Identify Drug Targets Research Articles

Network medicine informed multiomics integration identifies drug targets and repurposable medicines for Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a devastating, immensely complex neurodegenerative disease by lack of effective treatments. We developed a network medicine methodology via integrating human brain multi-omics data to prioritize drug targets and repurposable treatments for ALS. We leveraged non-coding ALS loci effects from genome-wide associated studies (GWAS) on human brain expression quantitative trait loci (QTL) (eQTL), protein QTL (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL). Using a network-based deep learning framework, we identified 105 putative ALS-associated genes enriched in known ALS pathobiological pathways. Applying network proximity analysis of predicted ALS-associated genes and drug-target networks under the human protein-protein interactome (PPI) model, we identified potential repurposable drugs (i.e., Diazoxide and Gefitinib) for ALS. Subsequent validation established preclinical evidence for top-prioritized drugs. In summary, we presented a network-based multi-omics framework to identify drug targets and repurposable treatments for ALS and other neurodegenerative disease if broadly applied.

Deciphering the genetic underpinnings of neuroticism: A Mendelian randomization study of druggable gene targets

BackgroundNeuroticism, known for its association with a greater risk of psychiatric conditions such as depression and anxiety, is a critical focus of research. MethodsCis-expression quantitative trait loci (eQTLs) from 31,684 whole blood samples provided by the eQTLGen Consortium, alongside data from a large neuroticism cohort, were analyzed to identify genes causally linked to neuroticism. To further explore the influence of gene expression changes on neuroticism, colocalization analysis was conducted. Identified drug targets were assessed for potential side effects using a phenome-wide association study (PheWAS). Additionally, we utilized multiple databases to explore the interactions between drugs and genes for drug prediction and assess the current medications for drug repurposing. ResultsThe analysis involved a total of 4473 druggable genes, with two-sample Mendelian randomization (MR) identifying 186 genes that are causally linked to neuroticism. Colocalization analysis highlighted 11 genes (TLR4, MMRN1, EP300, BRAF, ORM1, ACVR1B, LRRC17, NOS2, ADAMTS6, GPX1, and VCL) with a posterior probability of colocalization (PPH4) >0.8. PheWAS revealed that drugs targeting BRAF, LRRC17, ADAMTS6, and GPX1 were also associated with other traits. Notably, six of these genes (TLR4, MMRN1, BRAF, ACVR1B, NOS2, and GPX1) are already being explored for drug development in psychiatric and other diseases. ConclusionThis study pinpointed six genes as promising therapeutic targets for neuroticism. The repurposing and development of drugs targeting these genes hold potential for managing neuroticism and associated psychiatric disorders.

Proteome-wide Mendelian randomization and functional studies uncover therapeutic targets for polycystic ovarian syndrome.

Polycystic ovarian syndrome (PCOS) is an endocrine syndrome that affects a large portion of women worldwide. This proteogenomic and functional study aimed to uncover candidate therapeutic targets for PCOS. We comprehensively investigated the causal association between circulating proteins and PCOS using two-sample Mendelian randomization analysis. Cis-protein quantitative trait loci were derived from six genome-wide association studies (GWASs) on plasma proteome. Genetic associations with PCOS were obtained from a large-scale GWAS meta-analysis, FinnGen cohort, and UK Biobank. Colocalization analyses were performed to prioritize the causal role of candidate proteins. Protein-protein interaction (PPI) and druggability evaluation assessed the druggability of candidate proteins. We evaluated the enrichment of tier 1 and 2 candidate proteins in individuals with PCOS and a mouse model and explored the potential application of the identified drug target. Genetically predicted levels of 65 proteins exhibited associations with PCOS risk, with 30 proteins showing elevated levels and 35 proteins showing decreased levels linked to higher susceptibility. PPI analyses revealed that FSHB, POSTN, CCN2, and CXCL11 interacted with targets of current PCOS medications. Eighty medications targeting 20 proteins showed their potential for repurposing as therapeutic targets for PCOS. EGLN1 levels were elevated in granulosa cells and the plasma of individuals with PCOS and in the plasma and ovaries of dehydroepiandrosterone (DHEA)-induced PCOS mouse model. As an EGLN1 inhibitor, administration of roxadustat in the PCOS mouse model elucidated the EGLN1-HIF1α-ferroptosis axis in inducing PCOS and validated its therapeutic effect in PCOS. Our study identifies candidate proteins causally associated with PCOS risk and suggests that targeting EGLN1 provides a promising treatment strategy.

