Relationship between the molecular structure of duckweed starch and its in vitro enzymatic degradation kinetics

International Journal of Biological Macromolecules 139 (2019) 244–251

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International Journal of Biological Macromolecules journal homepage: http://www.elsevier.com/locate/ijbiomac

Relationship between the molecular structure of duckweed starch and its in vitro enzymatic degradation kinetics Marcia Maria de Souza Moretti a,b, Wenwen Yu b, Wei Zou b, Célia Maria Landi Franco a, Liliane Lazzari Albertin c, Peer M. Schenk d, Robert G. Gilbert b,e,⁎ a

São Paulo State University (UNESP), Institute of Biosciences, Humanities and Exact Sciences, São José do Rio Preto, SP, Brazil The University of Queensland, Centre for Nutrition and Food Sciences, Queensland Alliance for Agricultural and Food Innovation, Brisbane, QLD 4072, Australia São Paulo State University (UNESP), School of Natural Sciences and Engineering, Ilha Solteira, SP, Brazil d The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072, Australia e Joint International Research Laboratory of Agriculture and Agri-Product Safety, College of Agriculture, Yangzhou University, Yangzhou, Jiangsu Province 225009, China b c

a r t i c l e

i n f o

Article history: Received 14 April 2019 Received in revised form 29 July 2019 Accepted 30 July 2019 Available online 30 July 2019 Keywords: Duckweed Starch Size-exclusion chromatography (SEC) Molecular structure Starch degradation

a b s t r a c t Starch molecular structural effects in duckweed (Lemna minor and Landoltia punctata) controlling in vitro enzymatic degradation kinetics was studied. The molecular size distributions of fully-branched starches and the chain length distributions (CLDs) of enzymatically debranched duckweed starches were obtained using size-exclusionchromatography (SEC). The CLDs of both debranched amylose and amylopectin were fitted with models using biologically-meaningful parameters. While there were no significant correlations between amylose content and starch degradation rate, the total amounts of amylose with shorter chain length negatively correlated with undigested starch content, and the amount of amylopectin long chains negatively correlated with the degradation rate coefficient. This provides new knowledge for the utilization of duckweed starches in bioethanol production. © 2019 Elsevier B.V. All rights reserved.

1. Introduction Bioethanol can be produced from a variety of feedstocks used as carbon source, including first-generation feedstocks (such as sugarcane, corn, triticale, wheat and cassava) and second-generation feedstocks (agricultural and municipal waste, grasses and trees) [1,2]. However, there is a concern about the use of first-generation feedstocks for ethanol production, since they inevitably compete for cropland used for food/feed production and biodiverse landscapes, such as rainforests [3]. Duckweed, a floating aquatic plant of the Lamnaceae family, is a potential alternative to resolve this problem since it is used for the decontamination of wastewater by absorbing problematic minerals [4]. This usage however means that its starch cannot be used in the food or animal feed industries, but, by the same token, duckweed is a more promising raw material than corn for ethanol production. Duckweed starch content varies with species and growth conditions, ranging from 3% to 75% of dry weight [3]. Duckweed may produce biomass up to 64 g/gweek [5], which is higher than generally seen in larger aquatic or terrestrial plants. The starch production rate from duckweed can be as much ⁎ Corresponding author at: The University of Queensland, Centre for Nutrition and Food Sciences, Queensland Alliance for Agricultural and Food Innovation, Brisbane, QLD 4072, Australia. E-mail address: [email protected] (R.G. Gilbert).

https://doi.org/10.1016/j.ijbiomac.2019.07.206 0141-8130/© 2019 Elsevier B.V. All rights reserved.

