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In Silico Characterization of Growth Differentiation Factors as Inhibitors of TNF-Alpha and IL-6 in Immune-Mediated Inflammatory Disease Rheumatoid Arthritis.

ABSTRACT: Tumor necrosis factor alpha (TNF-α) plays a critical role in the progression of inflammation and affects the cells of the synovial membrane. Another key factor in the progression of rheumatoid inflammation is interleukin-6 (IL-6). Both TNF-α and IL-6 promote the proliferation of synovial membrane cells thus stimulating the production of matrix metalloproteinases and other cytotoxins and leading towards bone erosion and destruction of the cartilage. Growth differentiation factor-11 (GDF11) and growth differentiation factor-8 (GDF8) which is also known as myostatin are members of the transforming growth factor-β family and could be used as antagonists to inflammatory responses which are associated with rheumatoid arthritis. In the current study, to elucidate the evolutionary relationships of GDF11 with its homologs from other closely related organisms, a comprehensive phylogenetic analysis was performed. From the phylogram, it was revealed that the clade of Primates that belong to superorder Euarchontoglires showed close evolutionary relationships with order Cetartiodactyla of the Laurasiatheria superorder. Fifty tetrapeptides were devised from conserved regions of GDF11 which served as ligands in protein-ligand docking against TNF-α and IL-6 followed by drug scanning and ADMET profiling of best selected ligands. The peptides SAGP showed strong interactions with IL-6, and peptides AFDP and AGPC showed strong interactions with TNF-α, and all three peptides fulfilled all the pharmacokinetic parameters which are important for bioavailability. The potential of GDF8 as an antagonist to TNF-α and IL-6 was also explored using a protein-protein docking approach. The binding patterns of GDF8 with TNF-α and IL-6 showed that GDF8 could be used as a potential inhibitor of TNF-α and IL-6 to treat rheumatoid arthritis.

SUBMITTER: Mustafa G

PROVIDER: S-EPMC8019371 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:In clinical practice, approximately one-third of patients with rheumatoid arthritis (RA) respond insufficiently to TNF-α inhibitors (TNFis). The aim of the study was to explore the use of a metabolomics to identify predictors for the outcome of TNFi therapy, and study the metabolomic fingerprint in active RA irrespective of patients' response. In the metabolomic profiling, lipids, oxylipins, and amines were measured in serum samples of RA patients from the observational BiOCURA cohort, before start of biological treatment. Multivariable logistic regression models were established to identify predictors for good- and non-response in patients receiving TNFi (n = 124). The added value of metabolites over prediction using clinical parameters only was determined by comparing the area under receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive- and negative predictive value and by the net reclassification index (NRI). The models were further validated by 10-fold cross validation and tested on the complete TNFi treatment cohort including moderate responders. Additionally, metabolites were identified that cross-sectionally associated with the RA disease activity score based on a 28-joint count (DAS28), erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP). Out of 139 metabolites, the best-performing predictors were sn1-LPC(18:3-ω3/ω6), sn1-LPC(15:0), ethanolamine, and lysine. The model that combined the selected metabolites with clinical parameters showed a significant larger AUC-ROC than that of the model containing only clinical parameters (p = 0.01). The combined model was able to discriminate good- and non-responders with good accuracy and to reclassify non-responders with an improvement of 30% (total NRI = 0.23) and showed a prediction error of 0.27. For the complete TNFi cohort, the NRI was 0.22. In addition, 88 metabolites were associated with DAS28, ESR or CRP (p<0.05). Our study established an accurate prediction model for response to TNFi therapy, containing metabolites and clinical parameters. Associations between metabolites and disease activity may help elucidate additional pathologic mechanisms behind RA.

