Thanks to all of our collaborators who have assisted with sweet corn field collections, including Dominic Reisig and Dan Mott at North Carolina State University, and Pat Porter at Texas A&#38; M University in Lubbock, TX. Thanks to Fiona Clissold for helping to optimize the protocols and for providing edits to this manuscript. This work was supported in part by the Texas A&#38; M C. Everette Salyer Fellowship (Department of Entomology) and the Biotechnology Risk Assessment Grant Program competitive grant no. 2015-33522-24099 from the U.S. Department of Agriculture (awarded to GAS and STB).

1. Plant Collection and Processing
<ol>
	<li>Collect and process plant samples
	<ol>
		<li>After collecting plant samples, flash-freeze samples by dipping plant material into liquid nitrogen with forceps and store at -80 &#176;C. If the plant samples collected are too large to flash-freeze, quickly cool the samples using dry ice and transfer to a -80 &#176;C freezer as soon as possible. The macronutrient content of plant material can change after tissues are separated from the plant, so it is important to freeze plant samples as soon as possible after collection. 
		CAUTION: Liquid nitrogen can cause severe frostbite when in contact with skin. Please make sure to transport liquid nitrogen in approved containers and wear proper PPE during its handling, including cryo-gloves, eye googles, face shield, apron, and a lab coat.</li>
		<li>Lyophilize plant material using a freeze-dryer to ensure that the tissues are metabolically inactive while water is being removed. Dry until the mass of the sample stabilizes to ensure all water has been removed.</li>
		<li>Once samples are dry, grind into a fine powder using a grinder or mill. Store samples in a desiccating cabinet until analysis.</li>
	</ol>
	</li>
</ol>
2. Soluble Protein Assay
<ol>
	<li>Sample Preparation
	<ol>
		<li>Weigh out replicate samples of each tissue, approximately 20 mg each, into 1.5 mL polystyrene microcentrifuge tubes and label tubes. These samples will subsequently be referred to as unknown samples. Record the exact mass of each sample, as this information will be required to calculate the %soluble protein in each unknown sample. Use microcentrifuge tubes with locking lids, as non-locking lids may open during the subsequent heating step, resulting in loss of sample solution.</li>
	</ol>
	</li>
	<li>Solubilize and isolate unknown sample proteins
	<ol>
		<li>Using a micro-pipettor, add 500 &#181;L of 0.1 M NaOH to each tube. Close lids tightly and sonicate for 30 minutes. Preheat a hot water bath 90 &#176;C. 
		CAUTION: Sodium hydroxide is a strong base and can cause burns when in contact with skin. Although 0.1 M NaOH represents a low concentration, wear protective gloves to avoid skin irritation.</li>
		<li>Place tubes in a rack in the hot water bath for 15 minutes.</li>
		<li>Centrifuge tubes at 15,000 x g for 10 minutes. Pipette the supernatant liquid into new labeled microcentrifuge tubes, using a new pipette tip for each sample. Add 300 &#181;L of 0.1 M NaOH to the tube containing the pellet and centrifuge again at 15,000 x g for 10 minutes. Again, collect the supernatant liquid and combine it with the other supernatant fluid from the same sample. This is a potential stopping point, and samples can be stored at 4 &#176;C overnight if needed.</li>
	</ol>
	</li>
	<li>Precipitate plant proteins
	<ol>
		<li>Neutralize the pH of the supernatant solution by adding 11 &#181;L of 5.8 M HCl. Confirm a pH of ~7 by dipping litmus paper into each sample solution. 
		CAUTION: Hydrochloric acid is a strong corrosive acid that can cause burns when in contact with skin (skin application or inhalation). Please wear gloves to prevent skin irritation while handling HCl.</li>
		<li>Add 90 &#181;L of 100% trichloroacetic acid (TCA) to each tube and incubate tubes on ice for 30 minutes. The precipitation of proteins will turn the solution from clear to opaque. If this does not occur after 30 minutes, refrigerate the tubes for 1 hour. This is a potential stopping point, and samples can be stored at 4 &#176;C overnight if needed. 
		CAUTION: Trichloroacetic acid is corrosive. Please wear gloves when handling to prevent contact with skin and avoid inhalation.</li>
		<li>Centrifuge samples at 13,000 x g for 10 minutes at 4 &#176;C. Carefully remove TCA supernatant by suctioning it out using a vacuum line attached to a fine glass micropipette tip (the same tip can be used across samples). Do not disturb the protein pellet by avoiding heavy suction and keeping an appropriate distance between pipette tip and the pellet.</li>
		<li>Wash pellet quickly with 100 &#181;L of -20 &#176;C acetone. This step removes any remaining TCA from the pellet, which can interfere with the subsequent Bradford assay; however, acetone will start to dissolve the protein pellet if left in contact too long, so acetone should be added then quickly extracted from the pellet within 5 seconds.</li>
