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. 2018 Sep 4;5:180154. doi: 10.1038/sdata.2018.154

Single cell genomes of Prochlorococcus, Synechococcus, and sympatric microbes from diverse marine environments

Paul M Berube 1,a, Steven J Biller 1,*, Thomas Hackl 1, Shane L Hogle 1, Brandon M Satinsky 1, Jamie W Becker 1, Rogier Braakman 1, Sara B Collins 1, Libusha Kelly 2, Jessie Berta-Thompson 1, Allison Coe 1, Kristin Bergauer 3, Heather A Bouman 4, Thomas J Browning 5, Daniele De Corte 6, Christel Hassler 7, Yotam Hulata 8, Jeremy E Jacquot 9, Elizabeth W Maas 10, Thomas Reinthaler 3, Eva Sintes 3, Taichi Yokokawa 6, Debbie Lindell 8, Ramunas Stepanauskas 11, Sallie W Chisholm 1,12,b
PMCID: PMC6122165  PMID: 30179231

Abstract

Prochlorococcus and Synechococcus are the dominant primary producers in marine ecosystems and perform a significant fraction of ocean carbon fixation. These cyanobacteria interact with a diverse microbial community that coexists with them. Comparative genomics of cultivated isolates has helped address questions regarding patterns of evolution and diversity among microbes, but the fraction that can be cultivated is miniscule compared to the diversity in the wild. To further probe the diversity of these groups and extend the utility of reference sequence databases, we report a data set of single cell genomes for 489 Prochlorococcus, 50 Synechococcus, 9 extracellular virus particles, and 190 additional microorganisms from a diverse range of bacterial, archaeal, and viral groups. Many of these uncultivated single cell genomes are derived from samples obtained on GEOTRACES cruises and at well-studied oceanographic stations, each with extensive suites of physical, chemical, and biological measurements. The genomic data reported here greatly increases the number of available Prochlorococcus genomes and will facilitate studies on evolutionary biology, microbial ecology, and biological oceanography.

Subject terms: Microbial ecology, Coevolution, Biogeography, Marine biology, Genome

Background & Summary

Marine cyanobacteria within the genera Prochlorococcus and Synechococcus are estimated to be responsible for roughly 25% of ocean net primary productivity1. Prochlorococcus is the numerically dominant phototroph in oligotrophic subtropical gyres, which are among the largest contiguous biomes on Earth2. In these nutrient poor regimes, Prochlorococcus can account for over half of the chlorophyll3,4. While Prochlorococcus is generally restricted to open ocean habitats between 45oN and 40oS, Synechococcus has a much broader geographical distribution that extends to subpolar and coastal regions1. This difference in range is thought to be due in part to the greater phenotypic flexibility and regulatory capacity among Synechococcus, enabling acclimation to heterogeneous conditions5. By contrast, Prochlorococcus has a more streamlined genome adapted to less variable but nutrient depleted regions of the open ocean6. Although Prochlorococcus cells have the smallest genomes of known oxygenic phototrophs (~1.6–2.7 Mbp and ~2000–3000 genes), the global collective of this group harbors an immense diversity of protein encoding genes7. Recent estimates using 41 genomes of cultivated isolates suggested that the Prochlorococcus pan-genome–the complete set of genes harbored by all Prochlorococcus–contains more than 80,000 distinct genes6, many of which presumably play a role in adaptation to local environmental conditions. Only a small fraction of these genes have been catalogued, highlighting the potential for culture-independent single cell genomics to reveal new ecologically relevant functions among Prochlorococcus.

As a consequence of their abundance and global distribution, Prochlorococcus and Synechococcus perform key functions at the base of marine food webs, primarily the supply of fixed carbon to higher trophic levels. Much of this carbon is regenerated through respiration by co-occurring heterotrophic bacteria, such as the highly abundant SAR11 clade of marine Alphaproteobacteria (Candidatus Pelagibacter ubique). Many of these heterotrophic bacteria perform ecosystem services that in turn benefit the cyanobacterial populations. In particular, Prochlorococcus is highly sensitive to reactive oxygen species, such as hydrogen peroxide8, which can be detoxified by some heterotrophic community members. Abundant catalase encoding heterotrophs in oligotrophic environments, such as the SAR86 and SAR116 clades of marine proteobacteria9,10 and some sub-populations of SAR11 (ref. 11), are likely to be important community members that provide cross-protection for sensitive Prochlorococcus populations. Recent work further suggests that Prochlorococcus and the dominant heterotrophic cells in the oligotrophic ocean have evolved metabolic co-dependencies to maximize metabolic potential11. Thus, understanding the diversity of functions performed by sympatric heterotrophic cells is essential for understanding the ecology and evolution of Prochlorococcus and the combined impact these microbial groups have on ecosystem function and ocean biogeochemistry.

