Effects of a diverse prebiotic fibre blend on inflammation, the gut microbiota and affective symptoms in metabolic syndrome: a pilot open-label randomised controlled trial.

Emerging evidence suggests that low-grade systemic inflammation plays a key role in altering brain activity, behaviour and affect. Modulation of the gut microbiota using prebiotic fibre offers a potential therapeutic tool to regulate inflammation, mediated via the production of short-chain fatty acids (SCFA). However, the impact of prebiotic consumption on affective symptoms and the possible contribution from inflammation, gut symptoms and the gut microbiome are currently underexamined. In this 12-week study, the effects of a diverse prebiotic blend on inflammation, gut microbiota profiles and affective symptoms in a population with metabolic syndrome (MetS) were examined. Sixty males and females with MetS meeting the criteria for MetS were randomised into a treatment group (n 40), receiving 10 g per day of a diverse prebiotic blend and healthy eating advice, and a control group (n 20), receiving healthy eating advice only. Our results showed a significant reduction in high sensitivity C-reactive protein (hs-CRP) in the treatment (-0·58 [-9·96 to-2·63]) compared with control (0·37 [-3·64 to-3·32]), alongside significant improvements in self-reported affective scores in the treatment compared with the control group. While there were no differences in relative abundance between groups at week 12, there was a significant increase from baseline to week 12 in fecal Bifidobacterium and Parabacteroides in the treatment group, both of which are recognised as SCFA producers. Multivariate regression analyses further revealed an association between gastrointestinal symptoms and hs-CRP with affective scores. Together, this study provides preliminary support for a diverse prebiotic blend for mood, stress and anxiety.

Microbial community-scale metabolic modelling predicts personalized short-chain fatty acid production profiles in the human gut.

Microbially derived short-chain fatty acids (SCFAs) in the human gut are tightly coupled to host metabolism, immune regulation and integrity of the intestinal epithelium. However, the production of SCFAs can vary widely between individuals consuming the same diet, with lower levels often associated with disease. A systems-scale mechanistic understanding of this heterogeneity is lacking. Here we use a microbial community-scale metabolic modelling (MCMM) approach to predict individual-specific SCFA production profiles to assess the impact of different dietary, prebiotic and probiotic inputs. We evaluate the quantitative accuracy of our MCMMs using in vitro and ex vivo data, plus published human cohort data. We find that MCMM SCFA predictions are significantly associated with blood-derived clinical chemistries, including cardiometabolic and immunological health markers, across a large human cohort. Finally, we demonstrate how MCMMs can be leveraged to design personalized dietary, prebiotic and probiotic interventions aimed at optimizing SCFA production in the gut. Our model represents an approach to direct gut microbiome engineering for precision health and nutrition.

Effects of a diverse prebiotic fibre supplement on HbA1c, insulin sensitivity and inflammatory biomarkers in pre-diabetes: a pilot placebo-controlled randomised clinical trial.

Prebiotic fibre represents a promising and efficacious treatment to manage pre-diabetes, acting via complementary pathways involving the gut microbiome and viscosity-related properties. In this study, we evaluated the effect of using a diverse prebiotic fibre supplement on glycaemic, lipid and inflammatory biomarkers in patients with pre-diabetes. Sixty-six patients diagnosed with pre-diabetes (yet not receiving glucose-lowering medications) were randomised into treatment (thirty-three) and placebo (thirty-three) interventions. Participants in the treatment arm consumed 20 g/d of a diverse prebiotic fibre supplement, and participants in the placebo arm consumed 2 g/d of cellulose for 24 weeks. A total of fifty-one and forty-eight participants completed the week 16 and week 24 visits, respectively. The intervention was well tolerated, with a high average adherence rate across groups. Our results extend upon previous work, showing a significant change in glycated haemoglobin (HbA1c) in the treatment group but only in participants with lower baseline HbA1c levels (< 6 % HbA1c) (P = 0·05; treatment -0·17 ± 0·27 v. placebo 0·07 ± 0·29, mean ± sd). Within the whole cohort, we showed significant improvements in insulin sensitivity (P = 0·03; treatment 1·62 ± 5·79 v. placebo -0·77 ± 2·11) and C-reactive protein (P FWE = 0·03; treatment -2·02 ± 6·42 v. placebo 0·94 ± 2·28) in the treatment group compared with the placebo. Together, our results support the use of a diverse prebiotic fibre supplement for physiologically relevant biomarkers in pre-diabetes.

