# "Quasigenus" among Phycodnaviridae: A diversity of chlorophyte-infecting viruses in response to a dense algal culture in a high-rate algal pond

Emily E Chase, Thomas M Pitot, Sonia Monteil-Bouchard, Christelle Desnues, Guillaume Blanc

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Emily E Chase, Thomas M Pitot, Sonia Monteil-Bouchard, Christelle Desnues, Guillaume Blanc. "Quasigenus" among Phycodnaviridae: A diversity of chlorophyte-infecting viruses in response to a dense algal culture in a high-rate algal pond. 2023. hal-04281548

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1 "Quasigenus" among *Phycodnaviridae: A diversity of chlorophyte-infecting viruses in response to a
dense algal culture in a high-rate algal pond.

3

4 Emily E. Chase,¹,* Thomas M. Pitot,² Sonia Monteil-Bouchard,³ Christelle Desnues,³,⁴ and Guillaume Blanc³,,*

5

6 ¹Department of Microbiology, University of Tennessee, Knoxville, Tennessee 37996, USA

7 ²Department of Biochemistry, Microbiology and Bioinformatics, Laval University, Quebec, Canada

8 ³Microbiologie Environnementale Biotechnologie, Institut Méditerranéen d'Océanologie, Campus de Luminy,
163 Avenue de Luminy, 13009 Marseille, France

10 ⁴Institut hospitalo-universitaire (IHU) Méditerranée infection, 19-21 Boulevard Jean Moulin, 13005 Marseille,
France

12 *Corresponding authors

13

14 Abstract

This study approaches a high rate algal pond (HRAP) culture by metagenomic sequencing of the viral DNA
fraction, this includes the so-called giant virus fraction (phylum *Nucleocytoviricota*), with the goal of revealing
viruses coexisting within an intensified algal culture. A wealth of interesting novel viruses is revealed, including
members of *Nucleocytoviricota*, *Lavidaviridae*, and polinton-like viruses, which are taxa containing previously
characterized algal viruses. Our sequencing results are coupled with a virus targeted qPCR study and 18S rDNA
metabarcoding to elucidate potential virus-host interactions. Several species of green algae are identified
(Chlorophyta), likely representing the alternating dominant populations during the year of study. Finally, we
observe a bloom of viral diversity within the family *Phycodnaviridae* (*Nucleocytoviricota*), including highly
related but non-identical genotypes, appearing in the HRAP in September and October 2018. This bloom is most
likely the cause of a mass mortality event of the cultured algae that occurred during these same months. We
hypothesize that these related *Phycodnaviridae* lineages selectively infect different strains of the same algal
species of the Genus *Picochlorum* that have been identified in the HRAP by metabarcoding and coined this
phenomenon a "quasigenus" by analogy to the RNA virus quasispecies concept.

28

29 **Abbreviations:** High-rate algal pond (HRAP), Polinton-like viruses; PLVs, fluorescence activated cell sorting; FACS, ultraviromes; UV, megaviromes; MV, open reading frame (ORF), major capsid protein; MCP, DNA polymerase B; PolB, principal component analyses; PCA, similarity profile analysis; SIMPROF, auxiliary metabolic genes; AMG, restriction modification; RM

33

34 **Keywords:** *Nucleocytoviricota*, polinton-like viruses, virophage, metagenomics, microalgae

35

36
## 37 Introduction

The importance of viruses in aquatic systems is well documented from nutrient cycling and algal bloom control in the open ocean (Suttle and Wilhelm, 1999; Biggs et al., 2021), to modulating microbial communities in the deepest ocean (Jian et al., 2021), and throughout the open ocean water column (Luo et al., 2020). In relation to microalgae, specific viral groups of interest include members of Phylum *Nucleocytoviricota* or "giant viruses", virophages (*Lavidaviridae*), and polinton-like viruses. Family *Phycodnaviridae* and family *Mimiviridae* are two taxa within *Nucleocytoviricota* known to infect microalgae (Wilson et al., 2009; Claverie and Abergel, 2018).

Family *Lavidaviridae* are a group of viruses infecting *Nucleocytoviricota* in a tripartite/co-infection system between a giant virus and its host (Fischer, 2021). Previous work on family *Lavidaviridae* has given evidence for algal *Nucleocytoviricota* hosts of these tripartite systems (Yau et al., 2011), and recently characterised this interaction *in situ* with a *Chlorella* host (Sheng et al., 2022). Despite evidence of a virophage benefiting the cellular host indirectly by reducing *Nucleocytoviricota* production in co-infection (La Scola et al., 2008), and regulating host-virus dynamics (Yau et al., 2011), much is still unknown about family *Lavidaviridae*. Polintons (or Mavericks), are relatively large (up to 40 kb) DNA transposons and have been found in a substantial diversity of multicellular and unicellular species, including protists, fungi, beetles, fish, chicken, etc. (Kapitonov and Jurka, 2006). In 2015, a 31 kb DNA virus (TsV-N1) similar in structure to polintons and with which it shares a number of phylogenetically related genes, was identified as viral particles infecting the microalgae *Tetraselmis striata* (Pagarete et al., 2015). This confirmed that some polinton-like viruses (PLVs) behaved as genuine viruses (i.e., having infectious virions) and not just transposable elements (Koonin and Krupovic, 2017). Notably, the PLVs have some differences from polintons, including the absence of specific genes (see (Yutin et al., 2015)).

Recent metagenomic work concluded that there are at least eight major groups of PLVs and also uncovered many associated with microalgae (Bellas and Sommaruga, 2021). Interestingly, members of family *Lavidaviridae* can possess similarities to PLVs (Koonin and Krupovic, 2017) and PLVs also have potential as a co-infection with extended *Mimiviridae* species (Yutin et al., 2015), therefore could contribute to viral-host dynamics in a tripartite system as well.

Given the significance of viruses in major aquatic systems our study focuses on a specific system that is relatively unexplored until now; the viral community of an industrial high-rate algal pond (HRAP). The said HRAP hosts a non-specific polyculture of microalgae sourced from seawater of the Mediterranean Sea. Our objectives were to understand the diversity of microalgal viruses in the system alongside their dynamics during a culturing period. More specifically, the HRAP environment permitted a dense microalgal culture not unlike an algal bloom of a natural aquatic environment, consequently providing an opportunity to investigate a bloom-like situation and the viral community during this period. Each of the aforementioned virus groups (i.e., *Nucleocytoviricota*; family *Phycodnaviridae* and family *Mimiviridae*, family *Lavidaviridae*, and PLVs) were investigated within the
microalgae culturing HRAP of this study to gain an understanding of their role in a dense culture that we suggest
is analogous to a natural microalgal bloom.

