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Association of coral algal symbionts with a diverse viral community responsive to heat shock

BMC Microbiology volume 17, Article number: 174 (2017) Cite this article

Abstract

Background

Stony corals provide the structural foundation of coral reef ecosystems and are termed holobionts given they engage in symbioses, in particular with photosynthetic dinoflagellates of the genus Symbiodinium. Besides Symbiodinium, corals also engage with bacteria affecting metabolism, immunity, and resilience of the coral holobiont, but the role of associated viruses is largely unknown. In this regard, the increase of studies using RNA sequencing (RNA-Seq) to assess gene expression provides an opportunity to elucidate viral signatures encompassed within the data via careful delineation of sequence reads and their source of origin.

Results

Here, we re-analyzed an RNA-Seq dataset from a cultured coral symbiont (Symbiodinium microadriaticum, Clade A1) across four experimental treatments (control, cold shock, heat shock, dark shock) to characterize associated viral diversity, abundance, and gene expression. Our approach comprised the filtering and removal of host sequence reads, subsequent phylogenetic assignment of sequence reads of putative viral origin, and the assembly and analysis of differentially expressed viral genes. About 15.46% (123 million) of all sequence reads were non-host-related, of which <1% could be classified as archaea, bacteria, or virus. Of these, 18.78% were annotated as virus and comprised a diverse community consistent across experimental treatments. Further, non-host related sequence reads assembled into 56,064 contigs, including 4856 contigs of putative viral origin that featured 43 differentially expressed genes during heat shock. The differentially expressed genes included viral kinases, ubiquitin, and ankyrin repeat proteins (amongst others), which are suggested to help the virus proliferate and inhibit the algal host’s antiviral response.

Conclusion

Our results suggest that a diverse viral community is associated with coral algal endosymbionts of the genus Symbiodinium, which prompts further research on their ecological role in coral health and resilience.

Background

Coral reefs constitute one of the most diverse ecosystems on the planet providing ecological and economical value [1]. These ecosystems are under threat due to the effects of anthropogenic pollution, ocean acidification, and ocean warming, amongst others [1]. Stony corals are the foundation species of reef ecosystems as they provide the structural calcium carbonate framework. They constitute complex assemblages of organisms, termed holobionts, consisting of the animal host organism, photosynthetic dinoflagellates of the genus Symbiodinium, bacteria and other microbes, and viruses [2]. In the case of Symbiodinium the relationship is a mutualistic symbiosis, as the coral provides a sheltered and light-rich environment, and in return, the algal endosymbionts provide energy to the coral host in the form of photosynthates [3, 4]. The microbial community has been shown to be important for nutrient cycling, in particular nitrogen [5,6,7,8], pathogen defense [9], and coral health [10,11,12,13,14]. More recently, studies have started investigating the diversity and function of viruses of the coral holobiont [15,16,17,18,19].

Studies that aimed at characterizing viruses associated with the coral holobiont found a diverse viral community closely associated with coral hosts [16]. The coral surface mucus layer is of particular interest, given that the mucus layer serves as the outer barrier of the coral to the surrounding seawater [20, 21]. Indeed, viral abundances in the mucus layer have been reported to be greater than those in the surrounding seawater by as much as an order of magnitude [22]. Although the exact roles of viruses in the coral holobiont function remain unclear at present, research suggests that viruses might be associated with coral mortality and reef ecosystem decline [16]. For instance, viruses have been proposed to play a role in some coral diseases [19, 23, 24]. Viruses were also proposed to be involved to some degree in coral bleaching, based on a general increase in viral abundance in bleaching corals such as Diploria strigosa, Porites compressa, Acropora aspera, and Acropora millepora [15, 17, 25]. Further support comes from a recent study by Levin et al. [26]. In this study, viruses associated with Symbiodinium showed differential expression under temperature stress, in particular an increase of nucleocytoplasmic large DNA virus transcripts. However, further research is warranted to unequivocally establish a potential role of viruses in coral disease and coral bleaching.

The study by Levin et al. [26] was interesting in another regard as it assessed viral gene expression by using RNA-Seq data derived from Symbiodinium cultures, originally produced to assess algal gene expression [27]. This shows that data from next-generation sequencing studies can be used to investigate gene expression across the range of organisms captured (i.e., organisms associated with the target organism that are sequenced as ‘by-catch’), but only few studies have done so [28,29,30]. Spurred by the insight and feasibility of these recent studies and approaches, here we re-analyzed a previously published RNA-Seq dataset of cultured Symbiodinium microadriaticum (Clade A1) across four experimental conditions for the presence and diversity of viruses and viral gene expression [31]. Our approach included removal of algal host sequence data and phylogenetic annotation of assigned virus sequences as well as assembly and gene expression profiling of putative viral genes, providing a snapshot of virus diversity and viral gene expression. The applied procedure should be amenable to query other RNA-Seq datasets, in particular those obtained from coral holobionts, to extend the insights obtained here.

Methods

Algal culturing and experimental treatment

Culturing methods used to generate this dataset are described in detail in Liew et al. [31]. Briefly, individual colonies of S. microadriaticum (strain CCMP2467, Clade A1, from Bigelow National Center for Marine Algae and Microbiota) were selected from antibiotic-treated (kanamycin 50 μg/ml, ampicillin 100 μg/ml, and streptomycin 50 μg/ml) f/2-agar plates, and transferred into 200 ml of f/2 media without antibiotics for 2 weeks in non-treated Nunclon Δ TripleFlasks (Thermo Scientific, Waltham, MA). Cultures were exposed to a light intensity of 80 μmol photons m−2 s−1 on a 12 h:12 h day:night cycle (daytime was set from 6 am to 6 pm). All cultures were sampled at the same time of the day (noon). Cultures were incubated in triplicates under three coral bleaching-relevant stressors alongside a control treatment [31]. Experimental treatments were: cold shock (“16C”: 16 °C for 4 h prior to sampling), heat shock (“36C”: 36 °C for 4 h prior to sampling), and dark shock (“DS”: no light for 6 h prior to sampling). The control treatment (“Noon”) was sampled at 12 pm in the midst of the day cycle.

