Drug-related genomics in cancer and immunological diseases.
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ABSTRACT:
SUBMITTER: Wei JF
PROVIDER: S-EPMC4134793 | biostudies-literature | 2014
REPOSITORIES: biostudies-literature
Publications
Drug-related genomics in cancer and immunological diseases.
Wei Ji-Fu JF Wang Yong-Qing YQ Luo Huai-Rong HR
International journal of genomics 20140723
Similar Datasets
Project description:The application of structural genomics methods and approaches to proteins from organisms causing infectious diseases is making available the three dimensional structures of many proteins that are potential drug targets and laying the groundwork for structure aided drug discovery efforts. There are a number of structural genomics projects with a focus on pathogens that have been initiated worldwide. The Center for Structural Genomics of Infectious Diseases (CSGID) was recently established to apply state-of-the-art high throughput structural biology technologies to the characterization of proteins from the National Institute for Allergy and Infectious Diseases (NIAID) category A-C pathogens and organisms causing emerging, or re-emerging infectious diseases. The target selection process emphasizes potential biomedical benefits. Selected proteins include known drug targets and their homologs, essential enzymes, virulence factors and vaccine candidates. The Center also provides a structure determination service for the infectious disease scientific community. The ultimate goal is to generate a library of structures that are available to the scientific community and can serve as a starting point for further research and structure aided drug discovery for infectious diseases. To achieve this goal, the CSGID will determine protein crystal structures of 400 proteins and protein-ligand complexes using proven, rapid, highly integrated, and cost-effective methods for such determination, primarily by X-ray crystallography. High throughput crystallographic structure determination is greatly aided by frequent, convenient access to high-performance beamlines at third-generation synchrotron X-ray sources.
| S-EPMC2789569 | biostudies-literature
Project description:Therapeutic monoclonal antibodies (mAbs) have high efficacy in treating TNF α-related immunological diseases. Other than neutralizing TNF α, these IgG1 antibodies exert Fc receptor-mediated effector functions such as the complement-dependent cytotoxicity (CDC) and antibody-dependent cell cytotoxicity (ADCC). The crystallizable fragment (Fc) of these IgG1 contains a single glycosylation site at Asn 297/300 that is essential for the CDC and ADCC. Glycosylated antibodies lacking core fucosylation showed an improved ADCC. However, no structural data are available concerning the ligand-binding interaction of these mAbs used in TNF α-related diseases and the role of the fucosylation. We therefore used comparative modeling for generating complete 3D mAb models that include the antigen-binding fragment (Fab) portions of infliximab, complexed with TNF α (4G3Y.pdb), the Fc region of the human IGHG1 fucosylated (3SGJ) and afucosylated (3SGK) complexed with the Fc receptor subtype Fcγ RIIIA, and the Fc region of a murine immunoglobulin (1IGT). After few thousand steps of energy minimization on the resulting 3D mAb models, minimized final models were used to quantify interactions occurring between Fcγ RIIIA and the fucosylated/afucosylated Fc fragments. While fucosylation does not affect Fab-TNF α interactions, we found that in the absence of fucosylation the Fc-mAb domain and Fcγ RIIIA are closer and new strong interactions are established between G129 of the receptor and S301 of the Chimera 2 Fc mAb; new polar interactions are also established between the Chimera 2 Fc residues Y299, N300, and S301 and the Fcγ RIIIA residues K128, G129, R130, and R155. These data help to explain the reduced ADCC observed in the fucosylated mAbs suggesting the specific AA residues involved in binding interactions.
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Project description:In recent years, epigenetic modifications have been increasingly regarded as an important hallmark of cancer. Histone acetylation, as an important part of epigenetic modification, plays a key role in the progress, treatment, and prognosis of many cancers. In this study, based on the TCGA database, we performed LASSO regression and the Cox algorithm to establish a prognostic signature of ovarian cancer associated with histone acetylation modulator genes and verified it externally in the GEO database. Subsequently, we performed an immunological bioinformatics analysis of the model from multiple perspectives using the CIBERSORT algorithm, ESTIMATE algorithm, and TIDE algorithm to verify the accuracy of the model. Based on the prognostic model, we divided ovarian cancer patients into high-risk and low-risk groups, and assessed survival and the efficacy of accepting immunosuppressive therapy. In addition, based on the analysis of characteristics of the model, we also screened targeted drugs for high-risk patients and predicted potential drugs that inhibit platinum resistance through the connectivity map method. We ultimately constructed a histone acetylation modulator-related signature containing 10 histone acetylation modulators, among which HDAC1, HDAC10, and KAT7 can act as independent prognostic factors for ovarian cancer and are related to poor prognosis. In the analysis of the tumor microenvironment, the proportion of the B-infiltrating cells and the macrophages was significantly different between the high- and low-risk groups. Also, the samples with high-risk scores had higher tumor purity and lower immune scores. In terms of treatment, patients in the high-risk group who received immunotherapy had a higher likelihood of immune escape or rejection and were less likely to respond to platinum/paclitaxel therapy. Finally, we screened 20 potential drugs that could target the model for reference.
