OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders

Scientific Validation of Using Active Constituent as Research Focus in Traditional Chinese Medicine: Case Study of Pueraria lobata Intervention in Type 2 Diabetes

Objectives: Traditional Chinese Medicine (TCM) is recognized for its complex composition and multiple therapeutic targets. However, current pharmacological research often concentrates on extracts or individual components. The former approach faces numerous challenges, whereas the latter oversimplifies and disregards the synergistic effects among TCM components. This study aims to investigate the scientific validity of focusing on the active constituent in TCM efficacy research, using Pueraria lobata (P. lobata) as a case study. Methods: Through spectrum-effect correlation analysis, network pharmacology, and molecular docking, five active ingredients of P. lobata were identified: puerarin, formononetin, tuberosin, 4',7-dihdroxy-3'-methoxyisoflavone, and Daidzein-4,7-diglucoside. These ingredients were combined to form an active constituent, which was subsequently tested in vitro and in vivo. Results: In in vitro, the active constituent exhibited superior effects in enhancing glucose consumption and glycogen synthesis compared to both the P. lobata extract and individual components. In vivo experiments demonstrated that medium and high doses of the active constituent were significantly more effective than P. lobata extract, with effects comparable to those of metformin in reducing blood sugar levels. Conclusions: The active constituent effectively improves T2DM by lowering blood glucose levels, promoting glycogen synthesis, and modulating glycolipid metabolism. Both in vitro and in vivo studies indicate that it outperformed the P. lobata extract and individual components. This study establishes the scientific validity and feasibility of utilizing the active constituent as the focus for investigating the efficacy of TCM, thereby offering novel insights and a new research paradigm for future TCM investigations.

Unified Clinical Vocabulary Embeddings for Advancing Precision Medicine

Integrating clinical knowledge into AI remains challenging despite numerous medical guidelines and vocabularies. Medical codes, central to healthcare systems, often reflect operational patterns shaped by geographic factors, national policies, insurance frameworks, and physician practices rather than the precise representation of clinical knowledge. This disconnect hampers AI in representing clinical relationships, raising concerns about bias, transparency, and generalizability. Here, we developed a resource of 67,124 clinical vocabulary embeddings derived from a clinical knowledge graph tailored to electronic health record vocabularies, spanning over 1.3 million edges. Using graph transformer neural networks, we generated clinical vocabulary embeddings that provide a new representation of clinical knowledge by unifying seven medical vocabularies. These embeddings were validated through a phenotype risk score analysis involving 4.57 million patients from Clalit Healthcare Services, effectively stratifying individuals based on survival outcomes. Inter-institutional panels of clinicians evaluated the embeddings for alignment with clinical knowledge across 90 diseases and 3,000 clinical codes, confirming their robustness and transferability. This resource addresses gaps in integrating clinical vocabularies into AI models and training datasets, paving the way for knowledge-grounded population and patient-level models.

Quercetin alleviates ulcerative colitis through inhibiting CXCL8-CXCR1/2 axis: a network and transcriptome analysis

Introduction

Ulcerative colitis (UC) is a chronic inflammatory condition of the intestinal tract in which mucosal healing is a crucial measure of therapeutic efficacy. Quercetin, a flavonoid prevalent in various foods and traditional Chinese medicines, exhibits notable pharmacological properties, including antioxidant and anti-inflammatory activities. Consequently, it warrants investigation to determine its potential therapeutic effects on UC. The objective of this study was to investigate the effects and underlying mechanisms of quercetin in a murine model of UC.

Methods

A comprehensive approach integrating network predictions with transcriptomic analyses was employed to identify the potential targets and enriched pathways associated with quercetin in UC. Subsequently, the effects of quercetin on pathological morphology, inflammatory mediators, and mucosal barrier-associated proteins, as well as the identified potential targets and enriched pathways, were systematically investigated in a murine model of dextran sulfate sodium (DSS)-induced UC.

Results

Network analyses identified CXCL8 and its receptors, CXCR1 and CXCR2, as primary target genes for therapeutic intervention in UC. Further validation through transcriptomic analysis and immunofluorescence staining demonstrated significant upregulation of the CXCL8-CXCR1/2 axis in the intestinal tissues of patients with UC. Experimental investigations in animal models have shown that quercetin markedly alleviates DSS-induced symptoms in mice. This effect includes the restoration of colonic crypt architecture, normalization of goblet cell structure and density, reduction of inflammatory cell infiltration, and decreased concentrations of inflammatory mediators. Quercetin enhanced the expression of tight junction (TJ) proteins, including ZO-1, MUC2 (Mucin 2), and occludin, thereby preserving the integrity of the intestinal mucosal barrier. Additionally, it significantly diminished the levels of IL-17A, NF-κB, CXCL8, CXCR1, and CXCR2 in the colonic tissues of mice with UC.

Discussion

The ameliorative effects of quercetin on colon tissue damage in DSS-induced UC mice were significant, possibly due to its ability to inhibit the CXCL8-CXCR1/2 signaling axis. These findings provide a solid foundation for the clinical application and pharmaceutical advancement of quercetin.

Network Pharmacology, Molecular Docking, and In Vitro Insights into the Potential of Mitragyna speciosa for Alzheimer's Disease

Mitragyna speciosa Korth. Havil (MS) has a traditional use in relieving pain, managing hypertension, treating cough, and diarrhea, and as a morphine substitute in addiction recovery. Its potential in addressing Alzheimer's disease (AD), a neurodegenerative condition with no effective treatments, is under investigation. This study aims to explore MS mechanisms in treating AD through network pharmacology, molecular docking, and in vitro studies. Using network pharmacology, we identified 19 MS components that may affect 60 AD-related targets. The compound-target network highlighted significant interactions among 60 nodes and 470 edges, with an average node degree of 15.7. The KEGG enrichment analysis revealed Alzheimer's disease (hsa05010) as a relevant pathway. We connected 20 targets to tau and β-amyloid proteins through gene expression data from the AlzData database. Docking studies demonstrated high binding affinities of MS compounds like acetylursolic acid, beta-sitosterol, isomitraphylline, and speciophylline to AD-related proteins, such as AKT1, GSK3B, NFκB1, and BACE1. In vitro studies showed that ethanolic (EE), distilled water (DWE), and pressurized hot water (PHWE) extracts of MS-treated 100 μM H2O2-induced SH-SY5Y cells significantly reduced oxidative damage. This research underscores the multi-component, multi-target, and multi-pathway effects of MS on AD, providing insights for future research and potential clinical applications.

Mechanistic insights into the neurotoxicity of F53B: Effects on metabolic dysregulation and apoptosis of dopaminergic neurons

F53B (6:2 chlorinated polyfluorinated ether sulfonate), a substitute for perfluorooctane sulfonate (PFOS), is widely used as a chromium mist inhibitor in the electroplating industry. However, significant concern has arisen owing to its biological toxicity. Several studies on F53B toxicity in mammals have focused on hepatotoxicity, immunotoxicity, developmental toxicity, and reproductive toxicity, while its neurotoxic effects, especially in relation to neurodegenerative diseases such as Parkinson's disease (PD), remain unclear. In this study, we investigated the neurotoxic effects of F53B on dopaminergic neurons and explored its potential risk associated with PD in a cellular model. Potential target prediction and validation experiments demonstrated that F53B induced apoptosis in dopaminergic neurons. We also discovered that F53B triggered oxidative stress and inflammatory responses, and stimulated nitric oxide (NO) generation in the PD cellular model. Subsequently, untargeted metabolomics and lipidomics approaches were integrated to explore the molecular mechanisms underlying the response of dopaminergic neurons to F53B exposure. The results suggested that F53B disrupted arginine and proline metabolism, energy metabolism, and caused lipid dysregulation, particularly promoting the hydrolysis of sphingomyelin (SM) into ceramide (Cer). Overall, this study provides evidence that F53B exposure could increase the potential risk of PD and offers novel insights into its neurotoxicity mechanisms.

Pheno-Ranker: a toolkit for comparison of phenotypic data stored in GA4GH standards and beyond

BACKGROUND

Phenotypic data comparison is essential for disease association studies, patient stratification, and genotype-phenotype correlation analysis. To support these efforts, the Global Alliance for Genomics and Health (GA4GH) established Phenopackets v2 and Beacon v2 standards for storing, sharing, and discovering genomic and phenotypic data. These standards provide a consistent framework for organizing biological data, simplifying their transformation into computer-friendly formats. However, matching participants using GA4GH-based formats remains challenging, as current methods are not fully compatible, limiting their effectiveness.

RESULTS

Here, we introduce Pheno-Ranker, an open-source software toolkit for individual-level comparison of phenotypic data. As input, it accepts JSON/YAML data exchange formats from Beacon v2 and Phenopackets v2 data models, as well as any data structure encoded in JSON, YAML, or CSV formats. Internally, the hierarchical data structure is flattened to one dimension and then transformed through one-hot encoding. This allows for efficient pairwise (all-to-all) comparisons within cohorts or for matching of a patient's profile in cohorts. Users have the flexibility to refine their comparisons by including or excluding terms, applying weights to variables, and obtaining statistical significance through Z-scores and p-values. The output consists of text files, which can be further analyzed using unsupervised learning techniques, such as clustering or multidimensional scaling (MDS), and with graph analytics. Pheno-Ranker's performance has been validated with simulated and synthetic data, showing its accuracy, robustness, and efficiency across various health data scenarios. A real data use case from the PRECISESADS study highlights its practical utility in clinical research.

CONCLUSIONS

Pheno-Ranker is a user-friendly, lightweight software for semantic similarity analysis of phenotypic data in Beacon v2 and Phenopackets v2 formats, extendable to other data types. It enables the comparison of a wide range of variables beyond HPO or OMIM terms while preserving full context. The software is designed as a command-line tool with additional utilities for CSV import, data simulation, summary statistics plotting, and QR code generation. For interactive analysis, it also includes a web-based user interface built with R Shiny. Links to the online documentation, including a Google Colab tutorial, and the tool's source code are available on the project home page: https://github.com/CNAG-Biomedical-Informatics/pheno-ranker .