RNA extraction and sequencing

For RNA extractions, about 3 × 107 algal cells (200 ml of 1–2 × 105 cells/ml) were pelleted in 50 ml Falcon tubes at 500 g, decanted, washed, and re-pelleted at 1000 g. The pellets were drip-dried and subsequently snap-frozen with liquid nitrogen for subsequent processing. Total RNA was extracted from the cell pellets via grinding in liquid nitrogen. To aid grinding, 200–300 μl of 0.5 mm glass beads (BioSpec Products, Bartlesville, OK) were added into the mortar. Subsequently, the RNeasy Plant Mini Kit (Qiagen, Hilden, Germany) was used according to manufacturer’s instructions to extract RNA. The quality of RNA was assessed using a Bioanalyzer 2100 (Agilent, Santa Clara, CA) prior to sequencing library creation. Strand-specific RNA-Seq libraries were generated from oligodT-selected total RNA using the NEBNext Ultra Directional RNA Prep Kit (New England Biolabs, Ipswich, MA) according to manufacturer’s instructions. A total of 397 million paired-end read pairs (read length: 101 bp, library insert size: 180 bp) were sequenced and retrieved from the NCBI Short Read Archive (SRA) under BioProject accession PRJNA315758 [31].

Sequence data filtering

The software trimmomatic [32] was used for quality control and read trimming (settings: headcrop:6; lading:30; trailing:30; slidingwindow:4:30, minlen:35), while fastq-mcf [33] was utilized to remove Illumina adapters (minimum remaining length l = 50, quality threshold q = 20, minimum quality mean qual_mean = 20), retaining 609 of the 795 million sequence reads (397 million paired-end read pairs) (Additional file 1: Figure S1). The BBSplit script from BBMap-35.85 [34] was used to remove sequencing library spiked-in PhiX174 Illumina control sequences. The BBSplit script was also used to remove sequence reads that mapped to the genome of Symbiodinium microadriaticum [35, 36]. This yielded 123 million sequence reads (53 million read pairs and 17 million single reads, respectively) that were used for all subsequent analyses (Additional file 1: Figure S1).

Viral diversity and community composition

Filtered paired-end read pairs (see above) were classified with the software CLARK [37] (Version 1.2.3; default settings; databases: bacteria and viruses; NCBI RefSeq release 75, March 15, 2016). Read pairs from each sample (n = 12, i.e. 3 replicates over 4 experimental conditions) were annotated to the highest (taxon) level for classification of sequences as bacteria, archaea, and viruses, and counts for each taxon were summed up for a given treatment and over all treatments. Taxa for which the sum of sequence counts across all samples was <12 (i.e., average count in each replicate <1) were removed from subsequent analyses. Sequence counts of remaining taxa were then utilized to assess changes in viral communities across different experimental treatments. Sequence counts were normalized using the cumulative-sum scaling (CSS) method implemented in the R Bioconductor package metagenomeSeq (v 1.12.1) [38,39,40], and a zero-inflated log-normal model (fitFeatureModel in metagenomeSeq) was calculated. LIMMA empirical Bayes method [41] as implemented in metagenomeSeq was used to test for statistically significant differences for each pairwise comparison between the control (“Noon”) and remaining treatment conditions (cold shock “16C”, heat shock “36C”, dark shock “DS”) at an FDR-corrected cutoff of ≤ 0.1.

Viral gene assembly, annotation, and gene expression

Besides taxonomic assignment, retained sequence reads (see above) were utilized for a de novo assembly with rnaSPAdes-0.1.1 (default settings) [42]. Assembled contigs (> 200 bp) were annotated using Prokka [43] (settings: -- metagenome, −-norrna --notrna --kingdom Viruses, remaining settings: default; databases: see below), based on predicted coding sequences (CDS) inferred from Prodigal [44] through BLAST+ [45] searches against the viral RefSeq (Release 75, March 07, 2016) [46], the Tara Ocean Virome (TOV) database [47], and a quality-controlled set of viral core genes from UniProt (settings --kingdom viruses, provided by Prokka, release May 2016). Further, HMM profile databases consisting of viral pFAM [48], viral TIGRFAMs [49], vFAMs [50], and Viral OGs of EggNOG 4.5 [51] were queried using hmmscan from the HMMER 3.1 package [52]. For gene expression, retained sequence reads (see above) were mapped to all assembled contigs using Kallisto-0.42.5 [53] (settings: number of bootstrap samples = 10; for single reads: estimated average fragment length = 70, estimated standard deviation of fragment length = 18; remaining settings = default) to retrieve raw counts and Transcripts Per Million (TPM) estimates for each contig across all treatments and replicates. Gene expression was assessed using raw counts, normalized according to the cumulative-sum scaling (CSS) method (see above). A zero-inflated log-normal model was calculated. Differentially expressed genes between the control (“noon”) and remaining treatment conditions (cold shock “16C”, heat shock “36C”, and dark shock “DS”) were determined using the LIMMA Empirical Bayes method [41] as implemented in metagenomeSeq at an FDR ≤ 0.1. Following this procedure, only contigs containing at least one annotated viral gene (i.e., CDS) were considered in the viral gene expression analysis. For graphical visualization, TPM values of significantly differentially expressed virus genes were imported into the Multi Experiment Viewer (MeV) [54] and normalized by expression of genes across conditions (normalized expression = [(TPM) - MeanTPM(across conditions)]/[Standard deviationTPM(across conditions)]) for generation of an expression graph with the R package ggplot2 [55].

Results

Viral community composition

A total of 795 million sequence reads (i.e., 397 million paired-end read pairs) across 4 treatments (control, cold shock, heat shock, dark shock) with 3 replicates each (i.e., total of 12 samples), were sequenced. Of these, 123 million sequence reads were retained after quality control and removal of sequence reads that aligned to the genomes of Microvirus PhiX174 and S. microadriaticum (Table 1, Additional file 1: Figure S1). To assess viral community composition, 263,878 read pairs could be annotated to archaea, bacteria, and viruses (Table 1, Additional file 1: Figure S1). Viruses comprised 18.78%, bacteria 80.45%, and archaea 0.77% of these annotated read pairs (Table 1). Hence, archaea were rare and comprised a minor fraction of the microbial community, whereas bacteria made up the majority of annotated read pairs. In total 144 distinct viral taxa from 41 families (excluding unassigned and unclassified viruses) were identified across all samples (Additional file 2: Table S1).

Table 1 Sequence data overview and read-based taxonomic classification
Full size table

The most abundant viruses were members of the Potyviridae family (19.70% ± 1.21%) with a single species (Bidens mottle virus) comprising 19.10% ± 1.16% of all annotated read pairs under consideration (Fig. 1, Additional file 2: Table S1). The next five most abundant families were Picornaviridae (8.38% ± 0.57%), Herpesviridae (8.03% ± 0.51%), Bromoviridae (7.73% ± 0.51%), Astroviridae (7.27% ± 0.47%), and Partitiviridae (7.02% ± 0.41%) (Fig. 1, Additional file 2: Table S1). The viral community was dominated by ssRNA(+) viruses (62.54% ± 4.08%), while dsRNA viruses (22.49% ± 1.36%) and dsDNA viruses (14.88% ± 0.95%) were also present. RNA retroviruses (ssRNA(rt)) and ssDNA viruses were extremely rare (< 0.01% each) and none of ssRNA(−) and dsDNA(rt) were identified. Although some differences between control and treatment conditions could be observed, the abundance of viral species was overall similar and not significantly different (Fig. 1). We found the largest difference in the heat shock treatment (in comparison to the control), with the greatest abundance differences by Synechococcus phage ACG-2014f (log2-fold change −0.95), Pandoravirus salinus (log2-fold change −0.89), Mycobacterium phage DS6A (log2-fold change −0.70), White clover cryptic virus (log2-fold change −0.67), cowpox virus (log2-fold change −0.59), and Dulcamara mottle virus (log2-fold change −0.54) (Fig. 1, Additional file 2: Table S1). Interestingly, all of these viruses became less abundant in the heat shock treatment (Fig. 1, Additional file 2: Table S1).

Fig. 1
figure 1

Viral community associated with cultured Symbiodinium microadriatcum. Only viral taxa present with ≥ 12 sequence counts (i.e., average count in each replicate ≥ 1) are displayed. Upper portion depicts relative abundance of viral taxa in the control treatment (noon). Lower portion depicts log2-fold changes of taxa-based viral abundance for three shock treatments (cold shock “16C”, heat shock “36C”, and dark shock “DS”) in comparison to control condition

Full size image

Viral gene assembly and expression

To evaluate viral gene expression, 123 million retained sequence reads were assembled into 56,064 contigs (N50 = 1462 bp; > 1000 bp = 15,844 contigs) (Additional file 1: Figure S1). Of these, 5925 contigs were < 200 bp and were not considered for annotation. Of the remaining 50,139 contigs, we predicted 35,271 coding sequences (‘hypothetical proteins’), the majority of which (30,206) did not retrieve an annotation and were not considered for subsequent analyses. Viral gene annotations could be obtained for 4856 contigs. Of these, 2075 contigs were annotated with the viral RefSeq database, 2455 contigs with the Tara Ocean Virome (TOV) database, and 326 contigs with the vFam database.

To assess viral gene expression, all sequence reads were mapped against the 56,064 contigs and read counts and TPM values were determined (Additional file 1: Figure S1, Additional file 3: Table S2). We found no significant differences in gene expression between control and either cold shock or dark shock treatments for any of the 4856 contigs of putative viral origin. Conversely, for the heat shock treatment, we identified 43 contigs with viral genes that were differentially expressed (Fig. 2), of which 22 received a functional gene annotation (Table 2). Most of these were serine/threonine kinases, ankyrin repeat proteins, and ubiquitins. Similar to the results of viral abundance differences, these genes were downregulated in the heat shock treatment (in comparison to the control) with log2-fold changes ranging from −0.9 to −3.4, with the exception of an ubiquitin E3 ligase ICP0 that was upregulated in the heat shock treatment (log2-fold change 0.8) (Fig. 2, Table 2).

Fig. 2
figure 2

Gene graph of the 43 differentially expressed viral genes in cultures of S. microadriaticum across four experimental treatments (control “Noon”, dark shock “DS”, cold shock “16C”, heat shock “36C”). Only genes in the heat shock treatment displayed significant differential expression and were downregulated in comparison to the control (with the exception of one upregulated gene). Displayed are normalized TPM values over replicated treatments. Each gene is represented by a distinct color

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Table 2 Set of 22 functionally annotated differentially expressed viral genes in S. microadriaticum cultures under heat shock (4 h at 36 °C, FDR < 0.1) and associated log2-fold changes
Full size table

Discussion

The importance of bacterial and viral communities to host organisms is being increasingly recognized, but resources for the identification and characterization of viruses are still scarce, making it challenging to identify viruses using a bioinformatics approach [29, 47]. The NCBI RefSeq database, for instance, holds 5539 viral species, while the number of bacterial species is 40,984 (NCBI RefSeq Release 75). Despite these limitations, there is an increase in both the amount and accessibility of genomic and transcriptomic data available for viruses (e.g. NCBI RefSeq, vFam, Tara Ocean Virome, and others). In this regard, two recently published studies focused on identifying new viruses from metagenomic data substantially increased sequence data available for viruses [56, 57]. Although the technical advances of next-generation sequencing provide the basis to study ‘holotranscriptomes’ or ‘hologenomes’ (i.e., the transcriptome or genome of entire metaorganisms), comparatively few studies have focused on or incorporated viruses [28,29,30, 58].

To complement recent efforts, here we employed a bioinformatics approach to characterize viral community composition and gene expression from an existing RNA-Seq dataset of the coral symbiont Symbiodinium microadriaticum [31]. We relied on the circumstance that high throughput sequencing commonly produces data for not only the target organism, but other associated organisms (e.g., microbes and viruses) as well. Importantly, our intention was to annotate viral sequence reads affiliated with the ‘metaorganism’ Symbiodinium. Hence, we considered bacteriophages of metaorganism-associated bacteria a specific component of this. Notably, algal cultures were treated with prokaryote-specific antibiotics (kanamycin, streptomycin, and ampicillin) prior to experimental treatments. Thus, we anticipated an overall reduced bacterial contribution as well as a shifted community composition due to variations in susceptibility to the antibiotics by distinct bacteria and archaea. This in turn supposedly had effects on the abundance of assessed bacteriophages. Further, RNA preparations were oligodT-selected prior to library preparation. While dsDNA and ssRNA(+) viruses (including Herpesviridae, Mimiviridae, and Polydnaviridae) polyadenylate their mRNAs [59,60,61], not all viruses harbor polyadenylated mRNAs (e.g., some ssRNA(−) viruses). Hence, viral community composition using this RNA-Seq dataset is biased towards viruses that harbor polyadenylated mRNAs. In this regard, it is interesting to note that some ssRNA(+) genomes are polyadenylated, including viruses of the Potyviridae and Picornaviridae family, the two most abundant viral families in the here-analyzed data (Additional file 2: Table S1) [62, 63]. Thus, their particular abundance might come from the circumstance that genomic and transcriptomic sequence data for these viruses were captured. Further, retroviruses and other viral DNA integrated into the genome of S. microadriaticum were not assessed, due to our filtering procedure that removed sequences with similarity to the algal host genome. Last, it is important to keep in mind that no viral enrichment steps were applied during sample preparation, explaining the low number of sequences that were of putative viral origin. Still, 49,557 read pairs could be assigned to viruses, which allowed us to obtain an insight into the viral community associated with cultured S. microadriaticum, albeit being incomplete and likely biased towards viruses with polyadenylated mRNAs or genomes.

