Fatty Acid Synthase binds endogenous dsRNAs and dampens the innate immune response to exogenous dsRNAs by limiting their accumulation

Fatty Acid Synthase binds endogenous dsRNAs and dampens the innate immune response to exogenous dsRNAs by limiting their accumulation | 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 Fatty Acid Synthase binds endogenous dsRNAs and dampens the innate immune response to exogenous dsRNAs by limiting their accumulation View ORCID Profile Charline Pasquier , View ORCID Profile Mélanie Messmer , View ORCID Profile Lisanne Knol , View ORCID Profile Johana Chicher , View ORCID Profile Richard Patryk Ngondo , View ORCID Profile Sébastien Pfeffer , View ORCID Profile Erika Girardi doi: https://doi.org/10.1101/2025.07.16.662511 Charline Pasquier 1 UPR 9002 – Architecture et Réactivité de l’ARN, CNRS, Institut de Biologie Moléculaire et Cellulaire , 2 allée Konrad Roentgen, 67084 Strasbourg, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Charline Pasquier Mélanie Messmer 1 UPR 9002 – Architecture et Réactivité de l’ARN, CNRS, Institut de Biologie Moléculaire et Cellulaire , 2 allée Konrad Roentgen, 67084 Strasbourg, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mélanie Messmer Lisanne Knol 1 UPR 9002 – Architecture et Réactivité de l’ARN, CNRS, Institut de Biologie Moléculaire et Cellulaire , 2 allée Konrad Roentgen, 67084 Strasbourg, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lisanne Knol Johana Chicher 2 Plateforme Protéomique Strasbourg-Esplanade, CNRS UAR1589, Institut de Biologie Moléculaire et Cellulaire , 2 allée Konrad Roentgen, 67084 Strasbourg, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Johana Chicher Richard Patryk Ngondo 1 UPR 9002 – Architecture et Réactivité de l’ARN, CNRS, Institut de Biologie Moléculaire et Cellulaire , 2 allée Konrad Roentgen, 67084 Strasbourg, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Richard Patryk Ngondo Sébastien Pfeffer 1 UPR 9002 – Architecture et Réactivité de l’ARN, CNRS, Institut de Biologie Moléculaire et Cellulaire , 2 allée Konrad Roentgen, 67084 Strasbourg, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sébastien Pfeffer Erika Girardi 1 UPR 9002 – Architecture et Réactivité de l’ARN, CNRS, Institut de Biologie Moléculaire et Cellulaire , 2 allée Konrad Roentgen, 67084 Strasbourg, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Erika Girardi For correspondence: e.girardi{at}ibmc-cnrs.unistra.fr Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract In mammalian cells, the presence of double-stranded RNA (dsRNA) in the cytoplasm is a danger signal indicative of viral infection. Cells establish and maintain an antiviral state via the production of interferon (IFN) in response to the detection of viral dsRNAs by specialized RNA-binding proteins (RBPs). In addition to viral dsRNA, endogenous (endo) dsRNAs can also be expressed and recognized either causing autoimmune diseases or regulating physiological processes, depending on their abundance, localization and modification profile. Although evidence on the regulatory role of endo-dsRNAs has increasingly been reported, the mechanisms behind their association with host RBPs and their immunogenicity remains largely misunderstood. To identify new endo-dsRNA-associated proteins and regulatory pathways, we induced the accumulation of endo-dsRNAs in wild type (WT) and ADAR1 KO HCT116 cells treated with the DNA methyltransferase inhibitor 5-azacytidin (5-AZA) and isolated the endo-dsRNA-associated proteome. Among the interactors, we identified the Fatty Acid Synthase (FASN), a key enzyme involved in lipid metabolism. We demonstrated that FASN associates with endo-dsRNAs already in untreated WT HCT116 cells and that this interaction increases upon 5-AZA treatment and in the absence of ADAR1. Moreover, FASN depletion leads to the accumulation of cytoplasmic endo-dsRNAs in close proximity to mitochondria, induces the expression of IFN-stimulated genes (ISGs) such as IFIT1, increases type III IFN secretion and MAVS aggregation. This correlates to a strong reduction of Sindbis virus (SINV) replication in FASN KO HCT116 cells, where ISG expression is significantly induced. This increase in antiviral response is also observed upon poly I:C treatment of FASN KO HCT116 cells. Overall, our data indicate that the accumulation of endo-dsRNAs in FASN-depleted cells induce a stronger innate immune response to exogenous dsRNAs, suggesting a positive feedback loop dependent on FASN expression. Introduction The innate immune response, and more specifically the interferon (IFN) response in mammals, is one of the first defense against pathogens 1 . Activation of this response relies on the detection of pathogen-associated molecular patterns (PAMPs) by host pattern recognition receptors (PRRs). In the case of viral infection, one of the major PAMPs is double-stranded RNA (dsRNA), whose structure can be recognized independently of its sequence by RIG-I-like receptors (RLRs) in the cytoplasm 2 . RLR activation leads to their interaction with the adaptor mitochondrial antiviral-signalling protein (MAVS) located at the mitochondrial outer membrane. Subsequently, aggregation of MAVS leads to a protein phosphorylation cascade culminating in the expression and secretion of IFNs which will act in an autocrine and paracrine manner 3 . Type I and type III IFNs are robustly induced upon detection of viral nucleic acids in most human cell types, although type III IFN response is more specific of epithelial cells. Type I IFNs include 17 members in humans, compared to the 4 type III IFN members namely IFNL1, IFNL2, IFNL3, and IFNL4 4 . While type I IFNs trigger a stronger and faster response that resolves quickly, type III IFNs induce a weaker response that persists longer. Despite these differences and their receptor specificity, both pathways activate overlapping sets of interferon-stimulated genes (ISGs), such as ISG15 5 , IFIT1 6 , IFI27 or OAS3 7 , thereby establishing an antiviral state which ultimately leads to apoptotic death of the infected cell. While for many years the presence of dsRNA was thought to be uniquely associated with a pathogenic exogenous source, recent studies have shown that when cells are subjected to various forms of stress, they produce dsRNA of endogenous origin (endo-dsRNA). These molecules can originate from overlapping sense-antisense nuclear transcripts, as well as transposable elements (TEs) and mitochondrial bidirectional transcription 8 . Deregulation of endo-dsRNA accumulation has been observed following bacterial lipopolysaccharide (LPS) treatment 9 and viral infection 10 , indicating a possible contribution to innate immune defense. DNA methyltransferase inhibitors (DNMTi), used as chemotherapeutic agents, are also known to induce a state reminiscent of an antiviral response 11 , 12 . The 5-azacytidine (5-AZA) and 5-aza-2-deoxycytidine (DAC) are cytidine analogues that are incorporated into the genome during mitosis and blocks DNA methylation processes 13 . Genomic global demethylation by transient DAC low-dose exposure of colon cancer cells leads to both the re-expression of numerous tumor suppressors and TEs 12 . In particular, DNMTi treatments induce re-expression of endogenous retroviruses (ERVs) 11 , 12 and inverted Alu repeats (IR-Alus) 14 , which form long dsRNAs able to stimulate the IFN response via MDA5/MAVS/IRF7 activation 12 . This “viral mimicry” state due to the dsRNA induction would then act as a major mechanism for the DNMTi anti-tumor effect. DsRNA-binding proteins (dsRBPs) are emerging as crucial regulators of endo-dsRNA biology 15 . To prevent detrimental IFN activation, certain dsRBPs have been shown to regulate the immunogenicity of dsRNAs. The adenosine deaminase RNA specific ADAR1 is an enzyme capable of interacting with dsRNA and editing adenines to inosines, thereby destabilizing the canonical Watson-Crick base-pair pairing and preventing dsRNA detection by RLRs 16 , 17 . ADAR1 has two protein isoforms produced from two distinct promoters: ADAR1p110, which is constitutively expressed and localized in the nucleus, and ADAR1p150, which is interferon-inducible and predominantly localized in the cytoplasm 18 . Recently published work suggests that accumulation of unedited endogenous RNA duplexes in ADAR1 depletion could enhance DNMTi efficiency to treat cancer by triggering the dsRNA-mediated IFN response 14 . In addition, the interleukin enhanced factor 3 (ILF3) could compete with ADAR1 for binding to endo-dsRNAs and thereby prevent adenosine-to-inosine (A-to-I) editing 19 . The Heterogenous Nuclear Ribonucleoprotein C (hnRNPC) prevents dsRNAs leaving the nucleus via splicing regulation 20 , while PNPT1 avoids the leakage of mitochondrial dsRNAs (mt-dsRNAs) to the cytoplasm 21 . Despite these examples, the identity and role of dsRBPs remain poorly understood indicating the importance of better characterize new partners in the regulation of dsRNA expression and immunogenicity, In this study, we investigated the proteome associated to endo-dsRNAs in human colorectal cancer cell line depleted or not for ADAR1 and in 5-AZA-treated versus untreated condition. We optimized a method 22 based on anti-dsRNA-immunoprecipitation coupled to mass spectrometry (DRIMS) and adapted it to study the endo-dsRNA interactome. The proteomic analysis of the bound proteins revealed a number of key factors, among which the Fatty Acid Synthase (FASN) was the most significantly enriched one. This enzyme plays a crucial role in lipid metabolism and its overexpression has been associated with adverse cancer prognosis 23 . We demonstrate that FASN intervenes at two layers of dsRNA biology: 1) FASN is immunoprecipitated with