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Multiple fingerprint and fingerprint-activity relationship for quality assessment of polysaccharides from Flammulina velutipes
Journal Pre-proof Multiple fingerprint and fingerprint-activity relationship for quality assessment of polysaccharides from Flammulina velutipes Yutong Dong, Fei Pei, Anxiang Su, Katherine Z. Sanidad, Gaoxing Ma, Liyan Zhao, Qiuhui Hu PII:
S0278-6915(19)30734-3
DOI:
https://doi.org/10.1016/j.fct.2019.110944
Reference:
FCT 110944
To appear in:
Food and Chemical Toxicology
Received Date: 30 September 2019 Revised Date:
31 October 2019
Accepted Date: 5 November 2019
Please cite this article as: Dong, Y., Pei, F., Su, A., Sanidad, K.Z., Ma, G., Zhao, L., Hu, Q., Multiple fingerprint and fingerprint-activity relationship for quality assessment of polysaccharides from Flammulina velutipes, Food and Chemical Toxicology (2019), doi: https://doi.org/10.1016/ j.fct.2019.110944. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
1
Multiple fingerprint and fingerprint-activity relationship for quality assessment
2
of polysaccharides from Flammulina velutipes
3 4
Yutong Donga, Fei Peia, Anxiang Sub, Katherine Z. Sanidadc, Gaoxing Maa, Liyan
5
Zhaob, Qiuhui Hua,*
6 7
a
8
Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality
9
Control and Processing, Nanjing University of Finance and Economics, Nanjing
College of Food Science and Engineering/Collaborative Innovation Center for
10
210023, China.
11
b
12
210095, P. R. China.
13
c
14
USA.
College of Food Science and Technology, Nanjing Agricultural University, Nanjing
Department of Food Science, University of Massachusetts, Amherst 01003, MA,
15 16
*
17
E-mail: [email protected]
Corresponding author: Qiuhui Hu
18
1
19
Abstract
20
Polysaccharides are known as one of the most important bioactive compounds in
21
Flammulina velutipes. However, there is no accurate and comprehensive assessment
22
method to evaluate and authenticate F. velutipes polysaccharides (FVPs) from
23
different sources. In this study, a multiple fingerprint analysis method including
24
scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy
25
(FT-IR), and high-performance liquid chromatography (HPLC) was established. The
26
inhibitory activities of FVPs against HepG2 were measured and introduced into
27
multiple linear regression (MLR) analysis to investigate fingerprint-activity
28
relationship. The principal component analysis (PCA) scores showed that the
29
polysaccharides extracted from 20 batches of different F. velutipes were highly similar,
30
and substandard samples could be distinguished from the authentic polysaccharides
31
clearly. The glucuronic acid could be considered as a marker for discrimination of
32
white and yellow F. velutipes polysaccharides in HPLC fingerprints. Moreover, the
33
HPLC fingerprint-growth inhibitory activity relationship illuminated that
34
monosaccharides composition played an important role on the HepG2 growth
35
inhibitory activity, and activity-associated markers (mannose, rhamnose, xylose, and
36
galactose) were chosen to assess FVPs from different sources. The suggested HPLC
37
fingerprint-activity relationship method provides an integrated strategy for the
38
quality control of F. velutipes and its related products.
39
2
40
Keywords: Flammulina velutipes; polysaccharides; chemometrics;
41
fingerprint-activity relationship; quality control
3
42
1. Introduction
43
Substantial studies have shown that fungus-derived polysaccharides have a
44
variety of health-promoting effects, such as needle mushroom (Flammulina velutipes)
45
polysaccharides (FVPs), which possess antioxidant (Lin et al., 2016), antitumor
46
(Meng et al., 2016), immunomodulatory (Wang et al., 2018), and anti-inflammatory
47
activities (Wu et al., 2010). In this case, the polysaccharides are widely developed as
48
functional foods, dietary supplements, as well as therapeutic drugs (Aida et al., 2009;
49
Yu et al., 2018). However, the qualities of the polysaccharides and related products are
50
unstable, even existing the adulteration (Wu et al., 2015). Indeed, most
51
fungus-derived polysaccharides are complicated mixtures, containing multiple
52
polysaccharide molecules, and many of these structural features play critical roles in
53
the biological effects of the polysaccharides. Unfortunately, there are few available
54
methods for the analysis and authentication of such polysaccharides. To facilitate
55
product developments and quality control, it is of practical importance to develop a
56
simple, effective, and accurate method to characterize and authenticate the
57
fungus-derived polysaccharides.
58
Fingerprint analysis combined with chemometrics was an available method used
59
for identification and quantification of characteristic compounds approved by the
60
World Health Organization in 1991, and it has been reported efficient for the
61
characterization of the complex molecular system (Jing et al., 2014). Additionally,
62
chemometrics is a powerful method to analyze fingerprints based on chemical data,
4
63
including similarity analysis (SA), principal component analysis (PCA) (Liu et al.,
64
2015), thereby helping to analyze the raw data collected from fingerprints. Previous
65
studies have shown that the fingerprint analysis combined with chemometrics can be
66
applied for the analysis of agricultural products (such as coffee, wine, and wheat) with
67
different geographic origins (Zhao and Zhang, 2016). Indeed, as a kind of biopolymer,
68
the polysaccharides have complicated chemical structures, which are characterized by
69
the molecular weight distribution, monosaccharide composition, glycosidic linkage,
70
and secondary- and higher-order structures (Jing et al., 2015; Xiao and Jiang, 2015).
71
On the other hand, the information about fingerprint characteristics are sufficient to
72
evaluate activities because of the variation in components founded in fingerprints, so
73
the relationship between fingerprint and activity should be built to select the main
74
bioactivity compounds for targeted quality control. However, studies hardly focused
75
on the analysis of multiple fingerprint and investigated the fingerprint-activity
76
relationship of polysaccharides to establish an effective and quantitative approach for
77
the quality assessment.
78
In this study, a combination of scanning electron microscopy (SEM),
79
Fourier-transform infrared spectroscopy (FT-IR), and high-performance liquid
80
chromatography (HPLC) was proposed to perform multiple fingerprint analysis of 20
81
batches of FVPs, in order to establish an effective evaluation system based on
82
chemometrics for comparison of polysaccharides from F. velutipes as well as
83
identification of unknown sample. The relationship between 8 common peaks of
5
84
HPLC fingerprint and IC25 of polysaccharides was modeled by using multiple liner
85
regression (MLR) method. This study improved the evaluation system of bioactive
86
polysaccharides using the activity-associated markers, further meaningful to develop
87
the authentication and quality control of fungus-derived polysaccharides and its
88
related products.
89 90
2. Materials and methods
91
2.1. Materials and reagents
92
Twenty authentic batches of fresh F. velutipes (numbered 1-20), which have
93
different varieties and cultivation methods, as well as three unknown samples
94
(numbered 21-23), were purchased in different regions in China (see detailed sample
95
information in Table 1). Among the unknown samples, one of the samples is authentic
96
F. velutipes polysaccharides; the other two samples are substandard F. velutipes
97
polysaccharides.
98 99
Monosaccharides standards (rhamnose, glucose, mannose, galactose, fucose, arabinose, xylose, ribose, glucuronic acid, and galacturonic acid) and T-series dextran
100
standards (T-500, T-200, T-70, T-40 and T-10) were purchased from Sigma-Aldrich
101
(St. Louis, MO, USA). Glucosamine, galactosamine, and
102
1-Phenyl-3-menthyl-5-pyrazolone were purchased from Acros Organics (Shanghai,
103
China). All other chemicals and solvents were of analytical reagent grade.
104
6
105
2.2 Preparation of F. velutipes polysaccharides
106
The extraction of F. velutipes polysaccharides was performed using a modified
107
procedure according to our previous method (Du et al., 2016). Fresh F. velutipes were
108
hot-air dried at 60
109
Scientific Co. Ltd, Japan). Dried samples were ground into powder and passed
110
through a No.80 mesh. The powder (40 g) was dissolved in 1 L of deionized water by
111
stirring (using a magnetic stir bar) in a water bath at 80
112
room temperature, the supernatant was collected by centrifugation (8000 rpm, 10 min,
113
4
114
the extract was precipitated using 4-fold volume anhydrous ethanol at 4
115
Polysaccharide precipitate was centrifuged at 10,000 rpm for 20 min and washed
116
completely with anhydrous ethanol and acetone. The precipitate was then dialyzed
117
against deionized water (every 2 h) for a total of 72 h to remove small molecular
118
impurities. After vacuum concentration, the solution was lyophilized as F. velutipes
119
polysaccharides for subsequent analysis.
for 12 h in an electric vacuum drying oven (DNF610, YAMATO
for 4 h. After cooling to
) and deproteinized with sevag reagent (chloroform: n-butyl alcohol, 4:1). Then overnight.
120
Three unknown samples were purchased from commercial market, and soluble
121
corn starch and maltodextrin could be used as additives to add into related products.
122
Therefore, the quality of three unknown samples should be determined to further
123
support the established multiple fingerprint.
124 125
2.3 Determination of components in polysaccharides from F. velutipes
7
126
The total carbohydrate content was determined by the phenol-sulfuric acid
127
method at 490 nm with D-glucose as a standard (Dubois et al. 1956). The content of
128
uronic acid content was achieved by the sulfuric acid-carbazole method using
129
galacturonic acid as a standard (Karamanos et al. 1988). The protein content was
130
measured by the Coomassie brilliant blue G-250 method with bovine serum albumin
131
as a standard (Bradford et al. 1976).
132 133 134
2.4 Determination of molecular weight High-performance size-exclusion chromatography (HPSEC) on an Agilent 1200
135
system was used to determine the molecular weight of polysaccharides in this study.
