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Background Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer (BC) and lacks effective treatment options. Polyphyllin B (PB), a saponin extracted from Paris formosana Hayata, has shown potential in cancer therapy. However, its anti-TNBC effects and mechanisms are not well studied. This study investigated the effects and underlying mechanisms of PB in TNBC. Methods CCK8 assay, Wound healing assay, Transwell migration experiments, and clonogenic assay were performed to assess the effects of PB in three TNBC cells lines. Iron level, lipid ROS, and MDA levels were assessed as ferroptosis markers. Mechanistic studies were conducted using molecular docking, Western blot, immunofluorescence, RT-qPCR, Nrf2 overexpression assays and establishment of a TNBC mouse model. Results PB treatment resulted in significant cell death and inhibited the migration of TNBC cells. Accumulation of iron and MDA suggested that PB induces ferroptosis. In vitro and in vivo , PB downregulated the expression of nuclear factor E2-related factor 2 (NRF2) and sequestosome-1 (P62) and upregulated the expression of Kelch-like ECH-associated protein 1 (KEAP1). Furthermore, it modulated the expression of key ferroptosis regulators, including acyl-CoA synthetase long-chain family member 4 (ACSL4) and glutathione peroxidase 4 (GPX4). In a TNBC mouse model, PB suppressed tumor growth and increased malondialdehyde accumulation. Conclusion PB inhibits cell proliferation and migration and induces ferroptosis in TNBC through the inactivation of NRF2. PB is a promising candidate for TNBC treatment that warrants further investigation.

Abstract Functional magnetic resonance imaging (fMRI) data are inherently complex, characterized by high dimensionality, intricate inter-regional dependencies, and substantial individual variability across experimental paradigms. Traditional linear mixed models (LMMs) provide a principled framework that models population-level fixed effects while estimating variance components arising from subject-level random effects; however, they often fail to adequately capture nonlinear relationships inherent in neuroimaging data. To address these limitations, we introduce the nonlinear mixed model (NMM) approach, an innovative extension of the LMM framework that integrates neural networks to flexibly model complex fixed-effect relationships while preserving the random-effects structure to account for individual differences. NMM advances fMRI analysis by: (1) identifying robust functional connectivity (FC) patterns consistently observed across multiple paradigms; (2) leveraging SHapley Additive exPlanations (SHAP) analysis to provide post-hoc interpretability of the nonlinear fixed effects, quantifying how age, sex, and paradigm contribute to predicted FC and how these effects are distributed across large-scale brain networks; and (3) using subject-specific random effects as neural fingerprints that not only show systematic variability across attention and default mode systems but also predict standardized cognitive scores, demonstrating biological relevance. Applied to the Philadelphia Neurodevelopmental Cohort (PNC) across emotion, n-back, and resting-state paradigms, NMM achieved superior model fit relative to classical LMMs, as evidenced by lower mean squared error (MSE) in predicting FC. This framework offers a statistically rigorous and practically explainable approach for modeling large-scale FC from modest covariates while explicitly separating population-level effects from stable individual variability in functional brain organization.

Abstract Inflammatory bowel diseases (IBD), principally Crohn’s disease (CD) and ulcerative colitis (UC), are common chronic disorders involving inflammation and often progressive tissue damage. Genome-wide association studies have mapped many risk signals, but the causal variants, effector genes and relevant cellular contexts remain difficult to resolve, limiting mechanistic interpretation and therapeutic translation. Here we performed a multi-ancestry GWAS meta-analysis of 125,992 individuals with IBD and more than 1.2 million controls, identifying 619 independent association signals (374 novel) at 420 IBD regions that account for 77–80% of SNP-based heritability. Fine-mapping resolved 81 high-confidence variants, 41 not previously reported. Although most signals were shared between CD and UC, 39% showed IBD subtype specificity, with UC signals showing stronger enrichment in functional annotations from intestinal epithelial, secretory and enteroendocrine cells, and CD showing stronger genetic correlations with circulating inflammatory biomarkers, including C-reactive protein and glycoprotein acetylation. Latent causal modelling supported a causal effect of decreased high-density lipoprotein on CD risk. By integrating bulk and single-cell eQTL and pQTL resources using colocalisation and Mendelian randomisation, together with coding-variant evidence from exome sequencing, we prioritised 664 candidate effector genes across 341 signals, including 390 newly implicated IBD genes, revealing new biological mechanisms and candidate therapeutic targets supported by human genetics.

