Recent findings have demonstrated that the gut microbiome complements our human genome with at least 100-fold more genes. In contrast to our Homo sapiens-derived genes, the microbiome is much more ...plastic, and its composition changes with age and diet, among other factors. An altered gut microbiota has been associated with several diseases, including obesity and diabetes, but the mechanisms involved remain elusive. Here we discuss factors that affect the gut microbiome, how the gut microbiome may contribute to metabolic diseases, and how to study the gut microbiome. Next-generation sequencing and development of software packages have led to the development of large-scale sequencing efforts to catalog the human microbiome. Furthermore, the use of genetically engineered gnotobiotic mouse models may increase our understanding of mechanisms by which the gut microbiome modulates host metabolism. A combination of classical microbiology, sequencing, and animal experiments may provide further insights into how the gut microbiota affect host metabolism and physiology.
Bariatric surgery is currently the most effective procedure for the treatment of obesity. Given the role of the gut microbiota in regulating host metabolism and adiposity, we investigated the ...long-term effects of bariatric surgery on the microbiome of patients randomized to Roux-en-Y gastric bypass or vertical banded gastroplasty and matched for weight and fat mass loss. The two surgical procedures induced similar and durable changes on the gut microbiome that were not dependent on body mass index and resulted in altered levels of fecal and circulating metabolites compared with obese controls. By colonizing germ-free mice with stools from the patients, we demonstrated that the surgically altered microbiota promoted reduced fat deposition in recipient mice. These mice also had a lower respiratory quotient, indicating decreased utilization of carbohydrates as fuel. Our results suggest that the gut microbiota may play a direct role in the reduction of adiposity observed after bariatric surgery.
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•RYGB and VBG induce long-term alterations in the human gut microbiome•The changes in the microbiome do not depend on BMI•RYGB and VBG have different effects on bile acid and TMAO metabolism•The surgically altered microbiome contributes to fat mass regulation
Bariatric surgery durably alters the human gut microbiome. Here, Tremaroli et al. demonstrate that two types of bariatric surgery, Roux-en-Y gastric bypass and vertical banded gastroplasty, produce long-term alterations of the gut microbiome independently of BMI and that these alterations modulate host metabolism and fat mass deposition.
Type 2 diabetes (T2D) is a result of complex gene-environment interactions, and several risk factors have been identified, including age, family history, diet, sedentary lifestyle and obesity. ...Statistical models that combine known risk factors for T2D can partly identify individuals at high risk of developing the disease. However, these studies have so far indicated that human genetics contributes little to the models, whereas socio-demographic and environmental factors have greater influence. Recent evidence suggests the importance of the gut microbiota as an environmental factor, and an altered gut microbiota has been linked to metabolic diseases including obesity, diabetes and cardiovascular disease. Here we use shotgun sequencing to characterize the faecal metagenome of 145 European women with normal, impaired or diabetic glucose control. We observe compositional and functional alterations in the metagenomes of women with T2D, and develop a mathematical model based on metagenomic profiles that identified T2D with high accuracy. We applied this model to women with impaired glucose tolerance, and show that it can identify women who have a diabetes-like metabolism. Furthermore, glucose control and medication were unlikely to have major confounding effects. We also applied our model to a recently described Chinese cohort and show that the discriminant metagenomic markers for T2D differ between the European and Chinese cohorts. Therefore, metagenomic predictive tools for T2D should be specific for the age and geographical location of the populations studied.
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Dostopno za:
DOBA, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Recent advances in synthetic biology have transformed mushroom farming from a focus on traditional cultivation to comprehensive applications based on cutting-edge biotechnologies. Synthetic biology ...has promising applications in this field, including precision breeding, mining biosynthetic gene clusters, developing mushroom chassis cells, and constructing cell factories for high value-added products.
An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk ...chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed.
•Kinetic models can be used for rational design of cell factories.•Important kinetic modeling concepts are explained in detail.•Several applications of kinetic models for cell factory improvements are reviewed.•The advantages, challenges, and perspectives of kinetic modeling are discussed.
Recent findings have implicated the gut microbiota as a contributor of metabolic diseases through the modulation of host metabolism and inflammation. Atherosclerosis is associated with lipid ...accumulation and inflammation in the arterial wall, and bacteria have been suggested as a causative agent of this disease. Here we use shotgun sequencing of the gut metagenome to demonstrate that the genus Collinsella was enriched in patients with symptomatic atherosclerosis, defined as stenotic atherosclerotic plaques in the carotid artery leading to cerebrovascular events, whereas Roseburia and Eubacterium were enriched in healthy controls. Further characterization of the functional capacity of the metagenomes revealed that patient gut metagenomes were enriched in genes encoding peptidoglycan synthesis and depleted in phytoene dehydrogenase; patients also had reduced serum levels of β-carotene. Our findings suggest that the gut metagenome is associated with the inflammatory status of the host and patients with symptomatic atherosclerosis harbor characteristic changes in the gut metagenome.
