Identification Of Drug Targets Research Articles

Identification of novel drug targets for osteoarthritis by integrating genetics and proteomes from blood

BackgroundOsteoarthritis (OA) is a degenerative osteoarticular disease, involving genetic predisposition. How the risk variants confer the risk of OA through their effects on proteins remains largely unknown. Therefore, we aimed to discover new and effective drug targets for OA and its subtypes.MethodsA proteome-wide association study (PWAS) was performed based on OA and its subtypes genome-wide association studies (GWAS) summary datasets and the protein quantitative trait loci (pQTL) data. Subsequently, Mendelian randomization (MR) and colocalization analysis was conducted to estimate the associations between protein and OA risk. The replication analysis was performed in an independent dataset of human plasma pQTL data.ResultsThe abundance of seven proteins was causally related to OA, two proteins to knee OA and six proteins to hip OA, respectively. We replicated 2 of these proteins using an independent pQTL dataset. With the further support of colocalization, and higher ECM1 level was causally associated with a higher risk of OA and hip OA. Higher PCSK1 level was causally associated with a lower risk of OA. And higher levels of ITIH1, EFEMP1, and ERLEC1 were associated with decreased risk of hip OA.ConclusionOur study provides new insights into the genetic component of protein abundance in OA and a promising therapeutic target for future drug development.

Structural and Functional Annotations of Hypothetical Proteins of Candida auris for Novel Drug Target Identification: An insilico Approach

Candida auris is an emerging fungal pathogen that causes severe invasive infections in healthcare facilities which are difficult to control and treat due to its resistance to major antifungal drugs. A large fraction of C. auris proteins are uncharacterized and hypothetical, and structural and functional characterization of these proteins can aid in the selection of novel drug targets. The present study involves a computational approach for the structural and functional characterization of hypothetical proteins from Candida auris. After the sequenceretrieval, hypothetical proteins were predicted for physiochemical properties and subcellular localization, structurally modeled using I-TASSER, quality assessed through Verify3D, ERRAT, and PROCHECK, and functionally annotated to reveal conserved domains and roles in pathogen pathways. Finally, the immunogenic assessments, non-human homologous analysis, and druggability analysis reveal three hypothetical proteins of C. auris (KND95408.2, KND95415.2, and KND95429.2) as novel drug targets. Furthermore, the stable conformations of theseselected drug targets with the minimum root mean square deviations (RMSD) and fluctuation (RMSF) were analyzed by MD simulations. Conclusively, the functional annotations of these hypothetical proteins can help in understanding the disease mechanisms at the molecular level, as well as provide new targets for drug development against Candida auris.

Bioinformatics Analysis Screening and Identification of Key Biomarkers and Drug Targets in Human Glioblastoma.

Glioblastoma is the most common type of brain cancer, with a prognosis that is unfortunately poor. Despite considerable progress in the field, the intricate molecular basis of this cancer remains elusive. The aim of this study was to identify genetic indicators of glioblastoma and reveal the processes behind its development. The advent and integration of supercomputing technology have led to a significant advancement in gene expression analysis platforms. Microarray analysis has gained recognition for its pivotal role in oncology, crucial for the molecular categorization of tumors, diagnosis, prognosis, stratification of patients, forecasting tumor responses, and pinpointing new targets for drug discovery. Numerous databases dedicated to cancer research, including the Gene Expression Omnibus (GEO) database, have been established. Identifying differentially expressed genes (DEGs) and key genes deepens our understanding of the initiation of glioblastoma, potentially unveiling novel markers for diagnosis and prognosis, as well as targets for the treatment of glioblastoma. This research sought to discover genes implicated in the development and progression of glioblastoma by analyzing microarray datasets GSE13276, GSE14805, and GSE109857 from the GEO database. DEGs were identified, and a function enrichment analysis was performed. Additionally, a protein-protein interaction network (PPI) was constructed, followed by module analysis using the tools STRING and Cytoscape. The analysis yielded 88 DEGs, consisting of 66 upregulated and 22 downregulated genes. These genes' functions and pathways primarily involved microtubule activity, mitotic cytokinesis, cerebral cortex development, localization of proteins to the kinetochore, and the condensation of chromosomes during mitosis. A group of 27 pivotal genes was pinpointed, with biological process analysis indicating significant enrichment in activities, such as division of the nucleus during mitosis, cell division, maintaining cohesion between sister chromatids, segregation of sister chromatids during mitosis, and cytokinesis. The survival analysis indicated that certain genes, including PCNA clamp-associated factor (PCLAF), ribonucleoside- diphosphate reductase subunit M2 (RRM2), nucleolar and spindle-associated protein 1 (NUSAP1), and kinesin family member 23 (KIF23), could be instrumental in the development, invasion, or recurrence of glioblastoma. The identification of DEGs and key genes in this study advances our comprehension of the molecular pathways that contribute to the oncogenesis and progression of glioblastoma. This research provides valuable insights into potential diagnostic and therapeutic targets for glioblastoma.