endo-dsRNAs, and 2) its depletion causes dsRNA upregulation in HCT116 cells. Our data indicate that FASN KO cells are primed for the IFN response since they produce more type III IFN, express ISGs, such as IFIT1, and show increased MAVS aggregation at the mitochondrial surface compared to control cells at steady state. Concordantly, we showed a stronger innate immune response to dsRNAs of exogenous origin, such as poly I:C and viral dsRNAs from SINV infection in FASN KO cells. Strikingly, while viral production and viral RNA expression is strongly impaired in the absence of FASN, dsRNA accumulation remains high, suggesting that the endo-dsRNA accumulation and possibly their sensing in FASN-depleted conditions may trigger IFN activation. Results Endo-dsRNAs accumulate in 5-AZA-treated ADAR1 KO HCT116 cells To study the regulation of endo-dsRNAs, we first generated a model allowing us to monitor it. It was previously reported that DNMTi treatment was enhanced by the depletion of the ADAR1 deaminase in human cells which favors the accumulation of immunogenic endo-dsRNAs 14 . Using the CRISPR/Cas9 strategy, we first generated an ADAR1 knockout (KO) human colorectal cancer HCT116 cell line which was characterized at the genomic level ( Figure 1A ). As ADAR1p110 is ubiquitously expressed and ADAR1p150 is induced by type-I IFN response, we validate that the expression of both ADAR1 isoforms was impaired upon treatment with type I IFN (IFN alpha A2) ( Figure 1 B ). To verify the ability of WT and ADAR1 KO HCT116 cells to respond to IFN stimulation, we measured the expression of several ISGs, namely ISG15, IFIT1, OAS3 and IFI27 by RT-qPCR, upon type I IFN treatment by RT-qPCR ( Figure S1A ). Both WT and ADAR1 KO HCT116 cells exhibited a strong and comparable induction of ISG expression following type-I IFN stimulation. The amplitude and pattern of ISG upregulation were remarkably similar between the two cell lines, indicating that the absence of ADAR1 does not affect the ability of cells to respond to IFN. These results demonstrate that IFN signaling remains fully operational in ADAR1 KO cells. Moreover, we tested the innate immune response to exogenous dsRNAs using poly I:C, a synthetic dsRNA analog containing a polyinosine and polycytosine strands that mimics exogenous dsRNA accumulation upon viral infection. We observed the induction of ISG15 and IFN-lambda (IFNL1) following poly I:C transfection ( Figure S1B ), as well as an increase of IL-18 which may suggest the activation of the inflammasome pathway 24 . Download figure Open in new tab Figure 1: Endo-dsRNAs accumulation in ADAR1 KO HCT116 upon 5-AZA treatment with 500nM 5-AZA. A) Schematic representation of the ADAR1 CRISPR/Cas9 KO (ADAR1 KO) in HCT116 cells. Two gRNAs targeting the ADAR1 exon 2 (in pink and in blue, respectively) were used to delete a region of 414 bp. Indel event due to Cas9 cleavages in the KO is represented in orange. B) Western blot on ADAR1 and TUBULIN on lysates from ADAR1 KO and WT HCT116 with or without IFN-I treatment (1000U/mL, 24 hours treatments). C) ADAR1 KO and WT HCT116 cell viability monitored by MTT assay in control and 500nM 5-AZA treated cells over three days. Results represent the mean ± standard deviation (SD) of three biological replicates. Statistical analysis was performed using two-way ANOVA test with multiple comparison to WT NT (*= pval<0.05; **** = pval<0.00005). D) Representative confocal immunofluorescence images from WT and ADAR1 KO HCT116 cells treated (5-AZA) or not (NT) with 500nM 5-AZA. Samples were treated with E. coli RNase III (RNase III +) or mock-treated (RNase III -). Staining with mouse J2 anti-dsRNA antibody (in yellow) and with DAPI (in cyan) is shown. Scale bar: 10µM. E ) Relative integrated density was quantified in 20 areas per conditions using the Fiji software. Statistical analysis was performed using one-way ANOVA test (pval<0.05). We then treated our cell lines with 500nM of the DNMTi 5-AZA for 24 hours and we harvested the samples after four days to allow several cell divisions to take place for a proper 5-AZA incorporation into the genome. Under these conditions, we observed a significant reduction in cell viability ( Figure 1C ), which is consistent with previous reports in the literature 13 . However, the innate immune response was not triggered as indicated by the absence of induction of ISG15, IFIT1, OAS3 and IFI27 by RT-qPCR ( Figure S1C ). We then performed total RNA-seq analysis on RNA extracted from WT and ADAR1 KO HCT116 cells, treated or not with 500nM 5-AZA. The 5-AZA treatment resulted in overexpression of about 500 genes in both WT and in ADAR1 KO HCT116 cells ( Figure S1D, Table 1 and 2 ), which indicated an overall genome de-repression due to inhibition of DNA demethylation. About two-thirds of the 5-AZA-induced genes were shared in WT and ADAR1 KO conditions ( Figure S1E ). Nonetheless, gene set enrichment analysis did not identify any significantly enriched hallmark gene set, suggesting that the induced genes do not specifically belong to the inflammatory response and the IFN-a pathway. The accumulation of endo-dsRNAs in DNMTi-treated human cell lines was been reported in previous studies 11 , 12 , 14 . To test whether endogenous dsRNAs accumulation was also induced in our experimental conditions upon 5-AZA treatment in HCT116 cells, we performed immunostaining analysis using the J2 antibody specific for dsRNA structures longer than 40 base pairs 25 , 26 . While immunostaining showed no indication of a significant endo-dsRNA accumulation in ADAR-depleted or 5-AZA treated WT HCT116 cells, we quantified a significant accumulation in ADAR1 KO cell upon 500nM 5-AZA treatment compared to all the other conditions ( Figure 1D-E ). The specificity of the dsRNA immunostaining was validated by treatment with RNase III, an enzyme which specifically degrades RNA duplexes ( Figure 1D-E ). Overall, our results indicated a synergy between 5-AZA treatment and ADAR1 depletion in inducing a significant accumulation of endo-dsRNAs in HCT116 cells. Despite the lack of a strong transcriptomic IFN signature, our experimental set up combining DNMTi treatment and ADAR1 depletion allowed us to achieve sufficient endo-dsRNA accumulation to further study the endo-dsRNA interactome. Identification of FASN as an endo-dsRNA-associated factor To identify cellular proteins associating with endo-dsRNAs, we performed a pull-down of long dsRNA based on the dsRNA immunoprecipitation followed by proteomic analysis (DRIMS), an approach already established for dsRNA interactome analysis upon viral infection 22 . Given the low amount of endo-dsRNAs expressed in our experimental conditions, we optimized the protocol for the immunoprecipitation of endo-dsRNA (eDRIMS) in WT and ADAR1 KO HCT116 cells, treated or not with 5-AZA ( Figure 2A ) by starting from a larger number of cells. Immunoprecipitated proteins upon J2-IP and in the control IgG-IP samples were analyzed by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) and the protein enrichment of the J2-IP over the IgG-IP was analyzed in each condition ( Figure 2B-C , Figure S2A-B ). By analyzing the four experimental conditions, we identified a total of 29 proteins significantly enriched in the anti-dsRNA J2-IP over the control IgG-IP in at least one experimental condition ( Table 3 ). Overall, more than half of the identified proteins have already been reported to bind RNA, being involved in diverse aspects of RNA metabolism ( Table 3 ). Among them, we retrieved several known dsRNA-associated proteins, such as the ATP-dependent RNA helicase A (DHX9) 27 and hnRNPC 28 , which validated our approach. Download figure Open in new tab Figure 2. Identification of FASN as a novel endo-dsRNA interactant in HCT116 cells. A) Schematic representation of the eDRIMS proteomic approach on WT and ADAR1 KO HCT116 cells treated (5-AZA) or not (NT) with 5-AZA. Cells were immunoprecipitated using the anti-dsRNA J2 antibody coupled to magnetic beads (J2-IP). A mouse IgG antibody was used as a specificity control (IgG-IP). Liquid chromatography coupled to tandem mass spectrometry analysis (LC/MSMS) was employed to identify dsRNA associated proteins. B-C) Volcano plot showing the protein enrichment upon J2-IP over IgG-IP ( B ) in untreated HCT116 cells (WT NT) and ( C ) ADAR1 KO HCT116 cells treated with 5-AZA (ADAR1 KO 5-AZA) over three biological replicates. Purple dots represent proteins that are significantly enriched (FDR>1,3, log2FC>1). Red dots correspond to proteins with a coefficient of variation (CV) <50% on the IP-KO_AZA spectral counts. Grey dots represent non significantly enriched proteins. D) Western blot on pre-cleared lysates (INP), control IP (IgG-IP) or dsRNA IP (J2-IP) of WT and ADAR1 KO HCT116 cells, treated or not with 5-AZA. Primary antibodies against FASN, DHX9, hnRNPU, DDX5 and hnRNPC were used. E) Representative confocal microscopy images from Proximity Ligation Assay (PLA) using mouse J2 anti-dsRNAs and rabbit anti-FASN primary antibodies in WT and ADAR1 KO HCT116 cells, treated or not with 5-AZA. PLA signal is shown in yellow. DNA was stained with DAPI (in cyan), Scale bar: 10µM (n=1). F) Quantification of PLA puncta per cell was manually performed on 10 images for each condition. The Fatty Acid Synthase (FASN) was the top hit among the significantly dsRNA-enriched proteins in both untreated WT (WT NT) and 5-AZA-treated ADAR1 KO (ADAR1 KO 5-AZA) HCT116 cells ( Figure 2B-C ). FASN is a multienzyme that catalyzes the synthesis of palmitate from acetyl-CoA and malonyl-CoA in the presence of NADH into long chain saturated fatty acids 29 . This fatty acid is an essential component of lipid rafts in the plasma membrane and is necessary for