136
The polysaccharide powder (3 mg) was dissolved in deionized water (1 mL) and
137
filtered through a 0.45 µm membrane. Each sample solution prepared (20 µL) was
138
injected into Shodex OHpak SB-803 HQ column with SB-804 HQ and SB-805 HQ
139
(8.0 mm × 300 mm) and detected on RID. The system was maintained at 30
140
eluted with 0.1 M NaNO3 at a rate of 0.8 mL/min. The molecular weights of the
141
samples were calculated based on a standard curve derived from T-series dextran
142
standards (T-500, T-200, T-70, T-40, and T-10).
and
143 144 145 146
2.5 Scanning electron microscopy (SEM) analysis Scanning electron microscopy (TM3000 Tabletop Microscope, HITACHI, Japan) was used to examine the apparent morphology of F.velutipes polysaccharides.
8
147
Samples were fixed on specimen stubs using conductive double-sided tape and coated
148
with a gold layer using a sputter coater (BAL-TEC AG, Balzers, Liechtenstein) in a
149
vacuum environment. The acceleration voltage was set at 15 kV, and the morphology
150
of the polysaccharide was observed at different magnification times.
151 152
2.6 Fourier transform infrared spectroscopy (FT-IR) analysis
153
A Bruker Tensor27 (Ettlingen, Germany) was used to obtain FT-IR spectra to
154
identify functional groups. Each sample powder (2 mg) was ground with 20 mg
155
potassium bromide (KBr) in an agate mortar. Then the mixture was pressed into
156
pellets for recording spectra in the range of 4000-400 cm-1, was used a resolution of 4
157
cm-1 for 50 scans.
158
The similarity of FT-IR fingerprints was evaluated by calculating angle cosine
159
and correlation coefficient (R) using eq (1), (2). A spectrum can be recognized as a
160
vector, and the similarity between samples can be calculated according to the
161
formulas. When the value is close to 1, the two vectors are highly similar. =
162
163 164 165
∑
∑
[ ∈ (0,
× ∑
/2)] (1) R= ∑
∑
(
(
̅ )(
̅) ∑
(
)
)
(2)
166
9
167 168
2.7 Acid hydrolysis procedures of F. velutipes Polysaccharides solution (4 mg/mL, 100 µL) was hydrolyzed with 100 µL of 4 M
169
TFA in a small ampoule and the mixture was incubated at 110
for 2 h under nitrogen
170
atmosphere. After cooling to room temperature, hydrolysates were centrifuged at
171
4000 rpm for 5 min, methanol (200 µL) was added into the supernatants so the
172
mixture could be evaporated to remove residual TFA by blowing nitrogen atmosphere
173
and heating of water bath. This process of adding methanol and drying was conducted
174
to remove TFA completely.
175 176 177
2.8 Preparation of PMP derivatives Hydrolyzed polysaccharides or monosaccharide standards were dissolved in 100
178
µL of water, mixed with 100 µL of 0.6 M NaOH, and 200 µL of 0.5 M methanol
179
solution of 1-phenyl-3-methyl-5-pyrazolone (PMP). The reaction mixture was heated
180
to 70
181
temperature, the mixture was neutralized with 50 µL of 0.3 M HCl, and diluted to 1
182
mL using water. The reaction mixture was extracted by chloroform twice, and
183
centrifuged at 2000 rpm for 5 min to discard the chloroform phase. The process above
184
was repeated three times to extract derivatives for removal of the residual PMP
185
reagent. Finally, the extraction was filtered through a 0.45 µm hydrophobic membrane
186
for HPLC analysis.
in a water bath for 100 min before mixing absolutely. After cooling to room
187
10
188
2.9 RP-HPLC-DAD analysis
189
The prepared PMP derivatives were analyzed using an Agilent 1200 HPLC
190
system (Agilent, USA) equipped with a ZORBAX Eclipse XDB-C18 HPLC column
191
(4.6 mm × 250 mm). The mobile phase was 83% (v/v) 0.1 M phosphate buffer (pH 6.7)
192
and 17% (v/v) acetonitrile at a flow rate of 0.8 mL/min. The column temperature was
193
maintained at 25 °C, the sample injection volume was 20 µl and the UV detection
194
wavelength was set at 245 nm.
195 196 197
2.10 Cell viability assay by MTT The antitumor activity of FVPs and unknown samples on HepG2 was evaluated
198
by MTT assay. The 100 µL of the cells were plated in a 96-well plate at a density of
199
2×105 cells/mL. After incubating for 24 h, cells treated with different concentration
200
of polysaccharides samples (25, 50, 100, 200, 400 µg/mL) were cultured for 24 h.
201
Untreated cells were served as the negative control. At the end of the treatment, the
202
culture medium was discarded and 50 µL MTT (5 mg/mL, PBS) was added into
203
each well to incubate for 4 h. Then the supernatant was removed and 100 µL DMSO
204
was dissolved the formed formazan crystals. Absorbance was measured at 570 nm
205
using a microplate reader. The cell viability was expressed as follows: (./ − .1 ) cell viability (% control) = × 1003 (.2 − .1 )
206 207
Where As is the absorbance of treated cells; Ab is the absorbance of untreated cells; Ac is the absorbance without cells.
11
208 209 210
2.11 Statistical analysis Data were expressed as mean±standard deviation. One-way analysis of variance
211
(ANOVA) was used to identify differences between groups. Values of P<0.05 were
212
represented to be statistically significant. The similarity of FT-IR fingerprints was
213
evaluated using angle cosine and correlation coefficient method by MATLAB R2016a
214
and Excel, respectively. The similarity of HPLC fingerprints was submitted using the
215
Similarity Evaluation System for Chromatographic Fingerprint of traditional Chinese
216
medicine (TCM) designed by the China Pharmacopoeia Committee (Version 2012A).
217
Principal component analysis (PCA) of FT-IR and HPLC fingerprints were performed
218
by using SIMCA-P 14.1 (Umetrics AB, Sweden) and SPSS software (SPSS Inc.,
219
Chicago, IL, USA), respectively. The IC25 values (mg/mL) of samples against HepG2
220
cells and MLR analysis on the fingerprint-activity relationship were calculated using
221
SPSS software according to the cell viability.
222 223
3. Results and discussion
224
3.1 The components of FVPs
225
The total carbohydrate, uronic acid and protein contents are shown in Table 2.
226
The total carbohydrate contents were 55.07-71.93%, the content of uronic acid ranged
227
from 19.36-29.22%, and the protein contents of FVPs were 1.18-5.00%, respectively.
228
However, the significant differences of components cannot provide enough
12
229
information for evaluation of FVPs from different sources. Additionally, total
230
carbohydrate contents were superior to protein contents, indicating that
231
polysaccharides extracted from 20 batches of F.velutipes were reliable to establish
232
multiple fingerprints.
233 234 235
3.2 Distribution of molecular weight HPSEC was an efficient and common technique to analyze the molecular weight
236
distribution and polymer dispersity index of polysaccharides extracted from nature
237
resource (Wu et al., 2016). As shown in Table 1, the molecular weight distribution of
238
polysaccharides samples could be divided into four main fractions: <5 kDa, 10-50
239
kDa, 100-500 kDa, and 1000-5000 kDa. The molecular weight distribution of FVPs
240
were mainly in the range of 10-50 kDa and 100-500 kDa, which can be explained by
241
that the relative molecular weights of the two major fractions (FVP-1 and FVP-2)
242
obtained from F. velutipes polysaccharides were 28 kDa and 268 kDa in the previous
243
study (Yang et al., 2012). However, the molecular weight distribution results were
244
insufficient to assess the quality due to the flexible structure of crude polysaccharides,
245
so further studies are needed to distinguish polysaccharides from F. velutipes.
246 247 248 249
3.3 SEM analysis The obtained microstructure of FVPs were shown in Fig. 2. The results showed that the morphological structure of FVPs was highly similar, and the crude
13
250
polysaccharide is lamellar structured with the smooth surface. These findings were
251
different from those of Lentinus edodes polysaccharides, which appeared to have a
252
rough and unshaped surface (Yin et al., 2018). As a result, the regular morphology
253
could set a standard for quality control by comparing the microstructure of
254
polysaccharides samples with the standard microstructure of polysaccharides from F.
255
velutipes. However, the extraction method may affect the surface and structure of
256
polysaccharides, then the FT-IR and HPLC fingerprints were used to further analyze
257
for the comprehensive evaluation of polysaccharides from F. velutipes.
258 259
3.4 FT-IR fingerprints analysis
260
3.4.1 FT-IR fingerprints of F. velutipes polysaccharides
261
The FT-IR spectra were carried out in 4000 to 400 cm-1 to determine the
262
functional groups and chemical bands of FVPs (Fig. 2A). The established standard
263
FT-IR fingerprint (Fig. 2B) showed that the polysaccharides derived from 20 batches
264
of F. velutipes had 12 common and characteristic peaks at 3383.24 cm-1, 2932.73 cm-1,
265
2183.10 cm-1, 1647.48 cm-1, 1422.57 cm-1, 1372.21 cm-1, 1248.20 cm-1, 1201.75 cm-1,
266
1078.69 cm-1, 1039.72 cm-1, 889.75 cm-1, and 573.52 cm-1. The intense peak of FT-IR
267
spectra fingerprint at 3383 cm-1 was considered as O-H stretching vibration, and the
268
peak at 2933 cm-1 was the typical absorption of C-H, which was consistent with the
269
characteristic absorption peaks of polysaccharides (Chen et al., 2016). The bands at
270
1647 cm-1 and 1423 cm-1 were derived from carboxylate anions bonds (C=O)
14
271
(Romdhane et al., 2017). The bands within the range of 1420-1240 cm-1 can be
272
observed in fingerprints, which was due to C-O unsymmetrical stretching vibration
273
and the existence of O-H (Han et al., 2016). Two peaks at 1079 cm-1 and 1040 cm-1
274
indicated that the structure of FVPs contains furanose rings (Ma et al., 2014). The
275
peak at 890 cm-1 exhibited that the glycosyl residues of polysaccharides were
276
β-anomeric configuration (Mkadmini Hammi et al., 2016). These results
277
demonstrated that 12 common characteristic FT-IR signals were significant to build
278
chemometric models for quality control of polysaccharides from F. velutipes.
