PubChem BioAssay: 2017 update

FerroLigandDB: A Ferroptosis Ligand Database of Structure-Activity Relations

Ferroptosis is an iron-dependent programmed cell death characterized by lipid peroxidation that is linked to the pathophysiological processes in many diseases, such as neurodegenerative diseases, cancers, ischemia-reperfusion injuries, and organ damages. Many proteins are associated with ferroptosis signal transduction pathways. Novel chemical compounds are demanded to explore and regulate these pathways. Therefore, a ferroptosis ligand database, which holds relations among chemical structures, targets, bioactivities, and diseases, is needed for discovering and designing new ferroptosis regulators. This work reports FerroLigandDB, a manually curated database for small-molecular ferroptosis regulators. The database comprises 466 ferroptosis inducer entries (with 380 unique molecular structures) and 539 ferroptosis inhibitor entries (with 468 unique molecular structures) (note: one compound can be recorded as multiple entries due to the different assays). Each ferroptosis ligand entry is detailed with compound IDs, structure attributes, bioactivity values, test objects, target information, associated diseases, and references. The fields in the FerroLigandDB database implicitly contain relationships among chemical structures, bioactivities, targets, and diseases. Thus, FerroLigandDB is a comprehensive resource for scientists to design and discover novel ferroptosis regulators. The user interface of FerroLigandDB is implemented with query features and data visualization facilities. With compound identifiers, the compounds are linked to the records of other chemoinformatics databases (such as PubChem and SciFinder). The FerroLigandDB database is freely accessible at http://ferr.gulab.org.cn/.

PubChem synonym filtering process using crowdsourcing

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

Complete mitogenome and intra-family comparative mitogenomics showed distinct position of Pama Croaker Otolithoides pama

The Pama Croaker, Otolithoides pama, is an economically important fish species in Bangladesh. Intra-family similarities in morphology and typical barcode sequences of cox1 create ambiguities in its identification. Therefore, morphology and the complete mitochondrial genome of O. pama, and comparative mitogenomics within the family Sciaenidae have been studied. Extracted genomic DNA was subjected to Illumina-based short read sequencing for De-Novo mitogenome assembly. The complete mitogenome of O. pama (Accession: OQ784575.1) was 16,513 bp, with strong AC biasness and strand asymmetry. Relative synonymous codon usage (RSCU) among 13 protein-coding genes (PCGs) of O. pama was also analyzed. The studied mitogenomes including O. pama exhibited consistent sizes and gene orders, except for the genus Johnius which possessed notably longer mitogenomes with unique gene rearrangements. Different genetic distance metrics across 30 species of Sciaenidae family demonstrated 12S rRNA and the control region (CR) as the most conserved and variable regions, respectively, while most of the PCGs undergone a purifying selection. Different phylogenetic trees were congruent with one another, where O. pama was distinctly placed. This study would contribute to distinguishing closely related fish species of Sciaenidae family and can be instrumental in conserving the genetic diversity of O. pama.

Therapeutic targets of formononetin for treating prostate cancer at the single-cell level

Prostate cancer is one of the serious health problems of older male, about 13% of male was affected by prostate cancer. Prostate cancer is highly heterogeneity disease with complex molecular and genetic alterations. So, targeting the gene candidates in prostate cancer in single-cell level can be a promising approach for treating prostate cancer. In the present study, we analyzed the single cell sequencing data obtained from 2 previous reports to determine the differential gene expression of prostate cancer in single-cell level. By using the network pharmacology analysis, we identified the therapeutic targets of formononetin in immune cells and tissue cells of prostate cancer. We then applied molecular docking to determine the possible direct binding of formononetin to its target proteins. Our result identified a cluster of differential gene expression in prostate cancer which can serve as novel biomarkers such as immunoglobulin kappa C for prostate cancer prognosis. The result of network pharmacology delineated the roles of formononetin's targets such CD74 and THBS1 in immune cells' function of prostate cancer. Also, formononetin targeted insulin receptor and zinc-alpha-2-glycoprotein which play important roles in metabolisms of tissue cells of prostate cancer. The result of molecular docking suggested the direct binding of formononetin to its target proteins including INSR, TNF, and CXCR4. Finally, we validated our findings by using formononetin-treated human prostate cancer cell DU145. For the first time, our result suggested the use of formononetin for treating prostate cancer through targeting different cell types in a single-cell level.

Alpha-tocopherol inhibits ferroptosis and promotes neural function recovery in rats with spinal cord injury via downregulating Alox15

Spinal cord injury (SCI) is a type of central nervous system (CNS) injury in which ferroptosis is becoming a promising target for treatment. Alpha-tocopherol (Vitamin E, Vit E) is a compound with anti-ferroptosis activity. The mechanism of alpha-tocopherol in regulating ferroptosis after SCI has not been deeply studied. In this study, rats with SCI were treated by Alpha-tocopherol based on bioinformatic analysis and molecular docking prediction. Behavioral tests and histological findings showed that Alpha-tocopherol promoted neural function recovery and tissue repairment in rats with SCI. Subsequently, regulatory effects of Alpha-tocopherol on Alox15 and ferroptosis were detected and then localized by immunofluorescence. In vitro, alpha-tocopherol improved the ROS accumulation, iron overload, lipid peroxidation and mitochondrial dysfunction. The effects of Alpha-tocopherol on the expression of Alox15, Ptgs2 and 4Hne were validated in vitro. Finally, the inhibitory effects of Alpha-tocopherol on Alox15 and ferroptosis were weakened by the mutation of 87th residue of Alox15. In summary, alpha-tocopherol could alleviate SCI-induced ferroptosis by downregulating Alox15 to promote neural function recovery in rats with SCI. Findings in this study could help further our understanding on SCI-induced ferroptosis and provide a novel insight for treating SCI.

Precision unveiled: Synergistic genomic landscapes in breast cancer-Integrating single-cell analysis and decoding drug toxicity for elite prognostication and tailored therapeutics

BACKGROUND

Globally, breast cancer, with diverse subtypes and prognoses, necessitates tailored therapies for enhanced survival rates. A key focus is glutamine metabolism, governed by select genes. This study explored genes associated with T cells and linked them to glutamine metabolism to construct a prognostic staging index for breast cancer patients for more precise medical treatment.

METHODS

Two frameworks, T-cell related genes (TRG) and glutamine metabolism (GM), stratified breast cancer patients. TRG analysis identified key genes via hdWGCNA and machine learning. T-cell communication and spatial transcriptomics emphasized TRG's clinical value. GM was defined using Cox analyses and the Lasso algorithm. Scores categorized patients as TRG_high+GM_high (HH), TRG_high+GM_low (HL), TRG_low+GM_high (LH), or TRG_low+GM_low (LL). Similarities between HL and LH birthed a "Mixed" class and the TRG_GM classifier. This classifier illuminated gene variations, immune profiles, mutations, and drug responses.

RESULTS

Utilizing a composite of two distinct criteria, we devised a typification index termed TRG_GM classifier, which exhibited robust prognostic potential for breast cancer patients. Our analysis elucidated distinct immunological attributes across the classifiers. Moreover, by scrutinizing the genetic variations across groups, we illuminated their unique genetic profiles. Insights into drug sensitivity further underscored avenues for tailored therapeutic interventions.

CONCLUSION

Utilizing TRG and GM, a robust TRG_GM classifier was developed, integrating clinical indicators to create an accurate predictive diagnostic map. Analysis of enrichment disparities, immune responses, and mutation patterns across different subtypes yields crucial subtype-specific characteristics essential for prognostic assessment, clinical decision-making, and personalized therapies. Further exploration is warranted into multiple fusions between metrics to uncover prognostic presentations across various dimensions.

DPMGCDA: Deciphering circRNA-Drug Sensitivity Associations with Dual Perspective Learning and Path-Masked Graph Autoencoder

Accumulating evidence has indicated that the expression of circular RNAs (circRNAs) can affect the cellular sensitivity to drugs and significantly influence drug efficacy. However, traditional experimental approaches for validating these associations are resource-intensive and time-consuming. To address this challenge, we propose a computational framework termed DPMGCDA leveraging dual perspective learning and path-masked graph autoencoder to predict circRNA-drug sensitivity associations. Initially, we construct circRNA-circRNA fusion similarity networks and drug-drug fusion similarity networks using similarity network fusion, ensuring a comprehensive integration of information. Based on the above, we built the circRNA homogeneous graph, the drug homogeneous graph, and the circRNA-drug heterogeneous graph. Next, we form the initial node features in the circRNA-drug heterogeneous graph from the homogeneous graph-level perspective and the combined feature-level perspective and complete the prediction of potential associations using the path-masked graph autoencoder in both perspectives. The predictions under both perspectives are finally combined to obtain the final prediction score. Transductive setting experiments and inductive setting experiments all demonstrate that our method, DPMGCDA, outperforms state-of-the-art approaches. Additionally, we verify the necessity of employing dual perspective learning through ablation tests and analyze the effective encoding capability of the path-masked graph autoencoder for features through embedding visualization. Moreover, case studies on four drugs corroborate DPMGCDA's ability to identify potential circRNAs associated with new drugs.

