Stability of the human faecal microbiome in a cohort of adult men

Prediction of 24-Hour Urinary Sodium Excretion Using Machine-Learning Algorithms

BACKGROUND

Accurate quantification of sodium intake based on self-reported dietary assessments has been a persistent challenge. We aimed to apply machine-learning (ML) algorithms to predict 24-hour urinary sodium excretion from self-reported questionnaire information.

METHODS AND RESULTS

We analyzed 3454 participants from the NHS (Nurses' Health Study), NHS-II (Nurses' Health Study II), and HPFS (Health Professionals Follow-Up Study), with repeated measures of 24-hour urinary sodium excretion over 1 year. We used an ensemble approach to predict averaged 24-hour urinary sodium excretion using 36 characteristics. The TOHP-I (Trial of Hypertension Prevention I) was used for the external validation. The final ML algorithms were applied to 167 920 nonhypertensive adults with 30-year follow-up to estimate confounder-adjusted hazard ratio (HR) of incident hypertension for predicted sodium. Averaged 24-hour urinary sodium excretion was better predicted and calibrated with ML compared with the food frequency questionnaire (Spearman correlation coefficient, 0.51 [95% CI, 0.49-0.54] with ML; 0.19 [95% CI, 0.16-0.23] with the food frequency questionnaire; 0.46 [95% CI, 0.42-0.50] in the TOHP-I). However, the prediction heavily depended on body size, and the prediction of energy-adjusted 24-hour sodium excretion was modestly better using ML. ML-predicted sodium was modestly more strongly associated than food frequency questionnaire-based sodium in the NHS-II (HR comparing Q5 versus Q1, 1.48 [95% CI, 1.40-1.56] with ML; 1.04 [95% CI, 0.99-1.08] with the food frequency questionnaire), but no material differences were observed in the NHS or HPFS.

CONCLUSIONS

The present ML algorithm improved prediction of participants' absolute 24-hour urinary sodium excretion. The present algorithms may be a generalizable approach for predicting absolute sodium intake but do not substantially reduce the bias stemming from measurement error in disease associations.

24-hour urinary sodium and potassium excretions, plasma metabolomic profiles, and cardiometabolic biomarkers in US adults: A cross-sectional study

BACKGROUND

High sodium and low potassium intakes are associated with a higher risk of hypertension and cardiovascular disease (CVD), but there are limited data on the circulating metabolomics profiles of 24-hour urinary sodium and potassium excretions in free-living individuals.

OBJECTIVES

To characterize metabolomics signatures of a high-sodium and low-potassium diet in a cross-sectional study.

METHODS

In 1,028 healthy older adults from the Women's and Men's Lifestyle Validation Studies, we investigated the association of habitual sodium and potassium intakes measured by 2-4 24-hour urine samples with plasma metabolites (quantified using liquid chromatography-tandem mass spectrometry) and metabolomic pathways. Our primary exposures were energy-adjusted 24-hour urinary sodium excretion, potassium excretion, and sodium-to-potassium ratio, calculated based on energy expenditure derived from the Doubly Labelled Water method. Then we assessed the partial correlations of their metabolomics scores, derived from elastic net regressions, with cardiometabolic biomarkers.

RESULTS

Higher sodium excretion was associated with 38 metabolites including higher piperine, phosphatidylethanolamine, and C5:1 carnitine. In pathway analysis, higher sodium excretion was associated with enhanced biotin and propanoate metabolism, and enhanced degradation of lysine and branched-chain amino acids (BCAAs). Metabolites associated with higher potassium and lower sodium-to-potassium ratio included quinic acid and proline-betaine. After adjusting for confounding factors, the metabolomics score for sodium-to-potassium ratio positively correlated with fasting insulin (Spearman's rank correlation coefficient ρ=0.27), C-peptide (ρ=0.30), and triglyceride (ρ=0.46), and negatively with adiponectin (ρ=-0.40), and high-density lipoprotein (HDL) cholesterol (ρ=-0.42).

CONCLUSIONS

We discovered metabolites and metabolomics pathways associated with a high-sodium diet, including metabolites related to biotin, propanoate, lysine, and BCAA pathways. The metabolomics signature for a higher sodium-low potassium diet is associated with multiple components of elevated cardiometabolic risk.

A pervasive large conjugative plasmid mediates multispecies biofilm formation in the intestinal microbiota increasing resilience to perturbations

Although horizontal gene transfer is pervasive in the intestinal microbiota, we understand only superficially the roles of most exchanged genes and how the mobile repertoire affects community dynamics. Similarly, little is known about the mechanisms underlying the ability of a community to recover after a perturbation. Here, we identified and functionally characterized a large conjugative plasmid that is one of the most frequently transferred elements among Bacteroidales species and is ubiquitous in diverse human populations. This plasmid encodes both an extracellular polysaccharide and fimbriae, which promote the formation of multispecies biofilms in the mammalian gut. We use a hybridization-based approach to visualize biofilms in clarified whole colon tissue with unprecedented 3D spatial resolution. These biofilms increase bacterial survival to common stressors encountered in the gut, increasing strain resiliency, and providing a rationale for the plasmid's recent spread and high worldwide prevalence.

Taxonomic and metabolic development of the human gut microbiome across life stages: a worldwide metagenomic investigation

The human gut microbiota is a dynamic community of microorganisms that undergo variable changes over the entire life span. To thoroughly investigate the possible fluctuations of the microbiota throughout human life, we performed a pooled analysis of healthy fecal samples across different age groups covering the entire human life span. Our study integrated data from 79 publicly available studies and new stool samples from an Italian cohort, i.e., the Parma Microbiota project, resulting in 6,653 samples processed through the shotgun metagenomic approach. This approach has allowed species-level taxonomic reconstruction of the gut microbiota and investigation of its metabolic potential across the human life span. From a taxonomic point of view, our findings confirmed and detailed at species-level accuracy that the microbial richness of the gut microbiota gradually increases in the first stage of life, becoming relatively stable during adolescence. Moreover, the analysis identified the potential core microbiota representative of distinct age groups, revealing age-related bacterial patterns and the continuous rearrangement of the microbiota in terms of relative abundances across the life span rather than the acquisition and loss of taxa. Furthermore, the shotgun approach provided insights into the functional contribution of the human gut microbiome. The metagenomic analysis revealed functional age-related differences, particularly in carbohydrate and fiber metabolism, suggesting a co-evolution of the microbiome assembly with diet. Additionally, we identified correlations between vitamin synthesis, such as thiamine and niacin, and early life, suggesting a potential role of the microbiome in human physiology, in particular in the functions of the host's nervous and immune systems.

IMPORTANCE

In this study, we provided comprehensive insights into the dynamic nature of the human gut microbiota across the human life span. In detail, we analyzed a large data set based on a shotgun metagenomic approach, combining public data sets and new samples from the Parma Microbiota project and obtaining a detailed overview of the possible relationship between gut microbiota development and aging. Our findings confirmed the main stages in microbial richness development and revealed specific core microbiota associated with different age stages. Moreover, the shotgun metagenomic approach allowed the disentangling of the functional changes in the microbiome across the human life span, particularly in diet-related metabolism, which is probably correlated to bacterial co-evolution with dietary habits. Notably, our study also uncovered positive correlations with vitamin synthesis in early life, suggesting a possible impact of the microbiota on human physiology.

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

To understand the dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune, and clinical markers of microbiomes from four body sites in 86 participants over 6 years. We found that microbiome stability and individuality are body-site specific and heavily influenced by the host. The stool and oral microbiome are more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. We identify individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlate across body sites, suggesting systemic dynamics influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals show altered microbial stability and associations among microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.

Temporal gut microbiota variability and association with dietary patterns: From the one-year observational Diet, Cancer, and Health - Next Generations MAX study

BACKGROUND

Knowledge about the variability of gut microbiota within an individual over time is important to allow meaningful investigations of the gut microbiota in relation to diet and health outcomes in observational studies. Plant-based dietary patterns have been associated with a lower risk of morbidity and mortality and may alter gut microbiota in a favorable direction.

OBJECTIVES

To assess the gut microbiota variability during one year and investigate the association between adherence to diet indexes and the gut microbiota in a Danish population.

METHODS

Four hundred forty-four participants were included in the Diet, Cancer, and Health - Next Generations MAX study (DCH-NG MAX). Stool samples collected up to three times during a year were analyzed by 16S ribosomal ribonucleic acid gene sequencing. Diet was obtained by 24-hour dietary recalls. Intraclass correlation coefficient (ICC) was calculated to assess temporal microbial variability based on 214 individuals. Diet indexes (Nordic, Mediterranean, and plant-based diets) and food groups thereof were associated with gut microbiota using linear regression analyses.

RESULTS

We found that 91 out of 234 genera had an ICC >0.5. We identified three subgroups dominated by Bacteroides, Prevotella 9, and Ruminococcaceae and adherence to diet indexes differed between subgroups. Higher adherence to diet indexes was associated with the relative abundance of 22 genera. Across diet indexes, higher intakes of fruit, vegetables, whole grains/cereals, and nuts were most frequently associated with these genera.

CONCLUSIONS

In the DCH-NG MAX study, 39% of the genera had an ICC >0.5 over one year, suggesting that these genera could be studied with health outcomes in prospective analyses with acceptable precision. Adherence to the Nordic, Mediterranean, and plant-based diets differed between bacterial subgroups and was associated with a higher abundance of genera with fiber-degrading properties. Fruits, vegetables, whole grains/cereals, and nuts were frequently associated with these genera.

Temporal dynamics of gut microbiomes in non-industrialized urban Amazonia

Increasing levels of industrialization have been associated with changes in gut microbiome structure and loss of features thought to be crucial for maintaining gut ecological balance. The stability of gut microbial communities over time within individuals seems to be largely affected by these changes but has been overlooked among transitioning populations from low- to middle-income countries. Here, we used metagenomic sequencing to characterize the temporal dynamics in gut microbiomes of 24 individuals living an urban non-industrialized lifestyle in the Brazilian Amazon. We further contextualized our data with 165 matching longitudinal samples from an urban industrialized and a rural non-industrialized population. We show that gut microbiome composition and diversity have greater variability over time among non-industrialized individuals when compared to industrialized counterparts and that taxa may present diverse temporal dynamics across human populations. Enterotype classifications show that community types are generally stable over time despite shifts in microbiome structure. Furthermore, by tracking genomes over time, we show that levels of bacterial population replacements are more frequent among Amazonian individuals and that non-synonymous variants accumulate in genes associated with degradation of host dietary polysaccharides. Taken together, our results suggest that the stability of gut microbiomes is influenced by levels of industrialization and that tracking microbial population dynamics is important to understand how the microbiome will adapt to these transitions.IMPORTANCEThe transition from a rural or non-industrialized lifestyle to urbanization and industrialization has been linked to changes in the structure and function of the human gut microbiome. Understanding how the gut microbiomes changes over time is crucial to define healthy states and to grasp how the gut microbiome interacts with the host environment. Here, we investigate the temporal dynamics of gut microbiomes from an urban and non-industrialized population in the Amazon, as well as metagenomic data sets from urban United States and rural Tanzania. We showed that healthy non-industrialized microbiomes experience greater compositional shifts over time compared to industrialized individuals. Furthermore, bacterial strain populations are more frequently replaced in non-industrialized microbiomes, and most non-synonymous mutations accumulate in genes associated with the degradation of host dietary components. This indicates that microbiome stability is affected by transitions to industrialization, and that strain tracking can elucidate the ecological dynamics behind such transitions.

Dynamic nature of viral and bacterial communities in human faeces

Bacteriophages are a major component of the gut microbiome and are believed to play a role in establishment and stabilization of microbial communities by influencing taxonomic and functional diversity. We show that the activity of lytic and temperate phages can also significantly affect bacterial community structure in a model of extended colonic retention. Intact fresh human feces were incubated anaerobically at 37°C without homogenization and subjected to metagenomic sequencing. We observed subject-specific blooms and collapses of selected bacteriophage and bacterial populations within some individuals. Most notable were striking collapses of Prevotella populations accompanied by increases in specific bacteriophages. In a number of cases, we even observed a shift from one bacterial "enterotype" to another within 48 h. These results confirm that intact feces represents a highly dynamic ecological system and suggests that colonic retention time could have a profound effect on microbiome composition, including a significant impact by bacteriophages.

Longitudinal profiling of the intestinal microbiome in children with cystic fibrosis treated with elexacaftor-tezacaftor-ivacaftor

The intestinal microbiome influences growth and disease progression in children with cystic fibrosis (CF). Elexacaftor-tezacaftor-ivacaftor (ELX/TEZ/IVA), the newest pharmaceutical modulator for CF, restores the function of the pathogenic mutated CF transmembrane conductance regulator (CFTR) channel. We performed a single-center longitudinal analysis of the effect of ELX/TEZ/IVA on the intestinal microbiome, intestinal inflammation, and clinical parameters in children with CF. Following ELX/TEZ/IVA, children with CF had significant improvements in body mass index and percent predicted forced expiratory volume in one second, and required fewer antibiotics for respiratory infections. Intestinal microbiome diversity increased following ELX/TEZ/IVA coupled with a decrease in the intestinal carriage of Staphylococcus aureus, the predominant respiratory pathogen in children with CF. There was a reduced abundance of microbiome-encoded antibiotic resistance genes. Microbial pathways for aerobic respiration were reduced after ELX/TEZ/IVA. The abundance of microbial acid tolerance genes was reduced, indicating microbial adaptation to increased CFTR function. In all, this study represents the first comprehensive analysis of the intestinal microbiome in children with CF receiving ELX/TEZ/IVA.IMPORTANCECystic fibrosis (CF) is an autosomal recessive disease with significant gastrointestinal symptoms in addition to pulmonary complications. Recently approved treatments for CF, CF transmembrane conductance regulator (CFTR) modulators, are anticipated to substantially improve the care of people with CF and extend their lifespans. Prior work has shown that the intestinal microbiome correlates with health outcomes in CF, particularly in children. Here, we study the intestinal microbiome of children with CF before and after the CFTR modulator, ELX/TEZ/IVA. We identify promising improvements in microbiome diversity, reduced measures of intestinal inflammation, and reduced antibiotic resistance genes. We present specific bacterial taxa and protein groups which change following ELX/TEZ/IVA. These results will inform future mechanistic studies to understand the microbial improvements associated with CFTR modulator treatment. This study demonstrates how the microbiome can change in response to a targeted medication that corrects a genetic disease.

The Impact of the Gut Microbiome, Environment, and Diet in Early-Onset Colorectal Cancer Development

Traditionally considered a disease common in the older population, colorectal cancer is increasing in incidence among younger demographics. Evidence suggests that populational- and generational-level shifts in the composition of the human gut microbiome may be tied to the recent trends in gastrointestinal carcinogenesis. This review provides an overview of current research and putative mechanisms behind the rising incidence of colorectal cancer in the younger population, with insight into future interventions that may prevent or reverse the rate of early-onset colorectal carcinoma.

Functional and evolutionary significance of unknown genes from uncultivated taxa

Many of the Earth's microbes remain uncultured and understudied, limiting our understanding of the functional and evolutionary aspects of their genetic material, which remain largely overlooked in most metagenomic studies1. Here we analysed 149,842 environmental genomes from multiple habitats2-6 and compiled a curated catalogue of 404,085 functionally and evolutionarily significant novel (FESNov) gene families exclusive to uncultivated prokaryotic taxa. All FESNov families span multiple species, exhibit strong signals of purifying selection and qualify as new orthologous groups, thus nearly tripling the number of bacterial and archaeal gene families described to date. The FESNov catalogue is enriched in clade-specific traits, including 1,034 novel families that can distinguish entire uncultivated phyla, classes and orders, probably representing synapomorphies that facilitated their evolutionary divergence. Using genomic context analysis and structural alignments we predicted functional associations for 32.4% of FESNov families, including 4,349 high-confidence associations with important biological processes. These predictions provide a valuable hypothesis-driven framework that we used for experimental validatation of a new gene family involved in cell motility and a novel set of antimicrobial peptides. We also demonstrate that the relative abundance profiles of novel families can discriminate between environments and clinical conditions, leading to the discovery of potentially new biomarkers associated with colorectal cancer. We expect this work to enhance future metagenomics studies and expand our knowledge of the genetic repertory of uncultivated organisms.

