Seasonal Enhancement of the Viral Shunt Catalyzes a Subsurface Oxygen Maximum in the Sargasso Sea

Seasonal Enhancement of the Viral Shunt Catalyzes a Subsurface Oxygen Maximum in the Sargasso Sea | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Seasonal Enhancement of the Viral Shunt Catalyzes a Subsurface Oxygen Maximum in the Sargasso Sea Naomi E. Gilbert , View ORCID Profile Daniel Muratore , View ORCID Profile Camelia Shopen Gochev , View ORCID Profile Gary R. LeCleir , Shelby M. Cagle , Helena L. Pound , Christine L. Sun , Alfonso Carillo , Kimberley S. Ndlovu , View ORCID Profile Ilia Maidanik , Ashley R. Coenen , Lauren Chittick , View ORCID Profile Jennifer M. DeBruyn , View ORCID Profile Alison Buchan , View ORCID Profile Debbie Lindell , View ORCID Profile Matthew B. Sullivan , View ORCID Profile Joshua S. Weitz , View ORCID Profile Steven W. Wilhelm doi: https://doi.org/10.1101/2025.01.23.634377 Naomi E. Gilbert 1 Department of Microbiology, The University of Tennessee , Knoxville, TN, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel Muratore 2 School of Biology, Georgia Institute of Technology , Atlanta GA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Daniel Muratore Camelia Shopen Gochev 3 Faculty of Biology, Technion-Israel Institute of Technology , Haifa, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Camelia Shopen Gochev Gary R. LeCleir 1 Department of Microbiology, The University of Tennessee , Knoxville, TN, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gary R. LeCleir Shelby M. Cagle 1 Department of Microbiology, The University of Tennessee , Knoxville, TN, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Helena L. Pound 1 Department of Microbiology, The University of Tennessee , Knoxville, TN, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christine L. Sun 4 Department of Microbiology, The Ohio State University , Columbus OH, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alfonso Carillo 4 Department of Microbiology, The Ohio State University , Columbus OH, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kimberley S. Ndlovu 4 Department of Microbiology, The Ohio State University , Columbus OH, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ilia Maidanik 3 Faculty of Biology, Technion-Israel Institute of Technology , Haifa, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ilia Maidanik Ashley R. Coenen 5 School of Physics, Georgia Institute of Technology , Atlanta GA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lauren Chittick 4 Department of Microbiology, The Ohio State University , Columbus OH, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jennifer M. DeBruyn 6 Biosystems Engineering and Soil Science, The University of Tennessee , Knoxville, TN, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jennifer M. DeBruyn Alison Buchan 1 Department of Microbiology, The University of Tennessee , Knoxville, TN, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alison Buchan Debbie Lindell 3 Faculty of Biology, Technion-Israel Institute of Technology , Haifa, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Debbie Lindell Matthew B. Sullivan 4 Department of Microbiology, The Ohio State University , Columbus OH, USA 7 Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University , Columbus OH, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Matthew B. Sullivan Joshua S. Weitz 8 Department of Biology, University of Maryland, College Park , MD, US 9 Department of Physics, University of Maryland, College Park , MD, USA 10 University of Maryland Institute for Health Computing , North Bethesda, MD, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Joshua S. Weitz For correspondence: wilhelm{at}utk.edu jsweitz{at}umd.edu Steven W. Wilhelm 1 Department of Microbiology, The University of Tennessee , Knoxville, TN, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Steven W. Wilhelm For correspondence: wilhelm{at}utk.edu jsweitz{at}umd.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Subsurface oxygen maxima (SOMs) occur directly beneath the mixed layer of stratified water columns across oligotrophic open ocean basins. SOMs occur seasonally and are hypothesized to result from elevated microbial net primary productivity (NPP). Here, we set out to identify mechanistic drivers of the SOM near the Bermuda Atlantic Time Series (BATS) site in the Sargasso Sea in October 2019. Coupled time-series analysis of metatranscriptomics, flow cytometry, and family-specific cyanophage quantification revealed elevated Prochlorococcus abundances, cyanophage abundances, and cyanophage-specific transcriptional activity in the SOM. These findings spurred us to analyze historical oxygen saturation profiles at BATS – identifying a repeated, seasonal cycle in SOM emergence associated with elevated virus-like particles and Prochlorococcus numbers. Returning to the 2019 study site, we found that transcriptional markers for increased dissolved organic matter uptake by copiotrophic bacteria were enriched in the SOM, consistent with enhanced catabolic activity due to the viral shunt. In addition, Prochlorococcus exhibited enrichment in ammonium transport transcripts at the SOM, consistent with increased responsiveness to remineralization activity by heterotrophs. Altogether, these findings suggest that enhanced viral lysis leads to locally elevated nutrient recycling and oxygen production, further reinforcing hypotheses that viruses may play a critical role in the emergence of SOMs in the oligotrophic ocean. Introduction Marine microbial communities face novel physicochemical environments and potential stresses as a result of changing climate feedback 1 . In turn, shifts in microbially-induced ecosystem function may influence biogeochemical cycles of key elements, such as carbon and oxygen. Quantitative predictions of the feedback between the ocean and atmosphere require incorporating information on microbial processes and their response to physicochemical gradients in the water column. In the oligotrophic open ocean, a significant fraction of atmospheric carbon dioxide is drawn down by oxygenic photosynthesis by phytoplankton 2 . Moreover, oligotrophic regions such as the Sargasso Sea consistently exhibit subsurface zones harboring dense phototrophic populations 3 , 4 , concurrent with high oxygen concentrations. These regions are referred to as “subsurface oxygen maxima” (SOMs). SOMs have been observed across several oligotrophic open ocean regions including the North Atlantic Subtropical Gyre 5 , 6 , the permanently stratified tropical and subtropical areas of the Atlantic Ocean 7 , the South Pacific Ocean 8 , the Mediterranean Sea 9 , and sometimes even in large lakes 10 . SOMs can occur at 40-125 m depth, depending on the region and season 11 – 13 , and are formed between the upper mixed layer and the deep chlorophyll maximum 6 , 7 . The origins of SOMs remain controversial. Physics-based hypotheses suppose that highly oxygenated waters are subducted and remain at high oxygen saturation because of their inability to ventilate 5 , 9 . Biological hypotheses, supported by modeling and float-based studies in SOMs from multiple ocean regions, suggest enhanced primary production from microbial photosynthesis immediately below the