FDR regimes: FDR <0.05, FDR<0.10, and differentially expressed transcripts from the Bd-Ranavirus comparison at FDR<0.10 allocated to one of the other comparisons (S1 Text).

  FDR
Comparison <0.05 <0.10 allocated
Bd vs. control 120 315 360
Ranavirus vs. control 23 34 65
Bd vs. Ranavirus 58 136 n/a
Both pathogens vs. control 5 10 18
Total unique transcripts 174 419 407

† some transcripts are differentially expressed in more than one pairwise comparison. This total accounts for this repetition by counting each differentially expressed transcript once only.

Fig 2. Venn diagrams showing distribution of differentially expressed transcripts across comparisons of treatments.

Fig 2

Bd vs. control, Ranavirus vs. control, and Bd vs. Ranavirus at a) FDR < 0.05 & b) FDR < 0.10.

Exposure to Bd elicited far greater transcriptional divergence from controls than did exposure to Ranavirus (Table 2, Fig 3; 120 differentially expressed transcripts for Bd compared to 23 for Ranavirus at FDR<0.05). After allocation of the Bd vs. Ranavirus transcripts to one of the pathogen treatments, there were a total of 360 transcripts (294 up-regulated, 66 down-regulated, 122 annotated) for Bd and 65 (35 up-regulated, 30 down-regulated, 16 annotated) for Ranavirus (Fig 4). The overall tendency for up-regulated expression in Bd-exposed animals was not seen in Ranavirus-exposed animals (Fig 4). Amongst annotated transcripts that were differentially expressed after Bd challenge, interferon-induced proteins figure prominently through Interferon-stimulated 20kDa exonuclease-like 2 and multiple versions of Interferon-induced very large GTPase 1 proteins (Table 3).

Fig 3. Relative expression of differentially expressed (FDR < 0.05) transcripts (rows) across all samples (columns).

Fig 3

Dendrograms show relationships between samples based on expression values (top) and between transcripts based on comparative expression across samples (left). Bd = Batrachochytrium dendrobatidis, Con = control, Rv = Ranavirus.

Fig 4. Counts of up- and down-regulated transcripts in response to Bd and Ranavirus.

Fig 4

Data are summarized at alternate false-discovery rate levels (FDR < 0.05 & FDR < 0.10) and following re-allocation of transcripts in the Bd vs. Ranavirus comparison.

Table 3. Differentially expressed transcripts (all comparisons, FDR = 0.05, log-fold-change = 1) that were successfully annotated.

