Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents

Drug repurposing: databases and pipelines

The concept of drug repurposing is focused on the repositioning of drug molecules that have already undergone safety trials. There are different strategies for drug repurposing. Network-based strategy focuses on the evaluation of drug combinations in a molecular environment with multi-target hits and analysis of drug interactions. Implementation of any in silico strategy requires several databases and pipelines for executing the process of shortlisting appropriate drugs.

Efficacy of Danlou tablet on myocardial ischemia/ reperfusion injury assessed by network pharmacology and experimental verification

OBJECTIVE

To investigate the potential pharmacological mechanism of Danlou tablet (, DLT) with a long-term clinical application in the treatment of myocardial ischemia/reperfusion (I/R) injury through network pharmacology, molecular docking and experimental verification.

METHODS

The main chemical ingredients in DLT were retrieved from the Traditional Chinese Medicine (TCM) System Pharmacology Database, the TCM information database, the bioinformatics analysis tool for molecular mechanism of TCM, and HERB database. Disease targets of I/R were accessed from the databases of Online Mendelian Inheritance in Man, GeneCards, Therapeutic Target Database, and DisGeNET database. The overlaying genes of DLT and I/R were obtained from the Venny online platform. The core targets and protein-protein interaction network were constructed and analyzed via the Search Tool for the Retrieval of Interacting Genes Proteins database and Cytoscape software. Furthermore, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed by the Metascape platform. Based on the results, the component-target-pathway network was constructed and drafted via the Cytoscape software and the platform of Bioinformatics. Furthermore, we performed molecular docking to predict the binding information between chemical molecules and target proteins. Finally, oxygen-glucose deprivation/recovery (OGD/R)-induced H9c2 cardiomyocytes were used to validate the results of network pharmacology in vitro.

RESULTS

A total of 189 active chemical components in DLT and 849 correlative targets of I/R were screened. Of note, 133 overlaying genes found from the Venny online platform were concentrated into 28 core genes. Furthermore, the GO and KEGG pathway enrichment analysis presented that DLT might participate in 42 types of GO molecular functions, 747 types of GO biological processes, 19 types of GO cellular components, and 140 kinds of pathways to treat I/R. In the component-target-pathway network, the indirect relationship between herbs and their possible effective pathways was clarified. Based on the molecular docking, we speculated that Baicalein-prostaglandin G/H synthase 2 (PTGS2) with -3.24 kcal/mol, Luteolin-heat shock protein 90 alpha family class A member 1 (HSP90AA1) with -3.22 kcal/mol, Baicalein-HSP90AA1 with -3.13 kcal/mol, and Quercetin-HSP90AA1 with -3.05 kcal/mol possessed the strongest binding force of less than -3 kcal/mol, sequentially. Experimental verification showed that Quercetin, Luteolin, and Baicalein could increase the relative cell viability of OGD/R-stimulated cardiomyocytes, probably by suppressing PTGS2, and activating HSP90AA1 and estrogen receptor 1 expression.

CONCLUSIONS

We predicted the potential active compounds as the material basis of DLT that may provide a new approach to elucidate the novel pharmacological mechanism underlying the treatment of cardiac I/R damage.

Allosteric drugs: New principles and design approaches

Focusing on an important biomedical implication of allostery - design of allosteric drugs, we describe characteristics of allosteric sites, effectors, and their modes of actions distinguishing them from the orthosteric counterparts and calling for new principles and protocols in the quests for allosteric drugs. We show the importance of considering both binding affinity and allosteric signaling in establishing the structure-activity relationships (SARs) toward design of allosteric effectors, arguing that pairs of allosteric sites and their effector ligands - the site-effector pairs - should be generated and adjusted simultaneously in the framework of what we call directed design protocol. Key ideas and approaches for designing allosteric effectors including reverse perturbation, targeted and agnostic analysis are also discussed here. Several promising computational approaches are highlighted, along with the need for and potential advantages of utilizing generative models to facilitate discovery/design of new allosteric drugs.

Understanding apoptotic induction by Sargentodoxa cuneata-Patrinia villosa herb pair via PI3K/AKT/mTOR signalling in colorectal cancer cells using network pharmacology and cellular studies

ETHNOPHARMACOLOGICAL RELEVANCE

Sargentodoxa cuneata (Sargentodoxa cuneata (Oliv.) Rehder & E.H.Wilson, DXT)-Patrinia villosa(Patrinia villosa (Thunb.) Dufr, BJC) constitutes a commonly employed herb pair in Chinese medicine for colorectal cancer (CRC) treatment. Modern pharmacological investigations have revealed the anticancer activities of both Sargentodoxa cuneata and Patrinia villosa. Nevertheless, comprehensive studies are required to discern the specific antitumor active ingredients and mechanism of action when these two herbs are used in combination.

AIM OF THE STUDY

Through the integration of network pharmacology, molecular docking techniques, experimental assays, and bioinformatics analysis, our study aims to forecast the active ingredients, potential targets, and molecular mechanisms underlying the therapeutic efficacy of this herb pair against CRC.

MATERIALS AND METHODS

Plant names (1, Sargentodoxa cuneata (Oliv.) Rehder & E.H.Wilson; 2, Patrinia villosa (Thunb.) Dufr.) have been verified through WorldFloraOnline (www.worldFloraonline.org) and MPNs (http://mpns.kew.org). The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) were utilized for screening the active ingredients of the herb pair. The PharmMapper database was employed to predict the target proteins for each active ingredient. CRC-related targets were obtained from the Genecards database, Online Mendelian Inheritance in Man (OMIM) database, Disease Gene Network (DisGeNET) database, and Therapeutic Target Database (TTD). Common targets were identified by intersecting the target proteins of all active ingredients with CRC-related targets. Protein-protein interactions (PPI) for the common target proteins were constructed using the String database and Cytoscape 3.9.1 software. Network topology analysis facilitated the identification of core targets. These core targets were subjected to enrichment analysis of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) using the Metascape database. Molecular docking was performed using Discovery Studio 2019 to investigate the interactions between the active ingredients and core target proteins. The core targets were validated through bioinformatics analysis using GEPIA, HPA, and the cBioPortal database. Finally, a series of experiments were conducted to further validate the results in vitro.

RESULT

A total of 15 active ingredients and 255 herb targets were identified, resulting in 66 common targets in conjunction with 6113 disease targets. The PPI analysis highlighted AKT1, EGFR, CASP3, SRC, and ESR1 as core targets. KEGG enrichment analysis indicated significant enrichment in the PI3K-AKT signaling pathway, a pathway associated with cancer. Molecular docking experiments confirmed favorable interactions between dihydroguaiaretic acid and the core target proteins (AKT1, EGFR, CASP3, and ESR1). Bioinformatics analysis revealed differential expression of EGFR and CASP3 in normal and CRC tissues. Cellular experiments further verified that dihydroguaiaretic acid induces apoptosis in colorectal cancer cells through the PI3K-AKT signaling pathway.

CONCLUSION

Our network pharmacology study has elucidated that the Sargentodoxa cuneata-Patrinia villosa herb pair exerts the negative regulation of the PI3K/AKT/mTOR signaling pathway, ultimately leading to the induction of apoptosis in colorectal cancer cells. This research has predicted and validated the active ingredients, potential targets, and molecular mechanisms of Sargentodoxa cuneata-Patrinia villosa in the treatment of CRC, providing scientific evidence for the use of traditional Chinese medicine in managing CRC.

Large-scale computational modelling of the M1 and M2 synovial macrophages in rheumatoid arthritis

Macrophages play an essential role in rheumatoid arthritis. Depending on their phenotype (M1 or M2), they can play a role in the initiation or resolution of inflammation. The M1/M2 ratio in rheumatoid arthritis is higher than in healthy controls. Despite this, no treatment targeting specifically macrophages is currently used in clinics. Thus, devising strategies to selectively deplete proinflammatory macrophages and promote anti-inflammatory macrophages could be a promising therapeutic approach. State-of-the-art molecular interaction maps of M1 and M2 macrophages in rheumatoid arthritis are available and represent a dense source of knowledge; however, these maps remain limited by their static nature. Discrete dynamic modelling can be employed to study the emergent behaviours of these systems. Nevertheless, handling such large-scale models is challenging. Due to their massive size, it is computationally demanding to identify biologically relevant states in a cell- and disease-specific context. In this work, we developed an efficient computational framework that converts molecular interaction maps into Boolean models using the CaSQ tool. Next, we used a newly developed version of the BMA tool deployed to a high-performance computing cluster to identify the models' steady states. The identified attractors are then validated using gene expression data sets and prior knowledge. We successfully applied our framework to generate and calibrate the M1 and M2 macrophage Boolean models for rheumatoid arthritis. Using KO simulations, we identified NFkB, JAK1/JAK2, and ERK1/Notch1 as potential targets that could selectively suppress proinflammatory macrophages and GSK3B as a promising target that could promote anti-inflammatory macrophages in rheumatoid arthritis.