Identification of miRNA-TF Regulatory Pathways Related to Diseases from a Neuroendocrine-Immune Perspective

The neuroendocrine-immune (NEI) network is fundamental for maintaining body's homeostasis and health. While the roles of microRNAs (miRNAs) and transcription factors (TFs) in disease processes are well-established, their synergistic regulation within the NEI network has yet to be elucidated. In this study, we constructed a background NEI-related miRNA-TF regulatory network (NEI-miRTF-N) by integrating NEI signaling molecules (including miRNAs, genes, and TFs) and identifying miRNA-TF feed-forward loops. Our analysis reveals that the number of immune signaling molecules is the highest and suggests potential directions for signal transduction, primarily from the nervous system to both the endocrine and immune systems, as well as from the endocrine system to the immune system. Furthermore, disease-specific NEI-miRTF-Ns for depression, Alzheimer's disease (AD) and dilated cardiomyopathy (DCM) were constructed based on the known disease molecules and significantly differentially expressed (SDE) molecules. Additionally, we proposed a novel method using depth-first-search algorithm for identifying significantly dysregulated NEI-related miRNA-TF regulatory pathways (NEI-miRTF-Ps) and verified their reliability from multiple perspectives. Our study provides an effective approach for identifying disease-specific NEI-miRTF-Ps and offers new insights into the synergistic regulation of miRNAs and TFs within the NEI network. Our findings provide information for new therapeutic strategies targeting these regulatory pathways.

DRED: A Comprehensive Database of Genes Related to Repeat Expansion Diseases

Expansion of tandem repeats in genes often causes severe diseases, such as fragile X syndrome, Huntington's disease, and spinocerebellar ataxia. However, information on genes associated with repeat expansion diseases is scattered throughout the literature, systematic prediction of potential genes that may cause diseases via repeat expansion is also lacking. Here, we develop DRED, a Database of genes related to Repeat Expansion Diseases, as a manually-curated database that covers all known 61 genes related to repeat expansion diseases reported in PubMed and OMIM, along with the detailed repeat information for each gene. DRED also includes 516 genes with the potential to cause diseases via repeat expansion, which were predicted based on their repeat composition, genetic variations, genomic features, and disease associations. Various types of information on repeat expansion diseases and their corresponding genes/repeats are presented in DRED, together with links to external resources, such as NCBI and ClinVar. DRED provides user-friendly interfaces with comprehensive functions, and can serve as a central data resource for basic research and repeat expansion disease-related medical diagnosis. DRED is freely accessible at http://omicslab.genetics.ac.cn/dred, and will be frequently updated to include newly reported genes related to repeat expansion diseases.

Based on network pharmacology, molecular docking and experimental verification to reveal the mechanism of Andrographis paniculata against solar dermatitis

BACKGROUND

Solar dermatitis (SD) is an acute, damaging inflammation of the skin caused by UV exposure, especially UVB. Therefore, the discovery of novel anti-SD therapeutic agents is crucial. Andrographis paniculata (AP) is a medicinal plant with a wide range of pharmacological effects. Increased evidence shows that AP has potential therapeutic effects on SD. However, the therapeutic mechanisms of AP against SD have not yet been completely elucidated, which is an unexplored field.

PURPOSE

This study employed network pharmacology, molecular docking and experimental verification to ascertain the active constituents, possible targets, and biological pathways associated with AP in the treatment of SD.

METHODS

AP-related active ingredients and their potential targets were screened from TCMSP and Swiss Target Prediction database, respectively. Potential therapeutic targets of SD were collected using the GeneCards, DrugBank and OMIM databases. Then, we established protein-protein interaction (PPI), compound-target-disease (D-C-T-D) through Cytoscape to identify the major components, core targets of AP against SD. Next, the GO and KEGG pathway was identified by the David database of AP in the treatment of SD. Molecular docking techniques were used to estimate the binding force between the components and the hub genes. In this paper, we used UVB-irradiated HaCaT keratinocytes as an in vitro model and established the dorsal skin of UVB-irradiated ICR mice as an in vivo model to explore the mechanism for further verification.

RESULTS

There were 24 active components and 63 related target genes in AP against SD. PPI analysis showed that AKT-1, TNF-α, IL6, MMP9, EGFR, and PTGS2 shared the highest centrality among all target genes. KEGG pathway analysis revealed that the PI3K-Akt signaling pathway may be central in the anti-SD system. The molecular docking results showed that the main active components of AP have strong binding affinity with hub genes. In vitro results showed that WG had a protective effect on UVB-intervened HaCat cells. Western blot analysis showed that WG intervention achieved anti-inflammation by reducing the phosphorylated expression of AKT, PI3K proteins in the PI3K-AKT signaling pathway and downregulating the expression of TNF-α, IL-6, EGFR. Furthermore, Histological analysis confirmed that administration of WG to ICR mice significantly ameliorated UVB-induced skin roughness, epidermal thickening, disturbed collagen fiber alignment and wrinkles. Meanwhile, immunohistochemistry showed that administration of WG to ICR mice significantly reduced UVB-induced expression of MMP9, MPO, F4/80 in the skin. These results provide new insights into the contribution of WG to the development of clinical treatment modalities for UVB-induced SD.

CONCLUSION

The crucial element in the fight against SD is WG, with the primary route being PI3K/Akt. The main components and hub genes had robust binding abilities. In vitro and vivo experiments showed that WG could inhibit the expression level of the hub genes by inhibiting the PI3K/Akt pathway. In summary, the information presented in this study indicates that WG might be utilised as a treatment for UVB-induced SD.

Study of the multitarget mechanism of Astragalus (HUANGQI) in the treatment of Alzheimer's disease based on network pharmacology and molecular docking technology

CONTEXT

In China, HUANGQI is widely used for the treatment of Alzheimer's disease (AD). However, a comprehensive understanding of its mechanism of anti-AD effects is lacking.

OBJECTIVE

To explore the active ingredients of HUANGQI and its potential targets and mechanisms of action in AD.

MATERIALS AND METHODS

The active ingredients and targets of HUANGQI were screened from databases (TCSMP, ETCM, and BATMan), and AD-related genes were obtained from DrugBank and GeneCards. The same target genes were screened, and a drug-target disease network was constructed. The PPI network was constructed and GO and KEGG pathway enrichment analyses of the targets. The Cell Counting Kit-8 (CCK-8) assay was used to determine suitable HUANGQI treatment concentrations for HT-22 cells between 0-480 μg/mL. CCK-8, FITC-phalloidin and propidium iodide (PI) assays were used to examine the protective effect of (0, 60, 120, 240 μg/mL) of HUANGQI on 20 μM Aβ1-42-induced HT-22 cell cytotoxicity.

RESULTS

Twelve active ingredients of HUANGQI were selected, with 679 common targets associated with AD. GO and KEGG analysis revealed that the therapeutic mechanisms of HUANGQI involve TNF, AGE, the NF-κB pathway, and nuclear receptor activity-related processes. The CCK-8 assay indicated that HUANGQI was not cytotoxic to HT-22 cells at concentrations less than 240 μg/mL and was able to attenuate Aβ1-42-induced cellular damage (EC50 = 83.46 μg/mL). FITC-phalloidin and PI assays suggested that HUANGQI could alleviate 20 μM Aβ1-42-induced neuronal cell cytotoxicity in a dose-dependent manner.

CONCLUSION

HUANGQI has a protective effect on Aβ1-42-induced nerve cell injury; further mechanism research was needed.

Active Targets and Potential Mechanisms of Erhuang Quzhi Formula in Treating NAFLD: Network Analysis and Experimental Assessment

The purpose of this research was to investigate the main active components, potential targets of action, and pharmacological mechanisms of Erhuang Quzhi Formula (EHQZF) against NAFLD using network pharmacology, molecular docking, and experimental validation. The main active chemical components of EHQZF and the potential targets for treating NAFLD were extracted and analyzed. The PPI network diagram of "Traditional Chinese Medicine-Active Ingredients-Core Targets" was constructed and the GO, KEGG, and molecular docking analysis were carried out. Identification of components in traditional Chinese medicine compounds was conducted by LC-MS. NAFLD models were established and relevant pathologic indicators and Western blot were analyzed in vivo and ex vivo. Totally 8 herbs attributed to the liver meridian and 20 corresponding targets of NAFLD were obtained from EHQZF. Flavonoids and phenolic acids as the main components of EHQZF treated NAFLD through the MAPK/AKT signaling pathway. Pathway enrichment analysis focused on the MAPK/AKT signaling pathway and apoptosis signaling pathway. Molecular docking showed that Quercetin and Luteolin had stable binding structures with AKT1, STAT3, and other targets. Experiments showed that EHQZF reduced lipid accumulation, regulated changes in adipose tissue, inhibited the MAPK/AKT signaling pathway and exert multiple components, several targets, and multiple pathway interactions to treat NAFLD.

Machine learning based analysis of single-cell data reveals evidence of subject-specific single-cell gene expression profiles in acute myeloid leukaemia patients and healthy controls

Acute Myeloid Leukaemia (AML) is characterized by uncontrolled growth of immature myeloid cells, disrupting normal blood production. Treatment typically involves chemotherapy, targeted therapy, and stem cell transplantation but many patients develop chemoresistance, leading to poor outcomes due to the disease's high heterogeneity. In this study, we used publicly available single-cell RNA sequencing data and machine learning to classify AML patients and healthy, monocytes, dendritic and progenitor cells population. We found that gene expression profiles of AML patients and healthy controls can be classified at the individual level with high accuracy (>70 %) when using progenitor cells, suggesting the existence of subject-specific single cell transcriptomics profiles. The analysis also revealed molecular determinants of patient heterogeneity (e.g. TPSD1, CT45A1, and GABRA4) which could support new strategies for patient stratification and personalized treatment in leukaemia.

