Human Gut Microbiota from Autism Spectrum Disorder Promote Behavioral Symptoms in Mice.

Autism spectrum disorder (ASD) manifests as alterations in complex human behaviors including social communication and stereotypies. In addition to genetic risks, the gut microbiome differs between typically developing (TD) and ASD individuals, though it remains unclear whether the microbiome contributes to symptoms. We transplanted gut microbiota from human donors with ASD or TD controls into germ-free mice and reveal that colonization with ASD microbiota is sufficient to induce hallmark autistic behaviors. The brains of mice colonized with ASD microbiota display alternative splicing of ASD-relevant genes. Microbiome and metabolome profiles of mice harboring human microbiota predict that specific bacterial taxa and their metabolites modulate ASD behaviors. Indeed, treatment of an ASD mouse model with candidate microbial metabolites improves behavioral abnormalities and modulates neuronal excitability in the brain. We propose that the gut microbiota regulates behaviors in mice via production of neuroactive metabolites, suggesting that gut-brain connections contribute to the pathophysiology of ASD.

Extensive strain-level copy-number variation across human gut microbiome species.

Within each bacterial species, different strains may vary in the set of genes they encode or in the copy number of these genes. Yet, taxonomic characterization of the human microbiota is often limited to the species level or to previously sequenced strains, and accordingly, the prevalence of intra-species variation, its functional role, and its relation to host health remain unclear. Here, we present a comprehensive large-scale analysis of intra-species copy-number variation in the gut microbiome, introducing a rigorous computational pipeline for detecting such variation directly from shotgun metagenomic data. We uncover a large set of variable genes in numerous species and demonstrate that this variation has significant functional and clinically relevant implications. We additionally infer intra-species compositional profiles, identifying population structure shifts and the presence of yet uncharacterized variants. Our results highlight the complex relationship between microbiome composition and functional capacity, linking metagenome-level compositional shifts to strain-level variation.

Circuitry and dynamics of human transcription factor regulatory networks.

The combinatorial cross-regulation of hundreds of sequence-specific transcription factors (TFs) 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 TFs and to analyze the dynamics of these connections across 41 diverse cell and tissue types. We find that human TF 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 an extensive description of the circuitry, dynamics, and organizing principles of the human TF regulatory network.

A chemically controlled Cas9 switch enables temporal modulation of diverse effectors.

CRISPR-Cas9 has yielded a plethora of effectors, including targeted transcriptional activators, base editors and prime editors. Current approaches for inducibly modulating Cas9 activity lack temporal precision and require extensive screening and optimization. We describe a versatile, chemically controlled and rapidly activated single-component DNA-binding Cas9 switch, ciCas9, which we use to confer temporal control over seven Cas9 effectors, including two cytidine base editors, two adenine base editors, a dual base editor, a prime editor and a transcriptional activator. Using these temporally controlled effectors, we analyze base editing kinetics, showing that editing occurs within hours and that rapid early editing of nucleotides predicts eventual editing magnitude. We also reveal that editing at preferred nucleotides within target sites increases the frequency of bystander edits. Thus, the ciCas9 switch offers a simple, versatile approach to generating chemically controlled Cas9 effectors, informing future effector engineering and enabling precise temporal effector control for kinetic studies.

Human gut bacteria contain acquired interbacterial defence systems.

The human gastrointestinal tract consists of a dense and diverse microbial community, the composition of which is intimately linked to health. Extrinsic factors such as diet and host immunity are insufficient to explain the constituents of this community, and direct interactions between co-resident microorganisms have been implicated as important drivers of microbiome composition. The genomes of bacteria derived from the gut microbiome contain several pathways that mediate contact-dependent interbacterial antagonism1-3. Many members of the Gram-negative order Bacteroidales encode the type VI secretion system (T6SS), which facilitates the delivery of toxic effector proteins into adjacent cells4,5. Here we report the occurrence of acquired interbacterial defence (AID) gene clusters in Bacteroidales species that reside within the human gut microbiome. These clusters encode arrays of immunity genes that protect against T6SS-mediated intra- and inter-species bacterial antagonism. Moreover, the clusters reside on mobile elements, and we show that their transfer is sufficient to confer resistance to toxins in vitro and in gnotobiotic mice. Finally, we identify and validate the protective capability of a recombinase-associated AID subtype (rAID-1) that is present broadly in Bacteroidales genomes. These rAID-1 gene clusters have a structure suggestive of active gene acquisition and include predicted immunity factors of toxins derived from diverse organisms. Our data suggest that neutralization of contact-dependent interbacterial antagonism by AID systems helps to shape human gut microbiome ecology.

Gestational diabetes is driven by microbiota-induced inflammation months before diagnosis.

OBJECTIVE: Gestational diabetes mellitus (GDM) is a condition in which women without diabetes are diagnosed with glucose intolerance during pregnancy, typically in the second or third trimester. Early diagnosis, along with a better understanding of its pathophysiology during the first trimester of pregnancy, may be effective in reducing incidence and associated short-term and long-term morbidities. DESIGN: We comprehensively profiled the gut microbiome, metabolome, inflammatory cytokines, nutrition and clinical records of 394 women during the first trimester of pregnancy, before GDM diagnosis. We then built a model that can predict GDM onset weeks before it is typically diagnosed. Further, we demonstrated the role of the microbiome in disease using faecal microbiota transplant (FMT) of first trimester samples from pregnant women across three unique cohorts. RESULTS: We found elevated levels of proinflammatory cytokines in women who later developed GDM, decreased faecal short-chain fatty acids and altered microbiome. We next confirmed that differences in GDM-associated microbial composition during the first trimester drove inflammation and insulin resistance more than 10 weeks prior to GDM diagnosis using FMT experiments. Following these observations, we used a machine learning approach to predict GDM based on first trimester clinical, microbial and inflammatory markers with high accuracy. CONCLUSION: GDM onset can be identified in the first trimester of pregnancy, earlier than currently accepted. Furthermore, the gut microbiome appears to play a role in inflammation-induced GDM pathogenesis, with interleukin-6 as a potential contributor to pathogenesis. Potential GDM markers, including microbiota, can serve as targets for early diagnostics and therapeutic intervention leading to prevention.

MIMOSA2: a metabolic network-based tool for inferring mechanism-supported relationships in microbiome-metabolome data.

MOTIVATION: Recent technological developments have facilitated an expansion of microbiome-metabolome studies, in which samples are assayed using both genomic and metabolomic technologies to characterize the abundances of microbial taxa and metabolites. A common goal of these studies is to identify microbial species or genes that contribute to differences in metabolite levels across samples. Previous work indicated that integrating these datasets with reference knowledge on microbial metabolic capacities may enable more precise and confident inference of microbe-metabolite links. RESULTS: We present MIMOSA2, an R package and web application for model-based integrative analysis of microbiome-metabolome datasets. MIMOSA2 uses genomic and metabolic reference databases to construct a community metabolic model based on microbiome data and uses this model to predict differences in metabolite levels across samples. These predictions are compared with metabolomics data to identify putative microbiome-governed metabolites and taxonomic contributors to metabolite variation. MIMOSA2 supports various input data types and customization with user-defined metabolic pathways. We establish MIMOSA2's ability to identify ground truth microbial mechanisms in simulation datasets, compare its results with experimentally inferred mechanisms in honeybee microbiota, and demonstrate its application in two human studies of inflammatory bowel disease. Overall, MIMOSA2 combines reference databases, a validated statistical framework, and a user-friendly interface to facilitate modeling and evaluating relationships between members of the microbiota and their metabolic products. AVAILABILITY AND IMPLEMENTATION: MIMOSA2 is implemented in R under the GNU General Public License v3.0 and is freely available as a web server at http://elbo-spice.cs.tau.ac.il/shiny/MIMOSA2shiny/ and as an R package from http://www.borensteinlab.com/software_MIMOSA2.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrating phylogenetic and functional data in microbiome studies.

MOTIVATION: Microbiome functional data are frequently analyzed to identify associations between microbial functions (e.g. genes) and sample groups of interest. However, it is challenging to distinguish between different possible explanations for variation in community-wide functional profiles by considering functions alone. To help address this problem, we have developed POMS, a package that implements multiple phylogeny-aware frameworks to more robustly identify enriched functions. RESULTS: The key contribution is an extended balance-tree workflow that incorporates functional and taxonomic information to identify functions that are consistently enriched in sample groups across independent taxonomic lineages. Our package also includes a workflow for running phylogenetic regression. Based on simulated data we demonstrate that these approaches more accurately identify gene families that confer a selective advantage compared with commonly used tools. We also show that POMS in particular can identify enriched functions in real-world metagenomics datasets that are potential targets of strong selection on multiple members of the microbiome. AVAILABILITY AND IMPLEMENTATION: These workflows are freely available in the POMS R package at https://github.com/gavinmdouglas/POMS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The microbiome's fiber degradation profile and its relationship with the host diet.

BACKGROUND: The relationship between the gut microbiome and diet has been the focus of numerous recent studies. Such studies aim to characterize the impact of diet on the composition of the microbiome, as well as the microbiome's ability to utilize various compounds in the diet and produce metabolites that may be beneficial for the host. Consumption of dietary fibers (DFs)-polysaccharides that cannot be broken down by the host's endogenous enzymes and are degraded primarily by members of the microbiome-is known to have a profound effect on the microbiome. Yet, a comprehensive characterization of microbiome compositional and functional shifts in response to the consumption of specific DFs is still lacking. RESULTS: Here, we introduce a computational framework, coupling metagenomic sequencing with careful annotation of polysaccharide degrading enzymes and DF structures, for inferring the metabolic ability of a given microbiome sample to utilize a broad catalog of DFs. We demonstrate that the inferred fiber degradation profile (IFDP) generated by our framework accurately reflects the dietary habits of various hosts across four independent datasets. We further demonstrate that IFDPs are more tightly linked to the host diet than commonly used taxonomic and functional microbiome-based profiles. Finally, applying our framework to a set of ~700 metagenomes that represents large human population cohorts from 9 different countries, we highlight intriguing global patterns linking DF consumption habits with microbiome capacities. CONCLUSIONS: Combined, our findings serve as a proof-of-concept for the use of DF-specific analysis for providing important complementary information for better understanding the relationship between dietary habits and the gut microbiome.

Infants with cystic fibrosis have altered fecal functional capacities with potential clinical and metabolic consequences.

BACKGROUND: Infants with cystic fibrosis (CF) suffer from gastrointestinal (GI) complications, including pancreatic insufficiency and intestinal inflammation, which have been associated with impaired nutrition and growth. Recent evidence identified altered fecal microbiota taxonomic compositions in infants with CF relative to healthy infants that were characterized by differences in the abundances of taxa associated with GI health and nutrition. Furthermore, these taxonomic differences were more pronounced in low length infants with CF, suggesting a potential link to linear growth failure. We hypothesized that these differences would entail shifts in the microbiome's functional capacities that could contribute to inflammation and nutritional failure in infants with CF. RESULTS: To test this hypothesis, we compared fecal microbial metagenomic content between healthy infants and infants with CF, supplemented with an analysis of fecal metabolomes in infants with CF. We identified notable differences in CF fecal microbial functional capacities, including metabolic and environmental response functions, compared to healthy infants that intensified during the first year of life. A machine learning-based longitudinal metagenomic age analysis of healthy and CF fecal metagenomic functional profiles further demonstrated that these differences are characterized by a CF-associated delay in the development of these functional capacities. Moreover, we found metagenomic differences in functions related to metabolism among infants with CF that were associated with diet and antibiotic exposure, and identified several taxa as potential drivers of these functional differences. An integrated metagenomic and metabolomic analysis further revealed that abundances of several fecal GI metabolites important for nutrient absorption, including three bile acids, correlated with specific microbes in infants with CF. CONCLUSIONS: Our results highlight several metagenomic and metabolomic factors, including bile acids and other microbial metabolites, that may impact nutrition, growth, and GI health in infants with CF. These factors could serve as promising avenues for novel microbiome-based therapeutics to improve health outcomes in these infants.