279 280 281
3.4.2 Similarity analysis Cosine and correlation coefficient methods were used to calculate the similarity
282
of FT-IR spectra between each polysaccharides sample and the standard FT-IR
283
fingerprint based on the raw data of wave bands. Similarity analysis results showed
284
that the cosine values and correlation coefficients in the 20 batches of FT-IR spectra
285
were 0.9904 ± 0.0124 (n=20) and 0.9596 ± 0.0349 (n=20), ranging from 0.9474 to
286
0.9997 and 0.8882 to 0.9934, respectively. The similarity values between different
287
samples illustrated a strong correlation, which indicated that the standard FT-IR
288
fingerprint was reliable to apply for identification of polysaccharides from F.
289
velutipes.
290 291
3.4.3 Principal component analysis
15
292
The PCA is worked by SIMCA 14.4 to describe the relationship between
293
different polysaccharides samples, the spectral preprocessing method of standard
294
normal variate (SNV) was chosen to preprocess the original spectra before principal
295
component analysis (Li et al., 2016). The PCA score plot showed that the
296
polysaccharides from 20 batches of FVPs were clustered closely, indicating that
297
overall polysaccharides samples were similar in a high degree (Fig. 2C). The
298
preprocessed spectra data were evaluated by using two main PCA plots of FT-IR
299
spectra fingerprints, and the total variance of PC1 and PC2 accounted for 79.7% and
300
14.1%, respectively. However, the PCA analysis of FT-IR fingerprints could not be
301
used to discriminate different polysaccharides samples due to nuances between 20
302
batches of FVPs, thus the further HPLC fingerprints analysis were applied to
303
determine differences and similarities.
304 305
3.5 HPLC fingerprints analysis
306
3.5.1 HPLC fingerprints of F. velutipes polysaccharides
307
HPLC was used to analyze monosaccharide composition in 20 batches of FVPs,
308
in order to establish the standard HPLC fingerprint. HPLC analysis showed that there
309
are 8 common characteristic peaks, identified to be mannose, glucosamine, ribose,
310
rhamnose, glucose, galactose, xylose and fucose with the molar ratio of 8.04, 0.34.
311
0.59, 0.28, 68.28, 15.28, 2.14 and 5.04%, in the polysaccharides from 20 batches of F.
312
velutipes, which was in agreement with a previous study (see HPLC chromatograms
16
313
in Fig. 3A and the standard HPLC fingerprint in Fig. 3B) (Zhang et al., 2012). Notably,
314
polysaccharides from white F. velutipes contained glucuronic acid but
315
polysaccharides from yellow F. velutipes did not, indicating that glucuronic acid
316
could be considered as a specific marker to distinguish white and yellow F. velutipes.
317
And plus, the glucose peak was regarded as the reference peak, which is attributed to
318
suitable content, intensity and retention time in the whole chromatography. The
319
analysis of RRT (relative retention time) and RPA (relative peak area) statistic can
320
provide detailed information for the monosaccharide composition of FVPs from
321
different sources.
322 323 324
3.5.2 Similarity analysis Based on the HPLC chromatograms of polysaccharide samples and the standard
325
fingerprint, the similarity values were calculated by using the professional software
326
“Similarity Evaluation System for Chromatographic Fingerprint of traditional
327
Chinese medicine (TCM) (Version 2012A).” Similarity analysis showed that
328
correlation coefficients of polysaccharides from 20 batches of F. velutipes compared
329
with the standard fingerprint were 0.957 to 0.999. The high similarity in HPLC
330
fingerprints demonstrated that the standard fingerprint could represent most of the
331
characteristic features for polysaccharides from F. velutipes.
332 333
3.5.3 Principal component analysis (PCA)
17
334
The HPLC fingerprints of F. velutipes polysaccharides were evaluated by
335
principal component analysis (PCA) to outline multivariate differences and
336
quantitative determination between 20 batches of FVPs. The data matrix of relative
337
peak areas of characteristic peaks worked by SPSS showed that the first two PCs
338
accounted for 68.19 % and 11.97 % of the variance in the score plot of PCA (Fig. 3C).
339
Two main factors could be calculated by eqs. (3)-(4).
340
PC1 = 0.157 ∗ X1 + 0.140 ∗ X2 + 0.107 ∗ X3 + 0.081 ∗ X4 + 0.113 ∗ X5 −
341
0.159 ∗ X6 + 0.152 ∗ X7 + 0.129 ∗ X8 + 0.153 ∗
342
X9 (3)
343
PC2 = −0.161 ∗ X1 + 0.363 ∗ X2 + 0.394 ∗ X3 + 0.576 ∗ X4 − 0.342 ∗ X5 +
344
0.069 ∗ X6 − 0.042 ∗ X7 − 0.397 ∗ X8 − 0.048 ∗
345
X9 (4)
346
The X1 to X9 were mannose, glucosamine, ribose, rhamnose, glucose,
347
glucuronic acid, galactose, xylose, and fucose, respectively, in the above equations.
348
The PC1 was related well with X1, X6, X7 and X9 with the loadings of 0.157, 0.159,
349
0.152 and 0.153, and the equation of PC2 had an excellent explanation for X2, X3, X4,
350
X5, X8, indicated that these main factors could make contribution to classify F.
351
velutipes polysaccharides from different sources. The plot scores suggested that
352
polysaccharides from white and yellow F. velutipes could be identified depending on
353
the different monosaccharide composition.
354
18
355 356
3.6 Identification of unknown samples Three unknown F.velutipes polysaccharides products were purchased to verify
357
whether multiple fingerprints combined with chemometrics could be applied for
358
quality control. The appearance results showed that the morphology of unknown
359
sample 1 is similar to that of authentic F. velutipes, but the unknown samples 2 and 3
360
were suspected samples (Fig. 1 S21-23). The microstructure of soluble corn starch
361
and maltodextrin attached to the surface of polysaccharides particles could be seen in
362
the other two unknown samples, respectively, and this appearance was similar to the
363
adulterated polysaccharide reported in the previous study (Qian et al., 2009).
364
Furthermore, the FT-IR spectra of unknown sample 1 (S21) were similar to the
365
standard fingerprint, supporting that this sample could be F. velutipes polysaccharides,
366
though more data is required for identification. In contrast, the FT-IR spectra of
367
unknown samples 2 and 3 (S22 and S23) were different from the standard fingerprint:
368
unknown sample 2 showed stronger bands in the region of 1500 cm-1 to 500 cm-1, and
369
unknown sample 3 showed different peaks at in the region of 1500 cm-1 to 500 cm-1.
370
The cosine values and correlation coefficient of the unknown samples were 0.9820,
371
0.9830, 0.8334 and 0.9619, 0.6901, 0.6803, which demonstrated that the correlation
372
between unknown sample 1 and the standard FT-IR fingerprint was higher than that of
373
the other two unknown samples. Additionally, the HPLC chromatograms of three
374
unknown samples were similar to the HPLC fingerprint with the common
375
characteristic peaks, but the molar ration of peaks had a great discrepancy in unknown
19
376
samples 2 and 3 (Fig. 3D), and the similarity values of the unknown samples were
377
0.973, 0.879, and 0.878. In the principal component analysis of FT-IR fingerprints, the
378
unknown sample 1 was close to the class of authentic F. velutipes polysaccharides,
379
while unknown sample 2 and 3 were much more diffuse from entirety in a high degree
380
(Fig. 2C; Fig. 3C). Additionally, unknown sample 1 could be identified as a yellow F.
381
velutipes polysaccharides product according to the principal component analysis of
382
HPLC fingerprints. In summary, unknown samples 2 and 3 were substandard F.
383
velutipes polysaccharides products, thus multiple fingerprint combined with
384
chemometrics could be used to classify unknown samples successfully.
385 386
3.67 Cell growth inhibitory assay of FVPs and HPLC fingerprint-growth
387
inhibitory activity relationship
388
The 25% inhibition concentration (IC25) for polysaccharides from F. velutipes
389
was determined for HepG2 cells, and the lower value of IC25 indicated the stronger
390
cancer cell growth inhibitory activity. The inhibitory activity results of FVPs from
391
different sources showed that S10 exhibited the highest inhibitory effect on HepG2
392
(IC25=99.57 µg/mL), while the lowest antitumor activity was IC25 305.49 µg/mL
393
from S20 (Fig.4). However, the variation in inhibitory activity of FVPs were
394
affected by monosaccharides composition, and therefore HPLC fingerprint-growth
395
inhibitory relationship should be established to assess the quality of polysaccharides
396
from F. velutipes.
20
397
MLR method was used to establish quantitative fingerprint-activity relationship
398
model built with 8 common peaks of HPLC fingerprint and with IC25 of FVPs
399
against HepG2 cells. As is shown in Table 2, the determination coefficient of
400
regression model was 0.778, and P-value at the level of 0.05 implied statistically
401
significant. Meanwhile, the mean value of residuals was 1.22×10-13 closing to zero.
