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.
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.
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 "macronutrient" 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, 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 µg total proteins per microplate well or <25 µg/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 – 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.
1. Plant Collection and Processing
2. Soluble Protein Assay
3. Digestible Carbohydrate Assay
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 shows a summary of these corn samples and where they were collected. All varieties were collected at maturity, but due to developmental differences between varieties, they were not all collected at the same day after planting. We processed the ears into distinct tissues by quickly separating the husks (modified leaves surround the cob), and silks (shiny fibers between the husk and kernels) from the ear before storing all tissues at -80 °C as indicated in the Methods. Each tissue was then freeze-dried. Once dried, we separated the kernels from the base and tip of the ear by shaving off kernels from the top one-third (tip) and bottom one-third (base) of the cob. Next, all tissues were ground into a fine powder. We then analyzed husk, silk, tip kernel, and base kernel tissue samples for soluble protein and digestible carbohydrate content according to the procedures outlined in the Methods above. Given constraints on the amount of tissue available, we analyzed a total of 217 plant samples for both soluble protein and digestible carbohydrate content.
Soluble Protein
We ran nine sample plates through the spectrophotometer in total, and overall, standard curves had high correlation coefficients (r), with values between 0.985-1.00. Figure 1 shows the standard curve obtained with the highest (A) and lowest (B) r values to exhibit the variability we observed across plates. We calculated the %soluble protein for all samples using initial sample mass (Table 4) and then analyzed the data for statistical differences in soluble protein content between regions, varieties, and tissue types. Data were rank-transformed to meet normality assumptions when necessary.
We observed significant differences in soluble protein content between regions (Welch ANOVA; F(2, 133.5) = 4.303, P = 0.015), as a result, data from each region were subsequently analyzed separately. Minnesota samples showed a significant interaction between variety and tissue type on soluble protein content (two-way ANOVA; F(3, 64) = 16.51, P < 0.001). Most tissues had similar soluble protein content in both varieties, except for base kernels, where the sweet corn variety contained almost 7 times the amount compared to the field corn variety. For the field corn variety (Syngenta/NK-3122A-EZ), husks and silks were similar and had the lowest soluble protein content of all tissues. Kernels from the tip of the ear had the highest soluble protein content, and kernels from the base of the ear were intermediate (Figure 2a). For the sweet corn variety (Providence Bicolor), all tissues were distinct, with the husks and silks containing the lowest soluble protein content, and base kernels contained ~2.5 times more than tip kernels (Figure 2b).
North Carolina samples also showed a significant interaction between variety and tissue type (two-way ANOVA; F(3, 77) = 3.33, P < 0.024). Most tissues showed similar soluble protein content across varieties, except for tip kernels which exhibited a higher content in non-Bt variety (Sweet G90 Hybrid). For the Bt variety (Seedway Bt 1576) husks were the tissue with the lowest soluble protein content, followed by silks and tip kernels, which had a similar content. Base kernels had the highest soluble protein content but were not statistically different from that of tip kernels (Figure 2c). In the non-Bt variety (Sweet G90 Hybrid), all tissues were distinct except for tip and base kernels which were statistically similar. Husks had the lowest soluble protein content, followed by silks, and tip and base kernels which had the highest content (Figure 2d).
Texas samples showed significant differences in soluble protein content between varieties (two-way ANOVA; F(1, 76) = 12.91, P = 0.001) and tissues (two-way ANOVA; F(3, 76) = 21.90, P < 0.001), but no significant interaction (two-way ANOVA; F(3, 76) = 0.436, P = 0.728). Overall, the bicolor variety (Sh2 SS2742 NAT III) showed a lower average soluble protein content than the Silver Queen variety (TRTD F1 (su)). Across both varieties, husks had the lowest protein content, followed by silks, and then tip and base kernels, both of which had similarly high content (Figure 2e-f).
Digestible Carbohydrates
Because all samples were analyzed at one time, we only ran one standard curve. Figure 1c shows that the correlation coefficient was high, at 0.998. We calculated the %digestible carbohydrate content for all samples (Table 5) and then analyzed data for statistical differences in digestible carbohydrate content between regions, varieties, and tissue types. Data were rank-transformed when necessary to meet normality assumptions.
