Host-derived lipid transfer and metabolic reprogramming in a ciliate–algal symbiosis

Host-derived lipid transfer and metabolic reprogramming in a ciliate–algal symbiosis | 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 Host-derived lipid transfer and metabolic reprogramming in a ciliate–algal symbiosis View ORCID Profile Yan-Jun Chen , View ORCID Profile Chi-Yen Wei , View ORCID Profile Md Mostafa Kamal , Chia-Wei Hsu , Sung-Yun Hsiao , Der-Chuen Lee , Pei-Ling Wang , View ORCID Profile Jun-Yi Leu doi: https://doi.org/10.1101/2025.11.27.690892 Yan-Jun Chen 1 Institute of Molecular Biology, Academia Sinica , 11529, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yan-Jun Chen Chi-Yen Wei 1 Institute of Molecular Biology, Academia Sinica , 11529, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Chi-Yen Wei Md Mostafa Kamal 1 Institute of Molecular Biology, Academia Sinica , 11529, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Md Mostafa Kamal Chia-Wei Hsu 2 Agricultural Biotechnology Research Center, Academia Sinica , 11529, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sung-Yun Hsiao 3 Institute of Astronomy and Astrophysics, Academia Sinica , 11529, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Der-Chuen Lee 3 Institute of Astronomy and Astrophysics, Academia Sinica , 11529, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pei-Ling Wang 4 Institute of Oceanography, National Taiwan University , 106 Taipei, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jun-Yi Leu 1 Institute of Molecular Biology, Academia Sinica , 11529, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jun-Yi Leu For correspondence: jleu{at}as.edu.tw Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Symbiotic associations enable species to integrate complementary traits and adapt to novel environments. However, how the host and endosymbiont exchange nutrients remains poorly understood in most cases. Previously Paramecium bursaria cells were shown to reorganize lipid droplets to accommodate endosymbiotic Chlorella cells and interfering with lipid metabolism reduced the endosymbiont number. Here, we combined transcriptomics, lipidomics, imaging mass spectrometry, and stable-isotope tracing to investigate organic nutrient exchange in this symbiotic system. Our results reveal that endosymbiotic algae undergo extensive reprogramming of lipid metabolic pathways and accumulate markedly higher levels of triglycerides than free-living algae. Isotope-labeling experiments demonstrate that at least some of these lipids originate from the host, providing direct evidence for organic carbon transfer from Paramecium to its algal endosymbionts. Together, our results show that the establishment of symbiosis fundamentally reshapes algal lipid metabolism and uncover an unexpected host-to-symbiont lipid provisioning mechanism—opposite to the canonical direction of carbon flow observed in most photosynthetic symbioses. This work provides new insight into the metabolic principles that sustain and stabilize endosymbiotic partnerships. Introduction Symbiotic associations enable independent species to integrate complementary traits, thereby conferring adaptive advantages in competitive ecological contexts 1 . A particularly widespread and ecologically significant form of such interactions involves symbioses with photosynthetic partners, as observed in diverse taxa including ciliates, corals, hydras, jellyfish, and sea anemones. Among these, the mutualistic relationship between the unicellular ciliate Paramecium and the green alga Chlorella has served as a tractable model system for investigating host–symbiont interactions 2 , 3 . The persistence of these associations is critically dependent on efficient nutrient exchange between host and symbiont 4 – 6 . Despite longstanding interest, the cellular and molecular mechanisms that regulate nutrient trafficking in this partnership remain incompletely characterized, particularly with respect to fluxes from host to symbiont 3 . Elucidating these processes is essential for advancing our understanding of the regulatory principles that sustain photosymbiotic interactions. During the establishment of symbiosis, symbionts typically acquire nitrogen—primarily in the form of ammonium (NH₄⁺) and nitrate (NO₃⁻)—as well as phosphorus, largely supplied as phosphate (PO₄³⁻), from the host. In return, the host receives photosynthetically derived metabolites, including carbohydrates, amino acids, and lipids 7 . Among these, lipids and glucose constitute the principal nutrients translocated from symbiont to host. However, in the absence of comprehensive quantitative analyses and stable isotope–labeled metabolite tracing, the precise proportions and directionality of these nutrient fluxes remain unresolved. The importance of lipid exchange is underscored by evidence that lipid droplet formation in corals is strongly dependent on their symbiotic state 8 . Within the spectrum of lipids transferred, sterols play a particularly critical role, as all cnidarians are sterol-auxotrophic and must rely on dietary intake or symbiont-derived sterols to fulfill their metabolic requirements 9 . Despite this, the dynamics and mechanisms of lipid transfer in other symbiotic model systems remain poorly characterized, representing a significant gap in our current understanding of host–symbiont metabolic integration. Paramecium bursaria represents a classical model of endosymbiosis, harboring hundreds of algal endosymbionts within its cytoplasm. Both host and symbiont can be cultured independently under laboratory conditions, and aposymbiotic P. bursaria cells can be readily reinfected through co-culture with isolated algae 10 . Owing to this experimental tractability, the Paramecium – Chlorella association has been widely employed as a model system for investigating the cellular and evolutionary dynamics of endosymbiosis 11 . In the Paramecium bursaria–Chlorella symbiosis, the host provides organic nitrogen, especially arginine, which contributes to purine metabolism and nitrogen-rich molecules such as chlorophyll precursors. In return, the algal symbiont fixes inorganic carbon and transfers it mainly as carbohydrates, lipids, and amino acid skeletons 12 – 15 . Other studies have shown that maltose is the principal photosynthate released by Chlorella variabilis , with its release strongly dependent on light and low pH. Blue and red wavelengths are most effective, and inhibitor experiments indicate that PSI-driven cyclic electron flow and ATP generation are required. Maltose largely originates from newly synthesized starch, and its steady supply is essential for maintaining symbiosis, since depletion leads to reduced release and eventual digestion of the algae by the host 16 . Overall, while nitrogen and carbon exchanges are conserved across symbiotic lineages, strains differ in light-management strategies, with some enhancing carotenoid-based photoprotection and others increasing chlorophyll and electron transport activity. However, relatively few studies have demonstrated cases in which the host may provide organic carbon to its algal symbionts. Most research emphasizes the flow of carbon from photosynthetic partners to heterotrophic hosts, while evidence for host-to-symbiont carbon transfer remains limited and often indirect. Based on our previous studies, showing that the morphology and localization