as 28 t/ha-year, which is considerably greater than from corn (about 5 t/ha-year) [6]. Previous studies on duckweed starch have focused on growth conditions, starch accumulation and application as feedstock and/or for ethanol production [1,3,7–10]. There has been no systematic research on how the duckweed starch molecular structural influences starch enzymatic degradation rate (see e.g. [11]), which is of importance in determining the production of glucose, a key precursor in ethanol production. A study of the correlation between duckweed starch structural parameters and its degradation kinetics would help understand the role played by molecular structure in starch hydrolysis, and reveal structural features linked to higher yields and production rates of fermentable sugar. To understand this, starch molecular structures, specifically the molecular size distributions of the fully branched molecules and the chain length distributions (CLDs) of enzymatically debranched starches, were obtained using size exclusion chromatography (SEC, a type of gel permeation chromatography, GPC). The CLDs of both amylopectin and amylose were fitted by biosynthetic models [11–13]. The kinetics of the cooked duckweed starch degradation rates were estimated using logarithm-of-slope analysis (LOS) combined with a modified nonlinear least-square (NLLS) method, as reported previously [11]. This yields the values of k (the digestion rate coefficient) and C∞ (the residual undigested starch). Correlations between the structural parameters

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Table 1 Chemical composition and molecular structures of the starch samples⁎. Samples

COD.

Locations

Total starch (%)

Moisture content (%)

Protein content (%)

Amylose content (%)

Landoltia punctata Landoltia punctata Lemna minor Lemna minor Lemna minor

AU BR AU BR UQ

Watergarden Paradise (NSW, Australia) São Paulo State University (SP, Brazil) Fair Dinkum Seeds (QLD, Australia) São Paulo State University, (SP, Brazil) The University of Queensland, (QLD, Australia)

85.7 ± 0.7a 81.2 ± 0.1bc 83.0 ± 0.6ab 79.5 ± 0.9c 75.4 ± 0.9d

5.3 ± 0.2a 7.0 ± 0.1a 5.7 ± 1.4a 6.9 ± 0.7a 4.9 ± 0.3a

3.1 ± 0.0d 4.9 ± 0.0b 2.8 ± 0.0e 3.9 ± 0.0c 6.4 ± 0.0a

35.4 ± 0.0ab 39.2 ± 0.3a 33.0 ± 2.1bc 29.4 ± 0.7c 31.4 ± 0.8bc

⁎ Mean ± SD is calculated from duplicate measurements; values with different letters in the same column are significantly different with p b 0.05.

obtained from the model fitting and the digestion properties were then obtained using standard statistical methods. 2. Materials and methods 2.1. Materials Five duckweed samples, two from Brazil and three from Australia, were chosen in this study, as shown in Table 1. The samples were dried in an oven at 50 °C for 48 h and stored at room temperature for future starch extraction. Dimethyl sulfoxide (DMSO, GR grade) was from Merck Co. Inc.; LiBr (Reagent-Plus), protease from Streptomyces (Type XIV) and porcine pancreatic α-amylase were from Sigma-Aldrich (St. Louis, USA). Isoamylase (from Pseudomonas), total starch (AA/AMG) assay kit and amyloglucosidase were from Megazyme International Ltd. (Bray, Ireland). 2.5 g duckweed samples were ground using a 6850 Freezer/Mill cryogrinder (SPEX, Metuchen, USA) with liquid nitrogen (precool 2 min, 1 cycle, grinding for 5 min). The starch extraction process for enzyme hydrolysis and structural analysis was chosen to ensure complete solubilization of starch with minimal molecular degradation. Purified and solubilized starches were obtained using a slight modification of the method described by Syahariza et al. [14]. Briefly, 2.5 g of raw sample flour was deproteinated using protease from Streptomyces (Type XIV) (2.5 U/mL) in tricine buffer (15 mL, pH 7.5, 250 mM) for 30 min at 37 °C. The mixture was centrifuged at 10,000 ×g for 20 min, with the supernatant being discarded. The precipitate was mixed with sodium bisulfite solution (15 mL, 0.45% w/v) and incubated at 37 °C for 1 h followed by centrifuging at 10,000 ×g for 20 min, discarding the supernatant. The residue was then suspended in 20 mL of DMSO containing 0.5% w/w LiBr (DMSO/ LiBr) for 8 h at 80 °C. After centrifuging (10,000g, 10 min), the supernatant was collected and 80 mL of absolute ethanol (to a final concentration of 80%) was added. The starch precipitated was centrifuged at 10,000 ×g for 20 min. Then the starch was washed with absolute ethanol (15 mL), dried in an oven at 37 °C for 48 h and stored at room temperature for future analysis.