Network pharmacology provides new insights into the mechanism of traditional Chinese medicine and natural products used to treat pulmonary hypertension

BACKGROUND

Pulmonary hypertension (PH) is a rare cardiovascular disease with high morbidity and mortality rates. It is characterized by increased pulmonary arterial pressure. Current research into relevant therapeutic drugs and targets for PH, however, is insufficient still. Traditional Chinese medicine (TCM) and natural products have a long history as therapeutics for PH. Network pharmacology is an approach that integrates drug-target interactions and signaling pathways based on biomarkers information obtained from drug and disease databases. The concept of network pharmacology shows many similarities with the TCM philosophy. Network pharmacology help elucidate the mechanisms of TCM in PH. This review presents representative applications of network pharmacology in the study of the mechanisms of TCM and natural products for the treatment of PH.

METHODS

In this review, we used ("pulmonary hypertension" OR "pulmonary arterial hypertension" OR "chronic thromboembolic pulmonary hypertension") AND ("network pharmacology" OR "systematic pharmacology") as keywords to search for reports from PubMed, Web of Science, and Google Scholar databases from ten years ago. The studies were screened and those chosen are summarized here. The TCM and natural products inPH and their corresponding targets and signaling pathways are described. Additionally, we discuss the application of network pharmacology in the study of TCM in PH to provide insights for future application strategies.

RESULTS

Network pharmacology have shown that AKT-related pathways, HIF-1 signaling pathway, MAPK signaling pathway, TGF-β-Smad pathway, cell cycle-related pathways and inflammation-related pathways are the main signaling pathways enriched in the PH targets of TCM. Reservatrol, curcumol, genistin, formononetin, wogonin, luteolin, baicalein, berberine, triptolide and tanshinone llA are active ingredients specific for PH treatment. A number of databases and tools specific for the treatment of PH are used in network pharmacology and natural product research.

CONCLUSION

Through the reasonable combination of molecular docking, omics technology and bioinformatics technology, the mechanism of multi-targets can be explained more comprehensively. Analyzing the complex mechanism of TCM from the clinical perspective may be a potential development trend of network pharmacology. Combination of predicted targets and traditional pharmacology improves efficiency of drug development.

The unfolded features on the synchronized fashion of gut microbiota and Drynaria rhizome against obesity via integrated pharmacology

Drynaria rhizome (DR) is used as a natural remedy to ameliorate obesity (OB) in East Asia; in parallel, the gut microbiota (GM) might exert a positive impact on OB through their metabolites. This study elucidates the orchestrated effects of DR and GM on OB. DR-GM, - a key signaling pathway-target-metabolite (DGSTM) networks were used to unveil the relationship between DR and GM, and Molecular Docking Test (MDT) and Density Functional Theory (DFT) were adopted to underpin the uppermost molecules. The NR1H3 (target) - 3-Epicycloeucalenol (ligand), and PPARG (target) - Clionasterol (ligand) conjugates from DR, FABP3 (target) - Ursodeoxycholic acid, FABP4 (target) - Lithocholic acid (ligand) or Deoxycholic acid (ligand), PPARA (target) - Equol (ligand), and PPARD (target) - 2,3-Bis(3,4-dihydroxybenzyl)butyrolactone (ligand) conjugates from GM formed the most stable conformers via MDT and DFT. Overall, these findings suggest that DR-GM might be a promising ameliorator on PPAR signaling pathway against OB.

Study on the effect and mechanisms of piperine against cervical cancer based on network pharmacology and experimental validation

Piperine has immunomodulatory and anti-inflammatory properties, and its potential in treating cervical cancer needs further exploration. Using data from The Cancer Genome Atlas (TCGA), we identified immune-related differentially expressed genes (IRDEGs) in cervical cancer. Predicted targets of piperine were compared with cervical cancer-associated genes from various databases. Protein-protein interaction (PPI) network analysis, enrichment of GO and KEGG pathways, and molecular docking were performed. Kaplan-Meier survival analysis was done to assess prognostic significance. In vitro and in vivo experiments were conducted to confirm findings. We obtained 403 IRDEGs, 125 piperine targets, and 7037 cervical cancer genes. PPI network analysis revealed potential targets and pathways regulated by piperine. Molecular docking showed good binding activity of piperine with specific targets. In vitro, piperine inhibited cervical cancer cell proliferation, migration, and invasion, and promoted apoptosis. In vivo, piperine suppressed tumor growth and downregulated expression of IL-1β and NLRP3 in tumor cells. Piperine also downregulated expression of IL-17A, IL-21, IL-22, and RORγt, and decreased the number of Th17 cells in tumor tissues. Piperine may inhibit cervical cancer progression through modulation of Th17 cell activation mediated by the NLRP3/IL-1β axis. Further studies are warranted to explore the potential of piperine as an immunomodulatory agent in cervical cancer treatment.

Flavonoids from Rhododendron nivale Hook. f ameliorate alcohol-associated liver disease via activating the PPARα signaling pathway

BACKGROUND

Flavonoids are increasingly recognized for their potent antioxidant properties and potential therapeutic roles in the management of alcohol-associated liver disease (ALD). Extracts derived from Rhododendron nivale Hook. f. (FRN) have been shown to influence glutathione metabolism in aging animal models, exhibiting notable antioxidant effects. However, the specific impact of FRN on ALD remains insufficiently explored.

HYPOTHESIS/PURPOSE

This study seeks to elucidate the efficacy of FRN in alleviating the pathology associated with ALD, delving into the underlying molecular mechanisms that facilitate its protective effects.

STUDY DESIGN

We employed network pharmacology to predict the functional roles and pathway enrichments associated with FRN targets. Both a murine model of ALD and in vitro cellular models were utilized to clarify the mechanistic basis by which FRN mitigates ALD.

METHODS

FRN was extracted and characterized according to well-established methodologies outlined in our previous studies. Potential functions and pathways implicated by FRN were predicted through network pharmacology analyses. A combination of liver transcriptomics, targeted lipidomics, molecular biology techniques, and antagonists of relevant targets were employed to investigate the mechanisms through which FRN exerts its protective effects in ALD.

RESULTS

Network pharmacology identified multiple target genes modulated by FRN, particularly those within critical ALD-related signaling pathways, such as PPARα signaling and fatty acids (FAs) degradation. Notably, treatment with FRN in the ALD murine model led to a significant attenuation of hepatic lipid accumulation and a restoration of serum AST and ALT to baseline ranges. Subsequent validation through liver transcriptomics and molecular biology techniques revealed an upregulation of PPARα expression concomitant with a downregulation of ACSL1 in FRN-treated ALD mice. Targeted lipidomic and bioinformatic analyses demonstrated that FRN substantially reduced the accumulation of long-chain fatty acids in hepatocytes. Importantly, the reversal of FRN's protective effects on lipid accumulation through the PPARα antagonist GW6471 provides compelling evidence for the critical role of PPARα signaling modulation in mediating the beneficial impact of FRN on ALD.

CONCLUSION

Our research highlights FRN's capacity to alleviate ALD through PPARα pathway activation, paving the way for innovative treatment strategies. This underscores the significance of natural compounds in pharmacotherapy, suggesting that FRN may provide an effective alternative for managing ALD.

A consortium of Hordeum vulgare and gut microbiota against non-alcoholic fatty liver disease via data-driven analysis

Despite many recent studies on non-alcoholic fatty liver disease (NAFLD) therapeutics, the optimal treatment has yet to be determined. In this unfinished project, we combined secondary metabolites (SMs) from the gut microbiota (GM) and Hordeum vulgare (HV) to investigate their combinatorial effects via network pharmacology (NP). Additionally, we analyzed GM or barley - signalling pathways - targets - metabolites (GBSTMs) in combinatorial perspectives (HV, and GM). A total of 31 key targets were analysed via a protein-protein interaction (PPI) network, and JUN was identified as the uppermost target in NAFLD. On a bubble plot, we revealed that apelin signalling pathway, which had the lowest enrichment factor antagonize NAFLD. Holistically, we scrutinized GBSTM to identify key components (GM, signalling pathways, targets, and metabolites) associated with the Apelin signalling pathway. Consequently, we found that the primary GMs (Eubacterium limosum, Eggerthella sp. SDG-2, Alistipes indistinctus YIT 12060, Odoribacter laneus YIT 12061, Paraprevotella clara YIT 11840, Paraprevotella xylaniphila YIT 11841) to ameliorate NAFLD. The molecular docking test (MDT) suggested that tryptanthrin-JUN is an agonist, conversely, dihydroglycitein-HDAC5, 1,3-diphenylpropan-2-ol-NOS1, and (10[(Acetyloxy)methyl]-9-anthryl)methyl acetate-NOS2, which are antagonistic conformers in the apelin signalling pathway. Overall, these results suggest that combination therapy could be an effective strategy for treating NAFLD.

Genetic architecture of cerebrospinal fluid and brain metabolite levels and the genetic colocalization of metabolites with human traits

Brain metabolism perturbation can contribute to traits and diseases. We conducted a genome-wide association study for cerebrospinal fluid (CSF) and brain metabolite levels, identifying 205 independent associations (47.3% new signals, containing 11 new loci) for 139 CSF metabolites, and 32 independent associations (43.8% new signals, containing 4 new loci) for 31 brain metabolites. Of these, 96.9% (CSF) and 71.4% (brain) of the new signals belonged to previously analyzed metabolites in blood or urine. We integrated the metabolite quantitative trait loci (MQTLs) with 23 neurological, psychiatric and common human traits and diseases through colocalization to identify metabolites and biological processes implicated in these phenotypes. Combining CSF and brain, we identified 71 metabolite-trait associations, such as glycerophosphocholines with Alzheimer's disease, O-sulfo-L-tyrosine with Parkinson's disease, glycine, xanthine with waist-to-hip ratio and ergothioneine with inflammatory bowel disease. Our study expanded the knowledge of MQTLs in the central nervous system, providing insights into human traits.

Potential mechanism of Qinggong Shoutao pill alleviating age-associated memory decline based on integration strategy

CONTEXT

Qinggong Shoutao Wan (QGSTW) is a pill used as a traditional medicine to treat age-associated memory decline (AAMI). However, its potential mechanisms are unclear.