402
The R2 value of linear trend lines of normal probability plots was 0.984, which
403
demonstrated that residuals were normally distributed. These results proved that
404
HPLC-growth inhibitory activity regression model was reasonable. The effect of
405
significant factors on inhibitory activity can be ranked from low to high as follows:
406
X7 (xylose), X6 (galactose), X4 (rhamnose), and X1 (mannose) (Table 3). The content
407
of X4, X6, and X7 and had obviously negative correlation with inhibitory activity.
408
Nevertheless, the high content of X1 were observed to significantly inhibit the
409
proliferation of HepG2 cells, which is consistent with previous study (Jiang et al.,
410
2012).
411 412 413
4. Conclusion In summary, a comprehensive and efficient approach was successfully proposed
414
for quality control of polysaccharides from F. velutipes and its related products. The
415
SEM analysis was an intuitive method for the identification of polysaccharides but
416
limited by the extraction method of polysaccharides. The establishment of FT-IR and
417
HPLC fingerprints combined with chemometric methods (SA and PCA)
21
418
demonstrated that polysaccharides from the 20 batches of F. velutipes had unique
419
standard fingerprint characteristics, and the authentic F.velutipes polysaccharides of
420
three unknown samples can be identified successfully. However, HPLC fingerprints
421
analysis could further differentiate the white and yellow F. velutipes polysaccharides
422
in a high degree. The quantitative HPLC fingerprint-growth inhibitory activity
423
against HepG2 relationship were established to set up a time-saving activity
424
prediction and quality assessment of FVPs from different sources based on MLR
425
analysis. Overall, the applicable and accurate strategy is confirmed to evaluate
426
fungus-derived polysaccharides from different sources, thereby providing an
427
alternative for the quality control of polysaccharides products.
428 429
Acknowledgment
430
This work is financially supported by the Key Research and Development
431
Program of Jiangsu Province (BE2017374), the China Agriculture Research System
432
(CARS-20), and the Postgraduate Research & Practice Innovation Program of
433
Jiangsu Province (KYCX18_1405).
22
434
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545
Figure Captions
546
Fig. 1. The SEM graphics of polysaccharides from F. Velutipes and unknown
547
samples. The microstructures of S1-S20 were highly similar. The structure of S21
548
was close to polysaccharides samples (S1-S20), but the ones of S22 and S23 were
549
dramatically different and adulterate.
550 551
Fig. 2. Comparison of FT-IR spectra of polysaccharides from F. Velutipes. (A)
552
FT-IR fingerprints; (B) referential fingerprint from (A); (C) FT-IR spectra of three
553
unknow samples; (D) plot of PCA scores. S21 was similar to polysaccharides
554
samples (S1-S20), but S22 and S23 were much more diffused.
555 556
Fig. 3. The monosaccharide composition of polysaccharides from F. Velutipes.
557
(A) HPLC fingerprints; (B) referential fingerprint from (A); (C) HPLC profiles of
558
three unknow samples; (D) plot of PCA scores. Component: 1, mannose; 2,
559
glucosamine; 3, ribose; 4, rhamnose; 5, glucose; 6, galactose; 7, xylose; 8, fucose.
28
560
S21 was close to polysaccharides samples (S1-S20), but S22 and S23 were much
561
more diffused.
562 563
Fig. 4. HepG2 cell inhibition IC25 of polysaccharides from F. velutipes. S1-S20
564
represent different antitumor activities of 20 batches of FVPs from various sources.
565
Each value is expressed as mean ± standard deviation (n = 3). Different letters
566
indicate significance difference between different columns (P < 0.05).
29
Table 1 Molecular weight distribution of samples from different sources Number
Variety
Sources
Cultivation method
1
Xuerong, white
Dezhou, Shandong Province
Bottle cultivated
2
Chuanjin 11, white
Ande, Sichuan Province
Bottle cultivated
3
Chuanjin 33, white
Tangchang, Sichuan Province
Bag cultivated
4
Hualv, white
Siyang, Jiangsu Province
Bottle cultivated
5
Jin 19, white
Handan, Hebei province
Bottle cultivated
6
Jinbai 1, white
Shijiazhuang, Hebei province
Bag cultivated
7
Baijin Japan, white
Zhangzhou, Fujian Province
Bottle cultivated
8
Nvwa, white
Zhuhai, Guangdong Province
Bottle cultivated
Molecular weight (Da) 1,623,755 118,835 17,339 1990 1,533,362 132,974 15,638 1,150,118 199,478 15,921 1,154,566 259,032 12,758 1,251,461 265,744 13,894 1,235,162 527,488 17,683 1799 1,418,385 212,857 18,460 1,026,916 109,338 16,009
Area per centage (%) 12.34 62.20 17.77 7.70 26.69 38.60 34.71 20.68 29.02 50.30 31.43 23.19 45.37 10.58 37.17 52.25 46.01 15.98 30.28 7.72 24.31 32.39 43.31 30.77 26.46 42.78
Number
Variety
Sources
Cultivation method
9
Baixue 2, white
Huangshi, Hubei Province
Bag cultivated
10
Guoren, white
Baoji, Shanxi Province
Bottle cultivated
11
Chuanjin 3, yellow
Ande, Sichuan Province
Bag cultivated
12
Chuanjin 54, yellow
Tangchang, Sichuan Province
Bag cultivated
13
Su 6, yellow
Shijiazhuang, Hebei Province
Bag cultivated
14
2102, yellow
Handan, Hebei province
Bag cultivated
15
Jin 17, yellow
Xiamen, Fujian Province
Bottle cultivated
16
Jin 13, yellow
Gutian, Fujian Province
Bottle cultivated
17
Sanming 1, yellow
Puyang, Henan Province
Bag cultivated
Molecular weight (Da) 1,441,769 117,693 16,457 1,440,038 119,605 15,254 1,190,650 176,997 65,401 16,375 1,374,191 144,020 17,106 1,096,561 204,222 19,411 1,204,842 227,940 15,982 1,580,919 158,809 18,655 1,472,898 122,033 17,876 1,350,057 105,983 18,654
Area per centage (%) 12.79 39.32 47.89 15.06 55.81 29.14 9.83 26.40 29.98 33.79 25.99 41.17 32.84 35.10 41.78 23.13 41.49 37.94 20.58 24.03 48.71 27.26 14.43 61.28 24.29 19.10 49.63 31.26
Number
Variety
Sources
Cultivation method
18
New Su 6, yellow
Zhengzhou, Henan Province
Bag cultivated
19
Sanming B, yellow
Dezhou, Shandong Province
Bag cultivated
20
Jinza 19, yellow
Changzhou, Jiangsu Province
Bag cultivated
21
Unknown sample 1
-
-
22
Unknown sample 2
-
-
23
Unknown sample 3
-
-
Molecular weight (Da) 1,400,902 119,867 20,712 1,922,985 145,810 17,754 600,018 18,645 747,314 15,077 1,476,583 171,804 16,600 1,761,954 153,053 5,856
Area per centage (%) 17.94 46.19 35.87 12.21 54.27 33.52 53.81 46.20 48.09 51.91 12.36 70.12 17.42 7.16 41.46 51.38
Table 2 The components of FVPs Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Protein content (%)
Uronic acid content (%)
Total carbohydrate content (%)
2.06±0.17 1.18±0.09 1.77±0.06 2.81±0.57 2.81±0.25 2.27±0.14 4.50±0.19 3.84±0.31 5.00±0.44 4.13±0.38 3.67±0.25 2.06±0.14 2.38±0.03 2.44±0.17 3.50±0.26 4.58±0.35 2.38±0.20 3.43±0.30 1.53±0.01 2.64±0.13
22.40±0.55 29.22±0.84 20.66±0.21 21.56±0.91 24.69±1.27 20.87±0.66 22.63±0.33 23.79±0.45 23.39±0.96 23.76±0.38 24.75±0.41 24.31±0.82 21.75±0.42 23.64±1.12 21.27±0.47 24.46±0.42 23.78±0.64 22.69±0.46 24.10±0.25 19.36±0.31
58.68±1.65 57.49±1.73 56.04±0.97 71.93±1.82 58.04±2.02 57.93±1.77 71.54±1.83 60.75±2.23 71.16±1.68 56.32±2.05 55.07±1.72 69.04±1.69 70.04±1.99 62.91±1.69 70.02±1.89 69.75±1.59 70.86±1.91 69.86±1.56 66.91±2.11 63.46±1.78
Table 3 Results of multiple linear regression model
Regression models
Determination coefficient
P-value
HPLC fingerprint based model
0.778
0.009
Residuals Correlation coefficient Mean value of probability plot 1.22×10-13
0.984
Table 4 HPLC fingerprint-activity relationship and their corresponding parameters parameters Regression coefficient Standardized regression coefficient t-test P-value
constant 245.828
X1 -757.773
X2 475.785
X3 -71.469
X4 940.447
X5 -880.957
X6 5373.172
X7 3014.958
X8 -2195.325
0
-3.226
0.191
-0.041
0.544
-0.256
2.529
1.561
-1.053
8.420 4.00×10-6
-2.305 0.042
0.449 0.662
-0.182 0.859
2.675 0.022
-1.413 0.185
2.776 0.018
3.265 0.008
-0.883 0.396
Independent variables (X1-X7) represent mannose, glucosamine, ribose, rhamnose, glucose, galactose, xylose and fucose, respectively.
Fig. 1
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S21
S22 S11
S12
S13
S14
S15
S23 S16
S17
S18
S19
S20
Fig. 2
A
B
C
D
Fig. 3
A
B
C
D
Fig. 4
Highlights HPLC fingerprint-activity were established to set a new evaluation approach of polysaccharides from Flammulina Velutipes. Multiple fingerprint combined with chemometrics distinguished substandard samples from authentic samples. The activity-associated markers of HPLC fingerprints were chosen successfully by multiple liner regression analysis.
Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
S0278-6915(19)30734-3
DOI:
https://doi.org/10.1016/j.fct.2019.110944
Reference:
FCT 110944
To appear in:
Food and Chemical Toxicology
Received Date: 30 September 2019 Revised Date:
31 October 2019
Accepted Date: 5 November 2019
Please cite this article as: Dong, Y., Pei, F., Su, A., Sanidad, K.Z., Ma, G., Zhao, L., Hu, Q., Multiple fingerprint and fingerprint-activity relationship for quality assessment of polysaccharides from Flammulina velutipes, Food and Chemical Toxicology (2019), doi: https://doi.org/10.1016/ j.fct.2019.110944. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
1
Multiple fingerprint and fingerprint-activity relationship for quality assessment
2
of polysaccharides from Flammulina velutipes
3 4
Yutong Donga, Fei Peia, Anxiang Sub, Katherine Z. Sanidadc, Gaoxing Maa, Liyan
5
Zhaob, Qiuhui Hua,*
6 7
a
8
Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality
9
Control and Processing, Nanjing University of Finance and Economics, Nanjing
College of Food Science and Engineering/Collaborative Innovation Center for
10
210023, China.
11
b
12
210095, P. R. China.
13
c
14
USA.
College of Food Science and Technology, Nanjing Agricultural University, Nanjing
Department of Food Science, University of Massachusetts, Amherst 01003, MA,
15 16
*
17
E-mail: [email protected]
Corresponding author: Qiuhui Hu
18
1
19
Abstract
20
Polysaccharides are known as one of the most important bioactive compounds in
21
Flammulina velutipes. However, there is no accurate and comprehensive assessment
22
method to evaluate and authenticate F. velutipes polysaccharides (FVPs) from
23
different sources. In this study, a multiple fingerprint analysis method including
24
scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy
25
(FT-IR), and high-performance liquid chromatography (HPLC) was established. The
26
inhibitory activities of FVPs against HepG2 were measured and introduced into
27
multiple linear regression (MLR) analysis to investigate fingerprint-activity
28
relationship. The principal component analysis (PCA) scores showed that the
29
polysaccharides extracted from 20 batches of different F. velutipes were highly similar,
30
and substandard samples could be distinguished from the authentic polysaccharides
31
clearly. The glucuronic acid could be considered as a marker for discrimination of
32
white and yellow F. velutipes polysaccharides in HPLC fingerprints. Moreover, the
33
HPLC fingerprint-growth inhibitory activity relationship illuminated that
34
monosaccharides composition played an important role on the HepG2 growth
35
inhibitory activity, and activity-associated markers (mannose, rhamnose, xylose, and
36
galactose) were chosen to assess FVPs from different sources. The suggested HPLC
37
fingerprint-activity relationship method provides an integrated strategy for the
38
quality control of F. velutipes and its related products.
39
2
40
Keywords: Flammulina velutipes; polysaccharides; chemometrics;
41
fingerprint-activity relationship; quality control
3
42
1. Introduction
43
Substantial studies have shown that fungus-derived polysaccharides have a
44
variety of health-promoting effects, such as needle mushroom (Flammulina velutipes)
45
polysaccharides (FVPs), which possess antioxidant (Lin et al., 2016), antitumor
46
(Meng et al., 2016), immunomodulatory (Wang et al., 2018), and anti-inflammatory
47
activities (Wu et al., 2010). In this case, the polysaccharides are widely developed as
48
functional foods, dietary supplements, as well as therapeutic drugs (Aida et al., 2009;
49
Yu et al., 2018). However, the qualities of the polysaccharides and related products are
50
unstable, even existing the adulteration (Wu et al., 2015). Indeed, most
51
fungus-derived polysaccharides are complicated mixtures, containing multiple
52
polysaccharide molecules, and many of these structural features play critical roles in
53
the biological effects of the polysaccharides. Unfortunately, there are few available
54
methods for the analysis and authentication of such polysaccharides. To facilitate
55
product developments and quality control, it is of practical importance to develop a
56
simple, effective, and accurate method to characterize and authenticate the
57
fungus-derived polysaccharides.
58
Fingerprint analysis combined with chemometrics was an available method used
59
for identification and quantification of characteristic compounds approved by the
60
World Health Organization in 1991, and it has been reported efficient for the
61
characterization of the complex molecular system (Jing et al., 2014). Additionally,
62
chemometrics is a powerful method to analyze fingerprints based on chemical data,
4
63
including similarity analysis (SA), principal component analysis (PCA) (Liu et al.,
64
2015), thereby helping to analyze the raw data collected from fingerprints. Previous
65
studies have shown that the fingerprint analysis combined with chemometrics can be
66
applied for the analysis of agricultural products (such as coffee, wine, and wheat) with
67
different geographic origins (Zhao and Zhang, 2016). Indeed, as a kind of biopolymer,
68
the polysaccharides have complicated chemical structures, which are characterized by
69
the molecular weight distribution, monosaccharide composition, glycosidic linkage,
70
and secondary- and higher-order structures (Jing et al., 2015; Xiao and Jiang, 2015).
71
On the other hand, the information about fingerprint characteristics are sufficient to
72
evaluate activities because of the variation in components founded in fingerprints, so
73
the relationship between fingerprint and activity should be built to select the main
74
bioactivity compounds for targeted quality control. However, studies hardly focused
75
on the analysis of multiple fingerprint and investigated the fingerprint-activity
76
relationship of polysaccharides to establish an effective and quantitative approach for
77
the quality assessment.
78
In this study, a combination of scanning electron microscopy (SEM),
79
Fourier-transform infrared spectroscopy (FT-IR), and high-performance liquid
80
chromatography (HPLC) was proposed to perform multiple fingerprint analysis of 20
81
batches of FVPs, in order to establish an effective evaluation system based on
82
chemometrics for comparison of polysaccharides from F. velutipes as well as
83
identification of unknown sample. The relationship between 8 common peaks of
5
84
HPLC fingerprint and IC25 of polysaccharides was modeled by using multiple liner
85
regression (MLR) method. This study improved the evaluation system of bioactive
86
polysaccharides using the activity-associated markers, further meaningful to develop
87
the authentication and quality control of fungus-derived polysaccharides and its
88
related products.
89 90
2. Materials and methods
91
2.1. Materials and reagents
92
Twenty authentic batches of fresh F. velutipes (numbered 1-20), which have
93
different varieties and cultivation methods, as well as three unknown samples
94
(numbered 21-23), were purchased in different regions in China (see detailed sample
95
information in Table 1). Among the unknown samples, one of the samples is authentic
96
F. velutipes polysaccharides; the other two samples are substandard F. velutipes
97
polysaccharides.
98 99
Monosaccharides standards (rhamnose, glucose, mannose, galactose, fucose, arabinose, xylose, ribose, glucuronic acid, and galacturonic acid) and T-series dextran
100
standards (T-500, T-200, T-70, T-40 and T-10) were purchased from Sigma-Aldrich
101
(St. Louis, MO, USA). Glucosamine, galactosamine, and
102
1-Phenyl-3-menthyl-5-pyrazolone were purchased from Acros Organics (Shanghai,
103
China). All other chemicals and solvents were of analytical reagent grade.
104
6
105
2.2 Preparation of F. velutipes polysaccharides
106
The extraction of F. velutipes polysaccharides was performed using a modified
107
procedure according to our previous method (Du et al., 2016). Fresh F. velutipes were
108
hot-air dried at 60
109
Scientific Co. Ltd, Japan). Dried samples were ground into powder and passed
110
through a No.80 mesh. The powder (40 g) was dissolved in 1 L of deionized water by
111
stirring (using a magnetic stir bar) in a water bath at 80
112
room temperature, the supernatant was collected by centrifugation (8000 rpm, 10 min,
113
4
114
the extract was precipitated using 4-fold volume anhydrous ethanol at 4
115
Polysaccharide precipitate was centrifuged at 10,000 rpm for 20 min and washed
116
completely with anhydrous ethanol and acetone. The precipitate was then dialyzed
117
against deionized water (every 2 h) for a total of 72 h to remove small molecular
118
impurities. After vacuum concentration, the solution was lyophilized as F. velutipes
119
polysaccharides for subsequent analysis.
for 12 h in an electric vacuum drying oven (DNF610, YAMATO
for 4 h. After cooling to
) and deproteinized with sevag reagent (chloroform: n-butyl alcohol, 4:1). Then overnight.
120
Three unknown samples were purchased from commercial market, and soluble
121
corn starch and maltodextrin could be used as additives to add into related products.
122
Therefore, the quality of three unknown samples should be determined to further
123
support the established multiple fingerprint.
124 125
2.3 Determination of components in polysaccharides from F. velutipes
7
126
The total carbohydrate content was determined by the phenol-sulfuric acid
127
method at 490 nm with D-glucose as a standard (Dubois et al. 1956). The content of
128
uronic acid content was achieved by the sulfuric acid-carbazole method using
129
galacturonic acid as a standard (Karamanos et al. 1988). The protein content was
130
measured by the Coomassie brilliant blue G-250 method with bovine serum albumin
131
as a standard (Bradford et al. 1976).
132 133 134
2.4 Determination of molecular weight High-performance size-exclusion chromatography (HPSEC) on an Agilent 1200
135
system was used to determine the molecular weight of polysaccharides in this study.
