Brief Bioinform. 2024 Mar 27;25(3):bbae238. doi: 10.1093/bib/bbae238.

ABSTRACT

Identifying drug-target interactions (DTIs) holds significant importance in drug discovery and development, playing a crucial role in various areas such as virtual screening, drug repurposing and identification of potential drug side effects. However, existing methods commonly exploit only a single type of feature from drugs and targets, suffering from miscellaneous challenges such as high sparsity and cold-start problems. We propose a novel framework called MSI-DTI (Multi-Source Information-based Drug-Target Interaction Prediction) to enhance prediction performance, which obtains feature representations from different views by integrating biometric features and knowledge graph representations from multi-source information. Our approach involves constructing a Drug-Target Knowledge Graph (DTKG), obtaining multiple feature representations from diverse information sources for SMILES sequences and amino acid sequences, incorporating network features from DTKG and performing an effective multi-source information fusion. Subsequently, we employ a multi-head self-attention mechanism coupled with residual connections to capture higher-order interaction information between sparse features while preserving lower-order information. Experimental results on DTKG and two benchmark datasets demonstrate that our MSI-DTI outperforms several state-of-the-art DTIs prediction methods, yielding more accurate and robust predictions. The source codes and datasets are publicly accessible at https://github.com/KEAML-JLU/MSI-DTI.

PMID:38762789 | DOI:10.1093/bib/bbae238

Transl Psychiatry. 2024 May 18;14(1):204. doi: 10.1038/s41398-024-02915-x.

ABSTRACT

Decline in cognitive function is the most feared aspect of ageing. Poorer midlife cognitive function is associated with increased dementia and stroke risk. The mechanisms underlying variation in cognitive function are uncertain. Here, we assessed associations between 1160 proteins' plasma levels and two measures of cognitive function, the digit symbol substitution test (DSST) and the Montreal Cognitive Assessment in 1198 PURE-MIND participants. We identified five DSST performance-associated proteins (NCAN, BCAN, CA14, MOG, CDCP1), with NCAN and CDCP1 showing replicated association in an independent cohort, GS (N = 1053). MRI-assessed structural brain phenotypes partially mediated (8-19%) associations between NCAN, BCAN, and MOG, and DSST performance. Mendelian randomisation analyses suggested higher CA14 levels might cause larger hippocampal volume and increased stroke risk, whilst higher CDCP1 levels might increase intracranial aneurysm risk. Our findings highlight candidates for further study and the potential for drug repurposing to reduce the risk of stroke and cognitive decline.

PMID:38762535 | DOI:10.1038/s41398-024-02915-x

Int J Biol Macromol. 2024 May 16:132468. doi: 10.1016/j.ijbiomac.2024.132468. Online ahead of print.

ABSTRACT

The current outbreak of mpox presents a significant threat to the global community. However, the lack of mpox-specific drugs necessitates the identification of additional candidates for clinical trials. In this study, a network medicine framework was used to investigate poxviruses-human interactions to identify potential drugs effective against the mpox virus (MPXV). The results indicated that poxviruses preferentially target hubs on the human interactome, and that these virally-targeted proteins (VTPs) tend to aggregate together within specific modules. Comorbidity analysis revealed that mpox is closely related to immune system diseases. Based on predicted drug-target interactions, 268 drugs were identified using the network proximity approach, among which 23 drugs displaying the least side-effects and significant proximity to MPXV were selected as the final candidates. Lastly, specific drugs were explored based on VTPs, differentially expressed proteins, and intermediate nodes, corresponding to different categories. These findings provide novel insights that can contribute to a deeper understanding of the pathogenesis of MPXV and development of ready-to-use treatment strategies based on drug repurposing.

PMID:38761900 | DOI:10.1016/j.ijbiomac.2024.132468

Technol Health Care. 2024 Apr 25. doi: 10.3233/THC-248005. Online ahead of print.

