Integrative analysis and modeling of human immune responses and pathologies

人类免疫反应和病理学的综合分析和建模

• 批准号: 9354903
• 负责人: John Tsang
• 金额: $ 79.4万
• 依托单位: NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9354903
• 关键词: Adjuvant; Adult; Age; Antibodies; Antibody Response; B-Lymphocyte Subsets; B-Lymphocytes; Biological; Biological Assay; Biological Markers; Biological Models; Biological Response Modifiers; Biology; Blood; Blood Cells; Cells; Childhood; Clinic; Clinical; Cohort Studies; Collection; Color; Communities; Complex; Computer Analysis; Data; Data Analyses; Data Set; Databases; Diagnostic; Disease; Environmental Exposure; Ethics; Etiology; Evaluation; Experimental Designs; Extramural Activities; Flow Cytometry; Frequencies; Future; Gender; Gene Expression; Gene Expression Profile; Generations; Genes; Genetic; Genotype; Geographic Locations; Germ Lines; Goals; HIV; Health; Housing; Human; Immune; Immune response; Immune system; Immunity; Immunologic Monitoring; Immunology; Individual; Influenza A Virus, H5N1 Subtype; Influenza vaccination; Intervention; Laboratories; Maps; Measurement; Measures; Mediating; Mendelian disorder; Meta-Analysis; MicroRNAs; Modeling; Molecular; Myelogenous; National Institute of Allergy and Infectious Disease; Online Systems; Pathology; Pathway interactions; Patients; Pattern; Peripheral Blood Mononuclear Cell; Pharmaceutical Preparations; Phenotype; Plasmablast; Play; Population; Process; Public Housing; Retrieval; Role; Sampling; Seasons; Serologic tests; Serum; Sorting - Cell Movement; Steroids; System; Systems Biology; Technology; Therapeutic; Time; Tissues; Transcript; United States National Institutes of Health; Vaccination; Vaccines; Variant; Viral Load result; Virus; Whole Blood; Work; arm; base; biological systems; cell type; cohort; comparison group; computer framework; cytokine; data modeling; design; disease natural history; exhaustion; genome wide association study; genome-wide; human subject; immunoregulation; influenza virus vaccine; insight; novel; predicting response; predictive modeling; predictive signature; response; sample collection; seasonal influenza; tool; transcriptome; transcriptomics; vaccine response; vaccine trial; virtual; Adjuvant; Adult; Age; Antibodies; Antibody Response; B-Lymphocyte Subsets; B-Lymphocytes; Biological; Biological Assay; Biological Markers; Biological Models; Biological Response Modifiers; Biology; Blood; Blood Cells; Cells; Childhood; Clinic; Clinical; Cohort Studies; Collection; Color; Communities; Complex; Computer Analysis; Data; Data Analyses; Data Set; Databases; Diagnostic; Disease; Environmental Exposure; Ethics; Etiology; Evaluation; Experimental Designs; Extramural Activities; Flow Cytometry; Frequencies; Future; Gender; Gene Expression; Gene Expression Profile; Generations; Genes; Genetic; Genotype; Geographic Locations; Germ Lines; Goals; HIV; Health; Housing; Human; Immune; Immune response; Immune system; Immunity; Immunologic Monitoring; Immunology; Individual; Influenza A Virus, H5N1 Subtype; Influenza vaccination; Intervention; Laboratories; Maps; Measurement; Measures; Mediating; Mendelian disorder; Meta-Analysis; MicroRNAs; Modeling; Molecular; Myelogenous; National Institute of Allergy and Infectious Disease; Online Systems; Pathology; Pathway interactions; Patients; Pattern; Peripheral Blood Mononuclear Cell; Pharmaceutical Preparations; Phenotype; Plasmablast; Play; Population; Process; Public Housing; Retrieval; Role; Sampling; Seasons; Serologic tests; Serum; Sorting - Cell Movement; Steroids; System; Systems Biology; Technology; Therapeutic; Time; Tissues; Transcript; United States National Institutes of Health; Vaccination; Vaccines; Variant; Viral Load result; Virus; Whole Blood; Work; arm; base; biological systems; cell type; cohort; comparison group; computer framework; cytokine; data modeling; design; disease natural history; exhaustion; genome wide association study; genome-wide; human subject; immunoregulation; influenza virus vaccine; insight; novel; predicting response; predictive modeling; predictive signature; response; sample collection; seasonal influenza; tool; transcriptome; transcriptomics; vaccine response; vaccine trial; virtual

We have started by utilizing data generated at the Trans-NIH Center for Human Immunology (CHI) to assess the immune phenotypes of healthy individuals at baseline and after perturbations, particularly with seasonal influenza vaccination. The CHI has generated multiple types of measurements of peripheral blood mononuclear cells (PBMC), including microarray data for measuring transcript abundance, multiple panels of 15-color flow cytometry for assessing cell populations (and abundance of key markers), luminex assays for measuring serum cytokine concentrations, genome-wide genotyping, and immunological endpoints such as virus-specific antibody titers and B cell Elispots. We have successfully resolved a number of data analysis and modeling challenges and have been conducting integrative modeling projects using both in-house and public data sets to draw novel insights into human immunology. In addition to utilizing and integrating CHI and public data sets, we have initiated collaborative projects with both extramural and intramural colleagues by applying our human systems immunology approach to meta-analyze data from multiple cohorts as well as generating and integrating new data from both healthy and disease subjects. Recent highlights of our efforts include: 1. By utilizing vaccine perturbation data, we have developed a conceptual and methodological framework to quantify baseline and response variations at the level of genes, pathways, and cell populations in a cohort of individuals. Our framework takes advantage of such natural variations to systematically infer correlates, build predictive models of response quality after immune perturbation, and infer novel functional connections among various components in the human immune system. We have applied this framework to the influenza vaccination study utilizing antibody titer response as an exemplar endpoint. We confirmed previously known post-vaccination correlates based on gene expression and plasmablast frequency from day 7 samples. More importantly, using an approach that compensates for the influence of pre-existing serology, age and gender, we derived accurate predictive models of antibody responses using pre-vaccination data alone. This finding has obvious implications for the design of future vaccine trials and for developing a deeper understanding of the molecular and cellular parameters that contribute to robust vaccine responses. The robustness and translational potential of these findings is further emphasized by our discovery that the parameters playing the greatest role in correct response prediction are those with the most stable baseline values across individuals. This raises the prospect of monitoring immune health and predicting the quality of responses in the clinic via the evaluation of these baseline blood biomarkers. The conceptual and computational analysis framework we have developed can also be applied to systems and population level exploration in a number of medically relevant circumstances, including but not limited to the effects of drug intervention or natural disease history studies in humans. See Tsang JS. 2015 for details. 