STITCH: interaction networks of chemicals and proteins

Neuroprotective effects of Centella asiatica against LPS/amyloid beta-induced neurodegeneration through inhibition of neuroinflammation

Protein aggregation and microglia-mediated neuroinflammation are the major contributors to the progression of neurodegeneration. Currently, available drugs for neurodegenerative diseases have limited efficacy and are associated with several side effects; suggesting a need to discover novel therapeutic agents. Therefore, we aim to evaluate the neuroprotective effects of C. asiatica against amyloid beta (Aβ) and lipopolysaccharides (LPS)-induced neurodegeneration using human microglia and neuronal cell-based models. To identify potential molecular targets of C. asiatica, network pharmacology-based approaches were used along with molecular docking, followed by experimental validation via indirect ELISA, Western blotting, and indirect immunofluorescence assays. Our results from network pharmacology, molecular docking, and cell-based models, exhibited that AKT1, TNF-α, STAT3, CASP3, PTGS2, MAPK1, APP, and NF-κB are the potential molecular targets of C. asiatica. Further, we have found that C. asiatica treatment reduces LPS/Aβ-induced cell death, NO production, and LDH release in microglia and neuronal cells. The anti-neuroinflammatory effect of C. asiatica was further observed via the reduction of LPS, Aβ, and LPS+Aβ-induced neuroinflammatory markers; TNF-α, IL6, IL-1β, AKT1, INOS, NF-κB, MAPK3, and PTGS2 in microglia cells. Moreover, neurodegenerative and apoptotic markers; APP, α-syn, P-tau STAT3, and CASP3 were reduced upon C. asiatica treatment in neuronal cells, suggesting its neuroprotective properties. For the first time, we have shown the neuroprotective effects of C. asiatica against LPS, Aβ, and LPS+Aβ -induced neurodegeneration via inhibition of neuroinflammation and neurodegenerative markers. The outcomes of the study suggested that C. asiatica could be a promising candidate for neuroinflammation-mediated neurodegenerative diseases like Parkinson's and Alzheimer's.

The proteomic differences and expression of fatty acid-binding protein 6 (FABP6) associated with gastrointestinal injury in horses with oral administration of a clinical dose of phenylbutazone

BACKGROUND

Phenylbutazone (PBZ) can potentially induce gastrointestinal ulceration, and early detection of PBZ-induced gastroenteropathy will be useful for the diagnosis, treatment, and prevention of PBZ toxicity.

OBJECTIVES

To identify putative proteins associated with equine gastric ulcer syndrome after clinical dose (4.4 mg/kg) administration of PBZ by proteomic study.

STUDY DESIGN

In vivo experiments.

METHODS

Proteomic analysis using LC-MS/MS compared protein expression in serum and faeces of seven PBZ-treated horses with seven placebo-treated controls, and a novel putative biomarker was validated via enzyme-linked immunosorbent assay.

RESULTS

Differentially expressed proteins (DEPs) analysis on 5298 serum annotated proteins and 3538 faecal annotated proteins using the DESeq2 were performed between the control and treatment of EGUS groups. The results showed a list of 226 and 181 significant proteins in serum and faecal samples, respectively with a p adjust value <0.05. The proteomic serum and faeces samples were integrated into STITCH to illustrate PBZ interaction with bile acid homeostasis. FABP6 was significantly increased in PBZ-treated horses. The serum FABP6 concentration in the treatment group on Day 8 (1.80 ± 0.37 ng/mL) was higher than on Day 0 (1.15 ± 0.33 ng/mL, p = 0.01, 95% CI [-1.07, -0.25]). On Day 8, the serum FABP6 concentration in the treatment group was also higher than the control group (1.20 ± 0.48 ng/mL; p = 0.02, 95% CI [-1.10, -0.11]).

MAIN LIMITATIONS

Validation of all expressed proteins is a main limitation.

CONCLUSIONS

Administration of PBZ at a clinical dose of 4.4 mg/kg twice daily for 7 days may cause gastric mucosal damage. PBZ treatment increased the expression of SLC10A1 and FABP6, suggesting that early gastric mucosal injury may be linked to the bile acid pathway. Bile acids could potentially exacerbate PBZ-induced EGUS.

Molecular mechanisms of arecoline-induced oral cancer: a network toxicology and molecular docking techniques integrated analysis

The IARC classified betel nut as Group 1 carcinogen (2004) and arecoline as Group 2B carcinogen (2020), with approximately one-third of global oral cancer cases attributed to smokeless tobacco or betel nut consumption. While current evidence establishes an association between arecoline and oral cancer, the underlying molecular mechanisms remain complex and poorly elucidated. This study employs network toxicology integrated with molecular docking techniques to systematically investigate the potential molecular pathogenesis of arecoline-induced oral cancer, aiming to provide novel insights for targeted therapeutic strategies. The SMILES structure of arecoline was retrieved from PubChem for foundational data preparation. Toxicity profiling was conducted using ProTox-3.0 and ADMETlab databases. Potential targets of arecoline were identified via STITCH and SwissTargetPrediction. Oral cancer-related targets were collated from GeneCards, OMIM, and TTD. Intersection analysis between arecoline targets and oral cancer-associated targets was performed to identify shared targets, which were further utilized to construct compound-target regulatory network and subjected to PPI, GO, and KEGG analyses. Core targets driving oral cancer were screened using the cytoHubba plugin. Then, the correlation between core targets and immune cell infiltration in oral cancer was explored, and molecular docking validated the binding affinity of arecoline to core targets. Finally, Gromacs 2022.3 software was used to simulate the molecular dynamics of the complexes obtained by molecular docking for 100 ns. Using the STITCH and SwissTargetPrediction databases, a total of 46 potential targets of arecoline were identified. Concurrently, 2,375 oral cancer-related targets were retrieved from GeneCards, OMIM, and TTD. Intersection analysis of these two target sets yielded 26 overlapping targets. PPI analysis revealed that TP53, IL6, SNAI1, and CASP3 occupied central positions in the network, exhibiting extensive interactions with other target proteins. Enrichment analysis comprehensively elucidated the molecular functions, biological processes, cellular components, and associated pathways of these overlapping targets. Further screening using Cytoscape software identified four core targets: TP53, TNF, IL6, and CASP3. Immune infiltration analysis indicated that the expression levels of TP53, TNF, IL6, and CASP3 in oral cancer tissues were positively correlated with the infiltration levels of immune cells, including CD8 + T cells, Th1 cells, NK cells, and macrophages. Molecular docking experiments demonstrated strong binding activities between arecoline and TP53, IL6, and CASP3, while TNF also exhibited moderate binding affinity. Dynamic simulation further verified the stable binding of arecoline to TP53, TNF, IL6 and CASP3. Arecoline may induce oral cancer by acting on core targets including TP53, TNF, IL6, and CASP3, which interfere with normal cellular growth regulation, inflammatory responses, and apoptotic mechanisms. Therapeutic strategies targeting TP53, TNF, IL6, and CASP3 may represent novel research directions for clinical diagnosis and treatment of oral cancer.

Exposure to emerging water contaminants and human health risk: Cytotoxic and genotoxic effects of caffeine and diethyltoluamide (DEET) on eukaryotic cells

The presence of emerging pollutants in aquatic ecosystems due to human activities poses substantial concerns. While many studies explore detection and removal techniques for these compounds, conventional treatment methods often fail to address emerging pollutants. Moreover, there is a lack of legislation defining safe thresholds for these substances in water. Consequently, caffeine and N,N-diethyl-meta-toluamide (DEET) persist in surface waters, including treated sources, with limited understanding of their genomic effects on human cells. This study aimed to assess the cytotoxic and genotoxic effects of caffeine and DEET, individually and in combination, at concentrations detected in drinking water, using HepG2 cells. Additionally, through systems biology, we sought to understand the underlying molecular mechanisms of both substances. Cytotoxicity was evaluated using MTT and Trypan Blue assays, while genotoxicity was assessed using the comet assay. The chemoproteomic interaction network was constructed using STITCH and STRING databases, with subnetworks analyzed using Cytoscape plugins (MCODE, CentiScaPe, and BiNGO). Both compounds reduced HepG2 cell viability in a dose-dependent manner in both assays. Caffeine and DEET also induced DNA damage at all tested concentrations, including in co-exposure. Proteins related to the inflammatory response, signaling pathways, and xenobiotic metabolism were the main hub-bottlenecks of the chemoproteomic interaction network. These findings underscore the urgent need for further investigations into the presence of emerging pollutants in drinking water and their potential risks to human health.

Network pharmacology and experimental verification to explore the molecular mechanisms of Astragaloside IV against diabetic encephalopathy

PURPOSE

Diabetic encephalopathy (DE) is a neurological complication caused by diabetes mellitus, and its underlying mechanism has not been fully clarified. Astragaloside IV (AS-IV) has been demonstrated to have treatment effects on multiple neurologic diseases. The objective of this research is to explore the role and underlying mechanism of AS-IV in the treatment of DE, utilizing the methods of network pharmacology and experimental validation.

METHODS

Multiple public databases were used to search for the targets of AS-IV. Gene Expression Omnibus (GEO) dataset (GSE16135) was analyzed to identify differentially expressed genes (DEGs) in DE. The Venn diagram was employed to determine the intersecting genes. These genes were considered potential therapeutic targets of AS-IV in DE and were annotated using bioinformatics techniques. Subsequently, a protein-protein interaction (PPI) network was constructed utilizing Cytoscape software to identify the core targets of action. Additionally, molecular docking was conducted to validate the binding affinity of AS-IV to the main targets. Finally, we validated the predictive outcomes of network pharmacology in a DE rat model induced by intraperitoneal injection of streptozotocin (STZ).

