Showing papers by "Jeroen Raes published in 2021"

Large-scale association analyses identify host factors influencing human gut microbiome composition

Abstract: To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 of 410 genera were detected in more than 95% of samples. A genome-wide association study of host genetic variation regarding microbial taxa identified 31 loci affecting the microbiome at a genome-wide significant (P < 5 × 10−8) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (genome-wide association study signal: P = 1.28 × 10−20), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95 × 10−10 < P < 5 × 10−8) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization identified enrichment of microbiome trait loci in the metabolic, nutrition and environment domains and suggested the microbiome might have causal effects in ulcerative colitis and rheumatoid arthritis.

Monocyte-driven atypical cytokine storm and aberrant neutrophil activation as key mediators of COVID-19 disease severity.

Abstract: Epidemiological and clinical reports indicate that SARS-CoV-2 virulence hinges upon the triggering of an aberrant host immune response, more so than on direct virus-induced cellular damage. To elucidate the immunopathology underlying COVID-19 severity, we perform cytokine and multiplex immune profiling in COVID-19 patients. We show that hypercytokinemia in COVID-19 differs from the interferon-gamma-driven cytokine storm in macrophage activation syndrome, and is more pronounced in critical versus mild-moderate COVID-19. Systems modelling of cytokine levels paired with deep-immune profiling shows that classical monocytes drive this hyper-inflammatory phenotype and that a reduction in T-lymphocytes correlates with disease severity, with CD8+ cells being disproportionately affected. Antigen presenting machinery expression is also reduced in critical disease. Furthermore, we report that neutrophils contribute to disease severity and local tissue damage by amplification of hypercytokinemia and the formation of neutrophil extracellular traps. Together our findings suggest a myeloid-driven immunopathology, in which hyperactivated neutrophils and an ineffective adaptive immune system act as mediators of COVID-19 disease severity.

Local immune response to food antigens drives meal-induced abdominal pain

Abstract: Up to 20% of people worldwide develop gastrointestinal symptoms following a meal1, leading to decreased quality of life, substantial morbidity and high medical costs. Although the interest of both the scientific and lay communities in this issue has increased markedly in recent years, with the worldwide introduction of gluten-free and other diets, the underlying mechanisms of food-induced abdominal complaints remain largely unknown. Here we show that a bacterial infection and bacterial toxins can trigger an immune response that leads to the production of dietary-antigen-specific IgE antibodies in mice, which are limited to the intestine. Following subsequent oral ingestion of the respective dietary antigen, an IgE- and mast-cell-dependent mechanism induced increased visceral pain. This aberrant pain signalling resulted from histamine receptor H1-mediated sensitization of visceral afferents. Moreover, injection of food antigens (gluten, wheat, soy and milk) into the rectosigmoid mucosa of patients with irritable bowel syndrome induced local oedema and mast cell activation. Our results identify and characterize a peripheral mechanism that underlies food-induced abdominal pain, thereby creating new possibilities for the treatment of irritable bowel syndrome and related abdominal pain disorders.

Deep ocean metagenomes provide insight into the metabolic architecture of bathypelagic microbial communities.

Abstract: The deep sea, the largest ocean’s compartment, drives planetary-scale biogeochemical cycling. Yet, the functional exploration of its microbial communities lags far behind other environments. Here we analyze 58 metagenomes from tropical and subtropical deep oceans to generate the Malaspina Gene Database. Free-living or particle-attached lifestyles drive functional differences in bathypelagic prokaryotic communities, regardless of their biogeography. Ammonia and CO oxidation pathways are enriched in the free-living microbial communities and dissimilatory nitrate reduction to ammonium and H 2 oxidation pathways in the particle-attached, while the Calvin Benson-Bassham cycle is the most prevalent inorganic carbon fixation pathway in both size fractions. Reconstruction of the Malaspina Deep Metagenome-Assembled Genomes reveals unique non-cyanobacterial diazotrophic bacteria and chemolithoautotrophic prokaryotes. The widespread potential to grow both autotrophically and heterotrophically suggests that mixotrophy is an ecologically relevant trait in the deep ocean. These results expand our understanding of the functional microbial structure and metabolic capabilities of the largest Earth aquatic ecosystem.

