DOI: 10.5061/dryad.s4mw6m9h7
title: Overturning circulation structures the microbial functional seascape of the South Pacific

description: Global overturning circulation partitions the deep ocean into regions with
 unique physicochemical characteristics, but the extent to which these
 water masses represent distinct ecosystems remains unknown. Here, we
 integrate extensive genomic information with hydrography and water mass
 age to delineate microbial taxonomic and functional boundaries across the
 South Pacific. Prokaryotic richness steeply increases with depth in the
 surface ocean, forming a “phylocline”, below which richness is
 consistently high, dipping slightly in highly aged water. Reconstructed
 genomes self-organize into six spatially-distinct taxonomic cohorts and
 ten functionally-distinct biomes that are primarily structured by
 wind-driven circulation at the surface and density-driven circulation at
 depth. Overall, water physicochemistry, modulated at depth by water age,
 drives microbial diversity and functional potential in the pelagic ocean.
description: # Overturning circulation structures the microbial functional seascape of
 the South Pacific [Access this dataset on
 Dryad](https://doi.org/10.5061/dryad.s4mw6m9h7) This dataset supports the
 publication titled *"Overturning circulation structures the microbial
 functional seascape of the South Pacific"* by Kolody et al. (DOI
 10.1126/science.adv6903) and full methods are available in the
 supplementary text. It presents high-resolution genomicand amplicon data
 from the South Pacific Ocean in the context of the environmental data
 sampled along the GO-SHIP Pacific 18 (P18) transect. The data include
 prokaryotic metagenome-assembled genomes (MAGs), 16S and 18S rRNA gene
 amplicon data, functional annotations (KEGG, CAZy), and physical/chemical
 oceanographic metadata. These files collectively enabled the study of
 microbial diversity, biogeography, and functional potential as shaped by
 ocean overturning circulation and water mass age. --- ## Description of
 the data and file structure The dataset is divided into nine main data
 tables (Data S1–S9), each described below. All files are provided as
 `.csv` or `.tsv` tables and are linked to the analytical framework
 outlined in the main text and supplementary methods of the publication.
 Most large files are best explored in R or Python. ### **Data S1: Genome
 Abundance and Metadata** File: SD1_genome_classification_and_coverage.csv
 This table contains information for curated marine genomes (n = 307
 prokaryotic MAGs). Columns are: * genome: genome identifier. These are
 composed of the following fields, separated by underscores: library name,
 ggkbase taxonomic annotation, approximate % GC, and approximate genome
 coverage in the library the genome was isolated from. * library:
 sequencing library representing the sample for which the coverage will be
 given in the following column. . Library names have the following fields,
 separated by underscores: cruise line, cruise station, CTD cast number,
 niskin number, molecular sample identifier, size class from serial
 filtration (L= large, 0.5 um filter; S = small, 0.22 um filter), and
 collection depth in meters. Libraries from the second sequencing effort
 are appended with “_2”. * mean.coverage: mean normalized coverage of the
 given genome for the sample (library) in the proceeding column *
 GTDB.Tk.classification: Taxonomic assignment of the given genome via
 GTDB-Tk * percent.completeness: % genome completeness from checkM *
 percent.contamination: estimated % genome contamination from checkM * GC:
 genome GC content * genome.size: genome size in base pairs (bp) * contigs:
 number of contigs comprising the given genome * longest.contig.length:
 length of the longest contig in the genome * mean.contig.length: mean
 length of contigs in the genome * coding.density: genome coding density *
 translation.table: translation table used for gene prediction *
 predicted.genes: number of genes predicted in the genome * genome.quality:
 genome quality. Medium is ≥ 50% completeness, &lt; 10% contamination; High
 is &gt; 90% completeness, &lt; 5% contamination * cohort: WGCNA module
 (biogeographic cohort) membership of genome * age_bin: Fisher's Exact
 test-based assignment of genome to a water age group (&lt; 50 years, 50
 -1000 years, &gt; 1000 years) * age_bin_FDR: FDR-corrected p-value for
 Fisher's Exact test-based enrichment of genomes across water age
 groups * iRep: iRep genome replication rate estimates (NA = insufficient
 coverage) * iRep_r_squared: R-squared value for iRep replication rate
 estimate (NA = insufficient coverage) * Water_Mass: Fisher's Exact
 test-based assignment of genome to a water mass [Antarctic Bottom Water
 (AABW), Antarctic Intermediate Water (AAIW), Lower Circumpolar Deep Water
 (LCDW), Upper Circumpolar Deep Water (UCDW), and Upper Water (above 500
 m)] * Water_Mass_enrichment_odds_ratio: odds ratio associated with
 enrichment of genomes across water masses * Water_Mass_enrichment_FDR:
 FDR-corrected p-value for Fisher's Exact test-based enrichment of
 genomes across water masses ### **Data S2: 16S rRNA Amplicon Counts and
 Annotations** File: SD2_16S_counts_silva138_r1.csv This table describes
 the taxonomy and biogeography of prokaryotes detected via 16S rRNA
 metabarcoding on the P18 transect. Columns are: * Feature.ID: unique
 identifier for each organism (amplicon sequence variant: ASV) *
 molecular_sample_id: unique identifier for molecular sample collected *
 Confidence_silva: confidence score for SILVA v138 taxonomic annotations *
 raw_counts: non-normalized read copies of given ASV * copies_per_mL:
 estimated ASV copies contained in each milliliter of seawater filtered
 (see methods) * Taxon_silva: SILVA v138 taxonomic annotations ASVs *
 domain, phylum, class, order, family, genus: un-nested silva taxomic
 annotations * cohort: WGCNA module (biogeographic cohort) assignment for
 given ASV Best viewed using R due to file size. ### **Data S3: 18S rRNA
 Amplicon Counts and Annotations** File: SD3_18S_counts_pr2_r1.csv This
 table describes the taxonomy and biogeography of eukaryotes detected via
 18S rRNA metabarcoding on the P18 transect. It includes: * Feature.ID:
 unique identifier for each organism (amplicon sequence variant: ASV) *
 molecular_sample_id: unique identifier for molecular sample collected *
 Confidence_pr2: confidence score for Protist Ribosomal Reference (PR2)
 taxonomic annotations * raw_counts: non-normalized read copies of given
 ASV * copies_per_mL: estimated ASV copies contained in each milliliter of
 seawater filtered (see methods) * Taxon_pr2: Protist Ribosomal Reference
 (PR2) taxonomic annotations * kingdom, supergroup, division, class, order,
 family, genus, species: un-nested PR2 taxomic annotations * cohort: WGCNA
 module (biogeographic cohort) assignment for given ASV Best viewed in R
 due to large file size. ### **Data S4: Environmental Metadata** File:
 SD4_environmental_metadata_r1.csv This table describes the physical and
 chemical environment at the locations where molecular samples were
 collected. Full environmental metadata for the GO-SHIP P18 cruise are
 available through [CCHDO](https://cchdo.ucsd.edu/cruise/33RO20161119).