Binding Curve Viewer: Visualizing the Equilibrium and Kinetics of Protein-Ligand Binding and Competitive Binding

Understanding the thermodynamics and kinetics of the protein-ligand interaction is essential for biologists and pharmacologists. To visualize the equilibrium and kinetics of the binding reaction with 1:1 stoichiometry and no cooperativity, we obtained the exact relationship of the concentration of the protein-ligand complex and the time in the second-order binding process and numerically simulated the process of competitive binding. First, two common concerns in measuring protein-ligand interactions were focused on how to avoid the titration regime and how to establish the appropriate incubation time. Then, we gave examples of how the commonly used experimental conditions of [L]0 ≫ [P]0 and [I]0 ≫ [P]0 affected the estimation of the kinetic and thermodynamic properties. Theoretical inhibition curves were calculated, and the apparent IC50 and IC50 were estimated accordingly under predefined conditions. Using the estimated apparent IC50, we compared the apparent Ki and Ki calculated by using the Cheng-Prusoff equation, Lin-Riggs equation, and Wang's group equation. We also applied our tools to simulate high-throughput screening and compare the results of real experiments. The visualization tool for simulating the saturation experiment, kinetic experiments of binding and competitive binding, and inhibition curve, "Binding Curve Viewer," is available at www.eplatton.net/binding-curve-viewer.

Role of Inflammation in the Development of COVID-19 to Parkinson's Disease

Background

The coronavirus disease 2019 (COVID-19) can lead to neurological symptoms such as headaches, confusion, seizures, hearing loss, and loss of smell. The link between COVID-19 and Parkinson's disease (PD) is being investigated, but more research is needed for a definitive connection.

Methods

Datasets GSE22491 and GSE164805 were selected to screen differentially expressed gene (DEG), and immune infiltration and gene set enrichment analysis (GSEA) of the DEG were performed. WGCNA analyzed the DEG and selected the intersection genes. Potential biological functions and signaling pathways were determined, and diagnostic genes were further screened using gene expression and receiver operating characteristic (ROC) curves. Screening and molecular docking of ibuprofen as a therapeutic target. The effectiveness of ibuprofen was verified by constructing a PD model in vitro, and constructing "COVID19-PD" signaling pathway, and exploring the role of angiotensin-converting enzyme 2 (ACE2) in PD.

Results

A total of 13 DEG were screened from the GSE36980 and GSE5281 datasets. Kyoto encyclopedia of genes and genomes (KEGG) analysis showed that the DEG were mainly associated with the hypoxia-inducible factor (HIF-1), epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor resistance, etc. After analysis, it is found that ibuprofen alleviates PD symptoms by inhibiting the expression of nuclear factor kappa-B (NF-κB), interleukin-1β (IL-1β), IL-6, and tumor necrosis factor-α (TNF-α). Based on signal pathway construction, the importance of ACE2 in COVID-19-induced PD has been identified. ACE2 is found to have widespread distribution in the brain. In the 1-methyl-4-phenyl-1,2,3,6-te-trahydropyridine (MPTP)-induced ACE2-null PD mice model, more severe motor and non-motor symptoms, increased NF-κB p65 and α-synuclein (α-syn) expression with significant aggregation, decreased tyrosine hydroxylase (TH), severe neuronal loss, and neurodegenerative disorders.

Conclusion

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection increases the risk of PD through an inflammatory environment and downregulation of ACE2, providing evidence for the molecular mechanism and targeted therapy associated with COVID-19 and PD.

Unveiling the molecular complexity of proliferative diabetic retinopathy through scRNA-seq, AlphaFold 2, and machine learning

Background

Proliferative diabetic retinopathy (PDR), a major cause of blindness, is characterized by complex pathogenesis. This study integrates single-cell RNA sequencing (scRNA-seq), Non-negative Matrix Factorization (NMF), machine learning, and AlphaFold 2 methods to explore the molecular level of PDR.

Methods

We analyzed scRNA-seq data from PDR patients and healthy controls to identify distinct cellular subtypes and gene expression patterns. NMF was used to define specific transcriptional programs in PDR. The oxidative stress-related genes (ORGs) identified within Meta-Program 1 were utilized to construct a predictive model using twelve machine learning algorithms. Furthermore, we employed AlphaFold 2 for the prediction of protein structures, complementing this with molecular docking to validate the structural foundation of potential therapeutic targets. We also analyzed protein-protein interaction (PPI) networks and the interplay among key ORGs.

Results

Our scRNA-seq analysis revealed five major cell types and 14 subcell types in PDR patients, with significant differences in gene expression compared to those in controls. We identified three key meta-programs underscoring the role of microglia in the pathogenesis of PDR. Three critical ORGs (ALKBH1, PSIP1, and ATP13A2) were identified, with the best-performing predictive model demonstrating high accuracy (AUC of 0.989 in the training cohort and 0.833 in the validation cohort). Moreover, AlphaFold 2 predictions combined with molecular docking revealed that resveratrol has a strong affinity for ALKBH1, indicating its potential as a targeted therapeutic agent. PPI network analysis, revealed a complex network of interactions among the hub ORGs and other genes, suggesting a collective role in PDR pathogenesis.

Conclusion

This study provides insights into the cellular and molecular aspects of PDR, identifying potential biomarkers and therapeutic targets using advanced technological approaches.

The HABD: Home of All Biological Databases Empowering Biological Research With Cutting-Edge Database Systems

The emergence of computer technologies and computing power has led to the development of several database systems that provide standardized access to vast quantities of data, making it possible to collect, search, index, evaluate, and extract useful knowledge across various fields. The Home of All Biological Databases (HABD) has been established as a continually expanding platform that aims to store, organize, and distribute biological data in a searchable manner, removing all dead and non-accessible data. The platform meticulously categorizes data into various categories, such as COVID-19 Pandemic Database (CO-19PDB), Database relevant to Human Research (DBHR), Cancer Research Database (CRDB), Latest Database of Protein Research (LDBPR), Fungi Databases Collection (FDBC), and many other databases that are categorized based on biological phenomena. It currently provides a total of 22 databases, including 6 published, 5 submitted, and the remaining in various stages of development. These databases encompass a range of areas, including phytochemical-specific and plastic biodegradation databases. HABD is equipped with search engine optimization (SEO) analyzer and Neil Patel tools, which ensure excellent SEO and high-speed value. With timely updates, HABD aims to facilitate the processing and visualization of data for scientists, providing a one-stop-shop for all biological databases. Computer platforms, such as PhP, html, CSS, Java script and Biopython, are used to build all the databases. © 2024 Wiley Periodicals LLC.

Integrated analysis of single cell-RNA sequencing and Mendelian randomization identifies lactate dehydrogenase B as a target of melatonin in ischemic stroke

AIMS

Despite the success of single-cell RNA sequencing in identifying cellular heterogeneity in ischemic stroke, clarifying the mechanisms underlying these associations of differently expressed genes remains challenging. Several studies that integrate gene expression and gene expression quantitative trait loci (eQTLs) with genome wide-association study (GWAS) data to determine their causal role have been proposed.

METHODS

Here, we combined Mendelian randomization (MR) framework and single cell (sc) RNA sequencing to study how differently expressed genes (DEGs) mediating the effect of gene expression on ischemic stroke. The hub gene was further validated in the in vitro model.

RESULTS

We identified 2339 DEGs in 10 cell clusters. Among these DEGs, 58 genes were associated with the risk of ischemic stroke. After external validation with eQTL dataset, lactate dehydrogenase B (LDHB) is identified to be positively associated with ischemic stroke. The expression of LDHB has also been validated in sc RNA-seq with dominant expression in microglia and astrocytes, and melatonin is able to reduce the LDHB expression and activity in vitro ischemic models.

CONCLUSION

Our study identifies LDHB as a novel biomarker for ischemic stroke via combining the sc RNA-seq and MR analysis.

Dexamethasone protects against asthma via regulating Hif-1α-glycolysis-lactate axis and protein lactylation

PURPOSE

Asthma can not be eradicated till now and its control primarily relies on the application of corticosteroids. Recently, glycolytic reprogramming has been reportedly contributed to asthma, this study aimed to reveal whether the effect of corticosteroids on asthma control is related to their regulation of glycolysis and glycolysis-dependent protein lactylation.

METHODS

Ovalbumin (OVA) aeroallergen was used to challenge mice and stimulate human macrophage cell line THP-1 following dexamethasone (DEX) treatment. Airway hyperresponsiveness, airway inflammation, the expressions of key glycolytic enzymes and pyroptosis markers, the level of lactic acid, real-time glycolysis and oxidative phosphorylation (OXPHOS), and protein lactylation were analyzed.