distilled water was added, followed by the addition of a sodium acetate buffer (10 mL, 0.2 M, pH 6.0, 0.49 mM MgCl2, 200 mM CaCl2, and 0.02% NaN3) containing a mixture of α-amylase (0.5 U/mg of starch) and amyloglucosidase (0.3 U/mg of starch). The resulting solution was incubated at 37 °C in a water bath in a sealed flask, with stirring at 300 rpm. Aliquots of hydrolyzed starch (0.1 mL) were pipetted at regular intervals up to 420 min, and the hydrolysis was stopped by adding 0.9 mL of absolute ethanol. The mixed solution was centrifuged at 4000 ×g for 10 min. The percentage of starch hydrolysis were determined from the amount of glucose released in the supernatant, using a D-Glucose Assay Kit (Megazyme International Ltd., Bray, Ireland). The starch degradation data were fitted using a modified LOS method [16] in order to obtain the values of k and C∞. First estimates of these were calculated from the LOS slope and intercept, and to identify if there are multiple phases present during the in vitro enzymatic degradation experiments [17]. The k values and C∞ were then refined by using a non-linear least-squares (NLLS) method [17,18]. 2.4. The chain-length distributions of debranched starch The characterization of the debranched starch molecules used the method of Wang et al. [19]. Starches extracted from the flour were separated using a Waters SEC- MALLS system (Wyatt Technology), equipped with a refractive index detector (DRI). SEC separates by molecular size (not molecular weight), specifically the SEC hydrodynamic radius Rh. The SEC detector signal in terms of elution volume was converted to the weight distribution w(log Rh): the weight of polymers as a function of their size. For a complex branched polymer such as whole starch, there is no relation between size and molecular weight. However, for a linear polymer such as debranched starch, Rh can be converted to the corresponding degree of polymerization (DP) X using the Mark-Houwink relation, as described elsewhere [20], which gives w (logX), the weight distribution of chains (after debranching) containing

2.2. Chemical compositions of extracted duckweed starch The total starch content of the extracted duckweed starches was measured using a Total Starch Assay Kit (AA/AMG) (Megazyme International Ltd., Bray, Ireland); moisture content was measured by drying the samples in an oven at 110 °C overnight and recording the weight loss of moisture in duplicate measurements, following AACC-I method 4440.01; protein content was calculated from the nitrogen content, which were determined using a CNS2000 auto-analyzer (LECO, Saint Joseph, USA), with a conversion factor of 6.25. 2.3. In vitro starch enzymatic degradation In vitro starch enzymatic hydrolysis was carried out following a method described elsewhere [15] with slight modifications. Extracted starch (100 mg, dry weight basis) was suspended in 6 mL of deionized water at 100 °C for 30 min with stirring. After cooling to 37 °C, 4 mL of

Fig. 1. SEC weight CLDs, w(logX), of debranched starches whole DP region, normalized to the maximum of the amylopectin component, and an enlarged of the amylopectin region with DP b100. All data normalized to the height of the amylopectin (highest) peak.

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L. punctata AU L. punctata BR L. minor AU L. minor BR L. minor UQ

A

0.2

-9

0.1

-12

L. minor UQ2 6

1

53

0

100

X

B

0.1

2

3

4

5

logX

Experimental Data Overall fitting Region 1 Region 2 Region 3

0.3

Experimental Region 3 Broaden Region 3 Residual Region 3 Region 2 Broaden Region 2 Residual Region 2 Region 1 Broaden Region 1 Residual final

D

0.2

w(logX)

Nde(X)

Experimental Region 3 Broaden Region 3 Region 2 Broaden Region 2 Region 1 Broaden Region 1

C

w(logX)

log Nde(X)

-6

0.3

0.01

0.1 0.001

L. minor UQ2 0.0001

0

50

X

100

0

L. minor UQ2 2

3

4

logX

5

Fig. 2. SEC number CLD, Nde(X), of debranched duckweed starch chains as a function of DP X (A). Illustrating the application of the amylopectin fitting (B) and amylose-fitting in nonsubtractive (C) and subtractive method (D) for L. minor UQ2. The numbers 1–3 refer to features going from regions of the lower (left) to the higher (right) DP X. Data for all other samples are given in the Supporting Information (amylopectin fitting in Fig. S1 and amylose fitting in Fig. S2).