ABSTRACT

BACKGROUND: Drug repositioning (DR) refers to a method used to find new targets for existing drugs. This method can effectively reduce the development cost of drugs, save time on drug development, and reduce the risks of drug design. The traditional experimental methods related to DR are time-consuming, expensive, and have a high failure rate. Several computational methods have been developed with the increase in data volume and computing power. In the last decade, matrix factorization (MF) methods have been widely used in DR issues. However, these methods still have some challenges. (1) The model easily falls into a bad local optimal solution due to the high noise and high missing rate in the data. (2) Single similarity information makes the learning power of the model insufficient in terms of identifying the potential associations accurately.

OBJECTIVE: We proposed self-paced learning with dual similarity information and MF (SPLDMF), which introduced the self-paced learning method and more information related to drugs and targets into the model to improve prediction performance.

METHODS: Combining self-paced learning first can effectively alleviate the model prone to fall into a bad local optimal solution because of the high noise and high data missing rate. Then, we incorporated more data into the model to improve the model's capacity for learning.

RESULTS: Our model achieved the best results on each dataset tested. For example, the area under the receiver operating characteristic curve and the precision-recall curve of SPLDMF was 0.982 and 0.815, respectively, outperforming the state-of-the-art methods.

CONCLUSION: The experimental results on five benchmark datasets and two extended datasets demonstrated the effectiveness of our approach in predicting drug-target interactions.

PMID:38759038 | DOI:10.3233/THC-248005

Eur Respir J. 2024 May 16;63(5):2400678. doi: 10.1183/13993003.00678-2024. Print 2024 May.

NO ABSTRACT

PMID:38754951 | DOI:10.1183/13993003.00678-2024

Methods. 2024 May 14:S1046-2023(24)00129-4. doi: 10.1016/j.ymeth.2024.05.011. Online ahead of print.

ABSTRACT

Pathogenic bacteria represent a formidable threat to human health, necessitating substantial resources for prevention and treatment. With the escalating concern regarding antibiotic resistance, there is a pressing need for innovative approaches to combat these pathogens. Repurposing existing drugs offers a promising solution. Our present work hypothesizes that proteins harboring ligand-binding pockets with similar chemical environments may be able to bind the same drug. To facilitate this drug-repurposing strategy against pathogenic bacteria, we introduce an online server, PharmaRedefine. Leveraging a combination of sequence and structure alignment and protein pocket similarity analysis, this platform enables the prediction of potential targets in representative bacteria for specific FDA-approved drugs. This novel approach holds tremendous potential for drug repositioning that effectively combat infections caused by pathogenic bacteria. PharmaRedefine is freely available at http://guolab.mpu.edu.mo/pharmredefine.

PMID:38754711 | DOI:10.1016/j.ymeth.2024.05.011

Brief Bioinform. 2024 Mar 27;25(3):bbae231. doi: 10.1093/bib/bbae231.

ABSTRACT

Drug repurposing offers a viable strategy for discovering new drugs and therapeutic targets through the analysis of drug-gene interactions. However, traditional experimental methods are plagued by their costliness and inefficiency. Despite graph convolutional network (GCN)-based models' state-of-the-art performance in prediction, their reliance on supervised learning makes them vulnerable to data sparsity, a common challenge in drug discovery, further complicating model development. In this study, we propose SGCLDGA, a novel computational model leveraging graph neural networks and contrastive learning to predict unknown drug-gene associations. SGCLDGA employs GCNs to extract vector representations of drugs and genes from the original bipartite graph. Subsequently, singular value decomposition (SVD) is employed to enhance the graph and generate multiple views. The model performs contrastive learning across these views, optimizing vector representations through a contrastive loss function to better distinguish positive and negative samples. The final step involves utilizing inner product calculations to determine association scores between drugs and genes. Experimental results on the DGIdb4.0 dataset demonstrate SGCLDGA's superior performance compared with six state-of-the-art methods. Ablation studies and case analyses validate the significance of contrastive learning and SVD, highlighting SGCLDGA's potential in discovering new drug-gene associations. The code and dataset for SGCLDGA are freely available at https://github.com/one-melon/SGCLDGA.

PMID:38754409 | DOI:10.1093/bib/bbae231

Eur Urol Oncol. 2024 May 15:S2588-9311(24)00115-9. doi: 10.1016/j.euo.2024.05.001. Online ahead of print.

