Integrative Metabolomic-Proteomic Analysis Uncovers a New Therapeutic Approach in Targeting Rheumatoid Arthritis

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Using the most advanced omics technique, metabolomics and proteomics approach, we explored varied metabolites and proteins to identify unique metabolite-protein signatures involved in the disease pathogenesis of RA. Methods: Untargeted metabolomics (n=20) and proteomics (n=60) of RA patients’ plasma were carried out by HPLC/LC-MS/MS and SWATH, respectively and analyzed by Metaboanalyst. The targets of metabolite retrieved by PharmMapper were matched with SWATH data, and joint pathway analysis was carried out. An in-vitro study of metabolites in TNF-α induced SW982 cells was conducted by Western, RT-PCR, scratch, and ROS scavenging assay. The effect of GUDCA was also evaluated in the CIA rat model. Results: A Total of 82 metabolites and 231 differential proteins were revealed. Porphyrin and chlorophyll pathway and its metabolite Glycoursodeoxycholic acid (GUDCA) was significantly altered. In vitro analysis has shown that GUDCA reduces inflammation thus offering protection against ROS production and cell proliferation. PharmMapper analysis revealed that GUDCA was significantly linked with identified SWATH proteins insulin like growth factor-1(IGF1), and Transthyretin (TTR) and it upregulates the expression of IGF1 and downregulates the expression of TTR in both in vitro and in vivo models. Conclusion: GUDCA was found to possess antioxidative, antiproliferative properties and an effective anti-inflammatory property at a low dosage. It may be considered as a potential therapeutic option for reducing the inflammatory parameters associated with RA. Rheumatoid arthritis (RA) Metabolomics Metabolites Proteomics SWATH Inflammatory pathways CIA-Rat model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Rheumatoid arthritis (RA) is an autoimmune systemic disorder characterized by persistent inflammation resulting in the destruction of synovium, bone, and cartilage [ 1 ]. Multiple external/internal factors, including epigenetic, genetic, and environmental factors, trigger heterogeneous pathogenicity during disease progression [ 2 ]. The epidemiology of RA suggests its occurrence in nearly 5 per 1000 adults worldwide, and women are 2 to 3 times more prone to its development than men. Despite the availability of drugs and treatments, the insufficient diagnosis/prognosis led to improper treatment, resulting in the threatening aspect of the disease [ 1 ]. Various markers are used to diagnose this disease, often lacking sensitivity and specificity [ 3 ], and overlap with other similar diseases. Therefore, a definitive study of the prognosis and detection of the disease is required. Currently, the omics approach, expanding its roots to metabolomic tools is in demand to determine the directional changes of altered metabolism in disease to understand the molecular pathophysiology of the disease[ 4 ][ 5 ]. The range of these metabolic alterations and various altered molecules, such as metabolites and proteins, could be prominent markers of cytokine-mediated inflammatory processes in RA. Metabolomics, an emerging tool, has been recently employed to identify potential markers in multiple diseases, such as infectious diseases, cancer, inflammatory diseases, and coronary artery disease (CAD)[ 6 ]. Metabolites are small molecules formed as by-products from multiple metabolic pathways. Generally, these metabolites get altered much earlier than the onset of diseases; hence, identification of altered metabolites holds significant potential in associating genes and proteins in a disease state [ 6 ]. In contrast to genomics and proteomics, the focus of metabolomics is on connecting small molecules of biological pathways that are modulators of genes and proteins’ activity, allowing more precise identification of disease-associated phenotypes occurring within the biological system [ 7 ]. It is reported that metabolites are the responses of rapid physiological actions according to the disease activity. To manage numerous cellular processes and to regulate protein activities, interactions between proteins and metabolites are essential [ 8 ]. Reports have indicated that metabolic enzymes, transcription factors, transporters, and membrane receptors can be controlled by interactions with proteins and metabolites (PMIs) [ 9 ]. Therefore, we used a proteomic approach apart from metabolomics to identify the significantly differential proteins in RA plasma samples. Differential proteome profiling is a powerful tool for studying proteins and their alterations in specific disease conditions, providing various differentially expressed proteins (DEPs) associated with disease development [ 10 ]. Further, the integrated analysis of metabolite-protein interactome has been reported to characterize any biological process efficiently[ 4 ]. Therefore, we focused our attention on identifying novel differential metabolites linked to differential proteome profile of RA and explored their relationship and relative importance in determining disease severity. Plasma samples of RA patients and healthy control (HC) were used in our study to identify differential metabolites and proteins via HPLC-MS/MS and SWATH-MS analysis, respectively, followed by in-silico analysis to identify altered pathways. Relative expression of the metabolite-induced protein was validated in RA synovial fibroblast cells (SW982) through in vitro studies. This was followed by in vivo validation using a Collagen Induced Arthritis (CIA) rat model. In this study, Glycoursodeoxycholic acid (GUDCA) metabolite was identified as a prominent regulatory metabolite in RA. Upon treatment with GUDCA, it was found that there was an increased expression of IGF1, and a decreased expression of TTR, and the treatment had anti-inflammatory, antioxidative, and antiproliferative properties. GUDCA may, therefore, be considered a therapeutic potential molecule for RA. Materials and Methods 1. Clinical Samples Blood samples (n = 60) were collected from RA patients from the Department of Rheumatology, All India Institute of Medical Sciences (AIIMS), New Delhi, India[ 11 ]. Similarly, blood samples (n = 40) were collected from HC with no prior ailment and joint inflammation. The medical history of each patient was collected ( Supplementary Table 1 )[ 12 ]. See details in supplementary file). 2. Metabolomics analysis To analyze the differential metabolites, HPLC-MS/MS was carried out using RA (n = 20) and HC (n = 20) plasma samples. Two complimentary LC-MS/MS metabolomics methods were applied: HILIC and C18 chromatography. The raw LC-MS (.wiff files) data file was analysed by Peak View (ABSciex). Fold change criteria were considered to categorize upregulated (fold change ≥ 1.5) and downregulated (fold change ≤ 0.5) metabolites, respectively, and p-value < 0.05 was considered [ 13 ][ 14 ]. (See details in supplementary file). 3. Proteomics of plasma samples: SWATH-MS acquisition Plasma samples of RA (n = 60) and HC (n = 40) were taken, and a total of 70µg protein was estimated by BCA and digested overnight at 37°C with 0.1µg/µl trypsin (Promega, USA). [ 12 ] [ 15 ]. (See details in supplementary file). 4. Integration of metabolomics and proteomics Association analysis between metabolomics and proteomics data was performed using significant differential metabolites and protein profiles between RA and HC groups. Joint Pathway Analysis was carried out using MetaboAnalyst 5.0 ( https://www.metaboanalyst.ca/ ). It enabled us to visualize significant genes and metabolites that were enriched in a particular pathway and integrated the underlying relationships among differentially expressed metabolites and proteins [ 16 ]. 5. Target Prediction of metabolites The major concern in drug discovery is to validate the best-screened active compounds’ interaction with appropriate targets [ 17 ]. To identify the potential molecular targets of the screened metabolites, PharmMapper server database ( http://lilab.ecust.edu.cn/PharmMapper ) was used [ 18 ][ 19 ]. The gene targets were matched with protein profile identified by SWATH analysis. (See details in supplementary file) 6. Western Blot (WB) and enzyme-linked immunosorbent assay (ELISA) For WB analysis, 4 pooled plasma proteins of RA and HC were used. Each pooled sample consists of RA (n = 10) and HC (n = 10); thus, 40 RA and 40 HC samples were used, and 20µg of pooled plasma proteins were run on SDS-PAGE and anti-IGF1 (Santa Cruz, USA) (1:2000) as the primary antibody and anti-mouse (1:5000) as a secondary antibody were used. The indirect ELISA was performed using diluted (1µl/200µl) RA plasma (n = 60) and HC (n = 40), coated into 96-well micro-titer plates (Thermo Scientific, Nunc, USA), followed by incubation with primary antibody (Anti-IGF1) and secondary antibody (anti-mouse). The absorbance was observed at 495nm [ 12 ] [ 20 ]. (See details in supplementary file). 7. Peripheral Blood Mononuclear Cell (PBMC) Isolation, RNA isolation, WB, and qRT-PCR: PBMCs are the primary immune cells in the human body and offer discriminatory immune responses toward inflammation [ 5 ]. PBMCs were isolated by centrifuging RA blood (n = 6) and HC (n = 6) using histopaque reagent[ 20 ] and then used to perform WB with 3 pooled RA and HC samples, respectively (n = 2 in each pooled sample) [ 15 ]. Total RNA was extracted from PBMCs of HC (n = 6) and RA (n = 6) using Tri-Xtract Reagent (G-biosciences). GAPDH as an internal reference. Primers are shown in Supplementary Table 2 (See details in the supplementary file). Similarly, in our earlier study, protein and mRNA levels of TTR were checked in PBMCs of RA blood [ 20 ]. 8. Correlation analysis: The association of GUDCA levels with RA disease activity, specifically with ACCPA and DAS28-ESR scores was investigated [ 20 ][ 21 ]. Additionally, the relationship between IGF1 levels, (measured by ELISA), and RA disease activity (assessed by the DAS28-ESR score) was examined. (See details in the supplementary file) 9. In-Vitro analysis a) Human synovial fibroblast SW982 cell culture and MTT test: SW982 cells were cultured in DMEM media and treated with GUDCA metabolite (1–70µM range) for 24h in serum-free media [ 22 ]. Absorbance was measured at 540 nm See details in the supplementary file) b) Total protein extraction and Western blotting: SW982 cells were cultured and pre-treated with GUDCA (50µM- 6.25 µM) for 24h. Protein was extracted in RIPA buffer after 10 min induction with TNF-α (10ng/ml) [ 23 ]. The blot was incubated with anti-p65, anti-IGF1, and anti-TTR separately as primary antibodies [ 22 ]. (See details in supplementary file) c) Real-Time Quantitative Reverse Transcription PCR (qRT-PCR): SW982 cells were cultured and incubated with GUDCA (50µM) for 24h. The effect was investigated by TNF-α treatment (10 ng/ml) on GUDCA pretreated cells for 1h. Total RNA was isolated and subjected to cDNA preparation, and mRNA expression was evaluated and quantitated using 2 −ΔΔCT formula [ 22 ]. Human-specific primer sequences are shown in Supplementary Table 2. (See details in supplementary file) d) Scratch Assay analysis: SW982 cells were grown in a culture plate, the vertical scratch was drawn, and each scratch area was measured before and after the treatment with GUDCA (50µM). Bright-field images were taken at 0h and 48h and analyzed using a Nikon Eclipse 650 (NIKON, Tokyo, Japan) at ×10 magnification. The images were analyzed using ImageJ software [ 24 ]. (See details in supplementary file) e) Total Reactive oxygen species (ROS) estimation: SW982 cells were pretreated with GUDCA (50µM) with and without TNF-α (24h), followed by adding 10µM working solution of DCFH-DA into each well for 30 min. Fluorescence images were taken by ZOE Fluorescent Cell Imager and analyzed by ImageJ software (22). (See details in supplementary file) 10. In Vivo Studies a) Development of Collagen-Induced Arthritis (CIA) Rat Model Female Wistar rats (60-80g) were procured from the ICMR -National Institute of Nutrition in Hyderabad, India. The work design was approved by the Institute’s Animal Ethical Committee (IGIB/IAEC/3/3/Mar 2023). The animals were randomly divided into four groups (n = 4). The untreated group/ healthy control (HC) (Group 1), Collagen-Induced Arthritis (CIA) (Group 2), vehicle control (VC + CIA) (Group 3), and GUDCA treated (CIA + GUDCA) (Group 4). CIA rats (Except the HC group) were then induced with 2mg/ml collagen (Type II) from chicken (Sigma, USA). GUDCA was administered at 800µg/Kg of rat body weight mixed with corn oil/ benzyl alcohol (95:5 v/v) and was injected subcutaneously [ 25 ][ 26 ]. (See details in supplementary file) b) Measurement of CIA induction in experimental groups and Detection of RA Throughout the study, paw volume and arthritis index (AI) were assessed in individual animals to monitor disease progression from day 0th to day 28th. [ 27 ] Splenic index and liver index were calculated for each rat as the ratio of the spleen/liver: body weight. [ 28 ] (See details in supplementary file) c) Enzyme-linked immunosorbent Assay (ELISA) of cytokines in plasma Rat plasma was separated and added (100µl) to the pre-coated ELISA plate, followed by the manufacturer’s guidelines. TNFα, IL(Interleukin)1β, and IL-6 cytokines were quantified using ELISA kits (ELK Biotechnology, China) [ 12 ]. d) Hematoxylin and Eosin Staining (H & E) Rat synovium was sliced and fixed in 10% formalin, fixed in the paraffin block, sliced (5µm thick) using a microtome, and slides were prepared. Slides were viewed under a Nikon microscope. Images of the slides at 10X magnification were taken, and Image-J software was used to analyze the images. [ 25 ](See details in supplementary file) e) Western blot analysis of the CIA model rat plasma The blood samples were drawn through direct heart puncture and collected in EDTA coated vacutainer tubes (BD, Franklin Lakes, NJ, USA), and plasma was separated. Similarly, synovial tissue was collected and crushed in liquid nitrogen from each rat, and the lysate was prepared in RIPA buffer and centrifuge; further supernatant was stored at -80°C for further analysis. For WB analysis, the total protein (10µg) concentration was run on the gel as mentioned above. [ 29 ] (See details in supplementary file) f) Enzyme-linked immunosorbent Assay (ELISA) of synovium The indirect ELISA was performed using synovium lysate of all groups diluted (10µl/90µl) with coating buffer into 96-well micro-titer plates (Thermo Scientific, Nunc, USA) and incubated overnight at 4ºC and proceeded as mentioned above. [ 29 ] (See details in supplementary file) 11. Statistical analysis All non-parametric Mann–Whitney tests were performed using Graph pad Prism 9.0. The complete data set was analyzed using MetaboAnalyst 3.0. Pathway enrichment analysis was performed to find the related pathway with the altered metabolites. Statistical analysis was performed with the paired student’s t-test to compare the data between two groups, and ANOVA was used to compare data among multiple groups. The obtained p-values were represented by asterisks on the graph (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001). Each experiment was repeated at least three times. Results 1. Identification of differentially expressed metabolites in RA plasma: Using HPLC-MS/MS, a total of 2519 m/z (mass/charge ratio) were identified in RA plasma and represented by volcano plots. (Fig. 1 A) Among them, 685 m/z were found to be differentially regulated, 70 m/z were upregulated (Blue dots) and 47 m/z (green dots) were downregulated. (Fig. 1 B). The differentially regulated metabolites were further processed with the metabolomics library, from which 82 were annotated (Supplementary Table 3) with high confidence. Amongst these, 23 metabolites were found to be significantly regulated (7 downregulated and 2 upregulated), as depicted in the pictorial representation (Fig. 1C) ( Table 1) . A threshold of fold change criteria for upregulated (fold change ≥ 1.5) and downregulated (fold change ≤ 0.5) was considered for the identification of differential metabolites. 2. Analysis of differential metabolites: For the discriminant analysis of RA and HC, 82 annotated metabolites were opted for the OPLS-DA and PCA, used to recognize group differences. The analysis was normalized using Pareto scaling. 