Understanding the interactions between bacteria in the human gut through metabolic modeling. Sci Rep. 2013;3:2532. [PMID: 23982459 PMCID: PMC3755282 DOI: 10.1038/srep02532]

Methanobrevibacter smithii cell variants in human physiology and pathology: A review

Methanobrevibacter smithii (M. smithii), initially isolated from human feces, has been recognised as a distinct taxon within the Archaea domain following comprehensive phenotypic, genetic, and genomic analyses confirming its uniqueness among methanogens. Its diversity, encompassing 15 genotypes, mirrors that of biotic and host-associated ecosystems in which M. smithii plays a crucial role in detoxifying hydrogen from bacterial fermentations, converting it into mechanically expelled gaseous methane. In microbiota in contact with host epithelial mucosae, M. smithii centres metabolism-driven microbial networks with Bacteroides, Prevotella, Ruminococcus, Veillonella, Enterococcus, Escherichia, Enterobacter, Klebsiella, whereas symbiotic association with the nanoarchaea Candidatus Nanopusillus phoceensis determines small and large cell variants of M. smithii. The former translocate with bacteria to induce detectable inflammatory and serological responses and are co-cultured from blood, urine, and tissular abscesses with bacteria, prototyping M. smithii as a model organism for pathogenicity by association. The sources, mechanisms and dynamics of in utero and lifespan M. smithii acquisition, its diversity, and its susceptibility to molecules of environmental, veterinary, and medical interest still have to be deeply investigated, as only four strains of M. smithii are available in microbial collections, despite the pivotal role this neglected microorganism plays in microbiota physiology and pathologies.

Genome-scale models in human metabologenomics

Metabologenomics integrates metabolomics with other omics data types to comprehensively study the genetic and environmental factors that influence metabolism. These multi-omics data can be incorporated into genome-scale metabolic models (GEMs), which are highly curated knowledge bases that explicitly account for genes, transcripts, proteins and metabolites. By including all known biochemical reactions catalysed by enzymes and transporters encoded in the human genome, GEMs analyse and predict the behaviour of complex metabolic networks. Continued advancements to the scale and scope of GEMs - from cells and tissues to microbiomes and the whole body - have helped to design effective treatments and develop better diagnostic tools for metabolic diseases. Furthermore, increasing amounts of multi-omics data are incorporated into GEMs to better identify the underlying mechanisms, biomarkers and potential drug targets of metabolic diseases.

The Future Exploring of Gut Microbiome-Immunity Interactions: From In Vivo/Vitro Models to In Silico Innovations

Investigating the complex interactions between microbiota and immunity is crucial for a fruitful understanding progress of human health and disease. This review assesses animal models, next-generation in vitro models, and in silico approaches that are used to decipher the microbiome-immunity axis, evaluating their strengths and limitations. While animal models provide a comprehensive biological context, they also raise ethical and practical concerns. Conversely, modern in vitro models reduce animal involvement but require specific costs and materials. When considering the environmental impact of these models, in silico approaches emerge as promising for resource reduction, but they require robust experimental validation and ongoing refinement. Their potential is significant, paving the way for a more sustainable and ethical future in microbiome-immunity research.

Microbiota, natural products, and human health: exploring interactions for therapeutic insights

The symbiotic relationship between the human digestive system and its intricate microbiota is a captivating field of study that continues to unfold. Comprising predominantly anaerobic bacteria, this complex microbial ecosystem, teeming with trillions of organisms, plays a crucial role in various physiological processes. Beyond its primary function in breaking down indigestible dietary components, this microbial community significantly influences immune system modulation, central nervous system function, and disease prevention. Despite the strides made in microbiome research, the precise mechanisms underlying how bacterial effector functions impact mammalian and microbiome physiology remain elusive. Unlike the traditional DNA-RNA-protein paradigm, bacteria often communicate through small molecules, underscoring the imperative to identify compounds produced by human-associated bacteria. The gut microbiome emerges as a linchpin in the transformation of natural products, generating metabolites with distinct physiological functions. Unraveling these microbial transformations holds the key to understanding the pharmacological activities and metabolic mechanisms of natural products. Notably, the potential to leverage gut microorganisms for large-scale synthesis of bioactive compounds remains an underexplored frontier with promising implications. This review serves as a synthesis of current knowledge, shedding light on the dynamic interplay between natural products, bacteria, and human health. In doing so, it contributes to our evolving comprehension of microbiome dynamics, opening avenues for innovative applications in medicine and therapeutics. As we delve deeper into this intricate web of interactions, the prospect of harnessing the power of the gut microbiome for transformative medical interventions becomes increasingly tantalizing.

Revealing microbiota characteristics and predicting flavor-producing sub-communities in Nongxiangxing baijiu pit mud through metagenomic analysis and metabolic modeling

The microorganisms of the pit mud (PM) of Nongxiangxing baijiu (NXXB) have an important role in the synthesis of flavor substances, and they determine attributes and quality of baijiu. Herein, we utilize metagenomics and genome-scale metabolic models (GSMMs) to investigate the microbial composition, metabolic functions in PM microbiota, as well as to identify microorganisms and communities linked to flavor compounds. Metagenomic data revealed that the most prevalent assembly of bacteria and archaea was Proteiniphilum, Caproicibacterium, Petrimonas, Lactobacillus, Clostridium, Aminobacterium, Syntrophomonas, Methanobacterium, Methanoculleus, and Methanosarcina. The important enzymes ofPMwere in bothGH and GT familymetabolism. A total of 38 high-quality metagenome-assembled genomes (MAGs) were obtained, including those at the family level (n = 13), genus level (n = 17), and species level (n = 8). GSMMs of the 38 MAGs were then constructed. From the GSMMs, individual and community capabilities respectively were predicted to be able to produce 111 metabolites and 598 metabolites. Twenty-three predicted metabolites were consistent with the metabonomics detected flavors and served as targets. Twelve sub-community of were screened by cross-feeding of 38 GSMMs. Of them, Methanobacterium, Sphaerochaeta, Muricomes intestini, Methanobacteriaceae, Synergistaceae, and Caloramator were core microorganisms for targets in each sub-community. Overall, this study of metagenomic and target-community screening could help our understanding of the metabolite-microbiome association and further bioregulation of baijiu.

De Novo Multi-Omics Pathway Analysis Designed for Prior Data Independent Inference of Cell Signaling Pathways

New tools for cell signaling pathway inference from multi-omics data that are independent of previous knowledge are needed. Here, we propose a new de novo method, the de novo multi-omics pathway analysis (DMPA), to model and combine omics data into network modules and pathways. DMPA was validated with published omics data and was found accurate in discovering reported molecular associations in transcriptome, interactome, phosphoproteome, methylome, and metabolomics data, and signaling pathways in multi-omics data. DMPA was benchmarked against module discovery and multi-omics integration methods and outperformed previous methods in module and pathway discovery especially when applied to datasets of relatively low sample sizes. Transcription factor, kinase, subcellular location, and function prediction algorithms were devised for transcriptome, phosphoproteome, and interactome modules and pathways, respectively. To apply DMPA in a biologically relevant context, interactome, phosphoproteome, transcriptome, and proteome data were collected from analyses carried out using melanoma cells to address gamma-secretase cleavage-dependent signaling characteristics of the receptor tyrosine kinase TYRO3. The pathways modeled with DMPA reflected the predicted function and its direction in validation experiments.

