Novel network pharmacology methods for drug mechanism of action identification, pre-clinical drug screening and drug repositioning

The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Here we present two methodology innovations on network pharmacology modeling. (1) We hypothesize that the gene network associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. We proposed a novel in vivo genetic interaction between genes as ‘synergistic outcome determination’, in a similar way to ‘synthetic lethality’. We scanned above genetic interactions based on microarray profiling for cancer prognosis, and identified a cluster of important yet epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to this network, we could define a perturbation index for each drug based upon its characteristic perturbation pattern. Finally, by using this index, we significantly discriminated successful drugs from the candidate pool, and revealed the mechanisms of drug combinations. Thus, the prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies. Part of this work was published, and we will present new results on this project. (2) MicroRNAs (miRNAs) play a key role in the regulation of the transcriptome and have been identified as a key mediator in human disease and drug response. we introduced a novel concept, the Context-specific MiRNA activity (CoMi activity), to reflect a miRNA’s regulation effect on a context specific gene set .Using breast cancer as an example, we examined the CoMi activity based on a Gene Ontology (GO) term as context. Interestingly, we found that chemotherapeutic drug treatment can counteract the dis-regulated CoMi activity in the cancer-specific network. For instance, 100% of down-regulated CoMi activities in a “core” breast cancer network contains apoptosis-related GO terms that could be counteracted by Paclitaxel treatment. By defining a Stability Index for in silico drug screening, we found CoMi activity signatures strikingly outperformed the traditional CMAP method or mRNA-based signatures. Thus, the dynamic remodeling of context-specific miRNAs regulation network could reveal the hidden miRNAs that act as key mediators of drug action and facilitate in silico cancer drug screening

In silico Genetic Network Models for Pre-clinical Drug Prioritization

The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. Here we propose a novel in vivo genetic interaction which we call ‘synergistic outcome determination’ (SOD), a concept similar to ‘Synthetic Lethality’. SOD is defined as the synergy of a gene pair with respect to cancer patients' outcome, whose correlation with outcome is due to cooperative, rather than independent, contributions of genes. The method combines microarray gene expression data with cancer prognostic information to identify synergistic gene-gene interactions that are then used to construct interaction networks based on gene modules (a group of genes which share similar function). In this way, we identified a cluster of important epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network. Finally, by calculating this index for compounds in the NCI Standard Agent Database, we significantly discriminated successful drugs from a broad set of test compounds, and further revealed the mechanisms of drug combinations. Thus, prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies.
&#xa

Genome wide prediction of protein function via a generic knowledge discovery approach based on evidence integration

BACKGROUND: The automation of many common molecular biology techniques has resulted in the accumulation of vast quantities of experimental data. One of the major challenges now facing researchers is how to process this data to yield useful information about a biological system (e.g. knowledge of genes and their products, and the biological roles of proteins, their molecular functions, localizations and interaction networks). We present a technique called Global Mapping of Unknown Proteins (GMUP) which uses the Gene Ontology Index to relate diverse sources of experimental data by creation of an abstraction layer of evidence data. This abstraction layer is used as input to a neural network which, once trained, can be used to predict function from the evidence data of unannotated proteins. The method allows us to include almost any experimental data set related to protein function, which incorporates the Gene Ontology, to our evidence data in order to seek relationships between the different sets. RESULTS: We have demonstrated the capabilities of this method in two ways. We first collected various experimental datasets associated with yeast (Saccharomyces cerevisiae) and applied the technique to a set of previously annotated open reading frames (ORFs). These ORFs were divided into training and test sets and were used to examine the accuracy of the predictions made by our method. Then we applied GMUP to previously un-annotated ORFs and made 1980, 836 and 1969 predictions corresponding to the GO Biological Process, Molecular Function and Cellular Component sub-categories respectively. We found that GMUP was particularly successful at predicting ORFs with functions associated with the ribonucleoprotein complex, protein metabolism and transportation. CONCLUSION: This study presents a global and generic gene knowledge discovery approach based on evidence integration of various genome-scale data. It can be used to provide insight as to how certain biological processes are implemented by interaction and coordination of proteins, which may serve as a guide for future analysis. New data can be readily incorporated as it becomes available to provide more reliable predictions or further insights into processes and interactions

Pre-Clinical Drug Prioritization via Prognosis-Guided Genetic Interaction Networks

The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. Here we propose a novel in vivo genetic interaction which we call ‘synergistic outcome determination’ (SOD), a concept similar to ‘Synthetic Lethality’. SOD is defined as the synergy of a gene pair with respect to cancer patients' outcome, whose correlation with outcome is due to cooperative, rather than independent, contributions of genes. The method combines microarray gene expression data with cancer prognostic information to identify synergistic gene-gene interactions that are then used to construct interaction networks based on gene modules (a group of genes which share similar function). In this way, we identified a cluster of important epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network. Finally, by calculating this index for compounds in the NCI Standard Agent Database, we significantly discriminated successful drugs from a broad set of test compounds, and further revealed the mechanisms of drug combinations. Thus, prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies

Virus-Free and Live-Cell Visualizing SARS-CoV-2 Cell Entry for Studies of Neutralizing Antibodies and Compound Inhibitors

新型冠状病毒SARS-CoV-2在全球蔓延，给全球公共卫生带来严重威胁。快速研制疫苗、抗体和治疗药物成为科学界面临的重大挑战。由于SARS-CoV-2的高度传染性，采用病毒感染模型进行中和抗体及小分子抑制剂的药效评估需要在高等级生物安全实验室中进行，且常需要数天时间才能完成检测，限制了抗体和药物筛选的效率。发展快速、可视、不依赖于活病毒的新冠病毒入胞检测探针和细胞模型，对于加速新冠病毒抗体和药物的研究有重要意义。夏宁邵教授团队通过CHO真核表达系统高效表达制备出C端融合抗酸荧光蛋白Gamillus的重组新冠病毒spike蛋白STG。STG经SEC分子筛和冷冻电镜确认呈现与天然病毒刺突高度相似的三聚体结构，且与ACE2有很高的亲和力（18.2nM）。STG具备良好的细胞相容性和荧光性质，研究者进一步开发了可定量测定感染恢复期血清、疫苗免疫血清中和抗体（入胞阻断抗体）水平的CSBT检测方法。除了抗体检测评估方面的应用外，该研究发展的探针和模型还可用于筛选分析抑制新冠病毒入胞及胞内转运的小分子化合物。 我校博士后张雅丽，博士生王邵娟、巫洋涛，博士后侯汪衡、袁伦志和深圳市第三人民医院沈晨光博士为共同第一作者。厦门大学夏宁邵教授、袁权教授、程通教授为该论文共同通讯作者。The ongoing corona virus disease 2019 (COVID-19) pandemic, caused by SARS-CoV-2 infection, has resulted in hundreds of thousands of deaths. Cellular entry of SARS-CoV-2, which is mediated by the viral spike protein and ACE2 receptor, is an essential target for the development of vaccines, therapeutic antibodies, and drugs. Using a mammalian cell expression system,a genetically engineered sensor of fluorescent protein (Gamillus)-fused SARS-CoV-2 spike trimer (STG) to probe the viral entry process is developed.In ACE2-expressing cells, it is found that the STG probe has excellent performance in the live-cell visualization of receptor binding, cellular uptake, and intracellular trafficking of SARS-CoV-2 under virus-free conditions. The new system allows quantitative analyses of the inhibition potentials and detailed influence of COVID-19-convalescent human plasmas, neutralizing antibodies and compounds, providing a versatile tool for high-throughput screening and phenotypic characterization of SARS-CoV-2 entry inhibitors. This approach may also be adapted to develop a viral entry visualization system for other viruses.This study was supported by National Natural Science Foundation of China (81993149041 for N.X.; 81902057 for Y.Z.; 81871316 and U1905205 for Q.Y.), the National Science and Technology Major Project of Infectious Diseases (No. 2017ZX10304402‐002‐003 for T.C. and No. 2017ZX10202203‐009 for Q.Y.), the National Science and Technology Major Projects for Major New Drugs Innovation and Development (No. 2018ZX09711003‐005‐003 for T.C.), the Science and Technology Major Project of Fujian (2020YZ014001), the Science and Technology Major Project of Xiamen (3502Z2020YJ01), and the Guangdong Basic and Applied Basic Research Foundation (2020A1515010368 for C.S.). 该研究得到了国家自然科学基金、传染病防治国家科技重大专项、福建省应急科技攻关项目和厦门应急科技攻关项目的支持

Multi-scale network biology model & the model library

Multi-scale network biology model & the model librar

Protein-protein Interaction Reveals Synergistic Discrimination of Cancer Phenotype

Cancer is a disease associated with the deregulation of multiple gene networks. Microarray data has permitted researchers to identify gene panel markers for diagnosis or prognosis of cancer but these are not sufficient to make specific mechanistic assertions about phenotype switches. We propose a strategy to identify putative mechanisms of cancer phenotypes by protein-protein interactions (PPI). We first extracted the logic status of a PPI via the relative expression of the corresponding gene pair. The joint association of a gene pair on a cancer phenotype was calculated by entropy minimization and assessed using a support vector machine. A typical predictor is “If Src high-expression, and Cav-1 low-expression, then cancer.” We achieved 90% accuracy on test data with a majority of predictions associated with the MAPK pathway, focal adhesion, apoptosis and cell cycle. Our results can aid in the development of phenotype discrimination biomarkers and identification of putative therapeutic interference targets for drug development