biology experiment effect of temperature on enzyme activity

Practical: Investigating Temperature & Enzyme Activity ( Edexcel IGCSE Biology )

Revision note.

Biology Lead

Practical: Enzymes & Temperature

• Amylase is an enzyme that digests starch (a polysaccharide of glucose) into maltose (a disaccharide of glucose)
• Spotting tile
• Measuring cylinder
• Thermometer
• Starch solution
• Amylase solution
• Add 5cm 3 starch solution to a test tube and heat to a set temperature using beaker of water with a Bunsen burner
• Add a drop of Iodine to each of the wells of a spotting tile
• Use a syringe to add 2cm 3   a mylase to the starch solution and mix well
• Every minute, transfer a droplet of solution to a new well of iodine solution (which should turn blue-black)
• Repeat this transfer process until the iodine solution stops turning blue-black (this means the amylase has broken down all the starch )
• Record the time taken for the reaction to be completed
• Repeat the investigation for a range of temperatures (from 20°C to 60°C)

Investigating the effect of temperature on enzyme activity

Results and Analysis

• Amylase is an enzyme which breaks down starch
• The quicker the reaction is completed, the faster the enzyme is working
• This is because the enzyme is working at its fastest rate and has digested all the starch
• This is because the amylase enzyme is working slowly due to low kinetic energy and few collisions between the amylase and the starch
• This is because the amylase enzyme has become denatured and so can no longer bind with the starch or break it down

Limitations

• Note that there are several different ways in which the temperature could be controlled. The method described above is not very precise, an improvement would be to use water baths kept at each temperature
• The starch and amylase solutions that need to be used should be placed in a water bath and allowed to reach the temperature (using a thermometer to check) before being used
• A solution containing starch will be darker than a solution containing glucose (as a result of the colour change of iodine)
• The absorbance or transmission of light through the coloured solution can be measured using a colorimeter

Applying CORMS to practical work

• When working with practical investigations, remember to consider your CORMS evaluation

CORMS evaluation

• C - We are changing the temperature in each repeat
• O - This is not relevant to this investigation as we aren't using an organism
• R - We will repeat the investigation several times to make sure our results are reliable
• M1 - We will measure the time taken
• M2 - for the iodine to stop turning black
• S - We will control the concentration and volume of starch solution, iodine and amylase used in the investigation

Describing and explaining experimental results for enzyme experiments is a common type of exam question so make sure you understand what is happening and can relate this to changes in the active site of the enzyme when it has denatured, or if it is a low temperature , relate it to the amount of kinetic energy the molecules have.

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Lára graduated from Oxford University in Biological Sciences and has now been a science tutor working in the UK for several years. Lára has a particular interest in the area of infectious disease and epidemiology, and enjoys creating original educational materials that develop confidence and facilitate learning.

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• v.402(Pt 2); 2007 Mar 1

The dependence of enzyme activity on temperature: determination and validation of parameters

Michelle e. peterson.

*Department of Biological Sciences, University of Waikato, Private Bag 3105, Hamilton, 3240, New Zealand

Roy M. Daniel

Michael j. danson.

†Centre for Extremophile Research, Department of Biology and Biochemistry, University of Bath, Bath BA2 7AY, U.K.

Robert Eisenthal

‡Department of Biology and Biochemistry, University of Bath, Bath BA2 7AY, U.K.

Traditionally, the dependence of enzyme activity on temperature has been described by a model consisting of two processes: the catalytic reaction defined by Δ G Dagger cat , and irreversible inactivation defined by Δ G Dagger inact . However, such a model does not account for the observed temperature-dependent behaviour of enzymes, and a new model has been developed and validated. This model (the Equilibrium Model) describes a new mechanism by which enzymes lose activity at high temperatures, by including an inactive form of the enzyme (E inact ) that is in reversible equilibrium with the active form (E act ); it is the inactive form that undergoes irreversible thermal inactivation to the thermally denatured state. This equilibrium is described by an equilibrium constant whose temperature-dependence is characterized in terms of the enthalpy of the equilibrium, Δ H eq , and a new thermal parameter, T eq , which is the temperature at which the concentrations of E act and E inact are equal; T eq may therefore be regarded as the thermal equivalent of K m . Characterization of an enzyme with respect to its temperature-dependent behaviour must therefore include a determination of these intrinsic properties. The Equilibrium Model has major implications for enzymology, biotechnology and understanding the evolution of enzymes. The present study presents a new direct data-fitting method based on fitting progress curves directly to the Equilibrium Model, and assesses the robustness of this procedure and the effect of assay data on the accurate determination of T eq and its associated parameters. It also describes simpler experimental methods for their determination than have been previously available, including those required for the application of the Equilibrium Model to non-ideal enzyme reactions.

INTRODUCTION

The effect of temperature on enzyme activity has been described by two well-established thermal parameters: the Arrhenius activation energy, which describes the effect of temperature on the catalytic rate constant, k cat , and thermal stability, which describes the effect of temperature on the thermal inactivation rate constant, k inact . Anomalies arising from this description have been resolved by the development [ 1 ] and validation [ 2 ] of a new model (the Equilibrium Model) that more completely describes the effect of temperature on enzyme activity by including an additional mechanism by which enzyme activity decreases as the temperature is raised. In this model, the active form of the enzyme (E act ) is in reversible equilibrium with an inactive (but not denatured) form (E inact ), and it is the inactive form that undergoes irreversible thermal inactivation to the thermally denatured state (X):

Figure 1 shows the most obvious graphical effect of the Model, which is a temperature optimum ( T opt ) at zero time ( Figures 1 A and ​ and1B), 1 B), matching experimental observations [ 2 ]. In contrast, the ‘Classical Model’, which assumes a simple two-state equilibrium between an active and a thermally-denatured state (E act →X), and can be described in terms of only two parameters (the Arrhenius activation energy and the thermal stability), shows that when the data are plotted in three dimensions there is no T opt at zero time ( Figure 1 C).

( A ) Experimental data for alkaline phosphatase. The enzyme was assayed as described by Peterson et al. [ 2 ], and the data were smoothed as described here in the Experimental section; the data are plotted as rate (μM·s −1 ) against temperature (K) against time during assay (s). ( B ) The result of fitting the experimental data for alkaline phosphatase to the Equilibrium Model. Parameter values derived from this fitting are: Δ G ‡ cat , 57 kJ·mol −1 ; Δ G ‡ inact , 97 kJ·mol −1 ; Δ H eq , 86 kJ·mol −1 ; T eq , 333 K [ 2 ]. ( C ) The result of running a simulation of the Classical Model using the values of Δ G ‡ cat and Δ G ‡ inact derived from the fitting described above. The experimental data itself cannot be fitted to the Classical Model.

