Genetic and structural interpretation of NLRP1 FIIND domain variants in glioma in an Indian cohort: a pilot study
Chhipa, S.; Sonawane, O. A.; Revanth, M.; Kumar, D.; Sharma, J.; Acharya, S.; Jha, D.; Dey, S.; Yadav, P.; Jha, S.
Show abstract
Gliomas, particularly glioblastoma (GBM), are aggressive primary brain tumors associated with dysregulated NLR signaling, a pathway central to innate immunity and inflammation. NLRP1 triggers proinflammatory cytokine release and pyroptotic cell death via autoproteolytic cleavage. The FIIND missense variant rs11651270 (M1184V) may modulate this cleavage process. While NLRP1 polymorphisms are associated with various diseases and cancers, their specific impact on glioma remains to be investigated. In our study, five FIIND-domain single-nucleotide polymorphisms (SNPs) of NLRP1 rs371579423, rs58604457, rs57636751, rs11651270, and rs2301583, were investigated by Sanger sequencing in a clinical cohort of glioma patients and compared against population-matched controls from the GenomeIndia dataset (Rajasthan cohort) using genetic association models. Molecular dynamics simulations were performed to evaluate the structural effects of the missense variant rs11651270 (M1184V) in NLRP1 during pre- and post-cleavage states. The linked variants rs58604457 (G>A) and rs57636751 (C>T) exhibited complete linkage disequilibrium (r2=1.00) and were significantly associated with lower odds of glioma (odds ratio ~0.5). The missense variant rs11651270 (T>C) showed no association with glioma risk. Notably, the SNP rs2301583 had a higher allelic frequency in glioma cases despite being absent in the GenomeIndia population-based control catalogue. Molecular dynamics simulations revealed that the M1184V substitution stabilizes local FIIND architecture by preserving {beta}-strand organization through persistent interactions with neighboring residues. This study provides the first combined genetic and structural analysis of NLRP1 FIIND-domain variants in an Indian glioma cohort. These findings illustrate the potential value of integrating population-based genetic association with structural modelling to generate hypotheses and uncover potential functional mechanisms of inflammasome-gene variants in neuro-oncology.
Matching journals
The top 11 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- MTAP loss correlates with an immunosuppressive profile in GBM and its substrate MTA stimulates alternative macrophage polarization 93%
- Single-cell analysis reveals diversity of tumor-associated macrophages and their interactions with T lymphocytes in glioblastoma 92%
- Pro-tumoral Ca2+ signaling is dependent on Ca-alpha1T and Slowpoke channels in Drosophila melanogaster glioma 91%
Similar papers in this journal
Similar papers in this journal
- Connecting Patterns of Tumor Growth with Sex Differences in Extreme Survivorship for Primary Glioblastoma Patients 91%
- An integrative pan-cancer investigation reveals common genetic and transcriptional alterations of AMPK pathway genes as important predictors of clinical outcomes across major cancer types 90%
- Novel significant stage-specific differentially expressed genes in liver hepatocellular carcinoma 90%
Similar papers in this journal
- Disrupting Akt-Wnt/β-catenin signaling suppresses glioblastoma stem cell growth and tumor progression in immunocompetent mice 92%
- High Frequency and Unique Subtypes of Meningioma in Patients with BAP1 Tumor Predisposition Syndrome 89%
- Early Imaging Marker of Progressive Glioblastoma: a window of opportunity 89%
Similar papers in this journal
- Ensemble Machine Learning Approaches Predict Survival in Lower-Grade Glioma Based on Glycosphingolipid Gene Expression and Metabolic Modelling 92%
- Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection 89%
- Analysis of Mutations in Precision Oncology using The Automated, Accurate, and User-Friendly Web Tool PredictONCO 88%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.