BioTechnologia vol. 97(2) C pp. 137-144 C 2016 Journal of Biotechnology, Computational Biology and Bionanotechnology REVIEW PAPERS . DOI: 10.5114/bta.2016.60783 CC S. = BY NC ND Metabolomics and fluxomics in biotechnology: current trends WOJCIECH WOJTOWICZ, PIOTR MLYNARZ * Department of Bioorganic Chemistry, Wroclaw University of Technology, Wrocław, Poland Abstract Metabolomics and fluxomics are two very rapidly developing fields of science. They provide a wide range of infor- mation on different biological systems and are the last link in the biology “omics” studies chain: genomics-trans- criptomics-proteomics-metabolomics. Metabolomics belongs to systems biology and it refers to changes occurring in low molecular weight compounds (<1500 Da) and reveals significant information about the actual state of exa- mined organisms in relation to a reference group. Metabolic flux analysis provides very important information about the flux metabolites in a pathway of a living organism based on 13C enriched isotopically substrates. Both these sciences use very advanced mathematical approaches in order to gain the highest possible output origi- nating from living systems. All features of both methods allow their use in with adopting of appropriate me- thodology, in a particular field of biotechnology, including medicine, pharmaceutical science and industry, food processing, toxicology, plant cultivation, and animal breeding. Key words: metabolomics, fluxomics, biotechnology, metabolites Introduction specialized tools are necessary for its further develop- ment (Chmiel, 1994). This is why metabolomics and fluxo- Biotechnology is a field of science that utilizes living mics are now essential approaches in modern science. organisms or their derivatives (e.g., enzymes, natural Each of these “colors” of biotechnology is using meta- products) in technical applications to make, modify, or bolomics as a tool to obtain information about the relation- process products for specific uses (Chmiel, 1994). It is ship between small-molecule compounds in living orga- applicable in broadly defined drug research, cosmeto- nisms, their products, and needed substrates. Metabolo- logy, food industry, environment protection, and so on. mics approach combined with fluxomics, enable accurate The division of modern biotechnology is based on the tracking of changes in the distribution of low molecular area of application, which are assigned to a specific color weight compounds in biochemical pathways, simultane- that defines the field of utilization. Blue biotechnology is ously allowing for utilization of those relevant information linked and widely used in the aquatic environment con- in the improvement of biotechnological processes (Hou nected to marine organisms and marine renewable ener- et al., 2012). gy. Green biotechnology is used in agriculture to im- prove the biotechnological tools for crop and animal hus- Metabolomics bandry. Red biotechnology – or medical biotechnology – is used in health care, in processes such as drug deve- Metabolomics is one of the fastest growing “omics lopment, diagnostics and analysis of disease entities. sciences” and is a part of the systems biology. The term White biotechnology has been adopted by the industries, metabolomics was used for the first time in 2002 by and its main purpose is the creation and use of more O. Fiehn. The general assumption about metabolomics is cost – effective and better quality products. Due to the that it should enable the identification and quantification broad field of biotechnology applications, more often of changes occurring in the general set of metabolites * Corresponding author: Department of Bioorganic Chemistry, Wroclaw University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland; e-mail: [email protected] 138 W. Wojtowicz, P. Mlynarz Fig. 1. An example of metabolomics and fluxomics application in biotechnology – low molecular weight compounds (<1500 Da) – pre- liquid (LC) chromatography, or capillary electrophoresis sent in a tested sample (Fiehn, 2002). Due to the unique- (CE) (Vuckovic, 2012), and nuclear magnetic resonance ness of the biological material and its chemical comple- spectroscopy (NMR) with the use of different nuclei re- xity, there are some essential steps which should be per- sonance frequencies (Psychogios et al., 2011). These formed during the process of its preparation and evalua- analytical techniques allow the collection of large am- tion. ounts of data from each sample, however on different Sample storage and preparation are crucial, especial- “depths,” considering the compounds concentration. Be- ly when different phases of the investigated samples (so- cause the generated data matrices are extensive, there lid, liquid, and gas) are