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Leveraging the genetics of psychiatric disorders to prioritize potential drug targets and compounds.

Genetics can inform biologically relevant drug development and repurposing, which may improve patient care. Here, we leverage the genetics of psychiatric disorders to prioritize potential drug targets and compounds. We used the genome-wide association studies of four psychiatric disorders [attention deficit hyperactivity disorder (ADHD), bipolar disorder, depression, and schizophrenia] and genes encoding drug targets. We conducted drug enrichment analyses incorporating the novel and biologically specific GSA-MiXeR tool. We conducted multiple molecular trait analyses using large-scale transcriptomic and proteomic datasets sampled from brain and blood tissue. This included the novel use of the UK Biobank proteomic data for a proteome-wide association study of psychiatric disorders. With the accumulated evidence, we prioritize potential drug targets and compounds for each disorder. We reveal candidate drug targets associated with a single or multiple disorders that implicate glutamate signaling. Drug prioritization indicated genetic support for psychotropic medications, including several top-ranked antipsychotics for schizophrenia. We also observed genetic support for commonly used psychotropics for psychiatric treatment (e.g., clozapine, duloxetine, and lithium). Revealed opportunities for drug repurposing included cholinergic drugs for ADHD, estrogen modulators for depression, and matrix metalloproteinases for ADHD and depression. Our findings indicate the genetic liability to schizophrenia is associated with reduced brain and blood expression of CYP2D6, a gene encoding a metabolizer of drugs and neurotransmitters, suggesting a genetic risk for poor drug response and altered neurotransmission. Our extensive analyses highlight the utility of genetics for informing drug development and repurposing for psychiatric disorders, providing novel opportunities for improving patient outcomes. Depicted is the series of analyses conducted to generate a list of prioritized drug targets and compounds. First pairings of genome-wide association study (GWAS) traits with drugs are generated using enrichment analyses. Next, a series of molecular trait analyses is conducted to generate and rank a list of potential drug targets for each GWAS trait. Finally, enrichment and molecular trait results are combined to generate a ranked list of prioritized drugs for each GWAS trait based on supporting genetic evidence. ADHD = Attention deficit hyperactivity disorder, BIP = Bipolar disorder, DEP = Depression, SCZ = Schizophrenia, DBP = Diastolic blood pressure, T2D = Type 2 diabetes, RNA = ribonucleic acid, XWAS = both transcriptome and proteome-wide association studies, MR = Mendelian randomization, coloc = colocalization.

Humans

Drug target ontology to classify and integrate drug discovery data.

BACKGROUND: One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. RESULTS: As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. CONCLUSIONS: DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

Biological Ontologies

Integrated immunoinformatics for the design of novel multi-epitope vaccine and identification of new drug targets against Stenotrophomonas maltophilia, a multidrug-resistant superbug.

BACKGROUND: Stenotrophomonas maltophilia is a multidrug-resistant opportunistic pathogen causing severe hospital-acquired infections, especially in immunocompromised patients. The absence of an effective vaccine and rising antibiotic resistance underscore the need for novel interventions. This study employed an integrated reverse vaccinology and computational analyses to identify new immunogenic targets, design a multi-epitope vaccine (MEV), and propose potential drug targets. METHODS: A comprehensive immunoinformatics pipeline was employed to assess antigenicity, allergenicity, human similarity, and physicochemical properties of S. maltophilia proteins. Both B- and T-cell epitopes were screened; however, only the top B-cell epitopes were selected for MEV construction, given the extracellular nature of S. maltophilia. MEV-TLR interactions were analyzed through molecular docking and dynamics simulations. In parallel, cytoplasmic proteins were screened via a subtractive genomics approach to identify essential, non-human homologous, and non-microbiome-similar proteins, which were further evaluated for druggability and interaction networks to propose novel therapeutic targets. RESULTS: From a total of 4111 proteins, seven potential immunogenic targets were identified: GspD (WP_108270537.1), FhuE (WP_049451370.1), fimbrial protein (WP_012479122.1), TonB-dependent receptor (WP_169448402.1), TolC family protein (WP_108270106.1), autotransporter beta-barrel OMP (WP_169448945.1), and a hypothetical protein (WP_005407892.1). Subsequently, an MEV was designed using five immunogenic epitopes derived from four of these targets: WP_005407892.1 (ADQDSSNM), WP_049451370.1 (SGKAEQ and GEESKTPS), WP_108270537.1 (GVTSTQSDSERT), and WP_169448945.1 (RELGGDRNE). Molecular docking and molecular dynamics simulations demonstrated strong, stable, and feasible interactions between the MEV and TLR-2 and TLR-4 receptors. Moreover, nine novel drug targets were predicted for S. maltophilia, providing new therapeutic insights. CONCLUSION: The designed MEV and identified immunogenic targets represent promising vaccine candidates against S. maltophilia. Further in vitro and in vivo studies are essential to confirm their safety, immunogenicity, and protective efficacy. Additionally, subtractive genomics analysis revealed nine novel, non-homologous drug targets, offering safer and more specific therapeutic avenues.

