肠道共生微生物与健康和疾病

人体是个庞大的动态的微生物群落的天然寄居场所,人体的皮肤、口腔、消化道、呼吸道和生殖道等部位都寄生着大量的微生物.这些微生物与人体互惠互利,形成共生复合体.其中,肠道共生微生物与宿主的相关性及对宿主生理和病理状态的影响已经得到了很好的阐释.肠道共生微生物的主要功能是帮助宿主代谢,使得能量和可吸收的营养物质更好的被利用,为肠道上皮细胞提供营养,增强免疫功能,帮助寄主抵抗外来微生物的入侵.肠道菌群紊乱也是一些疾病的症状或诱发原因,比如肥胖、糖尿病和肠道炎症等.深入研究人类共生微生物与健康和疾病的关系,将为一些疾病的预防和治疗提供新的手段.     

低氧暴露对大鼠肠道微生物群落的影响

【背景】低氧暴露时机体会对物质的吸收和代谢作出调整,以维持自身的能量需求。肠道微生物参与了宿主众多生理过程,尤其在宿主消化、代谢、免疫等多方面发挥了重要作用。而目前对于低氧暴露过程中宿主肠道微生物群落结构和功能的动态变化,以及这些变化与宿主习服低氧之间的关系则少有报道。【目的】研究大鼠暴露于低氧环境后其肠道微生物群落的结构和多样性,探讨低氧暴露对宿主肠道微生物群落的影响,以及肠道微生物群落的变化与宿主代谢之间可能存在的关系。【方法】分别收集低氧暴露1、7、14、21和28 d的实验组(模拟海拔4 500 m)和对照组(43.5 m)的SD大鼠粪便样品,通过IlluminaHiSeq测序平台对样品进行16SrRNAV3-V4区测序,利用Uparse、Qiime、LEfSe和PICRUSt软件分析肠道微生物群落结构、丰度、多样性、组间差异,并利用KEGG数据库对肠道微生物的功能进行预测。【结果】不同低氧暴露时间组和对照组SD大鼠肠道微生物群落特征具有明显差异。低氧暴露组SD大鼠的肠道微生物群落中拟杆菌门(Bacteroidetes)、拟杆菌目(Bacteroidales)、拟杆菌科(Bacteroidaceae)、普氏菌科(Prevotellaceae)、普氏菌属(Prevotella)和普氏菌种(copri)的相对丰度较高且具有统计学意义。相应时间的对照组SD大鼠肠道微生物群落中瘤胃球菌科(Ruminococcaceae)、瘤胃球菌属(Ruminococcus)、梭菌纲(Clostridia)和梭菌目(Clostridiales)等相对丰度较高且具有统计学意义。功能预测发现遗传信息处理和代谢相关通路在低氧暴露组SD大鼠肠道微生物中明显富集。【结论】低氧暴露过程中SD大鼠肠道微生物群落结构和多样性呈动态变化,碳水化合物代谢相关菌群(普氏菌和拟杆菌)明显增加,可能参与了宿主能量代谢调整,有利于机体对低氧环境的习服。     

肠道微生物功能宏基因组学与代谢组学关联分析方法研究进展

近年来基于高通量基因测序的微生物组学研究极大加深了人们对微生物与健康和疾病关系的认识。然而基因测序方法不能直接测定微生物的功能活性,难以鉴定微生物中的关键功能分子,单独使用无法回答肠道微生物何种成员通过何种方式影响宿主等关键科学问题。单一组学研究弊端尽显,多组学联用势在必行。肠道微生物代谢组学以微生物群落所有小分子代谢物为研究对象,可发现肠道微生物随宿主病理生理变化的关键代谢物,为微生物组-宿主互作机制研究提供线索,成为微生物组学研究的重要补充。肠道微生物功能基因组学与代谢组学关联分析在宿主生理、疾病病理、药物药理等方面取得众多进展,展现良好应用前景。然而目前肠道微生物功能基因组学与代谢组学关联分析存在方法滥用、相关性结论与生物学知识相悖等突出问题。为帮助正确应用肠道微生物功能宏基因组学与代谢组学关联分析,本文综述了各种多组学数据整合分析方法的原理、优缺点与适用范围,并给出了应用建议。     

