Drug Targets Research Articles

Unique intestinal microflora and metabolic profile in different stages of hypertension reveal potential biomarkers for early diagnosis and prognosis.

Introduction. Hypertension is the most prevalent chronic disease and a major risk factor for cardiovascular and cerebrovascular diseases.Gap statement. However, there has been no substantial breakthrough in aetiology, new drug targets, and drug development of hypertension in recent 50 years.Research aim. Therefore, this study was to screen unique intestinal microbiome and serum metabolic biomarkers which can early diagnose and track the prognosis of hypertension patients in different periods, and analyse its underlying mechanisms and functions.Methods. Four groups of stool and serum samples, including healthy controls (HCs), prehypertension (PHT), hypertension (HT), and hypertension-related complications (HTC), were collected. Microbial diversity assessed using 16S rRNA sequencing. The metabolites in serum samples were detected through LC-MS/MS analysis.Results. The composition of gut microbiota in patients exhibited dissimilarities compared to that in healthy subjects, which was distinguished by Prevotella, Slackia, Enterococcus, Bifidobacterium, and Lactobacillales may be potential markers for tracking the progression of hypertension, and Bifidobacterium, Butyricimonas, Adlercreutzia, Faecalibacterium, Lactobacillus, Ruminococcus, Clostridium, and Acidaminococcus demonstrated diagnostic value. Meanwhile, tracking the dynamic changes of deoxycholic acid, 4-oxododecanedioic acid, and l-arginine can serve as biomarkers for early diagnosis, and investigation into the mechanism by which the intestinal microbiome influences the onset and progression of hypertension. In terms of pathogenesis, the findings revealed that Bifidobacterium may caused the changes of AST, indirect bilirubin, ALT, triglyceride and uric acid by affecting metabolites cis-7-hexadecenoic acid methyl ester and N1-acetylspermidine. Additionally, Coprococcus may cause changes in albumin through the influence of androsterone enanthate.Conclusions. These findings highlight that the unique intestinal microbiome and serum metabolic profile in different periods of hypertension will provide valuable insight for timely diagnosis and prognosis tracking in hypertension patients with promising clinical applications.

New Insights on the Therapeutic Potential of Runt-Related Transcription Factor2 for Osteoarthritis: Evidence from Mendelian Randomization.

Research has highlighted the role of runt-related transcription factor2 (Runx2) in the development of osteoarthritis (OA); however, its causal association remains unclear. This study aimed to explore whether Runx2 expression is causally associated with OA and assess its therapeutic potential for OA. Genetic proxy instruments for Runx2 expression were obtained from gene expression quantitative trait locus (eQTLs) study of eQTLGen Consortium (n = 31,684). Aggregated genome-wide association study (GWAS) data for OA (including all OA [177,517 cases and 649,173 controls], knee OA (KOA) [62,497 cases and 333,557 controls], and hip OA (HOA) [36,445 cases and 316,943 controls]) were extracted from the Genetics of Osteoarthritis Consortium. We integrated eQTLs data with OA GWAS data to estimate their causal association and to estimate the potential of Runx2 as a drug target in the treatment of OA using summary data-based Mendelian randomization (SMR) analysis. Furthermore, different OA GWAS data (including all OA [77,052 cases and 378,169 controls], KOA [24,955 cases and 378,169 controls], and HOA [15,704 cases and 378,169 controls]) derived from the GWAS Catalog database were used for replication study. SMR analysis showed that high expression levels of Runx2 were associated with an increased risk of all OA [odds ratio (OR) 1.044, 95% confidence interval (CI) 1.023-1.067; P = 5.03 × 10-5], KOA (OR1.040, 95%CI 1.006-1.075; P = 0.021), and HOA (OR1.067, 95%CI 1.022-1.113; P = 0.003). This suggests that Runx2 inhibitors may have promising potential for the treatment of OA. Notably, the causal effects of Runx2 with all OA (OR1.053, 95%CI 1.027-1.079; P = 3.95 × 10-5) and KOA (OR1.043, 95%CI 1.001-1.087; P = 0.045) were repeated in the replication study, but limited evidence supported the association of Runx2 expression levels with HOA (OR1.045, 95%CI 0.993-1.101; P = 0.094). Our analyses indicate a positive correlation between Runx2 expression and OA risk across all three phenotypes, suggesting the potential of Runx2 inhibitors in the treatment of OA and providing evidence from a genetic perspective.

