This publication was funded by the Enzymes in the Environment Research Coordination Network Research supported by the US National Science Foundation (DEB# 1021559). This research was supported by the US National Science Foundation (DEB# 1021559), and the US Department of Energy's Office of Science (Biological and Environmental Research). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the US NSF.

1. Assay Set-up
<ol>
	<li>Label 3 deep well plates: &#34;Sample&#34;, &#34;MUB standard&#34;, and &#34;MUC standard&#34;.</li>
	<li>Pour each standard (MUB of 0, 2.5, 5, 10, 25, 50, and 100 &#956;M) and substrate (BG, CB, NAG, PHOS, XYL, AG, LAP) into separate clean, prelabeled reservoirs (oriented in rows).</li>
	<li>Pipette the appropriate MUB standard into corresponding wells of MUB standard plate (Table 1).
	<ol>
		<li>Pipette 200 &#956;l of 0 &#956;M MUB into row A of MUB standard plate.</li>
		<li>Pipette 200 &#956;l of 2.5 &#956;M MUB into row B of MUB standard plate.</li>
		<li>Pipette 200 &#956;l of 5 &#956;M MUB into row C of MUB standard plate.</li>
		<li>Pipette 200 &#956;l of 10 &#956;M MUB into row D of MUB standard plate.</li>
		<li>Pipette 200 &#956;l of 25 &#956;M MUB into row E of MUB standard plate.</li>
		<li>Pipette 200 &#956;l of 50 &#956;M MUB into row F of MUB standard plate.</li>
		<li>Pipette 200 &#956;l of 100 &#956;M MUB into row G of MUB standard plate.</li>
		<li>Repeat steps&#160;1.3.1 - 1.3.8&#160;for MUC standards into corresponding wells of the MUC standard plate.</li>
	</ol>
	</li>
	<li>Prep soil slurries for each soil sample.
	<ol>
		<li>Weigh 2.75 g of field moist soil into blender.</li>
		<li>Add 91 ml of 50 mM buffer. Blend on high for 1 min.</li>
		<li>Pour blender contents into a clean glass bowl at least as wide as 8-channel pipette with a stir bar.</li>
		<li>Place on stir plate, mix soil slurry on low.</li>
		<li>Pipette 800 &#956;l of soil slurry into first column of the Sample plate (Table 2).</li>
		<li>Repeat into 1st column of MUB standard and MUC standard plates (Table 1).</li>
		<li>Rinse blender, stir plate and stir bar with DI H2O or buffer between soil samples.</li>
		<li>Repeat steps&#160;1.4.1 - 1.4.7)for each sample, moving to the next column with each subsequent soil sample (Tables 1 and&#160;2).</li>
	</ol>
	</li>
	<li>Pipette the appropriate substrate (in this example, 200 &#956;M concentrations) into corresponding wells of the Sample plate (Table 2).
	<ol>
		<li>Pipette 200 &#956;l of BG substrate into row A of Sample plate.</li>
		<li>Pipette 200 &#956;l of CB substrate into row B of Sample plate.</li>
		<li>Pipette 200 &#956;l of NAG substrate into row C of Sample plate.</li>
		<li>Pipette 200 &#956;l of PHOS substrate into row D of Sample plate.</li>
		<li>Pipette 200 &#956;l of XYL substrate into row E of Sample plate.</li>
		<li>Pipette 200 &#956;l of AG substrate into row F of Sample plate.</li>
		<li>Pipette 200 &#956;l of LAP substrate into row G of Sample plate.</li>
		<li>Repeat steps 1.5.1 - 1.5.7&#160;for each incubation temperature sample plate.</li>
	</ol>
	</li>
</ol>
2. Incubation of Assay Plates
<ol>
	<li>Seal the deep-well plates (&#34;Sample&#34;, &#34;MUB standard&#34;, and &#34;MUC standard&#34;) with plate mats.</li>
	<li>Invert each (sealed) plate by hand until the solution is thoroughly mixed (~10x).</li>
	<li>Place plates in appropriate incubator for required incubation time period (Table 3).</li>
	<li>Record initial time as Time 0. Also record the appropriate incubation times so you know when to remove the plate from the incubator (Table 3).</li>
	<li>At the end of each incubation period, centrifuge the sealed plates for 3 min at ~2,900 x g.</li>
	<li>After certification, transfer 250 &#956;l from each well of the incubated deep well plates into corresponding wells in black flat-bottomed 96-well plates (one black plate will be used for each incubated deep well plate). It is important to transfer samples from the incubated deep well plates into the same wells (i.e. A1&#8230;A12, etc.) in the black flat-bottomed 96-well plates (Table 1, 2).</li>
</ol>
3. Fluorescent Measurements on a Microplate Fluorometer
<ol>
	<li>Following manufacturer&#39;s instructions for the fluorometric plate reader used, set the excitation wavelength to 365 nm and emission wavelength to 450 nm (or use appropriate filters).</li>
	<li>Read the three plates on the fluorometer. Important: samples and standard plates (i.e. MUB or MUC) must be read using the same gain.
	<ol>
		<li>Set the spectrophotometer to automatic gain and read the MUC and MUB Standard plates.</li>
		<li>Set gain to manual, and decrease the value from optimal to the next lowest number, rounded to the nearest five, for each Standard plate. Repeat 2x for each plate.</li>
		<li>Set the gain to automatic and run the Sample plate. Rerun the Sample plate manually to match the highest gain for each of the Standard plates (i.e. MUB and MUC).</li>
	</ol>
	</li>
</ol>
4. Data Analysis
<ol>
	<li>Enter standard curve fluorescence data into spreadsheet for MUB (Table 4a) and MUC standard (Table 4b)
	<ol>
		<li>Convert (&#956;M) concentrations to (&#956;mol) (Tables 4a and 4b).</li>
	</ol>
	</li>
	<li>For each sample&#39;s standard curve fluorescence data, calculate the slope, y-intercept, and R2 values for MUB and MUC (&#956;mol) standard concentrations. Acceptable R2 values should exceed 0.98 for each sample (Tables 4a and&#160;4b). It may be useful to visualize standard curves in a scatter plot; with fluorescence reads plotted as the dependent variable (y-axis) and standard concentration (&#956;mol) plotted as the independent variable (x-axis) (Figure 1).</li>
	<li>You will use the MUB and MUC standard curve slope and y-intercept values to calculate the sample + substrate raw fluorescence data into potential EEAs. You must first enter the sample + substrate raw fluorescence data into a new spreadsheet (Table 5a). You will also need to enter the incubation time (hr) and soil dry weight for each sample (Table 5b). Note, the time-values entered in this example are 3 hr, because the samples were incubated at 25 &#176;C (Table 3).
	<ol>
		<li>Subtract the standard curve intercept values from the corresponding sample fluorescence values and then divide by the slope of the corresponding standard curve (Table 5c). To accomplish this, rearrange standard line equation to solve for sample enzyme concentration (x); x=(y-b)/m where: [y = sample fluorescence raw read; b = intercept from MUB or MUC Standard curve; m = slope from MUB or MUC Standard curve].</li>
		<li>Multiply sample &#956;mol, from step&#160;4.3.1&#160;above, by 91 ml (buffer volume used in soil slurry) (Table 5d).</li>
		<li>Divide value obtained in step&#160;4.3.2&#160;by the sample-specific incubation time and dry soil mass (Table 5e): [enzyme amt. (&#956;mol) x&#160;91 ml x&#160;incubation (hr) x&#160;dry soil (g) x&#160;0.8 ml x&#160;1,000] 
		Note: 0.8 ml is the volume of slurry used in each well, and 1,000 corrects for the actual soil amount in each well.</li>
		<li>Multiply the value obtained in step&#160;4.3.3&#160;by 1,000 to get the desired units (nmol activity/g dry soil/hr) (Table 5f).</li>
	</ol>
	</li>
</ol>

No conflicts of interest declared.

The laboratory-based measurement of potential soil EEAs can provide important insights into microbial responses to their abiotic environment, and their consequences for ecosystem functioning. The results of this example data set suggest that minimal differences exist in soil enzyme activity or kinetics among climate treatment plots. However, inverse trends among plots encourage further investigation of covariates that may influence the production of microbial EEAs such as soil moisture, soil pH or plant growth. Overall, assessing EEAs in terms of (1) overall EEA in soils, (2) EEA stoichiometry (3) Arrhenius plots/activation energy, and (4) Q10 provides a wide spectrum of approaches that can be indicative of ecosystem-level processes from which to robustly characterize soil ecosystem functional dynamics.
High-throughput fluorescence-based EEA assays are a useful tool that is widely used to examine potential EEAs in soils and other environments. Importantly, potential activities reflect the enzyme pool size, but do not by themselves quantify enzyme production or turnover rates46. Although the technique is relatively straightforward, seemingly minor differences among lab protocols can hamper the comparability of results 13. Unfortunately, we do not currently have suitable standardized positive controls for EEAs. The use of stoichiometric ratios is one approach for overcoming these challenges. Otherwise, the advent of high-throughput techniques has advanced the study of enzymes in the environment4. Careful interpretation of the data generated by these assays may elucidate important trends in microbial activity.
The robustness of the protocol stems from the ability to select for conditions specific to individual samples, but this can also result in limitations. A series of modifications will be required to ensure samples are accurately measured for individual field sites:
Buffer
The buffer you select will depend on the pH of the soil. Buffering also stabilizes the fluorescence intensity of the fluorescent standards, which is highly pH dependent27,47. The pH buffer that we typically use to make the soil slurry is a 50 mM sodium acetate or Tris buffer which is chosen for the buffer&#39;s particular acid dissociation constant (pKa) to best match soil - sample pH levels. Sodium acetate has a pKa of 4.76, and Tris has a pKa of 8.06 so the amounts of these two buffers will vary in order to reach the desired pKa for the individual sample. Phosphate buffer (pKa = 7.2) has been suggested for neutral/slightly basic soils. However, we caution to test for analytical variability in preliminary studies before using this buffer, as high phosphate concentrations may interfere with enzyme activity.
Handling and storage of fluorescent substrates
Fluorescence assays can suffer from interference caused by impurities and/or instability of many fluorescent compounds when exposed to light, so caution is required when handling fluorescent substrates. We strongly recommend minimizing any light exposure to fluorescently labeled substrates and MUB and MUC standards. Using amber glass bottles or covering the glassware and containers used for making and storing fluorescent substrates and standards is highly recommended; aluminum foil to wrap glassware and containers works well. Likewise, efficiently inoculating plates and transferring to dark incubators is best practice. We recommend storing substrates and standards (-20 &#176;C) for no longer than two months (while protecting them from light); and thawing substrates (5 &#176;C) ~24-48 hr prior to beginning the enzyme assay(s).
Design and Replication
To best account for well to well sample variations, implementing negative assay controls and (if possible) assay replicates is recommended. Variation typically occurs due to differences in the amount of soil particles in each well and pipetting errors. Therefore, strong mixing and good pipetting technique will substantially minimize well to well variation. Furthermore, we strongly suggest implementing a negative assay control (buffer + substrate solution) to monitor substrate inconsistencies over time. This can be easily monitored when reading the enzyme plates by comparing negative control wells (we typically use the last column on the 96-well plates). Negative substrate controls are typically stable, therefore the signal increases well above detection limit, it is indicative of contamination or substrate instability, requiring replacement of the substrate and/or standard solutions.
To maximize throughput, our protocol includes several potential EEAs in a single deep well microplate, although other protocols perform one type of assay per plate (i.e. one substrate per plate) since different EEAs occur at different rates. Regardless, enzyme optimization must be performed on soils before performing this high throughout approach or the single plate approach. Multiple substrates can be used on a single plate if the reaction rates are relatively consistent for each enzyme within the time period of the incubation.
Our protocol recommends ~2.75 g soil to make the slurry solution, while ~1 gram is recommended in other protocols 24. We suggest that using more soil (if possible) is an effective approach to better capture within-sample soil variation in enzyme activity levels. In this protocol, we incubate 800 &#956;l of soil slurry with 200 &#956;l substrate, while others only incubate 200 &#956;l of soil slurry with 50 &#956;l of substrates. This is simply a function of scaling up that ultimately does not change the measured activity. There are also practical advantages for using larger volumes. One, it is easier to avoid soil particulates while pipetting into corresponding black flat-bottomed 96-well plates before recording intensities on the fluorometer. Second, the additional volume is useful in the case of accidental spills while transferring the black flat-bottomed 96-well plates to the fluorometer before recording enzyme-related fluorescence intensities. Even slight deviations in volume will significantly decrease fluorescence among wells. Lastly, due to the high throughput nature of this protocol, we commonly elect to rely on experimental replication to represent variation in enzyme activity levels rather than performing assay replicates. It is always best practice to include analytical replicates, but in practice, we feel our protocol provides a balanced tradeoff between analytical and experimental replicates given limited resources. Likewise, our approach uses relatively more well homogenized soil (2.75 g) per assay25, which inherently decreases within-soil variations. The decision to use analytical replicates should be carefully considered by testing for analytical error in preliminary studies48. However, we recommend that experimental designs with fewer than 4 treatment-group replicates should strongly consider using assay replicates.
