It is well established that autism spectrum disorders (ASD) have a strong genetic component. However, for at least 70% of cases, the underlying genetic cause is unknown1. Under the hypothesis that de novo mutations underlie a substantial fraction of the risk for developing ASD in families with no previous history of ASD or related phenotypes—so-called sporadic or simplex families2,3, we sequenced all coding regions of the genome, i.e. the exome, for parent-child trios exhibiting sporadic ASD, including 189 new trios and 20 previously reported4. Additionally, we also sequenced the exomes of 50 unaffected siblings corresponding to these new (n = 31) and previously reported trios (n = 19)4, for a total of 677 individual exomes from 209 families. Here we show de novo point mutations are overwhelmingly paternal in origin (4:1 bias) and positively correlated with paternal age, consistent with the modest increased risk for children of older fathers to develop ASD5. Moreover, 39% (49/126) of the most severe or disruptive de novo mutations map to a highly interconnected beta-catenin/chromatin remodeling protein network ranked significantly for autism candidate genes. In proband exomes, recurrent protein-altering mutations were observed in two genes, CHD8 and NTNG1. Mutation screening of six candidate genes in 1,703 ASD probands identified additional de novo, protein-altering mutations in GRIN2B, LAMC3, and SCN1A. Combined with copy number variant (CNV) data, these results suggest extreme locus heterogeneity but also provide a target for future discovery, diagnostics, and therapeutics.

Autism Genes, Again and Again Despite recent advances in sequencing technologies and their lowered costs—effective, highly sensitive, and specific sequencing of multiple genes of interest from large cohorts remains expensive. O'Roak et al. (p. 1619 ; published online 15 November) modified molecular inversion probe methods for target-specific capture and sequencing to resequence candidate genes in thousands of patients. The technique was applied to 44 candidate genes to identify de novo mutations in a large cohort of individuals with and without autism spectrum disorder. The analysis revealed several de novo mutations in genes that together contribute to 1% of sporadic autism spectrum disorders, supporting the notion that multiple genes underlie autism-spectrum disorders.

Highlights d Mice harboring human ASD, but not TD, microbiomes exhibit ASD-like behaviors d ASD and TD microbiota produce differential metabolome profiles in mice d Extensive alternative splicing of risk genes in brains of mice with ASD microbiota d BTBR mice treated with 5AV or taurine improved repetitive and social behaviors

The human microbiome plays a key role in a wide range of hostrelated processes and has a profound effect on human health. Comparative analyses of the human microbiome have revealed substantial variation in species and gene composition associated with a variety of disease states but may fall short of providing a comprehensive understanding of the impact of this variation on the community and on the host. Here, we introduce a metagenomic systems biology computational framework, integrating metagenomic data with an in silico systems-level analysis of metabolic networks. Focusing on the gut microbiome, we analyze fecal metagenomic data from 124 unrelated individuals, as well as six monozygotic twin pairs and their mothers, and generate community-level metabolic networks of the microbiome. Placing variations in gene abundance in the context of these networks, we identify both gene-level and network-level topological differences associated with obesity and inflammatory bowel disease (IBD). We show that genes associated with either of these host states tend to be located at the periphery of the metabolic network and are enriched for topologically derived metabolic "inputs." These findings may indicate that lean and obese microbiomes differ primarily in their interface with the host and in the way they interact with host metabolism. We further demonstrate that obese microbiomes are less modular, a hallmark of adaptation to low-diversity environments. We additionally link these topological variations to community species composition. The system-level approach presented here lays the foundation for a unique framework for studying the human microbiome, its organization, and its impact on human health. W e humans are mostly microbes. Microbial communities populate numerous sites in the human anatomy and harbor over 100 trillion microbial cells (1). This complex ensemble of microorganisms, collectively known as the human microbiome, plays an essential role in our development, immunity, and nutrition, and has a tremendous impact on our health (2). Among the various body habitats, the most densely colonized is the distal gut. The normal gut flora alone consists of hundreds of bacterial species, collectively encoding an enormous gene set that is 150-fold larger than the set of human genes (3). The gut microbiome plays a key role in many essential processes, including vitamin and amino acid biosynthesis, dietary energy harvest, and immune development (4). Transferring a donor microbiota into a recipient can induce various donor phenotypes [including increased adiposity (5) and metabolic syndrome (6)] or prompt the recovery of a sick recipient (7), suggesting a promising avenue for clinical application via directed manipulation of the microbiome. Characterizing the capacity of the human microbiome, its interaction with the host, and its contribution to various disease states therefore has the potential to provide deep insight into both normal human physiology and human disease, and calls for a predictive systemslevel understandin...