of lipid droplets changes near endosymbionts in host cells compared to aposymbiotic cells, perturbing symbiotic cells with chemical inhibitors of lipid metabolism reduces symbiont number 17 , we hypothesis that the organic carbon transfer from host to symbionts maintain the symbiosis relationship. This asymmetry highlights a key gap in our understanding of nutrient reciprocity within the Paramecium bursaria–Chlorella system and other endosymbiotic associations. Recently, the application of multi-omics approaches has opened new opportunities to investigate such questions of bidirectional nutrient exchange in symbiotic systems. By integrating transcriptomics and metabolomics, researchers can simultaneously track host- and symbiont-derived pathways, identify candidate metabolites involved in reciprocal exchange, and resolve their regulation under different environmental conditions 18 – 20 . In this study, we conducted transcriptomics and lipidomics analysis on both symbiotic and free-living algal cells to examine gene regulation and lipid metabolite changes. Our data revealed that symbiotic algal cells were highly involved in several lipid metabolic processes. Lipidomics analysis showed that symbiotic algal cells were most enriched with triglycerides (TG). Furthermore, imaging mass spectrometry and isotope mass spectrometry analyses demonstrated metabolite transfer from the host to the symbionts. We also validated that the isotope signal was incorporated into many lipid species in symbiotic algal cells. Gene expression profiling demonstrated a significant upregulation of lipid metabolism pathways in symbiotic algal cells compared to free-living algae. Finally, our study demonstrated that the establishment of symbiosis profoundly reprograms algal lipid metabolism, promoting the accumulation and transfer of host-derived lipids to the symbionts. Unlike other photosynthetic symbioses, where algal partners typically provide carbon-rich metabolites to the host, our results reveal an inverse pattern in which a non-photosynthetic host supplies lipids to the algal symbiont, highlighting a unique metabolic dependency within this symbiotic association. Result Characterization of lipid droplet component in Paramecium bursaria Our previous studies revealed an upregulation of the triacylglycerol (TG) biosynthetic pathway in symbiotic Paramecium bursaria cells compared to their free-living counterparts. Furthermore, inhibition of host TG biosynthesis resulted in a significant reduction in lipid droplet formation, accompanied by the loss of symbiotic algal cells 17 . These findings suggest a possible lipid transfer from the host to its endosymbiotic algae. Based on high resolution scanning electron microscopy (SEM) observations ( Supplementary Figure 1 ), lipid droplets were frequently found attached to or fused with the symbiotic algal cells, supporting this hypothesis. To further investigate the composition of lipid droplets in P. bursaria , we isolated lipid droplets from both symbiotic and aposymbiotic cells and performed comprehensive lipidomic profiling using LC–MS/MS. In total, 778 lipid species were identified and classified into 40 subclasses ( Figure 1A ). Among these, TGs comprised 284 species, representing the largest proportion of the total lipidome. Following TGs, the predominant subclasses included phosphatidylethanolamine (PE), phosphatidylcholine (PC), ether-linked TG (etherTG), ether-linked PC (etherPC), and ether-linked PE (etherPE), each consisting of approximately 40–60 lipid species. Download figure Open in new tab Figure 1. Lipidomic profiling of lipid droplets in Paramecium bursaria . (A) Pie and bar charts showing the proportion and number of each lipid class identified in isolated lipid droplets. (B) Heatmap of Pearson correlation coefficient matrix of lipidomic profiles showing distinct clustering of four biological replicates from symbiotic and aposymbiotic P. bursaria lipid droplets, G: symbiotic, W: aposymbiotic. (C) Fold-change comparison of total lipid abundance between symbiotic and aposymbiotic P. bursaria lipid droplets. (D) Distribution of lipid class abundance in symbiotic and aposymbiotic P. bursaria lipid droplets, DK2g: symbiotic, DK2w: aposymbiotic. (E) Differential abundance of individual lipid species between symbiotic and aposymbiotic P. bursaria lipid droplets. We further quantified the lipid abundance of isolated lipid droplets in P. bursaria . Heatmap of Pearson correlation coefficient matrix revealed a clear distinction in lipid composition between symbiotic and aposymbiotic P. bursaria , indicating that symbiosis substantially alters lipid metabolism ( Figure 1B ). Overall, the total lipid content in symbiotic P. bursaria was 7.4-fold higher than that in aposymbiotic cells ( Figure 1C ), suggesting that symbiotic P. bursaria accumulates a significantly larger lipid reserve. According to the lipid distribution profile, triacylglycerol (TG) constituted the most dominant lipid class in both symbiotic and aposymbiotic cells, accounting for 89% and 56% of the total lipid content, respectively ( Figure 1D ). A detailed comparison of individual lipid subclasses revealed that the majority of TG species exhibited markedly higher abundance in symbiotic P. bursaria ( Figure 1E and Supplementary Figure 2 ). Collectively, these findings demonstrate that the establishment of symbiosis promotes extensive lipid accumulation—particularly of TG species—within the host, likely reflecting enhanced energy storage and lipid exchange between P. bursaria and its endosymbiotic algae. Lipid metabolic regulation in Chlorella variabilis To investigate the regulation of lipid metabolism in symbiotic algal cells, we performed RNA sequencing (RNA-seq) to compare the transcriptomic profiles of symbiotic and free-living Chlorella variabilis . Differential expression analysis, with an adjusted p-value cutoff of <0.05, identified 7582 differentially expressed transcripts. Gene Ontology (GO) enrichment analysis was subsequently conducted on both upregulated and downregulated transcripts in symbiotic C. variabilis cells. The resulting GO terms were visualized and clustered using Cytoscape 21 ( Figure 2A ). Among these, five lipid-related GO categories were particularly enriched: cellular lipid metabolic process, lipid biosynthetic process, fatty acid metabolic process, terpenoid metabolic process, and isoprenoid biosynthetic process. Within these categories, the majority of genes were upregulated in symbiotic algal cells ( Figure 2B ), indicating a global enhancement of lipid metabolism pathways under symbiotic conditions. Together, these data suggest that C. variabilis undergoes transcriptional upregulation of lipid biosynthesis and related metabolic processes in response to endosymbiosis with P. bursaria . Download figure Open in new tab Figure 2. Differential gene expression analysis of symbiotic versus free-living Chlorella variabilis. (A) Enriched Gene Ontology Biological Process (GOBP) terms among significantly different genes visualized as clustered networks generated using Cytoscape. (B) Bar chart showing genes with adjusted p < 0.05 and gene number values within enriched GOBP terms related to lipid metabolism. Lipid transport and uptake into Chlorella variabilis To determine whether algal cells are capable of exogenous lipid uptake, we supplied both free-living and symbiotic Chlorella