		<li>Allow acetone to evaporate by placing tubes in a fume hood or refrigerator. Then, dissolve the protein pellet by adding 1 mL of 0.1 M NaOH to each tube. Fully dissolving the pellet may require several rounds of heating in a hot water bath, vortexing, and sonicating. 
		Note: The longer the tubes sit in the fume hood or refrigerator and the drier the pellets get, the more difficult they will be to re-solubilize. It is suggested to dry the pellet for 30 minutes, and then check for the presence of acetone every 10-15 minutes until it is clear that the acetone has evaporated (no acetone fumes are detectable).</li>
	</ol>
	</li>
	<li>Mix standard solutions, dilute unknown sample solutions, and quantify the total protein content of unknown samples using the Bradford assay.
	<ol>
		<li>Prepare bovine immunoglobulin G (IgG) standard solutions according to the concentrations listed in Table 1 (lyophilized or IgG stock solution can be mixed with deionized water to achieve each concentration). Store standards at 4 &#176;C. In a 96-well plate, add 160 &#181;L of each IgG standard solution in triplicate starting at the A1 position of the well plate (0 &#181;g/&#181;L in A1, A2, A3 position, 0.0125 &#181;g/&#181;L in B1, B2, B3 positions, etc.).</li>
		<li>Prepare a diluted NaOH solution by adding 50 &#181;L of 0.1 M NaOH to 950 &#181;L of distilled water. As a blank negative control, add 60 &#181;L of this solution to the well plate in triplicate (G1, G2, G3 positions).</li>
		<li>Prepare dilutions of unknown samples by adding 50 &#181;L of each sample solution to 950 &#181;L of distilled water in a new 1.5 mL microcentrifuge tube. Then, add 60 &#181;L of each diluted sample to the well plate in triplicate, starting at the H1 position. A 96-well plate should allow for the analysis of 6 standards, 1 blank, and 25 unknown samples when read in triplicate. Each well plate analyzed should contain its own IgG standard and blank samples.</li>
		<li>Add 100 &#181;L of distilled water to all blank and unknown samples wells. Now each well should contain a total of 160 &#181;L. Using a multi-channel pipette (8 channels), add 40 &#181;L of Coomassie brilliant blue G-250 protein dye to every well in the first column of the well plate. Mix the dye with the samples by plunging several times. Repeat for all other columns, replacing pipette tips to avoid contamination. 
		CAUTION: Coomassie brilliant blue G-250 is an irritant and contact with skin, eyes, or lungs should be avoided. Please wear gloves to prevent contact with skin. If&#160;contact with skin occurs, this product will stain.</li>
		<li>Use a needle to pop any bubbles present in the wells, as they may interfere with the microplate spectrophotometer reading. Clean the needle to avoid contamination between wells. Let the microplate incubate at room temperature for 5 minutes.</li>
		<li>Using a microplate spectrophotometer, record the absorbance values for each well at 595 nm.</li>
		<li>Record the average absorbance for the three blank wells and subtract this value from each of the standard and unknown sample readings. Then, record the average absorbance for each standard and unknown sample. 
		Note: To calculate the amount of protein present in each unknown sample using the standard curve, it is easiest to regress the average absorbance of each standard against the micrograms of protein present in each standard, but one can&#160;plot the protein concentration (&#181;g/&#181;L) of each standard if desirable. The following calculations, however, refer to a standard curve based on micrograms, not concentrations (&#181;g/&#181;L).</li>
		<li>Plot the average absorbance of each standard against the total amount of protein present in each standard well (Table 1). Fit a linear regression line to these data and use the equation to calculate the amount of protein (&#181;g) in each unknown sample well based on the average absorbance (Figure 1a). 
		Note: The correlation coefficient (r value) of this line should be greater than 0.98 and routinely near 0.99. The standard regression will be specific to each plate, hence the unknown sample wells in each plate must be analyzed separately.</li>
		<li>Once the average amount of protein is calculated for each unknown sample well, use the following equation to calculate the total amount of protein (&#181;g) in each original sample: 
		&#160;&#160;&#160;&#160;&#160;Mp = (((Wp/60) x 1000)/50)*1000 
		&#160;&#160;&#160;&#160;&#160;Mp = &#181;g of protein in original sample 
		&#160;&#160;&#160;&#160;&#160;Wp = &#181;g of protein in unknown sample well
		<ol>
			<li>Then, use the following equation to determine the percentage of protein in each sample: 
			&#160;&#160;&#160;&#160;&#160;Pp = ((Mp/1000)/Mi)*100 
			&#160;&#160;&#160;&#160;&#160;Pp =%protein in sample 
			&#160;&#160;&#160;&#160;&#160;Mi = initial mass of sample (mg) 
			Note: In some cases, the sample may surpass the upper absorbance threshold. If so, sample solutions may require further dilution at step 2.4.3. 
