The blind mole rat (BMR), a long-living subterranean rodent, is an exceptional model for both aging and cancer research since they do not display age-related phenotypes or tumor formation. The Janus kinase–signal transducer and activator of transcription (JAK–STAT) signaling is a cytokine-stimulated pathway that has a crucial role in immune regulation, proliferation, and cytokine production. Therefore, the pathway has recently attracted interest in cellular senescence studies. Here, by using publicly available data, we report that JAK–STAT signaling was suppressed in the BMR in comparison to the mouse. Interestingly, our experimental results showed upregulated Jak1/2 expressions in BMR fibroblasts during the replicative senescence process. The transcriptomic analysis using publicly available data also demonstrated that various cytokines related to JAK–STAT signaling were upregulated in the late passage cells, while some other cytokines such as MMPs and SERPINs were downregulated, representing a possible balance of senescence-associated secretory phenotypes (SASPs) in the BMR. Finally, our proteomics data also confirmed cytokine-mediated signaling activation in senescent BMR fibroblasts. Together, our findings suggest the critical role of JAK–STAT and cytokine-mediated signaling pathways during cellular senescence, pointing to the possible contribution of divergent inflammatory factors to the superior resistance of aging and cancer in BMRs.

Progressive pseudorheumatoid dysplasia (PPD) is a rare autosomal recessive cartilage disorder caused by biallelic variants in CCN6, which encodes the matricellular protein WISP3. Although WISP3 is thought to contribute to extracellular matrix (ECM) homeostasis, its precise molecular role in PPD remains unclear. To elucidate how disease-associated CCN6 variants affect chondrocyte function, we overexpressed four variants—p.Cys52*, p.Tyr109*, p.Gly83Glu, and p.Cys114Trp—all located within the IGFBP domain, and evaluated their impact on parameters including redox balance, ER stress, ECM remodeling, gene expression, and protein–protein interactions. The p.Cys52* variant resulted in rapid degradation of WISP3, indicating a complete loss-of-function. The p.Tyr109* variant disrupted ECM regulation, markedly reducing protein interaction capacity, which was correlated with elevated mitochondrial ROS (mtROS) levels and triggered a strong response that led to programmed cell death. Although both missense variants yielded full-length proteins, their effects diverged significantly: p.Gly83Glu induced minor cellular alterations, whereas p.Cys114Trp caused severe protein destabilization, increased ROS accumulation, and high levels of ER stress. Proteomic analysis revealed that p.Cys114Trp acquired novel interaction partners, suggesting a potential gain-of-function mechanism. Collectively, these findings demonstrate that the functional consequences of CCN6 variants depend not only on variant type or domain location but also on their positional and structural context. The distinct cellular responses elicited by each variant underscore the importance of functional validation in modeling PPD pathogenesis and offer valuable biological and therapeutic perspectives.

We developed a pathogenicity classifier, named Var3PPred, for identifying pathogenic variants in genes associated with autoinflammatory disorders. Our comprehensive approach integrates protein-protein interaction analysis and 3D structural information. Initially, we collected a dataset of 702 missense disease-associated variants from 35 genes linked to systemic autoinflammatory diseases (SAIDs). This dataset, sourced from the Infevers database, served as the training and test sets. We used the SMOTE algorithm to balance the dataset comprising 130 benign and 572 pathogenic variations. Our approach included 3D docking analysis of protein-protein interactions, utilizing data from the STRING and Intact databases. We weighted ZDOCK and SPRINT values in accordance with HGPEC gene rank scores for robustness. Additionally, we integrated sequential and structural features, such as changes in folding free energies (ΔΔ G), accessible surface area, volume, per residue local distance difference test (pLDDT) scores, and position specific independent count (PSIC) scores. These features, calculated using PyRosetta and AF2 computed structures, provided insights into amino acid conservation at variant positions and the impact of variants on protein structure and stability. Through extensive hyperparameter tuning of six machine learning algorithms, we found the random forest classifier to be the most effective, yielding an AUROC of 99% on the test set. Var3PPred outperformed three other classifiers, SIFT, PolyPhen, and CADD, on an unseen test set of a SAID-related gene. This demonstrates its capacity for pathogenicity classification of SAID variations. The source code for Var3PPred and the predictions for all 420 missense variants of uncertain significance from the Infevers database are available on GitHub: (https://github.com/alperbulbul1/Var3PPred).

