Mitochondrial instability contributes to IFN-driven heart disease

Mitochondrial instability contributes to IFN-driven heart disease | 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 Mitochondrial instability contributes to IFN-driven heart disease Arati Naveen Kumar , Trinitee Oliver , Aleksandr B. Stotland , Richard Ainsworth , Caroline A. Jefferies doi: https://doi.org/10.1101/2025.09.15.674036 Arati Naveen Kumar 1 Kao Autoimmunity Institute, Division of Rheumatology, Department of Medicine, Cedars-Sinai Medical Center , Los Angeles, California 2 Department of Biomedical Sciences, Cedars-Sinai Medical Center , Los Angeles, California Find this author on Google Scholar Find this author on PubMed Search for this author on this site Trinitee Oliver 1 Kao Autoimmunity Institute, Division of Rheumatology, Department of Medicine, Cedars-Sinai Medical Center , Los Angeles, California 2 Department of Biomedical Sciences, Cedars-Sinai Medical Center , Los Angeles, California Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aleksandr B. Stotland 3 Barbra Streisand Women’s Heart Center, Smidt Heart Institute , Cedars-Sinai Medical Center, Los Angeles, CA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Richard Ainsworth 1 Kao Autoimmunity Institute, Division of Rheumatology, Department of Medicine, Cedars-Sinai Medical Center , Los Angeles, California 2 Department of Biomedical Sciences, Cedars-Sinai Medical Center , Los Angeles, California Find this author on Google Scholar Find this author on PubMed Search for this author on this site Caroline A. Jefferies 1 Kao Autoimmunity Institute, Division of Rheumatology, Department of Medicine, Cedars-Sinai Medical Center , Los Angeles, California 2 Department of Biomedical Sciences, Cedars-Sinai Medical Center , Los Angeles, California Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: caroline.jefferies{at}cshs.org Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Aims Type I interferons (IFNs) are linked to an increased risk of cardiovascular disease and are chronically elevated in systemic autoimmune diseases such as systemic lupus erythematosus (SLE). We evaluated the effect of chronic IFNa exposure on cardiac and cardiomyocyte function in order to better understand IFN-driven cardiac disease from the perspective of SLE. Methods and Results Administration of the TLR7/8 agonist, resiquimod, to C57BL6 mice, drove chronic IFN induction and reduced ejection fraction and fractional shortening compared to mice treated with vehicle control. Multiomic analysis demonstrated increased IFN signature in resiquimod treated hearts (transcriptomic and proteomic) and a decrease in genes and proteins representing electron transport chain (ETC) and cytochrome complex assembly. Integration of metabolomics with transcriptomics revealed pathways representing chemokine signaling, Toll-like receptor signaling, HIF-1 signaling and Dilated cardiomyopathy. As our in vivo model of IFN-driven SLE-like heart disease showed both proteomic and transcriptomic changes that reflected changes in mitochondrial and potentially cardiac function, we conducted an in vitro analysis of the effects of acute and chronic IFN on cardiomyocytes, the most energetically demanding cell type in the heart, and the cells most susceptible to stress and inflammation. As with our in vivo findings, proteomic and transcriptomic analysis of AC16 cardiomyocytes exposed to chronic IFNα indicated perturbation of mitochondrial pathways. Extracellular flux analysis of chronically exposed AC16 cells showed impaired basal respiration, maximal respiration, ATP production and non-glycolytic acidification, glycolysis and glycolytic capacity, indicating increased mitochondrial stress in response to chronic exposure to IFNa. MitoTracker green staining showed increased fragmentation and perinuclear localization in AC16 chronically exposed to IFN and an increase in mtDNA release and expression of proteins known to contribute to mtDNA release and detection. Conclusions Our results directly support a role for chronic IFN in driving cardiac dysfunction in both a mouse model of IFN-driven disease that mimics SLE and in cardiomyocytes, though enhanced mitochondrial stress, mtDNA release and potentially exacerbation of cGAS-STING-IFN axis. Translational Perspectives Many immune features of SLE such as elevated type I interferons, chronic inflammation, and persistent autoantibody production are strongly correlated with increased cardiovascular risk, but it remains difficult to prove direct biological causation. This study demonstrates that chronic IFN exposure induces mitochondrial dysfunction and mtDNA release in cardiomyocytes, directly complementing an in vivo model of SLE-cardiac disease. It suggests that targeting IFNs may reduce CVD risk in IFN-driven autoimmunity. Interferon cardiomyocytes mitochondria Introduction Heart disease is a significant and well-recognized complication of systemic lupus erythematosus (SLE), contributing substantially to increased morbidity and mortality in affected individuals ( 1 ). Patients with SLE have a markedly elevated risk of premature and accelerated atherosclerosis, resulting in heightened incidence of ischemic heart disease and myocardial infarction even at a young age ( 2 ). In addition to atherosclerosis, lupus can also cause myocarditis, pericarditis, valvular heart disease, and coronary microvascular dysfunction (CMD), reflecting the multifaceted ways autoimmunity and chronic inflammation can injure the heart ( 3 ). Many immune features of SLE such as elevated type I interferons, chronic inflammation, and persistent autoantibody production are strongly correlated with increased cardiovascular risk, but how they contribute to increased cardiovascular disease (CVD) risk in SLE is currently unknown. Type I interferons (IFN) play key roles not only in host defense against infections but also in the development and progression of various forms of heart disease. IFN signaling can be triggered by cardiac injury, ischemia, and viral infection, leading to activation of hundreds of interferon-stimulated genes (ISGs) and substantial shifts in cardiac immune and metabolic environments ( 4 ). In the heart, induction of the type I IFN response has been reported in ischemic and non-ischemic cardiomyopathy, as well as in viral myocarditis ( 5 ). The cardiac effects of interferons are context-dependent: while they are protective in viral myocarditis, in the absence of infection (such as ischemia or in pressure overload), persistent IFN signaling can promote adverse cardiac remodeling, ultimately accelerating heart failure ( 6 - 8 ). Our previous analysis of genes differentially expressed in whole blood RNA samples from SLE patients with coronary microvascular dysfunction (CMD), highlighted differences in IFN signaling, cytosolic RNA/DNA sensing pathways between patients with CMD versus those without ( 9 ). Interestingly our SLE patients with CMD on cardiac MRI had normal left ventricular function compared to our non-CMD cohort, potentially indicating that CMD and increased representation of genes associated with cytosolic nucleic acid sensing may reflect an earlier stage of CVD in CMD-SLE patients, although this remains to be defined. As the primary source of ATP in cardiomyocytes, mitochondria are essential for maintaining contractile function and cellular viability; their impairment disrupts energy production, leading to reduced cardiac output and myocyte death ( 10 ). In heart disease, mitochondrial dysfunction has emerged as a key pathogenic factor, underlying conditions such as heart failure, ischemic cardiomyopathy, and diabetic cardiomyopathy ( 10 ). Damaged mitochondria exhibit impaired oxidative phosphorylation, diminished ATP generation, and increased production of mitochondrial reactive oxygen species (mtROS), which drive oxidative stress and proteotoxic injury due to misfolded proteins in cardiomyocytes ( 11 ). Excessive mtROS activates signaling pathways that induce cell death, inflammation, and adverse cardiac remodeling, exacerbating cardiac dysfunction. Compromised mitochondrial dynamics such as imbalances in fusion, fission, and defective mitophagy lead to the accumulation of dysfunctional mitochondrial fragments, disrupt energy supply, and further contribute to myocardial injury ( 12 ). These maladaptive shifts in mitochondrial dynamics disrupt myocardial energetics and cellular survival mechanisms, promoting cardiac remodeling and the progression of heart failure ( 10 ). An additional consequence of mitochondrial stress is the release of mitochondrial DNA (mtDNA) into the cytoplasm, engaging cytosolic DNA sensors, such as cyclic GMP-AMP synthase (cGAS), and downstream activation of the stimulator of interferon genes (STING) pathway ( 13 ). mtDNA and mitochondrial DAMPs additionally activate toll-like receptors (TLRs) in plasmacytoid dendritic cells, setting off cascades of pro-inflammatory cytokine release, including IFN-α ( 13 ). Our previous work has shown that chronic IFN exposure of monocytes induces mitochondrial reprograming and epigenetic reprogramming of the IFN response ( 14 ). However, whether chronic IFN exposure triggers similar changes in the heart remains unknown. In this study we investigate the effects of chronic activation of TLR7/8 in a mouse model of IFN-driven SLE-like disease on cardiac function. Using this model we show that chronic IFN induction in mice results in defective heart function and decreased representation of pathways regulating mitochondrial function. Addressing how chronic IFN affects cardiomyocytes, the most energetically cell type in the heart, we find that chronic IFN induces proteomic and transcriptomic changes in AC16 cardiomyocytes consistent with inflammation and altered mitochondrial function. Mitochondrial changes are accompanied by increased mitochondrial fragmentation and increased release of mtDNA into the cytoplasm, thus contributing to a feed forward augmentation of chronic IFN effects. Thus targeting cytosolic RNA/DNA sensing and the type I IFN response may reduce CVD risk in SLE patients. Materials and Methods Animals Ethical Compliance: All procedures followed National Institutes of Health guidelines and received approval from the Cedars-Sinai Medical Center Institutional Animal Care and Use Committee under protocol IACUC-008858. Both male and female C57BL/6J wild-type mice (9-12 weeks old) underwent epicutaneous administration of 100 μg of Resiquimod dissolved in 30 μl of a 1:2 ethanol/acetone mixture. The solution was applied topically to the left ear three times weekly over a four-week period. Echocardiograms were performed on all animals, which were subsequently sacrificed at the study endpoint for organ and blood collection. Cell line AC16 human ventricular cardiomyocytes were maintained and utilized for experiments at passages 4 through 10 in 6-well plates (100,000 cells per well). The cells were cultured for two days in growth medium (DMEM/F12 supplemented with 12.5% FBS, 1% antibiotic-antimycotic, pH 7.4) until they reached 70–75% confluence. In-vitro model AC16 cardiomyocytes were seeded at 100,000 cells per well in complete DMEM and initially stimulated for 24 hours with IFNα (1000 U/ml) or PBS. After stimulation, the cells were washed and rested for 4–5 days. They were then washed again, replated at 100,000 cells per well, and subjected to a second 24-hour stimulation with IFNα (1000 U/ml) or PBS. Following this, cells were washed and collected for RNA and protein extraction. Real-time Quantitative Polymerase Chain Reaction (RT-qPCR) RNA was isolated from tissues and cultured cells with TRIzol reagent (Sigma-Aldrich) following the manufacturer’s instructions. Reverse transcription was performed, and quantitative real-time PCR results were analyzed using the ΔΔCt method, normalized to 18s rRNA expression. View this table: View inline View popup mRNA-Seq library preparation and sequencing RNA integrity was assessed on a 2100 Bioanalyzer using the Agilent RNA 6000 Nano Kit (Agilent Technologies), and RNA quantification was performed with the Qubit fluorometer and Qubit RNA HS Assay Kit (ThermoFisher Scientific). A total of 325 ng RNA per sample underwent mRNA purification using the NEBNext® Poly(A) mRNA Magnetic Isolation Module (New England Biolab Inc). Stranded RNA-Seq libraries were constructed with the IDT xGen Broad-Range RNA Library Prep Kit (Integrated DNA Technologies) and amplified by 12 PCR cycles. Library concentrations were determined with the Qubit 1X dsDNA HS Assay kit, and library sizes evaluated using the Agilent 4200 TapeStation with Agilent HS D1000 ScreenTape. Multiplexed libraries were pooled for sequencing on a NovaSeq X (Illumina) platform using single-end 75 bp reads at a depth of 30 million reads per sample. Bioinformatics and data analysis Raw reads obtained from RNA-Seq were aligned to the transcriptome using STAR (version 2.5.0) /RSEM (version 1.2.25) with default parameters, using a custom human GRCh38 transcriptome reference downloaded from https://www.gencodegenes.org , containing all protein coding and long non-coding RNA genes based on human GENCODE version 33 annotation. Expression counts for each gene in all samples were normalized by a modified trimmed mean of the M-values normalization method and the unsupervised principal component analysis (PCA) was performed with DESeq2 Bioconductor package version 1.42.0 in R version 4.3. Each gene was fitted into a negative binomial generalized linear model, and the Wald test was applied to assess the differential expressions between two sample groups by DESeq2. Benjamini and Hochberg procedure was applied to adjust for multiple hypothesis testing, and differential expression gene candidates were selected with a false discovery rate less than 0.05. For functional