Although studies using cultivated isolates have revealed much about the ecology of Prochlorococcus, Synechococcus, SAR11, and other important taxa that contribute to the function of marine ecosystems12, culture-independent studies have begun to reveal an astounding degree of diversity in the wild. In particular, recent advances in the genomics of single cells have uncovered previously unknown marine microbial phyla and functions13 and have identified a high degree of genome streamlining, mixotrophy, and metabolic specialization within bacterial cells of the surface ocean14. Single cell genomes of Prochlorococcus have revealed the existence of new clades with distinct ecological and physiological adaptations15 as well as a high degree of genomic and functional diversity among Prochlorococcus cells with nearly identical ribotypes16,17.

Single cell genomes of both abundant and rare taxa are useful for phylogenetic anchoring of metagenomic data sets and expanding our knowledge of previously undetected phylogenetic lineages and the functions they harbor. Casting a broad net in order to most effectively capture the diversity of cyanobacterial and sympatric heterotrophic microorganisms, we have obtained samples from 22 geographic locations across the world’s oceans (Fig. 1), representing 10 Longhurst biogeographical provinces18,19 (Table 1). Many of these samples were collected under the auspices of the BioGEOTRACES component of GEOTRACES20. From these samples, we report 738 single cell genome assemblies consisting of 489 Prochlorococcus, 50 Synechococcus, 82 SAR11, 17 SAR116, 16 SAR86, 9 extracellular virus particles, and 75 additional sympatric microorganisms. To aid in the identification of orthologous genes and facilitate comparative genomics studies, we have precomputed a set of cyanobacterial and cyanophage specific clusters of orthologous groups of proteins (CyCOGs). We expect these data to be useful for a variety of studies related to evolutionary biology, microbial ecology, and ocean biogeochemistry.

Figure 1. Map of sampling locations.

Figure 1

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Single cell genomes at each site are represented by miniaturized stacked dot-plots (each dot represents one single cell genome), with organism group indicated by color, and cells categorized as “undetermined” if robust placement within known phylogenetic groups failed due to low assembly completeness/quality or missing close references. Larger points correspond to stations on associated GEOTRACES cruises.

Table 1. Biosamples with associated cruise and geolocation metadata.