2D score plot of OPLS-DA analysis (Fig. 1D) shows a T score of 8% (variation between the groups) and an orthogonal T score of 12.4% (variation within the groups). The 3D score plot of PCA was generated, which showed the differential pattern of metabolites (Fig. 1E) . Principal components (PC) PC1, PC2, and PC3 accounted for 22.6%, 13.2%, and 11.2% of the total variance, respectively, indicating that the metabolites of RA samples were distinctly discriminated from the healthy samples. A variable importance projection (VIP) score plot was created to recognize the top discriminators, and 15 top metabolites of RA were revealed (Fig. 1 F). To figure out the most classified significant metabolites, the criteria of VIP values were set to 2 to obtain pre-selected metabolites. This clarifies the variance described by the top-tiered metabolites among RA and healthy groups. 3. Pathway enrichment analysis of metabolites: Pathway enrichment analysis depicts the changes in the patterns of metabolite concentration biologically and identifies the pathways that impact the metabolite pattern. We used Metaboanalyst 3.0 and revealed that altered metabolites were associated with Starch and sucrose, galactose, Porphyrin, and Phenylacetate metabolism. (Fig. 1G) Pathway enrichment ratios were also calculated by Metaboanalyst and visualized by bar graph. (Fig. 1H) 4. Identification of differentially expressed proteins (DEPs) in RA plasma: A total of 231 proteins were identified by SWATH-MS analysis (Supplementary Table 4) , depicted by the volcano plot. Amongst, 62 proteins (violet spots) were identified as significant DEPs in RA compared to control samples (Fig. 2A) , 29 out of 62 DEPs were upregulated (red spots), and 23 were downregulated (green spots) (Fig. 2B, 2C) ( Table 2) . A threshold of fold change criteria for upregulated (fold change ≥ 1.5) and downregulated (fold change ≤ 0.5) was considered significant for identified DEPs between the groups. 5. Joint pathway analysis of differential protein and metabolite profile: Joint pathway analysis is an integrative analysis to assess the commonly associated pathways between the differential profile of metabolites and proteins of RA patients. Differentially expressed proteins and metabolites significantly enriched in Arachidonic acid metabolism, porphyrin and chlorophyll metabolism, Aminoacyl-tRNA biosynthesis, glyoxylate and dicarboxylate metabolism, and phenylalanine pathways and were found to be interrelated with identified differential proteins and metabolites (Fig. 2D) 6. ACCPA Analysis: ACCPA levels are directly proportional to disease severity [ 30 ]. In this study, ACCPA levels analyzed in plasma from individuals with RA to establish the relationship between ACCPA and GUDCA levels as well as IGF1. Results suggested significantly higher levels of ACCPA in RA patients compared to the HC (Fig. 2 E ). 7. Comparative Analysis of DEPs with differential metabolites and their relation with Rheumatoid Arthritis: In the analysis, "Porphyrin metabolism" was found to be a significant pathway associated with RA. This pathway involved three identified metabolites (Glycocholic acid, Glycodeoxycholic acid, and Glycoursodeoxycholic acid), with Glycoursodeoxycholic acid (GUDCA) being a significantly downregulated metabolite (0.44-fold change). GUDCA was found to have a significant moderate negative correlation with ACCPA concentration (Fig. 2F) and DAS28-ESR activity score (Fig. 2G) . As a result, it was chosen for further analysis (Supplementary Table 3). The PharmMapper database predicted 300 gene targets of GUDCA metabolite (Supplementary Table 5). These target genes were first matched with DisGeNET database genes of RA. Then the common genes were matched with identified proteins in RA plasma by SWATH ( Supplementary Table 4) to deduce the potential metabolite-protein pair altered in RA (Supplementary Table 6). This comparative analysis revealed that IGF1, TTR, and SHBG were common target proteins of GUDCA. However, IGF1 and TTR were screened and selected for further study in RA condition based on their GDA score, which was found to be 0.05 and 0.02, respectively, and was also found to be significantly (0.42-fold) downregulated and upregulated (1.89-fold) respectively in SWATH data. (Supplementary Table 4) 8. Validation of target protein expression The expression of significantly differential target protein (IGF1) was validated in four pooled samples of RA and HC by WB. The densitometric analysis showed a significant downregulation of IGF1 level (p < 0.0349) in RA compared to HC (Fig. 3 A ), with a 1.6-fold change after normalization with total protein. Further, ELISA revealed a 1.2-fold significantly downregulated expression of IGF1 (p < 0.011) in RA plasma (n = 60) compared to HC (n = 40) (Fig. 3B) . The levels of IGF1 were found to have a significant negative correlation with DAS28-ESR (Fig. 3C). Similar IGF1 levels were confirmed in PBMCs of RA and found to be 3-fold downregulated (p < 0.0007) at the protein level by WB and 1.5-fold downregulated (p < 0.0001) at levels by qRT-PCR, compared to HC after normalization with β-actin used as a loading control. (Fig. 3D and 3E) . Similarly, the levels of TTR's protein and mRNA levels were verified in RA plasma and PBMCs, compared to HC, in our previous studies [ 20 ]. 9. Human synovial fibroblast (SW982) cytotoxicity analysis of GUDCA: Cell viability of human synovial fibroblast cells (SW982) pre-treated with GUDCA (1–70 µM/ml) was measured by MTT (Fig. 4A). The bar represented the percentage (%) of cell survivability after the induction of cells. The results showed that less than 50µM GUDCA concentration did not affect cell viability. 10. Effect of GUDCA on inflammatory condition and target proteins The expression level of NFκB/(p65), a prominent inflammatory mediator, was analyzed and validated in 24h pre-treated SW982 cells with GUDCA (range of 50µM- 6.25µM) followed by TNF-α (10ng/ml) induction for 10min. The densitometric analysis demonstrated a significantly decreased expression of NFκB (p65) (p < 0.0164) at 50µM of GUDCA concentration (Fig. 4B). The protein (p < 0.0159) expression of the IGF1 was upregulated (Fig. 4C) , whereas the TTR level was downregulated (p < 0.0193) (Fig. 4D) by GUDCA induction at 50µM as compared to the control + TNFα. Subsequently, GUDCA treatment at 50µM concentration also showed a significant rise in the mRNA expression of IGF1 (p < 0.0152) (Fig. 4 E) and a decrease in the mRNA expression of TTR (p < 0.0027) (Fig. 4 F) , IL-6 (p < 0.0001) (Fig. 4G) , IL1β(p < 0.0118) (Fig. 4H) and NFκB (p65) (p < 0.0005) (Fig. 4I) compared to control + TNFα. Thus, GUDCA pre-treatment significantly decreased the inflammation level compared to the control, concluding that GUDCA metabolite possesses anti-inflammatory properties. 11. GUDCA inhibits cell migration and invasion in synovial fibroblasts: The wound healing assay was carried out to determine the effect of GUDCA on cell migration and invasion capability. The results indicated a decrease in the migration of cells when treated with GUDCA(50µM) at 48h compared to the untreated cells (C) at the same time points, indicating that GUDCA can inhibit cell migration and invasion of cells. The gaps marked in the untreated control cells (C) and TNF-α induced SW982 cells (control + TNFα) were almost filled with the migration of cells, while the cell's migratory ability in the GUDCA(50µM)-treated group was observed to be significantly less. The inhibition of cell migration was 41.0% in TNF-α treated cells, 66.9% in control cells, and 85.9% after treatment with GUDCA (50µM) at 48h, indicating that GUDCA has the potential of an antiproliferative agent since it inhibits cell migration under inflammatory conditions. (Fig. 5A) . 12. Reactive Oxygen scavenging (ROS) ability of GUDCA : Oxidative stress was measured by estimating the total cellular ROS in GUDCA (50µM) pretreated cells (SW982) with/without TNF-α for 24h. Fluorescence signals of cells were measured by DCFHDA dye and showed highly induced intracellular ROS production in TNF-α induced cells compared to untreated control cells. GUDCA treatment, therefore, significantly (p ≤ 0.0004) inhibited the intracellular ROS production in TNF-α induced SW982 cells (Fig. 5B) . 13. CIA-Rat model Establishment, Amelioration of Clinical severity, by GUDCA Treatment The CIA rat model is widely used to mimic the RA condition [ 31 ] and the effect of GUDCA was investigated in the model. Images of rat paws taken on the 28th day of all groups Group 1 (HC), Group 2 (CIA), Group 3 (CIA + VC), and Group 4 (CIA + GUDCA) before scarification are shown (Fig. 6A) . We observed that Group 4 showed less redness and swelling compared to Groups 2 and 3. The development of arthritis was quantified by measuring the paw volume using plethysmometer twice weekly to confirm the onset of the disease. After day 14, the average paw volume decreased in Groups 4, whereas paw volume increased in Groups 2 and 3 (Fig. 6B) . CIA-induced arthritis was determined to progress successfully by measuring arthritis index (AI%) between the groups. (Fig. 6C) . AI was more in group 2 and 3, that was decreased in group 4. The increased presence of autoreactive B cells in RA leads to elevated production of immunoglobulins and enlargement of the spleen. In CIA rats, the liver and spleen are susceptible to chronic inflammation that can be measured as splenic and liver index. In Group 2, the splenic index (Fig. 6D) and liver index (Fig. 6E) increased compared to normal rats, which was found to be normalized by GUDCA, exhibiting protective effects. Pro-inflammatory cytokine levels were also quantitively measured in the rat plasma of all groups. Downregulation of pro-inflammatory cytokines (TNFα, IL-1β, IL-6) were also revealed in rat plasma in Groups 4 compared to Groups 2 and 3 (Fig. 6F) . 14. GUDCA ameliorated inflammatory stress of synovium To further validate the anti-inflammatory activity of GUDCA, histological tests were performed on rat synovium by H&E staining (Fig. 6G) . The pink color represents cytoplasm, which correlates with the synovium's inflammation. The purple color represents the number of nuclei present, used to determine the number of cells infiltrated into the given region and to quantify inflammation[ 31 ]. The H&E scan analysis revealed that the group injected with GUDCA (Group 4) exhibited much less cell infiltration compared to Groups 3 and 2. (Fig. 6H) This histological examination supports the idea that Group 4 significantly reduces inflammation in the CIA rats. 15. Effect of GUDCA on differential regulation of TTR and IGF1 in CIA-rat plasma The impact of GUDCA on the differential regulation of proteins was assessed through WB analysis in CIA rat plasma. The findings indicated that GUDCA was able to decrease the expression of TTR and increase the expression of IGF1 in Group 4. Densitometric analysis showed a significant increase in the expression of IGF1 (p = 0.0101; Fig. 6I) and a decrease in the expression of TTR (p = 0.0013; Fig. 6J ). Similar results were observed in the synovium of CIA rats using ELISA. The ELISA results demonstrated a significant reduction in TTR levels (Fig. 6 K ) and an increase in the expression of IGF1 in Group 4 compared to CIA (Fig. 6 L ). Therefore, we may conclude that GUDCA may have the ability to regulate the expression of these proteins. The increased levels of TTR and decreased levels of IGF1 are linked to inflammatory markers (IL-6, IL-1β) that contribute to the progression of RA. This data was validated for the first time in synovial fibroblast cells, showing that treatment with GUDCA in both the synovial fibroblast cells and the CIA rat model reduces the exacerbation of disease severity. Discussion RA holds a crucial social, economic, and physiological burden on affected individuals. It dramatically impacts people’s bone health, and deformity leads to disability[ 32 ] [ 1 ]. Prevalent systemic inflammation in RA is mediated by pro-inflammatory cytokines that affect different cellular metabolism [ 5 ][ 8 ]. We utilized metabolomics and proteomics techniques, and revealed 82 metabolites and 231 differentially regulated proteins in RA with high confidence. Differential molecular patterns of metabolites and proteins were integrated using biological pathways analysis [ 33 ] and joint pathway analysis [ 34 ] and revealed “Porphyrin metabolism” as a prominent pathway associated with RA. This pathway was associated with the metabolites Glycocholic acid, Glycodeoxycholic acid, and Glycoursodeoxycholic acid (GUDCA) (bile acids conjugates). The report indicates that Glycocholic acid and Glycodeoxycholic acid are vital in regulating bile acid synthesis, depending on the availability of cholesterol substrate, and significantly reduce HMG-CoA reductase activity [ 35 , 36 ]. GUDCA is obtained from the acyl glycine conjugate of ursodeoxycholic acid [ 37 ][ 38 ]which is reported to have a neuroprotective and anti-inflammatory agent [ 39 ]. Ursodeoxycholic acid can dissolve cholesterol gallstones and treat cholestatic liver disorders, atherosclerosis, steatosis, and liver fibrosis.[ 40 , 41 ]. It has been proven to effectively prevent pain and cartilage degeneration in cases of osteoarthritis (OA). Its chondroprotective properties work by effectively suppressing oxidative damage and inhibiting catabolic factors that are known to contribute to the pathogenesis of cartilage damage in OA [ 40 – 43 ]. In our study on RA, we found that the level of GUDCA showed a negative correlation with disease severity as assessed by ACCPA levels and DAS28-ESR activity score. This led us to suspect that GUDCA might hold promise as a new treatment for RA. As a result, we have chosen to further analyze the therapeutic potential of GUDCA. To explore the potential targets of GUDCA, PharmMapper analysis of GUDCA and comparative study with differential proteomics profile was attempted and revealed that IGF1 and TTR can be the appropriate targets of GUDCA. IGF1 has a link with inflammation and immuno-metabolism and is associated with bone and cartilage differentiation[ 44 ]. Its bioactivity is controlled by six IGF-binding proteins (IGFBP-1 to IGFBP-6). Low expression of IGF1 in the sera of RA patients increased with disease activity[ 45 ] supported by our SWATH data. Increased levels of IGF1 promote articular cartilage regeneration after injury[ 46 ]. Thus, the effect of GUDCA on the expression of IGF1 and its association in synovial fibroblast cells with inflammation related to RA pathogenesis was examined. Significantly decreased levels of IGF1 in RA plasma and PBMCs suggested its low availability in RA (Fig. 3 A, 3D ). The levels of IGF1 were found to have a negative correlation with the DAS28-ESR score (Fig. 3 C ). The report shows that differentiation of bone and cartilage tissue is hampered due to less availability of IGF1 in RA[ 46 ]. IGF1 is thus unable to participate in regulating immunity and inflammation, and that promotes the disease conditions. We also selected a significantly upregulated protein, TTR, from SWATH data, a pre-albumin secretory protein that transports retinol and thyroxin (T4). In our previous study, we discovered that the increased rate of TTR glycation, along with its binding with RAGE, serves as a trigger for inflammatory pathways through the activation of NF-kB. [ 20 ]. To confirm the therapeutic ability of GUDCA, the expression of IGF1 and TTR by in vitro studies was analyzed. Expression levels of p65 were reduced in the presence of GUDCA at both protein and mRNA levels. The simultaneous observation of increased IGF1 levels and decreased TTR levels after GUDCA treatment suggests that GUDCA actively regulates these levels, thus playing a significant role in inflammation. Also, GUDCA has been found to have an anti-proliferative and antioxidative effect on synovial fibroblast cells (Fig. 5 A, 5B ). Further, our findings were validated in the CIA rat model. GUDCA helped to improve disease symptoms in CIA rats by significantly reducing paw volume and arthritis index. There was also a significant decrease in immune cell infiltration in the treated group compared to the CIA group, as indicated by the H&E score (Fig. 6 G ). Additionally, analysis of CIA rat plasma showed that the levels of the proteins TTR and IGF1, which are altered by GUDCA concentration, indicated reduced pathogenesis of RA. Conclusion Our study demonstrated that GUDCA may have a protective effect on the disease development of RA pathogenesis. The untargeted metabolomics by HPLC-MS/MS revealed metabolites and DEPs of RA and laid the foundation for monitoring disease development based on the interplay of both biomolecules (metabolite and protein). The explanation of metabolic pathways in chronic inflammatory circumstances such as RA provided new insight into disease development. It offered a hopeful sign for the specific biomolecular marker identification for RA. Further, the mechanistic evaluation of GUDCA at the cellular level needs to be explored, and its therapeutic impact must be validated through clinical studies. These steps are crucial to fully understand the potential benefits of this metabolite and its impact on human health. Abbreviations