GEM-based computational modeling for exploring metabolic interactions in a microbial community

Microbial communities play fundamental roles in every complex ecosystem, such as soil, sea and the human body. The stability and diversity of the microbial community depend precisely on the composition of the microbiota. Any change in the composition of these communities affects microbial functions. An important goal of studying the interactions between species is to understand the behavior of microbes and their responses to perturbations. These interactions among species are mediated by the exchange of metabolites within microbial communities. We developed a computational model for the microbial community that has a separate compartment for exchanging metabolites. This model can predict possible metabolites that cause competition, commensalism, and mutual interactions between species within a microbial community. Our constraint-based community metabolic modeling approach provides insights to elucidate the pattern of metabolic interactions for each common metabolite between two microbes. To validate our approach, we used a toy model and a syntrophic co-culture of Desulfovibrio vulgaris and Methanococcus maripaludis, as well as another in co-culture between Geobacter sulfurreducens and Rhodoferax ferrireducens. For a more general evaluation, we applied our algorithm to the honeybee gut microbiome, composed of seven species, and the epiphyte strain Pantoea eucalypti 299R. The epiphyte strain Pe299R has been previously studied and cultured with six different phyllosphere bacteria. Our algorithm successfully predicts metabolites, which imply mutualistic, competitive, or commensal interactions. In contrast to OptCom, MRO, and MICOM algorithms, our COMMA algorithm shows that the potential for competitive interactions between an epiphytic species and Pe299R is not significant. These results are consistent with the experimental measurements of population density and reproductive success of the Pe299R strain.

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models

The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.

Whole-plant silage maize to improve fiber digestive characteristics and intestinal microbiota of Hezuo pigs

Introduction

Utilizing roughage resources is an effective approach to alleviate the shortage of corn-soybean feed and reducing the costs in the swine industry. Hezuo pig is one group of plateau type local Tibetan pig with strong tolerance to crude feeding. Nevertheless, current research on the roughage tolerance in Hezuo pigs and the microbiological mechanisms behind it is still minimally.This study explored the impact of various ratios of whole-plant silage (WPS) maize on the pH, cellulase activity, short-chain fatty acids (SCFAs), and intestinal microbiota in Hezuo pigs.

Methods

Thirty-two Hezuo pigs were randomly divided into four groups (n = 8). The control group received a basal diet, while experimental groups I, II, and III were given diets with incremental additions of 5%, 10%, and 15% air-dried WPS maize, respectively, for 120 days.

Results

The findings revealed that compared with the control group, in Group II, the pH of cecum and colon were notably decreased (p < 0.05), while acid detergent fiberdigestibility, the concentration of propionic and isobutyric acid in the cecum, and the concentration of isobutyric acid in the colon were significantly increased (p < 0.05). Also, carboxymethyl cellulase activity in the cecum in group II of Hezuo pigs was significantly higher than that in the other three groups (p < 0.05). Furthermore, the cecum microbiota showed a higher diversity in the group II of Hezuo pigs than that in the control group, as shown by the Simpson and Shannon indices. Specifically, 15 and 24 bacterial species showed a significant difference in relative abundance at the family and genus levels, respectively. Correlation analyses revealed significant associations between bacterial genera and SCFAs concentrations in the cecum. The abundance of Bacteroides and NK4A214_group was positively correlated with amounts of valeric and isovaleric acid but negatively with propionic acid (p < 0.05). The abundance of UCG-010 was positively linked with acetic acid and negatively correlated with butyric acid (p < 0.05). Actinobacillus abundance was positively associated with butyric acid levels (p < 0.05).

Discussion

In conclusion, a 10% WPS maize diet improved crude fiber digestibility by lowering cecal and colonic chyme pH, enhancing intestinal cellulase activity, improving SCFA production, and increasing intestinal microbiota diversity.

Hierarchical eco-evo dynamics mediated by the gut microbiome

The concept of eco-evolutionary (eco-evo) dynamics, stating that ecological and evolutionary processes occur at similar time scales and influence each other, has contributed to our understanding of responses of populations, communities, and ecosystems to environmental change. Phenotypes, central to these eco-evo processes, can be strongly impacted by the gut microbiome. The gut microbiome shapes eco-evo dynamics in the host community through its effects on the host phenotype. Complex eco-evo feedback loops between the gut microbiome and the host communities might thus be common. Bottom-up dynamics occur when eco-evo interactions shaping the gut microbiome affect host phenotypes with consequences at population, community, and ecosystem levels. Top-down dynamics occur when eco-evo dynamics shaping the host community structure the gut microbiome.

A mathematical model of Bacteroides thetaiotaomicron, Methanobrevibacter smithii, and Eubacterium rectale interactions in the human gut

The human gut microbiota is a complex ecosystem that affects a range of human physiology. In order to explore the dynamics of the human gut microbiota, we used a system of ordinary differential equations to model mathematically the biomass of three microorganism populations: Bacteroides thetaiotaomicron, Eubacterium rectale, and Methanobrevibacter smithii. Additionally, we modeled the concentrations of relevant nutrients necessary to sustain these populations over time. Our model highlights the interactions and the competition among these three species. These three microorganisms were specifically chosen due to the system's end product, butyrate, which is a short chain fatty acid that aids in developing and maintaining the intestinal barrier in the human gut. The basis of our mathematical model assumes the gut is structured such that bacteria and nutrients exit the gut at a rate proportional to its volume, the rate of volumetric flow, and the biomass or concentration of the particular population or nutrient. We performed global sensitivity analyses using Sobol' sensitivities to estimate the relative importance of model parameters on simulation results.

Effects of Low Protein Diet on Production Performance and Intestinal Microbial Composition in Pigs

In order to study the effects of a low protein diet on the production performance and intestinal microbiota composition of Hexi pigs, twenty-seven Hexi pigs with an initial body weight of 60.50 ± 2.50 kg were randomly divided into three groups (control group (CG), group 1 (G1), and group 2 (G2)) and participated in a 60-day finishing trial. The CG was fed a normal protein level diet with a protein level of 16.0%, and G1 and G2 were fed a low protein level diet with protein levels of 14.0% and 12.0%, respectively. The results showed that the low protein level diet had no significant effect on the production performance of Hexi pigs, compared with the CG, the slaughter rate of G1 and G2 increased by 2.49% (p > 0.05) and 6.18% (p > 0.05), the shear force decreased by 2.43% (p > 0.05) and 15.57% (p > 0.05), the cooking loss decreased by 24.02% (p < 0.05) and 21.09% (p > 0.05), and the cooking percentage increased by 13.20% (p > 0.05) and 11.59% (p > 0.05). From 45 min to 24 h and 48 h after slaughter, each group of pH decreased by 1.02, 0.66, and 0.42. For muscle flesh color, the lightness (L) increased by 13.31% (p > 0.05) and 18.01% (p > 0.05) in G1 and G2 and the yellowness (b) increased by 7.72% (p > 0.05) and 13.06% (p > 0.05). A low protein level diet can improve the intestinal flora richness and diversity of growing and finishing pigs. In the jejunum, the ACE index (899.95), Simpson index (0.90), and Shannon (4.75) index were higher in G1 than in the other groups, but the Chao1 index (949.92) was higher in G2 than in the remaining two groups. Proteobacteria, Actinobacteria, Euryarchaeota, and Verrucomicrobia were significantly higher in G1 than in the CG. The relative abundances of Lactobacillus, Terrisporobacter, and Megasphaera in G1 was significantly higher than in the CG (p < 0.05). In the cecum, the ACE index (900.93), Chao1 index (879.10), Simpson index (0.94), and Shannon (5.70) index were higher in G1 than in the remaining groups. The Spirochaetes in G2 were significantly higher than in the other groups, but the Verrucomicrobia was significantly lower than in the other groups. The relative abundances of Lactobacillus were higher in G1 and G2 than in the CG (p > 0.05). The relative abundances of unidentified_Clostridiales and Terrisporobacter in G2 were significantly lower than in the CG (p < 0.05). The relative abundance of Turicibacter in G1 was significantly lower than in the CG (p < 0.05). The relative abundances of other bacterial genera in G1 and G2 were increased by 30.81% (p > 0.05) and 17.98% (p > 0.05).