The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic ...differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV‐R) and germ‐free (GF) mice using gene expression data and tissue‐specific genome‐scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue‐specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV‐R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N‐acetylated AAs in the hepatic portal vein of CONV‐R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV‐R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice.
Synopsis
Tissue‐specific genome‐scale metabolic models (GEMs), transcriptomic and metabolomic analyses reveal global metabolic differences between conventionally raised and germ‐free mice and show that the gut microbiota affects host amino acid and glutathione metabolism.
A generic Mouse Metabolic Reaction GEM (MMR) is created using the mouse orthologs of human genes in HMR2.
Tissue‐specific GEMs for 28 mouse tissues are reconstructed and applied for the analysis of global gene expression data.
Microbial‐induced metabolic differences in the small intestine are simulated using the relative metabolic differences (RMetD) method.
The model predictions are validated by measuring amino acid levels in the portal vein.
Tissue‐specific genome‐scale metabolic models (GEMs), transcriptomic and metabolomic analyses reveal global metabolic differences between conventionally raised and germ‐free mice and show that the gut microbiota affects host amino acid and glutathione metabolism.
The gut microbiota has profound effects on host physiology but local host-microbial interactions in the gut are only poorly characterised and are likely to vary from the sparsely colonised duodenum ...to the densely colonised colon. Microorganisms are recognised by pattern recognition receptors such as Toll-like receptors, which signal through the adaptor molecule MyD88.
To identify host responses induced by gut microbiota along the length of the gut and whether these required MyD88, transcriptional profiles of duodenum, jejunum, ileum and colon were compared from germ-free and conventionally raised wild-type and Myd88-/- mice. The gut microbial ecology was assessed by 454-based pyrosequencing and viruses were analysed by PCR.
The gut microbiota modulated the expression of a large set of genes in the small intestine and fewer genes in the colon but surprisingly few microbiota-regulated genes required MyD88 signalling. However, MyD88 was essential for microbiota-induced colonic expression of the antimicrobial genes Reg3β and Reg3γ in the epithelium, and Myd88 deficiency was associated with both a shift in bacterial diversity and a greater proportion of segmented filamentous bacteria in the small intestine. In addition, conventionally raised Myd88-/- mice had increased expression of antiviral genes in the colon, which correlated with norovirus infection in the colonic epithelium.
This study provides a detailed description of tissue-specific host transcriptional responses to the normal gut microbiota along the length of the gut and demonstrates that the absence of MyD88 alters gut microbial ecology.
The human gut microbiome is known to be associated with various human disorders, but a major challenge is to go beyond association studies and elucidate causalities. Mathematical modeling of the ...human gut microbiome at a genome scale is a useful tool to decipher microbe-microbe, diet-microbe and microbe-host interactions. Here, we describe the CASINO (Community And Systems-level INteractive Optimization) toolbox, a comprehensive computational platform for analysis of microbial communities through metabolic modeling. We first validated the toolbox by simulating and testing the performance of single bacteria and whole communities in vitro. Focusing on metabolic interactions between the diet, gut microbiota, and host metabolism, we demonstrated the predictive power of the toolbox in a diet-intervention study of 45 obese and overweight individuals and validated our predictions by fecal and blood metabolomics data. Thus, modeling could quantitatively describe altered fecal and serum amino acid levels in response to diet intervention.
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•Community And Systems-level INteractive Optimization toolbox•Modeling the effect of diet and gene richness on the gut microbiota•Revealing altered amino acid and SCFA levels after diet interventions
Shoaie et al. describe a computational platform designed to elucidate the complex metabolic interactions between gut microbes, host, and diet. The model predictions are validated in humans and reveal how microbial gene richness and diet affect gut microbiota composition, as well as amino acid and SCFA levels.
For thousands of years, the yeast Saccharomyces cerevisiae (S. cerevisiae) has served as a cell factory for the production of bread, beer, and wine. In more recent years, this yeast has also served ...as a cell factory for producing many different fuels, chemicals, food ingredients, and pharmaceuticals. S. cerevisiae, however, has also served as a very important model organism for studying eukaryal biology, and even today many new discoveries, important for the treatment of human diseases, are made using this yeast as a model organism. Here a brief review of the use of S. cerevisiae as a model organism for studying eukaryal biology, its use as a cell factory, and how advances in systems biology underpin developments in both these areas, is provided.
Through engineering yeast metabolism it is possible to produce many different molecules finding applications as biofuels, chemicals, and pharmaceuticals. Relying on the extensive knowledge base of yeast, due to its many years of use as a model organism, has assisted in improving new design strategies, and also the advancement in systems biology has enabled the improved design of efficient cell factories.
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