TgA86 mouse model demonstrated potential markers for Bone Formation in Spondyloarthritis through ROS Detoxification, Folate Metabolism, and Inositol Phosphate Metabolism

TgA86 mouse model demonstrated potential markers for Bone Formation in Spondyloarthritis through ROS Detoxification, Folate Metabolism, and Inositol Phosphate Metabolism | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article TgA86 mouse model demonstrated potential markers for Bone Formation in Spondyloarthritis through ROS Detoxification, Folate Metabolism, and Inositol Phosphate Metabolism Sanghyeon Yu, Seokho Myung, Se Hee Kim, Sang-Hoon Lee, Jung Yeon Kim, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7116883/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Spondyloarthritis (SpA) represents a collection of inflammatory conditions distinguished by abnormal bone formation within the axial skeleton, ultimately resulting in ankylosis. The precise mechanisms responsible for this pathological bone development remain poorly understood. The TgA86 mouse model, featuring overexpression of a mutant human TNF transgene, accurately recapitulates essential features of human SpA, particularly spinal fusion. While mitochondrial metabolism has gained recognition for its role in SpA pathogenesis, the particular metabolic alterations that promote pathological bone formation have yet to be comprehensively examined. Methods Bulk RNA sequencing data obtained from TgA86 mice at 4 and 10 months (n = 5 per group) along with their corresponding controls (n = 5 per group) underwent analysis through a multi-system approach encompassing differential gene expression analysis, protein-protein interaction assessment, and metabolic flux modeling. Results During disease development, TgA86 mice exhibited an immunological transition characterized by enhanced innate immune activity and reduced adaptive immune signaling. This immune transformation occurred alongside transcriptional modifications indicating suppressed mitochondrial oxidative phosphorylation and widespread activation of carbohydrate, amino acid, lipid and fatty acid metabolic pathways. Furthermore, we detected reduced SOD1 expression (ROS detoxification) and MTHFD1 expression (folate metabolism), combined with elevated INPP1 expression (inositol phosphate metabolism). Immunohistochemical analysis validated these changes within nucleus pulposus and surrounding cartilage tissue. Conclusion The TgA86 mouse model successfully reproduces the immunometabolic disruption characteristic of SpA. The documented transcriptomic pattern, reflecting a shift toward glycolysis, indicates that metabolic restructuring serves as an important factor in disease advancement. Notably, the altered regulation of SOD1, MTHFD1, and INPP1 represents a potential mechanism underlying pathological bone formation. These observations highlight the intricate relationship between immune function, cellular energy production, and bone development in SpA. TgA86 spondyloarthritis RNA-sequencing ROS folate inositol phosphate Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Spondyloarthritis (SpA) is a family of diseases encompassing ankylosing spondylitis (AS), psoriatic arthritis, inflammatory-bowel disease (IBD)-associated arthritis, reactive arthritis, and undifferentiated SpA. These subtypes typically affect the spine and sacroiliac joints, with inflammation often progressing to new bone formation and ankylosis, distinguishing SpA from other inflammatory arthritides lacking pronounced axial involvement.[ 1 ] Tumor necrosis factor-alpha (TNF-α) exists in two bioactive forms, transmembrane (tmTNF-α) and soluble (sTNF-α), the latter cleaved by TNF-α-converting enzyme (TACE/ADAM17). Although TNF-overexpressing mouse models replicate various inflammatory features of spondyloarthritis (SpA), they typically fail to show the characteristic new bone formation of SpA.[ 2 ] In SpA, decreased sTNF-α, elevated tmTNF, and reduced ADAM17 activity fuel this imbalance.[ 3 , 4 ] The TgA86 model, which overexpresses a mutant murine TNF gene with a defective ADAM17 cleavage site, closely mimics vertebral fusion and sacroiliac joint ankylosis, making it invaluable for studying SpA pathogenesis.[ 5 ] Mitochondrial metabolism is increasingly recognized as critical to disease development.[ 6 ] Recent multi-omics advances spotlight immune-regulation genes and pathways related to bone remodeling in SpA, yet many investigations focus on a single system level, often overlooking metabolic flux.[ 7 ] Employing a multi-system-level approach, this study aims to elucidate key transcriptional and metabolic changes behind new bone formation in TgA86 mice. We compared mRNA expression, analyzed functional pathways, simulated metabolic flux, and examined protein–protein interaction networks. Through these methods, we identified significantly altered genes, metabolic reactions, and regulatory mechanisms that may distinguish TgA86 from normal samples, offering insights into SpA-associated bone overgrowth. MATERIALS AND METHODS Sample Preparation and RNA sequencing Mouse spine bones were isolated, flash-frozen in liquid nitrogen, and ground into powder. Total RNA was extracted from this powder using TRIzol Reagent (Thermo Fisher Scientific) according to the manufacturer's protocol and subsequently treated with DNase I. Depending on the analysis goal, mRNA was enriched (poly(A) selection) or rRNA was depleted (ribo-zero kit). The resulting RNA was fragmented, reverse transcribed into cDNA using random hexamers, ligated with unique adapters, and PCR amplified. Size-selected libraries (200–400 bp inserts) were then sequenced on an Illumina NovaSeq platform using paired-end reads for comprehensive transcriptome analysis. Data Processing Sequencing data quality was assessed using FASTQC (v0.11.7).[ 8 ] Reads were aligned to the GRCm39 Mus musculus genome with STAR aligner (v2.7.3a). Gene expression was quantified using HTSeq-Count (v0.12.4) in union mode to generate an expression matrix.[ 9 , 10 ] Raw counts were then normalized using the regularized log (rlog) transformation in DESeq2 (v1.44.0) to stabilize variance for downstream analyses.[ 11 ] Differential and enrichment analysis Differential expression analysis (TgA86 vs control) used DESeq2 (v1.44.0). Inflammatory genes were curated from MSigDB. DEGs were visualized with EnhancedVolcano (v1.24.0) .[ 12 ] Gene ontology analysis used ClusterProfiler's enrichGO (v4.6.2) to identify enriched pathways separately in each group.[ 13 ] GSEA (v4.3.2) examined pathway associations, visualized as networks using EnrichmentMap (v3.5.0) in Cytoscape.[ 14 , 15 , 16 ] Additionally, fgsea (v1.24.0) calculated Normalized Enrichment Scores for MitoCarta 3.0 modules, depicted as lollipop plots.[ 17 , 18 ] Energy-associated core gene expression comparisons were visualized using heatmaps.[ 19 ] Metabolic flux simulation The fundamental setup of our study was rigorously aligned with methodologies from prior research by conducting Flux Balance Analysis (FBA) with the cobrapy package in a Python 3.7.6 environment, utilizing the imm1415 metabolic base model.[ 20 , 21 , 22 ] We implemented two key constraints: (i) To assess the influence of specific enzymes on the relative reaction rates of metabolic pathways, enzyme expression levels were constrained using CORDA (v1.0.0). (ii) We manually constrained energy-associated metabolic pathways-such as ‘Oxidative Phosphorylation’, ’Glycolysis/Gluconeogenesis’, ‘Citric Acid Cycle’, ’CoA Biosynthesis’, ’CoA Catabolism’, ’Biomass and maintenance functions’, ’NAD Metabolism’, ’Fatty Acid Metabolism’, ’Fatty acid activation’, ’Fatty acid elongation’, and ‘Fatty acid oxidation’-to maximize their activity. Gene expression profiles were incorporated into the model for each sample, while parameters were consistently maintained across simulations, providing flux levels for all reactions. These flux levels were analyzed as grouped variables to compare the 'TgA86' and 'control' groups using a custom flux balance analysis. A non-parametric van der Waerden test was employed for comparison, utilizing the R package matrixTests (v. 0.1.9), and metabolic flux alterations were visualized with upset plots and Escher.[ 23 ] Immunochemistry Bone tissue from 4- or 10-month-old mice was fixed in 10% formalin (24–48 hrs) and decalcified in 10% EDTA (1–3 weeks, 4°C). Tissues were paraffin-embedded and sectioned at 4 µm. Sections were mounted, deparaffinized, rehydrated, and rinsed in PBS. Antigen retrieval involved heating in citrate (pH 6.0) or EDTA (pH 8.0) buffer (95°C, 15–20 min). Sections were blocked (5% BSA, 30–60 min, RT) and incubated with primary antibodies [INPP1(Novusbio, NBP1-314715), MTHFD1(Novusbio, NBP1-82612), SOD1(abcam, ab51245)] diluted in PBS (overnight, 4°C). After 3x PBS washes, HRP-conjugated secondary antibody was applied (1 hr, RT, dark). Following 3x PBS washes, signals were detected using DAB chromogenic staining, and slides were viewed by light microscopy. RESULTS Multi-system-level Analysis of SpA Progression As illustrated in Fig. 1 , five TgA86 and five control mouse samples were collected at 4 and 10 months. After RNA sequencing, differential expression analyses were performed to compare TgA86 and control samples using three gene sets: Differentially Expressed Genes (DEGs), a customized group of mitochondria-associated genes, and MitoCarta3.0. Pathway enrichment was assessed through Gene Set Enrichment Analysis (GSEA) and Fast GSEA (fgsea), while metabolic flux simulations identified key reactions altered in SpA progression. A refined list of potential targets was narrowed down via protein–protein interaction analysis and was validated using immunohistochemistry. Differential Gene Expression and Pathway Enrichment Analysis in the TgA86 Model Since there is currently no information available on the gene expression profiles of the TgA86 mouse model at 4 and 10 months, we initially investigated the number of significantly differentially expressed genes (DEGs) in the TgA86 model across this timeframe. Given that inflammation is a hallmark of spondyloarthritis (SpA), we also evaluated the number of inflammation-associated genes for the two distinct time points. As shown in Fig. 2 A, we identified 2,908 DEGs at 4 months and 2,024 DEGs at 10 months. Among these, 582 inflammation-associated DEGs were identified at 4 months, and 442 at 10 months. As expected, known inflammatory genes exhibited significant expression differences in the TgA86 model. Additionally, the identification of a sufficient number of DEGs supports the feasibility of various downstream analyses. In addition, to determine whether groups of the identified DEGs are collectively associated with functions classified in databases from previous studies, we conducted a Gene Ontology (GO) analysis. The comparative groups were as follows: TgA86 versus control at both 4 months and 10 months, and TgA86 at 4 months versus 10 months as depicted in Fig. 2 A. In the comparison between TgA86 and the control at 4 months, ‘positive regulation of cytokine production’ and ‘adaptive immune response’ were significantly upregulated in TgA86. From the 10-month comparison, an enhancement was observed in 'positive regulation of cell motility' and 'positive regulation of cell migration,' whose processes are associated with innate immunity, alongside 'inflammatory response' and 'immune effector process'. Comparing TgA at 10 months with TgA at 4 months revealed an enrichment of pathways involved in various intracellular signaling processes and innate immunity in the 10-month group. Taken together, these findings reaffirm that, as is well-known for SpA, the observed changes are predominantly associated not only with inflammation but also with various immune responses. However, this analytical method did not provide potential links associated with bone formation of the TgA86 model. Although the GO analysis revealed enrichment in diverse signaling processes beyond immune-associated responses (including inflammatory responses), we additionally conducted a gene set enrichment analysis (GSEA) to further investigate whether the groups of DEGs were associated with functions other than immune-associated responses from a different perspective (Fig. 2 B, supplementary table S1 and S2). Overall, as expected, a broad range of immune-related responses were either upregulated or downregulated in the TgA86 model at both 4- and 10-month time points. In addition, alterations in cell cycle–associated pathways were observed, aligning with previous microarray analyses that identified cell cycle regulation as one of the top enriched pathways in AS, consistent with the findings of Zhang et al.