Genomic revolution in parasitology: Unlocking the mysteries of parasites

Whole-genome sequencing has revolutionized parasitology, offering insights into parasites' biology, evolution, and potential interventions. This discussion explores the profound impact of genomics on parasitology, from democratizing research to understanding parasites' intricacies and their relationships with hosts. High-throughput sequencing has made genomics accessible globally, allowing labs worldwide to study parasitic organisms. Whole-genome sequencing provides a comprehensive view of parasite genes and regulatory elements, facilitating in-depth biological investigations. It helps identify drug targets and reveals the genetic basis of drug resistance, which is vital for new antiparasitic drug development. Comparative genomics is a powerful tool for understanding the evolutionary relationships among parasites. Researchers create robust phylogenetic trees that clarify parasite evolution by comparing genomic data. Case studies of malaria parasites, trypanosomes, schistosomes, and Leishmania species illustrate the insights gained. Genomics also sheds light on parasite life cycles, uncovering gene expression, regulatory networks, key proteins, and molecular markers. This informs our understanding of host-specific adaptations. Practical applications encompass drug target discovery, vaccine development, diagnostics, intervention strategies, understanding drug resistance, and disease eradication efforts. In conclusion, genomics is transforming parasitology, especially whole-genome sequencing and comparative genomics. It offers a holistic view of parasites and their evolution, paving the way for innovative antiparasitic strategies and contributing to the control and potential eradication of parasitic diseases.

Identifying Hub Genes and Metabolic Pathways in Collagen VI-Related Dystrophies: A Roadmap to Therapeutic Intervention

Collagen VI-related dystrophies (COL6RD) are a group of rare muscle disorders caused by mutations in specific genes responsible for type VI collagen production. It affects muscles, joints, and connective tissues, leading to weakness, joint problems, and structural issues. Currently, there is no effective treatment for COL6RD; its management typically addresses symptoms and complications. Therefore, it is essential to decipher the disease’s molecular mechanisms, identify drug targets, and develop effective treatment strategies to treat COL6RD. In this study, we employed differential gene expression analysis, weighted gene co-expression network analysis, and genome-scale metabolic modeling to investigate gene expression patterns in COL6RD patients, uncovering key genes, significant metabolites, and disease-related pathophysiological pathways. First, we performed differential gene expression and weighted gene co-expression network analyses, which led to the identification of 12 genes (CHCHD10, MRPS24, TRIP10, RNF123, MRPS15, NDUFB4, COX10, FUNDC2, MDH2, RPL3L, NDUFB11, PARVB) as potential hub genes involved in the disease. Second, we utilized a drug repurposing strategy to identify pharmaceutical candidates that could potentially modulate these genes and be effective in the treatment. Next, we utilized context-specific genome-scale metabolic models to compare metabolic variations between healthy individuals and COL6RD patients. Finally, we conducted reporter metabolite analysis to identify reporter metabolites (e.g., phosphatidates, nicotinate ribonucleotide, ubiquinol, ferricytochrome C). In summary, our analysis revealed critical genes and pathways associated with COL6RD and identified potential targets, reporter metabolites, and candidate drugs for therapeutic interventions.

Streamlined analysis of drug targets by proteome integral solubility alteration indicates organ-specific engagement

Proteins are the primary targets of almost all small molecule drugs. However, even the most selectively designed drugs can potentially target several unknown proteins. Identification of potential drug targets can facilitate design of new drugs and repurposing of existing ones. Current state-of-the-art proteomics methodologies enable screening of thousands of proteins against a limited number of drug molecules. Here we report the development of a label-free quantitative proteomics approach that enables proteome-wide screening of small organic molecules in a scalable, reproducible, and rapid manner by streamlining the proteome integral solubility alteration (PISA) assay. We used rat organs ex-vivo to determine organ specific targets of medical drugs and enzyme inhibitors to identify drug targets for common drugs such as Ibuprofen. Finally, global drug profiling revealed overarching trends of how small molecules affect the proteome through either direct or indirect protein interactions.

