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The human gut microbiota has now been associated with drug responses and efficacy, while chemical compounds present in these drugs can also impact the gut bacteria. However, drug–microbe interactions are still understudied in the clinical context, where polypharmacy and comorbidities co-occur. Here, we report relations between commonly used drugs and the gut microbiome. We performed metagenomics sequencing of faecal samples from a population cohort and two gastrointestinal disease cohorts. Differences between users and non-users were analysed per cohort, followed by a meta-analysis. While 19 of 41 drugs are found to be associated with microbial features, when controlling for the use of multiple medications, proton-pump inhibitors, metformin, antibiotics and laxatives show the strongest associations with the microbiome. We here provide evidence for extensive changes in taxonomy, metabolic potential and resistome in relation to commonly used drugs. This paves the way for future studies and has implications for current microbiome studies by demonstrating the need to correct for multiple drug use. Here, via a metagenomics analysis of population-based and disease cohorts, Vich Vila et al. study the impact of 41 commonly used medications on the taxonomic structures, metabolic potential and resistome of the gut microbiome, underscoring the importance of correcting for multiple drug use in microbiome studies.

The human gut microbiome is a complex ecosystem that is involved in its host’s metabolism, immunity and health. Although interindividual variations in gut microbial composition are mainly driven by environmental factors, some gut microorganisms are heritable and thus can be influenced by host genetics. In the past 5 years, 12 microbial genome-wide association studies (mbGWAS) with >1,000 participants have been published, yet only a few genetic loci have been consistently confirmed across multiple studies. Here we discuss the state of the art for mbGWAS, focusing on current challenges such as the heterogeneity of microbiome measurements and power issues, and we elaborate on potential future directions for genetic analysis of the microbiome. This Perspective discusses the analytical issues concerning heterogeneity and power encountered in microbial genome-wide association studies and highlights potential future directions for genetic analysis of the microbiome.

The relationship between gut microbial metabolism and mental health is one of the most intriguing and controversial topics in microbiome research. Bidirectional microbiota–gut–brain communication has mostly been explored in animal models, with human research lagging behind. Large-scale metagenomics studies could facilitate the translational process, but their interpretation is hampered by a lack of dedicated reference databases and tools to study the microbial neuroactive potential. Surveying a large microbiome population cohort (Flemish Gut Flora Project, n  = 1,054) with validation in independent data sets ( n total  = 1,070), we studied how microbiome features correlate with host quality of life and depression. Butyrate-producing Faecalibacterium and Coprococcus bacteria were consistently associated with higher quality of life indicators. Together with Dialister , Coprococcus spp. were also depleted in depression, even after correcting for the confounding effects of antidepressants. Using a module-based analytical framework, we assembled a catalogue of neuroactive potential of sequenced gut prokaryotes. Gut–brain module analysis of faecal metagenomes identified the microbial synthesis potential of the dopamine metabolite 3,4-dihydroxyphenylacetic acid as correlating positively with mental quality of life and indicated a potential role of microbial γ-aminobutyric acid production in depression. Our results provide population-scale evidence for microbiome links to mental health, while emphasizing confounder importance. Correlation of microbiome features with host quality of life and depression identified specific taxa and microbial pathways in two independent, large population cohorts, identifying links between microbial neuroactive potential and depression.

Contaminant sequences of external origin complicate the study of host-associated viromes, particularly in low-biomass samples obtained through viral-like particle (VLP) enrichment. However, the prevalence and impact of external contaminants on low-biomass samples are under-studied. Here, we analyze 1321 gut virome samples and 55 negative controls (NCs) from four early-life virome studies. Virus sequences identified in NCs, termed negativeome, were used as a proxy for the contamination assessment. We show that 61% of samples share at least one identical strain with negativeome, likely representing external contamination. While the median abundance of contaminant strains in these samples is only 1%, it ranges from 0 to 99% and exceeds 10% in 11% of infant samples. We further demonstrate that contamination is largely study-specific and has a greater impact on infant samples than on maternal samples. Based on our results, we propose a contamination assessment method using a publicly available database of sequences detected in NCs and a strain-level decontamination strategy. Contamination significantly impacts low-biomass studies. Here, the authors highlight its pervasive and study-specific effects, particularly on early-life virome samples, and offer practical strategies to mitigate contamination.

Fecal microbiome variation in the average, healthy population has remained under-investigated. Here, we analyzed two independent, extensively phenotyped cohorts: the Belgian Flemish Gut Flora Project (FGFP; discovery cohort; N = 1106) and the Dutch LifeLines-DEEP study (LLDeep; replication; N = 1135). Integration with global data sets (N combiend = 3948) revealed a 14-genera core microbiota, but the 664 identified genera still underexplore total gut diversity. Sixty-nine clinical and questionnaire-based covariates were found associated to microbiota compositional variation with a 92% replication rate. Stool consistency showed the largest effect size, whereas medication explained largest total variance and interacted with other covariate-microbiota associations. Early-life events such as birth mode were not reflected in adult microbiota composition. Finally, we found that proposed disease marker genera associated to host covariates, urging inclusion of the latter in study design.

Gut microbiota has been implicated in major diseases affecting the human population and has also been linked to triglycerides and high-density lipoprotein levels in the circulation. Recent development in metabolomics allows classifying the lipoprotein particles into more details. Here, we examine the impact of gut microbiota on circulating metabolites measured by Nuclear Magnetic Resonance technology in 2309 individuals from the Rotterdam Study and the LifeLines-DEEP cohort. We assess the relationship between gut microbiota and metabolites by linear regression analysis while adjusting for age, sex, body-mass index, technical covariates, medication use, and multiple testing. We report an association of 32 microbial families and genera with very-low-density and high-density subfractions, serum lipid measures, glycolysis-related metabolites, ketone bodies, amino acids, and acute-phase reaction markers. These observations provide insights into the role of microbiota in host metabolism and support the potential of gut microbiota as a target for therapeutic and preventive interventions. Here, the authors provide an in-depth study of the metabolome in two large population-based prospective cohorts and identify 32 microbial traits associated with various metabolic biomarkers and specific lipoprotein subfractions, providing insights into the role of microbiota in influencing host lipid levels.

The human gut harbors a complex ecosystem of microorganisms, including bacteria and viruses. With the rise of next-generation sequencing technologies, we have seen a quantum leap in the study of human-gut-inhabiting bacteria, yet the viruses that infect these bacteria, known as bacteriophages, remain underexplored. In this review, we focus on what is known about the role of bacteriophages in human health and the technical challenges involved in studying the gut virome, of which they are a major component. Lastly, we discuss what can be learned from studies of bacteriophages in other ecosystems.

The levels of the thousands of metabolites in the human plasma metabolome are strongly influenced by an individual’s genetics and the composition of their diet and gut microbiome. Here, by assessing 1,183 plasma metabolites in 1,368 extensively phenotyped individuals from the Lifelines DEEP and Genome of the Netherlands cohorts, we quantified the proportion of inter-individual variation in the plasma metabolome explained by different factors, characterizing 610, 85 and 38 metabolites as dominantly associated with diet, the gut microbiome and genetics, respectively. Moreover, a diet quality score derived from metabolite levels was significantly associated with diet quality, as assessed by a detailed food frequency questionnaire. Through Mendelian randomization and mediation analyses, we revealed putative causal relationships between diet, the gut microbiome and metabolites. For example, Mendelian randomization analyses support a potential causal effect of Eubacterium rectale in decreasing plasma levels of hydrogen sulfite—a toxin that affects cardiovascular function. Lastly, based on analysis of the plasma metabolome of 311 individuals at two time points separated by 4 years, we observed a positive correlation between the stability of metabolite levels and the amount of variance in the levels of that metabolite that could be explained in our analysis. Altogether, characterization of factors that explain inter-individual variation in the plasma metabolome can help design approaches for modulating diet or the gut microbiome to shape a healthy metabolome. The influence of an individual’s genetics, diet and gut microbiome on their plasma metabolome was studied in 1,368 individuals and Mendelian randomization and mediation analyses were used to unveil causal relationships between diet, the gut microbiome and the metabolome.

Host genetics are known to influence the gut microbiome, yet their role remains poorly understood. To robustly characterize these effects, we performed a genome-wide association study of 207 taxa and 205 pathways representing microbial composition and function in 7,738 participants of the Dutch Microbiome Project. Two robust, study-wide significant ( P  < 1.89 × 10 −10 ) signals near the LCT and ABO genes were found to be associated with multiple microbial taxa and pathways and were replicated in two independent cohorts. The LCT locus associations seemed modulated by lactose intake, whereas those at ABO could be explained by participant secretor status determined by their FUT2 genotype. Twenty-two other loci showed suggestive evidence ( P  < 5 × 10 −8 ) of association with microbial taxa and pathways. At a more lenient threshold, the number of loci we identified strongly correlated with trait heritability, suggesting that much larger sample sizes are needed to elucidate the remaining effects of host genetics on the gut microbiome. A genome-wide association study of 207 taxa and 205 pathways representing gut microbial composition and function from 7,738 individuals of the Dutch Microbiome Project identifies genetic associations at the LCT and ABO loci.

The gut microbiome is an ecosystem that involves complex interactions. Currently, our knowledge about the role of the gut microbiome in health and disease relies mainly on differential microbial abundance, and little is known about the role of microbial interactions in the context of human disease. Here, we construct and compare microbial co-abundance networks using 2,379 metagenomes from four human cohorts: an inflammatory bowel disease (IBD) cohort, an obese cohort and two population-based cohorts. We find that the strengths of 38.6% of species co-abundances and 64.3% of pathway co-abundances vary significantly between cohorts, with 113 species and 1,050 pathway co-abundances showing IBD-specific effects and 281 pathway co-abundances showing obesity-specific effects. We can also replicate these IBD microbial co-abundances in longitudinal data from the IBD cohort of the integrative human microbiome (iHMP-IBD) project. Our study identifies several key species and pathways in IBD and obesity and provides evidence that altered microbial abundances in disease can influence their co-abundance relationship, which expands our current knowledge regarding microbial dysbiosis in disease. Gut microbiome alterations have been linked to inflammatory bowel disease (IBD) and obesity. Here, the authors characterize the metagenomes of four large human cohorts and perform co-abundance network analysis showing that dysbiosis in disease is marked by the altered co-abundance relationships, suggesting that pathway coabundance networks are more heterogeneous than species network.