Drug targets

Association of genetically proxied cancer-targeted drugs with cardiovascular diseases through Mendelian randomization analysis.

BACKGROUND: Cancer-targeted therapies are progressively pivotal in oncological care. Observational studies underscore the emergence of cancer therapy-related cardiovascular toxicity (CTR-CVT), impacting patient outcomes. We aimed to investigate the causal relationship between different types of cancer-targeted therapies and cardiovascular disease (CVD) outcomes through a two-sample Mendelian randomization (MR) study. METHODS: This genome-wide association study was conducted using a two-sample Mendelian randomization framework. Genetic instruments for drug target gene expression were extracted from the eQTLGen consortium (31684 individuals, 37 cohorts). Genome-wide association study (GWAS) summary statistics for 19 cardiovascular diseases were derived from the FinnGen database. Primary analysis was carried out using the summary-data-based MR (SMR) method, with sensitivity analysis for validation. Colocalization analysis identifies shared causal variants between exposure eQTLs and CVD-associated single-nucleotide polymorphisms (SNPs). RESULTS: Among the 39 drug target genes, 8 were identified with detectable cis-eQTLs and were subsequently validated through positive control analysis for further investigation. In the SMR and sensitivity analyses, genetically proxied VEGFA inhibition showed significantly strong association with stroke (odds ratio [OR] = 1.17, 95% confidence interval [CI] = 1.09-1.26, p = 1.33 × 10- 5). Additionally, the inhibition of FGFR1, FLT1, and MAP2K2 exhibited suggestive association with corresponding cardiovascular disease outcomes. Nevertheless, only VEGFA expression and stroke shared a causal variant (93.6%), whereas FGFR1, MAP2K2, and FLT1 did not share causal variants with corresponding cardiovascular diseases in the colocalization analysis. CONCLUSIONS: This genetic association study revealed evidence supporting the genetic association between the use of VEGFA inhibitors and increased stroke risk, highlighting the need for enhanced pharmacovigilance. These findings underscore the delicate balance between cardiovascular toxicity risk and the benefits of cancer-targeted therapy.

Humans

Genetic evidence supports the prioritization of CD40 among prespecified immune-related candidate drug targets in myasthenia gravis.

AIM: To prioritize prespecified immune-related candidate drug targets in myasthenia gravis for further validation based on integrated genetic evidence. METHODS: We integrated drug-target Mendelian randomization (MR) using cis-expression quantitative trait loci (cis-eQTLs), protein-level MR of plasma CD40 abundance using plasma protein quantitative trait loci (pQTLs), and colocalization analyses to evaluate genetically proxied associations with overall MG, early-onset myasthenia gravis (EOMG), and late-onset myasthenia gravis (LOMG). RESULTS: In this study, CD40 showed the most consistent genetic evidence among the six prespecified targets. Effect estimates are reported as odds ratios (ORs) with 95% confidence intervals (CIs). Higher CD40 expression proxied by cis-eQTLs was associated with increased risk of overall MG (OR = 1.14, 95% CI: 1.05-1.24, Bonferroni-adjusted p = 0.022) and EOMG (OR = 1.32, 95% CI: 1.12-1.56, Bonferroni-adjusted p = 0.015). Genetically predicted higher plasma CD40 protein abundance was associated with increased overall MG risk (OR = 1.31, 95% CI: 1.08-1.57, Bonferroni-adjusted p = 0.010), whereas the protein-level MR result for EOMG was directionally consistent but not statistically significant. Colocalization analysis provided suggestive but not definitive evidence of colocalization between CD40 expression and EOMG risk. FCGRT, IL2RA, and SYK showed additional exploratory MR signals requiring further validation. CONCLUSION: CD40 showed the most consistent genetic support among the prespecified targets, supporting its prioritization for functional validation and further therapeutic investigation in MG.

