iMTBGO: An Algorithm for Integrating Metabolic Networks with Transcriptomes Based on Gene Ontology Analysis

Author(s): Zhitao Mao, Hongwu Ma*.

Journal Name: Current Genomics

Volume 20 , Issue 4 , 2019

Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Background: Constraint-based metabolic network models have been widely used in phenotypic prediction and metabolic engineering design. In recent years, researchers have attempted to improve prediction accuracy by integrating regulatory information and multiple types of “omics” data into this constraint-based model. The transcriptome is the most commonly used data type in integration, and a large number of FBA (flux balance analysis)-based integrated algorithms have been developed.

Methods and Results: We mapped the Kcat values to the tree structure of GO terms and found that the Kcat values under the same GO term have a higher similarity. Based on this observation, we developed a new method, called iMTBGO, to predict metabolic flux distributions by constraining reaction boundaries based on gene expression ratios normalized by marker genes under the same GO term. We applied this method to previously published data and compared the prediction results with other metabolic flux analysis methods which also utilize gene expression data. The prediction errors of iMTBGO for both growth rates and fluxes in the central metabolic pathways were smaller than those of earlier published methods.

Conclusion: Considering the fact that reaction rates are not only determined by genes/expression levels, but also by the specific activities of enzymes, the iMTBGO method allows us to make more precise predictions of metabolic fluxes by using expression values normalized based on GO.

Keywords: Transcriptome, gene ontology, metabolic network, constraint-based model, turnover number, flux balance analysis.