Discovery of the selective and nanomolar inhibitor of DPP-4 more potent than sitagliptin by structure-guided rational design

Various therapeutic targets and approaches are commonly employed in the management of Type 2 Diabetes. These encompass diverse groups of drugs that target different mechanisms involved in glucose regulation. Inhibition of the DPP-4 enzyme has been proven an excellent target for antidiabetic drug design. Our previous work on discovering multitarget antidiabetic drugs led to the identification of a gallic acid-thiazolidinedione hybrid as a potent DPP4 inhibitor (IC50 = 36 nM). In current research, our efforts resulted in a new dihydropyrimidine-based scaffold with enhanced DPP4 inhibition potential. After virtual evaluation, the designed molecules with excellent interaction patterns and binding energy values were synthesized in the wet laboratory. The inhibition potential of synthesized compounds was assessed against the DPP-4 enzyme. Compound 46 with single digit IC50 value 2 nM exhibited 4-fold and 18-fold higher activity than Sitagliptin and our previously reported hybrid respectively. Moreover, compounds 46, 47 and 50 have shown manyfold selectivity against DPP8 and DPP9. Further pretreatment with compounds 43, 45–47 and 50 (at doses of 10 and 20 mg/kg) in OGTT conducted on rats resulted in a significant decrease in the serum glucose levels compared to the control group. In the long-term STZ-induced diabetic rats, tested compound 50 performed similarly to the reference drug. Molecular dynamics simulations and in-silico molecular docking studies were employed to elucidate the time-dependent interactions of inhibitors within the active sites of DPP4. The compounds examined in this work might serve as a possible lead in the development of effective diabetic mellitus treatments.

Discovery of Aldehyde Dehydrogenase as a Potential Fungicide Target and Screening of its Natural Inhibitors against Fusarium verticillioides.

Fusarium verticillioides is the primary pathogen causing ear rot and stalk rot in corn (Zea mays). It not only affects yields but also produces mycotoxins endangering both human and animal health. Aldehyde dehydrogenase (ALDH) is essential for the oxidation of aldehydes in living organisms, making it a potential target for human drug design. However, there are limited reports on its function in plant pathogenic fungus. In this study, we analyzed the expression levels and gene knockout mutants, revealing that ALDH genes FvALDH-43 and FvALDH-96 in F. verticillioides played significant roles in pathogenicity and resistance to low-temperature stress by affecting antioxidant capacity. Virtual screening for natural product inhibitors and molecular docking were performed targeting FvALDH-43 and FvALDH-96. Following the biological activity analysis, three natural flavonoid compounds featuring a 2-hydroxyphenol chromene were identified. Among these, Taxifolin exhibited the highest biological activity and low toxicity. Both in vitro and in vivo biological evaluations confirmed that Taxifolin targeted ALDH and inhibited its activity. These findings indicate that aldehyde dehydrogenase may serve as a promising target for the design of novel fungicides.

Bioinformatics and Chemoinformatics Analysis Explored the Role of Linum usitatissimum in Diabetic Heart Conditions: Experimental Analysis in H9c2 Rat Embryonic Cardiomyocytes Cell Lines.