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## 75 Materials and methods

76

Sampling, filtration, nucleic acid extraction, and next-generation sequencing of HRAP sampled water

78

Water sampling and HRAP characteristics are as described in a previous publication (Chase et al., 2021). Samples were processed (e.g., centrifuged, filtered, etc.) in the same manner to produce an “ultravirome” (UV) of “small” DNA viruses (i.e., <0.2µm). As an attempt to isolate and subsequently sequence *Nucleocytoviricota*, fluorescence activated cell sorting (FACS) was carried out in the size fraction 0.2 µm – 1.2 µm labelled using SYBR-Green and analyzed according to the side scatter and FITC fluorescence parameters. The size fraction was obtained by filtering samples through a 1.2 µm pore filter (Sartorius ref :17593), then concentrated 25× on a tangential flow filtration column with a nominal pore size of 0.2 µm (Microkros ref: C02-P20U-05-N). Four gated populations, “megaviromes” (MV), corresponding to areas of the SSC-FITC plot (where giant viruses are generally observed (Khalil et al., 2016)) were sorted in each sample (Figure S1). Note that some bacterial populations may emerge in the same area (i.e., gated population) and thus are considered “contaminants” in the MVs. The number of isolated particles (events) per population ranged between 89 000 and 500 000. Lysis was carried out as detailed in (Chase et al., 2021), and nucleic acid was extracted using EZ1 Advanced XL extraction with a Virus Card (QIAGEN). Six of the 20 FACS populations did not generate sufficient DNA and were not subsequently sequenced. Library preparation and paired-end 2 × 250 bp Illumina MiSeq sequencing of the UVs were done as described in a previous publication (Chase et al., 2021). FACS population were amplified using Ready-To-Go Genomiphi V3 kit (GE Healthcare), followed by purification and Illumina HiSeq sequencing (paired-end 2 × 151 bp) performed by DOE Joint Genome Institute (JGI).

96

Quality control and contig assembly of metagenomes and putative taxonomic assignments

98

Quality control steps with raw reads were as described in previous work (Chase et al., 2021). Assemblies of UVs and MVs were done with SPAdes version 3.15.0 using the metaviral algorithm and the single cell algorithm respectively (Antipov et al., 2020). Only contigs over 2000 bp were kept for further analysis. Assembly statistics were done using metaQUAST version 5.1 (Mikheenko et al., 2016) and are reported in Table S1. Taxonomic assignments were given by an “open reading frames (ORFs) voting” procedure. To fulfill this, ORFs greater than 100 codons were extracted from each contig in the UVs and MVs, aligned against TrEMBL (Boeckmann et al., 2003) using MMSEQ (Steinegger and Söding, 2017) with e-value < 10⁻⁵ and the taxonomic information of the ORF best hits were recorded. Contig taxonomic classification was done using a majority rule criterion
over the ORF best hits. On average, 92.7% of the contigs were classified within the UVs and 97.1% within the MV contigs. Overall, 3335 contigs were specifically assigned to viruses (prokaryotic or eukaryotic) from which 36,525 ORFs were extracted. Further ORFs functional annotation was performed by protein alignment against the SWISS-PROT (Boutet et al., 2007) database using MMSEQS. We used the eggnog-mapper (Cantalapiedra et al., 2021) for assignment to COG (Tatusov et al., 2000) and KEGG (Kanehisa et al., 2016) functional categories.

*Identifying potential microalgae hosts using 18S rDNA metabarcoding of HRAP water*

To characterize the diversity of eukaryotic microorganisms by metabarcoding, DNA was extracted from 0.2 µm filtered water and the V4 variable region of the 18S ribosomal RNA gene was amplified using eukaryote-specific universal primers (forward CCAGCASCYGCGGTAATTCC and reverse ACTTTCGTTCTTGATYRA; (Guillou et al., 2013)). The obtained amplicons were then subcontracted by the Genotoul facility (Toulouse; get.genotoul.fr) which performed indexing and Illumina MiSeq sequencing. The sense and antisense sequences obtained were assembled and then clustered as ASVs (amplicon sequence variants) and identified at different taxonomic levels using the PR2 database version 4.12.0 (Guillou et al., 2013). These analyses were performed under R software version 3.6.2 using the *DADA2* package (Callahan et al., 2016).

*Phylogenetic construction of Phylum Nucleocytoviricota, PLVs, and family Lavidaviridae relationships*

A major capsid protein (MCP), DNA polymerase B (PolB), and ATPase tree were produced using publicly available data for *Nucleocytoviricota* using an alignment from MAFFT v.7 (Katoh and Standley, 2013), and FastTree (Price et al., 2009) with default settings. Additionally, a PLV tree (MCP based using publicly available data) was composed also using FastTree with default settings. MCP sequences were downloaded from National Centre for Biotechnology Information (NCBI) for family *Lavidaviridae*. Corresponding sequences were also aligned using MAFFT v.7 and the *Lavidaviridae* tree was produced using FastTree with default settings. All trees were run with 1000 bootstrap replicates. In all cases putative sequences from relevant groups recovered from the HRAP were included in the trees.

*Dynamics of putative viral targets by qPCR*

The selection of putative viruses for targeting, processing of raw water samples for qPCR, and the qPCR reaction setup were conducted in the same way as described in previous work (Chase et al., 2021) without the need for cDNA construction. Briefly, Primer3 (Untergasser et al., 2012) and PerlPrimer v.1.2.3 (Marshall, 2004) were used to produce efficient primers *in silico* before *in vitro* testing. The number of reaction cycles (total of 45) minus the Cq value were reported as the “inverted Cq”.
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Using correlation among viruses and potential hosts to uncover possible host-virus interactions

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Principle component analyses (PCA) were performed and visualised on a combined dataset of standardized metabarcoding (18S) and qPCR results using R package *factoextra*, using time points where both metabarcoding and qPCR results were available. Similarity profile analysis (SIMPROF) hierarchical clustering at $\alpha$=0.1 was conducted using R package *clustsig*, and correlation networks were visualised using Cytoscape v3.9.1 (Smoot et al., 2011). Publicly available sequenced alga of potential hosts (e.g., Picochlorum spp.) were checked for recent *Nucleocytoviricota* viral insertions using ViralRecall (Aylward and Moniruzzaman, 2021).