Soil, Buffer Volumes, and Substrate concentration optimization
The soil buffer:substrate concentration ratio is an important variable which strongly influences the measured fluorescence when performing enzyme assays. The amount of soil in the slurry added to assay or the concentration of substrate may need to be adjusted depending on the activity of enzymes in the soil sample. The amounts selected for this example were based on previous testing of these soils to ensure that substrate availability was nonlimiting, and that we were measuring maximum potential rates under the assay conditions (Vmax) 44,45. However, for soil samples that have high concentrations of enzymes, the amount of soil or substrate concentration must be increased. We have found that increasing substrate concentrations (and adjusting the standard curve accordingly) can affect linearity of the curve. Therefore, we recommend reducing soil amounts and/or proportional adjustments to buffer volumes as necessary. Regardless, it is important to optimize substrate concentration for each soil type assayed because measured EEAs can differ by an order of magnitude greater between saturated and sub-saturated substrate concentrations 26. Therefore differences among experimental treatments (etc.) are susceptible to type II statistical error and less likely to be detected at sub-saturating conditions, and statistical error27,35. To optimize substrate concentrations, preliminary enzyme assays should performed on representative soil samples using a wide range of substrate concentrations. After recording the fluorescence, simply plot the data (similarly to the standard curve example; Figure 1) to identify the substrate concentration (x-axis) that corresponds to enzyme activity (y-axis) where the slope levels off (~0). Likewise, the substrate concentration corresponding to the point where the slope levels off (~0) is a good indication of the optimal substrate concentration for that particular soil.
This assay ultimately measures fluorescence over a given time produced by the fluorogenic moiety that is cleaved from the substrate as a result of enzyme-mediated substrate depolymerization. Therefore, step 5 (substrate addition) is critical and must be performed as efficiently as possible in order to minimize the time between when the substrate is added to the soils and when the assays are incubated. Likewise, once the substrate comes in contact with the sample, enzymatic reactions will start to occur. We recommend using a multi-channel pipette for this reason. It is strongly suggested to become efficient using the multi-channel pipette prior to the day you are performing your enzyme assays. To accomplish this, you can practice pipetting with water until you can easily transfer volumes into the 96-well plates with your pipettor.
Soil Quenching
Quenching refers to decreasing fluorescence intensity caused by particulates and/or organic material in the soil slurries-incubations26. Quenching can be influenced by adjusting the soil:buffer ratios25. Due to background fluorescence from individual samples, it is critical to run standards with samples to account for background (quenching) fluorescence. Although some protocols use a single concentration after testing that the signal is linear, we strongly recommend implementing a standard quench control for each sample to best control for the effects of quenching. Failure to do so will result in a standard curve not applicable to the sample, and an incorrect estimation of enzymatic activity. The addition of standards to soil slurries is not time-sensitive, as the standard addition does not affect the background fluorescence of the sample.
NaOH addition
The addition of NaOH is used in some protocols to optimize fluorometric enzyme activity measurements because the fluorescent dye released from the synthetic substrates exhibits peak fluorescence at pH&#62;9.026,49. When considering this suggestion, the NaOH concentration necessary to the soil slurry pH (i.e. to pH&#62;9) will vary depending on the particular soil and the pH buffer used26. However, others argue that NaOH may not be necessary because signal intensity is generally very high even at lower pH, and because it introduces an additional source of measurement error. For example, the effect of NaOH additions on slurry pH and thus MUB or MUC fluorescence changes over time25. MUB linked substrates have been shown to demonstrate consistent increased fluorescence for 20 min following NaOH additions before levels taper; while MUC has demonstrated steady decreased fluorescence for up to 60 min26. Therefore, it is important to standardize the time between NaOH addition and fluorescence measurement. Alternatively, if fluorescence levels are sufficiently detectable without addition, conducting the assays without adding NaOH has been suggested as an equally acceptable alternative26.
Temperature
Temperature sensitivity should be taken into account when deciding incubation temperature. If the primary interest is understanding enzyme kinetics, using three or more temperatures as illustrated using Arrhenius plots (in the results section) is a robust approach. If the sample site has characteristically low temperature such as permafrost soil, then the duration of incubation may need to be extended to allow for the enzymes to react in the colder incubation temperatures. While traditional enzyme kinetics suggests that an increase in temperature should result in increased enzyme activity, we have found that enzymes may be site specific in terms of temperature sensitivity50. Therefore to understand site-specific enzyme activity potential it is critical that incubation temperature and duration be adjusted to reflect field site values.
Conclusion
EEs are critical drivers of biogeochemical processes in soils, and thus we need to be able to measure their activities. There are many challenges to measuring EEAs in soils, including interference and inhibition. Despite these challenges, standardized protocols (as the one described here) can be universally applied to measure EEAs for a wide range of enzymes. While it is fairly easy to generate quality data following these protocols, the interpretation of this data in an ecological context requires careful consideration of what these assays are really measuring, and how EEAs under assay conditions may differ from those under in situ conditions.

