Medication Use is Associated with Distinct Microbial Features in Anxiety and Depression.

This study investigated the relationship between gut microbiota and neuropsychiatric disorders (NPDs), specifically anxiety disorder (ANXD) and/or major depressive disorder (MDD), as defined by DSM-IV or V criteria. The study also examined the influence of medication use, particularly antidepressants and/or anxiolytics, classified through the Anatomical Therapeutic Chemical (ATC) Classification System, on the gut microbiota. Both 16S rRNA gene amplicon sequencing and shallow shotgun sequencing were performed on DNA extracted from 666 fecal samples from the Tulsa-1000 and NeuroMAP CoBRE cohorts. The results highlight the significant influence of medication use; antidepressant use is associated with significant differences in gut microbiota beta diversity and has a larger effect size than NPD diagnosis. Next, specific microbes were associated with ANXD and MDD, highlighting their potential for non-pharmacological intervention. Finally, the study demonstrated the capability of Random Forest classifiers to predict diagnoses of NPD and medication use from microbial profiles, suggesting a promising direction for the use of gut microbiota as biomarkers for NPD. The findings suggest that future research on the gut microbiota's role in NPD and its interactions with pharmacological treatments are needed.

Integrating research on bacterial pathogens and commensals to fight infections-an ecological perspective.

The incidence of antibiotic-resistant bacterial infections is increasing, and development of new antibiotics has been deprioritised by the pharmaceutical industry. Interdisciplinary research approaches, based on the ecological principles of bacterial fitness, competition, and transmission, could open new avenues to combat antibiotic-resistant infections. Many facultative bacterial pathogens use human mucosal surfaces as their major reservoirs and induce infectious diseases to aid their lateral transmission to new host organisms under some pathological states of the microbiome and host. Beneficial bacterial commensals can outcompete specific pathogens, thereby lowering the capacity of the pathogens to spread and cause serious infections. Despite the clinical relevance, however, the understanding of commensal-pathogen interactions in their natural habitats remains poor. In this Personal View, we highlight directions to intensify research on the interactions between bacterial pathogens and commensals in the context of human microbiomes and host biology that can lead to the development of innovative and sustainable ways of preventing and treating infectious diseases.

A modified Michaelis-Menten equation estimates growth from birth to 3 years in healthy babies in the USA.

BACKGROUND: Standard pediatric growth curves cannot be used to impute missing height or weight measurements in individual children. The Michaelis-Menten equation, used for characterizing substrate-enzyme saturation curves, has been shown to model growth in many organisms including nonhuman vertebrates. We investigated whether this equation could be used to interpolate missing growth data in children in the first three years of life and compared this interpolation to several common interpolation methods and pediatric growth models. METHODS: We developed a modified Michaelis-Menten equation and compared expected to actual growth, first in a local birth cohort (N = 97) then in a large, outpatient, pediatric sample (N = 14,695). RESULTS: The modified Michaelis-Menten equation showed excellent fit for both infant weight (median RMSE: boys: 0.22 kg [IQR:0.19; 90% < 0.43]; girls: 0.20 kg [IQR:0.17; 90% < 0.39]) and height (median RMSE: boys: 0.93 cm [IQR:0.53; 90% < 1.0]; girls: 0.91 cm [IQR:0.50;90% < 1.0]). Growth data were modeled accurately with as few as four values from routine well-baby visits in year 1 and seven values in years 1-3; birth weight or length was essential for best fit. Interpolation with this equation had comparable (for weight) or lower (for height) mean RMSE compared to the best performing alternative models. CONCLUSIONS: A modified Michaelis-Menten equation accurately describes growth in healthy babies aged 0-36 months, allowing interpolation of missing weight and height values in individual longitudinal measurement series. The growth pattern in healthy babies in resource-rich environments mirrors an enzymatic saturation curve.

The keystone gut species Christensenella minuta boosts gut microbial biomass and voluntary physical activity in mice.