病毒感染对宿主肠道微生态的影响

摘要：肠道菌群对宿主免疫系统的建立和发育起着重要的作用,与宿主的生理、病理等密切相关,对机体抗病毒作用具有一定的影响。病毒感染影响宿主肠道微生物群落,进而影响宿主机体营养物质的代谢及细胞免疫功能。本研究着重综述病毒感染对宿主肠道微生态及免疫的影响。     

病毒感染对宿主肠道微生态的影响

肠道菌群对宿主免疫系统的建立和发育起着重要的作用,与宿主的生理、病理等密切相关,对机体抗病毒作用具有一定的影响。病毒感染影响宿主肠道微生物群落,进而影响宿主机体营养物质的代谢及细胞免疫功能。本研究着重综述病毒感染对宿主肠道微生态及免疫的影响。     

膳食脂肪、肠道微生物与宿主健康的研究进展

作为三大主要营养物质之一,膳食脂肪为人体提供能量和营养。膳食脂肪摄入不当会破坏肠道微生物的稳态,影响宿主的代谢状况,增加慢性疾病发生的风险。建立疾病动物模型是研究肠道微生物与宿主健康的重要手段。文中综述了膳食脂质的数量和种类、肠道微生物和宿主代谢之间的相互作用及其可能的作用机制,阐述了基于不同的疾病动物模型,膳食脂质影响肠道微生物的结构和功能,以及对宿主代谢的调节,为深入了解膳食脂质、肠道微生态和宿主健康三者之间的关系提供了依据。     

反刍动物胃肠道微生物多样性研究进展

反刍动物胃肠道中庞大而复杂的微生物群落对饲料利用和宿主自身代谢有深远的影响。胃肠道微生物群落在亚种或菌株水平上表现出极大的多样性。研究反刍动物胃肠道微生物多样性有助于了解其结构、功能、影响因素以及可能的调控措施。但是,80%~90%的胃肠道微生物仍然无法培养,构成了反刍动物胃肠道微生物研究的瓶颈,分子生物学和生物信息学的快速发展为这个问题的解决提供了新的机遇。     

肠道微生态在营养与健康领域的研究进展

人体肠道拥有庞大而复杂的共生微生态,其群落的稳定状态影响机体的能量吸收、物质代谢及免疫调节等功能。肠道微生态的失衡与肥胖、抑郁症、糖尿病及相关代谢疾病的发生发展存在因果关系,但具体作用机制仍不明晰。肠道微生态与宿主之间存在完整的代谢系统并不断进行丰富的代谢交换,共同应对环境变化因素并影响宿主健康。饮食调控可干预宿主微生态的组成与数量,改善人体代谢。本文分别从膳食纤维、益生菌、粪菌移植、后生素等方面对肠道菌群进行个体化、精准、靶向的干预肠道微生物领域的相关研究,对多组学联合应用于微生物领域的组成和变化规律进行深层揭示。未来的研究热点应聚焦肠道干预方式的远期影响和安全性,控制并消除过程中的可能变异,制定精准高效的干预路径,为慢病防控与健康促进提供医学证据。     

食品添加剂对肠道微生物组的影响

肠道微生物参与宿主多种代谢途径的调节,对机体多种生理功能产生重要影响,并在许多慢性炎性疾病的发展中起核心作用,而饮食中的营养物质能够影响肠道微生物群落结构并为微生物的代谢提供底物。本文综述食品添加剂对肠道微生物组的影响,对预防和治疗与肠道微生物群落相关的许多疾提供科学依据。     

昆虫肠道微生物的研究进展和应用前景

昆虫肠道的独特结构和理化性质为多种多样的微生物定殖提供了特殊环境,肠道微生物的群落组成与宿主昆虫的生长发育、代谢繁殖等生命活动密切相关。种类丰富多样、生态位分布广泛的昆虫体内含有大量特化的肠道微生物群落,经过长期协同进化形成的共生关系具有多方面无可替代的优势。这种相对稳定的共生关系对昆虫整个生命周期具有极其重要的作用,肠道微生物不仅为宿主提供重要的营养物质、协助消化食物、提高宿主防御和解毒能力,还影响宿主昆虫的寿命、发育周期以及交配与繁殖能力等。同时,昆虫肠道微生物在农业、生态、医药以及能源环保等多个学科领域也显示出了巨大的应用前景。本文就昆虫肠道微生物群落的多样性、功能和影响肠道微生物生存因素,以及应用前景等方面进行综述,讨论了昆虫肠道微生物的最新研究进展。     

