Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data.

BACKGROUND: Oncometabolites, often generated as a result of a gene mutation, show pro-oncogenic function when abnormally accumulated in cancer cells. Identification of such mutation-associated metabolites will facilitate developing treatment strategies for cancers, but is challenging due to the large number of metabolites in a cell and the presence of multiple genes associated with cancer development. RESULTS: Here we report the development of a computational workflow that predicts metabolite-gene-pathway sets. Metabolite-gene-pathway sets present metabolites and metabolic pathways significantly associated with specific somatic mutations in cancers. The computational workflow uses both cancer patient-specific genome-scale metabolic models (GEMs) and mutation data to generate metabolite-gene-pathway sets. A GEM is a computational model that predicts reaction fluxes at a genome scale and can be constructed in a cell-specific manner by using omics data. The computational workflow is first validated by comparing the resulting metabolite-gene pairs with multi-omics data (i.e., mutation data, RNA-seq data, and metabolome data) from acute myeloid leukemia and renal cell carcinoma samples collected in this study. The computational workflow is further validated by evaluating the metabolite-gene-pathway sets predicted for 18 cancer types, by using RNA-seq data publicly available, in comparison with the reported studies. Therapeutic potential of the resulting metabolite-gene-pathway sets is also discussed. CONCLUSIONS: Validation of the metabolite-gene-pathway set-predicting computational workflow indicates that a decent number of metabolites and metabolic pathways appear to be significantly associated with specific somatic mutations. The computational workflow and the resulting metabolite-gene-pathway sets will help identify novel oncometabolites and also suggest cancer treatment strategies.

Efficient production of an antitumor precursor actinocin and other medicinal molecules from kynurenine pathway in Escherichia coli.

Kynurenine pathway has a potential to convert L-tryptophan into multiple medicinal molecules. This study aims to explore the biosynthetic potential of kynurenine pathway for the efficient production of actinocin, an antitumor precursor selected as a proof-of-concept target molecule. Kynurenine pathway is first constructed in Escherichia coli by testing various combinations of biosynthetic genes from four different organisms. Metabolic engineering strategies are next performed to improve the production by inhibiting a competing pathway, and enhancing intracellular supply of a cofactor S-adenosyl-L-methionine, and ultimately to produce actinocin from glucose. Metabolome analysis further suggests additional gene overexpression targets, which finally leads to the actinocin titer of 719 mg/L. E. coli strain engineered to produce actinocin is further successfully utilized to produce 350 mg/L of kynurenic acid, a neuroprotectant, and 1401 mg/L of 3-hydroxyanthranilic acid, an antioxidant, also from glucose. These competitive production titers demonstrate the biosynthetic potential of kynurenine pathway as a source of multiple medicinal molecules. The approach undertaken in this study can be useful for the sustainable production of molecules derived from kynurenine pathway, which are otherwise chemically synthesized.

A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis.

Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.

Machine learning-guided evaluation of extraction and simulation methods for cancer patient-specific metabolic models.

Genome-scale metabolic model (GEM) has been established as an important tool to study cellular metabolism at a systems level by predicting intracellular fluxes. With the advent of generic human GEMs, they have been increasingly applied to a range of diseases, often for the objective of predicting effective metabolic drug targets. Cancer is a representative disease where the use of GEMs has proved to be effective, partly due to the massive availability of patient-specific RNA-seq data. When using a human GEM, so-called context-specific GEM needs to be developed first by using cell-specific RNA-seq data. Biological validity of a context-specific GEM highly depends on both model extraction method (MEM) and model simulation method (MSM). However, while MEMs have been thoroughly examined, MSMs have not been systematically examined, especially, when studying cancer metabolism. In this study, the effects of pairwise combinations of three MEMs and five MSMs were evaluated by examining biological features of the resulting cancer patient-specific GEMs. For this, a total of 1,562 patient-specific GEMs were reconstructed, and subjected to machine learning-guided and biological evaluations to draw robust conclusions. Noteworthy observations were made from the evaluation, including the high performance of two MEMs, namely rank-based 'task-driven Integrative Network Inference for Tissues' (tINIT) or 'Gene Inactivity Moderated by Metabolism and Expression' (GIMME), paired with least absolute deviation (LAD) as a MSM, and relatively poorer performance of flux balance analysis (FBA) and parsimonious FBA (pFBA). Insights from this study can be considered as a reference when studying cancer metabolism using patient-specific GEMs.

Removal of aqueous-phase Pb(II), Cd(II), As(III), and As(V) by nanoscale zero-valent iron supported on exhausted coffee grounds.

Nanoscale zero-valent iron (NZVI) is recognized as an excellent adsorbent for metallic contaminants. Nevertheless, NZVI itself tends to agglomerate, so that its performance deterioriates without supporting materials. The use of exhausted coffee grounds as a supporting material for NZVI is expected to resolve this problem and provide the social benefits of waste minimization and resource recycling. In this study, NZVI was supported on exhausted coffee grounds (NZVI-Coffee ground) to enhance its dispersion. The aims of this study were to characterize NZVI-Coffee ground with a focus on atomic dispersion, evaluate NZVI-Coffee ground as an adsorbent for typical metallic contaminants and arsenic, and assess the effects of solution chemistry on the adsorption process. In order to achieve these goals, characterization, adsorption kinetics, adsorption equilibrium, and the effects of pH and temperature on adsorption were studied. Pb(II), Cd(II), As(III), and As(V) were selected as target contaminants. The characterization study showed that atomic dispersion was enhanced four-fold by supporting NZVI on coffee grounds. The enhanced dispersion resulted in rapid kinetic characteristics and large adsorption capacity. The optimum pH for adsorption of Pb(II) and Cd(II) was 4-6, and that for As(III) and As(V) was 2-4. The pH effect can be explained by surface protonation/deprotonation and adsorbate speciation. Only the adsorption of Pb(II) was an exothermic process; those of other species were endothermic. In every tested case, the adsorption process was spontaneous. According to the results, NZVI-Coffee ground is an effective adsorbent for the removal of aqueous phase Pb(II), Cd(II), As(III), and As(V).