Extracellular enzymes (EEs) produced by soil bacteria, fungi, and archaea are involved in innumerable biogeochemical processes, and are central to the processing, stabilization, and destabilization of soil organic matter and nutrient cycling in terrestrial ecosystems 1. By producing EEs, soil microbes decompose and transform polymeric organic matter into smaller soluble molecules, thereby liberating previously bound micro- and macronutrients, which allows plants and microbes to assimilate available nutrients from the soil. EEs have been studied for decades, primarily by measuring their activities in laboratory assays2-4, since it is very difficult to directly detect and quantify enzymes.
Extracellular enzyme activity (EEA) is most strongly controlled by the concentration of enzymes and corresponding substrates. The abundance of different C-, N-, and P-degrading enzymes in soils is controlled by numerous factors including microbial biomass, community composition, substrate availability, microclimate, and stoichiometric demands5,6. However, in situ EEAs within the soil environment are also affected by temperature7,8 , the binding of enzymes to soil clays and humic properties2, and diffusion constraints 9, which ultimately regulate the active enzyme pool, in terms of size, substrate availability, and turnover rates10-12. Therefore, acknowledging in situ soil conditions is critical when using laboratory enzyme assays to interpret soil microbial function across different environmental sites.
Many different classes of EEA can be quantified in laboratory assays using a variety of synthetic substrates (please refer to &#34;List of Reagents Table&#34; for more detail). Some protocols utilize substrates in assays that are coupled to a colorimetric reaction that can be detected with a spectrophotometer, while others, including the protocol we describe here, utilize substrates that are bound to a fluorescent moiety. Fluorescence EE assays are typically more sensitive (by an order of magnitude) than colorimetric assays (which use a chromogenic moiety linked with a synthetic substrate)12-14. Sensitivity in EEA detection includes two aspects: one is related to the quantity of the compound of interest detected and the other related to the lowest detectable potential enzyme activity. Methods for colorimetric P-nitrophenol (pNP)-based assays can be found in past works15,16. In brief, soil (typically sieved to &#60;2 mm and air-dried) is incubated at optimal or field-relevant temperature and pH. The rate at which the reaction product released is determined colorimetrically with a spectrophotometer14. The higher sensitivity of the fluorescence enzyme assays is due in part to the more sensitive detection of the fluorogenic moiety separation associated with substrate degradation rather than recording absorbance after the separation of a specific chromogenic moiety at a specific wavelength. The two most commonly used synthetic fluorescent indicators are 4-methylumbelliferone (MUB)17 and 7-amino-4-methylcoumarin (MUC)18,19. MUC-linked substrates are commonly associated with N-rich synthetic substrates such as proteins and/or amino acids. Fluorescent techniques were first developed for aquatic samples20,21, and their application to soils requires controls for signal quenching and interference22,23. Assays can either be conducted using traditional &#34;bench top&#34; chemistry with large volumes, or can be employed in microplate based protocols, with increased throughput but possibly higher measurement error. While there are several widely cited protocols for fluorescent detection of EEAs in soils24, many labs employ subtle variations on these protocols, often inadvertently or due to differences in laboratory equipment or reagents. Seemingly small differences in the details of protocols can strongly affect measured EEAs25,26 and the lack of standardized enzymes makes it challenging to calibrate assays between different laboratories. Thus, there is an important need for the dissemination of detailed protocols to encourage the standardization of EEA assays.
In our protocol, soil samples are prepared by combining soil samples with a pH buffer and homogenizing with a blender. The slurries are then inoculated with a nonlimiting amount of fluorescently labeled C-, N-, or P-rich substrate, chosen depending on the specific research question of interest. Using soil slurries in the enzyme assays serves as a control to minimize substrate diffusion limitations. The fluorescent moieties are quenched till they are cleaved from their respective substrates, and thus enzyme activity can be detected as the fluorescent dye is released from the substrate by an enzyme-catalyzed reaction. The increasing fluorescence intensity through time reflects the rate of the enzyme-catalyzed reaction.
The general concept of the fluorescence enzyme assay is that synthetic substrates bound with a fluorogenic moiety (fluorescent dye), are added to soil samples 27. During enzyme-catalyzed substrate degradation, the bond breaks between the fluorescent dye and the substrate. The fluorescent dye liberated from the substrate is consequently used as an indirect assessment of enzyme activity, and can be quantified using a microplate reader to detect the fluorescence intensity of the dye. In brief, fluorescence quantification is accomplished as the liberated dye emits light of one wavelength after absorbing light of a different wavelength. The fluorescence intensity is recorded by a plate reader capable of both excitation and detection. Enzyme activity can be subsequently quantified based on the known fluorescent-dye concentrations of the substrate (i.e. known quantities of synthetic substrate added to soil samples) along with referencing a standard dilution curve of fluorescence intensities for the specific fluorogenic moiety of the substrate used in the assay (i.e. 4-methylumbelliferone (MUB) or 7-amino-4-methylcoumarin (MUC)). (Please refer to the protocol section for specific details on enzyme activity quantification).
Laboratory soil enzyme assays are useful for assessing microbial community function, but there are several technical limitations that users should recognize10. Fluorescence assays can suffer from interference caused by impurities and/or instability of many fluorescent compounds when exposed to light, so caution is required when handling fluorescent substrates25. Soil particulates and/or organic material in the soil slurries can also interfere with fluorescence intensities, known as the quenching effect 26. Furthermore, laboratory enzyme assays only assesses potential EEAs under laboratory conditions. In vitro assays measure EEAs under conditions where substrate diffusion and abundance is nonlimiting. Therefore, the data provided by these assays may not be a good proxy for EEAs under in situ soil conditions10. Overall, enzyme activity is very useful for relative comparisons in which soil types are similar. However, when using this method to compare activities among soils that differ in physical or chemical properties, caution should be used. This is due to the fact that differences in soil type and temperature can drastically alter the state of in situ enzyme kinetics. Another limitation is that relatively few substrates are commercially available (compared to the natural environment). Furthermore, the synthetic substrates used for enzyme assays are relatively simple (easily soluble) may not accurately represent the soil substrates present or available in situ. Another factor to consider is that using soil slurries will incorporate the activity of some stabilized enzymes (i.e. immobilized by organic matter or clays) that may not be active under in situ conditions 2. Laboratory enzyme assays also do not provide information regarding the persistence of enzymes in the soil (enzyme turnover rates) or information regarding the specific microbial species that are producing soil enzymes.

