OP-Synthetic: identification of optimal genetic manipulations for the overproduction of native and non-native metabolites

doi:10.1007/s40484-014-0033-7

Quant. Biol.

2014, Vol. 2

Issue (3) : 100-109 https://doi.org/10.1007/s40484-014-0033-7

RESEARCH ARTICLE

OP-Synthetic: identification of optimal genetic manipulations for the overproduction of native and non-native metabolites

Honglei Liu, Yanda Li, Xiaowo Wang(

)

MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, TNLIST/Department of Automation, Tsinghua University, Beijing100084, China

Download: PDF(549 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Constraint-based flux analysis has been widely used in metabolic engineering to predict genetic optimization strategies. These methods seek to find genetic manipulations that maximally couple the desired metabolites with the cellular growth objective. However, such framework does not work well for overproducing chemicals that are not closely correlated with biomass, for example non-native biochemical production by introducing synthetic pathways into heterologous host cells. Here, we present a computational method called OP-Synthetic, which can identify effective manipulations (upregulation, downregulation and deletion of reactions) and produce a step-by-step optimization strategy for the overproduction of indigenous and non-native chemicals. We compared OP-Synthetic with several state-of-the-art computational approaches on the problems of succinate overproduction and N-acetylneuraminic acid synthetic pathway optimization in Escherichia coli. OP-Synthetic showed its advantage for efficiently handling multiple steps optimization problems on genome wide metabolic networks. And more importantly, the optimization strategies predicted by OP-Synthetic have a better match with existing engineered strains, especially for the engineering of synthetic metabolic pathways for non-native chemical production. OP-Synthetic is freely available at:http://bioinfo.au.tsinghua.edu.cn/member/xwwang/OPSynthetic/.

Keywords metabolic network flux analysis optimization

Corresponding Author(s): Xiaowo Wang

About author:

Tongcan Cui and Yizhe Hou contributed equally to this work.

Online First Date: 31 December 2014 Issue Date: 14 January 2015

Cite this article:

Honglei Liu,Yanda Li,Xiaowo Wang. OP-Synthetic: identification of optimal genetic manipulations for the overproduction of native and non-native metabolites[J]. Quant. Biol., 2014, 2(3): 100-109.

URL:

https://academic.hep.com.cn/qb/EN/10.1007/s40484-014-0033-7
https://academic.hep.com.cn/qb/EN/Y2014/V2/I3/100

Fig.1 OP-Synthetic flowchart.

Fig.2 Manipulation type.

	Approach	Manipulation	Succinate production ( $mmol ? gDW − 1 h − 1$ )	Computation time ( s)
CoreE. coli metabolic model	OptKnock GDLS GDBB OptForce OP-Synthetic^a	ACALD2x, CYTBD, FBP, GLUN, GND, LDH_D, SUCDi ACALD, GLUDy, PFL, PYK, ME2 NA AKGDH( $↑$ ), GAPD( $×$ ), FUM( $×$ ), PFL( $×$ ), ATPM( $×$ ) (step 1) FUM( $↑$ ) (step 2) MDH( $↑$ ) (step 3) PPC( $↑$ ) (step 4) PYK( $×$ ) (step 5) ALCD2x-ETOHt2r-EX_etoh(e)( $×$ ), ACALD ( $×$ )	10.29 9.92 NA 15* 10.30	21 3 NA 45831 30
E. coli iAF1260 metabolic model	OptKnock GDLS GDBB OptForce OP-Synthetic^a	ALCD2x, AP5AH, FE2t3pp, GLCDPP, PRATPP CO2tex, LDH_D, PFL, THD2pp, PPKr ALCD2x, GLCptspp, GLUDy, Pit2rpp, RPE MCOATA_f( $×$ ), PPA( $×$ ), GLYCLTDx( $↑$ ), NADH18pp( $↑$ ), TRSARr_f( $↑$ ) (step 1) FRD2( $↑$ ) (step 2) ETOHtex-ETOHt2rpp-EX_etoh(e) ( $×$ ) (step 3)ACALD( $×$ ) (step 4) PYK( $×$ ) (step 5) PPC( $↑$ )	10.79 9.77 9.42 15* 10.85	53553 49329 53 154583 514
E. coli iAF1260 metabolic model with addition of pyc	OptKnock GDLS GDBB OptForce OP-Synthetic^a	AP5AH, DDCAtexi, DTMPK, GLYK, MCITS, MLTG1, NOtpp, R1PK, URAtex ACALD, ACCOAL, EAR40x, EAR80x, Htex, ME2, PGI, PPKr, SUCOAS ACALD, LALDO2x, ALR4x OAADC, GART, THRA2i, GND, ME2, THD2pp 3HAD60( $×$ ), ACS( $×$ ), O2tex_f( $×$ ), PFL( $×$ ), PGAMT_f( $×$ ), GAPD6( $↑$ ), MALS( $↑$ ), MDH3( $↑$ ), NADH18pp( $↑$ ) (step 1) ACALD( $×$ ), ETOHtex-ETOHt2rpp-EX_etoh(e) ( $×$ ), ALCD2x( $×$ ) (step 2) FDR2( $↑$ ) (step 3) MDH( $↑$ ), PYC( $↑$ ) (step 4) EX_for(e)-FORtex ( $×$ ), FORtppi( $×$ ) (step 5) PFL ( $×$ ),	15.10 12.98 10.69 15* 15.15	62483 9395 136 154592 522