Cytokine storms and inflammation lead to heart failure (HF). Bioactive compounds, as complementary medicine, can be the primary source of compounds with anti-inflammatory properties. Linum usitatissimum (LiU) has antioxidant capacity and anti-inflammatory activity. Here, candidate hugeness was selected based on the in silico studies, bio-cheminformatics, and bioinformatic analysis for excremental validation. We selected the vital genes with differential expression from the GSE26887 dataset. Based on the bioinformatics analysis, several parameters are determined to choose switchable genes involved in diabetic HF (DHF). We designed the protein-protein interactions network to consider the nodes' degree, modularity, and betweenness centrality. Hence, we selected the interleukin (IL)-6 protein as a target for drug design and discovery to reduce diabetes complications in the heart. Here, H9c2 cell lines of rat embryonic cardiomyocytes induce HF using hyperglycemic and hyperlipidemic conditions. Real-time polymerase chain reaction evaluated the relative expression of SMAD7/NRF-2/STAT3. Furthermore, we assessed the concentration of IL-6 using the enzyme-linked immunosorbent assay technique. Based on the bioinformatic analysis, we found that IL-6 with the highest network parameters score might be presented as a druggable protein in the DHF. Bioactive compounds and phytochemicals have potential strategies to manage DHF. LiUs decreased the expression level of the SMAD7 (P <0.0001) and STAT3 (P < 0.0001), and increased the expression level of the NRF2 (P < 0.0001). In addition, LiUs significantly reduced the concentration of IL-6 (P < 0.0001). Our data proposed that LiUs regulated inflammation and triggered the antioxidant defense in HF. Moreover, LiUs could have potential approaches to managing and preventing DHF.

Sex- and age-associated factors drive the pathophysiology of MASLD.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is strongly associated with obesity. Sex and age affect MASLD prevalence and pathophysiology. The use of animal models fed Western-style diets is vital for investigating the molecular mechanisms contributing to metabolic dysregulation and for facilitating novel drug target identification. However, the sex-associated and age-associated mechanisms underlying the pathophysiology remain poorly understood. This knowledge gap limits the development of personalized sex-specific and age-specific drug treatments. Young (7wk) and aged (52wk) male and female mice were fed a high-fat diet (HFD) or low-fat diet. Liver metabolome (>600 molecules) and transcriptome profiles were analyzed. Male and female mice fed an HFD developed obesity, glucose intolerance, and hepatic steatosis. However, fasting blood glucose, insulin, and serum alanine aminotransferase levels were higher in males fed an HFD, indicating a more severe metabolic disease. In addition, males showed significant increases in liver diacylglycerides and glycosylceramides (known mediators of insulin resistance and fibrosis), and more changes in the transcriptome: extracellular matrix organization and proinflammatory genes were elevated only in males. In contrast, no major increase in damaging lipid classes was observed in females fed an HFD. However, aging affected the liver to a greater extent in females. Acylcarnitine levels were significantly reduced, suggestive of changes in fatty acid oxidation, and broad changes in the transcriptome were observed, including reduced oxidative stress response gene expression and alterations in lipid partitioning genes. Here, we show distinct responses to an HFD between males and females. Our study underscores the need for using both sexes in drug target identification studies, and characterizing the molecular mechanisms contributing to the MASLD pathophysiology in aging animals.

Integrating genome-wide CRISPR screens and in silico drug profiling for targeted antidote development.

Numerous toxins threaten humans, but specific antidotes are unavailable for most of them. Although CRISPR screening has aided the discovery of the mechanisms of some toxins, developing targeted antidotes remains a significant challenge. Recently, we established a systematic framework to develop antidotes by combining the identification of novel drug targets by using a genome-wide CRISPR screen with a virtual screen of drugs approved by the US Food and Drug Administration. This approach allows for a comprehensive understanding of toxin mechanisms at the whole-genome level and facilitates the identification of promising antidote drugs targeting specific molecules. Here, we present step-by-step instructions for executing genome-scale CRISPR-Cas9 knockout screens of toxins in HAP1 cells. We also provide detailed guidance for conducting an in silico drug screen and an in vivo drug validation. By using this protocol, it takes ~4 weeks to perform the genome-scale screen, 4 weeks for sequencing and data analysis, 4 weeks to validate candidate genes, 1 week for the virtual screen and 2 weeks for in vitro drug validation. This framework has the potential to accelerate the development of antidotes for a wide range of toxins and can rapidly identify promising drug candidates that are already known to be safe and effective. This could lead to the development of new antidotes much more quickly than traditional methods, protecting lives from diverse toxins and advancing human health.