Transcript ID Comparison logFC logCPM PValue FDR Bd1 Bd2 Bd3 Con1 Con2 Con3 Rv1 Rv2 Rv3 Name Accession Protein name Expect Value Associated with enriched GO term?
comp239933_c4_seq3 bd 10.91 1.33 5.03E-26 9.86E-22 4.41 2.28 2.54 0 0 0 0.4 3.79 0.9 AP4S1_BOVIN Q3ZBB6 AP-4 complex subunit sigma-1 E:5e-86
comp229785_c0_seq2 bd 6.08 5.80 3.42E-06 3.20E-03 1.25 112.48 1.45 0.35 0.63 0.76 2.42 1.03 0.5 PRVB_RANES P02617 Parvalbumin beta E:2e-55 yes
comp181434_c0_seq2 bd 4.41 2.49 2.27E-06 2.34E-03 1.88 29.12 1.47 0.6 0.5 0.44 0.68 0.51 0.65 ACT3_XENLA P04752 Actin, alpha skeletal muscle 3 E:0
comp103973_c0_seq2 bd 4.29 1.64 4.07E-07 6.34E-04 7.53 2.71 14.08 0.43 0.92 0.04 0.37 0.85 0 GVIN1_HUMAN Q7Z2Y8 Interferon-induced very large GTPase 1 E:2e-61
comp242668_c0_seq7 bd 3.08 1.02 6.67E-05 2.40E-02 1.98 1.53 1.81 0.35 0 0.34 0.92 0.45 0.21 GVIN1_HUMAN Q7Z2Y8 Interferon-induced very large GTPase 1 E:6e-88
comp242668_c0_seq10 bd 2.98 0.30 3.75E-05 1.84E-02 2.72 2.1 2.45 0.1 0.83 0.05 0.41 0.86 0 GVIN1_HUMAN Q7Z2Y8 Interferon-induced very large GTPase 1 E:3e-117
comp239447_c0_seq9 bd 2.97 0.98 2.19E-04 3.94E-02 1.8 3.46 1.04 0 0.57 0.29 0.24 1.32 0 ATF4_RAT Q9ES19 Cyclic AMP-dependent transcription factor ATF-4 E:9e-103
comp242668_c0_seq9 bd 2.67 0.53 6.01E-06 5.12E-03 3.53 2.87 5.16 0.29 0.46 1.31 2.5 1.01 1.49 GVIN1_HUMAN Q7Z2Y8 Interferon-induced very large GTPase 1 E:5e-85
comp91414_c0_seq1 bd 2.56 2.89 8.10E-06 6.62E-03 3.8 24.69 7.71 2.15 2.31 1.96 2.78 1.57 2.08 ALDOA_HUMAN P04075 Fructose-bisphosphate aldolase A E:1e-136
comp224799_c0_seq2 bd 2.56 0.11 1.02E-04 2.90E-02 6.6 2.21 6.65 1.08 1.5 0.3 0.38 1.97 0.41 GVIN1_MOUSE Q80SU7 Interferon-induced very large GTPase 1 E:7e-06
comp242668_c0_seq13 bd 2.45 4.11 9.82E-09 2.41E-05 9.74 6.38 10.96 1.29 1.64 2.63 6.22 2.19 3.1 GVIN1_MOUSE Q80SU7 Interferon-induced very large GTPase 1 E:1e-157
comp242668_c0_seq6 bd 2.43 2.88 8.95E-08 1.60E-04 5.27 2.75 5.09 0.79 1.23 0.67 1.57 1.49 0.39 GVIN1_HUMAN Q7Z2Y8 Interferon-induced very large GTPase 1 E:0
comp242668_c0_seq1 bd 2.42 4.39 4.85E-07 6.34E-04 12.38 7.36 16.85 1.4 3.81 2.5 5.6 4.53 1.79 GVIN1_HUMAN Q7Z2Y8 Interferon-induced very large GTPase 1 E:3e-68
comp511272_c0_seq1 bd 2.42 -0.05 1.61E-04 3.29E-02 2.32 2.68 8.25 0.95 1.11 0.71 0.48 1.96 2.44 ANGT_RAT P01015 Angiotensinogen E:2e-06 yes
comp242668_c0_seq3 bd 2.37 3.42 4.52E-07 6.34E-04 7.75 4.4 11.14 1.03 1.79 2.3 4.32 2.38 2.82 GVIN1_HUMAN Q7Z2Y8 Interferon-induced very large GTPase 1 E:0
comp242026_c1_seq5 bd 2.32 2.31 4.84E-06 4.32E-03 2.18 3.4 3.65 0.98 0.7 0.32 2.49 2.64 1.43 LIAS_XENLA Q6GQ48 Lipoyl synthase, mitochondrial E:0
comp283381_c0_seq1 bd 2.14 1.99 1.33E-04 3.13E-02 9.61 3.37 12.88 2.61 1.42 2.53 4.93 1.48 5.41 AGLUS_METJA Q58746 Archaeal glutamate synthase [NADPH] E:6e-154
comp351995_c0_seq1 bd 2.06 0.65 1.77E-04 3.47E-02 2.61 1.68 4.52 0.91 0.64 0.81 1.4 1.04 1.4 TCPZ_CHICK Q5ZJ54 T-complex protein 1 subunit zeta E:7e-112
comp349197_c0_seq1 bd 2.05 -1.04 2.73E-04 4.58E-02 1.53 1.23 2.2 0.42 0.42 0.46 0.88 0.45 0.7 GCP4_MOUSE Q9D4F8 Gamma-tubulin complex component 4 E:5e-58
comp90672_c0_seq1 bd 2.02 -0.55 1.37E-04 3.13E-02 2.37 1.33 2.17 0.53 0.53 0.53 1.02 0.85 1.07 PTN1_CHICK O13016 Tyrosine-protein phosphatase non-receptor type 1 E:2e-89
comp225219_c0_seq1 bd 2.01 1.84 1.10E-04 3.05E-02 10.54 6.86 11.4 1.85 4.49 1.52 2.8 3.22 0.24 GVIN1_HUMAN Q7Z2Y8 Interferon-induced very large GTPase 1 E:1e-36
comp241555_c2_seq2 bd 1.99 1.50 4.19E-05 1.96E-02 2.79 2.14 1.82 0.36 0.49 1.01 1.93 1.3 0.3 MESH1_XENTR Q28C98 Guanosine-3',5'-bis(diphosphate) 3'-pyrophosphohydrolase MESH1 E:3e-97
