Integrated bioinformatics reveals genetic links between visceral obesity and uterine tumors.

Visceral obesity (VO), characterized by excess fat around internal organs, is a recognized risk factor for gynecological tumors, including benign uterine leiomyoma (ULM) and malignant uterine leiomyosarcoma (ULS). Despite this association, the shared molecular mechanisms remain underexplored. This study utilizes an integrated bioinformatics approach to elucidate common molecular pathways and identify potential therapeutic targets linking VO, ULM, and ULS. We analyzed gene expression datasets from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) in each condition. We found 101, 145, and 18 DEGs in VO, ULM, and ULS, respectively, with 37 genes overlapping across all three conditions. Functional enrichment analysis revealed that these overlapping DEGs were significantly enriched in pathways related to cell proliferation, immune response, and transcriptional regulation, suggesting shared biological processes. Protein-protein interaction network analysis identified 14 hub genes, of which TOP2A, APOE, and TYMS showed significant differential expression across all three conditions. Drug-gene interaction analysis identified 26 FDA-approved drugs targeting these hub genes, highlighting potential therapeutic opportunities. In conclusion, this study uncovers shared molecular pathways and actionable drug targets across VO, ULM, and ULS. These findings deepen our understanding of disease etiology and offer promising avenues for drug repurposing. Experimental validation is needed to translate these insights into clinical applications and innovative treatments.

Identification and Functional Annotation of Hypothetical Proteins of Pan-Drug-Resistant Providencia rettgeri Strain MRSN845308 Toward Designing Antimicrobial Drug Targets.

Providencia rettgeri has increasingly been responsible for several infections, including urinary tract, post-burn wounds, neonatal sepsis, and others. The emergence of drug-resistant isolates of P rettgeri, accompanied by intrinsic and acquired antibiotic resistance, has exacerbated the challenge of treating such infections, necessitating the development of novel therapeutics. Hypothetical proteins (HPs) form a major portion of cellular proteins and can be targeted by these novel therapeutics. In this study, 410 HPs from a pan-drug-resistant (PDR) P rettgeri strain (MRSN845308) were functionally annotated and characterized by physicochemical properties, localization, virulence, essentiality, druggability, and functionality. Among 410 HPs, the VirulentPred 2.0 tool and VICMpred combinedly predicted 33 HPs as virulent, whereas 48 HPs were highly interacting proteins based on the STRING v12 database. BlastKOALA and eggNOG-mapper v2.1.12 predicted 13 HPs involved in several metabolic pathways like Riboflavin metabolism and Lipopolysaccharide biosynthesis. Overall, 83 HPs were selected as primary drug targets; however, only 80 remained after nonhomology searching and essentiality analysis. In addition, all were detected as novel drug targets according to DrugBank 5.1.12. Considering the potential of membrane and extracellular proteins, 29 HPs (extracellular, outer, and inner membrane) were selected based on the combined prediction from PSORTb v3.0.3, CELLO v.2.5, BUSCA, SOSUIGramN, and PSLpred. According to the prevalence of those HPs in different strains of P rettgeri sequences in National Center for Biotechnology Information Identical Protein Groups (NCBI-IPG), 5 HPs were selected as final drug targets. In addition, 5 other HPs annotated as transporter proteins were also added to the list. As no crystal structures of our targets are present, 3-dimensional structures of selected HPs were predicted by the AlphaFold Server powered by AlphaFold 3. Our findings might facilitate a better understanding of the mechanism of virulence and pathogenesis, and up-to-date annotations can make uncharacterized HPs easy to identify as targets for novel therapeutics.

Validation of TYK2 and exploration of PRSS36 as drug targets for psoriasis using Mendelian randomization.