ABSTRACT

Current standard-of-care systemic therapy options for locally advanced and metastatic bladder cancer (BC), which are predominantly based on cisplatin-gemcitabine combinations, are limited by significant treatment failure rates and frailty-based patient ineligibility. We previously addressed the urgent clinical need for better-tolerated BC therapeutic strategies using a drug screening approach, which identified outstanding antineoplastic activity of clofarabine in preclinical models of BC. To further assess clofarabine as a potential BC therapy component, we conducted head-to-head comparisons of responses to clofarabine versus gemcitabine in preclinical in vitro and in vivo models of BC, complemented by in silico analyses. In vitro data suggest a distinct correlation between the two antimetabolites, with higher cytotoxicity of gemcitabine, especially against several nonmalignant cell types, including keratinocytes and endothelial cells. Accordingly, tolerance of clofarabine (oral or intraperitoneal application) was distinctly better than for gemcitabine (intraperitoneal) in patient-derived xenograft models of BC. Clofarabine also exhibited distinctly superior anticancer efficacy, even at dosing regimens optimized for gemcitabine. Neither complete remission nor cure, both of which were observed with clofarabine, were achieved with any tolerable gemcitabine regimen. Taken together, our findings demonstrate that clofarabine has a better therapeutic window than gemcitabine, further emphasizing its potential as a candidate for drug repurposing in BC. PATIENT SUMMARY: We compared the anticancer activity of clofarabine, a drug used for treatment of leukemia but not bladder cancer, and gemcitabine, a drug currently used for chemotherapy against bladder cancer. Using cell cultures and mouse models, we found that clofarabine was better tolerated and more efficacious than gemcitabine, and even cured implanted tumors in mouse models. Our results suggest that clofarabine, alone or in combination schemes, might be superior to gemcitabine for the treatment of bladder cancer.

PMID:38755094 | DOI:10.1016/j.euo.2024.05.001

J Mol Model. 2024 May 16;30(6):170. doi: 10.1007/s00894-024-05967-4.

ABSTRACT

CONTEXT: In the pursuit of novel therapeutic possibilities, repurposing existing drugs has gained prominence as an efficient strategy. The findings from our study highlight the potential of repurposed drugs as promising candidates against receptor for advanced glycation endproducts (RAGE) that offer therapeutic implications in cancer, neurodegenerative conditions and metabolic syndromes. Through careful analyses of binding affinities and interaction patterns, we identified a few promising candidates, ultimately focusing on sertindole and temoporfin. These candidates exhibited exceptional binding affinities, efficacy, and specificity within the RAGE binding pocket. Notably, they displayed a pronounced propensity to interact with the active site of RAGE. Our investigation further revealed that sertindole and temoporfin possess desirable pharmacological properties that highlighted them as attractive candidates for targeted drug development. Overall, our integrated computational approach provides a comprehensive understanding of the interactions between repurposed drugs, sertindole and temoporfin and RAGE that pave the way for future experimental validation and drug development endeavors.

METHODS: We present an integrated approach utilizing molecular docking and extensive molecular dynamics (MD) simulations to evaluate the potential of FDA-approved drugs, sourced from DrugBank, against RAGE. To gain deeper insights into the binding mechanisms of the elucidated candidate repurposed drugs, sertindole and temoporfin with RAGE, we conducted extensive all-atom MD simulations, spanning 500 nanoseconds (ns). These simulations elucidated the conformational dynamics and stability of the RAGE-sertindole and RAGE-temoporfin complexes.

PMID:38753123 | DOI:10.1007/s00894-024-05967-4

Drug Target Insights. 2024 May 10;18:8-19. doi: 10.33393/dti.2024.3019. eCollection 2024 Jan-Dec.