CD40

Systematic meta-analysis of the toxicities and side effects of the targeted drug lenvatinib.

BACKGROUND: Lenvatinib, an effective targeted drug for various cancers, has clinical medication safety concerns due to its toxicities and side effects. OBJECTIVE: This study evaluated lenvatinib-induced any adverse events (any AEs) and nine aspects: vascular toxicities related to the circulatory system (vascular toxicities, blood system, and heart), toxicities of the skin and its appendages (skin/subcutaneous tissue and taste system), toxicities of the respiratory system (respiratory, thoracic, and mediastinal and respiratory tract), toxicities of the nervous system (nervous system and general), toxicities of the digestive system (gastrointestinal and liver), toxicities of the urinary system, toxicities of the endocrine and metabolic system (endocrine and metabolism/nutrition), toxicities of the musculoskeletal system, and other severe toxicities. Toxicities and side effects were stratified by severity into any and &#x2265;3 grades for analysis. PATIENTS/MATERIALS AND METHODS: Multiple databases were searched for lenvatinib cancer clinical studies (cohort studies and randomized controlled trials) from inception to December 31, 2024; toxicity and side effect data were extracted and analyzed. RESULTS: Nine high-quality studies were included, showing that lenvatinib is effective in cancers but has notable toxicities. Taking hypertension as an example, for any grade, the risk ratio (RR) was 2.34 with a 95% confidence interval (CI) of [2.09, 2.62], a Z-value of 14.74, and a P-value <0.00001; for grade &#x2265;3, the RR was 2.60 with a 95% CI of [2.21, 3.06], a Z-value of 11.44, and a P-value <0.00001. CONCLUSION: Lenvatinib is effective for cancer but toxic, and this study supports its rational clinical use.

Humans

Identifying potential drug targets for physical and cognitive frailty: an integrative analysis of CHARLS cohort, mendelian randomization, and gene colocalization.

With the aging of the population, frailty has become a common syndrome that severely affects the quality of life of older adults. This study aims to analyze the correlation between cognition and frailty, physical activity and frailty, and elucidate the potential pharmacological targets of cognitive frailty and physical frailty.We conducted logistic regression analyses using data from the China Health and Retirement Longitudinal Study (CHARLS) to examine the associations between total cognition and frailty, physical activity and frailty. Furthermore, summary-data-based Mendelian randomization (SMR) and two-sample Mendelian randomization (TSMR) were employed to explore potential pharmacological targets for frailty. Genes associated with physical frailty and cognitive frailty were identified, followed by analysis via colocalization analysis, phenome-wide association studies (PheWAS), and DsigDB drug prediction. Cross-sectional analysis of CHARLs revealed that total cognition(OR 0.93, 95% CI 0.92-0.95) and middle physical activity(OR 0.95, 95% CI 0.92-0.97) were negatively correlated with frailty. SMR identified 41 drug genes associated with frailty, and subsequent TSMR validation and co-localization analysis showed that 11 candidate genes exhibited strong colocalization (PP.H4&#x2009;>&#x2009;0.8). GRPEL 1, PABPC 4, and WBP 2NL were ultimately identified as potential drug targets associated with physical frailty, while LANCL1, LRPPRC, FADS1, and WBP2NL were identified as potential drug targets associated with cognitive frailty. Phenome-wide association analysis(PheWAS) did not reveal any significant associations between these genes and other phenotypes at the genome-wide significance threshold. Laudanosine, 25-hydroxycholesterol, and hexadecanal emerged as the top three candidate compounds for therapeutic intervention. We identified potential drug targets for physical frailty and cognitive frailty through comprehensive analysis and elucidated drugs associated with potentially relevant genetic markers, thereby laying the foundation for a deeper understanding of the mechanisms of frailty.