RESULTS

Through the application of network pharmacology and bioinformatics analyses, we discovered the top two hub targets (EGFR and JAK2). Subsequent molecular docking analysis showed that AS-IV was precisely located within the binding sites of both EGFR and JAK2, with binding energies of -8.18 kJ/mol and -10.94 kJ/mol, respectively. Behavioral experiments demonstrated that the treated rats showed improvements in cognitive impairment. Following AS-IV treatment, there was a significant reduction in amyloid-β (Aβ) plaques deposition and neurofibrillary tangles in the hippocampal tissue of DE rats. Furthermore, TUNEL staining and Western blot analyses demonstrated that AS-IV suppressed neuronal apoptosis and inhibited the activation of the EGFR/JAK2/STAT3 signaling pathway.

CONCLUSION

These results demonstrated that the AS-IV has the potential to improve cognitive impairment in DE rats by mitigating neuronal apoptosis through the EGFR/JAK2/STAT3 signaling pathway, which provides important implications for the treatment of DE.

Synthesis, X-ray diffraction, and computational studies of acyclovir and HBG analogs derived from Triazolyl-1,4-benzothiazine and their oxidized forms for breast cancer and SARS-CoV-2

This study presents a simple and efficient synthetic method for preparing a new series of acyclonucleosides derived from 1,4-benzothiazine and 1,4-benzothiazine-1,1-dioxide. These compounds feature the introduction of a 1,2,3-triazole-4-ylmethyl ring as a spacer between the heterocyclic bases and the pseudosugars of acyclovir (ACV) and hydroxybutylguanine (HBG). The acyclonucleosides were synthesized through copper-catalyzed 1,3-dipolar cycloaddition reactions between azides 8a and 8b and the N4-propargyl base 7. Following this, the deprotection of the acyclic chains and the oxidation of the sulfur to sulfone afforded the acyclonucleosides 9a,b-12a,b in satisfactory yields. The synthesized acyclonucleosides were characterized using 1H and 13C NMR spectroscopy. Moreover, the structure of 9b was confirmed by single-crystal X-ray diffraction analysis. The synthesized acyclonucleosides were evaluated through in silico studies, including network pharmacology for bioactivity, toxicity prediction, physicochemical properties, and ADMET analysis. Molecular docking studies revealed significant interactions, highlighting compound 11b's favorable binding with the target protein AKT1, achieving a binding energy of -6.43 kcal/mol, which is close to the Capivasertib standard. Similarly, compound 12b showed interactions akin to hydroxychloroquine, with a binding energy of -6.29 kcal/mol for the SARS-CoV-2 target protein. Molecular dynamics simulations further validated the stability of the ligand-protein complexes during 200 ns, as evidenced by acceptable RMSD and RMSF and Rg values. The post-dynamic, MMGBSA, PCA, FEL, PDF, and DCCM analyses of the AKT and SARS-CoV-2 protein-ligand complexes have provided comprehensive insights into their interactions with standard drugs, binding affinities, conformational dynamics, and structural stability. These studies are crucial for understanding the molecular mechanisms underlying drug efficacy and resistance, thereby informing the rational design of new inhibitors targeting AKT and SARS-CoV-2 proteins. Finally, the two most promising compounds, 11b and 12b, selected from the docking results, were analyzed using Density Functional Theory (DFT). These analyses revealed significant variations in their electronic properties, providing valuable insights into their reactivity, stability, and polarity.

Bryophyllum pinnatum Induces p53-Dependent Apoptosis of Colorectal Cancer Cells via Increased Intracellular ROS and G2/M Cell-Cycle Arrest In Vitro and Validated in Silico by Molecular Docking

Chemotherapy, radiotherapy and surgical treatments of cancer having several limitations and toxic side-effects, have led researchers to focus towards development of alternative natural plant-based therapeutics that can reduce disease severity. The present research work is mainly focussed towards identifying molecular mechanisms of apoptosis of colorectal cancer cells (HCT116) by perennial herb Bryophyllum pinnatum leaf-extract via both in vitro experimentations and in silico analysis. B. pinnatum leaf extract induced highest cytotoxicity at lowest dose (IC50:0.01 mg/mL) against HCT116 cells with 49.5% (p < 0.0001) cellular death, in comparison to other cancer cell lines. It has arrested HCT116 cell populations at G2/M cell-cycle phase and led to 10 folds (p < 0.0001) and 5.5 folds (p < 0.0001) increased intracellular ROS production in treated groups. ROS production might have led to significant 34.23% and 21.03% (p < 0.0001) apoptosis in treated cells, proved in vitro and in silico, with significant upregulation of p53 (p < 0.0001), BAX (p = 0.0252), CASPASE3 (p < 0.0001) and downregulation of BCL2 (p = 0.0058), leading to increased nuclear p53 (p = 0.0002) accumulation in treated cells, suggesting that the leaf-extract might have induced p53-dependent apoptosis of colorectal cancer cells. The phyto-extract also possess significant gene-modulatory potential as evident from qRT-PCR analysis of oncogenes and tumor suppressor genes. Leaf's bioactive phyto-constituents were elucidated by GC-MS and HPLC-ESI/MS analysis. In silico STITCH analysis provided significant network interactions between these bioactive phyto-compounds and studied proteins. Further Molecular Docking studies revealed strong binding between such docked complexes. Also, predicted major bioactive phyto-constituents of B. pinnatum leaf-extract such as Quercetin, Morin and β-Sitosterol have induced significant (p < 0.0001) apoptosis and increased intracellular ROS, validating their in silico interactions with studied proteins of HCT116 cells. All these studies together demonstrated ability of B. pinnatum to be used as a suitable natural phyto-therapeutic agent for development of chemo-preventive medications against colorectal cancer.

DTGHAT: multi-molecule heterogeneous graph transformer based on multi-molecule graph for drug-target identification

Introduction

Drug target identification is a fundamental step in drug discovery and plays a pivotal role in new therapies development. Existing computational methods focus on the direct interactions between drugs and targets, often ignoring the complex interrelationships between drugs, targets and various biomolecules in the human system.

Method

To address this limitation, we propose a novel prediction model named DTGHAT (Drug and Target Association Prediction using Heterogeneous Graph Attention Transformer based on Molecular Heterogeneous). DTGHAT utilizes a graph attention transformer to identify novel targets from 15 heterogeneous drug-gene-disease networks characterized by chemical, genomic, phenotypic, and cellular networks.

Result

In a 5-fold cross-validation study, DTGHAT achieved an area under the receiver operating characteristic curve (AUC) of 0.9634, which is at least 4% higher than current state-of-the-art methods. Characterization ablation experiments highlight the importance of integrating biomolecular data from multiple sources in revealing drug-target interactions. In addition, a case study on cancer drugs further validates DTGHAT's effectiveness in predicting novel drug target identification. DTGHAT is free and available at: https://github.com/stella-007/DTGHAT.git.

Exploring the efficacy of Benincasa hispida extract on obesity linked inflammatory bowel disease by integrating computational analysis and experimental validations

The association of obesity with inflammatory bowel disease (IBD) can be understood by the intricate role of pro- and anti-inflammatory cytokines, especially adipokines, which are secreted by adipose tissue and are responsible for IBD because of their structural similarity with tumor necrosis factor-alpha (TNF-α), an important cytokine involved in IBD pathogenesis. The current study was carried out to evaluate the therapeutic potential of Benincasa hispida in obesity-associated IBD. Approximately 18 compounds sourced from Benincasa hispida (Thunb.) were comprehensively analyzed, among which 11 presented favorable drug-likeness scores and adherence to Lipinski's Rule of Five. Various methodologies, including compound-gene set pathway enrichment analysis, network pharmacology, docking studies, and molecular dynamics simulations, have been employed. Safety assessments via Protox confirmed the nontoxic nature of these compounds, which is crucial for their therapeutic potential. Through Venn diagram analysis of the Gene Card and OMIM databases, proteins associated with obesity and IBD management were pinpointed. Pathway enrichment analysis revealed 810 targets across 192 distinct pathways, with 8 directly related to the pathogenesis of obesity and IBD. Notable therapeutic targets, such as MTOR, were identified through STRING and KEGG pathway database analyses, shedding light on the molecular pathways modulated by these protein targets. Interactions among compounds, proteins, and pathways were visualized via Cytoscape 3.6.1. Furthermore, the compounds were docked with the protein target via AutoDock 4.2, and the compound ajmalin exhibited the highest binding affinity with the MTOR protein, with a binding energy of -7.8 kcal/mol; later, a dynamic study was performed for the ajmaline and protein complex. These findings shed light on the potential efficacy of Benincasa hispida in targeting crucial pathways for managing obesity and IBD. Hence, in vivo studies involving Wistar rats exposed to microplastics and monosodium glutamate (MSG) were carried out to evaluate the potential of Benincasa hispida extracts in mitigating obesity-related IBD. Fecal lipid analysis revealed alterations associated with these conditions, whereas histopathological examinations of the liver and intestine revealed the inflammatory changes induced by MSG and microplastics. The protective effects of this extract on liver and intestinal histology suggest promising avenues for further investigations, emphasizing its potential as a therapeutic intervention for IBD and obesity.

The flavonoid hyperoside attenuates the toxic effect of cisplatin on the human ovarian granulosa cells: in vitro model study

Premature ovarian insufficiency/failure is a well-known long-term risk of chemotherapy including CDDP in women. Granulosa cells (GCs) are an essential ovarian cell type that promotes oocyte growth and is crucial for ovarian reproductive function. Hyperoside (HYP) is a flavonoid known for its beneficial pharmacological properties, including anti-inflammatory and antiapoptotic effects. Hence the current work aimed to evaluate the potential cytoprotective impact of HYP on CDDP-induced cytotoxicity in a human ovarian GCs cell line model via a wide range of assays including MTT, hormones secretion, ATP and mitochondrial membrane potential, reactive oxygen species, lipid peroxidation as well as antioxidant enzymes, Caspases, and Akt kinase activities. Forty-eight-hour exposure to 5-10µM CDDP resulted in reduction of GCs viability in a dose-dependent manner. HYP (40 µM) was found to ameliorate this CDDP -induced effect on GCs viability. CDDP in a concentration-dependent way, dramatically reduced cellular ATP, mitochondrial activities, cellular progesterone, and estradiol secretion. It also increased oxidative stress markers, cytochrome c levels, caspase -3.-8.-9, and Bax/Bcl2 ratio with decreased Akt kinase activity and its coding genes expression. These cytotoxic effects of CDDP on the treated GCs, were mitigated to varying degrees by HYP (40 µM). In conclusion, CDDP-induced cytotoxic effects on GCs seem to be the possible underlying cellular and molecular mechanisms of CDDP-induced ovarian insufficiency/failure. The study also demonstrated the therapeutic potential of HYP in mitigating CDDP-induced ovarian injury. Further studies are warranted to investigate the potential benefit of HYP as an adjuvant to CDDP treatment protocols to avoid adverse ovarian effects.

Cytosolic cytochrome c represses ferroptosis

The release of cytochrome c, somatic (CYCS) from mitochondria to the cytosol is an established trigger of caspase-dependent apoptosis. Here, we unveil an unexpected role for cytosolic CYCS in inhibiting ferroptosis-a form of oxidative cell death driven by uncontrolled lipid peroxidation. Mass spectrometry and site-directed mutagenesis revealed the existence of a cytosolic complex composed of inositol polyphosphate-4-phosphatase type I A (INPP4A) and CYCS. This CYCS-INPP4A complex is distinct from the CYCS-apoptotic peptidase activating factor 1 (APAF1)-caspase-9 apoptosome formed during mitochondrial apoptosis. CYCS boosts INPP4A activity, leading to increased formation of phosphatidylinositol-3-phosphate, which prevents phospholipid peroxidation and plasma membrane rupture, thus averting ferroptotic cell death. Unbiased screening led to the identification of the small-molecule compound 10A3, which disrupts the CYCS-INPP4A interaction. 10A3 sensitized cultured cells and tumors implanted in immunocompetent mice to ferroptosis. Collectively, these findings redefine our understanding of cytosolic CYCS complexes that govern diverse cell death pathways.

Integrated Network Pharmacology, Molecular Modeling, LC-MS Profiling, and Semisynthetic Approach for the Roots of Rubia tinctorum L. Metabolites in Cancer Treatment

Rubia tinctorum L. is one of the most widely used plants in folk medicine, with many reported pharmacological activities. One of these valuable activities is its anticancer efficacy. The aim of this study is to explore the multilevel mechanisms of R. tinctorum metabolites in cancer treatment using network pharmacology, together with molecular docking and in vitro studies. The network pharmacology analysis enabled us to reveal the hit anticancer R. tinctorum constituents, which were found to be acacetin, alizarin, anthragallol, 2-hydroxyanthraquinone, and xanthopurpurin. The most enriched cancer-linked target genes were PLCG1, BCL2, CYP1B1, NSD2, and ESR2. The pathways that were mostly involved in the anticancer mechanism of R. tinctorum metabolites were found to be metabolic pathways as well as pathways in cancer and apoptosis. Molecular docking of the identified hit anticancer constituents on the active sites of the most enriched genes unveiled that acacetin and alizarin possessed the lowest binding energies on the active sites of NSD2 and BCL2, respectively. While anthragallol showed the most stabilized interaction on the active sites of PLCG1, CYP1B1, and ESR2. Consequently, R. tinctorum extracts were evaluated for their in vitro cytotoxicity on a panel of cancerous cells. Among the tested R. tinctorum extracts, the chloroform extract was the strongest one with an IC50 = 3.987 μg/mL on the MCF-7 breast cancer cell line. Consequently, it was subjected to chromatographic separation and purification to isolate its major components with reported anticancer activity (scopoletin, rubiadin, chrysophanic acid, alizarin, purpurin, nor-damnacanthal, emodin, and rutin). Alizarin and purpurin constituted the main anthraquinones in R. tinctorum . Thus, they were quantified using LC/MS analysis. Moreover, a semisynthetic approach of alizarin toward the enhancement of its anticancer effect on the tested cancer cells was attained. Among the synthesized compounds, 2-methyl alizarin was the most active one with an IC50 = 8.878 μg/mL against the HepG2 cell line. This study provides deep insights into the anticancer mechanisms of R. tinctorum metabolites for the first time using network pharmacology and valorizes their significance as valuable anticancer agents.

DruGagent: Multi-Agent Large Language Model-Based Reasoning for Drug-Target Interaction Prediction

Advancements in large language models (LLMs) allow them to address diverse questions using human-like interfaces. Still, limitations in their training prevent them from answering accurately in scenarios that could benefit from multiple perspectives. Multi-agent systems allow the resolution of questions to enhance result consistency and reliability. While drug-target interaction (DTI) prediction is important for drug discovery, existing approaches face challenges due to complex biological systems and the lack of interpretability needed for clinical applications. DrugAgent is a multi-agent LLM system for DTI prediction that combines multiple specialized perspectives with transparent reasoning. Our system adapts and extends existing multi-agent frameworks by (1) applying coordinator-based architecture to the DTI domain, (2) integrating domain-specific data sources, including ML predictions, knowledge graphs, and literature evidence, and (3) incorporating Chain-of-Thought (CoT) and ReAct (Reason+Act) frameworks for transparent DTI reasoning. We conducted comprehensive experiments using a kinase inhibitor dataset, where our multi-agent LLM method outperformed the non-reasoning multi-agent model (GPT-4o mini) by 45% in F1 score (0.514 vs 0.355). Through ablation studies, we demonstrated the contributions of each agent, with the AI agent being the most impactful, followed by the KG agent and search agent. Most importantly, our approach provides detailed, human-interpretable reasoning for each prediction by combining evidence from multiple sources - a critical feature for biomedical applications where understanding the rationale behind predictions is essential for clinical decision-making and regulatory compliance. Code is available at https://anonymous.4open.science/r/DrugAgent-B2EA.

Artificial intelligence-based drug repurposing with electronic health record clinical corroboration: A case for ketamine as a potential treatment for amphetamine-type stimulant use disorder

BACKGROUND AND AIMS

Amphetamine-type stimulants are the second-most used illicit drugs globally, yet there are no US Food and Drug Administration (FDA)-approved treatments for amphetamine-type stimulant use disorders (ATSUD). The aim of this study was to utilize a drug discovery framework that integrates artificial intelligence (AI)-based drug prediction, clinical corroboration and mechanism of action analysis to identify FDA-approved drugs that can be repurposed for treating ATSUD.

DESIGN AND SETTING

An AI-based knowledge graph model was first utilized to prioritize FDA-approved drugs in their potential efficacy for treating ATSUD. Among the top 10 ranked candidate drugs, ketamine represented a novel candidate with few studies examining its effects on ATSUD. We therefore conducted a retrospective cohort study to assess the association between ketamine and ATSUD remission using US electronic health record (EHR) data. Finally, we analyzed the potential mechanisms of action of ketamine in the context of ATSUD.

PARTICIPANTS AND MEASUREMENTS

ATSUD patients who received anesthesia (n = 3663) or were diagnosed with depression (n = 4328) between January 2019 and June 2022. The outcome measure was the diagnosis of ATSUD remission within one year of the drug prescription.

FINDINGS

Ketamine for anesthesia in ATSUD patients was associated with greater ATSUD remission compared with other anesthetics: hazard ratio (HR) = 1.58, 95% confidence interval (CI) = 1.15-2.17. Similar results were found for ATSUD patients with depression when comparing ketamine with antidepressants and bupropion/mirtazapine with HRs of 1.51 (95% CI = 1.14-2.01) and 1.68 (95% CI = 1.18-2.38), respectively. Functional analyses demonstrated that ketamine targets several ATSUD-associated pathways including neuroactive ligand-receptor interaction and amphetamine addiction.

CONCLUSIONS

There appears to be an association between clinician-prescribed ketamine and higher remission rates in patients with amphetamine-type stimulant use disorders.

Arachidonic acid suppresses lung cancer cell growth and modulates lipid metabolism and the ERK/PPARγ signaling pathway

Lung cancer remains the leading cause of cancer-related mortality worldwide, necessitating the development of new treatment strategies. Arachidonic acid (ARA), a polyunsaturated fatty acid, shows promise in cancer therapy due to its potential anti-tumor effects, although its role in lung cancer remains unclear. This study investigated the effects and underlying mechanism of ARA on A549 and NCI-H1299 lung cancer cells. In vitro assays were used to assess cell viability, apoptosis, colony formation, lipid droplet formation, and changes in cellular lipid content. ARA dose-dependently suppressed cell viability, facilitated apoptosis, and suppressed colony formation in both lung cancer cell lines. Network pharmacology analysis was performed to identify potential gene targets and pathways, uncovering 61 overlapping genes between ARA and lung cancer-related targets, with mitogen-activated protein kinase 1 (MAPK1) emerging as a key gene. Enrichment analyses suggested that the effects of ARA might be mediated through lipid metabolism and the extracellular signal-regulated kinase (ERK)/peroxisome proliferator-activated receptor gamma (PPARγ) signaling pathway. In both lung cancer cell lines, ARA treatment inhibited lipid droplet formation and decreased the cellular lipids. Immunoblotting further confirmed that ARA treatment significantly increased ERK phosphorylation while reducing PPARγ and fatty acid synthase (FASN) protein levels. In vitro experiments using GW9662, a PPARγ antagonist, confirmed that inhibiting lipid droplet formation impairs lung cancer cell viability and promotes apoptosis. Furthermore, in vivo experiments demonstrated that ARA significantly reduced tumor size and weight in a lung cancer xenograft model, further validating its anti-tumor effects. The potential of ARA as a therapeutic agent for lung cancer might involve lipid metabolism and relevant signaling pathways. A future study exploring the full therapeutic potential of ARA and underlying mechanisms in lung cancer is needed.

A bioinformatics approach combined with experimental validation analyzes the efficacy of azithromycin in treating SARS-CoV-2 infection in patients with IPF and COPD These authors contributed equally: Yining Xie, Guangshu Chen, and Weiling Wu

The swift transmission rate and unfavorable prognosis associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have prompted the pursuit of more effective therapeutic interventions. Azithromycin (AZM) has garnered significant attention for its distinctive pharmacological mechanisms in the treatment of SARS-CoV-2. This study aims to elucidate the biological rationale for employing AZM in patients with chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) who are infected with SARS-CoV-2. Genetic data about COVID-19, COPD, and IPF were independently obtained from the GeneCards database. And 40 drug targets about AZM were retrieved from the STITCH database. The analysis revealed that 311 DEGs were common among COPD, IPF, and COVID-19, and we further found eight genes that interacted with AZM targets. We conducted an analysis of hub genes and their corresponding signaling pathways in these patient cohorts. Additionally, we explored the inhibitory effects of AZM on these hub genes. AZM demonstrated a significant inhibitory effect on eight key genes, except for AR and IL-17 A. These findings suggest that AZM may serve as a promising therapeutic agent for patients with COPD and IPF and SARS-CoV-2 infection.

Prediction of drug's anatomical therapeutic chemical (ATC) code by constructing biological profiles of ATC codes

BACKGROUND

The Anatomical Therapeutic Chemical (ATC) classification system, proposed and maintained by the World Health Organization, is among the most widely used drug classification schemes. Recently, it has become a key research focus in drug repositioning. Computational models often pair drugs with ATC codes to explore drug-ATC code associations. However, the limited information available for ATC codes constrains these models, leaving significant room for improvement.

RESULTS

This study presents an inference method to identify highly related target proteins, structural features, and side effects for each ATC code, constructing comprehensive biological profiles. Association networks for target proteins, structural features, and side effects are established, and a random walk with restart algorithm is applied to these networks to extract raw associations. A permutation test is then conducted to exclude false positives, yielding robust biological profiles for ATC codes. These profiles are used to construct new ATC code kernels, which are integrated with ATC code kernels from the existing model PDATC-NCPMKL. The recommendation matrix is subsequently generated using the procedures of PDATC-NCPMKL. Cross-validation results demonstrate that the new model achieves AUROC and AUPR values exceeding 0.96.

CONCLUSION

The proposed model outperforms PDATC-NCPMKL and other previous models. Analysis of the contributions of the newly added ATC code kernels confirms the value of biological profiles in enhancing the prediction of drug-ATC code associations.

Molecular insights of vitamin D receptor SNPs and vitamin D analogs: a novel therapeutic avenue for vitiligo

Vitamin D receptor (VDR) agonists play a pivotal role in modulating immune responses and promoting melanocyte survival, making them potential candidates for vitiligo treatment. The VDR gene is integral to mediating the effects of vitamin D in the immune system, and disruptions in its structure due to missense mutations may significantly contribute to the pathogenesis of vitiligo. Missense single-nucleotide polymorphisms (SNPs) can alter the amino acid sequence of the VDR protein, potentially affecting its ligand-binding affinity and downstream signaling. Investigating these missense SNPs provides critical insights into the genetic underpinnings of vitiligo and may help identify biomarkers for early detection and precision-targeted therapies. This study explored the therapeutic potential of vitamin D analogs in vitiligo management, with a particular focus on their binding interactions and molecular efficacy. Using molecular docking and virtual screening, 24 vitamin D analogs were evaluated. Calcipotriol exhibited the highest binding affinity (-11.4 kcal/mol) and unique interactions with key residues in the VDR ligand-binding domain. Additionally, an analysis of structural variations stemming from missense mutations in the VDR gene highlighted potential impacts on receptor-ligand interactions, further emphasizing the importance of genetic factors in treatment response. These findings underscore the potential of calcipotriol to promote melanogenesis and modulate pigmentation in vitiligo. A comparative analysis identified structural variations influencing the efficacy of other analogs, such as calcitriol and tacalcitol. Although the in silico methods provided valuable insights, the study acknowledges the limitations of excluding dynamic cellular environments and emphasizes the need for experimental validation. Overall, this study enhances our understanding of VDR-targeted therapies, and calcipotriol is a promising candidate for further development in the management of vitiligo.

Exploring disulfiram mechanisms in renal fibrosis: insights from biological data and computational approaches

Background

Disulfiram (DSF) is an anti-alcoholic drug that has been reported to inhibit the epithelial-to-mesenchymal transition and crosslinking during fibrosis, pyroptosis, and inflammatory NF-κB and Nrf-2 signaling pathways. However, there is insufficient evidence to support the mechanisms of DSF in preventing renal fibrosis (RF). Therefore, the current study aimed to elucidate the DSF-modulated targets and pathways in renal fibrosis.

Methods

The common proteins between DSF and RF were screened for protein-protein interaction, pathway enrichment, cluster, and gene ontology analysis. Molecular docking was executed for core genes using AutoDock Vina through the POAP pipeline. Molecular dynamics (MD) simulation (100 ns) was performed to infer protein-ligand stability, and conformational changes were analyzed by free energy landscape (FEL).

Results

A total of 78 targets were found to be common between DSF and RF, of which NFKB, PIK3CA/R1, MTOR, PTGS2, and MMP9 were the core genes. PI3K-Akt signaling followed by JAK-STAT, TNF, Ras, ErbB, p53, phospholipase D, mTOR, IL-17, NF-κB, AMPK, VEGF, and MAPK signaling pathways were modulated by DSF in RF. DSF showed a direct binding affinity with active site residues of core genes, and except for DSF with NF-κB, all other complexes, including the standard, were found to be stable during 100 ns MD simulation with minimal protein-ligand root mean squared deviation and residual fluctuations and higher compactness with broad conformational changes.

Conclusion

DSF protects against renal fibrosis, and this study paves the way for experimental investigation to repurpose DSF for treating RF.

Modeling the Interactions Between Chemicals and Proteins to Predict the Health Consequences of Air Pollution

The impacts of air pollution on human health have become a major concern, especially with rising greenhouse gas emissions and urban development. This study investigates the molecular mechanisms using the STITCH 4.0 and STRING 9.0 databases to analyze the interaction networks (PCI and PPI) associated with two air pollutants: carbon monoxide and hydrogen sulfide. The functional and pathway analysis related to these pollutants were performed by OmicsBox v.3.0. Additionally, critical proteins and their essential pathways were also identified by the Cytoscape networking tool v.3.10.3. AutoDock vina was employed to hypothetically determine the direct interactions of CO and H2S with the proteins that were found by STITCH. This study revealed that CO and H2S interacted with the different biological processes related to human health, including erythropoiesis, oxidative stress, energy production, amino acids metabolism, and multiple signaling pathways associated with respiratory, cardiovascular, and neurological functions. Six essential proteins were identified based on their degree of centrality, namely, FECH, HMOX1, ALB, CTH, CBS, and CBSL, which regulate various Reactome and KEGG pathways. Molecular docking analysis revealed that CO exhibited a strong interaction with ADI1, demonstrating a binding affinity of -1.9 kcal/mL. Alternately, the binding energy associated with the H2S interaction was notably weak (below -0.9 kcal/mL). This present research highlights the necessity for ongoing investigation into the molecular effects of air pollution to guide public health policies and interventions.

Optimizing Jasplakinolide delivery in rhabdomyosarcoma cells using pulsed electric fields (PEFs) for enhanced therapeutic impact

This study explores the combination of jasplakinolide with electroporation (JSP + EP), a method enhancing targeted molecule delivery. CHO-K1 (Chinese hamster ovarian), C2C12 (mouse myoblast), and RD (rhabdomyosarcoma) cells were treated with jasplakinolide (50 nM) in HEPES buffer and exposed to electrical pulses (0.8-1.2 kV/cm). Cell viability was measured via the MTS assay, cytoskeleton structure was assessed with confocal microscopy, and docking studies examined jasplakinolide-actin interactions. The combination of jasplakinolide and electric pulses synergistically affected RMS cells (Rhabdomyosarcoma), causing significant cytoskeletal changes and reduced viability. Docking studies revealed that jasplakinolide interacts with both monomeric and filamentous actin, highlighting a dual mechanism. Confocal imaging showed substantial actin cytoskeleton disruption in cancer cells, with minimal effects on normal cells. Jasplakinolide combined with electric pulses can specifically target cancer cells with less cytotoxicity to normal cells, potentially reducing side effects following the clinical procedure.

Prediction of adverse drug reactions based on pharmacogenomics combination features: a preliminary study

Introduction

Adverse Drug Reactions (ADRs), a widespread phenomenon in clinical drug treatment, are often associated with a high risk of morbidity and even death. Drugs and changes in gene expression are the two important factors that affect whether and how adverse reactions occur. Notably, pharmacogenomics data have recently become more available and could be used to predict ADR occurrence. However, there is a challenge in effectively analyzing the massive data lacking guidance on mutual relationship for ADRs prediction.

Methods

We constructed separate similarity features for drugs and ADRs using pharmacogenomics data from the Comparative Toxicogenomics Database [CTD, including Chemical-Gene Interactions (CGIs) and Gene-Disease Associations (GDAs)]. We proposed a novel deep learning architecture, DGANet, based on the constructed features for ADR prediction. The algorithm uses Convolutional Neural Networks (CNN) and cross-features to learn the latent drug-gene-ADR associations for ADRs prediction.

Results and Discussion

The performance of DGANet was compared to three state-of-the-art algorithms with different genomic features. According to the results, GDANet outperformed the benchmark algorithms (AUROC = 92.76%, AUPRC = 92.49%), demonstrating a 3.36% AUROC and 4.05% accuracy improvement over the cutting-edge algorithms. We further proposed new genomic features that improved DGANet's predictive capability. Moreover, case studies on top-ranked candidates confirmed DGANet's ability to predict new ADRs.

Mechanisms of Huhuang decoction in treating diabetic wounds: a network pharmacological and experimental study

Background: Huhuang (HH) decoction, a composition of seven traditional Chinese medicines, has demonstrated clinical efficacy in wound healing. However, its pharmacological foundation and potential mechanisms remain unclear. This study aimed to elucidate the mechanisms of action of HH decoction in the treatment of diabetic wounds. Methods: The chemical composition of HH decoction was analysed using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. The targets of the HH decoction in treating diabetic wounds were predicted using network pharmacology. The gene ontology and pathway enrichment analyses were performed using the DAVID functional annotation tool. The compound targets and PPI networks were established using Cytoscape. Molecular docking was implemented using the AutoDock Vina software. Experimental verification was performed on the target prediction of the HH decoction in treating diabetic wounds, both in vivo and in vitro. Results: The study identified 53 chemical components in HH decoction, with tetrahydropalmatine, emodin, rosmarinic acid, citric acid, berberine, and cryptotanshinone as key components for treating diabetic wounds. Twenty-one target genes were identified as core genes. Gene ontology analysis indicated that the therapeutic effects of HH on diabetic foot ulcers may occur through the regulation of cell proliferation, migration, and inflammation. Pathway enrichment was found to be mainly related to the HIF-1 and TNF signalling pathways. HH promoted proliferation, migration, and tube formation in vascular endothelial cells in vitro. Compared with the control group, the expression levels of HIF-1α, VEGF-α, cyclinD1 in the HH group were higher while the phosphorylation level of p65 in the HH group was significantly lower. The concentrations of IL-6, TNF-α, and IL-1β in wound tissue in the HH group were significantly lower than those in the control group. The expression levels of CD31, VEGF-α, Ki67 and HIF-1α in the wounds of diabetic rats in the HH group were higher than those in the control group. Conclusions: The HH decoction promotes diabetic wound healing via multiple components, targets, and pathways. It may enhance vascular endothelial cell proliferation via cyclinD1, promote vascularization through the HIF-1α/VEGF-α signalling pathway, and inhibit inflammation through NF-κB signalling pathways.

Fucoxanthin mitigates mercury-induced mitochondrial toxicity in the human ovarian granulosa cell line

Mercury (Hg) is known to be a hazardous toxin with a significant negative impact on female reproduction through mechanisms that remain unclear. The carotenoid fucoxanthin (FX) is an antioxidant with several positive effects on human health. This study aimed to examine the potential protective role of FX in reducing the Hg-induced bioenergetic disturbances in a human ovarian granulosa cell line model. (methods briefly) Hg was found to reduce the viability of granulosa cells in a concentration-dependent manner, with an estimated 72-hour EC50 of 10 µM. In contrast, FX (10 and 20 µM) improved cell viability. Hg (1 and 10 µM) significantly reduced cellular ATP levels, mitochondrial membrane potential, oxygen consumption rates, and lactate production. Additionally, Hg impaired the activities and kinetics of mitochondrial complexes I and III and reduced the expression of mitochondrial genes ND1, ND5, cytochrome B, cytochrome C oxidase, and ATP synthase subunits 6 and 8. According to tests on mitochondrial membranes, Hg increased membrane fluidity by reducing saturated fatty acid levels and increasing those of unsaturated fatty acids. Hg also promoted mitochondrial swelling and enhanced the inner mitochondrial membrane permeability to hydrogen and potassium ions. FX (10 µM) was shown to mitigate the negative effects of Hg on the viability of treated granulosa cells, bioenergetics parameters, and mitochondrial membrane integrity in a concentration-dependent manner. Based on these findings, bioenergetic disruption may be a key underlying cause of Hg-induced ovarian dysfunction. Furthermore, FX may have a potential therapeutic role in treating ovarian disorders caused by Hg-induced disruption of granulosa cell bioenergetics.

Dual Representation Learning for Predicting Drug-Side Effect Frequency Using Protein Target Information

Knowledge of unintended effects of drugs is critical in assessing the risk of treatment and in drug repurposing. Although numerous existing studies predict drug-side effect presence, only four of them predict the frequency of the side effects. Unfortunately, current prediction methods 1) do not utilize drug targets, 2) do not predict well for unseen drugs, and 3) do not use multiple heterogeneous drug features. We propose a novel deep learning-based drug-side effect frequency prediction model. Our model utilized heterogeneous features such as target protein information as well as molecular graph, fingerprints, and chemical similarity to create drug embeddings simultaneously. Furthermore, the model represents drugs and side effects into a common vector space, learning the dual representation vectors of drugs and side effects, respectively. We also extended the predictive power of our model to compensate for the drugs without clear target proteins using the Adaboost method. We achieved state-of-the-art performance over the existing methods in predicting side effect frequencies, especially for unseen drugs. Ablation studies show that our model effectively combines and utilizes heterogeneous features of drugs. Moreover, we observed that, when the target information given, drugs with explicit targets resulted in better prediction than the drugs without explicit targets.

Exploring the therapeutic potential of Rhododendron anthopogon D.Don essential oil constituents against lung cancer: A network pharmacology-based analysis with molecular docking and experimental studies

Rhododendron anthopogon D.Don is an evergreen shrub used by Himalayan healers to treat many ailments most notably lung problems. However, the mechanism by which R. anthopogon essential oil fights lung cancer has not been well studied. Here, in the present study, we used network pharmacology in combination with chemical profiling, molecular docking, and in-vitro experimental studies to uncover the mechanism of R. anthopogon essential oil constituents against lung cancer. By employing network pharmacology-based analysis, a total of 266 potential target genes obtained for 12 active components of R. anthopogon interacted with 260 common targets and 17,731 disease targets associated with lung cancer were retrieved. Using protein-protein interaction network (PPI), search tool for the retrieval of interacting genes/proteins (STRING) and database for annotation, visualization, and integrated discovery (DAVID) databases, we predicted that the main signaling pathways involved in the association of lung cancer with R. anthopogon essential oil constituents are the cancer signaling pathway and vascular endothelial growth factor and its receptor (VEGFR) cancer signalling pathway. Using TIMER 2.0 analysis and University of Alabama Cancer Database (UALCAN) findings, the expression pattern of EGFR was investigated across all TCGA (The cancer genome atlas) datasets. The study revealed that EGFR expression was elevated in various cancers especially in lung adenocarcinoma. Molecular docking analysis revealed that linalool, α-bisabolol, and guaiol possessed strong binding affinity with TNF-α, MAPK3, and EGFR protein drug targets. Our results predicted that TNF-α, MAPK3, and EGFR may be potential molecular targets of R. anthopogon essential oil constituents for the treatment of lung cancer. Furthermore, our study verified that R. anthopogon essential oil constituents inhibit proliferation, and induces apoptosis in lung cancer cell lines. Therefore, the present study highlights anti-lung cancer activity of the constituents of R. anthopogon essential oil and its potential involvement in comprehending therapeutic mechanism that may be applied in the lung cancer therapy.

Integrating network pharmacology, molecular docking and simulation approaches with machine learning reveals the multi-target pharmacological mechanism of Berberis integerrima against diabetic nephropathy

Diabetic nephropathy (DN) is one of the most feared complications of diabetes and key cause of end-stage renal disease (ESRD). Berberis integerrima has been widely used to treat diabetic complications, but exact molecular mechanism is yet to be discovered. Data on active ingredients of B. integerrima and target genes of both diabetic nephropathy and B.integerrima were obtained from public databases. Common results between B. integerrima and DN targets were used to create protein-protein interaction (PPI) network using STRING database and exported to Cytoscape software for the selection of hub genes based on degree of connectivity. Future, PPI network between constituents and overlapping targets was created using Cytoscape to investigate the network pharmacological effects of B. integerrima on DN. KEGG pathway analysis of core genes exposed their involvement in excess glucose-activated signaling pathway. Then, expression of core genes was validated through machine learning classifiers. Finally, PyRx and AMBER18 software was used for molecular docking and simulation. We found that Armepavine, Berberine, Glaucine, Magnoflorine, Reticuline, Quercetin inhibits the growth of diabetic nephropathy by affecting ICAM1, PRKCB, IKBKB, KDR, ALOX5, VCAM1, SYK, TBXA2R, LCK, and F3 genes. Machine learning revealed SYK and PRKCB as potential genes that could use as diagnostic biomarkers against DN. Furthermore, docking and simulation analysis showed the binding affinity and stability of the active compound with target genes. Our study revealed that B. integerrima has preventive effect on DN by acting on glucose-activated signaling pathways. However, experimental studies are needed to reveal biosafety profiles of B. integerrima in DN.Communicated by Ramaswamy H. Sarma.

Predictive cavity and binding site identification: Techniques and applications

Strategies for recognizing predictive cavities and binding site identification are decisive for drug discovery, molecular docking, and tracing protein-ligand interactions. The two major approaches experimental and computational strive for prognosticating and distinguishing protein's binding sites. Profuse diminutive molecules are associated with the binding sites and influence normal biological functioning. The various structure-based strategies such as molecular dynamics, docking simulations, algorithms for pocket identification, and homology modeling are covered under computational techniques, where they propound the exhaustive comprehension of possible binding pockets hinge on the structure of protein and its physiochemical properties. The various sequence-based approaches rely on the homogeneousness of the sequence and machine learning replicas edified on already known protein and ligand composites to anticipate the interactive sites of novel proteins. The high-resolution structural identification and foot printing of protein to map the confirmational changes attributable to ligand and binding sites can be identified through diverse experimental methods such as NMR spectroscopy, mass spectrometry, and x-ray crystallography. These techniques are pivotal for drug discovery and designing, as the efficiency and specificity of ligands can be amplified through virtual screening and structural-based drug designing. Moreover, the ongoing developments in this domain promise to drive the revolution and efficiency in drug discovery and research in molecular biology.

Multiscale Synergy Networks Offer Insights into Disease and Comorbidity Mechanisms

Complex diseases involve extensive metabolic interactions within intricate biological networks. Consequently, it is advantageous to analyze metabolic phenotype data through metabolite interactions rather than focus on individual metabolites in isolation. In this article, we propose a novel analysis strategy called SynNet, which constructs multiscale synergy networks associated with specific metabolic phenotypes, offering new perspectives on the metabolic response mechanisms of diseases, including the mechanisms underlying disease comorbidity. The SynNet strategy begins with the construction of a metabolite-level synergy network (m-SynNet). This network is based on the definition and identification of significant metabolite pair interactions that distinguish disease phenotypes. Subsequently, a pathway synergy effect is defined by mapping these synergistic metabolite pairs onto the predefined metabolic pathways and performing a hypergeometric test to assess the probability of these pairs affecting a given pathway pair. The resulting significant pathway pairs identified form a pathway-level synergy network (p-SynNet). Both m-SynNet and p-SynNet offer complementary insights into disease mechanisms that go beyond conventional metabolomics analysis. For example, nodes with high connectivity in m-/p-SynNet suggest a strong correlation with the phenotype, while shared pathways across different phenotypes offer clues about the mechanisms of disease comorbidity. We applied the SynNet strategy to two real-world metabolomic data sets of disease comorbidity and identified key pathways associated with disease comorbidity from the p-SynNet. The candidate pathways are supported by the existing literature. Thus, the SynNet strategy may represent an alternative approach for metabolomic data analysis, providing novel insights into disease mechanisms and comorbidity.

Afobazole: a potential drug candidate which can inhibit SARS CoV-2 and mimicry of the human respiratory pacemaker protein

In COVID-19 patients, respiratory failure was reported due to damage to the respiratory centers of the brainstem. Molecular mimicry of three brainstem pre-Botzinger complex proteins (DAB1, AIFM and SURF1) was regarded as the underlying reason for respiratory failure and the autoimmune neurological sequelae. Of the three brainstem proteins mimicked by SARS CoV-2, corresponding sequences to two of the mimicry peptides were located in the N-protein of SARS CoV-2. N-protein is important for viral RNA synthesis and genome packaging. Here, we have used molecular modeling, docking and MD simulations to discern potential drugs which can inhibit molecular mimicry of DAB1 by SARS CoV-2 and also eliminate it by interfering in genome packaging. The binding site (drug target) for molecular docking was defined as the amino acid sequence extending from position 168-185 of the N-protein which was a SLiM region and also included the mimicry hexapeptide. Molecular docking after MD simulations was used to discern probable inhibitors of the drug-target from FDA-approved neurological drugs in the Broad Institute's Drug Repurposing Hub. Our results revealed that an anti-anxiety drug afobazole qualified the ADMET parameters, formed a stable complex with the drug-target and exhibited the highest binding energy (-88.21 kJ/mol). This suggests that afobazole can be repurposed against SARS CoV-2 for disrupting molecular mimicry of human DAB1 protein and also eliminate the etiopathological agent by interfering in viral genome packaging.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-025-00316-6.

Identification of differentially expressed MiRNA clusters in cervical cancer

BACKGROUND

Aberrant miRNA expression has been associated with cervical cancer (CC) progression. The present study aimed to identify the miRNA clusters (MCs) altered in CC, identify their clinical utility, and understand their biological functions via computational analysis.

METHODS

We used small RNA sequencing and qRT‒PCR to identify and validate abnormally expressed MCs in cervical squamous cell carcinoma (CSCC) samples. We compared our data with publicly available CC datasets to identify the differentially expressed MCs in CC. The potential targets, pathways, biological functions, and clinical utility of abnormally expressed MCs were predicted via several computational tools.

RESULTS

Small RNA sequencing revealed that 229 miRNAs belonging to 48 MCs were significantly differentially expressed in CSCC (p-value ≤ 0.05). Validation by qRT‒PCR confirmed the downregulation of members of the miR-379/656, namely, hsa-miR-376c-3p (2.8-fold; p-value 0.03), hsa-miR-494-3p (3.4-fold; p-value 0.02), hsa-miR-495-3p (eightfold; p-value 0.01), and hsa-miR-409-3p (fivefold; p-value 0.03), in CSCC samples compared with normal samples. The prognostic model generated via miRNA expression and random forest analysis showed robust sensitivity and specificity (0.88 to 0.92) in predicting overall survival. In addition, we report 22 prognostically important miRNAs in CC. Pathway analysis revealed the enrichment of several cancer-related pathways, notably p53, the cell cycle, viral infection and MAPK signalling. CDC25A, CCNE1, E2F1, CCNE2, RBL1, E2F3, CDK2, RBL2, E2F2 and CCND2 were identified as the top ten gene targets of MC. Drug‒gene interaction analysis revealed enrichment of 548 approved drugs and 62 unique genes.

CONCLUSION

Our study identified MCs, their target genes, their prognostic utility, and their potential functions in CC and recommended their usefulness in CC management.

Remimazolam inhibits apoptosis of endothelial and epithelial cells by activating the PI3K/AKT pathway in acute lung injury

BACKGROUND

Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are significant burdens on global health. Remimazolam (REM), a novel sedative, has shown potential in its anti-inflammatory effects. However, a lack of evidence currently hinders our ability to determine if REM can improve ALI/ARDS.

METHODS

We initially evaluated REM's impact on lung injury in a lipopolysaccharide (LPS)-induced ALI mouse model. Subsequently, a network pharmacology (NP) strategy and ribonucleic acid-sequencing (RNA-seq) technique were used to investigate the potential molecular mechanisms underlying REM's action against ALI. Finally, we carried out in vivo and in vitro experiments to validate our findings on these mechanisms.

RESULTS

REM effectively mitigated lung injury in the mouse model. NP and RNA-seq analyses revealed significant enrichment of apoptosis-related pathways. Both in vivo and in vitro experiments revealed that REM significantly reduced levels of cleaved cysteine-aspartic acid-specific protease/proteinases 7 and 3 (cleaved Caspases-7 and -3) and cytochrome c (Cyt c) while enhancing the B-cell lymphoma 2 (Bcl-2)/Bcl-2-like protein 4 (Bax) ratio and phosphorylated protein kinase B (P-AKT) levels in lung tissue, endothelial cells, and epithelial cells. Furthermore, in vitro experiments confirmed that inhibiting the phosphoinositide 3-kinase (PI3K)/AKT pathway with LY294002 weakened REM's antiapoptotic effects. In addition, pretreatment with PK11195 (the ligand of 18-kDa translocator protein [TSPO]) attenuated REM's upregulation of the PI3K/AKT pathway and antiapoptotic effect in LPS-induced endothelial cells.

CONCLUSIONS

This study presents novel findings elucidating the beneficial effect of REM in ALI. This effect can be attributed to REM's ability to inhibit apoptosis by activating of the PI3K/AKT pathway in endothelial and epithelial cells. Additionally, REM targeted TSPO to regulate this pathway in endothelial cells. These results suggested a potential protective role for REM in ALI/ARDS management.

Quantifying hope: an EU perspective of rare disease therapeutic space and market dynamics

Rare diseases, affecting millions globally, pose a significant healthcare burden despite impacting a small population. While approximately 70% of all rare diseases are genetic and often begin in childhood, diagnosis remains slow and only 5% have approved treatments. The UN emphasizes improved access to primary care (diagnostic and potentially therapeutic) for these patients and their families. Next-generation sequencing (NGS) offers hope for earlier and more accurate diagnoses, potentially leading to preventative measures and targeted therapies. In here, we explore the therapeutic landscape for rare diseases, analyzing drugs in development and those already approved by the European Medicines Agency (EMA). We differentiate between orphan drugs with market exclusivity and repurposed existing drugs, both crucial for patients. By analyzing market size, segmentation, and publicly available data, this comprehensive study aims to pave the way for improved understanding of the treatment landscape and a wider knowledge accessibility for rare disease patients.

DeepCompoundNet: enhancing compound-protein interaction prediction with multimodal convolutional neural networks

Virtual screening has emerged as a valuable computational tool for predicting compound-protein interactions, offering a cost-effective and rapid approach to identifying potential candidate drug molecules. Current machine learning-based methods rely on molecular structures and their relationship in the network. The former utilizes information such as amino acid sequences and chemical structures, while the latter leverages interaction network data, such as protein-protein interactions, drug-disease interactions, and protein-disease interactions. However, there has been limited exploration of integrating molecular information with interaction networks. This study presents DeepCompoundNet, a deep learning-based model that integrates protein features, drug properties, and diverse interaction data to predict chemical-protein interactions. DeepCompoundNet outperforms state-of-the-art methods for compound-protein interaction prediction, as demonstrated through performance evaluations. Our findings highlight the complementary nature of multiple interaction data, extending beyond amino acid sequence homology and chemical structure similarity. Moreover, our model's analysis confirms that DeepCompoundNet gets higher performance in predicting interactions between proteins and chemicals not observed in the training samples.Communicated by Ramaswamy H. Sarma.

Network pharmacological mechanism and molecular experimental validation of artemisinin in the treatment of lung adenocarcinoma

BACKGROUND

Lung cancer is a medical ailment with high mortality and prevalence rates. Artemisinin (ART) and its derivatives exhibit anti-cancer properties against various malignancies, including lung cancer. However, further research is required to determine the precise anti-cancer mechanisms of ART. Hence, this study aimed to utilize network pharmacology to preliminarily investigate the therapeutic effectiveness and mode of action of this medication.

METHODS

Using a bioinformatics approach, five target proteins with the strongest connections were selected for docking. Gene enrichment analysis was performed, and the ART target proteins were predicted. Various methods, including methyl thiazolyl tetrazolium (MTT) assays, colony formation assays, microsphere formation assays, flow cytometry, and western blotting analysis, were employed to assess the impact of ART on the malignant characteristics of lung cancer cells.

RESULTS

Bioinformatic analysis identified 51 ART target genes in lung adenocarcinoma for further analysis. Pathway enrichment analysis of target genes revealed 639 enriched Gene Ontology-Biological Process (GO BP) and 17 enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. These findings imply that ART may control the IL-6 signaling pathway by focusing on important molecules such as CDK4 and IL-6. The ART-treated group experienced apoptosis induction, cell cycle arrest, and inhibition of cell proliferation and microsphere formation compared with the control group (p < 0.05, p < 0.01). Additionally, ART reduced the protein expression of CDK4, COX2, ERBB2, CD44, and EpCAM while increasing that of caspase 3, IL-6, p53, and SRC (p < 0.01).

CONCLUSION

ART inhibited the growth and stemness of HCC827 cells.

A Data-Driven Approach to Enhance the Prediction of Bacteria-Metabolite Interactions in the Human Gut Microbiome Using Enzyme Encodings and Metabolite Structural Embeddings

Background: The human gut microbiome is critical for host health by facilitating essential metabolic processes. Our study presents a data-driven analysis across 312 bacterial species and 154 unique metabolites to enhance the understanding of underlying metabolic processes in gut bacteria. The focus of the study was to create a strategy to generate a theoretical (negative) set for binary classification models to predict the consumption and production of metabolites in the human gut microbiome. Results: Our models achieved median balanced accuracies of 0.74 for consumption predictions and 0.95 for production predictions, highlighting the effectiveness of this approach in generating reliable negative sets. Additionally, we applied a kernel principal component analysis for dimensionality reduction. The consumption model with a polynomial kernel, and the production model with a radial basis function with 32 reduced features, showed median accuracies of 0.58 and 0.67, respectively. This demonstrates that biological information can still be captured, albeit with some loss, even after reducing the number of features. Furthermore, our models were validated on six previously unseen cases, achieving five correct predictions for consumption and four for production, demonstrating alignment with known biological outcomes. Conclusions: These findings highlight the potential of integrating data-driven approaches with machine learning techniques to enhance our understanding of gut microbiome metabolism. This work provides a foundation for creating bacteria-metabolite datasets to enhance machine learning-based predictive tools, with potential applications in developing therapeutic methods targeting gut microbes.

Integrative multi-omics analysis of autism spectrum disorder reveals unique microbial macromolecules interactions

INTRODUCTION

Gut microbiota alterations have been implicated in Autism Spectrum Disorder (ASD), yet the mechanisms linking these changes to ASD pathophysiology remain unclear.

OBJECTIVES

This study utilized a multi-omics approach to uncover mechanisms linking gut microbiota to ASD by examining microbial diversity, bacterial metaproteins, associated metabolic pathways and host proteome.

METHODS

The gut microbiota of 30 children with severe ASD and 30 healthy controls was analyzed. Microbial diversity was assessed using 16S rRNA V3 and V4 sequencing. A novel metaproteomics pipeline identified bacterial proteins, while untargeted metabolomics explored altered metabolic pathways. Finally, multi-omics integration was employed to connect macromolecular changes to neurodevelopmental deficits.

RESULTS

Children with ASD exhibited significant alterations in gut microbiota, including lower diversity and richness compared to controls. Tyzzerella was uniquely associated with the ASD group. Microbial network analysis revealed rewiring and reduced stability in ASD. Major metaproteins identified were produced by Bifidobacterium and Klebsiella (e.g., xylose isomerase and NADH peroxidase). Metabolomics profiling identified neurotransmitters (e.g., glutamate, DOPAC), lipids, and amino acids capable of crossing the blood-brain barrier, potentially contributing to neurodevelopmental and immune dysregulation. Host proteome analysis revealed altered proteins, including kallikrein (KLK1) and transthyretin (TTR), involved in neuroinflammation and immune regulation. Finally, multi-omics integration supported single-omics findings and reinforced the hypothesis that gut microbiota and their macromolecular products may contribute to ASD-associated symptoms.

CONCLUSIONS

The integration of multi-omics data provided critical evidence that alteration in gut microbiota and associated macromolecule production may play a role in ASD-related symptoms and co-morbidities. Key bacterial metaproteins and metabolites were identified as potential contributors to neurological and immune dysregulation in ASD, underscoring possible novel targets for therapeutic intervention.

MetaLigand: A database for predicting non-peptide ligand mediated cell-cell communication

Non-peptide ligands (NPLs), including lipids, amino acids, carbohydrates, and non-peptide neurotransmitters and hormones, play a critical role in ligand-receptor-mediated cell-cell communication, driving diverse physiological and pathological processes. To facilitate the study of NPL-dependent intercellular interactions, we introduce MetaLigand, an R-based and web-accessible tool designed to infer NPL production and predict NPL-receptor interactions using transcriptomic data. MetaLigand compiles data for 233 NPLs, including their biosynthetic enzymes, transporter genes, and receptor genes, through a combination of automated pipelines and manual curation from comprehensive databases. The tool integrates both de novo and salvage synthesis pathways, incorporating multiple biosynthetic steps and transport mechanisms to improve prediction accuracy. Comparisons with existing tools demonstrate MetaLigand's superior ability to account for complex biogenesis pathways and model NPL abundance across diverse tissues and cell types. Furthermore, analysis of single-nucleus RNA-seq datasets from age-related macular degeneration samples revealed that distinct retinal cell types exhibit unique NPL profiles and participate in specific NPL-mediated pathological cell-cell interactions. Finally, MetaLigand supports single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data, enabling the visualization of predicted NPL production levels and heterogeneity at single-cell resolution.

Invasive lobular carcinoma integrated multi-omics analysis reveals silencing of Arginosuccinate synthase and upregulation of nucleotide biosynthesis in tamoxifen resistance

Invasive Lobular Carcinoma (ILC), a distinct subtype of breast cancer is hallmarked by E-Cadherin loss, slow proliferation, and strong hormone receptor positivity. ILC faces significant challenges in clinical management due to advanced stage at diagnosis, late recurrence, and development of resistance to endocrine therapy - a cornerstone of ILC treatment. To elucidate the mechanisms underlying endocrine resistance in ILC, ILC cell lines (MDA-MB-134-VI, SUM44PE) were generated to be resistant to tamoxifen, a selective estrogen receptor modulator. The tamoxifen-resistant (TAMR) cells exhibit a 2-fold increase tamoxifen IC50 relative to parental cells. Metabolomics and RNA-sequencing revealed deregulation of alanine, aspartate, and glutamate metabolism, purine metabolism, and arginine and proline metabolism in TAMR cells. Among the fifteen commonly dysregulated genes in these pathways, low ASS1 expression was identified in the TAMR cells and was significantly correlated with poor outcome in ILC patients, specifically in the context of endocrine therapy. Our study reveals methylation mediated silencing of ASS1 in TAMR cells as a likely mechanism of downregulation. Demethylation restored ASS1 expression and correspondingly reduced tamoxifen IC50 toward parental levels. Nucleic acid biosynthesis is augmented in TAMR cells, evidenced by increase in nucleotide intermediates. Both TAMR cell lines demonstrated increased expression of several nucleic acid biosynthesis enzymes, including PAICS, PRPS1, ADSS2, CAD, and DHODH. Furthermore, CAD, the key multifunctional protein of de novo pyrimidine biosynthesis pathway is differentially activated in TAMR cells. Treating TAMR cell with Decitabine, a demethylating agent, or Farudodstat, a pyrimidine biosynthesis inhibitor, markedly augmented efficacy of tamoxifen. Collectively, our study unveils ASS1 downregulation as a novel mechanism underlying acquired tamoxifen resistance in ILC and establishes a metabolic link between ASS1 and nucleic acid biosynthesis. Restoring ASS1 expression or inhibiting pyrimidine biosynthesis restored tamoxifen sensitivity. ASS1 could be a potential biomarker and therapeutic target in tamoxifen resistant ILC patients, warranting further investigation.

Biological characteristics, immune infiltration and drug prediction of PANoptosis related genes and possible regulatory mechanisms in inflammatory bowel disease

PANoptosis is one of several modes of programmed cell death (PCD) and plays an important role in many inflammatory and immune diseases. The role of PANoptosis in inflammatory bowel disease (IBD) is currently unknown. Differentially expressed PANoptosis-related genes (DE-PRGs) were identified, and pathway enrichment analyses were performed. LASSO regression model construction, a nomogram model, calibration curves, ROC and DCA curves were used to evaluate the predictive value of the model. Predicts transcription factors (TFs) and small-molecule drugs of DE-PRGs were analysed. Model genes and immuno-infiltration were analysed. The PANoptosis features of IBD include 12 genes: OGT, TLR2, GZMB, TLR4, PPIF, YBX3, CASP5, BCL2L1, CASP6, MEFV, GSDMB and BAX. The enrichment analysis suggested that these genes were related to TNF signalling, NF-κB, pyroptosis and necroptosis. Machine learning identified three model genes: OGT, GZMB and CASP5. The nomogram model, calibration curves, ROC and DCA curves have strong predictive value. Immuno-infiltration analysis revealed that immune cell infiltration was increased in patients with IBD, and the model genes were closely related to the infiltration of various immune cells. The TFs associated with DE-PRGs were RELA, NFKB1, HIF1A, TP53 and SP1. In addition, the Connectivity Map (CMap) database identified the top 10 small-molecule compounds, including buspirone, chloroquine, spectinomycin and chlortetracycline. This study indicate that DE-PRGs model genes have good predictive ability for IBD. Moreover, PANoptosis may mediate the process of IBD through TNF signalling, NF-κB, pyroptosis, necroptosis and immune mechanisms. These results present a new horizon for the research and treatment of IBD.

Limosilactobacillus reuteri fermented brown rice alleviates anxiety improves cognition and modulates gut microbiota in stressed mice

Chronic stress disrupts gut microbiota homeostasis, contributing to anxiety and depression. This study explored the effects of Limosilactobacillus reuteri fermented brown rice (FBR) on anxiety using an ICR mouse chronic mild stress (CMS) model. Anxiety was assessed through body weight, corticosterone levels, neurotransmitter profiles, and behavioral tests. A four-week FBR regimen reduced corticosterone, restored neurotransmitters like gamma-aminobutyric acid (GABA) and serotonin, and improved anxiety-related behaviors. Metagenomic (16S rRNA) and metabolomic analyses revealed enhanced amino acid metabolism, energy metabolism, and short-chain fatty acid (SCFA) production in FBR-treated mice. FBR-enriched beneficial gut bacteria, aligning the microbiota profile with that of non-stressed mice. FBR also modulated GABA receptor-related gene expression, promoting relaxation. Network pharmacology identified quercetin, GABA, glutamic acid, phenylalanine, and ferulic acid as bioactive compounds with neuroprotective potential. These findings highlight FBR's potential as a gut-brain axis-targeted therapeutic for anxiety and stress-related disorders.

Drug Repurposing: Insights into Current Advances and Future Applications

Drug development is a complex and expensive process that involves extensive research and testing before a new drug can be approved for use. This has led to a limited availability of potential therapeutics for many diseases. Despite significant advances in biomedical science, the process of drug development remains a bottleneck, as all hypotheses must be tested through experiments and observations, which can be timeconsuming and costly. To address this challenge, drug repurposing has emerged as an innovative strategy for finding new uses for existing medications that go beyond their original intended use. This approach has the potential to speed up the drug development process and reduce costs, making it an attractive option for pharmaceutical companies and researchers alike. It involves the identification of existing drugs or compounds that have the potential to be used for the treatment of a different disease or condition. This can be done through a variety of approaches, including screening existing drugs against new disease targets, investigating the biological mechanisms of existing drugs, and analyzing data from clinical trials and electronic health records. Additionally, repurposing drugs can lead to the identification of new therapeutic targets and mechanisms of action, which can enhance our understanding of disease biology and lead to the development of more effective treatments. Overall, drug repurposing is an exciting and promising area of research that has the potential to revolutionize the drug development process and improve the lives of millions of people around the world. The present review provides insights on types of interaction, approaches, availability of databases, applications and limitations of drug repurposing.

Computational analysis of Ayurvedic herbs to explore their potential role as anti-cervical cancer agents

Cervical cancer is one of the common types of cancer in women. Treatment regimens include use of chemotherapy but it leads to certain side effects thereby creating a need for safer therapeutic options. Ayurveda has a great potential to provide better treatment strategies. In this study, computational approaches have been employed to investigate the molecular mechanism of anti-cervical cancer Ayurvedic herbs. Initially, Ayurvedic plants possessing anti-cervical cancer activities were obtained from literature. Bioactive compounds present in such plants were evaluated for drug-likeliness, biological functions and associations with cancer-related pathways. This resulted in the most promising drug-like bioactive compounds which were found to target cancer pathways like microRNA and proteoglycans, Human papillomavirus infection. Anti-cervical cancer activity possessing herbs derived from the study include Camellia sinensis, Equisetum arvense, Rosmarinus officinalis. Major bioactive compounds extracted from the enlisted herbs that contribute in promoting anti-cervical cancer effects include allicin, apigenin, and mataresinol. Overall, our study has provided insights into the scientific mechanism behind anti-cervical cancer activities of the indigenous herbs of Ayurveda. In addition, this study has also highlighted key bioactive compounds which have a potential in targeting cancer related pathways and thus can further be utilized to devise better therapeutics to cure cervical cancer.

Advancing COVID-19 Treatment: The Role of Non-covalent Inhibitors Unveiled by Integrated Machine Learning and Network Pharmacology

INTRODUCTION

The COVID-19 pandemic has necessitated rapid advancements in therapeutic discovery. This study presents an integrated approach combining machine learning (ML) and network pharmacology to identify potential non-covalent inhibitors against pivotal proteins in COVID-19 pathogenesis, specifically B-cell lymphoma 2 (BCL2) and Epidermal Growth Factor Receptor (EGFR).

METHODS

Employing a dataset of 13,107 compounds, ML algorithms such as k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes (NB) were utilized for screening and predicting active inhibitors based on molecular features. Molecular docking and molecular dynamics simulations, conducted over a 100 nanosecond period, enhanced the ML-based screening by providing insights into the binding affinities and interaction dynamics with BCL2 and EGFR. Network pharmacology analysis identified these proteins as hub targets within the COVID-19 protein-protein interaction network, highlighting their roles in apoptosis regulation and cellular signaling.

RESULTS

The identified inhibitors exhibited strong binding affinities, suggesting potential efficacy in disrupting viral life cycles and impeding disease progression. Comparative analysis with existing literature affirmed the relevance of BCL2 and EGFR in COVID-19 therapy and underscored the novelty of integrating network pharmacology with ML. This multidisciplinary approach establishes a framework for emerging pathogen treatments and advocates for subsequent in vitro and in vivo validation, emphasizing a multi-targeted drug design strategy against viral adaptability.

CONCLUSION

This study's findings are crucial for the ongoing development of therapeutic agents against COVID-19, leveraging computational and network-based strategies.

Deep metabolomics revealed trajectories of jasmonate signaling-mediated primary metabolism in Arabidopsis upon Spodoptera litura herbivory

Plants defend against chewing herbivores by up-regulating jasmonic acid (JA) signaling, which activates downstream signaling cascades and produces numerous secondary metabolites that act as defense molecules against the herbivores. Although secondary metabolism always remains a focus of research, primary metabolism is also reported to be realigned upon herbivory. However, JA signaling-mediated modulation of primary metabolites and their metabolic pathways in plants are mostly unexplored. Here, we applied gas chromatography-mass spectrometry-based untargeted metabolomics aided with computational statistical frameworks on wild type Arabidopsis, mutants of active JA receptor (i.e., CORONATINE-INSENSITIVE 1, COI1-1) and downstream transcription factor (i.e., MYC2) to navigate the JA signaling-mediated primary metabolism alterations during herbivory. Pathway and metabolite's chemical class enrichment analysis revealed JA signaling is crucial for constitutive as well as herbivore-induced primary metabolism and topology of their interaction networks. JA signaling majorly modulated alterations of sugars, amino acids and related metabolites. Herbivory-mediated sugar depletion and induction of methionine for aliphatic glucosinolates are also dependent on JA signaling. Taken together, our results demonstrate trails of JA signaling-mediated primary metabolic alterations associated with herbivory.

Phosphorylation of SNW1 protein associated with equine melanocytic neoplasm identified in serum and feces

Equine melanocytic neoplasm (EMN) represents a form of skin tumor observed predominantly in grey horses aged over 15 years. Despite its prevalence, current therapeutic and preventive strategies for EMN have been subject to limited investigation. This study endeavors to shed light on potential phosphoproteins present in equine serum and fecal samples, potentially linked to EMN, with a specific focus on functional interactions in EMN pathogenesis. We examined 50 samples (25 serum, 25 feces), divided into three groups based on EMN severity: normal (n = 16), mild (n = 18), and severe EMN (n = 16). Equine phosphoproteome analysis identified 2,359 annotated serum phosphoproteins and 2002 annotated fecal phosphoproteins through differentially expressed proteins (DEPs). KEGG analysis emphasized the role of environmental information processing. Notably, the integrin NF-kappaB binding P-TEFb to stimulate transcriptional elongation signaling pathway, involving SNW1 protein, was implicated in early stage of EMN development in both serum and fecal samples. This highlights SNW1's potential role in mediating transcriptional processes, offering a novel marker within environmental information processing. This study enhances understanding of EMN mechanisms in horses, suggesting early detection through non-invasive methods and identifying a functional pathway involving SNW1, which could inform future treatment and prevention strategies.

Exploring the anti-cancer properties of Carissa carandas as a multi-targeted approach against breast cancer

The escalating incidence of breast cancer globally presents a formidable challenge within oncology. Our research pursued an examination of the anti-cancer potential of Carissa carandas, a shrub traditionally used for medicinal purposes and known for its composition of vital nutrients and phytochemicals. We employed a network pharmacology strategy combined with molecular docking and molecular dynamics simulations to elucidate the intricate relationships between the phytochemical constituents of C. carandas, critical breast cancer proteins, and associated signaling pathways. The study highlighted a complex network of protein interactions, identifying AKT1, HIF1A, PTGS2, and GSK3B as key nodes within this network. These proteins are engaged by numerous investigated compounds from C. carandas and are fundamental in modulating crucial signaling pathways such as those involving Estrogen, HIF-1, Prolactin, VEGF, and Th17 cell differentiation-each of which plays a recognized role in breast cancer progression, affecting tumor growth, proliferation, and metastatic potential. Our analysis suggests that the phytochemicals in C. carandas may exert anti-cancer activity by synergistically modulating these pathways, highlighting the benefit of multi-targeted therapeutic approaches over single-targeted ones. In summary, through the application of advanced network pharmacology, molecular docking, MD simulations, and MM/PBSA analysis, our study offers a detailed exploration of the potential mechanisms by which C. carandas may exert anti-cancer effects. This sets a foundation for further in-depth experimental and clinical trials to validate these mechanisms and support the advancement of novel plant-derived therapeutic options towards breast cancer, with the possibility of significantly advancing the therapeutic options for this prevalent disease.

Nitazoxanide mitigates methotrexate hepatotoxicity in rats: role in inhibiting apoptosis and regulating endoplasmic reticulum stress

Objectives

Hepatotoxicity is a severe outcome of methotrexate (MTX) therapy, limiting its clinical use and contributing to its related morbidity and mortality. This study investigated the hepatoprotective effects of nitazoxanide (NTZ), an antiprotozoal drug, against MTX-induced hepatotoxicity and whether endoplasmic reticulum (ER) stress-modulation underlies the expected beneficial effects of NTZ.

Methods

Thirty-six rats were allocated to six groups, one control group and five MTX groups, where induction of hepatotoxicity was achieved via injecting MTX (20 mg/kg). Groups were assigned as MTX-vehicle, NTZ-100, and NTZ-200 groups (at 100 and 200 mg/kg/day, gavage, respectively), N-acetyl cysteine (NAC) group (500 mg/kg), and 4-phenyl butyric acid (4-PBA) group (150 mg/kg, i.p). Liver function enzymes in serum, hepatic oxidative stress, proinflammatory cytokines, apoptosis, and ER-stress biomarkers were assessed. A histopathological examination was performed.

Results

Treatment with NTZ lessened the serum liver enzymes, reduced malondialdehyde (lipid peroxidation product), enhanced antioxidant capacity, attenuated proinflammatory cytokines, and suppressed apoptosis. The protective effect of NTZ was dose-dependent, and the findings observed with the high-dose NTZ were similar to those obtained with the ER-stress inhibitor (4-PBA).

Conclusion

NTZ exerted a hepatoprotective effect in MTX-challenged rats that is mediated via modulation of ER stress and inhibiting apoptosis.