Abstract Rationale Interstitial lung abnormalities (ILA) are abnormal findings on chest CT scans that may represent early-stages of interstitial lung disease. The presence of fibrosis (traction bronchiectasis and/or honeycombing) identifies those with ILA at an increased risk for mortality, however, determining who will progress remains a challenge, and the role of blood-based biomarkers in ILA risk-stratification is unknown. Methods Data from four prospective cohort studies were used to identify associations between plasma proteins measured with DNA aptamers (SomaScan) and ILA, fibrosis, and mortality. The Genetic Epidemiology of COPD Study (COPDGene) was used to identify protein associations, and findings were replicated by meta-analysis of the Age, Gene/Environment Susceptibility-Reykjavik study (AGES-Reykjavik), SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS), and Framingham Heart Study (FHS). Protein associations with ILA and with fibrotic ILA compared to non-fibrotic ILA were performed using multivariable logistic regression; among those with ILA, Cox proportional hazards models were used to identify protein associations with mortality. Models were adjusted for age, gender, race, BMI, smoking status, pack-year smoking history, and for mortality, COPD. Benjamini-Hochberg false discovery rate p-values <0.05 were considered significant. Results 524 (10.7%) of 4,899 COPDGene participants had ILA, of whom 57 (10.9%) had fibrosis; of 8,146 AGES-Reykjavik, SPIROMICS and FHS participants included in the meta-analysis, 666 (8.2%) had ILA, of whom 198 (29.7%) had fibrosis. In COPDGene, 230 proteins were associated with ILA; the most significant of which were SFTPD (adjusted odds ratio per standard deviation [SD] of protein [OR]=2.21, p = 3.7E-41) and SFTPB (OR = 1.82, p = 7.9E-37). In the meta-analysis, 159 of the ILA-associated proteins were replicated. Of the 230 ILA-associated proteins in COPDGene, 9 were associated with fibrosis, and 5 of these 9 proteins were associated with mortality among those with ILA. The 5 proteins associated with all three outcomes were WFDC2 (ORILA=1.90, p = 2.6E-34; ORFibrosis=2.00, p = 0.0001; adjusted hazards of mortality per SD of protein [HR]=1.59, p = 3.4E-05), GDF15 (ORILA=1.67, p = 3.6E-23; ORFibrosis=1.78, p = 0.0006; HR = 1.63, p = 1.4E-06), SCGB3A1 (ORILA=1.52, p = 4.5e-14; ORFibrosis=1.80, p = 0.0002; HR = 1.25, p = 0.02), TREM1 (ORILA=1.44, p = 1.4e-14; ORFibrosis=1.62, p = 0.001; HR = 1.25, p = 0.02) ICAM5 (ORILA=1.42, p = 1.2e-11; ORFibrosis=1.79, p = 0.0001; HR = 1.31, p = 0.002). In the meta-analysis, 6 of the 9 fibrosis-associated proteins and all 5 ILA mortality-associated proteins replicated. Conclusion This represents the first plasma proteomic screen demonstrating reproducible biomarker associations not only with ILA presence, but also with measures of severity and adverse outcomes. These results provide compelling targets for further study, including their prospective evaluation as clinical biomarkers for early disease identification and prognostication. This abstract is funded by: Multiple Sources

