Algal Betaine Triggers Bacterial Hydrogen Peroxide (H2O2) Production that Promotes Algal Demise

Algal Betaine Triggers Bacterial Hydrogen Peroxide (H2O2) Production that Promotes Algal Demise | 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 Algal Betaine Triggers Bacterial Hydrogen Peroxide (H 2 O 2 ) Production that Promotes Algal Demise Delia A. Narváez-Barragán , Lilach Yuda , Dayana Yahalomi , Valeria Lipsman , Sergey Malitsky , Einat Segev doi: https://doi.org/10.1101/2025.05.07.652642 Delia A. Narváez-Barragán 1 Department of Plant and Environmental Sciences, Weizmann Institute of Science; Rehovot , 7610001, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lilach Yuda 1 Department of Plant and Environmental Sciences, Weizmann Institute of Science; Rehovot , 7610001, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dayana Yahalomi 2 Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science , Rehovot, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site Valeria Lipsman 1 Department of Plant and Environmental Sciences, Weizmann Institute of Science; Rehovot , 7610001, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sergey Malitsky 3 Department of Life Sciences Core Facilities, Weizmann Institute of Science , Rehovot, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site Einat Segev 1 Department of Plant and Environmental Sciences, Weizmann Institute of Science; Rehovot , 7610001, Israel Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: Einat.Segev{at}weizmann.ac.il Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT Hydrogen peroxide (H 2 O 2 ) plays various roles in the ocean, acting as a signaling molecule at low concentrations and causing oxidative stress when accumulated. While many marine microbes produce H 2 O 2 , its role in microbial interactions remains unclear. Here, we used transcriptomics, genetics, and metabolomics to study H 2 O 2 dynamics in the interaction between Emiliania huxleyi algae and Phaeobacter inhibens bacteria. We found that H 2 O 2 levels rise during algal death and that bacterial H 2 O 2 production triggers this demise. Manipulating H 2 O 2 levels shifted the outcome of the interaction. We also uncovered a link between H 2 O 2 and betaine metabolism: aging algae release betaine, which promotes bacterial H 2 O 2 production and, in turn, accelerates algal death. Genes involved in H 2 O 2 and betaine metabolism were upregulated in environmental samples from an algal bloom. Together, our findings identify H 2 O 2 and betaine as key molecules that modulate algal-bacterial interactions, potentially impacting microbial dynamics in marine ecosystems. INTRODUCTION Reactive oxygen species (ROS) perform a wide range of both harmful and beneficial roles across the tree of life 1 . In the marine environment, hydrogen peroxide (H 2 O 2 ) stands out as the most persistent ROS 1 , 2 . Researchers have detected H 2 O 2 throughout the water column, from sunlit surface waters to the deep ocean 3 , 4 , highlighting its stability and ecological relevance in marine systems. Among ROS, H 2 O 2 uniquely diffuses across cell membranes and persists both inside and outside cells due to its relatively low reactivity 1 , 2 . Biologically, H 2 O 2 plays diverse roles: at low concentrations, it functions as a signaling molecule, while at elevated levels, it induces oxidative stress and cellular damage 2 . H 2 O 2 forms through both abiotic and biotic processes. Abiotic production occurs via photooxidation of dissolved organic matter (DOM) and through rainfall events 4 , 5 . Biologically, organisms generate H 2 O 2 as a byproduct of aerobic respiration and photosynthesis. Several sources of extracellular H 2 O 2 production have been identified in marine systems, including phytoplankton and bacteria 6 – 9 . While both phytoplankton and bacteria produce H 2 O 2 , they must tightly regulate its extracellular levels to maintain intracellular redox balance, support essential cellular functions such as cell growth, and prevent oxidative damage 6 , 7 , 10 . Some phytoplankton species, however, are highly susceptible to H 2 O 2 and cannot manage harmful extracellular concentrations on their own. Instead, they depend on coexisting microbes, particularly heterotrophic bacteria, that have evolved strategies to degrade H 2 O 2 effectively 10 , 11 . These bacterial detoxification capabilities enhance community-level tolerance in environments with elevated H 2 O 2 and may create ecological dependencies that influence microbial interactions and shape community composition in the ocean 1 , 11 . Algal blooms serve as hotspots for H 2 O 2 production 1 , 7 , where the high density of microbial cells possibly allows H 2 O 2 released by one organism to directly affect its neighbors 12 . The rich biomass and DOM produced during blooms enhance both abiotic and biotic H 2 O 2 generation 1 , 7 . Heterotrophic bacteria associated with these blooms actively contribute to biotic production 9 . For example, gene expression data suggest that the roseobacter Ruegeria pomeroyi produces H 2 O 2 while degrading dimethylsulfoniopropionate (DMSP), a common algal metabolite abundant during blooms 13 . Bacteria also play a key role in degrading H 2 O 2 , helping to regulate its concentration in the environment 10 . Despite its potential significance in shaping bloom dynamics, microbial communities, and interspecies interactions, the role of H 2 O 2 in these processes remains poorly understood. The microalga Emiliania huxleyi (also known as Gephyrocapsa huxleyi 14 ) forms dense oceanic blooms that can persist for several weeks before collapsing due to nutrient limitation and viral infection 15 , 16 . Recent studies have also demonstrated that bacteria can terminate laboratory-induced E. huxleyi blooms 17 – 19 . In natural settings, E. huxleyi blooms host diverse bacterial communities, often dominated by members of the roseobacter group. One well-studied species from this group, Phaeobacter inhibens , frequently co-occurs with E. huxleyi at sea and displays a range of algal-bacterial interactions in laboratory cultures 17 – 24 . Initially, P. inhibens bacteria promote algal growth, but as the interaction progresses, bacteria shift towards antagonism. P. inhibens bacteria induce algal cell death through several mechanisms, including the production of roseobacticides 18 , nitric oxide (NO) 19 , and excessive levels of indole acetic acid (IAA) 17 . Although these are distinct pathogenicity pathways, they all lead to oxidative stress and trigger apoptosis-like programmed cell death in algae 17 , 19 , 24 . H 2 O 2 likely plays a role in this process, as supported by the observation that P. inhibens bacteria upregulate genes involved in H 2 O 2 detoxification in response to E. huxleyi -derived p -coumaric acid, the precursor of roseobacticides 25 . Here, we show that H 2 O 2 accumulates throughout the interaction between E. huxleyi and P. inhibens , with levels rising significantly during bacterial-induced algal death, suggesting a potential role in triggering algal demise. By manipulating H 2 O 2 concentrations in co-cultures, we directly influenced the outcome of the interaction. We also identified bacterial genes involved in H 2 O 2 metabolism and used genetic manipulation to demonstrate their functional contribution to algal death. In addition, we uncovered a previously unrecognized link between H 2 O 2 and betaine metabolism. Betaine, a well-known algal metabolite is produced by aging algae towards the end of the interaction, and stimulates H 2 O 2 production in bacteria. Modifying betaine levels altered the trajectory of the algal-bacterial relationship, suggesting that bacteria sense betaine as a proxy of the algal status. Our findings suggest that betaine is an age-related cue from algae, promoting bacterial H 2 O 2 production and ultimately triggering algal demise. RESULTS H 2 O 2 levels impact the algal fate during algal-bacterial interactions To determine the involvement of H 2 O 2 in the algal-bacterial interaction we first measured the extracellular H 2 O 2 levels at different algal growth phases, both in algal mono-cultures and in co-cultures with P. inhibens bacteria ( Fig. 1A ). Cultures were sampled at days 0, 