			Additional dilutions must then be accounted for in subsequent calculations. Some microplate reader brands also provide computer software that will automatically calculate the average protein concentration of each unknown sample based on the standard curve data.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
3. Digestible Carbohydrate Assay
<ol>
	<li>Sample Preparation
	<ol>
		<li>Weigh out replicate samples of each tissue, approximately 20 mg each, into glass 15 mL tubes with rubber-lined screw caps (100 mm length). These sample will subsequently be referred to as unknown samples. Label tubes with a waterproof marker or with labelling tape on the lids and record the exact mass of each sample, as this information will be required to calculate the %carbohydrates in each unknown sample.</li>
	</ol>
	</li>
	<li>Extract digestible carbohydrates from each unknown sample.
	<ol>
		<li>Add 1 mL of 0.1 M H2SO4 to each tube and screw caps onto tubes tightly. Place in a boiling water bath for 1 hour. 
		CAUTION: Sulfuric acid is very corrosive. Please wear gloves, eye googles, and an apron when handling H2SO4 and perform this step in a fume hood.</li>
		<li>Cool off tubes in a tepid water bath. Pour tube contents into labeled 1.5 mL microcentrifuge tubes (do not be concerned if some plant material remains in the glass tubes).</li>
		<li>Centrifuge tubes at 15,000 x g for 10 minutes. Remove the supernatant liquid with a micro-pipettor and place into new labeled 1.5 mL microcentrifuge tubes. Tubes can be refrigerated overnight at this point. If continuing with assay, refer to step 3.3.2.</li>
	</ol>
	</li>
	<li>Mix standard solutions and quantify the total digestible carbohydrate content of unknown samples.
	<ol>
		<li>Prepare D(+) glucose standard solutions with the concentrations listed in Table 2. Prepare six glass test tubes with 400 &#181;L of each standard solution.</li>
		<li>Pipette 15 &#181;L of each unknown sample into its own test tube and add 385 &#181;L of distilled water to each for a total volume of 400 &#181;L in each test tube. In a fume hood, add 400 &#181;L of 5% phenol to each standard and unknown sample test tube, and then quickly pipette 2 mL of concentrated H2SO4 into each tube. Do not touch the test tube contents with the pipette tip, just add to the solution surface (a repeater pipette works best for this).</li>
		<li>Let tubes incubate for 10 minutes, and then carefully vortex the tubes to mix contents. Incubate for an additional 30 minutes. 
		CAUTION: Phenol and sulfuric acid are corrosive irritants. To protect from exposure to skin, eyes, and inhalation, this step should be performed in a chemical fume hood using the proper PPE, which includes: face shield or eye goggles, rubber gloves, lab apron, and boots. Mixing phenol with sulfuric acid also results in an exothermic reaction, which will result in the tubes becoming hot.</li>
		<li>Pipette 800 &#181;L of sample from each tube into each of 3 polystyrene 1.5 mL semi-micro cuvettes (3 technical replicates per sample). Set the spectrophotometer to read at 490 nm. Calibrate to a blank cuvette containing distilled water before reading standards or unknown samples, and intermittently throughout sample readings. Run each cuvette through a spectrophotometer and record the absorbance. Average across technical replicates for each unknown sample. 
		Note: In some cases, samples may surpass the upper absorbance threshold. If so, samples should be diluted at step 3.2.3. Dilutions must also be accounted for in subsequent calculations.</li>
		<li>Calculate the average absorbance for each standard. 
		Note: To calculate the amount of total digestible carbohydrates present in each unknown sample using the standard curve, it is easiest to regress the average absorbance of each standard against the micrograms of D(+)glucose present in each standard, but one can also&#160;plot the D(+)glucose concentration (&#181;g/&#181;L) of each standard if desirable. The following calculations, however, refer to a standard curve based on micrograms, not concentrations (&#181;g/&#181;L).</li>
		<li>Calculate the standard curve by plotting the average absorbance against the total amount of D(+)glucose (&#181;g) in each standard solution. Fit a linear regression line to these data, and then use the following equation to calculate the total amount of carbohydrates (&#181;g) in each unknown sample: 
		&#160;&#160;&#160;&#160;&#160;Mc = (((Ax &#8211; b)/m)/(15))*1000 
		&#160;&#160;&#160;&#160;&#160;Mc = &#181;g of carbohydrates in sample 
		&#160;&#160;&#160;&#160;&#160;Ax = average absorbance of unknown sample 
		&#160;&#160;&#160;&#160;&#160;b = y-intercept (standard regression line) 
		&#160;&#160;&#160;&#160;&#160;m = slope (standard regression line) 
		The correlation coefficient of this line should be greater than 0.98 and routinely near 0.99. Then, use the following equation to determine the percentage of carbohydrates in each sample: 
		&#160;&#160;&#160;&#160;&#160;Pc = ((Mc/1000)/(Mi))*100 
		&#160;&#160;&#160;&#160;&#160;Pc =%carbohydrates 
		&#160;&#160;&#160;&#160;&#160;Mi = initial mass of sample (mg)</li>
	</ol>
	</li>
</ol>