Network analysis, in vivo, and in vitro experiments identified the mechanisms by which Piper longum L. [Piperaceae] alleviates cartilage destruction, joint inflammation, and arthritic pain

Osteoarthritis (OA) is characterized by irreversible joint destruction, pain, and dysfunction. Piper longum L. [Piperaceae] (PL) is an East Asian herbal medicine with reported anti-inflammatory, analgesic, antioxidant, anti-stress, and anti-osteoporotic effects. This study aimed to evaluate the efficacy of PL in inhibiting pain and progressive joint destruction in OA based on its anti-inflammatory activity, and to explore its potential mechanisms using in vivo and in vitro models of OA. We predicted the potential hub targets and signaling pathways of PL through network analysis and molecular docking. Network analysis results showed that the possible hub targets of PL against OA were F2R, F3, MMP1, MMP2, MMP9, and PTGS2. The molecular docking results predicted strong binding affinities for the core compounds in PL: piperlongumine, piperlonguminine, and piperine. In vitro experiments showed that PL inhibited the expression of LPS-induced pro-inflammatory factors, such as F2R, F3, IL-1β, IL-6, IL-17A, MMP-1, MMP-2, MMP-3, MMP-9, MMP-13, NOS2, PTGS2, PGE2, and TNF-β. These mechanisms and effects were dose-dependent in vivo models. Furthermore, PL inhibited cartilage degradation in an OA-induced rat model. Thus, this study demonstrated that multiple components of PL may inhibit the multilayered pathology of OA by acting on multiple targets and pathways. These findings highlight the potential of PL as a disease-modifying OA drug candidate, which warrants further investigation.

Calycosin ameliorates osteoarthritis by regulating the imbalance between chondrocyte synthesis and catabolism

Osteoarthritis (OA) is a severe chronic inflammatory disease. As the main active component of Astragalus mongholicus Bunge, a classic traditional ethnic herb, calycosin exhibits anti-inflammatory action and its mechanism of exact targets for OA have yet to be determined. In this study, we established an anterior cruciate ligament transection (ACLT) mouse model. Mice were randomized to sham, OA, and calycosin groups. Cartilage synthesis markers type II collagen (Col-2) and SRY-Box Transcription Factor 9 (Sox-9) increased significantly after calycosin gavage. While cartilage matrix degradation index cyclooxygenase-2 (COX-2), phosphor-epidermal growth factor receptor (p-EGFR), and matrix metalloproteinase-9 (MMP9) expression were decreased. With the help of network pharmacology and molecular docking, these results were confirmed in chondrocyte ADTC5 cells. Our results indicated that the calycosin treatment significantly improved cartilage damage, this was probably attributed to reversing the imbalance between chondrocyte synthesis and catabolism.

The potential mechanism of Aidi injection against neuroblastoma-an investigation based on network pharmacology analysis

Background: Aidi injection, a classic traditional Chinese medicine (TCM) formula, has been used on a broader scale in treating a variety of cancers. In this study, we aimed to explore the potential anti-tumor effects of Aidi injection in the treatment of neuroblastoma (NB) using network pharmacology (NP). Methods: To elucidate the anti-NB mechanism of Aidi injection, an NP-based approach and molecular docking validation were employed. The compounds and target genes were collected from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (BATMAN-TCM) database. The protein-protein interaction network was constructed using the STRING database. clusterProfiler (R package) was utilized to annotate the bioinformatics of hub target genes. The gene survival analysis was performed on R2, a web-based genomic analysis application. iGEMDOCK was used for molecular docking validation, and GROMACS was utilized to validate molecular docking results. Furthermore, we investigated the anticancer effects of gomisin B and ginsenoside Rh2 on human NB cells using a cell viability assay. The Western blot assay was used to validate the protein levels of target genes in gomisin B- and ginsenoside Rh2-treated NB cells. Results: A total of 2 critical compounds with 16 hub target genes were identified for treating NB. All 16 hub genes could potentially influence the survival of NB patients. The top three genes (EGFR, ESR1, and MAPK1) were considered the central hub genes from the drug-compound-hub target gene-pathway network. The endocrine resistance and estrogen signaling pathways were identified as the therapeutic pathways using the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Gomisin B and ginsenoside Rh2 showed a good binding ability to the target protein in molecular docking. The results of cell experiments showed the anti-NB effect of gomisin B and ginsenoside Rh2. In addition, the administration of gomisin B over-regulated the expression of ESR1 protein in MYCN-amplified NB cells. Conclusion: In the present study, we investigated the potential pharmacological mechanisms of Aidi against NB and revealed the anti-NB effect of gomisin B, providing clinical evidence of Aidi in treating NB and establishing baselines for further research.

Elucidating the susceptibility to breast cancer: an in-depth proteomic and transcriptomic investigation into novel potential plasma protein biomarkers

Objectives: This study aimed to identify plasma proteins that are associated with and causative of breast cancer through Proteome and Transcriptome-wide association studies combining Mendelian Randomization. Methods: Utilizing high-throughput datasets, we designed a two-phase analytical framework aimed at identifying novel plasma proteins that are both associated with and causative of breast cancer. Initially, we conducted Proteome/Transcriptome-wide association studies (P/TWAS) to identify plasma proteins with significant associations. Subsequently, Mendelian Randomization was employed to ascertain the causation. The validity and robustness of our findings were further reinforced through external validation and various sensitivity analyses, including Bayesian colocalization, Steiger filtering, heterogeneity and pleiotropy. Additionally, we performed functional enrichment analysis of the identified proteins to better understand their roles in breast cancer and to assess their potential as druggable targets. Results: We identified 5 plasma proteins demonstrating strong associations and causative links with breast cancer. Specifically, PEX14 (OR = 1.201, p = 0.016) and CTSF (OR = 1.114, p < 0.001) both displayed positive and causal association with breast cancer. In contrast, SNUPN (OR = 0.905, p < 0.001), CSK (OR = 0.962, p = 0.038), and PARK7 (OR = 0.954, p < 0.001) were negatively associated with the disease. For the ER-positive subtype, 3 plasma proteins were identified, with CSK and CTSF exhibiting consistent trends, while GDI2 (OR = 0.920, p < 0.001) was distinct to this subtype. In ER-negative subtype, PEX14 (OR = 1.645, p < 0.001) stood out as the sole protein, even showing a stronger causal effect compared to breast cancer. These associations were robustly supported by colocalization and sensitivity analyses. Conclusion: Integrating multiple data dimensions, our study successfully pinpointed plasma proteins significantly associated with and causative of breast cancer, offering valuable insights for future research and potential new biomarkers and therapeutic targets.

Exploring the potential mechanism and molecular targets of Taohong Siwu Decoction against deep vein thrombosis based on network pharmacology and analysis docking

This study aims to investigate the mechanism of Taohong Siwu Decoction (THSWD) against deep vein thrombosis (DVT) using network pharmacology and molecular docking technology. We used the Traditional Chinese Medicine Systems Pharmacology database and reviewed literature to identify the main chemical components of THSWD. To find targets for DVT, we consulted GeneCards, Therapeutic Target Database, and PharmGKB databases. We used Cytoscape 3.8.2 software to construct herb-disease-gene-target networks. Additionally, we integrated drug targets and disease targets on the STRING platform to create a protein-protein interaction network. Then, we conducted Kyoto Encyclopedia of Genes and Genomes and gene ontology analysis. Finally, We employed the molecular docking method to validate our findings. We identified 56 potential targets associated with DVT and found 61 effective components. beta-sitosterol, quercetin, and kaempferol were the most prominent among these components. Our analysis of the protein-protein interaction network revealed that IL6, L1B, and AKT1 had the highest degree of association. Gene ontology analysis showed that THSWD treatment for DVT may involve response to inorganic substances, negative regulation of cell differentiation, plasma membrane protein complex, positive regulation of phosphorylation, and signaling receptor regulator activity. Kyoto Encyclopedia of Genes and Genomes analysis indicated that lipid and atherosclerosis, pathways in cancer, as well as the PI3K-Akt pathway are the main signal pathways involved. Molecular docking results demonstrated strong binding affinity between beta-sitosterol, quercetin, kaempferol, and AKT1 proteins as well as IL1B and IL6 proteins. The main targets for THSWD treatment of DVT may include AKT1, IL1B, and IL6. Beta-sitosterol, quercetin, and kaempferol may be the active ingredients responsible for producing this effect. These compounds may slow down the progression of DVT by regulating the inflammatory response through the PI3K/Akt pathway.

Molecular mechanism of PSORI-CM01 for psoriasis by regulating the inflammatory cytokines network

ETHNOPHARMACOLOGICAL RELEVANCE

Psoriasis is an inflammatory skin disease, there is no radical cure. Traditional Chinese medicine has accumulated a lot of clinical experience in the treatment of psoriasis and developed a variety of treatment methods, among which Yinxieling optimization formula (PSORI-CM01) have a definite clinical effect in the treatment of psoriasis, but their mechanism of action is still unclear.

AIM OF THE STUDY

To investigate the molecular mechanism of the PSORI-CM01 in the treatment of psoriasis.

MATERIALS AND METHODS

Firstly, potential active compounds and key signaling pathways of PSORI-CM01 were explored by the systems pharmacology method. Then MTT assay was used to screen the potentially active compounds of PSORI-CM01, and explore the combined effects of potentially active compounds. The regulation of potentially active compounds on inflammatory factors were evaluated by a Human Th17 Magnetic Bead Panel. The regulation of PSORI-CM01 on key targets in the key signaling pathways were explored by qRT-PCR method. Finally, the molecular mechanism of PSORI-CM01 in the treatment of psoriasis was explained by the systems pharmacology method.

RESULTS

The potentially active compounds of PSORI-CM01 included gallic acid, liquiritigenin, rosmarinic acid, syringic acid, isoliquiritin apioside, caffeic acid, naringenin, cryptochlorogenic acid, (+)-taxifolin, p-coumaric acid, chlorogenic acid, fraxin, 5-hydroxymethylfurfural, lithospermic acid, isoliquiritigenin, salviandic acid B, octahydrocurcumin, catechin, syringaldehyde, methyl rosmarinate, paeonol, protocatechuic acid, astilbin, isoastilbin, isofraxidin and zederone. Both antagonistic and synergistic effects were determined in the combinations of active compounds. Most of the active compounds up-regulated IL-2, IL-6, IL-9 and TNF-α, and down-regulated IFN-γ, IL-1β, IL-2, IL-9, IL-10, IL-13, IL-15, IL-17F, IL-21, IL-22 and IL-27. The PI3K-Akt signaling pathway would be the key signaling pathway of PSORI-CM01. The qRT-PCR results showed that its compounds can effectively regulate the expression of key targets in this pathway.

CONCLUSIONS

The molecular mechanism of PSORI-CM01 for treating psoriasis would be mediated by regulating the network of inflammatory factors through the PI3K-Akt signaling pathway.

Altered metabolic profiles and targets relevant to the protective effect of acteoside on diabetic nephropathy in db/db mice based on metabolomics and network pharmacology studies

ETHNOPHARMACOLOGICAL RELEVANCE

Diabetic nephropathy (DN) was a major cause of end-stage renal failure and a common microvascular complication in patients with diabetes mellitus (DM). Acteoside (ACT) was the main ingredient extracted from the leaves of Rehmannia glutinosa, which had the functions of entering the lung, moisturizing the skin and relieving itching, nourishing yin and tonifying the kidney, cooling blood, and stopping bleeding. ACT had attracted worldwide interest because of its therapeutic effects on DM and its complications.

AIM OF THE STUDY

To clarify the metabolic profiles and targets of ACT in db/db mice based on metabolomics and network pharmacology studies.

MATERIALS AND METHODS

Db/db mice were used to observe the biochemical indices and histopathological changes in the kidney to evaluate the pharmacological effects of ACT on DN. Untargeted metabolomics studies were performed to investigate by UHPLC-LTQ-Orbitrap MS on urine, serum, and kidney samples. The key targets and pathways were analyzed by network pharmacology. For the pathways enriched by untargeted metabolomics, targeted metabolomics by UHPLC-QQQ-MS/MS was performed in kidney samples for validation. Sensitive biomarkers in kidney samples were evaluated. The effect of ACT on the improvement of DN from the perspective of metabolism of small molecules in vivo was described.

RESULTS

ACT could delay the progression of DN and improve the degree of histopathological damage to the kidney. The pathways were focused on amino acid metabolism by untargeted metabolomics. Through network pharmacology analysis, the effect pathways were related to signal transduction, carbohydrate, lipid, amino acid metabolism and mainly affected the endocrine and immune systems. Amino acid metabolism was disturbed in the kidney of db/db mice, which could be callback by ACT, such as tryptophan, glutamine, cysteine, leucine, threonine, proline, phenylalanine, histidine, serine, arginine, asparagine by targeted metabolomics.

CONCLUSIONS

In conclusion, this study provided strong support for ACT on DN treatment in clinics. Meanwhile, the Rehmannia glutinosa was used fully to raise the income level of farmers economically, while achieving the social benefit of empowering rural revitalization.

Effects of scutellarin on the mechanism of cardiovascular diseases: a review

Cardiovascular diseases represent a significant worldwide problem, jeopardizing individuals' physical and mental wellbeing as well as their quality of life as a result of their widespread incidence and fatality. With the aging society, the occurrence of Cardiovascular diseases is progressively rising each year. However, although drugs developed for treating Cardiovascular diseases have clear targets and proven efficacy, they still carry certain toxic and side effect risks. Therefore, finding safe, effective, and practical treatment options is crucial. Scutellarin is the primary constituent of Erigeron breviscapus (Vant.) Hand-Mazz. This article aims to establish a theoretical foundation for the creation and use of secure, productive, and logical medications for Scutellarin in curing heart-related illnesses. Additionally, the examination and analysis of the signal pathway and its associated mechanisms with regard to the employment of SCU in treating heart diseases will impart innovative resolving concepts for the treatment and prevention of Cardiovascular diseases.

VARIDT 3.0: the phenotypic and regulatory variability of drug transporter

The phenotypic and regulatory variability of drug transporter (DT) are vital for the understanding of drug responses, drug-drug interactions, multidrug resistances, and so on. The ADME property of a drug is collectively determined by multiple types of variability, such as: microbiota influence (MBI), transcriptional regulation (TSR), epigenetics regulation (EGR), exogenous modulation (EGM) and post-translational modification (PTM). However, no database has yet been available to comprehensively describe these valuable variabilities of DTs. In this study, a major update of VARIDT was therefore conducted, which gave 2072 MBIs, 10 610 TSRs, 46 748 EGRs, 12 209 EGMs and 10 255 PTMs. These variability data were closely related to the transportation of 585 approved and 301 clinical trial drugs for treating 572 diseases. Moreover, the majority of the DTs in this database were found with multiple variabilities, which allowed a collective consideration in determining the ADME properties of a drug. All in all, VARIDT 3.0 is expected to be a popular data repository that could become an essential complement to existing pharmaceutical databases, and is freely accessible without any login requirement at: https://idrblab.org/varidt/.

CMAUP database update 2024: extended functional and association information of useful plants for biomedical research

Knowledge of the collective activities of individual plants together with the derived clinical effects and targeted disease associations is useful for plant-based biomedical research. To provide the information in complement to the established databases, we introduced a major update of CMAUP database, previously featured in NAR. This update includes (i) human transcriptomic changes overlapping with 1152 targets of 5765 individual plants, covering 74 diseases from 20 027 patient samples; (ii) clinical information for 185 individual plants in 691 clinical trials; (iii) drug development information for 4694 drug-producing plants with metabolites developed into approved or clinical trial drugs; (iv) plant and human disease associations (428 737 associations by target, 220 935 reversion of transcriptomic changes, 764 and 154121 associations by clinical trials of individual plants and plant ingredients); (v) the location of individual plants in the phylogenetic tree for navigating taxonomic neighbors, (vi) DNA barcodes of 3949 plants, (vii) predicted human oral bioavailability of plant ingredients by the established SwissADME and HobPre algorithm, (viii) 21-107% increase of CMAUP data over the previous version to cover 60 222 chemical ingredients, 7865 plants, 758 targets, 1399 diseases, 238 KEGG human pathways, 3013 gene ontologies and 1203 disease ontologies. CMAUP update version is freely accessible at https://bidd.group/CMAUP/index.html.

Prognostic biomarker discovery based on proteome landscape of Chinese lung adenocarcinoma

Despite recent innovations in imaging and genomic screening promotes advance in diagnosis and treatment of lung adenocarcinoma (LUAD), there remains high mortality of LUAD and insufficient understanding of LUAD biology. Our previous study performed an integrative multi-omic analysis of LUAD, filling the gap between genomic alterations and their biological proteome effects. However, more detailed molecular characterization and biomarker resources at proteome level still need to be uncovered. In this study, a quantitative proteomic experiment of patient-derived benign lung disease samples was carried out. After that, we integrated the proteomic data with previous dataset of 103 paired LUAD samples. We depicted the proteomic differences between non-cancerous and tumor samples and among diverse pathological subtypes. We also found that up-regulated mitophagy was a significant characteristic of early-stage LUAD. Additionally, our integrative analysis filtered out 75 potential prognostic biomarkers and validated two of them in an independent LUAD serum cohort. This study provided insights for improved understanding proteome abnormalities of LUAD and the novel prognostic biomarker discovery offered an opportunity for LUAD precise management.

TTD: Therapeutic Target Database describing target druggability information

Target discovery is one of the essential steps in modern drug development, and the identification of promising targets is fundamental for developing first-in-class drug. A variety of methods have emerged for target assessment based on druggability analysis, which refers to the likelihood of a target being effectively modulated by drug-like agents. In the therapeutic target database (TTD), nine categories of established druggability characteristics were thus collected for 426 successful, 1014 clinical trial, 212 preclinical/patented, and 1479 literature-reported targets via systematic review. These characteristic categories were classified into three distinct perspectives: molecular interaction/regulation, human system profile and cell-based expression variation. With the rapid progression of technology and concerted effort in drug discovery, TTD and other databases were highly expected to facilitate the explorations of druggability characteristics for the discovery and validation of innovative drug target. TTD is now freely accessible at: https://idrblab.org/ttd/.

ADCdb: the database of antibody-drug conjugates

Antibody-drug conjugates (ADCs) are a class of innovative biopharmaceutical drugs, which, via their antibody (mAb) component, deliver and release their potent warhead (a.k.a. payload) at the disease site, thereby simultaneously improving the efficacy of delivered therapy and reducing its off-target toxicity. To design ADCs of promising efficacy, it is crucial to have the critical data of pharma-information and biological activities for each ADC. However, no such database has been constructed yet. In this study, a database named ADCdb focusing on providing ADC information (especially its pharma-information and biological activities) from multiple perspectives was thus developed. Particularly, a total of 6572 ADCs (359 approved by FDA or in clinical trial pipeline, 501 in preclinical test, 819 with in-vivo testing data, 1868 with cell line/target testing data, 3025 without in-vivo/cell line/target testing data) together with their explicit pharma-information was collected and provided. Moreover, a total of 9171 literature-reported activities were discovered, which were identified from diverse clinical trial pipelines, model organisms, patient/cell-derived xenograft models, etc. Due to the significance of ADCs and their relevant data, this new database was expected to attract broad interests from diverse research fields of current biopharmaceutical drug discovery. The ADCdb is now publicly accessible at: https://idrblab.org/adcdb/.

