Epigenome-Microbiome crosstalk: A new way to maintain cognition at old age

认知功能减退和痴呆症是世界范围内与老龄化相关的最重要的健康问题。生活方式的改变，如社交和体力活动--即使是在晚年开始的--对保持认知和健康衰老是有益的。肠道微生物群在调节大脑功能中的作用目前引起了人们的高度关注。肠道微生物区系感知并对环境信号作出反应，从而产生许多对寄主生理重要的代谢物。为了阐明肠道微生物组在老年将生活方式的改变转化为改善认知的作用，我们最近提交了一个项目(BA 2863/8-1，FR 1264/4-1和Ka 3541/6-1)。组学技术(转录组、元基因组和16S rRNA的测序)将被应用于分析不同年龄的小鼠，并随后进行认知调节干预，以建立微生物组-肠道-大脑沟通的电子多尺度模型。同样来自申请者的最近的工作表明，衰老与DNA甲基化的显著变化有关，微生物组强烈影响这种表观遗传机制。因此，在这个项目中，我们打算用结肠和大脑样本中的甲基组(减少代表性的亚硫酸氢盐和整个甲基组测序)的信息来补充我们的数据集。大脑样本的一个子集将接受特定细胞类型的分析。这些表观基因组数据将扩展和完善我们的多尺度代谢模型，使我们能够预测微生物组和大脑在衰老过程中的相互作用，它与认知功能的关系，以及它们如何受到保护认知的干预措施的影响。我们的模型将在未来作为合理设计基于微生物组的疗法以对抗衰老病理的重要基础。

Cognitive decline and dementia are among the most important aging-related health problems worldwide. Changes in lifestyle such as social interaction and physical activity – even if it starts late in life – are beneficial for maintaining cognition and healthy aging. The role of the gut microbiome in modulating brain function is currently of high interest. Gut microbiota sense and respond to environmental cues and as a result generate numerous metabolites important for host physiology. To elucidate the role of the gut microbiome in converting lifestyle changes into an improved cognition at old age we recently submitted a project (BA 2863/8-1, FR 1264/4-1 und KA 3541/6-1). Omics-technologies (sequencing of the transcriptome, the metagenome and the 16S rRNA) will be applied to analyse mice at different ages and following cognition-modulating interventions to build an in silico multi-scale model of microbiome-gut-brain communication. Very recent work, also from the applicants, has shown that aging is associated with pronounced changes in DNA-methylation and that the microbiome strongly influences this epigenetic mechanism. Thus, in this project we intend to complement our data set with information of the methylome (reduced representation bisulfite and whole methylome sequencing) in colon and brain samples. A subset of brain samples will be subjected to a cell-type-specific analysis. These epigenomic data will expand and complete our multi-scale metabolic model which will enable us to predict interactions between microbiome and brain in the course of aging, its relation to cognitive function and how they are influenced by interventions preserving cognition. Our model will serve in the future as essential basis for the rational design of microbiome-based therapies to counteract aging pathologies.