RESULTS

DEX significantly attenuated OVA-induced eosinophilic airway inflammation, including airway hyperresponsiveness, leukocyte infiltration, goblet cell hyperplasia, Th2 cytokines production and pyroptosis markers expression. Meanwhile, OVA-induced Hif-1α-glycolysis axis was substantially downregulated by DEX, which resulted in low level of lactic acid. Besides, key glycolytic enzymes in the lungs of asthmatic mice were notably co-localized with F4/80-positive macrophages, indicating metabolic shift to glycolysis in lung macrophages during asthma. This was confirmed in OVA-stimulated THP-1 cells that DEX treatment resulted in reductions in pyroptosis, glycolysis and lactic acid level. Finally, protein lactylation was found significantly increased in the lungs of asthmatic mice and OVA-stimulated THP-1 cells, which were both inhibited by DEX.

CONCLUSION

Our present study revealed that the effect of DEX on asthma control was associated with its suppressing of Hif-1α-glycolysis-lactateaxis and subsequent protein lactylation, which may open new avenues for the therapy of eosinophilic asthma.

Dissecting the shared genetic landscape of anxiety, depression, and schizophrenia

BACKGROUND

Numerous studies highlight the genetic underpinnings of mental disorders comorbidity, particularly in anxiety, depression, and schizophrenia. However, their shared genetic loci are not well understood. Our study employs Mendelian randomization (MR) and colocalization analyses, alongside multi-omics data, to uncover potential genetic targets for these conditions, thereby informing therapeutic and drug development strategies.

METHODS

We utilized the Consortium for Linkage Disequilibrium Score Regression (LDSC) and Mendelian Randomization (MR) analysis to investigate genetic correlations among anxiety, depression, and schizophrenia. Utilizing GTEx V8 eQTL and deCODE Genetics pQTL data, we performed a three-step summary-data-based Mendelian randomization (SMR) and protein-protein interaction analysis. This helped assess causal and comorbid loci for these disorders and determine if identified loci share coincidental variations with psychiatric diseases. Additionally, phenome-wide association studies, drug prediction, and molecular docking validated potential drug targets.

RESULTS

We found genetic correlations between anxiety, depression, and schizophrenia, and under a meta-analysis of MR from multiple databases, the causal relationships among these disorders are supported. Based on this, three-step SMR and colocalization analyses identified ITIH3 and CCS as being related to the risk of developing depression, while CTSS and DNPH1 are related to the onset of schizophrenia. BTN3A1, PSMB4, and TIMP4 were identified as comorbidity loci for both disorders. Molecules that could not be determined through colocalization analysis were also presented. Drug prediction and molecular docking showed that some drugs and proteins have good binding affinity and available structural data.

CONCLUSIONS

Our study indicates genetic correlations and shared risk loci between anxiety, depression, and schizophrenia. These findings offer insights into the underlying mechanisms of their comorbidities and aid in drug development.

Exploration of the potential causative genes for inflammatory bowel disease: Transcriptome-wide association analysis, Mendelian randomization analysis and Bayesian colocalisation

Background

Inflammatory bowel disease (IBD) poses a complex challenge due to its intricate underlying mechanisms, and curative treatments remain elusive. Consequently, there is an urgent need to identify genes causally associated with IBD.

Methods

We extracted blood eQTL data from the GTExv8.ALL.Whole_Blood database, genome-wide association studies (GWAS) summary statistics of IBD from the IEU GWAS database, and performed a three-fold analysis protocol, including transcriptome-wide association analysis, Mendelian randomisation analysis, Bayesian colocalisation, and subsequent potential therapeutic agents identification.

Results

We identified four pathogenic genes, namely CARD9, RTEL1, STMN3 and ARFRP1, that promote the development of IBD, encompassing both ulcerative colitis (UC) and Crohn's disease (CD). Notably, ARFRP1 exhibited the ability to suppress IBD (encompassing UC and CD) development. Regarding drug prediction, cyclophosphamide emerged as a promising novel therapeutic option for IBD, encompassing UC and CD.

Conclusion

We identified several potential genes related to IBD (UC and CD), including CARD9, RTEL1, STMN3 and ARFRP1, warranting further investigation in functional studies to elucidate underlying disease mechanisms. Additionally, clinical studies exploring the potential of cyclophosphamide as a treatment avenue for IBD are warranted.

Indole Propionic Acid Disturbs the Normal Function of Tryptophanyl-tRNA Synthetase in Mycobacterium tuberculosis

Tuberculosis (TB) is the leading infectious disease caused by Mycobacterium tuberculosis and the second-most contagious killer after COVID-19. The emergence of drug-resistant TB has caused a great need to identify and develop new anti-TB drugs with novel targets. Indole propionic acid (IPA), a structural analog of tryptophan (Trp), is active against M. tuberculosis in vitro and in vivo. It has been verified that IPA exerts its antimicrobial effect by mimicking Trp as an allosteric inhibitor of TrpE, which is the first enzyme in the Trp synthesis pathway of M. tuberculosis. However, other Trp structural analogs, such as indolmycin, also target tryptophanyl-tRNA synthetase (TrpRS), which has two functions in bacteria: synthesis of tryptophanyl-AMP by catalyzing ATP + Trp and producing Trp-tRNATrp by transferring Trp to tRNATrp. So, we speculate that IPA may also target TrpRS. In this study, we found that IPA can dock into the Trp binding pocket of M. tuberculosis TrpRS (TrpRSMtb), which was further confirmed by isothermal titration calorimetry (ITC) assay. The biochemical analysis proved that TrpRS can catalyze the reaction between IPA and ATP to generate pyrophosphate (PPi) without Trp as a substrate. Overexpression of wild-type trpS in M. tuberculosis increased the MIC of IPA to 32-fold, and knock-down trpS in Mycolicibacterium smegmatis made it more sensitive to IPA. The supplementation of Trp in the medium abrogated the inhibition of M. tuberculosis by IPA. We demonstrated that IPA can interfere with the function of TrpRS by mimicking Trp, thereby impeding protein synthesis and exerting its anti-TB effect.

Exploring the roles and potential therapeutic strategies of inflammation and metabolism in the pathogenesis of vitiligo: a mendelian randomization and bioinformatics-based investigation

Introduction

Vitiligo, a common autoimmune acquired pigmentary skin disorder, poses challenges due to its unclear pathogenesis. Evidence suggests inflammation and metabolism's pivotal roles in its onset and progression. This study aims to elucidate the causal relationships between vitiligo and inflammatory proteins, immune cells, and metabolites, exploring bidirectional associations and potential drug targets.

Methods

Mendelian Randomization (MR) analysis encompassed 4,907 plasma proteins, 91 inflammatory proteins, 731 immune cell features, and 1400 metabolites. Bioinformatics analysis included Protein-Protein Interaction (PPI) network construction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Subnetwork discovery and hub protein identification utilized the Molecular Complex Detection (MCODE) plugin. Colocalization analysis and drug target exploration, including molecular docking validation, were performed.

Results

MR analysis identified 49 proteins, 39 immune cell features, and 59 metabolites causally related to vitiligo. Bioinformatics analysis revealed significant involvement in PPI, GO enrichment, and KEGG pathways. Subnetwork analysis identified six central proteins, with Interferon Regulatory Factor 3 (IRF3) exhibiting strong colocalization evidence. Molecular docking validated Piceatannol's binding to IRF3, indicating a stable interaction.

Conclusion

This study comprehensively elucidates inflammation, immune response, and metabolism's intricate involvement in vitiligo pathogenesis. Identified proteins and pathways offer potential therapeutic targets, with IRF3 emerging as a promising candidate. These findings deepen our understanding of vitiligo's etiology, informing future research and drug development endeavors.

PLANET: A Multi-objective Graph Neural Network Model for Protein-Ligand Binding Affinity Prediction

Predicting protein-ligand binding affinity is a central issue in drug design. Various deep learning models have been published in recent years, where many of them rely on 3D protein-ligand complex structures as input and tend to focus on the single task of reproducing binding affinity. In this study, we have developed a graph neural network model called PLANET (Protein-Ligand Affinity prediction NETwork). This model takes the graph-represented 3D structure of the binding pocket on the target protein and the 2D chemical structure of the ligand molecule as input. It was trained through a multi-objective process with three related tasks, including deriving the protein-ligand binding affinity, protein-ligand contact map, and ligand distance matrix. Besides the protein-ligand complexes with known binding affinity data retrieved from the PDBbind database, a large number of non-binder decoys were also added to the training data for deriving the final model of PLANET. When tested on the CASF-2016 benchmark, PLANET exhibited a scoring power comparable to the best result yielded by other deep learning models as well as a reasonable ranking power and docking power. In virtual screening trials conducted on the DUD-E benchmark, PLANET's performance was notably better than several deep learning and machine learning models. As on the LIT-PCBA benchmark, PLANET achieved comparable accuracy as the conventional docking program Glide, but it only spent less than 1% of Glide's computation time to finish the same job because PLANET did not need exhaustive conformational sampling. Considering the decent accuracy and efficiency of PLANET in binding affinity prediction, it may become a useful tool for conducting large-scale virtual screening.