[1]
Llaneras, F.; Picó, J. Stoichiometric modelling of cell metabolism. J. Biosci. Bioeng., 2008, 105(1), 1-11.
[http://dx.doi.org/10.1263/jbb.105.1] [PMID: 18295713]
[2]
Fong, S.S.; Palsson, B.O. Metabolic gene-deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes. Nat. Genet., 2004, 36(10), 1056-1058.
[http://dx.doi.org/10.1038/ng1432] [PMID: 15448692]
[3]
Famili, I.; Forster, J.; Nielsen, J.; Palsson, B.O. Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. Proc. Natl. Acad. Sci. USA, 2003, 100(23), 13134-13139.
[http://dx.doi.org/10.1073/pnas.2235812100] [PMID: 14578455]
[4]
Edwards, J.S.; Palsson, B.O. Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions. BMC Bioinformatics, 2000, 1, 1.
[http://dx.doi.org/10.1186/1471-2105-1-1] [PMID: 11001586]
[5]
Beste, D.J.; Hooper, T.; Stewart, G.; Bonde, B.; Avignone-Rossa, C.; Bushell, M.E.; Wheeler, P.; Klamt, S.; Kierzek, A.M.; McFadden, J. GSMN-TB: A web-based genome-scale network model of Mycobacterium tuberculosis metabolism. Genome Biol., 2007, 8(5), R89.
[http://dx.doi.org/10.1186/gb-2007-8-5-r89] [PMID: 17521419]
[6]
Varma, A.; Palsson, B.O. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl. Environ. Microbiol., 1994, 60(10), 3724-3731.
[PMID: 7986045]
[7]
Edwards, J.S.; Ibarra, R.U.; Palsson, B.O. In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat. Biotechnol., 2001, 19(2), 125-130.
[http://dx.doi.org/10.1038/84379] [PMID: 11175725]
[8]
Becker, S.A.; Palsson, B.O. Context-specific metabolic networks are consistent with experiments. PLOS Comput. Biol., 2008, 4(5)e1000082
[http://dx.doi.org/10.1371/journal.pcbi.1000082] [PMID: 18483554]
[9]
Åkesson, M.; Förster, J.; Nielsen, J. Integration of gene expression data into genome-scale metabolic models. Metab. Eng., 2004, 6(4), 285-293.
[http://dx.doi.org/10.1016/j.ymben.2003.12.002] [PMID: 15491858]
[10]
Zur, H.; Ruppin, E.; Shlomi, T. iMAT: An integrative metabolic analysis tool. Bioinformatics, 2010, 26(24), 3140-3142.
[http://dx.doi.org/10.1093/bioinformatics/btq602] [PMID: 21081510]
[11]
Rossell, S.; Huynen, M.A.; Notebaart, R.A. Inferring metabolic states in uncharacterized environments using gene-expression measurements. PLOS Comput. Biol., 2013, 9(3)e1002988
[http://dx.doi.org/10.1371/journal.pcbi.1002988] [PMID: 23555222]
[12]
Jensen, P.A.; Papin, J.A. Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics, 2011, 27(4), 541-547.
[http://dx.doi.org/10.1093/bioinformatics/btq702] [PMID: 21172910]
[13]
Töpfer, N.; Jozefczuk, S.; Nikoloski, Z. Integration of time-resolved transcriptomics data with flux-based methods reveals stress-induced metabolic adaptation in Escherichia coli. BMC Syst. Biol., 2012, 6, 148.
[http://dx.doi.org/10.1186/1752-0509-6-148] [PMID: 23194026]
[14]
Lee, D.; Smallbone, K.; Dunn, W.B.; Murabito, E.; Winder, C.L.; Kell, D.B.; Mendes, P.; Swainston, N. Improving metabolic flux predictions using absolute gene expression data. BMC Syst. Biol., 2012, 6, 73.
[http://dx.doi.org/10.1186/1752-0509-6-73] [PMID: 22713172]
[15]
Kim, J.; Reed, J.L. RELATCH: Relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations. Genome Biol., 2012, 13(9), R78.
[http://dx.doi.org/10.1186/gb-2012-13-9-r78] [PMID: 23013597]
[16]
Navid, A.; Almaas, E. Genome-level transcription data of Yersinia pestis analyzed with a new metabolic constraint-based approach. BMC Syst. Biol., 2012, 6, 150.
[http://dx.doi.org/10.1186/1752-0509-6-150] [PMID: 23216785]
[17]
Collins, S.B.; Reznik, E.; Segrè, D. Temporal expression-based analysis of metabolism. PLOS Comput. Biol., 2012, 8(11)e1002781
[http://dx.doi.org/10.1371/journal.pcbi.1002781] [PMID: 23209390]
[18]
Colijn, C.; Brandes, A.; Zucker, J.; Lun, D.S.; Weiner, B.; Farhat, M.R.; Cheng, T.Y.; Moody, D.B.; Murray, M.; Galagan, J.E. Interpreting expression data with metabolic flux models: Predicting Mycobacterium tuberculosis mycolic acid production. PLOS Comput. Biol., 2009, 5(8)e1000489
[http://dx.doi.org/10.1371/journal.pcbi.1000489] [PMID: 19714220]
[19]