the palmitoylation of, among others, innate immunity effectors which ensures their stability and localization 30 . Interestingly, FASN overexpression and increased activity is also one of the most frequent phenotypic alterations in cancer cells 31 . The interaction between the most enriched proteins identified by eDRIMS in 5-AZA-treated ADAR1 KO HCT116 and the endo-dsRNAs was tested by western blot analysis ( Figure 2D ). We demonstrated the association of FASN, DHX9, hnRNPC, hnRNPU and DDX5 to the endo-dsRNAs in all experimental conditions ( Figure 2D and Table 3 ). These results indicate that the factors associated with endo-dsRNAs in 5-AZA treated ADAR1 KO cells can already bind to dsRNAs in untreated WT conditions. Since FASN was the most enriched factor in multiple conditions and its involvement in endo-dsRNA biology has not yet been characterized, we decided to focus on this candidate. We first investigated the localization of FASN by immunostaining. In both untreated and 5-AZA treated WT and ADAR1 KO HCT116 cells, FASN is diffusedly expressed in the cytoplasm, suggesting that it could interact with aberrantly expressed cytoplasmic endo-dsRNAs ( Figure S2C ). To validate the proximity between FASN and cytoplasmic endo-dsRNAs in cellulo , we performed a proximity ligation assay (PLA) between FASN and endo-dsRNAs ( Figure 2E ). The results show the FASN proximity with endo-dsRNAs already in untreated WT conditions with up to 6 puncta per cell ( Figure 2E-F ). We quantified an increase of interaction events in ADAR1 KO and 5-AZA treated WT HCT16 compared to untreated WT HCT116, with the strongest increase with up to 15 puncta per cell in 5-AZA treated ADAR1 KO HCT116 cells ( Figure 2F ). Given the similar FASN expression levels across conditions ( Figure S2C ), this is possibly due to the increased presence of endo-dsRNAs in this experimental condition. Altogether, our data show that the eDRIMS approach could be used to identify the endo-dsRNA interactome in HCT116 cells and to discover the association of the enzyme FASN to cytoplasmic RNA duplexes. FASN depletion leads to an accumulation of endo-dsRNAs and primes the cells for an amplified innate immune response To determine if, in addition to its known functions 29 , FASN has any impact on endo-dsRNA biology, we first performed knock-down experiments by transfecting FASN-specific (siFASN) or non-targeting control (siCTL) siRNAs in HCT116 cells and observed the endo-dsRNA accumulation by J2 immunofluorescence ( Figure S3A ). We detected a cytoplasmic accumulation of endo-dsRNAs in siFASN compared to the siCTL condition, indicating that FASN depletion results in dsRNA accumulation. A similar phenotype was observed in three other human cell lines, hepatoma Huh7.5.1 cells, breast cancer MDA-MB cells and lung cancer A549 cells ( Figure S3B ), although at different degrees. To better understand these phenotypic differences, we looked at FASN basal protein levels in those different cell lines ( Figure S3C ) and observed that FASN expression is lower in A549 and MDA-MB cells compared to HCT116 and Huh7.5.1 cells. These results suggest that dsRNA accumulation seems higher upon FASN knock-down in the cell lines which express FASN at higher levels ( Figure S3B and C ). To go further, we generated a FASN KO HCT116 clone using the CRISPR/Cas9 technology ( Figure 3A ). HCT116 cells stably expressing Streptococcus pyogenes (Sp)Cas9 32 were transduced with lentiviral particles expressing either a non-targeting control CRISPR guide (crg)RNA or a crgRNA targeting the FASN exon 7. Upon selection of stable cells, a single FASN KO clone was isolated for characterization (hereafter referred to as FASN KO HCT116 cells) and was compared to cells expressing the crgRNA non-targeting control (hereafter referred to as CTRL HCT116 cells). At the genomic level, an indel in the exon 7 leads to a frameshift in the ORF and the presence of an early stop codon in FASN KO HCT116 cells. We verified that FASN expression was impaired in the KO cells by western blot, while MAVS levels remained mostly unaffected ( Figure 3B , left panel ). In contrast, we tested whether FASN expression was altered upon treatment with type I IFN and observed a reduction in FASN level by western blot, while MAVS was strongly induced ( Figure 3B , right panel ). We then assessed the effect of FASN KO on cell viability by MTT assay over 72 hours and measured a significant reduction compared to CTRL HCT116 cells ( Figure S3D ) which is consistent with previous reports indicating that FASN inhibition reduces cell growth 23 , 33 – 35 . We also performed immunostaining analysis and observed a significant cytoplasmic endo-dsRNA accumulation in FASN KO HCT116 compared to CTRL HCT116 cells ( Figure 3C-D ), as already observed by siRNA knockdown in HCT116 and other cell lines ( Figure S3A-B ). Download figure Open in new tab Figure 3. Accumulation of endo-dsRNAs in FASN KO HCT116 cells correlates with higher type III IFN production and MAVS aggregation. A) Schematic representation of the FASN CRISPR/Cas9 KO (FASN KO) in HCT116 cells. One gRNA targeting the FASN exon 7 (in blue) was used to generate an INDEL which produces a substitution of G-to-A and an insertion of T (INDEL in red) causing the appearance of a premature STOP codon. B) Western blot on FASN, MAVS and GAPDH on lysates from CTRL HCT116 cells treated or not with IFN-I (1000U/mL) for 24 hours (left panel) and on CTRL and FASN KO HCT116 (right panel). C) Representative confocal co-immunostaining images using mouse J2 anti-dsRNAs (in yellow) and rabbit anti-FASN (in magenta) primary antibodies in CTRL and FASN KO HCT116 cells. DNA was stained with DAPI (cyan). The right panel corresponds to a zoom into the area indicated by a yellow square in the MERGE panel. Scale bar: 10µM. D ) The relative integrated density of dsRNA staining was quantified in 20 areas per conditions using the Fiji software. Statistical analysis was performed using t-test (pval<0.005). E) Volcano plots displaying the differences in gene expression between CTRL and FASN KO HCT116 cells over three biological replicates. Genes with an FDR value 1 and are displayed in red, genes with a log2 fold change < -1 are displayed in blue. All other genes are displayed in grey. Horizontal line represents FDR = 0.01, vertical lines represent a log2 fold change of -1 and 1. F) Type III IFN production in the supernatant of CTRL and FASN KO HCT116 cells monitored by measuring the optical density (OD) at 655 nm on HEK-Blue IFN-Lambda cells over three biological replicates. Statistical analysis was performed using t-test (pval<0.005) G) Representative confocal immunofluorescence analysis performed in CTRL and FASN KO HCT116 cells using mouse anti-MAVS antibody (in green) or MitoTracker dye (in magenta). Nuclei were stained with DAPI (in cyan). Scale bar: 10 μm. H) The relative integrated density of MAVS staining was quantified in 20 areas per conditions using the Image J software. Statistical analysis was performed using t-test (pval<0.00005). To characterize the transcriptome of FASN KO HCT116 cells and better understand the impact of FASN on endo-dsRNA accumulation and activation of innate immune pathways, we performed total RNA-seq on RNA extracted from FASN KO and CTRL HCT116 cells ( figure 3E and Table 4 ). The analysis of differential gene expression shows a significant reduction in the expression of about 195 genes and the overexpression of about 351 genes in FASN KO cells compared to CTRL HCT116 cells ( Figure 3E ). Among these transcripts, FASN mRNA is strongly downregulated in FASN KO cells, while Interferon Induced Protein With Tetratricopeptide Repeats 1 (IFIT1) is significantly upregulated. Gene set enrichment analysis of all significantly differentially expressed genes showed the highest enrichment scores for downregulated genes involved in oxidative phosphorylation and MYC targets, with a false discovery rate (FDR) below 5% ( Figure S3E ). Among the upregulated genes, the inflammatory response and lipid metabolism pathways showed some of the highest enrichment scores, even if their false discovery rate (FDR) was above 5% ( Figure S3E ). Interestingly, Gene Ontology enrichment analysis of cellular component GO terms for significantly downregulated genes in FASN KO versus CTRL HCT116 cells revealed mitochondria as a the most prominent feature ( Figure S3F ). To assess whether FASN KO cells have an altered IFN response, type III IFN production in the supernatants from KO and CTRL HCT116 cells was measured using HEK-Blue IFN-Lambda reporter cells. This analysis revealed that secretion of type III IFN was significantly higher in FASN KO compared to CTRL HCT116 cells ( Figure 3F ), suggesting that these cells are somehow primed for an amplified innate immune response. Since MAVS activation induces its local oligomerization and leads to the production of type I and type III IFN as well as ISGs 36 , we aimed at verifying whether any change of MAVS aggregation at the mitochondria outer membrane were detectable upon FASN depletion to further characterize this phenotype. As expected, immunostaining showed that MAVS co-localized with mitochondria in FASN KO and CTRL HCT116 cells ( Figure 3G ). Of note, a significant increase in the relative intensity of MAVS in FASN KO compared to CTRL HCT116 cells was quantified ( Figure 3H ). Given that MAVS expression levels are unaffected in FASN KO ( Figure 3B ), this result could suggest an aggregation of this protein in FASN-depleted cells. Interestingly, immunostaining analysis coupled to MitoTracker labelling indicates that endo-dsRNAs seem to be in proximity of mitochondria in FASN KO HCT116 cells ( Figure S3G-H ). Altogether, our data indicate