MGACL: Prediction Drug-Protein Interaction Based on Meta-Graph Association-Aware Contrastive Learning.

The identification of drug-target interaction (DTI) is crucial for drug discovery. However, how to reduce the graph neural network's false positives due to its bias and negative transfer in the original bipartite graph remains to be clarified. Considering that the impact of heterogeneous auxiliary information on DTI varies depending on the drug and target, we established an adaptive enhanced personalized meta-knowledge transfer network named Meta Graph Association-Aware Contrastive Learning (MGACL), which can transfer personalized heterogeneous auxiliary information from different nodes and reduce data bias. Meanwhile, we propose a novel DTI association-aware contrastive learning strategy that aligns high-frequency drug representations with learned auxiliary graph representations to prevent negative transfer. Our study improves the DTI prediction performance by about 3%, evaluated by analyzing the area under the curve (AUC) and area under the precision-recall curve (AUPRC) compared with existing methods, which is more conducive to accurately identifying drug targets for the development of new drugs.

Plasma Proteomics to Identify Drug Targets and Potential Drugs for Retinal Artery Occlusion: An Integrated Analysis in the UK Biobank.

Retinal artery occlusion (RAO), which is positively correlated with acute ischemic stroke (IS) and results in severe visual impairment, lacks effective intervention drugs. This study aims to perform integrated analysis using UK Biobank plasma proteome data of RAO and IS to identify potential targets and preventive drugs. A total of 7191 participants (22 RAO patients, 1457 IS patients, 8 individuals with both RAO and IS, and 5704 healthy age-gender-matched controls) were included in this study. Unique 1461 protein expression profiles of RAO, IS, and the combined data set, extracted from UK Biobank Plasma proteomics projects, were analyzed using both differential expression analysis and elastic network regression (Enet) methods to identify shared key proteins. Subsequent analyses, including single cell type expression assessment, pathway enrichment, and druggability analysis, were conducted for verifying shared key proteins and discovery of new drugs. Five proteins were found to be shared among the samples, with all of them showing upregulation. Notably, adhesion G-protein coupled receptor G1 (ADGRG1) exhibited high expression in glial cells of the brain and eye tissues. Gene set enrichment analysis revealed pathways associated with lipid metabolism and vascular regulation and inflammation. Druggability analysis unveiled 15 drug candidates targeting ADGRG1, which demonstrated protective effects against RAO, especially troglitazone (-8.5 kcal/mol). Our study identified novel risk proteins and therapeutic drugs associated with the rare disease RAO, providing valuable insights into potential intervention strategies.

Discovery of therapeutic targets in cancer using chromatin accessibility and transcriptomic data

Most cancer types lack targeted therapeutic options, and when first-line targeted therapies are available, treatment resistance is a huge challenge. Recent technological advances enable the use of assay for transposase-accessible chromatin with sequencing (ATAC-seq) and RNA sequencing (RNA-seq) on patient tissue in a high-throughput manner. Here, we present a computational approach that leverages these datasets to identify drug targets based on tumor lineage. We constructed gene regulatory networks for 371 patients of 22 cancer types using machine learning approaches trained with three-dimensional genomic data for enhancer-to-promoter contacts. Next, we identified the key transcription factors (TFs) in these networks, which are used to find therapeutic vulnerabilities, by direct targeting of either TFs or the proteins that they interact with. We validated four candidates identified for neuroendocrine, liver, and renal cancers, which have a dismal prognosis with current therapeutic options.

