| Literature DB >> 32581797 |
Hui Si Audrey Koh1,2, Jun Lu3,4,5,6, Weibiao Zhou1,2,7.
Abstract
Fucoidan refers to a group of sulfated polysaccharide that is commonly obtained from various species of brown seaweed. Fucoidan has gained increased popularity among researchers in the recent years due to its numerous biological activities, including its inhibitory effects against starch hydrolyzing enzymes such as α-amylase and α-glucosidase. This highlights the potential of fucoidan as an antidiabetic agent in the management and prevention of diabetes mellitus. In this study, the inhibitory effects of fucoidan isolated from the New Zealand Undaria pinnatifida seaweed species against three starch hydrolyzing enzymes-α-amylase, α-glucosidase, and amyloglucosidase-was investigated. It was demonstrated that while the fucoidan exhibited significant inhibitory effects against all the three starch hydrolases, it is an uncompetitive inhibitor of α-amylase and amyloglucosidase, and is a competitive inhibitor of α-glucosidase. Moreover, it exhibited significantly stronger inhibitory effects against α-glucosidase than α-amylase, thus having the desirable characteristics as an antidiabetic agent.Entities:
Keywords: Diabetes; Undaria pinnatifida; amyloglucosidase; fucoidan; α-amylase; α-glucosidase
Year: 2020 PMID: 32581797 PMCID: PMC7289976 DOI: 10.3389/fphar.2020.00831
Source DB: PubMed Journal: Front Pharmacol ISSN: 1663-9812 Impact factor: 5.810
Figure 1(A) Michaelis-Menten plot and (B) Lineweaver-Burk plot of fucoidan inhibition against α-amylase.
Inhibition activity of fucoidan against α-amylase—Maximal velocity (V) and Michaelis-Menten constant (K).
| Concentration of fucoidan (mg/ml) | ||
|---|---|---|
| Control | 3.274 ± 0.1007a | 5.893 ± 0.4187a |
| 0.5 | 2.948 ± 0.1016ab | 5.158 ± 0.4344ab |
| 1.0 | 2.834 ± 0.1022b | 5.577 ± 0.475ab |
| 1.5 | 2.058 ± 0.0631c | 4.136 ± 0.3418bc |
| 2.0 | 1.415 ± 0.05508d | 2.983 ± 0.3641c |
a–dValues are presented as mean with standard deviation (n = 9). Within each column, mean values with different superscript lowercase letters are statistically different (p < 0.05) across the different samples.
Figure 2(A) Michaelis-Menten plot and (B) Lineweaver-Burk plot of fucoidan inhibition against α-glucosidase.
Inhibition activity of fucoidan against α-glucosidase—Maximal velocity (V) and Michaelis-Menten constant (K).
| Concentration of fucoidan (mg/ml) | ||
|---|---|---|
| Control | 0.0126 ± 0.0016a | 6.594 ± 1.921a |
| 0.1 | 0.0131 ± 0.0014a | 7.458 ± 1.768a |
| 0.2 | 0.0129 ± 0.0004a | 7.767 ± 0.507a |
| 0.5 | 0.0108 ± 0.0005a | 17.16 ± 1.235ab |
| 1.0 | 0.0101 ± 0.0006a | 21.89 ± 2.007b |
a–bValues are presented as mean with standard deviation (n = 9). Within each column, mean values with different superscript lowercase letters are statistically different (p < 0.05) across the different samples.
Figure 3(A) Michaelis-Menten plot and (B) Lineweaver-Burk plot of fucoidan inhibition against amyloglucosidase.
Inhibition activity of fucoidan against amyloglucosidase—Maximal velocity (V) and Michaelis-Menten constant (K).
| Concentration of fucoidan (mg/ml) | ||
|---|---|---|
| Control | 0.962 ± 0.014a | 2.21 ± 0.13a |
| 0.5 | 0.938 ± 0.025a | 2.36 ± 0.24a |
| 1.0 | 0.832 ± 0.024b | 1.85 ± 0.23b |
| 1.5 | 0.781 ± 0.024c | 1.73 ± 0.24b |
| 2.0 | 0.753 ± 0.016c | 1.87 ± 0.17b |
a–cValues are presented as mean with standard deviation (n = 9). Within each column, mean values with different superscript lowercase letters are statistically different (p < 0.05) across the different samples.
Type of inhibition of fucoidan against α-amylase, α-glucosidase, and amyloglucosidase.
| [Substrate] mM | IC50 (mg/ml) | Type of inhibition | ||
|---|---|---|---|---|
| α-Amylase | 4.33 | 0.190 ± 0.005b | Uncompetitive | |
| α-Glucosidase | 10.00 | 0.137 ± 0.012a | Competitive | |
| Amyloglucosidase | 4.33 | 0.280 ± 0.016c | Uncompetitive | |
a–cValues are presented as mean with standard deviation (n = 9). Within each column, mean values with different superscript lowercase letters are statistically different (p < 0.05) across the different samples.