that FASN depletion causes the accumulation of cytoplasmic endo-dsRNAs in HCT116 cells and other cell lines tested. FASN KO reduces viability, displays increasing levels of specific ISGs, higher production of type III IFN and induces MAVS aggregation phenotype which could suggest a pre-activated antiviral innate immune response. FASN-depleted cells display a stronger innate immune response to exogenous dsRNAs of both synthetic and viral origin Given the observed phenotype of the FASN KO cells, we hypothesized that FASN depletion could boost the antiviral innate immune response to exogenous dsRNAs. We then stimulated the FASN KO and CTRL HCT116 cells by transfection of poly I:C for 6 hours. Interestingly, we observed a significant increase in type III IFN production in poly I:C-stimulated FASN KO cells compared to CTRL HCT116 cells ( Figure S4A ). To verify the increased ability of FASN KO HCT116 cells to respond to exogenous dsRNAs, we tested the induction of ISG15, OAS3, IL-18 and IFIT1 upon polyI:C treatment by RT-qPCR ( Figure S4B ). Although these mRNAs were already induced by poly I:C in CTRL HCT116 cells, their expression was even further upregulated in FASN KO cells. To test if such an amplified innate immune response to exogenous dsRNAs exists, we infected FASN KO and CTRL HCT116 with Sindbis virus (SINV), an arthropod-borne (arbo)virus with a positive single-stranded RNA genome, capped and polyadenylated, whose cytoplasmic replication result in strong accumulation of long dsRNAs 22 . A modified version of SINV which expresses the Green Fluorescent Protein (SINV-GFP) was used as a reporter for viral replication ( Figure S4C ). We observed a quantitative reduction of the GFP relative fluorescence in FASN KO compared to CTRL HCT116 cells over 48 hours of infection at a MOI of 0.1 by automated live cell imaging ( Figure 4A ). This observation correlates with a decrease in viral capsid protein production in FASN KO compared to CTRL HCT116 cells ( Figure 4B ). This was associated to a significantly decrease of viral genome and sub-genome levels ( Figure 4C ) and a reduction of viral titers ( Figure 4D ) in FASN KO compared to CTRL HCT116 cells as measured by plaque assay. We hypothesized that such a defect in SINV infection was linked to an overactivation of the antiviral immune response in FASN KO, as shown by the upregulation of ISG15, IL-18, IFIT1 and OAS3 measured by RT-qPCR ( Figure 4E ). Download figure Open in new tab Figure 4. SINV infection is impaired in FASN KO HCT116 cells. A) SINV-GFP infection kinetics in CTRL and FASN KO HCT116 cells. The relative GFP fluorescence area (expressed in percentage) as a function of time was measured after SINV GFP infection at an MOI of 0.1 every 6 h for 48 h with the CellcyteX automated cell counter and analyzer. Results represent the mean ± standard deviation (SD) of three biological replicates. B ) Western blot on lysates from CTRL and FASN KO HCT116 infected or not with SINV-GFP MOI 0.1, 24 hours) using rabbit anti-capsid antibody. C) RT-qPCR on SINV genome (SINV gRNA) or sub-genome (SINV sub-gRNA) on CTRL and FASN KO HCT116 infected or not (mock) with SINV-GFP MOI 0.1 for 24 hours. Results represent the mean ± standard deviation (SD) of three biological replicates. Gene expression was normalized over actin expression. Statistical analysis was performed using a t-test (** = pval<0.005, *** = pval<0.0005). D) Viral titers from supernatants of CTRL and FASN KO HCT116 cells infected with MOI 0.1 SINV-GFP for 24 hours as quantified by plaque assay. Results represent the mean ± standard deviation (SD) of three biological replicates. Statistical analysis was performed using a t-test (* = pval<0.05). E) RT-qPCR on four ISGs on CTRL and FASN KO HCT116 cells infected or not (mock) with SINV-GFP (MOI 0.1, 24 hours). Gene expression was normalized to actin expression. Results represent the mean ± standard deviation (SD) of three biological replicates. Statistical analysis was performed using a one-way ANOVA test with multiple comparison to CTRL mock (** = pval<0.005; **** = pval<0.0005). F ) Representative confocal co-immunostaining images using mouse J2 anti-dsRNAs (in yellow) and rabbit anti-FASN (in magenta) primary antibodies in CTRL and FASN KO HCT116 cells. DNA was stained with DAPI (cyan). Scale bar: 10µM. G) Epifluorescence microscopy analysis of SINV (+) RNA in SINV-infected CTRL and FASN KO HCT116 cells (MOI 0.1, 24 hpi) by RNA fluorescence in situ hybridization (FISH) (in red), DAPI staining (in cyan) and merge of the two channels are shown. Scale bar: 5 μm. We also analyzed dsRNA accumulation in FASN KO and CTRL HCT116 cells upon SINV infection by immunostaining using the J2 antibody. As expected, we could observe a strong accumulation of dsRNAs in SINV infected CTRL HCT116 cells, which is consistent with previous findings 22 ( Figure 4F ). Strikingly, we also detected a strong dsRNA accumulation in SINV-infected FASN KO cells, despite the impaired infection in FASN depleted cells. To further understand the origin of such RNA duplexes, we performed RNA-FISH experiments using probes specific for the plus strand viral RNAs (namely genomic and sub-genomic RNAs). We observed that viral RNA levels were strongly reduced in FASN KO conditions ( Figure 4G ), consistently with the results in Figure 4D . These data led us to hypothesize that dsRNA accumulation upon SINV infection in FASN KO cells could accounts mostly for endo-dsRNAs accumulating upon infection. Our results demonstrate that FASN KO HCT116 cells have a stronger activation of the antiviral immune response to exogenous sources of dsRNAs (e.g.: poly I:C and SINV infection). Consistent with these findings, SINV RNA replication, protein expression and particle production were reduced upon FASN depletion. Interestingly, despite reduced viral RNA levels, SINV-infected FASN KO HCT116 cells show persistently high levels of double-stranded RNAs (dsRNAs) relative to CTRL HCT116 cells, suggesting that impaired FASN function may promote the accumulation of endogenous dsRNAs. Discussion Throughout this work, we aimed to identify new cellular proteins able to regulate dsRNA accumulation and immunogenicity. Our study reveals a novel role for the lipid metabolism enzyme FASN 31 in the regulation of endo-dsRNA biology and innate immune response. We first demonstrated that the treatment with the DNMTi 5-AZA combined with ADAR1 depletion increased the accumulation of endo-dsRNAs. Although this experimental set-up resulted in a significant accumulation of cytoplasmic dsRNA, this condition was only marginally associated with innate immune pathway activation in our cellular model. This observation, partially discordant with some literature reports 11 , 12 , 14 , suggests that the accumulation of endogenous dsRNAs alone does not suffice, in this context, to elicit a robust IFN response. This apparent tolerance may reflect transcriptional inertia, active negative regulation of cytoplasmic sensors, or a cell line–specific adaptation. Nevertheless, this model proved useful as an experimental tool to artificially enrich cells in endo-dsRNA, facilitating their immunoprecipitation and interactome analysis. Using these experimental conditions, we isolated a set of cellular proteins associated with endo-dsRNAs by eDRIMS. Among the partners, we identified several well-characterized RBPs involved in dsRNA metabolism, such as ILF3 19 , HNRNPC 20 , and DHX9 27 , thereby validating the robustness of our approach. Unexpectedly, FASN emerged as one of the major interactors. FASN is a key enzyme in fatty acid metabolism, being the rate-limiting enzyme to generate palmitic acid, and plays a critical role in multiple cellular processes such as energy storage, membrane plasticity and protein palmitoylation 29 . Interestingly, although FASN was mostly pulled-down with dsRNAs upon 5-AZA treatment in ADAR1 KO cells, we observed that FASN already interacts with endo-dsRNA under untreated wild-type conditions, indicating that this association occurs independently of perturbed cellular states. To preserve the physiological context, we therefore chose to perform functional analyses of FASN in untreated cells expressing endogenous levels of ADAR1. This strategy aimed to minimize artifacts related to epigenetic destabilization or cellular stress and to evaluate the intrinsic role of FASN in basal regulation of endo-dsRNA and IFN response. Our western blot results suggest that FASN protein abundance is negatively regulated in response to IFN pathway activation. This is consistent with previous reports 37 , describing that FASN is down regulated upon IFN activation. We subsequently demonstrated that FASN depletion leads to an accumulation of cytoplasmic endogenous dsRNA in close proximity to mitochondria, accompanied by activation of type III IFN pathway. This activation indicated by type III IFN production, expression of specific ISGs such as IFIT1, and aggregation of the MAVS protein, leads to a “primed” cellular state that renders cells highly resistant to viral infection with SINV. This is particularly relevant since the primary sites of infection for this mosquito-transmitted arbovirus are epithelial tissues and were type III IFN is expressed as a major defense against these viral pathogens 4 . Our data also suggest that FASN acts as a proviral factor possibly by negatively regulating endo-dsRNA accumulation and immunogenicity. Our hypothesis is that reduced FASN expression levels promote the accumulation of endo-dsRNA and contribute to a slight activation of the IFN response under physiological conditions. The fact that FASN itself is downregulated at the protein level upon IFN stimulation suggests the existence of a positive