Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts

Novel therapeutic targets uncovered by genome-wide integrative analysis in bronchopulmonary dysplasia

BACKGROUND

Bronchopulmonary dysplasia (BPD) is the most common chronic respiratory disease in extremely premature infants. This study aims to identify gene expression dysregulation and explore various molecular pathways implicated in BPD.

METHODS

This study integrated BPD genome-wide association study (GWAS), single-cell transcriptomics (scRNA-seq), and Mendelian randomization (MR) analysis to investigate the causal relationship between gene expression and BPD.

RESULTS

Cell annotation and ligand-receptor analysis highlighted myofibroblasts as the most interactive cell type. Key genes, including CDH4, ENC1, and PAM, were identified as protective factors against BPD, while GRB10 was associated with increased disease risk. Immune metabolism-related pathways showed elevated activity of PAM, GRB10, and ENC1 in epithelial-mesenchymal transition. The Drug-Gene Interaction Database (DGIdb) predicted three drugs-LM10, navoximod, and ziprasidone-that potentially interact with these key genes.

CONCLUSION

This integrative genome-wide analysis provides valuable insights into the genetic mechanisms underlying BPD. The findings facilitate the identification of novel therapeutic targets and pave the way for personalized treatment strategies for affected neonates.

Unveiling the role of oxidative stress in ANCA-associated glomerulonephritis through integrated machine learning and bioinformatics analyses

Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a systemic autoimmune disease often leading to rapidly progressive glomerulonephritis. Oxidative stress plays a critical role in the development and progression of ANCA-associated glomerulonephritis (AAGN), but the underlying mechanisms remain poorly understood. Targeting genes related to oxidative stress may provide novel insights and supplementary therapeutic benefits for AAGN. In the current study, we obtained differentially expressed genes from AAGN-related microarray datasets in the Gene Expression Omnibus database, and oxidative stress-related genes (OSRGs) from the GeneCards and Gene Ontology databases to identify differentially expressed OSRGs. Then, by integrating weighted gene co-expression network analysis, and machine learning algorithms, we identified four upregulated hub OSRGs (all p < 0.01) with strong diagnostic potential (all AUC > 0.9)-CD44, ITGB2, MICB, and RAC2 - in the AAGN glomerular training dataset GSE104948 and validation dataset GSE108109, along with two hub OSRGs (all p < 0.05) with better diagnostic potential (all AUC > 0.7) - upregulated gene VCAM1 and downregulated gene VEGFA-in the AAGN tubulointerstitial training dataset GSE104954 and validation dataset GSE108112. The GSEA analysis suggested that these hub genes may play a role in inflammatory and immune response processes. Moreover, we constructed regulatory networks and identified drugs that potentially target these hub genes. It's to be noted that RAC2 and ITGB2 were associated with cyclophosphamide in the AAGN glomerular compartment, while VCAM1 and VEGFA were associated with dexamethasone in the tubulointerstitial compartment. This study offers novel insights into immune-associated OSRGs within the glomerular and tubulointerstitial compartments of AAGN which may serve as innovative targets for diagnosing and treating AAGN.

Exploring potential key genes and disease mechanisms in early-onset genetic epilepsy via integrated bioinformatics analysis

Epilepsy is a severe common neurological disease affecting all ages. Epilepsy with onset before the age of 5 years, designated early-onset epilepsy (EOE), is of special importance. According to previous studies, genetic factors contribute significantly to the pathogenesis of EOE that remains unclear and must be explored. So, a list of 229 well-selected EOE-associated genes expressed in the brain was created for the investigation of genetic factors and molecular mechanisms involved in its pathogenesis. Enrichment analysis showed that among significant pathways were nicotine addiction, GABAergic synapse, synaptic vesicle cycle, regulation of membrane potential, cholinergic synapse, dopaminergic synapse, and morphine addiction. Performing an integrated analysis as well as protein-protein interaction network-based approaches with the use of GO, KEGG, ClueGO, cytoHubba and 3 network metrics, 12 hub genes were identified, seven of which, CDKL5, GABRA1, KCNQ2, KCNQ3, SCN1A, SCN8A and STXBP1, were identified as key genes (via Venn diagram analysis). These key genes are mostly enriched in SNARE interactions in vesicular transport, regulation of membrane potential and synaptic vesicle exocytosis. Clustering analysis of the PPI network via MCODE showed significant functional modules, indicating also other pathways such as N-Glycan biosynthesis and protein N-linked glycosylation, retrograde endocannabinoid signaling, mTOR signaling and aminoacyl-tRNA biosynthesis. Drug-gene interaction analysis identified a number of drugs as potential medications for EOE, among which the non-FDA approved drugs azetukalner (under clinical development), indiplon and ICA-105665 and the FDA approved drugs retigabine, ganaxolone and methohexital.

Genome-wide association study -Driven drug repositioning for the treatment of insomnia

Insomnia is a prevalent sleep disorder characterized by difficulty initiating or maintaining sleep, leading to severe health complications, increased mortality, and substantial socioeconomic burdens. Despite therapeutic advancements, effective pharmacological interventions remain limited, necessitating alternative approaches for drug discovery. This study aimed to identify potential therapeutic targets for insomnia by integrating gene network analysis, genomic data, and bioinformatics-driven drug repurposing strategies, aligning with the United Nations' Sustainable Development Goal (SDG) 3: Good Health and Well-being. Insomnia-associated Single Nucleotide Polymorphisms (SNPs) were retrieved from the GWAS catalog, yielding 3,952 loci. Insomnia risk genes were identified by linking these loci to proximal SNPs (r2 ≥ 0.8) in Asian populations using HaploReg v4.2, resulting in 1,765 candidate genes. A bioinformatics pipeline incorporating ten functional annotations and drug-gene interaction was employed to prioritize gene targets and identify novel repurposed drugs with potential biological relevance to insomnia. Drug-Gene Interaction Database (DGIdb) analysis identified seven druggable targets among 27 biologically significant insomnia risk genes, corresponding to 12 existing drugs. Notably, NRXN1 emerged as a highly promising target due to its strong functional annotation score and its known interaction with Duloxetine hydrochloride and nicotine polacrilex. This study underscores the potential of bioinformatics-driven gene network analysis in identifying drug repurposing candidates for insomnia. Further experimental validation is warranted to elucidate the therapeutic mechanisms of NRXN1 modulation in insomnia treatment.

Deciphering the prognostic landscape of triple-negative breast cancer: A focus on immune-related hub genes and therapeutic implications

Triple-negative breast cancer (TNBC), known for its hostile nature and limited treatment modalities, has spurred researchers to explore novel approaches for enhancing clinical outcomes. Here, the study aimed to analyze transcriptomics data to identify immune-related hub genes associated with TNBC that might serve as prognostic biomarkers. Initially, we determined genes that were differentially expressed between TNBC and normal tissues by integrating microarray and RNA sequencing data. Then, through protein-protein interaction and module analysis, we identified five putative hub genes: AURKA, CCNB1, CDCA8, GAPDH, and TOP2A. Subsequently, gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that the hub genes were primarily involved in the progesterone-mediated oocyte maturation signaling pathway and oocyte meiosis. Additionally, we observed that these five hub genes were significantly elevated at both protein and mRNA levels in TNBC tissues and contributed to worse survival. Furthermore, the expression of these hub genes exhibited a strong positive association with immune-invading cells such as CD8 T cells, CD4 T cells, and dendritic cells. The analysis of the regulatory network revealed three transcription factors (YBX-1, E2F1, and E2F3) and three posttranscriptional regulators (hsa-mir-25-3p, hsa-mir-92a-3p, and hsa-let-7b-5p) of hub genes. Finally, we explored potential drug candidates for the hub genes using Drug-Gene Interaction Database and discovered that there are no FDA-approved drugs for CCNB1 and CDCA8, highlighting a promising area for future research. Taken together, our results will be of immense importance in addressing the intricacies of TNBC.

Integrative genome-wide association studies, proteome-wide Mendelian randomization, and single-cell RNA sequencing identify interleukin-6 receptor protein as a therapeutic target in aortic aneurysm

Aortic aneurysm (AA) is a genetic cardiovascular disease marked by progressive weakening and dilation of the aortic wall, often resulting in high mortality if untreated. Early diagnosis and prevention remain challenging. This study integrated genome-wide association studies (GWAS), proteome-wide Mendelian randomization (PW-MR), and single-cell RNA sequencing data from large-scale cohorts (FinnGen, deCODE Genetics, and UK Biobank Pharma Proteomics Project) to identify new biomarkers and therapeutic targets for AA. Analyzing genetic data from 8125 AA patients and 381,977 controls, we identified four significant loci (ADAMTS8, PLCE1, NOC3L, and SPSB1). PW-MR highlighted numerous plasma proteins, with interleukin-6 receptor (IL6R) showing strong association and colocalization in both deCODE and UK Biobank Pharma Proteomics Project (UKB-PPP) datasets. Multi-trait colocalization analysis supports IL6R's role, suggesting that drugs targeting IL6R, such as tocilizumab, may benefit AA treatment, though potential side effects warrant consideration. Single-cell analysis indicated that macrophages are crucial in AA progression, particularly through the IL6R-mediated inflammatory response. These findings emphasize IL6R as a potential target for early intervention and AA prevention. By integrating genetic associations, proteomic evidence, and single-cell insights, this study offers new strategies for identifying biomarkers, understanding disease mechanisms, and developing targeted therapies for aortic aneurysm.

In silico drug repurposing for potential HPV-induced skin wart treatment - A comparative transcriptome analysis

INTRODUCTION

Warts are dermal disorders resulting from HPV infection and can be transmitted by direct contact. Existing treatment approaches, such as topical treatment with salicylate, have low efficiency and demonstrate side effects. Thus, the discovery of potent drug treatments for skin warts is necessary. Here we propose the use of alternative medications for the possible treatment of skin warts with the help of comparative transcriptome analysis and drug repurposing approaches.

METHODS

Gene expression datasets related to HPV-induced warts and cervical cancer were extracted from the GEO database. Differentially expressed genes (DEGs) were identified using DESeq2 in the Galaxy database. Upregulated DEGs were assessed for druggability using the DGIdb tool. Gene ontology and enrichment analysis were performed to investigate the characteristics of druggable DEGs. A molecular docking virtual screening was conducted using PyRx software to identify potential therapeutic targets for skin warts. The interactions between selected drug candidates and the target protein were analyzed using the BIOVIA Discovery Studio. The physicochemical characteristics of potential pharmaceuticals were evaluated using the SwissADME database. Finally, the molecular dynamics (MD) simulation was performed to validate the stability and dynamic behavior of drug-protein interactions.