SUMMARY The combinatorial cross-regulation of hundreds of sequence-specific transcription factors defines a regulatory network that underlies cellular identity and function. Here we use genome-wide maps of in vivo DNaseI footprints to assemble an extensive core human regulatory network comprising connections among 475 sequence-specific transcription factors, and to analyze the dynamics of these connections across 41 diverse cell and tissue types. We find that human transcription factor networks are highly cell-selective and are driven by cohorts of factors that include regulators with previously unrecognized roles in control of cellular identity. Moreover, we identify many widely expressed factors that impact transcriptional regulatory networks in a cell-selective manner. Strikingly, in spite of their inherent diversity, all cell type regulatory networks independently converge on a common architecture that closely resembles the topology of living neuronal networks. Together, our results provide the first description of the circuitry, dynamics, and organizing principles of the human transcription factor regulatory network.

The human microbiome plays a key role in human health and is associated with numerous diseases. Metagenomic-based studies are now generating valuable information about the composition of the microbiome in health and in disease, demonstrating nonneutral assembly processes and complex co-occurrence patterns. However, the underlying ecological forces that structure the microbiome are still unclear. Specifically, compositional studies alone with no information about mechanisms of interaction, potential competition, or syntrophy, cannot clearly distinguish habitat-filtering and species assortment assembly processes. To address this challenge, we introduce a computational framework, integrating metagenomic-based compositional data with genome-scale metabolic modeling of species interaction. We use in silico metabolic network models to predict levels of competition and complementarity among 154 microbiome species and compare predicted interaction measures to species co-occurrence. Applying this approach to two large-scale datasets describing the composition of the gut microbiome, we find that species tend to co-occur across individuals more frequently with species with which they strongly compete, suggesting that microbiome assembly is dominated by habitat filtering. Moreover, species' partners and excluders exhibit distinct metabolic interaction levels. Importantly, we show that these trends cannot be explained by phylogeny alone and hold across multiple taxonomic levels. Interestingly, controlling for host health does not change the observed patterns, indicating that the axes along which species are filtered are not fully defined by macroecological host states. The approach presented here lays the foundation for a reverse-ecology framework for addressing key questions concerning the assembly of host-associated communities and for informing clinical efforts to manipulate the microbiome.T he human body is home to numerous microbial species and several complex microbial ecosystems. Advances in sequencing technologies and metagenomics now allow researchers to characterize the composition of species that inhabit the human body and the variation these communities exhibit in health and in disease (1-3). Specifically, recent studies of the microbiome have found tremendous variation among healthy individuals (1) and demonstrated clear associations between species composition and several host phenotypes including obesity (4, 5), inflammatory bowel disease (IBD) (2), and diabetes (6), as well as with external factors such as diet (7). These studies further demonstrated that, as in many other ecosystems, the composition of species in the microbiome exhibits distinct patterns that clearly deviate from a random distribution. For example, species composition in the human microbiome exhibits a significant checkerboard pattern, indicating pairs of taxa that exclude one another from shared environments (8, 9). These patterns are similar to those seen in macroecological communities, suggesting that similar pressures may act upon such m...

The Firmicutes are a phylum of bacteria that dominate numerous polymicrobial habitats of importance to human health and industry. Although these communities are often densely colonized, a broadly distributed contact-dependent mechanism of interbacterial antagonism utilized by Firmicutes has not been elucidated. Here we show that proteins belonging to the LXG polymorphic toxin family present in Streptococcus intermedius mediate cell contact- and Esx secretion pathway-dependent growth inhibition of diverse Firmicute species. The structure of one such toxin revealed a previously unobserved protein fold that we demonstrate directs the degradation of a uniquely bacterial molecule required for cell wall biosynthesis, lipid II. Consistent with our functional data linking LXG toxins to interbacterial interactions in S. intermedius, we show that LXG genes are prevalent in the human gut microbiome, a polymicrobial community dominated by Firmicutes. We speculate that interbacterial antagonism mediated by LXG toxins plays a critical role in shaping Firmicute-rich bacterial communities.DOI: http://dx.doi.org/10.7554/eLife.26938.001

Despite considerable genetic heterogeneity underlying neurodevelopmental diseases, there is compelling evidence that many disease genes will map to a much smaller number of biological subnetworks. We developed a computational method, termed MAGI (merging affected genes into integrated networks), that simultaneously integrates protein–protein interactions and RNA-seq expression profiles during brain development to discover “modules” enriched for de novo mutations in probands. We applied this method to recent exome sequencing of 1116 patients with autism and intellectual disability, discovering two distinct modules that differ in their properties and associated phenotypes. The first module consists of 80 genes associated with Wnt, Notch, SWI/SNF, and NCOR complexes and shows the highest expression early during embryonic development (8–16 post-conception weeks [pcw]). The second module consists of 24 genes associated with synaptic function, including long-term potentiation and calcium signaling with higher levels of postnatal expression. Patients with de novo mutations in these modules are more significantly intellectually impaired and carry more severe missense mutations when compared to probands with de novo mutations outside of these modules. We used our approach to define subsets of the network associated with higher functioning autism as well as greater severity with respect to IQ. Finally, we applied MAGI independently to epilepsy and schizophrenia exome sequencing cohorts and found significant overlap as well as expansion of these modules, suggesting a core set of integrated neurodevelopmental networks common to seemingly diverse human diseases.