 NA's indicate data was not collected for the given sample. Columns
 are: * molecular_sample_id: unique identifier for molecular sample
 collected * station_number, cast_number, niskin_id: station, CTD-cast
 number (in case of multiple casts at a given station), and niskin bottle
 identifiers given by the GO-SHIP program to differentiate samples *
 LATITUDE: latitude (degrees) * CTD_pressure: ambient pressure measurement
 from CTD probe (dbar) * DATE: date as year, month, day * TIME: time of CTD
 cast * LONGITUDE: longitude (degrees) * water_column_depth: water column
 depth in meters * CTD_temperature: ambient temperature of sample from CTD
 probe (ITS-90) * CTD_salinity: salinity measurement from CTD probe
 (PSS-78) * OXYGEN: oxygen (µmol/kg) * SILCATE: silicate (µmol/kg) *
 NITRAT: nitrate (µmol/kg) * NITRIT: nitrite (µmol/kg) * PHSPHT: phosphate
 (µmol/kg) * CFC.11: trichlorofluoromethane (CFC-11) in pmol/kg * CFC.12:
 dichlorodifluoromethane (CFC-12) in pmol/kg * SF6: sulfur hexafluoride
 concentration (fmol/kg) * total_carbon: total carbon (µmol/kg) *
 alkalinity: alkalinity (µmol/kg) * pH_seawater_sensor: pH measured by the
 seawater sensor * dissolved_organic_carbon: dissolved organic carbon *
 total_dissolved_nitrogen: total dissolved nitrogen * DELC14: δ¹³C (per
 mille) * DELC13: δ¹⁴C (per mille) * cal_BP: cal BP (calibrated years
 before the present) * radioc_years_before_2017: water age in years before
 present (defined as 2017- the year of collection) derived from radiocarbon
 data (see methods) * CFC11_age: water age (years before 2017) estimated
 from CFC-11 data (see methods) * CFC12_age: water age (years before 2017)
 estimated from CFC-12 data (see methods) * SF6_age: water age (years
 before 2017) estimated from SF6 data (see methods) * avg_atm_tracer_age:
 average atmospheric tracer age (years before 2017) * consensus_age:
 consensus age across atmospheric tracers and radiocarbon data (years
 before 2017) * avg_atm_tracer_age_interpolated: average atmospheric tracer
 age interpolated to cover more samples (years before 2017) *
 consensus_age_interpolated: consensus age across atmospheric tracers and
 radiocarbon data interpolated to cover more samples (years before 2017) *
 CCL4: carbon tetrachloride (pmol/kg) * N2O: nitrous oxide (nmol/kg) *
 total_dissolved_phosphorus: total dissolved phosphorus * AABW_fraction:
 mixing fraction of Antarctic Bottom Water * NPIW_fraction: mixing fraction
 of North Pacific Intermediate Water * LCDW_fraction: mixing fraction of
 Lower Circumpolar Deep Water * UCDW_fraction: mixing fraction of Upper
 Circumpolar Deep Water * AAIW_fraction: mixing fraction of Antarctic
 Intermediate Water * PDW_fraction: mixing fraction of Pacific Deep Water *
 closest_end_member: water mass with the highest mixing fraction for the
 given sample * closest_end_member_percent: percent of water in the sample
 estimated to originate from the closest end member (water mass) *
 Prochlorococcus_cell_counts: cell counts of Prochlorococcus from flow
 cytometry. Not collected for molecular samples 42, 45, 214, and 215 *
 Synechococcus_cell_counts: cell counts of Synechococcus from flow
 cytometry. Not collected for molecular samples 42, 45, 214, and 215 *
 sample_depth: depth at which the sample was collected (m) ### **Data S5:
 ASV Endemism** File: SD5_unique_ASV_percents_16S_18S.csv This table gives
 the number and percentage of 16S rRNA and 18S rRNA amplicon sequence
 variants (ASVs) unique to each sample. Missing 18S rRNA values correspond
 to samples for which 16S samples passed quality control thresholds but 18S
 rRNA samples did not (see Methods). Columns are: * molecular_sample_id:
 unique identifier for molecular sample collected * total_ASVs_16S: total
 16S rRNA ASVs * unique_ASVs_16S: number of 16S rRNA ASVs found only in the
 given sample * percent_ASVs_unique_16S: % of 16S rRNA ASVs unique to the
 given sample * total_ASVs_18S: total 18S rRNA ASVs * unique_ASVs_18S:
 number of 18S rRNA ASVs found only in the given sample *
 percent_ASVs_unique_18S: % of 18S rRNA ASVs unique to the given sample ###
 **Data S6: KEGG Orthologs in Functional Zones** File:
 SD6_KO_functional_zones.tsv This table describes the KEGG Orthologies
 (KOs) pertaining to each functional zone defined in the manuscript.