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## Results and Discussion

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154

Metagenomic sequencing data and biodiversity of putative DNA viruses in the HRAP

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In total five ultraviromes (UV) from different months of 2018 (i.e., April [04.17], May [05.17], July [07.05], September [09.11], and October [10.23]) were sequenced, and 14 megavirome (MV) populations based on FACS gated populations (see **Table S1**). Following genome assembly, the number of contigs greater than 2 kb ranged from 74 to 1,968 for the UV samples, and 180 to 10,045 for the MVs, with the average number per sample being 1221 and 3995 for the UV and MV respectively. The GC content in the UV ranged from 47% to 54%, whereas in the MV it ranged from 38% to 53%. Bacterial sequences were very abundant in all viromes (**Figure S2**). Regardless of potential contamination issues that are common in viral metagenomics (Jurasz et al., 2021), this result is not surprising for FAC-sorted MVs, as the size fraction of sorted particles includes that of small bacteria. For UVs, this could be due to leaks in the filtration or inefficient digestion of free DNA (lysed bacteria) present in the sample. Nevertheless, virus contigs still represented between 0.4% (05.17.P4) to 39% (07.05.UV) of the cumulated contig length. Eukaryotic virus sequences were generally dominant over prokaryotic virus sequences in MVs (the mean proportion was 2.5% for eukaryotic viruses versus 0.8% for prokaryotic viruses) while this trend was opposite in the UVs (mean frequency was 18.1% versus 0.6%, respectively).

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Using metagenomics, we employed two approaches to uncover DNA viruses of the HRAP, permitting the observation of small DNA viruses within a UV, and large DNA viruses (e.g., *Nucleocytoviricota*) through FACS populations and further processing (MV). The UVs are primarily composed of phages (e.g., *Siphoviridae*, *Podoviridae*, and *Myoviridae*), and unclassified prokaryotic viruses (**Figure 1 A**). Polinton-like viruses were also uncovered in the DNA UVs and MVs (appear in the “unclassified virus” category; see also phylogeny in **Figure S3**) and are discussed more below. Of special importance to our study, is our effort to retrieve *Nucleocytoviricota* by FACS methods, and we were able to recover a viral diversity community composition different than the UV fraction (**Figure 1 B**). Recent work (Palermo et al., 2021) on metagenomic sample filtration methods points out the almost definite loss of viral community taxonomic information in metagenomic studies

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179 where larger (> 0.45 µm pore size) water fractions are removed and left un-sequenced. No doubt, sequencing 180 this large viral fraction permitted higher recovery of *Nucleocytoviricota* compared to the UV method. A putative 181 member of family *Lavidaviridae* (a virophage) was detected and does not group closely with a *Lavidaviridae* 182 known to infect a *Chlorella* species (Figure S4). As stated previously, virophages are found in association with 183 algae infecting *Nucleocytoviricota*, and were recovered in our MV samples (Figure 1 B). This result will be 184 explored in more depth. 185

<figure><img src="image_2.png" /><figcaption>**Figure 1.** Proportion (by z-score) of viral taxonomic assignments to assembled (A) DNA ultravirome contigs by each metagenomic sample in 2018, and (B) proportion of viral taxonomic assignments to assembled metagenomes produced from FACS population, grouped together by sampling month. Please note (B) is only to showcase the diversity and is not a quantitative assessment as some sample months produced more FACS populations of interest, and therefore were attributed more sequencing. Additionally, these classifications are resulting from our “ORF voting” procedure. Total numbers of contigs attributed to each family are shown on the y-axis, and total number of contigs attributed to each month are shown on the x-axis.</figcaption></figure>

## Genetic potential of viral communities

Of 21,896 ORFs extracted from eukaryotic virus contigs, 5407 (25%) had a significant match in sequence databases, and 811 could be assigned to COG (Cluster of Orthologous Groups of proteins) functional categories (Table S2). Also, 198 ORFs were identified as encoding an MCP. For prokaryotic viruses contigs, 6586 (45%) of the 14,629 extracted ORFs had a match in sequence databases, of which 154 were against a capsid protein
and 728 were assigned to a COG functional category. Contigs of prokaryotic viruses were found to encode a higher proportion of proteins involved in DNA replication and repair, and in metabolisms of membranes and cell walls (Figure S5). One possible reason is that these viruses were mainly found in the UV fractions and therefore, likely had smaller genomes carrying genetic information more narrowly focused on essential viral functions, i.e., replication and entry into the cell. Conversely, eukaryotic viruses, which were more highly represented in the MV fractions, encoded a greater proportion of proteins involved in accessory metabolic functions, including nucleotide metabolism, protein translation and post-translation modification.

Genes of viral contigs that had significant matches in cellular organisms but not in viruses were further examined. These genes are probably derived from horizontal transfers from cellular hosts. They represent candidate auxiliary metabolic genes (AMGs) that have not yet been observed in viruses or whose sequence divergence is such that the homology relationship with another virus is no longer detectable by protein alignment.

Eukaryotic virus contigs carried 99 of these potential AMGs, 62 of which had an unknown function. Among the genes to which a function could be attributed, several were located on contigs generated from the 07.05.P3 population and encoded remarkable functions including a neutral sphingomyelinase which is a key enzyme in sphingolipid metabolism and is implicated in stress-induced signaling pathways (Choezom and Gross, 2022).

Another gene encoding a myosin motor domain-containing protein suggests that the corresponding virus might use actin-based movements in the infection process. Contigs from different populations of September (09.11) also encoded a variety of interesting proteins never yet observed in eukaryotic viruses. They include homologues of heme oxygenase and Phycocyanobilin:ferredoxin oxidoreductase which are both involved in synthesis of bilins which play multiple role in chlorophyll metabolism and signal transduction (Zhang et al., 2018), a CP12-like protein involved in regulation of the Calvin cycle responsible for CO₂ assimilation (Gontero and Maberly, 2012), a RNA polymerase subunit Rpb5, a phosphatidylserine decarboxylase involved in the synthesis of the abundant phospholipid phosphatidylethanolamine of mitochondrial membranes (Nerlich et al., 2007), a clavaminate synthase-like proteins potentially involved in the biosynthesis of clavulanic acid and other 5S clavams (Tahlan et al., 2004), which have antibacterial and antifungal activities, a non-histone chromosomal MC1 family protein whose archeal homologue is used for DNA packaging (De Vuyst et al., 2005), and a VIT1/CCC1 iron transporter family protein. We also found components of restriction modification (RM) system, including several endonucleases and DNA methyltransferases yet unseen in viruses. The presence of RM systems is unsurprising, given that they are found in members of *Nucleocytoviricota* (Filée, 2018), and more specifically have been documented in *Phycodnaviridae* (chloroviruses) (Coy et al., 2020). As previously summarized (Jeudy et al., 2020), nucleases within giant virus genomes could assist in the degradation of host DNA to permit recycling into viral particles, or inhibit host gene expression (Agarkova et al., 2006). Prokaryotic virus contigs encoded 242 proteins that match homologs in cellular organisms but not in viruses, of which most (209) had an unknown function. Interesting functions were identified, including a ABC transporter efflux protein encoded by a prokaryotic virus from the 09.11.P4 (MV) population, a N-acetylmuramoyl-L-alanine amidase (09.11.UV) involved in cleavage of cell-wall glycopetides, a helix-turn-helix (HTH) transcription regulator
(05.17.UV), a non-viral-type sialidase (10.23.UV) involved in the degradation of extracellular mucin, a RNA polymerase sigma factor (04.17.UV) and a trehalose utilization ThuA protein. Taken together, these results suggest that HRAP viruses encode a specific subset of regulatory and/or enzymatic proteins that differs from the proteomes of model viruses that serve as a template for comparative studies.