[ 37 ] However, a potential process, specifically the metabolic process, was concurrently observed, where the relationship between metabolic processes and bone formation in SpA, along with the underlying mechanisms, remains largely unexplored. Given that energy metabolism, as a subset of metabolic processes, is crucial for maintaining cellular homeostasis in conjunction with other metabolic processes, we focused our subsequent analysis on the alterations associated with energy metabolism.[ 24 ] Analysis of Energy Metabolism-Related Gene Expression in TgA86 Mice Mitochondria are central to energy metabolism, and considering their crucial role in bone formation, we investigated core gene expression profiles associated with energy metabolism whose gene list was provided from Guarnieri et al as illustrated in Fig. 3 A.[ 19 , 25 ] Comparative analysis of the core expression levels of energy metabolism-associated genes between the 4-month and 10-month TgA86 samples and the control, based on T-scores, revealed a general downregulation of genes associated with antioxidant defenses, including Sod1 and Gpx1, in both TgA86 samples. Additionally, genes involved in folate metabolism, such as Mthfd1, Mthfd2, and Shmt2, exhibited a significant decrease. These findings underscore the deep connection between these pathways and mitochondrial dysfunction. Conversely, we observed a notable upregulation in genes associated with the hypoxia-inducible factor (HIF) pathway, particularly Hif1a and Bnip3. To further analyze the alterations in mitochondrial energy metabolism from a different perspective, we implemented the fgsea package to estimate the activation levels of modules ( i.e. , gene groups) based on MitoCarta3.0. as shown in Fig. 3 B.[ 17 , 18 ] The activation of Oxidative Phosphorylation, which can be considered the primary functional pathway of mitochondria, was observed to be attenuated in TgA86 compared to the control at both 4 months and 10 months. This reduction in ATP production observed in the TgA86 model indicated an increased potential for metabolic dysregulation in SpA. Also, modules on mitochondrial central dogma unveiled significant downregulations in the TgA86 model at both 4 months and 10 months. Contrarily, the activation levels of diverse metabolic pathways were enhanced in the TgA86 model at both 4 months and 10 months. Specifically, metabolisms on creatine, choline, cholesterol, bile acid, steroid, catechol, and carnitine were further activated in the TgA86 model. This finding aligned with multiple previous studies that have reported elevated levels of creatine, cholesterol, choline, and carbohydrate metabolism products in the serum of SpA patients.[ 26 ] Identification of altered metabolic pathways associated with bone formation and inflammation in the TgA86 model Considering the potential association of metabolic alterations with the underlying mechanism of bone formation in the TgA86 model, we conducted metabolic flux simulations across all available metabolic reactions, utilizing our context-specific constraint-based metabolic modeling approach adapted from da Silveira WA et al .[ 20 ] Among the metabolic pathways with at least 10% of reactions displaying significant alterations, Carnitine shuttle, TCA cycle, Fatty acid elongation, Fatty acid oxidation, Keratan sulfate degradation, NAD metabolism, and Sphingolipid metabolism exhibited relatively more pronounced changes as shown in Fig. 4 . Among the list, keratan sulfate degradation unveiled the most significant alterations. This finding can be particularly considerable, as keratan sulfate has been implicated in bone formation processes.[ 27 ] Sphingolipid metabolism, which displayed the second most significant change, demonstrated associations with inflammatory responses, suggesting a potential link to SpA. Some other metabolic pathways ( i.e. , TCA cycle, NAD metabolism, and tyrosine metabolism) potentially associated with bone formation also demonstrated substantial alterations. Integrating protein level analysis using protein-protein interaction to identify candidate genes in SpA To identify potential functional genes in the TgA86 model, we conducted a comprehensive analysis of molecular alterations from both transcriptional and metabolic perspectives. Metabolically, we focused on the calculated flux differences in specific reactions between the TgA86 and control groups. Transcriptionally, we compared the expression levels of genes predicted to encode enzymes for those reactions. This multi-system-level analytical approach led to the identification of several metabolic reactions and their corresponding genes as potential markers for TgA86, as detailed in Additional file 1: Table 3. As depicted in Fig. 5 , a protein-protein interaction analysis indicates that these candidate genes have a high likelihood of interacting, as determined by various criteria such as network type, edge significance, interaction source, and minimum interaction score. While enhancing the precision of potential gene selection by considering the probability of identifying pertinent interactions, we further explored additional interaction traces with other proteins (or genes). Overall, a total of 28 genes were considered as candidates, among which 23 genes formed four distinct protein networks. The biggest network contained 16 genes ( i.e. , Aldh9a1, Aldh3a2, Aoc3, Aldh1a1, Mthfd1, Idh1, Ndufs3, Ndufb5, Sucla2, Prdx2, Gpx1, Sod1, Ucp2, Ptgs2, Sod3, and Enpp2) and the second biggest one included 3 genes ( i.e. ,Hexa, Manba, and Hexb), and the others had two genes ( i.e. , either Dut and Nme3, or Cdipt and Inpp1). The changes in transcriptional expression of the above genes during disease progression were further validated at the protein level through immunohistochemistry using commercially available antibodies. Among the 19 candidate genes, SOD1, INPP1, and MTHFD1 were selected for further investigation due to their known roles in oxidative stress, immune signaling, and metabolic pathways-mechanisms that plausibly contribute to the pathogenesis of SpA. Investigating the roles of SOD1, MTHFD1, and INPP1 in SpA pathogenesis As depicted in Fig. 6 A, considerable forward-direction alterations in the SPODM and SPODMx reactions associated with the Sod1 enzyme activity in the TgA86 model were detected at both 4 and 10 months, indicating its role in ROS detoxification. Previous studies have demonstrated that oxidative stress induces a decline in Sod1 levels, leading to reduced bone stiffness and decreased bone marrow density.[ 28 ] The association of decreased Sod1 with anaerobic conditions may suggest its potential role as a marker gene for ankylosis, a pathological reduction in bone density characteristic of AS. Substantial activation changes in the MTHFD reaction, mediated by the Mthfd1 enzyme, were observed within the folate metabolism as being backwarded in 4 months and forwarded in 10 months. Studies indicated that folate metabolism is linked to cytoskeletal remodeling, bone proliferation, and migration, with a particular result that a decrease in Mthfd1 was associated with an increase in serine metabolism, which regulates chondrocyte proliferation.[ 29 ] This aligns with our detection, where Mthfd1 staining in chondrocytes suggests that, in AS, Mthfd1 could be a pivotal regulator of chondrocyte proliferation via serine metabolism. Within inositol phosphate metabolism, a notable alteration in the MI14PPreaction mediated by Inpp1 was observed in the TgA86 model, shifting from a backward direction at 4 months to a forward direction at 10 months. Inositol phosphates are known to influence signaling pathways such as PI3K/AKT/mTOR, which are involved in regulating chondrocyte proliferation.[ 30 , 31 , 32 ] Given that lipid metabolism contributes to aberrant ossification in osteoarthritis (OA), the dynamic change in Inpp1-associated reactions suggests a potential role in chondrocyte activity and pathological bone formation in SpA.[ 33 ] Building on our comprehensive multi-system-level analysis, which identified INPP1, MTHFD1, and SOD1 as potential target genes of significant interest in the context of SpA, we conducted further validation through immunohistochemical staining. This validation process underscored the potential significance of these genes in the pathogenesis of SpA, highlighting their possible roles in the disease's molecular mechanisms. Consistent with our expectations, the expression of INPP1, indicated by brown nuclear staining, was elevated in the nucleus pulposus and adjacent cartilage of 10-month-old mice compared to 4-month-old mice. Conversely, the expression of MTHFD1 and SOD1 was slightly reduced in the nucleus pulposus and adjacent cartilage of 4-month-old mice compared to 10-month-old mice, as illustrated in Fig. 6 B. DISCUSSION To comprehensively address diverse biomedical research questions, recent studies have increasingly leveraged multi-omics data from various system levels to identify novel biomarkers. Note that metabolomics data acquisition has gained prominence for exploring metabolic alterations that potentially link genotype to phenotype. However, given the high costs and labor-intensive nature of obtaining metabolomics data, we have adopted an alternative analytical approach: metabolic flux modeling.[ 34 ] Utilizing the imm1415 base model, our simulation can concurrently examine changes in 2,775 metabolites and 3,726 metabolic reactions, providing a broader scope compared to metabolomics data obtained through gas/liquid chromatography–mass spectrometry. By integrating diverse analytical methods, including metabolic modeling, across multiple system levels, this study has identified activity changes in various molecules within the TgA86 model and facilitated the discovery of novel biomarkers. Gene expression of the TgA86 mouse model changed from cytokine production and adaptive immune response at 4 months to cell motility and migration associated with innate immunity at 10 months. In the aspect of energy metabolism, energy production of mitochondria decreased as down regulation of genes associated with OXPHOS. On the other hand, genes associated with carbohydrate, amino acid, lipid, and fatty acid metabolism increased. Regarding the mechanism of bone formation, decreased expression of SOD1 and MTHFD1, along with increased expression ofInpp1, was observed in chondrocytes. Unlike other autoimmune diseases such as rheumatoid arthritis or systemic lupus erythematosus, SpA is characterized by dysfunction in both innate and adaptive immunity. In addition to the IL-23/IL-17 axis, perpetuation of inflammation associated with T cells, NK cells, and macrophages was demonstrated in SpA patients.[ 35 ] In our study, GO analysis of the 4-month TgA86 mouse vertebral samples showed increased cytokine production, adaptive immune response and leukocyte mediated immunity with increased leukocyte migration. In 10-months mouse vertebral samples, genes related to inflammatory response, cell motility, and migration were significantly upregulated, suggesting progressive immune dysregulation. Mitochondria are the powerhouse of eukaryotic cell and major biological metabolic pathways in mitochondria are tightly coordinated tricarboxylic acid (TCA) cycle and oxidative phosphorylation (OXPHOS).[ 36 ] Cellular metabolisms produce energy fuel in a form of adenosine triphosphate (ATP) to maintain cellular homeostasis and functions, and also decide cell fate. Altered cellular metabolisms induce chronic diseases such as osteoarthritis, by disrupting energy production, biomolecule synthesis, mitochondria and reactive oxygen species (ROS) regulation, or dysregulation of energy-sensing signaling pathways.[ 37 ] In OA chondrocytes, majority of ATP is generated through glycolysis rather than OXPHOS.[ 38 ] A similar metabolic shift was observed in the TgA86 mouse model, where OXPHOS-related genes were downregulated and SOD1, a negative regulator of glycolysis, was decreased, as confirmed by immunohistochemistry. This suggests a glycolytic shift reminiscent of the Warburg effect, which is known to support ATP production under inflammatory or hypoxic conditions. Notably, T cells also require elevated glycolytic activity for activation, supporting a link between altered metabolism and immune activation in SpA.[ 39 ] Oxidative stress is associated with inflammation and increases in active disease status of SpA.[ 40 ] Excess ROS, such as nitric oxide, superoxide, and hydroxyl radicals, contribute to a pro-inflammatory microenvironment and mitochondrial dysfunction.[ 41 ] In TgA86 mice, ROS detoxification-related genes were upregulated in 10-month-old samples, while SOD1 expression was downregulated. Given that SOD1 is a key antioxidant enzyme converting superoxide to hydrogen peroxide, its reduction may contribute to sustained oxidative stress, promoting inflammation and pathological new bone formation.