Hepatocyte aquaporin 8-mediated water transport facilitates bile dilution and prevents gallstone formation in mice

Although water channel aquaporin-8 (AQP8) has been implicated in hepatic bile formation and liver diseases associated with abnormal bile flow in human and animal studies, direct evidence of its involvement in bile secretion is still lacking. This study aimed to determine the role of AQP8 in bile secretion and gallstone formation. We generated various transgenic knock-in and knockout mouse models and assessed liver AQP8 expression by immunostaining and immunoblotting, hepatic bile secretion by cannulation of the common bile duct, cholesterol gallstone formation by feeding a high-fat lithogenic diet, and identified regulatory small molecules by screening the organic fractions of cholagogic Chinese herbs and biochemical characterization. We identified a novel expression pattern of AQP8 protein in the canalicular membrane of approximately 50% of the liver lobules. AQP8-deficient mice exhibited impaired hepatic bile formation, characterized by the secretion of concentrated bile with a lower flow rate and higher levels of bile lipids than that of wild-type littermates. AQP8-/- mice showed accelerated gallstone formation, which was rescued by AAV-mediated hepatic expression of AQP8 or AQP1. Moreover, we identified a small molecule, scutellarin, that upregulates hepatocyte AQP8 expression in vitro and in vivo. In AQP8+/+ mice, scutellarin significantly increased bile flow, decreased bile lipid concentrations, and prevented gallstone formation compared to AQP8-/- mice. Molecular studies revealed that scutellarin promoted the ubiquitination and degradation of HIF-1α, a transcriptional negative regulator of AQP8, by disrupting its interactions with HSP90. AQP8 plays a crucial role in facilitating water transport and bile dilution during hepatic bile formation, thereby mitigating gallstone formation in mice. Small-molecule intervention validated hepatocyte AQP8 as a promising drug target for gallstone therapy. The incidence of gallstone disease is high, and current drug treatments for gallstones are very limited, necessitating the identification of novel drug targets for developing new drugs with universal applicability. To our knowledge, this is the first study to provide direct evidence that hepatic water channel AQP8 plays a key role in bile dilution and gallstone formation. Modulation of hepatic water transport may provide a universal therapeutic strategy for all types of gallstone diseases.

Essential cancer protein identification using graph-based random walk with restart

Protein-protein interaction (PPI) network analysis holds significant promise for cancer diagnosis and drug target identification. This paper introduces a novel random walk-based method called essential cancer protein identification using graph-based random walk with restart (EPI-GBRWR) to address this gap. This proposed method incorporates local and global topological features of proteins, enhancing the accuracy of essential protein identification in PPI networks. Starting with meticulous preprocessing of cancer gene datasets from NCBI, including breast, lung, colorectal, and ovarian cancers, and identifying a core set of common genes. The proposed method constructs PPI networks to capture complex protein interactions from these common cancer genes. Topological analysis, including a centrality measures matrix, is generated to perform the analysis to identify essential nodes. The study revealed that 40 essential proteins among breast, colorectal, lung and ovarian cancer showcase the potency of integrative methodologies in unravelling cancer complexity, signalling a transformative era in cancer research and treatment. The strength of the findings from the study has direct clinical relevance in cancer diseases. It contributes to the field of precision medicine to guide personalized treatment strategies.

MMD-DTA: A multi-modal deep learning framework for drug-target binding affinity and binding region prediction.

The prediction of drug-target affinity (DTA) plays a crucial role in drug development and the identification of potential drug targets. In recent years, computer-assisted DTA prediction has emerged as a significant approach in this field. In this study, we propose a multi-modal deep learning framework called MMD-DTA for predicting drug-target binding affinity and binding regions. The model can predict DTA while simultaneously learning the binding regions of drug-target interactions through unsupervised learning. To achieve this, MMD-DTA first uses graph neural networks and target structural feature extraction network to extract multi-modal information from the sequences and structures of drugs and targets. It then utilizes the feature interaction and fusion modules to generate interaction descriptors for predicting DTA and interaction strength for binding region prediction. Our experimental results demonstrate that MMD-DTA outperforms existing models based on key evaluation metrics. Furthermore, external validation results indicate that MMD-DTA enhances the generalization capability of the model by integrating sequence and structural information of drugs and targets. The model trained on the benchmark dataset can effectively generalize to independent virtual screening tasks. The visualization of drug-target binding region prediction showcases the interpretability of MMD-DTA, providing valuable insights into the functional regions of drug molecules that interact with proteins.