INTEDE 2.0: the metabolic roadmap of drugs

The metabolic roadmap of drugs (MRD) is a comprehensive atlas for understanding the stepwise and sequential metabolism of certain drug in living organisms. It plays a vital role in lead optimization, personalized medication, and ADMET research. The MRD consists of three main components: (i) the sequential catalyses of drug and its metabolites by different drug-metabolizing enzymes (DMEs), (ii) a comprehensive collection of metabolic reactions along the entire MRD and (iii) a systematic description on efficacy & toxicity for all metabolites of a studied drug. However, there is no database available for describing the comprehensive metabolic roadmaps of drugs. Therefore, in this study, a major update of INTEDE was conducted, which provided the stepwise & sequential metabolic roadmaps for a total of 4701 drugs, and a total of 22 165 metabolic reactions containing 1088 DMEs and 18 882 drug metabolites. Additionally, the INTEDE 2.0 labeled the pharmacological properties (pharmacological activity or toxicity) of metabolites and provided their structural information. Furthermore, 3717 drug metabolism relationships were supplemented (from 7338 to 11 055). All in all, INTEDE 2.0 is highly expected to attract broad interests from related research community and serve as an essential supplement to existing pharmaceutical/biological/chemical databases. INTEDE 2.0 can now be accessible freely without any login requirement at: http://idrblab.org/intede/.

BATMAN-TCM 2.0: an enhanced integrative database for known and predicted interactions between traditional Chinese medicine ingredients and target proteins

Traditional Chinese medicine (TCM) is increasingly recognized and utilized worldwide. However, the complex ingredients of TCM and their interactions with the human body make elucidating molecular mechanisms challenging, which greatly hinders the modernization of TCM. In 2016, we developed BATMAN-TCM 1.0, which is an integrated database of TCM ingredient-target protein interaction (TTI) for pharmacology research. Here, to address the growing need for a higher coverage TTI dataset, and using omics data to screen active TCM ingredients or herbs for complex disease treatment, we updated BATMAN-TCM to version 2.0 (http://bionet.ncpsb.org.cn/batman-tcm/). Using the same protocol as version 1.0, we collected 17 068 known TTIs by manual curation (with a 62.3-fold increase), and predicted ∼2.3 million high-confidence TTIs. In addition, we incorporated three new features into the updated version: (i) it enables simultaneous exploration of the target of TCM ingredient for pharmacology research and TCM ingredients binding to target proteins for drug discovery; (ii) it has significantly expanded TTI coverage; and (iii) the website was redesigned for better user experience and higher speed. We believe that BATMAN-TCM 2.0, as a discovery repository, will contribute to the study of TCM molecular mechanisms and the development of new drugs for complex diseases.

Medicine for chronic atrophic gastritis: a systematic review, meta- and network pharmacology analysis

PURPOSE

The aim of this study is to determine the effectiveness and reliability of adding traditional Chinese medicine (TCM) in the clinical intervention and explore mechanisms of action for chronic atrophic gastritis (CAG) through meta- and network pharmacology analysis (NPAs).

METHODS

A predefined search strategy was used to retrieve literature from PubMed, Embase database, Cochrane Library, China National Knowledge Infrastructure (CNKI), Chinese BioMedical Literature Database (CBM), Wan Fang Data and China Science and Technology Journal Database (VIP). After applying inclusion and exclusion criteria, a total of 12 randomized controlled trials (RCTs) were included for meta-analysis to provide clinical evidence of the intervention effects. A network meta-analysis using Bayesian networks was conducted to observe the relative effects of different intervention measures and possible ranking of effects. The composition of the TCM formulation in the experimental group was analysed, and association rule mining was performed to identify hub herbal medicines. Target genes for CAG were searched in GeneCards, Online Mendelian Inheritance in Man, PharmGKB, Therapeutic Target Database and DrugBank. A regulatory network was constructed to connect the target genes with active ingredients of the hub herbal medicines. Enrichment analyses were performed using the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) to examine the central targets from a comprehensive viewpoint. Protein-protein interaction networks (PPINs) were constructed to identify hub genes and conduct molecular docking with differentially expressed genes (DEGs) and corresponding active molecules.

RESULTS

A total of 1140 participants from 12 RCTs were included in the statistical analysis, confirming that the experimental group receiving the addition of TCM intervention had better clinical efficacy. Seven hub TCMs (Paeonia lactiflora, Atractylodes macrocephala, Pinellia ternata, Citrus reticulata, Codonopsis pilosula, Salvia miltiorrhiza and Coptis chinensis) were identified through association rule analysis of all included TCMs. Thirteen hub genes (CDKN1A, CASP3, STAT1, TP53, JUN, MAPK1, STAT3, MAPK3, MYC, HIF1A, FOS, MAPK14 and AKT1) were obtained from 90 gene PPINs. Differential gene expression analysis between the disease and normal gastric tissue identified MAPK1 and MAPK3 as the significant genes. Molecular docking analysis revealed that naringenin, luteolin and quercetin were the main active compounds with good binding activities to the two hub targets. GO analysis demonstrated the function of the targets in protein binding, while KEGG analysis indicated their involvement in important pathways related to cancer.

CONCLUSIONS

The results of a meta-analysis of 12 RCTs indicate that TCM intervention can improve the clinical treatment efficacy of CAG. NPAs identified seven hub TCM and 13 target genes associated with their actions, while bioinformatics analysis identified two DEGs between normal and CAG gastric tissues. Finally, molecular docking was employed to reveal the mechanism of action of the active molecules in TCM on the DEGs. These findings not only reveal the mechanisms of action of the active components of the TCMs, but also provide support for the development of new drugs, ultimately blocking the progression from chronic gastritis to gastric cancer.

Integrated Analysis of Single-Cell RNA-Seq and Bulk RNA-Seq Unravels the Molecular Feature of Tumor-Associated Macrophage of Acute Myeloid Leukemia

Background

The association between acute myeloid leukemia (AML) and macrophage remains to be deeply explored.

Methods

Gene expression profiles and clinical variable characteristics of AML patients were collected from TCGA, GEO, and TARGET databases. Consensus clustering was employed to construct the macrophage-related clusters. The macrophage-related index (MRI) was constructed using the LASSO and multivariate Cox analysis. The GSE71014 and TARGET datasets were utilized as external validation sets. Single-cell sequencing data for AML (GSE116256) was adopted to analyze modeled gene expression levels in cells.

Results

Two macrophage-related clusters with different prognostic and immune infiltration characteristics were constructed in AML. Cluster B had a poorer prognosis, more cancer-promoting pathway enrichment, and an immunosuppressive microenvironment. Relied on the MRI, patients of different groups showed different levels of immune infiltration, different mutations, and prognoses. LGALS1 and BCL2A1 may play roles in promoting cancer in AML, while ELANE may have a significant effect on suppressing cancer.

Conclusion

Macrophage-related genes (MRGs) had significant impacts on the occurrence and progression of AML. MRI may better evaluate the prognosis and immune features of AML patients.

Genetic effects of sequence-conserved enhancer-like elements on human complex traits

BACKGROUND

The vast majority of findings from human genome-wide association studies (GWAS) map to non-coding sequences, complicating their mechanistic interpretations and clinical translations. Non-coding sequences that are evolutionarily conserved and biochemically active could offer clues to the mechanisms underpinning GWAS discoveries. However, genetic effects of such sequences have not been systematically examined across a wide range of human tissues and traits, hampering progress to fully understand regulatory causes of human complex traits.

RESULTS

Here we develop a simple yet effective strategy to identify functional elements exhibiting high levels of human-mouse sequence conservation and enhancer-like biochemical activity, which scales well to 313 epigenomic datasets across 106 human tissues and cell types. Combined with 468 GWAS of European (EUR) and East Asian (EAS) ancestries, these elements show tissue-specific enrichments of heritability and causal variants for many traits, which are significantly stronger than enrichments based on enhancers without sequence conservation. These elements also help prioritize candidate genes that are functionally relevant to body mass index (BMI) and schizophrenia but were not reported in previous GWAS with large sample sizes.

CONCLUSIONS

Our findings provide a comprehensive assessment of how sequence-conserved enhancer-like elements affect complex traits in diverse tissues and demonstrate a generalizable strategy of integrating evolutionary and biochemical data to elucidate human disease genetics.

Unraveling the intercellular communication disruption and key pathways in Alzheimer's disease: an integrative study of single-nucleus transcriptomes and genetic association

BACKGROUND

Recently, single-nucleus RNA-seq (snRNA-seq) analyses have revealed important cellular and functional features of Alzheimer's disease (AD), a prevalent neurodegenerative disease. However, our knowledge regarding intercellular communication mediated by dysregulated ligand-receptor (LR) interactions remains very limited in AD brains.

METHODS

We systematically assessed the intercellular communication networks by using a discovery snRNA-seq dataset comprising 69,499 nuclei from 48 human postmortem prefrontal cortex (PFC) samples. We replicated the findings using an independent snRNA-seq dataset of 56,440 nuclei from 18 PFC samples. By integrating genetic signals from AD genome-wide association studies (GWAS) summary statistics and whole genome sequencing (WGS) data, we prioritized AD-associated Gene Ontology (GO) terms containing dysregulated LR interactions. We further explored drug repurposing for the prioritized LR pairs using the Therapeutic Targets Database.

RESULTS

We identified 190 dysregulated LR interactions across six major cell types in AD PFC, of which 107 pairs were replicated. Among the replicated LR signals, we found globally downregulated communications in the astrocytes-to-neurons signaling axis, characterized, for instance, by the downregulation of APOE-related and Calmodulin (CALM)-related LR interactions and their potential regulatory connections to target genes. Pathway analyses revealed 44 GO terms significantly linked to AD, highlighting Biological Processes such as 'amyloid precursor protein processing' and 'ion transmembrane transport,' among others. We prioritized several drug repurposing candidates, such as cromoglicate, targeting the identified dysregulated LR pairs.

CONCLUSIONS

Our integrative analysis identified key dysregulated LR interactions in a cell type-specific manner and the associated GO terms in AD, offering novel insights into potential therapeutic targets involved in disrupted cell-cell communication in AD.

Exploration of the Potential Mechanism of Yujin Powder Treating Dampness-heat Diarrhea by Integrating UPLC-MS/MS and Network Pharmacology Prediction

BACKGROUND

Yujin powder (YJP) is a classic prescription for treating dampness-heat diarrhea (DHD) in Traditional Chinese Medicine (TCM), but the main functional active ingredients and the exact mechanisms have not been systematically studied.

OBJECTIVES

This study aimed to preliminarily explore the potential mechanisms of YJP for treating DHD by integrating UPLC-MS/MS and network pharmacology methods.

METHODS

Ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) technology was used to determine the ingredients of YJP. And then, the targets of these components were predicted and screened from TCMSP, SwissTargetPrediction databases. The disease targets related to DHD were obtained by using the databases of GeneCards, OMIM, DisGeNET, TTD, and DrugBank. The protein-protein interaction networks (PPI) of YJP-DHD were constructed using the STRING database and Origin 2022 software to identify the cross-targets by screening the core-acting targets and a network diagram by Cytoscape 3.8.2 software was also constructed. Metascape database was used for performing GO and KEGG enrichment anlysis on the core genes. Finally, molecular docking was used to verify the results with AutoDock 4.2.6, AutoDock Tools 1.5.6, PyMOL 2.4.0, and Open Babel 2.3.2 software.

RESULTS

597 components in YJP were detected, and 153 active components were obtained through database screening, among them the key active ingredients include coptisine, berberine, baicalein, etc. There were 362 targets treating DHD, among them the core targets included TNF, IL-6, ALB, etc. The enriched KEGG pathways mainly involve PI3K-Akt, TNF, MAPK, etc. Molecular docking results showed that coptisine, berberine, baicalein, etc., had a strong affinity with TNF, IL-6, and MAPK14. Therefore, TNF, IL-6, MAPK14, ALB, etc., are the key targets of the active ingredients of YJP coptisine, baicalein, and berberine, etc. They have the potential to regulate PI3K-Akt, MAPK, and TNF signalling pathways. The component-target-disease network diagram revealed that YJP treated DHD through the effects of anti-inflammation, anti-diarrhea, immunoregulation, and improving intestinal mucosal injury.

CONCLUSION

It is demonstrated that YJP treats DHD mainly through the main active ingredients coptisine, berberine, baicalein, etc. comprehensively exerting the effects of anti-inflammation, anti-diarrhea, immunoregulation, and improving intestinal mucosal injury, which will provide evidence for further in-depth studying the mechanism of YJP treating DHD.

Multi-tissue transcriptome-wide association study reveals susceptibility genes and drug targets for insulin resistance-relevant phenotypes

AIM

Genome-wide association studies (GWAS) have identified multiple susceptibility loci associated with insulin resistance (IR)-relevant phenotypes. However, the genes responsible for these associations remain largely unknown. We aim to identify susceptibility genes for IR-relevant phenotypes via a transcriptome-wide association study.

MATERIALS AND METHODS

We conducted a large-scale multi-tissue transcriptome-wide association study for IR (Insulin Sensitivity Index, homeostasis model assessment-IR, fasting insulin) and lipid-relevant traits (high-density lipoprotein cholesterol, triglycerides, low-density lipoprotein cholesterol and total cholesterol) using the largest GWAS summary statistics and precomputed gene expression weights of 49 human tissues. Conditional and joint analyses were implemented to identify significantly independent genes. Furthermore, we estimated the causal effects of independent genes by Mendelian randomization causal inference analysis.

RESULTS

We identified 1190 susceptibility genes causally associated with IR-relevant phenotypes, including 58 genes that were not implicated in the original GWAS. Among them, 11 genes were further supported in differential expression analyses or a gene knockout mice database, such as KRIT1 showed both significantly differential expression and IR-related phenotypic effects in knockout mice. Meanwhile, seven proteins encoded by susceptibility genes were targeted by clinically approved drugs, and three of these genes (H6PD, CACNB2 and DRD2) have been served as drug targets for IR-related diseases/traits. Moreover, drug repurposing analysis identified four compounds with profiles opposing the expression of genes associated with IR risk.

CONCLUSIONS

Our study provided new insights into IR aetiology and avenues for therapeutic development.

Research on the Regulatory Mechanism of Ginseng on the Tumor Microenvironment of Colorectal Cancer based on Network Pharmacology and Bioinformatics Validation

BACKGROUND

A network pharmacology study on the biological action of ginseng in the treatment of colorectal cancer (CRC) by regulating the tumor microenvironment (TME).

OBJECTIVES

To investigate the potential mechanism of action of ginseng in the treatment of CRC by regulating TME.

METHODS

This research employed network pharmacology, molecular docking techniques, and bioinformatics validation. Firstly, the active ingredients and the corresponding targets of ginseng were retrieved using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), the Traditional Chinese Medicine Integrated Database (TCMID), and the Traditional Chinese Medicine Database@Taiwan (TCM Database@Taiwan). Secondly, the targets related to CRC were retrieved using Genecards, Therapeutic Target Database (TTD), and Online Mendelian Inheritance in Man (OMIM). Tertiary, the targets related to TME were derived from screening the GeneCards and National Center for Biotechnology Information (NCBI)-Gene. Then the common targets of ginseng, CRC, and TME were obtained by Venn diagram. Afterward, the Protein-protein interaction (PPI) network was constructed in the STRING 11.5 database, intersecting targets identified by PPI analysis were introduced into Cytoscape 3.8.2 software cytoHubba plugin, and the final determination of core targets was based on degree value. The OmicShare Tools platform was used to analyze the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the core targets. Autodock and PyMOL were used for molecular docking verification and visual data analysis of docking results. Finally, we verified the core targets by Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) databases in bioinformatics.

RESULTS

A total of 22 active ingredients and 202 targets were identified to be closely related to the TME of CRC. PPI network mapping identified SRC, STAT3, PIK3R1, HSP90AA1, and AKT1 as possible core targets. Go enrichment analysis showed that it was mainly involved in T cell co-stimulation, lymphocyte co-stimulation, growth hormone response, protein input, and other biological processes; KEGG pathway analysis found 123 related signal pathways, including EGFR tyrosine kinase inhibitor resistance, chemokine signaling pathway, VEGF signaling pathway, ErbB signaling pathway, PD-L1 expression and PD-1 checkpoint pathway in cancer, etc. The molecular docking results showed that the main chemical components of ginseng have a stable binding activity to the core targets. The results of the GEPIA database showed that the mRNA levels of PIK3R1 were significantly lowly expressed and HSP90AA1 was significantly highly expressed in CRC tissues. Analysis of the relationship between core target mRNA levels and the pathological stage of CRC showed that the levels of SRC changed significantly with the pathological stage. The HPA database results showed that the expression levels of SRC were increased in CRC tissues, while the expression of STAT3, PIK3R1, HSP90AA1, and AKT1 were decreased in CRC tissues.

CONCLUSION

Ginseng may act on SRC, STAT3, PIK3R1, HSP90AA1, and AKT1 to regulate T cell costimulation, lymphocyte costimulation, growth hormone response, protein input as a molecular mechanism regulating TME for CRC. It reflects the multi-target and multi-pathway role of ginseng in modulating TME for CRC, which provides new ideas to further reveal its pharmacological basis, mechanism of action and new drug design and development.

Cation-independent mannose 6-phosphate receptor: From roles and functions to targeted therapies

The cation-independent mannose 6-phosphate receptor (CI-M6PR) is a ubiquitous transmembrane receptor whose main intracellular role is to direct enzymes carrying mannose 6-phosphate moieties to lysosomal compartments. Recently, the small membrane-bound portion of this receptor has appeared to be implicated in numerous pathophysiological processes. This review presents an overview of the main ligand partners and the roles of CI-M6PR in lysosomal storage diseases, neurology, immunology and cancer fields. Moreover, this membrane receptor has already been noted for its strong potential in therapeutic applications thanks to its cellular internalization activity and its ability to address pathogenic factors to lysosomes for degradation. A number of therapeutic delivery approaches using CI-M6PR, in particular with enzymes, antibodies or nanoparticles, are currently being proposed.

Exploring the Mechanisms of Self-made Kuiyu Pingchang Recipe for the Treatment of Ulcerative Colitis and Irritable Bowel Syndrome using a Network Pharmacology-based Approach and Molecular Docking

BACKGROUND

Ulcerative colitis (UC) and irritable bowel syndrome (IBS) are common intestinal diseases. According to the clinical experience and curative effect, the authors formulated Kuiyu Pingchang Decoction (KYPCD) comprised of Paeoniae radix alba, Aurantii Fructus, Herba euphorbiae humifusae, Lasiosphaera seu Calvatia, Angelicae sinensis radix, Panax ginseng C.A. Mey., Platycodon grandiforus and Allium azureum Ledeb.

OBJECTIVE

The aim of the present study was to explore the mechanisms of KYPCD in the treatment of UC and IBS following the Traditional Chinese Medicine (TCM) theory of "Treating different diseases with the same treatment".

METHODS

The chemical ingredients and targets of KYPCD were obtained using the Traditional Chinese Medicine Systems Pharmacology database and analysis platform (TCMSP). The targets of UC and IBS were extracted using the DisGeNET, GeneCards, DrugBANK, OMIM and TTD databases. The "TCM-component-target" network and the "TCM-shared target-disease" network were imaged using Cytoscape software. The protein-protein interaction (PPI) network was built using the STRING database. The DAVID platform was used to analyze the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Using Autodock Tools software, the main active components of KYPCD were molecularly docked with their targets and visualized using PyMOL.

RESULTS

A total of 46 active ingredients of KYPCD corresponding to 243 potential targets, 1,565 targets of UC and 1,062 targets of IBS, and 70 targets among active ingredients and two diseases were screened. Core targets in the PPI network included IL6, TNF, AKT1, IL1B, TP53, EGFR and VEGFA. GO and KEGG enrichment analysis demonstrated 563 biological processes, 48 cellular components, 82 molecular functions and 144 signaling pathways. KEGG enrichment results revealed that the regulated pathways were mainly related to the PI3K-AKT, MAPK, HIF-1 and IL-17 pathways. The results of molecular docking analysis indicated that the core active ingredients of KYPCD had optimal binding activity to their corresponding targets.

CONCLUSION

KYPCD may use IL6, TNF, AKT1, IL1B, TP53, EGFR and VEGFA as the key targets to achieve the treatment of UC and IBS through the PI3K-AKT, MAPK, HIF-1 and IL-17 pathways.

Deciphering the Mechanism of Siwu Decoction Inhibiting Liver Metastasis by Integrating Network Pharmacology and In Vivo Experimental Validation

BACKGROUND

Siwu Decoction (SWD) is a well-known classical TCM formula that has been shown to be effective as a basis for preventing and reducing liver metastases (LM). However, the active ingredients and potential molecular mechanisms remain unclear.

OBJECTIVE

This study aimed to systematically analyze the active ingredients and potential molecular mechanisms of SWD on LM and validate mechanisms involved.

MATERIALS AND METHODS

The active ingredients in SWD were extracted by UHPLC-MS/MS in a latest study. Protox II was retrieved to obtain toxicological parameters to detect safety. Swiss Target Prediction database was exploited to harvest SWD targets. Five databases, Gene Cards, DisGeNET, Drugbank, OMIM, and TTD, were employed to filter pathogenic targets of LM. STRING database was utilized to construct the protein-protein interaction network for therapeutic targets, followed by Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. GEPIA database and the Human Protein Atlas were taken to observe the expression of core genes and proteins. ImmuCellAI algorithm was applied to analyze the immune microenvironment and survival relevant to core genes. Molecular docking was performed to verify the affinity of SWD effective ingredients to core targets. In vivo experiments were carried out to validate the anti-LM efficacy of SWD and verify the pivotal mechanisms of action.

RESULTS

Eighteen main bioactive phytochemicals identified were all non-hepatotoxic. PPI network acquired 118 therapeutic targets, of which VEGFA, CASP3, STAT3, etc. were identified as core targets. KEGG analysis revealed that HIF-1 pathway and others were critical. After tandem targets and pathways, HIF-1/VEGF was regarded as the greatest potential pathway. VEGFA and HIF-1 were expressed differently in various stages of cancer and normal tissues. There was a negative regulation of immunoreactive cells by VEGFA, which was influential for prognosis. Molecular docking confirmed the tight binding to VEGFA. This study revealed the exact effect of SWD against LM, and identified significant inhibition the expression of HIF-1α, VEGF, and CD31 in the liver microenvironment.

CONCLUSION

This study clarified the active ingredients of SWD, the therapeutic targets of LM and potential molecular mechanisms. SWD may protect against LM through suppressing HIF-1/VEGF pathway.

Text Mining and Drug Discovery Analysis: A Comprehensive Approach to Investigate Diabetes-Induced Osteoporosis

Purpose: Osteoporosis (OP) and diabetes are prevalent diseases in orthopedic and endocrinology departments, with OP potentially arising as a complication of diabetes. However, the mechanism underlying diabetes-induced osteoporosis (DOP) remains enigmatic, and drug discovery in this domain is restricted, hindering research into the DOP's etiology and treatment. With the ultimate goal of preventing OP in diabetic patients, the objective of this study is to mine the genes and pathways linked to DOP using bioinformatics and databases. Method: The present study employed text mining as the initial approach to retrieve genes commonly associated with diabetes and OP. Subsequently, functional annotation was conducted to investigate the roles and functionalities. In order to explore the interactions between proteins relevant to DOP, we constructed protein-protein interaction (PPI) networks. Furthermore, to obtain key genes and candidate drugs for DOP treatment, we conducted drug-gene interaction (DGI) analysis, complemented by a thorough examination of transcriptional factors (TFs)-miRNA co-regulation. Results: The results through text mining revealed 110 genes that are commonly associated with both diabetes and OP. Subsequent enrichment analysis narrowed down the list to 95 symbols that were involved in PPI analysis. After DGI analysis, we identified 7 genes targeted by 11 drugs, which represent candidates for treating DOP. Conclusion: This study unveils ANDECALIXIMAB, SILTUXIMAB, OLOKIZUMAB, SECUKINUMAB, and IXEKIZUMAB as promising potential drugs for DOP treatment, demonstrating the significance of utilizing text mining and pathway analysis to investigate disease mechanisms and explore existing therapeutic options.

A CNN-CBAM-BIGRU model for protein function prediction

Understanding a protein's function based solely on its amino acid sequence is a crucial but intricate task in bioinformatics. Traditionally, this challenge has proven difficult. However, recent years have witnessed the rise of deep learning as a powerful tool, achieving significant success in protein function prediction. Their strength lies in their ability to automatically learn informative features from protein sequences, which can then be used to predict the protein's function. This study builds upon these advancements by proposing a novel model: CNN-CBAM+BiGRU. It incorporates a Convolutional Block Attention Module (CBAM) alongside BiGRUs. CBAM acts as a spotlight, guiding the CNN to focus on the most informative parts of the protein data, leading to more accurate feature extraction. BiGRUs, a type of Recurrent Neural Network (RNN), excel at capturing long-range dependencies within the protein sequence, which are essential for accurate function prediction. The proposed model integrates the strengths of both CNN-CBAM and BiGRU. This study's findings, validated through experimentation, showcase the effectiveness of this combined approach. For the human dataset, the suggested method outperforms the CNN-BIGRU+ATT model by +1.0 % for cellular components, +1.1 % for molecular functions, and +0.5 % for biological processes. For the yeast dataset, the suggested method outperforms the CNN-BIGRU+ATT model by +2.4 % for the cellular component, +1.2 % for molecular functions, and +0.6 % for biological processes.

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.

Molecular mechanism of ChaiShi JieDu granule in treating dengue based on network pharmacology and molecular docking: A review

Dengue fever is a frequently occurring infectious disease caused by the Dengue virus, prevalent in tropical and subtropical regions. Chaishi Jiedu Granules (CSJD) is an empirical prescription of the Eighth Affiliated Hospital of Guangzhou Medical University in the treatment of dengue fever, which has been widely used in the treatment of dengue fever, and has shown good efficacy in improving the clinical symptoms of patients. This study aims to explore the molecular mechanism of CSJD in treating dengue fever using network pharmacology, molecular docking techniques, and virtual screening methods. The results showed that luteolin, quercetin and other compounds in CSJD could target important targets related to dengue virus, including STAT3, AKT1, TNF, IL-6, and other key genes, thus playing an antiviral role. Among them, luteolin and wogonin in CSJD also inhibited dengue virus replication and reduced inflammation, and showed good binding force with IL-6 and TNF. Therefore, this study provides an important reference for the development of CSJD as a potential drug for dengue fever treatment and a new perspective for research and development in this field.

Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics

Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.

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.

Mechanism of Huaiqihuang in treatment of diabetic kidney disease based on network pharmacology, molecular docking and in vitro experiment

BACKGROUND

This study aimed to investigate the active components, key targets, and potential molecular mechanisms Huaiqihuang (HQH) in the treatment of diabetic kidney disease (DKD) through network pharmacology, molecular docking, and in vitro experiments.

METHODS

The active components and potential targets of HQH were obtained from the TCMSP and HERB databases. The potential targets of DKD were obtained from the GeneCards, OMIM, DrugBank, and TTD databases. Protein interaction relationships were obtained from the STRING database, and a protein interaction network was constructed using Cytoscape software. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis was performed using the Metascape database. Molecular docking was performed using AutoDock software to verify the binding between key compounds and core target genes. In vitro experiments were conducted using human renal proximal tubular epithelial cells and various methods, such as CCK8, RT-PCR, immunofluorescence, and western blot, to evaluate the effects of HQH on inflammatory factors, key targets, and pathways.

RESULTS

A total of 48 active ingredients, 168 potential targets of HQH, and 1073 potential targets of DKD were obtained. A total of 118 potential targets, 438 biological processes, and 187 signal pathways were identified for the treatment of DKD. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis indicated that HQH may exert its therapeutic effects on DKD by regulating the expression of inflammatory factors through the nuclear factor kappa B (NF-κB) signaling pathway. The molecular docking results showed that β-sitosterol and baicalein had the highest binding affinity with key targets such as AKT1, IL6, TNF, PTGS2, IL1B, and CASP3, suggesting that they may be the most effective active ingredients of HQH in the treatment of DKD. In vitro experimental results demonstrated that HQH could enhance the viability of human renal proximal tubular epithelial cells inhibited by high glucose, decrease the levels of AKT1, TNF, IL6, PTGS2, IL1B, and CASP3, reduce the expression of NF-κB-P65 (P < .01), inhibit NF-κB-p65 nuclear translocation, and decrease chemokine expression (P < .01).

CONCLUSION

HQH may exert its therapeutic effects on DKD by inhibiting the NF-κB signaling pathway, reducing the level of pro-inflammatory cytokines, and alleviating the high glucose-induced injury of renal tubular epithelial cells.

Unveiling the mechanisms of nephrotoxicity caused by nephrotoxic compounds using toxicological network analysis

Billions of people worldwide have experienced irreversible kidney injuries, which is mainly attributed to the complexity of drug-induced nephrotoxicity. Consequently, there is an urgent need for uncovering the mechanisms of nephrotoxicity caused by compounds. In the present study, a network-based methodology was applied to explore the mechanisms of nephrotoxicity induced by specific compounds. Initially, a total of 42 nephrotoxic compounds and 60 kinds of syndromes associated with nephrotoxicity were collected from public resources. Afterward, network localization and separation algorithms were used to map the targets of compounds and diseases into the human interactome. By doing so, 199 statistically significant nephrotoxic networks displaying the interaction between compound targets and disease genes were obtained, which played pivotal roles in compounds-induced nephrotoxicity. Subsequently, enrichment analysis pinpointed core Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways that highlight commonalities in nephrotoxicity induced by nephrotoxic compounds. It was found that nephrotoxic compounds primarily induce nephrotoxicity by mediating the advanced glycosylation end products-receptor for advanced glycosylation end products signaling pathway in diabetic complications, human cytomegalovirus infection, lipid and atherosclerosis, Kaposi sarcoma-associated herpesvirus infection, apoptosis, and the phosphatidylinositol 3-kinase-Akt pathways. These results provide valuable insights for preventing drug-induced nephrotoxicity. Furthermore, the approaches we used are also helpful in conducting research on other kinds of toxicities.

The PENGUIN approach to reconstruct protein interactions at enhancer-promoter regions and its application to prostate cancer

We introduce Promoter-Enhancer-Guided Interaction Networks (PENGUIN), a method for studying protein-protein interaction (PPI) networks within enhancer-promoter interactions. PENGUIN integrates H3K27ac-HiChIP data with tissue-specific PPIs to define enhancer-promoter PPI networks (EPINs). We validated PENGUIN using cancer (LNCaP) and benign (LHSAR) prostate cell lines. Our analysis detected EPIN clusters enriched with the architectural protein CTCF, a regulator of enhancer-promoter interactions. CTCF presence was coupled with the prevalence of prostate cancer (PrCa) single nucleotide polymorphisms (SNPs) within the same EPIN clusters, suggesting functional implications in PrCa. Within the EPINs displaying enrichments in both CTCF and PrCa SNPs, we also show enrichment in oncogenes. We substantiated our identified SNPs through CRISPR/Cas9 knockout and RNAi screens experiments. Here we show that PENGUIN provides insights into the intricate interplay between enhancer-promoter interactions and PPI networks, which are crucial for identifying key genes and potential intervention targets. A dedicated server is available at https://penguin.life.bsc.es/ .

Tri©DB: an integrated platform of knowledgebase and reporting system for cancer precision medicine

BACKGROUND

With the development of cancer precision medicine, a huge amount of high-dimensional cancer information has rapidly accumulated regarding gene alterations, diseases, therapeutic interventions and various annotations. The information is highly fragmented across multiple different sources, making it highly challenging to effectively utilize and exchange the information. Therefore, it is essential to create a resource platform containing well-aggregated, carefully mined, and easily accessible data for effective knowledge sharing.

METHODS

In this study, we have developed "Consensus Cancer Core" (Tri©DB), a new integrative cancer precision medicine knowledgebase and reporting system by mining and harmonizing multifaceted cancer data sources, and presenting them in a centralized platform with enhanced functionalities for accessibility, annotation and analysis.

RESULTS

The knowledgebase provides the currently most comprehensive information on cancer precision medicine covering more than 40 annotation entities, many of which are novel and have never been explored previously. Tri©DB offers several unique features: (i) harmonizing the cancer-related information from more than 30 data sources into one integrative platform for easy access; (ii) utilizing a variety of data analysis and graphical tools for enhanced user interaction with the high-dimensional data; (iii) containing a newly developed reporting system for automated annotation and therapy matching for external patient genomic data. Benchmark test indicated that Tri©DB is able to annotate 46% more treatments than two officially recognized resources, oncoKB and MCG. Tri©DB was further shown to have achieved 94.9% concordance with administered treatments in a real clinical trial.

CONCLUSIONS

The novel features and rich functionalities of the new platform will facilitate full access to cancer precision medicine data in one single platform and accommodate the needs of a broad range of researchers not only in translational medicine, but also in basic biomedical research. We believe that it will help to promote knowledge sharing in cancer precision medicine. Tri©DB is freely available at www.biomeddb.org , and is hosted on a cutting-edge technology architecture supporting all major browsers and mobile handsets.

Research into the anti-pulmonary fibrosis mechanism of Renshen Pingfei formula based on network pharmacology, metabolomics, and verification of AMPK/PPAR-γ pathway of active ingredients

ETHNOPHARMACOLOGICAL RELEVANCE

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive disease with limited therapy. Renshen Pingfei Formula (RPFF), a classic Chinese medicine derivative formula, has been shown to exert therapeutic effects on IPF.

AIM OF THE STUDY

The study aimed to explore the anti-pulmonary fibrosis mechanism of RPFF through network pharmacology, clinical plasma metabolomics, and in vitro experiment.

METHODS

Network pharmacology was used to study the holistic pharmacological mechanism of RPFF in the treatment of IPF. The differential plasma metabolites for RPFF in the treatment of IPF were identified by untargeted metabolomics analysis. By integrated analysis of metabolomics and network pharmacology, the therapeutic target of RPFF for IPF and the corresponding herbal ingredients were identified. In addition, the effects of the main components of the formula, kaempferol and luteolin, which regulate the adenosine monophosphate (AMP)-activated protein kinase (AMPK)/peroxisome proliferator-activated receptor γ (PPAR-γ) pathway were observed in vitro according to the orthogonal design.

RESULTS

A total of 92 potential targets for RPFF in the treatment of IPF were obtained. The Drug-Ingredients-Disease Target network showed that PTGS2, ESR1, SCN5A, PPAR-γ, and PRSS1 were associated with more herbal ingredients. The protein-protein interaction (PPI) network identified the key targets of RPFF in IPF treatment, including IL6, VEGFA, PTGS2, PPAR-γ, and STAT3. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis acquired the main enriched pathways, and PPAR-γ involved in multiple signaling pathways, including the AMPK signaling pathway. Untargeted clinical metabolomics analysis revealed plasma metabolite variations in patients with IPF versus controls and before versus after RPFF treatment for patients with IPF. Six differential metabolites were explored as differential plasma metabolites for RPFF in IPF treatment. Combined with network pharmacology, a therapeutic target PPAR-γ of RPFF in IPF treatment and the corresponding herbal components were identified. Based on the orthogonal experimental design, the experiments showed that kaempferol and luteolin can decrease the mRNA and protein expression of α-smooth muscle actin (α-SMA), and the combination of lower dose can inhibit α-SMA mRNA and protein expression by promoting the AMPK/PPAR-γ pathway in transforming growth factor beta 1 (TGF-β1)-treated MRC-5 cells.

CONCLUSIONS

This study revealed that the therapeutic effects of RPFF are due to multiple ingredients and have multiple targets and pathways, and PPAR-γ is one of therapeutic targets for RPPF in IPF and involved in the AMPK signaling pathway. Two ingredients of RPFF, kaempferol and luteolin, can inhibit fibroblast proliferation and the myofibroblast differentiation of TGF-β1, and exert a synergistic effect through AMPK/PPAR-γ pathway activation.

Regulation of Peptidase Activity beyond the Active Site in Human Health and Disease

This comprehensive review addresses the intricate and multifaceted regulation of peptidase activity in human health and disease, providing a comprehensive investigation that extends well beyond the boundaries of the active site. Our review focuses on multiple mechanisms and highlights the important role of exosites, allosteric sites, and processes involved in zymogen activation. These mechanisms play a central role in shaping the complex world of peptidase function and are promising potential targets for the development of innovative drugs and therapeutic interventions. The review also briefly discusses the influence of glycosaminoglycans and non-inhibitory binding proteins on enzyme activities. Understanding their role may be a crucial factor in the development of therapeutic strategies. By elucidating the intricate web of regulatory mechanisms that control peptidase activity, this review deepens our understanding in this field and provides a roadmap for various strategies to influence and modulate peptidase activity.