X monomer units. A series of SEC columns (GRAM 100 and GRAM 1000 columns) placed in an oven at 80 °C were used to separate the debranched starch molecules. DMSO/LiBr solution was used as mobile phase with a flow rate of 0.6 mL/min. Data were analyzed as described previously, e.g. [21].

2.5. Amylose content The starch amylose content was determined from the SEC weight distribution of debranched starch molecules, following the method described by Vilaplana et al. [22], by dividing the area under the CLD for DP ≳ 100 (the DP at which is a clear divide between the amylose and amylopectin components) by the total area under the CLD.

2.6. Mathematical fitting of amylopectin and amylose CLDs The number distribution Nde(X) = X−2 w(logX) of amylopectin as a function of DP from SEC was fitted using the Wu-Gilbert model, using publicly available code [12]. The model assumes that the amylopectin CLD is controlled by several enzyme sets, comprising one each of an isoform of the three types of starch biosynthesis enzymes: starch synthase (SS), starch branching enzyme (SBE) and debranching enzyme (DBE). The activities of these then determine the CLD. The CLD from each enzyme set dominates (but not exclusively) the overall CLD over a given range of DP. Each enzyme set i appears in the final expression for its component of the CLD as a value of βi, the ratio of the activity of the SBE to that of SS in that set, and hi, the overall relative activity of the SS; the activity of the DBE does not appear explicitly, being constrained

Fig. 3. Parameters obtained from fitting the number CLD of duckweed starches to the mathematical model: amylopectin (Ap) and amylose (Am) parameters; β(1, 2, 3) and h(1, 2, 3). The numbers 1–3 refer to features going from regions of the lower to the higher DP X. Amylose parameters were obtained from the application of the non-subtractive and subtractive methods.

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by various biosynthetic requirements giving its activity in terms of that of the SBE. For the particular system studies here, which will be seen to be governed by three enzyme sets, the parameters are β(1–3) and h(1–3). The CLD of amylose as a function of DP was fitted using a new model, again with publicly available code [13,23]. The model also assumes that the CLD is governed by various enzyme sets, in this case each of which contains an isoform of SS (usually but not necessarily only GBSS) and an SBE. The final result is again a parameterization in terms of a βi and hi for each set, which have the same meaning as those in amylopectin. Subscripts AP and AM are used where necessary to distinguish amylopectin and amylose components where needed. 2.7. Statistical analysis For each sample, enzyme hydrolysis was measured and structural analyses were performed in duplicates. All data were reported as means and standard errors. One-way analysis of variance (ANOVA) and both Pearson and Spearman rank correlations were used.

while the other samples L. minor were collected from nutrientstarvation conditions. Transcriptome and proteome studies have revealed that, under starvation conditions, the expression profile of SS in duckweed can be affected [4,26,27]. The fitting to the amylose CLDs using the amylose biosynthesis model is shown in Fig. 2C and D for a typical fit and in the Supporting Information for fits to all the data. Significant differences (p b 0.05) are observed in the values of β(Am) and h(Am) between the duckweed samples (Fig. 3). Thus the relative activities of the enzyme synthesizing amylose of the regions with high, medium and lower chain length and the total amounts of amylose in these regions are different between the samples. Both amylose parameters obtained from L. minor UQ samples are significantly different compared to the other duckweed samples (Fig. 3), again ascribed to the environmental effects as above. Consistent with this inference, it has been reported [26] that the transcript encoding GBSS, a key enzyme in amylose biosynthesis, exhibited an expression level higher under nutrient-starvation conditions (from 79 Fragments Per Kilobase of transcripts per Million mapped fragments (FPKM) at 0 h to 432 FPKM after 24 h).