The viruses associated with cultures of S. microadriaticum constituted a highly diverse community. Community composition was similar across replicates within treatments and across treatments, thus indicating a stable viral community associated with the algal host (Fig. 1, Additional file 2: Table S1). In total, 41 different viral families were classified, yet only 7 of these families accounted for more than 5% of the community each, highlighting that the viral community was composed of a few abundant and many rare viruses. While previous studies elucidating viruses associated with Symbiodinium cultures also detected multiple viral taxa, they found a lower diversity (between 7 to 11 viral families), albeit on the same order of magnitude [26, 29, 30]. The most abundant virus in the here-assessed dataset was the Bidens mottle virus of the family Potyviridae. Viruses of the family Potyviridae are known to infect many different plants, especially economically important species [64,65,66]. To our knowledge, this is the first report of Potyviridae in association with Symbiodinium. Other highly abundant families included the Picornaviridae, Herpesviridae, Bromoviridae, Astroviridae, and Partitiviridae (in decreasing order of abundance). Previous studies investigating viruses associated with Symbiodinium found plant-infecting viruses of the family Phycodnaviridae [18, 26, 29, 30, 67], which were also present in our study, but at very low abundance.

Interestingly, two different pandoraviruses (Pandoravirus salinus and Pandoravirus dulcis) were classified. Pandoraviruses are among the largest known viruses, with a particle size > 0.7 μm and a genome size of 1.9 to 2.5 Mb (exceeding some eukaryotes) [68, 69]. Given that the majority of studies use size-based filtration, a filtration-free approach is arguably more likely to detect giant viruses, as suggested previously [26, 30], although Weynberg et al. [67] detected giant viruses of the family Mimiviridae, despite applying a size-based filtration.

Herpesviridae were the third most abundant virus family in the Symbiodinium cultures. Herpes-like viral structures have been described previously in healthy, bleached, and diseased corals [15, 17,18,19]. However, further analysis only revealed similarities in a few coding regions, suggesting a distinct relationship to known Herpesviruses [16, 25]. Interestingly, a study by Lawrence et al. [70] found viral structures of the same shape and size as the herpesvirus-like particles described above in transmission electron micrographs of a range of Symbiodinium cultures and in the nucleus of Symbiodinium cells. Additionally, recent studies detected viruses of the order Herpesvirales associated with Symbiodinium type C1 [67], as well as similarities with viruses of the Herpesviridae family of Symbiodinium type C3, originally isolated from jellyfish [30]. Accordingly, Herpesviruses previously identified in corals [15,16,17,18,19] might in fact be associated with their algal endosymbionts.

At large, we found stable gene expression of the viral community, which is in agreement with the results of the viral community composition and which parallels the overall stable gene expression of the Symbiodinium host [31]. We expected to see increased viral activity in the heat shock treatment, given that a temperature increase might trigger the lytic cycle [24, 71], but we only found few differentially expressed genes. At the same time, we only detected differential gene expression in the short-term heat shock experiment over four hours, and changes might become more apparent in experiments with longer treatment times. Correspondingly, our results suggest that the application of a short-term treatment (over hours), such as heat (“36C”), cold (“16C”), or dark (“DS”) shock, affect viral gene expression to a comparably small extent. Nevertheless, some viral genes were downregulated in the heat shock treatment (with the exception of one upregulated gene), while the cold and dark shock treatments did not show any effect on viral gene expression. Given that the amount of differentially expressed genes was < 1% of all genes assayed, we do not attribute this to a general shutdown of the host transcriptional machinery under heat shock, but rather as a viral response to the heat shock.

The differentially expressed viral genes included serine/threonine kinases, ankyrins, ABC transporters, and ubiquitins, of which ankyrins and ubiquitins have been reported as upregulated after UV exposure in C3 Symbiodinium cultures [30]. Further, kinases, ankyrins, and ABC transportes have been described in the virome associated with cultured Symbiodinium recently [67]. In the context of viral infections, kinases are known to impact and regulate the autophagy of cells [72,73,74], which in turn constitutes part of an innate and adaptive immune response of organisms [72, 75]. Two serine/threonine kinases (Us1 and Us3) of Herpesviruses were shown to inhibit interferons of the respective host, thus hinder the autophagy and immunity of the host cell [74, 76, 77]. Further, (de-)ubiquitination enzymes have a key role in antiviral immunity [78, 79]. Further, the replication of many viruses depends on the hydrolytic activity of the ubiquitin-proteasome system in order to make amino acids available for viral protein synthesis [80,81,82]. ABC transporters, however, are not known to affect viral reproduction, but might present an alternative mechanism to counteract the host’s innate immunity [83, 84]. Ankyrin repeat proteins are one of the most abundant and ubiquitous proteins in all kingdoms of life and harbor structurally and regulatory important roles, yet they are rare in viruses [81, 85]. Ankyrin repeat proteins have been described as being part of signaling networks during and after viral infections [85, 86] as well as to counteract the innate immunity of host cells by preventing apoptosis of the host and manipulating the host’s ubiquitination system [78, 81, 87]. Taken together, the cold shock and dark shock treatments did not elicit significant differences regarding viral gene expression, while the heat shock treatment resulted in the downregulation of some viral genes (< 1% of all genes assayed). Most of these genes seem to play a role in inhibiting the host’s antiviral response, but it remains to be determined whether this is due to the host or the viruses being affected by the heat shock. Of note, we did not observe significant viral abundance changes between treatments and individual viral taxa seem to be similar abundant across all treatments (Fig. 1, Additional file 2: Table S1). As such, the downregulation of genes could not be associated with the decreased abundance of certain viruses.