Psoriasis is a chronic inflammatory skin disorder with multiple causes, including genetic and environmental factors. Despite advances in treatment, there remains a need to identify novel therapeutic targets. A Mendelian randomization (MR) analysis was conducted to identify therapeutic targets for psoriasis. Data on cis-expression quantitative trait loci were obtained from the eQTLGen Consortium (n = 31,684). Summary statistics for psoriasis (outcome) were sourced from the GWAS Catalog with a sample size of 484,598, including 5,427 cases and 479,171 controls. Colocalization analysis was used to assess whether psoriasis risk and gene expression were driven by shared single nucleotide polymorphisms. Drug prediction and molecular docking were utilized to validate the pharmacological value of the drug targets. The MR analysis found that 81 drug targets were significantly associated, and two (TYK2 and PRSS36) were supported by colocalization analysis (PP.H4 > 0.80). Phenome-wide association studies did not show any associations with other traits at the gene level. Biologically, these genes were closely related to immune function. Molecular docking revealed strong binding with drugs and proteins, as supported by available structural data. This study validated TYK2 as a drug target for psoriasis, in line with its existing clinical use, including the development of decucravacitinib. PRSS36 is a potential novel target requiring further investigation.

Polypharmacological profiling across protein target families and cellular pathways using the multiplexed cell-based assay platform safetyProfiler reveals efficacy, potency and side effects of drugs.

Selectivity profiling is key for assessing the pharmacological properties of multi-target drugs. We have developed a cell-based and barcoded assay encompassing ten druggable targets, including G protein-coupled receptors (GPCRs), receptor tyrosine kinases (RTKs), nuclear receptors, a protease as well as their key downstream pathways and profiled 17 drugs in living cells for efficacy, potency, and side effects. Notably, this multiplex assay, termed safetyProfiler assay, enabled the simultaneous assessment of multiple target and pathway activities, shedding light on the polypharmacological profile of compounds. For example, the neuroleptics clozapine, paliperidone, and risperidone potently inhibited primary targets DRD2 and HTR2A as well as cAMP and calcium pathways. However, while paliperidone and risperidone also potently inhibited the secondary target ADRA1A and mitogen-activated protein kinase (MAPK) downstream pathways, clozapine only exhibited mild antagonistic effects on ADRA1A and lacked MAPK inhibition downstream of DRD2 and HTR2A. Furthermore, we present data on the selectivity for bazedoxifene, an estrogen receptor antagonist currently undergoing clinical phase 2 trials for breast cancer, on MAPK signaling. Additionally, precise potency data for LY2452473, an androgen receptor antagonist, that completed a phase 2 clinical trial for prostate cancer, are presented. The non-selective kinase inhibitor staurosporine was observed to potently inactivate the two RTKs EGFR and ERBB4 as well as MAPK signaling, while eliciting stress-related cAMP responses. Our findings underscore the value of comprehensive profiling in elucidating the pharmacological properties of established and novel therapeutics, thereby facilitating the development of novel multi-target drugs with enhanced efficacy and selectivity.

The riboflavin biosynthetic pathway as a novel target for antifungal drugs against Candida species.

In recent decades, there has been an increase in the occurrence of fungal infections; yet, the arsenal of drugs available to fight invasive infections remains very limited. The development of new antifungal agents is hindered by the restricted number of molecular targets that can be exploited, given the shared eukaryotic nature of fungi and their hosts which often leads to host toxicity. In this paper, we examine the riboflavin biosynthetic pathway as a potential novel drug target. Riboflavin is an essential nutrient for all living organisms. Its biosynthetic pathway does not exist in humans, who obtain riboflavin through their diet. Our findings demonstrate that all enzymes in the pathway are essential for Candida albicans, Candida glabrata, and Saccharomyces cerevisiae. Auxotrophic strains, which mimic a drug targeting the biosynthesis pathway, experience rapid mortality in the absence of supplemented riboflavin. Furthermore, RIB1 is essential for virulence in both C. albicans and C. glabrata in a systemic mouse model. The fungal burden of a RIB1 deletion strain is significantly reduced in the kidneys and brain of infected mice, and this reduction becomes more pronounced over time. Nevertheless, auxotrophic cells can still take up external riboflavin when supplemented. We identified Orf19.4337 as the riboflavin importer in C. albicans and named it Rut1. We found that Rut1 only facilitates growth at external riboflavin concentrations that exceed the physiological concentrations in the human body. This suggests that riboflavin uptake is unlikely to serve as a resistance mechanism against drugs targeting the biosynthesis pathway. Interestingly, the uptake system in S. cerevisiae is more effective than in C. albicans and C. glabrata, enabling an auxotrophic S. cerevisiae strain to outcompete an auxotrophic C. albicans strain in lower riboflavin concentrations. IMPORTANCE: Candida species are a common cause of invasive fungal infections. Candida albicans, in particular, poses a significant threat to immunocompromised individuals. This opportunistic pathogen typically lives as a commensal on mucosal surfaces of healthy individuals but it can also cause invasive infections associated with high morbidity and mortality. Currently, there are only three major classes of antifungal drugs available to treat these infections. In addition, the efficacy of these antifungal agents is restricted by host toxicity, suboptimal pharmacokinetics, a narrow spectrum of activity, intrinsic resistance of fungal species, such as Candida glabrata, to certain drugs, and the acquisition of resistance over time. Therefore, it is crucial to identify new antifungal drug targets with novel modes of action to add to the limited armamentarium.