Abstract Rationale Deficiency of alpha-1 antitrypsin (AAT), the protein encoded by SERPINA1, remains the strongest genetic risk factor for COPD. Individuals heterozygote for the type Z variant, termed the PI*MZ genotype (estimated global prevalence n = 35,000,000), experience varying respiratory morbidity beyond expected from tobacco smoking alone. AAT is a pleiotropic protein which inhibits not only neutrophil elastase but also neutrophil chemotaxis mediated by leukotriene B4 (LTB4) [by antagonizing the LTB4 receptor (LTB4R)] and interleukin-8 (IL-8). Although LTB-4 and IL-8 gene variants and protein levels have independently been implicated in COPD, their potential role in PI*MZ is unknown. Methods COPDGene and SPIROMICS are observational tobacco-smoke and COPD-enriched cohort studies. Participants were phenotyped by spirometry and CT imaging, with whole genome sequencing data available via TOPMed. We analyzed participants with PI*MM (COPDGene n = 9,732; SPIROMICS n = 2,105) and PI*MZ (COPDGene n = 300; SPIROMICS n = 83). LTB-4 (ALOX5AP, LTA4H, LTB4R) and IL-8 (CXCL8, CXCR1, CXCR2) associated gene variants (exonic and intronic) with a minor allele frequency of ≥ 0.05 were extracted. We assessed statistical interactions between MZ status and chest CT outcomes or FEV1% predicted using the SPAGxECCT package with adjustment for relatedness (genetic related matrix) and ancestry (top ten PCs). Baseline protein LTB4R (SPIROMICS only) and IL-8 (both cohorts) protein levels were measured via SomaScan, and statistical interactions between MZ status and AAT concentration were explored. Models were adjusted for age, sex, BMI, smoking status, and pack-years. Given the candidate gene discovery approach, significance was defined as nominal p-value<0.05 Results We identified the following number of variants in genes of the LTB-4 or IL-8 pathways: 87 shared between cohorts; 133 unique to COPDGene; 1 unique to SPIROMICS. LTB-4-related polymorphisms were associated (p < 0.05) with any radiographic outcomes in 22 variants (COPDGene) vs. 23 variants (SPIROMICS), with a single overlap (rs17074984 in ALOX5AP). Two LTB-4-pathway variants were associated with FEV1% predicted in SPIROMICS (both in ALOX5AP), but none in COPDGene. AAT plasma concentrations displayed a statistically significant interaction with FEV1% predicted (p = 0.046) and PRMemphysema (p = 0.013) for LTBR4, but not for IL-8. There was not an interaction by MM/MZ status and protein levels. Conclusions Predominantly LTB-4, and not IL-8, pathway-related genetic variants and protein levels may contribute to disease heterogeneity among PI*MZ individuals. The association of circulating LTB4R protein with reduced lung function, particularly among those with low AAT levels, further suggest interactions between AAT and the leukotriene pathway in PI*MZ individuals. Thus, PI*MZ individuals may uniquely derive benefit from leukotriene-targeting pharmacotherapy. This abstract is funded by: NIH NHLBI K23HL173570

Abstract Rationale To construct multi-trait polygenic scores (PRS) predicting chronic obstructive pulmonary disease (COPD) and exacerbations, validate their performance in diverse cohorts, and identify PRS-related proteins for potential therapeutic targeting. Methods PRSmix+, a multi-trait PRS framework, is used to train a composite PRS (PRSmulti) in COPDGene non-Hispanic white participants (n = 6,647). Associations of PRSmulti with COPD status (GOLD 2-4 vs. GOLD 0 or ICD) and exacerbation frequency were tested in COPDGene African American (n = 2,466), ECLIPSE (n = 1,858), MassGeneral Brigham Biobank (n = 15,152), and All of Us (n = 118,566). Protein prediction models were applied to GWAS summary statistics from traits contributing to PRSmulti and were validated with proteomic data in COPDGene (n = 5,173) and UK Biobank (n = 5,012). Results PRSmix+ selected 7 traits for PRSmulti. In multivariable models, PRSmulti was associated with COPD status (meta-analysis random effects (RE) OR 1.58 [95% CI: 1.28-1.94]) and exacerbation frequency (meta-analysis RE beta 0.21 [95% CI: 0.11-0.31]), with higher effect sizes observed in smoking-enriched cohorts. PRSmulti outperformed traditional single-trait PRS in all tested cohorts. Using protein prediction models, we identified 73 proteins associated with the PRS that were also validated with measured protein levels in COPDGene and UK biobank. Of these proteins, 25 were linked to approved or investigational drugs. Notable targets include AGER (RAGE), IL1RL1, and SCARF2, all implicated in COPD pathogenesis and exacerbations. Conclusions Multi-trait PRS improves prediction of COPD and exacerbation risk. Integration with proteomic data identifies druggable protein targets, offering a promising avenue for precision medicine in COPD management. Trial registration. COPDGene: NCT00608764; ECLIPSE: NCT00292552. This abstract is funded by: NIH and the COPD Foundation

