Dissecting the molecular mechanism of cepharanthine against COVID-19, based on a network pharmacology strategy combined with RNA-sequencing analysis, molecular docking, and molecular dynamics simulation

Network pharmacology and experimental verification revealing valnemulin alleviates DSS-induced ulcerative colitis by inhibiting intestinal senescence

Ulcerative colitis (UC) is a chronic inflammatory disease that is increasing in prevalence globally. Senescence is characterized by a specific chronic, low-grade, "sterile" inflammatory state known as inflammaging, suggesting that senescence may exacerbate the severity of UC. However, the link between UC and senescence remains unclear. Valnemulin (VAL) is a semi-synthetic derivative of a naturally occurring diterpenoid antibiotic (pleuromutilin), which can inhibit peptidyl transferase. Studies investigating the potential of valnemulin to inhibit senescence and alleviate colitis are currently limited. In this study, we revealed that dextran sulfate sodium (DSS), an inducer of UC, induces senescence in both colon epithelial NCM460 cells and colon tissues. Additionally, VAL, identified from a compound library, exhibited robust anti-senescence activity in DSS-treated NCM460 cells. Identified in our study as an anti-senescence agent, VAL effectively mitigated DSS-induced UC and colonic senescence in mice. Through network pharmacology analysis and experimental validation, the potential signaling pathway (AMPK/NF-κB) for VAL in treating UC was identified. We discovered that DSS significantly inhibited the AMPK signaling pathway and activated the NF-κB signaling pathway. However, supplementation with VAL remarkably restored AMPK activity and inhibited the NF-κB signaling pathway, which led to the inhibition of senescence. In summary, our study demonstrated that DSS-induced UC stimulates the senescence of colonic tissues, and VAL can effectively alleviate DSS-induced colonic damage and reduce colonic senescence. Our research findings provide a new perspective for targeting anti-senescence in the treatment of UC.

Anti-ageing mechanism of topical bioactive ingredient composition on skin based on network pharmacology

OBJECTIVE

To elucidate the anti-ageing mechanism of the combination of eight ingredients on the skin from a multidimensional view of the skin.

METHODS

The target pathway mechanisms of composition to delay skin ageing were investigated by a network pharmacology approach and experimentally validated at three levels: epidermal, dermal, and tissue.

RESULTS

We identified 24 statistically significant skin ageing-related pathways, encompassing crucial processes such as epidermal barrier repair, dermal collagen and elastin production, inhibition of reactive oxygen species (ROS), as well as modulation of acetylcholine and acetylcholine receptor binding. Furthermore, our in vitro experimental findings exhibited the following outcomes: the composition promotes fibroblast proliferation and the expression of barrier-related genes in the epidermis; it also stimulated the expression of collagen I, collagen III, and elastic fibre while inhibiting ROS and β-Gal levels in HDF cells within the dermis. Additionally, Spilanthol in the Acmella oleracea extract contained in the composition demonstrated neuro-relaxing activity in Zebrafish embryo, suggesting its potential as an anti-wrinkle ingredient at the hypodermis level.

CONCLUSIONS

In vitro experiments validated the anti-ageing mechanism of composition at multiple skin levels. This framework can be extended to unravel the functional mechanisms of other clinically validated compositions, including traditional folk recipes utilized in cosmeceuticals.

Genome assembly of Stephania longa provides insight into cepharanthine biosynthesis

Introduction

Stephania longa, a medicinal plant renowned for producing cepharanthine, has gained significance due to the compound's notable antiviral properties against SARS-CoV-2. However, a comprehensive genetic understanding of S. longa has been lacking. This study aimed to develop a high-quality, chromosome-level genome assembly to uncover the genetic intricacies and evolutionary narrative of this species. By integrating genomic data with metabolomic and transcriptomic analyses, we sought to identify key genes involved in cepharanthine biosynthesis.

Methods

We employed a multi-faceted approach comprising genome assembly, phylogenetic analysis, gene family dynamics investigation, metabolomic profiling, and gene expression analysis across various tissues of S. longa. This integrated strategy enabled the identification of key genes involved in cepharanthine biosynthesis and elucidated the species' evolutionary history.

Results

Our phylogenetic analysis clarified the placement of the genus Stephania within the Ranunculales order and revealed its notably high mutation rate. We identified gene family expansions and signs of positive selection likely contributing to Stephania's unique metabolic capabilities. Metabolomic profiling uncovered complex regulatory mechanisms orchestrating the biosynthesis and distribution of cepharanthine and related metabolites. Through the integration of genomic, transcriptomic, and metabolomic data, we identified genes with expression patterns and evolutionary trajectories suggesting pivotal roles in cepharanthine biosynthesis, including those involved in crucial biosynthetic steps.

Discussion

This comprehensive study, integrating genomic, metabolomic, and transcriptomic approaches, provides valuable insights into S. longa's biosynthetic potential. It not only enhances our understanding of the species but also establishes a foundation for future investigations into the biosynthesis and therapeutic exploitation of cepharanthine and related alkaloids.

Diagnostic implications of ubiquitination-related gene signatures in Alzheimer's disease

The purpose of this study was to explore the diagnostic implications of ubiquitination-related gene signatures in Alzheimer's disease. In this study, we first collected 161 samples from the GEO database (including 87 in the AD group and 74 in the normal group). Subsequently, through differential expression analysis and the iUUCD 2.0 database, we obtained 3450 Differentially Expressed Genes (DEGs) and 806 Ubiquitin-related genes (UbRGs). After taking the intersection, we obtained 128 UbR-DEGs. Secondly, by conducting GO and KEGG enrichment analysis on these 128 UbR-DEGs, we identified the main molecular functions and biological pathways related to AD. Furthermore, through the utilization of GSEA analysis, we have gained insight into the enrichment of functions and pathways within both the AD and normal groups. Further, using lasso regression analysis and cross-validation techniques, we identified 22 characteristic genes associated with AD. Subsequently, we constructed a logistic regression model and optimized it, resulting in the identification of 6 RUbR-DEGs: KLHL21, WDR82, DTX3L, UBTD2, CISH, and ATXN3L. In addition, the ROC result showed that the diagnostic model we built has excellent accuracy and reliability in identifying AD patients. Finally, we constructed a lncRNA-miRNA-mRNA (competing endogenous RNA, ceRNA) regulatory network for AD based on six RUbR-DEGs, further elucidating the interaction between UbRGs and lncRNA, miRNA. In conclusion, our findings will contribute to further understanding of the molecular pathogenesis of AD and provide a new perspective for AD risk prediction, early diagnosis and targeted therapy in the population.

An Automated Analysis Framework for Epidemiological Survey on COVID-19

For a long time, the prevention and control of COVID-19 has received significant attention. A crucial aspect of controlling the disease's spread is the epidemiological survey of patients and the subsequent analysis of epidemiological survey reports (case reports). However, current mainstream analysis approaches are all made manually. This manual method is time-consuming and manpower-intensive. This paper designs an automated visual epidemiological survey analysis (AVESA) framework for the epidemiological survey on COVID-19. AVESA designs a deep neural network for information extraction from case reports and automatically constructs an epidemiological knowledge graph based on predefined pattern. Moreover, a multi-dimensional knowledge reasoning model is developed for conducting knowledge reasoning in the complete COVID-19 epidemiological knowledge graph. In the entity extraction sub-task and multi-task extraction sub-task, AVESA achieved F1 scores of 85.12% and 92.29% respectively on the constructed dataset, significantly outperforming the standalone information extraction models. In full-graph computing, all three experiments align closely with manual analysis standards. In the risk analysis experiment, the weighted PageRank algorithm showed an average improvement of 11.21% in Top_Recall_n% over the standard PageRank algorithm. In the community detection experiment, the weighted Louvain algorithm showed a mere 4.34% community difference rate compared to manual analysis.

Unraveling the Mechanism of Xiaochaihu Granules in Alleviating Yeast-Induced Fever Based on Network Analysis and Experimental Validation

Xiaochaihu granules (XCHG) are extensively used to treat fever. Nevertheless, the underlying mechanism remains elusive. This study aimed to explore the potential of XCHG in mitigating yeast-induced fever and the underlying metabolic pathways. The chemical composition of XCHG was ascertained using ultra-fast liquid chromatography/quadrupole-time-of-flight tandem mass spectrometry (UFLC-Q-TOF-MS/MS), followed by integrated network analysis to predict potential targets. We then conducted experimental validation using pharmacological assays and metabolomics analysis in a yeast-induced mouse fever model. The study identified 133 compounds in XCHG, resulting in the development of a comprehensive network of herb-compound-biological functional modules. Subsequently, molecular dynamic (MD) simulations confirmed the stability of the complexes, including γ-aminobutyric acid B receptor 2 (GABBR2)-saikosaponin C, prostaglandin endoperoxide synthases (PTGS2)-lobetyolin, and NF-κB inhibitor IκBα (NFKBIA)-glycyrrhizic acid. Animal experiments demonstrated that XCHG reduced yeast-induced elevation in NFKBIA's downstream regulators [interleukin (IL)-1β and IL-8], inhibited PTGS2 activity, and consequently decreased prostaglandin E2 (PGE2) levels. XCHG also downregulated the levels of 5-hydroxytryptamine (5-HT), γ-aminobutyric acid (GABA), corticotropin releasing hormone (CRH), and adrenocorticotrophin (ACTH). These corroborated the network analysis results indicating XCHG's effectiveness against fever in targeting NFKBIA, PTGS2, and GABBR2. The hypothalamus metabolomics analysis identified 14 distinct metabolites as potential antipyretic biomarkers of XCHG. In conclusion, our findings suggest that XCHG alleviates yeast-induced fever by regulating inflammation/immune responses, neuromodulation, and metabolism modules, providing a scientific basis for the anti-inflammatory and antipyretic properties of XCHG.

Network pharmacology, molecular docking, and molecular dynamics simulation analysis reveal the molecular mechanism of halociline against gastric cancer

Halociline, a derivative of alkaloids, was isolated from the marine fungus Penicillium griseofulvum by our group. This remarkable compound exhibits promising antineoplastic activity, yet the precise molecular mechanisms underlying its anticancer properties remain enigmatic. To unravel these mechanisms, we employed an integrated approach of network pharmacology analysis, molecular docking simulations, and molecular dynamics simulations to explore halociline therapeutic targets for gastric cancer. The data from network pharmacology indicate that halociline targets MAPK1, MMP-9, and PIK3CA in gastric cancer cells, potentially mediated by diverse pathways including cancer, lipid metabolism, atherosclerosis, and EGFR tyrosine kinase inhibitor resistance. Notably, molecular docking and dynamics simulations revealed a high affinity between halociline and these targets, with free binding energies (ΔEtotal) of - 20.28, - 27.94, and - 25.97 kcal/mol for MAPK1, MMP-9, and PIK3CA, respectively. This study offers valuable insights into the potential molecular mechanism of halociline's inhibition of gastric cancer cells and serves as a valuable reference for future basic research efforts.

An Integrated Network Pharmacology and RNA-seq Approach for Exploring the Protective Effect of Andrographolide in Doxorubicin-Induced Cardiotoxicity

PURPOSE

Doxorubicin (Dox) is clinically limited due to its dose-dependent cardiotoxicity. Andrographolide (Andro) has been confirmed to exert cardiovascular protective activities. This study aimed to investigate protective effects of Andro in Dox-induced cardiotoxicity (DIC).

METHODS

The cardiotoxicity models were induced by Dox in vitro and in vivo. The viability and apoptosis of H9c2 cells and the myocardial function of c57BL/6 mice were accessed with and without Andro pretreatment. Network pharmacology and RNA-seq were employed to explore the mechanism of Andro in DIC. The protein levels of Bax, Bcl2, NLRP3, Caspase-1 p20, and IL-1β were qualified as well.

RESULTS

In vitro, Dox facilitated the downregulation of cell viability and upregulation of cell apoptosis, after Andro pretreatment, the above symptoms were remarkably reversed. In vivo, Andro could alleviate Dox-induced cardiac dysfunction and apoptosis, manifesting elevation of LVPWs, LVPWd, EF% and FS%, suppression of CK, CK-MB, c-Tnl and LDH, and inhibition of TUNEL-positive cells. Using network pharmacology, we collected and visualized 108 co-targets of Andro and DIC, which were associated with apoptosis, PI3K-AKT signaling pathway, and others. RNA-seq identified 276 differentially expressed genes, which were enriched in response to oxidative stress, protein phosphorylation, and others. Both network pharmacology and RNA-seq analysis identified Tap1 and Timp1 as key targets of Andro in DIC. RT-QPCR validation confirmed that the mRNA levels of Tap1 and Timp1 were consistent with the sequenced results. Moreover, the high expression of NLRP3, Caspase-1 p20, and IL-1β in the Dox group was reduced by Andro.

CONCLUSIONS

Andro could attenuate DIC through suppression of Tap1 and Timp1 and inhibition of NLRP3 inflammasome activation, serving as a promising cardioprotective drug.

Protective Effect and Related Mechanism of Modified Danggui Buxue Decoction on Retinal Oxidative Damage in Mice based on Network Pharmacology

INTRODUCTION

Age-related macular degeneration (AMD) is one of the common diseases that cause vision loss in the elderly, and oxidative stress has been considered a major pathogenic factor for AMD. Modified Danggui Buxue Decoction (RRP) has a good therapeutic effect on non-proliferatic diabetic retinopathy and can improve the clinical symptoms of patients.

METHODS

The key ingredients and core targets of RRP protecting retinal oxidative damage were obtained by Network pharmacology analysis. A mouse retinal oxidative damage model induced by tail vein injection of 1% NaIO3 solution (25 mg/kg) was treated with RRP for 4 weeks and used to verify the pharmacodynamics and related mechanism.

AIM

This study aimed to predict and verify the protective effect and mechanism of RRP on retinal oxidative damage in mice based on network pharmacology and animal experiments.

RESULTS

A total of 15 key active components included in RRP interacted with 57 core targets related to retinal oxidative damage (such as AKT1, NFE2L2, HMOX1), mainly involved in the AGE-RAGE signaling pathway in diabetic complications, PI3K-AKT signaling pathway and so on. Further studies in vivo found that RRP improved the retinal oxidative damage, increased the content of SOD and GSH, decreased the content of MDA in mouse serum, promoted the expression of p-PI3K, p-AKT, Nrf2, HO-1 and NQO1 proteins in the mouse retina, and inhibited the expression of Nrf2 in the cytoplasm.

CONCLUSION

This study revealed that RRP had a protective effect on oxidative damage of the retina in mice, and might exert anti-oxidative effect by activating the PI3K/Akt/Nrf2 signal pathway. This study provided scientific data for the further development of hospital preparations of RRP, and a good theoretical basis for the clinical application of RRP.

Dissecting the functional significance of HSP90AB1 and other heat shock proteins in countering glioblastomas and ependymomas using omics analysis and drug prediction using virtual screening

Heat shock proteins (HSPs) are the evolutionary family of proteins that are highly conserved and present widely in various organisms and play an array of important roles and cellular functions. Currently, very few or no studies are based on the systematic analysis of the HSPs in Glioblastoma (GBMs) and ependymomas. We performed an integrated omics analysis to predict the mutual regulatory differential HSP signatures that were associated with both glioblastoma and ependymomas. Further, we explored the various common dysregulated biological processes operating in both the tumors, and were analyzed using functional enrichment, gene ontology along with the pathway analysis of the predicted HSPs. We established an interactome network of protein-protein interaction (PPIN) to identify the hub HSPs that were commonly associated with GBMs and ependymoma. To understand the mutual molecular mechanism of the HSPs in both malignancies, transcription factors, and miRNAs overlapping with both diseases were explored. Moreover, a transcription factor-miRNAs-HSPs coregulatory network was constructed along with the prediction of potential candidate drugs that were based on perturbation-induced gene expression analysis. Based on the RNA-sequencing data, HSP90AB1 was identified as the most promising target among other predicted HSPs. Finally, the ranking of the drugs was arranged based on various drug scores. In conclusion, this study gave a spotlight on the mutual targetable HSPs, biological pathways, and regulatory signatures associated with GBMs and ependymoma with an improved understanding of crosstalk involved. Additionally, the role of therapeutics was also explored against HSP90AB1. These findings could potentially be able to explain the interplay of HSP90AB1 and other HSPs within these two malignancies.

Recent advances towards natural plants as potential inhibitors of SARS-Cov-2 targets

CONTEXT

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is still ongoing and currently the most striking epidemic disease. With the rapid global spread of SARS-CoV-2 variants, new antivirals are urgently needed to avert a more serious crisis. Inhibitors from traditional medicines or natural plants have shown promising results to fight COVID-19 with different mechanisms of action.

OBJECTIVES

To provide comprehensive and promising approaches to the medical community in the fight against this epidemic by reviewing potential plant-derived anti-SARS-CoV-2 inhibitors.

METHODS

Structural databases such as TCMSP (http://lsp.nwu.edu.cn/tcmsp.php), TCM Database @ Taiwan (http://tcm.cmu.edu.tw/), BATMAN-TCM (http://bionet.ncpsb.org/batman-tcm/) and TCMID (http://www.megabionet.org/tcmid/), as well as PubMed, Sci Finder, Research Gate, Science Direct, CNKI, Web of Science and Google Scholar were searched for relevant articles on TCMs and natural products against SARS-CoV-2.

RESULTS

Seven traditional Chinese medicines formulas have unique advantages in regulating the immune system for treating COVID-19. The plant-derived natural compounds as anti-SARS-CoV-2 inhibitors were identified based on 5 SARS-CoV-2 key proteins, namely, angiotensin-converting enzyme 2 (ACE2), 3 C-like protease (3CLpro), papain-like protease (PLpro), spike (S) protein, and nucleocapsid (N) protein.

CONCLUSIONS

A variety of natural products, such as flavonoids, terpenoids, phenols, and alkaloids, were identified, which could be used as potential SASR-Cov-2 inhibitors. These shed new light on the efficient discovery of SASR-Cov-2 inhibitors from natural products.

Integration of Omics Data and Network Models to Unveil Negative Aspects of SARS-CoV-2, from Pathogenic Mechanisms to Drug Repurposing

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 health emergency, affecting and killing millions of people worldwide. Following SARS-CoV-2 infection, COVID-19 patients show a spectrum of symptoms ranging from asymptomatic to very severe manifestations. In particular, bronchial and pulmonary cells, involved at the initial stage, trigger a hyper-inflammation phase, damaging a wide range of organs, including the heart, brain, liver, intestine and kidney. Due to the urgent need for solutions to limit the virus' spread, most efforts were initially devoted to mapping outbreak trajectories and variant emergence, as well as to the rapid search for effective therapeutic strategies. Samples collected from hospitalized or dead COVID-19 patients from the early stages of pandemic have been analyzed over time, and to date they still represent an invaluable source of information to shed light on the molecular mechanisms underlying the organ/tissue damage, the knowledge of which could offer new opportunities for diagnostics and therapeutic designs. For these purposes, in combination with clinical data, omics profiles and network models play a key role providing a holistic view of the pathways, processes and functions most affected by viral infection. In fact, in addition to epidemiological purposes, networks are being increasingly adopted for the integration of multiomics data, and recently their use has expanded to the identification of drug targets or the repositioning of existing drugs. These topics will be covered here by exploring the landscape of SARS-CoV-2 survey-based studies using systems biology approaches derived from omics data, paying particular attention to those that have considered samples of human origin.

In silico evidence implicating novel mechanisms of Prunella vulgaris L. as a potential botanical drug against COVID-19-associated acute kidney injury

COVID-19-associated acute kidney injury (COVID-19 AKI) is an independent risk factor for in-hospital mortality and has the potential to progress to chronic kidney disease. Prunella vulgaris L., a traditional Chinese herb that has been used for the treatment of a variety of kidney diseases for centuries, could have the potential to treat this complication. In this study, we studied the potential protective role of Prunella vulgaris in COVID-19 AKI and explored its specific mechanisms applied by network pharmacology and bioinformatics methods. The combination of the protein-protein interaction network and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment -target gene network revealed eight key target genes (VEGFA, ICAM1, IL6, CXCL8, IL1B, CCL2, IL10 and RELA). Molecular docking showed that all these eight gene-encoded proteins could be effectively bound to three major active compounds (quercetin, luteolin and kaempferol), thus becoming potential therapeutic targets. Molecular dynamics simulation also supports the binding stability of RELA-encoded protein with quercetin and luteolin. Together, our data suggest that IL6, VEGFA, and RELA could be the potential drug targets by inhibiting the NF-κB signaling pathway. Our in silico studies shed new insights into P. vulgaris and its ingredients, e.g., quercetin, as potential botanical drugs against COVID-19 AKI, and warrant further studies on efficacy and mechanisms.