<table border="1">
	<tbody>
		<tr>
			<td>Name of Material/ Equipment</td>
			<td>Company</td>
			<td>Catalogue number</td>
			<td>Comments</td>
		</tr>
		<tr>
			<td>Reagents: </td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>4-Methylumbelliferyl &#945;-D-glucopyranoside (AG)</td>
			<td>Sigma Aldrich</td>
			<td>M9766</td>
			<td>Sugar degradation</td>
		</tr>
		<tr>
			<td>4-Methylumbelliferyl &#946;-D-glucopyranoside (BG)</td>
			<td>Sigma Aldrich</td>
			<td>M3633</td>
			<td>Sugar degradation</td>
		</tr>
		<tr>
			<td>4-Methylumbelliferyl &#946;-D-cellobioside (CB)</td>
			<td>Sigma Aldrich</td>
			<td>M6018</td>
			<td>Cellulose degradation</td>
		</tr>
		<tr>
			<td>L-Leucine-7-amido-4-methylcoumarin hydrochloride (LAP)</td>
			<td>Sigma Aldrich</td>
			<td>L2145</td>
			<td>Protein degradation</td>
		</tr>
		<tr>
			<td>4-Methylumbelliferyl N-acetyl-&#946;-D-glucosaminide (NAG)</td>
			<td>Sigma Aldrich</td>
			<td>M2133</td>
			<td>Chitin degradation</td>
		</tr>
		<tr>
			<td>4-Methylumbelliferyl phosphate (PHOS)</td>
			<td>Sigma Aldrich</td>
			<td>M8883</td>
			<td>Phosphorus mineralization</td>
		</tr>
		<tr>
			<td>4-Methylumbelliferyl-&#946;-D-xylopyranoside (XYL)</td>
			<td>Sigma Aldrich</td>
			<td>M7008</td>
			<td>Hemicellulose degradation</td>
		</tr>
		<tr>
			<td>4-Methylumbelliferone (MUB)</td>
			<td>Sigma Aldrich</td>
			<td>M1381</td>
			<td></td>
		</tr>
		<tr>
			<td>7-Amino-4-methylcoumarin (MUC)</td>
			<td>Sigma Aldrich</td>
			<td>A9891</td>
			<td></td>
		</tr>
		<tr>
			<td>50 mM Sodium acetate anhydrous buffer</td>
			<td>Fisher Scientific</td>
			<td>S210-500</td>
			<td>acidic and neutral soils</td>
		</tr>
		<tr>
			<td>50 mM Tris base buffer</td>
			<td>Fisher Scientific</td>
			<td>BP154-1</td>
			<td>basic soils</td>
		</tr>
		<tr>
			<td>Equipment</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Bel-Art Scienceware disposable pipetting reservoirs</td>
			<td>Fisher Scientific</td>
			<td>14-512-65</td>
			<td>pipetting reservoir</td>
		</tr>
		<tr>
			<td>Electronic; Eppendorf Xplorer; 8-channel</td>
			<td>Fisher Scientific</td>
			<td>13-684-265</td>
			<td>8-channel pipette, range: 50-1,200 &#956;l</td>
		</tr>
		<tr>
			<td>TipOne 101-1,250 &#956;l extended length natural tips</td>
			<td>USA Scientific</td>
			<td>1112 - 1720</td>
			<td>10 racks of 96 tips (960 tips)</td>
		</tr>
		<tr>
			<td>Hotplate; heating surface: 10 in x 10 in.</td>
			<td>Fisher Scientific</td>
			<td>11-100-100SH</td>
			<td>Lab disc magnetic stir plate</td>
		</tr>
		<tr>
			<td>Waring blender</td>
			<td>Fisher Scientific</td>
			<td>14-509-7P</td>
			<td>Two-speed; 1 L (34 oz) stainless steel container</td>
		</tr>
		<tr>
			<td>BRAND Dispensette III bottle-top dispensers</td>
			<td>Fisher Scientific</td>
			<td>13-688-231</td>
			<td>Dispenser; range: 5-50 ml</td>
		</tr>
		<tr>
			<td>Wheaton Unispense &#956;P peristaltic pumps</td>
			<td>Fisher Scientific</td>
			<td>13-683-7</td>
			<td>Optional dispenser</td>
		</tr>
		<tr>
			<td>Fisherbrand magnetic stir bar</td>
			<td>Fisher Scientific</td>
			<td>1451363SIX</td>
			<td>Used to stirr soil slurry afer blending</td>
		</tr>
		<tr>
			<td>Pyrex glass bowls</td>
			<td>World Kitchen</td>
			<td>5304218</td>
			<td>Pyrex 10 oz rimmed custard cup</td>
		</tr>
		<tr>
			<td>Costar 96-well black solid plates</td>
			<td>Fisher Scientific</td>
			<td>07-200-590</td>
			<td>Used for plate reader step</td>
		</tr>
		<tr>
			<td>Costar 96-well assay blocks</td>
			<td>Fisher Scientific</td>
			<td>07-200-700</td>
			<td>V-bottom; 2 ml; sterile</td>
		</tr>
		<tr>
			<td>Thermo Scientific Nunc 96-well cap mats</td>
			<td>Fisher Scientific</td>
			<td>14-387-93</td>
			<td>Sterile 96-well cap mat for square wells</td>
		</tr>
		<tr>
			<td>Centrifuge equipped with holders for deep-well plates</td>
			<td>Thermo Scientific</td>
			<td>3121</td>
			<td>Centra-GP8 (this model is no longer available)</td>
		</tr>
		<tr>
			<td>Tecan Infinite 200 series multifunctional microplate reader</td>
			<td>Tecan</td>
			<td>30016058</td>
			<td>Plate reader (this model is no longer available)</td>
		</tr>
	</tbody>
</table>

high-throughput fluorometric measurement of potential soil extracellular enzyme activities