4, 7, 10, and 14 of cultivation, and H 2 O 2 levels were quantified using the fluorescent probe Amplex Red (see materials and methods). Our data show that algae in mono-cultures continuously produce H 2 O 2 , with levels increasing during the exponential growth phase and decreasing during the late stationary phase ( Fig. 1B ). However, in co-cultures with bacteria, a different H 2 O 2 profile is seen over time, with H 2 O 2 levels decreasing during the early stages of interaction (day 4, Fig. 1A-B ) but increasing as bacteria induce algal death (day 14, Fig. 1A-B ). These differences in H 2 O 2 profiles suggest that bacteria impact H 2 O 2 dynamics throughout the interaction with their algal host. Download figure Open in new tab Fig 1. Impacts of H 2 O 2 levels on algal-bacterial interaction. A) E. huxleyi algae grown alone (green bars) or in co-cultivation (brown) with P. inhibens bacteria (line) along 14 days. B) Extracellular H 2 O 2 concentration (µM) measured at days 0, 4, 7, 10 and 14, in cultures of E. huxleyi alone (green) or grown alongside bacteria (brown). C) Algae growth alone (green bar) or in co-cultivation (brown bar) with bacteria (lines). Cultures were supplemented with the H 2 O 2 scavenger Potassium Iodide (1mM KI, light green and yellow) in all time points, or with 1µM H 2 O 2 (dark green and gray) at days 7, 10 and 12 of the interaction. Error bars represent the standard deviation from the mean of at least 3 biological replicates. Box-plots show results from three independent experiments; black dots indicate individual measurements. Box-plot elements: center line - median; box limits -upper and lower quartiles; whiskers – min and max values. Normality was first assessed using a Shapiro–Wilk test, followed by an unpaired t-test to determine statistical differences between mono- and co-cultures at various time points. NS: Not significant, *p=0.01, **p=0.006. The involvement of oxidative stress in bacterial pathogenicity towards algae has been documented by us 17 , 19 and by others 24 , 25 . Since H 2 O 2 can be a prominent inducer of oxidative stress, we investigated whether H 2 O 2 levels affect the outcome of the algal-bacterial interaction, by manipulating H 2 O 2 levels. H 2 O 2 concentrations were either reduced by supplementing cultures with 1 mM potassium iodide (KI), a H 2 O 2 scavenger 26 , or increased by adding 1 µM H 2 O 2 . Our data show that algae were unaffected by both treatments when grown in mono-cultures ( Fig. 1C ). However, in co-cultures with bacteria, addition of the scavenger resulted in inhibition of the characteristic algal death, whereas the addition of exogenous H 2 O 2 accelerated algal death ( Fig. 1C ). These results demonstrate that the manipulation of H 2 O 2 levels impacts the algal fate during the algal-bacterial interaction. Bacteria modulate the algal H 2 O 2 -related gene expression The varying levels of H 2 O 2 during the algal-bacterial interaction could be the result of changes in H 2 O 2 production, as well as H 2 O 2 detoxification. Furthermore, these changes could be from either an algal or bacterial source. To understand the algal and bacterial H 2 O 2 metabolism, we first assessed changes in H 2 O 2 -associated processes in algae through expression analysis of indicative genes. E. huxleyi algae are known to produce and metabolize H 2 O 2 27 , 28 . To analyze genes involved in algal H 2 O 2 production and tolerance, we compiled a list of relevant genes by focusing on those associated with enzymatic reactions know to generate H 2 O 2 either directly or as a byproduct. These genes were identified through literature searches and their annotated functions 7 , 8 , 29 – 32 , as well as homology-based predictions within the MetaCyc database 33 (table S1). Then, we analyzed the expression patterns of these algal genes in algal mono-cultures and algal-bacterial co-cultures using a RNA-seq dataset that we previously generated 23 . Transcription patterns of algal genes involved in H 2 O 2 production and degradation were similar between mono and co-cultures during the early and late exponential phases, as well as the stationary growth phase of algal growth ( Fig. 2A ). During these growth stages, only one gene exhibited different expression patterns, superoxide dismutase ( sod ) was downregulated during the late exponential phase in co-cultures compared to algal mono-cultures ( Fig. 2A ). However, during algal demise in co-cultures, which corresponds to the late stationary phase in algal mono-cultures, various genes exhibited marked expression differences. In the presence of bacteria, several algal genes that are involved in H 2 O 2 production were significantly up-regulated, these include sod and the genes encoding the reactive oxygen species modulator 1 ( romo1 ), spermine oxidase-like ( smo ), FAD-linked sulfhydryl oxidase ( erv 1 ) and NADH dehydrogenases ( ndhs ) from the electron transport chain. The increased expression of these genes suggests increased production of H 2 O 2 , which may be involved in algal demise through the activation of programed cell death 34 – 37 . Additionally, we observed the downregulation of photosynthesis-related genes, likely due to ROS accumulation 38 . Simultaneously, downregulation of several algal genes involved in H 2 O 2 detoxification was observed, including genes encoding the glycolate oxidase ( gox ), which is involved in the activation of response genes 39 , protoporphyrinogen oxidase ( ppo ), cytochrome c peroxidase ( cpp ), glutathione peroxidases ( gpx ), and ascorbate peroxidase ( apx , Fig. 2A ). The decreased expression of these genes suggests a compromised algal H 2 O 2 -detoxification process 40 , 41 . These findings suggest that bacteria enhance the production and decrease the detoxification processes of H 2 O 2 in algae, thereby potentially accelerating algal demise. Download figure Open in new tab Fig 2. Algal and bacterial H 2 O 2 production and degradation. A) Heatmap showing the expression of algal genes at different growth phases in algal mono-cultures (designated A) or in co-cultures with bacteria (designated A+B). Top panel – H 2 O 2 production, lower panel - H 2 O 2 degradation. B) P. inhibens grown in minimal media supplemented with 0 (black), 10 (green), 100 (brown), 250 (yellow), 500 (gray) and 750 (blue) µM H 2 O 2 . Lines represent the average growth curve based on three biological replicates and shaded areas indicate standard deviation (SD). C) Extracellular H 2 O 2 concentration (µM) measured at 0, 3, 6 and 24 hours of bacterial cultivation, supplemented with 100 (brown) or 250 (yellow) µM H 2 O 2 , compared with media without bacterial addition (light and dark green). Lines represent bacterial growth. D) Heatmap showing the expression of bacterial H 2 O 2 tolerance genes at different growth phases in bacterial pure cultures (designated B) or growing in co-culture with algae (designated B+A). E) E. huxleyi algae in mono-cultures (green bar) or in co-cultures with P. inhibens WT (brown) or △pxn (yellow), and △katg (gray) mutants. Lines represent bacterial growth. In A and D heatmaps expression intensity is shown as Z score. Asterisks indicate significant differential gene expression (adjusted p -value <0.05), black asterisk indicate upregulation while pink asterisk downregulation. Gene descriptions and log2 fold changes are available at table S1-2. The underlying dataset was previously described in Sperfeld & Narvaez-Barragan et al . 