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Background: Polymorphisms in IL1B play a significant role in depression, multiple inflammatory-associated disorders, and susceptibility to infection. Functional non-synonymous SNPs (nsSNPs) result in changes in the encoded amino acids, potentially leading to structural and functional alterations in the mutant proteins. So far, most genetic studies have concentrated on SNPs located in the IL1B promoter region, without addressing nsSNPs and their association with multifactorial diseases. Therefore, this study aimed to explore the impact of deleterious nsSNPs retrieved from the dbSNP database on the structure and functions of the IL1B protein.
Results: Six web servers (SIFT, PolyPhen-2, PROVEAN, SNPs&GO, PHD-SNP, PANTHER) were used to analyze the impact of 222 missense SNPs on the function and structure of IL1B protein. Five novel nsSNPs (E100K, T240I, S53Y, D128Y, and F228S) were found to be deleterious and had a mutational impact on the structure and function of the IL1B protein. The I-mutant v2.0 and MUPro servers predicted that these mutations decreased the stability of the IL1B protein. Additionally, these five mutations were found to be conserved, underscoring their significance in protein structure and function. Three of them (T240I, D128Y, and F228S) were predicted to be cancer-causing nsSNPs. To analyze the behavior of the mutant structures under physiological conditions, we conducted a 50 ns molecular dynamics simulation using the WebGro online tool. Our findings indicate that the mutant values differ from those of the IL1B wild type in terms of RMSD, RMSF, Rg, SASA, and the number of hydrogen bonds.
Conclusions: This study provides valuable insights into nsSNPs located in the coding regions of IL1B, which lead to direct deleterious effects on the functional and structural aspects of the IL1B protein. Thus, these nsSNPs could be considered significant candidates in the pathogenesis of disorders caused by IL1B dysfunction, contributing to effective drug discovery and the development of precision medications. Thorough research and wet lab experiments are required to verify our findings. Moreover, bioinformatic tools were found valuable in the prediction of deleterious nsSNPs.
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http://dx.doi.org/10.1186/s12863-024-01233-x | DOI Listing |
J Biochem Mol Toxicol
September 2025
Department of Rehabilitation Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
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August 2025
Thailand Excellence Center for Tissue Engineering and Stem Cell, Department of Biochemistry, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.
A growing body of evidence indicates a high incidence of osteoarthritis (OA) in both weight-bearing and non-weight-bearing joints in obese patients. The levels of leptin in the synovial fluid of obese patients with OA are elevated compared to healthy people, suggesting that leptin may be a key factor of OA in obese individuals. Synovitis can occur at all stages of OA development, causing diseases progression.
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August 2025
Department of Science Education, Korea National University of Education, Cheongju-si, Republic of Korea;
Background/aim: Glioblastoma multiforme (GBM) is a highly aggressive, treatment-resistant brain tumor with a dismal prognosis, and identifying key molecules involved in its therapeutic resistance is essential to improve patient outcomes. This study was undertaken to identify hub genes associated with the radioresistance and temozolomide (TMZ) resistance of GBM using bioinformatics analysis.
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Commun Biol
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Department of Oral Biology, School and Hospital of Stomatology, Jilin University, Changchun, China.
The oral mucosa exhibits unique regenerative properties and distinct wound healing dynamics compared to facial skin, providing a valuable model for studying tissue-specific repair mechanisms. Using a rigorously controlled mouse model combined with single-cell transcriptomics, we identified a novel mechanism underlying this tissue-specific difference. Our study revealed a population of fibroblast progenitors in the oral buccal mucosa that rapidly activate and differentiate into mature fibroblasts, contributing to effective wound resolution.
View Article and Find Full Text PDFPLoS One
August 2025
School of Medicine, Shaoxing University, Shaoxing, Zhejiang, China.
This study aimed to identify potential interacting genes between abdominal aortic aneurysm (AAA) and periodontitis. To achieve this, we obtained datasets of AAA and periodontitis from the GEO database, conducted differential analysis on the AAA dataset, and performed weighted gene co-expression network analysis (WGCNA) on the periodontitis dataset to preliminarily identify interacting genes via intersection. Subsequently, we refined key candidate genes by constructing a PPI network and applying three machine learning algorithms.
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