研究生英文文献汇报系列2.pptx

Infant Gut Microbiome Associated With Cognitive Development 婴儿（2岁时）的认知发育与（特定种类的）肠道微生物有关 Biological Psychiatry, June 2017（online） IF:10.577 Yao xing,2018-01-02 Contens Background Objectives Methods Results Strengths and limitations Conclusions Words rodents（啮齿动物） Gut colonization（肠道微生物定植） MSEL: Mullen Scales of Early Learning（马伦早期学习量表） 16S ribosomal RNA amplicon sequencing（16s rRNA扩增子测序） alpha diversity（多样性： 多样性高，肠道菌群比较成熟） the University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山 分校） Co-occurrence Networks（共线/同现网络） Metagenome Prediction（宏基因组学预测，利用基因组学的研究策略研 究样品所包含的全部微生物的遗传组成及其群落功能） Faecalibacterium（普氏菌） Bacteroides（拟杆菌） Ruminococcaceae（瘤胃球菌/疣微菌科） Background 1.The gut microbiome is a complex microbial ecosystem that varies between individuals and may be a key modulator of neurodevelopment through the microbiome-gut-brain axis（肠脑轴）. 2.In rodents, experimental manipulations that alter the intestinal microbiota impact anxiety and depression- related behaviors . 3.In humans, altered microbial composition of the gut has been reported in children with autism and adults with depression and is linked to childhood temperament and adult cognition. 4.No studies have addressed when these relationships emerge or directly examined which brain regions may be involved. 5.A relationship may exist between concurrent(同 时发生的) gut colonization and brain development processes. Objectives 1.In this prospective, longitudinal study, the researchers tested whether the 1-year-old gut microbiome was associated with cognitive outcomes at 1 and 2 years of age. 2.They hypothesized that infant gut microbiome samples would cluster into groups of community similarity and that cognitive ability would differ between clusters.根据群落相似性，肠道菌群样本可以聚 类为不同的组，而聚类到不同组的婴儿可能具有不同的认知 能力 Methods 1. Study Population Recruited 89 1-year-old infants (twins and singletons) from two prospective longitudinal studies of early brain development at the University of North Carolina at Chapel Hill.两项关于早期大脑发育的前瞻性、纵向研究 Exclusion criteria for the parent studies included fetal ultrasound abnormalities and major medical illness of the mother. 2. Microbiome Analysis（微生物群分析方 法） Parents collected approximately 200 mg of fecal material from a single diaper, placed it in a tube filled with Allprotect reagent（试剂 ） and returned it through overnight shipping（连夜运送）. Once received, samples were stored at -80 C until analysis. MiSeq platform（用于对细菌种类进行鉴别和量化）, Quantitative Insights Into Microbial Ecology software（用于获取生物学信息、多样性 测定）, de novo algorithm（用于最优分类单位选取）, ChimeraSlayer （用于嵌合测序）. Clustering analysis was completed with distance metrics，Cluster scoring methods were used to assess the optimal number of clusters. 3. Genera Analysis and Co-occurrence Networks To better understand the unique community dynamics of each cluster, we used one representative genera from each cluster as a seed to generate co- occurrence networks via Spearman correlations. 4. Cognitive Testing The Mullen consists of five separate scales, with their own age-group standardized normative T scores and percentiles, and an Early Learning (Cognitive) Composite (ELC). The five scales measure gross motor, fine motor, visual reception, expressive language, and receptive language skills. The standardized T scores of four scales (gross motor not included) are combined to create the ELC. This provided cognitive data for 86 subjects at 1 year of age and 69 subjects at 2 years of age. 5. Structural Image Acquisition Siemens head-only 3T TIM-Trio scanner During unsedated natural sleep 婴儿在自然睡眠状态下 的大脑成像 Magnetization prepared rapid acquisition gradient- echo scans were obtained at years 1 and 2 A total of 38 infants at year 1 did not go to sleep or woke up in the scanner (success rate = 57%). A total of 61 infants at year 2 did not go to sleep, woke up in the scanner, or were lost to follow-up (success rate = 31%). 