Project description:Hayashi2013 - TNF-induced Signaling Dynamics Model (Model A) Tumor necrosis factor (TNF) plays a crucial role in inflammation and is associated with diseases such as rheumatoid arthritis and cancer. While TNF signaling pathways are well studied, targeted regulation of proinflammatory responses remains a challenge. In this study, Hayashi et al., (2013) developed a computational model to simulate TNF-induced activation of MAP kinase (p38), NF-κB, and proinflammatory gene expression in murine fibroblast cells. The model parameters were optimized to fit experimental data, ensuring accurate representation of TNF signaling dynamics. The simulations using in silico knockouts identified receptor-interacting protein 1 (RIP1) as a key regulator of TNF-driven proinflammatory responses. Experimental validation using Necrostatin-1, a RIP1 inhibitor, confirmed these predictions and demonstrated its potential as a therapeutic target for regulating proinflammatory mediators in TNF-driven diseases. This model is described in the article: Hayashi K, Piras V, Tabata S, Tomita M, Selvarajoo K. A systems biology approach to suppress TNF-induced proinflammatory gene expressions. Cell Commun Signal. 2013 Nov 7;11:84. doi: 10.1186/1478-811X-11-84. PMID: 24199619; PMCID: PMC3832246. Abstract: Background: Tumor necrosis factor (TNF) is a widely studied cytokine (ligand) that induces proinflammatory signaling and regulates myriad cellular processes. In major illnesses, such as rheumatoid arthritis and certain cancers, the expression of TNF is elevated. Despite much progress in the field, the targeted regulation of TNF response for therapeutic benefits remains suboptimal. Here, to effectively regulate the proinflammatory response induced by TNF, a systems biology approach was adopted. Results: We developed a computational model to investigate the temporal activations of MAP kinase (p38), nuclear factor (NF)-κB, and the kinetics of 3 groups of genes, defined by early, intermediate and late phases, in murine embryonic fibroblast (MEF) and 3T3 cells. To identify a crucial target that suppresses, and not abolishes, proinflammatory genes, the model was tested in several in silico knock out (KO) conditions. Among the candidate molecules tested, in silico RIP1 KO effectively regulated all groups of proinflammatory genes (early, middle and late). To validate this result, we experimentally inhibited TNF signaling in MEF and 3T3 cells with RIP1 inhibitor, Necrostatin-1 (Nec-1), and investigated 10 genes (Il6, Nfkbia, Jun, Tnfaip3, Ccl7, Vcam1, Cxcl10, Mmp3, Mmp13, Enpp2) belonging to the 3 major groups of upregulated genes. As predicted by the model, all measured genes were significantly impaired. Conclusions: Our results demonstrate that Nec-1 modulates TNF-induced proinflammatory response, and may potentially be used as a therapeutic target for inflammatory diseases such as rheumatoid arthritis and osteoarthritis. Figure 2B, obtained from the simulation of TNFR1 Model A, is highlighted here. This model was curated during the Hackathon hosted by BioMed X GmbH in 2024.