biosample project cruise_name cruise_id station bottle_id depth_m latitude_degrees_north longitude_degrees_east sample_date longhurst_province
SWC-01 CMORE BIGRAPA MV1015 1 100100213 20 −20.08 −70.8 11/19/2010 CHIL
SWC-02 CMORE BIGRAPA MV1015 1 100100203 55 −20.08 −70.8 11/19/2010 CHIL
SWC-03 CMORE BIGRAPA MV1015 4 100402922 14 −23.46 −88.77 11/29/2010 SPSG
SWC-04 CMORE BIGRAPA MV1015 4 100402914 112 −23.46 −88.77 11/29/2010 SPSG
SWC-05 CMORE BIGRAPA MV1015 7 100705722 14 −26.25 −103.96 12/8/2010 SPSG
SWC-06 CMORE BIGRAPA MV1015 7 100705705 180 −26.25 −103.96 12/8/2010 SPSG
SWC-07 HOT HOT214 KM0920 ALOHA 2140201221 5 23.75 −158 8/19/2009 NPTG
SWC-08 HOT HOT216 KOK0917 ALOHA 2160201214 100 23.75 −158 11/4/2009 NPTG
SWC-09 BATS BATS248 AE0916 BATS 1024800403 10 31.07 −64.17 7/14/2009 NASW
SWC-10 BATS BATS252 AE0926 BATS 1025200410 100 31.07 −64.17 11/7/2009 NASW
SWC-11 GEOTRACES GA02(L1) PE319 16 632891 8 36.2 −53.31 5/20/2010 NASW
SWC-12 GEOTRACES GA02(L2) PE321 25 633233 119 24.71 −67.07 6/17/2010 NATR
SWC-13 GEOTRACES GA02(L2) PE321 35 634604 100 9.55 −50.47 6/28/2010 NATR
SWC-14 GEOTRACES GA03(L1) KN199 7 841892 57.7 24 −22 10/24/2010 NATR
SWC-15 GEOTRACES GA03(L2) KN204 4 844349 90.8 38.32 −68.87 11/12/2011 GFST
SWC-16 GEOTRACES GA03(L2) KN204 16 845948 89.9 26.14 −44.83 11/30/2011 NASW
SWC-17 GEOTRACES GA03(L2) KN204 20 846242 99.7 22.33 −35.87 12/4/2011 NATR
SWC-18 GEOTRACES GA03(L2) KN204 24 846716 71.6 17.4 −24.5 12/10/2011 NATR
SWC-19 GEOTRACES GA10(L1) D357 9 237839 21.2 −34.98 16.02 11/10/2010 EAFR
SWC-20 GEOTRACES GP13(L1) SS2011 4 1223543 50.6 −30 156 5/16/2011 AUSE
SWC-21 GEOTRACES GP13(L1) SS2011 22 1222400 50.4 −30 174 5/24/2011 ARCH
SWC-22 GEOTRACES GP13(L1) SS2011 38 1224245 76 −32.5 −170 5/31/2011 SPSG
SWC-23 GEOTRACES GP13(L2) TAN1109 GT3 1153793 203 −32.5 −170 6/11/2011 SPSG
SWC-24 GEOTRACES GP13(L2) TAN1109 GT19 1156283 100 −32.5 −154 6/20/2011 SPSG
SWC-26 SCOPE GRADIENTS(1.0) KOK1606 4 10400223 5 28.14 −158 4/22/2016 NPTG
SWC-27 SCOPE GRADIENTS(1.0) KOK1606 4 10400206 90 28.14 −158 4/22/2016 NPTG
SWC-28 SCOPE GRADIENTS(1.0) KOK1606 6 10600223 5 32.7 −158 4/24/2016 NPPF
SWC-29 SCOPE GRADIENTS(1.0) KOK1606 6 10600207 60 32.7 −158 4/24/2016 NPPF
SWC-30 SCOPE GRADIENTS(1.0) KOK1606 9 10900223 5 36.57 −158 4/27/2016 NPPF
SWC-31 SCOPE GRADIENTS(1.0) KOK1606 9 10900207 65 36.57 −158 4/27/2016 NPPF
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Methods

Sample collection

Samples were collected on 13 cruises in the Pacific and Atlantic oceans and encompass 30 discrete biosamples (Table 1) from the following Longhurst Provinces: CHIL, Coastal-Chile-Peru Current Coastal Province; SPSG, Westerlies-S. Pacific Subtropical Gyre Province; NPTG, Trades-N. Pacific Tropical Gyre Province; NASW, Westerlies-N. Atlantic Subtropical Gyral Province (West) (STGW); NATR, Trades-N. Atlantic Tropical Gyral Province (TRPG); GFST, Westerlies-Gulf Stream Province; EAFR, Coastal-E. Africa Coastal Province; AUSE, Coastal-East Australian Coastal Province; ARCH, Trades-Archipelagic Deep Basins Province; NPPF, Westerlies-N. Pacific Polar Front Province. A minimum of 2 replicates of 1-2 mL of raw seawater was transferred to sterile cryovials with glycerol added as a cryoprotectant at a final concentration of 10%. Samples were flash frozen in liquid nitrogen and stored at -80 °C.

Single amplified genome (SAG) generation

The generation, identification, sequencing, and de novo assembly of SAGs were performed at the Bigelow Laboratory for Ocean Sciences’ Single Cell Genomics Center (scgc.bigelow.org). The cryopreserved samples were thawed and pre-screened through a 40 μm mesh size cell strainer (Becton Dickinson). Fluorescence-activated cell sorting (FACS) was performed using a BD InFlux Mariner flow cytometer equipped with a 488 nm laser for excitation and a 70 μm nozzle orifice (Becton Dickinson, formerly Cytopeia), as previously described16,21. The cytometer was triggered on side scatter, and the “single-1 drop” mode was used for maximal sort purity. For cyanobacteria, the sort gate was defined based on cellular pigment autofluorescence16. In order to discriminate heterotrophic bacteria and extracellular particles, environmental samples were incubated with the SYTO-9 DNA stain (5 μM final concentration; Thermo Fisher Scientific) for 10–60 min, after which the particle green fluorescence (proxy to nucleic acid content), light side scatter (proxy to size), and the ratio of green versus red fluorescence (for improved discrimination of cells from detrital particles) were used to define the sort gate21. Individual cells were deposited into 384-well plates (Table 2) containing 600 nL per well of 1x TE buffer and stored at -80°C until further processing. Of the 384 wells, 317 wells were dedicated for single particles, 64 wells were used as negative controls (no droplet deposition), and 3 wells received 10 particles each to serve as positive controls. BD FACS Sortware software was used to collect index sort data (indexed_facs_wga_summary.tsv, Data Citation 1), with FACS plots available from figshare (facs_ssc_fsc_plots.pdf, Data Citation 1). Diameters of sorted cells (indexed_facs_wga_summary.tsv, Data Citation 1) were determined using the FACS light forward scatter signal, which was calibrated against cells of microscopy-characterized laboratory cultures21. The DNA for each cell was amplified using either multiple displacement amplification (MDA) or WGA-X21, with amplification kinetics distributions for each plate available from figshare (kinetics_welltype_distributions_summary.pdf, Data Citation 1).

Table 2. Biosamples and sort gates associated with the plate identification numbers used as prefixes for genome names.

biosample Prochlorococcus Sort Gate Synechococcus Sort Gate Cyanobacteria Sort Gate Bacteria Sort Gate Extracellular Sort Gate
SWC-01 AG-311 n/a n/a AG-313 n/a
SWC-02 AG-315 AG-316 n/a AG-319 n/a
SWC-03 AG-321 AG-323 n/a AG-325 n/a
SWC-04 AG-331 n/a n/a AG-333 n/a
SWC-05 AG-335 n/a n/a AG-337 AG-339
SWC-06 AG-341 n/a n/a AG-343 AG-345
SWC-07 AG-347 n/a n/a AG-349 n/a
SWC-08 AG-402 n/a n/a AG-404 n/a
SWC-09 AG-355 n/a n/a AG-359 n/a
SWC-10 AG-363 n/a n/a AG-365 n/a
SWC-11 AG-388 n/a n/a AG-390 n/a
SWC-12 AG-412 n/a n/a AG-414 n/a
SWC-13 AG-409 n/a n/a AG-410 n/a
SWC-14 AG-418 AG-420 n/a AG-422 n/a
SWC-15 AG-424 n/a n/a AG-426 n/a
SWC-16 AG-429 n/a n/a AG-430 n/a
SWC-17 AG-432 n/a n/a AG-435 n/a
SWC-18 AG-436 n/a n/a AG-439 n/a
SWC-19 AG-442 AG-444a n/a AG-447 n/a
SWC-20 AG-449 AG-450 n/a AG-453a n/a
SWC-21 AG-455 n/a n/a AG-457a n/a
SWC-22 AG-459 n/a n/a AG-461 n/a
SWC-23 AG-463 n/a n/a AG-464 n/a
SWC-24 AG-469 n/a n/a AG-470 n/a
SWC-26 n/a n/a AG-670 n/a n/a
SWC-27 n/a n/a AG-673 n/a n/a
SWC-28 n/a n/a AG-676 n/a n/a
SWC-29 n/a n/a AG-679 n/a n/a
SWC-30 n/a n/a AG-683 n/a n/a
SWC-31 n/a n/a AG-686 n/a n/a
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aRibosomal RNA sequences only (16S-23S intergenic transcribed spacer or 16S).

Marker gene screening

Single cell MDA and WGA-X products were diluted 50x in UV-treated, 0.2 μm filtered water and then used as templates in real-time PCR, as previously described21. Heterotrophic bacteria were screened using 16S rRNA gene primers 27 F and 907 R. Cyanobacteria were analyzed using primers targeting the 16S-23S intergenic transcribed spacer (ITS) sequence16. The obtained PCR amplicons were sequenced from both ends using Sanger technology at GeneWiz (South Plainfield, NJ). The two reads were automatically aligned and the consensus was manually curated using Sequencher v4.7 (Gene Codes Corporation, Ann Arbor, MI, USA). Chimeric 16S rRNA sequences were identified using DECIPHER22 and removed. ITS and 16S rRNA sequences have been deposited with GenBank (Data Citation 2,Data Citations 3).