Humans

Kinome analysis of Madurella mycetomatis identified kinases in the cell wall integrity pathway as novel potential therapeutic drug targets in eumycetoma caused by Madurella mycetomatis.

Eumycetoma is a neglected tropical subcutaneous disease most commonly caused by the fungus Madurella mycetomatis. Currently, eumycetoma is treated by a combination of antifungal therapy and surgery, with limited success rates. To identify novel drug targets we used an in silico approach to determine the kinases present in M. mycetomatis genome and rank them as potential drug targets. In total 132 predicted kinases were identified in M. mycetomatis, of which 21 were predicted to be essential for fungal viability and 4 of these had no human orthologues. Two were linked to the Cell Wall Integrity (CWI) signalling pathway and were expressed in a Galleria mellonella infection model. Several kinase inhibitors were identified after in silico modelling, however only 8 were able to inhibit growth. Five had predicted binding affinity with components of the CWI. Altogether, the CWI shows potential as a drug target for further evaluation.

Madurella

DORSSAA: Drug-Target interactOmics Resource Based on Stability/Solubility Alteration Assay.

Advancements in high-throughput techniques such as Thermal Proteome Profiling and the high-throughput Proteome Integral Solubility Alteration assay have revolutionized our understanding of drug-protein interactions. Despite these innovations, the absence of an integrative platform for cross-study analysis of stability and solubility alteration data represents a significant bottleneck. To address this gap, we introduce Drug-target interactOmics Resource based on Stability/Solubility Alteration Assay (DORSSAA), an interactive and expandable web-based platform for the systematic analysis and visualization of proteome stability and solubility alteration assay datasets. Currently, DORSSAA features 1,135,985 records spanning 38 cell lines and organisms, 135 compounds, and 40,742 protein targets. Through its user-friendly interface, the resource supports comparative drug-protein interaction analysis and facilitates the discovery of actionable therapeutic targets. Through two case studies, methotrexate target profiling in A549 cells and combinatorial-therapy drug-target interactions in leukemia cell lines, we demonstrate DORSSAA's utility for identifying protein-drug interactions across diverse experimental contexts. This resource empowers researchers to accelerate drug discovery and enhance our understanding of protein behavior. Compared with data repositories and interaction databases, DORSSAA provides direct protein-level evidence of mechanisms of action with strict statistical control for each study. This enables more reliable identification of drug targets, off-target effects, and potential drug combinations.

Humans

Genetic and epigenetic underpinnings of biological aging: a multi-omics study integrating Mendelian randomization, spatial transcriptomics, and drug target discovery.

Inflammaging represents a hallmark of biological aging, yet the causal inflammatory mediators driving multi-dimensional epigenetic aging and their effector genes remain poorly characterized at the genetic level. We developed a four-tier analytical framework integrating causal screening, multi-omics effector gene mapping, spatial transcriptomics, and drug target evaluation. Two-sample Mendelian randomization (MR) of 91 circulating inflammatory proteins against six aging phenotypes identified IL-12B, IFNG, and IL-2 as the most robust pro-aging mediators with consistent effects across independent outcomes. Using multi-omics summary-based MR (SMR) as the core analytical engine, we integrated four-layer whole-blood molecular QTL resources eQTL (eQTLGen, n = 31,684), sQTL (GTEx, n = 755), pQTL (INTERVAL + SCALLOP, n = 34,232), and mQTL (McRae et al., n = 1,980) - with GWAS summary statistics for four epigenetic age acceleration measures. At a stringent threshold (P_SMR < 1&#xd7;10&#x207b;&#xb9;&#xb2;), seven high-confidence effector genes were identified: NHLRC1, TPMT, SELP, and RIPPLY3 for IEAA; ZNF373A and PLDN for HannumAA; and EDARADD for PhenoAA. The chromosome 6p21 NHLRC1-TPMT locus, overwhelmingly driven by methylation QTL signals (-log&#x2081;&#x2080;P = 26.06), emerged as the dominant genetic node of epigenetic aging. Spatial projection via gsMap onto a mouse E16.5 embryo atlas (121,767 cells) revealed preferential enrichment in smooth muscle and lung, with EDARADD showing marked specificity in mucosal epithelium. Cross-database drug target mining classified TPMT and SELP as repurposable known targets and NHLRC1 as a high-priority novel druggable candidate. This study provides multi-omics convergent causal evidence for inflammation-driven epigenetic aging and delivers genetically anchored targets for precision anti-aging intervention.