Project description:Hayashi2013 - Extended TNF-induced Signaling with Gene Expression Dynamics (Model B) Tumor necrosis factor (TNF) plays a crucial role in inflammation and is associated with diseases such as rheumatoid arthritis and cancer. While TNF signaling pathways are well studied, targeted regulation of proinflammatory responses remains a challenge. In this study, Hayashi et al., (2013) developed a computational model to simulate TNF-induced activation of MAP kinase (p38), NF-κB, and proinflammatory gene expression in murine fibroblast cells. The model parameters were optimized to fit experimental data, ensuring accurate representation of TNF signaling dynamics. The simulations using in silico knockouts identified receptor-interacting protein 1 (RIP1) as a key regulator of TNF-driven proinflammatory responses. Experimental validation using Necrostatin-1, a RIP1 inhibitor, confirmed these predictions and demonstrated its potential as a therapeutic target for regulating proinflammatory mediators in TNF-driven diseases. This model is described in the article: Hayashi K, Piras V, Tabata S, Tomita M, Selvarajoo K. A systems biology approach to suppress TNF-induced proinflammatory gene expressions. Cell Commun Signal. 2013 Nov 7;11:84. doi: 10.1186/1478-811X-11-84. PMID: 24199619; PMCID: PMC3832246. Abstract: Background: Tumor necrosis factor (TNF) is a widely studied cytokine (ligand) that induces proinflammatory signaling and regulates myriad cellular processes. In major illnesses, such as rheumatoid arthritis and certain cancers, the expression of TNF is elevated. Despite much progress in the field, the targeted regulation of TNF response for therapeutic benefits remains suboptimal. Here, to effectively regulate the proinflammatory response induced by TNF, a systems biology approach was adopted. Results: We developed a computational model to investigate the temporal activations of MAP kinase (p38), nuclear factor (NF)-κB, and the kinetics of 3 groups of genes, defined by early, intermediate and late phases, in murine embryonic fibroblast (MEF) and 3T3 cells. To identify a crucial target that suppresses, and not abolishes, proinflammatory genes, the model was tested in several in silico knock out (KO) conditions. Among the candidate molecules tested, in silico RIP1 KO effectively regulated all groups of proinflammatory genes (early, middle and late). To validate this result, we experimentally inhibited TNF signaling in MEF and 3T3 cells with RIP1 inhibitor, Necrostatin-1 (Nec-1), and investigated 10 genes (Il6, Nfkbia, Jun, Tnfaip3, Ccl7, Vcam1, Cxcl10, Mmp3, Mmp13, Enpp2) belonging to the 3 major groups of upregulated genes. As predicted by the model, all measured genes were significantly impaired. Conclusions: Our results demonstrate that Nec-1 modulates TNF-induced proinflammatory response, and may potentially be used as a therapeutic target for inflammatory diseases such as rheumatoid arthritis and osteoarthritis. Figure S3, obtained from the simulation of TNFR1 Model B, is highlighted here. This model was curated during the Hackathon hosted by BioMed X GmbH in 2024.

Project description:BACKGROUND: The gut microbiome and the host immune system interact synergistically to maintain intestinal health and defend against infections. Toll-like receptor 5 (TLR5), known for recognizing bacterial flagellin, plays a pivotal role in this regulatory network. Despite significant advances in understanding the gut microbiome and TLR5, the precise role of TLR5 in gut health and infection resistance remains unclear. This study aimed to elucidate the role of TLR5 in regulating gut microbiota and metabolites and their impact on resistance to Salmonella infection using a TLR5 intestinal-specific overexpression mouse model. METHODS: Fecal microbiota and metabolite transplantation experiments were conducted using feces from TLR5-overexpressing mice (TLR5+/+ group) and wild-type mice (WT group) into C57 mice, creating four experimental groups: Flora-TLR5-C57, Metabolite-TLR5-C57, Flora-WT-C57 and Metabolite-WT-C57. Mice were infected with Salmonella, and various parameters including survival time, fecal Salmonella load, and alterations in gut microbiota and metabolites were assessed. RESULTS: Despite notable shifts in gut microbiota composition and metabolite profiles induced by TLR5 overexpression, no significant differences in fecal Salmonella load or survival time were observed across the experimental groups. TLR5-overexpressing mice exhibited an enrichment of beneficial bacterial taxa and significant changes in metabolites; however, these alterations did not correlate with enhanced resistance to Salmonella infection. CONCLUSION: Our findings suggest that while TLR5 overexpression significantly modifies gut microbiota and metabolite profiles, these changes do not directly enhance resistance to Salmonella infection. The immune response to Salmonella is multifaceted and involves various components beyond the gut microbiota and metabolites. Further research is required to elucidate the detailed molecular pathways through which TLR5 influences infection outcomes and to explore potential combined therapeutic strategies for enhancing host defenses against enteric pathogens.

Dataset Information

Homo sapiens

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets