Oncogenesis. 2024 Nov 23;13(1):42. doi: 10.1038/s41389-024-00541-2.

ABSTRACT

Glioblastoma stem cells (GSCs) are pivotal in the recurrence and drug resistance of glioblastoma multiforme (GBM). However, precision therapeutic and diagnostic markers for GSCs have not been fully established. Here, using bioinformatics and experimental analysis, we identified P4HB, a protein disulfide isomerase, as a serum marker that maintains stemness in GSCs through the Wnt/β-catenin signaling pathway. Transcriptional silencing of P4HB induces apoptosis and diminishes stem cell-like characteristics in GSCs. Treatments with the chemical CCF624 or the China National Medical Products Administration (NMPA)-approved securinine significantly prolonged survival in patient-derived xenograft mouse models, underscoring P4HB's potential as a therapeutic target and presenting an expedited path to clinical application through drug repurposing. Additionally, elevated P4HB levels in patient serum were found to correlate with disease progression, underscoring its utility as a biomarker and its promise for precision medicine.

PMID:39580454 | DOI:10.1038/s41389-024-00541-2

Life Sci. 2024 Nov 21:123268. doi: 10.1016/j.lfs.2024.123268. Online ahead of print.

ABSTRACT

AIMS: While Ca2+ signaling plays a vital role in maintaining normal endothelial function and vascular activity, aberrant Ca2+ signaling in endothelial dysfunction is involved in the pathogenesis of inflammation. As a safe anti-psychotic drug to mobilize Ca2+ signaling, we repurposed spiperone as a potential drug for two intestinal epithelial injury related diseases, colitis and sepsis.

MATERIALS AND METHODS: Spiperone-induced vasorelaxation of human submucosal arterioles and mesenteric arterioles from wide-type and TRPV4 KO mice was determined by Mulvany-style wire myograph. The action of spiperone in HUVEC was tested by Ca2+ imaging and patch clamp, and its action on murine mesenteric arterioles was measured in vivo. LPS- and CLP-induced septic mice and DSS-induced colitic mice were used to examine the anti-inflammatory effects of spiperone.

KEY FINDINGS: Spiperone induced endothelium-dependent hyperpolarization (EDH)-mediated vasorelaxation of healthy arterioles with EC50 of ~50 nM predominately via PLC/IP3/IP3R pathway to induce endoplasmic reticulum (ER) Ca2+ release and further to promote Ca2+ entry via TRPV4-constituted SOCE. In both LPS- and CLP-induced septic mice, spiperone effectively prevented and treated sepsis by reducing serum proinflammatory factors, alleviating multiple organ dysfunction, rescuing the impaired EDH-mediated vasorelaxation and improving murine survival rate. Similarly, spiperone could also protect against murine colitis.

SIGNIFICANCE: We reveal new action mode and mechanism of spiperone to induce EDH-mediated vasorelaxation of both human and murine arterioles to protect against colitis and sepsis by innovatively inducing PLC/IP3R/Ca2+ signaling rather than canonically antagonizing GPCR. Spiperone could be repurposed as a potential new drug for the prevention/treatment of colitis and sepsis.

PMID:39580139 | DOI:10.1016/j.lfs.2024.123268

Cell. 2024 Nov 15:S0092-8674(24)01268-6. doi: 10.1016/j.cell.2024.10.045. Online ahead of print.

ABSTRACT

Large-scale proteomics studies can refine our understanding of health and disease and enable precision medicine. Here, we provide a detailed atlas of 2,920 plasma proteins linking to diseases (406 prevalent and 660 incident) and 986 health-related traits in 53,026 individuals (median follow-up: 14.8 years) from the UK Biobank, representing the most comprehensive proteome profiles to date. This atlas revealed 168,100 protein-disease associations and 554,488 protein-trait associations. Over 650 proteins were shared among at least 50 diseases, and over 1,000 showed sex and age heterogeneity. Furthermore, proteins demonstrated promising potential in disease discrimination (area under the curve [AUC] > 0.80 in 183 diseases). Finally, integrating protein quantitative trait locus data determined 474 causal proteins, providing 37 drug-repurposing opportunities and 26 promising targets with favorable safety profiles. These results provide an open-access comprehensive proteome-phenome resource (https://proteome-phenome-atlas.com/) to help elucidate the biological mechanisms of diseases and accelerate the development of disease biomarkers, prediction models, and therapeutic targets.

PMID:39579765 | DOI:10.1016/j.cell.2024.10.045

Comput Biol Chem. 2024 Nov 15;114:108283. doi: 10.1016/j.compbiolchem.2024.108283. Online ahead of print.

ABSTRACT

Methotrexate (MTX) is an antimetabolite drug that mimics folate and inhibits dihydrofolic acid reductase, resulting in the impairment of malignant growth in actively proliferating tissues. MTX is approved by the FDA for primarily treating non-Hodgkin lymphoma, lymphoblastic leukemia, and osteosarcoma. In addition, MTX is also prescribed as a preferred anti-rheumatic medication for the management of rheumatoid arthritis, including psoriasis, indicating that MTX has a multipronged mechanism of action. MTX is also known to exert anti-inflammatory effects, and interestingly, the role of C5a, a pro-inflammatory glycoprotein of the complement system, is well established in several chronic inflammatory diseases, including rheumatoid arthritis and psoriasis, through the recruitment of C5a receptors (C5aR1/C5aR2) expressed in both immune and non-immune cells. Notably, through drug repurposing studies, we have earlier shown that non-steroidal anti-inflammatory drugs (NSAIDS) can potentially neutralize the function of C5a. Though MTX binds to serum albumin and can affect the immune system, whether its interaction with C5a could be therapeutically beneficial due to the downregulation of both extracellular and intracellular signaling of C5a is not yet established in the literature. In the current study, we have hypothesized and provided preliminary evidence through computational studies that MTX can strongly bind to the hotspot regions on C5a involved in the interactions with its receptors, which is likely to alter the downstream signaling of C5a and contribute to the overall therapeutic efficacy of MTX.

PMID:39579472 | DOI:10.1016/j.compbiolchem.2024.108283

Cell Death Dis. 2024 Nov 22;15(11):853. doi: 10.1038/s41419-024-07251-y.

ABSTRACT

Ferroptosis is a regulated and non-apoptotic form of cell death mediated by iron-dependent peroxidation of polyunsaturated fatty acyl tails in phospholipids. Research of the past years has shed light on the occurrence of ferroptosis in organ injury and degenerative diseases of the brain, kidney, heart, and other tissues. Hence, ferroptosis inhibition may prove therapeutically beneficial to treat distinct diseases. In this study, we explored the ferroptosis-modulating activity of seratrodast, an inhibitor of thromboxane A2 (TXA2) receptor, which is approved in some countries for the treatment of asthma. Interestingly, seratrodast suppressed ferroptosis, but not apoptosis and necroptosis; thus, demonstrating selective anti-ferroptotic activity. While seratrodast itself does not inhibit lipid peroxidation, it exhibits potent radical-trapping antioxidant activity upon reduction to its corresponding hydroquinone form-analogously to ubiquinone and vitamin K. Importantly, seratrodast ameliorated the severity of renal ischemia-reperfusion injury in mice. Together, this study provides a drug repurposing case, where seratrodast-a marketed drug-can undergo fast-forward preclinical/clinical development for the inhibition of ferroptosis in distinct degenerative diseases.

PMID:39578434 | DOI:10.1038/s41419-024-07251-y

Comput Biol Med. 2024 Nov 21;184:109403. doi: 10.1016/j.compbiomed.2024.109403. Online ahead of print.