3. Results and discussion 3.2. Starch degradation and its relationship to starch structure 3.1. Starch molecular structure SEC weight distributions of debranched starch samples are shown in Fig. 1. As always seen for all except some high-amylose starches, these show the amylopectin chains with DP ≲ 100, separated from the longer chains, which are amylose, by the noticeable minimum in the weight CLD at DP ~ 100. All starch samples showed two peaks for amylopectin branches, as seen in many examples in the literature; the first component at X ≲ 34 is for chains confined to one lamella, and the second is for chains spanning more than one lamella. L. minor UQ showed a higher second peak of amylopectin (X ~45) than the others, i.e. more longer trans-lamellar chains. The amylose contents, Table 1, showed significant differences (p b 0.05) among the samples, varying between 29 and 39%, in the range reported in the literature [24,25]. While there could be some extra-long amylopectin chains which would elute in the same region as amylose (X ≳ 100), the number of these is miniscule, as seen by examination of accurate distributions for chains obtained using fluorophore-assisted carbohydrate electrophoresis (FACE). For example, Fig. 3 of Wang et al. [19], where the number distribution from FACE is plotted with a logarithmic axis and goes up to X ~180, one can see a distinct change in the shape of the distribution for X ≳ 100 (suggesting that the amylose chains start at X ~100), and a simple extrapolation of the last part of the amylopectin region below X = 100 into the amylose region (as partially shown here in Fig. 2) shows the number of these chains (which would be the extra-long amylopectin chains) is orders of magnitude less than those of amylose. Fig. 2A show the number CLDs of amylopectin extracted from different duckweeds. A typical fit to the amylopectin CLD using the amylopectin biosynthesis model [12] is shown in Fig. 2B, and fits to all the data is in the Supporting Information. Significant differences (p b 0.05) are observed in the fitted enzymatic activity parameters among the duckweed samples (Fig. 3) when comparing the same species, suggesting that the genetic variations and/or growing conditions affect the starch biosynthesis. In general, the β(Ap) values of L. minor UQ are smaller than the other duckweed samples. The lower activity ratio of SBE to SS in L. minor UQ sample provides a lower branching rate in this sample compared with the other duckweeds. Likewise, higher values of h(Ap, 2) and h(Ap, 3) were found in the L. minor UQ sample, meaning the contribution of each enzyme set to the amylopectin CLD was higher. Enzyme sets in the regions 2 and 3 have a lower SBE activity and, corresponding to a higher proportion of long amylopectin branches, as apparent in the SEC distributions (Fig. 1). We suggest that the difference in the activity ratio of SBE to SS among the samples may be associated to the growth condition of duckweed. L. minor UQ was collected from wastewater,

The gelatinization of starch is accompanied by the rupture and disintegration of the granular structure and melting of the crystalline structure. Gelatinization increases the susceptibility of starch to α-amylase and amyloglucosidase, since these enzymes are relatively large and they have limited access to starch granules: granule swelling can increase the surface area of starch granules accessible to enzymes [15,28]. The digestograms of the starches are shown in Fig. 4A, and the fits of these to the LOS and NLLS treatments in panels B and C of Fig. 4, respectively, with the fitting parameters in Table 2, which shows statistically significant differences between the digestograms of some of the samples. The LOS plot reveals a change of slope between 20 and 60 min of hydrolysis time, suggesting that samples are degraded in two separate phases, one fast and one slow (Fig. 4B), as reflected in the fitted rate coefficients (Fig. 4C). This may be ascribed to some starch being released into solution after gelatinization. The likely explanation [29] is that the activity of amyloglucosidase from A. niger is dependent on the degree of polymerization (DP) of the substrate. During starch degradation, there is a large number of subsites available with higher affinity to longer oligosaccharides compared with maltose. Zhang et al. [29] found that α-amylase and amyloglucosidase have antagonistic effects in the digestion of cooked starch to glucose. It is possible that even in the presence of α-amylase, amyloglucosidase will digest oligosaccharides (due to its higher affinity for longer substrates), and these would be rapidly digested in the first phase of the digestion. Guo et al. [30,31] also observed two phases during the starch hydrolysis of amylopectin from cereal starches when using amyloglucosidase from A. niger. The fitting of the digestograms is shown in Table 2. L. minor AU had higher rate coefficients for both phases than the other samples. On the other hand, less starch was degraded in this sample (more residual starch: C∞ = 59.4 and 16.7% for phases I and II, respectively) compared with the other samples. This means that not all the starch in this sample is available to the enzymes (α-amylase and amyloglucosidase), but those starches readily available for attack by enzymes are faster hydrolyzed, and/or it contains a higher content of readily available starches after gelatinization. As shown in Table 3, there is no correlation between the amylose content and the starch digestion rate coefficient. This result was different from the many previous studies which demonstrate that amylose content affects the starch degradation rate in cereal starches, e.g. [32]. Two possible explanations for this are as follows. (1) In this study, the starch is extracted with DMSO with lithium salt solution that has been shown to dissolve starch molecules completely, besides removing other biological components, for example, lipids that are known to form amylose-lipid complexes and, consequently, to interfere in the starch degradation rate [33,34]. (2) As mentioned before, the amyloglucosidase enzyme used in