Rheumatoid arthritis (RA); healthy control (HC); differentially expressed proteins (DEPs); Collagen Induced Arthritis (CIA); Radioimmunoprecipitation assay buffer (RIPA); Dulbecco's Modified Eagle Medium (DMEM); Glycoursodeoxycholic acid (GUDCA); American College of Rheumatology (ACR); European League Against Rheumatism (EULAR); glyceraldehyde-3-phosphate dehydrogenase (GAPDH); reactive oxygen species (ROS); dichlorodihydrofluorescein diacetate (DCFHDA); High Performance Liquid Chromatography-Mass Spectrometry (HPLC-MS) (HPLC/LC-MS/MS); Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH-MS); ethylenediamine tetra acetic acid (EDTA); liquid chromatography–tandem mass spectrometry (LC-MS/MS); hydrophilic interaction liquid chromatography (HILIC); National Institute of Standards and Technology (NIST); Bicinchoninic Acid (BCA); Gene Disease association (GDA); Sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE); polymerase chain reaction (PCR); 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT); Endoplasmic reticulum (ER); High fat diet (HFD); insulin-like growth factor-1 (IGF1); Receptor for Advanced Glycation Endproducts (RAGE); anti-citrullinated protein/peptide antibody (ACCPA); disease activity score - erythrocyte sedimentation rate (DAS28-ESR) scores; Dichloro-dihydro-fluorescein diacetate (DCFH-DA); Phosphate-buffered saline (PBS); Indian Council of Medical Research (ICMR) ; Projections to Latent Structures Discriminant Analysis (OPLS-DA); Principal component analysis (PCA); variable importance projection (VIP); sex hormone-binding globulin (SHBG); Electrochemiluminescence (ECL); Horseradish peroxidase (HRP); Hydroxymethylglutaryl-CoA (HMG-CoA). Declarations Ethics Statement The study protocol was ethically approved by AIIMS, New Delhi, India (Reg No IEC-37/07.02.2020, RP-15/2020) and the study protocols complied with the Declaration of Helsinki. The work design was approved by the Institute’s Animal Ethical Committee (IGIB/IAEC/3/3/Mar 2023). Acknowledgment We acknowledge the Council of Scientific and Industrial Research (CSIR), and DST- Science & Engineering research board (SERB), Government of India, New Delhi, India, for providing financial support. Lovely Joshi, Mohd Saquib, Ashish and Debolina received fellowship support from CSIR. Prachi Agnihotri, Swati Malik received a fellowship from DST. Mr. Praveen for mass spectrometer data acquisition and Mr. Pankaj Yadav for transporting biological samples from the hospital to the lab. We also thank CSIR-Institute of Genomics and Integrative Biology, Delhi, India to provide the research platform, AcSIR for academic support, and the Department of Rheumatology, All India Institute of Medical Sciences (AIIMS), New Delhi, India for providing patient’s sample. Funding Statement We would like to acknowledge the Department of Science and Technology (DST), Science & Engineering Research Board (SERB) CRG/2019/006398, New Delhi, India for financial support. Consent for publication: All the authors have agreed that the study should be submitted to Journal. Competing interests/ Author Declarations : The authors declare no conflict of interests. Authors' contributions: All authors have made substantial contributions to data analysis and took part in drafting the article or revising it critically for important intellectual content and agreed to submit it to the current journal. Prachi and Dr Sagarika Biswas made substantial contributions to the conception, design, drafting, acquisition of data, or analysis and interpretation of data. Swati, Lovely, Saquib, Ashish and Debolina contributed in drafting, data analysis, and sample handling. Dr Uma Kumar provided the biological samples. Dr Sagarika Biswas agreed to submit to the current journal, gave final approval for the version to be published, and agreed to be accountable for all aspects of the work. Authors' information: Council of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology, Mall Road, Delhi University Campus, Delhi, India, 110007, All India Institute of Medical Sciences, Ansari Nagar, New Delhi - 110029, India Availability of data and materials: For all original data and protocol, please contact Dr Sagarika Biswas ( [email protected] ). References Guo Q, Wang Y, Xu D, Nossent J, Pavlos NJ, Xu J. 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S.No. m/z Metabolite Fold change p value 1 538.3154 Cholylmethionine 3.177627 0.015285 2 566.3472 LPC 18:1 1.51379 0.051561 3 165.0999 6-Pentyl-2H-pyran-2-one 1.326574 0.034463 4 496.331 LPC 16:0 0.829247 0.035057 5 454.2848 1-palmitoyl-2-hydroxy-sn-glycero-3-phosphoethanolamine 0.780522 0.036636 6 790.5701 PC O-38:7 0.768475 0.046717 7 544.3292 LPC 20:4/0:0 0.757545 0.002489 8 246.1643 CAR 5:0 0.722267 0.015927 9 520.3286 LPC 18:2 0.707948 5.45E-05 10 524.363 LysoPC(0:0/18:0) 0.704678 0.024745 11 488.2879 GLYCOCHOLATE 0.685498 0.038619 12 786.591 1,2-dioleoyl-sn-glycero-3-phosphatidylcholine 0.670958 0.01464 13 482.3144 1-pentadecanoyl-2-hydroxy-sn-glycero-3-phosphocholine 0.657604 0.013365 14 480.2975 LPE 18:1 0.631667 0.000649 15 449.3838 Trihydroxycholestanoic acid 0.520398 0.007314 16 595.1513 Eriodictyol 7-O-neohesperidoside 0.500863 0.036249 17 258.9185 D-glucose 6-phosphate 0.490946 0.011213 18 246.9637 Asp-Asp 0.485635 0.000563 19 478.2915 1-Oleoyl-sn-glycero-3-phosphoethanolamine/LysoPE(18:1/0:0) 0.481877 0.02599 20 238.9864 Heptanedioic acid, 1-(2-cyclopentylidenehydrazide) 0.466123 0.006953 21 448.3038 Glycoursodeoxycholic acid 0.449878 0.007679 22 413.5848 5.alpha.-Pregnan-3.alpha.,17-diol-20-one 3-sulfate 0.367392 3.79E-06 23 464.2986 Glycocholic acid 0.197437 0.010649 Table 2 List of significantly 29 upregulated and 23 downregulated differentially regulated proteins with fold change and p-value. S.No Protein fold change pvalue 1 Apolipoprotein C-I 62.40570335 0.007758662 2 Alpha-1-acid glycoprotein 1 16.7214335 0.00553161 3 Apolipoprotein C-III 9.850330468 1.25567E-06 4 Complement C4-A 8.302636966 0.002453229 5 Complement C4-B 8.023461599 0.000342575 6 Inter-alpha-trypsin inhibitor heavy chain H4 6.888252287 0.008452104 7 Hemopexin 5.921200271 0.010553529 8 Apolipoprotein A-I 4.808899273 0.00343186 9 Complement C1r subcomponent 4.738477563 0.018669551 10 Ig kappa chain V-I region EU 3.814368748 0.012133217 11 Angiotensinogen 3.583448101 0.008352337 12 Fibrinogen alpha chain 3.47708266 0.026347715 13 Complement C1q subcomponent subunit B 3.417463883 0.011119125 14 CD5 antigen-like 3.100496169 0.033706855 15 Hemoglobin subunit beta 2.657607982 0.007641719 16 Alpha-1-antitrypsin 2.536239559 0.013834211 17 Serum paraoxonase/arylesterase 1 2.285135829 0.035481367 18 Cartilage acidic protein 1 2.195281727 0.027063039 19 Clusterin 2.19471443 0.001684064 20 Dipeptidyl peptidase 4 2.11232999 0.016101272 21 Inter-alpha-trypsin inhibitor heavy chain H3 2.056770621 0.012397008 22 Serum amyloid A-4 protein 2.055016548 0.043047554 23 Transthyretin 1.897613179 0.048515129 24 Protein AMBP 1.885350481 0.016866094 25 Alpha-1-antichymotrypsin 1.83019933 0.035537119 26 Complement component C9 1.814339568 0.049834854 27 Leucine-rich alpha-2-glycoprotein 1.717204308 0.03938007 28 Ceruloplasmin 1.694083887 0.003258636 29 Thyroxine-binding globulin 1.659605788 0.045789643 30 L-selectin 0.507441747 0.038241024 31 Biotinidase 0.48889523 0.047922207 32 N-acetylmuramoyl-L-alanine amidase 0.465354202 0.004123615 33 Ig heavy chain V-III region ZAP 0.454522581 0.047582617 34 Histidine-rich glycoprotein 0.45196089 0.013256396 35 Insulin-like growth factor I 0.42957407 0.016637558 36 72 kDa type IV collagenase 0.427441571 0.058048635 37 Afamin 0.427009471 0.025087744 38 Ig heavy chain V-III region LAY 0.409704949 0.023839039 39 Complement component C8 gamma chain 0.394727685 0.006947765 40 Eukaryotic initiation factor 4A-II 0.383639153 0.039364223 41 Heparin cofactor 2 0.35791362 0.001140975 42 Coagulation factor XII 0.35345247 0.039938135 43 C-reactive protein 0.299366867 0.006885079 44 Complement component C8 alpha chain 0.282144005 0.004202023 45 Selenoprotein P 0.279631127 0.003575084 46 ATP-binding cassette sub-family F member 1 0.220376839 0.02683651 47 Lumican 0.210071273 0.022956686 48 Monocyte differentiation antigen CD14 0.200887807 0.002936902 49 Fibulin-1 0.190617853 0.005701437 50 Coagulation factor XIII A chain 0.089036558 0.039629791 51 Fetuin-B 0.062425822 0.006111321 52 C4b-binding protein beta chain 0.052992974 0.000368021 Additional Declarations No competing interests reported. Supplementary Files SupplemenatryFile.docx Supplementarytable3.xlsx SupplementaryTable4.xlsx SupplementaryTable5.xlsx supplementaryfileuncroppedgelimages.docx Cite Share Download PDF Status: Published Journal Publication published 23 Dec, 2024 Read the published version in Arthritis Research & Therapy → Version 1 posted Editorial decision: Revision requested 23 Sep, 2024 Reviews received at journal 23 Sep, 2024 Reviews received at journal 06 Sep, 2024 Reviewers agreed at journal 03 Sep, 2024 Reviewers agreed at journal 03 Sep, 2024 Reviewers invited by journal 03 Sep, 2024 Editor assigned by journal 12 Aug, 2024 Submission checks completed at journal 12 Aug, 2024 First submitted to journal 08 Aug, 2024 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. 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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-4878563","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":350369314,"identity":"76193ed1-2616-4f2d-a0e6-9650193051e1","order_by":0,"name":"Prachi Agnihotri","email":"","orcid":"","institution":"Institute of Genomics and Integrative Biology","correspondingAuthor":false,"prefix":"","firstName":"Prachi","middleName":"","lastName":"Agnihotri","suffix":""},{"id":350369315,"identity":"0db0f127-648e-42af-934a-b810a2345449","order_by":1,"name":"Mohd Saquib","email":"","orcid":"","institution":"Institute of Genomics and Integrative Biology","correspondingAuthor":false,"prefix":"","firstName":"Mohd","middleName":"","lastName":"Saquib","suffix":""},{"id":350369316,"identity":"654dc317-f583-468b-973d-d9cc4363e6ab","order_by":2,"name":"Lovely Joshi","email":"","orcid":"","institution":"Institute of Genomics and Integrative Biology","correspondingAuthor":false,"prefix":"","firstName":"Lovely","middleName":"","lastName":"Joshi","suffix":""},{"id":350369317,"identity":"d2df7842-ba37-43cf-8ec0-670ba891f87a","order_by":3,"name":"Swati Malik","email":"","orcid":"","institution":"Institute of Genomics and Integrative Biology","correspondingAuthor":false,"prefix":"","firstName":"Swati","middleName":"","lastName":"Malik","suffix":""},{"id":350369318,"identity":"37acf6e1-35de-44c1-a1bb-3cadaa42f587","order_by":4,"name":"Debolina Chakraborty","email":"","orcid":"","institution":"Institute of Genomics and Integrative Biology","correspondingAuthor":false,"prefix":"","firstName":"Debolina","middleName":"","lastName":"Chakraborty","suffix":""},{"id":350369319,"identity":"17305a19-8293-439d-8075-361cf6e657a8","order_by":5,"name":"Ashish Sarkar","email":"","orcid":"","institution":"Institute of Genomics and Integrative Biology","correspondingAuthor":false,"prefix":"","firstName":"Ashish","middleName":"","lastName":"Sarkar","suffix":""},{"id":350369320,"identity":"262ca532-f209-426d-915a-7ac38f1d9c32","order_by":6,"name":"Uma Kumar","email":"","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Uma","middleName":"","lastName":"Kumar","suffix":""},{"id":350369321,"identity":"dfff0b5d-fd0e-487a-a9ca-aa451ebcdc2b","order_by":7,"name":"Sagarika Biswas","email":"data:image/png;base64,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","orcid":"","institution":"Institute of Genomics and Integrative Biology","correspondingAuthor":true,"prefix":"","firstName":"Sagarika","middleName":"","lastName":"Biswas","suffix":""}],"badges":[],"createdAt":"2024-08-08 06:29:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4878563/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4878563/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13075-024-03429-z","type":"published","date":"2024-12-23T15:57:32+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66117940,"identity":"f31ee754-86c6-487c-9d11-1455073a58b4","added_by":"auto","created_at":"2024-10-08 00:57:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":338952,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA) \u003c/strong\u003eVolcano plot showing altered metabolite features in the test set of RA and controls (fold change ≥1.5, fold change ≤ 0.5, p \u0026lt; 0.05). A total of 2519 m/z was plotted with 685 m/z significantly regulated metabolites (red dots). \u003cstrong\u003eB)\u003c/strong\u003e Graph depicting 70 significantly upregulated m/z (Blue dots) and 47 significantly downregulated (Green dots) m/z. \u003cstrong\u003eC) \u003c/strong\u003ePictorial representation of identified m/z downstream analysis with their significance level. Log2FC; log2 fold change, m/z; mass to charge ratio. \u003cstrong\u003eMultivariate analysis D)\u003c/strong\u003e 2D score plot showing the distinct distribution of RA cases and controls in the validation set as discriminated by the identified altered metabolites using OPLS-DA analysis.\u003cstrong\u003e E)\u003c/strong\u003e 3D plot showing the distinct distribution of RA cases and controls using PCA analysis.\u003cstrong\u003e F) \u003c/strong\u003eVariable importance in projection (VIP) plot depicting the top 15 discriminators for RA cases in the validation set.\u003cstrong\u003e G) \u003c/strong\u003ePathway analysis of identified metabolite set by metaboanalyst 5.0 \u003cstrong\u003eH)\u003c/strong\u003e Pathway enrichment analysis represented by Bar graph computing a single P value for each metabolic pathway. PC; principal component, VIP; variable importance in projection, OPLS-DA; Orthogonal partial least squares-discriminant analysis, RA; rheumatoid arthritis, HC; healthy control.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4878563/v1/dfd8d786543f8ba48aee2e50.png"},{"id":66118440,"identity":"ed47a16c-66dd-45cc-9986-7c7ac7208a02","added_by":"auto","created_at":"2024-10-08 01:05:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":176072,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProteomic analysis by SWATH A)\u003c/strong\u003e Volcano plot showing altered proteins in the test set of RA and HC (fold change ≥1.5, fold change ≤ 0.5, p \u0026lt; 0.05), A total of 62 significant proteins were represented by purple dots \u003cstrong\u003eB) \u003c/strong\u003eGraph depicting 29 significantly upregulated (red dots) proteins\u003cstrong\u003e \u003c/strong\u003eand 23 significantly downregulated proteins (green dots), rest proteins significant but did not fall under the selection criteria (Black dots) \u003cstrong\u003eC)\u003c/strong\u003e Pictorial representation of identified protein shown downstream analysis with their significance level \u003cstrong\u003eD) \u003c/strong\u003eJoint pathway analysis of identified metabolites and proteins by metaboanalyst. \u0026nbsp;\u003cstrong\u003eE)\u003c/strong\u003e Significant difference in ACCPA levels in RA (n=60) and HC (n=40) samples with estimation plot. \u003cstrong\u003eCorrelation analysis\u003c/strong\u003e \u003cstrong\u003eF) \u003c/strong\u003eCorrelation analysis between ACCPA and GUDCA levels: The ACCPA concentration negatively correlates with GUDCA with r = -0.5882 significantly. \u003cstrong\u003eG) \u003c/strong\u003eThe graph shows a negative correlation between GUDCA and DAS28-ESR score with r = -0.5749 in RA. \u0026nbsp;Log2FC; log2 fold change, RA; rheumatoid arthritis, HC; healthy control, ACCPA; anti-citrullinated protein/peptide antibody, DAS28-ESR; Disease Activity Score 28-joint count- erythrocyte sedimentation rate.