ABSTRACT

Drug repurposing is a strategic endeavor that entails the identification of novel therapeutic applications for pharmaceuticals that are already available in the market. Despite the advantageous nature of implementing this particular strategy owing to its cost-effectiveness and efficiency in reducing the time required for the drug discovery process, it is essential to bear in mind that there are various factors that must be meticulously considered and taken into account. Up to this point, there has been a noticeable absence of comprehensive analyses that shed light on the limitations of repurposing drugs. The primary aim of this review is to conduct a thorough illustration of the various challenges that arise when contemplating drug repurposing from a clinical perspective in three major fields-cardiovascular, cancer, and diabetes-and to further underscore the potential risks associated with the emergence of antimicrobial resistance (AMR) when employing repurposed antibiotics for the treatment of noninfectious and infectious diseases. The process of developing repurposed medications necessitates the application of creativity and innovation in designing the development program, as the body of evidence may differ for each specific case. In order to effectively repurpose drugs, it is crucial to consider the clinical implications and potential drawbacks that may arise during this process. By comprehensively analyzing these challenges, we can attain a deeper comprehension of the intricacies involved in drug repurposing, which will ultimately lead to the development of more efficacious and safe therapeutic approaches.

PMID:38751378 | PMC:PMC11094707 | DOI:10.33393/dti.2024.3019

Naunyn Schmiedebergs Arch Pharmacol. 2024 May 15. doi: 10.1007/s00210-024-03151-2. Online ahead of print.

ABSTRACT

Management of cancer is challenging due to non-targeting and high side effect issues. Drug repurposing is an innovative method for employing medications for other disease therapy in addition to their original use. Simvastatin, a 3-hydroxy-3-methylglutaryl coenzyme-A reductase inhibitor, is a lipid-lowering drug that is being studied for the treatment of cancer in various in vitro and in vivo models. Nanotechnology offers a potential platform for incorporation of drugs with enhanced pharmaceutical (solubility, release characteristics, stability, etc.) and biological characteristics (targeting, pharmacokinetic, pharmacodynamic). Utilizing a variety of resources such as Scopus, Springer, Web of Science, Elsevier, Bentham Science, Taylor & Francis, and PubMed, a thorough literature search was carried out by looking through electronic records published between 2003 and 2024. The keywords used were simvastatin, drug repurposing, anti-cancer simvastatin, pharmaceutical properties of simvastatin, simvastatin nanoformulations, simvastatin patents, clinical trials, etc. Numerous articles were looked for, filtered, checked out, and incorporated. Pure simvastatin has been researched as a repurposed medication for the treatment of cancer in several in vitro and in vivo models, such as carcinoma of the lung, colon, liver, prostate, breast, and skin. Simvastatin also incorporated into different nanocarriers (nanosuspensions, microparticles/nanoparticles, liposomes, and nanostructured lipid carriers) and showed improvement in solubility, bioavailability, drug loading, release kinetics, and targeting. Clinical trial and patent reports suggest potential of simvastatin in cancer therapy. The preclinical studies of pure simvastatin in in vitro and in vivo models showed the potential for its ability to inhibit cancer cell growth and further incorporation into nanoformulations strengthened its preclinical and pharmaceutical characteristics.

PMID:38748226 | DOI:10.1007/s00210-024-03151-2

Mol Biotechnol. 2024 May 15. doi: 10.1007/s12033-024-01186-7. Online ahead of print.

ABSTRACT

Langerhans cell histiocytosis (LCH) is a rare condition predominantly affecting young children. Activation of the MAPK pathway has offered key new insights into the pathogenesis of LCH; however, the precise mechanisms underlying its occurrence and development are still far from being completely elucidated. There is still a relapse/reactivation rate in patients with multisystem LCH. Therefore, this study aimed to investigate other potential LCH pathophysiologies and prospective therapeutic targets. The gene expression omnibus (GEO) database was used to retrieve gene expression profiles of LCH (GSE16395). Three distinct types of analyses were performed after identifying the common differentially expressed genes (DEGs) in LCH: hub gene identification, functional annotation, module construction, drug repositioning, and expression analysis via immunohistochemistry (IHC). We identified 417 common DEGs and 50 central hub genes. This functional study highlighted the significance of keratinization, skin development, and inflammation. In addition, we predicted new drug candidates (RS2 drugs targeting matrix metalloprotease1, MMP1) that could be used for LCH treatment. Finally, gene-miRNA and gene-TF networks and immune cell infiltration were analyzed for MMP1-related genes. MMP1 expression levels in LCH tissues were validated by IHC. Our study identified the central communal genes and novel drug candidates. These shared pathways and hub genes offer new perspectives on future mechanisms of action and therapeutic targets.