RESULTS

Based on the findings from gene expression profiling, Integrin Alpha-X (ITGAX, CD11c) has been identified as a candidate protein that is significantly upregulated in individuals afflicted with skin warts. Integrin Alpha-X plays a crucial role in mediating intercellular interactions during inflammatory processes and notably enhances the adhesion and chemotactic activity of monocytes. Through molecular docking, MD, and physicochemical analyses, it has been demonstrated that dihydroergotamine effectively inhibits the ITGAX protein, suggesting its potential as a therapeutic agent for the management of skin warts.

CONCLUSION

Dihydroergotamine can be repurposed as a potential drug in the treatment of skin warts by targeting Integrin Alpha-X protein.

Potential therapeutic targets for ovarian hyperstimulation syndrome revealed by proteome-wide mendelian randomization and colocalization analysis

Ovarian hyperstimulation syndrome (OHSS) is a severe complication associated with assisted reproductive technologies, characterized by metabolic, immune and vascular disorders. Understanding the molecular mechanisms underlying OHSS could reveal potential therapeutic targets and improve patient outcomes. In this study, We aimed to utilize proteome-wide Mendelian randomization (MR) and colocalization analysis to identify plasma proteins associated with OHSS and evaluate their potential as therapeutic targets through druggability assessment. We employed proteome-wide MR analysis summary data-based Mendelian randomization (SMR) analysis and phenome-wide association study (PheWAS) analysis to establish causal relationships between plasma proteins and OHSS. Colocalization analysis confirmed overlaps between proteins and genetic signals associated with OHSS. Pathway and network analyses were conducted to explore biological functions and protein interactions, while drug-target databases were queried for potential therapeutic interventions. Our results showed that 4 key proteins, including Suprabasin (SBSN), SLAMF4 (CD244), Enolase 3 (ENO3) and Thioredoxin domain-containing protein 12 (TXNDC12) were identified as significant contributors to OHSS. Pathway enrichment and interaction analyses further supported their involvement in metabolic, immune and structural pathways related to OHSS. Drug availability for colocalized proteins reveled potential drug targets for ENO3 (2-deoxy-D-glucose), CD244 (lenalidomide) and TXNDC12 (Auranofin), while no potential drug targets were identified for SBSN. Over all, our study identified15 plasma proteins, including SBSN, CD244, ENO3, and TXNDC12, as key contributors to the risk of OHSS through MR and colocalization analysis. These proteins were involved in metabolic regulation, immune response and antioxidant pathways, highlighting potential therapeutic targets and suggesting new directions for treatment strategies.

Insight into dysregulated VEGF-related genes in diabetic retinopathy through bioinformatic analyses

Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes mellitus. VEGF plays a pivotal role in the pathogenesis of DR. To characterize the VEGF-related genes in DR patients, the RNAseq dataset of DR and normal control were downloaded from the GEO database and analyzed using R package limma. The differentially expressed VEGFGs between DR and NC were identified, and their expression levels were verified through qRT-PCR and Western blotting. Enrichment analyses were performed to understand the key functions and involved pathways of DE-VEGFGs. A two-sample MR analysis was carried out to study the causal link between prostate cancer and DR. Next, we built a nomogram model to predict the risk of DR using the expression level of DE-VEGFGs. Additionally, we estimated the immune cell infiltration between clusters and calculated the correlation between DE-VEGFGs expression and immune cell infiltration in DR. The DGIdb database was used to identify potential target drug for DE-VEGFGs. Finally, we constructed a ceRNA regulation network with predictions from miRNA-mRNA interaction databases and miRNA-lncRNA interaction database. We identified six DE-VEGFGs that are involved in the regulation of the VEGF pathway. The two-sample MR analysis revealed a positive correlation between prostate cancer and the risk of DR. The nomogram which uses the DE-VEGFGs expression to predict the DR risk shows good performance based on the calibration curve and AUC value. Monocytes and T cells CD4 memory activated show different expression between DR and NC; meanwhile, these cell types were correlated with DE-VEGFGs. The drug-gene interaction network provides candidates for DR treatment, and the ceRNA regulation network suggests a potential biomarker for DR. Our study identified dysregulated VEGF-related genes in DR and emphasized their significance in the pathogenesis of DR. Additionally, our findings offer insights into their potential clinical predictive value, immune implications, targeting drug candidates, and regulatory network dynamics.

Explicating the transformative role of artificial intelligence in designing targeted nanomedicine

INTRODUCTION

Artificial intelligence (AI) has emerged as a transformative force in nanomedicine, revolutionizing drug delivery, diagnostics, and personalized treatment. While nanomedicine offers precise targeted drug delivery and reduced toxic effects, its clinical translation is hindered by biological complexity, unpredictable in vivo behavior, and inefficient trial-and-error approaches.

AREAS COVERED

This review covers the application of AI and Machine Learning (ML) across the nanomedicine development pipeline, starting from drug and target identification to nanoparticle design, toxicity prediction, and personalized dosing. Different AI/ML models like QSAR, MTK-QSBER, and Alchemite, along with data sources and high-throughput screening methods, have been explored. Real-world applications are critically discussed, including AI-assisted drug repurposing, controlled-release formulations, and cancer-specific delivery systems.

EXPERT OPINION

AI has emerged as an essential component in designing next-generation nanomedicine. Efficiently handling multidimensional datasets, optimizing formulations, and personalizing treatment regimens, it has sped up the innovation process. However, challenges like data heterogeneity, model transparency, and regulatory gaps remain. Addressing these hurdles through interdisciplinary efforts and emerging innovations like explainable AI and federated learning will pave the way for the clinical translation of AI-driven nanomedicine.

Integrated Bioinformatics Analysis and Cellular Experimental Validation Identify Lipoprotein Lipase Gene as a Novel Biomarker for Tumorigenesis and Prognosis in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the leading causes of death worldwide, and thus, more biomarker and therapeutic targets need to be explored. Herein, we aimed to explore new biomarkers of LUAD by integrating bioinformatics analysis with cell experiments. We firstly identified 266 druggable genes that were significantly differentially expressed between LUAD tissues and adjacent normal lung tissues. Among these genes, SMR analysis with p-value correction suggested that declining lipoprotein lipase (LPL) levels may be causally associated with an elevated risk of LUAD, which was corroborated by co-localization analysis. Analyses of clinical data showed that LPL in lung cancer tissues has considerable diagnostic value for LUAD, and elevated LPL levels were positively associated with improved patient survival outcomes. Cell experiments with an LPL activator proved these findings; the activator inhibited the proliferation and migration of lung cancer cells. Next, we found that LPL promoted the infiltration of immune cells such as DCs, IDCs, and macrophages in LUAD by mononuclear sequencing analysis and TIMER2.0. Meanwhile, patients with low levels of LPL expression demonstrated superior immunotherapeutic responses to anti-PD-1 therapy. We conclude that LPL acts as a diagnostic and prognostic marker for LUAD.

Exploration oxidative stress underlying gastroesophageal reflux disease and therapeutic targets identification: a multi-omics Mendelian randomization study

INTRODUCTION

Gastroesophageal reflux disease (GERD) is a chronic inflammatory gastrointestinal disease, which has no thoroughly effective or safe treatment. Elevated oxidative stress is a common consequence of chronic inflammatory conditions.

METHODS

We employed Summary-data based MR (SMR) analysis to assess the associations between gene molecular characteristics and GERD. Exposure data were the summary-level data on the levels of DNA methylation, gene expression, and protein expression, which obtained from related methylation, expression, and protein quantitative trait loci investigations (mQTL, eQTL, and pQTL). Outcome data, Genome-wide association study (GWAS) summary statistics of GERD, were extracted from the Ong's study (discovery), the Dönertaş's study (replication), and the FinnGen study (replication). Colocalization analysis was performed to determine if the detected signal pairs shared a causative genetic mutation. Oxidative stress related genes and druggable genes were imported to explore oxidative stress mechanism underlying GERD and therapeutic targets of GERD. The Drugbank database was utilized to conduct druggability evaluation.

RESULTS

After multi-omics SMR analysis and colocalization analysis, we identified seven key genes for GERD, which were SUOX and SERPING1, DUSP13, SULT1A1, LMOD1, UBE2L6, and PSCA. SUOX was screened out to be the mediator, which suggest that GERD is related to oxidative stress. SERPING1, SULT1A1, and PSCA were selected to be the druggable genes.

CONCLUSIONS

These findings offered strong support for the identification of GERD treatment targets in the future as well as for the study of the oxidative stress mechanism underlying GERD.

Identification of Key Genes and Potential Therapeutic Targets in Sepsis-Associated Acute Kidney Injury Using Transformer and Machine Learning Approaches

Sepsis-associated acute kidney injury (SA-AKI) is a life-threatening complication of sepsis, characterized by high mortality and prolonged hospitalization. Early diagnosis and effective therapy remain difficult despite extensive investigation. To address this, we developed an AI-driven integrative framework that combines a Transformer-based deep learning model with established machine learning techniques (LASSO, SVM-RFE, Random Forest and neural networks) to uncover complex, nonlinear interactions among gene-expression biomarkers. Analysis of normalized microarray data from GEO (GSE95233 and GSE69063) identified differentially expressed genes (DEGs), and KEGG/GO enrichment via clusterProfiler revealed key pathways in immune response, protein synthesis, and antigen presentation. By integrating multiple transcriptomic cohorts, we pinpointed 617 SA-AKI-associated DEGs-21 of which overlapped between sepsis and AKI datasets. Our Transformer-based classifier ranked five genes (MYL12B, RPL10, PTBP1, PPIA, and TOMM7) as top diagnostic markers, with AUC values ranging from 0.9395 to 0.9996 (MYL12B yielding 0.9996). Drug-gene interaction mining using DGIdb (FDR < 0.05) nominated 19 candidate therapeutics for SA-AKI. Together, these findings demonstrate that melding deep learning with classical machine learning not only sharpens early SA-AKI detection but also systematically uncovers actionable drug targets, laying groundwork for precision intervention in critical care settings.

Psychiatric genome-wide association study enrichment shows promise for future psychopharmaceutical discoveries

BACKGROUND

Innovation in psychiatric therapeutics has stagnated on known mechanisms. Psychiatric genome-wide association studies (GWAS) have identified hundreds of genome-wide significant (GWS) loci that have rapidly advanced our understanding of disease etiology. However, whether these results can be leveraged to improve clinical treatment for specific psychiatric disorders remains poorly understood.

METHODS

In this proof-of-principal evaluation of GWAS clinical utility, we test whether the targets of drugs used to treat Attention Deficit Hyperactivity Disorder (ADHD), Bipolar Disorder (BiP), Generalized Anxiety Disorder (GAD), Major Depressive Disorder (MDD), Post-Traumatic Stress Disorder (PTSD), Schizophrenia (SCZ), Substance Use Disorders (SUDs), and insomnia (INS), are enriched for GWAS meta-analysis findings.

RESULTS

The genes coding for treatment targets of medications used to SCZ, BiP, MDD, and SUDs (but not ADHD, PTSD, GAD, or INSOM) are enriched for GWS loci identified in their respective GWAS (ORs: 2.78-27.63; all ps <1.15e-3). Enrichment is largely driven by the presence of a GWS locus or loci within a gene coding for a drug target (i.e., proximity matching). Broadly, additional annotation (i.e., functional: Combined Annotation Dependent Depletion [CADD] scores, regulomeDB scores, eQTL, chromatin loop, and gene region; statistical: effect size of genome-wide significant SNPs; Z-score of SNPs; number of drug targets implicated by GWAS), with the exception of weighting by the largest SNP effect size, does not further improve enrichment across disorders. Evaluation of prior smaller GWAS reveal that more recent larger GWAS improve enrichment.

CONCLUSIONS

GWAS results may assist in the prioritization of medications for future psychopharmaceutical research.

Identifying Key Plasma Proteins in the Onset of Parkinson's Disease: Proteome-Wide Mendelian Randomization and Single-Cell RNA Sequencing Analysis

Currently, the pathogenesis of Parkinson's disease (PD) remains enigmatic, primarily due to the scarcity of definitive diagnostic markers, thereby hampering both diagnosis and treatment. The urgent need for accessible plasma markers and targeted therapeutic agents has prompted us to employ various methodologies. We leveraged Mendelian randomization analysis, colocalization analysis, SMR analysis, and the HEIDI test to delve into the causal relationships between 2923 plasma proteins in the UK Biobank and PD. Our findings revealed that 21 plasma proteins, including CTF1 and STX4, may demonstrate causal relationships with PD. Further single-cell and bioinformatics analyses have shed light on the fact that 18 of these proteins exhibit differential expression across various brain cell types in patients with PD. These proteins are involved in crucial biological processes, including peptide binding, amide binding, amyloid-beta binding, endocytic vesicle formation, and the functioning of early endosomes. Notably, the PPI network exhibited interactions between ITGAM and HLA-DRA, as well as APOE, while APOE displayed interactions with APOA1, and SERPINE2 interacted with VNN2. Furthermore, our study demonstrates that plasma proteins, including CTF1, STX4, HPGDS, and APOA1, exhibit therapeutic potential for drug development based on gene-drug interaction predictions. While these findings provide a theoretical basis for the exploration of diagnostic markers and potential therapeutic targets for PD, extensive experimental validation is essential to confirm their potential in the future.

Identification of therapeutic targets for neonatal respiratory distress: A systematic druggable genome-wide Mendelian randomization

Currently, there remains a significant gap in effective pharmacologic interventions for neonatal respiratory distress syndrome (NRDS). To address this critical unmet medical need, we aimed to systematically identify novel therapeutic targets and preventive strategies through comprehensive integration and analysis of multiple publicly accessible datasets. In this study, we employed an integrative approach combining druggable genome data, cis-expression quantitative trait loci (cis-eQTL) from human blood and lung tissues, and genome-wide association study summary statistics for neonatal respiratory distress. We performed two-sample Mendelian randomization (TSMR) analysis to investigate potential causal relationships between druggable genes and neonatal respiratory distress. To strengthen causal inference, we performed Bayesian co-localization analyses. Furthermore, we conducted phenome-wide Mendelian randomization (Phe-MR) to systematically evaluate potential side effects and alternative therapeutic indications associated with the identified candidate drug targets. Finally, we interrogated existing drug databases to identify actionable pharmacological agents targeting the identified genes. All 3 genes (LTBR, NAAA, CSNK1G2) were analyzed by Bayesian co-localization (PH4 > 75%). CSNK1G2 (lung eQTL, odds ratio [OR]: 0.419, 95% CI: 0.185-0.948, P = .037; blood eQTL, OR: 4.255, 95% CI: 1.346-13.455, P = .014; Gtex whole blood eQTL, OR: 4.966, 95% CI: 1.104-22.332, P = .037). LTBR (lung eQTL, OR: 0.550, 95% CI: 0.354-0.856, P = .008; blood eQTL, OR: 0.347, 95% CI: 0.179-0.671, P = .002; Gtex whole blood eQTL, OR: 0.059, 95% CI: 0.0.007-0.478, P = .008). NAAA (lung eQTL, OR: 0.717, 95% CI: 0.555-0.925, P = .011; Gtex whole blood eQTL, OR: 0.660, 95% CI: 0.476-0.913, P = .012). Drug repurposing analyses support the possibility that etanercept and asciminib hydrochloride may treat neonatal respiratory distress by activating LTBR. This study demonstrated that LTBR, NAAA, and CSNK1G2 may serve as promising biomarkers and therapeutic targets for NRDS.

Paracrine signaling mediators of vascular endothelial barrier dysfunction in sepsis: implications for therapeutic targeting

Vascular endothelial barrier disruption is a critical determinant of morbidity and mortality in sepsis. Whole blood represents a key source of paracrine signaling molecules inducing vascular endothelial barrier disruption in sepsis. This study analyzes whole-genome transcriptome data from sepsis patients' whole blood available in the NCBI GEO database to identify paracrine mediators of vascular endothelial barrier dysfunction, uncovering novel insights that may guide drug repositioning strategies. This study identifies the regulated expression of paracrine signaling molecules TFPI, MMP9, PROS1, JAG1, S1PR1, and S1PR5 which either disrupt or protect vascular endothelial barrier function in sepsis and could serve as potential targets for repositioning existing drugs. Specifically, TFPI (barrier protective), MMP9 (barrier destructive), PROS1 (barrier protective), and JAG1 (barrier destructive) are upregulated, while S1PR1 (barrier protective) and S1PR5 (barrier protective) are downregulated. Our observations highlight the importance of considering both protective and disruptive mediators in the development of therapeutic strategies to restore endothelial barrier integrity in septic patients. Identifying TFPI, MMP9, PROS1, JAG1, S1PR1, and S1PR5 as druggable paracrine regulators of vascular endothelial barrier function in sepsis could pave the way for precision medicine approaches, enabling personalized treatments that target specific mediators of endothelial barrier disruption to improve patient outcomes in sepsis.

Exploring the potential antidepressant mechanisms of ibuprofen and celecoxib based on network pharmacology and molecular docking

BACKGROUND

Evidence has shown that ibuprofen and celecoxib are effective in improving depressive symptoms, but their mechanisms of action are unclear. In this study, we aimed to determine the relationship between these two drugs and depressive disorder (DD) and elucidate potential mechanisms of action.

METHODS

Relevant targets for ibuprofen, celecoxib, and DD were obtained and screened from multiple online drug and disease public databases. A protein-protein interaction network was obtained. The Centiscape and CytoHubba plug-ins were applied to screen for core targets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed. Molecular docking was performed to predict the binding of ibuprofen and celecoxib to core targets. Examined the differences in core target protein expression between DD patients (DDs, n = 18) and healthy controls (HCs, n = 16) as a further experimental validation of the network pharmacology results.

RESULTS

In total, 220 potential targets for ibuprofen and 316 potential targets for celecoxib were identified and associated with DD. The antidepressant effects of both drugs involve many key targets in pathways such as "pathways in cancer" and "neuroactive ligand-receptor interaction," including ALB, BCL2, MAPK3, SRC, STAT3, EGFR, and PPARG. The binding affinity of ALB with ibuprofen is the strongest, and it is connected only by hydrophobic interactions. Celecoxib exhibits higher affinity at multiple targets such as SRC, EGFR, and PPARG, with stronger and more specific intermolecular interactions, including salt bridges and halogen bonds. Clinical trials have found that serum ALB expression in DDs is significantly lower than that in HCs (t = 6.653, p < 0.001), further confirming the potential role of ibuprofen in DD.

CONCLUSIONS

Ibuprofen and celecoxib primarily exert their antidepressant effects through targets and pathways related to inflammation, neural signaling, and cancer, with celecoxib showing a stronger potential antidepressant effect. The expression difference of the core target ALB between depression and healthy individuals further supports the potential effect of the drug on DD. Our findings propose new treatment strategies, support the link between inflammation and depression, and encourage reassessing existing medications for depression.

Circulating mediators linking cardiometabolic diseases to HFpEF: a mediation Mendelian randomization analysis

BACKGROUND

Heart failure with preserved ejection fraction (HFpEF) is an increasingly prevalent clinical syndrome with high morbidity and mortality. Although HFpEF frequently coexists with cardiometabolic diseases, the causal mechanisms and potential mediators remain poorly understood.

OBJECTIVES

This study aimed to identify cardiometabolic risk factors specifically driving HFpEF and to determine their underlying circulating mediators.

METHODS

We used two-sample Mendelian Randomization (MR) to analyze the effects of obesity, Type 2 diabetes, hypertension, chronic kidney disease (CKD), and dyslipidemia on HFpEF and heart failure with reduced ejection fraction (HFrEF) in large European-ancestry GWAS datasets. We then performed mediation MR to identify plasma proteins and metabolites that mediate the transition from each cardiometabolic disease to HFpEF, respectively. We applied multivariable MR to assess the impact of risk confounding on the results. Bioinformatic analyses were conducted to delineate mechanisms.

RESULTS

Cardiometabolic diseases had heterogeneous effects on HFpEF and HFrEF. Obesity and type 2 diabetes showed adjusted causal effects with HFpEF, hypertension showed potential relevance to HFpEF, whereas dyslipidemia and CKD did not. MR analysis identified 5 proteins that mediate obesity to HFpEF; 5 proteins that mediate type 2 diabetes to HFpEF. Further mediation MR analysis of obesity and T2D on HFrEF revealed heterogeneity in circulating mediators between metabolic HFpEF and HFrEF. Comprehensive bioinformatics analyses showed that IL1R1, together with other proteins such as TP53 and FGF19, orchestrates the inflammatory and fibrotic processes underlying HFpEF.

CONCLUSIONS

These findings suggest that metabolic HFpEF has distinct etiological features compared with HFrEF and is driven by complex, condition-specific mediators. IL1R1 mediates HFpEF in multiple metabolic risk states, suggesting a potential therapeutic target. Further translational studies are warranted to evaluate anti-inflammatory strategies targeting IL1R1 in HFpEF.

Post-genome-wide association study dissects genetic vulnerability and risk gene expression of Sjögren's disease for cardiovascular disease

OBJECTIVES

This study aims to clarify the genetic associations between Sjögren's Disease (SD) and cardiovascular disease (CVD) outcomes, and to conduct an in-depth exploration of specific pleiotropic susceptibility genes.

METHODS

We performed two-sample and multivariable Mendelian randomization (MR) analysis to investigate the association between SD and the risk of ischemic heart disease (IHD) and stroke. Linkage disequilibrium score regression (LDSC) and Bayesian co-localization analyses were employed to assess the genetic associations between traits. Cross-phenotype analyses were employed to identify shared variants and genes, followed by a Transcriptome-Wide Association Study (TWAS) and Multi-marker Analysis of Genomic Annotation (MAGMA) based on Multi-Trait Analysis of GWAS (MTAG) results. To validate the pleiotropic genes, we further analyzed tissue-specific differentially expressed genes (DEGs) related to SD using RNA sequencing data.

RESULTS

The two-sample and multivariable MR analyses revealed that SD confers a genetic vulnerability to IHD and stroke. LDSC and co-localization analyses indicated a strong genetic linkage between SD and CVDs. Cross-phenotype analyses identified 38 and 37 pleiotropic single nucleotide polymorphisms (SNPs) for SD-Stroke and SD-IHD, respectively, primarily located within the MHC class region on 6p21.32:33 loci. Additionally, TWAS and MAGMA analyses identified pleiotropic genes located outside the MHC regions-seven associated with stroke (UHRF1BP1, SNRPC, BLK, FAM167A, ARHGAP27, C8orf12, and PLEKHM1) and two associated with IHD (UHRF1BP1 and SNRPC). Proxy variants within these genes in SD suggested an increased causal risk for stroke or IHD. Co-localization analysis further reinforced that SD and stroke share significant SNPs within the loci of FAM167A, BLK, C8orf12, SNRPC, and UHRF1BP1. DEG analysis revealed a significant up-regulation of the identified genes in SD-specific tissues.

CONCLUSIONS

SD appears genetically predisposed to an increased risk of CVDs. Moreover, this research not only identified pleiotropic genes shared between SD and CVDs, but also, for the first time, detected key gene expressions that elevate CVD risk in SD patients-findings that may offer promising therapeutic targets for patient management.

Integrating HiTOP and RDoC frameworks part II: shared and distinct biological mechanisms of externalizing and internalizing psychopathology

BACKGROUND

The Hierarchical Taxonomy of Psychopathology (HiTOP) and Research Domain Criteria (RDoC) frameworks emphasize transdiagnostic and mechanistic aspects of psychopathology. We used a multi-omics approach to examine how HiTOP's psychopathology spectra (externalizing [EXT], internalizing [INT], and shared EXT + INT) map onto RDoC's units of analysis.

METHODS

We conducted analyses across five RDoC units of analysis: genes, molecules, cells, circuits, and physiology. Using genome-wide association studies from the companion Part I article, we identified genes and tissue-specific expression patterns. We used drug repurposing analyses that integrate gene annotations to identify potential therapeutic targets and single-cell RNA sequencing data to implicate brain cell types. We then used magnetic resonance imaging data to examine brain regions and circuits associated with psychopathology. Finally, we tested causal relationships between each spectrum and physical health conditions.

RESULTS

Using five gene identification methods, EXT was associated with 1,759 genes, INT with 454 genes, and EXT + INT with 1,138 genes. Drug repurposing analyses identified potential therapeutic targets, including those that affect dopamine and serotonin pathways. Expression of EXT genes was enriched in GABAergic, cortical, and hippocampal neurons, while INT genes were more narrowly linked to GABAergic neurons. EXT + INT liability was associated with reduced gray matter volume in the amygdala and subcallosal cortex. INT genetic liability showed stronger causal effects on physical health - including chronic pain and cardiovascular diseases - than EXT.

CONCLUSIONS

Our findings revealed shared and distinct pathways underlying psychopathology. Integrating genomic insights with the RDoC and HiTOP frameworks advanced our understanding of mechanisms that underlie EXT and INT psychopathology.

Genome-wide Mendelian randomization study identifies therapeutic targets for diabetic microangiopathy

AIMS

This study aims to identify potential therapeutic targets for diabetic microangiopathy by integrating genome-wide association studies (GWAS) and Mendelian randomization (MR) analyses.

METHODS

A comprehensive analysis of GWAS datasets on diabetic microangiopathy was conducted by using two-sample MR to determine the causal effects of blood-expressed druggable genes at both the transcriptional and protein levels. Co-localization analysis was conducted to validate gene-trait associations, while phenome-wide association studies (PheWAS) explored broader phenotypic implications. Additionally, protein-protein interaction (PPI) networks were constructed to elucidate gene interactions and molecular docking was conducted to determine therapeutic druggability.

RESULTS

Nine candidate therapeutic targets (PSORS1C3, HLA-C, RAMP1, CTSG, SREBF1, BTN3A2, PPA1, PRKD2, and PPIG) were identified, with co-localization analysis confirming their involvement in diabetic microangiopathy. Among them, HLA-C exhibited associations with additional traits, suggesting the specificity of the remaining targets. Functional enrichment analysis indicated a predominant involvement of immune-related pathways, underscoring their relevance to the pathogenesis of diabetic microangiopathy. Furthermore, molecular docking studies revealed strong binding affinities.

CONCLUSIONS

This study provides compelling genetic evidence supporting the role of immune-related druggable genes in diabetic microangiopathy and identifies novel therapeutic targets for intervention.

Exploring the regulatory mechanism of CCNA2 in colorectal cancer: insights from multiomics and experimental analysis

Colorectal cancer (CRC) is the third-most common cancer and the second-leading cause of mortality due to cancer worldwide. The underlying regulatory mechanism of CCNA2 in CRC was explored through multiomics and experimental analyses, thus facilitating diagnosis, therapy and prognosis. Two gene expression datasets (i.e., GSE9348 and GSE110223) were extracted from GEO. Differentially expressed genes (DEGs) were identified via GEO2R, which were used for enrichment analyses through DAVID. PPI network of DEGs was constructed by STRING, and the core genes were identified. CCNA2, a prognostic core gene for CRC, was validated in TCGA and HPA via transcriptomics and proteomics. ROC analysis was performed to evaluate the diagnostic value of CCNA2 in CRC. The therapeutic value of CCNA2 was evaluated in DGIdb through pharmacogenomics. The correlation between CCNA2 and immune infiltration was determined in TIMER by immunomics. TF-mRNA and miRNA-mRNA networks for CCNA2 were constructed in miRnet and miRDB via transcriptomics. The role and mechanism of CCNA2 in CRC were investigated both in vitro and in vivo. The miR-548x-3p/CCNA2 regulatory axis in CRC was investigated in vitro. CCNA2 showed excellent diagnostic, therapeutic, and prognostic value in CRC. CCNA2 was closely associated with tumor-infiltrating immunocytes, TFs, and miRNAs. The upregulation of CCNA2 was observed in CRC, and the knockdown of CCNA2 inhibited the proliferation, migration, and invasion while inducing apoptosis of CRC cells. The knockdown of CCNA2 could inhibit epithelial-mesenchymal transition (EMT) pathway. CCNA2 acted as a target of miR-548x-3p in regulating the biological behavior of CRC cells via the EMT-signaling pathway. CCNA2 is a potential biomarker for the diagnosis, treatment, and prognosis of CRC and is associated with immune infiltration, TF, and miRNA. The miR-548x-3p/CCNA2 axis plays a pivotal role in regulating the tumorigenesis of CRC through the EMT-signaling pathway.

Genome-wide association studies of binge eating behaviour and anorexia nervosa yield insights into the unique and shared biology of eating disorder phenotypes

Eating disorders -including anorexia nervosa (AN), bulimia nervosa, and binge eating disorder-are clinically distinct but exhibit symptom overlap and diagnostic crossover. Genomic analyses have mostly examined AN. We conducted the first genomic meta-analysis of binge eating behaviour (BE; 39,279 cases, 1,227,436 controls), alongside new analyses of AN (24,223 cases, 1,243,971 controls) and its subtypes (all European ancestries). We identified six loci associated with BE, including loci associated with higher body mass index (BMI) and impulse-control behaviours. AN GWAS yielded eight loci, validating six loci. Subsequent polygenic risk score analysis demonstrated an association with AN in two East Asian ancestry cohorts. BE and AN exhibited similar positive genetic correlations with psychiatric disorders, but opposing genetic correlations with anthropometric traits. Most of the genetic signal in BE and AN was not shared with BMI. We have extended eating disorder genomics beyond AN; future work will incorporate multiple diagnoses and global ancestries.

Identifying novel protein biomarkers with cross-psychiatric disorders effects and potential intervention targets: Evidence from proteomic-Mendelian randomization

Plasma proteins are the potential therapeutic targets for psychiatric disorders due to their important roles in signal transduction. We aimed to explore the plasma protein biomarkers with cross-psychiatric disorders effects. Proteome-wide Mendelian randomization (MR) and colocalization analyses were performed to investigate the potential causal relationship between plasma protein biomarkers and 12 psychiatric disorders and further identify the potential proteins with cross-effects. To assess the directionality and exclude potential reverse causation, Steiger directionality tests and reverse MR analyses were additionally conducted. Then, validation analysis was performed by employing summary data from cross-psychiatric disorder GWAS to validate the cross-psychiatric effects of proteins. Protein-protein interactions were conducted to evaluate the interaction between candidate proteins and druggability assessment was used to prioritize potential drug targets for psychiatric disorders. We identified novel plasma proteins that possessed cross-psychiatric disorder effects, especially BTN2A1 and BTN3A2 associated with major depressive disorder (MDD), schizophrenia (SCZ), and bipolar disorder (BIP); ITIH1, ITIH3, ITIH4 and FES associated with SCZ and BIP, and the cross-effects of these proteins on SCZ and BIP were confirmed by validation analyses. Steiger tests and reverse MR supported causal directionality. Besides, the protein-protein interactions (PPI) analysis indicated cross-effects proteins had significant interaction, especially ITIH1-ITIH3. The druggability assessment prioritized eight proteins, two of which (ITIH3 and NCAM1) has been targeted by antipsychotic drugs. Our findings provided insights into shared biological mechanisms underlying these conditions.

Integrating analysis of multi-omics summary data identifies novel plasma protein biomarkers and drug targets for bladder cancer

The plasma proteins are an important source of therapeutic targets. This study aims to address the diagnostic and therapeutic challenges of bladder cancer (BC) by using Mendelian randomization (MR) with a large sample size from multiple centers to identify the plasma proteins which are causally related to the pathogenesis of BC. Followed by merging nine plasma protein datasets from six studies, a total of 5538 plasma proteins and three BC datasets (ieu-b-4874, ukb-b-8193, FinnGen_R11_C3_ BLADDER_EXALL) were used to perform proteome‑wide MR to estimate the contribution of plasma proteins to BC, separately. To ensure the robustness of the results, Veen intersection operation on MR results revealed that 14 meaningful candidate pathogenic plasma proteins (ANKRD27, BIN1, FAHD1, IL17RB, MRPL21, PPT1, PSCA, SLC16A3, SLURP1, SPON2, TACSTD2, TMEM87B, YWHAB) were obtain from three datasets. Then, we validated these proteins through various methods, including meta-analysis, reverse MR, Bayesian co-localization analysis and summary-data-based MR (SMR), and pathogenic plasma proteins were divided into three layers according to the validation confidence. We then performed single-cell transcriptome analysis (Registration number: GSE222315), which showed that 13/14 candidate plasma proteins were expressed and 12 proteins were differentially expressed in at least one cell type. Finally, protein-protein interactions (PPI) analysis and druggability evaluation were performed to explore the relationship between the interaction of plasma protein markers and existing cancer drug targets. Summarily, our research uncovered 14 plasma protein biomarkers linked to BC risk, offering novel perspectives on the etiology and potential targets for developing screening biomarkers and therapeutic drugs for BC.

Functional dissection of noncoding variants associated with rheumatoid arthritis

OBJECTIVES

Noncoding variants are critical to our understanding of the genetic basis of diseases and disorders such as rheumatoid arthritis (RA). While genome-wide association studies have identified regions of the genome associated with disease, functional studies are still lagging that can identify potentially causative variants.

METHODS

In order to functionally fine-map RA-associated variants, we identified variants at enhancers marked in primary activated T helper cells and conducted massively parallel reporter assay in these cells.

RESULTS

We found that combinations of functional variant genotypes are often exclusive to patients with RA. We leveraged 3-dimensional genome architecture and expression quantitative trait loci data to identify target genes of enhancers exhibiting allelic differences in activity. We confirmed enhancer activity and target gene interactions by Clustered Regularly Interpaced Short Palindromic Repeats Cas9 (CRISPR-Cas9) deletion in primary T cells.

CONCLUSIONS

The identification of functional enhancer variants suggests possible causal variants, and their target genes reveal known and novel genes as likely drivers of RA, as well as a means for therapeutic intervention.

Systematic identification of cancer-type-specific drugs based on essential genes and validations in lung adenocarcinoma

Depicting a global landscape of essential gene-targeting drugs would provide more opportunities for cancer therapy. However, a systematic investigation on drugs targeting essential genes still has not been reported. We suppose that drugs targeting cancer-type-specific essential genes would generally have less toxicity than those targeting pan-cancer essential genes. A scoring function-based strategy was developed to identify cancer-type-specific targets and drugs. The EssentialitySpecificityScore ranked the essential genes in 19 cancer types, and 1151 top genes were identified as cancer-type-specific targets. Combining target-drug interaction databases with research/marketing status, 370 cancer-type-specific drugs were identified, bound to 100 out of all identified targets. Profiles of applied cancer types of identified targets and drugs illustrate the scoring strategy's effectiveness: most drugs apply to cancer types <10. Seven drugs with no previous anticancer evidence were validated in 11 lung adenocarcinoma cell lines, and lower inhibition rates (from 9.4% to 44.0%) were observed in 10 normal cell lines. This difference is statistically significant (Student's t-test, P ≤ .0001), confirming the rationality of our supposition. Our built EGKG (Essential Gene Knowledge Graph) forms a computational basis to uncover essential gene targets and drugs for specific cancer types. It is available at http://gepa.org.cn/egkg/. Also, our experimental result suggests that combining drugs with orthogonal essentiality may be an alternative way to improve anticancer effects while maintaining biocompatibility. The code and data are available at https://github.com/KKINGA1/EGKG_data_process.

Multiomic Underpinnings of Drug Targets for Intracranial Aneurysm: Evidence From Diversified Mendelian Randomization

AIMS

The absence of pharmaceutics poses challenges in preventing intracranial aneurysm (IA) progression and rupture. This research emphasized identifying drug targets for IA through a druggable genome-wide Mendelian randomization (MR) analysis.

METHODS

A two-sample MR analysis was performed leveraging cis-expression quantitative trait loci in the blood (n = 31,684) and arteries (n = 584) aligned with 5883 druggable genes as exposure and the largest IA summary statistics (n = 7495) as outcome. Bayesian colocalization analysis, plasma cis-protein quantitative trait loci (n = 35,559), and external IA cohorts (FinnGen, n = 2582; Zhou, n = 380) were used for validation. A phenome-wide MR (Phe-MR) incorporating 783 diseases uncovered side effects. Multivariable MR addressed unmeasured pleiotropy.

RESULTS

Five druggable genes in blood and one in the coronary artery showed significant association with IA risk (p-FDR ≤ 0.05). NT5C2, PRCP, and CRMP1 shared a common variant with IA (PPH4 ≥ 0.8). The external validation cohorts confirmed the effects of NT5C2 on IA (FinnGen cohort, Odds Ratio [OR], 0.81, 95% Confidential Interval [95% CI] 95% CI, 0.707-0.930; p = 0.003; Zhou cohort, OR, 0.68, 95% CI, 0.469-0.983; p = 0.041). The genetically predicted protein level of PRCP validated an inverse association with IA risk (OR, 0.734; 95% CI, 0.561-0.959; p = 0.023). The Phe-MR revealed insignificance for NT5C2 or PRCP. Direct causal effects on IA were 0.60 (95% CI, 0.457-0.797; p = 1.36E-05) for PRCP and 0.67 (95% CI, 0.527-0.860; p = 0.002) for NT5C2 after adjusting for IA modifiable risk factors.

CONCLUSIONS

NT5C2 and PRCP were identified as potential drug targets for IA, with effects independent of known modifiable risk factors. Targeting NT5C2 and PRCP appeared exclusively effective and safe.

Target Screening and Single Cell Analysis of Diabetic Retinopathy and Hepatocarcinoma

The association between liver cancer and diabetes has been a longstanding focus in medical research. Current evidence suggests that diabetes is an independent risk factor for the development of liver cancer. Diabetic retinopathy (DR), a prevalent neurovascular complication of diabetes, has yet to be fully characterised concerning liver cancer. Therefore, this study seeks to identify shared genes and pathways between liver cancer and DR to uncover potential therapeutic targets. Immune infiltration and cell communication in liver cancer were analysed using the GEO single-cell dataset GSM7494113. Single-cell RNA sequencing data from rat retinas were obtained from the GEO datasets GSE209872 and GSE160306. Ferritin phagocytosis-related genes were retrieved from the GeneCards database. The SeuratR package was employed for single-cell clustering analysis, while the CellChat package assessed differences in intercellular communication. Genes shared between DR and liver cancer were identified, and the DGIDB database was consulted to predict potential drug-gene interactions targeting membrane proteins involved in ferritin phagocytosis. Key ferritin phagocytosis (FRHG) genes were further validated using quantitative real-time polymerase chain reaction (qRT-PCR). After annotating the single-cell data through dimensionality reduction and clustering, the expression of genes associated with membrane protein-related ferritinophagy was notably elevated in both HCC and DR samples. Based on the expression of ferritinophagy-related genes, the ferritin deposition score in Müller cells from the DR group was significantly higher than that in the control group. Cell communication analysis revealed that central hub genes associated with ferritinophagy, such as PSAP and MK, along with other signalling pathways, were significantly upregulated in the high Müller group compared to the low Müller group. In contrast, VEGF expression was enhanced in the low Müller group. Importantly, the machine learning model constructed using these key hub genes demonstrated high diagnostic efficacy for both HCC and DR. Finally, by simulating a hyperosmotic diabetic microenvironment, we confirmed in vitro that high glucose conditions significantly stimulate the expression of the shared key hub genes in both HCC and DR. The present study identified the connection between ferritinophagy-related subgroups of cells and key hub genes in both HCC and DR, providing new insights into DR-associated biomarkers and the shared pathological regulatory pathways with HCC. These findings further suggest potential therapeutic targets for both diseases.

TARGET-SL: precision essential gene prediction using driver prioritisation and synthetic lethality

The ability to identify patient-specific vulnerabilities to guide cancer treatments is a vital area of research. However, predictive bioinformatics tools are difficult to translate into clinical applications due to a lack of in vitro and in vivo validation. While the increasing number of personalised driver prioritisation algorithms (PDPAs) report powerful patient-specific information, the results do not easily translate into treatment strategies. Critical in addressing this gap is the ability to meaningfully benchmark and validate PDPA predictions. To address this, we developed Tumour-specific Algorithm for Ranking GEnetic Targets via Synthetic Lethality (TARGET-SL), which utilises PDPA predictions to produce a ranked list of predicted essential genes that can be validated in vitro and in vivo. This framework employs a novel strategy to benchmark PDPAs, by comparing predictions with ground truth gene essentiality data from large-scale CRISPR-knockout and drug sensitivity screens. Importantly TARGET-SL identifies vulnerabilities that are more exclusive to individual tumours than predictions based on canonical driver genes. We further find that TARGET-SL is better at identifying sample-specific vulnerabilities than other similar tools.