OBJECTIVE

This study elucidates the possible mechanisms of QGSTW in treating AAMI.

MATERIALS AND METHODS

Network pharmacology and molecular docking approaches were utilized to identify the potential pathway by which QGSTW alleviates AAMI. C57BL/6J mice were divided randomly into control, model, and QGSTW groups. A mouse model of AAMI was established by d-galactose, and the pathways that QGSTW acts on to ameliorate AAMI were determined by ELISA, immunofluorescence staining and Western blotting after treatment with d-gal (100 mg/kg) and QGSTW (20 mL/kg) for 12 weeks.

RESULTS

Network pharmacology demonstrated that the targets of the active components were significantly enriched in the cAMP signaling pathway. AKT1, FOS, GRIN2B, and GRIN1 were the core target proteins. QGSTW treatment increased the discrimination index from -16.92 ± 7.06 to 23.88 ± 15.94% in the novel location test and from -19.54 ± 5.71 to 17.55 ± 6.73% in the novel object recognition test. ELISA showed that QGSTW could increase the levels of cAMP. Western blot analysis revealed that QGSTW could upregulate the expression of PKA, CREB, c-Fos, GluN1, GluA1, CaMKII-α, and SYN. Immunostaining revealed that the expression of SYN was decreased in the CA1 and DG.

DISCUSSION AND CONCLUSIONS

This study not only provides new insights into the mechanism of QGSTW in the treatment of AAMI but also provides important information and new research ideas for the discovery of traditional Chinese medicine compounds that can treat AAMI.

Biliverdinuria Caused by Exonic BLVRA Deletions in Two Dogs with Green Urine

Background/Objectives: In heme degradation, biliverdin reductase catalyzes the conversion of biliverdin to bilirubin. Defects in the biliverdin reductase A gene (BLVRA) causing biliverdinuria are extraordinarily rare in humans, and this inborn error of metabolism has not been reported in other mammals. The objective of this study was to diagnose biliverdinuria and identify the causal BLVRA variants in two adult mixed-breed dogs with life-long green urine. One of the dogs also had an unexplained regenerative anemia and mild hepatopathy. Methods: Clinicopathological evaluations, urinary mass spectroscopy, and molecular genetic studies were performed. Urine metabolic screening identified increased biliverdin concentrations in both cases relative to control dogs. Results: Whole genome and Sanger sequencing revealed that each case was homozygous for large deletions in BLVRA: UU_Cfam_GSD_1.0/canFam4 chr18:6,532,022-6,551,313 (19,292 bp) in Case 1 and chr18:6,543,863-6,545,908 (2046 bp) in Case 2. These variants were predicted to result in major BLVRA truncations (ENSCAFT00805017018.1 p.[Lys117-Lys296del] and p.[Ala154fs], respectively) and loss of enzyme function. In a genomic variant database, 671 dogs from 63 breeds had coverage over these regions, ruling out homozygosity for the BLVRA deletions. A gene defect for the regenerative anemia in Case 1 was not discovered. Conclusions: While expected to be rare, genotyping for the BLVRA deletions can be used to identify other affected and carrier dogs. This study illustrates the use of targeted metabolic and genomic screening as key diagnostic tools to diagnose a rare metabolic disorder. These are the first confirmed cases of biliverdinuria caused by BLVRA defects in non-human mammals.

Candidate SNP Markers Significantly Altering the Affinity of the TATA-Binding Protein for the Promoters of Human Genes Associated with Primary Open-Angle Glaucoma

Primary open-angle glaucoma (POAG) is the most common form of glaucoma. This condition leads to optic nerve degeneration and eventually to blindness. Tobacco smoking, alcohol consumption, fast-food diets, obesity, heavy weight lifting, high-intensity physical exercises, and many other bad habits are lifestyle-related risk factors for POAG. By contrast, moderate-intensity aerobic exercise and the Mediterranean diet can alleviate POAG. In this work, we for the first time estimated the phylostratigraphic age indices (PAIs) of all 153 POAG-related human genes in the NCBI Gene Database. This allowed us to separate them into two groups: POAG-related genes that appeared before and after the phylum Chordata, that is, ophthalmologically speaking, before and after the camera-type eye evolved. Next, in the POAG-related genes' promoters, we in silico predicted all 3835 candidate SNP markers that significantly change the TATA-binding protein (TBP) affinity for these promoters and, through this molecular mechanism, the expression levels of these genes. Finally, we verified our results against five independent web services-PANTHER, DAVID, STRING, MetaScape, and GeneMANIA-as well as the ClinVar database. It was concluded that POAG is likely to be a symptom of the human self-domestication syndrome, a downside of being civilized.

Spectrum and Prevalence of Rare APOE Variants and Their Association with Familial Dysbetalipoproteinemia

Familial dysbetalipoproteinemia (FD) is a highly atherogenic, prevalent genetically based lipid disorder. About 10% of FD patients have rare APOE variants associated with autosomal dominant FD. However, there are insufficient data on the relationship between rare APOE variants and FD. Genetic data from 4720 subjects were used to identify rare APOE variants and investigate their pathogenicity for autosomal dominant FD. We observed 24 variants in 86 unrelated probands. Most variants were unique (66.7%). Five identified APOE variants (p.Glu63ArgfsTer15, p.Gly145AlafsTer97, p.Lys164SerfsTer87, p.Arg154Cys, and p.Glu230Lys) are causal for autosomal dominant FD. One of them (p.Lys164SerfsTer87) was described for the first time. When we compared clinical data, it was found that carriers of pathogenic or likely pathogenic APOE variants had significantly higher triglyceride levels (median 5.01 mmol/L) than carriers of benign or likely benign variants (median 1.70 mmol/L, p = 0.034) and variants of uncertain significance (median 1.38 mmol/L, p = 0.036). For the first time, we estimated the expected prevalence of causal variants for autosomal dominant FD in the population sample: 0.27% (one in 619). Investigating the spectrum of APOE variants may advance our understanding of the genetic basis of FD and underscore the importance of APOE gene sequencing in patients with lipid metabolism disorders.

Post-transcriptional regulation supports the homeostatic expression of mature RNA

Gene expression regulation is a sophisticated, multi-stage process, and its robustness is critical to normal cell function and the survival of an organism. Previous studies indicate that differential gene expression at the RNA level is typically attenuated at the protein level through translational regulation. However, how post-transcriptional regulation (PTR) influences expression change during the RNA maturation process remains unclear. In this study, we investigated this by quantifying the magnitude of expression change in precursor RNA and mature RNA across a vast range of different biological conditions. We analyzed bulk tissue RNA sequencing data from 4689 samples, including healthy and diseased tissues from human, chimpanzee, rhesus macaque, and murine sources. We demonstrated that PTR tends to support homeostatic expression of mature RNA by amplifying normal tissue-specific expression of precursor RNA, while reducing expression change of precursor RNA in disease contexts. Our study provides insight into the general influence of PTR on gene expression homeostasis. Our analysis also suggests that intronic reads in RNA-seq studies may contain under-utilized information about disease associations. Additionally, our findings may assist in identifying new disease biomarkers and more effective ways of altering gene expression as a therapeutic strategy.

Predicting the therapeutic role and potential mechanisms of Indole-3-acetic acid in diminished ovarian reserve based on network pharmacology and molecular docking

BACKGROUND

Indole-3-acetic acid (IAA), an indole analog produced by intestinal microorganisms metabolizing tryptophan, has anti-inflammatory and antioxidant properties and thus has potential applications in ovarian protection, although the exact mechanism is unknown. The present study preliminarily investigated the pharmacological mechanism of IAA in alleviating diminished ovarian reserve (DOR) by network pharmacology and molecular docking.

METHODS

Relevant target proteins of IAA were searched in SwissTargetPrediction, PharmMapper, TargetNet, BATMAN-TCM, and SuperPred databases. The potential targets of DOR were obtained from GeneCards, DisGenet, OMIM, and Drugbank databases. Both common targets were then imported into the String website to construct a PPI network, and these targets were analyzed for GO and KEGG enrichment. Finally, we utilized molecular docking to validate the possible binding conformations between IAA and the candidate targets. We used in vitro experiments to preliminarily investigate the effects of IAA on DOR.

RESULTS

We obtained 88 potential targets for IAA and DOR interaction. We received 16 pivotal targets by constructed protein interaction screening. KEGG enrichment analysis mainly included the AGE-RAGE signaling pathway, IL-17 signaling pathway, Chemical carcinogenesis-reactive oxygen species in diabetic complications, etc. GO functional analysis showed that IAA treatment of DOR may involve biological processes such as response to external stimuli, hypoxia, gene expression, and regulation of enzyme activity. Molecular docking and in vitro experiments further revealed the potential effects of IAA on MMP2, TNF-α, AKT1, HSP90AA1, and NF-κ B.

CONCLUSION

We preliminarily revealed the potential protective effects of IAA against DOR through multiple targets and pathways, which provides a new research strategy for the molecular mechanism of IAA to alleviate DOR in the future. However, further studies need to demonstrate whether IAA can be used as a compound to prevent and treat DOR.

Network Pharmacology and Molecular Docking Reveal Anti-Asthmatic Potential of Zephyranthes rosea Lindl. in an Ovalbumin-Induced Asthma Model

Background: This study aimed to evaluate the anti-inflammatory effects of a Zephyranthes rosea in an ovalbumin-induced asthma model. Methods: Allergic asthma was induced in mice via intraperitoneal injection, followed by intranasal ovalbumin challenge. Methanolic extract of Z. rosea bulb was orally administered to asthmatic mice for 14 days. Hematological parameters for bronchoalveolar lavage fluid (BALF) and blood were analyzed. The mRNA expression levels of interleukins and transforming growth factor beta (TGF-β1) in lung tissues were determined using reverse transcriptase-polymerase chain reaction (RT-PCR). Network pharmacology analysis was used to find possible Z. rosea targets. After building a protein-protein interaction network to find hub genes, GO and KEGG enrichment analyses were carried out to determine the potential mechanism. In silico analysis was performed by Molecular Operating Environment. Results: GC-MS analysis of Z. rosea extract detected major classes of phytochemicals. Hematological parameters in blood and BALF from Z. rosea extract-treated animals were significantly reduced in a dose-dependent fashion. Histopathology revealed that Z. rosea bulb had an ameliorative effect on lung tissues. Moreover, treatment with Z. rosea bulb extract significantly restored the normal levels of IL-4, IL-6, IL-1β, IL-10, IL-13, and TGF-β1 in allergic asthmatic mice compared to the diseased group. In silico analysis, particularly of the binding affinities of Z. rosea bulb phytoconstituents for IL6, AKT1, and Src, supported in vivo results. Conclusions: These findings indicated that Z. rosea bulb extract significantly ameliorates cellular and molecular biomarkers of bronchial inflammation and could be a potential candidate for treating allergic asthma.

Protective effect of Shenqi Wenfei Formula against lipopolysaccharide/cigarette smoke-induced COPD in Rat based on gut microbiota and network pharmacology analysis

Introduction

The incidence of chronic obstructive pulmonary disease (COPD) appears to be increasing and evidence suggests that the intestinal flora may play a causative role in its development. Previous studies found that the Shenqi Wenfei Formula (SQWF) can regulate pyroptosis via the NLRP3/GSDMD pathway, thereby reducing the inflammatory response in the lungs of COPD model rats. However, there is no information on whether the drug's effects are associated with intestinal flora. Therefore, this study investigates whether the effects of SQWF are mediated through the regulation of intestinal flora, aiming to elucidate the underlying mechanisms of its therapeutic impact on COPD.

Methods

COPD was induced in rats using lipopolysaccharide and cigarette smoke, followed by intragastric administration of SQWF or physiological saline The targets of SQWF, associated signaling pathways, and key bacterial groups were investigated using 16S rRNA sequencing, network pharmacology, and bioinformatics techniques. The prediction results were validated using quantitative reverse transcription PCR, western blotting, and immunofluorescence, among other methods.

Results

SQWF treatment was found to alleviate COPD in model rats. Treatment was also observed to restore the balance of the intestinal flora in the rats, especially by reducing the abundance of g_Parabacteroides. Bioinformatics predictions identified g_Parabacteroides metabolites, RelA, HDAC1, and enriched neutrophil extracellular trap formation pathways as core targets of SQWF in COPD. qRT-PCR and Western blotting results showed that SQWF treatment reduced ReLA and HDAC1 mRNA and protein expression, along with decreased myeloperoxidase and neutrophil elastase levels in the nucleus.

Conclusion

Treatment with SQWF was found to restore the imbalance of intestinal g_Parabacteroides in COPD and also regulate the expression of the ReLA and HDAC1 genes, thereby reducing pulmonary neutrophil extracellular traps and alleviating lung inflammation.

Integrated systems pharmacology, molecular docking, and MD simulations investigation elucidating the therapeutic mechanisms of BHD in Alzheimer's disease treatment

Alzheimer's disease (AD) poses a longstanding health challenge, prompting a century-long exploration into its etiology and progression. Despite significant advancements in medical science, current AD treatments provide only symptomatic relief, urging a shift towards innovative paradigms. This study, departing from the amyloid hypothesis, integrates Systems Pharmacology, Molecular Docking and Molecular Dynamic Simulations to investigate a polyherbal phytoformulation (US 7,273,626 B2) rooted in Ayurveda for AD, consisting of Bacopa monnieri, Hippophae rhamnoides, and Dioscorea bulbifera (BHD). Diosgenin emerges as a crucial compound, aligning with previous studies, yet recognizing its limitations in explaining BHD's mechanism, this research delves into the intricate network of interactions. Protein-Protein Interaction (PPI) network analysis identifies hub genes (ALOX5, GSK3B, ACHE, SRC, AKT1, EGFR, PIK3R1, ESR1 and APP), suggesting a systems-level modulation of AD. Enrichment analyses unveil 370 AD-associated genes and key terms like "Cellular Response to Chemical Stimulus" and "Regulation of Biological Quality." KEGG pathway analysis underscores BHD's potential in Alzheimer's disease pathway (hsa05010), Endocrine resistance (hsa01522), and PI3K-Akt signaling (hsa04151). Molecular docking, carefully selecting compounds (Kaempferol, Quercetin, Myricetin, Isorhamnetin, Beta-Sitosterol, Stigmasterol, Emodin and Diosgenin) and top modulated targets, validates interactions with high dock scores, providing promising therapeutic avenues. Two core targets, Acetylcholinesterase (AChE) and Estrogen Receptor 1 (ESR1), were identified for further investigation due to their critical roles in Alzheimer's disease. To validate the molecular docking results, Molecular Dynamics (MD) simulations were performed on the AChE complexes with Myricetin, Beta-Sitosterol, and Stigmasterol, as well as the ESR1 complexes with Emodin, Diosgenin, and Beta-Sitosterol. These simulations were then compared to the interactions observed with the marketed drugs Donepezil and Estradiol, which are commonly used in Alzheimer's treatment. The MD simulations provided detailed insights into the stability and behavior of these complexes over time. The findings indicated that Myricetin and Emodin not only maintained stable interactions with AChE and ESR1 but also exhibited greater stability than Donepezil and Estradiol at specific time points and protein regions, as demonstrated by lower RMSD and RMSF values. These results suggest that natural compounds hold promise as potential therapeutic agents in the treatment of Alzheimer's disease, offering new avenues for drug development, while the formulation BHD shows potential as an adjuvant in integrative medicine alongside standard Alzheimer's treatments, effectively targeting related pathways and genes.

The effect of ethanol extracts of loulu flower on LPS-induced acute lung injury in mice

ETHNOPHARMACOLOGICAL RELEVANCE

In Mongolian medicine, Loulu flower (LLF), the dried inflorescence of Rhaponticum uniflorum (L.) DC. from the Compositae family, has been used to clear heat and relieve toxicity for millennia, particularly in the treatment of pneumonia.

AIM OF THIS STUDY

To reveal the effects of LLF on mice with lipopolysaccharide (LPS)-stimulated acute lung injury (ALI) and elucidate the underlying mechanisms.

MATERIALS AND METHODS

ALI was established in BALB/c mice via nasal drops administration of LPS (5 mg/kg). The mice were then orally administrated with various doses of LLF extracts and the positive drug dexamethasone (DEX, 5 mg/kg), once daily for seven consecutive days. Last day, after being stimulated with LPS for 6h, the mice were closed dislocation of cervical vertebra, the serum, bronchus alveolar lavage fluid (BALF) and lung tissue were put into the EP tube and stored at -80 °C for further analysis. The changes of histopathology were tested by hematoxylin and eosin stain (H&E), the levels of, IL-1β, IL-18, TNF-α and IL-4 in BALF and serum were measured by ELISA. The pathways related to the treatment of ALI were predicted by network pharmacology. The expression levels of TLR4/NF-κB and NLRP3 signaling pathway-associated proteins, COX-2 and ERK were tested by western blotting. The levels of P65 and NLRP3 in lung tissues were determined by immunofluorescence analysis.

RESULTS

LLF total extract and the extract parts could alleviate the inflammatory cell infiltration, thicken the alveolar walls in lung tissues, reduce the levels of IL-18, IL-1β in BALF, the TNF-α in both BALF and serum, meantime enhance the level of IL-4 in BALF and serum in mice with LPS-induced ALI. Our network pharmacology and comprehensive gene ontology analyses revealed the active constituents of LLF and the pathways, including TLR4/NF-κB, NLRP3 and MAPK signaling pathways, which play significant roles in ALI. Furthermore, both the total extract and its extraction portions suppressed the expressions of proteins related with the COX-2, p-ERK and TLR4/NF-κB signaling pathway (TLR4, p-IκB, p-p65), as well as the NLRP3 signaling pathway (NLRP3, cleaved caspase-1, caspase-1, IL-1β).

CONCLUSION

LLF could improve the pathological changes and reducing inflammatory reactions in mice induced by LPS. The mechanism may be related to the modulation of the TLR4/NLRP3 signaling pathways.

The role and mechanism of "eight famous herbals in Zhejiang" in cancer via network pharmacology and experimental validation

In recent years, some components and active ingredients from the herbal formula "eight famous herbals in Zhejiang" (Zhe-Ba-Wei) have been reported to possess antitumor properties. However, there is still no systemic study on the role and mechanism of Zhe-Ba-Wei in cancer. To systematically investigate the anticancer efficacy of Zhe-Ba-Wei, we first identified 17 reported active ingredients with gene targets associated with various types of tumors. Second, we screened these active ingredients and their responding multiple shared targets by analyzing the convergence of diverse and tumor-specific target sites and identified four crucial active ingredients (ferulic acid, quercetin, rutin, luteolin), which were characterized by 27 overlapping gene targets. Third, these 27 gene targets were subsequently mapped onto the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology term, and among the 27 total potential targets, 12 were involved in plasma membrane function. Fourth, we investigated the binding affinities between the four crucial active ingredients and their potential targets such as EGFR and MET, both of which are well-known oncogenes in various cancers. Subsequently, an investigation of the computational ADMET properties showed that most of these four ingredients exhibited good ADMET properties. Finally, we found that three active ingredients (ferulic acid, luteolin, and quercetin) could inhibit the proliferation of non-small cell lung cancer cells and decrease the protein expression of EGFR in a concentration-dependent manner. All these results shed light on the bioactive components, pharmacological effects, and drug development and utilization of Zhe-Ba-Wei, aiming to provide useful support for its further research and clinical application.

