FKBP11 targeted plasma cells promotes abdominal aortic aneurysm progression through an m6A-dependent mechanism

Objective Despite surgical advance, effective targeted drugs for non-surgical treatment of abdominal aortic aneurysm (AAA) are lacking because of the unclear pathogenesis of AAA. N6-methyladenosine (m6A) methylation, acknowledged for its pivotal influence on RNA metabolism, including aspects such as stability, transport, translation, and splicing, is largely implied for its role in AAA mechanism. This study aims to elucidate the involvement of m6A methylation in the progression of AAA through an integrative multi-omics and machine learning approach. Methods and Results We utilized methylated RNA immunoprecipitation sequencing (MeRIP-seq) to map the m6A methylation landscape in AAA tissues and combined this with RNA sequencing (RNA-seq) from the GEO database, to explore the interplay between m6A methylation and gene expression. A machine learning-based AAA m6A-related mRNA signature (AMRMS) was developed to predict the risk of AAA dilation. The AMRMS showed robust predictive power in distinguishing between patients with large and small AAAs. Notably, FKBP11 was identified as a key gene significantly influencing the predictive model, and up-regulated in large AAAs compared to its in small AAAs. Further single-cell RNA sequencing (scRNA-seq) and histological analysis highlighted the critical role of FKBP11 in mediating the endoplasmic reticulum stress of plasma cells within the AAA walls and its correlation with m6A methylation. Conclusions The m6A modification regulatory network plays a vital role in the progression of AAA, and the AMRMS offers promising potential in assessing the risk of AAA dilation. Our findings suggest that elevated FKBP11, by activating endoplasmic reticulum stress in plasma cells, may significantly contribute to AAA expansion. ### Competing Interest Statement The authors have declared no competing interest. * AAA : abdominal aortic aneurysm AMRMS : AAA m6A-related mRNA signature AUC : Area Under the Curve BP : biological process CBBs : Cell Barcoded Magnetic Beads CC : cellular component CCA : Canonical Correlation Analysis CMUaB : CMU Aneurysm Biobank CDS : coding sequence CTA : computed tomography angiography DEGs : differential expressed genes DMGs : differentially methylated genes ECs : endothelial cells Enet : Elastic Network ER : endoplasmic reticulum FC : Fold Change GBM : Generalized Boosted Regression Modeling glmBoost : Generalized Linear Model Boosting GO : gene ontology HC : healthy control IHC : Immunohistochemistry IP : immunoprecipitation KEGG : Kyoto encyclopedia of genes and genomes LDA : Linear Discriminant Analysis LOOCV : Leave-One-Out Cross-Validation m6A : N6-methyladenosine MASS : Multicenter Aneurysm Screening Study MeRIP-seq : methylated RNA immunoprecipitation with next-generation sequencing MF : molecular function MPs : mononuclear phagocytes NKCs/TCs : natural killer cells/T cells PCs : plasma cells PCA : principal Component Analysis PPI : Protein-Protein Interactions RF : Random Forest ROC : Receiver Operating Characteristic SMCs : smooth muscle cells SMGs : specific methylated genes Stepglm : Step Generalized Linear Model SVM : Support Vector Machine t-SNE : t-Distributed Stochastic Neighbor Embedding Teff : Effector T cells Th : Helper T cells Treg : Regulatory T cells UMI : unique molecular identifiers UPR : unfolded protein response XGBoost : eXtreme Gradient Boosting 3’UTR : 3’ untranslated region 5’UTR : 5’ untranslated region.