variabilis cells—isolated from P. bursaria —with oleic acid (FA 18:1). Remarkably, both free-living and symbiotic algal cells exhibited enlarged lipid droplets following oleic acid treatment, indicating that C. variabilis can actively uptake and incorporate exogenous fatty acids ( Figure 3A ). Notably, the lipid droplets in symbiotic algal cells were significantly larger than those in free-living cells, suggesting that symbiotic C. variabilis possesses an enhanced capacity for fatty acid uptake and lipid storage compared to its free-living counterpart. Download figure Open in new tab Figure 3. Incorporation of host-derived lipids into symbiotic Chlorella variabilis. (A) Transmission electron microscopy (TEM) images and quantitative analysis showing an increase in lipid droplet size in symbiotic Chlorella cells compared to free-living Chlorella cells. FL: free-living, SM: symbiotic, *** p-value < 0.001, Mann-Whitney U test, n=30 cells (B) Nanoscale secondary ion mass spectrometry (NanoSIMS) images showing incorporation of 13C isotope signals into symbiotic Chlorella cells after P. bursaria was fed with 13C-labeled oleic acid and quantitative analysis showing an increase in 13C/12C ratio in symbiotic Chlorella cells compared to free-living Chlorella cells. *** p-value < 0.001, Mann-Whitney U test, n=30 cells. (C) Elemental analysis–isotope ratio mass spectrometry (EA–IRMS) results showing a significantly higher 13C atom percentage in symbiotic Chlorella cells after P. bursaria was fed with 13C-labeled oleic acid. *** p-value < 0.001, Student’s t-tests, 4 biological replicates. To directly examine lipid transfer from the host to the symbiont, P. bursaria cells were first fed with oleic acid (FA 18:1), which led to an increase in lipid droplet size within the host ( Supplementary Figure 3 ), indicating incorporation of the fatty acid into host lipid stores. We then supplied P. bursaria with 13C-labeled oleic acid (13C18-oleic acid) and traced the isotope distribution after 24 hours. Isotope imaging revealed strong 13C enrichment in host lipid droplets, accompanied by a significant increase in the 13C/12C ratio within the symbiotic C. variabilis compared with the control ( Figure 3B ). Quantitative analysis using elemental analysis–isotope ratio mass spectrometry (EA–IRMS) further confirmed elevated 13C atom percentages in symbiotic algae ( Figure 3C ). These findings provide direct evidence that lipids synthesized or absorbed by P. bursaria can be transferred to its symbiotic C. variabilis , supporting the existence of an active lipid exchange mechanism within this endosymbiotic system. Lipid profiles Chlorella variabilis Given our evidence supporting lipid transfer from the P. bursaria host to its C. variabilis symbionts, we next examined whether lipid composition and abundance differed between symbiotic and free-living C. variabilis . Lipids were extracted from both cell types and subjected to comprehensive lipidomic profiling using LC–MS/MS. In total, 947 lipid species were identified and classified into 41 subclasses ( Figure 4A ). Among these, triacylglycerols (TGs) comprised 298 species, representing the largest proportion of the total lipidome. Other major subclasses included phosphatidylethanolamine (PE), phosphatidylcholine (PC), phosphatidylglycerol (PG), ether-linked TG (etherTG), ether-linked PC (etherPC), and ether-linked PE (etherPE), each containing approximately 40–80 lipid species. Download figure Open in new tab Figure 4. Lipidomic profiling of lipid in Chlorella variabilis . (A) Pie and bar charts showing the proportion and number of each lipid class identified in Chlorella cells. (B) Heatmap of Pearson correlation coefficient matrix of lipidomic profiles showing distinct clustering of four biological replicates from symbiotic and free-living Chlorella cells, FL: free-living, SM: symbiotic. (C) Fold-change comparison of total lipid abundance between symbiotic and free-living Chlorella cells. (D) Distribution of lipid class abundance in symbiotic and free-living Chlorella cells. (E) Differential abundance of individual lipid species between symbiotic and free-living Chlorella cells. Quantitative comparison of lipid abundance revealed pronounced metabolic differences between symbiotic and free-living C. variabilis . Heatmap of Pearson correlation coefficient matrix clearly distinguished the two lipidomes, indicating that symbiosis substantially alters algal lipid metabolism ( Figure 4B ). Overall, the total lipid content in symbiotic C. variabilis was 5.3-fold higher than that in free-living cells ( Figure 4C ), suggesting that symbiotic algae accumulate a considerably larger lipid reserve—an observation that mirrors the lipid enrichment seen in symbiotic P. bursaria . Lipid class distribution analysis showed that TGs were the most abundant lipid class in symbiotic C. variabilis , accounting for 34% of total lipids, compared with only 4.3% in free-living cells ( Figure 4D ). In contrast, phosphatidylcholine (PC) represented the dominant class in free-living C. variabilis , comprising approximately 45% of the total lipid content. Interestingly, ether-linked lysophosphatidylcholine (etherLPC) also accounted for a relatively high proportion (∼18%) of the total lipid content in symbiotic cells. A detailed comparison of individual lipid subclasses further also revealed that the majority of TG species were markedly more abundant in symbiotic C. variabilis ( Figure 4E and Supplementary Figure 4 ). Collectively, these findings demonstrate that symbiotic C. variabilis undergoes substantial lipid remodeling characterized by enhanced accumulation of TGs and ether-linked lipid species. This lipid enrichment likely reflects adaptive metabolic reprogramming to facilitate energy storage and metabolic exchange within the P. bursaria–Chlorella symbiotic partnership. Stable-isotope tracing reveals incorporation of host-derived lipids into symbiont lipids To further determine which symbiont lipid species incorporate host-derived fatty acids, we supplied P. bursaria with 13C-labeled oleic acid (13C18-oleic acid) and subsequently extracted lipids from the symbiotic C. variabilis cells for LC–MS/MS analysis. Lipidomic profiling was conducted in both positive and negative ionization modes to ensure broad lipid coverage. Peak annotation for unlabeled samples was performed using MS-DIAL 22 , while isotopologue extraction and labeling fraction quantification were carried out using El-Maven 23 . Isotopic natural abundance correction was applied with AccuCor 24 . In total, 772 lipid species were detected in the symbiotic C. variabilis lipidome after 13C-labeled oleic acid feeding, of which 273 exhibited significant 13C labeling following host supplementation with 13C18-oleic acid. Among the labeled species, triacylglycerols (TGs) showed the highest incorporation rate, with approximately 50% of TG molecules containing 13C-labeled fatty acid moieties. In addition to TGs, phospholipid classes such as phosphatidylcholine (PC), phosphatidylethanolamine (PE), and phosphatidylglycerol (PG), as well as glycolipid classes including monogalactosyldiacylglycerol (MGDG) and digalactosyldiacylglycerol (DGDG), displayed substantial isotopic enrichment ( Figure 5A ). Furthermore, we analyzed the isotopologue distribution across all labeled lipid species in symbiotic C. variabilis . The majority of labeled lipids exhibited low-mass