Identification of potential novel targets for treating inflammatory bowel disease using Mendelian randomization analysis.

BACKGROUND: Inflammatory bowel disease (IBD) is a complex autoimmune disorder, although some medications are available for its treatment. However, the long-term efficacy of these drugs remains unsatisfactory. Therefore, there is a need to develop novel drug targets for IBD treatment. METHODS: We conducted two-sample Mendelian randomization (MR) analysis using Genome-Wide Association Study (GWAS) data to assess the causal relationships between plasma proteins and IBD and its subtypes. Subsequently, the presence of shared genetic variants between the identified plasma proteins and traits was explored using Bayesian co-localization. Phenome-wide MR was used to evaluate evaluated adverse effects, and drug target databases were examined for therapeutic potential. RESULTS: Using the Bonferroni correction (P < 3.56e-05), 17 protein-IBD pairs were identified. Notably, the genetic associations of IBD shared a common variant locus (PP.H4 > 0.7) with five proteins (MST1, IL12B, HGFAC, FCGR2A, and IL18R1). As a subtype of IBD, ulcerative colitis shares common variant loci with FCGR2A, IL12B, and MST1. In addition, we found that ANGPTL3, IL18R1, and MST1 share a common variant locus with Crohn's disease. Furthermore, phenome-wide MR analysis revealed that except for ANGPTL3, no other proteins showed potential adverse effects. In the drug database, identified plasma proteins such as FCGR2A and IL18R1 were found to be potential drug targets for the treatment of IBD and its subtypes. CONCLUSION: Six proteins (FCGR2A, IL18R1, MST1, HGFAC, IL12B, and ANGPTL3) were identified as potential drug targets for the treatment of IBD and its subtypes.

Exploration of protein and genetic targets causing atrioventricular block: mendelian-randomization analyses based on eQTL data and pQTL data.

BACKGROUND: Atrioventricular block (AVB) is a heterogeneous group of arrhythmias. AVB can lead to sudden arrest of the heart and subsequent syncope or sudden cardiac death. Few scholars have investigated the underlying molecular mechanisms of AVB. Finding molecular markers can facilitate understanding of AVB and exploration of therapeutic targets. METHODS: Two-sample Mendelian randomization (MR) analysis was undertaken with inverse variance weighted (IVW) model and Wald ratio as the primary approach. Reverse MR analysis was undertaken to identify the associated protein targets and gene targets. Expression quantitative trait loci (eQTL) data from the eQTLGen database and protein quantitative trait loci (pQTL) data from three previous large-scale proteomic studies on plasma were retrieved as exposure data. Genome-wide association study (GWAS) summary data (586 cases and 379,215 controls) for AVB were retrieved from the UK Biobank database. Colocalization analyses were undertaken to identify the effect of filtered markers on outcome data. Databases (DrugBank, Therapeutic Target, PubChem) were used to identify drugs that interacted with targets. RESULTS: We discovered that 692 genes and 42 proteins showed a significant correlation with the AVB phenotype. Proteins (cadherin-5, sTie-1, Notch 1) and genes (DNAJC30, ABO) were putative molecules to AVB. Drug-interaction analyses revealed anticancer drugs such as tyrosine-kinase inhibitors and TIMD3 inhibitors could cause AVB. Other substances (e.g. toxins, neurological drugs) could also cause AVB. CONCLUSIONS: We identified the proteins (cadherin-5, sTie-1, Notch 1) and gene (DNAJC30, ABO) targets associated with AVB pathogenesis. Anticancer drugs (tyrosine-kinase inhibitors, TIMD3 inhibitors), toxins, or neurological drugs could also cause AVB.