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J., Sword, G. A., Lorch, P. D., Couzin, I. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cannibal+crickets+on+a+forced+march+for+protein+and+salt.">Cannibal crickets on a forced march for protein and salt.</a> Proceedings of the National Academy of Sciences of the United States of America. 103 (11), 4152-4156 (2006).</li><li>Lihoreau, M., Buhl, J., Sword, G. A., Raubenheimer, D., Simpson, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nutritional+ecology+beyond+the+individual:+a+conceptual+framework+for+integrating+nutrition+and+social+interactions.">Nutritional ecology beyond the individual: a conceptual framework for integrating nutrition and social interactions.</a> Ecology Letters. 18 (3), 273-286 (2015).</li><li>Deans, C. A., Behmer, S. T., Tessnow, A., Tamez-Guerra, P., Pusztai-Carey, M., Sword, G. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nutrition+affects+insect+susceptibility+to+Bt.">Nutrition affects insect susceptibility to Bt.</a> Scientific Reports. 7, 39705 (2017).</li></ol>

The authors have nothing to disclose.

By combining well-established colorimetric assays with effective plant-specific extraction protocols, the assays demonstrated here provide a reasonable and accurate method for measuring plant soluble protein and digestible carbohydrate content. Our results using corn as an exemplar illustrates how these protocols can be used to obtain precise measurements across different biologically-relevant spatial scales. For example, we were able to detect differences in plant soluble protein and digestible carbohydrate content betw...

Plant biomass forms the foundation of virtually all terrestrial food-webs. Plants acquire nutritional elements from the soil through their roots systems and utilize sunlight in their foliar tissues to synthesize biomolecules. In particular, carbon and nitrogen are used to create carbohydrates, proteins (comprised of amino acids), and lipids that are needed to build plant biomass (it should be noted that in plant physiology the term &#34;macronutrient&#34; often refers to soil elements, such as N, P, K, and S, however, throughout this paper this term will refer to biomolecules, such as proteins, carbohydrates, and lipids). When herbivores consume plant material, the macronutrients contained in plant tissues are broken down into their constituent parts and then used to drive the physiological processes of the consumer. In this way, plant macronutrients have a strong influence on consumer physiology along with important implications for higher order ecological interactions and food-web dynamics.
Across the animal kingdom, soluble protein and digestible carbohydrates are the macronutrients most closely tied to survival, reproduction, and performance1. Moreover, the majority of animals actively regulate their intake of these two macronutrients to meet their physiological demands1,2. This is particularly true for insect herbivores that detect the concentrations of sugars and amino acids in plant tissues, which in turn directs feeding behavior. As a result, plant soluble protein and digestible carbohydrate content has played a strong role in the evolution of plant-insect interactions.
While data on plant soluble protein and digestible carbohydrate content are relatively rare (but see6,7,8,9,10,11), there is a preponderance of data available on plant elemental content (carbon, nitrogen, and phosphorus). Largely this is because elements play a primary role in plant mineral nutrition3,4,5. Where elements are measured, correlations have been used to extrapolate the amount of soluble protein and digestible carbohydrate, but accurate calculations are often difficult to obtain. For instance, it is impossible to use carbon as an indicator of plant digestible carbohydrate content because carbon is ubiquitously present in all organic compounds. A stronger relationship exists between elemental nitrogen and plant soluble protein content, and generalized nitrogen-to-protein conversion factors are often utilized. However, there is strong evidence that nitrogen-to-protein conversions are highly species-specific12,13,14,15, making the use of generalized conversion likely inaccurate. Because of this, nitrogen-to-protein conversion factors often lack precision, particularly to the extent that is required for nutritional studies on herbivores. Also, the presence of N-containing plant allelochemicals,&#160;such as alkaloids and glucosinolates that are often toxic to herbivores, can confound these conversions.
Here, we offer two chemical assays for measuring the concentration of soluble proteins and digestible carbohydrates in plant tissues. These assays are presented separately, but it is suggested that they be used concurrently to analyze the same plant samples in order to achieve a more comprehensive analysis of plant macronutrients. Both employ similar methodologies, consisting of an extraction step, followed by quantification via absorbance. Plant sample prep is also identical for both protocols, making it easy to run both analyses in tandem. The utility of these assays do not stem from their novelty, as they rely on older, (Bradford, Jones, Dubois) well-established colorimetric assays16,17,18, but here we have organized a clear and easy-to-follow protocol that combines these methods with more obscure plant-specific extraction techniques17,19 in order to make the application of these assays more accessible to those in plant-relevant fields.
For both assays, plant macronutrients are first extracted physically by freezing, lyophilizing, and grinding the plant material. For the soluble protein assay, further chemical extraction is done17,19 through several rounds of vortexing and heating samples in NaOH solution. The well-known Bradford assay, utilizing Coomassie brilliant blue G-250, is then used to quantify soluble proteins and polypeptides between 3,000-5,000 Daltons16,17. This assay has a detection range between 1-20 &#181;g total proteins per microplate well or &#60;25 &#181;g/mL, but does not measure free amino acids. The extraction step of the digestible carbohydrate assay is based on the dilute acid method of Smith et al.20 and allows for the isolation of soluble sugars, starch, and fructosan &#8211; but not structural carbohydrates. A phenol-sulfuric acid quantification method is taken from Dubois et al.18 and measures all mono-, oligo-, and polysaccharides (as well as methyl derivatives). This assay is able to quantify specific sugars, but here we use it as an indicator of total digestible carbohydrate content (see Smith et al.20 for more detailed analysis). Together, these assays measure the two macronutrients that are strongly tied to plant eco-physiology and herbivore performance, providing important data on resource quality at the base of terrestrial food-webs. Presenting these protocols promotes the generation of plant macronutrient datasets in order to obtain a more thorough understanding of plant physiology, herbivore nutritional ecology, and plant-herbivore interactions.