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

To understand dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune and clinical markers of microbiomes from four body sites in 86 participants over six years. We found that microbiome stability and individuality are body-site-specific and heavily influenced by the host. The stool and oral microbiome were more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. Also, we identified individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlated across body sites, suggesting systemic coordination influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals showed altered microbial stability and associations between microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.

Study Highlights

The stability of the human microbiome varies among individuals and body sites.Highly individualized microbial genera are more stable over time.At each of the four body sites, systematic interactions between the environment, the host and bacteria can be detected.Individuals with insulin resistance have lower microbiome stability, a more diversified skin microbiome, and significantly altered host-microbiome interactions.

Microbiome-based correction for random errors in nutrient profiles derived from self-reported dietary assessments

Since dietary intake is challenging to directly measure in large-scale cohort studies, we often rely on self-reported instruments (e.g., food frequency questionnaires, 24-hour recalls, and diet records) developed in nutritional epidemiology. Those self-reported instruments are prone to measurement errors, which can lead to inaccuracies in the calculation of nutrient profiles. Currently, few computational methods exist to address this problem. In the present study, we introduce a deep-learning approach --- Microbiome-based nutrient profile corrector (METRIC), which leverages gut microbial compositions to correct random errors in self-reported dietary assessments using 24-hour recalls or diet records. We demonstrate the excellent performance of METRIC in minimizing the simulated random errors, particularly for nutrients metabolized by gut bacteria in both synthetic and three real-world datasets. Further research is warranted to examine the utility of METRIC to correct actual measurement errors in self-reported dietary assessment instruments.

Microbial diversity and ecological complexity emerging from environmental variation and horizontal gene transfer in a simple mathematical model

Microbiomes are generally characterized by high diversity of coexisting microbial species and strains that remains stable within a broad range of conditions. However, under fixed conditions, microbial ecology conforms with the exclusion principle under which two populations competing for the same resource within the same niche cannot coexist because the less fit population inevitably goes extinct. To explore the conditions for stabilization of microbial diversity, we developed a simple mathematical model consisting of two competing populations that could exchange a single gene allele via horizontal gene transfer (HGT). We found that, although in a fixed environment, with unbiased HGT, the system obeyed the exclusion principle, in an oscillating environment, within large regions of the phase space bounded by the rates of reproduction and HGT, the two populations coexist. Moreover, depending on the parameter combination, all three major types of symbiosis obtained, namely, pure competition, host-parasite relationship and mutualism. In each of these regimes, certain parameter combinations provided for synergy, that is, a greater total abundance of both populations compared to the abundance of the winning population in the fixed environments. These findings show that basic phenomena that are universal in microbial communities, environmental variation and HGT, provide for stabilization of microbial diversity and ecological complexity.

A metatranscriptomics strategy for efficient characterization of the microbiome in human tissues with low microbial biomass

The high background of host RNA poses a major challenge to metatranscriptome analysis of human samples. Hence, metatranscriptomics has been mainly applied to microbe-rich samples, while its application in human tissues with low ratio of microbial to host cells has yet to be explored. Since there is no computational workflow specifically designed for the taxonomic and functional analysis of this type of samples, we propose an effective metatranscriptomics strategy to accurately characterize the microbiome in human tissues with a low ratio of microbial to host content. We experimentally generated synthetic samples with well-characterized bacterial and host cell compositions, and mimicking human samples with high and low microbial loads. These synthetic samples were used for optimizing and establishing the workflow in a controlled setting. Our results show that the integration of the taxonomic analysis of optimized Kraken 2/Bracken with the functional analysis of HUMAnN 3 in samples with low microbial content, enables the accurate identification of a large number of microbial species with a low false-positive rate, while improving the detection of microbial functions. The effectiveness of our metatranscriptomics workflow was demonstrated in synthetic samples, simulated datasets, and most importantly, human gastric tissue specimens, thus providing a proof of concept for its applicability on mucosal tissues of the gastrointestinal tract. The use of an accurate and reliable metatranscriptomics approach for human tissues with low microbial content will expand our understanding of the functional activity of the mucosal microbiome, uncovering critical interactions between the microbiome and the host in health and disease.

Interplay between diet, circulating indolepropionate concentrations and cardiometabolic health in US populations

OBJECTIVES

To identify indolepropionate (IPA)-predicting gut microbiota species, investigate potential diet-microbiota interactions, and examine the prospective associations of circulating IPA concentrations with type 2 diabetes (T2D) and coronary heart disease (CHD) risk in free-living individuals.

DESIGN

We included 287 men from the Men's Lifestyle Validation Study, a substudy of the Health Professionals Follow-Up Study (HPFS), who provided up to two pairs of faecal samples and two blood samples. Diet was assessed using 7-day diet records. Associations between plasma concentrations of tryptophan metabolites and T2D CHD risk were examined in 13 032 participants from Nurses' Health Study (NHS), NHSII and HPFS.

RESULTS

We identified 17 microbial species whose abundance was significantly associated with plasma IPA concentrations. A significant association between higher tryptophan intake and higher IPA concentrations was only observed among men who had higher fibre intake and a higher microbial species score consisting of the 17 species (p-interaction<0.01). Dietary and plasma concentrations of tryptophan and most kynurenine pathway metabolites were positively associated with T2D risk (HRQ5 vs Q1 ranged from 1.17 to 1.46) while a significant inverse association was found for IPA (HRQ5 vs Q1 (95% CI) 0.70 (0.56 to 0.88)). No associations were found in CHD for any plasma tryptophan metabolites.

CONCLUSIONS

Specific microbial species and dietary fibre jointly predicted significantly higher circulating IPA concentrations at higher tryptophan intake. Dietary and plasma tryptophan, as well as its kynurenine pathway metabolites, demonstrated divergent associations from those for IPA, which was significantly predictive of lower risk of T2D.

Food & You: A digital cohort on personalized nutrition

Nutrition is a key contributor to health. Recently, several studies have identified associations between factors such as microbiota composition and health-related responses to dietary intake, raising the potential of personalized nutritional recommendations. To further our understanding of personalized nutrition, detailed individual data must be collected from participants in their day-to-day lives. However, this is challenging in conventional studies that require clinical measurements and site visits. So-called digital or remote cohorts allow in situ data collection on a daily basis through mobile applications, online services, and wearable sensors, but they raise questions about study retention and data quality. "Food & You" is a personalized nutrition study implemented as a digital cohort in which participants track food intake, physical activity, gut microbiota, glycemia, and other data for two to four weeks. Here, we describe the study protocol, report on study completion rates, and describe the collected data, focusing on assessing their quality and reliability. Overall, the study collected data from over 1000 participants, including high-resolution data of nutritional intake of more than 46 million kcal collected from 315,126 dishes over 23,335 participant days, 1,470,030 blood glucose measurements, 49,110 survey responses, and 1,024 stool samples for gut microbiota analysis. Retention was high, with over 60% of the enrolled participants completing the study. Various data quality assessment efforts suggest the captured high-resolution nutritional data accurately reflect individual diet patterns, paving the way for digital cohorts as a typical study design for personalized nutrition.

Walnut consumption and gut microbial metabolism: Results of an exploratory analysis from a randomized, crossover, controlled-feeding study

BACKGROUND & AIMS

The effect of walnut-related modulation of gut microbiota composition on microbiota functionality is unknown. The aim was to characterize the effect of a walnut-enriched diet (WD), compared to a fatty acid-matched diet devoid of walnuts (WFMD) and a diet where oleic acid replaces alpha-linolenic acid (ORAD), on bacterial gene expression.

METHODS

A 3-period, randomized, crossover, controlled-feeding study was conducted. Participants were provided a 2-week run-in standard western diet (SWD; 50% kcal carbohydrate, 16% protein, 34% fat, 12% SFA). Following the SWD in random sequence order, participants were provided the WD, WFMD, and ORAD (48% carbohydrate; 17% protein; fat 35%; 7% SFA). The WD contained 18% of energy from walnuts (57 g/d/2100 kcal). The WFMD and ORAD were devoid of walnuts; liquid non-tropical plant oils were included in these diets. Metatranscriptomic analyses were performed as an exploratory outcome.

