$$\rightleftharpoonup{xx}$$
$$\longleftharp{xx}$$,
$$\longrightharp{xx}$$,
Enzyme assays can be used to quantify potential EEAs and to compare activities among similar samples. Here, we present representative results from an experimental climate study comparing soils that experienced ambient climate conditions (ACN) to soils that were exposed to elevated CO2 and heating treatments (EHN) (Figures 2-6). Plant cover in all plots were characteristic of a northern mixed grass prairie dominated by the C4 grass Bouteloua gracilis (H.B.K) Lag. and two C3 grasses, Hesperostipa comata Trin and Rupr. and Pascopyrum smithii (Rydb.); about 20% of the vegetation is composed of sedges and forbs. More information regarding the site description and field experimental design can be found in references28-30. Soils were collected from two different depths (0-5 cm and 5-15 cm) within each of the treatment plots using a 1.5 cm diameter core to assess soil EEAs in response to altered climate conditions. In our examples (i.e. Figures 2-6), the sample size is (N = 3). Regardless of this minimal sample size, the variation is relatively small in most cases (as demonstrated by the error bars) and robustly reflects variability in potential EEAs among treatment plots. Analysis of Variance (ANOVA) and Tukey post hoc multiple comparisons were used to identify significant shifts in enzyme activity among treatment plots and soil depths.
The following representative results have been provided to demonstrate how this high-throughput, fluorometric assay can be used to test (1) overall EEA in soils, (2) how EEA stoichiometry can be indicative of ecosystem-level processes and (3) the relationship between incubation temperature and EEA. Soil EEA's are commonly studied to relate shifts in microbial function to soil nutrient cycling; useful indicators to assess microbial nutrient demands in response to climate change, plant community shifts, and more broadly ecosystem functioning31-33. EEA stoichiometry has been more recently adopted as an index to assess soil biochemical nutrient cycling by intersecting ecological stoichiometric theory and metabolic theory of ecology to assess potential microbial nutrient imbalances corresponding to environmental conditions5. Numerous studies have suggested that wide stoichiometric ratios are indicative of nutrient growth limitations34-36; and as soil nutrients become limited, microbes respond by allocating metabolic resources to produce specific enzymes to acquire deficient nutrients37. Ecoenzymatic C:N:P stoichiometry ratios are thus useful to identify relative shifts in potential microbial community nutrient demands in response to various environmental perturbations5. Lastly, temperature sensitivities of EEAs can be useful to assess how soil microbial community functional diversity is likely influenced by temperature shifts7,38. Enzyme temperature sensitivities can widely vary between soils for a single enzyme class, and microbial communities producing enzymes have demonstrated shifts in enzyme activity corresponding to shifts in climate from historic conditions7. Thus enzyme activity questions related to the thermal ecology of EEs can be a useful way to assess microbial functional dynamics and belowground ecosystem processes in response to climate changes39,40.
In this example, potential C-, N-, and P-enzyme acquisition activities assayed at 0-5 cm soil depths did not differ by experimental treatment (Figure 2a). However, at 5-15 cm soil depths, several potential EEAs did differ significantly (Figure 2b). For example, the C-degrading enzymes β-1,4-glucosidase and β-D-cellobiohydrolase were lower in the EHN plots (p ≤ 0.038; Figure 2b) compared to the ACN plots. The N and P mineralizing enzymes (β-1,4-N-acetylglucosaminidase and phosphatase, respectively) were also lower in the EHN plots (p ≤ 0.012; Figure 2b) compared to ACN at 5-15 cm soil depths.
Calculating and plotting the sum of all C-, N-, or P- cycling potential EEAs can be a useful approach to observe broader patterns regarding potential soil C-, N-, and/or P-cycles (Figure 3). In this example, the sum of β-1,4-glucosidase, β-D-cellobiohydrolase, β-Xylosidase, and α-1,4-glucosidase potential EEAs was calculated to represent potential C cycling activities. The sum of β-1,4-N-acetylglucosaminidase and L-leucine aminopeptidase was calculated to represent potential N cycling activities. Phosphatase was used to represent potential P cycling activities. In this example, potential EEAs for total C-, N- and P-cycling trended lower in the EHN plots compared to the ACN plots at the 5-15 cm soil depths (Figure 3). However, this trend was only significant for total N and P cycling activities (p ≤ 0.046; Figure 3). Soil EEAs did not significantly differ among the treatment plots at 0-5 cm soil depths (Figure 3). The findings for this example suggest contrasting trends in enzyme-activity functional group (i.e. C-, N-, or P-degrading enzymes) among the treatment plots (ACN vs. EHN) in response to soil depth. For example, C-, N- and P-degrading soil EEAs in the ambient plots (ACN) trended higher at lower depths compared to soils exposed to elevated CO2 and heating (EHN), which demonstrated an inverse pattern (Figure 3a-c).
Enzyme stoichiometry is another useful approach to assess potential enzyme activities in the environment (Figure 4). The microbial demand for nutrients is determined by the elemental stoichiometry of microbial biomass in relation to environmental nutrient availability32. Likewise, microbes produce specific enzymes (i.e. C-, N-, or P-degrading enzymes) to meet nutrient demands within their soil environments, also referred to as ecological stoichiometry41. The ratio of potential EEAs is one way to assess microbial nutrient demands. For example, a 1:1 ratio between two enzyme functional groups (for example in C:N nutrient acquisition) would suggest that the demand for N is high relative to the demand for C when considering microbial biomass C:N ratios at the community level is typically 8:142. In this example, soil enzyme stoichiometry C:N, C:P, or N:P activities significantly differ among the treatment plots at 0-5 cm soil depths (Figures 4a-c). However, potential enzyme C:P and N:P ratios were higher in the EHN plots compared to the ACN plots at the 5-15 cm soil depths (p = 0.05; Figures 4b and 4c). This observation suggests that there is relatively higher P mineralization EEAs compared to C and N EEA the ACN plots (compared to EHN) at the 5-15 cm soil depths. Enzyme C:N acquisition activity ratios demonstrated a similar trend to that demonstrated by total C EEAs with higher C:N due to higher potential C EEAs in the ACN plots at the lower depth compared to EHN (Figure 4a).
Temperature can strongly influence soil EEAs. Yet, in typical lab assays, soil enzymes are measured at a single temperature that may not correspond to in situ temperature conditions. Our fluorescence enzyme assay method allows us to consider in situ temperature effects by incorporating multiple laboratory incubation temperatures for comparison. Using laboratory incubations at multiple temperatures allows us to analyze temperature-dependent enzyme kinetics data using Arrhenius plot and Q10 calculations. Arrhenius plot are used to visualize activation energy; and are plotted using the logarithm of enzyme activity (y-axis dependent variable) as a function of the inverse temperature converted to degrees Kelvin (1/K) on the x-axis (i.e. independent variable; Figures 5a-c). Activation energy is commonly defined as the minimum energy required to catalyze a chemical reaction (i.e. degrade a given substrate into smaller products). For our purposes, activation energy serves as a proxy for the temperature sensitivity of enzyme catalyzed reactions. Higher activation energy indicates enzyme temperature sensitivity. Likewise, activation energies (i.e. Figures 5d-f) directly correspond to Q10 values (i.e. Figures 6a-c). A further explanation of formulas used to calculate activation energy and practical application can be found in many past works40,43-45. Arrhenius plots, activation energy, and Q10 plots provide redundant information and should not all be used in the same manuscript to present data for publication (Figures 5 and 6). Therefore, when using these techniques, it is necessary to choose the most appropriate plot type for your enzyme temperature - kinetics data10. All plot types (Arrhenius and Q10) were presented here for demonstration purposes; to provide visual examples of how to present enzyme results.
In our examples, we assessed potential enzyme kinetics for potential C-, N-, and P-EEAs among both treatment plots at the two soil depths (Figure 5; Figure 6). The finding demonstrated that the temperature sensitivity of EEAs was not significantly different among treatment plots at either soil depths for activation energy as demonstrated in the Arrhenius plots (Figures 5a-c) and corresponding activation energies (Figure d-f) and (not surprisingly) for Q10 plots (in this example between the 15 °C and 25 °C laboratory incubations; Figures 6a-c). This suggests that enzyme kinetics were not influenced by the field experimental treatments in this particular study.

