Search

Abstract Background Therapeutic targets supported by genetic evidence from genome-wide association studies (GWAS) show higher probability of success in clinical trials. GWAS is a powerful approach to identify links between genetic variants and phenotypic variation; however, identifying the genes driving associations identified in GWAS remains challenging. Integration of molecular quantitative trait loci (molQTL) such as expression QTL (eQTL) using mendelian randomization (MR) and colocalization analyses can help with the identification of causal genes. Careful interpretation remains warranted because eQTL can affect the expression of multiple genes within the same locus. Methods We used a combination of genomic features that include variant annotation, activity-by-contact maps, MR, and colocalization with molQTL to prioritize causal genes across 4,611 disease GWAS and meta-analyses from biobank studies, namely FinnGen, Estonian Biobank and UK Biobank. Results Genes identified using this approach are enriched for gold standard causal genes and capture known biological links between disease genetics and biology. In addition, we find that eQTL colocalizing with GWAS are statistically enriched for corresponding disease-relevant tissues. We show that predicted directionality from MR is generally consistent with matched drug mechanism of actions (> 85% for approved drugs). Compared to the nearest gene mapping method, genes supported by multi-omics evidences displayed higher enrichment in approved therapeutic targets (risk ratio 1.75 vs. 2.58 for genes with the highest level of support). Finally, using this approach, we detected anassociation between the IL6 receptor signal transduction gene IL6ST and polymyalgia rheumatica, an indication for which sarilumab, a monoclonal antibody against IL-6, has been recently approved. Conclusions Combining variant annotation, activity-by-contact maps, and molQTL increases performance to identify causal genes, while informing on directionality which can be translated to successful target identification and drug development.

Abstract Tumor energy metabolism plays a crucial role in the occurrence, progression, and drug resistance of tumors. The study of tumor energy metabolism has gradually become an emerging field of tumor treatment. Recent studies have shown that epigenetic regulation is closely linked to tumor energy metabolism, influencing the metabolic remodeling and biological traits of tumor cells. This review focuses on the primary pathways of tumor energy metabolism and explores therapeutic strategies to target these pathways. It covers key areas such as glycolysis, the Warburg effect, mitochondrial function, oxidative phosphorylation, and the metabolic adaptability of tumors. Additionally, this article examines the role of the epigenetic regulator SWI/SNF complex in tumor metabolism, specifically its interactions with glucose, lipids, and amino acids. Summarizing therapeutic strategies aimed at these metabolic pathways, including inhibitors of glycolysis, mitochondrial-targeted drugs, exploitation of metabolic vulnerabilities, and recent developments related to SWI/SNF complexes as potential targets. The clinical significance, challenges, and future directions of tumor metabolism research are discussed, including strategies to overcome drug resistance, the potential of combination therapy, and the application of new technologies.

Standard single-cell (sc) proteomics of disease states inferred from multicellular organs or organoids cannot currently be related to single-cell physiology. Here, a scPatch-Clamp/Proteomics platform is developed on single neurons generated from hiPSCs bearing an Alzheimer's disease (AD) genetic mutation and compares them to isogenic wild-type controls. This approach provides both current and voltage electrophysiological data plus detailed proteomics information on single-cells. With this new method, the authors are able to observe hyperelectrical activity in the AD hiPSC-neurons, similar to that observed in the human AD brain, and correlate it to ≈1400 proteins detected at the single neuron level. Using linear regression and mediation analyses to explore the relationship between the abundance of individual proteins and the neuron's mutational and electrophysiological status, this approach yields new information on therapeutic targets in excitatory neurons not attainable by traditional methods. This combined patch-proteomics technique creates a new proteogenetic-therapeutic strategy to correlate genotypic alterations to physiology with protein expression in single-cells.

Abstract Background Nonischemic cardiomyopathy (NISCM) is a clinical challenge with limited therapeutic targets. This study aims to identify promising drug targets for NISCM. Methods We utilized cis-pQTLs from the deCODE study, which includes data from 35,559 Icelanders, and SNPs from the FinnGen study, which includes data from 1,754 NISCM cases and 340,815 controls of Finnish ancestry. Mendelian randomization (MR) analysis was performed to estimate the causal relationship between circulating plasma protein levels and NISCM risk. Proteins with significant associations underwent false discovery rate (FDR) correction, followed by Bayesian colocalization analysis. The expression of top two proteins, LILRA5 and NELL1, was further analyzed using various NISCM datasets. Descriptions from the Human Protein Atlas (HPA) validated protein expression. The impact of environmental exposures on LILRA5 was assessed using the Comparative Toxicogenomics Database (CTD), and molecular docking identified the potential small molecule interactions. Results MR analysis identified 255 circulating plasma proteins associated with NISCM, with 16 remaining significant after FDR correction. Bayesian colocalization analysis identified LILRA5 and NELL1 as significant, with PP.H4 > 0.8. LILRA5 has a protective effect (OR = 0.758, 95% CI, 0.670–0.857) while NELL1 displays the risk effect (OR = 1.290, 95% CI, 1.199–1.387) in NISCM. Decreased LILRA5 expression was found in NISCM such as diabetic, hypertrophic, dilated, and inflammatory cardiomyopathy, while NELL1 expression increased in hypertrophic cardiomyopathy. HPA data indicated high LILRA5 expression in neutrophils, macrophages and endothelial cells within normal heart and limited NELL1 expression. Immune infiltration analysis revealed decreased neutrophil in diabetic cardiomyopathy. CTD analysis identified several small molecules that affect LILRA5 mRNA expression. Among these, Estradiol, Estradiol-3-benzoate, Gadodiamide, Topotecan, and Testosterone were found to stably bind to the LILRA5 protein at the conserved VAL-15 or THR-133 residues in the Ig-like C2 domain. Conclusion Based on European Ancestry Cohort, this study reveals that LILRA5 and NELL1 are potential therapeutic targets for NISCM, with LILRA5 showing particularly promising prospects in diabetic cardiomyopathy. Several small molecules interact with LILRA5, implying potential clinical implication.

Meningiomas, characterized primarily as benign intracranial or spinal tumors, present distinctive challenges due to their variable clinical behavior, with certain cases exhibiting aggressive features linked to elevated morbidity and mortality. Despite their prevalence, the underlying molecular mechanisms governing the initiation and progression of meningiomas remain insufficiently understood. MicroRNAs (miRNAs), small endogenous non-coding RNAs orchestrating post-transcriptional gene expression, have garnered substantial attention in this context. They emerge as pivotal biomarkers and potential therapeutic targets, offering innovative avenues for managing meningiomas. Recent research delves into the intricate mechanisms by which miRNAs contribute to meningioma pathogenesis, unraveling the molecular complexities of this enigmatic tumor. Meningiomas, originating from arachnoid meningothelial cells and known for their gradual growth, constitute a significant portion of intracranial tumors. The clinical challenge lies in comprehending their progression, particularly factors associated with brain invasion and heightened recurrence rates, which remain elusive. This comprehensive review underscores the pivotal role of miRNAs, accentuating their potential to advance our comprehension of meningioma biology. Furthermore, it suggests promising directions for developing diagnostic biomarkers and therapeutic interventions, holding the promise of markedly improved patient outcomes in the face of this intricate and variable disease.