Glucose Transporter and Sensor Mechanisms in Fungal Pathogens as Potential Drug Targets.

Fungal infections are emerging as major health challenges in recent years. The development of resistance against existing antifungal agents needs urgent attention and action. The limited classes of antifungal drugs available, their tendency to cause adverse effects, lack of effectiveness, etc., are the major limitations of current therapy. Thus, there is a pressing demand for new antifungal drug classes to cope with the present circumstances. Glucose is the key source of energy for all organisms, including fungi. Glucose plays a crucial role as a source of carbon and energy for processes like virulence, growth, invasion, biofilm formation, and resistance development. The glucose transport and sensing mechanisms are well developed in these organisms as an important strategy to sustain survival. Modulating these transport or sensor mechanisms may serve as an important strategy to inhibit fungal growth. Moreover, the structural difference between human and fungal glucose transporters makes them more appealing as drug targets. Limited literature is available for fungal glucose entry mechanisms. This review provides a comprehensive account of sugar transport mechanisms in common fungal pathogens.

Mechanistic and structural insights into vitamin B2 metabolizing enzyme riboflavin kinase from Leishmania donovani

Leishmania donovani relies on specific vitamins and cofactors crucial for its survival and pathogenesis. Tailoring therapies to disrupt these pathways offers a promising strategy for the treatment of Visceral Leishmaniasis. Current treatment regimens are limited due to drug resistance and high costs. The dependency of Leishmania parasites on Vitamin B2 and its metabolic products is not known. In this study, we have biochemically and biophysically characterized a Vitamin B2 metabolism enzyme, riboflavin kinase from L. donovani (LdRFK) which converts riboflavin (vitamin B2) into flavin mononucleotide (FMN). Sequence comparison with human counterpart reflects 31.58 % identity only, thus opening up the possibility of exploring it as drug target. The rfk gene was cloned, expressed and the recombinant protein was purified. Kinetic parameters of LdRFK were evaluated with riboflavin and ATP as substrates which showed differential binding affinity when compared with the human RFK enzyme. Thermal and denaturant stability of the enzyme was evaluated. The rfk gene was overexpressed in the parasites and its role in growth and cell cycle was evaluated. In the absence of crystal structure, homology modelling and molecular dynamic simulation studies were performed to predict LdRFK structure. The data shows differences in substrate binding between human and parasite enzyme. This raises the possibility of exploring LdRFK for specific designing of antileishmanial molecules. Gene disruption studies can further validate its candidature as antileishmanial target.

The Gene Network Correlation Analysis of Obesity to Type 1 Diabetes and Cardiovascular Disorders: An Interactome-Based Bioinformatics Approach.

The current study focuses on the importance of Protein-Protein Interactions (PPIs) in biological processes and the potential of targeting PPIs as a new treatment strategy for diseases. Specifically, the study explores the cross-links of PPIs network associated with obesity, type 1 diabetes mellitus (T1DM), and cardiac disease (CD), which is an unexplored area of research. The research aimed to understand the role of highly connected proteins in the network and their potential as drug targets. The methodology for this research involves retrieving genes from the NCBI online gene database, intersecting genes among three diseases (type 1 diabetes, obesity, and cardiovascular) using Interactivenn, determining suitable drug molecules using NetworkAnalyst, and performing various bioinformatics analyses such as Generic Protein-Protein Interactions, topological properties analysis, function enrichment analysis in terms of GO, and Kyoto Encyclopedia of Genes and Genomes (KEGG), gene co-expression network, and protein drug as well as protein chemical interaction network. The study focuses on human subjects. The results of this study identified 12 genes [VEGFA (Vascular Endothelial Growth Factor A), IL6 (Interleukin 6), MTHFR (Methylenetetrahydrofolate reductase), NPPB (Natriuretic Peptide B), RAC1 (Rac Family Small GTPase 1), LMNA (Lamin A/C), UGT1A1 (UDP-glucuronosyltransferase family 1 membrane A1), RETN (Resistin), GCG (Glucagon), NPPA (Natriuretic Peptide A), RYR2 (Ryanodine receptor 2), and PRKAG2 (Protein Kinase AMP-Activated Non-Catalytic Subunit Gamma 2)] that were shared across the three diseases and could be used as key proteins for protein-drug/chemical interaction. Additionally, the study provides an in-depth understanding of the complex molecular and biological relationships between the three diseases and the cellular mechanisms that lead to their development. Potentially significant implications for the therapy and management of various disorders are highlighted by the findings of this study by improving treatment efficacy, simplifying treatment regimens, cost-effectiveness, better understanding of the underlying mechanism of these diseases, early diagnosis, and introducing personalized medicine. In conclusion, the current study provides new insights into the cross-links of PPIs network associated with obesity, T1DM, and CD, and highlights the potential of targeting PPIs as a new treatment strategy for these prevalent diseases.