Table 1. Deep well plate design for fluorescence standard concentrations with soil samples. Template for organizing and loading fluorescence standards (MUB or MUC) with soil samples into the deep well plate prior to incubating. Note: The columns represent the same soil samples (800 μl of soil slurry additions). A gradient of fluorescence standard concentrations is loaded for each row (200 μl additions). Each cell represents fluorescence standard concentration plus soil slurry additions. For example, S1 - S12 represent soil samples (800 μl of soil slurry); MUB 0-100 μM = 4-Methylumbelliferone concentrations (200 μl additions). In this example, row H is open, and is consequently available to include a higher fluorescence standard concentration if needed. This decision to increase the standard curve concentrations may be necessary for higher potential enzyme activity (and/or the substrate concentrations used) for your specific soil samples. The potential enzyme activity for your samples should fall within the range of the standard curve values. Click here to view larger table.

Table 2. Deep well plate design for soil samples with substrate. Template for organizing and loading soil samples and substrates into the deep well plate prior to incubating. Note: The columns represent the same soil samples (800 μl of soil slurry additions). Different substrates are loaded across each row (200 μl additions). Each cell represents soil samples plus substrate addition. For example, S1 - S12 represent unique soil samples (800 μl of soil slurry). Substrates (200 μl additions) include: BG = 4-Methylumbelliferyl β-D-glucopyranoside; CB = 4-Methylumbelliferyl β-D-cellobioside; NAG = 4-Methylumbelliferyl N-acetyl-β-D-glucosaminide; PHOS = 4-Methylumbelliferyl phosphate: XYL = 4-Methylumbelliferyl-β-D-xylopyranoside; AG = 4-Methylumbelliferyl α-D-glucopyranoside; and LAP = L-Leucine-7-amido-4-methylcoumarin hydrochloride. In this example, row H is open, and is consequently available to include another substrate to assess another potential enzyme activity if desired. Click here to view larger table.
| Temperature | Time |
| 4 °C | ~23 hr |
| 15 °C | 6 hr |
| 25 °C | 3 hr |
| 35 °C | 1.5 hr |
Table 3. Incubator temperatures required for corresponding incubation time periods. Incubation temperatures and corresponding time periods for the enzyme assay procedure.

