ULK3

Genome-wide association studies (GWASs) have identified hundreds of risk loci for coronary artery disease (CAD). However, non-European populations are underrepresented in GWASs, and the causal gene-regulatory mechanisms of these risk loci during atherosclerosis remain unclear. We incorporated local ancestry and haplotypes to identify quantitative trait loci for expression (eQTLs) and splicing (sQTLs) in coronary arteries from 138 ancestrally diverse Americans. Of 2,132 eQTL-associated genes (eGenes), 47% were previously unreported in coronary artery; 19% exhibited cell-type-specific expression. Colocalization revealed subgroups of eGenes unique to CAD and blood pressure GWAS. Fine-mapping highlighted additional eGenes, including TBX20 and IL5. We also identified sQTLs for 1,690 genes, among which TOR1AIP1 and ULK3 sQTLs demonstrated the importance of evaluating splicing to accurately identify disease-relevant isoform expression. Our work provides a patient-derived coronary artery eQTL resource and exemplifies the need for diverse study populations and multifaceted approaches to characterize gene regulation in disease processes.

Drug repurposing provides a cost-effective approach to address the need for breast cancer prevention and therapeutics. We aimed to identify actionable druggable targets using Mendelian randomization (MR) and then validate the candidate drugs using population-based analyses.

We identified genetic instruments for 1406 actionable targets of approved non-oncological drugs based on gene expression, DNA methylation, and protein expression quantitative trait loci (eQTL, mQTL, and pQTL, respectively). Genome-wide association study (GWAS) summary statistics were obtained from the Breast Cancer Association Consortium (122,977 cases, 105,974 controls). We further conducted a nested case-control study using data retrieved from Swedish registers to validate the candidate drugs that were identified from MR analyses.

We identified six significant MR associations with gene expression levels (TUBB, MDM2, CSK, ULK3, MC1R and KCNN4) and two significant associations with gene methylation levels across 21 CpG islands (RPS23 and MAPT). Results from the nested case-control study showed that the use of raloxifene (targeting MAPT) was associated with 35% reduced breast cancer risk (odds ratio, OR, 0.65; 95% confidence interval, CI, 0.51-0.83). However, usage of estradiol, tolterodine, and nitrofurantoin (also targeting MAPT) was associated with increased breast cancer risk, with adjusted ORs and 95% CI of 1.10 (1.07-1.13), 1.16 (1.09-1.24), and 1.09 (1.05-1.13), respectively. The effect of raloxifene and nitrofurantoin lost significance in further validation analyses using active-comparator and new-user design.

This large-scale MR analysis, combined with population-based validation, identified eight druggable target genes for breast cancer and suggested that raloxifene is an effective chemoprevention against breast cancer.

Swedish Research Council, Cancerfonden, Crafoordska Stiftelsen, Allmänna Sjukhusets i Malmö Stiftelsen för bekämpande av cancer, 111 Project and MAS cancer.

Sepsis is a life-threatening disease with a high mortality in the intensive care unit (ICU), and autophagy plays an essential role in the development of sepsis. The purpose of this study was to identify potential autophagy-related genes in sepsis and their relationship with immune cell infiltration by bioinformatics analysis.

The messenger RNA (mRNA) expression profile of the GSE28750 data set was collected from the Gene Expression Omnibus (GEO) database. The potential differentially expressed autophagy-related genes of sepsis were screened with the "limma" package in R (The Foundation for Statistical Computing). The hub genes were selected by weighted gene coexpression network analysis (WGCNA) networks with Cytoscape, and functional enrichment analysis was performed. The expression level and diagnostic value of the hub genes were validated by Wilcoxon test and receiver operating characteristic (ROC) curve analysis of the GSE95233 data set. The compositional patterns of immune cell infiltration in sepsis were estimated using the CIBERSORT algorithm. Spearman rank correlation analysis was used to associate the identified biomarkers with infiltrating immune cells. A competing endogenous (ceRNA) network was constructed to predict related noncoding RNAs of identified biomarkers with the miRWalk platform.

In all, 80 differential autophagy-related genes were obtained. GABARAPL2, GAPDH, WDFY3, MAP1LC3B, DRAM1, WIPI1, and ULK3 were identified as hub genes and diagnostic biomarker groups for sepsis. In addition, 7 differentially infiltrated immune cells correlated with the hub autophagy-related genes were identified. The ceRNA network predicted 23 microRNAs and 122 long noncoding RNAs related to 5 hub autophagy-related genes.

GABARAPL2, GAPDH, WDFY3, MAP1LC3B, DRAM1, WIPI1, and ULK3 may influence the development of sepsis and have a vital impact on sepsis immune regulation as autophagy-related genes.