[ 42 ] In line with previous metabolomic studies in AS, which demonstrated distinct amino acid profiles between controls and patients, our data also revealed altered amino acid metabolism in TgA86 mice.[ 43 , 44 ] Among these, MTHFD1, a key enzyme involved in the de novo biosynthesis of serine from glucose, was significantly downregulated. Given that intracellular serine is essential for maintaining chondrocyte function, reduced MTHFD1 expression may contribute to cartilage degeneration observed in SpA.[ 45 ] Upregulation of fatty acid metabolism was observed in the present study. Fatty acids function as both structural components of membranes and key regulators of cellular energy and signaling. Inpp1, which was significantly upregulated in TgA86 mice, catalyzes the hydrolysis of polyphosphorylated inositols, generating second messengers such as diacylglycerol and Ins(1,4,5)P₃ that activate protein kinase C. These signaling pathways can modulate glucose metabolism and promote the synthesis of glucose-derived free fatty acids.[ 46 ] Furthermore, inositol phosphate signaling downstream of Inpp1 has been linked to enhanced lipid metabolism, supporting the notion of metabolic reprogramming toward lipid utilization.[ 47 ] Finally, chondrogenic signaling and hypoxia-related pathways appear to play a role in pathological bone formation. Increased BMP6/pSmad1/5 signaling and aberrant chondrocyte differentiation have been reported in early-phase AS.[ 48 , 49 ] Our study showed upregulation of hypoxia-inducible pathways in both 4- and 10-month TgA86 mice, suggesting a hypoxic microenvironment conducive to endochondral ossification.[ 50 ] Immunostaining for SOD1, MTHFD1, and Inpp1 revealed distinct expression patterns in TgA86 mice compared to controls, further supporting their involvement in chondrocyte differentiation and pathological bone formation. This study has several limitations that should be considered for future research. First, all transcriptomic results were derived from bulk RNA-seq data, which averages expression levels across heterogeneous cell populations. Further study is needed to discover cell-type–specific expression patterns. Second, we lacked a matched human RNA-sequencing dataset, preventing direct cross-species comparison and limiting the our findings to the murine observations. Finally, functional validation of the candidate genes and pathways identified here was outside the scope of this work; independent cohorts and complementary experimental approaches will be required to confirm and extend these results. Declarations Acknowledgements Not applicable. Authors' contributions SY, SM, and SHK contributed equally to this work. SY performed bioinformatics analysis and drafted the manuscript. SM conducted metabolic flux simulation and protein-protein interaction analysis and drafted the manuscript. SHK carried out immunohistochemistry experiments and drafted the manuscript. SHL and HIY provided clinical guidance and interpretation. JYK performed histopathological analysis. TK, HK, CS, and JK assisted with data analysis and visualization. DT, YZ, JS, and YL contributed to methodology development and data interpretation. MSK and KSK conceived the study, supervised the research, and revised the manuscript. All authors read and approved the final manuscript. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available due to institutional data sharing policies but are available from the corresponding author on reasonable request. Ethics approval All animal experiments were conducted in accordance with the guidelines for the care and use of laboratory animals and were approved by the Institutional Animal Care and Use Committee (IACUC) of Kyung Hee University Hospital at Gangdong (approval number: KHNMC AP 2024-0005). All procedures were performed following the institutional guidelines and relevant national regulations for animal research. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Dougados M, Baeten D, London. England), 377(9783), 2127–37. https://doi.org/10.1016/S0140-6736(11)60071-8 . Keffer J, Probert L, Cazlaris H, Georgopoulos S, Kaslaris E, Kioussis D, Kollias G. Transgenic mice expressing human tumour necrosis factor: a predictive genetic model of arthritis. EMBO J. 1991;10(13):4025–31. https://doi.org/10.1002/j.1460-2075.1991.tb04978.x . Vandooren B, Noordenbos T, Ambarus C, Krausz S, Cantaert T, Yeremenko N, Boumans M, Lutter R, Tak PP, Baeten D. Absence of a classically activated macrophage cytokine signature in peripheral spondylarthritis, including psoriatic arthritis. Arthritis Rheum. 2009;60(4):966–75. https://doi.org/10.1002/art.24406 . Kaaij MH, van Tok MN, Blijdorp IC, Ambarus CA, Stock M, Pots D, Knaup VL, Armaka M, Christodoulou-Vafeiadou E, van Melsen TK, Masdar H, Eskes HJPP, Yeremenko NG, Kollias G, Schett G, Tas SW, van Duivenvoorde LM, Baeten DLP. Transmembrane TNF drives osteoproliferative joint inflammation reminiscent of human spondyloarthritis. J Exp Med. 2020;217(10):e20200288. https://doi.org/10.1084/jem.20200288 . Christodoulou-Vafeiadou E, Geka C, Ntari L, Kranidioti K, Argyropoulou E, Meier F, Armaka M, Mourouzis I, Pantos C, Rouchota M, Loudos G, Denis MC, Karagianni N, Kollias G. Ectopic bone formation and systemic bone loss in a transmembrane TNF-driven model of human spondyloarthritis. Arthritis Res therapy. 2020;22(1):232. https://doi.org/10.1186/s13075-020-02327-4 . Spinelli JB, Haigis MC. The multifaceted contributions of mitochondria to cellular metabolism. Nat Cell Biol. 2018;20(7):745–54. https://doi.org/10.1038/s41556-018-0124-1 . Zhao ZB, Bian ZH, Lin ZM, Wu SF, Long J, Cui Y, Li Y, Li W, Sun EW, Lian ZX, He Y. Single-Cell Analysis of Patients with Axial Spondyloarthritis After Anti-TNFα Treatment: Experimental Data and Review of the Literature. Clin Rev Allergy Immunol. 2023;65(2):136–47. https://doi.org/10.1007/s12016-023-08959-z . Andrews S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online]. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinf (Oxford England). 2013;29(1):15–21. https://doi.org/10.1093/bioinformatics/bts635 . Putri GH, Anders S, Pyl PT, Pimanda JE, Zanini F. Analysing high-throughput sequencing data in Python with HTSeq 2.0. Bioinf (Oxford England). 2022;38(10):2943–5. https://doi.org/10.1093/bioinformatics/btac166 . Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. https://doi.org/10.1186/s13059-014-0550-8 . Blighe K, Rana S, Lewis M. (2024). EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling. R package version 1.24.0. https://github.com/kevinblighe/EnhancedVolcano Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L, Fu X, Liu S, Bo X, Yu G. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innov (Cambridge (Mass)). 2021;2(3):100141. https://doi.org/10.1016/j.xinn.2021.100141 . Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102(43):15545–50. https://doi.org/10.1073/pnas.0506580102 . Merico D, Isserlin R, Stueker O, Emili A, Bader GD. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS ONE. 2010;5(11):e13984. https://doi.org/10.1371/journal.pone.0013984 . Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504. https://doi.org/10.1101/gr.1239303 . Korotkevich G, Sukhov V, Budin N, Shpak B, Artyomov MN, Sergushichev A. Fast gene set enrichment analysis. bioRxiv. 2021;060012. https://doi.org/10.1101/060012 . Rath S, Sharma R, Gupta R, Ast T, Chan C, Durham TJ, Goodman RP, Grabarek Z, Haas ME, Hung WHW, Joshi PR, Jourdain AA, Kim SH, Kotrys AV, Lam SS, McCoy JG, Meisel JD, Miranda M, Panda A, Patgiri A, Mootha VK. MitoCarta3.0: an updated mitochondrial proteome now with sub-organelle localization and pathway annotations. Nucleic Acids Res. 2021;49(D1):D1541–7. https://doi.org/10.1093/nar/gkaa1011 . Guarnieri JW, Dybas JM, Fazelinia H, Kim MS, Frere J, Zhang Y, Soto Albrecht Y, Murdock DG, Angelin A, Singh LN, Weiss SL, Best SM, Lott MT, Zhang S, Cope H, Zaksas V, Saravia-Butler A, Meydan C, Foox J, Mozsary C, Wallace DC. Core mitochondrial genes are down-regulated during SARS-CoV-2 infection of rodent and human hosts. Sci Transl Med. 2023;15(708):eabq1533. https://doi.org/10.1126/scitranslmed.abq1533 . da Silveira WA, Fazelinia H, Rosenthal SB, Laiakis EC, Kim MS, Meydan C, Kidane Y, Rathi KS, Smith SM, Stear B, Ying Y, Zhang Y, Foox J, Zanello S, Crucian B, Wang D, Nugent A, Costa HA, Zwart SR, Schrepfer S, Beheshti A. Comprehensive Multi-omics Analysis Reveals Mitochondrial Stress as a Central Biological Hub for Spaceflight Impact. Cell. 2020;183(5):1185–e120120. https://doi.org/10.1016/j.cell.2020.11.002 . Ebrahim A, Lerman JA, Palsson BO, Hyduke DR. COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Syst Biol. 2013;7:74. https://doi.org/10.1186/1752-0509-7-74 . Sigurdsson MI, Jamshidi N, Steingrimsson E, Thiele I, Palsson BØ. A detailed genome-wide reconstruction of mouse metabolism based on human Recon 1. BMC Syst Biol. 2010;4:140. https://doi.org/10.1186/1752-0509-4-140 . King ZA, Dräger A, Ebrahim A, Sonnenschein N, Lewis NE, Palsson BO. Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways. PLoS Comput Biol. 2015;11(8):e1004321. https://doi.org/10.1371/journal.pcbi.1004321 . Zhang C, Shen L. Functional modules analysis based on protein-protein network analysis in ankylosing spondylitis. Eur Rev Med Pharmacol Sci. 2012;16(13):1821–7. Park-Min KH. Metabolic reprogramming in osteoclasts. Semin Immunopathol. 2019;41(5):565–72. https://doi.org/10.1007/s00281-019-00757-0 . Shen L, Yu Y, Karner CM. SLC38A2 provides proline and alanine to regulate postnatal bone mass accrual in mice. Front Physiol. 2022;13:992679. https://doi.org/10.3389/fphys.2022.992679 . Wakitani S, Nawata M, Kawaguchi A, Okabe T, Takaoka K, Tsuchiya T, Nakaoka R, Masuda H, Miyazaki K. Serum keratan sulfate is a promising marker of early articular cartilage breakdown. Rheumatology (Oxford). 2007;46(11):1652–6. https://doi.org/10.1093/rheumatology/kem220 . Montllor-Albalate C, Kim H, Thompson AE, Jonke AP, Torres MP, Reddi AR. Sod1 integrates oxygen availability to redox regulate NADPH production and the thiol redoxome. Proc Natl Acad Sci USA. 2022;119(1):e2023328119. https://doi.org/10.1073/pnas.2023328119 . Stegen S, Loopmans S, Stockmans I, Moermans K, Carmeliet P, Carmeliet G. De novo serine synthesis regulates chondrocyte proliferation during bone development and repair. Bone Res. 2022;10(1):14. https://doi.org/10.1038/s41413-021-00185-7 . Pandurangan AK, Ismail S, Esa NM, Munusamy MA. Inositol-6 phosphate inhibits the mTOR pathway and induces autophagy-mediated death in HT-29 colon cancer cells. Archives Med science: AMS. 2018;14(6):1281–8. https://doi.org/10.5114/aoms.2018.76935 . Benjamin DI, Louie SM, Mulvihill MM, Kohnz RA, Li DS, Chan LG, Sorrentino A, Bandyopadhyay S, Cozzo A, Ohiri A, Goga A, Ng SW, Nomura DK. Inositol phosphate recycling regulates glycolytic and lipid metabolism that drives cancer aggressiveness. ACS Chem Biol. 2014;9(6):1340–50. https://doi.org/10.1021/cb5001907 . Chen LL, Huang M, Tan JY, Chen XT, Lei LH, Wu YM, Zhang DY. PI3K/AKT pathway involvement in the osteogenic effects of osteoclast culture supernatants on preosteoblast cells. Tissue Eng Part A. 2013;19(19–20):2226–32. https://doi.org/10.1089/ten.TEA.2012.0469 . Wu X, Fan X, Crawford R, Xiao Y, Prasadam I. The Metabolic Landscape in Osteoarthritis. Aging disease. 2022;13(4):1166–82. https://doi.org/10.14336/AD.2021.1228 . Hoffmann MA, Nothias LF, Ludwig M, Fleischauer M, Gentry EC, Witting M, Dorrestein PC, Dührkop K, Böcker S. High-confidence structural annotation of metabolites absent from spectral libraries. Nat Biotechnol. 2022;40(3):411–21. https://doi.org/10.1038/s41587-021-01045-9 . Fei Y, Yu H, Wu Y, Gong S. The causal relationship between immune cells and ankylosing spondylitis: a bidirectional Mendelian randomization study. Arthritis Res therapy. 2024;26(1):24. https://doi.org/10.1186/s13075-024-03266-0 . Martínez-Reyes I, Chandel NS. Mitochondrial TCA cycle metabolites control physiology and disease. Nat Commun. 2020;11(1):102. https://doi.org/10.1038/s41467-019-13668-3 . Wu X, Fan X, Crawford R, Xiao Y, Prasadam I. The Metabolic Landscape in Osteoarthritis. Aging disease. 2022;13(4):1166–82. https://doi.org/10.14336/AD.2021.1228 . Martin JA, Martini A, Molinari A, Morgan W, Ramalingam W, Buckwalter JA, McKinley TO. Mitochondrial electron transport and glycolysis are coupled in articular cartilage. Osteoarthr Cartil. 2012;20(4):323–9. https://doi.org/10.1016/j.joca.2012.01.003 . Macintyre AN, Gerriets VA, Nichols AG, Michalek RD, Rudolph MC, Deoliveira D, Anderson SM, Abel ED, Chen BJ, Hale LP, Rathmell JC. The glucose transporter Glut1 is selectively essential for CD4 T cell activation and effector function. Cell Metabol. 2014;20(1):61–72. https://doi.org/10.1016/j.cmet.2014.05.004 . Solmaz D, Kozacı D, Sarı İ, Taylan A, Önen F, Akkoç N, Akar S. Oxidative stress and related factors in patients with ankylosing spondylitis. Eur J Rheumatol. 