Tab.1 Comparison of OptKnock, GDLS, GDBB, OptForce and OP-Synthetic in succinate overproduction.

Fig.3 Comparison of the number of reactions that can be verified by literature and running time using OptKnock, GDLS, GDBB, OptForce and OP-Synthetic.

Fig.4 The optimization procedure of N-acetylneuraminic acid by OP-Synthetic.

Step	Manipulations
1	G6PDA(nagB) ( $×$ ), AMANAPEr(nanE)( $×$ ), EDD( $×$ ), EX_neu(nanT)( $×$ ), AMANK(nanK)( $×$ ), GLUABUTt7pp( $×$ ), ETOHtex- ETOHt2rpp- EX_etoh(e)( $×$ ), AGDC( $×$ ), ABUTt2pp( $×$ )
2	EX_for(e)-FORtex( $×$ ), PFL( $×$ ), PYK( $×$ )
3	Act2rpp( $×$ ), ACtex-EX_ac(e)( $×$ )

Tab.2 The manipulations identified by OP-Synthetic for NeuAc yield optimization.

1	J. W. Lee,, D. Na,, J. M. Park,, J. Lee,, S. Choi, and S. Y. Lee, (2012) Systems metabolic engineering of microorganisms for natural and non-natural chemicals. Nat. Chem. Biol., 8, 536–546 https://doi.org/10.1038/nchembio.970 pmid: 22596205
2	K. L. J. Prather, and C. H. Martin, (2008) De novo biosynthetic pathways: rational design of microbial chemical factories. Curr. Opin.Biotechnol.,19, 468–474 https://doi.org/10.1016/j.copbio.2008.07.009 pmid: 18725289
3	H. Alper,, K. Miyaoku, and G. Stephanopoulos, (2005) Construction of lycopene-overproducing E. coli strains by combining systematic and combinatorial gene knockout targets. Nat. Biotechnol., 23, 612–616 https://doi.org/10.1038/nbt1083 pmid: 15821729
4	J. H. Park,, K. H. Lee,, T. Y. Kim, and S. Y. Lee, (2007) Metabolic engineering of Escherichia coli for the production of L-valine based on transcriptome analysis and in silico gene knockout simulation. Proc. Natl. Acad. Sci. USA, 104, 7797–7802 https://doi.org/10.1073/pnas.0702609104 pmid: 17463081
5	D.-K. Ro,, E. M. Paradise,, M. Ouellet,, K. J. Fisher,, K. L. Newman,, J. M. Ndungu,, K. A. Ho,, R. A. Eachus,, T. S. Ham,, J. Kirby,, et al. (2006) Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Nature, 440, 940–943 https://doi.org/10.1038/nature04640 pmid: 16612385
6	J. Kang,, P. Gu,, Y. Wang,, Y. Li,, F. Yang,, Q. Wang, and Q. Qi, (2012) Engineering of an N-acetylneuraminic acid synthetic pathway in Escherichia coli. Metab. Eng., 14, 623–629 https://doi.org/10.1016/j.ymben.2012.09.002 pmid: 23018051
7	E. J. Steen,, Y. Kang,, G. Bokinsky,, Z. Hu,, A. Schirmer,, A. McClure,, S. B. Del Cardayre, and J. D. Keasling, (2010) Microbial production of fatty-acid-derived fuels and chemicals from plant biomass. Nature, 463, 559–562 https://doi.org/10.1038/nature08721 pmid: 20111002
8	J. W. Lee,, T. Y. Kim,, Y.-S. Jang,, S. Choi, and S. Y. Lee, (2011) Systems metabolic engineering for chemicals and materials. Trends Biotechnol., 29, 370–378 https://doi.org/10.1016/j.tibtech.2011.04.001 pmid: 21561673
9	J. M. Park,, T. Y. Kim, and S. Y. Lee, (2009) Constraints-based genome-scale metabolic simulation for systems metabolic engineering. Biotechnol. Adv., 27, 979–988 https://doi.org/10.1016/j.biotechadv.2009.05.019 pmid: 19464354
10	S. J. Cox,, S. Shalel Levanon,, A. Sanchez,, H. Lin,, B. Peercy,, G. N. Bennett, and K.-Y. San, (2006) Development of a metabolic network design and optimization framework incorporating implementation constraints: a succinate production case study. Metab. Eng., 8, 46–57 https://doi.org/10.1016/j.ymben.2005.09.006 pmid: 16263313
11	H. Lin,, G. N. Bennett, and K.-Y. San, (2005) Metabolic engineering of aerobic succinate production systems in Escherichia coli to improve process productivity and achieve the maximum theoretical succinate yield. Metab. Eng., 7, 116–127 https://doi.org/10.1016/j.ymben.2004.10.003 pmid: 15781420
12	K. H. Lee,, J. H. Park,, T. Y. Kim,, H. U. Kim, and S. Y. Lee, (2007) Systems metabolic engineering of Escherichia coli for L-threonine production. Mol. Syst. Biol., 3, 149 https://doi.org/10.1038/msb4100196 pmid: 18059444