Aging

Mapping the Immune cell-specific gene regulatory network in bipolar disorder: A framework from scTWMR to exploratory drug-target annotation.

BACKGROUND: Although the involvement of the immune system in the genetic susceptibility of bipolar disorder (BD) is widely acknowledged, the causal relationship between gene expression in specific immune cell subtypes and BD requires systematic elucidation. METHODS: We implemented an analytical framework integrating single-cell transcriptome-wide Mendelian randomization (scTWMR) with colocalization analysis. This approach utilized cis-expression quantitative trait loci (cis-eQTLs) derived from 14 distinct immune cell types as instrumental variables to interrogate BD genome-wide association study (GWAS) summary statistics (comprising 41,917 cases and 371,549 controls). Subsequent investigations encompassed functional enrichment analysis, protein-protein interaction (PPI) network construction, phenome-wide association study (PheWAS), and performed an exploratory drug-target annotation. RESULTS: Our analysis identified 33 gene-immune cell associations. Colocalization analysis provided robust evidence (PPH4 > 90%) for shared causal variants implicating the MAD1L1, APOM, and NFKBIL1 loci. Significantly enriched biological pathways included cell cycle regulation, circadian rhythm entrainment, and neuroinflammation. The PPI network revealed a core regulatory module centered on histone-encoding and immune-related genes. Exploratory drug-target annotation nominated compounds for further investigation for compounds targeting APOM, TMEM258, and NFKBIL1. CONCLUSION: This study systematically delineates a genetically supported regulatory network of immune cell-specific gene expression in BD, predominantly implicating CD8&#x207a; effector T cells, plasma cells, and B cells. The findings corroborate established pathological pathways while uncovering novel cell type-specific therapeutic targets, thereby providing a genetic framework for prioritizing candidate targets for future investigation.

Bipolar disorder

Multi-omics Mendelian randomization integrating RNA-seq, eQTL and pQTL data revealed CPXM1 as a potential drug target for osteoporosis.

Osteoporosis, a prevalent skeletal disorder characterized by decreased bone mineral density and increased fracture risk, continues to be a major global health concern. Traditional treatments for osteoporosis have limited efficacy and safety profiles, highlighting the need for novel therapeutic targets. This study integrates multi-omics data, including RNA-seq, expression quantitative trait loci (eQTL), and protein quantitative trait loci (pQTL) data, through Mendelian randomization (MR) to identify potential drug targets for osteoporosis. By leveraging bidirectional two-sample MR analysis, we identified CPXM1 (Carboxypeptidase X, M14 family member 1) as a novel gene that is causally linked to osteoporosis risk. Through transcriptomic and proteomic validation, we demonstrate that CPXM1 was upregulated in aged bone tissues and osteoporotic conditions in both human and murine models. Gene set enrichment analysis (GSEA) revealed significant dysregulation of bone homeostasis pathways, including increased extracellular matrix degradation and suppression of osteoblast differentiation in aged mice. Furthermore, phenome-wide association studies (PheWAS) confirmed minimal off-target effects of CPXM1, reinforcing its potential as a therapeutic target. Finally, computational drug repurposing predicted several promising drug candidates, including Doxorubicin, 5-Fluorouracil, and 2-Methylcholine, which may target CPXM1 pathways for osteoporosis treatment. These findings highlight CPXM1 as a potential biomarker and therapeutic target, offering new avenues for osteoporosis therapy.

Osteoporosis

CASTER-DTA: Equivariant Graph Neural Networks for Predicting Drug-Target Affinity.