Cell selection

A selection of cyanobacterial and extracellular SAGs derived from the BiG-RAPA cruise (plates AG-311, AG-315, AG-321, AG-331, AG-335, AG-341, AG-316, AG-323, AG-339, and AG-345) and HOT and BATS cruises (plates AG-347, AG-355, AG-363, and AG-402) were chosen for sequencing based on their fast whole genome amplification, which correlates with good genome recovery in de novo assemblies21. Forty-eight additional cyanobacterial SAGs from plates AG-347, AG-355, AG-363, AG-402, AG-418, and AG-459 were selected based on the presence/absence of the narB marker gene as determined by a PCR screen using primer sequences 5’-CANTGGCAYACNATGAC-3’ and 5’-RAANCCCCARTGCATNGG-3’. All other cyanobacterial SAGs were selected based on ITS taxonomy with the aim of obtaining a diverse set of cyanobacterial single cell genomes from multiple geographic locations and depths. All heterotroph SAGs were selected based on the classification of 16S sequences using the Ribosomal Database Project (RDP) Release 11 (ref. 23). We focused on obtaining a diverse pool of heterotrophs, including those with poor representation in public databases, that co-occur with Prochlorococcus and Synechococcus (e.g. SAR11, SAR116, SAR86, marine Actinobacteria, OCS116, SAR202, and SAR324).

Genomic sequencing and de novo assembly

Illumina libraries were created, sequenced and de novo assembled as previously described21. Only contigs longer than 2,000 bp were retained. This workflow was evaluated for assembly errors using three bacterial benchmark cultures with diverse genome complexity and %GC, indicating 60% average genome recovery, no non-target and undefined bases, and average frequencies of misassemblies, indels and mismatches per 100 kbp: 1.5, 3.0 and 5.0 (ref. 21). Paired-end sequencing reads (Data Citation 4) and genome assemblies (Data Citations 5–7) have been deposited with NCBI.

Genome annotation

All genome assemblies were also deposited at the Joint Genome Institute’s Integrated Microbial Genomes (IMG) system and annotated using the JGI Microbial Genome Annotation Pipeline (MGAP v. 4)24,25. Assembled genome sequences, gene calls, and functional annotations are available from figshare (Data Citation 1) and IMG (https://img.jgi.doe.gov/). Data can also be viewed and analyzed within IMG/ProPortal (https://img.jgi.doe.gov/proportal). A table linking IMG accession numbers with genome assembly statistics is provided to facilitate use of these annotation data (genome_assembly_summary.tsv, Data Citation 1).

Phylogeny

In order to facilitate downstream analyses, we have inferred the phylogeny for the cyanobacterial genomes and heterotrophic bacterial genomes (Figs. 2 and 3). We used the PhyloSift software26 to identify and align a collection of core protein coding gene families from the single cell genomes. Briefly, PhyloSift uses LAST27 to identify 37 protein-coding marker genes28. The identified orthologous sequences are then aligned to marker gene HMM profiles using the hmmer software suite29 and concatenated into a reading-frame-aware nucleotide codon alignment. The alignments were then trimmed using the automated heuristic method -automated1 in trimAl v1.2 (ref. 30). The recovery of PhyloSift marker gene families ranged from 2–37 (median 31) families per heterotroph single cell genome and from 0–37 (median 27) families per cyanobacterial single cell genome. For phylogenetic inference, we included all single cell genomes with at least 2 PhyloSift marker families present in the final alignments, and 151 additional heterotroph genomes to provide greater context for the heterotroph tree (Data Citation 1). We made our marker-family selection criteria as inclusive as possible in order to convey general tree topology for the greatest possible proportion of the dataset, but we note that some of the most incomplete genomes may be subject to phylogenetic artefacts due to the large number of gaps in their alignments. Maximum Likelihood trees were inferred using raxmlHPC-PTHREADS-AVX v8.2.9 (ref. 31) using the GTRGAMMA model of rate heterogeneity for heterotrophs and the GTRCAT model for cyanobacteria. RAxML runs were conducted with rapid bootstrapping32, and the number of bootstrap trees was automatically determined using the extended majority rule criterion33 resulting in 300 and 100 bootstrap replicates for the cyanobacterial and heterotroph trees respectively. Lists of taxa used for the phylogenies as well as the alignments in FASTA format and trees in Newick format are available from figshare (Data Citation 1).

Figure 2. Maximum Likelihood phylogeny of cyanobacterial genomes.

Figure 2

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The phylogeny includes 66 Prochlorococcus isolate genomes, 27 Synechococcus isolate references, and 588 single cell genomes (533 of which are part of this project). Bootstrap values are represented by size-scaled dots at nodes. Bootstrap values less than 50 are omitted. Scale bar represents 0.1 nucleotide substitutions per sequence position. Phylogenetic clade membership is indicated by colored blocks and text labels. The three Synechococcus subclusters displayed are highlighted by dashed lines and a segmented outer ring. The tree is rooted at Synechococcus sp. WH5701 (subcluster 5.2). The underlying data set used for phylogenetic inference was a concatenated alignment of 2–37 PhyloSift marker gene families (see methods for details).