ABSTRACT

Drug repositioning offers promising prospects for accelerating drug discovery by identifying potential drug-disease associations (DDAs) for existing drugs and diseases. Previous methods have generated meta-path-augmented node or graph embeddings for DDA prediction in drug-disease heterogeneous networks. However, these approaches rarely develop end-to-end frameworks for path instance-level representation learning as well as the further feature selection and aggregation. By leveraging the abundant topological information in path instances, more fine-grained and interpretable predictions can be achieved. To this end, we introduce deep multiple instance learning into drug repositioning by proposing a novel method called MilGNet. MilGNet employs a heterogeneous graph neural network (HGNN)-based encoder to learn drug and disease node embeddings. Treating each drug-disease pair as a bag, we designed a special quadruplet meta-path form and implemented a pseudo meta-path generator in MilGNet to obtain multiple meta-path instances based on network topology. Additionally, a bidirectional instance encoder enhances the representation of meta-path instances. Finally, MilGNet utilizes a multi-scale interpretable predictor to aggregate bag embeddings with an attention mechanism, providing predictions at both the bag and instance levels for accurate and explainable predictions. Comprehensive experiments on five benchmarks demonstrate that MilGNet significantly outperforms ten advanced methods. Notably, three case studies on one drug (Methotrexate) and two diseases (Renal Failure and Mismatch Repair Cancer Syndrome) highlight MilGNet's potential for discovering new indications, therapies, and generating rational meta-path instances to investigate possible treatment mechanisms. The source code is available at https://github.com/gu-yaowen/MilGNet.

PMID:39577348 | DOI:10.1016/j.compbiomed.2024.109403

Redox Biol. 2024 Nov 6;78:103420. doi: 10.1016/j.redox.2024.103420. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD) is the most common neurodegenerative disease, characterized by memory loss, speech and motor defects, personality changes, and psychological disorders. The exact cause of AD remains unclear. Current treatments focus on maintaining neurotransmitter levels or targeting β-amyloid (Aβ) protein, but these only alleviate symptoms and do not reverse the disease. Developing new drugs is time-consuming, costly, and has a high failure rate. Utilizing multi-omics for drug repositioning has emerged as a new strategy. Based on transcriptomic perturbation data of over 40,000 drugs in human cells from the LINCS-L1000 database, our study employed the Jaccard index and hypergeometric distribution test for reverse transcriptional feature matching analysis, identifying Givinostat as a potential treatment for AD. Our research found that Givinostat improved cognitive behavior and brain pathology in models and enhanced hippocampal synaptic plasticity. Transcriptome sequencing revealed increased expression of mitochondrial respiratory chain complex proteins in the brains of APP/PS1 mice after Givinostat treatment. Functionally, Givinostat restored mitochondrial membrane potential, reduced reactive oxygen species, and increased ATP content in Aβ-induced HT22 cells. Additionally, it improved mitochondrial morphology and quantity in the hippocampus of APP/PS1 mice and enhanced brain glucose metabolic activity. These effects are linked to Givinostat promoting mitochondrial biogenesis and improving mitochondrial function. In summary, Givinostat offers a promising new strategy for AD treatment by targeting mitochondrial dysfunction.

PMID:39577323 | DOI:10.1016/j.redox.2024.103420

J Med Microbiol. 2024 Nov;73(11). doi: 10.1099/jmm.0.001929.