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the present study has a large number of subsites available with affinity to longer oligosaccharides so, in this case, the long branches of amylose would not negatively affect the starch degradation. Besides, the porcine pancreatic α-amylase used in the present study is one of the few enzymes known with a considerable level of multiple attack action and when this enzyme acts on a very long substrate, it has a random or multiple attack action [35]. For the same number of bonds broken, the molecular size of the amylose is decreased more rapidly by random hydrolysis than by the repetitive action. (3) The range of amylose contents in these duckweeds is comparatively small. The correlation between the starch hydrolysis rate and its molecular fine structure is shown in Table 3. The β(Ap, 2) and β(Ap, 3) parameters were positively correlated with the k value of phase II. The degradation parameters are correlated with structural features in enzyme sets in regions 2 and 3, suggesting that the amylopectin long chains contribute to a quicker degradation rate coefficient. As mentioned before, the glucoamylase has more affinity for longer oligosaccharides, so those long amylopectin chains that were not broken in phase I of the hydrolysis would be quickly degraded in phase II. On the other hand, h(Ap, 2) and h(Ap, 3) showed negative correlations with k, these parameters reflecting the proportion of trans-lamellar chains with DP between 34 and 70 and long trans-lamella chains with DP 70 ≤ X b 100, respectively. Various authors, e.g. [15,30], have observed similar correlation when studied the relationship between starch digestibility and amylopectin fine structure, which is attributed to retrogradation of long amylopectin chains, and consequently a higher resistance to enzymatic digestion. There was negative correlation between C∞ and h(Am, 1), suggesting that the total amounts of shorter amylose chains (around 100 to 390 DP) provide a lower undigested starch content. This could be because the shorter amylose chains are leached out into solution quicker and degraded first. The same negative correlation was observed by Yu et al. [11] when evaluating the amylose molecular structure and digestibility of cooked white rice starch. In the present study, no correlation is seen between amylopectin structural parameters and C∞, or between the amylose structural parameters and starch degradation rate coefficient. As is well known, the protein content can also influence the starch enzymatic degradation, as discussed for example by Zou et al. [36]. However, in the present study, the protein content and duckweed starch degradation did not show any significant correlation, as shown in Table 3. 3.3. The role of amylopectin and amylose in duckweed starch biosynthesis

Fig. 4. Digestograms of duckweed starches (A) (the dots were calculated from duplicate measurements), and the fitting of these digestograms to logarithm of slope fit (LOS) (B) and the non-linear least-squares fit (NLLS) (C).

In this study, a correlation between the amylopectin and amylose parameters was found (Tables S1 and S2 of the Supporting information), as seen elsewhere, e.g. [37]. There was significant negative correlation between β(Ap, 1) and β(Am, 2). This means that the ratio of activity of SBE to that of SS in CLDs of short amylopectin chains is negatively correlated with that of SBE to GBSS in medium-chain amylose. Likewise,

Table 2 Values of starch digestion rate constants k and estimated percentage of undigested starch C∞ observed during each starch hydrolysis phase (phase I and II)⁎. “Norm” and “Log” refer to NLLS fits of the normal and logarithmic forms of the data. Hydrolysis Phase

Samples

k (10−2 min−1)⁎⁎ Norm

(Phase I)

(Phase II)

L. L. L. L. L. L. L. L. L. L.

punctata (AU) punctata (BR) minor (AU) minor (BR) minor (UQ) punctata (AU) punctata (BR) minor (AU) minor (BR) minor (UQ)