| S-EPMC9510621 | biostudies-literature
Project description:Breast cancer is a heterogeneous disease that develops through a multistep process via the accumulation of genetic/epigenetic alterations in various cancer-related genes. Current treatment options for breast cancer patients include surgery, radiotherapy, and chemotherapy including conventional cytotoxic and molecular-targeted anticancer drugs for each intrinsic subtype, such as endocrine therapy and antihuman epidermal growth factor receptor 2 (HER2) therapy. However, these therapies often fail to prevent recurrence and metastasis due to resistance. Overall, understanding the molecular mechanisms of breast carcinogenesis and progression will help to establish therapeutic modalities to improve treatment. The recent development of comprehensive omics technologies has led to the discovery of driver genes, including oncogenes and tumor-suppressor genes, contributing to the development of molecular-targeted anticancer drugs. Here, we review the development of anticancer drugs targeting cancer-specific functional therapeutic targets, namely, MELK (maternal embryonic leucine zipper kinase), TOPK (T-lymphokine-activated killer cell-originated protein kinase), and BIG3 (brefeldin A-inhibited guanine nucleotide-exchange protein 3), as identified through comprehensive breast cancer transcriptomics.
| S-EPMC8384626 | biostudies-literature
Project description:Nuclear receptors (NRs) are ligand-regulated transcription factors that regulate metabolism, development and immunity. The NR superfamily is one of the major classes of drug targets for human diseases. Retinoic acid receptor-related orphan receptor (ROR) α, β and γ belong to the NR superfamily, and these receptors are still considered as 'orphan' receptors because the identification of their endogenous ligands has been controversial. Recent studies have demonstrated that these receptors are regulated by synthetic ligands, thus emerge as important drug targets for the treatment of multiple sclerosis, rheumatoid arthritis, psoriasis, etc. Studying the structural basis and ligand development of RORs will pave the way for a better understanding of the roles of these receptors in human diseases. Here, we review the structural basis, disease relevance, strategies for ligand identification, and current status of development of therapeutic ligands for RORs.
| S-EPMC4571318 | biostudies-literature
Project description:BackgroundCervical cancer (CC) is a common cancer in female, which is associated with problems like poor prognosis. Circular RNA (circRNA) is a kind of competing endogenous RNA (ceRNA) that has an important role in regulating microRNA (miRNA) in many cancers. The regulatory mechanisms of CC immune microenvironment and the transcriptome level remain to be fully explored.MethodsIn this study, we constructed the ceRNA network through the interaction data and expression matrix of circRNA, miRNA and mRNA. Meanwhile, based on the gene expression matrix, CIBERSORT algorithm was used to reveal contents of tumor-infiltrating immune cells (TIICs). Then, we screened prognostic markers based on ceRNA network and immune infiltration and constructed two nomograms. In order to find immunological differences between the high- and low-risk CC samples, we examined multiple immune checkpoints and predicted the effect of PD-L1 ICI immunotherapy. In addition, the sensitive therapeutics for high-risk patients were screened, and the potential agents with anti-CC activity were predicted by Connective Map (CMap).ResultsWe mapped a ceRNA network including 5 circRNAs, 17 miRNAs and 129 mRNAs. From the mRNA nodes of the network six genes and two kind of cells were identified as prognostic makers for CC. Among them, there was a significant positive correlation between CD8+ T cells and SNX10 gene. The results of TIDE and single sample GSEA (ssGSEA) showed that T cells CD8 do play a key role in inhibiting tumor progression. Further, our study screened 24 drugs that were more sensitive to high-risk CC patients and several potential therapeutic agents for reference.ConclusionsOur study identified several circRNA-miRNA-mRNA regulatory axes and six prognostic genes based on the ceRNA network. In addition, through TIIC, survival analysis and a series of immunological analyses, T cells were proved to be good prognostic markers, besides play an important role in the immune process. Finally, we screened 24 potentially more effective drugs and multiple potential drug compounds for high- and low-risk patients.