Uncovering the protective mechanism of baicalin in treatment of fatty liver based on network pharmacology and cell model of NAFLD

Excessive nonesterified fatty acids (NEFA) impair cellular metabolism and will induce fatty liver formation in dairy cows during the periparturient. Baicalin, an active flavonoid, has great potential efficacy in alleviating lipid accumulation and ameliorating the development of fatty liver disease. Nevertheless, its mechanism remains unclear. Here, the potential mechanism of baicalin on system levels was explored using network pharmacology and in vitro experiments. Firstly, the target of baicalin and fatty liver disease was predicted, and then the protein-protein interaction (PPI) network was constructed. In addition, the Kyoto Encyclopedia of Genes and Genomes (KEGG) (q-value) pathway enrichment is performed through the Database for Annotation, Visualization, and Integrated Discovery (DAVID) server. Finally, the results of the network analysis of the in vitro treatment of bovine hepatocytes by NEFA were confirmed. The results showed that 33 relevant targets of baicalin in the treatment of liver fatty were predicted by network pharmacology, and the top 20 relevant pathways were extracted by KEGG database. Baicalin treatment can reduce triglyceride (TAG) content and lipid droplet accumulation in NEFA-treated bovine hepatocytes, and the mechanism is related to inhibiting lipid synthesis and promoting lipid oxidation. The alleviating effect of baicalin on fatty liver may be related to the up-regulation of solute vector family member 4 (SLC2A4), Down-regulated AKT serine/threonine kinase 1 (AKT1), Peroxisome proliferator-activated receptor gamma (PPARG), Epidermal growth factor receptor (EGFR), tumor necrosis factor (TNF), Interleukin 6 (IL-6) were associated. These results suggested that baicalin may modulate key inflammatory markers, and lipogenesis processes to prevent fatty liver development in dairy cows.

Integrating Metabolomic Analysis, Network Pharmacology, and Molecular Docking to Underlying Pharmacological Mechanism and Ethnobotanical Rationalization for Diabetes Mellitus: Study on Medicinal Plant Fibraurea tinctoria Lour

INTRODUCTION

Fibraurea tinctoria Lour. has long been used in traditional medicine to treat diabetes mellitus (DM). However, a comprehensive scientific understanding of its potential active compounds and underlying pharmacological mechanisms still needs to be unveiled.

OBJECTIVE

This study, therefore, presents a novel approach by integrating metabolomic profiling, pharmacological network, and molecular docking analysis to investigate the potential of F. tinctoria as antidiabetes mellitus.

METHODS

Active compounds were obtained through analysis using ultrahigh-performance liquid chromatography-quadrupole-orbital ion trap-high resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS) and screening of active compounds using Lipinski rule of five and ADMET parameters. Potential targets of F. tinctoria compounds and DM-related targets were retrieved from public databases, such as DisGeNET, GeneCards, OMIM, PharmaGKB, and TTD. The targets' gene ontology (GO) was created using DAVID and protein-protein interactions using STRING. The plant-organ-compound-target-disease network was constructed using Cytoscape. Then, molecular docking analysis predicted and verified the interactions of essential bioactive compounds of F. tinctoria and DM core targets.

RESULTS

The network pharmacology approach identified 35 active compounds, 565 compound-related targets, and 17,289 DM-related targets. EGFR, HSP90AA1, ESR1, HSP90AB1, and GSK3B were the core targets, whereas isolariciresinol, cubebin, corypalmine, (-)-8-oxocanadine, and (+)-N-methylcoclaurine were the most active compounds of F. tinctoria with DM potential. GO functional enrichment analysis revealed 483 biological processes, 485 cellular components, and 463 molecular functions. REACTOME pathway enrichment analysis yielded 463 significantly enriched signaling pathways. Of these pathways, the cytokine signaling in the immune system pathway may play a key role in treating DM. The results of molecular docking analysis showed that the core targets of DM, such as 5gnk, 3o0i, 6psj, 5ucj, and 1q5k, bind stably to the analyzed bioactive compounds of F. tinctoria.

CONCLUSIONS

This study provides significant insights into the potential mechanism of F. tinctoria in treating DM. The main active compounds of F. tinctoria were found to interact with the core targets (EGFR, HSP90AA1, ESR1, HSP90AB1, and GSK3B) through the cytokine signaling pathway in the immune system, suggesting a potential therapeutic pathway for DM. However, it is essential to note that these findings are preliminary, and further research is necessary to validate them. Those research studies could involve in vitro and in vivo studies to confirm the bioactivity of the identified compounds and their interactions with the core targets. When the findings are confirmed, they could have significant clinical implications, potentially leading to developing new therapeutic strategies for DM.

Exploring the active ingredients and mechanisms of Liujunzi decoction in treating hepatitis B: a study based on network pharmacology, molecular docking, and molecular dynamics simulations

Liujunzi decoction (LJZD) is commonly used to treat hepatitis B virus (HBV), though its active ingredients and mechanisms are not fully known. This study identified core targets and active components of LJZD for treating hepatitis B (HB) through network pharmacology, molecular docking, and molecular dynamics simulation. Screening from databases yielded 533 active components, 2619 targets for LJZD, and 2910 for HB, with 891 intersecting targets. STRING and CytoHubba analyses identified AR and VDR as core targets, with key pathways including PI3K-Akt and MAPK. The findings clarify LJZD's multicomponent, multitarget mechanisms, supporting its clinical application for HB treatment.

Genome-wide screening reveals essential roles for HOX genes and imprinted genes during caudal neurogenesis of human embryonic stem cells

Mapping the essential pathways for neuronal differentiation can uncover new therapeutics and models for neurodevelopmental disorders. We thus utilized a genome-wide loss-of-function library in haploid human embryonic stem cells, differentiated into caudal neuronal cells. We show that essential genes for caudal neurogenesis are enriched for secreted and membrane proteins and that a large group of neurological conditions, including neurodegenerative disorders, manifest early neuronal phenotypes. Furthermore, essential transcription factors are enriched with homeobox (HOX) genes demonstrating synergistic regulation and surprising non-redundant functions between HOXA6 and HOXB6 paralogs. Moreover, we establish the essentialome of imprinted genes during neurogenesis, demonstrating that maternally expressed genes are non-essential in pluripotent cells and their differentiated germ layers, yet several are essential for neuronal development. These include Beckwith-Wiedemann syndrome- and Angelman syndrome-related genes, for which we suggest a novel regulatory pathway. Overall, our work identifies essential pathways for caudal neuronal differentiation and stage-specific phenotypes of neurological disorders.

Interpretation knowledge extraction for genetic testing via question-answer model

BACKGROUND

Sequencing-based genetic testing is widely used in biomedical research, including pathogenic microorganism detection with metagenomic next-generation sequencing (mNGS). The application of sequencing results to clinical diagnosis and treatment relies on various interpretation knowledge bases. Currently, the existing knowledge bases are primarily built through manual knowledge extraction. This method requires professionals to read extensive literature and extract relevant knowledge from it, which is time-consuming and costly. Furthermore, manual extraction unavoidably introduces subjective biases. In this study, we aimed to automatically extract knowledge for interpreting mNGS results.

METHOD

We propose a novel approach to automatically extract pathogenic microorganism knowledge based on the question-answer (QA) model. First, we construct a MicrobeDB dataset since there is no available pathogenic microorganism QA dataset for training the model. The created dataset contains 3,161 samples from 618 published papers covering 224 pathogenic microorganisms. Then, we fine-tune the selected baseline model based on MicrobeDB. Finally, we utilize ChatGPT to enhance the diversity of training data, and employ data expansion to increase training data volume.

RESULTS

Our method achieves an Exact Match (EM) and F1 score of 88.39% and 93.18%, respectively, on the MicrobeDB test set. We also conduct ablation studies on the proposed data augmentation method. In addition, we perform comparative experiments with the ChatPDF tool based on the ChatGPT API to demonstrate the effectiveness of the proposed method.

CONCLUSIONS

Our method is effective and valuable for extracting pathogenic microorganism knowledge.

Phenotypic spectrum of dual diagnoses in developmental disorders

As more patients receive genome-wide sequencing, the number of individuals diagnosed with multiple monogenic conditions is increasing. We sought to investigate the relative phenotypic contribution of dual diagnoses using both manual curation and computational approaches. First, we computed 1,003,236 semantic similarity scores for all possible pairs of 1,417 genes in the Developmental Disorder Gene2Phenotype (DDG2P) database using Human Phenotype Ontology terms. Next, for 62 probands with two molecular diagnoses in the Deciphering Developmental Disorders study, we computed semantic similarity scores between the probands' phenotypes and DDG2P phenotypes associated with the two disorders and compared the results with manual attribution of proband phenotypes to none, one, or both of the genes. We found a spectrum of phenotypic similarity for dual diagnoses, both across all DDG2P genes and within dual diagnosed probands, from phenotypically distinct through blended to indistinguishable conditions. Pairwise semantic similarity scores between two DDG2P genes were a good predictor of the extent of phenotypic blending observed in probands. Dual diagnoses involving genes linked with synergistic phenotypes can result in more extreme presentations while those involving antagonistic phenotypes have spuriously high pairwise semantic similarity scores despite a potentially milder atypical presentation. We suggest that the phenotypic contribution of two molecular diagnoses may contain discrete, synergistic, or antagonistic elements. Conceptual recognition of this phenotypic spectrum is important for making a final clinico-molecular diagnosis and providing accurate genetic counseling.

Causal relationships between diseases mined from the literature improve the use of polygenic risk scores

MOTIVATION

Identifying causal relations between diseases allows for the study of shared pathways, biological mechanisms, and inter-disease risks. Such causal relations can facilitate the identification of potential disease precursors and candidates for drug re-purposing. However, computational methods often lack access to these causal relations. Few approaches have been developed to automatically extract causal relationships between diseases from unstructured text, but they are often only focused on a small number of diseases, lack validation of the extracted causal relations, or do not make their data available.

RESULTS

We automatically mined statements asserting a causal relation between diseases from the scientific literature by leveraging lexical patterns. Following automated mining of causal relations, we mapped the diseases to the International Classification of Diseases (ICD) identifiers to allow the direct application to clinical data. We provide quantitative and qualitative measures to evaluate the mined causal relations and compare to UK Biobank diagnosis data as a completely independent data source. The validated causal associations were used to create a directed acyclic graph that can be used by causal inference frameworks. We demonstrate the utility of our causal network by performing causal inference using the do-calculus, using relations within the graph to construct and improve polygenic risk scores, and disentangle the pleiotropic effects of variants.