13	H. Alper,, Y.-S. Jin,, J. F. Moxley, and G. Stephanopoulos, (2005) Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli. Metab. Eng., 7, 155–164 https://doi.org/10.1016/j.ymben.2004.12.003 pmid: 15885614
14	C. Bro,, B. Regenberg,, J. Förster, and J. Nielsen, (2006) In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production. Metab. Eng., 8, 102–111 https://doi.org/10.1016/j.ymben.2005.09.007 pmid: 16289778
15	K. J. Kauffman,, P. Prakash, and J. S. Edwards, (2003) Advances in flux balance analysis. Curr. Opin.Biotechnol.,14, 491–496 https://doi.org/10.1016/j.copbio.2003.08.001 pmid: 14580578
16	J. D. Orth,, I. Thiele, and B. Ø. Palsson, (2010) What is flux balance analysis? Nat. Biotechnol., 28, 245–248 https://doi.org/10.1038/nbt.1614 pmid: 20212490
17	R. Mahadevan, and C. H. Schilling, (2003) The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab. Eng., 5, 264–276 https://doi.org/10.1016/j.ymben.2003.09.002 pmid: 14642354
18	D. Segrè,, D. Vitkup, and G. M. Church, (2002) Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl. Acad. Sci. USA, 99, 15112–15117 https://doi.org/10.1073/pnas.232349399 pmid: 12415116
19	N. D. Price,, J. L. Reed, and B. O. Palsson, (2004) Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat. Rev. Microbiol., 2, 886–897 https://doi.org/10.1038/nrmicro1023 pmid: 15494745
20	J. Kim, and J. L. Reed, (2010) OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains. BMC Syst. Biol., 4, 53 https://doi.org/10.1186/1752-0509-4-53 pmid: 20426856
21	L. Yang,, W. R. Cluett, and R. Mahadevan, (2011) EMILiO: a fast algorithm for genome-scale strain design. Metab. Eng., 13, 272–281 https://doi.org/10.1016/j.ymben.2011.03.002 pmid: 21414417
22	A. P. Burgard,, P. Pharkya, and C. D. Maranas, (2003) Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng., 84, 647–657 https://doi.org/10.1002/bit.10803 pmid: 14595777
23	P. Pharkya, and C. D. Maranas, (2006) An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. Metab. Eng., 8, 1–13 https://doi.org/10.1016/j.ymben.2005.08.003 pmid: 16199194
24	P. Pharkya,, A. P. Burgard, and C. D. Maranas, (2004) OptStrain: a computational framework for redesign of microbial production systems. Genome Res., 14, 2367–2376 https://doi.org/10.1101/gr.2872004 pmid: 15520298
25	G. Rockwell,, N. J. Guido,, and G. M. Church, (2013) Redirector: designing cell factories by reconstructing the metabolic objective. PLoS Comput. Biol., 9, e1002882 https://doi.org/10.1371/journal.pcbi.1002882 pmid: 23341769
26	H. S. Choi,, S. Y. Lee,, T. Y. Kim, and H. M. Woo, (2010) In silico identification of gene amplification targets for improvement of lycopene production. Appl. Environ. Microbiol., 76, 3097–3105 https://doi.org/10.1128/AEM.00115-10 pmid: 20348305
27	J. M. Park,, H. M. Park,, W. J. Kim,, H. U. Kim,, T. Y. Kim, and S. Y. Lee, (2012) Flux variability scanning based on enforced objective flux for identifying gene amplification targets. BMC Syst. Biol., 6, 106 https://doi.org/10.1186/1752-0509-6-106 pmid: 22909053
28	S. Ranganathan,, P. F. Suthers, and C. D. Maranas, (2010) OptForce: an optimization procedure for identifying all genetic manipulations leading to targeted overproductions. PLoS Comput. Biol., 6, e1000744 https://doi.org/10.1371/journal.pcbi.1000744 pmid: 20419153
29	C. Cotten, and J. L. Reed, (2013) Constraint-based strain design using continuous modifications (CosMos) of flux bounds finds new strategies for metabolic engineering. Biotechnol. J., 8, 595–604 https://doi.org/10.1002/biot.201200316 pmid: 23703951
30	D. S. Lun,, G. Rockwell,, N. J. Guido,, M. Baym,, J. A. Kelner,, B. Berger,, J. E. Galagan, and G. M. Church, (2009) Large-scale identification of genetic design strategies using local search. Mol. Syst. Biol., 5, 296 https://doi.org/10.1038/msb.2009.57 pmid: 19690565