RESULTS

The analytical sample included 35 participants (40% female) with a mean ± SD age of 43 ± 10 y and BMI of 30.3 ± 4.9 kg/m2. The ⍺-diversity of taxa actively expressing genes, assessed by observed species (p = 0.27) and Pielou's Evenness (p = 0.09), did not differ among the diets. The ⍺-diversity of actively expressed genes was greater following the WD compared to the WFMD and ORAD as assessed by the observed genes and Pielou's Evenness metrics (p < 0.05). β-Diversity of the actively expressed genes differed following the WD compared to the WFMD (p = 0.001) and ORAD (p = 0.001); β-diversity did not differ between the WFMD and ORAD. Active composition analyses showed increased Gordonibacter (p < 0.001) activity following the WD vs. the ORAD. Greater expression of many genes was observed following the WD compared to the WFMD and ORAD. Following the WD, greater expression of metabolism-related genes encoding glycine amidinotransferase (GATM; K00613) and arginine deiminase (K01478) was observed compared to the WFMD. Greater expression of glycine amidinotransferase (GATM; K00613) by Gordonibacter was also observed following the WD vs. the WFMD and ORAD.

CONCLUSION

Our results suggest walnut intake may increase endogenous production of homoarginine through gut microbiota-mediated upregulation of GATM, which is a novel mechanism by which walnuts may lower cardiovascular disease risk. However, given the exploratory nature replication is needed.

CLINICAL TRIAL REGISTRATION

Clinicaltrials.gov (NCT02210767).

Gut feelings: associations of emotions and emotion regulation with the gut microbiome in women

BACKGROUND

Accumulating evidence suggests that positive and negative emotions, as well as emotion regulation, play key roles in human health and disease. Recent work has shown the gut microbiome is important in modulating mental and physical health through the gut-brain axis. Yet, its association with emotions and emotion regulation are understudied. Here we examined whether positive and negative emotions, as well as two emotion regulation strategies (i.e. cognitive reappraisal and suppression), were associated with the gut microbiome composition and functional pathways in healthy women.

METHODS

Participants were from the Mind-Body Study (N = 206, mean age = 61), a sub-study of the Nurses' Health Study II cohort. In 2013, participants completed measures of emotion-related factors. Two pairs of stool samples were collected, 6 months apart, 3 months after emotion-related factors measures were completed. Analyses examined associations of emotion-related factors with gut microbial diversity, overall microbiome structure, and specific species/pathways and adjusted for relevant covariates.

RESULTS

Alpha diversity was negatively associated with suppression. In multivariate analysis, positive emotions were inversely associated with the relative abundance of Firmicutes bacterium CAG 94 and Ruminococcaceae bacterium D16, while negative emotions were directly correlated with the relative abundance of these same species. At the metabolic pathway level, negative emotions were inversely related to the biosynthesis of pantothenate, coenzyme A, and adenosine.

CONCLUSIONS

These findings offer human evidence supporting linkages of emotions and related regulatory processes with the gut microbiome and highlight the importance of incorporating the gut microbiome in our understanding of emotion-related factors and their associations with physical health.

Microbial-enrichment method enables high-throughput metagenomic characterization from host-rich samples

Host-microbe interactions have been linked to health and disease states through the use of microbial taxonomic profiling, mostly via 16S ribosomal RNA gene sequencing. However, many mechanistic insights remain elusive, in part because studying the genomes of microbes associated with mammalian tissue is difficult due to the high ratio of host to microbial DNA in such samples. Here we describe a microbial-enrichment method (MEM), which we demonstrate on a wide range of sample types, including saliva, stool, intestinal scrapings, and intestinal mucosal biopsies. MEM enabled high-throughput characterization of microbial metagenomes from human intestinal biopsies by reducing host DNA more than 1,000-fold with minimal microbial community changes (roughly 90% of taxa had no significant differences between MEM-treated and untreated control groups). Shotgun sequencing of MEM-treated human intestinal biopsies enabled characterization of both high- and low-abundance microbial taxa, pathways and genes longitudinally along the gastrointestinal tract. We report the construction of metagenome-assembled genomes directly from human intestinal biopsies for bacteria and archaea at relative abundances as low as 1%. Analysis of metagenome-assembled genomes reveals distinct subpopulation structures between the small and large intestine for some taxa. MEM opens a path for the microbiome field to acquire deeper insights into host-microbe interactions by enabling in-depth characterization of host-tissue-associated microbial communities.

Targeted curation of the gut microbial gene content modulating human cardiovascular disease

IMPORTANCE

One of the most-cited examples of the gut microbiome modulating human disease is the microbial metabolism of quaternary amines from protein-rich foods. By-products of this microbial processing promote atherosclerotic heart disease, a leading cause of human mortality globally. Our research addresses current knowledge gaps in our understanding of this microbial metabolism by holistically inventorying the microorganisms and expressed genes catalyzing critical atherosclerosis-promoting and -ameliorating reactions in the human gut. This led to the creation of an open-access resource, the Methylated Amine Gene Inventory of Catabolism database, the first systematic inventory of gut methylated amine metabolism. More importantly, using this resource we deliver here, we show for the first time that these gut microbial genes can predict human disease, paving the way for microbiota-inspired diagnostics and interventions.

Microbiomes, Their Function, and Cancer: How Metatranscriptomics Can Close the Knowledge Gap

The interaction between the microbial communities in the human body and the onset and progression of cancer has not been investigated until recently. The vast majority of the metagenomics research in this area has concentrated on the composition of microbiomes, attempting to link the overabundance or depletion of certain microorganisms to cancer proliferation, metastatic behaviour, and its resistance to therapies. However, studies elucidating the functional implications of the microbiome activity in cancer patients are still scarce; in particular, there is an overwhelming lack of studies assessing such implications directly, through analysis of the transcriptome of the bacterial community. This review summarises the contributions of metagenomics and metatranscriptomics to the knowledge of the microbial environment associated with several cancers; most importantly, it highlights all the advantages that metatranscriptomics has over metagenomics and suggests how such an approach can be leveraged to advance the knowledge of the cancer bacterial environment.

Mapping the T cell repertoire to a complex gut bacterial community

Certain bacterial strains from the microbiome induce a potent, antigen-specific T cell response1-5. However, the specificity of microbiome-induced T cells has not been explored at the strain level across the gut community. Here, we colonize germ-free mice with complex defined communities (roughly 100 bacterial strains) and profile T cell responses to each strain. The pattern of responses suggests that many T cells in the gut repertoire recognize several bacterial strains from the community. We constructed T cell hybridomas from 92 T cell receptor (TCR) clonotypes; by screening every strain in the community against each hybridoma, we find that nearly all the bacteria-specific TCRs show a one-to-many TCR-to-strain relationship, including 13 abundant TCR clonotypes that each recognize 18 Firmicutes. By screening three pooled bacterial genomic libraries, we discover that these 13 clonotypes share a single target: a conserved substrate-binding protein from an ATP-binding cassette transport system. Peripheral regulatory T cells and T helper 17 cells specific for an epitope from this protein are abundant in community-colonized and specific pathogen-free mice. Our work reveals that T cell recognition of commensals is focused on widely conserved, highly expressed cell-surface antigens, opening the door to new therapeutic strategies in which colonist-specific immune responses are rationally altered or redirected.

Current concepts, advances, and challenges in deciphering the human microbiota with metatranscriptomics

Metatranscriptomics refers to the analysis of the collective microbial transcriptome of a sample. Its increased utilization for the characterization of human-associated microbial communities has enabled the discovery of many disease-state related microbial activities. Here, we review the principles of metatranscriptomics-based analysis of human-associated microbial samples. We describe strengths and weaknesses of popular sample preparation, sequencing, and bioinformatics approaches and summarize strategies for their use. We then discuss how human-associated microbial communities have recently been examined and how their characterization may change. We conclude that metatranscriptomics insights into human microbiotas under health and disease have not only expanded our knowledge on human health, but also opened avenues for rational antimicrobial drug use and disease management.

Stability of human gut microbiome: Comparison of ecological modelling and observational approaches

The gut microbiome plays a pivotal role in the human body, and perturbations in its composition have been linked to various disorders. Stability is an essential property of a healthy human gut microbiome, which allows it to maintain its functional richness under the external influences. This property has been explored through two distinct methodologies - mathematical modelling based on ecological principles and statistical analysis drawn from observations in interventional studies. Here we conducted a meta-analysis aimed to compare the two approaches utilising the data from 9 interventional and time series studies encompassing 3512 gut microbiome profiles obtained via 16S rRNA gene sequencing. By employing the previously published compositional Lotka-Volterra method, we modelled the dynamics of the microbial community and evaluated ecological stability measures. These measures were compared to those based on observed microbiome changes. There was a substantial correlation between the outcomes of the two approaches. Particularly, local stability assessed within the ecological paradigm was positively correlated with observational stability measures accounting for the compositional nature of microbiome data. Additionally, we were able to reproduce the previously reported inverse relationship between the community's robustness to microorganism loss and local stability, attributed to the distinct impacts of coefficient characterising the network decomposition on these two stability assessments. Our findings demonstrate harmonisation between the ecological and observational approaches to microbiome analysis, advancing the understanding of healthy gut microbiome concept. This paves the way to develop efficient microbiome-targeting interventions for disease prevention and treatment.

Evaluation of Gut Microbiota Stability and Flexibility as a Response to Seasonal Variation in the Wild François' Langurs (Trachypithecus francoisi) in Limestone Forest

The coevolution between gut microbiota and the host markedly influences the digestive strategies of animals to cope with changes in food sources. We have explored the compositional structure and seasonal variation in the gut microbiota of François' langur in a limestone forest in Guangxi, southwest China, using 16S rRNA sequencing. Our results demonstrated that Firmicutes and Bacteroidetes were the dominant phyla in langurs, followed by Oscillospiraceae, Christensenellaceae, and Lachnospiraceae at the family level. The top five dominant phyla did not show significant seasonal variations, and only 21 bacterial taxa differed at the family level, indicating stability in gut the microbiota possibly with respect to foraging for several dominant plants and high-leaf feeding by the langurs. Moreover, rainfall and minimum humidity are important factors affecting the gut microbiota of the langurs, but they explain few changes in bacterial taxa. The activity budget and thyroid hormone levels of the langurs did not differ significantly between seasons, indicating that these langurs did not respond to seasonal changes in food by regulating behavior or reducing metabolism. The present study indicates that the gut microbiota's structure is related to digestion and energy absorption of these langurs, providing new perspectives on their adaptation to limestone forests. IMPORTANCE François' langur is a primate that particularly lives in karst regions. The adaptation of wild animals to karst habitats has been a hot topic in behavioral ecology and conservation biology. In this study, gut microbiota, behavior, and thyroid hormone data were integrated to understand the interaction of the langurs and limestone forests from the physiological response, providing basic data for assessing the adaptation of the langurs to the habitats. The responses of the langurs to environmental changes were explored from the seasonal variations in gut microbiota, which would help to further understand the adaptive strategies of species to environmental changes.

Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions

BACKGROUND

A growing body of evidence suggests that the gut microbiota is strongly linked to general human health. Microbiome-directed interventions, such as diet and exercise, are acknowledged as a viable and achievable strategy for preventing disorders and improving human health. However, due to the significant inter-individual diversity of the gut microbiota between subjects, lifestyle recommendations are expected to have distinct and highly variable impacts to the microbiome structure.

RESULTS

Here, through a large-scale meta-analysis including 1448 shotgun metagenomics samples obtained longitudinally from 396 individuals during lifestyle studies, we revealed Bacteroides stercoris, Prevotella copri, and Bacteroides vulgatus as biomarkers of microbiota's resistance to structural changes, and aromatic and non-aromatic amino acid biosynthesis as important regulator of microbiome dynamics. We established criteria for distinguishing between significant compositional changes from normal microbiota fluctuation and classified individuals based on their level of response. We further developed a machine learning model for predicting "responders" and "non-responders" independently of the type of intervention with an area under the curve of up to 0.86 in external validation cohorts of different ethnicities.

CONCLUSIONS

We propose here that microbiome-based stratification is possible for identifying individuals with highly plastic or highly resistant microbial structures. Identifying subjects that will not respond to generalized lifestyle therapeutic interventions targeting the restructuring of gut microbiota is important to ensure that primary end-points of clinical studies are reached. Video Abstract.

Nutritional redundancy in the human diet and its application in phenotype association studies

Studying human dietary intake may help us identify effective measures to treat or prevent many chronic diseases whose natural histories are influenced by nutritional factors. Here, by examining five cohorts with dietary intake data collected on different time scales, we show that the food intake profile varies substantially across individuals and over time, while the nutritional intake profile appears fairly stable. We refer to this phenomenon as 'nutritional redundancy' and attribute it to the nested structure of the food-nutrient network. This network enables us to quantify the level of nutritional redundancy for each diet assessment of any individual. Interestingly, this nutritional redundancy measure does not strongly correlate with any classical healthy diet scores, but its performance in predicting healthy aging shows comparable strength. Moreover, after adjusting for age, we find that a high nutritional redundancy is associated with lower risks of cardiovascular disease and type 2 diabetes.

Enterosignatures define common bacterial guilds in the human gut microbiome

The human gut microbiome composition is generally in a stable dynamic equilibrium, but it can deteriorate into dysbiotic states detrimental to host health. To disentangle the inherent complexity and capture the ecological spectrum of microbiome variability, we used 5,230 gut metagenomes to characterize signatures of bacteria commonly co-occurring, termed enterosignatures (ESs). We find five generalizable ESs dominated by either Bacteroides, Firmicutes, Prevotella, Bifidobacterium, or Escherichia. This model confirms key ecological characteristics known from previous enterotype concepts, while enabling the detection of gradual shifts in community structures. Temporal analysis implies that the Bacteroides-associated ES is "core" in the resilience of westernized gut microbiomes, while combinations with other ESs often complement the functional spectrum. The model reliably detects atypical gut microbiomes correlated with adverse host health conditions and/or the presence of pathobionts. ESs provide an interpretable and generic model that enables an intuitive characterization of gut microbiome composition in health and disease.

Gut microbiome composition may be an indicator of preclinical Alzheimer's disease

Alzheimer's disease (AD) pathology is thought to progress from normal cognition through preclinical disease and ultimately to symptomatic AD with cognitive impairment. Recent work suggests that the gut microbiome of symptomatic patients with AD has an altered taxonomic composition compared with that of healthy, cognitively normal control individuals. However, knowledge about changes in the gut microbiome before the onset of symptomatic AD is limited. In this cross-sectional study that accounted for clinical covariates and dietary intake, we compared the taxonomic composition and gut microbial function in a cohort of 164 cognitively normal individuals, 49 of whom showed biomarker evidence of early preclinical AD. Gut microbial taxonomic profiles of individuals with preclinical AD were distinct from those of individuals without evidence of preclinical AD. The change in gut microbiome composition correlated with β-amyloid (Aβ) and tau pathological biomarkers but not with biomarkers of neurodegeneration, suggesting that the gut microbiome may change early in the disease process. We identified specific gut bacterial taxa associated with preclinical AD. Inclusion of these microbiome features improved the accuracy, sensitivity, and specificity of machine learning classifiers for predicting preclinical AD status when tested on a subset of the cohort (65 of the 164 participants). Gut microbiome correlates of preclinical AD neuropathology may improve our understanding of AD etiology and may help to identify gut-derived markers of AD risk.

The gut microbiome modifies the associations of short- and long-term physical activity with body weight changes

BACKGROUND

The gut microbiome regulates host energy balance and adiposity-related metabolic consequences, but it remains unknown how the gut microbiome modulates body weight response to physical activity (PA).

METHODS

Nested in the Health Professionals Follow-up Study, a subcohort of 307 healthy men (mean[SD] age, 70[4] years) provided stool and blood samples in 2012-2013. Data from cohort long-term follow-ups and from the accelerometer, doubly labeled water, and plasma biomarker measurements during the time of stool collection were used to assess long-term and short-term associations of PA with adiposity. The gut microbiome was profiled by shotgun metagenomics and metatranscriptomics. A subcohort of 209 healthy women from the Nurses' Health Study II was used for validation.

RESULTS

The microbial species Alistipes putredinis was found to modify the association between PA and body weight. Specifically, in individuals with higher abundance of A. putredinis, each 15-MET-hour/week increment in long-term PA was associated with 2.26 kg (95% CI, 1.53-2.98 kg) less weight gain from age 21 to the time of stool collection, whereas those with lower abundance of A. putredinis only had 1.01 kg (95% CI, 0.41-1.61 kg) less weight gain (pinteraction = 0.019). Consistent modification associated with A. putredinis was observed for short-term PA in relation to BMI, fat mass%, plasma HbA1c, and 6-month weight change. This modification effect might be partly attributable to four metabolic pathways encoded by A. putredinis, including folate transformation, fatty acid β-oxidation, gluconeogenesis, and stearate biosynthesis.

CONCLUSIONS

A greater abundance of A. putredinis may strengthen the beneficial association of PA with body weight change, suggesting the potential of gut microbial intervention to improve the efficacy of PA in body weight management. Video Abstract.

Methods in Nutrition & Gut Microbiome Research: An American Society for Nutrition Satellite Session [13 October 2022]

The microbial cells colonizing the human body form an ecosystem that is integral to the regulation and maintenance of human health. Elucidation of specific associations between the human microbiome and health outcomes is facilitating the development of microbiome-targeted recommendations and treatments (e.g., fecal microbiota transplant; pre-, pro-, and post-biotics) to help prevent and treat disease. However, the potential of such recommendations and treatments to improve human health has yet to be fully realized. Technological advances have led to the development and proliferation of a wide range of tools and methods to collect, store, sequence, and analyze microbiome samples. However, differences in methodology at each step in these analytic processes can lead to variability in results due to the unique biases and limitations of each component. This technical variability hampers the detection and validation of associations with small to medium effect sizes. Therefore, the American Society for Nutrition (ASN) Nutritional Microbiology Group Engaging Members (GEM), sponsored by the Institute for the Advancement of Food and Nutrition Sciences (IAFNS), hosted a satellite session on methods in nutrition and gut microbiome research to review currently available methods for microbiome research, best practices, as well as tools and standards to aid in comparability of methods and results. This manuscript summarizes the topics and research discussed at the session. Consideration of the guidelines and principles reviewed in this session will increase the accuracy, precision, and comparability of microbiome research and ultimately the understanding of the associations between the human microbiome and health.

Stress, inflammation, microbiome and depression

Psychiatric disorders are mental illnesses involving changes in mood, cognition and behavior. Their prevalence has rapidly increased in the last decades. One of the most prevalent psychiatric disorders is major depressive disorder (MDD), a debilitating disease lacking efficient treatments. Increasing evidence shows that microbial and immunological changes contribute to the pathophysiology of depression and both are modulated by stress. This bidirectional relationship constitutes the brain-gut axis involving various neuroendocrine, immunological, neuroenterocrine and autonomic pathways. The present review covers the most recent findings on the relationships between stress, the gut microbiome and the inflammatory response and their contribution to depression.

An in vitro platform for study of the human gut microbiome under an oxygen gradient

The complex, dynamic environment of the human lower gastrointestinal tract is colonized by hundreds of bacterial species that impact health and performance. Ex vivo study of the functional interactions between microbial community members in conditions representative of those in the gut is an ongoing challenge. We have developed an in vitro 40-plex platform that provides an oxygen gradient to support simultaneous maintenance of microaerobic and anaerobic microbes from the gut microbiome that can aid in rapid characterization of microbial interactions and direct comparison of individual microbiome samples. In this report, we demonstrate that the platform more closely maintained the microbial diversity and composition of human donor fecal microbiome samples than strict anaerobic conditions. The oxygen gradient established in the platform allowed the stratification and subsequent sampling of diverse microbial subpopulations that colonize microaerobic and anaerobic micro-environments. With the ability to run forty samples in parallel, the platform has the potential to be used as a rapid screening tool to understand how the gut microbiome responds to environmental perturbations such as toxic compound exposure, dietary changes, or pharmaceutical treatments.

Endogenous small intestinal microbiome determinants of transient colonisation efficiency by bacteria from fermented dairy products: a randomised controlled trial

BACKGROUND

The effects of fermented food consumption on the small intestine microbiome and its role on host homeostasis are largely uncharacterised as our knowledge on intestinal microbiota relies mainly on faecal samples analysis. We investigated changes in small intestinal microbial composition and functionality, short chain fatty acid (SCFA) profiles, and on gastro-intestinal (GI) permeability in ileostomy subjects upon the consumption of fermented milk products.

RESULTS

We report the results from a randomised, cross-over, explorative study where 16 ileostomy subjects underwent 3, 2-week intervention periods. In each period, they consumed either milk fermented by Lacticaseibacillus rhamnosus CNCM I-3690, or milk fermented by Streptococcus thermophilus CNCM I-1630 and Lactobacillus delbrueckii subsp. bulgaricus CNCM I-1519, or a chemically acidified milk (placebo) daily. We performed metataxonomic, metatranscriptomic analysis, and SCFA profiling of ileostomy effluents as well as a sugar permeability test to investigate the microbiome impact of these interventions and their potential effect on mucosal barrier function. Consumption of the intervention products impacted the overall small intestinal microbiome composition and functionality, mainly due to the introduction of the product-derived bacteria that reach in several samples 50% of the total microbial community. The interventions did not affect the SCFA levels in ileostoma effluent, or gastro-intestinal permeability and the effects on the endogenous microbial community were negligible. The impact on microbiome composition was highly personalised, and we identified the poorly characterised bacterial family, Peptostreptococcaceae, to be positively associated with a low abundance of the ingested bacteria. Activity profiling of the microbiota revealed that carbon- versus amino acid-derived energy metabolism of the endogenous microbiome could be responsible for the individual-specific intervention effects on the small intestine microbiome composition and function, reflected also on urine microbial metabolites generated through proteolytic fermentation.

CONCLUSIONS

The ingested bacteria are the main drivers of the intervention effect on the small intestinal microbiota composition. Their transient abundance level is highly personalised and influenced by the energy metabolism of the ecosystem that is reflected by its microbial composition ( http://www.

CLINICALTRIALS

gov , ID NCT NCT02920294). Video Abstract.

Prospective and Cross-sectional Associations of the Rectal Tissue Microbiome with Colorectal Adenoma Recurrence

BACKGROUND

The gut microbiome is plausibly associated with colorectal cancer risk; however, previous studies mostly investigated this association cross-sectionally. We investigated cross-sectional and prospective associations of the rectal tissue microbiome with adenoma recurrence in the Polyp Prevention Trial (PPT).

METHODS

PPT is a 4-year randomized clinical trial of the effect of a dietary intervention on adenoma recurrence among community members. We extracted DNA from rectal biopsies at baseline, end of year 1, and end of year 4 among 455 individuals and sequenced the V4 region of the 16S rRNA gene. At each timepoint, we investigated associations of alpha diversity, beta diversity, and presence and relative abundance of select taxa with adenoma recurrence using multivariable logistic regression.

RESULTS

Variation in beta diversity was primarily explained by subject and minimally by year of collection or time between biopsy and colonoscopy. Cross-sectionally, year 4 alpha diversity was strongly, inversely associated with adenoma prevalence [ORQ3 vs. Q1 Shannon index = 0.40 (95% confidence interval, CI: 0.21-0.76)]. Prospective alpha diversity associations (i.e., baseline/year 1 alpha diversity with adenoma recurrence 3-4 years later) were weak or null, as were cross-sectional and prospective beta diversity-adenoma associations. Bacteroides abundance was more strongly, positively associated with adenoma prevalence cross-sectionally than prospectively.

CONCLUSIONS

Rectal tissue microbiome profiles may be associated with prevalent adenomas, with little evidence supporting prospective associations.

IMPACT

Additional prospective studies, with serial fecal and tissue samples, to explore microbiome-colorectal cancer associations are needed. Eventually, it may be possible to use microbiome characteristics as intervenable risk factors or screening tools.

Understanding human health through metatranscriptomics

Metatranscriptomics has revolutionized our ability to explore and understand transcriptional programs in microbial communities. Moreover, it has enabled us to gain deeper and more specific insight into the microbial activities in human gut, respiratory, oral, and vaginal communities. Perhaps the most important contribution of metatranscriptomics arises, however, from the analyses of disease-associated communities. We review the advantages and disadvantages of metatranscriptomics analyses in understanding human health and disease. We focus on human tissues low in microbial biomass and conditions associated with dysbiotic microbiota. We conclude that a more widespread use of metatranscriptomics and increased knowledge on microbe activities will uncover critical interactions between microbes and host in human health and provide diagnostic basis for culturing-independent, direct functional pathogen identification.

Controlling the human microbiome

We coexist with a vast number of microbes that live in and on our bodies. Those microbes and their genes are collectively known as the human microbiome, which plays important roles in human physiology and diseases. We have acquired extensive knowledge of the organismal compositions and metabolic functions of the human microbiome. However, the ultimate proof of our understanding of the human microbiome is reflected in our ability to manipulate it for health benefits. To facilitate the rational design of microbiome-based therapies, there are many fundamental questions to be addressed at the systems level. Indeed, we need a deep understanding of the ecological dynamics associated with such a complex ecosystem before we rationally design control strategies. In light of this, this review discusses progress from various fields, e.g., community ecology, network science, and control theory, that are helping us make progress toward the ultimate goal of controlling the human microbiome.

Physical activity and diet associations with the gut microbiota in the Coronary Artery Risk Development in Young Adults (CARDIA) study

BACKGROUND

Gut microbiota may influence metabolic pathways related to chronic health conditions. Evidence for physical activity and diet influences on gut microbial composition exists, but data from diverse population-based cohort studies are limited.

OBJECTIVES

We hypothesized that gut microbial diversity and genera are associated with physical activity and diet quality.

METHODS

Data were from 537 participants in the Coronary Artery Risk Development in Young Adults (CARDIA) Study, a prospective cohort, who attended the year 30 follow-up examination (2015-2016; aged 47-61 y; 45% Black race/55% White race; 45% men/55% women). The 16S ribosomal RNA marker gene was sequenced from stool DNA, and genus-level taxonomy was assigned. Within-person microbial diversity (α-diversity) was assessed with Shannon diversity index and richness scores; between-person diversity (β-diversity) measures were generated with principal coordinates analysis (PCoA). Current and long-term physical activity and diet quality measures were derived from data collected over 30 y of follow-up. Multivariable-adjusted regression analysis controlled for: sociodemographic variables (age, race, sex, education, and field center), other health behaviors (smoking, alcohol consumption, and medication use), and adjusted for multiple comparisons with the false discovery rate (<0.20).

RESULTS

Based on PCoA β-diversity, participants' microbial community compositions differed significantly (P < 0.001), with respect to both current and long-term physical activity and diet quality. α-Diversity was associated only with current physical activity (positively) in multivariable-adjusted analysis. Multiple genera (n = 45) were associated with physical activity and fewer with diet (n = 5), including positive associations with Lachnospiraceae UCG-001 and Ruminococcaceae IncertaeSedis with both behaviors.

CONCLUSIONS

Physical activity and diet quality were associated with gut microbial composition among 537 participants in the CARDIA study. Multiple genera were associated with physical activity. Physical activity and diet quality were associated with genera consistent with pathways related to inflammation and short-chain fatty acid production.

The person-to-person transmission landscape of the gut and oral microbiomes

The human microbiome is an integral component of the human body and a co-determinant of several health conditions1,2. However, the extent to which interpersonal relations shape the individual genetic makeup of the microbiome and its transmission within and across populations remains largely unknown3,4. Here, capitalizing on more than 9,700 human metagenomes and computational strain-level profiling, we detected extensive bacterial strain sharing across individuals (more than 10 million instances) with distinct mother-to-infant, intra-household and intra-population transmission patterns. Mother-to-infant gut microbiome transmission was considerable and stable during infancy (around 50% of the same strains among shared species (strain-sharing rate)) and remained detectable at older ages. By contrast, the transmission of the oral microbiome occurred largely horizontally and was enhanced by the duration of cohabitation. There was substantial strain sharing among cohabiting individuals, with 12% and 32% median strain-sharing rates for the gut and oral microbiomes, and time since cohabitation affected strain sharing more than age or genetics did. Bacterial strain sharing additionally recapitulated host population structures better than species-level profiles did. Finally, distinct taxa appeared as efficient spreaders across transmission modes and were associated with different predicted bacterial phenotypes linked with out-of-host survival capabilities. The extent of microorganism transmission that we describe underscores its relevance in human microbiome studies5, especially those on non-infectious, microbiome-associated diseases.

Microbiome epidemiology and association studies in human health

Studies of the human microbiome share both technical and conceptual similarities with genome-wide association studies and genetic epidemiology. However, the microbiome has many features that differ from genomes, such as its temporal and spatial variability, highly distinct genetic architecture and person-to-person variation. Moreover, there are various potential mechanisms by which distinct aspects of the human microbiome can relate to health outcomes. Recent advances, including next-generation sequencing and the proliferation of multi-omic data types, have enabled the exploration of the mechanisms that connect microbial communities to human health. Here, we review the ways in which features of the microbiome at various body sites can influence health outcomes, and we describe emerging opportunities and future directions for advanced microbiome epidemiology.

Impact of Data and Study Characteristics on Microbiome Volatility Estimates

The human microbiome is a dynamic community of bacteria, viruses, fungi, and other microorganisms. Both the composition of the microbiome (the microbes that are present and their relative abundances) and the temporal variability of the microbiome (the magnitude of changes in their composition across time, called volatility) has been associated with human health. However, the effect of unbalanced sampling intervals and differential read depth on the estimates of microbiome volatility has not been thoroughly assessed. Using four publicly available gut and vaginal microbiome time series, we subsampled the datasets to several sampling intervals and read depths and then compared additive, multiplicative, centered log ratio (CLR)-based, qualitative, and distance-based measures of microbiome volatility between the conditions. We find that longer sampling intervals are associated with larger quantitative measures of change (particularly for common taxa), but not with qualitative measures of change or distance-based volatility quantification. A lower sequencing read depth is associated with smaller multiplicative, CLR-based, and qualitative measures of change (particularly for less common taxa). Strategic subsampling may serve as a useful sensitivity analysis in unbalanced longitudinal studies investigating clinical associations with microbiome volatility.

Alterations in fecal β-defensin-3 secretion as a marker of instability of the gut microbiota

Compositional changes in the microbiota (dysbiosis) may be a basis for Irritable Bowel Syndrome (IBS), but biomarkers are currently unavailable to direct microbiota-directed therapy. We therefore examined whether changes in fecal β-defensin could be a marker of dysbiosis in a murine model. Experimental dysbiosis was induced using four interventions relevant to IBS: a mix of antimicrobials, westernized diets (high-fat/high-sugar and high salt diets), or mild restraint stress. Fecal mouse β-defensin-3 and 16S rRNA-based microbiome profiles were assessed at baseline and during and following these interventions. Each intervention, except for mild restraint stress, altered compositional and diversity profiles of the microbiota. Exposure to antimicrobials or a high-fat/high-sugar diet, but not mild restraint stress, resulted in decreased fecal β-defensin-3 compared to baseline. In contrast, exposure to the high salt diet increased β-defensin-3 compared to baseline. Mice exposed to the mix of antimicrobials showed the largest compositional changes and the most significant correlations between β-defensin-3 levels and bacterial diversity. The high salt diet was also associated with significant correlations between changes in β-defensin-3 and bacterial diversity, and this was not accompanied by discernible inflammatory changes in the host. Thus, dietary change or antimicrobial exposure, both recognized factors in IBS exacerbations, induced marked dysbiosis that was accompanied by changes in fecal β-defensin-3 levels. We propose that serial monitoring of fecal β-defensins may serve as a marker of dysbiosis and help identify those IBS patients who may benefit from microbiota-directed therapeutic interventions.

Time-scale analysis of the long-term variability of human gut microbiota characteristics in Chinese individuals

Studying the dynamics and stability of the human gut microbiota over time is important for exploring their relationship with human health and developing treatment strategies for putative microbiome-related ailments. Here, we collected stool samples from seven healthy Chinese subjects at 1-month intervals between 2016 and 2020. Sequencing and bioinformatics analyses revealed that the bacteria in the collected fecal samples fluctuated over time, and the extent of these changes increased over time. Further, the average shared proportion value obtained using Sourcetracker2 was 63.5% for samples collected from the same individual in the preceding month, and over a 3-year period, this value decreased to 40.7%. Furthermore, the proportion of different bacteria in the gut microbiota of the different subjects fluctuated to varying degrees. Therefore, our results suggested that it is important to consider the effect of time on gut microbiota composition when it is used to evaluate health. Our study opens up a new field of microbiota research, considering not just the instantaneous microbiota, but also the change of the gut microbiota over time.

Broad host range may be a key to long-term persistence of bacteriophages infecting intestinal Bacteroidaceae species

The longitudinal studies have found that the human gut microbiota is stable over time with some major bacterial lineages or even strains persisting for years. This was recently extended to gut bacteriophages using the metagenomic data. Here, we focused on cultivation of the major Bacteroidetes of human gut, the Bacteroides and Phocaeicola strains, and their bacteriophages from two healthy donors. The persistence of Bacteroides and Phocaeicola species and strains was confirmed. We isolated 28 genetically different phages grouped into seven distinct clusters, two of these were new. Moreover, the bacteriophages from several groups, although being genetically quite homogeneous, had the ability to infect the strains belonging to different species isolated from several sampling time-points and different donors. We propose that the ability to infect several host species, which differ in their nutritional niches, may promote long-term persistence of dominant gut bacteriophage groups.

The impact of Traditional Chinese Medicine on mouse gut microbiota abundances and interactions based on Granger causality and pathway analysis

Objectives

The intestinal microbiota is essential in absorbing nutrients and defending against pathogens and is associated with various diseases, including obesity, type 2 diabetes, and hypertension. As an alternative medicine, Traditional Chinese Medicine (TCM) has long been used in disease treatment and healthcare, partly because it may mediate gut microbiota. However, the specific effects of TCM on the abundance and interactions of microbiota remain unknown. Moreover, using TCM ingredients and data detailing changes in the abundance of gut microorganisms, we developed bioinformatic methods that decipher the impact of TCM on microorganism interactions.

Methods

The dynamics of gut microorganisms affected by TCM treatments is explored using a mouse model, which provided the abundance of 70 microorganisms over time. The Granger causality analysis was used to measure microorganism interactions. Novel "serial connection" and "diverging connection" models were used to identify molecular mechanisms underlying the impact of TCM on gut microorganism interactions, based on microorganism proteins, TCM chemical ingredients, and KEGG reaction equations.

Results

Codonopsis pilosula (Dangshen), Cassia twig (Gui Zhi), Radices saussureae (Mu Xiang), and Sijunzi Decoction did not cause an increase in the abundance of harmful microorganisms. Most TCMs decreased the abundance of Bifidobacterium pseudolongum, suggesting a Bifidobacterium pseudolongum supplement should be used during TCM treatment. The Granger causality analysis indicated that TCM treatment changes more than half the interactions between the 70 microorganisms, and "serial connection" and "diverging connection" models suggested that changes in interactions may be related to the reaction number connecting species proteins and TCM ingredients. From a species diversity perspective, a TCM decoction is better than a single herb for healthcare. The Sijunzi Decoction only significantly increased the abundance of Bifidobacterium pseudolongum and did not cause a decrease in the abundance of other species but was found to improve the alpha diversity with the lowest replacement rate.

Conclusions

Because most of the nine TCMs are medicinal and edible plants, we expect the methods and results presented can be used to optimize and integrate microbiota and TCMs into healthcare processes. Moreover, as a control study, these results can be combined with future disease mouse models to link variations in species abundance with particular diseases.

The Gut Microbiome and Colonic Motility Disorders: A Practical Framework for the Gastroenterologist

PURPOSE OF REVIEW

Colonic motility disorders may be influenced by the gut microbiota, which plays a role in modulating sensory and motor function. However, existing data are inconsistent, possibly due to complex disease pathophysiology, fluctuation in symptoms, and difficulty characterizing high-resolution taxonomic composition and function of the gut microbiome.

RECENT FINDINGS

Increasingly, human studies have reported associations between gut microbiome features and colonic motility disorders, such as irritable bowel syndrome and constipation. Several microbial metabolites have been identified as regulators of colonic motility in animal models. Modulation of the gut microbiota via dietary intervention, probiotics, and fecal microbiota transplant is a promising avenue for treatment for these diseases. An integration of longitudinal multi-omics data will facilitate further understanding of the causal effects of dysbiosis on disease. Further understanding of the microbiome-driven mechanisms underlying colonic motility disorders may be leveraged to develop personalized, microbiota-based approaches for disease prevention and treatment.

Machine learning on the road to unlocking microbiota's potential for boosting immune checkpoint therapy

The intestinal microbiota is a complex and diverse ecological community that fulfills multiple functions and substantially impacts human health. Despite its plasticity, unfavorable conditions can cause perturbations leading to so-called dysbiosis, which have been connected to multiple diseases. Unfortunately, understanding the mechanisms underlying the crosstalk between those microorganisms and their host is proving to be difficult. Traditionally used bioinformatic tools have difficulties to fully exploit big data generated for this purpose by modern high throughput screens. Machine Learning (ML) may be a potential means of solving such problems, but it requires diligent application to allow for drawing valid conclusions. This is especially crucial as gaining insight into the mechanistic basis of microbial impact on human health is highly anticipated in numerous fields of study. This includes oncology, where growing amounts of studies implicate the gut ecosystems in both cancerogenesis and antineoplastic treatment outcomes. Based on these reports and first signs of clinical benefits related to microbiota modulation in human trials, hopes are rising for the development of microbiome-derived diagnostics and therapeutics. In this mini-review, we're inspecting analytical approaches used to uncover the role of gut microbiome in immune checkpoint therapy (ICT) with the use of shotgun metagenomic sequencing (SMS) data.

MicrobiomeCensus estimates human population sizes from wastewater samples based on inter-individual variability in gut microbiomes

The metagenome embedded in urban sewage is an attractive new data source to understand urban ecology and assess human health status at scales beyond a single host. Analyzing the viral fraction of wastewater in the ongoing COVID-19 pandemic has shown the potential of wastewater as aggregated samples for early detection, prevalence monitoring, and variant identification of human diseases in large populations. However, using census-based population size instead of real-time population estimates can mislead the interpretation of data acquired from sewage, hindering assessment of representativeness, inference of prevalence, or comparisons of taxa across sites. Here, we show that taxon abundance and sub-species diversisty in gut-associated microbiomes are new feature space to utilize for human population estimation. Using a population-scale human gut microbiome sample of over 1,100 people, we found that taxon-abundance distributions of gut-associated multi-person microbiomes exhibited generalizable relationships with respect to human population size. Here and throughout this paper, the human population size is essentially the sample size from the wastewater sample. We present a new algorithm, MicrobiomeCensus, for estimating human population size from sewage samples. MicrobiomeCensus harnesses the inter-individual variability in human gut microbiomes and performs maximum likelihood estimation based on simultaneous deviation of multiple taxa’s relative abundances from their population means. MicrobiomeCensus outperformed generic algorithms in data-driven simulation benchmarks and detected population size differences in field data. New theorems are provided to justify our approach. This research provides a mathematical framework for inferring population sizes in real time from sewage samples, paving the way for more accurate ecological and public health studies utilizing the sewage metagenome.

Wastewater-based epidemiology (WBE) is an emerging field that employs sewage as aggregated samples of human populations. This approach is particularly promising for tracking diseases that can spread asymptomatically in large populations, such as the COVID-19. As a new type of biological data, sewage has its own unique challenges to utilize. While wastewater samples are usually assumed to represent large populations, the assumption is not guaranteed at locations closer to residences due to stochasticity in toilet flushes; thus, unlike epidemiological experiments collecting data from individuals, sample size, herein the human population size represented by a wastewater sample, is a fundamental yet difficult-to-characterize parameter for sewage samples. Researchers would need to aggregate data from large areas and week-long collection to stabilize data, during which, important spikes in small areas or short time scales may be lost. It also remains challenging to turn viral titers into case prevalences, evaluating representativeness, or comparing measurements across sites/studies.

This study provides a framework to estimate human population size from sewage utilizing human gut-associated microorganisms. Through analysis, we demonstrate that variance of taxon abundances and single-nucleotide polymorphism as two variables that change with population size. We provide a new tool MicrobiomeCensus that performs population size estimation from microbial taxon abundances. MicrobiomeCensus outperforms generic algorithms in terms of computational efficiency while at comparable or better accuracy. Using MicrobiomeCensus, we detected population size differences in sewage samples taken in Cambridge, MA, under two sampling approaches, i.e., “grab” or “composite” sampling. This study provides a framework to utilize individual-level microbiomes to learn from sewage, paving the way to prevalence estimation and improved spatio-temporal resolutions in WBE.