The gut bacteria of the family Christensenellaceae are consistently associated with metabolic health, but their role in promoting host health is not fully understood. Here, we explored the effect of Christensenella minuta amendment on voluntary physical activity and the gut microbiome. We inoculated male and female germ-free mice with an obese human donor microbiota together with live or heat-killed C. minuta for 28 days and measured physical activity in respirometry cages. Compared to heat-killed, the live-C. minuta treatment resulted in reduced feed efficiency and higher levels of physical activity, with significantly greater distance traveled for males and higher levels of small movements and resting metabolic rate in females. Sex-specific effects of C. minuta treatment may be in part attributable to different housing conditions for males and females. Amendment with live C. minuta boosted gut microbial biomass in both sexes, immobilizing dietary carbon in the microbiome, and mice with high levels of C. minuta lose more energy in stool. Live C. minuta also reduced within and between-host gut microbial diversity. Overall, our results showed that C. minuta acts as a keystone species: despite low relative abundance, it has a large impact on its ecosystem, from the microbiome to host energy homeostasis.IMPORTANCEThe composition of the human gut microbiome is associated with human health. Within the human gut microbiome, the relative abundance of the bacterial family Christensenellaceae has been shown to correlate with metabolic health and a lean body type. The mechanisms underpinning this effect remain unclear. Here, we show that live C. minuta influences host physical activity and metabolic energy expenditure, accompanied by changes in murine metabolism and the gut microbial community in a sex-dependent manner in comparison to heat-killed C. minuta. Importantly, live C. minuta boosts the biomass of the microbiome in the gut, and a higher level of C. minuta is associated with greater loss of energy in stool. These observations indicate that modulation of activity levels and changes to the microbiome are ways in which the Christensenellaceae can influence host energy homeostasis and health.

An integrated systems biology approach reveals differences in formate metabolism in the genus Methanothermobacter.

Methanogenesis allows methanogenic archaea to generate cellular energy for their growth while producing methane. Thermophilic hydrogenotrophic species of the genus Methanothermobacter have been recognized as robust biocatalysts for a circular carbon economy and are already applied in power-to-gas technology with biomethanation, which is a platform to store renewable energy and utilize captured carbon dioxide. Here, we generated curated genome-scale metabolic reconstructions for three Methanothermobacter strains and investigated differences in the growth performance of these same strains in chemostat bioreactor experiments with hydrogen and carbon dioxide or formate as substrates. Using an integrated systems biology approach, we identified differences in formate anabolism between the strains and revealed that formate anabolism influences the diversion of carbon between biomass and methane. This finding, together with the omics datasets and the metabolic models we generated, can be implemented for biotechnological applications of Methanothermobacter in power-to-gas technology, and as a perspective, for value-added chemical production.

Obesity is the main driver of altered gut microbiome functions in the metabolically unhealthy.

Obesity (OB) and cardiometabolic disease are major public health issues linked to changes in the gut microbiome. OB and poor cardiometabolic health status (CHS) are often comorbid, which hinders efforts to identify components of the microbiome uniquely linked to either one. Here, we used a deeply phenotyped cohort of 408 adults from Colombia, including subjects with OB, unhealthy CHS, or both, to validate previously reported features of gut microbiome function and diversity independently correlated with OB or CHS using fecal metagenomes. OB was defined by body mass index, waist circumference, and body fat; CHS as healthy or unhealthy according to blood biochemistry and anthropometric data. We found that OB, more so than metabolic status, drove associations with gut microbiome structure and functions. The microbiome of obese individuals with and without co-existing unhealthy CHS was characterized by reduced metagenomic diversity, reduced fermentative potential and elevated capacity to respond to oxidative stress and produce bacterial antigens. Disease-linked features were correlated with increased host blood pressure and inflammatory markers, and were mainly contributed by members of the family Enterobacteriaceae. Our results link OB with a microbiome able to tolerate an inflammatory and oxygenated gut state, and suggest that OB is the main driver of microbiome functional differences when poor CHS is a comorbidity.

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.

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.

Longitudinal comparison of the developing gut virome in infants and their mothers.

The human gut virome and its early life development are poorly understood. Prior studies have captured single-point assessments with the evolution of the infant virome remaining largely unexplored. We performed viral metagenomic sequencing on stool samples collected longitudinally from a cohort of 53 infants from age 2 weeks to 3 years (80.7 billion reads), and from their mothers (9.8 billion reads) to examine and compare viromes. The asymptomatic infant virome consisted of bacteriophages, nonhuman dietary/environmental viruses, and human-host viruses, predominantly picornaviruses. In contrast, human-host viruses were largely absent from the maternal virome. Previously undescribed, sequence-divergent vertebrate viruses were detected in the maternal but not infant virome. As infants aged, the phage component evolved to resemble the maternal virome, but by age 3, the human-host component remained dissimilar from the maternal virome. Thus, early life virome development is determined predominantly by dietary, infectious, and environmental factors rather than direct maternal acquisition.

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.

Silent recognition of flagellins from human gut commensal bacteria by Toll-like receptor 5.

Flagellin, the protein subunit of the bacterial flagellum, stimulates the innate immune receptor Toll-like receptor 5 (TLR5) after pattern recognition or evades TLR5 through lack of recognition. This binary response fails to explain the weak agonism of flagellins from commensal bacteria, raising the question of how TLR5 response is tuned. Here, we screened abundant flagellins present in metagenomes from human gut for both TLR5 recognition and activation and uncovered a class of flagellin-TLR5 interaction termed silent recognition. Silent flagellins were weak TLR5 agonists despite pattern recognition. Receptor activity was tuned by a TLR5-flagellin interaction distal to the site of pattern recognition that was present in Salmonella flagellin but absent in silent flagellins. This interaction enabled flagellin binding to preformed TLR5 dimers and increased TLR5 signaling by several orders of magnitude. Silent recognition by TLR5 occurred in human organoids and mice, and silent flagellin proteins were present in human stool. These flagellins were produced primarily by the abundant gut bacteria Lachnospiraceae and were enriched in nonindustrialized populations. Our findings provide a mechanism for the innate immune system to tolerate commensal-derived flagellins while remaining vigilant to the presence of flagellins produced by pathogens.

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.

Interpreting tree ensemble machine learning models with endoR.

Tree ensemble machine learning models are increasingly used in microbiome science as they are compatible with the compositional, high-dimensional, and sparse structure of sequence-based microbiome data. While such models are often good at predicting phenotypes based on microbiome data, they only yield limited insights into how microbial taxa may be associated. We developed endoR, a method to interpret tree ensemble models. First, endoR simplifies the fitted model into a decision ensemble. Then, it extracts information on the importance of individual features and their pairwise interactions, displaying them as an interpretable network. Both the endoR network and importance scores provide insights into how features, and interactions between them, contribute to the predictive performance of the fitted model. Adjustable regularization and bootstrapping help reduce the complexity and ensure that only essential parts of the model are retained. We assessed endoR on both simulated and real metagenomic data. We found endoR to have comparable accuracy to other common approaches while easing and enhancing model interpretation. Using endoR, we also confirmed published results on gut microbiome differences between cirrhotic and healthy individuals. Finally, we utilized endoR to explore associations between human gut methanogens and microbiome components. Indeed, these hydrogen consumers are expected to interact with fermenting bacteria in a complex syntrophic network. Specifically, we analyzed a global metagenome dataset of 2203 individuals and confirmed the previously reported association between Methanobacteriaceae and Christensenellales. Additionally, we observed that Methanobacteriaceae are associated with a network of hydrogen-producing bacteria. Our method accurately captures how tree ensembles use features and interactions between them to predict a response. As demonstrated by our applications, the resultant visualizations and summary outputs facilitate model interpretation and enable the generation of novel hypotheses about complex systems.

Codiversification of gut microbiota with humans.

The gut microbiomes of human populations worldwide have many core microbial species in common. However, within a species, some strains can show remarkable population specificity. The question is whether such specificity arises from a shared evolutionary history (codiversification) between humans and their microbes. To test for codiversification of host and microbiota, we analyzed paired gut metagenomes and human genomes for 1225 individuals in Europe, Asia, and Africa, including mothers and their children. Between and within countries, a parallel evolutionary history was evident for humans and their gut microbes. Moreover, species displaying the strongest codiversification independently evolved traits characteristic of host dependency, including reduced genomes and oxygen and temperature sensitivity. These findings all point to the importance of understanding the potential role of population-specific microbial strains in microbiome-mediated disease phenotypes.

Characterization of inositol lipid metabolism in gut-associated Bacteroidetes.

Inositol lipids are ubiquitous in eukaryotes and have finely tuned roles in cellular signalling and membrane homoeostasis. In Bacteria, however, inositol lipid production is relatively rare. Recently, the prominent human gut bacterium Bacteroides thetaiotaomicron (BT) was reported to produce inositol lipids and sphingolipids, but the pathways remain ambiguous and their prevalence unclear. Here, using genomic and biochemical approaches, we investigated the gene cluster for inositol lipid synthesis in BT using a previously undescribed strain with inducible control of sphingolipid synthesis. We characterized the biosynthetic pathway from myo-inositol-phosphate (MIP) synthesis to phosphoinositol dihydroceramide, determined the crystal structure of the recombinant BT MIP synthase enzyme and identified the phosphatase responsible for the conversion of bacterially-derived phosphatidylinositol phosphate (PIP-DAG) to phosphatidylinositol (PI-DAG). In vitro, loss of inositol lipid production altered BT capsule expression and antimicrobial peptide resistance. In vivo, loss of inositol lipids decreased bacterial fitness in a gnotobiotic mouse model. We identified a second putative, previously undescribed pathway for bacterial PI-DAG synthesis without a PIP-DAG intermediate, common in Prevotella. Our results indicate that inositol sphingolipid production is widespread in host-associated Bacteroidetes and has implications for symbiosis.

The developing infant gut microbiome: A strain-level view.

At birth, neonates provide a vast habitat awaiting microbial colonization. Microbiome assembly is a complex process involving microbial seeding and succession driven by ecological forces and subject to environmental conditions. These successional events not only significantly affect the ecology and function of the microbiome, but also impact host health. While the establishment of the infant microbiome has been a point of interest for decades, an integrated view focusing on strain level colonization has been lacking until recently. Technological and computational advancements enabling strain-level analyses of the infant microbiome have demonstrated the immense complexity of this system and allowed for an improved understanding of how strains of the same species spread, colonize, evolve, and affect the host. Here, we review the current knowledge of the establishment and maturation of the infant gut microbiome with particular emphasis on newer discoveries achieved through strain-centric analyses.

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.

Incorporating genome-based phylogeny and functional similarity into diversity assessments helps to resolve a global collection of human gut metagenomes.

Tree-based diversity measures incorporate phylogenetic or functional relatedness into comparisons of microbial communities. This can improve the identification of explanatory factors compared to tree-agnostic diversity measures. However, applying tree-based diversity measures to metagenome data is more challenging than for single-locus sequencing (e.g. 16S rRNA gene). Utilizing the Genome Taxonomy Database for species-level metagenome profiling allows for functional diversity measures based on genomic content or traits inferred from it. Still, it is unclear how metagenome-based assessments of microbiome diversity benefit from incorporating phylogeny or function into measures of diversity. We assessed this by measuring phylogeny-based, function-based and tree-agnostic diversity measures from a large, global collection of human gut metagenomes composed of 30 studies and 2943 samples. We found tree-based measures to explain phenotypic variation (e.g. westernization, disease status and gender) better or equivalent to tree-agnostic measures. Ecophylogenetic and functional diversity measures provided unique insight into how microbiome diversity was partitioned by phenotype. Tree-based measures greatly improved machine learning model performance for predicting westernization, disease status and gender, relative to models trained solely on tree-agnostic measures. Our findings illustrate the usefulness of tree- and function-based measures for metagenomic assessments of microbial diversity, which is a fundamental component of microbiome science.