An integrated systems biology approach reveals differences in formate metabolism in the genus Methanothermobacter

Methanogenesis allows methanogenic archaea to generate cellular energy for their growth while producing methane. Thermophilic hydrogenotrophic species of the genus Methanothermobacter have been recognized as robust biocatalysts for a circular carbon economy and are already applied in power-to-gas technology with biomethanation, which is a platform to store renewable energy and utilize captured carbon dioxide. Here, we generated curated genome-scale metabolic reconstructions for three Methanothermobacter strains and investigated differences in the growth performance of these same strains in chemostat bioreactor experiments with hydrogen and carbon dioxide or formate as substrates. Using an integrated systems biology approach, we identified differences in formate anabolism between the strains and revealed that formate anabolism influences the diversion of carbon between biomass and methane. This finding, together with the omics datasets and the metabolic models we generated, can be implemented for biotechnological applications of Methanothermobacter in power-to-gas technology, and as a perspective, for value-added chemical production.

Metabolic engineering of human gut microbiome: Recent developments and future perspectives

Many studies have demonstrated that the gut microbiota is associated with human health and disease. Manipulation of the gut microbiota, e.g. supplementation of probiotics, has been suggested to be feasible, but subject to limited therapeutic efficacy. To develop efficient microbiota-targeted diagnostic and therapeutic strategies, metabolic engineering has been applied to construct genetically modified probiotics and synthetic microbial consortia. This review mainly discusses commonly adopted strategies for metabolic engineering in the human gut microbiome, including the use of in silico, in vitro, or in vivo approaches for iterative design and construction of engineered probiotics or microbial consortia. Especially, we highlight how genome-scale metabolic models can be applied to advance our understanding of the gut microbiota. Also, we review the recent applications of metabolic engineering in gut microbiome studies as well as discuss important challenges and opportunities.

Non-invasive monitoring of microbiota and host metabolism using secondary electrospray ionization-mass spectrometry

The metabolic "handshake" between the microbiota and its mammalian host is a complex, dynamic process with major influences on health. Dissecting the interaction between microbial species and metabolites found in host tissues has been a challenge due to the requirement for invasive sampling. Here, we demonstrate that secondary electrospray ionization-mass spectrometry (SESI-MS) can be used to non-invasively monitor metabolic activity of the intestinal microbiome of a live, awake mouse. By comparing the headspace metabolome of individual gut bacterial culture with the "volatilome" (metabolites released to the atmosphere) of gnotobiotic mice, we demonstrate that the volatilome is characteristic of the dominant colonizing bacteria. Combining SESI-MS with feeding heavy-isotope-labeled microbiota-accessible sugars reveals the presence of microbial cross-feeding within the animal intestine. The microbiota is, therefore, a major contributor to the volatilome of a living animal, and it is possible to capture inter-species interaction within the gut microbiota using volatilome monitoring.

Dingxin recipe Ⅲ ameliorates hyperlipidemia injury in SD rats by improving the gut barrier, particularly the SCFAs/GPR43 pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Dingxin Recipe Ⅲ (DXR Ⅲ) is a traditional Chinese medicine compound used for hyperlipidemia treatment in clinical practice. However, its curative effects and pharmacological mechanisms in hyperlipidemia have not been clarified to date.

AIM OF THE STUDY

Studies have demonstrated that gut barrier was strongly implicated in lipid deposition. Based on gut barrier and lipid metabolism, this study examined the effects and molecular mechanisms of DXR Ⅲ in hyperlipidemia.

MATERIALS AND METHODS

The bioactive compounds of DXR Ⅲ were detected by ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry, and its effects were evaluated in high-fat diet-fed rats. Specifically, the serum levels of lipids and hepatic enzymes were measured using the appropriate kits; colon and liver sections were obtained for histological analyses; gut microbiota and metabolites were analyzed by 16S rDNA sequencing and liquid chromatography-MS/MS; and the expression of genes and proteins was determined by real-time quantitative polymerase chain reaction and western blotting and immunohistochemistry, respectively. The pharmacological mechanisms of DXR Ⅲ were further explored by fecal microbiota transplantation and short-chain fatty acid (SCFAs)-based interventions.

RESULTS

DXR Ⅲ treatment significantly downregulated serum lipid levels, mitigated hepatocyte steatosis and improved lipid metabolism. Moreover, DXR Ⅲ improved the gut barrier, specifically by improving the physical barrier in the colon, causing part composition changes in the gut microbiota, and increasing the serum SCFAs level. DXR Ⅲ also upregulated the expression of colon GPR43/GPR109A. Fecal microbiota transplantation from rats treated with DXR Ⅲ downregulated part hyperlipidemia-related phenotypes, while the SCFAs intervention significantly improved most of the hyperlipidemia-related phenotypes and upregulated the expression of GPR43. Moreover, both DXR Ⅲ and SCFAs upregulated the expression of colon ABCA1.

CONCLUSION

DXR Ⅲ protects against hyperlipidemia by improving the gut barrier, particularly the SCFAs/GPR43 pathway.

Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine

Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.

Genome-scale metabolic modeling and in silico analysis of opportunistic skin pathogen Cutibacterium acnes

Cutibacterium acnes, one of the most abundant skin microbes found in the sebaceous gland, is known to contribute to the development of acne vulgaris when its strains become imbalanced. The current limitations of acne treatment using antibiotics have caused an urgent need to develop a systematic strategy for selectively targeting C. acnes, which can be achieved by characterizing their cellular behaviors under various skin environments. To this end, we developed a genome-scale metabolic model (GEM) of virulent C. acnes, iCA843, based on the genome information of a relevant strain from ribotype 5 to comprehensively understand the pathogenic traits of C. acnes in the skin environment. We validated the model qualitatively by demonstrating its accuracy prediction of propionate and acetate production patterns, which were consistent with experimental observations. Additionally, we identified unique biosynthetic pathways for short-chain fatty acids in C. acnes compared to other GEMs of acne-inducing skin pathogens. By conducting constraint-based flux analysis under endogenous carbon sources in human skin, we discovered that the Wood-Werkman cycle is highly activated under acnes-associated skin condition for the regeneration of NAD, resulting in enhanced propionate production. Finally, we proposed potential anti-C. acnes targets by using the model-guided systematic framework based on gene essentiality analysis and protein sequence similarity search with abundant skin microbiome taxa.