Identification of potential therapeutic targets for skin cutaneous melanoma on the basic of transcriptomics.

Advanced skin cutaneous melanoma (SKCM) is responsible for the majority of skin cancer-related deaths. Apart from the rare BRAF V600F mutation, which can be targeted with specific drugs, there are currently no other novel effective therapeutic targets. We used SMR analysis with cis-expressed quantitative trait locus (cis-eQTL) as the exposure variable and SKCM as the outcome variable to identify potential therapeutic targets for SKCM. Colocalization assays and HEIDI tests are used to test whether SKCM risk and gene expression are driven by common SNPs. Replication analysis further validated the findings, and we also constructed protein-protein interaction networks to explore the relationship between the identified genes and known SKCM targets. Drug prediction and molecular docking further validated the medicinal value of drug targets. Transcriptome differential analysis further validated that there were differences between normal tissues and SKCM for the selected targets. We identified 13 genes significantly associated with the risk of SKCM, including five protective genes and eight harmful genes. The HEIDI test and co-localization analysis further indicates a causal association between genes (SOX4, MAFF) and SKCM, categorized as Class 1 evidence targets. The remaining 11 genes, except for HELZ2 show a moderately causal association with SKCM, categorized as Class 2 evidence targets. Target druggability predictions from DGIdb suggest that SOX4, MAFF, ACSF3, CDK10, SPG7, and TCF25 are likely to be future drug targets. The study provides genetic evidence for targeting available drug genes for the treatment of SKCM.

The Identification of Potential Anti-Depression/Anxiety Drug Targets by Stress-Induced Rat Brain Regional Proteome and Network Analyses.

Depression and anxiety disorders are prevalent stress-related neuropsychiatric disorders and involve multiple molecular changes and dysfunctions across various brain regions. However, the specific and shared pathophysiological mechanisms occurring in these regions remain unclear. Previous research used a rat model of chronic mild stress (CMS) to segregate and identify depression-susceptible, anxiety-susceptible, and insusceptible groups; then the proteomes of six distinct brain regions (the hippocampus, prefrontal cortex, hypothalamus, pituitary, olfactory bulb, and striatum) were separately and quantitatively analyzed. To gain a comprehensive and systematic understanding of the molecular abnormalities, this study aimed to investigate and compare differential proteomics data from the six regions. Differentially expressed proteins (DEPs) were identified in between specific regions and across all regions and subjected to a series of bioinformatics analyses. Regional comparisons showed that stress-induced proteomic changes and corresponding gene ontology and pathway enrichments were largely distinct, attributable to differences in cell populations, protein compositions, and brain functions of these areas. Additionally, a notable degree of overlap in the significantly enriched terms was identified, potentially suggesting strong connections in the enrichment across different regions. Furthermore, intra-regional and inter-regional protein-protein interaction networks and drug-target-DEP networks were constructed. Integrated analysis of the three association networks in the six regions, along with the DisGeNET database, identified ten DEPs as potential targets for anti-depression/anxiety drugs. Collectively, these findings revealed commonalities and differences across different brain regions at the protein level induced by CMS, and identified several novel protein targets for the development of new therapeutics for depression and anxiety.

Mitochondria-Targeted Multifunctional Nanoprodrugs by Inhibiting Metabolic Reprogramming for Combating Cisplatin-Resistant Lung Cancer.