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4878563/v1/fcedc8b08a6ac3bfa1ccc0cd.png"},{"id":66117937,"identity":"307d1c2b-3fb1-40b0-a5e4-d05007088150","added_by":"auto","created_at":"2024-10-08 00:57:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":191639,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA)\u003c/strong\u003e Western blot analysis of IGF1 in RA representing the level of IGF1 in RA (n=40) pooled plasma compared to HC (n=40) (p \u0026lt;0.0349) with 1.6-fold downregulated expression. \u003cstrong\u003eB) \u003c/strong\u003eThe expression of IGF1 by ELISA in RA Plasma (n = 60) and control (n = 40) indicated a significantly low-level expression with a 1.2-fold (p \u0026lt;0.0011) compared to HC. \u003cstrong\u003eC) \u003c/strong\u003eCorrelation analysis between IGF1 and DAS28-ESR score shows a significant negative correlation with r = -0.7097 in RA patients. \u003cstrong\u003eD)\u003c/strong\u003eWestern blot analysis of IGF1 in\u003cstrong\u003e \u003c/strong\u003ePBMCs representing the significantly downregulated expression of IGF1 (p\u0026lt;0.0007) in RA PBMCs compared to HC (n=6 each) \u003cstrong\u003eE)\u003c/strong\u003eSignificantly downregulated gene expression of IGF1 (p\u0026lt;0.0001) in RA PBMCs compared to HC (n=6 each) by qRT-PCR. The statistical significance was determined by Student's t-test, p \u0026lt; 0.05. RA; rheumatoid arthritis, HC; healthy control, Mw; molecular weight, IGF1;insulin-like growth factor-1; ELISA; Enzyme-Linked ImmunoSorbent Assay, PBMC; Peripheral blood mononuclear cells, qRT-PCR; Real-Time Quantitative Reverse Transcription PCR , * = P ≤ 0.05, ** = P ≤ 0.01, *** = P ≤ 0.001, ****=P≤ 0.0001\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4878563/v1/1f21b77ef4711a6c295f49a5.png"},{"id":66118441,"identity":"6fa2cc31-60a2-4c38-bdae-9d1c08116951","added_by":"auto","created_at":"2024-10-08 01:05:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":238308,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA)\u003c/strong\u003e The cell viability test by the MTT assay representing the cell viability results of SW982 cells treated with GUDCA (1-70 μM/ml) for 24h \u003cstrong\u003eB) \u003c/strong\u003eWestern Blot analysis- The expression level of p65, pre-treated with serial dilutions of GUDCA (50μM to 6.25μM) with or without TNF-α (10 ng/ml) for 10 min was observed significantly (p\u0026lt;0.0164) downregulated at 50μM of GUDCA in SW982 cells. \u003cstrong\u003eC)\u003c/strong\u003e Significantly (p\u0026lt;0.0159) upregulated expression of IGF1 analyzed by Western Blot in SW982 cells, pre-treated with 50μM GUDCA for 24h with or without TNF-α (10ng/ml) induction for 10 min \u003cstrong\u003eD) \u003c/strong\u003eSignificantly (p\u0026lt;0.0193)\u003cstrong\u003e \u003c/strong\u003edownregulated expression of TTR analyzed by Western Blot in SW982 cells, pre-treated with 50μM GUDCA for 24h with or without TNF-α (10 ng/ml) induction for 10 min. Densitometric analysis of GUDCA treatments normalized by GAPDH. \u003cstrong\u003eE) \u003c/strong\u003emRNA expression analysis of IGF1 by qRT-PCR in SW982 cells pretreated with GUDCA (50μM) for 24h and induction of TNF-α (10 ng/ml) for 1h. The mRNA expression of IGF1 was observed significantly (p\u0026lt;0.0152) upregulated\u003cstrong\u003e F) \u003c/strong\u003emRNA expression of TTR was observed significantly (p\u0026lt;0.0027) downregulated\u003cstrong\u003e G)\u003c/strong\u003e IL6 gene expression significantly (p\u0026lt;0.0001) downregulated \u003cstrong\u003eH) \u003c/strong\u003eIL1β gene expression significantly (p\u0026lt;0.0118) downregulated \u003cstrong\u003eI) \u003c/strong\u003ep65 gene expression significantly (p\u0026lt;0.0005) downregulated compared to control with TNF-α. The data normalized with GAPDH as an internal loading control, and the values presented as the mean ± SEM (n = 3). MTT; 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, GUDCA; Glycoursodeoxycholic acid, TTR; Transthyretin, SEM; Standard error of the mean, C; control cells without TNF-α and GUDCA, Mw; molecular weight, tc; toxic control, vc; vehicle control, IGF1;insulin like growth factor-1, IL1β; interleukinβ, IL6; interleukin 6, GAPDH; Glyceraldehyde 3-phosphate dehydrogenase, ns; non-significant, * = P ≤ 0.05, ** = P ≤ 0.01, *** = P ≤ 0.001, ****=P≤ 0.0001\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4878563/v1/02318fb2a3e19d274334c9b0.png"},{"id":66117942,"identity":"7c82d17f-0a0a-4bbe-9e12-484c1691ad1f","added_by":"auto","created_at":"2024-10-08 00:57:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":819815,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA) Scratch assay: \u003c/strong\u003eMigration ability of SW982 cells by Scratch assay after stimulation with GUDCA 50μM for 24h with or without TNF-α (10 ng/ml). The results showed that the %inhibition of migration in control was 66%, in TNF-αtreated cells 41%, and GUDCA treated cells 85% at 48h compared to 0h. ****\u003cem\u003ep\u003c/em\u003e ≤ 0.0001. \u003cstrong\u003eB) Totalcellular ROS\u003c/strong\u003e was analyzed after GUDCA (50μM) pretreatment for 24h with/without TNF-α (10ng/ml) using DCFHDA probe. GUDCA treatment inhibited ROS production observed by fluorescence intensity significantly (\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.0004) compared to TNF-α induced and control cells (C). Error bars represent the mean ± SEM from three independent experiments. \u0026nbsp;C; control cells without TNF-α and GUDCA, GUDCA; Glycoursodeoxycholic acid, ROS; reactive oxygen species, DCFHDA; dichlorodihydrofluorescein diacetate, ANOVA; Analysis of Variance, SEM; Standard error of the mean, ns; nonsignificant, * = P ≤ 0.05, ** = P ≤ 0.01, *** = P ≤ 0.001, ****=P≤ 0.0001\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4878563/v1/5e5f79380cf143399a106093.png"},{"id":66117945,"identity":"12a43edd-6d05-44a0-971c-cbc645937e18","added_by":"auto","created_at":"2024-10-08 00:57:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1876499,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of GUDCA on Collagen-Induced Arthritis Rat model: \u003cstrong\u003eA)\u003c/strong\u003e Visual representative of hind paw image of the rats from each group, where edema and redness were reduced in Groups 4 compared to Groups 2 and 3. \u003cstrong\u003eB)\u003c/strong\u003e Graphical representation of measured paw volume from day 0 to day 28, depicting the changes in paw volume in Group 4 compared to Groups 2 and 3. \u003cstrong\u003eC)\u003c/strong\u003e Shows the attenuation of clinical Arthritis index (%) was reduced upon treatment of GUDCA to the CIA group compared to control and indicates a significant difference between the groups. \u003cstrong\u003eD)\u003c/strong\u003e Effects of GUDCA on the spleen index of CIA mice on the day of scarification. The weight of spleens in CIA mice was significantly larger than those of normal mice represented as spleen weight/ body weight (g/Kg). Similarly, \u003cstrong\u003eE)\u003c/strong\u003e Liver index was calculated. \u003cstrong\u003eF)\u003c/strong\u003e Validation of pro-inflammatory parameter: the levels of the proinflammatory cytokine were measured by quantitative ELISA analysis in rat plasma in Groups 1 to 4, showing the downregulation of TNF α, IL-1β, and IL-6 levels in Groups 4 compared to Groups 2 and 3 \u003cstrong\u003eG) \u003c/strong\u003eThe H\u0026amp;E staining shows decreased cell inflammation (purple color) in Group 4 compared to Groups 2 and 3 \u003cstrong\u003eH) \u003c/strong\u003eThe analysis of cell infiltration in the synovium was measured as cell count by Image J and represented as H\u0026amp;E score found to be downregulated in groups 4 compared to groups 2 and 3.\u003cstrong\u003e \u003c/strong\u003eWestern Blot analysis of TTR and IGF1 in plasma of CIA-RAT Model \u003cstrong\u003eI)\u003c/strong\u003e The band intensities of the CIA group show a significantly decreased level of IGF1(p=0.05) in plasma compared to the control; the treatment with GUDCA effectively increased the expression level in plasma of CIA arthritis model of RA. \u003cstrong\u003eJ)\u003c/strong\u003e Similarly, a significantly increased level of TTR (p=0.05) shows expression in plasma, and the treatment with GUDCA effectively decreased the expression normalized by total protein. Indirect ELISA of Synovium \u003cstrong\u003eK) \u003c/strong\u003eSignificantly reduced level of TTR was found in the GUDCA treated group compared to CIA and VC group \u003cstrong\u003eL) \u003c/strong\u003eIncreased IGF1 expression after GUDCA treatment in rats compared to the CIA group. (Group1: Healthy or HC, Group2: CIA, Group3: CIA + VC, Group4: CIA + GUDCA, * = P ≤ 0.05, ** = P ≤ 0.01, *** = P ≤ 0.001).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4878563/v1/320e20e4bfd1acefe74138af.png"},{"id":72640750,"identity":"757335b0-2f16-498d-a500-1c08d7cb4fc8","added_by":"auto","created_at":"2024-12-30 16:09:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4707368,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4878563/v1/cab4ee14-6152-4131-b66c-bf987f4dcef9.pdf"},{"id":66118439,"identity":"1c40218c-3c37-4f05-88eb-2eba244c9f2e","added_by":"auto","created_at":"2024-10-08 01:05:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":70264,"visible":true,"origin":"","legend":"","description":"","filename":"SupplemenatryFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-4878563/v1/8fbe78a16a5f13dc76bbaa3f.docx"},{"id":66117936,"identity":"180733ba-2b3b-4130-b4bc-5c005af33694","added_by":"auto","created_at":"2024-10-08 00:57:35","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":25098,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4878563/v1/f60da52f2463255187b219a5.xlsx"},{"id":66117941,"identity":"5f9760fc-f2fa-418c-ae70-5a6dd713dd2c","added_by":"auto","created_at":"2024-10-08 00:57:35","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":22510,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4878563/v1/46e5fabc8646dc0a16f99af6.xlsx"},{"id":66118442,"identity":"310655f4-e64b-4d47-ba29-475ebded67c6","added_by":"auto","created_at":"2024-10-08 01:05:36","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":97398,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4878563/v1/0f261f70bcfef5003b9ef875.xlsx"},{"id":66117947,"identity":"3e9c8374-1e21-492b-b18a-57c6ff8a2f43","added_by":"auto","created_at":"2024-10-08 00:57:36","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":4772749,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfileuncroppedgelimages.docx","url":"https://assets-eu.researchsquare.com/files/rs-4878563/v1/55d821c36a9a441aad10ef2c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative Metabolomic-Proteomic Analysis Uncovers a New Therapeutic Approach in Targeting Rheumatoid Arthritis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRheumatoid arthritis (RA) is an autoimmune systemic disorder characterized by persistent inflammation resulting in the destruction of synovium, bone, and cartilage [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Multiple external/internal factors, including epigenetic, genetic, and environmental factors, trigger heterogeneous pathogenicity during disease progression [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The epidemiology of RA suggests its occurrence in nearly 5 per 1000 adults worldwide, and women are 2 to 3 times more prone to its development than men. Despite the availability of drugs and treatments, the insufficient diagnosis/prognosis led to improper treatment, resulting in the threatening aspect of the disease [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Various markers are used to diagnose this disease, often lacking sensitivity and specificity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and overlap with other similar diseases. Therefore, a definitive study of the prognosis and detection of the disease is required.\u003c/p\u003e \u003cp\u003eCurrently, the omics approach, expanding its roots to metabolomic tools is in demand to determine the directional changes of altered metabolism in disease to understand the molecular pathophysiology of the disease[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e][\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The range of these metabolic alterations and various altered molecules, such as metabolites and proteins, could be prominent markers of cytokine-mediated inflammatory processes in RA. Metabolomics, an emerging tool, has been recently employed to identify potential markers in multiple diseases, such as infectious diseases, cancer, inflammatory diseases, and coronary artery disease (CAD)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMetabolites are small molecules formed as by-products from multiple metabolic pathways. Generally, these metabolites get altered much earlier than the onset of diseases; hence, identification of altered metabolites holds significant potential in associating genes and proteins in a disease state [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In contrast to genomics and proteomics, the focus of metabolomics is on connecting small molecules of biological pathways that are modulators of genes and proteins\u0026rsquo; activity, allowing more precise identification of disease-associated phenotypes occurring within the biological system [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is reported that metabolites are the responses of rapid physiological actions according to the disease activity. To manage numerous cellular processes and to regulate protein activities, interactions between proteins and metabolites are essential [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Reports have indicated that metabolic enzymes, transcription factors, transporters, and membrane receptors can be controlled by interactions with proteins and metabolites (PMIs) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, we used a proteomic approach apart from metabolomics to identify the significantly differential proteins in RA plasma samples. Differential proteome profiling is a powerful tool for studying proteins and their alterations in specific disease conditions, providing various differentially expressed proteins (DEPs) associated with disease development [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Further, the integrated analysis of metabolite-protein interactome has been reported to characterize any biological process efficiently[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, we focused our attention on identifying novel differential metabolites linked to differential proteome profile of RA and explored their relationship and relative importance in determining disease severity.\u003c/p\u003e \u003cp\u003ePlasma samples of RA patients and healthy control (HC) were used in our study to identify differential metabolites and proteins via HPLC-MS/MS and SWATH-MS analysis, respectively, followed by \u003cem\u003ein-silico\u003c/em\u003e analysis to identify altered pathways. Relative expression of the metabolite-induced protein was validated in RA synovial fibroblast cells (SW982) through in vitro studies. This was followed by in vivo validation using a Collagen Induced Arthritis (CIA) rat model. In this study, Glycoursodeoxycholic acid (GUDCA) metabolite was identified as a prominent regulatory metabolite in RA. Upon treatment with GUDCA, it was found that there was an increased expression of IGF1, and a decreased expression of TTR, and the treatment had anti-inflammatory, antioxidative, and antiproliferative properties. GUDCA may, therefore, be considered a therapeutic potential molecule for RA.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e1. Clinical Samples\u003c/h2\u003e\n\u003cp\u003eBlood samples (n\u0026thinsp;=\u0026thinsp;60) were collected from RA patients from the Department of Rheumatology, All India Institute of Medical Sciences (AIIMS), New Delhi, India[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. Similarly, blood samples (n\u0026thinsp;=\u0026thinsp;40) were collected from HC with no prior ailment and joint inflammation. The medical history of each patient was collected (\u003cstrong\u003eSupplementary Table\u0026nbsp;1\u003c/strong\u003e)[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. See details in supplementary file).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2. Metabolomics analysis\u003c/h2\u003e\n\u003cp\u003eTo analyze the differential metabolites, HPLC-MS/MS was carried out using RA (n\u0026thinsp;=\u0026thinsp;20) and HC (n\u0026thinsp;=\u0026thinsp;20) plasma samples. Two complimentary LC-MS/MS metabolomics methods were applied: HILIC and C18 chromatography. The raw LC-MS (.wiff files) data file was analysed by Peak View (ABSciex). Fold change criteria were considered to categorize upregulated (fold change\u0026thinsp;\u0026ge;\u0026thinsp;1.5) and downregulated (fold change\u0026thinsp;\u0026le;\u0026thinsp;0.5) metabolites, respectively, and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e][\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. (See details in supplementary file).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e3. Proteomics of plasma samples: SWATH-MS acquisition\u003c/h2\u003e\n\u003cp\u003ePlasma samples of RA (n\u0026thinsp;=\u0026thinsp;60) and HC (n\u0026thinsp;=\u0026thinsp;40) were taken, and a total of 70\u0026micro;g protein was estimated by BCA and digested overnight at 37\u0026deg;C with 0.1\u0026micro;g/\u0026micro;l trypsin (Promega, USA). [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. (See details in supplementary file).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e4. Integration of metabolomics and proteomics\u003c/h2\u003e\n\u003cp\u003eAssociation analysis between metabolomics and proteomics data was performed using significant differential metabolites and protein profiles between RA and HC groups. Joint Pathway Analysis was carried out using MetaboAnalyst 5.