| S-EPMC9689098 | biostudies-literature
Project description:Understanding mechanisms by which genetic variants predispose to complications of infectious diseases can lead to important benefits including the development of biomarkers to prioritize vaccination or prophylactic therapy. Family studies, candidate genes in animal models, and the absence of well-defined risks where the complications are rare all can point to genetic predisposition. The most common approach to assessing genetic risk is to conduct an association study, which is a case control study using either a candidate gene approach or a genome wide approach. Although candidate gene variants may focus on potentially causal variants, because other variants across the genome are not tested these studies frequently cannot be replicated. Genome wide association studies need a sizable sample and usually do not identify causal variants but variants which may be in linkage disequilibrium to the actual causal variant. There are many pitfalls that can lead to bias in such studies, including misclassification of cases and controls, use of improper phenotypes, and genotyping errors. These studies have been limited to common genes and rare variants may not be detected. As the use of next generation sequencing becomes more common, it can be anticipated that more variants will be confirmed. The purpose of this review article is to address the issue of genomics in infectious diseases with an emphasis on the host. Although there are a plentitude of studies that focus on the molecular characteristics of pathogens, there are far fewer studies that address the role of human genetics in the predisposition to infection or more commonly its complications. This paper will review both the approaches used to study host genetics in humans and the pitfalls associated with some of these methods. The focus will be on human disease and therefore discussion of the use of animal models will be limited to those where there are genes that have been replicated in humans. The paper will focus on common genetic variants that account for complex traits such as infectious diseases using examples from flaviviruses.
| S-EPMC3848513 | biostudies-literature
Project description:Associations between genetic loci and increased susceptibility to autoimmune disease have been well characterized, however, translating this knowledge into mechanistic insight and patient benefit remains a challenge. While improvements in the precision, completeness and accuracy of our genetic understanding of autoimmune diseases will undoubtedly be helpful, meeting this challenge will require two interlinked problems to be addressed: first which of the highly correlated variants at an individual locus is responsible for increased disease risk, and second what are the downstream effects of this variant. Given that the majority of loci are thought to affect non-coding regulatory elements, the second question is often reframed as what are the target gene(s) and pathways affected by causal variants. Currently, these questions are being addressed using a wide variety of novel techniques and datasets. In many cases, these approaches are complementary and it is likely that the most accurate picture will be generated by consolidating information relating to transcription, regulatory activity, chromatin accessibility, chromatin conformation and readouts from functional experiments, such as genome editing and reporter assays. It is clear that it will be necessary to gather this information from disease relevant cell types and conditions and that by doing so our understanding of disease etiology will be improved. This review is focused on the field of autoimmune disease functional genomics with a particular focus on the most exciting and significant research to be published within the last couple of years.
| S-EPMC7530520 | biostudies-literature
Project description:BackgroundInterstitial lung diseases (ILDs), a diverse group of diffuse lung diseases, mainly affect the lung parenchyma. The low-throughput 'omics' technologies (genomics, transcriptomics, proteomics) and relative drug information have begun to reshaped our understanding of ILDs, whereas, these data are scattered among massive references and are difficult to be fully exploited. Therefore, we manually mined and summarized these data at a database (ILDGDB, http://ildgdb.org/ ) and will continue to update it in the future.Main bodyThe current version of ILDGDB incorporates 2018 entries representing 20 ILDs and over 600 genes obtained from over 3000 articles in four species. Each entry contains detailed information, including species, disease type, detailed description of gene (e.g. official symbol of gene), and the original reference etc. ILDGDB is free, and provides a user-friendly web page. Users can easily search for genes of interest, view their expression pattern and detailed information, manage genes sets and submit novel ILDs-gene association.ConclusionThe main principle behind ILDGDB's design is to provide an exploratory platform, with minimum filtering and interpretation, while making the presentation of the data very accessible, which will provide great help for researchers to decipher gene mechanisms and improve the prevention, diagnosis and therapy of ILDs.
| S-EPMC7731518 | biostudies-literature
Project description: Not available
| S-EPMC1564298 | biostudies-literature