136
The polysaccharide powder (3 mg) was dissolved in deionized water (1 mL) and
137
filtered through a 0.45 µm membrane. Each sample solution prepared (20 µL) was
138
injected into Shodex OHpak SB-803 HQ column with SB-804 HQ and SB-805 HQ
139
(8.0 mm × 300 mm) and detected on RID. The system was maintained at 30
140
eluted with 0.1 M NaNO3 at a rate of 0.8 mL/min. The molecular weights of the
141
samples were calculated based on a standard curve derived from T-series dextran
142
standards (T-500, T-200, T-70, T-40, and T-10).
and
143 144 145 146
2.5 Scanning electron microscopy (SEM) analysis Scanning electron microscopy (TM3000 Tabletop Microscope, HITACHI, Japan) was used to examine the apparent morphology of F.velutipes polysaccharides.
8
147
Samples were fixed on specimen stubs using conductive double-sided tape and coated
148
with a gold layer using a sputter coater (BAL-TEC AG, Balzers, Liechtenstein) in a
149
vacuum environment. The acceleration voltage was set at 15 kV, and the morphology
150
of the polysaccharide was observed at different magnification times.
151 152
2.6 Fourier transform infrared spectroscopy (FT-IR) analysis
153
A Bruker Tensor27 (Ettlingen, Germany) was used to obtain FT-IR spectra to
154
identify functional groups. Each sample powder (2 mg) was ground with 20 mg
155
potassium bromide (KBr) in an agate mortar. Then the mixture was pressed into
156
pellets for recording spectra in the range of 4000-400 cm-1, was used a resolution of 4
157
cm-1 for 50 scans.
158
The similarity of FT-IR fingerprints was evaluated by calculating angle cosine
159
and correlation coefficient (R) using eq (1), (2). A spectrum can be recognized as a
160
vector, and the similarity between samples can be calculated according to the
161
formulas. When the value is close to 1, the two vectors are highly similar. =
162
163 164 165
∑
∑
[ ∈ (0,
× ∑
/2)] (1) R= ∑
∑
(
(
̅ )(
̅) ∑
(
)
)
(2)
166
9
167 168
2.7 Acid hydrolysis procedures of F. velutipes Polysaccharides solution (4 mg/mL, 100 µL) was hydrolyzed with 100 µL of 4 M
169
TFA in a small ampoule and the mixture was incubated at 110
for 2 h under nitrogen
170
atmosphere. After cooling to room temperature, hydrolysates were centrifuged at
171
4000 rpm for 5 min, methanol (200 µL) was added into the supernatants so the
172
mixture could be evaporated to remove residual TFA by blowing nitrogen atmosphere
173
and heating of water bath. This process of adding methanol and drying was conducted
174
to remove TFA completely.
175 176 177
2.8 Preparation of PMP derivatives Hydrolyzed polysaccharides or monosaccharide standards were dissolved in 100
178
µL of water, mixed with 100 µL of 0.6 M NaOH, and 200 µL of 0.5 M methanol
179
solution of 1-phenyl-3-methyl-5-pyrazolone (PMP). The reaction mixture was heated
180
to 70
181
temperature, the mixture was neutralized with 50 µL of 0.3 M HCl, and diluted to 1
182
mL using water. The reaction mixture was extracted by chloroform twice, and
183
centrifuged at 2000 rpm for 5 min to discard the chloroform phase. The process above
184
was repeated three times to extract derivatives for removal of the residual PMP
185
reagent. Finally, the extraction was filtered through a 0.45 µm hydrophobic membrane
186
for HPLC analysis.
in a water bath for 100 min before mixing absolutely. After cooling to room
187
10
188
2.9 RP-HPLC-DAD analysis
189
The prepared PMP derivatives were analyzed using an Agilent 1200 HPLC
190
system (Agilent, USA) equipped with a ZORBAX Eclipse XDB-C18 HPLC column
191
(4.6 mm × 250 mm). The mobile phase was 83% (v/v) 0.1 M phosphate buffer (pH 6.7)
192
and 17% (v/v) acetonitrile at a flow rate of 0.8 mL/min. The column temperature was
193
maintained at 25 °C, the sample injection volume was 20 µl and the UV detection
194
wavelength was set at 245 nm.
195 196 197
2.10 Cell viability assay by MTT The antitumor activity of FVPs and unknown samples on HepG2 was evaluated
198
by MTT assay. The 100 µL of the cells were plated in a 96-well plate at a density of
199
2×105 cells/mL. After incubating for 24 h, cells treated with different concentration
200
of polysaccharides samples (25, 50, 100, 200, 400 µg/mL) were cultured for 24 h.
201
Untreated cells were served as the negative control. At the end of the treatment, the
202
culture medium was discarded and 50 µL MTT (5 mg/mL, PBS) was added into
203
each well to incubate for 4 h. Then the supernatant was removed and 100 µL DMSO
204
was dissolved the formed formazan crystals. Absorbance was measured at 570 nm
205
using a microplate reader. The cell viability was expressed as follows: (./ − .1 ) cell viability (% control) = × 1003 (.2 − .1 )
206 207
Where As is the absorbance of treated cells; Ab is the absorbance of untreated cells; Ac is the absorbance without cells.
11
208 209 210
2.11 Statistical analysis Data were expressed as mean±standard deviation. One-way analysis of variance
211
(ANOVA) was used to identify differences between groups. Values of P<0.05 were
212
represented to be statistically significant. The similarity of FT-IR fingerprints was
213
evaluated using angle cosine and correlation coefficient method by MATLAB R2016a
214
and Excel, respectively. The similarity of HPLC fingerprints was submitted using the
215
Similarity Evaluation System for Chromatographic Fingerprint of traditional Chinese
216
medicine (TCM) designed by the China Pharmacopoeia Committee (Version 2012A).
217
Principal component analysis (PCA) of FT-IR and HPLC fingerprints were performed
218
by using SIMCA-P 14.1 (Umetrics AB, Sweden) and SPSS software (SPSS Inc.,
219
Chicago, IL, USA), respectively. The IC25 values (mg/mL) of samples against HepG2
220
cells and MLR analysis on the fingerprint-activity relationship were calculated using
221
SPSS software according to the cell viability.
222 223
3. Results and discussion
224
3.1 The components of FVPs
225
The total carbohydrate, uronic acid and protein contents are shown in Table 2.
226
The total carbohydrate contents were 55.07-71.93%, the content of uronic acid ranged
227
from 19.36-29.22%, and the protein contents of FVPs were 1.18-5.00%, respectively.
228
However, the significant differences of components cannot provide enough
12
229
information for evaluation of FVPs from different sources. Additionally, total
230
carbohydrate contents were superior to protein contents, indicating that
231
polysaccharides extracted from 20 batches of F.velutipes were reliable to establish
232
multiple fingerprints.
233 234 235
3.2 Distribution of molecular weight HPSEC was an efficient and common technique to analyze the molecular weight
236
distribution and polymer dispersity index of polysaccharides extracted from nature
237
resource (Wu et al., 2016). As shown in Table 1, the molecular weight distribution of
238
polysaccharides samples could be divided into four main fractions: <5 kDa, 10-50
239
kDa, 100-500 kDa, and 1000-5000 kDa. The molecular weight distribution of FVPs
240
were mainly in the range of 10-50 kDa and 100-500 kDa, which can be explained by
241
that the relative molecular weights of the two major fractions (FVP-1 and FVP-2)
242
obtained from F. velutipes polysaccharides were 28 kDa and 268 kDa in the previous
243
study (Yang et al., 2012). However, the molecular weight distribution results were
244
insufficient to assess the quality due to the flexible structure of crude polysaccharides,
245
so further studies are needed to distinguish polysaccharides from F. velutipes.
246 247 248 249
3.3 SEM analysis The obtained microstructure of FVPs were shown in Fig. 2. The results showed that the morphological structure of FVPs was highly similar, and the crude
13
250
polysaccharide is lamellar structured with the smooth surface. These findings were
251
different from those of Lentinus edodes polysaccharides, which appeared to have a
252
rough and unshaped surface (Yin et al., 2018). As a result, the regular morphology
253
could set a standard for quality control by comparing the microstructure of
254
polysaccharides samples with the standard microstructure of polysaccharides from F.
255
velutipes. However, the extraction method may affect the surface and structure of
256
polysaccharides, then the FT-IR and HPLC fingerprints were used to further analyze
257
for the comprehensive evaluation of polysaccharides from F. velutipes.
258 259
3.4 FT-IR fingerprints analysis
260
3.4.1 FT-IR fingerprints of F. velutipes polysaccharides
261
The FT-IR spectra were carried out in 4000 to 400 cm-1 to determine the
262
functional groups and chemical bands of FVPs (Fig. 2A). The established standard
263
FT-IR fingerprint (Fig. 2B) showed that the polysaccharides derived from 20 batches
264
of F. velutipes had 12 common and characteristic peaks at 3383.24 cm-1, 2932.73 cm-1,
265
2183.10 cm-1, 1647.48 cm-1, 1422.57 cm-1, 1372.21 cm-1, 1248.20 cm-1, 1201.75 cm-1,
266
1078.69 cm-1, 1039.72 cm-1, 889.75 cm-1, and 573.52 cm-1. The intense peak of FT-IR
267
spectra fingerprint at 3383 cm-1 was considered as O-H stretching vibration, and the
268
peak at 2933 cm-1 was the typical absorption of C-H, which was consistent with the
269
characteristic absorption peaks of polysaccharides (Chen et al., 2016). The bands at
270
1647 cm-1 and 1423 cm-1 were derived from carboxylate anions bonds (C=O)
14
271
(Romdhane et al., 2017). The bands within the range of 1420-1240 cm-1 can be
272
observed in fingerprints, which was due to C-O unsymmetrical stretching vibration
273
and the existence of O-H (Han et al., 2016). Two peaks at 1079 cm-1 and 1040 cm-1
274
indicated that the structure of FVPs contains furanose rings (Ma et al., 2014). The
275
peak at 890 cm-1 exhibited that the glycosyl residues of polysaccharides were
276
β-anomeric configuration (Mkadmini Hammi et al., 2016). These results
277
demonstrated that 12 common characteristic FT-IR signals were significant to build
278
chemometric models for quality control of polysaccharides from F. velutipes.