mixed layer generates elevated oxygen autochthonously 8 , 9 , 12 , 13 . Despite the significance and ubiquity of SOMs across oligotrophic oceans, key biological mechanisms contributing to SOM formation remain elusive. A stable SOM occurs annually in the Sargasso Sea during the stratified summer and late fall – providing an opportunity to evaluate the joint impact of physical and biological mechanisms on SOM emergence and persistence 6 . The SOM observed at the Bermuda Atlantic Time-series Study (BATS) long-term oceanographic monitoring site has been associated with elevated Prochlorococcus cell abundances, heterotrophic bacterial densities, and particulate organic carbon 3 , 6 . Virus-like particle abundances (VLPs) have also been reported to reach their maximum levels beneath the mixed layer at BATS during the stratified fall, coinciding with the emergence of the SOM 3 . In theory, a peak in biomass of the dominant microbial populations at the SOM correlated to high VLPs would be consistent with the ‘viral shunt’ hypothesis, by which viral lysis enhances nutrient recycling and fuels primary production 14 – 17 . Moreover, historical reports of enhanced microbial and viral population abundances in the BATS SOM as well as the positive relationships between stratification intensity, SOM formation, and primary/secondary production led us to hypothesize that the BATS SOM harbors a microbial community associated with accelerated dissolved organic matter (DOM) recycling, coupled with enhanced viral lysis. Accelerated DOM recycling and enhanced nutrient retention mediated by viral infection could, in turn, result in oxygen accumulation in an isopycnal below the mixed layer, depending on balance of primary relative to secondary productivity. Here, we set out to test our hypothesis that SOMs are mediated by an enhanced viral shunt through microbial and biogeochemical characterization of a BATS-proximal SOM sampled during late stratification in October 2019. Over a six-day Lagrangian sampling effort, we collected day/night-resolving metatranscriptomes along with direct measurements of population-specific densities of the dominant taxa from depth profiles sampling above, at, and beneath the SOM. We quantified cyanophage abundances and infections in the SOM using the polony and iPolony methods 18 , 19 and community viromic assessment 20 , focusing on cyanophages capable of infecting Prochlorococcus . Our results suggest that enhanced viral lysis leads to tight feedback between photoautotrophs and heterotrophs and the repeated, seasonal emergence of SOMs in late fall associated with ocean stratification in an archetypal oligotrophic gyre. Seasonal stratification is accompanied with persistent oxygen saturation and particulate matter within the sub-mixed layer Depth profiles were collected every 4 h aboard the RV Atlantic Explorer following a Lagrangian cruise track starting at BATS (31° 40′ N, 64°10′ W) on October 12 th , 2019 and ending on October 17 th , 2019 ( Figure 1a & b). Density profiles derived from conductivity, temperature, density (CTD) measurements indicated a highly stratified upper water column with a stable mixed-layer depth around 50 m for the duration of the sampling period ( Figure 1c ). A stable subsurface peak in oxygen saturation was apparent in the isopycnal about 5 m beneath the mixed layer depth ( Figure 1d ). This layer, which we refer to as the “SOM”, was situated approximately 50 m above the deep chlorophyll maximum (DCM; Figure 1d,e , Supplementary Figure 1). The SOM also displayed the highest beam attenuation values, on average 1.52-fold higher than the DCM (sd ± 0.14), and 1.09-fold higher than the upper mixed layer (sd ± 0.04), suggesting peak concentrations of particulate organic matter (POM) in the 0.5-20 μm size range 21 , 22 ( Figure 1f & Supplementary Figure 1). CTD profiles measured every 4 h reveal a diel cycle in beam attenuation within the SOM (RAIN nonparametric test, p < 1e-10), suggesting these particles had growth/decay cycles and were not solely allochthonous detritus ( Figure 1f ). These observations are consistent with a previous analysis conducted at BATS which found enhanced colored dissolved organic matter (cDOM) levels associated with elevated bacterial abundance and production in and around the SOM layer 6 . The SOM during this study had a mean increase of 22.14 (sd ± 4.20) μmol O 2 /kg (11.57% increase) compared to the upper mixed layer (UML) and 17.43 (sd ± 5.66) μmol O 2 /kg higher (8.92% increase) than the DCM. These differences result in an average increase in oxygen saturation of 6.47% (sd ± 1.22%) in the SOM from the UML, and an increase in oxygen saturation of 13.9% (sd ± 1.93%) in the SOM compared to the DCM ( Figure 1d ). The chlorophyll fluorescence in the SOM, however, was only 26.9% (sd ± 4.47%) that of the DCM ( Figure 1e ). Download figure Open in new tab Figure 1. Depth resolved diel sampling during the 2019 fall cruise at BATS. a) Lagrangian sampling track starting at the BATS station (red point). Each black point represents a CTD-cast starting Oct. 12 th , 2019, at 20:00 local time (GMT-3) and ending on Oct. 17 th , 2019, at 08:00. b) Schematic of a single day in the time series sampling profile. The surface (SRF) and bottom of the mixed layer (BML) were sampled every four hours, while the subsurface oxygen maximum (SOM) and deep chlorophyll maximum (DCM) were sampled every twelve hours. Panels c-f show CTD profiles interpolated over the entire time series (taken every four hours from the surface to 200 m depth) for c) Sigma-theta (kg/m 3 ) d) Oxygen saturation (%) e) Chlorophyll fluorescence (mg/m 3 ) and f) Beam attenuation (1/m); with the mixed layer depth (as defined by 0.125 difference in sigma-theta from 10 m) indicated in white. Depth names are designated at their approximate location in bold. We assessed oceanographic climatological data collected since 1988 to evaluate whether a SOM is a recurring feature in the BATS water column. We analyzed > 6,000 CTD casts from monthly sampling efforts to search for a systematic pattern in sub-mixed-layer oxygen saturation. Using a standardized method of sensor data processing and thermodynamic conversions (see Methods), we confirmed an annually occurring SOM in the BATS climatology ( Figure 2a ). Using 5,072 CTD casts with high-quality optode data (verified by calibration against CTD oxygen measurements using the Winkler method - see Methods), we modeled the change in mean oxygen saturation in the 10 m beneath the mixed layer depth as a function of the time of year ( Figure 2b ). Nonlinear least squares fitting of a sinusoidal regression found an annual fluctuation in sub-mixed layer oxygen saturation of approximately 6.79% (6.71, 6.88; 95% CI, p < 1e-10), from a baseline of 100.50% (100.42,100.59; 95% CI, p < 1e-10), with an annual minimum in mid-February (decimal year=0.123, 0.121,0.125; 95% CI, p < 1e-10). Due to the symmetry of the sine function, this sinusoidal regression indicates a maximum in August (Supplementary Figure 2). These results suggest that on average, over the BATS climatology, the annual SOM’s intensity reaches its maximum in August. The maximum in sub-mixed layer oxygen saturation builds after the deep mixing period ends (typically in March-April) to reach a maximum