Microbes in soils and other environments produce extracellular enzymes to depolymerize and hydrolyze organic macromolecules so that they can be assimilated for energy and nutrients. Measuring soil microbial enzyme activity is crucial in understanding soil ecosystem functional dynamics. The general concept of the fluorescence enzyme assay is that synthetic C-, N-, or P-rich substrates bound with a fluorescent dye are added to soil samples. When intact, the labeled substrates do not fluoresce. Enzyme activity is measured as the increase in fluorescence as the fluorescent dyes are cleaved from their substrates, which allows them to fluoresce. Enzyme measurements can be expressed in units of molarity or activity. To perform this assay, soil slurries are prepared by combining soil with a pH buffer. The pH buffer (typically a 50 mM sodium acetate or 50 mM Tris buffer), is chosen for the buffer&#39;s particular acid dissociation constant (pKa) to best match the soil sample pH. The soil slurries are inoculated with a nonlimiting amount of fluorescently labeled (i.e. C-, N-, or P-rich) substrate. Using soil slurries in the assay serves to minimize limitations on enzyme and substrate diffusion. Therefore, this assay controls for differences in substrate limitation, diffusion rates, and soil pH conditions; thus detecting potential enzyme activity rates as a function of the difference in enzyme concentrations (per sample).
Fluorescence enzyme assays are typically more sensitive than spectrophotometric (i.e. colorimetric) assays,&#160;but can suffer from interference caused by impurities and the instability of many fluorescent compounds when exposed to light; so caution is required when handling fluorescent substrates. Likewise, this method only assesses potential enzyme activities under laboratory conditions when substrates are not limiting. Caution should be used when interpreting the data representing cross-site comparisons with differing temperatures or soil types, as in situ soil type and temperature can influence enzyme kinetics.

Watch this Scientific Journal Video about High-throughput Fluorometric Measurement of Potential Soil Extracellular Enzyme Activities at JoVE.com

High-throughput Fluorometric Measurement of Potential Soil Extracellular Enzyme Activities

To measure potential rates of soil extracellular enzyme activities, synthetic substrates that are bound to a fluorescent dye are added to soil samples. Enzyme activity is measured as the fluorescent dye is released from the substrate by an enzyme-catalyzed reaction, where higher fluorescence indicates more substrate degradation.

Microbes in soils and other environments produce extracellular enzymes to depolymerize and hydrolyze organic macromolecules so that they can be ...

high-throughput-fluorometric-measurement-potential-soil-extracellular

Research

JoVE Journal

Environment

46.3K 次观看.  Colorado State University. 测量土壤胞外酶活性的潜在利率，绑定到一个荧光染料的合成底物加入到土壤样品。酶的活性被测量为荧光染料从基板释放的酶催化反应，其中更高的荧光指示多底物降解。 

视频: High-throughput Fluorometric Measurement of Potential Soil Extracellular Enzyme Activities

Determination of Microbial Extracellular Enzyme Activity in Waters, Soils, and Sediments using High Throughput Microplate Assays

Much of the nutrient cycling and carbon processing in natural environments occurs through the activity of extracellular enzymes released by microorganisms. Thus, measurement of the activity of these extracellular enzymes can give insights into the rates of ecosystem level processes, such as organic matter decomposition or nitrogen and phosphorus mineralization. Assays of extracellular enzyme activity in environmental samples typically involve exposing the samples to artificial colorimetric or fluorometric substrates and tracking the rate of substrate hydrolysis. Here we describe microplate based methods for these procedures that allow the analysis of large numbers of samples within a short time frame. Samples are allowed to react with artificial substrates within 96-well microplates or deep well microplate blocks, and enzyme activity is subsequently determined by absorption or fluorescence of the resulting end product using a typical microplate reader or fluorometer. Such high throughput procedures not only facilitate comparisons between spatially separate sites or ecosystems, but also substantially reduce the cost of such assays by reducing overall reagent volumes needed per sample.

Microplate based procedures are described for the colorimetric or fluorometric analysis of extracellular enzyme activity. These procedures allow for the rapid assay of such activity in large numbers of environmental samples within a manageable time frame.

Much of the nutrient cycling and carbon  processing in natural environments occurs through the activity of extracellular  enzymes released by ...

Design and Operation of a Continuous 13C and 15N Labeling Chamber for Uniform or Differential, Metabolic and Structural, Plant Isotope Labeling

Tracing rare stable isotopes from plant material through the ecosystem provides the most sensitive information about ecosystem processes; from CO2 fluxes and soil organic matter formation to small-scale stable-isotope biomarker probing. Coupling multiple stable isotopes such as 13C with 15N, 18O or&#160;2H has the potential to reveal even more information about complex stoichiometric relationships during biogeochemical transformations. Isotope labeled plant material has been used in various studies of litter decomposition and soil organic matter formation1-4. From these and other studies, however, it has become apparent that structural components of plant material behave differently than metabolic components (i.e. leachable low molecular weight compounds) in terms of microbial utilization and long-term carbon storage5-7. The ability to study structural and metabolic components separately provides a powerful new tool for advancing the forefront of ecosystem biogeochemical studies. Here we describe a method for producing 13C and 15N labeled plant material that is either uniformly labeled throughout the plant or differentially labeled in structural and metabolic plant components.
Here, we present the construction and operation of a continuous 13C and 15N labeling chamber that can be modified to meet various research needs. Uniformly labeled plant material is produced by continuous labeling from seedling to harvest, while differential labeling is achieved by removing the growing plants from the chamber weeks prior to harvest. Representative results from growing Andropogon gerardii Kaw demonstrate the system&#39;s ability to efficiently label plant material at the targeted levels. Through this method we have produced plant material with a 4.4 atom%13C and 6.7 atom%15N uniform plant label, or material that is differentially labeled by up to 1.29 atom%13C and 0.56 atom%15N in its metabolic and structural components (hot water extractable and hot water residual components, respectively). Challenges lie in maintaining proper temperature, humidity, CO2 concentration,&#160;and light levels in an airtight 13C-CO2 atmosphere for successful plant production. This chamber description represents a useful research tool to effectively produce uniformly or differentially multi-isotope labeled plant material for use in experiments on ecosystem biogeochemical cycling.

