Modulation of Atlantic salmon (Salmo salar) gut microbiota composition and predicted metabolic capacity by feeding diets with processed black soldier fly (Hermetia illucens) larvae meals and fractions

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Effects of Yeast Species and Processing on Intestinal Health and Transcriptomic Profiles of Atlantic Salmon (Salmo salar) Fed Soybean Meal-Based Diets in Seawater

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SALARECON connects the Atlantic salmon genome to growth and feed efficiency

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Identification of Antimicrobial Drug Targets from Robustness Properties of Metabolic Networks

A reaction universe containing all 13,849 metabolic reactions known to exist was constructed and found to share many topological properties with real-world metabolic networks. Integration of the reaction universe into 43 different microbial genome-scale metabolic reconstructions led to improved viability and robustness. Five metabolic reactions remained essential in more than 70 % of these reconstructions after integration of the reaction universe and these absolutely superessential reactions were identified as potential targets for broad-spectrum antimicrobial drugs. One of the five reactions was involved in peptidoglycan biosynthesis and the remaining four were part of riboflavin metabolism. No reactions were absolutely superessential in all 43 cellular contexts, meaning that no set of reactions that are always essential in any metabolic network is likely to exist. Ten of the reconstructions into which the reaction universe was integrated were used to generate large ensembles of random viable metabolic networks. The method used for metabolic network randomization was evaluated and it was found that it produced networks with large fractions of blocked reactions. Aside from this, the reaction contents of random viable metabolic networks correlated very strongly with network size. Most importantly, small networks were less randomized than large ones. Even so, the increased size of the reaction universe relative to past studies allowed greater network randomization than what has previously been achieved. Many reactions that were essential or part of synthetic lethal pairs in random viable metabolic networks were capable of being so in all investigated cellular contexts. Based on this, it was postulated that essentiality and synthetic lethality is often caused by factors that are shared between different organisms and environments. Superessentiality indices, which indicate how frequently reactions are expected to be essential in metabolic networks in general, were calculated and found to correlate positively between cellular contexts. However, these correlations were only strong between indices obtained from very similar models, indicating that superessentiality is sensitive to cellular context. Also, a great deal of deviation between indices calculated in this study and previously reported ones was observed, primarily due to the increased size of the reaction universe. An average superessentiality index revealed that some reactions were highly superessential in all investigated cellular contexts and the ten reactions with highest average superessentiality indices, all of them involved in purine or histidine metabolism, were identified as potential antimicrobial drug targets. Synthetic lethality data obtained from random viable metabolic networks was used to construct graph representations of pairwise synthetic lethal interactions between reactions. All of these synthetic lethality networks contained a giant component in which most nodes were found and in all cases this giant component was highly clustered and single-scale and exhibited small-world properties. Indications of assortative network organization were also found. Finally, an algorithm was developed for identifying alternative metabolic pathways of essential reactions in metabolic networks and applied to all essential reactions in two models of potentially pathogenic bacteria. It was found that more than 500 alternative metabolic pathways existed in the reaction universe for most essential reactions in these models. The remaining essential reactions generally had few alternative pathways, most of which consisted of few reactions. Comparison to superessentiality indices showed that the key determinant for reaction superessentiality was most likely a combination of the number of alternative pathways and the lengths of these pathways

Large-scale metabolic modeling of microbial systems

Constraint-based models (CBMs) are key tools for elucidating the behavior of genome-scale metabolic networks, and their scope is steadily expanding towards larger systems in which multiple cells interact. High-throughput metabolomics and other system-level experimental approaches are also advancing rapidly, accumulating large amounts of data that promise to increase the predictive power of CBMs and in turn our understanding of metabolic systems. However, scaling to large systems and data integration are both fundamental challenges for which new computational approaches are required. We address this by introducing methods for data integration and large-scale modeling of metabolic networks. We show that these methods outperform the state of the art and use them to gain biological insights into two microbial systems: clinical strains of Mycobacterium tuberculosis and the defined gut microbiota Oligo-MM12. Human tuberculosis is caused by members of the M. tuberculosis complex (MTBC) that vary in virulence and transmissibility. While genome-wide association studies have uncovered several mutations conferring drug resistance, much less is known of the factors underlying other bacterial phenotypes. Variation in the outcome of tuberculosis infection and diseases has primarily been attributed to patient and environmental factors, but recent evidence indicates an additional role for the genetic diversity among MTBC clinical strains. Here, we integrated CBMs with growth, metabolomics, and genomics data to unravel the effect of genetic variation on strain-specific metabolic capabilities and vulnerabilities. To identify the functionality of single nucleotide polymorphisms (SNPs), we developed an approach based on flux balance analysis (FBA) that predicts the effects of SNPs on metabolism. Our predictions correctly classified SNP effects in pyruvate kinase and suggested a genetic basis for strain-specific inherent baseline susceptibility to the antibiotic para-aminosalicylic acid. The method is broadly applicable across microbial life, opening new possibilities for the development of more selective treatment strategies. Gut microbiotas are complex microbial communities in the human intestine that play key roles in health and disease. Although the microbial components of many gut microbiotas have been thoroughly investigated, we still know little about their interactions or how they lead to observed variations in composition and function. Defined gut microbiotas such as Oligo-MM12, a community of 12 bacterial strains covering five major phyla in the murine gut, offer a way to tackle the inherent complexity of natural microbiotas. To aid our understanding of metabolic interactions in Oligo-MM12, we built genome-scale CBMs of the Oligo-MM12 strains and used them to predict cross-feedings between strains. Using an ensemble modeling approach, we built 11 CBMs of each of the 12 strains and integrated them with growth and metabolomics data to make them condition-specific. The FBA-based approach previously applied to MTBC strains allowed us to identify metabolite cross-feedings that explained observed growth differences between strains in a rich medium. Our predictions recovered experimentally observed cross-feedings for 20 amino acids and central carbon metabolism intermediates, most of which would not have been detected without CBM ensembles. Our results show that data integration combined with ensemble modeling can be used to explain strain-specific growth in terms of metabolic interactions, paving the way for mechanistic understanding of gut microbiota functions. Pathway analysis can define CBM solutions in terms of metabolic pathways but is computationally hard because the number of pathways grows exponentially with network size. Here, we developed new concepts and methods that facilitate pathway analysis in large metabolic networks. We defined the minimal pathways (MPs) of a metabolic (sub)network as a subset of its elementary flux vectors and developed a computational approach that made it possible to find them in large networks. Efficient enumeration of MPs was achieved by combining this with computation of minimal cut sets in a separate optimization problem. We also found that a simple graph representation of MPs could accelerate enumeration by reducing problem sizes and that our approach could be randomized to allow random sampling of MPs. Our approach outperformed current methods and we showed that it allowed scalable pathway analysis by applying it to a range of microbial and mammalian genome-scale CBMs. We also demonstrated some of its possible applications by sampling random MPs from the central carbon metabolism of Escherichia coli in the context of a genome-scale CBM and by enumerating all minimal media for strains in Oligo-MM12

The Spleen as a Target to Characterize Immunomodulatory Effects of Down-Stream Processed Cyberlindnera jadinii Yeasts in Atlantic Salmon Exposed to a Dietary Soybean Meal Challenge

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Model-based integration of genomics and metabolomics reveals SNP functionality in Mycobacterium tuberculosis

Human tuberculosis is caused by members of the; Mycobacterium tuberculosis; complex (MTBC) that vary in virulence and transmissibility. While genome-wide association studies have uncovered several mutations conferring drug resistance, much less is known about the factors underlying other bacterial phenotypes. Variation in the outcome of tuberculosis infection and diseases has been attributed primarily to patient and environmental factors, but recent evidence indicates an additional role for the genetic diversity among MTBC clinical strains. Here, we used metabolomics to unravel the effect of genetic variation on the strain-specific metabolic adaptive capacity and vulnerability. To define the functionality of single-nucleotide polymorphisms (SNPs) systematically, we developed a constraint-based approach that integrates metabolomic and genomic data. Our model-based predictions correctly classify SNP effects in pyruvate kinase and suggest a genetic basis for strain-specific inherent baseline susceptibility to the antibiotic; para; -aminosalicylic acid. Our method is broadly applicable across microbial life, opening possibilities for the development of more selective treatment strategies

Effects of Yeast Species and Processing on Intestinal Health and Transcriptomic Profiles of Atlantic Salmon (Salmo salar) Fed Soybean Meal-Based Diets in Seawater

The objective of the current study was to examine the effects of yeasts on intestinal health and transcriptomic profiles from the distal intestine and spleen tissue of Atlantic salmon fed SBM-based diets in seawater. Cyberlindnera jadinii (CJ) and Wickerhamomyces anomalus (WA) yeasts were heat-inactivated with spray-drying (ICJ and IWA) or autolyzed at 50 °C for 16 h (ACJ and AWA), followed by spray-drying. Six diets were formulated, one based on fishmeal (FM), a challenging diet with 30% soybean meal (SBM) and four other diets containing 30% SBM and 10% of each of the four yeast fractions (i.e., ICJ, ACJ, IWA and AWA). The inclusion of CJ yeasts reduced the loss of enterocyte supranuclear vacuolization and reduced the population of CD8α labeled cells present in the lamina propria of fish fed the SBM diet. The CJ yeasts controlled the inflammatory responses of fish fed SBM through up-regulation of pathways related to wound healing and taurine metabolism. The WA yeasts dampened the inflammatory profile of fish fed SBM through down-regulation of pathways related to toll-like receptor signaling, C-lectin receptor, cytokine receptor and signal transduction. This study suggests that the yeast species, Cyberlindnera jadinii and Wickerhamomyces anomalus are novel high-quality protein sources with health-beneficial effects in terms of reducing inflammation associated with feeding plant-based diets to Atlantic salmon

Effect of yeast species and processing on intestinal microbiota of Atlantic salmon (Salmo salar) fed soybean meal-based diets in seawater

Abstract Background Yeasts are gaining attention as alternative ingredients in aquafeeds. However, the impact of yeast inclusion on modulation of intestinal microbiota of fish fed plant-based ingredients is limited. Thus, the present study investigates the effects of yeast and processing on composition, diversity and predicted metabolic capacity of gut microbiota of Atlantic salmon smolt fed soybean meal (SBM)-based diet. Two yeasts, Cyberlindnera jadinii (CJ) and Wickerhamomyces anomalus (WA), were produced in-house and processed by direct heat-inactivation with spray-drying (ICJ and IWA) or autolyzed at 50 °C for 16 h, followed by spray-drying (ACJ and AWA). In a 42-day feeding experiment, fish were fed one of six diets: a fishmeal (FM)-based diet, a challenging diet with 30% SBM and four other diets containing 30% SBM and 10% of each of the four yeast products (i.e., ICJ, ACJ, IWA and AWA). Microbial profiling of digesta samples was conducted using 16S rRNA gene sequencing, and the predicted metabolic capacities of gut microbiota were determined using genome-scale metabolic models. Results The microbial composition and predicted metabolic capacity of gut microbiota differed between fish fed FM diet and those fed SBM diet. The digesta of fish fed SBM diet was dominated by members of lactic acid bacteria, which was similar to microbial composition in the digesta of fish fed the inactivated yeasts (ICJ and IWA diets). Inclusion of autolyzed yeasts (ACJ and AWA diets) reduced the richness and diversity of gut microbiota in fish. The gut microbiota of fish fed ACJ diet was dominated by the genus Pediococcus and showed a predicted increase in mucin O-glycan degradation compared with the other diets. The gut microbiota of fish fed AWA diet was highly dominated by the family Bacillaceae. Conclusions The present study showed that dietary inclusion of FM and SBM differentially modulate the composition and predicted metabolic capacity of gut microbiota of fish. The inclusion of inactivated yeasts did not alter the modulation caused by SBM-based diet. Fish fed ACJ diet increased relative abundance of Pediococcus, and mucin O-glycan degradation pathway compared with the other diets