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• W2786799503 abstract "Metabolomics combined with systems biology can be used to identify endogenous metabolites that modulate protein expression. Recent examples include the 2-fold enhancements of pertussis toxin protein in vaccine production and myelin basic protein expression in oligodendrocyte maturation; both applied a metabolomics-systems strategy to identify active metabolites. Metabolomics combined with systems biology can be used to identify endogenous metabolites that modulate protein expression. Recent examples include the 2-fold enhancements of pertussis toxin protein in vaccine production and myelin basic protein expression in oligodendrocyte maturation; both applied a metabolomics-systems strategy to identify active metabolites. Endogenous metabolites play essential roles in the regulation of cellular activity through their interactions with genes, transcripts, and proteins; however, our ability to identify active metabolites to modulate these interactions is relatively limited. Two recent examples represent notable exceptions in which metabolomics and systems biology were combined to enhance protein expression: the first study identified metabolites that enhance the commercial production of a pertussis vaccine (Branco dos Santos et al., 2017Branco dos Santos F. Olivier B.G. Boele J. Smessaert V. De Rop P. Krumpochova P. Klau G.W. Giera M. Dehottay P. Teusink B. Goffin P. Probing the genome-scale metabolic landscape of Bordetella pertussis, the causative agent of whooping cough.Appl. Environ. Microbiol. 2017; (Published online August 25, 2017)https://doi.org/10.1128/AEM.01528-17Crossref PubMed Scopus (19) Google Scholar), and the second identified a single metabolite that modulates an active protein for multiple sclerosis neuron remyelination (Beyer et al., 2018Beyer B.A. Fang M. Sadrian B. Montenegro-Burke J.R. Plaisted W.C. Kok B.P.C. Saez E. Kondo T. Siuzdak G. Lairson L.L. Metabolomics-based discovery of a metabolite that enhances oligodendrocyte maturation.Nat. Chem. Biol. 2018; 14: 22-28Crossref PubMed Scopus (59) Google Scholar). Protein expression is intimately linked with the genome and the genetic information transmitted downstream; however, the role metabolomics and metabolites can play as a driving force in these upstream events is unappreciated (Figure 1, left). For example, the post-genome era is characterized by the rise of transcriptomics, proteomics, and metabolomics, all designed to elucidate the composition and, ultimately, the function behind their information content. In this context, the central dogma of biology (Crick, 1970Crick F. Central dogma of molecular biology.Nature. 1970; 227: 561-563Crossref PubMed Scopus (1691) Google Scholar) is that genome-encoded information is being transmitted downward to the transcriptome and proteome, leading to changes within the metabolic pool, the metabolome. Therefore, it is not surprising that the metabolome has been primarily linked with phenotypic changes while the transcriptome and proteome are seen as control centers. This downstream concept places metabolomics at the forefront of diagnostics and biomarker discovery, however, minimizing metabolites and their more significant role as initiators of bioactivity. Integrating metabolomics within a systems biology framework facilitates the elucidation of metabolites’ upstream impact and assists in the identification of biologically active metabolites that can promote protein expression and activity. To date, systems biology has not yet been effectively combined with metabolomics to identify metabolites that can modulate phenotype; a particularly interesting application is protein expression. The recent study by Branco dos Santos et al., 2017Branco dos Santos F. Olivier B.G. Boele J. Smessaert V. De Rop P. Krumpochova P. Klau G.W. Giera M. Dehottay P. Teusink B. Goffin P. Probing the genome-scale metabolic landscape of Bordetella pertussis, the causative agent of whooping cough.Appl. Environ. Microbiol. 2017; (Published online August 25, 2017)https://doi.org/10.1128/AEM.01528-17Crossref PubMed Scopus (19) Google Scholar used a genome-wide metabolic model of the human pathogen Bordetella pertussis to increase pertussis vaccine production (Figure 1, upper right). Initially, the systems biology-based model of the pathogen revealed an apparent gap in the theoretical versus experimentally observed nitrogen metabolism. Constraint-based analysis techniques allowed a comprehensive accounting of mass flow in the organism, and a model for Bordetella pertussis was constructed and validated, revealing a gap in the experimentally determined nitrogen output. Characterization of theoretical optimal flux distributions through the metabolic network was used to predict all possible nitrogen sinks compatible with the data; this list was subsequently used as a filter for untargeted metabolomics analysis of the growth media. By performing an iterative cycle in which metabolomics was used to help decipher the mass balance of the simulations, the authors could improve/curate the original computer model. In this way, quite unexpectedly, nucleobases and nucleosides were identified as substantial novel sinks of nitrogen, at concentrations ranging up to the millimolar level. In addition to identifying the relevant nitrogen metabolite sinks, the models were also used to investigate sulfur balance. In the particular case of pertussis toxin (PT), it was known that sulfate inhibits PT production, and that sulfate accumulated in the traditional growth medium through excess methionine and glutathione (in the medium). The metabolic map provided an alternative pathway that was previously unknown for inorganic sulfur assimilation via thiosulphate; thus, using thiosulphate as an alternative, together