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Prevalence of viral frequency-dependent infection in coastal marine prokaryotes revealed using monthly time series virome analysis

Viruses infecting marine prokaryotes have large impacts on the diversity and dynamics of their hosts. Model systems suggest viral infection is frequency-dependent and constrained by the virus-host encounter rate. However, it is unclear whether the frequency-dependent infection is pervasive among the abundant prokaryotic populations with different growth strategies (i.e. r-strategy and K-strategy). To address this question, we performed a comparison of prokaryotic and viral communities using 16S rRNA amplicon and virome sequencing based on samples collected monthly for two years at a Japanese coastal site, Osaka Bay. Concurrent seasonal shifts observed in prokaryotic and viral community dynamics indicated that abundances of viruses correlated with that of their predicted host phyla (or classes). Co-occurrence network analysis between abundant prokaryotes and viruses revealed 6 423 co-occurring pairs, suggesting a tight coupling of host and viral abundances and their “one to many” correspondence. Although dominant K-strategist like species, such as SAR11, showed few co-occurring viruses, a fast succession of their viruses suggests viruses infecting these populations changed continuously. Our results suggest the frequency-dependent viral infection prevailed in coastal marine prokaryotes regardless of host taxa and growth strategy.

Introduction reads (FPKM) for putative terL genes were calculated using in-house ruby scripts. 154 The mts-OBV contigs with complete viral genomic sequence set collected in a 155 previous study [44] were used for viral abundance estimation based on read mapping. The 156 complete viral genomic sequence belonged to one of the following two categories: (i) 1 157 811 environmental viral genomes (EVGs; all are circularly assembled genomes, 45 were 158 assembled in Osaka Bay in a previous study [44]) derived from marine virome studies; 159 (ii) 2 429 reference viral genomes (RVGs) of cultured dsDNA viruses. Genus-level 160 genomic OTUs (gOTUs) were previously assigned for complete genomes based on 161 genomic similarity score (SG) using ViPTree [47]. For the mts-OBV contigs, if a sequence 162 showed a high similarity to one of the complete genomes (with SG > 0.15), the sequence 163 was assigned to the gOTU of the most similar genome as previously described [32,44]. 164 Quality controlled virome reads were obtained through quality control steps as previously 165 described [44]. These reads were mapped against the viral genomic sequence set using 166 Bowtie2 software with the "--score-min L,0,-0.3" parameter [48]. FPKM values were 167 calculated using in-house ruby scripts. 168 Viral host prediction 169 viral genomic sequence set collected in a previous study [44]. If mts-OBV contigs were 171 classified into the same gOTU with the viruses with a known (via cultivation) or predicted 172 (by genomic content [44]) host group, the host group was assigned to the contigs. We also 173 compared similarity with mts-OBV contigs, the viral genomes deposited in a virus-host 174 database (as of October 2018), and recently reported isolates [49,50]. 175 In addition, for the viruses without assigned host groups via genomic similarity, 176 we performed in silico host prediction based on the nucleotide sequence similarity 177 between viruses and prokaryotes as previously described [30,51,52]. First, a total of 220 178 103 viral genomes/contigs derived from marine viromes were collected and used for the

Estimation of the growth strategy of ASVs 225
We established indexes for the approximation of the r (intrinsic rate of natural 226 increase) and K (carrying capacity) of each ASV by monitoring their monthly dynamics. 227 For the approximation of the r of each ASV, the maximum increase of the normalized 228 relative rank (0-1) per month was applied. Similarly, for the approximation of K for each 229 ASV, the length of continuously dominant month (>0.1% relative abundance, 1-18 230 months) of each ASV was applied. 231

Detection of SNPs 232
Reads were mapped to the viral contigs using Bowtie2 with a "--score-min L,0,-233   Alpha-diversity (Shannon index) of the viral community was significantly higher 272 than that of the prokaryotic community (p< 0.001, Supplementary Figures S2A-B). 273 Both richness and evenness were also significantly higher in the viral community than in 274 noted that prokaryotic diversity was evaluated via single marker gene analysis (i.e., 16S 276 rRNA) but viral diversity was evaluated via whole genome sequencing. Thus, the 277 methodological difference could have caused the relatively higher diversity of the viral 278 community. Another possible explanation for the higher viral diversity is that a 279 prokaryotic species can be infected by more than one viral species at each time point 280 (discussed below). 281