AVAILABILITY AND IMPLEMENTATION

The data are available through https://github.com/bio-ontology-research-group/causal-relations-between-diseases.

UHPLC-Q-Orbitrap HRMS and network analysis to explore the mechanisms of QiShenYiQi dripping pill for treating myocardial infarction

This study focused on examining the protection of QiShenYiQi dripping pills (QSYQ) against myocardial infarction (MI) and investigating its potential mechanisms. Ultra high performance liquid chromatography-q exactive-orbitrap high resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS) was employed to analyze potential active compounds of QSYQ. The targets of these compounds were predicted using an integrated in silico method and cross-referenced with relevant databases to identify associated pathways. Experimental validation was then conducted to confirm the accuracy of the systems pharmacology findings. In the end, network analysis combined with UHPLC screened 13 potential active compounds and obtained 99 targets for the intersection of potential active compounds and diseases. The enrichment analysis results indicated that the cyclic guanosine monophosphate-protein kinase G (cGMP-PKG) signaling pathway might be the mechanism of action of QSYQ in the treatment of MI. Experimental verification demonstrated that QSYQ could alleviate oxidative stress, promote vasodilation, and activate proteins related to the mitochondrial ATP-sensitive potassium channel (KATP) and nitric oxide (NO)-cGMP-PKG signaling pathway. This study provides insights into both the pathogenic mechanisms underlying MI and the molecular mechanisms through which QSYQ may confer protection. Given the role of PKG in regulating myocardial stiffness, it emerges as a promising therapeutic target for myocardial remodeling. We propose that the NO-cGMP-PKG and mitochondrial KATP pathways may serve as candidate therapeutic targets for the development of new interventions for MI.

Exploring genetic testing requests, genetic alterations and clinical associations in a cohort of children with autism spectrum disorder

Several studies show great heterogeneity in the type of genetic test requested and in the clinicopathological characteristics of patients with ASD. The following study aims, firstly, to explore the factors that might influence professionals' decisions about the appropriateness of requesting genetic testing for their patients with ASD and, secondly, to determine the prevalence of genetic alterations in a representative sample of children with a diagnosis of ASD. Methods: We studied the clinical factors associated with the request for genetic testing in a sample of 440 children with ASD and the clinical factors of present genetic alterations. Even though the main guidelines recommend genetic testing all children with an ASD diagnosis, only 56% of children with an ASD diagnosis were genetically tested. The prevalence of genetic alterations was 17.5%. These alterations were more often associated with intellectual disability and dysmorphic features. There are no objective data to explicitly justify the request for genetic testing, nor are there objective data to justify requesting one genetic study versus multiple studies. Remarkably, only 28% of males were genetically tested with the recommended tests (fragile X and CMA). Children with dysmorphic features and organic comorbidities were more likely to be genetic tested than those without. Previous diagnosis of ASD (family history of ASD) and attendance at specialist services were also associated with Genetically tested Autism Spectrum Disorder GTASD. Our findings emphasize the importance of establishing algorithms to facilitate targeted genetic consultation for individuals with ASD who are likely to benefit, considering clinical phenotypes, efficiency, ethics, and benefits.

Mutational landscape of risk variants in comorbid depression and obesity: a next-generation sequencing approach

Major depression (MD) and obesity are complex genetic disorders that are frequently comorbid. However, the study of both diseases concurrently remains poorly addressed and therefore the underlying genetic mechanisms involved in this comorbidity remain largely unknown. Here we examine the contribution of common and rare variants to this comorbidity through a next-generation sequencing (NGS) approach. Specific genomic regions of interest in MD and obesity were sequenced in a group of 654 individuals from the PISMA-ep epidemiological study. We obtained variants across the entire frequency spectrum and assessed their association with comorbid MD and obesity, both at variant and gene levels. We identified 55 independent common variants and a burden of rare variants in 4 genes (PARK2, FGF21, HIST1H3D and RSRC1) associated with the comorbid phenotype. Follow-up analyses revealed significantly enriched gene-sets associated with biological processes and pathways involved in metabolic dysregulation, hormone signaling and cell cycle regulation. Our results suggest that, while risk variants specific to the comorbid phenotype have been identified, the genes functionally impacted by the risk variants share cell biological processes and signaling pathways with MD and obesity phenotypes separately. To the best of our knowledge, this is the first study involving a targeted sequencing approach toward the study of the comorbid MD and obesity. The framework presented here allowed a deep characterization of the genetics of the co-occurring MD and obesity, revealing insights into the mutational and functional profile that underlies this comorbidity and contributing to a better understanding of the relationship between these two disabling disorders.

Insights into the Therapeutic Potential of Active Ingredients of Citri Reticulatae Pericarpium in Combatting Sarcopenia: An In Silico Approach

Sarcopenia is a systemic medical disorder characterized by a gradual decline in muscular strength, function, and skeletal muscle mass. Currently, there is no medication specifically approved for the treatment of this condition. Therefore, the identification of new pharmacological targets may offer opportunities for the development of novel therapeutic strategies. The current in silico study investigated the active ingredients and the mode of action of Citri Reticulatae Pericarpium (CRP) in addressing sarcopenia. The active ingredients of CRP and the potential targets of CRP and sarcopenia were determined using various databases. The STRING platform was utilized to construct a protein-protein interaction network, and the key intersecting targets were enriched through the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. Molecular docking was used to determine the binding interactions of the active ingredients with the hub targets. The binding affinities obtained from molecular docking were subsequently validated through molecular dynamics simulation analyses. Five active ingredients and 45 key intersecting targets between CRP and sarcopenia were identified. AKT1, IL6, TP53, MMP9, ESR1, NFKB1, MTOR, IGF1R, ALB, and NFE2L2 were identified as the hub targets with the highest degree node in the protein-protein interaction network. The results indicated that the targets were mainly enriched in PIK3-AKT, HIF-1, and longevity-regulating pathways. The active ingredients showed a greater interaction affinity with the hub targets, as indicated by the results of molecular docking and molecular dynamics simulations. Our findings suggest that the active ingredients of Citri Reticulatae Pericarpium, particularly Sitosterol and Hesperetin, have the potential to improve sarcopenia by interacting with AKT1 and MTOR proteins through the PI3K-AKT signaling pathway.

MeHA: A Computational Framework in Revealing the Genetic Basis of Animal Mental Health Traits Under an Intensive Farming System-A Case Study in Pigs

Intensively farmed animals such as pigs inevitably experience a certain degree of psychological stress, which leads to a reduction in production performance. Mental health traits are currently difficult to measure, resulting in a gap in understanding their genetic basis. To address this challenge, we propose a computational framework called mental health of animals (MeHA), capable of revealing genes related to animal mental health traits. Using MeHA, we identified 109 candidate genes associated with pig mental health and discovered their intricate connections with critical functions, such as memory, cognition, and neural development, which are essential components of mental health and cognitive performance. Importantly, our findings provide evidence of the potential impact of these genes on economically important traits, including meat quality and piglet survival. This research underscores the importance of genetic studies in enhancing our understanding of animal behavior and cognition, as well as promoting agricultural practices. By applying our approach to study the genetic basis of mental health in pigs as a case, we confirmed that our framework is an effective way to reveal genetic factors affecting animal mental health traits, which contributes to animal welfare and has potential implications for understanding human mental disorders.

Analyses of GWAS signal using GRIN identify additional genes contributing to suicidal behavior

Genome-wide association studies (GWAS) identify genetic variants underlying complex traits but are limited by stringent genome-wide significance thresholds. We present GRIN (Gene set Refinement through Interacting Networks), which increases confidence in the expanded gene set by retaining genes strongly connected by biological networks when GWAS thresholds are relaxed. GRIN was validated on both simulated interrelated gene sets as well as multiple GWAS traits. From multiple GWAS summary statistics of suicide attempt, a complex phenotype, GRIN identified additional genes that replicated across independent cohorts and retained biologically interrelated genes despite a relaxed significance threshold. We present a conceptual model of how these retained genes interact through neurobiological pathways that may influence suicidal behavior, and identify existing drugs associated with these pathways that would not have been identified under traditional GWAS thresholds. We demonstrate GRIN's utility in boosting GWAS results by increasing the number of true positive genes identified from GWAS results.

Network Pharmacology Combined with Experimental Validation to Investigate the Mechanism of the Anti-Hyperuricemia Action of Portulaca oleracea Extract

BACKGROUND/OBJECTIVES

Hyperuricemia (HUA) is a common metabolic disease caused by purine metabolic disorders in the body. Portulaca oleracea L. (PO) is an edible wild vegetable.

METHODS

In this study, the regulatory effect of PO on HUA and its potential mechanism were initially elucidated through network pharmacology and experimental validation.

RESULTS

The results showed that PO from Sichuan province was superior to the plant collected from other habitats in inhibiting xanthine oxidase (XOD) activity. Berberine and stachydrine were isolated and identified from PO for the first time by UPLC-Q-Exactive Orbitrap MS. The potential molecular targets and related signaling pathways were predicted by network pharmacology and molecular docking techniques. Molecular docking showed that berberine had strong docking activity with XOD, and the results of in vitro experiments verified this prediction. Through experimental analysis of HUA mice, we found that PO can reduce the production of uric acid (UA) in the organism by inhibiting XOD activity. On the other hand, PO can reduce the body 's reabsorption of urate and aid in its excretion out of the body by inhibiting the urate transporter proteins (GLUT9, URAT1) and promoting the high expression of urate excretory protein (ABCG2). The results of H/E staining showed that, compared with the positive drug (allopurinol and benzbromarone) group, there was no obvious renal injury in the middle- and high-dose groups of PO extract.

CONCLUSIONS

In summary, our findings reveal the potential of wild plant PO as a functional food for the treatment of hyperuricemia.

A validated heart-specific model for splice-disrupting variants in childhood heart disease

BACKGROUND

Congenital heart disease (CHD) is the most common congenital anomaly. Almost 90% of isolated cases have an unexplained genetic etiology after clinical testing. Non-canonical splice variants that disrupt mRNA splicing through the loss or creation of exon boundaries are not routinely captured and/or evaluated by standard clinical genetic tests. Recent computational algorithms such as SpliceAI have shown an ability to predict such variants, but are not specific to cardiac-expressed genes and transcriptional isoforms.