Figure 3. Maximum Likelihood phylogeny of heterotrophic bacterial single cell genomes and additional reference genomes.

Figure 3

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Bootstrap values are represented by size-scaled dots at nodes. Scale bar represents 0.3 nucleotide substitutions per sequence position. The eight taxonomic lineages of the single cells are colored and labeled. Additional marine Actinobacteria lineages are presented in grey to provide added context for the Sva0996 lineage. Numbers in parenthesis indicate the number of single cell genomes from each lineage relative to the total number of genomes in that lineage used to construct the tree. The underlying data set used for phylogenetic inference was a concatenated alignment of 2-37 PhyloSift marker gene families (see methods for details).

Cyanobacterial Clusters of Orthologous Groups of proteins (CyCOGs)

To provide an overview of functional composition of the genes present in the cyanobacterial and cyanophage genomes, we inferred clusters of orthologous groups of proteins, referred to here as CyCOGs. The underlying set of genomes includes Prochlorococcus, Synechococcus, cyanophages, and cyanobacterial virocells (genome assemblies containing both bacteria and phage genomes) from our data set. We also included publicly available genome data from IMG for Prochlorococcus, marine Synechococcus in subclusters 5.1, 5.2, and 5.3, and cyanophages isolated using these cyanobacteria as hosts. The following assemblies with likely heterotroph contamination were excluded: AG-418-M21 and scB245a_518D8 (ref. 16).

The clustering of the proteins was carried out with panX34, with parameters tuned to account for incompleteness of SAG genomes: --core_genome_threshold 0.5 --core_gene_strain_fpath strains_complete_95plus.txt (core genes are defined by being present in at least 50% of all genomes, and in every genome of >95% completeness). The workflow comprises an initial clustering step performed with MCL35 on all-versus-all alignments generated with DIAMOND36, followed by a phylogeny-aware postprocessing procedure to split paralogous groups34. This analysis yielded a total of 40,295 CyCOGs (cycogs.tsv, Data Citation 1), of which 23,427 are found in Prochlorococcus, 17,692 are found in Synechococcus, and 3,267 are found in cyanophage.

This is ProPortal CyCOGs version 6.0 (cycogs.tsv, Data Citation 1). Prior releases include versions 1, 3, 4, and 5 of ProPortal CyCOGs37–40. Version 2 is an unreleased set of CyCOGs developed for testing purposes only. Legacy CyCOG definitions are also available from IMG/ProPortal (https://img.jgi.doe.gov/proportal).

Code availability

No custom code was used in the generation or processing of the data. Software versions and the use of any adjustable variables and parameters are as follows:

DECIPHER 2.2.0 (ref. 22)

Trimmomatic 0.32 (ref. 41): -phred33 LEADING:0 TRAILING:5 SLIDINGWINDOW:4:15 MINLEN:36

kmernorm 1.05: -k 21 -t 30 -c 3 (http://sourceforge.net/projects/kmernorm)

SPAdes 3.0.0 (ref. 42): --careful --sc --phred-offset 33

SPAdes 3.9.0 (ref. 42): --careful --sc --phred-offset 33

PhyloSift 1.0.1 (ref. 26): search --isolate --besthit --threads 20

PhyloSift 1.0.1 (ref. 26): align --isolate --besthit --threads 20

trimAl 1.2 (ref. 30): -automated1

RAxML 8.2.9 (ref. 31): -T 20 -m GTRCAT -p 8048 -f a -x 39381 -# autoMRE

RAxML 8.2.9 (ref. 31): -T 20 -m GTRGAMMA -p 82748 -f a -x 34671 -# autoMRE

Data Records

File 1: Fluorescence-activated cell sorting (FACS) plots associated with the single cell genome assemblies can be found in facs_ssc_fsc_plots.pdf (Data Citation 1).

File 2: DNA amplification kinetics summaries associated with the single cell genome assemblies can be found in kinetics_platemap_summary.pdf (Data Citation 1).

File 3: DNA amplification kinetics distributions associated with the single cell genome assemblies can be found in kinetics_welltype_distributions_summary.pdf (Data Citation 1).

File 4: A complete list of genomes used for CyCOG annotations can be found in cycogs-genomes.tsv (Data Citation 1).