279 280 281
3.4.2 Similarity analysis Cosine and correlation coefficient methods were used to calculate the similarity
282
of FT-IR spectra between each polysaccharides sample and the standard FT-IR
283
fingerprint based on the raw data of wave bands. Similarity analysis results showed
284
that the cosine values and correlation coefficients in the 20 batches of FT-IR spectra
285
were 0.9904 ± 0.0124 (n=20) and 0.9596 ± 0.0349 (n=20), ranging from 0.9474 to
286
0.9997 and 0.8882 to 0.9934, respectively. The similarity values between different
287
samples illustrated a strong correlation, which indicated that the standard FT-IR
288
fingerprint was reliable to apply for identification of polysaccharides from F.
289
velutipes.
290 291
3.4.3 Principal component analysis
15
292
The PCA is worked by SIMCA 14.4 to describe the relationship between
293
different polysaccharides samples, the spectral preprocessing method of standard
294
normal variate (SNV) was chosen to preprocess the original spectra before principal
295
component analysis (Li et al., 2016). The PCA score plot showed that the
296
polysaccharides from 20 batches of FVPs were clustered closely, indicating that
297
overall polysaccharides samples were similar in a high degree (Fig. 2C). The
298
preprocessed spectra data were evaluated by using two main PCA plots of FT-IR
299
spectra fingerprints, and the total variance of PC1 and PC2 accounted for 79.7% and
300
14.1%, respectively. However, the PCA analysis of FT-IR fingerprints could not be
301
used to discriminate different polysaccharides samples due to nuances between 20
302
batches of FVPs, thus the further HPLC fingerprints analysis were applied to
303
determine differences and similarities.
304 305
3.5 HPLC fingerprints analysis
306
3.5.1 HPLC fingerprints of F. velutipes polysaccharides
307
HPLC was used to analyze monosaccharide composition in 20 batches of FVPs,
308
in order to establish the standard HPLC fingerprint. HPLC analysis showed that there
309
are 8 common characteristic peaks, identified to be mannose, glucosamine, ribose,
310
rhamnose, glucose, galactose, xylose and fucose with the molar ratio of 8.04, 0.34.
311
0.59, 0.28, 68.28, 15.28, 2.14 and 5.04%, in the polysaccharides from 20 batches of F.
312
velutipes, which was in agreement with a previous study (see HPLC chromatograms
16
313
in Fig. 3A and the standard HPLC fingerprint in Fig. 3B) (Zhang et al., 2012). Notably,
314
polysaccharides from white F. velutipes contained glucuronic acid but
315
polysaccharides from yellow F. velutipes did not, indicating that glucuronic acid
316
could be considered as a specific marker to distinguish white and yellow F. velutipes.
317
And plus, the glucose peak was regarded as the reference peak, which is attributed to
318
suitable content, intensity and retention time in the whole chromatography. The
319
analysis of RRT (relative retention time) and RPA (relative peak area) statistic can
320
provide detailed information for the monosaccharide composition of FVPs from
321
different sources.
322 323 324
3.5.2 Similarity analysis Based on the HPLC chromatograms of polysaccharide samples and the standard
325
fingerprint, the similarity values were calculated by using the professional software
326
“Similarity Evaluation System for Chromatographic Fingerprint of traditional
327
Chinese medicine (TCM) (Version 2012A).” Similarity analysis showed that
328
correlation coefficients of polysaccharides from 20 batches of F. velutipes compared
329
with the standard fingerprint were 0.957 to 0.999. The high similarity in HPLC
330
fingerprints demonstrated that the standard fingerprint could represent most of the
331
characteristic features for polysaccharides from F. velutipes.
332 333
3.5.3 Principal component analysis (PCA)
17
334
The HPLC fingerprints of F. velutipes polysaccharides were evaluated by
335
principal component analysis (PCA) to outline multivariate differences and
336
quantitative determination between 20 batches of FVPs. The data matrix of relative
337
peak areas of characteristic peaks worked by SPSS showed that the first two PCs
338
accounted for 68.19 % and 11.97 % of the variance in the score plot of PCA (Fig. 3C).
339
Two main factors could be calculated by eqs. (3)-(4).
340
PC1 = 0.157 ∗ X1 + 0.140 ∗ X2 + 0.107 ∗ X3 + 0.081 ∗ X4 + 0.113 ∗ X5 −
341
0.159 ∗ X6 + 0.152 ∗ X7 + 0.129 ∗ X8 + 0.153 ∗
342
X9 (3)
343
PC2 = −0.161 ∗ X1 + 0.363 ∗ X2 + 0.394 ∗ X3 + 0.576 ∗ X4 − 0.342 ∗ X5 +
344
0.069 ∗ X6 − 0.042 ∗ X7 − 0.397 ∗ X8 − 0.048 ∗
345
X9 (4)
346
The X1 to X9 were mannose, glucosamine, ribose, rhamnose, glucose,
347
glucuronic acid, galactose, xylose, and fucose, respectively, in the above equations.
348
The PC1 was related well with X1, X6, X7 and X9 with the loadings of 0.157, 0.159,
349
0.152 and 0.153, and the equation of PC2 had an excellent explanation for X2, X3, X4,
350
X5, X8, indicated that these main factors could make contribution to classify F.
351
velutipes polysaccharides from different sources. The plot scores suggested that
352
polysaccharides from white and yellow F. velutipes could be identified depending on
353
the different monosaccharide composition.
354
18
355 356
3.6 Identification of unknown samples Three unknown F.velutipes polysaccharides products were purchased to verify
357
whether multiple fingerprints combined with chemometrics could be applied for
358
quality control. The appearance results showed that the morphology of unknown
359
sample 1 is similar to that of authentic F. velutipes, but the unknown samples 2 and 3
360
were suspected samples (Fig. 1 S21-23). The microstructure of soluble corn starch
361
and maltodextrin attached to the surface of polysaccharides particles could be seen in
362
the other two unknown samples, respectively, and this appearance was similar to the
363
adulterated polysaccharide reported in the previous study (Qian et al., 2009).
364
Furthermore, the FT-IR spectra of unknown sample 1 (S21) were similar to the
365
standard fingerprint, supporting that this sample could be F. velutipes polysaccharides,
366
though more data is required for identification. In contrast, the FT-IR spectra of
367
unknown samples 2 and 3 (S22 and S23) were different from the standard fingerprint:
368
unknown sample 2 showed stronger bands in the region of 1500 cm-1 to 500 cm-1, and
369
unknown sample 3 showed different peaks at in the region of 1500 cm-1 to 500 cm-1.
370
The cosine values and correlation coefficient of the unknown samples were 0.9820,
371
0.9830, 0.8334 and 0.9619, 0.6901, 0.6803, which demonstrated that the correlation
372
between unknown sample 1 and the standard FT-IR fingerprint was higher than that of
373
the other two unknown samples. Additionally, the HPLC chromatograms of three
374
unknown samples were similar to the HPLC fingerprint with the common
375
characteristic peaks, but the molar ration of peaks had a great discrepancy in unknown
19
376
samples 2 and 3 (Fig. 3D), and the similarity values of the unknown samples were
377
0.973, 0.879, and 0.878. In the principal component analysis of FT-IR fingerprints, the
378
unknown sample 1 was close to the class of authentic F. velutipes polysaccharides,
379
while unknown sample 2 and 3 were much more diffuse from entirety in a high degree
380
(Fig. 2C; Fig. 3C). Additionally, unknown sample 1 could be identified as a yellow F.
381
velutipes polysaccharides product according to the principal component analysis of
382
HPLC fingerprints. In summary, unknown samples 2 and 3 were substandard F.
383
velutipes polysaccharides products, thus multiple fingerprint combined with
384
chemometrics could be used to classify unknown samples successfully.
385 386
3.67 Cell growth inhibitory assay of FVPs and HPLC fingerprint-growth
387
inhibitory activity relationship
388
The 25% inhibition concentration (IC25) for polysaccharides from F. velutipes
389
was determined for HepG2 cells, and the lower value of IC25 indicated the stronger
390
cancer cell growth inhibitory activity. The inhibitory activity results of FVPs from
391
different sources showed that S10 exhibited the highest inhibitory effect on HepG2
392
(IC25=99.57 µg/mL), while the lowest antitumor activity was IC25 305.49 µg/mL
393
from S20 (Fig.4). However, the variation in inhibitory activity of FVPs were
394
affected by monosaccharides composition, and therefore HPLC fingerprint-growth
395
inhibitory relationship should be established to assess the quality of polysaccharides
396
from F. velutipes.
20
397
MLR method was used to establish quantitative fingerprint-activity relationship
398
model built with 8 common peaks of HPLC fingerprint and with IC25 of FVPs
399
against HepG2 cells. As is shown in Table 2, the determination coefficient of
400
regression model was 0.778, and P-value at the level of 0.05 implied statistically
401
significant. Meanwhile, the mean value of residuals was 1.22×10-13 closing to zero.