23 . Exp: exponential, stat: stationary. In C and E error bars represent the standard deviation from the mean of 3 biological replicates. Bacteria can produce and degrade H 2 O 2 In addition to the algal impact on H 2 O 2 levels in co-cultures, bacteria may also contribute to the observed patterns. To test the bacterial influence on H 2 O 2 levels, we evaluated the ability of P. inhibens to metabolize extracellular H 2 O 2 . First, to mimic conditions in which bacteria experience extracellular H 2 O 2 from an algal source, we examined bacterial growth under increasing concentrations of extracellular H 2 O 2 in pure bacterial cultures with defined media. Our data show that P. inhibens bacteria can grow in H 2 O 2 concentrations as high as 500 µM ( Fig. 2B ). Next, to track the bacterial ability to degrade extracellular H 2 O 2 , we cultivated pure bacterial cultures in defined media supplemented with 100 µM or 250 µM H 2 O 2 and monitored the H 2 O 2 levels in these cultures. We observed that under both H 2 O 2 concentrations, the extracellular H 2 O 2 was completely depleted after 3 hours, as compared to a control sample that did not contain bacteria ( Fig. 2C ). After 24 hours, bacterial growth was evident, and H 2 O 2 was detected in bacterial cultures but not in the controls, where H 2 O 2 was abiotically degraded 1 . These findings suggest that P. inhibens bacteria can both degrade and produce H 2 O 2 . To understand the bacterial mechanisms involved in H 2 O 2 metabolism in the presence of algae, we analyzed the expression patterns of relevant bacterial genes. First, we compiled a list of genes that encode enzymes with established roles in the bacterial response towards H 2 O 2 41 (table S2). Expression analyses of these genes in bacterial pure cultures versus co-cultures with algal partners 23 revealed several genes encoding peroxiredoxins ( pxn ) that were expressed exclusively in the presence of algae (for example pxn 1 , Fig. 2D and table S2). A similar expression pattern was observed for ccp ( Fig. 2D ). Both pxn and ccp have been reported to be involved in the cell antioxidant defense 42 , 43 . Additionally, oxyR , encoding a H 2 O 2 sensor 44 and katG , encoding the major catalase involved in H 2 O 2 degradation 45 , 46 , were both upregulated in bacteria during the algal demise ( Fig. 2D ). To corroborate the involvement of the differentially expressed genes in the bacterial tolerance to H 2 O 2 , we deleted the genes pxn 1 and katG, and challenged the mutants with different H 2 O 2 concentrations (fig. S1). Our results show that the ΔkatG mutant was the most affected, exhibiting delayed growth under 100mM H 2 O 2 (fig. S1A) compared to the wild-type (WT), which was not affected by the same H 2 O 2 concentration ( Fig. 2B ). In contrast, the Δpxn 1 mutant was affected only under resource-limited conditions, such as reduced carbon source concentrations (fig. S2B and E). Limited nutrient bioavailability was previously reported to exacerbate bacterial susceptibility to H 2 O 2 1 , 47 . These findings highlight the importance of KatG for H 2 O 2 detoxification in P. inhibens and suggest that bacteria can activate different mechanism to respond to H 2 O 2 under various environmental conditions, such as variations in resource availability. Given the compromised ability of the mutant strains to degrade H 2 O 2 , we hypothesized that these mutants would exhibit different dynamics during their interaction with algae. We therefore cultivated mutant bacteria in co-cultures with algae and monitored the bacterial and algal growth ( Fig. 2E ). Our data show that co-cultures with the mutant strains Δpxn and ΔkatG exhibit accelerated algal death ( Fig. 2E ). As both Pxn and KatG are involved in H 2 O 2 degradation, their absence might result in H 2 O 2 accumulation, resulting in expedited algal death. To explore this possibility, we measured the H 2 O 2 levels in co-cultures with the mutant strains, specifically at day 12, which represents the stage of algal demise in co-cultures with mutant bacteria. Our results indicate higher H 2 O 2 concentrations in the co-cultures with Δpxn and ΔkatG mutants compared to co-cultures with WT strains (fig. S2). This suggests that impaired bacterial H 2 O 2 degradation leads to accumulation of H 2 O 2 in the context of the algal host, which accelerates algal death. These dynamics are similar to the effects observed upon addition of exogenous H 2 O 2 to algal cultures ( Fig. 1C ), highlighting the importance of maintaining balanced H 2 O 2 production and degradation processes during algal-bacterial interactions. H 2 O 2 from a bacterial source induces algal demise Various marine bacteria possess the genetic potential to produce H 2 O 2 8 , 9 , but whether they contribute to the extracellular H 2 O 2 pool during their interactions with algal partners is unknown. To investigate bacterial H 2 O 2 production, we monitored the levels of bacterially produced H 2 O 2 in pure bacterial cultures and in the context of algae. Distinguishing between the bacterial and algal H 2 O 2 sources in co-cultures is experimentally challenging. Therefore, we cultivated bacteria in 75% algal spent media (see materials and methods), allowing bacteria to sense the chemical repertoire of their algal host, but eliminating the active production of algal H 2 O 2 during cultivation. Our results revealed that H 2 O 2 levels increased over time in bacterial cultures that were supplemented with algal spent media but not in control bacterial cultures that were cultivated in a defined medium ( Fig. 3A ), suggesting that bacteria produce H 2 O 2 in the context of algae. Download figure Open in new tab Fig 3. H 2 O 2 produced by bacteria promotes algal death. A) Extracellular H 2 O 2 concentration (fmol/cell) measured in exponential P. inhibens bacteria grown in minimal media with 5.5 mM glucose as a carbon source (WT: green and Δsod mutant: yellow), or 75% spent media collected from algae at day 14 (brown). Box-plots show results from three independent experiments; black dots indicate individual measurements. Box-plot elements: center line - median; box limits -upper and lower quartiles; whiskers – min and max values. Normality was first assessed using a Shapiro–Wilk test, followed by a Mann-Whitney test to determine statistical differences between mono- and co-cultures at various time points. *P≤0.03. B) E. huxleyi grown alone (green bar) or in co-cultivation with P. inhibens WT (brown) or △sod mutant (yellow). Lines represent bacterial growth. Error bars represent the standard deviation from the mean of at least 3 biological replicates. To corroborate the bacterial H 2 O 2 production during co-cultures, we wanted to perturb the bacterial ability to generate H 2 O 2 and monitor the algal-bacterial interaction. Therefore, we deleted the bacterial gene that encodes a super oxide dismutase ( sod , table S2) which is essential in the conversion of a superoxide anion to H 2 O 2 and oxygen (2O 2 •− +2H + → H 2 O 2 +O 2 ) 48 . The deletion of this gene resulted in a delayed bacterial growth when grown in pure cultures with glucose as a carbon source (fig. S3A), possibly due to the role of superoxide in regulating growth 49 . Interestingly, these effects were less pronounced, or not observed at all, when the Δsod mutant was grown in algal spent media (fig. S3B) or in co-cultures with algae ( Fig. 3B ). Monitoring H 2 O 2 levels in pure bacterial cultures normalized by cell numbers, showed that the Δsod mutant produced less H 2 O 2 than the WT when cultivated in defined media or algal spent media ( Fig. 3A ). Furthermore, in co-cultures of the Δsod mutant with algae, a delayed algal death was observed ( Fig. 3B ), suggesting that bacterially produced H 2 O 2 contributes to promoting algal demise. Betaine induces bacterial H 2 O 2 production To understand the process of bacterial H 2 O 2 production during the algal-bacterial interaction, we aimed to identify the elements that regulate H 2 O 2 production in P. inhibens . It was previously suggested that H 2 O 2 might be produced by the degradation of DMSP. This was based on the co-expression of dmdA, the gene encoding the primary DMSP demethylase, and katG in environmental samples 13 . However, in our transcriptomic data 23 , we did not observe a correlation between the expression of katG and the genes encoding the DMSP-degrading enzymes DmdA 50 or Bmt 55 (table S3). Therefore, we explored other genetic modules that are potentially involved in bacterial H 2 O 2 production (table S2). We identified three operons encoding sarcosine oxidases ( sox 1 , sox 2 and sox 3 , Fig. 4A ) which, according to previous reports, can release H 2 O 2 as a byproduct during the degradation of betaine 51 , 52 . Betaine is an algal-secreted metabolite which is abundant in marine environments 53 . While the enzymatic machinery responsible for betaine degradation in P. inhibens has not been fully elucidated 54 , we observed that these bacteria can utilize betaine as a