Reduced Incidence of Prevotella and Other Fermenters in Intestinal Microflora of Autistic Children
TL;DR: Autism and accompanying GI symptoms were characterized by distinct and less diverse gut microbial compositions with lower levels of Prevotella, Coprococcus, and unclassified Veillonellaceae.
Abstract: High proportions of autistic children suffer from gastrointestinal (GI) disorders, implying a link between autism and abnormalities in gut microbial functions. Increasing evidence from recent high-throughput sequencing analyses indicates that disturbances in composition and diversity of gut microbiome are associated with various disease conditions. However, microbiome-level studies on autism are limited and mostly focused on pathogenic bacteria. Therefore, here we aimed to define systemic changes in gut microbiome associated with autism and autism-related GI problems. We recruited 20 neurotypical and 20 autistic children accompanied by a survey of both autistic severity and GI symptoms. By pyrosequencing the V2/V3 regions in bacterial 16S rDNA from fecal DNA samples, we compared gut microbiomes of GI symptom-free neurotypical children with those of autistic children mostly presenting GI symptoms. Unexpectedly, the presence of autistic symptoms, rather than the severity of GI symptoms, was associated with less diverse gut microbiomes. Further, rigorous statistical tests with multiple testing corrections showed significantly lower abundances of the genera Prevotella, Coprococcus, and unclassified Veillonellaceae in autistic samples. These are intriguingly versatile carbohydrate-degrading and/or fermenting bacteria, suggesting a potential influence of unusual diet patterns observed in autistic children. However, multivariate analyses showed that autism-related changes in both overall diversity and individual genus abundances were correlated with the presence of autistic symptoms but not with their diet patterns. Taken together, autism and accompanying GI symptoms were characterized by distinct and less diverse gut microbial compositions with lower levels of Prevotella, Coprococcus, and unclassified Veillonellaceae.
Citations
More filters
TL;DR: A gut-microbiome-brain connection in a mouse model of ASD is supported and a potential probiotic therapy for GI and particular behavioral symptoms in human neurodevelopmental disorders is identified.
2,507 citations
Cites background from "Reduced Incidence of Prevotella and..."
...Indeed, dysbiosis of the microbiota is implicated in the pathogenesis of several human disorders, including IBD, obesity, and cardiovascular disease (Blumberg and Powrie, 2012), and several studies report altered composition of the intestinal microbiota in ASD (Adams et al., 2011; Finegold et al., 2010; Finegold et al., 2012; Kang et al., 2013; Parracho et al., 2005;Williams et al., 2011; Williams et al., 2012)....
[...]
...…D’Eufemia et al., 1996; de Magistris et al., 2010) and microbiome alterations (Adams et al., 2011; Finegold C et al., 2010; Finegold et al., 2012; Kang et al., 2013; Parracho et al., 2005; Williams et al., 2011; Williams et al., 2012) are reported in several independent studies of ASD; however,…...
[...]
...…and cardiovascular disease (Blumberg and Powrie, 2012), and several studies report altered composition of the intestinal microbiota in ASD (Adams et al., 2011; Finegold et al., 2010; Finegold et al., 2012; Kang et al., 2013; Parracho et al., 2005;Williams et al., 2011; Williams et al., 2012)....
[...]
Journal Article•
TL;DR: FastTree as mentioned in this paper uses sequence profiles of internal nodes in the tree to implement neighbor-joining and uses heuristics to quickly identify candidate joins, then uses nearest-neighbor interchanges to reduce the length of the tree.
Abstract: Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement neighbor-joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest-neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N^2) space and O(N^2 L) time, but FastTree requires just O( NLa + N sqrt(N) ) memory and O( N sqrt(N) log(N) L a ) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 hours and 2.4 gigabytes of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 hours and 50 gigabytes of memory. In simulations, FastTree was slightly more accurate than neighbor joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.
2,436 citations
TL;DR: It is advocated that investigators avoid rarefying altogether and supported statistical theory is provided that simultaneously accounts for library size differences and biological variability using an appropriate mixture model.
Abstract: Current practice in the normalization of microbiome count data is inefficient in the statistical sense. For apparently historical reasons, the common approach is either to use simple proportions (which does not address heteroscedasticity) or to use rarefying of counts, even though both of these approaches are inappropriate for detection of differentially abundant species. Well-established statistical theory is available that simultaneously accounts for library size differences and biological variability using an appropriate mixture model. Moreover, specific implementations for DNA sequencing read count data (based on a Negative Binomial model for instance) are already available in RNA-Seq focused R packages such as edgeR and DESeq. Here we summarize the supporting statistical theory and use simulations and empirical data to demonstrate substantial improvements provided by a relevant mixture model framework over simple proportions or rarefying. We show how both proportions and rarefied counts result in a high rate of false positives in tests for species that are differentially abundant across sample classes. Regarding microbiome sample-wise clustering, we also show that the rarefying procedure often discards samples that can be accurately clustered by alternative methods. We further compare different Negative Binomial methods with a recently-described zero-inflated Gaussian mixture, implemented in a package called metagenomeSeq. We find that metagenomeSeq performs well when there is an adequate number of biological replicates, but it nevertheless tends toward a higher false positive rate. Based on these results and well-established statistical theory, we advocate that investigators avoid rarefying altogether. We have provided microbiome-specific extensions to these tools in the R package, phyloseq.