31	D. Egen, and D. S. Lun, (2012) Truncated branch and bound achieves efficient constraint-based genetic design. Bioinformatics, 28, 1619–1623 https://doi.org/10.1093/bioinformatics/bts255 pmid: 22543499
32	A. M. Feist,, C. S. Henry,, J. L. Reed,, M. Krummenacker,, A. R. Joyce,, P. D. Karp,, L. J. Broadbelt,, V. Hatzimanikatis, and B. Ø. Palsson, (2007) A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol. Syst. Biol., 3, 121 https://doi.org/10.1038/msb4100155 pmid: 17593909
33	K. Jantama,, M. J. Haupt,, S. A. Svoronos,, X. Zhang,, J. C. Moore,, K. T. Shanmugam, and L. O. Ingram, (2008) Combining metabolic engineering and metabolic evolution to develop nonrecombinant strains of Escherichia coli C that produce succinate and malate. Biotechnol.Bioeng., 99, 1140–1153 https://doi.org/10.1002/bit.21694 pmid: 17972330
34	A. M. Sánchez,, G. N. Bennett, and K.-Y. San, (2006) Batch culture characterization and metabolic flux analysis of succinate-producing Escherichia coli strains. Metab. Eng., 8, 209–226 https://doi.org/10.1016/j.ymben.2005.11.004 pmid: 16434224
35	P. A. Jensen, and J. A. Papin, (2011) Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics, 27, 541–547 https://doi.org/10.1093/bioinformatics/btq702 pmid: 21172910
36	R. U. Ibarra,, J. S. Edwards, and B. O. Palsson, (2002) Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature, 420, 186–189 https://doi.org/10.1038/nature01149 pmid: 12432395
37	A. M. Sánchez,, G. N. Bennett, and K. Y. San, (2005) Efficient succinic acid production from glucose through overexpression of pyruvate carboxylase in an Escherichia coli alcohol dehydrogenase and lactate dehydrogenase mutant. Biotechnol. Prog., 21, 358–365 https://doi.org/10.1021/bp049676e pmid: 15801771
38	A. M. Sánchez,, G. N. Bennett, and K.-Y. San,(2005) Novel pathway engineering design of the anaerobic central metabolic pathway in Escherichia coli to increase succinate yield and productivity. Metab. Eng., 7, 229–239 https://doi.org/10.1016/j.ymben.2005.03.001 pmid: 15885621
39	J. Schellenberger,, R. Que,, R. M. T. Fleming,, I. Thiele,, J. D. Orth,, A. M. Feist,, D. C. Zielinski,, A. Bordbar,, N. E. Lewis,, S. Rahmanian,, et al. (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat. Protoc., 6, 1290–1307 https://doi.org/10.1038/nprot.2011.308 pmid: 21886097
40	R. Schauer, (2000) Achievements and challenges of sialic acid research. Glycoconj. J., 17, 485–499 https://doi.org/10.1023/A:1011062223612 pmid: 11421344
41	B. Wang, (2009) Sialic acid is an essential nutrient for brain development and cognition. Annu. Rev. Nutr., 29, 177–222 https://doi.org/10.1146/annurev.nutr.28.061807.155515 pmid: 19575597
42	M. Ishikawa, and S. Koizumi, (2010) Microbial production of N-acetylneuraminic acid by genetically engineered Escherichia coli. Carbohydr. Res., 345, 2605–2609 https://doi.org/10.1016/j.carres.2010.09.034 pmid: 20971455
43	F. Tao,, Y. Zhang,, C. Ma, and P. Xu, (2011) One-pot bio-synthesis: N-acetyl-D-neuraminic acid production by a powerful engineered whole-cell catalyst. Sci. Rep., 1, 142 https://doi.org/10.1038/srep00142 pmid: 22355659
44	H. H. Wang,, F. J. Isaacs,, P. A. Carr,, Z. Z. Sun,, G. Xu,, C. R. Forest, and G. M. Church, (2009) Programming cells by multiplex genome engineering and accelerated evolution. Nature, 460, 894–898 https://doi.org/10.1038/nature08187 pmid: 19633652
45	N. E. Lewis,, K. K. Hixson,, T. M. Conrad,, J. A. Lerman,, P. Charusanti,, A. D. Polpitiya,, J. N. Adkins,, G. Schramm,, S. O. Purvine,, D. Lopez-Ferrer,, et al. (2010) Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol. Syst. Biol., 6, 390 https://doi.org/10.1038/msb.2010.47 pmid: 20664636
46	L. S. Qi,, M. H. Larson,, L. A. Gilbert,, J. A. Doudna,, J. S. Weissman,, A. P. Arkin, and W. A. Lim, (2013) Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell, 152, 1173–1183 https://doi.org/10.1016/j.cell.2013.02.022 pmid: 23452860