sole carbon source, although reaching lower yields compared to growth with glucose (fig. S4). Furthermore, P. inhibens bacteria exhibit various physiological responses to betaine 22 , 23 , 55 , suggesting that they possess the capacity to metabolize this compound. Interestingly, examining the genomic context of the sox operons revealed additional nearby elements related to H 2 O 2 production, such as the sod gene, which is a source of H 2 O 2 in P. inhibens ( Fig. 3 ), lactate dehydrogenase ( ldh ) that can form H 2 O 2 56 , as well as additional components involved in betaine degradation (fig. S5). This genomic proximity suggests a possible link between H 2 O 2 production and betaine metabolism. Download figure Open in new tab Fig 4. Betaine induces bacterial H 2 O 2 production driving algal demise. A) Heatmap showing the expression of bacterial genes involved in H 2 O 2 production at different growth phases in bacterial pure cultures (designated B) or in co-culture with algae (designated B+A). Expression intensity is shown as Z score. Asterisks indicate significant differential gene expression (adjusted p -value <0.05), black asterisk indicate upregulation while pink asterisk downregulation. Gene descriptions and log2 fold changes are available at table S2. The underlying dataset was previously described in Sperfeld & Narvaez-Barragan et al . 23 . Exp: exponential, stat: stationary. B) Extracellular H 2 O 2 concentration (fmol/cell) measured in pure bacterial cultures of WT bacteria cultivated in minimal media using glucose (green) or betaine (brown) as carbon source. Box-plot elements: center line - median; box limits -upper and lower quartiles; whiskers – min and max values. Normality was first assessed using a Shapiro–Wilk test, followed by a Mann-Whitney test to determine statistical differences between mono- and co-cultures at various time points. *P=0.0001. C) Intra-(green bars) and extra-cellular (brown bars) betaine levels produced by algae in monocultures, measured on days 0, 4, 7, 10, 12 and 14 of cultivation using liquid chromatography–mass spectrometry (LC–MS). D) Algae in monocultures (green bars) or in co-cultures with bacteria (brown bars) supplemented at days 7, 10 and 12 with 1 μM betaine (dark green bars for monocultures and gray bars for co-cultures). Lines represent bacterial growth. In C and D error bars represent the standard deviation from the mean of at least 3 biological replicates. Therefore, we examined the possible involvement of betaine in bacterial H 2 O 2 production. First, we examined the expression levels of the sox operons in pure bacterial cultures and in algal-bacterial cultures. Our data revealed that two of these operons, sox 1 and sox 2 , are highly expressed during interaction with algae but not when bacteria are grown in pure cultures ( Fig. 4A and table S2). To explore whether specifically betaine influences H 2 O 2 production in bacteria, we monitored H 2 O 2 levels in pure bacterial cultures cultivated in defined media with either glucose or betaine as a sole carbon source. We maintained the same carbon concentration across experiments and normalized the results by bacterial cell numbers. Our results indicate a roughly 2-fold increase in the levels of H 2 O 2 in cultures that were cultivated with betaine ( Fig 4B ). Next, we analyzed the impact of betaine on the relative expression of genes that are putatively involved in bacterial H 2 O 2 production. Therefore, pure bacterial cultures were cultivated with betaine or glucose as a sole carbon source, RNA was extracted, and expression levels were compared between cultures via qRT-PCR. According to our results, betaine strongly induces sox 1 expression (24-log 2 fold) but not sox 2 (table S4), suggesting that sox 1 may play a key role in betaine and H 2 O 2 metabolism in P. inhibens. However, as mentioned before, both sox operons were upregulated in the presence of algae ( Fig. 2A ). Additional H 2 O 2 -producing genes were also upregulated in response to betaine; sod 48 (2-log 2 fold), pyridoxamine 5’-phosphate oxidase ( pdxh 57 , 0.5-log 2 fold) and protoporphyrinogen oxidase ( hemJ 58 , 59 , 1-log 2 fold) (Fig. S6B). Overall, our data suggest that bacterial H 2 O 2 production is promoted by extracellular betaine. As betaine is a common algal metabolite 53 that appears to impact bacterial H 2 O 2 metabolism, we wished to characterize betaine levels in algal cultures of E. huxleyi. To this end, cells and supernatants were collected from algal mono-cultures on days 0, 4, 7, 10, 12 and 14, and subjected to intra- and extracellular betaine measurements using liquid chromatography–mass spectrometry (LC–MS) analysis ( Fig. 4C ). Our data demonstrate that algae produce intracellular betaine throughout algal growth and secrete increasing levels of betaine as the algal culture ages, during the stationary phase. Interestingly, in algal-bacterial co-cultures, algal demise due to bacteria is seen at day 14 of incubation ( Fig. 1A ) and appears to be preceded by a period in which algal betaine secretion is constantly high, as evident in algal mono-cultures ( Fig. 4C ). Next, we evaluated whether the concentration of secreted betaine observed in algal mono-cultures can drive bacterial pathogenicity, likely through bacterial H 2 O 2 production. Therefore, we calculated based on our LC-MS measurements that the average concentration of extracellular betaine in the stationary phase is 2.5 μM. Consequently, we supplemented algal-bacterial co-cultures with 1μM betaine at days 7, 10 and 12, time points at which algal demise is not yet seen in co-cultures. Betaine supplementation triggered earlier algal death compared to non-supplemented controls ( Fig. 4D ). In contrast, axenic algal cultures supplemented with betaine did not show signs of cell death ( Fig. 4D ), indicating that the effect depends on the presence of bacteria. These results suggest that betaine secretion by aging algae triggers bacterial H 2 O 2 production, which in turn can contribute to algal demise. Co-expression of bacterial H 2 O 2 and betaine related genes in the environment The novel metabolic connection between betaine and H 2 O 2 uncovered in this study could have environmental significance. Given that E. huxleyi algae and P. inhibens bacteria naturally co-occur in oceanic ecosystems 17 , 20 , and considering that other algal-associated bacteria possess similar genomic capabilities, 9 , 60 , 61 our findings may hold ecological relevance. However, bridging between simplified laboratory cultures and the complex marine environment is challenging. Yet, once a mechanism is revealed in a model system, indicative proxies can be measured at sea. Therefore, as a first step in elucidating the possible relevance of betaine-induced H 2 O 2 metabolism at sea, we explored the transcript abundance of bacterial H 2 O 2 and betaine-related genes in algal-rich ocean environments. Analysis of a metatranscriptomic dataset from the TARA Oceans expedition 62 showed a positive correlation between chlorophyll a (as an indicator of algal abundance), transcript abundance of bacterial genes related to betaine degradation ( mttb and soxD,G ), and transcript abundance of bacterial genes related to H 2 O 2 production ( hemJ and ldh ) (fig. S6). These observations suggest that bacterial betaine degradation and H 2 O 2 production are possibly enhanced in algal-rich environments. Next, we analyzed the expression patterns of bacterial genes related to H 2 O 2 degradation, H 2 O 2 production, and betaine metabolism during a natural algal bloom. Algal blooms naturally occur in the ocean due to an influx of inorganic nutrients and low abundance of predators reaching algal densities of at least 10 6 cells/L 63 . We re-analyzed a previously published transcriptomics dataset that followed bacterial gene expression during an algal bloom 64 . Our analysis revealed increased expression of genes involved in H 2 O 2 -degradation genes during the second half of the bloom (April 6th and 9th timepoints), with some genes maintaining high expression until algal demise ( Fig. 5 ). These data suggest that bacteria experience elevated H 2 O 2 levels during later phases of the algal bloom. Interestingly, the higher expression