IID – Strain or single cell identifier

GROUP – Prochlorococcus, Synechococcus, or Virus

IMG_ID – IMG genome identification number

TYPE – Single amplified genome (SAG) or cultured reference (ISOLATE)

JGI_GENOMEPORTAL_NAME – Name in the JGI Genome Portal

Completeness – Percent genome completeness determined by checkM

File 5: CyCOG definitions can be found in cycogs.tsv (Data Citation 1).

cycog_iid – Unique CyCOG identifier

cycog_num_taxa – Number of genomes containing CyCOG

cycog_num_genes – Number of genes encompassed by CyCOG

cycog_num_duplications – Number of paralogous genes within CyCOG

cycog_num_pro – Number of Prochlorococcus genes within CyCOG

cycog_num_syn – Number of Synechococcus genes within CyCOG

cycog_num_phage – Number of cyanophage/virus genes within CyCOG

cycog_cns_product – Consensus annotation for genes within CyCOG

cycog_genes – Comma delimited list of all genes found in CyCOG with the format of A_B, where A is the IID of the genome found in File 4 and B is the unique IMG gene ID.

File 6: Taxa used for the phylogeny of cyanobacteria can be found in cyanobacteria_phylogeny_taxa.tsv (Data Citation 1).

File 7: The reading-frame-aware nucleotide codon alignment used for phylogenetic inference of cyanobacterial taxa can be found in cyanobacteria_phylogeny_alignment.fna (Data Citation 1).

File 8: The maximum likelihood phylogenetic tree for cyanobacteria can be found in cyanobacteria_phylogeny_rootedtree.nwk (Data Citation 1).

File 9: Taxa used for the phylogeny of heterotrophic bacteria can be found in heterotroph_phylogeny_taxa.tsv (Data Citation 1).

File 10: The reading-frame aware nucleotide codon alignment used for phylogenetic inference of heterotrophic bacterial taxa can be found in heterotroph_phylogeny_alignment.fna (Data Citation 1).

File 11: The maximum likelihood phylogenetic tree for heterotrophic bacteria can be found in heterotroph_phylogeny_unrootedtree.nwk (Data Citation 1).

File 12: A bzip2 compressed tar archive containing IMG annotated genome assemblies, gene and protein sequences, and associated annotation files derived from the JGI Microbial Genome Annotation Pipeline (MGAP v. 4)24,25 (Data Citation 1).

.fna – Nucleic acid file in multi-fasta format

.genes.fna – Gene sequences in multi-fasta format

.genes.faa – Amino Acids file in mult-fasta format

.gff – Gene Information file in GFF3 format

.cog.tab.txt – COG hits in tab-delimited format

.intergenic.fna – Intergenic regions in multi-fasta format

.ipr.tab.txt – IPR hits in tab-delimited format

.ko.tab.txt – KO and EC annotation in tab-delimited format

.pfam.tab.txt – pFam hits in tab-delimited format

.signalp.tab.txt – Signal peptide annotation in tab-delimited format

.tigrfam.tab.txt – TigrFam hits in tab-delimited format

.tmhmm.tab.txt – Transmembrane helices in tab-delimited format

File 13: Indexed fluorescence activated cell sorting data, estimated cell sizes, and cross-over point (Cp) values for whole genome amplification can be found in indexed_facs_wga_summary.tsv (Data Citation 1).

File 14: IMG genome IDs, phylogenetic inference, usage notes, and genome assembly statistics can be found in genome_assembly_summary.tsv (Data Citation 1).

ITS and 16S sequences for all SAGs (including those that did not undergo whole genome sequencing) are available from GenBank under the accession numbers MG666579-MG668595 for ITS sequences (Data Citation 2) and MH074888-MH077527 for 16S sequences (Data Citation 3).

Paired-end sequencing reads in fastq format are available from the NCBI Sequence Read Archive (Data Citation 4).

Genome assemblies are available from GenBank (Data Citations 5–7).

Annotated genome assemblies and geolocation metadata are available from IMG (https://img.jgi.doe.gov/) and IMG/ProPortal (https://img.jgi.doe.gov/proportal).

Technical Validation

The quality of the sequencing reads was assessed using fastqc and the quality of the assembled genomes was assessed using checkM43 and tetramer frequency analysis as previously described14. This workflow was evaluated for assembly errors using a series of benchmark cultures with diverse genome complexity and %GC21.

Usage Notes

While the single cell genomes in this data set were screened for contamination that could have been introduced during cell sorting and DNA amplification, users should be aware that these screening procedures do not eliminate the potential for multiple genomes being present in the same assembly. Some single cell genomes may be derived from cells infected with a bacteriophage (i.e. virocells44) and thus contain both host and virus genomes. Other single cells may contain multiple genomes due to a close physical association between two cells that resulted in co-sorting and co-amplification of DNA. Given that many of these events are biologically meaningful, these genome assemblies were not removed from the data set or modified to separate multiple genomes. Based on our technical validation, we have identified possible virocells or co-sorted genomes in the data set (genome_assembly_summary.tsv, Data Citation 1).

Ancillary physical, chemical, and biological data associated with the data set can be accessed from C-MORE (http://hahana.soest.hawaii.edu/cmoreDS/), HOT (http://hahana.soest.hawaii.edu/hot/hot-dogs/), BATS (http://bats.bios.edu/), and GEOTRACES (https://www.bodc.ac.uk/geotraces/data/) using the sample metadata available in Tables 1 and 2. The U.S. Biological and Chemical Oceanography Data Management Office (BCO-DMO) can also be used to access associated data for HOT (https://www.bco-dmo.org/project/2101), BATS (https://www.bco-dmo.org/project/2124), and the U.S. GEOTRACES North Atlantic Transect (https://www.bco-dmo.org/project/2066).

Additional information

How to cite this article: Berube, P. M. et al. Single cell genomes of Prochlorococcus, Synechococcus, and sympatric microbes from diverse marine environments. Sci. Data 5:180154 doi: 10.1038/sdata.2018.154 (2018).

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Material

sdata2018154-isa1.zip (123.8KB, zip)

Acknowledgments

This work was supported in part by grants from the Simons Foundation (Life Sciences Project Award ID 337262, S.W.C.; SCOPE Award ID 329108, S.W.C.; Life Sciences Project Award ID 426570SP, E.V.A.), the Moore Foundation (Grant IDs GBMF495 and GBMF4511), and the National Science Foundation (OCE-1153588, OCE-1356460, and DBI-0424599 to S.W.C. and OCE-1335810 to R.S.). We are grateful for the support of chief scientists: E. Virginia Armbrust (University of Washington), Andrew Bowie (University of Tasmania), Philip Boyd (University of Tasmania), Edward Boyle (Massachusetts Institute of Technology), Gregory Cutter (Old Dominion University), Loes Gerringa (NIOZ Royal Netherlands Institute for Sea Research), Gideon Henderson (University of Oxford), William Jenkins (Woods Hole Oceanographic Institution), Daniel Repeta (Woods Hole Oceanographic Institution), and Micha Rijkenberg (NIOZ Royal Netherlands Institute for Sea Research), as well as Gerhard Herndl (University of Vienna), James Moffett (University of Southern California), and Hein de Baar (NIOZ Royal Netherlands Institute for Sea Research) for organizational and logistical support of bioGEOTRACES. We thank the HOT, BATS, and SCOPE cruise operations teams and Debbie A. Hulston (NIWA) for assistance with sampling, as well as the captains and crew of the R/V Kilo Moana, R/V Ka`imikai-O-Kanaloa, R/V Atlantic Explorer, R/V Pelagia, R/V Southern Surveyor, R/V Tangaroa, RRS James Cook, RRS Discovery, R/V Melville, and R/V Knorr. We also thank the staff of the Bigelow Laboratory for Ocean Sciences’ Single Cell Genomics Center for the generation of single cell genomics data. This paper is a contribution from the Simons Collaboration on Ocean Processes and Ecology (SCOPE) and from the NSF Center for Microbial Oceanography: Research and Education (C-MORE).

Footnotes

The authors declare no competing interests.

Data Citations

  1. Berube P. M., et al. . 2018. Figshare. https://doi.org/10.6084/m9.figshare.c.4037048
  2. 2017. GenBank. MG666579-MG668595
  3. 2018. GenBank. MH074888-MH077527
  4. 2018. NCBI Sequence Read Archive. SRP141175
  5. 2018. GenBank. QBVH00000000-QCVZ00000000
  6. 2018. GenBank. MH319718-MH319767
  7. 2018. GenBank. MH327275-MH327492

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Associated Data

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

Data Citations

  1. Berube P. M., et al. . 2018. Figshare. https://doi.org/10.6084/m9.figshare.c.4037048
  2. 2017. GenBank. MG666579-MG668595
  3. 2018. GenBank. MH074888-MH077527
  4. 2018. NCBI Sequence Read Archive. SRP141175
  5. 2018. GenBank. QBVH00000000-QCVZ00000000
  6. 2018. GenBank. MH319718-MH319767
  7. 2018. GenBank. MH327275-MH327492

Supplementary Materials

sdata2018154-isa1.zip (123.8KB, zip)

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