Prediction of Transcriptional Patterns of Gene Deletion Mutants by Modified Control Effective Flux

Quanyu Zhao1 Hiroyuki Kurata1

qyzhao@bio.kyutech.ac.jp kurata@bio.kyutech.ac.jp

1 Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4, Kawazu, Iizuka, Fukuoka, 820-8502, Japan

Keywords: gene expression, metabolic network, gene deletion mutant, control effective flux 1 Introduction

Metabolic Pathway Analysis (MPA) is important tool in the field of metabolic engineering. The possible pathway from substrates to products, or internal loops could be analyzed by Elementary Mode (EM) or Extreme Pathways (EP). An application of EMs is to predict transcriptional patterns for carbon source perturbation conditions by Control Effective Fluxes (CEFs) [1]. In our previous study [2], CEF is revised to gene modification conditions, for example, gene deletion or enzyme over-expression. The published gene expression data will vary for different sources due to the technical or biological reasons. In this study, the prediction capability of modified CEF (mCEF) is compared with the experimental data of different sources for pgi [3], sucA [4] and pykF [5] mutants of E. coli. The selection of cellular function is also discussed. 2 Method and Results 2.1 Metabolic network The metabolic network of Escherichia coli was shown in previous study [2]. There are 53 reactions and 1455 EMs. Glucose is the solo carbon source. EMs were calculated by CellNetAnalyzer. All programs were performed in Matlab (The Mathworks Inc.). 2.2 Modified algorithm of Control-Effective Flux (mCEF) The efficiency of the j-th EM is defined by:

( ),

,

,

CELLOBJ j jmj CELLOBJ

i j ii

p EA

pε

η

⋅=

⋅∑ (1)

where jEA is the relative enzyme activity related to the i-th reaction of a mutant to wild type; ,CELLOBJ jp is

the normalized element of the reaction of cellular function in the j-th EM; pi,j is element of EM matrix; η is about the modifications of reactions. The mCEF for the mutant and wild type and defined as:

( ), ,

max,

1( )

mj CELLOBJ i j i

ji m

CELLOBJ CELLOBJ j CELLOBJj

pmCEF mut

p

ε η

ε

⋅ ⋅=

∑∑ ∑

(2)

, ,

max,

( )1

( )j CELLOBJ i j

ji

CELLOBJ CELLOBJ j CELLOBJj

pmCEF w

p

ε

ε

⋅=

∑∑ ∑

(3)

The CEF ratio for a mutant to wild type, the relative change in a gene expression profile of a mutant to wild type, is provided by:

( )( , )

( )i

ii

mCEF mutw mut

mCEF wΘ = (4)

The cellular functions are both biomass formation and ATP generation for predicting relative gene expression

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of mutant to wild type in this section. Details of the algorithm are shown in our previous study [2]. The predicted results for pgi, sucA and pykF mutants of E. coli are shown in Figure 1. The experimental data is different from those in previous study [2]. The coefficients of determination, R2, are all more than 0.60 except the omitted points. The slopes are from 0.79 to 0.96. It is shown mCEF is effective for these mutants.

Figure 1: Prediction of gene expression patterns for mutants of E. coli by mCEF.

Next, one single cellular function (biomass formation, ATP generation or acetate production) was used in

equation (2) and (3) for these mutants. The results are shown in Table 1. It is interested that the result obtained using biomass formation as the single cellular function is closed to those adopted both biomass formation and ATP maintenance as cellular function acetate is the best cellular function for pykF mutant and ATP drain is the best one for the sucA mutant.

Table 1: Slope and R2 for the correlation of experimental and predicted transcript ratios by mCEF

Mutant Acetate production Biomass formation ATP drain slope R2 slope R2 slope R2 pgi 0.9529 0.5620 0.7936 0.6148 1.0125 0.1995 sucA 0.9519 0.5388 0.9575 0.6472 0.9868 0.7021 pykF 0.9152 0.7794 0.8816 0.6967 0.8914 0.6074

3 Discussions In this study, mCEF was used to predict transcriptional patterns for gene modification mutants of E. coli. The experimental data is from different references. It is proved mCEF is a potential tool for the correlation of transcriptional patterns. The prediction capability of mCEF relies on the metabolic models and selection of cellular functions. The most suitable cellular functions in mCEF indicate the dominant mechanism in the biological process. The algorithm of mCEF will be improved in the further study. References

[1] Stelling, J., Klamt, S., Bettenbrock, K., Schuster, S. and Gilles, E.D., Metabolic network structure determines key aspects of functionality and regulation, Nature, 420(6912):190-193, 2002.

[2] Zhao, Q.Y. and Kurata, H., Genetic Modification of Flux (GMF) for Flux Prediction of Mutants, Bioinformatics, 25(13):1702-1708, 2009.

[3] Kabir, M.M. and Shimizu, K., Gene expression patterns for metabolic pathway in pgi knockout Escherichia coli with and without phb genes based on RT-PCR, Journal of Biotechnology, 105: 11-31, 2003

[4] Li, M., Ho, P.Y., Yao, S.J., Shimizu, K., Effect of sucA or sucC gene knockout on the metabolism in Escherichia coli based on gene expressions, enzyme activities, intracellular metabolite concentrations and metabolic fluxes by C-13-labeling experiments, Biochemical Engineering Journal, 30(3):286-296, 2006.

[5] Siddiquee, K.A., Arauzo-Bravo, M.J., Shimizu, K., Effect of a pyruvate kinase (pykF-gene) knockout mutation on the control of gene expression and metabolic fluxes in Escherichia coli, FEBS Letters, 235(1):25-33, 2004.

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