402
The R2 value of linear trend lines of normal probability plots was 0.984, which
403
demonstrated that residuals were normally distributed. These results proved that
404
HPLC-growth inhibitory activity regression model was reasonable. The effect of
405
significant factors on inhibitory activity can be ranked from low to high as follows:
406
X7 (xylose), X6 (galactose), X4 (rhamnose), and X1 (mannose) (Table 3). The content
407
of X4, X6, and X7 and had obviously negative correlation with inhibitory activity.
408
Nevertheless, the high content of X1 were observed to significantly inhibit the
409
proliferation of HepG2 cells, which is consistent with previous study (Jiang et al.,
410
2012).
411 412 413
4. Conclusion In summary, a comprehensive and efficient approach was successfully proposed
414
for quality control of polysaccharides from F. velutipes and its related products. The
415
SEM analysis was an intuitive method for the identification of polysaccharides but
416
limited by the extraction method of polysaccharides. The establishment of FT-IR and
417
HPLC fingerprints combined with chemometric methods (SA and PCA)
21
418
demonstrated that polysaccharides from the 20 batches of F. velutipes had unique
419
standard fingerprint characteristics, and the authentic F.velutipes polysaccharides of
420
three unknown samples can be identified successfully. However, HPLC fingerprints
421
analysis could further differentiate the white and yellow F. velutipes polysaccharides
422
in a high degree. The quantitative HPLC fingerprint-growth inhibitory activity
423
against HepG2 relationship were established to set up a time-saving activity
424
prediction and quality assessment of FVPs from different sources based on MLR
425
analysis. Overall, the applicable and accurate strategy is confirmed to evaluate
426
fungus-derived polysaccharides from different sources, thereby providing an
427
alternative for the quality control of polysaccharides products.
428 429
Acknowledgment
430
This work is financially supported by the Key Research and Development
431
Program of Jiangsu Province (BE2017374), the China Agriculture Research System
432
(CARS-20), and the Postgraduate Research & Practice Innovation Program of
433
Jiangsu Province (KYCX18_1405).
22
434
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Figure Captions
546
Fig. 1. The SEM graphics of polysaccharides from F. Velutipes and unknown
547
samples. The microstructures of S1-S20 were highly similar. The structure of S21
548
was close to polysaccharides samples (S1-S20), but the ones of S22 and S23 were
549
dramatically different and adulterate.
550 551
Fig. 2. Comparison of FT-IR spectra of polysaccharides from F. Velutipes. (A)
552
FT-IR fingerprints; (B) referential fingerprint from (A); (C) FT-IR spectra of three
553
unknow samples; (D) plot of PCA scores. S21 was similar to polysaccharides
554
samples (S1-S20), but S22 and S23 were much more diffused.
555 556
Fig. 3. The monosaccharide composition of polysaccharides from F. Velutipes.
557
(A) HPLC fingerprints; (B) referential fingerprint from (A); (C) HPLC profiles of
558
three unknow samples; (D) plot of PCA scores. Component: 1, mannose; 2,
559
glucosamine; 3, ribose; 4, rhamnose; 5, glucose; 6, galactose; 7, xylose; 8, fucose.
28
560
S21 was close to polysaccharides samples (S1-S20), but S22 and S23 were much
561
more diffused.
562 563
Fig. 4. HepG2 cell inhibition IC25 of polysaccharides from F. velutipes. S1-S20
564
represent different antitumor activities of 20 batches of FVPs from various sources.
565
Each value is expressed as mean ± standard deviation (n = 3). Different letters
566
indicate significance difference between different columns (P < 0.05).
29
Table 1 Molecular weight distribution of samples from different sources Number
Variety
Sources
Cultivation method
1
Xuerong, white
Dezhou, Shandong Province
Bottle cultivated
2
Chuanjin 11, white
Ande, Sichuan Province
Bottle cultivated
3
Chuanjin 33, white
Tangchang, Sichuan Province
Bag cultivated
4
Hualv, white
Siyang, Jiangsu Province
Bottle cultivated
5
Jin 19, white
Handan, Hebei province
Bottle cultivated
6
Jinbai 1, white
Shijiazhuang, Hebei province
Bag cultivated
7
Baijin Japan, white
Zhangzhou, Fujian Province
Bottle cultivated
8
Nvwa, white
Zhuhai, Guangdong Province
Bottle cultivated
Molecular weight (Da) 1,623,755 118,835 17,339 1990 1,533,362 132,974 15,638 1,150,118 199,478 15,921 1,154,566 259,032 12,758 1,251,461 265,744 13,894 1,235,162 527,488 17,683 1799 1,418,385 212,857 18,460 1,026,916 109,338 16,009
Area per centage (%) 12.34 62.20 17.77 7.70 26.69 38.60 34.71 20.68 29.02 50.30 31.43 23.19 45.37 10.58 37.17 52.25 46.01 15.98 30.28 7.72 24.31 32.39 43.31 30.77 26.46 42.78
Number
Variety
Sources
Cultivation method
9
Baixue 2, white
Huangshi, Hubei Province
Bag cultivated
10
Guoren, white
Baoji, Shanxi Province
Bottle cultivated
11
Chuanjin 3, yellow
Ande, Sichuan Province
Bag cultivated
12
Chuanjin 54, yellow
Tangchang, Sichuan Province
Bag cultivated
13
Su 6, yellow
Shijiazhuang, Hebei Province
Bag cultivated
14
2102, yellow
Handan, Hebei province
Bag cultivated
15
Jin 17, yellow
Xiamen, Fujian Province
Bottle cultivated
16
Jin 13, yellow
Gutian, Fujian Province
Bottle cultivated
17
Sanming 1, yellow
Puyang, Henan Province
Bag cultivated
Molecular weight (Da) 1,441,769 117,693 16,457 1,440,038 119,605 15,254 1,190,650 176,997 65,401 16,375 1,374,191 144,020 17,106 1,096,561 204,222 19,411 1,204,842 227,940 15,982 1,580,919 158,809 18,655 1,472,898 122,033 17,876 1,350,057 105,983 18,654
Area per centage (%) 12.79 39.32 47.89 15.06 55.81 29.14 9.83 26.40 29.98 33.79 25.99 41.17 32.84 35.10 41.78 23.13 41.49 37.94 20.58 24.03 48.71 27.26 14.43 61.28 24.29 19.10 49.63 31.26
Number
Variety
Sources
Cultivation method
18
New Su 6, yellow
Zhengzhou, Henan Province
Bag cultivated
19
Sanming B, yellow
Dezhou, Shandong Province
Bag cultivated
20
Jinza 19, yellow
Changzhou, Jiangsu Province
Bag cultivated
21
Unknown sample 1
-
-
22
Unknown sample 2
-
-
23
Unknown sample 3
-
-
Molecular weight (Da) 1,400,902 119,867 20,712 1,922,985 145,810 17,754 600,018 18,645 747,314 15,077 1,476,583 171,804 16,600 1,761,954 153,053 5,856
Area per centage (%) 17.94 46.19 35.87 12.21 54.27 33.52 53.81 46.20 48.09 51.91 12.36 70.12 17.42 7.16 41.46 51.38
Table 2 The components of FVPs Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Protein content (%)
Uronic acid content (%)
Total carbohydrate content (%)
2.06±0.17 1.18±0.09 1.77±0.06 2.81±0.57 2.81±0.25 2.27±0.14 4.50±0.19 3.84±0.31 5.00±0.44 4.13±0.38 3.67±0.25 2.06±0.14 2.38±0.03 2.44±0.17 3.50±0.26 4.58±0.35 2.38±0.20 3.43±0.30 1.53±0.01 2.64±0.13
22.40±0.55 29.22±0.84 20.66±0.21 21.56±0.91 24.69±1.27 20.87±0.66 22.63±0.33 23.79±0.45 23.39±0.96 23.76±0.38 24.75±0.41 24.31±0.82 21.75±0.42 23.64±1.12 21.27±0.47 24.46±0.42 23.78±0.64 22.69±0.46 24.10±0.25 19.36±0.31
58.68±1.65 57.49±1.73 56.04±0.97 71.93±1.82 58.04±2.02 57.93±1.77 71.54±1.83 60.75±2.23 71.16±1.68 56.32±2.05 55.07±1.72 69.04±1.69 70.04±1.99 62.91±1.69 70.02±1.89 69.75±1.59 70.86±1.91 69.86±1.56 66.91±2.11 63.46±1.78
Table 3 Results of multiple linear regression model
Regression models
Determination coefficient
P-value
HPLC fingerprint based model
0.778
0.009
Residuals Correlation coefficient Mean value of probability plot 1.22×10-13
0.984
Table 4 HPLC fingerprint-activity relationship and their corresponding parameters parameters Regression coefficient Standardized regression coefficient t-test P-value
constant 245.828
X1 -757.773
X2 475.785
X3 -71.469
X4 940.447
X5 -880.957
X6 5373.172
X7 3014.958
X8 -2195.325
0
-3.226
0.191
-0.041
0.544
-0.256
2.529
1.561
-1.053
8.420 4.00×10-6
-2.305 0.042
0.449 0.662
-0.182 0.859
2.675 0.022
-1.413 0.185
2.776 0.018
3.265 0.008
-0.883 0.396
Independent variables (X1-X7) represent mannose, glucosamine, ribose, rhamnose, glucose, galactose, xylose and fucose, respectively.
Fig. 1
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S21
S22 S11
S12
S13
S14
S15
S23 S16
S17
S18
S19
S20
Fig. 2
A
B
C
D
Fig. 3
A
B
C
D
Fig. 4
Highlights HPLC fingerprint-activity were established to set a new evaluation approach of polysaccharides from Flammulina Velutipes. Multiple fingerprint combined with chemometrics distinguished substandard samples from authentic samples. The activity-associated markers of HPLC fingerprints were chosen successfully by multiple liner regression analysis.
Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: