Non-targeted metabolomic analysis of highland barley original powder and enzymatic hydrolysis powder (pullulanase combined with α-glucosidase)

doi:10.21203/rs.3.rs-2477398/v1

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Non-targeted metabolomic analysis of highland barley original powder and enzymatic hydrolysis powder (pullulanase combined with α-glucosidase)

https://doi.org/10.21203/rs.3.rs-2477398/v1

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Highland barley was used as raw material to prepare slow-digested starch by double-enzyme enzymatic hydrolysis process. The non-targeted metabolomics method was used to analyze the correlation between the differences in metabolites of small molecules before and after double-enzyme enzymatic hydrolysis, and to explore the effect on the metabolism of highland barley starch after double-enzyme enzymatic hydrolysis. UHPLC-QTOF-MS detection showed that the main up-regulated metabolites and metabolic pathways in enzymolysis group were mainly flavonoids, and the main down-regulated metabolites and metabolic pathways were mainly lipid metabolism. The results showed that compared with the control group, the enzymatic hydrolysis group regulated the content of sugars and flavonoids in the enzymatic hydrolysis starch by affecting the metabolic pathways such as glycolysis and flavonoid production, thereby affecting the starch characteristics of the double enzyme enzymatic hydrolysis of highland barley flour and making it produce a more gentle blood glucose response in the human body.

double-enzyme hydrolysis of highland barley starch

non-targeted metabolomics

differential metabolites

metabolic pathways

Highland barley, also known as naked barley, rice barley, or Huai wheat, is widely grown in high-altitude areas such as Tibet, Qinghai, Yunnan, and Ganzi Prefecture in Sichuan[1] It has high protein, high fiber, high vitamin, and low fat, the structural characteristics of low-sugar "three highs and two lows" ingredients[2]. With the continuous improvement of people's living standards, chronic diseases such as diabetes have become a major problem that threatens human health. Studies have shown that the glycemic index of barley is also lower than other grains, and its hypoglycemic effect may be induced by changes in the distribution of amino acids, biogenic amines, and organic acids improve insulin sensitivity[3, 4]. A 3-month clinical study conducted by Bi Mingxin et al[5]. also believed that highland barley oatmeal can significantly improve the glucose and lipid metabolism in patients with impaired fasting blood sugar.

Starch is the largest source of energy required in the human diet and is of great significance for maintaining the normal physiology and activity of our bodies[6]. Englyst et al[7].proposed to classify starch into three categories according to the bioavailability of starch under simulated conditions in vitro: fast-digesting starch, slow-digesting starch and resistant starch. Slowly digested starch has the physiological function of slow digestion in the body, so it can reduce the pressure on the blood sugar balance system and prevent chronic diseases such as metabolism. The enzymatic method will increase the content of slow-digestible starch in starch, thereby reducing the glycemic index, and this method also shows the advantages of high safety, environmental friendliness, strong specificity, and fewer by-products[8]. Through previous studies, it was found that enzymatic hydrolysis can increase the content of fast-digestible starch and reduce the digestibility of starch. Anpanyu [9] used α-amylase, β-amylase and pullulanase to prepare highland barley slowly digestible starch. It was found that the content of slowly digestible starch reached the highest value when the amount of pullulanase was 200 U; zhang [10] prepared highland barley chronically digestible starch by enzymatic hydrolysis and optimized the process. The results showed that the content of slowly digestible starch was 32.72% when the amount of pullulanase was 200 U, the enzymolysis time was 10 h, the refrigerated retrogradation time was 1 day and the starch concentration was 15%. Guraya et al. [11] treated rice starch with pullulanase and found that the content of slowly digestible starch increased.

Metabolomics is a science that studies the types, quantities and changes of metabolites (endogenous metabolites) with a molecular mass less than 1500 Da caused by external stimuli, pathophysiological changes and their own gene mutations. Metabolomics can be divided into targeted metabolomics and non-targeted metabolomics in terms of detection methods. Targeted metabolomics often requires higher levels of purification and selective extraction of metabolites. Untargeted metabolomics focuses on detecting as many metabolomes as possible without necessarily identifying or quantifying specific compounds. Metabolomics has emerged as an effective tool to address the future needs of agriculture and human nutrition. Ren Chuanying et al. used liquid chromatography-tandem mass spectrometry combined with non-targeted metabolomics technology to analyze the changes of metabolites before and after brown rice milling and the main metabolic pathways involved[12]. Mao Honglin et al. found that the metabolism levels of amino acids and nucleotides in De'ang sour tea and Pu'er tea are significantly different, and the pathways involved in amino acid metabolism and nucleotide metabolism are important for the taste quality of tea with different fermentation processes. Influence[13].

In recent years, most researchers have devoted themselves to exploring the preparation of slow-digestible starch and resistant starch by different physical and chemical methods. There are few studies on the metabolic mechanism of enzymatically hydrolyzed starch, especially the correlation analysis between slow-digestible starch and metabolism. Therefore, it is necessary to study its metabolic mechanism to provide a theoretical basis for slow-digesting starch to improve starch digestibility and reduce glycemic index.

Materials and Reagents

Highland barley powder: purchased from Qinghai Hanhe Biotechnology Co., Ltd.

Enzymatic hydrolysis of highland barley powder: laboratory-made. Under the conditions of pullulanase concentration of 200 U/g highland barley powder, α-glucosidase concentration of 80 U/g highland barley powder, enzymatic hydrolysis time of 3 h, enzymatic hydrolysis temperature of 55°C, and Solid-liquid ratio 1:15 g/mL, The content of slow-digestible starch in double-enzyme enzymatic hydrolysis of highland barley flour was 28.01%.

Instruments and equipment

AB Triple TOF 6600 mass spectrometer (AB SCIEX)

Agilent 1290 Infinity LC ultra-high pressure liquid chromatograph (Agilent)

Low temperature high-speed centrifuge (Eppendorf 5430R)

Chromatographic column: Waters, ACQUITY UPLC BEH C-18 1.7 µm, 2.1 mm×100 mm column Acetonitrile (Merck, 1499230-935)

Ammonium acetate (Sigma, 70221)

Methods

Extraction of metabolites

After the samples were slowly thawed at 4°C, an appropriate amount of samples was added to pre-cooled methanol/acetonitrile/water solution (2:2:1, v/v), mixed by vortex, sonicated at low temperature for 30 min, and allowed to stand at -20°C for 10 min. Centrifuge at 14,000 g at 4°C for 20 min, taked the supernatant and dried it in vacuo, added 100 µL of acetonitrile aqueous solution (acetonitrile: water = 1:1, v/v) to reconstitute, vortex, and centrifuge at 14,000 g at 4°C for 15 min. Supernatant injection analysis.

Chromatography-Mass Spectrometry

The samples were separated by an Agilent 1290 Infinity LC ultra-high performance liquid chromatography (UHPLC) C-18 column; the column temperature was 40°C; the flow rate was 0.4 mL/min; the injection volume was 2 µL; mobile phase composition A: water + 25 mM acetic acid Ammonium + 0.5% formic acid, B: methanol; the gradient elution procedure is as follows: 0–0.5 min, 5% B; 0.5–0 min, B linearly changes from 5–100%; 10.0–12.0 min, B maintained at 100%; 12.0–12.1 min, B linearly changed from 100–5%; 12.1–16 min, B maintained at 5%; the sample was placed in an autosampler at 4°C throughout the analysis. In order to avoid the influence caused by the fluctuation of the instrument detection signal, the continuous analysis of the samples is carried out in random order. QC samples are inserted into the sample queue to monitor and evaluate the stability of the system and the reliability of experimental data.

The primary and secondary spectra of the samples were collected using an AB Triple TOF 6600 mass spectrometer. The ESI source conditions after HILIC chromatographic separation are as follows: Ion Source Gas1: 60, Ion Source Gas2: 60, Curtain gas: 30, source temperature: 600℃, IonSapary Voltage Floating ± 5500 V (positive and negative modes); TOF MS scan m/z range: 60–1000 Da, production scan m/z range: 25–1000 Da, TOF MS scan accumulation time 0.20 s/spectra, production scan accumulation time 0.05 s/spectra; MS was acquired with information dependent acquisition (IDA) and used in high sensitivity mode, Declustering potential (DP): ±60 V (both positive and negative modes), Collision Energy: 35 ± 15 eV, IDA setting As follows Exclude isotopes within 4 Da, Candidate ions to monitor per cycle: 10.

Data processing and analysis

For untargeted metabolomics data, raw ms data (wiff. scan files) were converted to mzXML files using proteowizard msconvert, imported into freely available xcms software, and then used for alignment, peak detection and retention time correction. In addition, multivariate statistical analysis, including PCA, PLS-DA, and OPLS-DA, was performed using simca software (version 14.0), and used Heatmaps were drawn, and functional annotation of metabolites and enrichment analysis of metabolic pathways were evaluated using the database of the Kyoto gg Encyclopedia of Genes and Genomes

Untargeted metabolomic analysis

Sample analysis

Similarities and differences of samples were detected by PCA. The data acquisition performance of the equipment was monitored using quality control samples during the analysis. In both anion and cation detection modes, dense clustering of quality control samples shows that the variation caused by instrumental error is small throughout the test, and the test data is reliable. The PG group and the control group were distributed on the left and right sides of the PCA score graph, indicating a separation between the two (Fig. 1a and 2a).

Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA)

To optimize the separation between groups, the two groups of samples were further analyzed using PLS-DA and OPLS-DA after removal of the control samples. As shown in Fig. 1b,2b and Fig. 1c,2c, the PG group and the control group exhibited trends of intra-group aggregation and inter-group segregation in both modes, which was more pronounced in OPLS-DA. The results showed that the metabolic pattern of highland barley starch changed under the conditions of enzymatic hydrolysis. In order to avoid overfitting of the supervised model in the modeling process, the permutation test method is used to test the model to ensure the validity of the model. Figure 1d,2d and Fig. 1e,2e,show the PLS-DA (R²X = 0.992, R²Y = 1, q2 = 0.999) and OPLS-DA (R²X = 0.992, R²Y = 1, q2 = 0.999) of the PG group and the Control group in both anionic and cationic ion modes ); permutation test plots of PLS-DA (R²X = 0.953, R²Y = 0.998, q2 = 0.988) and OPLS-DA (R²X = 0.953, R²Y = 0.998, q2 = 0.987). As the replacement retention rate gradually decreased, the R² and Q² of the random model gradually decreased, indicating that there was no overfitting in the original model. This indicates that PLS-DA and OPLS-DA showed statistically significant separation of metabolites between groups.

Screening and identification of differential metabolites

Different metabolites with biological significance were found according to the projected variable importance (VIP) obtained by OPLS-DA. As shown in Table 1, in the anion detection mode, 39 differential metabolites were identified by matching with the database (VIP > 1.0, P < 0.05). Among them, 13 differential metabolites were related to the KEGG metabolic pathway, including Lipids and lipid-like molecules (gamma-Linolenic acid), phenylpropanoids and polyketides ((-)-epicatechin, apigenin, epigallocatechin, geranin, procyanidin B2), organic acids and derivatives (L-Pyroglutamic acid, L-Malic acid, Gluconic acid), organic oxygen compounds (D-Glucose, Stachyose, Trehalose, Raffinose). Among these metabolites, 5 metabolites were up-regulated and 34 were down-regulated. The up-regulated metabolites included flavonoids, benzene and substituted derivatives, organic oxygen compounds, etc. Down-regulated metabolites include hydroxyl acids and derivatives, flavonoids, etc.