isotopologues (M+1, M+2, and M+3), followed by a smaller but distinct group of higher-mass isotopologues (M+17 to M+18). This pattern suggests that host-derived fatty acids undergo partial oxidation within the algal cells, generating shorter-chain acyl-CoA intermediates that are subsequently incorporated into various lipid species. The presence of fully labeled isotopologues (M+17 and M+18) further indicates that intact oleic acid molecules can also be directly incorporated into algal lipids ( Figure 5B ). We next examined isotopologue distributions within the highly labeled lipid classes, including TG, PE, PC, PG, MGDG, and DGDG. Notably, in TGs, most lipid species exhibited isotopologues corresponding to M+18 and M+36, indicating the direct incorporation of one or two fully labeled oleic acid molecules into TG structures. This result strongly supports the notion that host-derived fatty acids are not only taken up by the symbiotic algae but are efficiently utilized for triacylglycerol biosynthesis ( Supplementary Figure 5A ). We also quantified the labeling fraction across different lipid species. In the TG class, the distribution analysis revealed that most lipid species exhibited a low labeling fraction, primarily within the 0–0.2 range, suggesting heterogeneous incorporation efficiency of 13C-labeled fatty acids among individual TG molecules ( Supplementary Figure 5B ). This is likely because TGs predominantly consist of long-chain fatty acids, making it difficult for all carbon atoms within each molecule to become fully labeled. Consistently, the corresponding mass spectra of labeled TGs clearly displayed characteristic isotopic peak patterns, further confirming the partial incorporation of 13C-labeled oleic acid into TG molecules ( Supplementary Figure 5C ). All these findings demonstrate that host-derived fatty acids and lipids are not confined to neutral lipid pools but are also incorporated into structural membrane and photosynthetic glycolipids of the algal symbiont. This broad incorporation pattern highlights the extensive metabolic integration between P. bursaria and C. variabilis , supporting host-to-symbiont lipid transfer as a defining feature of their endosymbiotic relationship. Moreover, the isotopic and isotopologue analyses indicate that symbiotic C. variabilis not only assimilates host-derived lipids but also metabolically processes them through oxidation and remodeling pathways before reassembling them into diverse lipid classes, underscoring the dynamic bidirectional nature of lipid metabolism within this mutualistic association. Download figure Open in new tab Figure 5. Incorporation of 13C isotope into lipid species of symbiotic Chlorella variabilis . (A) Number of significantly 13C-labeled lipid species across different lipid classes. (B) Total number of 13C-labeled isotopologues detected across all lipid species. Differential expression of lipid related pathway in symbiotic C. variabilis Given the pronounced lipid accumulation phenotype observed in symbiotic C. variabilis —including the expansion of lipid droplets and increased total lipid content compared with free-living cells—we next examined the underlying transcriptional regulation of lipid metabolism. To this end, we analyzed the transcriptomic profiles of symbiotic and free-living C. variabilis to identify differentially expressed genes associated with lipid metabolic pathways ( Figure 6A ). Download figure Open in new tab Figure 6. Gene regulation of lipid metabolic pathways and proposed model of host–symbiont lipid transfer. (A) Bar chart showing the log2(fold change) in expression of genes involved in lipid metabolism pathways in symbiotic Chlorella cells compared with free-living Chlorella cells. (B) Proposed model illustrating the transfer of host-derived lipids to the algal symbiont and subsequent lipid biosynthesis and remodeling within the symbiotic C. variabilis . The model summarizes the integration of host lipid supply with symbiont lipid synthesis and metabolic reprogramming. The results revealed that several key pathways related to lipid metabolism were significantly altered in symbiotic C. variabilis genes involved in fatty acid (FA) β-oxidation were markedly upregulated, suggesting enhanced lipid turnover and energy release through fatty acid catabolism, likely contributing to the remodeling of host-derived lipids. Concurrently, multiple enzymes within the triacylglycerol (TAG) biosynthetic pathway, including glycerol-3-phosphate acyltransferase (GPAT), Phosphatidic acid phosphatase (PAP) and diacylglycerol acyltransferase (DGAT), exhibited elevated expression, indicating an increased flux toward TAG synthesis and neutral lipid storage. Moreover, genes involved in glycolipid biosynthesis, particularly the monogalactosyldiacylglycerol (MGDG) and digalactosyldiacylglycerol (DGDG) pathways, were slightly upregulated, consistent with enhanced photosynthetic membrane remodeling in the symbiotic state. Likewise, some enzymes participating in phospholipid metabolism, including those responsible for phosphatidylethanolamine (PE) and phosphatidylcholine (PC) synthesis, were also more highly expressed, reflecting active membrane biogenesis and possible lipid exchange between the host and symbiont. Together, these transcriptomic findings reveal that symbiotic C. variabilis coordinates the upregulation of both catabolic and anabolic lipid pathways—simultaneously enhancing FA oxidation for energy production and promoting TAG, glycolipid, and phospholipid synthesis for storage and membrane assembly—underscoring a finely tuned metabolic reprogramming in response to endosymbiosis. In summary, our results provide both molecular and imaging evidence supporting the hypothesis that the P. bursaria host supplies lipids to its algal symbionts, establishing a model of metabolic integration between the two partners. As illustrated in ( Figure 6B ), the enhanced lipid accumulation observed in the algal cells likely results from increased lipid biosynthesis within the symbiont, driven by host-derived lipid transfer and coordinated metabolic regulation. Discussion Reciprocal Metabolic Exchange Underpinning the Paramecium–Chlorella Endosymbiosis Analysis of both symbiotic and free-living Chlorella variabilis transcriptomes, together with genomic and transcriptomic data from symbiotic and aposymbiotic Paramecium bursaria , has enabled the identification of genes involved in metabolic integration within this endosymbiosis. A study generated a high-quality genome assembly for P. bursaria and revealed distinctive metabolic gene features that differentiate it from other Paramecium species. Through transcriptomic profiling and RNAi-based functional analyses, the study demonstrated that the host regulates the abundance of its algal symbionts via glutamine-mediated signaling. Moreover, the authors reconstructed a detailed map of metabolic interactions between P. bursaria and C. variabilis , highlighting the extensive exchange of metabolites required to sustain the partnership. Together, these findings provide important insights into the molecular and metabolic basis of ciliate–algal endosymbiosis 25 . In our study, we analyzed the transcriptomes of symbiotic and free-living Chlorella variabilis and performed enrichment analyses to identify biological processes associated with the symbiotic state. Subsequent GO term clustering revealed