Human and Bacterial Molecular Pathways in Cancer Risk: Modulation by Diet

 描述(由申请人提供)：我们正在响应PAR-13-375、营养遗传学和营养研究的营养基因组学方法。这项修订后的提案得到了蛋白质组学、代谢组学、肠道微生物组、实验营养学和生物统计学方面的一组知名研究人员的支持，旨在通过一项受控喂养研究评估特定饮食模式对癌症风险途径生物标记物的影响。饮食对人类来说是一种复杂的暴露，人们认为饮食的某些方面在几种癌症的风险中发挥了作用。到目前为止，实验营养学的重点一直是特定营养物质或饮食成分对癌症预防或治疗机制的影响，而较少关注对多种同时暴露的营养物质的反应，例如在自由生活的人类食用食物时会发生的情况。观察性研究表明，我们还需要了解人们对饮食模式的反应，以便更好地针对癌症预防的公共健康建议。饮食成分具有多效性，并调节多种重要的致癌途径。蛋白质组学和代谢组学有能力捕捉到这一范围的反应。此外，肠道微生物组和肠道微生物代谢产物已被证明影响其中一些途径，并与几种癌症的风险有关。我们将使用精心设计和良好控制的喂养研究来解决以下目标：1)比较两种不同的饮食模式作为受控饮食喂养对癌症易感性重要调控途径的蛋白质组生物标记物的影响；2)比较两种饮食模式对(A)肠道微生物群落和肠道元基因组和元转录组以及(B)血浆代谢组的影响；以及3)探索饮食诱导的代谢组和代谢组的变化如何解释蛋白质组学指标的变化，这些指标也随着饮食的变化而改变。我们将使用完成的随机交叉喂养研究的数据和样本，碳水化合物和相关生物标记物(CARB)，其中80名参与者[40名正常体重(BMI 18.5-25.0 kg/m2)和40名超重至肥胖(BMI 28.0-40.0 kg/m2)]分别摄入两种对照饮食，每人28天。其中一种饮食富含全谷物、新鲜水果和蔬菜。另一种是用精制谷物代替全谷物，并提供主要来自高血糖指数食物来源的碳水化合物。所有食物都被提供给参与者，两种饮食中的能量含量和常量营养素成分是相同的。每个参与者都充当自己的对照。在每次饮食开始和结束时收集空腹血液和大便。我们将使用一种新的蛋白质抗体阵列来测量血浆蛋白质组，将使用16S rRNA测序、元基因组学和元转录组学来表征肠道微生物群落，并将使用有针对性的全球方法来测量血浆代谢组。我们将使用新的统计方法来整合各种组学数据，并使用最先进的单标记和路径丰富统计模型来测试相关性，这些统计模型可以根据人口统计学差异进行调整。

 DESCRIPTION (provided by applicant): We are responding to PAR-13-375, Nutrigenetics and Nutrigenomics Approaches for Nutrition Research. This amended proposal, supported by a team of well-established investigators in proteomics, metabolomics, gut microbiome, experimental nutrition, and biostatistics, aims to evaluate effects of specific diet patterns on biomarkers of cancer-risk pathways in a controlled feeding study. Diet is a complex exposure in humans, aspects of which are recognized to play a role in the risk of several cancers. To date, the focus of experimental nutrition has been on the impact of specific nutrients or dietary constituents on mechanisms of cancer prevention or treatment, with less attention placed on the response to multiple, concurrent nutrient exposures, as would occur in free-living humans consuming food. Observational studies suggest that we also need to understand the response to dietary patterns to better target public health recommendations for cancer prevention. Dietary constituents have pleiotropic effects and modulate a variety of pathways important in carcinogenesis. Proteomics and metabolomics have the capacity to capture this range of responses. Further, the gut microbiome and gut microbial metabolites have been shown to affect some of these pathways and have been implicated in the risk of several cancers. We will use a carefully designed, well-controlled feeding study to address the following aims: 1) Compare the effects of two distinct dietary patterns fed as controlled diets on proteomic biomarkers of regulatory pathways important in cancer susceptibility; 2) Compare the effects of the two dietary patterns on (a) gut microbial community and the gut metagenome and metatranscriptome and (b) the plasma metabolome; and 3) Explore what diet-induced changes in the metatranscriptome and metabolomics profiles explain the variation in proteomic measures that also changed in response to diet. We will use data and samples from a completed randomized cross-over feeding study, Carbohydrate and Related Biomarkers (CARB), where 80 participants [40 normal weight (BMI 18.5-25.0 kg/m2) and 40 overweight to obese (BMI 28.0-40.0 kg/m2)] consumed two controlled diets for 28 days each. One diet was high in whole grains, fresh fruits and vegetables. The other substituted refined grains for whole grains, and provided carbohydrates from mostly high-glycemic index food sources. All food was provided to participants and energy content and macronutrient composition was identical in both diets. Each participant served as their own control. Fasting blood and stool were collected at the beginning and end of each diet. We will use a novel protein antibody array to measure the plasma proteome, will characterize the gut microbial community using 16S rRNA sequencing, metagenomics and metatranscriptomics, and will measure the plasma metabolome using targeted and global approaches. We will use novel statistical methods for integrating the various omics data and test for associations using state-of-the- art single-marker and pathway-enrichment statistical models which adjust for demographic variation.