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242 *Biodiversity of potential alga hosts in the HRAP*

243

When considering only the top ten classifications of V4-18S ASVs in each sample month (**Figure 2 A**) the most abundant taxa are class *Trebouxiophyceae* (primarily family *Chlorellales*), and for the scope of this paper will be the focus. It is worth noting, the lack of diatoms is most likely due to the HRAP culture lacking in biogenic silica (i.e., no silica was added). Of specific interest to our study, several groups of unicellular microalgae are present (**Figure 2 A and B**). Including aforementioned family *Chlorellales*, alongside other members of phylum Chlorophyta; *Chlorodendraceae*, *Chlamydomonadales*, *Sphaeropleales*, *Pyramimonadales*, *Ulvophyceae*, and *Mamiellaceae* in the HRAP during the 2018 culture samples (highlighted in **Figure 2 B**). Given previously established associations with DNA viruses to Phylum Chlorophyta (Etten et al., 2020; Short et al., 2020), we focused mostly on these microalgae and not additional unicellular plankton identified in the HRAP by metabarcoding, as these species do not appear in substantial amounts (based on ASVs) in 2018 alongside our metagenomic study or present evidence of relationships with the putative viruses tracked in our study.

255

<figure><img src="image_3.png" /></figure>

256

**Figure 2.** Classification of HRAP eukaryotes by (A) proportion of amplicon sequence variants (ASVs) assigned to taxonomy of 18S metabarcoding data for each month of 2018 that also has metagenomic sampling, ASV from each month were summarised. Members of Phylum Chlorophyta are indicated on the legend with a
260 circle. Only the top ten ASV classifications for each month are represented in the schematic. Additionally, (B)

261 ASV counts of all Chlorophyta (excluding the abundant Class Trebouxiophyceae), this is not limited to the top
262 ten ASV classifications per month.

263

264 *Predicting potential microalgal hosts and their viruses.*

265

266 Using correlation of microalgal species and viral population presence we can try to resolve potential host and
267 virus relationships (Hingamp et al., 2013; Roux et al., 2017). As the approach is simply observing correlations
268 it cannot account for ecological nuances of host-virus relationships, namely it assumes that overlapping host and
269 virus presence and their relative abundances are an indicator of infection. With this understanding we interpreted
270 our results as a preliminary suggestion of possible host-virus relationships in the context of known viral ecology.

271 Correlation analyses were inconclusive for virus-host relationships outside of Chlorophyta alga, and
272 consequently we focused solely on this group.

273

274 Principal component analysis (PCA) with members of familys *Mimiviridae, Phycodnaviridae, and Lavidaviridae*
275 (sequenced within MVs) showed a strong positive correlation (i.e., higher covariance) among *Picochlorum* and
276 several members of these viruses (**Figure S6 A**), including a putative phycodnavirus that also had a strong
277 correlation with the detected member of family *Lavidaviridae* (i.e., the putative virophage). A strong correlation
278 was shown between a member of family *Mimiviridae* (M1) and the alga group *Dictyosphaerium* (**Figure S6 A**).

279 These relationships are primarily associated with September and October (2018) sampling. Although much less
280 supported by these principal components, there was an additional association between a member of family
281 *Mimiviridae* (M2) and an unknown species from the order *Chlorellales*. The PCA for polinton-like viruses
282 (PLVs) does not show a close correlation for some of the putative viruses (**Figure S6 B**). Some PLVs are
283 associated with genera *Dictyosphaerium* and *Mantoniella*, and to some extent the genus *Picochlorum*. However,
284 these variables contribute much less to the ordination than the examples highlighted within the
285 *Nucleocytoviricota* and *Lavidaviridae* PCA (**Figure S5 A**). Hierarchical clustering indicates a significant
286 grouping among previously described MV viruses and hosts (**Figure S7**; b, d, and e). Additionally, PLVs and
287 their associated PCA suggested hosts also produce significant groupings, although with slightly weaker
288 correlations (although still significant) in most cases (**Figure S8**) in relation to *Nucleocytoviricota* and
289 *Lavidaviridae* groupings. Correlations among both the MVs and PLVs with genus *Picochlorum* reflect an
290 apparent bloom of *Picochlorum* in the area the HRAP is stationed during these months, however in comparison
291 to the PLVs the correlation among 11 *Phycodnaviridae*, one *Mimiviridae*, and the putative virophage are
292 specifically much stronger (**Figure S7 b**). In the context of viral ecology, it seems more likely that correlations
293 found among *Nucleocytoviricota* and *Lavidaviridae* could imply potential hosts more readily than PLV and alga
294 correlations. We attribute this to the likely rapid proliferation of the *Nucleocytoviricota* groups after infection
295 (e.g., *Chlorella* virocells release PBCV-1 viral particles three hours post infection (Van Etten et al., 1983)). In
the case of PLVs, although relatively little is known, their dual lifestyle (Krupovic and Koonin, 2015; Yutin et al., 2015) of insertion, and later proliferation may require specific cues for a virocell to develop and release viral particles effectively uncoupling infection and release of viruses acutely, thereby confounding the interpretation of a correlation analysis. Although *Lavidaviridae* could be uncoupled in the same way due to viral integration capabilities, in this case a known trigger for viral particle production is present (i.e., co-infection with *Nucleocytoviricota*) and conclusively we expect all members of this tripartite infection to co-exist when *Lavidaviridae* are detected in the HRAP. In this scenario, detection of the *Lavidaviridae* indicates viral particles being produced and this would be in response to cellular hosts with a *Nucleocytoviricota* infection taking place (i.e., a virocell is formed). With ecology in mind, we expect to be able to produce some valid indication of both *Nucleocytoviricota* and *Lavidaviridae* alga hosts through correlation analyses, however PLV hosts indication are less straight forward. Additionally, some interactions could be occurring between PLVs and *Nucleocytoviricota*, but these were not explored given the lack of ecological information on these putative PLVs.