multiple functional groups that appear to be differentially regulated during symbiosis, highlighting broad transcriptional reprogramming in response to the host environment ( Figure 2A ). However, transcriptomic data alone cannot fully reveal the actual metabolic exchanges occurring between the host and the symbiont. To resolve the direction and magnitude of metabolite transfer, complementary metabolomic approaches are required. In particular, modern isotope-tracing techniques enable direct visualization of metabolite flow from host to symbiont or vice versa. A recent study demonstrated this by feeding Paramecium hosts with 15N-labeled organic nitrogen and 13C-labeled inorganic carbon sources, allowing the authors to trace metabolite fluxes incorporating into the symbiont. Their study identified time-resolved co-enriched metabolites, showing that 15N transfer to Chlorella primarily involves amino acid pathways, with downstream enrichment patterns pointing to arginine as the most likely N-exchange metabolite. The authors also detected enrichment in larger N-rich metabolites associated with algal growth, as well as 13C enrichment in P. bursaria metabolites linked to carbohydrate and lipid-related metabolism, indicating incorporation of symbiont-derived carbon into storage and central metabolic pathways 26 . In our previous work, we observed that lipid droplet morphology and positioning shift toward endosymbionts in symbiotic P. bursaria , and that chemically inhibiting host lipid metabolism reduces symbiont abundance, suggesting lipid-based carbon transfer 17 . In our mass-imaging experiment, 13C-oleic acid feeding revealed strong 13C enrichment in host lipid droplets and a corresponding increase in the 13C/12C ratio within Chlorella variabilis , supporting direct lipid-derived carbon transfer from host to symbiont ( Figure 3B ). Together, these findings support bidirectional metabolite exchange between P. bursaria and Chlorella , underscoring the tightly integrated metabolic coupling that sustains this endosymbiosis. Notably, our results highlight a host-to-symbiont lipid transfer, in which the non-photosynthetic host supplies lipids to its photosynthetic partner—contrasting with previous studies in other systems where lipids are primarily provided by the photosynthetic symbiont. How Lipid Transport and Regulation Shape Host–Symbiont Interactions Symbiosis with arbuscular mycorrhizal fungi (AMF) enhances nutrient acquisition in most land plants and has played a crucial role throughout the 450-million-year history of terrestrial plant evolution. In return for this nutritional benefit, host plants up-regulate lipid biosynthesis pathways and export lipids to their fungal partners. Disruption of transcription factors involved in lipid transfer leads to defects in both fungal development and symbiosis establishment 27 . During AMF symbiosis, high and highly specific expression of key lipid-biosynthetic enzymes—such as FatM in the plastid and RAM2 in the endoplasmic reticulum—enables host root cells to fine-tune lipid production and increase β-monoacylglycerol output 28 . These monoacylglycerols (MAGs) are then transported to the intraradical mycelium via STR/STR2 transporters, where they are taken up and used for triacylglycerol (TAG) synthesis 28 , 29 . Furthermore, mutations in lipid biosynthesis genes such as KASI and GPAT6 impair fungal development and reduce accumulation of hallmark 16:1ω5 fatty acids, further underscoring the essential role of host-derived lipids in sustaining AMF symbiosis 30 . Lipid exchange also plays pivotal regulatory roles in other symbiotic systems. In cnidarian–algal partnerships, algae transfer octadecanoid-derived oxylipins to their hosts, modulating inflammatory transcriptional pathways 31 , while coral–algal symbioses rely on the transfer of sterols mediated by atypical cholesterol-binding proteins 9 . Together, these examples illustrate that lipid transport is a widely conserved regulatory mechanism that shapes metabolic integration, communication, and stability across diverse host–symbiont interactions. As mentioned earlier, we propose a model of reverse lipid transfer from the host to the algal symbiont; however, direct evidence for the mechanism by how algal cells take up these host-derived lipids is still lacking. In mammalian systems, cells import fatty acids through the CD36 receptor 32 and acquire lipoproteins via endocytic pathways 33 , 34 . Additionally, phospholipid transporters—including flippases, floppases, and scramblases—play essential roles in maintaining membrane lipid asymmetry by moving phospholipids between bilayer leaflets. The conserved inner-leaflet localization of phosphatidylserine (PS) in corals and other eukaryotes underscores the tight regulation of lipid transport necessary to preserve membrane integrity and cellular homeostasis 35 , 36 . To explore whether a similar mechanism might exist in Chlorella , we searched for a CD36, ABCG transporter, phospholipid scramblase and p4-atpase flippase homolog and identified putative CD36-like and ABCG transporter, phospholipid scramblase and p4-atpase flippase genes. Transcriptome analysis showed that CD36-like, ABCG transporter and phospholipid scramblase genes are markedly upregulated in symbiotic Chlorella compared with free-living cells (Supplementary Fig. 6). In addition, a recent study demonstrated siderophore uptake via endocytosis in diatoms, indicating that algae are capable of employing endocytic pathways for nutrient acquisition 37 . Together, these observations suggest that Chlorella may use analogous receptor-mediated or endocytic mechanisms to facilitate lipid uptake during endosymbiosis. Reprogramming of symbiont lipid metabolism In figure 6B , our model illustrates how host-derived triacylglycerols (TAGs) or fatty acids (FAs) transferred into the algal symbiont can be incorporated into multiple metabolic pathways to support symbiont growth. After entering the algal cell, host-derived TAGs may be hydrolyzed into FAs and funneled into the acyl-CoA pool through β-oxidation. These acyl-CoAs can then be redirected toward membrane phospholipid synthesis (e.g., PE and PC), chloroplast galactolipid synthesis (MGDG and DGDG), or re-esterified into TAGs stored within the algal lipid droplets. By contributing to both structural lipid formation and energy-rich storage lipids, host-supplied fatty acids could help sustain symbiont metabolism and chloroplast function during endosymbiosis. This framework highlights how lipid trafficking from host to symbiont may integrate into core algal lipid pathways, ultimately supporting photosynthetic capacity and maintaining a stable symbiotic relationship. However, this model is based on 13C stable-isotope tracing, and further experimental validation is required to determine whether host-derived lipids directly and functionally support these metabolic pathways in the symbiont. Overall, our results provide a comprehensive lipidomic and transcriptomic comparison between free-living and symbiotic C. variabilis , revealing extensive lipid reprogramming in the symbiotic state. Furthermore, isotope imaging and 13C stable isotope-tracing experiments demonstrate lipid transfer from host lipid droplets to the algal symbiont, supporting the idea that host-derived lipids contribute to maintaining a stable endosymbiotic relationship. Materials and methods Cell culture P. bursaria cells were cultured in CGM medium (12 mM sodium acetate, 0.5% yeast extract) with Dryl’s buffer (2 mM sodium citrate, 1 mM NaH 2 PO 4 /Na 2 HPO 4 and 1.5 mM CaCl 2 ) and fed with Chlorogonium cells every five days. The cell cultures were kept at 23 °C with a light-to-dark cycle of 12 h:12 h. To obtain aposymbiotic P. bursaria strains, green symbiotic cells were treated with cycloheximide (100 μg/ml) for about two weeks, as described previously. The aposymbiotic cells were maintained under low-light conditions. Chlorella variabilis 2540 was cultivated in a modified CA medium. The medium was prepared by mixing 1 mL of 2% Ca(NO₃)₂·4H₂O, 1 mL of 10% KNO₃, 1 mL of 5% NH₄NO₃, 1 mL of 3% β-Na₂-glycerophosphate·5H₂O, 1 mL of 2% MgSO₄·7H₂O, 10 mL of 4% HEPES buffer, 1 mL of PIV metals solution, and 10 mL of Fe (as EDTA, 1:1 molar ratio). Vitamin supplements were added as follows: 10 μL of 1 mg/mL vitamin B₁₂ (final 10 μg/mL, 100× stock), 100 μL of 0.1 mg/mL biotin (final 1 μg/mL, 100× stock), and 10 μL of 1 mg/mL thiamine hydrochloride. The final volume was brought to 1 L with double-distilled water (ddH₂O), and the pH was adjusted to 7.2. The PIV metals solution was prepared by combining 10 mL of 1% Na₂EDTA·2H₂O, 1.96 mL of 1% FeCl₃·6H₂O, 0.36 mL of 1% MnCl₂·4H₂O, 0.11 mL of 2% ZnSO₄·7H₂O, 0.10 mL of 0.4% CoCl₂·6H₂O, and 0.10 mL of 0.25% Na₂MoO₄·2H₂O, and adjusting the total volume to 100 mL with ddH₂O. The Fe (as EDTA, 1:1 molar) solution was prepared by mixing 7.02 mL of 1% Fe(NH₄)₂(SO₄)₂·6H₂O with 6.6 mL of 1% Na₂EDTA·2H₂O, followed by adjusting the final volume to 100 mL with ddH₂O. C. variabilis genome assembly and annotation For the assembly of the reference genome, Illumina HiSeq 4000 paired-end sequencing and MinION sequencing read data of the C. variabilis NC64A (ATCC 50528) genome were obtained from NCBI BioProject PRJDB7392 38 . The quality of the short and long read data was evaluated using FastQC v0.11.5 ( http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ) and NanoQC v0.9.4 39 , respectively. Low-quality reads were trimmed using Trimmomatic v0.39 40 for short reads and NanoFilt v2.8.0 39 for long reads. Subsequently, a hybrid assembly of the C. variabilis genome was performed using MaSuRCA v4.1.0 41 with default parameters. The assembly quality was assessed using Quast v5.2.0 42 by comparing the indices with previous reports. The genome assembly was further improved using Redundans v2.0.1 43 under the scaffolding with long reads condition. The assembled genome, containing 260 contigs, has a total size of 44.2 Mb and a GC content of 67.1%. The N50 value of the genome is 296,995 bp. The mapping rate of RNA raw reads aligned with the assembled genome is around 88% using HISAT2 v2.2.1 44 . Additionally, the gene structure of the genome was predicted using Braker3 v3.0.8 45 , resulting in 12,785 predicted genes. The completeness of the gene predictions was evaluated using BUSCO v 5.7.1 46 with the Eukaryota odb10 dataset (255 single-copy orthologs), with approximately 90.58% of the complete genes matching the BUSCO profile. RNA isolation and sequencing To prepare the algal samples for RNA isolation, 3×10 8 algal cells from freshly isolated endosymbiotic algae (D0), short-term (D1, D2, D4, and D6), and long-term (FL) free-living algal cultures were collected according to the procedures for time-course re-infection assays. The algal cells were washed twice with 1X PBS buffer and resuspended in nuclease-free water to a total volume of 200 µL. All algal samples were processed in triplicate. For RNA isolation, 0.8 mL Tri reagent and 0.5 mL 0.01 mm glass beads were added to each sample, and the algal cells were homogenized at room temperature for 80 seconds using a bead beater, followed by immediate cooling on ice. The supernatants of the algal samples were transferred and centrifuged at 12,000 x g for 10 minutes at 4°C to eliminate algal debris and polysaccharides. Subsequently, the supernatants were transferred again, mixed with 50 µL of 0.5 M potassium acetate, incubated for 5 minutes at room temperature, and then 300 µL of chloroform was added and mixed vigorously using a vortex mixer for 3 minutes at room temperature. The samples were centrifuged at 21,000 x g for 20 minutes at 4°C to separate the phases, and the aqueous phase was carefully transferred into 0.6 mL of ice-cold 100% ethanol (EtOH). For the subsequent steps, the RNA was cleaned and collected using the RNeasy Mini Kit (Qiagen). After evaluating the RNA quality of all samples, library preparation and paired-end read sequencing of total RNA for the algal samples were performed using the SureSelect Strand-Specific RNA Library and NovaSeq X Plus. For each sample, 20 million reads were generated. Transcriptome analysis The quality of raw reads from all algal samples was evaluated using FastQC v0.11.5 ( http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ), and adapter contamination and low-quality reads were trimmed using Trimmomatic v0.39 40 . Filtered reads were quantified for gene expression in the different algal samples using Salmon v0.14.1 47 , and the mean TPM (Transcripts Per Million) value for each gene was calculated from triplicates for each algal sample condition. Gene features annotation and gene ontology (GO) enrichment analysis GO terms were predicted by InterProScan 48 and Blast2GO 49 . Fisher’s exact tests were applied to the annotation terms. The resulting p-values were corrected for multiple hypotheses using Bonferroni correction, and a cutoff of 5% FDR was applied Lipid droplet collection, lipid extraction and LC-MS/MS analysis For each biological replicate of lipid extraction from lipid droplet samples, approximately 1 × 10 5 Paramecium bursaria cells (corresponding to ∼1 L of culture volume) were harvested through a 10 μm nylon mesh sieve after removing cell debris with cheesecloth filtration. The collected cells were washed on the nylon mesh with Dryl’s solution and subsequently transferred into 15 mL centrifuge tubes. The washed cells were resuspended in lysis buffer containing 10 mM MgCl₂, 10 mM Tris (pH 7.5), and 250 mM sucrose, and lysed using a nitrogen cavitation bomb. The bomb was pressurized to 300 psi and maintained for 20 minutes, after which the pressure was slowly released to lyse the cells. Following cell disruption, the lysate was mixed with an equal volume of high sucrose buffer and centrifuged at 17,000 rpm for 1 hour at 4 °C using an SW41Ti rotor (Beckman). The topmost lipid layer was carefully collected and further purified by re-floating the lipid droplets through centrifugation at 13,000 × g for 10 minutes at 4 °C. The upper lipid droplet fraction was then collected for downstream lipid extraction analysis. For lipid extraction from Chlorella cells, approximately 2 × 10 6 free-living Chlorella variabilis cells were harvested by centrifugation at 8,000 × g, followed by two washes with Dryl’s buffer. Symbiotic Chlorella cells were isolated from P. bursaria by cell disruption through high-speed centrifugation at 20,000 × g for 20 minutes, followed by two washes with Dryl’s buffer and debris removal using a 10 μm nylon mesh. Lipid extraction was performed following a modified chloroform/methanol method. Isolated lipid droplets were resuspended to final volume about 500 µL in lysis buffer, while Chlorella cells were resuspended in 500 µL of Dryl’s buffer. Subsequently, 1 