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Interaction dynamics and virus–host range for estuarine actinophages captured by epicPCR

Nature Microbiology volume 6, pages 630–642 (2021)Cite this article

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Abstract

Viruses impact microbial diversity, gene flow and function through virus–host interactions. Although metagenomics surveys are rapidly cataloguing viral diversity, methods are needed to capture specific virus–host interactions in situ. Here, we leveraged metagenomics and repurposed emulsion paired isolation-concatenation PCR (epicPCR) to investigate viral diversity and virus–host interactions in situ over time in an estuarine environment. The method fuses a phage marker, the ribonucleotide reductase gene, with the host 16S rRNA gene of infected bacterial cells within emulsion droplets providing single-cell resolution for dozens of samples. EpicPCR captured in situ virus–host interactions for viral clades with no closely related database representatives. Abundant freshwater Actinobacteria lineages, in particular Rhodoluna sp., were the most common hosts for these poorly characterized viruses, with interactions correlated with environmental factors. Multiple methods used to identify virus–host interactions, including epicPCR, identified different and largely non-overlapping interactions within the vast virus–host interaction space. Tracking virus–host interaction dynamics also revealed that multi-host viruses had significantly longer periods with observed virus–host interactions, whereas single-host viruses were observed interacting with hosts at lower minimum abundances, suggesting more efficient interactions. Capturing in situ interactions with epicPCR revealed environmental and ecological factors shaping virus–host interactions, highlighting epicPCR as a valuable technique in viral ecology.

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Fig. 1: Global distribution and abundance of Chesapeake Bay viral populations.
Fig. 2: Seasonal dynamics of Chesapeake Bay bacterial and viral communities.
Fig. 3: EpicPCR identifies phage–host interactions in the environment without cultivation.
Fig. 4: Abundance, diversity and ecology of CSP-like actinophage virus–host interactions as determined by epicPCR in Chesapeake Bay from May to December 2018.
Fig. 5: Phage interactions with Rhodoluna host observed in Chesapeake Bay from May to August 2018 and again in December 2018, as revealed by epicPCR.
Fig. 6: Biological trade-offs and ecological patterns related to viral and bacterial interactions revealed by epicPCR.

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Data availability

Sequences associated with 16S rRNA libraries from environmental samples and incubation experiments, bacterial and viral shotgun libraries, and fusion amplicons from epicPCR, have been deposited in the NCBI under BioProject accession no. PRJNA599167. Water physicochemical measurements and qPCR data have been deposited in the BCO-DMO database under datasets 757405 and 821955. Datasets used in this analysis include GOV2.0, NCBI non-redundant nucleotide database (nr), Tampa Bay metagenomic libraries (BioProject accession nos. PRJNA28619, PRJNA47459 and PRJNA52403), and Damariscotta River Estuary, ME, USA (BioProject accession no. PRJNA357591). Source data are provided with this paper.

Code availability

No customized code was used in analysis of the data.

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Acknowledgements

We thank the Smithsonian Environmental Research Center and K. Lohan for providing access to their facilities during sample collection. This work was supported by the National Science Foundation Biological Oceanography (award nos. 1820652, 1829831 and 1756314) and a Gordon and Betty Moore Foundation Investigator award (no. 3790). Part of this project was conducted using computational resources at the Maryland Advanced Research Computing Center and the Ohio Supercomputer Center for high-performance computing.

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Authors and Affiliations

  1. Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, MD, USA

    Eric G. Sakowski, Keith Arora-Williams & Sarah P. Preheim

  2. Department of Microbiology, The Ohio State University, Columbus, OH, USA

    Funing Tian, Ahmed A. Zayed, Olivier Zablocki & Matthew B. Sullivan

  3. Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH, USA

    Matthew B. Sullivan

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  1. Eric G. Sakowski
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Contributions

E.G.S. and S.P.P. conceived the work. E.G.S. conducted all field work, epicPCR analysis and incubation experiments. E.G.S., K.A.W. and S.P.P. conducted the experimental and computational analysis for the bacterial metagenomic and 16S rRNA gene libraries. E.G.S., F.T., A.A.Z. and O.Z. conducted experimental and computational analysis for the viral metagenomic libraries. E.G.S., K.A.W. and S.P.P. conducted the bioinformatic host prediction. E.G.S. wrote the manuscript. E.G.S., A.A.Z., O.Z., M.B.S. and S.P.P. edited the manuscript.

Corresponding authors

Correspondence to Eric G. Sakowski or Sarah P. Preheim.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Map of the Chesapeake Bay with sampling site.

Map of the Chesapeake Bay with sampling site at the Smithsonian Environmental Research Center (SERC) in Edgewater, MD marked (SERC pier). Samples were taken off the SERC pier near the mouth of the Rhode River.

Extended Data Fig. 2 Distribution of viral populations in Chesapeake Bay samples collected across seasons and years.