comp229974_c0_seq1 bd 1.96 2.16 2.46E-06 2.41E-03 4.02 4.63 5.61 1.15 1.37 1.51 1.6 5.18 5.9 UCP2_CYPCA Q9W725 Mitochondrial uncoupling protein 2 E:1e-171
comp94070_c0_seq1 bd 1.92 1.66 1.57E-04 3.29E-02 9.58 4.32 11.86 1.91 2.74 2.97 3.68 2.42 4.34 CCNI_HUMAN Q14094 Cyclin-I E:1e-73
comp222706_c0_seq1 bd 1.83 2.97 1.33E-04 3.13E-02 10.14 7.78 22.32 4.03 4.51 4.21 6.39 6.42 7.46 I20L2_BOVIN Q2YDK1 Interferon-stimulated 20 kDa exonuclease-like 2 E:2e-82
comp229273_c0_seq1 bd 1.79 1.21 1.36E-04 3.13E-02 1.31 1.18 1.97 0.49 0.49 0.45 0.36 0.46 0.5 RN145_MOUSE Q5SWK7 RING finger protein 145 E:0
comp212366_c0_seq1 bd 1.78 3.49 1.39E-04 3.13E-02 20.21 14.26 43.21 7.65 8.68 9.17 11.28 10.4 17.08 SF3A1_BOVIN A2VDN6 Splicing factor 3A subunit 1 E:5e-51
comp197838_c0_seq1 bd 1.71 3.96 1.52E-04 3.29E-02 11.96 7.56 22.38 4.25 4.74 5.45 4.97 3.87 9.34 AGFG1_HUMAN P52594 Arf-GAP domain and FG repeat-containing protein 1 E:3e-174
comp225693_c0_seq1 bd 1.61 3.51 2.06E-04 3.82E-02 19.41 13.65 33.7 7.4 8.68 8.52 13.25 9.22 9.2 TDX_CYNPY Q90384 Peroxiredoxin E:9e-123
comp241884_c0_seq6 bd -1.66 2.25 4.48E-05 1.98E-02 0.58 0.6 0.77 2.19 2.2 2.37 1.52 2.12 0.42 METK1_HUMAN Q00266 S-adenosylmethionine synthase isoform type-1 E:0
comp242157_c2_seq1 bd -1.66 2.77 1.60E-04 3.29E-02 0.59 0.68 1.09 2.49 2.03 3.78 1.24 3.31 1.96 DNJB9_MOUSE Q9QYI6 DnaJ homolog subfamily B member 9 E:3e-115
comp230044_c0_seq1 bd -1.78 1.57 2.16E-04 3.92E-02 0.8 1.27 1.39 5.75 3.07 4.02 3.56 2.46 0.94 LDLR1_XENLA Q99087 Low-density lipoprotein receptor 1 E:2e-149 yes
comp234135_c0_seq1 bd -1.82 1.79 8.43E-05 2.58E-02 0.89 1.05 1.25 5.47 2.99 3.76 2.91 2.37 1.08 LDLR2_XENLA Q99088 Low-density lipoprotein receptor 2 E:0 yes
comp234135_c0_seq2 bd -1.90 1.21 8.52E-05 2.58E-02 1.93 1.43 1.41 8.4 4 6.96 4.33 3.49 1.56 LDLR1_XENLA Q99087 Low-density lipoprotein receptor 1 E:1e-41 yes
comp225798_c0_seq3 bd -1.93 3.57 1.12E-05 8.45E-03 6.06 3.65 4.11 26.68 11.04 19.18 15.04 25.74 4.57 GRP78_XENLA Q91883 78 kDa glucose-regulated protein E:7e-120
comp225798_c0_seq1 bd -2.07 6.00 2.81E-05 1.60E-02 7.83 5.24 12.8 28.98 26.02 68.9 21.59 44.4 21.21 GRP78_XENLA Q91883 78 kDa glucose-regulated protein E:0
comp235980_c0_seq10 bd -2.34 0.51 1.87E-04 3.60E-02 0.22 0.06 0.33 1.47 0.7 1.31 1.87 1.73 1.16 GPBP1_MOUSE Q6NXH3 Vasculin E:8e-146
comp233358_c0_seq1 bd -2.71 1.47 8.65E-09 2.41E-05 0.39 0.3 0.29 2.67 2.54 1.83 0.53 0.32 0 ACSM3_MOUSE Q3UNX5 Acyl-coenzyme A synthetase ACSM3, mitochondrial E:0
comp236082_c0_seq2 bd -2.95 2.10 9.57E-12 3.76E-08 0.33 0.42 0.28 2.7 3.32 2.55 5.57 1.28 1.75 FMO5_RABIT Q04799 Dimethylaniline monooxygenase [N-oxide-forming] 5 E:0
comp238162_c2_seq1 bd -3.34 -0.48 7.85E-08 1.54E-04 0.11 0.02 0.18 1.24 1.05 1.13 0.4 0.5 0.27 OVCA2_XENTR A4II73 Ovarian cancer-associated gene 2 protein homolog E:2e-110
comp239921_c0_seq17 bd -4.27 2.03 1.46E-05 1.06E-02 0.04 0 0.37 2.45 2.02 4.76 3.95 0.11 1.81 ECH1_RAT Q62651 Delta(3,5)-Delta(2,4)-dienoyl-CoA isomerase, mitochondrial E:6e-150
comp242544_c0_seq7 bd -6.32 1.26 4.00E-21 3.92E-17 0 0.07 0.04 2.48 3.86 2.68 0.34 4.06 1.03 CK054_XENLA Q6GME2 Ester hydrolase C11orf54 homolog E:0
comp234419_c0_seq2 bd -7.46 0.76 4.32E-07 6.34E-04 0.01 0 0 0 1.75 1.95 1.16 1.45 2.04 FA73B_XENLA Q6GR21 Protein FAM73B E:0
comp229785_c0_seq2 bd_rv 4.90 5.89 1.42E-04 4.89E-02 1.25 112.48 1.45 0.35 0.63 0.76 2.42 1.03 0.5 PRVB_RANES P02617 Parvalbumin beta E:2e-55 yes