ERT-GFAN: A multimodal drug–target interaction prediction model based on molecular biology and knowledge-enhanced attention mechanism

In drug discovery, precisely identifying drug–target interactions is crucial for finding new drugs and understanding drug mechanisms. Evolving drug/target heterogeneous data presents challenges in obtaining multimodal representation in drug–target prediction(DTI). To deal with this, we propose ‘ERT-GFAN’, a multimodal drug–target interaction prediction model inspired by molecular biology. Firstly, it integrates bio-inspired principles to obtain structure feature of drugs and targets using Extended Connectivity Fingerprints(ECFP). Simultaneously, the knowledge graph embedding model RotatE is employed to discover the interaction feature of drug–target pairs. Subsequently, Transformer is utilized to refine the contextual neighborhood features from the obtained structure feature and interaction features, and multi-modal high-dimensional fusion features of the three-modal information constructed. Finally, the final DTI prediction results are outputted by integrating the multimodal fusion features into a graphical high-dimensional fusion feature attention network (GFAN) using our innovative multimodal high-dimensional fusion feature attention. This multimodal approach offers a comprehensive understanding of drug–target interactions, addressing challenges in complex knowledge graphs. By combining structure feature, interaction feature, and contextual neighborhood features, ‘ERT-GFAN’ excels in predicting DTI. Empirical evaluations on three datasets demonstrate our method’s superior performance, with AUC of 0.9739, 0.9862, and 0.9667, AUPR of 0.9598, 0.9789, and 0.9750, and Mean Reciprocal Rank(MRR) of 0.7386, 0.7035, and 0.7133. Ablation studies show over a 5% improvement in predictive performance compared to baseline unimodal and bimodal models. These results, along with detailed case studies, highlight the efficacy and robustness of our approach.

A novel role of ADAMTS16 in renal fibrosis through activating TGF-β/Smad signaling

Chronic Kidney Disease (CKD) has emerged as a global public health concern, with its primary pathological basis being Renal Fibrosis (RF), crucial to halt its progression to End-Stage Renal Disease (ESRD). However, effective treatment options are currently lacking. Therefore, exploring the mechanisms of RF, identifying drug targets and diagnostic biomarkers are important. In this study, we identified ADAMTS16 as a newly expressed regulatory factor highly expressed in renal fibrosis tissue. ADAMTS16 interacts with latency-associated peptide (LAP)-transforming growth factor (TGF)-β, leading to the activation of TGF-β. Loss of ADAMTS16 expression effectively reduces TGF-β-dependent transcription activity. Furthermore, the use of RRFR tetrapeptide derived from ADAMTS16 can activate the TGF-β/Smad signaling axis, promoting RF. In summary, ADAMTS16 is induced in the progression of CKD, interacting with LAP-TGF-β and potentially activating SMAD2/3. Therefore, targeting ADAMTS16 may serve as a crucial new strategy to alleviate RF and treat CKD patients.

Master Regulators of Causal Networks in Intestinal- and Diffuse-Type Gastric Cancer and the Relation to the RNA Virus Infection Pathway.

Causal networks are important for understanding disease signaling alterations. To reveal the network pathways affected in the epithelial-mesenchymal transition (EMT) and cancer stem cells (CSCs), which are related to the poor prognosis of cancer, the molecular networks and gene expression in diffuse- and intestinal-type gastric cancer (GC) were analyzed. The network pathways in GC were analyzed using Ingenuity Pathway Analysis (IPA). The analysis of the probe sets in which the gene expression had significant differences between diffuse- and intestinal-type GC in RNA sequencing of the publicly available data identified 1099 causal networks in diffuse- and intestinal-type GC. Master regulators of the causal networks included lenvatinib, pyrotinib, histone deacetylase 1 (HDAC1), mir-196, and erb-b2 receptor tyrosine kinase 2 (ERBB2). The analysis of the HDAC1-interacting network identified the involvement of EMT regulation via the growth factors pathway, the coronavirus pathogenesis pathway, and vorinostat. The network had RNA-RNA interactions with microRNAs such as mir-10, mir-15, mir-17, mir-19, mir-21, mir-223, mir-25, mir-27, mir-29, and mir-34. The molecular networks revealed in the study may lead to identifying drug targets for GC.

Fluorescently labelled vedolizumab to visualise drug distribution and mucosal target cells in inflammatory bowel disease

ObjectiveImproving patient selection and development of biological therapies such as vedolizumab in IBD requires a thorough understanding of the mechanism of action and target binding, thereby providing individualised treatment strategies....