Coral bleaching (i.e., the loss of Symbiodinium) affects coral reefs on a global scale, and bleaching events are primarily due to an increase in seawater temperature [88]. Nevertheless, the mechanisms are not yet fully understood [1, 7, 89,90,91,92]. Heat stress has been previously implicated in the onset of viral outbreaks and it has been suggested that heat-induced increases in viral activity may be associated with some aspects of coral bleaching [15,16,17,18, 22, 25, 93]. Along these lines, Wilson et al. [94] showed temperature induction of viruses in Symbiodinium, and Levin et al. [26] found differential viral gene expression in Symbiodinium populations that coincided with their thermal tolerance. Conversely, Weynberg et al. [67] did not find differences in viral abundance in cultured Symbiodinium type C1 after exposure to elevated temperature. Here we found a relatively stable viral community, but could detect differences in the viral gene expression during a short-term heat shock treatment. Taken together, while a potential role of viruses in coral bleaching is compelling, conclusive data supporting this is unavailable at the moment. Future targeted efforts should help clarify the role of viruses associated with the coral holobiont and any presumptive impacts on coral health and resilience.

Conclusions

Despite the putative importance of viruses, comparatively few studies describe the viral diversity associated with eukaryotic hosts. Host-targeted RNA-Seq studies provide an opportunity to examine associated viral signatures. In this study, we used an oligodT-selected transcriptome dataset obtained from antibiotic-treated cultured Symbiodinium microadriaticum under three shock treatments and one control treatment to assess viral diversity and gene expression. From these data, we observed a stably associated and diverse viral community that is characterized by few abundant and many rare viral families. The most abundant viral family were the Potyviridae, which encompasses many known plant viruses, and has not been described in Symbiodinium before. Expression of genes in the viral community was primarily affected by heat shock and included processes targeting host antiviral immunity, but the relevance of this to the coral holobiont remains to be shown. Taken together, we present an approach to assess viral communities and gene expression from existing RNA-Seq data that can be applied to existing high throughput gene expression datasets.

Abbreviations

RNA-Seq:

RNA sequencing

References

  1. Hoegh-Guldberg O. Climate Change, coral bleaching and the future of the world's coral reef. CSIRO Australia. 1999;50:839–66.

    Google Scholar 

  2. Knowlton N, Rohwer F. Multispecies microbial mutualisms on coral reefs: the host as a habitat. Am Nat. 2003;162(4 Suppl):S51–62.

    Article  PubMed  Google Scholar 

  3. Burriesci MS, Raab TK, Pringle JR. Evidence that glucose is the major transferred metabolite in dinoflagellate-cnidarian symbiosis. J Exp Biol. 2012;215:3467–77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Falkowski PG, Dubinsky Z, Muscatine L, Porter JW. Light and the bioenergetics of a symbiotic coral. Bioscience. 1984;34:705–9.

    Article  CAS  Google Scholar 

  5. Rädecker N, Pogoreutz C, Voolstra CR, Wiedenmann J, Wild C. Nitrogen cycling in corals: The key to understanding holobiont functioning? Trends Microbiol. 2015;23:490–7.

    Article  PubMed  Google Scholar 

  6. Lesser MP, Mazel CH, Gorbunov MY, Falkowski PG. Discovery of symbiotic nitrogen-fixing cyanobacteria in corals. Science. 2004;305(5686):997–1000.

    Article  CAS  PubMed  Google Scholar 

  7. Pogoreutz C, Rädecker N, Cárdenas A, Gärdes A, Voolstra CR, Wild C. Sugar enrichment provides evidence for a role of nitrogen fixation in coral bleaching. Global Change Biology. 2017;23(9):3838-48. doi:10.1111/gcb.13695. Epub 2017 Apr 21.

  8. Pogoreutz C, Rädecker N, Cárdenas A, Gärdes A, Wild C, Voolstra CR. Nitrogen fixation aligns with nifH abundance and expression in two coral trophic functional groups. Front Microbiol. 2017;8:1187.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Ritchie KB. Regulation of microbial populations by coral surface mucus and mucus-associated bacteria. Mar Ecol Prog Ser. 2006;322:1–14.

    Article  CAS  Google Scholar 

  10. Rosenberg E, Koren O, Reshef L, Efrony R, Zilber-Rosenberg I. The role of microorganisms in coral health, disease and evolution. Nat Rev Microbiol. 2007;5(5):355–62.

    Article  CAS  PubMed  Google Scholar 

  11. Sunagawa S, DeSantis TZ, Piceno YM, Brodie EL, DeSalvo MK, Voolstra CR, Weil E, Andersen GL, Medina M. Bacterial diversity and White Plague Disease-associated community changes in the Caribbean coral Montastraea faveolata. ISME J. 2009;3(5):512–21.

    Article  CAS  PubMed  Google Scholar 

  12. Krediet CJ, Ritchie KB, Paul VJ, Teplitski M. Coral-associated micro-organisms and their roles in promoting coral health and thwarting diseases. Proc R Soc Lond B Biol Sci. 2013;280(1755):20122328.

    Article  Google Scholar 

  13. Ziegler M, Seneca FO, Yum LK, Palumbi SR, Voolstra CR. Bacterial community dynamics are linked to patterns of coral heat tolerance. Nat Commun. 2017;8:14213.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ben-Haim Y, Zicherman-Keren M, Rosenberg E. Temperature-regulated bleaching and lysis of the coral Pocillopora damicornis by the novel pathogen Vibrio coralliilyticus. Appl Environ Microbiol. 2003;69(7):4236–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Marhaver KL, Edwards RA, Rohwer F. Viral communities associated with healthy and bleaching corals. Environ Microbiol. 2008;10:2277–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Vega Thurber R, Payet JP, Thurber AR, Correa AMS. Virus–host interactions and their roles in coral reef health and disease. Nat Rev Microbiol. 2017;15:205–16.

    Article  Google Scholar 

  17. Vega Thurber RL, Barott KL, Hall D, Liu H, Rodriguez-Mueller B, Desnues C, Edwards RA, Haynes M, Angly FE, Wegley L, et al. Metagenomic analysis indicates that stressors induce production of herpes-like viruses in the coral Porites compressa. Proc Natl Acad Sci U S A. 2008;105:18413–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Correa AMS, Welsh RM, Vega Thurber RL. Unique nucleocytoplasmic dsDNA and +ssRNA viruses are associated with the dinoflagellate endosymbionts of corals. ISME J. 2013;7:13–27.

    Article  CAS  PubMed  Google Scholar 

  19. Soffer N, Brandt ME, Correa AMS, Smith TB, Thurber RV. Potential role of viruses in white plague coral disease. ISME J. 2014;8:271–83.