Accurately determining the binding affinity of a ligand with a protein is important for drug design, development, and screening. With the advent of accessible protein structure prediction methods such as AlphaFold, predicted protein 3D structures are readily available; however, methods for predicting binding affinity currently do not take full advantage of 3D protein information. Here, we present CASTER-DTA (Cross-Attention with Structural Target Equivariant Representations for Drug-Target Affinity), which uses an equivariant graph neural network to learn more robust protein representations alongside a standard graph neural network to learn molecular representations to predict drug-target affinity. We augment these representations by incorporating an attention-based mechanism between protein residues and drug atoms to improve interpretability. We show that CASTER-DTA represents a state-of-the-art improvement on multiple benchmarks for predicting drug-target affinity and that it generates novel insights for several related tasks. We then apply CASTER-DTA to create a large resource of the binding affinities of every FDA-approved drug against every protein in the human proteome and make these predictions freely available for download. We also make available a web server for researchers to apply a pretrained CASTER-DTA model for predicting binding affinities between arbitrary proteins and drugs.

deep learning

Association Analysis of the Circulating Proteome With Sarcopenia-Related Traits Reveals Potential Drug Targets for Sarcopenia.

BACKGROUND: Sarcopenia severely affects the physical health of the elderly. Currently, there is no specific drug available for sarcopenia. This study aims to identify pathogenic proteins and druggable targets for sarcopenia through Mendelian randomization (MR)-based analytical framework. METHODS: A sequential stepwise screening method that includes two-sample MR, Steiger filtering test and colocalization (MRSC) was applied to identify causal proteins associated with sarcopenia-related traits. In the MR analyses, 4372 circulating proteins with valid instrumental variables (IVs) from eight proteomic genome-wide association studies were utilized as exposures, and nine sarcopenia-related traits were utilized as outcomes. IVs were classified into cis-protein quantitative trait loci (pQTLs) and trans-pQTLs based on their positions. We conducted cis-only MRSC analyses and cis&#x2009;+&#x2009;trans MRSC analyses using cis-pQTLs and cis&#x2009;+&#x2009;trans pQTLs as IVs, respectively. Post-MRSC analyses were conducted on the prioritized findings of MRSC, including annotation of protein-altering variants (PAVs), assessment of overlap between pQTLs and expression quantitative trait loci (eQTLs), protein-protein interaction (PPI) analysis, pathway enrichment analysis and annotation of drug targets. Utilizing data from the UK Biobank, we performed an observational study to explore the associations between baseline circulating protein levels and the longitudinal changes in nine sarcopenia-related traits. RESULTS: A total of 181 causal associations for 65 proteins were prioritized by the cis-only MRSC analyses and 227 associations for 91 proteins were prioritized by the cis&#x2009;+&#x2009;trans MRSC analyses. Among the prioritized proteins, the majority of them employed non-PAVs as IVs and most of their cis-pQTLs overlapped with corresponding eQTLs and exhibited consistent directionality, with only one trans-pQTL overlapping with an eQTL. The PPI network of cis-only MRSC-prioritized proteins (p&#x2009;=&#x2009;4.04&#x2009;&#xd7;&#x2009;10-4) and cis&#x2009;+&#x2009;trans MRSC-prioritized proteins (p&#x2009;=&#x2009;8.76&#x2009;&#xd7;&#x2009;10-5) showed significantly more interactions than expected. Reactome, KEGG and GO pathway enrichment analyses for cis-only MRSC-prioritized proteins identified 52, 12 and 79 enriched pathways, respectively (adjusted p&#x2009;<&#x2009;0.05). For proteins identified by cis&#x2009;+&#x2009;trans MRSC analyses, only 15 pathways were enriched through the GO pathway enrichment analyses. In the observational study, 197 circulating proteins were identified to be associated with one or more sarcopenia-related traits (p&#x2009;<&#x2009;0.05/2923). Among them, the significant associations of CTSB (negative association) and ASGR1 (positive association) with sarcopenia-related traits were observed to have consistent directional associations in both MR-based studies and observational studies. Drug target annotations suggested that 52 MRSC-prioritized proteins and 145 biomarkers are drug targets or druggable. CONCLUSIONS: This study identified 89 potential pathogenic proteins and 197 candidate biomarkers for sarcopenia, providing valuable clues for the development of therapeutic drugs for sarcopenia.

Humans

The tissue-specific effects of glucose-lowering drug targets on aging mediated through DNA methylation: a multi-omics genetic study.

