Bridging the gap between gene expression and metabolic phenotype via kinetic models

BMC Systems Biology

Jaques Reifman¹  Anders Wallqvist¹  Francisco G Vital-Lopez¹ 

[1] DoD Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advance Technology Research Center, U.S. Army Medical Research and Materiel Command, Ft. Detrick, MD, 21702, USA

关键词: Metabolomics;    Fluxomics;    Transcriptomics;    S. cerevisiae;    Metabolic networks;    Kinetic models;    Gene expression;   

Others  :  1142622
DOI  :  10.1186/1752-0509-7-63

 received in 2012-09-27, accepted in 2013-06-27,  发布年份 2013

【 摘 要 】

Background

Despite the close association between gene expression and metabolism, experimental evidence shows that gene expression levels alone cannot predict metabolic phenotypes, indicating a knowledge gap in our understanding of how these processes are connected. Here, we present a method that integrates transcriptome, fluxome, and metabolome data using kinetic models to create a mechanistic link between gene expression and metabolism.

Results

We developed a modeling framework to construct kinetic models that connect the transcriptional and metabolic responses of a cell to exogenous perturbations. The framework allowed us to avoid extensive experimental characterization, literature mining, and optimization problems by estimating most model parameters directly from fluxome and transcriptome data. We applied the framework to investigate how gene expression changes led to observed phenotypic alterations of Saccharomyces cerevisiae treated with weak organic acids (i.e., acetate, benzoate, propionate, or sorbate) and the histidine synthesis inhibitor 3-aminotriazole under steady-state conditions. We found that the transcriptional response led to alterations in yeast metabolism that mimicked measured metabolic fluxes and concentration changes. Further analyses generated mechanistic insights of how S. cerevisiae responds to these stresses. In particular, these results suggest that S. cerevisiae uses different regulation strategies for responding to these insults: regulation of two reactions accounted for most of the tolerance to the four weak organic acids, whereas the response to 3-aminotriazole was distributed among multiple reactions. Moreover, we observed that the magnitude of the gene expression changes was not directly correlated with their effect on the ability of S. cerevisiae to grow under these treatments. In addition, we identified another potential mechanism of action of 3-aminotriazole associated with the depletion of tetrahydrofolate.

Conclusions

Our simulation results show that the modeling framework provided an accurate mechanistic link between gene expression and cellular metabolism. The proposed method allowed us to integrate transcriptome, fluxome, and metabolome data to determine and interpret important features of the physiological response of yeast to stresses. Importantly, given its flexibility and robustness, our approach can be applied to investigate the transcriptional-metabolic response in other cellular systems of medical and industrial relevance.