comp103973_c0_seq2 bd_rv 4.55 1.67 3.77E-07 5.28E-04 7.53 2.71 14.08 0.43 0.92 0.04 0.37 0.85 0 GVIN1_HUMAN Q7Z2Y8 Interferon-induced very large GTPase 1 E:2e-61
comp181434_c0_seq2 bd_rv 4.16 2.56 7.23E-06 5.46E-03 1.88 29.12 1.47 0.6 0.5 0.44 0.68 0.51 0.65 ACT3_XENLA P04752 Actin, alpha skeletal muscle 3 E:0
comp224799_c0_seq2 bd_rv 2.67 0.13 1.46E-04 4.94E-02 6.6 2.21 6.65 1.08 1.5 0.3 0.38 1.97 0.41 GVIN1_MOUSE Q80SU7 Interferon-induced very large GTPase 1 E:7e-06
comp214878_c0_seq1 bd_rv 2.65 3.17 1.25E-04 4.64E-02 16.34 12.63 9.1 10.8 0.63 0 0.39 1.37 4.6 MFAP4_BOVIN P55918 Microfibril-associated glycoprotein 4 E:7e-74
comp91414_c0_seq1 bd_rv 2.56 2.94 2.16E-05 1.32E-02 3.8 24.69 7.71 2.15 2.31 1.96 2.78 1.57 2.08 ALDOA_HUMAN P04075 Fructose-bisphosphate aldolase A E:1e-136
comp229401_c0_seq1 bd_rv 2.14 1.63 2.95E-05 1.70E-02 3.67 2.54 4.79 0.75 0.63 3.05 1.23 0.61 0.92 NB5R3_BOVIN P07514 NADH-cytochrome b5 reductase 3 E:4e-155
comp242668_c0_seq6 bd_rv 2.10 2.98 9.08E-05 3.71E-02 5.27 2.75 5.09 0.79 1.23 0.67 1.57 1.49 0.39 GVIN1_HUMAN Q7Z2Y8 Interferon-induced very large GTPase 1 E:0
comp215007_c0_seq2 bd_rv 2.09 -0.04 8.85E-05 3.69E-02 2.06 1.91 0.9 1.06 1.57 1.96 0.34 0.59 0.31 ABCA8_HUMAN O94911 ATP-binding cassette sub-family A member 8 E:1e-87
comp229273_c0_seq1 bd_rv 1.90 1.23 1.10E-04 4.31E-02 1.31 1.18 1.97 0.49 0.49 0.45 0.36 0.46 0.5 RN145_MOUSE Q5SWK7 RING finger protein 145 E:0
comp237274_c0_seq1 bd_rv 1.57 3.80 7.01E-05 3.12E-02 12.22 8.48 9.59 5.89 6.67 5.81 3.96 4.64 2.63 GLCTK_RAT Q0VGK3 Glycerate kinase E:5e-166
comp234903_c0_seq1 bd_rv -2.16 1.76 1.43E-05 1.00E-02 0.36 0.25 0.54 0.59 3.55 0.95 1.53 2.32 1.89 RBM5_XENTR A4IGK4 RNA-binding protein 5 E:0
comp203468_c0_seq1 bd_rv -2.49 1.52 6.96E-07 8.03E-04 1.47 1.27 0.69 9.75 0 6.96 4.56 8.09 8.14 IGJ_MOUSE P01592 Immunoglobulin J chain E:2e-44 yes
comp235980_c0_seq10 bd_rv -2.78 0.95 5.85E-06 4.59E-03 0.22 0.06 0.33 1.47 0.7 1.31 1.87 1.73 1.16 GPBP1_MOUSE Q6NXH3 Vasculin E:8e-146
comp236082_c0_seq2 bd_rv -2.95 2.16 6.42E-07 7.87E-04 0.33 0.42 0.28 2.7 3.32 2.55 5.57 1.28 1.75 FMO5_RABIT Q04799 Dimethylaniline monooxygenase [N-oxide-forming] 5 E:0
comp239921_c0_seq2 bd_rv -3.87 2.53 6.42E-05 3.00E-02 0.04 0.95 0.05 1.76 0.98 12.33 9.38 4.76 1.84 ECH1_RAT Q62651 Delta(3,5)-Delta(2,4)-dienoyl-CoA isomerase, mitochondrial E:6e-150
comp242544_c0_seq7 bd_rv -5.50 0.57 1.97E-08 4.84E-05 0 0.07 0.04 2.48 3.86 2.68 0.34 4.06 1.03 CK054_XENLA Q6GME2 Ester hydrolase C11orf54 homolog E:0
comp234419_c0_seq2 bd_rv -7.77 1.16 3.74E-23 7.35E-19 0.01 0 0 0 1.75 1.95 1.16 1.45 2.04 FA73B_XENLA Q6GR21 Protein FAM73B E:0
comp239933_c4_seq3 rv 9.74 0.31 1.57E-13 1.03E-09 4.41 2.28 2.54 0 0 0 0.4 3.79 0.9 AP4S1_BOVIN Q3ZBB6 AP-4 complex subunit sigma-1 E:5e-86
comp235822_c0_seq8 rv 4.32 2.04 7.02E-16 6.89E-12 0 1.01 0 0.23 0.06 0.16 2.78 3.2 2.94 NB5R3_BOVIN P07514 NADH-cytochrome b5 reductase 3 E:2e-161
comp236322_c0_seq1 rv 2.28 3.56 2.71E-08 1.33E-04 1.63 1.33 2.11 1.16 0.88 1.09 4.48 3.56 6.94 POL2_MOUSE P11369 Retrovirus-related Pol polyprotein LINE-1 E:3e-172
comp215007_c0_seq2 rv -1.89 -0.28 3.24E-05 3.09E-02 2.06 1.91 0.9 1.06 1.57 1.96 0.34 0.59 0.31 ABCA8_HUMAN O94911 ATP-binding cassette sub-family A member 8 E:1e-87
comp240300_c2_seq5 rv -1.97 2.66 3.58E-06 6.39E-03 5.84 3.81 2.69 3.15 8.03 7.01 1.41 1.61 1.52 CI064_HUMAN Q5T6V5 UPF0553 protein C9orf64 E:6e-167
comp227497_c0_seq1 rv -2.01 7.16 2.68E-06 5.25E-03 8.5 27.9 43.39 69.64 112.17 154.2 17.51 27.83 35.5 CP7A1_HUMAN P22680 Cholesterol 7-alpha-monooxygenase E:0