BACKGROUND: DNA methylation plays a key role in mediating the anti-aging effects of glucose-lowering drugs. This study aims to systematically explore the potential anti-aging effects of target genes of FDA-approved glucose-lowering drugs and the underlying epigenetic mediators. METHODS: We conducted a two-sample Mendelian randomization (MR) study to investigate the putative causal relationships between the gene expression levels of glucose-lowering drug targets and 10 aging-related phenotypes, followed by a two-step MR to estimate the mediation effect of DNA methylation. Drug candidates were selected according to the latest review of clinical drug use for type 2 diabetes, and their target genes were obtained from the DGIdb. Tissue-specific cis-expression quantitative trait loci (eQTLs) from GTEx Consortium were selected as genetic instruments to proxy the expression level of drug-target genes. Glycemic phenotypes were used as positive controls to validate the instruments. The cis- and trans-methylation QTLs of Cytosine-phosphate-Guanine sites near the drug target genes were obtained from GoDMC Consortium. Additionally, we performed enrichment analyses focused on tissue specificity and aging pathways to further corroborate our findings. RESULTS: We obtained 194 target genes interacting with 36 FDA-approved anti-diabetic drugs, of which the tissue-specific eQTLs were used to proxy the drug target effects. MR showed strong evidence that nine interacting genes of six glucose-lowering drugs showed anti-aging potential on one or more aging-related phenotypes mediated by DNA methylation: EHMT2, HSPA4, IGF2BP2, IRS1, LPL, NDUFAF1, NDUFS3, SLC22A3, and TCF7L2. These genes were distributed in 17 tissues, especially in the central nervous system, suggesting a potential neural component in their anti-aging effects. For instance, expression of EHMT2 in several brain basal ganglia regions, where the gene interacted with Tolazamide, showed a protective effect on frailty (odds ratio (OR) in caudate&#x2009;=&#x2009;1.02, 95%CI&#x2009;=&#x2009;1.01-1.04, FDR adjusted P&#x2009;=&#x2009;1.69&#x2009;&#xd7;&#x2009;10-2; OR in putamen&#x2009;=&#x2009;1.02, 95% CI&#x2009;=&#x2009;1.01-1.03, PFDR&#x2009;=&#x2009;3.37&#x2009;&#xd7;&#x2009;10-2, OR in nucleus accumbens&#x2009;=&#x2009;1.02, 95% CI&#x2009;=&#x2009;1.01-1.04, PFDR&#x2009;=&#x2009;3.37&#x2009;&#xd7;&#x2009;10-2). These associations were externally validated by searching literature evidence in existing EWAS and TWAS studies, as well as evidence from enrichment analyses. CONCLUSIONS: This study prioritizes nine glucose-lowering genes as anti-aging drug targets in specific tissues and prioritizes their epigenetic regulation through DNA methylation for future drug development.

DNA Methylation

An integrated in-silico approach for drug target identification in human pathogen Shigella dysenteriae.

Shigella dysenteriae, is a Gram-negative bacterium that emerged as the second most significant cause of bacillary dysentery. Antibiotic treatment is vital in lowering Shigella infection rates, yet the growing global resistance to broad-spectrum antibiotics poses a significant challenge. The persistent multidrug resistance of S. dysenteriae complicates its management and control. Hence, there is an urgent requirement to discover novel therapeutic targets and potent medications to prevent and treat this disease. Therefore, the integration of bioinformatics methods such as subtractive and comparative analysis provides a pathway to compute the pan-genome of S. dysenteriae. In our study, we analysed a dataset comprising 27 whole genomes. The S. dysenteriae strain SD197 was used as the reference for determining the core genome. Initially, our focus was directed towards the identification of the proteome of the core genome. Moreover, several filters were applied to the core genome, including assessments for non-host homology, protein essentiality, and virulence, in order to prioritize potential drug targets. Among these targets were Integration host factor subunit alpha and Tyrosine recombinase XerC. Furthermore, four drug-like compounds showing potential inhibitory effects against both target proteins were identified. Subsequently, molecular docking analysis was conducted involving these targets and the compounds. This initial study provides the list of novel targets against S. dysenteriae. Conclusively, future in vitro investigations could validate our in-silico findings and uncover potential therapeutic drugs for combating bacillary dysentery infection.