Ashburner, M.; Ball, C.A.; Blake, J.A.; Botstein, D.; Butler, H.; Cherry, J.M.; Davis, A.P.; Dolinski, K.; Dwight, S.S.; Eppig, J.T.; Harris, M.A.; Hill, D.P.; Issel-Tarver, L.; Kasarskis, A.; Lewis, S.; Matese, J.C.; Richardson, J.E.; Ringwald, M.; Rubin, G.M.; Sherlock, G. The Gene Ontology Consortium. Gene ontology: Tool for the unification of biology. Nat. Genet., 2000, 25(1), 25-29.
[http://dx.doi.org/10.1038/75556] [PMID: 10802651]
[20]
The Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still Going strong. Nucleic Acids Res., 2019, 47(D1), D330-D338.
[http://dx.doi.org/10.1093/nar/gky1055] [PMID: 30395331]
[21]
Placzek, S.; Schomburg, I.; Chang, A.; Jeske, L.; Ulbrich, M.; Tillack, J.; Schomburg, D. BRENDA in 2017: New perspectives and new tools in BRENDA. Nucleic Acids Res., 2017, 45(D1), D380-D388.
[http://dx.doi.org/10.1093/nar/gkw952] [PMID: 27924025]
[22]
Carrera, J.; Estrela, R.; Luo, J.; Rai, N.; Tsoukalas, A.; Tagkopoulos, I. An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli. Mol. Syst. Biol., 2014, 10, 735.
[http://dx.doi.org/10.15252/msb.20145108] [PMID: 24987114]
[23]
Huang, W.; Sherman, B.T.; Lempicki, R.A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res., 2009, 37(1), 1-13.
[http://dx.doi.org/10.1093/nar/gkn923] [PMID: 19033363]
[24]
Huang, W.; Sherman, B.T.; Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc., 2009, 4(1), 44-57.
[http://dx.doi.org/10.1038/nprot.2008.211] [PMID: 19131956]
[25]
Zhou, J.; Rudd, K.E. EcoGene 3.0. Nucleic Acids Res., 2013, 41(Database issue), D613-D624.
[PMID: 23197660]
[26]
Holm, A.K.; Blank, L.M.; Oldiges, M.; Schmid, A.; Solem, C.; Jensen, P.R.; Vemuri, G.N. Metabolic and transcriptional response to cofactor perturbations in Escherichia coli. J. Biol. Chem., 2010, 285(23), 17498-17506.
[http://dx.doi.org/10.1074/jbc.M109.095570] [PMID: 20299454]
[27]
Ishii, N.; Nakahigashi, K.; Baba, T.; Robert, M.; Soga, T.; Kanai, A.; Hirasawa, T.; Naba, M.; Hirai, K.; Hoque, A.; Ho, P.Y.; Kakazu, Y.; Sugawara, K.; Igarashi, S.; Harada, S.; Masuda, T.; Sugiyama, N.; Togashi, T.; Hasegawa, M.; Takai, Y.; Yugi, K.; Arakawa, K.; Iwata, N.; Toya, Y.; Nakayama, Y.; Nishioka, T.; Shimizu, K.; Mori, H.; Tomita, M. Multiple high-throughput analyses monitor the response of E. coli to perturbations. Science, 2007, 316(5824), 593-597.
[http://dx.doi.org/10.1126/science.1132067] [PMID: 17379776]
[28]
Motamedian, E.; Mohammadi, M.; Shojaosadati, S.A.; Heydari, M. TRFBA: An algorithm to integrate genome-scale metabolic and transcriptional regulatory networks with incorporation of expression data. Bioinformatics, 2017, 33(7), 1057-1063.
[http://dx.doi.org/10.1093/bioinformatics/btw772] [PMID: 28065897]
[29]
Machado, D.; Herrgård, M. Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLOS Comput. Biol., 2014, 10(4)e1003580
[http://dx.doi.org/10.1371/journal.pcbi.1003580] [PMID: 24762745]
[30]
Orth, J.D.; Conrad, T.M.; Na, J.; Lerman, J.A.; Nam, H.; Feist, A.M.; Palsson, B.O. A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011. Mol. Syst. Biol., 2011, 7, 535.
[http://dx.doi.org/10.1038/msb.2011.65] [PMID: 21988831]
[31]
Daran-Lapujade, P.; Jansen, M.L.A.; Daran, J.M.; van Gulik, W.; de Winde, J.H.; Pronk, J.T. Role of transcriptional regulation in controlling fluxes in central carbon metabolism of Saccharomyces cerevisiae. A chemostat culture study. J. Biol. Chem., 2004, 279(10), 9125-9138.
[http://dx.doi.org/10.1074/jbc.M309578200] [PMID: 14630934]
[32]
Daran-Lapujade, P.; Rossell, S.; van Gulik, W.M.; Luttik, M.A.H.; de Groot, M.J.L.; Slijper, M.; Heck, A.J.R.; Daran, J.M.; de Winde, J.H.; Westerhoff, H.V.; Pronk, J.T.; Bakker, B.M. The fluxes through glycolytic enzymes in Saccharomyces cerevisiae are predominantly regulated at posttranscriptional levels. Proc. Natl. Acad. Sci. USA, 2007, 104(40), 15753-15758.
[http://dx.doi.org/10.1073/pnas.0707476104] [PMID: 17898166]
[33]
Kochanowski, K.; Sauer, U.; Chubukov, V. Somewhat in control-the role of transcription in regulating microbial metabolic fluxes. Curr. Opin. Biotechnol., 2013, 24(6), 987-993.
[http://dx.doi.org/10.1016/j.copbio.2013.03.014] [PMID: 23571096]


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 20
ISSUE: 4
Year: 2019
Page: [252 - 259]
Pages: 8
DOI: 10.2174/1389202920666190626155130
Price: $58

Article Metrics

PDF: 29
HTML: 2
EPUB: 1
PRC: 1

Special-new-year-discount