SUMMARY Although autism spectrum disorder (ASD) is defined by core behavioral impairments, gastrointestinal (GI) symptoms are commonly reported. Subsets of ASD individuals display dysbiosis of the gut microbiota, and some exhibit increased intestinal permeability. Here we demonstrate GI barrier defects and microbiota alterations in a mouse model displaying features of ASD, maternal immune activation (MIA). Oral treatment of MIA offspring with the human commensal Bacteroides fragilis corrects gut permeability, alters microbial composition and ameliorates ASD-related defects in communicative, stereotypic, anxiety-like and sensorimotor behaviors. MIA offspring display an altered serum metabolomic profile, and B. fragilis modulates levels of several metabolites. Treating naïve mice with a metabolite that is increased by MIA and restored by B. fragilis causes behavioral abnormalities, suggesting that gut bacterial effects on the host metabolome impact behavior. Taken together, these findings support a gut-microbiome-brain connection in ASD and identify a potential probiotic therapy for GI and behavioral symptoms of autism.

A primary aim of microbial ecology is to determine patterns and drivers of community distribution, interaction, and assembly amidst complexity and uncertainty. Microbial community composition has been shown to change across gradients of environment, geographic distance, salinity, temperature, oxygen, nutrients, pH, day length, and biotic factors 1-6 . These patterns have been identified mostly by focusing on one sample type and region at a time, with insights extra polated across environments and geography to produce generalized principles. To assess how microbes are distributed across environments globally-or whether microbial community dynamics follow funda mental ecological 'laws' at a planetary scale-requires either a massive monolithic cross environment survey or a practical methodology for coordinating many independent surveys. New studies of microbial environments are rapidly accumulating; however, our ability to extract meaningful information from across datasets is outstripped by the rate of data generation. Previous meta analyses have suggested robust gen eral trends in community composition, including the importance of salinity 1 and animal association 2 . These findings, although derived from relatively small and uncontrolled sample sets, support the util ity of meta analysis to reveal basic patterns of microbial diversity and suggest that a scalable and accessible analytical framework is needed.The Earth Microbiome Project (EMP, http://www.earthmicrobiome. org) was founded in 2010 to sample the Earth's microbial communities at an unprecedented scale in order to advance our understanding of the organizing biogeographic principles that govern microbial commu nity structure 7,8 . We recognized that open and collaborative science, including scientific crowdsourcing and standardized methods 8 , would help to reduce technical variation among individual studies, which can overwhelm biological variation and make general trends difficult to detect 9 . Comprising around 100 studies, over half of which have yielded peer reviewed publications (Supplementary Table 1), the EMP has now dwarfed by 100 fold the sampling and sequencing depth of earlier meta analysis efforts 1,2 ; concurrently, powerful analysis tools have been developed, opening a new and larger window into the distri bution of microbial diversity on Earth. In establishing a scalable frame work to catalogue microbiota globally, we provide both a resource for the exploration of myriad questions and a starting point for the guided acquisition of new data to answer them. As an example of using this Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of r...

Background: Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses. Results: Effects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination metrics based on presence or absence. Alternate normalization measures are potentially vulnerable to artifacts due to library size. Effects on differential abundance testing: We build on a previous work to evaluate seven proposed statistical methods using rarefied as well as raw data. Our simulation studies suggest that the false discovery rates of many differential abundance-testing methods are not increased by rarefying itself, although of course rarefying results in a loss of sensitivity due to elimination of a portion of available data. For groups with large (~10×) differences in the average library size, rarefying lowers the false discovery rate. DESeq2, without addition of a constant, increased sensitivity on smaller datasets (<20 samples per group) but tends towards a higher false discovery rate with more samples, very uneven (~10×) library sizes, and/or compositional effects. For drawing inferences regarding taxon abundance in the ecosystem, analysis of composition of microbiomes (ANCOM) is not only very sensitive (for >20 samples per group) but also critically the only method tested that has a good control of false discovery rate. Conclusions: These findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study.

Deblur provides a rapid and sensitive means to assess ecological patterns driven by differentiation of closely related taxa. This algorithm provides a solution to the problem of identifying real ecological differences between taxa whose amplicons differ by a single base pair, is applicable in an automated fashion to large-scale sequencing data sets, and can integrate sequencing runs collected over time.

We continue to uncover a wealth of information connecting microbes in important ways to human and environmental ecology. As our scientific knowledge and technical abilities improve, the tools used for microbiome surveys can be modified to improve the accuracy of our techniques, ensuring that we can continue to identify groundbreaking connections between microbes and the ecosystems they populate, from ice caps to the human body. It is important to confirm that modifications to these tools do not cause new, detrimental biases that would inhibit the field rather than continue to move it forward. We therefore demonstrated that two recently modified primer pairs that target taxonomically discriminatory regions of bacterial and fungal genomic DNA do not introduce new biases when used on a variety of sample types, from soil to human skin. This confirms the utility of these primers for maintaining currently recommended microbiome research techniques as the state of the art.