【 授权许可】

   
2013 Vital-Lopez et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

【 参考文献 】

• [1]Feist AM, Herrgard MJ, Thiele I, Reed JL, Palsson BO: Reconstruction of biochemical networks in microorganisms. Nat Rev Microbiol 2009, 7:129-143.
• [2]Fang X, Wallqvist A, Reifman J: Modeling synergistic drug inhibition of Mycobacterium tuberculosis growth in murine macrophages. Mol Biosyst 2011, 7:2622-2636.
• [3]Becker SA, Feist AM, Mo ML, Hannum G, Palsson BO, Herrgard MJ: Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2007, 2:727-738.
• [4]Pharkya P, Burgard AP, Maranas CD: OptStrain: a computational framework for redesign of microbial production systems. Genome Res 2004, 14:2367-2376.
• [5]Liu L, Agren R, Bordel S, Nielsen J: Use of genome-scale metabolic models for understanding microbial physiology. FEBS Lett 2010, 584:2556-2564.
• [6]Schuster S, Dandekar T, Fell DA: Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. Trends Biotechnol 1999, 17:53-60.
• [7]Becker SA, Palsson BO: Context-specific metabolic networks are consistent with experiments. PLOS Comput Biol 2008, 4:e1000082.
• [8]Oberhardt MA, Goldberg JB, Hogardt M, Papin JA: Metabolic network analysis of Pseudomonas aeruginosa during chronic cystic fibrosis lung infection. J Bacteriol 2010, 192:5534-5548.
• [9]Shlomi T, Cabili MN, Herrgard MJ, Palsson BO, Ruppin E: Network-based prediction of human tissue-specific metabolism. Nat Biotechnol 2008, 26:1003-1010.
• [10]Fang X, Wallqvist A, Reifman J: Modeling phenotypic metabolic adaptations of Mycobacterium tuberculosis H37Rv under hypoxia. PLOS Comput Biol 2012, 8:e1002688.
• [11]Narendra V, Lytkin NI, Aliferis CF, Statnikov A: A comprehensive assessment of methods for de-novo reverse-engineering of genome-scale regulatory networks. Genomics 2010, 97:7-18.
• [12]Shlomi T, Eisenberg Y, Sharan R, Ruppin E: A genome-scale computational study of the interplay between transcriptional regulation and metabolism. Mol Syst Biol 2007, 3:101.
• [13]Covert MW, Xiao N, Chen TJ, Karr JR: Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics 2008, 24:2044-2050.
• [14]Kim J, Reed JL: OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains. BMC Syst Biol 2010, 4:53. BioMed Central Full Text
• [15]Lee JM, Min Lee J, Gianchandani EP, Eddy JA, Papin JA: Dynamic analysis of integrated signaling, metabolic, and regulatory networks. PLOS Comput Biol 2008, 4:e1000086.
• [16]Kotte O, Heinemann M: A divide-and-conquer approach to analyze underdetermined biochemical models. Bioinformatics 2009, 25:519-525.
• [17]Price ND, Reed JL, Palsson BO: Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol 2004, 2:886-897.
• [18]Gutenkunst RN, Waterfall JJ, Casey FP, Brown KS, Myers CR, Sethna JP: Universally sloppy parameter sensitivities in systems biology models. PLOS Comput Biol 2007, 3:1871-1878.
• [19]Liebermeister W, Klipp E: Bringing metabolic networks to life: convenience rate law and thermodynamic constraints. Theor Biol Med Model 2006, 3:41. BioMed Central Full Text
• [20]Tran LM, Rizk ML, Liao JC: Ensemble modeling of metabolic networks. Biophys J 2008, 95:5606-5617.
• [21]Jamshidi N, Palsson BO: Mass action stoichiometric simulation models: incorporating kinetics and regulation into stoichiometric models. Biophys J 2010, 98:175-185.
• [22]Smallbone K, Simeonidis E, Swainston N, Mendes P: Towards a genome-scale kinetic model of cellular metabolism. BMC Syst Biol 2010, 4:6. BioMed Central Full Text
• [23]Schwacke JH, Voit EO: Computation and analysis of time-dependent sensitivities in Generalized Mass Action systems. J Theor Biol 2005, 236:21-38.
• [24]Abbott DA, Knijnenburg TA, de-Poorter LM, Reinders MJ, Pronk JT, van-Maris AJ: Generic and specific transcriptional responses to different weak organic acids in anaerobic chemostat cultures of Saccharomyces cerevisiae. FEMS Yeast Res 2007, 7:819-833.
• [25]Moxley JF, Jewett MC, Antoniewicz MR, Villas-Boas SG, Alper H, Wheeler RT, Tong L, Hinnebusch AG, Ideker T, Nielsen J, Stephanopoulos G: Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p. Proc Natl Acad Sci USA 2009, 106:6477-6482.
• [26]Fournier ML, Paulson A, Pavelka N, Mosley AL, Gaudenz K, Bradford WD, Glynn E, Li H, Sardiu ME, Fleharty B, et al.: Delayed correlation of mRNA and protein expression in rapamycin-treated cells and a role for Ggc1 in cellular sensitivity to rapamycin. Mol Cell Proteomics 2010, 9:271-284.
• [27]Lee MV, Topper SE, Hubler SL, Hose J, Wenger CD, Coon JJ, Gasch AP: A dynamic model of proteome changes reveals new roles for transcript alteration in yeast. Mol Syst Biol 2011, 7:514.
• [28]Cleveland WS: Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 1979, 74:829-836.
• [29]Hinnebusch AG: Translational regulation of GCN4 and the general amino acid control of yeast. Annu Rev Microbiol 2005, 59:407-450.
• [30]AbdulHameed MD, Chaudhury S, Singh N, Sun H, Wallqvist A, Tawa GJ: Exploring polypharmacology using a ROCS-based target fishing approach. J Chem Inf Model 2012, 52:492-505.
• [31]Piper P, Mahe Y, Thompson S, Pandjaitan R, Holyoak C, Egner R, Muhlbauer M, Coote P, Kuchler K: The pdr12 ABC transporter is required for the development of weak organic acid resistance in yeast. EMBO J 1998, 17:4257-4265.
• [32]Hatzimanikatis V, Bailey JE: MCA has more to say. J Theor Biol 1996, 182:233-242.
• [33]Adiamah DA, Handl J, Schwartz JM: Streamlining the construction of large-scale dynamic models using generic kinetic equations. Bioinformatics 2010, 26:1324-1331.
• [34]Bruck J, Liebermeister W, Klipp E: Exploring the effect of variable enzyme concentrations in a kinetic model of yeast glycolysis. Genome Inform 2008, 20:1-14.
• [35]Teusink B, Passarge J, Reijenga CA, Esgalhado E, van der-Weijden CC, Schepper M, Walsh MC, Bakker BM, van-Dam K, Westerhoff HV, Snoep JL: Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry. Eur J Biochem 2000, 267:5313-5329.
• [36]Kummel A, Panke S, Heinemann M: Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Mol Syst Biol 2006, 2:2006.0034.
• [37]Heinrich R, Rapoport TA: A linear steady-state treatment of enzymatic chains. General properties, control and effector strength. Eur J Biochem 1974, 42:89-95.
• [38]Kacser H, Burns JA: The control of flux. Symp Soc Exp Biol 1973, 27:65-104.
• [39]Schwanhausser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J, Chen W, Selbach M: Global quantification of mammalian gene expression control. Nature 2011, 473:337-342.