enrichment analysis across sample groups, we conducted genes enrichment analysis using the R package “clusterProfiler v4.10.0” ( 15 ) and “pathfindR v2.3.1 ( 16 ). Data is included in supplementary data files 1-3. Gene set enrichment analysis (GSEA) was performed against the Reactome pathway database (version v2025.1) from MSigDB. Significance was assessed using 1,000 permutations, and the top-ranking pathways were identified based on the normalized enrichment score (NES) and a false discovery rate (FDR) q-value < 0.25. Venn diagrams were compiled from differentially expressed genes and proteins using Venny 2.1.0. Protein extraction from pellets Protein extraction from cell pellets was done by resuspending pellets in ice-cold RIPA buffer supplemented with a protease and phosphatase inhibitor cocktail to prevent protein degradation and preserve post-translational modifications. Samples were incubated on ice for 30 minutes with intermittent vortexing to ensure thorough lysis, followed by centrifugation at 14,000 x g for 15 minutes at 4°C. The clarified supernatant containing solubilized proteins was collected for downstream quantification and analyses. Mitoplex Tier 2 targeted proteomic analysis, as previously described by Stotland et al, was employed to quantify mitochondrial proteins. Experimenters were aware of group assignments during sample processing and analysis. Adipose tissue was lysed in 8 M urea with 1 M ammonium bicarbonate (pH 8.0), and protein concentration was determined using the Pierce BCA assay (Thermo Fisher Scientific). Samples were digested using the SP3 protocol: 20 μg of protein was adjusted to 60 μl with 6 M urea, 1 M ammonium bicarbonate, and 5% SDS lysis buffer. Reduction was performed with 16.8 μl of 200 mM dithiothreitol for 30 min at 37°C and 300 rpm, followed by alkylation with 21.2 μl of 400 mM IAA for 30 min in the dark at room temperature. Tris-HCl (pH 8) was added to reach 160 μl total volume, then 5 μl of a 10:1 mass ratio bead suspension (1:1 hydrophilic/hydrophobic beads, Cytiva) was added and vortexed. Samples were adjusted to 70% ACN and incubated for 18 min, followed by on-magnet solvent removal and rinses with 2× 80% ethanol and 2× ACN (200 μl each). The dried sample was suspended in 50 mM Tris-HCl (pH 8) with 10 mM CaCl2 and digested with trypsin at a 1:20 ratio. Samples underwent 5 min bath sonication before overnight incubation (18 h, 37°C, 1200 rpm). After digestion, beads were removed, and samples were brought to 0.1% FA and 2% DMSO, then spiked with a 1:250 dilution of stable isotope-labeled reference peptides as described in Stotland et al. Eight μg of digested peptides, run in duplicate technical replicates, were separated on a Shimadzu Prominence UFLCXR HPLC using a Waters Xbridge BEH30 C18 column (2.1 mm × 100 mm, 3.5 μm, 0.25 ml/min, 36°C) coupled to a SCIEX QTRAP 6500. Mobile phase A contained 2% acetonitrile, 98% water, and 0.1% formic acid; B was 95% acetonitrile, 5% water, and 0.1% formic acid. After loading and equilibrating at 5% B and eluting peptides with a linear 5–35% gradient over 30 min, the column was washed with 98% B for 10 min, returning to 5% for re-equilibration. A scheduled acquisition method was used, monitoring peptide fragments within a 2-min retention window. Raw data were processed in Skyline (version 21.1.0.146) to select peak boundaries and quantify peak areas. Peaks were manually inspected, and unusable data excluded. Automated peak integration was applied, and processed data were exported for further analysis in R (Stotland et al). Only fragments with a CV ≤ 20% across heavy standards were quantified. Endogenous-to-heavy standard abundance ratios were averaged to generate final protein quantification values. Technical replicate abundance ratios were calculated per sample. Statistical analysis was performed in MetaboAnalyst 6.0 ( http://www.metaboanalyst.ca ) Metabolomic Analyses (dMRM) Metabolites were extracted from frozen tissues using 800 μl of extraction buffer (80% methanol, 20% water, 0.8% ammonium bicarbonate) and homogenized on ice with a 5-second pulse at 50% power (Polytron PowerGen 125, Thermo Fisher Scientific). Samples were centrifuged at 21,000g for 10 min at 4°C, and supernatants were vacuum dried. Metabolite pellets were resuspended in methanol:water (20:80) for analysis on an Agilent 6470A triple quadrupole mass spectrometer (operated in negative mode) with an Agilent 1290 UHPLC and MassHunter dMRM database/method, detecting 219 polar metabolites per sample. The method utilizes ion-pair reversed-phase chromatography (IP-RP) for anionic/hydrophobic separation, with TBA as the ion pair reagent. Mobile phase A is water with 3% methanol, 10 mM TBA, 15 mM acetic acid. Phases B and D are isopropanol and acetonitrile, respectively; C is methanol with TBA and acetic acid. Separation was performed on an Agilent ZORBAX RRHD Extend-C18 (2.1 × 150 mm, 1.8 μm) with a matching guard column. The method leverages known retention times for dynamic MRM transitions. QQQ chromatograms were inspected in Agilent MassHunter Quantitative Analysis, and peaks manually checked. Data were analyzed in MetaboAnalyst 6.0. LC-MS/MS Analysis DIA analysis for heart and AC16 cell proteins was performed on an Orbitrap Ascend Tribrid (Thermo Scientific) mass spectrometer interfaced with a EASY-Spray™ ionization source (Thermo Scientific, ES081) coupled to Vanquish Neo ultra-high-pressure chromatography system with 0.1% formic acid in water as mobile phase A and 0.1% formic acid in acetonitrile as mobile phase B. Peptides were separated at constant flow rate of 1.20 µL/minute with a linearly increasing gradient of 8-34% B for 0-90 minutes, then flushed with 98% B from 90-112 minutes. The column used was Thermo Scientific™ µPac™ HPLC column with a 200cm bed length (P/N: COL-NANO200G1B). MS1 resolution was set to 120,000 with AGC target set to standard. RF Lens was set to 60% with a maximum injection time of 251 ms. Fragmented ions were detected across a scan range of 380-985 m/z with 60 non-overlapping data independent acquisition precursor windows of size 10 Da. MS2 resolution was set to 15,000 with a scan range of 145-1450 m/z, a normalized collision energy of 30%, and a normalized AGC target of 400% with a custom maximum injection time set to 25 ms. All data is acquired in profile mode using positive polarity. DIA analysis for heart and AC16 proteins was performed on an Orbitrap Astral (Thermo Scientific) mass spectrometer interfaced with an EASY-Spray™ nano-electrospray ionization source (Thermo Scientific, ES081) coupled to Vanquish Neo ultra-high-pressure chromatography system with 0.1% formic acid in water as mobile phase A and 0.1% formic acid in acetonitrile as mobile phase B. Peptides were separated at an initial flow rate of 3 µL/minute and 4% B for 0.5 minutes. At 0.5 minutes, the flow rate is decreased to 1.3 µL/minute and the gradient was