    Article  CAS  PubMed  Google Scholar 

  20. Glasl B, Herndl GJ, Frade PR. The microbiome of coral surface mucus has a key role in mediating holobiont health and survival upon disturbance. ISME J. 2016;10(9):2280–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Hadaidi G, Röthig T, Ziegler M, Voolstra CR. Stable mucus-associated bacterial communities in bleached and healthy corals of Porites lobata from the Arabian Seas. Sci Rep. 2017;7:45362. doi:10.1038/srep45362.

  22. Nguyen-Kim H, Bouvier T, Bouvier C, Doan-Nhu H, Nguyen-Ngoc L, Rochelle-Newall E, Baudoux AC, Desnues C, Reynaud S, Ferrier-Pages C, et al. High occurrence of viruses in the mucus layer of scleractinian corals. Environ Microbiol Rep. 2014;6:675–82.

    Article  PubMed  Google Scholar 

  23. Bettarel Y, Thuy NT, Huy TQ, Hoang PK, Bouvier T. Observation of virus-like particles in thin sections of the bleaching scleractinian coral Acropora cytherea. J Mar Biol Assoc U K. 2013;93:909–12.

    Article  Google Scholar 

  24. Weynberg KD, Voolstra CR, Neave MJ, Buerger P, van Oppen MJH. From cholera to corals: Viruses as drivers of virulence in a major coral bacterial pathogen. Sci Rep. 2015;5:17889.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Correa AMS, Ainsworth TD, Rosales SM, Thurber AR, Butler CR, Vega Thurber RL. Viral outbreak in corals associated with an in situ bleaching event: atypical herpes-Like viruses and a new megavirus infecting Symbiodinium. Front Microbiol. 2016;7:127.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Levin RA, Voolstra CR, Weynberg KD, van Oppen MJH. Evidence for a role of viruses in the thermal sensitivity of coral photosymbionts. ISME J. 2016;11:808–12.

    Article  PubMed  Google Scholar 

  27. Levin RA, Beltran VH, Hill R, Kjelleberg S, McDougald D, Steinberg PD, van Oppen MJ. Sex, cavengers, and chaperones: transcriptome secrets of divergent Symbiodinium thermal tolerances. Mol Biol Evol. 2016;33(9):2201–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Daniels CA, Baumgarten S, Yum LK, Michell CT, Bayer T, Arif C, Roder C, Weil E, Voolstra CR. Metatranscriptome analysis of the reef-building coral Orbicella faveolata indicates holobiont response to coral disease. Front Mar Sci. 2015;2;62. doi:10.3389/fmars.2015.00062.

  29. Wood-Charlson EM, Weynberg KD, Suttle C, Roux S, MJH v O. Metagenomic characterisation of viral communities in corals: mining biological signal from methodological noise. Environ Microbiol. 2015;17:1–21.

    Article  Google Scholar 

  30. Lawrence SA, Floge SA, Davy JE, Davy SK, Wilson WH. Exploratory analysis of Symbiodinium transcriptomes reveals potential latent infection by large dsDNA viruses. Environ Microbiol. 2017. doi:10.1111/1462-2920.13782. Epub ahead of print.

  31. Liew YJ, Li Y, Baumgarten S, Voolstra CR, Aranda M. Condition-specific RNA editing in the coral symbiont Symbiodinium microadriaticum. PLoS Genet. 2017;13(2):e1006619.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Aronesty E. ea-utils: Command-line tools for processing biological sequencing data. Durham: Expression Analysis; 2011.

    Google Scholar 

  34. Bushnell B: BBMap short-read aligner, and other bioinformatics tools. 2015.

    Google Scholar 

  35. Aranda M, Li Y, Liew YJ, Baumgarten S, Sim O, Wilson MC, Piel J, Ashoor H. Genomes of coral dinoflagellate symbionts highlight evolutionary adaptations conducive to a symbiotic lifestyle. Sci Rep. 2016;6:1–15.

    Article  Google Scholar 

  36. Liew YJ, Aranda M, Voolstra CR. Reefgenomics. Org-a repository for marine genomics data. Database. 2016;2016:baw152.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Ounit R, Wanamaker S, Close TJ, Lonardi S. CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers. BMC Genomics. 2015;16:236.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004;5(10):R80.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Paulson JN, Pop M, Bravo HC. metagenomeSeq: Statistical analysis for sparse high-throughput sequencing. Bioconductor. 2013:1–20.

  40. R Core Team. R. a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2013. https://www.r-project.org/.

  41. Smyth GK. Limma: Linear Models for Microarray Data. In: Gentleman R, Carey VJ, Huber W, Irizarry RA, Dudoit S, editors. Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Statistics for Biology and Health. New York: Springer; 2005.

  42. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.

    Article  CAS  PubMed  Google Scholar 

  44. Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinf. 2010;11(1):119.

    Article  Google Scholar 

  45. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL. BLAST+: architecture and applications. BMC Bioinf. 2009;10:421.

    Article  Google Scholar 

  46. O'Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, Rajput B, Robbertse B, Smith-White B, Ako-Adjei D, et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2016;44(D1):D733–45.

    Article  PubMed  Google Scholar 

  47. Brum JR, Ignacio-espinoza JC, Roux S, Doulcier G, Acinas SG, Alberti A, Chaffron S. Patterns and ecological drivers of ocean viral communities. Science. 2015;348(6237):1261498. -1261491-1261411

    Article  PubMed  Google Scholar 

  48. Finn RD, Coggill P, Eberhardt RY, Eddy SR, Mistry J, Mitchell AL, Potter SC, Punta M, Qureshi M, Sangrador-Vegas A, et al. The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 2016;44(D1):D279–85.

    Article  CAS  PubMed  Google Scholar 

  49. Haft DH, Selengut JD, Richter RA, Harkins D, Basu MK, Beck E. TIGRFAMs and Genome Properties in 2013. Nucleic Acids Res. 2013;41(Database issue):D387–95.

    Article  CAS  PubMed  Google Scholar 

  50. Skewes-Cox P, Sharpton TJ, Pollard KS, Derisi JL. Profile hidden Markov models for the detection of viruses within metagenomic sequence data. PLoS One. 2014;9:e105067.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Huerta-Cepas J, Szklarczyk D, Forslund K, Cook H, Heller D, Walter MC, Rattei T, Mende DR, Sunagawa S, Kuhn M, et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 2016;44(D1):D286–93.

    Article  CAS  PubMed  Google Scholar 

  52. Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7(10):e1002195.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Bray NL, Pimental H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016;34:525–8.

    Article  CAS  PubMed  Google Scholar 

  54. Saeed A, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M. TM4: a free, open-source system for microarray data management and analysis. BioTechniques. 2003;34(2):374.