C∞ (%)⁎⁎ Log

c

4.2 ± 0.1 4.2 ± 0.1c 5.7 ± 0.1a 4.0 ± 0.0c 4.7 ± 0.0b 1.1 ± 0.0a 0.9 ± 0.0b 1.1 ± 0.0a 0.9 ± 0.0b 0.9 ± 0.0b

Norm c

4.1 ± 0.1 4.3 ± 0.0c 5.8 ± 0.0a 3.9 ± 0.1d 4.9 ± 0.0b 1.1 ± 0.0a 0.9 ± 0.0b 1.1 ± 0.0a 0.9 ± 0.0b 0.9 ± 0.0b

Log c

50.7 ± 1.2 54.9 ± 0.2b 59.4 ± 0.4 a 49.0 ± 0.6c 58.4 ± 1.2ab 15.6 ± 0.2a 8.0 ± 1.0b 16.7 ± 0.1a 3.3 ± 1.2c 6.4 ± 0.2b

⁎ Data calculated using NLLS fit. ⁎⁎ Mean ± SD is calculated from measurements taken twice; values with different letters in the same column and phase are significantly different with p b 0.05.

50.5 ± 0.7c 55.4 ± 0.3b 59.8 ± 0.5a 48.2 ± 0.9c 59.0 ± 1.3a 15.9 ± 0.3a 8.3 ± 0.9b 16.9 ± 0.0a 2.9 ± 1.9c 6.2 ± 0.3bc

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Table 3 Correlation coefficients between digestion rates and molecular structural parameters of cooked duckweed starchesa. Structural attributes

Pearson correlations of duckweed samples⁎,⁎⁎ NLLS fit - Phase I k Norm

k

C∞ Log

Spearman correlations of duckweed samples

NLLS fit - Phase II

Log

Norm

k Log

Norm

NLLS fit - Phase II C∞

Log

Norm

k Log

Norm

C∞

Log

Norm

Debranched starch molecular structure Am content (%) −0.156 −0.083 0.101 Protein content (%) −0.164 −0.162 0.277

0.144 0.300

0.044 0.146 0.347 0.378 0.200 0.200 0.200 0.200 −0.154 0.564 0.600 0.600 −0.790 −0.796 −0.658 −0.650 −0.100 −0.100 −0.100 −0.100 −0.821 −0.821 −0.700 −0.700

Amylopectin fine structure (Ap) β(1) 0.145 0.055 β(2) 0.008 −0.078 β(3) −0.045 −0.130 h(1) −0.124 −0.050 h(2) −0.136 −0.046 h(3) 0.065 0.137

−0.338 −0.485 −0.531 0.283 0.394 0.487

−0.36 −0.501 −0.545 0.313 0.417 0.494

0.575 0.664 0.650 −0.537 −0.779 −0.587

0.587 0.685 0.672 −0.484 −0.781 −0.627

0.435 0.562 0.555 −0.35 −0.647 −0.539

0.432 0.557 0.550 −0.324 −0.635 −0.541

0.000 −0.200 −0.200 0.500 0.200 0.200

0.000 −0.200 −0.200 0.500 0.200 0.200

0.000 −0.200 −0.200 0.500 0.200 0.200

0.000 −0.200 −0.200 0.500 0.200 0.200

0.667 0.718 0.718 −0.616 −0.718 −0.718

0.564 0.718 0.718 −0.205 −0.718 −0.718

0.400 0.600 0.600 −0.100 −0.600 −0.600

0.400 0.600 0.600 −0.100 −0.600 −0.600

Amylose fine structure (Am) −0.081 β(3) β(2) −0.116 β(2) sub 0.018 β(1) sub 0.113 h(3) −0.003 h(2) 0.165 h(1) −0.315 h(2) sub −0.181 h(1) sub −0.046

