Stress-sensitive neural circuits change the gut microbiome via duodenal glands.

Negative psychological states impact immunity by altering the gut microbiome. However, the relationship between brain states and microbiome composition remains unclear. We show that Brunner's glands in the duodenum couple stress-sensitive brain circuits to bacterial homeostasis. Brunner's glands mediated the enrichment of gut Lactobacillus species in response to vagus nerve stimulation. Cell-specific ablation of the glands markedly suppressed Lactobacilli counts and heightened vulnerability to infection. In the forebrain, we mapped a vagally mediated, polysynaptic circuit connecting the central nucleus of the amygdala to Brunner's glands. Chronic stress suppressed central amygdala activity and phenocopied the effects of gland lesions. Conversely, excitation of either the central amygdala or parasympathetic vagal neurons activated Brunner's glands and reversed the effects of stress on the gut microbiome and immunity. The findings revealed a tractable brain-body mechanism linking psychological states to host defense.

Microbiome engineering: Taming the untractable.

In this issue of Cell, Jin et al. describe several innovative tools for microbiome engineering to enable in situ editing of complex communities. However, challenges remain to overcome the widespread genetic intractability of microbiome constituents.

Conducting a microbiome study.

Human microbiome research is an actively developing area of inquiry, with ramifications for our lifestyles, our interactions with microbes, and how we treat disease. Advances depend on carefully executed, controlled, and reproducible studies. Here, we provide a Primer for researchers from diverse disciplines interested in conducting microbiome research. We discuss factors to be considered in the design, execution, and data analysis of microbiome studies. These recommendations should help researchers to enter and contribute to this rapidly developing field.

Human genetics shape the gut microbiome.

Host genetics and the gut microbiome can both influence metabolic phenotypes. However, whether host genetic variation shapes the gut microbiome and interacts with it to affect host phenotype is unclear. Here, we compared microbiotas across >1,000 fecal samples obtained from the TwinsUK population, including 416 twin pairs. We identified many microbial taxa whose abundances were influenced by host genetics. The most heritable taxon, the family Christensenellaceae, formed a co-occurrence network with other heritable Bacteria and with methanogenic Archaea. Furthermore, Christensenellaceae and its partners were enriched in individuals with low body mass index (BMI). An obese-associated microbiome was amended with Christensenella minuta, a cultured member of the Christensenellaceae, and transplanted to germ-free mice. C. minuta amendment reduced weight gain and altered the microbiome of recipient mice. Our findings indicate that host genetics influence the composition of the human gut microbiome and can do so in ways that impact host metabolism.

Advancing the microbiome research community.

The human microbiome has become a recognized factor in promoting and maintaining health. We outline opportunities in interdisciplinary research, analytical rigor, standardization, and policy development for this relatively new and rapidly developing field. Advances in these aspects of the research community may in turn advance our understanding of human microbiome biology.

Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies.

Whether the human fetus and the prenatal intrauterine environment (amniotic fluid and placenta) are stably colonized by microbial communities in a healthy pregnancy remains a subject of debate. Here we evaluate recent studies that characterized microbial populations in human fetuses from the perspectives of reproductive biology, microbial ecology, bioinformatics, immunology, clinical microbiology and gnotobiology, and assess possible mechanisms by which the fetus might interact with microorganisms. Our analysis indicates that the detected microbial signals are likely the result of contamination during the clinical procedures to obtain fetal samples or during DNA extraction and DNA sequencing. Furthermore, the existence of live and replicating microbial populations in healthy fetal tissues is not compatible with fundamental concepts of immunology, clinical microbiology and the derivation of germ-free mammals. These conclusions are important to our understanding of human immune development and illustrate common pitfalls in the microbial analyses of many other low-biomass environments. The pursuit of a fetal microbiome serves as a cautionary example of the challenges of sequence-based microbiome studies when biomass is low or absent, and emphasizes the need for a trans-disciplinary approach that goes beyond contamination controls by also incorporating biological, ecological and mechanistic concepts.

Toward Microbiome Engineering: Expanding the Repertoire of Genetically Tractable Members of the Human Gut Microbiome.

Genetic manipulation is necessary to interrogate the functions of microbes in their environments, such as the human gut microbiome. Yet, the vast majority of human gut microbiome species are not genetically tractable. Here, we review the hurdles to seizing genetic control of more species. We address the barriers preventing the application of genetic techniques to gut microbes and report on genetic systems currently under development. While methods aimed at genetically transforming many species simultaneously in situ show promise, they are unable to overcome many of the same challenges that exist for individual microbes. Unless a major conceptual breakthrough emerges, the genetic tractability of the microbiome will remain an arduous task. Increasing the list of genetically tractable organisms from the human gut remains one of the highest priorities for microbiome research and will provide the foundation for microbiome engineering.

Host remodeling of the gut microbiome and metabolic changes during pregnancy.

Many of the immune and metabolic changes occurring during normal pregnancy also describe metabolic syndrome. Gut microbiota can cause symptoms of metabolic syndrome in nonpregnant hosts. Here, to explore their role in pregnancy, we characterized fecal bacteria of 91 pregnant women of varying prepregnancy BMIs and gestational diabetes status and their infants. Similarities between infant-mother microbiotas increased with children's age, and the infant microbiota was unaffected by mother's health status. Gut microbiota changed dramatically from first (T1) to third (T3) trimesters, with vast expansion of diversity between mothers, an overall increase in Proteobacteria and Actinobacteria, and reduced richness. T3 stool showed strongest signs of inflammation and energy loss; however, microbiome gene repertoires were constant between trimesters. When transferred to germ-free mice, T3 microbiota induced greater adiposity and insulin insensitivity compared to T1. Our findings indicate that host-microbial interactions that impact host metabolism can occur and may be beneficial in pregnancy.

The Relationship Between the Human Genome and Microbiome Comes into View.

The body's microbiome, composed of microbial cells that number in the trillions, is involved in human health and disease in ways that are just starting to emerge. The microbiome is assembled at birth, develops with its host, and is greatly influenced by environmental factors such as diet and other exposures. Recently, a role for human genetic variation has emerged as also influential in accounting for interpersonal differences in microbiomes. Thus, human genes may influence health directly or by promoting a beneficial microbiome. Studies of the heritability of gut microbiotas reveal a subset of microbes whose abundances are partly genetically determined by the host. However, the use of genome-wide association studies (GWASs) to identify human genetic variants associated with microbiome phenotypes has proven challenging. Studies to date are small by GWAS standards, and cross-study comparisons are hampered by differences in analytical approaches. Nevertheless, associations between microbes or microbial genes and human genes have emerged that are consistent between human populations. Most notably, higher levels of beneficial gut bacteria called Bifidobacteria are associated with the human lactase nonpersister genotype, which typically confers lactose intolerance, in several different human populations. It is time for the microbiome to be incorporated into studies that quantify interactions among genotype, environment, and the microbiome in order to predict human disease susceptibility.

ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning.

The number of published metagenome assemblies is rapidly growing due to advances in sequencing technologies. However, sequencing errors, variable coverage, repetitive genomic regions, and other factors can produce misassemblies, which are challenging to detect for taxonomically novel genomic data. Assembly errors can affect all downstream analyses of the assemblies. Accuracy for the state of the art in reference-free misassembly prediction does not exceed an AUPRC of 0.57, and it is not clear how well these models generalize to real-world data. Here, we present the Residual neural network for Misassembled Contig identification (ResMiCo), a deep learning approach for reference-free identification of misassembled contigs. To develop ResMiCo, we first generated a training dataset of unprecedented size and complexity that can be used for further benchmarking and developments in the field. Through rigorous validation, we show that ResMiCo is substantially more accurate than the state of the art, and the model is robust to novel taxonomic diversity and varying assembly methods. ResMiCo estimated 7% misassembled contigs per metagenome across multiple real-world datasets. We demonstrate how ResMiCo can be used to optimize metagenome assembly hyperparameters to improve accuracy, instead of optimizing solely for contiguity. The accuracy, robustness, and ease-of-use of ResMiCo make the tool suitable for general quality control of metagenome assemblies and assembly methodology optimization.