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca/\u003c/span\u003e\u003c/span\u003e). It enabled us to visualize significant genes and metabolites that were enriched in a particular pathway and integrated the underlying relationships among differentially expressed metabolites and proteins [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e5. Target Prediction of metabolites\u003c/h2\u003e\n\u003cp\u003eThe major concern in drug discovery is to validate the best-screened active compounds\u0026rsquo; interaction with appropriate targets [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. To identify the potential molecular targets of the screened metabolites, PharmMapper server database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://lilab.ecust.edu.cn/PharmMapper\u003c/span\u003e\u003c/span\u003e) was used [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e][\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. The gene targets were matched with protein profile identified by SWATH analysis. (See details in supplementary file)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e6. Western Blot (WB) and enzyme-linked immunosorbent assay (ELISA)\u003c/h2\u003e\n\u003cp\u003eFor WB analysis, 4 pooled plasma proteins of RA and HC were used. Each pooled sample consists of RA (n\u0026thinsp;=\u0026thinsp;10) and HC (n\u0026thinsp;=\u0026thinsp;10); thus, 40 RA and 40 HC samples were used, and 20\u0026micro;g of pooled plasma proteins were run on SDS-PAGE and anti-IGF1 (Santa Cruz, USA) (1:2000) as the primary antibody and anti-mouse (1:5000) as a secondary antibody were used. The indirect ELISA was performed using diluted (1\u0026micro;l/200\u0026micro;l) RA plasma (n\u0026thinsp;=\u0026thinsp;60) and HC (n\u0026thinsp;=\u0026thinsp;40), coated into 96-well micro-titer plates (Thermo Scientific, Nunc, USA), followed by incubation with primary antibody (Anti-IGF1) and secondary antibody (anti-mouse). The absorbance was observed at 495nm [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. (See details in supplementary file).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e7. Peripheral Blood Mononuclear Cell (PBMC) Isolation, RNA isolation, WB, and qRT-PCR:\u003c/h2\u003e\n\u003cp\u003ePBMCs are the primary immune cells in the human body and offer discriminatory immune responses toward inflammation [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]. PBMCs were isolated by centrifuging RA blood (n\u0026thinsp;=\u0026thinsp;6) and HC (n\u0026thinsp;=\u0026thinsp;6) using histopaque reagent[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e] and then used to perform WB with 3 pooled RA and HC samples, respectively (n\u0026thinsp;=\u0026thinsp;2 in each pooled sample) [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. Total RNA was extracted from PBMCs of HC (n\u0026thinsp;=\u0026thinsp;6) and RA (n\u0026thinsp;=\u0026thinsp;6) using Tri-Xtract Reagent (G-biosciences). GAPDH as an internal reference. Primers are shown in \u003cstrong\u003eSupplementary Table\u0026nbsp;2\u003c/strong\u003e (See details in the supplementary file). Similarly, in our earlier study, protein and mRNA levels of TTR were checked in PBMCs of RA blood [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e8. Correlation analysis:\u003c/h2\u003e\n\u003cp\u003eThe association of GUDCA levels with RA disease activity, specifically with ACCPA and DAS28-ESR scores was investigated [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e][\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, the relationship between IGF1 levels, (measured by ELISA), and RA disease activity (assessed by the DAS28-ESR score) was examined. (See details in the supplementary file)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e9.\u003c/strong\u003e \u003cstrong\u003eIn-Vitro\u003c/strong\u003e \u003cstrong\u003eanalysis\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003ea) Human synovial fibroblast SW982 cell culture and MTT test:\u003c/h2\u003e\n\u003cp\u003eSW982 cells were cultured in DMEM media and treated with GUDCA metabolite (1\u0026ndash;70\u0026micro;M range) for 24h in serum-free media [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. Absorbance was measured at 540 nm See details in the supplementary file)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eb) Total protein extraction and Western blotting:\u003c/h2\u003e\n\u003cp\u003eSW982 cells were cultured and pre-treated with GUDCA (50\u0026micro;M- 6.25 \u0026micro;M) for 24h. Protein was extracted in RIPA buffer after 10 min induction with TNF-\u0026alpha; (10ng/ml) [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. The blot was incubated with anti-p65, anti-IGF1, and anti-TTR separately as primary antibodies [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. (See details in supplementary file)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003ec) Real-Time Quantitative Reverse Transcription PCR (qRT-PCR):\u003c/h2\u003e\n\u003cp\u003eSW982 cells were cultured and incubated with GUDCA (50\u0026micro;M) for 24h. The effect was investigated by TNF-\u0026alpha; treatment (10 ng/ml) on GUDCA pretreated cells for 1h. Total RNA was isolated and subjected to cDNA preparation, and mRNA expression was evaluated and quantitated using 2\u003csup\u003e\u0026minus;\u0026Delta;\u0026Delta;CT\u003c/sup\u003e formula [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. Human-specific primer sequences are shown in \u003cstrong\u003eSupplementary Table\u0026nbsp;2.\u003c/strong\u003e (See details in supplementary file)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003ed) Scratch Assay analysis:\u003c/h2\u003e\n\u003cp\u003eSW982 cells were grown in a culture plate, the vertical scratch was drawn, and each scratch area was measured before and after the treatment with GUDCA (50\u0026micro;M). Bright-field images were taken at 0h and 48h and analyzed using a Nikon Eclipse 650 (NIKON, Tokyo, Japan) at \u0026times;10 magnification. The images were analyzed using ImageJ software [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. (See details in supplementary file)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003ee) Total Reactive oxygen species (ROS) estimation:\u003c/h2\u003e\n\u003cp\u003eSW982 cells were pretreated with GUDCA (50\u0026micro;M) with and without TNF-\u0026alpha; (24h), followed by adding 10\u0026micro;M working solution of DCFH-DA into each well for 30 min. Fluorescence images were taken by ZOE Fluorescent Cell Imager and analyzed by ImageJ software (22). (See details in supplementary file)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e10. In Vivo Studies\u003c/strong\u003e\u003c/p\u003e\n\u003cstrong\u003ea) Development of Collagen-Induced Arthritis (CIA) Rat Model\u003c/strong\u003e\u003cbr /\u003e\n\u003cp\u003eFemale Wistar rats (60-80g) were procured from the ICMR -National Institute of Nutrition in Hyderabad, India. The work design was approved by the Institute\u0026rsquo;s Animal Ethical Committee (IGIB/IAEC/3/3/Mar 2023). The animals were randomly divided into four groups (n\u0026thinsp;=\u0026thinsp;4). The untreated group/ healthy control (HC) (Group 1), Collagen-Induced Arthritis (CIA) (Group 2), vehicle control (VC\u0026thinsp;+\u0026thinsp;CIA) (Group 3), and GUDCA treated (CIA\u0026thinsp;+\u0026thinsp;GUDCA) (Group 4). CIA rats (Except the HC group) were then induced with 2mg/ml collagen (Type II) from chicken (Sigma, USA). GUDCA was administered at 800\u0026micro;g/Kg of rat body weight mixed with corn oil/ benzyl alcohol (95:5 v/v) and was injected subcutaneously [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e][\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. (See details in supplementary file)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003eb) Measurement of CIA induction in experimental groups and Detection of RA\u003c/h2\u003e\n\u003cp\u003eThroughout the study, paw volume and arthritis index (AI) were assessed in individual animals to monitor disease progression from day 0th to day 28th. [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e] Splenic index and liver index were calculated for each rat as the ratio of the spleen/liver: body weight. [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e] (See details in supplementary file)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003ec) Enzyme-linked immunosorbent Assay (ELISA) of cytokines in plasma\u003c/h2\u003e\n\u003cp\u003eRat plasma was separated and added (100\u0026micro;l) to the pre-coated ELISA plate, followed by the manufacturer\u0026rsquo;s guidelines. TNF\u0026alpha;, IL(Interleukin)1\u0026beta;, and IL-6 cytokines were quantified using ELISA kits (ELK Biotechnology, China) [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003ed) Hematoxylin and Eosin Staining (H \u0026amp; E)\u003c/h2\u003e\n\u003cp\u003eRat synovium was sliced and fixed in 10% formalin, fixed in the paraffin block, sliced (5\u0026micro;m thick) using a microtome, and slides were prepared. Slides were viewed under a Nikon microscope. Images of the slides at 10X magnification were taken, and Image-J software was used to analyze the images. [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e](See details in supplementary file)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003ee) Western blot analysis of the CIA model rat plasma\u003c/h2\u003e\n\u003cp\u003eThe blood samples were drawn through direct heart puncture and collected in EDTA coated vacutainer tubes (BD, Franklin Lakes, NJ, USA), and plasma was separated. Similarly, synovial tissue was collected and crushed in liquid nitrogen from each rat, and the lysate was prepared in RIPA buffer and centrifuge; further supernatant was stored at -80\u0026deg;C for further analysis. For WB analysis, the total protein (10\u0026micro;g) concentration was run on the gel as mentioned above. [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e] (See details in supplementary file)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n\u003ch2\u003ef) Enzyme-linked immunosorbent Assay (ELISA) of synovium\u003c/h2\u003e\n\u003cp\u003eThe indirect ELISA was performed using synovium lysate of all groups diluted (10\u0026micro;l/90\u0026micro;l) with coating buffer into 96-well micro-titer plates (Thermo Scientific, Nunc, USA) and incubated overnight at 4\u0026ordm;C and proceeded as mentioned above. [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e] (See details in supplementary file)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n\u003ch2\u003e11. Statistical analysis\u003c/h2\u003e\n\u003cp\u003eAll non-parametric Mann\u0026ndash;Whitney tests were performed using Graph pad Prism 9.0. The complete data set was analyzed using MetaboAnalyst 3.0. Pathway enrichment analysis was performed to find the related pathway with the altered metabolites. Statistical analysis was performed with the paired student\u0026rsquo;s t-test to compare the data between two groups, and ANOVA was used to compare data among multiple groups. The obtained p-values were represented by asterisks on the graph (*p\u0026thinsp;\u0026le;\u0026thinsp;0.05, **p\u0026thinsp;\u0026le;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026le;\u0026thinsp;0.001, ****p\u0026thinsp;\u0026le;\u0026thinsp;0.0001). Each experiment was repeated at least three times.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\n\u003ch2\u003e1. Identification of differentially expressed metabolites in RA plasma:\u003c/h2\u003e\n\u003cp\u003eUsing HPLC-MS/MS, a total of 2519 m/z (mass/charge ratio) were identified in RA plasma and represented by volcano plots. (Fig.\u0026nbsp;1\u003cstrong\u003eA)\u003c/strong\u003e Among them, 685 m/z were found to be differentially regulated, 70 m/z were upregulated (Blue dots) and 47 m/z (green dots) were downregulated. (Fig.\u0026nbsp;1\u003cstrong\u003eB).\u003c/strong\u003e The differentially regulated metabolites were further processed with the metabolomics library, from which 82 were annotated \u003cstrong\u003e(Supplementary Table\u0026nbsp;3)\u003c/strong\u003e with high confidence. Amongst these, 23 metabolites were found to be significantly regulated (7 downregulated and 2 upregulated), as depicted in the pictorial representation \u003cstrong\u003e(Fig.\u0026nbsp;1C)\u003c/strong\u003e (\u003cstrong\u003eTable\u0026nbsp;1)\u003c/strong\u003e. A threshold of fold change criteria for upregulated (fold change\u0026thinsp;\u0026ge;\u0026thinsp;1.5) and downregulated (fold change\u0026thinsp;\u0026le;\u0026thinsp;0.5) was considered for the identification of differential metabolites.\u003c/p\u003e\n\u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\n\u003ch2\u003e2. Analysis of differential metabolites:\u003c/h2\u003e\n\u003cp\u003eFor the discriminant analysis of RA and HC, 82 annotated metabolites were opted for the OPLS-DA and PCA, used to recognize group differences. The analysis was normalized using Pareto scaling. 2D score plot of OPLS-DA analysis \u003cstrong\u003e(Fig.\u0026nbsp;1D)\u003c/strong\u003e shows a T score of 8% (variation between the groups) and an orthogonal T score of 12.4% (variation within the groups). The 3D score plot of PCA was generated, which showed the differential pattern of metabolites \u003cstrong\u003e(Fig.\u0026nbsp;1E)\u003c/strong\u003e. Principal components (PC) PC1, PC2, and PC3 accounted for 22.6%, 13.2%, and 11.2% of the total variance, respectively, indicating that the metabolites of RA samples were distinctly discriminated from the healthy samples. A variable importance projection (VIP) score plot was created to recognize the top discriminators, and 15 top metabolites of RA were revealed (Fig.\u0026nbsp;1\u003cstrong\u003eF).\u003c/strong\u003e To figure out the most classified significant metabolites, the criteria of VIP values were set to 2 to obtain pre-selected metabolites. This clarifies the variance described by the top-tiered metabolites among RA and healthy groups.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n\u003ch2\u003e3. Pathway enrichment analysis of metabolites:\u003c/h2\u003e\n\u003cp\u003ePathway enrichment analysis depicts the changes in the patterns of metabolite concentration biologically and identifies the pathways that impact the metabolite pattern. We used Metaboanalyst 3.0 and revealed that altered metabolites were associated with Starch and sucrose, galactose, Porphyrin, and Phenylacetate metabolism. \u003cstrong\u003e(Fig.\u0026nbsp;1G)\u003c/strong\u003e Pathway enrichment ratios were also calculated by Metaboanalyst and visualized by bar graph. \u003cstrong\u003e(Fig.\u0026nbsp;1H)\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\n\u003ch2\u003e4. Identification of differentially expressed proteins (DEPs) in RA plasma:\u003c/h2\u003e\n\u003cp\u003eA total of 231 proteins were identified by SWATH-MS analysis \u003cstrong\u003e(Supplementary Table\u0026nbsp;4)\u003c/strong\u003e, depicted by the volcano plot. Amongst, 62 proteins (violet spots) were identified as significant DEPs in RA compared to control samples \u003cstrong\u003e(Fig.\u0026nbsp;2A)\u003c/strong\u003e, 29 out of 62 DEPs were upregulated (red spots), and 23 were downregulated (green spots) \u003cstrong\u003e(Fig.\u0026nbsp;2B, 2C)\u003c/strong\u003e (\u003cstrong\u003eTable\u0026nbsp;2)\u003c/strong\u003e. A threshold of fold change criteria for upregulated (fold change\u0026thinsp;\u0026ge;\u0026thinsp;1.5) and downregulated (fold change\u0026thinsp;\u0026le;\u0026thinsp;0.5) was considered significant for identified DEPs between the groups.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\n\u003ch2\u003e5. Joint pathway analysis of differential protein and metabolite profile:\u003c/h2\u003e\n\u003cp\u003eJoint pathway analysis is an integrative analysis to assess the commonly associated pathways between the differential profile of metabolites and proteins of RA patients. Differentially expressed proteins and metabolites significantly enriched in Arachidonic acid metabolism, porphyrin and chlorophyll metabolism, Aminoacyl-tRNA biosynthesis, glyoxylate and dicarboxylate metabolism, and phenylalanine pathways and were found to be interrelated with identified differential proteins and metabolites \u003cstrong\u003e(Fig.\u0026nbsp;2D)\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\n\u003ch2\u003e6. ACCPA Analysis:\u003c/h2\u003e\n\u003cp\u003eACCPA levels are directly proportional to disease severity [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. In this study, ACCPA levels analyzed in plasma from individuals with RA to establish the relationship between ACCPA and GUDCA levels as well as IGF1. Results suggested significantly higher levels of ACCPA in RA patients compared to the HC (Fig.