METHODS

We used genome sequencing (GS) (n = 1101 CHD probands) and myocardial RNA-Sequencing (RNA-Seq) (n = 154 CHD and n = 43 cardiomyopathy probands) to identify and validate splice disrupting variants, and to develop a heart-specific model for canonical and non-canonical splice variants that can be applied to patients with CHD and cardiomyopathy. Two thousand five hundred seventy GS samples from the Medical Genome Reference Bank were analyzed as healthy controls.

RESULTS

Of 8583 rare DNA splice-disrupting variants initially identified using SpliceAI, 100 were associated with altered splice junctions in the corresponding patient myocardium affecting 95 genes. Using strength of myocardial gene expression and genome-wide DNA variant features that were confirmed to affect splicing in myocardial RNA, we trained a machine learning model for predicting cardiac-specific splice-disrupting variants (AUC 0.86 on internal validation). In a validation set of 48 CHD probands, the cardiac-specific model outperformed a SpliceAI model alone (AUC 0.94 vs 0.67 respectively). Application of this model to an additional 947 CHD probands with only GS data identified 1% patients with canonical and 11% patients with non-canonical splice-disrupting variants in CHD genes. Forty-nine percent of predicted splice-disrupting variants were intronic and > 10 bp from existing splice junctions. The burden of high-confidence splice-disrupting variants in CHD genes was 1.28-fold higher in CHD cases compared with healthy controls.

CONCLUSIONS

A new cardiac-specific in silico model was developed using complementary GS and RNA-Seq data that improved genetic yield by identifying a significant burden of non-canonical splice variants associated with CHD that would not be detectable through panel or exome sequencing.

LDAGM: prediction lncRNA-disease asociations by graph convolutional auto-encoder and multilayer perceptron based on multi-view heterogeneous networks

BACKGROUND

Long non-coding RNAs (lncRNAs) can prevent, diagnose, and treat a variety of complex human diseases, and it is crucial to establish a method to efficiently predict lncRNA-disease associations.

RESULTS

In this paper, we propose a prediction method for the lncRNA-disease association relationship, named LDAGM, which is based on the Graph Convolutional Autoencoder and Multilayer Perceptron model. The method first extracts the functional similarity and Gaussian interaction profile kernel similarity of lncRNAs and miRNAs, as well as the semantic similarity and Gaussian interaction profile kernel similarity of diseases. It then constructs six homogeneous networks and deeply fuses them using a deep topology feature extraction method. The fused networks facilitate feature complementation and deep mining of the original association relationships, capturing the deep connections between nodes. Next, by combining the obtained deep topological features with the similarity network of lncRNA, disease, and miRNA interactions, we construct a multi-view heterogeneous network model. The Graph Convolutional Autoencoder is employed for nonlinear feature extraction. Finally, the extracted nonlinear features are combined with the deep topological features of the multi-view heterogeneous network to obtain the final feature representation of the lncRNA-disease pair. Prediction of the lncRNA-disease association relationship is performed using the Multilayer Perceptron model. To enhance the performance and stability of the Multilayer Perceptron model, we introduce a hidden layer called the aggregation layer in the Multilayer Perceptron model. Through a gate mechanism, it controls the flow of information between each hidden layer in the Multilayer Perceptron model, aiming to achieve optimal feature extraction from each hidden layer.

CONCLUSIONS

Parameter analysis, ablation studies, and comparison experiments verified the effectiveness of this method, and case studies verified the accuracy of this method in predicting lncRNA-disease association relationships.

Fisetin reduces ovalbumin-triggered airway remodeling by preventing phenotypic switching of airway smooth muscle cells

BACKGROUND

The transformation of airway smooth muscle cells (ASMCs) from a quiescent phenotype to a hypersecretory and hypercontractile phenotype is a defining feature of asthmatic airway remodeling. Fisetin, a flavonoid compound, possesses anti-inflammatory characteristics in asthma; yet, its impact on airway remodeling and ASMCs phenotype transition has not been investigated.

OBJECTIVES

This research seeked to assess the impact of fisetin on ovalbumin (OVA) induced asthmatic airway remodeling and ASMCs phenotype transition, and clarify the mechanisms through network pharmacology predictions as well as in vivo and in vitro validation.

METHODS

First, a fisetin-asthma-ASMCs network was constructed to identify potential targets. Subsequently, cellular and animal studies were carried out to examine the inhibitory effects of fisetin on airway remodeling in asthmatic mice, and to detemine how fisetin impacts the phenotypic transition of ASMCs.

RESULTS

Network analysis indicated that fisetin might affect asthma via mediating the phosphatidylinositol 3-kinase (PI3K)/ protein kinase B (AKT) pathway. Intraperitoneal administration of fisetin in vivo reduced airway inflammation and remodeling, as shown by reduced inflammatory cells, decreased T helper type 2 (Th2) cytokine release, diminished collagen accumulation, mitigated airway smooth muscle thickening, and decreased expression of osteopontin (OPN), collagen-I and α-smooth muscle actin (α-SMA). Moreover, fisetin suppressed the PI3K/AKT pathway in asthmatic lung tissue. According to the in vitro data, fisetin downregulated the expression of the synthetic phenotypic proteins OPN and collagen-I, contractile protein α-SMA, and inhibited cellular migration, potentially through the PI3K/AKT pathway.

CONCLUSION

These results suggest that fisetin inhibits airway remodeling in asthma by regulating ASMCs phenotypic shift, emphasizing that fisetin is a promising candidate for the treatment of airway smooth muscle remodeling.

Exploring the mechanism of Zhengxintai Formula for the treatment of coronary heart disease based on network pharmacology

Zhengxintai Formula (ZXT) has shown good effects in the clinical treatment of coronary atherosclerotic heart disease (CHD). However, its potential molecular mechanism for treating coronary heart disease is still unknown. The Traditional Chinese Medicine Systematic Pharmacology Database and Analysis Platform and literature reviews were used to determine the active components and targets of the 6 herbs used in ZXT. Next, we searched disease target databases for targets associated with CHD. Secondly, Cytoscape was used to map the "active compounds-target" network, "protein-protein interaction" network, and "compound-target-disease" network. After that, gene ontology analysis and the pathway analysis by the Kyoto Encyclopedia of Genes and Genomes were performed on the targets. Finally, molecular docking between the compounds and the targets was performed to verify their binding ability. The analysis obtained 116 active compounds of ZXT, corresponding to 611 targets. Thousand three hundred forty-five coronary heart disease targets were collected. Obtained 177 potential ZXT targets for coronary artery disease. Gene ontology analysis yielded 734 biological process entries, 84 cellular component entries, and 122 molecular function entries. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed the key pathways such as "Fluid shear stress and atherosclerosis," "Lipid and atherosclerosis", and "PI3K-Akt signaling pathway." The molecular docking results showed good binding between each screened core target and the core components. ZXT fulfills its role in the treatment of CHD through the core components and core targets that have been screened out, but the exact process still needs to be further investigated.

Incidental finding of a BRCA2 variant following whole genome sequencing to molecularly diagnose bilateral congenital cataracts

A male patient in his 20s with a history of bilateral congenital cataracts and nystagmus presented to the genetic eye disease clinic at Moorfields Eye Hospital to enquire about genetic testing for family decision-making and access to preimplantation genetic testing. He had a history of lensectomy with best-corrected visual acuities of logMAR 0.60 and 1.00 in the right and left eye. Whole genome sequencing (WGS) was conducted, which included targeted analysis of a panel of 115 lens-related genes and incidental findings, for which patients are unable to opt-out. Genetic testing identified the causative variant c.134T>C (p.Leu45Pro) in the CRYGC gene. A pathogenic variant in BRCA2 was also identified as a secondary finding. This was unexpected given the absence of a strong family history of breast or ovarian cancer. This case illustrates the importance of genetic counselling and informing patients about the implications of incidental findings that arise from WGS.

The power of hybridization capture - Illustrated using an expanded gene panel on 100 post mortem samples, focusing on sudden unexplained death

Sudden unexpected death (SUD) is an unexpected event that in many cases are caused by diseases with an underlying genetic background. Forensic molecular autopsy is an approach that has gained wide-spread attention, in part explained by the rapid progress of DNA sequencing techniques. The approach leverages genetic data in combination with medical autopsy findings in post-mortem samples to explore a potential underlying genetic cause of death. Traditional forensic approaches to molecular autopsy focus on a small panel of genes, say <200 genes, with strong association to heart conditions whereas clinical genetics tend to capture entire exomes while subsequently selecting targeted panels bioinformatically. The drop in price and the increased throughput has promoted wider exome sequencing as a viable method to discover genetic variants. We explore a targeted gene panel consisting of 2422 genes, selected based on their broad association to sudden unexplained death. A hybridization capture approach from Twist Bioscience based on double stranded DNA probes was used to target exons of the included genes. We selected and sequenced a total of 98 post-mortem samples from historical forensic autopsy cases where the cause of death could not be unambiguously determined based on medical findings and that had a previous negative molecular autopsy. In the current study, we focus on the performance of the hybridization capture technology on a 2422 gene panel and explore metrics related to sequencing success using a mid-end NextSeq 550 as well as a MiSeq FGx platform. With the latter we demonstrate that our sequence data benefits from 2×300 bp sequencing increasing coverage, in particular, for difficult regions where shadow coverage, i.e. regions outside the probes, are utilized. The results further illustrate a highly uniform capture across the panel of genes (mean fold80=1.5), in turn minimizing excessive sequencing costs to reach sufficient coverage, i.e. 20X. We outline a stepwise procedure to select genes associated with SUD through virtual bioinformatical panels extracting tier of genes with increasing strength of association to SUD. We propose some prioritization strategies to filter variants with highest potential and show that the number of high priority genetic variant requiring manual inspections is few (0-3 for all tiers of genes) when all filters are applied.