Dysosmobacter welbionis is a newly isolated human commensal bacterium preventing diet-induced obesity and metabolic disorders in mice

Abstract: Objective To investigate the abundance and the prevalence of Dysosmobacter welbionis J115T, a novel butyrate-producing bacterium isolated from the human gut both in the general population and in subjects with metabolic syndrome. To study the impact of this bacterium on host metabolism using diet-induced obese and diabetic mice. Design We analysed the presence and abundance of the bacterium in 11 984 subjects using four human cohorts (ie, Human Microbiome Project, American Gut Project, Flemish Gut Flora Project and Microbes4U). Then, we tested the effects of daily oral gavages with live D. welbionis J115T on metabolism and several hallmarks of obesity, diabetes, inflammation and lipid metabolism in obese/diabetic mice. Results This newly identified bacterium was detected in 62.7%–69.8% of the healthy population. Strikingly, in obese humans with a metabolic syndrome, the abundance of Dysosmobacter genus correlates negatively with body mass index, fasting glucose and glycated haemoglobin. In mice, supplementation with live D. welbionis J115T, but not with the pasteurised bacteria, partially counteracted diet-induced obesity development, fat mass gain, insulin resistance and white adipose tissue hypertrophy and inflammation. In addition, live D. welbionis J115T administration protected the mice from brown adipose tissue inflammation in association with increased mitochondria number and non-shivering thermogenesis. These effects occurred with minor impact on the mouse intestinal microbiota composition. Conclusions These results suggest that D. welbionis J115T directly and beneficially influences host metabolism and is a strong candidate for the development of next-generation beneficial bacteria targeting obesity and associated metabolic diseases.

Novel insights into the genetically obese (ob/ob) and diabetic (db/db) mice: two sides of the same coin.

Abstract: Leptin-deficient ob/ob mice and leptin receptor-deficient db/db mice are commonly used mice models mimicking the conditions of obesity and type 2 diabetes development. However, although ob/ob and db/db mice are similarly gaining weight and developing massive obesity, db/db mice are more diabetic than ob/ob mice. It remains still unclear why targeting the same pathway—leptin signaling—leads to the development of two different phenotypes. Given that gut microbes dialogue with the host via different metabolites (e.g., short-chain fatty acids) but also contribute to the regulation of bile acids metabolism, we investigated whether inflammatory markers, bacterial components, bile acids, short-chain fatty acids, and gut microbes could contribute to explain the specific phenotype discriminating the onset of an obese and/or a diabetic state in ob/ob and db/db mice. Six-week-old ob/ob and db/db mice were followed for 7 weeks; they had comparable body weight, fat mass, and lean mass gain, confirming their severely obese status. However, as expected, the glucose metabolism and the glucose-induced insulin secretion were significantly different between ob/ob and db/db mice. Strikingly, the fat distribution was different, with db/db mice having more subcutaneous and ob/ob mice having more epididymal fat. In addition, liver steatosis was more pronounced in the ob/ob mice than in db/db mice. We also found very distinct inflammatory profiles between ob/ob and db/db mice, with a more pronounced inflammatory tone in the liver for ob/ob mice as compared to a higher inflammatory tone in the (subcutaneous) adipose tissue for db/db mice. When analyzing the gut microbiota composition, we found that the quantity of 19 microbial taxa was in some way affected by the genotype. Furthermore, we also show that serum LPS concentration, hepatic bile acid content, and cecal short-chain fatty acid profiles were differently affected by the two genotypes. Taken together, our results elucidate potential mechanisms implicated in the development of an obese or a diabetic state in two genetic models characterized by an altered leptin signaling. We propose that these differences could be linked to specific inflammatory tones, serum LPS concentration, bile acid metabolism, short-chain fatty acid profile, and gut microbiota composition.

Human and preclinical studies of the host-gut microbiome co-metabolite hippurate as a marker and mediator of metabolic health.