<table><tbody><tr><td>microplate reader (spectrophotometer)</td><td>Bio-Rad</td><td>Model 680 XR</td><td></td></tr><tr><td>Bio-Rad Protein Assay Dye Reagent concentrate</td><td>Bio-Rad</td><td>#5000006</td><td>450mL</td></tr></tbody></table>

quantifying plant soluble protein and digestible carbohydrate content, using corn (zea mays) as an exemplar

Elemental data are commonly used to infer plant quality as a resource to herbivores. However, the ubiquity of carbon in biomolecules, the presence of nitrogen-containing plant defensive compounds, and variation in species-specific correlations between nitrogen and plant protein content all limit the accuracy of these inferences. Additionally, research focused on plant and/or herbivore physiology require a level of accuracy that is not achieved using generalized correlations. The methods presented here offer researchers a clear and rapid protocol for directly measuring plant soluble proteins and digestible carbohydrates, the two plant macronutrients most closely tied to animal physiological performance. The protocols combine well characterized colorimetric assays with optimized plant-specific digestion steps to provide precise and reproducible results. Our analyses of different sweet corn tissues show that these assays have the sensitivity to detect variation in plant soluble protein and digestible carbohydrate content across multiple spatial scales. These include between-plant differences across growing regions and plant species or varieties, as well as within-plant differences in tissue type and even positional differences within the same tissue. Combining soluble protein and digestible carbohydrate content with elemental data also has the potential to provide new opportunities in plant biology to connect plant mineral nutrition with plant physiological processes. These analyses also help generate the soluble protein and digestible carbohydrate data needed to study nutritional ecology, plant-herbivore interactions and food-web dynamics, which will in turn enhance physiology and ecological research.

To show the usefulness of these methods, we analyzed the soluble protein and digestible carbohydrate content of four different field and sweetcorn tissues that serve as distinct potential nutritional resources for insect herbivores. We collected ears of corn from three agricultural regions in the United States (Minnesota, North Carolina, and Texas), encompassing five different varieties of sweet corn (i.e., genotypes) and one variety of field corn as an outgroup. Table 3<...

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Quantifying Plant Soluble Protein and Digestible Carbohydrate Content, Using Corn Zea mays As an Exemplar

The protocols described herein provide a clear and approachable methodology for measuring soluble protein and digestible (non-structural) carbohydrate content in plant tissues. The ability to quantify these two plant macronutrients has significant implications for advancing the fields of plant physiology, nutritional ecology, plant-herbivore interactions and food-web ecology.

Quantifying Plant Soluble Protein and Digestible Carbohydrate Content, Using Corn (Zea mays) As an Exemplar

Elemental data are commonly used to infer plant quality as a resource to herbivores. However, the ubiquity of carbon in biomolecules, the presence of ...

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19.7K Views.  Texas A&M University. The protocols described herein provide a clear and approachable methodology for measuring soluble protein and digestible (non-structural) carbohydrate content in plant tissues. The ability to quantify these two plant macronutrients has significant implications for advancing the fields of plant physiology, nutritional ecology, plant-herbivore interactions and food-web ecology.

Video: Quantifying Plant Soluble Protein and Digestible Carbohydrate Content, Using Corn Zea mays As an Exemplar

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize (Zea mays L.)

Maize is an important grain crop in the United States and worldwide. However, maize grain must be processed prior to human consumption. Furthermore, whole grain composition and processing characteristics vary among maize hybrids and can impact the quality of the final processed product. Therefore, in order to produce healthier processed food products from maize, it is necessary to know how to optimize processing parameters for particular sets of germplasm to account for these differences in grain composition and processing characteristics. This includes a better understanding of how current processing techniques impact the nutritional quality of the final processed food product. Here, we describe a microscale protocol that both simulates the processing pipeline to produce cornflakes from large flaking grits and allows for the processing of multiple grain samples simultaneously. The flaking grits, the intermediate processed products, or final processed product, as well as the corn grain itself, can be analyzed for nutritional content as part of a high-throughput analytical pipeline. This procedure was developed specifically for incorporation into a maize breeding research program, and it can be modified for other grain crops. We provide an example of the analysis of insoluble-bound ferulic acid and p-coumaric acid content in maize. Samples were taken at five different processing stages. We demonstrate that sampling can take place at multiple stages during microscale processing, that the processing technique can be utilized in the context of a specialized maize breeding program, and that, in our example, most of the nutritional content was lost during food product processing.

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

Here, we present a microscale protocol for processing grain samples and for incorporating this microscale approach into a high-throughput analytical pipeline. This is a higher throughput adaptation of currently available protocols.

Maize is an important grain crop in the United States and worldwide. However, maize grain must be processed prior to human consumption. Furthermore, whole ...