Using our full qPCR dataset (Figures 3 and 4) and Chlorophyta metabarcoding results (Figure 5) we can further hypothesize virus-host interactions by using our correlation and clustering work as an initial indication of potential hosts. Overall viruses within hierarchically clustered groupings exhibited similar patterns of presence and absence based on qPCR results in both *Nucleocytoviricota* and *Lavidaviridae* (Figure 3), and PLV analyses (Figure 4). Notably, the *Nucleocytoviricota* and *Lavidaviridae* grouping (Figure S6 b) reflect *Picochlorum* ASVs quite closely (Figure 5 F), strengthening our hypothesis that members from this grouping could be infecting members of genus *Picochlorum*. Although overlap does occur between the single member of *Mimiviridae* in group d (Figure 3) and correlated algae from genera *Dictyosphaerium*, and *Mantoniella* (Figure S7 c,d), no obvious clarification is made by the full dataset and qPCR implied abundances are quite low. Finally, the single *Mimiviridae* in group e (Figure S7) has further evidence for infecting the “unassigned” *Chlorella* species (Figure 5 F), as previously indicated by our correlation analyses, given both of their nearly complete disappearance after May 2018.
<figure><img src="image_4.png" /></figure>

321
322

323 **Figure 3.** Putative Nucleocytoviricota and virophage tracking by qPCR throughout 2017 and 2018 samples. Where V, P, and M indicate potential virophage (family *Lavidaviridae*), family *Phycodnaviridae*, and family *Mimiviridae* respectively. An inverse Cq is calculated by number of cycles (total 45) minus the mean Cq value across technical triplicate reactions for each instance (e.g., viral target and sample data). Redlines indicate HRAP crash dates. Note, group c is not present given that no viruses were included during hierarchical clustering.
<figure><img src="image_5.png" /><figcaption>**Figure 4.** Putative polinton-like virus (PLV) tracking by qPCR throughout 2017 and 2018 samples. Where PLV indicates potential polinton-like viruses. An inverse Cq is calculated by number of cycles (total 45) minus the mean Cq value across technical triplicate reactions for each instance (e.g., viral target and sample data). Redlines indicate basin crash dates.</figcaption></figure>

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A

Class Chlorodendrophyceae; Order Chlorodendrales;
Family Chlorodendraceae

ASV
Counts

<figure><img src="image_6.png" /></figure>

Collection Date (mm-dd)

B

Class Chlorophyceae; Order Sphaeropleales;
Family Sphaeropleales

ASV
Counts

<figure><img src="image_7.png" /></figure>

C

Class Mamiellophyceae; Order Mamiellales;
Family Mamiellaceae

<figure><img src="image_8.png" /></figure>

D

Class Chlorophyceae; Order Chlamydomonadales;
Family Chlamydomonadale

<figure><img src="image_9.png" /></figure>

E

Class Trebouxiophyceae; Order Chlorellales;
Family Chlorellales

<figure><img src="image_10.png" /></figure>

Collection Date (mm-dd)

F

<figure><img src="image_11.png" /></figure>

GENUS

Collection Date (mm-dd)
**Figure 5.** Presence and relative amounts (based on amplicon sequence variants (ASVs) of 18S data) of potential microalgae hosts using 2018 data (with the exception of 2017 being included in **A**) collected by water samples of the basin system. (**A-E**) The most abundant potential microalgal hosts at the Family level are shown, and (**F**) Genus level for family *Chlorellales* and family *Sphaeropleales* (as indicated by E or B). ASV counts are indicated by colour legends. Instances where a new culture run is made, after a die-off, are indicated by a red line.

Interestingly, the PLVs most closely related to a known PLV infecting *Tetraselmis striata* (**Figure S3**; PLV 2, 5, and 7) do not correlate closely with *Tetraselmis* spp. in the HRAP based on our PCA analysis (**Figure S6 B**), however two out of three significantly cluster together (**Figure S8 b**). We cannot confidently conclude that none of the tracked PLVs infect *Tetraselmis* spp. not only because the theoretical decoupling of PLVs and hosts discussed above, but also because an important peak of *Tetraselmis* spp. ASVs occurs during a period where qPCR data is unavailable (mid-June to autumn 2017; **Table S3**). This peak would likely be an important part of the data to assess the specific relationship (if any) between PLV 2, 5, and 7 and *Tetraselmis* spp. Phylogenetically closely related PLVs could be infecting completely different organisms, therefore the relationship of PLVs 2, 5, 7, to TsV-N1 (and each other) does not completely confirm infection of the same species. Overall, correlating PLVs and hosts by these methods are made difficult by their ecology (and our minimal understanding of PLVs at present). No other classifications (by ASVs) outside of Chlorophyta correlate significantly (including hierarchical clustering analysis) with these PLVs (data not included), despite the discovery of PLVs infecting several groups outside of Chlorophyta (Bellas and Sommaruga, 2021). However, alternative hosts (i.e., not members of Chlorophyta) cannot be ruled out by these *in silico* analyses alone. An important point is that viruses found in different taxonomic groups can also be infecting the same organism (Bellas and Sommaruga, 2021), consequently it is difficult to infer a host of these tracked PLVs. Regardless of the true host, this HRAP appears to be an interesting and significant reservoir of PLVs, some quite likely to be infecting microalgae.