Abstract: Objective Gut microbial products are involved in regulation of host metabolism. In human and experimental studies, we explored the potential role of hippurate, a hepatic phase 2 conjugation product of microbial benzoate, as a marker and mediator of metabolic health. Design In 271 middle-aged non-diabetic Danish individuals, who were stratified on habitual dietary intake, we applied 1H-nuclear magnetic resonance (NMR) spectroscopy of urine samples and shotgun-sequencing-based metagenomics of the gut microbiome to explore links between the urine level of hippurate, measures of the gut microbiome, dietary fat and markers of metabolic health. In mechanistic experiments with chronic subcutaneous infusion of hippurate to high-fat-diet-fed obese mice, we tested for causality between hippurate and metabolic phenotypes. Results In the human study, we showed that urine hippurate positively associates with microbial gene richness and functional modules for microbial benzoate biosynthetic pathways, one of which is less prevalent in the Bacteroides 2 enterotype compared with Ruminococcaceae or Prevotella enterotypes. Through dietary stratification, we identify a subset of study participants consuming a diet rich in saturated fat in which urine hippurate concentration, independently of gene richness, accounts for links with metabolic health. In the high-fat-fed mice experiments, we demonstrate causality through chronic infusion of hippurate (20 nmol/day) resulting in improved glucose tolerance and enhanced insulin secretion. Conclusion Our human and experimental studies show that a high urine hippurate concentration is a general marker of metabolic health, and in the context of obesity induced by high-fat diets, hippurate contributes to metabolic improvements, highlighting its potential as a mediator of metabolic health.

Clinical practices underlie COVID-19 patient respiratory microbiome composition and its interactions with the host.

Abstract: Understanding the pathology of COVID-19 is a global research priority. Early evidence suggests that the respiratory microbiome may be playing a role in disease progression, yet current studies report contradictory results. Here, we examine potential confounders in COVID-19 respiratory microbiome studies by analyzing the upper (n = 58) and lower (n = 35) respiratory tract microbiome in well-phenotyped COVID-19 patients and controls combining microbiome sequencing, viral load determination, and immunoprofiling. We find that time in the intensive care unit and type of oxygen support, as well as associated treatments such as antibiotic usage, explain the most variation within the upper respiratory tract microbiome, while SARS-CoV-2 viral load has a reduced impact. Specifically, mechanical ventilation is linked to altered community structure and significant shifts in oral taxa previously associated with COVID-19. Single-cell transcriptomics of the lower respiratory tract of COVID-19 patients identifies specific oral bacteria in physical association with proinflammatory immune cells, which show higher levels of inflammatory markers. Overall, our findings suggest confounders are driving contradictory results in current COVID-19 microbiome studies and careful attention needs to be paid to ICU stay and type of oxygen support, as bacteria favored in these conditions may contribute to the inflammatory phenotypes observed in severe COVID-19 patients.

Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases.

Abstract: While metagenomic sequencing has become the tool of preference to study host-associated microbial communities, downstream analyses and clinical interpretation of microbiome data remains challenging due to the sparsity and compositionality of sequence matrices. Here, we evaluate both computational and experimental approaches proposed to mitigate the impact of these outstanding issues. Generating fecal metagenomes drawn from simulated microbial communities, we benchmark the performance of thirteen commonly used analytical approaches in terms of diversity estimation, identification of taxon-taxon associations, and assessment of taxon-metadata correlations under the challenge of varying microbial ecosystem loads. We find quantitative approaches including experimental procedures to incorporate microbial load variation in downstream analyses to perform significantly better than computational strategies designed to mitigate data compositionality and sparsity, not only improving the identification of true positive associations, but also reducing false positive detection. When analyzing simulated scenarios of low microbial load dysbiosis as observed in inflammatory pathologies, quantitative methods correcting for sampling depth show higher precision compared to uncorrected scaling. Overall, our findings advocate for a wider adoption of experimental quantitative approaches in microbiome research, yet also suggest preferred transformations for specific cases where determination of microbial load of samples is not feasible. Here, the authors use simulated quantitative gut microbial communities to benchmark the performance of 13 common data transformations in determining diversity as well as microbe-microbe and microbe-metadata associations, finding that quantitative approaches incorporating microbial load variation outperform computational strategies in downstream analyses, urging for a widespread adoption of quantitative approaches, or recommending specific computational transformations whenever determination of microbial load of samples is not feasible.

Disentangling environmental effects in microbial association networks.

Abstract: Background Ecological interactions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions through associations across time and space, which can be represented as association networks. Associations could result from either ecological interactions between microorganisms, or from environmental selection, where the association is environmentally driven. Therefore, before downstream analysis and interpretation, we need to distinguish the nature of the association, particularly if it is due to environmental selection or not. Results We present EnDED (environmentally driven edge detection), an implementation of four approaches as well as their combination to predict which links between microorganisms in an association network are environmentally driven. The four approaches are sign pattern, overlap, interaction information, and data processing inequality. We tested EnDED on networks from simulated data of 50 microorganisms. The networks contained on average 50 nodes and 1087 edges, of which 60 were true interactions but 1026 false associations (i.e., environmentally driven or due to chance). Applying each method individually, we detected a moderate to high number of environmentally driven edges-87% sign pattern and overlap, 67% interaction information, and 44% data processing inequality. Combining these methods in an intersection approach resulted in retaining more interactions, both true and false (32% of environmentally driven associations). After validation with the simulated datasets, we applied EnDED on a marine microbial network inferred from 10 years of monthly observations of microbial-plankton abundance. The intersection combination predicted that 8.3% of the associations were environmentally driven, while individual methods predicted 24.8% (data processing inequality), 25.7% (interaction information), and up to 84.6% (sign pattern as well as overlap). The fraction of environmentally driven edges among negative microbial associations in the real network increased rapidly with the number of environmental factors. Conclusions To reach accurate hypotheses about ecological interactions, it is important to determine, quantify, and remove environmentally driven associations in marine microbial association networks. For that, EnDED offers up to four individual methods as well as their combination. However, especially for the intersection combination, we suggest using EnDED with other strategies to reduce the number of false associations and consequently the number of potential interaction hypotheses. Video abstract.

Gut Microbiome Profiling Uncovers a Lower Abundance of Butyricicoccus in Advanced Stages of Chronic Kidney Disease

Abstract: Chronic kidney disease (CKD) is characterized by the accumulation of uremic toxins which exert deleterious effects on various organ systems. Several of these uremic toxins originate from the bacterial metabolization of aromatic amino acids in the colon. This study assessed whether the gut microbial composition varies among patients in different stages of CKD. Uremic metabolites were quantified by UPLC/fluorescence detection and microbial profiling by 16S rRNA amplicon sequencing. Gut microbial profiles of CKD patients were compared among stages 1–2, stage 3 and stages 4–5. Although a substantial inter-individual difference in abundance of the top 15 genera was observed, no significant difference was observed between groups. Bristol stool scale (BSS) correlated negatively with p-cresyl sulfate and hippuric acid levels, irrespective of the intake of laxatives. Butyricicoccus, a genus with butyrate-generating properties, was decreased in abundance in advanced stages of CKD compared to the earlier stages (p = 0.043). In conclusion, in this cross-sectional study no gradual differences in the gut microbial profile over the different stages of CKD were observed. However, the decrease in the abundance of Butyricicoccus genus with loss of kidney function stresses the need for more in-depth functional exploration of the gut microbiome in CKD patients not on dialysis.

Unstable regulatory T cells, enriched for naïve and Nrp1neg cells, are purged after fate challenge.

Abstract: Regulatory T cells (Tregs) are indispensable for the control of immune homeostasis and have clinical potential as a cell therapy for treating autoimmunity. Tregs can lose expression of the lineage-defining Foxp3 transcription factor and acquire effector T cell (Teff) characteristics, a process referred to as Treg plasticity. The extent and reversibility of such plasticity during immune responses remain unknown. Here, using a murine genetic fate-mapping system, we show that Treg stability is maintained even during exposure to a complex microbial/antigenic environment. Furthermore, we demonstrate that the observed plasticity of Tregs after adoptive transfer into a lymphopenic environment is a property limited to only a subset of the Treg population, with the nonconverting majority of Tregs being resistant to plasticity upon secondary stability challenge. The unstable Treg fraction is a complex mixture of phenotypically distinct Tregs, enriched for naive and neuropilin-1–negative Tregs, and includes peripherally induced Tregs and recent thymic emigrant Tregs. These results suggest that a “purging” process can be used to purify stable Tregs that are capable of robust fate retention, with potential implications for improving cell transfer therapy.

Null-model-based network comparison reveals core associations

Abstract: Microbial network construction and analysis is an important tool in microbial ecology. Such networks are often constructed from statistically inferred associations and may not represent ecological interactions. Hence, microbial association networks are error prone and do not necessarily reflect true community structure. We have developed anuran, a toolbox for investigation of noisy networks with null models. Such models allow researchers to generate data under the null hypothesis that all associations are random, supporting identification of nonrandom patterns in groups of association networks. This toolbox compares multiple networks to identify conserved subsets (core association networks, CANs) and other network properties that are shared across all networks. We apply anuran to a time series of fecal samples from 20 women to demonstrate the existence of CANs in a subset of the sampled individuals. Moreover, we use data from the Global Sponge Project to demonstrate that orders of sponges have a larger CAN than expected at random. In conclusion, this toolbox is a resource for investigators wanting to compare microbial networks across conditions, time series, gradients, or hosts.

Interactions between soil compositions and the wheat root microbiome under drought stress: From an in silico to in planta perspective.

Abstract: As wheat (Triticum aestivum) is an important staple food across the world, preservation of stable yields and increased productivity are major objectives in breeding programs. Drought is a global concern because its adverse impact is expected to be amplified in the future due to the current climate change. Here, we analyzed the effects of edaphic, environmental, and host factors on the wheat root microbiomes collected in soils from six regions in Belgium. Amplicon sequencing analysis of unplanted soil and wheat root endosphere samples indicated that the microbial community variations can be significantly explained by soil pH, microbial biomass, wheat genotype, and soil sodium and iron levels. Under drought stress, the biodiversity in the soil decreased significantly, but increased in the root endosphere community, where specific soil parameters seemingly determine the enrichment of bacterial groups. Indeed, we identified a cluster of drought-enriched bacteria that significantly correlated with soil compositions. Interestingly, integration of a functional analysis further revealed a strong correlation between the same cluster of bacteria and β-glucosidase and osmoprotectant proteins, two functions known to be involved in coping with drought stress. By means of this in silico analysis, we identified amplicon sequence variants (ASVs) that could potentially protect the plant from drought stress and validated them in planta. Yet, ASVs based on 16S rRNA sequencing data did not completely distinguish individual isolates because of their intrinsic short sequences. Our findings support the efforts to maintain stable crop yields under drought conditions through implementation of root microbiome analyses.

Specific contributions of segmental transit times to gut microbiota composition.

Abstract: We read with great interest the study by Asnicar and colleagues, describing the exploration of gut microbiota composition in relation to gut transit time using the ‘blue dye’ method.1 In agreement with previous research, the authors provided convincing evidence that longer gut transit times are linked to increasing relative abundances of microbial species (ie, Akkermansia muciniphila, Bacteroides spp. and Alistipes spp. ).1–3 However, despite practical advantages, the ‘blue dye’ method does not allow to distinguish between segmental transit times. In a recent study, we investigated segmental transit time and passage-related variation in pressure and pH in 22 individuals (11 subjects with normal weight and 11 subjects with obesity) using a wireless motility capsule (SmartPill©).4 During capsule passage, participants were asked to collect a faecal sample. Samples were subjected to 16S rRNA gene amplicon sequencing according to Valles-Colomer et al .5 Associations between clinical and SmartPill© variables and faecal microbiota community variation were assessed using single and stepwise multivariate distance-based redundancy analyses (dbRDA). Next, we explored associations between the microbiome covariates identified and participants’ enterotypes using Kruskal-Wallis test with post-hoc pairwise Dunn’s test.6 Finally, correlations between relative abundances of genera and significant variables were assessed …

Successional stages in infant gut microbiota maturation

Abstract: Background: Disturbances in the primary colonization of the infant gut can result in life-long consequences and have been associated with a range of host conditions. Although early life factors have been shown to affect the infant gut microbiota development, our current understanding of the human gut colonization in early life remains limited. To gain more insights in the unique dynamics of this rapidly evolving ecosystem, we investigated the microbiota over the first year of life in eight densely sampled infants (total number of samples, n=303). To evaluate gut microbiota maturation transition towards an adult configuration, we compared the microbiome composition of the infants to the Flemish Gut Flora Project population (n=1,106). Results: We observed the infant gut microbiota to mature through three distinct, conserved stages of ecosystem development. Across these successional gut microbiota maturation stages, genus predominance was observed to shift from Escherichia over Bifidobacterium to Bacteroides. Both disease and antibiotic treatment were observed to be associated occasionally with gut microbiota maturation stage regression, a transient setback in microbiota maturation dynamics. Although the studied microbiota trajectories evolved to more adult-like constellations, microbiome community typing against the background of the Flemish Gut Flora Project (FGFP) cohort clustered all infant samples within the (in adults) potentially dysbiotic Bact2 enterotype. Conclusion: We confirmed similarities between infant gut microbial colonization and adult dysbiosis. A profound knowledge about the primary gut colonization process in infants might provide crucial insights into how the secondary colonization of a dysbiotic adult gut can be redirected.

Disentangling environmental effects in microbial association networks

Abstract: Background Ecological interactions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions through associations across time and space, which can be represented as association networks. Associations could result from either ecological interactions between microorganisms, or from environmental selection, where the associations are environmentally-driven. Therefore, before downstream analysis and interpretation, we need to distinguish the nature of the association, particularly if it is due to environmental selection or not. Results We present EnDED (Environmentally-Driven Edge Detection), an implementation of four approaches as well as their combination to predict which links between microorganisms in an association network are environmentally-driven. The four approaches are Sign Pattern, Overlap, Interaction Information, and Data Processing Inequality. We tested EnDED on networks from simulated data of 50 microorganisms. The networks contained on average 50 nodes and 1087 edges, of which 60 were true interactions but 1026 false associations (i.e. environmentally-driven or due to chance). Applying each method individually, we detected a moderate to high number of environmentally-driven edges - 87% Sign Pattern and Overlap, 67% Interaction Information, and 44% Data Processing Inequality. Combining these methods in an intersection approach resulted in retaining more interactions, both true and false (32% of environmentally-driven associations). After validation with the simulated datasets, we applied EnDED on a marine microbial network inferred from 10 years of monthly observations of microbial-plankton abundance. The intersection combination predicted that 8.3% of the associations were environmentally-driven, while individual methods predicted 24.8% (Data Processing Inequality), 25.7% (Interaction Information), and up to 84.6% (Sign Pattern as well as Overlap). The fraction of environmentally-driven edges among negative microbial associations in the real network increased rapidly with the number of environmental factors. Conclusions To reach accurate hypotheses about ecological interactions, it is important to determine, quantify, and remove environmentally-driven associations in marine microbial association networks. For that, EnDED offers up to four individual methods as well as their combination. However, especially for the intersection combination, we suggest using EnDED with other strategies to reduce the number of false associations and consequently the number of potential interaction hypotheses.

Treponema peruense sp. nov., a commensal spirochaete isolated from human faeces.

Abstract: A Gram-stain-negative, obligatory anaerobic spirochaete (RCC2812T) was isolated from a faecal sample obtained from an individual residing in a remote Amazonian community in Peru. The bacterium showed highest 16S rRNA gene sequence similarity to the pig intestinal spirochete Treponema succinifaciens (89.48 %). Average nucleotide identity values between strain RCC2812T and all available Treponema genomes from validated type strains were all <73 %, thus clearly lower than the species delineation threshold. The DNA G+C content of RCC2812T was 41.24 mol%. Phenotypic characterization using the API-ZYM and API 20A systems confirmed the divergent position of this bacterium within the genus Treponema. Strain RCC2812T could be differentiated from the phylogenetically most closely related T. succinifaciens by the presence of alkaline phosphatase and α -glucosidase activities. Unlike T. succinifaciens, strain RCC2812T grew equally well with or without serum. Strain RCC2812T is the first commensal Treponema isolated from the human faecal microbiota of remote populations, and based on the collected data represents a novel Treponema species for which the name Treponema peruense sp. nov. is proposed. The type strain is RCC2812T (=LMG 31794T=CIP 111910T).

Temporal variability in quantitative human gut microbiome profiles and implications for clinical research.

Abstract: While clinical gut microbiota research is ever-expanding, extending reference knowledge of healthy between- and within-subject gut microbiota variation and its drivers remains essential; in particular, temporal variability is under-explored, and a comparison with cross-sectional variation is missing. Here, we perform daily quantitative microbiome profiling on 713 fecal samples from 20 Belgian women over six weeks, combined with extensive anthropometric measurements, blood panels, dietary data, and stool characteristics. We show substantial temporal variation for most major gut genera; we find that for 78% of microbial genera, day-to-day absolute abundance variation is substantially larger within than between individuals, with up to 100-fold shifts over the study period. Diversity, and especially evenness indicators also fluctuate substantially. Relative abundance profiles show similar but less pronounced temporal variation. Stool moisture, and to a lesser extent diet, are the only significant host covariates of temporal microbiota variation, while menstrual cycle parameters did not show significant effects. We find that the dysbiotic Bact2 enterotype shows increased between- and within-subject compositional variability. Our results suggest that to increase diagnostic as well as target discovery power, studies could adopt a repeated measurement design and/or focus analysis on community-wide microbiome descriptors and indices. Here, the authors report quantitative daily gut microbiome variation of individual gut bacterial abundances in healthy individuals, linked to changes in transit time and diet, highlighting the potential need for multiple samplings for microbiome target identification and the development and application of reliable microbiome diagnostics.