of bacterial H 2 O 2 -related genes appears to be independent of algal abundance. Altought algal growth is evident by March 26th and April 3rd, the induction of bacterial H 2 O 2 -degrading genes is not observed until April 6th. In line with this observation, bacterial H 2 O 2 -producing genes follow a similar trend, showing increased expression toward the end of the bloom, with pdxH , hemJ and sod exhibiting increased expression levels ( Fig. 5 ). This enhanced expression suggests a bacterial contribution to H 2 O 2 levels during the algal bloom. Notably, in our experimental system, these H 2 O 2 -producing genes were also induced by betaine (table S6), supporting a mechanistic link. Expression of genes in the sox operon, responsible for betaine-dependent H 2 O 2 production, was observed before and during algal demise, with sox G showing enhanced expression ( Fig. 5 ). Genes involved in bacterial betaine degradation were expressed throughout the bloom, possibly reflecting the dual role of betaine as carbon an nitrogen source 61 . This may explain the expression of betaine-related genes at the beginning of the bloom, consistent with observations that P. inhibens and other marine bacteria metabolize betaine to resume growth 23 . High expression of bacterial betaine-related genes was also observed at the end of the bloom. However, it is important to note that the sampled bloom was biphasic; an influx of nutrient-rich coastal waters on April 20th led to a second bloom phase 64 . The overlap between the demise of the first bloom and the onset of the second bloom may complicate interpretation of bacterial gene expression patterns at this timepoints. Download figure Open in new tab Fig 5. Co-expression of bacterial H 2 O 2 and betaine-related genes during a natural algal bloom. Expression profiles of 21 bacterial genes involved in H 2 O 2 degradation, H 2 O 2 production, and betaine metabolism across 10 time points during an algal bloom event (March to April 2020). Transcript abundance is presented as a Z-score normalized values. Algal abundance, represented by chlorophyll a (Chl a ) concentration, is shown in the green heatmap in the top of the figure alongside the sampling days. Data are based on metatranscriptomic analysis from Sidhu et al . 64 . Interestingly, the first bacterial enzymes in the pathway of betaine degradation, mttb 65 and bmt 55 , along with the transcriptional activator gbdr 66 , were expressed early in the bloom (March 26th and April 3rd timepoints), followed by the induction of sox genes, and then by increased expression of genes involved in H 2 O 2 -production during the bloom decline. This pattern suggests that bacterial betaine metabolism may precede and contribute to oxidative stress, ultimately playing a role in bloom collapse. Together, these environmental observations complement our laboratory findings, suggesting that bacterial betaine degradation and the associated H 2 O 2 production may influence the algal fate during natural bloom events, highligthing the broader ecological relevance of this metabolic connection. DISCUSSION Modulating H 2 O 2 levels changes the fate of algal-bacterial interactions Interactions between algae and bacteria rely on the exchange of nutrients and metabolites 67 . However, inorganic molecules are often overlooked in the context of this microbial communication, despite their ability to influence and shape microbial interactions 19 . Among these inorganic compounds, H 2 O 2 appears to markedly impact the E. huxleyi-P. inhibens interaction. H 2 O 2 levels measured during the interaction change in response to bacterial presence ( Fig 1A-B ), and manipulating H 2 O 2 levels, either by decreasing or increasing them, determines whether algae and bacteria will coexist or whether the algal population will collapse, respectively ( Fig. 1C ). Bacteria have the potential to act as key regulators of H 2 O 2 levels in natural aquatic systems 9 . We found that P. inhibens bacteria possess multiple strategies to detoxify H 2 O 2 ( Fig.2B -D). Disrupting the bacterial H 2 O 2 degradation capacity has direct consequences for the interaction, particularly impacting the fate of the algal partner ( Fig. 2E ). Consistent with environmental observations in other heterothropic bacteria 46 , KatG appers to function as the primary enzyme for H 2 O 2 degradation in P. inhibens (fig. S1). Although the Δkatg mutant shows impaired growth in minimal media supplemented with H 2 O 2 , its growth in co-culture with algae is comparable to the WT ( Fig. 2E ), suggesting that other detoxification mechanisms may compensate to maintain low intracelular bacterial H 2 O 2 concentrations 10 . We detected peroxiredoxin ( pxn1 ) expression only in the presence of the algal host ( Fig. 2D ), indicating that H 2 O 2 -degrading enzymes may exhibit functional redundancy that is dependent on the environmental context. Aditionally, pxn1 is further influenced by nutrient limitation coditions (fig. S1). This ability to maintain balanced redox in response to multiple environmental challenges, also observed in Vibrio vulnificus and E. coli 68 , 69 , may provide a competitive advantage for marine bacteria constantly exposed to fluctuating H 2 O 2 levels 4 . In contrast, algal H 2 O 2 -degrading enzymes expression does not appear to change in response to bacterial presence during exponential growth and the early stationary phase. However, during algal demise, different enzymes involved in H 2 O 2 degradation, including apx , ccp1,3 , and gpx 2-3 are downregulated ( Fig. 2A ). This response suggests that an impaired algal H 2 O 2 -detoxification process might be part of the bacterial pathogenic strategy 40 , contributing to H 2 O 2 accumulation and redox imbalance that leads to algal death. Additionally, H 2 O 2 homeostasis may become incleasingly important as the algal host ages. Indeed, in the bloom-forming diatom Coscinodiscus radiatus, ROS scavenging via extracellular vesicles is essential in late growth stages for preventing algal death 15 . These findings underscore the central role of H 2 O 2 levels in shaping algal-bacterial interactions, showing that redox balance is essential not only for individual fitness but also for determining the outcome of more complex interactions. As human activity continues to elevate oxidative stress in marine environments 1 , 2 , and H 2 O 2 is increasingly used to control harmful algal blooms 70 , it becomes impportant to understand how microbes respond to H 2 O 2 fluctuations. Both algae and bacteria contribute H 2 O 2 during their interaction Our results suggest a dynamic interplay between H 2 O 2 production and removal during the E. huxleyi – P. inhibens interaction. We observed two distinct shifts in extracellular H 2 O 2 levels during the algal-bacterial interaction; an initial decrease followed by a later accumulation ( Fig. 1B ). Algae produce higher levels of H 2 O 2 during exponential growth, but this production declines as they enter the stationary phase ( Fig. 1A–B ). During this transition, the expression of H 2 O 2 -degrading enzymes increases ( Fig. 2A ), while photosynthetic gene expression decreases ( Fig. 2A ), both indicators of algal aging 15 , 71 . These results suggest that E. huxleyi reduces H 2 O 2 production as it ages. However, in the presence of bacteria, algae may continue contributing to extracellular H 2 O 2 pool during their demise, as indicated by the overexpression of algal genes associated with H 2 O 2 production ( Fig 2A ). We also found that P. inhibens produces H 2 O 2 , with increasing levels in the presence of the algal host or host-derived metabolites ( Fig. 1B and 3A ). Interestingly, H₂O₂ appears to function as an important signal for bacterial growth. A Δ sod mutant, which produces lower levels of H 2 O 2 , grows more slowly than the WT in defined media (fig. S3A). This growth impairment may result from the accumulation of toxic superoxide (O₂⁻), as previously reported in sod mutants of E. coli and Salmonella typhimurium 72 , 73 . However, when the P. inhibens Δ sod mutant was grown in algal spent media containing host-derived H 2 O 2 , its exponential growth was comparable to the WT (fig. S3B). Although, towards the stationary phase, the