MLASM: Machine learning based prediction of anticancer small molecules

Cancer, being the second leading cause of death globally. So, the development of effective anticancer treatments is crucial in the field of medicine. Anticancer peptides (ACPs) have shown promising therapeutic potential in cancer treatment compared to traditional methods. However, the process of identifying ACPs through experimental means is often time-intensive and expensive. To overcome this issue, we employed a machine learning-based approach for the first time to develop an anticancer model using small molecules. Anticancer small molecules (ACSMs) are compounds that have been developed to target and inhibit cancer cells. In this study, we used 10,000 compounds to develop the machine learning models using five algorithms such as, Random Forest (RF), Light gradient boosting machine (LightGBM), K-nearest neighbors (KNN), Decision tree (DT) and Extreme Gradient Boosting (XGB). The developed models were evaluated using the test set and top three models were identified (RF, LightGBM and XGB). Furthermore, to validate the predictive performance of our models, we have performed external validation using an FDA approved anticancer compounds/drugs. Following this analysis, we found that our LightGBM model correctly predicted 9 compounds as active. However, RF and XGB exhibited some limitations by predicting 8 and 7 compounds as active out of 10, respectively. These results demonstrate that, when compared to RF and XGB, the LightGBM model showcase robust prediction capabilities, achieving a superior accuracy of 79% with an AUC of 0.88. These findings provide promising insights into the potential of our approach for predicting anticancer small molecules, highlighting the role of machine learning in advancing cancer treatment research.

Ropivacaine as a novel AKT1 specific inhibitor regulates the stemness of breast cancer

BACKGROUND

Ropivacaine, a local anesthetic, exhibits anti-tumor effects in various cancer types. However, its specific functions and the molecular mechanisms involved in breast cancer cell stemness remain elusive.

METHODS

The effects of ropivacaine on breast cancer stemness were investigated by in vitro and in vivo assays (i.e., FACs, MTT assay, mammosphere formation assay, transwell assays, western blot, and xenograft model). RNA-seq, bioinformatics analysis, Western blot, Luciferase reporter assay, and CHIP assay were used to explore the mechanistic roles of ropivacaine subsequently.

RESULTS

Our study showed that ropivacaine remarkably suppressed stem cells-like properties of breast cancer cells both in vitro and in vivo. RNA-seq analysis identified GGT1 as the downstream target gene responding to ropivacaine. High GGT1 levels are positively associated with a poor prognosis in breast cancer. Ropivacaine inhibited GGT1 expression by interacting with the catalytic domain of AKT1 directly to impair its kinase activity with resultant inactivation of NF-κB. Interestingly, NF-κB can bind to the promoter region of GGT1. KEGG and GSEA analysis indicated silence of GGT1 inhibited activation of NF-κB signaling pathway. Depletion of GGT1 diminished stem phenotypes of breast cancer cells, indicating the formation of NF-κB /AKT1/GGT1/NF-κB positive feedback loop in the regulation of ropivacaine-repressed stemness in breast cancer cells.

CONCLUSION

Our finding revealed that local anesthetic ropivacaine attenuated breast cancer stemness through AKT1/GGT1/NF-κB signaling pathway, suggesting the potential clinical value of ropivacaine in breast cancer treatment.

Comprehensive Analyses of PANoptosome with Potential Implications in Cancer Prognosis and Immunotherapy

Cell death resistance significantly contributes to poor therapeutic outcomes in various cancers. PANoptosis, a unique inflammatory programmed cell death (PCD) pathway activated by specific triggers and regulated by the PANoptosome, possesses key features of apoptosis, pyroptosis, and necroptosis, but these cannot be accounted for by any of the three PCD pathways alone. While existing studies on PANoptosis have predominantly centered on infectious and inflammatory diseases, its role in cancer malignancy has been understudied. In this comprehensive investigation, we conducted pan-cancer analyses of PANoptosome component genes across 33 cancer types. We characterized the genetic, epigenetic, and transcriptomic landscapes, and introduced a PANoptosome-related potential index (PANo-RPI) for evaluating the intrinsic PANoptosome assembly potential in cancers. Our findings unveil PANo-RPI as a prognostic factor in numerous cancers, including KIRC, LGG, and PAAD. Crucially, we established a significant correlation between PANo-RPI and tumor immune responses, as well as the infiltration of diverse lymphoid and myeloid cell subsets across nearly all cancer types. Moreover, a high PANo-RPI was consistently associated with improved immunotherapy response and efficacy, as evidenced by re-analysis of multiple immunotherapy cohorts. In conclusion, our study suggests that targeting PANoptosome components and modulating PANoptosis may hold tremendous therapeutic potential in the context of cancer.

Gramine improves sepsis-induced myocardial dysfunction by binding to NF-κB p105 and inhibiting its ubiquitination

BACKGROUND

Sepsis and its associated heart failure are among the leading causes of death. Gramine, a natural indole alkaloid, can be extracted from a wide variety of raw plants, and it exhibits therapeutic potential in pathological cardiac hypertrophy. However, the effect of gramine on inflammatory cardiomyopathy, particularly sepsis-induced myocardial injury, remains an unexplored area.

PURPOSE

To determine the role of gramine in sepsis-induced myocardial dysfunction and explore its underlying mechanism.

STUDY DESIGN AND METHODS

In mice, sepsis was established by intraperitoneally injecting lipopolysaccharide (LPS, 10 mg/kg). Subsequently, the effects of gramine administration (50 or 100 mg/kg) on LPS-triggered cardiac dysfunction in mice were investigated. For in vitro studies, isolated primary cardiomyocytes were used to assess the effect of gramine (25 or 50 µM) on LPS-induced apoptosis and inflammation. Additionally, molecular docking, co-immunoprecipitation and ubiquitination analyzes were conducted to explore the underlying mechanisms.

RESULTS

Gramine visibly ameliorated sepsis-induced cardiac dysfunction, inflammatory response, and mortality in vivo. Moreover, it significantly alleviated LPS-induced apoptotic and inflammatory responses in vitro. Furthermore, target prediction for gramine using the SuperPred website indicated that the nuclear factor NF-κB p105 subunit was one of the molecules ranked in priority order with a high model accuracy and a high probability score. Molecular docking studies demonstrated that gramine effectively docked to the death domain of NF-κB p105. Mechanistic studies revealed that gramine suppressed the processing of NF-κB p105 to p50 by inhibiting NF-κB p105 ubiquitination. Additionally, the protective effect of gramine on cardiac injury was almost abolished by overexpressing NF-κB p105.

CONCLUSION

Gramine is a promising bioactive small molecule for treating sepsis-induced myocardial dysfunction, which acts by docking to NF-κB p105 and inhibiting NF-κB p105 ubiquitination, thus preventing its processing to NF-κB p50. Therefore, gramine holds potential as a clinical drug for treating myocardial depression during sepsis.

Robinetin Alleviates Metabolic Failure in Liver through Suppression of p300-CD38 Axis

Metabolic abnormalities in the liver are closely associated with diverse metabolic diseases such as non-alcoholic fatty liver disease, type 2 diabetes, and obesity. The aim of this study was to evaluate the ameliorating effect of robinetin (RBN) on the significant pathogenic features of metabolic failure in the liver and to identify the underlying molecular mechanism. RBN significantly decreased triglyceride (TG) accumulation by downregulating lipogenesis-related transcription factors in AML-12 murine hepatocyte cell line. In addition, mice fed with Western diet (WD) containing 0.025% or 0.05% RBN showed reduced liver mass and lipid droplet size, as well as improved plasma insulin levels and homeostatic model assessment of insulin resistance (HOMA-IR) values. CD38 was identified as a target of RBN using the BioAssay database, and its expression was increased in OPA-treated AML-12 cells and liver tissues of WD-fed mice. Furthermore, RBN elicited these effects through its anti-histone acetyltransferase (HAT) activity. Computational simulation revealed that RBN can dock into the HAT domain pocket of p300, a histone acetyltransferase, which leads to the abrogation of its catalytic activity. Additionally, knock-down of p300 using siRNA reduced CD38 expression. The chromatin immunoprecipitation (ChIP) assay showed that p300 occupancy on the promoter region of CD38 was significantly decreased, and H3K9 acetylation levels were diminished in lipid-accumulated AML-12 cells treated with RBN. RBN improves the pathogenic features of metabolic failure by suppressing the p300-CD38 axis through its anti-HAT activity, which suggests that RBN can be used as a new phytoceutical candidate for preventing or improving this condition.

Deep reinforcement learning enables better bias control in benchmark for virtual screening

Virtual screening (VS) has been incorporated into the paradigm of modern drug discovery. This field is now undergoing a new wave of revolution driven by artificial intelligence and more specifically, machine learning (ML). In terms of those out-of-the-box datasets for model training or benchmarking, their data volume and applicability domain are limited. They are suffering from the biases constantly reported in the ML application. To address these issues, we present a novel benchmark named MUBDsyn. The utilization of synthetic decoys (i.e., presumed inactives) is the main feature of MUBDsyn, where deep reinforcement learning was leveraged for bias control during decoy generation. Then, we carried out extensive validations on this new benchmark. First, we confirmed that MUBDsyn was superior to the classical benchmarks in control of domain bias, artificial enrichment bias and analogue bias. Moreover, we found that the assessment of ML models based on MUBDsyn was less biased as revealed by the analysis of asymmetric validation embedding bias. In addition, MUBDsyn showed better setting of benchmarking challenge for deep learning models compared with NRLiSt-BDB. Overall, we have proven that MUBDsyn is the close-to-ideal benchmark for VS. The computational tool is publicly available for the easy extension of MUBDsyn.

Inhibition of IL-17 signaling in macrophages underlies the anti-arthritic effects of halofuginone hydrobromide: Network pharmacology, molecular docking, and experimental validation

BACKGROUND

Rheumatoid arthritis (RA) is a prevalent autoimmune disease marked by chronic synovitis as well as cartilage and bone destruction. Halofuginone hydrobromide (HF), a bioactive compound derived from the Chinese herbal plant Dichroa febrifuga Lour., has demonstrated substantial anti-arthritic effects in RA. Nevertheless, the molecular mechanisms responsible for the anti-RA effects of HF remain unclear.

METHODS

This study employed a combination of network pharmacology, molecular docking, and experimental validation to investigate potential targets of HF in RA.

RESULTS

Network pharmacology analyses identified 109 differentially expressed genes (DEGs) resulting from HF treatment in RA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses unveiled a robust association between these DEGs and the IL-17 signaling pathway. Subsequently, a protein-protein interaction (PPI) network analysis revealed 10 core DEGs, that is, EGFR, MMP9, TLR4, ESR1, MMP2, PPARG, MAPK1, JAK2, STAT1, and MAPK8. Among them, MMP9 displayed the greatest binding energy for HF. In an in vitro assay, HF significantly inhibited the activity of inflammatory macrophages, and regulated the IL-17 signaling pathway by decreasing the levels of IL-17 C, p-NF-κB, and MMP9.

CONCLUSION

In summary, these findings suggest that HF has the potential to inhibit the activation of inflammatory macrophages through its regulation of the IL-17 signaling pathway, underscoring its potential in the suppression of immune-mediated inflammation in RA.

The pursuit of accurate predictive models of the bioactivity of small molecules

Property prediction is a key interest in chemistry. For several decades there has been a continued and incremental development of mathematical models to predict properties. As more data is generated and accumulated, there seems to be more areas of opportunity to develop models with increased accuracy. The same is true if one considers the large developments in machine and deep learning models. However, along with the same areas of opportunity and development, issues and challenges remain and, with more data, new challenges emerge such as the quality and quantity and reliability of the data, and model reproducibility. Herein, we discuss the status of the accuracy of predictive models and present the authors' perspective of the direction of the field, emphasizing on good practices. We focus on predictive models of bioactive properties of small molecules relevant for drug discovery, agrochemical, food chemistry, natural product research, and related fields.

TRAF2 inhibits senescence in hepatocellular carcinoma cells via regulating the ROMO1/ NAD+/SIRT3/SOD2 axis

The suppression of tumor proliferation via cellular senescence has emerged as a promising approach for anti-tumor therapy. Tumor necrosis factor receptor-associated factor 2 (TRAF2), an adaptor protein involved in the NF-κB signaling pathway and reactive oxygen species (ROS) production, has been implicated in hepatocellular carcinoma (HCC) proliferation. However, little is currently known about whether TRAF2 promotes HCC development by inhibiting cellular senescence. Replicative senescence model and IR-induced mouse model demonstrated that TRAF2 expression was decrease in senescence cells or liver tissues. Depletion of TRAF2 could inhibit proliferation and arrest the cell cycle via activating p53/p21WAF1 and p16INK4a/pRb signaling pathways in HCC cells and eventually lead to cellular senescence. Mechanistically, TRAF2 deficiency increased the expression of mitochondrial protein reactive oxygen species modulator 1 (ROMO1) and subsequently activated the NAD+/SIRT3/SOD2 pathway to promote the production of ROS and cause mitochondrial dysfunction, which eventually contributed to DNA damage response (DDR). Our findings demonstrate that TRAF2 deficiency inhibits the proliferation of HCC by promoting senescence. Therefore, targeting TRAF2 through various approaches holds therapeutic potential for treating HCC.

Engineering serine hydroxymethyltransferases for efficient synthesis of L-serine in Escherichia coli

L-serine is a high-value amino acid widely used in the food, medicine, and cosmetic industries. However, the low yield of L-serine has limited its industrial production. In this study, a cellular factory for efficient synthesis of L-serine was obtained by engineering the serine hydroxymethyltransferases (SHMT). Firstly, after screening the SHMT from Alcanivorax dieselolei by genome mining, a mutant AdSHMTE266M with high thermal stability was identified through rational design. Subsequently, an iterative saturating mutant library was constructed by using coevolutionary analysis, and a mutant AdSHMTE160L/E193Q with enzyme activity 1.35 times higher than AdSHMT was identified. Additionally, the target protein AdSHMTE160L/E193Q/E266M was efficiently overexpressed by improving its mRNA stability. Finally, combining the substrate addition strategy and system optimization, the optimized strain BL21/pET28a-AdSHMTE160L/E193Q/E266M-5'UTR-REP3S16 produced 106.06 g/L L-serine, which is the highest production to date. This study provides new ideas and insights for the engineering design of SHMT and the industrial production of L-serine.

Environment relevant exposure of perfluorooctanoic acid accelerates the growth of hepatocellular carcinoma cells through mammalian target of rapamycin (mTOR) signal pathway

Perfluorooctanoic acid (PFOA), a synthetic alkyl chain fluorinated compound, has emerged as a persistent organic pollutant of grave concern, casting a shadow over both ecological integrity and humans. Its insidious presence raises alarms due to its capacity to bioaccumulate within the human liver, potentially paving the treacherous path toward liver cancer. Yet, the intricate mechanisms underpinning PFOA's role in promoting the growth of hepatocellular carcinoma (HCC) remain shrouded in ambiguity. Here, we determined the proliferation and transcription changes of HCC after PFOA exposure through integrated experiments including cell culture, nude mice tests, and colony-forming assays. Based on our findings, PFOA effectively promotes the proliferation of HCC cells within the experimental range of concentrations, both in vivo and in vitro. The proliferation efficiency of HCC cells was observed to increase by approximately 10% due to overexposure to PFOA. Additionally, the cancer weight of tumor-bearing nude mice increased by 87.0% (p < 0.05). We systematically evaluated the effects of PFOA on HCC cells and found that PFOA's exposure can selectively activate the PI3K/AKT/mTOR/4E-BP1 signaling pathway, thereby playing a pro-cancer effect on HCC cells Confirmation echoed through western blot assays and inhibitor combination analyses. These insights summon a response to PFOA's dual nature as both an environmental threat and a promoter of liver cancer. Our work illuminates the obscured domain of PFOA-induced hepatoxicity, shedding light on its ties to hepatocellular carcinoma progression.

Lipid metabolism-derived FAAH is a sensitive marker for the prognosis and immunotherapy of osteosarcoma patients

Lipid metabolism in cancer refers to the alterations in how cancer cells process and utilize lipids, a type of fat molecule. It was investigated how lipid metabolism relates to osteosarcoma. Genes relevant to lipid metabolism were gathered to create lipid metabolism-associated clusters and locate the dangerous marker. We investigated FAAH's prognostic significance, route annotation, immunotherapy response, and medication prediction. Besides, FAAH is proven to be a potent, dangerous marker that may promote growth and migration and inhibit the apoptosis of osteosarcoma. FAAH exhibits higher expression levels in tumor tissues as compared to normal tissues. In conclusion, FAAH is identified in this work as a potentially dangerous gene and immunotherapy determinant. This study requires more investigation to determine how FAAH influences the immune response in osteosarcoma.

The efficacy of sorafenib against hepatocellular carcinoma is enhanced by 5-aza-mediated inhibition of ID1 promoter methylation

Sorafenib resistance greatly restricts its clinical application in patients with hepatocellular carcinoma (HCC). Numerous studies have reported that ID1 exerts a crucial effect in cancer initiation and development. Our previous research revealed an inhibitory role of ID1 in sorafenib resistance. However, the upstream regulatory mechanism of ID1 expression is unclear. Here, we discovered that ID1 expression is negatively correlated with promoter methylation, which is regulated by DNMT3B. Knockdown of DNMT3B significantly inhibited ID1 methylation status and resulted in an increase of ID1 expression. The demethylating agent 5-aza-2'-deoxycytidine (5-aza) remarkably upregulated ID1 expression. The combination of 5-aza with sorafenib showed a synergistic effect on the inhibition of cell viability.

