Most of our knowledge about bacterial functional roles in microbiomes comes from bulk measurements. Yet microbial communities are complex ecosystems in which functionally distinct bacterial subpopulations with unique transcriptional states emerge across environmental niches and from interactions with other community members. Such heterogeneous transcriptional states are inherently missed by bulk measurements. To address this gap, we developed multispecies microbial split-pool ligation meta-transcriptomics (metaSPLiT), a scalable, instrument-free single-cell RNA sequencing approach for the microbiome. Using metaSPLiT, we profiled healthy human fecal microbiomes and reconstructed 21,598 single cell transcriptomes belonging to 70 unique bacterial species.

Despite extensive research, molecular differences in human populations and the influence of ancestry, age, geography, and diet are poorly understood. We performed comprehensive multiomics profiling (including genomics, transcriptomics, proteomics, metabolomics, lipidomics, metallomics, glycomics, and microbiomics) on samples from 322 healthy individuals of European, East Asian, and South Asian ancestry across multiple continents. We identified ethnicity-associated molecular features linked to host metabolism, autoimmune disease risk, drug metabolism, and neurodegenerative pathways. We uncovered ancestry- and geography-related molecular changes affecting metabolism, immune function, microbiome composition, and biological aging.

The human gut is colonized by trillions of microbes that influence the health of their human host. Whereas many bacterial species have now been linked to a variety of different diseases, the involvement of Archaea, an evolutionarily distinct group of microbes, in human disease remains elusive. By analyzing 19 independent clinical studies, we demonstrate that associations between Archaea and human diseases are widespread yet highly heterogeneous, with a pronounced and consistent enrichment of Methanobrevibacter smithii in colorectal cancer (CRC) patients. Metabolic modelling and in vitro co-culture identified distinct mutualistic interactions of M. smithii with CRC-causing bacteria such as Fusobacterium nucleatum, including metabolic enhancement. Metabolomics further reveal archaeal-derived compounds with tumor-modulating properties. Together, our results provide mechanistic insights into how the human gut archaeome may participate in CRC-associated microbial networks through metabolic cooperation with bacteria.

Diet is a key determinant of human and planetary health, but accurately measuring dietary intake remains challenging. Traditional self-reporting tools are imprecise, compromising our ability to accurately link diets with health outcomes. Modern technologies, including smartphone apps, image-based methods and biomarkers of food intake (BFIs), offer promise but bring their own caveats. App- and image-based methods reduce bias and reporting burden, but remain partly self-reported, and are thus prone to errors similar to those of traditional methods. Omics-based BFIs (that is, metabolites, food-related DNA or food proteins) are objective measures derived from biological samples; however, they mostly reflect recent intake, and require careful sampling alignment to estimate habitual diets. Here we discuss the drawbacks and opportunities for all dietary tools and propose strategies to integrate technologies along with multisampling for longitudinal measurements, for a new era in dietary assessment that can clarify the impact of diets, dietary components and dietary behaviour on human and planetary health.

MICOMWeb is a user-friendly website for modeling microbial community metabolism in the human gut. This website tackles three constraints when generating in silico metagenome-scale metabolic models: i) the prior Python user knowledge for metabolic modeling using flux balance analysis with the MICOM Python package, ii) predefined and user-defined diets to generate ad hoc metabolic models, and iii) the high-throughput computational infrastructure required to obtain the simulated growth and metabolic exchange fluxes, using real abundance from metagenomic shotgun or 16S amplicon sequencing; we present MICOMWeb’s features to easily run in silico experiments as a functional hypothesis generator for experimental validation on three previously published databases.

Clostridioides difficile (C. difficile) colonizes up to 40% of community-dwelling adults without causing disease but can eventually lead to infection (C. difficile infection [CDI]). There has been a lack of focus on how to prevent colonization and facilitate the successful clearance of C. difficile prior to the emergence of CDI. We show that microbial community-scale metabolic models (MCMMs) accurately predict C. difficile colonization susceptibility in vitro and in vivo, offering mechanistic insights into microbiota-specific interactions involving metabolites like succinate, trehalose, and ornithine. MCMMs reveal distinct C. difficile metabolic niches—two growth-associated and one non-growth-associated—observed across 15,204 individuals from five cohorts. We further demonstrate that MCMMs can predict personalized C. difficile growth suppression by a probiotic cocktail designed to replace fecal microbiota transplants (FMTs) for the treatment of recurrent CDI, and we identify new probiotic targets for future validation. MCMMs represent a powerful framework for predicting pathogen colonization and assessing probiotic efficacy across diverse microbiota contexts.

Absolute bacterial biomass estimation in the human gut is crucial for understanding microbiome dynamics and host-microbe interactions. Current methods for quantifying bacterial biomass in stool, such as flow cytometry, quantitative polymerase chain reaction (qPCR), or spike-ins, can be labor-intensive, costly, and confounded by factors like water content, DNA extraction efficiency, PCR inhibitors, and other technical challenges that add bias and noise. We propose a simple, cost-effective approach that circumvents some of these technical challenges: directly estimating bacterial biomass from metagenomes using bacterial-to-host (B:H) read count ratios.

The colonic epithelium plays a key role in the host-microbiome interactions, allowing uptake of various nutrients and driving important metabolic processes. To unravel detailed metabolic activities in the human colonic epithelium, our present study focuses on the generation of the first cell-type-specific genome-scale metabolic model (GEM) of human colonic epithelial cells, named iColonEpithelium.

GEMs are powerful tools for exploring reactions and metabolites at the systems level and predicting the flux distributions at steady state. Our cell-type-specific iColonEpithelium metabolic reconstruction captures genes specifically expressed in the human colonic epithelial cells. iColonEpithelium is also capable of performing metabolic tasks specific to the colonic epithelium. A unique transport reaction compartment has been included to allow for the simulation of metabolic interactions with the gut microbiome. We used iColonEpithelium to identify metabolic signatures associated with inflammatory bowel disease.

Dietary intake is tightly coupled to gut microbiota composition, human metabolism and the incidence of virtually all major chronic diseases. Dietary and nutrient intake are usually assessed using self-reporting methods, including dietary questionnaires and food records, which suffer from reporting biases and require strong compliance from study participants. Here, we present Metagenomic Estimation of Dietary Intake (MEDI): a method for quantifying food-derived DNA in human faecal metagenomes.

We show that DNA-containing food components can be reliably detected in stool-derived metagenomic data, even when present at low abundances (more than ten reads). We show how MEDI dietary intake profiles can be converted into detailed metabolic representations of nutrient intake. MEDI identifies the onset of solid food consumption in infants, shows significant agreement with food frequency questionnaire responses in an adult population and shows agreement with food and nutrient intake in two controlled-feeding studies. Finally, we identify specific dietary features associated with metabolic syndrome in a large clinical cohort without dietary records, providing a proof-of-concept for detailed tracking of individual-specific, health-relevant dietary patterns without the need for questionnaires.

Background

Infants exposed to HIV but uninfected have altered immune profiles which include heightened systemic inflammation. The mechanism(s) underlying this phenomenon is unknown. Here, we investigated differences in neonatal gut bacterial and viral microbiome and associations with inflammatory biomarkers in plasma. Further, we tested whether HIV exposure impacts antibody-microbiota binding in neonatal gut and whether antibodies in breast milk impact the growth of commensal bacteria.

Results

Neonates exposed to HIV but uninfected (nHEU) exhibited altered gut bacteriome and virome compared to unexposed neonates (nHU). In addition, HIV exposure differentially impacted IgA-microbiota binding in neonates. The relative abundance of Blautia spp. in the whole stool or IgA-bound microbiota was positively associated with plasma concentrations of C-reactive protein. Finally, IgA from the breast milk of mothers living with HIV displayed a significantly lower ability to inhibit the growth of Blautia coccoides which was associated with inflammation in nHEU.

The human gut pathogen Clostridioides difficile displays substantial inter-strain genetic variability and confronts a changeable nutrient landscape in the gut. We examined how human gut microbiota inter-species interactions influence the growth and toxin production of various C. difficile strains across different nutrient environments.

Negative interactions influencing C. difficile growth are prevalent in an environment containing a single highly accessible resource and sparse in an environment containing C. difficile-preferred carbohydrates. C. difficile toxin production displays significant community-context dependent variation and does not trend with growth-mediated inter-species interactions. C. difficile strains exhibit differences in interactions with Clostridium scindens and the ability to compete for proline. Further, C. difficile shows substantial differences in transcriptional profiles in co-culture with C. scindens or Clostridium hiranonis.

Bowel movement frequency (BMF) directly impacts the gut microbiota and is linked to diseases like chronic kidney disease or dementia. In particular, prior work has shown that constipation is associated with an ecosystem-wide switch from fiber fermentation and short-chain fatty acid production to more detrimental protein fermentation and toxin production. Here, we analyze multi-omic data from generally healthy adults to see how BMF affects their molecular phenotypes, in a pre-disease context.

Microbially derived short-chain fatty acids (SCFAs) in the human gut are tightly coupled to host metabolism, immune regulation and integrity of the intestinal epithelium. However, the production of SCFAs can vary widely between individuals consuming the same diet, with lower levels often associated with disease. A systems-scale mechanistic understanding of this heterogeneity is lacking. Here we use a microbial community-scale metabolic modelling (MCMM) approach to predict individual-specific SCFA production profiles to assess the impact of different dietary, prebiotic and probiotic inputs. We evaluate the quantitative accuracy of our MCMMs using in vitro and ex vivo data, plus published human cohort data.

Prior work has shown a positive scaling relationship between vertebrate body size, human height, and gut microbiome alpha diversity. This observation mirrors commonly observed species area relationships (SAR) in many other ecosystems. Here, we expand these observations to several large data sets, showing that this size-diversity scaling relationship is independent of relevant covariates, like diet, body mass index, age, sex, bowel movement frequency, antibiotic usage, and cardiometabolic health markers. Island biogeography theory (IBT), which predicts that larger islands tend to harbor greater species diversity through neutral demographic processes, provides a simple mechanism for positive SARs.

Background

The human upper respiratory tract (URT) microbiome, like the gut microbiome, varies across individuals and between health and disease states. However, study-to-study heterogeneity in reported case–control results has made the identification of consistent and generalizable URT-disease associations difficult.

Results

In order to address this issue, we assembled 26 independent 16S rRNA gene amplicon sequencing data sets from case–control URT studies, with approximately 2–3 studies per respiratory condition and ten distinct conditions covering common chronic and acute respiratory diseases. We leveraged the healthy control data across studies to investigate URT associations with age, sex, and geographic location, in order to isolate these associations from health and disease states.

Microbial consortia drive essential processes, ranging from nitrogen fixation in soils to providing metabolic breakdown products to animal hosts. However, it is challenging to translate the composition of microbial consortia into their emergent functional capacities. Community-scale metabolic models hold the potential to simulate the outputs of complex microbial communities in a given environmental context, but there is currently no consensus for what the fitness function of an entire community should look like in the presence of ecological interactions and whether community-wide growth operates close to a maximum.

Multiomic profiling is useful in characterizing heterogeneity of both health and disease states. Obesity exerts profound metabolic perturbation in individuals and is a risk factor for multiple chronic diseases. Here, we report a global atlas of cross-sectional and longitudinal changes associated with Body Mass Index (BMI) across 1,100+ blood analytes, as well as their correspondence to host genome and fecal microbiome composition, from a cohort of 1,277 individuals enrolled in a wellness program.

Variation in the blood metabolome is intimately related to human health. However, few details are known about the interplay between genetics and the microbiome in explaining this variation on a metabolite-by-metabolite level. Here, we perform analyses of variance for each of 930 blood metabolites robustly detected across a cohort of 1,569 individuals with paired genomic and microbiome data while controlling for a number of relevant covariates.

We find that 595 (64%) of these blood metabolites are significantly associated with either host genetics or the gut microbiome, with 69% of these associations driven solely by the microbiome, 15% driven solely by genetics and 16% under hybrid genome–microbiome control. Additionally, interaction effects, where a metabolite–microbe association is specific to a particular genetic background, are quite common, albeit with modest effect sizes. This knowledge will help to guide targeted interventions designed to alter the composition of the human blood metabolome.

Statins remain one of the most prescribed medications worldwide. While effective in decreasing atherosclerotic cardiovascular disease risk, statin use is associated with adverse effects for a subset of patients, including disrupted metabolic control and increased risk of type 2 diabetes.

We investigated the potential role of the gut microbiome in modifying patient responses to statin therapy across two independent cohorts (discovery n = 1,848, validation n = 991). Microbiome composition was assessed in these cohorts using stool 16S rRNA amplicon and shotgun metagenomic sequencing, respectively. Microbiome associations with markers of statin on-target and adverse effects were tested via a covariate-adjusted interaction analysis framework, utilizing blood metabolomics, clinical laboratory tests, genomics, and demographics data.

The gut microbiome has important effects on human health, yet its importance in human ageing remains unclear. In the present study, we demonstrate that, starting in mid-to-late adulthood, gut microbiomes become increasingly unique to individuals with age.

We leverage three independent cohorts comprising over 9,000 individuals and find that compositional uniqueness is strongly associated with microbially produced amino acid derivatives circulating in the bloodstream. In older age (over ~80 years), healthy individuals show continued microbial drift towards a unique compositional state, whereas this drift is absent in less healthy individuals.

Recent human feeding studies have shown how the baseline taxonomic composition of the gut microbiome can determine responses to dietary interventions, but the exact functional determinants underlying this phenomenon remain unclear.

In this study, we set out to better understand interactions between baseline BMI, metabolic health, diet, gut microbiome functional profiles, and subsequent weight changes in a human cohort that underwent a healthy lifestyle intervention. Overall, our results suggest that the microbiota may influence host weight loss responses through variable bacterial growth rates, dietary energy harvest efficiency, and immunomodulation.

Reconstructing metabolic reaction networks enables the development of testable hypotheses of an organism’s metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Gene–protein–reaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols2, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking3. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality.

Compositional changes in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn’s disease, and diabetes. However, connecting microbial community composition to ecosystem function remains a challenge. Here, we introduce MICOM, a customizable metabolic model of the human gut microbiome. By using a heuristic optimization approach based on L2 regularization, we were able to obtain a unique set of realistic growth rates that corresponded well with observed replication rates.

Rapid advances in DNA-sequencing and bioinformatics technologies in the past two decades have substantially improved understanding of the microbial world. This growing understanding relates to the vast diversity of microorganisms; how microbiota and microbiomes affect disease and medical treatment; how microorganisms affect the health of the planet; and the nascent exploration of the medical, forensic, environmental and agricultural applications of microbiome biotechnology.

Much of this work has been driven by marker-gene surveys (for example, bacterial/archaeal 16S rRNA genes, fungal internal-transcribed-spacer regions and eukaryotic 18S rRNA genes), which profile microbiota with varying degrees of taxonomic specificity and phylogenetic information. The field is now transitioning to integrate other data types, such as metabolite, metaproteome or metatranscriptome profiles.

Broad spectrum antibiotics cause both transient and lasting damage to the ecology of the gut microbiome. Antibiotic-induced loss of gut bacterial diversity has been linked to susceptibility to enteric infections. Prior work on subtherapeutic antibiotic treatment in humans and non-human animals has suggested that entire gut communities may exhibit tolerance phenotypes. In this study, we validate the existence of these community tolerance phenotypes in the murine gut and explore how antibiotic treatment duration or a diet enriched in antimicrobial phytochemicals might influence the frequency of this phenotype.