sharply increased linearly from 4-9% B for 0.5-0.6 minutes. At 0.6 minutes, the flow rate was again decreased to 0.8 µL/minute and the gradient was increased from 8-22.5% B for 0.6-13.9 minutes. At 13.9 minutes, the gradient was ramped up slightly faster, going from 22.5-35% B from 13.9-20.8 minutes. At 20.8 minutes, the flow rate was increased to 2 µL/minute and the gradient was linearly increased from 35-55% B from 20.8 minutes to 21.2 minutes. At 21.2 minutes, the column undergoes a wash of 99% B with a flow rate of 3 µL/minute before being equilibrated for the next run. The column used was PepSep C18 15cm x 150 µm, 1.9µm (Bruker, P/N: 1893471). Source parameters were set to a voltage of 2000 V and a capillary temperature of 280°C. MS1 scan range was set to 380-980 m/z and MS1 resolution was set to 240,000 with an AGC target set to ‘Custom’ and a normalized AGC target set to 500%. RF Lens was set to 40% with maximum injection time set to 5ms. Precursor mass range was set to 380-980 m/z with 199 non-overlapping data independent acquisition precursor windows of size 3 m/z. MS2 scan range was set to 100-1000 m/z and normalized HCD collision energy set to 25%. Maximum injection time was set to 5ms with AGC target set to ‘Custom’ and normalized AGC target set to 500%. All data is acquired in profile mode using positive polarity. Data Integration MetaboAnalyst 6.0 was used to integrate transcriptomic and metabolomic data in our study. Network-based multiomics data integration was applied to differentially expressed genes and metabolites using GO bP pathway library for annotation. Both metabolic and regulatory pathways identified were significantly altered (FDR q<0.02). The pathway impact value was also determined. An impact score 0.3 was used as cut-off. mtDNA Preparation Mitochondrial DNA (mtDNA) was isolated from cytosolic fraction of the cell samples using the Qiagen DNeasy Blood & Tissue Kit according to the manufacturer’s protocol. mtDNA collected was used for PCR to measure mitochondrial and housekeeping DNA coding regions. Oxygen Consumption and Acidification Analysis AC16 human ventricular cardiomyocytes were analyzed via real-time respirometry using a Seahorse XFe96 Analyzer (Seahorse Biosciences), after IFNα stimulation. Oxygen consumption rate (OCR) served as a measure of oxidative phosphorylation and Extracellular Acidification Rate (ECAR) primarily measures the rate of the glycolytic pathway. They were measured using the Mito Stress and Glycolytic Stress Tests respectively. Purified AC16 cells were washed with 10 ml DPBS, resuspended in XF RPMI medium (Agilent Technologies), and plated at 10,000 cells/well in 200 μl on XF96 plates and equilibrated at 37°C for 1 hour before analysis. Final concentrations of inhibitors for MitoStress assay were: 0.5 µM FCCP and 0.5 µM/0.5 µM antimycin/rotenone. Final concentrations of inhibitors for Glycolytic Stress Assay were: Glucose: 10 mM, Oligomycin: 1 μM, 2-deoxy-glucose (2-DG): 50 mM Statistical Analysis Data are shown as mean ± SD. Student’s unpaired t-test or one-way ANOVA with Bonferroni post-hoc testing were used for significance, with normality checked by Shapiro–Wilk test. Non-normally distributed data were analyzed by Mann–Whitney test or Kruskal–Wallis ANOVA with Dunn’s post-hoc test. All analyses were conducted in Prism v7.0a (GraphPad). Significance was defined as p < 0.05. Figure legend asterisks: *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001. Results Hasham et al showed that epicutaneous application of the TLR7/8 agonist R848, resiquimod, to CFN hybrid mice (cross of C57Bl6/J, FVB/NJ, and NOD/ShiLtJ) led to development of SLE-like autoimmune disease, characterized by the overproduction of type I interferons and IFN-stimulated genes. Importantly, the mice showed rapid progression from acute myocarditis to severe dilated cardiomyopathy and histopathology and revealed widespread heart inflammation resembling autoimmune pancarditis ( 17 ). Given our interest in cardiovascular complications in SLE and our hypothesis that type I IFNs may be driving some of the cardiovascular effects observed, we adopted this model for our study, selecting C57Bl/6 mice for the study. Mice were treated with 100µg of R848 or vehicle control, 3 times a week for 4 weeks. At the end of the 4 weeks, echocardiography was performed to determine heart function, after which the mice were euthanized and tissues harvested for analysis. R848-treated mice showed a decrease in both ejection fraction and fractional shortening that were both statistically significant, which indicates impaired cardiac contractile function, specifically left ventricular systolic dysfunction ( Figure 1A&B ). R848-treated mice had significantly larger spleens than vehicle-treated controls and increased expression of IFN stimulated genes in spleens (Supplemental Figure 1A - D). Hematoxylin & eosin staining of paraffin-embedded heart sections showed a significant increase in nuclei per um 2 of tissue when compared to that of the control mice indicating cellular hypertrophy ( Figure 1C&D ). Whereas Masson’s Trichrome staining to assess fibrosis did not show any apparent fibrosis in the hearts of the R848-treated mice when compared to the control mice hearts ( Figure 1E&F ). Download figure Open in new tab Figure 1. Resiquimod treatment induces cardiac inflammation and reduces cardiac function. (A&B) Echocardiograms were performed on control and R848-treated mice at 4-weeks endpoint before they were sacrificed. (A) Ejection fraction percentage (B) Fractional shortening percentage. Data represent mean ± SD. Statistical significance was determined using non-parametric Mann Whitney test. * p <0.05. (C-F) Paraffin-embedded heart sections from control and R848-treated mice were used for histology. (C) Hematoxylin & eosin staining of the heart sections; (D) Quantification of number of nuclei per uM of tissue (E) Masson’s Transcriptomic and proteomic analysis of heart tissue was conducted to obtain better insights into the molecular pathology of heart tissue chronically exposed to IFN-inducing stimuli. As expected, genes and proteins representing IFN stimulated genes (ISGs) and anti-viral immunity were overrepresented in our datasets (Data not shown). Gene set enrichment analysis of transcriptomic data using the Reactome pathway showed an enrichment in pathways representing neutrophil degranulation and immunoregulatory interactions between a lymphoid and non-lymphoid cell, whereas pathways representing mitochondrial translation and the Electron Transport pathway were underrepresented in heart tissue of R848-treated mice ( Figure 2A ). Proteomic analysis showed similar decreased enrichment of mitochondrial and bioenergetic pathways, and increased enrichment of pathways representing chemotaxis (ruffle organization), response to virus and immune activation ( Figure 2B ). Comparing differentially expressed genes (DEGs) and proteins (DEPs), 75 were found to be common to both sets, whilst 2801 identities were unique to DEGs and 375 unique to DEPs ( Figure 3A ). GSEA identified overlapping genes and proteins representing IFNa/b signaling (GBP2;IFIT1;IFIT3;ISG15;STAT1), regulation of complement cascade (C1QB;C1QC;C5AR1;CPN2), neutrophil degranulation (C5AR1;CD14;CD44;CD68; GSDMD; PTPRC;PYGL;RAP1B;RHOG), cytokine and innate immune signaling (CASP3; CD44; GBP2; GSDMD; ICAM1; IFIT1; IFIT3; IL16; ISG15; LBP; LYN; OSMR; RAP1B; STAT1) (Supplemental Figure 2A&B). Download figure Open in new tab Figure 2. Resiquimod-treatment results in transcriptomic and proteomic changes consistent with altered immune response and altered TCA cycle in the heart (A) RNA and (B) proteins were extracted from hearts of control and R848-treated mice and analyzed by RNAseq and Mass spectrometry, respectively (control n=10, R848-treated n= 9) (A, B) Gene set enrichment analysis (GSEA) was performed against the Reactome pathway database (version v2025.1) from MSigDB. Significance was assessed using 1,000 permutations, and the top-ranking pathways were identified based on the normalized enrichment score (NES) and a false discovery rate (FDR) q-value < 0.25 and are represented here. Right hand panel of (A) and (B) showing volcano with top genes highlighted; (C) Venn diagram showing numbers of overlapping and unique DEGs and DEPs; (D) GSEA performed against the Reactome pathway database for the DEGs and DEPs shared between both groups. Download figure Open in new tab Figure 3. Unique sets of genes and proteins are differentially expressed in resiquimod hearts. (A) Venn diagram showing numbers of overlapping and unique DEGs and DEPs from RNAseq and from hearts of resiquimod treated mice; (B) GSEA performed against the Reactome pathway database uniquely differentially expressed compared to DEPs shown as a volcano plot. Significance was assessed permutations, and the top-ranking pathways were identified based on the normalized enrichment score false discovery rate (FDR) q-value < 0.25 and are represented here; (C) Linear regression plot of Log2 Log2FC of DEPs; (D) Table identifying proteins whose expression is enhanced relative to genes (upper genes enhanced relative to proteins (lower panel). Assessing unique genes differentially expressed in mouse hearts, GSEA using the Reactome pathway database as a reference showed pathways such as FC gamma receptor dependent phagocytosis, hemostasis, platelet activation and aggregation and signaling by interleukins as being the enriched pathways ( Figure 3B ). A scatter plot of the Log2FC of commonly expressed genes (X-axis) vs the Log2FC of commonly expressed proteins was sued to determine whether specific genes or proteins were differentially stabilized. Only 2 proteins showed higher levels compared to the relative levels of the encoding mRNA and these were UHRF1 (a ubiquitin-like protein) and ABCB(9ATP cassette transporter 9) ( Figure 3D upper panel). Interestingly, UHRF1 protein levels are linked to heart health and cardiomyocyte survival, proliferation and repair ( 18 , 19 ). UHFR1 is an epigenetic cofactor, maintaining DNA methylation patterns via recruiting in DNMT1 ( 20 ). Thus the enhanced stability of UHRF1 protein may alter gene expression patterns through altered DNA methylation and act as a potentially protective mechanism against cardiomyocyte stress. Many of the genes that were more highly represented than their protein counterparts were immune mediated, and included LPS, Ly6E and TSPO, a mitochondrial transporter protein. These increased gene levels compared to proteins may indicate enhanced mRNA stability of decreased stability of the proteins encoded by these genes. The significance of this uncoupled regulation may again be protective, acting to limit inflammation. Metabolomic analysis of heart tissue showed an overrepresentation of pathways associated with purine, pyrimidine metabolism, amino acid synthesis and metabolism and the TCA cyclce (Supplemental Figure 3.A). Taurine and hypotaurine metabolism was also significantly enriched and was accompanied by a 32-fold increase in taurocholic acid in the hearts of resiquimod treated mice (Supplemental Figure 3B). Increased levels of Succinic acid may reflect mitochondrial function and disruptions in the TCA cycle – as indicated by both our proteomic and transcriptomic data. Elevated levels of succinate in young adults have also been associated with increased CVD risk ( 21 ). Others such as deoxyinosine and pyridoxic acid may reflect increased oxidative stress, whereas increased levels of amino acids (glutamic acid, threonine and acetylglutamic acid) may indicate increased catabolism of proteins. In cardiac tissue increases in these metabolites indicates ischemic stress. Of note, high levels of taurocholic acid arrhythmogenic, inducing atrial arrhythmias and altering ion channel function and are associated with atrial fibrillation. Integrated analysis of genomic and metabolic data using Network-based multi-omics data integration and the GO BP pathway for annotation revealed pathways representing NFkB and chemokine signaling, lipid and atherosclerosis and cardiomyopathy ( Figure 4A ). In addition features related to TLR signaling ( Figure 4B ) which is a positive validation of our analysis. Interactions between a lymphoid and non-lymphoid immune cells ( Figure 4C ) were also identified, suggesting that in resiquimod treated hearts, interactions between immune cells and non-immune cells (potentially cardiomyocytes) are increased, as would be expected under inflammatory conditions. Joint Pathway analysis of our data sets showed a strong concordance between genes associated with chemokine and innate immune signaling and specific metabolites (Supplemental Figure 3C). Download figure Open in new tab Figure 4. Integration of genomics and metabolomics highlights key pathways dysregulated in the resiquimod heart. (A-C) Metabolites were extracted from hearts of control and R848-treated mice and analyzed using mass spectroscopy (control n=5, R848-treated n= 8). Differential metabolite analysis was performed using the MetaboAnalyst 6.0 and genomic and metabolomic data integrated at the pathway level using pathway integration. (A) The output table of top pathways identified after integration,FDR<0.02; (B&C) Features identified as being up (green) or down (red) regulated in TOLL-Like receptor signalin (B) and Leukocyte transmigration KEGG pathways. These findings suggest that altered mitochondrial function in cardiomyocytes may underpin the reduced cardiac function observed. We therefore conducted an in vitro analysis of the effects of acute and chronic IFN on cardiomyocytes, the most energetically demanding cell type in the heart, and the cells most susceptible to stress and inflammation ( 22 ). Indeed, cardiomyocytes require high levels of ATP via oxidative phosphorylation