feedback mechanism which may amplify antiviral innate immunity to exogenous dsRNAs ( Figure 5 ). Download figure Open in new tab Figure 5. Hypothetical model of FASN–mediated regulation of the IFN response via endogenous dsRNA. (1) Once exogenous dsRNA of viral origin or synthetic analogs are introduced into the cytoplasm, (2) they are sensed by RIG-I-like receptors (RLRs), which trigger downstream signaling cascades. (3) The activation of RLRs promotes mitochondrial aggregation of the adaptor protein MAVS, (4) which in turn induces the expression of type III interferons (IFN-III). (5) Secreted type III IFN acts in an autocrine and paracrine manner. (6) Binding of IFN-III to its receptor (IFNLR) triggers downstream signaling and (7) the transcriptional activation of interferon-stimulated genes (ISGs), (8) establishing an antiviral state. (9) IFN signaling negatively regulates FASN protein expression. (10) Downregulation of FASN leads to the accumulation of endogenous dsRNAs, particularly in the proximity of mitochondria. (11) While it remains unclear whether FASN directly binds these endogenous dsRNAs (bi-directional red arrow), we hypothesize that they are sensed by RLRs, thereby amplifying the IFN response via a positive feedback loop, as indicated by "+" symbols on steps 3–7. This immunomodulatory role of FASN is echoed by recent observations showing that certain viruses have evolved strategies to induce FASN expression during infection 38 , 39 . More generally, metabolic reprogramming is commonly observed upon viral infection, allowing modulation of key cellular pathways 40 – 44 . It is thus conceivable that upregulation of FASN represents a viral strategy to inhibit IFN responses by maintaining endogenous dsRNA levels below the detection threshold. Consistently, a recent study in a bovine model of Bovine Diarrhea Border Virus (BDBV) infection reported that FASN depletion confers increased resistance to infection 38 , 39 , associated with exacerbated IFN pathway activation, thereby corroborating our findings. However, these results must be nuanced in light of the well-established functions of FASN in lipid metabolism and innate immunity. FASN has recently emerged as proviral host factor for Dengue virus (DENV), where it associates to viral replication sites to remodel cellular membranes and therefore support viral genome replication 45 . In line with these observations, FASN association to CHIKV genomic RNA has also been reported 47 , further supporting its involvement in RNA biology. FASN is also well known to catalyze the synthesis of palmitate and therefore promote protein palmitoylation, a post-translational modification crucial for the membrane localization and function of several key innate immunity effectors, including MAVS 48 , IFITM proteins 30 , and the NLRP3 sensor 50 , whose palmitoylation controls inflammasome activation. Given these pro-immunity roles, we initially anticipated that FASN depletion would attenuate antiviral responses. The observed opposite effect in our experimental work, namely a boosted activation of the ISGs upon poly I:C and SINV infection, revealed an unexpected functional complexity and that FASN fulfils multiple, sometimes opposing, roles depending on cellular contexts and RNA species involved. It is worth to mention that, while FASN is essential for the palmitoylation of CHIKV nsp1 and the formation of viral replication complexes 46 , SINV proteins are not palmitoylated 49 . Therefore, FASN may modulate SINV infection via a palmitoylation-independent mechanism. Previous work conducted in our laboratory suggests that FASN co-purifies with endogenous dsRNA under basal conditions, and while FASN is still detected in dsRNA pulldowns following viral infection, its association does not increase, indicating that FASN is not specifically enriched on viral dsRNA upon SINV infection 22 . This observation raises the hypothesis of FASN being associated with a particular subset of endogenously produced dsRNAs, which remains to be identified. Furthermore, it remains uncertain whether the RNAs bound to FASN under physiological conditions correspond to those accumulating upon FASN depletion. The identification of the RNA species directly bound by FASN would be extremely helpful to elucidate the molecular mechanisms underlying their modulatory role in immune responses. One of our hypotheses builds on the emerging paradigm that certain metabolic enzymes, historically considered as devoid of immune functions, may also act as RNA binding proteins. In this respect, their catalytic activity can be modulated through interactions with specific RNAs. FASN falls into this category of “moonlighting” metabolic enzymes 51 , capable of binding RNA via a non-canonical RNA-binding domain. Therefore, it will be important to investigate whether FASN ability to dampen the interferon response depends on the modulation of its enzymatic activity through dsRNA binding, or alternatively, on the capacity to sequester endogenous dsRNA from immune sensors. Materials and methods Cell culture, viral stocks and virus infection Cell lines were maintained at 37°C in a humidified atmosphere enriched with 5%CO2. HCT116 VERO E6 and HEK293T were cultured in Dulbecco’s modified Eagle medium (DMEM; Gibco; ThermoFisher Scientific) supplemented with 10% FBS (FBS, bioSera FB1090/500), HEK-Blue IFN-Lambda cells (Invivogen) were maintained in culture in DMEM (Gibco; ThermoFisher Scientific) supplemented with 10% heat-inactivated FBS, 10µg/ml of blasticidin (Invivogen), 1µg/ml of puromycin (Invivogen) and 100µg/ml of zeocin (Invivogen). Viral stocks available in the laboratory were produced from plasmids carrying a wild-type or a green fluorescent protein (GFP)-SINV genomic sequence, kindly provided by Dr. Carla Saleh (Pasteur Institute, Paris). Viral stocks were prepared as in 52 Cells were infected at a MOI of 10 - 1 and samples were harvested at 24 or 48 h post-infection as indicated in the figure legends. Cell treatments and transfection 5-AZA treatment ADAR1 KO or WT HCT116 cells were treated with 500nM or 1µM of 5-AZA (Sigma Aldrich) or with acetic acid. After 24 h, media were replaced by drug-free media. 72 h later, cells were harvested. IFN-I treatment ADAR1 KO or WT HCT116 cells were treated with 1000U/ml of IFN-Alpha a2 (11100-1, Pbl Assay Science). Cells were harvested 24 hours later. Poly I:C transfection Transfection complexes were prepared using poly I:C at either 2 or 20 µg/ml at final concentration and Lipofectamine 2000 transfection reagent (Thermo Fisher Scientific). The transfections were performed according to the manufacturer’s instructions and cells were harvested at 6 or 24 hours post treatment according to figure legends. siRNA transfection Transfection complexes were prepared using siRNA targeting FASN (siFASN) or non-targeting control (siCTL) at final concentration of 20nM and Lipofectamine 2000 transfection reagent (Thermo Fisher Scientific) diluted in Opti-MEM transfection media according to the manufacturer’s instructions. The complexes were added to cells and samples were fixed for immunofluorescence 24 hours later. ADAR1 CRISPR/Cas9 knockout CRISPR guide RNA (crgRNA) sequences targeting the human ADAR1 gene were taken from 53 : crgRNA#1 ADAR1 sense: 5’CACCGAAATGCTGTGCTAATTGACA3’; crgRNA#1 ADAR1 antisense: 5’AAACTGTCAATTAGCACAGCATTTC3’; crgRNA#2 ADAR1 sense:5’CACCGATGATGGCTGAAACTCACC3’; crgRNA#2 ADAR1 antisense: 5’AAACGGTGAGTTTCGAGCCATCATC3’. Briefly, the crgRNA#1 and #2 to be inserted into the pX459V2 plasmid were prepared by oligos annealing at 95°C during 10min and letting cool down overnight. In parallel, 3µg of pX45V2 were digested using BbsI restriction enzyme during 4 hours at 37°C. Digested plasmid was purified on agarose gel (Monarch DNA gel extraction Kit, NEB). Diluted oligo duplexes and 100 to 300ng of digested plasmid were ligated using the T4 DNA Fast Ligase (Invitrogen) and DH5alpha chemo-competent E. coli bacteria were transformed with ligation plasmids containing an ampicillin resistance gene under sterile conditions by heat shock. Transformed bacteria were selected on Lucia Bertani (LB) (Roth) medium + Ampicillin 100µg/mL and incubated at 37°C overnight. At least one clone per conditions were cultured overnight at 37°C in 100 mL LB+Ampicillin 100µg/mL. The bacteria were pelleted by centrifugation at 5000g for 15 minutes and plasmids were extracted from the bacterial pellet using midipreparations (Macherey-Nagel). The quantity of plasmid extracted was measured using Nanodrop. Plasmid sequence was verified by Sanger sequencing (Eurofins genomics). HCT116 cells were transfected with the two plasmids encoding for SpCas9 protein and containing crgRNA#1 and crgRNA#2, respectively. One day later, cells were treated with 1µg/ml of puromycin for 48 h. Surviving cells were diluted in DMEM, 10%FBS to obtain 0,5 cells/well in 96-well plates. Three weeks later, cellular genomic DNA was extracted from colonies. Cells were lysed in lysis buffer (50mM Tris-HCl [pH8,0]; 100mM EDTA [pH8,0]; 100mM NaCl; 1% SDS) containing 0,1mg of proteinase K and incubated overnight at 55°C. Genomic DNA was extracted using phenol/chloroform/isoamyl alcohol reagent. Then, 50ng of genomic DNA were amplified with the GoTaq DNA polymerase (Promega) using specific primers surrounding the deleted region: ADAR1 sense: 5’GTAAGACCAGACGGTCATAGC3’; ADAR1 antisense: 5’CGCTGATGGGGTTCTTCAGC3’. Wild-type genomic DNA was used as control template. The PCR reaction was loaded in 1% agarose gel and obtained amplicons were gel purified (Monarch kit: NEB) and sequenced by Sanger sequencing (Eurofins genomics). Endogenous double-stranded RNA immunoprecipitation and proteomic analysis (eDRIMS) Protein G Dynabeads (Invitrogen) were resuspend in FA lysis buffer (1mM EDT1 [pH8,0], 50mM HEPES-KOH [pH7,5], 140mM NaCl, 0,1% sodium deoxycholate [w/v], 1% triton X-100 [v/v], 1 tablet of commercial protease inhibitor cocktail (ROCHE)). 