Circulating Blood-Based Proteins in Psychopathology and Cognition: A Mendelian Randomization Study

Importance

Peripheral (blood-based) biomarkers for psychiatric illness could benefit diagnosis and treatment, but research to date has typically been low throughput, and traditional case-control studies are subject to potential confounds of treatment and other exposures. Large-scale 2-sample mendelian randomization (MR) can examine the potentially causal impact of circulating proteins on neuropsychiatric phenotypes without these confounds.

Objective

To identify circulating proteins associated with risk for schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) as well as cognitive task performance (CTP).

Design, Setting, and Participants

In a 2-sample MR design, significant proteomic quantitative trait loci were used as candidate instruments, obtained from 2 large-scale plasma proteomics datasets: the UK Biobank Pharma Proteomics Project (2923 proteins per 34 557 UK individuals) and deCODE Genetics (4719 proteins per 35 559 Icelandic individuals). Data analysis was performed from November 2023 to November 2024.

Exposure

Genetic influence on circulating levels of proteins in plasma.

Main Outcomes and Measures

Outcome measures were summary statistics drawn from recent large-scale genome-wide association studies for SCZ (67 323 cases and 93 456 controls), BD (40 463 cases and 313 436 controls), MDD (166 773 cases and 507 679 controls), and CTP (215 333 individuals). MR was carried out for each phenotype, and proteins that showed statistically significant (Bonferroni-corrected P < .05) associations from MR analysis were used for pathway, protein-protein interaction, drug target enrichment, and potential druggability analysis for each outcome phenotype separately.

Results

MR analysis revealed 113 Bonferroni-corrected associations (46 novel) involving 91 proteins across the 4 outcome phenotypes. Immune-related proteins, such as interleukins and complement factors, showed pleiotropic effects across multiple outcome phenotypes. Drug target enrichment analysis provided support for repurposing of anti-inflammatory agents for SCZ, amantadine for BD, retinoic acid for MDD, and duloxetine for CTP.

Conclusions and Relevance

Identifying potentially causal effects of circulating proteins on neuropsychiatric phenotypes suggests potential biomarkers and offers insights for the development of innovative therapeutic strategies. The study also reveals pleiotropic effects of many proteins across different phenotypes, indicating shared etiology among serious psychiatric conditions and cognition.

Association of Breast Cancer and Selective Estrogen Receptor Modulators on the Risk of Meningioma: Insights from Mendelian Randomization

Considering the potential links between breast cancer (BC), selective estrogen receptor modulators, and meningioma in previous epidemiology studies, this study aimed to investigate them through the Mendelian randomization approach. We extracted instrumental variables (IVs) of different subtypes of BC from the largest genome-wide association study. Gene targets of SERMs were obtained from the Drug-Gene Interaction Database. Mendelian randomization (MR) analysis applied inverse variance weighted approach to evaluate causality. A series of sensitivity analyses and reverse MR were used to evaluate the stability of the MR results. Genetically determined estrogen receptor (ER) positive BC, luminal A-like breast cancer (OR 1.17, 95% CI 1.04 to 1.32, p = 0.01), and luminal B-like breast cancer (OR 1.20, 95% CI 1.04 to 1.37, p = 0.009) were associated with an increased odds ratio of meningioma (OR 1.18, 95% CI 1.05 to 1.32, p = 0.005). Among SERM-targeted genes, CYP2D6 (OR 1.37, 95% CI 1.23 to 1.54, p = 4.15 × 10- 8), NGR1 (OR 1.15, 95% CI 1.10 to 1.20, p = 2.59 × 10- 11), and MAPT (OR 10.20, 95% CI 2.90 to 35.84, p = 0.0003) were associated with increased meningioma risk, while BRCA1 (OR 0.67, 95% CI 0.57 to 0.80, p = 4.88 × 10- 6) showed negative causal association with meningioma risk. The outcome of the sensitivity analysis and reverse MR analysis corroborated the findings. These findings suggested a causal relationship between BC and meningioma, and identified potential target genes associated with meningioma, which was beneficial to early identification and prevention of meningioma risk.

Identification of druggable genetic targets for prostate cancer risk based on mendelian randomization and single-cell RNA sequencing

PURPOSE

This study aimed to identify genetic targets linked to prostate cancer risk using advanced genetic analysis techniques.

OBJECTIVE

The goal was to conduct a comprehensive analysis using Mendelian Randomization (MR), colocalization, and single-cell RNA sequencing to identify druggable genes as potential therapeutic targets or diagnostic markers.

METHODS

The study involved selecting 2608 druggable genes by intersecting expression Quantitative Trait Loci (eQTLs) with druggable genome databases. MR analysis using prostate cancer GWAS data identified genes with causal associations to prostate cancer risk. Colocalization analysis confirmed shared genetic variants influencing both the exposure and outcome. Single-cell RNA sequencing assessed gene expression in prostate tumor cell types, while a phenome-wide association study (PheWAS) evaluated potential side effects.

RESULTS

MR analysis identified 58 genes associated with prostate cancer risk, with 12 validated by colocalization analysis. Five genes (BAK1, ATP1B2, PEMT, TPM3, ZDHHC7) demonstrated strong colocalization, indicating potential as drug targets. Single-cell RNA sequencing revealed their enrichment in prostate tumor T cells and macrophages. PheWAS suggested minimal side effects for most, except BAK1, which was linked to increased platelet counts.

CONCLUSION

This study identified several genetic targets associated with prostate cancer risk, highlighting the potential for targeted therapy. By integrating Mendelian randomization analysis, colocalization analysis, and single-cell RNA sequencing, the accuracy of target validation was improved, which may provide new directions for targeted therapy in prostate cancer.

Identifying therapeutic target genes for diabetic retinopathy using systematic druggable genome-wide Mendelian randomization

INTRODUCTION

The treatment and prevention of diabetic retinopathy (DR) remain significant challenges. Mendelian randomization (MR) has been widely used to explore novel therapeutic targets. In this study, we conducted a systematic druggable genome-wide MR analysis to explore potential therapeutic targets for DR.

METHODS

We obtained data on druggable genes and screened for genes within blood expression quantitative trait loci (eQTL), which were then subjected to MR analysis and colocalization analysis with DR genome-wide association studies data to identify genes strongly associated with DR. Additionally, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, protein-protein interaction (PPI) network construction, drug candidate prediction, and molecular docking were performed to provide valuable insights for the development of more effective and targeted therapeutic drugs.

RESULTS

MR analysis of blood eQTLs revealed 30 significant DR-associated druggable genes, with PRKAB1 (OR = 0.935, 95% CI: 0.892 to 0.980) and CNR1 (OR = 0.814, 95% CI: 0.696 to 0.951) being protective genes, whereas CACNA1E (OR = 1.282, 95% CI: 1.050 to 1.565), NME1 (OR = 1.198, 95% CI: 1.028 to 1.397), and CHRNA2 (OR = 1.192, 95% CI: 1.025 to 1.386) were associated with increased risk. KEGG analysis highlighted significant pathways, including adrenergic signaling in cardiomyocytes (hsa04261), the oxytocin signaling pathway (hsa04921), and arrhythmogenic right ventricular cardiomyopathy (hsa05412). PPI network analysis identified two key modules: one comprising BIN1, CDH2, ACTN1, EPAS1, CEBPA, and CTSD nodes, and the other consisting of CACNG6, CACNA1E, CACNA2D3, and RASGRP3 nodes. Drug candidate prediction suggested ethanol and isoflupredone as potential therapeutic interventions, and molecular docking revealed C5's strong protein binding affinity.

CONCLUSIONS

This study utilized MR and colocalization analysis to identify potential drug targets for DR. The findings provide promising leads for the treatments of DR, potentially reducing drug development costs.

Potential common mechanisms between primary Sjögren's syndrome and Hashimoto's thyroiditis: a public databases-based study

Objective

Primary Sjögren's syndrome (pSS) and Hashimoto thyroiditis (HT) can occur in the same patient population, but the mechanism of co-occurrence remains unknown. This study aims to explore the underlying mechanism.

Methods

We screened differentially expressed genes (DEGs) in the pSS and HT-related transcriptomic microarrays. Based on KEGG, PID, Reactome, and BioCarta enrichment analysis, pathway annotations were performed. A PPI network was developed using STRING. Betweenness, BottleNeck, MNC, Radiality EPC, and Stress topological analyses were performed to identify hub genes. Then, we used two more datasets to validate the key genes. Immune infiltration landscape of pSS and HT were profiled based on CIBERSORT, Xcell, MCPCounter, and EPIC. Correlation between T/B cells and key genes was performed. Single gene GSEA analysis was performed to further explore enriched pathways of key genes. Finally, we predicted the drugs of key genes and the cross-talk genes targeted in the protein domain.

Results

A total of 93 cross-talk genes were found. These genes were mainly related to the immune system. STAT1, CD8A, and PTPRC were identified as hub genes using six topological methods. STAT1 and PTPRC are considered key genes after in silico validation. STAT1 and PTPRC were linked to CD8+ Tcm and other immune cells in the pSS and HT dataset. GSEA analysis showed that STAT1 and PTPRC may play a role in pSS and HT through several pathways, including IFNγ response, IFNα response, allograft rejection, E2F targets, complement, G2M checkpoint, IL6-JAK-STAT3 signaling, KRAS signaling up, IL2-STAT5 signaling, IL6-JAK-STAT3-signaling, and inflammatory response. Guttiferone K and picoplatin may be the candidate drugs for the treatment of pSS and HT. Cross-talk genes were mainly enriched in IGc1, MHCIIα and SCY.

Conclusion

We analysed databases and gene expression data for pSS and HT. We identified two genes (STAT1, PTPRC) as potential biomarkers of disease activity in pSS and HT. We also gained new insights into the cellular and molecular mechanisms associated with pSS and HT. Based on the key genes and cross-talk genes, we predicted potential drugs and protein domains for pSS and HT.