Given the similarity of *Picochlorum* (**Figure 5 F**) and group c virus (**Figure 3**) dynamics (i.e., presence and abundance) with visual inspection and the results of our correlation analyses, the possibility of a previously undocumented infection of *Nucleocytoviricota* and a tripartite infection with a *Lavidaviridae* to a *Picochlorum* required further investigation. Analysis of eukaryotic genomes has revealed that endogenous *Nucleocytoviricota* are common in some lineages, particularly in green algae, and thus play an important role in host genome evolution (Blanc et al., 2015; Gallot-Lavallée and Blanc, 2017; Moniruzzaman et al., 2020). These viral insertions provide evidence of past interaction between the original virus and the organism that captured its DNA. Available *Picochlorum* genome assemblies were downloaded from NCBI GenBank to check for *Nucleocytovirivota* integrated *Nucleocytoviricota* genes/viral elements (GenBank accessions GCA011316045, GCA010909725, GCA00281821, GCA009650465, GCA_000876415). Several regions with *Nucleocytoviricota* signatures were recovered among these genomes (median size of ~15 kb; **Table S4**) using the dedicated
371 ViralRecall program (Aylward and Moniruzzaman, 2021), interestingly a *Picochlorum* genome recovered from the Mediterranean Sea (Costa Vermeille) (Krasovec et al., 2018) possessed a relatively large region (~94 kb) with both superfamily II helicase and D5 primase-helicase markers detected. These results suggest that *Nucleocytoviricota* do or have been infected with *Picochlorum* in the past (this particular sample was taken in June 2011), and specifically in the geographical area where the HRAP was situated.

376

377 Diversity of Phycodnaviridae may reflect diversity of the host

378

379 Of substantial interest in this study is the scale of *Phycodnaviridae* (phycodnaviruses) uncovered in our MVs (Figure 6). *Mimiviridae* representatives also appear, although in less abundance. It is known that *Phycodnaviridae* genomes of genera *Prasinovirus* and *Chlorovirus* contain multiple copies of the MCP gene (i.e., paralogs) (Clerissi et al., 2014), therefore the sheer number of MCP hits in this phylogeny may be at least partially explained by intragenome duplication. Nonetheless, as a core gene of *Nucleocytoviricota* (Yutin et al., 2009), putative MCPs are a vital finding in the HRAP because they signal true viral hits. With respect to DNA polymerase (PolB), a relatively robust single-copy gene for phylogenetic reconstruction of *Nucleocytoviricota* (Chen and Suttle, 1996; Clerissi et al., 2014), there are still a considerable number of putative *Phycodnaviridae* (19 total; Figure S9). Finally, a considerable number of ATPase sequences (another marker of *Nucleocytoviricota*) with *Phycodnaviridae* hits are also recovered from the MVs (18 total; Figure S10). Our results appear to suggest that in an environment such as an HRAP, where a dense culture of microalgae simulates an algal bloom, a viral “bloom” reflecting the algal abundance can occur. In a similar fashion to the RNA quasispecies (Andino and Domingo, 2015), the HRAP *Phycodnaviridae* form a repertoire of closely related genomes. RNA quasispecies are said to involve a high copy number of genome variants arising from the high mutation rates of RNA viruses (Duffy, 2018). This phenomenon produces so-called “mutant swarms” that foster unique dynamics between viruses where variants are acting “within replicate complexes, within cells, or outside cells” (Domingo et al., 2012; Domingo and Perales, 2019) in competition amongst themselves. The results of this, to the host(s)’ detriment and the virus population’s benefit, is the production of a “genome repertoire” (Domingo et al., 2020) in advance of a host’s response, or other factors that could affect the host-virus dynamics of a system in the future. However, instead of multiple viral, quasi-identical strain-like species of the RNA quasispecies, HRAP phycodnaviruses exhibited substantial genomic sequence divergence given dotplot alignments of contigs containing a single-copy DNAP gene (Figure S11). RNA viruses are capable of evolving as quasispecies because their error-prone (i.e., low fidelity) RNA polymerase, resulting in a mutational rate up to 1 million times that of their hosts (Duffy, 2018). *Nucleocytoviricota* do not feature the same high rate of mutation, largely attributed to high fidelity PolB repair machinery (Fischer et al., 2014; Redrejo-Rodríguez and Salas, 2014). Thus, it seems likely that the phycodnaviral lineages had begun to diverge well before they entered in the culture basin and on a larger time scale than between members of a typical RNA quasispecies. Assuming that the HRAP phycodnaviruses infect the same species they would have
407 to compete for access to the host. Under competitive conditions, the maintenance of a large diversity of viruses

408 is not the most likely scenario, since ecological theory rather predicts the hegemony of the most fit virus. A

409 possibly more likely scenario would be that the different viral lineages have evolved different strain specificity

410 within the same host species. Under the very favourable condition of a HRAP culture, multiple algal strains

411 could co-exist durably and support replication of a diversity of related viruses. Alternatively, this diversity

412 could reflect the diversity of closely related species present within one alga genus (i.e., *Picochlorum*).

413
<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

Putative *Phycodnaviridae*

<figure><img src="image_14.png" /></figure>

Putative *Mimiviridae*

<figure><img src="image_15.png" /></figure>

April Sample

<figure><img src="image_16.png" /></figure>

September Sample

<figure><img src="image_17.png" /></figure>

October Sample

Tree scale: 1
**Figure 6.** Phylogenetic relationship of *Nucleocytoviricota* found within the study (denoted as “unclassified”), and sequences obtained from NCBI GenBank (accession numbers as indicated) based on major capsid protein (MCP) amino acid sequence. Groups are assigned based on clades of closely related (by sequence similarity) novel *Nucleocytoviricota* for further study. Bootstrap support is indicated and based on 1000 replicates.

Within RNA quasispecies, diverse genotypes are permissible in order to handle the diversity of strains occurring within their host target species, such as the bloom species *Heterosigma akashiwo*, which has a wide distribution (Lawrence and Suttle, 2004). An array of virus strains is kept as a repertoire of infectious agents to handle the dynamics of their host strains. It may not be the case that new algal strains are developing, but rather a dance between dominant strains, and strains that are simply maintained, which changes over time or space. Although the exact mechanism is not the same between the HRAP phycodnaviruses and the RNA viruses, the pressure put on the viruses to evolve (i.e., the presence and maintenance of specific traits of a species pangenome) and the evolutionary response, or “solution”, are similar. The said pressure being the dynamics and repertoire of microalgae strains and closely related species (in our case members of family *Chlorellales* – potentially genus *Picochlorum*), and the solution being having a complementary genome repertoire of related viruses at hand; in other words a genome repertoire to handle host diversity similar to RNA quasispecies evolutionary “behaviour”.