Dysregulation along the gut microbiota-immune system axis after oral exposure to titanium dioxide nanoparticles: A possible environmental factor promoting obesity-related metabolic disorders

Food additives are one major hallmark of ultra-processed food in the Western-diet, a food habit often associated with metabolic disorders. Among these additives, the whitener and opacifying agent titanium dioxide (TiO₂) raises public health issues due to the ability of TiO₂ nanoparticles (NPs) to cross biological barriers and accumulate in different systemic organs like spleen, liver and pancreas. However before their systemic passage, the biocidal properties of TiO₂ NPs may alter the composition and activity of the gut microbiota, which play a crucial role for the development and maintenance of immune functions. Once absorbed, TiO₂ NPs may further interact with immune intestinal cells involved in gut microbiota regulation. Since obesity-related metabolic diseases such as diabetes are associated with alterations in the microbiota-immune system axis, this raises questions about the possible involvement of long-term exposure to food-grade TiO₂ in the development or worsening of these diseases. The current purpose is to review the dysregulations along the gut microbiota-immune system axis after oral TiO₂ exposure compared to those reported in obese or diabetic patients, and to highlight potential mechanisms by which foodborne TiO₂ NPs may increase the susceptibility to develop obesity-related metabolic disorders.

Metagenome-based metabolic modelling predicts unique microbial interactions in deep-sea hydrothermal plume microbiomes

Deep-sea hydrothermal vents are abundant on the ocean floor and play important roles in ocean biogeochemistry. In vent ecosystems such as hydrothermal plumes, microorganisms rely on reduced chemicals and gases in hydrothermal fluids to fuel primary production and form diverse and complex microbial communities. However, microbial interactions that drive these complex microbiomes remain poorly understood. Here, we use microbiomes from the Guaymas Basin hydrothermal system in the Pacific Ocean to shed more light on the key species in these communities and their interactions. We built metabolic models from metagenomically assembled genomes (MAGs) and infer possible metabolic exchanges and horizontal gene transfer (HGT) events within the community. We highlight possible archaea-archaea and archaea-bacteria interactions and their contributions to the robustness of the community. Cellobiose, D-Mannose 1-phosphate, O2, CO2, and H2S were among the most exchanged metabolites. These interactions enhanced the metabolic capabilities of the community by exchange of metabolites that cannot be produced by any other community member. Archaea from the DPANN group stood out as key microbes, benefiting significantly as acceptors in the community. Overall, our study provides key insights into the microbial interactions that drive community structure and organisation in complex hydrothermal plume microbiomes.

Synthetic microbial communities (SynComs) of the human gut: design, assembly, and applications

The human gut harbors native microbial communities, forming a highly complex ecosystem. Synthetic microbial communities (SynComs) of the human gut are an assembly of microorganisms isolated from human mucosa or fecal samples. In recent decades, the ever-expanding culturing capacity and affordable sequencing, together with advanced computational modeling, started a ''golden age'' for harnessing the beneficial potential of SynComs to fight gastrointestinal disorders, such as infections and chronic inflammatory bowel diseases. As simplified and completely defined microbiota, SynComs offer a promising reductionist approach to understanding the multispecies and multikingdom interactions in the microbe-host-immune axis. However, there are still many challenges to overcome before we can precisely construct SynComs of designed function and efficacy that allow the translation of scientific findings to patients' treatments. Here, we discussed the strategies used to design, assemble, and test a SynCom, and address the significant challenges, which are of microbiological, engineering, and translational nature, that stand in the way of using SynComs as live bacterial therapeutics.

Interactions between Culturable Bacteria Are Predicted by Individual Species' Growth

Predicting interspecies interactions is a key challenge in microbial ecology given that interactions shape the composition and functioning of microbial communities. However, predicting microbial interactions is challenging because they can vary considerably depending on species' metabolic capabilities and environmental conditions. Here, we employ machine learning models to predict pairwise interactions between culturable bacteria based on their phylogeny, monoculture growth capabilities, and interactions with other species. We trained our models on one of the largest available pairwise interactions data set containing over 7,500 interactions between 20 species from two taxonomic groups that were cocultured in 40 different carbon environments. Our models accurately predicted both the sign (accuracy of 88%) and the strength of effects (R² of 0.87) species had on each other's growth. Encouragingly, predictions with comparable accuracy could be made even when not relying on information about interactions with other species, which are often hard to measure. However, species' monoculture growth was essential to the model, as predictions based solely on species' phylogeny and inferred metabolic capabilities were significantly less accurate. These results bring us one step closer to a predictive understanding of microbial communities, which is essential for engineering beneficial microbial consortia. IMPORTANCE In order to understand the function and structure of microbial communities, one must know all pairwise interactions that occur between the different species within the community, as these interactions shape the community's structure and functioning. However, measuring all pairwise interactions can be an extremely difficult task especially when dealing with big complex communities. Because of that, predicting interspecies interactions is a key challenge in microbial ecology. Here, we use machine learning models in order to accurately predict the type and strength of interactions. We trained our models on one of the largest available pairwise interactions data set, containing over 7,500 interactions between 20 different species that were cocultured in 40 different environments. Our results show that, in general, accurate predictions can be made, and that the ability of each species to grow on its own in the given environment contributes the most to predictions. Being able to predict microbial interactions would put us one step closer to predicting the functionality of microbial communities and to rationally microbiome engineering.

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.

Influence of the Gut Microbiota on the Development of Neurodegenerative Diseases

Neurodegenerative disorders are marked by neuronal death over time, causing a variety of cognitive and motor dysfunctions. Protein misfolding, neuroinflammation, and mitochondrial and protein clearance system dysfunction have all been identified as common pathways leading to neurodegeneration in recent decades. An altered microbiome of the gut, which is considered to play a central role in diseases as well as health, has recently been identified as another potential feature seen in neurodegenerative disorders. An array of microbial molecules that are released in the digestive tract may mediate gut-brain connections and permeate many organ systems, including the nervous system. Furthermore, recent findings from clinical as well as preclinical trials suggest that the microbiota of the gut plays a critical part in gut-brain interplay and that a misbalance in the composition of the gut microbiome may be linked to the etiology of neurological disorders (majorly neurodegenerative health problems); the underlying mechanism of which is still unknown. The review aims to consider the association between the microbiota of the gut and neurodegenerative disorders, as well as to add to our understanding of the significance of the gut microbiome in neurodegeneration and the mechanisms that underlie it. Knowing the mechanisms behind the gut microbiome's role and abundance will provide us with new insights that could lead to novel therapeutic strategies.

Role of the Gut-Brain Axis, Gut Microbial Composition, Diet, and Probiotic Intervention in Parkinson's Disease

Parkinson's disease (PD) is the second-most prevalent neurodegenerative or neuropsychiatric disease, affecting 1% of seniors worldwide. The gut microbiota (GM) is one of the key access controls for most diseases and disorders. Disturbance in the GM creates an imbalance in the function and circulation of metabolites, resulting in unhealthy conditions. Any dysbiosis could affect the function of the gut, consequently disturbing the equilibrium in the intestine, and provoking pro-inflammatory conditions in the gut lumen, which send signals to the central nervous system (CNS) through the vagus enteric nervous system, possibly disturbing the blood-brain barrier. The neuroinflammatory conditions in the brain cause accumulation of α-syn, and progressively develop PD. An important aspect of understanding and treating the disease is access to broad knowledge about the influence of dietary supplements on GM. Probiotics are live microorganisms which, when administered in adequate amounts, confer a health benefit on the host. Probiotic supplementation improves the function of the CNS, and improves the motor and non-motor symptoms of PD. Probiotic supplementation could be an adjuvant therapeutic method to manage PD. This review summarizes the role of GM in health, the GM-brain axis, the pathogenesis of PD, the role of GM and diet in PD, and the influence of probiotic supplementation on PD. The study encourages further detailed clinical trials in PD patients with probiotics, which aids in determining the involvement of GM, intestinal mediators, and neurological mediators in the treatment or management of PD.