There were no significant differences in between regions (ANOVA; F(2, 216) = 1.47, P = 0.231), but for the sake of continuity with the protein analyses we again analyzed data from each region separately. Minnesota samples showed no significant differences in digestible carbohydrate content between varieties (two-way ANOVA; F(1, 64) = 0.00014, P = 0.990) or tissue type (two-way ANOVA; F(3, 64) = 0.818, P = 0.489). There was also no significant interaction between variety and tissue (two-way ANOVA; F(3, 64) = 2.26, P = 0.092). Figure 2a-b shows that all tissues exhibited an average content of 36.5% (± 0.53).
North Carolina samples showed a significant effect of tissue type on digestible carbohydrate content (two-way ANOVA; F(3, 77) = 3.99, P = 0.011), but no effect of variety (two-way ANOVA; F(1, 77) = 1.06, P = 0.307) or interaction (two-way ANOVA; F(3, 77) = 0.465, P = 0.708). Figure 2c-d shows that silks had the lowest digestible carbohydrate content, followed by husks, tip kernels, and base kernels. All tissues were statistically similar except for silks and base kernels.
Texas samples showed no significant effect of variety (two-way ANOVA; F(1, 76) = 0.834, P = 0.364) or tissue type (two-way ANOVA; F(3, 76) = 1.03, P = 0.385), but did show a significant interaction (two-way ANOVA; F(3, 76) = 3.34, P = 0.024). This effect was largely because base kernels in the bicolor variety (Sh2 SS2742 NAT III) had a significantly higher digestible carbohydrate content than those in the Silver Queen variety (TRTD F1 (su)). Figure 2e-f shows that there were no statistical differences in digestible carbohydrate content across tissue types in the Silver Queen variety (TRTD F1 (su)), but there was a significant difference between silk and base kernel tissues for the bicolor variety (Sh2 SS2742 NAT III).
Figure 1. Standard curves for macronutrient assays. (A) The standard curve for the soluble protein assay showing the plate with the highest correlation coefficient (best curve). (B) The standard curve for the soluble protein assay showing the plate with the lowest correlation coefficient (worst curve). (C) The standard curve for the digestible carbohydrate assay. Please click here to view a larger version of this figure.
Figure 2. Mean %soluble protein and %digestible carbohydrate content for each tissue type. (A) MN- Bt Syngenta/NK-3122A-EZ (field corn), (B) MN- non-Bt Providence Bicolor, (C) NC- Seedway Bt 1576, (D) NC- non-Bt Sweet G90 Hybrid, (E) TX- Sh2 SS2742 F1 NAT III Bicolor, (F) TX- Silver Queen TRTD F1 (su). Circles show raw data, while squares show the mean values. Green (husk), red (silk), yellow (tip kernels), and purple (base kernels). The dotted-lines connecting the origin to each square show the P:C ratio for each tissue (ratio is the slope of the dotted-line). Letters show post hoc results for protein differences along the top and carbohydrate differences along the side, with different letters representing significantly different values across tissues. Please click here to view a larger version of this figure.
IgG Concentration (ug/uL) | Protein in Standard Samples (ug) |
0.0000 | 0 |
0.0125 | 2 |
0.0250 | 4 |
0.0375 | 6 |
0.0500 | 8 |
0.1000 | 16 |
Table 1.Standard curve calculations for the soluble protein assay. The amount of protein in each standard is calculated by taking the concentration of each standard and multiplying it by the amount of standard solution in each well (160 µL).
D(+)glucose Concentration (ug/uL) | D(+)glucose in Standard Samples (ug) |
0.0000 | 0 |
0.0375 | 15 |
0.0750 | 30 |
0.1125 | 45 |
0.1500 | 60 |
0.1875 | 75 |
Table 2. Standard curve calculation for digestible carbohydrate assay. The amount of glucose in each standard is calculated by taking the concentration of each standard and multiplying it by the amount of standard solution in each test tube (400 µL).