0.134 0.197 0.278 0.373 0.014 −0.063 −0.612 −0.277 0.450

0.147 0.208 0.296 0.372 0.019 −0.071 −0.629 −0.278 0.469

−0.063 −0.332 −0.005 −0.263 −0.176 0.228 0.478 −0.168 −0.419

−0.079 −0.362 −0.012 −0.317 −0.138 0.257 0.537 −0.132 −0.483

0.031 −0.258 0.133 −0.254 −0.159 0.174 0.586 −0.201 −0.488

0.024 −0.265 0.130 −0.266 −0.141 0.184 0.591 −0.186 −0.485

−0.100 −0.300 0.500 −0.100 0.100 0.300 −0.300 −0.300 −0.100

−0.100 −0.300 0.500 −0.100 0.100 0.300 −0.300 −0.300 −0.100

−0.100 −0.300 0.500 −0.100 0.100 0.300 −0.600 −0.300 −0.100

−0.100 −0.300 0.500 −0.100 0.100 0.300 −0.600 −0.300 −0.100

0.205 −0.205 0.308 0.205 −0.205 0.205 0.154 −0.462 −0.421

−0.410 −0.616 0.103 −0.410 0.410 0.616 0.667 0.051 −0.421

−0.300 −0.500 0.300 −0.300 0.300 0.500 0.600 −0.100 −0.700

−0.300 −0.500 0.300 −0.300 0.300 0.500 0.600 −0.100 −0.700

−0.029 −0.056 0.085 0.163 −0.009 0.120 −0.331 −0.216 0.045

Norm

NLLS fit - Phase I C∞

Log

Norm

Log

a Results are based on duplicate measurements. ⁎ Correlation is significant at the 0.05 level. ⁎⁎ Correlation is significant at the 0.01 level.

β(Ap, 1), β(Ap, 2) and β(Ap, 3) were negatively correlated with h(Am, 1). That is, the parameters controlling the amount of short, medium and long amylopectin chains are negatively correlated with those of shorter amylose chains. GBSS together with SS catalyze the addition of ADPglucose onto the non-reducing ends of a starch molecule [38]. There were also positive correlations between the longer-chain amylopectin structural parameters (h(Ap, 2) and h(Ap, 3)) and the amylose parameters h(Am, 1), that is, the longer amylopectin chains are positively correlated with the amounts of amylose present in the lower DP region. This result is to some extent consistent with the model of starch biosynthesis proposed by van de Wal et al. [39] who analyzed polysaccharide biosynthesis under in vitro conditions using purified Chlamydomonas starch granules. The authors observed that the amylose is synthesized by extension and cleavage from amylopectin. Ball et al. [40] suggested that amylose could be synthesized at the expense of the long chains built by GBSSI on amylopectin.

4. Conclusion This study examines, for the first time, the fine molecular structure of amylose and amylopectin extracted from duckweed starch and its relationship to starch degradation. Amylose and amylopectin CLDs are different between the samples, suggesting, as is well accepted, that starch biosynthesis in duckweed is affected by their nutritional conditions during plant growth and development. Correlations between structural fitting parameters suggested that amylose is perhaps synthesized by extension from amylopectin. Hydrolysis results showed that the starch degradation rate is negatively affected by longer chains in the amylopectin, while the undigested starch content is negatively affected by shorter amylose chains. Furthermore, the kinetic profiles of the hydrolysis are also found to be dependent on the enzyme chosen. The results from this study shown that the amylose content was significantly higher between the L. punctata species; however, in vitro digestion results pointed that the digestion rate is not affected by the amylose content. This study can be used as a guide to selecting species for bioethanol production, which is important for optimal bioethanol production from

a plant that would otherwise be a waste product after use in water decontamination. Abbreviations AA alpha amylase AMG amyloglucosidase ANOVA analysis of variance ADP-glucose adenosine diphosphate glucose CLD chain-length distribution C∞ undigested starch DB degree of branching DBE starch debranching enzymes DMSO dimethyl sulfoxide DP degree of polymerization GBSS granule-bound starch synthase k degradation rate coefficient LOS logarithm of slope NLLS non-linear least squares SBE starch branching enzyme SEC size-exclusion chromatography SS starch synthase Compliance with ethical standards This article does not contain any studies with human participants or animals. Declaration of competing interest The authors declare that they have no conflict of interest. Acknowledgments The authors would like to thank Dr. Bernadine Flanagan for helpful discussions and MM thanks Timothy Phillips for English editing. RGG

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