Comparative analysis of metabolic network of pathogens

doi:10.1007/s11515-017-1440-8

Front. Biol.

2017, Vol. 12

Issue (2) : 139-150 https://doi.org/10.1007/s11515-017-1440-8

RESEARCH ARTICLE

Comparative analysis of metabolic network of pathogens

Kumar Gaurav, Yasha Hasija(

)

Department of Biotechnology, Delhi Technological University, Delhi – 110042, India

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Abstract

BACKGROUND: Metabolic networks are complex and system of highly connected chemical reactions and hence it needs a system level computational approach to identify the genotype- phenotype relationship. The study of essential genes and reactions and synthetic lethality of genes and reactions plays a crucial role in explaining functional links between genes and gene function predictions.

METHODS: Flux balance analysis (FBA) has been developed as a powerful method for the in silico analyses of metabolic networks. In this study, we present the comparative analysis of the genomic scale metabolic networks of the four microorganisms i.e.Salmonella typhimurium, Mycobacterium tuberculosis, Staphylococcus aureus,andHelicobacter pylori. The fluxes of all reaction were obtained and the growth rate of the organism was calculated by setting the biomass reaction as the objective function.

RESULTS & CONCLUSIONS:The average lethality fraction of all the four organisms studied ranged from 0.2 to 0.6. It was also observed that there are very few metabolites which are highly connected. Those metabolites that are highly connected are supposed to be the ‘global players’ similar to the hub protein in the protein – protein interaction network.

Keywords essential genes synthetic lethal genes metabolite connectivity robustness analysis

Corresponding Author(s): Yasha Hasija

Online First Date: 30 March 2017 Issue Date: 14 April 2017

Cite this article:

Kumar Gaurav,Yasha Hasija. Comparative analysis of metabolic network of pathogens[J]. Front. Biol., 2017, 12(2): 139-150.

URL:

https://academic.hep.com.cn/fib/EN/10.1007/s11515-017-1440-8
https://academic.hep.com.cn/fib/EN/Y2017/V12/I2/139

Tab.1 Growth rates of all the four organisms using FBA

Tab.2 Growth rates of Mycobacterium tuberculosis under the alternative substrates

Tab.3 Growth rates of Staphylococcus aureus under the alternative substrates

Tab.4 Growth rates of Helicobacter pylori under the alternative substrates

Tab.5 Growth rates of Salmonella typhimurium under the alternative substrates

Tab.6 Essential genes and essential reactions present in metabolic networks of given organisms.

Tab.7 Common essential reactions compared among four different organisms.

Tab.8 List of essential genes

Tab.9 List of essential reactions

Tab.10 Synthetic gene lethality.

Tab.11 Synthetic reaction lethality

Fig.1 Robustness analysis of metabolic network ofMycobacterium tuberculosiswith oxygen uptake rate fixed at 17 mmol/(gDW·h).

Fig.2 Robustness analysis of metabolic network ofMycobacterium tuberculosiswith glucose uptake rate fixed at 10 mmol/(gDW·h).

Fig.3 Robustness analysis of metabolic network ofStaphylococcus aureus with oxygen uptake rate fixed at 17 mmol/(gDW·h).

Fig.4 Robustness analysis of metabolic network ofStaphylococcus aureus with glucose uptake rate fixed at 10 mmol/(gDW·h).

Fig.5 Robustness analysis of metabolic network ofHelicobacter pylori with oxygen uptake rate fixed at 17 mmol/(gDW·h).

Fig.6 Robustness analysis of metabolic network ofHelicobacter pylori with glucose uptake rate fixed at 10 mmol/(gDW·h).

Fig.7 Robustness analysis of metabolic network ofSalmonellatyphimurium with oxygen uptake rate fixed at 17 mmol/(gDW·h).

Fig.8 Robustness analysis of metabolic network ofSalmonella typhimurium with glucose uptake rate fixed at 10 mmol/(gDW·h)

Fig.9 Phenotypic phase plane analysis of metabolic network ofMycobacterium tubeculosis.

Fig.10 Phenotypic phase plane analysis of metabolic network ofStaphylococcus aureus.

Fig.11 Phenotypic phase plane analysis of metabolic network ofHelicobacter pylori.

Fig.12 Phenotypic phase plane analysis of metabolic network ofSalmonella typhimurium.

Fig.13 Metabolite connectivity in metabolic network Mycobacterium tuberculosis.

Fig.14 Metabolite connectivity in metabolic network Staphylococcus aureus.

Fig.15 Metabolite connectivity in metabolic network Helicobacter pylori.

Fig.16 Metabolite connectivity in metabolic network Salmonella typhimurium.

Fig.17 Correlation between metabolite connectivity and average lethality of Mycobacterium tuberculosis.

Fig.18 Correlation between metabolite connectivity and average lethality of Staphylococcus aureus.

Fig.19 Correlation between metabolite connectivity and average lethality of Helicobacter pylori.

Fig.20 Correlation between metabolite connectivity and average lethality of Salmonella typhimurium.