\u0026nbsp;2\u003cstrong\u003eE\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e7. Comparative Analysis of DEPs with differential metabolites and their relation with Rheumatoid Arthritis:\u003c/h3\u003e\n\u003cp\u003eIn the analysis, \"Porphyrin metabolism\" was found to be a significant pathway associated with RA. This pathway involved three identified metabolites (Glycocholic acid, Glycodeoxycholic acid, and Glycoursodeoxycholic acid), with Glycoursodeoxycholic acid (GUDCA) being a significantly downregulated metabolite (0.44-fold change). GUDCA was found to have a significant moderate negative correlation with ACCPA concentration \u003cstrong\u003e(Fig.\u0026nbsp;2F)\u003c/strong\u003e and DAS28-ESR activity score \u003cstrong\u003e(Fig.\u0026nbsp;2G)\u003c/strong\u003e. As a result, it was chosen for further analysis \u003cstrong\u003e(Supplementary Table\u0026nbsp;3).\u003c/strong\u003e The PharmMapper database predicted 300 gene targets of GUDCA metabolite \u003cstrong\u003e(Supplementary Table\u0026nbsp;5).\u003c/strong\u003e These target genes were first matched with DisGeNET database genes of RA. Then the common genes were matched with identified proteins in RA plasma by SWATH (\u003cstrong\u003eSupplementary Table\u0026nbsp;4)\u003c/strong\u003e to deduce the potential metabolite-protein pair altered in RA \u003cstrong\u003e(Supplementary Table\u0026nbsp;6).\u003c/strong\u003e This comparative analysis revealed that IGF1, TTR, and SHBG were common target proteins of GUDCA. However, IGF1 and TTR were screened and selected for further study in RA condition based on their GDA score, which was found to be 0.05 and 0.02, respectively, and was also found to be significantly (0.42-fold) downregulated and upregulated (1.89-fold) respectively in SWATH data. \u003cstrong\u003e(Supplementary Table\u0026nbsp;4)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\n\u003ch2\u003e8. Validation of target protein expression\u003c/h2\u003e\n\u003cp\u003eThe expression of significantly differential target protein (IGF1) was validated in four pooled samples of RA and HC by WB. The densitometric analysis showed a significant downregulation of IGF1 level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0349) in RA compared to HC (Fig.\u0026nbsp;3\u003cstrong\u003eA\u003c/strong\u003e), with a 1.6-fold change after normalization with total protein. Further, ELISA revealed a 1.2-fold significantly downregulated expression of IGF1 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.011) in RA plasma (n\u0026thinsp;=\u0026thinsp;60) compared to HC (n\u0026thinsp;=\u0026thinsp;40) \u003cstrong\u003e(Fig.\u0026nbsp;3B)\u003c/strong\u003e. The levels of IGF1 were found to have a significant negative correlation with DAS28-ESR \u003cstrong\u003e(Fig.\u0026nbsp;3C).\u003c/strong\u003e Similar IGF1 levels were confirmed in PBMCs of RA and found to be 3-fold downregulated (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0007) at the protein level by WB and 1.5-fold downregulated (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) at levels by qRT-PCR, compared to HC after normalization with \u0026beta;-actin used as a loading control. \u003cstrong\u003e(Fig.\u0026nbsp;3D and 3E)\u003c/strong\u003e. Similarly, the levels of TTR's protein and mRNA levels were verified in RA plasma and PBMCs, compared to HC, in our previous studies [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\n\u003ch2\u003e9. Human synovial fibroblast (SW982) cytotoxicity analysis of GUDCA:\u003c/h2\u003e\n\u003cp\u003eCell viability of human synovial fibroblast cells (SW982) pre-treated with GUDCA (1\u0026ndash;70 \u0026micro;M/ml) was measured by MTT \u003cstrong\u003e(Fig.\u0026nbsp;4A).\u003c/strong\u003e The bar represented the percentage (%) of cell survivability after the induction of cells. The results showed that less than 50\u0026micro;M GUDCA concentration did not affect cell viability.\u003c/p\u003e\n\u003cdiv id=\"Sec32\" class=\"Section3\"\u003e\n\u003ch2\u003e10. Effect of GUDCA on inflammatory condition and target proteins\u003c/h2\u003e\n\u003cp\u003eThe expression level of NF\u0026kappa;B/(p65), a prominent inflammatory mediator, was analyzed and validated in 24h pre-treated SW982 cells with GUDCA (range of 50\u0026micro;M- 6.25\u0026micro;M) followed by TNF-\u0026alpha; (10ng/ml) induction for 10min. The densitometric analysis demonstrated a significantly decreased expression of NF\u0026kappa;B (p65) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0164) at 50\u0026micro;M of GUDCA concentration \u003cstrong\u003e(Fig.\u0026nbsp;4B).\u003c/strong\u003e The protein (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0159) expression of the IGF1 was upregulated \u003cstrong\u003e(Fig.\u0026nbsp;4C)\u003c/strong\u003e, whereas the TTR level was downregulated (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0193) \u003cstrong\u003e(Fig.\u0026nbsp;4D)\u003c/strong\u003e by GUDCA induction at 50\u0026micro;M as compared to the control\u0026thinsp;+\u0026thinsp;TNF\u0026alpha;. Subsequently, GUDCA treatment at 50\u0026micro;M concentration also showed a significant rise in the mRNA expression of IGF1 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0152) (Fig.\u0026nbsp;4\u003cstrong\u003eE)\u003c/strong\u003e and a decrease in the mRNA expression of TTR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0027) (Fig.\u0026nbsp;4\u003cstrong\u003eF)\u003c/strong\u003e, IL-6 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) \u003cstrong\u003e(Fig.\u0026nbsp;4G)\u003c/strong\u003e, IL1\u0026beta;(p\u0026thinsp;\u0026lt;\u0026thinsp;0.0118) \u003cstrong\u003e(Fig.\u0026nbsp;4H)\u003c/strong\u003e and NF\u0026kappa;B (p65) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0005) \u003cstrong\u003e(Fig.\u0026nbsp;4I)\u003c/strong\u003e compared to control\u0026thinsp;+\u0026thinsp;TNF\u0026alpha;. Thus, GUDCA pre-treatment significantly decreased the inflammation level compared to the control, concluding that GUDCA metabolite possesses anti-inflammatory properties.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec33\" class=\"Section3\"\u003e\n\u003ch2\u003e11. GUDCA inhibits cell migration and invasion in synovial fibroblasts:\u003c/h2\u003e\n\u003cp\u003eThe wound healing assay was carried out to determine the effect of GUDCA on cell migration and invasion capability. The results indicated a decrease in the migration of cells when treated with GUDCA(50\u0026micro;M) at 48h compared to the untreated cells (C) at the same time points, indicating that GUDCA can inhibit cell migration and invasion of cells. The gaps marked in the untreated control cells (C) and TNF-\u0026alpha; induced SW982 cells (control\u0026thinsp;+\u0026thinsp;TNF\u0026alpha;) were almost filled with the migration of cells, while the cell's migratory ability in the GUDCA(50\u0026micro;M)-treated group was observed to be significantly less. The inhibition of cell migration was 41.0% in TNF-\u0026alpha; treated cells, 66.9% in control cells, and 85.9% after treatment with GUDCA (50\u0026micro;M) at 48h, indicating that GUDCA has the potential of an antiproliferative agent since it inhibits cell migration under inflammatory conditions. \u003cstrong\u003e(Fig.\u0026nbsp;5A)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003e\u003cstrong\u003e12. Reactive Oxygen scavenging (ROS) ability of GUDCA\u003c/strong\u003e:\u003c/h3\u003e\n\u003cp\u003eOxidative stress was measured by estimating the total cellular ROS in GUDCA (50\u0026micro;M) pretreated cells (SW982) with/without TNF-\u0026alpha; for 24h. Fluorescence signals of cells were measured by DCFHDA dye and showed highly induced intracellular ROS production in TNF-\u0026alpha; induced cells compared to untreated control cells. GUDCA treatment, therefore, significantly (p\u0026thinsp;\u0026le;\u0026thinsp;0.0004) inhibited the intracellular ROS production in TNF-\u0026alpha; induced SW982 cells \u003cstrong\u003e(Fig.\u0026nbsp;5B)\u003c/strong\u003e.\u003c/p\u003e\n\u003ch3\u003e13. CIA-Rat model Establishment, Amelioration of Clinical severity, by GUDCA Treatment\u003c/h3\u003e\n\u003cp\u003eThe CIA rat model is widely used to mimic the RA condition [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e] and the effect of GUDCA was investigated in the model. Images of rat paws taken on the 28th day of all groups Group 1 (HC), Group 2 (CIA), Group 3 (CIA\u0026thinsp;+\u0026thinsp;VC), and Group 4 (CIA\u0026thinsp;+\u0026thinsp;GUDCA) before scarification are shown \u003cstrong\u003e(Fig.\u0026nbsp;6A)\u003c/strong\u003e. We observed that Group 4 showed less redness and swelling compared to Groups 2 and 3. The development of arthritis was quantified by measuring the paw volume using plethysmometer twice weekly to confirm the onset of the disease. After day 14, the average paw volume decreased in Groups 4, whereas paw volume increased in Groups 2 and 3 \u003cstrong\u003e(Fig.\u0026nbsp;6B)\u003c/strong\u003e. CIA-induced arthritis was determined to progress successfully by measuring arthritis index (AI%) between the groups. \u003cstrong\u003e(Fig.\u0026nbsp;6C)\u003c/strong\u003e. AI was more in group 2 and 3, that was decreased in group 4. The increased presence of autoreactive B cells in RA leads to elevated production of immunoglobulins and enlargement of the spleen. In CIA rats, the liver and spleen are susceptible to chronic inflammation that can be measured as splenic and liver index. In Group 2, the splenic index \u003cstrong\u003e(Fig.\u0026nbsp;6D)\u003c/strong\u003e and liver index \u003cstrong\u003e(Fig.\u0026nbsp;6E)\u003c/strong\u003e increased compared to normal rats, which was found to be normalized by GUDCA, exhibiting protective effects. Pro-inflammatory cytokine levels were also quantitively measured in the rat plasma of all groups. Downregulation of pro-inflammatory cytokines (TNF\u0026alpha;, IL-1\u0026beta;, IL-6) were also revealed in rat plasma in Groups 4 compared to Groups 2 and 3 \u003cstrong\u003e(Fig.\u0026nbsp;6F)\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv id=\"Sec36\" class=\"Section2\"\u003e\n\u003ch2\u003e14. GUDCA ameliorated inflammatory stress of synovium\u003c/h2\u003e\n\u003cp\u003eTo further validate the anti-inflammatory activity of GUDCA, histological tests were performed on rat synovium by H\u0026amp;E staining \u003cstrong\u003e(Fig.\u0026nbsp;6G)\u003c/strong\u003e. The pink color represents cytoplasm, which correlates with the synovium's inflammation. The purple color represents the number of nuclei present, used to determine the number of cells infiltrated into the given region and to quantify inflammation[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. The H\u0026amp;E scan analysis revealed that the group injected with GUDCA (Group 4) exhibited much less cell infiltration compared to Groups 3 and 2. \u003cstrong\u003e(Fig.\u0026nbsp;6H)\u003c/strong\u003e This histological examination supports the idea that Group 4 significantly reduces inflammation in the CIA rats.\u003c/p\u003e\n\u003cdiv id=\"Sec37\" class=\"Section3\"\u003e\n\u003ch2\u003e15. Effect of GUDCA on differential regulation of TTR and IGF1 in CIA-rat plasma\u003c/h2\u003e\n\u003cp\u003eThe impact of GUDCA on the differential regulation of proteins was assessed through WB analysis in CIA rat plasma. The findings indicated that GUDCA was able to decrease the expression of TTR and increase the expression of IGF1 in Group 4. Densitometric analysis showed a significant increase in the expression of IGF1 (p\u0026thinsp;=\u0026thinsp;0.0101; \u003cstrong\u003eFig.\u0026nbsp;6I)\u003c/strong\u003e and a decrease in the expression of TTR (p\u0026thinsp;=\u0026thinsp;0.0013; \u003cstrong\u003eFig.\u0026nbsp;6J\u003c/strong\u003e). Similar results were observed in the synovium of CIA rats using ELISA. The ELISA results demonstrated a significant reduction in TTR levels (Fig.\u0026nbsp;6\u003cstrong\u003eK\u003c/strong\u003e) and an increase in the expression of IGF1 in Group 4 compared to CIA (Fig.\u0026nbsp;6\u003cstrong\u003eL\u003c/strong\u003e). Therefore, we may conclude that GUDCA may have the ability to regulate the expression of these proteins.\u003c/p\u003e\n\u003cp\u003eThe increased levels of TTR and decreased levels of IGF1 are linked to inflammatory markers (IL-6, IL-1\u0026beta;) that contribute to the progression of RA. This data was validated for the first time in synovial fibroblast cells, showing that treatment with GUDCA in both the synovial fibroblast cells and the CIA rat model reduces the exacerbation of disease severity.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eRA holds a crucial social, economic, and physiological burden on affected individuals. It dramatically impacts people\u0026rsquo;s bone health, and deformity leads to disability[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Prevalent systemic inflammation in RA is mediated by pro-inflammatory cytokines that affect different cellular metabolism [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e][\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe utilized metabolomics and proteomics techniques, and revealed 82 metabolites and 231 differentially regulated proteins in RA with high confidence. Differential molecular patterns of metabolites and proteins were integrated using biological pathways analysis [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and joint pathway analysis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and revealed \u0026ldquo;Porphyrin metabolism\u0026rdquo; as a prominent pathway associated with RA. This pathway was associated with the metabolites Glycocholic acid, Glycodeoxycholic acid, and Glycoursodeoxycholic acid (GUDCA) (bile acids conjugates).\u003c/p\u003e \u003cp\u003eThe report indicates that Glycocholic acid and Glycodeoxycholic acid are vital in regulating bile acid synthesis, depending on the availability of cholesterol substrate, and significantly reduce HMG-CoA reductase activity [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. GUDCA is obtained from the acyl glycine conjugate of ursodeoxycholic acid [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e][\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]which is reported to have a neuroprotective and anti-inflammatory agent [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Ursodeoxycholic acid can dissolve cholesterol gallstones and treat cholestatic liver disorders, atherosclerosis, steatosis, and liver fibrosis.[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. It has been proven to effectively prevent pain and cartilage degeneration in cases of osteoarthritis (OA). Its chondroprotective properties work by effectively suppressing oxidative damage and inhibiting catabolic factors that are known to contribute to the pathogenesis of cartilage damage in OA [\u003cspan additionalcitationids=\"CR41 CR42\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study on RA, we found that the level of GUDCA showed a negative correlation with disease severity as assessed by ACCPA levels and DAS28-ESR activity score. This led us to suspect that GUDCA might hold promise as a new treatment for RA. As a result, we have chosen to further analyze the therapeutic potential of GUDCA.\u003c/p\u003e \u003cp\u003eTo explore the potential targets of GUDCA, PharmMapper analysis of GUDCA and comparative study with differential proteomics profile was attempted and revealed that IGF1 and TTR can be the appropriate targets of GUDCA. IGF1 has a link with inflammation and immuno-metabolism and is associated with bone and cartilage differentiation[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Its bioactivity is controlled by six IGF-binding proteins (IGFBP-1 to IGFBP-6). Low expression of IGF1 in the sera of RA patients increased with disease activity[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] supported by our SWATH data. Increased levels of IGF1 promote articular cartilage regeneration after injury[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Thus, the effect of GUDCA on the expression of IGF1 and its association in synovial fibroblast cells with inflammation related to RA pathogenesis was examined.\u003c/p\u003e \u003cp\u003eSignificantly decreased levels of IGF1 in RA plasma and PBMCs suggested its low availability in RA (Fig.\u0026nbsp;3\u003cb\u003eA, 3D\u003c/b\u003e). The levels of IGF1 were found to have a negative correlation with the DAS28-ESR score (Fig.\u0026nbsp;3\u003cb\u003eC\u003c/b\u003e). The report shows that differentiation of bone and cartilage tissue is hampered due to less availability of IGF1 in RA[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. IGF1 is thus unable to participate in regulating immunity and inflammation, and that promotes the disease conditions.