6. Image Analysis Brain tissue was classified as gray matter, white matter, and cerebrospinal fluid. ITK-SNAP software Obtained measures of intracranial volume, total gray matter, total white matter, total cerebrospinal fluid, lateral ventricle volume, and 90-region gray matter volumes at 1 year of age for 46 subjects and at 2 years of age for 27 subjects. 扫描结果提供了颅内容量、总灰 质、总白质、总脑脊液、侧脑室体积和90区域灰质体积等信 息 7.Association of Cognitive and Brain Outcomes With Cluster Membership and Alpha Diversity Linear mixed effect models were used to test for effects of ： 1. cognitive performance and brain volumes 2. cluster membership on alpha diversity Likelihood ratio tests were used to test the significance of the coefficient of each cluster and alpha diversity measure. Secondary analyses were adjusted for multiple comparisons 7.Association of Cognitive and Brain Outcomes With Cluster Membership and Alpha Diversity Sensitivity analyses Raw scores T scores Demographic and medical variables associated with cluster membership or alpha diversity and neuroimaging or cognitive outcomes were included as covariates 8.Metagenome Prediction From Phylogenetic Investigation of Communities by Reconstruction of Unobserved States PICRUSt （生物信息软件）was used to predict meta- genome functional content from the 16S ribosomal RNA sequencing data. Results 1.Infant Microbiota Cluster Into Three Groups Cluster 1 (C1) characterized by a relatively high abundance of Faecalibacterium;第一组的普氏菌属细 菌丰度较高 Cluster 2 (C2) by a relatively high abundance of Bacteroides;第二组的拟杆菌属细菌丰度较高 Cluster 3 (C3) by a relatively high abundance of an unnamed genus in the family Ruminococcaceae.第三组以瘤胃球菌菌科一种未命名的 细菌属的相对丰度较高为主要特征 2. Identification of Predictor Covariates C2组（拟杆菌 丰度高）的婴 儿在1岁时更 少的是通过剖 腹产出生、更 多的是母乳喂 养。 2. Identification of Predictor Covariates Paternal ethnicity in C2 was 90% white compared with C3 and C1. 2. Identification of Predictor Covariates Infants in C2 were more likely to be breastfed at 1 year of age . 3. Cognitive Abilities Differ Between Clusters 4. Alpha Diversity Correlates With Cognitive Performance Four measures of alpha diversity, Chao1 (CH1), observed species (OS), Shannon Index (SI), and Faiths Phylogenetic Diversity (FPD) were tested for associations with the ELC. Alpha diversity measures accounted for 5% to 23% of the variance in Mullen scores. FPD, SI, OS, and CH1 were all significantly different between clusters, with C1 showing the greatest alpha diversity and C2 showing the least. 在2岁时，肠道菌群-多样性高与Mullen早期学习量表总得 分以及视觉感知和语言表达评分较低有关。C1组的-多样 性最高，而C2组-多样性最低。 我们通常会认为肠道菌群多样性更高的婴儿可能会有更好的 认知发育，因为多样性的肠道菌群通常会创造一个更稳定的 功能群体，但是结果却是相反，为什么会这样？ 4. Alpha Diversity Correlates With Cognitive Performance 5. Predicted Metagenome of Gut Microbiota Differs Between Clusters C2 showed the highest performance on the Mullen Scales, and therefore we were particularly interested in metabolic differences unique to this group； C2 had increased genes involved in metabolism of biotin, lipoic acid, folate, and ubiquinone and other terpenoid ； Genes involved in bacterial chemotaxis, flagellar assembly, bacterial motility proteins, and cytoskeleton proteins were decreased. C2组肠道微生物中，与叶酸、生物素代谢有关的基因增加 了，而与细菌致病性（如细菌的趋化性、细菌的运动）相关的基因减少 了； 可能的解释是，多样化的菌群可能会含有一些对大脑发育具有潜在的负 面影响的致病细菌，或者说在一个多样化的菌群中，所有的细菌必须共 享资源，因此有益菌可利用的资源减少。 Conclusion 1. Microbial composition of the human gut at 1 year of age predicts cognitive performance at 2 years of age, particularly in the area of communicative behavior. 2. Results may have implications for developmental disorders characterized by cognitive or la