mutant reached a lower maximal yield, similar to what has been reported for other sod mutants, which exhibit increased sensitivity during this phase 72 , 73 . Likewise, in co-culture with E. huxleyi , where H 2 O 2 is continuously released, the mutant grows at levels comparable to the WT ( Fig. 3B ). These suggest that external H 2 O 2 may partially compensate for reduced endogenuos production. Similar responses have been observed in other roseobacters, such as Ruegeria pomeroyi and Roseobacter sp. strain AzwK-3b ., where exposure to O₂⁻ and H 2 O 2 influence growth and redox regulation 49 . These findings suggest that P. inhibens, and potentially other marine bacteria, may constantly secrete H 2 O 2 as a self-generated signal and increase its production upon encountering a host or host-derived cues. Our results indicate that both algae and bacteria release H 2 O 2 into their shared environment and respond to changes in its concentration. This bidirectional exchange suggests a chemical feedback loop in which H 2 O 2 is not only a metabolic byproduct but also a central component of cross-kingdom inorganic communication. H 2 O 2 from a bacterial source is involved in pathogenicity In the algal-bacterial interaction studied here, H 2 O 2 accumulation may be the signal that drives the shift from coexistence to collapse. The Δ sod mutant is impaired in H 2 O 2 production and causes a delayed algal death ( Fig. 3 ), suggesting that bacterial-derived H 2 O 2 contributes to promoting algal demise. For bacterial H 2 O 2 to impact neighboring algal cells, close proximity and the formation of gradients within the phycosphere appear to be essential 12 , 67 . During algal demise, bacterial cells increase their attachment to the algal surface 17 , potentially allowing localized concentrations of inorganic molecules to act as signals that amplify PCD-like responses in algae 16 , 19 , 74 . Additionally, some algal genes involved in H₂O₂ production are upregulated primarily in response to bacterial pathogenicity ( Fig. 2A ). This raises the possibility that bacteria manipulate the algal oxidative metabolism to amplify the H₂O₂ signal and trigger a PCD-like process, as previously observed during E. huxleyi viral infections 16 and upon exposure to the inorganic molecule NO 19 . Interestingly, NO and H₂O₂ are known to act synergistically to modulate responses in plants 75 , and similar interactions may occur in algae, which show comparable sensitivity to oxygen and nitrogen species 19 . Moreover, bacterial roseobacticides that kill the algal host, induce expression of bacterial H₂O₂ defense mechanisms 25 , suggesting that roseobacticides production may be coupled with an increase in H₂O₂ levels. Together, these findings point to the orchestration of multiple bacterial pathways involved in algal death, highlighting the complex molecular interplay underlying bacterial pathogenicity. Notably, other heterotrophic bacteria that interact with algae also possess the capacity to produce H₂O₂ 9 , suggesting that this phenomenon may be widespread in the ocean. As a result, the potential implications of fluctuating H₂O₂ levels, particularly in the context of host-microbe interactions, has likely been underestimated. Betaine signals algal aging and triggers a corresponding bacterial response Bacteria use a variety of algal-derived metabolites, and specific host signals can influence bacterial behavior to promote either mutualistic or pathogenic interactions 17 – 19 , 67 . Shifting from mutualism to pathogenicity likely depends on the bacterial ability to sense and respond to cues that reflect the physiological state of the host 19 , 76 . We found that E. huxleyi increases betaine production and secretion as it enters the stationary phase ( Fig. 4C ), suggesting that betaine levels are correlated with algal aging. In co-culture, P. inhibens responded with increased expression of betaine degradation genes during the stationary phase ( Fig. 4A ). This dose-dependent response suggests that bacteria may sense elevated betaine levels as a cue of algal senescence and adjust their metabolism accordingly. A similar pattern has been observed in the bloom-forming diatom Coscinodiscus radiatus, where rising betaine concentrations were associated with the onset of senecense 15 . Interestingly, several algal-derived compounds that are associated with bacterial-induced cell death also accumulate during the late stages of algal growth. These algal compounds include 1) extracellular nitrite, which is reduced by bacteria to NO 19 , 2) p -coumaric acid, the degradation product of the algal cell wall and the precursor for roseobacticide biosynthesis 25 , 3) tryptophan, which promotes enhanced bacterial production of indole-3-acetic acid (IAA) 17 , and 4) DMSP which accumulates toward the end of E. huxleyi growth and triggers pathogenic responses in specific Sulfitobacter strains 76 . When considered alongside these senescence-associated algal metabolites, betaine emerges as a novel link between algal physiology and bacterial pathogenicity. Betaine not only supports bacterial growth (fig. S4), but also influences the expression of bacterial genes involved in promoting algal demise ( Fig. 3A and table S4). These findings support that bacteria can sense distinct metabolic fingerprints of their host 77 to shift their behavior accordingly. Moreover, we found that bacterial genes involved in H 2 O 2 and betaine metabolism are co-expressed in association with phytoplankton (fig. S6) and during algal blooms ( Fig. 5 ), suggesting that this form of chemical communication may extend beyond the P. innhibens – E. huxleyi system. Interestingly, in other microalgae such as Chrysochromulina sp. and Amphidinium carterae, betaine levels peak earlier, before the stationary phase 78 , suggesting that betaine signaling may vary across taxa. To conclude, our findings deepen the understanding of the molecular mechanisms that shape algal-bacterial interactions, and highlight H₂O₂ and betaine as key regulatory molecules. This insight is particularly important in the context of climate change, which is expected to alter microbial betaine degradation 53 and, in turn, impact both betaine and H₂O₂ concentrations in the ocean, potentially influencing microbial dynamics across multiple scales. MATERIALS AND METHODS Strains and growth conditions The bacterial strain Phaeobacter inhibens DSM 17395 was obtained from the German Collection of Microorganism and Cell Cultures (DSMZ, Braunschweig, Germany). It was grown in either ½ YTSS medium (yeast extract, 2 g/L; trypton, 1.25 g/L; sea salts; 20 g/L), or artificial seawater (ASW) medium based on the protocol of Goyet and Poisson 79 . ASW contained mineral salts (NaCl, 409.41 mM; Na 2 SO 4 , 28.22 mM; KCl, 9.08 mM; KBr, 0.82 mM; NaF, 0.07 mM; Na 2 CO 3 , 0.20 mM; NaHCO 3 , 2 mM; MgCl · 6 H 2 O, 50.66 mM; CaCl 2 , 10.2 mM, SrCl 2 · 6 H 2 O, 0.09 mM), L1 trace elements (Na 2 EDTA · 2H 2 O, 4.36 mg/L; FeCl 3 · 6 H 2 O, 3.15 mg/L; MnCl 2 · 4 H 2 O, 178.1 μg/L; ZnSO 4 · 7 H 2 O, 23 μg/L; CoCl 2 · 6 H 2 O, 11.9 μg/L; CuSO 4 · 5 H 2 O, 2.5 μg/L; Na 2 MoO 4 · 2 H 2 O, 19.9 μg/L; H 2 SeO 3 , 1.29 μg/L; NiSO 4 · 6 H 2 O, 2.63 μg/L; Na 3 VO 4 , 1.84 μg/L; K 2 CrO 4 , 1.94 μg/L), L1 nutrients (NaNO 3 , 882 μM; NaH 2 PO 4 · 2 H 2 O, 36.22 μM), 5 mM NH 4 Cl, 33 mM Na 2 SO 4 , and 5.5 mM Glucose, adjusted to a pH 8.2 with HCl. For especific experiments, the carbon source was replaced with either 1mM glucose or 6.6mM betaine. P. inhibens mutants mutants strains Δkatg and Δpxn were supplemented with 30 μg/ml gentamycin, and Δsod with 150 μg/ml kanamycin. When indicated 1 mM potassium iodide (KI, Sigma-Aldrich) or H 2 O 2 (Bio-Lab ltd) was added to the ASW medium. The algal strain E. huxleyi CCMP3266 was obtained from the National Center for Marine Algae and Microbiota (Bigelow Laboratory for Ocean Sciences). Algae were cultured in ASW medium at 18 °C under a light–dark cycle of 18 h of light and 6 h of dark, with an illumination intensity of 150 mmol m −2 s −1 . Absence of bacteria in axenic algal cultures was monitored periodically by plating on 1⁄2 YTSS plates and by microscopy. For experiments using algal spent media, E. huxleyi cultures were grown for 14 days under