mL of pre-cooled chloroform/methanol (2:1, v/v) was added to each sample. The mixtures were gently agitated for 1 hour on ice to ensure complete lipid extraction. After incubation, 0.25 mL of cold double-distilled water (ddH₂O) was added, and the samples were vortexed for 10 minutes while kept on ice. Phase separation was achieved by centrifugation at 13,000 rpm for 10 minutes at 4 °C. The upper hydrophilic layer was carefully discarded, and the lower hydrophobic (organic) phase was transferred to a new microcentrifuge tube, taking care to avoid disturbing the interfacial protein disc. The collected organic phase was dried using a SpeedVac concentrator and stored at –80 °C until further analysis. Prior to LC–MS/MS analysis, the dried lipid extracts were reconstituted in 80 µL of chloroform/methanol (2:1, v/v) containing a lipid isotope internal standard mixture (Avanti research Cat. A83707). The samples were then centrifuged at 13,000 rpm for 10 minutes at 4 °C, and the resulting supernatant was collected for LC–MS/MS analysis. Lipidomic profiling was conducted utilizing an Orbitrap Fusion Lumos Tribrid MS (Thermo Fisher, Waltham, MA, USA) coupled with a Vanquish Horizon UHPLC system (Thermo Fisher) equipped a BEH C18 column (100 × 2.1 mm, 1.7 µm; Waters). Lipid separation was achieved through a 14 min gradient employing 40% acetonitrile and 10% ACN/90% isopropanol with 10 mM ammonium formate at a flowrate of 0.5 mL/min. Both MSs were operated in a data-dependent acquisition mode (DDA) featuring one MS scans and multiple MS/MS scans at a fixed cycle time of 0.4 sec. MS/MS spectra were acquired under collision-induced dissociation (CID, 25 and 35 eV) and higher-energy collisional dissociation (HCD, normalized collision energy 25 and 45, stepped ±15%). Raw MS data were processed using MS-DIAL software (version 4.90) 50 . The parameters for peak detection were set as follows: minimum peak height 30000 amplitude, minimum peak width of 10 scans, and peak smoothing using a linear weighted moving average over four scans. Metabolite identification was performed using a combined MS/MS spectral library composed of the NIST Tandem Mass Spectral Library and the MassBank of North America (MoNA). Lipid identification employed the LipidBlast MS/MS library supplemented with in-house predicted retention times. Mass tolerances were set to 0.008 Da for MS1 and 0.05 Da for MS/MS, with a retention-time tolerance of 0.5 min for lipids. A score cutoff of 75 was applied. Stable isotope tracing by LC-MS/MS and EA-IRMS analysis P. bursaria cells were fed with 0.1mM 13C-labeled oleic acid (13C18-oleic acid, MedChemExpress, Cat. HY-N1446S2) for 24 hours. For EA-IRMS analysis, algae within Paramecium were dried and weighed to the required mass, then enclosed in tin capsules for subsequent analysis. During the analysis, international isotope ratio standards USGS40, USGS62, and USGS65 (United States Geological SurveyReston Stable Isotope Laboratory) were inserted as reference materials for calibration and calculation. The isotope ratio analysis was performed using an EA–Conflo–IRMS system. Samples were combusted into gas form by an elemental analyzer (EA; Thermo Flash2000), transferred through a Conflo IV interface (Thermo), and analyzed using an isotope ratio mass spectrometer (Thermo Delta V Advantage). The EA system was connected to two high-purity gases: oxygen (5N), which assisted the combustion of the samples, and helium (5N), which served as the carrier gas. When the sample dropped into the combustion tube heated to 1050 °C, it was oxidized to produce gaseous compounds including NOₓ, CO₂, SO₂, and H₂O. The SO₂ was absorbed by silver cobalt oxide packed within the combustion tube. The remaining gases (NOₓ, CO₂, and H₂O) then entered the reduction tube, where they were converted into N₂, CO₂, and H₂O. Subsequently, the gas stream was passed through a water removal column containing magnesium perchlorate, which absorbed H₂O, leaving only N₂ and CO₂. These purified gases were then introduced into the isotope ratio mass spectrometer, where the isotopic ratios of N₂ and CO₂ were precisely measured. For isotope-tracing LC-MS/MS analysis, the procedure was same as previous LC-MS/MS analysis and the data were analyzed by El-MAVEN software with isotope correction by IsoCorrectoR software 51 . Isotopologue peak intensities corresponding to M, M+1, up to M+N were extracted for each metabolite from the LC–MS/MS datasets. Quantification of 13C incorporation was performed by summing the intensities of all labeled isotopologues (M+1 through M+N), yielding the total labeled signal. To normalize for variations in metabolite abundance across samples, the fractional 13C labeling was calculated as the ratio of the summed labeled isotopologues to the total isotopologue signal (M to M+N). For downstream comparisons, fractional labeling values were stratified into two experimental groups: 13C-labeled and control. For each metabolite, the mean fractional labeling of the 13C-labeled group was contrasted with that of the control group. The difference between these means (Δ labeling = mean_C13 − mean_control) was used as the metric of 13C enrichment. Statistical evaluation of group-wise differences was conducted independently for each metabolite. Depending on data distribution and sample size, either an unpaired two-tailed Student’s t-test or a Mann–Whitney U test was applied. The resulting p-values were adjusted for multiple hypothesis testing using the Benjamini–Hochberg false discovery rate (FDR) correction. A metabolite was designated as significantly 13C-labeled if it satisfied both of the following criteria: (1) the mean fractional labeling in the 13C-labeled group exceeded that of the control group (Δ labeling > 0); and (2) the FDR-adjusted p-value was < 0.05. Confocal microscopy For lipid droplet staining, cells were similarly fixed with 4% formaldehyde for 10 minutes and stained with LipidSpot™ dye (1000X dilution; Cat. # 70065-T, Biotium, CA, USA) for 30 minutes at room temperature. Fluorescence signals were detected using a confocal microscope (ZEISS LSM 780 Upright Confocal Microscope (ZEISS, Oberkochen, Germany) with an excitation wavelength of 440 nm and an emission wavelength of 585 nm. TEM and SEM imaging Samples were fixed in 2.5% glutaraldehyde prepared in 0.05 M cacodylate buffer (pH 7.2) using rapid microwave irradiation (PELCO 3451 laboratory microwave system; Ted Pella) for one cycle of 1 min on – 1 min off – 1 min on at 250 W, with a ColdSpot module (Ted Pella) maintained at 10 °C. After rinsing in 0.05 M cacodylate buffer, the samples were postfixed with 1% OsO₄ in the same buffer (pH 7.2) using one microwave cycle of 1 min on – 1 min off – 1 min on – 1 min off at 100 W. The samples were then rinsed with double-distilled water and dehydrated through a graded ethanol series (one cycle of 1 min on – 1 min off – 1 min on at 100 W). Subsequently, the samples were infiltrated with a graded series of Spurr resin (3 min each at 250 W), embedded in plastic capsules, and polymerized at 70 °C for 12 h. Then, Ultrathin sections (∼100 nm thick) were prepared using a Leica EM UC6/UC7 ultramicrotome. The TEM images were examined using a Tecnai G2 Spirit TWIN transmission electron microscope (Thermo Fisher Scientific) operated at 120 kV, equipped with a Gatan CCD camera (Model SC1000; 4008 × 2672 active pixels). A single-tilt holder, CompuStage