Viral populations were defined as contigs >5kb with < 95% average nucleotide identity across 80% of the contig. Population distributions were determined by mapping reads from each time point to population representative contigs. Shared viral populations indicate those populations that were observed across multiple time points. a, Shared viral populations between Chesapeake Bay samples collected from May 2017 to December 2018. Viral population distributions were also compared to a sample collected from the same site in December 2012. The number of viral populations observed differed between each timepoint, resulting in non-reciprocal comparisons. Self-self comparisons completely overlapped. Vertical and horizontal (below each bar cluster) bar color represent the date of sampling for each group, according to the figure legend. b, Comparison of mean viral community similarity between samples from the same season (spring, winter; n = 6 comparisons) versus between seasons (n = 19 comparisons). There was a significantly higher proportion of viral populations that were shared by samples from the same season compared to samples from different seasons (two-tailed Mann-Whitney U, p = 0.0003). Box and whisker markers represent the minimum, first quartile, median, third quartile, and maximum values. c, Comparison of mean viral community similarity between samples from the same year (2018, n = 12 comparisons) versus samples from different years (2012, 2017, and 2018; n = 13 comparisons). There was no observed difference in the proportion of shared viral populations from the same year versus across years (two-tailed Mann-Whitney U, p > 0.05). Box and whisker markers represent the minimum, first quartile, median, third quartile, and maximum values.

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Extended Data Fig. 3 Abundance and diversity of viral populations with RNR alpha subunit genes in the Chesapeake Bay.

Abundance and diversity of viral populations with RNR alpha subunit genes in the Chesapeake Bay from samples collected between May 2017 and December 2018. 634 RNR gene homologs were found across 10,858 total viral populations. a, The proportion of viral populations > 5kb with identified RNR genes at each sample time point. The total number of viral populations with identified RNR alpha subunit genes at each time point are indicated in parentheses. b, The predicted relative abundance of viral populations > 5kb with identifiable RNR alpha subunit genes in the Chesapeake Bay. Relative abundance was predicted by read mapping to all viral populations > 5kb. c, Maximum likelihood tree displaying the diversity of RNR alpha subunit genes in the Chesapeake Bay. RNR alpha subunit peptides from UniRef were clustered with translated Chesapeake Bay cellular metagenome (> 0.2 µm), viral metagenome (< 0.2 µm), and amplicon RNR sequences at 50% aa identity. Sequences were aligned with MAFFT and trimmed to 407C to 596P in E. coli. Primers designed to amplify cyanosiphoviruses and cyanopodoviruses were limited to amplifying RNR genes within this monophyletic clade. Scale bar represents amino acid substitutions per site. d, Gene counts per mL of ‘Cyano SP-like’ RNR genes in the Chesapeake Bay from May to December 2018 (n = 17 biologically independent samples). Gene counts were quantified with qPCR. Error bars are SE.

Extended Data Fig. 4 Correlation of pairwise shared populations determined by comparing viral contigs and alpha subunit RNR genes.

Spearman’s Rank correlation of pairwise shared populations determined by comparing viral contigs and alpha subunit RNR genes. Shared viral populations between libraries from December 2012, May 2017, May 2018, August 2018, and December 2018 were compared. Each point represents the proportion of viral populations that were shared between two sample time points based on comparison of contigs > 5kb (x axis) and comparison of RNR genes only (y axis) (for example 50% of May 2017 viral populations were shared with May 2018 by analysis of contigs, 57% of May 2017 viral populations were shared with May 2018 by analysis of RNR genes only). RNR genes alone captured the seasonal diversity observed from viral contigs, making it a good marker gene for viral population diversity (Spearman’s Rho = 0.93, p = 0).

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Extended Data Fig. 5 Host predictions for Chesapeake Bay viral populations from 2018 virome libraries.

Host predictions for Chesapeake Bay viral populations from spring (May), summer (August), and winter (December) 2018 virome libraries. a, Predicted host and phage family based on RNR homology. 634 RNR gene homologs were found across 10,858 total viral populations. RNR nucleotide sequences were aligned with MAFFT using the peptide sequences as a guide in TranslatorX. Only the 437 RNR sequences that spanned the same region (460A to 693I in the E. coli class I alpha RNR peptide) were further analyzed to avoid possibly double-counting partial RNR sequences on separate contigs. RNR homology was queried by BLASTn against the NCBI nr database. Only top hits with e values < 1E-10 were classified. The total number of RNR sequences analyzed at each time point are indicated in parentheses. b, Mean nucleotide identity between Chesapeake Bay RNR sequences and reference sequence top hits. Only Chesapeake Bay RNR sequences sharing at least 65% nucleotide identity over 90% of the sequence with a reference sequence were assigned a reference hit. The number of RNR sequences are indicated in parentheses. Box and whisker markers represent the minimum, first quartile, median, third quartile, and maximum values.

Extended Data Fig. 6 Rank abundance curve of viral populations and RNR sequences in an example viral community.

All of the Cyano SP-like Actinophage RNR sequences (dark lines highlighted with arrows) were ranked in the rare tails of the two viral communities and are shown in the blown-out, log-transformed insets. Only the two paired long-read and short-read metagenomes are shown here with similar results being observed for the rest of the short-read-only viromes.