Project description:BackgroundRheumatoid arthritis (RA) is an autoimmune disease. Bone marrow mesenchymal stem cells (BMSCs) have multilineage differentiation and anti-inflammatory potential, and small interfering RNAs (siRNAs) can inhibit the target gene expression, which make them suitable for ameliorating RA. The current study was aimed to explore the effect and potential mechanisms of siRNAs targeting IL-1β/TNF-α combined with BMSCs transplantation in ameliorating RA in rats.MethodsCollagen-induced arthritis (CIA) model rats were randomly divided into five groups: PBS (Model control group), methotrexate (Positive drug treatment group), BMSCs (BMSCs transplantation group), siRNA (IL-1β/TNF-α siRNAs injection group), siRNA + BMSCs (Both IL-1β/TNF-α siRNAs injection and BMSCs transplantation group). After treatment for 0, 7, 14, 21, 28 days, the ameliorating effect was comprehensively assessed through results of the body weight, toe swelling value, the immobility time of forced swimming, the serum concentrations of IL-1β and TNF-α, knee joint DR-X imaging and pathological analysis as well as of IL-1β, TNF-α and NF-κB mRNA expression in spleen tissue. Furthermore, the potential underlying mechanism involving the NF-κB signaling pathways was also explored.ResultsCompared with the PBS group, BMSCs, siRNA, siRNA + BMSCs treatment groups showed significant lower toe swelling value, immobility time, spleen index, serum contents of IL-1β and TNF-α. In addition, the DR-X results showed that the knee carton surface tended to smoothing without bone hyperplasia, suggesting that these three treatments were all able to successfully ameliorate RA symptoms. In addition, compared with the PBS group, the protein expression of p-NF-κB-p65 was significantly reduced in the knees of siRNA + BMSCs rats. BMSCs labeled with BrdU were also found in the knees of rats. Moreover, the mRNA expression of IL-1β, TNF-α and NF-κB-P65 in spleen tissue of siRNA + BMSCs rats were all significantly inhibited.ConclusionsOur results demonstrated for the first time that siRNA + BMSCs was able to ameliorate RA inflammation by inhibiting the activation of NF-κB signaling pathways and reducing the erosion of articular cartilage, and siRNA + BMSCs treatment showed synergism effects in helping ameliorating the inflammation and cartilage repair of RA rats. Therefore, the results of our present study provide a new idea for gene and stem cell therapy for RA.

Project description:Background: Rheumatoid arthritis (RA) is a chronic inflammatory disorder primarily targeting joints, significantly impacting patients' quality of life. The introduction of tumor necrosis factor-alpha (TNF-α) inhibitors has markedly improved RA management by reducing inflammation. However, these medications are associated with adverse skin reactions, which can vary greatly among patients due to genetic differences. Objectives: This study aimed to identify risk factors associated with skin adverse events by TNF-α in RA patients. Methods: A cohort study was conducted, encompassing patients with RA who were prescribed TNF-α inhibitors. This study utilized machine learning algorithms to analyze genetic data and identify markers associated with skin-related adverse events. Various machine learning algorithms were employed to predict skin and subcutaneous tissue-related outcomes, leading to the development of a risk-scoring system. Multivariable logistic regression analysis identified independent risk factors for skin and subcutaneous tissue-related complications. Results: After adjusting for covariates, individuals with the TT genotype of rs12551103, A allele carriers of rs13265933, and C allele carriers of rs73210737 exhibited approximately 20-, 14-, and 10-fold higher incidences of skin adverse events, respectively, compared to those with the C allele, GG genotype, and TT genotype. The machine learning algorithms used for risk prediction showed excellent performance. The risk of skin adverse events among patients receiving TNF-α inhibitors varied based on the risk score: 0 points, 0.6%; 2 points, 3.6%; 3 points, 8.5%; 4 points, 18.9%; 5 points, 36.7%; 6 points, 59.2%; 8 points, 90.0%; 9 points, 95.7%; and 10 points, 98.2%. Conclusions: These findings, emerging from this preliminary study, lay the groundwork for personalized intervention strategies to prevent TNF-α inhibitor-associated skin adverse events. This approach has the potential to improve patient outcomes by minimizing the risk of adverse effects while optimizing therapeutic efficacy.

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In Silico Characterization of Growth Differentiation Factors as Inhibitors of TNF-Alpha and IL-6 in Immune-Mediated Inflammatory Disease Rheumatoid Arthritis.

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