How to address the resistance of cisplatin (CDDP) has always been a clinical challenge. The resistance mechanism of platinum-based drugs is very complex, including nuclear DNA damage repair, apoptosis escape, and tumor metabolism reprogramming. Tumor cells can switch between mitochondrial oxidative phosphorylation (OXPHOS) and glycolysis and develop resistance to chemotherapy drugs through metabolic variability. In addition, due to the lack of histone protection and a relatively weak damage repair ability, mitochondrial DNA (mtDNA) is more susceptible to damage, which in turn affects mitochondrial OXPHOS and can become a potential target for platinum-based drugs. Therefore, mitochondria, as targets of anticancer drugs, have become a hot topic in tumor resistance research. This study constructed a self-assembled nanotargeted drug delivery system LND-SS-Pt-TPP/HA-CD. β-Cyclodextrin-grafted hydronic acid (HA-CD)-encapsulated prodrug nanoparticles can target CD44 on the tumor surface and further deliver the prodrug to intracellular mitochondria through a triphenylphosphine group (TPP+). Disulfide bonds can be selectively degraded by glutathione (GSH) in mitochondria, releasing lonidamine (LND) and the cisplatin prodrug (Pt(IV)). Under the action of GSH and ascorbic acid, Pt(IV) is further reduced to cisplatin (Pt(II)). Cisplatin can cause mtDNA damage, induce mitochondrial dysfunction and mitophagy, and then affect mitochondrial OXPHOS. Meanwhile, LND can reduce the hexokinase II (HK II) level, induce destruction of mitochondria, and block energy supply by glycolysis inhibition. Ultimately, this self-assembled nano targeted delivery system can synergistically kill cisplatin-resistant lung cancer cells, which supplies an overcome cisplatin resistance choice via the disrupt mitochondria therapy.

Integrated network pharmacology and molecular docking to investigate the potential mechanism of Tufuling on Alzheimer's disease

ObjectiveThis study aimed to investigate the mechanism of Tu Fu Ling in treating Alzheimer's disease (AD) using network pharmacology and molecular docking. MethodsThe TCMSP and Swiss target prediction databases were utilized to confirm the active components of Tu Fu Ling and their corresponding targets, with target gene names converted using the UniProt database. Genes related to AD were collected from DisGeNET, GeneCards, and the Open Target Platform databases. Common target genes between the disease and the drug were obtained using Venny 2.1 tools and visualized using Cytoscape software. Protein-protein interaction (PPI) data were further analyzed to determine correlations between common target genes, and GO and KEGG pathway enrichment analyses were performed for intersecting genes. Finally, PYmol, AutoDock Tool, Discovery Studio 2020, and PyRx software were used for preliminary computer virtual verification and visualization of active drug ingredients and target proteins. ResultsNine active ingredients meeting the screening criteria yielded a total of 168 genes after removing duplicates. A total of 3833 target genes were collected, with 129 overlapping target genes identified. GO enrichment analysis identified 643 biological processes, 82 cellular components, and 147 molecular functions. KEGG pathway enrichment analysis also revealed a pathway closely related to AD (hsa05010: Alzheimer's disease). In molecular docking analysis, the binding affinity between the 9 active ingredients and 10 core targets ranged from −3.5 to −12.3 kcal/mol, indicating strong binding. ConclusionThis study preliminarily verified the combination of Tu Fu Ling's screened active ingredient and the calculated core target, suggesting a potential mechanism of action to improve the symptoms of AD patients through multi-target and multi-pathway approaches. This provides a valuable reference for further exploration of the pharmacological mechanism of AD and the formulation of drug therapy.

Investigating the potential mechanisms of Litsea cubeba essential oil for anti-melanoma through experimental validation, network pharmacology, and molecular docking analysis

Abstract Objectives Melanoma remains a challenge due to the lack of effective and low-toxicity treatments. Litsea cubeba essential oil (LEO), known for its tyrosinase inhibitory activity, has shown promise as an anti-melanoma compound, although robust scientific evidence is lacking. Methods We conducted GC-MS analysis to identify the major components of LEO and screened for effective components were further evaluated on A375 and HaCaT cells using the CCK-8 assay. Network pharmacology was employed to predict potential targets using PharmMapper and SwissTarget Prediction databases, with melanoma-related targets sourced from the GeneCards database. Protein–protein interaction (PPI) network was created using STRING and Cytoscape. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed through the DAVI database. Additionally, we constructed a natural product-main components-core targets-pathways-disease (NMCPD) network in Cytoscape and conducted molecular docking using PyMOL and Autodock Vina. Results GC-MS analysis revealed neral (cis-citral) and geranial (trans-citral) as the primary active components of LEO. Cell assays demonstrated that a citral mixture, in combination with LEO, effectively inhibited A375 cell growth with IC50 values of 35.94 ± 1.23 μM and 12.00 ± 0.96 μg/mL, while exhibiting minimal toxicity to HaCaT cells with IC50 values of 67.72 ± 2.96 μM and 22.32 ± 2.53 μg/mL. Screening identified10 hub targets among 190 common targets between drug and disease-related targets. KEGG pathway enrichment analysis suggested therapeutic effects of citral on melanoma by modulating signaling pathways. Molecular docking revealed strong binding affinity of neral and geranial with RXRA and ESR1, suggesting that citral, the principal LEO component, regulates multiple pathways for potential melanoma therapy. Conclusions These findings support the potential utility of LEO as a treatment for melanoma and highlight the importance of exploring tyrosinase inhibitors for the development of novel anti-melanoma drugs.