Lignin Down-regulation of Zea mays via dsRNAi and Klason Lignin Analysis

To facilitate the use of lignocellulosic biomass as an alternative bioenergy resource, during biological conversion processes, a pretreatment step is needed to open up the structure of the plant cell wall, increasing the accessibility of the cell wall carbohydrates. Lignin, a polyphenolic material present in many cell wall types, is known to be a significant hindrance to enzyme access. Reduction in lignin content to a level that does not interfere with the structural integrity and defense system of the plant might be a valuable step to reduce the costs of bioethanol production. In this study, we have genetically down-regulated one of the lignin biosynthesis-related genes, cinnamoyl-CoA reductase (ZmCCR1) via a double stranded RNA interference technique. The ZmCCR1_RNAi construct was integrated into the maize genome using the particle bombardment method. Transgenic maize plants grew normally as compared to the wild-type control plants without interfering with biomass growth or defense mechanisms, with the exception of displaying of brown-coloration in transgenic plants leaf mid-ribs, husks, and stems. The microscopic analyses, in conjunction with the histological assay, revealed that the leaf sclerenchyma fibers were thinned but the structure and size of other major vascular system components was not altered. The lignin content in the transgenic maize was reduced by 7-8.7%, the crystalline cellulose content was increased in response to lignin reduction, and hemicelluloses remained unchanged. The analyses may indicate that carbon flow might have been shifted from lignin biosynthesis to cellulose biosynthesis. This article delineates the procedures used to down-regulate the lignin content in maize via RNAi technology, and the cell wall compositional analyses used to verify the effect of the modifications on the cell wall structure.

Lignin Down-regulation of Zea mays via dsRNAi and Klason Lignin Analysis

A double stranded RNA interference (dsRNAi) technique is employed to down-regulate the maize cinnamoyl coenzyme A reductase (ZmCCR1) gene to lower plant lignin content. Lignin down-regulation from the cell wall is visualized by microscopic analyses and quantified by the Klason method. Compositional changes in hemicellulose and crystalline cellulose are analyzed.

To facilitate the use of lignocellulosic biomass as an alternative bioenergy resource, during biological conversion processes, a pretreatment step is ...

Bioengineering

Estimation of Crystalline Cellulose Content of Plant Biomass using the Updegraff Method

Cellulose is the most abundant polymer on Earth generated by photosynthesis and the main load-bearing component of cell walls. The cell wall plays a significant role in plant growth and development by providing strength, rigidity, rate and direction of cell growth, cell shape maintenance, and protection from biotic and abiotic stressors. The cell wall is primarily composed of cellulose, lignin, hemicellulose and pectin. Recently plant cell walls have been targeted for the second-generation biofuel and bioenergy production. Specifically, the cellulose component of the plant cell wall is used for the production of cellulosic ethanol. Estimation of cellulose content of biomass is critical for fundamental and applied cell wall research. The Updegraff method is simple, robust, and the most widely used method for the estimation of crystalline cellulose content of plant biomass. The alcohol insoluble crude cell wall fraction upon treatment with Updegraff reagent eliminates the hemicellulose and lignin fractions. Later, the Updegraff reagent resistant cellulose fraction is subjected to sulfuric acid treatment to hydrolyze the cellulose homopolymer into monomeric glucose units. A regression line is developed using various concentrations of glucose and used to estimate the amount of the glucose released upon cellulose hydrolysis in the experimental samples. Finally, the cellulose content is estimated based on the amount of glucose monomers by colorimetric anthrone assay.

The Updegraff method is the most widely used method for the cellulose estimation. The main purpose of this demonstration is to provide a detailed Updegraff protocol for estimation of cellulose content in plant biomass samples.

Cellulose is the most abundant polymer on Earth generated by photosynthesis and the main load-bearing component of cell walls. The cell wall plays a ...

Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling

The characterization of gene expression is dependent on RNA quality. In germinating, developing and mature cereal seeds, the extraction of high-quality RNA is often hindered by high starch and sugar content. These compounds can reduce both the yield and the quality of the extracted total RNA. The deterioration in quantity and quality of total RNA can subsequently have a significant impact on the downstream transcriptomic analyses, which may not accurately reflect the spatial and/or temporal variation in the gene expression profile of the samples being tested. In this protocol, we describe an optimized method for extraction of total RNA with sufficient quantity and quality to be used for whole transcriptome analysis of cereal grains. The described method is suitable for several downstream applications used for transcriptomic profiling of developing, germinating, and mature cereal seeds. The method of transcriptome profiling using a microarray platform is shown. This method is specifically designed for gene expression profiling of cereals with described genome sequences. The detailed procedure from microarray handling to final quality control is described. This includes cDNA synthesis, cRNA labelling, microarray hybridization, slide scanning, feature extraction, and data quality validation. The data generated by this method can be used to characterize the transcriptome of cereals during germination, in various stages of grain development, or at different biotic or abiotic stress conditions. The results presented here exemplify high-quality transcriptome data amenable for downstream bioinformatics analyses, such as the determination of differentially expressed genes (DEGs), characterisation of gene regulatory networks, and conducting transcriptome-wide association study (TWAS).

A method for transcriptome profiling of cereals is presented. The microarray-based gene expression profiling starts with the isolation of high-quality total RNA from cereal grains and continues with the generation of cDNA. After cRNA labelling and microarray hybridization, recommendations are given for signal detection and quality control.

The characterization of gene expression is dependent on RNA quality. In germinating, developing and mature cereal seeds, the extraction of high-quality ...