    CAS  PubMed  Google Scholar 

  55. Wickham H. ggplot2: elegant graphics for data analysis. New-York: Springer-Verlag; 2009.

    Book  Google Scholar 

  56. Paez-Espino D, Eloe-Fadrosh EA, Pavlopoulos GA, Thomas AD, Huntemann M, Mikhailova N, Rubin E, Ivanova NN, Kyrpides NC. Uncovering earth’s virome. Nature. 2016;536(7617):425–30.

    Article  CAS  PubMed  Google Scholar 

  57. Roux S, Brum JR, Dutilh BE, Sunagawa S, Duhaime MB, Loy A, Poulos BT, Solonenko N, Lara E, Poulain J, et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature. 2016;537(7622):689–93.

    Article  CAS  PubMed  Google Scholar 

  58. Shi M, Lin X-d, Tian J-h, Chen L-j, Chen X, Li C-x, Qin X-c, Li J, Cao J-p, Eden J-s, et al. Redefining the invertebrate RNA virosphere. Nature. 2016;540(7634):539–43.

    Article  CAS  Google Scholar 

  59. Tycowski KT, Shu MD, Borah S, Shi M, Steitz JA. Conservation of a triple-helix-forming RNA stability element in noncoding and genomic RNAs of diverse viruses. Cell Rep. 2012;2:26–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Majerciak V, Ni T, Yang W, Meng B, Zhu J, Zheng ZM. A viral genome landscape of RNA polyadenylation from KSHV latent to lytic infection. PLoS Pathog. 2013;9:e1003749.

    Article  PubMed  PubMed Central  Google Scholar 

  61. te Velthuis AJW, Fodor E. Influenza virus RNA polymerase: insights into the mechanisms of viral RNA synthesis. Nat Rev Microbiol. 2016;14:479–93.

    Article  Google Scholar 

  62. Adams MJ, Antoniw JF, Beaudoin F. Overview and analysis of the polyprotein cleavage sites in the family Potyviridae. Mol Plant Pathol. 2005;6(4):471–87.

    Article  CAS  PubMed  Google Scholar 

  63. Le Gall O, Christian P, Fauquet CM, King AMQ, Knowles NJ, Nakashima N, Stanway G, Gorbalenya AE. Picornavirales, a proposed order of positive-sense single-stranded RNA viruses with a pseudo-T = 3 virion architecture. Arch Virol. 2008;153(4):715–27.

    Article  PubMed  Google Scholar 

  64. Gibbs AJ, Ohshima K, Phillips MJ, Gibbs MJ. The prehistory of potyviruses: their initial radiation was during the dawn of agriculture. PLoS One. 2008;3:e2523.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Alexander HM, Mauck KE, Whitfield AE, Garrett KA, Malmstrom CM. Plant-virus interactions and the agro-ecological interface. Eur J Plant Pathol. 2014;138:529–47.

    Article  Google Scholar 

  66. Malmstrom CM, Alexander HM. Effects of crop viruses on wild plants. Curr Opinion Virol. 2016;19:30–6.

    Article  Google Scholar 

  67. Weynberg KD, Neave M, Clode PL, Voolstra CR, Brownlee C, Laffy P, Webster NS, Levin RA, Wood-Charlson EM, van Oppen MJH. Prevalent and persistent viral infection in cultures of the coral algal endosymbiont Symbiodinium. Coral Reefs. 2017;36,3:773–84.

  68. Philippe N, Legendre M, Doutre G, Couté Y, Poirot O, Lescot M, Arslan D, Seltzer V, Bertaux L, Bruley C, et al. Pandoraviruses: Amoeba viruses with genomes up to 2.5 Mb reaching that of parasitic eukaryotes. Science. 2013;341:281–6.

    Article  CAS  PubMed  Google Scholar 

  69. Yutin N, Koonin EV. Pandoraviruses are highly derived phycodnaviruses. Biol Direct. 2013;8:25.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Lawrence SA, Wilson WH, Davy JE, Davy SK. Latent virus-like infections are present in a diverse range of Symbiodinium spp. (Dinophyta). J Phycol. 2014;50:984–97.

    Article  PubMed  Google Scholar 

  71. Mojica KDA, Brussaard CPD. Factors affecting virus dynamics and microbial host-virus interactions in marine environments. FEMS Microbiol Ecol. 2014;89:495–515.

    Article  CAS  PubMed  Google Scholar 

  72. Virgin HW, Levine B. Autophagy genes in immunity. Nat Immunol. 2009;10:461–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Lussignol M, Queval C, Bernet-Camard M-F, Cotte-Laffitte J, Beau I, Codogno P, Esclatine A. The herpes simplex virus 1 Us11 protein inhibits autophagy through its interaction with the protein kinase PKR. J Virol. 2013;87:859–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Su C, Zhan G, Zheng C. Evasion of host antiviral innate immunity by HSV-1, an update. Virol J. 2016;13:38.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Lupfer C, Thomas PG, Anand PK, Vogel P, Milasta S, Martinez J, Huang G, Green M, Kundu M, Chi H, et al. Receptor interacting protein kinase 2-mediated mitophagy regulates inflammasome activation during virus infection. Nat Immunol. 2013;14:480–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Wang S, Wang K, Lin R, Zheng C. Herpes simplex virus 1 serine/threonine kinase US3 hyperphosphorylates IRF3 and inhibits beta interferon production. J Virol. 2013;87:12814–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Zhang M, Liu Y, Wang P, Guan X, He S, Luo S, Li C, Hu K, Jin W, Du T, et al. HSV-2 immediate-early protein US1 inhibits IFN-β production by suppressing association of IRF-3 with IFN-β promoter. J Immunol. 2015;194:3102–15.

    Article  CAS  PubMed  Google Scholar 

  78. Al-Khodor S, Price CT, Kalia A, Abu Kwaik Y. Functional diversity of ankyrin repeats in microbial proteins. Trends Microbiol. 2009;18:132–9.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Zhong H, Wang D, Fang L, Zhang H, Luo R, Shang M, Ouyang C, Ouyang H, Chen H, Xiao S. Ubiquitin-specific proteases 25 negatively regulates virus-induced type I interferon signaling. PLoS One. 2013;8:1–14.

