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: 8556055
• 负责人: John Tsang
• 金额: $ 13.97万
• 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/8556055
• 关键词: African American; Age; Animal Model; Antibodies; Antibody Formation; Autoimmune Diseases; Autoimmune Process; B-Lymphocytes; Biological Assay; Biological Markers; Blood; Cells; Clinic; Collaborations; Color; Communicable Diseases; Computer Analysis; Computer Simulation; Cytokine Gene; Data; Data Analyses; Data Set; Disease; Evaluation; Flow Cytometry; Frequencies; Future; Gender; Gene Expression; Genes; Genetic; Genome; Genotype; Goals; Haplotypes; Health; Human; Immune; Immune response; Immune system; Immunologic Monitoring; Immunology; In Vitro; Individual; Infection; Influenza vaccination; Intervention; Knowledge; Link; Malignant Neoplasms; Measurement; Measures; Metabolic; Metabolic Diseases; Methods; Modeling; Molecular; Mus; Network-based; PTPN22 gene; Pathology; Pathway interactions; Peripheral Blood Mononuclear Cell; Pharmaceutical Preparations; Phenotype; Plasmablast; Play; Population; Preventive; Process; Public Domains; Public Housing; Relative (related person); Role; Sampling; Serologic tests; Serum; Signal Transduction; Specimen; System; Techniques; Technology; Therapeutic; Tissues; Transcript; United States National Institutes of Health; Vaccination; Vaccine Design; Vaccines; Variant; Virus; Work; base; cohort; cytokine; data modeling; design; disease natural history; flu; genetic analysis; genetic variant; genome wide association study; genome-wide; human data; in vivo; novel; predictive modeling; research study; response

We have been primarily utilizing human data generated by the trans-NIH Center for Human Immunology (CHI) to assess the immune phenotypes of healthy individuals at baseline and after flu 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 tackled a number of data analysis and modeling challenges in the past year and initiated integrative modeling projects that involved both in-house and public data sets. These include: 1. By utilizing vaccine perturbation data, we 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 and to take advantage of these variations to systematically infer correlates, build predictive models of response quality after vaccination, and infer novel functional connectivities in the human immune system. By applying this framework using antibody titer response from the CHI flu study as an exemplar endpoint, we confirmed previously known post-vaccination correlates based on gene expression and plasmablasts frequency from day 7 samples. More importantly, using an approach that compensates for the influence of pre-existing serology, age and gender, we describe accurate predictive models of antibody responses using pre-vaccination non-antigen specific cell population frequencies alone. This latter finding of response predictors in baseline measurements 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 immune responses in the clinic via the evaluation of these 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. 2. We developed a novel method to integrate cytokine, gene expression and cell population data to infer functional linkages between cytokines, genes and cell populations. Cytokine functions are often studied in vitro; our approach provides direct inference of cytokine functions in vivo by utilizing human population variations. 3. Given the presence of African American individuals in our cohort, we developed a more accurate and robust approach to perform genetic imputation to infer the missing genotypes of admixed populations. Our approach achieves better accuracy, especially for more difficult to impute loci, by customizing the imputation panels for individual haplotype blocks across the genome. 4. We have conducted genetic analysis linking genetic variants to PBMC gene expression and cell populations. These include hypothesis driven analysis that involved well known variants such as PTPN22 (in collaboration with Erik Petersen and Robert Carter) as well as genome-wide associations. While signals tend to be weak given the relative small size of the cohort, we have been developing and applying network based approaches to infer the effects of multiple genetic variants on multiple phenotypes to boost power. We are also in the process of integrating numerous genome-wide association study data sets (GWAS) of immune relevant phenotypes (including autoimmune, metabolic and infectious diseases) with our data set to decipher the molecular, cellular and immunological underpinnings of a number of common diseases.

我们主要利用跨NIH人类免疫学中心（CHI）生成的人类数据来评估基线和流感疫苗后健康个体的免疫表型。 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。在过去的一年中，我们成功地解决了许多数据分析和建模挑战，并启动了涉及内部和公共数据集的综合建模项目。其中包括： 1。利用疫苗扰动数据，我们开发了一个概念和方法学框架，以量化一个个体的基因，途径和细胞群体的基线和响应变化，并利用这些变化来系统地推断出相关性，在疫苗接种后构建响应质量的预测模型，并推断出新型的响应质量和新型功能性连接性。通过使用CHI流感研究中的抗体滴度响应作为示例终点应用此框架，我们证实了先前已知的疫苗接种后，基于基因表达和plasmablast频率从第7天样本开始就相关。更重要的是，使用一种补偿预先存在的血清学，年龄和性别影响的方法，我们仅使用预疫苗接种的非抗原特异性细胞群频率来描述抗体反应的准确预测模型。后者在基线测量中对响应预测变量的发现对未来疫苗试验的设计以及对有助于强大疫苗反应的分子和细胞参数的更深入了解具有明显的影响。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。这增加了监测免疫健康并通过评估这些血液生物标志物的诊所中免疫反应质量的前景。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。 2。我们开发了一种新颖的方法，将细胞因子，基因表达和细胞群数据整合到推断细胞因子，基因和细胞群之间的功能联系。经常在体外研究细胞因子功能。我们的方法通过利用人群变异来直接推断体内细胞因子功能。 3。鉴于非裔美国人在我们的队列中存在，我们开发了一种更准确，更强大的方法来执行遗传插补，以推断混合种群的缺失基因型。我们的方法可以通过定制整个基因组的单个单倍型块的插补面板来实现更好的准确性，尤其是对于更难估算基因座的精度。 4。我们进行了遗传分析，将遗传变异与PBMC基因表达和细胞群联系起来。其中包括假设驱动的分析，涉及众所周知的变体，例如PTPN22（与Erik Petersen和Robert Carter合作）以及全基因组的关联。鉴于队列的相对小尺寸，信号往往很弱，但我们一直在开发和应用基于网络的方法来推断多种遗传变异对多种表型的影响以增强功率。我们还在将众多全基因组关联研究数据集（GWAS）整合到免疫相关表型（包括自身免疫性，代谢性和感染性疾病）的过程中，我们的数据集以破译分子，细胞和免疫学基础许多常见疾病的基础。