Rapidly expanding knowledge on the role of the gut microbiome in health and disease

The human gut is colonized by a wide diversity of micro-organisms, which are now known to play a key role in the human host by regulating metabolic functions and immune homeostasis. Many studies have indicated that the genomes of our gut microbiota, known as the gut microbiome or our “other genome” could play an important role in immune-related, complex diseases, and growing evidence supports a causal role for gut microbiota in regulating predisposition to diseases. A comprehensive analysis of the human gut microbiome is thus important to unravel the exact mechanisms by which the gut microbiota are involved in health and disease. Recent advances in next-generation sequencing technology, along with the development of metagenomics and bioinformatics tools, have provided opportunities to characterize the microbial communities. Furthermore, studies using germ-free animals have shed light on how the gut microbiota are involved in autoimmunity. In this review we describe the different approaches used to characterize the human microbiome, review current knowledge about the gut microbiome, and discuss the role of gut microbiota in immune homeostasis and autoimmunity. Finally, we indicate how this knowledge could be used to improve human health by manipulating the gut microbiota. This article is part of a Special Issue entitled: From Genome to Function.     

元基因组学及其在转化医学中的应用

微生物群落遍布于人体的每个角落,与人共生并对人体健康产生重要和深刻的影响。与人类共生的全部微生物的基因组总和称为“元基因组”或“人类第二基因组”。研究人体微生物群落及相关元基因组数据,对转化医学领域的基础研究和临床应用具有重要的价值。通过对生物医学相关的高通量元基因组数据进行分析,不仅能为基础医学研究向医学临床应用转化提供新思路和新方法,而且具有广阔的应用前景。基于新一代测序技术产生的数据,元基因组分析技术和方法能够弥补以往人体微生物先培养后鉴定方法的缺陷,同时能有效鉴定和分析微生物群落的组成及功能,从而进一步探究和揭示微生物群落与机体生理状态之间的关系,为解决许多医学领域的难题提供了全新的切入角度和思维方法。文章系统介绍了元基因组研究的现状,包括元基因组的方法概念和研究进展,并以元基因组在医学研究中的应用为着眼点,综述了元基因组在转化医学方面的研究进展,进一步阐述了元基因组研究在转化医学应用领域中具有的重要地位。     

The dynamic microbiome

While our genomes are essentially static, our microbiomes are inherently dynamic. The microbial communities we harbor in our bodies change throughout our lives due to many factors, including maturation during childhood, alterations in our diets, travel, illnesses, and medical treatments. Moreover, there is mounting evidence that our microbiomes change us, by promoting health through their beneficial actions or by increasing our susceptibility to diseases through a process termed dysbiosis. Recent technological advances are enabling unprecedentedly detailed studies of the dynamics of the microbiota in animal models and human populations. This review will highlight key areas of investigation in the field, including establishment of the microbiota during early childhood, temporal variability of the microbiome in healthy adults, responses of the microbiota to intentional perturbations such as antibiotics and dietary changes, and prospective analyses linking changes in the microbiota to host disease status. Given the importance of computational methods in the field, this review will also discuss issues and pitfalls in the analysis of microbiome time-series data, and explore several promising new directions for mathematical model and algorithm development.     

