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Showing 981 to 984 of 5186 entries
Published on February 17, 2022
The Multifaceted Roles of Mast Cells in Immune Homeostasis, Infections and Cancers.
Abstract: Mast cells (MCs) play important roles in normal immune responses and pathological states. The location of MCs on the boundaries between tissues and the external environment, including gut mucosal surfaces, lungs, skin, and around blood vessels, suggests a multitude of immunological functions. Thus, MCs are pivotal for host defense against different antigens, including allergens and microbial pathogens. MCs can produce and respond to physiological mediators and chemokines to modulate inflammation. As long-lived, tissue-resident cells, MCs indeed mediate acute inflammatory responses such as those evident in allergic reactions. Furthermore, MCs participate in innate and adaptive immune responses to bacteria, viruses, fungi, and parasites. The control of MC activation or stabilization is a powerful tool in regulating tissue homeostasis and pathogen clearance. Moreover, MCs contribute to maintaining the homeostatic equilibrium between host and resident microbiota, and they engage in crosstalk between the resident and recruited hematopoietic cells. In this review, we provide a comprehensive overview of the functions of MCs in health and disease. Further, we discuss how mouse models of MC deficiency have become useful tools for establishing MCs as a potential cellular target for treating inflammatory disorders.
Published on February 15, 2022
Patient-specific Boolean models of signalling networks guide personalised treatments.
Abstract: Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients. A total of 488 prostate samples were used to build patient-specific models and compared to available clinical data. Additionally, eight prostate cell line-specific models were built to validate our approach with dose-response data of several drugs. The effects of single and combined drugs were tested in these models under different growth conditions. We identified 15 actionable points of interventions in one cell line-specific model whose inactivation hinders tumorigenesis. To validate these results, we tested nine small molecule inhibitors of five of those putative targets and found a dose-dependent effect on four of them, notably those targeting HSP90 and PI3K. These results highlight the predictive power of our personalised Boolean models and illustrate how they can be used for precision oncology.
Published on February 14, 2022
D3PM: a comprehensive database for protein motions ranging from residue to domain.
Abstract: BACKGROUND: Knowledge of protein motions is significant to understand its functions. While currently available databases for protein motions are mostly focused on overall domain motions, little attention is paid on local residue motions. Albeit with relatively small scale, the local residue motions, especially those residues in binding pockets, may play crucial roles in protein functioning and ligands binding. RESULTS: A comprehensive protein motion database, namely D3PM, was constructed in this study to facilitate the analysis of protein motions. The protein motions in the D3PM range from overall structural changes of macromolecule to local flip motions of binding pocket residues. Currently, the D3PM has collected 7679 proteins with overall motions and 3513 proteins with pocket residue motions. The motion patterns are classified into 4 types of overall structural changes and 5 types of pocket residue motions. Impressively, we found that less than 15% of protein pairs have obvious overall conformational adaptations induced by ligand binding, while more than 50% of protein pairs have significant structural changes in ligand binding sites, indicating that ligand-induced conformational changes are drastic and mainly confined around ligand binding sites. Based on the residue preference in binding pocket, we classified amino acids into "pocketphilic" and "pocketphobic" residues, which should be helpful for pocket prediction and drug design. CONCLUSION: D3PM is a comprehensive database about protein motions ranging from residue to domain, which should be useful for exploring diverse protein motions and for understanding protein function and drug design. The D3PM is available on www.d3pharma.com/D3PM/index.php .
Published on February 14, 2022
Executable network of SARS-CoV-2-host interaction predicts drug combination treatments.
Abstract: The COVID-19 pandemic has pushed healthcare systems globally to a breaking point. The urgent need for effective and affordable COVID-19 treatments calls for repurposing combinations of approved drugs. The challenge is to identify which combinations are likely to be most effective and at what stages of the disease. Here, we present the first disease-stage executable signalling network model of SARS-CoV-2-host interactions used to predict effective repurposed drug combinations for treating early- and late stage severe disease. Using our executable model, we performed in silico screening of 9870 pairs of 140 potential targets and have identified nine new drug combinations. Camostat and Apilimod were predicted to be the most promising combination in effectively supressing viral replication in the early stages of severe disease and were validated experimentally in human Caco-2 cells. Our study further demonstrates the power of executable mechanistic modelling to enable rapid pre-clinical evaluation of combination therapies tailored to disease progression. It also presents a novel resource and expandable model system that can respond to further needs in the pandemic.