Strategies for the Identification and Assessment of Bacterial Strains with Specific Probiotic Traits

Early in the 1900s, it was proposed that health could be improved and senility delayed by manipulating gut microbiota with the host-friendly bacteria found in yogurt. Later, in 1990, the medical community reconsidered this idea and today probiotics represent a developed area of research with a billion-dollar global industry. As a result, in recent decades, increased attention has been paid to the isolation and characterization of novel probiotic bacteria from fermented foods and dairy products. Most of the identified probiotic strains belong to the lactic acid bacteria group and the genus Bifidobacterium. However, current molecular-based knowledge has allowed the identification and culture of obligatory anaerobic commensal bacteria from the human gut, such as Akkermansia spp. and Faecalibacterium spp., among other human symbionts. We are aware that the identification of new strains of these species does not guarantee their probiotic effects and that each effect must be proved through in vitro and in vivo preclinical studies before clinical trials (before even considering it as a probiotic strain). In most cases, the identification and characterization of new probiotic strain candidates may lack the appropriate set of in vitro experiments allowing the next assessment steps. Here, we address some innovative strategies reported in the literature as alternatives to classical characterization: (i) identification of alternatives using whole-metagenome shotgun sequencing, metabolomics, and multi-omics analysis; and (ii) probiotic characterization based on molecular effectors and/or traits to target specific diseases (i.e., inflammatory bowel diseases, colorectal cancer, allergies, among others).

Metabolic modeling of the International Space Station microbiome reveals key microbial interactions

BACKGROUND

Recent studies have provided insights into the persistence and succession of microbes aboard the International Space Station (ISS), notably the dominance of Klebsiella pneumoniae. However, the interactions between the various microbes aboard the ISS and how they shape the microbiome remain to be clearly understood. In this study, we apply a computational approach to predict possible metabolic interactions in the ISS microbiome and shed further light on its organization.

RESULTS

Through a combination of a systems-based graph-theoretical approach, and a constraint-based community metabolic modeling approach, we demonstrated several key interactions in the ISS microbiome. These complementary approaches provided insights into the metabolic interactions and dependencies present amongst various microbes in a community, highlighting key interactions and keystone species. Our results showed that the presence of K. pneumoniae is beneficial to many other microorganisms it coexists with, notably those from the Pantoea genus. Species belonging to the Enterobacteriaceae family were often found to be the most beneficial for the survival of other microorganisms in the ISS microbiome. However, K. pneumoniae was found to exhibit parasitic and amensalistic interactions with Aspergillus and Penicillium species, respectively. To prove this metabolic prediction, K. pneumoniae and Aspergillus fumigatus were co-cultured under normal and simulated microgravity, where K. pneumoniae cells showed parasitic characteristics to the fungus. The electron micrography revealed that the presence of K. pneumoniae compromised the morphology of fungal conidia and degenerated its biofilm-forming structures.

CONCLUSION

Our study underscores the importance of K. pneumoniae in the ISS, and its potential positive and negative interactions with other microbes, including potential pathogens. This integrated modeling approach, combined with experiments, demonstrates the potential for understanding the organization of other such microbiomes, unravelling key organisms and their interdependencies. Video Abstract.

Metabolic modeling of the International Space Station microbiome reveals key microbial interactions

BACKGROUND

Recent studies have provided insights into the persistence and succession of microbes aboard the International Space Station (ISS), notably the dominance of Klebsiella pneumoniae. However, the interactions between the various microbes aboard the ISS and how they shape the microbiome remain to be clearly understood. In this study, we apply a computational approach to predict possible metabolic interactions in the ISS microbiome and shed further light on its organization.

RESULTS

Through a combination of a systems-based graph-theoretical approach, and a constraint-based community metabolic modeling approach, we demonstrated several key interactions in the ISS microbiome. These complementary approaches provided insights into the metabolic interactions and dependencies present amongst various microbes in a community, highlighting key interactions and keystone species. Our results showed that the presence of K. pneumoniae is beneficial to many other microorganisms it coexists with, notably those from the Pantoea genus. Species belonging to the Enterobacteriaceae family were often found to be the most beneficial for the survival of other microorganisms in the ISS microbiome. However, K. pneumoniae was found to exhibit parasitic and amensalistic interactions with Aspergillus and Penicillium species, respectively. To prove this metabolic prediction, K. pneumoniae and Aspergillus fumigatus were co-cultured under normal and simulated microgravity, where K. pneumoniae cells showed parasitic characteristics to the fungus. The electron micrography revealed that the presence of K. pneumoniae compromised the morphology of fungal conidia and degenerated its biofilm-forming structures.

CONCLUSION

Our study underscores the importance of K. pneumoniae in the ISS, and its potential positive and negative interactions with other microbes, including potential pathogens. This integrated modeling approach, combined with experiments, demonstrates the potential for understanding the organization of other such microbiomes, unravelling key organisms and their interdependencies. Video Abstract.

A protocol for the cultivation and monitoring of ileal gut microbiota surrogates

AIMS

This research aimed to develop and validate a cultivation and monitoring protocol that is suitable for a surrogate microbial community that accounts for the gut microbiota of the ileum of the small intestine.

METHODS AND RESULTS

Five bacterial species have been selected as representatives of the ileal gut microbiota and a general anaerobic medium (MS-BHI, as minimally supplemented BHI) has been constructed and validated against BCCM/LGM recommended and commercial media. Moreover, appropriate selective/differential media have been investigated for monitoring each ileal gut microbiota surrogate. Results showed that MS-BHI was highly efficient in displaying individual and collective behavior of the ileal gut microbiota species, when compared with other types of media. Likewise, the selective/differential media managed to identify and describe the behavior of their targeted species.

CONCLUSIONS

MS-BHI renders a highly efficient, inexpensive and easy-to-prepare cultivation and enumeration alternative for the surrogate ileal microbiota species. Additionally, the selective/differential media can identify and quantify the bacteria of the surrogate ileal microbial community.

SIGNIFICANCE AND IMPACT OF STUDY

The selected gut microbiota species can represent an in vitro ileal community, forming the basis for future studies on small intestinal microbiota. MS-BHI and the proposed monitoring protocol can be used as a standard for gut microbiota studies that utilize conventional microbiological techniques.

Nutritional Interactions between Bacterial Species Colonising the Human Nasal Cavity: Current Knowledge and Future Prospects

The human nasal microbiome can be a reservoir for several pathogens, including Staphylococcus aureus. However, certain harmless nasal commensals can interfere with pathogen colonisation, an ability that could be exploited to prevent infection. Although attractive as a prophylactic strategy, manipulation of nasal microbiomes to prevent pathogen colonisation requires a better understanding of the molecular mechanisms of interaction that occur between nasal commensals as well as between commensals and pathogens. Our knowledge concerning the mechanisms of pathogen exclusion and how stable community structures are established is patchy and incomplete. Nutrients are scarce in nasal cavities, which makes competitive or mutualistic traits in nutrient acquisition very likely. In this review, we focus on nutritional interactions that have been shown to or might occur between nasal microbiome members. We summarise concepts of nutrient release from complex host molecules and host cells as well as of intracommunity exchange of energy-rich fermentation products and siderophores. Finally, we discuss the potential of genome-based metabolic models to predict complex nutritional interactions between members of the nasal microbiome.