Extended Data Fig. 7 k-mer-based host predictions for Chesapeake Bay viral populations assembled from shotgun metagenomics sequence data.

Half of all assembled viral populations > 5kb had a significant top hit to a putative host in the host database (see Materials and Methods; upper panel left). Actinobacteria were overrepresented as putative hosts for Chesapeake Bay viral contigs relative to all Chesapeake Bay metagenome-assembled genomes (MAGs; upper panel right). If the composition of predicted top hosts and the abundance of those in database were very similar, it would suggest that the probability of being a predicted host would scale with the abundance in the database, potentially creating false-positive associations. However, the enrichment of MAGs in the top host predictions (upper panel middle) compared to the number of MAGs in the database (lower panel left) and the enrichment of Actinobacteria within MAGs predicted as hosts (upper panel right) compared to the composition of the MAG dataset (lower panel right) is consistent with substantial viral pressure on Actinobacteria populations in this environment.

Extended Data Fig. 8 Viral community composition of populations based on top predicted host by in silico host prediction.

a, Viral community composition of populations with a Chesapeake Bay MAG as a top predicted host. Contigs with a Chesapeake Bay MAG as a top predicted host represented 20% of all viral contigs with a significant (p < 0.05) host prediction (n = 937). The total number of viral populations with a Chesapeake Bay MAG as a top host prediction are indicated in parentheses for each sample timepoint. b, Viral community composition of all contigs with a significant (p < 0.05) host prediction (n = 4,644). The total number of viral populations with a significant host prediction are indicated in parentheses for each sample timepoint.

Extended Data Fig. 9 Predictions of virus-host associations by various bioinformatics approaches largely identify unique interactions within virus-host interaction space.

a, Bioinformatics approaches identifying viral populations predicted to infected the observed metagenome assembled genomes (MAGs). Three different approaches were applied to infer viral populations that infect MAGs; Markov model-based method (WIsH, blue), CRISPER spacer homology match (CRISPR, red) and tRNA homology matches (tRNA, yellow). Numbers within each non-overlapping shaded region show how many MAGs were uniquely predicted as hosts with each method. MAGs predicted as hosts from multiple different methods are found within the overlapping shaded region (for example 2 of the same MAGs were predicted as hosts by WIsH and CRISPR in the red and blue overlapping region). Numbers in parentheses indicate how many of the shared predictions match the same viral population. In all cases, none of the viral populations predicted to infect MAGs were identical between methods. b, Bioinformatics approaches identifying host taxonomy for observed viral populations. Three different approaches were applied to infer host taxonomy; Markov model-based method (WIsH, blue), RNR homology match (RNR, red) and tRNA homology matches (tRNA, yellow). Numbers within each non-overlapping shaded region show how many viral population predictions were unique for each method. Viral populations with hosts predicted from multiple different methods are found within the overlapping shaded region. Numbers in parentheses indicate how many overlapping predictions match at the genus (first) and phylum (second) level. For example, 36 of the same viral populations had host taxonomy predicted by WIsH and RNR (red and blue overlapping region). However, while there were 36 shared predictions, only two of these host predictions were concordant at the genus or phylum level (5.6%).

Extended Data Fig. 10 The impact of viruses on Chesapeake Bay summer bacterial populations.

Bacterial communities sampled in July 2019 were incubated for 75 hours with or without viruses. a, Bacterial community composition prior to incubation. b, Growth of bacterial communities in incubations with viruses (n = 6 incubations of the same initial sample) and without viruses (n = 6 incubations of the same initial sample). Bacterial abundances were quantitated with qPCR and are reported as the mean fold-change of the bacterial community relative to the starting community abundance. Error bars are SD. Asterix indicates significant (two-tailed Mann-Whitney U test, p = 0.02 at 52 hours; p = 0.005 at 75 hours) difference in fold change between no virus and with virus incubations. c, Phylogeny and putative resistance or susceptibility of OTUs to viruses in the incubation experiment. Only OTUs with significantly (FDR p < 0.05) greater growth in one of the treatments (without viruses or with viruses) are depicted. OTUs were first filtered for detectable growth during the incubation (as determined by relative abundance of each OTU and community absolute abundances from qPCR). OTUs that displayed growth in at least four treatment replicates (without viruses or with viruses) were assessed for significantly (FDR p < 0.05) greater growth in one of the treatments using a Kruskal-Wallis test. In total, 89/ 2,664 OTUs passed filtering criteria and were identified as displaying significantly higher growth in one of the treatments. Putative susceptibility was calculated as (OTU Abundance Fold Change Without viruses)/(OTU Abundance Fold Change With Viruses). Higher values indicate greater presumed susceptibility to viral-mediated mortality.

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Sakowski, E.G., Arora-Williams, K., Tian, F. et al. Interaction dynamics and virus–host range for estuarine actinophages captured by epicPCR. Nat Microbiol 6, 630–642 (2021). https://doi.org/10.1038/s41564-021-00873-4

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