PMID:38748071 | DOI:10.1007/s12033-024-01186-7

Naunyn Schmiedebergs Arch Pharmacol. 2024 May 14. doi: 10.1007/s00210-024-03137-0. Online ahead of print.

ABSTRACT

The emergence of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak, which causes COVID-19, had a devastating impact on both people's lives and the global economy. During the course of the pandemic, the lack of specific drugs or treatments tailored for COVID-19 led to extensive repurposing of existing drugs in the pursuit of effective treatments. Some drug molecules demonstrated efficacy, while others proved ineffective. In this context, the approach of drug repurposing emerged as a novel strategy for combating COVID-19. Repurposed drugs and biologics have shown effectiveness, leading to improved clinical outcomes among patients with COVID-19. Similarly, It is equally important to assess the risk-benefit ratio associated with drugs and biologics adapted for COVID-19 treatment. Herein, we primarily focus on evaluating adverse drug events linked to repurposed COVID-19 medications, repurposed biologics, and COVID-specific drug molecules.

PMID:38743117 | DOI:10.1007/s00210-024-03137-0

Arthritis Res Ther. 2024 May 13;26(1):99. doi: 10.1186/s13075-024-03327-4.

ABSTRACT

OBJECTIVES: This study aims to elucidate the transcriptomic signatures and dysregulated pathways in patients with Systemic Lupus Erythematosus (SLE), with a particular focus on those persisting during disease remission.

METHODS: We conducted bulk RNA-sequencing of peripheral blood mononuclear cells (PBMCs) from a well-defined cohort comprising 26 remission patients meeting the Low Lupus Disease Activity State (LLDAS) criteria, 76 patients experiencing disease flares, and 15 healthy controls. To elucidate immune signature changes associated with varying disease states, we performed extensive analyses, including the identification of differentially expressed genes and pathways, as well as the construction of protein-protein interaction networks.

RESULTS: Several transcriptomic features recovered during remission compared to the active disease state, including down-regulation of plasma and cell cycle signatures, as well as up-regulation of lymphocytes. However, specific innate immune response signatures, such as the interferon (IFN) signature, and gene modules involved in chromatin structure modification, persisted across different disease states. Drug repurposing analysis revealed certain drug classes that can target these persistent signatures, potentially preventing disease relapse.

CONCLUSION: Our comprehensive transcriptomic study revealed gene expression signatures for SLE in both active and remission states. The discovery of gene expression modules persisting in the remission stage may shed light on the underlying mechanisms of vulnerability to relapse in these patients, providing valuable insights for their treatment.

PMID:38741185 | DOI:10.1186/s13075-024-03327-4

BMC Med Inform Decis Mak. 2024 May 13;24(1):122. doi: 10.1186/s12911-024-02531-1.

ABSTRACT

MOTIVATION: Drug repurposing speeds up the development of new treatments, being less costly, risky, and time consuming than de novo drug discovery. There are numerous biological elements that contribute to the development of diseases and, as a result, to the repurposing of drugs.

METHODS: In this article, we analysed the potential role of protein sequences in drug repurposing scenarios. For this purpose, we embedded the protein sequences by performing four state of the art methods and validated their capacity to encapsulate essential biological information through visualization. Then, we compared the differences in sequence distance between protein-drug target pairs of drug repurposing and non - drug repurposing data. Thus, we were able to uncover patterns that define protein sequences in repurposing cases.

RESULTS: We found statistically significant sequence distance differences between protein pairs in the repurposing data and the rest of protein pairs in non-repurposing data. In this manner, we verified the potential of using numerical representations of sequences to generate repurposing hypotheses in the future.

PMID:38741115 | DOI:10.1186/s12911-024-02531-1

Mol Divers. 2024 May 13. doi: 10.1007/s11030-024-10880-2. Online ahead of print.