Inferring circRNA-drug sensitivity associations via dual hierarchical attention networks and multiple kernel fusion

Increasing evidence has shown that the expression of circular RNAs (circRNAs) can affect the drug sensitivity of cells and significantly influence drug efficacy. Therefore, research into the relationships between circRNAs and drugs can be of great significance in increasing the comprehension of circRNAs function, as well as contributing to the discovery of new drugs and the repurposing of existing drugs. However, it is time-consuming and costly to validate the function of circRNA with traditional medical research methods. Therefore, the development of efficient and accurate computational models that can assist in discovering the potential interactions between circRNAs and drugs is urgently needed. In this study, a novel method is proposed, called DHANMKF , that aims to predict potential circRNA-drug sensitivity interactions for further biomedical screening and validation. Firstly, multimodal networks were constructed by DHANMKF using multiple sources of information on circRNAs and drugs. Secondly, comprehensive intra-type and inter-type node representations were learned using bi-typed multi-relational heterogeneous graphs, which are attention-based encoders utilizing a hierarchical process. Thirdly, the multi-kernel fusion method was used to fuse intra-type embedding and inter-type embedding. Finally, the Dual Laplacian Regularized Least Squares method (DLapRLS) was used to predict the potential circRNA-drug sensitivity associations using the combined kernel in circRNA and drug spaces. Compared with the other methods, DHANMKF obtained the highest AUC value on two datasets. Code is available at https://github.com/cuntjx/DHANMKF .

Myricetin suppresses traumatic brain injury-induced inflammatory response via EGFR/AKT/STAT pathway

Traumatic brain injury (TBI) is a common disease in neurosurgery with a high fatality and disability rate which imposes a huge burden on society and patient's family. Inhibition of neuroinflammation caused by microglia activation is a reasonable strategy to promote neurological recovery after TBI. Myricetin is a natural flavonoid that has shown good therapeutic effects in a variety of neurological disease models, but its therapeutic effect on TBI is not clear. We demonstrated that intraperitoneal injection of appropriate doses of myricetin significantly improved recovery of neurological function after TBI in Sprague Dawley rats and inhibited excessive inflammatory responses around the lesion site. Myricetin dramatically reduced the expression of toxic microglia markers generated by TBI and LPS, according to the outcomes of in vivo and in vitro tests. In particular, the expression of inducible nitric oxide synthase, cyclooxygenase 2, and some pro-inflammatory cytokines was reduced, which protected learning and memory functions in TBI rats. Through network pharmacological analysis, we found that myricetin may inhibit microglia hyperactivation through the EGFR-AKT/STAT pathway. These findings imply that myricetin is a promising treatment option for the management of neuroinflammation following TBI.

MNCLCDA: predicting circRNA-drug sensitivity associations by using mixed neighbourhood information and contrastive learning

BACKGROUND

circRNAs play an important role in drug resistance and cancer development. Recently, many studies have shown that the expressions of circRNAs in human cells can affect the sensitivity of cells to therapeutic drugs, thus significantly influencing the therapeutic effects of these drugs. Traditional biomedical experiments required to verify this sensitivity relationship are not only time-consuming but also expensive. Hence, the development of an efficient computational approach that can accurately predict the novel associations between drug sensitivities and circRNAs is a crucial and pressing need.

METHODS

In this research, we present a novel computational framework called MNCLCDA, which aims to predict the potential associations between drug sensitivities and circRNAs to assist with medical research. First, MNCLCDA quantifies the similarity between the given drug and circRNA using drug structure information, circRNA gene sequence information, and GIP kernel information. Due to the existence of noise in similarity information, we employ a preprocessing approach based on random walk with restart for similarity networks to efficiently capture the useful features of circRNAs and drugs. Second, we use a mixed neighbourhood graph convolutional network to obtain the neighbourhood information of nodes. Then, a graph-based contrastive learning method is used to enhance the robustness of the model, and finally, a double Laplace-regularized least-squares method is used to predict potential circRNA-drug associations through the kernel matrices in the circRNA and drug spaces.

RESULTS

Numerous experimental results show that MNCLCDA outperforms six other advanced methods. In addition, the excellent performance of our proposed model in case studies illustrates that MNCLCDA also has the ability to predict the associations between drug sensitivity and circRNA in practical situations.

CONCLUSIONS

After a large number of experiments, it is illustrated that MNCLCDA is an efficient tool for predicting the potential associations between drug sensitivities and circRNAs, thereby can provide some guidance for clinical trials.

Molecular docking and simulation studies of Chloroquine, Rimantadine and CAP-1 as potential repurposed antivirals for decapod iridescent virus 1 (DIV1)

Drug repurposing is a methodology of identifying new therapeutic use for existing drugs. It is a highly efficient, time and cost-saving strategy that offers an alternative approach to the traditional drug discovery process. Past in-silico studies involving molecular docking have been successful in identifying potential repurposed drugs for the various treatment of diseases including aquaculture diseases. The emerging shrimp hemocyte iridescent virus (SHIV) or Decapod iridescent virus 1 (DIV1) is a viral pathogen that causes severe disease and high mortality (80 %) in farmed shrimps caused serious economic losses and presents a new threat to the shrimp farming industry. Therefore, effective antiviral drugs are critically needed to control DIV1 infections. The aim of this study is to investigate the interaction of potential existing antiviral drugs, Chloroquine, Rimantadine, and CAP-1 with DIV1 major capsid protein (MCP) with the intention of exploring the potential of drug repurposing. The interaction of the DIV1 MCP and three antivirals were characterised and analysed using molecular docking and molecular dynamics simulation. The results showed that CAP-1 is a more promising candidate against DIV1 with the lowest binding energy of -8.46 kcal/mol and is more stable compared to others. We speculate that CAP-1 binding may induce the conformational changes in the DIV1 MCP structure by phosphorylating multiple residues (His123, Tyr162, and Thr395) and ultimately block the viral assembly and maturation of DIV1 MCP. To the best of our knowledge, this is the first report regarding the structural characterisation of DIV1 MCP docked with repurposing drugs.

Study of the curative effect of Zhang's Xibi formula and its underlying mechanism involving inhibition of inflammatory responses and delay of knee osteoarthritis

OBJECTIVE

To verify the clinical efficacy of Zhang's Xibi formula (ZSXBF) and explain the mechanism underlying its therapeutic effect.

METHODS

Preliminary elucidation of the clinical efficacy of ZSXBF in treating KOA in self-control studies, exploration of its mechanism of action with network pharmacology methods, and validation in animal experiments.

RESULTS

In clinical studies, ZSXBF administration effectively improved patient quality of life and reduce pain. Network pharmacology was used to explore the possible mechanisms underlying its treatment effect, and after verification in clinical experience and animal experiments, it was found that ZSXBF regulated the expression of immune-related proteins such as IL-17, ERK1, and TP53 in mouse knee joints.

CONCLUSION

ZSXBF, which is a traditional Chinese medicine compound that is used to clear heat and detoxify, can effectively improve the clinical symptoms of KOA patients, and its underlying mechanism includes the regulation of human immune-related proteins.

Multimodal learning in clinical proteomics: enhancing antimicrobial resistance prediction models with chemical information

MOTIVATION

Large-scale clinical proteomics datasets of infectious pathogens, combined with antimicrobial resistance outcomes, have recently opened the door for machine learning models which aim to improve clinical treatment by predicting resistance early. However, existing prediction frameworks typically train a separate model for each antimicrobial and species in order to predict a pathogen's resistance outcome, resulting in missed opportunities for chemical knowledge transfer and generalizability.

RESULTS

We demonstrate the effectiveness of multimodal learning over proteomic and chemical features by exploring two clinically relevant tasks for our proposed deep learning models: drug recommendation and generalized resistance prediction. By adopting this multi-view representation of the pathogenic samples and leveraging the scale of the available datasets, our models outperformed the previous single-drug and single-species predictive models by statistically significant margins. We extensively validated the multi-drug setting, highlighting the challenges in generalizing beyond the training data distribution, and quantitatively demonstrate how suitable representations of antimicrobial drugs constitute a crucial tool in the development of clinically relevant predictive models.

AVAILABILITY AND IMPLEMENTATION

The code used to produce the results presented in this article is available at https://github.com/BorgwardtLab/MultimodalAMR.