with a careful balancing of amino acid requirements based on the metabolomics analysis of biomass composition, resulted in balanced growth conditions. Overall, metabolomics analysis of the fermentations, combined with a systems biology approach, led to a 2.4-fold increase in PT production from a simpler medium. In a second example, metabolomics was employed in combination with pathway analysis to identify metabolites that modulate the process of oligodendrocyte differentiation and/or maturation, an important regenerative process associated with demyelinating diseases (Beyer et al., 2018Beyer B.A. Fang M. Sadrian B. Montenegro-Burke J.R. Plaisted W.C. Kok B.P.C. Saez E. Kondo T. Siuzdak G. Lairson L.L. Metabolomics-based discovery of a metabolite that enhances oligodendrocyte maturation.Nat. Chem. Biol. 2018; 14: 22-28Crossref PubMed Scopus (59) Google Scholar). Mass spectrometry-based metabolomics was used to investigate the mechanism, and whether endogenous metabolites could impact oligodendrocyte precursor cell (OPC) differentiation. Taurine and creatine pathways were found to be the most highly altered events associated with OPC differentiation, where taurine and hypotaurine increased by over 20- and 10-fold, respectively, and metabolites in the creatine pathway were also upregulated by at least an order of magnitude. Quantitative analysis of associated metabolites was performed on both the upstream and downstream parts of the taurine and creatine pathways. When added exogenously at physiologically relevant concentrations, taurine was found to dramatically enhance drug-induced OPC differentiation and facilitate the in vitro myelination of co-cultured axons. Mechanistically, taurine-induced OPC differentiation- and myelination-enhancing activities appeared to be driven by taurine’s ability to increase serine levels, which is an initial building block required for the synthesis of the glycosphingolipid components of myelin. A key outcome in the taurine-induced differentiation process was the increased generation of myelin basic protein (Figure 1, lower right) by 2.5-fold, a protein directly associated with the myelination of neurons. The primary motivation for integrating metabolomics and systems biology has been in the generation of more comprehensive metabolic maps focusing primarily on altering metabolite levels; this coincides with recent reports indicating that our knowledge about metabolic biological activity is rapidly increasing (Husted et al., 2017Husted A.S. Trauelsen M. Rudenko O. Hjorth S.A. Schwartz T.W. GPCR-mediated signaling of metabolites.Cell Metab. 2017; 25: 777-796Abstract Full Text Full Text PDF PubMed Scopus (289) Google Scholar). A first step in the direction of providing metabolic activity has recently been described and incorporated into XCMS Online, where metabolite selection is made on multiple selection criteria including statistical significance, pathway analysis, and multi-omics screening. However, as we are in the early stages a more sophisticated level of metabolite selection will likely evolve. Current systems biology models concentrate on production of metabolites and flux with criteria that have yet to be fully understood, including how proteins are biosynthesized, their folding requirements, and what the production inhibitory effects are. Ultimately, metabolomics-guided systems biology-based models will provide the necessary framework for the integration of all aforementioned approaches, in that it should allow predicting metabolic requirements for the production of key active metabolites. In turn, this will allow us to perturb metabolism and concurrently shape the proteomic landscape to our needs. The examples described above underscore the utility of integrating metabolomics and systems biology to decipher and advance metabolite-induced protein expression. Metabolites are a logical source to enhance and understand these biological systems, especially as their bioactivities are becoming more apparent (C. Guijas et al., unpublished data), as is the systems-wide role they are playing (Huan et al., 2017Huan T. Forsberg E.M. Rinehart D. Johnson C.H. Ivanisevic J. Benton H.P. Fang M. Aisporna A. Hilmers B. Poole F.L. et al.Systems biology guided by XCMS Online metabolomics.Nat. Methods. 2017; 14: 461-462Crossref PubMed Scopus (125) Google Scholar). A significant challenge will be in deciphering which metabolites are best suited to alter the system, a challenge that will likely be solved by combining seemingly disparate information, including statistical analysis, pathway data, and other ‘omic technologies, and integrating this knowledge into existing databases. This is a task well suited for machine learning-type approaches, once enough data are available to provide meaningful information (Sastry et al., 2017Sastry A. Monk J. Tegel H. Uhlen M. Palsson B.O. Rockberg J. Brunk E. Machine learning in computational biology to accelerate high-throughput protein expression.Bioinformatics. 2017; 33: 2487-2495Crossref PubMed Scopus (8) Google Scholar). For example, combining genome-wide metabolic models with experimental metabolomics data will likely lead to improved models and the identification of metabolites with unique activities. Most intriguing to us is that unlike other ‘omic approaches, the beauty of metabolomics and activity testing is that metabolites are readily commercially accessible and generally inexpensive, and can directly impact the system quickly. This is also intriguing because as technologists, we are no longer just passive observers but instead active participants." @default.
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• W2786799503 title "Metabolite-Induced Protein Expression Guided by Metabolomics and Systems Biology" @default.
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