ABSTRACT

There are many genes that produce proteins related to diseases and these proteins can be targeted with drugs as a potential therapeutic approach. Recent advancement in drug discovery techniques have created new opportunities for treating variety of diseases by targeting disease-related proteins. Structure-based drug discovery is a faster and more cost-effective approach than traditional methods. SHP2 phosphatase, encoded by the PTPN11 gene, has been the focus of much attention due to its involvement in many types of diseases. The biological function of SHP2 is enabled mostly by protein-protein interaction through its SH2 domains. In this study, we report the identification of a potential small molecule inhibitor for the N-SH2 domain of SHP2 by structure-based drug discovery approach. We utilized molecular docking studies, followed by molecular dynamics simulations and MM/PBSA calculations, to analyze compounds retrieved from the Broad's Drug Repurposing Hub and ZINC15 databases. We selected 10 hit compounds with the best docking scores from the libraries and examined their binding properties in the N-SH2 domain. We found that compound CID 60838 (Irinotecan) was the most suitable compound with a binding free energy value of - 64.45 kcal/mol and significant interactions with the target residues in the domain.

PMID:38739228 | DOI:10.1007/s11030-024-10880-2

Bioinform Adv. 2024 Mar 9;4(1):vbae038. doi: 10.1093/bioadv/vbae038. eCollection 2024.

ABSTRACT

MOTIVATION: Medications can have unexpected effects on disease, including not only harmful drug side effects, but also beneficial drug repurposing. These effects on disease may result from hidden influences of drugs on disease gene networks. Then, discovering how biological effects of drugs relate to disease biology can both provide insight into the mechanism of latent drug effects, and can help predict new effects.

RESULTS: Here, we develop Draphnet, a model that integrates molecular data on 429 drugs and gene associations of nearly 200 common phenotypes to learn a network that explains drug effects on disease in terms of these molecular signals. We present evidence that our method can both predict drug effects, and can provide insight into the biology of unexpected drug effects on disease. Using Draphnet to map a drug's known molecular effects to downstream effects on the disease genome, we put forward disease genes impacted by drugs, and we suggest a new grouping of drugs based on shared effects on the disease genome. Our approach has multiple applications, including predicting drug uses and learning drug biology, with implications for personalized medicine.

AVAILABILITY AND IMPLEMENTATION: Code to reproduce the analysis is available at https://github.com/RDMelamed/drug-phenome.

PMID:38736684 | PMC:PMC11087821 | DOI:10.1093/bioadv/vbae038

Chembiochem. 2024 May 12:e202300816. doi: 10.1002/cbic.202300816. Online ahead of print.

ABSTRACT

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

PMID:38735845 | DOI:10.1002/cbic.202300816

Chem Pharm Bull (Tokyo). 2024 May 11. doi: 10.1248/cpb.c23-00718. Online ahead of print.

ABSTRACT

Using (S)-decursinol isolated from root of AGN, we semi-synthesized and evaluated a series of both enantiomerically pure decursin derivatives for their antiproliferative activities against A549 human lung cancer cells. All synthesized compounds showed a broad spectrum of inhibitory activities against the growth of A549 cells. Especially, compound (S)-2d with (E)-(furan-3-yl)acryloyl group showed the most potent activity (IC50: 14.03 µM) against A549 cancer cells as compared with the reference compound, decursin (IC50: 43.55 µM) and its enantiomer, (R)-2d (IC50: 151.59 µM). Western blotting assays indicated that (S)-2d more strongly inhibited JAK1 and STAT3 phosphorylation than decursin in a dose-dependent manner, while having no effect on CXCR7 overexpression and total STAT3 level. In addition, (S)-2d induced cell cycle arrest at G1 phase and subsequent apoptotic cell death in A549 cancer cells. Our combined analysis of molecular docking studies and biological data suggests that the inhibition of JAK1 with (S)-2d resulted in loss of STAT3 phosphorylation and inhibition of cell growth in A549 cancer cells. These overall results strongly suggest that (S)-2d (MRC-D-004) as a novel JAK1 inhibitor may have therapeutic potential in the treatment of A549 human lung cancers by targeting the JAK1/STAT3 signaling pathway.

PMID:38735699 | DOI:10.1248/cpb.c23-00718

Trends Genet. 2024 May 10:S0168-9525(24)00095-7. doi: 10.1016/j.tig.2024.04.008. Online ahead of print.

ABSTRACT

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

PMID:38734482 | DOI:10.1016/j.tig.2024.04.008