Table 1

The negative detection mode (opls-da vip > 1, p value < 0.05), the differential metabolites of enzymatic hydrolysis of highland barley powder were studied in detail
Name	adduct	m/z	rt(min)	VIP	Fold change	Variation	p-value
(-)-Epicatechin	[M-H]-	289.07156	2.809	3.446483926	0.047124696	↓	2.85023E-15
Nystose	[M-H]-	711.22223	0.673	1.884029667	0.018140886	↓	4.29346E-14
Epigallocatechin	[M-H]-	305.0654	1.94	1.457363206	0.004744973	↓	8.19879E-14
(2S,3S,4S,5R,6S)-3,4,5-Trihydroxy-6 -[5-hydroxy-2-(4-hydroxyphenyl)-6-methoxy-4-oxochromen-7-yl]oxyoxane-2-carboxylic acid	[M-H]-	475.08737	4.494	1.196902241	0.109614142	↓	9.06315E-14
Dimorphecolic acid	[M-H]-	295.22916	9.915	3.1492803	0.296490224	↓	9.55879E-14
9-HOTrE	[M-H]1-	293.21262	9.165	1.679152834	0.317792001	↓	1.38389E-13
[3-[2-Aminoethoxy(hydroxy)phosphoryl]oxy-2-hydroxypropyl] octadeca-9,12-dienoate	[M-H]-	476.2774	10.515	3.468110314	0.270945281	↓	2.98062E-13
Hydron;nonanedioate	[M-H]-	187.09837	5.067	1.103199872	0.100934839	↓	5.91873E-13
L-Pyroglutamic acid	[M-H]-	128.03435	0.688	1.121168248	0.027392905	↓	6.02342E-13
Lactose	[M-H]-	387.11432	0.658	6.058193744	0.111504921	↓	1.14351E-12
Procyanidin B2	[M-H]-	577.13635	2.382	3.602385527	0.002536368	↓	1.59637E-12
Trehalose	[M-H]-	341.112	0.659	5.456679882	0.206693496	↓	2.05188E-12
Bavachin	[M-H]-	323.13562	2.726	1.481558198	0.000292826	↓	2.94275E-12
Xanthorhamnin	[M-H]-	769.22083	4.773	1.176377243	0.054824501	↓	5.17346E-12
FA 18:1;3O	[M-H]-	329.23199	7.285	1.434624998	0.288840431	↓	1.55831E-11
L-Malic acid	[M-H]-	133.01324	0.657	2.73397094	0.00229625	↓	2.98909E-11
Fumaric acid (not validated)	[M-H]-	115.00341	0.639	1.495327979	0.013087708	↓	5.81406E-11
Raffinose	[M-H]-	503.16055	0.666	3.541632442	0.073348568	↓	7.26814E-11
Apigenin	[M-H]-	269.04559	6.211	1.911242699	3.35774162	↑	7.36692E-11
4-Hydroxy-5-[(2S,3R,4S,5S,6R)-3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxy-1H-benzo[f][2]benzofuran-3-one	[M-H]-	377.08414	0.654	2.792969744	0.215352588	↓	2.64941E-10
Gluconic acid	[M-H]-	195.05064	0.625	1.698743566	0.084579487	↓	5.8884E-10
Stachyose	[M-H]-	665.21631	0.673	1.433406038	0.053727126	↓	1.40185E-09
Tubuloside A	[M-H]-	827.27338	0.678	1.018637883	0.031227582	↓	2.29666E-09
Phosphatidylcholine lyso 16:0	[M-CH3]-	480.31055	10.788	1.302602501	0.439067711	↓	3.24154E-09
(Z)-5,8,11-Trihydroxyoctadec-9-enoic acid	[M-H]-	329.23221	7.658	1.529075115	0.249575064	↓	4.05833E-09
(2Z)-4,6-Dihydroxy-2-[(4-hydroxy-3,5-dimethoxyphenyl)methylidene]-1-benzofuran-3-one	[M-H]-	329.06702	6.233	3.983452553	1.383372348	↑	1.75721E-08
FA 18:2;O	[M-H]-	295.22684	11.867	1.202267104	0.649513406	↓	1.9305E-08
Luteolin	[M-H]-	285.04068	5.799	1.9869953	0.701054733	↑	2.14058E-07
3-Phenoxybenzoic acid	[M-H]-	213.05565	4.95	1.323728565	1.504013752	↑	4.11093E-07
FA 18:0;O	[M-H]-	299.25613	11.156	1.124046492	0.202392854	↑	1.80656E-06
PG 36:4	[M-H]-	769.50232	12.499	1.121064744	0.766497762	↑	8.82294E-06
PE 36:3	[M-H]-	740.52411	12.734	1.643899895	0.666542597	↑	3.25296E-05
D-Glucose	[M-H]-	179.05573	0.652	1.836605904	1.169281969	↑	3.45763E-05
Kaempferol 3,7-diglucoside	[M-H]-	609.34161	7.477	2.991711928	1.165011072	↑	6.56922E-05
PI 34:2	[M-H]-	833.52142	12.985	1.477633663	0.393677199	↓	0.000606235
Linoleic acid	[M-H]-	279.23108	12.372	1.526504634	0.928854244	↑	0.011912493
Flavone base + 4O, C-Hex-dHex	[M-H]-	593.15295	5.352	1.333552772	0.086798914	↓	0.025719417
Maysin	[M-H]-	575.14008	5.345	2.740124396	0.066813033	↓	0.029996321
Gingerol	[M-H]-	293.17523	7.478	7.246968799	0.96249008	↑	0.031715141