the conditions described above. Cultures were collected and filtered (0.2 µM pore filter,Thermo Fisher Scientific) to remove algal cells. The pH of the resulting spent media was adjusted to 8 and re-filtered to ensure sterility. For bacterial growth experiments, 75% algal spent media was mixed with 25% ASW (v/v, ratio of 3:1), and supplemented with L1 trace elements, L1 nutrients, 5.5 mM glucose, 33 mM Na 2 SO 4 , and 5 mM NH 4 Cl. Bacterial growth curves P. inhibens was streaked from glycerol stocks onto ½ YTSS agar plates containing antibiotics when required and incubated at 30 °C for 48 hours. A single colony was then used to inoculate a 10 mL ASW medium for a pre-culture, which was grown at 30 °C with shaking at 130 rpm for 48 hours to reach stationary phase. Cells were then diluted to an OD 600 of 0.01 in either 75% algal spent medium or fresh ASW and supplemented, when indicated, with various concentrations of H 2 O 2 (Bio-Lab ltd) or sterile water as control. Diluted cultures were transferred into a 96-well microtiter plate (150 µl per well) and overlaid with 50 µl hexadecane (Thermo Fisher Scientific) to prevent evaporation. Bacterial growth was monitored at 30°C using an Infinite 200 Pro M Plex plate reader (Tecan Group Ltd., Männedorf, Switzerland) with alternating cycles of 5 sec shaking and 59:55 min incubation. Absorbance at 600 nm was measured after each shaking cycle and multiplied by a factor of 3.86 to reflect optical density measurements performed in 1 cm cuvettes. Background absorbance, which is the measured OD 600 of the culture media without bacterial addition, was subtracted from all measurements. Bacterial and algal growth during co-cultivation For co-culture experiments, 30mL of ASW medium were inoculated with 10 4 E. huxleyi cells, from late exponential phase inoculum, and incubated without shaking under the same conditions described above for algal cultures. After four days of algal growth, cultures were inoculated with 20 ul of P. inhibens , which had been pre-cultured for 48h in ASW, diluted to an OD 600 of 0.01, and further diluted to a final concentration of 10 4 cells/mL. Bacterial growth was monitored at the indicated timepoints by serially diluting co-culture samples and plating them on 1⁄2 YTSS plates, with antibiotics when necessary. After two days of incubation at 30°C, colony-forming units (CFUs) were counted to calculate the total bacterial abundance. Algal growth in cultures was monitored using a CellStream CS-100496 flow cytometer (Merck, Darmstadt, Germany), with excitation at 561nm and emission at 702 nm. For each sample, 30,000 events were recorded, and algal cells were gated based on event size and fluorescence intensity. Extracelullar H 2 O 2 measurements Extracellular H 2 O 2 was quantified using the fluorescent probe Amplex red (Sigma-Aldrich), following the manufacturers instructions. Briefly, 100 μl of culture sample was centrifuged for 30 sec at 4000 rpm. Then, 10 μl of the supernatant was mixed with 40 μl of 1X Amplex reaction buffer in a μClear 96-well plate (Greiner Bio-One, Germany). Following that, 50 μl of 100 μM Amplex reagent was added to each well. The reaction was incubated in the dark for 30 min. Fluorescence was measured using an Infinite 200 Pro M Plex plate reader (Tecan Group Ltd., Männedorf, Switzerland) with excitation at 590nm and 530 nm emission. Background fluorescence was determined using control wells containing culture media, either minimal media or 75% spent media, without bacterial addition, and was subtracted from all individual measurements. H 2 O 2 depletion by bacteria Bacterial pre-cultures (prepared as described in Bacterial growth curves section) were diluted to an OD 600 of 0.01 in 30 mL of fresh ASW medium. Cultures were supplemented with either 100 or 250 μM H 2 O 2 , or sterile water as control. Bacterial cultures were incubated at 30 °C with shaking at 130 rpm. Samples were collected at 0, 3, 6 and 24 h. H 2 O 2 concentrations were measured as described in Extracelullar H 2 O 2 measurements section. Bacterial growth (OD 600 ) was monitored using an Ultrospec 2100 pro spectrophotometer (Biochrom, Cambridge, UK). Co-culture RNA-sequencing The RNA-sequencing dataset used in this study was previously published by our group 23 . Briefly, co-cultures and pure cultures containing either E. huxleyi or P. inhibens were cultivated as described in Bacterial and algal growth during co-cultivation section. Cells were harvested by centrifugation at 4 °C, and total RNA was extracted using the ISOLATE II RNA Mini Kit (Meridian Bioscience, OH, USA). Ribosomal rRNA depletion was performed using Pan-Bacteria and custom probes designed for E. huxleyi . RNA libraries were deep sequenced on a NovaSeq 6000 system using a 100 cycles S2 Kit (Illumina, San Diego, CA, USA) in paired-end mode. Quality-filtered and trimmed reads were mapped to the E. huxleyi synthetic genome assembly (sGenome) 80 or the P. inhibens DSM 17395 genome (accession: GCF_000154765.2). Raw sequencing data are available under BioProject accession PRJNA976961. Differential expression of algal genes in co-culture vs pure cultures was assessed using DESeq2. To compare absolute transcript abundances, feature counts were converted to transcripts per kilobase million (TPM), accounting for gene length and sequencing depth. Differential expression of bacterial genes was further analyzed using the limma-voom pipeline in R (v4.1.1) with the Bioconductor package limma 81 . Only genes with log₂ fold changes (logFC) and adjusted p -values <0.05 (Benjamini–Hochberg correction) were considered significant. P. inhibens pxn, katG, and sod null mutants The primers and plasmids used for the KO constructs are described in table S5. DNA manipulation and cloning PCR was performed using Phusion High Fidelity DNA polymerase (Thermo Fisher Scientific), according with manufacturer recommended PCR conditions. PCR-amplified DNA was cleaned with NucleoSpin Gel and PCR Clean-up kit (MACHEREY-NAGEL, Düren, Germany). For creation of katg null mutant ( Δkatg ) cells (ES329), ∼1000 bp regions upstream and downstream of the katG gene (accession: PGA1_RS19020) were amplified by PCR, using the primers 1-4. The gentamycin resistance marker of pBBR1MCS5 82 was amplified using primers 13 and 14. The PCR-amplified fragments (upstream region + gentamycin resistance + downstream region) were assembled and cloned into the pCR™8/GW/TOPO® vector (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA) using restriction-free cloning 83 , generating the plasmid pDN22. For creation of pxn null mutant ( Δpxn ) cells (ES151), ∼1000 bp regions upstream and downstream of the pxn gene (accession: PGA1_RS07285) were amplified by PCR, using the primers 7-10. The gentamycin resistance marker of pBBR1MCS5 82 was amplified using primers 13 and 14. The PCR-amplified fragments (upstream region + gentamycin resistance + downstream region) were assembled and cloned pCR™8/GW/TOPO® vector (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA) using restriction-free cloning 83 , generating the plasmid pLY1. For creation of sod null mutant ( Δsod ) cells (ES330), ∼1000 bp regions upstream and downstream of the sod gene (accession: PGA1_RS09485) were amplified by PCR, using the primers 17-20. The kanamycin resistance marker of pYDR1 19 was amplified using primers 23 and 24. The PCR-amplified fragments (upstream region + kanamycin resistance + downstream region) were assembled and cloned into the pCR™8/GW/TOPO® vector (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA) using restriction-free cloning 83 , generating the plasmid pDN23. P. inhibens electrocompetent cells (300 μl) were transformed with 10 μg of the constructed plasmids by a pulse of 2.5 kV (Bio Rad), and cells were selected on ½ YTSS plates containing 30 μg/ml Gentamycin or 150 μg/ml Kanamycin. Successful null mutants were verified in single cell clones by PCR (5-6 and 15-16 for Δkatg , 11-12 and 15-16 for Δpxn, and 21-22 and 25-26 for Δsod ) and sanger sequencing. P. inhibens gene expression Bacterial pre-cultures (prepared as described in Bacterial growth curves section) of P. inhibens were diluted to an OD 600 of 0.01 in 30 mL of fresh ASW medium containing either 5.5 mM glucose