GMEmbeddings: An R Package to Apply Embedding Techniques to Microbiome Data

Large-scale microbiome studies investigating disease-inducing microbial roles base their findings on differences between microbial count data in contrasting environments (e.g., stool samples between cases and controls). These microbiome survey studies are often impeded by small sample sizes and database bias. Combining data from multiple survey studies often results in obvious batch effects, even when DNA preparation and sequencing methods are identical. Relatedly, predictive models trained on one microbial DNA dataset often do not generalize to outside datasets. In this study, we address these limitations by applying word embedding algorithms (GloVe) and PCA transformation to ASV data from the American Gut Project and generating translation matrices that can be applied to any 16S rRNA V4 region gut microbiome sequencing study. Because these approaches contextualize microbial occurrences in a larger dataset while reducing dimensionality of the feature space, they can improve generalization of predictive models that predict host phenotype from stool associated gut microbiota. The GMEmbeddings R package contains GloVe and PCA embedding transformation matrices at 50, 100 and 250 dimensions, each learned using ∼15,000 samples from the American Gut Project. It currently supports the alignment, matching, and matrix multiplication to allow users to transform their V4 16S rRNA data into these embedding spaces. We show how to correlate the properties in the new embedding space to KEGG functional pathways for biological interpretation of results. Lastly, we provide benchmarking on six gut microbiome datasets describing three phenotypes to demonstrate the ability of embedding-based microbiome classifiers to generalize to independent datasets. Future iterations of GMEmbeddings will include embedding transformation matrices for other biological systems. Available at: https://github.com/MaudeDavidLab/GMEmbeddings.

Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review

Through the developments of Omics technologies and dissemination of large-scale datasets, such as those from The Cancer Genome Atlas, Alzheimer’s Disease Neuroimaging Initiative, and Genotype-Tissue Expression, it is becoming increasingly possible to study complex biological processes and disease mechanisms more holistically. However, to obtain a comprehensive view of these complex systems, it is crucial to integrate data across various Omics modalities, and also leverage external knowledge available in biological databases. This review aims to provide an overview of multi-Omics data integration methods with different statistical approaches, focusing on unsupervised learning tasks, including disease onset prediction, biomarker discovery, disease subtyping, module discovery, and network/pathway analysis. We also briefly review feature selection methods, multi-Omics data sets, and resources/tools that constitute critical components for carrying out the integration.

In vitro interaction network of a synthetic gut bacterial community

A key challenge in microbiome research is to predict the functionality of microbial communities based on community membership and (meta)-genomic data. As central microbiota functions are determined by bacterial community networks, it is important to gain insight into the principles that govern bacteria-bacteria interactions. Here, we focused on the growth and metabolic interactions of the Oligo-Mouse-Microbiota (OMM¹²) synthetic bacterial community, which is increasingly used as a model system in gut microbiome research. Using a bottom-up approach, we uncovered the directionality of strain-strain interactions in mono- and pairwise co-culture experiments as well as in community batch culture. Metabolic network reconstruction in combination with metabolomics analysis of bacterial culture supernatants provided insights into the metabolic potential and activity of the individual community members. Thereby, we could show that the OMM¹² interaction network is shaped by both exploitative and interference competition in vitro in nutrient-rich culture media and demonstrate how community structure can be shifted by changing the nutritional environment. In particular, Enterococcus faecalis KB1 was identified as an important driver of community composition by affecting the abundance of several other consortium members in vitro. As a result, this study gives fundamental insight into key drivers and mechanistic basis of the OMM¹² interaction network in vitro, which serves as a knowledge base for future mechanistic in vivo studies.

Bacteria and Methanogens in the Human Microbiome: a Review of Syntrophic Interactions

Methanogens are microorganisms belonging to the Archaea domain and represent the primary source of biotic methane. Methanogens encode a series of enzymes which can convert secondary substrates into methane following three major methanogenesis pathways. Initially recognized as environmental microorganisms, methanogens have more recently been acknowledged as host-associated microorganisms after their detection and initial isolation in ruminants in the 1950s. Methanogens have also been co-detected with bacteria in various pathological situations, bringing their role as pathogens into question. Here, we review reported associations between methanogens and bacteria in physiological and pathological situations in order to understand the metabolic interactions explaining these associations. To do so, we describe the origin of the metabolites used for methanogenesis and highlight the central role of methanogens in the syntrophic process during carbon cycling. We then focus on the metabolic abilities of co-detected bacterial species described in the literature and infer from their genomes the probable mechanisms of their association with methanogens. The syntrophic interactions between bacteria and methanogens are paramount to gut homeostasis. Therefore, any dysbiosis affecting methanogens might impact human health. Thus, the monitoring of methanogens may be used as a bio-indicator of dysbiosis. Moreover, new therapeutic approaches can be developed based on their administration as probiotics. We thus insist on the importance of investigating methanogens in clinical microbiology.

NCMW: A Python Package to Analyze Metabolic Interactions in the Nasal Microbiome

The human upper respiratory tract is the reservoir of a diverse community of commensals and potential pathogens (pathobionts), including Streptococcus pneumoniae (pneumococcus), Haemophilus influenzae, Moraxella catarrhalis, and Staphylococcus aureus, which occasionally turn into pathogens causing infectious diseases, while the contribution of many nasal microorganisms to human health remains undiscovered. To better understand the composition of the nasal microbiome community, we create a workflow of the community model, which mimics the human nasal environment. To address this challenge, constraint-based reconstruction of biochemically accurate genome-scale metabolic models (GEMs) networks of microorganisms is mandatory. Our workflow applies constraint-based modeling (CBM), simulates the metabolism between species in a given microbiome, and facilitates generating novel hypotheses on microbial interactions. Utilizing this workflow, we hope to gain a better understanding of interactions from the metabolic modeling perspective. This article presents nasal community modeling workflow (NCMW)—a python package based on GEMs of species as a starting point for understanding the composition of the nasal microbiome community. The package is constructed as a step-by-step mathematical framework for metabolic modeling and analysis of the nasal microbial community. Using constraint-based models reduces the need for culturing species in vitro, a process that is not convenient in the environment of human noses.

Availability: NCMW is freely available on the Python Package Index (PIP) via pip install NCMW. The source code, documentation, and usage examples (Jupyter Notebook and example files) are available at https://github.com/manuelgloeckler/ncmw.

Mitochondria-Microbiota Interaction in Neurodegeneration

Alzheimer’s and Parkinson’s are the two best-known neurodegenerative diseases. Each is associated with the excessive aggregation in the brain and elsewhere of its own characteristic amyloid proteins. Yet the two afflictions have much in common and often the same amyloids play a role in both. These amyloids need not be toxic and can help regulate bile secretion, synaptic plasticity, and immune defense. Moreover, when they do form toxic aggregates, amyloids typically harm not just patients but their pathogens too. A major port of entry for pathogens is the gut. Keeping the gut’s microbe community (microbiota) healthy and under control requires that our cells’ main energy producers (mitochondria) support the gut-blood barrier and immune system. As we age, these mitochondria eventually succumb to the corrosive byproducts they themselves release, our defenses break down, pathogens or their toxins break through, and the side effects of inflammation and amyloid aggregation become problematic. Although it gets most of the attention, local amyloid aggregation in the brain merely points to a bigger problem: the systemic breakdown of the entire human superorganism, exemplified by an interaction turning bad between mitochondria and microbiota.