in August, until it dissipates at the onset of the next deep mixing event in winter ( Figure 2a ). This timing further implies that enhanced oxygenation is not solely due to physical entrainment of oxygenated waters from deep mixing alone. Download figure Open in new tab Figure 2. Historical analysis of the sub-surface oxygen maximum at BATS. a) Interannual mean oxygen profiles from the BATS CTD record from 1996-2019. Oxygen data are binned by 5 m depth windows and 12-day means throughout the year. The black line indicates interannual average mixed layer depth for that 10-day window as determined by 0.125 change in sigma-theta from a reference pressure of 10 db. Data were retrieved from http://bats.bios.edu/bats-data/b ) Annual BATS oxygen profiles by month of year. Data are binned into 2 m depth bins and color indicates smoothed oxygen concentration as determined by CTD optodes. Black line indicates mixed-layer depth as determined by a 0.125 difference in sigma-theta from a reference pressure of 1 0 db. White bars indicate missing data for that month. SOM Microbial Community Activity is More Similar to the Mixed Layer than the Deep Chlorophyll Maximum We collected metatranscriptomes from depth profiles every 12 h (day = 08:00, night = 20:00) during our Lagrangian sampling effort in October 2019 ( Figure 1b ). These samples allowed us to compare the expression of 17,798,931 genes across eukaryotic plankton, bacteria, and viruses between the SOM, the UML, and the DCM. We constructed a PCA ordination of metatranscriptome gene expression profiles (Supplementary Figure 3). The axis explaining most of the variation between metatranscriptomes (54.67%) separated the DCM samples from the other depths, meaning that SOM metatranscriptomes more closely resemble samples from the surface (SRF) and the base of the mixed layer (BML) than the DCM (Supplementary Figure 3). The second PCA axis, explaining 19.86% of total variance, established a gradient from SRF samples (highest PC2 values) to BML to SOM samples (lowest PC2 values, Supplementary Figure 3). Metatranscriptomic profiles suggest the SOM microbial community transcription resembled the SRF/BML more closely than it does the DCM, with some divergence in community-level expression profiles. Next, we evaluated to what extent differences in community expression were due to difference in microbial community composition across these depth layers versus differences in the relative expression of different functional genes among shared taxa. We applied hierarchical clustering to taxon-specific expression of rpoB and RPB1 , a pair of core conserved housekeeping genes (RNA polymerase subunit B) spanning Archaea ( rpoB ), Bacteria ( rpoB ), and Eukaryota ( RPB1 ) ( Figure 3 ). The taxonomic distribution of rpoB/RPB1 transcripts in the SOM more closely resembled that of the surface and BML than the DCM (Supplementary Figure 3). The SRF/BML and SOM samples were characterized by high relative rpoB transcript abundances of the High-Light II Prochlorococcus ecotypes, and rpoB transcripts assigned to the orders Flavobacteriales and Rhodospirillales ( Figure 3 ). The SOM samples had elevated relative rpoB transcript abundances for the Protobacteria orders Pseudomonadales, Oceanospirillales, and Desulfuromonadales, the Bacteriodetes order Cytophagales, and Prochlorococcus High-Light I ecotypes relative to the SRF/BML samples ( Figure 3 , Supplementary Figure 4). Statistical assessment of contig-level rpoB and RPB1 abundance differences with depth also identified significantly (BH-adjusted p<0.1) elevated transcript abundances in the SOM of contigs with rpoBs assigned to known copiotrophic taxa ( e.g., Rhodobacterales, Flavobacteriales, Oceanospirillales, Cytophagales; Supplementary Figure 5a). These taxa are associated with environments analogous to phytoplankton blooms, for example, where algal-derived organic matter primarily supplies these rapid DOM recyclers 23 – 26 . A seasonal sub-mixed-layer Rhodobacteraceae maximum has also been previously observed at BATS 3 . We observe genome “streamlined” heterotrophs such as SAR116, SAR86, and SAR92 27 , that are primarily active in the SRF/BML relative to the SOM ( Figure 3 ). These results suggest that the community composition of active microbes at the SOM, as defined by the relative proportions of housekeeping gene transcription, resembles the SRF/BML more than the DCM, but the SOM had increased transcripts of Prochlorococcus and putative copiotrophic bacteria. Download figure Open in new tab Figure 3. The SOM resembles the upper mixed layer, with key differences in taxonomic composition. Hierarchically clustered heatmap of rpoB/RPB1 normalized transcripts (variance stabilizing transformation, VST) averaged across depth/time collected (day = 8 am, night = 8 pm) and standardized by row (Z-score, ([Observed VST – Mean VST]/standard deviation). VST values are summed across order-level taxonomy for prokaryotes, except for Prochlorococcus and Synechococcus, which are summed to the ecotype-level. VST values are summed across class-level taxonomy for eukaryotes. Each row is color-coded by domain-level taxonomic information. The bar plot to the left shows the total VST for each row across all samples. We then compared the changes in population structure as inferred through the housekeeping gene analysis to depth profiles of cell counts as determined by flow cytometry. Prochlorococcus , which had increased transcriptional abundance in the SOM, also had increased cell abundances compared to the SRF (BH-adjusted p<0.0001), BML (BH-adjusted p=0.0012) and DCM (BH-adjusted p=0.0229; Figure 4 ). Sub-surface mixed-layer peaks in Prochlorococcus high-light ecotype abundances have been observed previously at BATS and the North Pacific Subtropical Gyre 3 , 4 . Heterotrophic bacterial cell abundances were also elevated at the SOM compared to the SRF (BH-adjusted p=0.0351), BML (BH-adjusted p=0.0165), and DCM (BH-adjusted p<0.0001; Figure 4 ). Oxygen saturation above 100% in this isopycnal can come from the imbalance of primary production (O 2 supply) to respiration (O 2 demand). While both primary producers and heterotrophs had overall increased abundances in the SOM, the relative increase of Prochlorococcus was higher, with a mean heterotrophic bacterium-to- Prochlorococcus ratio of 4.4 as opposed to 8.2 in the mixed layer (Supplementary Table 1). This suggests that oxygen supersaturation in the SOM could arise from disproportionate increases in Prochlorococcus (driving increased primary production) relative to heterotrophic bacteria. Overall, the joint analysis of expression and composition indicates that the SOM supports a microbial community containing similar taxa to the SRF/BML, but in an alternative functional state maintaining higher population densities of heterotrophic bacteria and Prochlorococcus . Download figure Open in new tab Figure 4. Bulk cellular abundances of Prochlorococcus and heterotrophic bacteria. Each circle is an independent sample collected at 8 am or 8 pm at each of the 4 depths and were measured using flow cytometry. Taxa shared between the mixed layer and SOM are enriched in transporter transcripts related to nitrogen recycling We compared functional gene expression profiles of the SRF, BML, SOM, and DCM with