As shown in Table 2, In cation detection mode, 34 differential metabolites were identified by matching against the database (VIP > 1.0, P < 0.05). Among them, 7 differential metabolites were related to the KEGG metabolic pathway, including organic nitrogen compounds (choline, alpha-Linolenoyl ethanolamide), organic oxygen compounds (sucrose), organic acids and their derivatives (N,N-Dimethylarginine, 4-Guanidinobutyric acid), Phenylpropanoids and polyketides ( Biochanin A 7-O-beta-d-glucoside-6 " -O-malonate ), nucleosides, nucleotides and analogues ( Cyclic AMP ) Among these metabolites, 12 metabolites were up-regulated and 22 were down-regulated. The up-regulated metabolites include isoprene lipids, peptide mimics, etc. The down-regulated metabolites include organic oxygen compounds, fatty acyl, etc.

Table 2

The positive detection mode (opls-da vip > 1, p value < 0.05), the differential metabolites of enzymatic hydrolysis of highland barley powder were studied in detail
Name	adduct	m/z	rt(min)	VIP	Fold change	Variation	p-value
Phenylbutyrylglutamine	[M + H]+	293.1604	3.075	2.531248905	0.003903474	↓	3.92393E-17
Feruloyl lysine	[M + H]+	323.172	3.338	2.246101033	0.069069118	↓	9.23129E-17
2-(6-Aminopurin-9-yl)-5-(hydroxymethyl)oxolane-3,4-diol	[M + H]+	268.10413	1.944	1.121847272	0.156354357	↓	6.50774E-16
beta-Maltose	[M + H]+	343.12317	0.662	1.460233039	0.046422054	↓	2.3069E-15
Betaine	[M + H]+	118.08568	0.643	5.64539765	0.061609564	↓	2.60796E-15
Feruloyl agmatine	[M + H]+	307.1767	3.742	1.384636512	0.078509009	↓	6.93505E-14
alpha-Linolenoyl ethanolamide	[M + H]+	322.27231	8.975	1.403782895	0.154790416	↓	1.24133E-13
Choline	[M]+	104.10628	0.662	2.924582075	0.345913074	↓	1.33574E-13
Maltotriose	[M + Na]+	527.15857	0.686	1.769157317	0.346595626	↓	2.3205E-13
Steviol	[M + H]+	319.22534	9.57	1.047087644	0.567461397	↓	1.1252E-12
N,N-Dimethylarginine	[M + H]+	203.15067	0.748	1.043853234	0.038495255	↓	1.20307E-12
Glyceryl monooleate	[M + H]+	357.29947	11.289	2.008084293	0.464566677	↓	2.67875E-12
1-O-linoleoyl-3-O-beta-D-galactopyranosyl-syn-glycerol	[M + NH4]+	534.36261	10.592	1.254295407	0.10749055	↓	2.99043E-12
5-dodecyl-4-hydroxy-4-methylcyclopent-2-en-1-one	[M + H-H2O]+	263.23468	12.4	1.830536702	7.572333708	↑	3.13985E-12
[5-(2-Hydroxy-2,5,5,8a-tetramethyl-3,4,4a,6,7,8-hexahydro-1H-naphthalen-1-yl)-3-methylpentyl] acetate	[M-H2O + H]+	335.29221	12.091	1.420276012	38.61167874	↑	9.23025E-12
n-Oleoylethanolamine	[M + H]+	326.30615	11.081	1.452149851	0.164196386	↓	1.29795E-11
Surfactin	[M + H]+	1036.68787	11.41	1.001254911	57.59420717	↑	1.01505E-10