and use fatty acids as the main substrates ( 23 ). AC16 human ventricular cardiomyocytes are a widely used immortalized cell line derived from adult human ventricular heart tissue through fusion with SV40-transformed human fibroblasts ( 24 ). These cells exhibit key cardiac markers—contractile proteins, gap junction proteins, and transcription factors indicative of cardiomyocyte identity and have been recently used to study mitochondrial metabolism and hypoxic signaling in response to hypoxia and reoxygenation ( 25 ). Importantly, AC16 cells responded to IFNa (1000u/ml) and induced expression of IFN stimulated genes (Supplemental figure 4). We next structured our in-vitro model in a way that allowed for us to study the effects of both acute and chronic exposure to type I IFN ( Figure 4 .2A). AC16 cells were stimulated with 1000ug/ml of IFNα for 24-hours. The cells were then washed twice with PBS to remove the IFNα and allowed to either rest for 4 days (primed) or rested (3 days) and then restimulated for another 24-hours post rest (primed + IFN, chronic IFN). In addition, we treated cells with IFNα for 24-hours to assess the effects of acute IFNα treatment on the cells (acute IFN). All cells were seeded at the same time and harvested on the same day ( Figure 5A ). Using this treatment protocol we first looked at the effects of primed, acute or chronic IFN on gene expression in the AC16 cells. qPCR of RNA isolated from the different groups compared gene expression of ISGs such as MX1, IRF7, ISG15, IFI44, and IFI27 across the 4 groups (unstimulated, acute IFN, primed IFN and chronic IFN). ISGs were upregulated immediately after stimulation with IFNα (acute IFN), and in the primed + IFN group (chronic IFN) but this increase was not observed in the primed cells nor did priming increase the IFN response in the primed + IFN (chronic) group (Supplemental Figure 5). This suggested that transcriptional responses to IFNα in AC16 were acute and not sustained and also suggested that priming with IFNα did not induce a ‘memory’ response as we had previously observed for primary monocytes ( 26 ). Download figure Open in new tab Figure 5. Acute and chronic IFNα exposure in AC16 cardiomyocytes resulted in differential changes in gene and protein expression (A) Schematic showing AC16 treatment and generation of our 4 groups (n=3 for each) Control (CC), primed (IC), Acute IFN (CI) and primed and restimulated with IFN (II); (B) Bar plot showing the total number of DEGs up (orange) and down (blue) in our different groups; (C) Venn diagram showing the numbers of common and unique DEGs between CI vs CC and II vs CC; (D) Differentially expressed proteins in our 4 groups were analyzed to determine the overlapping and unique pathways upregulated between CI vs CC and II vs CC. Over representation analysis ORA against the REACTOME database was performed; (E) Gene set enrichment analysis (GSEA) was performed for II vs CI against the Reactome pathway database (version v2025.1). The top-ranking pathways represented here were identified based on normalized enrichment score (NES) and a false discovery rate (FDR) q-value < 0.05. To get better insights into the effect of IFN on cardiomyocytes we assessed transcriptomic and proteomic changes in the AC16 cells due to acute and chronic type I IFN. Figure 5B shows a bar plot of total numbers of differentially expressed genes in acute IFN, primed and primed + IFN vs Ctrl (n=3 per group). Both the acute (IFN for 24 hours) and the primed+IFN (mimicking chronic IFN) saw the most transcriptional changes. Priming alone showed only sustained increases in gene expression, the majority of which were ISGs (data not shown). To understand whether priming induced any longterm changes and could be enhanced by a second stimulation with IFN 4 days after priming, we compared the DEGs between acute IFN (CI compared to control) versus primed plus retimulated with IFN II compared to control). As shown in the Venn diagram ( Figure 5C ), the majority of genes were common between both and represented IFN signaling, cytokine signaling, and the adaptive immune system (Supplemental figure 7). A similar analysis of differentially expressed proteins amongst our different conditions showed that pathways associated with tissue migration, hormone metabolism, immunoregulatory interactions between a lymphoid and non-lymphoid cell ( Figure 5D ) being unique to II vs CC. Direct comparison of DEP between CI and II demonstrated mitochondrial related pathways as being enriched in the chronically activated AC16s ( Figure 5E ). In summary our results show that priming with IFN induces a sustained effect on AC16 cells, particularly at a protein level and that these changes are consistent with mitochondrial dysregulation. Therefore, we next set out to establish if IFNα exposure affected the metabolic activity of cardiomyocytes using the AC16 cells. To measure if there was a change in metabolic activity of these cells due to acute and chronic IFN exposure, we conducted mitochondrial and glycolysis stress assays using the Seahorse EFA analyzer to determine the bioenergetics of a cell by assessing oxygen consumption rate (OCR) and extracellular acidification rate (ECAR), respectively. We observed that primed and chronic exposure groups show impaired oxidative phosphorylation and glycolysis, and decreased basal respiration, maximal respiration, ATP production ( Figure 6A-D ). This indicates mitochondrial dysfunction which could be due to accumulation of dysfunctional mitochondria, increased mitochondrial fission leading to fragmented organelles with impaired function, or a dysregulation between mitochondrial quantity and quality. To address the direct effect IFNα on the mitochondria in AC16 cells we conducted a targeted mass spectrometry assay termed Mitoplex which quantifies the levels of 37 proteins critical to carbon metabolism and overall mitochondrial function ( 27 ). Using this approach, we saw a significant increase in mitochondrial proteins in the primed and primed+IFN groups compared to either control or acute IFNα stimulated cells ( Figure 6E ). These changes in mitochondrial proteins spanned different mitochondrial pathways such as TCA cycle (SDHA, MDHM, ACON), electron transport chain (SODM), mitochondrial membrane transport (TIM50, COX2), mitochondrial transcription (TFAM) and fission (DRP1) (Supplemental Figure 6). The increase in mitochondrial proteins may suggest the cells are attempting to adapt or compensate for stress due to chronic IFN exposure by increasing the abundance of mitochondrial machinery, defects in mitochondrial function or integrity could ultimately be limiting both aerobic and anaerobic ATP generation. Download figure Open in new tab Figure 6. Chronic IFNα stimulation of AC16 cells impairs mitochondrial respiration and induces mitochondrial stress and mtDNA release. Mitochondrial stress test was used to compare