ABSTRACT

Introduction. Mucormycosis is an aggressive, angioinvasive infection associated with high morbidity and mortality. The disease remains difficult to treat, with limited available antifungal drugs. Consequently, there is an urgent need to develop alternate therapeutics against mucormycosis. In an earlier study, we demonstrated that the non-steroidal anti-inflammatory drug diflunisal impacted the actin cytoskeleton and quorum sensing and inhibited the formation of filopodia-/cytoneme-like extensions in Rhizopus arrhizus.Hypothesis. The non-steroidal anti-inflammatory drug diflunisal could exhibit potential antifungal activity.Aim. This study aimed to investigate the plausible antifungal activity of diflunisal against a range of medically important Mucorales and its combination effect with antifungal drugs.Methodology. The antifungal activity of diflunisal against Rhizopus arrhizus, Lichtheimia corymbifera, Rhizomucor pusillus, Cunninghamella bertholletiae, Mucor indicus, Mucor irregularis and Apophysomyces elegans was evaluated by broth microdilution assay. Allied salicylates were also screened. A combination assay with amphotericin B deoxycholate and posaconazole was performed by fractional inhibitory concentration test.Results. Exposure to diflunisal inhibited Rhizopus arrhizus spore germination in a dose-dependent manner. MICs of diflunisal against different Mucorales ranged from 64 to 2048 µg ml-1. Remarkably low levels of diflunisal (0.03-2 µg ml-1), depending on the strain/species tested, improved the antifungal activity of amphotericin B against mucoralean fungi by twofold (ΣFIC ≈ 0.5-0.508; P<0.01). Field-emission scanning electron micrographs further confirmed these observations. MICs of posaconazole were unchanged by this compound.Conclusion. Considering that amphotericin B remains the first-line drug against mucormycosis and exhibits dose-dependent side effects in clinical practice, especially nephrotoxicity, the observed additive interaction at remarkably low, clinically achievable levels of diflunisal demonstrates its potential utility as an adjunct therapy against mucoralean fungi.

PMID:39576272 | DOI:10.1099/jmm.0.001929

Neurochem Res. 2024 Nov 22;50(1):28. doi: 10.1007/s11064-024-04255-0.

ABSTRACT

Carbonyl stress refers to the excessive accumulation of advanced glycation end products (AGEs) in mammalian tissues. This phenomenon plays a significant role in the pathogenesis of various diseases, including diabetes, chronic renal failure, arteriosclerosis, and central nervous system (CNS) disorders. We have previously demonstrated that an increase in glutathione concentration, dependent on the nuclear factor erythroid 2-related factor 2 (Nrf2) system, provides a potent cytoprotective effect against Methylglyoxal (MGO)-induced carbonyl stress. Meanwhile, dimethyl fumarate (DMF), known for its Nrf2-activating effects, was recently approved as a treatment for multiple sclerosis (MS), a neurodegenerative disease. DMF is a first line therapy for relapsing-remitting MS and may also be effective for other neurodegenerative conditions. However, the detailed mechanisms by which DMF mitigates neurodegenerative pathologies remain unclear. This study investigates the impact of DMF on anticarbonyl activity and its underlying mechanism focusing on the accumulation of carbonyl protein in the cell. MGO, a glucose metabolite, was used to induce carbonylation in the neuronal cell line. MGO is a typical carbonyl compound that readily reacts with arginine and lysine residues to form AGE-modified proteins. Methylglyoxal-derived hydroimidazolone 1 (MG-H1) often forms uncharged, hydrophobic residues on the protein surface, which can affect protein distribution and lead to misfolding. Our findings indicate that DMF increases levels of glutathione (GSH), glutamate cysteine ligase modifier subunit (GCLM), and nuclear Nrf2 in SH-SY5Y cells. Importantly, DMF pretreatment significantly reduced the accumulation of MG-H1-modified proteins. Furthermore, this effect of DMF was diminished when Nrf2 expression was suppressed and when GCL, a rate-limiting enzyme in GSH synthesis, was inhibited. Thus, the increase in GSH levels, leading to the activation of the Nrf2 pathway, a key factor in DMF's ability to suppress the accumulation of MG-H1-modified proteins. This study is the first to demonstrate that DMF possesses strong anticarbonyl stress activity in neuronal cells. Therefore, future research may extend the application of DMF to other CNS diseases associated with carbonyl stress, such as Alzheimer's and Parkinson's disease.

PMID:39576418 | DOI:10.1007/s11064-024-04255-0

J Biomol Struct Dyn. 2024 Nov 21:1-18. doi: 10.1080/07391102.2024.2425404. Online ahead of print.