Functional genetics reveals the contribution of delta opioid receptor to type 2 diabetes and beta-cell function

Functional genetics has identified drug targets for metabolic disorders. Opioid use impacts metabolic homeostasis, although mechanisms remain elusive. Here, we explore the OPRD1 gene (encoding delta opioid receptor, DOP) to understand its impact on type 2 diabetes. Large-scale sequencing of OPRD1 and in vitro analysis reveal that loss-of-function variants are associated with higher adiposity and lower hyperglycemia risk, whereas gain-of-function variants are associated with lower adiposity and higher type 2 diabetes risk. These findings align with studies of opium addicts. OPRD1 is expressed in human islets and beta cells, with decreased expression under type 2 diabetes conditions. DOP inhibition by an antagonist enhances insulin secretion from human beta cells and islets. RNA-sequencing identifies pathways regulated by DOP antagonism, including nerve growth factor, circadian clock, and nuclear receptor pathways. Our study highlights DOP as a key player between opioids and metabolic homeostasis, suggesting its potential as a therapeutic target for type 2 diabetes.

Bis-benzylisoquinoline alkaloids inhibit African swine fever virus internalization and replication by impairing late endosomal/lysosomal function.

African swine fever (ASF), caused by the African swine fever virus (ASFV), is a highly infectious disease afflicting domestic pigs and wild boars. It exhibits an alarming acute infection fatality rate of up to 100%. Regrettably, no commercial vaccines or specific drugs for combating this disease are currently available. This study evaluated the anti-ASFV activities in porcine alveolar macrophages, 3D4/21 cells, and PK-15 cells of four bis-benzylisoquinoline alkaloids (BBAs): cepharanthine (CEP), tetrandrine, fangchinoline, and iso-tetrandrine. Furthermore, we demonstrated that CEP, which exhibited the highest selectivity index (SI = 81.31), alkalized late endosomes/lysosomes, hindered ASFV endosomal transport, disrupted virus uncoating signals, and thereby inhibited ASFV internalization. Additionally, CEP disrupted ASFV DNA synthesis, leading to the inhibition of viral replication. Moreover, berbamine was labeled with NBD to synthesize a fluorescent probe to study the cellular location of these BBAs. By co-staining with Lyso-Tracker and lysosome-associated membrane protein 1, we demonstrated that BBAs target the endolysosomal compartments for the first time. Our data together indicated that BBAs are a class of natural products with significant inhibitory effects against ASFV infection. These findings suggest their potential efficacy as agents for the prevention and control of ASF, offering valuable references for the identification of potential drug targets.IMPORTANCEThe urgency and severity of African swine fever (ASF) underscore the critical need for effective interventions against this highly infectious disease, which poses a grave threat to domestic pigs and wild boars. Our study reveals the potent anti-African swine fever virus (ASFV) efficacy of bis-benzylisoquinoline alkaloids (BBAs), particularly evident in the absence of progeny virus production under a 5 µM concentration treatment. The structural similarity among cepharanthine, tetrandrine, fangchinoline, and iso-tetrandrine, coupled with their analogous inhibitory stages and comparable selectivity indexes, strongly suggests a shared antiviral mechanism within this drug category. Further investigation revealed that BBAs localize to lysosomes and inhibit the internalization and replication of ASFV by disrupting the endosomal/lysosomal function. These collective results have profound implications for ASF prevention and control, suggesting the potential of the investigated agents as prophylactic and therapeutic measures. Furthermore, our study offers crucial insights into identifying drug targets and laying the groundwork for innovative interventions.

Crystal structure combined with metabolomics and biochemical studies indicates that FAM3A participates in fatty acid beta-oxidation upon binding of acyl-L-carnitine

As the first member of the family with sequence similarity 3 (FAM3), FAM3A promotes synthesis of ATP in mitochondria of hepatic cells and cells from other organs. Dysregulations of FAM3A are involved in the development of diabetes and nonalcoholic fatty liver disease (NAFLD). So far, the molecule mechanism under the physiological and pathological functions of FAM3A is largely unexplored. Here, we determined the crystal structure of FAM3A at high resolution of 1.38Å, complexed with an unknown-source compound which was characterized through metabolomics and confirmed as methacholine by thermal shift assay and surface plasmon resonance (SPR). Exploration for natural ligands of FAM3A was conducted through the same molecular interaction assays. The observed binding of acyl-L-carnitine molecules indicated FAM3A participating in fatty acid beta-oxidation. Knockdown and rescue assays coupled with fatty acid oxidation determination confirmed the role of FAM3A in beta-oxidation. This investigation reveals the molecular mechanism for the biological function of FAM3A and would provide basis for identifying drug target for treatment of diabetes and NAFLD.