Design and Operation of a Continuous 13C and 15N Labeling Chamber for Uniform or Differential, Metabolic and Structural, Plant Isotope Labeling

This method explains how to build and operate a continuous 13C and 15N isotope labeling chamber for uniform or differential plant tissue labeling. Representative results from metabolic and structural labeling of Andropogon gerardii are discussed.

Tracing rare stable isotopes from plant material through the ecosystem provides the most sensitive information about ecosystem processes; from CO2 fluxes ...

Soil Sampling and Isolation of Entomopathogenic Nematodes (Steinernematidae, Heterorhabditidae)

Entomopathogenic nematodes (a.k.a. EPN) represent a group of soil-inhabiting nematodes that parasitize a wide range of insects. These nematodes belong to two families: Steinernematidae and Heterorhabditidae. Until now, more than 70 species have been described in the Steinernematidae and there are about 20 species in the Heterorhabditidae. The nematodes have a mutualistic partnership with Enterobacteriaceae bacteria and together they act as a potent insecticidal complex that kills a wide range of insect species.
Herein, we focus on the most common techniques considered for collecting EPN from soil. The second part of this presentation focuses on the insect-baiting technique, a widely used approach for the isolation of EPN from soil samples, and the modified White trap technique which is used for the recovery of these nematodes from infected insects. These methods and techniques are key steps for the successful establishment of EPN cultures in the laboratory and also form the basis for other bioassays that consider these nematodes as model organisms for research in other biological disciplines. The techniques shown in this presentation correspond to those performed and/or designed by members of S. P. Stock laboratory as well as those described by various authors.

Soil Sampling and Isolation of Entomopathogenic Nematodes Steinernematidae, Heterorhabditidae

Entomopathogenic nematodes are soil-inhabiting roundworms that parasitize a wide range of insects. We demonstrate sampling methods used for the isolation of these nematodes from the soil using two techniques: the insect baiting and the modified White trap, for the recovery of nematodes from soil samples and infected insect cadavers, respectively.

Entomopathogenic nematodes (a.k.a. EPN) represent a group of soil-inhabiting nematodes that parasitize a wide range of insects. These nematodes belong to ...

Measurement of Greenhouse Gas Flux from Agricultural Soils Using Static Chambers

Measurement of greenhouse gas (GHG) fluxes between the soil and the atmosphere, in both managed and unmanaged ecosystems, is critical to understanding the biogeochemical drivers of climate change and to the development and evaluation of GHG mitigation strategies based on modulation of landscape management practices. The static chamber-based method described here is based on trapping gases emitted from the soil surface within a chamber and collecting samples from the chamber headspace at regular intervals for analysis by gas chromatography. Change in gas concentration over time is used to calculate flux. This method can be utilized to measure landscape-based flux of carbon dioxide, nitrous oxide, and methane, and to estimate differences between treatments or explore system dynamics over seasons or years. Infrastructure requirements are modest, but a comprehensive experimental design is essential. This method is easily deployed in the field, conforms to established guidelines, and produces data suitable to large-scale GHG emissions studies.

This article showcases the static chamber-based method for measurement of greenhouse gas flux from soil systems. With relatively modest infrastructure investments, measurements may be obtained from multiple treatments/locations and over timeframes ranging from hours to years.

Measurement of greenhouse gas (GHG) fluxes between the soil and the atmosphere, in both managed and unmanaged ecosystems, is critical to understanding the ...

Semi-High Throughput Screening for Potential Drought-tolerance in Lettuce (Lactuca sativa) Germplasm Collections

This protocol describes a method by which a large collection of the leafy green vegetable lettuce (Lactuca sativa L.) germplasm was screened for likely drought-tolerance traits. Fresh water availability for agricultural use is a growing concern across the United States as well as many regions of the world. Short-term drought events along with regulatory intervention in the regulation of water availability coupled with the looming threat of long-term climate shifts that may lead to reduced precipitation in many important agricultural regions has increased the need to hasten the development of crops adapted for improved water use efficiency in order to maintain or expand production in the coming years. This protocol is not meant as a step-by-step guide to identifying at either the physiological or molecular level drought-tolerance traits in lettuce, but rather is a method developed and refined through the screening of thousands of different lettuce varieties. The nature of this screen is based in part on the streamlined measurements focusing on only three water-stress indicators: leaf relative water content, wilt, and differential plant growth following drought-stress. The purpose of rapidly screening a large germplasm collection is to narrow the candidate pool to a point in which more intensive physiological, molecular, and genetic methods can be applied to identify specific drought-tolerant traits in either the lab or field. Candidates can also be directly incorporated into breeding programs as a source of drought-tolerance traits.

Semi-High Throughput Screening for Potential Drought-tolerance in Lettuce Lactuca sativa Germplasm Collections

This protocol was developed to screen a large germplasm collection of the leafy vegetable lettuce (Lactuca sativa L.) for drought-tolerance, in order to identify a small candidate pool of lettuce for use in physiological, molecular, and genetic studies to identify underlying drought-tolerance traits along with breeding programs.

This protocol describes a method by which a large collection of the leafy green vegetable lettuce (Lactuca sativa L.) germplasm was screened for likely ...

A Gusseted Thermogradient Table to Control Soil Temperatures for Evaluating Plant Growth and Monitoring Soil Processes

Thermogradient tables were first developed in the 1950s primarily to test seed germination over a range of temperatures simultaneously without using a series of incubators. A temperature gradient is passively established across the surface of the table between the heated and cooled ends and is lost quickly at distances above the surface. Since temperature is only controlled on the table surface, experiments are restricted to shallow containers, such as Petri dishes, placed on the table. Welding continuous aluminum vertical strips or gussets perpendicular to the surface of a table enables temperature control in depth via convective heat flow. Soil in the channels between gussets was maintained across a gradient of temperatures allowing a greater diversity of experimentation. The gusseted design was evaluated by germinating oat, lettuce, tomato, and melon seeds. Soil temperatures were monitored using individual, battery-powered dataloggers positioned across the table. LED lights installed in the lids or along the sides of the gradient table create a controlled temperature chamber where seedlings can be grown over a range of temperatures. The gusseted design enabled accurate determination of optimum temperatures for fastest germination rate and the highest percentage germination for each species. Germination information from gradient table experiments can help predict seed germination and seedling growth under the adverse soil conditions often encountered during field crop production. Temperature effects on seed germination, seedling growth, and soil ecology can be tested under controlled conditions in a laboratory using a gusseted thermogradient table.