2016;3(1):20–4. https://doi.org/10.5152/eurjrheum.2015.0031 . Ye G, Xie Z, Zeng H, Wang P, Li J, Zheng G, Wang S, Cao Q, Li M, Liu W, Cen S, Li Z, Wu Y, Ye Z, Shen H. Oxidative stress-mediated mitochondrial dysfunction facilitates mesenchymal stem cell senescence in ankylosing spondylitis. Cell Death Dis. 2020;11(9):775. https://doi.org/10.1038/s41419-020-02993-x . Park JH, Elpers C, Reunert J, McCormick ML, Mohr J, Biskup S, Schwartz O, Rust S, Grüneberg M, Seelhöfer A, Schara U, Boltshauser E, Spitz DR, Marquardt T. SOD1 deficiency: a novel syndrome distinct from amyotrophic lateral sclerosis. Brain. 2019;142(8):2230–7. https://doi.org/10.1093/brain/awz182 . Huang T, Pu Y, Wang X, Li Y, Yang H, Luo Y, Liu Y. Metabolomic analysis in spondyloarthritis: A systematic review. Front Microbiol. 2022;13:965709. https://doi.org/10.3389/fmicb.2022.965709 . Zhou Y, Zhang X, Chen R, Han S, Liu Y, Liu X, Gao M, Yang C, Lu D, Sun B, Chen H. Serum amino acid metabolic profiles of ankylosing spondylitis by targeted metabolomics analysis. Clin Rheumatol. 2020;39(8):2325–36. https://doi.org/10.1007/s10067-020-04974-z . Stegen S, Loopmans S, Stockmans I, Moermans K, Carmeliet P, Carmeliet G. De novo serine synthesis regulates chondrocyte proliferation during bone development and repair. Bone Res. 2022;10(1):14. https://doi.org/10.1038/s41413-021-00185-7 . Benjamin DI, Louie SM, Mulvihill MM, Kohnz RA, Li DS, Chan LG, Sorrentino A, Bandyopadhyay S, Cozzo A, Ohiri A, Goga A, Ng SW, Nomura DK. Inositol phosphate recycling regulates glycolytic and lipid metabolism that drives cancer aggressiveness. ACS Chem Biol. 2014;9(6):1340–50. https://doi.org/10.1021/cb5001907 . Ghoshal S, Tyagi R, Zhu Q, Chakraborty A. Inositol hexakisphosphate kinase-1 interacts with perilipin1 to modulate lipolysis. Int J Biochem Cell Biol. 2016;78:149–55. https://doi.org/10.1016/j.biocel.2016.06.018 . Yu T, Zhang J, Zhu W, Wang X, Bai Y, Feng B, Zhuang Q, Han C, Wang S, Hu Q, An S, Wan M, Dong S, Xu J, Weng X, Cao X. Chondrogenesis mediates progression of ankylosing spondylitis through heterotopic ossification. Bone Res. 2021;9(1):19. https://doi.org/10.1038/s41413-021-00140-6 . Shao F, Liu Q, Zhu Y, Fan Z, Chen W, Liu S, Li X, Guo W, Feng GS, Yu H, Xu Q, Sun Y. Targeting chondrocytes for arresting bony fusion in ankylosing spondylitis. Nat Commun. 2021;12(1):6540. https://doi.org/10.1038/s41467-021-26750-6 . Hirao M, Tamai N, Tsumaki N, Yoshikawa H, Myoui A. Oxygen tension regulates chondrocyte differentiation and function during endochondral ossification. J Biol Chem. 2006;281(41):31079–92. https://doi.org/10.1074/jbc.M602296200 . Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7116883","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492721478,"identity":"55759649-2a77-4039-8a6e-2a821e9fbe28","order_by":0,"name":"Sanghyeon Yu","email":"","orcid":"","institution":"Kyung Hee University Hospital at Gangdong","correspondingAuthor":false,"prefix":"","firstName":"Sanghyeon","middleName":"","lastName":"Yu","suffix":""},{"id":492721479,"identity":"5a84e5a8-257a-4286-9233-605b09fdf87e","order_by":1,"name":"Seokho Myung","email":"","orcid":"","institution":"Kyung Hee University Hospital at Gangdong","correspondingAuthor":false,"prefix":"","firstName":"Seokho","middleName":"","lastName":"Myung","suffix":""},{"id":492721480,"identity":"b0a041aa-776f-4577-97ec-144a5e0cb8d5","order_by":2,"name":"Se Hee Kim","email":"","orcid":"","institution":"Kyung Hee University","correspondingAuthor":false,"prefix":"","firstName":"Se","middleName":"Hee","lastName":"Kim","suffix":""},{"id":492721481,"identity":"81f2e9e1-1fab-4442-88dc-ecd83837281e","order_by":3,"name":"Sang-Hoon Lee","email":"","orcid":"","institution":"Kyung Hee University","correspondingAuthor":false,"prefix":"","firstName":"Sang-Hoon","middleName":"","lastName":"Lee","suffix":""},{"id":492721482,"identity":"6c709e3a-2412-411a-9008-a896e18ca2f4","order_by":4,"name":"Jung Yeon Kim","email":"","orcid":"","institution":"Inje University Sanggye Paik Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jung","middleName":"Yeon","lastName":"Kim","suffix":""},{"id":492721483,"identity":"93123d80-5643-4beb-8808-942286172f10","order_by":5,"name":"Taeyoon Kim","email":"","orcid":"","institution":"Kyung Hee University Hospital at Gangdong","correspondingAuthor":false,"prefix":"","firstName":"Taeyoon","middleName":"","lastName":"Kim","suffix":""},{"id":492721484,"identity":"b782ca71-232c-408c-885b-84670def1eb4","order_by":6,"name":"Hojung Kim","email":"","orcid":"","institution":"Kyung Hee University Hospital at Gangdong","correspondingAuthor":false,"prefix":"","firstName":"Hojung","middleName":"","lastName":"Kim","suffix":""},{"id":492721485,"identity":"0987e863-ba0f-409b-aff6-6e264fd7ec24","order_by":7,"name":"Chaerim Song","email":"","orcid":"","institution":"Kyung Hee University Hospital at Gangdong","correspondingAuthor":false,"prefix":"","firstName":"Chaerim","middleName":"","lastName":"Song","suffix":""},{"id":492721486,"identity":"8546d6b4-47ff-4d52-921a-434f9431ba3a","order_by":8,"name":"Hyung-In Yang","email":"","orcid":"","institution":"Kyung Hee University","correspondingAuthor":false,"prefix":"","firstName":"Hyung-In","middleName":"","lastName":"Yang","suffix":""},{"id":492721487,"identity":"561c911c-c7fd-48d5-8e6f-fa7b0110ba07","order_by":9,"name":"Junghyun Kim","email":"","orcid":"","institution":"Hankuk University of Foreign Studies","correspondingAuthor":false,"prefix":"","firstName":"Junghyun","middleName":"","lastName":"Kim","suffix":""},{"id":492721488,"identity":"aac6a238-f8f7-4466-b0da-1b42ae406a4d","order_by":10,"name":"Deanne Taylor","email":"","orcid":"","institution":"Children's Hospital of Philadelphia","correspondingAuthor":false,"prefix":"","firstName":"Deanne","middleName":"","lastName":"Taylor","suffix":""},{"id":492721489,"identity":"1e2e84cc-e6fe-4a5c-adcf-277001efb2c1","order_by":11,"name":"Yuanchao Zhang","email":"","orcid":"","institution":"Children's Hospital of Philadelphia","correspondingAuthor":false,"prefix":"","firstName":"Yuanchao","middleName":"","lastName":"Zhang","suffix":""},{"id":492721490,"identity":"1373949d-6dbc-48bf-8f36-72ebfaea1af5","order_by":12,"name":"Jaeyong Shin","email":"","orcid":"","institution":"Yonsei University","correspondingAuthor":false,"prefix":"","firstName":"Jaeyong","middleName":"","lastName":"Shin","suffix":""},{"id":492721491,"identity":"cf6aedc6-c671-4a16-983f-8d2b3d36ab4f","order_by":13,"name":"Yoonsung Lee","email":"","orcid":"","institution":"Kyung Hee University Hospital at Gangdong","correspondingAuthor":false,"prefix":"","firstName":"Yoonsung","middleName":"","lastName":"Lee","suffix":""},{"id":492721492,"identity":"602ca97d-02c7-4ab9-b3b6-c306d72dbe78","order_by":14,"name":"Man S. Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYJACxgYDBjkJCQjHgGgtxqRqYWBInEG0Ft32s4c/zijYlj5zdo8Bw48aBmPzBgJazM7kpUluMLidO1vmjAFjzzEGM5kDhLQcyDFjfADUMk8ix4CBt4HBRoKQw8zOvzH+CNSSLgfUwviXKC03cgxADkuQBmphBtpiRoSWN2aSMwxuG86ckVZwWOaYhDERDssx/tjz57a8xI3kjQ/f1NgYziCkBQUcYGAgaMcoGAWjYBSMAmIAALr2PTS8JTuMAAAAAElFTkSuQmCC","orcid":"","institution":"Kyung Hee University Hospital at Gangdong","correspondingAuthor":true,"prefix":"","firstName":"Man","middleName":"S.","lastName":"Kim","suffix":""},{"id":492721494,"identity":"0d344f48-b7ed-4176-bbc3-222bd270991b","order_by":15,"name":"Kyoung Soo Kim","email":"","orcid":"","institution":"Kyung Hee University","correspondingAuthor":false,"prefix":"","firstName":"Kyoung","middleName":"Soo","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2025-07-14 04:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7116883/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7116883/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87926054,"identity":"4b04206a-952a-468c-a4bb-3753d34e4aa1","added_by":"auto","created_at":"2025-07-30 12:38:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":433795,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive overview of the workflow, encompassing sample collection through to experimental validation.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7116883/v1/67c240dbbe990d40e97aa6b6.png"},{"id":87925418,"identity":"a70d8ba1-31dc-4342-a50f-f7bde8fb2fa6","added_by":"auto","created_at":"2025-07-30 12:30:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":685810,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential Expression and Enrichment Analysis between TgA86 and Control Groups. (A) A volcano plot displaying the differentially expressed genes between TgA86 and control groups, with statistically significant genes highlighted in red (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 and |log2FoldChange| \u0026gt; 1). (B) A dot plot presenting the top 10 enriched gene ontology pathways identified from the differentially expressed genes in A (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). (C) A network visualization showing the interconnections between selected gene ontology pathways enriched in the differential expression analysis comparing TgA86 and control.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7116883/v1/67fc682d60455d280de5e89d.png"},{"id":87927833,"identity":"55ce0c07-5b16-40c6-91a4-fdb342fb213b","added_by":"auto","created_at":"2025-07-30 12:54:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":613531,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap illustrating t-scores and gene expression profiles within curated mitochondrial energy metabolism-associated gene sets. (B) Lollipop plot showing normalized enrichment scores of the MitoCarta3.0 gene set, computed using the fgsea R package.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7116883/v1/0f32a10461727d97e1f65c9b.png"},{"id":87927054,"identity":"eb019bcd-7f95-4a54-a769-92624a9896a9","added_by":"auto","created_at":"2025-07-30 12:46:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":282931,"visible":true,"origin":"","legend":"\u003cp\u003eAn upset plot displaying the number of reactions exhibiting significant changes (P \u0026lt; 0.1, van der Waerden test) with a minimum of three altered reactions.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7116883/v1/67d338455296e4dbc798a8c9.png"},{"id":87926061,"identity":"e3768e85-4578-426d-ab38-71967ed4dbb9","added_by":"auto","created_at":"2025-07-30 12:38:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":194447,"visible":true,"origin":"","legend":"\u003cp\u003eThe PPI (protein-protein interaction) network shows protein interactions linked with collected as candidate RNAs based on metabolic flux simulations.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7116883/v1/917e81ee3dfbc713dfc417e8.png"},{"id":87925431,"identity":"fc779b12-669b-4484-a67a-89b62f00fd55","added_by":"auto","created_at":"2025-07-30 12:30:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":370965,"visible":true,"origin":"","legend":"\u003cp\u003eCombined analysis of metabolic flux simulation and immunohistochemical validation. (A) Metabolic flux map illustrating key differences in reaction activities between the TgA86 model and control mice, visualized using Escher. (B) Representative immunohistochemical staining images demonstrating expression patterns of selected marker genes (INPP1, MTHFD1, and SOD1).\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7116883/v1/13d359966b2518cf2d077abf.png"},{"id":91294145,"identity":"e430b22c-51e4-46ab-a6c5-452941470518","added_by":"auto","created_at":"2025-09-15 02:31:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3184348,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7116883/v1/1c1df526-9729-49a2-9a52-41734453da3e.pdf"},{"id":87925414,"identity":"d487a575-fefc-4d34-bc83-6cf2eeea6753","added_by":"auto","created_at":"2025-07-30 12:30:00","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26602,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7116883/v1/17a1b1e8199a7916f0167868.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"TgA86 mouse model demonstrated potential markers for Bone Formation in Spondyloarthritis through ROS Detoxification, Folate Metabolism, and Inositol Phosphate Metabolism","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSpondyloarthritis (SpA) is a family of diseases encompassing ankylosing spondylitis (AS), psoriatic arthritis, inflammatory-bowel disease (IBD)-associated arthritis, reactive arthritis, and undifferentiated SpA. These subtypes typically affect the spine and sacroiliac joints, with inflammation often progressing to new bone formation and ankylosis, distinguishing SpA from other inflammatory arthritides lacking pronounced axial involvement.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eTumor necrosis factor-alpha (TNF-α) exists in two bioactive forms, transmembrane (tmTNF-α) and soluble (sTNF-α), the latter cleaved by TNF-α-converting enzyme (TACE/ADAM17). Although TNF-overexpressing mouse models replicate various inflammatory features of spondyloarthritis (SpA), they typically fail to show the characteristic new bone formation of SpA.