ABSTRACT

COX-2 and 5-LOX are major enzymes implicated in inflammatory processes and have a crucial role in the pathogenesis of inflammatory disorders and malignancies. Designing antagonists that may concurrently interact with several receptors is a viable technique; thus, blocking these two targets with a single chemical compound might provide an efficient therapeutic approach. In-silico approaches have been employed to find polypharmacological inhibitors, especially for drug repurposing and multitarget drug design. Here, virtual screening of designed oxygen-containing heterocyclic series from prior literature was used to locate a feasible dual inhibitor against COX-2 and 5-LOX. Among these, 5-phenyl-2-(pyridin-3-yl)oxazol-4-yl cyclohexyl(methyl)sulfamate (N14) and 5-phenyl-2-(pyridin-4-yl)oxazol-4-yl benzenesulfonate (N16) was found to more promising with good interaction energy against COX-2 (-9.5 and -9.4 kcal/mol) and 5-LOX (-8.6 and -7.6 kcal/mol). Additionally, it also fulfilled the ADME/T parameters revealed to be drug-like, as anticipated by Lipinski's rule of five and Veber's rule. Furthermore, the molecular dynamics, free binding energy and post-processing analysis indicate N14 and N16 appears as a promising candidates with a novel molecular scaffold that could be examined further as a polypharmacological anticancer therapeutic candidate to explore further for the development.

PMID:39573889 | DOI:10.1080/07391102.2024.2425404

RSC Adv. 2024 Nov 20;14(50):37114-37130. doi: 10.1039/d4ra03571b. eCollection 2024 Nov 19.

ABSTRACT

Drug developers are currently focusing on investigating alternative strategies, such as "drug repositioning", to address issues associated with productivity, regulatory obstacles, and the steadily rising cost of pharmaceuticals. Repositioning is the best strategy to stop searching for new drugs because it takes less time and money to investigate new indications for already approved or unsuccessful drugs. Although there are several potent Topo II inhibitors available on the market as important drugs used in the therapy of many types of cancer, more may be required in the future. The current inhibitors have drawbacks including acquired resistance and unfavorable side effects such as cardiotoxicity and subsequent malignancy. A substantial body of research documented the cytotoxic potential of experimental fluoroquinolones (FQs) on tumor cell lines and their remarkable efficacy against eukaryotic Topo II in addition to optimized physical and metabolic characteristics. The FQ scaffold has a unique ability to potentially resolve every major issue associated with traditional Topo II inhibitors while maintaining a highly desirable profile in crucial drug-likeness parameters; therefore, there is a significant chance that FQs will be repositioned as anticancer candidates. This review offers a summary of the most recent research on the anticancer potential of FQs that was published in 2023. Along with discussing structural activity relationship studies and the mechanism underlying their antiproliferative activity, this review aims to provide up-to-date information that will spur the development of more potent FQs as viable cancer treatment candidates.

PMID:39569131 | PMC:PMC11578043 | DOI:10.1039/d4ra03571b

Front Pharmacol. 2024 Nov 6;15:1459822. doi: 10.3389/fphar.2024.1459822. eCollection 2024.

ABSTRACT

The rise of β-Lactamase mediated antibiotic resistance is a major concern for public health; hence, there is an urgent need to find new treatment approaches. Structure-guided drug repurposing offers a promising approach to swiftly deliver essential therapeutics in the fight against escalating antibiotic resistance. Here, a structure-guided virtual screening approach was used involving drug profiling, molecular docking, and molecular dynamics (MD) simulation to identify existing drugs against β-Lactamase-associated drug resistance. We exploited a large panel of FDA-approved drugs to an extensive in silico analysis to ascertain their ability to inhibit β-Lactamase. First, molecular docking investigations were performed to assess the binding affinities and interactions of screened molecules with the active site of β-Lactamase enzymes. Out of all the screened candidates, Aristospan was identified to possess promising characteristics, which include appropriate drug profiles, high binding specificity, and efficiency towards the binding pocket of β-Lactamase. Further analysis showed that Aristospan possesses several desirable biological characteristics and tends to bind to the β-Lactamase binding site. To explore the interactions further, the best docking pose of Aristospan was selected for MD simulations to assess the thermodynamic stability of the drug-enzyme complex and its conformational changes over 500 ns. The MD simulations in independent replica runs demonstrated that the β-Lactamase-Aristospan complex was stable in the 500 ns trajectory. These enlightening results suggest that Aristospan may harbor the potential for further evolution into a possible β-Lactamase inhibitor, with potential applications in overcoming antibiotic resistance in both Gram-positive and Gram-negative bacteria.

PMID:39568577 | PMC:PMC11576302 | DOI:10.3389/fphar.2024.1459822

Patterns (N Y). 2024 Oct 31;5(11):101093. doi: 10.1016/j.patter.2024.101093. eCollection 2024 Nov 8.

ABSTRACT

Computational analyses of transcriptomic data have dramatically improved our understanding of complex diseases. However, such approaches are limited by small sample sets of disease-affected material. We asked if a variational autoencoder trained on large groups of healthy human RNA sequencing (RNA-seq) data can capture the fundamental gene regulation system and generalize to unseen disease changes. Importantly, we found this model to successfully compress unseen transcriptomic changes from 25 independent disease datasets. We decoded disease-specific signals from the latent space and found them to contain more disease-specific genes than the corresponding differential expression analysis in 20 of 25 cases. Finally, we matched these disease signals with known drug targets and extracted sets of known and potential pharmaceutical candidates. In summary, our study demonstrates how data-driven representation learning enables the arithmetic deconstruction of the latent space, facilitating the dissection of disease mechanisms and drug targets.

PMID:39568475 | PMC:PMC11573900 | DOI:10.1016/j.patter.2024.101093

Expert Opin Drug Discov. 2024 Nov 21:1-15. doi: 10.1080/17460441.2024.2430955. Online ahead of print.

ABSTRACT

INTRODUCTION: Mapping the interactions between pharmaceutical compounds and their molecular targets is a fundamental aspect of drug discovery and repurposing. Drug-target interactions are important for elucidating mechanisms of action and optimizing drug efficacy and safety profiles. Several computational methods have been developed to systematically predict drug-target interactions. However, computational and experimental validation of the drug-target predictions greatly vary across the studies.

AREAS COVERED: Through a PubMed query, a corpus comprising 3,286 articles on drug-target interaction prediction published within the past decade was covered. Natural language processing was used for automated abstract classification to study the evolution of computational methods, validation strategies and performance assessment metrics in the 3,286 articles. Additionally, a manual analysis of 259 studies that performed experimental validation of computational predictions revealed prevalent experimental protocols.

EXPERT OPINION: Starting from 2014, there has been a noticeable increase in articles focusing on drug-target interaction prediction. Docking and regression stands out as the most commonly used techniques among computational methods, and cross-validation is frequently employed as the computational validation strategy. Testing the predictions using multiple, orthogonal validation strategies is recommended and should be reported for the specific target prediction applications. Experimental validation remains relatively rare and should be performed more routinely to evaluate biological relevance of predictions.

PMID:39568436 | DOI:10.1080/17460441.2024.2430955

Br J Haematol. 2024 Nov 20. doi: 10.1111/bjh.19912. Online ahead of print.

ABSTRACT

This commentary discusses the therapeutic potential of drug repurposing in sickle cell disease, highlighting the efficacy of hydroxyurea in enhancing fetal haemoglobin and the work of Raz et al. discussing the potential of using memantine for improving cognitive function, while emphasizing the need for further research. Commentary on: Raz et al. Memantine treatment in sickle cell disease: A 1-year study of its effects on cognitive functions and neural processing. Br J Haematol 2024 (Online ahead of print). doi: 10.1111/bjh.19866.

PMID:39568202 | DOI:10.1111/bjh.19912

BMC Med. 2024 Nov 20;22(1):547. doi: 10.1186/s12916-024-03760-x.

ABSTRACT

BACKGROUND: We systematically reviewed Mendelian randomization (MR) studies and summarized evidence on the potential effects of different antihypertensive drugs on health.

METHODS: We searched PubMed and Embase for MR studies evaluating the effects of antihypertensive drug classes on health outcomes until 22 May 2024. We extracted data on study characteristics and findings, assessed study quality, and compared the evidence with that from randomized controlled trials (RCTs).

RESULTS: We identified 2643 studies in the search, of which 37 studies were included. These studies explored a wide range of health outcomes including cardiovascular diseases and their risk factors, psychiatric and neurodegenerative diseases, cancer, immune function and infection, and other outcomes. There is strong evidence supporting the protective effects of genetically proxied antihypertensive drugs on cardiovascular diseases. We found strong protective effects of angiotensin-converting enzyme (ACE) inhibitors on diabetes whereas beta-blockers showed adverse effects. ACE inhibitors might increase the risk of psoriasis, schizophrenia, and Alzheimer's disease but did not affect COVID-19. There is strong evidence that ACE inhibitors and calcium channel blockers (CCBs) are beneficial for kidney and immune function, and CCBs showed a safe profile for disorders of pregnancy. Most studies have high quality. RCT evidence supports the beneficial effects of ACE inhibitors and CCBs on stroke, diabetes, and kidney function. However, there is a lack of reliable RCTs to confirm the associations with other diseases.

CONCLUSIONS: Evidence of the benefits and off-target effects of antihypertensive drugs contribute to clinical decision-making, pharmacovigilance, and the identification of drug repurposing opportunities.

PMID:39567981 | DOI:10.1186/s12916-024-03760-x

Comput Biol Med. 2024 Nov 19;184:109415. doi: 10.1016/j.compbiomed.2024.109415. Online ahead of print.

ABSTRACT

Gastric cancer (GC) is a common cancer worldwide. Therefore, searching for effective treatments is essential, and drug repositioning can be a promising strategy to find new potential drugs for GC therapy. For the first time, we sought to identify molecular alterations and validate new mechanisms related to Mebendazole (MBZ) treatment in GC cells through transcriptome analysis using microarray technology. Data revealed 1066 differentially expressed genes (DEGs), of which 345 (2.41 %) genes were upregulated, 721 (5.04 %) genes were downregulated, and 13,231 (92.54 %) genes remained unaltered after MBZ exposure. The overexpressed genes identified were CCL2, IL1A, and CDKN1A. In contrast, the H3C7, H3C11, and H1-5 were the top 3 underexpressed genes. Gene set enrichment analysis (GSEA) identified 8 pathways significantly overexpressed in the treated group (p < 0.05 and FDR<0.25). The validation of the expression of top desregulated genes by RT-qPCR confirmed the transcriptome results, where MBZ increased the CCL2, IL1A, and CDKN1A and reduced the H3C7, H3C11, and H1-5 transcript levels. Expression analysis in samples from TCGA databases correlated that the lower ILI1A and higher H3C11 and H1-5 gene expression are associated with decreased overall survival rates in patients with GC, indicating that MBZ treatment can improve the prognosis of patients. Thus, the data demonstrated that the drug MBZ alters the transcriptome of the AGP-01 lineage, mainly modulating the expression of histone proteins and inflammatory cytokines, indicating a possible epigenetic and immunological effect on tumor cells, these findings highlight new mechanisms of action related to MBZ treatment. Additional studies are still needed to better clarify the epigenetic and immune mechanism of MBZ in the therapy of GC.

PMID:39566281 | DOI:10.1016/j.compbiomed.2024.109415

Parasitol Res. 2024 Nov 20;123(11):387. doi: 10.1007/s00436-024-08389-6.

ABSTRACT

Acanthamoeba castellanii is the causative pathogen of a severe eye infection, known as Acanthamoeba keratitis and a life-threatening brain infection, named granulomatous amoebic encephalitis. Current treatments are problematic and costly and exhibit limited efficacy against Acanthamoeba parasite, especially the cyst stage. In parallel to drug discovery and drug repurposing efforts, drug modification is also an important approach to tackle infections, especially against neglected parasites such as free-living amoebae: Acanthamoeba. In this study, we determined whether modifying pentamidine and doxycycline through chitosan-functionalized graphene oxide loading enhances their anti-amoebic effects. Various concentrations of doxycycline, pentamidine, graphene oxide, chitosan-functionalized graphene oxide, and chitosan-functionalized graphene oxide loaded with doxycycline and pentamidine were investigated for amoebicidal effects against pathogenic A. castellanii belonging to the T4 genotype. Lactate dehydrogenase assays were performed to determine toxic effects of these various drugs and nanoconjugates against human cells. The findings revealed that chitosan-functionalized graphene oxide loaded with doxycycline demonstrated potent amoebicidal effects. Nanomaterials significantly (p < 0.05) inhibited excystation and encystation of A. castellanii without exhibiting toxic effects against human cells in a concentration-dependent manner, as compared with other formulations. These results indicate that drug modifications coupled with nanotechnology may be a viable avenue in the rationale development of effective therapies against Acanthamoeba infections.

PMID:39565414 | DOI:10.1007/s00436-024-08389-6

ACS Med Chem Lett. 2024 Oct 2;15(11):1907-1917. doi: 10.1021/acsmedchemlett.4c00358. eCollection 2024 Nov 14.

ABSTRACT

Diabetes mellitus (DM) is a global health concern, and dipeptidyl peptidase-4 (DPP-4) is a key therapeutic target. The study used three machine learning and deep learning models to predict potential DPP-4 inhibitors using a curated data set of 6,750 compounds. The models included support vector machine (SVM), random forest (RF), naive Bayes (NB), and multitask deep neural network (MTDNN). The MTDNN model demonstrated strong predictive performance, achieving 98.62% train accuracy and 98.42% test accuracy for predicting DPP-4 inhibitors and a correlation coefficient of 0.979 for training and 0.977 for the test data set, with low training and test errors while predicting corresponding IC50 values. The MTDNN model predicted potential inhibitors using an external data set of FDA-approved drugs, identifying 100 compounds. Among these, five compounds stood out with promising molecular docking and dynamic profiles, suggesting their potential as repurposed drugs for targeting DPP-4 and offering hope for the future of diabetes treatment.

PMID:39563823 | PMC:PMC11571088 | DOI:10.1021/acsmedchemlett.4c00358

Bioinformatics. 2024 Nov 19:btae692. doi: 10.1093/bioinformatics/btae692. Online ahead of print.

ABSTRACT

MOTIVATION: Drug repositioning, identifying novel indications for approved drugs, is a cost-effective strategy in drug discovery. Despite numerous proposed drug repositioning models, integrating network-based features, differential gene expression, and chemical structures for high-performance drug repositioning remains challenging.

RESULTS: We propose a comprehensive deep pre-training and fine-tuning framework for drug repositioning, termed DrugRepPT. Initially, we design a graph pre-training module employing model-augmented contrastive learning on a vast drug-disease heterogeneous graph to capture nuanced interactions and expression perturbations after intervention. Subsequently, we introduce a fine-tuning module leveraging a graph residual-like convolution network to elucidate intricate interactions between diseases and drugs. Moreover, a Bayesian multi-loss approach is introduced to balance the existence and effectiveness of drug treatment effectively. Extensive experiments showcase the efficacy of our framework, with DrugRepPT exhibiting remarkable performance improvements compared to SOTA baseline methods (Improvement 106.13% on Hit@1 and 54.45% on mean reciprocal rank). The reliability of predicted results is further validated through two case studies, ie, gastritis and fatty liver, via literature validation, network medicine analysis, and docking screening.

AVAILABILITY AND IMPLEMENTATION: The code and results are available at https://github.com/2020MEAI/DrugRepPT.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:39563444 | DOI:10.1093/bioinformatics/btae692