In addition to the obvious differences in the graphs representing the two models, it has been observed experimentally that at any temperature above the maximum enzyme activity, the loss of activity attributable to the shift in the E act /E inact equilibrium is very fast (<1 s) relative to the loss of activity due to thermal denaturation (shown in Figure 1 by the lines of rate against time) [ 2 ]. This and other evidence to date [ 2 ] suggest that the phenomenon described by the model (i.e. the E act /E inact equilibrium) arises from localized conformational changes rather than global changes in structure. However, the extent of the conformational change, and the extent to which it could be described as a partial unfolding, is not yet established.

The equilibrium between the active and inactive forms of the enzyme can be characterized in terms of the enthalpy of the equilibrium, Δ H eq , and a new thermal parameter, T eq , which is the temperature at which the concentrations of E act and E inact are equal; T eq can therefore be regarded as the thermal equivalent of K m . T eq has both fundamental and technological significance. It has important implications for our understanding of the effect of temperature on enzyme reactions within the cell and of enzyme evolution in response to temperature, and will possibly be a better expression of the effect of environmental temperature on the evolution of the enzyme than thermal stability. T eq thus provides an important new parameter for matching an enzyme's properties to its cellular and environmental function. T eq must also be considered in engineering enzymes for biotechnological applications at high temperatures [ 3 ]. Enzyme engineering is frequently directed at stabilizing enzymes against denaturation; however, raising thermal stability may not enhance high temperature activity if T eq remains unchanged.

The detection of the reversible enzyme inactivation, which forms the basis of the Equilibrium Model, requires careful acquisition and processing of assay data due to the number of conflicting influences that arise when increasing the temperature of an enzyme assay. Determination of T eq to date has used continuous assays, because this method produces progress curves directly and obviates the need to perform separate activity and stability experiments, and has utilized enzymes whose reactions are essentially irreversible (far from reaction equilibrium), do not show any substrate or product inhibition and remain saturated with substrate throughout the assay. However, there are a large number of enzymes that do not fit these criteria, narrowing the potential utility of determining T eq . The present paper describes methods for the reliable determination of T eq under ideal or non-ideal enzyme reaction conditions, using either continuous or discontinuous assays, and outlines the assay data required for accurate determination of T eq and the thermodynamic constants (Δ G ‡ cat , Δ G ‡ inact and Δ H eq ) associated with the model [ 2 ]; it also introduces a method of fitting progress curves directly to the Equilibrium Model and determines the robustness of the data-fitting procedures. The results show directly how the Equilibrium Model parameters are affected by the data.

The methods described in the present paper allow the determination of the new parameters Δ H eq and T eq , required for any description of the way in which temperature affects enzyme activity. In addition, they facilitate the straightforward and simultaneous determination of Δ G ‡ cat and Δ G ‡ inact under relatively physiological conditions. They therefore have the potential to be of considerable value in the pure and applied study of enzymes.

EXPERIMENTAL

Aryl-acylamidase (aryl-acylamide amidohydrolyase; EC 3.5.1.13) from Pseudomonas fluorescens , β-lactamase (β-lactamhydrolase; EC 3.5.2.6) from Bacillus cereus and p NPP ( p -nitrophenylphosphate) were purchased from Sigma–Aldrich. p NAA ( p -nitroacetanilide) was obtained from Merck, wheat germ acid phosphatase [orthophosphoric-monoester phosphohydrolase (acid optimum); EC 3.1.3.2] from Serva Electrophoresis and nitrocefin from Oxoid. All other chemicals used were of analytical grade.

Instrumentation

All enzymic activities were measured using a Thermospectronic™ Helios γ-spectrophotometer equipped with a Thermospectronic™ single-cell Peltier-effect cuvette holder. This system was networked to a computer installed with Vision32™ (version 1.25, Unicam) software including the Vision Enhanced Rate program capable of recording absorbance changes over time intervals down to 0.125 s.

Temperature control

The temperature of each assay was recorded directly, using a Cole-Parmer Digi-Sense® thermocouple thermometer accurate to ±0.1% of the reading and calibrated using a Cole–Parmer NIST (National Institute of Standards and Technology)-traceable high-resolution glass thermometer. The temperature probe was placed inside the cuvette adjacent to the light path during temperature equilibration before the initiation of the reaction and again immediately after completion of each enzyme reaction. Measurements of temperature were also taken at the top and bottom of the cuvette to check for temperature gradients. Where the temperature measured before and after the reaction differed by more than 0.1 °C, the reaction was repeated.

Assay conditions

Assays at high temperature (and over any wide temperature range) can sometimes pose special problems and may need additional care [ 4 – 6 ]. Quartz cuvettes were used in all experiments for their relatively quick temperature equilibration and heat-retaining capacity. Where required, a plastic cap was fitted to the cuvette to prevent loss of solvent due to evaporation (at higher temperatures), or a constant stream of a dry inert gas (e.g. nitrogen) was blown across the cuvette to prevent condensation at temperatures below ambient. Buffers were adjusted to the appropriate pH value at the assay temperature, using a combination electrode calibrated at this temperature. Where very low concentrations of enzyme were used, salts or low concentrations of non-ionic detergents were added to prevent loss of protein to the walls of the cuvette.

Substrate concentrations were maintained at not less than 10 times the K m to ensure that the enzyme remained saturated with substrate for the assay duration. Where these concentrations could not be maintained (e.g. because of substrate solubility), tests were conducted to confirm that there was no decrease in rate over the assay period arising from substrate depletion. In addition, K m values over the full temperature range examined were determined. Since K m values for enzymes tend to rise with temperature [ 7 , 8 ], in some cases dramatically, this is particularly important. Any decrease in rate at higher temperatures that is caused by an increase in K m at higher temperatures is a potential source of large errors.

Assay reactions were initiated by the rapid addition of a few microlitres of chilled enzyme, so that the addition had no significant effect on the temperature of the solution inside the cuvette.

Enzyme assays

Aryl-acylamidase activity was measured by following the increase in absorbance at 382 nm (ϵ 382 =18.4 mM −1 ·cm −1 ) corresponding to the release of p -nitroaniline from the p NAA substrate [ 9 ]. Reaction mixtures contained 0.1 M Tris/HCl, pH 8.6, 0.75 mM p NAA and 0.003 units of enzyme. One unit is defined as the amount of enzyme required to catalyse the hydrolysis of 1 μmol of p NAA per min at 37 °C.