Microbial community-scale metabolic modeling predicts personalized short chain fatty acid production profiles in the human gut.

Microbially-derived short chain fatty acids (SCFAs) in the human gut are tightly coupled to host metabolism, immune regulation, and integrity of the intestinal epithelium. However, the production of SCFAs can vary widely between individuals consuming the same diet, with lower levels often associated with disease. A systems-scale mechanistic understanding of this heterogeneity is lacking. We present a microbial community-scale metabolic modeling (MCMM) approach to predict individual-specific SCFA production profiles. We assess the quantitative accuracy of our MCMMs using in vitro, ex vivo, and in vivo data. Next, we show how MCMM SCFA predictions are significantly associated with blood-derived clinical chemistries, including cardiometabolic and immunological health markers, across a large human cohort. Finally, we demonstrate how MCMMs can be leveraged to design personalized dietary, prebiotic, and probiotic interventions that optimize SCFA production in the gut. Our results represent an important advance in engineering gut microbiome functional outputs for precision health and nutrition.

Perspective: Leveraging the Gut Microbiota to Predict Personalized Responses to Dietary, Prebiotic, and Probiotic Interventions.

Humans often show variable responses to dietary, prebiotic, and probiotic interventions. Emerging evidence indicates that the gut microbiota is a key determinant for this population heterogeneity. Here, we provide an overview of some of the major computational and experimental tools being applied to critical questions of microbiota-mediated personalized nutrition and health. First, we discuss the latest advances in in silico modeling of the microbiota-nutrition-health axis, including the application of statistical, mechanistic, and hybrid artificial intelligence models. Second, we address high-throughput in vitro techniques for assessing interindividual heterogeneity, from ex vivo batch culturing of stool and continuous culturing in anaerobic bioreactors, to more sophisticated organ-on-a-chip models that integrate both host and microbial compartments. Third, we explore in vivo approaches for better understanding of personalized, microbiota-mediated responses to diet, prebiotics, and probiotics, from nonhuman animal models and human observational studies, to human feeding trials and crossover interventions. We highlight examples of existing, consumer-facing precision nutrition platforms that are currently leveraging the gut microbiota. Furthermore, we discuss how the integration of a broader set of the tools and techniques described in this piece can generate the data necessary to support a greater diversity of precision nutrition strategies. Finally, we present a vision of a precision nutrition and healthcare future, which leverages the gut microbiota to design effective, individual-specific interventions.

Functional heterogeneity in the fermentation capabilities of the healthy human gut microbiota.

The human gut microbiota is known for its highly heterogeneous composition across different individuals. However, relatively little is known about functional differences in its ability to ferment complex polysaccharides. Through ex vivo measurements from healthy human donors, we show that individuals vary markedly in their microbial metabolic phenotypes (MMPs), mirroring differences in their microbiota composition, and resulting in the production of different quantities and proportions of Short Chain Fatty Acids (SCFAs) from the same inputs. We also show that aspects of these MMPs can be predicted from composition using 16S rRNA sequencing. From experiments performed using the same dietary fibers in vivo, we demonstrate that an ingested bolus of fiber is almost entirely consumed by the microbiota upon passage. We leverage our ex vivo data to construct a model of SCFA production and absorption in vivo, and argue that inter-individual differences in quantities of absorbed SCFA are directly related to differences in production. Though in vivo studies are required to confirm these data in the context of the gut, in addition to in vivo read outs of SCFAs produced in response to specific fiber spike-ins, these data suggest that optimizing SCFA production in a given individual through targeted fiber supplementation requires quantitative understanding of their MMP.

Prebiotics and Community Composition Influence Gas Production of the Human Gut Microbiota.

Prebiotics confer benefits to human health, often by promoting the growth of gut bacteria that produce metabolites valuable to the human body, such as short-chain fatty acids (SCFAs). While prebiotic selection has strongly focused on maximizing the production of SCFAs, less attention has been paid to gases, a by-product of SCFA production that also has physiological effects on the human body. Here, we investigate how the content and volume of gas production by human gut microbiota are affected by the chemical composition of the prebiotic and the community composition of the microbiota. We first constructed a linear system model based on mass and electron balance and compared the theoretical product ranges of two prebiotics, inulin and pectin. Modeling shows that pectin is more restricted in product space, with less potential for H2 but more potential for CO2 production. An ex vivo experimental system showed pectin degradation produced significantly less H2 than inulin, but CO2 production fell outside the theoretical product range, suggesting fermentation of fecal debris. Microbial community composition also impacted results: methane production was dependent on the presence of Methanobacteria, while interindividual differences in H2 production during inulin degradation were driven by a Lachnospiraceae taxon. Overall, these results suggest that both the chemistry of the prebiotic and the composition of the microbiota are relevant to gas production. Metabolic processes that are relatively prevalent in the microbiome, such as H2 production, will depend more on substrate, while rare metabolisms such as methanogenesis depend more strongly on microbiome composition.IMPORTANCE Prebiotic fermentation in the gut often leads to the coproduction of short-chain fatty acids (SCFAs) and gases. While excess gas production can be a potential problem for those with functional gut disorders, gas production is rarely considered during prebiotic design. In this study, we combined the use of theoretical models and an ex vivo experimental platform to illustrate that both the chemical composition of the prebiotic and the community composition of the human gut microbiota can affect the volume and content of gas production during prebiotic fermentation. Specifically, more prevalent metabolic processes such as hydrogen production were strongly affected by the oxidation state of the probiotic, while rare metabolisms such as methane production were less affected by the chemical nature of the substrate and entirely dependent on the presence of Methanobacteria in the microbiota.

Exploiting the gut microbiota's fermentation capabilities towards disease prevention.

One of the crucial roles played in the context of human physiology by the human gut microbiota is to ferment resistant polysaccharides and dietary fibres in the colon. Even though it has long been presumed that these processes play fundamental roles in regulating human health, we remain unable to treat or even diagnose deficiencies in microbial fermentation. In part, this relatively slow progress can be attributed to the fact that studying the gut microbiota and its metabolic properties has until now heavily relied on next generation sequencing and case-control cohorts to identify differentially abundant genes, pathways or organisms in the context of a particular clinical indication. Unfortunately, these methods and studies do not allow us to rigorously probe the functional and metabolic phenotype of a microbiota, or for elucidating its mechanisms of action on the host. To improve our clinical control over these fermentation processes, it is critical that we improve our quantitative, mechanistic understanding of their impact on host physiology. In this review, we provide an overview of our current understanding of the roles microbial fermentation processes play in human health in the context of disease prevention. We then describe the evidence linking these processes with depression and anxiety-related conditions, and use these complex disorders as a framework for illustrating the fact that achieving a clinical vision that exploits microbial fermentation towards human health will depend on thoughtful multi-disciplinary collaboration between clinical research, systems biology, and the pharmaceutical and analytical sciences.

Comparison of Bioinformatics Pipelines and Operating Systems for the Analyses of 16S rRNA Gene Amplicon Sequences in Human Fecal Samples.

Amplicon high-throughput sequencing of 16S ribosomal RNA (rRNA) gene is currently the most widely used technique to investigate complex gut microbial communities. Microbial identification might be influenced by several factors, including the choice of bioinformatic pipelines, making comparisons across studies difficult. Here, we compared four commonly used pipelines (QIIME2, Bioconductor, UPARSE and mothur) run on two operating systems (OS) (Linux and Mac), to evaluate the impact of bioinformatic pipeline and OS on the taxonomic classification of 40 human stool samples. We applied the SILVA 132 reference database for all the pipelines. We compared phyla and genera identification and relative abundances across the four pipelines using the Friedman rank sum test. QIIME2 and Bioconductor provided identical outputs on Linux and Mac OS, while UPARSE and mothur reported only minimal differences between OS. Taxa assignments were consistent at both phylum and genus level across all the pipelines. However, a difference in terms of relative abundance was identified for all phyla (p < 0.013) and for the majority of the most abundant genera (p < 0.028), such as Bacteroides (QIIME2: 24.5%, Bioconductor: 24.6%, UPARSE-linux: 23.6%, UPARSE-mac: 20.6%, mothur-linux: 22.2%, mothur-mac: 21.6%, p < 0.001). The use of different bioinformatic pipelines affects the estimation of the relative abundance of gut microbial community, indicating that studies using different pipelines cannot be directly compared. A harmonization procedure is needed to move the field forward.

Modular genetic design of multi-domain functional amyloids: insights into self-assembly and functional properties.

Engineering functional amyloids through a modular genetic strategy represents new opportunities for creating multifunctional molecular materials with tailored structures and performance. Despite important advances, how fusion modules affect the self-assembly and functional properties of amyloids remains elusive. Here, using Escherichia coli curli as a model system, we systematically studied the effect of flanking domains on the structures, assembly kinetics and functions of amyloids. The designed amyloids were composed of E. coli biofilm protein CsgA (as amyloidogenic cores) and one or two flanking domains, consisting of chitin-binding domains (CBDs) from Bacillus circulans chitinase, and/or mussel foot proteins (Mfps). Incorporation of fusion domains did not disrupt the typical ß-sheet structures, but indeed affected assembly rate, morphology, and stiffness of resultant fibrils. Consequently, the CsgA-fusion fibrils, particularly those containing three domains, were much shorter than the CsgA-only fibrils. Furthermore, the stiffness of the resultant fibrils was heavily affected by the structural feature of fusion domains, with ß-sheet-containing domains tending to increase the Young's modulus while random coil domains decreasing the Young's modulus. In addition, fibrils containing CBD domains showed higher chitin-binding activity compared to their CBD-free counterparts. The CBD-CsgA-Mfp3 construct exhibited significantly lower binding activity than Mfp5-CsgA-CBD due to inappropriate folding of the CBD domain in the former construct, in agreement with results based upon molecular dynamics modeling. Our study provides new insights into the assembly and functional properties of designer amyloid proteins with increasing complex domain structures and lays the foundation for the future design of functional amyloid-based structures and molecular materials.

Transmission of human-associated microbiota along family and social networks.

The human microbiome, described as an accessory organ because of the crucial functions it provides, is composed of species that are uniquely found in humans1,2. Yet, surprisingly little is known about the impact of routine interpersonal contacts in shaping microbiome composition. In a relatively 'closed' cohort of 287 people from the Fiji Islands, where common barriers to bacterial transmission are absent, we examine putative bacterial transmission in individuals' gut and oral microbiomes using strain-level data from both core single-nucleotide polymorphisms and flexible genomic regions. We find a weak signal of transmission, defined by the inferred sharing of genotypes, across many organisms that, in aggregate, reveals strong transmission patterns, most notably within households and between spouses. We were unable to determine the directionality of transmission nor whether it was direct. We further find that women harbour strains more closely related to those harboured by their familial and social contacts than men, and that transmission patterns of oral-associated and gut-associated microbiota need not be the same. Using strain-level data alone, we are able to confidently predict a subset of spouses, highlighting the role of shared susceptibilities, behaviours or social interactions that distinguish specific links in the social network.

Analysis of 1135 gut metagenomes identifies sex-specific resistome profiles.