In vitro cancer cell line luminescence-based validation of anticancer phytocompounds obtained from Leucas biflora against HELA cervical and A549 lung cancer cells.

Current research aims to screen the anticancer prospective of Leucas biflora phytocompounds against apoptotic regulator target protein essential for cancer progression. In gas chromatography-mass spectrometry analysis major phytocompounds such as tetracosahexaene, squalene, phytol, 22-stigmasten-3-one, stigmasterol, fluorene, and 1,4-dihydro were identified in ethanolic leaf extract of Leucas biflora. In vitro, the free radical scavenging potential of ethanolic leaf extract of Leucas biflora was examined through its DPPH and ABTS radical scavenging potential IC50 value 15.35 and 13.20 μg/ml, respectively. Dose-dependent cytotoxicity was monitored against both A549 lung cancer and HELA cervical cancer cells. Leucas biflora ethanolic leaf extract highly reduces the cell viability of both HELA and A549 cells in in vitro cytotoxicity assays. Leucas biflora ethanolic extract produces 23.76% and 29.76% viability rates against A549 lung and HELA cervical cancer cell lines, and their IC50 values differ slightly at 95.80 and 90.40 μg/ml, respectively. In molecular docking analysis lung cancer target protein-ligand complex 5Y9T-16132746 showed a maximum score of -14 kcal/mol by exhibiting stable binding affinity and interactions among all screened complexes. Based on docking score nine phytocompounds from Leucas biflora and two reference standard drugs were chosen for further analysis. Further validation reveals that the fluorene, 1,4-dihydro possess good ADMET, Bioactivity and density functional theory indices.

Small Molecule Inhibitors against the Bacterial Pathogen Brucella.

Brucellosis remains one of the major zoonotic diseases worldwide. As a causative agent of brucellosis, it has many ways to evade recognition by the immune system, allowing it to replicate and multiply in the host, causing significant harm to both humans and animals. The pathogenic mechanism of Brucella has not been elucidated, making the identification of drug targets from the pathogenic mechanism a challenge. Metalloenzymatic targets and some protein targets unique to Brucella are exploitable in the development of inhibitors against this disease. The development of specific small molecule inhibitors is urgently needed for brucellosis treatment due to the antibiotic resistance of Brucella. This review summarizes the research on small molecule inhibitors of Brucella, which could be instructive for subsequent studies.

Chemical coverage of human biological pathways

Chemical probes and chemogenomic compounds are valuable tools to link gene to phenotype, explore human biology, and uncover novel targets for precision medicine. The mission of the Target 2035 initiative is to discover chemical tools for all human proteins by the year 2035. Here, we draw a landscape of the current chemical coverage of human biological pathways. Although available chemical tools target only 3% of the human proteome, they already cover 53% of human biological pathways and represent a versatile toolkit to dissect a vast portion of human biology. Pathways targeted by existing drugs might be enriched in unknown but valid drug targets and could be prioritized in future Target 2035 efforts.

Potential drug targets for osteoporosis identified: A Mendelian randomization study