. bd = Bd vs. control, rv = Ranavirus vs. control, bd_rv = Bd vs. Ranavirus

Five transcripts were significantly differentially expressed in both the Ranavirus vs. control and Bd vs. control sets (FDR<0.05). Three of these were up-regulated after exposure to both pathogens (including the two with the highest logFC change values of all transcripts in both treatments) and two were down-regulated. The AP-4 complex subunit sigma-1 is the only one of these five transcripts that was successfully annotated, and was associated with the highest logFC increase in both treatments (10.9 and 9.74 fold up-regulation in Bd, and Ranavirus treatments respectively). At the FDR threshold of p<0.10, ten transcripts were significantly differentially expressed in both the Bd vs. control and Ranavirus vs. control comparisons. Of these, four were up-regulated in both comparisons and six down-regulated. Four annotations in addition to the AP-4 complex were obtained, including (i) Cholesterol 7-alpha-monooxygenase which may be involved in xenobiotic metabolism, (ii) Acyl-coenzyme A synthetase ACSM3, a mitochondrial gene involved in lipid and/or fatty acid metabolism, (iii) Protein CutA homolog possibly involved in metal ion response, and (iv) Protein FAM136A, another mitochondrial protein. Differentially expressed transcripts with annotation are summarized in Table 3, and the full data is available in S1 File.

GO term enrichment

Enriched GO terms (Table 4; adjusted P-value<0.05) clustered in cell signalling, immunity, inflammation and metabolism. In total, 103 GO terms were significantly enriched in animals challenged with Bd (at 5% level after adjusting for multiple comparisons) compared to the reference set (see Table 4). When parent GO terms were examined, 14 of the 20 differentially expressed transcripts under the “metabolic process” parent GO term (GO:0008152) were up-regulated in Bd compared to controls. All transcripts related to “Biological regulation” (GO:0065007) were up-regulated. Immune system processes (GO:0006958), inflammatory responses (GO:0006954) and response to stimulus (GO:0050896) were also generally up-regulated (driven by complement activation) though there was also a down-regulation of Immunoglobulin J chain. No GO terms were enriched in the Ranavirus challenged animals, and no GO terms (regardless of treatment) were significantly enriched when differentially expressed transcripts in the FDR < 0.05 lists only were used for GO enrichment analyses.