Host-mycobiome metabolic interactions in health and disease

Fungal communities (mycobiome) have an important role in sustaining the resilience of complex microbial communities and maintenance of homeostasis. The mycobiome remains relatively unexplored compared to the bacteriome despite increasing evidence highlighting their contribution to host-microbiome interactions in health and disease. Despite being a small proportion of the total species, fungi constitute a large proportion of the biomass within the human microbiome and thus serve as a potential target for metabolic reprogramming in pathogenesis and disease mechanism. Metabolites produced by fungi shape host niches, induce immune tolerance and changes in their levels prelude changes associated with metabolic diseases and cancer. Given the complexity of microbial interactions, studying the metabolic interplay of the mycobiome with both host and microbiome is a demanding but crucial task. However, genome-scale modelling and synthetic biology can provide an integrative platform that allows elucidation of the multifaceted interactions between mycobiome, microbiome and host. The inferences gained from understanding mycobiome interplay with other organisms can delineate the key role of the mycobiome in pathophysiology and reveal its role in human disease.

Correlation between Gut Microbiota, its Metabolic Products, and their Association with Liver Enzymes among Sample of Egyptian Females

Background and Aim: The gut microbiota appears to play a critical role in the pathogenesis of obesity, liver metabolism and the associated diseases. The present study aimed to identify the existing gut microbiota enterotypes and its metabolic products profiles among a sample of normal weight and obese Egyptian females, and to investigate the correlation between gut microbiota; body mass index andliver enzymes among them.Methods: A case-control cross-sectional study, included 112 Egyptian females; 82obese and30 normal weight; with age ranged from 25 up to 60 years. For each participant, anthropometric measurements (weight, height and BMI), laboratory investigations (AST, ALT, SCFA, CRP) and microbiota analysis were done. Results: The obese females had higher significant values of CRP,AST, ALTand SCFA. In addition, obese females had insignificant higher values of log Bacteroidetes, log firmicutes, log firmicutes/ Bacteroidetes ratio, and log lactobacillus, and insignificant lower values of log bifidobacteria; than normal weight group.Among normal weight group, Lactobacillus shad significant positive correlations with SCFA, Bifidobacteria and Firmicutes, and significant negative correlations with AST, ALTand CRP. Bifidobacteria had significant negative correlations with Ht and ALT. Bacteroidetes bacteria had significant positive correlations with SCFA, and significant negative correlations with age and height. Firmicutes bacteria had significant negative correlations with AST and ALT. Firmicutes / Bacteroidetes Ratio had significant negative correlations with AST, ALTand SCFA. Among obese group, Lactobacillus and Bifidobacteria had significant negative correlations with Firmicutes / Bacteroidetes Ratio however; these correlations were insignificant among normal weight group. Moreover, there were insignificant correlations between any type of studied microbiota and any of the anthropometric or laboratory parameters; except Firmicutes bacteria had significant negative correlations with ALT.Conclusion: The beneficial Lactobacillus and bifidobacteria have its good impact in improving obesity status, liver function in form of ALT.

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

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.

INFORMATION CONTENT OF HIGH-ORDER ASSOCIATIONS OF THE HUMAN GUT MICROBIOTA NETWORK

The human gastrointestinal tract is an environment that hosts an ecosystem of microorganisms essential to human health. Vital biological processes emerge from fundamental inter- and intra-species molecular interactions that influence the assembly and composition of the gut microbiota ecology. Here we quantify the complexity of the ecological relationships within the human infant gut microbiota ecosystem as a function of the information contained in the nonlinear associations of a sequence of increasingly-specified maximum entropy representations of the system. Our paradigm frames the ecological state, in terms of the presence or absence of individual microbial ecological units that are identified by amplicon sequence variants (ASV) in the gut microenvironment, as a function of both the ecological states of its neighboring units and, in a departure from standard graphical model representations, the associations among the units within its neighborhood. We characterize the order of the system based on the relative quantity of statistical information encoded by high-order statistical associations of the infant gut microbiota.

Engineering Bacteroides thetaiotaomicron to produce non-native butyrate based on a genome-scale metabolic model-guided design

Bacteroides thetaiotaomicron represents a major symbiont of the human gut microbiome that is increasingly viewed as a promising candidate strain for microbial therapeutics. Here, we engineer B. thetaiotaomicron for heterologous production of non-native butyrate as a proof-of-concept biochemical at therapeutically relevant concentrations. Since B. thetaiotaomicron is not a natural producer of butyrate, we heterologously expressed a butyrate biosynthetic pathway in the strain, which led to the production of butyrate at the final concentration of 12 mg/L in a rich medium. Further optimization of butyrate production was achieved by a round of metabolic engineering guided by an expanded genome-scale metabolic model (GEM) of B. thetaiotaomicron. The in silico knock-out simulation of the expanded model showed that pta and ldhD were the potent knock-out targets to enhance butyrate production. The maximum titer and specific productivity of butyrate in the pta-ldhD double knockout mutant increased by nearly 3.4 and 4.8 folds, respectively. To our knowledge, this is the first engineering attempt that enabled butyrate production from a non-butyrate producing commensal B. thetaiotaomicron. The study also highlights that B. thetaiotaomicron can serve as an effective strain for live microbial therapeutics in human.

Topological Data Analysis Highlights Novel Geographical Signatures of the Human Gut Microbiome

Background: There is growing interest in the connection between the gut microbiome and human health and disease. Conventional approaches to analyse microbiome data typically entail dimensionality reduction and assume linearity of the observed relationships, however, the microbiome is a highly complex ecosystem marked by non-linear relationships. In this study, we use topological data analysis (TDA) to explore differences and similarities between the gut microbiome across several countries. Methods: We used curated adult microbiome data at the genus level from the GMrepo database. The dataset contains OTU and demographical data of over 4,400 samples from 19 studies, spanning 12 countries. We analysed the data with tmap, an integrative framework for TDA specifically designed for stratification and enrichment analysis of population-based gut microbiome datasets. Results: We find associations between specific microbial genera and groups of countries. Specifically, both the USA and UK were significantly co-enriched with the proinflammatory genera Lachnoclostridium and Ruminiclostridium, while France and New Zealand were co-enriched with other, butyrate-producing, taxa of the order Clostridiales. Conclusion: The TDA approach demonstrates the overlap and distinctions of microbiome composition between and within countries. This yields unique insights into complex associations in the dataset, a finding not possible with conventional approaches. It highlights the potential utility of TDA as a complementary tool in microbiome research, particularly for large population-scale datasets, and suggests further analysis on the effects of diet and other regionally varying factors.

Synergies of Systems Biology and Synthetic Biology in Human Microbiome Studies

A number of studies have shown that the microbial communities of the human body are integral for the maintenance of human health. Advances in next-generation sequencing have enabled rapid and large-scale quantification of the composition of microbial communities in health and disease. Microorganisms mediate diverse host responses including metabolic pathways and immune responses. Using a system biology approach to further understand the underlying alterations of the microbiota in physiological and pathological states can help reveal potential novel therapeutic and diagnostic interventions within the field of synthetic biology. Tools such as biosensors, memory arrays, and engineered bacteria can rewire the microbiome environment. In this article, we review the computational tools used to study microbiome communities and the current limitations of these methods. We evaluate how genome-scale metabolic models (GEMs) can advance our understanding of the microbe-microbe and microbe-host interactions. Moreover, we present how synergies between these system biology approaches and synthetic biology can be harnessed in human microbiome studies to improve future therapeutics and diagnostics and highlight important knowledge gaps for future research in these rapidly evolving fields.