\u003c/p\u003e \u003cp\u003eWe also selected a significantly upregulated protein, TTR, from SWATH data, a pre-albumin secretory protein that transports retinol and thyroxin (T4). In our previous study, we discovered that the increased rate of TTR glycation, along with its binding with RAGE, serves as a trigger for inflammatory pathways through the activation of NF-kB. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. To confirm the therapeutic ability of GUDCA, the expression of IGF1 and TTR by \u003cem\u003ein vitro\u003c/em\u003e studies was analyzed. Expression levels of p65 were reduced in the presence of GUDCA at both protein and mRNA levels. The simultaneous observation of increased IGF1 levels and decreased TTR levels after GUDCA treatment suggests that GUDCA actively regulates these levels, thus playing a significant role in inflammation. Also, GUDCA has been found to have an anti-proliferative and antioxidative effect on synovial fibroblast cells (Fig.\u0026nbsp;5\u003cb\u003eA, 5B\u003c/b\u003e). Further, our findings were validated in the CIA rat model. GUDCA helped to improve disease symptoms in CIA rats by significantly reducing paw volume and arthritis index. There was also a significant decrease in immune cell infiltration in the treated group compared to the CIA group, as indicated by the H\u0026amp;E score (Fig.\u0026nbsp;6\u003cb\u003eG\u003c/b\u003e). Additionally, analysis of CIA rat plasma showed that the levels of the proteins TTR and IGF1, which are altered by GUDCA concentration, indicated reduced pathogenesis of RA.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study demonstrated that GUDCA may have a protective effect on the disease development of RA pathogenesis. The untargeted metabolomics by HPLC-MS/MS revealed metabolites and DEPs of RA and laid the foundation for monitoring disease development based on the interplay of both biomolecules (metabolite and protein). The explanation of metabolic pathways in chronic inflammatory circumstances such as RA provided new insight into disease development. It offered a hopeful sign for the specific biomolecular marker identification for RA. Further, the mechanistic evaluation of GUDCA at the cellular level needs to be explored, and its therapeutic impact must be validated through clinical studies. These steps are crucial to fully understand the potential benefits of this metabolite and its impact on human health.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eRheumatoid arthritis (RA); healthy control (HC); differentially expressed proteins (DEPs); Collagen Induced Arthritis (CIA); Radioimmunoprecipitation assay buffer (RIPA); Dulbecco\u0026apos;s Modified Eagle Medium (DMEM); Glycoursodeoxycholic acid (GUDCA); American College of Rheumatology (ACR); European League Against Rheumatism (EULAR); \u0026nbsp;glyceraldehyde-3-phosphate dehydrogenase (GAPDH); reactive oxygen species (ROS); dichlorodihydrofluorescein diacetate (DCFHDA); High Performance Liquid Chromatography-Mass Spectrometry (HPLC-MS) (HPLC/LC-MS/MS); Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH-MS); ethylenediamine tetra acetic acid (EDTA); liquid chromatography\u0026ndash;tandem mass spectrometry (LC-MS/MS); hydrophilic interaction liquid chromatography (HILIC); National Institute of Standards and Technology (NIST); Bicinchoninic Acid (BCA); Gene Disease association (GDA); Sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE); polymerase chain reaction (PCR); \u0026nbsp;3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT); Endoplasmic reticulum (ER); High fat diet (HFD); insulin-like growth factor-1 (IGF1); Receptor for Advanced Glycation Endproducts (RAGE); anti-citrullinated protein/peptide antibody (ACCPA); disease activity score - erythrocyte sedimentation rate (DAS28-ESR) scores; Dichloro-dihydro-fluorescein diacetate (DCFH-DA); Phosphate-buffered saline (PBS); Indian Council of Medical Research (ICMR) ; Projections to Latent Structures Discriminant Analysis (OPLS-DA); Principal component analysis (PCA); variable importance projection (VIP); sex hormone-binding globulin (SHBG); Electrochemiluminescence (ECL); Horseradish peroxidase (HRP); Hydroxymethylglutaryl-CoA (HMG-CoA).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was ethically approved by AIIMS, New Delhi, India (Reg No IEC-37/07.02.2020, RP-15/2020) and the study protocols complied with the Declaration of Helsinki.\u0026nbsp;The work design was approved by the Institute\u0026rsquo;s Animal Ethical Committee (IGIB/IAEC/3/3/Mar 2023).\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the Council of Scientific and Industrial Research (CSIR), and DST- Science \u0026amp; Engineering research board (SERB), Government of India, New Delhi, India, for providing financial support. Lovely Joshi, Mohd Saquib, Ashish and Debolina received fellowship support from CSIR. Prachi Agnihotri, Swati Malik received a fellowship from DST. Mr. Praveen for mass spectrometer data acquisition and Mr. Pankaj Yadav for transporting biological samples from the hospital to the lab. We also thank CSIR-Institute of Genomics and Integrative Biology, Delhi, India to provide the research platform, AcSIR for academic support, and the Department of Rheumatology,\u0026nbsp;All India Institute of Medical Sciences (AIIMS), New Delhi, India for providing patient\u0026rsquo;s sample.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the Department of Science and Technology (DST), Science \u0026amp; Engineering Research Board (SERB) CRG/2019/006398, New Delhi, India for financial support. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors have agreed that the study should be submitted to Journal.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eCompeting interests/\u003c/strong\u003e \u003cstrong\u003eAuthor Declarations\u003c/strong\u003e:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflict of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have made substantial contributions to data analysis and took part in drafting the article or revising it critically for important intellectual content and agreed to submit it to the current journal. Prachi and Dr Sagarika Biswas made substantial contributions to the conception, design, drafting, acquisition of data, or analysis and interpretation of data. Swati, Lovely, Saquib, Ashish and Debolina contributed in drafting, data analysis, and sample handling. Dr Uma Kumar provided the biological samples. Dr Sagarika Biswas agreed to submit to the current journal, gave final approval for the version to be published, and agreed to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information:\u0026nbsp;\u003c/strong\u003eCouncil of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology, Mall Road, Delhi University Campus, Delhi, India, 110007, All India Institute of Medical Sciences, Ansari Nagar, New Delhi - 110029, India\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eFor all original data and protocol, please contact Dr Sagarika Biswas (\u003ca href=\"mailto:[email protected]\"\[email protected]\u003c/a\u003e).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGuo Q, Wang Y, Xu D, Nossent J, Pavlos NJ, Xu J. Rheumatoid arthritis: pathological mechanisms and modern pharmacologic therapies. 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Inappropriate serum levels of IGF-I and IGFBP‐3 in patients with rheumatoid arthritis. Rheumatology [Internet]. 2002 [cited 2023 Jun 16];41:352\u0026ndash;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dx.doi.org/10.1093/rheumatology/41.3.352\u003c/span\u003e\u003cspan address=\"10.1093/rheumatology/41.3.352\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee H, Suh YS, Lee S, Il, Cheon YH, Kim M, Noh HS et al. Serum IGF-1 in patients with rheumatoid arthritis: correlation with disease activity. BMC Res Notes [Internet]. 2022 [cited 2023 Jun 16];15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/35382860/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/35382860/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWenwei T, Cheung PT, Lau YL. IGF-I increases interferon-gamma and IL-6 mRNA expression and protein production in neonatal mononuclear cells. Pediatr Res [Internet]. 1999 [cited 2023 Jun 16];46:748\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/10590034/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/10590034/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eList of metabolites annotated with high confidence in which 23 metabolites were differentially regulated by NIST library and MS-Dial 5.0.\u003c/div\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eS.No.\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003em/z\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eMetabolite\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eFold change\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003ep value\u003c/div\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e538.3154\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCholylmethionine\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3.177627\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.015285\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e566.3472\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eLPC 18:1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.51379\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.051561\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e165.0999\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e6-Pentyl-2H-pyran-2-one\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.326574\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.034463\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e496.331\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eLPC 16:0\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.829247\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.035057\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e5\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e454.2848\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1-palmitoyl-2-hydroxy-sn-glycero-3-phosphoethanolamine\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.780522\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.036636\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e6\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e790.5701\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003ePC O-38:7\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.768475\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.046717\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e7\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e544.3292\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eLPC 20:4/0:0\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.757545\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.002489\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e8\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e246.1643\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCAR 5:0\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.722267\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.015927\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e9\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e520.3286\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eLPC 18:2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.707948\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e5.45E-05\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e10\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e524.363\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eLysoPC(0:0/18:0)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.704678\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.024745\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e11\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e488.2879\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eGLYCOCHOLATE\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.685498\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.038619\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e12\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e786.591\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1,2-dioleoyl-sn-glycero-3-phosphatidylcholine\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.670958\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.01464\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e13\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e482.3144\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1-pentadecanoyl-2-hydroxy-sn-glycero-3-phosphocholine\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.657604\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.013365\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e14\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e480.2975\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eLPE 18:1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.631667\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.000649\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e15\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e449.3838\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eTrihydroxycholestanoic acid\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.520398\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.007314\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e16\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e595.1513\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eEriodictyol 7-O-neohesperidoside\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.500863\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.036249\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e17\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e258.9185\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eD-glucose 6-phosphate\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.490946\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.011213\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e18\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e246.9637\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAsp-Asp\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.485635\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.000563\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e19\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e478.2915\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1-Oleoyl-sn-glycero-3-phosphoethanolamine/LysoPE(18:1/0:0)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.481877\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.02599\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e238.9864\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eHeptanedioic acid, 1-(2-cyclopentylidenehydrazide)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.466123\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.006953\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e21\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e448.3038\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eGlycoursodeoxycholic acid\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.449878\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.007679\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e22\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e413.5848\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e5.alpha.-Pregnan-3.alpha.,17-diol-20-one 3-sulfate\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.367392\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3.79E-06\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e23\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e464.2986\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eGlycocholic acid\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.197437\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.010649\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eList of significantly 29 upregulated and 23 downregulated differentially regulated proteins with fold change and p-value.