Table 4. Enriched GO terms associated with differentially expressed transcripts in the Bd vs. control comparison (adjusted P-value <0.05).

GO term description adjusted PValue pValue GO ID Total Annotated seqs Individual GO term total Total DE set Individual GO term total in DE set
cellular ketone metabolic process 5.51E-08 1.64E-10 GO:0042180 36661 1876 92 23
organic acid metabolic process 5.51E-08 1.73E-10 GO:0006082 36661 1881 92 23
oxoacid metabolic process 5.51E-08 1.05E-10 GO:0043436 36661 1834 92 23
carboxylic acid metabolic process 5.51E-08 1.05E-10 GO:0019752 36661 1834 92 23
negative regulation of endopeptidase activity 1.04E-05 4.08E-08 GO:0010951 36661 130 92 7
monocarboxylic acid metabolic process 1.11E-05 5.23E-08 GO:0032787 36661 1047 92 15
small molecule metabolic process 5.84E-05 3.21E-07 GO:0044281 36661 5068 92 32
negative regulation of peptidase activity 1.14E-04 7.16E-07 GO:0010466 36661 198 92 7
regulation of lipid biosynthetic process 1.46E-04 1.03E-06 GO:0046890 36661 209 92 7
succinate metabolic process 1.62E-04 1.27E-06 GO:0006105 36661 9 92 3
complement activation, classical pathway 3.95E-04 3.41E-06 GO:0006958 36661 161 92 6
humoral immune response mediated by circulating immunoglobulin 4.03E-04 3.79E-06 GO:0002455 36661 164 92 6
humoral immune response 4.07E-04 4.15E-06 GO:0006959 36661 258 92 7
positive regulation of G-protein coupled receptor protein signaling pathway 5.09E-04 5.59E-06 GO:0045745 36661 46 92 4
positive regulation of lipid storage 8.41E-04 9.90E-06 GO:0010884 36661 53 92 4
regulation of triglyceride biosynthetic process 9.83E-04 1.23E-05 GO:0010866 36661 56 92 4
complement activation, alternative pathway 1.19E-03 1.59E-05 GO:0006957 36661 124 92 5
activation of plasma proteins involved in acute inflammatory response 1.39E-03 2.08E-05 GO:0002541 36661 221 92 6
complement activation 1.39E-03 1.97E-05 GO:0006956 36661 219 92 6
immunoglobulin mediated immune response 2.25E-03 3.53E-05 GO:0016064 36661 243 92 6
B cell mediated immunity 2.29E-03 3.78E-05 GO:0019724 36661 246 92 6
negative regulation of hydrolase activity 2.42E-03 4.32E-05 GO:0051346 36661 508 92 8
regulation of triglyceride metabolic process 2.42E-03 4.37E-05 GO:0090207 36661 77 92 4
fatty acid metabolic process 2.59E-03 5.09E-05 GO:0006631 36661 674 92 9
regulation of endopeptidase activity 2.59E-03 5.09E-05 GO:0052548 36661 381 92 7
regulation of peptidase activity 2.71E-03 5.52E-05 GO:0052547 36661 386 92 7
lipid metabolic process 3.48E-03 7.65E-05 GO:0006629 36661 2370 92 17
lymphocyte mediated immunity 3.48E-03 7.59E-05 GO:0002449 36661 279 92 6
regulation of lipid storage 3.53E-03 8.04E-05 GO:0010883 36661 90 92 4
regulation of activation of membrane attack complex 3.70E-03 9.28E-05 GO:0001969 36661 6 92 2
positive regulation of complement activation 3.70E-03 9.28E-05 GO:0045917 36661 6 92 2
positive regulation of activation of membrane attack complex 3.70E-03 9.28E-05 GO:0001970 36661 6 92 2
regulation of lipid metabolic process 3.83E-03 9.91E-05 GO:0019216 36661 424 92 7
positive regulation of glucose transport 4.54E-03 1.21E-04 GO:0010828 36661 100 92 4
protein maturation by peptide bond cleavage 4.85E-03 1.45E-04 GO:0051605 36661 314 92 6
adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains 4.85E-03 1.37E-04 GO:0002460 36661 311 92 6
protein maturation 4.85E-03 1.35E-04 GO:0051604 36661 446 92 7
cellular respiration 4.85E-03 1.43E-04 GO:0045333 36661 197 92 5
regulation of glucose transport 5.26E-03 1.61E-04 GO:0010827 36661 202 92 5
sterol metabolic process 5.74E-03 1.80E-04 GO:0016125 36661 327 92 6
leukocyte mediated immunity 7.24E-03 2.33E-04 GO:0002443 36661 343 92 6
adaptive immune response 8.24E-03 2.72E-04 GO:0002250 36661 353 92 6
acute inflammatory response 8.60E-03 3.11E-04 GO:0002526 36661 362 92 6
positive regulation of type II hypersensitivity 8.60E-03 3.38E-04 GO:0002894 36661 11 92 2
positive regulation of type IIa hypersensitivity 8.60E-03 3.38E-04 GO:0001798 36661 11 92 2
positive regulation of myeloid leukocyte mediated immunity 8.60E-03 3.38E-04 GO:0002888 36661 11 92 2
regulation of type II hypersensitivity 8.60E-03 3.38E-04 GO:0002892 36661 11 92 2
regulation of type IIa hypersensitivity 8.60E-03 3.38E-04 GO:0001796 36661 11 92 2
activation of immune response 8.60E-03 3.32E-04 GO:0002253 36661 517 92 7
respiratory electron transport chain 8.60E-03 3.09E-04 GO:0022904 36661 52 92 3
steroid metabolic process 1.01E-02 4.03E-04 GO:0008202 36661 534 92 7
translation 1.04E-02 4.26E-04 GO:0006412 36661 539 92 7
generation of precursor metabolites and energy 1.06E-02 4.40E-04 GO:0006091 36661 714 92 8
regulation of G-protein coupled receptor protein signaling pathway 1.06E-02 4.51E-04 GO:0008277 36661 141 92 4
regulation of acute inflammatory response to antigenic stimulus 1.20E-02 5.56E-04 GO:0002864 36661 14 92 2
positive regulation of acute inflammatory response to antigenic stimulus 1.20E-02 5.56E-04 GO:0002866 36661 14 92 2
positive regulation of hypersensitivity 1.20E-02 5.56E-04 GO:0002885 36661 14 92 2
regulation of hypersensitivity 1.20E-02 5.56E-04 GO:0002883 36661 14 92 2
positive regulation of protein amino acid phosphorylation 1.20E-02 5.19E-04 GO:0001934 36661 399 92 6
organic acid biosynthetic process 1.21E-02 5.81E-04 GO:0016053 36661 568 92 7
carboxylic acid biosynthetic process 1.21E-02 5.81E-04 GO:0046394 36661 568 92 7
protein processing 1.45E-02 7.04E-04 GO:0016485 36661 423 92 6
positive regulation of inflammatory response to antigenic stimulus 1.45E-02 7.31E-04 GO:0002863 36661 16 92 2
energy derivation by oxidation of organic compounds 1.45E-02 7.30E-04 GO:0015980 36661 426 92 6
positive regulation of phosphorylation 1.54E-02 7.85E-04 GO:0042327 36661 432 92 6
regulation of inflammatory response to antigenic stimulus 1.79E-02 9.28E-04 GO:0002861 36661 18 92 2
regulation of transport 2.08E-02 1.14E-03 GO:0051049 36661 1705 92 12
cellular lipid metabolic process 2.08E-02 1.12E-03 GO:0044255 36661 1701 92 12
positive regulation of phosphorus metabolic process 2.08E-02 1.16E-03 GO:0010562 36661 466 92 6
positive regulation of phosphate metabolic process 2.08E-02 1.16E-03 GO:0045937 36661 466 92 6
cholesterol metabolic process 2.08E-02 1.11E-03 GO:0008203 36661 309 92 5
positive regulation of protein maturation by peptide bond cleavage 2.24E-02 1.27E-03 GO:0010954 36661 21 92 2
positive regulation of immune response 2.26E-02 1.29E-03 GO:0050778 36661 652 92 7
tricarboxylic acid cycle 2.48E-02 1.44E-03 GO:0006099 36661 88 92 3
electron transport chain 2.51E-02 1.48E-03 GO:0022900 36661 194 92 4
regulation of cellular ketone metabolic process 2.57E-02 1.53E-03 GO:0010565 36661 196 92 4
cellular amino acid biosynthetic process 2.63E-02 1.59E-03 GO:0008652 36661 198 92 4
acetyl-CoA catabolic process 2.67E-02 1.63E-03 GO:0046356 36661 92 92 3
small molecule catabolic process 2.74E-02 1.70E-03 GO:0044282 36661 1314 92 10
negative regulation of catalytic activity 2.80E-02 1.76E-03 GO:0043086 36661 886 92 8
positive regulation of extracellular matrix constituent secretion 3.60E-02 2.51E-03 GO:0003331 36661 1 92 1
regulation of extracellular matrix constituent secretion 3.60E-02 2.51E-03 GO:0003330 36661 1 92 1
physiological cardiac muscle hypertrophy 3.60E-02 2.51E-03 GO:0003301 36661 1 92 1
cell growth involved in cardiac muscle cell development 3.60E-02 2.51E-03 GO:0061049 36661 1 92 1
dicarboxylic acid metabolic process 3.60E-02 2.51E-03 GO:0043648 36661 107 92 3
fibroblast proliferation 3.60E-02 2.51E-03 GO:0048144 36661 1 92 1
coenzyme catabolic process 3.60E-02 2.38E-03 GO:0009109 36661 105 92 3
response to muscle activity involved in regulation of muscle adaptation 3.60E-02 2.51E-03 GO:0014873 36661 1 92 1
aerobic respiration 3.60E-02 2.38E-03 GO:0009060 36661 105 92 3
cellular amino acid metabolic process 3.71E-02 2.62E-03 GO:0006520 36661 739 92 7
regulation of immune response 3.76E-02 2.69E-03 GO:0050776 36661 949 92 8
positive regulation of B cell mediated immunity 3.95E-02 2.94E-03 GO:0002714 36661 32 92 2
positive regulation of immunoglobulin mediated immune response 3.95E-02 2.94E-03 GO:0002891 36661 32 92 2
positive regulation vascular endothelial growth factor production 3.95E-02 2.94E-03 GO:0010575 36661 32 92 2
regulation of vascular endothelial growth factor production 3.95E-02 2.94E-03 GO:0010574 36661 32 92 2
purine ribonucleoside triphosphate metabolic process 4.11E-02 3.09E-03 GO:0009205 36661 567 92 6
positive regulation of acute inflammatory response 4.30E-02 3.32E-03 GO:0002675 36661 34 92 2
purine nucleoside triphosphate metabolic process 4.30E-02 3.34E-03 GO:0009144 36661 576 92 6
oxidation reduction 4.30E-02 3.33E-03 GO:0055114 36661 243 92 4
ribonucleoside triphosphate metabolic process 4.63E-02 3.64E-03 GO:0009199 36661 586 92 6
positive regulation of humoral immune response 4.68E-02 3.71E-03 GO:0002922 36661 36 92 2
cofactor catabolic process 4.76E-02 3.81E-03 GO:0051187 36661 124 92 3
heterocycle metabolic process 4.96E-02 4.01E-03 GO:0046483 36661 1242 92 9

Discussion

We investigated the response of an anuran host (R. temporaria) to the fungal pathogen Bd and the viral pathogen, Ranavirus, and detected a significant transcriptional response to Bd. Enriched GO terms involved the major arms of the immune response (innate and adaptive immunity and complement activation) as well as metabolic processes. Elements of the adaptive immune response were significantly differentially expressed in animals exposed to Bd and those exposed to Ranavirus, and this transcriptional response occurred at only four days post-exposure and before signs of disease consistent with ranavirosis were observed. Despite this, the overall response to Ranavirus was extremely limited. Variation between pools within treatments resulted in low power to detect differential expression when accounting for multiple comparisons (False Discovery Rate). However, this conservative approach allows confidence in the results and likely includes the genes with the largest transcriptional changes.