gene enrichment analyses based on order-level (for heterotrophic bacteria), class-level (Eukaryotes), or genus-level (for Cyanobacteria, combining all Prochlorococcus ecotypes) aggregates of functional genes annotated via KEGG orthology [KO, 28 ]. Using ratios of KO transcript abundances to the corresponding rpoB / RPB1 (“ rpoB ratio”; see Methods), we identified 604 KO-annotated genes that had either significantly (BH-adjusted p < 0.1) increased (193) or decreased (411 ) transcription relative to that taxon’s rpoB/RPB1 expression only at the SOM ( i.e. , was higher or lower at the SOM compared to all three other depths), spanning eukaryotic and prokaryotic taxa (Supplementary Figure 6 & Supplementary Table 2). Out of the 604 KO-annotated genes, 25 were assigned to Prochlorococcus (Supplementary Figure 7a & Supplementary Table 2). Three of these had significantly higher transcript abundances at the SOM relative to the other depths (Supplementary Figure 7a & Supplementary Table 2). These are mrp (ATP-binding protein involved in chromosome partitioning; K03593), rpmG (large subunit ribosomal protein L33; K02913), and amt (ammonium transporter, K03320; Supplementary Figure 7a & Supplementary Table 2). The Prochlorococcus ammonium transporter, amt , had the highest rpoB ratio at the SOM (∼2.5 amt:rpoB ) compared to all other Prochlorococcus KOs. Prochlorococcus amt expression also increased at dusk in the mixed layer, but not at the DCM (Supplementary Figure 7b). Seventy-four KO-annotated genes belonging to the KEGG “transport” category assigned to eukaryotes, heterotrophic bacteria, and cyanobacteria were identified as having either significantly (BH-adjusted p<0.1) higher or lower transcript values at the SOM ( Table 1 & Supplementary Table 2). Transporter transcripts for inorganic nutrients such as iron/other metals, phosphate, and nitrate were lower at the SOM ( Table 1 ). Heterotrophic bacterial transcripts significantly were significantly higher at the SOM included transporters for (nitrogen-containing) organic substrates, including polar/branched-chain amino acids, oligopeptides, and polyamines ( Table 1 ). While expression cannot directly be substituted for biochemical activity 29 , we identified higher transcription related to uptake of organic nitrogen-containing molecules among heterotrophs and enhanced ammonium uptake by Prochlorococcus (the latter of which is mentioned above), alongside decreased expression of transporters for other nutrients by all taxonomic groups. This result is consistent with increased uptake of organic nitrogen-containing molecules among heterotrophs and ammonium uptake among Prochlorococcus at the SOM. View this table: View inline View popup Table 1. Summary of transporters with transcript-ratios detected as significantly and uniquely increased or decreased at the SOM during the day (8 am time points). The enhanced amt expression by Prochlorococcus at the SOM could be due to different phenomena. Increased transcript levels could be a response to nitrogen starvation 30 due to rapid drawdown of nitrogen sources (including ammonium) by competing cells. Alternatively, this could be a response to enhanced ammonium availability. While amt transcription has been shown to be constitutively high for some Prochlorococcus strains 30 – 32 , the addition of ammonium after severe nitrogen starvation resulted in an increase in transcript levels 31 . Ammonium is not part of the standard suite of inorganic nutrients measured via monthly cruise expeditions at BATS, so historical observations of ammonium concentrations in the SOM are sparse 6 , 33 . Further, because the realized concentration of nutrients in situ is influenced by input, production, drawdown, and abiotic transformation, extrapolating realized nutrient availability from measured nutrient concentrations remains challenging. Since ntcA transcription is low in the presence of sufficient ammonium 30 , 31 , the combined finding of significantly lower ntcA transcript levels (Suppl. Fig. 7a) and significantly higher amt transcript levels normalized to rpoB transcripts, suggests a higher flux of ammonium and a role for this nitrogen acquisition pathway for Prochlorococcus at the SOM. We propose that the enhanced amt expression at the SOM is likely due to a combination of physicochemical and biological factors, where Prochlorococcus satisfies its nitrogen demand under competition via rapid ammonium assimilation, as suggested in a previous studies 31 , 34 . Indeed, transcript levels of transporters for other inorganic nutrients at the SOM suggests reduced nutrient stress relative to other photic depths, assuming expression is induced in the presence of their substrates. Vertical injection of ammonium towards the SOM from depth is possible, however enrichment in amt transcripts is concentrated at the SOM and not at the DCM, suggesting a different supply mechanism. Prochlorococcus amt transcript levels were higher at dusk for both the SOM and SRF/BML (Supplementary Figure 7b). Phototrophic amt expression has been shown to occur mainly at dusk, and was linked to enhanced ribosome and protein synthesis expression at night 35 . These patterns are also consistent with diel expression of Prochlorococcus amt in laboratory experiments 32 . The transport of organic compounds by heterotrophic bacteria at the SOM likely satisfies their carbon and nitrogen demand under competition, as copiotrophic bacteria are suited to use diverse inorganic and organic resources for growth 26 . Collectively, these observations relate to each other in the context of the microbial loop – heterotrophic bacteria catabolize DOM, excreting ammonium as a byproduct, fueling carbon fixation by primary producers, who would then release DOM 36 , 37 . In this case, we hypothesize that heterotrophic degradation of organic matter provided additional ammonium that supported Prochlorococcus growth. Our hypothesis led us to ask - what was the source of DOM for heterotrophs? Elevated signatures of viral infection of prokaryotes in the SOM Virus-mediated mortality of Prochlorococcus is hypothesized to be a significant contributor to the release of DOM into the environment 14 , 15 , 38 . Virus-induced lysis and release of DOM is challenging to estimate directly 19 , hence proxies for the strength of potential viral lysis are often used, including viral abundance, the percentage of infected cells when possible, and intracellular viral transcriptional activity. Here, we quantified cyanophage abundance in the water column using the polony method 18 and found that, on-average, T4-like cyanophages were more abundant in the SOM than in the mixed layer by 3.12-fold (BH-adjusted p=0.0163) and 5.54-fold (BH-adjusted p=0.0007) relative to the SRF and the BML respectively ( Figure 5a , Supplementary Table 1). Likewise, via the iPolony method 19 , we found that the total number of infected cells was higher in the SOM by 11.70-fold (BH-adjusted p<0.0001) and 3.44-fold (BH-adjusted p=0.014) than in the SRF and BML respectively ( Figure 5b , Supplementary Table 1). This equates to roughly 2.8% of Prochlorococcus cells infected by T4-like cyanophages at the SOM (Supplementary Figure 8). Download figure Open in new tab Figure 5. T4- and T7-like cyanophages targeting Prochlorococcus