2,184 citations
Cites background from "Reduced Incidence of Prevotella and..."
...In many cases researchers have also failed to repeat the random subsampling step or record the pseudorandom number generation seed/process — both of which are essential for reproducibility....
[...]
TL;DR: This review focuses on studies in humans to describe challenges and propose strategies that leverage existing knowledge to move rapidly from correlation to causation and ultimately to translation into therapies.
Abstract: Our understanding of the link between the human microbiome and disease, including obesity, inflammatory bowel disease, arthritis and autism, is rapidly expanding. Improvements in the throughput and accuracy of DNA sequencing of the genomes of microbial communities that are associated with human samples, complemented by analysis of transcriptomes, proteomes, metabolomes and immunomes and by mechanistic experiments in model systems, have vastly improved our ability to understand the structure and function of the microbiome in both diseased and healthy states. However, many challenges remain. In this review, we focus on studies in humans to describe these challenges and propose strategies that leverage existing knowledge to move rapidly from correlation to causation and ultimately to translation into therapies.
1,359 citations
TL;DR: The findings suggest that the intestinal microbiome is altered in PD and is related to motor phenotype, and the suitability of the microbiome as a biomarker is warranted.
Abstract: In the course of Parkinson's disease (PD), the enteric nervous system (ENS) and parasympathetic nerves are amongst the structures earliest and most frequently affected by alpha-synuclein pathology. Accordingly, gastrointestinal dysfunction, in particular constipation, is an important non-motor symptom in PD and often precedes the onset of motor symptoms by years. Recent research has shown that intestinal microbiota interact with the autonomic and central nervous system via diverse pathways including the ENS and vagal nerve. The gut microbiome in PD has not been previously investigated. We compared the fecal microbiomes of 72 PD patients and 72 control subjects by pyrosequencing the V1-V3 regions of the bacterial 16S ribosomal RNA gene. Associations between clinical parameters and microbiota were analyzed using generalized linear models, taking into account potential confounders. On average, the abundance of Prevotellaceae in feces of PD patients was reduced by 77.6% as compared with controls. Relative abundance of Prevotellaceae of 6.5% or less had 86.1% sensitivity and 38.9% specificity for PD. A logistic regression classifier based on the abundance of four bacterial families and the severity of constipation identified PD patients with 66.7% sensitivity and 90.3% specificity. The relative abundance of Enterobacteriaceae was positively associated with the severity of postural instability and gait difficulty. These findings suggest that the intestinal microbiome is altered in PD and is related to motor phenotype. Further studies are warranted to elucidate the temporal and causal relationships between gut microbiota and PD and the suitability of the microbiome as a biomarker.
1,325 citations
Cites background from "Reduced Incidence of Prevotella and..."
...However, low Prevotellaceae levels do not seem to be specific for PD, because they have been reported in patients with autism and type 1 diabetes.(61,71) Based on these observations, high fecal abundance of Prevotellaceae could be a useful biomarker to exclude PD....
[...]
...Decreased levels of Prevotella and increased abundance of Enterobacteriaceae in feces of autistic children support the relevance of these bacteria in CNS disorders.(61,62) Prevotella is a commensal microbe in the colon that not only can degrade a broad spectrum of plant polysaccharides and mucin glycoproteins in the mucosal layer of the gut, but also may interact with the immune system....
[...]
...Prevotella is a commensal microbe in the colon that not only can degrade a broad spectrum of plant polysaccharides and mucin glycoproteins in the mucosal layer of the gut, but also may interact with the immune system.(61,63-65) Prevotella is the main contributor of one of the recently suggested gut microbiome enterotypes....
[...]
References
More filters
TL;DR: An overview of the analysis pipeline and links to raw data and processed output from the runs with and without denoising are provided.
Abstract: Supplementary Figure 1 Overview of the analysis pipeline. Supplementary Table 1 Details of conventionally raised and conventionalized mouse samples. Supplementary Discussion Expanded discussion of QIIME analyses presented in the main text; Sequencing of 16S rRNA gene amplicons; QIIME analysis notes; Expanded Figure 1 legend; Links to raw data and processed output from the runs with and without denoising.
28,911 citations
"Reduced Incidence of Prevotella and..." refers methods in this paper
...We measured phylogenetic diversity (PD) index by using Quantitative Insights Into Microbial Ecology (QIIME) software package [29]....
[...]
University of Massachusetts Amherst1, University of Michigan2, University of New Mexico3, University of British Columbia4, Texas A&M University5, University of Minnesota6, University of Warwick7, Dalhousie University8, Colorado School of Mines9, University of Ljubljana10, Graz University of Technology11, Louisiana State University12
TL;DR: M mothur is used as a case study to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the α and β diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments.