Perhaps the most notable result is the strong up-regulation of elements of the adaptive immunity, in response to either pathogen. The highest log fold-change for both pathogen treatments was successfully annotated to the AP-4 complex subunit sigma-1 gene (AP4S1). This is a subunit of a protein coat that is involved in targeting proteins from the trans-Golgi network to the endosomal-lysosomal system (http://www.uniprot.org/uniprot/Q9Y587). The trans-Golgi network is the location for the loading of cytokines (cytokines modulate the balance between the humoral and cell-based adaptive immune responses) with signal peptides into vesicles or carriers for delivery to the cell surface or other organelles [43]. The endosomal-lysosomal proteolysis system appears to be crucial in the immune system, in particular by binding class II MHC molecules to create ligands for antigen recognition by the T lymphocyte system. This process of antigen processing is important for immunity to pathogens as well as for the identification of self-peptides [44]. This result indicates that AP4S1 may be a particularly important candidate gene for future studies of amphibian host-pathogen biology. To date, we are unaware of any studies focusing on this gene in this context.

It is also notable that Interferon-stimulated 20 kDa exonuclease-like 2 and multiple versions of an Interferon-induced very large GTPase 1 were significantly up-regulated in the Bd treatment (Table 2). Interferon-induced very large GTPases are thought to mobilise effectors against a broad range of invading pathogens [45]. This result suggests that we have sampled these frogs after an initial innate response and at the point of mobilising other pathways. The result further suggests that these hosts are mounting a major phagocytic response to Bd challenge, an anti-fungal response which has previously been shown to be unimpaired during experimental chytrid infection [46].

We found a much stronger transcriptional response to Bd exposure than to Ranavirus. Our results broadly reflect what is seen in the wild. R. temporaria populations in the UK have undergone serious declines in the face of recurrent FV3-like Ranavirus die-offs [5] and are involved in multi-host mass mortality events associated with infection with a newly emerging Ranavirus lineage in Spain [7]. This host is highly susceptible to Ranavirus and it seems likely that infected individuals are failing to mount an effective immune response. In contrast, common frogs are considered relatively resistant to Bd [9], which may reflect effective immunity. Our observations of a weak transcriptional response to Ranavirus are therefore consistent with the higher pathogenicity of Ranavirus relative to Bd in this host as well as potential immune evasion of Ranavirus (reviewed in [17]).

However, the limited overall response to Ranavirus challenge that we detected was surprising in the light of previous work. Cotter et al. exposed Ambystomatid salamanders to ATV and used a custom microarray to measure host response in spleens at time-points between 24 hours and 6 days [15]. Ambystomatids are naturally infected with ATV in North America and are highly susceptible [47] but immune response (including phagocytosis, cytokine signalling, and complement activation) was detected as early as 24 hours post exposure and transcriptional response increased through the six day experimental period [15]. Differences in methodological approach and host-pathogen system might explain the contrasting findings. Tissue type, dose and exposure route all varied between studies and the host species investigated are highly divergent. The viruses utilised are also divergent. ATV is closely related to fish Ranaviruses and may represent a recent host jump [48]. ATV also seems more specialized for salamanders over other amphibians [49]. On the other hand, FV3-like viruses have been more frequently implicated in multi-host die-offs [2] (and may therefore represent better immune evaders) and are the more derived lineage of amphibian-like ranaviruses.

Previous work has shown increased expression of Immunoglobulin Y and activation-induced cytidine deaminase (AID) after Ranavirus (FV3) exposure in Xenopus laevis, indicating that B-cells are activated, and antibodies to Ranavirus have been detected two to six months post-exposure in this host species [5052]. As well as this adaptive immune response, there was a rapid up-regulation of pro-inflammatory genes (Arginase 1, IL-1B, TNF-a, largely produced by macrophages), and an increased abundance of macrophages and antimicrobial peptides, which are known to inactivate FV3 in vitro [5254]. Other studies indicate that we might expect to detect changes in MHC class I gene expression under Ranavirus infection, however it is clear that MHC expression is age-dependent. Pre-metamorphic Xenopus tadpoles do not express MHC class Ia genes, while adults do–in juveniles, the stage used in our experiment, expression is weaker than in adults [55]. We saw evidence for an adaptive immune response being initiated (AP4S1 up-regulation) but little evidence of further responses in our Ranavirus vs. control comparison.

Rosenblum et al. also reported significant transcriptional changes in Interferon-related genes in Bd-exposed animals, although they found many of these were only expressed at 16 days as opposed to 3 days post-exposure [12]. Our data demonstrates that in R. temporaria, these pathways are already active four days post-exposure. Bd may suppress T-cell mediated responses to infection [13] and resistance to Bd may be partly due to an ability to overcome this [11]. Here we have demonstrated the enrichment of lymphocyte and leukocyte mediated immunity in R. temporaria. In addition to immunological responses, Rosenblum et al. reported differential expression in a range of skin integrity, cellular stress, and homeostasis genes—the majority of these detected 16 days post-exposure [12]. Bd disruption of host skin integrity is a key disease process [10] and resistant hosts may mitigate this effect through increased expression of genes contributing to skin structure [11]. Although we sampled livers and not skin, we also observed this type of structural response to Bd through up-regulation of genes involved in fibroblast proliferation as well as up-regulation of actin and Galectin-3.