Cholangiocarcinoma is a rare but highly lethal cancer of the biliary epithelium, marked by heterogeneous molecular subtypes, unclear etiology, and poor five-year survival, highlighting the need for new diagnostic and therapeutic strategies; therefore, this study integrates genomic, transcriptomic, single-cell, methylomic, and molecular-dynamics data to pinpoint pathogenic variants. We performed an integrative multi-omics analysis of publicly available datasets. Somatic variants from 23 tumor samples in The Cancer Genome Atlas were annotated with 11 pathogenicity tools (AUC ≥ 0.86 across EVE, REVEL, SIFT, AlphaMissense, DEOGEN2 were the most stringent). Differential gene expression was assessed in matched bulk RNA-seq (tumor vs. non-tumor) using DESeq2 with Benjamini–Hochberg FDR correction. A single-cell RNA-seq dataset comprising 23,782 cells from an intrahepatic cholangiocarcinoma was clustered with marker genes identified by Wilcoxon rank-sum tests. Illumina 450 K methylation arrays (52 tumors, 12 normal livers) were analyzed with limma and DMRcate to detect differentially methylated probes and regions. AlphaFold3 models of wild-type and MAP2K1R49C were subjected to 50 ns all-atom molecular-dynamics simulations in GROMACS; conformational shifts were quantified by RMSD/RMSF and stability tested with FoldX5. Twenty-four tumor-specific missense variants were detected. The four highest-confidence pathogenic substitutions (EVE, REVEL, SIFT, AlphaMissense, DEOGEN2) occurred in TUBB3, FLNC, ABCA1, and MAP2K1. Bulk RNA-seq confirmed significant dysregulation of these genes and enrichment of extracellular-matrix organization, cytoskeletal remodeling, MAPK signaling, and cholesterol-efflux pathways. Single-cell analysis resolved 23 transcriptionally distinct clusters; proliferative malignant cholangiocytes selectively over-expressed ABCA1 and MAP2K1, indicating tumor-cell specificity. Methylome profiling identified 148,928 DMPs and 7040 DMRs; promoter hypomethylation of TUBB3 and ABCA1 correlated with their transcriptional activation. Substituting Arg-49 with Cys in MAP2K1 dismantles the Arg-centred hydrogen-bond/salt-bridge cluster, reduces hydrophobic packing, and, corroborated by 50 ns MD (Welch’s t = −58.06, p = 3.17 × 10−230) and FoldX5 (ΔΔG = +2.3 kcal mol−1), significantly destabilises the protein, manifesting as higher backbone RMSD and increased local flexibility relative to wild type. This multi-omics, public data-driven synthesis delineates a coherent network of genomic, epigenomic, transcriptomic, and structural vulnerabilities, offering a rational framework for therapeutic targeting of cholangiocarcinoma. This study reveals novel bile duct-associated variations that expand our understanding of cholangiocarcinoma pathogenesis and provide potential targets for precision medicine approaches.

HerCanPred, a machine-learning-based pathogenicity classifier specifically optimized for 63 cancer-predisposition genes, was developed to improve the interpretation of missense variants in hereditary cancer syndromes. This model integrates sequence conservation with structural features derived from AlphaFold2 (AF2) structures. HerCanPred achieved a strong performance, outperforming 23 established predictors. SHAP analysis identified AF2-derived structural features, specifically local pLDDT confidence scores and relative solvent accessible area, as the strongest predictors of variant impact. Benchmarking the strengths and limitations of HerCanPred against existing methods showed that misclassification of pathogenic variants was concentrated in disordered and surface-exposed regions, whereas benign failures were more broadly distributed. HerCanPred and three established predictors were also applied to over 57,000 variants of uncertain significance (VUS) from the same gene set. Notably, 166 VUS were reassigned as pathogenic and 75 as benign, with an enrichment of the NF1, FH, and MLH1 genes. By combining gene-specific training with 3D structural information, HerCanPred provides a robust framework for reducing diagnostic uncertainty. Our findings demonstrate that targeted, structure-aware tools can contribute to resolving VUS, providing a rational basis for systematic variant reinterpretation and more informed medical management in hereditary cancer care.

Multiple sclerosis (MS) is an immune-mediated central nervous system disease with mapped genetic risk loci but unresolved cross-layer regulatory mechanisms. We harmonised 30 primary public MS datasets spanning bulk transcriptomics (552 samples, 367 MS/185 healthy control [HC]), DNA methylation arrays (475 samples, 244 MS/231 HC) with sorted-cell whole-genome bisulfite sequencing (WGBS) assessment, single-cell RNA sequencing (RNA-seq; 517,533 cells from 81 donors), and cerebrospinal fluid (CSF) and brain white-matter proteomes, and UK Biobank Pharma Proteomics Project (UK Biobank-PPP) plasma proteomic statistics (407 MS/39,979 HC) for external validation. We prioritised MS-associated genes showing inverse-concordant, cohort-level methylation–expression associations and orthogonal support across layers. After harmonised reprocessing and per-stratum differential testing, a bulk-RNA × methylation inverse-concordance filter followed by an independent proteomic and/or donor-level single-cell anchor identified two Tier-1 candidates (IKZF1’s anchor is a candidate-panel-adjusted donor-level pseudobulk association, not transcriptome-wide significance): ITGB2 and IKZF1. ITGB2 showed the strongest cross-layer support: RNA down-regulation, promoter hypermethylation and reduced plasma abundance in UK Biobank MS cases, a pattern consistent with altered leukocyte-integrin adhesion. Pathway and network analyses linked the candidates predominantly to leukocyte and lymphocyte activation and differentiation, leukocyte-integrin adhesion, and interferon and cytokine-signalling immune modules. These findings nominate MS-relevant regulatory candidates and provide a reusable multi-omics framework for hypothesis generation and validation.