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] In SpA, decreased sTNF-α, elevated tmTNF, and reduced ADAM17 activity fuel this imbalance.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] The TgA86 model, which overexpresses a mutant murine TNF gene with a defective ADAM17 cleavage site, closely mimics vertebral fusion and sacroiliac joint ankylosis, making it invaluable for studying SpA pathogenesis.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eMitochondrial metabolism is increasingly recognized as critical to disease development.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Recent multi-omics advances spotlight immune-regulation genes and pathways related to bone remodeling in SpA, yet many investigations focus on a single system level, often overlooking metabolic flux.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] Employing a multi-system-level approach, this study aims to elucidate key transcriptional and metabolic changes behind new bone formation in TgA86 mice. We compared mRNA expression, analyzed functional pathways, simulated metabolic flux, and examined protein\u0026ndash;protein interaction networks. Through these methods, we identified significantly altered genes, metabolic reactions, and regulatory mechanisms that may distinguish TgA86 from normal samples, offering insights into SpA-associated bone overgrowth.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cem\u003eSample Preparation and RNA sequencing\u003c/em\u003e\u003c/p\u003e\u003cp\u003eMouse spine bones were isolated, flash-frozen in liquid nitrogen, and ground into powder. Total RNA was extracted from this powder using TRIzol Reagent (Thermo Fisher Scientific) according to the manufacturer's protocol and subsequently treated with DNase I. Depending on the analysis goal, mRNA was enriched (poly(A) selection) or rRNA was depleted (ribo-zero kit). The resulting RNA was fragmented, reverse transcribed into cDNA using random hexamers, ligated with unique adapters, and PCR amplified. Size-selected libraries (200\u0026ndash;400 bp inserts) were then sequenced on an Illumina NovaSeq platform using paired-end reads for comprehensive transcriptome analysis.\u003c/p\u003e\u003cp\u003e\u003cem\u003eData Processing\u003c/em\u003e\u003c/p\u003e\u003cp\u003eSequencing data quality was assessed using FASTQC (v0.11.7).[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Reads were aligned to the GRCm39 Mus musculus genome with STAR aligner (v2.7.3a). Gene expression was quantified using HTSeq-Count (v0.12.4) in union mode to generate an expression matrix.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Raw counts were then normalized using the regularized log (rlog) transformation in DESeq2 (v1.44.0) to stabilize variance for downstream analyses.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e\u003cp\u003e\u003cem\u003eDifferential and enrichment analysis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eDifferential expression analysis (TgA86 vs control) used DESeq2 (v1.44.0). Inflammatory genes were curated from MSigDB. DEGs were visualized with EnhancedVolcano (v1.24.0) .[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Gene ontology analysis used ClusterProfiler's enrichGO (v4.6.2) to identify enriched pathways separately in each group.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] GSEA (v4.3.2) examined pathway associations, visualized as networks using EnrichmentMap (v3.5.0) in Cytoscape.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] Additionally, fgsea (v1.24.0) calculated Normalized Enrichment Scores for MitoCarta 3.0 modules, depicted as lollipop plots.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Energy-associated core gene expression comparisons were visualized using heatmaps.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\u003cp\u003e\u003cem\u003eMetabolic flux simulation\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe fundamental setup of our study was rigorously aligned with methodologies from prior research by conducting Flux Balance Analysis (FBA) with the cobrapy package in a Python 3.7.6 environment, utilizing the imm1415 metabolic base model.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] We implemented two key constraints: (i) To assess the influence of specific enzymes on the relative reaction rates of metabolic pathways, enzyme expression levels were constrained using CORDA (v1.0.0). (ii) We manually constrained energy-associated metabolic pathways-such as \u0026lsquo;Oxidative Phosphorylation\u0026rsquo;, \u0026rsquo;Glycolysis/Gluconeogenesis\u0026rsquo;, \u0026lsquo;Citric Acid Cycle\u0026rsquo;, \u0026rsquo;CoA Biosynthesis\u0026rsquo;, \u0026rsquo;CoA Catabolism\u0026rsquo;, \u0026rsquo;Biomass and maintenance functions\u0026rsquo;, \u0026rsquo;NAD Metabolism\u0026rsquo;, \u0026rsquo;Fatty Acid Metabolism\u0026rsquo;, \u0026rsquo;Fatty acid activation\u0026rsquo;, \u0026rsquo;Fatty acid elongation\u0026rsquo;, and \u0026lsquo;Fatty acid oxidation\u0026rsquo;-to maximize their activity. Gene expression profiles were incorporated into the model for each sample, while parameters were consistently maintained across simulations, providing flux levels for all reactions. These flux levels were analyzed as grouped variables to compare the 'TgA86' and 'control' groups using a custom flux balance analysis. A non-parametric van der Waerden test was employed for comparison, utilizing the R package matrixTests (v. 0.1.9), and metabolic flux alterations were visualized with upset plots and Escher.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\u003cp\u003e\u003cem\u003eImmunochemistry\u003c/em\u003e\u003c/p\u003e\u003cp\u003eBone tissue from 4- or 10-month-old mice was fixed in 10% formalin (24\u0026ndash;48 hrs) and decalcified in 10% EDTA (1\u0026ndash;3 weeks, 4\u0026deg;C). Tissues were paraffin-embedded and sectioned at 4 \u0026micro;m. Sections were mounted, deparaffinized, rehydrated, and rinsed in PBS. Antigen retrieval involved heating in citrate (pH 6.0) or EDTA (pH 8.0) buffer (95\u0026deg;C, 15\u0026ndash;20 min). Sections were blocked (5% BSA, 30\u0026ndash;60 min, RT) and incubated with primary antibodies [INPP1(Novusbio, NBP1-314715), MTHFD1(Novusbio, NBP1-82612), SOD1(abcam, ab51245)] diluted in PBS (overnight, 4\u0026deg;C). After 3x PBS washes, HRP-conjugated secondary antibody was applied (1 hr, RT, dark). Following 3x PBS washes, signals were detected using DAB chromogenic staining, and slides were viewed by light microscopy.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cem\u003eMulti-system-level Analysis of SpA Progression\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, five TgA86 and five control mouse samples were collected at 4 and 10 months. After RNA sequencing, differential expression analyses were performed to compare TgA86 and control samples using three gene sets: Differentially Expressed Genes (DEGs), a customized group of mitochondria-associated genes, and MitoCarta3.0. Pathway enrichment was assessed through Gene Set Enrichment Analysis (GSEA) and Fast GSEA (fgsea), while metabolic flux simulations identified key reactions altered in SpA progression. A refined list of potential targets was narrowed down via protein\u0026ndash;protein interaction analysis and was validated using immunohistochemistry.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eDifferential Gene Expression and Pathway Enrichment Analysis in the TgA86 Model\u003c/em\u003e\u003c/p\u003e\u003cp\u003eSince there is currently no information available on the gene expression profiles of the TgA86 mouse model at 4 and 10 months, we initially investigated the number of significantly differentially expressed genes (DEGs) in the TgA86 model across this timeframe. Given that inflammation is a hallmark of spondyloarthritis (SpA), we also evaluated the number of inflammation-associated genes for the two distinct time points. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, we identified 2,908 DEGs at 4 months and 2,024 DEGs at 10 months. Among these, 582 inflammation-associated DEGs were identified at 4 months, and 442 at 10 months. As expected, known inflammatory genes exhibited significant expression differences in the TgA86 model. Additionally, the identification of a sufficient number of DEGs supports the feasibility of various downstream analyses.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn addition, to determine whether groups of the identified DEGs are collectively associated with functions classified in databases from previous studies, we conducted a Gene Ontology (GO) analysis. The comparative groups were as follows: TgA86 versus control at both 4 months and 10 months, and TgA86 at 4 months versus 10 months as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA. In the comparison between TgA86 and the control at 4 months, \u0026lsquo;positive regulation of cytokine production\u0026rsquo; and \u0026lsquo;adaptive immune response\u0026rsquo; were significantly upregulated in TgA86. From the 10-month comparison, an enhancement was observed in 'positive regulation of cell motility' and 'positive regulation of cell migration,' whose processes are associated with innate immunity, alongside 'inflammatory response' and 'immune effector process'. Comparing TgA at 10 months with TgA at 4 months revealed an enrichment of pathways involved in various intracellular signaling processes and innate immunity in the 10-month group. Taken together, these findings reaffirm that, as is well-known for SpA, the observed changes are predominantly associated not only with inflammation but also with various immune responses. However, this analytical method did not provide potential links associated with bone formation of the TgA86 model.\u003c/p\u003e\u003cp\u003eAlthough the GO analysis revealed enrichment in diverse signaling processes beyond immune-associated responses (including inflammatory responses), we additionally conducted a gene set enrichment analysis (GSEA) to further investigate whether the groups of DEGs were associated with functions other than immune-associated responses from a different perspective (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2).\u003c/p\u003e\u003cp\u003eOverall, as expected, a broad range of immune-related responses were either upregulated or downregulated in the TgA86 model at both 4- and 10-month time points. In addition, alterations in cell cycle\u0026ndash;associated pathways were observed, aligning with previous microarray analyses that identified cell cycle regulation as one of the top enriched pathways in AS, consistent with the findings of Zhang et al.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eHowever, a potential process, specifically the metabolic process, was concurrently observed, where the relationship between metabolic processes and bone formation in SpA, along with the underlying mechanisms, remains largely unexplored. Given that energy metabolism, as a subset of metabolic processes, is crucial for maintaining cellular homeostasis in conjunction with other metabolic processes, we focused our subsequent analysis on the alterations associated with energy metabolism.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e\u003cp\u003e\u003cem\u003eAnalysis of Energy Metabolism-Related Gene Expression in TgA86 Mice\u003c/em\u003e\u003c/p\u003e\u003cp\u003eMitochondria are central to energy metabolism, and considering their crucial role in bone formation, we investigated core gene expression profiles associated with energy metabolism whose gene list was provided from \u003cem\u003eGuarnieri et al\u003c/em\u003e as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eComparative analysis of the core expression levels of energy metabolism-associated genes between the 4-month and 10-month TgA86 samples and the control, based on T-scores, revealed a general downregulation of genes associated with antioxidant defenses, including Sod1 and Gpx1, in both TgA86 samples. Additionally, genes involved in folate metabolism, such as Mthfd1, Mthfd2, and Shmt2, exhibited a significant decrease. These findings underscore the deep connection between these pathways and mitochondrial dysfunction. Conversely, we observed a notable upregulation in genes associated with the hypoxia-inducible factor (HIF) pathway, particularly Hif1a and Bnip3.\u003c/p\u003e\u003cp\u003eTo further analyze the alterations in mitochondrial energy metabolism from a different perspective, we implemented the fgsea package to estimate the activation levels of modules (\u003cem\u003ei.e.