Acid phosphatase activity was measured discontinuously using p NPP as substrate [ 10 ]. Reaction mixtures (1 ml) contained 0.1 M sodium acetate, pH 5.0, 10 mM p NPP and 8 μ-units of enzyme. The assay was stopped using 0.5 ml of 1 M NaOH. The amount of p -nitrophenol released was measured at 410 nm (ϵ 410 =18.4 mM −1 ·cm −1 ). One unit is defined as the amount of enzyme that hydrolyses 1 μmol of p NPP to p -nitrophenol per min at 37 °C.

β-Lactamase activity was measured by following the increase in absorbance at 485 nm (ϵ 485 =20.5 mM −1 ·cm −1 ) associated with the hydrolysis of the β-lactam ring of nitrocefin [ 11 ]. Reaction mixtures contained 0.05 M sodium phosphate, pH 7.0, 1 mM EDTA, 0.1 mM nitrocefin and 0.003 units of enzyme. One unit is defined as the amount of enzyme that will hydrolyse the β-lactam ring of 1 μmol of cephalosporin per min at 25 °C.

Protein determination

Protein concentrations claimed by the manufacturers (determined by Biuret) were checked using the far-UV method of Scopes [ 12 ].

Data capture and analysis

For each enzyme, reaction-progress curves at a variety of temperatures were collected; the time interval was set so that an absorbance reading was collected every 1 s. Three progress curves were collected at each temperature; where the slope for these triplicates deviated by more than 10%, the reactions were repeated.

When required, the initial (zero time) rate of reaction for each assay triplicate was determined using the linear search function in the Vision32™ rate program.

Although earlier determinations of Δ G ‡ cat , Δ G ‡ inact , Δ H eq and T eq used initial parameter estimates derived from the calculation of rates from progress curves (described in [ 2 ]), more recent analysis of results indicates that the method described below is simpler and equally accurate.

Using the values for Δ G ‡ cat (80 kJ·mol −1 ), Δ G ‡ inact (95 kJ·mol −1 ), Δ H eq (100 kJ·mol −1 ) and T eq (320 K) described in the original paper [ 1 ] as initial parameter estimates (deemed to be ‘typical’ or ‘plausible’ values for each of the parameters) and the concentration of protein in each assay (expressed in mol·l −1 ), the experimental data were fitted to the Equilibrium Model using MicroMath® Scientist® for Windows software (version 2.01, MicroMath Scientific Software).

The values for each parameter were first ‘improved’ by Simplex searching [ 13 , 14 ]. The experimental data were then fitted to the Equilibrium Model using the parameters derived from the Simplex search, employing an iterative non-linear minimization of least squares. This minimization utilizes Powell's algorithm [ 15 ] to find a local minimum, possibly a global minimum, of the sum of squared deviations between the experimental data and the model calculations.

In each case, the fitting routine was set to take minimum and maximum iterative step-sizes of 1×10 −12 and 1 respectively. The sum of squares goal (the termination criterion for the fitting routine) was set to 1×10 −12 .

The S.D. values in the Tables refer to the fit of the data to the model. On the basis of the variation between the individual triplicate rates from which the parameters are derived for all the enzymes we have assayed so far, we find that the experimental errors in the determination of Δ G ‡ cat , Δ G ‡ inact and T eq are less than 0.5%, and less than 6% in the determination of Δ H eq .

A stand-alone Matlab® [version 7.1.0.246 (R14) Service Pack 3; Mathworks] application, enabling the facile derivation of the Equilibrium Model parameters from a Microsoft® Office Excel file of experimental progress curves (product concentration against time) can be obtained on CD from R.M.D. This application is suitable for computers running Microsoft® Windows XP, and is for non-commercial research purposes only.

RESULTS AND DISCUSSION

The Equilibrium Model has four data inputs: enzyme concentration, temperature, concentration of product and time. From the last two, an estimate of the rate of reaction (in M·s −1 ) can be obtained. In describing the effect of temperature on catalytic activity, the rate of the catalytic reaction is the measurement of interest. The quantitative expression of the dependence of rate on temperature, T , and time, t , is given by eqn (1) :

where k B is Boltzmann's constant and h is Planck's constant. This is the expression that we have used in our proposal [ 1 ] and validation [ 2 ] of the Equilibrium Model to date. Experimentally, however, rates are rarely measured directly; rather, product concentration is determined at increasing times, either by continuous or discontinuous assay, giving a series of progress curves. The quantitative expression relating the product concentration, time and temperature for the Equilibrium Model can be obtained by integrating eqn (1) , giving eqn (2) :

We find that data processed as enzyme rates using eqn (1) , or as product concentration changes using eqn (2) , give essentially the same results. However, since eqn (2) involves a more direct measurement, the experimental protocol used in the present study involves measuring progress curves of product concentration against time at different temperatures and fitting these data to eqn (2) .

Robustness of the fitted constants

If the enzyme preparation used in the determination of T eq is not pure, then overestimation of the enzyme concentration is likely. Few methods of determining protein concentration give answers that are correct in absolute terms; apart from any limitations in terms of sensitivity and interferences, most are based on a comparison with a standard of uncertain equivalence to the enzyme under investigation. The determination of enzyme concentration is thus a potential source of error.

To determine how dependent the fitted constants are on the accuracy of the enzyme concentration, data for β-lactamase [ 2 ] were fitted against the experimentally determined progress curves with the enzyme concentration reduced 2-, 5- and 10-fold compared with that determined experimentally ( Table 1 ). It is evident that errors in determining enzyme concentration have little effect upon parameter determination, except, of course, in respect of Δ G ‡ cat , which is reduced as the model attempts to relate the reduced enzyme concentration to the observed rates of reaction. Even with changing the enzyme concentration 5-fold, errors in the values for Δ G ‡ inact , Δ H eq and T eq are small.

The experimental data for β-lactamase were used to generate the Equilibrium Model parameters as described in the Experimental section. Changes were then made to the experimentally determined enzyme concentration to determine the dependence of the fitted constants on the accuracy of the protein concentration. Parameter values are ±S.D.