to be taken into consideration. is a need to use mathematical methods, which enable di- The chosen methods should allow for obtaining the best mensionality reduction and allow to visualize the obtai- possible metabolic “snapshot” with the minimum exter- ned data. As a result of this need, the chemometric ap- nal influence on an overall compound composition. Ano- proach became widely applied in metabolomics (Holmes ther important element is the choice of an appropriate and Antti, 2002); however, basic statistics is also used analytical technique that facilitates the broadest and the for comparing concentrations of metabolites in different most accurate analysis of a biological matrix. The me- groups (Deja et al., 2013). Commonly used chemometric thod of sample preparation and the analytical technique methods can be categorized as either supervised or must ensure high reproducibility, which enables further unsupervised, with Principal Component Analysis (PCA), comparative analyzes (Fiehn, 2002); therefore, special Robust Principal Component Analysis (RPCA), and Hie- protocols were proposed in various areas of biotechno- rarchical cluster analysis (Uarrota et al., 2014) being logy (Vuckovic, 2012). unsupervised, and Partial least squares Discriminant With the development of metabolomics, the terms Analysis (PLS-DA) (Wold et al., 2001) and Orthogonal Par- metabolic fingerprint and metabolic footprint have now tial least squares Discriminant Analysis (OPLS-DA) as become commonly used. They are based on the general supervised methods. assumptions of metabolomics; however, the overall pic- The possibilities of analyzing, testing, and data visu- ture has the reduced scope to just small molecule com- alization are broad and still expanding due to the growth pounds of the diverse origins. Particularly in cells type in the field of chemometrics. studies, metabolic fingerprinting refers directly to the Metabolomics studies enable new and rapid diag- whole set of intracellular metabolites (Lin et al., 2007), nostic methods to be used in various cases. They are ex- while metabolic footprinting applies to a set of extra- tensively used in cancer research (Deja et al., 2013; cellular metabolites (Pope et al., 2007). Fong et al., 2011; Hirayama et al., 2009) and might po- Many analytical techniques are applied in metabolo- tentially support histopathological examination. These mics studies due to the vast amount of chemical com- studies can be a supporting tool for final medical diagno- pounds and their different groups. However, the most sis, or they can become an alternative or even an inde- commonly used are mass spectrometry (MS) combined pendent diagnostic method (Spratlin et al., 2009). Com- with a separation technique, for example, gas (GC), monly evaluated biological materials in metabolomics Metabolomics and fluxomics in biotechnology: current trends 139 Fig. 2. Two-dimensional projection of PCA (unsupervised), PLS-DA (supervised), and OPLS-DA (supervised) chemometric models studies are serum and urine, which very often reflects with the influence of environmental factors on metabo- the biochemical changes occurring in organisms. There- lome changes of a modified line with regard to the fol- fore, it may be an option for basic diagnostics and might lowing year’s crops. This approach was applied in a case enable rapid and less-invasive screening tests for pa- study of field-grown, genetically modified wheat, in which tients. At the same time, metabolomics permits tracking it has been shown – based on a metabolomics analysis – disease changes and development or anticipating the that the place and year of cultivation have had a signi- trend of the disease’s progression (Zhang et al., 2012; ficant impact on the plants’ metabolome (Baker et al., Dawiskiba et al., 2014; Zabek et al., 2016). 2006). The evaluation of differences in the metabolome Metabolomics enabled the determination of the crops was successfully used for differentiating wild type seeds metabolome (phenotyping) by differentiating the compo- from their biosynthetic mutants (Bottcher et al., 2008). sition of low molecular weight compounds in soybean The metabolomics approach can also be used direc- (Lin et al., 2014), rice (Hu et al., 2014) and beans culti- tly in the biotechnological industry, where microbial se- vars (Mensack et al., 2010), subsequently enabled the condary metabolites are extremely important. It allows differentiation of regional products in terms of their to track the changes of conditions in which the selected origin (Zieliński et al., 2014) or the impact of stressors microbial strains may be more relevant than others (Hou on living organisms (Dita et al., 2006). Metabolomics et al., 2012). Therefore, the identification and classifica-