Molecular mechanisms of radiation resistance in colorectal cancer: in silico identification of AURKA, BIRC5 and PLK1 proteins as potential biomarkers

PURPOSE

The development of radiation resistance by tumor cells severely affects the survival of colorectal cancer patients. The aim of this work is to study the molecular mechanisms involved in the resistance to radiotherapy treatment in colorectal cancer and the identification of key genes as possible biomarkers.

METHODS

Data mining was performed in PubMed with the keywords 'colorectal neoplasms', 'radiotherapy', and 'resistance', generating a total of 242 articles in which a series of inclusion and exclusion criteria were applied to select the articles of interest. Then, an in-silico analysis of the selected genes was performed with the bioinformatic tools: GeneCodis, Metascape, KEGG, REACTOME, STRING, STITCH, CHEA3, DGIdb, CTD, and GEPIA.

RESULTS

Different mechanisms and genes involved in radiation resistance were described. These are related to evasion of apoptosis, cell cycle dysregulation, epithelial-mesenchymal transition, and repair of DNA breaks, with the last one being the most relevant and influential. The In-silico study carried out with 21 genes involved in radiation resistance showed the implication of FoxO signaling and EGFR tyrosine kinase inhibitor resistance as the most enriched pathways. In addition, the study identified the key proteins AURKA, BIRC5, and PLK1, showing multiple interacting chemicals and drugs; such as tamoxifen, omacetaxine mepesuccinate, and hydroxyzine pamoate, among others.

CONCLUSION

The identification of multiple transcription factors that regulate the expression of these key genes as well as the validation in patient samples where higher expression is observed in tumor patients, conserved across tumor stages I-IV, suggests their potential as possible biomarkers.

Exploring the Regulatory Effect of Hydroxytyrosol on Ovarian Inflammaging Through Autophagy-Targeted Mechanisms: A Bioinformatics Approach

Background/Objectives: Ovarian aging represents a critically important aspect of female senescence. It not only denotes the loss of fertility but is also accompanied by a series of physiological changes and the aging of other organs. Hydroxytyrosol (HT), a natural polyphenolic phytocompound, has been demonstrated to exhibit remarkable effects in regulating autophagy, inflammation, and the aging process. However, the relationship between HT and ovarian aging, as well as the specific underlying mechanisms, remains poorly understood. Methods: In this study, network pharmacology, molecular docking, and molecular dynamics simulation were employed to explore the regulatory effect of HT on ovarian inflammaging via autophagy-targeted mechanisms. Results: Through network pharmacology analysis, this study successfully identified 10 hub genes associated with ovarian aging regulation. Notably, four out of the top five hub genes were found to be closely related to autophagy regulatory pathways. Further investigation revealed the pivotal role of ATG7: HT may regulate ovarian inflammaging through activating the FIP200 (focal adhesion kinase family interacting protein of 200 kD)-dependent non-canonical selective autophagy pathway. The results of molecular docking indicated that ATG7 has a strong binding ability with HT. Molecular dynamics simulation further verified the binding stability between the two. Conclusions: By analysis, a possible pathway for HT to regulate ovarian inflammaging via non-canonical selective autophagy was explored, providing cues for further research.

Drug Design in the Age of Network Medicine and Systems Biology: Transcriptomics Identifies Potential Drug Targets Shared by Sarcoidosis and Pulmonary Hypertension

Network medicine considers the interconnectedness of human diseases and their underlying molecular substrates. In this context, sarcoidosis and pulmonary hypertension (PH) have long been thought of as distinct diseases, but there is growing evidence of shared molecular mechanisms. This study reports on common differentially expressed genes (DEGs), regulatory elements, and pathways between the two diseases. Publicly available transcriptomic datasets for sarcoidosis (GSE157671) and PH (GSE236251) were retrieved from the Gene Expression Omnibus database. DEGs were identified using GEO2R, followed by pathway enrichment and gene interaction analyses via GeneMANIA and STRING. Importantly, a total of 13 common DEGs were identified between sarcoidosis and PH, with 7 upregulated and 6 downregulated genes. The SMAD2/3 nuclear pathway was a shared enriched pathway, suggesting a role in fibrosis and immune regulation. There were also divergences between sarcoidosis and PH. For example, gene set enrichment analysis indicated significant associations of the IFN-gamma signaling pathway with sarcoidosis and the TNF-alpha signaling with PH. miRNA network analysis identified hsa-miR-34a-5p, hsa-let-7g-5p, and hsa-miR-19a-3p as key shared regulators linked to common genes in both sarcoidosis and PH. Finally, DGIdb analysis revealed potential therapeutic candidates targeting these genes in both diseases. This study contributes to the field of drug design and discovery from a network medicine standpoint. The shared molecular links uncovered between sarcoidosis and PH in this study point to several potential biomarkers and therapeutic targets. Further experimental validation and translational medical research are called for diagnostics and drugs, which can effectively and safely help the clinical management of both diseases.

Exploring the Interconnections Between Mitochondrial Dysfunction and Polycystic Ovary Syndrome: A Comprehensive Integrated Analysis

Polycystic ovary syndrome (PCOS) is a leading cause of anovulatory infertility and is strongly linked to mitochondrial dysfunction (MD) in reproductive-age women. MD contributes to excessive reactive oxygen species (ROS) accumulation, exacerbating disease progression. This study aimed to identify key MD-related genes (MDRGs) involved in PCOS through bioinformatics analyses and experimental validation. Two PCOS transcriptome datasets (GSE34526 and GSE5850) were analyzed to identify differentially expressed genes (DEGs), which were then intersected with MDRGs to obtain MD-related DEGs (MDDEGs). Functional enrichment (GO, KEGG, GSEA) and protein-protein interaction (PPI) network analyses identified eight hub MDDEGs (MMP9, PPP1 CA, PSMD12, LIFR, PRKAA1, ITGAM, SUCLA2, GPBAR1). A rat PCOS model was established to validate hub gene expression via RT-qPCR, western blotting, and immunohistochemistry. The experimental data confirmed that seven hub genes exhibited consistent expression patterns with GSE34526 (P < 0.05), while only PRKAA1 and LIFR matched GSE5850 findings. Additionally, ROC analysis for the five most significant genes (LIFR, PBK, PRKAA1, RCAN1, MMP9) demonstrated promising diagnostic value (AUC > 0.85). This study highlights the role of MD in shaping the immune microenvironment of PCOS and identifies novel molecular targets for potential therapeutic interventions.

Shared circulating diagnostic biomarkers and molecular mechanisms in ischemic stroke and systemic lupus erythematosus

Introduction

Ischemic stroke, a prevalent cerebrovascular disorder characterized by reduced cerebral blood flow, and systemic lupus erythematosus (SLE), an autoimmune disease affecting various organs, are suspected to share overlapping etiological mechanisms and genetic predispositions. This study aimed to identify shared diagnostic biomarkers and molecular mechanisms by analyzing datasets from the GEO database.

Methods

We pinpointed differentially expressed genes using the limma package and identified co-expression modules associated with both conditions using Weighted Gene Coexpression Network Analysis. Pathway enrichment analysis was conducted using GO and KEGG to identify co-driver genes. LASSO regression was applied to evaluate potential diagnostic markers, and immune cell infiltration was quantified using the CIBERSORT computational method. A middle cerebral artery occlusion (MCAO) mouse model was developed to assess core gene expression in vivo.

Results

We identified 69 shared driver genes linked to stroke and SLE, which were narrowed down to the top 10 genes through a Protein-Protein Interaction network analysis with Cytoscape. LASSO regression selected EIF2AK2, PARP9, and IFI27 as diagnostic biomarkers, supported by ROC curve analysis. Immune cell infiltration profiles were nearly identical between ischemic stroke and SLE. 9.4T MR imaging, H&E and Nissl staining confirmed ischemic stroke in the MCAO model, and qPCR analysis confirmed elevated expression of the three hub genes.

Discussion

Our findings provide evidence for common diagnostic indicators and disease mechanisms in ischemic stroke and SLE, offering novel insights for potential therapeutic strategies targeting their shared immune cell infiltration microenvironments.

The Identification of Biomarkers and Therapeutic Targets for Diabetic Kidney Disease by Integrating the Proteome with the Genome

Background: The blood proteome is a major source of biomarkers and therapeutic targets. We conducted a proteome-wide Mendelian randomization (MR) study to identify cardiometabolic protein markers for diabetic kidney disease (DKD). Methods: We measured all 369 proteins in the Olink Explore 384 Cardiometabolic and Cardiometabolic panel of 500 patients with type 2 diabetes from 11 communities in Shanghai. Protein quantitative trait loci (pQTLs) were derived by coupling genomic and proteomic data. Cis-pQTLs identified for proteins were used as instrumental variables in MR analyses of DKD risk, and the outcome data were obtained from 8401 Japanese individuals with type 2 diabetes (2809 cases and 5592 controls). Replication MR analysis was performed in the UK Biobank Pharma Proteomics Project (UKB-PPP). Colocalization analysis and the Heidi test were used to examine whether the identified proteins and DKD shared causal variants. Results: Among the 369 proteins, we identified 66 independent cis-pQTLs for 64 proteins. MR analysis suggested that two cardiometabolic proteins (UMOD and SIRPA) may play a causal role in increasing DKD risk, with UMOD showing replication in UKB-PPP. Bayesian colocalization further supported the causal effects of these proteins. Additional analyses indicated that UMOD is highly expressed in renal macrophages. Further downstream analyses suggested that UMOD could be a potential novel target and that SIRPA could be a potential repurposing target for DKD; however, further validation is needed. Conclusions: By integrating proteomic and genetic data from patients with type 2 diabetes, we identified two protein biomarkers potentially associated with DKD risk. These findings provide insights into DKD pathophysiology and therapeutic target development, but further replication and functional studies are needed to confirm these associations.