Enzyme ChE, cholinergic therapy and molecular docking: Significant considerations and future perspectives.

Enzyme Che plays an essential role in cholinergic and non-cholinergic functions. It is present in the fertilized/unfertilized eggs and sperm of different species. Inclusion criteria for data collection from electronic databases NCBI and Google Scholar are enzyme AChE/BChE, cholinergic therapy, genomic organization and gene transcription, enzyme structure, biogenesis, transport, processing and localization, molecular signaling and biological function, polymorphism and influencing factors. Enzyme Che acts as a signaling receptor during hematopoiesis, protein adhesion, amyloid fiber formation, neurite outgrowth, bone development, and maturation, explaining the activity out of synaptic neurotransmission. Polymorphism in the Che genes correlates to various diseases and diverse drug responses. In particular, change accompanies cancer, neurodegenerative, and cardiovascular disease. Literature knowledge indicates the importance of Che inhibitors that influence biochemical and molecular pathways in disease treatment, genomic organization, gene transcription, structure, biogenesis, transport, processing, and localization of Che enzyme. Enzyme Che polymorphism changes indicate the possibility of efficient and new inhibitor drug target mechanisms in diverse research areas.

Unraveling the Core Components and Critical Targets of Houttuynia cordata Thunb. in Treating Non-small Cell Lung Cancer through Network Pharmacology and Multi-omics Analysis.

OBJECTIVE: This study aimed to preliminary explore the molecular mechanisms of Houttuynia cordata Thunb. (H. cordata; Saururaceae) in treating non-small cell lung cancer (NSCLC), with the goal of screening drug potential targets for clinical drug development. METHODS: This study employed a multi-omics and multi-source data integration approach to identify potential therapeutic targets of H. cordata against NSCLC from the TCMSP database, GEO database, BioGPS database, Metascape database, and others. Meanwhile, target localization was performed, and its possible mechanisms of action were predicted. Furthermore, dynamics simulations and molecular docking were used for verification. Multiomics analysis was used to confirm the selected key genes' efficacy in treating NSCLC. RESULTS: A total of 31 potential therapeutic targets, 8 key genes, and 5 core components of H. cordata against NSCLC were screened out. These potential therapeutic targets played a therapeutic role mainly by regulating lipid and atherosclerosis, the TNF signaling pathway, the IL-17 signaling pathway, and others. Molecular docking indicated a stable combination between MMP9 and quercetin. Finally, through multi-omics analysis, it was found that the expression of some key genes was closely related not only to the progression and prognosis of NSCLC but also to the level of immune infiltration. CONCLUSION: Through comprehensive network pharmacology and multi-omics analysis, this study predicts that the core components of H. cordata play a role in treating NSCLC by regulating lipid and atherosclerosis, as well as the TNF signaling pathway. Among them, the anti-NSCLC activity of isoramanone is reported for the first time.

Gut microbiota metabolites: potential therapeutic targets for Alzheimer's disease?

Background: Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive decline in cognitive function, which significantly increases pain and social burden. However, few therapeutic interventions are effective in preventing or mitigating the progression of AD. An increasing number of recent studies support the hypothesis that the gut microbiome and its metabolites may be associated with upstream regulators of AD pathology. Methods: In this review, we comprehensively explore the potential mechanisms and currently available interventions targeting the microbiome for the improvement of AD. Our discussion is structured around modern research advancements in AD, the bidirectional communication between the gut and brain, the multi-target regulatory effects of microbial metabolites on AD, and therapeutic strategies aimed at modulating gut microbiota to manage AD. Results: The gut microbiota plays a crucial role in the pathogenesis of AD through continuous bidirectional communication via the microbiota-gut-brain axis. Among these, microbial metabolites such as lipids, amino acids, bile acids and neurotransmitters, especially sphingolipids and phospholipids, may serve as central components of the gut-brain axis, regulating AD-related pathogenic mechanisms including ß-amyloid metabolism, Tau protein phosphorylation, and neuroinflammation. Additionally, interventions such as probiotic administration, fecal microbiota transplantation, and antibiotic use have also provided evidence supporting the association between gut microbiota and AD. At the same time, we propose an innovative strategy for treating AD: a healthy lifestyle combined with targeted probiotics and other potential therapeutic interventions, aiming to restore intestinal ecology and microbiota balance. Conclusion: Despite previous efforts, the molecular mechanisms by which gut microbes act on AD have yet to be fully described. However, intestinal microorganisms may become an essential target for connecting the gut-brain axis and improving the symptoms of AD. At the same time, it requires joint exploration by multiple centers and multiple disciplines.