Bu-Shen-Ning-Xin decoction ameliorates premature ovarian insufficiency by suppressing oxidative stress through rno_circRNA_012284/rno_miR-760–3p/HBEGF pathway

BackgroundPOI (premature ovarian insufficiency) refers to premature and rapid decline of ovarian reserve function in women before the age of 40, which can be manifested as menstrual disorders, endocrine abnormalities and low fertility. Bu-Shen-Ning-Xin decoction (BSNXD) has been found to have therapeutic effects on POI. Nevertheless, how it exerts therapeutic effects remains elusive. PurposeThis research aims to clarify the pharmacological mechanisms of BSNXD. MethodsWe applied Ultra Performance Liquid Chromatography (UPLC) to identify the main components of BSNXD.4-vinylcyclohexene diepoxide(VCD)was used to induce POI models. ELISA detected the serum level of hormones. H&E staining evaluated the morphology of ovarian tissues.CircRNA and mRNA expression profiles in the ovaries of both POI rats and those treated with BSNXD were detected. Then, dysregulated circRNAs and mRNAs that were potentially altered by BSNXD were screened. Network pharmacology analysis was performed to identify drug targets of BSNXD active ingredients. A circRNA-miRNA-mRNA network and an oxidative stress(OS)-related subnetwork were constructed. Expression of rno_circRNA_012284, rno_miR-760–3p, and HBEGF(Heparin-binding epidermal growth factor-like growth factor) was measured by RT-PCR and their binding were verified by dual-luciferase reporter assays. ROS was measured through DCFH-DA fluorescence probes. The HBEGF target was selected for molecular docking with key active ingredients.Surface plasmon resonance(SPR) was applied to verify the binding ability and affinity between components and HBEGF. ResultsUPLC analysis indicated that 6 chemical compounds including berberine, paeoniflorin, morroniside,gallic acid, loganin, baicalin were identified.Elevated FSH and LH levels, suppressed E2 and AMH levels in the serum, and inhibited follicles and corpus luteums in the ovarian tissues of VCD-induced rats were notably reversed by BSNXD.In total, 992 up- and 1135 down-regulated circRNAs, and 205 up- and 243 down-regulated mRNAs were found in POI rat ovaries following BSNXD administration. Furthermore, 198 drug targets of BSNXD were identified. An OS-related and BSNXD-targeted ceRNA subnetwork composed of rno_circRNA_012284/rno_miR-760–3p/HBEGF was established. rno_circRNA_012284 and HBEGF were up-regulated and rno_miR-760–3p was down-regulated in POI ovarian granulosa cells (OGCs) after BSNXD administration. rno_circRNA_012284 was a sponge of rno_miR-760–3p to elevate HBEGF expression. Moreover, rno_circRNA_012284 overexpression alleviated POI-induced excessive ROS generation in ovarian granulosa cells, while rno_circRNA_012284 inhibition exerted the opposite effect. Finally,molecular docking speculated active ingredients of each herb acted on HBEGF to reduce the OS. SPR tests showed that Berberine,Baicalein,Quercetin,Pachymic acid,Paeoniflorin exhibited satisfying affinity with HBEGF protein. ConclusionThis study demonstrates that BSNXD ameliorates POI partly by attenuating OS in ovarian granulosa cells via rno_circRNA_012284/rno_miR-760–3p/HBEGF axis, uncovering the pharmacological mechanisms of BSNXD in alleviating POI.