Decoding potential host protein targets against Flaviviridae using protein-protein interaction network

Flaviviridae family comprises some of the most vulnerable viruses known for causing widespread outbreaks, high mortality rates, and severe long-term health complications in humans. Viruses like Dengue (DENV), Zika (ZIKV) and Hepatitis C (HCV) are endemic across the globe, especially in tropical and subtropical regions, infecting multiple tissues and leading to significant health crises. Investigating virus-host interactions across tissues can reveal tissue-specific drug targets and aid antiviral drug repurposing. In this study, we employed a multi-step computational approach to construct a comprehensive virus-human interactome by integrating virus-host protein-protein interactions (PPIs) with tissue-specific gene expression profiles to study key protein targets associated with Flaviviridae infections. Mapping drug-target predictions revealed druggable proteins - CCNA2 in peripheral blood mononuclear cells (PBMC) and EIF2S2, CDK7 and CARS in the liver, with Tamoxifen, Sirolimus, Entrectinib and L-cysteine as potential repurposable drugs, respectively. Further protein-ligand docking and molecular dynamics (MD) simulations were conducted to assess the stability of the complexes. These findings highlight common druggable human targets exploited by DENV, ZIKV and HCV, providing a foundation for broad-spectrum antiviral therapies. By focusing on shared host pathways and targets in viral replication, we propose promising drug candidates, supporting the development of unified therapeutic strategies against Flaviviridae viruses.

Genomic and GEO data integration identifies PDGFB as a potential therapeutic target for sepsis

Sepsis is a major contributor to global health loss, yet effective therapeutic options remain scarce. This study aims to identify potential therapeutic targets for sepsis. We integrated data from the druggable genome, expression quantitative trait loci (eQTLs) from human blood, and genome-wide association studies on sepsis. Mendelian randomization (MR) was employed to investigate causal relationships between drug target genes and sepsis. The eQTLGen Consortium data served as the discovery set and was validated using genotype-tissue expression (GTEx) eQTLs. Sensitivity and colocalization analyses were conducted to support causal inferences. Additionally, phenome-wide MR (Phe-MR) was used to assess potential side effects of druggable genes. The expression levels of the target genes were validated using the GSE154918 dataset. In the discovery MR analysis phase, we identified 26 potential targets with significant expression in blood (PFDR < 0.05). PDGFB and BPI were further validated in the replication MR analysis. Colocalization analysis provided strong evidence (PPH4 > 0.75) supporting PDGFB as a therapeutic candidate for sepsis. Phe-MR analysis suggested that targeting PDGFB is unlikely to cause adverse effects. PDGFB downregulation was confirmed in sepsis groups via the GEO dataset. PDGFB is identified as a promising druggable target for sepsis treatment, supported by strong evidence of its therapeutic potential.

GraphPINE: Graph Importance propagation for interpretable drug response prediction

Explainability is necessary for many tasks in biomedical research. Recent explainability methods have focused on attention, gradient, and Shapley value. These do not handle data with strong associated prior knowledge and fail to constrain explainability results based on known relationships between predictive features. We propose GraphPINE, a graph neural network (GNN) architecture leveraging domain-specific prior knowledge to initialize node importance optimized during training for drug response prediction. Typically, a manual post-prediction step examines literature (i.e., prior knowledge) to understand returned predictive features. While node importance can be obtained for gradient and attention after prediction, node importance from these methods lacks complementary prior knowledge; GraphPINE seeks to overcome this limitation. GraphPINE differs from other GNN gating methods by utilizing an LSTM-like sequential format. We introduce an importance propagation layer that unifies 1) updates for feature matrix and node importance and 2) uses GNN-based graph propagation of feature values. This initialization and updating mechanism allows for informed feature learning and improved graph representation. We apply GraphPINE to cancer drug response prediction using drug screening and gene data collected for over 5,000 gene nodes included in a gene-gene graph with a drug-target interaction (DTI) graph for initial importance. The gene-gene graph and DTIs were obtained from curated sources and weighted by article count discussing relationships between drugs and genes. GraphPINE achieves a PR-AUC of 0.894 and ROC-AUC of 0.796 across 952 drugs. Code is available at https://anonymous.4open.science/r/GraphPINE-40DE.

Cytotoxic Effect of Escitalopram/Etoposide Combination on Etoposide-Resistant Lung Cancer

Background: Antidepressants are a class of pharmaceuticals utilized for the management of many psychiatric disorders, including depression. A considerable number of antidepressants, particularly selective serotonin reuptake inhibitors (SSRIs), have been documented to demonstrate significant anticancer properties in various cancer cell lines. Objectives: The aim of this study was to evaluate the selective cytotoxic and apoptotic effects of escitalopram oxalate (ES) alone and in combination with etoposide (ET) on ET-resistant A549 (A549/90E) lung cancer cells. Methods: The cytotoxic effects of the drugs were determined by CCK-8, trypan blue, and neutral red assays. Apoptosis was observed by Annexin V fluorescein isothiocyanate (FITC)/PI and mitochondrial membrane potential (ΔΨm) assays. Moreover, the effects of the drugs, alone and in combination, on apoptosis-related proteins, caspase-3, PTEN, and resistance-related P-gP were determined by ELISA. The relationship between drugs and lung cancer was determined with protein-protein interaction (PPI) network analysis. Results: Our results revealed that ES significantly exerted cytotoxic effects on both wild-type and A549/90E cells compared with BEAS-2B cells. The IC50 values of 48.67 and 51.6 μg/mL obtained for ET and ES, respectively, at the end of 24 h of incubation for A549 cells were applied reciprocally for each cell by including BEAS-2B together with the 2xIC50 and ½ IC50 values. The results of each combination were statistically evaluated with combination indices (CIs) obtained using the Compusyn synergistic effect analysis program. Combination doses with a synergistic effect in A549 and A549/90E cells and an antagonistic effect in BEAS-2B cells have been determined as ½ IC50 for ET and ½ IC50 for ES. ET ½ IC50, ES ½ IC50, and an ET ½ IC50 + ES ½ IC50 combination caused 18.37%, 55.19%, and 57.55% death in A549 cells, whereas they caused 44.9%, 22.4%, and 51.94% death in A549/90E cells, respectively. In A549 cells, the combination of ES ½ IC50 and ET ½ IC50 caused increased levels of caspase-3 (p < 0.01) and P-gP (p < 0.001), while PTEN levels remained unchanged. The combination resulted in an increase in caspase-3 (p < 0.001) and PTEN (p < 0.001) amounts, alongside a decrease in P-gP (p < 0.01) levels in A549/90E cells. The death mechanism induced by the combination was found to be apoptotic by Annexin V-FITC and ΔΨm assays. Conclusions: Based on our findings, ES was observed to induce cytotoxic and apoptotic activities in A549/90E cells in vitro. ES in combination therapy is considered to be effective to overcome ET resistance by reducing the amount of P-gP in A549/90E cells.

A large-scale genome-wide study of gene-sleep duration interactions for blood pressure in 811,405 individuals from diverse populations

Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discovered 22 novel gene-sleep duration interaction loci for blood pressure, mapped to 23 genes. Investigating these genes' functional implications shed light on neurological, thyroidal, bone metabolism, and hematopoietic pathways that necessitate future investigation for blood pressure management that caters to sleep health lifestyle. Non-overlap between short sleep (12) and long sleep (10) interactions underscores the plausible nature of distinct influences of both sleep duration extremes in cardiovascular health. Several of our loci are specific towards a particular population background or sex, emphasizing the importance of addressing heterogeneity entangled in gene-environment interactions, when considering precision medicine design approaches for blood pressure management.

Genetically Proxied Antiplatelet Drug Target Perturbation and Risk of Aneurysmal Subarachnoid Hemorrhage: A Mendelian Randomization Analysis

BACKGROUND

The impact of antiplatelet drugs (APDs) on the rupture risk of unruptured intracranial aneurysms (uIAs) remains controversial. This study aimed to evaluate the causal effects of APDs on aneurysmal subarachnoid hemorrhage (aSAH) and uIA.

METHODS

A two-sample Mendelian randomization (TSMR) analysis examined associations between genetically proxied platelet reactivity and aSAH. The therapeutic inhibition of platelet aggregation by 5 widely used APDs was proxied by expression quantitative trait loci from eqtlGen consortium and Genotype-Tissue Expression project v8 consortium and protein quantitative trait loci from deCODE database. Causal effects were estimated with summary-data-based MR, TSMR, colocalization analysis, and sensitivity analysis. Mediation MR analysis explored potential pathways.

RESULTS

The platelet reactivity was inversely associated with the risk of aSAH, exhibiting no discernible heterogeneity or pleiotropic effects (odds ratio, 0.883; 95% confidential interval, 0.833-0.936; P = 2.67E-05). No causal effects on the aSAH and uIA were observed for the majority of APD target genes by summary-data-based MR, TSMR, and colocalization analysis. However, elevated genetic expression of platelet endothelial aggregation receptor 1 was associated with increased platelet reactivity with an odds ratio of 1.46 (β1=0.375, se=0.072; P = 1.99E-07), and this elevation showed significant inverse association with aSAH risks (β2=-0.125, se=0.030; P = 2.67E-05).

CONCLUSIONS

The platelet reactivity was inversely associated with aSAH risk. However, APDs were not identified as either risk or protective agents for aSAH or uIA. Targeting platelet endothelial aggregation receptor 1 might reduce platelet reactivity and increase aSAH risk, highlighting the need for further research.

Antiviral Potential and In Silico Insights of Polyphenols as Sustainable Phytopharmaceuticals: A Comprehensive Review

Polyphenols, particularly flavonoids, are reported to have health-promoting, disease-preventing abilities and several polyphenols having a wide spectrum of antiviral activities can be explored for preventive and/or therapeutic purposes. We have compiled the updated literature of diverse polyphenols active against common viral diseases, including herpes, hepatitis, influenza, rota and SARS-corona-viruses. The antiviral activity of bioactive polyphenols depends on the hydroxyl and ester groups of polyphenol molecules, as compounds with five or more hydroxyl groups and three specific methoxy groups showed antiviral potential, like anti-rabies activity. This comprehensive review will explore selective polyphenols isolated from common ethnomedicinal or food plants. Comparing bioactivities of structurally related polyphenols and using bioinformatics studies, we have explored the three most promising phyto-antivirals, including chrysin, resveratrol and quercetin, available in many foods and medicinal plants. Quercetin showed a maximum interaction score with human genes. We also explore the intricate structure-activity relationship between these polyphenols and pathogenic viruses with their mechanisms of antiviral action in selected virus models. Here, we report the promising potential of some phyto-polyphenols in the management of viral diseases through an in-depth analysis of the structure and bioactivity of these compounds.

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.