Identifying new biomarkers and potential therapeutic targets for breast cancer through the integration of human plasma proteomics: a Mendelian randomization study and colocalization analysis.

Background: The proteome is a crucial reservoir of targets for cancer treatment. While some targeted therapies have been developed, there are still significant challenges in early diagnosis and treatment, highlighting the need to identify new biomarkers and therapeutic targets for breast cancer. Therefore, we conducted a comprehensive proteome-wide Mendelian randomization (MR) study to identify novel biomarkers and potential therapeutic targets for breast cancer. Methods: Protein quantitative trait locus (pQTL) data were extracted from two published plasma proteome-wide association studies. Genetic variants associated with breast cancer were obtained from the Breast Cancer Association Consortium, which included 133,384 cases and 113,789 controls, and the Finnish cohort study, comprising 18,786 cases and 182,927 controls. We employed summary-based MR and colocalization methods to identify potential drug targets for breast cancer, which were subsequently validated using a two-sample MR approach. Finally, a protein-protein interaction (PPI) network was constructed to detect interactions between the identified proteins and existing cancer drug targets. Results: Gene-predicted levels of ten proteins were associated with breast cancer risk. Decreased levels of CASP8, DDX58, CPNE1, ULK3, PARK7, and BTN2A1, as well as increased levels of TNFRSF9, TNXB, DNPH1, and TLR1, were linked to an elevated risk of breast cancer. Among these, CASP8 and DDX58 were supported by tier-one evidence, while CPNE1, ULK3, PARK7, and TNFRSF9 received tier-two evidence support. The remaining proteins, TNXB, BTN2A1, DNPH1, and TLR1, were supported by tier-three evidence. CASP8, DDX58, CPNE1, ULK3, PARK7, and TNFRSF9 have already been identified as targets in drug development and potential therapeutic targets for breast cancer treatment. Additionally, ULK3 showed promise as a prognostic biomarker for breast cancer. Conclusions: The present study identified several novel potential drug targets and biomarkers for breast cancer, providing new insights into its diagnosis and treatment. The integration of PPI and druggability evaluations enhances the prioritization of these therapeutic targets, paving the way for future drug development efforts.

AI approaches for the discovery and validation of drug targets.

Artificial intelligence (AI) holds immense promise for accelerating and improving all aspects of drug discovery, not least target discovery and validation. By integrating a diverse range of biological data modalities, AI enables the accurate prediction of drug target properties, ultimately illuminating biological mechanisms of disease and guiding drug discovery strategies. Despite the indisputable potential of AI in drug target discovery, there are many challenges and obstacles yet to be overcome, including dealing with data biases, model interpretability and generalisability, and the validation of predicted drug targets, to name a few. By exploring recent advancements in AI, this review showcases current applications of AI for drug target discovery and offers perspectives on the future of AI for the discovery and validation of drug targets, paving the way for the generation of novel and safer pharmaceuticals.

Molecular targets in SARS-CoV-2 infection: An update on repurposed drug candidates.

The 2019 widespread contagion of the human coronavirus novel type (SARS-CoV-2) led to a pandemic declaration by the World Health Organization. A daily increase in patient numbers has formed an urgent necessity to find suitable targets and treatment options for the novel coronavirus (COVID-19). Despite scientists' struggles to discover quick treatment solutions, few effective specific drugs are approved to control SARS-CoV-2 infections thoroughly. Drug repositioning or Drug repurposing and target-based approaches are promising strategies for facilitating the drug discovery process. Here, we review current in silico, in vitro, in vivo, and clinical updates regarding proposed drugs for prospective treatment options for COVID-19. Drug targets that can direct pharmaceutical sciences efforts to discover new drugs against SARS-CoV-2 are divided into two categories: Virus-based targets, for example, Spike glycoprotein and Nucleocapsid Protein, and host-based targets, for instance, inflammatory cytokines and cell receptors through which the virus infects the cell. A broad spectrum of drugs has been found to show anti-SARS-CoV-2 potential, including antiviral drugs and monoclonal antibodies, statins, anti-inflammatory agents, and herbal products.