are abundant in the extracellular fraction and actively infecting at the SOM. a) Abundance of free T4-like cyanophages,and b) Number of Prochlorococcus cells infected by T4-like cyanophages, across depth. c) Abundance of free T7-like cyanophages, and d) Number of Prochlorococcus cells infected by T7-like cyanophage across depth. Free cyanophages were quantified by the Polony method which measures phage DNA in the <0.2 µm fraction, while the number of infected cells was determined by the iPolony method which quantifies the number of Prochlorococcus with intracellular phage DNA. See Supplementary Figure 8 for the percent of Prochlorococcus infected by T4-like and T7-like cyanophages. Additionally, we detected higher free T7-like cyanophages in the SOM by 19.62-fold (BH-adjusted p<0.0001), 15.42-fold (BH-adjusted p=0.0002), and 2.79-fold (BH-adjusted p=0.1524) relative to the SRF, BLM and DCM respectively ( Figure 5c , Supplementary Table 1). The total number of T7-like cyanophage-infected Prochlorococcus cells was also higher in the SOM by 11.26-fold (BH-adjusted p<0.0001) and 5.42-fold (BH-adjusted p=0.0057) relative to the SRF and BML respectively ( Figure 5d , Supplementary Table 1). This equates to ∼4.1% of the population of Prochlorococcus being infected by T7-like cyanophages at the SOM (Supplementary Figure 8). Together, ∼7 % of Prochlorococcus was infected by cyanophages in the SOM. We also assessed the intracellular expression of assembled virus Operational Taxonomic Units (vOTUs) from collected viromes, comparing their expression values across depth. First, we assigned vOTUs to T4- and T7-like phage groups broadly infecting prokaryotes based on the presence of hallmark genes and found that a wide range of T4- and T7-like vOTUs had the highest expression values at the SOM compared to the mixed layer (Supplementary Figure 9, see Methods for hallmark gene details). Only a smaller subset of mostly T7-like vOTUs had expression values localized to the DCM (Supplementary Figure 9). Next, using phylogenies of translated hallmark genes for T4-like (Gp23) and T7-like (DNA Pol A) phages, we assessed putative vOTUs related to isolated cyanophages infecting Prochlorococcus and Synechococcus hosts (Supplementary Figures 10 & 11). Putative phage scaffolds related to T4- like cyanophage isolates (Supplementary Figure 10) had 0.7 - 3.76 and 0.96 - 11.02 log 2 fold-change more transcripts at the SOM compared to the SRF and the DCM, respectively (Supplementary Table 3). Putative phage scaffolds related to T7-like cyanophage isolates (Supplementary Figure 11) had 2.06 - 6.23 and 1.43 - 6.23 log 2 fold-change more transcripts at the SOM compared to the SRF and the DCM (Supplementary Table 3). Although T7-like cyanophage abundance was highest at the SOM, the total number of T7-cyanophage infected cells at the SOM was similar to the DCM ( Figure 5d , Supplementary Table 1). We also found transcripts for other phages related to isolates known to infect heterotrophic bacteria that were significantly elevated at the SOM (Supplementary Figure 10 & 11), suggesting that enhanced viral replication may be expanded to bacterial taxa beyond cyanobacteria at the SOM. The combined results indicate elevated viral infection and production of Prochlorococcus cyanophages as well as elevated levels of heterotrophic phage in the SOM. Previous analysis found increases in virus-like particles (VLPs), representing the total dsDNA virioplankton particle concentration, in the SOM over a period of 10 years at BATS concomitant with increases in Prochlorococcus and bulk- Rhodobacteraceae cell abundances 3 (see Supplementary Figure 12 for reanalysis of these data with BATS oxygen profile data incorporated). The elevated standing stock of viral particles and evidence of heightened levels of viral infection during our Lagrangian cruise suggest that a large population of extracellular virions are maintained by infection and replication at the SOM. To date, all known Prochlorococcus phages are obligately lytic 39 , 40 , and lytic cyanophage have been well documented at BATS 41 . Indeed, the position of the SOM in the water column may favor lytic reproduction of viruses, as light levels are attenuated at this depth, reducing the likelihood of viral particle inactivation via UV-induced damage 42 . Additionally, enhanced nutrient availability supplied to the host could play a role in promoting lytic infection at the SOM [reviewed in 43 ]. Altogether, these results suggest the SOM harbors increased viral infection across multiple bacterial taxa, ranging from cyanobacterial to heterotrophic bacterial hosts. Conclusions: Toward a Theory of an Enhanced Viral Shunt in the SOM In this paper, we explored potential ecological mechanisms underlying the recurrent feature of a SOM in the Sargasso Sea. We did so by assessing both cellular and viral activity proxies of the current BATS Lagrangian dataset along with detailed historical abundance data. Through our analysis of historical BATS data to trace the emergence of the SOM over the past 30 years, we found corroborating evidence for a relationship between the SOM, Prochlorococcus populations, and virioplankton abundances. By contextualizing our case study with long-term macroscopic dynamics, we propose that the SOM contains a microbial community with enhanced net primary production, accelerated by a seasonal viral shunt, wherein enhanced viral infection of primary producers stimulates heterotrophic organic matter remineralization, providing inorganic nutrients to fuel primary production and oxygen accumulation. Previous studies have provided evidence that SOMs are associated with enhanced DOM levels in the form of cDOM, which has been shown to stimulate the growth of heterotrophic bacteria 44 . Other studies have shown bioavailable cDOM was enriched in the lysate of Prochlorococcus after infection by cyanophage 45 . Metabolomic analysis of marine bacterial lysates has also been shown to be enriched in amino acids and other organic nitrogen substrates upon infection by Synechococcus phage 46 and heterotrophic bacteriophages 47 – 52 . Catabolism of organic matter by heterotrophic bacteria produces abundant inorganic nitrogen in the form of ammonium, which Prochlorococcus competitively sequesters to satisfy its nitrogen demand 34 . Indeed, laboratory studies have demonstrated that DOM enrichment, driven by viral lysis, supported phytoplankton growth by supplying excess ammonium 17 , 53 . Additionally, the increased dusk Prochlorococcus amt expression coinciding with enhanced beam attenuation at dusk suggest a potential coupling of these processes at the SOM. Indeed, the SOM in the stratified season likely supports optimal lytic viral replication, where light is sufficient to allow phototrophic metabolism, yet attenuated to prevent viral particle degradation. Additionally, our transcriptomics data suggest alleviated nutrient limitation below the mixed layer, which likely contributes to enhanced microbial abundances and viral reproduction. Overall, our combined results suggest that the SOM in the Sargasso Sea arises as a recurrent biogeochemical feature from the combination of seasonal shifts in physical water column structure and enhanced microbial activity, at least partly mediated by viral