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

Convert-Pheno: A software toolkit for the interconversion of standard data models for phenotypic data

Efficient sharing and integration of phenotypic data is crucial for advancing biomedical research and enhancing patient outcomes in precision medicine and public health. To achieve this, the health data community has developed standards to promote the harmonization of variable names and values. However, the use of diverse standards across different research centers can hinder progress. Here we present Convert-Pheno, an open-source software toolkit that enables the interconversion of common data models for phenotypic data such as Beacon v2 Models, CDISC-ODM, OMOP-CDM, Phenopackets v2, and REDCap. Along with the software, we have created a detailed documentation that includes information on deployment and installation.

The Potential Mechanism of Liujunzi Decoction in the Treatment of Breast Cancer based on Network Pharmacology and Molecular Docking Technology

BACKGROUND

Liujunzi Decoction (LJZD) is a potential clinical treatment for Breast Cancer (BC), but the active ingredients and mechanisms underlying its effectiveness remain unclear.

OBJECTIVE

The study aimed to investigate the target gene of LJZD compatibility and the possible mechanism of action in the treatment of breast cancer by using network pharmacology and molecular docking.

METHODS

Based on TCMSP, ETCM, and BATMAN database searching and screening to obtain the ingredients of LJZD, the related targets were obtained. Breast cancer-related targets were collected through GEO, Geencards, OMIM, and other databases, and drug-disease Venn diagrams were drawn by R. The PPI network map was constructed by using Cytoscape. The intersecting targets were imported into the STRING database, and the core targets were analyzed and screened. The intersected targets were analyzed by the DAVID database for GO and KEGG enrichment. AutoDock Vina and Gromacs were used for molecular docking and simulation of the core targets and active ingredients.

RESULTS

126 active ingredients of LJZD were obtained; 241 targets related to breast cancer were sought after screening, and 180 intersection targets were identified through Venn diagram analysis. The core targets were FOS and ESR1. KEGG enrichment analysis mainly involved PI3K/Akt, MAPK, and other signaling pathways.

CONCLUSION

This study has explored the possible targets and signaling pathways of LJZD in treating breast cancer through network pharmacology and bioinformatics analysis. Molecular docking and simulation have further validated the potential mechanism of action of LJZD in breast cancer treatment, providing essential experimental data for future studies.

Exploring the Molecular Mechanism of Niuxi-Mugua Formula in Treating Coronavirus Disease 2019 via Network Pharmacology, Computational Biology, and Surface Plasmon Resonance Verification

BACKGROUND

In China, Niuxi-Mugua formula (NMF) has been widely used to prevent and treat coronavirus disease 2019 (COVID-19). However, the mechanism of NMF for treating COVID-19 is not yet fully understood.

OBJECTIVE

This study aimed to explore the potential mechanism of NMF for treating COVID- 19 by network pharmacology, computational biology, and surface plasmon resonance (SPR) verification.

MATERIALS AND METHODS

The NMF-compound-target network was constructed to screen the key compounds, and the Molecular Complex Detection (MCODE) tool was used to screen the preliminary key genes. The overlapped genes (OGEs) and the preliminary key genes were further analyzed by enrichment analysis. Then, the correlation analysis of immune signatures and the preliminary key genes was performed. Molecular docking and molecular dynamic (MD) simulation assays were applied to clarify the interactions between key compounds and key genes. Moreover, the SPR interaction experiment was used for further affinity kinetic verification.

RESULTS

Lipid and atherosclerosis, TNF, IL-17, and NF-kappa B signaling pathways were the main pathways of NMF in the treatment of COVID-19. There was a positive correlation between almost the majority of immune signatures and all preliminary key genes. The key compounds and the key genes were screened out, and they were involved in the main pathways of NMF for treating COVID-19. Moreover, the binding affinities of most key compounds binding to key genes were good, and IL1B-Quercetin had the best binding stability. SPR analysis further demonstrated that IL1B-Quercetin showed good binding affinity.

CONCLUSION

Our findings provided theoretical grounds for NMF in the treatment of COVID-19.

Network Pharmacology, Molecular Docking and Experimental Verification Revealing the Mechanism of Fule Cream against Childhood Atopic Dermatitis

BACKGROUND

The Fule Cream (FLC) is an herbal formula widely used for the treatment of pediatric atopic dermatitis (AD), however, the main active components and functional mechanisms of FLC remain unclear. This study performed an initial exploration of the potential acting mechanisms of FLC in childhood AD treatment through analyses of an AD mouse model using network pharmacology, molecular docking technology, and RNA-seq analysis.

MATERIALS AND METHODS

The main bioactive ingredients and potential targets of FLC were collected from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) and SwissTargetPrediction databases. An herb-compound-target network was built using Cytoscape 3.7.2. The disease targets of pediatric AD were searched in the DisGeNET, Therapeutic Target Database (TTD), OMIM, DrugBank and GeneCards databases. The overlapping targets between the active compounds and the disease were imported into the STRING database for the construction of the protein-protein interaction (PPI) network. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the intersection targets were performed, and molecular docking verification of the core compounds and targets was then performed using AutoDock Vina 1.1.2. The AD mouse model for experimental verification was induced by MC903.

RESULTS

The herb-compound-target network included 415 nodes and 1990 edges. Quercetin, luteolin, beta-sitosterol, wogonin, ursolic acid, apigenin, stigmasterol, kaempferol, sitogluside and myricetin were key nodes. The targets with higher degree values were IL-4, IL-10, IL-1α, IL-1β, TNFα, CXCL8, CCL2, CXCL10, CSF2, and IL-6. GO enrichment and KEGG analyses illustrated that important biological functions involved response to extracellular stimulus, regulation of cell adhesion and migration, inflammatory response, cellular response to cytokine stimulus, and cytokine receptor binding. The signaling pathways in the FLC treatment of pediatric AD mainly involve the PI3K-Akt signaling pathway, cytokine‒cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and NF-κB signaling pathway. The binding energy scores of the compounds and targets indicate a good binding activity. Luteolin, quercetin, and kaempferol showed a strong binding activity with TNFα and IL-4.

CONCLUSION

This study illustrates the main bioactive components and potential mechanisms of FLC in the treatment of childhood AD, and provides a basis and reference for subsequent exploration.

Exploration of the effect and mechanism of Scutellaria barbata D. Don in the treatment of ovarian cancer based on network pharmacology and in vitro experimental verification

The mortality rate of ovarian cancer is the highest among gynecological cancers, posing a serious threat to women health and life. Scutellaria barbata D. Don (SBD) can effectively treat ovarian cancer. However, its mechanism of action is unclear. The aim of this study was to elucidate the mechanism of SBD in the treatment of ovarian cancer using network pharmacology, and to verify the experimental results using human ovarian cancer SKOV3 cells. The Herb and Disease Gene databases were searched to identify common targets of SBD and ovarian cancer. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and Protein-Protein Interaction (PPI) network analyses were performed to identify the potential molecular mechanisms behind SBD. Finally, the molecular docking and main possible pathways were verified by experimental studies. Cell proliferation, the mRNA expression level of key genes and signaling pathway were all investigated and evaluated in vitro. A total of 29 bioactive ingredients and 137 common targets in SBD were found to inhibit ovarian cancer development. The active ingredients identified include quercetin, luteolin, and wogonin. Analysis of the PPI network showed that AKT1, VEGFA, JUN, TNF, and Caspase-3 shared centrality among all target genes. The results of the KEGG pathway analysis indicated that the cancer pathway, PI3K-Akt signaling pathway, and MAPK signaling pathways mediated the effects of SBD against ovarian cancer progression. Cell experiments showed that quercetin, luteolin, and wogonin inhibited the proliferation and clone formation of SKOV3 cells and regulated mRNA expression of 5 key genes by inhibiting PI3K/Akt signaling pathway. Our results demonstrate that SBD exerted anti-ovarian cancer effects through its key components quercetin, luteolin and wogonin. Mechanistically, its anti-cancer effects were mediated by inhibition of the PI3K/Akt signaling pathways. Therefore, SBD might be a candidate drug for ovarian cancer treatment.

Identifying the natural products in the treatment of atherosclerosis by increasing HDL-C level based on bioinformatics analysis, molecular docking, and in vitro experiment

BACKGROUND

Previous studies have demonstrated that high-density lipoprotein cholesterol (HDL-C) plays an anti-atherosclerosis role through reverse cholesterol transport. Several studies have validated the efficacy and safety of natural products in treating atherosclerosis (AS). However, the study of raising HDL-C levels through natural products to treat AS still needs to be explored.

METHODS

The gene sets associated with AS were collected and identified by differential gene analysis and database query. By constructing a protein-protein interaction (PPI) network, the core submodules in the network are screened out. At the same time, by calculating node importance (Nim) in the PPI network of AS disease and combining it with Kyoto Encyclopedia of genes and genomes (KEGG) pathways enrichment analysis, the key target proteins of AS were obtained. Molecular docking is used to screen out small natural drug molecules with potential therapeutic effects. By constructing an in vitro foam cell model, the effects of small molecules on lipid metabolism and key target expression of foam cells were investigated.

RESULTS

By differential gene analysis, 451 differential genes were obtained, and a total of 313 disease genes were obtained from 6 kind of databases, then 758 AS-related genes were obtained. The enrichment analysis of the KEGG pathway showed that the enhancement of HDL-C level against AS was related to Lipid and atherosclerosis, Cholesterol metabolism, Fluid shear stress and atherosclerosis, PPAR signaling pathway, and other pathways. Then we intersected 31 genes in the core module of the PPI network, the top 30 genes in Nims, and 32 genes in the cholesterol metabolism pathway, and finally found 3 genes. After the above analysis and literature collection, we focused on the following three related gene targets: APOA1, LIPC, and CETP. Molecular docking showed that Genistein has a good binding affinity for APOA1, CETP, and LIPC. In vitro, experiments showed that Genistein can up-regulated APOA1, LIPC, and CETP levels.

CONCLUSIONS

Based on our research, Genistein may have the effects of regulating HDL-C and anti-atherosclerosis. Its mechanism of action may be related to the regulation of LIPC, CETP, and APOA1 to improve lipid metabolism.

Evaluation of noninvasive biospecimens for transcriptome studies

Transcriptome studies disentangle functional mechanisms of gene expression regulation and may elucidate the underlying biology of disease processes. However, the types of tissues currently collected typically assay a single post-mortem timepoint or are limited to investigating cell types found in blood. Noninvasive tissues may improve disease-relevant discovery by enabling more complex longitudinal study designs, by capturing different and potentially more applicable cell types, and by increasing sample sizes due to reduced collection costs and possible higher enrollment from vulnerable populations. Here, we develop methods for sampling noninvasive biospecimens, investigate their performance across commercial and in-house library preparations, characterize their biology, and assess the feasibility of using noninvasive tissues in a multitude of transcriptomic applications. We collected buccal swabs, hair follicles, saliva, and urine cell pellets from 19 individuals over three to four timepoints, for a total of 300 unique biological samples, which we then prepared with replicates across three library preparations, for a final tally of 472 transcriptomes. Of the four tissues we studied, we found hair follicles and urine cell pellets to be most promising due to the consistency of sample quality, the cell types and expression profiles we observed, and their performance in disease-relevant applications. This is the first study to thoroughly delineate biological and technical features of noninvasive samples and demonstrate their use in a wide array of transcriptomic and clinical analyses. We anticipate future use of these biospecimens will facilitate discovery and development of clinical applications.

Discussion on the mechanism of Lingguizhugan Decoction in treating hypertension based on network pharmacology and molecular simulation technology

To explore the mechanism of Lingguizhugan Decoction in treating hypertension based on network pharmacology and molecular simulation. The active ingredients and potential targets were screened by the Systematic Pharmacological Analysis Platform of Traditional Chinese Medicine (TCMSP). Hypertension-related targets were obtained from OMIM and GeneCards databases. Common targets between drug and hypertension were screened in the Venny platform. A protein-protein interaction (PPI) network was constructed in the STRING database using intersection targets. Key targets in PPI network were analyzed by Cytoscape. R language program was used for Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Finally, the binding abilities of the main active ingredients to critical targets were verified by molecular simulation. Naringenin, quercetin, kaempferol, and β-sitosterol in Lingguizhugan Decoction, and potential targets such as STAT3, AKT1, TNF, IL6, JUN, PTGS2, MMP9, CASP3, TP53, and MAPK3, were screened out. KEGG Enrichment analysis revealed that the common targets of Lingguizhugan Decoction and hypertension are mainly involved in the lipid and atherosclerosis signaling pathway, AGE-RAGE signaling pathway in diabetic complications, fluid shear stress and atherosclerosis, and IL17 signaling pathway. The molecular simulation results showed that naringenin-MAPK3, quercetin-MMP9, quercetin-PTGS2, and quercetin-TP53 were the top four in the docking scores. Naringenin-MAPK3 and quercetin-MMP9 were stable, with binding free energies of -27.97 ± 1.41 kcal/mol and -21.15 ± 3.17 kcal/mol, respectively. The possible mechanism of Lingguizhugan Decoction in treating hypertension is characterized of multi-component, multi-target, and multi-pathway.Communicated by Ramaswamy H. Sarma.

AAAKB: A manually curated database for tracking and predicting genes of Abdominal aortic aneurysm (AAA)

Abdominal aortic aneurysm (AAA), an extremely dangerous vascular disease with high mortality, causes massive internal bleeding due to aneurysm rupture. To boost the research on AAA, efforts should be taken to organize and link the information about AAA-related genes and their functions. Currently, most researchers screen through genetic databases manually, which is cumbersome and time-consuming. Here, we developed "AAAKB" a manually curated knowledgebase containing genes, SNPs and pathways associated with AAA. In order to facilitate researchers to further explore the mechanism network of AAA, AAAKB provides predicted genes that are potentially associated with AAA. The prediction is based on the protein interaction information of genes collected in the database, and the random forest algorithm (RF) is used to build the prediction model. Some of these predicted genes are differentially expressed in patients with AAA, and some have been reported to play a role in other cardiovascular diseases, illustrating the utility of the knowledgebase in predicting novel genes. Also, AAAKB integrates a protein interaction visualization tool to quickly determine the shortest paths between target proteins. As the first knowledgebase to provide a comprehensive catalog of AAA-related genes, AAAKB will be an ideal research platform for AAA. Database URL: http://www.lqlgroup.cn:3838/AAAKB/.

Mapping protein states and interactions across the tree of life with co-fractionation mass spectrometry

We present CFdb, a harmonized resource of interaction proteomics data from 411 co-fractionation mass spectrometry (CF-MS) datasets spanning 21,703 fractions. Meta-analysis of this resource charts protein abundance, phosphorylation, and interactions throughout the tree of life, including a reference map of the human interactome. We show how large-scale CF-MS data can enhance analyses of individual CF-MS datasets, and exemplify this strategy by mapping the honey bee interactome.

Integrating network pharmacology and experimental verification to explore the mechanism of Tripterygium wilfordii in ankylosing spondylitis

OBJECTIVE

This study aimed to validate the mechanism of triptolide in treating ankylosing spondylitis (AS) through network pharmacology, molecular docking, and in vitro experiments.

METHODS

We gathered AS-related genes using databases including DrugBank, OMIM, GeneCards, TTD and DisGeNET. TCMSP database was used to collect Tripterygium wilfordii (TWHF)-related data. Additionally, the potential targets of TWHF in treating AS were predicted by consulting databases such as Venny, String, Cytoscape, and Cytohubba. Subsequently, a protein-protein interaction network was created and the gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed by metascape database. After selecting the most active ingredient of TWHF, molecular docking was performed to confirm the predicted results. Furthermore, we explore the potential mechanism of the most active ingredient of TWHF in the treatment of AS in vitro.

RESULT

By integrating the results of network pharmacological analysis, 62 genes were found to be strongly associated with AS, such as STAT3, TNF, MMP9, VEGFA, CXCL8, PTGS2, etc. Triptolide (TP) is one of the most active ingredients in TWHF. The enrichment analysis indicated that 292 biological processes and 132 signaling pathways were involved, with the T helper 17 cells cell differentiation pathway as the key pathway. TP was selected for molecular docking and in vitro experiments. The molecular docking results indicated that TP had excellent affinity with 6 key targets. Further, flow cytometry, cell counting assay, and ELISA demonstrated that the serum level of IL-17 was higher in AS patients compared to XXX, and 25 μg/mL TP was the optimal intervention concentration. RT-qPCR and Western blotting further verified that TP could inhibit the activation of RORγt and the JAK2/STAT3 signaling pathway.

CONCLUSION

In conclusion, based on network pharmacology, molecular docking, and experimental verification in vitro, we proposed that the TP can inhibit the activation of RORγt and the JAK2/STAT3 signaling pathway and inhibit the differentiation of T helper 17 cells cells. The article provide a theoretical basis for further development and utilization of TWHF in AS management.

A Panel-Agnostic Strategy 'HiPPo' Improves Diagnostic Efficiency in the UK Genomic Medicine Service

Genome sequencing is available as a clinical test in the UK through the Genomic Medicine Service (GMS). The GMS analytical strategy predominantly filters genome data on preselected gene panels. Whilst this reduces variants requiring assessment by reporting laboratories, pathogenic variants outside applied panels may be missed, and variants in genes without established disease-gene relationships are largely ignored. This study compares the analysis of a research exome to a GMS clinical genome for the same patients. For the research exome, we applied a panel-agnostic approach filtering for variants with High Pathogenic Potential (HiPPo) using ClinVar, allele frequency, and in silico prediction tools. We then restricted HiPPo variants to Gene Curation Coalition (GenCC) disease genes. These results were compared with the GMS genome panel-based approach. Twenty-four participants from eight families underwent parallel research exome and GMS genome sequencing. Exome HiPPo analysis identified a similar number of variants as the GMS panel-based approach. GMS genome analysis returned two pathogenic variants and one de novo variant. Exome HiPPo analysis returned the same variants plus an additional pathogenic variant and three further de novo variants in novel genes, where case series are underway. When HiPPo was restricted to GenCC disease genes, statistically fewer variants required assessment to identify more pathogenic variants than reported by the GMS, giving a diagnostic rate per variant assessed of 20% for HiPPo versus 3% for the GMS. With UK plans to sequence 5 million genomes, strategies are needed to optimise genome analysis beyond gene panels whilst minimising the burden of variants requiring clinical assessment.

Integrated bioinformatics and network pharmacology to explore the therapeutic target and molecular mechanisms of Bailing capsule on polycystic ovary syndrome

BACKGROUND

Polycystic ovary syndrome (PCOS) is a complex endocrine and metabolic disorder that is common in women of reproductive age. The clinical features of PCOS include hyperandrogenemia and polycystic ovarian changes. Bailing capsule (BL), a proprietary Chinese medicine that contains fermented Cordyceps sinensis powder, has been applied to treat PCOS. However, the specific active ingredients of BL and its mechanisms of action are yet to be elucidated.

METHODS

Initially, the effectiveness of BL on PCOS model mice was evaluated. Subsequently, the active ingredients of BL were searched in the TCMSP and TCM Systems Pharmacology databases, and their targets were predicted using Swiss Target Prediction and SEA databases. Furthermore, the GEO gene database was used to screen for differentially expressed genes (DEGs) related to PCOS. Data from Gene Card, OMIM, DDT, and Drugbank databases were then combined to establish a PCOS disease gene library. Cross targets were imported into the STRING database to construct a protein-protein interaction network. In addition, GO and KEGG pathway enrichment analyses were performed using Metascape and DAVID databases and visualized using Cytoscape software and R 4.2.3. The core targets were docked with SYBYL-X software, and their expressions in PCOS mice were further verified using qPCR.

RESULTS

The core active ingredients of BL were identified to be linoleyl acetate, cholesteryl palmitate, arachidonic acid, among others. Microarray data sets from four groups containing disease and normal samples were obtained from the GEO database. A total of 491 DEGs and 106 drug-disease cross genes were selected. Estrous cycle and ovarian lesions were found to be improved in PCOS model mice following BL treatment. While the levels of testosterone, progesterone, and prolactin decreased, that of estradiol increased. qPCR findings indicated that the expressions of JAK2, PPARG, PI3K, and AKT1 were upregulated, whereas those of ESR1 and IRS1 were downregulated in PCOS model mice. After the administration of BL, the expressions of associated genes were regulated. This study demonstrated that BL exerted anti-PCOS effects via PIK3CA, ESR1, AKT, PPARG, and IRS1 targets affecting PI3K-Akt signaling pathways.

DISCUSSION

This research clarified the multicomponent, multitarget, and multichannel action of BL and provided a theoretical reference for further investigations on its pharmacological basis and molecular mechanisms against PCOS.

The OREGANO knowledge graph for computational drug repurposing

Drug repositioning is a faster and more affordable solution than traditional drug discovery approaches. From this perspective, computational drug repositioning using knowledge graphs is a very promising direction. Knowledge graphs constructed from drug data and information can be used to generate hypotheses (molecule/drug - target links) through link prediction using machine learning algorithms. However, it remains rare to have a holistically constructed knowledge graph using the broadest possible features and drug characteristics, which is freely available to the community. The OREGANO knowledge graph aims at filling this gap. The purpose of this paper is to present the OREGANO knowledge graph, which includes natural compounds related data. The graph was developed from scratch by retrieving data directly from the knowledge sources to be integrated. We therefore designed the expected graph model and proposed a method for merging nodes between the different knowledge sources, and finally, the data were cleaned. The knowledge graph, as well as the source codes for the ETL process, are openly available on the GitHub of the OREGANO project ( https://gitub.u-bordeaux.fr/erias/oregano ).

Finding information about uncharacterized Drosophila melanogaster genes

Genes that have been identified in the genome but remain uncharacterized with regards to function offer an opportunity to uncover novel biological information. Novelty is exciting but can also be a barrier. If nothing is known, how does one start planning and executing experiments? Here, we provide a recommended information-mining workflow and a corresponding guide to accessing information about uncharacterized Drosophila melanogaster genes, such as those assigned only a systematic coding gene identifier. The available information can provide insights into where and when the gene is expressed, what the function of the gene might be, whether there are similar genes in other species, whether there are known relationships to other genes, and whether any other features have already been determined. In addition, available information about relevant reagents can inspire and facilitate experimental studies. Altogether, mining available information can help prioritize genes for further study, as well as provide starting points for experimental assays and other analyses.

Bioinformatics analysis and in vivo validation study of Ophiocordyceps sinensis (Berk.)G.H.Sungetal against lung adenocarcinoma

ETHNOPHARMACOLOGICAL RELEVANCE

Lung adenocarcinoma (LUAD) is one of the main types of lung cancer. Ophiocordyceps sinensis has many potentially useful pharmacologic features, such as lung protection, and both anti-inflammatory and antioxidant activities.

AIM OF THE STUDY

This study was conducted to investigate-using bioinformatics and in vivo experimental validation-the possible role of O. sinensis against LUAD.

MATERIALS AND METHODS

We obtained important targets of O. sinensis for the treatment of LUAD using network pharmacology techniques and deep mining of the TCGA database, and validated them by molecular docking techniques and in vivo experiments.

RESULTS

Through bioinformatics analysis and research, we screened BRCA1 and CCNE1 as important biomarkers for LUAD and as core targets of O. sinensis against LUAD. The non-small cell lung cancer signaling pathway, PI3K-Akt signaling pathway, and HIF-1 signaling pathway are potentially important pathways of O. sinensis against LUAD. The molecular docking results showed good binding between the active components in O. sinensis and the two core targets, and the in vivo experimental validation results indicated that O. sinensis had good inhibitory effects in the Lewis lung cancer (LLC) model.

CONCLUSIONS

BRCA1 and CCNE1 are crucial biomarkers for LUAD and are important targets for O. sinensis to exert anti-LUAD effects.

Mechanisms of Si-Wu Decoction in the treatment of ulcerative colitis revealed by network pharmacology and experimental verification

ETHNOPHARMACOLOGICAL RELEVANCE

Si-Wu Decoction (SWD) is a traditional Chinese medicine decoction. SWD is commonly used to treat blood deficiency syndrome. It is also used to treat some ulcerative colitis (UC) patients now, but the mechanism of action remains unclear.

AIM OF THE STUDY

This study explored the efficacy and mechanism of action of SWD in treating UC based on network pharmacology and related experimental validation.

MATERIALS AND METHODS

Several databases were used to screen SWD for major active ingredients, targets of the ingredients, and UC disease genes. Cytoscape 3.8.2 software was used for topological analysis to construct the drug-compound-disease gene-target relationship network. The String database platform was used to construct the target protein interaction network. The DAVID (Database for Annotation, Visualization and Integrated Discovery) database was used to perform the GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis for the key targets. DSS (Dextran Sulfate Sodium)-induced UC mouse model was used to evaluate the in-vivo activity of SWD. Western Blot analysis and quantitative polymerase chain reaction were performed to verify the targets in the related pathways.

RESULTS

Network pharmacology revealed that the SWD targeted pathway network involved 12 core targets and 15 major pathways. SWD may play a part by targeting key targets such as nuclear factor-kappaB (NF-κB), Janus kinase (JAK)-signal transducer and activator of transcription 3 (STAT3) pathway, and several mitogenic pathways. We showed that SWD largely restored the colorectal structure in UC model mice. Compared to the model group, the SWD group showed reduced infiltration of inflammatory cells. SWD significantly decreased the mRNA levels of IL-6 (Interleukin-6), TNF-α (Tumor necrosis factor-alpha), IL-1b (Interleukin-1beta) and other pro-inflammatory factors. Western Blot results showed that SWD concentration-dependently inhibited STAT3 and NF-κB activation in DSS-treated colon tissue.

CONCLUSION

Our findings suggest that SWD treats UC by inhibiting STAT3 and NF-κB signaling pathways, reducing the expression of inflammatory cytokines, and improving epithelial repair in experimental colitis, thus shedding light on the mechanisms by which SWD exerts its effects on UC.

Transcriptome analyses of murine right and left maxilla-mandibular complex

OBJECTIVE

The objective of the study was to investigate differential gene expression between murine right and left maxilla-mandibular (MxMn) complexes.

SETTING AND SAMPLE POPULATION

Wild-type (WT) C57BL/6 embryonic (E) day 14.5 (n = 3) and 18.5 (n = 3) murine embryos.

METHODS

The E14.5 and 18.5 embryos were harvested and hemi-sectioned the MxMn complexes into right and left halves in the mid-sagittal plane. We isolated total RNA using Trizol reagent and further purified using the RNA-easy kit (QIAGEN). We confirmed equal expression of house-keeping genes in right and left halves using RT-PCR and then performed paired-end whole mRNA sequencing in LC Sciences (Houston, TX) followed by differential transcript analyses (>1 or <-1 log fold change; p < .05; q < .05; and FPKM >0.5 in 2/3 samples). The Mouse Genome Informatics and Online Mendelian Inheritance in Man databases as well as gnomAD constraint scores were used to prioritize differentially expressed transcripts.

RESULTS

There were 19 upregulated and 19 downregulated transcripts at E14.5 and 8 upregulated and 17 downregulated transcripts at E18.5 time-points. These differentially expressed transcripts were statistically significant and shown to be associated with craniofacial phenotypes in mouse models. These transcripts also have significant gnomAD constraint scores and are enriched in biological processes critical for embryogenesis.

CONCLUSIONS

We identified significant differential expression of transcripts between E14.5 and 18.5 murine right and left MxMn complexes. These findings when extrapolated to humans, they may provide a biological basis for facial asymmetry. Further experiments are required to validate these findings in murine models with craniofacial asymmetry.

Frailty and sarcopenia: A bibliometric analysis of their association and potential targets for intervention

Frailty and sarcopenia are age-related diseases, and exhibit a concomitant relationship, as they share many common clinical features and etiological factors. Transitions within frailty status would be influenced by the presence of sarcopenia. Investigating their association to devise efficacious intervention and management strategies for geriatric patients is imperative, given their potentially unfavorable outcomes. In this study, the literature on sarcopenia and frailty was screened in the Web of Science core collection database over the past 30 years to ascertain the link between them through bibliometric analysis and the exploration of disease-related molecular pathways within the GeneCards and OMIM databases was conducted. Per inclusion and exclusion criteria, 3889 literature sources were selected for subsequent analysis. Keywords, including "cirrhosis" and "postoperative complications," represent the current and potential future research trends and focal points in this field. Moreover, 63 common potential targets between the two diseases were identified. Their pathogenesis involved cellular aging and endocrine metabolism regulation pathways, including AMPK, cell senescence, and the endocrine resistance pathway. This study identified an intimate correlation between frailty and sarcopenia in pathogenesis, prevention, and treatment measures, establishing a foundation for exploring shared prevention and treatment strategies for these two disorders.

Whole-Exome Sequencing Among School-Aged Children With High Myopia

Importance

High myopia (HM) is one of the leading causes of visual impairment worldwide. Genetic factors are known to play an important role in the development of HM.

Objective

To identify risk variants in a large HM cohort and to examine the implications of genetic testing of schoolchildren with HM.

Design, Setting, and Participants

This cohort study retrospectively reviewed whole-exome sequencing (WES) results in 6215 schoolchildren with HM who underwent genetic testing between September 2019 and July 2020 in Wenzhou City, China. HM is defined as a spherical equivalent refraction (SER) of -6.00 diopters (D) or less. The study setting was a genetic testing laboratory and a multicenter school census. Data were analyzed from July 2021 to June 2022.

Main Outcomes and Measures

The frequency and distribution of positive germline variants, the percentage of individuals with HM in both eyes, and subsequent variant yield for common high myopia (CHM; -8.00 D ≤ SER ≤ -6.00 D), ultra myopia (UM; -10.00 D ≤ SER < -8.00 D), and extreme myopia (EM; SER < -10.00 D).

Results

Of the 6215 schoolchildren with HM, 3278 (52.74%) were male. Their mean (SD) age was 14.87 (2.02) years, including 355 students in primary school, 1970 in junior high school, and 3890 in senior high school. The mean (SD) SER was -7.51 (-1.36) D for the right eye and -7.46 (-1.34) D for the left eye. Among schoolchildren with HM, genetic testing yielded 271 potential pathogenic variants in 75 HM candidate genes in 964 diagnoses (15.52%). A total of 36 known variants were found in 490 HM participants (7.88%) and 235 protein-truncating variants (PTVs) in 506 participants (8.14%). Involved variant yield was significantly positively associated with SER (Cochran-Armitage test for trend Z = 2.5492; P = .01), which ranged from 7.66% in the CHM group, 8.70% in the UM group, to 11.90% in the EM group. We also found that primary school students with EM had the highest variant yield of PTVs (8 of 35 students [22.86%]), which was 1.77 and 4.78 times that of the UM and CHM, respectively.

Conclusions and Relevance

In this cohort study of WES for HM, several potential pathogenic variants were identified in a substantial number of schoolchildren with HM. The high variation frequency in younger students with EM can provide clues for genetic screening and clinical examinations of HM to promote long-term follow-up assessment.

Virtual screening combined with experimental verification reveals the potential mechanism of Fuzitang decoction against Gouty Arthritis

Background

and Purpose: Fuzitang decoction (FZT), a classic prescription of traditional Chinese medicine (TCM), has excellent efficacy in treating gouty arthritis (GA). However, the underlying molecular mechanism remains obscure. In the present study, we aimed to explore the underlying mechanisms of FZT in treating GA by virtual screening combined with experimental verification.

Methods

In this study, the active components of FZT and their corresponding targets were screened from the TCMSP database and TargetNet database. Then, the potential targets of FZT against GA were retrieved from multiple databases to generate a network. Protein-protein interaction, herbal-component-target, Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were applied to identify potential targets and related signaling pathways. Furthermore, molecular docking simulation was applied to identify the interactions between the drug and targets. Finally, in vitro experiments were conducted to validate the potential targets and signaling pathways.

Results

In the present study, several crucial components, including kaempferol, luteolin, catechin, deoxyandrographolide, and perlolyrine in FZT, were obtained through network pharmacology, and several potential targets to treat GA were developed, such as PPARG, CYP3A4, PTGS2 (known as COX2), VEGFA, and CYP1A1. Experimental validation suggested that deoxyandrographolide significantly suppressed the expression of IL-1β, COX2, NLRP3 and IL-6 in inflammatory monocyte cells.

Conclusions

Our results identified a novel anti-inflammatory compound, deoxyandrographolide, which helps to explain the potential mechanism of FZT in treating GA and provides evidence to support FZT's clinical use.

Assessment of the anti-inflammatory mechanism of quercetin 3,7-dirhamnoside using an integrated pharmacology strategy

Pouzolzia zeylanica (L.) Benn. is a Chinese herbal medicine widely used for its anti-inflammatory and pus-removal properties. To explore its potential anti-inflammatory mechanism, quercetin 3,7-dirhamnoside (QDR), the main flavonoid component of P. zeylanica (L.) Benn., was extracted and purified. The potential anti-inflammatory targets of QDR were predicted using network analysis. These potential targets were verified using molecular docking, molecular dynamics simulations, and in vitro experiments. Consequently, 342 potential anti-inflammatory QDR targets were identified. By analyzing the intersection between the protein-protein interaction and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, we identified several potential protein targets of QDR, including RAC-alpha serine/threonine-protein kinase (AKT1), Ras-related C3 botulinum toxin substrate 1 (RAC1), nitric oxide synthase 3 (NOS3), serine/threonine-protein kinase mTOR (mTOR), epidermal growth factor receptor (EGFR), growth factor receptor-bound protein 2 (GRB2), and endothelin-1 receptor (EDNRA). QDR has anti-inflammatory activity and regulates immune responses and apoptosis through chemokines, Phosphatidylinositol 3-kinase 3(PI3K)/AKT, cAMP, T-cell receptor, and Ras signaling pathways. Molecular docking analysis showed that QDR has good binding abilities with AKT1, mTOR, and NOS3. In addition, molecular dynamics simulations demonstrated that the protein-ligand complex systems formed between QDR and AKT1, mTOR, and NOS3 have high dynamic stability, and their protein-ligand complex systems possess strong binding ability. In RAW264.7 macrophages, QDR significantly inhibited lipopolysaccharides (LPS)-induced inducible nitric oxide synthase expression, nitric oxide (NO) release and the generation of proinflammatory cytokines IL-6, IL-1β, and TNF-α. QDR downregulated the expression of p-AKT1(Ser473)/AKT1 and p-mTOR (Ser2448)/mTOR, and upregulated the expression of NOS3, Rictor, and Raptor. This indicates that the anti-inflammatory mechanisms of QDR involve regulation of AKT1 and mTOR to prevent apoptosis and of NOS3 which leads to the release of endothelial NO. Thus, our study elucidated the potential anti-inflammatory mechanism of QDR, the main flavonoid found in P. zeylanica (L.) Benn.

The mechanisms of Huangqi Guizhi Wuwu decoction in treating ischaemic stroke based on network pharmacology and experiment verification

CONTEXT

Huangqi Guizhi Wuwu Decoction (HGWD) is effective in treating ischaemic stroke (IS). However, its mechanism of action is still unclear.

OBJECTIVE

Network pharmacology integrated with in vivo experiments were used to clarify the underlying mechanisms of HGWD for treating IS.

MATERIALS AND METHODS

TCMSP, GeneCards, OMIM and STRING were used to retrieve and construct visual protein interaction networks for the key targets. The AutoDock tool was used for molecular docking between key targets and active compounds. The neuroprotective effect of HGWD were verified in a middle cerebral artery occlusion (MCAO) model rat. The Sprague-Dawley (SD) rats were divided into sham, model, low-dose (5 g/kg, i.g.), high-dose (20 g/kg, i.g.), and nimodipine (20 mg/kg, i.g.) groups once daily for 7 days. The neurological scores, brain infarct volumes, lipid peroxidation, inflammatory cytokines, Nissl bodies, apoptotic neurons, and signalling pathways were all investigated and evaluated in vivo.

RESULTS

Network pharmacology identified 117 HGWD targets related to IS and 36 candidate compounds. GO and KEGG analyses showed that HGWD anti-IS effects were mainly associated with PI3K-Akt and HIF-1 signalling pathways. HGWD effectively reduced the cerebral infarct volumes (19.19%), the number of apoptotic neurons (16.78%), and the release of inflammatory cytokines, etc. in MCAO rats. Furthermore, HGWD decreased the levels of HIF-1A, VEGFA, Bax, cleaved caspase-3, p-MAPK1, and p-c-Jun while increasing the expression of p-PI3K, p-AKT1, and Bcl-2.

DISCUSSION AND CONCLUSION

This study initially elucidated the mechanism of HGWD anti-IS, which contributed to the further promotion and secondary development of HGWD in clinical practice.

Network pharmacology integrated with molecular docking technology to reveal the potential mechanism of Shuganfang against drug-induced liver injury

This study aimed to investigate the active composition and mechanism of the Shuganfang (SGF) in treating drug-induced liver injury (DILI) using network pharmacology and molecular docking. The potential active ingredients and targets of SGF were obtained from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) database. DILI-related targets were queried from various databases including GEO, GeneCards, OMIM, NCBI, and DisGeNET. The STRING database was used to establish a protein-protein interaction (PPI) network. DAVID was utilized for conducting gene ontology (GO) function enrichment and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses. The data visualization and analysis of herb-ingredient-target and disease-pathway-target-ingredient networks were conducted using Cytoscape software (version 3.7.2). PyMoL and AutoDock software was used to select the best binding target for molecular docking. A total of 177 active ingredients,126 targets and 10112 disease targets were obtained, including 122 intersection targets. The identified potential active ingredients consisted of quercetin, kaempferol, luteolin, tanshinone IIa, nobiletin, isorhamnetin, beta-sitosterol and naringenin. The core targets implicated in the study were IL6, estrogen receptor 1 (ESR1), hypoxia-inducible factor alpha subunit 1 (HIF1A), MYC and vascular endothelial growth factor A (VEGFA). KEGG analysis revealed that the treatment of DILI with SGF mainly acted through apoptosis, the PI3K-Akt signaling pathway, and the tumor necrosis factor (TNF) signaling pathway. Furthermore, the binding affinities between the potential ingredients and the core targets were subsequently confirmed through molecular docking experiments. The findings indicated that the docking outcomes remained consistent and demonstrated a favorable capacity for binding. SGF exerts a therapeutic effect on DILI through multiple active ingredients, multiple targets and multiple pathways. Our findings contribute to a positive investigation and establish a theoretical basis for further extensive exploration of SGF as a potential treatment for DILI in future research.

Data collection on rare bone and mineral conditions in Europe: The landscape of registries and databases

BACKGROUND

knowledge on the natural history of rare diseases is necessary to improve outcomes. Disease registries may play a key role in covering these unmet needs in the rare bone and mineral community.

OBJECTIVE

to map existing bone and mineral conditions registries in Europe and their characteristics.

METHODS

online survey about the use of registries/databases and their characteristics. This survey was disseminated among members of the European Reference Network on Rare Bone Diseases (ERN BOND) and non-ERN experts in the field of bone and mineral conditions as well as patient organisations.

RESULTS

sixty-three responses from health care providers (HCPs) and 10 responses from patient groups (PGs) were collected. The response rate for ERN BOND members was 55%. Of 63 HCPs, 37 declared using a registry. Osteogenesis imperfecta (OI) was the most registered condition. We mapped 3 international registries, all were disease-specific.

CONCLUSIONS

There is a need for developing a common high-quality platform for registering rare bone and mineral conditions.

Improving the filtering of false positive single nucleotide variations by combining genomic features with quality metrics

MOTIVATION

Technical errors in sequencing or bioinformatics steps and difficulties in alignment at some genomic sites result in false positive (FP) variants. Filtering based on quality metrics is a common method for detecting FP variants, but setting thresholds to reduce FP rates may reduce the number of true positive variants by overlooking the more complex relationships between features. The goal of this study is to develop a machine learning-based model for identifying FPs that integrates quality metrics with genomic features and with the feature interpretability property to provide insights into model results.

RESULTS

We propose a random forest-based model that utilizes genomic features to improve identification of FPs. Further examination of the features shows that the newly introduced features have an important impact on the prediction of variants misclassified by VEF, GATK-CNN, and GARFIELD, recently introduced FP detection systems. We applied cost-sensitive training to avoid errors in misclassification of true variants and developed a model that provides a robust mechanism against misclassification of true variants while increasing the prediction rate of FP variants. This model can be easily re-trained when factors such as experimental protocols might alter the FP distribution. In addition, it has an interpretability mechanism that allows users to understand the impact of features on the model's predictions.

AVAILABILITY AND IMPLEMENTATION

The software implementation can be found at https://github.com/ideateknoloji/FPDetect.

Network pharmacology integrated with experimental verification reveals the antipyretic characteristics and mechanism of Zi Xue powder

CONTEXT

Zi Xue Powder (ZXP) is a traditional formula for the treatment of fever. However, the potential mechanism of action of ZXP remains unknown.

OBJECTIVE

This study elucidates the antipyretic characteristics of ZXP and the mechanism by which ZXP alleviates fever.

MATERIALS AND METHODS

The key targets and underlying fever-reducing mechanisms of ZXP were predicted using network pharmacology and molecular docking. The targets of ZXP anti-fever active ingredient were obtained by searching TCMSP, STITCH and HERB. Moreover, male Sprague-Dawley rats were randomly divided into four groups: control, lipopolysaccharide (LPS), ZXP (0.54, 1.08, 2.16 g/kg), and positive control (acetaminophen, 0.045 g/kg); the fever model was established by intraperitoneal LPS injection. After the fever model was established at 0.5 h, the rats were administered treatment by gavage, and the anal temperature changes of each group were observed over 10 h after treatment. After 10 h, ELISA and Western blot analysis were used to further investigate the mechanism of ZXP.

RESULTS

Network pharmacology analysis showed that MAPK was a crucial pathway through which ZXP suppresses fever. The results showed that ZXP (2.16 g/kg) decreased PGE2, CRH, TNF-a, IL-6, and IL-1β levels while increasing AVP level compared to the LPS group. Furthermore, the intervention of ZXP inhibited the activation of MAPK pathway in LPS-induced fever rats.

CONCLUSIONS

This study provides new insights into the mechanism by which ZXP reduces fever and provides important information and new research ideas for the discovery of antipyretic compounds from traditional Chinese medicine.

Network pharmacology and molecular docking elucidate potential mechanisms of Eucommia ulmoides in hepatic ischemia-reperfusion injury

Eucommia ulmoides (EU) and its diverse extracts have demonstrated antioxidative, anti-inflammatory, and cytoprotective properties against hepatic ischemia-reperfusion injury (HIRI). However, the primary constituents of EU and their putative mechanisms remain elusive. This study aims to explore the potential mechanisms of EU in the prevention and treatment of HIRI by employing network pharmacology and molecular docking methodologies. The main components and corresponding protein targets of EU were searched in the literature and TCMSP, and the compound target network was constructed by Cytoscape 3.9.1. Liver ischemia-reperfusion injury targets were searched in OMIM and GeneCards databases. The intersection points of compound targets and disease targets were obtained, and the overlapping targets were imported into the STRING database to construct the PPI network. We further analyzed the targets for GO and KEGG enrichment. Finally, molecular docking studies were performed on the core targets and active compounds. The component-target network unveiled a total of 26 efficacious bioactive compounds corresponding to 207 target proteins. Notably, the top-ranking compounds based on degree centrality were quercetin, β-sitosterol, and gallic acid. Within the PPI network, the highest degree centrality encompassed RELA, AKT1, TP53. GO and KEGG enrichment analysis elucidated that EU in HIRI primarily engaged in positive regulation of gene expression, positive transcriptional regulation via RNA polymerase II promoter, negative modulation of apoptotic processes, positive regulation of transcription from DNA templates, and drug responsiveness, among other biological processes. Key pathways included cancer pathways, RAGE signaling pathway, lipid metabolism, atherosclerosis, TNF signaling pathway, PI3K-Akt signaling pathway, and apoptotic pathways. Molecular docking analysis revealed robust affinities between quercetin, β-sitosterol, gallic acid, and RELA, AKT1, TP53, respectively. This study reveals EU exhibits substantial potential in mitigating and treating HIRI through multifaceted targeting and involvement in intricate signaling pathways.

Integrated Network Pharmacology, Molecular Docking and Animal Experiment to Explore the Efficacy and Potential Mechanism of Baiyu Decoction Against Ulcerative Colitis by Enema

Background

Baiyu Decoction (BYD), a clinical prescription of traditional Chinese medicine, has been proven to be valuable for treating ulcerative colitis (UC) by enema. However, the mechanism of BYD against UC remains unclear.

Purpose

A combination of bioinformatics methods including network pharmacology and molecular docking and animal experiments were utilized to investigate the potential mechanism of BYD in the treatment of UC.

Materials and Methods

Firstly, the representative compounds of each herb in BYD were detected by liquid chromatography-mass spectrometry. Subsequently, we predicted the core targets and potential pathways of BYD for treating UC through network pharmacology. And rat colitis model was established with dextran sodium sulfate. UC rats were subjected to BYD enema administration, during which we recorded body weight changes, disease activity index, and colon length to assess the effectiveness of BYD. Besides, quantitative real-time PCR, western blotting, ELISA and immunofluorescence were used to detect intestinal inflammatory factors, intestinal barrier biomarkers and TOLL-like receptor pathway in rats. Finally, the core components and targets of BYD were subjected to molecular docking so as to further validate the results of network pharmacology.

Results

A total of 41 active compositions and 203 targets related to BYD-UC were subjected to screening. The results of bioinformatics analysis showed that quercetin and kaempferol may be the main compounds. Additionally, AKT1, IL-6, TP53, TNF and IL-1β were regarded as potential therapeutic targets. KEGG results explained that TOLL-like receptor pathway might play a pivotal role in BYD protecting against UC. In addition, animal experiments and molecular docking validated the network pharmacology results. BYD enema treatment can reduce body weight loss, lower disease activity index score, reverse colon shortening, relieve intestinal inflammation, protect intestinal barrier, and inhibit TOLL-like receptor pathway in UC rats. Besides, molecular docking suggested that quercetin and kaempferol docked well with TLR4, AKT1, IL-6, TP53.

Conclusion

Utilizing network pharmacology, animal studies, and molecular docking, enema therapy with BYD was confirmed to have anti-UC efficacy by alleviating intestinal inflammation, protecting the intestinal barrier, and inhibiting the TOLL-like receptor pathway. Researchers should focus not only on oral medications but also on the rectal administration of medications in furtherance of the cure of ulcerative colitis.

Genome-wide identification of dominant polyadenylation hexamers for use in variant classification

Polyadenylation is an essential process for the stabilization and export of mRNAs to the cytoplasm and the polyadenylation signal hexamer (herein referred to as hexamer) plays a key role in this process. Yet, only 14 Mendelian disorders have been associated with hexamer variants. This is likely an under-ascertainment as hexamers are not well defined and not routinely examined in molecular analysis. To facilitate the interrogation of putatively pathogenic hexamer variants, we set out to define functionally important hexamers genome-wide as a resource for research and clinical testing interrogation. We identified predominant polyA sites (herein referred to as pPAS) and putative predominant hexamers across protein coding genes (PAS usage >50% per gene). As a measure of the validity of these sites, the population constraint of 4532 predominant hexamers were measured. The predominant hexamers had fewer observed variants compared to non-predominant hexamers and trimer controls, and CADD scores for variants in these hexamers were significantly higher than controls. Exome data for 1477 individuals were interrogated for hexamer variants and transcriptome data were generated for 76 individuals with 65 variants in predominant hexamers. 3' RNA-seq data showed these variants resulted in alternate polyadenylation events (38%) and in elongated mRNA transcripts (12%). Our list of pPAS and predominant hexamers are available in the UCSC genome browser and on GitHub. We suggest this list of predominant hexamers can be used to interrogate exome and genome data. Variants in these predominant hexamers should be considered candidates for pathogenic variation in human disease, and to that end we suggest pathogenicity criteria for classifying hexamer variants.

Based on network pharmacology and molecular docking to explore the molecular mechanism of Ginseng and Astragalus decoction against postmenopausal osteoporosis

Traditional Chinese medicine suggests that Ginseng and Astragalus Decoction (GAD) may effectively treat postmenopausal osteoporosis (PMO). However, the exact mechanism of action for GAD remains unclear. This study aims to utilize network pharmacology and molecular docking technology to explore the potential mechanism of GAD in treating PMO. The main chemical components of GAD were identified by consulting literature and traditional Chinese medicine systems pharmacology database. GeneCards and online mendelian inheritance in man were used to identify PMO disease targets, and Cytoscape 3.8.2 software was used to construct a herb-disease-gene-target network. The intersection of drug targets and disease targets was introduced into the search tool for the retrieval of interacting genes platform to construct a protein-protein interaction network. Additionally, we further conducted gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses, followed by molecular docking between active ingredients and core protein targets. We have identified 59 potential targets related to the treatment of PMO by GAD, along with 33 effective components. Quercetin and kaempferol are the compounds with higher degree. In the protein-protein interaction network, IL6, AKT1, and IL1B are proteins with high degree. The enrichment analysis of gene ontology and KEEG revealed that biological processes involved in treating PMO with GAD mainly include response to hormones, positive regulation of phosphorylation, and regulation of protein homodimerization activity. The signal pathways primarily include Pathways in cancer, PI3K-Akt signaling pathway, and AGE-RAGE signaling pathway. Molecular docking results indicate that kaempferol and quercetin have a high affinity for IL6, AKT1, and IL1B. Our research predicts that IL6, AKT1, and IL1B are highly likely to be potential targets for treating PMO with GAD. PI3K/AKT pathway and AGE-ARGE pathway may play an important role in PMO.

Multi-level advances in databases related to systems pharmacology in traditional Chinese medicine: a 60-year review

The therapeutic effects of traditional Chinese medicine (TCM) involve intricate interactions among multiple components and targets. Currently, computational approaches play a pivotal role in simulating various pharmacological processes of TCM. The application of network analysis in TCM research has provided an effective means to explain the pharmacological mechanisms underlying the actions of herbs or formulas through the lens of biological network analysis. Along with the advances of network analysis, computational science has coalesced around the core chain of TCM research: formula-herb-component-target-phenotype-ZHENG, facilitating the accumulation and organization of the extensive TCM-related data and the establishment of relevant databases. Nonetheless, recent years have witnessed a tendency toward homogeneity in the development and application of these databases. Advancements in computational technologies, including deep learning and foundation model, have propelled the exploration and modeling of intricate systems into a new phase, potentially heralding a new era. This review aims to delves into the progress made in databases related to six key entities: formula, herb, component, target, phenotype, and ZHENG. Systematically discussions on the commonalities and disparities among various database types were presented. In addition, the review raised the issue of research bottleneck in TCM computational pharmacology and envisions the forthcoming directions of computational research within the realm of TCM.

Network pharmacology and molecular docking reveal potential mechanism of esculetin in the treatment of ulcerative colitis

Ulcerative colitis (UC) is a chronic inflammatory bowel disease of the colonic mucosa. Esculetin is a type of natural coumarin that has many pharmacological activities such as antioxidant, anticancer, anti-inflammatory, etc. A previous study showed that esculetin improved intestinal inflammation and reduced serum proinflammatory cytokines in UC. The present study aimed to utilize network pharmacology and molecular docking to explore the potential mechanism of esculetin against UC. The potential gene targets of esculetin were predicted through SwissTargetPrediction and Super-PRED web servers. UC-related genes were obtained from DisGeNet, OMIM, and GeneCards databases. The overlap between gene targets of esculetin and UC-related genes were identified as the potential targets of esculetin against UC. The interaction between these overlapping genes was analyzed by the STRING database and the core genes were identified by Cytoscape platform. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of the core genes were then performed. And the results of these analyses were further confirmed through molecular docking. A total of 50 overlapping genes were identified as the potential action targets of esculetin against UC. Among them, 10 genes (AKT1, STAT1, CCND1, SRC, PTGS2, EGFR, NFKB1, ESR1, MMP9, SERPINE1) were finally identified as the core genes. The Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis results showed that the top signaling pathway associated with the core genes of esculetin against UC was the prolactin (PRL) signaling pathway. Molecular docking results showed that esculetin has a strong binding affinity to the core genes, as well as PRL and prolactin receptor. This study suggests that esculetin may have a crucial impact on UC through the PRL signaling pathway and provides insights into the potential mechanism of esculetin in the treatment of UC, which may shed light on the mechanism and treatment of UC.

Network pharmacology: a bright guiding light on the way to explore the personalized precise medication of traditional Chinese medicine

Network pharmacology can ascertain the therapeutic mechanism of drugs for treating diseases at the level of biological targets and pathways. The effective mechanism study of traditional Chinese medicine (TCM) characterized by multi-component, multi-targeted, and integrative efficacy, perfectly corresponds to the application of network pharmacology. Currently, network pharmacology has been widely utilized to clarify the mechanism of the physiological activity of TCM. In this review, we comprehensively summarize the application of network pharmacology in TCM to reveal its potential of verifying the phenotype and underlying causes of diseases, realizing the personalized and accurate application of TCM. We searched the literature using "TCM network pharmacology" and "network pharmacology" as keywords from Web of Science, PubMed, Google Scholar, as well as Chinese National Knowledge Infrastructure in the last decade. The origins, development, and application of network pharmacology are closely correlated with the study of TCM which has been applied in China for thousands of years. Network pharmacology and TCM have the same core idea and promote each other. A well-defined research strategy for network pharmacology has been utilized in several aspects of TCM research, including the elucidation of the biological basis of diseases and syndromes, the prediction of TCM targets, the screening of TCM active compounds, and the decipherment of mechanisms of TCM in treating diseases. However, several factors limit its application, such as the selection of databases and algorithms, the unstable quality of the research results, and the lack of standardization. This review aims to provide references and ideas for the research of TCM and to encourage the personalized and precise use of Chinese medicine.

Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases

Understanding phenotype-to-genotype relationships is a grand challenge of 21st century biology with translational implications. The recently proposed "omnigenic" model postulates that effects of genetic variation on traits are mediated by core-genes and -proteins whose activities mechanistically influence the phenotype, whereas peripheral genes encode a regulatory network that indirectly affects phenotypes via core gene products. Here, we develop a positive-unlabeled graph representation-learning ensemble-approach based on a nested cross-validation to predict core-like genes for diverse diseases using Mendelian disorder genes for training. Employing mouse knockout phenotypes for external validations, we demonstrate that core-like genes display several key properties of core genes: Mouse knockouts of genes corresponding to our most confident predictions give rise to relevant mouse phenotypes at rates on par with the Mendelian disorder genes, and all candidates exhibit core gene properties like transcriptional deregulation in disease and loss-of-function intolerance. Moreover, as predicted for core genes, our candidates are enriched for drug targets and druggable proteins. In contrast to Mendelian disorder genes the new core-like genes are enriched for druggable yet untargeted gene products, which are therefore attractive targets for drug development. Interpretation of the underlying deep learning model suggests plausible explanations for our core gene predictions in form of molecular mechanisms and physical interactions. Our results demonstrate the potential of graph representation learning for the interpretation of biological complexity and pave the way for studying core gene properties and future drug development.

Evaluation of the Effect and Mechanism of Sanhuang Ointment on MRSA Infection in the Skin and Soft Tissue via Network Pharmacology

Introduction

Skin and soft tissue infection (SSTI) is a frequently encountered clinical disease, and Sanhuang ointment, a traditional Chinese medicine, is used to treat it. However, the pharmacological effect of Sanhuang ointment on SSTI and its underlying mechanism remains unclear. Here, we investigate the protective effect of Sanhuang ointment on Methicillin-resistant Staphylococcus aureus (MRSA) infection in the skin and soft tissues and the underlying mechanism by network pharmacological analysis, followed by in vivo experimental validation.

Methods

Via network pharmacology, the active components and disease targets of Sanhuang ointment were screened and intersected for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. A rat model of skin and soft tissue infection was established, and pathological features were observed. Large, medium, and small-dose groups (1 g, 0.5 g, and 0.25 g/animal, with the total amount of Vaseline, dispensed 1 g/animal) of Sanhuang ointment were prepared and Mupirocin ointment was used as a positive control (0.5 g/animal, with the total amount of Vaseline, dispensed 1 g/animal). The expressions of key proteins of the IL-17/NF-κB signaling pathway and downstream inflammatory factors were analyzed by histomorphological analysis, enzyme-linked immunosorbent assay, polymerase chain reaction, and Western blotting.

Results

In all, 119 active components and 275 target genes of Sanhuang ointment were identified and intersected with MRSA infection-related genes via network pharmacology analysis, and 34 target genes of Sanhuang ointment were found to be involved in skin and soft tissue infections with MRSA. Sanhuang ointment (1 g/mouse) could effectively ameliorate histopathological changes and significantly inhibit the expression of key proteins involved in the IL-17/NF-κB signaling pathway and downstream inflammatory factors (p < 0.05).

Conclusion

Sanhuang ointment has a protective effect on MRSA infection and inhibits inflammation by inhibiting the IL-17/NF-κB signaling pathway. Our findings are important for the secondary development and new drug development of Sanhuang ointment.

An In Silico Study for Expanding the Utility of Cannabidiol in Alzheimer's Disease Therapeutic Development

Cannabidiol (CBD), a major non-psychoactive component of the cannabis plant, has shown therapeutic potential in Alzheimer's disease (AD). In this study, we identified potential CBD targets associated with AD using a drug-target binding affinity prediction model and generated CBD analogs using a genetic algorithm combined with a molecular docking system. As a result, we identified six targets associated with AD: Endothelial NOS (ENOS), Myeloperoxidase (MPO), Apolipoprotein E (APOE), Amyloid-beta precursor protein (APP), Disintegrin and metalloproteinase domain-containing protein 10 (ADAM10), and Presenilin-1 (PSEN1). Furthermore, we generated CBD analogs for each target that optimize for all desired drug-likeness properties and physicochemical property filters, resulting in improved pIC50 values and docking scores compared to CBD. Molecular dynamics (MD) simulations were applied to analyze each target's CBD and highest-scoring CBD analogs. The MD simulations revealed that the complexes of ENOS, MPO, and ADAM10 with CBD exhibited high conformational stability, and the APP and PSEN1 complexes with CBD analogs demonstrated even higher conformational stability and lower interaction energy compared to APP and PSEN1 complexes with CBD. These findings demonstrated the capable binding of the six identified targets with CBD and the enhanced binding stability achieved with the developed CBD analogs for each target.

Naturally occurring canine laminopathy leading to a dilated and fibrosing cardiomyopathy in the Nova Scotia Duck Tolling Retriever

Dilated cardiomyopathy (DCM) is characterized by decreased systolic function and dilation of one or both ventricles, often leading to heart failure or sudden death. Two 10-month-old sibling Nova Scotia Duck Tolling Retrievers (NSDTR) died acutely with evidence of dilated cardiomyopathy with myocardial fibrosis. Association analysis using two cases and 35 controls identified three candidate regions homozygous in the two cases. Whole genome sequencing identified a frameshift deletion in the LMNA gene (NC_049228.1:g.41688530del, NP_001274080:p.(Asp576ThrfsTer124)). Three retrospectively identified NSDTRs with sudden death before 2 years of age and severe myocardial fibrosis were also homozygous for the deletion. One 5 year old with sudden death and myocardial fibrosis was heterozygous for the deletion. This variant was not identified in 722 dogs of other breeds, nor was it identified to be homozygous in 784 NSDTR. LMNA codes for lamin A/C proteins, which are type V intermediate filaments that provide structural support to the nuclear membrane. In humans, LMNA variants can cause DCM with sudden death as well as diseases of striated muscles, lipodystrophy, neuropathies, and accelerated aging disorders. This frameshift deletion is predicted to affect processing of prelamin A into lamin A. Pedigree analysis in the NSDTR and functional evaluation of heterozygotes is consistent with a predominantly recessive mode of inheritance and possibly low penetrance in heterozygotes in contrast to people, where most pathogenic LMNA variants are dominantly inherited.

Development of a highly optimized procedure for the discovery of RNA G-quadruplexes by combining several strategies

RNA G-quadruplexes (rG4s) are non-canonical secondary structures that are formed by the self-association of guanine quartets and that are stabilized by monovalent cations (e.g. potassium). rG4s are key elements in several post-transcriptional regulation mechanisms, including both messenger RNA (mRNA) and microRNA processing, mRNA transport and translation, to name but a few examples. Over the past few years, multiple high-throughput approaches have been developed in order to identify rG4s, including bioinformatic prediction, in vitro assays and affinity capture experiments coupled to RNA sequencing. Each individual approach had its limits, and thus yielded only a fraction of the potential rG4 that are further confirmed (i.e., there is a significant level of false positive). This report aims to benefit from the strengths of several existing approaches to identify rG4s with a high potential of being folded in cells. Briefly, rG4s were pulled-down from cell lysates using the biotinylated biomimetic G4 ligand BioTASQ and the sequences thus isolated were then identified by RNA sequencing. Then, a novel bioinformatic pipeline that included DESeq2 to identify rG4 enriched transcripts, MACS2 to identify rG4 peaks, rG4-seq to increase rG4 formation probability and G4RNA Screener to detect putative rG4s was performed. This workflow uncovers new rG4 candidates whose rG4-folding was then confirmed in vitro using an array of established biophysical methods. Clearly, this workflow led to the identification of novel rG4s in a highly specific and reliable manner.

Inhibition of Caspase-1-mediated pyroptosis promotes osteogenic differentiation, offering a therapeutic target for osteoporosis

BACKGROUND

Pyroptosis, an emerging inflammatory form of cell death, has been previously demonstrated to stimulate a massive inflammatory response, thus hindering the osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs). Nevertheless, the impact of pyroptosis in thwarting osteogenic differentiation and exacerbating the advancement of osteoporosis (OP) remains enigmatic.

METHODS

We evaluated the expression levels of pyroptosis-associated indicators, including NOD-like receptor family pyrin domain-containing protein 3 (NLRP3), CASPASE-1, IL-1β, and IL-18, in specimens obtained from femoral heads of OP patients, as well as in an ovariectomy-induced mouse model of OP. Subsequently, the precise roles of pyroptosis in osteogenic differentiation were investigated using bioinformatics analysis, alongside morphological and biochemical assessments.

RESULTS

The pivotal pyroptotic proteins, including NLRP3, Caspase-1, IL-1β, and IL-18, exhibited significant upregulation within the bone tissue samples of clinical OP cases, as well as in the femoral tissues of ovariectomy (OVX)-induced mouse OP model, displaying a negatively associated with compromised osteogenic capacity, as represented by lessened bone mass, suppressed expression of osteogenic proteins such as Runt-related transcription factor 2 (RUNX2), Alkaline phosphatase (ALP), Osterix (OSX), and Osteopontin (OPN), and increased lipid droplets. Moreover, bioinformatics analysis substantiated shared gene expression patterns between pyroptosis and OP pathology, encompassing NLRP3, Caspase-1, IL-1β, IL-18, etc. Furthermore, our in vitro investigation using ST2 cells revealed that dexamethasone treatment prominently induced pyroptosis while impeding osteogenic differentiation. Notably, gene silencing of Caspase-1 effectively counteracted the inhibitory effects of dexamethasone on osteogenic differentiation, as manifested by increased ALP activity and enhanced expression of RUNX2, ALP, OSX, and OPN.

CONCLUSION

Our findings unequivocally underscore that inhibition of Caspase-1-mediated pyroptosis promotes osteogenic differentiation, providing a promising therapeutic target for managing OP.

Artificial Intelligence Optical Biopsy for Evaluating the Functional State of Wounds

INTRODUCTION

The clinical characterization of the functional status of active wounds in terms of their driving cellular and molecular biology remains a considerable challenge that currently requires excision via a tissue biopsy. In this pilot study, we use convolutional Siamese neural network (SNN) architecture to predict the functional state of a wound using digital photographs of wounds in a canine model of volumetric muscle loss (VML).

METHODS

Digital images of VML injuries and tissue biopsies were obtained in a standardized fashion from an established canine model of VML. Gene expression profiles for each biopsy site were obtained using RNA sequencing. These profiles were converted to functional profiles by a manual review of validated gene ontology databases in which we determined a hierarchical representation of gene functions based on functional specificity. An SNN was trained to regress functional profile expression values, informed by an image segment showing the surface of a small tissue biopsy.

RESULTS

The SNN was able to predict the functional expression of a range of functions based with error ranging from ∼5% to ∼30%, with functions that are most closely associated with the early state of wound healing to be those best-predicted.

CONCLUSIONS

These initial results suggest promise for further research regarding this novel use of machine learning regression on medical images. The regression of functional profiles, as opposed to specific genes, both addresses the challenge of genetic redundancy and gives a deeper insight into the mechanistic configuration of a region of tissue in wounds.

Identification of variants in genes associated with hypertrophic cardiomyopathy in Mexican patients

The objective of this work was to identify genetic variants in Mexican patients diagnosed with hypertrophic cardiomyopathy (HCM). According to world literature, the genes mainly involved are MHY7 and MYBPC3, although variants have been found in more than 50 genes related to heart disease and sudden death, and to our knowledge there are no studies in the Mexican population. These variants are reported and classified in the ClinVar (PubMed) database and only some of them are recognized in the Online Mendelian Information in Men (OMIM). The present study included 37 patients, with 14 sporadic cases and 6 familial cases, with a total of 21 index cases. Next-generation sequencing was performed on a predesigned panel of 168 genes associated with heart disease and sudden death. The sequencing analysis revealed twelve (57%) pathogenic or probably pathogenic variants, 9 of them were familial cases, managing to identify pathogenic variants in relatives without symptoms of the disease. At the molecular level, nine of the 12 variants (75%) were single nucleotide changes, 2 (17%) deletions, and 1 (8%) splice site alteration. The genes involved were MYH7 (25%), MYBPC3 (25%) and ACADVL, KCNE1, TNNI3, TPM1, SLC22A5, TNNT2 (8%). In conclusion; we found five variants that were not previously reported in public databases. It is important to follow up on the reclassification of variants, especially those of uncertain significance in patients with symptoms of the condition. All patients included in the study and their relatives received family genetic counseling.

Exploring the potential mechanisms of Tongmai Jiangtang capsules in treating diabetic nephropathy through multi-dimensional data

Background

Diabetic nephropathy (DN) is a prevalent and debilitating disease that represents the leading cause of chronic kidney disease which imposes public health challenges Tongmai Jiangtang capsule (TMJT) is commonly used for the treatment of DN, albeit its underlying mechanisms of action are still elusive.

Methods

This study retrieved databases to identify the components and collect the targets of TMJT and DN. Target networks were constructed to screen the core components and targets. Samples from the GEO database were utilized to perform analyses of targets and immune cells and obtain significantly differentially expressed core genes (SDECGs). We also selected a machine learning model to screen the feature genes and construct a nomogram. Furthermore, molecular docking, another GEO dataset, and Mendelian randomization (MR) were utilized for preliminary validation. We subsequently clustered the samples based on SDECG expression and consensus clustering and performed analyses between the clusters. Finally, we scored the SDECG score and analyzed the differences between clusters.

Results

This study identified 13 SDECGs between DN and normal groups which positively regulated immune cells. We also identified five feature genes (CD40LG, EP300, IL1B, GAPDH, and EGF) which were used to construct a nomogram. MR analysis indicated a causal link between elevated IL1B levels and an increased risk of DN. Clustering analysis divided DN samples into four groups, among which, C1 and CI were mainly highly expressed and most immune cells were up-regulated. C2 and CII were the opposite. Finally, we found significant differences in SDECG scores between C1 and C2, CI and CII, respectively.

Conclusion

TMJT may alleviate DN via core components (e.g. Denudatin B, hancinol, hirudinoidine A) targeting SDECGs (e.g. SRC, EGF, GAPDH), with the involvement of feature genes and modulation of immune and inflammation-related pathways. These findings have potential implications for clinical practice and future investigations.

Integrative analyses highlight functional regulatory variants associated with neuropsychiatric diseases

Noncoding variants of presumed regulatory function contribute to the heritability of neuropsychiatric disease. A total of 2,221 noncoding variants connected to risk for ten neuropsychiatric disorders, including autism spectrum disorder, attention deficit hyperactivity disorder, bipolar disorder, borderline personality disorder, major depression, generalized anxiety disorder, panic disorder, post-traumatic stress disorder, obsessive-compulsive disorder and schizophrenia, were studied in developing human neural cells. Integrating epigenomic and transcriptomic data with massively parallel reporter assays identified differentially-active single-nucleotide variants (daSNVs) in specific neural cell types. Expression-gene mapping, network analyses and chromatin looping nominated candidate disease-relevant target genes modulated by these daSNVs. Follow-up integration of daSNV gene editing with clinical cohort analyses suggested that magnesium transport dysfunction may increase neuropsychiatric disease risk and indicated that common genetic pathomechanisms may mediate specific symptoms that are shared across multiple neuropsychiatric diseases.

Establishment and characterization of CSCRi006-A: an induced pluripotent stem cell line generated from a patient with Diamond-Blackfan Anemia (DBA) carrying ribosomal protein S19 (RPS19) mutation

Diamond-Blackfan anemia (DBA) is a congenital hypoplastic anemia characterized by ineffective erythropoiesis. DBA is majorly caused by mutations in the ribosomal protein (RP) genes (Gadhiya and Wills in Diamond-Blackfan Anemia, https://www.statpearls.com/ ; 2023). A suitable disease model that yields a continuous supply of erythroid cells is required to study disease pathogenesis and drug discovery. Toward this, we reprogrammed dermal fibroblasts from a DBA patient with a heterozygous mutation c.22-23delAG in the RPS19 gene identified through exome sequencing. To generate induced pluripotent stem cells (iPSCs), we induced episomal expression of the reprogramming factors OTC3/4, L-MYC, LIN28, SOX2, and KLF4, and a p53 shRNA2. The DBA-iPSC line CSCRi006-A generated during this study was extensively characterized for its pluripotency and genome stability. The clone retained normal karyotype and showed high expression levels of pluripotency markers, OCT4, NANOG, SOX2, TRA-I-60, TRA-I-81, and SSEA4. It could differentiate into cells originating from all three germ cell layers, as identified by immunostaining for SOX17 (endoderm), Brachyury (mesoderm), and PAX6 (ectoderm). IPSCs provide a renewable source of cells for in vitro disease modeling. CSCRi006-A, a thoroughly characterized iPSC line carrying heterozygous RPS19 c.22-23delAG mutation, is a valuable cell line for the disease modeling of DBA. This iPSC line can be differentiated into different blood cell types to study the mechanisms of disease development and identify potential treatments.

Insight into the Interaction Mechanism of Vitamin D against Metabolic Syndrome: A Meta-Analysis and In Silico Study

As a dietary supplement or functional food additive, vitamin D (VD) deficiency may impact extra-skeletal functions associated with metabolic syndrome (MetS) risk factors. However, the precise effects and mechanisms of VD supplementation on dyslipidemia and insulin resistance in MetS subjects remain controversial. Here, we investigate potential therapeutic targets, pathways and mechanisms of VD against MetS through a comprehensive strategy including meta-analysis, network pharmacology analysis, molecular docking, dynamics simulations, and quantum chemical calculations. Our results reveal that VD supplementation significantly reduces triglyceride levels, fasting glucose, and insulin concentrations in subjects, thereby improving insulin homeostasis to some extent. We theoretically identify 14 core MetS-associated targets. Notably, VD exhibits substantial interactions with three targets (PPARγ, FABP4, and HMGCR) in the PPAR signaling pathway, indicating that VD can modulate this pathway. Van der Waals forces predominantly stabilize the complexes formed between VD and the three targets. Nonetheless, to provide valuable insights for personalized MetS management, further research is necessary to confirm our findings, emphasizing the importance of exploring genetic variability in VD response. In conclusion, our study contributes insights into the mechanisms of VD in preventing and treating MetS through dietary supplementation, promoting the development of VD-based functional foods or nutritious diets.

Mechanism of Artemisia annua L. in the treatment of acute myocardial infarction: network pharmacology, molecular docking and in vivo validation

This study was to evaluate the potential mechanism of action of Artemisia annua L. (A. annua) in the treatment of acute myocardial infarction (AMI) using network pharmacology, molecular docking and in vivo experiments. 22 active chemical compounds and 193 drug targets of A. annua were screened using the Traditional Chinese Medicine System Pharmacological (TCMSP) database. 3876 disease targets were also collected. Then 158 intersection targets between AMI and A. annua were obtained using R 4.2.0 software. String database was used to construct the protein-protein interaction (PPI) network and 6 core targets (MAPK1, TP53, HSP90AA1, RELA, AKT1, and MYC) were screened. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the R package. GO enrichment results were mainly related to cell responses to chemical stress and cell membrane microregions. KEGG pathways were mainly involved in lipids, atherosclerosis and fluid shear stress. In addition, molecular docking between A. annua active compounds and core targets showed high binding activity. As for in vivo validation, A. annua extract showed significant effects on improving post-infarction ventricular function, delaying ventricular remodeling, and reducing myocardial fibrosis and apoptosis. This study has revealed the potential components and molecular mechanisms of A. annua in the treatment of AMI. Our work also showed that A. annua has great effect on reducing myocardial fibrosis and scar area after infarction.

Rapid and efficient LC-MS/MS diagnosis of inherited metabolic disorders: a semi-automated workflow for analysis of organic acids, acylglycines, and acylcarnitines in urine

OBJECTIVES

The analysis of organic acids in urine is an important part of the diagnosis of inherited metabolic disorders (IMDs), for which gas chromatography coupled with mass spectrometry is still predominantly used.

METHODS

Ultra-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for urinary organic acids, acylcarnitines and acylglycines was developed and validated. Sample preparation consists only of dilution and the addition of internal standards. Raw data processing is quick and easy using selective scheduled multiple reaction monitoring mode. A robust standardised value calculation as a data transformation together with advanced automatic visualisation tools are applied for easy evaluation of complex data.

RESULTS

The developed method covers 146 biomarkers consisting of organic acids (n=99), acylglycines (n=15) and acylcarnitines (n=32) including all clinically important isomeric compounds present. Linearity with r2>0.98 for 118 analytes, inter-day accuracy between 80 and 120 % and imprecision under 15 % for 120 analytes were achieved. Over 2 years, more than 800 urine samples from children tested for IMDs were analysed. The workflow was evaluated on 93 patient samples and ERNDIM External Quality Assurance samples involving a total of 34 different IMDs.

CONCLUSIONS

The established LC-MS/MS workflow offers a comprehensive analysis of a wide range of organic acids, acylcarnitines and acylglycines in urine to perform effective, rapid and sensitive semi-automated diagnosis of more than 80 IMDs.

Mechanism and ingredients prediction of Radix Salviae-Angelicae Sinensis Radix-Lycii Fructus-Rehmanniae Radix Praeparata-Ginkgo Folium for retinitis pigmentosa therapy using network pharmacology and molecular docking analysis

Background

Radix Salviae (Danshen)-Angelicae Sinensis Radix (Danggui)-Lycii Fructus (Gouqizi)-Rehmanniae Radix Praeparata (Shudihuang)-Ginkgo Folium (Yinxinye) (RALRG) are commonly used herbs in China that have shown positive effects on retinitis pigmentosa (RP). However, little research has been performed on the impact of RALRG and RP. Herein, this study aimed to predict the mechanism and potential components of RALRG in treating RP.

Methods

The ingredients of RALRG were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP); the potential targets of RP and RALRG were obtained from TCMSP, GeneCards, and the Online Mendelian Inheritance in Man (OMIM) database. A protein-protein interaction (PPI) network was constructed to visualize PPIs. The functional enrichment was performed with the R program. A visual RALRG-RP-pathway pharmacology network was established by Cytoscape 3.9.1. Molecular docking was used to perform molecular docking and calculate the binding affinity.

Results

A total of 132 effective active ingredients in RALRG with 248 target genes were screened; 92 intersection target genes were acquired from the intersection of RP- and RALRG-related genes. Gene Ontology (GO) enrichment indicated that these intersection targets were mainly involved in oxidative stress, metal ion response, and chemical stress. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that the PI3K-AKT, cellular senescence, and MAPK signaling pathways were closely related to the therapy of RP. In addition, a potential pharmacology network for RALRG-RP-pathway was constructed. AKT1 and JUN were considered the primary targets. Luteolin, quercetin, and kaempferol were identified as the vital three active ingredients.

Conclusions

RALRG was found to be the main regulator for oxidative stress and PI3K/AKT signaling pathways. Luteolin, quercetin, and kaempferol were three promising complementary ingredients for RP treatment. This study may provide a theoretical basis for applying RALRG to screen potential drugs for RP.

Protein modeling and in silico analysis to assess pathogenicity of ABCA4 variants in patients with inherited retinal disease

Purpose

The retina-specific ABCA transporter, ABCA4, plays an essential role in translocating retinoids required by the visual cycle. ABCA4 genetic variants are known to cause a wide range of inherited retinal disorders, including Stargardt disease and cone-rod dystrophy. More than 1,400 ABCA4 missense variants have been identified; however, more than half of these remain variants of uncertain significance (VUS). The purpose of this study was to employ a predictive strategy to assess the pathogenicity of ABCA4 variants in inherited retinal diseases using protein modeling and computational approaches.

Methods

We studied 13 clinically well-defined patients with ABCA4 retinopathies and identified the presence of 10 missense variants, including one novel variant in the ABCA4 gene, by next-generation sequencing (NGS). All variants were structurally analyzed using AlphaFold2 models and existing experimental structures of human ABCA4 protein. The results of these analyses were compared with patient clinical presentations to test the effectiveness of the methods employed in predicting variant pathogenicity.

Results

We conducted a phenotype-genotype comparison of 13 genetically and phenotypically well-defined retinal disease patients. The in silico protein structure analyses we employed successfully detected the deleterious effect of missense variants found in this affected patient cohort. Our study provides American College of Medical Genetics and Genomics (ACMG)-defined supporting evidence of the pathogenicity of nine missense ABCA4 variants, aligning with the observed clinical phenotypes in this cohort.

Conclusions

In this report, we describe a systematic approach to predicting the pathogenicity of ABCA4 variants by means of three-dimensional (3D) protein modeling and in silico structure analysis. Our results demonstrate concordance between disease severity and structural changes in protein models induced by genetic variations. Furthermore, the present study suggests that in silico protein structure analysis can be used as a predictor of pathogenicity and may facilitate the assessment of genetic VUS.