\u003c/em\u003e, gene groups) based on MitoCarta3.0. as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] The activation of Oxidative Phosphorylation, which can be considered the primary functional pathway of mitochondria, was observed to be attenuated in TgA86 compared to the control at both 4 months and 10 months. This reduction in ATP production observed in the TgA86 model indicated an increased potential for metabolic dysregulation in SpA. Also, modules on mitochondrial central dogma unveiled significant downregulations in the TgA86 model at both 4 months and 10 months. Contrarily, the activation levels of diverse metabolic pathways were enhanced in the TgA86 model at both 4 months and 10 months. Specifically, metabolisms on creatine, choline, cholesterol, bile acid, steroid, catechol, and carnitine were further activated in the TgA86 model. This finding aligned with multiple previous studies that have reported elevated levels of creatine, cholesterol, choline, and carbohydrate metabolism products in the serum of SpA patients.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e\u003cp\u003e\u003cem\u003eIdentification of altered metabolic pathways associated with bone formation and inflammation in the TgA86 model\u003c/em\u003e\u003c/p\u003e\u003cp\u003eConsidering the potential association of metabolic alterations with the underlying mechanism of bone formation in the TgA86 model, we conducted metabolic flux simulations across all available metabolic reactions, utilizing our context-specific constraint-based metabolic modeling approach adapted from \u003cem\u003eda Silveira WA et al\u003c/em\u003e.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Among the metabolic pathways with at least 10% of reactions displaying significant alterations, Carnitine shuttle, TCA cycle, Fatty acid elongation, Fatty acid oxidation, Keratan sulfate degradation, NAD metabolism, and Sphingolipid metabolism exhibited relatively more pronounced changes as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAmong the list, keratan sulfate degradation unveiled the most significant alterations. This finding can be particularly considerable, as keratan sulfate has been implicated in bone formation processes.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] Sphingolipid metabolism, which displayed the second most significant change, demonstrated associations with inflammatory responses, suggesting a potential link to SpA. Some other metabolic pathways (\u003cem\u003ei.e.\u003c/em\u003e, TCA cycle, NAD metabolism, and tyrosine metabolism) potentially associated with bone formation also demonstrated substantial alterations.\u003c/p\u003e\u003cp\u003e\u003cem\u003eIntegrating protein level analysis using protein-protein interaction to identify candidate genes in SpA\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo identify potential functional genes in the TgA86 model, we conducted a comprehensive analysis of molecular alterations from both transcriptional and metabolic perspectives. Metabolically, we focused on the calculated flux differences in specific reactions between the TgA86 and control groups. Transcriptionally, we compared the expression levels of genes predicted to encode enzymes for those reactions. This multi-system-level analytical approach led to the identification of several metabolic reactions and their corresponding genes as potential markers for TgA86, as detailed in Additional file 1: Table\u0026nbsp;3.\u003c/p\u003e\u003cp\u003eAs depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, a protein-protein interaction analysis indicates that these candidate genes have a high likelihood of interacting, as determined by various criteria such as network type, edge significance, interaction source, and minimum interaction score. While enhancing the precision of potential gene selection by considering the probability of identifying pertinent interactions, we further explored additional interaction traces with other proteins (or genes).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOverall, a total of 28 genes were considered as candidates, among which 23 genes formed four distinct protein networks. The biggest network contained 16 genes (\u003cem\u003ei.e.\u003c/em\u003e, Aldh9a1, Aldh3a2, Aoc3, Aldh1a1, Mthfd1, Idh1, Ndufs3, Ndufb5, Sucla2, Prdx2, Gpx1, Sod1, Ucp2, Ptgs2, Sod3, and Enpp2) and the second biggest one included 3 genes (\u003cem\u003ei.e.\u003c/em\u003e,Hexa, Manba, and Hexb), and the others had two genes (\u003cem\u003ei.e.\u003c/em\u003e, either Dut and Nme3, or Cdipt and Inpp1).\u003c/p\u003e\u003cp\u003eThe changes in transcriptional expression of the above genes during disease progression were further validated at the protein level through immunohistochemistry using commercially available antibodies. Among the 19 candidate genes, SOD1, INPP1, and MTHFD1 were selected for further investigation due to their known roles in oxidative stress, immune signaling, and metabolic pathways-mechanisms that plausibly contribute to the pathogenesis of SpA.\u003c/p\u003e\u003cp\u003e\u003cem\u003eInvestigating the roles of SOD1, MTHFD1, and INPP1 in SpA pathogenesis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAs depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, considerable forward-direction alterations in the SPODM and SPODMx reactions associated with the Sod1 enzyme activity in the TgA86 model were detected at both 4 and 10 months, indicating its role in ROS detoxification. Previous studies have demonstrated that oxidative stress induces a decline in Sod1 levels, leading to reduced bone stiffness and decreased bone marrow density.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] The association of decreased Sod1 with anaerobic conditions may suggest its potential role as a marker gene for ankylosis, a pathological reduction in bone density characteristic of AS.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSubstantial activation changes in the MTHFD reaction, mediated by the Mthfd1 enzyme, were observed within the folate metabolism as being backwarded in 4 months and forwarded in 10 months. Studies indicated that folate metabolism is linked to cytoskeletal remodeling, bone proliferation, and migration, with a particular result that a decrease in Mthfd1 was associated with an increase in serine metabolism, which regulates chondrocyte proliferation.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] This aligns with our detection, where Mthfd1 staining in chondrocytes suggests that, in AS, Mthfd1 could be a pivotal regulator of chondrocyte proliferation via serine metabolism.\u003c/p\u003e\u003cp\u003eWithin inositol phosphate metabolism, a notable alteration in the MI14PPreaction mediated by Inpp1 was observed in the TgA86 model, shifting from a backward direction at 4 months to a forward direction at 10 months. Inositol phosphates are known to influence signaling pathways such as PI3K/AKT/mTOR, which are involved in regulating chondrocyte proliferation.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] Given that lipid metabolism contributes to aberrant ossification in osteoarthritis (OA), the dynamic change in Inpp1-associated reactions suggests a potential role in chondrocyte activity and pathological bone formation in SpA.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eBuilding on our comprehensive multi-system-level analysis, which identified INPP1, MTHFD1, and SOD1 as potential target genes of significant interest in the context of SpA, we conducted further validation through immunohistochemical staining. This validation process underscored the potential significance of these genes in the pathogenesis of SpA, highlighting their possible roles in the disease's molecular mechanisms. Consistent with our expectations, the expression of INPP1, indicated by brown nuclear staining, was elevated in the nucleus pulposus and adjacent cartilage of 10-month-old mice compared to 4-month-old mice. Conversely, the expression of MTHFD1 and SOD1 was slightly reduced in the nucleus pulposus and adjacent cartilage of 4-month-old mice compared to 10-month-old mice, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eTo comprehensively address diverse biomedical research questions, recent studies have increasingly leveraged multi-omics data from various system levels to identify novel biomarkers. Note that metabolomics data acquisition has gained prominence for exploring metabolic alterations that potentially link genotype to phenotype. However, given the high costs and labor-intensive nature of obtaining metabolomics data, we have adopted an alternative analytical approach: metabolic flux modeling.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] Utilizing the imm1415 base model, our simulation can concurrently examine changes in 2,775 metabolites and 3,726 metabolic reactions, providing a broader scope compared to metabolomics data obtained through gas/liquid chromatography\u0026ndash;mass spectrometry. By integrating diverse analytical methods, including metabolic modeling, across multiple system levels, this study has identified activity changes in various molecules within the TgA86 model and facilitated the discovery of novel biomarkers.\u003c/p\u003e\u003cp\u003eGene expression of the TgA86 mouse model changed from cytokine production and adaptive immune response at 4 months to cell motility and migration associated with innate immunity at 10 months. In the aspect of energy metabolism, energy production of mitochondria decreased as down regulation of genes associated with OXPHOS. On the other hand, genes associated with carbohydrate, amino acid, lipid, and fatty acid metabolism increased. Regarding the mechanism of bone formation, decreased expression of SOD1 and MTHFD1, along with increased expression ofInpp1, was observed in chondrocytes.\u003c/p\u003e\u003cp\u003eUnlike other autoimmune diseases such as rheumatoid arthritis or systemic lupus erythematosus, SpA is characterized by dysfunction in both innate and adaptive immunity. In addition to the IL-23/IL-17 axis, perpetuation of inflammation associated with T cells, NK cells, and macrophages was demonstrated in SpA patients.[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] In our study, GO analysis of the 4-month TgA86 mouse vertebral samples showed increased cytokine production, adaptive immune response and leukocyte mediated immunity with increased leukocyte migration. In 10-months mouse vertebral samples, genes related to inflammatory response, cell motility, and migration were significantly upregulated, suggesting progressive immune dysregulation.\u003c/p\u003e\u003cp\u003eMitochondria are the powerhouse of eukaryotic cell and major biological metabolic pathways in mitochondria are tightly coordinated tricarboxylic acid (TCA) cycle and oxidative phosphorylation (OXPHOS).[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] Cellular metabolisms produce energy fuel in a form of adenosine triphosphate (ATP) to maintain cellular homeostasis and functions, and also decide cell fate. Altered cellular metabolisms induce chronic diseases such as osteoarthritis, by disrupting energy production, biomolecule synthesis, mitochondria and reactive oxygen species (ROS) regulation, or dysregulation of energy-sensing signaling pathways.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] In OA chondrocytes, majority of ATP is generated through glycolysis rather than OXPHOS.[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] A similar metabolic shift was observed in the TgA86 mouse model, where OXPHOS-related genes were downregulated and SOD1, a negative regulator of glycolysis, was decreased, as confirmed by immunohistochemistry. This suggests a glycolytic shift reminiscent of the Warburg effect, which is known to support ATP production under inflammatory or hypoxic conditions. Notably, T cells also require elevated glycolytic activity for activation, supporting a link between altered metabolism and immune activation in SpA.[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eOxidative stress is associated with inflammation and increases in active disease status of SpA.[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] Excess ROS, such as nitric oxide, superoxide, and hydroxyl radicals, contribute to a pro-inflammatory microenvironment and mitochondrial dysfunction.[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] In TgA86 mice, ROS detoxification-related genes were upregulated in 10-month-old samples, while SOD1 expression was downregulated. Given that SOD1 is a key antioxidant enzyme converting superoxide to hydrogen peroxide, its reduction may contribute to sustained oxidative stress, promoting inflammation and pathological new bone formation.[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eIn line with previous metabolomic studies in AS, which demonstrated distinct amino acid profiles between controls and patients, our data also revealed altered amino acid metabolism in TgA86 mice.