5-(5-Methoxycarbonyl-5,8a-dimethyl-2-methylidene-3,4,4a,6,7,8-hexahydro-1H-naphthalen-1-yl)-3-methylpentanoic acid	[M + H]+	351.25278	9.181	1.247353424	0.429408817	↓	3.17406E-10
(2-Hydroxy-3-octadeca-6,9,12-trienoyloxypropyl) 2-(trimethylazaniumyl)ethyl phosphate	[M + H]+	518.32227	10.104	2.274145447	0.463054007	↓	4.88507E-10
2-(((R)-2,3-Dihydroxypropyl)phosphoryloxy)-N,N,N-trimethylethanaminium	[M]+	258.11105	0.619	1.881624012	0.080824773	↓	1.19845E-09
Sucrose	[M + Na]+	365.10562	0.661	4.753818957	6.65279446	↑	2.32119E-08
1-Hexadecanoyl-sn-glycero-3-phosphoethanolamine	[M + H]+	454.2901	10.802	1.217417143	0.326799432	↓	5.46184E-08
(6beta,22E)-6-Hydroxystigmasta-4,22-dien-3-one	[M + H]+	427.35568	12.39	1.414627471	1.329728779	↑	9.04358E-08
beta-Gentiobiose	[M + NH4]+	360.15021	0.656	1.13694152	0.698248743	↓	1.59354E-07
4-Guanidinobutyric acid	[M + H]+	146.09116	0.705	1.008797731	0.031308841	↓	5.59524E-07
Avocadene 4-acetate	[M + Na]+	351.25171	12.896	1.319152241	1.191310088	↑	1.98448E-05
N-Desmethyltramadol	[M + H]+	250.17816	6.972	1.190142318	1.353080555	↑	2.22753E-05
Fibleucin	[M + H]+	357.18555	5.475	1.493693437	0.060528217	↓	0.000537587
Biochanin A 7-O-beta-d-glucoside-6''-O-malonate	[M + H]+	533.4165	12.83	1.087377858	1.174500524	↑	0.000634626
Cyclic AMP	[M + H]+	330.3374	10.291	1.200940559	0.607386376	↓	0.001095802
alpha-Spinasterol	[M + H-H2O]+	395.36487	12.677	1.210028951	1.339489288	↑	0.004537202
2,4-Dihydroxyheptadecyl acetate	[M-H2O + H]+	313.27072	13.825	1.412480674	1.489043385	↑	0.006251986
6-{9a,11a-dimethyl-1H,2H,3H,3aH,3bH,4H,8H,9H,9bH,10H,11H-cyclopenta[a]phenanthren-1-yl}-3-ethyl-2-methylheptane	[M + H]+	397.3815	14.043	1.235515418	1.256297606	↑	0.046051593
L-Glutathione (oxidized form)	[M + H]+	613.47644	13.401	1.062153722	1.112520503	↑	0.047324414

In order to more intuitively show the relationship between samples and the expression differences of metabolites between different samples, a heat map analysis was performed on the expression of significantly different metabolites enriched in pathways in two modes of anion and cation (Fig. 3). Each row in the figure represents a differential metabolite, each column represents a sample, and each of the control group and the PG group has 3 samples. Blue is the low-expressing substance, and red is the high-expressing metabolite. From blue to red, the expression level of the substance is from low to high. The PG group and the Contral group were clearly separated, indicating that there were significant differences between the two groups, and the screened metabolites with significant differences could be used as markers to distinguish the two groups.

Functional annotation of differential metabolites and their enrichment analysis and kegg metabolic pathway analysis

Comprehensive analysis of differential metabolites in both cation and anion modes to ensure comprehensiveness and improve confidence. Functional annotation of differential metabolites revealed that 138 metabolic pathways were integrated, covering 20 metabolites. Among them, metabolic pathways mainly include Flavonoid biosynthesis, GIucagon signaling pathway, Taste transduction, and ABC transporters (Fig. 4). Differential abundance analysis (DA) showed that at least 61 metabolites out of 57 metabolic pathways were captured (P < 0.05), of which 56 metabolic pathways were high (<-0.5 DA score, blue) and 1 metabolic pathways were low (> 0.5 da score, red) (Fig. 5). The up-regulated metabolic pathways mainly include Isoflavonoid biosynthesis. Down-regulated metabolic pathways mainly include Glucagon signaling pathway, Cholinergic synapse, etc.

With the rapid development of my country's economy and the change of people's living habits, chronic diseases such as diabetes have become the main problems that endanger the health of the population. Highland barley has attracted more and more people's attention because of its characteristics of "three highs and two lows". As a new type of functional food, slow-digesting starch has slow digestion and absorption[14], maintains postprandial blood glucose homeostasis[15], improves postprandial glycemic load[16], controls diabetes and reduces the risk of disease[17].

Pullulanase is a hydrolysis of pullulan and amylopectin in the α-1,6 glycosidic bond, conducive to the formation of slow digestion starch crystal structure. Glucosyltransferases transfer free glucose residues to other chains by catalyzing the hydrolysis of α-1,4 glycosidic bonds and transfer reactions to form new α-l,6 glycosidic bonds, thereby increasing branch density and changing starch digestion characteristics [18].The differential metabolite gamma-Linolenic acid is metabolized in the body to eicosatrienoic acid (bihomo-γ-linolenic acid), arachidonic acid and other eicosanoid precursors, which have anti-inflammatory, anti-HIV, diabetes and heart disease important physiological functions such as vascular diseases[19].Flavonoids are a class of natural colored compounds with a C6-C3-C6 molecular structure, which are basically yellow and light yellow[20]. Flavonoids have functions such as lowering blood sugar, anti-tumor, and anti-oxidation[21]. Compared with Control, the up-regulation of flavonoids in AG group also explained that after double-enzyme hydrolysis, the starch properties of highland barley changed. Change, the content of slow-digestible starch increases, so that the release of glucose is slow, resulting in a relatively slow blood sugar response level in the human body, resulting in a decrease in the postprandial glycemic index, thus achieving the purpose of preventing diabetes.

This study was based on liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) non-targeted metabolomics analysis of differential metabolites in the Control group and the AG group. PCA analysis showed that under the cation and anion models, the Control group and the AG group were significantly different are separate. The principal component analysis correctly reflected the accumulation pattern and specificity of the metabolites in the corresponding samples, which indicated that there were metabolomic differences between the Control group and the AG group. These differential metabolites contain more flavonoids, which is consistent with Chen et al[22]. found that after highland barley was digested in the gastrointestinal tract in vitro, the content of phenolics, flavonoids and antioxidant activity were increased, and the bioavailability enhanced. Zhang et al[23]. found that phloretin, protocatechuic acid, p-coumaric acid and ferulic acid were the main phenolic acids, and catechin was the main flavonoid after different processing methods were used to simulate digestion in vitro. class substancesare consistent.

