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Antibiotic resistance is a global health challenge, involving the transfer of bacteria and genes between humans, animals and the environment. Although multiple barriers restrict the flow of both bacteria and genes, pathogens recurrently acquire new resistance factors from other species, thereby reducing our ability to prevent and treat bacterial infections. Evolutionary events that lead to the emergence of new resistance factors in pathogens are rare and challenging to predict, but may be associated with vast ramifications. Transmission events of already widespread resistant strains are, on the other hand, common, quantifiable and more predictable, but the consequences of each event are limited. Quantifying the pathways and identifying the drivers of and bottlenecks for environmental evolution and transmission of antibiotic resistance are key components to understand and manage the resistance crisis as a whole. In this Review, we present our current understanding of the roles of the environment, including antibiotic pollution, in resistance evolution, in transmission and as a mere reflection of the regional antibiotic resistance situation in the clinic. We provide a perspective on current evidence, describe risk scenarios, discuss methods for surveillance and the assessment of potential drivers, and finally identify some actions to mitigate risks.In this Review, Larsson and Flach discuss the drivers of and bottlenecks for environmental evolution and transmission of antibiotic resistance, and they explore environmental surveillance strategies that could complement clinical surveillance systems.

The gut microbiota contributes to host physiology through the production of a myriad of metabolites. These metabolites exert their effects within the host as signalling molecules and substrates for metabolic reactions. Although the study of host–microbiota interactions remains challenging due to the high degree of crosstalk both within and between kingdoms, metabolite-focused research has identified multiple actionable microbial targets that are relevant for host health. Metabolites, as the functional output of combined host and microorganism interactions, provide a snapshot in time of an extraordinarily complex multi-organism system. Although substantial work remains towards understanding host–microbiota interactions and the underlying mechanisms, we review the current state of knowledge for each of the major classes of microbial metabolites with emphasis on clinical and translational research implications. We provide an overview of methodologies available for measurement of microbial metabolites, and in addition to discussion of key challenges, we provide a potential framework for integration of discovery-based metabolite studies with mechanistic work. Finally, we highlight examples in the literature where this approach has led to substantial progress in understanding host–microbiota interactions.The gut microbiota contributes to host physiology through the production of a myriad of metabolites. In this Review, Bäckhed and colleagues discuss the major classes of microbial metabolites, highlight examples of how microbial metabolites affect host health and provide a potential framework for integration of discovery-based metabolite studies with mechanistic work.

Key Points The gastrointestinal tract presents a continuous secreted and cell surface barrier to potential enteric pathogens. Specialized gastrointestinal epithelial cells secrete large amounts of mucin glycoproteins and antimicrobial molecules that, together, form the mucus barrier to infection. Although the lumen of the gastrointestinal tract contains large numbers of commensal microorganisms, the inner layers of mucus are sterile. Secreted mucins are large, heavily O -glycosylated glycoproteins that are produced by goblet cells. During their biosynthesis, mucins homo-oligomerize into complex polymeric networks that, when secreted, give mucus its viscoelastic properties. Antimicrobial molecules are produced throughout the gastrointestinal tract but particularly by the specialized Paneth cells in the small intestine. These molecules target different classes of pathogens and help keep the inner mucus layer sterile. Cell surface mucins are heavily O -glycosylated transmembrane glycoproteins that are present on the apical surface of all gastrointestinal epithelial cells. These mucins limit binding of pathogens to epithelial cells by steric hindrance and by acting as releasable decoys for microbial adhesins. Deficiencies in secreted or cell surface mucins in animal models lead to increased pathology during infection. Pathogens have evolved multiple strategies to penetrate the mucosal barrier, including: disruption and penetration of the mucus, avoidance of the mucus barrier, and disruption of epithelial integrity and epithelial production of barrier components. The production of components of the mucus barrier is influenced by the normal microbiota and by both innate and adaptive immune responses to pathogens. There are changes in the rate of mucus production and the content of mucus in response to infection; these factors are components of the mechanism of clearance of enteric pathogens and parasites. The mucus barrier provides a crucial defence against commensal microorganisms and enteric pathogens. In this Review, McGuckin and colleagues describe the structure of the mucus barrier and discuss how the composition of the mucus layer is regulated under normal conditions and in response to infection. The extracellular secreted mucus and the cell surface glycocalyx prevent infection by the vast numbers of microorganisms that live in the healthy gut. Mucin glycoproteins are the major component of these barriers. In this Review, we describe the components of the secreted and cell surface mucosal barriers and the evidence that they form an effective barricade against potential pathogens. However, successful enteric pathogens have evolved strategies to circumvent these barriers. We discuss the interactions between enteric pathogens and mucins, and the mechanisms that these pathogens use to disrupt and avoid mucosal barriers. In addition, we describe dynamic alterations in the mucin barrier that are driven by host innate and adaptive immune responses to infection.

Type 1 diabetes (T1D) is an autoimmune disease that targets pancreatic islet beta cells and incorporates genetic and environmental factors 1 , including complex genetic elements 2 , patient exposures 3 and the gut microbiome 4 . Viral infections 5 and broader gut dysbioses 6 have been identified as potential causes or contributing factors; however, human studies have not yet identified microbial compositional or functional triggers that are predictive of islet autoimmunity or T1D. Here we analyse 10,913 metagenomes in stool samples from 783 mostly white, non-Hispanic children. The samples were collected monthly from three months of age until the clinical end point (islet autoimmunity or T1D) in the The Environmental Determinants of Diabetes in the Young (TEDDY) study, to characterize the natural history of the early gut microbiome in connection to islet autoimmunity, T1D diagnosis, and other common early life events such as antibiotic treatments and probiotics. The microbiomes of control children contained more genes that were related to fermentation and the biosynthesis of short-chain fatty acids, but these were not consistently associated with particular taxa across geographically diverse clinical centres, suggesting that microbial factors associated with T1D are taxonomically diffuse but functionally more coherent. When we investigated the broader establishment and development of the infant microbiome, both taxonomic and functional profiles were dynamic and highly individualized, and dominated in the first year of life by one of three largely exclusive Bifidobacterium species ( B. bifidum , B. breve or B. longum ) or by the phylum Proteobacteria. In particular, the strain-specific carriage of genes for the utilization of human milk oligosaccharide within a subset of B. longum was present specifically in breast-fed infants. These analyses of TEDDY gut metagenomes provide, to our knowledge, the largest and most detailed longitudinal functional profile of the developing gut microbiome in relation to islet autoimmunity, T1D and other early childhood events. Together with existing evidence from human cohorts 7 , 8 and a T1D mouse model 9 , these data support the protective effects of short-chain fatty acids in early-onset human T1D. An analysis of more than 10,000 metagenomes from the TEDDY study provides a detailed functional profile of the gut microbiome in relation to islet autoimmunity, and supports the protective effects of short-chain fatty acids in early-onset type 1 diabetes.

Discharge of treated sewage leads to release of antibiotic resistant bacteria, resistance genes and antibiotic residues to the environment. However, it is unclear whether increased abundance of antibiotic resistance genes in sewage and sewage-impacted environments is due to on-site selection pressure by residual antibiotics, or is simply a result of fecal contamination with resistant bacteria. Here we analyze relative resistance gene abundance and accompanying extent of fecal pollution in publicly available metagenomic data, using crAssphage sequences as a marker of human fecal contamination (crAssphage is a bacteriophage that is exceptionally abundant in, and specific to, human feces). We find that the presence of resistance genes can largely be explained by fecal pollution, with no clear signs of selection in the environment, with the exception of environments polluted by very high levels of antibiotics from manufacturing, where selection is evident. Our results demonstrate the necessity to take into account fecal pollution levels to avoid making erroneous assumptions regarding environmental selection of antibiotic resistance. Increased abundance of antibiotic resistance genes in the environment may be due to selection pressure by residual antibiotics, or to contamination with resistant bacteria from human faeces. Here, Karkman et al. analyze metagenomic data and find evidence supporting the second scenario in most cases.

In 2013, a Lancet Infectious Diseases Commission described the state of antimicrobial resistance worldwide. Since then, greater awareness of the public health ramifications of antimicrobial resistance has led to national actions and global initiatives, including a resolution at the high-level meeting of the UN General Assembly in 2016. Progress in addressing this issue has ranged from a ban on irrational drug combinations in India to commitments to ban colistin as a growth promoter in animals, improve hospital infection control, and implement better antimicrobial stewardship. Funds have been mobilised, and regulatory barriers to new antibiotic development have been relaxed. These efforts have been episodic and uneven across countries, however. Sustained funding for antimicrobial resistance and globally harmonised targets to monitor progress are still urgently needed. Except for in a few leading countries, antimicrobial resistance has not captured the sustained focus of national leaders and country-level actors, including care providers.

The development of the microbiome from infancy to childhood is dependent on a range of factors, with microbial–immune crosstalk during this time thought to be involved in the pathobiology of later life diseases 1 – 9 such as persistent islet autoimmunity and type 1 diabetes 10 – 12 . However, to our knowledge, no studies have performed extensive characterization of the microbiome in early life in a large, multi-centre population. Here we analyse longitudinal stool samples from 903 children between 3 and 46 months of age by 16S rRNA gene sequencing ( n  = 12,005) and metagenomic sequencing ( n  = 10,867), as part of the The Environmental Determinants of Diabetes in the Young (TEDDY) study. We show that the developing gut microbiome undergoes three distinct phases of microbiome progression: a developmental phase (months 3–14), a transitional phase (months 15–30), and a stable phase (months 31–46). Receipt of breast milk, either exclusive or partial, was the most significant factor associated with the microbiome structure. Breastfeeding was associated with higher levels of Bifidobacterium species ( B. breve and B. bifidum ), and the cessation of breast milk resulted in faster maturation of the gut microbiome, as marked by the phylum Firmicutes. Birth mode was also significantly associated with the microbiome during the developmental phase, driven by higher levels of Bacteroides species (particularly B . fragilis ) in infants delivered vaginally. Bacteroides was also associated with increased gut diversity and faster maturation, regardless of the birth mode. Environmental factors including geographical location and household exposures (such as siblings and furry pets) also represented important covariates. A nested case–control analysis revealed subtle associations between microbial taxonomy and the development of islet autoimmunity or type 1 diabetes. These data determine the structural and functional assembly of the microbiome in early life and provide a foundation for targeted mechanistic investigation into the consequences of microbial–immune crosstalk for long-term health. Metagenomic sequencing analysis of stool samples from 903 children as part of the TEDDY study shows that breastfeeding was the most important factor associated with microbiome structure, and the cessation of breast milk resulted in faster maturation of the gut microbiome.

Humans host complex microbial communities believed to contribute to health maintenance and, when in imbalance, to the development of diseases. Determining the microbial composition in patients and healthy controls may thus provide novel therapeutic targets. For this purpose, high-throughput, cost-effective methods for microbiota characterization are needed. We have employed 454-pyrosequencing of a hyper-variable region of the 16S rRNA gene in combination with sample-specific barcode sequences which enables parallel in-depth analysis of hundreds of samples with limited sample processing. In silico modeling demonstrated that the method correctly describes microbial communities down to phylotypes below the genus level. Here we applied the technique to analyze microbial communities in throat, stomach and fecal samples. Our results demonstrate the applicability of barcoded pyrosequencing as a high-throughput method for comparative microbial ecology.

Abstract Antibiotic resistance and its wider implications present us with a growing healthcare crisis. Recent research points to the environment as an important component for the transmission of resistant bacteria and in the emergence of resistant pathogens. However, a deeper understanding of the evolutionary and ecological processes that lead to clinical appearance of resistance genes is still lacking, as is knowledge of environmental dispersal barriers. This calls for better models of how resistance genes evolve, are mobilized, transferred and disseminated in the environment. Here, we attempt to define the ecological and evolutionary environmental factors that contribute to resistance development and transmission. Although mobilization of resistance genes likely occurs continuously, the great majority of such genetic events do not lead to the establishment of novel resistance factors in bacterial populations, unless there is a selection pressure for maintaining them or their fitness costs are negligible. To enable preventative measures it is therefore critical to investigate under what conditions and to what extent environmental selection for resistance takes place. In addition, understanding dispersal barriers is not only key to evaluate risks, but also to prevent resistant pathogens, as well as novel resistance genes, from reaching humans. This review defines which ecological and environmental factors are important for the development of antibiotic resistance in human pathogens, and suggests some possible mitigation strategies to delay and reduce increased resistance.

ObjectiveThe effects of dietary interventions on gut bacteria are ambiguous. Following a previous intervention study, we aimed to determine how differing diets impact gut bacteria and if bacterial profiles predict intervention response.DesignSixty-seven patients with IBS were randomised to traditional IBS (n=34) or low fermentable oligosaccharides, disaccharides, monosaccharides and polyols (FODMAPs) (n=33) diets for 4 weeks. Food intake was recorded for 4 days during screening and intervention. Faecal samples and IBS Symptom Severity Score (IBS-SSS) reports were collected before (baseline) and after intervention. A faecal microbiota dysbiosis test (GA-map Dysbiosis Test) evaluated bacterial composition. Per protocol analysis was performed on 61 patients from whom microbiome data were available.ResultsResponders (reduced IBS-SSS by ≥50) to low FODMAP, but not traditional, dietary intervention were discriminated from non-responders before and after intervention based on faecal bacterial profiles. Bacterial abundance tended to be higher in non-responders to a low FODMAP diet compared with responders before and after intervention. A low FODMAP intervention was associated with an increase in Dysbiosis Index (DI) scores in 42% of patients; while decreased DI scores were recorded in 33% of patients following a traditional IBS diet. Non-responders to a low FODMAP diet, but not a traditional IBS diet had higher DI scores than responders at baseline. Finally, while a traditional IBS diet was not associated with significant reduction of investigated bacteria, a low FODMAP diet was associated with reduced Bifidobacterium and Actinobacteria in patients, correlating with lactose consumption.ConclusionsA low FODMAP, but not a traditional IBS diet may have significant impact on faecal bacteria. Responsiveness to a low FODMAP diet intervention may be predicted by faecal bacterial profiles.Trial registration numberNCT02107625.