COMPONENT ANALYSIS
Z. Herke 1, T. Cserny 2, B. Magyar 3, Zs. I. Németh 1
1Institute for Chemistry, Faculty of Forestry, University of West Hungary, H-9400, 4 Bajcsy-Zs, Sopron, Hungary, herkezoli@emk.nyme.hu
2Hungarian Geological Society, H-1015, 12 Csalogány, Budapest, Hungary
3Elgoscar-2000 Ltd, H-1134, 1-3 Klapka, Budapest, Hungary
Introduction
Bioremediation technologies have been utilized expansively in the applications of environmental remediation for degradation of the hydrocarbons pollution in soil and in groundwater [1]. In every such a biotechnology system where enzymes are as biocatalysts the velocity of biochemical transformation are regulated by the activation and/or inhibition of enzyme reactions. Based on the dependency of biochemical reactions on the enzyme substrate and inhibitor concentrations as well as on the physical and chemical quality of the process agent, the speed of degradation can be optimized and the bioremediation efficiency can in turn be maximized. The presence of the enzyme inhibitor or activator is able to reveal the changes of the monotony of the decomposition. Having the degradation data se-ries of the pollutants correlated to each other, extra information can be obtained about the existence or lack of modifying the enzyme activities. The modification of the monotony in the kinetic data series is due to some modification of the enzyme activity. Applicability of correlation and principal component analysis is illustrated by some examples of assessments of both model data set and experi-mental results.
Materials and Methods
The concentration alteration of the substrates can be modelled as quasi kind of Michaelis-Menten reaction mechanism. Decreasing the amount of the com-pounds in time can be expressed by equation
(1)
, where S symbolizes the initial substrate, KM is the Michaelis constant of bioca-talysis, vmax is maximal reaction rate of the biodegradation. The kinetic curves of model substrates subjected (S”A” and S”B”) to model enzymatic decomposition
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are depicted in Figure 1.These kinetic results are involved the feasible mecha-nisms of various reversible inhibitions and enzyme activation during demolition of the model substrates. Different solutions of degradation model have also been generated by Scilab algorithms. A realistic noise (amplitude: 2.5 % of concentra-tion mean) was superposed to the data series of the substrate.
Figure 1. Kinetic curves of model substrate S”A” under inhibition (S2INH-S4INH) and activation (S5ACT-S7ACT) effect
The initial components (benzaldehyde, ethyl acetate, toluene, cyclohexanol, di-chloromethane) were possible environmental pollutant in the enzyme catalytic exper-iments. The enzyme product extracted from earthworth (Lumbricus rubellus) that is used in technological degradation of hydrocarbon pollutants. The decomposition of the components were investigated separately about each other with only the enzyme extract ‒ these were the kinetic data without inhibition or the reference ‒ simultane-ously in the interest of activation and inhibition effect the compounds were mixed with each other and with other compounds (e.g. NaCl, glutathione). To monitor the concentrations of the substrates, gas chromatograph-mass spectrometer (GC-MS) an-alytical technique (SHIMADZU GC-MS QP2010, AOC-5000 injector) was applied.
Results
Correlation analysis of kinetic curves for detection of the inhibition
Appearance of activation modifying effects can be made to be detected by assessing the correlations of kinetic data series belonging to different substrates, the linear re-gression parameters are changed significantly by the competitive inhibition of S”B”
model substrate. The inhibition effect increased the slopes of the regressions while they decreased the intercepts in accordance with the reference curve (Figure 2.).
Figure 2. Correlations between model substrate S”A” and S”B”
The regression of benzaldehyde and toluene experimental data series de-picted in Figure 3. „Reference” means data free from activity modifications and
“Inhibition” means the compounds was investigated in the presence of each other. The variation of the regression parameters are in accordance with the model re-sults, which suppose inhibition relationship between benzaldehyde and toluene.
Figure 3. Correlations between benzaldehyde and toluene
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Detection of enzymatic mechanisms by principal component analysis
The substrates taking part in various kinds of biodegradation process are specific points on the loadings plot of P1 and P2 components. The position of the reference substrate point (S”A”) and those of the others under inhibition or activation are shown in Figure 4.
Figure 4. Model substrate points under inhibition and activation effects The position of various substrates points under inhibition and activation ef-fects are depicted on Figure 5. The first principal component decreases in the case of inhibition effect (BAINH, EAINH) furthermore, it increases under activation effect (BAACT) and these substrate points are significantly farther from the ref-erence point (BAINERT, EAINERT).
Figure 5. Experimental substrate points under inhibition and activation effects
Conclusion
In this study alternative possibilities of the detection of various enzyme mech-anisms are represented both model and experimental results by the application of multivariate data analysis. Since the significant alteration of the monotony of degradation kinetic can derived from the appearances of inhibition and/or acti-vation mechanisms. The inhibition effects can be deduced from the correlations of the kinetic curves or from the regressions of correlated data. Significant de-viation of the regressions of the kinetic curves can be rendered by covariance analysis (ANCOVA). Principal component analysis (PCA) is also suitable for the detection of the effects of enzyme inhibition or activation. The substrates under some types of enzyme mechanism can separate their points on the loading plot of first two components (PC1, PC2) from the reference points which are not sub-jected to the effects altering the monotony of kinetic curve. The depicted experi-mental results on are in tune with the literature references [2, 3]. If the presented methods are used simultaneously then extra information will be obtained from the enzyme catalytic mechanisms, which can efficiently contribute to the success of the environmental strategies.
References
Alcalde M., Ferrer M., Plou J. F., Ballesteros A. (1999): Environmental biocatalysis: from remediation with enzymes to novel green processes, Trends in Biotechnology 24, pp. 281-287
Yeum S.H., Yoo Y. J. (1997): Overcoming the inhibition eff ects of metal ions in the degradation of benzene and toluene by Alcalygenes xylososidans Y234, Korean Journal of Chemisty Engineering 14 (3), pp. 204-208
van Iersel M. F. M., Eppink M. H. M., van Berkel W. J. H., Rombouts F. M., Abee T.
(1997): Purifi cation and Characterization of a Novel NADP-Dependent Branched-Chain Alcohol Dehydrogenase from Saccharomyces cerevisiae, Applied and Environmental Microbiology 63, pp. 4079-4082