NOX1-mediated oxidative stress induces chondrocyte ferroptosis by inhibiting the Nrf2/HO-1 pathway

Osteoarthritis (OA) is a common joint disease associated with the aging of the population, and it reduces the quality of life of patients. It is characterized by the destruction of articular cartilage and the secretion of inflammatory cytokines. Owing to the unclear pathogenesis of OA, current treatment methods have significant limitations. Oxidative stress has been revealed to play an important role in the development of OA. Our experiments indicated that the levels of GSH decreased and the level of MDA increased in chondrocytes, which induced ferroptosis in chondrocytes in OA. We also revealed that ferroptosis was the main mechanism of cartilage destruction caused by the addition of the ferroptosis activator erastin and the ferroptosis inhibitor ferrostatin-1. NOX1 is the main modulator of oxidative stress by increasing the generation of reactive oxidative species (ROS). We suppressed the expression of NOX1 in chondrocytes through cell transfection. The expression of collagen II and MMP13, and the secretion of IL-1β and TNF-α were reversed. An increase in the mitochondrial membrane potential and a decrease in the level of intracellular ROS indicate an improvement in oxidative damage. Additionally, we determined the effect of the Nrf2/HO-1 pathway on NOX1-mediated chondrocyte injury. We found that NOX1 inhibited the expression of Nrf2/HO-1, but the activation of Nrf2 improved the oxidative damage to chondrocytes in vivo and vitro. This study revealed that NOX1-mediated oxidative stress induces chondrocyte ferroptosis by inhibiting the Nrf2/HO-1 pathway. Our findings contribute to revealing the pathogenesis of OA, providing targets for drug design and optimizing the clinical treatment of OA.

Human Genetics and Genomics for Drug Target Identification and Prioritization: Open Targets' Perspective.

Open Targets, a consortium among academic and industry partners, focuses on using human genetics and genomics to provide insights to key questions that build therapeutic hypotheses. Large-scale experiments generate foundational data, and open-source informatic platforms systematically integrate evidence for target-disease relationships and provide dynamic tooling for target prioritization. A locus-to-gene machine learning model uses evidence from genome-wide association studies (GWAS Catalog, UK BioBank, and FinnGen), functional genomic studies, epigenetic studies, and variant effect prediction to predict potential drug targets for complex diseases. These predictions are combined with genetic evidence from gene burden analyses, rare disease genetics, somatic mutations, perturbation assays, pathway analyses, scientific literature, differential expression, and mouse models to systematically build target-disease associations (https://platform.opentargets.org). Scored target attributes such as clinical precedence, tractability, and safety guide target prioritization. Here we provide our perspective on the value and impact of human genetics and genomics for generating therapeutic hypotheses.

AI-recognized mitochondrial phenotype enables identification of drug targets.

AI-recognized mitochondrial phenotype enables identification of drug targets.

A genome-scale metabolic model of a globally disseminated hyperinvasive M1 strain of Streptococcus pyogenes.

Streptococcus pyogenes is responsible for a range of diseases in humans contributing significantly to morbidity and mortality. Among more than 200 serotypes of S. pyogenes, serotype M1 strains hold the greatest clinical relevance due to their high prevalence in severe human infections. To enhance our understanding of pathogenesis and discovery of potential therapeutic approaches, we have developed the first genome-scale metabolic model (GEM) for a serotype M1 S. pyogenes strain, which we name iYH543. The curation of iYH543 involved cross-referencing a draft GEM of S. pyogenes serotype M1 from the AGORA2 database with gene essentiality and autotrophy data obtained from transposon mutagenesis-based and growth screens. We achieved a 92.6% (503/543 genes) accuracy in predicting gene essentiality and a 95% (19/20 amino acids) accuracy in predicting amino acid auxotrophy. Additionally, Biolog Phenotype microarrays were employed to examine the growth phenotypes of S. pyogenes, which further contributed to the refinement of iYH543. Notably, iYH543 demonstrated 88% accuracy (168/190 carbon sources) in predicting growth on various sole carbon sources. Discrepancies observed between iYH543 and the actual behavior of living S. pyogenes highlighted areas of uncertainty in the current understanding of S. pyogenes metabolism. iYH543 offers novel insights and hypotheses that can guide future research efforts and ultimately inform novel therapeutic strategies.IMPORTANCEGenome-scale models (GEMs) play a crucial role in investigating bacterial metabolism, predicting the effects of inhibiting specific metabolic genes and pathways, and aiding in the identification of potential drug targets. Here, we have developed the first GEM for the S. pyogenes highly virulent serotype, M1, which we name iYH543. The iYH543 achieved high accuracy in predicting gene essentiality. We also show that the knowledge obtained by substituting actual measurement values for iYH543 helps us gain insights that connect metabolism and virulence. iYH543 will serve as a useful tool for rational drug design targeting S. pyogenes metabolism and computational screening to investigate the interplay between inhibiting virulence factor synthesis and growth.

Identification of novel drug targets to counteract efflux pump mediated multidrug resistance in Acinetobacter baumannii

The emergence of multidrug-resistant (MDR) Acinetobacter baumannii poses an escalating threat to the healthcare system worldwide. A significant factor contributing to increasing resistance is the overexpression of chromosomally encoded efflux pumps, which expel antibiotics from bacterial cells, thereby rendering treatments less effective. The efflux pumps not only mediate resistance to antibiotics through drug efflux but also work synergistically with other resistance mechanisms, thereby doubling the resistance. Despite their crucial role in antibiotic resistance, understanding of the structure, function, mechanisms of action, and regulation of efflux pumps remains limited, which is necessary for devising effective strategies to restore drug susceptibility and to combat MDR isolates. In this context, the present study evaluated the prevalence of efflux pump overexpression in clinical A. baumannii isolates using phenotypic and genotypic methods and identified potential therapeutic targets employing a network-based approach. A total of 172 A. baumannii isolates were collected and subjected to antibiotic susceptibility tests using the Kirby-Bauer disk diffusion method. All the isolates were found to be MDR, with 94.76 % showing resistance to carbapenems. Efflux pump overexpression was detected in 54.65 % of isolates using the Ethidium-Bromide Agar Cartwheel method, and efflux pump inhibitory activity was observed in 68.71 % of isolates using cyanide m-chlorophenylhydrazone (CCCP). A total of thirteen efflux pump genes were detected in the tested isolates using diagnostic PCR, which were considered for interaction network analysis using STRING. Clustering analysis of the merged network identified two highly interconnected clusters, each comprising functional partners crucial for efflux pump function and regulation. Key hub genes, including AdeB, AdeJ, AdeK, AdeC, macB, tolC, AIL80285.1, AdeR, and AdeS, were identified as primary targets due to their significant influence on the network. Additionally, 24 clustered genes were pinpointed as potential drug targets for developing novel therapeutics to combat the formidable challenge of efflux pump-mediated MDR in A. baumannii.

Spatial transcriptomic characterization of pathologic niches in IPF.

Despite advancements in antifibrotic therapy, idiopathic pulmonary fibrosis (IPF) remains a medical condition with unmet needs. Single-cell RNA sequencing (scRNA-seq) has enhanced our understanding of IPF but lacks the cellular tissue context and gene expression localization that spatial transcriptomics provides. To bridge this gap, we profiled IPF and control patient lung tissue using spatial transcriptomics, integrating the data with an IPF scRNA-seq atlas. We identified three disease-associated niches with unique cellular compositions and localizations. These include a fibrotic niche, consisting of myofibroblasts and aberrant basaloid cells, located around airways and adjacent to an airway macrophage niche in the lumen, containing SPP1+ macrophages. In addition, we identified an immune niche, characterized by distinct lymphoid cell foci in fibrotic tissue, surrounded by remodeled endothelial vessels. This spatial characterization of IPF niches will facilitate the identification of drug targets that disrupt disease-driving niches and aid in the development of disease relevant in vitro models.

Identification of Potential Drug Targets for Myopia Through Mendelian Randomization.