Table 4. MUB and MUC standard curve calculations. Demonstrates how to organize a) MUB and b) MUC raw fluorescence data (μmol) to subsequently calculate slope, y-intercept, and R-squared values. To calculate slope, y-intercept, and R-squared values from your standard concentration curves, select (or plot) the MUB and/or MUC raw fluorescence data as the dependent variable (y-axis) and standard concentration (μmol) as the independent variable (x-axis). Note: MUB = 4-Methylumbelliferone; MUC = 7-Amino-4-methylcoumarin. Click here to view larger table.

Table 5. Enzyme activity calculations. Demonstrates how to a) organize sample raw fluorescence data and subsequently perform the steps needed (b-f) to calculate EEAs into: μmol activity/g dry soil/hr. Note: S1 - S12 represent unique soil samples. Substrates include: BG = 4-Methylumbelliferyl β-D-glucopyranoside; CB = 4-Methylumbelliferyl β-D-cellobioside; NAG = 4-Methylumbelliferyl N-acetyl-β-D-glucosaminide; PHOS = 4-Methylumbelliferyl phosphate: XYL = 4-Methylumbelliferyl-β-D-xylopyranoside; AG = 4-Methylumbelliferyl α-D-glucopyranoside; and LAP = L-Leucine-7-amido-4-methylcoumarin hydrochloride. Click here to view larger table.

Figure 1. MUB standard curve plot. Scatterplot visualization of standard curves with raw fluorescence data as the dependent variable (y-axis) and standard concentration (μmol) as the independent variable (x-axis).

Figure 2. C, N and P cycling enzyme activities. Potential EEAs at the Prairie Heating and Elevated CO2 Enrichment (PHACE) site in control plots (ACN: exposed to ambient climate conditions) and treatment plots (EHN: exposed to increased temperature and elevated CO2). Soil enzymes were assayed at a) 0-5 cm soil depths and b) 5-15 cm soil depths. Vertical bars represent mean ± S.E. Note: ACN = ambient - climate plots; EHN = elevated CO2 and heating plots. Click here to view larger figure.

Figure 3. Total C, N and P cycling enzyme stoichiometric ratios. The sum of potential EEAs involved in the acquisition of a) C, b) N, and c) P- for both treatment groups in the PHACE experiment at 0-5 cm and 5-15 cm soil depths. Vertical bars represent mean ± S.E. Note: ACN = ambient - climate plots; EHN = elevated CO2 and heating plots. Click here to view larger figure.

Figure 4. Enzyme stoichiometry for total C, N, and P cycling enzyme activities. Enzyme acquisition activity stoichiometric ratios at the Prairie Heating and Elevated CO2 Enrichment (PHACE) site in control plots (ACN: exposed to ambient climate conditions) and treatment plots (EHN: exposed to increased temperature and elevated CO2). Total soil a) C:N, b) C:P and c) N:P was plotted for both treatment groups at 0-5 cm and 5-15 cm soil depths. Vertical bars represent mean ± S.E. Note: ACN = ambient - climate plots; EHN = elevated CO2 and heating plots. Click here to view larger figure.

Figure 5. Total C, N and P cycling enzyme activation energies. Temperature sensitivity of potential EEAs displayed using Arrhenius plots for total a) C, b) N, and c) P degrading enzymes; as well as the corresponding activation energy values for total d) C, e) N, and f) P degrading enzymes among treatment plots and soil depth at the Prairie Heating and Elevated CO2 Enrichment (PHACE) site. Vertical bars for activation energy (i.e. d-f) represent mean ± S.E. Note: ACN = ambient - climate plots; EHN = elevated CO2 and heating plots. For publication, it is important to note that the author would typically choose to present either Arrhenius plots (i.e. a-c) or activation energy values (i.e. d-f), but not both plot- types. Click here to view larger figure.

Figure 6. Total C, N and P cycling enzyme Q10 (15-25 °C). Temperature sensitivities of potential EEAs measured as Q10, based on laboratory incubations at 15 °C and 25 °C for total a) C, b) N, and c) P degrading enzymes among treatment plots and soil depth at the Prairie Heating and Elevated CO2 Enrichment (PHACE) site. Vertical bars for Q10 represent mean ± S.E. Note: ACN = ambient - climate plots; EHN = elevated CO2 and heating plots. Click here to view larger figure.