Several gastrointestinal diseases show a sex imbalance, although the underlying (patho)physiological mechanisms behind this are not well understood. The gut microbiome may be involved in this process, forming a complex interaction with host immune system, sex hormones, medication and other environmental factors. Here we performed sex-specific analyses of fecal microbiota composition in 1135 individuals from a population-based cohort. The overall gut microbiome composition of females and males was significantly different (p = 0.001), with females showing a greater microbial diversity (p = 0.009). After correcting for the effects of intrinsic factors, smoking, diet and medications, female hormonal factors such as the use of oral contraceptives and undergoing an ovariectomy were associated with microbial species and pathways. Females had a higher richness of antibiotic-resistance genes, with the most notable being resistance to the lincosamide nucleotidyltransferase (LNU) gene family. The higher abundance of resistance genes is consistent with the greater prescription of the Macrolide-Lincosamide-Streptogramin classes of antibiotics to females. Furthermore, we observed an increased resistance to aminoglycosides in females with self-reported irritable bowel syndrome. These results throw light upon the effects of common medications that are differentially prescribed between sexes and highlight the importance of sex-specific analysis when studying the gut microbiome and resistome.

Gut microbiota composition and functional changes in inflammatory bowel disease and irritable bowel syndrome.

Changes in the gut microbiota have been associated with two of the most common gastrointestinal diseases, inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS). Here, we performed a case-control analysis using shotgun metagenomic sequencing of stool samples from 1792 individuals with IBD and IBS compared with control individuals in the general population. Despite substantial overlap between the gut microbiome of patients with IBD and IBS compared with control individuals, we were able to use gut microbiota composition differences to distinguish patients with IBD from those with IBS. By combining species-level profiles and strain-level profiles with bacterial growth rates, metabolic functions, antibiotic resistance, and virulence factor analyses, we identified key bacterial species that may be involved in two common gastrointestinal diseases.

Predictability and persistence of prebiotic dietary supplementation in a healthy human cohort.

Dietary interventions to manipulate the human gut microbiome for improved health have received increasing attention. However, their design has been limited by a lack of understanding of the quantitative impact of diet on a host's microbiota. We present a highly controlled diet perturbation experiment in a healthy, human cohort in which individual micronutrients are spiked in against a standardized background. We identify strong and predictable responses of specific microbes across participants consuming prebiotic spike-ins, at the level of both strains and functional genes, suggesting fine-scale resource partitioning in the human gut. No predictable responses to non-prebiotic micronutrients were found. Surprisingly, we did not observe decreases in day-to-day variability of the microbiota compared to a complex, varying diet, and instead found evidence of diet-induced stress and an associated loss of biodiversity. Our data offer insights into the effect of a low complexity diet on the gut microbiome, and suggest that effective personalized dietary interventions will rely on functional, strain-level characterization of a patient's microbiota.

Endospores and other lysis-resistant bacteria comprise a widely shared core community within the human microbiota.

Endospore-formers in the human microbiota are well adapted for host-to-host transmission, and an emerging consensus points to their role in determining health and disease states in the gut. The human gut, more than any other environment, encourages the maintenance of endospore formation, with recent culture-based work suggesting that over 50% of genera in the microbiome carry genes attributed to this trait. However, there has been limited work on the ecological role of endospores and other stress-resistant cellular states in the human gut. In fact, there is no data to indicate whether organisms with the genetic potential to form endospores actually form endospores in situ and how sporulation varies across individuals and over time. Here we applied a culture-independent protocol to enrich for endospores and other stress-resistant cells in human feces to identify variation in these states across people and within an individual over time. We see that cells with resistant states are more likely than those without to be shared among multiple individuals, which suggests that these resistant states are particularly adapted for cross-host dissemination. Furthermore, we use untargeted fecal metabolomics in 24 individuals and within a person over time to show that these organisms respond to shared environmental signals, and in particular, dietary fatty acids, that likely mediate colonization of recently disturbed human guts.

Designing fecal microbiota transplant trials that account for differences in donor stool efficacy.