Shigella dysenteriae

Genetically Proxied Inhibition of Cholesterol-Lowering Drug Targets and Survival in HPV-Positive and Non-HPV-Driven Head and Neck Cancer: A Multicentre MR Study.

BACKGROUND: Cholesterol pathways may influence head and neck squamous cell carcinoma (HNSCC) progression, but evidence on prognosis is inconsistent. We used Mendelian randomization (MR) to test whether genetically proxied inhibition of major low-density lipoprotein cholesterol (LDL-C)-lowering drug targets and circulating lipid traits affects overall survival (OS) in HPV-positive and non-HPV-driven HNSCCs. METHODS: We proxied lifelong LDL-C lowering using 55 cis-acting single-nucleotide polymorphisms in HMGCR, NPC1L1, PCSK9, and LDL-receptor (LDLR) from the updated 2021 Global Lipids Genetics Consortium and instrumented circulating lipid traits. Two-sample MR estimated effects on OS in 4,869 multicentre HNSCC cases (1,291 HPV-positive; 3,578 non-HPV-driven) using minimally adjusted Cox models. Sensitivity analyses additionally adjusted for tumor stage and treatment, assessed collider bias using an external HNSCC incidence genome-wide association study, and examined between-center heterogeneity and colocalization. RESULTS: Using the updated Global Lipids Genetic Consortium 2021 instruments, genetically proxied HMGCR inhibition showed a directionally protective but nonsignificant association with OS in HPV-positive oropharyngeal HNSCC in the primary analysis [inverse variance weighted (IVW) HR = 0.19; 95% confidence interval (CI), 0.03-1.18; P = 0.08], with directionally concordant weighted median results. No corresponding protective association was observed for HMGCR in non-HPV-driven disease (IVW HR = 1.68; 95% CI, 0.78-3.63; P = 0.19). No clear evidence of association was observed for NPC1L1, PCSK9, LDLR, or circulating lipid traits in either HPV stratum. Colocalization did not support a shared causal variant. CONCLUSIONS: These analyses provide suggestive evidence that genetically proxied HMGCR inhibition may influence survival in HPV-positive oropharyngeal HNSCC. IMPACT: HMGCR-related pathways may be relevant to prognosis in HPV-positive oropharyngeal HNSCC, whereas clear survival effects of other cholesterol-lowering targets were not supported.

Humans

Common Molecular Mechanisms and Candidate Drug Targets in Type 2 Diabetes Mellitus and Atherosclerotic Cardiovascular Disease.

This study examined the mechanisms underlying the comorbidity between type 2 diabetes mellitus (T2DM) and atherosclerotic cardiovascular disease (ASCVD), while identifying potential therapeutic targets. Common differentially expressed genes (C-DEGs) between T2DM and ASCVD were extracted from the GSE78721 and GSE12288 datasets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, protein-protein interaction (PPI) network construction, hub gene identification, and Drug-Gene Interaction Database (DGIdb) analysis were conducted. The association between hub C-DEGs and immune-infiltrating cells was analyzed using the CIBERSORT method. Expression levels of hub C-DEGs were quantified through qRT-PCR and Western blot analyses. A total of 32 C-DEGs were identified, comprising 20 upregulated and 12 downregulated genes. C-DEGs were predominantly enriched in key pathways, including viral myocarditis, arrhythmogenic right ventricular cardiomyopathy, hypertrophic cardiomyopathy, and dilated cardiomyopathy. PPI analysis revealed 29 nodes and 39 edges, leading to the identification of eight hub C-DEGs (HSP90B1, PLAU, SLPI, TOP3A, NCF4, PRF1, TUBA1C, and CS) across both datasets. Furthermore, hub C-DEGs (TOP3A, SLPI, NCF4, PRF1, and PLAU) demonstrated significant correlations with immune-infiltrating cell levels. Drugs specifically targeting these hub C-DEGs present promising candidates for the treatment of T2DM and ASCVD. Additionally, the expression of hub C-DEGs at both mRNA and protein levels was validated in patients with T2DM and ASCVD. An integrated bioinformatics analysis facilitated the screening of candidate therapeutic targets, mechanisms, and drugs for T2DM and ASCVD, offering new insights into molecular therapies for these conditions.

Diabetes Mellitus, Type 2