Inferring dynamic signatures of microbes in complex host ecosystems

The human gut microbiota comprise a complex and dynamic ecosystem that profoundly affects host development and physiology. Standard approaches for analyzing time-series data of the microbiota involve computation of measures of ecological community diversity at each time-point, or measures of dissimilarity between pairs of time-points. Although these approaches, which treat data as static snapshots of microbial communities, can identify shifts in overall community structure, they fail to capture the dynamic properties of individual members of the microbiota and their contributions to the underlying time-varying behavior of host ecosystems. To address the limitations of current methods, we present a computational framework that uses continuous-time dynamical models coupled with Bayesian dimensionality adaptation methods to identify time-dependent signatures of individual microbial taxa within a host as well as across multiple hosts. We apply our framework to a publicly available dataset of 16S rRNA gene sequences from stool samples collected over ten months from multiple human subjects, each of whom received repeated courses of oral antibiotics. Using new diversity measures enabled by our framework, we discover groups of both phylogenetically close and distant bacterial taxa that exhibit consensus responses to antibiotic exposure across multiple human subjects. These consensus responses reveal a timeline for equilibration of sub-communities of micro-organisms with distinct physiologies, yielding insights into the successive changes that occur in microbial populations in the human gut after antibiotic treatments. Additionally, our framework leverages microbial signatures shared among human subjects to automatically design optimal experiments to interrogate dynamic properties of the microbiota in new studies. Overall, our approach provides a powerful, general-purpose framework for understanding the dynamic behaviors of complex microbial ecosystems, which we believe will prove instrumental for future studies in this field.     

Colliding and interacting microbiomes and microbial communities - consequences for human health

Living ‘things’ coexist with microorganisms, known as the microbiota/microbiome that provides essential physiological functions to its host. Despite this reliance, the microbiome is malleable and can be altered by several factors including birth-mode, age, antibiotics, nutrition, and disease. In this minireview, we consider how other microbiomes and microbial communities impact the host microbiome and the host through the concept of microbiome collisions (initial exposures) and interactions. Interactions include changes in host microbiome composition and functionality and/or host responses. Understanding the impact of other microbiomes and microbial communities on the microbiome and host are important considering the decline in human microbiota diversity in the developed world – paralleled by the surge of non-communicable, inflammatory-based diseases. Thus, surrounding ourselves with rich and diverse beneficial microbiomes and microbial communities to collide and interact with should help to diminish the loss in microbial diversity and protect from certain diseases. In the same vein, our microbiomes not only influence our health but potentially the health of those close to us. We also consider strategies for enhanced host microbiome collisions and interactions through the surrounding environment that ensure increased microbiome diversity and functionality contributing to enhanced symbiotic return to the host in terms of health benefit.     

Investigating the biological and clinical significance of human dysbioses

Culture-independent microbiological technologies that interrogate complex microbial populations without prior axenic culture, coupled with high-throughput DNA sequencing, have revolutionized the scale, speed and economics of microbial ecological studies. Their application to the medical realm has led to a highly productive merger of clinical, experimental and environmental microbiology. The functional roles played by members of the human microbiota are being actively explored through experimental manipulation of animal model systems and studies of human populations. In concert, these studies have appreciably expanded our understanding of the composition and dynamics of human-associated microbial communities (microbiota). Of note, several human diseases have been linked to alterations in the composition of resident microbial communities, so-called dysbiosis. However, how changes in microbial communities contribute to disease etiology remains poorly defined. Correlation of microbial composition represents integration of only two datasets (phenotype and microbial composition). This article explores strategies for merging the human microbiome data with multiple additional datasets (e.g. host single nucleotide polymorphisms and host gene expression) and for integrating patient-based data with results from experimental animal models to gain deeper understanding of how host-microbe interactions impact disease.     

Modelling the effect of birth and feeding modes on the development of human gut microbiota

The human gut microbiota is transmitted from mother to infant through vaginal birth and breastfeeding. Bifidobacterium, a genus that dominates the infants’ gut, is adapted to breast milk in its ability to metabolize human milk oligosaccharides; it is regarded as a mutualist owing to its involvement in the development of the immune system. The composition of microbiota, including the abundance of Bifidobacteria, is highly variable between individuals and some microbial profiles are associated with diseases. However, whether and how birth and feeding practices contribute to such variation remains unclear. To understand how early events affect the establishment of microbiota, we develop a mathematical model of two types of Bifidobacteria and a generic compartment of commensal competitors. We show how early events affect competition between mutualists and commensals and microbe-host-immune interactions to cause long-term alterations in gut microbial profiles. Bifidobacteria associated with breast milk can trigger immune responses with lasting effects on the microbial community structure. Our model shows that, in response to a change in birth environment, competition alone can produce two distinct microbial profiles post-weaning. Adding immune regulation to our competition model allows for variations in microbial profiles in response to different feeding practices. This analysis highlights the importance of microbe–microbe and microbe–host interactions in shaping the gut populations following different birth and feeding modes.