\u003c/div\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eS.No\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eProtein\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003efold change\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epvalue\u003c/div\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eApolipoprotein C-I\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e62.40570335\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.007758662\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAlpha-1-acid glycoprotein 1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e16.7214335\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.00553161\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eApolipoprotein C-III\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e9.850330468\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.25567E-06\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eComplement C4-A\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e8.302636966\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.002453229\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e5\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eComplement C4-B\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e8.023461599\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.000342575\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e6\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eInter-alpha-trypsin inhibitor heavy chain H4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e6.888252287\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.008452104\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e7\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eHemopexin\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e5.921200271\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.010553529\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e8\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eApolipoprotein A-I\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e4.808899273\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.00343186\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e9\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eComplement C1r subcomponent\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e4.738477563\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.018669551\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e10\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eIg kappa chain V-I region EU\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3.814368748\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.012133217\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e11\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAngiotensinogen\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3.583448101\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.008352337\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e12\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eFibrinogen alpha chain\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3.47708266\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.026347715\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e13\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eComplement C1q subcomponent subunit B\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3.417463883\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.011119125\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e14\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCD5 antigen-like\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3.100496169\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.033706855\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e15\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eHemoglobin subunit beta\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2.657607982\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.007641719\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e16\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAlpha-1-antitrypsin\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2.536239559\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.013834211\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e17\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSerum paraoxonase/arylesterase 1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2.285135829\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.035481367\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e18\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCartilage acidic protein 1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2.195281727\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.027063039\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e19\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eClusterin\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2.19471443\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.001684064\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eDipeptidyl peptidase 4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2.11232999\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.016101272\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e21\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eInter-alpha-trypsin inhibitor heavy chain H3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2.056770621\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.012397008\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e22\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSerum amyloid A-4 protein\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2.055016548\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.043047554\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e23\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eTransthyretin\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.897613179\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.048515129\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e24\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eProtein AMBP\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.885350481\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.016866094\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e25\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAlpha-1-antichymotrypsin\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.83019933\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.035537119\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e26\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eComplement component C9\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.814339568\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.049834854\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e27\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eLeucine-rich alpha-2-glycoprotein\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.717204308\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.03938007\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e28\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCeruloplasmin\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.694083887\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.003258636\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e29\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eThyroxine-binding globulin\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.659605788\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.045789643\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e30\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eL-selectin\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.507441747\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.038241024\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e31\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eBiotinidase\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.48889523\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.047922207\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e32\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eN-acetylmuramoyl-L-alanine amidase\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.465354202\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.004123615\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e33\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eIg heavy chain V-III region ZAP\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.454522581\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.047582617\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e34\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eHistidine-rich glycoprotein\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.45196089\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.013256396\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e35\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eInsulin-like growth factor I\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.42957407\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.016637558\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e36\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e72 kDa type IV collagenase\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.427441571\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.058048635\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e37\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAfamin\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.427009471\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.025087744\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e38\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eIg heavy chain V-III region LAY\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.409704949\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.023839039\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e39\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eComplement component C8 gamma chain\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.394727685\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.006947765\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e40\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eEukaryotic initiation factor 4A-II\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.383639153\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.039364223\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e41\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eHeparin cofactor 2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.35791362\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.001140975\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e42\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCoagulation factor XII\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.35345247\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.039938135\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e43\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eC-reactive protein\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.299366867\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.006885079\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e44\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eComplement component C8 alpha chain\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.282144005\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.004202023\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e45\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSelenoprotein P\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.279631127\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.003575084\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e46\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eATP-binding cassette sub-family F member 1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.220376839\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.02683651\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e47\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eLumican\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.210071273\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.022956686\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e48\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eMonocyte differentiation antigen CD14\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.200887807\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.002936902\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e49\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd 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class=\"SimplePara\"\u003e0.000368021\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"arthritis-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arrt","sideBox":"Learn more about [Arthritis Research \u0026 Therapy](http://arthritis-research.biomedcentral.com/)","snPcode":"13075","submissionUrl":"https://submission.nature.com/new-submission/13075/3","title":"Arthritis Research \u0026 Therapy","twitterHandle":"@ArthritisRes","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Rheumatoid arthritis (RA), Metabolomics, Metabolites, Proteomics, SWATH, Inflammatory pathways, CIA-Rat model","lastPublishedDoi":"10.21203/rs.3.rs-4878563/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4878563/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective: Rheumatoid arthritis (RA) is a chronic inflammatory condition that, despite available approaches to manage the disease, lacks an efficient treatment and timely diagnosis. Using the most advanced omics technique, metabolomics and proteomics approach, we explored varied metabolites and proteins to identify unique metabolite-protein signatures involved in the disease pathogenesis of RA.\u003c/p\u003e\n\u003cp\u003eMethods: Untargeted metabolomics (n=20) and proteomics (n=60) of RA patients’ plasma were carried out by HPLC/LC-MS/MS and SWATH, respectively and analyzed by Metaboanalyst. The targets of metabolite retrieved by PharmMapper were matched with SWATH data, and joint pathway analysis was carried out. An \u003cem\u003ein-vitro \u003c/em\u003estudy of metabolites in TNF-α induced SW982 cells was conducted by Western, RT-PCR, scratch, and ROS scavenging assay. The effect of GUDCA was also evaluated in the CIA rat model.\u003c/p\u003e\n\u003cp\u003eResults: A Total of 82 metabolites and 231 differential proteins were revealed. Porphyrin and chlorophyll pathway and its metabolite Glycoursodeoxycholic acid (GUDCA) was significantly altered. In vitro analysis has shown that GUDCA reduces inflammation thus offering protection against ROS production and cell proliferation. PharmMapper analysis revealed that GUDCA was significantly linked with identified SWATH proteins insulin like growth factor-1(IGF1), and Transthyretin (TTR) and it upregulates the expression of IGF1 and downregulates the expression of TTR in both in vitro and in vivo models.\u003c/p\u003e\n\u003cp\u003eConclusion: \u0026nbsp;GUDCA was found to possess antioxidative, antiproliferative properties and an effective anti-inflammatory property at a low dosage. It may be considered as a potential therapeutic option for reducing the inflammatory parameters associated with RA.\u003c/p\u003e","manuscriptTitle":"Integrative Metabolomic-Proteomic Analysis Uncovers a New Therapeutic Approach in Targeting Rheumatoid Arthritis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-08 00:57:30","doi":"10.21203/rs.3.rs-4878563/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-23T22:54:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-23T14:17:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-06T07:22:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198373433004666669597535526310654436792","date":"2024-09-03T11:41:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285853135529044925909844920806485896394","date":"2024-09-03T10:52:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-03T05:35:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-12T07:53:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-12T05:58:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Arthritis Research \u0026 Therapy","date":"2024-08-08T06:27:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"arthritis-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arrt","sideBox":"Learn more about [Arthritis Research \u0026 Therapy](http://arthritis-research.biomedcentral.com/)","snPcode":"13075","submissionUrl":"https://submission.nature.com/new-submission/13075/3","title":"Arthritis Research \u0026 Therapy","twitterHandle":"@ArthritisRes","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fa9e4e1f-bdab-4d98-9394-fba6973ddc15","owner":[],"postedDate":"October 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-30T16:05:01+00:00","versionOfRecord":{"articleIdentity":"rs-4878563","link":"https://doi.org/10.1186/s13075-024-03429-z","journal":{"identity":"arthritis-research-and-therapy","isVorOnly":false,"title":"Arthritis Research \u0026 Therapy"},"publishedOn":"2024-12-23 15:57:32","publishedOnDateReadable":"December 23rd, 2024"},"versionCreatedAt":"2024-10-08 00:57:30","video":"","vorDoi":"10.1186/s13075-024-03429-z","vorDoiUrl":"https://doi.org/10.1186/s13075-024-03429-z","workflowStages":[]},"version":"v1","identity":"rs-4878563","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4878563","identity":"rs-4878563","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}