infection. Critically, our analysis of -omics, polony/iPolony, and historical data demonstrate how the viral shunt may play a crucial role in driving SOM biogeochemical dynamics. As stratification is expected to intensify and deep mixing anticipated to weaken due to increased annual sea surface temperatures 54 , 55 , we expect emergent effects on the formation and relative importance of the SOM, and consequently the viral shunt, to biogeochemical fluxes of carbon and oxygen 56 . Our results emphasize the need to incorporate viral infection into changing ocean modeling and prediction as a critical mechanism for understanding carbon remineralization dynamics in the subsurface of oligotrophic oceans. Future efforts should include targeted quantification of bulk- and single-cell viral infection, using a combination of high-throughput ( e.g., single-cell transcriptomics) and host-resolved laboratory infection assays ( e.g., iPolony) to more accurately quantify viral contributions to SOM biogeochemistry. Such efforts will enable better modeling and prediction of mutual feedbacks between climate variability, SOM formation, and the macroscale function of SOMs. Methods Sampling design and AE1926 CTD data processing At the onset of the study, a surface buoy with an underwater drogue (at ∼30 m depth) was deployed to allow us to follow the same “patch” of water for the duration of the study. Water samples were collected using a CTD-rosette equipped with 24 x 12-L Niskin bottles. Depths for each cast were chosen based on in situ CTD oceanographic parameters to allow for focus on water column features: this included the top (surface, SRF) and bottom (BML) of the upper mixed layer, one depth within the highly oxygenated zone below the mixed layer (subsurface oxygen maxima, SOM), and one depth within the zone with the highest chlorophyll fluorescence values (the deep chlorophyll maxima, DCM). For each cast the CTD was deployed to at least 500 m to collect data on physical water column structure. CTD profiles used in Figure 1 are based on the water column profiles collected every 4h during the sampling campaign. Water samples were collected from four main depths every 12 h starting at 8:00 ADT (GMT-3): ∼5 m depth (SRF), ∼40-50 m depth (BML), ∼42 – 60 m depth (SOM) and 105-120 m depth (DCM). CTD data were retrieved as deposited in BCO-DMO 57 . Measurements were binned to the nearest 0.5 m of depth to standardize across casts, then a Nadaray-Watson kernel smoothing filter with bandwidth of 5 db was applied to each variable to remove noise and spikiness. Mixed layer depth for each cast was calculated using the criterion of an increase in potential density of 0.125 from a reference pressure of 10db to account for instrumental noise in the surface 58 – 61 . Notably, this MLD criterion tends to identify the potential density surface directly below which oxygen saturation % increases to over 100%, operationally capturing the SOM depth as the first isopycnal (within 0.1 kg/m 3 ) below the MLD as defined using the 0.125 kg/m 3 change from surface criterion. This mixed layer depth criterion is presented as a preset commonly used in oceanographic timeseries data archives [cite HOT-DOGS MLD page]. For statistical comparison of depth layers and depth layer-integrated time series analysis, smoothed data were averaged into depth bins representative of those layers – the mixed layer was binned from 5 to 40 m, the DCM from 120 to 100 m, and the SOM from 60 to 50 m. Beam attenuation, chlorophyll fluorescence, oxygen concentration, and oxygen saturation were all averaged (mean) across these layers. The beam attenuation measures particles within the 0.5-20 μm size range 21 , 22 . For diel periodicity analysis, data were initially detrended using a linear model, and then the nonparametric rhythmicity detection method ‘rain’ 62 was run in ‘independent’ mode with a test period of 24 hours. BATS CTD data processing CTD cast data from October 1988 through December 2019 were downloaded from the API available at http://bats.bios.edu/bats-data/ in ASCII format. After initial reformatting to account for inconsistencies in file formats between casts, any cast with faulty or missing data for conductivity, temperature, or pressure, as well as casts missing data from the top 10 db of pressure were removed so that all casts could have the same thermodynamic calculation processing. The remaining 5,270 CTD profiles were then processed using methods from the Gibbs Seawater Toolbox ( https://www.teos-10.org/pubs/gsw/html/gsw_front_page.html ) using the python 3.4.0 distribution through R via the reticulate package v1.24 ( https://github.com/rstudio/reticulate ). Briefly, height from surface of geoid, potential salinity, absolute salinity, conservative temperature, density (sigma-theta), oxygen solubility, and oxygen saturation were calculated using pressure, longitude, latitude, temperature, conductivity, and oxygen CTD data. Mixed layer depth was calculated using a change in sigma-theta of 0.125 from a reference pressure of 10db. Bottle data, including flow cytometry data, were also downloaded from the BATS API and matched to corresponding casts and depths. The quality of CTD optode data was assessed through a systematic comparison of CTD oxygen values to bottle measurements using the Winkler method 63 . A linear regression was used to assess the correspondence between methods. The fit was [O 2 µM via CTD optode] = 7.85 + 0.964*[O 2 µM via Winkler method] with an adjusted R 2 = 0.959. Any cast with a measurement containing a standardized residual of 3 or greater, indicating a large discrepancy between CTD optode and bottle oxygen measurements, was discarded for further analysis (total of 26 / 5,270 casts). Prochlorococcus- virus seasonal relationship modeling VLP counts associated with BATS monthly time series cruises from Parsons et al., 2012 were retrieved from supplementary information and matched to corresponding BATS time series cruise bottle and CTD data, processed as described in the above section. Altogether, 104 observations were paired resulting in Prochlorococcus flow cytometric counts from BATS publicly available bottle data, oxygen concentrations from BATS CTD data, and VLP counts. Data were separated by month to estimate monthly varying effects on the relationship between Prochlorococcus and VLPs. For each month, a type-II regression model (major axis estimation procedure) was fit using the R v4.2.1 package lmodel2 v.1.7-3 64 for log 10 Prochlorococcus counts and log 10 VLPs. The slope of this linear model on log-log data is equivalent to the exponent of a power-law relationship between Prochlorococcus counts and VLP counts. Parameter estimate uncertainty and model significance were assessed using a permutation test with 10,000 permutations per model. Metatranscriptome sampling, processing, and bioinformatic analysis Seawater was filtered through a 0.2-µm pore-size Sterivex™ filtration unit. Residual water was removed by pushing air through the filter with a 60 ml syringe, and the filter immediately transferred to a -80 °C freezer. RNA was extracted using a publicly available phenol-chloroform based protocol 65 with DNA contamination removed using the Turbo DNA-free™ kit (Ambion®). Metatranscriptome libraries were prepared by reducing