[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] Among these, MTHFD1, a key enzyme involved in the de novo biosynthesis of serine from glucose, was significantly downregulated. Given that intracellular serine is essential for maintaining chondrocyte function, reduced MTHFD1 expression may contribute to cartilage degeneration observed in SpA.[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eUpregulation of fatty acid metabolism was observed in the present study. Fatty acids function as both structural components of membranes and key regulators of cellular energy and signaling. Inpp1, which was significantly upregulated in TgA86 mice, catalyzes the hydrolysis of polyphosphorylated inositols, generating second messengers such as diacylglycerol and Ins(1,4,5)P₃ that activate protein kinase C. These signaling pathways can modulate glucose metabolism and promote the synthesis of glucose-derived free fatty acids.[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] Furthermore, inositol phosphate signaling downstream of Inpp1 has been linked to enhanced lipid metabolism, supporting the notion of metabolic reprogramming toward lipid utilization.[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eFinally, chondrogenic signaling and hypoxia-related pathways appear to play a role in pathological bone formation. Increased BMP6/pSmad1/5 signaling and aberrant chondrocyte differentiation have been reported in early-phase AS.[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] Our study showed upregulation of hypoxia-inducible pathways in both 4- and 10-month TgA86 mice, suggesting a hypoxic microenvironment conducive to endochondral ossification.[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] Immunostaining for SOD1, MTHFD1, and Inpp1 revealed distinct expression patterns in TgA86 mice compared to controls, further supporting their involvement in chondrocyte differentiation and pathological bone formation.\u003c/p\u003e\u003cp\u003eThis study has several limitations that should be considered for future research. First, all transcriptomic results were derived from bulk RNA-seq data, which averages expression levels across heterogeneous cell populations. Further study is needed to discover cell-type\u0026ndash;specific expression patterns. Second, we lacked a matched human RNA-sequencing dataset, preventing direct cross-species comparison and limiting the our findings to the murine observations. Finally, functional validation of the candidate genes and pathways identified here was outside the scope of this work; independent cohorts and complementary experimental approaches will be required to confirm and extend these results.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSY, SM, and SHK contributed equally to this work. SY performed bioinformatics analysis and drafted the manuscript. SM conducted metabolic flux simulation and protein-protein interaction analysis and drafted the manuscript. SHK carried out immunohistochemistry experiments and drafted the manuscript. SHL and HIY provided clinical guidance and interpretation. JYK performed histopathological analysis. TK, HK, CS, and JK assisted with data analysis and visualization. DT, YZ, JS, and YL contributed to methodology development and data interpretation. MSK and KSK conceived the study, supervised the research, and revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to institutional data sharing policies but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal experiments were conducted in accordance with the guidelines for the care and use of laboratory animals and were approved by the Institutional Animal Care and Use Committee (IACUC) of Kyung Hee University Hospital at Gangdong (approval number: KHNMC AP 2024-0005). All procedures were performed following the institutional guidelines and relevant national regulations for animal research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDougados M, Baeten D, London. England), 377(9783), 2127\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(11)60071-8\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(11)60071-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKeffer J, Probert L, Cazlaris H, Georgopoulos S, Kaslaris E, Kioussis D, Kollias G. Transgenic mice expressing human tumour necrosis factor: a predictive genetic model of arthritis. EMBO J. 1991;10(13):4025\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/j.1460-2075.1991.tb04978.x\u003c/span\u003e\u003cspan address=\"10.1002/j.1460-2075.1991.tb04978.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVandooren B, Noordenbos T, Ambarus C, Krausz S, Cantaert T, Yeremenko N, Boumans M, Lutter R, Tak PP, Baeten D. Absence of a classically activated macrophage cytokine signature in peripheral spondylarthritis, including psoriatic arthritis. Arthritis Rheum. 2009;60(4):966\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/art.24406\u003c/span\u003e\u003cspan address=\"10.1002/art.24406\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaaij MH, van Tok MN, Blijdorp IC, Ambarus CA, Stock M, Pots D, Knaup VL, Armaka M, Christodoulou-Vafeiadou E, van Melsen TK, Masdar H, Eskes HJPP, Yeremenko NG, Kollias G, Schett G, Tas SW, van Duivenvoorde LM, Baeten DLP. Transmembrane TNF drives osteoproliferative joint inflammation reminiscent of human spondyloarthritis. J Exp Med. 2020;217(10):e20200288. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1084/jem.20200288\u003c/span\u003e\u003cspan address=\"10.1084/jem.20200288\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChristodoulou-Vafeiadou E, Geka C, Ntari L, Kranidioti K, Argyropoulou E, Meier F, Armaka M, Mourouzis I, Pantos C, Rouchota M, Loudos G, Denis MC, Karagianni N, Kollias G. Ectopic bone formation and systemic bone loss in a transmembrane TNF-driven model of human spondyloarthritis. Arthritis Res therapy. 2020;22(1):232. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13075-020-02327-4\u003c/span\u003e\u003cspan address=\"10.1186/s13075-020-02327-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSpinelli JB, Haigis MC. The multifaceted contributions of mitochondria to cellular metabolism. Nat Cell Biol. 2018;20(7):745\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41556-018-0124-1\u003c/span\u003e\u003cspan address=\"10.1038/s41556-018-0124-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao ZB, Bian ZH, Lin ZM, Wu SF, Long J, Cui Y, Li Y, Li W, Sun EW, Lian ZX, He Y. Single-Cell Analysis of Patients with Axial Spondyloarthritis After Anti-TNFα Treatment: Experimental Data and Review of the Literature. Clin Rev Allergy Immunol. 2023;65(2):136\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12016-023-08959-z\u003c/span\u003e\u003cspan address=\"10.1007/s12016-023-08959-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndrews S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online]. Available online at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/span\u003e\u003cspan address=\"http://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinf (Oxford England). 2013;29(1):15\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bioinformatics/bts635\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/bts635\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePutri GH, Anders S, Pyl PT, Pimanda JE, Zanini F. Analysing high-throughput sequencing data in Python with HTSeq 2.0. Bioinf (Oxford England). 2022;38(10):2943\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bioinformatics/btac166\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btac166\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLove MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13059-014-0550-8\u003c/span\u003e\u003cspan address=\"10.1186/s13059-014-0550-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlighe K, Rana S, Lewis M. (2024). EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling. R package version 1.24.0. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/kevinblighe/EnhancedVolcano\u003c/span\u003e\u003cspan address=\"https://github.com/kevinblighe/EnhancedVolcano\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L, Fu X, Liu S, Bo X, Yu G. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innov (Cambridge (Mass)). 2021;2(3):100141. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.xinn.2021.100141\u003c/span\u003e\u003cspan address=\"10.1016/j.xinn.2021.100141\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSubramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102(43):15545\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.0506580102\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0506580102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMerico D, Isserlin R, Stueker O, Emili A, Bader GD. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS ONE. 2010;5(11):e13984. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0013984\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0013984\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498\u0026ndash;504. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/gr.1239303\u003c/span\u003e\u003cspan address=\"10.1101/gr.1239303\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKorotkevich G, Sukhov V, Budin N, Shpak B, Artyomov MN, Sergushichev A. Fast gene set enrichment analysis. bioRxiv. 2021;060012. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/060012\u003c/span\u003e\u003cspan address=\"10.1101/060012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRath S, Sharma R, Gupta R, Ast T, Chan C, Durham TJ, Goodman RP, Grabarek Z, Haas ME, Hung WHW, Joshi PR, Jourdain AA, Kim SH, Kotrys AV, Lam SS, McCoy JG, Meisel JD, Miranda M, Panda A, Patgiri A, Mootha VK. MitoCarta3.0: an updated mitochondrial proteome now with sub-organelle localization and pathway annotations. Nucleic Acids Res. 2021;49(D1):D1541\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkaa1011\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkaa1011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuarnieri JW, Dybas JM, Fazelinia H, Kim MS, Frere J, Zhang Y, Soto Albrecht Y, Murdock DG, Angelin A, Singh LN, Weiss SL, Best SM, Lott MT, Zhang S, Cope H, Zaksas V, Saravia-Butler A, Meydan C, Foox J, Mozsary C, Wallace DC. Core mitochondrial genes are down-regulated during SARS-CoV-2 infection of rodent and human hosts. Sci Transl Med. 2023;15(708):eabq1533. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/scitranslmed.abq1533\u003c/span\u003e\u003cspan address=\"10.1126/scitranslmed.abq1533\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eda Silveira WA, Fazelinia H, Rosenthal SB, Laiakis EC, Kim MS, Meydan C, Kidane Y, Rathi KS, Smith SM, Stear B, Ying Y, Zhang Y, Foox J, Zanello S, Crucian B, Wang D, Nugent A, Costa HA, Zwart SR, Schrepfer S, Beheshti A. Comprehensive Multi-omics Analysis Reveals Mitochondrial Stress as a Central Biological Hub for Spaceflight Impact. Cell. 2020;183(5):1185\u0026ndash;e120120. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2020.11.002\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2020.11.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEbrahim A, Lerman JA, Palsson BO, Hyduke DR. COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Syst Biol. 2013;7:74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1752-0509-7-74\u003c/span\u003e\u003cspan address=\"10.1186/1752-0509-7-74\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSigurdsson MI, Jamshidi N, Steingrimsson E, Thiele I, Palsson B\u0026Oslash;. A detailed genome-wide reconstruction of mouse metabolism based on human Recon 1. BMC Syst Biol. 2010;4:140. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1752-0509-4-140\u003c/span\u003e\u003cspan address=\"10.1186/1752-0509-4-140\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKing ZA, Dr\u0026auml;ger A, Ebrahim A, Sonnenschein N, Lewis NE, Palsson BO. Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways. PLoS Comput Biol. 2015;11(8):e1004321. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pcbi.1004321\u003c/span\u003e\u003cspan address=\"10.1371/journal.pcbi.1004321\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang C, Shen L. Functional modules analysis based on protein-protein network analysis in ankylosing spondylitis. Eur Rev Med Pharmacol Sci. 2012;16(13):1821\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePark-Min KH. Metabolic reprogramming in osteoclasts. Semin Immunopathol. 2019;41(5):565\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00281-019-00757-0\u003c/span\u003e\u003cspan address=\"10.1007/s00281-019-00757-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShen L, Yu Y, Karner CM. SLC38A2 provides proline and alanine to regulate postnatal bone mass accrual in mice. Front Physiol. 2022;13:992679. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphys.2022.992679\u003c/span\u003e\u003cspan address=\"10.3389/fphys.2022.992679\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWakitani S, Nawata M, Kawaguchi A, Okabe T, Takaoka K, Tsuchiya T, Nakaoka R, Masuda H, Miyazaki K. Serum keratan sulfate is a promising marker of early articular cartilage breakdown. Rheumatology (Oxford). 2007;46(11):1652\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/rheumatology/kem220\u003c/span\u003e\u003cspan address=\"10.1093/rheumatology/kem220\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMontllor-Albalate C, Kim H, Thompson AE, Jonke AP, Torres MP, Reddi AR. Sod1 integrates oxygen availability to redox regulate NADPH production and the thiol redoxome. Proc Natl Acad Sci USA. 2022;119(1):e2023328119. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.2023328119\u003c/span\u003e\u003cspan address=\"10.1073/pnas.2023328119\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStegen S, Loopmans S, Stockmans I, Moermans K, Carmeliet P, Carmeliet G. De novo serine synthesis regulates chondrocyte proliferation during bone development and repair. Bone Res. 2022;10(1):14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41413-021-00185-7\u003c/span\u003e\u003cspan address=\"10.1038/s41413-021-00185-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePandurangan AK, Ismail S, Esa NM, Munusamy MA. Inositol-6 phosphate inhibits the mTOR pathway and induces autophagy-mediated death in HT-29 colon cancer cells. Archives Med science: AMS. 2018;14(6):1281\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5114/aoms.2018.76935\u003c/span\u003e\u003cspan address=\"10.5114/aoms.2018.76935\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBenjamin DI, Louie SM, Mulvihill MM, Kohnz RA, Li DS, Chan LG, Sorrentino A, Bandyopadhyay S, Cozzo A, Ohiri A, Goga A, Ng SW, Nomura DK. Inositol phosphate recycling regulates glycolytic and lipid metabolism that drives cancer aggressiveness. ACS Chem Biol. 2014;9(6):1340\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/cb5001907\u003c/span\u003e\u003cspan address=\"10.1021/cb5001907\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen LL, Huang M, Tan JY, Chen XT, Lei LH, Wu YM, Zhang DY. PI3K/AKT pathway involvement in the osteogenic effects of osteoclast culture supernatants on preosteoblast cells. Tissue Eng Part A. 2013;19(19\u0026ndash;20):2226\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1089/ten.TEA.2012.0469\u003c/span\u003e\u003cspan address=\"10.1089/ten.TEA.2012.0469\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu X, Fan X, Crawford R, Xiao Y, Prasadam I. The Metabolic Landscape in Osteoarthritis. Aging disease. 2022;13(4):1166\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14336/AD.2021.1228\u003c/span\u003e\u003cspan address=\"10.14336/AD.2021.1228\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoffmann MA, Nothias LF, Ludwig M, Fleischauer M, Gentry EC, Witting M, Dorrestein PC, D\u0026uuml;hrkop K, B\u0026ouml;cker S. High-confidence structural annotation of metabolites absent from spectral libraries. Nat Biotechnol. 2022;40(3):411\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41587-021-01045-9\u003c/span\u003e\u003cspan address=\"10.1038/s41587-021-01045-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFei Y, Yu H, Wu Y, Gong S. The causal relationship between immune cells and ankylosing spondylitis: a bidirectional Mendelian randomization study. Arthritis Res therapy. 2024;26(1):24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13075-024-03266-0\u003c/span\u003e\u003cspan address=\"10.1186/s13075-024-03266-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMart\u0026iacute;nez-Reyes I, Chandel NS. Mitochondrial TCA cycle metabolites control physiology and disease. Nat Commun. 2020;11(1):102. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-019-13668-3\u003c/span\u003e\u003cspan address=\"10.1038/s41467-019-13668-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu X, Fan X, Crawford R, Xiao Y, Prasadam I. The Metabolic Landscape in Osteoarthritis. Aging disease. 2022;13(4):1166\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14336/AD.2021.1228\u003c/span\u003e\u003cspan address=\"10.14336/AD.2021.1228\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartin JA, Martini A, Molinari A, Morgan W, Ramalingam W, Buckwalter JA, McKinley TO. Mitochondrial electron transport and glycolysis are coupled in articular cartilage. Osteoarthr Cartil. 2012;20(4):323\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.joca.2012.01.003\u003c/span\u003e\u003cspan address=\"10.1016/j.joca.2012.01.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMacintyre AN, Gerriets VA, Nichols AG, Michalek RD, Rudolph MC, Deoliveira D, Anderson SM, Abel ED, Chen BJ, Hale LP, Rathmell JC. The glucose transporter Glut1 is selectively essential for CD4 T cell activation and effector function. Cell Metabol. 2014;20(1):61\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cmet.2014.05.004\u003c/span\u003e\u003cspan address=\"10.1016/j.cmet.2014.05.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSolmaz D, Kozacı D, Sarı İ, Taylan A, \u0026Ouml;nen F, Akko\u0026ccedil; N, Akar S. Oxidative stress and related factors in patients with ankylosing spondylitis. Eur J Rheumatol. 2016;3(1):20\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5152/eurjrheum.2015.0031\u003c/span\u003e\u003cspan address=\"10.5152/eurjrheum.2015.0031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYe G, Xie Z, Zeng H, Wang P, Li J, Zheng G, Wang S, Cao Q, Li M, Liu W, Cen S, Li Z, Wu Y, Ye Z, Shen H. Oxidative stress-mediated mitochondrial dysfunction facilitates mesenchymal stem cell senescence in ankylosing spondylitis. Cell Death Dis. 2020;11(9):775. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41419-020-02993-x\u003c/span\u003e\u003cspan address=\"10.1038/s41419-020-02993-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePark JH, Elpers C, Reunert J, McCormick ML, Mohr J, Biskup S, Schwartz O, Rust S, Gr\u0026uuml;neberg M, Seelh\u0026ouml;fer A, Schara U, Boltshauser E, Spitz DR, Marquardt T. SOD1 deficiency: a novel syndrome distinct from amyotrophic lateral sclerosis. Brain. 2019;142(8):2230\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/brain/awz182\u003c/span\u003e\u003cspan address=\"10.1093/brain/awz182\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang T, Pu Y, Wang X, Li Y, Yang H, Luo Y, Liu Y. Metabolomic analysis in spondyloarthritis: A systematic review. Front Microbiol. 2022;13:965709. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2022.965709\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2022.965709\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou Y, Zhang X, Chen R, Han S, Liu Y, Liu X, Gao M, Yang C, Lu D, Sun B, Chen H. Serum amino acid metabolic profiles of ankylosing spondylitis by targeted metabolomics analysis. Clin Rheumatol. 2020;39(8):2325\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10067-020-04974-z\u003c/span\u003e\u003cspan address=\"10.1007/s10067-020-04974-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStegen S, Loopmans S, Stockmans I, Moermans K, Carmeliet P, Carmeliet G. De novo serine synthesis regulates chondrocyte proliferation during bone development and repair. Bone Res. 2022;10(1):14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41413-021-00185-7\u003c/span\u003e\u003cspan address=\"10.1038/s41413-021-00185-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBenjamin DI, Louie SM, Mulvihill MM, Kohnz RA, Li DS, Chan LG, Sorrentino A, Bandyopadhyay S, Cozzo A, Ohiri A, Goga A, Ng SW, Nomura DK. Inositol phosphate recycling regulates glycolytic and lipid metabolism that drives cancer aggressiveness. ACS Chem Biol. 2014;9(6):1340\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/cb5001907\u003c/span\u003e\u003cspan address=\"10.1021/cb5001907\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGhoshal S, Tyagi R, Zhu Q, Chakraborty A. Inositol hexakisphosphate kinase-1 interacts with perilipin1 to modulate lipolysis. Int J Biochem Cell Biol. 2016;78:149\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biocel.2016.06.018\u003c/span\u003e\u003cspan address=\"10.1016/j.biocel.2016.06.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu T, Zhang J, Zhu W, Wang X, Bai Y, Feng B, Zhuang Q, Han C, Wang S, Hu Q, An S, Wan M, Dong S, Xu J, Weng X, Cao X. Chondrogenesis mediates progression of ankylosing spondylitis through heterotopic ossification. Bone Res. 2021;9(1):19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41413-021-00140-6\u003c/span\u003e\u003cspan address=\"10.1038/s41413-021-00140-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShao F, Liu Q, Zhu Y, Fan Z, Chen W, Liu S, Li X, Guo W, Feng GS, Yu H, Xu Q, Sun Y. Targeting chondrocytes for arresting bony fusion in ankylosing spondylitis. Nat Commun. 2021;12(1):6540. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-021-26750-6\u003c/span\u003e\u003cspan address=\"10.1038/s41467-021-26750-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHirao M, Tamai N, Tsumaki N, Yoshikawa H, Myoui A. Oxygen tension regulates chondrocyte differentiation and function during endochondral ossification. J Biol Chem. 2006;281(41):31079\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1074/jbc.M602296200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M602296200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"TgA86, spondyloarthritis, RNA-sequencing, ROS, folate, inositol phosphate","lastPublishedDoi":"10.21203/rs.3.rs-7116883/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7116883/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSpondyloarthritis (SpA) represents a collection of inflammatory conditions distinguished by abnormal bone formation within the axial skeleton, ultimately resulting in ankylosis. The precise mechanisms responsible for this pathological bone development remain poorly understood. The TgA86 mouse model, featuring overexpression of a mutant human TNF transgene, accurately recapitulates essential features of human SpA, particularly spinal fusion. While mitochondrial metabolism has gained recognition for its role in SpA pathogenesis, the particular metabolic alterations that promote pathological bone formation have yet to be comprehensively examined.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eBulk RNA sequencing data obtained from TgA86 mice at 4 and 10 months (n\u0026thinsp;=\u0026thinsp;5 per group) along with their corresponding controls (n\u0026thinsp;=\u0026thinsp;5 per group) underwent analysis through a multi-system approach encompassing differential gene expression analysis, protein-protein interaction assessment, and metabolic flux modeling.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eDuring disease development, TgA86 mice exhibited an immunological transition characterized by enhanced innate immune activity and reduced adaptive immune signaling. This immune transformation occurred alongside transcriptional modifications indicating suppressed mitochondrial oxidative phosphorylation and widespread activation of carbohydrate, amino acid, lipid and fatty acid metabolic pathways. Furthermore, we detected reduced SOD1 expression (ROS detoxification) and MTHFD1 expression (folate metabolism), combined with elevated INPP1 expression (inositol phosphate metabolism). Immunohistochemical analysis validated these changes within nucleus pulposus and surrounding cartilage tissue.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe TgA86 mouse model successfully reproduces the immunometabolic disruption characteristic of SpA. The documented transcriptomic pattern, reflecting a shift toward glycolysis, indicates that metabolic restructuring serves as an important factor in disease advancement. Notably, the altered regulation of SOD1, MTHFD1, and INPP1 represents a potential mechanism underlying pathological bone formation. These observations highlight the intricate relationship between immune function, cellular energy production, and bone development in SpA.\u003c/p\u003e","manuscriptTitle":"TgA86 mouse model demonstrated potential markers for Bone Formation in Spondyloarthritis through ROS Detoxification, Folate Metabolism, and Inositol Phosphate Metabolism","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 12:29:55","doi":"10.21203/rs.3.rs-7116883/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"25a5a5d0-090d-48bd-9930-f3776cade25b","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-15T02:23:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-30 12:29:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7116883","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7116883","identity":"rs-7116883","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}