Modeling approaches for probing cross-feeding interactions in the human gut microbiome

Impact of a legumes diet on the human gut microbiome articulated with fecal and plasma metabolomes: A pilot study

BACKGROUND & AIMS

Legumes intake is known to be associated with several health benefits the origins of which is still a matter of debate. This paper addresses a pilot small cohort to probe for metabolic aspects of the interplay between legumes intake, human metabolism and gut microbiota.

METHODS

Untargeted nuclear magnetic resonance (NMR) metabolomics of blood plasma and fecal extracts was carried out, in tandem with qPCR analysis of feces, to assess the impact of an 8-week pilot legumes diet intervention on the fecal and plasma metabolomes and gut microbiota of 19 subjects.

RESULTS

While the high inter-individual variability hindered the detection of statistically significant changes in the gut microbiome, increased fecal glucose and decreased threonine levels were noted. Correlation analysis between the microbiome and fecal metabolome lead to putative hypotheses regarding the metabolic activities of prevalent bacteria groups (Clostridium leptum subgroup, Roseburia spp., and Faecalibacterium prausnitzii). These included elevated fecal glucose as a preferential energy source, the involvement of valerate/isovalerate and reduced protein degradation in gut microbiota. Plasma metabolomics advanced mannose and betaine as potential markers of legume intake and unveiled a decrease in formate and ketone bodies, the latter suggesting improved energy utilization through legume carbohydrates. Amino acid metabolism was also apparently affected, as suggested by lowered urea, histidine and threonine levels.

CONCLUSIONS

Despite the high inter-individual gut microbiome variability characterizing the small cohort addressed, combination of microbiological measurements and untargeted metabolomics unveiled several metabolic effects putatively related to legumes intake. If confirmed in larger cohorts, our findings will support the inclusion of legumes in diets and contribute valuable new insight into the origins of associated health benefits.

ScyNet: Visualizing interactions in community metabolic models

Motivation

Genome-scale community metabolic models are used to gain mechanistic insights into interactions between community members. However, existing tools for visualizing metabolic models only cater to the needs of single organism models.

Results

ScyNet is a Cytoscape app for visualizing community metabolic models, generating networks with reduced complexity by focusing on interactions between community members. ScyNet can incorporate the state of a metabolic model via fluxes or flux ranges, which is shown in a previously published simplified cystic fibrosis airway community model.

Availability and implementation

ScyNet is freely available under an MIT licence and can be retrieved via the Cytoscape App Store (apps.cytoscape.org/apps/scynet). The source code is available at Github (github.com/univieCUBE/ScyNet).

Software sensor to monitor a synthetic microbial community emulating the dietary fiber impact on the gut microbiota

Human health is related to the gut microbiota, a complex ecosystem integrated by microorganisms and host cells. In vivo study of microbiota implies sophisticated analytical techniques, making it hard to study its dynamic changes. In vitro studies have proved helpful in deciphering interactions using representative synthetic microbial communities. Its characterization still depends on multiple sensors and analytical and molecular techniques. This work presents a SynCom including three bacteria with inulin as a fiber source to synthesize acetic, propionic, and butyric acids. A software sensor based on the Differential Neural Networks was implemented to estimate the SynCom dynamic. Species growth and byproduct concentration were estimated from available information (pH and optical density). We assessed the approach based on reported datasets; less than 5% of the error was achieved in less than 0.5 h, and we proved that our algorithm is suitable in noisy environments. This approach could accelerate the dynamic description of gut microbiota and reduce cost and time investment.

Screening Microbial Interactions During Inulin Utilization Reveals Strong Competition and Proteomic Changes in Lacticaseibacillus paracasei M38

Competition for resources is a common microbial interaction in the gut microbiome. Inulin is a well-studied prebiotic dietary fiber that profoundly shapes gut microbiome composition. Several community members and some probiotics, such as Lacticaseibacillus paracasei, deploy multiple molecular strategies to access fructans. In this work, we screened bacterial interactions during inulin utilization in representative gut microbes. Unidirectional and bidirectional assays were used to evaluate the effects of microbial interactions and global proteomic changes on inulin utilization. Unidirectional assays showed the total or partial consumption of inulin by many gut microbes. Partial consumption was associated with cross-feeding of fructose or short oligosaccharides. However, bidirectional assays showed strong competition from L. paracasei M38 against other gut microbes, reducing the growth and quantity of proteins found in the latter. L. paracasei dominated and outcompeted other inulin utilizers, such as Ligilactobacillus ruminis PT16, Bifidobacterium longum PT4, and Bacteroides fragilis HM714. The importance of strain-specific characteristics of L. paracasei, such as its high fitness for inulin consumption, allows it to be favored for bacterial competence. Proteomic studies indicated an increase in inulin-degrading enzymes in co-cultures, such as β-fructosidase, 6-phosphofructokinase, the PTS D-fructose system, and ABC transporters. These results reveal that intestinal metabolic interactions are strain-dependent and might result in cross-feeding or competition depending on total or partial consumption of inulin. Partial degradation of inulin by certain bacteria favors coexistence. However, when L. paracasei M38 totally degrades the fiber, this does not happen. The synergy of this prebiotic with L. paracasei M38 could determine the predominance in the host as a potential probiotic.

Using metabolic networks to predict cross-feeding and competition interactions between microorganisms

Understanding the interactions between microorganisms and their impact on bacterial behavior at the community level is a key research topic in microbiology. Different methods, relying on experimental or mathematical approaches based on the diverse properties of bacteria, are currently employed to study these interactions. Recently, the use of metabolic networks to understand the interactions between bacterial pairs has increased, highlighting the relevance of this approach in characterizing bacteria. In this study, we leverage the representation of bacteria through their metabolic networks to build a predictive model aimed at reducing the number of experimental assays required for designing bacterial consortia with specific behaviors. Our novel method for predicting cross-feeding or competition interactions between pairs of microorganisms utilizes metabolic network features. Machine learning classifiers are employed to determine the type of interaction from automatically reconstructed metabolic networks. Several algorithms were assessed and selected based on comprehensive testing and careful separation of manually compiled data sets obtained from literature sources. We used different classification algorithms, including K Nearest Neighbors, XGBoost, Support Vector Machine, and Random Forest, tested different parameter values, and implemented several data curation approaches to reduce the biological bias associated with our data set, ultimately achieving an accuracy of over 0.9. Our method holds substantial potential to advance the understanding of community behavior and contribute to the development of more effective approaches for consortia design.IMPORTANCEUnderstanding bacterial interactions at the community level is critical for microbiology, and leveraging metabolic networks presents an efficient and effective approach. The introduction of this novel method for predicting interactions through machine learning classifiers has the potential to advance the field by reducing the number of experimental assays required and contributing to the development of more effective bacterial consortia.

PyCoMo: a python package for community metabolic model creation and analysis

SUMMARY

PyCoMo is a python package for quick and easy generation of genome-scale compartmentalized community metabolic models that are compliant with current openCOBRA file formats. The resulting models can be used to predict (i) the maximum growth rate at a given abundance profile, (ii) the feasible community compositions at a given growth rate, and (iii) all exchange metabolites and cross-feeding interactions in a community metabolic model independent of the abundance profile; we demonstrate PyCoMo's capability by analysing methane production in a previously published simplified biogas community metabolic model.

AVAILABILITY AND IMPLEMENTATION

PyCoMo is freely available under an MIT licence at http://github.com/univieCUBE/PyCoMo, the Python Package Index, and Zenodo.

Genome-scale metabolic modeling of the human milk oligosaccharide utilization by Bifidobacterium longum subsp. infantis

Bifidobacterium longum subsp. infantis is a representative and dominant species in the infant gut and is considered a beneficial microbe. This organism displays multiple adaptations to thrive in the infant gut, regarded as a model for human milk oligosaccharides (HMOs) utilization. These carbohydrates are abundant in breast milk and include different molecules based on lactose. They contain fucose, sialic acid, and N-acetylglucosamine. Bifidobacterium metabolism is complex, and a systems view of relevant metabolic pathways and exchange metabolites during HMO consumption is missing. To address this limitation, a refined genome-scale network reconstruction of this bacterium is presented using a previous reconstruction of B. infantis ATCC 15967 as a template. The latter was expanded based on an extensive revision of genome annotations, current literature, and transcriptomic data integration. The metabolic reconstruction (iLR578) accounted for 578 genes, 1,047 reactions, and 924 metabolites. Starting from this reconstruction, we built context-specific genome-scale metabolic models using RNA-seq data from cultures growing in lactose and three HMOs. The models revealed notable differences in HMO metabolism depending on the functional characteristics of the substrates. Particularly, fucosyl-lactose showed a divergent metabolism due to a fucose moiety. High yields of lactate and acetate were predicted under growth rate maximization in all conditions, whereas formate, ethanol, and 1,2-propanediol were substantially lower. Similar results were also obtained under near-optimal growth on each substrate when varying the empirically observed acetate-to-lactate production ratio. Model predictions displayed reasonable agreement between central carbon metabolism fluxes and expression data across all conditions. Flux coupling analysis revealed additional connections between succinate exchange and arginine and sulfate metabolism and a strong coupling between central carbon reactions and adenine metabolism. More importantly, specific networks of coupled reactions under each carbon source were derived and analyzed. Overall, the presented network reconstruction constitutes a valuable platform for probing the metabolism of this prominent infant gut bifidobacteria.IMPORTANCEThis work presents a detailed reconstruction of the metabolism of Bifidobacterium longum subsp. infantis, a prominent member of the infant gut microbiome, providing a systems view of its metabolism of human milk oligosaccharides.

Effect of a multi-strain probiotic mixture consumption on anxiety and depression symptoms induced in adult mice by postnatal maternal separation

BACKGROUND

Intestinal microbial composition not only affects the health of the gut but also influences centrally mediated systems involved in mood, through the "gut-brain" axis, a bidirectional communication between gut microbiota and the brain. In this context, the modulation of intestinal microbiota and its metabolites through the administration of probiotics seems to represent a very promising approach in the treatment of the central nervous system alterations. Early postnatal life is a critical period during which the brain undergoes profound and essential modulations in terms of maturation and plasticity. Maternal separation (MS), i.e., the disruption of the mother-pup interaction, represents a pivotal paradigm in the study of stress-related mood disorders, by inducing persistent changes in the immune system, inflammatory processes, and emotional behavior in adult mammals.

RESULTS

We conducted experiments to investigate whether sustained consumption of a multi-strain probiotic formulation by adult male mice could mitigate the effects of maternal separation. Our data demonstrated that the treatment with probiotics was able to totally reverse the anxiety- and depressive-like behavior; normalize the neuro-inflammatory state, by restoring the resting state of microglia; and finally induce a proneurogenic effect. Mice subjected to maternal separation showed changes in microbiota composition compared to the control group that resulted in permissive colonization by the administered multi-strain probiotic product. As a consequence, the probiotic treatment also significantly affected the production of SCFA and in particular the level of butyrate.

CONCLUSION

Gut microbiota and its metabolites mediate the therapeutic action of the probiotic mix on MS-induced brain dysfunctions. Our findings extend the knowledge on the use of probiotics as a therapeutic tool in the presence of alterations of the emotional sphere that significantly impact on gut microbiota composition. Video Abstract.

Gut microbiome-metabolome interactions predict host condition

BACKGROUND

The effect of microbes on their human host is often mediated through changes in metabolite concentrations. As such, multiple tools have been proposed to predict metabolite concentrations from microbial taxa frequencies. Such tools typically fail to capture the dependence of the microbiome-metabolite relation on the environment.

RESULTS

We propose to treat the microbiome-metabolome relation as the equilibrium of a complex interaction and to relate the host condition to a latent representation of the interaction between the log concentration of the metabolome and the log frequencies of the microbiome. We develop LOCATE (Latent variables Of miCrobiome And meTabolites rElations), a machine learning tool to predict the metabolite concentration from the microbiome composition and produce a latent representation of the interaction. This representation is then used to predict the host condition. LOCATE's accuracy in predicting the metabolome is higher than all current predictors. The metabolite concentration prediction accuracy significantly decreases cross datasets, and cross conditions, especially in 16S data. LOCATE's latent representation predicts the host condition better than either the microbiome or the metabolome. This representation is strongly correlated with host demographics. A significant improvement in accuracy (0.793 vs. 0.724 average accuracy) is obtained even with a small number of metabolite samples ([Formula: see text]).

CONCLUSION

These results suggest that a latent representation of the microbiome-metabolome interaction leads to a better association with the host condition than any of the two separated or the simple combination of the two. Video Abstract.

Next-generation prebiotics: Maillard-conjugates of 2'-fucosyllactose and lactoferrin hydrolysates beneficially modulate gut microbiome composition and health promoting activity in a murine model

Current prebiotics are predominantly carbohydrates. However, great competition exists among gut microbes for the scarce protein in the colon, as most consumed protein is digested and absorbed in the small intestine. Herein we evaluated in-vivo novel next-generation prebiotics: protein-containing-prebiotics, for selectively-targeted delivery of protein to colonic probiotics, to boost their growth. This system is based on micellar-particles, composed of Maillard-glycoconjugates of 2'-Fucosyllactose (2'-FL, human-milk-oligosaccharide) shell, engulfing lactoferrin peptic-then-tryptic hydrolysate (LFH) core. This core-shell structure lowers protein-core digestibility, while the prebiotic glycans are hypothesized to serve as molecular-recognition ligands for selectively targeting probiotics. To study the efficacy of this novel prebiotic, we fed C57BL/6JRccHsd mice with either 2'-FL-LFH Maillard-glycoconjugates, unconjugated components (control), or saline (blank). Administration of 2'-FL-LFH significantly increased the levels of short-chain-fatty-acids (SCFAs)-producing bacterial families (Ruminococcaceae, Lachnospiraceae) and genus (Odoribacter) and the production of the health-related metabolites, SCFAs, compared to the unconjugated components and to saline. The SCFAs-producing genus Prevotella significantly increased upon 2'-FL-LFH consumption, compared to only moderate increase in the unconjugated components. Interestingly, the plasma-levels of inflammation-inducing lipopolysaccharides (LPS), which indicate increased gut-permeability, were significantly lower in the 2'-FL-LFH group compared to the unconjugated-components and the saline groups. We found that Maillard-glycoconjugates of 2'-FL-LFH can serve as novel protein-containing prebiotics, beneficially modulating gut microbial composition and its metabolic activity, thereby contributing to host health more effectively than the conventional carbohydrate-only prebiotics.

Microbial Imbalance and Intestinal Permeability in the Pathogenesis of Rheumatoid Arthritis: A Mechanism Review with a Focus on Bacterial Translocation, Citrullination, and Probiotic Intervention

This review aims to investigate the role of intestinal permeability (IP) in rheumatoid arthritis (RA), following the hypotheses that leakage of intestinal microbes can influence increased citrullination of peptides leading to anti-citrullinated protein antibody (ACPA) production and inflammation in RA; and that leaked microbes can migrate to the peripheral joints, leading to immune responses and synovitis in peripheral joints. This review explored the evidence for the link between microbial dysbiosis and increased IP in the inflammatory state in RA, as well as the role of increased citrullination and bacterial translocation in the link between microbiota and immune responses in RA. Furthermore, this research aims to evaluate the potential effect of probiotics on RA symptoms and pathogenesis via proposed mechanisms, including the support of microbial balance and suppression of inflammatory factors in RA. A systematic literature search was conducted in three tranches (review, mechanism, intervention). 71 peer-reviewed papers met the inclusions criteria and are summarized in a narrative analysis. Primary studies were critically appraised, synthesized and their relevance to clinical practice evaluated. Evidence found in this mechanism review consistently supported intestinal dysbiosis and increased IP in arthritis. An altered intestinal microbiome was demonstrated in RA with specific microbes such as Collinsella and Eggerthella correlating with increased IP, mucosal inflammation, and immune responses. Hypercitrullination and ACPA production correlated with arthritic symptoms and intestinal microbes were shown to influence hypercitrullination. Some in vitro and animal studies demonstrated a link between leakage of microbes and bacterial translocation, but further research is needed to elucidate the link between IP and citrullination. Probiotic intervention studies evidenced reductions in inflammatory markers IL-6 and TNFα, associated with proliferation of synovial tissue and pain perception in RA joint inflammation. Despite some conflict in the literature, probiotics may present a promising nutritional intervention in the suppression of both, disease activity and inflammatory markers.Key teaching pointsThere is evidence for a dysbiotic profile of the RA gut with specific RA-associated microbes.Increased intestinal permeability and leakage of PAD enzyme facilitates citrullination of peptides.Hypercitrullination and ACPA production correlate to arthritic signs.Microbial leakage and translocation plays a role in the pathogenesis of RA.Probiotics (e.g. L. Casei 01) may reduce inflammation and ameliorate RA symptoms.

Gut microbial metabolic signatures in diabetes mellitus and potential preventive and therapeutic applications

Diabetes mellitus can be subdivided into several categories based on origin and clinical characteristics. The most common forms of diabetes are type 1 (T1D), type 2 diabetes (T2D) and gestational diabetes mellitus (GDM). T1D and T2D are chronic diseases affecting around 537 million adults worldwide and it is projected that these numbers will increase by 12% over the next two decades, while GDM affects up to 30% of women during pregnancy, depending on diagnosis methods. These forms of diabetes have varied origins: T1D is an autoimmune disease, while T2D is commonly associated with, but not limited to, certain lifestyle patterns and GDM can result of a combination of genetic predisposition and pregnancy factors. Despite some pathogenic differences among these forms of diabetes, there are some common markers associated with their development. For instance, gut barrier impairment and inflammation associated with an unbalanced gut microbiota and their metabolites may be common factors in diabetes development and progression. Here, we summarize the microbial signatures that have been linked to diabetes, how they are connected to diet and, ultimately, the impact on metabolite profiles resulting from host-gut microbiota-diet interactions. Additionally, we summarize recent advances relating to promising preventive and therapeutic interventions focusing on the targeted modulation of the gut microbiota to alleviate T1D, T2D and GDM.

Evolution of a cross-feeding interaction following a key innovation in a long-term evolution experiment with Escherichia coli

The evolution of a novel trait can profoundly change an organism's effects on its environment, which can in turn affect the further evolution of that organism and any coexisting organisms. We examine these effects and feedbacks following the evolution of a novel function in the Long-Term Evolution Experiment (LTEE) with Escherichia coli. A characteristic feature of E. coli is its inability to grow aerobically on citrate (Cit-). Nonetheless, a Cit+ variant with this capacity evolved in one LTEE population after 31 000 generations. The Cit+ clade then coexisted stably with another clade that retained the ancestral Cit- phenotype. This coexistence was shaped by the evolution of a cross-feeding relationship based on C4-dicarboxylic acids, particularly succinate, fumarate, and malate, that the Cit+ variants release into the medium. Both the Cit- and Cit+ cells evolved to grow on these excreted resources. The evolution of aerobic growth on citrate thus led to a transition from an ecosystem based on a single limiting resource, glucose, to one with at least five resources that were either shared or partitioned between the two coexisting clades. Our findings show that evolutionary novelties can change environmental conditions in ways that facilitate diversity by altering ecosystem structure and the evolutionary trajectories of coexisting lineages.

Multi-omics in Crohn's disease: New insights from inside

Crohn's disease (CD) is an inflammatory bowel disease (IBD) with complex clinical manifestations such as chronic diarrhea, weight loss and hematochezia. Despite the increasing incidence worldwide, cure of CD remains extremely difficult. The rapid development of high-throughput sequencing technology with integrated-omics analyses in recent years has provided a new means for exploring the pathogenesis, mining the biomarkers and designing targeted personalized therapeutics of CD. Host genomics and epigenomics unveil heredity-related mechanisms of susceptible individuals, while microbiome and metabolomics map host-microbe interactions in CD patients. Proteomics shows great potential in searching for promising biomarkers. Nonetheless, single omics technology cannot holistically connect the mechanisms with heterogeneity of pathological behavior in CD. The rise of multi-omics analysis integrates genetic/epigenetic profiles with protein/microbial metabolite functionality, providing new hope for comprehensive and in-depth exploration of CD. Herein, we emphasized the different omics features and applications of CD and discussed the current research and limitations of multi-omics in CD. This review will update and deepen our understanding of CD from integration of broad omics spectra and will provide new evidence for targeted individualized therapeutics.

Microbial interactions and the homeostasis of the gut microbiome: the role of Bifidobacterium

The human gut is home to trillions of microorganisms that influence several aspects of our health. This dense microbial community targets almost all dietary polysaccharides and releases multiple metabolites, some of which have physiological effects on the host. A healthy equilibrium between members of the gut microbiota, its microbial diversity, and their metabolites is required for intestinal health, promoting regulatory or anti-inflammatory immune responses. In contrast, the loss of this equilibrium due to antibiotics, low fiber intake, or other conditions results in alterations in gut microbiota composition, a term known as gut dysbiosis. This dysbiosis can be characterized by a reduction in health-associated microorganisms, such as butyrate-producing bacteria, enrichment of a small number of opportunistic pathogens, or a reduction in microbial diversity. Bifidobacterium species are key species in the gut microbiome, serving as primary degraders and contributing to a balanced gut environment in various ways. Colonization resistance is a fundamental property of gut microbiota for the prevention and control of infections. This community competes strongly with foreign microorganisms, such as gastrointestinal pathogens, antibiotic-resistant bacteria, or even probiotics. Resistance to colonization is based on microbial interactions such as metabolic cross-feeding, competition for nutrients, or antimicrobial-based inhibition. These interactions are mediated by metabolites and metabolic pathways, representing the inner workings of the gut microbiota, and play a protective role through colonization resistance. This review presents a rationale for how microbial interactions provide resistance to colonization and gut dysbiosis, highlighting the protective role of Bifidobacterium species.

"What I cannot create, I do not understand": elucidating microbe-microbe interactions to facilitate plant microbiome engineering

Plant-microbe interactions are important for both physiological and pathological processes. Despite the significance of plant-microbe interactions, microbe-microbe interactions themselves represent an important, complex, dynamic network that warrants deeper investigation. To understand how microbe-microbe interactions affect plant microbiomes, one approach is to systematically understand all the factors involved in successful engineering of a microbial community. This follows the physicist Richard Feynman's declaration: "what I cannot create, I do not understand". This review highlights recent studies that focus on aspects that we believe are important for building (ergo understanding) microbe-microbe interactions in the plant environment, including pairwise screening, intelligent application of cross-feeding models, spatial distributions of microbes, and understudied interactions between bacteria and fungi, phages, and protists. We offer a framework for systematic collection and centralized integration of data of plant microbiomes that could organize all the factors that can help ecologists understand microbiomes and help synthetic ecologists engineer beneficial microbiomes.

Microbial metabolites as modulators of the infant gut microbiome and host-microbial interactions in early life

The development of infant gut microbiome is a pivotal process affecting the ecology and function of the microbiome, as well as host health. While the establishment of the infant microbiome has been of interest for decades, the focus on gut microbial metabolism and the resulting small molecules (metabolites) has been rather limited. However, technological and computational advances are now enabling researchers to profile the plethora of metabolites in the infant gut, allowing for improved understanding of how gut microbial-derived metabolites drive microbiome community structuring and host-microbial interactions. Here, we review the current knowledge on development of the infant gut microbiota and metabolism within the first year of life, and discuss how these microbial metabolites are key for enhancing our basic understanding of interactions during the early life developmental window.

Pharmacomicrobiomics and type 2 diabetes mellitus: A novel perspective towards possible treatment

Type 2 diabetes mellitus (T2DM), a major driver of mortality worldwide, is more likely to develop other cardiometabolic risk factors, ultimately leading to diabetes-related mortality. Although a set of measures including lifestyle intervention and antidiabetic drugs have been proposed to manage T2DM, problems associated with potential side-effects and drug resistance are still unresolved. Pharmacomicrobiomics is an emerging field that investigates the interactions between the gut microbiome and drug response variability or drug toxicity. In recent years, increasing evidence supports that the gut microbiome, as the second genome, can serve as an attractive target for improving drug efficacy and safety by manipulating its composition. In this review, we outline the different composition of gut microbiome in T2DM and highlight how these microbiomes actually play a vital role in its development. Furthermore, we also investigate current state-of-the-art knowledge on pharmacomicrobiomics and microbiome's role in modulating the response to antidiabetic drugs, as well as provide innovative potential personalized treatments, including approaches for predicting response to treatment and for modulating the microbiome to improve drug efficacy or reduce drug toxicity.

Bioactive composition and modulatory effects of Hed-Tean-Rad Mushroom, Macrocybe crassa on gut microbiota

Macrocybe crassa (or Tricholoma crassum) is a nutrient-dense wild edible mushroom native to Thailand. The mushroom extract and its constituents have remarkable biological characteristics, but the influence of the powder on the human gut microbiota is unknown. This study investigated the bioactive composition and modulatory properties of M. crassa powder on gut microbial composition and short-chain fatty acids (SCFA) production. The fermentation of M. crassa powder by human intestinal microbiota released SCFA, mainly acetic acid, propionic acid and butyric acid. M. crassa powder significantly modulated the microbiota by increasing the relative abundances of Bifidobacterium, Lactobacillus/Enterococcus group, Atopobium, Bacteroidaceae/Prevotellaceae, and C. coccoides. F. prausnitzii, Roseburia genus, C. histolyticum and C. cluster IX, similar to that of Fructooligosaccharides (FOS). With M. crassa powder, high content of propionic acid was observed, as well as a number of Bacteroidaceae/Prevotellaceae and C. cluster IX. On the other hand, FOS caused a high acetic acid concentration and a population of Bifidobacterium spp., Atopobium cluster, Bacteroidaceae/Prevotellaceae, and C. coccoides. Therefore, this work will significantly contribute to filling the knowledge gap and revealing the significance of M. crassa in the pharmaceutical industry.

Metabolic Modeling and Bidirectional Culturing of Two Gut Microbes Reveal Cross-Feeding Interactions and Protective Effects on Intestinal Cells

The gut microbiota is constituted by thousands of microbial interactions, some of which correspond to the exchange of metabolic by-products or cross-feeding. Inulin and xylan are two major dietary polysaccharides that are fermented by members of the human gut microbiota, resulting in different metabolic profiles. Here, we integrated community modeling and bidirectional culturing assays to study the metabolic interactions between two gut microbes, Phocaeicola dorei and Lachnoclostridium symbiosum, growing in inulin or xylan, and how they provide a protective effect in cultured cells. P. dorei (previously belonging to the Bacteroides genus) was able to consume inulin and xylan, while L. symposium only used certain inulin fractions to produce butyrate as a major end product. Constrained-based flux simulations of refined genome-scale metabolic models of both microbes predicted high lactate and succinate cross-feeding fluxes between P. dorei and L. symbiosum when growing in each fiber. Bidirectional culture assays in both substrates revealed that L. symbiosum growth increased in the presence of P. dorei. Carbohydrate consumption analyses showed a faster carbohydrate consumption in cocultures compared to monocultures. Lactate and succinate concentrations in bidirectional cocultures were lower than in monocultures, pointing to cross-feeding as initially suggested by the model. Butyrate concentrations were similar across all conditions. Finally, supernatants from both bacteria cultured in xylan in bioreactors significantly reduced tumor necrosis factor-α-induced inflammation in HT-29 cells and exerted a protective effect against the TcdB toxin in Caco-2 epithelial cells. Surprisingly, this effect was not observed in inulin cocultures. Overall, these results highlight the predictive value of metabolic models integrated with microbial culture assays for probing microbial interactions in the gut microbiota. They also provide an example of how metabolic exchange could lead to potential beneficial effects in the host. IMPORTANCE Microbial interactions represent the inner connections in the gut microbiome. By integrating mathematical modeling tools and microbial bidirectional culturing, we determined how two gut commensals engage in the exchange of cross-feeding metabolites, lactate and succinate, for increased growth in two fibers. These interactions underpinned butyrate production in cocultures, resulting in a significant reduction in cellular inflammation and protection against microbial toxins when applied to cellular models.

Inulin from Globe Artichoke Roots: A Promising Ingredient for the Production of Functional Fresh Pasta

Globe artichoke roots represent an alternative and sustainable source for inulin extraction and are well-noted for their technological and functional properties. Therefore, the aim of our study was to exploit inulin with high degree of polymerization as a replacement of durum wheat semolina for the production of functional fresh pasta. The effect of increased level of substitution (5, 10, 15%) on cooking, structural, sensory, and nutritional properties were evaluated and compared with a control sample consisting exclusively of durum wheat semolina. Inulin addition caused changes to internal structure as evaluated by scanning electron microscopy. The enriched samples showed a lower swelling index, an increasing cooking time, and values of cooking loss (2.37–3.62%), mainly due to the leaching of inulin into the cooking water. Cooked and raw enriched pasta was significantly darker and firmer than the control, but the sensory attributes were not negatively affected, especially at 5 and 10% of substitution levels. The increase of dietary fiber content in enriched pasta (3.44–12.41 g/100 g) resulted in a significant reduction of glycaemic index (pGI) and starch hydrolysis (HI). After gastrointestinal digestion, inulin-enriched pasta increased prebiotic growth able to significantly reduce E. coli cell density.

Exploring Bacterial Attributes That Underpin Symbiont Life in the Monogastric Gut

The large bowel of monogastric animals, such as that of humans, is home to a microbial community (microbiota) composed of a diversity of mostly bacterial species. Interrelationships between the microbiota as an entity and the host are complex and lifelong and are characteristic of a symbiosis. The relationships may be disrupted in association with disease, resulting in dysbiosis. Modifications to the microbiota to correct dysbiosis require knowledge of the fundamental mechanisms by which symbionts inhabit the gut. This review aims to summarize aspects of niche fitness of bacterial species that inhabit the monogastric gut, especially of humans, and to indicate the research path by which progress can be made in exploring bacterial attributes that underpin symbiont life in the gut.

Microbiome systems biology advancements for natural well-being

Throughout the years all data from epidemiological, physiological and omics have suggested that the microbial communities play a considerable role in modulating human health. The population of microorganisms residing in the human intestine collectively known as microbiota presents a genetic repertoire that is higher in magnitude than the human genome. They play an essential role in host immunity and neuronal signaling. Rapid enhancement of sequence based screening and development of humanized gnotobiotic model has sparked a great deal of interest among scientists to probe the dynamic interactions of the commensal bacteria. This review focuses on systemic analysis of the gut microbiome to decipher the complexity of the host-microbe intercommunication and gives a special emphasis on the evolution of targeted precision medicine through microbiome engineering. In addition, we have also provided a comprehensive description of how interconnection between metabolism and biochemical reactions in a specific organism can be obtained from a metabolic network or a flux balance analysis and combining multiple datasets helps in the identification of a particular metabolite. The review highlights how genetic modification of the critical components and programming the resident microflora can be employed for targeted precision medicine. Inspite of the ongoing debate on the utility of gut microbiome we have explored on the probable new therapeutic avenues like FMT (Fecal microbiota transplant) can be utilized. This review also recapitulates integrating human-relevant 3D cellular models coupled with computational models and the metadata obtained from interventional and epidemiological studies may decipher the complex interactome of diet-microbiota-disease pathophysiology. In addition, it will also open new avenues for the development of therapeutics derived from microbiome or implementation of personalized nutrition. In addition, the identification of biomarkers can also help towards the development of new diagnostic tools and eventually will lead to strategic management of the disease. Inspite of the ongoing debate on the utility of the gut microbiome we have explored how probable new therapeutic avenues like FMT (Fecal microbiota transplant) can be utilized. This review also summarises integrating human-relevant 3D cellular models coupled with computational models and the metadata obtained from interventional and epidemiological studies may decipher the complex interactome of diet- microbiota-disease pathophysiology. In addition, it will also open new avenues for the development of therapeutics derived from the microbiome or implementation of personalized nutrition. In addition, the identification of biomarkers can also help towards the development of new diagnostic tools and eventually will lead to strategic management of disease.

Dynamic proteome allocation regulates the profile of interaction of auxotrophic bacterial consortia

Microbial ecosystems are composed of multiple species in constant metabolic exchange. A pervasive interaction in microbial communities is metabolic cross-feeding and occurs when the metabolic burden of producing costly metabolites is distributed between community members, in some cases for the benefit of all interacting partners. In particular, amino acid auxotrophies generate obligate metabolic inter-dependencies in mixed populations and have been shown to produce a dynamic profile of interaction that depends upon nutrient availability. However, identifying the key components that determine the pair-wise interaction profile remains a challenging problem, partly because metabolic exchange has consequences on multiple levels, from allocating proteomic resources at a cellular level to modulating the structure, function and stability of microbial communities. To evaluate how ppGpp-mediated resource allocation drives the population-level profile of interaction, here we postulate a multi-scale mathematical model that incorporates dynamics of proteome partition into a population dynamics model. We compare our computational results with experimental data obtained from co-cultures of auxotrophic Escherichia coli K12 strains under a range of amino acid concentrations and population structures. We conclude by arguing that the stringent response promotes cooperation by inhibiting the growth of fast-growing strains and promoting the synthesis of metabolites essential for other community members.

Gut associated metabolites and their roles in Clostridioides difficile pathogenesis

The nosocomial pathogen Clostridioides difficile is a burden to the healthcare system. Gut microbiome disruption, most commonly by broad-spectrum antibiotic treatment, is well established to generate a state that is susceptible to CDI. A variety of metabolites produced by the host and/or gut microbiota have been shown to interact with C. difficile. Certain bile acids promote/inhibit germination while other cholesterol-derived compounds and amino acids used in the Stickland metabolic pathway affect growth and CDI colonization. Short chain fatty acids maintain intestinal barrier integrity and a myriad of other metabolic compounds are used as nutritional sources or used by C. difficile to inhibit or outcompete other bacteria in the gut. As the move toward non-antibiotic CDI treatment takes place, a deeper understanding of interactions between C. difficile and the host's gut microbiome and metabolites becomes more relevant.