ribosomal RNA using the QIAGEN’s FastSelect kit (5S/16S/23S for bacterial rRNA depletion) and sequenced (2 x 151 nt) using the low-input protocol for total RNA on the Illumina Novaseq S4 platform under the DOE Joint Genome Institute (JGI) Community Sequence Proposal ID# 505733. For samples with < 10 ng of RNA total, the ultra-low input protocol was used (13 PCR cycles versus 10 cycles for standard low-input). Specific library preparation methods used for each sample can be found on the JGI Genome Portal (Project ID 505733). Raw read filtering and trimming were done using BBDuk v38.67 and BBMap v38.84 from the BBtools packages 66 . Trimmed filtered reads were combined across samples and assembled using MEGAHIT v1.2.9 67 . MetaGeneMark v3.38 was used to call open reading frames (ORFs) with a kmer size parameter as --k-list 23,43,63,83,103,123. For the cellular community, trimmed filtered reads were mapped to the combined assembly using BBMap v38.84 66 with default parameters, and tabulated using featureCounts 68 . ORF protein sequences were annotated using eggNOG-mapper v2.1.4 69 for functional annotation, and aligned to the PhyloDB database ( https://github.com/allenlab/PhyloDB ) using the software package EUKulele 70 for taxonomic annotation of bacteria, eukaryotes, and archaea. ORFs were filtered with average read counts ≥ 10 across the dataset. Variance stabilizing transformation (VST) using the DESeq2 v.1.34.0 R-package was used for read normalization 71 . We used the Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology annotations 28 to explore actively transcribed metabolic pathways. Normalized read counts for each KEGG orthologue were summed for the genes assigned to each taxon within each sample, resulting in order-level signals for heterotrophic bacteria, genus-level for cyanobacteria, and class-level for eukaryotes. To assess differences between depth/time, we calculated the ratio of each KEGG KO VST to the corresponding order-level summed VST of rpoB/RPB1 ’s (DNA-directed RNA polymerase subunit-beta/large subunit, K04043 and K03006) which assesses relative taxonomic contribution to each sample 72 , resulting in a “KO vst: rpoB ratio” to correct for positive correlations between increases in gene abundance with taxon abundance. This approach has been recommended for adjusting the gene transcript abundance to the taxon-level RNA estimate within each sample 73 when paired metagenomic data are not available. Metatranscriptome recruitment to Single-Celled-Amplified genome (SAGS) of Prochlorococcus To resolve spatial and temporal patterns in Prochlorococcus ecotypes, we competitively recruited metatranscriptome reads to publicly available SAGS from Berube et al. 74 . SAG genomic assemblies, gene annotations from the IMG Annotation Pipeline version 4 (accommodating phylogenetic information pertaining to ecotype and clade of each SAG) and the cyanobacterial concatenated phylogenetic tree were retrieved via: https://figshare.com/articles/dataset/File_12_Genome_sequences_and_annotations/60 07223?backTo=/collections/Single_cell_genomes_of_i_Prochlorococcus_i_i_Synechoco ccus_i_and_sympatric_microbes_from_diverse_marine_environments. Trimmed, filtered reads were competitively mapped to a concatenated file containing all SAG genomic assemblies using BBMap v38.84 66 with default parameters and tabulated using the GFF file gene coordinates with featureCounts 68 . Only SAGS that had ≥ 10% of their genes mapped to transcripts were considered ‘detected’ to avoid capturing spurious read recruitments 75 and genes with < 20 reads mapped across the entire time series were filtered out. Reads were normalized using the VST method 71 . Statistical analysis of metatranscriptome data Statistical analyses were performed using the R Statistical Software v4.1.3 76 . Principal components analysis (prcomp() in R) was performed on VST normalized values across all ORFs detected in the metatranscriptome assembly. The Kruskal-Wallis test followed by Dunn’s multiple comparison was used to determine significance of trends between depths (SRF, BML, SOM, DCM), separating 08:00 and 20:00 time points. The Benjamini–Hochberg adaptive false discovery rate (FDR) control procedure was implemented using a significance threshold of FDR□=□10% (P□<□0.1). To be considered significantly elevated or depleted at the SOM, the mean VST value (KO VST: rpoB ratio) at the SOM had to be either higher or lower (with an adjusted p-value of ≤ 0.1) than the SRF, BML and DCM. Fold-change values for vOTU scaffolds harboring DNA_Pol_A were done using DESeq2 v.1.34.0 71 on scaffold-summed raw counts for the following comparisons: SOM versus SRF and SOM versus DCM samples collected at 8 AM. Virome sample collection, processing, and bioinformatic analysis Seawater (10 L) was 0.22-μm filtered to remove bacteria, and the remaining viruses then concentrated from the filtrate using iron chloride flocculation 77 followed by storage at 4 °C. Filters were cut in half and viruses resuspended from one half in ascorbic-EDTA buffer (0.1 M EDTA, 0.2 M Mg, 0.2 M ascorbic acid, pH 6.0). Viral particles were concentrated using Amicon Ultra 100 kDa centrifugal devices (Millipore), treated with DNase I (100 U/mL) followed by the addition of 0.1 M EDTA and 0.1 M EGTA to halt enzyme activity 20 , and DNA was extracted using Wizard PCR Preps DNA Purification Resin and Mini-columns (Promega, Cat. #A7181 and A7211 respectively) after Henn et al., (2010) 78 . All samples from the BATS virome were sequenced at ∼144 M reads per sample on an Illumina platform at the DOE Joint Genome Institute. Raw reads from all 39 samples in the BATS viromes dataset went through quality control using BBDuk ( https://jgi.doe.gov/data-and-tools/software-tools/bbtools/ ). Adaptors and Phix174 reads were removed (ktrim=r minlength=30 k=23 mink=11 hdist=1 hist2=1) and reads trimmed (qtrim=rl maq=20 maxns=0 minlength=30 trimq=20). Reads were assembled individually using MegaHIT 1.2.9 67 and those ≥ 1.5 kbp in length were piped through VirSorter2 79 and CheckV 80 following the viral sequence identification SOP 81 . Resulting viral contigs were clustered into viral populations (vOTUs) at ≥ 95% identity and ≥ 80% coverage using ClusterGenomes ( https://github.com/simroux/ClusterGenomes ). This resulted in a total of 44,819 vOTUs of which 13,369 were ≥ 10 kbp. After dereplication, contigs were piped through Virsorter2 for the second time with the --prep-for-dramv flag. The contigs were annotated using the DRAM annotator for viromes ( https://github.com/WrightonLabCSU/DRAM ). Trimmed filtered metatranscriptome reads were mapped to the ≥ 10kbp vOTU database using BBMap v38.84 66 with the minimum read identity set to 95% and normalized using the VST method 71 . To assess broad phylogenetic associations of vOTU contigs, T4-like and T7-like phage hallmark proteins were searched across the ≥ 5 kbp vOTU annotations. The following PFAMs were used for identifying T4-like contigs: gp23 (T4 major capsid protein, PF07068), gp32 (DNA-binding protein, PF08804), gp45-slide_C (sliding DNA clamp, PF09116), GPW_gp25 (tail sheath gpW/gp25-like domain, PF04965), Phage_gp53 (Base plate wedge protein 53, PF11246), Phage_sheath_1 (Phage tail sheath protein subtilisin-like domain, PF04984), Phage_T4_gp19 (T4-like virus