Previous work has shown that particular MHC class IIB alleles are associated with host resistance to Bd across a range of hosts varying in their susceptibility to the pathogen [56]. Rosenblum et al. reported down-regulation of MHC class I and II in the spleen and up-regulation of MHC class I and II in the skin in R. mucosa experimentally infected with Bd, however these were predominantly shown after 16 days post-exposure and not after only three days post-exposure [12]. We saw no changes in MHC expression in animals exposed to Bd.

A more relaxed false discovery rate threshold is expected to yield an increased number of differentially expressed genes but this effect appeared stronger in our Bd treatment. The relatively large number of genes withstanding the less stringent FDR correction (0.1) relative to the more conservative one (0.05) suggests that a large number of genes were differentially expressed between treatments but that the amplitude of this change was only modest. Our experimental design involved sampling animals at an early time-point, four days after exposure and prior to any observed mortality or observed signs of disease. This design has enabled identification of changes due to pathogen exposure rather than merely identifying differences between healthy and dying hosts. Previous experiments have pointed to reduced transcriptional responses at early time points post-exposure (3 days) relative to late time-points (16 days) in Rana species exposed to Bd, with the majority of transcriptional changes only becoming significant later [12]. Innate immune response to Ranavirus in X. laevis peaks at six days post-exposure, with adaptive immunity still detectable at 2–6 months post-exposure [57]. In contrast, we have demonstrated that certain adaptive host transcriptional responses do occur early for both pathogens examined.

This study demonstrates the utility of using RNAseq with non-model organisms to identify loci that may be important in host responses to pathogens. Amphibians are a highly threatened group, faced with catastrophic declines driven in part by emerging infectious diseases. Whole genome data is currently only available for a single amphibian genus (Xenopus). As such, our transcriptome data for R. temporaria provides a valuable resource for another genus [58], much-needed insight into amphibian immunity, as well as information on specific responses to the two most important multi-host pathogens affecting amphibians. We were also able to identify candidate genes that could serve as markers for understanding the impacts of disease in wild populations and the adaptive potential of populations that are under threat. While this study has provided crucial insights into amphibian gene expression following exposure to pathogens, comparisons across life-history stages, time points post-exposure, and source populations with different previous infection statuses and genetic diversity will all be necessary to gain a more complete picture of the transcriptional responses to amphibian disease. We foresee this line of research offering exciting possibilities for the selection of individuals with disease resistance for captive breeding programmes for conservation.

Supporting Information

S1 Text. Re-allocating differentially expressed transcripts in the Bd vs. Ranavirus comparison.

(DOCX)

S1 Table. Sample RNA concentrations.

(DOCX)

S2 Table. CEGMA output summary; results of filtering assembly on CEG coverage.

A) Full assembly, B) Filtered assembly on FPKM> = 1 for all replicates within at least one treatment.

(DOCX)

S1 Fig. Expression pattern of re-allocated Bd vs. Ranavirus transcripts.

Annotated transcripts from the Bd vs. Ranavirus (FDR<0.10) comparison that were also found in either Bd vs. control, Ranavirus vs. control or both prior to FDR filtering (protein name of best blast hit given).

(DOCX)

S1 File. Full list of differentially transcribed genes.

(CSV)

Acknowledgments

This work was carried out under Home Office license (Project Licence numbers PPL 80/2214 and PPL 80/2466) and was subject to review by the Institute of Zoology Ethics Committee and the University of Exeter Ethical Review Board.

We thank the Exeter Sequencing Service, the National Institute for Health Research University College London Hospitals Biomedical Research Centre, and Computational core facilities at the University of Exeter. We also thank Chris Durrant for help in preparing samples for sequencing and two anonymous reviewers whose comments improved the manuscript.

Data Availability

All raw data is available through EMBL-EBI Array Express, accession number E-MTAB-3632.

Funding Statement

Marie Curie Intra-European Fellowship EIDpop, (http://ec.europa.eu/research/mariecurieactions/about-mca/actions/ief/index_en.htm), grant number 327293, AGFG, Royal Society Research Grant, (https://royalsociety.org/grants/schemes/research-grants/), grant number RG130460, AGFG), Zoological Society of London internal funding, TWJG, European Research Council, (http://erc.europa.eu/funding-and-grants), grant number 260801-BIG-IDEA, FB, the sequencing was supported by the Wellcome Trust Institutional Strategic Support Fund (http://www.wellcome.ac.uk/Funding/WTP057769.htm, grant number WT097835MF), Wellcome Trust Multi User Equipment Award (http://www.wellcome.ac.uk/Funding/Biomedical-science/Funding-schemes/Strategic-awards-and-initiatives/WTDV031728.htm, grant number WT101650MA) and Biotechnology and Biological Sciences, Research Council (BBSRC) Strategic Longer and Larger grants (sLoLas) (http://www.bbsrc.ac.uk/funding/grants/lola/lola-index.aspx, grant number BB/K003240/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Text. Re-allocating differentially expressed transcripts in the Bd vs. Ranavirus comparison.

(DOCX)

S1 Table. Sample RNA concentrations.

(DOCX)

S2 Table. CEGMA output summary; results of filtering assembly on CEG coverage.

A) Full assembly, B) Filtered assembly on FPKM> = 1 for all replicates within at least one treatment.

(DOCX)

S1 Fig. Expression pattern of re-allocated Bd vs. Ranavirus transcripts.

Annotated transcripts from the Bd vs. Ranavirus (FDR<0.10) comparison that were also found in either Bd vs. control, Ranavirus vs. control or both prior to FDR filtering (protein name of best blast hit given).

(DOCX)

S1 File. Full list of differentially transcribed genes.

(CSV)

Data Availability Statement

All raw data is available through EMBL-EBI Array Express, accession number E-MTAB-3632.


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