Abstract Rationale Alpha-1 antitrypsin (AAT) deficiency, primarily caused by homozygous SERPINA1 Z mutation, increases the risk of COPD. Individuals carrying one Z and one normal M allele (MZ) have higher COPD risk than MM individuals, although many never develop disease. COPD is a polygenic condition influenced by numerous genetic factors across the genome and by environmental exposures such as smoking. We aimed to examine how polygenic factors and smoking modify risk of airflow obstruction in MZ compared with MM. Methods We included participants with the MZ and MM genotypes from the Framingham Heart Study. We first tested the association between AAT genotype (MZ coded as 1 and MM as 0) and FEV1/FVC, stratified by ever-smoking status, and further evaluated interactions between AAT genotype and smoking pack-years. Next, for each participant we calculated a polygenic risk score (PRS) for FEV1/FVC, which was derived from external genome-wide association studies in the UK Biobank and SpiroMeta Consortium. To reflect risk for airflow limitation, higher PRSs correspond to lower FEV1/FVC. We then tested the interaction between the PRS and AAT genotype, stratified by ever-smoking. Models were adjusted for age, sex, height, current smoking, pack-years (among ever-smokers), familial relatedness, and in PRS models, principal components of genetic ancestry. Results Among 4,220 participants (mean age 53.7 years, 54.2% female, 50.2% ever-smokers, mean FEV1/FVC 74.6%), 133 (3.2%) had MZ genotype. In never-smokers, MZ individuals had 1.72% higher FEV1/FVC compared with MM (P = 0.032), whereas no difference was observed among ever-smokers (β= -0.39%; P = 0.65). Cumulative smoking modified the effect of MZ genotype on FEV1/FVC (interaction β= -0.13% per pack-year; P = 0.003); this interaction persisted among ever-smokers (β= -0.11% per pack-year; P = 0.044). A one-standard-deviation increase in the FEV1/FVC PRS was associated with lower FEV1/FVC in both never- (β= -2.37%; P < 0.0001) and ever-smokers (β= -2.46%; P < 0.0001). A higher PRS was disproportionately associated with reduced FEV1/FVC in MZ compared to MM (interaction β= -1.87% per standard-deviation PRS increase, P = 0.027) among ever-smokers, but not in never-smokers (P = 0.15). Conclusions In this community-based cohort, the AAT MZ genotype was associated with higher FEV1/FVC among non-smokers compared to MM. The interaction between MZ genotype and pack-years suggested a dose-dependent modifying effect of smoking on the MZ-related COPD risk. Among ever-smokers, higher polygenic risk further amplified susceptibility to airflow obstruction in MZ compared with MM. These findings highlight the roles of both polygenic and environmental risk factors in COPD susceptibility in MZ. Replication of the results is needed. This abstract is funded by: This work is supported by an Alpha-1 Foundation Grant and a Boston University Department of Medicine Career Investment Award

Abstract Glioblastoma (GBM) is the most common and lethal primary malignant brain tumor in adults, with median survival remaining approximately 12–15 months despite aggressive multimodal therapy. Therapeutic resistance and tumor recurrence are driven in part by limited drug penetration across the blood–brain barrier (BBB) and the persistence of brain cancer stem cells (BCSCs), highlighting the need for brain-penetrant therapeutic platforms capable of achieving sustained intratumoral delivery. Here, we developed a dendrimer-based nanotherapeutic by conjugating metformin to a fourth-generation hydroxyl-terminated polyamidoamine dendrimer (P4-MET) to enhance intracranial bioavailability and therapeutic efficacy in GBM. P4-MET exhibited favorable pharmacokinetic properties, including prolonged retention within the tumor microenvironment, and demonstrated enhanced cytotoxicity against GBM cell lines relative to free metformin (f-MET). Mechanistical studies with transcriptomic profiling by RNA sequencing revealed distinct treatment-associated molecular signatures, identifying BOLA2B as the most significantly differentially expressed gene between treatment groups. Specifically, BOLA2B expression was markedly elevated in f-MET–treated cells but not so following P4-MET treatment. Given the established association of BOLA2B with mTORC1 signaling and GPX4-mediated ferroptosis resistance, these findings suggest that P4-MET may, at least in part, enhance therapeutic efficacy by modulating ferroptosis-associated pathways. In orthotopic GBM models, combination treatment with P4-MET and radiotherapy (RT) significantly prolonged overall survival and increased tumor cell death compared with either monotherapy alone, consistent with a synergistic radiosensitizing effect. Importantly, P4-MET demonstrated minimal systemic toxicity, supporting its favorable therapeutic index and translational potential. Collectively, these findings establish P4-MET as a brain-penetrant nanomedicine platform that improves metformin delivery, modulates ferroptosis-related signaling networks, and potentiates radiotherapeutic response in GBM. This study highlights the potential of dendrimer-enabled metabolic nanotherapies to overcome therapeutic resistance in malignant brain tumors.