[1]

Supplementary Material

Download

[1]	Saroj K Biswas, Nasir U Ahmed. Mathematical modeling and optimal intervention of COVID-19 outbreak[J]. Quant. Biol., 2021, 9(1): 84-92.
[2]	Jiyu Fan, Ailing Fu, Le Zhang. Progress in molecular docking[J]. Quant. Biol., 2019, 7(2): 83-89.
[3]	Wen Yang, Xia Wang, Kenan Li, Yuanru Liu, Ying Liu, Rui Wang, Honglin Li. Pharmacodynamics simulation of HOEC by a computational model of arachidonic acid metabolic network[J]. Quant. Biol., 2019, 7(1): 30-41.
[4]	Weizhong Tu, Shaozhen Ding, Ling Wu, Zhe Deng, Hui Zhu, Xiaotong Xu, Chen Lin, Chaonan Ye, Minlu Han, Mengna Zhao, Juan Liu, Zixin Deng, Junni Chen, Dong-Qing Wei, Qian-Nan Hu. SynBioEcoli: a comprehensive metabolism network of engineered E. coli in three dimensional visualization[J]. Quant. Biol., 2017, 5(1): 99-104.
[5]	Haiyan Liu. On statistical energy functions for biomolecular modeling and design[J]. Quant. Biol., 2015, 3(4): 157-167.

Viewed

Full text

Abstract

Cited

Shared

Discussed