tail tube protein gp19, PF06841), T4_baseplate (T4 bacteriophage base plate protein, PF12322), T4_gp59_N (gp41 DNA helicase, PF08993), T4_gp9_10 (Baseplate wedge protein gp10, PF07880), T4_neck-protein (Neck protein gp14, PF11649), T4_tail_cap (tail-tube assembly protein gp48, PF11091), T4-gp15_tss (T4-like virus Myoviridae tail sheath stabiliser , PF16724). The following PFAMs were used for identifying T7-like contigs: Phage_T7_Capsid (Phage T7 capsid assembly protein, PF05396), Phage_T7_tail (Phage T7 tail fibre protein, PF03906). Phylogenetic trees of DNA polymerase A (DNA_pol_A, PF00476) and T4 Major capsid proteins (gp23, PF07068) predicted from the vOTU contigs were constructed, with references downloaded from NCBI RefSeq representative of diverse phage families. Here, vOTU DNA_pol_A sequences >700 aa or gp23 sequences >450 aa in length were aligned to reference sequences in MEGA7 82 using ClustalW 83 and trimmed using trimAl (v.1.2; -gappyout method 84 . The maximum-likelihood tree was constructed in PhyML 85 with the LG model, and the Shimodaira-Hasegawa (SH)-like approximate likelihood ratio test (aLRT-SH-like). Remaining DNA_pol_A sequences < 700 aa or gp23 sequences <450 aa were placed on the tree using pplacer 86 and visualized and annotated using ITOL v.4 87 . Flow cytometry and statistical analysis Samples for flow cytometry were collected in duplicate after prefiltration through a 20 µm mesh and fixed in 0.125% glutaraldehyde for 15 min in the dark, frozen in liquid nitrogen, and stored at -80° C until analysis. The samples were run using an Influx DB flow cytometer (BD Biosciences) equipped with a small particle detector, a 488-nm and a 457-nm laser, and a 70- μm nozzle tip. Samples were weighed to determine the volume analyzed by the sorter. Synechococcus and Prochlorococcus cell abundances were determined based on autofluorescence and size. Prochlorococcus was detected by red fluorescence of chlorophyll a (emission at 692/640 nm) while Synechococcus was detected by orange fluorescence of phycoerythrin (emission at 580/30 nm). Total bacteria were enumerated by staining with 10 −4 diluted stock of SYBR Green I (Invitrogen), followed by a 15 min dark incubation. Total bacteria were detected using green fluorescence excited with the 488 nm laser and detection at emission wavelengths of 530/20 nm. Heterotrophic bacterial counts were quantified as the difference between the total bacteria less the abundance of the cyanobacteria. To determine infection levels of Prochlorococcus by the iPolony method (see below), Prochlorococcus were sorted using 1.0 drop purity mode with the same parameters described above (Mruwat et al., 2021). Using Prism (v 9.5.1), the Kruskal-Wallis test followed by Dunn’s multiple comparison was used to determine significance of trends across depth (SRF, BML, SOM, DCM) for each taxonomic group, integrating time. The Benjamini–Hochberg adaptive false discovery rate (FDR) control procedure was implemented using a significance threshold of FDR□=□10% (P□<□0.1). Polony detection of cyanophage and statistical analysis Samples for cyanophage enumeration were filtered through a 0.2 µm syringe filter and the filtrate was frozen at -80 °C without fixative. Quantification of T4- and T7-like cyanophage abundances was done using the polony method following Baran et al. 18 for T7-likes and Goldin et al. 88 for T4-likes. Degenerate 5’-acrydite-modified primers and the 0.2 μm filtrate from seawater samples were incorporated into an acrylamide gel prior to polymerization within a 40 µm-deep well etched into microscope slides (Thermo Fisher Scientific). Subsequently, other PCR reagents and the unmodified degenerate primer were diffused into the gel post-polymerization. DNA amplification was conducted using slide PCR, and amplicons were detected through hybridization with fluorescent probes using a GenePix 4000B microarray scanner (Axon Instruments). The primers and probe for T7-like cyanophages targeted the DNA polymerase gene ( DNApol ), for both clade A and clade B phages and are reported together as T7-like cyanophages. Notably, clade A cyanophages were found to be a minor contributor to T7-like cyanophages in this study, and they are reported together with clade B. Cyanophages belonging to the newly described clade C were not quantified using this method since they lack the DNA polymerase gene 89 . For T4-like cyanophage quantification, the primers and probes targeted the g20 portal protein gene. Samples for quantifying infection were collected and fixed as described for flow cytometry analysis. To evaluate the direct impact of viruses on cyanobacteria, we employed the iPolony method 19 which analyzes the presence of viral DNA within cyanobacteria cells. In this approach Prochlorococcus cells are sorted by flow cytometry (see above) and immediately embedded in polyacrylamide gels at an average of four thousand cells per gel. Subsequently, virus DNA was amplified and detected within the cells using the same amplification and hybridization procedures as those described for free cyanophages, with the exception that cells were not pretreated with EDTA for analysis of infection by T4-like cyanophages. For further details refer to Mruwat et al. (2021). Using Prism (v 9.5.1), the Kruskal-Wallis test followed by Dunn’s multiple comparison was used to determine significance of trends across depth (SRF, BML, SOM, DCM) for T7- and T4-like polony or iPolony values, integrating time. The Benjamini–Hochberg adaptive false discovery rate (FDR) control procedure was implemented using a significance threshold of FDR□=□10% (P□<□0.1). Data Availability Raw metatranscriptomic and viromic sequencing data is available through the JGI under project ID #505733. All cruise data is available via BC-DMO (See Wilhelm et al. 57 ). Statistics and figures code base and all data will be made publicly available shortly and in the meantime please contact SWW for any data or code requests. The authors declare no conflicts of interest. Acknowledgements The authors would like to thank the captain and crew of the RV Atlantic Explorer for conducting cruise AE1926, Rod Johnson and the entire BATS team for advice and assistance, Yotam Hulata for helping with flow cytometry and Shay Kirzner for helping with Prochlorococcus sorting. This work was funded by NSF grant OCE-1829641to S.W.W., Simons Foundation grant 735077 to S.W.W, NSF grant OCE-1829636 to J.S.W., Simons Foundation grant 721231 to J.S.W., the Blaise Pascal Institute Chair of Excellence award at the Institut de Biologie of the ‘Ecole Normale Sup’erieure to J.S.W, Simons Foundation Life grants 529554 and 735081 to D.L., Israel Science Foundation grant 2679/20 to D.L., NSF grant OCE-1737237 to A.B., and the Omidyar Complexity Postdoctoral Fellowship (by the Santa Fe Institute) to D.M. Sequencing was provided by the Joint Genome Institute CSP grant 505733 to M.S. and C.S. Footnotes ↵ c School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA References 1. ↵ Cavicchioli , R. et al. Scientists’ warning to humanity: microorganisms and climate change . Nature Reviews Microbiology 17 , 569 – 586 ( 2019 ). OpenUrl CrossRef PubMed 2. ↵ Field , C.B. , Behrenfeld , M.J. , Randerson , J.T. & Falkowski , P . 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