Cancer Metabolism: A Modeling Perspective

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

The gut microbiota modulates host amino acid and glutathione metabolism in mice

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

The gut microbiota modulates host amino acid and glutathione metabolism in mice

Abstract 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

New study on interactional effects of grouting pressure on the displacement of nailing bond

The maximum displacement in a soil nail bond system considering the pull-out, overburden load and grouting pressure effects has been evaluated. The Pull-out tests were carried out in five sites that located in Tehran, Iran. Moreover, additional pull-out test data from South Korea is considered. The displacement of the nailing system due to gravity and pressure grouting has been measured. Based on achieved data, four practical relationships between bound strength and pull-out displacements are developed. The parameters overburden load, grouting pressure, borehole diameter, moisture content and soil’s strength parameters have been chosen as the major inputs for the relationships. The correlation coefficients of the linear relationship range have been achieved between 0.89 and 0.99. While by using the multi-layer neural network for estimating it has been illustrated approximately 0.95

Quantifying diet-induced metabolic changes of the human gut microbiome

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 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

Heterogeneity of human metabolism in health and disease: a modelling perspective

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

Irritable bowel syndrome and microbiome; Switching from conventional diagnosis and therapies to personalized interventions

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

Identifying anti-growth factors for human cancer cell lines through genome-scale metabolic modeling

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