50µL of beads per IP were blocked with 10µg/mL yeast tRNA (Invitrogen) for 1 hour at room temperature. 10µL of uncoupled beads per IP were kept at 4°C and used for a pre-clearing of the cell lysates. Then, two micrograms of mouse anti-dsRNA J2 antibody (Jenna BioScience) or mouse anti-IgG antibody (Cell Signaling) were bound to remaining 40µL of beads overnight at 4°C. A total of 90% confluent WT or ADAR1 KO HCT116 cells treated or not with 5-AZA in 15 cm 2 dishes were washed with 1X cold PBS. Cells were scraped and spun at 1000 rpm at 4°C for 5 minutes. Each cell pellet was lysed in 1ml of nuclei lysis buffer (50 mM Tris-HCl [pH 8,0], 10 mM EDTA [pH 8,0], 1% SDS [w/v], 1X protease inhibitor cocktail (ROCHE)) and incubated on ice for 10min and diluted 5-fold with FA lysis buffer containing Ribolock RNase inhibitor (Invitrogen). After adding 10mM MgCL2, 5mM CaCl2, 1µL of Ribolock and 1 µL of DNase the samples were treated for 30 minutes at 37°C. EDTA (20mM) was added to stop the reaction and a spin at 13 000 rpm at 4°C for 5min was performed. Supernatant was carefully transferred to new tubes and blocked beads for pre-clearing (see above) were added and incubated 1 hour at 4° to pre-clear the samples. Subsequently, 40 µL of lysates out of 1mL were kept aside and treated as input samples for subsequent analyses. The remaining supernatants were added to the coupled beads (IgG or J2) and incubated at 4°C overnight. A magnetic rack was used to wash the beads. The beads were washed twice with 1mL of FA lysis buffer and twice with 1mL TE buffer (10 mM EDTA [pH 8,0], 100mM Tris-HCL [pH 8,0]). 80% of beads were kept for protein extraction by incubated it 10 min at 95°C with 40 µL 2XSDS Laemmli loading buffer (120 mM Tris-HCl [pH 6,8], 20% glycerol, 4% SDS, 0,04% bromophenol blue) and 20% of beads were kept for RNA extraction using 500 µL of Trizol Reagent® (Invitrogen, Thermo Fisher Scientific) according to the manufacturer’s instruction. Proteins were analyzed by silver staining after separation by SDS-PAGE (Bio-Rad) using the SilverQuest Silver Staining Kit (Invitrogen) according to manufacturer’s advice. Protein samples were prepared for LC-MS/MS analysis as described in 54 . Samples were analyzed with 160-minute gradients on the Qexactive + (ThermoFisher Scientific). The data were then subjected to a Mascot search with the Swissprot human bank (Mascot V.2.8, database v.2023_01) and the spectra were validated with a false positive rate (FDR) of less than 1% at PSMs and protein level, a score>25, and a validation at dataset level. The total spectral counts were then validated by statistical analyses: after a DEseq2 normalization of the data matrix, the spectral count values were submitted to a negative-binomial test using an edgeR GLM regression through R (R v3.2.5). For each identified protein, an adjusted pvalue (adjp) corrected by Benjamini–Hochberg was calculated, as well as a protein fold-change (FC). The results are presented in a Volcano plot using protein log2 fold changes and their corresponding adjusted (-log10adjp) to highlight upregulated and downregulated proteins. Due to stringent purification conditions, the number of purified proteins was low which impacted the reproducibility of the quantifications. To select candidates, on top of the significant p-values, we chose the proteins with the lowest coefficients of variation (CV<50% on the IP-KO_AZA spectral counts). Cloning of crgRNA targeting FASN EX7 in pKLV CRISPR guide RNA (crgRNA) sequences targeting the human FASN gene were taken from the human sgRNA Brunello lentiviral library 55 : crgRNA_FASN sens: 5’CACCGATGTATTCAAATGACTCAGGT3’ crgRNA_FASN antisens: 5’TAAAACCTGAGTCATTTGAATACATC3’ Briefly, the crgRNA#1 and #2 to be inserted into the pKLV (#50946 Addgene) plasmid were prepared by oligo annealing at 95°C for 10min and cooled down overnight. In parallel, 3µg of the pKLV-U6gRNA (BbsI)-pGKpuro2ABFP plasmid (#62348; Addgene) were digested using BbsI restriction enzyme for 4 hours at 37°C. Digested plasmid was purified on agarose gel (Monarch DNA gel extraction Kit, NEB). Diluted oligo duplexes and 100 to 300 ng of digested plasmid were ligated with the T4 DNA Fast Ligase (Invitrogen) using manufacturer’s instructions and DH5alpha chemo-competent E. coli bacteria were transformed with ligation plasmids containing an ampicillin resistance gene under sterile conditions by heat shock. Transformed bacteria were selected on Lucia Bertani (LB) (Roth) medium + Ampicillin 100µg/mL and incubated at 37°C overnight. At least one clone per conditions was cultured overnight at 37°C in 100 mL LB+Ampicillin 100µg/mL. The bacteria were pelleted by centrifugation at 5000g for 15 minutes and plasmids were extracted from the bacterial pellet using midipreparations (Macherey-Nagel). The quantity of pKLV-based plasmid extracted was measured using Nanodrop. Plasmid sequence was verified by Sanger sequencing (Eurofins genomics). Lentiviral production Lentiviral supernatants were obtained by transfecting HEK293T cells with 1.7 µg of pKLV transfer vector carrying the indicate transgene (crgRNA non targeting control or crgRNAEX7 FASN), 0.33 µg and 1.33 µg of the packaging plasmids pCMV-VSV-G (Addgene #8454) and psPAX2 (Addgene #12260), respectively, with Lipofectamine 2000 reagent (Invitrogen, Fisher Scientific). Briefly, one well from a 6-well plate with 600 000 cells was transfected for each lentiviral production. After 48 h, the medium containing viral particles was collected and filtered with a 0.45 µm PES filter and immediately used for transduction. Generation of CRISPR/Cas9 FASN KO HCT116 cells Then, 100 000 Cas9-HCT116 32 cells grown in Blasticidine 10µg/mL were transduced in a 6-well-plate by adding either 500 µL of pKLV empty or pKLV crgRNA_FASN EX10 lentiviral supernatant to 500 µL of DMEM supplemented with 10% FBS and 4 µg/mL polybrene (Merck, Sigma-Aldrich). One day later, the medium was raplaced. After 48h, 1 µg/mL puromycin was added to select the resistant cells, which were kept under constant antibiotic selection and maintained in culture with palmitic acid (P0500-10G, Sigma). Surviving cells were diluted in DMEM, 10%FBS to obtain 0,5 cells/well in 96-well plates and cultured for at least 3 weeks, after which genomic DNA was extracted from colonies. Cells were lysed in (50mM Tris-HCl [pH8,0]; 100mM EDTA [pH8,0]; 100mM NaCl; 1% SDS containing 0,1mg of proteinase K and incubated overnight at 55°C. Genomic DNA was extracted using phenol/chloroform/isoamyl alcohol reagent (Roth) and amplified using GoTaq DNA polymerase (Promega) using specific primers surrounding the mutation site: FASN sense: 5’TGTACGCCACCTCCTGAA3’ FASN antisense: 5’TGATGCCATTCAGCTCCTGG3’ Wild-type genomic DNA was used as control template. PCR reactions were loaded in 1% agarose gel and obtained amplicons were gel purified (Monarch DNA gel extraction Kit, NEB) and sequenced by Sanger sequencing (Eurofins genomics). HEK-Blue assay Ten thousand cells/well of HEK-blue IFN-Lambda cells (Invivogen) were plated in 96-well plates in DMEM complemented with 10% heat-inactivated FBS. The day after, cells were incubated with 50µL of the supernatant of tested cell lines and quantified for 24 h. The QUANTI-Blue reagent (Invivogen) was prepared following the manufacturer’s instruction. Next, 135µL of QUANTI-Blue were mixed to 25µL of HEK-blue IFN-Lambda supernatants in 96-well plates. The plates were incubated for 5min at 37°C and absorbance at 655nm was measured with the spectrometer iMark™ Bio-Rad. Protein extraction and western blotting Cells were collected in 300 to 500µL of lysis buffer (50mM tris-HCl [pH 7,5], 150mM NaCl; 5mM EDTA, 0,05%SDS, 1%Triton X-100, 1 tablet of commercial protease inhibitor cocktail (Sigma-Aldrich) and incubated for 30min on ice. Cell lysates were collected after 15 000 g centrifugation for 5 minutes and concentration was determined using the Bradford method (Bio-Rad). After protein quantification, 30µg of protein samples were heated in 1X Laemmli buffer at 95°C for 5 minutes and separated on continuous or discontinuous (Bio-Rad), denaturing SDS-PAGE using 5% SDS-PAGE stacking gels and 10% SDS-PAGE resolving gels during 1 hour at 140V in Tris-HCl, Glycine, SDS buffer. After separation, proteins were transferred during 1 hour at 100V to 0,45µm nitrocellulose membranes (GE healthcare), in Tris-HCl, Glycine, 20% Ethanol. Ponceau S staining was used to validate the protein transfer. Membranes were blocked with 5% milk-1X PBS containing 0,2% Tween 20 for 1 hour at room temperature and incubated with the indicated primary antibody overnight at 4°C. Membranes were washed three times with PBS containing 0,2% Tween 20 and incubated with indicated secondary antibody during 1 hour at room temperature. After three washes with PBS containing 0,2% Tween 20 and 1X PBS, protein signals were detected by chemiluminescence using ECL (Covalab) and Fusion FX imaging system device (Vilber). View this table: View inline View popup Download powerpoint Mitotracker staining and co-immunostaining Cells were grown in 8-well LabTek slide (Merck Millipore). For mitochondria labeling, 40 000 cells were incubated with 400nM MitoTracker Green FM (Signaling Technology) diluted in DMEM, 10% FBS at 37°C for 30 min before fixation. Cells were fixed with 4% formaldehyde (Merck) diluted in PBS 1X for 15 minutes at room temperature and washed three times with PBS 1X. For RNase III treatment, cells were incubated after fixation with PBS 1X; 0,1% Triton for 5min to permeabilize and 10U