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 human microbiome has been linked to several diseases. Gastrointestinal diseases are still one of the most prominent area of study in host-microbiome interactions however the underlying microbial ...mechanisms in these disorders are not fully established. Irritable bowel syndrome (IBS) remains as one of the prominent disorders with significant changes in the gut microbiome composition and without definitive treatment. IBS has a severe impact on socio-economic and patient's lifestyle. The association studies between the IBS and microbiome have shed a light on relevance of microbial composition, and hence microbiome-based trials were designed. However, there are no clear evidence of potential treatment for IBS. This review summarizes the epidemiology and socioeconomic impact of IBS and then focus on microbiome observational and clinical trials. At the end, we propose a new perspective on using data-driven approach and applying computational modelling and machine learning to design microbiome-aware personalized treatment for IBS.
Human cancer cell lines are used as important model systems to study molecular mechanisms associated with tumor growth, hereunder how genomic and biological heterogeneity found in primary tumors ...affect cellular phenotypes. We reconstructed Genome scale metabolic models (GEMs) for eleven cell lines based on RNA-Seq data and validated the functionality of these models with data from metabolite profiling. We used cell line-specific GEMs to analyze the differences in the metabolism of cancer cell lines, and to explore the heterogeneous expression of the metabolic subsystems. Furthermore, we predicted 85 antimetabolites that can inhibit growth of, or even kill, any of the cell lines, while at the same time not being toxic for 83 different healthy human cell types. 60 of these antimetabolites were found to inhibit growth in all cell lines. Finally, we experimentally validated one of the predicted antimetabolites using two cell lines with different phenotypic origins, and found that it is effective in inhibiting the growth of these cell lines. Using immunohistochemistry, we also showed high or moderate expression levels of proteins targeted by the validated antimetabolite. Identified anti-growth factors for inhibition of cell growth may provide leads for the development of efficient cancer treatment strategies.
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.
Tumor cells alter their metabolism to maintain unregulated cellular proliferation and survival, but this transformation leaves them reliant on constant supply of nutrients and energy. In addition to ...the widely studied dysregulated glucose metabolism to fuel tumor cell growth, accumulating evidences suggest that utilization of amino acids and lipids contributes significantly to cancer cell metabolism. Also recent progresses in our understanding of carcinogenesis have revealed that cancer is a complex disease and cannot be understood through simple investigation of genetic mutations of cancerous cells. Cancer cells present in complex tumor tissues communicate with the surrounding microenvironment and develop traits which promote their growth, survival, and metastasis. Decoding the full scope and targeting dysregulated metabolic pathways that support neoplastic transformations and their preservation requires both the advancement of experimental technologies for more comprehensive measurement of omics as well as the advancement of robust computational methods for accurate analysis of the generated data. Here, we review cancer-associated reprogramming of metabolism and highlight the capability of genome-scale metabolic modeling approaches in perceiving a system-level perspective of cancer metabolism and in detecting novel selective drug targets.
Metabolism is broadly defined as the sum of biochemical reactions within cells that are involved in maintaining the living state of the organism. Profound importance of metabolism comes from the fact ...that it is the sole source of energy that allows life to resist to be degraded into entropy. Human metabolism is a complex interactive network consisting of highly regulated functional pathways, impacting or been impacted by many other cellular process. Internal or external perturbations may cause dysfunction of some of these functional or regulatory pathways and may lead to the rise of abnormal phenotypes. Many human diseases associated with irregular metabolic transformations that perturb normal physiology and lead to phenotype dysfunction. Discovering how biological systems reorganize their activities to force specific phenotypic transformation, e.g., normal to cancer/diabetes/obesity, is a main challenge in life science. This thesis is dedicated to investigating genome-scale metabolic transformations from health to disease states, with specific focus on non-symmetric reprogramming in cancer metabolism.The human gut microbiome has been associated with a variety of human diseases, but to go beyond association studies and elucidate causalities is a major challenge. We developed a comprehensive computational platform, CASINO (Community and Systems-level Interactive Optimization), for simulation of the microbial communities using genome-scale metabolic modeling. We demonstrated the power of the toolbox in predicting metabolic interactions between gut microbiota and host, through a diet-intervention study of obese and overweight individuals. Our modeling platform could provide a quantitative description of the altered plasma and fecal amino acid levels in response to dietary intervention. Next, we proceed to investigate heterogeneity of cancer related metabolic transformations at the genome-scale. First, we reconstructed genome scale metabolic models (GEMs) for eleven human cancer cell lines based on RNA-Seq data. We used the generated models to investigate inter-cell line heterogeneity of metabolic reprogramming and also to identify potential anti-growth factors. This was followed by two consecutive studies on two main subtypes of the non-small cell lung cancer, lung adenocarcinoma (LAC) and lung squamous cell carcinoma (SCC), by generating RNA sequencing (RNAseq) data for cancer biopsies and for normal tissue samples. We followed a systemic approach to investigate the heterogeneity and direction of the metabolic transformation in lung cancer at three levels of biochemical organization: global metabolic network level, individual biochemical pathways level and at the level of specific enzymatic reactions. We observed large heterogeneity in the expression of enzymes involved in the majority of the metabolic pathways, and identified significant association between some of these variations and patient prognosis. Our findings provide mechanistic insights into complex metabolic behavior of tumors and may be used to develop more effective diagnostic and prognostic methods.