Region | Variety | Location | N |
Minnesota | Bt Syngenta/NK-3122A-EZ (field corn) | Rosemount, MN (44.7070, -93.1073) | 10 |
non-Bt Providence Bicolor | Rosemount, MN (44.7070, -93.1073) | 10 | |
North Carolina | non-Bt Sweet G90 Hybrid | Rocky Mount, NC (35.8918, -77.6780) | 10 |
Seedway Bt 1576 | Edenton, NC (36.1758, -76.7057) | 10 | |
Texas | Sh2 SS2742 F1 NAT III Bicolor | Lubbock, TX (33.6935, -101.8249) | 10 |
Silver Queen TRTD F1 (su) | Lubbock, TX (33.6935, -101.8249) | 10 |
Table 3. Summary of the corn sampling location and varieties. For each region and variety combination, 10 ears were collected and four tissues were analyzed: husk, silks, tip kernels, and base kernels.
Region | Variety | % soluble protein | |||
husk | silks | tip kernel | base kernel | ||
Minnesota | Bt Syngenta/NK-3122A-EZ (field corn) | 3.29 ± 0.38 | 4.57 ± 1.27 | 14.74 ± 1.54 | 7.07 ± 0.84 |
non-Bt Providence Bicolor | 3.35 ± 0.58 | 6.71 ± 0.70 | 17.87 ± 3.85 | 48.41 ± 2.85 | |
North Carolina | non-Bt Sweet G90 Hybrid | 2.90 ± 0.60 | 6.97 ± 0.63 | 12.95 ± 0.86 | 12.23 ± 0.83 |
Seedway Bt 1576 | 5.48 ± 0.73 | 9.08 ± 0.62 | 10.75 ± 0.93 | 12.76 ± 1.16 | |
Texas | Sh2 SS2742 F1 NAT III Bicolor | 7.74 ± 1.03 | 12.15 ± 0.63 | 14.79 ± 0.69 | 14.88 ± 0.48 |
Silver Queen TRTD F1 (su) | 6.06 ± 1.05 | 8.17 ± 0.79 | 12.95 ± 1.19 | 12.32 ± 1.23 |
Table 4. Mean protein values for each region, variety, and tissue type. Mean percentages (by dry mass) are shown ± 1 SE.
Region | Variety | % digestible carbohydrates | |||
husk | silks | tip kernel | base kernel | ||
Minnesota | Bt Syngenta/NK-3122A-EZ (field corn) | 37.55 ± 0.88 | 32.31 ± 1.38 | 36.79 ± 1.60 | 38.66 ± 1.57 |
non-Bt Providence Bicolor | 37.30 ± 0.82 | 37.31 ± 2.30 | 35.80 ± 1.77 | 34.83 ± 1.37 | |
North Carolina | non-Bt Sweet G90 Hybrid | 35.79 ± 0.81 | 35.28 ± 1.17 | 36.13 ± 0.91 | 39.47 ± 1.18 |
Seedway Bt 1576 | 35.75 ± 0.58 | 34.91 ± 0.76 | 35.73 ± 1.35 | 37.29 ± 1.13 | |
Texas | Sh2 SS2742 F1 NAT III Bicolor | 35.55 ± 0.87 | 33.40 ± 1.10 | 34.85 ± 1.01 | 39.14 ± 1.22 |
Silver Queen TRTD F1 (su) | 34.63 ± 1.13 | 33.42 ± 2.33 | 35.16 ± 1.16 | 33.93 ± 1.03 |
Table 5. Mean carbohydrate values for each region, variety, and tissue type. Mean percentages (by dry mass) are shown ± 1 SE.
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 between geographic regions, varieties (or genotypes), tissue types, and even spatially segregated tissues. Both assays can be done using common laboratory equipment and reagents, requiring only basic laboratory skills, and can analyze a relatively large number of samples (50-75) within a short timeframe.
Although relatively easy to perform, some steps are more critical than others, and if done incorrectly can limit the accuracy of the results. For example, it is imperative that plant materials are handled properly during the sampling stage. Even dissected plant tissue will remain metabolically active until exposed to a lethal temperature, and during this period plant macronutrient content can change. As a result, long time periods between plant sampling and freezing (either by liquid N or freezer storage) can increase the likelihood that sample plant macronutrient content may not reflect the content that was present at the time of sampling.