2. Applying a similar human immune profiling approach, we have assessed the effect of steroids on the human immune system. See Olnes and Kotliarov et. al. 2016 for details. 3. By utilizing natural variation among human subjects, we have developed a de novo approach of inferring predictive models of cell population frequencies using gene expression data alone (e.g., those from heterogeneous tissues such as PBMCs and whole blood). Our approach infers such predictive models from gene expression and flow phenotyping data alone without a priori information on the gene expression pattern of individual cell types or subsets. By using baseline data from our vaccine study, we have successfully derived cell-frequency predictive models for more than 20 immune cell subsets spanning the T, B and myeloid lineages. We have applied these models to predict the cell frequency changes across more than 100 whole blood or PBMC gene expression comparisons involving diverse diseases with or without apparent immunological conditions. 4. Since we routinely use and analyze publicly available data to argument data generated in-house, we have developed a web-based framework (including both user interfaces and database components) to facilitate search, retrieval, annotating, meta-analysis, and gene-expression signature generation. At the core of our tool are interfaces for creating, annotating, and sharing (among the user community) of which groups of samples can be compared to form classical gene-expression signatures or expression difference profiles the latter can be used to integrate across data sets and studies to generate virtual perturbation profiles across all genes. Since annotating such comparison groups is often one of the most time-consuming steps of reusing existing data, our framework provides functions for users to share their own annotations and search for others annotations. Our tool was designed for experimental biologist to take full advantage of reusing and sharing large-scale data for obtaining biological insights. (See Shah, Guo and Wendelsdorf et al. 2016) 5. Together with colleagues at the Human Immunology Project Consortium (HIPC), we have been performing meta-analysis of multiple human influenza vaccination data sets to derive common predictive signatures of vaccine responses using pre-vaccination gene-expression data. We have successfully uncovered both gene- and gene module-based predictive signatures for younger subjects using data from several study cohorts spanning multiple seasons and geographic locations. 6. Together with colleagues at the CHI, we have begun analyzing the multi-modal data obtained from the H5N1 adjuvanted vaccine systems biology study. The data were obtained at baseline and from multiple time-points post vaccination. The vaccine together with the adjuvant were administered in one of the arms of the study, while subjects in the second arm only had the vaccine without the adjuvant. One of the goals is to evaluate the effect of the adjuvant. We are developing a novel analysis framework to extract, in an unsupervised manner, as much information about the response dynamics as possible. And then we will correlate the distinct patterns of dynamical responses to biological variables, including the adjuvant status and antibody responses. 7. Together with NIH clinical colleagues studying immune-mediated monogenic diseases, we have begun to collect baseline samples from different patient groups and are in the process of phenotyping them using modern, multiplexed approaches such as blood and cell subset profiling, immune cell phenotyping, assessing circulating serum cytokines. One of the key goals is to obtain an integrative understanding of similarities and differences across disease groups and to further assess whether data from such a collection can help dissect genetically more complex diseases. 8. We also utilize some of our approaches, for example, profiling immune cell transcriptomes, and computational analyses to investigate human immunology and biology. For example, we have been working with Dr. Susan Moir from the Laboratory of Immunoregulation of NIAID to assess B cell subsets from HIV patients her laboratory has been sorting different B cell subsets from both HIV patients and healthy controls. The goal is to assess whether certain B cell subsets display distinct transcriptomic signature, e.g., exhaustion, in HIV patients and whether that correlates with viral load. 9. We continue to collaborate on efforts to assess microRNA functions in humans, e.g., see Zhang et al. 2015 and Wagschal et al. 2015.

我们首先利用在Trans-NIH人类免疫学中心（CHI）生成的数据来评估基线和扰动后健康个体的免疫表型，尤其是季节性流感疫苗接种。 The CHI has generated multiple types of measurements of peripheral blood mononuclear cells (PBMC), including microarray data for measuring transcript abundance, multiple panels of 15-color flow cytometry for assessing cell populations (and abundance of key markers), luminex assays for measuring serum cytokine concentrations, genome-wide genotyping, and immunological endpoints such as virus-specific antibody滴度和B细胞Elispots。我们已经成功解决了许多数据分析和建模挑战，并一直使用内部和公共数据集进行综合建模项目，以吸引对人类免疫学的新见解。除了利用和集成CHI和公共数据集外，我们还通过将我们的人类系统免疫学方法应用于来自多个同类群体的荟萃分析，并从健康和疾病受试者中产生和整合新数据，从而与壁外和壁内同事一起启动了协作项目。我们努力的最新亮点包括： 1。利用疫苗扰动数据，我们开发了一个概念和方法学框架，以量化一个个体同类基因，途径和细胞群体的基线和响应变化。我们的框架利用这种自然变化系统地推断出相关性，在免疫扰动后建立响应质量的预测模型，并推断人类免疫系统中各个组件之间的新功能连接。我们已经将该框架应用于流感疫苗接种研究，该研究利用抗体滴度响应作为示例端点。我们证实了先前已知的疫苗接种后，基于第7天样本从基因表达和浆膜频率基于基因表达和浆膜频率相关。更重要的是，使用一种方法来补偿先前存在的血清学，年龄和性别的影响，我们仅使用疫苗接种数据就得出了抗体反应的准确预测模型。这一发现对未来疫苗试验的设计具有明显的影响，并对有助于强大疫苗反应的分子和细胞参数有更深入的了解。我们发现，在正确的响应预测中起着最大作用的参数是那些个人之间最稳定的基线值，这些发现的鲁棒性和翻译潜力进一步强调了。这提高了监测免疫健康并通过评估这些基线血液生物标志物的诊所反应质量的前景。在许多医学上相关的情况下，我们已经开发的概念和计算分析框架也可以应用于系统和人群级别的探索，包括但不限于人类药物干预或自然疾病史研究的影响。参见Tsang JS。 2015年的详细信息。 2。采用类似的人类免疫分析方法，我们评估了类固醇对人免疫系统的影响。参见Olnes和Kotliarov等。 al。 2016年有关详细信息。 3。通过利用人类受试者之间的自然变异，我们开发了一种新的方法，仅使用基因表达数据来推断细胞群频率的预测模型（例如，来自异质组织（例如PBMC和全血）的预测模型）。我们的方法仅靠基因表达和流动表型数据来渗透这种预测模型，而没有有关单个细胞类型或亚群的基因表达模式的先验信息。通过使用我们的疫苗研究中的基线数据，我们成功地衍生出了跨越T，B和髓样谱系的20多个免疫细胞子集的细胞频率预测模型。我们已经应用了这些模型来预测100多个全血或PBMC基因表达比较的细胞频率变化，涉及有或没有明显的免疫条件的多种疾病。 4。由于我们通常将公开可用的数据用于内部生成的参数数据，因此我们开发了一个基于Web的框架（包括用户界面和数据库组件）来促进搜索，检索，注释，荟萃分析，荟萃分析和基因签名签名生成。 我们工具的核心是用于创建，注释和共享的接口（在用户社区中），可以将哪些样品组与形式的经典基因表达签名或表达差异概况进行比较。由于注释这种比较组通常是重复现有数据的最耗时的步骤之一，因此我们的框架为用户提供了共享自己的注释并搜索其他注释的功能。我们的工具是为实验生物学家而设计的，可以充分利用重复使用和共享大规模数据以获得生物学见解。 （请参阅Shah，Guo和Wendelsdorf等人，2016年） 5。与人类免疫学项目联盟（HIPC）的同事一起，我们一直对多种人类流感疫苗接种数据集进行荟萃分析，以通过疫苗接种前的基因表达数据来得出疫苗反应的共同预测性特征。我们使用跨越多个季节和地理位置的几个研究队列的数据，成功地发现了基因和基因模块的基因和基因模块的预测特征。 6。与CHI的同事一起，我们开始分析从H5N1辅助疫苗系统生物学研究中获得的多模式数据。数据是在基线和疫苗接种后多个时间点获得的。疫苗与佐剂一起施用在研究的一个臂中，而第二臂的受试者只有没有辅助剂的疫苗。目标之一是评估佐剂的效果。我们正在开发一个新颖的分析框架，以无监督的方式提取有关响应动态的信息。然后，我们将将动态响应的不同模式与生物学变量（包括辅助状态和抗体反应）相关联。 7。与研究免疫介导的单基因疾病的NIH临床同事一起，我们开始从不同患者群体中收集基线样品，并正在使用现代的多重方法（例如血液和细胞子群）（例如免疫细胞表）进行表型，评估循环血清细胞因子。关键目标之一是获得对疾病群体之间的相似性和差异的综合理解，并进一步评估该收集的数据是否可以帮助剖析遗传上更复杂的疾病。 8。我们还利用了我们的一些方法，例如分析免疫细胞转录组和计算分析来研究人类的免疫学和生物学。例如，我们一直在与NIAID免疫调节实验室的Susan Moir博士合作，以评估来自HIV患者的B细胞亚群，她的实验室一直在对HIV患者和健康对照组的不同B细胞子集进行分类。目的是评估某些B细胞子集是否显示出不同的转录组特征，例如疲劳，HIV患者以及这是否与病毒载量相关。 9。我们继续努力评估人类的microRNA功能，例如，参见Zhang等。 2015年和Wagschal等人。 2015。