1	Feist and Palsson (2008). The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli. Nature Biotechnol, 226(6):659–667
2	Alper H, Jin Y S, Moxley J F, Stephanopoulos G (2005). Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli. Metab Eng, 7(3): 155–164 https://doi.org/10.1016/j.ymben.2004.12.003 pmid: 15885614
3	Plaimas K, Eils R, König R . Identifying essential genes in bacterial metabolic networks with machine learning methods. BMC systems biology. 2010 May 3;4(1):1
4	Chowdhury R, Chowdhury A, Maranas C D (2015). Using gene essentiality and synthetic lethality information to correct yeast and CHO cell genome-scale models. Metabolites, 5(4): 536–570 https://doi.org/10.3390/metabo5040536 pmid: 26426067
5	Becker S A, Palsson B Ø (2005). Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation. BMC Microbiol, 5(1): 8
6	Schilling C H , Covert M W , Famili I , Church G M , Edwards J S , Palsson B O (2002). Genome-scale metabolic model of Helicobacter pylori 26695. J Bacteriol, 184(16):4582–4593
7	Deutscher D, Meilijson I, Kupiec M , Ruppin E (2006). Multiple knockout analysis of genetic robustness in the yeast metabolic network. Nat Genet, 38(9): 993–998 https://doi.org/10.1038/ng1856 pmid: 16941010
8	Edwards J S, Ramakrishna R, Palsson B O (2001). Characterizing the metabolic phenotype: a phenotype phase plane analysis. Biotechnol Bioeng, 77(1):27–36 pmid: 11745171
9	Hamilton J J, Reed J L (2012). Identification of functional differences in metabolic networks using comparative genomics and constraint-based models. PLoS One, 7(4): e34670 https://doi.org/10.1371/journal.pone.0034670 pmid: 22666308
10	Heinemann M, Kummel A, Ruinatscha R , Panke S (2005). In silico genome-scale reconstruction and validation of the Staphylococcus aureus metabolic network. Biotechnol Bioeng, 92(7):850–64
11	Jamshidi N, Palsson B Ø (2007). Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets. BMC Syst Biol, 1(1): 26 https://doi.org/10.1186/1752-0509-1-26 pmid: 17555602
12	Schellenberger J, Que R, Fleming R M , Thiele I , Orth J D , Feist A M , Zielinski D C , Bordbar A , Lewis N E , Rahmanian S , Kang J, Hyduke D R, Palsson B Ø (2011). Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc, 6(9):1290–1307
13	Keating S M (2006). SBMLToolbox: an SBML toolbox for MATLAB users. Bioinformatics, 22(10):1275–1277
14	Masel J, Siegal M L (2010). Robustness: mechanisms and consequences. Trends Genet, 25(9):395–403
15	Raman K, Chandra N (2009). Flux balance analysis of biological systems: applications and challenges. Brief Bioinform, 10(4):435–449
16	Kauffman K J, Prakash P, Edwards J S (2003). Advances in flux balance analysis. Curr Opin Biotechnol, 14(5): 491–496 https://doi.org/10.1016/j.copbio.2003.08.001 pmid: 14580578
17	McClelland M, Sanderson K E, Spieth J, Clifton S W , Latreille P , Courtney L , Porwollik S , Ali J, Dante M, Du F , Hou S, Layman D, Leonard S , Nguyen C , Scott K , Holmes A , Grewal N , Mulvaney E , Ryan E, Sun H, Florea L , Miller W , Stoneking T , Nhan M, Waterston R, Wilson R K (2001). Complete genome sequence of Salmonella enterica serovar Typhimurium LT2. Nature, 413(6858):852–856
18	Nijman S M B (2011). Synthetic lethality: general principles, utility and detection using genetic screens in human cells. FEBS Lett, 585(1): 1–6 https://doi.org/10.1016/j.febslet.2010.11.024 pmid: 21094158
19	Pratapa A, Balachandran S, Raman K (2015). Fast-SL: an efficient algorithm to identify synthetic lethal sets in metabolic networks. Bioinformatics, 31(20): 3299–3305 https://doi.org/10.1093/bioinformatics/btv352 pmid: 26085504
20	Raghunathan A, Reed J, Shin S , Palsson B , Daefler S (2009). Constraint-based analysis of metabolic capacity of Salmonella typhimurium during host-pathogen interaction. BMC Syst Biol, 3(1): 38 https://doi.org/10.1186/1752-0509-3-38 pmid: 19356237
21	Raman K, Rajagopalan P, Chandra N (2005). Flux balance analysis of mycolic acid pathway: targets for anti-tubercular drugs. PLOS Comput Biol, 1(5): e46 https://doi.org/10.1371/journal.pcbi.0010046 pmid: 16261191
22	Suthers P F, Zomorrodi A, Maranas C D (2009). Genome-scale gene/reaction essentiality and synthetic lethality analysis. Mol Syst Biol, 5: 301 https://doi.org/10.1038/msb.2009.56 pmid: 19690570
23	Thiele I, Hyduke D R, Steeb B, Fankam G , Allen D K , Bazzani S , Charusanti P , Chen F C , Fleming R M , Hsiung C A , De Keersmaecker S C , Liao Y C , Marchal K , Mo M L , Özdemir E , Raghunathan A , Reed J L , Shin S I , Sigurbjörnsdóttir S , Steinmann J , Sudarsan S , Swainston N , Thijs I M , Zengler K , Palsson B O , Adkins J N , Bumann D (2011). A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2. BMC Syst Biol, 5: 8
24	Thiele I, Vo TD, Price ND , Palsson B Ø (2005). Expanded metabolic reconstruction of Helicobacter pylori (iIT341 GSM/GPR): an in silico genome-scale characterization of single- and double-deletion mutants. J Bacteriol, 187(16):5818–5830

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