Steps 2.3.3-2.3.5 in the protein protocol are particularly important for a successful outcome, as these steps deal with the precipitated proteins. Care must be taken to avoid losing any of the protein pellet when vacuuming the supernatant TCA from the microcentrifuge tubes, because doing so will result in an underestimation of protein content. It is also important when washing the protein pellet with acetone to do so very quickly. Acetone can degrade the pellet if left in contact for more than several seconds. We suggest limiting contact between acetone and the pellet to less than 5 seconds. Finally, when drying the pellet, it is important to take care to only allow enough time for the acetone to evaporate. If the pellet is left to dry for too long, it becomes very difficult to resuspend in NaOH. We suggest drying the pellet for 30 minutes, and then checking for the presence of acetone, either by visually observing liquid in the tube or by carefully detecting the smell of acetone fumes. Continue checking the pellet every 10-15 minutes until the acetone has evaporated.
As outlined in Bradford 197616, the quantification steps using Coomassie brilliant blue G-250 dye allow for high sensitivity, with a standard deviation of only 5 µg protein/mL, high protein-dye complex stability, and limited interference by non-protein compounds. This assay has a detection range between 1-20 µg total proteins per microplate well (low-concentration assay) or a maximum of 25 µg/mL; however, any concentration that exceeds the highest standard should be diluted and re-analyzed for the most accurate results (the protocol produces an excess of solution in case such dilutions and re-analysis is necessary). There is a bias for the dye to preferentially bind to basic amino acids, such as arginine, and aromatic amino acid residues; however, the Bradford assay remains the most accurate and easy to use method for total protein quantification in mixed samples.
The digestible carbohydrate assay is a fast, cost-effective, and simple method for quantifying plant saccharides while excluding structural carbohydrates (such as cellulose), which are indigestible by most herbivores. The most problematic steps in the carbohydrate assays are steps 3.2.1 and 3.3.2-3.3.4. Here, it is critical to keep tubes upright and to tighten the screwcaps well when boiling, as the introduction of any water will dilute samples and affect accurate quantification. Also, care must be taken when working with phenol and concentrated sulfuric acid, as both are highly corrosive. It should be noted that we advocate recording sample absorbance at 490 nm, which is the maximum absorbance for hexoses, but not other sugars, such as pentoses and uronic acids which have a maximum absorbance at 480 nm20,21. For sugar mixtures in plant samples, 490 nm provides an appropriate wavelength for quantifying overall carbohydrate content22,23,24, but for a more detailed analysis of different sugars see20,21. It should also be noted that Masuko et al.23 provides a streamlined microplate method for phenol-sulfuric acid carbohydrate analysis.
Another notable method for estimating plant nutrient content25,26,27 is near-infrared spectroscopy (NIRS). NIRS technology is widely used in agricultural and food production. This alternative technique is noninvasive, nondestructive, takes a fraction of the time involved in any wet chemistry method, and can be applied at different scales from a landscape down to an individual piece of plant tissue. This technique measures nutrients indirectly and relies on accurate wet chemistry measurements of the plant chemical of interest for calibration. The methods described herein will therefore have a critical place in the future of plant nutrient analysis, ensuring that calibration is based on soluble and digestible macronutrient quantification and not biased by non-nutritive elemental surrogates.
The methods presented for measuring plant soluble protein and digestible carbohydrate content have significant implications for the environmental and biological sciences. Despite a wealth of information on the elemental composition of plant tissues, information on plant macronutrient content is severely lacking. Given the limitations that exist in correlating elemental measures with macronutrient content and acknowledging the strong relationship between plant nutritional content and higher order ecological processes, obtaining this kind of data is essential for advancing the fields of plant physiology, nutritional ecology, plant-herbivore interactions, food-web dynamics11,28,29,30. It is our hope that providing a clear and approachable methodology for measuring plant soluble protein and digestible carbohydrate content will encourage researchers to collect and incorporate this kind of data into future research.
The authors have nothing to disclose.
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& 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& 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).
microplate reader (spectrophotometer) | Bio-Rad | Model 680 XR | |
Bio-Rad Protein Assay Dye Reagent concentrate | Bio-Rad | #5000006 | 450mL |