or 6.6 mM betaine as the sole carbon source. Cultures were incubated at 30 °C with shaking at 130 rpm. After 22h cells were harvested by centrifugation at 4000 rpm for 10 min at 4°C. Cell pellets were resuspended in RNA lysis buffer (RLT, QIAGEN) containing 1% β-mercaptoethanol, transferred to tubes containing 300 mg of 100 µm acid-washed Low Binding Silica Beads (SPEX SamplePrep), and disrupted via bead beating at 30 mHz for 3 min. Total RNA was extracted using the Isolate II RNA Mini Kit (Meridian Bioscience, London, UK), followed by a second DNAse treatment using the TURBO DNase kit (Thermo Fisher Scientific) and further purified with RNAClean XP magnetic beads (Beckman Coulter). Total RNA was measured with the Qubit RNA HS Assay (Invitrogen, Thermo Fisher Scientific). RNA concentrations were quantified using the Quibit RNA HS Assay Kit (Invitrogen, Thermo Fisher Scientific). Equal amounts of RNA from each sample were used for cDNA synthesis with Superscript IV (Thermo Fisher Scientific). Quantitative PCR (qPCR) was performed in 384-well plates using the SensiFAST SYBR Lo-ROX Kit (Meridian Bioscience) on a QuantStudio 5 qPCR cycler (Applied Biosystems, Foster City, CA, USA). The program consisted of 40 amplification cycles following the enzyme requirements. Primers are listed in table S5. Efficiencies were determined using standard curves generated from serial dilutions of pooled cDNA. Only primer pairs with ≥85% efficiency were considered. Gene expression was calculated using the Comparative CT (ΔΔCT) method and normalized to the housekeeping genes gyrA and recA 22 . Betaine quantification from algal cultures E. huxleyi cultures were grown as described in the Strains and growth conditions section. At defined timepoints (0, 4, 7, 10, 12 and 14 days), algal cell concentrations were quantified using a CellStream flow cytometer, as decribed in the Bacterial and algal growth during co-cultivation section. For each time point a full culture flask was used. For extracellular betaine measurements, 400 µl of culture were transferred to an eppendorf tube and centrifuged at 4000rpm for 5 min at 4°C. After centrifugation, 200 µl of the uppermost supernatant was transferred to a new tube and snap-frozen in liquid nitrogen. For intracellular betaine measurements, the remaining algal culture (∼29.450 mL) was centrifuged at 4000rpm for 5 min at 4°C. The supernatant was discarded, and the cell pellet was resuspended in 1mL of ASW, transferred to an eppendorf tube, and centrifuged again as described above. The supernatant was discarded using a pipette tip, and the pellet was snap-frozen in liquid nitrogen. All samples were stored at −80°C until for biological replicates were collected and processed together. For metabolomics analysis, 750 µL of MeOH: DDW 7:3 containing 1.333 ug/ml 13 C 5 15 N-Betaine (Sigma-Aldrich) as internal standard, was added to the samples. After vortexing and three 10-minute sonication cycles, samples were centrifuged for 10 minutes at 14,000 rpm and 4°C, and the supernatant was collected. The pellets were re-extracted with 250 µL MeOH:DDW (7:3), followed by three 10-minute sonication cycles and centrifugation. Then the supernatants were combined with those from the first extraction, centrifuged again and transferred to vials. Samples with high concentration of betaine were diluted 20 times. The measurement of betaine was performed as described at Zheng et al . 84 with minor modifications described below. Briefly, analysis was performed using UPLC-ESI-MS/MS equipped with Acquity UPLC I class system (Waters). The LC separation was done using the Atlantis Premier BEH Z-HILIC (100 mm × 2.1 mm) (Waters). The Mobile phase B: acetonitrile and Mobile phase A: 20 mM ammonium carbonate with 0.1% ammonia hydroxide in water:acetonitrile (80:20, v/v). The flow rate was kept at 200 μl min−1 and gradient as follow: 0-2min 75% of B, 14 min 25% of B, 18 min 25% of B, 19 min 75% of B, for 4 min. Column temperature was set to 45°C and injection volume was 0.5 µl. MS detector (Waters TQ-XS) was equipped with ESI source. The source and de-solvation temperatures were maintained at 150°C and 600°C, respectively. The capillary voltage was set to 1.0 kV. Nitrogen was used as de-solvation gas and cone gas at the flow rate of 1000 L*h-1 and 150 L*h-1, respectively. Additional MS parameters are summarized in table S6. Data were processed with the MassLynx software with Targetlynx (Waters). Quantification of betaine was performed against an external calibration curve of Betaine (Sigma-Aldrich). Transcript abundance of H 2 O 2 and betaine related genes in algal-rich ocean layers Transcript abundances for orthologous groups (OG) of genes related to H 2 O 2 and betaine metabolism were obtained from a previously published dataset from the TARA Oceans expedition 62 and functionally annotated in the current study. Briefly, samples were collected from 126 globally distributed oceanic stations across different dephts. RNA was sequenced using an Illumina HiSeq2000 system in a paired-end mode. Sequencing reads were pre-procesed to generate High-quality (HQ) reads, which were then assembled into gene-encoding sequences to create the Ocean Microbial Reference Gene Catalog version 2 (OM-RGC.v2). Read counts were normalized by gene length and summarized based on EggNOG gene families (aligned to EggNOG version 3 database), and then divided by the transcript abundance of a constitutively expressed marker gene. DESeq2 was used for variance stabilization. The resulting transcript abundances are represented as log2-transformed values, indicating relative transcript numbers per cell. We focused on the samples from light-penetrated, epipelagic zones, specifically surface waters (SRF), the deep chlorophyll maximum (DCM), and the mixed layer. Clorophyll a concentrations, used as a proxy for algal density, were obtained for each sample from the corresponding dataset ( https://doi.org/10 . 5281/zenodo.3473199) and included included in our analysis. To examine the relationship between gene expression and algal abundance, we used Pearson correlation analyses with the cor.test() function from the stats package in R. TPM-normalized gene expression values were correlated with chlorophyll a concentrations across samples. For each gene, we calculated a correlation coefficient ( r ) and corresponding p -value, which was adjusted for multiple testing using the Bonferroni method. Genes with adjusted p -values <0.05 were considered significantly correlated with chlorophyll a . H 2 O 2 -related genes co-expression analysis during a natural bloom For the bloom expression analysis, we used a previously published metatranscriptomic dataset 64 , where transcriptomics reads were mapped to metagenome-assembled genomes (MAGs), and gene expression was normalized as transcripts per million (TPM). Relative expression of bacterial genes was assessed by assigning TPM values to specific MAGs and normalizing by genome size. The 46 most highly expressed MAGs were selected for further analysis, representing the following taxonomic groups: Gammaproteobacteria (36.95%) , Bacteroidia (32.6%) , Alphaproteobacteria (21.73%), Acidimicrobiia (4.34%), Actinomycetia (2.17%) and Verrucomicrobiae (2.17%). We focused on the expression profiles of 21 genes related to H 2 O 2 degradation and production, as well as betaine metabolism. Gene-level expression data were aggregated and summarized across 10 time points during the bloom, where chlorophyll a , representing algal abundance, was also measured. To visualize temporal gene expression patterns, we generated a heatmap using Z-score normalized values. Z-scores were calculated by subtracting the mean and dividing by the standard deviation for each gene, enabling comparison of relative expression levels across time points. AUTHOR CONTRIBUTIONS D.A.N.-B. and E.S. designed the study and wrote the manuscript. D.A.N.-B. and L.Y. connducted and analyzed the experimental work. D. Y. and V. L. analyzed the environmental data. S. M. performed the metabolomics analysis. COMPETING INTERESTS The authors declare no competing interests. ACKNOWLEDGEMENTS D.A.N.-B. received the Armando and Maria Jinich Fellowship. 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