rotation holder, and Gatan 626 cryo specimen holder were used for imaging as appropriate. The SEM imaging was acquired using FEI Helios NanoLab 660 (Thermo Fisher Scientific) in HR mode with the circular backscatter detector (CBS) using a current of 0.2 nA, a dwell time of 30 μs, and 4 mm working distance. Stage bias (-1.5 kV) was applied to enhance the SNR with the primary beam energy set at 4.5 keV such that the landing energy was 3.0 keV. The signal on all four segments of the CBS detector was summed. In all SEM images, the contrast was inverted to resemble the TEM-like images. Nano-SIMS imaging P. bursaria cells were fed with 0.1mM 13C-labeled oleic acid (13C18-oleic acid) for 24 hours. The distribution of carbon isotopic ratio on thin slides of Paramecium was analyzed by NanoSIMS 50 L (Cameca-Ametek, Gennevilliers, France) housed in Academia Sinica, Taiwan. Microtomed thin slides of paramecium were placed on 10mm diameter silicon wafer. Each 50 × 50 μm square area of interested was presputtered by 165 – 172 pA of Cs+ ion beam for 1 minutes and then sputtered by 2.8 – 2.9 pA Cs+ ion beam for 45 minutes to obtain image data at 512 x 512 pixels resolution. Secondary ions of 16O-, 12C2−, 12C13C−, 12C14N−, 31P- and 32S− were collected simultaneously by multiple electron multipliers. 30 um entrance slit and 150 um aperture slit were applied to reach a mass resolving power(M/△M) of 10000. Images of secondary ions and carbon isotopic ratio of symbiotic algae were processed by L’Image software (developed by Larry Nittler, Arizona State University, United State of America). More than 10 5 counts of 12C2-were collected in each region of symbiotic algae. Data availability The mass spectrometry lipidomic data have been deposited in the Metabolomics Workbench. The mass spectrometry lipidomic data analyzed in this study are provided as Supplementary Data. Author contribution JYL conceived the study. YJC and JYL designed analysis and interpreted results. YJC, CYW, CWH, SYH and PLW performed the experiments. YJC, CYW, KMM, CWH, SYH, DCL and PLW performed data analysis and conducted a content review of the manuscript. YJC and JYL wrote the paper. All authors read and approved the final manuscript. Declaration The authors declare no competing interests. Supplementary figure legends Download figure Open in new tab Supplementary figure 1. Ultrastructural interaction between host lipid droplets and algal symbionts. Scanning electron microscopy (SEM) images showing lipid droplets fusing with the perialgal vacuole (PV) membrane and physically attaching to the surface of the symbiotic algal cell. Download figure Open in new tab Supplementary figure 2. Differential abundance of lipid classes in symbiotic versus aposymbiotic Paramecium bursaria lipid droplets. Fold-change comparison of the abundance of each lipid class between symbiotic and aposymbiotic P. bursaria lipid droplets. Download figure Open in new tab Supplementary figure 3. Incorporation of exogenous fatty acids into host lipid droplets. Lipid droplet enlargement observed in Paramecium bursaria after feeding with oleic acid, demonstrating incorporation of exogenous fatty acid into host lipid droplets. Download figure Open in new tab Supplementary figure 4. Differential abundance of lipid classes in symbiotic versus free-living Chlorella variabilis . Fold-change comparison of the abundance of each lipid class between symbiotic and free-living Chlorella variabilis . Download figure Open in new tab Supplementary Figure 5. Example of isotopologue labeling patterns of 13C-labeled lipids in symbiotic Chlorella variabilis . (A) Number of 13C-labeled isotopologues identified within major lipid classes, including TG, PE, PG, PC, MGDG, and DGDG. (B) Frequency distribution of 13C labeling fractions for lipid species in TG, PE, PG, PC, MGDG, and DGDG classes. (C) Representative mass spectra showing the 13C18-labeled isotopologue peak compared with the unlabeled (12C) oleic acid–fed control group. Download figure Open in new tab Supplementary Figure 6. Differential expression of lipid transport genes between free-living and endosymbiotic states. Lipid transport–related genes exhibit altered expression profiles when comparing in free-living and endosymbiotic Chlorella variabilis . *** p-value < 0.001, Student’s t-tests, 3 biological replicates. Supplementary data Supplementary Data 1: Lipidome intensity data Supplementary Data 2: Transcriptome data Supplementary Data 3: EA–IRMS data Supplementary Data 4: El-Maven processing data Supplementary Data 5: Isotope correction data Acknowledgements We thank members of the Leu lab for helpful discussion and comments on the manuscript. We also thank the Academia Sinica IMB Imaging Core for TEM imaging experiments, Academia Sinica Research Center for Applied Science for SEM imaging experiments and Academia Sinica Metabolomics Core for lipidomics experiments. This work was supported by Academia Sinica of Taiwan (grant no. AS-IA-110-L01 and AS-GCS-113-L03) and the National Science and Technology Council of Taiwan (NSTC 113-2326-B-001-002). KMM was supported by an NSTC postdoctoral fellowship (NSTC 113-2811-B-001-065). Funder Information Declared Academia Sinica , AS-IA-110-L01 , AS-GCS-113-L03 National Science and Technology Council, https://ror.org/02kv4zf79 , NSTC 113-2326-B-001-002 Footnotes ↵ 5 Lead Contact References 1. ↵ Akçay , E . ( 2015 ). Evolutionary models of mutualism . Mutualism . Oxford University Press . 2. ↵ Dziallas , C. , Allgaier , M. , Monaghan , M.T. , and Grossart , H.P . ( 2012 ). Act together-implications of symbioses in aquatic ciliates . Front Microbiol 3 , 288 . doi: 10.3389/fmicb.2012.00288 . OpenUrl CrossRef PubMed 3. ↵ Jenkins , B.H . ( 2024 ). Mutualism on the edge: Understanding the Paramecium–Chlorella symbiosis . PLOS Biology 22 , e3002563 . doi: 10.1371/journal.pbio.3002563 . OpenUrl CrossRef PubMed 4. ↵ Dean , A.D. , Minter , E.J.A. , Sørensen , M.E.S. , Lowe , C.D. , Cameron , D.D. , Brockhurst , M.A. , and Jamie Wood , A . ( 2016 ). 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Share Host-derived lipid transfer and metabolic reprogramming in a ciliate–algal symbiosis Yan-Jun Chen , Chi-Yen Wei , Md Mostafa Kamal , Chia-Wei Hsu , Sung-Yun Hsiao , Der-Chuen Lee , Pei-Ling Wang , Jun-Yi Leu bioRxiv 2025.11.27.690892; doi: https://doi.org/10.1101/2025.11.27.690892 Share This Article: Copy Citation Tools Host-derived lipid transfer and metabolic reprogramming in a ciliate–algal symbiosis Yan-Jun Chen , Chi-Yen Wei , Md Mostafa Kamal , Chia-Wei Hsu , Sung-Yun Hsiao , Der-Chuen Lee , Pei-Ling Wang , Jun-Yi Leu bioRxiv 2025.11.27.690892; doi: https://doi.org/10.1101/2025.11.27.690892 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Microbiology Subject Areas All Articles Animal Behavior and Cognition (7621) Biochemistry (17645) Bioengineering (13867) Bioinformatics (41872) Biophysics (21416) Cancer Biology (18549) Cell Biology (25443) Clinical Trials (138) Developmental Biology (13360) Ecology (19866) Epidemiology (2067) Evolutionary Biology (24289) Genetics (15587) Genomics (22470) Immunology (17706) Microbiology (40314) Molecular Biology (17142) Neuroscience (88456) Paleontology (666) Pathology (2826) Pharmacology and Toxicology (4815) Physiology (7634) Plant Biology (15111) Scientific Communication and Education (2042) Synthetic Biology (4285) Systems Biology (9812) Zoology (2268)