Seasonal dynamics of prokaryotic and viral communities 282
We investigated seasonal dynamics of prokaryotic and viral communities using  Table S3, 291 Supplementary Figure S3). The similarity between samples was systematically lower 292 for the viral community than that for the prokaryotic community (Figure 1, discussed 293 below). The viral community composition was significantly correlated with the 294 prokaryotic community composition, as well as the seasonal environmental variables 295 (Mantel rho = 0.504, p < 0.01, Supplementally Table S3 ). 296 Given that each virus can only propagate in its specific host, and thereby the viral 297 community composition is shaped by prokaryotic community composition, abundance of 298 each virus might reflect the abundance of its host. To test this hypothesis, compositions 299 of prokaryotic and viral communities were compared using the information of predicted 300 viral hosts (mostly host phylum-or class level composition). Putative host groups of 301 viruses were predicted using four commonly used genome-based in silico prediction 302 methods (similarity with known viruses, CRISPR-spacer match, tRNA match, and 303 genome homology Major phyla (or classes for Proteobacteria) in the prokaryotic community did not 322 change drastically but the relative abundance of several phyla (classes) exhibited 323 remarkable seasonal dynamics (Figure 2). The seasonal dynamics of the predicted viral 324 hosts resembled the seasonal dynamics of prokaryotes (Figure 2). For example, 325 Cyanobacteria (79% of reads were assigned to OTU_8, Synechococcus) dominated in 326 summer (up to 9.6% and 22.6% of the community in June 2015 and July 2016, 327 respectively, Figure 2) and Synechococcus virus abundance also increased in summer (up 2) and SAR11 viruses (from 9 to 22% of the community, Figure 2) showed changes over 334 time but they were always abundant throughout the observed period. Therefore, virally 335 community appear to generally follow the dynamics of their host. 336 However, viral abundance did not always match with their putative host 337 abundance (Supplementally Figure S4). For example, the proportion of putative γ-338 Proteobacteria viruses was lower compared with that of γ-Proteobacteria and the 339 proportion of putative δ-Proteobacteria viruses was much higher compared with that of 340 δ-Proteobacteria (Figure 2). The lack of a tight correlation between viral and host 341 abundance may not be surprising. The host prediction based on genome analysis in this 342 study was mostly at the phylum or class level except for contigs showing similarity with 343 cultured viruses, such as Synechococcus/Prochlorococcus cyanoviruses, while typical 344 prokaryotic viruses could only infect specific host species or strains. Further, although 345 our analysis annotated putative hosts at nearly 60% of the viral community, remaining 346 populations without host prediction may lead to the underestimation of viruses infecting 347 some taxa. The difference in burst sizes among viruses, which have been estimated to 348 range from 6 to 300 in the marine environment [74], can also influence the estimation of 349 viral abundance. Next, to investigate whether viral abundance increased according to 350 specific host abundance, we statistically examined associations (i.e. co-occurrence) 351 between the viruses and ASVs extracted from the abundant 73 prokaryotic OTUs . 352

Co-occurrence network analysis between the abundant prokaryotes and viruses 353
To examine the dynamics of closely related (nearly strain-level) variants within The community of viruses showed a higher alpha-diversity that the community 384 of prokaryotes (Supplementary Figure S2), and the co-occurrence analysis indicated 385 one-to-many associations between the host and viral populations (median 16 viral species 386 per a prokaryotic ASV). This suggests that one abundant prokaryotic ASV can interact 387 with multiple viral species. Note that the numbers of co-occurring viral species were 388 overestimated since each contig could be a partial genome fragment derived from the 389 same viral genome (average completeness of mts-OBV contigs was 39%, 390 Supplementary Table S4). However, the contigs classified into different genera (average 391 8 gOTUs) often co-occurred with an ASV. Next, we characterized the "one to many" 392 virus-host interaction network (i.e. how many viruses co-occurred with each ASV) with 393 respect to their host taxa and host growth strategy. 394 The number of co-occurring viral species for prokaryotic ASVs was generally  viruses. Next, we examined the number of co-occurring viruses among prokaryotic ASVs 412 classified in the same taxa depending on the growth strategy to solve this issue. 413

Characterization of the virus-host interaction by host growth strategy 414
The growth strategy (r or K) of each prokaryotic ASV was defined by the indexes that we 415 introduced (see methods). According to these, 13 ASVs were determined as K-strategist-416 like ASVs (i.e. K-index>12, r-index< 0.1). Among the 13 ASVs, seven were classified taxa previously predicted as r-strategist (i.e. Flavobacteriaceae, Rhodobacteraceae, 419 Vibrio, and Marine Group II) were classified into the r-strategist-like ASVs (K-index<3, 420 r-index>0.5, total 33 ASVs) (Supplementally Figure S10). Generally, r-strategist-like 421 ASVs, such as members of Bacteroidetes, showed a large number of co-occurring viral 422 species (Supplementally Figure S10). In contrast, K-strategist-like ASVs of 423 Synechococcus and SAR11 showed relatively few co-occurring viral species 424 (Supplementally Figure S10). The most abundant ASV of Synechococcus (ASV8-1, 425 making up 76.7% of the whole cyanobacterial reads) and SAR11 (ASV1-1, occupied 7-426 64% of whole SAR11 reads of each month) showed 7 and 16 co-occurring viruses, 427 respectively, even though 183 cyanoviruses and 500 SAR11 viruses were detected during 428 the observation (Supplementally Figure S10). 429 If a temporal switch of virus-host pairs occurred, co-occurrence analysis may fail 430 to detect virus-host associations.. Therefore, we compared dynamics of the two dominant 431 prokaryotic ASVs and viral species that did not co-occur with their predicted hosts. Therefore, SNPs from mts-OBV contigs with more than ten coverages (2 356 contigs) 460 were calculated. We observed an increase of intrapopulation genetic diversity (SNPs 461 quantified by average genomic entropy) as a function of overall population abundance 462 regardless of their host taxa (Supplementary Figure S11). This result corroborates the 463 notion that contact-rate is the key parameter for the viral reproduction regardless of 464 whether they show a long term co-occurrence pattern with their hosts. 465

Ecological interpretation inferred from virus-host dynamics 466
There are at least three possible mechanisms of the above-mentioned virus-host 467 pair switch (Figure 5). First, more closely related prokaryotic populations that cannot be 468 differentiated by the 16S rRNA gene polymorphism could co-occur with viruses. 469 Previous studies focusing on the polymorphism of ITS sequences (ITS-ASV) in SAR11 470 and Cyanobacteria reported that ITS-ASV dynamics correlate more with viral dynamics, 28 inferred from T4-like viral marker genes, than 16S-ASV dynamics of these taxa [7,27].