of RNase III (Invitrogen)/well were added for 20min at room temperature. Cells were then washed 3 times with PBS 1X. Cells were blocked using blocking buffer (0,1% Triton X-100, 5% goat serum, PBS 1X) for 1 to 3 hours at room temperature. After incubation with primary antibodies diluted in blocking buffer for 1 to 3 hours in the dark at room temperature, cells were washed three times with 0,1% Triton X-100, PBS 1X and incubated with secondary antibody coupled with Alexa-488 (A11008, Invitrogen) and Alexa-594 (A11032, Invitrogen) diluted at 1:1000 in blocking buffer for 1 hour in the dark at room temperature. Cells were washed 3 times with 0,1% Triton X-100, PBS 1X. DAPI (D1306, Invitrogen, Thermo Fisher Scientific) staining was performed for 5 minutes to reveal the nuclei. Slides were mounted with Fluoromount-G mounting media (Invitrogen, Thermo Fisher Scientific) and observed by confocal microscopy (LSM780, Zeiss). Images were analysed using the open-source image analysis Fiji or Image J software (1.54p) software and fluorescence intensity profiles were obtained. The mean of the relative integrated intensity in 10 to 20 cells was calculated using the same size of Region Of Interest (ROI). The images of each color channel were merge. The lookup tables (LUT) function was used to obtain the different colors. Invert LUT command was used to invert the colors without changing the pixel values. View this table: View inline View popup Download powerpoint RNA-FISH Mock or SINV-WT infected (MOI 0.1, 24hpi) WT or FASN KO HCT116 cells were grown on 18 mm round cover glass in 12-well cell culture plates. Cells were fixed with 70% ethanol for at least 1 hr at 4°C and incubated overnight at room temperature with the SINV genome specific LGC BiosearchTechnologies’ Stellaris RNA FISH Probe diluted in RNA FISH hybridization buffer (Stellaris, Biosearch technologies). DAPI staining was performed for 30 min to reveal the nuclei (D1306, Invitrogen, Thermo Fisher Scientific). Slides were mounted on coverslips with Fluoromount-G mounting anti-fading media (Invitrogen, Thermo Fisher Scientific) and observed by epifluorescence microscopy (Olympus BX51). Images were analyzed using Image J software software 1.54P. The images of each color channel were merge. The lookup tables (LUT) function was used to obtain the different colors. Invert LUT command was used to invert the colors without changing the pixel values. Proximity Ligation Assay In situ PLA was performed on fixed HCT116 cells with DuoLink PLA technology probes and reagents (Sigma-Aldrich), following the manufacturers protocol. First, cells were permeabilized with PBS + Triton X-100 0.1% for 10 min. After two PBS washes, the cells were incubated with blocking solution for 1 hour at 37°C. The primary antibodies were diluted (anti-1: 400, anti-FASN 1:400) in Duolink® Antibody Diluent according manufactured instructions for 1 h at room temperature. The slides were washed twice for 5 min with buffer A between each step. Then, cells were incubated with the PLA PLUS and MINUS probes for 60 min at 37°C. The ligation step was performed for 30 min at 37°C. The amplification step was done during 100 min at 37°C. After two washes of 10 min with buffer B 1X, the slides were washed with wash buffer B at 0.01X for 1 minute and mounted with Duolink in situ mounting medium containing DAPI. Proximity ligation assay imaging was performed with a confocal laser scanning microscope (LSM780, Zeiss). J2-FASN PLA puncta quantification was manually performed using the FIJI software. Quantification was plotted as the number of puncta per field. Ten fields representing 10 different cells were analyzed per conditions. Puncta were isolated and quantified by analyzing particle function and using the same threshold. MTT assay Metabolic activity was determined by MTT assay and used to determine cellular viability. Ten thousand HCT116 WT and derived cells were cultured in 200 μl medium in a 96 well plate. Cells were incubated with 180µL of media supplemented with 20µL of the yellow tetrazole substrate compound MTT (Termo Fisher Scientific) (5 mg/ml) for 3 hours. The reaction was stopped by adding 100 μl SDS solution (10% (w/v)) and the absorbance at 595 nm was measured after dissolution of formazan crystals using a spectrofluorometer SAFAS FLX-Xenius. Plaque assay To assess the amount of infectious viral particles, a total of 1 million Vero-E6 cells were seeded in a 96 well plates. The day after, the cells were infected for 1 hour with 10-fold serial dilutions of the viral supernatants. Then, the viral supernatant was removed and 100µL of a 2.5% carboxymethyl cellulose solution was added on the infected cells, which were incubated at 37°C in a humidified atmosphere of 5% CO2. Plaques were counted manually under the microscope at 48 hpi. Live-cell imaging 250 000 cells/well WT or FASN KO HCT116 cells were seeded in 12-wells plate and infected with SINV-GFP at an MOI of 0.1. Uninfected cells were used as control. GFP fluorescence and phase contrast were observed using the automated CellcyteX live-cell imaging system (Discover Echo). 4 images per well (10X objective) were acquired every 6 h for 48 h and were analyzed with the Cellcyte Studio software to determine the GFP relative intensity. The results of three biological replicates were analyzed. RNA extraction Total RNA was extracted using Trizol Reagent® (Invitrogen, Thermo Fisher Scientific) according to the manufacturer’s instruction and quantified using the spectrofluorometer Denovix DS-11FX + . RNA-sequencing of ADAR1 KO vs WT -/+ 5-AZA RNA from WT and ADAR1 KO HCT116 cells in an 80% confluent 6-well plate format, either treated or not treated with 0.5 mM 5-AZA for 24 h, was extracted using Trizol Reagent® (Invitrogen, Thermo Fisher Scientific) according to the manufacturer’s instruction. Library preparation with the Illumina Stranded Total RNA Prep Ligation with Ribo-Zero Plus kit and paired-end sequencing (2x 100 nt) using the Illumina NextSeq 2000 system were carried out by the GenomEast sequencing platform (Strasbourg, France) to a read depth of at least 100M reads per sample on the biological triplicates. The quality and genomic origin of the sequencing reads were evaluated with FastQC 56 (v0.12.1) and FastQ Screen 57 (v0.15.3). Subsequently, STAR 58 (v2.7.10b) was used to align the reads against the Gencode GRCh38 primary genome assembly 59 (v44) with the options -- outSAMstrandField intronMotif, --outFilterType BySJout, and -quantMode GeneCounts. DESeq2 60 (v.144.0) was used to detect differences in gene expression. Gene counts were taken directly from the STAR output and all samples were analysed together in the same object for normalisation and dispersion estimation. Only genes with a minimum average read count of 20 were included in the analysis. Differential gene expression between conditions was analysed using a two-tailed Wald test, with independent filtering disabled. The log2 fold change between conditions was estimated using Approximate Posterior Estimation 61 . Genes with a maximum FDR of 0.01 and a minimal absolute estimated log2 fold change of 0.5 were labelled as significant. Clusterprofiler 62 (v4.12.0) was used for hypergeometric enrichment analysis of significant genes using the Hallmark molecular signature database 63 , 64 . All genes included in the gene expression analysis were used as background genes. Venn diagrams were generated on Venny 2.1 ( https://bioinfogp.cnb.csic.es/tools/venny/index.html ) by using the significantly upregulated transcripts identified in 5-AZA vs untreated WT HCT116 cells and 5-AZA vs untreated ADAR1KO HCT116 cells. RNA-sequencing of FASN KO vs WT RNA from WT and FASN KO HCT116 cells in an 80% confluent 6-well plate format, was extracted for library preparation and sequencing in biological triplicate. Ribo-depletion and stranded library preparation was performed at BGI Genomics (Hong Kong) and paired-end sequencing (2x 100 nt) using the DNBSEQ system were carried out by BGI Genomics (Hong Kong) to a read depth of at least 50M reads per sample. RNA-seq data from WT and FASN KO HCT116 cells were processed and analyzed using the nf-core/rnaseq pipeline (v3.18) 65 . Following quality control with FastQC, reads were aligned to the GRCh38.p14 reference genome using the STAR aligner, and gene quantification was performed with Salmon using the GENCODE v47 basic annotation. Differential gene expression analysis was conducted using DESeq2 66 and data exploration and visualisation were performed using the DEBrowser interface 67 . All differentially expressed genes with an adjusted p -value < 0.1 were used for gene set enrichment analysis (GSEA) using the WebGestalt tool 68 , with log2fold change used as the ranking metric. Enrichment was assessed against the Molecular Signatures Database (MSigDB) Hallmark gene set collection 63 and built-in non-redundant Gene Ontology databases. RT-qPCR For total RNA analysis, 1µg or 500ng of total RNA were treated with DNase I (Invitrogen, Thermo Fisher Scientific) and then reverse transcribed using oligo(N9) primer with the SuperScript IV reverse transcriptase (Invitrogen, Thermo Fisher Scientific) according to the manufacturer’s instruction. Quantitative Real-time (q)PCR analysis was performed and analyzed using CFX96 touch Real-Time PCR machine (Bio-Rad). cDNA diluted 1:10 was amplified using the Maxima SYBR Green qPCR Master mix (K0253, Thermo Fisher Scientific) and specific primers at an annealing temperature of 60°C. The results were normalized to the housekeeping gene using the comparative Ct method (ΔΔCt). For better visualisation of the data, a multiple factor of 10 is applied to the results so that the data can be represented using a logarithmic