Eubacterium rectale Attenuates HSV-1 Induced Systemic Inflammation in Mice by Inhibiting CD83

The purpose of this study was to determine whether administration of the microorganism Eubacterium rectale (E. rectale) could regulate dendritic cell (DC) activation and systemic inflammation in herpes simplex virus type 1-induced Behçet's disease (BD). E. rectale, butyrate-producing bacteria, was administered to BD mice. Peripheral blood leukocytes (PBL) and lymph node cells were isolated and analyzed by flow cytometry. 16S rRNA metagenomic analysis was performed in the feces of mice to determine the differences in the composition of the microbial population between normal and BD mice. Serum cytokine levels were measured by enzyme-linked immunosorbent assay. The frequency of DC activation marker CD83 positive cells was significantly increased in PBL of BD mice. Frequencies of CD83+ cells were also significantly increased in patients with active BD. 16S rRNA metagenomic analysis revealed different gut microbiota composition between normal and BD mice. The administration of E. rectale to BD mice reduced the frequency of CD83+ cells and significantly increased the frequency of NK1.1+ cells with the improvement of symptoms. The co-administration of colchicine and E. rectale also significantly reduced the frequency of CD83+ cells. Differences in gut microbiota were observed between normal mice and BD mice, and the administration of E. rectale downregulated the frequency of CD83, which was associated with BD deterioration. These data indicate that E. rectale could be a new therapeutic adjuvant for BD management.

Dynamic metabolic modelling predicts efficient acetogen-gut bacterium cocultures for CO-to-butyrate conversion

AIMS

While gas-fermenting acetogens have been engineered to secrete non-native metabolites such as butyrate, acetate remains the most thermodynamically favourable product. An alternative to metabolic engineering is to exploit native capabilities for CO-to-acetate conversion by coculturing an acetogen with a second bacterium that provides efficient acetate-butyrate conversion.

METHODS AND RESULTS

We used dynamic metabolic modelling to computationally evaluate the CO-to-butyrate conversion capabilities of candidate coculture systems by exploiting the diversity of human gut bacteria for anaerobic synthesis of butyrate from acetate and ethanol. A preliminary screening procedure based on flux balance analysis was developed to identify 48 gut bacteria which satisfied minimal growth rate and acetate-to-butyrate conversion requirements when cultured on minimal medium containing acetate and a simple sugar not consumed by the paired acetogen. A total of 170 acetogen/gut bacterium/sugar combinations were dynamically simulated for continuous growth using a 70/30 CO/CO₂ feed gas mixture and minimal medium computationally determined for each combination.

CONCLUSIONS

While coculture systems involving the acetogens Eubacterium limosum or Blautia producta yielded low butyrate productivities and CO-to-ethanol conversion had minimal impact on system performance, dynamic simulations predicted a large number of promising coculture designs with Clostridium ljungdahlii or C. autoethanogenum as the CO-to-acetate converter. Pairings with the gut bacterium Clostridium hylemonae or Roseburia hominis were particularly promising due to their ability to generate high butyrate productivities over a range of dilution rates with a variety of sugars. The higher specific acetate secretion rate of C. ljungdahlii proved more beneficial than the elevated growth rate of C. autoethanogenum for coculture butyrate productivity.

SIGNIFICANCE AND IMPACT OF THE STUDY

Our study demonstrated that metabolic modelling could provide useful insights into coculture design that can guide future experimental studies. More specifically, our predictions generated several favourable designs, which could serve as the first coculture systems realized experimentally.

CODY enables quantitatively spatiotemporal predictions on in vivo gut microbial variability induced by diet intervention

Quantitatively understanding and predicting spatiotemporal dynamics of microbiota is imperative for development of tailored microbiome-directed therapeutics treatments. However, the complexity of microbial variations, due to interactions with the host, other microbes, and environmental factors, makes it challenging to identify how microbiota colonize in the human gut. Here, we describe a novel multiscale framework for COmputing the DYnamics of the gut microbiota (CODY), which enables the quantification of spatiotemporal-specific variations of gut microbiome abundance profiles, without a prior knowledge of microbiome interactions. Importantly, the predictive power of CODY is demonstrated using cross-sectional data from two longitudinal metagenomics studies—the microbiota development during early infancy and during short-term diet intervention of obese adults.

Microbial variations in the human gut are harbored in temporal and spatial heterogeneity, and quantitative prediction of spatiotemporal dynamic changes in the gut microbiota is imperative for development of tailored microbiome-directed therapeutics treatments, e.g. precision nutrition. Given the high-degree complexity of microbial variations, subject to the dynamic interactions among host, microbial, and environmental factors, identifying how microbiota colonize in the gut represents an important challenge. Here we present COmputing the DYnamics of microbiota (CODY), a multiscale framework that integrates species-level modeling of microbial dynamics and ecosystem-level interactions into a mathematical model that characterizes spatial-specific in vivo microbial residence in the colon as impacted by host physiology. The framework quantifies spatiotemporal resolution of microbial variations on species-level abundance profiles across site-specific colon regions and in feces, independent of a priori knowledge. We demonstrated the effectiveness of CODY using cross-sectional data from two longitudinal metagenomics studies—the microbiota development during early infancy and during short-term diet intervention of obese adults. For each cohort, CODY correctly predicts the microbial variations in response to diet intervention, as validated by available metagenomics and metabolomics data. Model simulations provide insight into the biogeographical heterogeneity among lumen, mucus, and feces, which provides insight into how host physical forces and spatial structure are shaping microbial structure and functionality.

Anorexia nervosa and gut microbiota: A systematic review and quantitative synthesis of pooled microbiological data

BACKGROUND

Alterations of gut microbiota may play a role in Anorexia Nervosa (AN) through perturbations of the gut-brain axis. Some studies found differences in the gut microbiota of patients with AN compared to healthy controls, but results are heterogeneous. The aim of this work was to systematically review the existing studies comparing gut microbial composition in AN and healthy controls, and to perform a quantitative synthesis of the pooled clinical and microbiological data, when available.

METHODS

A comprehensive literature search was performed to identify human studies investigating relationships between AN and gut microbiota. Microbiome datasets from studies were pooled and analysed focusing on alpha and beta-diversity and the relative abundance of microbial species in patients' gut microbiota compared to healthy controls.

RESULTS

Nine studies were eligible for the systematic review, of which 4 were included in the quantitative synthesis. Preserved alpha-diversity and decreased beta-diversity in AN emerged from the qualitative synthesis, while a slight increase of alpha-diversity (d < 0.4) and comparable beta-diversity were reported by the quantitative synthesis. Out of the 46 common species compared, three had a large combined effect size (d ≥ 0.9) to differentiate patients from controls, namely Alistipes, Parabacterioides and Roseburia. The latter was also correlated with BMI (ρ = 0.29).

CONCLUSIONS

The decrease of butyrate-producing species and the increase of mucine-degrading species may represent hallmarks of the gut microbiota alterations in AN, and therefore potentially interesting therapeutic targets. The heterogeneity of clinical and methodological characteristics hampers the generalizability of the results. Standardized research methods could improve comparability among studies to better identify the alterations of gut microbiota in AN.