The purpose of this study was to identify potential drug targets for myopia and explore underlying mechanisms. Mendelian randomization (MR) was implemented to assess the effect of 2684 pharmacologically targetable genes in the blood and retina on the risk of myopia from a genomewide association study (GWAS) for age-at-onset of spectacle wearing-inferred mean spherical equivalent (MSE; discovery cohort, N = 287,448, European), which was further validated in a GWAS for autorefraction-measured MSE (replication cohort, N = 95,619, European). The reliability of the identified significant potential targets was strengthened by colocalization analysis. Additionally, enrichment analysis, protein-protein interaction network, and molecular docking were performed to explore the functional roles and the druggability of these targets. This systematic drug target identification has unveiled 6 putative genetically causal targets for myopia-CD34, CD55, Wnt3, LCAT, BTN3A1, and TSSK6-each backed by colocalization evidence in adult blood eQTL datasets. Functional analysis found that dopaminergic neuron differentiation, cell adhesion, Wnt signaling pathway, and plasma lipoprotein-associated pathways may be involved in myopia pathogenesis. Finally, drug prediction and molecular docking corroborated the pharmacological value of these targets with LCAT demonstrating the strongest binding affinity. Our study not only opens new avenues for the development of therapeutic interventions for myopia but may also help to understand the underlying mechanisms of myopia.

RELA-mediated upregulation of LINC03047 promotes ferroptosis in silica-induced pulmonary fibrosis via SLC39A14

Long non-coding RNAs play a key role in silicosis, a fatal fibrotic lung disease, and there is an urgent need to develop new treatment targets. Long intergenic non-protein-coding RNA 3047 (LINC03047) is associated with cancer, but its role and mechanism in the progression of silicosis require further elucidation. This study investigated the function of LINC03047 in the epithelial-mesenchymal transition (EMT) during silicosis progression. LINC03047 expression was upregulated in SiO2-treated BEAS-2B and A549 cells, promoting SiO2-induced ferroptosis and subsequent EMT. Moreover, knockdown of LINC03047 significantly decreased the expression of solute carrier family 39 member 14 (SLC39A14), a ferrous iron transporter, and inhibition of SLC39A14 alleviated the ferroptosis and EMT caused by LINC03047 overexpression. We further investigated that NF-κB p65 (RELA) was critical for LINC03047 transcription in SiO2-treated BEAS-2B and A549 cells. In vivo experiments showed that SLC39A14 deficiency improved SiO2-induced lipid peroxidation and EMT. Collectively, our study reveals the function of the RELA/LINC03047/SLC39A14 axis in SiO2-induced ferroptosis and EMT, thereby contributing to the identification of novel drug targets for silicosis therapy.

Nanogel: A versatile drug delivery system for the treatment of various diseases and their future perspective.

Nanogel (NG) drug delivery systems have emerged as promising tools for targeted and controlled drug release, revolutionizing treatment approaches across various diseases.Their unique physicochemical properties, such as nano size, high surface area,biocompatibility, stability, and tunable drug release, make themideal carriers for a wide range of therapeutic agents. Nanogels (NGs), characterized by their 3D network of crosslinked polymers, offer unique edges like high drug loading capacity, controlled release, and targeted delivery. Additionally, the diverse applications of NGs in medical therapeutics highlight their versatility and potential impact on improving patient outcomes. Their application spans cancer treatment, infectious diseases, and chronic conditions, allowing for precise drug delivery to specific tissues or cells, minimizing side effects, and enhancing therapeutic efficacy. Despite their potential, challenges such as scalability, manufacturing reproducibility, and regulatory hurdles must be addressed. Achieving clinical translation requires overcoming these obstacles to ensure therapeutic payloads' safe and efficient delivery. Strategies such as surface modification and incorporating stimuli-responsive elements enhanced NG performance and addressed specific therapeutic challenges. Advances in nanotechnology, biomaterials, and targeted drug design offer opportunities to improve the performance of NGs and address current limitations. Tailoring NGs for exploring combination therapies and integrating diagnostics for real-time monitoring represent promising avenues for future research. In conclusion, NG drug delivery systems have demonstrated tremendous potential in diverse disease applications. Overcoming challenges and leveraging emerging technologies will pave the way for their widespread clinical implementation, ushering in a new era of precision medicine and improved patient care.