Published on February 14, 2022
A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals.
Abstract: To accelerate biomedical research process, deep-learning systems are developed to automatically acquire knowledge about molecule entities by reading large-scale biomedical data. Inspired by humans that learn deep molecule knowledge from versatile reading on both molecule structure and biomedical text information, we propose a knowledgeable machine reading system that bridges both types of information in a unified deep-learning framework for comprehensive biomedical research assistance. We solve the problem that existing machine reading models can only process different types of data separately, and thus achieve a comprehensive and thorough understanding of molecule entities. By grasping meta-knowledge in an unsupervised fashion within and across different information sources, our system can facilitate various real-world biomedical applications, including molecular property prediction, biomedical relation extraction and so on. Experimental results show that our system even surpasses human professionals in the capability of molecular property comprehension, and also reveal its promising potential in facilitating automatic drug discovery and documentation in the future.
Published on February 12, 2022
Screening of antibacterial compounds with novel structure from the FDA approved drugs using machine learning methods.
Abstract: Bacterial infection is one of the most important factors affecting the human life span. Elderly people are more harmed by bacterial infections due to their deficits in immunity. Because of the lack of new antibiotics in recent years, bacterial resistance has increasingly become a serious problem globally. In this study, an antibacterial compound predictor was constructed using the support vector machines and random forest methods and the data of the active and inactive antibacterial compounds from the ChEMBL database. The results showed that both models have excellent prediction performance (mean accuracy >0.9 and mean AUC >0.9 for the two models). We used the predictor to screen potential antibacterial compounds from FDA-approved drugs in the DrugBank database. The screening results showed that 1087 small-molecule drugs have potential antibacterial activity and 154 of them are FDA-approved antibacterial drugs, which accounts for 76.2% of the approved antibacterial drugs collected in this study. Through molecular fingerprint similarity analysis and common substructure analysis, we screened 8 predicted antibacterial small-molecule compounds with novel structures compared with known antibacterial drugs, and 5 of them are widely used in the treatment of various tumors. This study provides a new insight for predicting antibacterial compounds by using approved drugs, the predicted compounds might be used to treat bacterial infections and extend lifespan.
Published on February 12, 2022
Multimodal reasoning based on knowledge graph embedding for specific diseases.
Abstract: MOTIVATION: Knowledge Graph (KG) is becoming increasingly important in the biomedical field. Deriving new and reliable knowledge from existing knowledge by knowledge graph embedding technology is a cutting-edge method. Some add a variety of additional information to aid reasoning, namely multimodal reasoning. However, few works based on the existing biomedical KGs are focused on specific diseases. RESULTS: This work develops a construction and multimodal reasoning process of Specific Disease Knowledge Graphs (SDKGs). We construct SDKG-11, a SDKG set including five cancers, six non-cancer diseases, a combined Cancer5, and a combined Diseases11, aiming to discover new reliable knowledge and provide universal pre-trained knowledge for that specific disease field. SDKG-11 is obtained through original triplet extraction, standard entity set construction, entity linking, and relation linking. We implement multimodal reasoning by reverse-hyperplane projection for SDKGs based on structure, category, and description embeddings. Multimodal reasoning improves pre-existing models on all SDKGs using entity prediction task as the evaluation protocol. We verify the model's reliability in discovering new knowledge by manually proofreading predicted drug-gene, gene-disease, and disease-drug pairs. Using embedding results as initialization parameters for the biomolecular interaction classification, we demonstrate the universality of embedding models. AVAILABILITY: The constructed SDKG-11 and the implementation by TensorFlow are available from https://github.com/ZhuChaoY/SDKG-11. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Published on February 11, 2022
African Genomic Medicine Portal: A Web Portal for Biomedical Applications.