Identification of Peptidoglycan Glycosyltransferase FtsI as a Potential Drug Target against Salmonella Enteritidis and Salmonella Typhimurium Serovars Through Subtractive Genomics, Molecular Docking and Molecular Dynamics Simulation Approaches.

INTRODUCTION: Salmonella enterica serovar Enteritidis and Salmonella enterica serovar Typhimurium are among the main causative agents of nontyphoidal Salmonella infections, imposing a significant global health burden. The emergence of antibiotic resistance in these pathogens underscores the need for innovative therapeutic strategies. OBJECTIVE: To identify proteins as potential drug targets against Salmonella Enteritidis and Salmonella Typhimurium serovars using In silico approaches. METHODS: In this study, a subtractive genomics approach was employed to identify potential drug targets. The whole proteome of Salmonella enteritidis PT4 and Salmonella typhimurium (D23580), containing 393 and 478 proteins, respectively, was analyzed through subtractive genomics to identify human homologous proteins of the pathogen and also the proteins linked to shared metabolic pathways of pathogen and its host. RESULTS: Subsequent analysis revealed 19 common essential proteins shared by both strains. To ensure hostspecificity, we identified 10 non-homologous proteins absent in humans. Among these proteins, peptidoglycan glycosyltransferase FtsI was pivotal, participating in pathogen-specific pathways and making it a promising drug target. Molecular docking highlighted two potential compounds, Balsamenonon A and 3,3',4',7-Tetrahydroxyflavylium, with strong binding affinities with FtsI. A 100 ns molecular dynamics simulation having 10,000 frames substantiated the strong binding affinity and demonstrated the enduring stability of the predicted compounds at the docked site. CONCLUSION: The findings in this study provide the foundation for drug development strategies against Salmonella infections, which can contribute to the prospective development of natural and cost-effective drugs targeting Salmonella Enteritidis and Salmonella Typhimurium.

A glance at structural biology in advancing rice blast fungus research.

The filamentous fungus Magnaporthe oryzae is widely recognized as a notorious plant pathogen responsible for causing rice blasts. With rapid advancements in molecular biology technologies, numerous regulatory mechanisms have been thoroughly investigated. However, most recent studies have predominantly focused on infection-related pathways or host defence mechanisms, which may be insufficient for developing novel structure-based prevention strategies. A substantial body of literature has utilized cryo-electron microscopy and X-ray diffraction to explore the relationships between functional components, shedding light on the identification of potential drug targets. Owing to the complexity of protein extraction and stochastic nature of crystallization, obtaining high-quality structures remains a significant challenge for the scientific community. Emerging computational tools such as AlphaFold for structural prediction, docking for interaction analysis, and molecular dynamics simulations to replicate in vivo conditions provide novel avenues for overcoming these challenges. In this review, we aim to consolidate the structural biological advancements in M. oryzae, drawing upon mature experimental experiences from other species such as Saccharomyces cerevisiae and mammals. We aim to explore the potential of protein construction to address the invasion and proliferation of M. oryzae, with the goal of identifying new drug targets and designing small-molecule compounds to manage this disease.

Identifying genetically-supported drug repurposing targets for non-small cell lung cancer through mendelian randomization of the druggable genome.