baseline levels of (A) oxygen consumption rates (OCR) (B) Basal respiration (C) Maximal respiration (D) ATP production values. Data represent mean ± SD of reads n=5 samples from one experiment, and are representative of data (A-D) n=5; (E) Proteins were isolated for Mitoplex assay, a targeted quantification of 37 mitochondrial proteins. A heatmap of the 37 mitochondrial proteins expression across the 4 groups was generated using MetaboAnalyst; (F) MitoTracker Green, a cell-permeant fluorescent dye that stains mitochondria in live cells was used to stain the four groups of AC16 cells. Quantification To investigate what effect (if any) IFN was having on mitochondrial mass or location, we stained the cells with MitoTracker Green, a cell-permeant fluorescent dye that stains mitochondria in live cells and can be used to measure mitochondrial content of cells and image size and localization ( Figure 6F ). We observed that primed and primed + IFN cells seem to show more fragmentation as the mitochondria appear more punctate. Secondly we also observed that primed and primed + IFN (chronic) groups showed more mitochondria around the nuclei than the cells acutely stimulated with IFN. This is in keeping with the fact that mitochondria located around the nucleus supply ATP and metabolic intermediates directly to support transcription and mRNA processing, so the presence of increased perinuclear mitochondria could be linked to cellular transcriptional activity and reflecting the cell’s increased transcriptional demands due to the stress of the chronic IFN exposure ( 28 ). Another measure of mitochondrial stress is increased release of mt nucleic acids (mtRNA/DNA) into the cytosol. These can then activate RNA sensing (RIG-I, IFIH1) and DNA sensing (cGAS-STING) pathways to increase IFN and inflammatory gene expression or to enhance cell death and stress. We had observed evidence of increased representation of these pathways in our chronically stimulated AC16 cells, in addition to increased levels of proteins that regulate mtDNA release (VDAC1&2) and a decrease in fumarate hydratase that is associated with increase mtRNA release ( 29 ). Thus in line with these observations we observed that AC16 cells chronically exposed to IFNa showed increased release of mtDNA in to the cytosol ( Figure 6G ). Therefore in summary, our data show that chronic IFN exposure alters innate immune signaling pathways and mitochondrial function and potentially dynamics, leading to enhanced stress and mtDNA release. These findings have important implications for autoimmune driven CVD, specifically implicating the IFN system and cytosolic RNA and DNA sensing pathways in altering mitochondrial dynamics and ultimately cardiomyocyte function. Discussion Systemic effects of type I interferons on heart function was assessed using the resiquimod mouse model and both ejection fraction and fractional shortening were reduced which indicates a reduction in cardiac output due to possible left ventricular dysfunction. We also observed cellular hypertrophy in the heart sections from the resiquimod-treated mice using histology. This suggests an adaptive but pathological response of cardiomyocytes to ongoing stress, linking the immune activation to adverse structural remodeling. Transcriptomic and proteomic profiling of the heart tissue establishes the resiquimod treatment resulted in an enrichment of interferon-driven immune pathways such as T cell responses and myeloid differentiation. Simultaneously, we observed a suppression of fundamental mitochondrial metabolic processes including the TCA cycle and electron transport chain and the mechanistic connection between the two could indicate a shift from oxidative energy metabolism toward immunometabolic reprogramming in the resiquimod-treated mouse hearts. Metabolomic analysis further reveals accumulation of ischemia-associated intermediates such as succinic acid, deoxyinosine and pyridoxic acid, and pathway enrichment analysis highlights dysregulation of purine metabolism, pentose phosphate pathway, and the malate-aspartate shuttle. These results suggest that systemic type I interferon exposure can remodel cardiac function and metabolism and implicates cross-talk between immune activation, mitochondrial energy failure, and metabolite signaling as key contributors to interferon-driven heart disease. In response to ischemic injury, metabolic stress, or systemic inflammation, immune cells such as macrophages, T cells, B cells, and neutrophils infiltrate the myocardium and undergo shifts in core metabolic pathways by moving between oxidative phosphorylation and glycolysis according to their activation and functional state. Metabolic imbalances themselves can also induce systemic inflammation, which disrupts heart structure and function by promoting maladaptive remodeling through cytokines, excess metabolic substrates, and paracrine signaling ( 30 ). These shared inflammatory pathways in cardiac cells cause progressive damage, leading to metabolic cardiomyopathy and, clinically, to heart failure with preserved ejection fraction (HFpEF)( 30 ). Obesity and diabetes share cellular changes resembling those seen in HFpEF, such as oxidative stress, mitochondrial dysfunction, lipotoxicity, hypertrophy, ECM buildup, and microvascular disease ( 31 ). One highly novel finding from our metabolomics data was the observation of a dramatic increase in taurocholic acid levels, a bile acid, in the hearts of resiquimod treated mice. Bile acid metabolism is strongly linked to cardiovascular diseases (CVDs) and metabolic disorders either by directly interacting with heart muscle cells and modifying contraction and electrical conduction or indirectly by modulating metabolic processes such as heart function regulation, cholesterol metabolism, and atherosclerotic plaque development ( 32 , 33 ). TCA specifically can induce arythmias and contractile dysfunction by altering calcium ion dynamics in cardiomyocytes ( 34 ). Other studies have linked excess bile acids with reduced fatty acid metabolism in cardiomyocytes, directly linking bile acids to reduced cardiomyocyte function ( 33 ). In SLE, patients have been shown to have elevated TCA in their feces, potentially driven by an imbalance in the gut microbiome ( 35 ). However, to date no link between TCA and SLE heart involvement has been suggested, and ours is the first study to identify an IFN-driven increase in heart tissue in resiquimod treated mice. Although more work needs to be done definitively establish it, our findings suggest a novel link between bile acid metabolism and cardiac dysfunction in the context of chronic interferon-driven disease. Our multiomic analysis of the heart tissue from resiquimod treated mice strongly implicated cardiomyocytes as the cell type undergoing pathologic changes in the heart. Transcriptomic analyses showed that both acute and chronic IFNα elevated ISG expression