Evaluating the causal effect of circulating proteome on the risk of osteoarthritis-related traits

OBJECTIVES

This study aims to identify circulating proteins that are causally associated with osteoarthritis (OA)-related traits through Mendelian randomisation (MR)-based analytical framework.

METHODS

Large-scale two-sample MR was employed to estimate the effects of thousands of plasma proteins on 12 OA-related traits. Additional analyses including Bayesian colocalisation, Steiger filtering analysis, assessment of protein-altering variants and mapping expression quantitative trait loci to protein quantitative trait loci were performed to investigate the reliability of the MR findings; protein-protein interaction, pathway enrichment analysis and evaluation of drug targets were conducted to deepen the understanding and identify potential therapeutic targets of OA.

RESULTS

Dozens of circulating proteins were identified to have putatively causal effects on OA-related traits, and a majority of these proteins were either drug targets or considered druggable.

CONCLUSIONS

Through MR analysis, we have identified numerous plasma proteins associated with OA-related traits, shedding light on protein-mediated mechanisms and offering promising therapeutic targets for OA.

Profiling the genome and proteome of metabolic dysfunction-associated steatotic liver disease identifies potential therapeutic targets

BACKGROUND & AIMS

Metabolic dysfunction-associated steatotic liver disease (MASLD) affects over 25% of the population and currently has no effective treatments. Plasma proteins with causal evidence may represent promising drug targets. We aimed to identify plasma proteins in the causal pathway of MASLD and explore their interaction with obesity.

METHODS

We analysed 2,941 plasma proteins in 43,978 European participants from UK Biobank. We performed genome-wide association study (GWAS) for all MASLD-associated proteins and created the largest MASLD GWAS (109,885 cases/1,014,923 controls). We performed Mendelian Randomization (MR) and integrated proteins and their encoding genes in MASLD ranges to identify candidate causal proteins. We then validated them through independent replication, exome sequencing, liver imaging, bulk and single-cell gene expression, liver biopsies, pathway, and phenome-wide data. We explored the role of obesity by MR and multivariable MR across proteins, body mass index, and MASLD.

RESULTS

We found 929 proteins associated with MASLD, reported five novel genetic loci associated with MASLD, and identified 17 candidate MASLD protein targets. We identified four novel targets for MASLD (CD33, GRHPR, HMOX2, and SCG3), provided protein evidence supporting roles of AHCY, FCGR2B, ORM1, and RBKS in MASLD, and validated nine previously known targets. We found that CD33, FCGR2B, ORM1, RBKS, and SCG3 mediated the association of obesity and MASLD, and HMOX2, ORM1, and RBKS had effect on MASLD independent of obesity.

CONCLUSIONS

This study identified new protein targets in the causal pathway of MASLD, providing new insights into the multi-omics architecture and pathophysiology of MASLD. These findings advise further therapeutic interventions for MASLD.

EMNPD: a comprehensive endophytic microorganism natural products database for prompt the discovery of new bioactive substances

The discovery and utilization of natural products derived from endophytic microorganisms have garnered significant attention in pharmaceutical research. While remarkable progress has been made in this field each year, the absence of dedicated open-access databases for endophytic microorganism natural products research is evident. To address the increasing demand for mining and sharing of data resources related to endophytic microorganism natural products, this study introduces EMNPD, a comprehensive endophytic microorganism natural products database comprising manually curated data. Currently, EMNPD offers 6632 natural products from 1017 endophytic microorganisms, targeting 1286 entities (including 94 proteins, 282 cell lines, and 910 species) with 91 diverse bioactivities. It encompasses the physico-chemical properties of natural products, ADMET information, quantitative activity data with their potency, natural products contents with diverse fermentation conditions, systematic taxonomy, and links to various well-established databases. EMNPD aims to function as an open-access knowledge repository for the study of endophytic microorganisms and their natural products, thereby facilitating drug discovery research and exploration of bioactive substances. The database can be accessed at http://emnpd.idrblab.cn/ without the need for registration, enabling researchers to freely download the data. EMNPD is expected to become a valuable resource in the field of endophytic microorganism natural products and contribute to future drug development endeavors.

Draft genome sequence of Staphylococcus gallinarum MTR_B001 strain isolated from breast muscle of a chicken in Bangladesh

We announce the genome sequence of the Staphylococcus gallinarum MTR_B001 strain isolated from the breast muscle of a chicken in 2022 in Bangladesh. This assembled genome had an estimated length of 2,889,393 bp (with 50× genome coverage), 15 contigs, 36 predicted antibiotic resistance genes, and 27 predicted virulence factor genes.

Integrating Metabolomics and Network Pharmacology to Explore the Mechanism of Xiao-Yao-San in the Treatment of Inflammatory Response in CUMS Mice

Depression can trigger an inflammatory response that affects the immune system, leading to the development of other diseases related to inflammation. Xiao-Yao-San (XYS) is a commonly used formula in clinical practice for treating depression. However, it remains unclear whether XYS has a modulating effect on the inflammatory response associated with depression. The objective of this study was to examine the role and mechanism of XYS in regulating the anti-inflammatory response in depression. A chronic unpredictable mild stress (CUMS) mouse model was established to evaluate the antidepressant inflammatory effects of XYS. Metabolomic assays and network pharmacology were utilized to analyze the pathways and targets associated with XYS in its antidepressant inflammatory effects. In addition, molecular docking, immunohistochemistry, Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR), and Western Blot were performed to verify the expression of relevant core targets. The results showed that XYS significantly improved depressive behavior and attenuated the inflammatory response in CUMS mice. Metabolomic analysis revealed the reversible modulation of 21 differential metabolites by XYS in treating depression-related inflammation. Through the combination of liquid chromatography and network pharmacology, we identified seven active ingredients and seven key genes. Furthermore, integrating the predictions from network pharmacology and the findings from metabolomic analysis, Vascular Endothelial Growth Factor A (VEGFA) and Peroxisome Proliferator-Activated Receptor-γ (PPARG) were identified as the core targets. Molecular docking and related molecular experiments confirmed these results. The present study employed metabolomics and network pharmacology analyses to provide evidence that XYS has the ability to alleviate the inflammatory response in depression through the modulation of multiple metabolic pathways and targets.

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.

A phenome-wide approach to identify causal risk factors for deep vein thrombosis

Deep vein thrombosis (DVT) is the formation of a blood clot in a deep vein. DVT can lead to a venous thromboembolism (VTE), the combined term for DVT and pulmonary embolism, a leading cause of death and disability worldwide. Despite the prevalence and associated morbidity of DVT, the underlying causes are not well understood. Our aim was to leverage publicly available genetic summary association statistics to identify causal risk factors for DVT. We conducted a Mendelian randomization phenome-wide association study (MR-PheWAS) using genetic summary association statistics for 973 exposures and DVT (6,767 cases and 330,392 controls in UK Biobank). There was evidence for a causal effect of 57 exposures on DVT risk, including previously reported risk factors (e.g. body mass index-BMI and height) and novel risk factors (e.g. hyperthyroidism and varicose veins). As the majority of identified risk factors were adiposity-related, we explored the molecular link with DVT by undertaking a two-sample MR mediation analysis of BMI-associated circulating proteins on DVT risk. Our results indicate that circulating neurogenic locus notch homolog protein 1 (NOTCH1), inhibin beta C chain (INHBC) and plasminogen activator inhibitor 1 (PAI-1) influence DVT risk, with PAI-1 mediating the BMI-DVT relationship. Using a phenome-wide approach, we provide putative causal evidence that hyperthyroidism, varicose veins and BMI enhance the risk of DVT. Furthermore, the circulating protein PAI-1 has a causal role in DVT aetiology and is involved in mediating the BMI-DVT relationship.

USP4 promotes the proliferation, migration, and invasion of esophageal squamous cell carcinoma by targeting TAK1

Ubiquitin-specific protease 4 (USP4) represents a potential oncogene involved in various human cancers. Nevertheless, the biological roles and precise mechanism of USP4 in esophageal squamous cell carcinoma (ESCC) progression are not understood. Here, USP4 expression was found to be markedly upregulated in ESCC tumor tissues and cells. Loss- and gain-of-function assays suggested that USP4 silencing inhibited ESCC cell proliferation, migration, and invasion, while USP4 overexpression promoted these behaviors. Consistently, USP4 silencing repressed tumor growth and metastasis in an ESCC nude mouse model in vivo. As a target molecule of USP4, transforming growth factor-β-activated kinase 1 (TAK1) also showed high expression in ESCC. Moreover, we observed that USP4 specifically interacted with TAK1 and stabilized TAK1 protein levels via deubiquitination in ESCC cells. Importantly, USP4 promotes ESCC proliferation, migration, and invasion via the MEK/ERK signaling pathway and can be inhibited by U0126. Neutral red (NR), an inhibitor of USP4 can suppress ESCC progression in vitro and in vivo. Overall, this study revealed that USP4/TAK1 plays crucial roles in ESCC progression by modulating proliferation, migration, and invasion, and USP4 might be a potential therapeutic target in ESCC.