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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.

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Background: The infant gut microbiome is a complex community that influences short- and long-term health. Its assembly and composition are governed by variables such as the feeding type. Breast milk provides infants an important supply of human milk oligosaccharides (HMO), a broad family of carbohydrates comprising neutral, fucosylated, and sialylated molecules. There is a positive association between HMOs and the overrepresentation of Bifidobacterium species in the infant gut, which is sustained by multiple molecular determinants present in the genomes of these species. Infant-gut-associated Bifidobacterium species usually share a similar niche and display similar HMO inclinations, suggesting they compete for these resources. There is also strong evidence of cross-feeding interactions between HMO-derived molecules and bifidobacteria. Methods: In this study, we screened for unidirectional and bidirectional interactions between Bifidobacterium and other species using individual HMO. Bifidobacterium bifidum and Bacteroides thetaiotaomicron increased the growth of several other species when their supernatants were used, probably mediated by the partial degradation of HMO. In contrast, Bifidobacterium longum subsp. infantis. supernatants did not exhibit positive growth. Results: Bifidobacterium species compete for lacto-N-tetraose, which is associated with reduced bidirectional growth. The outcome of these interactions was HMO-dependent, in which the two species could compete for one substrate but cross-feed on another. 2'-fucosyllactose and lacto-N-neotetraose are associated with several positive interactions that generally originate from the partial degradation of these HMOs. Conclusion: This study presents evidence for complex interactions during HMO utilization, which can be cooperative or competitive, depending on the nature of the HMO. This information could be useful for understanding how breast milk supports the growth of some Bifidobacterium species, shaping the ecology of this important microbial community.

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The intestinal microbiome is a community of anaerobic microorganisms whose activities significantly impact human health. Its composition can be modulated by consuming foods rich in dietary fiber, such as xylan, a complex polysaccharide that can be considered an emerging prebiotic. In this work, we evaluated how certain gut bacteria acted as primary degraders, fermenting dietary fibers, and releasing metabolites that other bacteria can further use. Different bacterial strains of Lactobacillus, Bifidobacterium, and Bacteroides were evaluated for their ability to consume xylan and interact with one another. Results from unidirectional assays gave indications of possible cross-feeding between bacteria using xylan as a carbon source. Bidirectional assays showed that Bifidobacterium longum PT4 increased its growth in the presence of Bacteroides ovatus HM222. Proteomic analyses indicated that B. ovatus HM222 synthesizes enzymes facilitating xylan degradation, such as ß-xylanase, arabinosidase, L-arabinose isomerase, and xylosidase. Interestingly, the relative abundance of these proteins remains largely unaffected in the presence of Bifidobacterium longum PT4. In the presence of B. ovatus, B. longum PT4 increased the production of enzymes such as α-L-arabinosidase, L-arabinose isomerase, xylulose kinase, xylose isomerase, and sugar transporters. These results show an example of positive interaction between bacteria mediated by xylan consumption. Bacteroides degraded this substrate to release xylooligosaccharides, or monosaccharides (xylose, arabinose), which might support the growth of secondary degraders such as B. longum.

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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.

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Interactions between species are essential in ecosystems, but sometimes competition dominates over mutualism. The transition between mutualism-competition can have several implications and consequences, and it has hardly been studied in experimental settings. This work studies the mutualism between cross-feeding bacteria in strains that supply an essential amino acid for their mutualistic partner when both strains are exposed to antimicrobials. When the strains are free of antimicrobials, we found that, depending on the amount of amino acids freely available in the environment, the strains can exhibit extinction, mutualism, or competition. The availability of resources modulates the behavior of both species. When the strains are exposed to antimicrobials, the population dynamics depend on the proportion of bacteria resistant to the antimicrobial, finding that the extinction of both strains is eminent for low levels of the resource. In contrast, competition between both strains continues for high levels of the resource. An optimal control problem was then formulated to reduce the proportion of resistant bacteria, which showed that under cooperation, both strains (sensitive and resistant) are immediately controlled, while under competition, only the density of one of the strains is decreased. In contrast, its mutualist partner with control is increased. Finally, using our experimental data, we did parameters estimation in order to fit our mathematical model to the experimental data.

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Microbial communities perform emergent activities that are essentially different from those carried by their individual members. The gut microbiome and its metabolites have a significant impact on the host, contributing to homeostasis or disease. Food molecules shape this community, being fermented through cross-feeding interactions of metabolites such as lactate, acetate, and amino acids, or products derived from macromolecule degradation. Mathematical and experimental approaches have been applied to understand and predict the interactions between microorganisms in complex communities such as the gut microbiota. Rational and mechanistic understanding of microbial interactions is essential to exploit their metabolic activities and identify keystone taxa and metabolites. The latter could be used in turn to modulate or replicate the metabolic behavior of the community in different contexts. This review aims to highlight recent experimental and modeling approaches for studying cross-feeding interactions within the gut microbiome. We focus on short-chain fatty acid production and fiber fermentation, which are fundamental processes in human health and disease. Special attention is paid to modeling approaches, particularly kinetic and genome-scale stoichiometric models of metabolism, to integrate experimental data under different diet and health conditions. Finally, we discuss limitations and challenges for the broad application of these modeling approaches and their experimental verification for improving our understanding of the mechanisms of microbial interactions.

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Consumption of flavonoids has been associated with protection against cardiovascular and neurodegenerative diseases. Most dietary flavonoids are subjected to bacterial transformations in the gut where they are converted into biologically active metabolites that are more bioavailable and have distinct effects relative to the parent compounds. While some of the pathways involved in the breakdown of flavonoids are emerging, little it is known about the impact of carbon source availability and community dynamics on flavonoid metabolism. This is relevant in the gut where there is a fierce competition for nutrients. In this study, we show that metabolism of one of the most commonly consumed flavonoids, quercetin, by the gut-associated bacterium Eubacterium ramulus is dependent on interspecies cross-feeding interactions when starch is the only energy source available. E. ramulus can degrade quercetin in the presence of glucose but is unable to use starch for growth or quercetin degradation. However, the starch-metabolizing bacterium Bacteroides thetaiotaomicron, which does not metabolize quercetin, stimulates degradation of quercetin and butyrate production by E. ramulus via cross-feeding of glucose and maltose molecules released from starch. These results suggest that dietary substrates and interactions between species modulate the degradation of flavonoids and production of butyrate, thus shaping their bioavailability and bioactivity, and likely impacting their health-promoting effects in humans.

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The aim of this work was to study the metabolic and microbial community dynamics during dark co-fermentation of 80% tequila vinasse and 20% nixtamalization wastewater (w/w). Batch co-fermentations were performed in a 3-L well-mixed reactor at 35 °C and pH 5.5. In correspondence to Illumina MiSeq sequencing and reactor monitoring, changes in metabolites and microbial communities were characterized by three main stages: (i) a first stage during which lactate and acetate producers dominated and consumed the major part of fermentable carbohydrates, (ii) a second stage in which lactate and acetate were consumed by emerging hydrogen-producing bacteria (HPB) in correlation with bioH2 (100 NmL/L-h or 1200 NmL/Lreactor) and butyrate production, and (iii) a third stage during which non-HPB outcompeted HPB after bioH2 production ceased. Altogether, the results of this study suggest that cooperative interactions between lactate producers and lactate- and acetate-consuming HPB could be attributed to lactate- and acetate-based cross-feeding interactions.

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