similarly, which is in line with literature reporting strong ISG induction in cardiomyocytes following type I interferon signaling and myocardial injury ( 7 ). Notably, chronic IFNα exposure led to distinct upregulation of genes related to cell migration and metabolic regulation, while primed and control samples showed top enrichment in ISGs and downregulation of genes involved in cholesterol biosynthesis. Recent studies have shown Toll-like receptors and interferons as mediators of the reprogramming of cholesterol homeostasis and regulation of sterol metabolic pathways in immune cells has been as acknowledged an integral response to host defense ( 36 , 37 ). At the proteomic level, primed cells compared to controls exhibited enrichment of pathways such as ‘mitochondrial protein degradation’, ‘mitochondrial translation’, and ‘respiration and respiratory electron transport’. Consistent upregulation of ISGs was seen with both acute and chronic IFNα exposure, but GSEA revealed features unique to the chronic IFN exposure that overlapped with the pathways observed in the primed cells, hence IFN priming contributed to those differentiating pathways. One study in Chaga’s disease observed that AC16 cells co-exposed to IFN-γ and TNF showed protein and gene expression changes associated with mitochondrial dysfunction, altered fatty acid metabolism and cardiac cell death ( 38 ). IFNα not only induces the expression of RIG-I and cGAS, but these sensors also detect cytosolic nucleic acids such as mitochondrial DNA released during stress or injury and trigger further type I interferon production ( 39 - 41 ). Both the cGAS-STING and RIG-I pathways participate in cardiac injury and remodeling by promoting chronic inflammation and innate immune activation in the heart, forming a feed-forward loop that can exacerbate myocardial damage ( 42 , 43 ). Our findings are novel in showing that IFNα priming changes the functional responses of human ventricular cardiomyocytes beyond ISG induction, particularly impacting mitochondrial organization, protein turnover, and cholesterol metabolism. This may provide new mechanistic insight into how chronic IFN exposure shapes cardiomyocyte function and contributes to the heart defects observed in lupus and interferon-driven heart disease models. In line with mitochondrial effects of chronic IFNa, Mitostress and Glycolysis stress assays showed that chronic IFN exposure resulted in decrease in both oxidative phosphorylation and glycolysis. This is interesting since during heart disease and cellular stress, cardiomyocytes undergo metabolic reprogramming, shifting from reliance on oxidative phosphorylation (OXPHOS) toward increased glycolysis to meet energy demands ( 44 ). This switch promotes cell survival under hypoxic or ischemic conditions but contributes to impaired contractile function and disease progression in the failing heart ( 45 ). The dual metabolic suppression of OXPHOS and glycolysis reflects a loss of metabolic flexibility and an inability to compensate for energy deficits through glycolysis ( 46 ). This is observed in advanced heart failure, drug-induced cardiotoxicity, or extreme metabolic stress, where mitochondrial function is severely impaired and cellular energy generation is stalled ( 47 , 48 ). A targeted mass spectrometry assay for 34 mitochondrial proteins shows that chronic IFN also increased many mitochondrial proteins involved in the TCA cycle (SDHA, MDHM, ACON), electron transport chain (SODM), mitochondrial membrane transport (TIM50, COX2), mitochondrial transcription (TFAM) and fission (DRP1) ( 27 ). This suggests that chronic IFN signaling drives compensatory adaptations in cardiomyocytes, enhancing mitochondrial content and machinery to meet increased energy and metabolic demands or counteract stress. The cGAS-STING and RIG-I signaling pathways are critical mediators of innate immune responses triggered by mitochondrial DNA (mtDNA). Cardiac stress, injury, or mitochondrial dysfunction can lead to the escape of mtDNA into the cytosol, where it is sensed by cGAS, activating the STING pathway and promoting a cascade of inflammatory responses, including increased production of type I interferons and pro-inflammatory cytokines ( 42 ). This heightened inflammation has been shown to drive pathological cardiac remodeling, hypertrophy, fibrosis, and dysfunction, and pharmacologic or genetic inhibition of cGAS-STING signaling can ameliorate these effects in experimental models ( 49 ). Similarly, RIG-I, another key cytosolic sensor, detects nucleic acids (including mtRNA) and orchestrates inflammatory responses in cardiac fibroblasts by increasing the production of cytokines such as IL-6 and IL-8, contributing to endothelial and myocardial injury, and ultimately advancing cardiac pathology ( 50 ). Together, these innate sensing pathways represent crucial molecular links between mitochondrial damage, persistent inflammation, and the onset and progression of heart disease. Our proteomics analysis revealed that both acute and chronic IFNα exposure lead to elevated levels of STING1 and RIG-I, with STING1 remaining persistently high in the primed group compared to control cells. To further investigate, we assessed cytosolic mtDNA release by isolating the cytosolic fraction from AC16 cardiomyocytes and we found that the primed and primed + IFN groups exhibited a marked increase in these mitochondrial DNA sequences in the cytosol, demonstrating that prolonged IFNα exposure promotes substantial mtDNA release in cardiomyocytes. While this observation is in line with literature showing that cGAS-STING and RIG-I pathways can be activated by mtDNA release during cardiac stress, driving type I interferon signaling and inflammation, persistent STING1 elevation and robust mtDNA release in cardiomyocytes represents a novel mechanistic insight into how systemic interferon signaling may drive heart disease in SLE and present targets for therapeutic intervention. Funder Information Declared NIH Common Fund, https://ror.org/001d55x84 , R01AI164504 Congressionally Directed Medical Research Programs , W81XWH-18-1-0709 Californa institute of regenerative medicine Footnotes The authors have declared that no conflict of interest exists. Sources of Funding This work was supported by research funding from NIH R01AI164504 (C. Jefferies), the Office of the Assistant Secretary of Defense for Health Affairs through the Department of Defense Lupus Research Program (LRP) , Award Number W81XWH-18-1-0709 (C. Jefferies), and CIRM. References 1. ↵ Schoenfeld SR , Kasturi S , Costenbader KH . The epidemiology of atherosclerotic cardiovascular disease among patients with SLE: a systematic review . Semin Arthritis Rheum . 2013 ; 43 ( 1 ): 77 – 95 . OpenUrl CrossRef PubMed 2. ↵ Esdaile JM , Abrahamowicz M , Grodzicky T , Li Y , Panaritis C , du Berger R , et al. 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