Abstract: Genomics data are currently being produced at unprecedented rates, resulting in increased knowledge discovery and submission to public data repositories. Despite these advances, genomic information on African-ancestry populations remains significantly low compared with European- and Asian-ancestry populations. This information is typically segmented across several different biomedical data repositories, which often lack sufficient fine-grained structure and annotation to account for the diversity of African populations, leading to many challenges related to the retrieval, representation and findability of such information. To overcome these challenges, we developed the African Genomic Medicine Portal (AGMP), a database that contains metadata on genomic medicine studies conducted on African-ancestry populations. The metadata is curated from two public databases related to genomic medicine, PharmGKB and DisGeNET. The metadata retrieved from these source databases were limited to genomic variants that were associated with disease aetiology or treatment in the context of African-ancestry populations. Over 2000 variants relevant to populations of African ancestry were retrieved. Subsequently, domain experts curated and annotated additional information associated with the studies that reported the variants, including geographical origin, ethnolinguistic group, level of association significance and other relevant study information, such as study design and sample size, where available. The AGMP functions as a dedicated resource through which to access African-specific information on genomics as applied to health research, through querying variants, genes, diseases and drugs. The portal and its corresponding technical documentation, implementation code and content are publicly available.
Published on February 11, 2022
Activation of ABCC Genes by Cisplatin Depends on the CoREST Occurrence at Their Promoters in A549 and MDA-MB-231 Cell Lines.
Abstract: Although cisplatin-based therapies are common among anticancer approaches, they are often associated with the development of cancer drug resistance. This phenomenon is, among others, caused by the overexpression of ATP-binding cassette, membrane-anchored transporters (ABC proteins), which utilize ATP to remove, e.g., chemotherapeutics from intracellular compartments. To test the possible molecular basis of increased expression of ABCC subfamily members in a cisplatin therapy mimicking model, we generated two cisplatin-resistant cell lines derived from non-small cell lung cancer cells (A549) and triple-negative breast cancer cells (MDA-MB-231). Analysis of data for A549 cells deposited in UCSC Genome Browser provided evidence on the negative interdependence between the occurrence of the CoREST complex at the gene promoters and the overexpression of ABCC genes in cisplatin-resistant lung cancer cells. Pharmacological inhibition of CoREST enzymatic subunits-LSD1 and HDACs-restored gene responsiveness to cisplatin. Overexpression of CoREST-free ABCC10 in cisplatin-resistant phenotypes was caused by the activity of EP300 that was enriched at the ABCC10 promoter in drug-treated cells. Cisplatin-induced and EP300-dependent transcriptional activation of ABCC10 was only possible in the presence of p53. In summary, the CoREST complex prevents the overexpression of some multidrug resistance proteins from the ABCC subfamily in cancer cells exposed to cisplatin. p53-mediated activation of some ABCC genes by EP300 occurs once their promoters are devoid of the CoREST complex.
Published on February 11, 2022
An effector index to predict target genes at GWAS loci.
Abstract: Drug development and biological discovery require effective strategies to map existing genetic associations to causal genes. To approach this problem, we selected 12 common diseases and quantitative traits for which highly powered genome-wide association studies (GWAS) were available. For each disease or trait, we systematically curated positive control gene sets from Mendelian forms of the disease and from targets of medicines used for disease treatment. We found that these positive control genes were highly enriched in proximity of GWAS-associated single-nucleotide variants (SNVs). We then performed quantitative assessment of the contribution of commonly used genomic features, including open chromatin maps, expression quantitative trait loci (eQTL), and chromatin conformation data. Using these features, we trained and validated an Effector Index (Ei), to map target genes for these 12 common diseases and traits. Ei demonstrated high predictive performance, both with cross-validation on the training set, and an independently derived set for type 2 diabetes. Key predictive features included coding or transcript-altering SNVs, distance to gene, and open chromatin-based metrics. This work outlines a simple, understandable approach to prioritize genes at GWAS loci for functional follow-up and drug development, and provides a systematic strategy for prioritization of GWAS target genes.
Showing 981 to 984 of 5186 entries