Biochemistry

Extraction and Quantification of Soluble, Radiolabeled Inositol Polyphosphates from Different Plant Species using SAX-HPLC

The phosphate esters of myo-inositol, also termed inositol phosphates (InsPs), are a class of cellular regulators playing important roles in plant physiology. Due to their negative charge, low abundance and susceptibility to hydrolytic activities, the detection and quantification of these molecules is challenging. This is particularly the case for highly phosphorylated forms containing &#8216;high-energy&#8217; diphospho bonds, also termed inositol pyrophosphates (PP-InsPs). Due to its high sensitivity, strong anion exchange high-performance liquid chromatography (SAX-HPLC) of plants labeled with [3H]-myo-inositol is currently the method of choice to analyze these molecules. By using [3H]-myo-inositol to radiolabel plant seedlings, various InsP species including several non-enantiomeric isomers can be detected and discriminated with high sensitivity. Here, the setup of a suitable SAX-HPLC system is described, as well as the complete workflow from plant cultivation, radiolabeling and InsP extraction to the SAX-HPLC run and subsequent data analysis. The protocol presented here allows the discrimination and quantification of various InsP species, including several non-enantiomeric isomers and of the PP-InsPs, InsP7 and InsP8, and can be easily adapted to other plant species. As examples, SAX-HPLC analyses of Arabidopsis thaliana and Lotus japonicus seedlings are performed and complete InsP profiles are presented and discussed. The method described here represents a promising tool to better understand the biological roles of InsPs in plants.

Here we describe strong anion exchange high-performance liquid chromatography of [3H]-myo-inositol-labeled seedlings which is a highly sensitive method to detect and quantify inositol polyphosphates in plants.

The phosphate esters of myo-inositol, also termed inositol phosphates (InsPs), are a class of cellular regulators playing important roles in plant ...

Comprehensive Compositional Analysis of Plant Cell Walls (Lignocellulosic biomass) Part II: Carbohydrates

The need for renewable, carbon neutral, and sustainable raw materials for industry and society has become one of the most pressing issues for the 21st century. This has rekindled interest in the use of plant products as industrial raw materials for the production of liquid fuels for transportation2 and other products such as biocomposite materials6. Plant biomass remains one of the greatest untapped reserves on the planet4. It is mostly comprised of cell walls that are composed of energy rich polymers including cellulose, various hemicelluloses, and the polyphenol lignin5 and thus sometimes termed lignocellulosics. However, plant cell walls have evolved to be recalcitrant to degradation as walls contribute extensively to the strength and structural integrity of the entire plant. Despite its necessary rigidity, the cell wall is a highly dynamic entity that is metabolically active and plays crucial roles in numerous cell activities such as plant growth and differentiation5. Due to the various functions of walls, there is an immense structural diversity within the walls of different plant species and cell types within a single plant4. Hence, depending of what crop species, crop variety, or plant tissue is used for a biorefinery, the processing steps for depolymerisation by chemical/enzymatic processes and subsequent fermentation of the various sugars to liquid biofuels need to be adjusted and optimized. This fact underpins the need for a thorough characterization of plant biomass feedstocks. Here we describe a comprehensive analytical methodology that enables the determination of the composition of lignocellulosics and is amenable to a medium to high-throughput analysis (Figure 1). The method starts of with preparing destarched cell wall material. The resulting lignocellulosics are then split up to determine its monosaccharide composition of the hemicelluloses and other matrix polysaccharides1, and its content of crystalline cellulose7. The protocol for analyzing the lignin components in lignocellulosic biomass is discussed in Part I3.

Comprehensive Compositional Analysis of Plant Cell Walls Lignocellulosic biomass Part II: Carbohydrates

Plant biomass is a major carbon-neutral renewable resource that could be used for the production of biofuels. Plant biomass consists mainly of cell walls, a structurally complex composite material termed lignocellulosics. Here we describe a protocol for a comprehensive analysis of the content and composition of wall derived carbohydrates.

The need for renewable, carbon neutral, and sustainable raw materials for industry and society has become one of the most pressing issues for the 21st ...

