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.

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.

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.

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.

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.