Background: Lung cancer is responsible for most cancer-related deaths, and non-small cell lung cancer (NSCLC) accounts for the majority of cases. Targeted therapy has made promising advancements in systemic treatment for NSCLC over the last two decades, but inadequate drug targets with clinically proven survival benefits limit its universal application in clinical practice compared to chemotherapy and immunotherapy. There is an urgent need to explore new drug targets to expand the beneficiary group. This study aims to identify druggable genes and to predict the efficacy and prognostic value of the corresponding targeted drugs in NSCLC. Methods: Two-sample mendelian randomization (MR) of druggable genes was performed to predict the efficacy of their corresponding targeted therapy for NSCLC. Subsequent sensitivity analyses were performed to assess potential confounders. Accessible RNA sequencing data were incorporated for subsequent verifications, and Kaplan-Meier survival curves of different gene expressions were used to explore the prognostic value of candidate druggable genes. Results: MR screening encompassing 4,863 expression quantitative trait loci (eQTL) and 1,072 protein quantitative trait loci (pQTL, with 453 proteins overlapping) were performed. Seven candidate druggable genes were identified, including CD33, ENG, ICOSLG and IL18R1 for lung adenocarcinoma, and VSIR, FSTL1 and TIMP2 for lung squamous cell carcinoma. The results were validated by further transcriptomic investigations. Conclusions: Drugs targeting genetically supported genomes are considerably more likely to yield promising efficacy and succeed in clinical trials. We provide compelling genetic evidence to prioritize drug development for NSCLC.

Pomalidomide sensitizes lung cancer cells to TRAIL/CDDP-induced apoptosis via directly targeting electron transfer flavoprotein alpha subunit.

Immunomodulatory drugs (IMiDs) represented by thalidomide exhibit benefits when combined with other chemotherapeutic drugs for patients with lung cancer, which inspired the exploration of combining pomalidomide with another agent to treat lung cancer as it is more potent than thalidomide. However, the drugs that can be combined with pomalidomide to benefit patients and related mechanisms remain unclear. Here, we performed a proteomic analysis based on the streptavidin pull-down to identify the potential target of pomalidomide in non-small cell lung cancer (NSCLC). In this work, electron transfer flavoprotein alpha subunit (ETFA), an important enzyme involved in electron transport in the respiratory chains was identified as a crucial cellular target of pomalidomide in NCI-H460 cells. Using apoptosis model and combination analyses, we found that pomalidomide directly targeted ETFA, and increased ATP generation, thereby significantly promoting tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)-induced apoptosis. Specific knockdown of ETFA could effectively eliminate the promoting effect of pomalidomide on energy production. Furthermore, respiratory chain inhibitors can effectively block cell apoptosis induced by TRAIL and pomalidomide. These results suggested that pomalidomide may promote apoptosis by facilitating energy production by targeting ETFA and thus enhanced the anticancer effects of chemotherapeutic drugs. It is noteworthy that pomalidomide noticeably increased the anticancer efficacy of cisplatin (CDDP) in NCI-H460 xenograft model with the main mechanisms by inducing apoptosis. Collectively, our data not only provide new insights into the anticancer mechanisms of pomalidomide but also reflect translational prospects of combining pomalidomide with CDDP for NSCLC treatment.

Proteome-wide Mendelian randomization identified potential drug targets for migraine.

BACKGROUND: Migraine is a highly prevalent and complex neurovascular disease. However, the currently available therapeutic drugs often fall to adequately meet clinical needs due to limited effectiveness and numerous undesirable side effects. This study aims to identify putative novel targets for migraine treatment through proteome-wide Mendelian randomization (MR). METHODS: We utilized MR to estimate the causal effects of plasma proteins on migraine and its two subtypes, migraine with aura (MA) and without aura (MO). This analysis integrated plasma protein quantitative trait loci (pQTL) data with genome-wide association studies (GWAS) findings for these migraine phenotypes. Moreover, we conducted a phenome-wide MR assessment, enrichment analysis, protein-protein interaction networks construction, and mediation MR analysis to further validate the pharmaceutical potential of the identified protein targets. RESULTS: We identified 35 protein targets for migraine and its subtypes (p < 8.04 × 10-6), with prioritized targets showing minimal side effects. Phenome-wide MR identified novel protein targets-FCAR, UBE2L6, LATS1, PDCD1LG2, and MMP3-that have no major disease side effects and interacted with current acute migraine medication targets. Additionally, MMP3, PDCD1LG2, and HBQ1 interacted with current preventive migraine medication targets. The causal effects of plasma protein on migraine were partly mediated by plasma metabolites (proportion of mediation from 3.8% to 21.0%). CONCLUSIONS: A set of potential protein targets for migraine and its subtypes were identified. These proteins showed rare side effects and were responsible for biological mechanisms involved in migraine pathogenesis, indicating priority for the development of migraine treatments.