Data sampling requirements: sampling rate

The increasing need for automation in enzyme assays has led to the development of instruments that use sampling techniques to assay enzymes at different times. Additionally, some assays are difficult to carry out continuously. It is therefore important to know whether the fitting procedures described herein are sufficiently robust to deal with discontinuous data collection. To determine the sampling requirement, progress curves for the reaction catalysed by aryl-acylamidase were collected in triplicate at 1 s intervals over a 25 min period at a variety of temperatures. Progress curves were then manipulated by the successive removal of a proportion of the data points to determine the effect of sampling rate on the fitting of the data to the Equilibrium Model and on the resulting parameters ( Table 2 ). Using the 1 s sampling interval as a reference, the absolute values of Δ G ‡ cat , Δ G ‡ inact , Δ H eq and T eq are essentially the same at all sampling rates (up to a 150 s interval), despite the increase in the S.D. values as the sampling interval increases. The results indicate that discontinuous enzyme assays can be used for the determination of T eq . The minimum number of points per progress curve required to give accurate values for the parameters will depend upon the length of the assay and the curvature of the progress curve, but, as expected, the larger number of data points arising from continuous assays give more accurate results. The results also show that accuracy is not dominated by a requirement for ‘early’ data, taken very soon after zero time, and that the S.D. provides a good guide to the accuracy of the parameters.

Progress curves for aryl-acylamidase, collected over 25 min and at ten different temperatures, were used to generate the Equilibrium Model parameters as described in the Experimental section. Experimental data points were then successively removed to give the effect of reduced frequency of data points to determine the effect of various sampling rates on the final parameter values. Parameter values are means±S.D.

The results presented above imply that the parameters can be obtained accurately from as few as ten data points (sampling only every 150 s in the case of the 1500 s aryl-acylamidase assays). We would expect the ‘data sampling’ shown in Table 2 to be a satisfactory proxy for a discontinuous assay. However, this was confirmed using another enzyme. Acid phosphatase was incubated with the substrate p NPP for a total assay duration of 30 min, and the reaction was sampled in triplicate every 60 s, stopped with NaOH, and the absorbance was read at 410 nm. Three progress curves (absorbance against time) at each temperature were generated from the triplicate absorbance values obtained when the reaction was stopped. Product concentrations (expressed in mol·l −1 ) were then calculated for each absorbance reading, and the data set was fitted to the Equilibrium Model as described previously and compared with data obtained in a continuous assay [ 2 ]. Taking experimental error into account, the parameter values generated from fitting these data ( Table 3 ) indicate no significant difference between the two methods, except in the case of Δ G ‡ inact . The increased value of the errors on each parameter determined using the discontinuous data indicate that, as expected, continuous assays give more accurate results.

Acid phosphatase was assayed discontinuously over a period of 30 min with a sampling rate of 60 s and at 5 °C intervals from 20 to 80 °C (13 temperature points). The results of fitting data for the same enzyme over the same temperature range and using the same intervals, but using a continuous assay (effective sampling rate of 1 s) have been included for comparison [ 2 ]. The progress curves generated for both methods were fitted to the Equilibrium Model and the parameters generated as described in the Experimental section. Parameter values are means±S.D.

Data sampling requirements: temperature range

Progress curves at 12 temperatures were collected for acid phosphatase [ 2 ]. Analysis of the initial rate of reaction (i.e. at zero time) shows three points above the temperature at which maximum product is formed ( Figure 2 ). By sequentially truncating the data set from the highest or the lowest temperature point and re-fitting the resulting data sets, we gain some insight into the dependence of the fitting routine and accurate estimation of the parameters on the data points above and below the T opt ( Table 4 ).

Acid phosphatase was assayed continuously as described by Peterson et al. [ 2 ]. For each triplicate progress curve, the initial rate of reaction was determined using the linear search function in the programme, Vision32™. The data are plotted as rate (μM·s −1 ) against temperature (K).

A full set of experimental data for acid phosphatase was used to generate the Equilibrium Model parameters as described in the Experimental section. Temperature points above the T opt ( Figure 2 ) were sequentially truncated from the complete data set to determine the influence of data points above the T opt on the final parameter values. Temperature points were also sequentially truncated from the lowest temperature point to the highest from the complete data set (12 temperature points) to determine how many points, in total, below the T opt are required for the accurate determination of T eq and the other thermodynamic parameters. In this case, each data set included all temperature points above the T opt . Parameter values are means±S.D.

For data truncated from the highest temperature point, the values of Δ G ‡ cat , Δ G ‡ inact and T eq do not vary greatly with the various data treatments. However, for the fit excluding the last three temperature points, there is a substantial loss in accuracy for the Equilibrium Model parameter, Δ H eq . This difference is not reflected in the S.D. values. Figure 3 , which illustrates the differences in each fitting of the truncated data sets to the Equilibrium Model presented as a three-dimensional plot of rate (μM·s −1 ) against temperature (K) against time (s), shows the reason for this. The plots indicate that when only one or two data points are removed, there is little difference in the shape of the plot when the data are simulated in three dimensions, but without a data point above the T opt , the equilibrium model effectively relapses towards the Classical Model ( Figure 1 ), with a sharp decline in Δ H eq , even though a reasonable value for T eq has been obtained. These results suggest that it is possible to obtain acceptable estimates of the parameters with only one temperature point above the T opt .

Acid phosphatase was assayed as described by Peterson et al. [ 2 ]. Temperature points above the T opt (see Figure 2 ) were sequentially truncated from the complete data set to determine the influence of data points above the T opt on the final parameter values. Illustrated here are the results plotted as rate (μM·s −1 ) against temperature (K) against time (s) for the fit of acid phosphatase data to the Equilibrium Model using ( A ) the full data set, ( B ) the data set excluding the last data point, ( C ) the data set excluding the last two data points, and ( D ) the data set excluding the last three data points.

All the foregoing discussion is based on an ab initio presumption that the temperature-dependence of enzyme activity is described by the Equilibrium Model. Of the 50 or so enzymes studied in detail by us, all follow the model. However, data that do not show clear evidence of a T opt when initial (zero time) rates are plotted against temperature may in fact be fitted equally well by the simpler Classical Model. In this situation, it would be foolhardy to carry out the procedure described in the present paper. It must therefore be stressed that if only one or two points above the T opt are determined, the measured initial rates at those temperatures must be sufficiently lower than that at T opt for the assumption of the Equilibrium Model to be justified. Ideally, two or more rate measurements above T opt showing a clear trend of falling rates should be obtained to apply the Equilibrium Model with confidence.

For data sequentially truncated from the lowest temperature point to the highest, a trend in the parameter values was seen ( Table 4 ). Parameter values were maintained close to the ‘complete’ data set level down to eight points; below eight points, values moved outside the S.D. limits, but were still relatively close in all cases.