Flavonoids exist in a variety of vegetables, fruits, Chinese herbal medicines and other plants, and have various biological activities such as antiviral, anti-inflammatory, antioxidant, hypoglycemic, preventing angiogenesis, and anticancer[24]. In vitro studies by Decharneux et al[25]. confirmed that flavonoids such as kaempferol, quercetin, 7, 8-diquercetin and d-catechin in plants can protect the lysosomal membrane and inhibit the release of glycosidase. After enzymatic hydrolysis, the flavonoids in highland barley starch increase, which can effectively inhibit the rise of blood sugar. Michae et al[26]. found that geranialin can reduce blood sugar levels in diabetic rats, improve blood lipid disorders, and protect the liver of diabetic rats, and proposed that its hypoglycemic pathway may be related to promoting pancreatic regeneration, increasing insulin secretion and activating glucose. Pro-synthase, etc. Studies have found that plant flavonoids can prevent diabetes and its complications[27, 28], Zhang et al[29]. found that the differential metabolite gentiobiose has functions such as regulating the balance of intestinal flora and promoting calcium and phosphorus absorption. Crowd eats.

In conclusion, this study shows that it is feasible to prepare highland barley slow-digestible starch by double-enzyme hydrolysis method. The non-targeted omics method was used to analyze the correlation between the metabolite differences of the small molecular substances in the Control group and the PG group before and after enzymatic hydrolysis. In the anion mode, 39 differential metabolites (including 5 up-regulated and 34 down-regulated) were detected in the Control group and the PG group, and the up-regulated metabolites included flavonoids, benzene and substituted derivatives, organic oxygen compounds. Down-regulated metabolites include hydroxyl acids and derivatives, flavonoids, etc. In the positive mode, 34 differential metabolites (including 12 up-regulated and 22 down-regulated) were detected in the Control group and the PG group, and the up-regulated metabolites included lipids and lipid molecules,Down-regulated metabolites include organic oxygen compounds, fatty acyl, etc. Pathway enrichment analysis of differential metabolites through the KEGG database found that the up-regulated metabolic pathways mainly include flavonoid biosynthesis. The down-regulated metabolic pathways include Glucagon signaling pathway, Cholinergic synapse and other signaling pathways. The results showed that compared with the PG group, the differential metabolites in the Control group were mainly down-regulated by affecting glucose metabolism, glycolysis and flavonoids.

PCA, principal component analysis, PLS-DA, partial least squares discriminant analysis, OPLS-DA, orthogonal partial least squares discriminant analysis, PTS, phosphotransferase system, UHPLC, ultra-high performance liquid chromatography, IDA, Information dependent acquisition, VIP, projected variable importance.

Availability of data and materials

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no conflicts of interest.

Funding

This work was supported by the Qinghai Provincial Natural Science Foundation Project-Youth (Grant No.: 2020-ZJ-975Q) and the Independent Project of State Key Laboratory of Sanjiangyuan Ecology and Plateau Agriculture and Animal Husbandry (2019-ZZ-03)

Authors' contributions

Yan Li analyzed the data, interpreted the results, and wrote the manuscript. Zhenzhen Yuan revised the manuscript and provided financial support. Kangna Sun, Xiaofan Song and Fuzhang Chen for data sorting and analysis.

Acknowledgments

We acknowledge the Qinghai University Agriculture and Animal Husbandry Laboratory for the support of experimental instruments and equipment, and Shanghai Zhongke New Life Technology Co., Ltd. for technical support.

Author information

Yan Li contributed to this work as the first authorAuthor contributions.

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Non-targeted metabolomic analysis of highland barley original powder and enzymatic hydrolysis powder (pullulanase combined with α-glucosidase)

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Abstract

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Introduction

Materials And Methods

Materials and Reagents

Instruments and equipment

Methods

Extraction of metabolites

Chromatography-Mass Spectrometry

Data processing and analysis

Untargeted metabolomic analysis

Sample analysis

Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA)

Screening and identification of differential metabolites

Functional annotation of differential metabolites and their enrichment analysis and kegg metabolic pathway analysis

Discussion

Conclusions

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