BackgroundOsteoporosis is a prevalent global health condition, primarily affecting the aging population, and several therapies for osteoporosis have been widely used. However, available drugs for osteoporosis are far from satisfactory because they cannot alleviate disease progression. This study aimed to explore potential drug targets for osteoporosis through Mendelian randomization analysis. MethodsUsing cis-expression quantitative trait loci (cis-eQTL) data of druggable genes and two genome-wide association studies (GWAS) datasets related to osteoporosis (UK Biobank and FinnGen cohorts), we employed mendelian randomization (MR) analysis to identify the druggable genes with causal relationships with osteoporosis. Subsequently, a series of follow-up analyses were conducted, such as colocalization analysis, cell-type specificity analysis, and correlation analysis with risk factors. The association between potential drug targets and osteoporosis was validated by qRT-PCR. ResultsSix druggable genes with causal relationships with osteoporosis were identified and successfully replicated, including ACPP, DNASE1L3, IL32, PPOX, ST6GAL1, and TGM3. Cell-type specificity analysis revealed that PPOX and ST6GAL1 were expressed in all cell types in the bone samples, while IL32, ACPP, DNASE1L3, and TGM3 were expressed in specific cell types. The GWAS data showed there were seven risk factors for osteoporosis, including vitamin D deficiency, COPD, physical activity, BMI, MMP-9, ALP and PTH. Furthermore, ACPP was associated with vitamin D deficiency and COPD; DNASE1L3 was linked to physical activity; IL32 correlated with BMI and MMP-9; and ST6GAL1 was related to ALP, physical activity, and MMP-9. Among these risk factors, only MMP-9 had a high genetic correlation with osteoporosis. The results of qRT-PCR demonstrated that IL32 was upregulated while ST6GAL1 was downregulated in peripheral blood of osteoporosis patients. ConclusionOur findings suggested that those six druggable genes offer potential drug targets for osteoporosis and require further clinical investigation, especially IL32 and ST6GAL1.

Determination of metal ion transport rate of human ZIP4 using stable zinc isotopes

The essential microelement zinc is absorbed in the small intestine mainly by the zinc transporter ZIP4, a representative member of the Zrt/Irt-like protein (ZIP) family. ZIP4 is reportedly upregulated in many cancers, making it a promising oncology drug target. To date, there have been no reports on the turnover number of ZIP4, which is a crucial missing piece of information needed to better understand the transport mechanism. In this work, we used a non-radioactive zinc isotope, 70Zn, and inductively coupled plasma mass spectrometry (ICP-MS) to study human ZIP4 (hZIP4) expressed in HEK293 cells. Our data showed that 70Zn can replace the radioactive 65Zn as a tracer in kinetic evaluation of hZIP4 activity. This approach, combined with the quantification of the cell surface expression of hZIP4 using biotinylation or surface-bound antibody, allowed us to estimate the apparent turnover number of hZIP4 to be in the range of 0.08-0.2 s-1. The turnover numbers of the truncated hZIP4 variants are significantly smaller than that of the full-length hZIP4, confirming a crucial role for the extracellular domain in zinc transport. Using 64Zn and 70Zn, we measured zinc efflux during the cell-based transport assay and found that it has little effect on the zinc import analysis under these conditions. Finally, we demonstrated that use of laser ablation (LA) ICP-TOF-MS on samples applied to a solid substrate significantly increased the throughput of the transport assay. We envision that the approach reported here can be applied to the studies of metal transporters beyond the ZIP family.

Viral DNA polymerase structures reveal mechanisms of antiviral drug resistance.

DNA polymerases are important drug targets, and many structural studies have captured them in distinct conformations. However, a detailed understanding of the impact of polymerase conformational dynamics on drug resistance is lacking. We determined cryoelectron microscopy (cryo-EM) structures of DNA-bound herpes simplex virus polymerase holoenzyme in multiple conformations and interacting with antivirals in clinical use. These structures reveal how the catalytic subunit Pol and the processivity factor UL42 bind DNA to promote processive DNA synthesis. Unexpectedly, in the absence of an incoming nucleotide, we observed Pol in multiple conformations with the closed state sampled by the fingers domain. Drug-bound structures reveal how antivirals may selectively bind enzymes that more readily adopt the closed conformation. Molecular dynamics simulations and the cryo-EM structure of a drug-resistant mutant indicate that some resistance mutations modulate conformational dynamics rather than directly impacting drug binding, thus clarifying mechanisms that drive drug selectivity.

MultiModRLBP: A Deep Learning Approach for Multi-Modal RNA-Small Molecule Ligand Binding Sites Prediction.

This study aims to tackle the intricate challenge of predicting RNA-small molecule binding sites to explore the potential value in the field of RNA drug targets. To address this challenge, we propose the MultiModRLBP method, which integrates multi-modal features using deep learning algorithms. These features include 3D structural properties at the nucleotide base level of the RNA molecule, relational graphs based on overall RNA structure, and rich RNA semantic information. In our investigation, we gathered 851 interactions between RNA and small molecule ligand from the RNAglib dataset and RLBind training set. Unlike conventional training sets, this collection broadened its scope by including RNA complexes that have the same RNA sequence but change their respective binding sites due to structural differences or the presence of different ligands. This enhancement enables the MultiModRLBP model to more accurately capture subtle changes at the structural level, ultimately improving its ability to discern nuances among similar RNA conformations. Furthermore, we evaluated MultiModRLBP on two classic test sets, Test18 and Test3, highlighting its performance disparities on small molecules based on metal and non-metal ions. Additionally, we conducted a structural sensitivity analysis on specific complex categories, considering RNA instances with varying degrees of structural changes and whether they share the same ligands. The research results indicate that MultiModRLBP outperforms the current state-of-the-art methods on multiple classic test sets, particularly excelling in predicting binding sites for non-metal ions and instances where the binding sites are widely distributed along the sequence. MultiModRLBP also can be used as a potential tool when the RNA structure is perturbed or the RNA experimental tertiary structure is not available. Most importantly, MultiModRLBP exhibits the capability to distinguish binding characteristics of RNA that are structurally diverse yet exhibit sequence similarity. These advancements hold promise in reducing the costs associated with the development of RNA-targeted drugs.

Targeting Catechol-O-Methyltransferase Induces Mitochondrial Dysfunction and Enhances the Efficacy of Radiotherapy in Glioma.

Radiotherapy (RT) is commonly used to try to eliminate any remaining tumor cells following surgical resection of glioma. However, tumor recurrence is prevalent, highlighting the unmet medical need to develop therapeutic strategies to enhance the efficacy of RT in glioma. Focusing on the radiosensitizing potential of currently approved drugs known to cross the blood-brain barrier can facilitate rapid clinical translation. Here, we assessed the role of catechol-o-methyltransferase (COMT), a key enzyme to degrade catecholamines and a drug target for Parkinson's disease, in glioma treatment. Analysis of TCGA data showed significantly higher COMT expression levels in both low-grade glioma and glioblastoma compared to normal brain tissues. Inhibition of COMT by genetic knockout or FDA-approved COMT inhibitors significantly sensitized glioma cells to RT in vitro and in vivo. Mechanistically, COMT inhibition in glioma cells led to mitochondria dysfunction and increased mitochondrial RNA release into the cytoplasm, activating the cellular antiviral double-stranded RNA sensing pathway and type I interferon (IFN) response. Elevated type I IFNs stimulated the phagocytic capacity of microglial cells, enhancing RT efficacy. Given the long-established safety record of the COMT inhibitors, these findings provide a solid rationale to evaluate them in combination with RT in glioma patients.

Highlights from the Top 100 Most Influential Articles Regarding the Nuclear Receptor PPAR-γ: A Bibliometric Analysis.

PPAR-γ is one of three members of the PPAR group of the nuclear receptor superfamily and plays an important regulatory role as a ligand-dependent transcription factor. This study aimed to identify the top 100 most influential articles in the field of PPAR-γ. We hypothesized that a bibliometric and scientometric analysis of the PPAR-γ research field could render trends that provide researchers and funding agencies valuable insight into the history of the field, and potential future directions. A literature search of publications was carried out using the Web of Science (WOS) and Scopus database based on specific subject words on September 11, 2023. Articles were listed in descending order of the number of citations. Statistical analysis was performed on the data of the top 100 cited articles in terms of year of publication, journal, research direction, institution, author, and country. Meanwhile, co-authorship networks and co-citation networks were constructed by using VOSviewer software, and keywords were analyzed for cooccurrence. A total of 9,456 articles regarding PPAR-γ were identified and analyzed based on the WOS database, and the top 100 cited articles in the field of PPAR-γ were ranked by citation. The most cited article was published in 1998, with 2,571 citations and a density of 102.80 citations/ year. Of the 100 articles, Harvard University was the institution with the highest number of articles published. Spiegelman, B. M. was the author with the highest number of articles published. Using the VOSviewer software, we found that the most used keywords were geneexpression, activated receptor-gamma, and adipocyte differentiation. PPAR-γ, one of the most widely studied transcription factors, is an important drug target for many diseases. Therefore, screening for small molecule compounds targeting PPAR-γ remains of great value. The present study identified the top 100 most influential articles in the field of PPAR-γ, which help global researchers to better understand research perspectives and develop future research directions of PPAR-γ.

Mendelian Randomization Unveils Drug Targets for IgA Nephropathy.

Mendelian Randomization Unveils Drug Targets for IgA Nephropathy.