Fecal microbiota transplantation is a highly effective intervention for patients suffering from recurrent Clostridium difficile, a common hospital-acquired infection. Fecal microbiota transplantation's success as a therapy for C. difficile has inspired interest in performing clinical trials that experiment with fecal microbiota transplantation as a therapy for other conditions like inflammatory bowel disease, obesity, diabetes, and Parkinson's disease. Results from clinical trials that use fecal microbiota transplantation to treat inflammatory bowel disease suggest that, for at least one condition beyond C. difficile, most fecal microbiota transplantation donors produce stool that is not efficacious. The optimal strategies for identifying and using efficacious donors have not been investigated. We therefore examined the optimal Bayesian response-adaptive strategy for allocating patients to donors and formulated a computationally tractable myopic heuristic. This heuristic computes the probability that a donor is efficacious by updating prior expectations about the efficacy of fecal microbiota transplantation, the placebo rate, and the fraction of donors that produce efficacious stool. In simulations designed to mimic a recent fecal microbiota transplantation clinical trial, for which traditional power calculations predict [Formula: see text] statistical power, we found that accounting for differences in donor stool efficacy reduced the predicted statistical power to [Formula: see text]. For these simulations, using the heuristic Bayesian allocation strategy more than quadrupled the statistical power to [Formula: see text]. We use the results of similar simulations to make recommendations about the number of patients, the number of donors, and the choice of clinical endpoint that clinical trials should use to optimize their ability to detect if fecal microbiota transplantation is effective for treating a condition.

Meta-analysis of gut microbiome studies identifies disease-specific and shared responses.

Hundreds of clinical studies have demonstrated associations between the human microbiome and disease, yet fundamental questions remain on how we can generalize this knowledge. Results from individual studies can be inconsistent, and comparing published data is further complicated by a lack of standard processing and analysis methods. Here we introduce the MicrobiomeHD database, which includes 28 published case-control gut microbiome studies spanning ten diseases. We perform a cross-disease meta-analysis of these studies using standardized methods. We find consistent patterns characterizing disease-associated microbiome changes. Some diseases are associated with over 50 genera, while most show only 10-15 genus-level changes. Some diseases are marked by the presence of potentially pathogenic microbes, whereas others are characterized by a depletion of health-associated bacteria. Furthermore, we show that about half of genera associated with individual studies are bacteria that respond to more than one disease. Thus, many associations found in case-control studies are likely not disease-specific but rather part of a non-specific, shared response to health and disease.

Identifying predictive features of Clostridium difficile infection recurrence before, during, and after primary antibiotic treatment.

BACKGROUND: Colonization by the pathogen Clostridium difficile often occurs in the background of a disrupted microbial community. Identifying specific organisms conferring resistance to invasion by C. difficile is desirable because diagnostic and therapeutic strategies based on the human microbiota have the potential to provide more precision to the management and treatment of Clostridium difficile infection (CDI) and its recurrence. METHODS: We conducted a longitudinal study of adult patients diagnosed with their first CDI. We investigated the dynamics of the gut microbiota during antibiotic treatment, and we used microbial or demographic features at the time of diagnosis, or after treatment, to predict CDI recurrence. To check the validity of the predictions, a meta-analysis using a previously published dataset was performed. RESULTS: We observed that patients' microbiota "before" antibiotic treatment was predictive of disease relapse, but surprisingly, post-antibiotic microbial community is indistinguishable between patients that recur or not. At the individual OTU level, we identified Veillonella dispar as a candidate organism for preventing CDI recurrence; however, we did not detect a corresponding signal in the conducted meta-analysis. CONCLUSION: Although in our patient population, a candidate organism was identified for negatively predicting CDI recurrence, results suggest the need for larger cohort studies that include patients with diverse demographic characteristics to generalize species that robustly confer colonization resistance against C. difficile and accurately predict disease relapse.

Synbiotic approaches to human health and well-being.

Synbiotics refer to combinations of probiotics and prebiotics that act synergistically to confer health benefits to the host. As a therapeutic strategy, they provide a gentle yet powerful method for modulating the composition and metabolic output of the human gut microbiota. In the context of achieving the UN Sustainable Development Goals, synbiotics have the potential to act as cost-effective prophylactic measures against a variety of human ailments, ranging from infant diarrhoea to metabolic and inflammatory diseases in adults, by maintaining commensal microbial communities and metabolic networks that are conducive to human health.