    CAS  Google Scholar 

  80. Van Sant C, Hagglund R, Lopez P, Roizman B. The infected cell protein 0 of herpes simplex virus 1 dynamically interacts with proteasomes, binds and activates the cdc34 E2 ubiquitin-conjugating enzyme, and possesses in vitro E3 ubiquitin ligase activity. Proc Natl Acad Sci U S A. 2001;98:8815–20.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Herbert MH, Squire CJ, Mercer AA. Poxviral ankyrin proteins. Viruses. 2015;7:709–38.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Karpe YA, Pingale KD, Kanade GD. Activities of proteasome and m-calpain are essential for Chikungunya virus replication. Virus Genes. 2016;52,5:716–21.

  83. Seyffer F, Tampe R. ABC transporters in adaptive immunity. Biochim Biophys Acta. 1850;2015:449–60.

    Google Scholar 

  84. Verweij MC, Dl H, Griffin BD, Luteijn RD, Davison AJ, Ressing ME, EJHJ W. Viral inhibition of the transporter associated with antigen processing (TAP): A striking example of functional convergent evolution. PLoS Pathog. 2015;11(4):e1004743.

    Article  PubMed  PubMed Central  Google Scholar 

  85. Guo CJ, Chen WJ, Yuan LQ, Yang LS, Weng SP, Yu XQ, He JG. The viral ankyrin repeat protein (ORF124L) from infectious spleen and kidney necrosis virus attenuates nuclear factor-kB activation and interacts with ikB kinase b. J Gen Virol. 2011;92:1561–70.

    Article  CAS  PubMed  Google Scholar 

  86. Blanié S, Gelfi J, Bertagnoli S, Camus-bouclainville C. MNF, an ankyrin repeat protein of myxoma virus, is part of a native cellular SCF complex during viral infection. Virol J. 2010;7:1–5.

    Article  Google Scholar 

  87. Revilla Y, Callejo M, Rodriguez JM, Culebras E, Nogal ML, Salas ML, Viñuela E, Fresno M. Inhibition of nuclear factor kB activation by a virus-encoded I-kappaB-like protein. J Biol Chem. 1998;273:5405–11.

    Article  CAS  PubMed  Google Scholar 

  88. Hughes TP, Kerry JT, Álvarez-Noriega M, Álvarez-Romero JG, Anderson KD, Baird AH, Babcock RC, Beger M, Bellwood DR, Berkelmans R, et al. Global warming and recurrent mass bleaching of corals. Nature. 2017;543(7645):373–7.

    Article  CAS  PubMed  Google Scholar 

  89. Rowan R. Coral bleaching: thermal adaptation in reef coral symbionts. Nature. 2004;430:742.

    Article  CAS  PubMed  Google Scholar 

  90. DeSalvo MK, Voolstra CR, Sunagawa S, Schwarz JA, Stillman JH, Coffroth MA, Szmant AM, Medina M. Differential gene expression during thermal stress and bleaching in the Caribbean coral Montastraea faveolata. Mol Ecol. 2008;17:3952–71.

    Article  CAS  PubMed  Google Scholar 

  91. Sweet MJ, Brown BE. Coral responses to anthropogenic stress in the twenty-first century: an ecophysiological perspective. Oceanogr Mar Biol Annu Rev. 2016;54:271–314.

    Google Scholar 

  92. Pogoreutz C, Rädecker N, Cárdenas A, Gärdes A, Wild C, Voolstra CR. Nitrogen Fixation Aligns with nifH Abundance and Expression in Two Coral Trophic Functional Groups. Front Microbiol. 2017;8:1187. doi:10.3389/fmicb.2017.01187.

  93. Nguyen-kim H, Bouvier T, Bouvier C, Bui VN, Le-lan H, Bettarel Y. Viral and bacterial epibionts in thermally-stressed corals. J Marine Sci Eng. 2015;3:1272–86.

    Article  Google Scholar 

  94. Wilson WH, Francis I, Ryan K, Davy SK. Temperature induction of viruses in symbiotic dinoflagellates. Aquat Microb Ecol. 2001;25:99–102.

    Article  Google Scholar 

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Acknowledgements

This study is based on a thesis for fulfillment of a Bachelor of Science (Biology) degree at the University of Bremen by JDB under supervision of Prof. Dr. Christian R Voolstra and Prof. Dr. Andreas Dotzauer.

Funding

Research reported in this publication was supported by a Science and Engineering Engagement and Development (SEED) fund (FCS/1/2449–01-01: The role of viruses in structuring the marine environment of the Red Sea) to the Red Sea Research Center at KAUST. JDB was funded by a Visiting Student Research Program (VSRP) fellowship by King Abdullah University of Science and Technology (KAUST). The funders had no role in the design of the study and collection, analysis, and interpretation of data, and in writing the manuscript.

Availability of data and materials

Sequence data used in this study are accessible at NCBI, BioProject ID PRJNA315758.

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Authors and Affiliations

  1. Red Sea Research Center, Division of Biological and Environmental Science and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia

    Jan D. Brüwer, Shobhit Agrawal, Yi Jin Liew, Manuel Aranda & Christian R. Voolstra

  2. Department of Biology and Chemistry, University of Bremen, 28359, Bremen, Germany

    Jan D. Brüwer

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  1. Jan D. Brüwer
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Contributions

CRV designed and conceived the study. MA, YJL, CRV provided tools and reagents. JDB, SA, CRV generated data and analyzed data. JDB, SA, CRV wrote the manuscript. YJL, MA provided comments to the manuscript. All authors approved the final manuscript.

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Correspondence to Christian R. Voolstra.

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Additional files

Additional file 1: Figure S1.

Analysis pipeline to assess viral diversity and gene expression from RNA-Seq data. After sequence read filtering, the pipeline is split for viral community and differential gene expression examination. Software used depicted in italics with specified settings (if other than default). (PDF 436 kb)

Additional file 2: Table S1.

Taxonomic diversity of viral classified sequences. Paired reads were classified with CLARK. Shown are CSS-normalized counts over viral taxa for each replicate of four experimental conditions: control, cold shock (“16C”), heat shock (“36C”), and dark shock (“DS”). (XLSX 38 kb)

Additional file 3: Table S2.

Viral gene expression in Transcripts Per Million (TPM). TPM values for all contigs with viral genes (n = 4856) for each replicate of four experimental conditions: control, cold shock (“16C”), heat shock (“36C”), and dark shock (“DS”). (XLSX 825 kb)

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Brüwer, J.D., Agrawal, S., Liew, Y.J. et al. Association of coral algal symbionts with a diverse viral community responsive to heat shock. BMC Microbiol 17, 174 (2017). https://doi.org/10.1186/s12866-017-1084-5

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