Traditional thermogradient tables create a range of temperatures across the surface. Welding gussets perpendicular to the surface of a thermogradient table will control temperature in depth increasing possible research applications.

Thermogradient tables were first developed in the 1950s primarily to test seed germination over a range of temperatures simultaneously without using a ...

Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management

Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes in elevation are related to changes in near-surface soil carbonate (CaCO3) profiles. The objective is to describe a simple conceptual model and detailed protocol for repeatable field and laboratory measurements of these quantities. Here, accurate elevation is measured using a ground-based differential global positioning system (GPS); other data acquisition methods could be applied to the same basic method. Soil samples are collected from prescribed depth intervals and analyzed in the lab using an efficient and precise modified pressure-calcimeter method for quantitative analysis of inorganic carbon concentration. Standard statistical methods are applied to point data, and representative results show significant correlations between changes in soil surface layer CaCO3 and changes in elevation consistent with the conceptual model; CaCO3 generally decreased in depositional areas and increased in erosional areas. Maps are derived from point measurements of elevation and soil CaCO3 to aid analyses. A map of erosional and depositional patterns at the study site, a rain-fed winter wheat field cropped in alternating wheat-fallow strips, shows the interacting effects of water and wind erosion affected by management and topography. Alternative sampling methods and depth intervals are discussed and recommended for future work relating soil erosion and deposition to soil CaCO3.

Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes in elevation are related to changes in near-surface soil carbonates. Repeatable methods for field and laboratory measurements of these quantities and data analysis methods are described here.

Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes ...

A 3-dimensional (3D)-printed Template for High Throughput Zebrafish Embryo Arraying

The zebrafish is a globally recognized fresh water organism frequently used in developmental biology, environmental toxicology, and human disease related research fields. Thanks to its unique features, including large fecundity, embryo translucency, rapid and simultaneous development, etc., zebrafish embryos are often used for large scale toxicity assessment of chemicals and drug/compound screening. A typical screening procedure involves adult zebrafish spawning, embryos selection, and arraying the embryos into multi-well plates. From there, embryos are subjected to exposure and the toxicity of chemical, or the effectiveness of the drugs/compounds can be evaluated relatively quickly based on phenotypic observations. Among these processes, embryos arraying is one of the most time-consuming and labor-intensive steps that limits the throughput level. In this protocol, we present an innovative approach that makes use of a 3D-printed arraying template coupled with vacuum manipulation to speed up this laborious step. The protocol herein describes the overall design of the arraying template, a detailed experimental setup and step-by-step procedure, followed by representative results. When implemented, this approach should prove beneficial in a variety of research applications using zebrafish embryos as testing subjects.

A 3-dimensional 3D-printed Template for High Throughput Zebrafish Embryo Arraying

Here, we present a protocol to design and fabricate a zebrafish embryo arraying template, followed by a detailed procedure on the use of such template for high throughput zebrafish embryo arraying into a 96-well plate.

The zebrafish is a globally recognized fresh water organism frequently used in developmental biology, environmental toxicology, and human disease related ...

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Landscape topography is a critical factor affecting soil formation and plays an important role in determining soil properties on the earth surface, as it regulates the gravity-driven soil movement induced by runoff and tillage activities. The recent application of Light Detection and Ranging (LiDAR) data holds promise for generating high spatial resolution topographic metrics that can be used to investigate soil property variability. In this study, fifteen topographic metrics derived from LiDAR data were used to investigate topographic impacts on redistribution of soil and spatial distribution of soil organic carbon (SOC). Specifically, we explored the use of topographic principal components (TPCs) for characterizing topography metrics and stepwise principal component regression (SPCR) to develop topography-based soil erosion and SOC models at site and watershed scales. Performance of SPCR models was evaluated against stepwise ordinary least square regression (SOLSR) models. Results showed that SPCR models outperformed SOLSR models in predicting soil redistribution rates and SOC density at different spatial scales. Use of TPCs removes potential collinearity between individual input variables, and dimensionality reduction by principal component analysis (PCA) diminishes the risk of overfitting the prediction models. This study proposes a new approach for modeling soil redistribution across various spatial scales. For one application, access to private lands is often limited, and the need to extrapolate findings from representative study sites to larger settings that include private lands can be important.

Landscape processes are critical components of soil formation and play important roles in determining soil properties and spatial structure in landscapes. We propose a new approach using stepwise principal component regression to predict soil redistribution and soil organic carbon across various spatial scales.

Landscape topography is a critical factor affecting soil formation and plays an important role in determining soil properties on the earth surface, as it ...

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

Applying toxicity testing of chemicals in higher order organisms, such as mice or rats, is time-consuming and expensive, due to their long lifespan and maintenance issues. On the contrary, the nematode Caenorhabditis elegans (C. elegans) has advantages to make it an ideal choice for toxicity testing: a short lifespan, easy cultivation, and efficient reproduction. Here, we describe a protocol for the automatic phenotypic profiling of C. elegans in a 384-well plate. The nematode worms are cultured in a 384-well plate with liquid medium and chemical treatment, and videos are taken of each well to quantify the chemical influence on 33 worm features. Experimental results demonstrate that the quantified phenotype features can classify and predict the acute toxicity for different chemical compounds and establish a priority list for further traditional chemical toxicity assessment tests in a rodent model.

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

A quantitative method has been developed to identify and predict the acute toxicity of chemicals by automatically analyzing the phenotypic profiling of Caenorhabditis elegans. This protocol describes how to treat worms with chemicals in a 384-well plate, capture videos, and quantify toxicological related phenotypes.

Applying toxicity testing of chemicals in higher order organisms, such as mice or rats, is time-consuming and expensive, due to their long lifespan and ...