The results of this data manipulation suggest that data at eight temperatures with two points above T opt (showing a clear downwards trend) are sufficient to yield parameter values for Δ G ‡ cat , Δ G ‡ inact , Δ H eq and T eq with reasonable precision.

Enzymes operating under ‘non-ideal’ conditions: the use of initial rates

To use data from progress curves collected over extended periods of time for valid fitting to the Equilibrium Model requires that any decrease in activity observed is due solely to thermal factors and not to some other process. This means that the enzyme and its reaction be ‘ideal’; that is, the enzyme is not product inhibited, the reaction is essentially irreversible and the enzyme operates at V max for the entire assay. To date, the enzymes that we have fitted to the Equilibrium Model have been chosen to meet, or come very close to meeting, these criteria over the 3–5 min duration of the assay.

However, many enzyme reactions are necessarily assayed under non-ideal conditions. For example, the reaction may be sufficiently reversible that the back reaction contributes to the observed rate during the assay and/or the products of the reaction may be inhibitors of the enzyme. Application of the Equilibrium Model to these non-ideal enzyme reactions can usually be achieved by restricting assays to the initial rate of reaction. Setting t =0 in eqn (1) gives eqn (3) below. Using this, it is possible to fit the experimental data for zero time (i.e. initial rates) to the Equilibrium Model to determine Δ G ‡ cat , Δ H eq and T eq , although the time-dependent thermal denaturation parameter, Δ G ‡ inact , cannot be determined. At t =0,

Another circumstance where ‘non-ideality’ may occur is when the decrease of rate during the assay is partially due to substrate depletion. If the enzyme is saturated at the start of the assay, lowering the enzyme concentration or increasing the sensitivity of the assay may remove this problem. In either case, using initial rates will allow the equilibrium model to be applied. However, if insufficient substrate is present at zero time to saturate the enzyme, either because of, e.g., solubility limitations, or as a result of increases in K m [ 7 , 8 ] as the temperature is altered, then considerable errors may arise. Even here, it may be possible, if the K m is known at each temperature, to obtain reasonable approximations of the initial rates at saturation by calculating the degree of saturation using the relationship v / V max =S/( K m +S), and applying the appropriate corrections.

To simulate the determination of the Equilibrium Model parameters for an enzyme that operates under non-ideal conditions, initial rates of reaction were calculated from each progress curve in the β-lactamase data set [ 2 ] and fitted to the modified zero-time version of the Equilibrium Model using the Scientist® software ( Table 5 ). No significant differences in any of the parameters determined this way were found, suggesting that this manner of determination is potentially as accurate as fitting the entire time course to the Equilibrium Model for the determination of Δ G ‡ cat , Δ H eq and T eq .

To simulate the determination of the Equilibrium Model parameters for an enzyme that operates under non-ideal conditions, initial rates of reaction were calculated from each progress curve in the β-lactamase data set [ 2 ] using the linear search function in the programme, Vision32™, and fitted to the Equilibrium Model via eqn (3) . Parameters calculated for the complete data set (entire time course) have been included for comparison [ 2 ]. Parameter values are means±S.D.

Conclusions

To date, determination of the parameters associated with the Equilibrium Model for individual enzymes has involved continuous assays with collection of data at 1 s intervals over 5 min periods at 2–3 °C temperature intervals over at least a 40 °C range, with each temperature run being carried out in triplicate; i.e. processing approx. 15000 data points gathered in approx. 50 experimental runs [ 2 ]. Using a simple technique of fitting the raw data (product concentration against time) to the Equilibrium Model, we have shown that data collection (and thus labour) can be reduced considerably without compromising the accuracy of the derived parameters. Accurate results require preferably more than one data point taken above T opt and more than eight temperature points in total. Major errors in enzyme determination affect only the determination of Δ G ‡ cat . Although continuous assays will give the most accurate results, Δ G ‡ cat , Δ G ‡ inact , Δ H eq and T eq can be determined accurately using discontinuous assays. Among other things, this will allow the determination of the parameters of enzymes from extreme thermophiles; since T opt for these enzymes may be above 100 °C, and since few continuous assay methods are practical at such temperatures, most such assays will have to be discontinuous [ 4 ]. Finally, we have demonstrated that the use of initial, zero-time rates enables the ready determination of the Equilibrium Model parameters (except Δ G ‡ inact ) of most non-ideal enzyme reactions.

The method described here enables the determination of the new fundamental enzyme thermal parameters arising from the Equilibrium Model. It should be noted that the Equilibrium Model itself enables an accurate description of the effect of temperature on enzyme activity, but does not purport to describe the molecular basis of this behaviour. Evidence so far ([ 2 ], and M. E. Peterson, C. K. Lee, C. Monk and R. M. Daniel, unpublished work) suggests that the conformational changes between the active and inactive forms of the enzyme described by the model are local rather than global, and possibly quite slight. The model, and the work described here, provides the foundation, and one of the tools needed to determine the molecular basis of these newly described properties of enzymes. The focus of future work must now be to apply the appropriate physicochemical techniques to determine the precise nature of this proposed structural change.

Acknowledgments

This work was supported by the Royal Society of New Zealand's International Science and Technology Linkages Fund, and the Marsden Fund.

The effect of temperature on enzyme activity: new insights and their implications

• Published: 13 September 2007
• Volume 12 , pages 51–59, ( 2008 )

Cite this article

• Roy M. Daniel 1 ,
• Michael J. Danson 2 ,
• Robert Eisenthal 3 ,
• Charles K. Lee 1 &
• Michelle E. Peterson 1  

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The two established thermal properties of enzymes are their activation energy and their thermal stability. Arising from careful measurements of the thermal behaviour of enzymes, a new model, the Equilibrium Model, has been developed to explain more fully the effects of temperature on enzymes. The model describes the effect of temperature on enzyme activity in terms of a rapidly reversible active-inactive transition, in addition to an irreversible thermal inactivation. Two new thermal parameters, T eq and Δ H eq , describe the active–inactive transition, and enable a complete description of the effect of temperature on enzyme activity. We review here the Model itself, methods for the determination of T eq and Δ H eq , and the implications of the Model for the environmental adaptation and evolution of enzymes, and for biotechnology.

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Acknowledgements

We thank the Royal Society of New Zealand’s Marsden Fund and the National Science Foundation (Biocomplexity 0120648) for financial support.

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Department of Biological Sciences, University of Waikato, Private Bag 3105, Hamilton, 3240, New Zealand

Roy M. Daniel, Charles K. Lee & Michelle E. Peterson

Centre for Extremophile Research, Department of Biology and Biochemistry, University of Bath, Bath, BA2 7AY, UK

Michael J. Danson

Department of Biology and Biochemistry, University of Bath, Bath, BA2 7AY, UK

Robert Eisenthal

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Correspondence to Roy M. Daniel .

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Daniel, R.M., Danson, M.J., Eisenthal, R. et al. The effect of temperature on enzyme activity: new insights and their implications. Extremophiles 12 , 51–59 (2008). https://doi.org/10.1007/s00792-007-0089-7

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Published : 13 September 2007

Issue Date : January 2008

DOI : https://doi.org/10.1007/s00792-007-0089-7

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The Effects of Temperature and pH on Enzymatic Activity

Learning Objectives

After completing the lab, the student will be able to:

• Measure enzymatic activity of the enzyme lactase over time and represent it graphically.
• Monitor the effects of environmental conditions on enzymatic activity.

Activity 2: Pre-Assessment

• Which environmental conditions could alter the rate at which an enzymatic reaction takes place? Why would this occur?
• Which environmental conditions could affect an enzyme’s active site? Why would this occur?
• Discuss the answers to questions 1 and 2 with the class.

Activity 2: The Effects of Temperature and pH on Enzymatic Activity

What types of environmental factors may affect enzymatic activity? Why? Several factors known to affect enzymatic activity are temperature, pH, and substrate concentration. In a typical chemical reaction, increasing temperature causes the substrates to become more energetic and hence more likely to bump into each other in solution. However, changes in temperature can cause an enzyme to denature , which changes the three-dimensional structure of the enzyme molecule. In addition, cellular enzymes each work within a certain pH range because the side chains within their active sites are optimized for efficient catalysis and are thus quite sensitive to changes in pH. Different enzymes may have different pH ranges and pH optima , conditions under which they work maximally; while many enzymes work best around a neutral pH, some are adapted to an acidic pH, while others are adapted to a basic pH.

Safety Precautions

• Goggles should be worn at all times while in laboratory.
• No open-toe shoes worn in laboratory.
• Measure fluids carefully using graduated cylinders to avoid breakage and spillage.
• Be careful not to touch solutions of concentrated acids and bases directly.
• Take precautions when using a hot plate and touching hot glassware.
• Inform your teacher immediately of any broken glassware as it could cause injuries.
• Clean up any spilled fluids to prevent other people from slipping.

For this activity, you will need the following:

• Graduated cylinder
• Buffer solutions of pH 3.0, pH 4.0, pH 5.0, pH 6.0, pH 7.0, pH 8.0, and pH 9.0
• Lactase (obtained from laboratory supply company)
• Stirring rod
• Labeling pencil
• Refrigerator set to approximately 4 °C
• Incubator set to 37 °C
• Thermometers
• Glucose test strips
• Graph paper

For this activity, you will work in pairs .

Structured Inquiry: Temperature

Step 1: Prepare a large beaker of boiling tap water on a hot plate. Prepare five identical test tubes, each containing 2 mL of milk. Label five test tubes accordingly with each of the following temperatures: 0°C (ice bath), 4°C (refrigerator), room temperature, 37°C, and 100°C (boiling temperature). Place one tube of milk at each of the five temperatures. Create a data table to enter your results for each of these test tubes over time. Measure the room temperature using a thermometer.

Step 2: Hypothesize/Predict: Based upon your knowledge of enzymes and the effects of temperature on their activity, rank the tubes from fastest (1) to slowest (5) glucose production predicted over time after the addition of lactase. Add your predictions to the data table you created in step 1.

Step 3: Student-led Planning: Discuss with your partner how you could use the data you collect to calculate a rate of lactase activity for each temperature.

Step 4: Make your lactase enzyme solution per your teacher’s instructions. Add 1 mL of the lactase enzyme solution to each of the five tubes listed above and immediately start timing. Immediately after adding the lactase enzyme solution, determine the glucose concentration in each tube using glucose test strips and the color chart that came with the test strips. Record this in your data table.

Monitor the temperatures of each of these locations, both before and after the experiment using thermometers.

Step 5: Every 3 minutes for 15 minutes, record the concentrations of glucose in each tube using the color chart that came with the test strips and record in your data table.

Step 6: Critical Analysis: Calculate the rate of enzymatic activity for lactase at each temperature using the method you devised in step 3. Using graph paper, graph your data of rates of lactase activity versus temperature. Which is the independent variable? Which is the dependent variable? Are the predictions you made in step 2 supported by your data? Explain how you know in your notebook.

Guided Inquiry: Temperature

Step 1: Hypothesize/Predict: Based on the data already collected, predict a temperature range that includes the optimal temperature for lactase activity. How do you think you could more finely pinpoint the optimal temperature for lactase activity? Write your ideas in your notebook.

Step 2: Student-led Planning: Determine how you could change the set-up of your test tubes to determine the optimal temperature for lactase activity. Once your teacher approves, create a table to record your data, prepare your test tubes per your design, and record data on glucose production every 3 minutes for 15 minutes. Determine the rates of lactase enzyme activity under each of your chosen conditions as you did in the Structured Inquiry. Graph the rates of lactase enzyme activity versus temperature and estimate the optimal temperature.

Step 3: Critical Analysis: Are the predictions you made in step 1 supported by your data? Is there any way you can improve your experiment? Discuss your answer with your lab partner and write it in your notebook.

Structured Inquiry: pH

Step 1: Prepare three test tubes, each containing 2 mL of milk, and label the three tubes as follows: 4.0 (acidic), 7.0 (neutral), and 9.0 (basic). To the first test tube, add 1 mL of pH 4.0 buffer. To the second test tube, add 1 mL of pH 7.0 buffer. To the third test tube, add 1 mL of pH 9.0 buffer. Create a data table to enter your results for each of these test tubes over time.

Step 2: Hypothesize/Predict: Based upon your knowledge of enzymes and the effects of pH on their activity, order the tubes from highest (1) to lowest (3) glucose production predicted over time. Add your predictions to the data table you created in step 1.

Step 3: Student-led Planning: Discuss with your partner how to calculate a rate of lactase activity for each pH.

Step 4: Make your lactase enzyme solution per your teacher’s instructions. Add 1 mL of the lactase enzyme solution to each of the three tubes listed above and immediately start timing. Immediately after adding the lactase enzyme solution, determine the glucose concentration in each tube using glucose test strips and the color chart that came with the test strips. Record this in your data table.

Step 6: Critical Analysis: Calculate the rate of enzymatic activity for lactase at each pH. Using graph paper, graph your data of rates of lactase activity versus pH. Which is the independent variable? Which is the dependent variable? Are the predictions you made in step 2 supported by your data? Explain how you know in your notebook.

Guided Inquiry: pH

Step 1: Hypothesize/Predict: Based on the data already collected, predict a pH range that includes the optimal pH for lactase activity. How do you think you could more finely pinpoint the optimal pH for lactase activity? Write your ideas in your notebook.

Step 2: Student-led Planning: Determine how you would change the set-up of your test tubes to determine the optimal pH for lactase activity. Once your teacher approves, create a table to record your data, prepare your test tubes per your design, and record data on glucose production every 3 minutes for 15 minutes. Determine the rates of lactase enzyme activity under each of your chosen conditions. Graph the rates of lactase enzyme activity versus pH and estimate the optimal pH.

Step 3: Critical Analysis: Are the predictions you made in step 1 supported by your data? Is there any way you could improve your experiment? Discuss your answer with your lab partner and write it in your notebook.

Assessments

• If an enzyme has an optimal activity at 25°C, what do you think will happen to the enzyme’s activity if the temperature is raised to 37°C? Why?
• If an enzyme has a largely acidic active site, what do you think will happen to the enzyme’s activity if the pH is made basic? Why?

Lab Manual for Biology Part I Copyright © 2022 by LOUIS: The Louisiana Library Network is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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The effect of temperature on enzyme activity: new insights and their implications

Affiliation.

• 1 Department of Biological Sciences, University of Waikato, Private Bag 3105, Hamilton 3240, New Zealand. [email protected]
• PMID: 17849082
• DOI: 10.1007/s00792-007-0089-7

The two established thermal properties of enzymes are their activation energy and their thermal stability. Arising from careful measurements of the thermal behaviour of enzymes, a new model, the Equilibrium Model, has been developed to explain more fully the effects of temperature on enzymes. The model describes the effect of temperature on enzyme activity in terms of a rapidly reversible active-inactive transition, in addition to an irreversible thermal inactivation. Two new thermal parameters, Teq and Delta Heq, describe the active-inactive transition, and enable a complete description of the effect of temperature on enzyme activity. We review here the Model itself, methods for the determination of Teq and Delta Heq, and the implications of the Model for the environmental adaptation and evolution of enzymes, and for biotechnology.

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Practical Biology

A collection of experiments that demonstrate biological concepts and processes.

Observing earthworm locomotion

Practical Work for Learning

Published experiments

Factors affecting enzyme activity.

Enzymes are sophisticated catalysts for biological processes. These practicals (and the practicals at intermediate level) give you opportunities to explore how enzyme activity changes in different conditions.

Enzyme experiments often provide real ‘messy’ data, because their activity can change dramatically from one lesson to the next.

Experiments

• Investigating the effect of pH on amylase activity
• Microscale investigations of catalase activity in plant extracts
• Investigating effect of temperature on the activity of lipase
• Investigating an enzyme-controlled reaction: catalase and hydrogen peroxide concentration
• Investigating effect of concentration on the activity of trypsin

Optional Lab Activities

Lab objectives.

At the conclusion of the lab, the student should be able to:

• define the following terms: metabolism, reactant, product, substrate, enzyme, denature
• describe what the active site of an enzyme is (be sure to include information regarding the relationship of the active site to the substrate)
• describe the specific action of the enzyme catalase, include the substrate and products of the reaction
• list what organelle catalase can be found in every plant or animal cell
• list the factors that can affect the rate of a chemical reaction and enzyme activity
• explain why enzymes have an optimal pH and temperature to ensure greatest activity (greatest functioning) of the enzyme (be sure to consider how virtually all enzymes are proteins and the impact that temperature and pH may have on protein function)
• explain why the same type of chemical reaction performed at different temperatures revealed different results/enzyme activity
• explain why warm temperatures (but not boiling) typically promote enzyme activity but cold temperature typically
• decreases enzyme activity
• explain why increasing enzyme concentration promotes enzyme activity
• explain why the optimal pH of a particular enzyme promotes its activity
• if given the optimal conditions for a particular enzyme, indicate which experimental conditions using that particular enzyme would show the greatest and least enzyme activity

Introduction

Hydrogen peroxide is a toxic product of many chemical reactions that occur in living things. Although it is produced in small amounts, living things must detoxify this compound and break down hydrogen peroxide into water and oxygen, two non-harmful molecules. The organelle responsible for destroying hydrogen peroxide is the peroxisome using the enzyme catalase. Both plants and animals have peroxisomes with catalase. The catalase sample for today’s lab will be from a potato.

Enzymes speed the rate of chemical reactions. A catalyst is a chemical involved in, but not consumed in, a chemical reaction. Enzymes are proteins that catalyze biochemical reactions by lowering the activation energy necessary to break the chemical bonds in reactants and form new chemical bonds in the products. Catalysts bring reactants closer together in the appropriate orientation and weaken bonds, increasing the reaction rate. Without enzymes, chemical reactions would occur too slowly to sustain life.

The functionality of an enzyme is determined by the shape of the enzyme. The area in which bonds of the reactant(s) are broken is known as the active site. The reactants of enzyme catalyzed reactions are called substrates. The active site of an enzyme recognizes, confines, and orients the substrate in a particular direction.

Enzymes are substrate specific, meaning that they catalyze only specific reactions. For example, proteases (enzymes that break peptide bonds in proteins) will not work on starch (which is broken down by the enzyme amylase). Notice that both of these enzymes end in the suffix -ase. This suffix indicates that a molecule is an enzyme.

Environmental factors may affect the ability of enzymes to function. You will design a set of experiments to examine the effects of temperature, pH, and substrate concentration on the ability of enzymes to catalyze chemical reactions. In particular, you will be examining the effects of these environmental factors on the ability of catalase to convert H 2 O 2 into H 2 O and O 2 .

The Scientific Method

As scientists, biologists apply the scientific method. Science is not simply a list of facts, but is an approach to understanding the world around us. It is use of the scientific method that differentiates science from other fields of study that attempt to improve our understanding of the world.

The scientific method is a systematic approach to problem solving. Although some argue that there is not one single scientific method, but a variety of methods; each of these approaches, whether explicit or not, tend to incorporate a few fundamental steps: observing, questioning, hypothesizing, predicting, testing, and interpreting results of the test. Sometimes the distinction between these steps is not always clear. This is particularly the case with hypotheses and predictions. But for our purposes, we will differentiate each of these steps in our applications of the scientific method.

You are already familiar with the steps of the scientific method from previous lab experiences. You will need to use your scientific method knowledge in today’s lab in creating hypotheses for each experiment, devising a protocol to test your hypothesis, and analyzing the results. Within the experimentation process it will be important to identify the independent variable, the dependent variable, and standardized variables for each experiment.

Part 1: Observe the Effects of Catalase

• Obtain two test tubes and label one as A and one as B.
• Use your ruler to measure and mark on each test tube 1 cm from the bottom.
• Fill each of two test tubes with catalase (from the potato) to the 1 cm mark
• Add 10 drops of hydrogen peroxide to the tube marked A.
• Add 10 drops of distilled water to the tube marked B.
• Bubbling height tube A
• Bubbling height tube B
• What happened when H 2 O 2 was added to the potato in test tube A?
• What caused this to happen?
• What happened in test tube B?
• What was the purpose of the water in tube B?

Part 2: Effects of pH, Temperature, and Substrate Concentration

Observations.

From the introduction and your reading, you have some background knowledge on enzyme structure and function. You also just observed the effects of catalase on the reaction in which hydrogen peroxide breaks down into water and oxygen.

From the objectives of this lab, our questions are as follows:

• How does temperature affect the ability of enzymes to catalyze chemical reactions?
• How does pH affect the ability of enzymes to catalyze chemical reactions?
• What is the effect of substrate concentration on the rate of enzyme catalyzed reactions?

Based on the questions above, come up with some possible hypotheses. These should be general, not specific, statements that are possible answers to your questions.

• Temperature hypothesis
• pH hypothesis
• Substrate concentration hypothesis

Test Your Hypotheses

Based on your hypotheses, design a set of experiments to test your hypotheses. Use your original experiment to shape your ideas. You have the following materials available:

• Catalase (from potato)
• Hydrogen peroxide
• Distilled water
• Hot plate (for boiling water)
• Acidic pH solution
• Basic pH solution
• Thermometer
• Ruler and wax pencil

Write your procedure to test each hypothesis. You should have three procedures, one for each hypothesis. Make sure your instructor checks your procedures before you continue.

• Procedure 1: Temperature
• Procedure 2: pH
• Procedure 3: Concentration

Record your results—you may want to draw tables. Also record any observations you make. Interpret your results to draw conclusions.

• Do your results match your hypothesis for each experiment?
• Do the results reject or fail to reject your hypothesis and why?
• What might explain your results? If your results are different from your hypothesis, why might they differ? If the results matched your predictions, hypothesize some mechanisms behind what you have observed.

Communicating Your Findings

Scientists generally communicate their research findings in written reports. Save the things that you have done above. You will be use them to write a lab report a little later in the course.

Sections of a Lab Report

• Title Page:  The title describes the focus of the research. The title page should also include the student’s name, the lab instructor’s name, and the lab section.
• Introduction:  The introduction provides the reader with background information about the problem and provides the rationale for conducting the research. The introduction should incorporate and cite outside sources. You should avoid using websites and encyclopedias for this background information. The introduction should start with more broad and general statements that frame the research and become more specific, clearly stating your hypotheses near the end.
• Methods:  The methods section describes how the study was designed to test your hypotheses. This section should provide enough detail for someone to repeat your study. This section explains what you did. It should not be a bullet list of steps and materials used; nor should it read like a recipe that the reader is to follow. Typically this section is written in first person past tense in paragraph form since you conducted the experiment.
• Results:  This section provides a written description of the data in paragraph form. What was the most reaction? The least reaction? This section should also include numbered graphs or tables with descriptive titles. The objective is to present the data, not interpret the data. Do not discuss why something occurred, just state what occurred.
• Discussion:  In this section you interpret and critically evaluate your results. Generally, this section begins by reviewing your hypotheses and whether your data support your hypotheses. In describing conclusions that can be drawn from your research, it is important to include outside studies that help clarify your results. You should cite outside resources. What is most important about the research? What is the take-home message? The discussion section also includes ideas for further research and talks about potential sources of error. What could you improve if you conducted this experiment a second time?
• Biology 101 Labs. Authored by : Lynette Hauser. Provided by : Tidewater Community College. Located at : http://www.tcc.edu/ . License : CC BY: Attribution
• BIOL 160 - General Biology with Lab. Authored by : Scott Rollins. Provided by : Open Course Library. Located at : http://opencourselibrary.org/biol-160-general-biology-with-lab/ . License : CC BY: Attribution

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Expression of glod4 in the testis of the qianbei ma goat and its effect on leydig cells.

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1. introduction, 2. materials and methods, 2.1. tissue collection, 2.2. cell culture and transfection, 2.3. plasmid construction, 2.4. total rna was extracted and reverse-transcribed, 2.5. real-time polymerase chain reaction, 2.6. western-blot, 2.7. immunohistochemistry, 2.8. subcellular localization, 2.9. cell proliferation analysis, 2.10. steroid assay, 2.11. flow cytometry analysis, 2.12. statistics, 3.1. localization of glod4 in the testis of qianbei ma goat, 3.2. expression patterns of the glod4 at different developmental stages in qianbei ma goat testicles, 3.3. effect of glod4 on the proliferation of goat leydig cells, 3.4. effect of glod4 on cell cycle progress of goat leydig cells, 3.5. promotion of testosterone hormone secretion by glod4, 4. discussion, 5. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Wang, J.; Chen, X.; Sun, W.; Tang, W.; Chen, J.; Zhang, Y.; Li, R.; Wang, Y. Expression of GLOD4 in the Testis of the Qianbei Ma Goat and Its Effect on Leydig Cells. Animals 2024 , 14 , 2611. https://doi.org/10.3390/ani14172611

Wang J, Chen X, Sun W, Tang W, Chen J, Zhang Y, Li R, Wang Y. Expression of GLOD4 in the Testis of the Qianbei Ma Goat and Its Effect on Leydig Cells. Animals . 2024; 14(17):2611. https://doi.org/10.3390/ani14172611

Wang, Jinqian, Xiang Chen, Wei Sun, Wen Tang, Jiajing Chen, Yuan Zhang, Ruiyang Li, and Yanfei Wang. 2024. "Expression of GLOD4 in the Testis of the Qianbei Ma Goat and Its Effect on Leydig Cells" Animals 14, no. 17: 2611. https://doi.org/10.3390/ani14172611

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