 Columns are: * kegg_id: KO identifier * kegg_hit: description of KO
 functions * genomes: number of genomes each KO is found in. *
 total.library.normalized.coverage: Library-normalized cumulative coverage
 of each KO across genomes. * Functional_Zone: Which of the 10 functional
 zones (determined via WGCNA) each KO pertains to. This is a large file
 best viewed in R or similar rather than excel. In cases where a gene was
 annotated as multiple KOs, KOs are given separated by commas and
 descriptions are separated by semicolons. Because of the presence of
 commas it is recommended to import into R using the following options:
 `sd6 &lt;- read.table("SD6.tsv", sep = "\t", header =
 T, comment.char = "", quote = "")` ### **Data S7:
 Eukaryotic KO Enrichment by Water Mass** File:
 SD7_euk_LPI_genes_water_masses_sig_ko_fisher.tsv This table gives the
 statistical enrichment/depletion of KOs from eukaryotic scaffolds across
 five water masses: Antarctic Bottom Water (AABW), Antarctic Intermediate
 Water (AAIW), Lower Circumpolar Deep Water (LCDW), Upper Circumpolar Deep
 Water (UCDW), and Upper Water (above 500 m). FDR-adjusted p-values were
 determined using Fisher's tests. Because of punctuation present in KO
 descriptions it is recommended to import into R using the following
 options: `sd7 &lt;- read.table("SD7.tsv", sep = "\t",
 header = T, comment.char = "", quote = "")`. Columns
 are: * KO: KO identifier * KO_description: description of KO functions *
 Water_Mass: Water mass that the given KO was found to have a significant
 association with * Odds_Ratio: Odds ratio of association between KO and
 water mass * Association: Whether the KO is enriched or depleted in the
 given water mass * Adjusted_P_Value: FDR-adjusted p-value from
 Fisher's test ### **Data S8: Prokaryotic KO Enrichment by Water
 Mass** File: SD8_wm_KO_enrichment.tsv This table gives the statistical
 enrichment/depletion of KOs from prokaryotic genomes across five water
 masses: Antarctic Bottom Water (AABW), Antarctic Intermediate Water
 (AAIW), Lower Circumpolar Deep Water (LCDW), Upper Circumpolar Deep Water
 (UCDW), and Upper Water (above 500 m). FDR-adjusted p-values were
 determined using Fisher's tests. Because of punctuation present in KO
 descriptions it is recommended to import into R using the following
 options: `sd8 &lt;- read.table("SD8.tsv", sep = "\t",
 header = T, comment.char = "", quote = "")`. Columns
 are: * kegg_hit: description of KO functions * kegg_id: KO identifier *
 Water_Mass: Water mass that the given KO was found to have a significant
 association with * Odds_Ratio: Odds ratio of association between KO and
 water mass * Association: Whether the KO is enriched or depleted in the
 given water mass * Adjusted_P_Value: FDR-adjusted p-value from
 Fisher's test ### **Data S9: CAZy Enrichment by Water Mass** File:
 SD9_CAZy_enrichments_by_wm.csv This table has Carbohydrate-Active enZYme
 (CAZy) gene families that are significantly enriched or depleted in
 prokaryotic genomes pertaining to any of the five water masses: Antarctic
 Bottom Water (AABW), Antarctic Intermediate Water (AAIW), Lower
 Circumpolar Deep Water (LCDW), Upper Circumpolar Deep Water (UCDW), and
 Upper Water (above 500 m). CAZys were annotated using DRAM. FDR-adjusted
 p-values were determined using Fisher's tests. Columns are: * cazy:
 description of Carbohydrate-Active enZYme (CAZy). In some cases multiple
 CAZys will be given as a single entry (separated by a semicolon) because
 genes had multiple annotations * Water_Mass: Water mass that the given
 CAZy was found to have a significant association with * Odds_Ratio: Odds
 ratio of association between CAZy and water mass * Association: Whether
 the CAZy is enriched or depleted in the given water mass *
 Adjusted_P_Value: FDR-adjusted p-value from Fisher's test --- ##
 Sharing/Access Information **Other public data access links:** * Full
 environmental metadata for the GO-SHIP P18 cruise:\ [CCHDO P18 cruise
 data](https://cchdo.ucsd.edu/cruise/33RO20161119) **Primary source data
 used in this study:** * Physical and chemical parameters: CTD profiles and
 bottle data from publicly available CCHDO data (see above). * Genome
 sequences and metagenomic assemblies: Created from this study, using raw
 reads from metagenomes sequenced on Illumina NovaSeq. * Ribosomal RNA
 amplicon data: 16S and 18S amplicons sequenced using Illumina MiSeq and
 HiSeq platforms.