In a study of *Prasinoviruses* found in *Ostreococcus* spp. (Yau et al., 2018), authors investigated a mechanism of dynamics by which *Nucleocytoviricota* and their hosts interactions centred around the host species (a microalgae species or strain) being resistant or susceptible to virus infection. Simplified, it was shown that in different environments, and in presence or absence of viruses, different microalgal life history strategies involving virus-susceptible and virus-resistant microalgae would be favoured or disfavoured. We thereby suggest that these phycodnaviruses forming this level of diversity in association with a dense bloom could be defined as a “quasigenus”, akin to the RNA quasispecies. This quasigenus consist of a group of related, but individual distinct, *Phycodnaviridae* that are capable of maintenance during a dense bloom of their host alga. Alga strains within one alga species can be virus susceptible or resistant (e.g., with the system of *Aureococcus anophagefferens* and AaV (Gobler et al., 2007; Brown and Bidle, 2014; Gann et al., 2022)), thereby a dense culture with different strains could support a group of closely related viruses (the “quasigenus”) that are capable of infecting some (one or more) strains of an alga species and not others. It will be interesting to determine if this “quasigenus” phenomenon also happens so prominently during algal blooms in natural environments or if it meets only in the very favourable conditions of the HRAP. Although the concept of several different viruses infecting one species is not new, it is specifically intriguing that the maintenance of several phycodnaviruses could reflect the persistence of algal strains within possibly one host species.

*Viruses may be a factor of mass mortality and bloom control within the HRAP*

Several culture crashes occurred in the HRAP throughout 2017 and 2018, some were during periods where our dataset is not at a high enough resolution to make implications about whether viruses were a significant factor
or not. For example, *Mimiviridae* 1 (Figure 3 e) and PLV 8 (Figure 4 c) are the only viruses tracked that seemingly persists before the July 2018 crash (excluding 2017 data), although not many samples were collected near the July 2018 crash. The resolution around the three crashes occurring in Autumn 2018 are quite well defined overall, and consequently the abundance of viruses (Figure 3 b, Figure 4 a) appear to coincide well with these crashes, thereby we hypothesize that the group of mostly phycodnaviruses are contributing to the crash of primarily *Picochlorum*. This is not unusual as viruses are known to participate in bloom termination (Gastrich et al., 2004; Lawrence and Suttle, 2004; Brussard et al., 2005). In context of the co-occurring PLVs, it is possible they are simply present and absent based on the presence and the removal of their potential hosts (e.g., induced by other predators, parasites or fluctuating seasonal conditions) and the overall crash and re-initiation of the system, but more significantly we do not know the effect of these PLVs on their hosts and if PLVs can contribute to or cause bloom termination in marine environments (e.g., if these PLVs lyse their hosts versus behave like *Lavidaviridae* with a tripartite infection system). Broadly speaking, the HRAP may not completely reflect the trajectory of Mediterranean sourced water used to initiate the HRAP culture, given that some algae only appear around times of culture restarts and then disappear after (family *Mamiellaceae* and *Chlamydomonadale*; Figure 5), however for the microalgae that persist and the viruses occurring alongside there appears to be an algal bloom like maintenance and resulting virus bloom termination.

## Conclusion

Overall, this study demonstrates the incredible diversity and dynamics of DNA viruses within an HRAP. Ultimately, we suggest that the HRAP environment can mimic a marine microalgae bloom. This resulting diversity of putative *Phycodnaviridae* contains a repertoire of viruses available to infect, at least one genera (and potentially the strains of one or more species) of microalgae, giving way to the concept “quasisgenus” of viruses. Although the exact progression to reach this result are different than that of RNA quasispecies, we cannot deny similarities in the end results of this potential virus-host dynamic. Ultimately, it appears that viruses (specifically those involved in a “quasisgenus” behaviour) could be meaningfully contributing to the termination of an algal bloom in the HRAP system.

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655

656 Acknowledgments

657 The authors thank the members of the VASCO2 consortium and the company COLDEP for their support and

658 permission to access the cultures. We also acknowledges support by the Institut Français de Recherche pour

659 l'Exploitation de la Mer (IFREMER) and work done by Angélique Gobet. Including the Ifremer Palavas-les-

660 Flots raceway facility and GeT-PLaGe platform (Genotoul). We also wish to specifically acknowledge our late

661 co-author, Dr. Christelle Desnues, who brought compassion, excellence, and inspiration to research during her

662 life.

663

664 Author Contributions

665 Conceptualization, C.D., G.B., E.E.C., and S.M.-B.; Methodology, C.D., G.B., S.M.-B., E.E.C.; Software,

666 E.E.C., and G.B.; Validation, formal analyses, and investigation, G.B., C.D. and E.E.C.; Resources, C.D. and

667 G.B.; Data Curation, E.E.C. and G.B.; Writing—Original Draft Preparation, E.E.C.; Writing—Review &

668 Editing, G.B., C.D., S.M.-B., T.M.P, and E.E.C.; Visualization, E.E.C.; Supervision, C.D., G.B., and S.M.-B.;

669 Project Administration, C.D., and G.B.; Funding Acquisition, G.B., and C.D. All authors have read and agreed

670 to the published version of the manuscript.

671

672 Funding

673 E.E.C, G.B, and C.D received funding from the European Union's Horizon 2020 research and innovation

674 programme under the Marie Skłodowska-Curie grant agreement No713750, with the financial support of the

675 Regional Council of Provence-Alpes-Côte d'Azur and with the financial support of the A*MIDEX (n° ANR-

676 11-IDEX-0001-02), funded by the Investissements d'Avenir project funded by the French Government, managed

677 by the French National Research Agency (ANR). The Phycovir project leading to this publication has received

678 funding from Excellence Initiative of Aix-Marseille University—A*MIDEX, a French “Investissements

679 d'Avenir" program. Part of the sequencing was conducted by the U.S. Department of Energy Joint Genome

680 Institute under CSP Proposal: 10.46936/10.25585/60000928, a DOE Office of Science User Facility
681 (https://ror.org/04xm1d337), is supported by the Office of Science of the U.S. Department of Energy operated
682 under Contract No. DE-AC02-05CH11231.

683

684 **Data Availability**

685 Data are available through and hosted by the NCBI SRA portal under the BioProject I PRJNA751746 (MiSeq
686 ultraviromes), and by the JGI Genome Portal under the JGI Proposal Id: 504991 (HiSeq megaviromes).

687

688 **Supplementary Materials**

689 Table S1: Assembly statistics, Table S2: functional annotation of viral contigs, Table S3: ASV counts, Table S4: information on predicted viral regions in *Picochlorum* genomes. Figure S1: Fluorescent activated cell sorting of 690 megaviral populations, Figure S2: Broad taxonomic classification of contigs for all sequencing dataset, Figure 691 S3: Putative polinton-like viruses (PLVs) recovered from HRAP metagenomes, Figure S4: Putative 692 *Lavidaviridae* assembled from HRAP assemblies, Figure S5: Principle component analyses, Figure S6: 693 Hierarchical clustering of potential hosts and viruses. Figure S7. Hierarchical clustering of potential hosts and 694 polinton-like viruses (PL). Figure S8: Putative *Nucleocytoviricota* phylogeny based on the DNA polymerase B 695 gene, Figure S9. Putative Nucleocytoviricota phylogeny based on the ATPase gene. Figure S10. Dotplot 696 alignments of *Phycodnaviridae* contigs containing a PolB gene. 697

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724 Supplemental Figures

725

<figure><img src="image_18.png" /></figure>

726

727

728 **Figure S1.** Fluorescent activated cell sorting by side scatter (SSC) and FITC (fluorescein isothiocyanate; excitation and emission spectrum peak wavelengths of ~495 nm and ~519 nm).

729 Sample dates are displayed above each sample, and populations (“P”) are indicated. The sized fraction for the sample water is between 0.2 µm –1.2 µm (labelled with SYBR-Green).

732
Proportion of contigs

<figure><img src="image_19.png" /></figure>

Taxonomic classification

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735 S2. Broad taxonomic classification of contigs above 2 kb for all megaviromes sequenced and a

736 combined ultravirome sequencing dataset.

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742
<figure><img src="image_20.png" /><figcaption>**Figure S3.** Putative polinton-like viruses (PLVs) recovered from HRAP metagenomes (indicated in red). The PLV TsV-N1 is indicated and the green line covers the PLVs that are clustering with it phylogenetically. The phylogeny based on the MCP gene. PLVs used in qPCR tracking are indicated in blue. Pol 8 is excluded due to the sequence dissimilarity relative to the rest of the tracked PLVs. Bootstrap values above 0.70 are displayed. All MCP (excluding those in red) were extracted from public databases (see Chapter 3 for more detailed methods).</figcaption></figure>

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<figure><img src="image_21.png" /><figcaption>**Figure S4.** Putative *Lavidaviridae* (in blue) assembled from HRAP assemblies. Phylogeny is based on major capsid protein sequenced retrieved from NCBI GenBank. Bootstrap probabilities (1000 replicates) are displayed.</figcaption></figure>

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<figure><img src="image_22.png" /><figcaption>**Figure S5.** Principle component analyses using combined and normalised qPCR data and 18S ASVs (i.e. metabarcoding) on tracked putative viruses and potential alga (Chlorophyta) hosts for (A) *Mimiviridae* (M), *Phycodnaviridae* (P), and virophage (Viro), and (B) polinton-like viruses (PL). Cos2 reports the strength of the principle component for the observations (i.e. virus or potential hosts), where a higher value depicts a stronger relationship between them or a “good representation”. The vector length of each observations represents the contribution they make to the ordination. Dates are represented by numbered objects, where 2017 is composed of 1–5 (April is 1 and 2, May is 3, June is 4 and 5), and 2018 is composed of 6–16 (April is 6, May is 7–9, August is 9, September Is 10–13, and October is 14–16).</figcaption></figure>

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<figure><img src="image_23.png" /><figcaption>Figure S6. Hierarchical clustering (SIMPROF, □ = 0.05) of potential hosts (Chlorophyta) and viruses of interest; *Phycodnaviridae* (P), *Mimiviridae* (M), and virophage (V). Predicted groupings are depicted (**a**–**e**) using a network visualisation. Darker colours represented a higher correlation between group members, where 1 is an exact correlation and 0 is no correlation. Chlorophyta species are abbreviated as follows: Pic; *Picochlorum*, Dict; *Dictyosphaerium*, Man; *Mantoniella*, Pyr; *Pyramimonas*, Mic; *Micromonas*, Chl; *Chlorella*, ChD; *Chlorodendrales*, ChN; *Chlorella* not assigned, Sch; *Schizochlamydella*, Tet; *Tetraselmis*, Dun; *Dunaliella*, and ChM; *Chlamydomonas*.</figcaption></figure>
<figure><img src="image_24.png" /><figcaption>**Figure S7.** Hierarchical clustering (SIMPROF, □ = 0.05) of potential hosts (Chlorophyta) and viruses of interest; polinton-like viruses (PL). Predicted groupings are depicted (**a**–**e**) using a network visualisation. Darker colours represented a higher correlation between group members, where 1 is an exact correlation and 0 is no correlation. Chlorophyta species are abbreviated as follows: Pic; *Picochlorum*, Dict; *Dictyosphaerium*, Man; *Mantoniella*, Sch; *Schizochlamydella*, Dun; *Dunaliella*, Chl; *Chlorella*, ChD; *Chlorodendrales*, Tet; *Tetraselmis*, ChM; *Chlamydomonas*, and ChN; *Chlorella* not assigned.</figcaption></figure>
<figure><img src="image_25.png" /></figure>

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790 **Figure S8.** Putative *Nucleocytoviricota* phylogeny based on the DNA polymerase B gene (polB).

791 Reference sequences were downloaded by NCBI GenBank (accession numbers as indicated) and

792 sequenced recovered from the HRAP are included (in bold). Bootstrap values above 0.70 are

793 displayed.

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795
<figure><img src="image_26.png" /><figcaption>Figure S9. Putative *Nucleocytoviricota* phylogeny based on the ATPase gene. Reference sequences were downloaded by NCBI GenBank (accession numbers as indicated) and sequenced recovered from the HRAP are included (in bold). Bootstrap values above 0.70 are displayed.</figcaption></figure>

796
<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /><figcaption>**Figure S10.** Dotplot alignments of *Phycodnaviridae* contigs containing a PolB gene. (A) 8 × 8 dotplot alignments of contig nucleotide sequences. (B) Dotplot representation of contig's translated nucleotide sequence matches identified by TBLASTX (red for forward matches, green for reverse matches). Position of the PolB gene on individual contigs is shown by yellow rectangles on the plot diagonal. Additional ORFs with a significant match to viruses or cellular organisms in TrEMBL are shown by red and grey rectangles respectively, and ORFs > 150 codons without match are shown with open rectangles. Contig orders are identical along the x- and y-axis in **A** and **B**. Only contigs greater than 10 kb were used in the analysis. Altogether, a residual gene collinearity is generally observed between *Phycodnaviridae* contigs, with only very few inversions (green matches), however nucleotide sequence conservation is frequently interrupted outside of coding sequences indicating that intergenic sequences have diverged to the point that no significant similarity is yet detectable.</figcaption></figure>