New cytotoxic dammarane type saponins from Ziziphus spina-christi

Cancer is the world's second-leading cause of death. Drug development efforts frequently focus on medicinal plants since they are a valuable source of anticancer medications. A phytochemical investigation of the edible Ziziphus spina-christi (F. Rhamnaceae) leaf extract afforded two new dammarane type saponins identified as christinin E and F (1, 2), along with the known compound christinin A (3). Different cancer cell lines, such as lung cancer (A549), glioblastoma (U87), breast cancer (MDA-MB-231), and colorectal carcinoma (CT-26) cell lines, were used to investigate the extracted compounds' cytotoxic properties. Our findings showed significant effects on all the tested cell lines at varying concentrations (1, 5, 10, and 20 µg/mL). The three compounds exhibited potent activity at low concentrations (< 10 μg/mL), as evidenced by their low IC50 values. To further investigate the complex relationships between these identified cancer-relevant biological targets and to identify critical targets in the pathogenesis of the disease, we turned to network pharmacology and in silico-based investigations. Following this, in silico-based analysis (e.g., inverse docking, ΔG calculation, and molecular dynamics simulation) was performed on the structures of the isolated compounds to identify additional potential targets for these compounds and their likely interactions with various signalling pathways relevant to this disease. Based on our findings, Z. spina-christi's compounds showed promise as potential anti-cancer therapeutic leads in the future.

Advancing Computational Toxicology by Interpretable Machine Learning

Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in humans. Computational toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict the toxicity potentials of chemicals. Although the applications of ML- and DL-based computational models in chemical toxicity predictions are attractive, many toxicity models are "black boxes" in nature and difficult to interpret by toxicologists, which hampers the chemical risk assessments using these models. The recent progress of interpretable ML (IML) in the computer science field meets this urgent need to unveil the underlying toxicity mechanisms and elucidate the domain knowledge of toxicity models. In this review, we focused on the applications of IML in computational toxicology, including toxicity feature data, model interpretation methods, use of knowledge base frameworks in IML development, and recent applications. The challenges and future directions of IML modeling in toxicology are also discussed. We hope this review can encourage efforts in developing interpretable models with new IML algorithms that can assist new chemical assessments by illustrating toxicity mechanisms in humans.

PLP2 Could Be a Prognostic Biomarker and Potential Treatment Target in Glioblastoma Multiforme

Objective

This study aimed to discern the association between PLP2 expression, its biological significance, and the extent of immune infiltration in human GBM.

Methods

Utilizing the GEPIA2 and TCGA databases, we contrasted the expression levels of PLP2 in GBM against normal tissue. We utilized GEPIA2 and LinkedOmics for survival analysis, recognized genes co-expressed with PLP2 via cBioPortal and GEPIA2, and implemented GO and KEGG analyses. The STRING database facilitated the construction of protein-protein interaction networks. We evaluated the relationship of PLP2 with tumor immune infiltrates using ssGSEA and the TIMER 2.0 database. An IHC assay assessed PLP2 and PDL-1 expression in GBM tissue, and the Drugbank database aided in identifying potential PLP2-targeting compounds. Molecular docking was accomplished using Autodock Vina 1.2.2.

Results

PLP2 expression was markedly higher in GBM tissues in comparison to normal tissues. High PLP2 expression correlated with a decrease in overall survival across two databases. Functional analyses highlighted a focus of PLP2 functions within leukocyte. Discrepancies in PLP2 expression were evident in immune infiltration, impacting CD4+ T cells, neutrophils, myeloid dendritic cells, and macrophages. There was a concomitant increase in PLP2 and PD-L1 expression in GBM tissues, revealing a link between the two. Molecular docking with ethosuximide and praziquantel yielded scores of -7.441 and -4.295 kcal/mol, correspondingly.

Conclusion

PLP2's upregulation in GBM may adversely influence the lifespan of GBM patients. The involvement of PLP2 in pathways linked to leukocyte function is suggested. The positive correlation between PLP2 and PD-L1 could provide insights into PLP2's role in glioma modulation. Our research hints at PLP2's potential as a therapeutic target for GBM, with ethosuximide and praziquantel emerging as potential treatment candidates, especially emphasizing the potential of these compounds in GBM treatment targeting PLP2.

Proteomic identification of metabolic changes in Paracoccidioides brasiliensis induced by a nitroheteroarylchalcone

Aim: To access the metabolic changes caused by a chalcone derivative (LabMol-75) through a proteomic approach. Methods: Proteomic analysis was performed after 9 h of Paracoccidioides brasiliensis yeast (Pb18) cell incubation with the LabMol-75 at MIC. The proteomic findings were validated through in vitro and in silico assays. Results: Exposure to the compound led to the downregulation of proteins associated with glycolysis and gluconeogenesis, β-oxidation, the citrate cycle and the electron transport chain. Conclusion: LabMol-75 caused an energetic imbalance in the fungus metabolism and deep oxidative stress. Additionally, the in silico molecular docking approach pointed to this molecule as a putative competitive inhibitor of DHPS.

Inflammation-related signature for prognostic prediction, tumor immune, genomic heterogeneity, and drug choices in prostate cancer: Integrated analysis of bulk and single-cell RNA-sequencing

Background

Prostate cancer (PCa) ranks as the second most prevalent malignancy among males on a global scale. Accumulating evidence suggests that inflammation has an intricate relationship with tumorigenesis, tumor progression and tumor immune microenvironment. However, the overall impact of inflammation-related genes on the clinical prognosis and tumor immunity in PCa remains unclear.

Methods

Machine learning methods were utilized to construct and validate a signature using The Cancer Genome Atlas (TCGA) for training, while the Memorial Sloan Kettering Cancer Center (MSKCC) and GSE70769 cohorts for independent validation. The efficacy of the signature in predicting outcomes and its clinical utility were assessed through a series of investigations encompassing in vitro experiments, survival analysis, and nomogram development. The association between the signature and precision medicine was explored via tumor immunity, genomic heterogeneity, therapeutic response, and molecular docking analyses, using bulk and single-cell RNA-sequencing data.

Results

We identified 7 inflammation-related genes with prognostic significance and developed an inflammation-related prognostic signature (IRPS) with 6 genes. Furthermore, we demonstrated that both the IRPS and a nomogram integrating risk score and pathologic T stage exhibited excellent predictive ability for the survival outcomes in PCa patients. Moreover, the IRPS was found to be significantly associated with the tumor immune, genomic heterogeneity, therapeutic response, and drug selection.

Conclusion

IRPS can serve as a reliable predictor for PCa patients. The signature may provide clinicians with valuable information on the efficacy of therapy and help personalize treatment for PCa patients.

Analysis of damage-associated molecular patterns in amyotrophic lateral sclerosis based on ScRNA-seq and bulk RNA-seq data

Background

Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disorder characterized by the progressive loss of motor neurons. Despite extensive research, the exact etiology of ALS remains elusive. Emerging evidence highlights the critical role of the immune system in ALS pathogenesis and progression. Damage-Associated Molecular Patterns (DAMPs) are endogenous molecules released by stressed or damaged cells, acting as danger signals and activating immune responses. However, their specific involvement in ALS remains unclear.

Methods

We obtained single-cell RNA sequencing (scRNA-seq) data of ALS from the primary motor cortex in the Gene Expression Omnibus (GEO) database. To better understand genes associated with DAMPs, we performed analyses on cell-cell communication and trajectory. The abundance of immune-infiltrating cells was assessed using the single-sample Gene Set Enrichment Analysis (ssGSEA) method. We performed univariate Cox analysis to construct the risk model and utilized the least absolute shrinkage and selection operator (LASSO) analysis. Finally, we identified potential small molecule drugs targeting ALS by screening the Connectivity Map database (CMap) and confirmed their potential through molecular docking analysis.

Results

Our study annotated 10 cell types, with the expression of genes related to DAMPs predominantly observed in microglia. Analysis of intercellular communication revealed 12 ligand-receptor pairs in the pathways associated with DAMPs, where microglial cells acted as ligands. Among these pairs, the SPP1-CD44 pair demonstrated the greatest contribution. Furthermore, trajectory analysis demonstrated distinct differentiation fates of different microglial states. Additionally, we constructed a risk model incorporating four genes (TRPM2, ROCK1, HSP90AA1, and HSPA4). The validity of the risk model was supported by multivariate analysis. Moreover, external validation from dataset GSE112681 confirmed the predictive power of the model, which yielded consistent results with datasets GSE112676 and GSE112680. Lastly, the molecular docking analysis suggested that five compounds, namely mead-acid, nifedipine, nifekalant, androstenol, and hydrastine, hold promise as potential candidates for the treatment of ALS.

Conclusion

Taken together, our study demonstrated that DAMP entities were predominantly observed in microglial cells within the context of ALS. The utilization of a prognostic risk model can accurately predict ALS patient survival. Additionally, genes related to DAMPs may present viable drug targets for ALS therapy.

Fusobacterium nucleatum upregulates MMP7 to promote metastasis-related characteristics of colorectal cancer cell via activating MAPK(JNK)-AP1 axis

BACKGROUND

Colorectal cancer (CRC) is the third most common malignant tumor. Fusobacterium nucleatum (F. nucleatum) is overabundant in CRC and associated with metastasis, but the role of F. nucleatum in CRC cell migration and metastasis has not been fully elucidated.