Pantothenate kinase: A promising therapeutic target against pathogenic Clostridium species

Current treatment of clostridial infections includes broad-spectrum antibiotics and antitoxins, yet antitoxins are ineffective against all Clostridium species. Moreover, rising antimicrobial resistance (AMR) threatens treatment effectiveness and public health. This study therefore aimed to discover a common drug target for four pathogenic clostridial species, Clostridium botulinum, C. difficile, C. tetani, and C. perfringens through an in-silico core genomic approach. Using four reference genomes of C. botulinum, C. difficile, C. tetani, and C. perfringens, we identified 1484 core genomic proteins (371/genome) and screened them for potential drug targets. Through a subtractive approach, four core proteins were finally identified as drug targets, represented by type III pantothenate kinase (CoaX) and, selected for further analyses. Interestingly, the CoaX is involved in the phosphorylation of pantothenate (vitamin B5), which is a critical precursor for coenzyme A (CoA) biosynthesis. Investigation of druggability analysis on the identified drug target reinforces CoaX as a promising novel drug target for the selected Clostridium species. During the molecular screening of 1201 compounds, a known agonist drug compound (Vibegron) showed strong inhibitory activity against targeted clostridial CoaX. Additionally, we identified tazobactam, a beta-lactamase inhibitor, as effective against the newly proposed target, CoaX. Therefore, identifying CoaX as a single drug target effective against all four clostridial pathogens presents a valuable opportunity to develop a cost-effective treatment for multispecies clostridial infections.

Unraveling genetic threads: Identifying novel therapeutic targets for allergic rhinitis through Mendelian randomization

BackgroundAllergic rhinitis (AR) is a pervasive global health issue, and currently, there is a scarcity of targeted drug therapies available. This study aims to identify potential druggable target genes for AR using Mendelian randomization (MR) analysis. MethodsMR analysis was conducted to assess the causal effect of expression quantitative trait loci (eQTL) in the blood on AR. Data on AR were collected from 2 datasets: FinnGen(R9) (11,009 cases and 359,149 controls) and UK Biobank (25,486 cases and 87,097 controls). Colocalization analysis was utilized to assess the common causal genetic variations between the identified drug target genes and AR. We also employed available genome-wide association studies (GWAS) data to gauge the impact of druggable genes on AR biomarkers and other allergic diseases. ResultsThis study employs MR to analyze the relationship between 3410 druggable genes and AR. After Bonferroni correction, 10 genes were found to be significantly associated with AR risk (P < 0.05/3410). Colocalization analysis revealed a significant causal relationship between the expression variation of CFL1 and EFEMP2 genes and AR, sharing direct causal variants (colocalization probability PP.H3 + PP.H4 > 0.8), highlighting their importance as potential therapeutic targets for AR. The CFL1 gene showed a causal link with levels of thymic stromal lymphopoietin (TSLP), eosinophil count, and interleukin-13 (IL-13) (P = 0.016, 7.45E-16, 0.00091, respectively). EFEMP2 was also causally related to eosinophil count, IL-13, and interleukin-17 (IL-17) (P = 0.00012, 0.00091, 0.032, respectively). PheWAS analysis revealed significant associations of CFL1 with asthma, whereas EFEMP2 showed associations with both asthma and eczema. Protein-Protein Interaction (PPI) network analysis further unveiled the direct interactions of EFEMP2 and CFL1 with proteins related to immune regulation and inflammatory responses, with 77.64% of the network consisting of direct bindings, indicating their key roles in modulating AR-related immune and inflammatory responses. Notably, there was an 8.01% significant correlation between immune-related pathways and genes involved in inflammatory responses. ConclusionThese genes present notable associations with AR biomarkers and other autoimmune diseases, offering valuable targets for developing new AR therapies.

MvG-NRLMF: Multi-view graph neighborhood regularized logistic matrix factorization for identifying drug–target interaction

Traditional methods for predicting drug–target interactions (DTIs) have significant room for improvement in terms of time period and monetary overhead. At present, machine learning-based approaches are commonly used in the drug discovery field. In this study, a multi-view graph neighborhood regularized logical matrix factorization (MvG-NRLMF) model was proposed to predict unknown DTIs. Multiple similarity matrices (kernels) were constructed from the space of drugs and targets, the corresponding Laplacian matrices were generated, and these were fused. Finally, the MvG-NRLMF model was adjusted using an alternating gradient ascent procedure for training. On the four benchmark datasets, our method was competitive, and on some datasets, our method even outperformed existing models.