Abstract
Intraspecific genomic exchanges happen frequently between bacteria living in the same natural environment and can also be performed artificially in the laboratory for basic research or genetic/metabolic engineering purposes. In silico metabolic reconstruction and simulation of the metabolism of the hybrid strains that result from these processes can be used to predict the phenotypic outcome of such genomic rearrangements; this can be especially helpful as a designing tool in the purview of synthetic biology. However, reconstructing the metabolism of a bacterium with a hybrid genome through in silico approaches is not a trivial task, as it requires taking into account the complex relationships existing between metabolic genes and how they change (or remain unchanged) when new genes are placed in a different genomic context. Furthermore, in order to “mix” the metabolic models of different bacterial strains one needs at least two different metabolic models to begin with, and reconstructing a genome-scale model from the ground up is a challenging task itself, requiring an intensive manual effort and a great deal of information. In this chapter, we propose two general protocols to address the aforementioned issues of: (1) quickly generating strain-specific metabolic models, given the relevant genomic sequence and an already existing, high-quality metabolic model of a different strain belonging to the same species, and (2) reconstructing the metabolic model of a hybrid strain containing genomic elements from two different parental strains.
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References
Tang N, Ma S, Tian J (2013) New tools for cost-effective DNA synthesis. In: Synthetic biology. Academic Press, New York, NY, pp 3–21
Medini D, Donati C, Tettelin H, Masignani V, Rappuoli R (2005) The microbial pan-genome. Curr Opin Genet Dev 15:589–594. https://doi.org/10.1016/j.gde.2005.09.006
Wiedenbeck J, Cohan FM (2011) Origins of bacterial diversity through horizontal genetic transfer and adaptation to new ecological niches. FEMS Microbiol Rev 35:957–976. https://doi.org/10.1111/j.1574-6976.2011.00292.x
Galardini M, Brilli M, Spini G, Rossi M, Roncaglia B, Bani A, Chiancianesi M, Moretto M, Engelen K, Bacci G, Pini F, Biondi EG, Bazzicalupo M, Mengoni A (2015) Evolution of intra-specific regulatory networks in a multipartite bacterial genome. PLoS Comput Biol 11:e1004478. https://doi.org/10.1371/journal.pcbi.1004478
Wiechert W (2002) Modeling and simulation: tools for metabolic engineering. J Biotechnol 94:37–63. https://doi.org/10.1016/S0168-1656(01)00418-7
Zhang C, Hua Q (2016) Applications of genome-scale metabolic models in biotechnology and systems medicine. Front Physiol 6:1–8. https://doi.org/10.3389/fphys.2015.00413
Zou W, Liu L, Zhang J, Yang H, Zhou M, Hua Q, Chen J (2012) Reconstruction and analysis of a genome-scale metabolic model of the vitamin C producing industrial strain Ketogulonicigenium vulgare WSH-001. J Biotechnol 161:42–48. https://doi.org/10.1016/j.jbiotec.2012.05.015
Biggs MB, Medlock GL, Kolling GL, Papin JA (2015) Metabolic network modeling of microbial communities. Wiley Interdiscip Rev Syst Biol Med 7:317–334. https://doi.org/10.1002/wsbm.1308
Henry CS, Bernstein HC, Weisenhorn P, Taylor RC, Lee JY, Zucker J, Song HS (2016) Microbial community metabolic modeling: a community data-driven network reconstruction. J Cell Physiol 231:2339–2345. https://doi.org/10.1002/jcp.25428
Sung J, Hale V, Merkel AC, Kim PJ, Chia N (2016) Metabolic modeling with Big Data and the gut microbiome. Appl Transl Genomics 10:10. https://doi.org/10.1016/j.atg.2016.02.001
Thiele I, Palsson BØ (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 5:93–121. https://doi.org/10.1038/nprot.2009.203