Metabolism is broadly defined as the sum of biochemical reactions within cells that are involved in maintaining the living state of the organism. Profound importance of metabolism comes from the fact ...that it is the sole source of energy that allows life to resist to be degraded into entropy. Human metabolism is a complex interactive network consisting of highly regulated functional pathways, impacting or been impacted by many other cellular process. Internal or external perturbations may cause dysfunction of some of these functional or regulatory pathways and may lead to the rise of abnormal phenotypes. Many human diseases associated with irregular metabolic transformations that perturb normal physiology and lead to phenotype dysfunction. Discovering how biological systems reorganize their activities to force specific phenotypic transformation, e.g., normal to cancer/diabetes/obesity, is a main challenge in life science. This thesis is dedicated to investigating genome-scale metabolic transformations from health to disease states, with specific focus on non-symmetric reprogramming in cancer metabolism.The human gut microbiome has been associated with a variety of human diseases, but to go beyond association studies and elucidate causalities is a major challenge. We developed a comprehensive computational platform, CASINO (Community and Systems-level Interactive Optimization), for simulation of the microbial communities using genome-scale metabolic modeling. We demonstrated the power of the toolbox in predicting metabolic interactions between gut microbiota and host, through a diet-intervention study of obese and overweight individuals. Our modeling platform could provide a quantitative description of the altered plasma and fecal amino acid levels in response to dietary intervention. Next, we proceed to investigate heterogeneity of cancer related metabolic transformations at the genome-scale. First, we reconstructed genome scale metabolic models (GEMs) for eleven human cancer cell lines based on RNA-Seq data. We used the generated models to investigate inter-cell line heterogeneity of metabolic reprogramming and also to identify potential anti-growth factors. This was followed by two consecutive studies on two main subtypes of the non-small cell lung cancer, lung adenocarcinoma (LAC) and lung squamous cell carcinoma (SCC), by generating RNA sequencing (RNAseq) data for cancer biopsies and for normal tissue samples. We followed a systemic approach to investigate the heterogeneity and direction of the metabolic transformation in lung cancer at three levels of biochemical organization: global metabolic network level, individual biochemical pathways level and at the level of specific enzymatic reactions. We observed large heterogeneity in the expression of enzymes involved in the majority of the metabolic pathways, and identified significant association between some of these variations and patient prognosis. Our findings provide mechanistic insights into complex metabolic behavior of tumors and may be used to develop more effective diagnostic and prognostic methods.
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.
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.
Lightweight fill can be advantageous in embankment construction for the purposes of reducing the (i) bearing pressures on the underlying soil foundation, (ii) destabilizing moments for constructed ...earthen slopes, and (iii) earth pressures acting behind retaining walls. This paper investigates the merits/limitations of particulate expanded polystyrene (EPS) beads mixed with clayey sand (CS) soil as lightweight fill, considering both geotechnical and environmental perspectives. The bench-scale geotechnical testing programme included standard Proctor (SP) compaction, California bearing ratio (CBR), direct shear (sheardox), oedometer and permeability testing performed on two different gradation CS soils amended with 0.5, 1.5 and 3.0 wt.% EPS, investigating two nominal bead sizes equivalent to poorly-graded medium and coarse sands. Compared to the unamended soils, the compacted dry density substantially decreased with increasing EPS beads content, from 2.09 t/m3 (0 wt.% EPS) to as low as 0.33 t/m3 for 3 wt.% (73 v.%) of larger-sized EPS beads. However, from analyses of the test results for the investigated 50 to 400 kPa applied stress range, even 0.5 wt.% (21 v.%) EPS beads caused a substantial mechanical failure, with a drastic decay of the CBR and compressibility parameters for the studied CS soils. Given the more detrimental environmental cost of leaving myriads of separate EPS beads mixed forever among the soil, it is concluded that the approach of adding particulate EPS beads to soils for producing uncemented lightened fill should not be employed in geotechnical engineering practice.
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