Abstract: mothur aims to be a comprehensive software package that allows users to use a single piece of software to analyze community sequence data. It builds upon previous tools to provide a flexible and powerful software package for analyzing sequencing data. As a case study, we used mothur to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the alpha and beta diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments. This analysis of more than 222,000 sequences was completed in less than 2 h with a laptop computer.
17,350 citations
"Reduced Incidence of Prevotella and..." refers methods in this paper
...We denoised PCR artifacts by eliminating sequences with low qualities [25]....
[...]
...We used the Mothur software to obtain rarefaction curves and ecological indices of Chao1 estimator and Shannon diversity/ richness [25]....
[...]
TL;DR: UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters and offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets.
Abstract: Motivation: Biological sequence data is accumulating rapidly, motivating the development of improved high-throughput methods for sequence classification.
Results: UBLAST and USEARCH are new algorithms enabling sensitive local and global search of large sequence databases at exceptionally high speeds. They are often orders of magnitude faster than BLAST in practical applications, though sensitivity to distant protein relationships is lower. UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters. UCLUST offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets.
Availability: Binaries are available at no charge for non-commercial use at http://www.drive5.com/usearch
Contact: [email protected]
Supplementary information:Supplementary data are available at Bioinformatics online.
17,301 citations
"Reduced Incidence of Prevotella and..." refers methods in this paper
...To obtain the operational taxonomic units (OTUs), we clustered the sequencing readouts at 90, 95, and 97% similarity with UCLUST algorithm [28], which are roughly equivalent to the taxonomic terms of family, genus, and species, respectively....
[...]
...We randomly subsampled the lowest number (15,991 sequence reads per sample) ten times, combined all sequences, and clustered sequences at 90, 95, and 97 % similarity by using UCLUST algorithm (Edgar 2010)....
[...]
01 Jan 2007
TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.
Abstract: Recently there has been a lot of interest in “ensemble learning” — methods that generate many classifiers and aggregate their results. Two well-known methods are boosting (see, e.g., Shapire et al., 1998) and bagging Breiman (1996) of classification trees. In boosting, successive trees give extra weight to points incorrectly predicted by earlier predictors. In the end, a weighted vote is taken for prediction. In bagging, successive trees do not depend on earlier trees — each is independently constructed using a bootstrap sample of the data set. In the end, a simple majority vote is taken for prediction. Breiman (2001) proposed random forests, which add an additional layer of randomness to bagging. In addition to constructing each tree using a different bootstrap sample of the data, random forests change how the classification or regression trees are constructed. In standard trees, each node is split using the best split among all variables. In a random forest, each node is split using the best among a subset of predictors randomly chosen at that node. This somewhat counterintuitive strategy turns out to perform very well compared to many other classifiers, including discriminant analysis, support vector machines and neural networks, and is robust against overfitting (Breiman, 2001). In addition, it is very user-friendly in the sense that it has only two parameters (the number of variables in the random subset at each node and the number of trees in the forest), and is usually not very sensitive to their values. The randomForest package provides an R interface to the Fortran programs by Breiman and Cutler (available at http://www.stat.berkeley.edu/ users/breiman/). This article provides a brief introduction to the usage and features of the R functions.
14,830 citations
"Reduced Incidence of Prevotella and..." refers methods in this paper
...adjust for multiple testing correction; glm for logistic regression; the glmperm package for permutation test for regression [34]; the randomForest package for Random Forests classification [35])....
[...]
TL;DR: The Illumina-based metagenomic sequencing, assembly and characterization of 3.3 million non-redundant microbial genes, derived from 576.7 gigabases of sequence, from faecal samples of 124 European individuals are described, indicating that the entire cohort harbours between 1,000 and 1,150 prevalent bacterial species and each individual at least 160 such species.
Abstract: To understand the impact of gut microbes on human health and well-being it is crucial to assess their genetic potential. Here we describe the Illumina-based metagenomic sequencing, assembly and characterization of 3.3 million non-redundant microbial genes, derived from 576.7 gigabases of sequence, from faecal samples of 124 European individuals. The gene set, ~150 times larger than the human gene complement, contains an overwhelming majority of the prevalent (more frequent) microbial genes of the cohort and probably includes a large proportion of the prevalent human intestinal microbial genes. The genes are largely shared among individuals of the cohort. Over 99% of the genes are bacterial, indicating that the entire cohort harbours between 1,000 and 1,150 prevalent bacterial species and each individual at least 160 such species, which are also largely shared. We define and describe the minimal gut metagenome and the minimal gut bacterial genome in terms of functions present in all individuals and most bacteria, respectively
9,268 citations
"Reduced Incidence of Prevotella and..." refers background in this paper
...[47] found 25 percent fewer genes in the gut of patients with irritable bowel syndrome than in healthy controls....
[...]