Fleischmann RD, Adams MD, White O, Clayton RA, Kirkness EF, Kerlavage AR, Bult CJ, Tomb JF, Dougherty BA, Merrick JM, Mckenney K, Sutton G, Fitzhugh W, Fields C, Gocayne JD, Scott J, Shirley R, Liu LI, Glodek A, Kelley JM, Weidman JF, Phillips CA, Spriggs T, Hedblom E, Cotton MD, Utterback TR, Hanna MC, Nguyen DT, Saudek DM, Brandon RC, Fine LD, Fritchman JL, Fuhrmann JL, Geoghagen NSM, Gnehm CL, Mcdonald LA, Small KV, Fraser CM, Smith HO, Venter JC (1995) Whole-genome random sequencing and assembly of haemophilus-influenzae Rd. Science 269:496–512. https://doi.org/10.1126/science.7542800
The C. elegans Sequencing Consortium (1998) Genome sequence of the nematode C. elegans: a platform for investigating biology. Science 282:2012–2018. https://doi.org/10.1126/science.282.5396.2012
Edwards JS, Palsson BØ (2000) The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc Natl Acad Sci U S A 97:5528–5533. https://doi.org/10.1073/pnas.97.10.5528
Förster J, Famili I, Fu P, Palsson B, Nielsen J (2003) Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res 13:244–253. https://doi.org/10.1101/gr.234503
Jamshidi N, Palsson BØ (2007) Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets. BMC Syst Biol 1:26. https://doi.org/10.1186/1752-0509-1-26
Rentzsch R, C a O (2009) Protein function prediction--the power of multiplicity. Trends Biotechnol 27:210–219. https://doi.org/10.1016/j.tibtech.2009.01.002
Aziz RK, Bartels D, Best AA, DeJongh M, Disz T, Edwards RA, Formsma K, Gerdes S, Glass EM, Kubal M, Meyer F, Olsen GJ, Olson R, Osterman AL, Overbeek RA, McNeil LK, Paarmann D, Paczian T, Parrello B, Pusch GD, Reich C, Stevens R, Vassieva O, Vonstein V, Wilke A, Zagnitko O (2008) The RAST Server: rapid annotations using subsystems technology. BMC Genomics 9:75. https://doi.org/10.1186/1471-2164-9-75
Aziz RK, Devoid S, Disz T, Edwards RA, Henry CS, Olsen GJ, Olson R, Overbeek R, Parrello B, Pusch GD, Stevens RL, Vonstein V, Xia F (2012) SEED servers: high-performance access to the SEED genomes, annotations, and metabolic models. PLoS One 7:e48053. https://doi.org/10.1371/journal.pone.0048053
Henry CS, DeJongh M, Best AA, Frybarger PM, Linsay B, Stevens RL (2010) High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol 28:977–982. https://doi.org/10.1038/nbt.1672
Sonnhammer ELL, Östlund G (2015) InParanoid 8: orthology analysis between 273 proteomes, mostly eukaryotic. Nucleic Acids Res 43:D234–D239. https://doi.org/10.1093/nar/gku1203
Fitch WM (1970) Distinguishing homologous from analogous proteins. Syst Zool 19:99–113. https://doi.org/10.2307/2412448
Koonin EV (2005) Orthologs, paralogs, and evolutionary genomics. Annu Rev Genet 39:309–338. https://doi.org/10.1146/annurev.genet.39.073003.114725
Madden T (2013) The BLAST sequence analysis tool. BLAST Seq Anal Tool 1–17.
Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S, Kang J, Hyduke DR, Palsson BØ (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
Ebrahim A, Lerman JA, Palsson BO, Hyduke DR (2013) COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Syst Biol 7:74. https://doi.org/10.1186/1752-0509-7-74
Bornstein BJ, Keating SM, Jouraku A, Hucka M (2008) LibSBML: an API library for SBML. Bioinformatics 24:880–881. https://doi.org/10.1093/bioinformatics/btn051
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Vignolini, T., Mengoni, A., Fondi, M. (2018). Template-Assisted Metabolic Reconstruction and Assembly of Hybrid Bacterial Models. In: Fondi, M. (eds) Metabolic Network Reconstruction and Modeling. Methods in Molecular Biology, vol 1716. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7528-0_8
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DOI: https://doi.org/10.1007/978-1-4939-7528-0_8
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