METHODS

Differential gene analysis, protein-protein interaction, robust rank aggregation analysis, functional enrichment analysis, and gene set variation analysis were used to figure out the potential vital genes and biological functions affected by F. nucleatum infection. The 16S rDNA sequencing and q-PCR were used to detect the abundance of F. nucleatum in tissues and stools. Then, we assessed the effect of F. nucleatum on CRC cell migration by wound healing and transwell assays, and confirmed the role of Matrix metalloproteinase 7 (MMP7) induced by F. nucleatum in cell migration. Furthermore, we dissected the mechanisms involved in F. nucleatum induced MMP7 expression. We also investigated the MMP7 expression in clinical samples and its correlation with prognosis in CRC patients. Finally, we screened out potential small molecular drugs that targeted MMP7 using the HERB database and molecular docking.

RESULTS

F. nucleatum infection altered the gene expression profile and affected immune response, inflammation, biosynthesis, metabolism, adhesion and motility related biological functions in CRC. F. nucleatum was enriched in CRC and promoted the migration of CRC cell by upregulating MMP7 in vitro. MMP7 expression induced by F. nucleatum infection was mediated by the MAPK(JNK)-AP1 axis. MMP7 was highly expressed in CRC and correlated with CMS4 and poor clinical prognosis. Small molecular drugs such as δ-tocotrienol, 3,4-benzopyrene, tea polyphenols, and gallic catechin served as potential targeted therapeutic drugs for F. nucleatum induced MMP7 in CRC.

CONCLUSIONS

Our study showed that F. nucleatum promoted metastasis-related characteristics of CRC cell by upregulating MMP7 via MAPK(JNK)-AP1 axis. F. nucleatum and MMP7 may serve as potential therapeutic targets for repressing CRC advance and metastasis.

DDI-GCN: Drug-drug interaction prediction via explainable graph convolutional networks

Drug-drug interactions (DDI) may lead to unexpected side effects, which is a growing concern in both academia and industry. Many DDIs have been reported, but the underlying mechanisms are not well understood. Predicting and understanding DDIs can help researchers to improve drug safety and protect patient health. Here, we introduce DDI-GCN, a method that utilizes graph convolutional networks (GCN) to predict DDIs based on chemical structures. We demonstrate that this method achieves state-of-the-art prediction performance on the independent hold-out set. It can also provide visualization of structural features associated with DDIs, which can help us to study the underlying mechanisms. To make it easy and accessible to use, we developed a web server for DDI-GCN, which is freely available at http://wengzq-lab.cn/ddi/.

Identifying a baicalein-related prognostic signature contributes to prognosis prediction and tumor microenvironment of pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is one of the most malignant and lethal human cancers in the world due to its high metastatic potential, and patients with PDAC have a poor prognosis, yet quite little is understood regarding the underlying biological mechanisms of its high metastatic capacity. Baicalein has a dramatic anti-tumor function in the treatment of different types of cancer. However, the therapeutic effects of baicalein on human PDAC and its mechanisms of action have not been extensively understood. In order to explore the biological characteristic, molecular mechanisms, and potential clinical value of baicalein in inhibiting the metastatic capacity of PDAC. We performed several in vitro, in vivo, and in silico studies. We first examined the potential regulation of baicalein in the metastatic capacity of PDAC cells. We showed that baicalein could dramatically suppress liver metastasis of PDAC cells with highly metastatic potential in mice model. The high-throughput sequencing analysis was employed to explore the biological roles of baicalein in PDAC cells. We found that baicalein might be involved in the infiltration of Cancer-Associated Fibroblasts (CAF) in PDAC. Moreover, a baicalein-related risk model and a lncRNA-related model were built by Cox analysis according to the data set of PDAC from TCGA database which suggested a clinical value of baicalein. Finally, we revealed a potential downstream target of baicalein in PDAC, we proposed that baicalein might contribute to the infiltration of CAF via FGFBP1. Thus, we uncovered a novel role for baicalein in regulation of PDAC liver metastasis that may contribute to its anti-cancer effect. We proposed that baicalein might suppress PDAC liver metastasis via regulation of FGFBP1-mediated CAF infiltration. Our results provide a new perspective on clinical utility of baicalein and open new avenues for the inhibition of liver-metastasis of PDAC.

Impacts of Indoor Dust Exposure on Human Colonic Cell Viability, Cytotoxicity and Apoptosis

INTRODUCTION

Environmental exposure to indoor dust is known to be associated with myriad health conditions, especially among children. Established routes of exposure include inhalation and non-dietary ingestion, which result in the direct exposure of gastrointestinal epithelia to indoor dust. Despite this, little prior research is available on the impacts of indoor dust on the health of human gastrointestinal tissue.

METHODS

Cultured human colonic (CCD841) cells were exposed for 24 h to standard trace metal dust (TMD) and organic contaminant dust (OD) samples at the following concentrations: 0, 10, 25, 50, 75, 100, 250, and 500 µg/mL. Cell viability was assessed using an MTT assay and protease analysis (glycyl-phenylalanyl-aminofluorocoumarin (GF-AFC)); cytotoxicity was assessed with a lactate dehydrogenase release assay, and apoptosis was assessed using a Caspase-Glo 3/7 activation assay.

RESULTS

TMD and OD decreased cellular metabolic and protease activity and increased apoptosis and biomarkers of cell membrane damage (LDH) in CCD841 human colonic epithelial cells. Patterns appeared to be, in general, dose-dependent, with the highest TMD and OD exposures associated with the largest increases in apoptosis and LDH, as well as with the largest decrements in metabolic and protease activities.

CONCLUSIONS

TMD and OD exposure were associated with markers of reduced viability and increased cytotoxicity and apoptosis in human colonic cells. These findings add important information to the understanding of the physiologic effects of indoor dust exposure on human health. The doses used in our study represent a range of potential exposure levels, and the effects observed at the higher doses may not necessarily occur under typical exposure conditions. The effects of long-term, low-dose exposure to indoor dust are still not fully understood and warrant further investigation. Future research should explore these physiological mechanisms to further our understanding and inform public health interventions.

DruMAP: A Novel Drug Metabolism and Pharmacokinetics Analysis Platform

We developed a novel drug metabolism and pharmacokinetics (DMPK) analysis platform named DruMAP. This platform consists of a database for DMPK parameters and programs that can predict many DMPK parameters based on the chemical structure of a compound. The DruMAP database includes curated DMPK parameters from public sources and in-house experimental data obtained under standardized conditions; it also stores predicted DMPK parameters produced by our prediction programs. Users can predict several DMPK parameters simultaneously for novel compounds not found in the database. Furthermore, the highly flexible search system enables users to search for compounds as they desire. The current version of DruMAP comprises more than 30,000 chemical compounds, about 40,000 activity values (collected from public databases and in-house data), and about 600,000 predicted values. Our platform provides a simple tool for searching and predicting DMPK parameters and is expected to contribute to the acceleration of new drug development. DruMAP can be freely accessed at: https://drumap.nibiohn.go.jp/.

Pan-Cancer Analysis Reveals CENPI as a Potential Biomarker and Therapeutic Target in Adrenocortical Carcinoma

Background

Centromere protein I (CENPI) has been shown to affect the tumorigenesis of breast and colorectal cancers. However, its biological role and prognostic value in other kinds of cancer, especially adrenocortical carcinoma (ACC), remained to be further investigated.

Methods

Various bioinformatics tools were adopted for exploring the significance of differential expression of CENPI in several malignant tumors from databases such as Depmap portal, GTEx, and TCGA. ACC was selected for further analyzed, and information such as clinicopathological features, the prognostic outcome of diverse subgroups, differentially expressed genes (DEGs), co-expression genes, as well as levels of tumor-infiltrating immune cells (TIIC), was extracted from multiple databases. To verify the possibility of CENPI as a therapeutic target in ACC, drug sensitivity assay and si-RNA mediate knockdown of CENPI were carried out.

Results

The pan-cancer analyses showed that the CENPI mRNA expression levels differed significantly among most cancer types. Additionally, a high precision in cancer prediction and close relation with cancer survival indicated that CENPI could be a potential candidate biomarker to diagnose and predict cancer prognosis. In ACC, CENPI was closely related to multiple clinical characteristics, such as pathological stage and primary therapy outcome. High CENPI levels predicted poor overall survival (OS), progression-free interval (PFI), and disease-specific survival (DSS) of ACC patients, particularly for different clinical subgroups. Moreover, the expression of CENPI showed positive relationship to Th2 cells but negatively related to most of the TIICs. Furthermore, drug sensitivity assay showed that vorinostat inhibit CENPI expression and ACC cell growth. Additionally, si-RNA mediated knockdown of CENPI inhibited ACC cell growth and invasion and showed synergistic anti-proliferation effect with AURKB inhibitor barasertib.

Conclusion

Pan-cancer analysis demonstrated that CENPI is a potential diagnostic and prognostic biomarker in various cancers as well as an anti-ACC therapeutic target.

AI in drug discovery and its clinical relevance

The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.