scale. View this table: View inline View popup Statistical analysis Statistical analyses were performed using GraphPad Prism 10 software. Unless otherwise stated, a paired Student’s t-test or one-way ANOVA test with multiple comparisons were performed. The number of replicates per experiment and the specific statistical tests used are stated in the figure legends. Data availability The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD065901. The RNA sequencing datasets of this study are currently being submitted to NCBI’s Gene Expression Omnibus 69 . Author contributions Charline Pasquier : Conceptualization; Formal analysis; Validation; Investigation; Methodology; Writing—original draft; Writing—review and editing. Mélanie Messmer : Formal analysis; Validation; Investigation; Methodology; Writing—review and editing. Lisanne Knol : Data curation; Software; Formal analysis; Investigation; Methodology; Writing—review and editing. Patryk Ngondo : Data curation; Software; Formal analysis; Visualization; Writing—review and editing. Johana Chicher : Data curation; Software; Formal analysis; Investigation; Methodology; Writing—review and editing. Sebastien Pfeffer : Funding acquisition; Supervision; Writing—review and editing; Erika Girardi: Conceptualization; Supervision; Funding acquisition; Project administration; Formal analysis; Investigation; Methodology; Validation; Writing—original draft; Writing—review and editing. Download figure Open in new tab Figure S1: Effect of IFN-I, poly I:C or 5-AZA treatment on ISG expression in WT or ADAR1 KO HCT116 cells. A-C) Relative ISGs expression in WT and ADAR1 KO HCT116 cells ( A ) treated or not with IFN-I (1000U/mL, 24 hours), ( B ) transfected with poly I:C (20µg/mL, 24 hours) or ( C ) treated or not with 500nM 5-AZA, measured by RT-qPCR. Gene expression was normalized over the ACTIN ( A-B ) or GAPDH ( C ) housekeeping gene expression. Results represent the mean ± standard deviation (SD) of three biological replicates (n = 3). Statistical analysis was performed using one-way ANOVA test with multiple comparison to WT NT (*= pval<0.05; **=pval<0.005; ***= pval<0.0005; **** = pval< 0.00005). D ) Volcano plots displaying the differences in gene expression between 5-AZA and untreated WT HCT116 cells (left panel) and 5-AZA vs untreated ADAR1KO HCT116 cells over three biological replicates (right panel). Genes with an FDR value = 0.5 and are displayed in red, genes with a log2 foldchange <= -0.5 are displayed in blue. All other genes are displayed in grey. Horizontal line represents FDR = 0.01, vertical lines represent a log2 fold change of -0.5 and 0.5. E ) Venn diagram showing the number of overlapping and unique significantly upregulated transcripts identified in 5-AZA vs untreated WT HCT116 cells (blue) and 5-AZA vs untreated ADAR1KO HCT116 cells (yellow) and the corresponding percentages. Download figure Open in new tab Figure S2. Endo-dsRNA enrichment in ADAR1 KO NT and 5-AZA WT HCT116 cells. A-B) Volcano plot showing the protein enrichment upon J2-IP over IgG-IP in ( A ) untreated ADAR1 KO HCT116 cells (ADAR1 KO NT) and ( B ) WT HCT116 cells treated with 5-AZA (WT 5-AZA) over three biological replicates. Purple dots represent proteins that are significantly enriched (FDR>1,3, log2FC>1). C) Representative confocal immunofluorescence images from WT and ADAR1 KO HCT116 cells treated (5-AZA) or not (NT) with 500nM 5-AZA. Staining with mouse J2 anti-dsRNA antibody (in yellow), rabbit anti-FASN (in blue) and with DAPI (in cyan) is shown. Scale bar: 10µM. Download figure Open in new tab Figure S3. Characterization of FASN KO HCT116 cells and siRNAFASN-depleted human cell types. A) Representative confocal co-immunofluorescence images from HCT116 cells transfected with anti-FASN siRNA (siFASN) or non-targeting siRNA (siCTL) using mouse J2 anti-dsRNA antibody (in yellow) or rabbit anti-FASN (in magenta). DNA was stained with DAPI (in cyan). Scale bar: 10µM. B) Representative confocal immunofluorescence images from MDA-MB, Huh7.5.1 and A549 cells transfected anti-FASN siRNA (siFASN) or non-targeting siRNA (siCTL) using mouse J2 anti-dsRNA antibody (in yellow). DNA was stained with DAPI (cyan). Scale bar: 10µM (n=1). C) Western blot on lysates from HCT116, MDA-MB, Huh7.5.1 and A549 cells using antibodies against FASN and TUBULIN. D ) CTRL and FASN KO HCT116 cell viability monitored by MTT assay. Results represent the mean ± standard deviation (SD) of three biological replicates. Statistical analysis was performed using two-way ANOVA test (*** = pval<0.0005; **** = pval<0.00005). E-F ) Gene set enrichment analysis using Hallmark gene set ( E ) or using GO cellular components ( F ) made with down and upregulated genes from FASN KO versus CTRL HCT116 cells. Genes with an FDR value 0.5 or <-0.5 were selected as genes of interest. The pathways with the highest score are represented and significant (FDR<=) is shown in dark red or dark blue. G) Representative confocal immunofluorescence analysis performed in CTRL and FASN KO HCT116 cells using mouse anti-dsRNA antibody (in yellow) or MitoTracker dye (in magenta). Nuclei were stained with DAPI (in cyan). Scale bar: 10 μm. H ) Fluorescence intensity profiles of J2 (yellow), MitoTracker (magenta) and DAPI (cyan) along the white line of 20 µm represented on the merge panel in ( G ). Download figure Open in new tab Figure S4. FASN KO cells show a stronger IFN response to poly I:C than CTRL HCT116 cells. A) Type III IFNs production by FASN KO and CTRL HCT116 cells upon poly I:C transfection (2µg/mL, 6 hours) monitored by HEK-Blue assay. Statistical analysis was performed using a Kruskal-Wallis test with multiple comparison to CTRL NT (* = pval<0.05) B) RT-qPCR on four ISGs from FASN KO and CTRL HCT116 cells transfected with poly I:C (2µg/mL, 6 hours). Gene expression was normalized to actin expression. Results represent the mean ± standard deviation (SD) of three biological replicates. Statistical analysis was performed using a one-way ANOVA test with multiple comparison to CTRL NT (** = pval<0.005). C ) Schematic representation of SINV-GFP genomic and sub-genomic RNAs. Genomic and sub-genomic viral RNAs are capped (m7G) and polyadenylated (A(n)). ORF1 codes for the non-structural proteins necessary for RNA replication (dark blue). ORF2 codes for the structural proteins (light blue). GFP is shown in green. Acknowledgements We thank all members of the laboratory for fruitful discussions, as well as Rebecca Pohen and Agathe Hunckler for technical assistance. This work of the Interdisciplinary Thematic Institute IMCbio+, as part of the ITI 2021-2028 program of the University of Strasbourg, CNRS and Inserm, was supported by IdEx Unistra (ANR-10-IDEX-0002), by SFRI-STRAT’US project (ANR-20-SFRI-0012), EUR IMCBio (IMCBio ANR-17-EURE-0023) and Equipex Insectarium ANR-11-EQPX-0022 under the framework of the French Investments for the Future Program (to SP). The mass spectrometry instrumentations were funded by the University of Strasbourg, IdEx “Equipement mi-lourd” 2015. Initial RNA sequencing was performed by the GenomEast platform, a member of the ‘France Génomique’ consortium (ANR-10-INBS-0009). CP was funded by a doctoral fellowship from the imcBio and by the Fondation ARC pour la Recherche contre le Cancer. This work received financial support from the Agence Nationale de la Recherche (EndoDRAI project ANR-22-CE15-0011-01 (to E.G.) and was supported by the Idex 2022 Attractivité grant under the framework of the IdeX University of Strasbourg (to E.G.). Funder Information Declared Agence Nationale de la Recherche , ANR-22-CE15-0011-01 Fondation ARC pour la Recherche sur le Cancer, https://ror.org/0489qz649 Idex 2022 Attractivité grant, University of Strasbourg Bibliography 1. ↵ Stetson , D.B. , and Medzhitov , R . ( 2006 ). Type I Interferons in Host Defense . Immunity 25 , 373 – 381 . doi: 10.1016/j.immuni.2006.08.007 . OpenUrl CrossRef PubMed Web of Science 2. ↵ Schlee , M. , and Hartmann , G . ( 2016 ). Discriminating self from non-self in nucleic acid sensing . Nat Rev Immunol 16 , 566 – 580 . doi: 10.1038/nri.2016.78 . OpenUrl CrossRef PubMed 3. ↵ Wu , J. , and Chen , Z.J . ( 2014 ). Innate Immune Sensing and Signaling of Cytosolic Nucleic Acids . Annu. Rev. 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Share Fatty Acid Synthase binds endogenous dsRNAs and dampens the innate immune response to exogenous dsRNAs by limiting their accumulation Charline Pasquier , Mélanie Messmer , Lisanne Knol , Johana Chicher , Richard Patryk Ngondo , Sébastien Pfeffer , Erika Girardi bioRxiv 2025.07.16.662511; doi: https://doi.org/10.1101/2025.07.16.662511 Share This Article: Copy Citation Tools Fatty Acid Synthase binds endogenous dsRNAs and dampens the innate immune response to exogenous dsRNAs by limiting their accumulation Charline Pasquier , Mélanie Messmer , Lisanne Knol , Johana Chicher , Richard Patryk Ngondo , Sébastien Pfeffer , Erika Girardi bioRxiv 2025.07.16.662511; doi: https://doi.org/10.1101/2025.07.16.662511 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 Immunology Subject Areas All Articles Animal Behavior and Cognition (7633) Biochemistry (17680) Bioengineering (13889) Bioinformatics (41927) Biophysics (21445) Cancer Biology (18585) Cell Biology (25491) Clinical Trials (138) Developmental Biology (13373) Ecology (19897) Epidemiology (2067) Evolutionary Biology (24308) Genetics (15606) Genomics (22496) Immunology (17736) Microbiology (40385) Molecular Biology (17175) Neuroscience (88583) Paleontology (666) Pathology (2830) Pharmacology and Toxicology (4822) Physiology (7641) Plant Biology (15149) Scientific Communication and Education (2045) Synthetic Biology (4293) Systems Biology (9822) Zoology (2271)