Biology

High-throughput Saccharification Assay for Lignocellulosic Materials

Polysaccharides that make up plant lignocellulosic biomass can be broken down to produce a range of sugars that subsequently can be 
used in establishing a biorefinery. These raw materials would constitute a new industrial platform, which is both sustainable and carbon neutral, to replace 
the current dependency on fossil fuel. The recalcitrance to deconstruction observed in lignocellulosic materials is produced by several intrinsic properties 
of plant cell walls. Crystalline cellulose is embedded in matrix polysaccharides such as xylans and arabinoxylans, and the whole structure is encased by the 
phenolic polymer lignin, that is also difficult to digest 1. In order to improve the digestibility of plant materials we need to discover the main bottlenecks 
for the saccharification of cell walls and also screen mutant and breeding populations to evaluate the variability in saccharification 2. These tasks require 
a high throughput approach and here we present an analytical platform that can perform saccharification analysis in a 96-well plate format. This platform has 
been developed to allow the screening of lignocellulose digestibility of large populations from varied plant species. We have scaled down the reaction volumes 
for gentle pretreatment, partial enzymatic hydrolysis and sugar determination, to allow large numbers to be assessed rapidly in an automated system.
This automated platform works with milligram amounts of biomass, performing ball milling under controlled conditions to reduce the plant 
materials to a standardised particle size in a reproducible manner. Once the samples are ground, the automated formatting robot dispenses specified and recorded 
amounts of material into the corresponding wells of 96 deep well plate (Figure 1). Normally, we dispense the same material into 4 wells to have 4 replicates for 
analysis. Once the plates are filled with the plant material in the desired layout, they are manually moved to a liquid handling station (Figure 2). In this 
station the samples are subjected to a mild pretreatment with either dilute acid or alkaline and incubated at temperatures of up to 90&deg;C. The pretreatment solution 
is subsequently removed and the samples are rinsed with buffer to return them to a suitable pH for hydrolysis. The samples are then incubated with an enzyme
mixture for a variable length of time at 50&deg;C. An aliquot is taken from the hydrolyzate and the reducing sugars are automatically determined by the MBTH 
colorimetric method.

A simple, rapid method for determining the saccharification potential of large numbers of plant biomass samples is described. The automated platform for this analysis involves the preparation of the plant biomass for analysis in 96 well plates and the subsequent performance of pretreatment, hydrolysis and quantification of the sugars released.

Polysaccharides that make up plant lignocellulosic biomass can be broken down to produce a range of sugars that subsequently can be 
used in establishing ...

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Measuring quantitative plant phenotypes, or traits, is essential for understanding plant responses to environmental factors, including biotic and abiotic stresses. However, traditional methods of measuring plants can be time-consuming, inaccurate, and destructive, particularly when assessing traits such as biomass and height on an individual basis. Image-based analysis offers an opportunity to reduce labor, improve accuracy and precision, and enable repeated measurements of the same plant over multiple time points. While high-throughput phenotyping facilities provide a solution, they are often prohibitively expensive and limited in availability worldwide. A method is described for capturing plant images using low-cost single-board computers and digital cameras within a photo studio setup, accompanied by an image analysis pipeline based on the free and open-source package PlantCV. This method offers a fast and accurate protocol for image capture, labeling, and transfer, along with best practices for ensuring high-quality image acquisition. Through image analysis, more than 10 phenotypes were quantified for all images in the experiment simultaneously, including traits such as plant size, height, and color. These methods were used to characterize the response of the Zea mays (maize) inbred variety B73 to heat, drought, and combined heat and drought abiotic stress conditions.

A method is presented for low-cost imaging of plants to measure phenotypes, along with best practices for image capture and an image analysis pipeline for quantifying plant traits. These methods were applied to measure maize (Zea mays) phenotypes under heat, drought, and combined abiotic stress conditions.

Measuring quantitative plant phenotypes, or traits, is essential for understanding plant responses to environmental factors, including biotic and abiotic ...

Soluble Protein Assay to Quantify Soluble Protein Content in Plant Tissues

Source: Deans, C. A. et al. <a href="https://app.jove.com/v/58164">Quantifying Plant Soluble Protein and Digestible Carbohydrate Content, Using Corn (Zea mays) As an Exemplar.</a> J. Vis. Exp. (2018)
This video describes a colorimetric method to quantify the soluble protein content in plant tissues. The method uses Coomassie Brilliant Blue G-250 dye, which interacts with the soluble protein to form a blue-colored stable protein-dye complex, proportional to the soluble protein content in the tissue.

Source: Deans, C. A. et al. Quantifying Plant Soluble Protein and Digestible Carbohydrate Content, Using Corn (Zea mays) As an Exemplar. J. Vis. Exp. ...

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Biological Techniques

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Biochemical assays

Colorimetric Assay for the Quantification of Non-Structural Carbohydrates in Plant Tissues

Source: Deans, C. A. et al. <a href="https://app.jove.com/v/58164">Quantifying Plant Soluble Protein and Digestible Carbohydrate Content, Using Corn (Zea mays) As an Exemplar.</a> J. Vis. Exp. (2018).
This video describes the method to quantify the digestible carbohydrates present in plant tissues. This helps to collect data from various plant tissues to study nutritional ecology, plant-herbivore interactions, and food-web dynamics.

Quantifying Plant Soluble Protein and Digestible Carbohydrate Content, Using Corn (Zea mays) As an Exemplar

Summary

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Chapters in this video

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize (Zea mays L.)

Lignin Down-regulation of Zea mays via dsRNAi and Klason Lignin Analysis

Estimation of Crystalline Cellulose Content of Plant Biomass using the Updegraff Method

Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling

Extraction and Quantification of Soluble, Radiolabeled Inositol Polyphosphates from Different Plant Species using SAX-HPLC

Comprehensive Compositional Analysis of Plant Cell Walls (Lignocellulosic biomass) Part II: Carbohydrates

High-throughput Saccharification Assay for Lignocellulosic Materials

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Soluble Protein Assay to Quantify Soluble Protein Content in Plant Tissues

Colorimetric Assay for the Quantification of Non-Structural Carbohydrates in Plant Tissues