PubChem in 2021: new data content and improved web interfaces

Targeting bacterial growth in biofilm conditions: rational design of novel inhibitors to mitigate clinical and food contamination using QSAR

Antimicrobial resistance (AMR) is a pressing global issue exacerbated by the abuse of antibiotics and the formation of bacterial biofilms, which cause up to 80% of human bacterial infections. This study presents a computational strategy to address AMR by developing three novel quantitative structure-activity relationship (QSAR) models based on molecular topology to identify potential anti-biofilm and antibacterial agents. The models aim to determine the chemo-topological pattern of Gram (+) antibacterial, Gram (-) antibacterial, and biofilm formation inhibition activity. The models were applied to the virtual screening of a commercial chemical database, resulting in the selection of 58 compounds. Subsequent in vitro assays showed that three of these compounds exhibited the most promising antibacterial activity, with potential applications in enhancing food and medical device safety.

Molecular mechanisms underlying Tao-Hong-Si-Wu decoction treating hyperpigmentation based on network pharmacology, Mendelian randomization analysis, and experimental verification

CONTEXT

Hyperpigmentation, a common skin condition marked by excessive melanin production, currently has limited effective treatment options.

OBJECTIVE

This study explores the effects of Tao-Hong-Si-Wu decoction (THSWD) on hyperpigmentation and to elucidate the underlying mechanisms.

MATERIALS AND METHODS

We employed network pharmacology, Mendelian randomization, and molecular docking to identify THSWD's hub targets and mechanisms against hyperpigmentation. The Cell Counting Kit-8 (CCK-8) assay determined suitable THSWD treatment concentrations for PIG1 cells. These cells were exposed to graded concentrations of THSWD-containing serum (2.5%, 5%, 10%, 15%, 20%, 30%, 40%, and 50%) and treated with α-MSH (100 nM) to induce an in vitro hyperpigmentation model. Assessments included melanin content, tyrosinase activity, and Western blotting.

RESULTS

ALB, IL6, and MAPK3 emerged as primary targets, while quercetin, apigenin, and luteolin were the core active ingredients. The CCK-8 assay indicated that concentrations between 2.5% and 20% were suitable for PIG1 cells, with a 50% cytotoxicity concentration (CC50) of 32.14%. THSWD treatment significantly reduced melanin content and tyrosinase activity in α-MSH-induced PIG1 cells, along with downregulating MC1R and MITF expression. THSWD increased ALB and p-MAPK3/MAPK3 levels and decreased IL6 expression in the model cells.

DISCUSSION AND CONCLUSION

THSWD mitigates hyperpigmentation by targeting ALB, IL6, and MAPK3. This study paves the way for clinical applications of THSWD as a novel treatment for hyperpigmentation and offers new targeted therapeutic strategies.

The PP2A inhibitor LB-100 mitigates lupus nephritis by suppressing tertiary lymphoid structure formation

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by multi-organ involvement and autoantibody production. Patients with SLE face a substantial risk of developing lupus nephritis (LN), which imposes a substantial burden on both patients and their families. Protein phosphatase 2A (PP2A) is a widely distributed serine/threonine phosphatase that participates in regulating multiple signaling pathways. Inhibition of PP2A has been implicated in the treatment of various diseases. LB-100, a small molecule inhibitor of PP2A, has demonstrated anti-tumor therapeutic effects and high safety profile in preclinical experiments. However, the role of PP2A and its inhibitor has been insufficiently studied in LN. In this study, we assessed the potential effects of LB-100 in both MRL/lpr mice and R848-induced BALB/c mice. Our findings indicated that LB-100 administration led to reduced spleen enlargement, decreased deposition of immune complexes, ameliorated renal damage, and improved kidney function in both spontaneous and R848-induced lupus mouse models. Importantly, we observed the formation of tertiary lymphoid structures (TLSs) in the kidneys of two distinct lupus mouse models. The levels of signature genes of TLS were elevated in the kidneys of lupus mice, whereas LB-100 mitigated chemokine production and inhibited TLS formation. In addition, we confirmed that inhibition or knockdown of PP2A reduced the production of T cell-related chemokines by renal tubular epithelial cells (RTEC). In summary, our study highlighted the renal protective potential of the PP2A inhibitor LB-100 in two distinct lupus mouse models, suggesting its potential as a novel strategy for treating LN and other autoimmune diseases.

Leonurus japonicus Houtt. modulates neuronal apoptosis in intracerebral hemorrhage: Insights from network pharmacology and molecular docking

ETHNOPHARMACOLOGICAL RELEVANCE

Leonurus japonicus Houtt. (Labiatae), commonly known as Chinese motherwort, is a herbaceous flowering plant that is native to Asia. It is widely acknowledged in traditional medicine for its diuretic, hypoglycemic, antiepileptic properties and neuroprotection. Currently, Leonurus japonicus (Leo) is included in the Pharmacopoeia of the People's Republic of China. Traditional Chinese Medicine (TCM) recognizes Leo for its myriad pharmacological attributes, but its efficacy against ICH-induced neuronal apoptosis is unclear.

AIMS OF THE STUDY

This study aimed to identify the potential targets and regulatory mechanisms of Leo in alleviating neuronal apoptosis after ICH.

MATERIALS AND METHODS

The study employed network pharmacology, UPLC-Q-TOF-MS technique, molecular docking, pharmacodynamic studies, western blotting, and immunofluorescence techniques to explore its potential mechanisms.

RESULTS

Leo was found to assist hematoma absorption, thus improving the neurological outlook in an ICH mouse model. Importantly, molecular docking highlighted JAK as Leo's potential therapeutic target in ICH scenarios. Further experimental evidence demonstrated that Leo adjusts JAK1 and STAT1 phosphorylation, curbing Bax while augmenting Bcl-2 expression.

CONCLUSION

Leo showcases potential in mitigating neuronal apoptosis post-ICH, predominantly via the JAK/STAT mechanism.

Exploring the potential molecular intersection of stroke and major depression disorder

Stroke and major depression disorder are common neurological diseases, and a large number of clinical studies have shown that there is a close relationship between the two diseases, but whether the two diseases are linked at the genetic level needs to be further explored. The purpose of this study was to explore the comorbidity mechanism of stroke and major depression by using bioinformatics technology and animal experiments. From the GEO database, we gathered transcriptome data of stroke and depression mice (GSE104036, GSE131712, GSE81672, and GSE146845) and identified comorbid gene set through edgR and WGCNA analyses. Further analysis revealed that these genes were enriched in pathways associated with cell death. Programmed cell death gene sets (PCDGs) are generated from genes related to apoptosis, necroptosis, pyroptosis and autophagy. The intersection of PCDGs and comorbid gene set resulted in two hub genes, Mlkl and Nlrp3. Single-cell sequencing analysis indicated that Mlkl and Nlrp3 are mainly influential on endothelial cells and microglia, suggesting that the impairment of these two cell types may be a factor in the relationship between stroke and major depression. This was experimentally confirmed by RT-PCR and immunofluorescence staining. Our research revealed that two specific genes, namely, Mlkl and Nlrp3, play crucial roles in the complex mechanism that links stroke and major depression. Additionally, we have predicted six possible therapeutic agents and the outcomes of docking simulations of target proteins and drug molecules.

PI3 kinase inhibitor PI828 uncouples aminergic GPCRs and Ca2+ mobilization irrespectively of its primary target

The phosphoinositide 3-kinase (PI3K) is involved in regulation of multiple intracellular processes. Although the inhibitory analysis is generally employed for validating a physiological role of PI3K, increasing body of evidence suggests that PI3K inhibitors can exhibit PI3K-unrelated activity as well. Here we studied Ca2+ signaling initiated by aminergic agonists in a variety of different cells and analyzed effects of the PI3K inhibitor PI828 on cell responsiveness. It turned out that PI828 inhibited Ca2+ transients elicited by acetylcholine (ACh), histamine, and serotonin, but did not affect Ca2+ responses to norepinephrine and ATP. Another PI3K inhibitor wortmannin negligibly affected Ca2+ signaling initiated by any one of the tested agonists. Using the genetically encoded PIP3 sensor PH(Akt)-Venus, we confirmed that both PI828 and wortmannin effectively inhibited PI3K and ascertained that this kinase negligibly contributed to ACh transduction. These findings suggested that PI828 inhibited Ca2+ responses to aminergic agonists tested, involving an unknown cellular mechanism unrelated to the PI3K inhibition. Complementary physiological experiments provided evidence that PI828 could inhibit Ca2+ signals induced by certain agonists, by acting extracellularly, presumably, through their surface receptors. For the muscarinic M3 receptor, this possibility was verified with molecular docking and molecular dynamics. As demonstrated with these tools, wortmannin could be bound in the extracellular vestibule at the muscarinic M3 receptor but this did not preclude binding of ACh to the M3 receptor followed by its activation. In contrast, PI828 could sterically block the passage of ACh into the allosteric site, preventing activation of the muscarinic M3 receptor.

Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science

In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and Machine Learning (ML) with cheminformatics has proven to be a powerful combination. Cheminformatics, which combines the principles of computer science and chemistry, is used to extract chemical information and search compound databases, while the application of AI and ML allows for the identification of potential hit compounds, optimization of synthesis routes, and prediction of drug efficacy and toxicity. This collaborative approach has led to the discovery, preclinical evaluations and approval of over 70 drugs in recent years. To aid researchers in the pursuit of new drugs, this article presents a comprehensive list of databases, datasets, predictive and generative models, scoring functions and web platforms that have been launched between 2021 and 2022. These resources provide a wealth of information and tools for computer-assisted drug development, and are a valuable asset for those working in the field of cheminformatics. Overall, the integration of AI, ML and cheminformatics has greatly advanced the drug discovery process and continues to hold great potential for the future. As new resources and technologies become available, we can expect to see even more groundbreaking discoveries and advancements in these fields.Communicated by Ramaswamy H. Sarma.

Network pharmacology and experimental evidence: ERK/CREB/BDNF signaling pathway is involved in the antidepressive roles of Kaiyu Zhishen decoction

ETHNOPHARMACOLOGICAL RELEVANCE

Major Depressive Disorder (MDD) emerges as a complex psychosomatic condition, notable for its considerable suicidality and mortality rates. Increasing evidence suggests the efficacy of Chinese herbal medicine in mitigating depression symptoms and offsetting the adverse effects associated with conventional Western therapeutics. Notably, clinical trials have revealed the adjunctive antidepressant potential of Kaiyu Zhishen Decoction (KZD) alongside Western medication. However, the standalone antidepressant efficacy of KZD and its underlying mechanisms merit in-depth investigation.

AIM OF THE STUDY

This research aims to elucidate the impact of KZD on MDD and delineate its mechanistic pathways through integrated network pharmacological assessments and empirical in vitro and in vivo analyses.

MATERIALS AND METHODS

To ascertain the optimal antidepressant dosage and mechanism of KZD, a Chronic Unpredictable Mild Stress (CUMS)-induced depression model in mice was established to evaluate depressive behaviors. High-Performance Liquid Chromatography (HPLC) and network pharmacological approaches were employed to predict KZD's antidepressant mechanisms. Subsequently, hippocampal samples were subjected to 4D-DIA proteomic sequencing and validated through Western blot, immunofluorescence, Nissl staining, and pathway antagonist applications. Additionally, cortisol-stimulated PC12 cells were utilized to simulate neuronal damage, analyzing protein and mRNA levels of MAPK-related signals and cell proliferation markers.

RESULTS

The integration of network pharmacology and HPLC identified kaempferol and quercetin as KZD's principal active compounds for MDD treatment. Proteomic and network pharmacological KEGG pathway analyses indicated the MAPK signaling pathway as a critical regulatory mechanism for KZD's therapeutic effect on MDD. KZD was observed to mitigate CUMS-induced upregulation of p-ERK/ERK, CREB, and BDNF protein expressions in hippocampal cells by attenuating oxidative stress, thereby ameliorating neuronal damage and exerting antidepressant effects. The administration of PD98059 counteracted KZD's improvements in depression-like behaviors and downregulated p-ERK/ERK and BDNF protein expressions in the hippocampus.

CONCLUSIONS

This investigation corroborates KZD's pivotal, dose-dependent role in antidepressant activity. Both in vivo and in vitro experiments demonstrate KZD's capacity to modulate the ERK-CREB-BDNF signaling pathway by diminishing ROS expression induced by oxidative stress, enhancing neuronal repair, and thus, manifesting antidepressant properties. Accordingly, KZD represents a promising herbal candidate for further antidepressant research.

Alpha-cyperone mitigates renal ischemic injury via modulation of HDAC-2 expression in diabetes: Insights from molecular dynamics simulations and experimental evaluation

Chronic diabetes mellitus is reported to be associated with acute kidney injury. The enzyme histone deacetylase-2 (HDAC-2) was found to be upregulated in diabetes-related kidney damage. Alpha-cyperone (α-CYP) is one of the active ingredients of Cyperus rotundus that possesses antioxidant and anti-inflammatory effects. We evaluated the effect of α-CYP on improving oxidative stress and tissue inflammation following renal ischemia/reperfusion (I/R) injury in diabetic rats. The effect of α-CYP on HDAC-2 expression in renal homogenates and in the NRK-52 E cell line was evaluated following renal I/R injury and high glucose conditions, respectively. Molecular docking was used to investigate the binding of α-CYP with the HDAC-2 active site. Both renal function and oxidative stress were shown to be impaired in diabetic rats due to renal I/R injury. Significant improvements in kidney/body weight ratio, creatinine clearance, serum creatinine, blood urea nitrogen (BUN), and uric acid were observed in diabetic rats treated with α-CYP (50 mg/kg) two weeks prior to renal I/R injury. α-CYP treatment also improved histological alterations in renal tissue and lowered levels of malondialdehyde, myeloperoxidase, and hydroxyproline. Treatment with α-CYP suppressed the increased HDAC-2 expression in the renal tissue of diabetic rats and in the NRK-52 E cell line. The molecular docking reveals that α-CYP binds to HDAC-2 with good affinity, ascertained by molecular dynamics simulations and binding free energy analysis. Overall, our data suggest that α-CYP can effectively prevent renal injury in diabetic rats by regulating oxidative stress, tissue inflammation, fibrosis and inhibiting HDAC-2 activity.

Advancements in hand-drawn chemical structure recognition through an enhanced DECIMER architecture

Accurate recognition of hand-drawn chemical structures is crucial for digitising hand-written chemical information in traditional laboratory notebooks or facilitating stylus-based structure entry on tablets or smartphones. However, the inherent variability in hand-drawn structures poses challenges for existing Optical Chemical Structure Recognition (OCSR) software. To address this, we present an enhanced Deep lEarning for Chemical ImagE Recognition (DECIMER) architecture that leverages a combination of Convolutional Neural Networks (CNNs) and Transformers to improve the recognition of hand-drawn chemical structures. The model incorporates an EfficientNetV2 CNN encoder that extracts features from hand-drawn images, followed by a Transformer decoder that converts the extracted features into Simplified Molecular Input Line Entry System (SMILES) strings. Our models were trained using synthetic hand-drawn images generated by RanDepict, a tool for depicting chemical structures with different style elements. A benchmark was performed using a real-world dataset of hand-drawn chemical structures to evaluate the model's performance. The results indicate that our improved DECIMER architecture exhibits a significantly enhanced recognition accuracy compared to other approaches. SCIENTIFIC CONTRIBUTION: The new DECIMER model presented here refines our previous research efforts and is currently the only open-source model tailored specifically for the recognition of hand-drawn chemical structures. The enhanced model performs better in handling variations in handwriting styles, line thicknesses, and background noise, making it suitable for real-world applications. The DECIMER hand-drawn structure recognition model and its source code have been made available as an open-source package under a permissive license.

Scaffold-Hopped Compound Identification by Ligand-Based Approaches with a Prospective Affinity Test

Scaffold-hopped (SH) compounds are bioactive compounds structurally different from known active compounds. Identifying SH compounds in the ligand-based approaches has been a central issue in medicinal chemistry, and various molecular representations of scaffold hopping have been proposed. However, appropriate representations for SH compound identification remain unclear. Herein, the ability of SH compound identification among several representations was fairly evaluated based on retrospective validation and prospective demonstration. In the retrospective validation, the combinations of two screening algorithms and four two- and three-dimensional molecular representations were compared using controlled data sets for the early identification of SH compounds. We found that the combination of the support vector machine and extended connectivity fingerprint with bond diameter 4 (SVM-ECFP4) and SVM and the rapid overlay of chemical structures (SVM-ROCS) showed a relatively high performance. The compounds that were highly ranked by SVM-ROCS did not share substructures with the active training compounds, while those ranked by SVM-ECFP4 were mostly recombinant. In the prospective demonstration, 93 SH compounds were prepared by screening the Namiki database using SVM-ROCS, targeting ABL1 inhibitors. The primary screening using surface plasmon resonance suggested five active compounds; however, in the competitive binding assays with adenosine triphosphate, no hits were found.

BMSC loaded photo-crosslinked hyaluronic acid/collagen hydrogel incorporating FG4592 for enhanced cell proliferation and nucleus pulposus differentiation

Intervertebral disc degeneration arises from damage or degeneration of the nucleus pulposus (NP). In this study, we developed a photo-crosslinkable hydrogel incorporating FG4592 to support the growth and differentiation of bone-marrow-derived mesenchymal stem cells (BMSC). Initially, hyaluronic acid was modified with tyramine and combined with collagen to introduce riboflavin as a photo-crosslinker. This hydrogel transitioned from liquid to gel upon exposure to blue light in 3 min. The results showed that the hydrogel was biodegradable and had mechanical properties comparable to those of human NP tissues. Scanning electron microscopy after BMSC seeding in the hydrogel revealed an even distribution, and cells adhered to the collagen fibers in the hydrogel with minimal cell mortality. The effect of FG4592 on BMSC proliferation and differentiation was examined, revealing the capability of FG4592 to promote BMSC proliferation and direct differentiation resembling human NP cells. After cultivating BMSCs in the photo-crosslinked hydrogel, there was an upregulation in the expression of glycosaminoglycans, aggrecan, type II collagen, and keratin 19 proteins. Cross-species analyses of rat and human BMSCs revealed consistent results. For potential clinical applications, BMSC loaded with photo-crosslinked hydrogels can be injected into damaged intervertebral disc to facilitate NP regeneration.

Pan-cancer analysis of super-enhancer-induced LINC00862 and validation as a SIRT1-promoting factor in cervical cancer and gastric cancer

Immune checkpoints inhibitors are effective but it needs more precise biomarkers for patient selection. We explored the biological significance of LINC00862 in pan-cancer by bioinformatics. And we studied its regulatory mechanisms using chromatin immunoprecipitation and RNA immunoprecipitation assays etc. TCGA and single-cell sequencing data analysis indicated that LINC00862 was overexpressed in the majority of tumor and stromal cells, which was related with poor prognosis. LINC00862 expression was related with immune cell infiltration and immune checkpoints expression, and had a high predictive value for immunotherapy efficacy. Mechanistically, LINC00862 competitively bound to miR-29c-3p to unleash SIRT1's tumor-promoting function. SIRT1 inhibitor-EX527 were screened by virtual screening and verified by in vitro and vivo assays. Notably, acetyltransferase P300-mediated super-enhancer activity stimulated LINC00862 transcription. Collectively, LINC00862 could be a diagnostic and prognostic biomarker. LINC00862 could also be a predictive biomarker for immunotherapy efficacy. Super-enhancer activity is the driver for LINC00862 overexpression in cervical cancer and gastric cancer.

β-Sitosterol alleviates the malignant phenotype of hepatocellular carcinoma cells via inhibiting GSK3B expression

To explore the effects of β-Sitosterol upon hepatocellular carcinoma cell proliferation, apoptosis, migration, invasion, and epithelial-mesenchymal transition (EMT), and to investigate the underlying mechanism using network pharmacology. Human hepatocellular carcinoma cell lines (Huh-7 and HCCLM3) were expose to gradient concentrations of β-Sitosterol (5 μg/mL, 10 μg/mL, and 20 μg/mL). Cell viability and proliferation were assessed using MTT, CCK-8, colony formation, and EdU assays.Flow cytometry was employed to evaluate cell cycle and apoptosis. Scratch and Transwell assays were performed, respectively, to detect cell migration and invasion. The levels of apoptosis-associated proteins (BAX, BCL2, and cleaved caspase3) as well as EMT-associated proteins (E-cadherin, N-cadherin, Snail, and Vimentin) were detected in Huh-7 and HCCLM3 cell lines using Western blot analysis. The drug target gene for β-Sitosterol was screened via PubChem and subsequently evaluated for expression in the GSE112790 dataset. In addition, the expression level of glycogen synthase kinase 3 beta (GSK3B) within the Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) database was analyzed, along with its correlation to the survival outcomes of patients with hepatocellular carcinoma. The diagnostic efficiency of GSK3B was assessed by analyzing the ROC curve. Subsequently, Huh-7 and HCCLM3 cell lines were transfected with the overexpression vector of GSK3B and then treated with β-Sitosterol to further validate the association between GSK3B and β-Sitosterol. GSK3B demonstrated a significantly elevated expression in patients with hepatocellular carcinoma, which could predict hepatocellular carcinoma patients' impaired prognosis based on GEO dataset and TCGA database. GSK3B inhibitor (CHIR-98014) notably inhibited cell proliferation and invasion, promoted cell apoptosis and cell cycle arrest at G0/G1 phase in hepatocellular carcinoma cells. β-Sitosterol treatment further promoted the efffects of GSK3B inhibitor on hepatocellular carcinoma cells. GSK3B overexpression has been found to enhance the proliferative and invasive capabilities of hepatocellular carcinoma cells. Furthermore it has been observed that GSK3B overexpression, it has been obsear can partially reverse the inhibitory effect of β-Sitosterol upon hepatocellular. β-Sitosterol suppressed hepatocellular carcinoma cell proliferation and invasion, and enhanced apoptosis via inhibiting GSK3B expression.

Unveiling the molecular basis of paracetamol-induced hepatotoxicity: Interaction of N-acetyl-p-benzoquinone imine with mitochondrial succinate dehydrogenase

Background and aim

N-acetyl-p-benzoquinoneimine (NAPQI), a toxic byproduct of paracetamol (Acetaminophen, APAP), can accumulate and cause liver damage by depleting glutathione and forming protein adducts in the mitochondria. These adducts disrupt the respiratory chain, increasing superoxide production and reducing ATP. The goal of this study was to provide computational proof that succinate dehydrogenase (SDH), a subunit of complex II in the mitochondrial respiratory chain, is a favorable binding partner for NAPQI in this regard.

Method

Molecular docking, molecular dynamics simulation, protein-protein interaction networks (PPI), and KEGG metabolic pathway analysis were employed to identify binding characteristics, interaction partners, and their associations with metabolic pathways. A lipid membrane was added to the experimental apparatus to mimic the natural cellular environment of SDH. This modification made it possible to develop a context for investigating the role and interactions of SDH within a cellular ecosystem that was more realistic and biologically relevant.

Result

The molecular binding affinity score for APAP and NAPQI with SDH was predicted -6.5 and -6.7 kcal/mol, respectively. Furthermore, RMSD, RMSF, and Rog from the molecular dynamics simulations study revealed that NAPQI has slightly higher stability and compactness compared to APAP at 100 ns timeframe with mitochondrial SDH.

Conclusion

This study serves to predict the mechanistic process of paracetamol toxicity by using different computational approaches. In addition, this study will provide information about the drug target against APAP hepatotoxicity.

Identification of pseudobaptigenin as a novel polyphenol-based multi-target antagonist of different hormone receptors for breast cancer therapeutics

Breast cancer (BC) is one of the most prevalent cancers in the world and is one of the major reasons for the death of women worldwide. BC is majorly categorized based on the presence or absence of three cell receptors ER, PR and HER2. The latest treatment for BC involves interfering with the production and action of hormones such as estrogen and progesterone. These hormones bind with receptors such as ER and PR and enhance the growth and proliferation of the BC cells. Although the available are effective, the increasing resistance and side effects related to hormonal imbalance are significant and hence there is a need for designing. On the other hand, plant-derivative products have gained a lot of popularity for their promising anti-cancerous activities. Polyphenols are one such group of plant derivatives that have proven to be useful against cancer. In the present study, an in-silico approach was used to search for a polyphenol that can inhibit ER. In this work, a total of 750 polyphenols were taken into consideration. This number was narrowed down to 55, based on their ADMET properties. These 55 polyphenols were then docked to the receptors, ER, PR and HER2. The molecular docking was followed by Molecular Dynamics (MD) simulations. Based on molecular docking and MD simulation results it was concluded that Pseudobaptigenin has the potential to be an inhibitor of ER, PR and HER2.Communicated by Ramaswamy H. Sarma.

In silico modelling of ciprofloxacin specific aptamer for the development of high-performance biosensor

Ciprofloxacin (CFX), a widely used fluoroquinolone antibiotic, is critical in healthcare settings for treating patients. However, improper treatment of wastewater from these facilities can lead to environmental contamination with CFX. This underscores the need for an efficient, straightforward method for early detection. In this study, a DNA aptamer was selected through a hierarchical docking workflow, and the stability and interactions were assessed by Molecular Dynamics (MD) simulation. The aptamer-CFX complex that showed the most promise had a docking score of -8.596 kcal/mol and was further analyzed using MD simulation and MM/PBSA. Based on the overall results, the identified ssDNA sequence length of 60 nt (CAGCGCTAGGGCTTTTAGCGTAATGGGTAGGGTGGTGCGGTGCAGATATCGGAATTGGTG) was immobilized over a gold transducer surface through the self-assembled monolayer (SAM; Au-S-ssDNA) method. The ssDNA-modified surface has demonstrated a high affinity towards CFX, which is confirmed by cyclic voltammogram (CV) and electrochemical impedance spectroscopy measurements (EIS). The DNA-aptamer modified electrode demonstrated a good linear range (10 × 10-9 - 200 × 10-9 M), detection limit (1.0 × 10-9 M), selectivity, reproducibility, and stability. The optimized DNA-aptamer-based CFX sensor was further utilized for the accurate determination of CFX with good recoveries in real samples.

ISTransbase: an online database for inhibitor and substrate of drug transporters

Drug transporters, integral membrane proteins found throughout the human body, play critical roles in physiological and biochemical processes through interactions with ligands, such as substrates and inhibitors. The extensive and disparate data on drug transporters complicate understanding their complex relationships with ligands. To address this challenge, it is essential to gather and summarize information on drug transporters, inhibitors and substrates, and simultaneously develop a comprehensive and user-friendly database. Current online resources often provide fragmented information and have limited coverage of drug transporter substrates and inhibitors, highlighting the need for a specialized, comprehensive and openly accessible database. ISTransbase addresses this gap by amassing a substantial amount of data from literature, government documents and open databases. It includes 16 528 inhibitors and 4465 substrates of 163 drug transporters from 18 different species, resulting in a total of 93 841 inhibitor records and 51 053 substrate records. ISTransbase provides detailed insights into drug transporters and their inhibitors/substrates, encompassing transporter and molecule structure, transporter function and distribution, as well as experimental methods and results from transport or inhibition experiments. Furthermore, ISTransbase offers three search strategies that allow users to retrieve drugs and transporters based on multiple selectable constraints, as well as perform checks for drug-drug interactions. Users can also browse and download data. In summary, ISTransbase (https://istransbase.scbdd.com/) serves as a valuable resource for accurately and efficiently accessing information on drug transporter inhibitors and substrates, aiding researchers in exploring drug transporter mechanisms and assisting clinicians in mitigating adverse drug reactions Database URL: https://istransbase.scbdd.com/.

Innovative Use of an Injectable, Self-Healing Drug-Loaded Pectin-Based Hydrogel for Micro- and Supermicro-Vascular Anastomoses

Microvascular surgery plays a crucial role in reconnecting micrometer-scale vessel ends. Suturing remains the gold standard technique for small vessels; however, suturing the collapsed lumen of microvessels is challenging and time-consuming, with the risk of misplaced sutures leading to failure. Although multiple solutions have been reported, the emphasis has predominantly been on resolving challenges related to arteries rather than veins, and none has proven superior. In this study, we introduce an innovative solution to address these challenges through the development of an injectable lidocaine-loaded pectin hydrogel by using computational and experimental methods. To understand the extent of interactions between the drug and the pectin chain, molecular dynamics (MD) simulations and quantum mechanics (QM) calculations were conducted in the first step of the research. Then, a series of experimental studies were designed to prepare lidocaine-loaded injectable pectin-based hydrogels, and their characterization was performed by using Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and rheological analysis. After all the results were evaluated, the drug-loaded pectin-based hydrogel exhibiting self-healing properties was selected as a potential candidate for in vivo studies to determine its performance during operation. In this context, the hydrogel was injected into the divided vessel ends and perivascular area, allowing for direct suturing through the gel matrix. While our hydrogel effectively prevented vasospasm and facilitated micro- and supermicro-vascular anastomoses, it was noted that it did not cause significant changes in late-stage imaging and histopathological analysis up to 6 months. We strongly believe that pectin-based hydrogel potentially enhanced microlevel arterial, lymphatic, and particularly venous anastomoses.

Postbiotics Prepared Using Lactobacillus reuteri Ameliorates Ethanol-Induced Liver Injury by Regulating the FXR/SHP/SREBP-1c Axis

SCOPE

While probiotics-based therapies have exhibited potential in alleviating alcohol-associated liver disease (ALD), the specific role of postbiotics derived from Lactobacillus reuteri (L. reuteri) in ALD remains elusive. This study aims to investigate the impact of postbiotics on ameliorating alcohol-induced hepatic steatosis and the underlying mechanisms.

METHODS AND RESULTS

Using network pharmacology, the study elucidates the targets and pathways impacted by postbiotics from L. reuteri, identifying the farnesoid X receptor (FXR) as a promising target for postbiotics against ALD, and lipid metabolism and alcoholism act as crucial pathways associated with postbiotics-targeting ALD. Furthermore, the study conducts histological and biochemical analyses coupled with LC/MS to evaluate the protective effects and mechanisms of postbiotics against ALD. Postbiotics may modulate bile acid metabolism in vivo by regulating FXR signaling, activating the FXR/FGF15 pathway, and influencing the enterohepatic circulation of bile acids (BAs). Subsequently, postbiotics regulate hepatic FXR activated by BAs and modulate the expression of FXR-mediated protein, including short regulatory partner (SHP) and sterol regulatory element binding protein-1c (SREBP-1c), thereby ameliorating hepatic steatosis in mice with ALD.

CONCLUSION

Postbiotics effectively alleviate ethanol-induced hepatic steatosis by regulating the FXR/SHP/SREBP-1c axis, as rigorously validated in both in vivo and in vitro.

Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery

Artificial intelligence transforms drug discovery, with phenotype-based approaches emerging as a promising alternative to target-based methods, overcoming limitations like lack of well-defined targets. While chemical-induced transcriptional profiles offer a comprehensive view of drug mechanisms, inherent noise often obscures the true signal, hindering their potential for meaningful insights. Here, we highlight the development of TranSiGen, a deep generative model employing self-supervised representation learning. TranSiGen analyzes basal cell gene expression and molecular structures to reconstruct chemical-induced transcriptional profiles with high accuracy. By capturing both cellular and compound information, TranSiGen-derived representations demonstrate efficacy in diverse downstream tasks like ligand-based virtual screening, drug response prediction, and phenotype-based drug repurposing. Notably, in vitro validation of TranSiGen's application in pancreatic cancer drug discovery highlights its potential for identifying effective compounds. We envisage that integrating TranSiGen into the drug discovery and mechanism research holds significant promise for advancing biomedicine.

Neuroprotective Effect and Mechanism of Tanreqing Injection on Ischemic Stroke: Insights from Network Pharmacology and in vivo Experiments

OBJECTIVE

To explore the neuroprotective effects and mechanism of Tanreqing Injection (TRQ) on treating ischemic stroke based on network pharmacology and in vivo experimental validation.

METHODS

The chemical compounds of TRQ were retrieved based on published data, with targets retrieved from PubChem, Therapeutic Target Database and DrugBank. Network visualization and analysis were performed using Cytoscape, with protein-protein interaction networks derived from the STRING database. Enrichment analysis was performed using Kyoto Encyclopedia of Genes Genomes pathway and Gene Ontology analysis. In in vivo experiments, the middle cerebral artery occlusion (MCAO) model was used. Infarct volume was determined by 2,3,5-triphenyltetrazolium hydrochloride staining and protein expressions were analyzed by Western blot. Molecular docking was performed to predict ligand-receptor interactions.

RESULTS

We screened 81 chemical compounds in TRQ and retrieved their therapeutic targets. Of the targets, 116 were therapeutic targets for stroke. The enrichment analysis showed that the apelin signaling pathway was a key pathway for ischemic stroke. Furthermore, in in vivo experiment we found that administering with intraperitoneal injection of 2.5 mL/kg TRQ every 6 h could significantly reduce the infarct volume of MCAO rats (P<0.05). In addition, protein levels of the apelin receptor (APJ)/phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) pathway were increased by TRQ (P<0.05). In addition, 41 chemical compounds in TRQ could bind to APJ.

CONCLUSIONS

The neuroprotective effect of TRQ may be related to the APJ/PI3K/AKT signaling pathway. However, further studies are needed to confirm the findings.

Exploring the action mechanism of Gegensan in the treatment of alcoholic liver disease based on network pharmacology and bioinformatics

Gegensan (GGS) has been reported for the treatment of alcoholic liver disease (ALD), but its therapeutic mechanism is still unclear. This paper aims to determine the therapeutic mechanism and targets of action of GGS on alcoholic liver disease utilizing network pharmacology and bioinformatics. The active ingredients in GGS were screened in the literature and databases, and common targets of ALD were then obtained from public databases to construct the network diagram of traditional Chinese medicine-active ingredient targets. Based on the common targets, Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to find target enrichment pathways, and the core targets were screened out by combining differential analysis and protein-protein interaction network analysis. Molecular docking was performed to verify the binding effect between the core targets and the corresponding active ingredients. ALD and GGS have 84 common targets, corresponding to 91 active ingredients. After subsequent differential analysis and protein-protein interaction network analysis, 10 core targets were identified. Gene Ontology and KEGG enrichment analyses showed that the main BPs corresponding to the common targets included the response to lipopolysaccharide, inflammatory response, etc. The KEGG pathways involved in the regulation of the common targets included the lipid-atherosclerosis pathway and the alcoholic liver disease pathway, etc. Further molecular docking showed that the core targets CYP1A1, CYP1A2, CXCL8, ADH1C, MMP1, SERPINE1, COL1A1, APOB, MMP1, and their corresponding 4 active ingredients, Naringenin, Kaempferol, Quercetin, and Stigmasterol, have a greater docking potential. The above results suggest that GGS can regulate lipid metabolism and inflammatory response in the ALD process, and alleviate the lipid accumulation and oxidative stress caused by ethanol. This study analyzed the core targets and mechanisms of action of GGS on ALD, which provides certain theoretical support for the further development of GGS in the treatment of ALD, and provides a reference for the subsequent research on the treatment of ALD.

S. C, Gangadhar S, A. S, Murali M, Shivamallu C, Achar RR, Silina E, Stupin V, Manturova N, Shati AA, Alfaifi MY, Elbehairi SEI, Kollur SP, Amruthesh KN. Exploration of Type III effector Xanthomonas outer protein Q (XopQ) inhibitor from Picrasma quassioides as an antibacterial agent using chemoinformatics analysis

The present study was focused on exploring the efficient inhibitors of closed state (form) of type III effector Xanthomonas outer protein Q (XopQ) (PDB: 4P5F) from the 44 phytochemicals of Picrasma quassioides using cutting-edge computational analysis. Among them, Kumudine B showed excellent binding energy (-11.0 kcal/mol), followed by Picrasamide A, Quassidine I and Quassidine J with the targeted closed state of XopQ protein compared to the reference standard drug (Streptomycin). The molecular dynamics (MD) simulations performed at 300 ns validated the stability of top lead ligands (Kumudine B, Picrasamide A, and Quassidine I)-bound XopQ protein complex with slightly lower fluctuation than Streptomycin. The MM-PBSA calculation confirmed the strong interactions of top lead ligands (Kumudine B and QuassidineI) with XopQ protein, as they offered the least binding energy. The results of absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis confirmed that Quassidine I, Kumudine B and Picrasamide A were found to qualify most of the drug-likeness rules with excellent bioavailability scores compared to Streptomycin. Results of the computational studies suggested that Kumudine B, Picrasamide A, and Quassidine I could be considered potential compounds to design novel antibacterial drugs against X. oryzae infection. Further in vitro and in vivo antibacterial activities of Kumudine B, Picrasamide A, and Quassidine I are required to confirm their therapeutic potentiality in controlling the X. oryzae infection.

Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction

The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein-ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.

PubChem synonym filtering process using crowdsourcing

PubChem ( https://pubchem.ncbi.nlm.nih.gov ) is a public chemical information resource containing more than 100 million unique chemical structures. One of the most requested tasks in PubChem and other chemical databases is to search chemicals by name (also commonly called a "chemical synonym"). PubChem performs this task by looking up chemical synonym-structure associations provided by individual depositors to PubChem. In addition, these synonyms are used for many purposes, including creating links between chemicals and PubMed articles (using Medical Subject Headings (MeSH) terms). However, these depositor-provided name-structure associations are subject to substantial discrepancies within and between depositors, making it difficult to unambiguously map a chemical name to a specific chemical structure. The present paper describes PubChem's crowdsourcing-based synonym filtering strategy, which resolves inter- and intra-depositor discrepancies in synonym-structure associations as well as in the chemical-MeSH associations. The PubChem synonym filtering process was developed based on the analysis of four crowd-voting strategies, which differ in the consistency threshold value employed (60% vs 70%) and how to resolve intra-depositor discrepancies (a single vote vs. multiple votes per depositor) prior to inter-depositor crowd-voting. The agreement of voting was determined at six levels of chemical equivalency, which considers varying isotopic composition, stereochemistry, and connectivity of chemical structures and their primary components. While all four strategies showed comparable results, Strategy I (one vote per depositor with a 60% consistency threshold) resulted in the most synonyms assigned to a single chemical structure as well as the most synonym-structure associations disambiguated at the six chemical equivalency contexts. Based on the results of this study, Strategy I was implemented in PubChem's filtering process that cleans up synonym-structure associations as well as chemical-MeSH associations. This consistency-based filtering process is designed to look for a consensus in name-structure associations but cannot attest to their correctness. As a result, it can fail to recognize correct name-structure associations (or incorrect ones), for example, when a synonym is provided by only one depositor or when many contributors are incorrect. However, this filtering process is an important starting point for quality control in name-structure associations in large chemical databases like PubChem.

The immune cells have complex causal regulation effects on cancers

BACKGROUND

There was a large body of evidence linking immune cells to cancer risk. However, the causal relationship between immune cells, cancer, and what genes play an important role is unclear.

METHODS

In this study, we performed comprehensive two-sample Mendelian randomization analysis (TSMR) to determine the causal relationship between immune cells and common cancers. We also performed Multimarker Analysis of Genomic Annotation (MAGMA) on immune cells causally associated with cancer to identify their relevant genes and used data summary-based MR (SMR) analysis to investigate the causal relationship between their gene expression, methylation, and cancer, and further used drug prediction and molecular docking to validate the medicinal value of the targets. Finally, reverse TSMR analysis was performed on cancer and immune cells to rule out reverse causality.

RESULTS

After FDR correction (PFDR < 0.05), the results showed that 2 immune cells were associated with lung cancer risk, and 1 immune cell was significantly associated with pancreatic cancer risk. The expression of OSBPL10, CHD4, SMDT1, PHETA2, and NAGA was positively and causally related to the risk of lung cancer by SMR analysis and HEIDI test. We also found that increased expression of ANP32E decreased the risk of pancreatic cancer and that the methylation level of OSBPL10, CHD4, SULF2, CENPM, and CYP2D6 had a causal association with lung cancer. The methylation level of FCGR3A was causally associated with pancreatic cancer. The results of molecular docking indicated a strong affinity between the drugs and proteins that possessed existing structural information.

CONCLUSION

This data-driven Mendelian randomization (MR) study demonstrates the causal role of immune cells in cancers. In addition, this study identifies candidate genes that may be potential anti-cancer drug targets.

Molecular mechanisms of quetiapine bidirectional regulation of bipolar depression and mania based on network pharmacology and molecular docking: Evidence from computational biology

BACKGROUND

Quetiapine monotherapy is recommended as the first-line option for acute mania and acute bipolar depression. However, the mechanism of action of quetiapine is unclear. Network pharmacology and molecular docking were employed to determine the molecular mechanisms of quetiapine bidirectional regulation of bipolar depression and mania.

METHODS

Putative target genes for quetiapine were collected from the GeneCard, SwissTargetPrediction, and DrugBank databases. Targets for bipolar depression and bipolar mania were identified from the DisGeNET and GeneCards databases. A protein-protein interaction (PPI) network was generated using the String database and imported into Cytoscape. DAVID and the Bioinformatics platform were employed to perform the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the top 15 core targets. The drug-pathway-target-disease network was constructed using Cytoscape. Finally, molecular docking was performed to evaluate the interactions between quetiapine and potential targets.

RESULTS

Targets for quetiapine actions against bipolar depression (126 targets) and bipolar mania (81 targets) were identified. Based on PPI and KEGG pathway analyses, quetiapine may affect bipolar depression by targeting the MAPK and PI3K/AKT insulin signaling pathways via BDNF, INS, EGFR, IGF1, and NGF, and it may affect bipolar mania by targeting the neuroactive ligand-receptor interaction signaling pathway via HTR1A, HTR1B, HTR2A, DRD2, and GRIN2B. Molecular docking revealed good binding affinity between quetiapine and potential targets.

LIMITATIONS

Pharmacological experiments should be conducted to verify and further explore these results.

CONCLUSIONS

Our findings suggest that quetiapine affects bipolar depression and bipolar mania through distinct biological core targets, and thus through different mechanisms. Furthermore, our results provide a theoretical basis for the clinical use of quetiapine and possible directions for new drug development.

Stachydrine hydrochloride protects the ischemic heart by ameliorating endoplasmic reticulum stress through a SERCA2a dependent way and maintaining intracellular Ca2+ homeostasis

This study aimed to explore the effects and mechanism of action of stachydrine hydrochloride (Sta) against myocardial infarction (MI) through sarcoplasmic/endoplasmic reticulum stress-related injury. The targets of Sta against MI were screened using network pharmacology. C57BL/6 J mice after MI were treated with saline, Sta (6 or 12 mg kg-1) for 2 weeks, and adult mouse and neonatal rat cardiomyocytes (AMCMs and NRCMs) were incubated with Sta (10-4-10-6 M) under normoxia or hypoxia for 2 or 12 h, respectively. Echocardiography, Evans blue, and 2,3,5-triphenyltetrazolium chloride (TTC) staining were used for morphological and functional analyses. Endoplasmic reticulum stress (ERS), unfolded protein reaction (UPR), apoptosis signals, cardiomyocyte contraction, and Ca2+ flux were detected using transmission electron microscopy (TEM), western blotting, immunofluorescence, and sarcomere and Fluo-4 tracing. The ingredient-disease-pathway-target network revealed targets of Sta against MI were related to apoptosis, Ca2+ homeostasis and ERS. Both dosages of Sta improved heart function, decreased infarction size, and potentially increased the survival rate. Sta directly alleviated ERS and UPR and elicited less apoptosis in the border myocardium and hypoxic NRCMs. Furthermore, Sta upregulated sarcoplasmic reticulum Ca2+-ATPase 2a (SERCA2a) in both ischaemic hearts and hypoxic NRCMs, accompanied by restored sarcomere shortening, resting intracellular Ca2+, and Ca2+ reuptake time constants (Tau) in Sta-treated hypoxic ARCMs. However, 2,5-di-t-butyl-1,4-benzohydroquinone (BHQ) (25 μM), a specific SERCA inhibitor, totally abolished the beneficial effect of Sta in hypoxic cardiomyocytes. Sta protects the heart from MI by upregulating SERCA2a to maintain intracellular Ca2+ homeostasis, thus alleviating ERS-induced apoptosis.

Mechanism of 7H-Dibenzo[c,g]carbazole metabolism in cytochrome P450 1A1: Insights from computational studies

7H-Dibenzo[c,g]carbazole (DBC) is a prevalent environmental contaminant that induces tumorigenesis in several experimental animals. Recently, it has been utilized to develop high-performance solar cells and organic phosphorescent materials. It is imperative to strengthen investigations of DBC metabolism to understand its potential risks to human health. In this study, human CYP1A1 was employed as the metabolic enzyme to investigate the metabolic mechanism of DBC by molecular docking, molecular dynamics (MD) simulation, and quantum mechanical (QM) calculation. The results indicate that DBC binds to CYP1A1 in two modes (mode 1 and mode 2) mainly through nonpolar solvation energies (ΔGnonpolar). The formation of the two binding modes is attributed to the anchoring effect of the hydrogen bond formed by DBC with Asp320 (mode 1) or Ser116 (mode 2). Mode 1 is a "reactive" conformation, while mode 2 is not considered a "reactive" conformation. C5 is identified as the dominant site, and the pyrrole nitrogen cannot participate in the metabolism. DBC is metabolized mainly by a distinct electrophilic addition-rearrangement mechanism, with an energy barrier of 21.74 kcal/mol. The results provide meaningful insights into the biometabolic process of DBC and contribute to understanding its environmental effects and health risks.

Design and Characterization of a Generalist Biosensor for Indole Derivatives

Transcription factor (TF)-based biosensors are useful synthetic biology tools for applications in a variety of areas of biotechnology. A major challenge of biosensor circuits is the limited repertoire of identified and well-characterized TFs for applications of interest, in addition to the challenge of optimizing selected biosensors. In this work, we implement the IclR family repressor TF TtgV from Pseudomonas putida DOT-T1E as an indole-derivative biosensor in Escherichia coli. We optimize the genetic circuit utilizing different components, providing insights into biosensor design and expanding on previous studies investigating this TF. We discover novel physiologically relevant ligands of TtgV, such as skatole. The broad specificity of TtgV makes it a useful target for directed evolution and protein engineering toward desired specificity. TtgV, as an indole-derivative biosensor, is a promising genetic component for the detection of compounds with biological activities relevant to health and the gut microbiome.

KCNE4-dependent modulation of Kv1.3 pharmacology

The voltage-dependent potassium channel Kv1.3 is a promising therapeutic target for the treatment of autoimmune and chronic inflammatory disorders. Kv1.3 blockers are effective in treating multiple sclerosis (fampridine) and psoriasis (dalazatide). However, most Kv1.3 pharmacological antagonists are not specific enough, triggering potential side effects and limiting their therapeutic use. Functional Kv are oligomeric complexes in which the presence of ancillary subunits shapes their function and pharmacology. In leukocytes, Kv1.3 associates with KCNE4, which reduces the surface abundance and enhances the inactivation of the channel. This mechanism exerts profound consequences on Kv1.3-related physiological responses. Because KCNE peptides alter the pharmacology of Kv channels, we studied the effects of KCNE4 on Kv1.3 pharmacology to gain insights into pharmacological approaches. To that end, we used margatoxin, which binds the channel pore from the extracellular space, and Psora-4, which blocks the channel from the intracellular side. While KCNE4 apparently did not alter the affinity of either margatoxin or Psora-4, it slowed the inhibition kinetics of the latter in a stoichiometry-dependent manner. The results suggested changes in the Kv1.3 architecture in the presence of KCNE4. The data indicated that while the outer part of the channel mouth remains unaffected, KCNE4 disturbs the intracellular architecture of the complex. Various leukocyte types expressing different Kv1.3/KCNE4 configurations participate in the immune response. Our data provide evidence that the presence of these variable architectures, which affect both the structure of the complex and their pharmacology, should be considered when developing putative therapeutic approaches.

Thai traditional medicines reduce CD147 levels in lung cells: Potential therapeutic candidates for cancers, inflammations, and COVID-19

ETHNOPHARMACOLOGICAL RELEVANCE

The cluster of differentiation 147 (CD147) is identified as the signaling protein relevant importantly in various cancers, inflammations, and coronavirus disease 2019 (COVID-19) via interacting with extracellular cyclophilin A (CypA). The reduction of CD147 levels inhibits the progression of CD147-associated diseases. Thai traditional medicines (TTMs): Keaw-hom (KH), Um-ma-ruek-ka-wa-tee (UM), Chan-ta-lee-la (CT), and Ha-rak (HR) have been used as anti-pyretic and anti-respiratory syndromes caused from various conditions including cancers, inflammations, and infections. Thus, these medicines would play a crucial role in the reduction of CD147 levels.

AIM OF THE STUDY

This article aimed to investigate the effects of KH, UM, CT, and HR for reducing the CD147 levels through in vitro study. Additionally, in silico study was employed to screen the active compounds reflexing the reduction of CD147 levels.

MATERIALS AND METHODS

The immunofluorescent technique was used to evaluate the reduction of CD147 levels in human lung epithelial cells (BEAS-2B) stimulated with CypA for eight extracts of KH, UM, CT, and HR obtained from water decoction (D) and 70% ethanol maceration (M) including, KHD, UMD, CTD, HRD, KHM, UMM, CTM, and HRM.

RESULTS

UM extracts showed the most efficiency for reduction of CD147 levels in the cytoplasm and perinuclear of BEAS-2B cells stimulated with CypA. Phenolic compounds composing polyphenols, polyphenol sugars, and flavonoids were identified as the major chemical components of UMD and UMM. Further, molecular docking calculations identified polyphenol sugars as CypA inhibitors.

CONCLUSIONS

UMD and UMM are potential for reduction of CD147 levels which provide a useful information for further development of UM as potential therapeutic candidates for CD147-associated diseases such as cancers, inflammations, and COVID-19.

Identification of drug targets for Sjögren's syndrome: multi-omics Mendelian randomization and colocalization analyses

Background

Targeted therapy for Sjögren's syndrome (SS) has become an important focus for clinicians. Multi-omics-wide Mendelian randomization (MR) analyses have provided new ideas for identifying potential drug targets.

Methods

We conducted summary-data-based Mendelian randomization (SMR) analysis to evaluate therapeutic targets associated with SS by integrating DNA methylation, gene expression and protein quantitative trait loci (mQTL, eQTL, and pQTL, respectively). Genetic associations with SS were derived from the FinnGen study (discovery) and the GWAS catalog (replication). Colocalization analyses were employed to determine whether two potentially relevant phenotypes share the same genetic factors in a given region. Moreover, to delve deeper into potential regulation among DNA methylation, gene expression, and protein abundance, we conducted MR analysis to explore the causal relationship between candidate gene methylation and expression, as well as between gene expression and protein abundance. Drug prediction and molecular docking were further employed to validate the pharmacological activity of the candidate drug targets.

Results

Upon integrating the multi-omics data, we identified three genes associated with SS risk: TNFAIP3, BTN3A1, and PLAU. The methylation of cg22068371 in BTN3A1 was positively associated with protein levels, consistent with the negative effect of cg22068371 methylation on the risk of SS. Additionally, positive correlations were observed between the gene methylation of PLAU (cg04939496) and expression, as well as between expression and protein levels. This consistency elucidates the promotional effects of PLAU on SS risk at the DNA methylation, gene expression, and protein levels. At the protein level, genetically predicted TNFAIP3 (OR 2.47, 95% CI 1.56-3.92) was positively associated with SS risk, while BTN3A1 (OR 2.96E-03, 95% CI 2.63E-04-3.33E-02) was negatively associated with SS risk. Molecular docking showed stable binding for candidate drugs and target proteins.

Conclusion

Our study reveals promising therapeutic targets for the treatment of SS, providing valuable insights into targeted therapy for SS. However, further validation through future experiments is warranted.

Integrating Network Pharmacology and Experimental Validation to Decipher the Mechanism of Action of Astragalus-Atractylodes Herb Pair in Treating Hepatocellular Carcinoma

Purpose

Traditional Chinese medicine (TCM) therapy is an important means to treat hepatocellular carcinoma (HCC), Astragalus (Latin name: Hedysarum Multijugum Maxim; Chinese name: Huangqi, HQ) and Atractylodes (Latin name: Atractylodes Macrocephala Koidz; Chinese name: Baizhu, BZ) (HQBZ), a classic herb pair, is often used in combination to HCC. However, the main components and potential mechanisms of HQBZ therapy in HCC remain unclear. This study aimed to identify the potential active ingredients and molecular mechanisms of action of HQBZ in HCC treatment.

Methods

The HQBZ-Compound-Target-HCC network and HQBZ-HCC transcriptional regulatory network were constructed to screen the core active compound components and targets of HQBZ therapy for HCC. Molecular docking techniques are used to verify the stability of binding core active compound components to targets. GO and KEGG enrichment analysis were used to explore the signaling pathway of HQBZ in HCC treatment, the mechanism of HQBZ treatment of HCC was verified based on in vivo H22 tumor bearing mice and in vitro cell experiments.

Results

Network pharmacology and molecular docking studies showed that HQBZ treatment of HCC was related to the targeted regulation of IL-6 and STAT3 by the active compound biatractylolide, KEGG pathway enrichment analysis suggest that HQBZ may play a role in the treatment of HCC through IL-6/STAT3 signaling pathway. In vitro experiment results proved that HQBZ could regulate IL-6/STAT3 signaling pathway transduction on CD8+T cells, inhibit CD8+T cell exhaustion and restore the function of exhausted CD8+T cells. In vivo experiment results proved that HQBZ can regulate IL-6/STAT3 signaling pathway transduction in H22 liver cancer model mouse tumor tissue, increased the proportion of tumor infiltrating CD8+T cells.

Conclusion

This study found that HQBZ may play a therapeutic role in HCC by targeting IL-6 and STAT3 through biatractylolide, its mechanism of action is related to regulating IL-6/STAT3 signaling pathway, reversing T cell failure and increasing tumor infiltration CD8+T cells.

Assessing the anticholinergic cognitive burden classification of putative anticholinergic drugs using drug properties

AIMS

This study evaluated the use of machine learning to leverage drug absorption, distribution, metabolism and excretion (ADME) data together with physicochemical and pharmacological data to develop a novel anticholinergic burden scale and compare its performance to previously published scales.

METHODS

Experimental and in silico ADME, physicochemical and pharmacological data were collected for antimuscarinic activity, blood-brain barrier penetration, bioavailability, chemical structure and P-glycoprotein (P-gp) substrate profile. These five drug properties were used to train an unsupervised model to assign anticholinergic burden scores to drugs. The model performance was evaluated through 10-fold cross-validation and compared with the clinical Anticholinergic Cognitive Burden (ACB) scale and nonclinical Anticholinergic Toxicity Scores (ATS) scale, which is based primarily on muscarinic binding affinity.

RESULTS

In silico software (ADMET Predictor) used for screening drugs for their blood-brain barrier (BBB) penetration correctly identified some drugs that do not cross the BBB. The mean area under the curve for the unsupervised and ACB scale based on the five selected variables was 0.76 and 0.64, respectively. The unsupervised model agreed with the ACB scale on the classification of more than half of the drugs (49 of 88) agreed on the classification of less than half the drugs in the ATS scale (12 of 25).

CONCLUSIONS

Our findings suggest that the commonly used ACB scale may misclassify certain drugs due to their inability to cross the BBB. By contrast, the ATS scale would misclassify drugs solely depending on muscarinic binding affinity without considering other drug properties. Machine learning models can be trained on these features to build classification models that are easy to update and have greater generalizability.

Integrated bioinformatics and validation reveal PTGS2 and its related molecules to alleviate TNF-α-induced endothelial senescence

Accumulative evidences have indicated the interaction between cellular senescence and ferroptosis. This study intends to investigate the ferroptosis-related molecular markers in TNF-α-induced endothelial senescence. The microarray expression dataset (GSE195517) was used to identify the differently expressed ferroptosis-related genes (DEFRGs) through weighted gene co-expressed network analysis (WGCNA). GO and KEGG were performed to explore the biological function. Furthermore, hub genes were identified after protein-protein interaction (PPI) analysis and validated through real-time qPCR (RT-qPCR). Then, a drug-gene network was established to predict potential drugs for the hub genes. Seven DEFRGs were recognized in the TNF-α-induced HUVEC senescence. Moreover, four hub genes (PTGS2, TNFAIP3, CXCL2, and IL6 are upregulated) were identified by PPI analysis and validated by RT-qPCR. Further analysis exhibited that PTGS2 was subcellularly located in the plasma membrane. Furthermore, after aminosalicylic acid (ASA) was identified as ferroptosis inhibitor for targeting PTGS2 in senescent HUVECs, 5-ASA and 4-ASA were verified to alleviate TNF-α-induced HUVEC senescence through ferroptosis. PTGS2 might play a role in TNF-α-induced HUVEC senescence and ASA may be the potential drug for alleviating TNF-α-induced HUVEC senescence through ferroptosis.

Potential inhibitors of VEGFR1, VEGFR2, and VEGFR3 developed through Deep Learning for the treatment of Cervical Cancer

Cervical cancer stands as a prevalent gynaecologic malignancy affecting women globally, often linked to persistent human papillomavirus infection. Biomarkers associated with cervical cancer, including VEGF-A, VEGF-B, VEGF-C, VEGF-D, and VEGF-E, show upregulation and are linked to angiogenesis and lymphangiogenesis. This research aims to employ in-silico methods to target tyrosine kinase receptor proteins-VEGFR-1, VEGFR-2, and VEGFR-3, and identify novel inhibitors for Vascular Endothelial Growth Factors receptors (VEGFRs). A comprehensive literary study was conducted which identified 26 established inhibitors for VEGFR-1, VEGFR-2, and VEGFR-3 receptor proteins. Compounds with high-affinity scores, including PubChem ID-25102847, 369976, and 208908 were chosen from pre-existing compounds for creating Deep Learning-based models. RD-Kit, a Deep learning algorithm, was used to generate 43 million compounds for VEGFR-1, VEGFR-2, and VEGFR-3 targets. Molecular docking studies were conducted on the top 10 molecules for each target to validate the receptor-ligand binding affinity. The results of Molecular Docking indicated that PubChem IDs-71465,645 and 11152946 exhibited strong affinity, designating them as the most efficient molecules. To further investigate their potential, a Molecular Dynamics Simulation was performed to assess conformational stability, and a pharmacophore analysis was also conducted for indoctrinating interactions.

Mechanism of network pharmacology of Erzhi Pill and Erxian Decoction in treating climacteric syndrome with "treating the same disease with different methods": A review

Network pharmacology and molecular docking methods were used in the present study to clarify the molecular mechanism of two traditional Chinese medicine prescriptions of climacteric syndrome. Based on oral availability and drug similarity, the main active components of Erzhi Pill and Erxian Decoction were screened through the platform of traditional Chinese medicine system pharmacology. The target database of climacteric syndrome was established by using GENECARD, OMIM, PharmGKB, Targets and Drugbank. The "component - target" network diagram was constructed using Cytoscape software (version 3.8.2). Topology analysis, module analysis, and GO and KEGG enrichment analyses were used to explore the core target and action pathway of Erzhi Pill-Erxian Decoction for treating climacteric syndrome of same disease with different treatments. There were 16 active components and 103 corresponding targets found in Erzhi Pill; 69 active components and 121 corresponding targets were found in Erxian Decoction; and 100 potential targets were found in Erzhi Pill and Erxian Decoction. Through network analysis, topology and module analysis, TP53, AKT1, Jun, ESR1, IL1B, CASP3, MMP9, PTGS2, HIF1A, MYC and EGFR could be considered as potential targets of the 2 prescriptions for alleviating climacteric syndrome. The effects of Erzhi pill and Erxian Decoction on climacteric syndrome are mainly in the pathway of lipid and atherosclerosis, AGE-RAGE signaling pathway and PI3K-Akt signaling pathway in diabetic complications. The active components in Erzhi Pill - Erxian Decoction, such as quercetin, show considerable potential as a candidate drug for the treatment of climacteric syndrome.

Screening of Mpro Protease (SARS-CoV-2) Covalent Inhibitors from an Anthocyanin-Rich Blueberry Extract Using an HRMS-Based Analytical Platform

BACKGROUND

The viral main protease (Mpro) of SARS-CoV-2 has been recently proposed as a key target to inhibit virus replication in the host. Therefore, molecules that can bind the catalytic site of Mpro could be considered as potential drug candidates in the treatment of SARS-CoV-2 infections. Here we proposed the application of a state-of-the-art analytical platform which combines metabolomics and protein structure analysis to fish-out potential active compounds deriving from a natural matrix, i.e., a blueberry extract.

METHODS

The experiments focus on finding MS covalent inhibitors of Mpro that contain in their structure a catechol/pyrogallol moiety capable of binding to the nucleophilic amino acids of the enzyme's catalytic site.

RESULTS

Among the potential candidates identified, the delphinidin-3-glucoside showed the most promising results. Its antiviral activity has been confirmed in vitro on Vero E6 cells infected with SARS-CoV-2, showing a dose-dependent inhibitory effect almost comparable to the known Mpro inhibitor baicalin. The interaction of delphinidin-3-glucoside with the Mpro pocket observed was also evaluated by computational studies.

CONCLUSIONS

The HRMS analytical platform described proved to be effective in identifying compounds that covalently bind Mpro and are active in the inhibition of SARS-CoV-2 replication, such as delphinidin-3-glucoside.

Reproductive chemical database: a curated database of chemicals that modulate protein targets regulating important reproductive biological processes

Recent studies have shifted the spotlight from adult disease to gametogenesis and embryo developmental events, and these are greatly affected by various environmental chemicals, such as drugs, metabolites, pollutants, and others. Growing research has highlighted the critical importance of identifying and understanding the roles of chemicals in reproductive biology. However, the functions and mechanisms of chemicals in reproductive processes remain incomplete. We developed a comprehensive database called the Reproductive Chemical Database (RCDB) ( https://yu.life.sjtu.edu.cn/ChenLab/RCDB ) to facilitate research on chemicals in reproductive biology. This resource is founded on rigorous manual literature extraction and precise protein target prediction methodologies. This database focuses on the delineation of chemicals associated with phenotypes, diseases, or endpoints intricately associated with four important reproductive processes: female and male gamete generation, fertilization, and embryo development in human and mouse. The RCDB encompasses 93 sub-GO processes, and it revealed 1447 intricate chemical-biological process interactions. To date, the RCDB has meticulously cataloged and annotated 830 distinct chemicals, while also predicting 614 target proteins from a selection of 3800 potential candidates. Additionally, the RCDB offers an online predictive tool that empowers researchers to ascertain whether specific chemicals play discernible functional roles in these reproductive processes. The RCDB is an exhaustive, cross-platform, manually curated database, which provides a user-friendly interface to search, browse, and use reproductive processes modulators and their comprehensive related information. The RCDB will help researchers to understand the whole reproductive process and related diseases and it has the potential to promote reproduction research in the pharmacological and pathophysiological areas.

Remapping the Chemical Space and the Pharmacological Space of Drugs: What Can We Expect from the Road Ahead?

This work examines the current landscape of drug discovery and development, with a particular focus on the chemical and pharmacological spaces. It emphasizes the importance of understanding these spaces to anticipate future trends in drug discovery. The use of cheminformatics and data analysis enabled in silico exploration of these spaces, allowing a perspective of drugs, approved drugs after 2020, and clinical candidates, which were extracted from the newly released ChEMBL34 (March 2024). This perspective on chemical and pharmacological spaces enables the identification of trends and areas to be occupied, thereby creating opportunities for more effective and targeted drug discovery and development strategies in the future.

OPTICS: An interactive online platform for photosensory and bio-functional proteins in optogenetic systems

High-precise modulation of bio-functional proteins related to signaling is crucial in life sciences and human health. The cutting-edge technology of optogenetics, which combines optical method with genetically encoded protein expression, pioneered new pathways for the control of cellular bio-functional proteins (CPs) using optogenetic tools (OTs) in spatial and temporal. Over the past decade, hundreds of optogenetic systems (OSs) have been developed for various applications from living cells to freely moving organisms. However, no database has been constructed to comprehensively provide the valuable information of OSs yet. In this work, a new database named OPTICS (an interactive online platform for photosensory and bio-functional proteins in optogenetic systems) is introduced. Our OPTICS is unique in (i) systematically describing diverse OSs from the perspective of photoreceptor-based classification and mechanism of action, (ii) featuring the detailed biophysical properties and functional data of OSs, (iii) providing the interaction between OT and CP for each OS referring to distinct applications in research, diagnosis, and therapy, and (iv) enabling a light response property-based search against all OSs in the database. Since the information on OSs is essential for rapid and predictable design of optogenetic controls, the comprehensive data provided in OPTICS lay a solid foundation for the future development of novel OSs. OPTICS is freely accessible without login requirement at https://idrblab.org/optics/.

Identification of Prospective Ebola Virus VP35 and VP40 Protein Inhibitors from Myxobacterial Natural Products

The Ebola virus (EBOV) is a lethal pathogen causing hemorrhagic fever syndrome which remains a global health challenge. In the EBOV, two multifunctional proteins, VP35 and VP40, have significant roles in replication, virion assembly, and budding from the cell and have been identified as druggable targets. In this study, we employed in silico methods comprising molecular docking, molecular dynamic simulations, and pharmacological properties to identify prospective drugs for inhibiting VP35 and VP40 proteins from the myxobacterial bioactive natural product repertoire. Cystobactamid 934-2, Cystobactamid 919-1, and Cittilin A bound firmly to VP35. Meanwhile, 2-Hydroxysorangiadenosine, Enhypyrazinone B, and Sorangiadenosine showed strong binding to the matrix protein VP40. Molecular dynamic simulations revealed that, among these compounds, Cystobactamid 919-1 and 2-Hydroxysorangiadenosine had stable interactions with their respective targets. Similarly, molecular mechanics Poisson-Boltzmann surface area (MMPBSA) calculations indicated close-fitting receptor binding with VP35 or VP40. These two compounds also exhibited good pharmacological properties. In conclusion, we identified Cystobactamid 919-1 and 2-Hydroxysorangiadenosine as potential ligands for EBOV that target VP35 and VP40 proteins. These findings signify an essential step in vitro and in vivo to validate their potential for EBOV inhibition.

Vitamin D Relieves Epilepsy Symptoms and Neuroinflammation in Juvenile Mice by Activating the mTOR Signaling Pathway via RAF1: Insights from Network Pharmacology and Molecular Docking Studies

Epilepsy is a common neurological disorder, and the exploration of potential therapeutic drugs for its treatment is still ongoing. Vitamin D has emerged as a promising treatment due to its potential neuroprotective effects and anti-epileptic properties. This study aimed to investigate the effects of vitamin D on epilepsy and neuroinflammation in juvenile mice using network pharmacology and molecular docking, with a focus on the mammalian target of rapamycin (mTOR) signaling pathway. Experimental mouse models of epilepsy were established through intraperitoneal injection of pilocarpine, and in vitro injury models of hippocampal neurons were induced by glutamate (Glu) stimulation. The anti-epileptic effects of vitamin D were evaluated both in vivo and in vitro. Network pharmacology and molecular docking analysis were used to identify potential targets and regulatory pathways of vitamin D in epilepsy. The involvement of the mTOR signaling pathway in the regulation of mouse epilepsy by vitamin D was validated using rapamycin (RAPA). The levels of inflammatory cytokines (TNF-α, IL-1β, and IL-6) were assessed by enzyme-linked immunosorbent assay (ELISA). Gene and protein expressions were detected by quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot, respectively. The terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate nick end-labeling (TUNEL) staining was used to analyze the apoptosis of hippocampal neurons. In in vivo experiments, vitamin D reduced the Racine scores of epileptic mice, prolonged the latency of epilepsy, and inhibited the production of TNF-α, IL-1β, and IL-6 in the hippocampus. Furthermore, network pharmacology analysis identified RAF1 as a potential target of vitamin D in epilepsy, which was further confirmed by molecular docking analysis. Additionally, the mTOR signaling pathway was found to be involved in the regulation of mouse epilepsy by vitamin D. In in vitro experiments, Glu stimulation upregulated the expressions of RAF1 and LC3II/LC3I, inhibited mTOR phosphorylation, and induced neuronal apoptosis. Mechanistically, vitamin D activated the mTOR signaling pathway and alleviated mouse epilepsy via RAF1, while the use of the pathway inhibitor RAPA reversed this effect. Vitamin D alleviated epilepsy symptoms and neuroinflammation in juvenile mice by activating the mTOR signaling pathway via RAF1. These findings provided new insights into the molecular mechanisms underlying the anti-epileptic effects of vitamin D and further supported its use as an adjunctive therapy for existing anti-epileptic drugs.

DL-3-n-Butylphthalide Ameliorates Post-stroke Emotional Disorders by Suppressing Neuroinflammation and PANoptosis

Post-stroke emotional disorders such as post-stroke anxiety and post-stroke depression are typical symptoms in patients with stroke. They are closely associated with poor prognosis and low quality of life. The State Food and Drug Administration of China has approved DL-3-n-butylphthalide (NBP) as a treatment for ischemic stroke (IS). Clinical research has shown that NBP alleviates anxiety and depressive symptoms in patients with IS. Therefore, this study explored the role and molecular mechanisms of NBP in cases of post-stroke emotional disorders using network pharmacology and experimental validation. The results showed that NBP treatment significantly increased the percentage of time spent in the center of the middle cerebral artery occlusion (MCAO) rats in the open field test and the percentage of sucrose consumption in the sucrose preference test. Network pharmacology results suggest that NBP may regulate neuroinflammation and cell death. Further experiments revealed that NBP inhibited the toll-like receptor 4/nuclear factor kappa B signaling pathway, decreased the level of pro-inflammatory cytokines, including tumor necrosis factor-α, interleukin-1β, and interleukin-6, and M1-type microglia markers (CD68, inducible nitric oxide synthase), and reduced the expression of PANoptosis-related molecules including caspase-1, caspase-3, caspase-8, gasdermin D, and mixed lineage kinase domain-like protein in the hippocampus of the MACO rats. These findings demonstrate that the mechanisms through which NBP ameliorates post-stroke emotional disorders in rats are associated with inhibiting neuroinflammation and PANoptosis, providing a new strategy and experimental basis for treating post-stroke emotional disorders.

OLB-AC: toward optimizing ligand bioactivities through deep graph learning and activity cliffs

MOTIVATION

Deep graph learning (DGL) has been widely employed in the realm of ligand-based virtual screening. Within this field, a key hurdle is the existence of activity cliffs (ACs), where minor chemical alterations can lead to significant changes in bioactivity. In response, several DGL models have been developed to enhance ligand bioactivity prediction in the presence of ACs. Yet, there remains a largely unexplored opportunity within ACs for optimizing ligand bioactivity, making it an area ripe for further investigation.

RESULTS

We present a novel approach to simultaneously predict and optimize ligand bioactivities through DGL and ACs (OLB-AC). OLB-AC possesses the capability to optimize ligand molecules located near ACs, providing a direct reference for optimizing ligand bioactivities with the matching of original ligands. To accomplish this, a novel attentive graph reconstruction neural network and ligand optimization scheme are proposed. Attentive graph reconstruction neural network reconstructs original ligands and optimizes them through adversarial representations derived from their bioactivity prediction process. Experimental results on nine drug targets reveal that out of the 667 molecules generated through OLB-AC optimization on datasets comprising 974 low-activity, noninhibitor, or highly toxic ligands, 49 are recognized as known highly active, inhibitor, or nontoxic ligands beyond the datasets' scope. The 27 out of 49 matched molecular pairs generated by OLB-AC reveal novel transformations not present in their training sets. The adversarial representations employed for ligand optimization originate from the gradients of bioactivity predictions. Therefore, we also assess OLB-AC's prediction accuracy across 33 different bioactivity datasets. Results show that OLB-AC achieves the best Pearson correlation coefficient (r2) on 27/33 datasets, with an average improvement of 7.2%-22.9% against the state-of-the-art bioactivity prediction methods.

AVAILABILITY AND IMPLEMENTATION

The code and dataset developed in this work are available at github.com/Yueming-Yin/OLB-AC.

Analysis of environmental pollutant Bisphenol F elicited prostate injury targets and underlying mechanisms through network toxicology, molecular docking, and multi-level bioinformatics data integration

Bisphenol F (BPF) has gained prominence as an alternative to bisphenol A (BPA) in various manufacturing applications, yet being detected in diverse environments and posed potential public health risk. This research aims to elucidate the putative toxic targets and underlying molecular mechanisms of prostate injury induced by exposure to BPF through multi-level bioinformatics data, integrating network toxicology and molecular docking. Systematically leveraging multilevel databases, we determined 276 targets related to BPF and prostate injury. Subsequent screenings through STRING and Cytoscape tool highlighted 27 key targets, including BCL2, HSP90AA1, MAPK3, ESR1, and CASP3. GO and KEGG enrichment analyses demonstrated enrichment of targets involved in apoptosis, abnormal hormonal activities, as well as cancer-related signal transduction cascades, ligand-receptor interaction networks, and endocrine system signaling pathways. Molecular docking simulations conducted via Autodock corroborated high-affinity binding interaction between BPF and key targets. The results indicate that BPF exposure can contribute to the initiation and progression of prostate cancer and prostatic hyperplastic by modulating apoptosis and proliferation, altering nerve function in blood vessel endothelial cells, and disrupting androgen metabolism. This study offers theoretical underpinnings for comprehending the molecular mechanisms implicated in BPF-elicited prostatic toxicity, while concomitantly establishing foundational framework for the development of prophylactic and therapeutic strategies for prostatic injuries related to polycarbonate and epoxy resin plastics incorporated with BPF, as well as environments afflicted by elevated levels of these compounds.

Study on the mechanism of quercetin in Sini Decoction Plus Ginseng Soup to inhibit liver cancer and HBV virus replication through CDK1

BACKGROUND

To explore the anti-tumor and anti-virus key active ingredients of Sini Decoction Plus Ginseng Soup (SNRS) and their mechanisms.

METHODS

The main ingredients of SNRS were analyzed by network pharmacology, and quercetin was identified as the key active ingredient. Then, we obtained the targets of quercetin by using Drugbank, PharmMapper, and SwissTargetPrediction databases. Then, the targets of HBV-related hepatocellular carcinoma (HBV-related HCC) were obtained by using Genecards database. In addition, using the gene expression profiles of HBV-related HCC patients in GEO database and the genes with the greatest survival difference in GEPIA 2 database identified the potential targets of quercetin. In addition, the mechanism of potential genes was studied through GO, KEGG analysis, and PPI network. Using AUC and survival analysis to evaluate the diagnostic and prognostic value of cyclin-dependent kinase 1 (CDK1) and CCNB1. Finally, the effects of quercetin on proliferation of Hep3B and HepG2215 cells and the level of CDK1 and CCNB1 were verified in vitro. ELISA was used to measure the expression levels of hepatitis B surface antigen (HBsAg) and hepatitis B e antigen (HBeAg) after the intervention by quercetin for 24 h and 48 h in HepG2215 cell.

RESULTS

The first 10 key ingredients of SNRS were identified, and quercetin was the most key ingredient. The 101 potential quercetin targets were identified for the treatment of HBV-related HCC. GO and KEGG showed that 101 potential target enrichment in cancer and cell cycle regulation. By Venn analysis, CDK1 and CCNB1 were intersection targets, which could be used as potential targets for the action of quercetin on HBV-related HCC. Moreover, the expression of CDK1 and CCNB1 was highly expressed in the high-risk group, while the OS rate was low. The 1-year, 3-year and 5-year area under the curve (AUC) curves of CDK1 and CCNB1 were 0.724, 0.676, 0.622 and 0.745, 0.678, 0.634, respectively. Moreover, experimental results also showed that quercetin inhibited cell proliferation and reduced CDK1 expression in Hep3B and HepG2215 cells. The expressions of HBsAg and HBeAg in HepG2215 cell supernatant and cell gradually decreased with the increase of intervention time of quercetin and CDK1 inhibitor.

CONCLUSIONS

Quercetin is a key ingredient of anti-HBV-related HCC activity and inhibits HBV replication in SNRS by inhibiting CDK1.

Structural Antagonism-Aided Conformational Regulation Enables an Aptamer-Loop G-Quadruplex Modular Sensor of β-Lactoglobulin

A simple, reliable method for identifying β-lactoglobulin (β-LG) in dairy products is needed to protect those with β-LG allergies. A common, practical strategy for target detection is designing simplified nucleic acid nanodevices by integrating functional components. This work presents a label-free modular β-LG aptasensor consisting of an aptamer-loop G-quadruplex (G4), the working conformation of which is regulated by conformational antagonism to ensure respective module functionality and the related signal transduction. The polymorphic conformations of the module-fused sequence are systematically characterized, and the cause is revealed as shifting antagonistic equilibrium. Combined with conformational folding dynamics, this helped regulate functional conformations by fine-tuning the sequences. Furthermore, the principle of specific β-LG detection by parallel G4 topology is examined as binding on the G4 aptamer loop by β-LG to reinforce the G4 topology and fluorescence. Finally, a label-free, assembly-free, succinct, and turn-on fluorescent aptasensor is established, achieving excellent sensitivity across five orders of magnitude, rapidly detecting β-LG within 22-min. This study provides a generalizable approach for the conformational regulation of module-fused G4 sequences and a reference model for creating simplified sensing devices for a variety of targets.